Figure_3C+D_characteristics_of_gorg-only_clusters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GORG CDS sequences used for this analysis were generated by Prokka during SCGC's standard SAG assembly and annotation pipeline.  \n",
    "\n",
    "Default prokka parameters were used to call genes.  \n",
    "\n",
    "All CDS from all GORG SAGs were then combined into one fasta file and clustered using mmseqs2 as follows:\n",
    "\n",
    "<!---\n",
    "pbs sub found at:\n",
    "/mnt/scgc/scgc_nfs/lab/julia/pbs_subs/190212_mmseq_gorg_m80.sh\n",
    "-->\n",
    "\n",
    "```\n",
    "cd /mnt/scgc/simon/simonsproject/gorg-clustering\n",
    "\n",
    "name=gorg_sag_orfs\n",
    "qual=80minid_m80\n",
    "threads=50\n",
    "\n",
    "faa=data/${name}.faa\n",
    "db=analyses/${name}.db\n",
    "db_clustered=analyses/${name}_${qual}.db_clustered\n",
    "rep_seqs=analyses/${name}_${qual}.reps\n",
    "out_fasta=analyses/${name}_${qual}.faa\n",
    "out_tsv=analyses/${name}_${qual}.tsv\n",
    "tmp_dir=data/tmp2/\n",
    "\n",
    "mkdir $tmp_dir\n",
    "\n",
    "mmseqs createdb $faa $db\n",
    "mmseqs linclust --threads $threads -v 1 --min-seq-id 0.6 --kmer-per-seq 80 $db $db_clustered $tmp_dir\n",
    "mmseqs result2repseq $db $db_clustered $rep_seqs  --threads $threads\n",
    "mmseqs result2flat $db $db $rep_seqs $out_fasta --use-fasta-header\n",
    "mmseqs createtsv $db $db $db_clustered $out_tsv --threads $threads -v 1\n",
    "```  \n",
    "\n",
    "Functions were assigned for each GORG cluster as follows:\n",
    "\n",
    "*for each cluster, choose a random representative sequence within the group, and then pull out the annotation for that sequence from the prokka .gff file for the SAG from which it came.  snakemake script can be found in ```./scripts/190228_gorg_extract_functions.snakemake```*\n",
    "\n",
    "Output table: ```\"/mnt/scgc/simon/simonsproject/gorg-clustering/analyses/snakemake_cluster_tbl_rand_orf.tsv\"```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "import os.path as op\n",
    "from collections import Counter\n",
    "import math\n",
    "\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sklearn.metrics\n",
    "\n",
    "def fit(df, x, y):\n",
    "    \n",
    "    def _check_fit(p, x, y):\n",
    "        predicted = [p(i) for i in x]\n",
    "        observed = y\n",
    "        residuals = [x-y for x, y in zip(predicted, observed)]\n",
    "        r2 = sklearn.metrics.r2_score(observed, predicted)\n",
    "        stdev = np.std(residuals)\n",
    "        return r2, stdev\n",
    "\n",
    "    x = [float(i) for i in df[x].values]\n",
    "    y = [float(i) for i in df[y].values]\n",
    "\n",
    "    z = np.polyfit(x, y, 1)\n",
    "    p = np.poly1d(z)\n",
    "    r2_order1, sd_order1 = _check_fit(p, x, y)\n",
    "    return p, r2_order1, sd_order1\n",
    "\n",
    "sns.set_style('white')\n",
    "\n",
    "wd = \"/mnt/scgc/simon/simonsproject/gorg-clustering/\"\n",
    "os.chdir(wd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_orf_names = ['cluster','total_members','rand_orf','eC_number','product','Name']\n",
    "df = pd.read_csv(\"analyses/snakemake_cluster_tbl_rand_orf.tsv\", sep=\"\\t\",names=rand_orf_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# identify clusters characterized as \"hypothetical protein\"\n",
    "df['hypothetical?'] = ['Hypothetical' if 'hypothetical protein' in l else \"Not hypothetical\" for l in df['product']]\n",
    "\n",
    "# identify singleton clusters\n",
    "df['single'] = ['Singletons' if i == 1 else 'Small_Clust' if i < 10 else 'Clusters' for i in df['total_members']]\n",
    "\n",
    "# identify annotated sequences based on whether or not gene name assigned\n",
    "df['annotated'] = ['not annotated' if type(i) == float else 'annotated' for i in df['Name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# incorporate protein sequence lengths into table\n",
    "\n",
    "# below file created by script: scripts/190208_prot_sizes.py\n",
    "aa_sizes_file = \"analyses/gorg_sag_orfs_sizes.csv\"\n",
    "sizes = []\n",
    "orfs = []\n",
    "\n",
    "with open(aa_sizes_file) as ih:\n",
    "    for count, l in enumerate(ih):\n",
    "        size = int(l.strip().split(\",\")[-1])\n",
    "        orf = l.split(\" \")[0]\n",
    "        sizes.append(size)\n",
    "        orfs.append(orf)\n",
    "        \n",
    "aa_sizes = pd.DataFrame(data={'orf':orfs, \"size\":sizes})\n",
    "\n",
    "\n",
    "# original cluster file from mmseqs2\n",
    "tsv_file = 'analyses/gorg_sag_orfs_80minid_m80.tsv'\n",
    "cdf = pd.read_csv(tsv_file, sep=\"\\t\", names = ['c1','c2'])\n",
    "cdf['sag'] = [i.split(\".\")[0].split(\"_\")[0] for i in cdf['c2']]\n",
    "cdf.rename(columns={'c1':'cluster','c2':'orf'}, inplace=True)\n",
    "\n",
    "csdf = cdf.merge(aa_sizes, on='orf', how='left')\n",
    "\n",
    "csdf['size1'] = [int(i) for i in csdf['size']]\n",
    "\n",
    "clust_sizes = csdf.groupby('cluster')['size1'].mean().reset_index().rename(columns={\"size1\":'average_size'})\n",
    "clust_stdev = csdf.groupby('cluster')['size1'].std().reset_index().rename(columns={\"size1\":'stdev_size'})\n",
    "\n",
    "adf = df.merge(clust_sizes, on='cluster', how='left').merge(clust_stdev, on='cluster', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adf['log_avg_size'] = [math.log(i, 10) for i in adf['average_size']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>cluster</th>\n",
       "      <th>total_members</th>\n",
       "      <th>rand_orf</th>\n",
       "      <th>eC_number</th>\n",
       "      <th>product</th>\n",
       "      <th>Name</th>\n",
       "      <th>hypothetical?</th>\n",
       "      <th>single</th>\n",
       "      <th>annotated</th>\n",
       "      <th>average_size</th>\n",
       "      <th>stdev_size</th>\n",
       "      <th>log_avg_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AG-470-O10_00608</td>\n",
       "      <td>35</td>\n",
       "      <td>AH-326-P14_00216</td>\n",
       "      <td>2.4.1.-</td>\n",
       "      <td>Rhamnosyltransferase WbbL</td>\n",
       "      <td>wbbL</td>\n",
       "      <td>Not hypothetical</td>\n",
       "      <td>Clusters</td>\n",
       "      <td>annotated</td>\n",
       "      <td>297.485714</td>\n",
       "      <td>8.406480</td>\n",
       "      <td>2.473466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AG-893-N16_00541</td>\n",
       "      <td>11</td>\n",
       "      <td>AG-893-I22_00083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hypothetical protein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hypothetical</td>\n",
       "      <td>Clusters</td>\n",
       "      <td>not annotated</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.492760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AH-321-G20_00914</td>\n",
       "      <td>1</td>\n",
       "      <td>AH-321-G20_00914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hypothetical protein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hypothetical</td>\n",
       "      <td>Singletons</td>\n",
       "      <td>not annotated</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.488551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AG-447-L15_00133</td>\n",
       "      <td>34</td>\n",
       "      <td>AG-333-O07_00325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tRNA-modifying protein YgfZ</td>\n",
       "      <td>ygfZ</td>\n",
       "      <td>Not hypothetical</td>\n",
       "      <td>Clusters</td>\n",
       "      <td>annotated</td>\n",
       "      <td>276.264706</td>\n",
       "      <td>2.502227</td>\n",
       "      <td>2.441325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AG-896-G08_00719</td>\n",
       "      <td>3</td>\n",
       "      <td>AG-896-G08_00719</td>\n",
       "      <td>2.5.1.3</td>\n",
       "      <td>Thiamine-phosphate synthase</td>\n",
       "      <td>thiE</td>\n",
       "      <td>Not hypothetical</td>\n",
       "      <td>Small_Clust</td>\n",
       "      <td>annotated</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.851258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cluster  total_members          rand_orf eC_number  \\\n",
       "0  AG-470-O10_00608             35  AH-326-P14_00216   2.4.1.-   \n",
       "1  AG-893-N16_00541             11  AG-893-I22_00083       NaN   \n",
       "2  AH-321-G20_00914              1  AH-321-G20_00914       NaN   \n",
       "3  AG-447-L15_00133             34  AG-333-O07_00325       NaN   \n",
       "4  AG-896-G08_00719              3  AG-896-G08_00719   2.5.1.3   \n",
       "\n",
       "                       product  Name     hypothetical?       single  \\\n",
       "0    Rhamnosyltransferase WbbL  wbbL  Not hypothetical     Clusters   \n",
       "1         hypothetical protein   NaN      Hypothetical     Clusters   \n",
       "2         hypothetical protein   NaN      Hypothetical   Singletons   \n",
       "3  tRNA-modifying protein YgfZ  ygfZ  Not hypothetical     Clusters   \n",
       "4  Thiamine-phosphate synthase  thiE  Not hypothetical  Small_Clust   \n",
       "\n",
       "       annotated  average_size  stdev_size  log_avg_size  \n",
       "0      annotated    297.485714    8.406480      2.473466  \n",
       "1  not annotated    311.000000    0.000000      2.492760  \n",
       "2  not annotated    308.000000         NaN      2.488551  \n",
       "3      annotated    276.264706    2.502227      2.441325  \n",
       "4      annotated     71.000000    0.000000      1.851258  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_counts = adf.groupby('single')['total_members'].sum()\n",
    "clust_counts = adf.groupby('single')['total_members'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 8.2 million ORFs from GORG belonging to 1.1 million clusters.\n",
      "Of these clusters 61.3 % consist of only one sequence.\n",
      "However, the majority, 78.6 %, of sequences belong to clusters containing more than one sequence.\n"
     ]
    }
   ],
   "source": [
    "print(\"There are\", round(adf['total_members'].sum() / 1000000, 1), \"million ORFs from GORG belonging to\", round(len(adf) / 1000000, 1), \"million clusters.\")\n",
    "print(\"Of these clusters\", round((clust_counts['Singletons'] / len(adf)) * 100, 1), \"% consist of only one sequence.\")\n",
    "print(\"However, the majority,\", round((seq_counts['Clusters'] / adf['total_members'].sum()) * 100, 1),\"%, of sequences belong to clusters containing more than one sequence.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/scgc_nfs/opt/common/anaconda3/4.0.0/lib/python3.5/site-packages/seaborn-0.7.1-py3.5.egg/seaborn/categorical.py:637: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  kde_data = remove_na(group_data[hue_mask])\n",
      "/mnt/scgc_nfs/opt/common/anaconda3/4.0.0/lib/python3.5/site-packages/seaborn-0.7.1-py3.5.egg/seaborn/categorical.py:932: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  violin_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2aab63602f98>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qem43VzClI1ewx+Me4FEmtxZ5SS1Ix9kH6pfmbySVS/Hdhf3Nlm7VVSqTwcaN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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab636020f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot('annotated',\n",
    "            'log_avg_size',\n",
    "            hue = 'single', \n",
    "            data = adf[(adf['total_members'] > 10)|(adf['total_members'] == 1)]).set(xlabel = \"Annotation Status\",\n",
    "                           ylabel = \"Log(Amino Acid Sequence Length)\")\n",
    "plt.legend(loc=2)\n",
    "#plt.savefig(\"analyses/plots/gorg-clustering_single_vs_multi_size_violinplots.png\", dpi = 300)\n",
    "#plt.savefig(\"analyses/plots/gorg-clustering_single_vs_multi_size_violinplots.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>single</th>\n",
       "      <th>total_clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Clusters</td>\n",
       "      <td>96397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Singletons</td>\n",
       "      <td>654009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Small_Clust</td>\n",
       "      <td>316045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        single  total_clusters\n",
       "0     Clusters           96397\n",
       "1   Singletons          654009\n",
       "2  Small_Clust          316045"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count singles and clusters\n",
    "\n",
    "single_cluster_count = df.groupby('single')['cluster'].count().reset_index().rename(columns={'cluster':'total_clusters'})\n",
    "single_cluster_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count occurence of different EC values within singletons versus clustered sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colors = [\"#cc535c\",\n",
    "\"#4ab1a9\",\n",
    "\"#bd5db0\",\n",
    "\"#76a44a\",\n",
    "\"#7179cb\",\n",
    "\"#c3843e\",\n",
    "\"#cc5143\",\n",
    "\"#000000\"]\n",
    "cdict = dict(zip([str(i) for i in np.arange(1,8)],colors))\n",
    "cdict['None'] = \"#000000\"\n",
    "\n",
    "ecdict = {'1':'Oxidoreductase',\n",
    "         '2':'Transferase',\n",
    "         '3':'Hydrolase',\n",
    "         '4': 'Lyase',\n",
    "         '5': 'Isomerase',\n",
    "         '6': 'Ligase',\n",
    "         '7': 'Translocase'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize counts\n",
    "\n",
    "ec_count = df.fillna('None').groupby(['single','eC_number'])['cluster'].count().reset_index()\n",
    "ec_count['proportion'] = [i['cluster'] / single_cluster_count[single_cluster_count['single'] == i['single']]['total_clusters'].values[0] for l, i in ec_count.iterrows()]\n",
    "\n",
    "for i in range(1,5):\n",
    "    col = \"ec{}\".format(i)\n",
    "    ec_count[col] = [\".\".join(j.split(\".\")[0:i]) if j != \"None\" else \"None\" for j in ec_count['eC_number'] ]\n",
    "\n",
    "ec3 = ec_count.groupby(['single','ec3'])['proportion'].sum().reset_index().pivot(index='ec3',columns='single',values='proportion').reset_index().fillna(0)\n",
    "\n",
    "ec3['level1'] = [i.split(\".\")[0] for i in ec3['ec3']]\n",
    "ec3['level1_color'] = ['#000000' if i == \"None\" else cdict[i] for i in ec3['level1']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'y = 0.649x + -0.0001'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit linear regression\n",
    "\n",
    "r2 = round(fit(ec3, 'Clusters','Singletons')[1],3)\n",
    "r2_statement = \"r2 = {}\".format(r2)\n",
    "\n",
    "ec3fit = fit(ec3[ec3['ec3'] != 'None'], 'Clusters','Singletons')\n",
    "ec_eq = 'y = {}x + {}'.format(round(ec3fit[0][1],3), round(ec3fit[0][0], 5))\n",
    "ec_eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2aab0e0e03c8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j4wkNDdVb2sjNzY2oqCi++uordX6ij48PHh4elCpVihkzZpCQkICdnR3nzp1j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\nFEIIIYQQJ0UCSSGEEEIIcVL+f6IRSPmFw2KXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab0e351f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Patch\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "p = sns.regplot('Clusters',\n",
    "            'Singletons',\n",
    "            data=ec3[ec3['ec3'] != 'None'], \n",
    "            fit_reg = True, \n",
    "            scatter_kws={\"s\":100, \n",
    "                         'facecolors':ec3['level1_color']})\n",
    "\n",
    "p.set_xlabel('Proportion Clusters with e.c. annotation', fontsize=14) \n",
    "p.set_ylabel('Proportion Singletons with e.c. annotation', fontsize=14)\n",
    "p.set_title('E.C. Annotation Counts\\nat 3 levels of e.c. annotation', fontsize=18)\n",
    "p.tick_params(labelsize=14)\n",
    "\n",
    "plt.xlim(0,)\n",
    "plt.ylim(0,)\n",
    "\n",
    "legend_elements = [Patch(facecolor=cdict[i], edgecolor=cdict[i], label=ecdict[i]) for i in ec3['level1'].unique() if i != 'None']\n",
    "plt.legend(handles=legend_elements, loc=2,\n",
    "fancybox=False, shadow=False, frameon=False, ncol=2, fontsize=14)\n",
    "\n",
    "plt.text(0.015, 0.005, r2_statement + \"\\n\" + ec_eq, fontsize = 18)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}