EDA on hapstats data generated from phase stitcher.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>totalPhasedBlocks</th>\n",
       "      <th>totalUnPhasedBlocks</th>\n",
       "      <th>totalVarsPhasedBlocks</th>\n",
       "      <th>totalVarsUnPhasedBlocks</th>\n",
       "      <th>phasedBlocksPI</th>\n",
       "      <th>unphasedBlocksPI</th>\n",
       "      <th>numVarsInPhasedBlock</th>\n",
       "      <th>numVarsInUnPhasedBlock</th>\n",
       "      <th>log2oddsInPhasedBlock</th>\n",
       "      <th>log2oddsInUnPhasedBlock</th>\n",
       "      <th>totalNumOfBlock</th>\n",
       "      <th>totalNumOfVars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>-342.702</td>\n",
       "      <td>-24.111</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>9,8,10,7,11</td>\n",
       "      <td>.</td>\n",
       "      <td>94,78,10,69,25</td>\n",
       "      <td>.</td>\n",
       "      <td>734.993,-393.969,53.979,612.634,-172.04</td>\n",
       "      <td>.</td>\n",
       "      <td>5</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>5,3,4,6</td>\n",
       "      <td>.</td>\n",
       "      <td>6,12,14,17</td>\n",
       "      <td>.</td>\n",
       "      <td>38.99,-58.008,131.935,-39.412</td>\n",
       "      <td>.</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>10</td>\n",
       "      <td>17,18,16,21,20</td>\n",
       "      <td>22,19</td>\n",
       "      <td>22,11,16,12,6</td>\n",
       "      <td>3,7</td>\n",
       "      <td>-129.685,-27.222,-65.413,-28.642,65.812</td>\n",
       "      <td>0.0,0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>0</td>\n",
       "      <td>13,15,12,14</td>\n",
       "      <td>.</td>\n",
       "      <td>56,25,96,107</td>\n",
       "      <td>.</td>\n",
       "      <td>-556.01,-254.929,1599.221,-532.147</td>\n",
       "      <td>.</td>\n",
       "      <td>4</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>103</td>\n",
       "      <td>71</td>\n",
       "      <td>25,27,23,28,31</td>\n",
       "      <td>24,26,29,30</td>\n",
       "      <td>6,12,23,37,25</td>\n",
       "      <td>12,31,19,9</td>\n",
       "      <td>121.272,-125.845,-146.213,131.782,-33.848</td>\n",
       "      <td>0.0,1.97,-19.022,0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>53</td>\n",
       "      <td>35</td>\n",
       "      <td>-362.144</td>\n",
       "      <td>6.283</td>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHROM  totalPhasedBlocks  totalUnPhasedBlocks  totalVarsPhasedBlocks  \\\n",
       "0      1                  1                    1                     60   \n",
       "1      2                  5                    0                    276   \n",
       "2      3                  4                    0                     49   \n",
       "3      4                  5                    2                     67   \n",
       "4      5                  4                    0                    284   \n",
       "5      7                  5                    4                    103   \n",
       "6      8                  1                    1                     53   \n",
       "\n",
       "   totalVarsUnPhasedBlocks  phasedBlocksPI unphasedBlocksPI  \\\n",
       "0                        6               1                2   \n",
       "1                        0     9,8,10,7,11                .   \n",
       "2                        0         5,3,4,6                .   \n",
       "3                       10  17,18,16,21,20            22,19   \n",
       "4                        0     13,15,12,14                .   \n",
       "5                       71  25,27,23,28,31      24,26,29,30   \n",
       "6                       35              33               32   \n",
       "\n",
       "  numVarsInPhasedBlock numVarsInUnPhasedBlock  \\\n",
       "0                   60                      6   \n",
       "1       94,78,10,69,25                      .   \n",
       "2           6,12,14,17                      .   \n",
       "3        22,11,16,12,6                    3,7   \n",
       "4         56,25,96,107                      .   \n",
       "5        6,12,23,37,25             12,31,19,9   \n",
       "6                   53                     35   \n",
       "\n",
       "                       log2oddsInPhasedBlock log2oddsInUnPhasedBlock  \\\n",
       "0                                   -342.702                 -24.111   \n",
       "1    734.993,-393.969,53.979,612.634,-172.04                       .   \n",
       "2              38.99,-58.008,131.935,-39.412                       .   \n",
       "3    -129.685,-27.222,-65.413,-28.642,65.812                 0.0,0.0   \n",
       "4         -556.01,-254.929,1599.221,-532.147                       .   \n",
       "5  121.272,-125.845,-146.213,131.782,-33.848    0.0,1.97,-19.022,0.0   \n",
       "6                                   -362.144                   6.283   \n",
       "\n",
       "   totalNumOfBlock  totalNumOfVars  \n",
       "0                2              66  \n",
       "1                5             276  \n",
       "2                4              49  \n",
       "3                7              77  \n",
       "4                4             284  \n",
       "5                9             174  \n",
       "6                2              88  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('tests/outdir/egout4/ms02g_haplotype_stats.txt', sep='\\t')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar', x='CHROM', y = 'totalPhasedBlocks')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5Vl1G1JOBTbEKIiIiNYuqqM0sDRgEnPjZNSIiElPRjqj/FZgKHI5dFBERqUmtizKZ2WBgm7uvM7MbTrHfOGAcQHp6ekPlkxgL4TMHz2Xn3Pl/NKWO+++KTY4zTDQj6h7ALWZWBrwC9DGz/3/8Tu7+rLvnuXteampqA8cUETl31VrU7v6gu6e5ewZwO7Dc3e+IeTIREQF0HbWISPDq9MEB7v428HZMkoiISI00ohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAJXa1GbWTMz+28zKzazDWb2WGMEExGRiKQo9vkK6OPue80sGSgws9fdfXWMs4mICFEUtbs7sLfq2+SqXx7LUCIi8jdRzVGbWaKZFQHbgLfc/Y8xTSUiItWiKmp3r3T3XCAN6G5mnY/fx8zGmdlaM1tbUVHRwDFFRM5ddbrqw913AiuA/jXc9qy757l7XmpqagPFExGRaK76SDWzFlVfnwf0Bd6LcS4REakSzVUfbYEXzCyRSLH/m7sviW0sERE5IpqrPtYDXRohi4iI1EDvTBQRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCVytRW1ml5rZCjPbaGYbzGxyYwQTEZGIpCj2OQTc7+6FZnYhsM7M3nL3jTHOJiIiRDGidvct7l5Y9fUeYBPw9VgHExGRiDrNUZtZBtAF+GNM0oiIyAmimfoAwMyaA4uAe919dw23jwPGAaSnpzdYQJEG9WhKHfffFZscInUQ1YjazJKJlPSL7v5qTfu4+7PunufueampqQ2ZUUTknBbNVR8GPA9scvcnYh9JRESOFs2IugcwEuhjZkVVvwbGOJeIiFSpdY7a3QsAa4QsIiJSA70zUUQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCVytRW1mvzazbWZW0hiBRETkWNGMqOcC/WOcQ0RETqLWonb3lcDnjZBFRERqoDlqEZHAJTXUgcxsHDAOID09vU73zZi2NOp9y5oNr9OxeXRX3fYXkXPGmdI9DTaidvdn3T3P3fNSU1Mb6rAiIuc8TX2IiAQumsvzXgZWAR3MrNzM7op9LBEROaLWOWp3H9YYQUREpGaa+hARCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCZyKWkQkcCpqEZHAqahFRAKnohYRCVxURW1m/c3sfTP70MymxTqUiIj8Ta1FbWaJwCxgANAJGGZmnWIdTEREIqIZUXcHPnT3j939r8ArwJDYxhIRkSPM3U+9g9nfAf3dfWzV9yOBa9x9wnH7jQPGVX3bAXi/4eMCcDGwPUbHbgzKH1/KH19ncv5YZ7/M3VNruiGpoR7B3Z8Fnm2o452Mma1197xYP06sKH98KX98ncn545k9mqmPT4FLj/o+rWqbiIg0gmiKeg1wpZldbmZNgNuB/4htLBEROaLWqQ93P2RmE4A3gUTg1+6+IebJTi7m0ysxpvzxpfzxdSbnj1v2Wl9MFBGR+NI7E0VEAqeiFhEJnIpaRCRwDXYddayYWUci74T8etWmT4H/cPdN8Ut17qg6/18H/ujue4/a3t/d34hfsuiYWXfA3X1N1dIH/YH33P21OEerMzOb5+6j4p3jdJjZ9UTe5Vzi7r+Nd57amNk1wCZ3321m5wHTgK7ARuD/uvuuRs0T8ouJZvZ9YBiRt62XV21OI3KJ4CvuPj1e2erLzEa7+5x45zgVM5sE3ANsAnKBye7+m6rbCt29axzj1crMfkRkjZok4C3gGmAF0Bd4090fj2O8UzKz4y+BNeBbwHIAd7+l0UPVgZn9t7t3r/r6u0SeR4uBfsB/hv5v18w2ADlVV709C3wJLATyq7bf1qh5Ai/qUiDT3Q8et70JsMHdr4xPsvozs83unh7vHKdiZu8C17r7XjPLIPJEne/uT5rZn9y9S3wTnlpV/lygKfAZkHbUCOmP7p4dz3ynYmaFREZvzwFOpKhfJjJIwd1/F790tTv6+WFma4CB7l5hZhcAq909K74JT83MNrn7VVVfHzMoMbMid89tzDyhT30cBtoBnxy3vW3VbUEzs/Unuwlo3ZhZTlPCkekOdy8zsxuAhWZ2GZE/Q+gOuXsl8KWZfeTuuwHcfb+Zhf78yQMmAz8Aprh7kZntD72gj5JgZi2JvA5m7l4B4O77zOxQfKNFpeSo//UWm1meu681s28AB2u7c0MLvajvBf7LzD4A/ly1LR34P8CEk90pIK2Bm4AvjttuwB8aP06dbTWzXHcvAqgaWQ8Gfg0EPSKq8lczO9/dvwSuPrLRzFII/Ae9ux8GZpjZv1f9vpXw/70eLQVYR+S57mbW1t23mFlzzowf8mOBJ83sh0QWYlplZn8m0kNjGztM0FMfAGaWQORFiKNfTFxTNVIKmpk9D8xx94IabnvJ3YfHIVbUzCyNyKj0sxpu6+Huv49DrKiZWVN3/6qG7RcDbd393TjEOi1mNgjo4e4PxTtLfZjZ+UBrd/+feGeJhpldBFxO5IdkubtvjUuO0ItaRORcp+uoRUQCp6IWEQmcilpEJHAqahGRwKmoRUQC979Oz/SMYn4P2QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['totalPhasedBlocks', 'totalUnPhasedBlocks']].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['totalVarsPhasedBlocks', 'totalVarsUnPhasedBlocks']].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['totalNumOfBlock'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['totalPhasedBlocks', 'totalUnPhasedBlocks']].plot(kind='line')"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "76fecdcc5948a122d6459e2081f75ede80f8418a8099f56ea59c608eb267b4e7"
  },
  "kernelspec": {
   "display_name": "Python 3.10.2 ('.env': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}