Plotting_Size_Histograms_for_SVs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1008,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 939,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cat  yes_DUP_processed.bed > yesmaybeDUP_curator1_dec292020.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 987,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"yesmaybeDUP_curator1_dec292020.csv\", \n",
    "                  names=[\"Chrom\", \"Start\", \"Stop\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cat yesallintersectDUP.txt | sed 's/_/ /g' | sed 's/ /,/g' > yesallintersectDUP.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 989,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chrom</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>402034</td>\n",
       "      <td>402320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>628959</td>\n",
       "      <td>629159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>1217735</td>\n",
       "      <td>1218005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>2537129</td>\n",
       "      <td>2539744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>5918650</td>\n",
       "      <td>5918773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>CM004552.1</td>\n",
       "      <td>3605616</td>\n",
       "      <td>3605719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>CM004553.1</td>\n",
       "      <td>2222223</td>\n",
       "      <td>2222342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>CM004553.1</td>\n",
       "      <td>2830984</td>\n",
       "      <td>2831303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>CM004553.1</td>\n",
       "      <td>3114555</td>\n",
       "      <td>3114959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>CM004553.1</td>\n",
       "      <td>3155223</td>\n",
       "      <td>3155632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>287 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Chrom    Start     Stop\n",
       "0    CM004525.1   402034   402320\n",
       "1    CM004525.1   628959   629159\n",
       "2    CM004525.1  1217735  1218005\n",
       "3    CM004525.1  2537129  2539744\n",
       "4    CM004525.1  5918650  5918773\n",
       "..          ...      ...      ...\n",
       "282  CM004552.1  3605616  3605719\n",
       "283  CM004553.1  2222223  2222342\n",
       "284  CM004553.1  2830984  2831303\n",
       "285  CM004553.1  3114555  3114959\n",
       "286  CM004553.1  3155223  3155632\n",
       "\n",
       "[287 rows x 3 columns]"
      ]
     },
     "execution_count": 989,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 990,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Size']=df['Stop']-df['Start']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 991,
   "metadata": {},
   "outputs": [],
   "source": [
    "Size = df['Size'].tolist()\n",
    "s = sorted(Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 992,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2121873"
      ]
     },
     "execution_count": 992,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max = df['Size'].max()\n",
    "max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 993,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 993,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min = df['Size'].min()\n",
    "min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13290.170731707318"
      ]
     },
     "execution_count": 994,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mean = df['Size'].mean()\n",
    "Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "345.0"
      ]
     },
     "execution_count": 995,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = df['Size'].median()\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 996,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "287"
      ]
     },
     "execution_count": 996,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = df['Size'].count()\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Rejected_Deletions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>4482951.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>11273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>13324.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Rejected_Deletions\n",
       "Max length              4482951.0\n",
       "Min length                   23.0\n",
       "Mean length               11273.0\n",
       "Median length               376.0\n",
       "Count                     13324.0"
      ]
     },
     "execution_count": 997,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d = {'Rejected_Deletions': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "#rejecteddels = pd.DataFrame(data=d)\n",
    "\n",
    "#rejecteddels= df.round()\n",
    "rejecteddels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "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>Uncurated DEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>142501103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>319153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>15029.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Uncurated DEL\n",
       "Max length       142501103.0\n",
       "Min length              23.0\n",
       "Mean length         319153.0\n",
       "Median length          376.0\n",
       "Count                15029.0"
      ]
     },
     "execution_count": 998,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d = {'Uncurated DEL': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "#uncurateddels = pd.DataFrame(data=d)\n",
    "\n",
    "#uncurateddels = df.round()\n",
    "uncurateddels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 999,
   "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>Curated DEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>6096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>259.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>1243.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Curated DEL\n",
       "Max length          6096.0\n",
       "Min length            25.0\n",
       "Mean length          259.9\n",
       "Median length        109.0\n",
       "Count               1243.0"
      ]
     },
     "execution_count": 999,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d = {'Curated DEL': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "\n",
    "#yesalldels = pd.DataFrame(data=d)\n",
    "#yesalldels = df.round(1)\n",
    "yesalldels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Curated DEL 1cur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>6096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>256.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>2457.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Curated DEL 1cur\n",
       "Max length               6096.0\n",
       "Min length                 25.0\n",
       "Mean length               256.7\n",
       "Median length             106.0\n",
       "Count                    2457.0"
      ]
     },
     "execution_count": 1000,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d = {'Curated DEL 1cur': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "#curateddelsG = pd.DataFrame(data=d)\n",
    "#curateddelsG = df.round(1)\n",
    "curateddelsG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1001,
   "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",
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       "    }\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>Curated_Inversions_G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>2057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>87.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Curated_Inversions_G\n",
       "Max length                   2057.0\n",
       "Min length                     37.0\n",
       "Mean length                    87.4\n",
       "Median length                 376.0\n",
       "Count                         177.0"
      ]
     },
     "execution_count": 1001,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d = {'Curated_Inversions_G': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "#curatedinvsG = pd.DataFrame(data=d)\n",
    "#curatedinvsG = df.round(1)\n",
    "curatedinvsG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 960,
   "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>Curated DUP 1cur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>2121873.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>13290.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Curated DUP 1cur\n",
       "Max length            2121873.0\n",
       "Min length                 95.0\n",
       "Mean length             13290.2\n",
       "Median length             345.0\n",
       "Count                     287.0"
      ]
     },
     "execution_count": 960,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d = {'Curated DUP 1cur': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "#curateddupsG = pd.DataFrame(data=d)\n",
    "#curateddupsG = df.round(1)\n",
    "curateddupsG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 961,
   "metadata": {},
   "outputs": [],
   "source": [
    "#d = {'Curated INV': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "#curatedinvs = pd.DataFrame(data=d)\n",
    "#curatedinvs = df.round(1)\n",
    "#curatedinvs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 962,
   "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>Curated DUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>161.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Curated DUP\n",
       "Max length           411.0\n",
       "Min length            98.0\n",
       "Mean length          161.8\n",
       "Median length        126.0\n",
       "Count                 37.0"
      ]
     },
     "execution_count": 962,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d = {'Curated DUP': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "#curateddups = pd.DataFrame(data=d)\n",
    "#curateddups = df.round(1)\n",
    "curateddups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 963,
   "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>Uncurated DUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>120312679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>1497612.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>1349.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>3430.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Uncurated DUP\n",
       "Max length       120312679.0\n",
       "Min length              79.0\n",
       "Mean length        1497612.7\n",
       "Median length         1349.5\n",
       "Count                 3430.0"
      ]
     },
     "execution_count": 963,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d = {'Uncurated DUP': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "#uncurateddups = pd.DataFrame(data=d)\n",
    "#uncurateddups = df.round(1)\n",
    "uncurateddups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 964,
   "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>Uncurated INV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>76386443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>639090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>1188.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Uncurated INV\n",
       "Max length        76386443.0\n",
       "Min length              33.0\n",
       "Mean length         639090.0\n",
       "Median length           83.0\n",
       "Count                 1188.0"
      ]
     },
     "execution_count": 964,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d = {'Uncurated INV': [max, min, Mean, median, count]}\n",
    "#df = pd.DataFrame(data=d, index=['Max length', 'Min length', 'Mean length','Median length', 'Count'])\n",
    "\n",
    "#uncuratedinvs = pd.DataFrame(data=d)\n",
    "#uncuratedinvs = df.round(1)\n",
    "uncuratedinvs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 965,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merged = pd.concat([yesalldels,uncurateddels,curatedinvs,uncuratedinvs,curateddups,uncurateddups], axis=1)\n",
    "#merged\n",
    "\n",
    "#merged.to_csv(\"size_table.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 966,
   "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>Curated DEL 1cur</th>\n",
       "      <th>Uncurated DEL</th>\n",
       "      <th>Curated_Inversions_G</th>\n",
       "      <th>Uncurated INV</th>\n",
       "      <th>Curated DUP 1cur</th>\n",
       "      <th>Uncurated DUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Max length</th>\n",
       "      <td>6096.0</td>\n",
       "      <td>142501103.0</td>\n",
       "      <td>2057.0</td>\n",
       "      <td>76386443.0</td>\n",
       "      <td>2121873.0</td>\n",
       "      <td>120312679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min length</th>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean length</th>\n",
       "      <td>256.7</td>\n",
       "      <td>319153.0</td>\n",
       "      <td>87.4</td>\n",
       "      <td>639090.0</td>\n",
       "      <td>13290.2</td>\n",
       "      <td>1497612.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median length</th>\n",
       "      <td>106.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>1349.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>2457.0</td>\n",
       "      <td>15029.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>1188.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>3430.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Curated DEL 1cur  Uncurated DEL  Curated_Inversions_G  \\\n",
       "Max length               6096.0    142501103.0                2057.0   \n",
       "Min length                 25.0           23.0                  37.0   \n",
       "Mean length               256.7       319153.0                  87.4   \n",
       "Median length             106.0          376.0                 376.0   \n",
       "Count                    2457.0        15029.0                 177.0   \n",
       "\n",
       "               Uncurated INV  Curated DUP 1cur  Uncurated DUP  \n",
       "Max length        76386443.0         2121873.0    120312679.0  \n",
       "Min length              33.0              95.0           79.0  \n",
       "Mean length         639090.0           13290.2      1497612.7  \n",
       "Median length           83.0             345.0         1349.5  \n",
       "Count                 1188.0             287.0         3430.0  "
      ]
     },
     "execution_count": 966,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged2 = pd.concat([curateddelsG,uncurateddels,curatedinvsG,uncuratedinvs,curateddupsG,uncurateddups], axis=1)\n",
    "merged2\n",
    "\n",
    "#merged2.to_csv(\"size_table2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 930,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "merged2.to_csv(\"size_table.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 931,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr style=\"text-align: right;\">\\n      <th></th>\\n      <th>Curated DEL 1cur</th>\\n      <th>Uncurated DEL</th>\\n      <th>Curated_Inversions_G</th>\\n      <th>Uncurated INV</th>\\n      <th>Curated DUP 1cur</th>\\n      <th>Uncurated DUP</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>Max length</th>\\n      <td>6096.0</td>\\n      <td>142501103.0</td>\\n      <td>2057.0</td>\\n      <td>76386443.0</td>\\n      <td>2121873.0</td>\\n      <td>120312679.0</td>\\n    </tr>\\n    <tr>\\n      <th>Min length</th>\\n      <td>25.0</td>\\n      <td>23.0</td>\\n      <td>37.0</td>\\n      <td>33.0</td>\\n      <td>95.0</td>\\n      <td>79.0</td>\\n    </tr>\\n    <tr>\\n      <th>Mean length</th>\\n      <td>256.7</td>\\n      <td>319153.0</td>\\n      <td>87.4</td>\\n      <td>639090.0</td>\\n      <td>13290.2</td>\\n      <td>1497612.7</td>\\n    </tr>\\n    <tr>\\n      <th>Median length</th>\\n      <td>106.0</td>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n      <td>83.0</td>\\n      <td>345.0</td>\\n      <td>1349.5</td>\\n    </tr>\\n    <tr>\\n      <th>Count</th>\\n      <td>2457.0</td>\\n      <td>15029.0</td>\\n      <td>177.0</td>\\n      <td>1188.0</td>\\n      <td>287.0</td>\\n      <td>3430.0</td>\\n    </tr>\\n  </tbody>\\n</table>'"
      ]
     },
     "execution_count": 931,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "htmlpretty = merged2.to_html()\n",
    "htmlpretty\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#merged.to_csv(\"size_table.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 849,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr style=\"text-align: right;\">\\n      <th></th>\\n      <th>Curated DEL</th>\\n      <th>Uncurated DEL</th>\\n      <th>Curated INV</th>\\n      <th>Uncurated INV</th>\\n      <th>Curated DUP</th>\\n      <th>Uncurated DUP</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>Max length</th>\\n      <td>6096.0</td>\\n      <td>142501103.0</td>\\n      <td>107.0</td>\\n      <td>76386443.0</td>\\n      <td>411.0</td>\\n      <td>120312679.0</td>\\n    </tr>\\n    <tr>\\n      <th>Min length</th>\\n      <td>25.0</td>\\n      <td>23.0</td>\\n      <td>49.0</td>\\n      <td>33.0</td>\\n      <td>98.0</td>\\n      <td>79.0</td>\\n    </tr>\\n    <tr>\\n      <th>Mean length</th>\\n      <td>259.9</td>\\n      <td>319153.0</td>\\n      <td>68.2</td>\\n      <td>639090.0</td>\\n      <td>161.8</td>\\n      <td>1497612.7</td>\\n    </tr>\\n    <tr>\\n      <th>Median length</th>\\n      <td>109.0</td>\\n      <td>376.0</td>\\n      <td>63.0</td>\\n      <td>83.0</td>\\n      <td>126.0</td>\\n      <td>1349.5</td>\\n    </tr>\\n    <tr>\\n      <th>Count</th>\\n      <td>1243.0</td>\\n      <td>15029.0</td>\\n      <td>13.0</td>\\n      <td>1188.0</td>\\n      <td>37.0</td>\\n      <td>3430.0</td>\\n    </tr>\\n  </tbody>\\n</table>'"
      ]
     },
     "execution_count": 849,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "htmlpretty = merged.to_html()\n",
    "htmlpretty\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr style=\"text-align: right;\">\\n      <th></th>\\n      <th>Curated_Deletions</th>\\n      <th>Curated_Deletions_G</th>\\n      <th>Uncurated_Deletions</th>\\n      <th>Rejected_Deletions</th>\\n      <th>Curated_Inversions</th>\\n      <th>Uncurated_Inversions</th>\\n      <th>Curated_Inversions_G</th>\\n      <th>Curated_Duplications</th>\\n      <th>Uncurated_Duplications</th>\\n      <th>Curated_Duplications_G</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>Max length</th>\\n      <td>6096.0</td>\\n      <td>6096.0</td>\\n      <td>142501103.0</td>\\n      <td>4482951.0</td>\\n      <td>107.0</td>\\n      <td>76386443.0</td>\\n      <td>2057.0</td>\\n      <td>411.0</td>\\n      <td>120312679.0</td>\\n      <td>2121873.0</td>\\n    </tr>\\n    <tr>\\n      <th>Min length</th>\\n      <td>25.0</td>\\n      <td>25.0</td>\\n      <td>23.0</td>\\n      <td>23.0</td>\\n      <td>49.0</td>\\n      <td>33.0</td>\\n      <td>37.0</td>\\n      <td>98.0</td>\\n      <td>79.0</td>\\n      <td>95.0</td>\\n    </tr>\\n    <tr>\\n      <th>Mean length</th>\\n      <td>259.9</td>\\n      <td>256.7</td>\\n      <td>319153.0</td>\\n      <td>11273.0</td>\\n      <td>68.2</td>\\n      <td>639090.0</td>\\n      <td>87.4</td>\\n      <td>161.8</td>\\n      <td>1497612.7</td>\\n      <td>13290.2</td>\\n    </tr>\\n    <tr>\\n      <th>Median length</th>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n      <td>376.0</td>\\n    </tr>\\n    <tr>\\n      <th>Count</th>\\n      <td>1243.0</td>\\n      <td>2457.0</td>\\n      <td>15029.0</td>\\n      <td>13324.0</td>\\n      <td>13.0</td>\\n      <td>1188.0</td>\\n      <td>177.0</td>\\n      <td>37.0</td>\\n      <td>3430.0</td>\\n      <td>287.0</td>\\n    </tr>\\n  </tbody>\\n</table>\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "cat size.md | sed ':a;N;$!ba;s/\\n/   /g' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_html_pretty(merged, filename='/tmp/out.html', title=''):\n",
    "    '''\n",
    "    Write an entire dataframe to an HTML file\n",
    "    with nice formatting.\n",
    "    Thanks to @stackoverflowuser2010 for the\n",
    "    pretty printer see https://stackoverflow.com/a/47723330/362951\n",
    "    '''\n",
    "    ht = ''\n",
    "    if title != '':\n",
    "        ht += '<h2> %s </h2>\\n' % title\n",
    "    ht += merged.to_html(classes='wide', escape=False)\n",
    "\n",
    "    with open(filename, 'w') as f:\n",
    "         f.write(HTML_TEMPLATE1 + ht + HTML_TEMPLATE2)\n",
    "            \n",
    "HTML_TEMPLATE1 = '''\n",
    "<html>\n",
    "<head>\n",
    "<style>\n",
    "  h2 {\n",
    "    text-align: center;\n",
    "    font-family: Helvetica, Arial, sans-serif;\n",
    "  }\n",
    "  table { \n",
    "    margin-left: auto;\n",
    "    margin-right: auto;\n",
    "  }\n",
    "  table, th, td {\n",
    "    border: 1px solid black;\n",
    "    border-collapse: collapse;\n",
    "  }\n",
    "  th, td {\n",
    "    padding: 5px;\n",
    "    text-align: center;\n",
    "    font-family: Helvetica, Arial, sans-serif;\n",
    "    font-size: 90%;\n",
    "  }\n",
    "  table tbody tr:hover {\n",
    "    background-color: #dddddd;\n",
    "  }\n",
    "  .wide {\n",
    "    width: 90%; \n",
    "  }\n",
    "</style>\n",
    "</head>\n",
    "<body>\n",
    "'''\n",
    "\n",
    "HTML_TEMPLATE2 = '''\n",
    "</body>\n",
    "</html>\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "intermediate_html = '/tmp/intermediate.html'\n",
    "to_html_pretty(merged,intermediate_html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the html file to a pdf file using weasyprint\n",
    "import weasyprint\n",
    "out_pdf= 'demo.pdf'\n",
    "weasyprint.HTML(intermediate_html).write_pdf(out_pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***NOW PLOT HISTOGRAMS*** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1012,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filter for SVs only below 2000 base pairs in length\n",
    "Size_filtered =  [x for x in Size if x <= 5000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1013,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Size_filtered, bins = 100)\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "red_patch = mpatches.Patch(color='red', label='Curated Duplications (287, single curator)')\n",
    "\n",
    "plt.legend(handles=[red_patch])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat yesallintersect.bed | sed 's/\\t/,/'g > yesallintersect_DEL.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"yesallintersect_DEL.csv\", \n",
    "                  names=[\"Chrom\", \"Start\", \"Stop\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chrom</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>450631</td>\n",
       "      <td>450686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>2793449</td>\n",
       "      <td>2793535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>3367261</td>\n",
       "      <td>3367328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>5558808</td>\n",
       "      <td>5559097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>7403899</td>\n",
       "      <td>7405237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>CM004550.1</td>\n",
       "      <td>4313940</td>\n",
       "      <td>4314149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>CM004552.1</td>\n",
       "      <td>1630828</td>\n",
       "      <td>1630907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>CM004553.1</td>\n",
       "      <td>2614566</td>\n",
       "      <td>2614634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>CM004553.1</td>\n",
       "      <td>2844947</td>\n",
       "      <td>2845034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>CM004554.1</td>\n",
       "      <td>868672</td>\n",
       "      <td>869052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1243 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Chrom    Start     Stop\n",
       "0     CM004525.1   450631   450686\n",
       "1     CM004525.1  2793449  2793535\n",
       "2     CM004525.1  3367261  3367328\n",
       "3     CM004525.1  5558808  5559097\n",
       "4     CM004525.1  7403899  7405237\n",
       "...          ...      ...      ...\n",
       "1238  CM004550.1  4313940  4314149\n",
       "1239  CM004552.1  1630828  1630907\n",
       "1240  CM004553.1  2614566  2614634\n",
       "1241  CM004553.1  2844947  2845034\n",
       "1242  CM004554.1   868672   869052\n",
       "\n",
       "[1243 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Stop'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/envs/jupyterlab/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\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.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Stop'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-702-33eb242f3e57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Size'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Stop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Start'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/envs/jupyterlab/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2904\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2905\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2906\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2907\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2908\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/jupyterlab/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Stop'"
     ]
    }
   ],
   "source": [
    "df['Size']=df['Stop']-df['Start']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Size'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/envs/jupyterlab/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\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.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Size'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-701-8a9be92c32c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mSize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Size'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/jupyterlab/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2904\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2905\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2906\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2907\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2908\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/jupyterlab/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Size'"
     ]
    }
   ],
   "source": [
    "Size = df['Size'].tolist()\n",
    "s = sorted(Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 979,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filter for SVs only below 2000 base pairs in length\n",
    "Size_filtered =  [x for x in Size if x <= 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 984,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Size_filtered, bins = 100)\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "red_patch = mpatches.Patch(color='red', label='Uncurated Inversions')\n",
    "\n",
    "plt.legend(handles=[red_patch])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "\n",
    "SV=DEL\n",
    "\n",
    "cat uncurated_all_${SV}.bed | awk '{print $1, $2, $3}' | sed 's/ /,/'g > uncurated_all_${SV}.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"uncurated_all_DEL.csv\", \n",
    "                  names=[\"Chrom\", \"Start\", \"Stop\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chrom</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>69685</td>\n",
       "      <td>70178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>70489</td>\n",
       "      <td>413631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>83292</td>\n",
       "      <td>83620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>83758</td>\n",
       "      <td>84400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CM004525.1</td>\n",
       "      <td>84072</td>\n",
       "      <td>84367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15024</th>\n",
       "      <td>CM004554.1</td>\n",
       "      <td>3378163</td>\n",
       "      <td>3378562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15025</th>\n",
       "      <td>CM004554.1</td>\n",
       "      <td>3458698</td>\n",
       "      <td>3461289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15026</th>\n",
       "      <td>CM004554.1</td>\n",
       "      <td>3461380</td>\n",
       "      <td>3466508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15027</th>\n",
       "      <td>CM004554.1</td>\n",
       "      <td>3479906</td>\n",
       "      <td>3486030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15028</th>\n",
       "      <td>CM004554.1</td>\n",
       "      <td>3514583</td>\n",
       "      <td>3515112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15029 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Chrom    Start     Stop\n",
       "0      CM004525.1    69685    70178\n",
       "1      CM004525.1    70489   413631\n",
       "2      CM004525.1    83292    83620\n",
       "3      CM004525.1    83758    84400\n",
       "4      CM004525.1    84072    84367\n",
       "...           ...      ...      ...\n",
       "15024  CM004554.1  3378163  3378562\n",
       "15025  CM004554.1  3458698  3461289\n",
       "15026  CM004554.1  3461380  3466508\n",
       "15027  CM004554.1  3479906  3486030\n",
       "15028  CM004554.1  3514583  3515112\n",
       "\n",
       "[15029 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Size']=df['Stop']-df['Start']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 981,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Size'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/envs/jupyterlab/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\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.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Size'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-981-8a9be92c32c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mSize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Size'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/jupyterlab/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2904\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2905\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2906\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2907\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2908\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/jupyterlab/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Size'"
     ]
    }
   ],
   "source": [
    "Size = df['Size'].tolist()\n",
    "s = sorted(Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 982,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filter for SVs only below 2000 base pairs in length\n",
    "Size_filtered =  [x for x in Size if x <= 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 983,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Size_filtered, bins = 100)\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "red_patch = mpatches.Patch(color='red', label='Deletions (Uncurated all)')\n",
    "\n",
    "plt.legend(handles=[red_patch])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}