{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Pygor Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the pygor3 Tutorial.\n",
    "\n",
    "Pygor3 is an open source project and Python package that allows to analyze infer, evaluate and generate V(D)J sequences, by using IGoR's.\n",
    "\n",
    "Pygor3 could help you to get simple calculations and visualizations of the statistics in VDJ recombination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IgorModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An IGoR model's encapsulates the Bayesian network probabilistic parameters of a V(D)J recombination process. \n",
    "IGoR is shipped with a set of default models.\n",
    "\n",
    "As an example lets load the recombination model for a human $\\beta$ T-cell receptor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading Parms filename from:  /home/olivares/.local/share/igor/models/human/tcr_beta/models/model_parms.txt\n",
      "Reading Marginals filename from:  /home/olivares/.local/share/igor/models/human/tcr_beta/models/model_marginals.txt\n"
     ]
    }
   ],
   "source": [
    "import pygor3 as p3\n",
    "mdl_hb = p3.get_default_IgorModel(\"human\", \"tcr_beta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (d_gene: 3, d_5_del: 21, d_3_del: 21)&gt;\n",
       "array([[[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 3.19224e-01,\n",
       "         2.89631e-01, 2.11165e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 6.86291e-08, ..., 1.38170e-01,\n",
       "         3.02534e-01, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 1.09220e-03, ..., 4.41026e-02,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        ...,\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00]],\n",
       "\n",
       "       [[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 2.11468e-03,\n",
       "         5.71094e-03, 7.95666e-02],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.50811e-02,\n",
       "         8.09057e-02, 7.76708e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 3.94418e-06, ..., 2.35577e-03,\n",
       "         5.88810e-02, 7.62736e-02],\n",
       "...\n",
       "        [0.00000e+00, 0.00000e+00, 1.25405e-01, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00]],\n",
       "\n",
       "       [[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.51398e-04,\n",
       "         1.89857e-02, 6.63111e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.29730e-02,\n",
       "         2.58015e-02, 9.39715e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.20776e-02,\n",
       "         1.25424e-01, 1.52907e-01],\n",
       "        ...,\n",
       "        [0.00000e+00, 0.00000e+00, 1.62236e-01, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00]]])\n",
       "Coordinates:\n",
       "  * d_gene        (d_gene) int64 0 1 2\n",
       "    lbl__d_gene   (d_gene) object &#x27; TRBD1*01&#x27; &#x27; TRBD2*01&#x27; &#x27; TRBD2*02&#x27;\n",
       "    seq__d_gene   (d_gene) object &#x27;GGGACAGGGGGC&#x27; ... &#x27;GGGACTAGCGGGAGGG&#x27;\n",
       "  * d_5_del       (d_5_del) int64 0 1 2 3 4 5 6 7 8 ... 13 14 15 16 17 18 19 20\n",
       "    lbl__d_5_del  (d_5_del) int64 -4 -3 -2 -1 0 1 2 3 ... 9 10 11 12 13 14 15 16\n",
       "  * d_3_del       (d_3_del) int64 0 1 2 3 4 5 6 7 8 ... 13 14 15 16 17 18 19 20\n",
       "    lbl__d_3_del  (d_3_del) int64 -4 -3 -2 -1 0 1 2 3 ... 9 10 11 12 13 14 15 16\n",
       "Attributes:\n",
       "    nickname:    d_3_del\n",
       "    event_type:  Deletion\n",
       "    seq_type:    D_gene\n",
       "    seq_side:    Three_prime\n",
       "    priority:    5\n",
       "    parents:     [&#x27;d_gene&#x27;, &#x27;d_5_del&#x27;]\n",
       "    childs:      []</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>d_gene</span>: 3</li><li><span class='xr-has-index'>d_5_del</span>: 21</li><li><span class='xr-has-index'>d_3_del</span>: 21</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8fbfbfed-21e1-4f4d-baec-0e718f7a12f8' class='xr-array-in' type='checkbox' checked><label for='section-8fbfbfed-21e1-4f4d-baec-0e718f7a12f8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 1.647e-08 0.004823 1.081e-09 ... 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 3.19224e-01,\n",
       "         2.89631e-01, 2.11165e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 6.86291e-08, ..., 1.38170e-01,\n",
       "         3.02534e-01, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 1.09220e-03, ..., 4.41026e-02,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        ...,\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00]],\n",
       "\n",
       "       [[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 2.11468e-03,\n",
       "         5.71094e-03, 7.95666e-02],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.50811e-02,\n",
       "         8.09057e-02, 7.76708e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 3.94418e-06, ..., 2.35577e-03,\n",
       "         5.88810e-02, 7.62736e-02],\n",
       "...\n",
       "        [0.00000e+00, 0.00000e+00, 1.25405e-01, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00]],\n",
       "\n",
       "       [[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.51398e-04,\n",
       "         1.89857e-02, 6.63111e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.29730e-02,\n",
       "         2.58015e-02, 9.39715e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.20776e-02,\n",
       "         1.25424e-01, 1.52907e-01],\n",
       "        ...,\n",
       "        [0.00000e+00, 0.00000e+00, 1.62236e-01, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00]]])</pre></div></div></li><li class='xr-section-item'><input id='section-9c16ed3c-107e-4e95-bbf9-49eb94305598' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c16ed3c-107e-4e95-bbf9-49eb94305598' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>d_gene</span></div><div class='xr-var-dims'>(d_gene)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-e21a8dee-0068-4c28-8fa0-d38a3345ca6c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e21a8dee-0068-4c28-8fa0-d38a3345ca6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c5bf00b-9e43-4435-aa3a-a1969cc00c28' class='xr-var-data-in' type='checkbox'><label for='data-6c5bf00b-9e43-4435-aa3a-a1969cc00c28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lbl__d_gene</span></div><div class='xr-var-dims'>(d_gene)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27; TRBD1*01&#x27; &#x27; TRBD2*01&#x27; &#x27; TRBD2*02&#x27;</div><input id='attrs-f1045410-c4e4-4baa-97b5-472dd98b47d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1045410-c4e4-4baa-97b5-472dd98b47d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2637686a-4698-4908-b49d-a39a9e1b93e1' class='xr-var-data-in' type='checkbox'><label for='data-2637686a-4698-4908-b49d-a39a9e1b93e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27; TRBD1*01&#x27;, &#x27; TRBD2*01&#x27;, &#x27; TRBD2*02&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seq__d_gene</span></div><div class='xr-var-dims'>(d_gene)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;GGGACAGGGGGC&#x27; ... &#x27;GGGACTAGCGGG...</div><input id='attrs-ed8fb216-ce12-4e27-9bd8-cbfb2918936d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed8fb216-ce12-4e27-9bd8-cbfb2918936d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe3c49ef-f9e6-4a3e-bcb5-42910bf582f7' class='xr-var-data-in' type='checkbox'><label for='data-fe3c49ef-f9e6-4a3e-bcb5-42910bf582f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;GGGACAGGGGGC&#x27;, &#x27;GGGACTAGCGGGGGGG&#x27;, &#x27;GGGACTAGCGGGAGGG&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>d_5_del</span></div><div class='xr-var-dims'>(d_5_del)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 15 16 17 18 19 20</div><input id='attrs-3f2c55c2-9849-434d-b0be-28dcba549a60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f2c55c2-9849-434d-b0be-28dcba549a60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7704af9-a504-4f61-b02c-0ca6fa457491' class='xr-var-data-in' type='checkbox'><label for='data-d7704af9-a504-4f61-b02c-0ca6fa457491' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lbl__d_5_del</span></div><div class='xr-var-dims'>(d_5_del)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4 -3 -2 -1 0 1 ... 12 13 14 15 16</div><input id='attrs-99e186c7-8cec-404c-b345-c79d36b15f00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99e186c7-8cec-404c-b345-c79d36b15f00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77fc2eb7-558c-42e3-8516-3eaef807d431' class='xr-var-data-in' type='checkbox'><label for='data-77fc2eb7-558c-42e3-8516-3eaef807d431' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-4, -3, -2, -1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "       13, 14, 15, 16])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>d_3_del</span></div><div class='xr-var-dims'>(d_3_del)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 15 16 17 18 19 20</div><input id='attrs-b2d40e54-0cf8-4b3a-b604-2e16f424c7fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2d40e54-0cf8-4b3a-b604-2e16f424c7fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b48a04be-5cbd-4cb0-abf4-869a6f259122' class='xr-var-data-in' type='checkbox'><label for='data-b48a04be-5cbd-4cb0-abf4-869a6f259122' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lbl__d_3_del</span></div><div class='xr-var-dims'>(d_3_del)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4 -3 -2 -1 0 1 ... 12 13 14 15 16</div><input id='attrs-cfb4349f-db80-4492-a807-7176bd9cdc6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfb4349f-db80-4492-a807-7176bd9cdc6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ad68428-084b-4ce4-8573-deb80250fda9' class='xr-var-data-in' type='checkbox'><label for='data-3ad68428-084b-4ce4-8573-deb80250fda9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-4, -3, -2, -1,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "       13, 14, 15, 16])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbc22f59-64e6-4d5a-984f-4079435eeee7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fbc22f59-64e6-4d5a-984f-4079435eeee7' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>nickname :</span></dt><dd>d_3_del</dd><dt><span>event_type :</span></dt><dd>Deletion</dd><dt><span>seq_type :</span></dt><dd>D_gene</dd><dt><span>seq_side :</span></dt><dd>Three_prime</dd><dt><span>priority :</span></dt><dd>5</dd><dt><span>parents :</span></dt><dd>[&#x27;d_gene&#x27;, &#x27;d_5_del&#x27;]</dd><dt><span>childs :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (d_gene: 3, d_5_del: 21, d_3_del: 21)>\n",
       "array([[[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 3.19224e-01,\n",
       "         2.89631e-01, 2.11165e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 6.86291e-08, ..., 1.38170e-01,\n",
       "         3.02534e-01, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 1.09220e-03, ..., 4.41026e-02,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        ...,\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00]],\n",
       "\n",
       "       [[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 2.11468e-03,\n",
       "         5.71094e-03, 7.95666e-02],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.50811e-02,\n",
       "         8.09057e-02, 7.76708e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 3.94418e-06, ..., 2.35577e-03,\n",
       "         5.88810e-02, 7.62736e-02],\n",
       "...\n",
       "        [0.00000e+00, 0.00000e+00, 1.25405e-01, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00]],\n",
       "\n",
       "       [[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.51398e-04,\n",
       "         1.89857e-02, 6.63111e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.29730e-02,\n",
       "         2.58015e-02, 9.39715e-01],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.20776e-02,\n",
       "         1.25424e-01, 1.52907e-01],\n",
       "        ...,\n",
       "        [0.00000e+00, 0.00000e+00, 1.62236e-01, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00],\n",
       "        [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "         0.00000e+00, 0.00000e+00]]])\n",
       "Coordinates:\n",
       "  * d_gene        (d_gene) int64 0 1 2\n",
       "    lbl__d_gene   (d_gene) object ' TRBD1*01' ' TRBD2*01' ' TRBD2*02'\n",
       "    seq__d_gene   (d_gene) object 'GGGACAGGGGGC' ... 'GGGACTAGCGGGAGGG'\n",
       "  * d_5_del       (d_5_del) int64 0 1 2 3 4 5 6 7 8 ... 13 14 15 16 17 18 19 20\n",
       "    lbl__d_5_del  (d_5_del) int64 -4 -3 -2 -1 0 1 2 3 ... 9 10 11 12 13 14 15 16\n",
       "  * d_3_del       (d_3_del) int64 0 1 2 3 4 5 6 7 8 ... 13 14 15 16 17 18 19 20\n",
       "    lbl__d_3_del  (d_3_del) int64 -4 -3 -2 -1 0 1 2 3 ... 9 10 11 12 13 14 15 16\n",
       "Attributes:\n",
       "    nickname:    d_3_del\n",
       "    event_type:  Deletion\n",
       "    seq_type:    D_gene\n",
       "    seq_side:    Three_prime\n",
       "    priority:    5\n",
       "    parents:     ['d_gene', 'd_5_del']\n",
       "    childs:      []"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb['d_3_del']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the composition of the Bayesina network we can use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nDh48SFFREbfddm18P0PYNx5D5AvEbAXv9XqpqalhxYoVvPrqqwBTCr0vdIT6kVewmwpjsoBmU7IJaf1UD7/Izal/Q5KSfk2ZMYGfP3+e/Px8Kisrx0VuCHtxYYj8BjATwfv9foQQWK1WCgsLpxR6SOuj3vsKNiUvpivkViWDgNZJvffHlKV8GUn66JDElQJftmwZQgjOnz9PfX09zc3NhrAXGYbIbzBTCf5HP/oR4XAYk8lEZmbmpEIXQqfB+xMkJExy7KOS2ORchiJn6Q0dJNt2JzBR4EVFRfh8Pvr6+qitreVf/uVfeOSRRwxhLzIMkS8ixgSfn5/Pm2++ic1mY2BggKamJpxOJ263m5dffhkYFbonWstwpAqHUhyX9kiShE3Opdn3H2RYbyca1vn+97/P8ePHSUpK4tixYwBkZWVRXl5Ofn7+tJk8DG4MhsgXIWNum1lZWWRlZaHrOoODg/T09NDZ2clf//Vf097ezubfGsEkxTeAoEm2EVJ76PEe57/+xascOXKEjIwM8vLyKC8vx+l0IkkSuq5TXV2Nz+dbVM5ABobIFyWVlZVEIhFaW1sBxkXkdrtZtWoVZrMZJSnIYOQsDqUg7u0xS8l0BT+gqKiInJwcfD4fg4ODDA8PMzw8jKZpmM1mfD4fNTU1k66yG9w4DJEvQlwuF1u3bqWoqIjs7GzS0tJIT08nOTl5PJPr6+/8BD1ngFvWX5taJ9ZYZDcB0cZfffV/oUijeb2i0ShDQ0MMDg4yMDBAR0cH7e3thMPh61gzWGgMkS9Cnn/++euWiejDMMvEkXNlbGU9qHbjNI/mnDObzePTCYPFjSHyBOHQoUNUVlbicDjIy8sjrA1iInPSsh0tQ+x77yLRiEbpTTmcPdbG6pty2PbJNfhGwhzcXUdHyzAms0xpeQ633VWCLEv8+w+OEPRHWLsul7rqHlxuG488XYHdaWGwP8RP3vz/GOlNwmq1snnzZu64444F/hYM5kJsYscaxJXu7m4++OADnE4nmzdvprGxEYE6Ye96DFXV2fXmBYL+KLfesYyeDs+Ez3e/dYH25iEqNhZQvDKD00dbqT7VMeH+SFijaHk6fd1eas52ouuC3b9poL+vny1btlBQUMD777/PxYsX4/7sBvPHEHkC0NzcDMDmzZu59dZbueWWW6YsOzwQIOCPULIqg4qNBWzcUjz+WTSi0dU+TCSiceJgM9VnOgFob/koBZYkwV0PlnLThnwAvJ4QwwMBPENhfL4ge/bsGXd2aWxsjPGTGsQDY7ieoMhSEkKos75PCMjIcnLHvSvGr5ktH51SM5kUTCYZSZbGywNISCxfUcKDd//ueFljqywxMESeABQXFwNw5MgRhBCcPn2aJCUdFd81Zd3pduwOC831/VSd7KD+Qs/4Z2aLQl6hm672YbrahnG4kuhu9+BOt5Odlzxl/e50O8mpVno7/HR1dWE2m2lsbGTt2rXGwlsCYAzXE4CcnBweeOABfD4fR48epbi4GIucMmlZk0nmvsfWkmQzc+ZoK+lZo71t0uUooNsfXUtJaSbnT3VweG8DnuEgWblTCxwASef+J1dQXLSG/fv3s3fvXiKRiCHwBGFBorWO8b3vfY/Pf/7zpKRM/gM1mDlR3c/Bjv8LC5kTAjxYkhRaGwYBUEwy54630dY8xCNPV7Bs5bWnyWZCUOvGbalgTcofx6TtH3cOHTqEz+fjgQceWJD6jOF6gmKWHRx728TFhl2Y5Y964sd/Zz2+kRAnDrWgRjRcKVa23rdqzgKH0cgxuTbDJz1RMUSewDz1yPOc6v477ErueIin9EwH+UVuKjbGxt01og1jU3JINq+KiT2DhccQeQKzsmgTcupTdAd34TAti7l9XWiExSAVrq9NuidvkBjEVeQ1NTUMDg6O/7uxsZHDhw+Pb72YzWY2bdqELBs/oLlS5PgtBsOnCGtDJCmpMbMrhCCgtZJve4gUy+qY2f04MjQ0NO5bAFBVVUUwGMRu/yhMV2lpadwWMuMq8srKSt555x3cbjcwKvqBgQEsFguBQICCggI2btwYzyYseUyynTUpf8Le+j8jNTmKyzb/H4oQgq7B8yQnraQo46kYtPLjzdDQEP/wD/8wHuiyt7cXVVU5d+4cMBrq62/+5m/iJvK4dqGPPPIIbreboqIiiouLSU1NHf9/m83GU089haIsTKqYpczJQy20Hb4J5DAhrXdetoTQ8Gst2ORlHN7hortrIEat/PhSUlLCpk2bsNvtFBcXk5ubS3Z2NsXFxbjdbtasWUNFRUXc6o+ryAsKCrj99tvp7u6ecN3n85GWlsbtt98ez+o/Fnz44YdUVVXx2U+/wIbMb5KkZOBTm9BFZNa2IroHn9ZCjm0bW0v+lscf/TQ/+9nP6OjouP7NBlMiSRJPPfXUeNy+KxkaGuKZZ56J65Q17pPhxx57jEgkgq7r49f6+vp4+umnMZvjG8B/qTB2XvtqxgT+uc99DqfTid2Uz82pX2eZ41OE9F58ajNR3XvND+tKhNAJaX341GYkSaHC/descH0Wk2yjtLSUJ554YlKhCyGorq5G07SYP+9SZMWKFZSXl9PX1zd+bXh4mIKCAtatWxfXuuMu8qt7c6MXnz1vv/02X//612loaBi/drXAx5AlMwWOR9mY/hLLnb+PJJkIaG341VZ8ajM+tQWf2oxfbcGvthHQ2nCZV1Dm/nM2pL1IimXthLonE7oQgtdff51vf/vb1NfXL8yXkOBM1psvRC8OC7SF9thjj3H06NHxWGVf/OIXjV58hkSjUQ4fPozNZuPFF1/kL//yL2lvb59U4Fdilp3k2u8lx3YPUeElqHYS0vrQRAgJBZNsx6bkYjPljEd7mYorhf47v/M7nDhxgh07dmCxWDh27BirVxur7zNhrDcfO4NQVlYW914cFsh3faw3HxwcxO12G734LKivrycUCpGZmYndbudP//RP2bNnz7QCvxJJkrDIyaRY1pBtu5M8+/3k2reRad2M07zsugIfo7S0lMcff5yvfe1rvPrqq+MLSIcOHUJVZ38a7uPIWG8eDAbx+XwL0ovDAh5Qeeyxx5AkiSeeeMLoxWfB8ePHMZlGB1zDw8P4fD7a2tro6em5zp2xRQjBhQsXxoM3BgIBLBYLoVBowjTCYHpWrFhBSUkJGRkZC9KLwwIcUNGFoD+kMRjWeef999l2zz1kOK1k20yY5Y9H8H1dqIS0HqL6CDoaMiYscipWJXNaT7JoNMqXvvQlUlNT6ezspLe3l/Xr1xMIBAgEAvzlX/4lK1asmPL+WDE2B9+xYwfFxcV4PB5qa2upqKjA6/Vy11138eyzz05rQxNhgmo3qvAj0JExY1UyscipH5skDHpARe0PUn+hjmAwQMWGdZjSrciO+HZ6cRF5RBOcHwqzpytA3UiUqC5GQw6O/S3F6P8XOUzclW1nU6YVp3lpeb1FdT8D4RP0hvbji7YA+uU84Yx+D0IgSyacplXk2O4hNenma4bOFy5c4Dvf+Q5CiHGBj/WejY2N5OTk8Hd/93cTPKfiwalTp3jxxRdJSUkhOzsbRVEYGBigtraW1atXo2ka3//+98dHHGOEtUH6QofoCx8moHYiIcHl/47+6ASKZCPFspYc2zaSzauRr5NmOZEQQqD1Bgme7CVUPYQ2HL5q7CyBLpBdZpLWpGLflIUpL/Zx9GMqck0X7O0O8OsWH/6ojsssk2KRMU3SY+tC4I3qDEd0FEni3lwbTxa5cCS42FU9SHvgDbqCu9BFFIucillyTpojXBcqUX2EqBhBkewUOh4j13Y/sjQqlldffZXXXnsNXddZs2YNHo8HXddxOp1s3bqVTZs2sXz58rg/UyQS4fz58xw6dIjTp0+j6zo222haprq6OtLS0vjmN785vgAX1gZp8f+SvtARJCQsctqUSSB0ESGsD6GJIElyOsXO3yY9aVPC9+7RLj/et5uJNHlBkVBSLEhJyjXPJYRARHR0TxihCsx5DlyPFmNZ5opZW2Im8q6AysuXPFzyRMi1K9hMMxerqgs6gxrJZpnnV6dQ5p7ZYtBiwxO5SN3IvxLWh7AruRPOeV8PTYQIqF24zCWsSv4CZpHF008/TXd3N6WlpaSmprJ161Y2btxIcXHxDfMUDAaDXLhwYVzwQ0ND1NXV8YUvfIE/+ZM/oT90hAbfTxBo2OTcWR1siepeQnofGUmbWO58Fovijt+DxAkR1fFXduHb3YaUpKCkJs34hSWEQPdE0P1RHHfm4dhWgJw0/79zTER+0RPhu+eHkCXIsspzfgt7IqNz92dXJrM9155Qb/Pu4Ic0eF/BIqXO+ccphCCs96ETwTLwOP/Pl/8Xn/nMZ9i6desNFfZUjAn+17/+NXX1l/j2vz5OZ/A9bPLcc6QLIQjonZglB+XuF6bNj77Y0EMqnv+oI3xxGFO2HWmOo1Kh6ag9AcyFLlKfXT3vOfu8RV7nifB3VYOkmGWSLfMfakc0QZtf5XOrktme55i3vYWgJ7ifOu8PsSsFM96Smo6o7iOsD3CT+4VrnFMWI0IImnw/ozP4Hk6leNKpyWwJaf1IkkSF+2vYTDkxaGV80SMaw/9+kUjDCKYYdVBqTwBTtp3U59Yi2+fu0jIvVQ6HNV6qHoqZwAEsikShw8Sr9V4ueWbvf73QeKON1HtfiZnAYdSRJUlO44LnHwhri/+ASG/oYEwFDqP50QUatZ7vo83BD3+h8X3QRqTeEzOBA5iy7US7/Iy83jSta/L1mLMyhRD8e+MIEV3ETOBjWBSJVIvEv170EFT1699wg9BEmLqRH2KWkmMm8DHMsguBToP3VYRYvN9BSOunyfdv2OS8mAl8DKucRUDroN3/VkztxppI0wiBg12YcmI/xTTl2Amd7SdUPXj9wlMwZ3VWDUU41hcizx6feaI7SWEgrPFuuz8u9mNBV2AnQa2LJCUtLvZtci6DkdMMhE/FxX4saPb+HIHAJNviYt+uFNAeeJOAujhPwglVx/OrBuRkC5IS+50hSZJQ0q14f9OEHpqbZ+GcW/VOu48U89wX2WZCrk3h/Q4/oUXYm2siQkfgXWxy/OaLkiSRJKXSEXh7XsO1eBHUehmInIrrdyBLJmRJoTu4L251zIdI4wjaYAgl2RK3OmS7CT2oErowdP3Ck90/l5s6Ayq1w1HSkuK7p21RJMI6nBlcfOlwhyPnUYUfRbbGtR6z7ManNuFXW69b1uud/lhprOkLHkRCinv8N6ucTU9oH1F98Y3q/Ae75rUoNlPkZDOBDzvn9PedU+uqh8IgMaEX7750nqZjB4gEA5iTrGSUrGLVlvuQZuCAHw0GOPiTf8KdV8j6x39nwmcuk8Sh3iCbs0aHgz/+8Y9pbm7mhRdeiLun12QcOnSIDz74gPXbw+Suubb+yl111NX0Eo2oOFxJrNtUOJ5X7Ho01PbxwevVbNxSzKatxcDodywhMxSpGk8bDPCNb3yDrKwsvvjFL9LT08Pbb7/NsWPH+Na3vkVm5uTZTmPF2Hew6u4G1lRcu8W15+1aLp6fGCjk8//31vEED9PR2+XlV6+eHM/CCqPHZ4VQ8UbrSUv6yN/7pZdeIhAI8NWvfnWeTzRzhoeHeemllygtLeUzT36aSIMHU85Hv4Oq9lreObtnwj1P3fowq3JKZmT/e+//EJvFyh/dO9FNWHaYUbsDaAMhTBk2fvOb33DmzBmef/558vKm32ack8hrPRGcponDdMVkoaBiI2arla4L5+isPkNKTgHZq8rmUsU4LrNMgzeKEGLR7JsLIfCrrZjlwms+c6VYue3O0T/oyUPNHNhZR/HKDJzJc1+YM0lORiIXwPHohOsjIyO88sorHDhwAJPJhKqqCxbEQRMRosKDIk3ucWe1mbnz/o/COJvm68koyfjVlgkivxHY7XaeeeYZXC4Xal/wms5ujPvKt2K3jHZMOSnzf+mO1aH1j4p8Nkwr8l/+8pdcuHCBr3zlKzgcDnbu3MnBgwdp3/g0mTkT52GZy0vRohHUSARPdwee7o5pRekb6OXC7rcI+73klU3M0qmGQ9Qf3stASwMIgchdTs9NnybHFZ/FnetxdW5wnQhR4UXmWuGu21RIOKQSCka5cLaTgH/67Z+6mh4O7WlAViSWrZiYAGGw38/BXfX0dA0jW07w29u38IlPfIKenh5Onz5Nb28vN910EwUFBSiKQltbW0yf+0qu/g5U3QtIU/6NzRaFZSvSMc1g3aa9eTSfuhodzad+JWP51Fubu7BZenjwDjvbtm27YRF+A4EAO3bsoLS0lCdXbYcplouKMwpx25NR5OkXpkeCXt44vZM+7wDri8onfKbpGvsvHqWmsw5VUykwZfJ4XSo5a2YXlXdakVdUVFBdXU1tbS233norNTU1ZGRk0JycwWS7Zk3HDtBedRKArJVryFw+dTCB2r3vEBgepOS2Oxnp6ZzwWf2hPfTU1VBw80YkSeL88SPs3J3Bs09+clYPFwvGcoNnZWVx++23s2/fvsvx06b+8b7x8zP09/qQJNiybeWUvXjAF2HfOxdJspm49Y5lVJ38aAVZ1wXv/+d5olGNW24vpqO7mf/4xc94/fXXaWpqorGxkYyMDFwuF37/6Fw1EAjQ1dUV8968t7eXHTt2kJGRwfLlyzl8+DBe3xC6NvWCqG8kxI++dwCTIrNmXS5b71s56felqjq737qAGtW47a7lNNRODES5+60L9Pd4qdhYSDCgUVlZicvlWhQxCbSRCJIy+W/g5f3/gSRJLEvP55F123EmTT613FVdScdQN3es3EAgEiKiRrFZRtd5Dtef4ljjGdYXleNIsnO0+gTv7n2f5x6bXZCOaUW+atUqbDYbNTU15ObmMjQ0xD33buPEFG/wvPJbcOcvo+P8KfoaL5G7ppXUguJryqnhEL7+XlJy8ilafztBzxD9TXXjnw+0NCB0nbYzx4DR4XFLU9OsHixWXJkbfMOGDXg8Hnbv67h8lmpy7nqglBFPkGMHmjh1pJWVa7OwOa5dfe3pHEHVdNZV5FK2Pg9Jktj33kVgNM/48FAQgGMHmojqwzScO0d7Wye5ubmEw2H6+/upq/voexsaGmLXrl0xzzXX2NhIXV0ddrudtrY2fD4fLa0tlPom//nkL3OTV+QmKcnE2eNtnD/VQVaui9U3XbsKP5ZPvbQsm4pb80lNt/PmL84CH+VTFwJOHmwDBHn2chobGxeFyJnkXZpqT+G+8q2k2JKp723mbGsN+y8e5ZGb753URNtgJy6rk7tWb0bTNc61XRj/rKG3BYAzraMx24Wm0tTVMutmTityRVEoLy/n1KlTnDx5EkmSWHdzBcr5MLoQyFcJ3e5Ow+5OQzGbGWpvpr+5blKRzwSL3cHabaM9d2dQ476Kuefyij0KY4clJyM7P5ns/GSGBwKcONRCZ5uHFWvmNi8rKknj5k0FBLRObvnDv2V4aIQDBw7wk5/8hOTkZDZs2DD+wm1ra+PZZ58lJye2W1pHjhzBarXy+OOPs2HDBvbs2cP7ewIkp3RNWv5KMUsSdLV7GOoPzKnusXzqm+7ORZYtlLs/S1LS4jjAJCXJCH3i76AgLZeCtFwACtPyONtaw4BvHo4ssszTGx9BlmS0wRCWNe5Z27juwltFRQUnTpzg1KlTFBUVkZqaSp6jH7+q4zJ/JPILu9/CkZaB2Wan68Lom9iemjF5pUlWnBnZjPR20Xrm6DXD9fRlK+i+eJ7+lnocqZkMdXTRZeuDtStn/YDzZbLc4CbJimA00umV20fhkMquN2ooujy3vnCuC0mC1PTJh2rZecmYFJnaqi6cyUkThuvudDvuVBtd7R6yC62YLDZONp5m7dq1fPnLX6alpYX29naam5ux2+1xTSM86XcwzQGU1392hqIVadhsZs6f6hh/1sm4Op/6lcP1K/Opd7T1kpNWztmWs2RkZJCfP7Mdi3hiSr92+/SD8/uxmi2kOtw09o5ue+a5s6e0UZSez6XuRvZfPEIgEpqwRbYiaxndnl6q2y+yLKOAnt4uIlm9rGfyUcFUXHf1oqioCLfbjRCCm2++GYDVyWZ80YnzMcVsoe3scS4d2Ekk4Kfols3kla2f0u6aex7C7k6j7cwxrK6Jw8uVn9hG7tqb6Wu4yIWDuzENdbOqpHhWDxYrJssNLkkKNiULVUzct5UViUhE5dj+Ro7sa8BqM7Ptk2tJy5z8oI3daeGeR1YjBJw+2jpBCLIs8eBTN5GTn8yJww2cPxQYzwkuSRKpqals27aNb3zjG6xatYrm5maCweCCfQcmyQnIk7rcutNsnD/ZwYEP6giHVT6xbQUlpVO88E0y2x8dzad++kjLeD71Ma7Mp35iXx+Dg4M3VOBjIpQkCVOW7Zppa4YrjUvdTXxwfj9tg52sKyrjztKppxbby7aQn5rD6ZZqTLKCWfmo371j5QZuX34LbYNd7Ko+QNNQO8vLZp94ctan0KLRKEc6vfzvS8Msc4w2SDaZUaaI26ZFI+hXBfpTzBZk08x27zr8Knfl2Hh25eLKaX6x/6d0+ndiUz76wVmSFJQpXBtDwShXf9NWm2lG24I+tZlVri+QZdsy6edCCJqbm9m/fz9PP/30jAI8xoITXf+VQKQbyxVuvdN9B+GQin7V8Ha68lfiU1u4OfXruMzxD5IxFcFgkJMnT7Jr1y42b97MnZ/YSu+Lp1BSzEjmUYGalcl1ENWiRLWJOpiu/NUITUftC5L11xtn7Xwz633ygwcPsnPPXi60+2kxSSiSRPHGLRRvnPwHWFe5i+6L5ydcW3Pvw+Ssvn5aGCEEUQFbsxfe6eV67Hu9h6O1Z7BITaMTT0Zzg+cXuSctv+MnJ/F6QhOu/Zc/2kxyyvQec7pQkSUTqdPsD0uSRElJCSUlM3O4iBWH39I4fekwFtk9fm267+C9/zxPZ9vwhGvTlR8jqo9gU7Jxmorn1d750tLSwp49eygoKCAcDvM/vvv3hFs8RFu9yE4zW1ZtYmvppknvPdpwhoN1xydcm6781WiDYWw3Z8zJu27WPfnQ0BBDQ0O82eqlsjdEnk3BmuzGluyetLx/qJ+I3zfhmj01gyTH9Xub4bCGy6LwzVvSF40jzBgdHR2c7f2fhNV+LJeziWZkO7HaJn8zd7V70K7ywc8pSMF0nQg6fq2d7KQ7WZH8+7FpeAxp62jiWOfXMcvO8VN4030HvV1eIuGJvdl05cfwqU2scH2eHNvdsWl4DBjTgToYYuiVCyiZNlKdKbjtk689DAdGGA6MTLjmtidPWf5KhBConQHS/qgcS9Hsw0LNOWhEf0jjqyf7SbVIswr1NFN0IWj2qfz5TancnBZf//C54onUUjX833EqRTE/ZgmjR1lDeh+3pP63RRs4oSuwiwbfqziVkri8iCP6MBIy69P+Nm4n3eaL51cNhE73Y8qNz4hT7QtiWebC/bk1c/qO56zODKvC761w0RXU4nIooiOgcVe2jYrUxbFdMhkpljXk2u4joMX+GORofvAOShyfWbQCB8i23UuyuZSQPr9sqpMhhEZYG2RV8vOLVuAAroeKkGwKuj8ac9t6WAMEyU8un/NLdF5d8NZsGxvSrbT5Yyv03qBGqkXhM8uTF90w/WqWOZ7GqmQR0mKb7CCgtZNqqSDbNrvtkoVGlhRWuf4PQCeie2JmVwgdn9ZCvuMRUixrYmY3HsgOM8mfWoE2HL4sytggVB2tL0jyE8tR5tHZzUvksiTxh6tTWJVspjVGQu8JalgUib+oSE2IWOwm2U6Z+89RJDtBrfv6N1yHscMvTtMySpO/mBBxyG2mHMrcf4EqfES0uZ15vpLRHOnNZFnvZJnjUzFoYfyxlqaS/PQKtL7gnIM7XImIaES7/DgfLsK6fvLtx5kSk2itIVXnh5c8HO8PkWNTsM9hjh7VBR1+lXyHmT8td5M5g2OJi4mwNsAFzz/iV1uwK/mzCsc8hqoHCWqdpCVtoDT5D+cc8fRG4Y02Uet5iajuw67kz+mceUT3ENEHyLM9xDLnbyfES+5KgucH8LxWj6TIKOkzD8d8JepgCBHWSH6sBNttWfMezcYs7roQgiN9IX5SP0JY08m2KlhnIHZVF/QENaK64IllTj5Z4MQyhdP/YkcXUToC79Hm/zWSpGCVs2Ykdk2ECWo9yJKJ5c5nybJuiXsghngR1X20+F6jJ/QhimTDKmfN6FlU3U9I78Miu1mV/Dxuy/yOKN9I1IEQI79uJNLgQXaakVMs1xWqEALdG0UfiWAqcJDy9ErMObF5ycc8TdJQWONAd4D3OwME1NH0SC6zhN0kI49mhSGsjWZPUQUoEtyZbWNbrp1C59JIhBhQO+kO7qYntB8h1Mupgl0okhUJGYGOpgeI4gOhI0s28mz3k227iyRlMfnozw0hBN5oHZ3BnQyGT17OfZaEWXYiS0lISAihoQo/qvAhgCQ5lXz7J8mwbsYsJ0Yo7ukQuiB8cYjA/k4iraNbyFKSgmw3IV3u/ISmowdURGh0Hm/OtWO/Mw9redp4mVgQt4SHEU1waSRCszfCBU+UzoBKRBeYZIkMi8Jqt4XlLjNrUiwJMfeeC1Hdz0i0Fm+0iZFoLWGt77JzixmbkkuKeS1OczHJltUxj/a6WAhrg4xEL+GN1jMSvUREH0YIHUVKwmEuItm8BqepGJd5ZcINzWfCWD60SLuPSNMIapsPPTA6Z5esCuYCJ5aSZMwFzpiGc76SuGc1NTAwuLEszS7UwMBgHEPkBgZLnMTapzKYFULoqMMNRIebiA5eIDrSilBDIMkoFhem1FLMqSuxpJaiOOJ3Ht3gxmLMyZcguhoi1HmYYOO7aIE+kCQksx3J/FGedKFFEFEvQguBkLBkVmAveRBzetmi9zI0mB2GyJcYkcFLjJz9V/RgH7ItA9ly/VNLQujowQH0iBdr7m04y34PxeqOf2MNFgRD5EsEIXT8db8hUPc6ktWNYp1d2N5RGwLN14EkW0i59UtY0he3z7jBzDBEvgQQQsdX/e8EW3aiJC9Dkue31KJHvOihQVI2foWkzJti1EqDG4Wxur4E8Ne/SaBlJ0pKybwFDiBbXMjWdEZOfI+oZ/YhgA0WF4bIE5zocBOBul9jSl4WU3932eIEs52Rs/+K0KbPAmOwuDFEnsAILcLIuR8iWVwx6cGvRrGlo/na8Te8E3PbBguHIfIEJtx7Fs3XiWKL36EWxVlAsPEt9MjiSxtsMDMMkScoQgiCje8iJcU3VLWkmBG6Rrj7+PULGyxKDJEnKJq/m+hIE3KSO+51ybY0As0fxL0eg/hgiDxBUb1tICbPjf3dVw/wg9eOTHlvVV03L768j2NVM0t1LJmdaP5u9Ojc8pkZ3FgMkSco6lADzDD7xtUUZqfw+L1lrCic2Vx+7EWi+TqvU9JgMWIcUEkgfvnLX3LhwgW+8pWvEB1pYf/ZPo7XXuJ3P7meD0800jvo55Y1ede109bj4Z39tdx72wrS3XZ+8NoR/MEI60pzqW7owe2y8fT9FTjtFipPNXO6toOgd4DME//CA08+R0XF9bPfGCwejJ48gaioqEDXdWpraxF6mIutw6S77Rw/305Hzwi3luUTjqhEo7MPC6yqOuGoxvLCdLr7vZy92EkwHOXg6WYyUh08eFshFWtK4hJj3yC+GCJPIFatWoXNZqOmpobufj8eb4jyldm0dg3jciRx98bl3H/HKuZyiEyS4MFPlLJh7WgCR48vhMWk4LBZGBwO0N7vx+VysXbt2hg/lUG8MUSeQCiKQnl5OU1NTZxr9ILQKFs+de7r2WAyKZhMMrI8+oYQAhRF5rnf2sgd65cBEu/uPcabb74Zk/oMFg5D5AnG2JD9fEuQ/HQLKS4ry/LceP1hPjzRyM7DddekSJ4r4YjK3mMNSJJETpoNiz0Vr9cbG+MGC4Yh8gSjqKgIt9uNZHFRVjKaF3z77SvJz07m9IVOTIp83UypM0WWJUb8YT48VseeU92kZxWwbdu2mNg2WDiMo6YJitAi9O/5MrLFhWSaPOurpo0upl2JLElYk2a3qaJ5O7AW3oWr/Nk5t9fgxmFsoSUokmLBVvwAgfrXMSUXTVqmo3eEn79zZsK1wlw3v/vI+hnXI4RA6FFshffMvbEGNxRD5AmMLX8LwYY3EVoYSbk2OUNmmoPffmjdhGtJs+zF9UAvlrQ1KK6CebXV4MZhDNcTnEDT+/hqfjoaMCLGARiFFkb395J653/D5MyNqW2DhcNYeEtwbMu2Y0otRffPP23ylQiho3nbcaz5jCHwBMcQeYIjySZSbvkiksWJ5u+JiU0hdFRPM9bCe7AtM1bTEx1juL5E0AJ9DB//e7RAP4prbrnBYTRmu+brwF60DWf5s3GJOGOwsBgiX0LoES++2l8QatuPbEtHnkVACSF0dH8PQui4yv4L1sK7EjZHusFEDJEvMYQQRPrO4bvwczR/NyhJKLa0SVffhRCIaAA9NAAILFkbcK39DIo9c+EbbhA3DJEvUYTQiQ7VE2r7kEh/FXrEC5d7ZknA6B9dYHJkY8ndjC1/C4o940Y22SBOGCL/GCCEQERG0AK9o+GVJRnJZENx5CBP4S1nsHQwRG5gsMQxVlYMDJY4xv7IEmdsqK76OtECfQg1CLKCbHKgOHMxOXKRTNcuyhksHQyRL1H0iJdQ13GCTe+hBftHLwp9NASMACQYHcgJLBll2Isv5yaXlRvXaIO4YMzJlxhCixJoeg9//esgNGRrOpLZMaVf+1huchENINvSSK74AywZZQvcaoN4Yoh8CaF62xk5+79RR9pQnPlIswzZrIc96MFBbMu24Vj9aWSzLU4tNVhIDJEvEaJDdQwf+x8gm+flzCJ0Dc3XhillJe6Nfzqa3dQgoTFW15cA0eEmho99B8nsnLe3miQrKK5lqJ4mPCdfQldDMWqlwY3CEHmCo0cDeE7/E5jsyEnJMbEpSRKKq4DoUD3+S7+KiU2DG4ch8gTHf+lX6KFBFGtqTO2OCr2QYNP7RAYvxtS2wcJiiDyBUUdaCbbuQXHGJzSTJCtI1jS8Va8ghB6XOgzijyHyBCbYtg9JNsV1b1uxutH8PUSH6uJWh0F8MUSeoOgRP6G2A8j22GRQmQ7JZCXYvCvu9RjEB0PkCca3v/1tXnrpJdSRZoTQrhu5ZccHVbz48j483hAeb4gXX97Hjg+qZlWnbMsk3HsGoU+fSHHfvn184xvfoKamZlb2DeKL4daaoKgjrbO+x2Y18/i9ZTjtllndJ8kKCA0t0GsEdUxADJEvEq7MPe5wONi5cycHDx7kueeeY9euXfT09LBx48bx8tGhS0hmxzV2VFXn3YMXqWvppyA7haj6Ue8bDEV5Y28NKwrTKcxx8/b+Ws7XdbOhLJ+6llH/9sfuWTvhs88+cSu5GS5e+sVZ0k78T/7863+Ppmns3buXqqoq/H4/+fn5PPfcc/H/kgzmhDFcXyRcmXscoKamhszMTA4fPkxbWxu333474XCYSCQCgBYeQZKvdVs9XdtJTX0PJfmpLMt109k7ct26ewa8rF+Th9cfpvJ085TlhB4FoLKyksrKSrKysnjkkUfIzTV698WM0ZMvEq7MPZ6bm8vQ0BDbt2/n4MGDJCcns337djRN49SpU5fvmNwbua17GIB7Nq0gNdlGfesA7T2eaevecksxJflpHDrTwoh3Cg83MR4ziosXLyJJEs888wxJScYx1cWO0ZMvEq7MPX7y5EkkSaKiomLK8pLJhhDTL4TNFGvS6IhAlqXxV8fYoTVdF6iqjqoJMI6hJiSGyBcRY0P2U6dOjacoLikpYWRkhN27d/POO+8wdp7I7F6JiPqusVGU6wZg3/EGjp5rpbPv+sP1yUhxjsZ+q67v5sMTjQgBsnn0sMrq1asRQrBjxw5Onz7Ne++9N6c6DBYGY7i+iBgT9vDwMDfffDMADz30ED6fj+PHj7Nu3TrM5tFe15xSPDqEvor1q/Po6vdS39pPJKqRm+mio2f2Ql+3Oo+GtgFqGnopX5mFySSP+8Zv3bqVaDRKVVUVTU1N5Ofnz/2hDeKOcdQ0QYn4+uj84M9QkgvGkyDMJff4TNCCg5gc2aTe8bWY2zaIP0ZPnqB09vv5wXt9aMEL48Po2eYenykiMoKt/LMxt2uwMBg9eYISDAZpqTnAyJkfoLgKkJBISjKRm+GKaT16NICI+snY9j0kZXZONAaLA6MnT1BsNhurN9yHR68iOngRxZkX8zqEEOj+blzrnjcEnsAYq+sJjCTJuMp/HwA9Goy5fd3fiSVrHdb8T8TctsHCYYg8wVHsGbgq/gDN34XQwjGzqwX6kUwOXDd9zshumuAYf70lgDXvdpIrPo/q7UCPBuZtT/P3IEky7ttfQLGlx6CFBjcSY+FtCRHqPoH33I9A15CdeVPGWp8KoUVQfR2Yk4tJueWPURxZcWqpwUJiiHyJoQUH8Va/SqT3NChWFHvWdSPH6GoIPdCLhISj9ClsJQ9e95y6QeJgiHwJIoRAHW4g2LKHUNeRsatIJvuo/7kA9AhCDV1OY2zHXvIQ1vw7jOH5EsQQ+RJHD4+gettRve1EhxsQWggJCSnJjTl1JSZnPiZXwayzrRgkDobIDQyWOMbquoHBEsdYXfmYIIRAqEGEFkGSJCSTzfBi+5hgiHwJo0e8hLtPEuk/T3SoDj0ywuXE5IDA5MjDlFZKUtYtWDLKjBX1JYoxJ1+CqP4eAo1vE2o/CEJHMjtGs5MqSeN750IIRNSPHvGCFkFOSsa+/GFshfcimYyQTksJQ+RLCKFrBNv346v5KRIgO3Jm3Dvr0QB6oBfFmUvyzc9jdpfEt7EGC4Yh8iWCroYYOfMDIj2nUJx5SCbrnOxowQFExIuz/LPYiu6dtdecweLDEPkSQKhhPCdfIjJ4EcVVOG9hCi2M5u3AWfZ72EseiFErDW4UxhZagiOEwHv+J0QGamMicABJSUJxFeCr+XdCPWfm30iDG4oh8gQn3HuGYEclSnJsBD6GpFiQ7Zn4ql4eXZwzSFgMkScwesSPr+rl0UMocTjzLVtc6NEAvgu/iLltg4XDEHkCE+o+hh7xjW6PxQnFlU+o4yBacCBudRjEF0PkCYoQOsHGd5FtGXGtR5JkkCRCHYfjWo9B/DBEnqCoI61owT5ki5NjVW28+PI+quq6+cFrR/juqwemvK+qrpsXX97Hsaq2Gdcl2zIJtuzC2IhJTAw/xgRF9bZPen375pVo2tRiLMxO4fF7y8hKm/kQXzJZ0YJ9iMgIUlLKrNtqcGMxRJ6gVO59lwMfXsCZ3EPOFbHWdx+pJxCKsqYkc9L72no8vLO/lntvW0G6284PXjuCPxhhXWku1Q09uF02nr6/AqfdQuWpZk7XdhCOaDiUAA8797HhzicW6hENYoQxXE9Auru72bXvCE6HnY3lBbR0Ds3LnqrqhKMaywvT6e73cvZiJ8FwlIOnm8lIdfDQllLKStxoobklTzS4sRgiT0Cam5sBjVvL8li/Jo+K0tx52ZMkePATpWxYO5q40OMLYTEpOGwWBocDtPd4cNksrFlZOP/GGyw4hsgTFmXSrKZzwWRSRrOWymMn1EBRZJ77rY3csX4ZADuPt/H2LmOFPRExRJ6AFBcXI1ucnKhu5UxtJ1WXumJeRziisvdYA5IkkZPhQlEU/JGYV2OwABgiT0BycnJ44MGH8PuDnKzpoDDXPf6ZEIJYeLfKssSIP8yHxxvZdaSOVKeZ7Q88Nn/DBguOcQotQYkONzF06JuYUkaH07ouaGgf4PU91aS7HXz2sQ2Eo9qEe+aav1xXg6CGSd/2PePoaQJibKElKKaUZci29HG31khU4z93nifFZeXeTSvo6B3h5++cmXDPXPOX64E+7KueNASeoBg9eQITaNmDr/rfxnvzKwmGo/T0+yZcm0v+ciF0NG8b6Xf/DxR7fF1oDeKD0ZMnMNbc2/Bf+tWkh1RsSWaK81PnXYfm68Sad4ch8ATGWHhLYGSLE1fF59ECvQihx9y+HvEhKUk413wm5rYNFg5D5AlOUvYGrPl3oHnbYnqARGhR9EAvroo/QE5Kjpldg4XHEHmCI0kSrpuew5y2Gs3bGhOhCy2C6m3FWfa7WHM2xKCVBjcSY+FtiaBHg4yc+RcivWdRnPlzjp2uhQYRIQ/Osv+Crfh+Y0V9CWCIfAkhdI1gyx78tf8Bknw57vr0ucnH0NUgur8HxZ5F8rrnMaeujHNrDRYKQ+RLENXXTaDhTUKdhwEdyexCtrgm5D4bzY0WQES8CDWEZHHhKHkI67LtyHOM2W6wODFEvoTRwx5C3SeI9FURHa5HRANIgAAQAsWRhTltNUlZG7BklBs5ypcohsg/JozmPvMhtChczmpq9NgfDwyRGxgscYwtNAODJY7h1rrE0SN+VF876kgbqqcZoQYACdmaijl1JYojF5Mr38hNvoQxhutLECEEqqeJYOteQp2HxiPISIoVLm+pCS0CWmR0fm524Fj+MEl5d6BY5+/vbrC4MES+xNBCw/iq/41w9wkkUxKyPeu6vbQeDaIH+0CSca7+FLZl242efQlhiHwJEeo5hffsD0GoyI68WXurCS2C5uvA5F5Jyvo/QrFPHtbZILEwRL5ECLbtx3vuR8iObGSzY852hBDo/m4kkw337X+FyZkTw1Ya3AiM1fUlQKj7BN5zP0Jx5s9L4DB64EVx5oIeZfjYi2ih+cV0N7jxGCJPcLTgAN6zPxz1U5/joZTJkO2ZiIgP7/mfGDnQEhxD5AmMEDre868CAtlsj7l92ZlHpOcUoa4jMbdtsHAYIk9gooOXiPSdQXbML4PKVEiShOzIwVfz09EtN4OExBB5AhNs3olkssf1zLdstiMiPiL95+NWh0F8MUSeoGihIcK9p5EXYJtLsrgINL0f93oM4oPh8bDIOXToEB988AFPPvkk+/btIxAI8NWvfhXV0wKAJE39nt7xQRUNbQP80ac3A/CD146wojCdZx6omFUbZGsq0aE6hBaZcCb9Svbt28e+ffv49Kc/TVlZ2azsG8QXQ+QJxMMPP4ymjWZFiY40AzMfptusZh6/twynfXKRTsfoi0RC83djSi6a9f0GNxZD5IuQQ4cOUVlZicPhIC8vb/z6u+++SyAQoKysDHWoHumqWOuqqvPuwYvUtfRTkJ1CVP0oTVIwFOWNvTWsKEynMMfN2/trOV/XzYayfOpa+gF47J61Ez777BO3kpvh4ruvHiBJ+PiL9T1Ijnz27t1LVVUVfr+f/Px8nnvuuYX5YgzmhDEnX2R0d3fzwQcf4HQ62bx5M42NjZOW09UAkjQxftvp2k5q6nsoyU9lWa6bzt6R69bXM+Bl/Zo8vP4wlaebpykpQItQWVlJZWUlWVlZPPLII+Tmxmdl3yB2GD35IqO5uRmAzZs3s2HDBjweD/v375+k5LVD9bbuYQDu2bSC1GQb9a0DtPd4pq1vyy3FlOSncehMCyPe0NQFJUCSuHjxIpIk8cwzz5CUFDvnG4P4YfTkCYpscSH06LztWJNG47rJssSYX9vYjpyuC1RVR1U1QEIywkUlJEZPvsgoLi4G4MiRIwghOH369KTlLGmrifSdA1v6+LWiXDd1Lf3sO95AXmYynX3XH65PRopzVMzV9d3UyvLl4+gSiiOH1atX09nZyY4dOygrK6Onp4eHHnpoTvUYLAxGT77IyMnJ4YEHHsDn83H06NFx0cPoCbExxxeTq/Cae9evzqNsZTbNnUM0dw6Rmzm7DKZjrFudR26mi5qGXoQQmBQJkFDsWWzdupWtW7fS29vL22+/TVdX15zqMFg4jKOmCYCu69TV1fHaa6+RmZnJF77wBYLeAQb2vYDsyEaSTciShDUpPgMzzd+DOX0t7lu/FBf7BvHFGK4nAJFIhJ///Oe43W4eeOAB2tra+PGPf0y4dxh15ByyJZnCXDe/+8j6uNQv1CD24vvjYtsg/hg9eQISDAbp6uoi6u3Ac+J7KM48rNYkcjPmNjyfDj00jKSYSbv7xWm96wwWL0ZPnoDYbDaWL18OLMerNBJs+xBTckbM6xG6hh4awr35rwyBJzDGXy7BcZQ+g5yUghYajrltzdeGbdl2LOlrY27bYOEwRJ7gyBYHKRv+BKI+9Ig3ZnZVbzumlOU4Vn8qZjYNbgzGnNzAYIlj9OQGBkscQ+QGBkscQ+QGBkscQ+QGBkscQ+QGBkscQ+QGBkuc/x+k2qNCDKub3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_Bayes_network()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly we can do the same for a human $\\alpha$ T-cell receptor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading Parms filename from:  /home/olivares/.local/share/igor/models/human/tcr_alpha/models/model_parms.txt\n",
      "Reading Marginals filename from:  /home/olivares/.local/share/igor/models/human/tcr_alpha/models/model_marginals.txt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_ha = p3.get_default_IgorModel(\"human\", \"tcr_alpha\")\n",
    "mdl_ha.plot_Bayes_network()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that for a $\\beta$ T-cell receptor we get a VDJ model and for the $\\alpha$ only a VJ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An IgorModel internally has two structure based in IGoR's model files:\n",
    " - model_parms.txt that contains the information about the events name, nickname, priority, and the dependencies between each other.\n",
    " - model_marginals.txt which contains the conditional probabilities for each event.\n",
    " - CDR3 anchors files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IgorModel_Parms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".xdata['v_3_del', 'j_choice', 'd_5_del', 'vd_dinucl', 'dj_dinucl', 'd_gene', 'v_choice', 'd_3_del', 'j_5_del', 'dj_ins', 'vd_ins']\n"
     ]
    }
   ],
   "source": [
    "print(mdl_hb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<pygor3.IgorIO.IgorModel_Parms at 0x7fac64302950>,\n",
       " <pygor3.IgorIO.IgorModel_Marginals at 0x7fac6459d710>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.parms, mdl_hb.marginals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a pandas dataframe of the realizations of an event just use the nickname of the event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>value</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GGGACAGGGGGC</td>\n",
       "      <td>TRBD1*01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GGGACTAGCGGGGGGG</td>\n",
       "      <td>TRBD2*01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GGGACTAGCGGGAGGG</td>\n",
       "      <td>TRBD2*02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               value       name\n",
       "id                             \n",
       "0       GGGACAGGGGGC   TRBD1*01\n",
       "1   GGGACTAGCGGGGGGG   TRBD2*01\n",
       "2   GGGACTAGCGGGAGGG   TRBD2*02"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.parms['d_gene']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'event_type': 'GeneChoice',\n",
       " 'seq_type': 'D_gene',\n",
       " 'seq_side': 'Undefined_side',\n",
       " 'priority': 6,\n",
       " 'realizations': [{'id': 0, 'value': 'GGGACAGGGGGC', 'name': ' TRBD1*01'},\n",
       "  {'id': 1, 'value': 'GGGACTAGCGGGGGGG', 'name': ' TRBD2*01'},\n",
       "  {'id': 2, 'value': 'GGGACTAGCGGGAGGG', 'name': ' TRBD2*02'}],\n",
       " 'name': 'GeneChoice_D_gene_Undefined_side_prio6_size3',\n",
       " 'nickname': 'd_gene'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event = mdl_hb.parms.get_Event('d_gene')\n",
    "event.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All parameters of the events have 3 columns id, value and name. For the GeneChoice events like 'd_gene'(above) the value is the sequence and the name the description of the sequence and for Deletion events like 'd_3_del' the value is an integer of the posible deletions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td></td>\n",
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      ],
      "text/plain": [
       "    value name\n",
       "id            \n",
       "0      -4     \n",
       "1      -3     \n",
       "2      -2     \n",
       "3      -1     \n",
       "4       0     \n",
       "5       1     \n",
       "6       2     \n",
       "7       3     \n",
       "8       4     \n",
       "9       5     \n",
       "10      6     \n",
       "11      7     \n",
       "12      8     \n",
       "13      9     \n",
       "14     10     \n",
       "15     11     \n",
       "16     12     \n",
       "17     13     \n",
       "18     14     \n",
       "19     15     \n",
       "20     16     "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.parms['d_3_del']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that for Deletion event negative values mean palidromic insertions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aditionally the anchors dataframe are stores in parms.df_V_anchors for CDR3 V anchors and parms.df_J_anchors for J with gene (gene name) as index in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anchor_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K02545|TRBJ1-1*01|Homo sapiens|F|J-REGION|749..796|48 nt|3| | | | |48+0=48| | |</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K02545|TRBJ1-2*01|Homo sapiens|F|J-REGION|886..933|48 nt|3| | | | |48+0=48| | |</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499..1548|50 nt|2| | | | |50+0=50| | |</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M14158|TRBJ1-4*01|Homo sapiens|F|J-REGION|2095..2145|51 nt|3| | | | |51+0=51| | |</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M14158|TRBJ1-5*01|Homo sapiens|F|J-REGION|2368..2417|50 nt|2| | | | |50+0=50| | |</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M14158|TRBJ1-6*01|Homo sapiens|F|J-REGION|2859..2911|53 nt|2| | | | |53+0=53| | |</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L36092|TRBJ1-6*02|Homo sapiens|F|J-REGION|643043..643095|53 nt|2| | | | |53+0=53| | |</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X02987|TRBJ2-1*01|Homo sapiens|F|J-REGION|800..849|50 nt|2| | | | |50+0=50| | |</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X02987|TRBJ2-2*01|Homo sapiens|F|J-REGION|995..1045|51 nt|3| | | | |51+0=51| | |</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X02987|TRBJ2-3*01|Homo sapiens|F|J-REGION|1282..1330|49 nt|1| | | | |49+0=49| | |</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X02987|TRBJ2-4*01|Homo sapiens|F|J-REGION|1432..1481|50 nt|2| | | | |50+0=50| | |</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X02987|TRBJ2-5*01|Homo sapiens|F|J-REGION|1553..1600|48 nt|3| | | | |48+0=48| | |</th>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>X02987|TRBJ2-6*01|Homo sapiens|F|J-REGION|1673..1725|53 nt|2| | | | |53+0=53| | |</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M14159|TRBJ2-7*01|Homo sapiens|F|J-REGION|2316..2362|47 nt|2| | | | |47+0=47| | |</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X02987|TRBJ2-7*02|Homo sapiens|ORF|J-REGION|1890..1936|47 nt|2| | | | |47+0=47| | |</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    anchor_index\n",
       "gene                                                            \n",
       "K02545|TRBJ1-1*01|Homo sapiens|F|J-REGION|749.....            17\n",
       "K02545|TRBJ1-2*01|Homo sapiens|F|J-REGION|886.....            17\n",
       "M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499....            19\n",
       "M14158|TRBJ1-4*01|Homo sapiens|F|J-REGION|2095....            20\n",
       "M14158|TRBJ1-5*01|Homo sapiens|F|J-REGION|2368....            19\n",
       "M14158|TRBJ1-6*01|Homo sapiens|F|J-REGION|2859....            22\n",
       "L36092|TRBJ1-6*02|Homo sapiens|F|J-REGION|64304...            22\n",
       "X02987|TRBJ2-1*01|Homo sapiens|F|J-REGION|800.....            19\n",
       "X02987|TRBJ2-2*01|Homo sapiens|F|J-REGION|995.....            20\n",
       "X02987|TRBJ2-3*01|Homo sapiens|F|J-REGION|1282....            18\n",
       "X02987|TRBJ2-4*01|Homo sapiens|F|J-REGION|1432....            19\n",
       "X02987|TRBJ2-5*01|Homo sapiens|F|J-REGION|1553....            17\n",
       "X02987|TRBJ2-6*01|Homo sapiens|F|J-REGION|1673....            22\n",
       "M14159|TRBJ2-7*01|Homo sapiens|F|J-REGION|2316....            16\n",
       "X02987|TRBJ2-7*02|Homo sapiens|ORF|J-REGION|189...            16"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.parms.df_J_anchors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IgorModel_Marginals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IgorModel_Marginals contains the conditional probabilities for each event. \n",
    "\n",
    "> :ATTENTION: **IGoR's model_marginals.txt stores conditional probabilities of the defined events, not marginals probabilities.**\n",
    "\n",
    "The conditional probability as a numpy array can be access with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.28586e-01, 1.04003e-01, 4.10916e-02, ..., 0.00000e+00,\n",
       "        5.36364e-02, 3.73186e-02],\n",
       "       [1.66126e-01, 7.89615e-02, 1.13701e-02, ..., 2.46074e-02,\n",
       "        6.49049e-02, 7.46140e-02],\n",
       "       [1.66156e-01, 7.90103e-02, 1.13744e-02, ..., 2.45354e-02,\n",
       "        6.49334e-02, 7.46554e-02],\n",
       "       ...,\n",
       "       [1.24050e-01, 4.76394e-02, 0.00000e+00, ..., 3.52196e-02,\n",
       "        5.51254e-02, 6.01472e-02],\n",
       "       [3.64941e-06, 2.04721e-09, 0.00000e+00, ..., 3.41728e-01,\n",
       "        6.84855e-02, 8.22442e-02],\n",
       "       [1.06077e-01, 1.13773e-01, 4.03129e-02, ..., 2.20679e-02,\n",
       "        7.16947e-02, 5.81105e-02]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.marginals['j_choice']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataArrays with parms and marginals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mdl_hb object encapsulates the information about IGoR's model, like the Bayes network and the corresponding conditional probabilities for each event and the information about the parms and marginals can be access directly from the xarray in IgorModel without using IgorModel_Parms and IgorModel_Marginals like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the conditional probabilities associated with this model we can the DataArray as\n",
    "\n",
    "IgorModel['event_nickname']\n",
    "\n",
    "For instance from this Bayesian network, we can see that for this model the choice of V ('v_choice') or the number of insertions between the V and D segments are independent of the rest of events. Hence, \n",
    "\n",
    "$P(\\text{v_choice})$ = mdl_hb['v_choice']\n",
    "\n",
    "However, for events like 'd_gene' or 'j_5_del' there are some conditional dependencies, therefore the notation in pygor \n",
    "\n",
    "$P(\\text{d_gene}| \\text{v_choice}, \\text{j_choice})$ = mdl_hb['d_gene']\n",
    "\n",
    "The get the dependencies information there is variable parents as attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (v_choice: 89)&gt;\n",
       "array([4.88741e-03, 9.32369e-03, 9.32259e-03, 1.30320e-02, 3.43430e-04,\n",
       "       8.50694e-03, 7.71250e-03, 6.12276e-04, 5.06104e-03, 4.59289e-05,\n",
       "       4.48245e-03, 8.16181e-03, 7.00053e-04, 7.77164e-03, 1.16174e-02,\n",
       "       1.16158e-02, 1.13872e-02, 1.06555e-02, 2.36870e-03, 2.28368e-02,\n",
       "       1.56715e-04, 4.58447e-03, 0.00000e+00, 4.88782e-05, 0.00000e+00,\n",
       "       1.54500e-02, 2.74050e-02, 5.18979e-03, 4.80289e-03, 1.62765e-01,\n",
       "       6.61229e-02, 2.07174e-02, 7.36226e-04, 2.47846e-37, 1.07472e-02,\n",
       "       2.03820e-02, 9.53245e-03, 9.53006e-03, 2.35607e-03, 2.39208e-03,\n",
       "       2.39208e-03, 2.45219e-03, 1.66904e-02, 2.98807e-03, 2.98804e-03,\n",
       "       1.59156e-02, 1.25468e-02, 9.06711e-03, 9.06936e-03, 9.76534e-02,\n",
       "       9.57746e-03, 9.17011e-03, 1.14472e-02, 1.14458e-02, 9.77554e-03,\n",
       "       1.89924e-02, 3.97949e-04, 1.70242e-03, 8.91336e-03, 6.73926e-03,\n",
       "       6.69167e-03, 2.94257e-02, 1.38534e-02, 4.19227e-03, 4.19275e-03,\n",
       "       2.48630e-03, 4.83399e-04, 1.29795e-07, 6.49975e-05, 1.94736e-02,\n",
       "       1.94728e-02, 0.00000e+00, 3.91595e-03, 3.96230e-02, 0.00000e+00,\n",
       "       0.00000e+00, 6.73805e-05, 2.90073e-02, 9.28490e-03, 5.32118e-03,\n",
       "       8.18432e-03, 1.07035e-03, 3.64764e-04, 3.26790e-03, 3.26801e-03,\n",
       "       3.26801e-03, 3.26809e-03, 8.23600e-04, 1.56399e-02])\n",
       "Coordinates:\n",
       "  * v_choice       (v_choice) int64 0 1 2 3 4 5 6 7 ... 81 82 83 84 85 86 87 88\n",
       "    lbl__v_choice  (v_choice) object &#x27;U66059|TRBV1*01|Homo sapiens|P|V-REGION...\n",
       "    seq__v_choice  (v_choice) object &#x27;GATACTGGAATTACCCAGACACCAAAATACCTGGTCACA...\n",
       "Attributes:\n",
       "    nickname:    v_choice\n",
       "    event_type:  GeneChoice\n",
       "    seq_type:    V_gene\n",
       "    seq_side:    Undefined_side\n",
       "    priority:    7\n",
       "    parents:     []\n",
       "    childs:      [&#x27;v_3_del&#x27;, &#x27;d_gene&#x27;, &#x27;j_choice&#x27;]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>v_choice</span>: 89</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ebb71c22-ae5e-46df-9b04-85cf732aa008' class='xr-array-in' type='checkbox' checked><label for='section-ebb71c22-ae5e-46df-9b04-85cf732aa008' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.004887 0.009324 0.009323 0.01303 ... 0.003268 0.0008236 0.01564</span></div><div class='xr-array-data'><pre>array([4.88741e-03, 9.32369e-03, 9.32259e-03, 1.30320e-02, 3.43430e-04,\n",
       "       8.50694e-03, 7.71250e-03, 6.12276e-04, 5.06104e-03, 4.59289e-05,\n",
       "       4.48245e-03, 8.16181e-03, 7.00053e-04, 7.77164e-03, 1.16174e-02,\n",
       "       1.16158e-02, 1.13872e-02, 1.06555e-02, 2.36870e-03, 2.28368e-02,\n",
       "       1.56715e-04, 4.58447e-03, 0.00000e+00, 4.88782e-05, 0.00000e+00,\n",
       "       1.54500e-02, 2.74050e-02, 5.18979e-03, 4.80289e-03, 1.62765e-01,\n",
       "       6.61229e-02, 2.07174e-02, 7.36226e-04, 2.47846e-37, 1.07472e-02,\n",
       "       2.03820e-02, 9.53245e-03, 9.53006e-03, 2.35607e-03, 2.39208e-03,\n",
       "       2.39208e-03, 2.45219e-03, 1.66904e-02, 2.98807e-03, 2.98804e-03,\n",
       "       1.59156e-02, 1.25468e-02, 9.06711e-03, 9.06936e-03, 9.76534e-02,\n",
       "       9.57746e-03, 9.17011e-03, 1.14472e-02, 1.14458e-02, 9.77554e-03,\n",
       "       1.89924e-02, 3.97949e-04, 1.70242e-03, 8.91336e-03, 6.73926e-03,\n",
       "       6.69167e-03, 2.94257e-02, 1.38534e-02, 4.19227e-03, 4.19275e-03,\n",
       "       2.48630e-03, 4.83399e-04, 1.29795e-07, 6.49975e-05, 1.94736e-02,\n",
       "       1.94728e-02, 0.00000e+00, 3.91595e-03, 3.96230e-02, 0.00000e+00,\n",
       "       0.00000e+00, 6.73805e-05, 2.90073e-02, 9.28490e-03, 5.32118e-03,\n",
       "       8.18432e-03, 1.07035e-03, 3.64764e-04, 3.26790e-03, 3.26801e-03,\n",
       "       3.26801e-03, 3.26809e-03, 8.23600e-04, 1.56399e-02])</pre></div></div></li><li class='xr-section-item'><input id='section-55f1b53c-435f-4b0a-9472-3f357dc250ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55f1b53c-435f-4b0a-9472-3f357dc250ac' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 83 84 85 86 87 88</div><input id='attrs-09bc0c74-a081-4dfe-b320-3cefd5c62f45' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09bc0c74-a081-4dfe-b320-3cefd5c62f45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fca23e1b-37bd-4f87-9658-cc38c747193c' class='xr-var-data-in' type='checkbox'><label for='data-fca23e1b-37bd-4f87-9658-cc38c747193c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lbl__v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;U66059|TRBV1*01|Homo sapiens|P|...</div><input id='attrs-02f1862b-fe2b-4593-82da-fa370d9c88e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02f1862b-fe2b-4593-82da-fa370d9c88e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1709c532-b852-401a-9bbd-ab68e88fba37' class='xr-var-data-in' type='checkbox'><label for='data-1709c532-b852-401a-9bbd-ab68e88fba37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;U66059|TRBV1*01|Homo sapiens|P|V-REGION|91723..92006|284 nt|1| | | | |284+0=284| | |&#x27;,\n",
       "       &#x27;U66059|TRBV10-1*01|Homo sapiens|F|V-REGION|214801..215087|287 nt|1| | | | |287+0=287| | |&#x27;,\n",
       "       &#x27;AF009660|TRBV10-1*02|Homo sapiens|F|V-REGION|54913..55199|287 nt|1| | | | |287+0=287| | |&#x27;,\n",
       "       &#x27;AF009660|TRBV10-1*04|Homo sapiens|F|V-REGION|54913..55199|287 nt|1| Found in Data! Indiv 7. Also found by BLAST | | | |287+0=287| | |&#x27;,\n",
       "       &#x27;U66059|TRBV10-2*01|Homo sapiens|F|V-REGION|239867..240153|287 nt|1| | | | |287+0=287| | |&#x27;,\n",
       "       &#x27;U03115|TRBV10-3*01|Homo sapiens|F|V-REGION|14880..15166|287 nt|1| | | | |287+0=287| | |&#x27;,\n",
       "       &#x27;U17047|TRBV10-3*02|Homo sapiens|F|V-REGION|84..365|282 nt|1| | | | |282+0=282| Extended by 2 | |&#x27;,\n",
       "       &#x27;M33233|TRBV11-1*01|Homo sapiens|F|V-REGION|747..1036|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;U66059|TRBV11-2*01|Homo sapiens|F|V-REGION|248813..249102|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &quot;M33235|TRBV11-2*02|Homo sapiens|[F]|V-REGION|171..455|285 nt|1| | | | |285+0=285| Edited and Extended, partial in 3&#x27;| |&quot;,\n",
       "       &#x27;X58796|TRBV11-2*03|Homo sapiens|(F)|V-REGION|81..365|285 nt|1| | | | |285+0=285| | |&#x27;,\n",
       "       &#x27;U03115|TRBV11-3*01|Homo sapiens|F|V-REGION|25513..25802|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &quot;X07224|TRBV12-1*01|Homo sapiens|P|V-REGION|381..667|287 nt|1| | | | |287+0=287|partial in 5&#x27;| |&quot;,\n",
       "       &quot;X06936|TRBV12-2*01|Homo sapiens|P|V-REGION|391..677|287 nt|1| | | | |287+0=287|partial in 5&#x27;| |&quot;,\n",
       "       &#x27;X07192|TRBV12-3*01|Homo sapiens|F|V-REGION|426..715|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;K02546|TRBV12-4*01|Homo sapiens|F|V-REGION|158..447|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;M14264|TRBV12-4*02|Homo sapiens|(F)|V-REGION|58..345|288 nt|1| | | | |288+0=288| | |&#x27;,\n",
       "       &#x27;X07223|TRBV12-5*01|Homo sapiens|F|V-REGION|392..681|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;U03115|TRBV13*01|Homo sapiens|F|V-REGION|6502..6788|287 nt|1| | | | |287+0=287| | |&#x27;,\n",
       "       &#x27;X06154|TRBV14*01|Homo sapiens|F|V-REGION|283..572|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "...\n",
       "       &#x27;X61443|TRBV7-2*02|Homo sapiens|F|V-REGION|192..475|284 nt|1| | | | |284+0=284| Extended by 6 | |&#x27;,\n",
       "       &#x27;U07975|TRBV7-2*03|Homo sapiens|F|V-REGION|7806..8090|285 nt|1| | | | |285+0=285| Extended by 2 | |&#x27;,\n",
       "       &#x27;X61443|TRBV7-2*05|Homo sapiens|F|V-REGION|192..475|284 nt|1| | | | |284+0=284| Discovered in Data, Possible Error, Not found in BLAST | |&#x27;,\n",
       "       &#x27;X61440|TRBV7-3*01|Homo sapiens|F|V-REGION|748..1037|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;M97943|TRBV7-3*02|Homo sapiens|ORF|V-REGION|404..693|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;AF009660|TRBV7-3*03|Homo sapiens|ORF|V-REGION|39374..39663|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;X74843|TRBV7-3*04|Homo sapiens|(F)|V-REGION|140..426|287 nt|1| | | | |287+0=287| Extended by 3 | |&#x27;,\n",
       "       &#x27;L36092|TRBV7-4*01|Homo sapiens|F|V-REGION|270051..270340|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;L36092|TRBV7-6*01|Homo sapiens|F|V-REGION|307097..307386|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;L36092|TRBV7-7*01|Homo sapiens|F|V-REGION|326549..326838|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;L36092|TRBV7-7*03|Homo sapiens|F|V-REGION|326549..326838|290 nt|1| | | | |290+0=290| Discovered Allele in Data | Found by BLAST |&#x27;,\n",
       "       &#x27;M11953|TRBV7-8*01|Homo sapiens|F|V-REGION|215..504|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;X61441|TRBV7-8*02|Homo sapiens|F|V-REGION|497..786|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;L36092|TRBV7-9*01|Homo sapiens|F|V-REGION|364320..364609|290 nt|1| | | | |290+0=290| | |&#x27;,\n",
       "       &#x27;M14261|TRBV7-9*04|Homo sapiens|(F)|V-REGION|58..342|285 nt|1| | | | |285+0=285| | |&#x27;,\n",
       "       &#x27;M27385|TRBV7-9*05|Homo sapiens|(F)|V-REGION|34..321|288 nt|1| | | | |288+0=288| Edited | |&#x27;,\n",
       "       &#x27;X74844|TRBV7-9*06|Homo sapiens|(F)|V-REGION|100..387|288 nt|1| | | | |288+0=288| Edited | |&#x27;,\n",
       "       &#x27;L14854|TRBV7-9*07|Homo sapiens|(F)|V-REGION|1..203|203 nt|1| | | | |203+0=203| Edited | |&#x27;,\n",
       "       &#x27;U66059|TRBV9*01|Homo sapiens|F|V-REGION|206836..207121|286 nt|1| | | | |286+0=286| | |&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seq__v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;GATACTGGAATTACCCAGACACCAAAATACC...</div><input id='attrs-eeac2b9a-4a6a-48fd-a633-64d2e0a50f62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eeac2b9a-4a6a-48fd-a633-64d2e0a50f62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d95edb20-52d3-40c5-a3fe-c022ee87d542' class='xr-var-data-in' type='checkbox'><label for='data-d95edb20-52d3-40c5-a3fe-c022ee87d542' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;GATACTGGAATTACCCAGACACCAAAATACCTGGTCACAGCAATGGGGAGTAAAAGGACAATGAAACGTGAGCATCTGGGACATGATTCTATGTATTGGTACAGACAGAAAGCTAAGAAATCCCTGGAGTTCATGTTTTACTACAACTGTAAGGAATTCATTGAAAACAAGACTGTGCCAAATCACTTCACACCTGAATGCCCTGACAGCTCTCGCTTATACCTTCATGTGGTCGCACTGCAGCAAGAAGACTCAGCTGCGTATCTCTGCACCAGCAGCCAAGA&#x27;,\n",
       "       &#x27;GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCAAGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGGAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGGAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGTAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGGAATCACCCAGAGCCCAAGATACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGATGTGTCACCAGACTTGGAGCCACAGCTATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCTATTACTCAGCAGCTGCTGATATTACAGATAAAGGAGAAGTCCCCGATGGCTATGTTGTCTCCAGATCCAAGACAGAGAATTTCCCCCTCACTCTGGAGTCAGCTACCCGCTCCCAGACATCTGTGTATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGGAATCACCCAGAGCCCAAGACACAAGGTCACAGAGACAGGAACACCAGTGACTCTGAGATGTCACCAGACTGAGAACCACCGCTATATGTACTGGTATCGACAAGACCCGGGGCATGGGCTGAGGCTGATCCATTACTCATATGGTGTTAAAGATACTGACAAAGGAGAAGTCTCAGATGGCTATAGTGTCTCTAGATCAAAGACAGAGGATTTCCTCCTCACTCTGGAGTCCGCTACCAGCTCCCAGACATCTGTGTACTTCTGTGCCATCAGTGAGTC&#x27;,\n",
       "       &#x27;GCTGGAATCACCCAGAGCCCAAGACACAAGGTCACAGAGACAGGAACACCAGTGACTCTGAGATGTCATCAGACTGAGAACCACCGCTATATGTACTGGTATCGACAAGACCCGGGGCATGGGCTGAGGCTGATCCATTACTCATATGGTGTTAAAGATACTGACAAAGGAGAAGTCTCAGATGGCTATAGTGTCTCTAGATCAAAGACAGAGGATTTCCTCCTCACTCTGGAGTCCGCTACCAGCTCCCAGACATCTGTGTACTTCTGTGCCATCAGTGAGTC&#x27;,\n",
       "       &#x27;GAAGCTGAAGTTGCCCAGTCCCCCAGATATAAGATTACAGAGAAAAGCCAGGCTGTGGCTTTTTGGTGTGATCCTATTTCTGGCCATGCTACCCTTTACTGGTACCGGCAGATCCTGGGACAGGGCCCGGAGCTTCTGGTTCAATTTCAGGATGAGAGTGTAGTAGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAGAGCTTGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAAAGCTTGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAAAGCTTGAGAACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAACCTGCAAAGCTTGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTGGTTCAGTCTCCCAGATATAAGATTATAGAGAAAAAACAGCCTGTGGCTTTTTGGTGCAATCCTATTTCTGGCCACAATACCCTTTACTGGTACCTGCAGAACTTGGGACAGGGCCCGGAGCTTCTGATTCGATATGAGAATGAGGAAGCAGTAGACGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAGAGCTTGGGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GCTGGTGTTATCCAGTCACCCAGGCACAAAGTGACAGAGATGGGACAATCAGTAACTCTGAGATGCGAACCAATTTCAGGCCACAATGATCTTCTCTGGTACAGACAGACCTTTGTGCAGGGACTGGAATTGCTGAATTACTTCTGCAGCTGGACCCTCGTAGATGACTCAGGAGTGTCCAAGGATTGATTCTCAGCACAGATGCCTGATGTATCATTCTCCACTCTGAGGATCCAGCCCATGGAACCCAGGGACTTGGGCCTATATTTCTGTGCCAGCAGCTTTGC&#x27;,\n",
       "       &#x27;GCTGGCATTATCCAGTCACCCAAGCATGAGGTGACAGAAATGGGACAAACAGTGACTCTGAGATGTGAGCCAATTTTTGGCCACAATTTCCTTTTCTGGTACAGAGATACCTTCGTGCAGGGACTGGAATTGCTGAGTTACTTCCGGAGCTGATCTATTATAGATAATGCAGGTATGCCCACAGAGCGATTCTCAGCTGAGAGGCCTGATGGATCATTCTCTACTCTGAAGATCCAGCCTGCAGAGCAGGGGGACTCGGCCGTGTATGTCTGTGCAAGTCGCTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGCCATGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGCCACAACTCCCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGACACGACTACCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGACATGACTACCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAGGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTAGAGTCACCCAGACACCAAGGCACAAGGTGACAGAGATGGGACAAGAAGTAACAATGAGATGTCAGCCAATTTTAGGCCACAATACTGTTTTCTGGTACAGACAGACCATGATGCAAGGACTGGAGTTGCTGGCTTACTTCCGCAACCGGGCTCCTCTAGATGATTCGGGGATGCCGAAGGATCGATTCTCAGCAGAGATGCCTGATGCAACTTTAGCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTATTTTTGTGCTAGTGGTTTGGT&#x27;,\n",
       "       &#x27;GCTGCTGGAGTCATCCAGTCCCCAAGACATCTGATCAAAGAAAAGAGGGAAACAGCCACTCTGAAATGCTATCCTATCCCTAGACACGACACTGTCTACTGGTACCAGCAGGGTCCAGGTCAGGACCCCCAGTTCCTCATTTCGTTTTATGAAAAGATGCAGAGCGATAAAGGAAGCATCCCTGATCGATTCTCAGCTCAACAGTTCAGTGACTATCATTCTGAACTGAACATGAGCTCCTTGGAGCTGGGGGACTCAGCCCTGTACTTCTGTGCCAGCAGCTTAGG&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTACTCAGTTCCCCAGCCACAGCGTAATAGAGAAGGGCCAGACTGTGACTCTGAGATGTGACCCAATTTCTGGACATGATAATCTTTATTGGTATCGACGTGTTATGGGAAAAGAAATAAAATTTCTGTTACATTTTGTGAAAGAGTCTAAACAGGATGAGTCCGGTATGCCCAACAATCGATTCTTAGCTGAAAGGACTGGAGGGACGTATTCTACTCTGAAGGTGCAGCCTGCAGAACTGGAGGATTCTGGAGTTTATTTCTGTGCCAGCAGCCAAGA&#x27;,\n",
       "...\n",
       "       &#x27;GGAGCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAAAGGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAGAGGCTGGGGCAGGGCCTGGAGTTTTTAATTTACTTCCAAGGCAACAGTGCACCAGACAAATCAGGGCTGCCCAGTGATCGCTTCTCTGCAGAGAGGACTGGGGAATCCGTCTCCACTCTGACGATCCAGCGCACACAGCAGGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAAAGGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAGAGGCTGGGGCAGGGCCTGGAGTTTTTAATTTACTTCCAAGGCAACAGTGCACCAGACAAATCAGGGCTGCCCAGTGATCGCTTCTCTGCAGAGAGGACTGGGGAATCCGTCTCCACTCTGACGATCCAGCGCACACAGCAGGAGGACTCGGCCGTGTATCTCTGTACCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGAGCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAAAGGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAGAGGCTGGGGCAGGGCCTGGAGTTTTTAATTTACTTCCAAGGCAACAGTGCACCAGACAAATCAGGGCTGCCCAGTGATCGCTTCTCTGCAGAGAGGACTGGGGAATCCGTCTCCACTCTGACGATCCAGCGCACATAGCAGGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAATATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAACGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCGGGGGGACTCAGCCGTGTATCTCTGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAAGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAAAGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCCGTGTATCTCCGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAAGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAAAGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCCGCGTATCTCCGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAATATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAACGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCGGGGGGACTCTGCCGTGTATCTCTGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGTCGCAAAGAGGGGACGGGATGTAGCTCTCAGGTGTGATTCAATTTCGGGTCATGTAACCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCTCAGAGGTTCTGACTTACTCCCAGAGTGATGCTCAACGAGACAAATCAGGGCGGCCCAGTGGTCGGTTCTCTGCAGAGAGGCCTGAGAGATCCGTCTCCACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCTGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCTCCCAGGTACAAAGTCACAAAGAGGGGACAGGATGTAGCTCTCAGGTGTGATCCAATTTCGGGTCATGTATCCCTTTATTGGTACCGACAGGCCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCAATTATGAAGCCCAACAAGACAAATCAGGGCTGCCCAATGATCGGTTCTCTGCAGAGAGGCCTGAGGGATCCATCTCCACTCTGACGATCCAGCGCACAGAGCAGCGGGACTCGGCCATGTATCGCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCTCCCAGGTACAAAGTCACAAAGAGGGGACAGGATGTAACTCTCAGGTGTGATCCAATTTCGAGTCATGCAACCCTTTATTGGTATCAACAGGCCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCAATTATGAAGCTCAACCAGACAAATCAGGGCTGCCCAGTGATCGGTTCTCTGCAGAGAGGCCTGAGGGATCCATCTCCACTCTGACGATTCAGCGCACAGAGCAGCGGGACTCAGCCATGTATCGCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCTCCCAGGTACAAAGTCACAAAGAGGGGACAGGATGTAACTCTCAGGTGTGATCCAATTTCGAGTCATGCAACCCTTTATTGGTATCAACAGGCCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCAATTATGAAGCTCAACCAGACAAATCAGGGCTGCCCAGTGATCGGTTCTCTGCAGAGAGGCCTGAGGGATCCATCTCCACTCTGACGATTCAGCGCACAGAGCAGCGGGACTCAGCCATGTATCGCTGTGCTAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCCCCTAGGTACAAAGTCGCAAAGAGAGGACAGGATGTAGCTCTCAGGTGTGATCCAATTTCGGGTCATGTATCCCTTTTTTGGTACCAACAGGCCCTGGGGCAGGGGCCAGAGTTTCTGACTTATTTCCAGAATGAAGCTCAACTAGACAAATCGGGGCTGCCCAGTGATCGCTTCTTTGCAGAAAGGCCTGAGGGATCCGTCTCCACTCTGAAGATCCAGCGCACACAGCAGGAGGACTCCGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCCCCTAGGTACAAAGTCGCAAAGAGAGGACAGGATGTAGCTCTCAGGTGTGATCCAATTTCGGGTCATGTATCCCTTTTTTGGTACCAACAGGCCCTGGGGCAGGGGCCAGAGTTTCTGACTTATTTCCAGAATGAAGCTCAACTAGACAAATCGGGGCTGCCCAGTGATCGCTTCTTTGCAGAAAGGCCTGAGGGATCCGTCTCCACTCTGAAGATCCAGCGCACACAGAAGGAGGACTCCGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTTTCTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;ATATCTGGAGTCTCCCACAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGAACCCTGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTGGAAAAATCAGGGCTGCTCAGTGATCGGATCTCTGCAGAGAGGCCTAAGGGATCTTTCTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTCTCTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTCTTTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;CACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTTTCTCCACCTTGGAGATCCAGCGCACAGAGGAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATTCTGGAGTCACACAAACCCCAAAGCACCTGATCACAGCAACTGGACAGCGAGTGACGCTGAGATGCTCCCCTAGGTCTGGAGACCTCTCTGTGTACTGGTACCAACAGAGCCTGGACCAGGGCCTCCAGTTCCTCATTCAGTATTATAATGGAGAAGAGAGAGCAAAAGGAAACATTCTTGAACGATTCTCCGCACAACAGTTCCCTGACTTGCACTCTGAACTAAACCTGAGCTCTCTGGAGCTGGGGGACTCAGCTTTGTATTTCTGTGCCAGCAGCGTAG&#x27;],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9809d889-7a70-45a8-be99-69f085195f5d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9809d889-7a70-45a8-be99-69f085195f5d' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>nickname :</span></dt><dd>v_choice</dd><dt><span>event_type :</span></dt><dd>GeneChoice</dd><dt><span>seq_type :</span></dt><dd>V_gene</dd><dt><span>seq_side :</span></dt><dd>Undefined_side</dd><dt><span>priority :</span></dt><dd>7</dd><dt><span>parents :</span></dt><dd>[]</dd><dt><span>childs :</span></dt><dd>[&#x27;v_3_del&#x27;, &#x27;d_gene&#x27;, &#x27;j_choice&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (v_choice: 89)>\n",
       "array([4.88741e-03, 9.32369e-03, 9.32259e-03, 1.30320e-02, 3.43430e-04,\n",
       "       8.50694e-03, 7.71250e-03, 6.12276e-04, 5.06104e-03, 4.59289e-05,\n",
       "       4.48245e-03, 8.16181e-03, 7.00053e-04, 7.77164e-03, 1.16174e-02,\n",
       "       1.16158e-02, 1.13872e-02, 1.06555e-02, 2.36870e-03, 2.28368e-02,\n",
       "       1.56715e-04, 4.58447e-03, 0.00000e+00, 4.88782e-05, 0.00000e+00,\n",
       "       1.54500e-02, 2.74050e-02, 5.18979e-03, 4.80289e-03, 1.62765e-01,\n",
       "       6.61229e-02, 2.07174e-02, 7.36226e-04, 2.47846e-37, 1.07472e-02,\n",
       "       2.03820e-02, 9.53245e-03, 9.53006e-03, 2.35607e-03, 2.39208e-03,\n",
       "       2.39208e-03, 2.45219e-03, 1.66904e-02, 2.98807e-03, 2.98804e-03,\n",
       "       1.59156e-02, 1.25468e-02, 9.06711e-03, 9.06936e-03, 9.76534e-02,\n",
       "       9.57746e-03, 9.17011e-03, 1.14472e-02, 1.14458e-02, 9.77554e-03,\n",
       "       1.89924e-02, 3.97949e-04, 1.70242e-03, 8.91336e-03, 6.73926e-03,\n",
       "       6.69167e-03, 2.94257e-02, 1.38534e-02, 4.19227e-03, 4.19275e-03,\n",
       "       2.48630e-03, 4.83399e-04, 1.29795e-07, 6.49975e-05, 1.94736e-02,\n",
       "       1.94728e-02, 0.00000e+00, 3.91595e-03, 3.96230e-02, 0.00000e+00,\n",
       "       0.00000e+00, 6.73805e-05, 2.90073e-02, 9.28490e-03, 5.32118e-03,\n",
       "       8.18432e-03, 1.07035e-03, 3.64764e-04, 3.26790e-03, 3.26801e-03,\n",
       "       3.26801e-03, 3.26809e-03, 8.23600e-04, 1.56399e-02])\n",
       "Coordinates:\n",
       "  * v_choice       (v_choice) int64 0 1 2 3 4 5 6 7 ... 81 82 83 84 85 86 87 88\n",
       "    lbl__v_choice  (v_choice) object 'U66059|TRBV1*01|Homo sapiens|P|V-REGION...\n",
       "    seq__v_choice  (v_choice) object 'GATACTGGAATTACCCAGACACCAAAATACCTGGTCACA...\n",
       "Attributes:\n",
       "    nickname:    v_choice\n",
       "    event_type:  GeneChoice\n",
       "    seq_type:    V_gene\n",
       "    seq_side:    Undefined_side\n",
       "    priority:    7\n",
       "    parents:     []\n",
       "    childs:      ['v_3_del', 'd_gene', 'j_choice']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb['v_choice']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot directly from the xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fac3b5ef210>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb['v_choice'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or use the a model method to get the plot with the corresponding labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1296x1080 with 1 Axes>,\n",
       " <AxesSubplot:title={'center':'$P($v_choice$)$'}>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_Event('v_choice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 720x1440 with 6 Axes>,\n",
       " array([<AxesSubplot:title={'center':'$P($d_gene$ = $  TRBD1*01 $|$v_choice,j_choice$)$'}, xlabel='j_choice', ylabel='v_choice'>,\n",
       "        <AxesSubplot:title={'center':'$P($d_gene$ = $  TRBD2*01 $|$v_choice,j_choice$)$'}, xlabel='j_choice', ylabel='v_choice'>,\n",
       "        <AxesSubplot:title={'center':'$P($d_gene$ = $  TRBD2*02 $|$v_choice,j_choice$)$'}, xlabel='j_choice', ylabel='v_choice'>],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6O0Gz1bMDNNk3O0CLtdY8KDtC3W1aKTtCi+u3PDs7Qt1BJZokd/QWJ2VHaLH4dWdlR6ibuvLT2RFa7FiixbunZAdoctg5O2ZHKKHBKCD7sZA4AAAABkj1S2AAAIDKoAUSAAAAk1D1S2AAAIDKGIwWyMq8gn9Kujw7ROHbKzyYHaHVYUdlJ6ibqguyI9Qt1t7ZEVr8Z3aAJlN0f3aEFuuveXp2hLqXzng0O0LDe8/LTtDi7We9JztC3RFbXpwdocU5+o/sCHW7TT86O0Ldc8r08yRJpbnQVGXKr1HRhQ0AAICuVL8EBgAAqIzB6MKmBRIAAABdcUR5Fj8di+1qBE1Qle9hv9nOjoAOles9/PPsAE1elx2gtMr28x3f+EB2hLrlDvxGdoS6adkB2iySF0TE3MwM9pyQJvr9cnDfX1f121ABAAAqgy5sAAAATELVL4EBAAAqYzBaIKv/CgAAACql+uVXZV7BFltsoaGhoewYqJByTcxAdTBxpQr4+R7dI+WZz1M6JZt7VWmVKSABAACqbzC6sJlEAwAAgK5UvwQGAACojMFogaz+KwAAAKiMwSgg6cIGAABAV6pfAgMAAFQGLZAAAACYhKpfAgMAAFTGYLRAVuYV3L5A+mxJFgA9RGdnR2hzbHaAulfpx9kR6u7KDtBmmewATR7PDtDm6j1Oz45QN/f0PbIj1A0denh2hBYX/vAt2RHqtt3lrOwILf7ym1dmR6g79rJNsyPUfTc7QGlVpvwaFV3YAAAA6Er1S2AAAIDKoAsbAAAAXRmMApIubAAAAHSlMiXwslOf1Atn3ZEdQ5IUJ34lO0IL73hudoS6b29+aXaEuhddek12hBbxphJNo3m6ZP87rl6On21JGrpuw+wIDQ+ukJ2gxbaf3jU7QsPjH8tO0OIl01+THaEuLr01O0Ld16c9kR2hhTfJTiDRAgkAAIBJqfolMAAAQGUMRgtk9V8BAABApVS//KILGwAAAF2pTAm84kuX1huHVs+OUTgnO0CLiOwE5RTaPDsCKul92QHQiWUey07Qgt/DVVGGS9r1vwvb9k6Svi5piqRjI+LItu3rSDpB0orFPgdHxJiXe6IFEgAAYEDZniLpW5J2lrSxpL1tb9y22yGSTomIzSTtJenb4x23Mi2QAAAA1df3FsgtJV0fETdIku2TJe0u6eqmfULS8Lphz5F023gHpYAEAADom74XkGtJuqXp9kJJL2vb51OSzrH9AUnLSdphvIP29BXY3kjSj5ruep6kQyWdWNy/rqQbJe0ZEfePdayrF0gvKcPQBUl/0UXZEVrExR/IjlDnLX+RHaHJUHaAFp/Ua7Mj1D2VHaDN57/80ewIDTc8LztBw9xyvYfXeud3siPUXfLy32VHaLHW73bOjlD3hxdckh2h7sZ7ZmVHmCxm2W7+hTEvIuZ18fi9JR0fEV+xvbWk79neJCKeHu0BPS0gI+JaSZtK9T74WyX9VNLBks6LiCNtH1zcPqiXWQAAAPL1pAXynoiYO8q2WyWt3XR7TnFfs/0k7SRJEfFH29MlzZJ012hP2M9JNNtL+ntE3KRa3/sJxf0nSNqjjzkAAAASTZ3gjzFdImkD2+vZnqbaJJn5bfvcrFqdJtsvlDRd0t1jHbSfBeRekk4qvp4dEbcXX98hafZID7C9v+0h20OLx34dAAAAaBMRiyUdIOlsSdeoNtv6KtuH296t2O0jkt5t+3LVarV9I8ZenKovoziLinc3SR9v3xYRYXvEkEX//TxJmuG5rLIFAAAqrv/rQBZrOp7Vdt+hTV9fLWmbbo7ZrxbInSVdGhF3FrfvtL2GJBWfR+1jBwAAQLn0q4DcW43ua6nW975P8fU+ks7oUw4AAIBEwy2QfRsD2RM9f1bby0l6jaT/bLr7SEmn2N5P0k2S9ux1DgAAgHz978LuhZ6/goh4RNIqbffdq2K2DwAAAKql+iUwAABAZQxGC6THmaVdGqPN1M5QtnNml+QSPSrXuSnTeZGkOP/V2RHK69XnZyeoK9P7pkw/T8AgsL1gjAW3+5ThpdE2IXoCzOn766p+CQwAAFAp1S+/qv8KAAAAKmMwurD7eSUaAAAADIDKlMBbbLGFhoaGsmOUEuOkRsZ5wZLgfQOgtwajBbL6rwAAAKAyBqOApAsbAAAAXal+CQwAAFAZtEACAABgEqpMCbxgwYLSLPDLIPvRleV7hGrhZwrdOnPthdkRWux6y5zsCHX8Hi67wWiBrP4rAAAAqBBryoQeL+NfcLqwAQAA0BVaIAEAAPpoYtsfpcUTfLxO0AIJAACArlSmBXKLdVbV0MFvyo5R87PdshO08G4/y45Qd/yK92dHqLt80YrZEVr8z3bnZ0eoW/n8N2dHaPXzXbMTNLzuzOwEpcXkjNFFyii0kV3w/L9lR6h74NEZ2RFa7HHbWtkRZA1GC2RlCkgAAICq60UBmYEubAAAAHSFFkgAAIA+GoQWyMoUkAtuXkl+379lx5Ak/WzORtkR2pRnDOQPSzTu8BytlB2hxU43/Dk7Qt0Fz/9jdoRWvzktO0Gddz0uO0JdPPhf2RFK67GdfpEdoYW9QXaEurPWOS87Qt1um1+aHaHVbdkB6MIGAADAJFWZFkgAAICqG4wLGdICCQAAgC4NQhEMAABQGYMwBpICcglMWerp7AgAAKCCmEQDAACASYkWSAAAgD6iBRIAAACTDi2QAAAAfTIoYyArU0A+R8vrFdo+O4Yk6RM3Zydot0l2gLp7swM0OXrGrdkRWrz2xqOzI5TXF8vxsy1JccRPsyM0fOyL2QlaRLwtO0LDB47PTtAi3nVQdoS6zY5dJztC3f+7uTxZymJQCki6sAEAANCVyrRAAgAADIJBaIGkgAQAAOgTurABAAAwKVWmBfIpSYuyQxTeN/Ph7Agt/vTwX7Ij1O202l3ZEeq+e9dq2RFarK2PZEeoeyo7QJtbP/G57Ah1N565a3aEunUPOSI7QovN/J7sCHVv0AHZEVocpouyI9Ttnh2gybTsACVkVaj4GgMtkAAAAOjKIBTBAAAAlcEYyA7YXtH2qbb/avsa21vbXtn2ubb/Vnxeqdc5AAAAsg1PopnIjwz9aIH8uqRfRsSbbE+TNEPSJySdFxFH2j5Y0sGSxlyFdaakl/c8amd+//DM7AgtTpCzI9R9566/ZkeoiwNPzo7Q4o/n7Jgdoe6pp8s1euXaEp2bjZ53Q3aEhqWezk7Q4s/xt+wIdW/zBtkRWkSsnB2hbqXy/EnQ8tkB0DM9/Sti+zmSXinpOEmKiCciYpFqY3xPKHY7QdIevcwBAABQFoPQAtnrZoj1JN0t6bu2/2z7WNvLSZodEbcX+9whaXaPcwAAAGCC9LqAnCppc0lHR8Rmkh5Rrbu6LiJCUoz0YNv72x6yPfSo7u5xVAAAgN4alDGQvS4gF0paGBHDC2SdqlpBeaftNSSp+Dzi4oERMS8i5kbE3BlatcdRAQAAemtQCsieTqKJiDts32J7o4i4VtL2kq4uPvaRdGTx+YxxjyVpcS/DYvCUbKJImSzm3IyuTOemZJNoyuSf2QFKrEwXCngiOwB6ph+zsD8g6QfFDOwbJP2Hai2fp9jeT9JNkvbsQw4AAIB0g7AOZM8LyIi4TNLcETZt3+vnBgAAKJPhLuyqK1FfDQAAAKqASxkCAAD0iTUYxVdlXsOcFRfpS9udlh1DkuTT3pwdocRekB2gbr1vjrg6VJobtXN2hCZlm5K2enaAurjk9vF36pfPb5mdoNVry3MVrtP0eHaEFnuW6Mo4/5kdoMmM7ABtDs8OMEAqU0ACAAAMgkEYA0kBCQAA0CdMogEAAMCkVJkWyAWL7pZP+7/sGJKkHfVkdoQWZ5doqF9xgaFS+Me7js2O0MLHvic7QmlF7J4doc52doS6iF9lR2hRpnNTNj/WY9kR6iL+lB2htA4vyVuYFkgAAABMOpVpgQQAAKi6QRkDSQEJAADQJ4NSQNKFDQAAgK5UqAXyQUm/zA4hSTpHJRmFW2Bc+8h87LuzI6BjJZoJhlFFXJcdoUl5Fu6WyjbBiJ+nshuEFsgKFZAAAADVRhc2AAAAJiVaIAEAAPrEGoziixZIAAAAdKUyRfAq2kK7aig7hiTpa9udnx2hxUpv+352hDq/c7nsCHVnr/uR7AgtXntjmSb1LM4O0Orzn8hOUHf3thdkR2h47zHZCVr4mEeyI9T9cNY+2RHa7JQdoGHvk7MTNDw6IztBKQ3CGMjKFJAAAABVxyQaAAAATEq0QAIAAPTJoLRAVqaAvF/Sj7NDFFY9f7vsCC2+tO2F2RGavD87QN2OLz83O0KrG7fKTtBkxewAre4pzyLMP/pTeb5P779g2+wILeLo92VHaPj3C7MTtNh7zmuzI9R9/Ki9siPUlWg0JiZYZQpIAACAQUALJAAAADo2KF3YTKIBAABAVyggAQAA+mjKBH+Mx/ZOtq+1fb3tg0fZZ0/bV9u+yvYPxztmZbqwX7Dsozp5o8uyY0iStrls0+wILb586HOyI9S9R7OzI9T5+ztnR2izQXaA0rr6wqWzI9Q99Nj07AgN//ue7AQtPvie8ixs/vVT35QdocXH33RqdoS672YHwJj63YVte4qkb0l6jaSFki6xPT8irm7aZwNJH5e0TUTcb3u18Y5LCyQAAMDg2lLS9RFxQ0Q8odrk+N3b9nm3pG9FxP2SFBF3jXfQyrRAAgAAVJ3V9+JrLUm3NN1eKOllbftsKEm2f69aA+mnIuKXYx2UAhIAAKDaZtkearo9LyLmdfH4qaqNs9pW0hxJv7H94ohYNNYDAAAA0Cc9GAN5T0TMHWXbrZLWbro9p7iv2UJJF0XEk5L+Yfs61QrKS0Z7QsZAAgAA9MnwJJo+zsK+RNIGttezPU3SXpLmt+1zumqtj7I9S7Uu7RvGOigFJAAAwICKiMWSDpB0tqRrJJ0SEVfZPtz2bsVuZ0u61/bVki6Q9N8Rce9Yx6ULGwAAoE8yrkQTEWdJOqvtvkObvg5JHy4+OkILJAAAALpSmRbIR59YRpfeuG52DEm1lTjL5DR9NDtC3TEqz+K+W+q52RFavHlqeRZ8v31xuX70V5zxu+wIdXtt9afsCA0vL895kaT/3PzS7AgNp70xO0GLI+XsCHUvVmRHqJuTHaDNL7IDFAbhWtjl+isCAAAwwDK6sHuBLmwAAAB0hRZIAACAPpqy1NMTe8AJPlwnel5A2r5R0kOSnpK0OCLm2l5Z0o8krSvpRkl7Dl9/EQAAYFBZQQHZhVdHxD1Ntw+WdF5EHGn74OL2QWMdYIqf1vLTH+tlxo49lR2g1GZmB6iblh2gzT9LNHHln9kB2qyx4qLsCHV/uv752RHq1l1YrikIq8x8ODtCw4xHsxOU1piL9/XZstkB0DNZf9F2V7HiuaQTJF2ocQpIAACAqrOlqRPdApmgH5NoQtI5thfY3r+4b3ZE3F58fYek2SM90Pb+todsDz3wdJn+pwIAAJi8+tEC+fKIuNX2apLOtf3X5o0REbZHXLQqIuZJmidJz1/6peVZ2AoAAGAJTfgYyAQ9LyAj4tbi8122fyppS0l32l4jIm63vYaku3qdAwAAIJvdg0k0CXpaQNpeTtJSEfFQ8fWOkg6XNF/SPpKOLD6fMd6xFi1eWj+/Y/Vexu3YGfp5doQWZbrqwKuzAzT5ZXaANvOzA5TY5Tevkx2h7u93rZYdoW7r3708O0KL2RtfnR2h7sJf7ZAdocXBJfo9XLYJhGVycXaAAdLrFsjZkn5qe/i5fhgRv7R9iaRTbO8n6SZJe/Y4BwAAQDqLLuxxRcQNkl46wv33Stq+l88NAACA3ijPwnQAAAADjzGQfbX6jEf1kRdcmh1DkvSdS8s1LukvemF2hLorPvH27Ah1K3zuE9kRWlyn/82O0GRxdoAWmx5xdnaEursO/EZ2hNLyMe/NjlAXZ+2cHaHFtju/MTtC3Yf9ruwIdctkByghezC6sPuxDiQAAAAGSGVaIAEAAKquJ9fCTtBRAWl7hqSPSFonIt5tewNJG0XEmT1NBwAAMEgmWRf2dyU9Lmnr4vatko7oSSIAAACUWqdd2OtHxJtt7y1JEfGoi8Ud+2UpP63lpz/Wz6ccQ7kmIEhXZgdo8oXsACU2JztAk5K9h58uz3DsNVZclB2hYWq5vk8Rt2VHaPj5u7MTtJmZHaBuSnaAJmXKUhaWNHUStUA+YXtZqbbUvu31VWuRBAAAwCTTaQvkYapdGW5t2z+QtI2kfXsVCgAAYBBNqkk0EXGu7UslbaVa6+sHI+KeniYDAAAYNJNpEo3tN0haHBE/L2ZeL7a9R0+TAQAAoJQ67sKOiJ8O34iIRbYPk3R6T1KN4C+P3KV1/vD/+vV0Y3qnTsqO0OI7KtFg8t3+kp2g7qHPPZkdoYV1WHaEutC92RFa3fGL7AR1L973+OwIdffP3y07Qospr7kqO0LdCi/YKTtCC++6d3aEuph3fnaEhtvWzE7Q4rOfyk4wOF3YnU6iGWk/FiEHAACYhDotAodsf1XSt4rb75e0oDeRAAAABtSAjIHstID8gKT/kfSj4va5qhWRAAAA6JA1iQrIiHhE0sE9zjKm5y89R1+b/aXMCHWvX7hedoQWEf/IjlBn35odoW5HrZUdocU35g5lRyivO1bPTlC376c+lR2h7vg//Gt2hBb+1xdlR6iLL5asE+ybp2cnqPvdl1+YHaHu8cVlG+326ewAA2PM76ztoyLiQ7Z/pmIR8WYRUa4R3gAAACU2KJNoxvvX4HvF5y/3OggAAMDAmwxjICNiQfH517anSdqw2HRtRJRrjRQAAAD0RUeDE2xvK+kESTeqNv5zbdv7RMRvepYMAABgwFihqYPeAtnkK5J2jIhrJcn2hpJOkrRFr4K1u/vJaTp24Zx+Pd2Y1lV5Jq2UzyHZAerO0V+zI7TY6Hd/zo7QMHVxdoJWU8qT5w3zbsuO0LD1ztkJWsQBK2VHaNjs/7ITtPhkdoAmr7juvdkRmkzPDoAe6bSAXHq4eJSkiLjO9tI9ygQAADCQPBnGQDYZsn2spO8Xt98qiTVJAAAAujSZCsj3qrZw+IHF7d9K+nZPEgEAAKDUOl1I/HFJXy0+AAAAsAQmyzqQkiTb20j6lKTnNj8mIp7Xm1gAAAADaJKNgTxO0n9JWiDpqd7FAQAAQNl1WkA+EBG/6GkSAACAATcpurBtb158eYHtL0k6TdLjw9sj4tIeZgMAAEAJjdcC+ZW223Obvg5J201snNE9oMd1hv7er6cb0wu1fnaEFi9zdoKG09f8bHaEut1v/VF2hBbr+I/ZEUrr5vm7ZUeoe/XGB46/U7984ZHsBK0O+VZ2grobt547/k599Fmtlx2hLj5aor9RKzyYnaCFD81OMEnWgYyIV/crCAAAwGQwCAXkUp3sZPtztldsur2S7SN6lgoAAACl1VEBKWnniFg0fCMi7pe0S08SAQAADCgrNHWppyf0I0OnBeQU28sM37C9rKRlxtgfAAAAA6rTZXx+IOk8298tbv+HpBN6E2lkK2kZvbYkk1c2L9nYhf9+dEZ2hDpP/5/sCHUH+pPZEVrcohLNdiqbO96dnaBuzq92yI5Q9+A7v5MdodXsO7IT1K37yi9mR2gRf9wqO0LdCSu9KztCiX0mO8DkmEQzLCK+YPtyScO/WT8TEWf3LhYAAMAgmgTrQDaLiF9K+uVI22z/MSK2nrBUAAAAKK2OC8hxTJ+g4wAAAAysSdWF3YEYa6PtKZKGJN0aEbvaXk/SyZJWUe362m+PiCcmKAsAAEBpUUB27oOSrpG0QnH7C5K+FhEn2z5G0n6Sjh7rAE9JeqCnETt339OdTl7vk0UrZidoMis7QB3/kVRIid7Dq2QHQGceo+NrNA+UaGLlIBRKGFmnC4l/2PZaY+0yxmPnSHqdpGOL21btEoinFrucIGmPTnIAAABUmYtJNBP5kaHTprTlJZ1j+7e2D7A9u23728d47FGSPiZp+BWuImlRRCwubi+UNGJxant/20O2h57Q3R1GBQAAQC91VEBGxKcj4kWS3i9pDUm/tv2rpu1XjvQ427tKuisiFixJuIiYFxFzI2LuNK26JIcAAAAojeFJNFVvgex2DORdku6QdK+k1TrYfxtJu9neRbWZ2itI+rqkFW1PLVoh50i6tcscqRaPvwsAAMAIBmMdyE7HQL7P9oWSzlOtC/rdEfGS8R4XER+PiDkRsa6kvSSdHxFvlXSBpDcVu+0j6YwlyA4AAIAEnbZAri3pQxFx2QQ970GSTrZ9hKQ/Szpugo4LAABQWrY0dQBaIDu9lOHHn+0TRcSFki4svr5B0pbP9pgAAADov36tAwkAADDpWYOxPiYF5BJYY2rJptHMLtOS2QuzA9Tdmx0AndvwuuwEdXdlB2g2/bHsBOVVtgs6lMicle/LjlBXukLptuwA0qSaRAMAAAAMowUSAACgT4bXgaw6CkgAAIC+CXkACki6sAEAANCVyrRAPqjF+kVJpkU8tHiV7AgtPuL7syPU7ajPZEeo++jGV2dHaLH51ZEdoW6NFRdlR2h1zg+zE9Q9ctYu2REafrlTdoIWe74xO0HDgRvumB2hxSvs7Ah1v3re37Mj1P3ziWnZEcrHkmiBBAAAwGRTmRZIAACAgTAALZAUkAAAAP3ioIAEAEweU7IDACgNCkgAAIB+GoAWSCbRAAAAoCu0QAIAAPTNYIyBpAUSAACgX4bXgZzIj/Ge0t7J9rW2r7d98Bj7/ZvtsD13vGNWpgVyFU3VrirHAt6/zg7QJqI8C1TbJ2RHqLvy6n2yI7R4TXaAJh9f9JXsCC32eWy97Ah1Z7zr2OwIdbu/48TsCC1OKs+vGl38osXZEVrsrPKcnN/ckJ2g4ansAJDtKZK+pdqfoYWSLrE9PyKubttveUkflHRRJ8elBRIAAKCf+tsCuaWk6yPihoh4QtLJknYfYb/PSPqCpMc6egndvF4AAAA8C8PrQE5sATnL9lDTx/5Nz7iWpFuabi8s7mtEsjeXtHZE/LzTl1GZLmwAAACM6J6IGHfc4khsLyXpq5L27eZxFJAAAAD91N9Z2LdKWrvp9pzivmHLS9pE0oW2JWl1SfNt7xYRQ6MdtDIF5L26Syfo69kxJEkb6oPZEVos5ewEzcrzlrqt5ecl349behByLa/PZEdodd/p2QnqXr3x1ePv1CfnnLxXdoQWO37+9uwIdVu+8jfZEVr8/Hnlmbny9jN3zY5QV46pr5PeJZI2sL2eaoXjXpLeMrwxIh6QNGv4tu0LJX10rOJRKtNfewAAgIHX33UgI2Kx7QMkna3aFUm/ExFX2T5c0lBEzF+S41JAAgAA9MvwOpB9FBFnSTqr7b5DR9l3206OySxsAAAAdKUyLZBbbLGahobKNfYQz2S/LTtCXZkWWMd49sgOULdCdoAmO2YHKLOj35OdoEWZhqJ/PztAiX2jFN8oLmUIAACASagyLZAAAACVlzAGshcoIAEAAPppAApIurABAADQlcq0QC5YsEDFCukl8KvsAC0its+OUFemiSsfL8vbpfD58pya0vnjC/+aHaFu62tekB2hvC7fNDtBw0svy05QWuX5W4mRDcYkmsoUkAAAAJU3IGMg6cIGAABAV2iBBAAA6JvB6MKmBRIAAABdqVAL5FqSPpAdQpK0rsozaUWSXlGi8dJn73hOdoS6KSW7jsf/Lf9wdoS6aVMXZ0doceqi8kxc+dnR78uOUHfm5z6RHaHF6xdenh2h7vPZAdr8+5YXZ0eoe4/KM2NvpzVvy47QYo/b1sqOMDBjICtUQAIAAAyAASgg6cIGAABAV3raAml7uqTfSFqmeK5TI+Iw2+tJOlnSKpIWSHp7RDzRyywAAAD5BmMSTa+7sB+XtF1EPGx7aUm/s/0LSR+W9LWIONn2MZL2k3T02IeaJeldPY7bmTWyA7T57du+nx2hbq3vvy07Ql053i0N73t4ZnaEusV6KjtCiy+W6JfpH79xYHaEum02vC47QosjFpZnbN2cle/LjtBih4u3zI5Qd6PuzY5Qd8xt92dHQI/0tAs7aoZnDixdfISk7SSdWtx/gqQ9epkDAACgFIYn0UzkR4KeT6KxPUW1burnS/qWpL9LWhQRw9NAF6o2xRoAAGDADUYXds8n0UTEUxGxqaQ5kraU1PF6Hbb3tz1ke0glapIHAACYzPq2jE9ELLJ9gaStJa1oe2rRCjlH0q2jPGaepHmSZG9ansE3AAAAS4J1IMdne1VJTxbF47KSXiPpC5IukPQm1WZi7yPpjPGOtYqmalet0su4Hft7doA2Hy/RxJUPleiH4ntPl2uVqsW6KTtCk3ItAfvfHz4pO0Ld33/zyuwIdSvt9MvsCC0+uell2REaVrsrO0GL15y1S3aEuktueF52hLppU5fLjtDitTdmJyiU6G/lkur1X5E1JJ1QjINcStIpEXGm7aslnWz7CEl/lnRcj3MAAABggvS0gIyIKyRtNsL9N6g2HhIAAGASYRINAAAAJqFyDYQCAAAYZEyi6a+nVbusTRmU6xoe0pElmpwxtPFD2RHqPnZl2YbWHpMdoLQevOxn2RHqDinRFUW+PfPh8Xfqo5XPf212hLrvrXxndoQWb7t35ewIdQuXezQ7Qt3PH52RHaGE6MIGAADAJFSZFkgAAIDKG5AubFogAQAA0JXKtEBa0pTsEIWy5Bi2qZ6bHaHJldkBSmzF7ABNFmUHQAeeKtli+KiGMr1vyjZnoDQGoAWyMgUkAABA9TGJBgAAAJMQLZAAAAD9MiCTaCggAQAA+mYwurArU0Dep4f1A/0+O4Yk6Y3aJjtCiw2zAzSZe+VK2RHqvjn989kRWhz72NeyI9SVbSLY9373WHaEupOu3CQ7QsOXP5qdoEV8473ZEeruPLVcE/bsc7Mj1K2vz2RHqGMZ8cFVmQISAACg8gakC5tJNAAAAOgKLZAAAAD9NAAtkBSQAAAAfcMkmr6aoZl6YUkmr5ymn2RHaPOu7AB1sW95Joro+ddnJ2jxv4cckR2h7vHsAG3+5Xk3ZEdouGzT7AR199y4bnaEFh89ft/sCHXHX71xdoRWG2+UnaDuey/4a3aEukefmJYdocUOJfpVU3WVKSABAAAqj0k0AAAAmIxogQQAAOgbxkD21Qu3kIaGslMM+7fsAG3KlgcjueKT2QnKrEzj2b6fHaBu1luzE7Q6PjtAi6uzA7SIyE6Ajjg7gOjCBgAAwORUmRZIAACAgTAALZAUkAAAAH0zGGMg6cIGAABAV2iBBAAA6Bcm0QAAAGAyogUSAACgbwZjDCQFJAAAQL/QhQ0AAIDJiBZIAACAfqIFEgAAAJMNLZAAAAD9YibRAAAAoFsUkP1z6wLpEGenqNlmnZuzI7TY5eYvZUeoO2ud/86OUHfUzetkR2hxdmQnQCfs8ryH49j7syO08LuOy45QF8EP1Gj2LMnfSkmalh0APVOZAhIAAKD66MIGAABAN1gHEgAAAJNRTwtI22vbvsD21bavsv3B4v6VbZ9r+2/F55V6mQMAAKAcii7sifxI4F4ORLa9hqQ1IuJS28tLWiBpD0n7SrovIo60fbCklSLioHGOVZoR0wzeBgCgemwviIi5mRnmPn/FGPrKthN6TO9xRt9fV0/HQEbE7ZJuL75+yPY1ktaStLukbYvdTpB0oaQxC0gAAIDKG5AxkH2bRGN7XUmbSbpI0uyiuJSkOyTNHuUx+0vavy8BAQAA+mEACsi+TKKxPVPSTyR9KCIebN4Wtf7gEfuEI2JeRMzNbm4GAABAQ89bIG0vrVrx+IOIOK24+07ba0TE7cU4ybt6nQMAACAf60COy7YlHSfpmoj4atOm+ZL2kXRk8fmM8Y41Q1vohRrqSc5urVeiVf4l6XOz7smOUPeWex7JjlC3pZ6bHaHFqf/6h+wIdev84ZLsCC3i8HuzI9Std+jh2RHq/vG7l2dHaLXlxdkJ6hbvcXp2hBZTf75LdoS6H88uT5vMNXetlh0BPdLrFshtJL1d0l9sX1bc9wnVCsdTbO8n6SZJe/Y4BwAAQD4m0YwvIn6n2qkayfa9fG4AAIDyGYwubK5EAwAAgK5U5lrYj+oeLdBx2TEkSRGvzI7QZoPsAHVv8U3ZEeouet/PsyO08Lffnx2htHzowuwITcozyPn/dnooO0KLdz80MztC3dRy/XirNuS/LHbIDtDkV9kByocubAAAAHRtAApIurABAADQFQpIAACAvikm0Uzkxzhs72T7WtvX2z54hO0ftn217Stsn2d73HXwKCABAAAGlO0pkr4laWdJG0va2/bGbbv9WdLciHiJpFMlfXG841ZmDOSmyy+j8/9lvewYkqSlXZ5JK5L05GWbZkeoi3cdkB2h7oPffl92hBZDm5Rn8tXcK1+cHaHFoVorO0Ld4SrPwvzvPupD2RFabO9jsyPUnVeyn+/aVXnLYZ0SzedZNjtAm+vKMEmu/5NotpR0fUTcIEm2T5a0u6Srh3eIiAua9v+TpLeNd9DKFJAAAADV15N1IGfZbr5c37yImFd8vZakW5q2LZT0sjGOtZ+kX4z3hBSQAAAA1XZPRMx9tgex/TZJcyW9arx9KSABAAD6pf9d2LdKWrvp9pzivha2d5D0SUmviojHxzsok2gAAAAG1yWSNrC9nu1pkvaSNL95B9ubSfpfSbtFxF2dHLQyLZBXPLS85py/XXYMSdJijVuY95U3fXd2hLq99K7sCHUn66nsCC1+f+Um2RHqtlB5BvxL0qcPPTw7Qt0HflOeyU5araPf431z3jfLM0lOJZo8KEkbl2BuxrD/zA7QpFy/haXDsgMM62MLZEQstn2ApLMlTZH0nYi4yvbhkoYiYr6kL0maKenHxVWVbo6I3cY6bmUKSAAAgOrrySSasZ8x4ixJZ7Xdd2jT111f/5IubAAAAHSFFkgAAIB+6f8kmp6oTAH5tKR/Zoeo+1N2gDYvzw5QV55RftJUTcmO0KJMacqURZL02PTsBHWX37xOdoS67Us0blaStMKD2QkanpiWnaDF3dkBSqpsYyDLof9d2L1AFzYAAAC6UpkWSAAAgMobkC5sWiABAADQFVogAQAA+mkAWiApIAEAAPqGSTQAAACYhGiBBAAA6Bcm0QAAAGAyogUSAACgbwZjDGRlCsjVJe2bHaLw+VO+lR2h1b+fkp2g7uotLs2OUPfJvU7OjtDCH9smO0KTmdkBWm13fnaCuu03vjo7QsPzbshO0OoVv8lO0PCZQ7MTtLhHn86OULf5Ov+RHaHuTyW6slNp0IUNAACAyagyLZAAAAADYQBaICkgAQAA+mYwxkA6IrIzdMR2aYJW5ZxlsJ0doa5s36cynZuyKdv3qjQ+fmR2glafPzg7Qd1tr/hddoQWa/725dkR0AHbCyJibmaGuS9eJobmrzWhx/Tz/tH310ULJAAAQL8wiQYAAACTES2QAAAAfTMYYyApIAEAAPplQLqwK1NAbvG8lTR05A7ZMSRJLynZXIgrPveJ7Ah1EQ9lR6gr26SVs9a5KTtC3bLTnsiO0Oro92UnqLvth2/JjlC35nYl+z6VyJrfODA7Qgv7/uwITbbNDoBJoDIFJAAAwEAYgBZIJtEAAACgK7RAAgAA9M1gTKLpaQuk7e/Yvsv2lU33rWz7XNt/Kz6v1MsMAAAApTE8iWYiPzJeRi+vAGH7lZIelnRiRGxS3PdFSfdFxJG2D5a0UkQcNP6x1g/pcz3L2p29sgOUVqmuKPKjcn2fvNe3siOUVsSJ2RHqLtzg9dkR6rb92/bZEdqUZyKYdGl2gBb7e/PsCHXzTn1TdoTS8pt+kn8lmk2XjqFzJ7btzKvd3ffX1dMWyIj4jaT72u7eXdIJxdcnSNqjlxkAAADKIwaiBTJjDOTsiLi9+PoOSbNH29H2/pL2r92a1fNgAAAAPTUg60CmzsKOWn/nqH2eETEvIubWmmVX6GMyAAAAjKanYyAlyfa6ks5sGgN5raRtI+J222tIujAiNhrvOHPnzo2hoaGeZgWAMi1AX6oxxSVTpu9T2fC+GZ3t/DGQm02NoQtnTugxveIDgzUGchTzJe1TfL2PpDMSMgAAAGAJ9XQMpO2TVLum0izbCyUdJulISafY3k+1KX179jIDAABAaXgw1oHsaQEZEXuPsqlsa1MAAAD0xwAUkFzKEAAAAF2pzKUMFyy4Qvba2TEKC7MDlFbE27Ij1C1+3WgN4DmWPmvcuWJ9tDg7QIvY8/LsCHXxjhPG36lvXpQdoNWXXpedoK5sE0VOWvWe7AhNdsgOgDHRhQ0AAIBusA4kAAAAJiNaIAEAAPqJFkgAAABMNhVqgZyq8lwPu1yTaMo0mLxcV4f4fnaAFmX6PpWNXZ7vVcRnsiM0fPa67AQtfMhnsyM0fOxL2QlalOnnu3bBN5QW60ACAACgawNQQNKFDQAAgK7QAgkAANA3ljzR5deTE3y88VWogJwl6V3ZIQp7ZQdo8fESDTuMA7+eHaHukG8cmB2hhV2mBarfkx2gRRxQnvFsG5fo5+nqQ6ZnR2hRpnF++shXsxO0uTQ7QN2BuiU7Qt2M7ABtjlSJfsArrkIFJAAAQNUtJWnmBB/z/gk+3vgoIAEAAPpmiqTlJ/iY/S8gmUQDAACArtACCQAA0De96MLuvwoVkFNUnhNeroXEL9cq2RHqbhmamx2h7vLsAG1maZ/sCHX3lGwSjR4rz2SR8vw0SXq4LL/zhpVnooimP5adoIX9nOwIdUfPeDQ7Qt3Mkn2fdF92AKk3Xdj9Rxc2AAAAulKhFkgAAICqG4wubFogAQAA0BVaIAEAAPpmMMZAVqaA3GKLVTQ0VJ5JCGVil2dl/XX+kJ2goVRXzSidf2YHKK3f/l92gmYfyg5QWhefXp6JV5IUsX52hJIq17Vo3l6KP5d0YQMAAGASqkwLJAAAQPUNRhc2LZAAAADoSmVaIK9cIG1UirEL0u+3vTA7QpvyLNQa+56UHaHu+6uUYsXYurcdcUh2hIYSLdwtSbptzewEddt/+aPZEerOO3+77AgtvF15BojGp07JjtDqzRtnJ6jb85Q9syPUPZEdoJSW0iC0QFamgAQAAKi+Ml1Zb8nRhQ0AAICu0AIJAADQN4OxjA8FJAAAQN8MxizsyhSQj+sxXadrs2NIkla98LjsCC3eqW2zI9Ttffy+2RHqTtZ/ZUdo8fb3/S07QpPF2QFavF9fzY5Qd96x78qO0HDaG7MTtIiP/jQ7Qt39v3lldoQWnynRhKcf6/LsCE0qU2agS3xnAQAA+mYwurCZRAMAAICu0AIJAADQN4yBBAAAQFcGowubAnIAPJUdAAAATCoUkAAAAH0zGF3YTKIBAABAV2iBBAAA6JvBaIFMKyBt7yTp66qdyWMj4six9p+m6VpTG/Ul23hu1MuzI7Q4PrITlNOBL/zP7Agt/vWvt2ZHaLJDdoAW73rJFdkRGv71D9kJGvY7NjtBaa30vXdkR2jx1fPKs5D4V7/79ewIDRdvmZ2ghY/JTiANyiSalC5s21MkfUvSzpI2lrS37Y0zsgAAAKA7WS2QW0q6PiJukCTbJ0vaXdLVSXkAAAD6oP9d2OP1+tpeRtKJkraQdK+kN0fEjWMdM6uAXEvSLU23F0p6WftOtveXtL8kTdE6/UkGAADQM/3twm7q9X2NavXWJbbnR0Rzo91+ku6PiOfb3kvSFyS9eazjlnoWdkTMi4i5ETF3ilbNjgMAAFA19V7fiHhC0nCvb7PdJZ1QfH2qpO1te6yDZrVA3ipp7abbc4r7RvWEFtxzo3zTs3zeWZLueZbHKB37PRNxmIE8NxOEczO6CTk3m13x0gmIMkEmbjT2BJybMX9/V9Vg/jy943sTcZQBPDffnagDTdS5ee4EHONZWbBgwdm2Z03wYafbHmq6PS8i5hVfd9LrW98nIhbbfkDSKhrjnGcVkJdI2sD2eqoVjntJestYD4iIZ90EaXsoIuY+2+MMIs7N6Dg3o+PcjI5zMzLOy+g4N6MbpHMTETtlZ5gIKV3YEbFY0gGSzpZ0jaRTIuKqjCwAAAADrJNe3/o+tqdKeo5qk2lGlbYOZEScJemsrOcHAACYBDrp9Z0vaR9Jf5T0JknnR8SYq0xPtivRzBt/l0mLczM6zs3oODej49yMjPMyOs7N6Dg3S6gY0zjc6ztF0nci4irbh0saioj5ko6T9D3b10u6T7Uic0wep8AEAAAAWpR6GR8AAACUDwUkAAAAujJpCkjbO9m+1vb1tg/OzlMWtte2fYHtq21fZfuD2ZnKxPYU23+2fWZ2ljKxvaLtU23/1fY1trfOzlQWtv+r+Fm60vZJtqdnZ8pi+zu277J9ZdN9K9s+1/bfis8rZWbMMsq5+VLxM3WF7Z/aXjExYpqRzk3Tto/Yjh6so4guTYoCsukyPjurtkzw3rYnbrngalss6SMRsbGkrSS9n3PT4oOqLTWFVl+X9MuIeIGkl4pzJEmyvZakAyXNjYhNVBuwPu5g9AF2vKT2Ne8OlnReRGwg6bzi9mR0vJ55bs6VtElEvETSdZI+3u9QJXG8nnluZHttSTtKurnfgfBMk6KAVGeX8ZmUIuL2iLi0+Poh1QqBtXJTlYPtOZJeJ+nY7CxlYvs5kl6p2qw9RcQTEbEoNVS5TJW0bLGW2gxJtyXnSRMRv1FtRmez5kumnSBpj35mKouRzk1EnFOskyxJf1Jtvb5JZ5T3jSR9TdLHJDH7twQmSwE50mV8KJLa2F5X0maSLkqOUhZHqfbL6unkHGWznqS7JX236N4/1vZy2aHKICJulfRl1VpIbpf0QESck5uqdGZHxO3F13dImp0ZpsTeKekX2SHKwvbukm6NiMuzs6BmshSQGIftmZJ+IulDEfFgdp5stneVdFdELMjOUkJTJW0u6eiI2EzSI5q83ZAtivF8u6tWZK8paTnbb8tNVV7FQsW0JrWx/UnVhhf9IDtLGdieIekTkg7NzoKGyVJAdnIZn0nL9tKqFY8/iIjTsvOUxDaSdrN9o2pDHraz/f3cSKWxUNLCiBhuqT5VtYIS0g6S/hERd0fEk5JOk/SvyZnK5k7ba0hS8fmu5DylYntfSbtKeut4VwKZRNZX7Z+yy4vfyXMkXWp79dRUk9xkKSDrl/GxPU21Qe3zkzOVgm2rNpbtmoj4anaesoiIj0fEnIhYV7X3y/kRQUuSpIi4Q9Ittjcq7tpe0tWJkcrkZklb2Z5R/GxtLyYYtRu+ZJqKz2ckZikV2zupNmxmt4h4NDtPWUTEXyJitYhYt/idvFDS5sXvIiSZFAVkMSh5+DI+10g6JSKuyk1VGttIertqLWyXFR+7ZIdC6X1A0g9sXyFpU0mfy41TDkWr7KmSLpX0F9V+x07aS7DZPkm1a+tuZHuh7f0kHSnpNbb/plqL7ZGZGbOMcm6+KWl5SecWv4uPSQ2ZZJRzg5LhUoYAAADoyqRogQQAAMDEoYAEAABAVyggAQAA0BUKSAAAAHSFAhIAAABdoYAEAABAVyggAaSy/Ycu97/Q9twu9p9r+xvdJwMAjGZqdgAAk1tE9PRSfxExJGmol88BAJMNLZAAUtl+eIxtB9n+i+3LbTdfseTfbV9s+zrbryj2nW77u8X+f7b96uL+bW2fWXw9s2mfK2z/W3H/jrb/aPtS2z+2PbOHLxkAKo8WSAClZHtnSbtLellEPGp75abNUyNiy+Kym4epdkm890uKiHix7RdIOsf2hm2H/R9JD0TEi4vnWMn2LEmHSNohIh6xfZCkD0s6vLevEACqiwISQFntIOm7EfGoJEXEfU3bTis+L5C0bvH1yyX9v2Lfv9q+SVJ7AbmDpL2Gb0TE/bZ3lbSxpN/blqRpql2HFwAwCgpIAFX0ePH5KT3732OWdG5E7P0sjwMAkwZjIAGU1bmS/sP2DElq68IeyW8lvbXYd0NJ60i6doRjvn/4hu2VJP1J0ja2n1/ct9wIXd8AgCYUkACyxYh3RvxS0nxJQ7Yvk/TRcY7zbUlL2f6LpB9J2jciHm/b5whJK9m+0vblkl4dEXdL2lfSSbavUK37+gVL+mIAYDJwxIi/uwGg52yvIunSiHhudhYAQOdogQSQwvaaqrX2fTk7CwCgO7RAAkhl+8WSvtd29+MR8bKMPACA8VFAAgAAoCt0YQMAAKArFJAAAADoCgUkAAAAukIBCQAAgK5QQAIAAKArFJAAAADoCgUkAAAAukIBCQAAgK5QQAIAAKArFJAAAADoCgUkAAAAukIBiUnH9udtf2iUbcfbPqLPkVAStj9l+1M9PP6NtndYwsdeZXvbCc7zrI6Z/XpsX2z7Rc/mGACWDAUkBo7tlWyH7YdtP2r7tuGC0faqkt4h6X9TQ5ZIcZ6GP562/c+m228t9rmx6f47ikJ7ZtMxmrffb/vnttceYftDthfZ/oPt99heqmmfZWwfZ/umYr/LbO/clnXf9qLD9sq2f2r7keKxb2nbfoDtIduP2z5+Qk9eH0XEiyLiwrIfs8/P/WVJh09AHABdooDEINpU0t0RMTMiZkh6r6Sv2Z4jaV9JZ0XEPxPzlUpxnmZGxExJN0t6fdN9P2ja9fXFPptK2kzSx9sONbx9DUl3Svp/I2xfXtJzJR0p6SBJxzVtnyrpFkmvkvQcSYdIOsX2urb/0/Ybiv3cdvtbkp6QNFvSWyUd3dYqdZukIyR9p4vTgmqYL+nVtlfPDgJMNhSQGESbSrqk6fZFxedpknaW9OvhDbY3s31p0eL1I0nTO3kC25vb/nPxuB/b/lFz17ftNW3/xPbdtv9h+8CmbTfa/qjtK2w/UDx2eiePLYOIuEPS2aqd55G2PybpVEkbj7L9gYiYL+nNkvaxvUlx/yMR8amIuDEino6IMyX9Q9IWqhV/60v6oKTPSVos6Qzby0n6N0n/ExEPR8TvVCsq3t70fKdFxOmS7u3mddo+yPapbfd93fY3xnnc2rZPK75/99r+Ztsum47xvX+h7QuLVtqrbO/WtK3eXTzWc3Tz/nEHXdBlfj3Fe22BpNeO9RoATDwKSAyizSRdLEm2V5T0WdX+yPxD0oslXVtsmybpdEnfk7SypB+rVoyMqXjcTyUdXzzuJElvaNq+lKSfSbpc0lqStpf0IdvNf+T2lLSTpPUkvUS1ltFOHyvbZxZ/lEf6OLODc7TEipbcnSVdP8r2GaoVh38a6zgRcbGkhZJeMcpxZkvaUNJVww+R5OLz08XnDSUtjojrmh56uaSJGBd3sqRdbC9f5Jmi2vfth6M9oNjnTEk3SVpXte/hyW27jfa9X1q17/05klaT9AFJP7C9UafP0en7p1MVeT3XSHrpkrw+AEuOAhKDaFNJ/237PtUKx1Ct+zQkrSjpoWK/rSQtLemoiHgyIk5Va8vlaLZSrbv1G8XjTlNRsBb+RdKqEXF4RDwRETdI+j9JezXt842IuC0i7lPtD+SmXTxWEbFrRKw4yseunZykJXC67YdU62a+S9JhI2xfJOkBSa+R9KUOjnmbakV4i6L4+IGkEyLir5Leqdo/AEdJ+qSkZSTtIWmmpAfbHv6ApOU7eUFjiYibJF2qxj8H20l6NCLGKoy3lLSmpP8uWlQfK1pFm432vd9KtddzZPG9P1+1wmrvLp6jo/dPF6rweh5S7ecaQB9NzQ4ATCTby0h6oaT1ImLhCLvcr0ZxsaakW4vCcthNHTzNSI+7penr50pasyimhk2R9Num23c0ff1occxOH5tlj4j4le1XqdYKN0vSohG2T5G0u6Rf29646PIezVqS7mu+o2h1+p5q4xoPkKSI+N9i2761m3FMcXszSSu0HXMFNf5JeLZ+qFrBc6Kkt2iM1sfC2pJuiojFY+wz2vd+TUm3RMTTTdtvUu0cdfocE/3+qcLrWV6t70MAfUALJAbNJpIeGaV4lKQrVOv2lKTbJa1l203b1+ngOUZ63NpNX98i6R9trYLLR8QuHRy7o8fa/oVbZ083f/yig+dZYhHxa9W67788yvanilbZpyS9fLTj2P4X1YqJ3zXdZ9Um1syW9G8R8WTbsY9vm7l7naSptjdouu+lanR7P1s/lrRt0W3/Bo1fQN4iaR3bS/LP+W2S1nbTzHTV3o+3dvEcz+a9N5IqvJ4XqtbFDaCPKCAxaDbT2MXDWarN8pWkP6o2GeNA20vbfqNq3Wnj+aNqxdEBtqfa3r3tcRdLesi1SRjL2p5ie5OiYBpPR4+NiJ2bZ0+3few8yrEn0lGSXmP7GWPPXLO7pJVUG5/Wvn0F27uqNs7t+xHxl6bNR6tWELw+OpgpHxGPSDpN0uG2l7O9jWqtn99rer6pxcSOKZKm2J7eaUEUEXdLulDSd1UrZJ7xetpcrNo/GEcWeaYXmTpxkWoteB8r3o/bSnq9njnmcKznGPP949ryS8d3mKcKr2e6apOszu3iNQGYABSQGDSbSrpyjO0nqjYxYtmIeELSG1Ub9H+fahM/ThvvCZoet59qXWdvU21s1+PF9qck7Vpk+YekeyQdq9rSNOMde4kf209FYXWipEOb7v6Z7YdVG5P4WUn7RMRVbduHx1B+UtJXJf3H8Ebbz5X0n6q99jvcthblGN4naVnVxmWeJOm9bc97iKR/SjpYte/VP4v7OvVDSTto/NbH4e/f6yU9X7UlkRaq9r4aV/G+er1qE5TukfRtSe8oxoB29BwdvH/WlvT7TvJU5PW8XtKFEXFbp68JwMRw6zAuYPDZ/pykuyLiqAk85kWSjomI707UMdF/Lq5CExGfyk0y8VxbPeBySS8ZHhpg+2ZJb4uI36SGW0LFz91+ETHWP40AeoBJNJh0IuITz/YYxUSSa1VrEXmrasuX/PLZHhfolaJF8IXDt127KtOqkm7MyvRsRcTLsjMAkxVd2MAIbK8zxiSVdSRtpFprziJJH5H0poi4PTMzJsSFxceoOnhvlF4xhvBvKq4WVPXXA2Bstr9j+y7bI7bWF2PXv2H7etcuDLD5uMekCxsAAGBw2X6lpIclnRgRm4ywfRfVFvvfRdLLJH19vBZ+WiABAAAGWDHO+b4xdtldteIyioslrGh7jbGOWZkxkLNmzYh1121fLzhJ3JWdoLyempKdoGHqzOwErZ6aqLWtJ4DL1fPw2LUbjb9Tn9z/6IzsCHVlm1q8xRbZCRoeueqx7Agtllvn5uwIDfevlJ2g4YmlsxO0WPDA3++JiFUzM9g9+QV8laTmH4p5ETGvi8evpdYLYiws7ht1aFZlCsh1111BQ0P7ZMeoefzr2QnK68GSFPmStGqny9X1yf2/zk7QMHWsC4v039Xbfj87Qt2pl4479KdvPpUdoM3QUHaChotfdHV2hBZbfuPA7AgNp74pO0HDbWuOv08fef7unVxtrA8muvxa/FhEzJ3gg46JLmwAAIDJ7Va1XlFtjp551agWFJAAAAB9Y9VaICfy41mbL+kdxWzsrSQ9MN7KIpXpwpaWlrR6doiaZco19qZUUkeWlFyJhiWVzYsu9fg79c2c7ABN2q/6l+u1HV/FsPfOjrKtMPSr7AANqz9jkm2eh0s2Fn1+doActk+StK2kWbYXSjpMtcJKEXGMapf53UXS9apdgvQ/Rj5SQ4UKSAAAgKobboHsn4jYe5ztIen93RyTAhIAAKBv+l9A9gJjIAEAANCV6pfAAAAAlTEYLZAVegVPSrojOwQq5bnZAdqUZPmxEvrJ6uW5jPhzZ92THaFuiw2vy47Q6ifZAZp8/ZDsBC1e8qGjsiPUXXHsVtkRGmY+nJ2gzUXZAQZGhQpIAACAQVD98qv6rwAAAKAyBqMLm0k0AAAA6Er1S2AAAIDKGIwWyOq/AgAAgMoYjAKSLmwAAAB0pfolMAAAQGXQAgkAAIBJqPolMAAAQKVUv/yq/isAAACoDLqwAQAAMAlVvwQGAACoDFogAQAAMAlVvwQGUHlTlno6O0I5Pc3/+MDgGYwWyOq/AgAAgMoYjAKSf28BAADQleqXwAAAAJVS/fKr+q8AAACgMgajC7tCr2COpC9kh0Cl3JQdAB3a47a1siM03JYdoMmV2QHaODtAQ0RkR2hxxQezEzTYx2VHwCRQoQISAACg6gajBZJJNAAAAOhK9UtgAACAyhiMFsjqvwIAA+Dh7ABNTs4O0OSA7ABtHssOUGeXaECmpPjD1tkR6so2PrRMyvG+GYwCki5sAAAAdKX6JTAAAEBl0AIJAACASajnBaTt/7J9le0rbZ9ke7rt9WxfZPt62z+yPa3XOQAAAMph6gR/9F9Pn9X2WpIOlLRxRPzT9imS9pK0i6SvRcTJto+RtJ+ko3uZZWK9LztAm29nBwCelTji89kR6k466kPZEer23vY52RFabVKilc0fnpmdoNXWH81O0HD5ptkJGhZXv6t24tGF3ampkpa1PVXSDEm3S9pO0qnF9hMk7dGHHAAAAJgAPS2BI+JW21+WdLOkf0o6R9ICSYsiYnGx20JJJbqOGQAAQK/QAjku2ytJ2l3SepLWlLScpJ26ePz+todsD9199909SgkAANAvwwVktcdA9roLewdJ/4iIuyPiSUmnSdpG0opFl7YkzZF060gPjoh5ETE3IuauuuqqPY4KAACATvS6bL1Z0la2Z6jWhb29pCFJF0h6k2qXfNhH0hnjH+p2SZ/pVc4ulWvSSjlW1q/hCghYIltenJ2gbruNr86O0LDH6dkJWr31+9kJGt5frt/DpfLSm7MTNNzLaoHPRBf2uCLiItUmy1wq6S/F882TdJCkD9u+XtIqko7rZQ4AAABMnJ6XwBFxmKTD2u6+QdKWvX5uAACA8ql+C2T1XwEAAEBl0IUNAACASahCJfBSkkp25YGSYOIKKu+Hb8lOUDe7RJNo/u89x2RHaPHut2YnaPj4t8t1RbDPfys7QZOfbpudoOGJsl2p+EfZAUQLJAAAACal6pfAAAAAlTEYLZDVfwUAAACVQQHZX0/fJj3UvhpQksVlWdC8cPXG2QkatvlddoImb88O0OrcO7MTlNZll345O0Ldpt88IDtC3XXHvCc7Qqv/Oio7Qd0r13ljdoQ262QHaLhs0+wEDQ+Xbe5CGcZADobqFJAAAAADofrlV/VfAQAAQGUMRhc2s7ABAADQleqXwAAAAJUxGC2Qrsoi1HPnzo2hoaHsGBiH7ewIdVV5b6Nc7xtp9ewATe7IDtCCn6nRPb9Eb+Hrh+ZmR2h4ulwdnd7ykgURkXqC7DkhfWCCj3pw319X9UtgAACAyhiMFsjqvwIAAIDKGIwCslxtywAAACi96pfAAAAAlVL98qv6rwClwiB7LAneN6i660v1FmbC6ehKNNup4iggAQAA+mYwxkBW/xUAAABUxmAUkEyiAQAAQFeqXwIDAABUxmC0QFb/FQAAAFTGYBSQdGEDAACgK9UvgQEAACqDFkgAAABMQhUqgRdKOig7REl9ITtAk62yAzT5UHaAVn87NDtBw1JPZydosfjAb2RHqJu63fnZEequPXmv7AgtNipRnvN2PCc7Qovt/75sdoSGL3w9O0HDthdmJyipCpVfo6j+KwAAAKgMurABAAAwCVW/BAYAAKiMwWiBrP4rAAAAqAwKyP66abr07g2yU0iSfjx/t+wILf79zuwEDRdu8P3sCHXb/nKn7AitNrg+O0Fp/edZ2QkavnPW7dkRmjyVHaDV80s0UUTfyw7QIrRCdoQ6H/yK7AhN/iU7QJs3ZQcYGNUpIAEAACpvMFogmUQDAACArlS/BAYAAKiU6pdfjojsDB2ZO3duDA0NZccAAAAVZXtBRMzNzfCCkI6b4KO+vO+viy5sAACAAWZ7J9vX2r7e9sEjbF/H9gW2/2z7Ctu7jHfM6rehAgAAVEZ/J9HYniLpW5Jeo9p1oS+xPT8irm7a7RBJp0TE0bY3lnSWpHXHOi4tkAAAAINrS0nXR8QNEfGEpJMl7d62T0j1taieI+m28Q7a0xLY9kaSftR01/MkHSrpxOL+dSXdKGnPiLi/l1kAAADy9aQFcpbt5oki8yJiXvH1WpJuadq2UNLL2h7/KUnn2P6ApOUk7TDeE/a0gIyIayVtKtWbUG+V9FNJB0s6LyKOLPriD5Z00NgHu1R6clov43Zu6ZuyE7RZIztAw+PTsxM0LPOT7ARtXpcdoLQOcHaChn9mB2jyhW0vzI7QYtYB38yO0HD987MTtDroyOwEDf85b/x9+uV5N2QnKKGeFJD3PMtJNHtLOj4ivmJ7a0nfs71JRDw92gP62YW9vaS/R8RNqjWdnlDcf4KkPfqYAwAAYLK4VdLaTbfnFPc120/SKZIUEX+UNF3SrLEO2s8Cci9JJxVfz46I4euF3SFp9kgPsL2/7SHbQ3ffU43lhgAAAMY2dYI/xnSJpA1sr2d7mmr12Py2fW5WraFPtl+oWgF591gH7UsBWQTeTdKP27dFbSHKEavDiJgXEXMjYu6qs0rUxwUAALBEhruw+1NARsRiSQdIOlvSNarNtr7K9uG2dyt2+4ikd9u+XLXGvn1jnIXC+zWPfGdJl0bEncXtO22vERG3215D0l19ygEAADCpRMRZqi3N03zfoU1fXy1pm26O2a8u7L3V6L6Wak2n+xRf7yPpjD7lAAAASNTfFshe6XkBaXs51RavPK3p7iMlvcb231SbKl6i6WsAAAAYS8/L1oh4RNIqbffdq2KwJgAAwOTR3yvR9Er1XwEAAEBlUED2lzeXlh4afz/kWuax7ASlZf89O0JpxekfyY7QsPvp2Qnq7FdnR2h1YXaAhnEmiPadzUohmFyqU0ACAAAMhOqXX/1cSBwAAAADoPolMAAAQGUwBhIAAABdoYAE0IWI9bMjlNjp2QHQgbJNXCkTzk01MNlp4lBAAgAA9A0tkAAAAOjKYBSQzMIGAABAV6pfAgMAAFTGYLRAVv8VAMCAYmLG6P7+souzI7RY/6ItsyMAfUUBCQAA0EfWlAk9Xsa/mhSQAAAAfTSx5aO0eIKP1wkm0QAAAKAr1WmBvPtm6ej3Zaeoee+3sxOU1v3bn58doW6ldW7OjtDqu/tmJyiv774zO0HDH/41O0Gdj313doQWZRqTWbYxh2VaoDquL9FFCx6emZ2gdCxaIAEAADAJVacFEgAAoOJ60QKZgQISAACgjwahgKQLGwAAAF2pTgvkzIelf/1DdgpJLGA7lpVK8j2SJH1ml+wEbf6WHaC0vv/RL2dHqHvbDc/LjtBw7PezE5TXm0/JTtCiTBOMbnvF77Ij1J1/9cbZEdqskh2ALmwAAAB0ZzAuZEgXNgAAALo0CEUwAABAZQxCFzYtkAAAAOgKLZAAAAB9wiQaAAAAdG0QCki6sAEAANAVWiABAAD6ZFC6sF2mxU/HMnfu1BgaWiE7Rs2d07ITtJp9R3aCknpDdoA238sOUF4Lts1O0DDz4ewEDb97eXaCVvt9JjtB3e82eig7QouXX7thdoSGW+dkJ2h4eGZ2ghZ+wbULImJuZoZlPDfW1NCEHvNGue+vixZIAACAPhmUFkgKSAAAgD4ahAKSSTQAAADoCi2QAAAAfUIXdt89R9Iu2SFqVvtBdoLyenx6doKGZR7LTtDm7dkBmiyfHaDFgn2Pz45Qt9oKD2ZHqPvT9c/PjtDi35/4VHaEupdvtVV2hFZ/2zU7QcM962QnwBisShVfo6ILGwAAAF0ZhCIYAACgMgahC5sWSAAAAHSl5wWk7RVtn2r7r7avsb217ZVtn2v7b8XnlXqdAwAAINvwJJqJ/MjQjy7sr0v6ZUS8yfY0STMkfULSeRFxpO2DJR0s6aAxj/Lkg9Kd5/Y8bEdmP52doLyWmZ2doOEfz8tO0Gq9/8hO0KRcV4e4t0RXq7jmtjWzI9R9+76VsyO0+Pf3fjs7QsPmJZtEs8Gl2QkaZpboqjiLGSk3Erqwx2H7OZJeKek4SYqIJyJikaTdJZ1Q7HaCpD16mQMAAAATp9f/Gqwn6W5J37X9UkkLJH1Q0uyIuL3Y5w5JIzZb2d5f0v6StM4chmsCAIBqG5R1IHtdlU2VtLmkoyNiM0mPqNZdXRcRISlGenBEzIuIuRExd9WVKSABAADKoNctkAslLYyIi4rbp6pWQN5pe42IuN32GpLu6nEOTEZP809HVTxVou/VlKXKM8b5qewAZTZ1cXaC8irRe7hUWUqCFsgORMQdkm6xvVFx1/aSrpY0X9I+xX37SDqjlzkAAADKglnYnfmApB8UM7BvkPQfqhWup9jeT9JNkvbsQw4AAABMgJ4XkBFxmaS5I2zavtfPDQAAUCaD0oXNAk0AAAB9Yg1G8VWd17D0S6XZQ9kpMK6bsgM0rJ8doN3fsgOU1s7XPz87Qt3G057IjlC3dnaANvZ62RHq4tQtsiO0KlOc2XdkJygxZwcYGNUpIAEAAAbAIHRhl2ftDAAAAFQCLZAAAAB9wiQaAAAAdI0CEqVgl2dQcO3KlBjZBtkBSsvTnsyOUMdbeCz/yA5QXj/fNTtBw+v+OztBEyYPDioKSAAAgD4ZlC5sJtEAAACgK7RAAgAA9MmgtEBSQA4Axh0Cg6lM45slKRaulR2hznNuzY7Qoky/h8v2vsEzDUIBSRc2AAAAukILJAAAQJ/QhQ0AAICuWINRfNGFDQAAgK5UqAj+q6StskMUrssO0Oa+7AAldVB2gDarZwdosnx2gBa3vvy32RHq/v6yi7Mj1MVbfpAdodVab8lOUHfHK3+THaHNw9kB6uKKF2dHaJj2RHaCFn7BtdkRJA1GFzYtkAAAAOhKhVogAQAAqo1JNAAAAOjKoBSQdGEDAACgKxVqgXxS0u3ZIWqeLM9gaUnSD/fNTtCwz0uzEzT5QnaAVnevlp2g4bHp2QlafOJ3N2dHqDs+/pYdoe4Ql2fSiiQd8YPy/O6bvfod2RFaffbI7AQNn7wiO0GJleMqPbRAAgAAYNKpUAskAABAtQ3KGEgKSAAAgD6igOyneFJ6/M7sFDVl6/jf5/PZCZqskR2g4Z8zshO0WvXR7ASldfznPpEdoWHvl2QnqPuuNsiO0OKIC3bLjtCw4l7ZCVrdvE52gobfvjI7QcPTZfuDOTnZ3knS11WrXY+NiGcM2rW9p6RPSQpJl0fEmIOwq1NAAgAAVFy/u7BtT5H0LUmvkbRQ0iW250fE1U37bCDp45K2iYj7bY8765MCEgAAoE+svhdfW0q6PiJukCTbJ0vaXdLVTfu8W9K3IuJ+SYqIu8Y7KG3LAAAA1TbL9lDTx/5N29aSdEvT7YXFfc02lLSh7d/b/lPR5T0mWiABAAD6qAdd2PdExNxn8fipkjaQtK2kOZJ+Y/vFEbFotAfQAgkAADC4bpW0dtPtOcV9zRZKmh8RT0bEPyRdJ409i48CEgAAoE+GJ9FM5Mc4LpG0ge31bE+TtJek+W37nK5a66Nsz1KtS/uGsQ5KFzYAAECf9HsWdkQstn2ApLOLp/5ORFxl+3BJQxExv9i2o+2rJT0l6b8j4t6xjksBCQAAMMAi4ixJZ7Xdd2jT1yHpw8VHRyggAQAA+ogr0fST15WW+Vx2isLy2QHalOjqL3pfdoCGZT+bnaDNi7IDNHlVdoAWp33j0PF36pM3vuvY7Ah1H5m6ODtCq4dnZidoeN6Yw7P676BnXNgjz+9PzE7QwJVoBlZ1CkgAAICK6/cYyF6hgAQAAOijKUs9PbEHnODDdaLnBaTtGyU9pNqsnsURMdf2ypJ+JGldSTdK2nP48jkAAAAot361QL46Iu5pun2wpPMi4kjbBxe3D+pTlglQonFApVOm8aFlyiKVK0+Zski3L1oxO0LD9MeyE9StseKi7AitHp2RnaBh5sPZCcqrTOfmiWnZCUrHClogn4XdVSxYKekESReqUgUkAABA92xp6kQXkAn6MT0qJJ1je0HTxb1nR8Ttxdd3SJrdhxwAAACYAP1ogXx5RNxqezVJ59r+a/PGiAjbMdIDi4Jzf0laZ51ZvU8KAADQYxPehZ2g5y2QEXFr8fkuST+VtKWkO22vIUnF57tGeey8iJgbEXNXXbVcY7YAAAAmq562QNpeTtJSEfFQ8fWOkg5X7SLe+0g6svh8xvhHu0dSWRb4fV12gDY/yg7QpEyF/teyA7Qp04Lv12UHaPH+vU7OjtCw5m3ZCepWW+HB7AitdvhVdoKGw8uz+Lwk6dfbZido2Lg872EtZrXAdnYPJtEk6PV3drakn9oefq4fRsQvbV8i6RTb+0m6SdKePc4BAACQzhqMLuyeFpARcYOkl45w/72Stu/lcwMAAKA3aFsGAADom8HowuYq5wAAAOhKhVogZ0jaIjtE4efZAdqUaGB7qZyZHaDVA0PZCRqeLtf/jvse/9PsCHXH37dydoS6f3neDdkRWq1yz/j79MkJJy7KjtBinyMOyY7QcOO62QlK7Pbxd+kxmzGQAAAA6EJPLmWYoKNmCNszbP+P7f8rbm9ge9feRgMAAEAZddoC+V1JCyRtXdy+VdKPVbo+QgAAgBKbZF3Y60fEm23vLUkR8aiLxR37Z4qkmf19ylGVaUFojO7h7ACtFq2YnaChZGMgp2UHaHJnib5Pg/BHZtIo2c8UysuSpg7Az3an7/gnbC8rKSTJ9vqSHu9ZKgAAAJRWpy2Qh0n6paS1bf9A0jaS9u1VKAAAgEE0KJNoOiogI+Jc25dK2kq11tcPRkR51nMAAABA33RUQNp+g6TzI+Lnxe0Vbe8REaf3MhwAAMBAmWSTaA6LiPpKvxGxyPZhkk7vSaoR3SbFYf17urG4+t/4yWGD7ACtnsuC76O5KTtAk9nvODE7Qt33P/bF7AgtnnjOg9kR6t75xtOzI7R6zqLsBHUL3nJldoS6LV5yRXaENm/NDjAwXdidTqIZaT8WIQcAAJiEOi0Ch2x/VdK3itvvV21dSAAAAHRqQLqwO22B/ICkJyT9qPh4XLUiEgAAAJNMp7OwH5F0cI+zAAAADDRrMFogxywgbR8VER+y/TMVi4g3i4jdepbsGbaQPNS/p8MSent2gIZzS3ZliOc/LztBw/THshO0eMOM67MjNOy3Q3aCuu+9a+XsCC3OfqI81wzytHOzI7SIo1bMjlC37LTzsyPU/f3652dHKJ1BmUQzXgvk94rPX+51EAAAAFTDmAVkRCwoPv/a9jRJGxabro2IJ3sdDgAAYKAMyCSaThcS31bSCZJuVK37fm3b+0TEb3qWDAAAYMBYoamTpYCU9BVJO0bEtZJke0NJJ0naolfBnuk6SWUZm3R6doAW9tbZEeoiPpkdoeE1e2UnaHNQdoDSeu+jPx1/pz55z6//LztC3Tn6dHaEFvtOeyI7Qt3a2QHaff9t2QnqNl6weXaE8nJ2gMHRaQG59HDxKEkRcZ3tpXuUCQAAYCB5MnVhq7aQ+LGSvl/cfqskpkQDAABMQp0WkO9VbeHwA4vbv5X07Z4kAgAAGGCTpgUyIh6X9NXiAwAAAEtgsqwDKUmyvY2kT0l6bvNjIqJEKyMDAACgHzrtwj5O0n9JWiDpqd7FAQAAGGCTbBLNAxHxi54mAQAAQCWMdy3s4cWkLrD9JUmnSXp8eHtEXNrDbAAAAANlsoyB/Erb7blNX4ek7SY2DgAAwOCaFOtARsSr+xVkfM+VdHR2iJrbNxx/nz6KU16eHaHhyXdkJ2hYumRXovn1RdkJGkr2y+srUz+bHaHutkOemx2hbguV6Gdb0gklujLOr5739uwIrd7/QHaChj/+a3aChntmZSdAjyzVyU62P2d7xabbK9k+omepAAAABtSUpZ6e0I8MHRWQknaOiEXDNyLifkm79CQRAADAgLJCU5d6ekI/MnRaQE6xvczwDdvLSlpmjP0BAAAwoDpdxucHks6z/d3i9n9IOqE3kUbx2N+ka1/f16cc1XVzx9+nnxZ3+m3sg/tWzk7QMDs7QJtXXZidoLT+tDg7QcOH33VsdoS6K39XrjGQEYdlR6jbw9kJWm2v72VHaLjtTdkJGqY9kZ2gdCbFJJphEfEF25dL2qG46zMRcXbvYgEAAKCsOm66iohfSvrlSNts/zEitp6wVAAAAANpcqwD2anpE3QcAACAgTUoXdidTqIZT4y10fYU23+2fWZxez3bF9m+3vaPbE+boBwAAADosX7NvvigpGskrVDc/oKkr0XEybaPkbSfxlsl3JKmlmSk/ep3ZCdoNf2x7AQNj87IToAKemF2gGa8hythZnaAMlvztuwEDUyiGdGkaYG0/WHba421yxiPnSPpdZKOLW5btUsgnlrscoKkPTrJAQAAgHydtkAuL+kc2/dJ+pGkH0fEnU3bx7qm1FGSPlYcQ5JWkbQoIoabExdKGrE4tb2/pP0laZ01S7RUDQAAwBLwgEyi6agFMiI+HREvkvR+SWtI+rXtXzVtv3Kkx9neVdJdEbFgScJFxLyImBsRc1ddecqSHAIAAKA0hifRVP1Sht02690l6Q5J90parYP9t5G0m+1dVJupvYKkr0ta0fbUohVyjqRbu8wBAACAJJ2OgXyf7QslnadaF/S7I+Il4z0uIj4eEXMiYl1Je0k6PyLeKukCScNL5e8j6YwlyA4AAFAxMalaINeW9KGIuGyCnvcgSSfbPkLSnyUdN0HHBQAAKC1bmjoAYyA7vZThx5/tE0XEhZIuLL6+QdKWz/aYAAAA6D+mNgMAAPSJNRjrQFangFzq6fIsmH3bmtkJWg3NzU7Q8KGjshOggk7ODtDk0ys8mB2h7nE9nh2hzTLZAeqq88crwcUl6uArywVA6pZoURiMgJ9BAACAvhmMdSApIAEAAPpkeB3IqutoGR8AAABgGC2QAAAAfRPyALRAVqeAXHp5aa1XZ6eomf6L7AStdj89O0GTQ7IDNNkhO0CrBYuyEzTct3J2ghbXHvLK7Ah1f/nyR7Mj1P1szt3ZEdrMyQ5Qd/yJ78iO0ObE7AANe56SnaBh5sPZCVodkB1AtWnYA1BA0oUNAACArlSnBRIAAGAQ0AIJAACAyYYWSAAAgH5xDEQLJAUkAABAPw1AAUkXNgAAALpCCyQAAEDfDEYXNi2QAAAAA8z2TravtX297YPH2O/fbIftueMds0ItkPdJT52UHaJmlcOyE7T69bbZCRrmDmUnaFju2OwErbbYKztBeX2/PIswv/iAb2ZHqPv9gd/IjtDC/kB2hLp4247ZEVq9PTtAk9vWzE7Q8HTZ2qkuyQ7Q94XEbU+R9C1Jr5G0UNIltudHxNVt+y0v6YOSLurkuGX7zgIAAAy2pZ6e2I+xbSnp+oi4ISKekHSypN1H2O8zkr4g6bGOXkI3rxcAAAClM8v2UNPH/k3b1pJ0S9PthcV9dbY3l7R2RPy80yesUBc2AABAxfVmHch7ImLccYsjxrGXkvRVSft28zgKSAAAgH7q7yzsWyWt3XR7TnHfsOUlbSLpQtuStLqk+bZ3i4hRJzZUqIDcQppSkgka/3hedoJWr7ohOwHwrNx547rZEep+fvoe2RHq3vPt92VHaPGOH74lO0LDh0edSJriwg22yo5Qt+3HNs+O0PDgCtkJ2pRgEk3/XSJpA9vrqVY47iWp/sMcEQ9ImjV82/aFkj46VvEoVaqABAAAqLr+rgMZEYttHyDpbElTJH0nIq6yfbikoYiYvyTHpYAEAAAYYBFxlqSz2u47dJR9t+3kmBSQAAAA/dLndSB7hQISAACgbwbjUoYUkEtiPSatABNp9q9fmR2h7p3ZAVocnx2gxYx9shM0K9eVaLb9W3aCZvOyA5TXR7+SnWBgUEACAAD0y4B0YXMlGgAAAHSFFkgAAIB+GoAWSApIAOkuftHV2RHqtrxq4+wIpWU/lR2hLmJKdgRgCQ3GJBq6sAEAANAVWiABAAD6ZUAm0VBAAgAA9A1d2AAAAJiEKtQCeZmklbNDFNbIDtDq3juzEzSs8GB2goanS/b/0TKfz05QWlu+65bsCA0/vTE7Qd1tX/1wdoQWES/PjtBw2OHZCVpNfyw7QcMep2cnaLhrtewE5TMgXdgl+wsLAACAsqtQCyQAAMAAGIAWSApIAACAvmESzbhsT7d9se3LbV9l+9PF/evZvsj29bZ/ZHtaL3MAAABg4vS6BfJxSdtFxMO2l5b0O9u/kPRhSV+LiJNtHyNpP0lHj32oKZKW73HcTpUlR2GVDbMTNNk8O0CTY7MDtCnT96lk1rwoO0HDjEezE9Q9sbhknUS3zslO0LDhttkJWr3p1OwEDfeVZcKppJkPZycoHybRjC9qht89SxcfIWk7ScM/bSdI2qOXOQAAADBxev7vre0pkhZIer6kb0n6u6RFEbG42GWhpLVGeez+kvaXpHXW4bqnAACg6gZjDGTPC8iIeErSprZXlPRTSS/o4rHzJM2TpLlzl4meBAQAAOiXAenC7tsAm4hYZPsCSVtLWtH21KIVco6kW8c/wgyVZ3zdz7MDtCnRwub3/zo7QcNKX8xO0Cpen52gtA7Zqzy/TI/Y85TsCHWH/Gmr7AgtPl+ic/Prv3bcFtEXb9vkyuwIDefsmJ2g4bHp2QnafCY7wMDo9SzsVYuWR9leVtJrJF0j6QJJbyp220fSGb3MAQAAUBpLPT2xHwl63QK5hqQTinGQS0k6JSLOtH21pJNtHyHpz5KO63EOAACAEmAM5Lgi4gpJm41w/w2StuzlcwMAAKA3SrbIGAAAwABjEk2/ra/aJO4SuKhcA9v1sj9lJ2hYvFp2giYfyw7QytX/hdErH9r2wuwIDd95Z3aCureWbBLN2r//1+wIda/b/vzsCK1eell2goYv/jA7QcOml2UnQI9UqIAEAACoOsZAAgAAoBsD0oXd02V8AAAAMHhogQQAAOinAWiBpIBcEk/TcFsND2cHQBUNwC/2yWAK36fRlencTF2cnQA9QgEJAADQN0yiAQAAQDeYRAMAAIDJqEItkPdJOjk7RM3WB2YnKK9pT2QnaHi8ZP8fLXNQdoLSmrXh+tkRGk7eKztB3ate8NfsCK3+sW12groVtirPgu81O2QHaNjlrOwEDYsrVGb0DV3YAAAA6AZd2AAAAJiMaIEEAADoJ1ogAQAAMNlUqAXyaZVmYei/H5mdoNX65Rn0r1PflJ2gYb9jsxOgU5u+LztBww6/yk5Qd/kXP5YdocXW652dHaHutgvvzI7QYs3sAM1K9B4ehJa2icckGgAAAHSDSTQAAACYjGiBBAAA6JvB6MKmBRIAAABdqVAL5CxJ78oOUbN+SXKUERNXsCTe++3sBE3Kk2Xra7ITlNeav90gO0J5rXpXdoISc3aAgRkDWaECEgAAYAAMQAFJFzYAAAC6QgskAABA3wzGJBoKSAAAgH4ZkDGQdGEDAACgK7RAAgAA9M1gdGHTAgkAAICu0AIJAADQLwMyBpICEgAAoJ8GoICkCxsAAABdoQUSAACgX8wkGgAAAExCtEACAAD00wC0QFJALonDDs9O0OrTh2YnAJ4V++PZEeoiPp8doe4Xz705O0KLnW9aJzsCOrJVdoAml2YHKCG6sAEAADAJ0QIJAADQL6wDCQAAgO4MRhd2TwtI22tLOlHSbEkhaV5EfN32ypJ+JGldSTdK2jMi7u9llgnFmENgQpVp3GGZ7HLzc7MjtHJ2gIaIyI5QYn/KDlBiJXoTV1yvx0AulvSRiNhYtVG977e9saSDJZ0XERtIOq+4DQAAMNiGu7An8iNBTwvIiLg9Ii4tvn5I0jWS1pK0u6QTit1OkLRHL3MAAABg4vRtDKTtdSVtJukiSbMj4vZi0x2qdXGP9Jj9Je0vSeusw/IRAABgAAzAGMi+LONje6akn0j6UEQ82LwtagNZRhzMEhHzImJuRMxdddVV+5AUAACgl2IgurB73gJpe2nViscfRMRpxd132l4jIm63vYaku8Y/0u2SPtOznN25LjtAm+9lByipt2cHaMP3aVT7nJidoOGQI7IT1B23wgPZEVqc/+AK2REaLt80O0Grl16WnaAJC4mj93raAmnbko6TdE1EfLVp03xJ+xRf7yPpjF7mAAAAKIUBmUTT6xbIbVRrBvqL7cuK+z4h6UhJp9jeT9JNkvbscQ4AAABMkJ4WkBHxO42+6NL2vXxuAACA8mEhcQAAAHSDSxn22eP3Sn8rySSEDco2iQYj+vT62QlaHZYdoMROeEd2giblybLXVudkR2jxzrN3zI5Qt1l9VFQ5/LlUF8bhSjSj40o0E6U6BSQAAMAgGIAWyL6sAwkAAAApYx1I2zvZvtb29bafcflo2x+2fbXtK2yfZ/u54x2TAhIAAGBA2Z4i6VuSdpa0saS9bW/cttufJc2NiJdIOlXSF8c7bnW6sB9aXvrNK7NT1GywQ3aCNr/KDtBw0luyEzQc9sPsBOjUZw7NTtCw6WXZCepuvOdT2RFabPyFZzRcpPnzvPuyI7SZlx2g4aISLSS+uDplRt/0fxLNlpKuj4gbJMn2yZJ2l3T18A4RcUHT/n+S9LbxDkoLJAAAQLXNsj3U9LF/07a1JN3SdHthcd9o9pP0i/GekH8NAAAA+qYn60DeExFzn+1BbL9N0lxJrxpvXwpIAACAful/F/atktZuuj2nuK+F7R0kfVLSqyLi8fEOShc2AADA4LpE0ga217M9TdJekuY372B7M0n/K2m3iLirk4NWpwVy1mJpv3uzUxT2yg5QXnt/JTtBk4OyA7RZkB2gtF57aHkmgp29x+nZEerecOnm2RFaHPfwzOwIdRusfkd2hBazd10zO0LD+ftmJ2go3XqHv88OUNPH8xIRi20fIOlsSVMkfScirrJ9uKShiJgv6UuSZkr6sW1JujkidhvruNUpIAEAANC1iDhL0llt9x3a9HXXy8tQQAIAAPRNTybR9B0FJAAAQL/0fxJNTzCJBgAAAF2pUAvkUqqN7yyDsuQoozJdpadkk53+0tHEtkmpVD9RKzyYnaDuNdkB2kybujg7Qt0TZbvCSZnyzFmYnaBhAFraJh5d2AAAAOgGXdgAAACYjGiBBAAA6CdaIAEAADDZ0AIJAADQN0yiAQAAQDeYRAMAAIDJiBZIAACAvqELu78ee1S6ZkF2ippZZ2cnaLVqmRbMvio7QHm9ODtAs4ezA7SYnx2g2Zc/mp2g7vcnviM7QotvXf2i7Ah18c7jsiO0Wvvm7AR1f//mxdkR6tZ/3g3ZEdr8IDvAwKhOAQkAAFB1AzIGkgISAACgnwaggGQSDQAAALpCCyQAAEDfMImmv6a/SHrhUHYK4Fn4n+wApbVYzo7QsGpkJ6ibkx2gzZ+jPOfm2n8p19+DjbIDNHn+xS/LjtBQnvk85TEgYyDpwgYAAEBXqtMCCQAAUHmD0YVNCyQAAAC6QgskgHTbqTxj63T0+7IT1D2lb2dHaHHPqy/MjlD3v0PbZkdo8VXdnh2hLko0VrVs7BKMtx6QMZAUkAAAAP00AAUkXdgAAADoCi2QAAAAfcMkmnHZ/o7tu2xf2XTfyrbPtf234vNKvcwAAACAidXrFsjjJX1T0olN9x0s6byIONL2wcXtg8Y/1AOSfj7xCZfI67IDlNhW2QEaLsoO0OZlf8pOUFrn64XZEeouO+ZH2RHq/pgdoM2sC7bNjlD3Na+RHaHFV/Wi7Ah1e/hX2RHqnsoOUEYDMommpy2QEfEbSfe13b27pBOKr0+QtEcvMwAAAJRH0YU9kR8JMibRzI6I4fUO7pA0e7Qdbe9ve8j20N13P9CfdAAAABhT6izsqC1WNeqCVRExLyLmRsTcVVd9Th+TAQAA9MBwF3bFWyAzZmHfaXuNiLjd9hqS7krIAAAAkGMAxkBmFJDzJe0j6cji8xmdPGjBgutl79rLXB1jlf+xlGiiyMuyA6Bzf80OULfp5S/JjlC3qAxXzWhRnt99jZFQaHd6eb5NpVO6H6kK62kBafskSdtKmmV7oaTDVCscT7G9n6SbJO3ZywwAAACl4cFYB7KnBWRE7D3Kpu17+bwAAADoHa5EAwAA0E+0QPbPFltsoaGhoewYGM/PyzFOVZK07YXZCVot93B2gtL6ytQnsyPU/XepxkhdOf4u/fTj8ow48p4/zo7QIuLC7Ah19hHZEZoszg5QQoPRhZ26jA8AAACqpzItkAAAAJXHpQwBAAAwGdECCQAA0E8D0AJJAYmJ9bozsxOggj5825rZEeq82t3ZEeq2LtHC3ZKkfz8lO0Ed13MYHYusj86+MDvCwKwDSRc2AAAAukILJAAAQD8NQAskBSQAAEDfWPJEl1/9X0uXLmwAAAB0hRZITKy9T85O0HDUh7ITtJp9R3aC0jpktbuyI9TFXatlR6j71jqPZUdoMz07QN0HS3XFIOkbWik7Ql2c8y/ZERoenZGdoISWkjRzgo95/wQfb3y0QAIAAKArtEACAAD0zRRJy0/wMfvfAkkBCQAA0De96MLuPwpITKzVSzTO7+GS/YDOzg5QXtOyAzSbujg7Qd2cle/LjtCmPAu+P5wdoE2pxh3+9QXZCRpKNwbyjOwAA4MCEgAAoG960YXdf0yiAQAAQFdogQQAAOgbxkACAACgK4PRhe2IyM7QEdulCVqVcwag2uzNsiO0iPhzdoS6P77wr9kRWmx9TYkmrmBUthdExNzMDHPnrhRDQ9tO6DHt0/v+umiBBAAA6Bu6sAEAANCVwejCZhY2AAAAukILJAAAQN8spUFogaxMAbnFFhtqaGhedozCydkBWj31tuwEDVM2yk7Q5PbsAK0u2DQ7QcOiFbMTtLps0+wEDVtenJ2g7r7tvpIdodU1G2cnqFttheOzI7Qp0e+bv7w2O0HD4sqUGegS31kAAIC+mSIm0QAAAKALgzELm0k0AAAA6EqFWiAflvSb7BCFkv3nMGVxdoKGe2dlJ2iYVqLzIkmv/nJ2gvJa833ZCRrO3DU7Qd3lN6+THaHF/9v46uwIdT+5dPPsCK1+PyM7QcPTK2YnaHiadqpnYhkfAAAATEIVaoEEAACousEYA0kBCQAA0Dd0YQMAAGASogVyiVT/PwcAAJCBLmwAAAB0hS5sAAAATEK0QAIAAPQNLZDPiu2dbF9r+3rbB2flAAAAQHdSWiBtT5H0LUmvkbRQ0iW250fEGJc5WEPS//QlH56FVe7JTtBw0VbZCVq9rGRXziiTk/fKTlC31lEfyo5Qd5uWzY7QIuKf2RHqXutLsyO0ODuyEzT5/CeyEzQsnJOdoM2vswOISTTPzpaSro+IGyTJ9smSdpdUnutkAQAATLj+d2Hb3knS14snPzYijmzbvoykEyVtIeleSW+OiBvHOmZWF/Zakm5pur2wuK+F7f1tD9keuvvuu/sWDgAAYBA09fruLGljSXvb3rhtt/0k3R8Rz5f0NUlfGO+4pZ5EExHzJM2TpLlz55apgwAAAGAJ9L0Lu5Ne390lfar4+lRJ37TtiBi19soqIG+VtHbT7TnFfaNasGDBPbZvepbPO0tSiQbplQrnZnQTdG787A9RPgP4vvmviTrQwJ0be0LewwN3XiRpYk7NYJ6bCTJR5+a5E3CMZ2XBggVn2541wYedbnuo6fa8ohFOGrnX92Vtj6/vExGLbT8gaRWNcc6zCshLJG1gez3VCse9JL1lrAdExKrP9kltD0XE3Gd7nEHEuRkd52Z0nJvRcW5GxnkZHedmdIN0biJip+wMEyFlDGRELJZ0gKSzJV0j6ZSIuCojCwAAwADrpNe3vo/tqZKeo9pkmlGljYGMiLMknZX1/AAAAJNAJ72+8yXtI+mPkt4k6fyxxj9KJZ9E0wPzxt9l0uLcjI5zMzrOzeg4NyPjvIyOczM6zs0SKsY0Dvf6TpH0nYi4yvbhkoYiYr6k4yR9z/b1ku5Trcgck8cpMAEAAIAWaZcyBAAAQDVRQAIAAKArk6aAtL2T7WttX2/74Ow8ZWF7bdsX2L7a9lW2P5idqUxsT7H9Z9tnZmcpE9sr2j7V9l9tX2N76+xMZWH7v4qfpSttn2R7enamLLa/Y/su21c23bey7XNt/634vFJmxiyjnJsvFT9TV9j+qe0VEyOmGencNG37iO3owTqK6NKkKCA7vIzPZLVY0kciYmNJW0l6P+emxQdVW2oKrb4u6ZcR8QJJLxXnSJJkey1JB0qaGxGbqDZgfdzB6APseEnta94dLOm8iNhA0nnF7cnoeD3z3JwraZOIeImk6yR9vN+hSuJ4PfPcyPbaknaUdHO/A+GZJkUBqabL+ETEE5KGL+Mz6UXE7RFxafH1Q6oVAs+4LvlkZHuOpNdJOjY7S5nYfo6kV6o2a08R8URELEoNVS5TJS1brKU2Q9JtyXnSRMRvVJvR2Wx3SScUX58gaY9+ZiqLkc5NRJxTrJMsSX9Sbb2+SWeU941Uu0bzxyQx+7cEJksBOdJlfCiS2theV9Jmki5KjlIWR6n2y+rp5Bxls56kuyV9t+jeP9b2ctmhyiAibpX0ZdVaSG6X9EBEnJObqnRmR8Ttxdd3SJqdGabE3inpF9khysL27pJujYjLs7OgZrIUkBiH7ZmSfiLpQxHxYHaebLZ3lXRXRCzIzlJCUyVtLunoiNhM0iOavN2QLYrxfLurVmSvKWk522/LTVVexULFtCa1sf1J1YYX/SA7SxnYniHpE5IOzc6ChslSQHZyGZ9Jy/bSqhWPP4iI07LzlMQ2knazfaNqQx62s/393EilsVDSwogYbqk+VbWCEtIOkv4REXdHxJOSTpP0r8mZyuZO22tIUvH5ruQ8pWJ7X0m7SnrreFcCmUTWV+2fssuL38lzJF1qe/XUVJPcZCkg65fxsT1NtUHt85MzlYJtqzaW7ZqI+Gp2nrKIiI9HxJyIWFe198v5EUFLkqSIuEPSLbY3Ku7aXtLViZHK5GZJW9meUfxsbS8mGLUbvmSais9nJGYpFds7qTZsZreIeDQ7T1lExF8iYrWIWLf4nbxQ0ubF7yIkmRQFZDEoefgyPtdIOiUirspNVRrbSHq7ai1slxUfu2SHQul9QNIPbF8haVNJn8uNUw5Fq+ypki6V9BfVfsdO2kuw2T5JtWvrbmR7oe39JB0p6TW2/6Zai+2RmRmzjHJuvilpeUnnFr+Lj0kNmWSUc4OS4VKGAAAA6MqkaIEEAADAxKGABAAAQFcoIAEAANAVCkgAAAB0hQISAAAAXaGABAAAQFcoIAGksv2HLve/0PbcLvafa/sb3ScDAIxmanYAAJNbRPT0Un8RMSRpqJfPAQCTDS2QAFLZfniMbQfZ/ovty203X7Hk321fbPs6268o9p1u+7vF/n+2/eri/m1tn1l8PbNpnyts/1tx/462/2j7Uts/tj2zhy8ZACqPFkgApWR7Z0m7S3pZRDxqe+WmzVMjYsvispuHqXZJvPdLioh4se0XSDrH9oZth/0fSQ9ExIuL51jJ9ixJh0jaISIesX2QpA9LOry3rxAAqosCEkBZ7SDpuxHxqCRFxH1N204rPi+QtG7x9csl/b9i37/avklSewG5g6S9hm9ExP22d5W0saTf25akaapdhxcAMAoKSABV9Hjx+Sk9+99jlnRuROz9LI8DAJMGYyABlNW5kv7D9gxJauvCHslvJb212HdDSetIunaEY75/+IbtlST9SdI2tp9f3LfcCF3fAIAmFJAAssWId0b8UtJ8SUO2L5P00XGO821JS9n+i6QfSdo3Ih5v2+cISSvZvtL25ZJeHRF3S9pX0km2r1Ct+/oFS/piAGAycMSIv7sBoOdsryLp0oh4bnYWAEDnaIEEkML2mqq19n05OwsAoDu0QAJIZfvFkr7XdvfjEfGyjDwAgPFRQAIAAKArdGEDAACgKxSQAAAA6AoFJAAAALpCAQkAAICuUEACAACgKxSQAAAA6AoFJAAAALpCAQkAAICuUEACAACgKxSQAAAA6AoFJAAAALpCAYlJx/bnbX9olG3H2z6iz5FQErY/ZftTPTz+jfb/b+/e4y2d6/6Pvz9mDMaMZhjnmTEqlCS0c4hbcroRBvmJUkRJUUnKlENuqaZyK8VN04GpZGJymGqcKpIS9pDTiMRghsHEOMaY5vP7Y12zrnVt+7DWzF7r873Wej0fj/3Y63Cta72vtfbe67O/p8t2XcbH3mtmOw1ynuXaZ/TxmNmtZva25dkHgGVDAYm2Y2ajzczN7EUze9nMHl9aMJrZmpI+IukHoSETkr1OS7+WmNm/a65/KNtmTs3t87NCe0TNPmrvf9bMfmtm43q5/wUzW2hmfzGzo81shZptVjKzH5vZI9l2fzOzPXtkPbxn0WFmq5vZ5Wb2UvbYDzayz7Jw97e5+w2p77PFz32mpNMHIQ6ABlFAoh1tIelpdx/h7sMlfVLSd8xsrKTDJc10938H5ktK9jqNcPcRkh6VtE/NbRfVbLpPts0WkraU9KUeu1p6/7qSnpT0/V7uHylpA0mTJZ0o6cc19w+V9Jik90h6g6STJV1iZhPM7BNmtn+2nfW4fq6kRZLWlvQhSefVtEr1uc8GXiKka4ak95rZOtFBgE5DAYl2tIWk22qu35J9HyZpT0l/XHqHmW1pZrdnrVO/lLRyPU9gZluZ2R3Z4y41s1/Wdn2b2Xpm9isze9rMHjazz9TcN8fMTjCzu8zsueyxK9fz2BS4+3xJ16jyOvd2/yuSpkvatI/7n3P3GZI+IOkwM9ssu/0ldz/N3ee4+xJ3/42khyW9U9JPJL1J0mclfV3SYklXmtmqkt4v6RR3f9Hdb1KlqPhwHfvsl5mdaGbTe9x2tpl9b4DHjTOzy7L3719mdk6PTbbo571/q5ndkLXS3mtm+9bcV+0u7u85Gvn5sTq6oFM+nuxnbZak/+7vGAAMPgpItKMtJd0qSWY2StLXVPmQeVjS2yXdn903TNIVkn4maXVJl6pSjPQre9zlki7MHnexpP1r7l9B0q8l3SlpfUm7SDrOzGo/5A6StIekDSVtrkrLaL2PlZn9JvtQ7u3rN3W8Rsssa8ndU9KDfdw/XJXi8K/97cfdb5U0V9J/9bGftSVtLOnepQ+RZNn3Jdn3jSUtdvcHah56p6Rex8X1ss/+TJO0l5mNzB47RJX37Rd9PSDb5jeSHpE0QZX3cFqPzfp671dU5b2/VtJakj4t6SIz26Te56j356deJTme+yS9Y1mOD8Cyo4BEO9pC0hfM7BlVCkdXpfvUJY2S9EK23baSVpT0XXd/zd2nq9hy2ZdtVeka/V72uMuUFayZd0la091Pd/dF7v6QpB9KOrhmm++5++Pu/owqH5BbNPBYufve7j6qj6+963mRlsEVZvaCKl3CT0n6Si/3L5T0nKTdJH27jn0+rkoRXpAVHxdJmuruf5d0hCr/AHxX0kmSVpK0n6QRkp7v8fDnJI2sY5/9cvdHJN2u/J+DnSW97O79FcZbS1pP0hey1s9XslbRWn2999tmxzM5e+//oEphdUgDz1HXz08DynA8L6jyew2ghYZGBwAGk5mtJOmtkjZ097m9bPKs8uJiPUnzssJyqUfqeJreHvdYzeUNJK2XFVNLDZH0p5rr82suv5zts97HRtnP3X9nZu9RpRVujKSFvdw/RNJESX80s02zLu++rC/pmdobslann6kyrvFYSXL3H2T3HV656udn17eUtFqPfa6m/J+EPvdZp1+oUvD8VNIH1U/rY2acpEfcfXE/2/T13q8n6TF3X1Jz/yOqvEb1Psdg//yU4XhGqvhzCKAFaIFEu9lM0kt9FI+SdJcqXZiS9ISk9c3Mau4fX8dz9Pa4cTWXH5P0cI9WwZHuvlcd+67rsWZ2lRVnT9d+XVXH8ywzd/+jKt33Z/Zx/3+yVtn/SNqhr/2Y2btUKSZuqrnNVJlYs7ak97v7az32fWGPmbsPSBpqZhvV3PYO1XRRD7TPAVwqaaes235/DVxAPiZpvJktyz/nj0saZzUz01X5eZzXwHMsz89eb8pwPG9VpYsbQAtRQKLdbKn+x7fNVGVGriTdrMpkjM+Y2YpmdoAq3WkDuVmV4uhYMxtqZhN7PO5WSS9YZRLGKmY2xMw2ywqmgdT1WHffs2amdM+vVixT811Ju5nZ68aeWcVESaNVGZ/W8/7VzGxvVca5/dzd7665+zxVCoJ9vI6Z8u7+kqTLJJ1uZqua2faqtH7+bFn32WP/T0u6QdIFqhQyrzueHm5V5R+MyVmelbNM9bhFlRa8L2Y/jztJ2kevH3PY33P0+/NjleWXLqwzTxmOZ2VVJkRd18AxARgEFJBoN1tIuqef+3+qysSIVdx9kaQDVBn0/4wqEz8uG+gJah53pCpdZ4eqMrbr1ez+/0jaO8vysKQFkn6kyjIyA+17mR/bSllh9VNJp9bc/Gsze1GVMYlfk3SYu9/b4/6lYyhPknSWpI8uvdPMNpD0CVWOfb71WIuyH5+StIoq4zIvlvTJpc+7HPus9QtJu2rg1sel798+kt6sypJIc1X5uRpQ9nO1jyoTlBZI+j9JH+k5XrO/56jj52ecpD/Xk6ckx7OPpBvc/fF6jwnA4LDiMC6g/ZnZ1yU95e7fHcR93iLpfHe/YLD2idaz7Cw07n5abJLBZ5XVA+6UtPnSbnwze1TSoe5+Y2i4ZZT93h3p7v390wigCZhEg47j7l9e3n1kE0nuV6VF5EOqLF9y9fLuF2iWrEXwrUuvW+WsTGtKmhOVaXm5+zbRGYBORRc20AszG9/PJJXxkjZRpTVnoaTPSzrQ3Z+IzIxBcUP21ac6fjaSl40h/IeyswWV/XgA9M/MfmJmT5lZr6312dj175nZg1Y5McBWA+6TLmwAAID2ZWY7SnpR0k/dfbNe7t9LlcX+95K0jaSzB2rhpwUSAACgjWXjnJ/pZ5OJqhSX7pWTJYwys3X722dpxkCOWXW4Txg1KjpGxZgF0QmKnhkdnSA3vKFVUprruZ7rSwdbrecJUwKtsGTgbVppxIjoBLmXE3qfhvd6OvFAL0cHqDE8OkDRvP7Wy2+xtRPKsiStdqpZd7+2wN3XjMxgZs3o+r1X0is116e4+5QGHr++iifEmJvd1ufQrNIUkBNGjVL30R+LjlHxsR9FJyiatqxnKWuCze+KTpC7dvfoBEW7/i46QW54SoWApO3rXVqwBe5IaEnBLbujE/Rwe3SAGgMO0WqtL02OTpA7odc1/mO8nFahb+Mfq+dsY2X0irt3tfIJS1NAAgAAtIfBLr/6O9toXeapeEa1sXr9WaMK0mpbBgAAaGumSgE5mF/LbYakj2SzsbeV9NxAK4vQAgkAANDGzOxiSTtJGmNmcyV9RdKKkuTu56tymt+9JD2oymDnj/a+p1xpCshZj4+WnVrXGbSazt/4UHSEos+dFZ0gTY8eEZ2gaLdroxOgHlvuGJ0gXafMjE6Q+2piYyC/sV10ghoJTfRcIzpATxYdQHkLZOu4+yED3O+Sjmlkn3RhAwAAoCGlaYEEAAAov9a3QDZD+Y8AAACgNNqjgKQLGwAAAA0pTQn8zjVeVvc+t0XHqEhtEeakpLPI+s3f+mJ0hILtDhgVHSG3aFh0gqI1E5qYdv3/RifIvfd90QmKUloU+msnRycoOiml2SLviQ6AAZWm/OoTLZAAAABoSPlLYAAAgNJojzGQ5T8CAACA0miPApIubAAAADSk/CUwAABAabRHC2T5jwAAAKA02qOApAsbAAAADSl/CQwAAFAq5S+/aIEEAABAQ8pfAgMAAJRGe4yBLP8RAAAAlEZ7FJB0YQMAAKAh5S+BAQAASqM9WiDLdQRLaDAFAACIVq4CEgAAoNRogQQAAEDDyl9+0ScMAACAhpi7R2eoS1dXl3d3d0fHSJLZPtERqtx/HR0BAAJ8NTpAjVOiAyTLzGa5e1dshtVc2maQ9/q7lh9X+dtQAQAASqM9xkDShQ0AAICGlL8EBgAAKA1aIAEAANCBSlQCL5H0YnSIzIjoAD3MiQ6QJLNDoiMUuF8cHSFdlx8QnSC32T3RCXIb/TE6QQ/rRgdIltm+0RGqSjI3toO1Rwtk+Y8AAACgNNqjgKQLGwAAAA0pfwkMAABQKuUvv5reAmlmnzOze83sHjO72MxWNrMNzewWM3vQzH5pZsOanQMAAACDo6klsJmtL+kzkjZ193+b2SWSDpa0l6TvuPs0Mztf0pGSzut3Z3Oflr5wfjPj1m9RWvWu+93REXKf+250giqf+6foCEXX7R6dILfb1OgERevMj06QmzAnOkHuusOiExTduGN0gtyLaU1mfKuOi46Q+/fw6AS5xeVvaRt8jIGs11BJq5jZUEnDJT0haWdJ07P7p0rarwU5AAAAgi0tIAfzq/WaWkC6+zxJZ0p6VJXC8TlJsyQtdPfF2WZzJa3f2+PN7Cgz6zaz7qdfTmUJHwAAgM7W1ALSzEZLmihpQ0nrSVpV0h71Pt7dp7h7l7t3rTk8re4KAACAxrVHC2Szn3VXSQ+7+9OSZGaXSdpe0igzG5q1Qo6VNG+gHc16ck3ZmZ9sath67axVoyMU/P7s6AQ1vvNCdIIac6MDFNjYr0VHqPHG6AAF7l+PjpB7rTs6QdUPD7gsOkLBx19I6B/5q/aKTlAw+zvHRUfIffvY6ARVi2/YKTpCD++LDiDGQNbnUUnbmtlwMzNJu0iaLel6SQdm2xwm6com5wAAAMAgaWoJ7O63mNl0SbdLWizpDklTJP1W0jQzOyO77cfNzAEAAJCO8rdANv0I3P0rkr7S4+aHJG3d7OcGAADA4Ct/CQwAAFAa7TEGsjRH8M7xj6n7y5+PjlHx6PjoBAVmJ0VHqHLfJjpCbt4PohMU+Pl7R0fIfeLP0QmK5u0bnSC3/qLoBFUf2+k30REKzl1l1+gIVcf8e2Z0hKJTzohOkDti+sDbtMjQgy6JjlA0ITqA1C4FZCsWEgcAAEAbKX8JDAAAUBq0QAIAAKADlb8EBgAAKI32aIEszxGs9oK08x+iU1T8/NDoBAWPvjuhCREXnxOdIHfIx6ITFG12YXSC3J+Oi05QtMPj0QlyV+4XnaDKfn1qdISCY7RydIR0nZzQJJrFCX20D10cnSBRCb1Hy4gubAAAADSk/CUwAABAadCFDQAAgIZQQLbWSptJG3VHp6j4n+gAReOSyvPu6ADp2j6xMZno3cToAOkys+gIVe4eHaFopVeiE+RWGhadIPefJdEJ0CTlKSABAABKrz1aIJlEAwAAgIaUvwQGAAAojfZogSz/EQAAAJRK+cuv8h8BAKAlkpu4gj4sig6QGxIdoKd0JoKVHQUkAABAy7RHFzaTaAAAANCQ8pfAAAAApdEeLZDlPwIAAIDSaI8Cki5sAAAANKT8JTAAAEBptEcLZPmPAAAAoDTao4CkCxsAAAANKU8J/OhT0qfPiU5R8cVvRScoGjs3OkHu54dGJ8gtTuzH+91/iU6QG7o4OkHRsIQWPh43OzpB7pZdoxMUzdg3OkHuuO9GJyi4aYeboiNU7XD/ttERarwYHSBRiX0+LQNaIAEAANCQ8pfAAAAApdEeYyDLfwQAAACl0R4FJF3YAAAAaEh5SuDxc6XvHx+douJXCQ0kl6Rxj0YnyH34iegENdIavP012yg6QtWQ6AA9TPLoBIna5mfRCYq2mRWdoMb3ogMU7HD/b6Mj1HgmOkDCLDqAaIEEAABARyp/CQwAAFAa7dECWf4jAAAAKJXyl190YQMAAKAhJSqBt5LUHR2i4v3RAVK2bnSAZJ3ERBE0LJ2JVxWp5QHKqPVd2Ga2h6SzVZlD+SN3n9zj/vGSpkoalW0zyd1n9rfPEhWQAAAAZdfaAtLMhkg6V9JukuZKus3MZrh77XlbT5Z0ibufZ2abSpopaUJ/+6ULGwAAoH1tLelBd3/I3RdJmiZpYo9tXNJq2eU3SHp8oJ02tQQ2s00k/bLmpjdKOlXST7PbJ0iaI+kgd3+2mVkAAADiNaUFcoyZ1Y7zm+LuU7LL60t6rOa+uZK26fH40yRda2aflrSqpF0HesKmFpDufr+kLaRqE+o8SZdLmiTp9+4+2cwmZddP7Hdnzz8gXbd7M+PWb7XnoxMU2LZfi45Q5Y9+NDpC7uXh0QmKTj81OkFuhSXRCYp+dkR0ghqLogPkLkjpdZH00Q2jE+Su+XN0gqL//n50gty/totOkHtxRHSCTrHA3buW4/GHSLrQ3f/XzLaT9DMz28zd+/ywaGUX9i6S/unuj6jSdDo1u32qpP1amAMAACDI0hbIwfzq1zxJ42quj81uq3WkpEskyd1vlrSypDH97bSVBeTBki7OLq/t7kvPeTdf0totzAEAABCopQXkbZI2MrMNzWyYKvXYjB7bPKpKQ5/M7K2qFJBP97fTlhSQWeB9JV3a8z53d1UGb/b2uKPMrNvMup9+7rUmpwQAAGgv7r5Y0rGSrpF0nyqzre81s9PNbN9ss89L+riZ3alKY9/hWX3Wp1bNI99T0u3u/mR2/UkzW9fdnzCzdSU91duDsgGgUySpa+PVWEUPAACUXOvXgczWdJzZ47ZTay7PlrR9I/tsVRf2Icq7r6VK0+lh2eXDJF3ZohwAAABYTk0vgc1sVVUWr/xEzc2TJV1iZkdKekTSQc3OAQAAEK/1LZDN0PQjcPeXJK3R47Z/KRusCQAA0Dnao4DkTDQAAABoSPlLYAAAgNJojxZIG2CWdjK6urq8u7t74A0RysyiI1Sl97P9YnSAZKX1c7NqdAQATWJms5bzjC2DkOEdLl07yHtdp+XHRRc2AAAAGlL+NlQAAIDSaI8ubFogAQAA0JDyl8BISnrjDlMyIjpAsvixAdA52qMFsvxHAAAAUBrtUUDShQ0AAICGlL8EBgAAKI32aIEs/xEAAACUBgVkx0pp0WOJiSsov5R+p1L6fUrpdUlNSu+TlNZ7ldprg/ZEAQkAANBCpiGDur+IfxmYRAMAAICG0AIJAADQQoPb/igtHuT91YMCEgAAoEVMFJCt9c+F0v5XRKfILIgOgBKaOnphdISqw569MzpCwV/ecl90hKpj05kLwWSIEknqvTrljOgEubWeik6AJilPAQkAAFByzWiBjMAkGgAAADSEFkgAAIAWaocWyPIUkG9aVbp8m+gUkiTXGtERUEI7bzo7OkLVP7dZJTpCwXa3bh0doWq7kZ+MjlDjgegAPVweHSD38BujExRt+OnoBLmv7hWdoMYL0QGKPvP96AR0YQMAAKAzlacFEgAAoOTa40zY7XEMAAAApUEXNgAAADoOLZAAAAAtwiQaAAAAdCRaIAEAAFqoHVogKSABAABahC5sAAAAdKTytEAumCddcFJ0iorN74pOUPTOXaIT5L5p0QlyD6V1popx00+LjpBbktj/jiN/HZ2g6toNN4iOULX7wdOiIxTY5NHREap8/krREQqOtc9FR6g6529bREfILRoWnSA5tEACAACgI5WnBRIAAKANtEMLJAUkAABAi7RLFzYF5LL4+aHRCYp+ktAYk6GLoxPkxiyITlDU3RWdIJfaGMjnPxmdoGr3h9eLjpD79fDoBAXuV0RHqJHQ+yTpHP9qdITc/a9EJ8gNWxSdAE1CAQkAANAipvYovtrhGAAAAEqjHbqwE+vHAgAAQOqaXkCa2Sgzm25mfzez+8xsOzNb3cyuM7N/ZN/TWVwMAACgSZZOohnMrwit6MI+W9LV7n6gmQ2TNFzSlyX93t0nm9kkSZMkndj/biydgf+LE+v5P/dT0Qlyj0yITlB1935XREcoWOumHaIjVA1ZYUl0hIIx1+8UHaFGQpMhdv9adIICs+ujI1T57RtFRyiwrR6LjlDlc1aNjpBL5XMbg66p76yZvUHSjpJ+LEnuvsjdF0qaKGlqttlUSfs1MwcAAEAqaIEc2IaSnpZ0gZm9Q9IsSZ+VtLa7P5FtM1/S2r092MyOknSUJI1fPaH/qAAAAJZBu6wD2ey25aGStpJ0nrtvKeklVbqrq9zdJXlvD3b3Ke7e5e5da45cpclRAQAAUI9mF5BzJc1191uy69NVKSifNLN1JSn7/lSTcwAAAIRjEk0d3H2+mT1mZpu4+/2SdpE0O/s6TNLk7PuVdexNSmzgP9KW2kSR1PIApcbvU99Sem2YRNO2WjGd+NOSLspmYD8k6aOqtHxeYmZHSnpE0kEtyAEAABCuHcZANr2AdPe/SertJMC7NPu5AQAAUsIkGgAAAHSkxFbE7tv8RyZo8hE/iY4hSZrU65xxSJI2mBOdoGrTI46NjlB0zLzoBAnbKTpA7vI7ohPk9r8mOkGB+4vREWq8LzpAgZ9wZnSE3MlrRSfILRoWnaCHQ6IDyFSi4qsf7XAMAAAApUEXNgAAADoOLZAAAAAtwiQaAAAAdKTStECu8867Nal7g+gYFad8PDpB0VdPjk5Q47fRAaqOPfac6AgF5+ywRXSEdL0jOkCN/S+LTlBlZtERCipnnkWvvj0xOkGV2b7REWqMiA6QpHZogSxNAQkAAFB2dGEDAACgI9ECCQAA0CK0QAIAAKAjlagF8u2SuqNDVHw1OkDK0jk7xDnJjff/W3QAlExqk1bMfhAdocr9E9ERetgoOkCV+33REZKVysS0dmiBLFEBCQAAUG50YQMAAKAj0QIJAADQIqb2KL7KcwxPPiZ95/joFJKkOZccFB2hYMu/bhsdoerZ51eLjlC1+OBp0REKhv72+egINV6MDlB0zfToBLn/fiw6Qe6acdEJCnzuPdERaqQ2BvJt0QFy+38tOkFu6OLoBEmiCxsAAAAdpzwtkAAAACXHJBoAAAB0JFogAQAAWqRdWiDLU0COeEna9q/RKSRJE5ak1XD77IQ50RFyszeNTlB1ysy9oiMUfCM6QMr+O50F6PX+m6MT5FZ+JTpBgf3iO9ERqvyuzaMjFP1mYnSC3IiEJskl8rldlch8vXYoINOqhAAAAJC88rRAAgAAlFy7dGHTAgkAAICGUEACAAC00JBB/hqIme1hZveb2YNmNqmPbQ4ys9lmdq+Z/WKgfZanC3v+OtKZJ0SnqDj19OgERR8c8H1undNOi05Q9Y25Y6MjFN08PjpBLrGJYEnl+dUvoxPUWDc6QIEff1Z0hNz5R0cnKDo3nTOCSbtGB6gxMjpA0bHRAVrfhW1mQySdK2k3SXMl3WZmM9x9ds02G0n6kqTt3f1ZM1troP0m9FcbAAAAg2xrSQ+6+0PuvkjSNEk9lw34uKRz3f1ZSXL3pwbaaXlaIAEAAErO1JTia4yZdddcn+LuU7LL60t6rOa+uZK26fH4jSXJzP6sSgPpae5+dX9PSAEJAADQQk3owl7g7l3L8fihkjaStJOksZJuNLO3u/vC/h4AoBVSGucHAC3xQnQASPMkjau5Pja7rdZcSbe4+2uSHjazB1QpKG/ra6d8ogEAALTI0kk0LZyFfZukjcxsQzMbJulgSTN6bHOFKq2PMrMxqnRpP9TfTikgAQAA2pS7L1Zl/vk1ku6TdIm732tmp5vZvtlm10j6l5nNlnS9pC+4+7/62y9d2AAAAC0ScSYad58paWaP206tueySjs++6kIBCQAA0ELtcCrDchWQqUxCmLHvwNu00ikJLWz+qS2iE+Rm7hWdoGj2ptEJcsMWRSco+uanohPkbkloEeYHNo5OUPTi1tEJcpvfFZ2gh4R+hp9bEp0g9+KI6AQ9PBsdoG2Uq4AEAAAosYgu7GZIpEkPAAAAZUELJAAAQAsNWWGQhxkEjFpoegFpZnNUWUn0P5IWu3uXma0u6ZeSJkiaI+mgpedfBAAAaFcmp4BswHvdfUHN9UmSfu/uk81sUnb9xP524EtMixcNa2bGutFs249E3iNJaWWRpKGLoxPkUsqSmpQmGK32fHSCoudXi06Qe2b16ATpGuziZHmklAWDKqoWmqhsxXNJUyXdoAEKSAAAgLIzk4a2QWHdigLSJV1rZi7pB+4+RdLa7v5Edv98SWv39kAzO0rSUZI0fuU1WxAVAACguQa9CztAKwrIHdx9npmtJek6M/t77Z3u7llx+TpZsTlFkt75ho163QYAAACt1fQC0t3nZd+fMrPLJW0t6UkzW9fdnzCzdSU9NdB+bOQLGrrTDc0NW6+DLolOUHRRv+c7b617DohOkNvhpugERaMWRidI17PjoxPklrwxOkFus3uiExTcPGlydISq7W7cMTpC0Z9viE6Qu+vQ6AS55MZb/zA6gMyaMIkmQFPXgTSzVc1s5NLLknaXdI+kGZIOyzY7TNKVzcwBAACAwdPsFsi1JV1uZkuf6xfufrWZ3SbpEjM7UtIjkg5qcg4AAIBwJsZADsjdH5L0jl5u/5ekXZr53AAAAOmhCxsAAAAdqDRrYr/67Gj9c/qB0TEkSW/a9XfREYo+tEl0gqq9bFJ0hKqZqQ2yf19Kje4jowMUPXZjdILcO98anSD3+ycG3qaFtrvvLdERcld1RScosL3S+f32q6+LjpBbXJoyo2XM2qMLmxZIAAAANKSufw3MbLikz0sa7+4fN7ONJG3i7r9pajoAAIA20pRzYQeot235AkmzJG2XXZ8n6VJJFJAAAAD16rAu7De5+7ckvSZJ7v6yKjPRAQAA0GHqbYFcZGarqHJea5nZmyS92rRUvfiPr6AXXlm5lU/ZtyWJDR299O7oBGlqg//wmmdEdICi1H6n0KvKx0Aa/Or3REdIV0oTV1LKkgiTNLQNPp/qfWe/IulqSePM7CJJ20s6vFmhAAAA2lFHjYF09+vM7HZJ26pSPH/W3Rc0NRkAAACSVO8s7P0l/cHdf5tdH2Vm+7n7Fc0MBwAA0FbaZBJN3V3Y7n750ivuvtDMviLpiqak6sXwTVfRFt2bt+rp+nfMEdEJis48ITpB1VX6Y3SE3PbPRico+uUt0Qlyw1+OTlC0z5bRCXIT/190gtyVO0cnKHBPaOzslftFJyhw/3x0hNxVv49OkFs4KjoBmqTeArK3Ee6MjAUAAGhAR42BlNRtZmdJOje7fowq60ICAACgXm3ShV3v2hmflrRI0i+zr1dVKSIBAADQYeqdhf2SpElNzgIAANDWTO3RAtlvAWlm33X348zs18oWEa/l7vs2LVkPr973kv65za2terp+vemWYdERCsz+HR2hyu/6dHSE3L+ejE5QdGNCjfYvD49OULTTZ6IT5DZ7V3SC3MPHRSco2vDP0QlyE8+LTtDDrtEBcnum9tok5PCp0QnaxkAtkD/Lvp/Z7CAAAADtriMm0bj7rOz7H81smKSNs7vud/fXmh0OAACgrbTJJJp6FxLfSdJUSXNU6b4fZ2aHufuNTUsGAACAJNW7jM//Strd3e+XJDPbWNLFkt7ZrGAAAADtxuQa2iktkJJWXFo8SpK7P2BmKzYpU69WWuNfetNHftrKp+zbeWm98e6nR0eoMS06QI3/ig5QYP+X0qiPtCYY+RfHRkfIbfxAdILchg9FJ0jXD46OTlB0cHd0gtzQhM7stKTe1QI7h3VSF7YqC4n/SNLPs+sfkpTQbwsAAABapd4C8pOqLBy+dK2NP0n6v6YkAgAAaGMd0wLp7q9KOiv7AgAAQAerdxb29pJOk7RB7WPc/Y3NiQUAANB+OmIdyBo/lvQ5SbMk/ad5cQAAANpYh02iec7dr2pqEgAAAJTCQOfC3iq7eL2ZfVvSZZJeXXq/u9/exGwAAABtpVO6sP+3x/WumssuaefBjQMAAIDUDXQu7Pe2KsiAVvm3tNk90SkqRi2MTtDDs9EBcp84KTpBlU35dnSEAl9yZXSEXGqL+966dXSC3Ad/EZ2gxoXRAdL1ic9HJyi649boBFXPnnBmdISq4cMWRUfoYc/oAG2zkHhdnyJm9nUzG1VzfbSZndG0VAAAAG1qyApLBvUrQr3NEHu6+8KlV9z9WUl7NSURAAAAklbvLOwhZrZStqC4zGwVSSs1LxYAAED7MbmGtkEXdr0F5EWSfm9mF2TXPyppanMiAQAAtKd2GQNZ76kMv2lmd0raNbvpq+5+TfNi9cIkpfKCv7JydIKC99sj0RGqfuXRCXK+uN7/j1rEjohOkBsSHaCH7YZFJ6iR2qB/9G6j6AAFttUd0RGq/IbToyPkEvu81NXRAdpH3Z+w7n61+njpzexmd99u0FIBAAC0pfZYB3Kw1vJI7F8MAAAANMtg9fEl1HEJAACQpo4aA7m8zGyIpG5J89x9bzPbUNI0SWtImiXpw+7e/8AjcymVBUlfHBGdoODO6ACpGro4OgHq9WpCC5uzvgSWgXtC7SiXHxCdIJfaGMhEtEMBWe9C4seb2fr9bTLALj4r6b6a69+U9B13f7Mqp1E5sp4cAAAAiFfvv/0jJV1rZn8ys2PNbO0e93+4rwea2VhJ75P0o+y6qXIO7enZJlMl7ddIaAAAgDKybBJNR5yJxt3/x93fJukYSetK+qOZ/a7m/v5OUv1dSV+UtPQI15C00N2X9i/OldRr66aZHWVm3WbW/fTC1+qJCgAAgCZrdAzkU5LmS/qXpLUG2tjM9pb0lLvPMrOdGg3n7lMkTZGkrreOSGiACQAAQOM6ahKNmX1K0kGS1pR0qaSPu/vsOh66vaR9zWwvVZb6WU3S2ZJGmdnQrBVyrKR5yxIeAACgXDprHchxko5z97e5+2l1Fo9y9y+5+1h3nyDpYEl/cPcPSbpe0oHZZodJurLB3AAAAAhS76kMvzTIz3uipGlmdoakOyT9eJD3DwAAkBwzaWgbtEC27GTB7n6DpBuyyw9J2rpVzw0AAJACU3uMgUxo9V4AAACUQctaIJfbEktnRfsRL0YnKPhodICChF6bxN4n9GOlLaITAMulssRxGvyut0dHyC0aFp0gQZ01iQYAAACQVKYWSAAAgJLrqHUgAQAAMBhcRgHZQkOWpDOm7fnVohMUnJTSOXq+clZ0gqpXZ6c10X+lyw+IjpBL7Y/X41tFJ6jxqegAuQ/+IjpB0emnRifI7fyH6AQF7gn9If7CmdEJcqnMXaiaFR2gbZSngAQAACg7U3r/xC8DJtEAAACgIbRAAgAAtFIbtEBSQAIAALSKOQUk8DpLGBWBkuNnGGXHzzBagAISAACglWiBBAAAQP3aowubdm4AAIA2ZmZ7mNn9ZvagmU3qZ7v3m5mbWddA+6QFEgAAoFVavA6kmQ2RdK6k3STNlXSbmc1w99k9thsp6bOSbqlnv+UpIF8aLnUPWBC3xurPRCcoMLszOkKVezqr/K908eyBN2qlP+wcnSB36unRCYru2jw6QW7lV6IT5Ia/HJ2g6LTTohPkUusCPOWM6AS5Xf8SnSCX2vt0TnSAEFtLetDdH5IkM5smaaKknh+SX5X0TUlfqGendGEDAAC00gpLBverf+tLeqzm+tzstioz20rSOHf/bb2HUJ4WSAAAgLJrzjqQY8ysu+b6FHefUlccsxUknSXp8EaekAISAACg3Ba4e1/j/OZJGldzfWx221IjJW0m6QYzk6R1JM0ws33dvbYoLShPAfmG56U9ro5OUbFoWHSCAp/2jegIufvuj06QO+TU6ARFi2dGJ8jN3Cs6QdG+M6IT5EYnNMb5ut2jExRMPeiS6AhV+3f1+bkWYrXrzo2OkPNEPivRt9aODb1N0kZmtqEqhePBkj649E53f07SmKXXzewGSSf0VzxKjIEEAABoW+6+WNKxkq6RdJ+kS9z9XjM73cz2Xdb9lqcFEgAAoPRav5C4u8+UNLPHbb1207n7TvXskwISAACgVVq8DmSz0IUNAACAhpSnBXLY26UN0ho0nYyNogOgLh8+ODoByma3a6MTFBz2bHSCWrtGB+ghoTwWHSBlKbw47XEu7PIUkAAAAGVHFzYAAAA6ES2QAAAArUQLJAAAADoNLZDL4ratoxMUvevW6ARp+vDPoxMU/ezQ6AQomey0YslwHx8docYj0QGAZcQkGgAAADSCSTQAAADoRLRAAgAAtAxd2C32T0n7R4eoWDAmOkHRH3eKTlD1+MlnREeoWm/fu6IjFB0yLTpBbnFiv/qX3hadoGrOdon8nZHk/nh0hB7WjQ6Q88Q60G7cMTpBbtGw6AS5x9eLToAmSexTBAAAoI21yRhICkgAAIBWaoMCMrE+AAAAAKSuqS2QZraypBslrZQ913R3/4qZbShpmqQ1JM2S9GF3X9TMLAAAAPGYRFOPVyXt7O4vmtmKkm4ys6skHS/pO+4+zczOl3SkpPP63dMr/5buv6/Jceu0cKvoBAUXf+Sn0RGqDjn/6OgIubs2j05QdGhCC5sPXRydoOjO+dEJqiYc8Wx0hBrTowMU/fDA6AS5Td8dnaAopd+prRM6ucQqG0YnKDo8OoDaZgxkU7uwveLF7OqK2ZdL2ln5X8apkvZrZg4AAAAMnqZPojGzIap0U79Z0rmqrMez0N2X/rs2V9L6fTz2KElHSdL49ZjvAwAAyq49urCbPonG3f/j7ltIGitpa0lvaeCxU9y9y9271hw9pFkRAQAA0ICWNeu5+0Izu17SdpJGmdnQrBVyrKR5rcoBAAAQpk3GQDZ7Fvaakl7LisdVJO0m6ZuSrpd0oCozsQ+TdOWAO1s4Wrpiv+aFbcQbH4pOUHDIsedER8g99MboBLnxj0YnKNo8sTPjpGTc3tEJcuMTOmOQ3hcdoCilyRmjFkYnKDrr+OgEuZRem2EssNIrCsgBrStpajYOcgVJl7j7b8xstqRpZnaGpDsk/bjJOQAAADBImlpAuvtdkrbs5faHVBkPCQAA0EHaYxINU5sBAABahTGQLfbaUGn+OtEpKlJaEFqSVns+OkFuekILDf8wOkBPX40OkK5/vyk6QW7mAdEJch86ODpB0Tv6P99DJ7PvjYyOUOVnp/TH74XoAD0kNFa15MpTQAIAAJRee3RhN30dSAAAALQXWiABAABahTGQAAAAaBgFZCuZtCSRHvfU3vi7No9OgLrcHh2gxlbRAQAAJVaiAhIAAKDsmEQDAACADkQLJAAAQKswiQYAAACNaY8u7PIUkGs+LR19fnSKip8cEZ2g6PH1ohPkzjwhOkHulISySNLGN0YnyC25NDpB0WHvj05Qdf5RP4iOUHX0Vl3REQrO7+qOjlB19NV7REco8KfWjI5Q44noAOgA5SkgAQAAyo4ubAAAADSsDQpIZmEDAACgIeVpgXxhhHTjjtEpKk7aLTpBDyOjA+S+cnx0gqp3n3FydISCv6Q0PjS1/36vuyc6QdUjLw+PjpB76z7RCQqOTmUcuiRNOzg6QdG5x0UnyB2zRnSCXGp/a5LQHpNoaIEEAABAQ8rTAgkAAFB2TKIBAABAY+jCBgAAQAcyd4/OUJeuri7v7k5nEVsAAFAuZjbL3UNX6O+asKZ3n7T/oO7Tjvphy4+LFkgAAAA0hDGQAAAArdQGYyApIAEAAFqGSTQAAADoQLRALgMzi45QUJaJUAAAdDzWgQQAAEBj6MIGAABAB6IFEgAAoFXowu5cjDkEAACdjAISAACglWiBBAAAQN2MSTQAAADoQLRAAgAAtFIbtECWp4B8+BnpQ7+ITlFxxsnRCYrGLIhOUHX/zn+IjlC1yR5XR0cosDNOiY6QLF+wRnSEXEp/2O/aPDpB0XtuiE5QxQkd+nHusdEJckMXRydAk5SngAQAACi99hgDSQEJAADQKm2yDiSTaAAAANCQphaQZjbOzK43s9lmdq+ZfTa7fXUzu87M/pF9H93MHAAAAGnIurAH8yuANXPgr5mtK2ldd7/dzEZKmiVpP0mHS3rG3Seb2SRJo939xP721dXV5d3d3U3LivaT2iB7lENSkyEADCozm+XuXZEZujYa5d1n7Tio+7R9f93y42pqC6S7P+Hut2eXX5B0n6T1JU2UNDXbbKoqRSUAAABKoGWTaMxsgqQtJd0iaW13fyK7a76ktft4zFGSjpKk8ePHtyAlAABAkzGJpj5mNkLSryQd5+7P197nlf6iXvuM3H2Ku3e5e9eaa67ZgqQAAAAYSNNbIM1sRVWKx4vc/bLs5ifNbF13fyIbJ/nUgDtaMEe64IjmBW3E6s9EJyh48qzjoyNUrf3lr0dHqPILD4uOUHDZpMnREaoO+Ou20RGKfrdrdILcvLHRCXI37RCdoOjl4dEJcom9NvaTI6MjVPlHpg68Uau88aHoBAlqj3Ugmz0L2yT9WNJ97n5WzV0zJC39dD9M0pXNzAEAAJCEpetAlnwWdrNbILeX9GFJd5vZ37LbvixpsqRLzOxISY9IOqjJOQAAADBImlpAuvtNqtTavdmlmc8NAACQHrqwAQAA0IHKcy7sMROkj/4kOoUkqbImejrcr4iOkPvTydEJcgtHRScoOOCJdaIj1JgTHaBovb2iE+TWnxudIPeB6ABFKS3O798aEx2hIK0F6N8WHaDGvdEBik77n+gEbXMu7PIUkAAAAO2gDQpIurABAADQEApIAACAlvGWL+NjZnuY2f1m9qCZTerl/uPNbLaZ3WVmvzezDQbaJwUkAABAq7R4HUgzGyLpXEl7StpU0iFmtmmPze6Q1OXum0uaLulbAx1GecZALnhE+vHHolNIkvymd0RHKDBLZx12P36/6Ai5xCbR3LTJA9ERqna4f+PoCEV7/i46QZouPyA6QYH7DdERqha/76XoCAUrfjGhCUavrBQdIbckobMXda6tJT3o7g9JkplNkzRR0uylG7j79TXb/1XSoQPttDwFJAAAQOk1ZR3IMWbWXXN9irtPyS6vL+mxmvvmStqmn30dKemqgZ6QAhIAAKDcFrh71/LuxMwOldQl6T0DbUsBCQAA0CqtXwdynqRxNdfHZrcVmNmukk6S9B53f3WgnZangFzpFenND0anqFg0LDpBgT/0uegIufOPjk6QO/zC6AQFO+x+bXSE3MWJzZ875JnoBLmn14pOkNv/jdEJiv50SnSCqqFnLoiOUOCT3x4dIbckkc9KqS3WO2yK1r4ut0nayMw2VKVwPFjSB2s3MLMtJf1A0h7u/lQ9O03sUwQAAACDxd0XSzpW0jWS7pN0ibvfa2anm9m+2WbfljRC0qVm9jczmzHQfsvTAgkAAFB6TZlE0/8zus+UNLPHbafWXN610X3SAgkAAICG0AIJAADQKq2fRNMU5SkgU3rB15kfnaDo0fHRCdKU2GQndXUPvA3ijVoYnaDGC9EBihatG50gN+LF6ATpeny96AS5VD63qx6ODqCILuxmoAsbAAAADSlPCyQAAEDZpdSjuhwoIAEAAFqpDQpIurABAADQEFogAQAAWoZJNAAAAOhAtEACAAC0CpNoAAAA0Bi6sAEAANCBytMCuXiotGBMdApJ0uE73hgdoeBCj06Qu/bwOdERqk781hejIxTcMXdsdITcktT+d5wWHaDqs8MWRUeoOvv51aIjFO1yb3SC3Hmfik5QdMBl0Qly4x6NTpAwiw7QNl3YqX2KAAAAIHHlaYEEAABoB23QAkkBCQAA0DLtMYmmPAXkqDdJ+6cxxiSlMYep2f3hCdERqnaPDvA6c6MDoA7fS2GMVObskfyx6dMnH4hO0MP86ABAS5WngAQAACi7NplEQwEJAADQMu3Rhc0sbAAAADSEFkgAAIBWoQu7tWbNklZIZGz7kjNPiI5QsM0JZ0ZHqLpVK0ZHqHJ/LTpCwf3v6o6OUPXvRcOiIxRscef50RGqfNoHoiPUeDE6QMHP10hnkfVDpyXWgbbbb6MT5F59f3QCdIDSFJAAAABtgRZIAAAA1I9JNAAAAOhATS0gzewnZvaUmd1Tc9vqZnadmf0j+z66mRkAAACSsXQSzWB+RRyGe/POdGBmO6oyCvyn7r5Zdtu3JD3j7pPNbJKk0e5+4kD76tp8Je/+9bpNy9qQDeZEJ0iW2XujI1SdNzyhQe2Sjp52cHSEdO0zIzpBmv68Q3SCAtvhlugIVe6PRkco+sb3oxPkPvO96ATJshEvzXL3rsgMXVus6N3XDW7bma31dMuPq6ktkO5+o6Rnetw8UdLU7PJUSfs1MwMAAEA6vC1aICMm0azt7k9kl+dLWruvDc3sKElHSdL49Ye0IBoAAEATtck6kKGTaLzSf95nH7q7T3H3LnfvWnN1CkgAAIAURLRAPmlm67r7E2a2rqSn6nrUsLdLG6SzEDN65359dIRkmf06OkKymjgUu9y2vyk6QQHvUz++9PXoBDVSypKaRM5IQgvkMpkh6bDs8mGSrgzIAAAA0HrWHmMgm72Mz8WSbpa0iZnNNbMjJU2WtJuZ/UPSrtl1AAAAlERTu7Dd/ZA+7tqlmc8LAACQLLqwAQAA0GlKdC7sZyRNiw6RYUHovqyTyPhkSbpzxxujIxT4tbtFR8gl99/vP6ID1NgoOkDukQnRCQpePfr86AhVK121R3SEglf3vDo6QtVKV6W1AD16ao9zYZeogAQAACg51oEEAABAJ6IFEgAAoJVogQQAAECnKVEL5IuSUpkUwSSavsxP6kwVO0YHKDC7LjpCstwTmriSkg3mRCcoWOmq6ATpWvnqPaMjVHnfZwhGCoxJNAAAAGhUGxSQdGEDAACgIbRAAgAAtIxJNtjl12uDvL+BlaeAfPU56eFEFmq9Z9/oBEWXHBSdIHdwKou9S3p8vegEBf73TaIj5Da5PjpBD9+JDpCbdVF0gtyms6MTFK3ycnSCGgn9rZHkntC4wzu3iE6QG7o4OkGCVpA0YpD3+ewg729gdGEDAACgIeVpgQQAACi9IZJGDvI+aYEEAABA4miBBAAAaJlmjIFsvfIUkG7SomHRKSpWez46QdGEOdEJcmPnRifIrf5MdIKim3aITpBLaD5PxcbRAXILR0UnyKXyNy9jw0dHR6hyPy86QrpGLYxOkGuD9Q4HXzO6sFuPLmwAAAA0pDwtkAAAAKXXHl3YtEACAACgIbRAAgAAtEx7jIEsTwG58mbSJt3RKSpSm4DwnugAtU6ODpCu7aIDpOx90QFyuySURZ+KDlDg/n/REVCPDeZEJ0iYRQcQXdgAAADoSOVpgQQAACg9urABAADQkBVEAdlKCx6RfnhUdApJ0rVf/3J0hILdr909OkLVxe/+S3SEqkMOnB4doeDmG3aKjlC10TrzoyMUjNlrZnSE3NHnRyfITZ4UnaDoa/tHJ8j9+enoBAWTd7gpOkLVpFu3jo6QW/mV6ARokvIUkAAAAKU3REyiAQAAQMehBRIAAKBl2mMZHwpIAACAlmEWdmut+pK09a3RKSRJuz/8uegIRcccF52g6pApaUx0kiTtf1l0goLtkloUOrE/XjenMwFBx58VnSCX2iQaLYgOkJt2TnSCgkk37RAdIfeuND4r05TCQuLtoTwFJAAAQOm1Rxc2k2gAAADQEFogAQAAWoYxkAAAAGhIe3RhU0C2gxWWRCcAAAAdhAISAACgZejCBgAAQEPao4AMm4VtZnuY2f1m9qCZpbbYGQAAAPoQ0gJpZkMknStpN0lzJd1mZjPcfXafD3phhJTKQq0jro5OUGDnXBEdIZfQ2r5+5gnREYp27I5OkEttoeG13hydIJfS4t1r3B2doMAsnUWY3cdHR+jhkegAuQ9cEp0gd+D06AQJao9JNFEtkFtLetDdH3L3RZKmSZoYlAUAAAANiBoDub6kx2quz5W0TVAWAACAFmn9GEgz20PS2dmT/8jdJ/e4fyVJP5X0Tkn/kvQBd5/T3z6TnkRjZkdJOkqSxo8uf3MvAADodK3twq5z2OCRkp519zeb2cGSvinpA/3tN6oLe56kcTXXx2a3Fbj7FHfvcveuNUes0rJwAAAAbaKeYYMTJU3NLk+XtIsNMOjZ3H3Qkw7EzIZKekDSLqoUjrdJ+qC739vPY57W8o9SHiNpwXLuo13x2vSN16ZvvDZ947XpHa9L33ht+jZYr80G7r7mIOxnmZnZ1aocz2BaWdIrNdenuPuU7PkOlLSHu38su/5hSdu4+7E1me7JtpmbXf9ntk2fr3lIF7a7LzazYyVdo0p//E/6Kx6zxyz3G25m3e7etbz7aUe8Nn3jtekbr03feG16x+vSN16bvrXTa+Pue0RnGAxhYyDdfaakmVHPDwAA0AHqGTa4dJu5WS/xG1SZTNOnsIXEAQAA0HS3SdrIzDY0s2GSDpY0o8c2MyQdll0+UNIffIAxjknPwm6CKdEBEsZr0zdem77x2vSN16Z3vC5947XpG6/NMupr2KCZnS6p291nSPqxpJ+Z2YOSnlGlyOxXyCQaAAAAlBdd2AAAAGgIBSQAAAAa0jEFpJntYWb3m9mDZjYpOk8qzGycmV1vZrPN7F4z+2x0ppSY2RAzu8PMfhOdJSVmNsrMppvZ383sPjPbLjpTKszsc9nv0j1mdrGZrRydKYqZ/cTMnsrWmFt62+pmdp2Z/SP7PjoyY5Q+XptvZ79Td5nZ5WY2KjBimN5em5r7Pm9mbmaDvY4iGtQRBWTNaXz2lLSppEPMbNPYVMlYLOnz7r6ppG0lHcNrU/BZSfdFh0jQ2ZKudve3SHqHeI0kSWa2vqTPSOpy981UGbA+4GD0NnahpJ5r3k2S9Ht330jS77PrnehCvf61uU7SZu6+uSon2/hSq0Ml4kK9/rWRmY2TtLukR1sdCK/XEQWk6juNT0dy9yfc/fbs8guqFALrx6ZKg5mNlfQ+ST+KzpISM3uDpB1VmbUnd1/k7gtDQ6VlqKRVsrXUhkt6PDhPGHe/UZUZnbVqT5k2VdJ+rcyUit5eG3e/1t0XZ1f/qsp6fR2nj58bSfqOpC9KYvZvAjqlgFxf0mM11+eKIul1zGyCpC0l3RIcJRXfVeWP1ZLgHKnZUNLTki7Iuvd/ZGarRodKgbvPk3SmKi0kT0h6zt2vjU2VnLXd/Yns8nxJa0eGSdgRkq6KDpEKM5soaZ673xmdBRWdUkBiAGY2QtKvJB3n7s9H54lmZntLesrdZ0VnSdBQSVtJOs/dt5T0kjq3G7IgG883UZUiez1Jq5rZobGp0pUtVExrUg9mdpIqw4suis6SAjMbLunLkk6NzoJcpxSQ9ZzGp2OZ2YqqFI8Xuftl0XkSsb2kfc1sjipDHnY2s5/HRkrGXElz3X1pS/V0VQpKSLtKetjdn3b31yRdJundwZlS86SZrStJ2fengvMkxcwOl7S3pA8NdCaQDvImVf4puzP7mzxW0u1mtk5oqg7XKQVkPafx6UhmZqqMZbvP3c+KzpMKd/+Su4919wmq/Lz8wd1pSZLk7vMlPWZmm2Q37SJpdmCklDwqaVszG579bu0iJhj1VHvKtMMkXRmYJSlmtocqw2b2dfeXo/Okwt3vdve13H1C9jd5rqStsr9FCNIRBWQ2KHnpaXzuk3SJu98bmyoZ20v6sCotbH/LvvaKDoXkfVrSRWZ2l6QtJH09Nk4aslbZ6ZJul3S3Kn9jO/YUbGZ2saSbJW1iZnPN7EhJkyXtZmb/UKXFdnJkxih9vDbnSBop6brsb/H5oSGD9PHaIDGcyhAAAAAN6YgWSAAAAAweCkgAAAA0hAISAAAADaGABAAAQEMoIAEAANAQCkgAAAA0hAISQCgz+0uD299gZl0NbN9lZt9rPBkAoC9DowMA6Gzu3tRT/bl7t6TuZj4HAHQaWiABhDKzF/u570Qzu9vM7jSz2jOW/D8zu9XMHjCz/8q2XdnMLsi2v8PM3pvdvpOZ/Sa7PKJmm7vM7P3Z7bub2c1mdruZXWpmI5p4yABQerRAAkiSme0paaKkbdz9ZTNbvebuoe6+dXbaza+ockq8YyS5u7/dzN4i6Voz27jHbk+R9Jy7vz17jtFmNkbSyZJ2dfeXzOxEScdLOr25RwgA5UUBCSBVu0q6wN1fliR3f6bmvsuy77MkTcgu7yDp+9m2fzezRyT1LCB3lXTw0ivu/qyZ7S1pU0l/NjNJGqbKeXgBAH2ggARQRq9m3/+j5f87ZpKuc/dDlnM/ANAxGAMJIFXXSfqomQ2XpB5d2L35k6QPZdtuLGm8pPt72ecxS6+Y2WhJf5W0vZm9Obtt1V66vgEANSggAUTzXm90v1rSDEndZvY3SScMsJ//k7SCmd0t6ZeSDnf3V3tsc4ak0WZ2j5ndKem97v60pMMlXWxmd6nSff2WZT0YAOgE5t7r324AaDozW0PS7e6+QXQWAED9aIEEEMLM1lOlte/M6CwAgMbQAgkglJm9XdLPetz8qrtvE5EHADAwCkgAAAA0hC5sAAAANIQCEgAAAA2hgAQAAEBDKCABAADQkP8PzeOeLoVeqGEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_Event('d_gene')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marginal Probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With IGoR provide us the conditional probabilities of the events defined in the Bayesian network. \n",
    "\n",
    "So we can calculate marginal probabilities, i.e.\n",
    "\n",
    "$P(\\text{j_choice}) = \\sum_{\\text{v_choice}}P(\\text{j_choice}, \\text{v_choice})$\n",
    "\n",
    "and using the Bayes theorem\n",
    "\n",
    "$ P(\\text{j_choice}, \\text{v_choice}) = P(\\text{j_choice} | \\text{v_choice}) \\times P(\\text{v_choice})$\n",
    "\n",
    "we get,\n",
    "\n",
    "$P(\\text{j_choice}) = \\sum_{\\text{v_choice}} P(\\text{j_choice} | \\text{v_choice}) \\times P(\\text{v_choice})$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<xarray.DataArray (j_choice: 15)>\n",
       " array([0.11962186, 0.10538077, 0.02016188, 0.05684014, 0.09945672,\n",
       "        0.03726745, 0.0322361 , 0.12542452, 0.05034454, 0.09853381,\n",
       "        0.01948227, 0.07500318, 0.0190534 , 0.0709002 , 0.07029376])\n",
       " Coordinates:\n",
       "   * j_choice       (j_choice) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14\n",
       "     lbl__j_choice  (j_choice) object 'K02545|TRBJ1-1*01|Homo sapiens|F|J-REGI...\n",
       "     seq__j_choice  (j_choice) object 'TGAACACTGAAGCTTTCTTTGGACAAGGCACCAGACTCA...,\n",
       " [<matplotlib.lines.Line2D at 0x7fac1dacf7d0>])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "P_marginal_j_choice = xr.dot(mdl_hb['j_choice'], mdl_hb['v_choice'])\n",
    "P_marginal_j_choice, P_marginal_j_choice.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But when a model is loaded with pygor3 the marginals are calculated automatically using a variable elimination process\n",
    "and are store in the Pmarginal variable\n",
    "\n",
    "IgorModel.Pmarginal['event_nickname']\n",
    "\n",
    "For this case\n",
    "\n",
    "$ P(\\text{'j_choice'}) = $ mdl_hb.Pmarginal['j_choice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (d_gene: 3)&gt;\n",
       "array([0.56140871, 0.2361217 , 0.20247255])\n",
       "Coordinates:\n",
       "  * d_gene       (d_gene) int64 0 1 2\n",
       "    lbl__d_gene  (d_gene) object &#x27; TRBD1*01&#x27; &#x27; TRBD2*01&#x27; &#x27; TRBD2*02&#x27;\n",
       "    seq__d_gene  (d_gene) object &#x27;GGGACAGGGGGC&#x27; ... &#x27;GGGACTAGCGGGAGGG&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>d_gene</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-34d8700f-4c6c-4860-bd6b-2380142e0447' class='xr-array-in' type='checkbox' checked><label for='section-34d8700f-4c6c-4860-bd6b-2380142e0447' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.5614 0.2361 0.2025</span></div><div class='xr-array-data'><pre>array([0.56140871, 0.2361217 , 0.20247255])</pre></div></div></li><li class='xr-section-item'><input id='section-d921445d-eb85-4c35-8d34-60811f4c6961' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d921445d-eb85-4c35-8d34-60811f4c6961' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>d_gene</span></div><div class='xr-var-dims'>(d_gene)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-8d6ae773-9eff-4c9d-9e64-1d484a08aade' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d6ae773-9eff-4c9d-9e64-1d484a08aade' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f364e921-631f-4d34-8e43-0b3e37605782' class='xr-var-data-in' type='checkbox'><label for='data-f364e921-631f-4d34-8e43-0b3e37605782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lbl__d_gene</span></div><div class='xr-var-dims'>(d_gene)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27; TRBD1*01&#x27; &#x27; TRBD2*01&#x27; &#x27; TRBD2*02&#x27;</div><input id='attrs-0218506a-444e-475f-a16d-0e8266e47ee3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0218506a-444e-475f-a16d-0e8266e47ee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-638c47d2-10d4-4dbc-ad4a-55239d07f523' class='xr-var-data-in' type='checkbox'><label for='data-638c47d2-10d4-4dbc-ad4a-55239d07f523' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27; TRBD1*01&#x27;, &#x27; TRBD2*01&#x27;, &#x27; TRBD2*02&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seq__d_gene</span></div><div class='xr-var-dims'>(d_gene)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;GGGACAGGGGGC&#x27; ... &#x27;GGGACTAGCGGG...</div><input id='attrs-c15dfc35-5b70-4728-81da-7abc3eabf1ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c15dfc35-5b70-4728-81da-7abc3eabf1ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d446803a-0453-4e9b-91bd-19ca138e672f' class='xr-var-data-in' type='checkbox'><label for='data-d446803a-0453-4e9b-91bd-19ca138e672f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;GGGACAGGGGGC&#x27;, &#x27;GGGACTAGCGGGGGGG&#x27;, &#x27;GGGACTAGCGGGAGGG&#x27;],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-581bba75-12b3-499a-93b7-16211b702f25' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-581bba75-12b3-499a-93b7-16211b702f25' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (d_gene: 3)>\n",
       "array([0.56140871, 0.2361217 , 0.20247255])\n",
       "Coordinates:\n",
       "  * d_gene       (d_gene) int64 0 1 2\n",
       "    lbl__d_gene  (d_gene) object ' TRBD1*01' ' TRBD2*01' ' TRBD2*02'\n",
       "    seq__d_gene  (d_gene) object 'GGGACAGGGGGC' ... 'GGGACTAGCGGGAGGG'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_Event_Marginal('d_gene')\n",
    "mdl_hb.Pmarginal['d_gene']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='d 3 del', ylabel='P'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_Event_Marginal('d_3_del')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you modifiy the conditional probabilities, to re-calculate the marginals, use the method generate_Pmarginals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl_hb.generate_Pmarginals()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joint Probabilities\n",
    "Pygor3 also have a method to calculate the joint probabilities of events\n",
    "\n",
    "IgorModel.get_P_joint(['event_nickname_1', 'event_nickname_2', ...])\n",
    "\n",
    "> :WARNING: **Be carefull with this function a the computer memory consumption could increase if more than 2 events are requested**.\n",
    "\n",
    "$P(\\text{'v_choice'}, \\text{'j_choice'}) = $mdl_hb.get_P_joint(['v_choice', 'j_choice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       ".xr-var-data,\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (v_choice: 89, j_choice: 15)&gt;\n",
       "array([[6.28453463e-04, 5.08306114e-04, 2.00831906e-04, ...,\n",
       "        0.00000000e+00, 2.62143710e-04, 1.82391677e-04],\n",
       "       [1.54890932e-03, 7.36213918e-04, 1.06011576e-04, ...,\n",
       "        2.29432410e-04, 6.05154843e-04, 6.95679479e-04],\n",
       "       [1.54900602e-03, 7.36581450e-04, 1.06039046e-04, ...,\n",
       "        2.28733827e-04, 6.05348578e-04, 6.95983268e-04],\n",
       "       ...,\n",
       "       [4.05407259e-04, 1.55690174e-04, 0.00000000e+00, ...,\n",
       "        1.15101267e-04, 1.80155267e-04, 1.96566942e-04],\n",
       "       [3.00565952e-09, 1.68608515e-12, 0.00000000e+00, ...,\n",
       "        2.81447867e-04, 5.64048061e-05, 6.77364474e-05],\n",
       "       [1.65903669e-03, 1.77940202e-03, 6.30491475e-04, ...,\n",
       "        3.45140763e-04, 1.12130116e-03, 9.08844648e-04]])\n",
       "Coordinates:\n",
       "  * v_choice       (v_choice) int64 0 1 2 3 4 5 6 7 ... 81 82 83 84 85 86 87 88\n",
       "    seq__v_choice  (v_choice) object &#x27;GATACTGGAATTACCCAGACACCAAAATACCTGGTCACA...\n",
       "  * j_choice       (j_choice) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14\n",
       "    seq__j_choice  (j_choice) object &#x27;TGAACACTGAAGCTTTCTTTGGACAAGGCACCAGACTCA...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>v_choice</span>: 89</li><li><span class='xr-has-index'>j_choice</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3e5de63f-86fb-4cf7-b818-9d18f12a8d4d' class='xr-array-in' type='checkbox' checked><label for='section-3e5de63f-86fb-4cf7-b818-9d18f12a8d4d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0006285 0.0005083 0.0002008 ... 0.0003451 0.001121 0.0009088</span></div><div class='xr-array-data'><pre>array([[6.28453463e-04, 5.08306114e-04, 2.00831906e-04, ...,\n",
       "        0.00000000e+00, 2.62143710e-04, 1.82391677e-04],\n",
       "       [1.54890932e-03, 7.36213918e-04, 1.06011576e-04, ...,\n",
       "        2.29432410e-04, 6.05154843e-04, 6.95679479e-04],\n",
       "       [1.54900602e-03, 7.36581450e-04, 1.06039046e-04, ...,\n",
       "        2.28733827e-04, 6.05348578e-04, 6.95983268e-04],\n",
       "       ...,\n",
       "       [4.05407259e-04, 1.55690174e-04, 0.00000000e+00, ...,\n",
       "        1.15101267e-04, 1.80155267e-04, 1.96566942e-04],\n",
       "       [3.00565952e-09, 1.68608515e-12, 0.00000000e+00, ...,\n",
       "        2.81447867e-04, 5.64048061e-05, 6.77364474e-05],\n",
       "       [1.65903669e-03, 1.77940202e-03, 6.30491475e-04, ...,\n",
       "        3.45140763e-04, 1.12130116e-03, 9.08844648e-04]])</pre></div></div></li><li class='xr-section-item'><input id='section-0e1a1b14-15e6-4ec6-9d90-833eee3319dc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e1a1b14-15e6-4ec6-9d90-833eee3319dc' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 83 84 85 86 87 88</div><input id='attrs-f1ac24da-5712-475e-82c3-6e8f2b14a6cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1ac24da-5712-475e-82c3-6e8f2b14a6cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-232a412f-f6db-4d68-8157-09bb47c2b8d4' class='xr-var-data-in' type='checkbox'><label for='data-232a412f-f6db-4d68-8157-09bb47c2b8d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seq__v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;GATACTGGAATTACCCAGACACCAAAATACC...</div><input id='attrs-f91ab3a0-71de-43ae-96ba-31d3ffc4bfa6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f91ab3a0-71de-43ae-96ba-31d3ffc4bfa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ab72f89-b30c-416b-811a-66cf2a6cda26' class='xr-var-data-in' type='checkbox'><label for='data-6ab72f89-b30c-416b-811a-66cf2a6cda26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;GATACTGGAATTACCCAGACACCAAAATACCTGGTCACAGCAATGGGGAGTAAAAGGACAATGAAACGTGAGCATCTGGGACATGATTCTATGTATTGGTACAGACAGAAAGCTAAGAAATCCCTGGAGTTCATGTTTTACTACAACTGTAAGGAATTCATTGAAAACAAGACTGTGCCAAATCACTTCACACCTGAATGCCCTGACAGCTCTCGCTTATACCTTCATGTGGTCGCACTGCAGCAAGAAGACTCAGCTGCGTATCTCTGCACCAGCAGCCAAGA&#x27;,\n",
       "       &#x27;GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCAAGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGGAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGGAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGTAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGGAATCACCCAGAGCCCAAGATACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGATGTGTCACCAGACTTGGAGCCACAGCTATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCTATTACTCAGCAGCTGCTGATATTACAGATAAAGGAGAAGTCCCCGATGGCTATGTTGTCTCCAGATCCAAGACAGAGAATTTCCCCCTCACTCTGGAGTCAGCTACCCGCTCCCAGACATCTGTGTATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGGAATCACCCAGAGCCCAAGACACAAGGTCACAGAGACAGGAACACCAGTGACTCTGAGATGTCACCAGACTGAGAACCACCGCTATATGTACTGGTATCGACAAGACCCGGGGCATGGGCTGAGGCTGATCCATTACTCATATGGTGTTAAAGATACTGACAAAGGAGAAGTCTCAGATGGCTATAGTGTCTCTAGATCAAAGACAGAGGATTTCCTCCTCACTCTGGAGTCCGCTACCAGCTCCCAGACATCTGTGTACTTCTGTGCCATCAGTGAGTC&#x27;,\n",
       "       &#x27;GCTGGAATCACCCAGAGCCCAAGACACAAGGTCACAGAGACAGGAACACCAGTGACTCTGAGATGTCATCAGACTGAGAACCACCGCTATATGTACTGGTATCGACAAGACCCGGGGCATGGGCTGAGGCTGATCCATTACTCATATGGTGTTAAAGATACTGACAAAGGAGAAGTCTCAGATGGCTATAGTGTCTCTAGATCAAAGACAGAGGATTTCCTCCTCACTCTGGAGTCCGCTACCAGCTCCCAGACATCTGTGTACTTCTGTGCCATCAGTGAGTC&#x27;,\n",
       "       &#x27;GAAGCTGAAGTTGCCCAGTCCCCCAGATATAAGATTACAGAGAAAAGCCAGGCTGTGGCTTTTTGGTGTGATCCTATTTCTGGCCATGCTACCCTTTACTGGTACCGGCAGATCCTGGGACAGGGCCCGGAGCTTCTGGTTCAATTTCAGGATGAGAGTGTAGTAGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAGAGCTTGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAAAGCTTGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAAAGCTTGAGAACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAACCTGCAAAGCTTGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTGGTTCAGTCTCCCAGATATAAGATTATAGAGAAAAAACAGCCTGTGGCTTTTTGGTGCAATCCTATTTCTGGCCACAATACCCTTTACTGGTACCTGCAGAACTTGGGACAGGGCCCGGAGCTTCTGATTCGATATGAGAATGAGGAAGCAGTAGACGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAGAGCTTGGGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GCTGGTGTTATCCAGTCACCCAGGCACAAAGTGACAGAGATGGGACAATCAGTAACTCTGAGATGCGAACCAATTTCAGGCCACAATGATCTTCTCTGGTACAGACAGACCTTTGTGCAGGGACTGGAATTGCTGAATTACTTCTGCAGCTGGACCCTCGTAGATGACTCAGGAGTGTCCAAGGATTGATTCTCAGCACAGATGCCTGATGTATCATTCTCCACTCTGAGGATCCAGCCCATGGAACCCAGGGACTTGGGCCTATATTTCTGTGCCAGCAGCTTTGC&#x27;,\n",
       "       &#x27;GCTGGCATTATCCAGTCACCCAAGCATGAGGTGACAGAAATGGGACAAACAGTGACTCTGAGATGTGAGCCAATTTTTGGCCACAATTTCCTTTTCTGGTACAGAGATACCTTCGTGCAGGGACTGGAATTGCTGAGTTACTTCCGGAGCTGATCTATTATAGATAATGCAGGTATGCCCACAGAGCGATTCTCAGCTGAGAGGCCTGATGGATCATTCTCTACTCTGAAGATCCAGCCTGCAGAGCAGGGGGACTCGGCCGTGTATGTCTGTGCAAGTCGCTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGCCATGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGCCACAACTCCCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGACACGACTACCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGACATGACTACCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAGGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTAGAGTCACCCAGACACCAAGGCACAAGGTGACAGAGATGGGACAAGAAGTAACAATGAGATGTCAGCCAATTTTAGGCCACAATACTGTTTTCTGGTACAGACAGACCATGATGCAAGGACTGGAGTTGCTGGCTTACTTCCGCAACCGGGCTCCTCTAGATGATTCGGGGATGCCGAAGGATCGATTCTCAGCAGAGATGCCTGATGCAACTTTAGCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTATTTTTGTGCTAGTGGTTTGGT&#x27;,\n",
       "       &#x27;GCTGCTGGAGTCATCCAGTCCCCAAGACATCTGATCAAAGAAAAGAGGGAAACAGCCACTCTGAAATGCTATCCTATCCCTAGACACGACACTGTCTACTGGTACCAGCAGGGTCCAGGTCAGGACCCCCAGTTCCTCATTTCGTTTTATGAAAAGATGCAGAGCGATAAAGGAAGCATCCCTGATCGATTCTCAGCTCAACAGTTCAGTGACTATCATTCTGAACTGAACATGAGCTCCTTGGAGCTGGGGGACTCAGCCCTGTACTTCTGTGCCAGCAGCTTAGG&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTACTCAGTTCCCCAGCCACAGCGTAATAGAGAAGGGCCAGACTGTGACTCTGAGATGTGACCCAATTTCTGGACATGATAATCTTTATTGGTATCGACGTGTTATGGGAAAAGAAATAAAATTTCTGTTACATTTTGTGAAAGAGTCTAAACAGGATGAGTCCGGTATGCCCAACAATCGATTCTTAGCTGAAAGGACTGGAGGGACGTATTCTACTCTGAAGGTGCAGCCTGCAGAACTGGAGGATTCTGGAGTTTATTTCTGTGCCAGCAGCCAAGA&#x27;,\n",
       "...\n",
       "       &#x27;GGAGCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAAAGGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAGAGGCTGGGGCAGGGCCTGGAGTTTTTAATTTACTTCCAAGGCAACAGTGCACCAGACAAATCAGGGCTGCCCAGTGATCGCTTCTCTGCAGAGAGGACTGGGGAATCCGTCTCCACTCTGACGATCCAGCGCACACAGCAGGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAAAGGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAGAGGCTGGGGCAGGGCCTGGAGTTTTTAATTTACTTCCAAGGCAACAGTGCACCAGACAAATCAGGGCTGCCCAGTGATCGCTTCTCTGCAGAGAGGACTGGGGAATCCGTCTCCACTCTGACGATCCAGCGCACACAGCAGGAGGACTCGGCCGTGTATCTCTGTACCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGAGCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAAAGGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAGAGGCTGGGGCAGGGCCTGGAGTTTTTAATTTACTTCCAAGGCAACAGTGCACCAGACAAATCAGGGCTGCCCAGTGATCGCTTCTCTGCAGAGAGGACTGGGGAATCCGTCTCCACTCTGACGATCCAGCGCACATAGCAGGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAATATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAACGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCGGGGGGACTCAGCCGTGTATCTCTGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAAGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAAAGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCCGTGTATCTCCGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAAGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAAAGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCCGCGTATCTCCGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAATATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAACGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCGGGGGGACTCTGCCGTGTATCTCTGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGTCGCAAAGAGGGGACGGGATGTAGCTCTCAGGTGTGATTCAATTTCGGGTCATGTAACCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCTCAGAGGTTCTGACTTACTCCCAGAGTGATGCTCAACGAGACAAATCAGGGCGGCCCAGTGGTCGGTTCTCTGCAGAGAGGCCTGAGAGATCCGTCTCCACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCTGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCTCCCAGGTACAAAGTCACAAAGAGGGGACAGGATGTAGCTCTCAGGTGTGATCCAATTTCGGGTCATGTATCCCTTTATTGGTACCGACAGGCCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCAATTATGAAGCCCAACAAGACAAATCAGGGCTGCCCAATGATCGGTTCTCTGCAGAGAGGCCTGAGGGATCCATCTCCACTCTGACGATCCAGCGCACAGAGCAGCGGGACTCGGCCATGTATCGCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCTCCCAGGTACAAAGTCACAAAGAGGGGACAGGATGTAACTCTCAGGTGTGATCCAATTTCGAGTCATGCAACCCTTTATTGGTATCAACAGGCCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCAATTATGAAGCTCAACCAGACAAATCAGGGCTGCCCAGTGATCGGTTCTCTGCAGAGAGGCCTGAGGGATCCATCTCCACTCTGACGATTCAGCGCACAGAGCAGCGGGACTCAGCCATGTATCGCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCTCCCAGGTACAAAGTCACAAAGAGGGGACAGGATGTAACTCTCAGGTGTGATCCAATTTCGAGTCATGCAACCCTTTATTGGTATCAACAGGCCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCAATTATGAAGCTCAACCAGACAAATCAGGGCTGCCCAGTGATCGGTTCTCTGCAGAGAGGCCTGAGGGATCCATCTCCACTCTGACGATTCAGCGCACAGAGCAGCGGGACTCAGCCATGTATCGCTGTGCTAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCCCCTAGGTACAAAGTCGCAAAGAGAGGACAGGATGTAGCTCTCAGGTGTGATCCAATTTCGGGTCATGTATCCCTTTTTTGGTACCAACAGGCCCTGGGGCAGGGGCCAGAGTTTCTGACTTATTTCCAGAATGAAGCTCAACTAGACAAATCGGGGCTGCCCAGTGATCGCTTCTTTGCAGAAAGGCCTGAGGGATCCGTCTCCACTCTGAAGATCCAGCGCACACAGCAGGAGGACTCCGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCCCCTAGGTACAAAGTCGCAAAGAGAGGACAGGATGTAGCTCTCAGGTGTGATCCAATTTCGGGTCATGTATCCCTTTTTTGGTACCAACAGGCCCTGGGGCAGGGGCCAGAGTTTCTGACTTATTTCCAGAATGAAGCTCAACTAGACAAATCGGGGCTGCCCAGTGATCGCTTCTTTGCAGAAAGGCCTGAGGGATCCGTCTCCACTCTGAAGATCCAGCGCACACAGAAGGAGGACTCCGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTTTCTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;ATATCTGGAGTCTCCCACAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGAACCCTGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTGGAAAAATCAGGGCTGCTCAGTGATCGGATCTCTGCAGAGAGGCCTAAGGGATCTTTCTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTCTCTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTCTTTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;CACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTTTCTCCACCTTGGAGATCCAGCGCACAGAGGAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATTCTGGAGTCACACAAACCCCAAAGCACCTGATCACAGCAACTGGACAGCGAGTGACGCTGAGATGCTCCCCTAGGTCTGGAGACCTCTCTGTGTACTGGTACCAACAGAGCCTGGACCAGGGCCTCCAGTTCCTCATTCAGTATTATAATGGAGAAGAGAGAGCAAAAGGAAACATTCTTGAACGATTCTCCGCACAACAGTTCCCTGACTTGCACTCTGAACTAAACCTGAGCTCTCTGGAGCTGGGGGACTCAGCTTTGTATTTCTGTGCCAGCAGCGTAG&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j_choice</span></div><div class='xr-var-dims'>(j_choice)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14</div><input id='attrs-66c8b885-626a-4094-afa4-8363b904a981' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66c8b885-626a-4094-afa4-8363b904a981' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4f2ebcc-caca-4b7c-b27a-bb3b4e9b67cd' class='xr-var-data-in' type='checkbox'><label for='data-c4f2ebcc-caca-4b7c-b27a-bb3b4e9b67cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seq__j_choice</span></div><div class='xr-var-dims'>(j_choice)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;TGAACACTGAAGCTTTCTTTGGACAAGGCAC...</div><input id='attrs-bf911862-70ce-4601-bc2f-d17670294cff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf911862-70ce-4601-bc2f-d17670294cff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c06cdaa2-7e27-41e1-a5d2-f225124f5b02' class='xr-var-data-in' type='checkbox'><label for='data-c06cdaa2-7e27-41e1-a5d2-f225124f5b02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;TGAACACTGAAGCTTTCTTTGGACAAGGCACCAGACTCACAGTTGTAG&#x27;,\n",
       "       &#x27;CTAACTATGGCTACACCTTCGGTTCGGGGACCAGGTTAACCGTTGTAG&#x27;,\n",
       "       &#x27;CTCTGGAAACACCATATATTTTGGAGAGGGAAGTTGGCTCACTGTTGTAG&#x27;,\n",
       "       &#x27;CAACTAATGAAAAACTGTTTTTTGGCAGTGGAACCCAGCTCTCTGTCTTGG&#x27;,\n",
       "       &#x27;TAGCAATCAGCCCCAGCATTTTGGTGATGGGACTCGACTCTCCATCCTAG&#x27;,\n",
       "       &#x27;CTCCTATAATTCACCCCTCCACTTTGGGAATGGGACCAGGCTCACTGTGACAG&#x27;,\n",
       "       &#x27;CTCCTATAATTCACCCCTCCACTTTGGGAACGGGACCAGGCTCACTGTGACAG&#x27;,\n",
       "       &#x27;CTCCTACAATGAGCAGTTCTTCGGGCCAGGGACACGGCTCACCGTGCTAG&#x27;,\n",
       "       &#x27;CGAACACCGGGGAGCTGTTTTTTGGAGAAGGCTCTAGGCTGACCGTACTGG&#x27;,\n",
       "       &#x27;AGCACAGATACGCAGTATTTTGGCCCAGGCACCCGGCTGACAGTGCTCG&#x27;,\n",
       "       &#x27;AGCCAAAAACATTCAGTACTTCGGCGCCGGGACCCGGCTCTCAGTGCTGG&#x27;,\n",
       "       &#x27;ACCAAGAGACCCAGTACTTCGGGCCAGGCACGCGGCTCCTGGTGCTCG&#x27;,\n",
       "       &#x27;CTCTGGGGCCAACGTCCTGACTTTCGGGGCCGGCAGCAGGCTGACCGTGCTGG&#x27;,\n",
       "       &#x27;CTCCTACGAGCAGTACTTCGGGCCGGGCACCAGGCTCACGGTCACAG&#x27;,\n",
       "       &#x27;CTCCTACGAGCAGTACGTCGGGCCGGGCACCAGGCTCACGGTCACAG&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48d7ca67-5ee9-4533-b21f-fafbfaf657b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48d7ca67-5ee9-4533-b21f-fafbfaf657b9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (v_choice: 89, j_choice: 15)>\n",
       "array([[6.28453463e-04, 5.08306114e-04, 2.00831906e-04, ...,\n",
       "        0.00000000e+00, 2.62143710e-04, 1.82391677e-04],\n",
       "       [1.54890932e-03, 7.36213918e-04, 1.06011576e-04, ...,\n",
       "        2.29432410e-04, 6.05154843e-04, 6.95679479e-04],\n",
       "       [1.54900602e-03, 7.36581450e-04, 1.06039046e-04, ...,\n",
       "        2.28733827e-04, 6.05348578e-04, 6.95983268e-04],\n",
       "       ...,\n",
       "       [4.05407259e-04, 1.55690174e-04, 0.00000000e+00, ...,\n",
       "        1.15101267e-04, 1.80155267e-04, 1.96566942e-04],\n",
       "       [3.00565952e-09, 1.68608515e-12, 0.00000000e+00, ...,\n",
       "        2.81447867e-04, 5.64048061e-05, 6.77364474e-05],\n",
       "       [1.65903669e-03, 1.77940202e-03, 6.30491475e-04, ...,\n",
       "        3.45140763e-04, 1.12130116e-03, 9.08844648e-04]])\n",
       "Coordinates:\n",
       "  * v_choice       (v_choice) int64 0 1 2 3 4 5 6 7 ... 81 82 83 84 85 86 87 88\n",
       "    seq__v_choice  (v_choice) object 'GATACTGGAATTACCCAGACACCAAAATACCTGGTCACA...\n",
       "  * j_choice       (j_choice) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14\n",
       "    seq__j_choice  (j_choice) object 'TGAACACTGAAGCTTTCTTTGGACAAGGCACCAGACTCA..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_V_J = mdl_hb.get_P_joint(['v_choice', 'j_choice'])\n",
    "P_V_J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fac1c1fc8d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "P_V_J.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fac1c0e7310>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb['j_choice'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Editing a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A model can be edited manually if necessary. In cases like a long gene description or to add new anchors to the existing genomic templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'V':                                                  name  \\\n",
       " id                                                      \n",
       " 0   U66059|TRBV1*01|Homo sapiens|P|V-REGION|91723....   \n",
       " 1   U66059|TRBV10-1*01|Homo sapiens|F|V-REGION|214...   \n",
       " 2   AF009660|TRBV10-1*02|Homo sapiens|F|V-REGION|5...   \n",
       " 3   AF009660|TRBV10-1*04|Homo sapiens|F|V-REGION|5...   \n",
       " 4   U66059|TRBV10-2*01|Homo sapiens|F|V-REGION|239...   \n",
       " ..                                                ...   \n",
       " 84  M14261|TRBV7-9*04|Homo sapiens|(F)|V-REGION|58...   \n",
       " 85  M27385|TRBV7-9*05|Homo sapiens|(F)|V-REGION|34...   \n",
       " 86  X74844|TRBV7-9*06|Homo sapiens|(F)|V-REGION|10...   \n",
       " 87  L14854|TRBV7-9*07|Homo sapiens|(F)|V-REGION|1....   \n",
       " 88  U66059|TRBV9*01|Homo sapiens|F|V-REGION|206836...   \n",
       " \n",
       "                                                 value  anchor_index  \n",
       " id                                                                   \n",
       " 0   GATACTGGAATTACCCAGACACCAAAATACCTGGTCACAGCAATGG...           NaN  \n",
       " 1   GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAG...         270.0  \n",
       " 2   GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAG...         270.0  \n",
       " 3   GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAG...         270.0  \n",
       " 4   GATGCTGGAATCACCCAGAGCCCAAGATACAAGATCACAGAGACAG...         270.0  \n",
       " ..                                                ...           ...  \n",
       " 84  ATATCTGGAGTCTCCCACAACCCCAGACACAAGATCACAAAGAGGG...         273.0  \n",
       " 85  GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGG...         273.0  \n",
       " 86  GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGG...         273.0  \n",
       " 87  CACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAG...         189.0  \n",
       " 88  GATTCTGGAGTCACACAAACCCCAAAGCACCTGATCACAGCAACTG...         270.0  \n",
       " \n",
       " [89 rows x 3 columns],\n",
       " 'D':          name             value\n",
       " id                             \n",
       " 0    TRBD1*01      GGGACAGGGGGC\n",
       " 1    TRBD2*01  GGGACTAGCGGGGGGG\n",
       " 2    TRBD2*02  GGGACTAGCGGGAGGG,\n",
       " 'J':                                                  name  \\\n",
       " id                                                      \n",
       " 0   K02545|TRBJ1-1*01|Homo sapiens|F|J-REGION|749....   \n",
       " 1   K02545|TRBJ1-2*01|Homo sapiens|F|J-REGION|886....   \n",
       " 2   M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       " 3   M14158|TRBJ1-4*01|Homo sapiens|F|J-REGION|2095...   \n",
       " 4   M14158|TRBJ1-5*01|Homo sapiens|F|J-REGION|2368...   \n",
       " 5   M14158|TRBJ1-6*01|Homo sapiens|F|J-REGION|2859...   \n",
       " 6   L36092|TRBJ1-6*02|Homo sapiens|F|J-REGION|6430...   \n",
       " 7   X02987|TRBJ2-1*01|Homo sapiens|F|J-REGION|800....   \n",
       " 8   X02987|TRBJ2-2*01|Homo sapiens|F|J-REGION|995....   \n",
       " 9   X02987|TRBJ2-3*01|Homo sapiens|F|J-REGION|1282...   \n",
       " 10  X02987|TRBJ2-4*01|Homo sapiens|F|J-REGION|1432...   \n",
       " 11  X02987|TRBJ2-5*01|Homo sapiens|F|J-REGION|1553...   \n",
       " 12  X02987|TRBJ2-6*01|Homo sapiens|F|J-REGION|1673...   \n",
       " 13  M14159|TRBJ2-7*01|Homo sapiens|F|J-REGION|2316...   \n",
       " 14  X02987|TRBJ2-7*02|Homo sapiens|ORF|J-REGION|18...   \n",
       " \n",
       "                                                 value  anchor_index  \n",
       " id                                                                   \n",
       " 0    TGAACACTGAAGCTTTCTTTGGACAAGGCACCAGACTCACAGTTGTAG            17  \n",
       " 1    CTAACTATGGCTACACCTTCGGTTCGGGGACCAGGTTAACCGTTGTAG            17  \n",
       " 2   CTCTGGAAACACCATATATTTTGGAGAGGGAAGTTGGCTCACTGTT...            19  \n",
       " 3   CAACTAATGAAAAACTGTTTTTTGGCAGTGGAACCCAGCTCTCTGT...            20  \n",
       " 4   TAGCAATCAGCCCCAGCATTTTGGTGATGGGACTCGACTCTCCATC...            19  \n",
       " 5   CTCCTATAATTCACCCCTCCACTTTGGGAATGGGACCAGGCTCACT...            22  \n",
       " 6   CTCCTATAATTCACCCCTCCACTTTGGGAACGGGACCAGGCTCACT...            22  \n",
       " 7   CTCCTACAATGAGCAGTTCTTCGGGCCAGGGACACGGCTCACCGTG...            19  \n",
       " 8   CGAACACCGGGGAGCTGTTTTTTGGAGAAGGCTCTAGGCTGACCGT...            20  \n",
       " 9   AGCACAGATACGCAGTATTTTGGCCCAGGCACCCGGCTGACAGTGCTCG            18  \n",
       " 10  AGCCAAAAACATTCAGTACTTCGGCGCCGGGACCCGGCTCTCAGTG...            19  \n",
       " 11   ACCAAGAGACCCAGTACTTCGGGCCAGGCACGCGGCTCCTGGTGCTCG            17  \n",
       " 12  CTCTGGGGCCAACGTCCTGACTTTCGGGGCCGGCAGCAGGCTGACC...            22  \n",
       " 13    CTCCTACGAGCAGTACTTCGGGCCGGGCACCAGGCTCACGGTCACAG            16  \n",
       " 14    CTCCTACGAGCAGTACGTCGGGCCGGGCACCAGGCTCACGGTCACAG            16  }"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.genomic_dataframe_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we make a copy of genomic_dataframe_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "0     U66059|TRBV1*01|Homo sapiens|P|V-REGION|91723....\n",
       "1     U66059|TRBV10-1*01|Homo sapiens|F|V-REGION|214...\n",
       "2     AF009660|TRBV10-1*02|Homo sapiens|F|V-REGION|5...\n",
       "3     AF009660|TRBV10-1*04|Homo sapiens|F|V-REGION|5...\n",
       "4     U66059|TRBV10-2*01|Homo sapiens|F|V-REGION|239...\n",
       "                            ...                        \n",
       "84    M14261|TRBV7-9*04|Homo sapiens|(F)|V-REGION|58...\n",
       "85    M27385|TRBV7-9*05|Homo sapiens|(F)|V-REGION|34...\n",
       "86    X74844|TRBV7-9*06|Homo sapiens|(F)|V-REGION|10...\n",
       "87    L14854|TRBV7-9*07|Homo sapiens|(F)|V-REGION|1....\n",
       "88    U66059|TRBV9*01|Homo sapiens|F|V-REGION|206836...\n",
       "Name: name, Length: 89, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import copy\n",
    "genomic_dict = copy.deepcopy(mdl_hb.genomic_dataframe_dict)\n",
    "genomic_dict['V']['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we change the long description names using v_genLabel function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "genomic_dict['V']['name'] = p3.v_genLabel(genomic_dict['V']['name'])\n",
    "genomic_dict['J']['name'] = p3.v_genLabel(genomic_dict['J']['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, update the the genomic_dataframe_dict with this new dict, by using the set_genomic_dataframe_dict method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl_hb.set_genomic_dataframe_dict(genomic_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>name</th>\n",
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       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GATACTGGAATTACCCAGACACCAAAATACCTGGTCACAGCAATGG...</td>\n",
       "      <td>TRBV1*01</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAG...</td>\n",
       "      <td>TRBV10-1*01</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAG...</td>\n",
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       "      <th>...</th>\n",
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       "      <th>84</th>\n",
       "      <td>ATATCTGGAGTCTCCCACAACCCCAGACACAAGATCACAAAGAGGG...</td>\n",
       "      <td>TRBV7-9*04</td>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGG...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGG...</td>\n",
       "      <td>TRBV7-9*06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>CACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAG...</td>\n",
       "      <td>TRBV7-9*07</td>\n",
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       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>GATTCTGGAGTCACACAAACCCCAAAGCACCTGATCACAGCAACTG...</td>\n",
       "      <td>TRBV9*01</td>\n",
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       "<p>89 rows × 2 columns</p>\n",
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       "                                                value         name\n",
       "id                                                                \n",
       "0   GATACTGGAATTACCCAGACACCAAAATACCTGGTCACAGCAATGG...     TRBV1*01\n",
       "1   GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAG...  TRBV10-1*01\n",
       "2   GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAG...  TRBV10-1*02\n",
       "3   GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAG...  TRBV10-1*04\n",
       "4   GATGCTGGAATCACCCAGAGCCCAAGATACAAGATCACAGAGACAG...  TRBV10-2*01\n",
       "..                                                ...          ...\n",
       "84  ATATCTGGAGTCTCCCACAACCCCAGACACAAGATCACAAAGAGGG...   TRBV7-9*04\n",
       "85  GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGG...   TRBV7-9*05\n",
       "86  GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGG...   TRBV7-9*06\n",
       "87  CACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAG...   TRBV7-9*07\n",
       "88  GATTCTGGAGTCACACAAACCCCAAAGCACCTGATCACAGCAACTG...     TRBV9*01\n",
       "\n",
       "[89 rows x 2 columns]"
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     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "mdl_hb.parms['v_choice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (v_choice: 89)&gt;\n",
       "array([4.88741e-03, 9.32369e-03, 9.32259e-03, 1.30320e-02, 3.43430e-04,\n",
       "       8.50694e-03, 7.71250e-03, 6.12276e-04, 5.06104e-03, 4.59289e-05,\n",
       "       4.48245e-03, 8.16181e-03, 7.00053e-04, 7.77164e-03, 1.16174e-02,\n",
       "       1.16158e-02, 1.13872e-02, 1.06555e-02, 2.36870e-03, 2.28368e-02,\n",
       "       1.56715e-04, 4.58447e-03, 0.00000e+00, 4.88782e-05, 0.00000e+00,\n",
       "       1.54500e-02, 2.74050e-02, 5.18979e-03, 4.80289e-03, 1.62765e-01,\n",
       "       6.61229e-02, 2.07174e-02, 7.36226e-04, 2.47846e-37, 1.07472e-02,\n",
       "       2.03820e-02, 9.53245e-03, 9.53006e-03, 2.35607e-03, 2.39208e-03,\n",
       "       2.39208e-03, 2.45219e-03, 1.66904e-02, 2.98807e-03, 2.98804e-03,\n",
       "       1.59156e-02, 1.25468e-02, 9.06711e-03, 9.06936e-03, 9.76534e-02,\n",
       "       9.57746e-03, 9.17011e-03, 1.14472e-02, 1.14458e-02, 9.77554e-03,\n",
       "       1.89924e-02, 3.97949e-04, 1.70242e-03, 8.91336e-03, 6.73926e-03,\n",
       "       6.69167e-03, 2.94257e-02, 1.38534e-02, 4.19227e-03, 4.19275e-03,\n",
       "       2.48630e-03, 4.83399e-04, 1.29795e-07, 6.49975e-05, 1.94736e-02,\n",
       "       1.94728e-02, 0.00000e+00, 3.91595e-03, 3.96230e-02, 0.00000e+00,\n",
       "       0.00000e+00, 6.73805e-05, 2.90073e-02, 9.28490e-03, 5.32118e-03,\n",
       "       8.18432e-03, 1.07035e-03, 3.64764e-04, 3.26790e-03, 3.26801e-03,\n",
       "       3.26801e-03, 3.26809e-03, 8.23600e-04, 1.56399e-02])\n",
       "Coordinates:\n",
       "  * v_choice       (v_choice) int64 0 1 2 3 4 5 6 7 ... 81 82 83 84 85 86 87 88\n",
       "    lbl__v_choice  (v_choice) object &#x27;TRBV1*01&#x27; &#x27;TRBV10-1*01&#x27; ... &#x27;TRBV9*01&#x27;\n",
       "    seq__v_choice  (v_choice) object &#x27;GATACTGGAATTACCCAGACACCAAAATACCTGGTCACA...\n",
       "Attributes:\n",
       "    nickname:    v_choice\n",
       "    event_type:  GeneChoice\n",
       "    seq_type:    V_gene\n",
       "    seq_side:    Undefined_side\n",
       "    priority:    7\n",
       "    parents:     []\n",
       "    childs:      [&#x27;v_3_del&#x27;, &#x27;d_gene&#x27;, &#x27;j_choice&#x27;]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>v_choice</span>: 89</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0d068411-73b9-4243-8f22-294e06c6bce4' class='xr-array-in' type='checkbox' checked><label for='section-0d068411-73b9-4243-8f22-294e06c6bce4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.004887 0.009324 0.009323 0.01303 ... 0.003268 0.0008236 0.01564</span></div><div class='xr-array-data'><pre>array([4.88741e-03, 9.32369e-03, 9.32259e-03, 1.30320e-02, 3.43430e-04,\n",
       "       8.50694e-03, 7.71250e-03, 6.12276e-04, 5.06104e-03, 4.59289e-05,\n",
       "       4.48245e-03, 8.16181e-03, 7.00053e-04, 7.77164e-03, 1.16174e-02,\n",
       "       1.16158e-02, 1.13872e-02, 1.06555e-02, 2.36870e-03, 2.28368e-02,\n",
       "       1.56715e-04, 4.58447e-03, 0.00000e+00, 4.88782e-05, 0.00000e+00,\n",
       "       1.54500e-02, 2.74050e-02, 5.18979e-03, 4.80289e-03, 1.62765e-01,\n",
       "       6.61229e-02, 2.07174e-02, 7.36226e-04, 2.47846e-37, 1.07472e-02,\n",
       "       2.03820e-02, 9.53245e-03, 9.53006e-03, 2.35607e-03, 2.39208e-03,\n",
       "       2.39208e-03, 2.45219e-03, 1.66904e-02, 2.98807e-03, 2.98804e-03,\n",
       "       1.59156e-02, 1.25468e-02, 9.06711e-03, 9.06936e-03, 9.76534e-02,\n",
       "       9.57746e-03, 9.17011e-03, 1.14472e-02, 1.14458e-02, 9.77554e-03,\n",
       "       1.89924e-02, 3.97949e-04, 1.70242e-03, 8.91336e-03, 6.73926e-03,\n",
       "       6.69167e-03, 2.94257e-02, 1.38534e-02, 4.19227e-03, 4.19275e-03,\n",
       "       2.48630e-03, 4.83399e-04, 1.29795e-07, 6.49975e-05, 1.94736e-02,\n",
       "       1.94728e-02, 0.00000e+00, 3.91595e-03, 3.96230e-02, 0.00000e+00,\n",
       "       0.00000e+00, 6.73805e-05, 2.90073e-02, 9.28490e-03, 5.32118e-03,\n",
       "       8.18432e-03, 1.07035e-03, 3.64764e-04, 3.26790e-03, 3.26801e-03,\n",
       "       3.26801e-03, 3.26809e-03, 8.23600e-04, 1.56399e-02])</pre></div></div></li><li class='xr-section-item'><input id='section-0d740624-b2ed-422e-82e0-e01cdeb03fe4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0d740624-b2ed-422e-82e0-e01cdeb03fe4' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 83 84 85 86 87 88</div><input id='attrs-0956ad3f-0f6a-42a1-851a-60653c2d77b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0956ad3f-0f6a-42a1-851a-60653c2d77b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68ac2d9f-5e22-4336-881e-1e72a1526c01' class='xr-var-data-in' type='checkbox'><label for='data-68ac2d9f-5e22-4336-881e-1e72a1526c01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lbl__v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;TRBV1*01&#x27; ... &#x27;TRBV9*01&#x27;</div><input id='attrs-a8a44b06-1127-441e-a0f5-a72a62bc7ecc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8a44b06-1127-441e-a0f5-a72a62bc7ecc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e965abfd-4b5f-40d8-b810-562f8c314086' class='xr-var-data-in' type='checkbox'><label for='data-e965abfd-4b5f-40d8-b810-562f8c314086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;TRBV1*01&#x27;, &#x27;TRBV10-1*01&#x27;, &#x27;TRBV10-1*02&#x27;, &#x27;TRBV10-1*04&#x27;,\n",
       "       &#x27;TRBV10-2*01&#x27;, &#x27;TRBV10-3*01&#x27;, &#x27;TRBV10-3*02&#x27;, &#x27;TRBV11-1*01&#x27;,\n",
       "       &#x27;TRBV11-2*01&#x27;, &#x27;TRBV11-2*02&#x27;, &#x27;TRBV11-2*03&#x27;, &#x27;TRBV11-3*01&#x27;,\n",
       "       &#x27;TRBV12-1*01&#x27;, &#x27;TRBV12-2*01&#x27;, &#x27;TRBV12-3*01&#x27;, &#x27;TRBV12-4*01&#x27;,\n",
       "       &#x27;TRBV12-4*02&#x27;, &#x27;TRBV12-5*01&#x27;, &#x27;TRBV13*01&#x27;, &#x27;TRBV14*01&#x27;,\n",
       "       &#x27;TRBV15*01&#x27;, &#x27;TRBV15*02&#x27;, &#x27;TRBV15*03&#x27;, &#x27;TRBV16*01&#x27;, &#x27;TRBV17*01&#x27;,\n",
       "       &#x27;TRBV18*01&#x27;, &#x27;TRBV19*01&#x27;, &#x27;TRBV2*01&#x27;, &#x27;TRBV2*03&#x27;, &#x27;TRBV20-1*01&#x27;,\n",
       "       &#x27;TRBV23-1*01&#x27;, &#x27;TRBV24-1*01&#x27;, &#x27;TRBV25-1*01&#x27;, &#x27;TRBV26*01&#x27;,\n",
       "       &#x27;TRBV27*01&#x27;, &#x27;TRBV28*01&#x27;, &#x27;TRBV29-1*01&#x27;, &#x27;TRBV29-1*02&#x27;,\n",
       "       &#x27;TRBV3-1*01&#x27;, &#x27;TRBV3-1*02&#x27;, &#x27;TRBV3-2*01&#x27;, &#x27;TRBV3-2*02&#x27;,\n",
       "       &#x27;TRBV30*01&#x27;, &#x27;TRBV30*02&#x27;, &#x27;TRBV30*04&#x27;, &#x27;TRBV4-1*01&#x27;, &#x27;TRBV4-2*01&#x27;,\n",
       "       &#x27;TRBV4-3*01&#x27;, &#x27;TRBV4-3*02&#x27;, &#x27;TRBV5-1*01&#x27;, &#x27;TRBV5-3*01&#x27;,\n",
       "       &#x27;TRBV5-3*02&#x27;, &#x27;TRBV5-4*01&#x27;, &#x27;TRBV5-4*02&#x27;, &#x27;TRBV5-5*01&#x27;,\n",
       "       &#x27;TRBV5-6*01&#x27;, &#x27;TRBV5-7*01&#x27;, &#x27;TRBV5-8*01&#x27;, &#x27;TRBV6-1*01&#x27;,\n",
       "       &#x27;TRBV6-2*01&#x27;, &#x27;TRBV6-3*01&#x27;, &#x27;TRBV6-4*01&#x27;, &#x27;TRBV6-5*01&#x27;,\n",
       "       &#x27;TRBV6-6*01&#x27;, &#x27;TRBV6-6*02&#x27;, &#x27;TRBV6-7*01&#x27;, &#x27;TRBV6-8*01&#x27;,\n",
       "       &#x27;TRBV6-9*01&#x27;, &#x27;TRBV7-1*01&#x27;, &#x27;TRBV7-2*01&#x27;, &#x27;TRBV7-2*02&#x27;,\n",
       "       &#x27;TRBV7-2*03&#x27;, &#x27;TRBV7-2*05&#x27;, &#x27;TRBV7-3*01&#x27;, &#x27;TRBV7-3*02&#x27;,\n",
       "       &#x27;TRBV7-3*03&#x27;, &#x27;TRBV7-3*04&#x27;, &#x27;TRBV7-4*01&#x27;, &#x27;TRBV7-6*01&#x27;,\n",
       "       &#x27;TRBV7-7*01&#x27;, &#x27;TRBV7-7*03&#x27;, &#x27;TRBV7-8*01&#x27;, &#x27;TRBV7-8*02&#x27;,\n",
       "       &#x27;TRBV7-9*01&#x27;, &#x27;TRBV7-9*04&#x27;, &#x27;TRBV7-9*05&#x27;, &#x27;TRBV7-9*06&#x27;,\n",
       "       &#x27;TRBV7-9*07&#x27;, &#x27;TRBV9*01&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seq__v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;GATACTGGAATTACCCAGACACCAAAATACC...</div><input id='attrs-7aa10157-bace-4cd8-bb98-63b51dd05e6c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7aa10157-bace-4cd8-bb98-63b51dd05e6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fbfa7c5-80d5-4d18-9c50-b33702d4ca9f' class='xr-var-data-in' type='checkbox'><label for='data-4fbfa7c5-80d5-4d18-9c50-b33702d4ca9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;GATACTGGAATTACCCAGACACCAAAATACCTGGTCACAGCAATGGGGAGTAAAAGGACAATGAAACGTGAGCATCTGGGACATGATTCTATGTATTGGTACAGACAGAAAGCTAAGAAATCCCTGGAGTTCATGTTTTACTACAACTGTAAGGAATTCATTGAAAACAAGACTGTGCCAAATCACTTCACACCTGAATGCCCTGACAGCTCTCGCTTATACCTTCATGTGGTCGCACTGCAGCAAGAAGACTCAGCTGCGTATCTCTGCACCAGCAGCCAAGA&#x27;,\n",
       "       &#x27;GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCAAGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGGAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGGAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGAAATCACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGTAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGGAATCACCCAGAGCCCAAGATACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGATGTGTCACCAGACTTGGAGCCACAGCTATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCTATTACTCAGCAGCTGCTGATATTACAGATAAAGGAGAAGTCCCCGATGGCTATGTTGTCTCCAGATCCAAGACAGAGAATTTCCCCCTCACTCTGGAGTCAGCTACCCGCTCCCAGACATCTGTGTATTTCTGCGCCAGCAGTGAGTC&#x27;,\n",
       "       &#x27;GATGCTGGAATCACCCAGAGCCCAAGACACAAGGTCACAGAGACAGGAACACCAGTGACTCTGAGATGTCACCAGACTGAGAACCACCGCTATATGTACTGGTATCGACAAGACCCGGGGCATGGGCTGAGGCTGATCCATTACTCATATGGTGTTAAAGATACTGACAAAGGAGAAGTCTCAGATGGCTATAGTGTCTCTAGATCAAAGACAGAGGATTTCCTCCTCACTCTGGAGTCCGCTACCAGCTCCCAGACATCTGTGTACTTCTGTGCCATCAGTGAGTC&#x27;,\n",
       "       &#x27;GCTGGAATCACCCAGAGCCCAAGACACAAGGTCACAGAGACAGGAACACCAGTGACTCTGAGATGTCATCAGACTGAGAACCACCGCTATATGTACTGGTATCGACAAGACCCGGGGCATGGGCTGAGGCTGATCCATTACTCATATGGTGTTAAAGATACTGACAAAGGAGAAGTCTCAGATGGCTATAGTGTCTCTAGATCAAAGACAGAGGATTTCCTCCTCACTCTGGAGTCCGCTACCAGCTCCCAGACATCTGTGTACTTCTGTGCCATCAGTGAGTC&#x27;,\n",
       "       &#x27;GAAGCTGAAGTTGCCCAGTCCCCCAGATATAAGATTACAGAGAAAAGCCAGGCTGTGGCTTTTTGGTGTGATCCTATTTCTGGCCATGCTACCCTTTACTGGTACCGGCAGATCCTGGGACAGGGCCCGGAGCTTCTGGTTCAATTTCAGGATGAGAGTGTAGTAGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAGAGCTTGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAAAGCTTGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAAAGCTTGAGAACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAACCTGCAAAGCTTGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTGGTTCAGTCTCCCAGATATAAGATTATAGAGAAAAAACAGCCTGTGGCTTTTTGGTGCAATCCTATTTCTGGCCACAATACCCTTTACTGGTACCTGCAGAACTTGGGACAGGGCCCGGAGCTTCTGATTCGATATGAGAATGAGGAAGCAGTAGACGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAGAGCTTGGGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GCTGGTGTTATCCAGTCACCCAGGCACAAAGTGACAGAGATGGGACAATCAGTAACTCTGAGATGCGAACCAATTTCAGGCCACAATGATCTTCTCTGGTACAGACAGACCTTTGTGCAGGGACTGGAATTGCTGAATTACTTCTGCAGCTGGACCCTCGTAGATGACTCAGGAGTGTCCAAGGATTGATTCTCAGCACAGATGCCTGATGTATCATTCTCCACTCTGAGGATCCAGCCCATGGAACCCAGGGACTTGGGCCTATATTTCTGTGCCAGCAGCTTTGC&#x27;,\n",
       "       &#x27;GCTGGCATTATCCAGTCACCCAAGCATGAGGTGACAGAAATGGGACAAACAGTGACTCTGAGATGTGAGCCAATTTTTGGCCACAATTTCCTTTTCTGGTACAGAGATACCTTCGTGCAGGGACTGGAATTGCTGAGTTACTTCCGGAGCTGATCTATTATAGATAATGCAGGTATGCCCACAGAGCGATTCTCAGCTGAGAGGCCTGATGGATCATTCTCTACTCTGAAGATCCAGCCTGCAGAGCAGGGGGACTCGGCCGTGTATGTCTGTGCAAGTCGCTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGCCATGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGCCACAACTCCCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGACACGACTACCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGACATGACTACCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAGGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTAGAGTCACCCAGACACCAAGGCACAAGGTGACAGAGATGGGACAAGAAGTAACAATGAGATGTCAGCCAATTTTAGGCCACAATACTGTTTTCTGGTACAGACAGACCATGATGCAAGGACTGGAGTTGCTGGCTTACTTCCGCAACCGGGCTCCTCTAGATGATTCGGGGATGCCGAAGGATCGATTCTCAGCAGAGATGCCTGATGCAACTTTAGCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTATTTTTGTGCTAGTGGTTTGGT&#x27;,\n",
       "       &#x27;GCTGCTGGAGTCATCCAGTCCCCAAGACATCTGATCAAAGAAAAGAGGGAAACAGCCACTCTGAAATGCTATCCTATCCCTAGACACGACACTGTCTACTGGTACCAGCAGGGTCCAGGTCAGGACCCCCAGTTCCTCATTTCGTTTTATGAAAAGATGCAGAGCGATAAAGGAAGCATCCCTGATCGATTCTCAGCTCAACAGTTCAGTGACTATCATTCTGAACTGAACATGAGCTCCTTGGAGCTGGGGGACTCAGCCCTGTACTTCTGTGCCAGCAGCTTAGG&#x27;,\n",
       "       &#x27;GAAGCTGGAGTTACTCAGTTCCCCAGCCACAGCGTAATAGAGAAGGGCCAGACTGTGACTCTGAGATGTGACCCAATTTCTGGACATGATAATCTTTATTGGTATCGACGTGTTATGGGAAAAGAAATAAAATTTCTGTTACATTTTGTGAAAGAGTCTAAACAGGATGAGTCCGGTATGCCCAACAATCGATTCTTAGCTGAAAGGACTGGAGGGACGTATTCTACTCTGAAGGTGCAGCCTGCAGAACTGGAGGATTCTGGAGTTTATTTCTGTGCCAGCAGCCAAGA&#x27;,\n",
       "...\n",
       "       &#x27;GGAGCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAAAGGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAGAGGCTGGGGCAGGGCCTGGAGTTTTTAATTTACTTCCAAGGCAACAGTGCACCAGACAAATCAGGGCTGCCCAGTGATCGCTTCTCTGCAGAGAGGACTGGGGAATCCGTCTCCACTCTGACGATCCAGCGCACACAGCAGGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAAAGGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAGAGGCTGGGGCAGGGCCTGGAGTTTTTAATTTACTTCCAAGGCAACAGTGCACCAGACAAATCAGGGCTGCCCAGTGATCGCTTCTCTGCAGAGAGGACTGGGGAATCCGTCTCCACTCTGACGATCCAGCGCACACAGCAGGAGGACTCGGCCGTGTATCTCTGTACCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGAGCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAAAGGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAGAGGCTGGGGCAGGGCCTGGAGTTTTTAATTTACTTCCAAGGCAACAGTGCACCAGACAAATCAGGGCTGCCCAGTGATCGCTTCTCTGCAGAGAGGACTGGGGAATCCGTCTCCACTCTGACGATCCAGCGCACATAGCAGGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAATATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAACGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCGGGGGGACTCAGCCGTGTATCTCTGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAAGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAAAGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCCGTGTATCTCCGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAAGATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAAAGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCCGCGTATCTCCGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGGGAAAATATGTAGAGCTCAGGTGTGATCCAATTTCAGGTCATACTGCCCTTTACTGGTACCGACAAAGCCTGGGGCAGGGCCCAGAGTTTCTAATTTACTTCCAAGGCACGGGTGCGGCAGATGACTCAGGGCTGCCCAACGATCGGTTCTTTGCAGTCAGGCCTGAGGGATCCGTCTCTACTCTGAAGATCCAGCGCACAGAGCGGGGGGACTCTGCCGTGTATCTCTGTGCCAGCAGCTTAAC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGTCGCAAAGAGGGGACGGGATGTAGCTCTCAGGTGTGATTCAATTTCGGGTCATGTAACCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCTCAGAGGTTCTGACTTACTCCCAGAGTGATGCTCAACGAGACAAATCAGGGCGGCCCAGTGGTCGGTTCTCTGCAGAGAGGCCTGAGAGATCCGTCTCCACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCTGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCTCCCAGGTACAAAGTCACAAAGAGGGGACAGGATGTAGCTCTCAGGTGTGATCCAATTTCGGGTCATGTATCCCTTTATTGGTACCGACAGGCCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCAATTATGAAGCCCAACAAGACAAATCAGGGCTGCCCAATGATCGGTTCTCTGCAGAGAGGCCTGAGGGATCCATCTCCACTCTGACGATCCAGCGCACAGAGCAGCGGGACTCGGCCATGTATCGCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCTCCCAGGTACAAAGTCACAAAGAGGGGACAGGATGTAACTCTCAGGTGTGATCCAATTTCGAGTCATGCAACCCTTTATTGGTATCAACAGGCCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCAATTATGAAGCTCAACCAGACAAATCAGGGCTGCCCAGTGATCGGTTCTCTGCAGAGAGGCCTGAGGGATCCATCTCCACTCTGACGATTCAGCGCACAGAGCAGCGGGACTCAGCCATGTATCGCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCTCCCAGGTACAAAGTCACAAAGAGGGGACAGGATGTAACTCTCAGGTGTGATCCAATTTCGAGTCATGCAACCCTTTATTGGTATCAACAGGCCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCAATTATGAAGCTCAACCAGACAAATCAGGGCTGCCCAGTGATCGGTTCTCTGCAGAGAGGCCTGAGGGATCCATCTCCACTCTGACGATTCAGCGCACAGAGCAGCGGGACTCAGCCATGTATCGCTGTGCTAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCCCCTAGGTACAAAGTCGCAAAGAGAGGACAGGATGTAGCTCTCAGGTGTGATCCAATTTCGGGTCATGTATCCCTTTTTTGGTACCAACAGGCCCTGGGGCAGGGGCCAGAGTTTCTGACTTATTTCCAGAATGAAGCTCAACTAGACAAATCGGGGCTGCCCAGTGATCGCTTCTTTGCAGAAAGGCCTGAGGGATCCGTCTCCACTCTGAAGATCCAGCGCACACAGCAGGAGGACTCCGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GGTGCTGGAGTCTCCCAGTCCCCTAGGTACAAAGTCGCAAAGAGAGGACAGGATGTAGCTCTCAGGTGTGATCCAATTTCGGGTCATGTATCCCTTTTTTGGTACCAACAGGCCCTGGGGCAGGGGCCAGAGTTTCTGACTTATTTCCAGAATGAAGCTCAACTAGACAAATCGGGGCTGCCCAGTGATCGCTTCTTTGCAGAAAGGCCTGAGGGATCCGTCTCCACTCTGAAGATCCAGCGCACACAGAAGGAGGACTCCGCCGTGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTTTCTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;ATATCTGGAGTCTCCCACAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGAACCCTGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTGGAAAAATCAGGGCTGCTCAGTGATCGGATCTCTGCAGAGAGGCCTAAGGGATCTTTCTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTCTCTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATACTGGAGTCTCCCAGAACCCCAGACACAAGATCACAAAGAGGGGACAGAATGTAACTTTCAGGTGTGATCCAATTTCTGAACACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTCTTTCCACCTTGGAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;CACAACCGCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCCCAGAGTTTCTGACTTACTTCCAGAATGAAGCTCAACTAGAAAAATCAAGGCTGCTCAGTGATCGGTTCTCTGCAGAGAGGCCTAAGGGATCTTTCTCCACCTTGGAGATCCAGCGCACAGAGGAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCTTAGC&#x27;,\n",
       "       &#x27;GATTCTGGAGTCACACAAACCCCAAAGCACCTGATCACAGCAACTGGACAGCGAGTGACGCTGAGATGCTCCCCTAGGTCTGGAGACCTCTCTGTGTACTGGTACCAACAGAGCCTGGACCAGGGCCTCCAGTTCCTCATTCAGTATTATAATGGAGAAGAGAGAGCAAAAGGAAACATTCTTGAACGATTCTCCGCACAACAGTTCCCTGACTTGCACTCTGAACTAAACCTGAGCTCTCTGGAGCTGGGGGACTCAGCTTTGTATTTCTGTGCCAGCAGCGTAG&#x27;],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37c09478-d2b9-4114-a983-682f5fe30f04' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37c09478-d2b9-4114-a983-682f5fe30f04' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>nickname :</span></dt><dd>v_choice</dd><dt><span>event_type :</span></dt><dd>GeneChoice</dd><dt><span>seq_type :</span></dt><dd>V_gene</dd><dt><span>seq_side :</span></dt><dd>Undefined_side</dd><dt><span>priority :</span></dt><dd>7</dd><dt><span>parents :</span></dt><dd>[]</dd><dt><span>childs :</span></dt><dd>[&#x27;v_3_del&#x27;, &#x27;d_gene&#x27;, &#x27;j_choice&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (v_choice: 89)>\n",
       "array([4.88741e-03, 9.32369e-03, 9.32259e-03, 1.30320e-02, 3.43430e-04,\n",
       "       8.50694e-03, 7.71250e-03, 6.12276e-04, 5.06104e-03, 4.59289e-05,\n",
       "       4.48245e-03, 8.16181e-03, 7.00053e-04, 7.77164e-03, 1.16174e-02,\n",
       "       1.16158e-02, 1.13872e-02, 1.06555e-02, 2.36870e-03, 2.28368e-02,\n",
       "       1.56715e-04, 4.58447e-03, 0.00000e+00, 4.88782e-05, 0.00000e+00,\n",
       "       1.54500e-02, 2.74050e-02, 5.18979e-03, 4.80289e-03, 1.62765e-01,\n",
       "       6.61229e-02, 2.07174e-02, 7.36226e-04, 2.47846e-37, 1.07472e-02,\n",
       "       2.03820e-02, 9.53245e-03, 9.53006e-03, 2.35607e-03, 2.39208e-03,\n",
       "       2.39208e-03, 2.45219e-03, 1.66904e-02, 2.98807e-03, 2.98804e-03,\n",
       "       1.59156e-02, 1.25468e-02, 9.06711e-03, 9.06936e-03, 9.76534e-02,\n",
       "       9.57746e-03, 9.17011e-03, 1.14472e-02, 1.14458e-02, 9.77554e-03,\n",
       "       1.89924e-02, 3.97949e-04, 1.70242e-03, 8.91336e-03, 6.73926e-03,\n",
       "       6.69167e-03, 2.94257e-02, 1.38534e-02, 4.19227e-03, 4.19275e-03,\n",
       "       2.48630e-03, 4.83399e-04, 1.29795e-07, 6.49975e-05, 1.94736e-02,\n",
       "       1.94728e-02, 0.00000e+00, 3.91595e-03, 3.96230e-02, 0.00000e+00,\n",
       "       0.00000e+00, 6.73805e-05, 2.90073e-02, 9.28490e-03, 5.32118e-03,\n",
       "       8.18432e-03, 1.07035e-03, 3.64764e-04, 3.26790e-03, 3.26801e-03,\n",
       "       3.26801e-03, 3.26809e-03, 8.23600e-04, 1.56399e-02])\n",
       "Coordinates:\n",
       "  * v_choice       (v_choice) int64 0 1 2 3 4 5 6 7 ... 81 82 83 84 85 86 87 88\n",
       "    lbl__v_choice  (v_choice) object 'TRBV1*01' 'TRBV10-1*01' ... 'TRBV9*01'\n",
       "    seq__v_choice  (v_choice) object 'GATACTGGAATTACCCAGACACCAAAATACCTGGTCACA...\n",
       "Attributes:\n",
       "    nickname:    v_choice\n",
       "    event_type:  GeneChoice\n",
       "    seq_type:    V_gene\n",
       "    seq_side:    Undefined_side\n",
       "    priority:    7\n",
       "    parents:     []\n",
       "    childs:      ['v_3_del', 'd_gene', 'j_choice']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb['v_choice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error_type': 'SingleErrorRate', 'error_values': '0.000396072'}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.ErrorRate_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pygor has a methods to create default VDJ and VJ models from a dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_V_gene_dict = {\n",
    "    'name':'my_pseudo_TRBV',\n",
    "    'value':'AAACCCTTTGGGACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGTAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC',\n",
    "    'anchor_index': 270\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>name</th>\n",
       "      <th>value</th>\n",
       "      <th>anchor_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRBV11-2*03</td>\n",
       "      <td>GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAA...</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRBV11-3*01</td>\n",
       "      <td>GAAGCTGGAGTGGTTCAGTCTCCCAGATATAAGATTATAGAGAAAA...</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRBV12-1*01</td>\n",
       "      <td>GCTGGTGTTATCCAGTCACCCAGGCACAAAGTGACAGAGATGGGAC...</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRBV12-2*01</td>\n",
       "      <td>GCTGGCATTATCCAGTCACCCAAGCATGAGGTGACAGAAATGGGAC...</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRBV12-3*01</td>\n",
       "      <td>GATGCTGGAGTTATCCAGTCACCCCGCCATGAGGTGACAGAGATGG...</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TRBV12-4*01</td>\n",
       "      <td>GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGG...</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>my_pseudo_TRBV</td>\n",
       "      <td>AAACCCTTTGGGACCCAGAGCCCAAGACACAAGATCACAGAGACAG...</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name                                              value  \\\n",
       "id                                                                      \n",
       "0      TRBV11-2*03  GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAA...   \n",
       "1      TRBV11-3*01  GAAGCTGGAGTGGTTCAGTCTCCCAGATATAAGATTATAGAGAAAA...   \n",
       "2      TRBV12-1*01  GCTGGTGTTATCCAGTCACCCAGGCACAAAGTGACAGAGATGGGAC...   \n",
       "3      TRBV12-2*01  GCTGGCATTATCCAGTCACCCAAGCATGAGGTGACAGAAATGGGAC...   \n",
       "4      TRBV12-3*01  GATGCTGGAGTTATCCAGTCACCCCGCCATGAGGTGACAGAGATGG...   \n",
       "5      TRBV12-4*01  GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGG...   \n",
       "6   my_pseudo_TRBV  AAACCCTTTGGGACCCAGAGCCCAAGACACAAGATCACAGAGACAG...   \n",
       "\n",
       "    anchor_index  \n",
       "id                \n",
       "0          273.0  \n",
       "1          273.0  \n",
       "2          270.0  \n",
       "3          270.0  \n",
       "4          273.0  \n",
       "5          273.0  \n",
       "6          270.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_V = genomic_dict['V'].loc[10:15]\n",
    "df_V = df_V.append(new_V_gene_dict, ignore_index=True)\n",
    "df_V.index.name='id'\n",
    "df_V"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use this new genomic templates to create a new model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygor3.IgorIO.IgorModel at 0x7fac17fdd490>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_mdl_0 = p3.IgorModel.make_default_VDJ(df_V, genomic_dict['D'], genomic_dict['J'])\n",
    "new_mdl_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1296x1080 with 1 Axes>,\n",
       " <AxesSubplot:title={'center':'$P($v_choice$)$'}>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_mdl_0.plot_Event('v_choice')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the new model will initiate with a uniform probability, that can be used to infer a new model from data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conditional and marginal probabilities can be exported as pdf files with plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fln_output_prefix = \"mdl_hb\"\n",
    "mdl_hb.export_plot_Pconditionals(fln_output_prefix+\"_CP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl_hb.export_plot_Pmarginals(fln_output_prefix+\"_MP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A model can be exported in IGoR's format with write_model method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing model parms in file  new_model_parms.txt\n",
      "Writing model marginals in file  new_model_marginals.txt\n",
      "Writing gene anchor's in file  new_V_anchors.csv\n",
      "Writing gene anchor's in file  new_J_anchors.csv\n"
     ]
    }
   ],
   "source": [
    "new_mdl_0.write_model('new_model_parms.txt', 'new_model_marginals.txt',\n",
    "                      fln_V_gene_CDR3_anchors='new_V_anchors.csv', \n",
    "                      fln_J_gene_CDR3_anchors='new_J_anchors.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "@Event_list\n",
      "#GeneChoice;V_gene;Undefined_side;7;v_choice\n",
      "%TRBV11-2*03;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAACCTGCAAAGCTTGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA;0\n",
      "%TRBV11-3*01;GAAGCTGGAGTGGTTCAGTCTCCCAGATATAAGATTATAGAGAAAAAACAGCCTGTGGCTTTTTGGTGCAATCCTATTTCTGGCCACAATACCCTTTACTGGTACCTGCAGAACTTGGGACAGGGCCCGGAGCTTCTGATTCGATATGAGAATGAGGAAGCAGTAGACGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAGAGCTTGGGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA;1\n",
      "%TRBV12-1*01;GCTGGTGTTATCCAGTCACCCAGGCACAAAGTGACAGAGATGGGACAATCAGTAACTCTGAGATGCGAACCAATTTCAGGCCACAATGATCTTCTCTGGTACAGACAGACCTTTGTGCAGGGACTGGAATTGCTGAATTACTTCTGCAGCTGGACCCTCGTAGATGACTCAGGAGTGTCCAAGGATTGATTCTCAGCACAGATGCCTGATGTATCATTCTCCACTCTGAGGATCCAGCCCATGGAACCCAGGGACTTGGGCCTATATTTCTGTGCCAGCAGCTTTGC;2\n",
      "%TRBV12-2*01;GCTGGCATTATCCAGTCACCCAAGCATGAGGTGACAGAAATGGGACAAACAGTGACTCTGAGATGTGAGCCAATTTTTGGCCACAATTTCCTTTTCTGGTACAGAGATACCTTCGTGCAGGGACTGGAATTGCTGAGTTACTTCCGGAGCTGATCTATTATAGATAATGCAGGTATGCCCACAGAGCGATTCTCAGCTGAGAGGCCTGATGGATCATTCTCTACTCTGAAGATCCAGCCTGCAGAGCAGGGGGACTCGGCCGTGTATGTCTGTGCAAGTCGCTTAGC;3\n",
      "%TRBV12-3*01;GATGCTGGAGTTATCCAGTCACCCCGCCATGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGCCACAACTCCCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC;4\n",
      "%TRBV12-4*01;GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGACACGACTACCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC;5\n",
      "%my_pseudo_TRBV;AAACCCTTTGGGACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGTAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC;6\n",
      "#GeneChoice;J_gene;Undefined_side;7;j_choice\n"
     ]
    }
   ],
   "source": [
    "!head new_model_parms.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gene;anchor_index\n",
      "TRBV11-2*03;273\n",
      "TRBV11-3*01;273\n",
      "TRBV12-1*01;270\n",
      "TRBV12-2*01;270\n",
      "TRBV12-3*01;273\n",
      "TRBV12-4*01;273\n",
      "my_pseudo_TRBV;270\n"
     ]
    }
   ],
   "source": [
    "!head new_V_anchors.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading Parms filename from:  new_model_parms.txt\n",
      "Reading Marginals filename from:  new_model_marginals.txt\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (v_choice: 7)&gt;\n",
       "array([0.14285714, 0.14285714, 0.14285714, 0.14285714, 0.14285714,\n",
       "       0.14285714, 0.14285714])\n",
       "Coordinates:\n",
       "  * v_choice       (v_choice) int64 0 1 2 3 4 5 6\n",
       "    lbl__v_choice  (v_choice) object &#x27;TRBV11-2*03&#x27; ... &#x27;my_pseudo_TRBV&#x27;\n",
       "    seq__v_choice  (v_choice) object &#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATA...\n",
       "Attributes:\n",
       "    nickname:    v_choice\n",
       "    event_type:  GeneChoice\n",
       "    seq_type:    V_gene\n",
       "    seq_side:    Undefined_side\n",
       "    priority:    7\n",
       "    parents:     []\n",
       "    childs:      [&#x27;j_choice&#x27;, &#x27;d_gene&#x27;, &#x27;v_3_del&#x27;]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>v_choice</span>: 7</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5a304ac1-6268-4c58-b3ee-f725a291fd1a' class='xr-array-in' type='checkbox' checked><label for='section-5a304ac1-6268-4c58-b3ee-f725a291fd1a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.1429 0.1429 0.1429 0.1429 0.1429 0.1429 0.1429</span></div><div class='xr-array-data'><pre>array([0.14285714, 0.14285714, 0.14285714, 0.14285714, 0.14285714,\n",
       "       0.14285714, 0.14285714])</pre></div></div></li><li class='xr-section-item'><input id='section-a24f6077-8087-4223-a6ff-c2e690119bcb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a24f6077-8087-4223-a6ff-c2e690119bcb' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6</div><input id='attrs-bae3c500-2bbd-4bc2-a0cc-ed54db364987' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bae3c500-2bbd-4bc2-a0cc-ed54db364987' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07a2ebf9-f9f9-4c3b-9f26-587842f66dc0' class='xr-var-data-in' type='checkbox'><label for='data-07a2ebf9-f9f9-4c3b-9f26-587842f66dc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lbl__v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;TRBV11-2*03&#x27; ... &#x27;my_pseudo_TRBV&#x27;</div><input id='attrs-1c66a853-d00f-4795-8059-22baec86cc99' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c66a853-d00f-4795-8059-22baec86cc99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d368d3-63a1-4023-a5ab-ce2b0a8f4f7f' class='xr-var-data-in' type='checkbox'><label for='data-42d368d3-63a1-4023-a5ab-ce2b0a8f4f7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;TRBV11-2*03&#x27;, &#x27;TRBV11-3*01&#x27;, &#x27;TRBV12-1*01&#x27;, &#x27;TRBV12-2*01&#x27;,\n",
       "       &#x27;TRBV12-3*01&#x27;, &#x27;TRBV12-4*01&#x27;, &#x27;my_pseudo_TRBV&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seq__v_choice</span></div><div class='xr-var-dims'>(v_choice)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATA...</div><input id='attrs-d447ec51-e3c9-4caf-83e4-9846e0b26a32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d447ec51-e3c9-4caf-83e4-9846e0b26a32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7aa74caa-9030-4efe-87cb-e05ae32dab2a' class='xr-var-data-in' type='checkbox'><label for='data-7aa74caa-9030-4efe-87cb-e05ae32dab2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAAGGCAGAGTGTGGCTTTTTGGTGCAATCCTATATCTGGCCATGCTACCCTTTACTGGTACCAGCAGATCCTGGGACAGGGCCCAAAGCTTCTGATTCAGTTTCAGAATAACGGTGTAGTGGATGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAACCTGCAAAGCTTGAGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GAAGCTGGAGTGGTTCAGTCTCCCAGATATAAGATTATAGAGAAAAAACAGCCTGTGGCTTTTTGGTGCAATCCTATTTCTGGCCACAATACCCTTTACTGGTACCTGCAGAACTTGGGACAGGGCCCGGAGCTTCTGATTCGATATGAGAATGAGGAAGCAGTAGACGATTCACAGTTGCCTAAGGATCGATTTTCTGCAGAGAGGCTCAAAGGAGTAGACTCCACTCTCAAGATCCAGCCTGCAGAGCTTGGGGACTCGGCCGTGTATCTCTGTGCCAGCAGCTTAGA&#x27;,\n",
       "       &#x27;GCTGGTGTTATCCAGTCACCCAGGCACAAAGTGACAGAGATGGGACAATCAGTAACTCTGAGATGCGAACCAATTTCAGGCCACAATGATCTTCTCTGGTACAGACAGACCTTTGTGCAGGGACTGGAATTGCTGAATTACTTCTGCAGCTGGACCCTCGTAGATGACTCAGGAGTGTCCAAGGATTGATTCTCAGCACAGATGCCTGATGTATCATTCTCCACTCTGAGGATCCAGCCCATGGAACCCAGGGACTTGGGCCTATATTTCTGTGCCAGCAGCTTTGC&#x27;,\n",
       "       &#x27;GCTGGCATTATCCAGTCACCCAAGCATGAGGTGACAGAAATGGGACAAACAGTGACTCTGAGATGTGAGCCAATTTTTGGCCACAATTTCCTTTTCTGGTACAGAGATACCTTCGTGCAGGGACTGGAATTGCTGAGTTACTTCCGGAGCTGATCTATTATAGATAATGCAGGTATGCCCACAGAGCGATTCTCAGCTGAGAGGCCTGATGGATCATTCTCTACTCTGAAGATCCAGCCTGCAGAGCAGGGGGACTCGGCCGTGTATGTCTGTGCAAGTCGCTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGCCATGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGCCACAACTCCCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;GATGCTGGAGTTATCCAGTCACCCCGGCACGAGGTGACAGAGATGGGACAAGAAGTGACTCTGAGATGTAAACCAATTTCAGGACACGACTACCTTTTCTGGTACAGACAGACCATGATGCGGGGACTGGAGTTGCTCATTTACTTTAACAACAACGTTCCGATAGATGATTCAGGGATGCCCGAGGATCGATTCTCAGCTAAGATGCCTAATGCATCATTCTCCACTCTGAAGATCCAGCCCTCAGAACCCAGGGACTCAGCTGTGTACTTCTGTGCCAGCAGTTTAGC&#x27;,\n",
       "       &#x27;AAACCCTTTGGGACCCAGAGCCCAAGACACAAGATCACAGAGACAGGAAGGCAGGTGACCTTGGCGTGTCACCAGACTTGGAACCACAACAATATGTTCTGGTATCGACAAGACCTGGGACATGGGCTGAGGCTGATCCATTACTCATATGGTGTTCACGACACTAACAAAGGAGAAGTCTCAGATGGCTACAGTGTCTCTAGATCAAACACAGAGGACCTCCCCCTCACTCTGTAGTCTGCTGCCTCCTCCCAGACATCTGTATATTTCTGCGCCAGCAGTGAGTC&#x27;],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6fc4da7d-82b2-4735-a527-27652ca1a7d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6fc4da7d-82b2-4735-a527-27652ca1a7d4' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>nickname :</span></dt><dd>v_choice</dd><dt><span>event_type :</span></dt><dd>GeneChoice</dd><dt><span>seq_type :</span></dt><dd>V_gene</dd><dt><span>seq_side :</span></dt><dd>Undefined_side</dd><dt><span>priority :</span></dt><dd>7</dd><dt><span>parents :</span></dt><dd>[]</dd><dt><span>childs :</span></dt><dd>[&#x27;j_choice&#x27;, &#x27;d_gene&#x27;, &#x27;v_3_del&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (v_choice: 7)>\n",
       "array([0.14285714, 0.14285714, 0.14285714, 0.14285714, 0.14285714,\n",
       "       0.14285714, 0.14285714])\n",
       "Coordinates:\n",
       "  * v_choice       (v_choice) int64 0 1 2 3 4 5 6\n",
       "    lbl__v_choice  (v_choice) object 'TRBV11-2*03' ... 'my_pseudo_TRBV'\n",
       "    seq__v_choice  (v_choice) object 'GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATA...\n",
       "Attributes:\n",
       "    nickname:    v_choice\n",
       "    event_type:  GeneChoice\n",
       "    seq_type:    V_gene\n",
       "    seq_side:    Undefined_side\n",
       "    priority:    7\n",
       "    parents:     []\n",
       "    childs:      ['j_choice', 'd_gene', 'v_3_del']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_0 = p3.IgorModel('new_model_parms.txt', 'new_model_marginals.txt',\n",
    "                      fln_V_gene_CDR3_anchors='new_V_anchors.csv', \n",
    "                      fln_J_gene_CDR3_anchors='new_J_anchors.csv')\n",
    "mdl_0['v_choice']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IgorModel can also be exported in separated csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl_0.export_csv('initial_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Insertions;P(vd_ins);P(dj_ins)\n",
      "0;0.024390243902439025;0.024390243902439025\n",
      "1;0.024390243902439025;0.024390243902439025\n",
      "2;0.024390243902439025;0.024390243902439025\n",
      "3;0.024390243902439025;0.024390243902439025\n",
      "4;0.024390243902439025;0.024390243902439025\n",
      "5;0.024390243902439025;0.024390243902439025\n",
      "6;0.024390243902439025;0.024390243902439025\n",
      "7;0.024390243902439025;0.024390243902439025\n",
      "8;0.024390243902439025;0.024390243902439025\n"
     ]
    }
   ],
   "source": [
    "!head initial_P__insertions.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ";TRBJ1-1*01;TRBJ1-2*01;TRBJ1-3*01;TRBJ1-4*01;TRBJ1-5*01;TRBJ1-6*01;TRBJ1-6*02;TRBJ2-1*01;TRBJ2-2*01;TRBJ2-3*01;TRBJ2-4*01;TRBJ2-5*01;TRBJ2-6*01;TRBJ2-7*01;TRBJ2-7*02\n",
      "TRBV11-2*03;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667\n",
      "TRBV11-3*01;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667\n",
      "TRBV12-1*01;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667\n",
      "TRBV12-2*01;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667\n",
      "TRBV12-3*01;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667\n",
      "TRBV12-4*01;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667\n",
      "my_pseudo_TRBV;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667;0.06666666666666667\n"
     ]
    }
   ],
   "source": [
    "!head initial_P__j_choice__G__v_choice.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entropy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ H = - \\sum_x P_(x) \\log(P(x)) $ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5.252905287497762"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "-np.dot(mdl_hb['v_choice'], np.nan_to_num(np.log2(mdl_hb['v_choice']), neginf=0, nan=0) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "array(5.25291424)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a48b3c0e-70a7-4135-bc09-9ef6c63c3dbb' class='xr-array-in' type='checkbox' checked><label for='section-a48b3c0e-70a7-4135-bc09-9ef6c63c3dbb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.253</span></div><div class='xr-array-data'><pre>array(5.25291424)</pre></div></div></li><li class='xr-section-item'><input id='section-66b64360-ac06-4bff-92e6-31f6b42d6561' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66b64360-ac06-4bff-92e6-31f6b42d6561' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-421d939a-b47a-43b8-96ff-96ebbf629683' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-421d939a-b47a-43b8-96ff-96ebbf629683' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(5.25291424)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.get_entropy_event('v_choice')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n"
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       "array(0.0779008)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e40c57a9-ec57-4038-beb5-b743f2722c7a' class='xr-array-in' type='checkbox' checked><label for='section-e40c57a9-ec57-4038-beb5-b743f2722c7a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0779</span></div><div class='xr-array-data'><pre>array(0.0779008)</pre></div></div></li><li class='xr-section-item'><input id='section-59bbeaed-323a-4b92-a004-4e8814579f45' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59bbeaed-323a-4b92-a004-4e8814579f45' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-df7604b4-6221-460b-a220-c9ab85224ff1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-df7604b4-6221-460b-a220-c9ab85224ff1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "<xarray.DataArray ()>\n",
       "array(0.0779008)"
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     },
     "execution_count": 45,
     "metadata": {},
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   "source": [
    "I_V_J = mdl_hb.get_mutual_information_events('v_choice', 'j_choice')\n",
    "I_V_J"
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      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
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      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
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       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00]])\n",
       "Coordinates:\n",
       "  * x        (x) &lt;U8 &#x27;v_choice&#x27; &#x27;d_gene&#x27; &#x27;j_choice&#x27; ... &#x27;vd_ins&#x27; &#x27;dj_ins&#x27;\n",
       "  * y        (y) &lt;U8 &#x27;v_choice&#x27; &#x27;d_gene&#x27; &#x27;j_choice&#x27; ... &#x27;vd_ins&#x27; &#x27;dj_ins&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'mutual_information'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 9</li><li><span class='xr-has-index'>y</span>: 9</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6668fe8e-4038-45ff-96c9-51e3d6e37742' class='xr-array-in' type='checkbox' checked><label for='section-6668fe8e-4038-45ff-96c9-51e3d6e37742' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.02748 0.0779 0.3342 0.002422 0.003846 ... 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[0.00000000e+00, 2.74757454e-02, 7.79007955e-02, 3.34244341e-01,\n",
       "        2.42224927e-03, 3.84618632e-03, 3.11240332e-03, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [2.74757454e-02, 0.00000000e+00, 1.54509182e-01, 7.40373019e-04,\n",
       "        1.50139212e-01, 2.66774661e-01, 4.92915719e-03, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [7.79007955e-02, 1.54509182e-01, 0.00000000e+00, 7.48363481e-04,\n",
       "        9.41603438e-03, 8.72144895e-03, 3.15006638e-01, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [3.34244341e-01, 7.40373019e-04, 7.48363481e-04, 0.00000000e+00,\n",
       "        5.45436417e-05, 7.33990325e-05, 2.35845118e-05, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [2.42224927e-03, 1.50139212e-01, 9.41603438e-03, 5.45436417e-05,\n",
       "        0.00000000e+00, 4.49012987e-01, 2.87003080e-04, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [3.84618632e-03, 2.66774661e-01, 8.72144895e-03, 7.33990325e-05,\n",
       "        4.49012987e-01, 0.00000000e+00, 2.74201841e-04, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [3.11240332e-03, 4.92915719e-03, 3.15006638e-01, 2.35845118e-05,\n",
       "        2.87003080e-04, 2.74201841e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00]])</pre></div></div></li><li class='xr-section-item'><input id='section-c59d2a15-583a-4792-8277-dedce9110806' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c59d2a15-583a-4792-8277-dedce9110806' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;v_choice&#x27; &#x27;d_gene&#x27; ... &#x27;dj_ins&#x27;</div><input id='attrs-2e0e75c5-540d-41a5-ada1-2b9d232ffab9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e0e75c5-540d-41a5-ada1-2b9d232ffab9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6d530dc-e2f7-4450-8d0a-56325b34d4d7' class='xr-var-data-in' type='checkbox'><label for='data-a6d530dc-e2f7-4450-8d0a-56325b34d4d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;v_choice&#x27;, &#x27;d_gene&#x27;, &#x27;j_choice&#x27;, &#x27;v_3_del&#x27;, &#x27;d_3_del&#x27;, &#x27;d_5_del&#x27;,\n",
       "       &#x27;j_5_del&#x27;, &#x27;vd_ins&#x27;, &#x27;dj_ins&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;v_choice&#x27; &#x27;d_gene&#x27; ... &#x27;dj_ins&#x27;</div><input id='attrs-267180a5-75e0-4170-bb9a-be4379c2ad4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-267180a5-75e0-4170-bb9a-be4379c2ad4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9b4056a-c654-479e-9c63-4f1a3b186d9e' class='xr-var-data-in' type='checkbox'><label for='data-c9b4056a-c654-479e-9c63-4f1a3b186d9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;v_choice&#x27;, &#x27;d_gene&#x27;, &#x27;j_choice&#x27;, &#x27;v_3_del&#x27;, &#x27;d_3_del&#x27;, &#x27;d_5_del&#x27;,\n",
       "       &#x27;j_5_del&#x27;, &#x27;vd_ins&#x27;, &#x27;dj_ins&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20d01690-a2a8-46d4-988c-c5f0af079f0d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-20d01690-a2a8-46d4-988c-c5f0af079f0d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'mutual_information' (x: 9, y: 9)>\n",
       "array([[0.00000000e+00, 2.74757454e-02, 7.79007955e-02, 3.34244341e-01,\n",
       "        2.42224927e-03, 3.84618632e-03, 3.11240332e-03, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [2.74757454e-02, 0.00000000e+00, 1.54509182e-01, 7.40373019e-04,\n",
       "        1.50139212e-01, 2.66774661e-01, 4.92915719e-03, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [7.79007955e-02, 1.54509182e-01, 0.00000000e+00, 7.48363481e-04,\n",
       "        9.41603438e-03, 8.72144895e-03, 3.15006638e-01, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [3.34244341e-01, 7.40373019e-04, 7.48363481e-04, 0.00000000e+00,\n",
       "        5.45436417e-05, 7.33990325e-05, 2.35845118e-05, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [2.42224927e-03, 1.50139212e-01, 9.41603438e-03, 5.45436417e-05,\n",
       "        0.00000000e+00, 4.49012987e-01, 2.87003080e-04, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [3.84618632e-03, 2.66774661e-01, 8.72144895e-03, 7.33990325e-05,\n",
       "        4.49012987e-01, 0.00000000e+00, 2.74201841e-04, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [3.11240332e-03, 4.92915719e-03, 3.15006638e-01, 2.35845118e-05,\n",
       "        2.87003080e-04, 2.74201841e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00]])\n",
       "Coordinates:\n",
       "  * x        (x) <U8 'v_choice' 'd_gene' 'j_choice' ... 'vd_ins' 'dj_ins'\n",
       "  * y        (y) <U8 'v_choice' 'd_gene' 'j_choice' ... 'vd_ins' 'dj_ins'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_mi = mdl_hb.get_mutual_information()\n",
    "da_mi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_mutual_information(da_mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading Parms filename from:  /home/olivares/.local/share/igor/models/human/tcr_beta/models/model_parms.txt\n",
      "Reading Marginals filename from:  /home/olivares/.local/share/igor/models/human/tcr_beta/models/model_marginals.txt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
       "array(0.0779008)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-56b7bbef-f8d7-41e9-bce7-338c9d727434' class='xr-array-in' type='checkbox' checked><label for='section-56b7bbef-f8d7-41e9-bce7-338c9d727434' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0779</span></div><div class='xr-array-data'><pre>array(0.0779008)</pre></div></div></li><li class='xr-section-item'><input id='section-fbc1ba12-eff6-40cc-9876-b45a1bb33c21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fbc1ba12-eff6-40cc-9876-b45a1bb33c21' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-99d2cab5-035f-447c-ad4a-9fea7fd94079' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-99d2cab5-035f-447c-ad4a-9fea7fd94079' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(0.0779008)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_nickname1 = 'v_choice'\n",
    "event_nickname2 = 'j_choice'\n",
    "\n",
    "mdl = p3.get_default_IgorModel(\"human\", \"tcr_beta\")\n",
    "\n",
    "da_P_x_y = mdl.get_P_joint([event_nickname1, event_nickname2])\n",
    "da_P_x = mdl.Pmarginal[event_nickname1]\n",
    "da_P_y = mdl.Pmarginal[event_nickname2]\n",
    "\n",
    "da_P_x_times_P_y = (da_P_x*da_P_y)\n",
    "da_P_x_times_P_y\n",
    "\n",
    "da_log_P_ratio = xr.zeros_like(da_P_x_y)\n",
    "\n",
    "da_log_P_ratio.values = np.nan_to_num(\n",
    "    np.log2(da_P_x_y / da_P_x_times_P_y), nan=0.0, neginf=0.0\n",
    ")\n",
    "\n",
    "# da_log_Value.values = np_log_Value\n",
    "xr.dot( da_P_x_y, da_log_P_ratio )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n"
     ]
    }
   ],
   "source": [
    "da_mi = mdl_hb.get_mutual_information()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_mutual_information(da_mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n",
      "/home/olivares/anaconda3/envs/statbiophys-dev/lib/python3.7/site-packages/xarray/core/computation.py:739: RuntimeWarning: divide by zero encountered in log2\n",
      "  result_data = func(*input_data)\n"
     ]
    }
   ],
   "source": [
    "da_mi = mdl_hb.get_mutual_information()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_mutual_information(da_mi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen in the Bayesian network, the number of deletions in V depends on choosen V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select events by probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see which combinations of V and D are not possible, for our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>lbl__v_choice</th>\n",
       "      <th>seq__v_choice</th>\n",
       "      <th>lbl__d_gene</th>\n",
       "      <th>seq__d_gene</th>\n",
       "      <th>P_joint_V_D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_choice</th>\n",
       "      <th>d_gene</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">22</th>\n",
       "      <th>0</th>\n",
       "      <td>TRBV15*03</td>\n",
       "      <td>GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTG...</td>\n",
       "      <td>TRBD1*01</td>\n",
       "      <td>GGGACAGGGGGC</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRBV15*03</td>\n",
       "      <td>GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTG...</td>\n",
       "      <td>TRBD2*01</td>\n",
       "      <td>GGGACTAGCGGGGGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRBV15*03</td>\n",
       "      <td>GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTG...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>GGGACTAGCGGGAGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">24</th>\n",
       "      <th>0</th>\n",
       "      <td>TRBV17*01</td>\n",
       "      <td>GAGCCTGGAGTCAGCCAGACCCCCAGACACAAGGTCACCAACATGG...</td>\n",
       "      <td>TRBD1*01</td>\n",
       "      <td>GGGACAGGGGGC</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRBV17*01</td>\n",
       "      <td>GAGCCTGGAGTCAGCCAGACCCCCAGACACAAGGTCACCAACATGG...</td>\n",
       "      <td>TRBD2*01</td>\n",
       "      <td>GGGACTAGCGGGGGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRBV17*01</td>\n",
       "      <td>GAGCCTGGAGTCAGCCAGACCCCCAGACACAAGGTCACCAACATGG...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>GGGACTAGCGGGAGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">33</th>\n",
       "      <th>1</th>\n",
       "      <td>TRBV26*01</td>\n",
       "      <td>GATGCTGTAGTTACACAATTCCCAAGACACAGAATCATTGGGACAG...</td>\n",
       "      <td>TRBD2*01</td>\n",
       "      <td>GGGACTAGCGGGGGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRBV26*01</td>\n",
       "      <td>GATGCTGTAGTTACACAATTCCCAAGACACAGAATCATTGGGACAG...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>GGGACTAGCGGGAGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">67</th>\n",
       "      <th>0</th>\n",
       "      <td>TRBV6-9*01</td>\n",
       "      <td>AATGCTGGTGTCACTCAGACCCCAAAATTCCACATCCTGAAGACAG...</td>\n",
       "      <td>TRBD1*01</td>\n",
       "      <td>GGGACAGGGGGC</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRBV6-9*01</td>\n",
       "      <td>AATGCTGGTGTCACTCAGACCCCAAAATTCCACATCCTGAAGACAG...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>GGGACTAGCGGGAGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">71</th>\n",
       "      <th>0</th>\n",
       "      <td>TRBV7-2*03</td>\n",
       "      <td>GCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAA...</td>\n",
       "      <td>TRBD1*01</td>\n",
       "      <td>GGGACAGGGGGC</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRBV7-2*03</td>\n",
       "      <td>GCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAA...</td>\n",
       "      <td>TRBD2*01</td>\n",
       "      <td>GGGACTAGCGGGGGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRBV7-2*03</td>\n",
       "      <td>GCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAA...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>GGGACTAGCGGGAGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">74</th>\n",
       "      <th>0</th>\n",
       "      <td>TRBV7-3*02</td>\n",
       "      <td>GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...</td>\n",
       "      <td>TRBD1*01</td>\n",
       "      <td>GGGACAGGGGGC</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRBV7-3*02</td>\n",
       "      <td>GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...</td>\n",
       "      <td>TRBD2*01</td>\n",
       "      <td>GGGACTAGCGGGGGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRBV7-3*02</td>\n",
       "      <td>GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>GGGACTAGCGGGAGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">75</th>\n",
       "      <th>0</th>\n",
       "      <td>TRBV7-3*03</td>\n",
       "      <td>GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...</td>\n",
       "      <td>TRBD1*01</td>\n",
       "      <td>GGGACAGGGGGC</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRBV7-3*03</td>\n",
       "      <td>GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...</td>\n",
       "      <td>TRBD2*01</td>\n",
       "      <td>GGGACTAGCGGGGGGG</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRBV7-3*03</td>\n",
       "      <td>GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>GGGACTAGCGGGAGGG</td>\n",
       "      <td>0.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                lbl__v_choice  \\\n",
       "v_choice d_gene                 \n",
       "22       0          TRBV15*03   \n",
       "         1          TRBV15*03   \n",
       "         2          TRBV15*03   \n",
       "24       0          TRBV17*01   \n",
       "         1          TRBV17*01   \n",
       "         2          TRBV17*01   \n",
       "33       1          TRBV26*01   \n",
       "         2          TRBV26*01   \n",
       "67       0         TRBV6-9*01   \n",
       "         2         TRBV6-9*01   \n",
       "71       0         TRBV7-2*03   \n",
       "         1         TRBV7-2*03   \n",
       "         2         TRBV7-2*03   \n",
       "74       0         TRBV7-3*02   \n",
       "         1         TRBV7-3*02   \n",
       "         2         TRBV7-3*02   \n",
       "75       0         TRBV7-3*03   \n",
       "         1         TRBV7-3*03   \n",
       "         2         TRBV7-3*03   \n",
       "\n",
       "                                                     seq__v_choice  \\\n",
       "v_choice d_gene                                                      \n",
       "22       0       GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTG...   \n",
       "         1       GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTG...   \n",
       "         2       GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTG...   \n",
       "24       0       GAGCCTGGAGTCAGCCAGACCCCCAGACACAAGGTCACCAACATGG...   \n",
       "         1       GAGCCTGGAGTCAGCCAGACCCCCAGACACAAGGTCACCAACATGG...   \n",
       "         2       GAGCCTGGAGTCAGCCAGACCCCCAGACACAAGGTCACCAACATGG...   \n",
       "33       1       GATGCTGTAGTTACACAATTCCCAAGACACAGAATCATTGGGACAG...   \n",
       "         2       GATGCTGTAGTTACACAATTCCCAAGACACAGAATCATTGGGACAG...   \n",
       "67       0       AATGCTGGTGTCACTCAGACCCCAAAATTCCACATCCTGAAGACAG...   \n",
       "         2       AATGCTGGTGTCACTCAGACCCCAAAATTCCACATCCTGAAGACAG...   \n",
       "71       0       GCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAA...   \n",
       "         1       GCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAA...   \n",
       "         2       GCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGGGAA...   \n",
       "74       0       GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...   \n",
       "         1       GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...   \n",
       "         2       GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...   \n",
       "75       0       GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...   \n",
       "         1       GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...   \n",
       "         2       GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...   \n",
       "\n",
       "                lbl__d_gene       seq__d_gene  P_joint_V_D  \n",
       "v_choice d_gene                                             \n",
       "22       0         TRBD1*01      GGGACAGGGGGC          0.0  \n",
       "         1         TRBD2*01  GGGACTAGCGGGGGGG          0.0  \n",
       "         2         TRBD2*02  GGGACTAGCGGGAGGG          0.0  \n",
       "24       0         TRBD1*01      GGGACAGGGGGC          0.0  \n",
       "         1         TRBD2*01  GGGACTAGCGGGGGGG          0.0  \n",
       "         2         TRBD2*02  GGGACTAGCGGGAGGG          0.0  \n",
       "33       1         TRBD2*01  GGGACTAGCGGGGGGG          0.0  \n",
       "         2         TRBD2*02  GGGACTAGCGGGAGGG          0.0  \n",
       "67       0         TRBD1*01      GGGACAGGGGGC          0.0  \n",
       "         2         TRBD2*02  GGGACTAGCGGGAGGG          0.0  \n",
       "71       0         TRBD1*01      GGGACAGGGGGC          0.0  \n",
       "         1         TRBD2*01  GGGACTAGCGGGGGGG          0.0  \n",
       "         2         TRBD2*02  GGGACTAGCGGGAGGG          0.0  \n",
       "74       0         TRBD1*01      GGGACAGGGGGC          0.0  \n",
       "         1         TRBD2*01  GGGACTAGCGGGGGGG          0.0  \n",
       "         2         TRBD2*02  GGGACTAGCGGGAGGG          0.0  \n",
       "75       0         TRBD1*01      GGGACAGGGGGC          0.0  \n",
       "         1         TRBD2*01  GGGACTAGCGGGGGGG          0.0  \n",
       "         2         TRBD2*02  GGGACTAGCGGGAGGG          0.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pjoint_V_D = mdl_hb.get_P_joint(['v_choice', 'd_gene'])\n",
    "\n",
    "da_tmp = Pjoint_V_D.where(Pjoint_V_D == 0)\n",
    "\n",
    "df = da_tmp.to_dataframe('P_joint_V_D').dropna()\n",
    "df #.to_csv('bibibi.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
       "array(0.)\n",
       "Coordinates:\n",
       "    v_choice       int64 22\n",
       "    lbl__v_choice  object &#x27;TRBV15*03&#x27;\n",
       "    seq__v_choice  object &#x27;GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTGGAAA...\n",
       "Attributes:\n",
       "    nickname:    v_choice\n",
       "    event_type:  GeneChoice\n",
       "    seq_type:    V_gene\n",
       "    seq_side:    Undefined_side\n",
       "    priority:    7\n",
       "    parents:     []\n",
       "    childs:      [&#x27;v_3_del&#x27;, &#x27;d_gene&#x27;, &#x27;j_choice&#x27;]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6690305f-27ad-45a7-8c21-137cfcd1586b' class='xr-array-in' type='checkbox' checked><label for='section-6690305f-27ad-45a7-8c21-137cfcd1586b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0</span></div><div class='xr-array-data'><pre>array(0.)</pre></div></div></li><li class='xr-section-item'><input id='section-c362e709-176b-42f4-a968-e4e7fd497445' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c362e709-176b-42f4-a968-e4e7fd497445' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>v_choice</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>22</div><input id='attrs-72509d5f-0f1f-4575-bb3f-281ac969fe1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72509d5f-0f1f-4575-bb3f-281ac969fe1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fcc40d1-4ccc-40f2-9ec1-479bbc8cd07d' class='xr-var-data-in' type='checkbox'><label for='data-4fcc40d1-4ccc-40f2-9ec1-479bbc8cd07d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(22)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lbl__v_choice</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;TRBV15*03&#x27;</div><input id='attrs-660eb168-70ec-41eb-b4da-e42beeb8d53e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-660eb168-70ec-41eb-b4da-e42beeb8d53e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8577fa0c-52b8-4fd6-a278-8f9956d8a96c' class='xr-var-data-in' type='checkbox'><label for='data-8577fa0c-52b8-4fd6-a278-8f9956d8a96c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;TRBV15*03&#x27;, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seq__v_choice</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;GATGCCATGGTCATCCAGAACCCAAGATACC...</div><input id='attrs-ed224ce6-2c03-409c-ab88-dea82b05d2ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed224ce6-2c03-409c-ab88-dea82b05d2ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaa6efa4-4206-408d-8d4a-9783ce61c148' class='xr-var-data-in' type='checkbox'><label for='data-eaa6efa4-4206-408d-8d4a-9783ce61c148' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTGGAAAGCCAGTGACCCTGAGTTGTTCTCAGACTTTGAACCATAACGTCATGTACTGGTACCAGCAGAAGTCAAGTCAGGCCCCAAAGCTGCTGTTCCACTACTATAACAAAGATTTTAACAATGAAGCAGACACCCCTGATAACTTCCAATCCAGGAGGCCGAACACTTCTTTCTGCTTTCTAGACATCCGCTCACCAGGCCTGGGGGACGCAGCCATGTACCAGTGTGCCACCAGCAGAGA&#x27;,\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b37a16d0-2cbd-4c3c-9788-8b692eb98501' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b37a16d0-2cbd-4c3c-9788-8b692eb98501' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>nickname :</span></dt><dd>v_choice</dd><dt><span>event_type :</span></dt><dd>GeneChoice</dd><dt><span>seq_type :</span></dt><dd>V_gene</dd><dt><span>seq_side :</span></dt><dd>Undefined_side</dd><dt><span>priority :</span></dt><dd>7</dd><dt><span>parents :</span></dt><dd>[]</dd><dt><span>childs :</span></dt><dd>[&#x27;v_3_del&#x27;, &#x27;d_gene&#x27;, &#x27;j_choice&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(0.)\n",
       "Coordinates:\n",
       "    v_choice       int64 22\n",
       "    lbl__v_choice  object 'TRBV15*03'\n",
       "    seq__v_choice  object 'GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTGGAAA...\n",
       "Attributes:\n",
       "    nickname:    v_choice\n",
       "    event_type:  GeneChoice\n",
       "    seq_type:    V_gene\n",
       "    seq_side:    Undefined_side\n",
       "    priority:    7\n",
       "    parents:     []\n",
       "    childs:      ['v_3_del', 'd_gene', 'j_choice']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb['v_choice'].loc[22] #, mdl_hb['v_choice'].loc[67] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                                                    TRBV15*03\n",
       "value           GATGCCATGGTCATCCAGAACCCAAGATACCGGGTTACCCAGTTTG...\n",
       "anchor_index                                                270.0\n",
       "Name: 22, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.genomic_dataframe_dict['V'].loc[22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use a default IGoR's model to generate sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing model parms in file  ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata/models/model_parms.txt\n",
      "Writing model marginals in file  ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata/models/model_marginals.txt\n",
      "Writing gene anchor's in file  ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata/ref_genome/V_gene_CDR3_anchors.csv\n",
      "Writing gene anchor's in file  ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata/ref_genome/J_gene_CDR3_anchors.csv\n",
      "Writing model parms in file  ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata//models/model_parms.txt\n",
      "Writing model marginals in file  ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata//models/model_marginals.txt\n",
      "Writing gene anchor's in file  ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata//ref_genome/V_gene_CDR3_anchors.csv\n",
      "Writing gene anchor's in file  ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata//ref_genome/J_gene_CDR3_anchors.csv\n",
      "/home/olivares/.local/bin/igor -set_wd ./igor_generating_3z_y24jv -batch dataIGoRBTnea5JaoG -set_custom_model ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata//models/model_parms.txt ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata//models/model_marginals.txt -set_CDR3_anchors  --V ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata//ref_genome/V_gene_CDR3_anchors.csv --J ./igor_generating_3z_y24jv/dataIGoRBTnea5JaoG_mdldata//ref_genome/J_gene_CDR3_anchors.csv -generate 10 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nt_sequence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq_index</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAGGCTGGAGTCACTCAAACTCCAAGATATCTGATCAAAACGAGAG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GAAGCTGGAGTTACTCAGTTCCCCAGCCACAGCGTAATAGAGAAGG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GGAGCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GATGCTGATGTTACCCAGACCCCAAGGAATAGGATCACAAAGACAG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AAGGCTGGAGTCACTCAAACTCCAAGATATCTGATCAAAACGAGAG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GAAACGGGAGTTACGCAGACACCAAGACACCTGGTCATGGGAATGA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CATGCCAAAGTCACACAGACTCCAGGACATTTGGTCAAAGGAAAAG...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 nt_sequence\n",
       "seq_index                                                   \n",
       "0          AAGGCTGGAGTCACTCAAACTCCAAGATATCTGATCAAAACGAGAG...\n",
       "1          GAAGCTGGAGTTACTCAGTTCCCCAGCCACAGCGTAATAGAGAAGG...\n",
       "2          GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...\n",
       "3          GGAGCTGGAGTCTCCCAGTCCCCCAGTAACAAGGTCACAGAGAAGG...\n",
       "4          GAAGCTGGAGTTGCCCAGTCTCCCAGATATAAGATTATAGAGAAAA...\n",
       "5          GGTGCTGGAGTCTCCCAGACCCCCAGTAACAAGGTCACAGAGAAGG...\n",
       "6          GATGCTGATGTTACCCAGACCCCAAGGAATAGGATCACAAAGACAG...\n",
       "7          AAGGCTGGAGTCACTCAAACTCCAAGATATCTGATCAAAACGAGAG...\n",
       "8          GAAACGGGAGTTACGCAGACACCAAGACACCTGGTCATGGGAATGA...\n",
       "9          CATGCCAAAGTCACACAGACTCCAGGACATTTGGTCAAAGGAAAAG..."
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seqs = p3.generate(10, mdl_hb)\n",
    "df_seqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One sequence evaluation\n",
    "Let's consider that we have a sequence of a TCR $\\beta$ receptor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "str_seq_hb = \"ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGTATCGTCAGTTCCCGAAACAGAGTCTCATGCTGATGGCAACTTCCAATGAGGGCTCCAAGGCCACATACGAGCAAGGCGTCGAGAAGGACAAGTTTCTCATCAACCATGCAAGCCTGACCTTGTCCACTCTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCTGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTTGGAG\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A classical approach would be make alignments of the VDJ segments and consider the maximun alignment of the segments as uniquely determinated construction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: SHOW NAIVE ALIGNMENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TODO: Simple explantion of the inference process "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate VDJ model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading Parms filename from:  /home/olivares/.local/share/igor/models/human/tcr_beta/models/model_parms.txt\n",
      "Reading Marginals filename from:  /home/olivares/.local/share/igor/models/human/tcr_beta/models/model_marginals.txt\n"
     ]
    }
   ],
   "source": [
    "mdl = p3.get_default_IgorModel(\"human\", \"tcr_beta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function evaluate in module pygor3.IgorIO:\n",
      "\n",
      "evaluate(input_sequences: Union[str, pandas.core.frame.DataFrame, numpy.ndarray, pathlib.Path], mdl: pygor3.IgorIO.IgorModel, N_scenarios=None, igor_wd=None, airr_format=True, batch_clean=True)\n",
      "    Evaluate input sequences with provided model\n",
      "    :param input_sequences:Union[str, pd.DataFrame, np.ndarray, Path]\n",
      "    :param mdl:IgorModel\n",
      "    :param batch_clean: Remove all temporary files True by default.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(p3.evaluate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation AIRR format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing model parms in file  ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/models/model_parms.txt\n",
      "Writing model marginals in file  ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/models/model_marginals.txt\n",
      "Writing gene anchor's in file  ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/V_gene_CDR3_anchors.csv\n",
      "Writing gene anchor's in file  ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/J_gene_CDR3_anchors.csv\n",
      "/home/olivares/.local/bin/igor -set_wd ./igor_evaluating_1b6e6i7z -batch dataIGoRyNbbuLilFX -read_seqs ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFXinput_sequences.csv\n",
      "/home/olivares/.local/bin/igor -set_wd ./igor_evaluating_1b6e6i7z -batch dataIGoRyNbbuLilFX -set_genomic  --V ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/genomicVs.fasta --D ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/genomicDs.fasta --J ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/genomicJs.fasta -set_CDR3_anchors  --V ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/V_gene_CDR3_anchors.csv --J ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/J_gene_CDR3_anchors.csv -align  --all  \n",
      "/home/olivares/.local/bin/igor -set_wd ./igor_evaluating_1b6e6i7z -batch dataIGoRyNbbuLilFX -set_custom_model ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/models/model_parms.txt ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/models/model_marginals.txt -evaluate  -output  --scenarios 10  --Pgen  \n",
      "./igor_evaluating_1b6e6i7z/aligns/dataIGoRyNbbuLilFX_indexed_CDR3s.csv\n",
      "('',)\n",
      "Incorrect number of bindings supplied. The current statement uses 5, and there are 1 supplied.\n",
      "Loading Gene templates ...\n",
      "V ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/genomicVs.fasta\n",
      "J ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/genomicJs.fasta\n",
      "D ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/genomicDs.fasta\n",
      "loading Anchors data ...\n",
      "Loading Gene Anchors from  ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/V_gene_CDR3_anchors.csv\n",
      "Loading Gene Anchors from  ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/ref_genome/J_gene_CDR3_anchors.csv\n",
      "./igor_evaluating_1b6e6i7z/aligns/dataIGoRyNbbuLilFX_V_alignments.csv\n",
      "['']\n",
      "['']\n",
      "['']\n",
      "Alignments loaded in database in ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX.db\n",
      "Reading Parms filename from:  ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/models/model_parms.txt\n",
      "Reading Marginals filename from:  ./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_mdldata/models/model_marginals.txt\n",
      "Loading parms to database\n",
      "('v_choice', 'v_3_del')\n",
      "('v_choice', 'd_gene')\n",
      "('v_choice', 'j_choice')\n",
      "('d_gene', 'd_3_del')\n",
      "('d_gene', 'd_5_del')\n",
      "('j_choice', 'j_5_del')\n",
      "('j_choice', 'd_gene')\n",
      "('d_5_del', 'd_3_del')\n",
      "Loading marginals to database\n",
      "./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_output/best_scenarios_counts.csv\n",
      "events_name_nickname_dict {'GeneChoice_V_gene_Undefined_side_prio7_size89': 'v_choice', 'GeneChoice_J_gene_Undefined_side_prio7_size15': 'j_choice', 'GeneChoice_D_gene_Undefined_side_prio6_size3': 'd_gene', 'Deletion_V_gene_Three_prime_prio5_size21': 'v_3_del', 'Deletion_D_gene_Five_prime_prio5_size21': 'd_5_del', 'Deletion_J_gene_Five_prime_prio5_size23': 'j_5_del', 'Deletion_D_gene_Three_prime_prio5_size21': 'd_3_del', 'Insertion_VD_genes_Undefined_side_prio4_size31': 'vd_ins', 'DinucMarkov_VD_genes_Undefined_side_prio3_size16': 'vd_dinucl', 'Insertion_DJ_gene_Undefined_side_prio2_size31': 'dj_ins', 'DinucMarkov_DJ_gene_Undefined_side_prio1_size16': 'dj_dinucl'}\n",
      "./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_output/Pgen_counts.csv\n",
      "./igor_evaluating_1b6e6i7z/dataIGoRyNbbuLilFX_output/Pgen_counts.csv\n",
      "('',)\n",
      "Incorrect number of bindings supplied. The current statement uses 2, and there are 1 supplied.\n",
      "----- Marginals -----\n",
      "d_3_del\n",
      "d_5_del\n",
      "d_gene\n",
      "dj_dinucl\n",
      "dj_ins\n",
      "j_5_del\n",
      "j_choice\n",
      "v_3_del\n",
      "v_choice\n",
      "vd_dinucl\n",
      "vd_ins\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...</td>\n",
       "      <td>TRBD1*01</td>\n",
       "      <td>M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...</td>\n",
       "      <td>GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT</td>\n",
       "      <td>CSAGEGQPGNTIYF</td>\n",
       "      <td>288M</td>\n",
       "      <td>4M</td>\n",
       "      <td>48M</td>\n",
       "      <td>1440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>288</td>\n",
       "      <td>61</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293</td>\n",
       "      <td>295</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>GGGGAA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>AGC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.034684</td>\n",
       "      <td>4.871310e-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...</td>\n",
       "      <td>TRBD1*01</td>\n",
       "      <td>M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...</td>\n",
       "      <td>GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT</td>\n",
       "      <td>CSAGEGQPGNTIYF</td>\n",
       "      <td>287M</td>\n",
       "      <td>4M</td>\n",
       "      <td>48M</td>\n",
       "      <td>1435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>287</td>\n",
       "      <td>61</td>\n",
       "      <td>285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292</td>\n",
       "      <td>294</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>TGGGGA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>CAGC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.028806</td>\n",
       "      <td>4.871310e-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...</td>\n",
       "      <td>GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT</td>\n",
       "      <td>CSAGEGQPGNTIYF</td>\n",
       "      <td>288M</td>\n",
       "      <td>3M</td>\n",
       "      <td>48M</td>\n",
       "      <td>1440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>288</td>\n",
       "      <td>61</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293</td>\n",
       "      <td>294</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>GGGGAA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>CAGC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.023637</td>\n",
       "      <td>4.871310e-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...</td>\n",
       "      <td>GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT</td>\n",
       "      <td>CSAGEGQPGNTIYF</td>\n",
       "      <td>287M</td>\n",
       "      <td>4M</td>\n",
       "      <td>48M</td>\n",
       "      <td>1435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>287</td>\n",
       "      <td>61</td>\n",
       "      <td>285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292</td>\n",
       "      <td>294</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>TGGGGA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>CAGC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.022053</td>\n",
       "      <td>4.871310e-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...</td>\n",
       "      <td>GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT</td>\n",
       "      <td>CSAGEGQPGNTIYF</td>\n",
       "      <td>288M</td>\n",
       "      <td>4M</td>\n",
       "      <td>48M</td>\n",
       "      <td>1440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>288</td>\n",
       "      <td>61</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>288</td>\n",
       "      <td>290</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>AGGGCAGC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.017562</td>\n",
       "      <td>4.871310e-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...</td>\n",
       "      <td>TRBD2*02</td>\n",
       "      <td>M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...</td>\n",
       "      <td>GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT</td>\n",
       "      <td>CSAGEGQPGNTIYF</td>\n",
       "      <td>288M</td>\n",
       "      <td>3M</td>\n",
       "      <td>48M</td>\n",
       "      <td>1440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>288</td>\n",
       "      <td>61</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292</td>\n",
       "      <td>293</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>GGGGA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>GCAGC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.015878</td>\n",
       "      <td>4.871310e-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sequence_id                                           sequence rev_comp  \\\n",
       "0            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "1            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "2            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "3            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "4            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "5            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "6            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "7            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "8            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "9            0  ATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGT...        F   \n",
       "\n",
       "   productive                                             v_call     d_call  \\\n",
       "0         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD1*01   \n",
       "1         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD2*02   \n",
       "2         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD1*01   \n",
       "3         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD2*02   \n",
       "4         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD1*01   \n",
       "5         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD1*01   \n",
       "6         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD2*02   \n",
       "7         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD2*02   \n",
       "8         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD2*02   \n",
       "9         NaN  M11955|TRBV20-1*01|Homo sapiens|F|V-REGION|427...   TRBD2*02   \n",
       "\n",
       "                                              j_call  \\\n",
       "0  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "1  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "2  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "3  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "4  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "5  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "6  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "7  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "8  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "9  M14158|TRBJ1-3*01|Homo sapiens|F|J-REGION|1499...   \n",
       "\n",
       "                                  sequence_alignment  germline_alignment  \\\n",
       "0  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "1  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "2  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "3  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "4  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "5  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "6  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "7  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "8  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "9  GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTG...                 NaN   \n",
       "\n",
       "                                     junction     junction_aa v_cigar d_cigar  \\\n",
       "0  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    288M      4M   \n",
       "1  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    288M      4M   \n",
       "2  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    288M      3M   \n",
       "3  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    288M      3M   \n",
       "4  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    288M      4M   \n",
       "5  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    287M      4M   \n",
       "6  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    288M      3M   \n",
       "7  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    287M      4M   \n",
       "8  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    288M      4M   \n",
       "9  TGCAGTGCTGGGGAAGGGCAGCCTGGAAACACCATATATTTT  CSAGEGQPGNTIYF    288M      3M   \n",
       "\n",
       "  j_cigar  v_score  v_identity  v_support  v_sequence_start  v_sequence_end  \\\n",
       "0     48M     1440         NaN        NaN                 2             288   \n",
       "1     48M     1440         NaN        NaN                 2             288   \n",
       "2     48M     1440         NaN        NaN                 2             288   \n",
       "3     48M     1440         NaN        NaN                 2             288   \n",
       "4     48M     1440         NaN        NaN                 2             288   \n",
       "5     48M     1435         NaN        NaN                 2             287   \n",
       "6     48M     1440         NaN        NaN                 2             288   \n",
       "7     48M     1435         NaN        NaN                 2             287   \n",
       "8     48M     1440         NaN        NaN                 2             288   \n",
       "9     48M     1440         NaN        NaN                 2             288   \n",
       "\n",
       "   v_germline_start  v_germline_end  v_alignment_start  v_alignment_end  \\\n",
       "0                61             286                NaN              NaN   \n",
       "1                61             286                NaN              NaN   \n",
       "2                61             286                NaN              NaN   \n",
       "3                61             286                NaN              NaN   \n",
       "4                61             286                NaN              NaN   \n",
       "5                61             285                NaN              NaN   \n",
       "6                61             286                NaN              NaN   \n",
       "7                61             285                NaN              NaN   \n",
       "8                61             286                NaN              NaN   \n",
       "9                61             286                NaN              NaN   \n",
       "\n",
       "   d_score  d_identity  d_support  d_sequence_start  d_sequence_end  \\\n",
       "0       20         NaN        NaN               292             294   \n",
       "1       20         NaN        NaN               292             294   \n",
       "2       15         NaN        NaN               296             297   \n",
       "3       15         NaN        NaN               293             294   \n",
       "4       20         NaN        NaN               293             295   \n",
       "5       20         NaN        NaN               292             294   \n",
       "6       15         NaN        NaN               293             294   \n",
       "7       20         NaN        NaN               292             294   \n",
       "8       20         NaN        NaN               288             290   \n",
       "9       15         NaN        NaN               292             293   \n",
       "\n",
       "   d_germline_start  d_germline_end  d_alignment_start  d_alignment_end  \\\n",
       "0                 7              10                NaN              NaN   \n",
       "1                14              17                NaN              NaN   \n",
       "2                 6               8                NaN              NaN   \n",
       "3                15              17                NaN              NaN   \n",
       "4                10              13                NaN              NaN   \n",
       "5                 7              10                NaN              NaN   \n",
       "6                11              13                NaN              NaN   \n",
       "7                14              17                NaN              NaN   \n",
       "8                11              14                NaN              NaN   \n",
       "9                14              16                NaN              NaN   \n",
       "\n",
       "   j_score  j_identity  j_support  j_sequence_start  j_sequence_end  \\\n",
       "0      240         NaN        NaN                 7              53   \n",
       "1      240         NaN        NaN                 7              53   \n",
       "2      240         NaN        NaN                 3              49   \n",
       "3      240         NaN        NaN                 6              52   \n",
       "4      240         NaN        NaN                 6              52   \n",
       "5      240         NaN        NaN                 7              53   \n",
       "6      240         NaN        NaN                 6              52   \n",
       "7      240         NaN        NaN                 7              53   \n",
       "8      240         NaN        NaN                11              57   \n",
       "9      240         NaN        NaN                 7              53   \n",
       "\n",
       "   j_germline_start  j_germline_end  j_alignment_start  j_alignment_end  \\\n",
       "0                 4              51                NaN              NaN   \n",
       "1                 4              51                NaN              NaN   \n",
       "2                 4              51                NaN              NaN   \n",
       "3                 4              51                NaN              NaN   \n",
       "4                 4              51                NaN              NaN   \n",
       "5                 4              51                NaN              NaN   \n",
       "6                 4              51                NaN              NaN   \n",
       "7                 4              51                NaN              NaN   \n",
       "8                 4              51                NaN              NaN   \n",
       "9                 4              51                NaN              NaN   \n",
       "\n",
       "   sequence_aa  vj_in_frame  stop_codon  complete_vdj  locus  \\\n",
       "0          NaN          NaN         NaN           NaN    NaN   \n",
       "1          NaN          NaN         NaN           NaN    NaN   \n",
       "2          NaN          NaN         NaN           NaN    NaN   \n",
       "3          NaN          NaN         NaN           NaN    NaN   \n",
       "4          NaN          NaN         NaN           NaN    NaN   \n",
       "5          NaN          NaN         NaN           NaN    NaN   \n",
       "6          NaN          NaN         NaN           NaN    NaN   \n",
       "7          NaN          NaN         NaN           NaN    NaN   \n",
       "8          NaN          NaN         NaN           NaN    NaN   \n",
       "9          NaN          NaN         NaN           NaN    NaN   \n",
       "\n",
       "   sequence_alignment_aa  n1_length        np1  np1_aa  np1_length  n2_length  \\\n",
       "0                    NaN          5      GGGGA     NaN           5          4   \n",
       "1                    NaN          5      GGGGA     NaN           5          4   \n",
       "2                    NaN          9  GGGGAAGGG     NaN           9          1   \n",
       "3                    NaN          6     GGGGAA     NaN           6          3   \n",
       "4                    NaN          6     GGGGAA     NaN           6          3   \n",
       "5                    NaN          6     TGGGGA     NaN           6          4   \n",
       "6                    NaN          6     GGGGAA     NaN           6          4   \n",
       "7                    NaN          6     TGGGGA     NaN           6          4   \n",
       "8                    NaN          1          G     NaN           1          8   \n",
       "9                    NaN          5      GGGGA     NaN           5          5   \n",
       "\n",
       "        np2  np2_aa  np2_length  p3v_length  p5d_length  p3d_length  \\\n",
       "0      CAGC     NaN           4           0           0           0   \n",
       "1      CAGC     NaN           4           0           0           0   \n",
       "2         C     NaN           1           0           0           0   \n",
       "3       AGC     NaN           4           0           0           1   \n",
       "4       AGC     NaN           3           0           0           0   \n",
       "5      CAGC     NaN           4           0           0           0   \n",
       "6      CAGC     NaN           4           0           0           0   \n",
       "7      CAGC     NaN           4           0           0           0   \n",
       "8  AGGGCAGC     NaN           8           0           0           0   \n",
       "9     GCAGC     NaN           5           0           0           0   \n",
       "\n",
       "   p5j_length  scenario_rank  scenario_proba_cond_seq          pgen  quality  \\\n",
       "0           0              1                 0.146984  4.871310e-13      NaN   \n",
       "1           0              2                 0.112527  4.871310e-13      NaN   \n",
       "2           0              3                 0.062955  4.871310e-13      NaN   \n",
       "3           0              4                 0.060409  4.871310e-13      NaN   \n",
       "4           0              5                 0.034684  4.871310e-13      NaN   \n",
       "5           0              6                 0.028806  4.871310e-13      NaN   \n",
       "6           0              7                 0.023637  4.871310e-13      NaN   \n",
       "7           0              8                 0.022053  4.871310e-13      NaN   \n",
       "8           0              9                 0.017562  4.871310e-13      NaN   \n",
       "9           0             10                 0.015878  4.871310e-13      NaN   \n",
       "\n",
       "   quality_alignment  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "5                NaN  \n",
       "6                NaN  \n",
       "7                NaN  \n",
       "8                NaN  \n",
       "9                NaN  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scenarios = p3.evaluate(str_seq_hb, mdl) # , N_scenarios=20\n",
    "df_scenarios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation IGoR format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing model parms in file  ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/models/model_parms.txt\n",
      "Writing model marginals in file  ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/models/model_marginals.txt\n",
      "Writing gene anchor's in file  ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/ref_genome/V_gene_CDR3_anchors.csv\n",
      "Writing gene anchor's in file  ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/ref_genome/J_gene_CDR3_anchors.csv\n",
      "/home/olivares/.local/bin/igor -set_wd ./igor_evaluating_mdsvldqo -batch dataIGoRQVzl4XnKII -read_seqs ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKIIinput_sequences.csv\n",
      "/home/olivares/.local/bin/igor -set_wd ./igor_evaluating_mdsvldqo -batch dataIGoRQVzl4XnKII -set_genomic  --V ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/ref_genome/genomicVs.fasta --D ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/ref_genome/genomicDs.fasta --J ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/ref_genome/genomicJs.fasta -set_CDR3_anchors  --V ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/ref_genome/V_gene_CDR3_anchors.csv --J ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/ref_genome/J_gene_CDR3_anchors.csv -align  --all  \n",
      "/home/olivares/.local/bin/igor -set_wd ./igor_evaluating_mdsvldqo -batch dataIGoRQVzl4XnKII -set_custom_model ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/models/model_parms.txt ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_mdldata/models/model_marginals.txt -evaluate  -output  --scenarios 10  --Pgen  \n",
      "igor_fln_generated_realizations_werr:  ./igor_evaluating_mdsvldqo/dataIGoRQVzl4XnKII_output/best_scenarios_counts.csv\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scenario_rank</th>\n",
       "      <th>scenario_proba_cond_seq</th>\n",
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       "      <td>2</td>\n",
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       "      <td>9</td>\n",
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      ],
      "text/plain": [
       "           scenario_rank  scenario_proba_cond_seq  v_choice  j_choice  d_gene  \\\n",
       "seq_index                                                                       \n",
       "0                      1                 0.146984        29         2       0   \n",
       "0                      2                 0.112527        29         2       2   \n",
       "0                      3                 0.062955        29         2       0   \n",
       "0                      4                 0.060409        29         2       2   \n",
       "0                      5                 0.034684        29         2       0   \n",
       "0                      6                 0.028806        29         2       0   \n",
       "0                      7                 0.023637        29         2       2   \n",
       "0                      8                 0.022053        29         2       2   \n",
       "0                      9                 0.017562        29         2       2   \n",
       "0                     10                 0.015878        29         2       2   \n",
       "\n",
       "           v_3_del  d_5_del  j_5_del  d_3_del  vd_ins  \\\n",
       "seq_index                                               \n",
       "0                9        9        6        7       5   \n",
       "0                9       16        6        4       5   \n",
       "0                9        8        6        9       9   \n",
       "0                9       17        6        3       6   \n",
       "0                9       12        6        4       6   \n",
       "0               10        9        6        7       6   \n",
       "0                9       13        6        8       6   \n",
       "0               10       16        6        4       6   \n",
       "0                9       13        6        7       1   \n",
       "0                9       16        6        5       5   \n",
       "\n",
       "                             vd_dinucl  dj_ins                 dj_dinucl  \\\n",
       "seq_index                                                                  \n",
       "0                      [2, 2, 2, 2, 0]       4              [1, 2, 0, 1]   \n",
       "0                      [2, 2, 2, 2, 0]       4              [1, 2, 0, 1]   \n",
       "0          [2, 2, 2, 2, 0, 0, 2, 2, 2]       1                       [1]   \n",
       "0                   [2, 2, 2, 2, 0, 0]       3                 [1, 2, 0]   \n",
       "0                   [2, 2, 2, 2, 0, 0]       3                 [1, 2, 0]   \n",
       "0                   [3, 2, 2, 2, 2, 0]       4              [1, 2, 0, 1]   \n",
       "0                   [2, 2, 2, 2, 0, 0]       4              [1, 2, 0, 1]   \n",
       "0                   [3, 2, 2, 2, 2, 0]       4              [1, 2, 0, 1]   \n",
       "0                                  [2]       8  [1, 2, 0, 1, 2, 2, 2, 0]   \n",
       "0                      [2, 2, 2, 2, 0]       5           [1, 2, 0, 1, 2]   \n",
       "\n",
       "          Mismatches  Pgen_estimate  \n",
       "seq_index                            \n",
       "0                 []   4.871310e-13  \n",
       "0                 []   4.871310e-13  \n",
       "0                 []   4.871310e-13  \n",
       "0                 []   4.871310e-13  \n",
       "0                 []   4.871310e-13  \n",
       "0                 []   4.871310e-13  \n",
       "0                 []   4.871310e-13  \n",
       "0                 []   4.871310e-13  \n",
       "0                 []   4.871310e-13  \n",
       "0                 []   4.871310e-13  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scenarios = p3.evaluate(str_seq_hb, mdl_hb, airr_format=False) # , N_scenarios=20\n",
    "df_scenarios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ck3s8G/iPEnGOyz2+u9Q+wOfSv+Jjp5TJreTrK82m377UNrKZ8Z8ts+1HZG1BbkxxVpP1kK4oIn7Juv2su9q/5PMjIhYCB5VYf1bucan+3UT2YZuvK3PKY1j7AZg7AVcU9yNPM+T360bu9wD5D6z8dO74UuP3IrnnSlGsKWXWv7WrPMzMzMzMzMzMbGD4QyttsPgJ6/bd3uBIagaujoh/ltoeEfcCN6fWLfVkbkTcBBARcyPikwOdkJmZmZmZmZmZ1SbP8LZBIfWlLjsLWdLngfcVrb48zUauC6ktyS+62OeictskvQP4VtHqhRFxeIl9x5L1Py92QBc90/tcRPysP89nZmZmZmZmZmb1ywVvqwndacOyoYuImWR91Luz77PAlKomZGZmZmZmZmZm1s/c0sTMzMzMzMzMzMzM6oIL3mZmZmZmZmZmZmZWF1zwNjMzMzMzMzMzM7O6oIgY6BzMzOrW1KlTY9asWQOdxqB01E9vB2DGx18/wJmY1ZELrh3oDDZYa054rKrxGzm5qvHNzMzMbHDx/5krk3RPREwttc0zvM3MzMzMzMzMzMysLrjgbWZmZmZmZmZmZmZ1wQVvMzMzMzMzMzMzM6sLLnibmZmZmZmZmZmZWV1wwdvMzMzMzMzMzMzM6oIL3mZmZmZmZmZmZmZWF5oGOgEzMzMz61/L16xm5rIneWL1ClrUSKPEviM3Ycdhowd9/FrOvWDB/HYun7Gc6SduzLhxjX0W18zMzMzMXPA2s0FA0s3ANyNiZm7dacDrgG8B84D5wDDgJeDHEXFJFzE3B86LiCP7MM9JKZcFadUdETG9r+KbmfWHiGDG84+we8tYjhgzCYAXOtpZsOrFQR+/lnPPa53bzlYTmmids4o37z+8T2ObmZmZmW3o3NLEzAaDy4Cji9YdndYDPBwRe0TEjmn9aZI+XClgRDzVl8XunIcjYkr652K3mdWche3LaVQDU0eMe2Xd6KZmpo0YP+jj13LuBe3tweOLVnPwISNobW3vs7hmZmZmZpZxwdvMBoPfAe+W1AyvzKTeHPhb8Y4R8QjwSeCUSgElTZI0Nz0+TtKVkv4k6V+Svp3WN0q6RNJcSXMknd63l2VmNvgs7Whj0yEtNRm/lnMvWDC/ncnbDGHs2EaGtzSw+KmOqp7PzMzMzGxD45YmZjbgIuI5SXcB7wSuIZvF/duICEmlDrkX2KGHp5kC7AGsAhZI+gHwWmCLiNgFQNLo9HV6yuv8EnG2lnQfsAz4QkS8qihvZlZLrnvxcR5PM5s/Nu51NRW/FnNvndvOPtOGAbDTLs20zm1ns839K7mZmZmZWV/xb9dmNlgU2poUCt4frbBvySp4F26KiBcBJD0ITARagcmp+H0dcAOULXQDLAYmRMSzkvYCrpa0c0QsWyc56QTgBIAJEyb0IlUzs+oZ39TCvJfX9qR+98ZbsbKzgwueWVDhqMERv5ZzB2hr6+TRhatZsmQNAjoj+4F2wNtaKPMGr5mZmZmZ9ZBbmpjZYHENcICkPYHhEXFPhX33IPvwyJ5YlXu8BmiKiOeB3YFbgOnAhZUCRMSqiHg2Pb4HeBjYvsR+F0TE1IiYOn583/V9NTPrC1s3j6QjOrl7xdJX1q2OzpqIX8u5A8x7sJ1ddxvKKaeN5uTTRnPq6aMZPaaBRYvc1sTMzMzMrK94hreZDQoRsVzSzcBFrP2wyldJ/b3PAX6QlvcBToqID/X0nJLGAe0RcYWkBcD/dbH/eOC5iFgjaTKwHfBIT89rZjaQJHHUmMnMXPYEt61YwvCGJprVwIGjNh/08Ws5d8jamey737B11u2wYzOtc9qZOHFIn5zDzMzMzGxD54K3mQ0mlwFXkbU0ydsm9c0eBrwEnBcRl6RtE4C2Xp5vC+BiSYW/djkTKvbwfhNwtqTVQCcwPSKe6+W5zcwGzKjGIRw5ZuuajF/LuR9z7EavWlfo521mZmZmZn3DBW8zGzQi4mqK+nNHxKNAS4XDpgE/KhHrUWCX9PgS4JLctoNzu+5Z4tiSPbwj4grgigq5mJmZmZmZmZnZAHLB28xqWkScMdA5mJmZmZmZmZnZ4OAPrTQzMzMzMzMzMzOzuuCCt5mZmZmZmZmZmZnVBRe8zczMzMzMzMzMzKwuuOBtZmZmZmZmZmZmZnXBBW8zMzMzMzMzMzMzqwsueJuZmZmZmZmZmZlZXWga6ATMzMzMrI+ccMhAZ7DBaqxy/DX8oGqxGy+YWLXYdcGvKzMzM7Oa4hneZmZmZmZmZmZmZlYXXPA2MzMzMzMzMzMzs7rggreZmZmZmZmZmZmZ1QUXvM3MzMzMzMzMzMysLrjgbWZmZmZmZmZmZmZ1wQVvMzMzMzMzMzMzM6sLTQOdgJmZmZmZ9Y0F89u5fMZypp+4MePGNfZp7OVrVjNz2ZM8sXoFLWqkUWLfkZuw47DRjm9mZmZmg0a3Z3hLulPSbEmLJC1Nj2dLmiTpUUlzJD0g6a+SJuaOW5P2u1/SvZL2TesnSWqTdJ+keZLuknRc7rgdJN0uaZWkT+XWK309q2j5IEkLJD0k6bO5/U9K60LSuArXt4ekn5fZ9milYweSpNdI+rOkf6WvY9L6gyWdXeG4wyR9qcy25V2cszCmsyVd24Nc3yLpD+u7T9rvVElzJbVKOi23/hxJb61w3Pckvamn55V0aHp+z5Y0S9Ibusoxd+wlko7sg33OyN33uWkcXiOpWdKtkrp8A0vSLel1Uojz2rT+rPzrL7f/JElzu4jZ5T4ljrlE0sJcHlO62H9i+v4xO4359Ny2R8scc1b+e0dv9+luHmZmZoNB69x2tprQROucVX0aNyKY8fwjTGweyamv3ZkTxu/AEaO3Ztma1Y5vZmZmZoNKtwveETEtIqYAXwJmRMSU9O/RtMv+EbEbcAvwhdyhbWm/3YEzgf/JbXs4IvaIiB2Bo4HTJH04bXsOOAU4pyiVD0o6Axgm6dNpuRH4EfBOYCfg/ZJ2Svv/AzgQeKyLS/wccF6XN2Lw+SxwU0RsB9yUlgGuA94jaXiZ4z4N/LiX52zLjf8hvYzRa5J2AT4G7APsDhwsadu0+QesvQfFx40F/iMibu3FaW8Cdk+vgY8AF/YixnqJiO8U7jvZa+mvEfFcRLSn/I7qZqgP5sZvSbXy7YYzcnnM7mLfxcDr07VPAz4rafNqJziI8zAzM3uV9vbg8UWrOfiQEbS2tvdp7IXty2lUA1NHrJ0DMrqpmWkjxju+mZmZmQ0q1ejhfTuwRZltGwHPl9oQEY8AnyQrchMRSyLibmB10X7/BzwBnAEsSsv7AA9FxCOp+Pcb4NC0/325onxJkkYBu0XE/Wl5rKQb0gzOCwF1cfwX06zZv0u6rDBjVNI2kv4k6R5Jf5O0Q1p/iaTzJN0m6ZH8zN40i/fuNJv4K5XOmxwKXJoeXwoclq47yN58OLhEvtsDqyLimbS8tbLZ9HMkfa0b5+w2ZTPv50u6F3hvbv0ISRcpm9l/n6RDexB2R+DOiFgZER3AXwuxI+IxYKykTUscdwTwp65yKyUilqd7CjACiHL7KvPD9Jy4EXhtbtteyv4K4h5JMyVt1r1LfpX3A5fllq8GPtjLWADLgbZcjvdLuh84sbCDpEZJ38k9Pz++HufrkYhoj4jCVLWhrPu9a2kux89L+qekvwOvy60v+Vrs4zzMzMwG1IL57UzeZghjxzYyvKWBxU919FnspR1tbDqkpc/i1Vt8MzMzMxs8qlGsOYis+FbQkv78fz7ZrNivVjj2XqBiIUrSB4Atge8AE9LyFsDjud2eoHzRvZSpQL4lw5eBv0fEzsBVwIQK+exNVkjdnWyG+dTc5guAkyNiL+BTrDujejPgDWQF6W+mWG8HtiMr4E8B9lJqvyHp+jIzSTeJiMXp8b+BTXLbZgFvLHHMfmT3uuD7wE8iYleyGaxdGaasrccdkg4rt5OkYcDPgPcAewH5IvTngb9ExD7A/sB3JI0oOn5qesOh2FzgjemNieHAu4CtctvvTddYbD/gnm7kVu56Dk/P4+vIZnmXczhZsXUn4ENAoY3PELIZ6Eem58RFwNdLnOdsSWVnzqdrPgi4Ird6LrB3V9eQXJxek1+UspZAEXFORMwobCd73u5edNxHgRcjYu90ro9J2rrSiSTNrrD566lwfq6koV0lLWkrSQ+Qvda/FRFPpdz3Ttv3IvtLkSlkz4n8/aj0Wix1rkNUpiVQuTyK9jkhvUZmLV269NVBzMzMqqB1bjs775L9SN1pl2Za5/btLO+86158nPOXzuNnzyxwfDMzMzMbVPryQytvlvQaspmiX8ytb0t//o+k1wO/SC0pSqk4kzq5LCJC0lkR8e1UsDtifRInKz7nq1JvYu2M4esklZyVnuwHXBMRLwMvS/o9gKSRZIXOy1NNEbIZoQVXR0Qn8KCkQpH67enffWl5JFkB/NaIeFdXF5HuS37m8RKgVJG8+Hr3Y+09/CXwrS5ONTEinpQ0GfiLpDkR8XCJ/XYAFkbEvwAk/R9wQtr2duAQre2fPIyiNxYiYhZwfHHQiJgn6VvADcAKYDawJrdLd667Um4lRcRVwFXpTYivkrXKKeVNZM/TNcBTkv6S1r8O2AX4c3pONFLiDYaIKNlbPec9wD8i4rncMWsktUsaFREvVTj2g2nsRpEVzI8BflHYKGk0MDrX9uWXZG/kQDZmu+X+ImFjsufnP8udrPDaL+FMsjdomsmK0Z8ByvacT7EeT+ffHLha0u8i4uncLm8EroqIlelark1fu3otljrXtUDJ/vTdyIOIuCBdF1OnTi371wBmZmZ9pa2tk0cXrmbJkjUI6IzsF+sD3tZC7udfr41vamHeyy++svzujbdiZWcHF/RRwbjW45uZmZnZ4NGXBe/9gReAXwFfIWtPso6IuF3Zhz+Wa5a3BzCv0kkKbSUi4qzCsqQnWXeG75bAkz3IvY2s4NqXGoAXKhT88p8kpNzX/4mIn/bgPE9L2iwiFqf2GPmezMNIbSqKtJEVK/O6XZSLiCfT10ck3UI2bqUK3pUIOCIi1vlfRq7431UOPwd+no75Btms/oJK173e4xwRt0qaLGlcoS1MNwlojYjXr2cKR7NuO5OCocDLlQ7Mjd1Lkn5N9tcEv6h0TI7IZknPXGelNKmbx+fzKBT6V0m6mGzWdXePfUrZh2S+EfhdNw7p6rXYK73Iw8zMrGrmPdjOrrsN5d3vWfsHc7+4ZBmLFnUwceKQ9Y6/dfNI/hKd3L1iKXunvtero3O949ZLfDMzMzMbPPq0pUnqp3wa8KE023sdqW9uI/BsiW2TyD6g8ge9OPXdwHbKelE3kxUES87OLGMesG1u+VbgAymvdwJjKhz7D7IPhxyWZpIeDBARy4CFkt6X4khScYuIYjOBj6Q4SNpC0mu7OOZa4Nj0+Fjgmty27Vm3VUtB8fX+g+yeQRd9oCWNKbSfSG9e7Ac8WGb3+cAkSduk5ffnts0ETi601JC0R6XzlsjjtenrBLLZ+L/Obe7OdVfKrdT5ts3luidZcflVz+PkVuAoZT2vNyN7MwhgATA+/aUDkoZI2rnSeUvksTHwZtYd58IHcj4TEavT8vwSxzalMSu0VzmYovsUES8AL0h6Q1qVfz7MBD6RjkXS9sVtaHpwHZulryLrOz83Le8j6VUFeElbSmpJj8eQtQMqnpJ1K3CYpJY0g/096Zp681osl3d38jAzM+t3rXPb2WHHdQvbO+zYTOucvmlrIomjxkzmsfblfH9JKz97ZgFXv/AYB47qm89urvX4ZmZmZjZ49OUMbyCbuSnpMrIPu/sqqYd32izg2NR+AWAbSfeRzbp9CTgvIi4BUPahg7PIPuiyU9JpwE6peFV8zg5JJ5EV5BqBiyKiNcU5Bfg0WY/mByRdHxHHFx0/X9LGuXYQXwEuk9QK3AYsqnC9d6fWCQ8ATwNzgMLfS34Q+ImkLwBDyD5M8/4KsW6QtCNwe7o/y4H/ByyRdD1wfIl+wd8Efivpo8BjwH/mtu1P1jqi2K3A/0pSmjF/KvBrSZ+hqJBawo7ATyV1kr1h8s2IKFnwjoiXJZ0AXCdpJfA3YFTa/FXge2Rj0gAspOgDNiVNBaYXj1dyRSryrgZOTIXaQiF3W7LnTrHrgI8DF3aRWylHkL2Rs5pspvhRuQ+xLHYV8FayNwIWkX2QKxHRntqBnJcK103pHrQWXffZwKzUVqPY4cANEbGiaP3+6foKb0SU+tvlocDMdI8agRvJ+pgX+zBwkbL2ODfk1l8ITALuTYXqpaQPSS1H0uwyM6t/JWl8ynM2MD2tn0Dp2fk7kj1nIx1zTkTMye8QEfdKmkH2GltC9kZYQY9ei8p6qE8t0V6myzzMzMwGwjHHbvSqdftM69s/YBzVOIQjx1T8+I4NOr6ZmZmZDQ4qX7PbsEg6HXgpIkp9SGJXx46MiOXKPkzwVuCEiLi3q+OqKbUG+XVEHFBm+/eB30fEjf2bWXVJOhzYMyK+WGb734GDCwXyeiHpSuCzEfFPSQcDkyPivIHOq6ckfQf4ZUQ8MNC59JWpU6fGrFml3n+xo356OwAzPr6+XX7MzOrfml79EWT3NF4wsWqx68IJZT9L3MzMzKxq/H/myiTdExFTS23r8xneNewnwPt6eewFknYim6l+6UAXu5MJwH9X2P4NYFo/5dKfmoD/rbD9v8nuzQv9kk0/SG18ro6IfwJExB8GOKVei4gzBjoHMzMzMzMzMzOrXS54JxHxMvDLcttT+4ybSmw6ICI+ULXEeiki7u5i+9NU6HMuaVdefT9WRUTJIrmkq4DivxH9TPEHHFZbRFzexfY7K22X9GGyFi95/4iIE0vs26N7VC0R0U73P3jSzMzMzMzMzMysbrng3U0R8SwwZaDz6C+pL/GUHux/ePWy6T8RcTFwcTf37dE9MjMzMzMzMzMzs+pqGOgEzMzMzMzMzMzMzMz6ggveZmZmZmZmZmZmZlYXXPA2MzMzMzMzMzMzs7rgHt5mZmZmZoNcIydXL/gJ1QtdD05vP71qsc9pnly12ACNF0ysavzTj7u5qvHPbT63qvHNzMysPnmGt5mZmZmZmZmZmZnVBRe8zczMzMzMzMzMzKwuuOBtZmZmZmZmZmZmZnXBBW8zMzMzMzMzMzMzqwsueJuZmZmZmZmZmZlZXXDB28zMzMzMzMzMzMzqQtNAJ2BmZmZmZlbrtFy03NhC4+JGYlgQw4O2A9voHNvZJ/EXzG/n8hnLmX7ixowb19gnMQuWr1nNzGVP8sTqFbSokUaJfUduwo7DRvdJ/GrfGzMzM7M8F7zNbFCStAaYAwwBOoBfAOdGRKektwCfioiDKxy/OXBeRBxZhdwmAA8CZ0XEOX0d38zMzGpMwIgrRtC+azsrD1sJQMPTDWilYGzfnKJ1bjtbTWiidc4q3rz/8L4JCkQEM55/hN1bxnLEmEkAvNDRzoJVL/bRCap/b8zMzMzy3NLEzAartoiYEhE7A28D3gl8ubsHR8RT1Sh2J98F/lil2GZmZlZjmh5rgkZo37P9lXWdm3SyZqs1fRK/vT14fNFqDj5kBK2t7V0f0AML25fTqAamjhj3yrrRTc1MGzG+T+JX+96YmZmZFXPB28wGvYhYApwAnCRJ3TlG0iRJc9Pj4yRdKelPkv4l6dtpfaOkSyTNlTRH0undiHsYsBBo7fUFmZmZWV1pWNpAx6YdVYu/YH47k7cZwtixjQxvaWDxU313rqUdbWw6pKXP4hWr9r0xMzMzK+aWJmZWEyLiEUmNwGt7GWIKsAewClgg6Qcp1hYRsQuApNHp6/R0zvPzASSNBD5DNuP8U73Mw8zMzKxHWue2s8+0YQDstEszrXPb2Wzz6vxX7roXH+fxNOv7Y+NeV5VzmJmZmVWTC95mtqG4KSJeBJD0IDCRbJb25FT8vg64AV5d6M45i6yP+PJKE80lnUA2I50JEyb0Vf5mZmY2SHWO76R5QXNVYre1dfLowtUsWbIGAZ0BAg54Wwvd/MO3isY3tTDv5bX9ut+98Vas7OzggmcWrHdsqO69MTMzMyvFLU3MrCZImgysAZb0MsSq3OM1QFNEPA/sDtwCTAcu7CLGNODbkh4FTgM+J+mk4p0i4oKImBoRU8eP75v+l2ZmZjZ4dUzsgA5ovm9tYbdhSQONjzeud+x5D7az625DOeW00Zx82mhOPX00o8c0sGhR37QJ2bp5JB3Ryd0rlr6ybnV09klsqO69MTMzMyvFM7zNbNCTNB44H/hhRER+NpOkfYCTIuJDvYg7DmiPiCskLQD+r9L+EfHG3LFnAcsj4oc9Pa+ZmZnVGcGKI1bQcmMLQ+8YCk3QuXEnbQe2rXfo1rnt7LvfsHXW7bBjM61z2pk4cch6x5fEUWMmM3PZE9y2YgnDG5poVgMHjtp8vWNnJ6jevTEzMzMrxQVvMxusWiTNBoYAHcAvge+mbU2snbE9Aejt/5i2AC6WVPhrlzOhfA9vMzMzs3JiVLDy8JV9HveYYzd61bpCP+++MqpxCEeO2bpPY+ZV696YmZmZleKCt5kNShFR6e9cdwYeTo+nAT8qcfyjwC7p8SXAJbltB+d23bPEsV0WuiPirK72MTMzMzMzMzOz/uWCt5nVFEk/Jytk/ydARJwxsBmZmZmZmZmZmdlg4YK3mdWUiPjoQOdgZmZmZmZmZmaDU0PXu5iZmZmZmZmZmZmZDX4ueJuZmZmZmZmZmZlZXXDB28zMzMzMzMzMzMzqggveZmZmZmZmZmZmZlYXXPA2MzMzMzMzMzMzs7rQNNAJmJmZmZmZDVbnNp870Cn03gnVDX8uh1Q1/untp1ct9iN/qF5sgGuemV3V+NW25oTHBjqFQetT7Y9UNf45zZOrFrvauVdbNe8NwHuvPLRqsa987zVViw3VzR2qm3+1c69lDz2zCoBDr1xUlfi1/rOqEs/wNjMzMzMzMzMzM7O64IK3mZmZmZmZmZmZmdUFF7zNzMzMzMzMzMzMrC644G1mZmZmZmZmZmZmdcEFbzMzMzMzMzMzMzOrCy54m5mZmZmZmZmZmVldaBroBMzMzMzMzMx6S8tFy40tNC5uJIYFMTxoO7CNzrGd6xW3seNltls4E4AhHW0EoqNpGAALtjmYaGhc79yXr1nNzGVP8sTqFbSokUaJfUduwo7DRq937P6Iv2B+O5fPWM70Ezdm3Lj1vx/1FL9az8uCat+bauZf7XsD1bs//fF9oZZzh9rMv9r3ptbj1+LPKhe8zczMzMzMrDYFjLhiBO27trPysJUANDzdgFYKxq5f6DVNw5i/3aEAbPb0faxpaGLJ+F3XN+NXRAQznn+E3VvGcsSYSQC80NHOglUv1kR8gNa57Ww1oYnWOat48/7D+yxuzcev4vOyoKr3ppr598O9gerdn2p/X4Dazh1qM/9q35tajl+rP6vc0qQOSBoraXb6929JT+aWI32dK+n3kkanYyZJakvb7pd0m6TXpW1vkfSH9HgHSbdLWiXpUxVyOFTSAyneLElvSOuVvp5VtHyspH+lf8fm4nxd0uOSlndxzYdJ+lJ6PF3Sh3p4z165xh4cc6GknXpyTBfxhku6TtJ8Sa2SvpnbdpKkj1Q49rTCNUs6W9KBFfa9RNLC3HNiSg9yfFTSuPXdJ+13cu5av53W7Srpkm7msiZ3Ddfm1t8iaVKJ/Y+T9MMuYna5T9H+W0m6WdKD6TpO7e6xZmZmZtb3mh5rgkZo37P9lXWdm3SyZqs1A5hV9yxsX06jGpg6Yu2v0qObmpk2YnxNxG9vDx5ftJqDDxlBa2t71wdsQPGr/bys9r2pZv798Zqt9v2pplrOHWo/f3u1Wv1Z5RnedSAingWmwCuF5eURcU5aXh4RhW2XAicCX0+HPpzb9nHgc8CxrOs54BTgsC7SuAm4NiJC0m7Ab4EdgNMkLQNGSPo68FdJs4AvA1OBAO6RdG1EPA/8Hvgh8K8uzvdp4JB0/ed3sW+fiIjjqxD2nIi4WVIzcJOkd0bEH4GLgH+kr+uQ1AR8BNgz5fWlbpznjIj4XR/m3SOS9gcOBXaPiFWSXgsQEXMkbSlpQkQs6iJMW+H5OoA6gP+OiHsljSJ77v45Ih4c4LzMzMzMNkgNSxvo2LRjoNPolaUdbWw6pKVm4y+Y387kbYYwdmwjw1saWPxUB5tt3nclhlqOX+3nZbXvTTXz74/XbLXvTzXVcu5Q+/nbq9XqzyrP8N6w3A5sUWbbRsDzxSsjYklE3A2srhQ4IpZHRKTFEWSFbCLiXGA8WdH8TxFxA/AO4M8R8Vwqcv8ZOCjtf0dELK50LknbA6si4pm0fFYXs8+3lXRjmsl+r6Rt0qaRkn6XZh3/Kjf7/ABJ90maI+kiSUPT+lskTU2PD0qx7pd0U1o3Iu1/Vzr+0C7u2cqIuDk9bgfuBbYsbAMelbRPiUPfCtwbER3pvJdIOrLSubpL2V8L3JBmL18IKLft/6Vrmy3pp5J60gTqE8A3I2IVZM+r3LbfA0evR9rPAWtSjh+W9E9JdwH75XIfL+kKSXenf/uVC1ZJRCyOiHvT45eAeZR/TZmZmZmZddt1Lz7O+Uvn8bNnFtRE/Na57ey8y1AAdtqlmda5fTubs9bjV1Mt594favn+1HLuUPv5W9dq5WeV32bZQKTi5AHAz3Ort5E0GxgFDAemrec5Dgf+B3gt8O607lRgKXAecJCkYWQFwsdzhz5Bz4qG+5EVh7vrV2TF1qvS+RuArYA9gJ2Bp8hmU++XZp9fAhwQEf+U9AuyYu33ctc5HvgZ8KaIWCjpNWnT54G/RMRHlLWOuUvSjcDGwIUR8a5yCab93wN8P7d6FvBG4K4S139PD64f4OvKWsDcBHy2UHgu4cvA3yPibEnvBj6a8tsROArYLyJWS/ox8EHgF0XXcT1wfEQ8VRR3e+CNaZb/y8Cn0hsphev8LPDtLq5hWBqfDrLxvBogIt6bzr0Z8BVgL+BF4GbgvnTs94FzI+LvkiYAM4Edy50ovbExvdKs/tRGZQ/gzhLbTgBOAJgwYUIXl2VmZmZmvdU5vpPmBc0DnUavjG9qYd7La3uUvnvjrVjZ2cEFfVREqGb8trZOHl24miVL1iCgM7KZMge8rYU0j2iDjl/N52W1c4fq5l/t12x/3J9qqeXcofbzt9Jq9WeVC971ryUVtbcgm43659y2fEuTo4ALSDOteyMirgKukvQm4KvAgcB5qc3JWRFxVppFvXtvz5FsRlZE71JqO7FFyo2IeDmtB7grIp5Iy7OBScBLwMKI+GcKUWgD871c2P8Abo2IhSnmc2n924FDcrPNhwETImIeUKnY3QRcRnavHsltWkLWFqbYZmRj2V1nAv8GmsnG+DPA2WX2fRPwXoCIuE5SYdb/AWSF5LvTvWtJ+a2jQlG/CXgN2b3bG/itpMnprwKWAJt34zomRsSTkiYDf5E0JyIezm2fBtwSEUsBJM0gK7RD9lzcKfdDdiNJI8udKCJmAZWK3SOBK4DTImJZieMvILvXTJ06NYq3m5mZmVnf6JjYAbdA833NtO+RzSRsWNKAVmnQ9/Heunkkf4lO7l6xlL1Tr9LV0VkT8ec92M6uuw3l3e8Z8cq6X1yyjEWLOpg4ccgGH7+az8tq5w7Vzb/ar9n+uD/VUsu5Q+3nb6XV6s8qF7zrX1tETJE0nGxW64lks62LXQtc3N2gkk4EPpYW35Wf0RsRt0qaLGlcoe1IRJyVvoakJ4G35MJtCdzS7SuCNrJZ0+srP8t5Dev/ehBwRET09G2oC4B/RcT3itYPI7vWYm1pW7fkWsSsknQxULb9SwUCLo2IM3txLGSz+K9MBe67JHUC48jeuCh3neuIiCfT10ck3UI2u/rhiget1QD8R+ENj4LevMssaQhZsftXEXFljwOYmZmZWd8RrDhiBS03tjD0jqHQBJ0bd9J2YJe/Xg44SRw1ZjIzlz3BbSuWMLyhiWY1cOCo7swFGdj4rXPb2Xe/df9LssOOzbTOae+Twlatx6/m87LquUN1X1dVfs32y/2pklrOHWo/fyutVn9WueC9gYiIlZJOAa5O7SiKvYHuFw+JiB8BPyosS9qWbMZ4SNoTGAo8W+bwmcA3JI1Jy28nm4XcXfOA/9fNPF+S9ISkwyLi6tSPu1Lv6QXAJEnbRsRDwDHAX4v2uQP4saStCy1N0izvmcDJkk5O92GPiLiPCiR9jax4X2o28fZkrVaKzQO2rRS36BybRcTiNLv+MGBuhd1vBT4AfE3SO4HCGN0EXCPp3IhYktq4jIqIx7qZxtXA/sDNynqwNwPPpG3bF3JKPctPiogPFV3DGGBl+sDLcWRtXYpboNwJfF/SWGAZ8D7g/rTtBuBk4Dsp3pSImN3N3PN5iKwt0LyI+G5PjzczMzOzvhejgpWHr6zqORZvskdV4o5qHMKRY7auSuxqxj/m2I1etW6fad2ek1P38aF6z8v+yB2q+7qqZuz+uj/Q998Xajl3qP38+yN2rcavxZ9V/tDKDUgqvj4AvD+t2iZ9AOH9wDdYW3RtIs1+lrSppCeATwJfSMXjV38XgyOAuak1yI+Ao3IfYlmcx3NkLU/uTv/OLrQFkfTtdL7h6VxnlQhxK7CH1p2eW6ltxDHAKZIeAG4DNi23Y5oB/GHgcklzgE7g/KJ9lpL1Z74y3bsZadNXgSHAA5Ja0zKSNk+9rdchaUuyvt87AfemscgXvvdj3RY0BX8kaz2yTlrlrgn4VbqWOWSzqr9WYd+vAG9K+b8XWAQQEQ8CXwBuSPfxz2StVYqv6XpJpd6GuwiYLGku8Bvg2NzzY3/guvR4AqVne+8IzEr3+2ayHt4P5ndIM9nPIvtw1n+wbtuXU4Cpkh6Q9CAwvcI9QNJUZR/aWWw/sufTW9N4zZZUtl2NmZmZmZmZmZn1L8/wrjOF1iG55ZFFy+/JLbaUCbMzabZ3RPybrOVIV+f9FvCtHuR5EVkRtHj9p4FPd3HsyvRhkAcANwJjgbIzjSPiX8Bbi1Y/Qq6NSkSclHt8E1m7jOI4b8k9/iNZ4Tm/vQ34eInjnqJED+/UP7xkTw1JewCtEfGqWfIR8ZikZyVtl65tLPDcq4Ks3b/42stK53t7mW0zWFvcz6+flHtcsvgbEe2UmJWfZtxPBU5Lq6aR+8uB3PG3Abt2I/+LKdGaJ7XWOarE+kvIPqS0eH3JHt4R8XfKjJmZmZmZmZmZmQ08z/C2dUj6OVlLi1cVHQeZb5DNAv8qWZH02gHOp6+NA75YYftngc0kXQQMB/7eL1n1vQnAZyOiAyAizoiIBwY4JzMzMzMzMzMzq1Ge4W3riIiPDnQO3RERT5MVua8lFYYl/Yis5UTe99Os35oSEaVameS3LyDrN35rYZ2kq4DipkefiYiZxcdL+jBwatHqf0TEib3LuHfSDPV/9ec5zczMzMzMzMysfrngbXWjv4u1g01EHN6DfUu2/jAzMzMzMzMzM6tlbmliZmZmZmZmZmZmZnXBBW8zMzMzMzMzMzMzqwsueJuZmZmZmZmZmZlZXVBEDHQOZmZ1S9JS4LGBzsO6ZRzwzEAnYVXhsa1fHtv65HGtXx7b+uWxrV8e2/rkca1fG9LYToyI8aU2uOBtZmYGSJoVEVMHOg/rex7b+uWxrU8e1/rlsa1fHtv65bGtTx7X+uWxzbiliZmZmZmZmZmZmZnVBRe8zczMzMzMzMzMzKwuuOBtZmaWuWCgE7Cq8djWL49tffK41i+Pbf3y2NYvj2198rjWL48t7uFtZmZmZmZmZmZmZnXCM7zNzMzMzMzMzMzMrC644G1mZnVP0laSbpb0oKRWSaem9VMk3SFptqRZkvZJ6yXpPEkPSXpA0p4DewVWTi/G9i2SXkzrZ0v60sBegZVTYWx3l3S7pDmSfi9po9wxZ6bX7QJJ7xi47K2Sno6tpEmS2nKv2/MH9gqsFEnDJN0l6f40rl9J67eWdGd6bc6Q1JzWD03LD6Xtkwb0AqysXoztcZKW5l6zxw/sFVg5Fcb2pDSuIWlcbn//jlwDejGu/v24RlQY21+l33/nSrpI0pC0foN9zbqliZmZ1T1JmwGbRcS9kkYB9wCHAd8Dzo2IP0p6F/DpiHhLenwy8C5gGvD9iJg2MNlbJb0Y27cAn4qIgwcoZeumCmN7KdkY/lXSR4CtI+KLknYCLgP2ATYHbgS2j4g1A3MFVk4vxnYS8IeI2GXAkrYuSRIwIiKWp/9o/x04FfgkcGVE/Ca9WXF/RPxE0n8Bu0XEdElHA4dHxFEDdwVWTi/G9jhgakScNHBZW3dUGNtVwPPALWRj+Uza378j14BejOtb8O/HNaHC2L4G+GPa7dfAren78Qb7mvUMbzMzq3sRsTgi7k2PXwLmAVsAARRmh24MPJUeHwr8IjJ3AKNTgcYGmV6MrdWICmO7PXBr2u3PwBHp8aHAbyJiVUQsBB4iK37bINOLsbUakH5mLk+LQ9K/AN4K/C6tv5TszQ3IXrOXpse/Aw5I/5G3QaYXY2s1otzYRsR9EfFoiUP8O3IN6MW4Wo2oMLbXp20B3AVsmfbZYF+zLnibmdkGJc0U3AO4EzgN+I6kx4FzgDPTblsAj+cOeyKts0Gsm2ML8Pr0Z4B/lLRzvydqPVY0tq1kv7wDvA/YKj3267YGdXNsAbaWdJ+kv0p6Y/9mad0lqVHSbGAJ2ZsWDwMvRERH2iX/unzlNZu2vwiM7deErdt6OLYAR6Q/n/+dpK2wQat4bCPizgq7+2dtjejhuIJ/P64ZlcY2zfo+BvhTWrXBvmZd8DYzsw2GpJHAFcBpEbEM+ARwekRsBZwO/Hwg87Pe68HY3gtMjIjdgR8AVw9AutYDJcb2I8B/SboHGAW0D2R+1ns9GNvFwISI2IOshcKvlevdboNHRKyJiClkM8v2AXYY2Iysr/RwbH8PTIqI3ciK45dW2NcGWPHYSnL7qDrQw3H178c1pIux/TFZO5O/DUhyg4gL3mZmtkFI73ZfAfwqIq5Mq48FCo8vZ237gydZd2bhlmmdDUI9GduIWFb4M8CIuB4Ykv/QHhtcSo1tRMyPiLdHxF5kPbsfTrv7dVtDejK2qU3Ns+nxPWn99gOTuXVHRLwA3Ay8nuzPp5vSpvzr8pXXbNq+MfBs/2ZqPdWdsY2IZyNiVVp/IbBXf+dpPZcb24Mq7OaftTWmO+Pq349rU/HYSvoyMJ5sckDBBvuadcHbzMzqXuoJ+nNgXkR8N7fpKeDN6fFbgX+lx9cCH0qfav0fwIsRsbjfErZu6+nYStq00CNW0j5kvwu5wDIIlRtbSa9NXxuALwDnp03XAkdLGippa2A7sh6GNsj0dGwljZfUmB5PJhvbR/o7b6ssjdPo9LgFeBtZf/abgSPTbscC16TH16Zl0va/pN6jNsj0dGyL+sMekva1QajM2M6vcIh/R64BPR1X/35cO8qNraTjgXcA74+IztwhG+xrtqnrXczMzGrefmS9zOakfmcAnwM+Bnw/zU56GTghbbue7JOsHwJWAh/u12ytJ3o6tkcCn5DUAbQBR7vAMmiVG9vtJJ2Ylq8ELgaIiFZJvwUeBDqAEyNiTf+mbN3Uo7EF3gScLWk10AlMj4jn+jFf657NgEvTmxMNwG8j4g+SHgR+I+lrwH2sbTH1c+CXkh4CngOOHoikrVt6OranSDqE7Hvxc8BxA5CzdU+5sT0F+DSwKfCApOsj4nj8O3Kt6Om4+vfj2lFubDuAx4Db03sXV0bE2WzAr1n5OWxmZmZmZmZmZmZm9cAtTczMzMzMzMzMzMysLrjgbWZmZmZmZmZmZmZ1wQVvMzMzMzMzMzMzM6sLLnibmZmZmZmZmZmZWV1wwdvMzMzMzMzMzMzM6oIL3mZmZmZmZmZmZmZWF1zwNjMzMzMzMzMzM7O64IK3mZmZmZmZmZmZmdWF/w/9yI8Lk7ZGMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ii, ps_scenario in df_scenarios.iterrows():\n",
    "    mdl_hb.plot_scenario(ps_scenario) #, nt_lim=(200,340))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1440x720 with 1 Axes>, <AxesSubplot:>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dict_scenario_fict = {\n",
    "            'scenario_rank' : 1,\n",
    "            'scenario_proba_cond_seq' : 0.9999,\n",
    "            'v_choice' : 61,\n",
    "            'j_choice' : 1,\n",
    "            'd_gene' : 0,\n",
    "            'v_3_del': 0,\n",
    "            'd_5_del': 1,\n",
    "            'j_5_del': 8,\n",
    "            'd_3_del': 6,\n",
    "            'vd_ins' : 1,\n",
    "            'vd_dinucl': [1],\n",
    "            'dj_ins': 2,\n",
    "            'dj_dinucl': [0, 3],\n",
    "            'Mismatches' : [],\n",
    "            'norm_scenario_proba_cond_seq': 0.000225\n",
    "        }\n",
    "ps_scenario_fict = pd.Series(dict_scenario_fict)\n",
    "mdl_hb.plot_scenario(ps_scenario_fict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('gene_template',\n",
       "              'GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTGGAACCTCTGTGAAGATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGTATCGTCAGTTCCCGAAACAGAGTCTCATGCTGATGGCAACTTCCAATGAGGGCTCCAAGGCCACATACGAGCAAGGCGTCGAGAAGGACAAGTTTCTCATCAACCATGCAAGCCTGACCTTGTCCACTCTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCTGCAGTGCTAGAGA'),\n",
       "             ('int_gene_5_del', 0),\n",
       "             ('int_gene_3_del', 5),\n",
       "             ('palindrome_5_end', ''),\n",
       "             ('gene_ini', 0),\n",
       "             ('gene_end', 288),\n",
       "             ('gene_cut',\n",
       "              'GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTGGAACCTCTGTGAAGATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGTATCGTCAGTTCCCGAAACAGAGTCTCATGCTGATGGCAACTTCCAATGAGGGCTCCAAGGCCACATACGAGCAAGGCGTCGAGAAGGACAAGTTTCTCATCAACCATGCAAGCCTGACCTTGTCCACTCTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCTGCAGTGCT'),\n",
       "             ('palindrome_3_end', ''),\n",
       "             ('gene_segment',\n",
       "              'GGTGCTGTCGTCTCTCAACATCCGAGCTGGGTTATCTGTAAGAGTGGAACCTCTGTGAAGATCGAGTGCCGTTCCCTGGACTTTCAGGCCACAACTATGTTTTGGTATCGTCAGTTCCCGAAACAGAGTCTCATGCTGATGGCAACTTCCAATGAGGGCTCCAAGGCCACATACGAGCAAGGCGTCGAGAAGGACAAGTTTCTCATCAACCATGCAAGCCTGACCTTGTCCACTCTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCTGCAGTGCT'),\n",
       "             ('gene_description',\n",
       "              \"TRBV20-1*01 (v_choice: 29) (v_3_del: 9), 3'del : 5\")])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scenario_aln_fict = mdl_hb.get_df_scenario_aln_from_scenario(ps_scenario_fict)\n",
    "df_scenario_aln_fict\n",
    "mdl_hb.get_gene_segment_dict('V', ps_scenario)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>value</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GGGACAGGGGGC</td>\n",
       "      <td>TRBD1*01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GGGACTAGCGGGGGGG</td>\n",
       "      <td>TRBD2*01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GGGACTAGCGGGAGGG</td>\n",
       "      <td>TRBD2*02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               value       name\n",
       "id                             \n",
       "0       GGGACAGGGGGC   TRBD1*01\n",
       "1   GGGACTAGCGGGGGGG   TRBD2*01\n",
       "2   GGGACTAGCGGGAGGG   TRBD2*02"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.parms['d_gene']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method plot_scenario in module pygor3.IgorIO:\n",
      "\n",
      "plot_scenario(ps_scenario, nt_lim: Union[NoneType, tuple, list] = None, show_CDR3=True, ax=None) method of pygor3.IgorIO.IgorModel instance\n",
      "    Return matplotlib fig, ax\n",
      "    :param ps_scenario: Pandas Series scenario\n",
      "    :param nt_lim:Union[None,tuple,list] region limits to show the scenario alignment\n",
      "    default give boundaries around CDR3, if no anchors in model, show the whole scenario\n",
      "    :param show_CDR3: Show CDR3 lines default(=True)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mdl_hb.plot_scenario)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observables from scenarios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predefine Functions for Scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "igor_fln_generated_realizations_werr:  best_scenarios_counts.csv\n"
     ]
    }
   ],
   "source": [
    "fln_scenarios = \"best_scenarios_counts.csv\"\n",
    "df_scenarios_many = mdl_hb.get_dataframe_scenarios(fln_scenarios)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairwise Probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_P_from_scenarios_cols in module pygor3.IgorIO:\n",
      "\n",
      "get_P_from_scenarios_cols(df_scenarios, colname_list) method of pygor3.IgorIO.IgorModel instance\n",
      "    Return xarray with marginalize probabilities of listed columns in dataframe scenarios df_scenarios\n",
      "    :param df_scenarios: Scenarios with normalize probability. Loaded with self.get_dataframe_scenarios()\n",
      "    :param colname_list: List of variables preserve for marginalization\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mdl_hb.get_P_from_scenarios_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fac14e44f10>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ryqQdoECRa62nSzrPY869necBVK9PIFYSHV+VygIs3S1PPtk+y7n6FnJd51M7Sl3HjYsAbF8NXD3Wc5K+b/u1Yz03QIEihBB6o46LApaw2XhPbITXIoQQ2qtb01MJY058hggUIYQwSh37KDoRgSKEEEbYSAOFxnticALF2odh1dmd5zPnxM7zyGVVpo2LcplWsQ7bSqnYdrM5NmPKqkrb1uaRM1BIOpRiy4XJwLm2zxjx/FTgImBPYBVwhO37JU2jWProd4DP2z6hKc0NwAzWL+x3iO1OlkM6erwnerEfRQghDJScGxdJmgx8GjgMmAscKWnktpQLgNW2dwHOAs5Mx5+mGAr2l+Nkf5TtV6dbyyAhaR9Jt0j6X0nPSHpO0rr1nmzfOV7aCBQhhDBC5mXG9wKW2b4v7clzKTBvxDnzWD/57XLgQEmy/aTt71IEjE59CjgS+DGwOfAnFAGsrcFpepqyPUwrtSLu4JiWqxmsOtX1QtWasHI0G1XtPeWS6bNz03V58tk/1/pnnc+jmMA/x+mSmndVW2h7YdPjmUDzpuLLgb1H5LHuHNtrJT0GTANWtnntCyQ9B3wFOD1t2zAu28skTbb9XEp7K3BKm9cYoEARQgg9NIE+ipW2x1rPrtuOsr1C0gsoAsXRFP0c43kqLc56m6R/opjFXKpVaYACRaaZ2Vm+GVbtG3xdVakDOdfvvGqfnUw1pf3rVePKPOppBdC8V+ysdGysc5ZLmgJsRdGpPS7bK9LPJyR9gaKJq1WgOJoiMJwAfCC93jtanL/OAAWKEELojcxLeNwC7CppJ4qAMB9494hzGnv0fB94J/CtVs1IKZhsbXulpE2AN9NmDwXbD6QaxWzgCmBp6jNpq1SgkLQF8CFgR9vHSdoVmGP7q2XShxDCIMlZo0h9DicA16Rsz7d9l6TTgGHbi4DzgIslLQN+SRFMirIU25a+ENhU0luBQyj28bkmBYnJFEHinJbvSXoT8FngJ+kt7iTpT21/o917KFujuABYDDQWjFoBfBloGyjSm3wCeA5Ya3tI0rbAlygi2/3A4bZXt85pBiW3d20jR9U/15jxqi2klksdN/mp2jUO3TZ50m/KnVjiNNtfB74+4tipTfefBt41TtrZ42S7Z7kCrvMJ4ADbywAk7Qx8DWgbKMoOj93Z9j8BzwLYfooWs/jGcEAa59vo8DkZuN72rsD16XEIIVSCMJMn/abUbYA80QgSyX0UX+LbKlujeCbthmRYF4nWTKiIzzcPeH26fyFwA/DhDvIbUFX6xpxTfPseHHVcEr5zEkwZrCAwLklvT3eHJX0duIzif/m7KPpP2iobKD5KsYb5DpIuAfYFji2Z1sC1kgx8Lo0v3t72g+n5h4Dtx0oo6XjgeIAdd9yq5MuFEELnBqy20MofNN1/GPi9dP9RWiwt3qxUoLB9naQlwD4UTU4n2m43EaRhvzTW90XAdZKet1OKbacgMtbrLgQWAgxNe6k5eqeSLzm+pff+d8d5zLklU9/CTfvkyWfh8Xny+cjpefKZXvaj0dpnd/xZlnzee/WhHeex9IOfzFASmHNLpuH252T6nR+3Q/tzyrjy7e3PKWFNrs9yhybJbDplbb+LkYXt93SaR6k+Cklvo+iI/loa6bQ29b631TTW9xHgSoqxvg9LmpHyngF0spBVCCFkVr8+CkmzJF0p6ZF0+4qkWWXSlu3M/qjtxxoPbP+KojmqXcG2TLMGkbQlxbCuO1k/Zpj086qS5QghhK6TqF2goBi9ugh4cbr9VzrWVtk+irECSpm02wNXSmqc/wXbV0u6BbhM0gKK8cCHt81pp23h4j8sWdzxzek4B8jWCb1/nmyYm2m58ml5moxyee+S6uwLPeeWXJ2+mZqejlvY/pxSMr2vt70mSzZT39Z5MyEwsTGZYyb3oAWBMraz3RwYPi/ppDIJywaKYUmfZP1Kg++nmFfRku37gFeNcXwVcGDJ1w4hhN5SrTqzG1ZJ+kPgi+nxkbRZJqShbKD4c4rxb19Kj6+jCBY9VMe1njINKcxWE6jYOkRz7ut3CSqsYp/BbDIunNEBUZ/hsU3+GPh3iv0uDPw3UKqju+yopyeJSXEhhI2EajTqCdZtnvQPtt+yIelbBgpJ/2r7JEn/RZps12xDXzSEEKqsbn0Utp+T9BJJm5ZdCLBZuxrFxennv0y8aLnlanrKIVd1vSrvJ7dc16dKzSJVKgtUrzxVW7ess42LatpHcR/wPUmLgCcbB223nSTUMlDYXpx+3piWp31Zemqp7Wc3vLwhhFBdopaB4ifpNgl4wUQSll1m/PUUazLdT3ENd5B0jO1c+xaWkGv12Kp9E6uSil2bVZmG/eaQbdvaXNe4Gp2+6yx9aZ58pp+dJ58O1a3pCcD2xwEkvbB46FILAkL5UU+fAA6xvTS90MsohlhNdJnbEEKoPMlMrVFnNoCkIYoJdo1J0I8Bf9xoOWqlbKDYpBEkAGz/T9owI4QQakf17KM4H/gz2zcBSNqPInC8sl3CiUy4Oxf4z/T4KGB4AwragRlUZw5EHTtrK6iu80OyyLVEfaZrM6dqAzw67MymloHiuUaQALD9XUmlqk1lA8X7KCbY/UV6fBPwHxMqYgghDIg69lEAN0r6HEW3gYEjgBsk7QFge8l4CdVi/+5KGRqa6eHh6MwOg66uM6qrRdLiph01J2zuFi/3F3b9YvsTgdfc/qqOXqtXJH27xdO2/Ybxniw76mlf4GPAS5rT2M401CGEEKqjjjUK2we0ej6NZL1wrOfKNj2dB3yAYiHA5yZWvFxqOOEu1/DPaY/nyadyKvSteVWmYZu5+l2WVmdlXSDjZznXMOTOSNRqCY+STqSYBjFK2UDxmO1v5CtPCCFUl3AdFwVsZ9zF2dut9bRHuvttSf8MXAGsaTzfqvMjhBAGVU2Hx7Yzbod1uxrFJ0Y8bu6wMTBu50eztHLhMLDC9psl7QRcCkyjaM46uv1CVXvQ8xG548rUBFbb4Z8VajLKZVrF3lOuJqNcKtJklE/9+ihK2LAaRbvOjwk4EbgHeGF6fCZwlu1LJX0WWAB8JtNrhRBCRzbSGsX3xnui7KinfwD+Ke2VjaRtgA/Z/kiJtLOANwF/D3xQxb6obwDenU65kGJEVZtAUaWNi6o2uahq5amaKr2vitVMKqciq8dSv0AhaSuK/7WNTZhvBE6z/RiA7RPGSzvWXthjOawRJFKGq4E3lkz7r8BfA42rPg34le3GkILlwMyxEko6XtKwpOFHH31yrFNCCCG7SWnjojK3AXI+8DhweLo9TrGER1tlRz1NljTV9hoASZsDU9slkvRm4BHbi9MKtBNieyGwEIoJdxNNH0IIG6aWfRQ7235H0+OPS7qtTMKygeIS4HpJjejzHsYZbzvCvsBbJL0R2Iyij+JsYGtJU1KtYhawon1WudZ6qpKqLTmda/2gqqnb56aK6tX8KdVyz+xfS9rP9ndh3UTqX5dJWHbP7DMl/Qg4KB36O9vXlEh3CnBKKtTrgb+0fZSkLwPvpBj5dAxwVZlyhBBCL9R046L3ARemvgoBvwSOLZOwbI0C21cDV4/1nKTv235t2byADwOXSjoduJVi5ncbVerMrpq61gSq1tkfuq8qndn1a3qyfRvwqrRxEbZLL+lQOlC0sVm7E2zfANyQ7t8H7JXptUMIISvJTBmsjupxSfpD2/8p6YMjjgMZ9syegOhoDiHUh4D61Ci2TD8ntE92s1yBYoDk6PiNpp4Qnq+GzYQ1CRS2P5dWx3jc9lkbkkepeRSSPihpzLkOjVM25MVDCKGS5CJQlLkNANvPAUduaPqyNYoXANdK+iXwJeDLth9uev7oDS1A71XoG0uub2FVWwK7lmr4jTmrGr6vjEFA0qEUUwMmA+faPmPE81OBi4A9gVXAEbbvlzQNuBz4HeDzzbOnJe0JfB7YHPg6cKJb70T3PUmfovgfvm4Gc5nFXcsOj/04xeSMV1Jsn3ejpOW2D0rP31kmnxBCGAzOFihSs8+ngYMpVqK4RdIi23c3nbYAWG17F0nzKdbDOwJ4miIK755uzT4DHAfcTBEoDgVabQfx6vTztKZjpRZ3nWgfxSPAQxQR70UTTNuZp1fB0ks6z2fOfZ3nke3bU658MtUoKqeOEwmjH6i1ilwfAflGPe0FLEujPZF0KTAPaA4U8yjWYYKiBvEpSbL9JPBdSbs8r3jSDOCFtn+QHl8EvJUWgaKTRV7LLgr4ZxRrg2wHfBk4bkQ0DCGE+tCEahTTJTXvgbAwLT/UMBP4edPj5cDeI/JYd47ttZIeo1gXb7z24Jkpn+Y8W/UjAyDpTcDLaZrSYPu08VMUytYodgBOShM2Qgih/soHipW2h9qf1l9pS4ctgAOAcylWx/hhmbRl+yhO2eDS5bLZMxVrNgqDI0dzRq4mkVzNYHVslsup85nZGTuzV1B82W4Ya327xjnLJU0BtqJo4m+V56w2eY70u7ZfKel22x+X9Ala92msU3aZ8RBC2Hg0JtzlGR57C7CrpJ0kbQrMBxaNOGcRxbp3UHzT/1arEUy2HwQel7RP2uPnj2i/Zl5jAcCnJL0YeJZitdW2NsIJdyGE0E6+GkXqczgBuIZieOz5tu+SdBowbHsRxXp3F0taRrFY3/xGekn3U6y8vamktwKHpD7iP2P98Nhv0L528FVJWwP/DCwp3iTnlnkPEShCCGGkvKOesP11iiGszcdObbr/NPCucdLOHuf4MKOHzLYqQ6P99CuSvgps1tjdrp0IFCGEMErWPorKkPS7wGzS/35J2L6oXboBChR7AMNtz2ovVydgDpk6SL/z5jz5vC1PNtWTYwBDRcb3r1O1QRm5rk9FrnO9FgUEQNLFwM7AbcBz6bApZoS3NECBIoQQeqWWNYohYG6bZT7GFIEihBBGqmGNArgT+G2KXeAmpKuBQtJmwHeAqem1Lrf9UUk7UWyDOg1YDBxtu64DukMIA8dZO7MrYjpwt6QfAmsaB22/pV3Cbtco1gBvsP2/kjahWLPkG8AHgbNsX5pmCy6gWOAqhBD6r541io9taMKuBorUFva/6eEm6dZYrfDd6fiFFG+gZaB4+p6nWPo7nXdmzzn+2I7z4LhMHYk5FjkEmJtr2a2KLXt+9z558pk7vfM8Vr608zwA5lSks3adTIM7lma6PndnGpjBlR2mr18fhe0bJb0E2NX2NyVtQTGvo62uz8yWNFnSbRQrz14H/AT4le1GvW7cxawkHS9pWNLw6rWru13UEEIo5J2ZXQmSjqNYmfZz6dBM4P+VSdv1zuy0s9Kr04zAK4HdJpB2IbAQQNrJuw3f1XF59hpe2P6kNm7e7XUd5wHAwo9kyeazV7w9Sz7v/dmOWfLJRa+7KUs+vqLzcb96+8UZSgK+d88s+XD33Dz5vO6FefL57HuzZPONTJ/lzmsUDFQQKOn9FEue3wxg+8eSSm0X0bNRT7Z/JenbwGuBrSVNSbWKMotZhRBCD9Wv6QlYY/uZYmkoSIsPlhoq2+1RT9sBz6YgsTnFDk9nAt+mWPjqUoqFsNotZsWee05jePiYdqf1yHfyZLN/nmzem+fLLuMvfd8f61cc6FTnfUr5ynJvnmyy9XW8Jk82Z52UJZvDzsqSTdF01FH6Wo56ulHS3wCbSzqYYq2o/yqTsNs1ihnAhWkrwEnAZba/Kulu4FJJpwO3UiyIFUII1VDPUU8nU4wwvQP4U4q1p/q/KKDt2xnjK0vaEnCvbr52CCF0pH6BYnOKlWvPgXV7eW8OPNUu4eDMzF57G6zKMMxxWpWaV2LzmcFRtbWVcpWnZms0ZVPLPorrgYNYP2Vhc+Ba4HfbJRycQBFCCL1Sz6anzWw3ggRpIvQWZRJufIEiS63kxM7zAKr3LbVick3cm5bhOi8tPaq7tTlH5cmnct/gM32Wc/3OO1XPzuwnJe1hewmApD1Zv+tdSxtfoAghhDLqV6M4CfiypF9Q1Jl+GziiTMIIFCGEMEr9+ihs3yJpN2BOOrTU9rNl0m58gWJljqanqnUkVkyO5j2AaY/nyadS17liTTRVa0bN9rfV4USKGvZRSHoXcLXtOyV9BNhD0umNpqhWur7WUwghDB7Xbq0n4G9tPyFpP+BAivlrpVbtjkARQggj1XBRQNZvf/om4BzbX6PkGP2Nr+kphBDaquWopxWSPkdaSknSVEpWFqJGEUIII9WzRnE4cA3w+7Z/BWwL/FXjSUnbjJcwahQhhDBKLUc9PQVc0fT4QZ6/f/b1wB5jpR2cQDHl1TCt8x3umFbDETBVWwqkUsukZDKn/Sk9VbXRStlU5O+zhqOeShh3qNjgBIoQQuiljS9QjLs3RQSKEEIYSfVreupEBIoQQhillqOe2ulP05OkHYCLgO0pqjULbZ8taVvgS8Bs4H7gcNuru1mWEEIoraZ9FGlk0w40/e9vmpl94Hjpul2jWAt8yPYSSS8AFku6DjgWuN72GZJOpth56cNdLksIIZRUv6YnSX9H8b/3J6zvjzDwBgDbvxwvbbd3uFs3/CpNHb8HmAnMA16fTrsQuIEIFCGEqqhnjeJwYGfbEx7e2LM+CkmzKbZFvRnYPgURgIcomqbGSnM8cDzAjjtuRZahc0sv6TyPORUbUnjTPnnymZtrMb9Mw2Nz7QExPUN5sg1HzTT8M9uigHmyqd6OexnUL1DcCWwNPDLRhD0JFJJ+C/gKcJLtx6X1fSa2LWnMYVm2FwILAYaGZo47dCuEELKq58ZF/wjcKulOYE3joO23tEvY9UAhaROKIHGJ7caswIclzbD9oKQZbECECyGE7qlfHwVFM/+ZwB3AhN5cV9d6UlF1OA+4x/Ynm55aBByT7h8DXNXNcoQQwoRkXutJ0qGSlkpalgbwjHx+qqQvpedvTk31jedOSceXSvr9puP3S7pD0m2Syixb8ZTtf7P9bds3Nm5lyt/tGsW+wNHAHZJuS8f+BjgDuEzSAuABik6WNmaQpR20Uv0Lmdpj9/9OnnyqZs69/S5Bk1xt51Xb4KdqMi0j0+nGRZCtRiFpMvBpilVblwO3SFpk++6m0xYAq23vImk+xTf/IyTNBeYDLwdeDHxT0stsN5YMP8B22U64myT9I8UX9eamp7YbF3V71NN3Gf83Nu6Y3RBC6K+sTU97Acts3wcg6VKKkZ/NgWIe8LF0/3LgU6lFZh5wqe01wE8lLUv5fX8DyvGa9LN59Mu64bGtDM7M7LW35dlis1IL1lVtpEjFvqVWakvVqv2ucqnY77wqJjY8dvqIpp+FaSBOw0zg502PlwN7j8hj3Tm210p6jGJM2kzgByPSzkz3DVybBgN9bsRrPv/tFLWaRbbPKvummg1OoAghhF6Z2KinlbaHulmccexne4WkFwHXSbrX9pjt0Lafk3QkEIEihBCyydf0tIJi2YyGWenYWOcslzQF2ApY1Sqt7cbPRyRdSdEk1arD8nuSPkWxfNKTjYN976PIKtd+FFlE80FPZGkygmpdnyqVBar3Wa5KebL2UdwC7CppJ4p/8vOBd484pzES9PvAO4FvpTlmi4AvSPokRWf2rsAPJW0JTEorXmwJHAKc1qYcr04/m8+rWR9FCCH0SsYlPFKfwwkU25BOBs63fZek04Bh24sophFcnDqrf0kRTEjnXUbR8b0WeH9qRtoeuDJNXp4CfMH21W3KccCGvgfZgzHheWhopoeH39vvYiR17disqxy/r5oOGMimWrssSlrcSb/B0NwtPHxJuSVktMetHb1Wr0jaCvgo8Lp06EbgNNuPtUvb1Ql3IYQwkDJPuKuI84EnKOatHQ48DlxQJuEANT1lmnCXZYht51kUqlYzqdq33SrVuOp6bSr2vnINie5YLdd62tn2O5oef7xpInRLUaMIIYSR6lmj+LWk/RoPJO0L/LpMwgGqUYQQQq/UclHA9wEXpr4KgNWsX3OvpQEKFA+SpbqdZV+BilXXa6tqTXM55HpPVbs2udawypMNnNpZ8npuXHQP8E/AzhT7UjwGvBW4vV3CAQoUIYTQQ/ULFFcBvwKWMHrCX0sRKEIIYaR6blw0y/ahG5IwOrNDCGEU17Ez+78lvWJDEg5QjSLT8NgsKtauW9f26myqNOGuaqr2u8ol+ijGsB9wrKSfUuxHIYrdqF/ZLmFXA4Wk84E3A4/Y3j0d25ZiUarZwP3A4bZXd7McIYQwYfULFIdtaMJuNz19HhjZJnYycL3tXYHr0+MQQqiQ+jU92X5grFuZtN3e4e47zXu/JvOA16f7FwI3AB/uZjnyq1p1vWpNRlVrpqna76tKqvbZqYh6Nj1tsH70UWxv+8F0/yFg+/FOlHQ8cDzAjjvu2IOihRACdR31tMH6OurJxdK14y5fa3uh7SHbQ9ttt10PSxZC2OjVrOmpE/2oUTwsaYbtByXNAB7pQxlCCGF8quUSHhusH4GisZPTGennVX0oQ+iqXO3VufY4yFGemrXBh/YiUKzT7eGxX6TouJ4uaTnFphlnAJdJWgA8QLEuegghVEjUKJp1e9TTkeM8dWA3XzeEEDoSo56eZ4BmZocQQo/EqKfniUARQghjiRrFOgMUKJaQp3OzSp2SVSpLFdVxAmDVfudVK09VCFT23+OzXS1JFQxQoAghhF6ZBPxWyXPrv1RdBIoQQhhlMvCCkudGoKiQPYDhDPnkaIaoY5MIVG/dn7pe5zC+XHNnOrUJxdYGZfysmwWphAEKFCGE0CsTaXqqvwgUIYQwykSanupvIwwUVRrlUaWy1Fkdr3PVmglzeSZTPuowfdQomm2EgSKEENqJGkWzAQoUVZpHUbVvYXX9dlnX95VDHd8TVGfgwSQiUKw3QIEihBB6ZRPgt/tdiMqIQBFCCKNE01OzAQoUVZpHUTVVm29QtXkUVRmbD9W7NrlU7bNzaofpozO72QAFihBC6JWoUTTrW6CQdChwNsVv5FzbZ/Tmlav0Taxi38JWnZ0nn2lVqwnkGHJZtZpoxT47lavVdipqFM36EigkTQY+DRwMLAdukbTI9t39KE8IITzfpsCL+12IyuhXjWIvYJnt+wAkXQrMA1oEijoOj61S7QaYtrLfJRgh1+SrHCr2u6qtevZRtGtBkTQVuAjYE1gFHGH7/vTcKcAC4DngL2xfUybPnPoVKGYCP296vBzYu09lCSGEEfL1UZRsQVkArLa9i6T5wJnAEZLmAvOBl1NUcb4p6WUpTc9aZSrdmS3peOB4gB137HNhQggbkaw1ijItKPOAj6X7lwOfkqR0/FLba4CfSlqW8qNEntn0K1CsAHZoejwrHXse2wuBhQCSHpWefaBNvtOBNu0nnVZJS+dRoiw9FeVprUrlqVJZYDDL85JOXmDx4sXXSJpe8vTNJDWP3V+Y/nc1lGlBWXeO7bWSHgOmpeM/GJF2Zrrfs1aZfgWKW4BdJe1EESDmA+9ulcD2du0ylTRseyhPETtTpbJAlKedKpWnSmWBjbM8tg/tZv6Dpi+BIkXME4BrKBoDz7d9Vz/KEkIIXVamBaVxznJJU4CtKDq1W6Vt2yqTy6RuZdyO7a/bfpntnW3/fb/KEUIIXbauBUXSphQtKItGnLMIOCbdfyfwLdtOx+dLmppaYHYFflgyz2wq3Zm9ARa2P6VnqlQWiPK0U6XyVKksEOXpyHgtKJJOA4ZtLwLOAy5OndW/pPjHTzrvMopO6rXA+20/B9DLVhkVQSuEEEIYW9+ankIIIQyGCBQhhBBaqkWgkHSopKWSlkk6uc9l2UHStyXdLekuSSf2szypTJMl3SrpqxUoy9aSLpd0r6R7JL22z+X5QPo93Snpi5I26/Hrny/pEUl3Nh3bVtJ1kn6cfm7T5/L8c/p93S7pSklb96ssTc99SJInMNchdGDgA0XT9PjDgLnAkWnae7+sBT5key6wD/D+PpcH4ETgnj6XoeFs4GrbuwGvoo/lkjQT+AtgyPbuFJ2C83tcjM8DI8fsnwxcb3tX4Pr0uJ/luQ7Y3fYrgf8BTuljWZC0A3AI8LMelWOjN/CBgqbp8bafARpT2fvC9oO2l6T7T1D8I5zZOlX3SJoFvAk4t19laCrLVsDrKEZ4YPsZ27/qa6GKkX+bp7HrWwC/6OWL2/4OxSiXZvOAC9P9C4G39rM8tq+1vTY9/AHFmP2+lCU5C/hrIEbi9EgdAsVY0+P79o+5maTZwGuAm/tYjH+l+KP6TR/L0LAT8ChwQWoKO1fSlv0qjO0VwL9QfDN9EHjM9rX9Kk+T7W0/mO4/BGzfz8KM8MfAN/r14pLmASts/6hfZdgY1SFQVJKk3wK+Apxk+/E+leHNwCO2F/fj9ccwhWJP28/Yfg3wJL1tVnme1PY/jyKAvRjYUtIf9qs8Y0mTrirxzVnS/6VoWr2kT6+/BfA35FmwLUxAHQJFqQUGe0nSJhRB4hLbV/SxKPsCb5F0P0WT3Bsk/Wcfy7McWG67UcO6nCJw9MtBwE9tP2r7WeAK4Hf7WJ6GhyXNAEg/H+lzeZB0LPBm4Cj3b/LVzhRB/UfpMz0LWCLpt/tUno1GHQJFT6eyt5OWBj4PuMf2J/tVDgDbp9ieZXs2xXX5lu2+fWO2/RDwc0lz0qED6dKyyCX9DNhH0hbp93Yg1ej0b17O4Rjgqj6WpbFBzl8Db7H9VL/KYfsO2y+yPTt9ppcDe6TPVeiigQ8UqZOtMZX9HuCyPi8wuC9wNMW399vS7Y19LE/V/DlwiaTbgVcD/9CvgqSazeUU2yfeQfH30NPlISR9Efg+MEfSckkLgDOAgyX9mKLW06P95Mctz6codvG5Ln2eP9vHsoQ+iCU8QgghtDTwNYoQQgjdFYEihBBCSxEoQgghtBSBIoQQQksRKEIIIbQUgSKEEEJLEShCz0n67wmef4OkoQmcPyTp3yZeshDCWOq2Z3YYALa7ukyG7WFguJuvEcLGJGoUoeck/W+L5z4s6Q5JP5LUPCP5XZJ+KOl/JO2fzt1M0gXp/FslHZCOv76xSZOk32o653ZJ70jHD5H0fUlLJH05LeIYQhhD1ChCZUg6jGI1171tPyVp26anp9jeKy2H8lGKpS3eT7HA6isk7QZcK+llI7L9W4rlw1+RXmObtCvaR4CDbD8p6cPAB4HTuvsOQxhMEShClRwEXNBYeM5286Y1jVV4FwOz0/39gH9P594r6QFgZKA4iKZd62yvTsuvzwW+V6wFyKYUawqFEMYQgSIMijXp53N0/rkVcJ3tIzvMJ4SNQvRRhCq5DnhP2qCGEU1PY7kJOCqd+zJgR2DpGHm+v/EgbVb0A2BfSbukY1uO0WQVQkgiUIR+GHPJYttXU+zFMCzpNuAv2+TzH8AkSXcAXwKOtb1mxDmnA9tIulPSj4ADbD8KHAt8MS13/n1gtw19MyHUXSwzHnpK0jRgie2X9LssIYRyokYRekbSiym+vf9Lv8sSQigvahSh5yS9Arh4xOE1tvfuR3lCCK1FoAghhNBSND2FEEJoKQJFCCGEliJQhBBCaCkCRQghhJb+P4DqSs4BhTZ2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pjoint_V_J_scens = mdl_hb.get_P_from_scenarios_cols(df_scenarios_many, ['v_choice', 'j_choice'])\n",
    "Pjoint_V_J_scens.plot(cmap='gnuplot2_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fac14d4ef50>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.get_P_joint(['v_choice', 'j_choice']).plot(cmap='gnuplot2_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual Information from scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_mutual_information_events_from_df_scenarios in module pygor3.IgorIO:\n",
      "\n",
      "get_mutual_information_events_from_df_scenarios(df_scenarios, event_nickname_x, event_nickname_y) method of pygor3.IgorIO.IgorModel instance\n",
      "    Return mutual information in log10 of the desired events\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mdl_hb.get_mutual_information_events_from_df_scenarios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mutual_information ( v_choice ,  j_choice ):  0.6392623620052954\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;norm_scenario_proba_cond_seq&#x27; ()&gt;\n",
       "array(0.63926236)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'norm_scenario_proba_cond_seq'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5bc9f9ca-76be-4d5e-a7e6-3113ab67a0a2' class='xr-array-in' type='checkbox' checked><label for='section-5bc9f9ca-76be-4d5e-a7e6-3113ab67a0a2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.6393</span></div><div class='xr-array-data'><pre>array(0.63926236)</pre></div></div></li><li class='xr-section-item'><input id='section-58b16542-941d-4c08-a9b3-191d7676c84a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-58b16542-941d-4c08-a9b3-191d7676c84a' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-4c357fd9-23ed-4db7-8a38-ca4faf6ae021' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4c357fd9-23ed-4db7-8a38-ca4faf6ae021' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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       "<xarray.DataArray 'norm_scenario_proba_cond_seq' ()>\n",
       "array(0.63926236)"
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     },
     "execution_count": 74,
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   "source": [
    "mdl_hb.get_mutual_information_events_from_df_scenarios(df_scenarios_many, 'v_choice', 'j_choice')"
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     "text": [
      "mutual_information ( v_choice ,  j_choice ):  0.6392623620052954\n",
      "mutual_information ( v_choice ,  d_gene ):  0.08894391386660068\n",
      "mutual_information ( v_choice ,  v_3_del ):  0.7021801087511944\n",
      "mutual_information ( v_choice ,  d_5_del ):  0.35886454309758736\n",
      "mutual_information ( v_choice ,  j_5_del ):  0.4667910640736827\n",
      "mutual_information ( v_choice ,  d_3_del ):  0.3381390129055559\n",
      "mutual_information ( v_choice ,  vd_ins ):  0.36950632876861056\n",
      "mutual_information ( v_choice ,  dj_ins ):  0.34050716113668833\n",
      "mutual_information ( j_choice ,  d_gene ):  0.16839619808892387\n",
      "mutual_information ( j_choice ,  v_3_del ):  0.1373839342864651\n",
      "mutual_information ( j_choice ,  d_5_del ):  0.11353204598699257\n",
      "mutual_information ( j_choice ,  j_5_del ):  0.44550937210770414\n",
      "mutual_information ( j_choice ,  d_3_del ):  0.11443530326748617\n",
      "mutual_information ( j_choice ,  vd_ins ):  0.11203857283894578\n",
      "mutual_information ( j_choice ,  dj_ins ):  0.09776360260846355\n",
      "mutual_information ( d_gene ,  v_3_del ):  0.017241089783731553\n",
      "mutual_information ( d_gene ,  d_5_del ):  0.36172414085319887\n",
      "mutual_information ( d_gene ,  j_5_del ):  0.021864784540005937\n",
      "mutual_information ( d_gene ,  d_3_del ):  0.22353112818847967\n",
      "mutual_information ( d_gene ,  vd_ins ):  0.013194437391113211\n",
      "mutual_information ( d_gene ,  dj_ins ):  0.01294449164566809\n",
      "mutual_information ( v_3_del ,  d_5_del ):  0.06598437525781697\n",
      "mutual_information ( v_3_del ,  j_5_del ):  0.09843108497391649\n",
      "mutual_information ( v_3_del ,  d_3_del ):  0.06941838791192181\n",
      "mutual_information ( v_3_del ,  vd_ins ):  0.10582382685367009\n",
      "mutual_information ( v_3_del ,  dj_ins ):  0.07374102134403618\n",
      "mutual_information ( d_5_del ,  j_5_del ):  0.08378406617127063\n",
      "mutual_information ( d_5_del ,  d_3_del ):  0.4950198124493378\n",
      "mutual_information ( d_5_del ,  vd_ins ):  0.10780812230061945\n",
      "mutual_information ( d_5_del ,  dj_ins ):  0.07617536406698441\n",
      "mutual_information ( j_5_del ,  d_3_del ):  0.08559953020573469\n",
      "mutual_information ( j_5_del ,  vd_ins ):  0.07299823195715721\n",
      "mutual_information ( j_5_del ,  dj_ins ):  0.10957597446165623\n",
      "mutual_information ( d_3_del ,  vd_ins ):  0.06822031197593291\n",
      "mutual_information ( d_3_del ,  dj_ins ):  0.08640307827393552\n",
      "mutual_information ( vd_ins ,  dj_ins ):  0.06747319489954777\n"
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   "source": [
    "da_mi_scenarios = mdl_hb.get_mutual_information_from_df_scenarios(df_scenarios_many)"
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;mutual_information&#x27; (x: 9, y: 9)&gt;\n",
       "array([[0.        , 0.63926236, 0.08894391, 0.70218011, 0.35886454,\n",
       "        0.46679106, 0.33813901, 0.36950633, 0.34050716],\n",
       "       [0.63926236, 0.        , 0.1683962 , 0.13738393, 0.11353205,\n",
       "        0.44550937, 0.1144353 , 0.11203857, 0.0977636 ],\n",
       "       [0.08894391, 0.1683962 , 0.        , 0.01724109, 0.36172414,\n",
       "        0.02186478, 0.22353113, 0.01319444, 0.01294449],\n",
       "       [0.70218011, 0.13738393, 0.01724109, 0.        , 0.06598438,\n",
       "        0.09843108, 0.06941839, 0.10582383, 0.07374102],\n",
       "       [0.35886454, 0.11353205, 0.36172414, 0.06598438, 0.        ,\n",
       "        0.08378407, 0.49501981, 0.10780812, 0.07617536],\n",
       "       [0.46679106, 0.44550937, 0.02186478, 0.09843108, 0.08378407,\n",
       "        0.        , 0.08559953, 0.07299823, 0.10957597],\n",
       "       [0.33813901, 0.1144353 , 0.22353113, 0.06941839, 0.49501981,\n",
       "        0.08559953, 0.        , 0.06822031, 0.08640308],\n",
       "       [0.36950633, 0.11203857, 0.01319444, 0.10582383, 0.10780812,\n",
       "        0.07299823, 0.06822031, 0.        , 0.06747319],\n",
       "       [0.34050716, 0.0977636 , 0.01294449, 0.07374102, 0.07617536,\n",
       "        0.10957597, 0.08640308, 0.06747319, 0.        ]])\n",
       "Coordinates:\n",
       "  * x        (x) &lt;U8 &#x27;v_choice&#x27; &#x27;j_choice&#x27; &#x27;d_gene&#x27; ... &#x27;vd_ins&#x27; &#x27;dj_ins&#x27;\n",
       "  * y        (y) &lt;U8 &#x27;v_choice&#x27; &#x27;j_choice&#x27; &#x27;d_gene&#x27; ... &#x27;vd_ins&#x27; &#x27;dj_ins&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'mutual_information'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 9</li><li><span class='xr-has-index'>y</span>: 9</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-08b41876-bef7-4ea7-b125-79bba5b22af6' class='xr-array-in' type='checkbox' checked><label for='section-08b41876-bef7-4ea7-b125-79bba5b22af6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.6393 0.08894 0.7022 0.3589 ... 0.07618 0.1096 0.0864 0.06747 0.0</span></div><div class='xr-array-data'><pre>array([[0.        , 0.63926236, 0.08894391, 0.70218011, 0.35886454,\n",
       "        0.46679106, 0.33813901, 0.36950633, 0.34050716],\n",
       "       [0.63926236, 0.        , 0.1683962 , 0.13738393, 0.11353205,\n",
       "        0.44550937, 0.1144353 , 0.11203857, 0.0977636 ],\n",
       "       [0.08894391, 0.1683962 , 0.        , 0.01724109, 0.36172414,\n",
       "        0.02186478, 0.22353113, 0.01319444, 0.01294449],\n",
       "       [0.70218011, 0.13738393, 0.01724109, 0.        , 0.06598438,\n",
       "        0.09843108, 0.06941839, 0.10582383, 0.07374102],\n",
       "       [0.35886454, 0.11353205, 0.36172414, 0.06598438, 0.        ,\n",
       "        0.08378407, 0.49501981, 0.10780812, 0.07617536],\n",
       "       [0.46679106, 0.44550937, 0.02186478, 0.09843108, 0.08378407,\n",
       "        0.        , 0.08559953, 0.07299823, 0.10957597],\n",
       "       [0.33813901, 0.1144353 , 0.22353113, 0.06941839, 0.49501981,\n",
       "        0.08559953, 0.        , 0.06822031, 0.08640308],\n",
       "       [0.36950633, 0.11203857, 0.01319444, 0.10582383, 0.10780812,\n",
       "        0.07299823, 0.06822031, 0.        , 0.06747319],\n",
       "       [0.34050716, 0.0977636 , 0.01294449, 0.07374102, 0.07617536,\n",
       "        0.10957597, 0.08640308, 0.06747319, 0.        ]])</pre></div></div></li><li class='xr-section-item'><input id='section-d5e471af-de4f-4b0f-b897-34e6e5b6bb54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d5e471af-de4f-4b0f-b897-34e6e5b6bb54' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;v_choice&#x27; &#x27;j_choice&#x27; ... &#x27;dj_ins&#x27;</div><input id='attrs-26ecd21c-51ae-41e3-a089-5c061b150057' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26ecd21c-51ae-41e3-a089-5c061b150057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04483664-b012-454e-a4f3-8cdbb1e9c94f' class='xr-var-data-in' type='checkbox'><label for='data-04483664-b012-454e-a4f3-8cdbb1e9c94f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;v_choice&#x27;, &#x27;j_choice&#x27;, &#x27;d_gene&#x27;, &#x27;v_3_del&#x27;, &#x27;d_5_del&#x27;, &#x27;j_5_del&#x27;,\n",
       "       &#x27;d_3_del&#x27;, &#x27;vd_ins&#x27;, &#x27;dj_ins&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;v_choice&#x27; &#x27;j_choice&#x27; ... &#x27;dj_ins&#x27;</div><input id='attrs-bb3cbb4d-dc73-4a84-b633-611ce313e169' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb3cbb4d-dc73-4a84-b633-611ce313e169' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbce366e-7790-4574-a2ed-626b150d012c' class='xr-var-data-in' type='checkbox'><label for='data-bbce366e-7790-4574-a2ed-626b150d012c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;v_choice&#x27;, &#x27;j_choice&#x27;, &#x27;d_gene&#x27;, &#x27;v_3_del&#x27;, &#x27;d_5_del&#x27;, &#x27;j_5_del&#x27;,\n",
       "       &#x27;d_3_del&#x27;, &#x27;vd_ins&#x27;, &#x27;dj_ins&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c54740a2-55ec-4a5b-8254-f5295cb393fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c54740a2-55ec-4a5b-8254-f5295cb393fa' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'mutual_information' (x: 9, y: 9)>\n",
       "array([[0.        , 0.63926236, 0.08894391, 0.70218011, 0.35886454,\n",
       "        0.46679106, 0.33813901, 0.36950633, 0.34050716],\n",
       "       [0.63926236, 0.        , 0.1683962 , 0.13738393, 0.11353205,\n",
       "        0.44550937, 0.1144353 , 0.11203857, 0.0977636 ],\n",
       "       [0.08894391, 0.1683962 , 0.        , 0.01724109, 0.36172414,\n",
       "        0.02186478, 0.22353113, 0.01319444, 0.01294449],\n",
       "       [0.70218011, 0.13738393, 0.01724109, 0.        , 0.06598438,\n",
       "        0.09843108, 0.06941839, 0.10582383, 0.07374102],\n",
       "       [0.35886454, 0.11353205, 0.36172414, 0.06598438, 0.        ,\n",
       "        0.08378407, 0.49501981, 0.10780812, 0.07617536],\n",
       "       [0.46679106, 0.44550937, 0.02186478, 0.09843108, 0.08378407,\n",
       "        0.        , 0.08559953, 0.07299823, 0.10957597],\n",
       "       [0.33813901, 0.1144353 , 0.22353113, 0.06941839, 0.49501981,\n",
       "        0.08559953, 0.        , 0.06822031, 0.08640308],\n",
       "       [0.36950633, 0.11203857, 0.01319444, 0.10582383, 0.10780812,\n",
       "        0.07299823, 0.06822031, 0.        , 0.06747319],\n",
       "       [0.34050716, 0.0977636 , 0.01294449, 0.07374102, 0.07617536,\n",
       "        0.10957597, 0.08640308, 0.06747319, 0.        ]])\n",
       "Coordinates:\n",
       "  * x        (x) <U8 'v_choice' 'j_choice' 'd_gene' ... 'vd_ins' 'dj_ins'\n",
       "  * y        (y) <U8 'v_choice' 'j_choice' 'd_gene' ... 'vd_ins' 'dj_ins'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_mi_scenarios#.to_dataframe().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_mutual_information(da_mi_scenarios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mdl_hb.plot_mutual_information(da_mi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean and Variance of values in scenarios dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest way to get the realization values of events in scenario is using the function\n",
    "IgorModel.get_realization_value_from_df_scenarios(df_scenarios, 'event_nickname')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_realization_value_from_df_scenarios in module pygor3.IgorIO:\n",
      "\n",
      "get_realization_value_from_df_scenarios(df_scenarios, event_nickname) method of pygor3.IgorIO.IgorModel instance\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mdl_hb.get_realization_value_from_df_scenarios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seq_index\n",
       "998        GGGACAGGGGGC\n",
       "998        GGGACAGGGGGC\n",
       "998        GGGACAGGGGGC\n",
       "998        GGGACAGGGGGC\n",
       "998        GGGACAGGGGGC\n",
       "             ...       \n",
       "703    GGGACTAGCGGGGGGG\n",
       "703    GGGACTAGCGGGGGGG\n",
       "703    GGGACTAGCGGGGGGG\n",
       "703        GGGACAGGGGGC\n",
       "703        GGGACAGGGGGC\n",
       "Length: 10000, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.get_realization_value_from_df_scenarios(df_scenarios_many, 'd_gene')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seq_index\n",
       "998    15\n",
       "998    15\n",
       "998    15\n",
       "998    15\n",
       "998    15\n",
       "       ..\n",
       "703     4\n",
       "703     5\n",
       "703     4\n",
       "703     4\n",
       "703     4\n",
       "Length: 10000, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.get_realization_value_from_df_scenarios(df_scenarios_many, 'j_5_del')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the values are assigned to a column in scenarios dataframe (df_scenarios_many) the weighted average can be calculated using the IgorModel.w_mean_df_scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_j_5_del = mdl_hb.get_realization_value_from_df_scenarios(df_scenarios_many, 'j_5_del')\n",
    "df_scenarios_many['j_5_del_value'] = ps_j_5_del\n",
    "# ps_j_5_del"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method w_mean_df_scenarios in module pygor3.IgorIO:\n",
      "\n",
      "w_mean_df_scenarios(column_name: str, df_scenarios: pandas.core.frame.DataFrame) method of pygor3.IgorIO.IgorModel instance\n",
      "    Return weighted mean with the normalized probabilities for each\n",
      "    scenario (norm_scenario_proba_cond_seq)\n",
      "    :param column_name: column name of df_scenario to calculate the average\n",
      "    :param df_scenarios: Scenarios with normalize probability. Loaded with self.get_dataframe_scenarios()\n",
      "\n",
      "Help on method w_variance_df_scenarios in module pygor3.IgorIO:\n",
      "\n",
      "w_variance_df_scenarios(colname_1: str, df_scenarios: pandas.core.frame.DataFrame) method of pygor3.IgorIO.IgorModel instance\n",
      "    Return weighted covariance with the normalized probabilities of the column names given for each\n",
      "    scenario (norm_scenario_proba_cond_seq)\n",
      "    :param colname_1: column name of df_scenario to calculate the weighted covariance\n",
      "    :param colname_2: column name of df_scenario to calculate the weighted covariance\n",
      "    :param df_scenarios: Scenarios with normalize probability. Loaded with self.get_dataframe_scenarios()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mdl_hb.w_mean_df_scenarios)\n",
    "help(mdl_hb.w_variance_df_scenarios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.157669017539739"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.w_mean_df_scenarios('j_5_del_value', df_scenarios_many)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.537416194979743"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.w_variance_df_scenarios('j_5_del_value', df_scenarios_many)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method get_observable_from_df_scenarios in module pygor3.IgorIO:\n",
      "\n",
      "get_observable_from_df_scenarios(observable_function, df_scenarios: pandas.core.frame.DataFrame) method of pygor3.IgorIO.IgorModel instance\n",
      "    Return a pandas series with the calculated observable over the df_scenarios dataframe.\n",
      "    :param observable_function: This function should use the varibles with self.realization\n",
      "    :param df_scenarios: Scenarios dataframe loaded with self.get_dataframe_scenarios.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mdl_hb.get_observable_from_df_scenarios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_total_insertions(ps_scenario):\n",
    "    my_internal = np.zeros(len(mdl.parms['vd_ins']))\n",
    "    vd_ins = mdl.realization(ps_scenario, 'vd_ins')\n",
    "    dj_ins = mdl.realization(ps_scenario, 'dj_ins')\n",
    "    # my_internal[vd_ins.id] = 1\n",
    "    # return my_internal \n",
    "    return vd_ins.value + dj_ins.value\n",
    "\n",
    "def f_total_deletions(ps_scenario):\n",
    "    v_3_del = mdl.realization(ps_scenario, 'v_3_del')\n",
    "    d_5_del = mdl.realization(ps_scenario, 'd_5_del')\n",
    "    d_3_del = mdl.realization(ps_scenario, 'd_3_del')\n",
    "    j_5_del = mdl.realization(ps_scenario, 'j_5_del')\n",
    "    return v_3_del.value + d_5_del.value + d_3_del.value + j_5_del.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_insertions = mdl.w_average_function_df_scenarios(f_total_insertions, df_scenarios_many)\n",
    "mean_deletions = mdl.w_average_function_df_scenarios(f_total_deletions, df_scenarios_many)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10.202437426325016, 15.645906964009958)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_insertions, mean_deletions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_give_thename(ps_scenario):\n",
    "    v_choice = mdl_hb.realization(ps_scenario, 'v_choice')\n",
    "    return v_choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seq_index\n",
       "998    TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...\n",
       "998    TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...\n",
       "998    TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...\n",
       "998    TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...\n",
       "998    TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...\n",
       "                             ...                        \n",
       "703    TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...\n",
       "703    TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...\n",
       "703    TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...\n",
       "703    TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...\n",
       "703    TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...\n",
       "Length: 10000, dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_hb.get_observable_from_df_scenarios(f_give_thename, df_scenarios=df_scenarios_many)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>scenario_rank</th>\n",
       "      <th>scenario_proba_cond_seq</th>\n",
       "      <th>v_choice</th>\n",
       "      <th>j_choice</th>\n",
       "      <th>d_gene</th>\n",
       "      <th>v_3_del</th>\n",
       "      <th>d_5_del</th>\n",
       "      <th>j_5_del</th>\n",
       "      <th>d_3_del</th>\n",
       "      <th>vd_ins</th>\n",
       "      <th>vd_dinucl</th>\n",
       "      <th>dj_ins</th>\n",
       "      <th>dj_dinucl</th>\n",
       "      <th>Mismatches</th>\n",
       "      <th>norm_scenario_proba_cond_seq</th>\n",
       "      <th>j_5_del_value</th>\n",
       "      <th>v_call</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1</td>\n",
       "      <td>0.783497</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>[1, 1, 2, 3]</td>\n",
       "      <td>7</td>\n",
       "      <td>[2, 2, 2, 2, 0, 2, 0]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>15</td>\n",
       "      <td>TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2</td>\n",
       "      <td>0.076884</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>[1, 1, 2, 3]</td>\n",
       "      <td>8</td>\n",
       "      <td>[2, 2, 2, 2, 0, 2, 0, 2]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>15</td>\n",
       "      <td>TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>3</td>\n",
       "      <td>0.043634</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>[1, 1, 2, 3, 2]</td>\n",
       "      <td>7</td>\n",
       "      <td>[2, 2, 2, 2, 0, 2, 0]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>15</td>\n",
       "      <td>TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>4</td>\n",
       "      <td>0.023517</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>[1, 1, 1, 2, 3]</td>\n",
       "      <td>7</td>\n",
       "      <td>[2, 2, 2, 2, 0, 2, 0]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>15</td>\n",
       "      <td>TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>5</td>\n",
       "      <td>0.021809</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>[1, 1, 2, 3, 2, 0]</td>\n",
       "      <td>7</td>\n",
       "      <td>[2, 2, 2, 2, 0, 2, 0]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>15</td>\n",
       "      <td>TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>6</td>\n",
       "      <td>0.021822</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>[2, 2, 1, 0, 3, 3]</td>\n",
       "      <td>5</td>\n",
       "      <td>[2, 0, 3, 1, 1]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>4</td>\n",
       "      <td>TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>7</td>\n",
       "      <td>0.016150</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>[2, 1, 0, 3, 3]</td>\n",
       "      <td>6</td>\n",
       "      <td>[2, 2, 0, 3, 1, 1]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>5</td>\n",
       "      <td>TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>8</td>\n",
       "      <td>0.013252</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>[2, 1, 0, 3, 3]</td>\n",
       "      <td>6</td>\n",
       "      <td>[2, 0, 3, 1, 1, 2]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>4</td>\n",
       "      <td>TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>9</td>\n",
       "      <td>0.013172</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>[2, 2, 1, 0, 3, 3, 2, 1]</td>\n",
       "      <td>4</td>\n",
       "      <td>[2, 0, 3, 1]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>4</td>\n",
       "      <td>TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>10</td>\n",
       "      <td>0.010846</td>\n",
       "      <td>45</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>[2, 1, 0, 3, 3, 2, 1, 2]</td>\n",
       "      <td>4</td>\n",
       "      <td>[2, 0, 3, 1]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>4</td>\n",
       "      <td>TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           scenario_rank  scenario_proba_cond_seq  v_choice  j_choice  d_gene  \\\n",
       "seq_index                                                                       \n",
       "998                    1                 0.783497        77         4       0   \n",
       "998                    2                 0.076884        77         4       0   \n",
       "998                    3                 0.043634        77         4       0   \n",
       "998                    4                 0.023517        77         4       0   \n",
       "998                    5                 0.021809        77         4       0   \n",
       "...                  ...                      ...       ...       ...     ...   \n",
       "703                    6                 0.021822        45        12       1   \n",
       "703                    7                 0.016150        45        12       1   \n",
       "703                    8                 0.013252        45        12       1   \n",
       "703                    9                 0.013172        45        12       0   \n",
       "703                   10                 0.010846        45        12       0   \n",
       "\n",
       "           v_3_del  d_5_del  j_5_del  d_3_del  vd_ins  \\\n",
       "seq_index                                               \n",
       "998              9        6       19        9       4   \n",
       "998              9        6       19       10       4   \n",
       "998              9        7       19        9       5   \n",
       "998             10        6       19        9       5   \n",
       "998              9        8       19        9       6   \n",
       "...            ...      ...      ...      ...     ...   \n",
       "703              6       11        8        7       6   \n",
       "703              5       11        9        7       5   \n",
       "703              5       11        8        8       5   \n",
       "703              6       11        8        4       8   \n",
       "703              5       12        8        4       8   \n",
       "\n",
       "                          vd_dinucl  dj_ins                 dj_dinucl  \\\n",
       "seq_index                                                               \n",
       "998                    [1, 1, 2, 3]       7     [2, 2, 2, 2, 0, 2, 0]   \n",
       "998                    [1, 1, 2, 3]       8  [2, 2, 2, 2, 0, 2, 0, 2]   \n",
       "998                 [1, 1, 2, 3, 2]       7     [2, 2, 2, 2, 0, 2, 0]   \n",
       "998                 [1, 1, 1, 2, 3]       7     [2, 2, 2, 2, 0, 2, 0]   \n",
       "998              [1, 1, 2, 3, 2, 0]       7     [2, 2, 2, 2, 0, 2, 0]   \n",
       "...                             ...     ...                       ...   \n",
       "703              [2, 2, 1, 0, 3, 3]       5           [2, 0, 3, 1, 1]   \n",
       "703                 [2, 1, 0, 3, 3]       6        [2, 2, 0, 3, 1, 1]   \n",
       "703                 [2, 1, 0, 3, 3]       6        [2, 0, 3, 1, 1, 2]   \n",
       "703        [2, 2, 1, 0, 3, 3, 2, 1]       4              [2, 0, 3, 1]   \n",
       "703        [2, 1, 0, 3, 3, 2, 1, 2]       4              [2, 0, 3, 1]   \n",
       "\n",
       "          Mismatches  norm_scenario_proba_cond_seq  j_5_del_value  \\\n",
       "seq_index                                                           \n",
       "998               []                      0.000801             15   \n",
       "998               []                      0.000079             15   \n",
       "998               []                      0.000045             15   \n",
       "998               []                      0.000024             15   \n",
       "998               []                      0.000022             15   \n",
       "...              ...                           ...            ...   \n",
       "703               []                      0.000030              4   \n",
       "703               []                      0.000022              5   \n",
       "703               []                      0.000018              4   \n",
       "703               []                      0.000018              4   \n",
       "703               []                      0.000015              4   \n",
       "\n",
       "                                                      v_call  \n",
       "seq_index                                                     \n",
       "998        TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...  \n",
       "998        TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...  \n",
       "998        TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...  \n",
       "998        TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...  \n",
       "998        TRBV7-4*01;GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGT...  \n",
       "...                                                      ...  \n",
       "703        TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...  \n",
       "703        TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...  \n",
       "703        TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...  \n",
       "703        TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...  \n",
       "703        TRBV4-1*01;ACTGAAGTTACCCAGACACCAAAACACCTGGTCAT...  \n",
       "\n",
       "[10000 rows x 17 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scenarios_many['v_call'] = mdl_hb.get_observable_from_df_scenarios(f_give_thename, df_scenarios=df_scenarios_many)\n",
    "df_scenarios_many"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('GGTGCTGGAGTCTCCCAGTCCCCAAGGTACAAAGTCGCAAAGAGGGGACGGGATGTAGCTCTCAGGTGTGATTCAATTTCGGGTCATGTAACCCTTTATTGGTACCGACAGACCCTGGGGCAGGGCTCAGAGGTTCTGACTTACTCCCAGAGTGATGCTCAACGAGACAAATCAGGGCGGCCCAGTGGTCGGTTCTCTGCAGAGAGGCCTGAGAGATCCGTCTCCACTCTGAAGATCCAGCGCACAGAGCAGGGGGACTCAGCTGTGTATCTCTGTGCCAGCAGCTTAGC',\n",
       " 77,\n",
       " 'TRBV7-4*01')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps_scenario = df_scenarios_many.iloc[0]\n",
    "realiz = mdl_hb.realization(ps_scenario, 'v_choice')\n",
    "realiz.value, realiz.id, realiz.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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