{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 205,

   "id": "third-detector",

   "metadata": {},

   "outputs": [],

   "source": [

    "nb_snsps = 100\n",

    "phenotypes = None\n",

    "\n",

    "# phenotypes = ['BMD_1_100']"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 206,

   "id": "streaming-definition",

   "metadata": {},

   "outputs": [],

   "source": [

    "import math\n",

    "import pandas as pd\n",

    "\n",

    "if phenotypes is None:\n",

    "    # That means all\n",

    "    import glob\n",

    "\n",

    "    csvs_ge = glob.glob('out/ge_*.csv')\n",

    "    csvs_lm = glob.glob('out/lm_*.csv')\n",

    "    \n",

    "    names_ge = [x[7:].split(\".\")[0] for x in csvs_ge]\n",

    "    names_lm = [x[7:].split(\".\")[0] for x in csvs_lm]\n",

    "    phenotypes = set(names_ge).intersection(names_lm)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 207,

   "id": "moderate-eight",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Best variant and it's pvalue for each phenotype\n"

     ]

    },

    {

     "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>pheno</th>\n",

       "      <th>p</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>10</th>\n",

       "      <td>testisweight_1_79646</td>\n",

       "      <td>4.521024e-18</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>19</th>\n",

       "      <td>abBMD_1_79646</td>\n",

       "      <td>1.110221e-14</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>27</th>\n",

       "      <td>tibia_1_79646</td>\n",

       "      <td>1.843551e-08</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>D1TOTDIST10_1_79646</td>\n",

       "      <td>5.792651e-08</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>37</th>\n",

       "      <td>D1ctrtime0to30_1_79646</td>\n",

       "      <td>6.901068e-08</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>D2TOTDIST25_1_79646</td>\n",

       "      <td>7.173318e-08</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>7</th>\n",

       "      <td>soleus_1_79646</td>\n",

       "      <td>8.342285e-08</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>D2hact0to15_1_79646</td>\n",

       "      <td>1.678204e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>42</th>\n",

       "      <td>D2ctrtime0to15_1_79646</td>\n",

       "      <td>1.861225e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>34</th>\n",

       "      <td>D3TOTDIST15_1_79646</td>\n",

       "      <td>3.161540e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>13</th>\n",

       "      <td>gastroc_1_79646</td>\n",

       "      <td>5.044782e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>30</th>\n",

       "      <td>D3vact0to15_1_79646</td>\n",

       "      <td>5.469865e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>60</th>\n",

       "      <td>EDL_1_79646</td>\n",

       "      <td>5.569678e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>41</th>\n",

       "      <td>D3TOTDIST25_1_79646</td>\n",

       "      <td>5.849949e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>16</th>\n",

       "      <td>D2hact0to30_1_79646</td>\n",

       "      <td>5.898484e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>58</th>\n",

       "      <td>D1totaldist0to15_1_79646</td>\n",

       "      <td>7.152997e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>43</th>\n",

       "      <td>D2TOTDIST5_1_79646</td>\n",

       "      <td>8.427636e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>51</th>\n",

       "      <td>D3totaldist0to30_1_79646</td>\n",

       "      <td>9.002542e-07</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>59</th>\n",

       "      <td>pp12PPIavg_1_79646</td>\n",

       "      <td>1.194845e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>5</th>\n",

       "      <td>D1ctrtime0to15_1_79646</td>\n",

       "      <td>1.262349e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>66</th>\n",

       "      <td>startle_1_79646</td>\n",

       "      <td>1.300837e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>15</th>\n",

       "      <td>TA_1_79646</td>\n",

       "      <td>1.374193e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>49</th>\n",

       "      <td>D3vact0to30_1_79646</td>\n",

       "      <td>1.384376e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>8</th>\n",

       "      <td>D1totaldist0to30_1_79646</td>\n",

       "      <td>1.508047e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>56</th>\n",

       "      <td>D3TOTDIST30_1_79646</td>\n",

       "      <td>1.905392e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>14</th>\n",

       "      <td>AvAltContextD3_1_79646</td>\n",

       "      <td>1.946489e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>54</th>\n",

       "      <td>sacweight_1_79646</td>\n",

       "      <td>2.519069e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>D1TOTDIST15_1_79646</td>\n",

       "      <td>2.704936e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>47</th>\n",

       "      <td>D2TOTDIST15_1_79646</td>\n",

       "      <td>2.749202e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>29</th>\n",

       "      <td>D2ctrtime0to30_1_79646</td>\n",

       "      <td>2.758986e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>61</th>\n",

       "      <td>D3ctrtime0to15_1_79646</td>\n",

       "      <td>2.861410e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>32</th>\n",

       "      <td>D3TOTDIST5_1_79646</td>\n",

       "      <td>2.992418e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>65</th>\n",

       "      <td>D2vact0to30_1_79646</td>\n",

       "      <td>3.072521e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>23</th>\n",

       "      <td>D3TOTDIST20_1_79646</td>\n",

       "      <td>3.278628e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>36</th>\n",

       "      <td>D1hact0to15_1_79646</td>\n",

       "      <td>3.315070e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>38</th>\n",

       "      <td>D3ctrtime0to30_1_79646</td>\n",

       "      <td>3.331037e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>33</th>\n",

       "      <td>AvToneD3_1_79646</td>\n",

       "      <td>3.555516e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>52</th>\n",

       "      <td>taillength_1_79646</td>\n",

       "      <td>3.605960e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>12</th>\n",

       "      <td>D2vact0to15_1_79646</td>\n",

       "      <td>3.704444e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>35</th>\n",

       "      <td>bw1_1_79646</td>\n",

       "      <td>4.694337e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>9</th>\n",

       "      <td>PPIavg_1_79646</td>\n",

       "      <td>4.943340e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>28</th>\n",

       "      <td>D2totaldist0to15_1_79646</td>\n",

       "      <td>4.998420e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>63</th>\n",

       "      <td>D3totaldist0to15_1_79646</td>\n",

       "      <td>5.237983e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>25</th>\n",

       "      <td>AvContextD2_1_79646</td>\n",

       "      <td>5.414419e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>22</th>\n",

       "      <td>D1hact0to30_1_79646</td>\n",

       "      <td>6.372553e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>46</th>\n",

       "      <td>plantaris_1_79646</td>\n",

       "      <td>6.387974e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>57</th>\n",

       "      <td>pp6PPIavg_1_79646</td>\n",

       "      <td>6.947052e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>44</th>\n",

       "      <td>D1TOTDIST25_1_79646</td>\n",

       "      <td>7.262472e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>64</th>\n",

       "      <td>D2TOTDIST10_1_79646</td>\n",

       "      <td>7.333816e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>55</th>\n",

       "      <td>D3hact0to30_1_79646</td>\n",

       "      <td>7.936938e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>31</th>\n",

       "      <td>D3hact0to15_1_79646</td>\n",

       "      <td>7.968502e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>20</th>\n",

       "      <td>PreTrainD1_1_79646</td>\n",

       "      <td>8.610831e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>24</th>\n",

       "      <td>AvToneD1_1_79646</td>\n",

       "      <td>9.298063e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>62</th>\n",

       "      <td>pp3PPIavg_1_79646</td>\n",

       "      <td>9.343953e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>53</th>\n",

       "      <td>PPIweight_1_79646</td>\n",

       "      <td>9.463255e-06</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>45</th>\n",

       "      <td>D1vact0to30_1_79646</td>\n",

       "      <td>1.020309e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>48</th>\n",

       "      <td>D2totaldist0to30_1_79646</td>\n",

       "      <td>1.022280e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>11</th>\n",

       "      <td>D1vact0to15_1_79646</td>\n",

       "      <td>1.196616e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>39</th>\n",

       "      <td>D3TOTDIST10_1_79646</td>\n",

       "      <td>1.212057e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>D2TOTDIST30_1_79646</td>\n",

       "      <td>1.515221e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>50</th>\n",

       "      <td>fastglucose_1_79646</td>\n",

       "      <td>1.578499e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>21</th>\n",

       "      <td>D2TOTDIST20_1_79646</td>\n",

       "      <td>1.616208e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>6</th>\n",

       "      <td>D1TOTDIST5_1_79646</td>\n",

       "      <td>1.629690e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>18</th>\n",

       "      <td>bw0_1_79646</td>\n",

       "      <td>1.894521e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>40</th>\n",

       "      <td>D1TOTDIST30_1_79646</td>\n",

       "      <td>2.025443e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>26</th>\n",

       "      <td>p120b4_1_79646</td>\n",

       "      <td>2.308234e-05</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>17</th>\n",

       "      <td>D1TOTDIST20_1_79646</td>\n",

       "      <td>2.548773e-05</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                       pheno             p\n",

       "10      testisweight_1_79646  4.521024e-18\n",

       "19             abBMD_1_79646  1.110221e-14\n",

       "27             tibia_1_79646  1.843551e-08\n",

       "0        D1TOTDIST10_1_79646  5.792651e-08\n",

       "37    D1ctrtime0to30_1_79646  6.901068e-08\n",

       "4        D2TOTDIST25_1_79646  7.173318e-08\n",

       "7             soleus_1_79646  8.342285e-08\n",

       "2        D2hact0to15_1_79646  1.678204e-07\n",

       "42    D2ctrtime0to15_1_79646  1.861225e-07\n",

       "34       D3TOTDIST15_1_79646  3.161540e-07\n",

       "13           gastroc_1_79646  5.044782e-07\n",

       "30       D3vact0to15_1_79646  5.469865e-07\n",

       "60               EDL_1_79646  5.569678e-07\n",

       "41       D3TOTDIST25_1_79646  5.849949e-07\n",

       "16       D2hact0to30_1_79646  5.898484e-07\n",

       "58  D1totaldist0to15_1_79646  7.152997e-07\n",

       "43        D2TOTDIST5_1_79646  8.427636e-07\n",

       "51  D3totaldist0to30_1_79646  9.002542e-07\n",

       "59        pp12PPIavg_1_79646  1.194845e-06\n",

       "5     D1ctrtime0to15_1_79646  1.262349e-06\n",

       "66           startle_1_79646  1.300837e-06\n",

       "15                TA_1_79646  1.374193e-06\n",

       "49       D3vact0to30_1_79646  1.384376e-06\n",

       "8   D1totaldist0to30_1_79646  1.508047e-06\n",

       "56       D3TOTDIST30_1_79646  1.905392e-06\n",

       "14    AvAltContextD3_1_79646  1.946489e-06\n",

       "54         sacweight_1_79646  2.519069e-06\n",

       "1        D1TOTDIST15_1_79646  2.704936e-06\n",

       "47       D2TOTDIST15_1_79646  2.749202e-06\n",

       "29    D2ctrtime0to30_1_79646  2.758986e-06\n",

       "61    D3ctrtime0to15_1_79646  2.861410e-06\n",

       "32        D3TOTDIST5_1_79646  2.992418e-06\n",

       "65       D2vact0to30_1_79646  3.072521e-06\n",

       "23       D3TOTDIST20_1_79646  3.278628e-06\n",

       "36       D1hact0to15_1_79646  3.315070e-06\n",

       "38    D3ctrtime0to30_1_79646  3.331037e-06\n",

       "33          AvToneD3_1_79646  3.555516e-06\n",

       "52        taillength_1_79646  3.605960e-06\n",

       "12       D2vact0to15_1_79646  3.704444e-06\n",

       "35               bw1_1_79646  4.694337e-06\n",

       "9             PPIavg_1_79646  4.943340e-06\n",

       "28  D2totaldist0to15_1_79646  4.998420e-06\n",

       "63  D3totaldist0to15_1_79646  5.237983e-06\n",

       "25       AvContextD2_1_79646  5.414419e-06\n",

       "22       D1hact0to30_1_79646  6.372553e-06\n",

       "46         plantaris_1_79646  6.387974e-06\n",

       "57         pp6PPIavg_1_79646  6.947052e-06\n",

       "44       D1TOTDIST25_1_79646  7.262472e-06\n",

       "64       D2TOTDIST10_1_79646  7.333816e-06\n",

       "55       D3hact0to30_1_79646  7.936938e-06\n",

       "31       D3hact0to15_1_79646  7.968502e-06\n",

       "20        PreTrainD1_1_79646  8.610831e-06\n",

       "24          AvToneD1_1_79646  9.298063e-06\n",

       "62         pp3PPIavg_1_79646  9.343953e-06\n",

       "53         PPIweight_1_79646  9.463255e-06\n",

       "45       D1vact0to30_1_79646  1.020309e-05\n",

       "48  D2totaldist0to30_1_79646  1.022280e-05\n",

       "11       D1vact0to15_1_79646  1.196616e-05\n",

       "39       D3TOTDIST10_1_79646  1.212057e-05\n",

       "3        D2TOTDIST30_1_79646  1.515221e-05\n",

       "50       fastglucose_1_79646  1.578499e-05\n",

       "21       D2TOTDIST20_1_79646  1.616208e-05\n",

       "6         D1TOTDIST5_1_79646  1.629690e-05\n",

       "18               bw0_1_79646  1.894521e-05\n",

       "40       D1TOTDIST30_1_79646  2.025443e-05\n",

       "26            p120b4_1_79646  2.308234e-05\n",

       "17       D1TOTDIST20_1_79646  2.548773e-05"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    }

   ],

   "source": [

    "best_pheno_snp = pd.DataFrame(columns=('pheno', 'p'))\n",

    "\n",

    "for i,p in enumerate(phenotypes):\n",

    "    ge_snps = pd.read_csv(f\"out/ge_{p}.csv\", nrows=3)\n",

    "    best_pheno_snp.loc[i] = [p, ge_snps['p'][0]]\n",

    "\n",

    "best_pheno_snp = best_pheno_snp.sort_values(by=['p'])\n",

    "print(\"Best variant and it's pvalue for each phenotype\")\n",

    "pd.set_option('display.max_rows', None)\n",

    "display(best_pheno_snp)\n",

    "pd.set_option('display.max_rows', 60)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 212,

   "id": "small-musician",

   "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>pheno</th>\n",

       "      <th>p</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>10</th>\n",

       "      <td>testisweight_1_79646</td>\n",

       "      <td>4.521024e-18</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>19</th>\n",

       "      <td>abBMD_1_79646</td>\n",

       "      <td>1.110221e-14</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>27</th>\n",

       "      <td>tibia_1_79646</td>\n",

       "      <td>1.843551e-08</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>D1TOTDIST10_1_79646</td>\n",

       "      <td>5.792651e-08</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>37</th>\n",

       "      <td>D1ctrtime0to30_1_79646</td>\n",

       "      <td>6.901068e-08</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>D2TOTDIST25_1_79646</td>\n",

       "      <td>7.173318e-08</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>7</th>\n",

       "      <td>soleus_1_79646</td>\n",

       "      <td>8.342285e-08</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                     pheno             p\n",

       "10    testisweight_1_79646  4.521024e-18\n",

       "19           abBMD_1_79646  1.110221e-14\n",

       "27           tibia_1_79646  1.843551e-08\n",

       "0      D1TOTDIST10_1_79646  5.792651e-08\n",

       "37  D1ctrtime0to30_1_79646  6.901068e-08\n",

       "4      D2TOTDIST25_1_79646  7.173318e-08\n",

       "7           soleus_1_79646  8.342285e-08"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    }

   ],

   "source": [

    "# We look only at phenotypes that give a pvalue larger than 1e-8\n",

    "display(best_pheno_snp[best_pheno_snp['p']<1e-7])\n",

    "phenotypes = list(best_pheno_snp[best_pheno_snp['p']<1e-7]['pheno'])"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 213,

   "id": "demanding-season",

   "metadata": {},

   "outputs": [],

   "source": [

    "# Find the closest results between ge (gemma) and lm (linear model)\n",

    "diff_phenos = pd.DataFrame(columns=('pheno', 'diff'))\n",

    "\n",

    "for i,p in enumerate(phenotypes):\n",

    "    ge_snps = pd.read_csv(f\"out/ge_{p}.csv\", nrows=nb_snsps)\n",

    "    lm_snps = pd.read_csv(f\"out/lm_{p}.csv\", nrows=nb_snsps)\n",

    "    \n",

    "    # Compute how far the SNP-s in gemma (best result) are from the ones in lm \n",

    "    total_diffs = 0\n",

    "    for idx,row in ge_snps.iterrows():\n",

    "        lookup_snp = lm_snps[lm_snps['snp']==row['snp']]\n",

    "        if lookup_snp.shape[0] > 0:\n",

    "            total_diffs += abs(lookup_snp.iloc[0,0]-row[0])\n",

    "        else:\n",

    "            total_diffs += nb_snsps\n",

    "    diff_phenos.loc[i] = [p, total_diffs]"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 214,

   "id": "assisted-jenny",

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "                    pheno  diff\n",

      "1           abBMD_1_79646   911\n",

      "6          soleus_1_79646   968\n",

      "4  D1ctrtime0to30_1_79646  1037\n",

      "2           tibia_1_79646  2835\n",

      "0    testisweight_1_79646  3228\n",

      "5     D2TOTDIST25_1_79646  3672\n",

      "3     D1TOTDIST10_1_79646  4768\n"

     ]

    }

   ],

   "source": [

    "pd.set_option('display.max_rows', None)\n",

    "print(diff_phenos.sort_values(by=['diff']))\n",

    "pd.set_option('display.max_rows', 60)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 218,

   "id": "liberal-costa",

   "metadata": {},

   "outputs": [

    {

     "data": {

      "image/png": 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\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    },

    {

     "data": {

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\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    },

    {

     "data": {

      "image/png": 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\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    },

    {

     "data": {

      "image/png": 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\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    },

    {

     "data": {

      "image/png": 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3Sd+VtH3xoZmZWRHynPH/DFgtaQzweWAh8KtCozIzs8LkSfzrUtcKE4EfR8RPgMHFhmVmZkXJcx//SklfBo4D3i6pHzCg2LDMzKwoec74PwC8AHw0dcMwEji70KjMzKwwXSb+lOwvAraUdBjwYkS4jd/MrI/Kc1fPicBc4N+Ao4BbJX206MDMzKwYedr4vwDsFRHLASQNA2YD5xcZmJmZFSNPG/9yYGXF9MpUZmZmfVCeM/4HgTmSriZ7WtZE4C5JnwOIiO8WGJ+ZmfWyPIl/YRratD04xffym5n1QV0m/oj4Ri0CMTOz2ugy8Ut6E3AqMKpy+Yg4IMe6i8iuCbxE9gvgFklDgUvT9hYBx0TE090P3czMeiJPU89lwDnAuWQJvLveGRFPVkxPBWZGxJmSpqbpL/Vgu2Zm1gN5Ev+6iPhZL9Y5EZiQxqcDs3DiNzOrmTy3c14r6SRJIyQNbRtybj+AGyTdLmlKKhseEUvS+OPA8PZWlDRF0jxJ85YtW5azOjMz60qeM/7J6e8XKsoC2CHHuvtFxGOS3gDcKOlvlTMjIiRFeytGxDRgGkBLS0u7y5iZWffluatndE83HhGPpb9PSLoK2AdYKmlERCyRNAJ4oqfbNzOz7svTV8/Rkgan8a9KulLSXjnW26xivc2AdwF3A9fw6reIybz6uwAzM6uBPG38/xURKyXtBxwEnEd2l09XhgN/kdRK1snb7yLieuBM4GBJD6Ttndmz0M3MrCfytPG33cL5XmBaRPxO0hldrRQRDwFj2ilfDhzYrSjNzKzX5Dnjf0zSz8keyPJ7SQNzrmdmZg0oTwI/Bvhf4N0RsQIYymvv8DEzsz4kz109q4ErK6aXAEs6XsPMzBqZm2zMzErGid/MrGQ6TfyS+ku6qVbBmJlZ8TpN/BHxEvCypC1qFI+ZmRUsz338q4AFkm4EnmsrjIiTC4vKzMwKkyfxX0nFXT1mZta35bmdc7qkTYDtIuLvNYjJzMwKlKeTtsOA+cD1aXqspGsKjsvMzAqS53bO08i6U14BEBHzydcXv5mZNaA8iX9tRDxTVfZyEcGYmVnx8lzcvUfSh4D+knYGTgZmFxuWmZkVJc8Z/6eB3YAXgIuBZ4FTCozJzMwKlLeTtv+UdFY2GSuLD8vMzIqS566evSUtAO4i+yFXq6TxeStI3T7cKem6ND1a0hxJD0q6VNLGPQ/fzMy6K09Tz3nASRExKiJGAZ8EftmNOj4D3FcxfRbwvYjYCXgaOKEb2zIzsw2UJ/G/FBE3t01ExF+AdXk2Lmkk2SMbz03TAg4ALk+LTAeO6Ea8Zma2gTps45c0Lo3+KT168WIgyB7BOCvn9r8PfBEYnKaHASsiou2DYzGwTQf1TwGmAGy33XY5qzMzs650dnH3O1XTX68Yj642LOl9wBMRcbukCd0NLCKmAdMAWlpauqzPzMzy6TDxR8Q7N3Db+wKHSzoUaAI2B34ADJG0UTrrHwk8toH1mJlZN3R5O6ekIcCHgVGVy3fVLXNEfBn4ctrGBODUiDhW0mXAUcAlwGTg6h5FbmZmPZLnl7u/B24FFtA7XTV8CbhE0hnAnWR3DZmZWY3kSfxNEfG5DakkImaRLghHxENknb6ZmVkd5Lmd89eSPiZphKShbUPhkZmZWSHynPG/CJwN/Cev3s0TuGtmM7M+KU/i/zywU0Q8WXQwZmZWvDxNPQ8Cq4sOxMzMaiPPGf9zwHxJN5F1zQx0fTunmZk1pjyJ/7dpMDOz14E8/fFPr0UgZmZWG3l+ufsw7fTNExG+q8fMrA/K09TTUjHeBBwN+D5+M7M+qsu7eiJiecXwWER8n6yPfTMz64PyNPWMq5jsR/YNIM83BTMza0B5Enhlv/zrgEXAMYVEY2ZmhctzV8+G9stvZmYNJE9Tz0DgSNbvj//04sIyM7Oi5GnquRp4Bridil/umplZ35Qn8Y+MiEMKj8TMzGoiTydtsyXt0d0NS2qSNFdSq6R7JH0jlY+WNEfSg5IulbRxt6M2M7Mey5P49wNul/R3SXdJWiDprhzrvQAcEBFjgLHAIZLeCpwFfC8idgKeBk7oYexmZtYDeZp63tOTDUdEAKvS5IA0BHAA8KFUPh04DfhZT+owM7Puy3M75yM93bik/mQXhXcCfgIsBFZExLq0yGJgmw7WnQJMAdhuu+16GoKZmVXJ09TTYxHxUkSMBUaSPWB9l26sOy0iWiKipbm5uagQzcxKp9DE3yYiVgA3AW8Dhkhq+6YxEnisFjGYmVmmsMQvqVnSkDS+CXAwcB/ZB8BRabHJZL8TMDOzGimys7URwPTUzt8P+E1EXCfpXuASSWcAdwLnFRiDmZlVKSzxR8RdwF7tlD9E1t5vZmZ1UJM2fjMzaxxO/GZmJePEb2ZWMk78ZmYl48RvZlYyTvxmZiXjxG9mVjJO/GZmJePEb2ZWMk78ZmYl48RvZlYyTvxmZiXjxG9mVjJO/GZmJePEb2ZWMk78ZmYlU+SjF7eVdJOkeyXdI+kzqXyopBslPZD+bllUDGZmtr4iz/jXAZ+PiF2BtwKflLQrMBWYGRE7AzPTtJmZ1UhhiT8ilkTEHWl8JdmD1rcBJgLT02LTgSOKisHMzNZXkzZ+SaPInr87BxgeEUvSrMeB4R2sM0XSPEnzli1bVoswzcxKofDEL2kQcAVwSkQ8WzkvIgKI9taLiGkR0RIRLc3NzUWHaWZWGoUmfkkDyJL+jIi4MhUvlTQizR8BPFFkDGZm9lpF3tUj4Dzgvoj4bsWsa4DJaXwycHVRMZiZ2fo2KnDb+wLHAQskzU9lXwHOBH4j6QTgEeCYAmMwM7MqhSX+iPgLoA5mH1hUvWZm1jn/ctfMrGSc+M3MSsaJ38ysZJz4zcxKxonfzKxknPjNzErGid/MrGSc+M3MSsaJ38ysZJz4zcxKxonfzKxknPjNzErGid/MrGSc+M3MSqbI/vitpD7206fWK5u7+qT1ylpPvaQW4ZhZFZ/xm5mVTJGPXjxf0hOS7q4oGyrpRkkPpL9bFlW/mZm1r8gz/guAQ6rKpgIzI2JnYGaaNjOzGios8UfEn4Hqxt6JwPQ0Ph04oqj6zcysfbVu4x8eEUvS+OPA8I4WlDRF0jxJ85YtW1ab6MzMSqBuF3cjIoDoZP60iGiJiJbm5uYaRmZm9vpW68S/VNIIgPT3iRrXb2ZWerVO/NcAk9P4ZODqGtdvZlZ6Rd7OeTHwV+DNkhZLOgE4EzhY0gPAQWnazMxqqLBf7kbEBzuYdWBRdZqZWdf8y10zs5Jx4jczKxknfjOzknHiNzMrGSd+M7OSceI3MysZJ34zs5Jx4jczKxknfjOzknHiNzMrGSd+M7OSceI3MysZJ34zs5Jx4jczK5nCumU2s75rzLcntVveeuolNY7k9eljP31qvbK5q09ar6yo/e0zfjOzkqnLGb+kQ4AfAP2BcyOisCdx1fuTtafaixv6RuxmfUFfzQ29oeZn/JL6Az8B3gPsCnxQ0q61jsPMrKzq0dSzD/BgRDwUES8ClwAT6xCHmVkpKSJqW6F0FHBIRJyYpo8D3hIRn6pabgowJU2+GVgOPFnLWLthKxoztkaNCxo3tkaNCxo3tkaNCxo3tlrFtX1ENFcXNuxdPRExDZjWNi1pXkS01DGkDjVqbI0aFzRubI0aFzRubI0aFzRubPWOqx5NPY8B21ZMj0xlZmZWA/VI/LcBO0saLWljYBJwTR3iMDMrpZo39UTEOkmfAv6X7HbO8yPinhyrTut6kbpp1NgaNS5o3NgaNS5o3NgaNS5o3NjqGlfNL+6amVl9+Ze7ZmYl48RvZlYyDZH4JR0i6e+SHpQ0tZ35x0taJml+Gk6smDdZ0gNpmFzjuL5XEdP9klZUzHupYl6vXryWdL6kJyTd3cF8SfphivsuSeMq5hW2v3LGdmyKaYGk2ZLGVMxblMrnS5pX47gmSHqm4j37WsW8To+DGsT2hYq47k7H1tA0r8h9tq2kmyTdK+keSZ9pZ5maH2s546rXcZYntroda6+IiLoOZBd4FwI7ABsDrcCuVcscD/y4nXWHAg+lv1um8S1rFVfV8p8mu1DdNr2qwH22PzAOuLuD+YcCfwAEvBWYU/T+6kZs/9pWJ1m3HXMq5i0CtqrTPpsAXLehx0ERsVUtexjwxxrtsxHAuDQ+GLi/nf/Nmh9rOeOq13GWJ7a6HWttQyOc8W9IFw7vBm6MiKci4mngRuCQOsX1QeDiXqq7UxHxZ6D9XtwyE4FfReZWYIikERS7v3LFFhGzU90At5L9jqNwOfZZRwrvYqSbsdXyOFsSEXek8ZXAfcA2VYvV/FjLE1cdj7M8+6wjNevOphES/zbAoxXTi2l/Rx2ZvrpdLqntB2B51y0yLiRtD4wG/lhR3CRpnqRbJR3RSzHl1VHsRe6vnjiB7GyxTQA3SLpdWZcdtfY2Sa2S/iBpt1TWMPtM0qZkyfOKiuKa7DNJo4C9gDlVs+p6rHUSV6W6HGddxFbXY61hu2yoci1wcUS8IOk/gOnAAXWOqdIk4PKIeKmibPuIeEzSDsAfJS2IiIV1iq/hSHon2T/kfhXF+6V99gbgRkl/S2fDtXAH2Xu2StKhwG+BnWtUd16HAbdEROW3g8L3maRBZB82p0TEs7257Q2RJ656HWddxFb3Y60Rzvi77MIhIpZHxAtp8lxgfN51i4yrwiSqvn5HxGPp70PALLJP/lrpKPaG6C5D0p5k7+PEiFjeVl6xz54AriL76lsTEfFsRKxK478HBkjaigbZZ0lnx1kh+0zSALIENiMirmxnkbocazniqttx1lVsDXGsFXHhoDsD2beOh8iaStouaOxWtcyIivH3A7fGqxeQHia7eLRlGh9aq7jScruQXSxSRdmWwMA0vhXwAL1/QXAUHV+ofC+vveA2t+j91Y3YtgMeBP61qnwzYHDF+GyyXlxrFdcb295DskTwj7T/ch0HRcaW5m9Bdh1gs1rts/T6fwV8v5Nlan6s5YyrLsdZztjqeqxFRP2beqKDLhwknQ7Mi4hrgJMlHQ6sIzv4j0/rPiXpm2T9/wCcHq/9Glx0XJCdhV0S6V1M/gX4uaSXyb5VnRkR9/ZGXACSLia7M2ArSYuBrwMDUtznAL8nu9viQWA18JE0r7D91Y3YvgYMA34qCWBdZL0UDgeuSmUbARdFxPU1jOso4BOS1gHPA5PSe9rTLkZ6MzbITnhuiIjnKlYtdJ8B+wLHAQskzU9lXyFLqvU81vLEVZfjLGdsdTvW2rjLBjOzkmmENn4zM6shJ34zs5Jx4jczKxknfjOzknHiNzNrMOqi476qZbeXNDP1bDBLUpfdUzjxmzUYSedK2jWNf6Vq3uz6RGU1dgH5+zb6Nll/SXsCpwP/09UKvp3TrIFJWhURg+odh9Ve6uvnuojYPU3vCPwEaCb7zcTHIuJvku4h+xHao8p+oPBMRGze2bZ9xm8NTdJ/pf7J/yLpYkmnpvIdJV2fOtq6WdIuqfwCZf3Dz5b0kKSjUrkkna2sP/sFkj6QyidI+pOkq9PyZyrry31uWm7HtFyzpCsk3ZaGfduJ9fi0nVnK+qD/esW8z6W675Z0SirbTNLvUmddd1fENEtSi6QzgU2U9dk+I81bleP1zFLWmeHfJM1IycD6vmnApyNiPHAq8NNU3gr8Wxp/PzBY0rBOt1TEz4E9eOiNAdgbmA80kfVt/gBwapo3E9g5jb+F1Ec92Vfky8hOanYl6+YW4EiyroH7k/168x9kfadPAFak8YFkfaN8I63zGdJP74GLyDr3guxXmPe1E+/xwBKyX4xuAtwNtJD1LbWArIuAQcA9ZH03HQn8omL9LdLfWUBLGl9VVceqHK/nGbJ+XvoBf22L20PfGqjoxiMdN8+n/4e24b40b2vgSuBO4AdkvXoO6Wzbde+ywawT+wJXR8QaYI2ka+GVng//Fbis4mR2YMV6v42Il4F7JQ1PZfuR9fD6ErBU0p/IPlieBW6LiCVp2wuBG9I6C4B3pvGDgF0r6ttc0qBInW1VuDFSh2CSrkz1BnBVpO4WUvnbgeuB70g6i+wr/c3d2DedvZ65EbE41TWfLIH8pRvbtsbTD1gREWOrZ0TEP0ln/Ol/48iIWNHZxpz4rS/q8J8geaFiPE8zR+XyL1dMv8yr/yP9gLemD6HOVF806/AiWkTcr+xRhYcCZ0iaGRGn54i3K5Wv5yX8f97nRcSzkh6WdHREXJaa7/aMiFZlPXs+lU52vgyc39X23MZvjewW4DBJTelM5n2Q/RMAD0s6Gl5p7x7TyXYAbgY+IKm/pGayxx3O7UYsN5A9XpNU59gOljtY0lBJmwBHpNdwM3CEpE0lbUbWDnuzpK2B1RFxIXA22eMXq61V1s1vb78ea2DKOu77K/BmSYslnQAcC5wgqZWsubDt6VwTgL9Lup+s2e+/u9q+zwSsYUXEbcoeVH8XsJSs6eWZNPtY4GeSvkrWk+UlZBe5OnIV8La0TABfjIjH2y4K53Ay8BNJd5H93/wZ+Hg7y80l64t9JHBhRMyD7KIzrybmcyPiTknvBs5W1ovrWuAT7WxvGnCXpDsi4thefD3WwCLigx3MWu8Wz4i4HLi8O9v37ZzW0Nra0ZU9dvDPwJRIzzRtNJKOJ7so+6l6x2LWGZ/xW6ObpuzHTE3A9EZN+mZ9ic/4zcxKxhd3zcxKxonfzKxknPjNzErGid/MrGSc+M3MSub/AJj6IDMnRUR6AAAAAElFTkSuQmCC\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    },

    {

     "data": {

      "image/png": 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\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    },

    {

     "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 numpy as np\n",

    "import matplotlib.pyplot as plt\n",

    "\n",

    "# Display the distribution of the most probable SNP-s per genome location\n",

    "diff_phenos = pd.DataFrame(columns=('pheno', 'diff'))\n",

    "\n",

    "chrs_lengths = [247249719.00,242951149.00,199501827.00,191273063.00,180857866.00,170899992.00,158821424.00,146274826.00,140273252.00,\n",

    "                135374737.00,134452384.00,132349534.00,114142980.00,106368585.00,100338915.00,88827254.00,78774742.00,76117153.00,63811651.00]\n",

    "abs_pos = [0] + [sum(chrs_lengths[0:i]) for i in range(1,20)]\n",

    "\n",

    "for i,p in enumerate(phenotypes):\n",

    "    ge_snps = pd.read_csv(f\"out/ge_{p}.csv\", nrows=nb_snsps)\n",

    "    lm_snps = pd.read_csv(f\"out/lm_{p}.csv\", nrows=nb_snsps)\n",

    "    \n",

    "    # We adjust positions to be absolute\n",

    "    lm_snps_abs = lm_snps.apply(lambda row: row['pos'] + abs_pos[row['chr']], axis=1)\n",

    "    ge_snps_abs = ge_snps.apply(lambda row: row['pos'] + abs_pos[row['chr']], axis=1)\n",

    "\n",

    "    fig, ax = plt.subplots()\n",

    "\n",

    "    a_heights, a_bins = np.histogram(lm_snps_abs, bins=20)\n",

    "    b_heights, b_bins = np.histogram(ge_snps_abs, bins=a_bins)\n",

    "\n",

    "    width = (a_bins[1] - a_bins[0])/3\n",

    "\n",

    "    ax.bar(a_bins[:-1], a_heights, width=width, facecolor='cornflowerblue',label='ge')\n",

    "    ax.bar(b_bins[:-1]+width, b_heights, width=width, facecolor='seagreen',label='lm')\n",

    "    ax.legend()\n",

    "    ax.set_xlabel('genome position')\n",

    "    ax.set_ylabel('number snps')\n",

    "    plt.title(p)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 216,

   "id": "difficult-passion",

   "metadata": {},

   "outputs": [

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       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>Unnamed: 0</th>\n",

       "      <th>chr</th>\n",

       "      <th>pos</th>\n",

       "      <th>log10p</th>\n",

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       "      <td>2.918000e-07</td>\n",

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       "      <td>9689084</td>\n",

       "      <td>4.389747</td>\n",

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       "      <td>rs46668932</td>\n",

       "      <td>4.089124e-05</td>\n",

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       "      <td>4.381706</td>\n",

       "      <td>rs234246831</td>\n",

       "      <td>4.152346e-05</td>\n",

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       "    <tr>\n",

       "      <th>98</th>\n",

       "      <td>99</td>\n",

       "      <td>13</td>\n",

       "      <td>114350559</td>\n",

       "      <td>4.372380</td>\n",

       "      <td>rs48974653</td>\n",

       "      <td>4.242482e-05</td>\n",

       "    </tr>\n",

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       "      <th>99</th>\n",

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       "      <td>9266275</td>\n",

       "      <td>4.335957</td>\n",

       "      <td>rs232497156</td>\n",

       "      <td>4.613629e-05</td>\n",

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       "<p>100 rows × 6 columns</p>\n",

       "</div>"

      ],

      "text/plain": [

       "    Unnamed: 0  chr        pos    log10p             snp             p\n",

       "0            1   13  113859598  7.078715      rs30535702  8.342285e-08\n",

       "1            2   13  114170966  6.792659     rs222759307  1.611912e-07\n",

       "2            3    5   67675886  6.740378  cfw-5-67675886  1.818116e-07\n",

       "3            4   13  112989997  6.534915     rs108106346  2.918000e-07\n",

       "4            5   13  113611365  6.274347      rs47870536  5.316831e-07\n",

       "..         ...  ...        ...       ...             ...           ...\n",

       "95          96   13    9689084  4.389747      rs45880155  4.076175e-05\n",

       "96          97   13    9271219  4.388370      rs46668932  4.089124e-05\n",

       "97          98   13    9266074  4.381706     rs234246831  4.152346e-05\n",

       "98          99   13  114350559  4.372380      rs48974653  4.242482e-05\n",

       "99         100   13    9266275  4.335957     rs232497156  4.613629e-05\n",

       "\n",

       "[100 rows x 6 columns]"

      ]

     },

     "metadata": {},

     "output_type": "display_data"

    }

   ],

   "source": [

    "display(ge_snps)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "id": "mexican-appliance",

   "metadata": {},

   "outputs": [],

   "source": []

  }

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