pufferfish-krakmap-ISMB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from scipy.stats import spearmanr\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from math import log\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Reference name to Taxa Id file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ref = \"/mnt/scratch2/avi/meta-map/kraken/KrakenDB/seqid2taxid.map\"\n",
    "with open(ref) as f:\n",
    "    refId2TaxId = pd.read_csv(f, header=None, sep=\"\\t\", names=[\"refName\", \"refId\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Taxonomy Tree\n",
    "### Taxum ID , Taxum Rank , Taxum Parent ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf = \"/mnt/scratch2/avi/meta-map/kraken/KrakenDB/taxonomy/nodes.dmp\"\n",
    "taxa = []\n",
    "id2rank = {}\n",
    "with open(tf) as f:\n",
    "    for line in f:\n",
    "        toks = line.rstrip(\"\\t|\\n\").split(\"\\t|\\t\")\n",
    "        taxa += [[int(toks[0]), int(toks[1]), toks[2]]]\n",
    "        id2rank[int(toks[0])] = toks[2]\n",
    "taxa_df = pd.DataFrame.from_records(taxa)\n",
    "taxa_df.columns = [\"taxaId\", \"pid\", \"rank\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taxaId</th>\n",
       "      <th>pid</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no rank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>131567</td>\n",
       "      <td>superkingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>335928</td>\n",
       "      <td>genus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>species</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>32199</td>\n",
       "      <td>species</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   taxaId     pid          rank\n",
       "0       1       1       no rank\n",
       "1       2  131567  superkingdom\n",
       "2       6  335928         genus\n",
       "3       7       6       species\n",
       "4       9   32199       species"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxa_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mard(df, c1, c2):\n",
    "    a = abs(df[c1]-df[c2])/(df[c1]+df[c2])\n",
    "    return a.sum()/a.count()\n",
    "def calc_precRecall(truth_pred):\n",
    "    alltruth = truth_pred['trueCount'].sum()\n",
    "    tp = truth_pred[['predCount', 'trueCount']].min(axis=1).sum()\n",
    "    recall = tp/max(alltruth, 1)\n",
    "    predictedP = truth_pred['predCount'].sum()\n",
    "    prec = tp/max(predictedP, 1)\n",
    "    f1 = 2*(prec*recall)/max((prec+recall), 1)\n",
    "    return [prec, recall, f1]\n",
    "def calc_presAbs_precRecall(truth_pred):\n",
    "    alltruth = len(truth_pred[truth_pred['trueId'] != 0])\n",
    "    tp = len(truth_pred[(truth_pred['predId'] != 0) & (truth_pred['trueId'] != 0)])\n",
    "    recall = tp/max(alltruth, 1)\n",
    "    predictedP = len(truth_pred[truth_pred['predId'] != 0])\n",
    "    prec = tp/max(predictedP, 1)\n",
    "    f1 = 2*(prec*recall)/max((prec+recall), 1)\n",
    "    return [prec, recall, f1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# assumption:\n",
    "# 0: id, 1: rank, 2: count, 3: aggregatedCount\n",
    "def truth_aggregate(taxa_df, df, keepAll = False):\n",
    "    res = defaultdict(lambda:0, {})\n",
    "    for index, row in df.iterrows():\n",
    "        nodeId = row.iloc[0]\n",
    "        count = row.iloc[2]\n",
    "        res[(nodeId, row.iloc[1])] = count # id, rank --> aggregated count\n",
    "        # increase the count of any inner node that is an ancestor of this node up until root\n",
    "        while (nodeId != 1):\n",
    "            parId = taxa_df[taxa_df['taxaId'] == nodeId].pid.values[0]\n",
    "            pnode = taxa_df[taxa_df['taxaId'] == parId]\n",
    "            res[(parId, pnode['rank'].values[0])] += count\n",
    "            nodeId = parId\n",
    "    res_list = []\n",
    "    for k, v in res.items(): # put the three values in the list\n",
    "        res_list.append([k[0], k[1], v])\n",
    "    return pd.DataFrame(res_list, columns=['trueId', 'trueRank', 'trueCount']) # convert it to a dataframe and return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# assumption:\n",
    "# 0: id, 1: rank, 2: count\n",
    "def aggregate(taxa_df, df, level = 'species', keepAll = False):\n",
    "    res = defaultdict(lambda:0, {})\n",
    "    for index, row in df.iterrows():\n",
    "        node = [row.iloc[0], row.iloc[1], row.iloc[2]]\n",
    "        while (node[1] != level and node[0] != 1):\n",
    "            parId = taxa_df[taxa_df['taxaId'] == node[0]].pid.values[0]\n",
    "            pnode = taxa_df[taxa_df['taxaId'] == parId]\n",
    "            node = [pnode.taxaId.values[0], pnode['rank'].values[0], node[2]]\n",
    "        if node[1] == level:\n",
    "            res[node[0]] += node[2]\n",
    "    return pd.DataFrame(list(res.items()), columns=['trueId', 'trueCount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "truthfile = '/mnt/scratch2/avi/meta-map/kraken/meta/truth.txt'\n",
    "truth = pd.read_csv(truthfile, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taxid</th>\n",
       "      <th>counts</th>\n",
       "      <th>species</th>\n",
       "      <th>size</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79329</td>\n",
       "      <td>24666</td>\n",
       "      <td>Chitinophaga pinensis</td>\n",
       "      <td>9127347</td>\n",
       "      <td>Huttenhower_HC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1836</td>\n",
       "      <td>22195</td>\n",
       "      <td>Saccharopolyspora erythraea</td>\n",
       "      <td>8079083</td>\n",
       "      <td>Huttenhower_HC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152480</td>\n",
       "      <td>20654</td>\n",
       "      <td>Burkholderia ambifaria</td>\n",
       "      <td>8095900</td>\n",
       "      <td>Huttenhower_HC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>182640</td>\n",
       "      <td>20483</td>\n",
       "      <td>Kribbella flavida</td>\n",
       "      <td>7579488</td>\n",
       "      <td>Huttenhower_HC1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80866</td>\n",
       "      <td>18289</td>\n",
       "      <td>Delftia acidovorans</td>\n",
       "      <td>6823175</td>\n",
       "      <td>Huttenhower_HC1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    taxid  counts                      species     size          dataset\n",
       "0   79329   24666        Chitinophaga pinensis  9127347  Huttenhower_HC1\n",
       "1    1836   22195  Saccharopolyspora erythraea  8079083  Huttenhower_HC1\n",
       "2  152480   20654       Burkholderia ambifaria  8095900  Huttenhower_HC1\n",
       "3  182640   20483            Kribbella flavida  7579488  Huttenhower_HC1\n",
       "4   80866   18289          Delftia acidovorans  6823175  Huttenhower_HC1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cedar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HC1\n",
      "spearman: 0.8896335210894745\n",
      "pearson: 0.9231588029692139\n",
      "mard: 0.39739670997904925\n",
      "HC2\n",
      "spearman: 0.8981036390945314\n",
      "pearson: 0.9247070025910785\n",
      "mard: 0.39510266478969913\n",
      "LC1\n",
      "spearman: 0.6777658632469074\n",
      "pearson: 0.9766616947159219\n",
      "mard: 0.2624311758931174\n",
      "LC2\n",
      "spearman: 0.9835165342845381\n",
      "pearson: 0.999916358905192\n",
      "mard: 0.27412189803431203\n",
      "LC3\n",
      "spearman: 0.9284039508324152\n",
      "pearson: 0.9998235801595162\n",
      "mard: 0.3273878618875248\n",
      "LC4\n",
      "spearman: 0.9892289424797295\n",
      "pearson: 0.9999704601336549\n",
      "mard: 0.1836100526482864\n",
      "LC5\n",
      "spearman: 0.385267828718259\n",
      "pearson: 0.9111521142577906\n",
      "mard: 0.8016507793480583\n",
      "LC6\n",
      "spearman: 0.8899856330197731\n",
      "pearson: 0.9982562945050177\n",
      "mard: 0.23743233406383168\n",
      "LC7\n",
      "spearman: 0.8516239316239316\n",
      "pearson: 0.9992721349504279\n",
      "mard: 0.21448492189793453\n",
      "LC8\n",
      "spearman: 0.7225493757376887\n",
      "pearson: 0.9918100498716004\n",
      "mard: 0.4814111346065421\n"
     ]
    }
   ],
   "source": [
    "for dataset in ['HC1', 'HC2', 'LC1', 'LC2', 'LC3', 'LC4', 'LC5', 'LC6', 'LC7', 'LC8']:\n",
    "    bracken = pd.read_csv('/mnt/scratch2/fatemeh/krakpuff/kraken/bracken/{}_species_abundance.txt'.format(dataset), sep='\\t')\n",
    "    bracken_truth=pd.merge(truth[truth['dataset'] == 'Huttenhower_'+dataset], bracken, how='outer', left_on='taxid', right_on='taxonomy_id').fillna(0)\n",
    "    print(dataset)\n",
    "    print('spearman: {}'.format(bracken_truth[['counts', 'new_est_reads']].corr(method='spearman')['counts']['new_est_reads']))\n",
    "    print('pearson: {}'.format(bracken_truth[['counts', 'new_est_reads']].corr(method='pearson')['counts']['new_est_reads']))\n",
    "    print('mard: {}'.format(mard(bracken_truth, 'counts', 'new_est_reads')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HC1\n",
      "spearman: 0.9982774037450032\n",
      "pearson: 0.9993551058581268\n",
      "mard: 0.6043304826003315\n",
      "HC2\n",
      "spearman: 0.9985846259778514\n",
      "pearson: 0.9991791005453481\n",
      "mard: 0.029420698857918846\n",
      "LC1\n",
      "spearman: 1.0\n",
      "pearson: 0.9999987703278822\n",
      "mard: 0.028687433373794913\n",
      "LC2\n",
      "spearman: 0.9991304347826087\n",
      "pearson: 0.9999973915759475\n",
      "mard: 0.02629295794952144\n",
      "LC3\n",
      "spearman: 1.0\n",
      "pearson: 0.9999938900564196\n",
      "mard: 0.02930787876515854\n",
      "LC4\n",
      "spearman: 1.0\n",
      "pearson: 0.9999995540865234\n",
      "mard: 0.06976236479582905\n",
      "LC5\n",
      "spearman: 0.6487859966120836\n",
      "pearson: 0.9668014050911022\n",
      "mard: 0.20351789180654214\n",
      "LC6\n",
      "spearman: 0.9973913043478261\n",
      "pearson: 0.9999972480128437\n",
      "mard: 0.043073973128333574\n",
      "LC7\n",
      "spearman: 1.0\n",
      "pearson: 0.9999989586146082\n",
      "mard: 0.02692023110374409\n",
      "LC8\n",
      "spearman: 0.9970355731225297\n",
      "pearson: 0.9999973115611752\n",
      "mard: 0.044373319058279376\n"
     ]
    }
   ],
   "source": [
    "for dataset in ['HC1', 'HC2', 'LC1', 'LC2', 'LC3', 'LC4', 'LC5', 'LC6', 'LC7', 'LC8']:\n",
    "    print(dataset)\n",
    "    cedar = pd.read_csv('/mnt/scratch2/fatemeh/krakpuff/singleEnd/krakpuff_output/cedar/{}_species_unfilt.cedar'.format(dataset), sep='\\t')\n",
    "    cedar_truth=pd.merge(truth[truth['dataset'] == 'Huttenhower_'+dataset], cedar, how='outer', left_on='taxid', right_on='taxaId') \n",
    "    print('spearman: {}'.format(cedar_truth[['counts', 'count']].corr(method='spearman')['counts']['count']))\n",
    "    print('pearson: {}'.format(cedar_truth[['counts', 'count']].corr(method='pearson')['counts']['count']))\n",
    "    print('mard: {}'.format(mard(cedar_truth, 'counts', 'count')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# for dataset in ['HC1', 'HC2', 'LC2', 'LC3', 'LC4', 'LC7', 'LC8']:\n",
    "#     print(dataset)\n",
    "#     cedar = pd.read_csv('/mnt/scratch2/fatemeh/krakpuff/singleEnd/krakpuff_output/cedar/{}_species_unfilt_noEq.cedar'.format(dataset), sep='\\t')\n",
    "#     cedar_truth=pd.merge(truth[truth['dataset'] == 'Huttenhower_'+dataset], cedar, how='outer', left_on='taxid', right_on='taxaId') \n",
    "#     print('spearman: {}'.format(cedar_truth[['counts', 'count']].corr(method='spearman')['counts']['count']))\n",
    "#     print('pearson: {}'.format(cedar_truth[['counts', 'count']].corr(method='pearson')['counts']['count']))\n",
    "#     print('mard: {}'.format(mard(cedar_truth, 'counts', 'count')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate aggregated values at different levels of the truth once and for all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#ds_truth = {}\n",
    "#for dataset in ['HC1', 'HC2', 'LC1', 'LC2', 'LC3', 'LC4', 'LC5', 'LC6', 'LC7', 'LC8']:\n",
    "    \n",
    "#     dataset_truth = truth[truth['dataset'] == 'Huttenhower_{}'.format(dataset)]\n",
    "#     dataset_truth = pd.merge(dataset_truth, taxa_df, left_on='taxid', right_on='taxaId')\n",
    "#     dataset_truth = dataset_truth[['taxaId', 'rank', 'counts']]\n",
    "#     dataset_truth.columns = [['trueId', 'rank', 'trueCount']]\n",
    "#     ds_truth[dataset] = truth_aggregate(taxa_df, dataset_truth)\n",
    "#     for level in ['species', 'genus', 'family', 'order', 'class', 'phylum']:\n",
    "#         key = dataset + '-' + level\n",
    "#         ds_truth[key] = aggregate(taxa_df, dataset_truth, level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for k, v in ds_truth.items():\n",
    "#     v.to_csv('/mnt/scratch2/fatemeh/krakpuff/truth_files/{}.truth'.format(k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds_truth = {}\n",
    "for dataset in ['HC1', 'HC2', 'LC1', 'LC2', 'LC3', 'LC4', 'LC5', 'LC6', 'LC7', 'LC8']:\n",
    "    for level in ['species', 'genus', 'family', 'order', 'class', 'phylum']:\n",
    "        key = dataset + '-' + level\n",
    "        ds_truth[key] = pd.DataFrame.from_csv('/mnt/scratch2/fatemeh/krakpuff/truth_files/{}.truth'.format(key))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def merge_clark_results(clarkf, clarkl):\n",
    "#clarkf = '/mnt/scratch2/fatemeh/krakpuff/clark_reports/{}_species.rpt'\n",
    "    clark_df = {}\n",
    "    clark_spec_ag = {}\n",
    "    for dataset in ['HC1', 'HC2', 'LC1', 'LC2', 'LC3', 'LC4', 'LC5', 'LC6', 'LC7', 'LC8']:\n",
    "        print(dataset)\n",
    "        clark = pd.read_csv(clarkf.format(dataset))\n",
    "        clark['TaxID'] = clark['TaxID'].apply(pd.to_numeric, errors='coerce')\n",
    "        clark['TaxID'] = clark['TaxID'].fillna(0.0).astype(int)\n",
    "        clark = pd.merge(clark, taxa_df, left_on='TaxID', right_on='taxaId')\n",
    "        clark_spec_ag[dataset] = truth_aggregate(taxa_df, clark[['TaxID', 'rank', 'Count']])\n",
    "        clark_spec_ag[dataset].columns = ['predId', 'rank', 'predCount']\n",
    "        clark_l = pd.DataFrame(columns=['TaxID', 'rank', 'Count'])\n",
    "        #clarkl = '/mnt/scratch2/fatemeh/krakpuff/clark_reports/{}_{}.rpt'\n",
    "        for level in ['genus', 'family', 'order', 'class', 'phylum']:\n",
    "            print(level)\n",
    "            clark= pd.read_csv(clarkl.format(dataset, level))\n",
    "            clark['TaxID'] = clark['TaxID'].apply(pd.to_numeric, errors='coerce')\n",
    "            clark['TaxID'] = clark['TaxID'].fillna(0.0).astype(int)\n",
    "            clark = pd.merge(clark, taxa_df, left_on='TaxID', right_on='taxaId')\n",
    "            clark_l = clark_l.append(clark[['TaxID', 'rank', 'Count']])\n",
    "        print(clark_l['rank'].unique())\n",
    "        clark_ag = pd.merge(clark_spec_ag[dataset], clark_l, left_on='predId', right_on='TaxID', how='outer').fillna(0)\n",
    "        clark_ag['totCount'] = clark_ag[['predCount', 'Count']].max(axis=1)\n",
    "        clark_ag['id'] = clark_ag[['predId', 'TaxID']].max(axis=1)\n",
    "        clark_ag = pd.merge(clark_ag[['id', 'totCount']], taxa_df, left_on='id', right_on='taxaId')\n",
    "        clark_df[dataset] = clark_ag[['id', 'rank', 'totCount']]\n",
    "        clark_df[dataset].columns = [['predId', 'predRank', 'predCount']] \n",
    "    return clark_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prepare(truthf, krakenf, pufferf, clark1_df, statf):\n",
    "    levels = ['species', 'genus', 'family', 'order', 'class', 'phylum']\n",
    "    stats = []\n",
    "    ds_puffer = {}\n",
    "    ds_kraken = {}\n",
    "    ds_clark = {}\n",
    "    for dataset in ['HC1', 'HC2', 'LC1', 'LC2', 'LC3', 'LC4', 'LC5', 'LC6', 'LC7', 'LC8']:\n",
    "        print(dataset)\n",
    "        ds_truth[dataset] = pd.DataFrame.from_csv(truthf.format(dataset))\n",
    "        #krakenf = '/mnt/scratch2/avi/meta-map/kraken/krakOut/{}.rpt'.format(dataset)\n",
    "        krakenf_formatted = krakenf.format(dataset)\n",
    "        kraken = pd.read_csv(krakenf_formatted, sep=\"\\t\", header=None)\n",
    "        kraken.columns = ['totalPerc', 'totalCount', 'count', 'r', 'id', 'rankScientificName']\n",
    "        kraken = kraken[['id', 'totalCount']]\n",
    "        kraken = pd.merge(kraken, taxa_df, left_on='id', right_on='taxaId')\n",
    "        kraken = kraken[['id','rank', 'totalCount']]\n",
    "        kraken.columns = ['predId', 'predRank', 'predCount']\n",
    "\n",
    "        #pufferf = '/mnt/scratch2/fatemeh/krakpuff/{}_species0_2.out'.format(dataset)\n",
    "        pufferf_formatted = pufferf.format(dataset)\n",
    "        puffer = pd.read_csv(pufferf_formatted, sep=\"\\t\")\n",
    "        puffer = puffer[puffer['taxaId'] > 0]\n",
    "        puffer.columns = ['predId', 'predRank', 'predDirectCount', 'predCount']\n",
    "       \n",
    "        #clark_df = pd.read_csv(clarkf.format(dataset))\n",
    "        #clark_df['TaxID'] = clark_df['TaxID'].apply(pd.to_numeric, errors='coerce')\n",
    "        #clark_df['TaxID'] = clark_df['TaxID'].fillna(0.0).astype(int)\n",
    "        #clark = pd.merge(clark_df, taxa_df, left_on='TaxID', right_on='taxaId')\n",
    "        #clark = clark[['TaxID', 'rank', 'Count']]\n",
    "        #clark.columns = ['predId', 'predRank', 'predCount']\n",
    "        \n",
    "        ds_truth_puffer_joined = pd.merge(ds_truth[dataset], puffer, how='outer', left_on='trueId', right_on='predId').fillna(0)\n",
    "        #ds_truth_puffer_joined.columns = ['trueId', 'trueCount', 'predId', 'predCount']\n",
    "        puff_pears = ds_truth_puffer_joined[['trueCount', 'predCount']].corr(method='pearson')['predCount']['trueCount']\n",
    "        puff_spear = ds_truth_puffer_joined[['trueCount', 'predCount']].corr(method='spearman')['predCount']['trueCount']\n",
    "        puff_mard = mard(ds_truth_puffer_joined, 'trueCount', 'predCount')\n",
    "        puff_precrecf1 = calc_precRecall(ds_truth_puffer_joined)\n",
    "        puff_presAb_precrecf1 = calc_presAbs_precRecall(ds_truth_puffer_joined)\n",
    "        \n",
    "        ds_truth_kraken_joined = pd.merge(ds_truth[dataset], kraken, how='outer', left_on='trueId', right_on='predId').fillna(0)\n",
    "        #ds_truth_kraken_joined.columns = ['trueId', 'trueCount', 'predId', 'predCount']\n",
    "        kraken_pears = ds_truth_kraken_joined[['trueCount', 'predCount']].corr(method='pearson')['predCount']['trueCount']\n",
    "        kraken_spear = ds_truth_kraken_joined[['trueCount', 'predCount']].corr(method='spearman')['predCount']['trueCount']\n",
    "        kraken_mard = mard(ds_truth_kraken_joined, 'trueCount', 'predCount')\n",
    "        kraken_precrecf1 = calc_precRecall(ds_truth_kraken_joined)\n",
    "        kraken_presAb_precrecf1 = calc_presAbs_precRecall(ds_truth_kraken_joined)\n",
    "        \n",
    "        ds_truth_clark_joined = pd.merge(ds_truth[dataset], clark1_df[dataset], how='outer', left_on='trueId', right_on='predId').fillna(0)\n",
    "        #ds_truth_kraken_joined.columns = ['trueId', 'trueCount', 'predId', 'predCount']\n",
    "        clark_pears = ds_truth_clark_joined[['trueCount', 'predCount']].corr(method='pearson')['predCount']['trueCount']\n",
    "        clark_spear = ds_truth_clark_joined[['trueCount', 'predCount']].corr(method='spearman')['predCount']['trueCount']\n",
    "        clark_mard = mard(ds_truth_clark_joined, 'trueCount', 'predCount')\n",
    "        clark_precrecf1 = calc_precRecall(ds_truth_clark_joined)\n",
    "        clark_presAb_precrecf1 = calc_presAbs_precRecall(ds_truth_clark_joined)\n",
    "\n",
    "        stats += [(dataset, 'all',\n",
    "                    puff_pears, puff_spear, puff_mard, \n",
    "                   puff_precrecf1[0], puff_precrecf1[1], puff_precrecf1[2],\n",
    "                   puff_presAb_precrecf1[0], puff_presAb_precrecf1[1], puff_presAb_precrecf1[2],\n",
    "                    kraken_pears, kraken_spear, kraken_mard, \n",
    "                   kraken_precrecf1[0], kraken_precrecf1[1], kraken_precrecf1[2],\n",
    "                  kraken_presAb_precrecf1[0], kraken_presAb_precrecf1[1], kraken_presAb_precrecf1[2],\n",
    "                  clark_pears, clark_spear, clark_mard, \n",
    "                   clark_precrecf1[0], clark_precrecf1[1], clark_precrecf1[2],\n",
    "                  clark_presAb_precrecf1[0], clark_presAb_precrecf1[1], clark_presAb_precrecf1[2])]\n",
    "        \n",
    "        \n",
    "        ############# NO RANK #################\n",
    "        key = dataset + '-norank'\n",
    "        ds_truth[key] = ds_truth[dataset][~ds_truth[dataset]['trueRank'].isin(levels)]\n",
    "        ds_puffer[key] = puffer[~puffer['predRank'].isin(levels)][['predId', 'predCount']]#aggregate(taxa_df, puffer, level)\n",
    "        ds_kraken[key] = kraken[~kraken['predRank'].isin(levels)][['predId', 'predCount']]#aggregate(taxa_df, kraken, level)\n",
    "        ds_truth_puffer_joined = pd.merge(ds_truth[key], ds_puffer[key], how='outer', left_on='trueId', right_on='predId').fillna(0)\n",
    "        #ds_truth_puffer_joined.columns = ['trueId', 'trueCount', 'predId', 'predCount']\n",
    "        puff_pears = ds_truth_puffer_joined[['trueCount', 'predCount']].corr(method='pearson')['predCount']['trueCount']\n",
    "        puff_spear = ds_truth_puffer_joined[['trueCount', 'predCount']].corr(method='spearman')['predCount']['trueCount']\n",
    "        puff_mard = mard(ds_truth_puffer_joined, 'trueCount', 'predCount')\n",
    "        puff_precrecf1 = calc_precRecall(ds_truth_puffer_joined)\n",
    "        puff_presAb_precrecf1 = calc_presAbs_precRecall(ds_truth_puffer_joined)\n",
    "\n",
    "        ds_truth_kraken_joined = pd.merge(ds_truth[key], ds_kraken[key], how='outer', left_on='trueId', right_on='predId').fillna(0)\n",
    "        #ds_truth_kraken_joined.columns = ['trueId', 'trueCount', 'predId', 'predCount']\n",
    "        kraken_pears = ds_truth_kraken_joined[['trueCount', 'predCount']].corr(method='pearson')['predCount']['trueCount']\n",
    "        kraken_spear = ds_truth_kraken_joined[['trueCount', 'predCount']].corr(method='spearman')['predCount']['trueCount']\n",
    "        kraken_mard = mard(ds_truth_kraken_joined, 'trueCount', 'predCount')\n",
    "        kraken_precrecf1 = calc_precRecall(ds_truth_kraken_joined)\n",
    "        kraken_presAb_precrecf1 = calc_presAbs_precRecall(ds_truth_kraken_joined)\n",
    "\n",
    "        stats += [(dataset, 'norank',\n",
    "                puff_pears, puff_spear, puff_mard, \n",
    "                   puff_precrecf1[0], puff_precrecf1[1], puff_precrecf1[2],\n",
    "                   puff_presAb_precrecf1[0], puff_presAb_precrecf1[1], puff_presAb_precrecf1[2],\n",
    "                kraken_pears, kraken_spear, kraken_mard, \n",
    "                   kraken_precrecf1[0], kraken_precrecf1[1], kraken_precrecf1[2],\n",
    "                  kraken_presAb_precrecf1[0], kraken_presAb_precrecf1[1], kraken_presAb_precrecf1[2],\n",
    "                  0,0,0,0,0,0,0,0,0)]\n",
    "            \n",
    "        for level in levels:\n",
    "            #print(level)\n",
    "            key = dataset + '-' + level\n",
    "            ds_truth[key] = pd.read_csv(truthf.format(key))\n",
    "            #ds_truth[key] = aggregate(taxa_df, dataset_truth, level)\n",
    "            ds_puffer[key] = puffer[puffer['predRank'] == level][['predId', 'predCount']]#aggregate(taxa_df, puffer, level)\n",
    "            ds_kraken[key] = kraken[kraken['predRank'] == level][['predId', 'predCount']]#aggregate(taxa_df, kraken, level)\n",
    "            ds_clark[key] = clark1_df[dataset][clark1_df[dataset]['predRank'] == level][['predId', 'predCount']]\n",
    "            \n",
    "            ds_truth_puffer_joined = pd.merge(ds_truth[key], ds_puffer[key], how='outer', left_on='trueId', right_on='predId').fillna(0)\n",
    "            #ds_truth_puffer_joined.columns = ['trueId', 'trueCount', 'predId', 'predCount']\n",
    "            puff_pears = ds_truth_puffer_joined[['trueCount', 'predCount']].corr(method='pearson')['predCount']['trueCount']\n",
    "            puff_spear = ds_truth_puffer_joined[['trueCount', 'predCount']].corr(method='spearman')['predCount']['trueCount']\n",
    "            puff_mard = mard(ds_truth_puffer_joined, 'trueCount', 'predCount')\n",
    "            puff_precrecf1 = calc_precRecall(ds_truth_puffer_joined)\n",
    "            puff_presAb_precrecf1 = calc_presAbs_precRecall(ds_truth_puffer_joined)\n",
    "            \n",
    "            ds_truth_kraken_joined = pd.merge(ds_truth[key], ds_kraken[key], how='outer', left_on='trueId', right_on='predId').fillna(0)\n",
    "            #ds_truth_kraken_joined.columns = ['trueId', 'trueCount', 'predId', 'predCount']\n",
    "            kraken_pears = ds_truth_kraken_joined[['trueCount', 'predCount']].corr(method='pearson')['predCount']['trueCount']\n",
    "            kraken_spear = ds_truth_kraken_joined[['trueCount', 'predCount']].corr(method='spearman')['predCount']['trueCount']\n",
    "            kraken_mard = mard(ds_truth_kraken_joined, 'trueCount', 'predCount')\n",
    "            kraken_precrecf1 = calc_precRecall(ds_truth_kraken_joined)\n",
    "            kraken_presAb_precrecf1 = calc_presAbs_precRecall(ds_truth_kraken_joined)\n",
    "            \n",
    "            ds_truth_clark_joined = pd.merge(ds_truth[key], ds_clark[key], how='outer', left_on='trueId', right_on='predId').fillna(0)\n",
    "            #ds_truth_kraken_joined.columns = ['trueId', 'trueCount', 'predId', 'predCount']\n",
    "            clark_pears = ds_truth_clark_joined[['trueCount', 'predCount']].corr(method='pearson')['predCount']['trueCount']\n",
    "            clark_spear = ds_truth_clark_joined[['trueCount', 'predCount']].corr(method='spearman')['predCount']['trueCount']\n",
    "            clark_mard = mard(ds_truth_clark_joined, 'trueCount', 'predCount')\n",
    "            clark_precrecf1 = calc_precRecall(ds_truth_clark_joined)\n",
    "            clark_presAb_precrecf1 = calc_presAbs_precRecall(ds_truth_clark_joined)\n",
    "            \n",
    "            stats += [(dataset, level,\n",
    "                    puff_pears, puff_spear, puff_mard, \n",
    "                       puff_precrecf1[0], puff_precrecf1[1], puff_precrecf1[2],\n",
    "                       puff_presAb_precrecf1[0], puff_presAb_precrecf1[1], puff_presAb_precrecf1[2],\n",
    "                    kraken_pears, kraken_spear, kraken_mard, \n",
    "                       kraken_precrecf1[0], kraken_precrecf1[1], kraken_precrecf1[2],\n",
    "                      kraken_presAb_precrecf1[0], kraken_presAb_precrecf1[1], kraken_presAb_precrecf1[2],\n",
    "                      clark_pears, clark_spear, clark_mard, \n",
    "                       clark_precrecf1[0], clark_precrecf1[1], clark_precrecf1[2],\n",
    "                      clark_presAb_precrecf1[0], clark_presAb_precrecf1[1], clark_presAb_precrecf1[2])]\n",
    "    stats_df = pd.DataFrame.from_records(stats, columns = ['dataset', 'level', \n",
    "                                   'puff_pearson', 'puff_spearman', 'puff_mard', \n",
    "                                                           'puff_cnt_prec', 'puff_cnt_rec', 'puff_cnt_f1',\n",
    "                                                           'puff_prec', 'puff_rec', 'puff_f1',\n",
    "                                     'kraken_pearson', 'kraken_spearman', 'kraken_mard', \n",
    "                                                           'kraken_cnt_prec', 'kraken_cnt_rec', 'kraken_cnt_f1',\n",
    "                                                           'kraken_prec', 'kraken_rec', 'kraken_f1',\n",
    "                                                          'clark_pearson', 'clark_spearman', 'clark_mard', \n",
    "                                                           'clark_cnt_prec', 'clark_cnt_rec', 'clark_cnt_f1',\n",
    "                                                           'clark_prec', 'clark_rec', 'clark_f1']) \n",
    "    stats_df['krakpuff_mard_diff'] = stats_df['kraken_mard']-stats_df['puff_mard']\n",
    "    stats_df['puffkrak_spearman_diff'] = stats_df['puff_spearman']-stats_df['kraken_spearman']\n",
    "    stats_df['puffkrak_pearson_diff'] = stats_df['puff_pearson']-stats_df['kraken_pearson']\n",
    "    stats_df['puffkrak_cnt_f1_diff'] = stats_df['puff_cnt_f1']-stats_df['kraken_cnt_f1']\n",
    "    stats_df['puffkrak_f1_diff'] = stats_df['puff_f1']-stats_df['kraken_f1']\n",
    "    \n",
    "    stats_df.to_csv(statf)\n",
    "    return (stats_df, ds_kraken, ds_puffer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def draw_plots(stats_df):\n",
    "\n",
    "    datasets = ['HC1', 'HC2', 'LC1', 'LC2', 'LC3', 'LC4', 'LC5', 'LC6', 'LC7', 'LC8']\n",
    "    levels = ['all', 'species']#, 'genus', 'family', 'order', 'class', 'phylum', 'norank']\n",
    "    statistics = ['spearman', 'pearson', 'mard', 'f1', 'cnt_f1']\n",
    "\n",
    "\n",
    "    for st in statistics:\n",
    "        print('\\n\\n{}'.format(st))\n",
    "        plt.subplots(3, 3, figsize=[30, 10])\n",
    "        index = np.arange(len(datasets))\n",
    "        bar_width = 0.35\n",
    "        opacity = 0.8\n",
    "\n",
    "        i = 1\n",
    "        for level in levels:\n",
    "            plt.subplot(3, 3, i)\n",
    "            plt.bar(index, stats_df[stats_df['level'] == level]['puff_{}'.format(st)], bar_width,\n",
    "                             alpha=opacity,\n",
    "                             color='b',\n",
    "                             label='Puffer')\n",
    "\n",
    "            plt.bar(index + bar_width, stats_df[stats_df['level'] == level]['kraken_{}'.format(st)], bar_width,\n",
    "                             alpha=opacity,\n",
    "                             color='g',\n",
    "                             label='Kraken')\n",
    "\n",
    "            plt.bar(index + 2 * bar_width, stats_df[stats_df['level'] == level]['clark_{}'.format(st)], bar_width,\n",
    "                             alpha=opacity,\n",
    "                             color='r',\n",
    "                             label='Clark')\n",
    "\n",
    "            #plt.xlabel('Datasets', fontsize=14)\n",
    "            plt.ylabel(st, fontsize=20)\n",
    "            #plt.title('Scores by person')\n",
    "            plt.xticks(index + bar_width, stats_df[stats_df['level'] == level]['dataset'])\n",
    "            plt.legend()\n",
    "            plt.title(level, fontsize=22)\n",
    "            i += 1\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw_box_plots(stats_df, levels, xaxis, hues, title):\n",
    "\n",
    "    datasets = ['HC1', 'HC2', 'LC1', 'LC2', 'LC3', 'LC4', 'LC5', 'LC6', 'LC7', 'LC8']\n",
    "    #levels = ['all', 'species']#, 'genus', 'family', 'order', 'class', 'phylum', 'norank']\n",
    "    statistics = ['spearman', 'pearson', 'mard', 'f1']#, 'cnt_f1']\n",
    "\n",
    "    for st in statistics:\n",
    "        print('\\n\\n{}'.format(st))\n",
    "        sb.boxplot(x=xaxis, y=st, hue=hues, data=stats_df[stats_df['level'].isin(levels)], linewidth=2)\n",
    "        plt.legend()\n",
    "        #plt.title(st, fontsize=22)  \n",
    "        plt.ylabel(st.title())\n",
    "        plt.savefig(\"{}_{}.pdf\".format(title, st))\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def unpivot(stats_df):\n",
    "    stats = ['spearman', 'pearson', 'mard', 'cnt_prec', 'cnt_rec', 'cnt_f1', 'prec', 'rec', 'f1']\n",
    "    tools = ['puff', 'kraken', 'clark']\n",
    "    unpiv_stats = pd.DataFrame(columns = ['tools', 'dataset', 'level']+stats)\n",
    "    #print(unpiv_stats.dtypes)\n",
    "    i = 0\n",
    "    for idx, r in stats_df.iterrows():\n",
    "        for tool in tools:\n",
    "            colls = ['dataset', 'level']\n",
    "            for s in stats:\n",
    "                colls += ['{}_{}'.format(tool, s)]\n",
    "            #print('{}: {}'.format(i, tool))\n",
    "            unpiv_stats.loc[i] = [tool] + list(r[colls])\n",
    "            i+=1\n",
    "    return unpiv_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# No Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "No Filtering\n",
      "\n",
      "\n",
      "spearman\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fatemeh/miniconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77591e9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "pearson\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEKCAYAAAA4t9PUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f776869a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "mard\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJDViYEiSGjEwJEmNGBiSpEYMDElSI/8fmNbLEo6B6mEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7768572860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "f1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf01o5JkQDjkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f778e8fcd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "spearman\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7759efd7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "pearson\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f777eb224a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "mard\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIgkKCiIikqCgICIiCf8fM3/JkCfQytcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7758c5bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "f1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFBRERCRFQUFERFIUFEREJEVBQUREUv4/wUDNdRraPAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f778f63d978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "spearman\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE7RJREFUeJzt3X+QXWd93/H3h7VN4uDgBm1okCwkNwJiqCFhrRRK6YaE\nRIATxSXFskkMJhlVKXZopk3wZKaFDk1Ch2knTuygakA1jh2rKT8FEZWJPYuJJxStHfmHhN2qClgS\nSby2Z0xMKbbsb/+4R8fXq9Xuldmzd7X7fs3c2Xue85x7v3dnZz/3Oc/5kapCkiSA5wy7AEnS4mEo\nSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqXVaVy+cZDtwIfBgVb1ihvUBrgbeDPxf\n4J1Vdedcr7tixYpas2bNPFcrSUvbHXfc8VBVjc7Vr7NQAK4DrgGuP8H6NwHrmsePAx9ufs5qzZo1\nTE5OzlOJkrQ8JPn6IP06231UVbcBj8zSZSNwffV8GTg7yQ91VY8kaW7DnFNYCRzqWz7ctEmShuSU\nmGhOsjnJZJLJqampYZcjSUvWMEPhCHBO3/Kqpu04VbWtqsaqamx0dM55EknSszTMUNgJXJaefwQ8\nWlV/PcR6JGnZ6/KQ1JuAcWBFksPA+4DTAapqK7CL3uGoB+gdknp5V7VIkgbTWShU1SVzrC/g3V29\nvyTp5HV5noJOYHx8fNglLDoTExPDLkESp8jRR5KkheFIYQgWy7fiYyOWxVKPpOFzpCBJahkKkqSW\noSBJahkKkqSWoSBJahkKkqSWoSBJai278xQ8m/h4/k6e5jkbWu4cKUiSWstupHDM313wrmGXoEXk\nrD3bh12CtCg4UpAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktToNhSQbktyf5ECSq2ZY//eS\nfCrJ3Um+kuQVXdYjSZpdZ6GQZAS4FngTcB5wSZLzpnX7LWBvVZ0PXAZc3VU9kqS5dTlSWA8cqKqD\nVfU4sAPYOK3PecCtAFV1H7AmyQs7rEmSNIsuQ2ElcKhv+XDT1u8u4J8BJFkPvBhYNf2FkmxOMplk\ncmpqqqNyJUnDnmj+IHB2kr3AlcBfAk9O71RV26pqrKrGRkdHF7pGSVo2urwg3hHgnL7lVU1bq6q+\nCVwOkCTAXwEHO6xJkjSLLkcKe4B1SdYmOQPYBOzs75Dk7GYdwK8AtzVBIUkags5GClV1NMkVwG5g\nBNheVfuSbGnWbwV+BPhYkgL2Ab/cVT2SpLl1ej+FqtoF7JrWtrXv+V8AL+myBknS4JbtTXa8qYok\nHW/YRx9JkhaRZTtS8Hac6ufIUepxpCBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqTW\nsj15zZOVJOl4jhQkSa1lN1KYmJgYdgmLxvj4OODvRNLTHClIklqGgiSpZShIklqGgiSpZShIklqd\nhkKSDUnuT3IgyVUzrH9+ks8muSvJviSXd1mPJGl2nYVCkhHgWuBNwHnAJUnOm9bt3cD+qnolMA78\npyRndFWTJGl2XY4U1gMHqupgVT0O7AA2TutTwFlJAjwPeAQ42mFNkqRZdBkKK4FDfcuHm7Z+1wA/\nAnwDuAd4T1U91WFNkqRZDHui+WeAvcCLgFcB1yT5/umdkmxOMplkcmpqaqFrlKRlo8tQOAKc07e8\nqmnrdznwyeo5APwV8LLpL1RV26pqrKrGRkdHOytYkpa7LkNhD7Auydpm8ngTsHNanweAnwRI8kLg\npcDBDmuSJM2iswviVdXRJFcAu4ERYHtV7UuypVm/FfgAcF2Se4AA762qh7qqSZI0u06vklpVu4Bd\n09q29j3/BvDTXdYgSRrcsCeaJUmLiKEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKk\nlqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoNHApJRpK8\nKMnqY48BttmQ5P4kB5JcNcP630iyt3ncm+TJJD9wsh9CkjQ/ThukU5IrgfcBfws81TQXcP4s24wA\n1wJvBA4De5LsrKr9x/pU1YeADzX9fxb49ap65Fl8DknSPBgoFID3AC+tqodP4rXXAweq6iBAkh3A\nRmD/CfpfAtx0Eq9/yhofHx92Cc+wGOqZmJgYdgmSGHz30SHg0ZN87ZXNdsccbtqOk+RMYAPwiZN8\nD0nSPBp0pHAQmEjyp8B3jjVW1X+epzp+Frj9RLuOkmwGNgOsXj3nVMai57diSYvVoCOFB4AvAGcA\nZ/U9ZnMEOKdveVXTNpNNzLLrqKq2VdVYVY2Njo4OWLIk6WQNNFKoqn//LF57D7AuyVp6YbAJuHR6\npyTPB/4p8IvP4j0kSfNo0KOPRoHfBF4OfM+x9qp6w4m2qaqjSa4AdgMjwPaq2pdkS7N+a9P1IuDm\nqvrWs/sIkqT5Muicwo3AfwMuBLYA7wCm5tqoqnYBu6a1bZ22fB1w3YB1SJI6NOicwguq6qPAE1X1\nxap6F3DCUYIk6dQ06EjhiebnXyd5C/ANwDOPJWmJGTQU/kMzIfyvgT8Avh/49c6qkiQNxaBHH32u\nefoo8BPdlSNJGqZBjz5aC1wJrOnfpqp+rpuyJEnDMOjuo08DHwU+y9MXxJMkLTGDhsL/q6rf77QS\nSdLQDRoKVyd5H3Azz7z20Z2dVCVJGopBQ+EfAr9E79yE/vspeK6CJC0hg4bCPwfOrarHuyxGkjRc\ng57RfC9wdpeFSJKGb9CRwtnAfUn28Mw5BQ9JlaQlZNBQeF+nVUiSFoU5QyHJCPD+qvJMZkla4uac\nU6iqJ4GnmmsfSZKWsEF3Hz0G3JPkC0B7M5yq+rVOqpIkDcWgofDJ5iFJWsIGvUrqx7ouRJI0fINe\nJXUd8LvAeTzzHs3ndlSXJGkIBj157b8CHwaO0rufwvXADV0VJUkajkFD4Xur6hYgVfX1qno/8Ja5\nNkqyIcn9SQ4kueoEfcaT7E2yL8kXBy9dkjTfBp1o/k6S5wD/O8kVwBHgebNt0JzfcC3wRuAwsCfJ\nzqra39fnbOAPgQ1V9UCSH3w2H0KSND8GHSm8BzgT+DXg1cAvAu+YY5v1wIGqOthcSG8HsHFan0uB\nT1bVAwBV9eCghUuS5t+gRx/tAUjyVFVdPuBrrwQO9S0fBn58Wp+XAKcnmQDOAq6uquunv1CSzcBm\ngNWrVw/49pKkkzXQSCHJa5LsB+5rll+Z5A/n4f1PozfyeAvwM8C/TfKS6Z2qaltVjVXV2Ojo6Dy8\nrSRpJoPuPvo9ev+0HwaoqruA18+xzRHgnL7lVU1bv8PA7qr6VlU9BNwGvHLAmiRJ82zQUKCqDk1r\nenKOTfYA65KsTXIGsAnYOa3PZ4DXJTktyZn0di99ddCaJEnza9Cjjw4leS1QSU6nN/E86z/vqjra\nHKm0GxgBtlfVviRbmvVbq+qrSf4HcDe923x+pKrufbYfRpL03UlVzd0pWQFcDfwUvdHFbuA9VfVw\nt+Udb2xsrCYnJxf6bSXplJbkjqoam6vfoEcfPQS8/buuSpK0qA169NG5ST6bZCrJg0k+k8TrHknS\nEjPoRPMfA38C/BDwIuC/Azd1VZQkaTgGDYUzq+qPqupo87iBvqulSpKWhkGPPvp8c0G7HUABFwO7\nkvwAQFU90lF9kqQFNGgovK35ubn5mebnJnoh4fyCJC0Bs4ZCkguAQ1W1tll+B/BW4GvA+x0hSNLS\nMtecwn8BHgdI8np6d1/7GPAosK3b0iRJC22u3UcjfaOBi4FtVfUJ4BNJ9nZbmiRpoc01UhhJciw4\nfhK4tW/doPMRkqRTxFz/2G8CvpjkIeDbwJcAkvwwvV1IkqQlZNZQqKrfTnILvZPWbq6nL5T0HODK\nrouTJC2sOXcBVdWXZ2j7X92UI0kapoHvpyBJWvoMBUlSy1CQJLU8rFTSM4yPjw+7hEVnYmJi2CUs\nGEcKkqSWIwVJz7AYvhUfG60shlqWm05HCkk2JLk/yYHm0tvT148neTTJ3ubx77qsR5I0u85GCklG\ngGuBNwKHgT1JdlbV/mldv1RVF3ZVhyRpcF2OFNYDB6rqYFU9Tu8GPRs7fD9J0nepy1BYCRzqWz7c\ntE332iR3J/l8kpd3WI8kaQ7Dnmi+E1hdVY8leTPwaWDd9E5JNtPc9W316tULW6EkLSNdjhSOAOf0\nLa9q2lpV9c2qeqx5vgs4PcmK6S9UVduqaqyqxkZHRzssWZKWty5DYQ+wLsnaJGfQu5/zzv4OSf5+\nkjTP1zf1PNxhTZKkWXS2+6iqjia5AtgNjADbq2pfki3N+q3ALwC/muQovfs1bOq7PLckaYF1OqfQ\n7BLaNa1ta9/za4BruqxBkjQ4L3MhSWoZCpKklqEgSWoN+zwFSQ0vWX08fydPW6iLAzpSkCS1HClI\ni8z1b/BUHT3tsltfsKDv50hBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQy\nFCRJLUNBktQyFCRJLUNBktQyFCRJrU5DIcmGJPcnOZDkqln6XZDkaJJf6LIeSdLsOguFJCPAtcCb\ngPOAS5Kcd4J+/xG4uataJEmD6XKksB44UFUHq+pxYAewcYZ+VwKfAB7ssBZJ0gC6DIWVwKG+5cNN\nWyvJSuAi4MMd1iFJGtCwJ5p/D3hvVT01W6ckm5NMJpmcmppaoNIkafnp8h7NR4Bz+pZXNW39xoAd\nSQBWAG9OcrSqPt3fqaq2AdsAxsbGqrOKJWmZ6zIU9gDrkqylFwabgEv7O1TV2mPPk1wHfG56IEiS\nFk5noVBVR5NcAewGRoDtVbUvyZZm/dau3ls6lV126wuGXYKWsS5HClTVLmDXtLYZw6Cq3tllLZKk\nuXUaCpJO3vVveHjYJWgRWeiR47CPPpIkLSKGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSp\nZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklreeU1aZLxHs4bJkYIkqdXp\nSCHJBuBqYAT4SFV9cNr6jcAHgKeAo8C/qqo/77ImabGamJgYdgmLxvj4OODvZBg6C4UkI8C1wBuB\nw8CeJDuran9ft1uAnVVVSc4H/gR4WVc1SZJm1+Xuo/XAgao6WFWPAzuAjf0dquqxqqpm8fuAQpI0\nNF2GwkrgUN/y4abtGZJclOQ+4E+Bd830Qkk2J5lMMjk1NdVJsZKkRTDRXFWfqqqXAT9Pb35hpj7b\nqmqsqsZGR0cXtkBJWka6DIUjwDl9y6uathlV1W3AuUlWdFiTJGkWXR59tAdYl2QtvTDYBFza3yHJ\nDwP/p5lo/jHgucDDHdYkaQ7HjvxZDBZLLcvpKKjOQqGqjia5AthN75DU7VW1L8mWZv1W4K3AZUme\nAL4NXNw38SxJWmA51f4Hj42N1eTk5LDLkKRTSpI7qmpsrn5Dn2iWJC0ehoIkqWUoSJJahoIkqWUo\nSJJahoIkqWUoSJJahoIkqXXKnbyWZAr4+rDrWEJWAA8NuwhpBv5tzq8XV9WcVxQ95UJB8yvJ5CBn\nOUoLzb/N4XD3kSSpZShIklqGgrYNuwDpBPzbHALnFCRJLUcKkqSWoaBWkucm+bMke5NcnOSfJNnX\nLH/vsOvTqSXJmiT3LvS2+u50eTtOnXp+FKCqXgWQZCvwu1V1w1Cr0pKU5LSqOjrsOvRMjhSWsObb\n1n1Jbkzy1SQfT3Jmkq8lWdH0GUsykeQHgRuAC5qRwb8A3gZ8IMmNw/wcOvUlOTfJXyb5jSQ7k9wK\n3JLkeUluSXJnknuSbJxl2wuSjCT5UJI9Se5u/k5JMt78HX+8728+C/5BlwBHCkvfS4Ffrqrbk2wH\n/uVMnarqwSS/AvybqroQIMlrgM9V1ccXrlwtNUleCuwA3klvNPpjwPlV9UiS04CLquqbzReVLyfZ\nOdO2VXVXks3Ao1V1QZLnArcnubnp/qPAy4FvALcD/xj484X5lEuHI4Wl71BV3d48vwF43TCL0bIz\nCnwGeHtV3dW0faGqHmmeB/idJHcDfwasBF44y7Y/DVyWZC/wP4EXAOuadV+pqsNV9RSwF1jT3cda\nuhwpLH3Tjzku4ChPfyH4noUtR8vMo8AD9L6M7G/avtW3/u30/vm/uqqeSPI1nv6bnGnbAFdW1e7+\nN0kyDnynr+lJ/P/2rDhSWPpWN7uBAC6lN5z+GvDqpu2twyhKy8bjwEX0vt1fOsP65wMPNoHwE8CL\n59h2N/CrSU4HSPKSJN/XXfnLj0m69N0PvLuZT9gPfBj4CvDRJB8AJoZYm5aBqvpWkguBLwB/NG31\njcBnk9wDTAL3nWjbJI8BH6G3W+jOZiJ5Cvj5jj/CsuIZzUtYkjX0JopfMeRSJJ0i3H0kSWo5UpAk\ntRwpSJJahoIkqWUoSJJahoI0iyRnJ5nx0iADbOuVPnXKMRSk2Z3NCa4XJS1FhoI0uw8C/6C5cuyH\nmse9zRU9LwZIz3Ht/ZK8PMlXmte5O8m6495JWgQ8o1ma3VXAK6rqVUneCmwBXgmsAPYkuQ14LfCq\nGdr7bQGurqobk5wBjCzYJ5BOgiMFaXCvA26qqier6m+BLwIXzNLe7y+A30ryXuDFVfXthSxcGpSh\nIC2Aqvpj4OeAbwO7krxhyCVJMzIUpNn9HXBW8/xLwMXN3b9GgdfTu7jgidpbSc4FDlbV79O7R8D5\nC/UBpJPhnII0i6p6OMntzaGlnwfuBu6id1+K36yqv0nyKeA1M7Sv6XuptwG/lOQJ4G+A31nAjyEN\nzGsfSZJa7j6SJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlS6/8D4wZpCr2SrxgAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f778ed82c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "pearson\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFplJREFUeJzt3X+QXeV93/H3RysT28REdFkzLcIIJ5ggY4zttVy7tkcO\nKBWJB2zSicF2pRCoUG3AdSdxKdPGk8nUJZOMJ2iGmsg2rYixaUrDFKdUWJgqJB7HsLKEQPyYUQUG\nkWLWm8bEPxJY6ds/7kFciZX2IOvs3dW+XzM7uud5nnPv92ru7Gef85x7TqoKSZKms2DQBUiS5gYD\nQ5LUioEhSWrFwJAktWJgSJJaMTAkSa0YGJKkVgwMSVIrBoYkqZWFgy7gSDrhhBNqyZIlgy5DkuaM\nLVu2fK+qRtqMPaoCY8mSJYyNjQ26DEmaM5J8p+1YD0lJkloxMCRJrRgYkqRWDAxJUisGhiSplc4C\nI8mNSZ5J8uBB+pNkXZKdSbYneWtf38okjzZ9V3dVoySpvS5nGP8FWHmI/vOA05qfNcDnAJIMAdc3\n/UuBi5Ms7bBOSVILnX0Po6ruSbLkEEMuAG6q3j1i/zLJoiT/EFgC7KyqXQBJbmnGPtRVrQDLly/v\n8uk1x23evHnQJUgDN8g1jJOAJ/u2dzdtB2ufUpI1ScaSjI2Pj3dSqCTpKPimd1WtB9YDjI6O1uE+\nj39Bvuizn/0sX/3qVzn//PP55Cc/OehyJM0Sg5xhPAWc3Le9uGk7WLtmwMTEBBs3bqSq2LhxIxMT\nE4MuSdIsMcgZxu3AFc0axTuA71fV/00yDpyW5FR6QXER8OEB1jmvbNiwgb179wKwZ88ebrrpJmcZ\nAlznm8p8OzLR5Wm1XwG+CZyeZHeSS5OsTbK2GXIHsAvYCXwe+BhAVU0CVwB3Ag8Df1xVO7qqU/u7\n6667mJycBGBycpJNmzYNuCJJs0WXZ0ldPE1/AR8/SN8d9AJFM+zcc8/ljjvuYHJykoULF7JixYpB\nl6RZYrb8Nf3CTGe21DOf+E1v7Wf16tUsWND7WAwNDbFq1aoBVyRptjAwtJ/h4WFWrlxJElauXMnw\n8PCgS5I0S8z502p15K1evZrHH3/c2YWk/RgYeonh4WHWrVs36DIkzTIekpIktWJgSJJaMTAkSa0Y\nGJKkVgwMSVIrBoYkqRUDQ5LUioEhSWrFwJAktWJgSJJaMTAkSa0YGHqJiYkJrrrqKm/PKmk/BoZe\nYsOGDTzwwAPcdNNNgy5F0ixiYGg/ExMTbNy4kapi48aNzjIk7WNgaD8bNmxg7969AOzZs8dZhqR9\nDAzt56677mJychKAyclJNm3aNOCKJM0WBob2c+6557JwYe++WgsXLmTFihUDrkjSbNFpYCRZmeTR\nJDuTXD1F//FJbkuyPcm9Sc7s6/tEkgeT7Ejyr7qsUy9avXo1Cxb0PhZDQ0PeplXSPp0FRpIh4Hrg\nPGApcHGSpQcMuwbYVlVnAauA65p9zwT+BbAMeDPw/iQ/11WtetHw8DArV64kCStXrmR4eHjQJUma\nJbqcYSwDdlbVrqp6DrgFuOCAMUuBuwGq6hFgSZITgTOAb1XVj6pqEvgz4MIOa1Wf1atX86Y3vcnZ\nhaT9dBkYJwFP9m3vbtr63U8TBEmWAacAi4EHgfckGU7yauCXgJOnepEka5KMJRkbHx8/wm9hfhoe\nHmbdunXOLiTtZ9CL3tcCi5JsA64EtgJ7quph4HeBrwEbgW3AnqmeoKrWV9VoVY2OjIzMUNmSNP8s\n7PC5n2L/WcHipm2fqnoWuAQgSYDHgF1N3xeBLzZ9n6E3Q5EkDUiXM4z7gNOSnJrkGOAi4Pb+AUkW\nNX0AlwH3NCFCktc2/76O3mGrL3dYqyRpGp3NMKpqMskVwJ3AEHBjVe1Isrbpv4He4vaGJAXsAC7t\ne4r/nmQYeB74eFX9TVe1SpKm1+UhKarqDuCOA9pu6Hv8TeANB9n3PV3WJkl6eQa96C1JmiMMDElS\nKwaGJKkVA0OS1IqBIUlqxcCQJLViYEiSWjEwJEmtdPrFPUlHxvLlywddwqzj/8mLNm/ePCOv4wxD\nktSKMwxpDrnpFyYGXYJmkVV3z+w9a5xhSJJaMTAkSa0YGJKkVgwMSVIrBoYkqRUDQ5LUioEhSWrF\nwJAktWJgSJJaMTAkSa10GhhJViZ5NMnOJFdP0X98ktuSbE9yb5Iz+/o+mWRHkgeTfCXJK7usVZJ0\naJ0FRpIh4HrgPGApcHGSpQcMuwbYVlVnAauA65p9TwKuAkar6kxgCLioq1olSdPrcoaxDNhZVbuq\n6jngFuCCA8YsBe4GqKpHgCVJTmz6FgKvSrIQeDXwVx3WKkmaRpdXqz0JeLJvezfwjgPG3A9cCPx5\nkmXAKcDiqtqS5PeBJ4AfA1+rqq9N9SJJ1gBrAF73utcd2XcgzTIzfXVSqd+gF72vBRYl2QZcCWwF\n9iQ5nt5s5FTgHwHHJvnoVE9QVeurarSqRkdGRmaqbkmad7qcYTwFnNy3vbhp26eqngUuAUgS4DFg\nF/BPgceqarzp+xPgXcCXOqxXmvW8H4b6HU33w7gPOC3JqUmOobdofXv/gCSLmj6Ay4B7mhB5AvjH\nSV7dBMk5wMMd1ipJmkZnM4yqmkxyBXAnvbOcbqyqHUnWNv03AGcAG5IUsAO4tOn7VpJbgW8Dk/QO\nVa3vqlZJ0vQ6vUVrVd0B3HFA2w19j78JvOEg+34a+HSX9UmS2hv0orckaY4wMCRJrRgYkqRWDAxJ\nUisGhiSpFQNDktSKgSFJasXAkCS1YmBIkloxMCRJrRgYkqRWDAxJUisGhiSpFQNDktSKgSFJaqX1\n/TCSDAEn9u9TVU90UZQkafZpFRhJrqR3M6PvAnub5gLO6qguSdIs03aG8Qng9KryDvTSAK26e3jQ\nJWgea7uG8STw/S4LkSTNbm1nGLuAzUn+J/D3LzRW1Wc7qUrSfjZv3jzoEmaN5cuXA/6fDELbwHii\n+Tmm+ZEkzTOtAqOqfhsgyU832z9os1+SlcB1wBDwhaq69oD+44EbgZ8F/g749ap6MMnpwH/tG/p6\n4Leq6g/avK6kI++Fv+xni9lQz3yb5bRaw0hyZpKtwA5gR5ItSd44zT5DwPXAecBS4OIkSw8Ydg2w\nrarOAlbRCxeq6tGqOruqzgbeBvwIuO1lvC9J0hHW9pDUeuBfV9X/BkiyHPg88K5D7LMM2FlVu5p9\nbgEuAB7qG7MUuBagqh5JsiTJiVX13b4x5wD/p6q+07JWSR2Yb39N66XaniV17AthAVBVm4Fjp9nn\nJHpnV71gd9PW737gQoAky4BTgMUHjLkI+MrBXiTJmiRjScbGx8enKUmSdLjaBsauJP++mQEsSfLv\n6J059ZO6FliUZBtwJbAV2PNCZ5JjgPOB/3awJ6iq9VU1WlWjIyMjR6AkSdJU2h6S+nXgt4E/abbv\nadoO5Sng5L7txU3bPlX1LHAJQJIAj7F/EJ0HfPuAQ1SSpAFoe5bU/wOugn2L2cc2v+wP5T7gtCSn\n0guKi4AP9w9Isgj4UVU9B1wG3HPA817MIQ5HSZJmTtuzpL6c5LgkxwIPAA8l+c1D7VNVk8AVwJ3A\nw8AfV9WOJGuTrG2GnQE8mORRerOJT/S95rHACl6c1UiSBihVNf2gZFtVnZ3kI8BbgauBLc3psLPG\n6OhojY2NDboMSZozkmypqtE2Y9suer8iySuADwC3V9Xz9K5WK0maJ9oGxg3A4/ROpb0nySnAdGsY\nkqSjyLSL3kkWAN+tqpP62p4A3tdlYZKk2WXaGUZV7QU+dUBbNYvakqR5ou0hqbuS/EaSk5P8gxd+\nOq1MkjSrtP3i3oeafz/e11b0riIrSZoH2n5x79SuC5EkzW5tZxgkOZPe1WVf+UJbVd3URVGSpNmn\nVWAk+TSwnF5g3EHvW9l/ARgYkjRPtF30/mf07kvxdFVdArwZ+JnOqpIkzTptA+PHzem1k0mOA55h\n/yvRSpKOcm3XMMaaK8t+HtgC/AD4ZmdVSZJmnbZnSX2seXhDko3AcVW1vbuyJEmzTdvLmyfJR5P8\nVlU9DvxNc0tVSdI80XYN4z8B76R3QyOAvwWu76QiSdKs1HYN4x1V9dYkW6F3B77mftuSpHmi7Qzj\n+ebWrAWQZATY21lVkqRZp21grANuA16b5D/Q+9LeZzqrSpI067Q9S+rmJFvofXkvwAeq6uFOK5Mk\nzSqHDIwkrwTWAj8HPAD8offBkKT5abpDUhuAUXphcR7w+51XJEmalaYLjKVV9dGq+kN615N678t5\n8iQrkzyaZGeSq6foPz7JbUm2J7m3uSLuC32Lktya5JEkDyd558t5bUnSkTVdYDz/woOXeyiqOavq\nenozk6XAxUmWHjDsGmBbVZ0FrAKu6+u7DthYVT9P72KHrplI0gBNt+j95iTPNo8DvKrZDr1bex93\niH2XATurahdAkluAC4CH+sYsBa6l92SPJFmS5ETg7+jNZn6t6XsOeO7lvDFJ0pF1yBlGVQ1V1XHN\nz2uqamHf40OFBcBJwJN927ubtn73AxcCNJcaOQVYDJwKjAP/OcnWJF9IcuxUL5JkTZKxJGPj4+PT\nlCRJOlxtv4fRlWuBRUm2AVcCW4E99GY+bwU+V1VvAX4IvGQNBKCq1lfVaFWNjoyMzFDZkjT/tL5F\n62F4iv3vmbG4adunqp4FLoHeBQ6Bx4BdwKuB3VX1rWborRwkMCRJM6PLGcZ9wGlJTm2uO3URcHv/\ngOZMqBeuSXUZcE9VPVtVTwNPJjm96TuH/dc+JEkzrLMZRlVNJrkCuBMYAm6sqh1J1jb9NwBnABuS\nFLADuLTvKa4Ebm4CZRfNTESSNBipqkHXcMSMjo7W2NjYoMuQpDkjyZaqGm0zdtCL3pKkOcLAkCS1\nYmBIkloxMCRJrRgYkqRWDAxJUisGhiSpFQNDktSKgSFJasXAkCS1YmBIkloxMCRJrRgYkqRWDAxJ\nUisGhiSpFQNDktSKgSFJasXAkCS1YmBIkloxMCRJrRgYkqRWOg2MJCuTPJpkZ5Krp+g/PsltSbYn\nuTfJmX19jyd5IMm2JGNd1ilJmt7Crp44yRBwPbAC2A3cl+T2qnqob9g1wLaq+mCSn2/Gn9PX/76q\n+l5XNUqS2utyhrEM2FlVu6rqOeAW4IIDxiwF7gaoqkeAJUlO7LAmSdJh6jIwTgKe7Nve3bT1ux+4\nECDJMuAUYHHTV8BdSbYkWXOwF0myJslYkrHx8fEjVrwkaX+DXvS+FliUZBtwJbAV2NP0vbuqzgbO\nAz6e5L1TPUFVra+q0aoaHRkZmZGiJWk+6mwNA3gKOLlve3HTtk9VPQtcApAkwGPArqbvqebfZ5Lc\nRu8Q1z0d1itJOoQuZxj3AaclOTXJMcBFwO39A5IsavoALgPuqapnkxyb5DXNmGOBXwQe7LBWSdI0\nOpthVNVkkiuAO4Eh4Maq2pFkbdN/A3AGsCFJATuAS5vdTwRu6006WAh8uao2dlWrJGl6qapB13DE\njI6O1tiYX9mQpLaSbKmq0TZjB73oLUmaIwwMSVIrBoYkqRUDQ5LUioEhSWrFwJAktWJgSJJaMTAk\nSa0YGJKkVgwMSVIrBoYkqRUDQ5LUioEhSWrFwJAktWJgSJJaMTAkSa0YGJKkVgwMSVIrBoYkqRUD\nQ5LUioEhSWql08BIsjLJo0l2Jrl6iv7jk9yWZHuSe5OceUD/UJKtSf60yzolSdPrLDCSDAHXA+cB\nS4GLkyw9YNg1wLaqOgtYBVx3QP8ngIe7qlGS1F6XM4xlwM6q2lVVzwG3ABccMGYpcDdAVT0CLEly\nIkCSxcAvA1/osEZJUktdBsZJwJN927ubtn73AxcCJFkGnAIsbvr+APgUsPdQL5JkTZKxJGPj4+NH\nom5J0hQGveh9LbAoyTbgSmArsCfJ+4FnqmrLdE9QVeurarSqRkdGRjouV5Lmr4UdPvdTwMl924ub\ntn2q6lngEoAkAR4DdgEfAs5P8kvAK4Hjknypqj7aYb2SpEPocoZxH3BaklOTHANcBNzePyDJoqYP\n4DLgnqp6tqr+bVUtrqolzX53GxaSNFidzTCqajLJFcCdwBBwY1XtSLK26b8BOAPYkKSAHcClXdUj\nSfrJpKoGXcMRMzo6WmNjY4MuQ5LmjCRbqmq0zdhBL3pLkuYIA0OS1IqBIUlqxcCQJLViYEiSWjEw\nJEmtGBiSpFYMDElzysTEBFdddRUTExODLmXeMTAkzSnr169n+/btrF+/ftClzDsGhqQ5Y2Jigk2b\nNgGwadMmZxkzzMCQNGesX7+evXt7t8jZu3evs4wZZmBImjO+/vWvH3Jb3TIwJM0ZB14s9Wi6eOpc\nYGBImjPOOeec/bbPPffcAVUyPxkYkuaMyy+/nAULer+2FixYwJo1awZc0fxiYEiaM4aHh/fNKlas\nWMHw8PCAK5pfuryntyQdcZdffjlPP/20s4sBMDAkzSnDw8OsW7du0GXMSx6SkiS1YmBIkloxMCRJ\nrRgYkqRWcjR9UzLJOPCdQddxlDgB+N6gi5AOws/nkXNKVY20GXhUBYaOnCRjVTU66Dqkqfj5HAwP\nSUmSWjEwJEmtGBg6GG80oNnMz+cAuIYhSWrFGYYkqRUDQ9NK8lNJ7kqyLcmHkrwnyY5m+1WDrk9z\nS5IlSR6c6X31k/Pig2rjLQBVdTZAkhuA/1hVXxpoVToqJVlYVZODrkMv5QxjHmr+Snskyc1JHk5y\na5JXJ3k8yQnNmNEkm5O8FvgS8PZmRnE58KvA7yS5eZDvQ3Nfktcn2ZrkN5PcnuRu4OtJfjrJ15N8\nO8kDSS44xL5vTzKU5PeS3Jdke/M5Jcny5nN8a99nPjP+Ro8SzjDmr9OBS6vqG0luBD421aCqeibJ\nZcBvVNX7AZK8E/jTqrp15srV0SbJ6cAtwK/Rm8W+FTirqv46yULgg1X1bPNHzF8muX2qfavq/iRr\ngO9X1duT/BTwjSRfa4a/BXgj8FfAN4B/AvzFzLzLo4szjPnryar6RvP4S8C7B1mM5p0R4H8AH6mq\n+5u2TVX1183jAJ9Jsh24CzgJOPEQ+/4isCrJNuBbwDBwWtN3b1Xtrqq9wDZgSXdv6+jmDGP+OvB8\n6gImefGPiFfObDmaZ74PPEHvD5WHmrYf9vV/hF4wvK2qnk/yOC9+JqfaN8CVVXVn/4skWQ78fV/T\nHvy9d9icYcxfr2sOLQF8mN4U/XHgbU3brwyiKM0bzwEfpDcr+PAU/T8DPNOExfuAU6bZ907gXyZ5\nBUCSNyQ5trvy5yeTdv56FPh4s37xEPA54F7gi0l+B9g8wNo0D1TVD5O8H9gE/NEB3TcDX03yADAG\nPHKwfZP8APgCvUNN324WtceBD3T8FuYdv+k9DyVZQm/R+swBlyJpDvGQlCSpFWcYkqRWnGFIklox\nMCRJrRgYkqRWDAzpMCRZlGTKy6m02NcrrmpOMjCkw7OIg1x/SzpaGRjS4bkW+NnmCr6/1/w82FxZ\n9UMA6XlJe78kb0xyb/M825Oc9pJXkmYJv+ktHZ6rgTOr6uwkvwKsBd4MnADcl+Qe4F3A2VO091sL\nXFdVNyc5BhiasXcgvUzOMKSf3LuBr1TVnqr6LvBnwNsP0d7vm8A1Sf4NcEpV/XgmC5deDgNDGqCq\n+jJwPvBj4I4kvzDgkqSDMjCkw/O3wGuax38OfKi569sI8F56F3I8WPs+SV4P7KqqdfTu8XDWTL0B\n6eVyDUM6DFU1keQbzemx/wvYDtxP774in6qqp5PcBrxzivYlfU/1q8A/T/I88DTwmRl8G9LL4rWk\nJEmteEhKktSKgSFJasXAkCS1YmBIkloxMCRJrRgYkqRWDAxJUisGhiSplf8Prx1MyXbZTpcAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7760004dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "mard\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7759b39160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "f1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBUlSYyhIkhpDQZLUGAqSpMZQkCQ1/w83tjdJdmnXfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f775b79a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n\\nNo Filtering\")\n",
    "clark_df = merge_clark_results('/mnt/scratch2/fatemeh/krakpuff/clark_reports/{}_species.rpt', \n",
    "                               '/mnt/scratch2/fatemeh/krakpuff/clark_reports/{}_{}.rpt')\n",
    "#(stats_df, kraken, puffer) = prepare('/mnt/scratch2/fatemeh/krakpuff/truth_files/{}.truth',\n",
    "#                                            '/mnt/scratch2/avi/meta-map/kraken/krakOut/{}_unfilt.rpt', \n",
    "#                                                  '/mnt/scratch2/fatemeh/krakpuff/{}_species_unfilt.out', \n",
    "#                                      clark_df,\n",
    "#                                      '/mnt/scratch2/fatemeh/krakpuff/statSummary/unfiltered.stat')\n",
    "#draw_plots(stats_df)\n",
    "#unpivot_stat = unpivot(stats_df)\n",
    "#unpivot_stat\n",
    "levels = ['all']\n",
    "draw_box_plots(unpivot_stat[unpivot_stat['level'] == 'all'], levels, 'tools', None, '/mnt/scratch2/fatemeh/krakpuff/statSummary/plots/no_filt_all')\n",
    "levels = ['species', 'genus', 'family', 'order', 'class', 'phylum']\n",
    "draw_box_plots(unpivot_stat, levels, 'level', 'tools','/mnt/scratch2/fatemeh/krakpuff/statSummary/plots/no_filt_levels')\n",
    "levels = ['norank']\n",
    "draw_box_plots(unpivot_stat[unpivot_stat['spearman'] != 0], levels, 'tools', None, '/mnt/scratch2/fatemeh/krakpuff/statSummary/plots/no_filt_norank')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter coverage less than 20 percent normalized by read len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "20%\n",
      "HC1\n",
      "HC2\n",
      "LC1\n",
      "LC2\n",
      "LC3\n",
      "LC4\n",
      "LC5\n",
      "LC6\n",
      "LC7\n",
      "LC8\n",
      "\n",
      "\n",
      "spearman\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACGgAAALICAYAAAApXFQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAAzs/wBk045Z1YKwjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f775ba01550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "pearson\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACGgAAALICAYAAAApXFQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f776d44dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "mard\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACGgAAALICAYAAAApXFQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAMDANGgAAAAAAAAAAAzs/wO8HD0CiWRNwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7759b91cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "f1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACGgAAALICAYAAAApXFQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/0RXygWLfweSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f776882ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "cnt_f1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACGgAAALICAYAAAApXFQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f775a55e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n\\n20%\")\n",
    "(stats20_df, kraken20, puffer20) = prepare('/mnt/scratch2/fatemeh/krakpuff/truth_files/{}.truth',\n",
    "                                           '/mnt/scratch2/avi/meta-map/kraken/krakOut/{}.rpt', \n",
    "                                                 '/mnt/scratch2/fatemeh/krakpuff/{}_species0_2.out',\n",
    "                                           '/mnt/scratch2/avi/meta-map/clark/{}.rpt',\n",
    "                                    '/mnt/scratch2/fatemeh/krakpuff/statSummary/puff20_krak20.stat')\n",
    "draw_plots(stats20_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter coverage less than 44 nucleotides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "44Nuc\n",
      "HC1\n",
      "HC2\n",
      "LC1\n",
      "LC2\n",
      "LC3\n",
      "LC4\n",
      "LC5\n",
      "LC6\n",
      "LC7\n",
      "LC8\n",
      "\n",
      "\n",
      "spearman\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fatemeh/miniconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nASsKfFzCrM+jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f178985f860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "pearson\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEKCAYAAAA4t9PUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQgYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f178985f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "mard\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNf8PQQC2aQakoJcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1786102470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "f1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17860c0470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "spearman\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmVmek4KZmeU5KZiZWd7/B6tuivdFiFv7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f178600dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "pearson\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWSr/BfjtknnonobnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1785feaf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "mard\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f178587ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "f1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17858c4d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "spearman\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCpKklqEgSWr9f2bGN/TUjB4dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f178578f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "pearson\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGBpJREFUeJzt3X+w3XV95/HnixupSrVhL5HpEiTRIhIB0V5jddWJSrqX\nrgNKOxWsTUphkVXAdad1WWa2jtNZl512nJIZVpoqu5eKsl12mY0uDSalKa1jJRcJhPBjJhsRkla5\nptVUq4WbvPeP8w2cXELuNzHfe+7NfT5m7uScz+fz/Z73Ye7wup/v5/sjVYUkSdM5btAFSJLmBgND\nktSKgSFJasXAkCS1YmBIkloxMCRJrRgYkqRWDAxJUisGhiSplQWDLuBoOumkk2rJkiWDLkOS5oz7\n7rvvu1W1qM3YYyowlixZwvj4+KDLkKQ5I8m32o71kJQkqRUDQ5LUioEhSWrFwJAktWJgSJJa6Sww\nktyc5KkkD71Af5KsSbI9yYNJ3tjXN5rksabv2q5qlCS11+UM478Do4foPx84vfm5AvgMQJIh4Mam\nfxlwSZJlHdYpSWqhs+swquqeJEsOMeRC4JbqPSP2r5MsTPKzwBJge1XtAEhyWzP24a5qBVixYkWX\nu9cct2nTpkGXIA3cINcwTgGe7Hu/s2l7ofaDSnJFkvEk4xMTE50UKkk6Bq70rqq1wFqAkZGROtL9\n+Bfkcz796U/zpS99iQsuuICPfexjgy5H0iwxyBnGLuDUvveLm7YXatcM2L17N+vXr6eqWL9+Pbt3\n7x50SZJmiUHOMNYBVzVrFG8Gvl9Vf5tkAjg9yVJ6QXEx8IEB1jmvjI2NsW/fPgD27t3LLbfc4ixD\ngOt8BzPfjkx0eVrtF4GvAWck2ZnksiRXJrmyGXInsAPYDvwR8GGAqpoErgLuAh4B/qSqtnVVpw60\nceNGJicnAZicnGTDhg0DrkjSbNHlWVKXTNNfwEdeoO9OeoGiGXbeeedx5513Mjk5yYIFC1i5cuWg\nS9IsMVv+mt4/05kt9cwnXumtA6xevZrjjuv9WgwNDbFq1aoBVyRptjAwdIDh4WFGR0dJwujoKMPD\nw4MuSdIsMedPq9XRt3r1ah5//HFnF5IOYGDoeYaHh1mzZs2gy5A0y3hISpLUioEhSWrFwJAktWJg\nSJJaMTAkSa0YGJKkVgwMSVIrBoYkqRUDQ5LUioEhSWrFwJAktWJgSJJaMTD0PLt37+aaa67xed6S\nDmBg6HnGxsbYunUrt9xyy6BLkTSLGBg6wO7du1m/fj1Vxfr1651lSHqWgaEDjI2NsW/fPgD27t3r\nLEPSszoNjCSjSR5Lsj3JtQfpPzHJHUkeTHJvkrP6+j6a5KEk25L82y7r1HM2btzI5OQkAJOTk2zY\nsGHAFUmaLToLjCRDwI3A+cAy4JIky6YMuw7YUlXnAKuAG5ptzwL+NbAceD3wniQ/11Wtes55553H\nggW9BzEuWLCAlStXDrgiSbNFlzOM5cD2qtpRVU8DtwEXThmzDLgboKoeBZYkORk4E/h6Vf1jVU0C\nfwFc1GGtaqxevZrjjuv9WgwNDflcb0nP6jIwTgGe7Hu/s2nr9wBNECRZDpwGLAYeAt6eZDjJS4Ff\nAk492IckuSLJeJLxiYmJo/wV5p/h4WFGR0dJwujoKMPDw4MuSdIsMehF7+uBhUm2AFcD9wN7q+oR\n4L8AXwHWA1uAvQfbQVWtraqRqhpZtGjRDJV9bFu9ejVnn322swtJB1jQ4b53ceCsYHHT9qyq2gNc\nCpAkwDeBHU3f54DPNX2fojdD0QwYHh5mzZo1gy5D0izT5QxjM3B6kqVJjgcuBtb1D0iysOkDuBy4\npwkRkryi+feV9A5bfaHDWiVJ0+hshlFVk0muAu4ChoCbq2pbkiub/pvoLW6PJSlgG3BZ3y7+V5Jh\n4BngI1X1va5qlSRNr8tDUlTVncCdU9pu6nv9NeA1L7Dt27usTZJ0eAa96C1JmiMMDElSKwaGJKkV\nA0OS1IqBIUlqxcCQJLXS6Wm1ko6OFStWDLqEWcf/Js/ZtGnTjHyOMwxJUivOMKQ55JZ3+chcPWfV\n3TN7N2lnGJKkVgwMSVIrBoYkqRUDQ5LUioEhSWrFwJAktWJgSJJaMTAkSa0YGJKkVrzSW5pDZvrK\nXqmfMwxJUiudzjCSjAI3AEPAZ6vq+in9JwI3A68Gfgz8ZlU91PR9DLgcKGArcGlV/bjLeqXZzntJ\nqd8xcy+pJEPAjcD5wDLgkiTLpgy7DthSVecAq+iFC0lOAa4BRqrqLHqBc3FXtUqSptflIanlwPaq\n2lFVTwO3ARdOGbMMuBugqh4FliQ5uelbALwkyQLgpcDfdFirJGkaXQbGKcCTfe93Nm39HgAuAkiy\nHDgNWFxVu4DfB54A/hb4flV95WAfkuSKJONJxicmJo7yV5Ak7TfoRe/rgYVJtgBXA/cDe5u1jQuB\npcA/B05I8sGD7aCq1lbVSFWNLFq0aKbqlqR5p8tF713AqX3vFzdtz6qqPcClAEkCfBPYAfxL4JtV\nNdH0/W/grcDnO6xXknQIXc4wNgOnJ1ma5Hh6i9br+gckWdj0Qe+MqHuaEHkC+IUkL22C5N3AIx3W\nKkmaRmczjKqaTHIVcBe9s5xurqptSa5s+m8CzgTGkhSwDbis6ft6ktuBbwCT9A5Vre2qVknS9Dq9\nDqOq7gTunNJ2U9/rrwGveYFtPwF8osv6JEntDXrRW5I0RxgYkqRWDAxJUisGhiSpFQNDktSKgSFJ\nasXAkCS1YmBIkloxMCRJrRgYkqRWDAxJUisGhiSpFQNDktSKgSFJaqXT25tLOrpW3T086BI0jznD\nkCS14gxDmgM2bdo06BJmjRUrVgD+NxmE1oGRZAg4uX+bqnqii6IkSbNPq8BIcjW9x6V+B9jXNBdw\nTkd1SZpl9v9lP1vMhnrm2yyn7Qzjo8AZVbW7y2IkSbNX28B4Evj+4e48yShwAzAEfLaqrp/SfyJw\nM/Bq4MfAb1bVQ0nOAP5H39BXAb9TVX9wuDVIOjrm21/Ter62gbED2JTk/wL/tL+xqj79Qhs0ax43\nAiuBncDmJOuq6uG+YdcBW6rqfUle24x/d1U9Bpzbt59dwB3tv5Yk6Whre1rtE8AG4HjgZX0/h7Ic\n2F5VO6rqaeA24MIpY5YBdwNU1aPAkiQnTxnzbuD/VdW3WtYqSepAqxlGVX0SIMlPN+9/0GKzU+gd\nytpvJ/DmKWMeAC4C/jLJcuA0YDG9xfX9Lga+2KZOSVJ3Ws0wkpyV5H5gG7AtyX1JXncUPv96YGGS\nLcDVwP3A3r7PPR64APifh6jtiiTjScYnJiaOQkmSpINpu4axFvh3VfXnAElWAH8EvPUQ2+wCTu17\nv7hpe1ZV7QEubfYZ4Jv01kv2Ox/4RlX1zzgOUFVrm/oYGRmpdl9HknS42q5hnLA/LACqahNwwjTb\nbAZOT7K0mSlcDKzrH5BkYdMHcDlwTxMi+12Ch6MkaVZofZZUkv8I/HHz/oMcOBN4nqqaTHIVcBe9\n02pvrqptSa5s+m8CzgTGkhS9w12X7d8+yQn0zrD60GF8H0lSR1I1/VGc5nqJTwJva5ruAT5ZVX/f\nYW2HbWRkpMbHxwddhiTNGUnuq6qRNmPbniX198A1zc6H6B2i2nPorSRJx5K2Z0l9IcnLm8NEW4GH\nk/x2t6VJkmaTtovey5oZxXuBPwWWAr/eWVWSpFmnbWC8KMmL6AXGuqp6ht7daiVJ80TbwLgJeJze\nqbT3JDkNcA1DkuaRaRe9kxwHfKeqTulrewJ4Z5eFSZJml2lnGFW1D/j4lLaqqsnOqpIkzTptD0lt\nTPJbSU5N8s/2/3RamSRpVml7pff7m38/0tdW9B5sJEmaB9peuLe060IkSbNb2xkGSc6i98CjF+9v\nq6pbuihKkjT7tAqMJJ8AVtALjDvp3Xb8rwADQ5LmibaL3r9C71Gp366qS4HXAz/TWVWSpFmnbWD8\nqDm9djLJy4GnOPDhSJKkY1zbNYzxJAvpPWXvPuAHwNc6q0qSNOu0PUvqw83Lm5KsB15eVQ92V5Yk\nabZpe3vzJPlgkt+pqseB7yVZ3m1pkqTZpO0axn8F3kLvGdsA/wDc2ElFkqRZqe0axpur6o1J7ofe\nE/iSHN9hXZKkWabtDOOZ5tGsBZBkEbCvs6okSbNO28BYA9wBvCLJf6J30d6nptsoyWiSx5JsT3Lt\nQfpPTHJHkgeT3NtcTb6/b2GS25M8muSRJG9pWaskqQNtz5K6Ncl99C7eC/DeqnrkUNs0M5IbgZXA\nTmBzknVV9XDfsOuALVX1viSvbca/u+m7AVhfVb/SHP566eF8MUnS0XXIwEjyYuBK4OeArcAfHsZz\nMJYD26tqR7Ov24ALgf7AWAZcD1BVjyZZkuRk4MfAO4DfaPqeBp5u+bmSpA5Md0hqDBihFxbnA79/\nGPs+BXiy7/3Opq3fA8BFAM1puqcBi4GlwATw35Lcn+SzSU44jM+WJB1l0wXGsqr6YFX9Ib37Sb3j\nKH/+9cDCJFuAq4H7gb30Zj5vBD5TVW8Afgg8bw0EIMkVScaTjE9MTBzl8iRJ+00XGM/sf3EEj2Td\nxYH3m1rctD2rqvZU1aVVdS6wClgE7KA3G9lZVV9vht5OL0Cep6rWVtVIVY0sWrToMEuUJLU13aL3\n65PsaV4HeEnzPvQe7f3yQ2y7GTg9yVJ6QXEx8IH+Ac39qf6xWaO4HLinqvYAe5I8meSMqnqM3kL4\nw0iSBuaQgVFVQ0e646qaTHIVcBcwBNxcVduSXNn03wScCYwlKWAbcFnfLq4Gbm3OkNoBXHqktUiS\nfnKpqkHXcNSMjIzU+Pj4oMuQpDkjyX1VNdJmbNsL9yRJ85yBIUlqxcCQJLViYEiSWjEwJEmtGBiS\npFYMDElSKwaGJKkVA0OS1IqBIUlqxcCQJLViYEiSWjEwJEmtGBiSpFYMDElSKwaGJKkVA0OS1IqB\nIUlqxcCQJLViYEiSWuk0MJKMJnksyfYk1x6k/8QkdyR5MMm9Sc7q63s8ydYkW5KMd1mnJGl6C7ra\ncZIh4EZgJbAT2JxkXVU93DfsOmBLVb0vyWub8e/u639nVX23qxolSe11OcNYDmyvqh1V9TRwG3Dh\nlDHLgLsBqupRYEmSkzusSZJ0hLoMjFOAJ/ve72za+j0AXASQZDlwGrC46StgY5L7klzRYZ2SpBY6\nOyTV0vXADUm2AFuB+4G9Td/bqmpXklcAG5I8WlX3TN1BEyZXALzyla+cobIlaf7pcoaxCzi17/3i\npu1ZVbWnqi6tqnOBVcAiYEfTt6v59yngDnqHuJ6nqtZW1UhVjSxatOjofwtJEtBtYGwGTk+yNMnx\nwMXAuv4BSRY2fQCXA/dU1Z4kJyR5WTPmBOAXgYc6rFWSNI3ODklV1WSSq4C7gCHg5qraluTKpv8m\n4ExgLEkB24DLms1PBu5Isr/GL1TV+q5qlSRNL1U16BqOmpGRkRof95INSWoryX1VNdJmrFd6S5Ja\nMTAkSa0YGJKkVgwMSVIrBoYkqRUDQ5LUioEhSWrFwJAktWJgSJJaMTAkSa0YGJKkVgwMSVIrBoYk\nqRUDQ5LUioEhSWrFwJAktWJgSJJaMTAkSa0YGJKkVgwMSVIrnQZGktEkjyXZnuTag/SfmOSOJA8m\nuTfJWVP6h5Lcn+TLXdYpSZpeZ4GRZAi4ETgfWAZckmTZlGHXAVuq6hxgFXDDlP6PAo90VaMkqb0u\nZxjLge1VtaOqngZuAy6cMmYZcDdAVT0KLElyMkCSxcC/Aj7bYY2SpJa6DIxTgCf73u9s2vo9AFwE\nkGQ5cBqwuOn7A+DjwL4Oa5QktTToRe/rgYVJtgBXA/cDe5O8B3iqqu6bbgdJrkgynmR8YmKi43Il\naf5a0OG+dwGn9r1f3LQ9q6r2AJcCJAnwTWAH8H7ggiS/BLwYeHmSz1fVB6d+SFWtBdYCjIyMVAff\nQ5JEtzOMzcDpSZYmOR64GFjXPyDJwqYP4HLgnqraU1X/oaoWV9WSZru7DxYWkqSZ09kMo6omk1wF\n3AUMATdX1bYkVzb9NwFnAmNJCtgGXNZVPZKkn0yqjp2jOCMjIzU+Pj7oMiRpzkhyX1WNtBk76EVv\nSdIcYWBIkloxMCRJrRgYkqRWDAxJUisGhiSpFQNDktSKgSFJasXAkCS1YmBImlN2797NNddcw+7d\nuwddyrxjYEiaU8bGxti6dSu33HLLoEuZdwwMSXPG7t27Wb9+PVXF+vXrnWXMMAND0pwxNjbGvn29\nh3Du3bvXWcYMMzAkzRkbN25kcnISgMnJSTZs2DDgiuYXA0PSnHHeeecxNDQEwNDQECtXrhxwRfOL\ngSFpzli9ejX7n+FTVaxatWrAFc0vBoYkqRUDQ9KcMTY2xnHH9f63ddxxx7noPcMMDElzhoveg2Vg\nSJozzjvvPBYsWADAggULXPSeYZ0GRpLRJI8l2Z7k2oP0n5jkjiQPJrk3yVlN+4ub9w8k2Zbkk13W\nKWluWL169bOHpIaGhlz0nmGdBUaSIeBG4HxgGXBJkmVThl0HbKmqc4BVwA1N+z8B76qq1wPnAqNJ\nfqGrWiXNDcPDw4yOjpKE0dFRhoeHB13SvNLlDGM5sL2qdlTV08BtwIVTxiwD7gaoqkeBJUlOrp4f\nNGNe1PxUh7VKmiNWr17N2Wef7exiALoMjFOAJ/ve72za+j0AXASQZDlwGrC4eT+UZAvwFLChqr7e\nYa2S5ojh4WHWrFnj7GIABr3ofT2wsAmGq4H7gb0AVbW3qs6lFyDL969vTJXkiiTjScYnJiZmqm5J\nmne6DIxdwKl97xc3bc+qqj1VdWkTDKuARcCOKWO+B/w5MHqwD6mqtVU1UlUjixYtOpr1S5L6dBkY\nm4HTkyxNcjxwMbCuf0CShU0fwOXAPVW1J8miJAubMS8BVgKPdlirJGkaC7racVVNJrkKuAsYAm6u\nqm1Jrmz6bwLOBMaSFLANuKzZ/Geb9iF6ofYnVfXlrmqVJE0v+2/kdSxIMgF8a9B1HCNOAr476CKk\nF+Dv59FzWlW1Op5/TAWGjp4k41U1Mug6pIPx93MwBn2WlCRpjjAwJEmtGBh6IWsHXYB0CP5+DoBr\nGJKkVpxhSJJaMTA0rSQ/lWRjki1J3p/k7c1t57c0F1ZKrSVZkuShmd5WP7nOLtzTMeUNAM0tXEhy\nE/Cfq+rzA61Kx6QkC6pqctB16PmcYcxDzV9pjya5NckjSW5P8tIkjyc5qRkzkmRTklcAnwfe1Mwo\nPgT8KvC7SW4d5PfQ3JfkVUnuT/LbSdYluRv4syQ/neTPknwjydYkUx+N0L/tm5q7W/9eks3NA9k+\n1IxZ0fwe3973O58Z/6LHCGcY89cZwGVV9dUkNwMfPtigqnoqyeXAb1XVewCSvAX4clXdPnPl6liT\n5Ax6z8n5DXqz2DcC51TV3yVZALyvubfcScBfJ1l3sG2r6oEkVwDfr6o3Jfkp4KtJvtIMfwPwOuBv\ngK8C/wL4q5n5lscWZxjz15NV9dXm9eeBtw2yGM07i4D/A/xaVT3QtG2oqr9rXgf4VJIHgY30nqVz\n8iG2/UVgVfOohK8Dw8DpTd+9VbWzqvYBW4Al3X2tY5szjPlr6vnUBUzy3B8RL57ZcjTPfB94gt4f\nKg83bT/s6/81esHw81X1TJLHee538mDbBri6qu7q/5AkK+g98nm/vfj/vSPmDGP+emVzaAngA/Sm\n6I8DP9+0/fIgitK88TTwPnqzgg8cpP9ngKeasHgnvadxHmrbu4B/k+RFAElek+SE7sqfn0za+esx\n4CPN+sXDwGeAe4HPJfldYNMAa9M8UFU/TPIeYAPwx1O6bwW+lGQrMM6U5+H0b5vkB8Bn6R1q+kaz\nqD0BvLfjrzDveKX3PJRkCb1F64M+9laSDsZDUpKkVpxhSJJacYYhSWrFwJAktWJgSJJaMTCkI5Bk\nYZKD3k6lxbbecVVzkoEhHZmFvMD9t6RjlYEhHZnrgVc3d/D9vebnoebOqu8HSM/z2vsleV2Se5v9\nPJjk9Od9kjRLeKW3dGSuBc6qqnOT/DJwJfB64CRgc5J7gLcC5x6kvd+VwA1VdWuS44GhGfsG0mFy\nhiH95N4GfLGq9lbVd4C/AN50iPZ+XwOuS/LvgdOq6kczWbh0OAwMaYCq6gvABcCPgDuTvGvAJUkv\nyMCQjsw/AC9rXv8l8P7mqW+LgHfQu5HjC7U/K8mrgB1VtYbeMx7OmakvIB0u1zCkI1BVu5N8tTk9\n9k+BB4EH6D1X5ONV9e0kdwBvOUj7kr5d/Srw60meAb4NfGoGv4Z0WLyXlCSpFQ9JSZJaMTAkSa0Y\nGJKkVgwMSVIrBoYkqRUDQ5LUioEhSWrFwJAktfL/AQtQ7H7IW0P8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1785b51588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "mard\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKEiSGkNBktT8H/8CpLUQP/zzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17898a01d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "f1\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1789877208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n\\n44Nuc\")\n",
    "#clark_filtered_df = merge_clark_results('/mnt/scratch2/fatemeh/krakpuff/clark_reports/{}_species_filtered.rpt', \n",
    "#                               '/mnt/scratch2/fatemeh/krakpuff/clark_reports/{}_{}_filtered.rpt')\n",
    "\n",
    "(stats44_df, kraken20, puffer44) = prepare('/mnt/scratch2/fatemeh/krakpuff/truth_files/{}.truth',\n",
    "                                           '/mnt/scratch2/avi/meta-map/kraken/krakOut/{}.rpt', \n",
    "                                           '/mnt/scratch2/fatemeh/krakpuff/{}_species44.out', \n",
    "                                           clark_filtered_df,\n",
    "                                    '/mnt/scratch2/fatemeh/krakpuff/statSummary/puff44_krak20.stat')\n",
    "#draw_plots(stats44_df)\n",
    "#stats44_df\n",
    "unpivot_stat44 = unpivot(stats44_df)\n",
    "unpivot_stat44\n",
    "levels = ['all']\n",
    "draw_box_plots(unpivot_stat44, levels, 'tools', None, '/mnt/scratch2/fatemeh/krakpuff/statSummary/plots/filt44_all')\n",
    "levels = ['species', 'genus', 'family', 'order', 'class', 'phylum']\n",
    "draw_box_plots(unpivot_stat44, levels, 'level', 'tools','/mnt/scratch2/fatemeh/krakpuff/statSummary/plots/filt44_levels')\n",
    "levels = ['norank']\n",
    "draw_box_plots(unpivot_stat44[unpivot_stat44['spearman'] != 0], levels, 'tools', None, '/mnt/scratch2/fatemeh/krakpuff/statSummary/plots/filt44_norank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80, 80\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clark_spearman_x</th>\n",
       "      <th>clark_spearman_y</th>\n",
       "      <th>clark_mard_x</th>\n",
       "      <th>clark_mard_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.774005</td>\n",
       "      <td>0.741106</td>\n",
       "      <td>0.762081</td>\n",
       "      <td>0.786294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.663851</td>\n",
       "      <td>0.611742</td>\n",
       "      <td>0.844524</td>\n",
       "      <td>0.868907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.703962</td>\n",
       "      <td>0.683599</td>\n",
       "      <td>0.807562</td>\n",
       "      <td>0.818976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.844987</td>\n",
       "      <td>0.831040</td>\n",
       "      <td>0.689987</td>\n",
       "      <td>0.704391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.918182</td>\n",
       "      <td>0.906415</td>\n",
       "      <td>0.573461</td>\n",
       "      <td>0.596292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.954090</td>\n",
       "      <td>0.945227</td>\n",
       "      <td>0.493516</td>\n",
       "      <td>0.513371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.974714</td>\n",
       "      <td>0.948683</td>\n",
       "      <td>0.449922</td>\n",
       "      <td>0.528504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.768215</td>\n",
       "      <td>0.731000</td>\n",
       "      <td>0.766120</td>\n",
       "      <td>0.793192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.675273</td>\n",
       "      <td>0.617579</td>\n",
       "      <td>0.838554</td>\n",
       "      <td>0.867507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.686685</td>\n",
       "      <td>0.662571</td>\n",
       "      <td>0.814186</td>\n",
       "      <td>0.827613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.833875</td>\n",
       "      <td>0.815911</td>\n",
       "      <td>0.693308</td>\n",
       "      <td>0.709442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.913436</td>\n",
       "      <td>0.900401</td>\n",
       "      <td>0.588304</td>\n",
       "      <td>0.609972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.928649</td>\n",
       "      <td>0.911789</td>\n",
       "      <td>0.574449</td>\n",
       "      <td>0.605981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.984975</td>\n",
       "      <td>0.974689</td>\n",
       "      <td>0.394953</td>\n",
       "      <td>0.441462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.808416</td>\n",
       "      <td>0.767170</td>\n",
       "      <td>0.741355</td>\n",
       "      <td>0.787806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.654456</td>\n",
       "      <td>0.596923</td>\n",
       "      <td>0.840393</td>\n",
       "      <td>0.891517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.750960</td>\n",
       "      <td>0.718225</td>\n",
       "      <td>0.831326</td>\n",
       "      <td>0.848475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.830770</td>\n",
       "      <td>0.802250</td>\n",
       "      <td>0.731538</td>\n",
       "      <td>0.760333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.883610</td>\n",
       "      <td>0.857167</td>\n",
       "      <td>0.652335</td>\n",
       "      <td>0.687543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.992060</td>\n",
       "      <td>0.983247</td>\n",
       "      <td>0.285744</td>\n",
       "      <td>0.353837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989305</td>\n",
       "      <td>0.294583</td>\n",
       "      <td>0.353467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.903167</td>\n",
       "      <td>0.875707</td>\n",
       "      <td>0.634893</td>\n",
       "      <td>0.674072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.832202</td>\n",
       "      <td>0.756510</td>\n",
       "      <td>0.744031</td>\n",
       "      <td>0.803922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.884587</td>\n",
       "      <td>0.853934</td>\n",
       "      <td>0.693475</td>\n",
       "      <td>0.718391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.906436</td>\n",
       "      <td>0.890194</td>\n",
       "      <td>0.638536</td>\n",
       "      <td>0.663561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.869839</td>\n",
       "      <td>0.867439</td>\n",
       "      <td>0.661767</td>\n",
       "      <td>0.666788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.727647</td>\n",
       "      <td>0.680046</td>\n",
       "      <td>0.812213</td>\n",
       "      <td>0.836172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.617438</td>\n",
       "      <td>0.540671</td>\n",
       "      <td>0.878594</td>\n",
       "      <td>0.906369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.555886</td>\n",
       "      <td>0.533568</td>\n",
       "      <td>0.887150</td>\n",
       "      <td>0.894912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.743121</td>\n",
       "      <td>0.718716</td>\n",
       "      <td>0.812987</td>\n",
       "      <td>0.823758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.812933</td>\n",
       "      <td>0.800990</td>\n",
       "      <td>0.671794</td>\n",
       "      <td>0.683605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.946651</td>\n",
       "      <td>0.938173</td>\n",
       "      <td>0.429534</td>\n",
       "      <td>0.476965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.873589</td>\n",
       "      <td>0.872794</td>\n",
       "      <td>0.485771</td>\n",
       "      <td>0.485771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.544953</td>\n",
       "      <td>0.527392</td>\n",
       "      <td>0.870876</td>\n",
       "      <td>0.890140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.385737</td>\n",
       "      <td>0.375319</td>\n",
       "      <td>0.939013</td>\n",
       "      <td>0.953339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.505624</td>\n",
       "      <td>0.484547</td>\n",
       "      <td>0.905086</td>\n",
       "      <td>0.915031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.539539</td>\n",
       "      <td>0.523461</td>\n",
       "      <td>0.877019</td>\n",
       "      <td>0.886540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.657396</td>\n",
       "      <td>0.639690</td>\n",
       "      <td>0.775632</td>\n",
       "      <td>0.796053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.924685</td>\n",
       "      <td>0.898782</td>\n",
       "      <td>0.586917</td>\n",
       "      <td>0.625644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.820153</td>\n",
       "      <td>0.775197</td>\n",
       "      <td>0.768125</td>\n",
       "      <td>0.796177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.704328</td>\n",
       "      <td>0.616600</td>\n",
       "      <td>0.856472</td>\n",
       "      <td>0.891055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.729677</td>\n",
       "      <td>0.700401</td>\n",
       "      <td>0.848876</td>\n",
       "      <td>0.858121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.823967</td>\n",
       "      <td>0.786420</td>\n",
       "      <td>0.772136</td>\n",
       "      <td>0.787317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.863244</td>\n",
       "      <td>0.846461</td>\n",
       "      <td>0.685857</td>\n",
       "      <td>0.695167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.963015</td>\n",
       "      <td>0.960992</td>\n",
       "      <td>0.530384</td>\n",
       "      <td>0.546128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.984369</td>\n",
       "      <td>0.981366</td>\n",
       "      <td>0.371634</td>\n",
       "      <td>0.406038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.891557</td>\n",
       "      <td>0.873032</td>\n",
       "      <td>0.689280</td>\n",
       "      <td>0.718128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.885358</td>\n",
       "      <td>0.845913</td>\n",
       "      <td>0.768835</td>\n",
       "      <td>0.816630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.907870</td>\n",
       "      <td>0.894319</td>\n",
       "      <td>0.693944</td>\n",
       "      <td>0.722078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.776771</td>\n",
       "      <td>0.762933</td>\n",
       "      <td>0.783646</td>\n",
       "      <td>0.795532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.922528</td>\n",
       "      <td>0.915648</td>\n",
       "      <td>0.600792</td>\n",
       "      <td>0.623006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.958089</td>\n",
       "      <td>0.949158</td>\n",
       "      <td>0.514371</td>\n",
       "      <td>0.531083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.977525</td>\n",
       "      <td>0.985506</td>\n",
       "      <td>0.392774</td>\n",
       "      <td>0.352292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.726437</td>\n",
       "      <td>0.687036</td>\n",
       "      <td>0.833744</td>\n",
       "      <td>0.856107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.614949</td>\n",
       "      <td>0.548469</td>\n",
       "      <td>0.890519</td>\n",
       "      <td>0.917234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.650572</td>\n",
       "      <td>0.626206</td>\n",
       "      <td>0.881417</td>\n",
       "      <td>0.889681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.743549</td>\n",
       "      <td>0.723738</td>\n",
       "      <td>0.811818</td>\n",
       "      <td>0.818527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.827561</td>\n",
       "      <td>0.824203</td>\n",
       "      <td>0.730219</td>\n",
       "      <td>0.730218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.914776</td>\n",
       "      <td>0.911375</td>\n",
       "      <td>0.584464</td>\n",
       "      <td>0.620598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.878146</td>\n",
       "      <td>0.872704</td>\n",
       "      <td>0.719033</td>\n",
       "      <td>0.737764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    clark_spearman_x  clark_spearman_y  clark_mard_x  clark_mard_y\n",
       "0           0.774005          0.741106      0.762081      0.786294\n",
       "2           0.663851          0.611742      0.844524      0.868907\n",
       "3           0.703962          0.683599      0.807562      0.818976\n",
       "4           0.844987          0.831040      0.689987      0.704391\n",
       "5           0.918182          0.906415      0.573461      0.596292\n",
       "6           0.954090          0.945227      0.493516      0.513371\n",
       "7           0.974714          0.948683      0.449922      0.528504\n",
       "8           0.768215          0.731000      0.766120      0.793192\n",
       "10          0.675273          0.617579      0.838554      0.867507\n",
       "11          0.686685          0.662571      0.814186      0.827613\n",
       "12          0.833875          0.815911      0.693308      0.709442\n",
       "13          0.913436          0.900401      0.588304      0.609972\n",
       "14          0.928649          0.911789      0.574449      0.605981\n",
       "15          0.984975          0.974689      0.394953      0.441462\n",
       "16          0.808416          0.767170      0.741355      0.787806\n",
       "18          0.654456          0.596923      0.840393      0.891517\n",
       "19          0.750960          0.718225      0.831326      0.848475\n",
       "20          0.830770          0.802250      0.731538      0.760333\n",
       "21          0.883610          0.857167      0.652335      0.687543\n",
       "22          0.992060          0.983247      0.285744      0.353837\n",
       "23          1.000000          0.989305      0.294583      0.353467\n",
       "24          0.903167          0.875707      0.634893      0.674072\n",
       "26          0.832202          0.756510      0.744031      0.803922\n",
       "27          0.884587          0.853934      0.693475      0.718391\n",
       "28          0.906436          0.890194      0.638536      0.663561\n",
       "29          0.869839          0.867439      0.661767      0.666788\n",
       "32          0.727647          0.680046      0.812213      0.836172\n",
       "34          0.617438          0.540671      0.878594      0.906369\n",
       "35          0.555886          0.533568      0.887150      0.894912\n",
       "36          0.743121          0.718716      0.812987      0.823758\n",
       "..               ...               ...           ...           ...\n",
       "45          0.812933          0.800990      0.671794      0.683605\n",
       "46          0.946651          0.938173      0.429534      0.476965\n",
       "47          0.873589          0.872794      0.485771      0.485771\n",
       "48          0.544953          0.527392      0.870876      0.890140\n",
       "50          0.385737          0.375319      0.939013      0.953339\n",
       "51          0.505624          0.484547      0.905086      0.915031\n",
       "52          0.539539          0.523461      0.877019      0.886540\n",
       "53          0.657396          0.639690      0.775632      0.796053\n",
       "54          0.924685          0.898782      0.586917      0.625644\n",
       "56          0.820153          0.775197      0.768125      0.796177\n",
       "58          0.704328          0.616600      0.856472      0.891055\n",
       "59          0.729677          0.700401      0.848876      0.858121\n",
       "60          0.823967          0.786420      0.772136      0.787317\n",
       "61          0.863244          0.846461      0.685857      0.695167\n",
       "62          0.963015          0.960992      0.530384      0.546128\n",
       "63          0.984369          0.981366      0.371634      0.406038\n",
       "64          0.891557          0.873032      0.689280      0.718128\n",
       "66          0.885358          0.845913      0.768835      0.816630\n",
       "67          0.907870          0.894319      0.693944      0.722078\n",
       "68          0.776771          0.762933      0.783646      0.795532\n",
       "69          0.922528          0.915648      0.600792      0.623006\n",
       "70          0.958089          0.949158      0.514371      0.531083\n",
       "71          0.977525          0.985506      0.392774      0.352292\n",
       "72          0.726437          0.687036      0.833744      0.856107\n",
       "74          0.614949          0.548469      0.890519      0.917234\n",
       "75          0.650572          0.626206      0.881417      0.889681\n",
       "76          0.743549          0.723738      0.811818      0.818527\n",
       "77          0.827561          0.824203      0.730219      0.730218\n",
       "78          0.914776          0.911375      0.584464      0.620598\n",
       "79          0.878146          0.872704      0.719033      0.737764\n",
       "\n",
       "[67 rows x 4 columns]"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clark_filtered_df\n",
    "tt = pd.merge(clark_filtered_df['HC1'], clark_df['HC1'], left_on=['predId'], right_on=['predId'], how='outer').fillna(0)\n",
    "tt[['predCount_x', 'predCount_y']].corr(method='spearman')\n",
    "print('{}, {}'.format(len(stats44_df), len(stats_df)))\n",
    "t = pd.merge(stats44_df, stats_df, left_on=['level', 'dataset'], right_on=['level', 'dataset'])\n",
    "t[t['clark_spearman_x'] != t['clark_spearman_y']][['clark_spearman_x', 'clark_spearman_y', 'clark_mard_x', 'clark_mard_y']]\n",
    "#print(len(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = pd.DataFrame.from_csv('/mnt/scratch2/fatemeh/krakpuff/statSummary/before_debug/unfiltered.stat')\n",
    "b = pd.DataFrame.from_csv('/mnt/scratch2/fatemeh/krakpuff/statSummary/no_repeated_ref/unfiltered.stat')\n",
    "c = pd.DataFrame.from_csv('/mnt/scratch2/fatemeh/krakpuff/statSummary/skip1/unfiltered.stat')\n",
    "d = pd.DataFrame.from_csv('/mnt/scratch2/fatemeh/krakpuff/statSummary/unfiltered.stat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>level</th>\n",
       "      <th>puff_pearson</th>\n",
       "      <th>puff_spearman</th>\n",
       "      <th>puff_mard</th>\n",
       "      <th>puff_cnt_prec</th>\n",
       "      <th>puff_cnt_rec</th>\n",
       "      <th>puff_cnt_f1</th>\n",
       "      <th>puff_prec</th>\n",
       "      <th>puff_rec</th>\n",
       "      <th>...</th>\n",
       "      <th>kraken_cnt_rec</th>\n",
       "      <th>kraken_cnt_f1</th>\n",
       "      <th>kraken_prec</th>\n",
       "      <th>kraken_rec</th>\n",
       "      <th>kraken_f1</th>\n",
       "      <th>krakpuff_mard_diff</th>\n",
       "      <th>puffkrak_spearman_diff</th>\n",
       "      <th>puffkrak_pearson_diff</th>\n",
       "      <th>puffkrak_cnt_f1_diff</th>\n",
       "      <th>puffkrak_f1_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HC1</td>\n",
       "      <td>all</td>\n",
       "      <td>0.999726</td>\n",
       "      <td>0.819785</td>\n",
       "      <td>0.713138</td>\n",
       "      <td>0.999760</td>\n",
       "      <td>0.918042</td>\n",
       "      <td>0.957160</td>\n",
       "      <td>0.305651</td>\n",
       "      <td>0.986188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919683</td>\n",
       "      <td>0.923420</td>\n",
       "      <td>0.165431</td>\n",
       "      <td>0.986188</td>\n",
       "      <td>0.283333</td>\n",
       "      <td>0.131168</td>\n",
       "      <td>0.191755</td>\n",
       "      <td>0.001221</td>\n",
       "      <td>0.033740</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HC1</td>\n",
       "      <td>species</td>\n",
       "      <td>0.956346</td>\n",
       "      <td>0.691920</td>\n",
       "      <td>0.820025</td>\n",
       "      <td>0.998595</td>\n",
       "      <td>0.843147</td>\n",
       "      <td>0.914311</td>\n",
       "      <td>0.197154</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.844487</td>\n",
       "      <td>0.914554</td>\n",
       "      <td>0.156200</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.269071</td>\n",
       "      <td>0.037050</td>\n",
       "      <td>0.073275</td>\n",
       "      <td>-0.001967</td>\n",
       "      <td>-0.000243</td>\n",
       "      <td>0.058632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HC1</td>\n",
       "      <td>genus</td>\n",
       "      <td>0.993907</td>\n",
       "      <td>0.774309</td>\n",
       "      <td>0.749951</td>\n",
       "      <td>0.999484</td>\n",
       "      <td>0.922207</td>\n",
       "      <td>0.959292</td>\n",
       "      <td>0.260417</td>\n",
       "      <td>0.974026</td>\n",
       "      <td>...</td>\n",
       "      <td>0.922971</td>\n",
       "      <td>0.959371</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.974026</td>\n",
       "      <td>0.355450</td>\n",
       "      <td>0.041006</td>\n",
       "      <td>0.065952</td>\n",
       "      <td>-0.000188</td>\n",
       "      <td>-0.000079</td>\n",
       "      <td>0.055509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HC1</td>\n",
       "      <td>family</td>\n",
       "      <td>0.994245</td>\n",
       "      <td>0.904803</td>\n",
       "      <td>0.609469</td>\n",
       "      <td>0.999776</td>\n",
       "      <td>0.923717</td>\n",
       "      <td>0.960242</td>\n",
       "      <td>0.416149</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.924990</td>\n",
       "      <td>0.960815</td>\n",
       "      <td>0.374302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.544715</td>\n",
       "      <td>0.038910</td>\n",
       "      <td>0.029599</td>\n",
       "      <td>-0.000270</td>\n",
       "      <td>-0.000573</td>\n",
       "      <td>0.043004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HC1</td>\n",
       "      <td>order</td>\n",
       "      <td>0.996273</td>\n",
       "      <td>0.950263</td>\n",
       "      <td>0.492391</td>\n",
       "      <td>0.999899</td>\n",
       "      <td>0.924848</td>\n",
       "      <td>0.960910</td>\n",
       "      <td>0.539326</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.926405</td>\n",
       "      <td>0.961725</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.671329</td>\n",
       "      <td>0.031526</td>\n",
       "      <td>0.009694</td>\n",
       "      <td>-0.000160</td>\n",
       "      <td>-0.000814</td>\n",
       "      <td>0.029401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HC1</td>\n",
       "      <td>class</td>\n",
       "      <td>0.999387</td>\n",
       "      <td>0.979559</td>\n",
       "      <td>0.392751</td>\n",
       "      <td>0.999974</td>\n",
       "      <td>0.925808</td>\n",
       "      <td>0.961463</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.927662</td>\n",
       "      <td>0.962456</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.014195</td>\n",
       "      <td>0.005669</td>\n",
       "      <td>-0.000015</td>\n",
       "      <td>-0.000993</td>\n",
       "      <td>0.011414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  dataset    level  puff_pearson  puff_spearman  puff_mard  puff_cnt_prec  \\\n",
       "0     HC1      all      0.999726       0.819785   0.713138       0.999760   \n",
       "1     HC1  species      0.956346       0.691920   0.820025       0.998595   \n",
       "2     HC1    genus      0.993907       0.774309   0.749951       0.999484   \n",
       "3     HC1   family      0.994245       0.904803   0.609469       0.999776   \n",
       "4     HC1    order      0.996273       0.950263   0.492391       0.999899   \n",
       "5     HC1    class      0.999387       0.979559   0.392751       0.999974   \n",
       "\n",
       "   puff_cnt_rec  puff_cnt_f1  puff_prec  puff_rec        ...         \\\n",
       "0      0.918042     0.957160   0.305651  0.986188        ...          \n",
       "1      0.843147     0.914311   0.197154  0.970000        ...          \n",
       "2      0.922207     0.959292   0.260417  0.974026        ...          \n",
       "3      0.923717     0.960242   0.416149  1.000000        ...          \n",
       "4      0.924848     0.960910   0.539326  1.000000        ...          \n",
       "5      0.925808     0.961463   0.634146  1.000000        ...          \n",
       "\n",
       "   kraken_cnt_rec  kraken_cnt_f1  kraken_prec  kraken_rec  kraken_f1  \\\n",
       "0        0.919683       0.923420     0.165431    0.986188   0.283333   \n",
       "1        0.844487       0.914554     0.156200    0.970000   0.269071   \n",
       "2        0.922971       0.959371     0.217391    0.974026   0.355450   \n",
       "3        0.924990       0.960815     0.374302    1.000000   0.544715   \n",
       "4        0.926405       0.961725     0.505263    1.000000   0.671329   \n",
       "5        0.927662       0.962456     0.619048    1.000000   0.764706   \n",
       "\n",
       "   krakpuff_mard_diff  puffkrak_spearman_diff  puffkrak_pearson_diff  \\\n",
       "0            0.131168                0.191755               0.001221   \n",
       "1            0.037050                0.073275              -0.001967   \n",
       "2            0.041006                0.065952              -0.000188   \n",
       "3            0.038910                0.029599              -0.000270   \n",
       "4            0.031526                0.009694              -0.000160   \n",
       "5            0.014195                0.005669              -0.000015   \n",
       "\n",
       "   puffkrak_cnt_f1_diff  puffkrak_f1_diff  \n",
       "0              0.033740          0.183333  \n",
       "1             -0.000243          0.058632  \n",
       "2             -0.000079          0.055509  \n",
       "3             -0.000573          0.043004  \n",
       "4             -0.000814          0.029401  \n",
       "5             -0.000993          0.011414  \n",
       "\n",
       "[6 rows x 25 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.019160\n",
       "1     0.013405\n",
       "2     0.017190\n",
       "3     0.018631\n",
       "4     0.026917\n",
       "5     0.053939\n",
       "6     0.092662\n",
       "7     0.011009\n",
       "8     0.007769\n",
       "9     0.013136\n",
       "10    0.015578\n",
       "11    0.011574\n",
       "12   -0.000024\n",
       "13   -0.000019\n",
       "14    0.032237\n",
       "15    0.020543\n",
       "16    0.025137\n",
       "17    0.023831\n",
       "18    0.024594\n",
       "19    0.137690\n",
       "20    0.222695\n",
       "21   -0.000271\n",
       "22    0.017137\n",
       "23   -0.005662\n",
       "24   -0.008366\n",
       "25   -0.000497\n",
       "26   -0.030833\n",
       "27   -0.044688\n",
       "28    0.022110\n",
       "29    0.007670\n",
       "        ...   \n",
       "40   -0.038124\n",
       "41   -0.044209\n",
       "42    0.011825\n",
       "43    0.007413\n",
       "44    0.006312\n",
       "45    0.022841\n",
       "46    0.008073\n",
       "47   -0.037894\n",
       "48   -0.000070\n",
       "49    0.010580\n",
       "50    0.002042\n",
       "51    0.013028\n",
       "52    0.004120\n",
       "53    0.021726\n",
       "54    0.020855\n",
       "55   -0.000244\n",
       "56   -0.045677\n",
       "57   -0.037883\n",
       "58   -0.046723\n",
       "59   -0.037429\n",
       "60   -0.041545\n",
       "61    0.000091\n",
       "62   -0.053435\n",
       "63    0.008436\n",
       "64    0.002865\n",
       "65    0.006488\n",
       "66    0.015254\n",
       "67    0.022575\n",
       "68    0.000043\n",
       "69   -0.035659\n",
       "Name: puff_mard, Length: 70, dtype: float64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kkk = c.puff_mard-b.puff_mard\n",
    "kkk[kkk != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "after_bug = {}\n",
    "with open('/mnt/scratch2/fatemeh/krakpuff/mapping_dmps/LC4.dmp') as f:\n",
    "    line = f.readline()\n",
    "    while line:\n",
    "        splitted = line.split()\n",
    "        l = []\n",
    "        for i in range(int(splitted[1])):\n",
    "            line = f.readline()\n",
    "            l += [line.split()[0]]\n",
    "        #if (splitted[0] in after_bug):\n",
    "        #    print('shouldnt {}'.format(splitted[0]))\n",
    "        after_bug[splitted[0]] = l\n",
    "        line = f.readline()       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "before_bug = {}\n",
    "with open('/mnt/scratch2/LC4.dmp') as f:\n",
    "    line = f.readline()\n",
    "    while line:\n",
    "        splitted = line.split()\n",
    "        l = []\n",
    "        for i in range(int(splitted[1])):\n",
    "            line = f.readline()\n",
    "            l += [line.split()[0]]\n",
    "        before_bug[splitted[0]] = l\n",
    "        line = f.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pzu_111491_111670_8e62b:0,1\n",
      "pzu_2703637_2703793_153bd:0,1\n",
      "pzu_1255774_1255944_3d32d:0,1\n",
      "pzu_2945802_2945978_2dd40:1,0\n",
      "pzu_1484500_1484678_89975:0,1\n",
      "pzu_2750703_2750884_36398:1,0\n",
      "pzu_3324642_3324789_706ef:1,0\n",
      "pzu_68754_68901_60ed6:0,1\n",
      "pzu_1563937_1564121_118dc:0,1\n",
      "pzu_1211775_1211974_252b3:0,1\n",
      "pzu_3540931_3541097_a019d:0,1\n",
      "pzu_1926279_1926470_2218b:2,1\n",
      "har_1949026_1949213_198c9:1,0\n",
      "xca_294301_294475_7dd0:2,1\n",
      "pzu_1676826_1676998_4d651:1,0\n"
     ]
    }
   ],
   "source": [
    "for k, v in after_bug.items():\n",
    "    if len(before_bug[k]) != len(v):\n",
    "        print('{}:{},{}'.format(k, len(before_bug[k]), len(v)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gi|197103466|ref|NC_011144.1|']\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(after_bug['pzu_111491_111670_8e62b'])\n",
    "print(before_bug['pzu_111491_111670_8e62b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clark_hc1 = pd.read_csv('/mnt/scratch2/avi/meta-map/clark/HC1.rpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f775a1ff3c8>]"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4XHd97/H3V4s3SZZtbZYXWZIlb4njxFYcJyTB2YNJ\nCQECyS1boPgGCKXtBQrkuaVwe7lQLi1QluDSQKCBsCXEFCchTmlCSOzEdryvsizbkrXb2q392z80\nNoqRbEszmjMjfV7Po0cz5xzr9/WZmfOZ8zvLz9wdEREZfxKCLkBERIKhABARGacUACIi45QCQERk\nnFIAiIiMUwoAEZFxSgEgIjJOKQBERMYpBYCIyDiVFHQB55OZmen5+flBlyEiEje2bt1a7+5ZF7Ns\nTAdAfn4+W7ZsCboMEZG4YWZHL3ZZdQGJiIxTCgARkXFKASAiMk4pAERExikFgIjIOBV2AJjZXDP7\nnZntNbM9ZvbxQZYxM/uGmZWa2U4zWx5uuyIiEp5InAbaA/wvd99mZmnAVjN71t33DljmTUBx6Ocq\n4Duh3yIiEpCwA8Ddq4Cq0OMWM9sHzAYGBsCdwA+9f/zJTWY2zcxyQ/9WRGRc6+nto+LUacob2iiv\nb+N0dx8fXj1/1NuN6IVgZpYPXAFsPmfWbOD4gOcVoWl/EgBmthZYC5CXlxfJ8kREAuPu1LV0Ulbf\nxpH6NsrqWkO/2zh2sp2evj+Oz56dNpH731iImY1qTRELADNLBX4J/JW7N4/077j7OmAdQElJiUas\nF5G40trZQ3l9G2XnbOSP1LfR2tlzdrkJSQkUZKSwcGYat186k/zMFAoyU5iXMYWs1ImjvvGHCAWA\nmSXTv/F/1N0fH2SRSmDugOdzQtNEROJSc0c3pbWtHKpp4VBNKwdrWymtaeFEU8fZZcxgVvpkCrNS\nePvy2RRkplCYlUpBZgqzp00mIWH0N/LnE3YAWH9M/Ruwz93/aYjF1gMPmNlj9B/8bVL/v4jEg+aO\nbg7VhDb0ta0crGmhtLaVqgEb+olJCRRlp7KyYAZF2anMz0qlICuF/IwUJiUnBlj9+UViD+ANwHuA\nXWa2PTTts0AegLs/BGwA1gClQDtwXwTaFRGJmO7ePsrq2thX1czeqmb2VTVzqKaV6uY/bugnJfdv\n6FcVZlCck0pxdhoLclKZM30KiQF/mx+JSJwF9CJw3v956Oyfj4bblohIJDS1d5/dyJ/Z4B+qaaWr\ntw/o758vzk7lmvkZFOekUZydyoKcNGZPnxyXG/qhxPTtoEVEwuHu1DR3sqOikT2VTaGNfguVjafP\nLpOZOoHFuVO57w35LM6dyuLcqRRmpZCcOPZvlKAAEJExo6G1k50VTeysaGJXZSM7Kpqoa+kEIMFg\nflYqK+ZN592r5rFk1lQW56aRnTYp4KqDowAQkbjU2tnDjuON7KhoZFdoo3/mm72FNvbXFWVy2Zx0\nls6ZxiWzpsb0AdkgKABEJOa5O8dPnmbrsZNsPXqKrUcbOVDdzJlrp+ZlTGH5vOm8/5p8ls5J59LZ\n6aRO1ObtQrSGRCTmdPb0squiia1HT7HtWP8Gv761vysndWISV+RN49Ybi1k+bzrL5qQzbcqEgCuO\nTwoAEQlcV08fOyoa2XS4gU1HGth69BQd3f1n5MzLmML1xZksnzedFfOmsyAnbUydiRMkBYCIRF1X\nTx87KxrZVNbAprKTbDl68uwGf3HuVO5dmcdVBRmsmDedrLSJAVc7dikARGTUuTuH61p5/mA9Lxys\n45UjJznd3QvAoplp3HNlHqsKM7iqYAbTU9SdEy0KABEZFU2nu3mptJ7nD9bx+0P1Z8/QKcxM4Z0l\nc7h6fqY2+AFTAIhIxJTWtvLs3hqe21fDa8cb6e1z0iYmcU1RBh+5YT7XF2cxd8aUoMuUEAWAiIxY\nb5/z2rFTPLu3hmf31lBW3wbAJbOm8uE3zueNC7O4fO60cXFVbTxSAIjIsHT19PFiaR1P767mP/fX\nUt/aRVKCcfX8DN7/hnxuXpzDrGmTgy5TLoICQEQuqKe3j01lJ/n1jhM8vaeaptPdpE1MYvWibG5Z\nksPqhVlMnZQcdJkyTAoAERmUu7Pl6CnWbz/Bhl1VNLR1kToxiVuX5HDHslyuLcpiQpK6duKZAkBE\nXudE42ke31bBL7ZWUN7QzqTkBG5anMOfXTaL1QuzdD+dMSRSQ0I+DNwB1Lr7pYPMXw08CRwJTXrc\n3b8QibZFJHwd3b38dm8NP99ynBdL63GHVYUz+NiNxdx+6UxSdF+dMSlSr+oPgG8CPzzPMr939zsi\n1J6IRMDxk+38+6ajPPbqcZpOdzN72mQ+dmMx71g+h7wMna451kUkANz9BTPLj8TfEpHR5e78obSB\nH7xUznP7a0gw4/ZLZvI/rsrj6sKMwAcql+iJ5n7dNWa2E6gEPuHue6LYtsi419Hdy8+3VvDIS+WU\n1raSkTKBj64u4s9X5ZGbrtM2x6NoBcA2IM/dW81sDfAroHiwBc1sLbAWIC8vL0rliYxdzR3d/Ojl\no3z/D0eob+3isjnpfPXuZbz5slwd0B3nohIA7t484PEGM/u2mWW6e/0gy64D1gGUlJR4NOoTGYsa\nWjt5+A9H+OFLR2np7OGNC7L4yOr5rCyYgZm6eSRKAWBmM4Ead3czWwkkAA3RaFtkvGnp6OZff3+E\n7/2+jNPdvay5NJcPr57PpbPTgy5NYkykTgP9CbAayDSzCuBzQDKAuz8EvAP4sJn1AKeBe9xd3+5F\nIqiju5cfvXyUb/9XKafau3nz0lz++pYFFGWnBl2axKhInQV07wXmf5P+00RFJMLcnfU7TvClp/ZT\n1dTBdcWZfOq2RSydo2/8cn66ukMkjh2obuHvntzN5iMnuXT2VL569zKuKcoMuiyJEwoAkTjU3NHN\n1549xCMvl5M2KYn/e9el3HNlnsbKlWFRAIjEmf/cX8NnHt9FbUsn91yZx6duW6hRtWREFAAicaKp\nvZvP/8ceHt9WycKcNNa9p4Rlc6cFXZbEMQWASBzYXNbAxx/bTl1rJw/cUMTHbipiYpIu4pLwKABE\nYlhvn/PN/yzl688dJG/GFJ74yDVcNkff+iUyFAAiMepkWxcP/HgbLx1u4K2Xz+If7lpKqm7LLBGk\nd5NIDNpf3cxfPLKF2pZO/vHtl3F3yRzdvkEiTgEgEmM27q3h44+9RsrEJH72P6/mch3olVGiABCJ\nIT955RiffWIXS2ens+49JcxMnxR0STKGKQBEYsR3nz/M/3tqPzcszOLbf76CyRN0lo+MLgWASMDc\nnf//2wN863eHueOyXP7pnZczISkh6LJkHFAAiATsaxsP8a3fHebelXP5h7cu1e0cJGoUACIB+v4f\njvD15w7xjhVz+OJdS3Wmj0SV9jNFAvLEaxV8/td7uXVJDl96mzb+En0KAJEAvHy4gU/8fCdXF2bw\njXuvIClRH0WJvoi868zsYTOrNbPdQ8w3M/uGmZWa2U4zWx6JdkXiUVXTaR748TbyM6aw7r0rNDC7\nBCZSXzt+ANx+nvlvAopDP2uB70SoXZG40tXTx0ce3UZHdy/ffc8K0iYlB12SjGMRCQB3fwE4eZ5F\n7gR+6P02AdPMLDcSbYvEk//zH3t57VgjX7l7GUXZaUGXI+NctDoeZwPHBzyvCE0TGTc27KriR5uO\nsvb6QtYs1fcfCV7MHXkys7VmtsXMttTV1QVdjkhE1LV08uATu7hsTjqfvG1h0OWIANELgEpg7oDn\nc0LT/oS7r3P3EncvycrKikpxIqPtf/9qN21dvXz17mUk64wfiRHReieuB94bOhtoFdDk7lVRalsk\nUL/bX8vTe6r5+E3FFOeo319iR0SuBDaznwCrgUwzqwA+ByQDuPtDwAZgDVAKtAP3RaJdkVjX0d3L\n59bvYX5WCh+6rjDockReJyIB4O73XmC+Ax+NRFsi8eT7fyjn2Ml2Hv2Lq3SDN4k5ekeKjJKm9m6+\n81+l3LgomzcUZQZdjsifUACIjJLvPH+Yls4enfUjMUsBIDIKTrV18chL5bxl2SwW504NuhyRQSkA\nREbBjzYd5XR3Lx9ZXRR0KSJDUgCIRNjprl5+8FI5Ny7KZuFMnfYpsUsBIBJhT7xWycm2LtZer9M+\nJbYpAEQi7KevHmNhThpXFcwIuhSR81IAiETQ3hPN7Kho4l1XztUIXxLzFAAiEfSzLceZkJjAXVfo\nZrcS+xQAIhHS3dvHr7ZXcuslOUxPmRB0OSIXpAAQiZBXjpyksb2bOy6bFXQpIhdFASASIU/vrmZS\ncgJvXKDbmEt8UACIREBfn/PMnmpWL8hm8gQN8i7xQQEgEgGvHW+ktqWT2y+dGXQpIhdNASASAc8f\nqCXB4IZF2UGXInLRFAAiEbCp7CRLZ6eTPjk56FJELlpEAsDMbjezA2ZWamafHmT+ajNrMrPtoZ+/\ni0S7IrGgo7uX7ccbuaowI+hSRIYl7BHBzCwR+BZwC1ABvGpm69197zmL/t7d7wi3PZFYs+3YKbp6\n+1hVqFs/SHyJxB7ASqDU3cvcvQt4DLgzAn9XJC5sLjtJgkFJvgJA4kskAmA2cHzA84rQtHNdY2Y7\nzewpM7tkqD9mZmvNbIuZbamrq4tAeSKja/ORBi6Zlc7USer/l/gSrYPA24A8d78M+BfgV0Mt6O7r\n3L3E3UuysnRBjcS2nt4+XjvWyJX69i9xKBIBUAnMHfB8TmjaWe7e7O6toccbgGQz0yjZEvfKG9rp\n7Onjklka9lHiTyQC4FWg2MwKzGwCcA+wfuACZjbTQvfGNbOVoXYbItC2SKAOVLcAaOQviUthnwXk\n7j1m9gDwDJAIPOzue8zs/tD8h4B3AB82sx7gNHCPu3u4bYsE7UB1M4kJRlF2atCliAxb2AEAZ7t1\nNpwz7aEBj78JfDMSbYnEkv3VLeRnTGFSsu7/I/FHVwKLhOFATQuLZqr/X+KTAkBkhNq7ejh2sl39\n/xK3FAAiI3SwphV3HQCW+KUAEBmhA9XNACxSAEicUgCIjFB5QzvJicbc6VOCLkVkRBQAIiNU09xB\ndtokEhIs6FJERkQBIDJCtc2d5EydGHQZIiOmABAZoermDnKmTgq6DJERUwCIjFCNAkDinAJAZATa\nu3po6ehRAEhcUwCIjEBNcyeAjgFIXFMAiIxATXMHgPYAJK4pAERGQAEgY4ECQGQE/hgA6gKS+KUA\nEBmBmuZOpkxIJHViRO6oLhKIiASAmd1uZgfMrNTMPj3IfDOzb4Tm7zSz5ZFoVyQoNc0dzJw6idBA\ndyJxKewAMLNE4FvAm4AlwL1mtuScxd4EFId+1gLfCbddkSDVNHeQre4fiXOR2ANYCZS6e5m7dwGP\nAXees8ydwA+93yZgmpnlRqBtkUDUNHfqALDEvUgEwGzg+IDnFaFpw11GJC64+9kuIJF4FnMHgc1s\nrZltMbMtdXV1QZcj8ieaTnfT2dNHtgJA4lwkAqASmDvg+ZzQtOEuA4C7r3P3EncvycrKikB5IpGl\nq4BlrIhEALwKFJtZgZlNAO4B1p+zzHrgvaGzgVYBTe5eFYG2RaLuzDUA6gKSeBf2Sczu3mNmDwDP\nAInAw+6+x8zuD81/CNgArAFKgXbgvnDbFQlKta4CljEiIlexuPsG+jfyA6c9NOCxAx+NRFsiQasN\nBUBWmrqAJL7F3EFgkVhX3dzB9CnJTEpODLoUkbAoAESGSdcAyFihABAZptrmDp0CKmOCAkBkmKqb\nO5ipU0BlDFAAiAxDb59T16IuIBkbFAAiw9DQ2kmfoy4gGRMUACLDUK2LwGQMUQCIDINuAyFjiQJA\nZBg0FrCMJQoAkWGoae4gwSAzVXsAEv8UACLDUFrbSt6MKSQmaChIiX8KAJFhOFDdwsKZaUGXIRIR\nCgCRi9TR3Ut5QxsLZ04NuhSRiFAAiFykQzWt9Dks1h6AjBEKAJGLtL+6GUBdQDJmKABELtKB6hYm\nJScwLyMl6FJEIiKsAWHMbAbwUyAfKAfe6e6nBlmuHGgBeoEedy8Jp12RIByoaaE4O01nAMmYEe4e\nwKeB59y9GHgu9HwoN7j75dr4S7zaV6UzgGRsCTcA7gQeCT1+BHhrmH9PJCbVtnRQ39rJIgWAjCHh\nBkCOu1eFHlcDOUMs58BGM9tqZmvDbFMk6l45chKAkvwZAVciEjkXPAZgZhuBmYPMenDgE3d3M/Mh\n/sy17l5pZtnAs2a2391fGKK9tcBagLy8vAuVJxIVm8oaSJmQyKWzdA2AjB0XDAB3v3moeWZWY2a5\n7l5lZrlA7RB/ozL0u9bMngBWAoMGgLuvA9YBlJSUDBUoIlG1uewkJfkzSErUiXMydoT7bl4PvC/0\n+H3Ak+cuYGYpZpZ25jFwK7A7zHZFoqa+tZNDta1cVajuHxlbwg2ALwG3mNkh4ObQc8xslpltCC2T\nA7xoZjuAV4DfuPvTYbYrEjVn+v9XFWYEXIlIZIV1HYC7NwA3DTL9BLAm9LgMWBZOOyJBeulwPZOT\nE1k6Oz3oUkQiSh2aIufR1+ds3FvL9QsySVb/v4wxekeLnMeOikaqmzu4/dLBToQTiW8KAJHzeHpP\nNUkJxo2LhrrERSR+KQBEhuDuPLO7mmuKMkmfnBx0OSIRpwAQGcLuymbKG9q5/RJ1/8jYpAAQGcJP\ntxxjYlICb74sN+hSREaFAkBkEKe7ennytRO8eWmuun9kzFIAiAxiw64qWjp7eNeVc4MuRWTUKABE\nzuHu/PDlcgozU1hZoNs/yNilABA5x8tlDeyoaOKD1xVgptG/ZOxSAIic47vPl5GZOoG3L58TdCki\no0oBIDLA7somnj9Yx31vKGBScmLQ5YiMKgWAyABffno/06Yk856r5wVdisioUwCIhLxUWs/vD9Xz\nwA1FTJ2kUz9l7FMAiAC9fc4Xn9rHrPRJvHuVvv3L+KAAEAEe3XyU3ZXNfPbNi9X3L+NGWAFgZneb\n2R4z6zOzkvMsd7uZHTCzUjP7dDhtikRabUsHX3nmANcWZfLmpbrtg4wf4e4B7AbexhADvAOYWSLw\nLeBNwBLgXjNbEma7IhHh7nz28d109vTx+Tsv0Xn/Mq6EFQDuvs/dD1xgsZVAqbuXuXsX8BhwZzjt\nikTK49sq2bivhk/eupD5WalBlyMSVdE4BjAbOD7geUVo2qDMbK2ZbTGzLXV1daNenIxfR+rb+Nz6\nPVyZP50PXFsQdDkiUXfBQeHNbCMw2A3RH3T3JyNdkLuvA9YBlJSUeKT/vghAR3cvH310G4kJxtfu\nuYLEBHX9yPhzwQBw95vDbKMSGHhLxTmhaSKBcHf+9pc72VvVzPfeW8LsaZODLkkkENHoAnoVKDaz\nAjObANwDrI9CuyKD+trGQzy5/QSfvG0hNy/RWL8yfoV7GuhdZlYBXA38xsyeCU2fZWYbANy9B3gA\neAbYB/zM3feEV7bIyPzqtUq+/twh3rFiDh9ZPT/ockQCdcEuoPNx9yeAJwaZfgJYM+D5BmBDOG2J\nhOul0no+9YudrCqcwRfvWqpTPmXc05XAMi68dLieDzzyKvmZU3jo3SuYkKS3vog+BTLmvXy4gQ/8\n4FXyZkzhxx9axbQpE4IuSSQmKABkTPv9oTru+8ErZzf+makTgy5JJGYoAGTM+vmW49z3/VfJz0jR\nxl9kEGEdBBaJRe7ON54r5Z83HuQNRRl8590rdH9/kUEoAGRMae/q4bOP7+JX20/wtuWz+dLbLtMB\nX5EhKABkzDhS38b9P9rKwdoWPnHrAj56Q5FO9RQ5DwWAjAlP7ariU7/YSWKi8ch9K7l+QVbQJYnE\nPAWAxLWWjm4+/+u9/GJrBcvmpPOtP1/OnOlTgi5LJC4oACRubS5r4BO/2EHlqdN87MYi/vKmYpIT\n1d8vcrEUABJ3mk5386Wn9vOTV46RN2MKP7//albMmxF0WSJxRwEgccPd+c2uKr7w673Ut3byoesK\n+OtbFjBlgt7GIiOhT47Ehd2VTXzh13t5pfwkS3Kn8m/vu5Klc9KDLkskrikAJKbVtnTwlacP8Itt\nFcyYMoEv3rWUd105VyN4iUSAAkBiUlN7N997sYyHXzxCV28fH7qukAduLNIVvSIRFFYAmNndwN8D\ni4GV7r5liOXKgRagF+hx95Jw2pWxq6Wjm4dfLOd7L5bR0tHDmqUz+eRtiyjITAm6NJExJ9w9gN3A\n24DvXsSyN7h7fZjtyRjV1N7NjzaV870Xj9DY3s0tS3L465sXsGTW1KBLExmzwh0RbB+gy+1lxCob\nT/Pwi0f4ySvHaO/q5YaFWfzNLQt1gFckCqJ1DMCBjWbWC3zX3ddFqV2JUbsqmvj+H46wfscJHHjL\nsll86LpCfeMXiaILBoCZbQRmDjLrQXd/8iLbudbdK80sG3jWzPa7+wtDtLcWWAuQl5d3kX9e4kFH\ndy+/3nGCf998jB3HG5kyIZH3Xp3PB67N1+0bRAJwwQBw95vDbcTdK0O/a83sCWAlMGgAhPYO1gGU\nlJR4uG1L8Mrr23h081F+tqWCptPdFGWn8vm3XMJdy2frrB6RAI16F5CZpQAJ7t4Senwr8IXRbleC\n1dbZw1O7q/nl1gpeLmsgKcG47ZKZvHvVPFYVztBxI5EYEO5poHcB/wJkAb8xs+3ufpuZzQK+5+5r\ngBzgidAHPgn4sbs/HWbdEoN6+5yXDzfw+LYKntpdzenuXuZlTOFvblnAPVfOJXvqpKBLFJEBwj0L\n6AngiUGmnwDWhB6XAcvCaUdil7uzq7KJ3+yqYv32E1Q1dZA2KYm3XjGbty+fzYp50/VtXyRG6Upg\nGbaBG/0Nu6o4fvI0SQnGdcWZPPjmxdy8OIdJyYlBlykiF6AAkIvS09vH1qOneG5/LU/t/uNG/9ri\nTD52YzG3Lslh2pQJQZcpIsOgAJAhNXd088LBOp7bV8vvDtTS2N5NcqJxzXxt9EXGAgWAnOXuHKlv\n4/mDdWzcV8PmspP09DnTpyRz46Jsbl6cw3XFmaTp1E2RMUEBMM41tnfxh9IGXiyt44WD9VQ2ngag\nKDuVD15XwM2Lc1ieN123XxYZgxQA40xnTy/bjzXyYmk9LxyqZ2dFI+6QNjGJa4oyuH/1fK4vzmRe\nhu6+KTLWKQDGuI7uXrYdPcWmIyfZXNbAa8cb6erpI8Hg8rnT+Msbi7l+QSbL5kwjSQOqi4wrCoAx\npq2zh61HT7H5SAOby06yo6KR7l4nwWDJrKm8Z9U8riqYwVWFGaRPVl++yHimAIhj7s6xk+1sO3aK\n1441su3YKfZVtdDb5yQmGJfOTucDbyjgqsIZlOTP0H13ROR1FABxpK2zhx0Vjbx2rJHXQhv9hrYu\nAFImJLJs7jTuf2MhKwsyWDFvOqkT9fKKyNC0hYhRXT19HKxpYVdlE7sqm9h+rJH91c30he6PWpiV\nwg2LsrkibxrL86azICdNZ+qIyLAoAGJAZ08vB6tbz27sd1c2caC6ha7ePgDSJiWxbM40HrihiCvm\nTeeKudN0AZaIhE0BEGUtHd0cqG5hX3ULe0/0b/APVLfQ3dv/1X7qpCSWzknnvmvzWTo7nUtnpTMv\nY4puqCYiEacAGCW9fc7Rhjb2VbWwv7r57O+KU6fPLpM+OZmls9P54LWFLJ2dztLZ6cydMVkbexGJ\nCgVAmNydutZOSmtbOVDdwv7Qhv5ATQsd3f1dOIkJRmFmClfkTefelXkszk1j0cyp5KZP0sZeRAKj\nALhIfX3OiabTlNa2nv05FPrddLr77HIZKRNYnDuVd181j0W5U1k0M42i7FTdHllEYk64I4J9Bfgz\noAs4DNzn7o2DLHc78HUgkf6Rwr4UTrujqae3j2Mn289u4A+f+V3XSntX79nlMlImUJSdyh2X5VKc\nnUpRdhoLZqaSnaZRr0QkPoS7B/As8Bl37zGzLwOfAf524AJmlgh8C7gFqABeNbP17r43zLZHrK/P\nqWruoLy+jbL6NspDP0fq2zh2sp2evj+ORZ+bPomi7FTedeVcirP7v80XZacyI0Vn4YhIfAt3SMjf\nDni6CXjHIIutBEpDQ0NiZo8BdwKjGgBn+uaP1LVR3jBwQ99OeUMbnT19Z5edlJxAfkYKi3LTuP3S\nmRRkplCck8b8rBTd+lhExqxIHgP4APDTQabPBo4PeF4BXBXBdl+np7ePu779EmV1rbQN6LJJTjTy\nZkyhIDOV6xdkUpCZSn7mFAoyU8hJm0SCLqISkXHmggFgZhuBmYPMetDdnwwt8yDQAzwabkFmthZY\nC5CXlzfsf5+UmEBRdior5k2nIDOF/MwUCjNTmDVtsq6UFREZ4IIB4O43n2++mb0fuAO4yd19kEUq\ngbkDns8JTRuqvXXAOoCSkpLB/t4F/fO7Lh/JPxMRGVfCugF86OyeTwFvcff2IRZ7FSg2swIzmwDc\nA6wPp10REQlfuCOAfBNIA541s+1m9hCAmc0ysw0A7t4DPAA8A+wDfubue8JsV0REwhTuWUBFQ0w/\nAawZ8HwDsCGctkREJLI0BqCIyDilABARGacUACIi45QCQERknFIAiIiMUzb4tVuxwczqgKMj/OeZ\nQH0Ey4kU1TV8sVqb6hoe1TV8I6ltnrtnXcyCMR0A4TCzLe5eEnQd51Jdwxertamu4VFdwzfatakL\nSERknFIAiIiMU2M5ANYFXcAQVNfwxWptqmt4VNfwjWptY/YYgIiInN9Y3gMQEZHziPsAMLO7zWyP\nmfWZWck58z5jZqVmdsDMbhswfYWZ7QrN+4aZjfpIMWb209AdU7ebWbmZbQ9Nzzez0wPmPTTatZxT\n19+bWeWA9tcMmDfo+otSXV8xs/1mttPMnjCzaaHpga6vUA23h9ZJqZl9OtrtD6hjrpn9zsz2hj4D\nHw9NH/I1jXJ95aHP2XYz2xKaNsPMnjWzQ6Hf06Nc08IB62W7mTWb2V8Fsc7M7GEzqzWz3QOmDbl+\nRuXz6O5x/QMsBhYC/wWUDJi+BNgBTAQKgMNAYmjeK8AqwICngDdFueavAn8XepwP7A5w/f098IlB\npg+5/qJU161AUujxl4Evx8j6Sgyti0JgQmgdLQmollxgeehxGnAw9LoN+poGUF85kHnOtH8EPh16\n/Okzr2vC3k1vAAADh0lEQVSAr2U1MC+IdQZcDywf+H4eav2M1ucx7vcA3H2fux8YZNadwGPu3unu\nR4BSYKWZ5QJT3X2T96/ZHwJvjVa9ob2NdwI/iVabIzTo+otW4+7+W+8fSwJgE/0jycWClUCpu5e5\nexfwGP3rKurcvcrdt4Uet9A/3sbsIGoZhjuBR0KPHyGKn71B3AQcdveRXmwaFnd/ATh5zuSh1s+o\nfB7jPgDOY7DB6GeHfioGmR4t1wE17n5owLSC0G7n82Z2XRRrOeNjoa6Whwfscg61/oLwAfr31M4I\ncn3F0no5y8zygSuAzaFJg72m0ebARjPbav1jfQPkuHtV6HE1kBNMaUD/6IQDv4jFwjobav2Myvsu\nLgLAzDaa2e5BfgL55jWUi6zzXl7/pqsC8tz9cuBvgB+b2dQo1vUd+rszLg/V8tVIth1GXWeWeRDo\nAR4NTRr19RVvzCwV+CXwV+7eTICv6TmuDb1ObwI+ambXD5wZ2gMP5DRE6x+e9i3Az0OTYmWdnRWN\n9RPWiGDR4hcYmH4IQw1GX8nruxPOO0j9cFyoTjNLAt4GrBjwbzqBztDjrWZ2GFgAbIlETRdT14D6\n/hX4j9DTodZfxFzE+no/cAdwU+jDEJX1dQGjvl6Gw8yS6d/4P+rujwO4e82A+QNf06hy98rQ71oz\ne4L+LosaM8t196pQd2xtELXRH0rbzqyrWFlnDL1+RuV9Fxd7ACO0HrjHzCaaWQFQDLwS2r1qNrNV\nof749wJPRqmmm4H97n62C8rMsswsMfS4MFRnWZTqIfQmO+Mu4MwZCYOuvyjWdTvwKeAt7t4+YHqg\n6wt4FSg2s4LQt8h76F9XURd6//4bsM/d/2nA9KFe02jWlmJmaWce039Qfzf96+p9ocXeR/Q+e+d6\n3Z54LKyzkKHWz+h8HqN95H0UjqTfRX9/WCdQAzwzYN6D9B8tP8CAM32AEvpf4MP0D2xvUar1B8D9\n50x7O7AH2A5sA/4syuvvR8AuYGfoTZZ7ofUXpbpK6e/z3B76eSgW1leohjX0n3FzGHgw2u0PqONa\n+rsIdg5YT2vO95pGsbZC+s9a2RF6vR4MTc8AngMOARuBGQHUlgI0AOkDpkV9ndEfQFVAd2gb9sHz\nrZ/R+DzqSmARkXFqLHcBiYjIeSgARETGKQWAiMg4pQAQERmnFAAiIuOUAkBEZJxSAIiIjFMKABGR\nceq/AUGnVWZ9WgvQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f776d609588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sign(a):\n",
    "    if a > 0:\n",
    "        return 1\n",
    "    if a <= 0:\n",
    "        return -1\n",
    "difference = list(range(-100, 100, 1))\n",
    "\n",
    "log_modulus = [sign(x)*log(abs(x)+1, 10) for x in difference]\n",
    "plt.plot(difference, log_modulus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                         object\n",
       "TaxID                         int64\n",
       "Lineage                      object\n",
       "Count                         int64\n",
       "Proportion_All(%)           float64\n",
       "Proportion_Classified(%)     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clark_df = pd.read_csv('/mnt/scratch2/avi/meta-map/clark/HC1.rpt')\n",
    "clark_df['TaxID'] = clark_df['TaxID'].apply(pd.to_numeric, errors='coerce')\n",
    "clark_df['TaxID'] = clark_df['TaxID'].fillna(0.0).astype(int)\n",
    "clark_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "682\n",
      "681\n",
      "Name                         object\n",
      "TaxID                         int64\n",
      "Lineage                      object\n",
      "Count                         int64\n",
      "Proportion_All(%)           float64\n",
      "Proportion_Classified(%)     object\n",
      "taxaId                        int64\n",
      "pid                           int64\n",
      "rank                         object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(len(clark_df))\n",
    "c_df = pd.merge(clark_df, taxa_df, left_on='TaxID', right_on='taxaId')\n",
    "print(len(c_df))\n",
    "print(c_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>TaxID</th>\n",
       "      <th>Lineage</th>\n",
       "      <th>Count</th>\n",
       "      <th>Proportion_All(%)</th>\n",
       "      <th>Proportion_Classified(%)</th>\n",
       "      <th>taxaId</th>\n",
       "      <th>pid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  TaxID  Lineage  Count  Proportion_All(%)  \\\n",
       "rank                                                      \n",
       "species   681    681      681    681                681   \n",
       "\n",
       "         Proportion_Classified(%)  taxaId  pid  \n",
       "rank                                            \n",
       "species                       681     681  681  "
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_df.groupby('rank').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>readId</th>\n",
       "      <th>readLen</th>\n",
       "      <th>predId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>taf_993394_993580_3f3dc/1</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lps_1012348_1012526_c9a0/2</td>\n",
       "      <td>100</td>\n",
       "      <td>1590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vpa.II_1755137_1755333_409/1</td>\n",
       "      <td>100</td>\n",
       "      <td>670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mac_4502468_4502637_321a9/2</td>\n",
       "      <td>100</td>\n",
       "      <td>2214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>xca_1318293_1318458_4af66/2</td>\n",
       "      <td>74</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         readId  readLen  predId\n",
       "0     taf_993394_993580_3f3dc/1       51     NaN\n",
       "1    lps_1012348_1012526_c9a0/2      100  1590.0\n",
       "2  vpa.II_1755137_1755333_409/1      100   670.0\n",
       "3   mac_4502468_4502637_321a9/2      100  2214.0\n",
       "4   xca_1318293_1318458_4af66/2       74   339.0"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clark_mapping = pd.read_csv('/mnt/scratch2/avi/meta-map/clark/HC1.csv')\n",
    "clark_mapping.columns = ['readId', 'readLen', 'predId']\n",
    "clark_mapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clark_mapping_ = pd.merge(clark_mapping, taxa_df, left_on='predId', right_on='taxaId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['species'], dtype=object)"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clark_mapping_['rank'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   totperc  totCnt  k1cnt typ    k1id                         name  taxaId  \\\n",
      "0    81.50  814977      0   -       1                         root       1   \n",
      "1    81.50  814977     22   -  131567           cellular organisms  131567   \n",
      "2    76.26  762643    841   D       2                     Bacteria       2   \n",
      "3    38.06  380612   1006   P    1224               Proteobacteria    1224   \n",
      "4    18.78  187808    564   C    1236          Gammaproteobacteria    1236   \n",
      "\n",
      "      pid          rank  \n",
      "0       1       no rank  \n",
      "1       1       no rank  \n",
      "2  131567  superkingdom  \n",
      "3       2        phylum  \n",
      "4    1224         class  \n",
      "     k2id    k2cnt     perc   k2rank                         name\n",
      "0   79329  20440.0  0.02448  species        Chitinophaga pinensis\n",
      "1    1836  18431.0  0.02207  species  Saccharopolyspora erythraea\n",
      "2  182640  16905.0  0.02025  species            Kribbella flavida\n",
      "3  152480  13938.0  0.01669  species       Burkholderia ambifaria\n",
      "4     986  13660.0  0.01636  species    Flavobacterium johnsoniae\n"
     ]
    }
   ],
   "source": [
    "k1 = pd.read_csv('/mnt/scratch2/avi/meta-map/kraken/krakOut/{}.rpt'.format('HC1'), sep='\\t', header=None)\n",
    "k2 = pd.read_csv('/home/fatemeh/others_projects/mason_eval/species/Huttenhower_HC1_KrakenFiltered.txt', sep='\\t', header=None)\n",
    "k1.columns=['totperc', 'totCnt', 'k1cnt', 'typ', 'k1id', 'name']\n",
    "k2.columns=['k2id', 'k2cnt', 'perc', 'k2rank', 'name']\n",
    "k1 = pd.merge(k1, taxa_df, left_on='k1id', right_on='taxaId')\n",
    "print(k1.head(5))\n",
    "print(k2.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "252\n",
      "1043\n",
      "272\n"
     ]
    }
   ],
   "source": [
    "print(len(k2))\n",
    "k1_species = k1[k1[\"rank\"] == 'species']\n",
    "print(len(k1))\n",
    "print(len(k1_species))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k1id</th>\n",
       "      <th>k2id</th>\n",
       "      <th>rank</th>\n",
       "      <th>k2rank</th>\n",
       "      <th>k1cnt</th>\n",
       "      <th>totCnt</th>\n",
       "      <th>k2cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>470934.0</td>\n",
       "      <td>470934.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10813.0</td>\n",
       "      <td>10812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>553.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>8491.0</td>\n",
       "      <td>10349.0</td>\n",
       "      <td>10352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>592316.0</td>\n",
       "      <td>592316.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28151.0</td>\n",
       "      <td>28151.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11279.0</td>\n",
       "      <td>11277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>614.0</td>\n",
       "      <td>614.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>615.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>82996.0</td>\n",
       "      <td>82996.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>244366.0</td>\n",
       "      <td>244366.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>2605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>573.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>17.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>548.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1134687.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>species</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>54291.0</td>\n",
       "      <td>54291.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>67825.0</td>\n",
       "      <td>67825.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>28901.0</td>\n",
       "      <td>28901.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>891974.0</td>\n",
       "      <td>891974.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>28141.0</td>\n",
       "      <td>28141.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>636.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>1711.0</td>\n",
       "      <td>1710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>67780.0</td>\n",
       "      <td>67780.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1263550.0</td>\n",
       "      <td>1263550.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>628.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>680.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13050.0</td>\n",
       "      <td>13023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>670.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>7655.0</td>\n",
       "      <td>10309.0</td>\n",
       "      <td>10299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>663.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>150340.0</td>\n",
       "      <td>150340.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>672.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>7891.0</td>\n",
       "      <td>11281.0</td>\n",
       "      <td>11281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>666.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>8537.0</td>\n",
       "      <td>8725.0</td>\n",
       "      <td>8730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1116375.0</td>\n",
       "      <td>1116375.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29494.0</td>\n",
       "      <td>29494.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>251</th>\n",
       "      <td>64160.0</td>\n",
       "      <td>64160.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>3498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>34105.0</td>\n",
       "      <td>34105.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>167965.0</td>\n",
       "      <td>167965.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2214.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12869.0</td>\n",
       "      <td>12862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2208.0</td>\n",
       "      <td>2208.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10943.0</td>\n",
       "      <td>10942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2209.0</td>\n",
       "      <td>2209.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>311400.0</td>\n",
       "      <td>311400.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4720.0</td>\n",
       "      <td>4720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>55802.0</td>\n",
       "      <td>55802.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4613.0</td>\n",
       "      <td>4613.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>187878.0</td>\n",
       "      <td>187878.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>4401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>163003.0</td>\n",
       "      <td>163003.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>172049.0</td>\n",
       "      <td>172049.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>246969.0</td>\n",
       "      <td>246969.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>1042877.0</td>\n",
       "      <td>1042877.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>2317.0</td>\n",
       "      <td>2317.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3942.0</td>\n",
       "      <td>3942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>224719.0</td>\n",
       "      <td>224719.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>2180.0</td>\n",
       "      <td>2180.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2843.0</td>\n",
       "      <td>2843.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>83171.0</td>\n",
       "      <td>83171.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3347.0</td>\n",
       "      <td>3347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2190.0</td>\n",
       "      <td>2190.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>644281.0</td>\n",
       "      <td>644281.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>species</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4088.0</td>\n",
       "      <td>3917.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>184117.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>species</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2746.0</td>\n",
       "      <td>0</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>0.0</td>\n",
       "      <td>364410.0</td>\n",
       "      <td>0</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0.0</td>\n",
       "      <td>121277.0</td>\n",
       "      <td>0</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>0.0</td>\n",
       "      <td>86662.0</td>\n",
       "      <td>0</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>0.0</td>\n",
       "      <td>99007.0</td>\n",
       "      <td>0</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>0.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>0.0</td>\n",
       "      <td>70771.0</td>\n",
       "      <td>0</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1772.0</td>\n",
       "      <td>0</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>0</td>\n",
       "      <td>species</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>281 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          k1id       k2id     rank   k2rank   k1cnt   totCnt    k2cnt\n",
       "0     470934.0   470934.0  species  species     0.0  10813.0  10812.0\n",
       "1        553.0      553.0  species  species  8491.0  10349.0  10352.0\n",
       "2     592316.0   592316.0  species  species     1.0      1.0      1.0\n",
       "3          9.0        9.0  species  species     0.0      1.0      1.0\n",
       "4      28151.0    28151.0  species  species     0.0  11279.0  11277.0\n",
       "5        614.0      614.0  species  species     0.0      8.0      8.0\n",
       "6        615.0      615.0  species  species     1.0      3.0      2.0\n",
       "7      82996.0    82996.0  species  species     1.0      2.0      2.0\n",
       "8     244366.0   244366.0  species  species     0.0   2601.0   2605.0\n",
       "9        573.0      573.0  species  species    17.0     28.0     28.0\n",
       "10       548.0      548.0  species  species     1.0      2.0      1.0\n",
       "11   1134687.0        0.0  species        0     2.0      2.0      0.0\n",
       "12     54291.0    54291.0  species  species     0.0      2.0      2.0\n",
       "13     67825.0    67825.0  species  species     0.0      1.0      1.0\n",
       "14       550.0      550.0  species  species     1.0      1.0      1.0\n",
       "15     28901.0    28901.0  species  species     0.0      1.0      2.0\n",
       "16    891974.0   891974.0  species  species     1.0      1.0      1.0\n",
       "17     28141.0    28141.0  species  species     1.0      1.0      1.0\n",
       "18       636.0      636.0  species  species  1303.0   1711.0   1710.0\n",
       "19     67780.0    67780.0  species  species     0.0     11.0     11.0\n",
       "20   1263550.0  1263550.0  species  species     0.0      1.0      1.0\n",
       "21       628.0      628.0  species  species     0.0      1.0      1.0\n",
       "22       680.0      680.0  species  species     0.0  13050.0  13023.0\n",
       "23       670.0      670.0  species  species  7655.0  10309.0  10299.0\n",
       "24       663.0      663.0  species  species     0.0      3.0      3.0\n",
       "25    150340.0   150340.0  species  species     1.0      1.0      1.0\n",
       "26       672.0      672.0  species  species  7891.0  11281.0  11281.0\n",
       "27       666.0      666.0  species  species  8537.0   8725.0   8730.0\n",
       "28   1116375.0  1116375.0  species  species     2.0      2.0      2.0\n",
       "29     29494.0    29494.0  species  species     0.0      1.0      2.0\n",
       "..         ...        ...      ...      ...     ...      ...      ...\n",
       "251    64160.0    64160.0  species  species     0.0   3498.0   3498.0\n",
       "252    34105.0    34105.0  species  species     0.0      1.0      1.0\n",
       "253   167965.0   167965.0  species  species     0.0      1.0      1.0\n",
       "254     2214.0     2214.0  species  species     0.0  12869.0  12862.0\n",
       "255     2208.0     2208.0  species  species     0.0  10943.0  10942.0\n",
       "256     2209.0     2209.0  species  species     0.0      1.0      2.0\n",
       "257   311400.0   311400.0  species  species     0.0   4720.0   4720.0\n",
       "258    55802.0    55802.0  species  species     0.0   4613.0   4613.0\n",
       "259   187878.0   187878.0  species  species     0.0   4401.0   4401.0\n",
       "260   163003.0   163003.0  species  species     1.0      1.0      1.0\n",
       "261   172049.0   172049.0  species  species     0.0      1.0      1.0\n",
       "262   246969.0   246969.0  species  species     1.0      1.0      1.0\n",
       "263  1042877.0  1042877.0  species  species     1.0      1.0      1.0\n",
       "264     2317.0     2317.0  species  species     0.0   3942.0   3942.0\n",
       "265   224719.0   224719.0  species  species     1.0      1.0      1.0\n",
       "266     2180.0     2180.0  species  species     0.0   2843.0   2843.0\n",
       "267    83171.0    83171.0  species  species     0.0   3347.0   3347.0\n",
       "268     2190.0     2190.0  species  species     0.0      1.0      1.0\n",
       "269   644281.0   644281.0  species  species     1.0      1.0      1.0\n",
       "270     2277.0     2277.0  species  species     0.0   4088.0   3917.0\n",
       "271   184117.0        0.0  species        0     0.0      1.0      0.0\n",
       "272        0.0     2746.0        0  species     0.0      0.0   9123.0\n",
       "273        0.0   364410.0        0  species     0.0      0.0   6061.0\n",
       "274        0.0   121277.0        0  species     0.0      0.0   3254.0\n",
       "275        0.0    86662.0        0  species     0.0      0.0     13.0\n",
       "276        0.0    99007.0        0  species     0.0      0.0      4.0\n",
       "277        0.0      571.0        0  species     0.0      0.0      2.0\n",
       "278        0.0    70771.0        0  species     0.0      0.0      1.0\n",
       "279        0.0     1772.0        0  species     0.0      0.0      1.0\n",
       "280        0.0      588.0        0  species     0.0      0.0      1.0\n",
       "\n",
       "[281 rows x 7 columns]"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k1k2 = pd.merge(k1_species, k2, left_on='k1id', right_on='k2id', how='outer').fillna(0)\n",
    "k1k2[['k1id', 'k2id', 'rank', 'k2rank', 'k1cnt', 'totCnt', 'k2cnt']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k1id</th>\n",
       "      <th>k2id</th>\n",
       "      <th>totCnt</th>\n",
       "      <th>k2cnt</th>\n",
       "      <th>k1k2_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>470934.0</td>\n",
       "      <td>470934.0</td>\n",
       "      <td>10813.0</td>\n",
       "      <td>10812.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>553.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>10349.0</td>\n",
       "      <td>10352.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28151.0</td>\n",
       "      <td>28151.0</td>\n",
       "      <td>11279.0</td>\n",
       "      <td>11277.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>615.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>244366.0</td>\n",
       "      <td>244366.0</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>2605.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>548.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1134687.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>28901.0</td>\n",
       "      <td>28901.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>636.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>1711.0</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>680.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>13050.0</td>\n",
       "      <td>13023.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>670.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>10309.0</td>\n",
       "      <td>10299.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>666.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>8725.0</td>\n",
       "      <td>8730.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29494.0</td>\n",
       "      <td>29494.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>404011.0</td>\n",
       "      <td>404011.0</td>\n",
       "      <td>11981.0</td>\n",
       "      <td>11982.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>192073.0</td>\n",
       "      <td>192073.0</td>\n",
       "      <td>10007.0</td>\n",
       "      <td>10011.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>60478.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1033846.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>339.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>10874.0</td>\n",
       "      <td>10876.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>29447.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>314722.0</td>\n",
       "      <td>314722.0</td>\n",
       "      <td>7567.0</td>\n",
       "      <td>7573.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>354.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>11812.0</td>\n",
       "      <td>11810.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>287.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>316.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>47880.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>80679.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1033854.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>727.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>4048.0</td>\n",
       "      <td>4049.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>158080.0</td>\n",
       "      <td>158080.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>91844.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>283734.0</td>\n",
       "      <td>283734.0</td>\n",
       "      <td>5802.0</td>\n",
       "      <td>5797.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1590.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>7231.0</td>\n",
       "      <td>7222.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>33959.0</td>\n",
       "      <td>33959.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>4109.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1308.0</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>3687.0</td>\n",
       "      <td>3674.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1314.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>3046.0</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>-96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1307.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>1525.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>5950.0</td>\n",
       "      <td>5951.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>1496.0</td>\n",
       "      <td>1496.0</td>\n",
       "      <td>9229.0</td>\n",
       "      <td>9210.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>39483.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>1764.0</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>11600.0</td>\n",
       "      <td>11603.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>1717.0</td>\n",
       "      <td>1717.0</td>\n",
       "      <td>5558.0</td>\n",
       "      <td>5557.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1836.0</td>\n",
       "      <td>1836.0</td>\n",
       "      <td>18432.0</td>\n",
       "      <td>18431.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>182640.0</td>\n",
       "      <td>182640.0</td>\n",
       "      <td>16904.0</td>\n",
       "      <td>16905.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>131568.0</td>\n",
       "      <td>131568.0</td>\n",
       "      <td>11079.0</td>\n",
       "      <td>11078.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>68909.0</td>\n",
       "      <td>68909.0</td>\n",
       "      <td>7239.0</td>\n",
       "      <td>7240.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>321332.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2214.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>12869.0</td>\n",
       "      <td>12862.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2208.0</td>\n",
       "      <td>2208.0</td>\n",
       "      <td>10943.0</td>\n",
       "      <td>10942.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2209.0</td>\n",
       "      <td>2209.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2277.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>4088.0</td>\n",
       "      <td>3917.0</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>184117.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2746.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9123.0</td>\n",
       "      <td>-9123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>0.0</td>\n",
       "      <td>364410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6061.0</td>\n",
       "      <td>-6061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0.0</td>\n",
       "      <td>121277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3254.0</td>\n",
       "      <td>-3254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>0.0</td>\n",
       "      <td>86662.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>0.0</td>\n",
       "      <td>99007.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>0.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>0.0</td>\n",
       "      <td>70771.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1772.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          k1id      k2id   totCnt    k2cnt  k1k2_diff\n",
       "0     470934.0  470934.0  10813.0  10812.0        1.0\n",
       "1        553.0     553.0  10349.0  10352.0       -3.0\n",
       "4      28151.0   28151.0  11279.0  11277.0        2.0\n",
       "6        615.0     615.0      3.0      2.0        1.0\n",
       "8     244366.0  244366.0   2601.0   2605.0       -4.0\n",
       "10       548.0     548.0      2.0      1.0        1.0\n",
       "11   1134687.0       0.0      2.0      0.0        2.0\n",
       "15     28901.0   28901.0      1.0      2.0       -1.0\n",
       "18       636.0     636.0   1711.0   1710.0        1.0\n",
       "22       680.0     680.0  13050.0  13023.0       27.0\n",
       "23       670.0     670.0  10309.0  10299.0       10.0\n",
       "27       666.0     666.0   8725.0   8730.0       -5.0\n",
       "29     29494.0   29494.0      1.0      2.0       -1.0\n",
       "32    404011.0  404011.0  11981.0  11982.0       -1.0\n",
       "33    192073.0  192073.0  10007.0  10011.0       -4.0\n",
       "34     60478.0       0.0      1.0      0.0        1.0\n",
       "38   1033846.0       0.0      1.0      0.0        1.0\n",
       "39       339.0     339.0  10874.0  10876.0       -2.0\n",
       "41     29447.0       0.0      1.0      0.0        1.0\n",
       "42    314722.0  314722.0   7567.0   7573.0       -6.0\n",
       "44       354.0     354.0  11812.0  11810.0        2.0\n",
       "45       300.0     300.0      3.0      1.0        2.0\n",
       "46       287.0     287.0      2.0      1.0        1.0\n",
       "47       316.0     316.0      4.0      3.0        1.0\n",
       "48     47880.0       0.0      2.0      0.0        2.0\n",
       "53     80679.0       0.0      1.0      0.0        1.0\n",
       "54   1033854.0       0.0      1.0      0.0        1.0\n",
       "56       727.0     727.0   4048.0   4049.0       -1.0\n",
       "60    158080.0  158080.0     25.0      1.0       24.0\n",
       "61     91844.0       0.0      1.0      0.0        1.0\n",
       "..         ...       ...      ...      ...        ...\n",
       "158   283734.0  283734.0   5802.0   5797.0        5.0\n",
       "168     1590.0    1590.0   7231.0   7222.0        9.0\n",
       "169    33959.0   33959.0   4300.0   4109.0      191.0\n",
       "175     1308.0    1308.0   3687.0   3674.0       13.0\n",
       "176     1314.0    1314.0   3046.0   3142.0      -96.0\n",
       "181     1307.0       0.0      3.0      0.0        3.0\n",
       "194     1525.0    1525.0   5950.0   5951.0       -1.0\n",
       "195     1496.0    1496.0   9229.0   9210.0       19.0\n",
       "199    39483.0       0.0      1.0      0.0        1.0\n",
       "200     1764.0    1764.0  11600.0  11603.0       -3.0\n",
       "208     1717.0    1717.0   5558.0   5557.0        1.0\n",
       "210     1836.0    1836.0  18432.0  18431.0        1.0\n",
       "212   182640.0  182640.0  16904.0  16905.0       -1.0\n",
       "214   131568.0  131568.0  11079.0  11078.0        1.0\n",
       "221    68909.0   68909.0   7239.0   7240.0       -1.0\n",
       "227   321332.0       0.0      1.0      0.0        1.0\n",
       "254     2214.0    2214.0  12869.0  12862.0        7.0\n",
       "255     2208.0    2208.0  10943.0  10942.0        1.0\n",
       "256     2209.0    2209.0      1.0      2.0       -1.0\n",
       "270     2277.0    2277.0   4088.0   3917.0      171.0\n",
       "271   184117.0       0.0      1.0      0.0        1.0\n",
       "272        0.0    2746.0      0.0   9123.0    -9123.0\n",
       "273        0.0  364410.0      0.0   6061.0    -6061.0\n",
       "274        0.0  121277.0      0.0   3254.0    -3254.0\n",
       "275        0.0   86662.0      0.0     13.0      -13.0\n",
       "276        0.0   99007.0      0.0      4.0       -4.0\n",
       "277        0.0     571.0      0.0      2.0       -2.0\n",
       "278        0.0   70771.0      0.0      1.0       -1.0\n",
       "279        0.0    1772.0      0.0      1.0       -1.0\n",
       "280        0.0     588.0      0.0      1.0       -1.0\n",
       "\n",
       "[97 rows x 5 columns]"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k1k2['k1k2_diff'] = k1k2['totCnt'] - k1k2['k2cnt']\n",
    "k1k2[k1k2['k1k2_diff'] != 0][['k1id', 'k2id', 'totCnt', 'k2cnt', 'k1k2_diff']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hc11 = pd.read_csv('~/others_projects/mason_eval/benchmarking_metagenomic_classifiers.git/truth/Huttenhower_HC1_abund.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "totalCnt = truth[truth['dataset'] == 'Huttenhower_HC1'].counts.sum()\n",
    "truth['fraction'] = truth['counts']/totalCnt\n",
    "truth['level'] = 'species'\n",
    "#truth.head()\n",
    "toSave=truth[['taxid', 'counts', 'fraction', 'level', 'species']]\n",
    "toSave.to_csv('~/others_projects/mason_eval/benchmarking_metagenomic_classifiers.git/truth/species_Huttenhower_HC1_abund.txt', sep='\\t', header=None, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k1 = pd.read_csv('/mnt/scratch2/avi/meta-map/kraken/krakOut/{}.rpt'.format('HC1'), sep='\\t', header=None)\n",
    "k1.columns=['totperc', 'totCnt', 'k1cnt', 'typ', 'k1id', 'name']\n",
    "k1 = pd.merge(k1, taxa_df, left_on='k1id', right_on='taxaId')\n",
    "k1.head()\n",
    "k1['totperc'] = k1['totperc']/100\n",
    "k1[k1['rank'] == 'species'][['taxaId', 'totCnt', 'totperc', 'rank', 'name']].to_csv('~/others_projects/mason_eval/benchmarking_metagenomic_classifiers.git/dataset/species_HC1_kraken.txt', \n",
    "       sep='\\t', header=None, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 days 00:02:01\n",
      "0 days 00:02:01\n",
      "00:02:01\n"
     ]
    }
   ],
   "source": [
    "from string import Template\n",
    "\n",
    "class DeltaTemplate(Template):\n",
    "    delimiter = \"%\"\n",
    "def delta2str(x, pos):\n",
    "    x = int(x/1000000000)\n",
    "    hours, rem = divmod(x, 3600)\n",
    "    minutes, seconds = divmod(rem, 60)\n",
    "    return '{:02d}:{:02d}:{:02d}'.format(int(hours), int(minutes), int(seconds))\n",
    "def strfdelta(tdelta, fmt):\n",
    "    print(tdelta)\n",
    "    d = {\"D\": tdelta.days}\n",
    "    \n",
    "    hours, rem = divmod(tdelta.seconds, 3600)\n",
    "    d[\"H\"] = '{:02d}'.format(hours)\n",
    "    minutes, seconds = divmod(rem, 60)\n",
    "    d[\"M\"] = '{:02d}'.format(minutes)\n",
    "    d[\"S\"] = '{:02d}'.format(seconds)\n",
    "    t = DeltaTemplate(fmt)\n",
    "    return t.substitute(**d)\n",
    "print(res.Duration[1])\n",
    "print(strfdelta(res.Duration[1], '%H:%M:%S'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "idxsps = pd.read_csv('/home/fatemeh/pufferfish_ismb_plots/query_time.csv')\n",
    "res=idxsps.melt(id_vars=[idxsps.columns[0]], var_name='Data sets', value_name='Durationt')\n",
    "res.columns = ['Tools', res.columns[1], res.columns[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res['Duration']=pd.to_timedelta(res['Durationt'], unit='s', box=True)\n",
    "#res['Duration']=pd.to_datetime(res['Duration'], format=\"%H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAAGQCAYAAAAjsQW6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAAAAAMCQ/wOS8pk2qOp1owAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69258d2748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20,5])\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel('Data Sets', fontsize=24, labelpad=40)\n",
    "plt.ylabel('Duration', fontsize=24)\n",
    "#plt.legend(prop={'size':30})\n",
    "snb.set_style(\"whitegrid\")\n",
    "snbplot = snb.barplot(x='Data sets', y='Duration', hue='Tools', data=res, palette='Set3')\n",
    "for p in snbplot.patches:\n",
    "    height = p.get_height()\n",
    "    snbplot.text(p.get_x()+p.get_width()/2.,\n",
    "            height + 600000000000,\n",
    "            #height + 30000,\n",
    "            #'{:,}'.format(int(height)),\n",
    "            delta2str(height, 0),\n",
    "            ha=\"center\", rotation=45, fontsize=16) \n",
    "    #print('{},{}'.format(row['Memory(MB)'], row['Data sets']))\n",
    "    #snbplot.text(row['Memory(MB)'],row['Data sets'], row['Memory(MB)'], color='black', ha=\"center\")\n",
    "\n",
    "#plt.gcf().\n",
    "## Rotate date labels automatically\n",
    "#snbplot.yaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))\n",
    "snbplot.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(delta2str))\n",
    "plt.legend(fontsize=14)\n",
    "plt.tight_layout()\n",
    "\n",
    "#snbplot.get_figure().autofmt_xdate()\n",
    "snbplot.get_figure().savefig('/home/fatemeh/pufferfish_ismb_plots/query_time.png', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "truth=pd.read_table('/mnt/scratch2/cedar/input_files/i100_taxonomy.truth')\n",
    "puff=pd.read_table('/mnt/scratch2/cedar/results/script_results/i100_31_sr1_puff/out.tab')\n",
    "cedar=pd.read_table('/mnt/scratch2/cedar/results/script_results/i100_31_sr1_cedar/out.tab')\n",
    "salmon=pd.read_table('/mnt/scratch2/cedar/results/script_results/i100_31_sr1_salmon/out.tab')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "truth:26667004.0\n",
      "puff:56445.95999999999\n",
      "salmon:239832.969\n",
      "cedar:241480.000432\n"
     ]
    }
   ],
   "source": [
    "print('truth:{}'.format(truth['NumCounts'].sum()))\n",
    "print('puff:{}'.format(puff['NumCounts'].sum()))\n",
    "print('salmon:{}'.format(salmon['NumCounts'].sum()))\n",
    "print('cedar:{}'.format(cedar['NumCounts'].sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>truth</th>\n",
       "      <th>puff</th>\n",
       "      <th>salmon</th>\n",
       "      <th>cedar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acinetobacter baumannii</td>\n",
       "      <td>262164.0</td>\n",
       "      <td>549.513</td>\n",
       "      <td>2184.705</td>\n",
       "      <td>6958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alkalilimnicola ehrlichii</td>\n",
       "      <td>230160.0</td>\n",
       "      <td>509.162</td>\n",
       "      <td>2165.511</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alkaliphilus metalliredigens</td>\n",
       "      <td>374667.0</td>\n",
       "      <td>781.633</td>\n",
       "      <td>3245.585</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bacillus anthracis</td>\n",
       "      <td>408054.0</td>\n",
       "      <td>511.744</td>\n",
       "      <td>3667.453</td>\n",
       "      <td>2804.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bacillus cereus</td>\n",
       "      <td>1320777.0</td>\n",
       "      <td>2352.731</td>\n",
       "      <td>11637.614</td>\n",
       "      <td>7408.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Species      truth      puff     salmon   cedar\n",
       "0       Acinetobacter baumannii   262164.0   549.513   2184.705  6958.0\n",
       "1     Alkalilimnicola ehrlichii   230160.0   509.162   2165.511   528.0\n",
       "2  Alkaliphilus metalliredigens   374667.0   781.633   3245.585   990.0\n",
       "3            Bacillus anthracis   408054.0   511.744   3667.453  2804.0\n",
       "4               Bacillus cereus  1320777.0  2352.731  11637.614  7408.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truth = truth.groupby('Species').sum().reset_index()\n",
    "puff = puff.groupby('Species').sum().reset_index()\n",
    "al=pd.merge(truth, puff, on='Species', how='outer')[['Species', 'NumCounts_x', 'NumCounts_y']].fillna(0)\n",
    "al.columns = ['Species', 'truth', 'puff']\n",
    "salmon = salmon.groupby('Species').sum().reset_index()\n",
    "al=pd.merge(al, salmon, on='Species', how='outer')[['Species', 'truth', 'puff', 'NumCounts']].fillna(0)\n",
    "al.columns = ['Species', 'truth', 'puff', 'salmon']\n",
    "cedar = cedar.groupby('Species').sum().reset_index()\n",
    "al=pd.merge(al, cedar, on='Species', how='outer')[['Species', 'truth', 'puff', 'salmon', 'NumCounts']].fillna(0)\n",
    "al.columns = ['Species', 'truth', 'puff', 'salmon', 'cedar']\n",
    "al.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>truth</th>\n",
       "      <th>puff</th>\n",
       "      <th>salmon</th>\n",
       "      <th>cedar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acinetobacter baumannii</td>\n",
       "      <td>262164.0</td>\n",
       "      <td>549.513</td>\n",
       "      <td>2184.705</td>\n",
       "      <td>6958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alkalilimnicola ehrlichii</td>\n",
       "      <td>230160.0</td>\n",
       "      <td>509.162</td>\n",
       "      <td>2165.511</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alkaliphilus metalliredigens</td>\n",
       "      <td>374667.0</td>\n",
       "      <td>781.633</td>\n",
       "      <td>3245.585</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bacillus anthracis</td>\n",
       "      <td>408054.0</td>\n",
       "      <td>511.744</td>\n",
       "      <td>3667.453</td>\n",
       "      <td>2804.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bacillus cereus</td>\n",
       "      <td>1320777.0</td>\n",
       "      <td>2352.731</td>\n",
       "      <td>11637.614</td>\n",
       "      <td>7408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bacillus clausii</td>\n",
       "      <td>286614.0</td>\n",
       "      <td>576.886</td>\n",
       "      <td>2582.661</td>\n",
       "      <td>742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bacillus halodurans</td>\n",
       "      <td>274294.0</td>\n",
       "      <td>589.009</td>\n",
       "      <td>2407.603</td>\n",
       "      <td>918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bacillus subtilis</td>\n",
       "      <td>272560.0</td>\n",
       "      <td>600.297</td>\n",
       "      <td>2461.188</td>\n",
       "      <td>808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bacillus thuringiensis</td>\n",
       "      <td>372187.0</td>\n",
       "      <td>1461.580</td>\n",
       "      <td>3265.563</td>\n",
       "      <td>3364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bacteroides thetaiotaomicron</td>\n",
       "      <td>402214.0</td>\n",
       "      <td>796.490</td>\n",
       "      <td>3396.130</td>\n",
       "      <td>893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bordetella bronchiseptica</td>\n",
       "      <td>466134.0</td>\n",
       "      <td>979.148</td>\n",
       "      <td>4155.232</td>\n",
       "      <td>1009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Borreliella bavariensis</td>\n",
       "      <td>81441.0</td>\n",
       "      <td>160.747</td>\n",
       "      <td>769.000</td>\n",
       "      <td>166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Buchnera aphidicola</td>\n",
       "      <td>26587.0</td>\n",
       "      <td>57.289</td>\n",
       "      <td>236.000</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Burkholderia pseudomallei</td>\n",
       "      <td>464987.0</td>\n",
       "      <td>993.777</td>\n",
       "      <td>4196.764</td>\n",
       "      <td>1040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Campylobacter concisus</td>\n",
       "      <td>160562.0</td>\n",
       "      <td>315.104</td>\n",
       "      <td>1408.462</td>\n",
       "      <td>328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Candidatus Blochmannia pennsylvanicus</td>\n",
       "      <td>53307.0</td>\n",
       "      <td>90.016</td>\n",
       "      <td>436.017</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chlamydia caviae</td>\n",
       "      <td>75947.0</td>\n",
       "      <td>167.524</td>\n",
       "      <td>714.322</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Chlamydia pneumoniae</td>\n",
       "      <td>79974.0</td>\n",
       "      <td>159.476</td>\n",
       "      <td>710.307</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Chlamydia trachomatis</td>\n",
       "      <td>73440.0</td>\n",
       "      <td>152.000</td>\n",
       "      <td>723.370</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Chromobacterium violaceum</td>\n",
       "      <td>336880.0</td>\n",
       "      <td>678.581</td>\n",
       "      <td>2945.185</td>\n",
       "      <td>734.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Clostridium beijerinckii</td>\n",
       "      <td>426880.0</td>\n",
       "      <td>937.255</td>\n",
       "      <td>3903.908</td>\n",
       "      <td>1172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Clostridium botulinum</td>\n",
       "      <td>286907.0</td>\n",
       "      <td>628.296</td>\n",
       "      <td>2553.101</td>\n",
       "      <td>778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Clostridium novyi</td>\n",
       "      <td>215627.0</td>\n",
       "      <td>457.466</td>\n",
       "      <td>1913.693</td>\n",
       "      <td>681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Corynebacterium efficiens</td>\n",
       "      <td>237814.0</td>\n",
       "      <td>496.705</td>\n",
       "      <td>2175.350</td>\n",
       "      <td>1272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Corynebacterium urealyticum</td>\n",
       "      <td>174534.0</td>\n",
       "      <td>381.776</td>\n",
       "      <td>1590.093</td>\n",
       "      <td>412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Coxiella burnetii</td>\n",
       "      <td>137654.0</td>\n",
       "      <td>282.046</td>\n",
       "      <td>1153.000</td>\n",
       "      <td>372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Cupriavidus necator</td>\n",
       "      <td>29627.0</td>\n",
       "      <td>67.000</td>\n",
       "      <td>284.000</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Cyanothece sp. ATCC 51142</td>\n",
       "      <td>378402.0</td>\n",
       "      <td>770.400</td>\n",
       "      <td>3276.000</td>\n",
       "      <td>846.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cytophaga hutchinsonii</td>\n",
       "      <td>312400.0</td>\n",
       "      <td>668.083</td>\n",
       "      <td>2782.000</td>\n",
       "      <td>683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Erwinia tasmaniensis</td>\n",
       "      <td>328615.0</td>\n",
       "      <td>685.823</td>\n",
       "      <td>2961.160</td>\n",
       "      <td>826.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>Turicibacter sp. HGF1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>Tyzzerella nexilis</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>Veillonella atypica</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>Veillonella dispar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>Veillonella parvula</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>Veillonella seminalis</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>Veillonella sp. 3_1_44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>Veillonella sp. 6_1_27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>Veillonella sp. ACP1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Veillonella sp. HPA0037</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Veillonella sp. oral taxon 158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Veillonella sp. oral taxon 780</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>Weissella paramesenteroides</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>[Bacteroides] pectinophilus</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>[Clostridium] asparagiforme</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>[Clostridium] citroniae</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>[Clostridium] clostridioforme</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>[Clostridium] hiranonis</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>[Clostridium] innocuum</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>[Clostridium] leptum</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>[Clostridium] methylpentosum</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>[Clostridium] spiroforme</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>[Eubacterium] infirmum</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>[Eubacterium] nodatum</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>[Eubacterium] rectale</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>[Eubacterium] siraeum</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>[Eubacterium] yurii</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>[Propionibacterium] humerusii</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>[Propionibacterium] namnetense</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>[Ruminococcus] torques</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>609 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Species      truth      puff     salmon  \\\n",
       "0                  Acinetobacter baumannii   262164.0   549.513   2184.705   \n",
       "1                Alkalilimnicola ehrlichii   230160.0   509.162   2165.511   \n",
       "2             Alkaliphilus metalliredigens   374667.0   781.633   3245.585   \n",
       "3                       Bacillus anthracis   408054.0   511.744   3667.453   \n",
       "4                          Bacillus cereus  1320777.0  2352.731  11637.614   \n",
       "5                         Bacillus clausii   286614.0   576.886   2582.661   \n",
       "6                      Bacillus halodurans   274294.0   589.009   2407.603   \n",
       "7                        Bacillus subtilis   272560.0   600.297   2461.188   \n",
       "8                   Bacillus thuringiensis   372187.0  1461.580   3265.563   \n",
       "9             Bacteroides thetaiotaomicron   402214.0   796.490   3396.130   \n",
       "10               Bordetella bronchiseptica   466134.0   979.148   4155.232   \n",
       "11                 Borreliella bavariensis    81441.0   160.747    769.000   \n",
       "12                     Buchnera aphidicola    26587.0    57.289    236.000   \n",
       "13               Burkholderia pseudomallei   464987.0   993.777   4196.764   \n",
       "14                  Campylobacter concisus   160562.0   315.104   1408.462   \n",
       "15   Candidatus Blochmannia pennsylvanicus    53307.0    90.016    436.017   \n",
       "16                        Chlamydia caviae    75947.0   167.524    714.322   \n",
       "17                    Chlamydia pneumoniae    79974.0   159.476    710.307   \n",
       "18                   Chlamydia trachomatis    73440.0   152.000    723.370   \n",
       "19               Chromobacterium violaceum   336880.0   678.581   2945.185   \n",
       "20                Clostridium beijerinckii   426880.0   937.255   3903.908   \n",
       "21                   Clostridium botulinum   286907.0   628.296   2553.101   \n",
       "22                       Clostridium novyi   215627.0   457.466   1913.693   \n",
       "23               Corynebacterium efficiens   237814.0   496.705   2175.350   \n",
       "24             Corynebacterium urealyticum   174534.0   381.776   1590.093   \n",
       "25                       Coxiella burnetii   137654.0   282.046   1153.000   \n",
       "26                     Cupriavidus necator    29627.0    67.000    284.000   \n",
       "27               Cyanothece sp. ATCC 51142   378402.0   770.400   3276.000   \n",
       "28                  Cytophaga hutchinsonii   312400.0   668.083   2782.000   \n",
       "29                    Erwinia tasmaniensis   328615.0   685.823   2961.160   \n",
       "..                                     ...        ...       ...        ...   \n",
       "579                  Turicibacter sp. HGF1        0.0     0.000      0.000   \n",
       "580                     Tyzzerella nexilis        0.0     0.000      0.000   \n",
       "581                    Veillonella atypica        0.0     0.000      0.000   \n",
       "582                     Veillonella dispar        0.0     0.000      0.000   \n",
       "583                    Veillonella parvula        0.0     0.000      0.000   \n",
       "584                  Veillonella seminalis        0.0     0.000      0.000   \n",
       "585                 Veillonella sp. 3_1_44        0.0     0.000      0.000   \n",
       "586                 Veillonella sp. 6_1_27        0.0     0.000      0.000   \n",
       "587                   Veillonella sp. ACP1        0.0     0.000      0.000   \n",
       "588                Veillonella sp. HPA0037        0.0     0.000      0.000   \n",
       "589         Veillonella sp. oral taxon 158        0.0     0.000      0.000   \n",
       "590         Veillonella sp. oral taxon 780        0.0     0.000      0.000   \n",
       "591            Weissella paramesenteroides        0.0     0.000      0.000   \n",
       "592            [Bacteroides] pectinophilus        0.0     0.000      0.000   \n",
       "593            [Clostridium] asparagiforme        0.0     0.000      0.000   \n",
       "594                [Clostridium] citroniae        0.0     0.000      0.000   \n",
       "595          [Clostridium] clostridioforme        0.0     0.000      0.000   \n",
       "596                [Clostridium] hiranonis        0.0     0.000      0.000   \n",
       "597                 [Clostridium] innocuum        0.0     0.000      0.000   \n",
       "598                   [Clostridium] leptum        0.0     0.000      0.000   \n",
       "599           [Clostridium] methylpentosum        0.0     0.000      0.000   \n",
       "600               [Clostridium] spiroforme        0.0     0.000      0.000   \n",
       "601                 [Eubacterium] infirmum        0.0     0.000      0.000   \n",
       "602                  [Eubacterium] nodatum        0.0     0.000      0.000   \n",
       "603                  [Eubacterium] rectale        0.0     0.000      0.000   \n",
       "604                  [Eubacterium] siraeum        0.0     0.000      0.000   \n",
       "605                    [Eubacterium] yurii        0.0     0.000      0.000   \n",
       "606          [Propionibacterium] humerusii        0.0     0.000      0.000   \n",
       "607         [Propionibacterium] namnetense        0.0     0.000      0.000   \n",
       "608                 [Ruminococcus] torques        0.0     0.000      0.000   \n",
       "\n",
       "      cedar  \n",
       "0    6958.0  \n",
       "1     528.0  \n",
       "2     990.0  \n",
       "3    2804.0  \n",
       "4    7408.0  \n",
       "5     742.0  \n",
       "6     918.0  \n",
       "7     808.0  \n",
       "8    3364.0  \n",
       "9     893.0  \n",
       "10   1009.0  \n",
       "11    166.0  \n",
       "12     59.0  \n",
       "13   1040.0  \n",
       "14    328.0  \n",
       "15     91.0  \n",
       "16    168.0  \n",
       "17    160.0  \n",
       "18    153.0  \n",
       "19    734.0  \n",
       "20   1172.0  \n",
       "21    778.0  \n",
       "22    681.0  \n",
       "23   1272.0  \n",
       "24    412.0  \n",
       "25    372.0  \n",
       "26     68.0  \n",
       "27    846.0  \n",
       "28    683.0  \n",
       "29    826.0  \n",
       "..      ...  \n",
       "579     1.0  \n",
       "580     4.0  \n",
       "581    11.0  \n",
       "582     9.0  \n",
       "583     5.0  \n",
       "584     6.0  \n",
       "585     7.0  \n",
       "586     6.0  \n",
       "587     2.0  \n",
       "588    11.0  \n",
       "589     4.0  \n",
       "590     7.0  \n",
       "591     5.0  \n",
       "592     1.0  \n",
       "593     1.0  \n",
       "594     1.0  \n",
       "595     1.0  \n",
       "596     1.0  \n",
       "597     1.0  \n",
       "598    36.0  \n",
       "599    12.0  \n",
       "600     1.0  \n",
       "601     1.0  \n",
       "602     1.0  \n",
       "603     2.0  \n",
       "604     1.0  \n",
       "605     4.0  \n",
       "606     0.0  \n",
       "607     3.0  \n",
       "608     3.0  \n",
       "\n",
       "[609 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}