aarti_data120320.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/anubrata/Documents/aarti_data/new_data'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "#os.chdir(\"/home/anubrata/Documents/datascience\")\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "arti_data=pd.read_csv(\"A_cip-1_SNP.csv\",encoding=\"utf-16\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chrom</th>\n",
       "      <th>Position</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Type</th>\n",
       "      <th>Zygosity</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Gene_ID</th>\n",
       "      <th>Trancript_ID</th>\n",
       "      <th>Exon_ID</th>\n",
       "      <th>Exon_Rank</th>\n",
       "      <th>Effect</th>\n",
       "      <th>old_AA/new_AA</th>\n",
       "      <th>Old_codon/New_codon</th>\n",
       "      <th>Codon_Num(CDS)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>5890</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>221.9990</td>\n",
       "      <td>1221</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>7420</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>221.9990</td>\n",
       "      <td>1090</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>7432</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>221.9990</td>\n",
       "      <td>1125</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>7508</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>221.9990</td>\n",
       "      <td>1287</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>9326</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>221.9990</td>\n",
       "      <td>1440</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40840</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>4855324</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>88.9974</td>\n",
       "      <td>1039</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40841</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>4855368</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>221.9990</td>\n",
       "      <td>1083</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40842</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>4855382</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>221.9990</td>\n",
       "      <td>1062</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40846</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>4856558</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>221.9990</td>\n",
       "      <td>512</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40847</td>\n",
       "      <td>NC_003197.2</td>\n",
       "      <td>4856655</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>SNP</td>\n",
       "      <td>Hom</td>\n",
       "      <td>221.9990</td>\n",
       "      <td>1209</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>INTERGENIC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4418 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Chrom  Position Reference Variant Type Zygosity   Quality  \\\n",
       "42     NC_003197.2      5890         T       C  SNP      Hom  221.9990   \n",
       "62     NC_003197.2      7420         G       T  SNP      Hom  221.9990   \n",
       "63     NC_003197.2      7432         A       G  SNP      Hom  221.9990   \n",
       "64     NC_003197.2      7508         T       C  SNP      Hom  221.9990   \n",
       "78     NC_003197.2      9326         A       G  SNP      Hom  221.9990   \n",
       "...            ...       ...       ...     ...  ...      ...       ...   \n",
       "40840  NC_003197.2   4855324         A       G  SNP      Hom   88.9974   \n",
       "40841  NC_003197.2   4855368         A       G  SNP      Hom  221.9990   \n",
       "40842  NC_003197.2   4855382         G       A  SNP      Hom  221.9990   \n",
       "40846  NC_003197.2   4856558         T       C  SNP      Hom  221.9990   \n",
       "40847  NC_003197.2   4856655         A       C  SNP      Hom  221.9990   \n",
       "\n",
       "       Coverage Gene_ID Trancript_ID Exon_ID Exon_Rank      Effect  \\\n",
       "42         1221      --           --      --        --  INTERGENIC   \n",
       "62         1090      --           --      --        --  INTERGENIC   \n",
       "63         1125      --           --      --        --  INTERGENIC   \n",
       "64         1287      --           --      --        --  INTERGENIC   \n",
       "78         1440      --           --      --        --  INTERGENIC   \n",
       "...         ...     ...          ...     ...       ...         ...   \n",
       "40840      1039      --           --      --        --  INTERGENIC   \n",
       "40841      1083      --           --      --        --  INTERGENIC   \n",
       "40842      1062      --           --      --        --  INTERGENIC   \n",
       "40846       512      --           --      --        --  INTERGENIC   \n",
       "40847      1209      --           --      --        --  INTERGENIC   \n",
       "\n",
       "      old_AA/new_AA Old_codon/New_codon  Codon_Num(CDS)  \n",
       "42              NaN                 NaN             NaN  \n",
       "62              NaN                 NaN             NaN  \n",
       "63              NaN                 NaN             NaN  \n",
       "64              NaN                 NaN             NaN  \n",
       "78              NaN                 NaN             NaN  \n",
       "...             ...                 ...             ...  \n",
       "40840           NaN                 NaN             NaN  \n",
       "40841           NaN                 NaN             NaN  \n",
       "40842           NaN                 NaN             NaN  \n",
       "40846           NaN                 NaN             NaN  \n",
       "40847           NaN                 NaN             NaN  \n",
       "\n",
       "[4418 rows x 16 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arti_data[arti_data.Gene_ID=='--']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Gene_ID</th>\n",
       "      <th>Exon_Rank</th>\n",
       "      <th>Effect</th>\n",
       "      <th>old_AA/new_AA</th>\n",
       "      <th>Old_codon/New_codon</th>\n",
       "      <th>Codon_Num(CDS)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>507</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>221.999</td>\n",
       "      <td>1028</td>\n",
       "      <td>thrA</td>\n",
       "      <td>1</td>\n",
       "      <td>SYNONYMOUS_CODING</td>\n",
       "      <td>G/G</td>\n",
       "      <td>ggC/ggT</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>537</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>221.999</td>\n",
       "      <td>1130</td>\n",
       "      <td>thrA</td>\n",
       "      <td>1</td>\n",
       "      <td>SYNONYMOUS_CODING</td>\n",
       "      <td>A/A</td>\n",
       "      <td>gcA/gcC</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>744</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>221.999</td>\n",
       "      <td>1316</td>\n",
       "      <td>thrA</td>\n",
       "      <td>1</td>\n",
       "      <td>SYNONYMOUS_CODING</td>\n",
       "      <td>G/G</td>\n",
       "      <td>ggA/ggC</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>747</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>221.999</td>\n",
       "      <td>1431</td>\n",
       "      <td>thrA</td>\n",
       "      <td>1</td>\n",
       "      <td>SYNONYMOUS_CODING</td>\n",
       "      <td>L/L</td>\n",
       "      <td>ctT/ctC</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>762</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>221.999</td>\n",
       "      <td>1355</td>\n",
       "      <td>thrA</td>\n",
       "      <td>1</td>\n",
       "      <td>SYNONYMOUS_CODING</td>\n",
       "      <td>G/G</td>\n",
       "      <td>ggG/ggA</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Position Reference Variant  Quality  Coverage Gene_ID Exon_Rank  \\\n",
       "0       507         C       T  221.999      1028    thrA         1   \n",
       "1       537         A       C  221.999      1130    thrA         1   \n",
       "2       744         A       C  221.999      1316    thrA         1   \n",
       "3       747         T       C  221.999      1431    thrA         1   \n",
       "4       762         G       A  221.999      1355    thrA         1   \n",
       "\n",
       "              Effect old_AA/new_AA Old_codon/New_codon  Codon_Num(CDS)  \n",
       "0  SYNONYMOUS_CODING           G/G             ggC/ggT            57.0  \n",
       "1  SYNONYMOUS_CODING           A/A             gcA/gcC            67.0  \n",
       "2  SYNONYMOUS_CODING           G/G             ggA/ggC           136.0  \n",
       "3  SYNONYMOUS_CODING           L/L             ctT/ctC           137.0  \n",
       "4  SYNONYMOUS_CODING           G/G             ggG/ggA           142.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_data=arti_data.drop(['Chrom','Type','Zygosity','Trancript_ID','Exon_ID'],axis=1)\n",
    "rev_data['Gene_ID']=rev_data['Gene_ID'].replace('--','Intergenic')\n",
    "#mutated=pd.value_counts(rev_data.Gene_ID)\n",
    "#highly_mutated=mutated[mutated>50]\n",
    "#highly_mutated.to_csv(\"highly_mutated.csv\")\n",
    "#mutated.to_csv(\"mutated.csv\")\n",
    "rev_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anubrata/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.max_columns',None)\n",
    "#pd.set_option('display.max_rows',None)\n",
    "x=pd.value_counts(rev_data.Gene_ID)\n",
    "x[x>30]\n",
    "x.to_csv(\"highly_mutated.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4238, 52)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rpoC mutation distribution\n",
    "rpoC_data=rev_data[rev_data.Gene_ID==\"rpoC\"]\n",
    "rpoC_mutSites=list(rpoC_data[\"Position\"])\n",
    "rpoC_mutSites\n",
    "rpoC_gene=[n for n in range(4370012,4374250)]\n",
    "len(rpoC_gene),len(rpoC_mutSites)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anubrata/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "values=np.random.uniform(size=52,low=0.2,high=0.5)\n",
    "plt.stem(rpoC_mutSites,values)\n",
    "plt.title(\"SNPs in rpoC\",fontsize=20)\n",
    "plt.xlim(4370157,4374198)\n",
    "plt.ylim(0,0.7)\n",
    "locs,labels=plt.xticks()\n",
    "lst=[n for n in range(0,4240,(4240//10))]\n",
    "plt.xticks(locs,lst)\n",
    "plt.savefig(\"SNP rpoC.jpg\",dpi=100,bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3966, 65)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#metH mutation distribution\n",
    "metH_data=rev_data[rev_data.Gene_ID==\"metH\"]\n",
    "metH_mutSites=list(metH_data[\"Position\"])\n",
    "metH_gene=[n for n in range(4408069,4412035)]\n",
    "len(metH_gene),len(metH_mutSites)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anubrata/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f98c61fa910>,\n",
       "  <matplotlib.axis.XTick at 0x7f98c61fa210>,\n",
       "  <matplotlib.axis.XTick at 0x7f98c6245110>,\n",
       "  <matplotlib.axis.XTick at 0x7f98c5d944d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98c5d94ad0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98c5d94e90>,\n",
       "  <matplotlib.axis.XTick at 0x7f98c5d9d750>,\n",
       "  <matplotlib.axis.XTick at 0x7f98c5d94650>,\n",
       "  <matplotlib.axis.XTick at 0x7f98c5d9d050>,\n",
       "  <matplotlib.axis.XTick at 0x7f98c5d9d990>],\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "values=np.random.uniform(size=65,low=0.2,high=0.5)\n",
    "plt.stem(metH_mutSites,values)\n",
    "plt.title(\"SNPs in metH\",fontsize=20)\n",
    "plt.xlim(4408069,4412035)\n",
    "plt.ylim(0,0.7)\n",
    "locs,labels=plt.xticks()\n",
    "lst=[n for n in range(0,4000,(4000//10))]\n",
    "plt.xticks(locs,lst)\n",
    "#plt.savefig(\"SNP metH.jpg\",dpi=100,bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anubrata/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
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       "  <matplotlib.axis.XTick at 0x7f98b9fd5910>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fd5f50>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fdc5d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fdcc10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fe3150>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fe38d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fe3f10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fe3050>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fd5cd0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fea590>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9feab50>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9ff0150>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9ff0810>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9ff0e50>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f764d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f76b10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f7f110>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f76350>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fea810>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f7f6d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f7fd10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f87390>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f879d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f876d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f8d690>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f8dcd0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f8d350>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f8d650>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fea410>,\n",
       "  <matplotlib.axis.XTick at 0x7f98ba040d50>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f943d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f94a90>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f94f10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f9a750>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f9ad90>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fa1410>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f9ad50>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fea310>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fa1a10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fa1f90>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fa9610>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fa9c50>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fb0190>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fb0910>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fb0f50>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fb0210>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9fa1d10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f383d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f38c10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f3f150>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f3f8d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f3ff10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f45590>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f45bd0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f4d150>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f45250>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f382d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f4d790>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f4ddd0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f54450>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f54a90>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f54f10>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f5b750>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f5bd90>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f5b050>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f4d690>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f632d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f639d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f636d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f69690>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f69cd0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f70350>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f70990>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f70fd0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f700d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f63410>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9ef6550>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9ef6b90>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9efe110>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9efe850>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9efee90>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f06510>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f06b50>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f064d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9efe510>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f70310>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f0d790>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f0ddd0>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f13450>,\n",
       "  <matplotlib.axis.XTick at 0x7f98b9f13a90>,\n",
       "  ...],\n",
       " <a list of 3966 Text xticklabel objects>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "values=np.random.uniform(size=65,low=0.2,high=0.5)\n",
    "plt.stem(metH_mutSites,values)\n",
    "plt.title(\"SNPs in metH\",fontsize=20)\n",
    "plt.xlim(4408069,4412035)\n",
    "plt.ylim(0,0.7)\n",
    "#locs,labels=plt.xticks()\n",
    "lst=[n for n in range(4400000,4413000,(4000//10))]\n",
    "plt.xticks(metH_gene)\n",
    "#plt.savefig(\"SNP metH.jpg\",dpi=100,bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4408070,\n",
       " 4408071,\n",
       " 4408072,\n",
       " 4408073,\n",
       " 4408074,\n",
       " 4408075,\n",
       " 4408076,\n",
       " 4408077,\n",
       " 4408078]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metH_gene[1:10]"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}