Merge haplotypes stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#example \n",
    "from phase_extender.hapstats import compute_haplotype_stats, compute_stats_df, plot_all_data, plot_hist_one_chr, plot_bar_hapstats\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initial_df_path = 'tempdir/msplots/initial_haplotype_stats_MA625.txt'\n",
    "# final_df_path ='tempdir/msplots/final_haplotype_stats_MA625.txt'\n",
    "# outputdir='tempdir/MA625_logs'\n",
    "# soi='MA625'\n",
    "initial_df_path = '../TestSwitchErrors/data/SetB/phased_NA12891_SetB_run01/initial_haplotype_stats_NA12891.txt'\n",
    "final_df_path1 = \"../TestSwitchErrors/data/SetB/phased_NA12891_SetB_run01/final_haplotype_stats_NA12891.txt\"\n",
    "outputdir='tempdir/NA12891/setB/iter01'\n",
    "\n",
    "soi= 'NA12891'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(outputdir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>CHROM</th>\n",
       "      <th>NA12891:PI</th>\n",
       "      <th>num_Vars_by_PI</th>\n",
       "      <th>range_of_PI</th>\n",
       "      <th>total_haplotypes</th>\n",
       "      <th>total_Vars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,...</td>\n",
       "      <td>5,6,3,3,3,4,5,3,4,8,7,4,5,7,3,4,6,3,4,5,5,9,5,...</td>\n",
       "      <td>18860,39198,6180,11396,15999,1754,11424,1913,1...</td>\n",
       "      <td>1933</td>\n",
       "      <td>9666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHROM                                         NA12891:PI  \\\n",
       "0     20  1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,...   \n",
       "\n",
       "                                      num_Vars_by_PI  \\\n",
       "0  5,6,3,3,3,4,5,3,4,8,7,4,5,7,3,4,6,3,4,5,5,9,5,...   \n",
       "\n",
       "                                         range_of_PI  total_haplotypes  \\\n",
       "0  18860,39198,6180,11396,15999,1754,11424,1913,1...              1933   \n",
       "\n",
       "   total_Vars  \n",
       "0        9666  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initial_hap_df = pd.read_csv('tempdir/sp76/initial_haplotype_stats_Sp76.txt', sep='\\t')\n",
    "initial_hap_df = pd.read_csv(initial_df_path, sep='\\t')\n",
    "\n",
    "initial_hap_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_all_data(initial_hap_df, soi=soi, prefix='initial',outputdir=outputdir ,logscale_x=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>CHROM</th>\n",
       "      <th>NA12891:PI</th>\n",
       "      <th>num_Vars_by_PI</th>\n",
       "      <th>range_of_PI</th>\n",
       "      <th>total_haplotypes</th>\n",
       "      <th>total_Vars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1.0,5.0,9.0,12.0,13.0,17.0,20.0,21.0,22.0,24.0...</td>\n",
       "      <td>17,15,19,4,19,13,5,5,14,7,6,6,4,17,22,5,61,15,...</td>\n",
       "      <td>83618,40234,56755,57384,147745,83182,27164,476...</td>\n",
       "      <td>207</td>\n",
       "      <td>9666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHROM                                         NA12891:PI  \\\n",
       "0     20  1.0,5.0,9.0,12.0,13.0,17.0,20.0,21.0,22.0,24.0...   \n",
       "\n",
       "                                      num_Vars_by_PI  \\\n",
       "0  17,15,19,4,19,13,5,5,14,7,6,6,4,17,22,5,61,15,...   \n",
       "\n",
       "                                         range_of_PI  total_haplotypes  \\\n",
       "0  83618,40234,56755,57384,147745,83182,27164,476...               207   \n",
       "\n",
       "   total_Vars  \n",
       "0        9666  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_hap_df = pd.read_csv(final_df_path1, sep='\\t', )\n",
    "final_hap_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cnBwNHjxEpUp5a/v2bfrxx4O655579fXXX+mrr2KVnp6usmXLasqUmfnaxubNmxQbu0FJSUl68slnFBDQXitXfvC7sb744jNt3rxRaWlpSkxM1MCBT+qhhzo5xjlz5rSmTo1STk6ObDabRox4QfHx53TkyI+aPDlSXbt218mTJzR06Ajl5ORo4MC+io5+RdHRkSpfvrzi48+pVasH9PTTQ3X27C+aPn2KMjIuq1ix4nrxxXEqW7acIiMjlJqaqsuXL+upp55Vy5at//A+psgBAIWmf/9w/fTTEaWmpqp581bq2bOP4uPP6dlnn9SHH65Wq1Zt1KnTI6pYsaKSkpI0Z858ubm5adSoYTpwYF++tuHn56eIiJf13Xe7tHz5Ej3wQECuY6Wnp2v27L8rMfGiBg9+QgEBDzrG+fvf56hHj95q1+4hHT58SNOmReutt5aqVq06Gj16nPz8/BQe3k/PPDNM27dvU7NmzeXl5aVffjmtWbPmqlQpbz377JM6dOigli1brNDQXmrTpq127dqhBQvmKSxsoJKSkvTqq6/r4sWLOnHi2E3ZxxQ5AKDQHTsWp0ceeVSS5OdXUSVLltLFixcc97u5ucnT01MTJ45XiRIldO7cOWVnZ+dr7Lp160uSypevoMuXL+c5VtOmzeTm5iZf3/IqXdpHiYmJjnGOHj2qJk2aSZJq166rc+fOXrOdkiVLqWnTZtqxY5vWrVujAQMGS5Jq1qwjH58ykqQGDRrp+PGj+vnnI1q69J9atmyxJMnd3UP33ltTwcGPa+LE8crOzlZoaO8b3Y3XRZHD5W72yTYA8sfZiWmFwWZzkzF2Va9eQ7t3f686deopPv6cLl1Klo9PGdlsNhlj15Ejh7Vp00YtWrRYly9f1qBB/W5gG9fezmusQ4cOSrry2X1qaqrKlSvnuO+ee+7Rnj3/VUDAgzp8+JB8fctLuvJLht1ulyR16xaiZcsWKykpUbVq1daZM6d17FicLl++LE9PT+3fv1ddunRTtWr3qE+ffrrvviY6duyo/vvfb/XTT0eUlpaqGTNeU0JCgoYMCVfbtu0KumsdKHIAQKEpV66csrKylZqaou++26mNGzcoIyNDL744Xh4eHmrQoJEWLJinCRNiVKJECQ0ZEi7pyuy6oCfBValSNdexLlw4rxEjhiglJUXPPz9G7u7/+0a/oUNH6pVXJuu9995Vdna2xo59WZLUqFFjTZ48QbNnz1PDho106tQJhYT0cKzn6empl18eowsXLuihhzqpdu06Gjp0hF59dZoyMzOVkXFZI0a8oCpVquqf//yHvvxyvex2uwYNerpAz++3bMYYc1NGciG+j9zamJEDRaMoZuS3Emd/ypYfdrtdQ4YMcnwmfubMaU2YME7/+Mc7Ny/odeT1feTMyAEAt7xx40YrOTnpmmXe3t6aNm2WyzKcPn1K48aNVpcu3VSqlLfLtusMM3K4HDNyoGjc6TNyK8trRs4FYQAAsDCKHAAACyu0z8izsrI0btw4nTp1SpmZmRoyZIhq1aqliIgI2Ww21a5dWxMmTJCbm5vmzZunjRs3ysPDQ+PGjVPjxo0LKxYAALeVQivyNWvWqGzZspoxY4YSExPVvXt31atXTyNHjlSrVq0UGRmpDRs2yN/fXzt27NCKFSt05swZDR8+XCtXriysWAAA3FYKrcgfffRRBQYGSpKMMXJ3d9e+ffvUsmVLSVL79u319ddfq0aNGgoICJDNZpO/v79ycnJ04cIF+fr6FlY0AABuG4VW5KVKlZIkpaSk6LnnntPIkSP1yiuvyPb/l+ApVaqULl26pJSUFJUtW/aa9S5dupRnkZcrV1IeHu653g8A+L28znyGdRXq35GfOXNGQ4cOVd++fdWtWzfNmDHDcV9qaqp8fHzk7e19zdfDpaamqnTpvN9sFy+mFVpmALhd8ae71lUkf36WkJCg8PBwjR49WqGhV77wvUGDBtq+fbskadOmTWrevLmaNWumLVu2yG636/Tp07Lb7RxWBwAgnwptRr5gwQIlJydr/vz5mj9/viRp/Pjxmjx5smbNmqV7771XgYGBcnd3V/PmzdWrVy/Z7XZFRkYWViQAAG47XNkNLseV3YCiwZXdrIsruwEAcJuiyAEAsDCKHAAAC6PIAQCwMIocAAALo8gBALAwihwAAAujyAEAsDCKHAAAC6PIAQCwMIocAAALo8gBALAwihwAAAujyAEAsDCKHAAAC6PIAQCwMIocAAALo8gBALAwihwAAAujyAEAsDCKHAAAC6PIAQCwMIocAAALo8gBALAwihwAAAujyAEAsDCPwhx89+7dmjlzppYuXaq//e1vSkhIkCSdOnVKTZo00ezZszVkyBBdvHhRnp6eKlasmN58883CjAQAwG2l0Ip80aJFWrNmjUqUKCFJmj17tiQpKSlJ/fv319ixYyVJx44d09q1a2Wz2QorCgAAt61CO7RerVo1zZ0793fL586dq379+qlixYpKSEhQcnKynnnmGfXp00exsbGFFQcAgNtSoc3IAwMDdfLkyWuWnT9/Xtu2bXPMxrOyshQeHq7+/fsrKSlJffr0UePGjVW+fPk8xy5XrqQ8PNwLKzoA3Jb8/EoXdQQUgkL9jPy3PvvsM3Xt2lXu7ldKuEKFCurdu7c8PDxUvnx51a9fX3FxcU6L/OLFNFfEBYDbSnz8paKOgALK65cwl561vm3bNrVv395xe+vWrRoxYoQkKTU1VYcPH9a9997rykgAAFiaS2fkcXFxqlq1quP2gw8+qC1btqhnz55yc3PTqFGj5Ovr68pIAABYms0YY4o6xI3i8JC1hU/7sqgjAHektyM6FnUEFNAtc2gdAADcXBQ5AAAWRpEDAGBhFDkAABZGkQMAYGEUOQAAFkaRAwBgYRQ5AAAWRpEDAGBhFDkAABZGkQMAYGEUOQAAFkaRAwBgYRQ5AAAWRpEDAGBhFDkAABZGkQMAYGEUOQAAFkaRAwBgYRQ5AAAWRpEDAGBhFDkAABZGkQMAYGEUOQAAFkaRAwBgYRQ5AAAWVqhFvnv3boWFhUmS9u/fr3bt2iksLExhYWFat26dJGnevHkKDQ1V7969tWfPnsKMAwDAbcejsAZetGiR1qxZoxIlSkiS9u3bp4EDByo8PNzxmH379mnHjh1asWKFzpw5o+HDh2vlypWFFQkAgNtOoc3Iq1Wrprlz5zpu7927Vxs3btRf//pXjRs3TikpKfr2228VEBAgm80mf39/5eTk6MKFC4UVCQCA206hzcgDAwN18uRJx+3GjRurR48eatSokd544w39/e9/V+nSpVW2bFnHY0qVKqVLly7J19c3z7HLlSspDw/3wooOALclP7/SRR0BhaDQivy3OnfuLB8fH8f/R0dHq1OnTkpNTXU8JjU1VaVLO3+jXbyYVmg5AeB2FR9/qagjoIDy+iXMZWetDxo0yHEy27Zt29SwYUM1a9ZMW7Zskd1u1+nTp2W3253OxgEAwP+4bEY+ceJERUdHy9PTUxUqVFB0dLS8vb3VvHlz9erVS3a7XZGRka6KAwDAbcFmjDFFHeJGcXjI2sKnfVnUEYA70tsRHYs6Agrolji0DgAAbj6nRZ6YmKitW7dKkhYuXKjnnntOR44cKfRgAADAOadF/vzzz+vnn3/W1q1b9dlnn6ljx46aMGGCK7IBAAAnnBZ5UlKS+vXrpw0bNigkJETdu3dXenq6K7IBAAAnnBa53W7X3r17tX79enXo0EEHDhxQTk6OK7IBAAAnnP752ejRozV9+nSFh4eratWq6tmzp8aOHeuKbAAAwAmnRd6mTRs1btxYJ06ckDFG77zzjkqWLOmKbAAAwAmnh9a3bdum7t2769lnn1V8fLw6deqkLVu2uCIbAABwwmmRz5o1S8uXL5ePj48qVqyopUuXavr06a7IBgAAnMjXyW5+fn6O27Vq1SrUQAAAIP+cfkZ+1113KTY2VjabTcnJyVq2bJn8/f1dkQ0AADjhdEYeFRWlTz75RGfOnFHnzp114MABRUVFuSIbAABwwumMvHz58po+fboOHjwoDw8P1a1bVzabzRXZAACAE06L/Ouvv9aYMWNUsWJF2e12JScna86cOWrcuLEr8gEAgDw4LfKpU6fqzTffVL169SRJP/zwgyZMmKCPP/640MMBAIC8Of2M3MvLy1HiknTfffcVaiAAAJB/TmfkjRs31vjx49WzZ0+5u7tr7dq1uvvuu7Vz505JUosWLQo9JAAAuD6nRf7TTz9JkmbOnHnN8tdff102m01LliwpnGQAAMCpfH1pCie2AQBwa3Ja5DNnztTFixcVHBys4ODga67yBgAAipbTIl+yZIlOnTql1atXa9CgQapcubJCQkLUqVMneXp6uiIjAADIhdOz1iXp7rvvVvfu3dW1a1cdPnxYS5cuVdeuXfXFF18Udj4AAJAHpzPyDz/8UGvWrFF8fLy6d++u5cuX66677tLZs2cVEhKizp07uyInAAC4DqdFvmvXLg0fPlytWrW6ZnmlSpU0YcKEQgsGAACcc3povXTp0r8r8TFjxkiSAgMDCycVAADIl1xn5OPHj9eJEye0d+9eHT582LE8Oztbly5dckk4AACQt1yLfMiQITp16pRiYmI0bNgwx3J3d3fVrFnTJeEAAEDecj20XqVKFbVq1Upr1qxR2bJl9eOPP2r//v0qWbKkypYtm6/Bd+/erbCwMEnSgQMH1LdvX4WFhWnQoEFKSEiQJE2ePFmPP/64wsLCFBYWxmwfAIAb4PRkt9WrV2vu3Ll6+OGHZbfbNXToUD377LMKDQ3Nc71FixZpzZo1KlGihCQpJiZGL7/8surXr6/3339fixYt0tixY7Vv3z69+eab8vX1vTnPCACAO4jTIn/77be1YsUKlStXTpL0zDPPqH///k6LvFq1apo7d65efPFFSdKsWbNUsWJFSVJOTo6KFSsmu92uY8eOKTIyUgkJCQoNDXU6LgAA+B+nRW632x0lLkm+vr6y2WxOBw4MDNTJkycdt6+W+Hfffad3331Xy5YtU1pamvr166eBAwcqJydH/fv3V6NGja752tTrKVeupDw83J1mAAD8j59f6aKOgELgtMjr1q2rmJgYx0z5o48+clq0uVm3bp3eeOMN/eMf/5Cvr6+jvK8efm/durUOHjzodPyLF9MKtH0AuJPFx3MOklXl9UuY078jnzx5sjw9PTVu3DiNHTtWHh4eBboQzOrVq/Xuu+9q6dKlqlq1qiTp6NGj6tOnj3JycpSVlaXvvvtODRs2vOGxAQC4UzmdkRcvXtzxOXdB5eTkKCYmRpUrV9bw4cMlSS1atNBzzz2n4OBg9ezZU56engoODlbt2rX/0LYAALiT2Iwx5np31KtX77qfhRtjZLPZdODAgUIPlxsOD1lb+LQvizoCcEd6O6JjUUdAAeV1aD3XGfnBgwcLJQwAALh5nB5aT05O1ty5c/XNN9/Iw8ND7du315AhQ1S8eHFX5AMAAHlwerLb6NGj5eHhoZkzZ2rq1KlKS0vT+PHjXZENAAA44XRGfurUKS1cuNBxe/z48eratWuhhgIAAPnjdEZevXp17dq1y3H74MGDql69eqGGAgAA+eN0Rn78+HH169dPNWrUkLu7u+Li4lSmTBl17NhRNptNGzZscEVOAABwHU6LfMGCBa7IAQAACsBpkfv5+emrr75SamqqpCsXdzl58qRGjBhR6OEAAEDenBb5sGHDlJ6eruPHj6t58+bauXOnmjZt6oJoAADAGacnu8XFxWnJkiXq3LmznnzySa1YsULnzp1zRTYAAOCE0yIvX768bDabatSooUOHDqlSpUrKzMx0RTYAAOCE00PrtWvXVnR0tPr06aMXXnhB586dU1ZWliuyAQAAJ5zOyCdOnKigoCDVqlVLzz33nM6dO6dXX33VFdkAAIATTovc3d1d5cqV065du1S6dGkFBgYqKSnJFdkAAIATTg+tv/zyy9q0aZOqVavmWGaz2bRkyZJCDQYAAJxzWuTbtm3TF198IS8vL1fkAQAAN8DpofXKlSsrIyPDFVkAAMANynVGPnbsWElXruQWHBys5s2by93d3XH/1KlTCz8dAADIU65F3rJly2v+CwAAbj25FnlISIgrcwAAgAJw+hk5AAC4deVa5MeOHXNlDgAAUAC5FvnIkSMlSc8++6yrsgAAgBuU62fkbm5u6tOnjw4dOqT+/fv/7n4uCAMAQNHLtcgXL16sAwcOaPz48Ro2bJgrMwEAgHzKtci9vb3VokULvf/++5Kk3bt3KycnR02bNlWFChVcFhAAAOTO6Vnr+/btU3BwsD7++GOtWrVKjz32mGJjY/M1+O7duxUWFibpyslzffr0Ud++fTVhwgTZ7XZJ0rx58xQaGqrevXtrz549f+CpAABw53F6rfXZs2dr+fLlqlq1qiTpxIkTGjZsmDp06JDneosWLdKaNWtUokQJSVeuBDdy5Ei1atVKkZGR2rBhg/z9/bVjxw6tWLFCZ86c0fDhw7Vy5cqb8LQAALgzOJ2RZ2dnO0pckqpWreqYTeelWrVqmjt3ruP2vn37HFeJa9++vbZu3apvv/1WAQEBstls8vf3V05Oji5cuFCQ5wEAwB3J6Yzc399f77zzjkJDQyVJH330ke6++26nAwcGBurkyZOO28YY2Ww2SVKpUqV06dIlpaSkqGzZso7HXF3u6+ub59jlypWUh4d7no8BAFzLz690UUdAIXBa5DExMYqOjtaCBQtkjFHr1q0VFRV1wxtyc/vf5D81NVU+Pj7y9vZWamrqNctLl3b+Rrt4Me2Gtw8Ad7r4+EtFHQEFlNcvYU6LvHz58pozZ84fDtGgQQNt375drVq10qZNm9S6dWtVq1ZNM2bM0KBBg/TLL7/Ibrc7nY0DAID/cVrkN8uYMWP08ssva9asWbr33nsVGBgod3d3NW/eXL169ZLdbldkZKSr4gAAcFuwGWNMUYe4URwesrbwaV8WdQTgjvR2RMeijoACyuvQutOz1mfPnn1TwwAAgJvHaZHHxsbKgpN2AADuCE4/Iy9btqweffRRNWzYUMWKFXMsnzp1aqEGAwAAzjkt8pCQEFfkAAAABZCvIj958qSOHDmigIAAnTlz5porvQEAgKLj9DPydevWaciQIYqJiVFSUpJ69+6t1atXuyIbAABwwmmRL1q0SO+9955KlSql8uXLa9WqVfrHP/7himwAAMAJp0Xu5uYmb29vx+2KFStec7lVAABQdJx+Rl67dm29++67ys7O1oEDB7R8+XLVq1fPFdkAAIATTqfWkZGROnv2rIoVK6Zx48bJ29tbEyZMcEU2AADghNMZecmSJfXcc8/pz3/+szw9PXXPPffI3Z2vEAUA4FbgtMh37NihF198Ub6+vjLGKDU1Va+++qruu+8+V+QDAAB5cFrk06ZN08KFC1W3bl1J0g8//KBJkybpo48+KvRwAAAgb/k6/fxqiUvSfffdp5ycnEILBAAA8i/XGfnOnTslSTVq1FBkZKRCQ0Pl4eGhTz75hMPqAADcInIt8tdff/2a2zNmzHD8v81mK7xEAAAg33It8qVLl7oyBwAAKACnJ7vt2rVLixcvVlJS0jXLlyxZUmihAABA/jgt8oiICA0bNkz+/v6uyAMAAG6A0yKvVKmSunfv7oIoAADgRjkt8rCwML3wwgtq3bq1PDz+93DKHQCAoue0yJcvXy5J+vbbb69ZTpEDAFD0nBZ5fHy8/v3vf7siCwAAuEFOr+zWvHlzxcbGKjs72xV5AADADXA6I4+NjdWKFSuuWWaz2XTgwIFCCwUAAPLHaZFv2bLFFTkAAEABOC3yefPmXXf5sGHDbnoYAABwY5wW+a9lZWVp8+bNatKkSYE29vHHH2vVqlWSpIyMDB04cECzZs3SK6+8osqVK0uShg8frpYtWxZofAAA7jQ2Y4y5kRUyMzMVHh6ud9999w9teNKkSapXr55Onz6tBg0aKDAwMN/rxsdf+kPbRtEKn/ZlUUcA7khvR3Qs6ggoID+/0rnel6/vI/+11NRUnT59+g8F+uGHH3TkyBH16tVL+/bt08qVK9W3b19NmzaNs+MBALgBTg+td+zY0fG1pcYYJScnKzw8/A9tdOHChRo6dKgkqW3btnr44YdVpUoVTZgwQe+//7769euX5/rlypWUh4f7H8oAAHeavGZ1sC6nRf7rrzO12Wzy8fGRt7d3gTeYnJysuLg4tW7dWpL0l7/8RT4+PpKkTp066fPPP3c6xsWLaQXePgDcqfhY0rry+iUsX1+asmXLFiUmJl6zvKCXaN25c6fatGkj6coM/7HHHtP777+vu+66S9u2bVPDhg0LNC4AAHcip0X+/PPP6/Tp06pZs6bjELtU8CKPi4tTlSpVJF2Z4U+ePFnDhg1T8eLFVbNmTfXs2bNA4wIAcCdyWuSHDh3SZ599dtM2+OSTT15zOyAgQAEBATdtfAAA7iROz1qvWbOmzp0754osAADgBjmdkV++fFmPPvqo6tSpIy8vL8fyJUuWFGowAADgnNMif/rpp12RAwAAFIDTIudyqQAA3Lpu+MpuAADg1kGRAwBgYRQ5AAAWRpEDAGBhFDkAABZGkQMAYGEUOQAAFkaRAwBgYRQ5AAAWRpEDAGBhFDkAABZGkQMAYGEUOQAAFkaRAwBgYRQ5AAAWRpEDAGBhFDkAABZGkQMAYGEUOQAAFkaRAwBgYRQ5AAAWRpEDAGBhHq7eYEhIiLy9vSVJVapUUa9evRQTEyN3d3cFBARo2LBhro4EAIBlubTIMzIyZIzR0qVLHcuCg4M1d+5cVa1aVU899ZT279+vBg0auDIWAACW5dJD6wcPHlR6errCw8PVv39/7dy5U5mZmapWrZpsNpsCAgK0detWV0YCAMDSXDojL168uAYNGqQePXro6NGjGjx4sHx8fBz3lypVSidOnHA6TrlyJeXh4V6YUQHgtuPnV7qoI6AQuLTIa9SooerVq8tms6lGjRoqXbq0EhMTHfenpqZeU+y5uXgxrRBTAsDtKT7+UlFHQAHl9UuYSw+tf/TRR5o2bZok6ezZs0pPT1fJkiV1/PhxGWO0ZcsWNW/e3JWRAACwNJfOyENDQzV27Fj16dNHNptNU6ZMkZubm1544QXl5OQoICBATZo0cWUkAAAszaVF7uXlpVdfffV3yz/88ENXxgAA4LbBBWEAALAwihwAAAujyAEAsDCKHAAAC6PIAQCwMIocAAALo8gBALAwihwAAAujyAEAsDCKHAAAC6PIAQCwMIocAAALo8gBALAwihwAAAujyAEAsDCKHAAAC6PIAQCwMIocAAALo8gBALAwihwAAAujyAEAsDCKHAAAC6PIAQCwMIocAAALo8gBALAwihwAAAvzcOXGsrKyNG7cOJ06dUqZmZkaMmSIKleurKefflr33HOPJKlPnz7q0qWLK2MBAGBZLi3yNWvWqGzZspoxY4YSExPVvXt3DR06VAMHDlR4eLgrowAAcFtwaZE/+uijCgwMlCQZY+Tu7q69e/cqLi5OGzZsUPXq1TVu3Dh5e3u7MhYAAJZlM8YYV280JSVFQ4YMUc+ePZWZmam6deuqUaNGeuONN5ScnKwxY8bkuX52do48PNxdlBY3W7fnVxd1BOCO9MmrwUUdAYXApTNySTpz5oyGDh2qvn37qlu3bkpOTpaPj48kqXPnzoqOjnY6xsWLaYUdEwBuO/Hxl4o6AgrIz690rve59Kz1hIQEhYeHa/To0QoNDZUkDRo0SHv27JEkbdu2TQ0bNnRlJAAALM2lM/IFCxYoOTlZ8+fP1/z58yVJERERmjJlijw9PVWhQoV8zcgBAMAVRfIZ+R/F4SFrC5/2ZVFHAO5Ib0d0LOoIKKBb5tA6AAC4uShyAAAsjCIHAMDCKHIAACyMIgcAwMIocgAALIwiBwDAwihyAAAsjCIHAMDCKHIAACyMIgcAwMIocgAALIwiBwDAwihyAAAsjCIHAMDCKHIAACyMIgcAwMIocgAALIwiBwDAwihyAAAsjCIHAMDCKHIAACyMIgcAwMIocgAALIwiBwDAwihyAAAsjCIHAMDCPIo6gCTZ7XZNnDhRhw4dkpeXlyZPnqzq1asXdSwAAG55t8SMfP369crMzNQHH3yg559/XtOmTSvqSAAAWMItUeTffvut2rVrJ0lq2rSp9u7dW8SJAACwhlvi0HpKSoq8vb0dt93d3ZWdnS0Pj+vH8/Mr7apoKASfvBpc1BEA4LZxS8zIvb29lZqa6rhtt9tzLXEAAPA/t0SRN2vWTJs2bZIkff/996pTp04RJwIAwBpsxhhT1CGunrX+448/yhijKVOmqGbNmkUdCwCAW94tUeQAAKBgbolD6wAAoGAocgAALIwiBwDAwihyAAAsjD/WBpCr9evXa9u2bbp06ZJ8fHz0pz/9SY8++qhsNltRRwPw/zhrHcB1TZo0SXa7Xe3bt1epUqWUmpqqTZs2KTs7WzExMUUdD8D/Y0YO4LoOHz6sd99995plnTp1Uu/evYsoEYDr4TNyANdlt9u1a9eua5bt3LlTnp6eRZQIwPVwaB3AdR0/flxTp07V/v37ZYyRm5ub6tevr5EjR6pu3bpFHQ/A/+PQOoDrOnLkiA4ePChPT0/97W9/05///GdJUv/+/bVkyZIiTgfgKoocwHUtWLBAq1evVk5OjkaMGKHMzEyFhISIg3jArYUiB3Bdnp6e8vHxkSTNnz9fTzzxhCpXrsyfngG3GE52A3Bdd999t6ZOnaq0tDR5e3tr3rx5ioqK0s8//1zU0QD8CkUO4LqmTJmiunXrOmbglStX1pIlSxQUFFTEyQD8GmetAwBgYczIAQCwMIocAAALo8gBiwoLC9P27duLOgaAIkaRAwBgYZzsBliAMUYzZ87U+vXr5e7url69emn9+vWqVKmSfv75ZyUlJWn8+PHq2LGjIiIilJiYqGPHjmn06NHy9fVVTEyMMjIyVK5cOUVFRal69eoKCwtT/fr1tW3bNl2+fFkvvfSSli5dqiNHjmjAgAEaMGCA0tPT9dJLL+nQoUOy2WwaNGiQunfvroMHDyoyMlLZ2dkqVqyYpk6dqnvuuUexsbGaM2eO7Ha7qlatqqioKFWoUEEdO3ZUUFCQNm7cKHd3d40aNUpvv/22jh07pjFjxqhLly5KSEhQZGSkfvnlF9lsNj3//PN64IEHtG3bNs2YMUOSVKZMGb366qvy9fUt4lcEuIUYALe8devWmd69e5uMjAyTkpJiHnvsMRMYGGgmTZpkjDHmyy+/NI8//rgxxpgxY8aYMWPGGGOMycjIMB06dDC7d+92jHP1cf369TMxMTHGGGPmzp1rHn74YZOWlmZOnjxpmjdvbowx5pVXXjHR0dHGGGPOnz9vOnbsaA4cOGAiIiLMunXrjDHGrF271qxatcokJCSYgIAAc+LECWOMMYsWLTLDhw83xhjToUMH88477xhjjImIiDB9+vQxWVlZZvv27SY4ONgYY8zIkSPN+vXrjTHGnD171nTq1MlcunTJ9OvXz5F/8eLFZvPmzYWxiwHL4tA6YAE7d+5UUFCQvLy8VKpUKa1evVp+fn56+OGHJUm1atXSxYsXHY9v3LixJOno0aPy8fFx3A4KCtLx48d16dIlSVL79u0lSf7+/mrSpIlKlCihu+++W8nJyZKkb775RqGhoZIkX19fderUSTt27NCDDz6o6OhojRs3Tp6enurWrZv27Nmjxo0bq0qVKpKkXr166ZtvvnFk+vW2WrRoIQ8PD/n7+zu2tXXrVr3++usKDg7W4MGDlZ2drRMnTqhTp04aNmyYoqKiVLNmTQUEBBTOTgYsiku0Ahbg4XHtj+rJkyeVlpYmd3d3SfrdZVOLFy8u6cpXkf6WMUY5OTmSdM1Xkv52G1cfe711H330Ud1///2KjY3V4sWL9dVXX6lDhw6/e2x2drbjtrNt2e12LV68WGXLlpUknT17VhUqVFD9+vXVoUMHxcbGasaMGdqzZ4+GDBnyu/WBOxUzcsACWrRooS+++EJZWVlKT0/Xk08+qbNnzzpd795771ViYqL27NkjSVq3bp38/f0dZelM69at9dFHH0mSLly4oA0bNqhly5YaOXKk9uzZo969e2vEiBHav3+/mjRpot27d+vkyZOSpA8++ECtWrXK93Ns3bq1li9fLunKN6899thjSk9PV48ePZSamur43H7//v35HhO4EzAjByygc+fO2rt3rx5//HHZ7Xb1799f//73v52u5+XlpdmzZys6Olrp6ekqU6aMZs+ene/tDh06VBMnTlS3bt2Uk5OjZ555Rg0bNtQzzzyj8ePHa/78+XJ3d1dERIQqVKigqKgoDRs2TFlZWfL391dMTEy+t/XSSy8pMjJS3bp1kyRNnz5d3t7eGjVqlCIiIuTh4aFixYpp0qRJ+R4TuBNw1joAABbGoXUAACyMIgcAwMIocgAALIwiBwDAwihyAAAsjCIHAMDCKHIAACyMIgcAwML+DxI082lZSBXRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_data(final_hap_df, soi, prefix='final',outputdir=outputdir,logscale_x=True,show_plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_df = pd.concat([initial_hap_df, final_hap_df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_df[\"stage\"] = [\"initial\", \"final\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>CHROM</th>\n",
       "      <th>NA12891:PI</th>\n",
       "      <th>num_Vars_by_PI</th>\n",
       "      <th>range_of_PI</th>\n",
       "      <th>total_haplotypes</th>\n",
       "      <th>total_Vars</th>\n",
       "      <th>stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,...</td>\n",
       "      <td>5,6,3,3,3,4,5,3,4,8,7,4,5,7,3,4,6,3,4,5,5,9,5,...</td>\n",
       "      <td>18860,39198,6180,11396,15999,1754,11424,1913,1...</td>\n",
       "      <td>1933</td>\n",
       "      <td>9666</td>\n",
       "      <td>initial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>1.0,5.0,9.0,12.0,13.0,17.0,20.0,21.0,22.0,24.0...</td>\n",
       "      <td>17,15,19,4,19,13,5,5,14,7,6,6,4,17,22,5,61,15,...</td>\n",
       "      <td>83618,40234,56755,57384,147745,83182,27164,476...</td>\n",
       "      <td>207</td>\n",
       "      <td>9666</td>\n",
       "      <td>final</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHROM                                         NA12891:PI  \\\n",
       "0     20  1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,...   \n",
       "1     20  1.0,5.0,9.0,12.0,13.0,17.0,20.0,21.0,22.0,24.0...   \n",
       "\n",
       "                                      num_Vars_by_PI  \\\n",
       "0  5,6,3,3,3,4,5,3,4,8,7,4,5,7,3,4,6,3,4,5,5,9,5,...   \n",
       "1  17,15,19,4,19,13,5,5,14,7,6,6,4,17,22,5,61,15,...   \n",
       "\n",
       "                                         range_of_PI  total_haplotypes  \\\n",
       "0  18860,39198,6180,11396,15999,1754,11424,1913,1...              1933   \n",
       "1  83618,40234,56755,57384,147745,83182,27164,476...               207   \n",
       "\n",
       "   total_Vars    stage  \n",
       "0        9666  initial  \n",
       "1        9666    final  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_df.to_csv(outputdir+'/merged_stats.tsv', index=False, sep='\\t')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.10.2 ('.env': venv)",
   "language": "python",
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  "language_info": {
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