{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "wDupRemoveDir='/cellar/users/btsui/all_seq_snp/Homo_sapiens_all_merged_snp.TCGA.with_pcr_rm.pickle'\n",
    "\n",
    "woDupRemoveDir='/cellar/users/btsui/all_seq_snp/Homo_sapiens_all_merged_snp.TCGA.pickle'\n",
    "\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "wDupRemoved=pd.read_pickle(wDupRemoveDir).loc['TCGA']\n",
    "\n",
    "ProcessedRunDigits=wDupRemoved.index.get_level_values('Run_digits').unique()\n",
    "\n",
    "woDupRemoved=pd.read_pickle(woDupRemoveDir)\n",
    "\n",
    "m=woDupRemoved.index.get_level_values('Run_digits').isin(ProcessedRunDigits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "woDupRemoved_inDf=woDupRemoved[m].loc['TCGA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
      "\n",
      " 99%|█████████▉| 449/452 [48:40<00:19,  6.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|█████████▉| 450/452 [48:44<00:12,  6.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|█████████▉| 451/452 [48:49<00:06,  6.50s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "100%|██████████| 452/452 [48:55<00:00,  6.49s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    }
   ],
   "source": [
    "corrDict={}\n",
    "for queryUUID in tqdm(ProcessedRunDigits):\n",
    "    woDupRemoved_inDf_tmp=woDupRemoved_inDf['ReadDepth'].loc[queryUUID]\n",
    "    wDupRemoved_inDf_tmp=wDupRemoved['ReadDepth'].loc[queryUUID]\n",
    "    mergedDf=np.log10(pd.DataFrame({'wo':woDupRemoved_inDf_tmp,\n",
    "                  'w':wDupRemoved_inDf_tmp})+1).dropna()\n",
    "    r,p=stats.pearsonr(mergedDf['wo'],mergedDf['w'])\n",
    "    #print (r)\n",
    "    \n",
    "    ### fit for high allelic read counts\n",
    "    mergedDfh=mergedDf[mergedDf['wo']>2]\n",
    "    l_h =stats.linregress(mergedDfh['wo'],mergedDfh['w'])\n",
    "    l_h.slope\n",
    "    \n",
    "    \n",
    "    mergedDfl=mergedDf[mergedDf['wo']<2]\n",
    "    l_l =stats.linregress(mergedDfl['wo'],mergedDfl['w'])\n",
    "    #print(l_l.slope)\n",
    "    corrDict[queryUUID]={'overall_r':r,'l_l_slope':l_l.slope,\n",
    "                        'l_h_slope':l_h.slope}\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mergedStatDf.loc[ProcessedRunDigits[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "mergedStatDf=pd.DataFrame(corrDict).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "l_h_slope    0.337517\n",
       "l_l_slope    0.889199\n",
       "overall_r    0.986250\n",
       "Name: 0.5, dtype: float64"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedStatDf.quantile(0.5,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "l_h_slope    0.281840\n",
       "l_l_slope    0.877407\n",
       "overall_r    0.982995\n",
       "Name: 0.025, dtype: float64"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedStatDf.quantile(0.025,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b5171677eb8>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(3,2))\n",
    "ax=sns.boxplot(data=mergedStatDf[['l_h_slope','l_l_slope']])\n",
    "ax.set_title('slope')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes=mergedStatDf[['l_h_slope','l_l_slope']].hist(sharey=True)\n",
    "for ax in axes.flatten():\n",
    "    ax.grid(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'r_l' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-255-6401eb861faf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr_l\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'r_l' is not defined"
     ]
    }
   ],
   "source": [
    "r_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "corrS=pd.Series(corrDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9828144840044503, 0.9831999657939651, 0.990159227830007)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrS.quantile(0.025),corrS.quantile(0.05),corrS.quantile(1-0.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tmpMergedDf=(mergedDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.8857445760190211, intercept=0.035048564933380266, rvalue=0.989165368230053, pvalue=0.0, stderr=0.0003237167782106638)\n",
      "LinregressResult(slope=0.30473605250695773, intercept=1.2763011970689413, rvalue=0.7949899160609349, pvalue=0.0, stderr=0.000864140900160121)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b5171cc34e0>]"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.jointplot(data=mergedDf,x='wo',y='w',kind='hex',xlim=[0,4],ylim=[0,4],size=5,stat_func=None)\n",
    "g.set_axis_labels(xlabel='without duplicate removal\\n log10(allelic read count)',ylabel='with duplicate removal\\nlog10(allelic read count)')\n",
    "#g.ax_joint.axhline(6)\n",
    "\n",
    "base=3\n",
    "cutoff=2\n",
    "tmpSubDf=tmpMergedDf[(tmpMergedDf.mean(axis=1)<=cutoff)]\n",
    "l_l=stats.linregress(tmpSubDf['wo'],tmpSubDf['w'])\n",
    "print(l_l)\n",
    "g.ax_joint.plot(\n",
    "    np.arange(0,cutoff+1,),np.arange(0,cutoff+1)*l_l.slope+l_l.intercept,alpha=0.5,linestyle='-.',color='black')\n",
    "\n",
    "\n",
    "cutoff=2\n",
    "tmpSubDf=tmpMergedDf[(tmpMergedDf.mean(axis=1)>cutoff)]\n",
    "l_h=stats.linregress(tmpSubDf['wo'],tmpSubDf['w'])\n",
    "print(l_h)\n",
    "g.ax_joint.plot(\n",
    "    np.arange(0,cutoff+1)+cutoff,np.arange(cutoff,cutoff+cutoff+1)*l_h.slope+l_h.intercept,linestyle='--',color='red')\n",
    "\n",
    "\n",
    "#g.savefig('./')\n",
    "#g.ax_joint.grid(True)\n",
    "#g.ax_joint.axvline(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05092114, 0.86934295])"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,cutoff+1)*l_l.slope+l_l.intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#woDupRemoved_inDf_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "woDupRemoved_inDf_DepthS=(woDupRemoved_inDf_tmp.groupby(['Chr','Pos']).sum())\n",
    "wDupRemoved_inDf_DepthS=wDupRemoved_inDf_tmp.groupby(['Chr','Pos']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#wDupRemoved_inDf_DepthS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpI=woDupRemoved_inDf_tmp.index.to_frame().set_index(['Chr','Pos']).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "woDupRemoved_inDf_DepthS_algned=woDupRemoved_inDf_DepthS[tmpI]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpI=wDupRemoved_inDf_tmp.index.to_frame().set_index(['Chr','Pos']).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "wDupRemoved_inDf_DepthS_algned=wDupRemoved_inDf_DepthS[tmpI]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "woDupRemoved_inDf_AllelicFreqS=woDupRemoved_inDf_tmp/woDupRemoved_inDf_DepthS_algned.values\n",
    "wDupRemoved_inDf_AllelicFreqS=wDupRemoved_inDf_tmp/wDupRemoved_inDf_DepthS_algned.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.1 s, sys: 540 ms, total: 13.7 s\n",
      "Wall time: 2.27 s\n"
     ]
    }
   ],
   "source": [
    "%time allelicDf=pd.DataFrame({'woDupRemoved':woDupRemoved_inDf_AllelicFreqS,'wDupRemoved':wDupRemoved_inDf_AllelicFreqS}).dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load in dbSNP vcf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "inVcfDir='/data/cellardata/users/btsui/dbsnp/Homo_sapiens/All_20170710.f1_byte2_not_00.vcf.gz' \n",
    "vcfDf=pd.read_csv(inVcfDir,sep='\\t',header=None)\n",
    "vcfDf.columns=['Chr','Pos','RsId','RefBase','AltBase','','','Annot']\n",
    "vcfDf['Chr']=vcfDf['Chr'].astype(np.str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "allelicDfResetDf=allelicDf.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "refI=vcfDf.set_index(['Chr','Pos','RefBase']).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ChrPosI=allelicDfResetDf.set_index(['Chr','Pos','base']).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_m=ChrPosI.isin(refI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "allelicDfResetDf['is_ref']=ref_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(0, 1, 11)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "allelicDfResetDf['wDupRemoved_bin']=np.digitize(allelicDfResetDf['wDupRemoved'],bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(allelicDfResetDf['woDupRemoved']-allelicDfResetDf['wDupRemoved']).abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "allelicDfResetDf['abs_diff']=(allelicDfResetDf['woDupRemoved']-allelicDfResetDf['wDupRemoved']).abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chr  Pos   \n",
       "1    14727     700\n",
       "     14727     700\n",
       "     630825      7\n",
       "     630833      5\n",
       "     850609      1\n",
       "Name: ReadDepth, dtype: uint16"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woDupRemoved_inDf_DepthS_algned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "allelicDfResetDf['woDupRemoved_inDf_DepthS']=woDupRemoved_inDf_DepthS[ChrPosI].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "allelicDfResetDf['woDupRemoved_inDf_DepthS_log10']=np.log10(allelicDfResetDf['woDupRemoved_inDf_DepthS']+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "binsize=1\n",
    "allelicDfResetDf['woDupRemoved_inDf_DepthS_bin']=(allelicDfResetDf['woDupRemoved_inDf_DepthS_log10']/binsize).astype(int)*binsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rLgYOHMjw4cOprKykvLy81VtZWRm9e/c+NNxW2+62UF2/fj2PPvoo9fX19OvXj9GjRzNy5EgGDx7MwYMH2bJlC7W1tbz66qssXryYiooKvvjFL57woblixQqGDRtG//79D4077bTTWLlyJStXrnQwOBgAUAvjms+ZjrRB0vXA9QCjR48+9sqaef7559m6dWtqXFlZGcOHD6e6upqqqir69u3b5jT279/P1q1b2bRpE++88w4HDx5k586d7Ny5k3Xr1rFp06bjFgzLli3jySefzGz6ffv2ZciQIQwbNox9+/YBMGHCBLZt28aePXtYs2ZNZu993XXX5b6AGTBgAAMGDGDnzp1UVFRQVVVFXV1dqi996NCh7Nmzh3fffZfKysp2/35OBBFBeXl6sVJWVkavXr1KZsdrlqSWFnfdT9bBUAuMKno8EtjUSptaSeXAQGBn8wlFxAxgBkBNTU2Xx+4NN9zA1q1beeeddw7dNm/eTG1t7aGf3vXr14+Kiooj/vDr6+vZu3dv6lcpAwYMYNiwYalbVVVVV5fdqiFDhmQ6/QMHDrB169YjwjRrvXv3ZtCgQcf1PVsydOhQbr75ZhYuXMicOXNYtGhRi+1OPfVUrrrqKiZMmFAyC4VjMXbsWFavXs2BAwcOBeHGjRupr6/PPczzUvy9l8rfQNbBsBgYJ2kssBG4EvibZm1mAp8HFgCfBubksX+hvLyc6upqqqurU+P37duXCostW7YcsVOxb9++TJo06VAADB06NLUpnYfq6mruvPPOo379wYMHaWxsTHUzNR9uS3F3UfPhsrKykvkHaUt5eTnnn38+NTU17Nx5xLoMkqiqqjpiReJENmXKFFauXMmKFSuoqamhrq6OFStWMGTIECZNmpR3ebko/lvvbl2ircn0LzbZZ3AjMBsoAx6MiDck3QEsiYiZwL8Aj0haT2FL4cosa+qsk08+mTPOOCO3fQN56dWrF7169aJ37955l9Lt9evX74gVip5q6NChXHTRRcydO5dRo0axadMm9u/fz7XXXlsyC8WuVvwDlFJZIcp8VSYiZgGzmo27vWh4P/CZrOsws+PjwgsvZNmyZSxatIjGxkbOPfdcRowYkXdZuSkOxFL5lWLPjHAzy0zv3r05//zzaWhoICK44IIL8i4pV8VhUCrB0HM6P83suDn77LNZs2YNQ4cOZeDAgXmXkysHg5kZcNJJJ3HNNdfkXUa3UPzjg1L5IYK7kszMMuRgMDOzFAeDmZmlFIdBqfxktzSqNDMrUaWylVDMwWBmlqFSPEjUwWBmliFvMZiZWYqDwczMUhwMZmaWUipHOxdzMJiZZcg7n83MLMVbDGZmluJgMDOzFAeDmZmlOBjMzCylVM6PVKz0KjYzKyEOBjMzS2nqSho/fnzOlXRc6R2SZ2ZWQsrKyrjllluoqKjIu5QOczCYmWWsqqoq7xI6xV1JZmaW4mAwM7MUB4OZmaU4GMzMLMXBYGZmKQ4GMzNLcTCYmVmKIiLvGjpN0jZgQ85lVALbc66hu/C8OMzz4jDPi8O6y7w4LSLaPaiiJIOhO5C0JCJq8q6jO/C8OMzz4jDPi8NKbV64K8nMzFIcDGZmluJgOHoz8i6gG/G8OMzz4jDPi8NKal54H4OZmaV4i8HMzFIcDEdB0lRJayWtl3Rb3vXkRdKDkrZKWpl3LXmTNErSXEmrJb0h6ea8a8qLpH6Sfi3p9WRe/EPeNeVNUpmk1yQ9k3ctHeFg6CRJZcD9wKXARGCapIn5VpWbh4CpeRfRTTQAX42ICcC5wA09+O/iAPDxiPhj4MPAVEnn5lxT3m4GVuddREc5GDpvMrA+It6MiDrgMeDynGvKRUS8BOzMu47uICI2R8SryfC7FBYCI/KtKh9RsC952Du59didmZJGAv8e+H7etXSUg6HzRgBvFz2upYcuAKxlksYA5wCL8q0kP0nXyTJgK/B8RPTYeQF8G/g74GDehXSUg6Hz1MK4Hrs2ZGmSTgb+H3BLROzNu568RERjRHwYGAlMljQp75ryIOkvgK0RsTTvWjrDwdB5tcCooscjgU051WLdiKTeFELhhxHxk7zr6Q4iYjfwIj13X9R5wGWSfkeh2/njkh7Nt6T2ORg6bzEwTtJYSX2AK4GZOddkOZMk4F+A1RHxT3nXkydJVZIGJcMfAC4B1uRbVT4i4usRMTIixlBYVsyJiKtzLqtdDoZOiogG4EZgNoUdjE9ExBv5VpUPST8GFgDjJdVK+kLeNeXoPOBzFNYIlyW3T+VdVE6GA3MlLaewIvV8RJTEzzStwEc+m5lZircYzMwsxcFgZmYpDgYzM0txMJiZWYqDwczMUhwMZmaW4mCwoyLpd5JWJLdVkv5RUt9jmN50SRuT3/+vkjStK+vtapKukXRfG8//raT/1M40LpK0Jzkd81pJLyWnUGh6vkrSouT5C1qZRld/D7dIOqno8b5W2o2X9GLyfa2W1OoVytqaV5JeOdpaLTsOBjsWUyLiLApnnD2dY7984T3J+XUuBx5ITjFRkiLi/0TEwx1oOi8izomI8cBNwH2SLk6euxhYkzw/r41pdOX3cAtwUrut4F6S7ys51fh3j+bNIuJPj+Z1li0HQw8n6e8k3ZQM3yNpTjJ8saRHJU1L1kZXSrqrpWkkp1j+W+AKSacma8LPFL3HfZKuSYZ/J+mu5EIuv5Z0RgvT+w3wPnBK8poPSvq5pKWS5kk6Mxn/kKTvJRfIeVPShcnFg1ZLeqjo/Y/4DJK+KOnuojbXSPpuMnx1UtsySQ8k1+BA0rWS1kn6FYUjnduar9MlfS0ZfrHoM69rbe0/IpYBdwA3SvowcDfwqaSOD7T1fsnrU99D8t63SlosabmSC+ZIGiNpjaQfJOOfknRS8ndQTeGo5blFn+VOFS66s1DS0GT0cArnDWt67xXtlDcq+Q7XSvpm0bT3JfcXJfPpqaS2H0pq6YSVdhw4GOwloGlBVQOcnKypnw/8BrgL+DiFC658VNIVLU0kOZPoW8C4Drzn3oiYDNxH4ZTEKZL+HfCbiNiajJoBfCkiPgJ8DfjnouanJPV9Gfg34B7gQ8BZkj4sqbqVz/AU8FdF0/lr4HFJE5Lh85Ktl0bgKknDgX+gEAifoHCRps4oTz7zLcA322j3KnBmEhK3A48na+V/6MibFH8Pkj5J4fuYTOGzf0TSnyVNxwMzIuJsYC/wXyPiXgonhJwSEVOSdv2BhclFd14CrkvG3wPMkfScpC8rOTdSGyYDVyV1fEZSTQttzqEwfyZS2PJpM3wtOw4GW0phgTGAwpW3FlAIiAuA3cCLEbEtOUfUD4E/a3VKLZ+SvCU/Lrr/k6LxX5a0lsJ1DKbDodNY/ynwpArn93+Awtpqk3+LwnldVgBbImJFRBwE3gDGAB9t6TNExDbgTUnnShpMYUE5n0L3zUeAxcn7XUxhIfWxounUAY938LM2aTrb6tKkrtZ0xVpy0zQ+mdxeIwkcDgf32xExPxl+lMKKQEvqgKatv0O1R8S/AhOAJ4GLgIXt7Nt4PiJ2JAH3k1be79cRUZt8f8toez5ZhsrzLsDyFRH1KpwS+FrgFWA5MAX4IPB7CgvJdiXBMgZYR2GNvXilo1/zt21l+J6I+F+S/gp4WNIHk+nsTtbeW3IguT9YNNz0uJzCJTdb8zjwWQpn/nw6IiLpvvhBRHy92ee7gmO77kZTbY20/X93DsdwCchm34OA/xERDzRrM4YjP0trn60+Dp9QLVV7RGwCHgQeVOG635MohEdLOvJ+xd9fe/PJMuQtBoNCF8HXkvt5FPqplwELgQslVSb97NOAXzV/cbJW/8/ATyNiF7ABmCipr6SBFNa6i/110f2C5tNLrmWwBPh8U9eIpM8k7yVJf9yJz7aojc/wE+CKZFzTFsALwKclDUne71RJpyXTuUjS4KSr7TOdqKFDJJ0N/HcK1xQ/mtc3/x5mA/85GY+kEU2fCxgtqWlrbRrwcjL8LjCgA+81NZkPSBoGDAY2tvGSTyTz8gMU5vn8NtpazpzIBoUw+AawICLek7Sfwq9lNkv6OjCXwtrnrIj4WdHr5iZr2L2Ap4FvAUTE25KeoLD18RsKXRnF+kpalLyutZ+l3gH8SNL/pdA3/T1Jf0/h+sGPAa935IO19RkiYpekVcDEiPh1Mm5V8j6/kNQLqAduiIiFkqZTCLLNFLpmyjpSQzsukPQahV8CbQVuiogXOjmN1r6HXyT7TBYk+3H3AVdTWBtfDXxe0gMUvqPvJdOaATwnaXPRfoaWfBL4TvK3AnBrRLzTRvuXgUeAM4AfRcSSTn5GO4582m07rpJuq5qI2J53LT1V0pX0TET0yMttWvvclWRmZineYjA7BpK+wZH7G56MiDu7+H0WAc1/9fO5Dhw/cNxI+nMKPw0u9lZE/GUe9djRczCYmVmKu5LMzCzFwWBmZikOBjMzS3EwmJlZioPBzMxS/j/BIrcjdbdLOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(data=allelicDfResetDf,x=\"woDupRemoved_inDf_DepthS_bin\", y=\"abs_diff\",\n",
    "                     inner=None, color=\".8\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Chr</th>\n",
       "      <th>Pos</th>\n",
       "      <th>base</th>\n",
       "      <th>woDupRemoved</th>\n",
       "      <th>wDupRemoved</th>\n",
       "      <th>is_ref</th>\n",
       "      <th>wDupRemoved_bin</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>woDupRemoved_inDf_DepthS</th>\n",
       "      <th>woDupRemoved_inDf_DepthS_bin</th>\n",
       "      <th>woDupRemoved_inDf_DepthS_log</th>\n",
       "      <th>woDupRemoved_inDf_DepthS_log10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14727</td>\n",
       "      <td>A</td>\n",
       "      <td>0.062857</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.022857</td>\n",
       "      <td>700</td>\n",
       "      <td>2</td>\n",
       "      <td>2.845718</td>\n",
       "      <td>2.845718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>14727</td>\n",
       "      <td>G</td>\n",
       "      <td>0.937143</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>0.022857</td>\n",
       "      <td>700</td>\n",
       "      <td>2</td>\n",
       "      <td>2.845718</td>\n",
       "      <td>2.845718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>630825</td>\n",
       "      <td>T</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.903090</td>\n",
       "      <td>0.903090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>630833</td>\n",
       "      <td>C</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.778151</td>\n",
       "      <td>0.778151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>850609</td>\n",
       "      <td>T</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.301030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Chr     Pos base  woDupRemoved  wDupRemoved  is_ref  wDupRemoved_bin  \\\n",
       "0   1   14727    A      0.062857     0.085714   False                1   \n",
       "1   1   14727    G      0.937143     0.914286    True               10   \n",
       "2   1  630825    T      1.000000     1.000000    True               11   \n",
       "3   1  630833    C      1.000000     1.000000    True               11   \n",
       "4   1  850609    T      1.000000     1.000000    True               11   \n",
       "\n",
       "   abs_diff  woDupRemoved_inDf_DepthS  woDupRemoved_inDf_DepthS_bin  \\\n",
       "0  0.022857                       700                             2   \n",
       "1  0.022857                       700                             2   \n",
       "2  0.000000                         7                             0   \n",
       "3  0.000000                         5                             0   \n",
       "4  0.000000                         1                             0   \n",
       "\n",
       "   woDupRemoved_inDf_DepthS_log  woDupRemoved_inDf_DepthS_log10  \n",
       "0                      2.845718                        2.845718  \n",
       "1                      2.845718                        2.845718  \n",
       "2                      0.903090                        0.903090  \n",
       "3                      0.778151                        0.778151  \n",
       "4                      0.301030                        0.301030  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allelicDfResetDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    .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>Chr</th>\n",
       "      <th>Pos</th>\n",
       "      <th>base</th>\n",
       "      <th>woDupRemoved</th>\n",
       "      <th>wDupRemoved</th>\n",
       "      <th>is_ref</th>\n",
       "      <th>wDupRemoved_bin</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>woDupRemoved_inDf_DepthS</th>\n",
       "      <th>woDupRemoved_inDf_DepthS_bin</th>\n",
       "      <th>woDupRemoved_inDf_DepthS_log</th>\n",
       "      <th>woDupRemoved_inDf_DepthS_log10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14727</td>\n",
       "      <td>A</td>\n",
       "      <td>0.062857</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.022857</td>\n",
       "      <td>700</td>\n",
       "      <td>2</td>\n",
       "      <td>2.845718</td>\n",
       "      <td>2.845718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>14727</td>\n",
       "      <td>G</td>\n",
       "      <td>0.937143</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>0.022857</td>\n",
       "      <td>700</td>\n",
       "      <td>2</td>\n",
       "      <td>2.845718</td>\n",
       "      <td>2.845718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>630825</td>\n",
       "      <td>T</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.903090</td>\n",
       "      <td>0.903090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>630833</td>\n",
       "      <td>C</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.778151</td>\n",
       "      <td>0.778151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>850609</td>\n",
       "      <td>T</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.301030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Chr     Pos base  woDupRemoved  wDupRemoved  is_ref  wDupRemoved_bin  \\\n",
       "0   1   14727    A      0.062857     0.085714   False                1   \n",
       "1   1   14727    G      0.937143     0.914286    True               10   \n",
       "2   1  630825    T      1.000000     1.000000    True               11   \n",
       "3   1  630833    C      1.000000     1.000000    True               11   \n",
       "4   1  850609    T      1.000000     1.000000    True               11   \n",
       "\n",
       "   abs_diff  woDupRemoved_inDf_DepthS  woDupRemoved_inDf_DepthS_bin  \\\n",
       "0  0.022857                       700                             2   \n",
       "1  0.022857                       700                             2   \n",
       "2  0.000000                         7                             0   \n",
       "3  0.000000                         5                             0   \n",
       "4  0.000000                         1                             0   \n",
       "\n",
       "   woDupRemoved_inDf_DepthS_log  woDupRemoved_inDf_DepthS_log10  \n",
       "0                      2.845718                        2.845718  \n",
       "1                      2.845718                        2.845718  \n",
       "2                      0.903090                        0.903090  \n",
       "3                      0.778151                        0.778151  \n",
       "4                      0.301030                        0.301030  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allelicDfResetDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b5172505898>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myBin=2\n",
    "n_bins=100\n",
    "fig, ax = plt.subplots(figsize=(3, 2))\n",
    "# plot the cumulative histogram\n",
    "# \"xx to xx read \"\n",
    "for myBin in np.arange(0,3):\n",
    "    x=allelicDfResetDf['abs_diff'][(allelicDfResetDf['woDupRemoved_inDf_DepthS_bin']==myBin)&(~allelicDfResetDf.is_ref)]#.sample(1000)\n",
    "    n, bins, patches = ax.hist(x, n_bins, normed=1, histtype='step',\n",
    "                               cumulative=True, label='{} to {}'.format(10**(myBin),10**(myBin+1)),range=[0,1.1])\n",
    "ax.set_xlim([-0.02,1.0])\n",
    "ax.set_ylim([0.5,1.02])\n",
    "ax.set_ylabel('Cumulative % of variants')\n",
    "ax.set_xlabel('Absoluate differenece in allelic fraction\\n due to duplicates')\n",
    "ax.legend(loc=(0.3,0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Absoluate differenece in allelic fraction due to duplicates')"
      ]
     },
     "execution_count": 121,
     "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": [
    "#x, 0 to 1\n",
    "ax=sns.distplot(allelicDfResetDf['abs_diff'][allelicDfResetDf['woDupRemoved_inDf_DepthS_bin']==2].sample(200),\n",
    "             hist_kws=dict(cumulative=True),\n",
    "             kde_kws=dict(cumulative=True),\n",
    "               )\n",
    "\n",
    "ax.set_ylabel('Cumulative % of samples')\n",
    "ax.set_ylabel('Absoluate differenece in allelic fraction due to duplicates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b52769376a0>"
      ]
     },
     "execution_count": 94,
     "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": [
    "sns.boxplot(data=allelicDfResetDf,x='woDupRemoved_inDf_DepthS_bin',y='abs_diff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b52768af9b0>"
      ]
     },
     "execution_count": 91,
     "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": [
    "##\n",
    "sns.stripplot(data=allelicDfResetDf,x='woDupRemoved_inDf_DepthS_bin',y='abs_diff',jitter=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b52723dec50>"
      ]
     },
     "execution_count": 68,
     "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": [
    "ax=.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Chr</th>\n",
       "      <th>Pos</th>\n",
       "      <th>base</th>\n",
       "      <th>woDupRemoved</th>\n",
       "      <th>wDupRemoved</th>\n",
       "      <th>is_ref</th>\n",
       "      <th>wDupRemoved_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14727</td>\n",
       "      <td>A</td>\n",
       "      <td>0.062857</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>14727</td>\n",
       "      <td>G</td>\n",
       "      <td>0.937143</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>630825</td>\n",
       "      <td>T</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>630833</td>\n",
       "      <td>C</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>850609</td>\n",
       "      <td>T</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Chr     Pos base  woDupRemoved  wDupRemoved  is_ref  wDupRemoved_bin\n",
       "0   1   14727    A      0.062857     0.085714   False                1\n",
       "1   1   14727    G      0.937143     0.914286    True               10\n",
       "2   1  630825    T      1.000000     1.000000    True               11\n",
       "3   1  630833    C      1.000000     1.000000    True               11\n",
       "4   1  850609    T      1.000000     1.000000    True               11"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allelicDfResetDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b5272168668>"
      ]
     },
     "execution_count": 65,
     "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": [
    "sns.boxplot(data=allelicDfResetDf[~allelicDfResetDf.is_ref],x='wDupRemoved_bin',y='woDupRemoved')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "object of too small depth for desired array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-7937bfdb0e4e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdigitize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mallelicDfResetDf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wDupRemoved'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: object of too small depth for desired array"
     ]
    }
   ],
   "source": [
    "np.digitize(allelicDfResetDf['wDupRemoved'],bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#allelicDfResetDf['wDupRemoved']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/cellar/users/btsui/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2b5271f9af98>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=allelicDfResetDf[(~allelicDfResetDf['is_ref'])&(allelicDfResetDf['woDupRemoved']>0)&(allelicDfResetDf['woDupRemoved']<1.0)],x='woDupRemoved',y='wDupRemoved',kind='hex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th>woDup</th>\n",
       "      <th>wDup</th>\n",
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       "    <tr>\n",
       "      <th>Chr</th>\n",
       "      <th>Pos</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <th>C</th>\n",
       "      <td>0.581967</td>\n",
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       "    <tr>\n",
       "      <th>T</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <th rowspan=\"30\" valign=\"top\">Y</th>\n",
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       "      <td>0.904762</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">26508094</th>\n",
       "      <th>A</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26509986</th>\n",
       "      <th>C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26510310</th>\n",
       "      <th>G</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26510337</th>\n",
       "      <th>A</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26510425</th>\n",
       "      <th>G</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">26515535</th>\n",
       "      <th>A</th>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">26515550</th>\n",
       "      <th>C</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">26515553</th>\n",
       "      <th>A</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">26515558</th>\n",
       "      <th>A</th>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26530858</th>\n",
       "      <th>G</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26530883</th>\n",
       "      <th>A</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26530902</th>\n",
       "      <th>C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26541498</th>\n",
       "      <th>C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26541512</th>\n",
       "      <th>A</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26543653</th>\n",
       "      <th>C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26554088</th>\n",
       "      <th>A</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">26557077</th>\n",
       "      <th>C</th>\n",
       "      <td>0.053763</td>\n",
       "      <td>0.013158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.946237</td>\n",
       "      <td>0.986842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">26557102</th>\n",
       "      <th>C</th>\n",
       "      <td>0.747664</td>\n",
       "      <td>0.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.252336</td>\n",
       "      <td>0.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26566504</th>\n",
       "      <th>G</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26567812</th>\n",
       "      <th>C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26567857</th>\n",
       "      <th>C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26568527</th>\n",
       "      <th>C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>237318 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      woDup      wDup\n",
       "Chr Pos      base                    \n",
       "1   14727    A     0.062857  0.085714\n",
       "             G     0.937143  0.914286\n",
       "    630825   T     1.000000  1.000000\n",
       "    630833   C     1.000000  1.000000\n",
       "    850609   T     1.000000  1.000000\n",
       "    948136   G     1.000000  1.000000\n",
       "    955964   G     1.000000  1.000000\n",
       "    970788   G     1.000000  1.000000\n",
       "    1013541  C     0.581967  0.510204\n",
       "             T     0.409836  0.489796\n",
       "    1014143  C     1.000000  1.000000\n",
       "    1014228  G     1.000000  1.000000\n",
       "    1014316  C     0.997067  1.000000\n",
       "    1014359  G     1.000000  1.000000\n",
       "    1020217  G     1.000000  1.000000\n",
       "    1020221  C     1.000000  1.000000\n",
       "    1020239  G     1.000000  1.000000\n",
       "    1022188  A     1.000000  1.000000\n",
       "    1022225  A     0.007092  0.008929\n",
       "             G     0.992908  0.991071\n",
       "    1022260  C     0.997783  1.000000\n",
       "    1022313  A     0.993318  1.000000\n",
       "    1040679  C     1.000000  1.000000\n",
       "    1041174  C     1.000000  1.000000\n",
       "    1041183  C     1.000000  1.000000\n",
       "    1041218  A     0.066667  0.076923\n",
       "             C     0.933333  0.923077\n",
       "    1041249  C     1.000000  1.000000\n",
       "    1041582  C     1.000000  1.000000\n",
       "    1041583  A     0.987013  1.000000\n",
       "...                     ...       ...\n",
       "Y   26420477 G     0.888889  0.904762\n",
       "    26508094 A     0.750000  0.750000\n",
       "             G     0.250000  0.250000\n",
       "    26509986 C     1.000000  1.000000\n",
       "    26510310 G     1.000000  1.000000\n",
       "    26510337 A     1.000000  1.000000\n",
       "    26510425 G     1.000000  1.000000\n",
       "    26515535 A     0.181818  0.181818\n",
       "             G     0.818182  0.818182\n",
       "    26515550 C     0.833333  0.833333\n",
       "             T     0.166667  0.166667\n",
       "    26515553 A     0.833333  0.833333\n",
       "             G     0.166667  0.166667\n",
       "    26515558 A     0.866667  0.857143\n",
       "             C     0.133333  0.142857\n",
       "    26530858 G     1.000000  1.000000\n",
       "    26530883 A     1.000000  1.000000\n",
       "    26530902 C     1.000000  1.000000\n",
       "    26541498 C     1.000000  1.000000\n",
       "    26541512 A     1.000000  1.000000\n",
       "    26543653 C     1.000000  1.000000\n",
       "    26554088 A     1.000000  1.000000\n",
       "    26557077 C     0.053763  0.013158\n",
       "             T     0.946237  0.986842\n",
       "    26557102 C     0.747664  0.769231\n",
       "             T     0.252336  0.230769\n",
       "    26566504 G     1.000000  1.000000\n",
       "    26567812 C     1.000000  1.000000\n",
       "    26567857 C     1.000000  1.000000\n",
       "    26568527 C     1.000000  1.000000\n",
       "\n",
       "[237318 rows x 2 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### variant allele only\n",
    "sns.jointplot(data=allelicDf,x='woDup',y='wDup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wo_allele</th>\n",
       "      <th>w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>wo_allele</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.99838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.99838</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           wo_allele        w\n",
       "wo_allele    1.00000  0.99838\n",
       "w            0.99838  1.00000"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allelicDf.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (447902,2) (3,) (447902,2) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-118-13ce9989fae3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m tmpDf2=pd.DataFrame({'wo_alle':woDupRemoved_inDf_tmp,'wo_allel_site_depth':woDupRemoved_inDf_DepthS_algned,\n\u001b[0;32m----> 2\u001b[0;31m                   'w':wDupRemoved_inDf_tmp})\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    346\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    349\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_init_dict\u001b[0;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    457\u001b[0m             \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_arrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_arrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m   7316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7317\u001b[0m     \u001b[0;31m# don't force copy because getting jammed in an ndarray anyway\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7318\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_homogenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7320\u001b[0m     \u001b[0;31m# from BlockManager perspective\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_homogenize\u001b[0;34m(data, index, dtype)\u001b[0m\n\u001b[1;32m   7614\u001b[0m                 \u001b[0;31m# Forces alignment. No need to copy data since we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7615\u001b[0m                 \u001b[0;31m# are putting it into an ndarray later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7616\u001b[0;31m                 \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7617\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7618\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, index, **kwargs)\u001b[0m\n\u001b[1;32m   3320\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reindex'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3321\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3322\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3324\u001b[0m     def drop(self, labels=None, axis=0, index=None, columns=None,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3683\u001b[0m         \u001b[0;31m# perform the reindex on the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3684\u001b[0m         return self._reindex_axes(axes, level, limit, tolerance, method,\n\u001b[0;32m-> 3685\u001b[0;31m                                   fill_value, copy).__finalize__(self)\n\u001b[0m\u001b[1;32m   3686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3687\u001b[0m     def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   3696\u001b[0m             \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3697\u001b[0m             new_index, indexer = ax.reindex(labels, level=level, limit=limit,\n\u001b[0;32m-> 3698\u001b[0;31m                                             tolerance=tolerance, method=method)\n\u001b[0m\u001b[1;32m   3699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3700\u001b[0m             \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, target, method, level, limit, tolerance)\u001b[0m\n\u001b[1;32m   2091\u001b[0m                     indexer = self.get_indexer(target, method=method,\n\u001b[1;32m   2092\u001b[0m                                                \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2093\u001b[0;31m                                                tolerance=tolerance)\n\u001b[0m\u001b[1;32m   2094\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2095\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cannot handle a non-unique multi-index!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_indexer\u001b[0;34m(self, target, method, limit, tolerance)\u001b[0m\n\u001b[1;32m   2040\u001b[0m                                       'for MultiIndex; see GitHub issue 9365')\n\u001b[1;32m   2041\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2042\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2044\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_ensure_platform_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.BaseMultiIndexCodesEngine.get_indexer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.BaseMultiIndexCodesEngine._extract_level_codes\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_codes_to_ints\u001b[0;34m(self, codes)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;31m# Shift the representation of each level by the pre-calculated number\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;31m# of bits:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mcodes\u001b[0m \u001b[0;34m<<=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0;31m# Now sum and OR are in fact interchangeable. This is a simple\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (447902,2) (3,) (447902,2) "
     ]
    }
   ],
   "source": [
    "tmpDf2=pd.DataFrame({'wo_alle':woDupRemoved_inDf_tmp,'wo_allel_site_depth':woDupRemoved_inDf_DepthS_algned,\n",
    "                  'w':wDupRemoved_inDf_tmp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time allelicFracDf=tmpDf2.groupby(['Chr','Pos']).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.9681818181818183"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.873/0.220"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmpDf10=woDupRemoved_inDf.loc['51da3bb2-6045-4f15-a7da-0dec84dda0ed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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></th>\n",
       "      <th>features</th>\n",
       "      <th>ReadDepth</th>\n",
       "      <th>AverageBaseQuality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chr</th>\n",
       "      <th>Pos</th>\n",
       "      <th>base</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">14727</th>\n",
       "      <th>A</th>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>95</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630825</th>\n",
       "      <th>T</th>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630833</th>\n",
       "      <th>C</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948136</th>\n",
       "      <th>G</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955964</th>\n",
       "      <th>G</th>\n",
       "      <td>102</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970788</th>\n",
       "      <th>G</th>\n",
       "      <td>39</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013541</th>\n",
       "      <th>C</th>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014143</th>\n",
       "      <th>C</th>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1014228</th>\n",
       "      <th>A</th>\n",
       "      <td>28</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>41</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014316</th>\n",
       "      <th>C</th>\n",
       "      <td>96</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014359</th>\n",
       "      <th>G</th>\n",
       "      <td>83</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020217</th>\n",
       "      <th>G</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020221</th>\n",
       "      <th>C</th>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020239</th>\n",
       "      <th>G</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022188</th>\n",
       "      <th>A</th>\n",
       "      <td>87</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1022225</th>\n",
       "      <th>G</th>\n",
       "      <td>121</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022260</th>\n",
       "      <th>C</th>\n",
       "      <td>204</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1022313</th>\n",
       "      <th>A</th>\n",
       "      <td>217</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041218</th>\n",
       "      <th>C</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041249</th>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041582</th>\n",
       "      <th>C</th>\n",
       "      <td>48</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1041583</th>\n",
       "      <th>A</th>\n",
       "      <td>48</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041648</th>\n",
       "      <th>G</th>\n",
       "      <td>75</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">MT</th>\n",
       "      <th>15553</th>\n",
       "      <th>G</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15572</th>\n",
       "      <th>T</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15579</th>\n",
       "      <th>A</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15607</th>\n",
       "      <th>A</th>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15615</th>\n",
       "      <th>G</th>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15637</th>\n",
       "      <th>C</th>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15649</th>\n",
       "      <th>A</th>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15670</th>\n",
       "      <th>T</th>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15682</th>\n",
       "      <th>A</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15746</th>\n",
       "      <th>A</th>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15758</th>\n",
       "      <th>A</th>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15784</th>\n",
       "      <th>T</th>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15812</th>\n",
       "      <th>G</th>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15833</th>\n",
       "      <th>C</th>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15848</th>\n",
       "      <th>A</th>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15884</th>\n",
       "      <th>G</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15890</th>\n",
       "      <th>C</th>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15923</th>\n",
       "      <th>A</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15927</th>\n",
       "      <th>G</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15928</th>\n",
       "      <th>G</th>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15932</th>\n",
       "      <th>T</th>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15943</th>\n",
       "      <th>T</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15950</th>\n",
       "      <th>G</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15965</th>\n",
       "      <th>A</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15967</th>\n",
       "      <th>G</th>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15990</th>\n",
       "      <th>C</th>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16188</th>\n",
       "      <th>C</th>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16278</th>\n",
       "      <th>C</th>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16390</th>\n",
       "      <th>G</th>\n",
       "      <td>18</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16519</th>\n",
       "      <th>T</th>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "features          ReadDepth  AverageBaseQuality\n",
       "Chr Pos     base                               \n",
       "1   14727   A             9                  27\n",
       "            G            95                  32\n",
       "            T             1                   2\n",
       "    630825  T             4                  34\n",
       "    630833  C             4                  33\n",
       "    948136  G             4                  33\n",
       "    955964  G           102                  32\n",
       "    970788  G            39                  32\n",
       "    1013541 C            39                  31\n",
       "    1014143 C            51                  31\n",
       "    1014228 A            28                  33\n",
       "            G            41                  33\n",
       "    1014316 C            96                  29\n",
       "    1014359 G            83                  28\n",
       "    1020217 G             1                  26\n",
       "    1020221 C             1                  33\n",
       "    1020239 G             1                  22\n",
       "    1022188 A            87                  31\n",
       "    1022225 G           121                  31\n",
       "            T             1                  19\n",
       "    1022260 C           204                  29\n",
       "    1022313 A           217                  31\n",
       "            G             1                   6\n",
       "            T             1                   2\n",
       "    1041218 C             1                  31\n",
       "    1041249 C             2                  11\n",
       "    1041582 C            48                  30\n",
       "    1041583 A            48                  28\n",
       "            C             1                   2\n",
       "    1041648 G            75                  29\n",
       "...                     ...                 ...\n",
       "MT  15553   G            15                  34\n",
       "    15572   T             8                  32\n",
       "    15579   A             8                  32\n",
       "    15607   A             5                  31\n",
       "    15615   G             2                  34\n",
       "    15637   C             1                  36\n",
       "    15649   A             2                  35\n",
       "    15670   T             8                  31\n",
       "    15682   A             8                  32\n",
       "    15746   A             9                  33\n",
       "    15758   A            13                  32\n",
       "    15784   T            12                  33\n",
       "    15812   G            12                  30\n",
       "    15833   C             8                  33\n",
       "    15848   A             6                  32\n",
       "    15884   G             1                  34\n",
       "    15890   C             1                  33\n",
       "    15923   A             4                  33\n",
       "    15927   G             4                  33\n",
       "    15928   G             4                  34\n",
       "    15932   T             4                  32\n",
       "    15943   T             3                  33\n",
       "    15950   G             4                  33\n",
       "    15965   A             4                  33\n",
       "    15967   G             4                  32\n",
       "    15990   C             1                  33\n",
       "    16188   C            15                  26\n",
       "    16278   C            30                  33\n",
       "    16390   G            18                  33\n",
       "    16519   T             7                  32\n",
       "\n",
       "[242114 rows x 2 columns]"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpDf10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1014359</th>\n",
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       "      <th>1020217</th>\n",
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       "    <tr>\n",
       "      <th>1020221</th>\n",
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       "      <th>1020239</th>\n",
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       "      <td>1.0</td>\n",
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       "      <th>1022188</th>\n",
       "      <td>87.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1022225</th>\n",
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       "    <tr>\n",
       "      <th>1022260</th>\n",
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       "      <td>204.0</td>\n",
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       "    <tr>\n",
       "      <th>1022313</th>\n",
       "      <td>217.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>1041218</th>\n",
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       "      <th>15607</th>\n",
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       "    <tr>\n",
       "      <th>15637</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>15649</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>15670</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>15682</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>15746</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15758</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15784</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15812</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15833</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15848</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15884</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15890</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15923</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15927</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15928</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15932</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15943</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15950</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15965</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15967</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15990</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16188</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16278</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16390</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16519</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205539 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "base             A      C      G     T\n",
       "Chr Pos                               \n",
       "1   14727      9.0    0.0   95.0   1.0\n",
       "    630825     0.0    0.0    0.0   4.0\n",
       "    630833     0.0    4.0    0.0   0.0\n",
       "    948136     0.0    0.0    4.0   0.0\n",
       "    955964     0.0    0.0  102.0   0.0\n",
       "    970788     0.0    0.0   39.0   0.0\n",
       "    1013541    0.0   39.0    0.0   0.0\n",
       "    1014143    0.0   51.0    0.0   0.0\n",
       "    1014228   28.0    0.0   41.0   0.0\n",
       "    1014316    0.0   96.0    0.0   0.0\n",
       "    1014359    0.0    0.0   83.0   0.0\n",
       "    1020217    0.0    0.0    1.0   0.0\n",
       "    1020221    0.0    1.0    0.0   0.0\n",
       "    1020239    0.0    0.0    1.0   0.0\n",
       "    1022188   87.0    0.0    0.0   0.0\n",
       "    1022225    0.0    0.0  121.0   1.0\n",
       "    1022260    0.0  204.0    0.0   0.0\n",
       "    1022313  217.0    0.0    1.0   1.0\n",
       "    1041218    0.0    1.0    0.0   0.0\n",
       "    1041249    0.0    2.0    0.0   0.0\n",
       "    1041582    0.0   48.0    0.0   0.0\n",
       "    1041583   48.0    1.0    0.0   0.0\n",
       "    1041648    0.0    0.0   75.0   1.0\n",
       "    1041950    0.0   48.0    0.0   0.0\n",
       "    1042136    0.0    0.0    0.0  33.0\n",
       "    1042190   15.0    0.0    0.0   0.0\n",
       "    1043223    0.0    9.0    0.0   0.0\n",
       "    1043248    0.0   16.0    0.0   0.0\n",
       "    1043288    0.0    0.0   28.0   0.0\n",
       "    1043382    0.0    0.0   12.0   0.0\n",
       "...            ...    ...    ...   ...\n",
       "MT  15553      0.0    0.0   15.0   0.0\n",
       "    15572      0.0    0.0    0.0   8.0\n",
       "    15579      8.0    0.0    0.0   0.0\n",
       "    15607      5.0    0.0    0.0   0.0\n",
       "    15615      0.0    0.0    2.0   0.0\n",
       "    15637      0.0    1.0    0.0   0.0\n",
       "    15649      2.0    0.0    0.0   0.0\n",
       "    15670      0.0    0.0    0.0   8.0\n",
       "    15682      8.0    0.0    0.0   0.0\n",
       "    15746      9.0    0.0    0.0   0.0\n",
       "    15758     13.0    0.0    0.0   0.0\n",
       "    15784      0.0    0.0    0.0  12.0\n",
       "    15812      0.0    0.0   12.0   0.0\n",
       "    15833      0.0    8.0    0.0   0.0\n",
       "    15848      6.0    0.0    0.0   0.0\n",
       "    15884      0.0    0.0    1.0   0.0\n",
       "    15890      0.0    1.0    0.0   0.0\n",
       "    15923      4.0    0.0    0.0   0.0\n",
       "    15927      0.0    0.0    4.0   0.0\n",
       "    15928      0.0    0.0    4.0   0.0\n",
       "    15932      0.0    0.0    0.0   4.0\n",
       "    15943      0.0    0.0    0.0   3.0\n",
       "    15950      0.0    0.0    4.0   0.0\n",
       "    15965      4.0    0.0    0.0   0.0\n",
       "    15967      0.0    0.0    4.0   0.0\n",
       "    15990      0.0    1.0    0.0   0.0\n",
       "    16188      0.0   15.0    0.0   0.0\n",
       "    16278      0.0   30.0    0.0   0.0\n",
       "    16390      0.0    0.0   18.0   0.0\n",
       "    16519      0.0    0.0    0.0   7.0\n",
       "\n",
       "[205539 rows x 4 columns]"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpDf10.unstack().fillna(0)['ReadDepth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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