{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load contig profiles and binning results.\n",
    "# TODO: Make profiles and binning results the same for all algos.\n",
    "profile = pd.read_csv(\"/Users/tanunia/PycharmProjects/biolab_scripts/canopy_profiles.in\", sep=\" \", header=None)\n",
    "clusters = pd.read_csv(\"/Users/tanunia/PycharmProjects/biolab_scripts/canopy_binning.tsv\", sep=\"\\t\", header=None)\n",
    "\n",
    "# Add binning column to profile\n",
    "clusters = clusters.rename(columns={1:'contig', 0:'color'})\n",
    "cols = clusters.columns\n",
    "clusters = clusters[cols[::-1]]\n",
    "clusters[\"color\"] = clusters[\"color\"].apply(lambda x: int(x[3:]))\n",
    "profile = profile.rename(columns={0:'contig'})\n",
    "profile = pd.merge(clusters, profile, on='contig')\n",
    "#profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>16</th>\n",
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       "      <td>874229.000000</td>\n",
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       "      <th>mean</th>\n",
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       "      <td>7.021815</td>\n",
       "      <td>9.682457</td>\n",
       "      <td>16.023759</td>\n",
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       "      <td>9.773265</td>\n",
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       "      <td>19.277283</td>\n",
       "      <td>14.636003</td>\n",
       "      <td>11.643889</td>\n",
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       "      <th>std</th>\n",
       "      <td>175.732041</td>\n",
       "      <td>22.349057</td>\n",
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       "      <td>35.921960</td>\n",
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       "      <td>32.773979</td>\n",
       "      <td>50.958231</td>\n",
       "      <td>34.555440</td>\n",
       "      <td>51.378334</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
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       "      <td>127.000000</td>\n",
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       "      <td>21.000000</td>\n",
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       "               color              1              2              3  \\\n",
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       "min         1.000000       0.000000       0.000000       0.000000   \n",
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       "75%       127.000000       7.000000       8.000000      21.000000   \n",
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       "\n",
       "                   4              5              6              7  \\\n",
       "count  874229.000000  874229.000000  874229.000000  874229.000000   \n",
       "mean       12.826631      11.029218       9.773265      15.424325   \n",
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       "min         0.000000       0.000000       0.000000       0.000000   \n",
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       "\n",
       "                   8              9             10             11  \\\n",
       "count  874229.000000  874229.000000  874229.000000  874229.000000   \n",
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       "50%         4.000000       5.000000       5.000000       9.000000   \n",
       "75%        10.000000      14.000000      12.000000      24.000000   \n",
       "max      3737.000000    4412.000000    3137.000000    3693.000000   \n",
       "\n",
       "                  12             13             14             15  \\\n",
       "count  874229.000000  874229.000000  874229.000000  874229.000000   \n",
       "mean       19.183485      13.395388      19.277283      14.636003   \n",
       "std        50.958231      34.555440      51.378334      27.745981   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         3.000000       2.000000       3.000000       4.000000   \n",
       "50%         9.000000       5.000000       7.000000       8.000000   \n",
       "75%        20.000000      14.000000      18.000000      20.000000   \n",
       "max      6464.000000    3996.000000    5213.000000    3440.000000   \n",
       "\n",
       "                  16  \n",
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       "50%         4.000000  \n",
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     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Information about profile\n",
    "profile.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>350711.000000</td>\n",
       "      <td>350711.000000</td>\n",
       "      <td>350711.000000</td>\n",
       "      <td>350711.000000</td>\n",
       "      <td>350711.000000</td>\n",
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       "      <td>350711.000000</td>\n",
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       "      <td>350711.000000</td>\n",
       "      <td>350711.000000</td>\n",
       "      <td>350711.000000</td>\n",
       "      <td>350711.000000</td>\n",
       "      <td>350711.000000</td>\n",
       "      <td>350711.000000</td>\n",
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       "      <td>6.711198</td>\n",
       "      <td>12.784426</td>\n",
       "      <td>22.158333</td>\n",
       "      <td>16.605630</td>\n",
       "      <td>12.973206</td>\n",
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       "      <td>13.463379</td>\n",
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       "      <td>28.524683</td>\n",
       "      <td>19.643273</td>\n",
       "      <td>17.859403</td>\n",
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       "      <th>std</th>\n",
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       "      <td>17.495651</td>\n",
       "      <td>47.772374</td>\n",
       "      <td>29.768226</td>\n",
       "      <td>30.134884</td>\n",
       "      <td>21.407159</td>\n",
       "      <td>19.749674</td>\n",
       "      <td>28.541668</td>\n",
       "      <td>32.811062</td>\n",
       "      <td>27.869045</td>\n",
       "      <td>18.852696</td>\n",
       "      <td>21.706461</td>\n",
       "      <td>36.840619</td>\n",
       "      <td>32.843480</td>\n",
       "      <td>54.120753</td>\n",
       "      <td>19.491147</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>21.000000</td>\n",
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       "      <td>122.000000</td>\n",
       "      <td>922.000000</td>\n",
       "      <td>2890.000000</td>\n",
       "      <td>965.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>1290.000000</td>\n",
       "      <td>1281.000000</td>\n",
       "      <td>1273.000000</td>\n",
       "      <td>1922.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1492.000000</td>\n",
       "      <td>925.000000</td>\n",
       "      <td>2252.000000</td>\n",
       "      <td>1641.000000</td>\n",
       "      <td>3422.000000</td>\n",
       "      <td>897.000000</td>\n",
       "      <td>2124.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               color              1              2              3  \\\n",
       "count  350711.000000  350711.000000  350711.000000  350711.000000   \n",
       "mean       21.828813       6.711198      12.784426      22.158333   \n",
       "std        21.429488      17.495651      47.772374      29.768226   \n",
       "min         1.000000       0.000000       0.000000       0.000000   \n",
       "25%         6.000000       0.000000       0.000000       4.000000   \n",
       "50%        15.000000       3.000000       4.000000      12.000000   \n",
       "75%        30.000000       7.000000      12.000000      30.000000   \n",
       "max       122.000000     922.000000    2890.000000     965.000000   \n",
       "\n",
       "                   4              5              6              7  \\\n",
       "count  350711.000000  350711.000000  350711.000000  350711.000000   \n",
       "mean       16.605630      12.973206      11.563843      18.747259   \n",
       "std        30.134884      21.407159      19.749674      28.541668   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         2.000000       2.000000       2.000000       3.000000   \n",
       "50%         7.000000       6.000000       6.000000       8.000000   \n",
       "75%        15.000000      12.000000      10.000000      23.000000   \n",
       "max      1499.000000    1290.000000    1281.000000    1273.000000   \n",
       "\n",
       "                   8              9             10             11  \\\n",
       "count  350711.000000  350711.000000  350711.000000  350711.000000   \n",
       "mean       14.536362      19.205451      13.463379      21.720180   \n",
       "std        32.811062      27.869045      18.852696      21.706461   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         3.000000       4.000000       4.000000       7.000000   \n",
       "50%         6.000000      10.000000       9.000000      17.000000   \n",
       "75%        13.000000      23.000000      18.000000      31.000000   \n",
       "max      1922.000000    1199.000000    1492.000000     925.000000   \n",
       "\n",
       "                  12             13             14             15  \\\n",
       "count  350711.000000  350711.000000  350711.000000  350711.000000   \n",
       "mean       23.329459      19.492075      28.524683      19.643273   \n",
       "std        36.840619      32.843480      54.120753      19.491147   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         6.000000       5.000000       6.000000       7.000000   \n",
       "50%        13.000000      10.000000      14.000000      13.000000   \n",
       "75%        29.000000      19.000000      28.000000      28.000000   \n",
       "max      2252.000000    1641.000000    3422.000000     897.000000   \n",
       "\n",
       "                  16  \n",
       "count  350711.000000  \n",
       "mean       17.859403  \n",
       "std        33.627913  \n",
       "min         0.000000  \n",
       "25%         3.000000  \n",
       "50%         7.000000  \n",
       "75%        21.000000  \n",
       "max      2124.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leave only clusters with significant contig length in profile\n",
    "\n",
    "#CANOPY: bin_info$third_largest > 3 000 000\n",
    "cag_str = \"CAG0001 CAG0002 CAG0004 CAG0003 CAG0005 CAG0008 CAG0007 CAG0006 CAG0010 CAG0015 CAG0014 CAG0009 CAG0012 CAG0074 CAG0018 CAG0040 CAG0016 CAG0029 CAG0013 CAG0017 CAG0021 CAG0020 CAG0085 CAG0019 CAG0028 CAG0047 CAG0057 CAG0032 CAG0039 CAG0027 CAG0024 CAG0122 CAG0062 CAG0048 CAG0030 CAG0022 CAG0025 CAG0056 CAG0071 CAG0077 CAG0049 CAG0034 CAG0023 CAG0051 CAG0036 CAG0059\"\n",
    "filter1 = [int(x[3:]) for x in cag_str.split(\" \")]\n",
    "#CONCOCT: bin_info$third_largest > 20 000 000 \n",
    "#filter1 = [89, 243, 312, 278, 109, 250, 60, 59, 195, 277, 190, 394, 311, 301, 333, 51, 143, 327, 338, 147, 256, 163, 18, 141, 134, 317, 81, 371, 288, 216, 388, 135, 71, 341, 367, 92, 232, 119, 252, 293, 361, 350, 168]\n",
    "profile = profile[profile[\"color\"].isin(filter1)]\n",
    "\n",
    "# New profile info\n",
    "profile.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get fraction of profile - profile_small. Normalize profile_small data (like in CONCOCT) and convert it to numpy array\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "profile_small = profile.sample(frac=0.1)\n",
    "data = profile_small.as_matrix(columns = profile.columns[2:])\n",
    "v = (1.0/2000)\n",
    "data = data + v\n",
    "along_Y = np.apply_along_axis(sum, 0, data)\n",
    "data = data/along_Y[None, :]\n",
    "along_X = np.apply_along_axis(sum, 1, data)\n",
    "data = data/along_X[:, None]\n",
    "data = np.log(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run bht-sne\n",
    "path_bhtsne = '/Users/tanunia/PycharmProjects/biolab_t-sne/'\n",
    "\n",
    "# Save profile_small to tsv file\n",
    "np.savetxt(\"data.in\", data, delimiter=\"\\t\")\n",
    "\n",
    "import sys, os\n",
    "os.system(path_bhtsne + 'bhtsne.py -p 50 -m 1000 -i data.in -o data.out')\n",
    "\n",
    "# Load coordinates from data.out\n",
    "ar = np.loadtxt(\"data.out\", delimiter=\"\\t\")\n",
    "len(ar[:, 0])\n",
    "\n",
    "# Save bhtsne result to profile_small\n",
    "profile_small[\"x\"] = ar[:, 0]\n",
    "profile_small[\"y\"] = ar[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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/PhzspKEVJLw4aB51JfXt19UIGi1bpkaXKPFahVUpeerK35FE0dIAMmPGRt55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BGGg34k4cLtVjmwo+6F+dN/fUtlbp2POdhUYI3H56rjn61dtuiedbw183vyt5XSmyqOJ4eBlGGt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\nQYgOPwiCoEH4ByA09CcrSjRZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127b81710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw bhtsne result for a fraction of profile - profile_small\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "color = profile_small[\"color\"].tolist()\n",
    "mx_color = max(color)\n",
    "plt.scatter(profile_small[\"x\"], profile_small[\"y\"], c=[cm.spectral(float(i) /mx_color) for i in color])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run PCA on profile_small\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "pcaed = pca.fit(data).transform(data)\n",
    "profile_small[\"x_pca\"] = pcaed[:, 0]\n",
    "profile_small[\"y_pca\"] = pcaed[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6vf1CQLY7hnvSr4XSeNh+AiBqDxKXtg5t77UxJHwy/P0pQbyLh6NvjcM2chlK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\nwrewsLA4SrAUvoWFhcVRwv8HB5WW1am5QHYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128162650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw PCA for profile_small\n",
    "plt.scatter(profile_small[\"x_pca\"], profile_small[\"y_pca\"], c=[cm.spectral(float(i) /mx_color) for i in color])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-550c05617b60>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"color\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Number of contigs in each bin\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "# Draw histogram with number of contigs in each bin\n",
    "plt.hist(profile[\"color\"], align='left', bins = 400)\n",
    "plt.title(\"Number of contigs in each bin\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</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",
       "      <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>1</th>\n",
       "      <td>8.876186</td>\n",
       "      <td>18.261282</td>\n",
       "      <td>51.187960</td>\n",
       "      <td>6.134054</td>\n",
       "      <td>12.633700</td>\n",
       "      <td>5.477037</td>\n",
       "      <td>6.014585</td>\n",
       "      <td>5.304814</td>\n",
       "      <td>6.874191</td>\n",
       "      <td>12.439046</td>\n",
       "      <td>29.793732</td>\n",
       "      <td>22.665219</td>\n",
       "      <td>8.702589</td>\n",
       "      <td>11.272143</td>\n",
       "      <td>18.419851</td>\n",
       "      <td>3.956024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.989724</td>\n",
       "      <td>1.488277</td>\n",
       "      <td>7.840371</td>\n",
       "      <td>21.469497</td>\n",
       "      <td>5.473986</td>\n",
       "      <td>6.484202</td>\n",
       "      <td>27.589086</td>\n",
       "      <td>8.524538</td>\n",
       "      <td>36.255537</td>\n",
       "      <td>13.439261</td>\n",
       "      <td>16.893876</td>\n",
       "      <td>16.096439</td>\n",
       "      <td>17.427745</td>\n",
       "      <td>16.574618</td>\n",
       "      <td>21.106419</td>\n",
       "      <td>25.184315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.720807</td>\n",
       "      <td>12.429749</td>\n",
       "      <td>28.174160</td>\n",
       "      <td>5.869552</td>\n",
       "      <td>8.563257</td>\n",
       "      <td>5.155427</td>\n",
       "      <td>7.075434</td>\n",
       "      <td>5.348820</td>\n",
       "      <td>7.066567</td>\n",
       "      <td>11.246722</td>\n",
       "      <td>28.066567</td>\n",
       "      <td>20.510428</td>\n",
       "      <td>12.535407</td>\n",
       "      <td>14.223367</td>\n",
       "      <td>19.688710</td>\n",
       "      <td>5.910453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.762003</td>\n",
       "      <td>1.769082</td>\n",
       "      <td>14.549234</td>\n",
       "      <td>30.423671</td>\n",
       "      <td>8.715794</td>\n",
       "      <td>9.015703</td>\n",
       "      <td>42.401146</td>\n",
       "      <td>11.751899</td>\n",
       "      <td>45.332861</td>\n",
       "      <td>20.204209</td>\n",
       "      <td>22.506758</td>\n",
       "      <td>23.953855</td>\n",
       "      <td>23.170228</td>\n",
       "      <td>23.510362</td>\n",
       "      <td>27.957910</td>\n",
       "      <td>33.567834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.803744</td>\n",
       "      <td>2.260464</td>\n",
       "      <td>5.384254</td>\n",
       "      <td>9.709994</td>\n",
       "      <td>6.948961</td>\n",
       "      <td>2.923761</td>\n",
       "      <td>7.159026</td>\n",
       "      <td>2.579015</td>\n",
       "      <td>17.632118</td>\n",
       "      <td>6.025057</td>\n",
       "      <td>10.539294</td>\n",
       "      <td>11.215262</td>\n",
       "      <td>12.417924</td>\n",
       "      <td>20.254556</td>\n",
       "      <td>11.322110</td>\n",
       "      <td>21.699245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.597182</td>\n",
       "      <td>11.346204</td>\n",
       "      <td>19.132968</td>\n",
       "      <td>8.455170</td>\n",
       "      <td>5.968345</td>\n",
       "      <td>7.536916</td>\n",
       "      <td>9.468041</td>\n",
       "      <td>6.318810</td>\n",
       "      <td>9.369510</td>\n",
       "      <td>12.423428</td>\n",
       "      <td>41.204192</td>\n",
       "      <td>28.746239</td>\n",
       "      <td>18.627272</td>\n",
       "      <td>21.547352</td>\n",
       "      <td>27.557788</td>\n",
       "      <td>10.249935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.218029</td>\n",
       "      <td>10.157114</td>\n",
       "      <td>23.773194</td>\n",
       "      <td>5.336961</td>\n",
       "      <td>3.908277</td>\n",
       "      <td>5.085754</td>\n",
       "      <td>7.689371</td>\n",
       "      <td>4.638199</td>\n",
       "      <td>6.644870</td>\n",
       "      <td>9.314052</td>\n",
       "      <td>27.603528</td>\n",
       "      <td>21.129202</td>\n",
       "      <td>12.519881</td>\n",
       "      <td>14.149741</td>\n",
       "      <td>16.151058</td>\n",
       "      <td>8.002107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.575741</td>\n",
       "      <td>10.346771</td>\n",
       "      <td>22.638090</td>\n",
       "      <td>5.676162</td>\n",
       "      <td>3.974290</td>\n",
       "      <td>5.669085</td>\n",
       "      <td>8.581475</td>\n",
       "      <td>4.796650</td>\n",
       "      <td>7.207829</td>\n",
       "      <td>9.250739</td>\n",
       "      <td>28.592135</td>\n",
       "      <td>22.005285</td>\n",
       "      <td>13.801397</td>\n",
       "      <td>15.090119</td>\n",
       "      <td>17.547433</td>\n",
       "      <td>8.376870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.283090</td>\n",
       "      <td>17.775891</td>\n",
       "      <td>46.993908</td>\n",
       "      <td>6.290751</td>\n",
       "      <td>6.759553</td>\n",
       "      <td>4.634946</td>\n",
       "      <td>4.491508</td>\n",
       "      <td>3.912036</td>\n",
       "      <td>6.604947</td>\n",
       "      <td>5.856747</td>\n",
       "      <td>15.012368</td>\n",
       "      <td>18.485785</td>\n",
       "      <td>6.398652</td>\n",
       "      <td>8.899114</td>\n",
       "      <td>15.555289</td>\n",
       "      <td>4.008030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.452875</td>\n",
       "      <td>0.668882</td>\n",
       "      <td>5.485553</td>\n",
       "      <td>15.041078</td>\n",
       "      <td>5.360934</td>\n",
       "      <td>7.015324</td>\n",
       "      <td>15.554694</td>\n",
       "      <td>9.098680</td>\n",
       "      <td>6.356780</td>\n",
       "      <td>10.844457</td>\n",
       "      <td>4.134404</td>\n",
       "      <td>9.089634</td>\n",
       "      <td>4.111142</td>\n",
       "      <td>7.369242</td>\n",
       "      <td>10.711437</td>\n",
       "      <td>6.492015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.246084</td>\n",
       "      <td>15.033800</td>\n",
       "      <td>32.710120</td>\n",
       "      <td>5.181884</td>\n",
       "      <td>12.197754</td>\n",
       "      <td>3.953421</td>\n",
       "      <td>3.386129</td>\n",
       "      <td>8.057811</td>\n",
       "      <td>4.936727</td>\n",
       "      <td>5.820899</td>\n",
       "      <td>18.617374</td>\n",
       "      <td>10.184151</td>\n",
       "      <td>5.140045</td>\n",
       "      <td>15.910655</td>\n",
       "      <td>12.137778</td>\n",
       "      <td>5.062036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.584997</td>\n",
       "      <td>0.062744</td>\n",
       "      <td>2.609888</td>\n",
       "      <td>7.971209</td>\n",
       "      <td>10.116311</td>\n",
       "      <td>5.365451</td>\n",
       "      <td>11.266231</td>\n",
       "      <td>3.404336</td>\n",
       "      <td>10.007112</td>\n",
       "      <td>4.673205</td>\n",
       "      <td>6.870383</td>\n",
       "      <td>5.352833</td>\n",
       "      <td>6.724363</td>\n",
       "      <td>4.729984</td>\n",
       "      <td>6.320257</td>\n",
       "      <td>5.385639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.285632</td>\n",
       "      <td>3.370204</td>\n",
       "      <td>39.660790</td>\n",
       "      <td>31.097938</td>\n",
       "      <td>63.584399</td>\n",
       "      <td>27.368937</td>\n",
       "      <td>47.739140</td>\n",
       "      <td>17.804931</td>\n",
       "      <td>22.058417</td>\n",
       "      <td>14.028805</td>\n",
       "      <td>31.784768</td>\n",
       "      <td>24.160963</td>\n",
       "      <td>15.563083</td>\n",
       "      <td>13.804010</td>\n",
       "      <td>13.530706</td>\n",
       "      <td>54.312478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.791718</td>\n",
       "      <td>22.044100</td>\n",
       "      <td>41.488028</td>\n",
       "      <td>5.091253</td>\n",
       "      <td>5.368434</td>\n",
       "      <td>3.731248</td>\n",
       "      <td>4.200220</td>\n",
       "      <td>3.388468</td>\n",
       "      <td>6.180186</td>\n",
       "      <td>5.273027</td>\n",
       "      <td>14.283289</td>\n",
       "      <td>17.374908</td>\n",
       "      <td>5.786343</td>\n",
       "      <td>7.882604</td>\n",
       "      <td>14.754825</td>\n",
       "      <td>2.979721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9.841274</td>\n",
       "      <td>4.174403</td>\n",
       "      <td>39.461849</td>\n",
       "      <td>21.626332</td>\n",
       "      <td>50.364483</td>\n",
       "      <td>46.409553</td>\n",
       "      <td>44.900184</td>\n",
       "      <td>35.059767</td>\n",
       "      <td>52.550276</td>\n",
       "      <td>23.415432</td>\n",
       "      <td>33.419473</td>\n",
       "      <td>87.867238</td>\n",
       "      <td>54.242866</td>\n",
       "      <td>77.074342</td>\n",
       "      <td>25.267238</td>\n",
       "      <td>38.063686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.076575</td>\n",
       "      <td>20.318730</td>\n",
       "      <td>104.826513</td>\n",
       "      <td>77.196962</td>\n",
       "      <td>49.132814</td>\n",
       "      <td>44.755586</td>\n",
       "      <td>94.593648</td>\n",
       "      <td>44.417524</td>\n",
       "      <td>24.696334</td>\n",
       "      <td>65.645870</td>\n",
       "      <td>55.305800</td>\n",
       "      <td>102.823123</td>\n",
       "      <td>82.419784</td>\n",
       "      <td>109.871579</td>\n",
       "      <td>66.819106</td>\n",
       "      <td>110.729977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.117764</td>\n",
       "      <td>2.163362</td>\n",
       "      <td>24.956252</td>\n",
       "      <td>67.610626</td>\n",
       "      <td>87.484668</td>\n",
       "      <td>49.998282</td>\n",
       "      <td>67.801216</td>\n",
       "      <td>55.489691</td>\n",
       "      <td>59.346815</td>\n",
       "      <td>26.625033</td>\n",
       "      <td>27.430611</td>\n",
       "      <td>32.191250</td>\n",
       "      <td>10.565028</td>\n",
       "      <td>22.097674</td>\n",
       "      <td>32.502776</td>\n",
       "      <td>19.317209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.393682</td>\n",
       "      <td>0.055353</td>\n",
       "      <td>2.096665</td>\n",
       "      <td>6.177805</td>\n",
       "      <td>5.615364</td>\n",
       "      <td>4.633590</td>\n",
       "      <td>7.547185</td>\n",
       "      <td>4.919401</td>\n",
       "      <td>8.712569</td>\n",
       "      <td>5.193061</td>\n",
       "      <td>5.590117</td>\n",
       "      <td>6.207371</td>\n",
       "      <td>5.638585</td>\n",
       "      <td>3.272310</td>\n",
       "      <td>6.886324</td>\n",
       "      <td>4.478871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.525765</td>\n",
       "      <td>3.095343</td>\n",
       "      <td>10.604712</td>\n",
       "      <td>28.575365</td>\n",
       "      <td>37.072472</td>\n",
       "      <td>9.322816</td>\n",
       "      <td>33.526178</td>\n",
       "      <td>16.519565</td>\n",
       "      <td>36.144530</td>\n",
       "      <td>21.231744</td>\n",
       "      <td>26.399146</td>\n",
       "      <td>23.185726</td>\n",
       "      <td>12.706944</td>\n",
       "      <td>32.464040</td>\n",
       "      <td>22.168641</td>\n",
       "      <td>25.041196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.743260</td>\n",
       "      <td>4.832874</td>\n",
       "      <td>5.839154</td>\n",
       "      <td>2.948683</td>\n",
       "      <td>9.521599</td>\n",
       "      <td>1.167279</td>\n",
       "      <td>3.499847</td>\n",
       "      <td>8.151808</td>\n",
       "      <td>14.434436</td>\n",
       "      <td>18.306985</td>\n",
       "      <td>25.753370</td>\n",
       "      <td>24.048100</td>\n",
       "      <td>17.901501</td>\n",
       "      <td>90.245558</td>\n",
       "      <td>31.771293</td>\n",
       "      <td>5.635110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.001719</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.001563</td>\n",
       "      <td>3.793438</td>\n",
       "      <td>4.291719</td>\n",
       "      <td>4.475625</td>\n",
       "      <td>2.629375</td>\n",
       "      <td>5.270313</td>\n",
       "      <td>2.415313</td>\n",
       "      <td>2.826406</td>\n",
       "      <td>4.040938</td>\n",
       "      <td>8.905938</td>\n",
       "      <td>0.847812</td>\n",
       "      <td>4.703906</td>\n",
       "      <td>4.397969</td>\n",
       "      <td>4.439375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.381265</td>\n",
       "      <td>0.067379</td>\n",
       "      <td>5.667543</td>\n",
       "      <td>2.866886</td>\n",
       "      <td>5.008546</td>\n",
       "      <td>11.233689</td>\n",
       "      <td>5.872638</td>\n",
       "      <td>8.505012</td>\n",
       "      <td>6.408381</td>\n",
       "      <td>10.862284</td>\n",
       "      <td>6.733607</td>\n",
       "      <td>14.372227</td>\n",
       "      <td>3.506984</td>\n",
       "      <td>4.217420</td>\n",
       "      <td>6.728348</td>\n",
       "      <td>4.541988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.950365</td>\n",
       "      <td>2.161189</td>\n",
       "      <td>1.699535</td>\n",
       "      <td>42.691567</td>\n",
       "      <td>6.211487</td>\n",
       "      <td>10.543161</td>\n",
       "      <td>35.040007</td>\n",
       "      <td>1.148406</td>\n",
       "      <td>8.081673</td>\n",
       "      <td>9.018260</td>\n",
       "      <td>10.304449</td>\n",
       "      <td>4.331341</td>\n",
       "      <td>101.074037</td>\n",
       "      <td>144.088313</td>\n",
       "      <td>24.463977</td>\n",
       "      <td>12.969290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.016312</td>\n",
       "      <td>5.696904</td>\n",
       "      <td>7.138981</td>\n",
       "      <td>50.494840</td>\n",
       "      <td>11.181092</td>\n",
       "      <td>9.333056</td>\n",
       "      <td>28.123502</td>\n",
       "      <td>10.745506</td>\n",
       "      <td>63.328063</td>\n",
       "      <td>20.450566</td>\n",
       "      <td>15.501831</td>\n",
       "      <td>8.434920</td>\n",
       "      <td>49.988182</td>\n",
       "      <td>53.852530</td>\n",
       "      <td>34.231858</td>\n",
       "      <td>61.643975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.941512</td>\n",
       "      <td>10.158678</td>\n",
       "      <td>26.509085</td>\n",
       "      <td>4.395224</td>\n",
       "      <td>11.370998</td>\n",
       "      <td>2.870912</td>\n",
       "      <td>3.107285</td>\n",
       "      <td>3.234124</td>\n",
       "      <td>4.751860</td>\n",
       "      <td>8.971102</td>\n",
       "      <td>18.993424</td>\n",
       "      <td>10.510988</td>\n",
       "      <td>8.261983</td>\n",
       "      <td>10.290881</td>\n",
       "      <td>16.632808</td>\n",
       "      <td>1.611005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.080410</td>\n",
       "      <td>6.656999</td>\n",
       "      <td>18.259986</td>\n",
       "      <td>2.532824</td>\n",
       "      <td>6.433831</td>\n",
       "      <td>1.617749</td>\n",
       "      <td>1.951893</td>\n",
       "      <td>1.697985</td>\n",
       "      <td>2.944772</td>\n",
       "      <td>4.763807</td>\n",
       "      <td>12.032998</td>\n",
       "      <td>4.697291</td>\n",
       "      <td>4.873394</td>\n",
       "      <td>7.738277</td>\n",
       "      <td>10.461619</td>\n",
       "      <td>1.374957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>12.270294</td>\n",
       "      <td>10.462736</td>\n",
       "      <td>17.751749</td>\n",
       "      <td>1.906053</td>\n",
       "      <td>2.322078</td>\n",
       "      <td>1.808782</td>\n",
       "      <td>2.543212</td>\n",
       "      <td>2.295836</td>\n",
       "      <td>1.231805</td>\n",
       "      <td>1.188768</td>\n",
       "      <td>10.370364</td>\n",
       "      <td>7.140133</td>\n",
       "      <td>2.849020</td>\n",
       "      <td>4.938418</td>\n",
       "      <td>3.887684</td>\n",
       "      <td>2.684220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.137901</td>\n",
       "      <td>2.511828</td>\n",
       "      <td>11.139828</td>\n",
       "      <td>2.696163</td>\n",
       "      <td>4.295602</td>\n",
       "      <td>4.462590</td>\n",
       "      <td>16.943052</td>\n",
       "      <td>8.039776</td>\n",
       "      <td>17.726827</td>\n",
       "      <td>20.556685</td>\n",
       "      <td>29.304363</td>\n",
       "      <td>27.276327</td>\n",
       "      <td>34.198703</td>\n",
       "      <td>40.283161</td>\n",
       "      <td>49.789732</td>\n",
       "      <td>20.057123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.916786</td>\n",
       "      <td>6.804878</td>\n",
       "      <td>8.044118</td>\n",
       "      <td>4.256456</td>\n",
       "      <td>0.710904</td>\n",
       "      <td>1.270803</td>\n",
       "      <td>3.299857</td>\n",
       "      <td>1.842898</td>\n",
       "      <td>2.596664</td>\n",
       "      <td>4.058465</td>\n",
       "      <td>13.060079</td>\n",
       "      <td>3.050574</td>\n",
       "      <td>4.385760</td>\n",
       "      <td>4.974354</td>\n",
       "      <td>8.554699</td>\n",
       "      <td>1.846664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>59.526995</td>\n",
       "      <td>18.241201</td>\n",
       "      <td>50.925557</td>\n",
       "      <td>43.806523</td>\n",
       "      <td>51.430440</td>\n",
       "      <td>69.373503</td>\n",
       "      <td>43.467846</td>\n",
       "      <td>126.274185</td>\n",
       "      <td>34.428966</td>\n",
       "      <td>67.276212</td>\n",
       "      <td>63.054358</td>\n",
       "      <td>163.466372</td>\n",
       "      <td>23.613230</td>\n",
       "      <td>44.510779</td>\n",
       "      <td>65.316750</td>\n",
       "      <td>15.552792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4.169038</td>\n",
       "      <td>0.842416</td>\n",
       "      <td>9.648808</td>\n",
       "      <td>14.825420</td>\n",
       "      <td>17.146499</td>\n",
       "      <td>20.670608</td>\n",
       "      <td>23.284316</td>\n",
       "      <td>32.354702</td>\n",
       "      <td>22.204138</td>\n",
       "      <td>7.848328</td>\n",
       "      <td>16.880103</td>\n",
       "      <td>16.831147</td>\n",
       "      <td>6.981711</td>\n",
       "      <td>14.136708</td>\n",
       "      <td>11.754849</td>\n",
       "      <td>6.418252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.466091</td>\n",
       "      <td>0.278227</td>\n",
       "      <td>1.852339</td>\n",
       "      <td>13.839564</td>\n",
       "      <td>10.562277</td>\n",
       "      <td>6.755025</td>\n",
       "      <td>28.641743</td>\n",
       "      <td>17.244035</td>\n",
       "      <td>71.874319</td>\n",
       "      <td>46.774939</td>\n",
       "      <td>38.575991</td>\n",
       "      <td>22.364832</td>\n",
       "      <td>121.582754</td>\n",
       "      <td>258.355439</td>\n",
       "      <td>40.232012</td>\n",
       "      <td>164.324629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3.466692</td>\n",
       "      <td>0.080431</td>\n",
       "      <td>3.399319</td>\n",
       "      <td>0.991105</td>\n",
       "      <td>1.827214</td>\n",
       "      <td>49.035011</td>\n",
       "      <td>2.293149</td>\n",
       "      <td>6.952498</td>\n",
       "      <td>4.045420</td>\n",
       "      <td>3.310371</td>\n",
       "      <td>12.964799</td>\n",
       "      <td>5.512491</td>\n",
       "      <td>0.866011</td>\n",
       "      <td>0.801287</td>\n",
       "      <td>7.362604</td>\n",
       "      <td>0.928274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.380402</td>\n",
       "      <td>1.216092</td>\n",
       "      <td>2.614818</td>\n",
       "      <td>2.163513</td>\n",
       "      <td>1.371241</td>\n",
       "      <td>13.117905</td>\n",
       "      <td>16.182633</td>\n",
       "      <td>12.895041</td>\n",
       "      <td>11.481179</td>\n",
       "      <td>7.604063</td>\n",
       "      <td>42.336985</td>\n",
       "      <td>31.820554</td>\n",
       "      <td>20.550289</td>\n",
       "      <td>25.567815</td>\n",
       "      <td>26.991436</td>\n",
       "      <td>21.536746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>121.099206</td>\n",
       "      <td>382.128336</td>\n",
       "      <td>104.400896</td>\n",
       "      <td>188.980240</td>\n",
       "      <td>5.042779</td>\n",
       "      <td>71.577918</td>\n",
       "      <td>135.162762</td>\n",
       "      <td>200.053779</td>\n",
       "      <td>148.892850</td>\n",
       "      <td>8.436341</td>\n",
       "      <td>76.961703</td>\n",
       "      <td>133.352007</td>\n",
       "      <td>132.365248</td>\n",
       "      <td>147.416174</td>\n",
       "      <td>35.548992</td>\n",
       "      <td>0.536769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2.542787</td>\n",
       "      <td>6.437042</td>\n",
       "      <td>4.536064</td>\n",
       "      <td>1.443969</td>\n",
       "      <td>2.351263</td>\n",
       "      <td>1.246944</td>\n",
       "      <td>2.383456</td>\n",
       "      <td>1.362877</td>\n",
       "      <td>3.133659</td>\n",
       "      <td>3.697229</td>\n",
       "      <td>11.426650</td>\n",
       "      <td>2.861043</td>\n",
       "      <td>4.262632</td>\n",
       "      <td>5.899552</td>\n",
       "      <td>8.562958</td>\n",
       "      <td>3.708028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.121143</td>\n",
       "      <td>19.624062</td>\n",
       "      <td>14.381985</td>\n",
       "      <td>12.325063</td>\n",
       "      <td>0.032736</td>\n",
       "      <td>4.366138</td>\n",
       "      <td>20.628649</td>\n",
       "      <td>5.116555</td>\n",
       "      <td>36.307131</td>\n",
       "      <td>8.160342</td>\n",
       "      <td>9.880108</td>\n",
       "      <td>7.239366</td>\n",
       "      <td>13.688699</td>\n",
       "      <td>13.484779</td>\n",
       "      <td>19.002711</td>\n",
       "      <td>21.630734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>8.096633</td>\n",
       "      <td>0.388282</td>\n",
       "      <td>12.994097</td>\n",
       "      <td>3.941627</td>\n",
       "      <td>3.318540</td>\n",
       "      <td>16.433100</td>\n",
       "      <td>0.650853</td>\n",
       "      <td>56.710319</td>\n",
       "      <td>2.859641</td>\n",
       "      <td>44.544600</td>\n",
       "      <td>5.201793</td>\n",
       "      <td>21.594447</td>\n",
       "      <td>0.349147</td>\n",
       "      <td>0.690643</td>\n",
       "      <td>12.655662</td>\n",
       "      <td>0.188675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.585809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007761</td>\n",
       "      <td>0.002217</td>\n",
       "      <td>4.278271</td>\n",
       "      <td>7.796674</td>\n",
       "      <td>0.002217</td>\n",
       "      <td>4.324612</td>\n",
       "      <td>0.003326</td>\n",
       "      <td>3.960976</td>\n",
       "      <td>1.947228</td>\n",
       "      <td>4.638581</td>\n",
       "      <td>0.001109</td>\n",
       "      <td>4.477827</td>\n",
       "      <td>0.010421</td>\n",
       "      <td>0.217073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>9.002233</td>\n",
       "      <td>28.178835</td>\n",
       "      <td>50.646350</td>\n",
       "      <td>0.884572</td>\n",
       "      <td>3.313686</td>\n",
       "      <td>0.803751</td>\n",
       "      <td>6.336906</td>\n",
       "      <td>0.882340</td>\n",
       "      <td>6.095557</td>\n",
       "      <td>3.698370</td>\n",
       "      <td>17.841259</td>\n",
       "      <td>4.257424</td>\n",
       "      <td>14.322170</td>\n",
       "      <td>32.066086</td>\n",
       "      <td>3.711766</td>\n",
       "      <td>5.126367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>3.852505</td>\n",
       "      <td>9.924165</td>\n",
       "      <td>3.733998</td>\n",
       "      <td>2.778293</td>\n",
       "      <td>2.651670</td>\n",
       "      <td>1.100649</td>\n",
       "      <td>3.850186</td>\n",
       "      <td>3.188312</td>\n",
       "      <td>7.121289</td>\n",
       "      <td>7.048469</td>\n",
       "      <td>12.543367</td>\n",
       "      <td>11.084416</td>\n",
       "      <td>18.497913</td>\n",
       "      <td>78.724026</td>\n",
       "      <td>17.065863</td>\n",
       "      <td>4.556586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.026564</td>\n",
       "      <td>9.219712</td>\n",
       "      <td>2.927756</td>\n",
       "      <td>2.005214</td>\n",
       "      <td>0.779543</td>\n",
       "      <td>0.533515</td>\n",
       "      <td>4.837388</td>\n",
       "      <td>6.061072</td>\n",
       "      <td>32.991063</td>\n",
       "      <td>6.736097</td>\n",
       "      <td>31.253972</td>\n",
       "      <td>14.201341</td>\n",
       "      <td>22.141758</td>\n",
       "      <td>1.370655</td>\n",
       "      <td>14.240566</td>\n",
       "      <td>26.971698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.005896</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.681364</td>\n",
       "      <td>0.001794</td>\n",
       "      <td>1.869777</td>\n",
       "      <td>0.952320</td>\n",
       "      <td>0.013074</td>\n",
       "      <td>14.330428</td>\n",
       "      <td>1.042041</td>\n",
       "      <td>1.482440</td>\n",
       "      <td>1.107665</td>\n",
       "      <td>14.001025</td>\n",
       "      <td>17.977954</td>\n",
       "      <td>7.421687</td>\n",
       "      <td>2.789797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2.173670</td>\n",
       "      <td>6.897074</td>\n",
       "      <td>18.482979</td>\n",
       "      <td>2.410106</td>\n",
       "      <td>5.388032</td>\n",
       "      <td>1.591489</td>\n",
       "      <td>1.782181</td>\n",
       "      <td>1.816489</td>\n",
       "      <td>2.523138</td>\n",
       "      <td>3.285638</td>\n",
       "      <td>10.931649</td>\n",
       "      <td>3.776596</td>\n",
       "      <td>3.994415</td>\n",
       "      <td>8.679255</td>\n",
       "      <td>9.338032</td>\n",
       "      <td>1.817819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1.108329</td>\n",
       "      <td>0.008160</td>\n",
       "      <td>0.013787</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>0.002814</td>\n",
       "      <td>0.030107</td>\n",
       "      <td>0.105234</td>\n",
       "      <td>0.090884</td>\n",
       "      <td>2.783343</td>\n",
       "      <td>0.154192</td>\n",
       "      <td>0.193022</td>\n",
       "      <td>0.143500</td>\n",
       "      <td>7.643500</td>\n",
       "      <td>6.826393</td>\n",
       "      <td>22.131683</td>\n",
       "      <td>41.960889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0.014581</td>\n",
       "      <td>0.002430</td>\n",
       "      <td>2.062778</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>0.443499</td>\n",
       "      <td>0.200486</td>\n",
       "      <td>1.331308</td>\n",
       "      <td>0.012961</td>\n",
       "      <td>1.962333</td>\n",
       "      <td>0.383556</td>\n",
       "      <td>0.021871</td>\n",
       "      <td>0.232888</td>\n",
       "      <td>10.975699</td>\n",
       "      <td>7.136087</td>\n",
       "      <td>6.498177</td>\n",
       "      <td>5.916565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1           2           3           4          5          6   \\\n",
       "color                                                                         \n",
       "1        8.876186   18.261282   51.187960    6.134054  12.633700   5.477037   \n",
       "2        2.989724    1.488277    7.840371   21.469497   5.473986   6.484202   \n",
       "3        3.720807   12.429749   28.174160    5.869552   8.563257   5.155427   \n",
       "4        5.762003    1.769082   14.549234   30.423671   8.715794   9.015703   \n",
       "5        2.803744    2.260464    5.384254    9.709994   6.948961   2.923761   \n",
       "6        0.597182   11.346204   19.132968    8.455170   5.968345   7.536916   \n",
       "7        2.218029   10.157114   23.773194    5.336961   3.908277   5.085754   \n",
       "8        1.575741   10.346771   22.638090    5.676162   3.974290   5.669085   \n",
       "9        5.283090   17.775891   46.993908    6.290751   6.759553   4.634946   \n",
       "10       3.452875    0.668882    5.485553   15.041078   5.360934   7.015324   \n",
       "12       8.246084   15.033800   32.710120    5.181884  12.197754   3.953421   \n",
       "13       0.584997    0.062744    2.609888    7.971209  10.116311   5.365451   \n",
       "14      10.285632    3.370204   39.660790   31.097938  63.584399  27.368937   \n",
       "15       5.791718   22.044100   41.488028    5.091253   5.368434   3.731248   \n",
       "16       9.841274    4.174403   39.461849   21.626332  50.364483  46.409553   \n",
       "17       8.076575   20.318730  104.826513   77.196962  49.132814  44.755586   \n",
       "18       3.117764    2.163362   24.956252   67.610626  87.484668  49.998282   \n",
       "19       2.393682    0.055353    2.096665    6.177805   5.615364   4.633590   \n",
       "20       3.525765    3.095343   10.604712   28.575365  37.072472   9.322816   \n",
       "21       5.743260    4.832874    5.839154    2.948683   9.521599   1.167279   \n",
       "22       0.001719    0.000313    0.001563    3.793438   4.291719   4.475625   \n",
       "23       3.381265    0.067379    5.667543    2.866886   5.008546  11.233689   \n",
       "24       3.950365    2.161189    1.699535   42.691567   6.211487  10.543161   \n",
       "25       7.016312    5.696904    7.138981   50.494840  11.181092   9.333056   \n",
       "27       3.941512   10.158678   26.509085    4.395224  11.370998   2.870912   \n",
       "28       2.080410    6.656999   18.259986    2.532824   6.433831   1.617749   \n",
       "29      12.270294   10.462736   17.751749    1.906053   2.322078   1.808782   \n",
       "30       2.137901    2.511828   11.139828    2.696163   4.295602   4.462590   \n",
       "32       1.916786    6.804878    8.044118    4.256456   0.710904   1.270803   \n",
       "34      59.526995   18.241201   50.925557   43.806523  51.430440  69.373503   \n",
       "36       4.169038    0.842416    9.648808   14.825420  17.146499  20.670608   \n",
       "39       0.466091    0.278227    1.852339   13.839564  10.562277   6.755025   \n",
       "40       3.466692    0.080431    3.399319    0.991105   1.827214  49.035011   \n",
       "47       0.380402    1.216092    2.614818    2.163513   1.371241  13.117905   \n",
       "48     121.099206  382.128336  104.400896  188.980240   5.042779  71.577918   \n",
       "49       2.542787    6.437042    4.536064    1.443969   2.351263   1.246944   \n",
       "51       0.121143   19.624062   14.381985   12.325063   0.032736   4.366138   \n",
       "56       8.096633    0.388282   12.994097    3.941627   3.318540  16.433100   \n",
       "57       0.585809    0.000000    0.007761    0.002217   4.278271   7.796674   \n",
       "59       9.002233   28.178835   50.646350    0.884572   3.313686   0.803751   \n",
       "62       3.852505    9.924165    3.733998    2.778293   2.651670   1.100649   \n",
       "71       0.026564    9.219712    2.927756    2.005214   0.779543   0.533515   \n",
       "74       0.005896    0.000000    0.000000    3.681364   0.001794   1.869777   \n",
       "77       2.173670    6.897074   18.482979    2.410106   5.388032   1.591489   \n",
       "85       1.108329    0.008160    0.013787    0.001688   0.002814   0.030107   \n",
       "122      0.014581    0.002430    2.062778    0.000810   0.443499   0.200486   \n",
       "\n",
       "               7           8           9          10         11          12  \\\n",
       "color                                                                         \n",
       "1        6.014585    5.304814    6.874191  12.439046  29.793732   22.665219   \n",
       "2       27.589086    8.524538   36.255537  13.439261  16.893876   16.096439   \n",
       "3        7.075434    5.348820    7.066567  11.246722  28.066567   20.510428   \n",
       "4       42.401146   11.751899   45.332861  20.204209  22.506758   23.953855   \n",
       "5        7.159026    2.579015   17.632118   6.025057  10.539294   11.215262   \n",
       "6        9.468041    6.318810    9.369510  12.423428  41.204192   28.746239   \n",
       "7        7.689371    4.638199    6.644870   9.314052  27.603528   21.129202   \n",
       "8        8.581475    4.796650    7.207829   9.250739  28.592135   22.005285   \n",
       "9        4.491508    3.912036    6.604947   5.856747  15.012368   18.485785   \n",
       "10      15.554694    9.098680    6.356780  10.844457   4.134404    9.089634   \n",
       "12       3.386129    8.057811    4.936727   5.820899  18.617374   10.184151   \n",
       "13      11.266231    3.404336   10.007112   4.673205   6.870383    5.352833   \n",
       "14      47.739140   17.804931   22.058417  14.028805  31.784768   24.160963   \n",
       "15       4.200220    3.388468    6.180186   5.273027  14.283289   17.374908   \n",
       "16      44.900184   35.059767   52.550276  23.415432  33.419473   87.867238   \n",
       "17      94.593648   44.417524   24.696334  65.645870  55.305800  102.823123   \n",
       "18      67.801216   55.489691   59.346815  26.625033  27.430611   32.191250   \n",
       "19       7.547185    4.919401    8.712569   5.193061   5.590117    6.207371   \n",
       "20      33.526178   16.519565   36.144530  21.231744  26.399146   23.185726   \n",
       "21       3.499847    8.151808   14.434436  18.306985  25.753370   24.048100   \n",
       "22       2.629375    5.270313    2.415313   2.826406   4.040938    8.905938   \n",
       "23       5.872638    8.505012    6.408381  10.862284   6.733607   14.372227   \n",
       "24      35.040007    1.148406    8.081673   9.018260  10.304449    4.331341   \n",
       "25      28.123502   10.745506   63.328063  20.450566  15.501831    8.434920   \n",
       "27       3.107285    3.234124    4.751860   8.971102  18.993424   10.510988   \n",
       "28       1.951893    1.697985    2.944772   4.763807  12.032998    4.697291   \n",
       "29       2.543212    2.295836    1.231805   1.188768  10.370364    7.140133   \n",
       "30      16.943052    8.039776   17.726827  20.556685  29.304363   27.276327   \n",
       "32       3.299857    1.842898    2.596664   4.058465  13.060079    3.050574   \n",
       "34      43.467846  126.274185   34.428966  67.276212  63.054358  163.466372   \n",
       "36      23.284316   32.354702   22.204138   7.848328  16.880103   16.831147   \n",
       "39      28.641743   17.244035   71.874319  46.774939  38.575991   22.364832   \n",
       "40       2.293149    6.952498    4.045420   3.310371  12.964799    5.512491   \n",
       "47      16.182633   12.895041   11.481179   7.604063  42.336985   31.820554   \n",
       "48     135.162762  200.053779  148.892850   8.436341  76.961703  133.352007   \n",
       "49       2.383456    1.362877    3.133659   3.697229  11.426650    2.861043   \n",
       "51      20.628649    5.116555   36.307131   8.160342   9.880108    7.239366   \n",
       "56       0.650853   56.710319    2.859641  44.544600   5.201793   21.594447   \n",
       "57       0.002217    4.324612    0.003326   3.960976   1.947228    4.638581   \n",
       "59       6.336906    0.882340    6.095557   3.698370  17.841259    4.257424   \n",
       "62       3.850186    3.188312    7.121289   7.048469  12.543367   11.084416   \n",
       "71       4.837388    6.061072   32.991063   6.736097  31.253972   14.201341   \n",
       "74       0.952320    0.013074   14.330428   1.042041   1.482440    1.107665   \n",
       "77       1.782181    1.816489    2.523138   3.285638  10.931649    3.776596   \n",
       "85       0.105234    0.090884    2.783343   0.154192   0.193022    0.143500   \n",
       "122      1.331308    0.012961    1.962333   0.383556   0.021871    0.232888   \n",
       "\n",
       "               13          14         15          16  \n",
       "color                                                 \n",
       "1        8.702589   11.272143  18.419851    3.956024  \n",
       "2       17.427745   16.574618  21.106419   25.184315  \n",
       "3       12.535407   14.223367  19.688710    5.910453  \n",
       "4       23.170228   23.510362  27.957910   33.567834  \n",
       "5       12.417924   20.254556  11.322110   21.699245  \n",
       "6       18.627272   21.547352  27.557788   10.249935  \n",
       "7       12.519881   14.149741  16.151058    8.002107  \n",
       "8       13.801397   15.090119  17.547433    8.376870  \n",
       "9        6.398652    8.899114  15.555289    4.008030  \n",
       "10       4.111142    7.369242  10.711437    6.492015  \n",
       "12       5.140045   15.910655  12.137778    5.062036  \n",
       "13       6.724363    4.729984   6.320257    5.385639  \n",
       "14      15.563083   13.804010  13.530706   54.312478  \n",
       "15       5.786343    7.882604  14.754825    2.979721  \n",
       "16      54.242866   77.074342  25.267238   38.063686  \n",
       "17      82.419784  109.871579  66.819106  110.729977  \n",
       "18      10.565028   22.097674  32.502776   19.317209  \n",
       "19       5.638585    3.272310   6.886324    4.478871  \n",
       "20      12.706944   32.464040  22.168641   25.041196  \n",
       "21      17.901501   90.245558  31.771293    5.635110  \n",
       "22       0.847812    4.703906   4.397969    4.439375  \n",
       "23       3.506984    4.217420   6.728348    4.541988  \n",
       "24     101.074037  144.088313  24.463977   12.969290  \n",
       "25      49.988182   53.852530  34.231858   61.643975  \n",
       "27       8.261983   10.290881  16.632808    1.611005  \n",
       "28       4.873394    7.738277  10.461619    1.374957  \n",
       "29       2.849020    4.938418   3.887684    2.684220  \n",
       "30      34.198703   40.283161  49.789732   20.057123  \n",
       "32       4.385760    4.974354   8.554699    1.846664  \n",
       "34      23.613230   44.510779  65.316750   15.552792  \n",
       "36       6.981711   14.136708  11.754849    6.418252  \n",
       "39     121.582754  258.355439  40.232012  164.324629  \n",
       "40       0.866011    0.801287   7.362604    0.928274  \n",
       "47      20.550289   25.567815  26.991436   21.536746  \n",
       "48     132.365248  147.416174  35.548992    0.536769  \n",
       "49       4.262632    5.899552   8.562958    3.708028  \n",
       "51      13.688699   13.484779  19.002711   21.630734  \n",
       "56       0.349147    0.690643  12.655662    0.188675  \n",
       "57       0.001109    4.477827   0.010421    0.217073  \n",
       "59      14.322170   32.066086   3.711766    5.126367  \n",
       "62      18.497913   78.724026  17.065863    4.556586  \n",
       "71      22.141758    1.370655  14.240566   26.971698  \n",
       "74      14.001025   17.977954   7.421687    2.789797  \n",
       "77       3.994415    8.679255   9.338032    1.817819  \n",
       "85       7.643500    6.826393  22.131683   41.960889  \n",
       "122     10.975699    7.136087   6.498177    5.916565  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at mean frequencies in each sample for each bin\n",
    "profile.groupby('color').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "214731"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique profiles in profile\n",
    "len(-profile.groupby(profile.columns.tolist()[2:],as_index=False).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make new dataframe - only unique profiles and run bhtsne on it\n",
    "new_profile = profile.drop_duplicates(profile.columns.tolist()[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "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>color</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "      <td>214731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.129800</td>\n",
       "      <td>7.893974</td>\n",
       "      <td>14.111134</td>\n",
       "      <td>20.949183</td>\n",
       "      <td>18.048246</td>\n",
       "      <td>13.387517</td>\n",
       "      <td>12.819826</td>\n",
       "      <td>19.863946</td>\n",
       "      <td>16.551271</td>\n",
       "      <td>19.722951</td>\n",
       "      <td>13.629411</td>\n",
       "      <td>21.115116</td>\n",
       "      <td>24.002212</td>\n",
       "      <td>20.522281</td>\n",
       "      <td>29.781308</td>\n",
       "      <td>19.061570</td>\n",
       "      <td>17.828744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.749713</td>\n",
       "      <td>20.238912</td>\n",
       "      <td>56.429132</td>\n",
       "      <td>31.039870</td>\n",
       "      <td>33.749958</td>\n",
       "      <td>22.096052</td>\n",
       "      <td>21.876617</td>\n",
       "      <td>30.797125</td>\n",
       "      <td>37.646332</td>\n",
       "      <td>29.446247</td>\n",
       "      <td>20.329130</td>\n",
       "      <td>22.523138</td>\n",
       "      <td>40.986660</td>\n",
       "      <td>34.669525</td>\n",
       "      <td>53.139510</td>\n",
       "      <td>20.306207</td>\n",
       "      <td>31.855980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>122.000000</td>\n",
       "      <td>922.000000</td>\n",
       "      <td>2890.000000</td>\n",
       "      <td>965.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>1290.000000</td>\n",
       "      <td>1281.000000</td>\n",
       "      <td>1273.000000</td>\n",
       "      <td>1922.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1492.000000</td>\n",
       "      <td>925.000000</td>\n",
       "      <td>2252.000000</td>\n",
       "      <td>1641.000000</td>\n",
       "      <td>3422.000000</td>\n",
       "      <td>897.000000</td>\n",
       "      <td>2124.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               color              1              2              3  \\\n",
       "count  214731.000000  214731.000000  214731.000000  214731.000000   \n",
       "mean       22.129800       7.893974      14.111134      20.949183   \n",
       "std        20.749713      20.238912      56.429132      31.039870   \n",
       "min         1.000000       0.000000       0.000000       0.000000   \n",
       "25%         5.000000       0.000000       0.000000       3.000000   \n",
       "50%        17.000000       3.000000       3.000000       9.000000   \n",
       "75%        32.000000       7.000000      10.000000      28.000000   \n",
       "max       122.000000     922.000000    2890.000000     965.000000   \n",
       "\n",
       "                   4              5              6              7  \\\n",
       "count  214731.000000  214731.000000  214731.000000  214731.000000   \n",
       "mean       18.048246      13.387517      12.819826      19.863946   \n",
       "std        33.749958      22.096052      21.876617      30.797125   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         3.000000       3.000000       2.000000       3.000000   \n",
       "50%         7.000000       6.000000       6.000000       8.000000   \n",
       "75%        17.000000      12.000000      11.000000      25.000000   \n",
       "max      1499.000000    1290.000000    1281.000000    1273.000000   \n",
       "\n",
       "                   8              9             10             11  \\\n",
       "count  214731.000000  214731.000000  214731.000000  214731.000000   \n",
       "mean       16.551271      19.722951      13.629411      21.115116   \n",
       "std        37.646332      29.446247      20.329130      22.523138   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         3.000000       4.000000       4.000000       7.000000   \n",
       "50%         6.000000      10.000000       8.000000      15.000000   \n",
       "75%        13.000000      23.000000      18.000000      30.000000   \n",
       "max      1922.000000    1199.000000    1492.000000     925.000000   \n",
       "\n",
       "                  12             13             14             15  \\\n",
       "count  214731.000000  214731.000000  214731.000000  214731.000000   \n",
       "mean       24.002212      20.522281      29.781308      19.061570   \n",
       "std        40.986660      34.669525      53.139510      20.306207   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         6.000000       4.000000       5.000000       7.000000   \n",
       "50%        12.000000      10.000000      13.000000      12.000000   \n",
       "75%        27.000000      19.000000      30.000000      27.000000   \n",
       "max      2252.000000    1641.000000    3422.000000     897.000000   \n",
       "\n",
       "                  16  \n",
       "count  214731.000000  \n",
       "mean       17.828744  \n",
       "std        31.855980  \n",
       "min         0.000000  \n",
       "25%         4.000000  \n",
       "50%         7.000000  \n",
       "75%        22.000000  \n",
       "max      2124.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_profile.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_profile = new_profile.sample(frac=0.1)\n",
    "data = new_profile.as_matrix(columns = new_profile.columns[2:])\n",
    "v = (1.0/2000)\n",
    "data = data + v\n",
    "along_Y = np.apply_along_axis(sum, 0, data)\n",
    "data = data/along_Y[None, :]\n",
    "along_X = np.apply_along_axis(sum, 1, data)\n",
    "data = data/along_X[:, None]\n",
    "data = np.log(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length of values does not match length of index",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-e02eb57c1b22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mnew_profile\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mar2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0mnew_profile\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mar2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2427\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2428\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2429\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2431\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2494\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2495\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2496\u001b[0m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2497\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value, broadcast)\u001b[0m\n\u001b[1;32m   2664\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2665\u001b[0m             \u001b[0;31m# turn me into an ndarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2666\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_sanitize_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2667\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2668\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_sanitize_index\u001b[0;34m(data, index, copy)\u001b[0m\n\u001b[1;32m   2877\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2878\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2879\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Length of values does not match length of '\u001b[0m \u001b[0;34m'index'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2881\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPeriodIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Length of values does not match length of index"
     ]
    }
   ],
   "source": [
    "np.savetxt(\"data2.in\", data, delimiter=\"\\t\")\n",
    "\n",
    "path_bhtsne = '/Users/tanunia/PycharmProjects/biolab_t-sne/'\n",
    "import sys, os\n",
    "os.system(path_bhtsne + 'bhtsne.py -p 50 -m 3000 -i data2.in -o data_canopy2.out')\n",
    "\n",
    "ar2 = np.loadtxt(\"data2.out\", delimiter=\"\\t\")\n",
    "len(ar2[:, 0])\n",
    "\n",
    "new_profile[\"x\"] = ar2[:, 0]\n",
    "new_profile[\"y\"] = ar2[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.scatter(new_profile[\"x\"], new_profile[\"y\"], c=new_profile[\"color\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_profile[\"color\"].value_counts()"
   ]
  }
 ],
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