proof-of-concept-plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from custom import load_sequence_and_metadata, seq2chararray, encode_array, compute_seq_lengths, get_density_interval\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_context('talk')\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load data\n",
    "sequences, metadata = load_sequence_and_metadata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rLuUPu9cy/eARfCNN4LespZMLZzWw1X2LuPaNRhoV+GWUAj4REREREUnrujdX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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5433d03780>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 288,
       "width": 446
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metadata['Year'] = metadata['Collection Date'].apply(lambda x: x.year)\n",
    "ax = metadata.groupby('Year').count()['Name'].plot()\n",
    "ax.set_title('Number of Sequences Per Year')\n",
    "ax.set_ylabel('Number of Sequences')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.62802434, -4.02198646, -0.79146308]),\n",
       " array([ 3.00617146,  0.3219904 ,  3.30449963]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_coords = pd.read_csv('data/metadata_with_embeddings.csv')\n",
    "array = meta_coords[['coords0', 'coords1', 'coords2']]\n",
    "\n",
    "lowp, highp = get_density_interval(99, array)\n",
    "lowp, highp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>coords0</th>\n",
       "      <th>coords1</th>\n",
       "      <th>coords2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.918940</td>\n",
       "      <td>-1.174675</td>\n",
       "      <td>0.871852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.288200</td>\n",
       "      <td>-1.232958</td>\n",
       "      <td>0.798981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.831635</td>\n",
       "      <td>-1.563137</td>\n",
       "      <td>2.395415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.006171</td>\n",
       "      <td>-0.969287</td>\n",
       "      <td>1.302068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.470726</td>\n",
       "      <td>0.321990</td>\n",
       "      <td>3.304500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.227757</td>\n",
       "      <td>-1.556512</td>\n",
       "      <td>1.652497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.022043</td>\n",
       "      <td>-2.085446</td>\n",
       "      <td>2.359227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.148276</td>\n",
       "      <td>-1.450171</td>\n",
       "      <td>1.902529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.518494</td>\n",
       "      <td>-0.856068</td>\n",
       "      <td>1.071871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.227757</td>\n",
       "      <td>-1.556512</td>\n",
       "      <td>1.652497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.118792</td>\n",
       "      <td>-1.631921</td>\n",
       "      <td>2.102484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.801143</td>\n",
       "      <td>-1.598644</td>\n",
       "      <td>1.551610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.470726</td>\n",
       "      <td>0.321990</td>\n",
       "      <td>3.304500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.118792</td>\n",
       "      <td>-1.631921</td>\n",
       "      <td>2.102484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.441803</td>\n",
       "      <td>-0.911016</td>\n",
       "      <td>1.256986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.801143</td>\n",
       "      <td>-1.598644</td>\n",
       "      <td>1.551610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.470726</td>\n",
       "      <td>0.321990</td>\n",
       "      <td>3.304500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.046337</td>\n",
       "      <td>-1.960277</td>\n",
       "      <td>2.297898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.118792</td>\n",
       "      <td>-1.631921</td>\n",
       "      <td>2.102484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.470726</td>\n",
       "      <td>0.321990</td>\n",
       "      <td>3.304500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.046337</td>\n",
       "      <td>-1.960277</td>\n",
       "      <td>2.297898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.118792</td>\n",
       "      <td>-1.631921</td>\n",
       "      <td>2.102484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.118792</td>\n",
       "      <td>-1.631921</td>\n",
       "      <td>2.102484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.771851</td>\n",
       "      <td>-1.085633</td>\n",
       "      <td>1.298355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.493231</td>\n",
       "      <td>-1.324761</td>\n",
       "      <td>1.873742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.106338</td>\n",
       "      <td>-1.567368</td>\n",
       "      <td>2.446098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.022043</td>\n",
       "      <td>-2.085446</td>\n",
       "      <td>2.359227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.137974</td>\n",
       "      <td>-1.954751</td>\n",
       "      <td>2.048233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.803096</td>\n",
       "      <td>-1.060387</td>\n",
       "      <td>1.415775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.557137</td>\n",
       "      <td>-0.937651</td>\n",
       "      <td>1.780598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13343</th>\n",
       "      <td>-0.023649</td>\n",
       "      <td>-2.053885</td>\n",
       "      <td>2.349242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13344</th>\n",
       "      <td>-0.096931</td>\n",
       "      <td>-1.959386</td>\n",
       "      <td>2.210831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13345</th>\n",
       "      <td>-0.145467</td>\n",
       "      <td>-1.361705</td>\n",
       "      <td>1.142086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13346</th>\n",
       "      <td>0.078348</td>\n",
       "      <td>-1.477327</td>\n",
       "      <td>2.253168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13347</th>\n",
       "      <td>-0.741128</td>\n",
       "      <td>-2.192901</td>\n",
       "      <td>2.914381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13348</th>\n",
       "      <td>-0.534840</td>\n",
       "      <td>-2.141206</td>\n",
       "      <td>2.858298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13349</th>\n",
       "      <td>1.179843</td>\n",
       "      <td>0.191758</td>\n",
       "      <td>2.854454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13350</th>\n",
       "      <td>0.184662</td>\n",
       "      <td>-1.652853</td>\n",
       "      <td>2.476905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13351</th>\n",
       "      <td>-0.010600</td>\n",
       "      <td>-1.319169</td>\n",
       "      <td>2.427523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13352</th>\n",
       "      <td>-0.024472</td>\n",
       "      <td>-1.635905</td>\n",
       "      <td>1.815091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13353</th>\n",
       "      <td>-0.579322</td>\n",
       "      <td>-0.916727</td>\n",
       "      <td>1.031197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13354</th>\n",
       "      <td>0.479878</td>\n",
       "      <td>-2.350614</td>\n",
       "      <td>2.619806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13355</th>\n",
       "      <td>0.151900</td>\n",
       "      <td>-1.849754</td>\n",
       "      <td>2.196189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13356</th>\n",
       "      <td>0.249665</td>\n",
       "      <td>-1.980036</td>\n",
       "      <td>2.394748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13357</th>\n",
       "      <td>0.068630</td>\n",
       "      <td>-1.922177</td>\n",
       "      <td>2.133150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13358</th>\n",
       "      <td>0.273144</td>\n",
       "      <td>-2.067154</td>\n",
       "      <td>2.432444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13359</th>\n",
       "      <td>-0.261155</td>\n",
       "      <td>-1.289115</td>\n",
       "      <td>1.311853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13360</th>\n",
       "      <td>0.135078</td>\n",
       "      <td>-1.212672</td>\n",
       "      <td>1.505061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13361</th>\n",
       "      <td>0.902746</td>\n",
       "      <td>-0.476329</td>\n",
       "      <td>2.201073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13362</th>\n",
       "      <td>0.862971</td>\n",
       "      <td>-3.604417</td>\n",
       "      <td>0.606099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13363</th>\n",
       "      <td>1.387338</td>\n",
       "      <td>-1.326220</td>\n",
       "      <td>1.562776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13364</th>\n",
       "      <td>1.401712</td>\n",
       "      <td>-1.112762</td>\n",
       "      <td>1.710803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13365</th>\n",
       "      <td>0.945322</td>\n",
       "      <td>-3.376436</td>\n",
       "      <td>0.436639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13366</th>\n",
       "      <td>0.580914</td>\n",
       "      <td>-3.405534</td>\n",
       "      <td>0.482366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13367</th>\n",
       "      <td>2.803096</td>\n",
       "      <td>-1.060387</td>\n",
       "      <td>1.415775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13368</th>\n",
       "      <td>1.193006</td>\n",
       "      <td>-1.211506</td>\n",
       "      <td>2.009974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13369</th>\n",
       "      <td>1.327766</td>\n",
       "      <td>-0.830434</td>\n",
       "      <td>2.577443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13370</th>\n",
       "      <td>2.722209</td>\n",
       "      <td>-1.042559</td>\n",
       "      <td>1.311239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13371</th>\n",
       "      <td>1.223868</td>\n",
       "      <td>-1.283525</td>\n",
       "      <td>2.154937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13372</th>\n",
       "      <td>1.327765</td>\n",
       "      <td>-0.830435</td>\n",
       "      <td>2.577443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13373 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        coords0   coords1   coords2\n",
       "0      0.918940 -1.174675  0.871852\n",
       "1      1.288200 -1.232958  0.798981\n",
       "2      0.831635 -1.563137  2.395415\n",
       "3      3.006171 -0.969287  1.302068\n",
       "4      1.470726  0.321990  3.304500\n",
       "5      1.227757 -1.556512  1.652497\n",
       "6      0.022043 -2.085446  2.359227\n",
       "7     -0.148276 -1.450171  1.902529\n",
       "8      1.518494 -0.856068  1.071871\n",
       "9      1.227757 -1.556512  1.652497\n",
       "10    -0.118792 -1.631921  2.102484\n",
       "11     1.801143 -1.598644  1.551610\n",
       "12     1.470726  0.321990  3.304500\n",
       "13    -0.118792 -1.631921  2.102484\n",
       "14     2.441803 -0.911016  1.256986\n",
       "15     1.801143 -1.598644  1.551610\n",
       "16     1.470726  0.321990  3.304500\n",
       "17    -0.046337 -1.960277  2.297898\n",
       "18    -0.118792 -1.631921  2.102484\n",
       "19     1.470726  0.321990  3.304500\n",
       "20    -0.046337 -1.960277  2.297898\n",
       "21    -0.118792 -1.631921  2.102484\n",
       "22    -0.118792 -1.631921  2.102484\n",
       "23     2.771851 -1.085633  1.298355\n",
       "24     1.493231 -1.324761  1.873742\n",
       "25     1.106338 -1.567368  2.446098\n",
       "26     0.022043 -2.085446  2.359227\n",
       "27    -0.137974 -1.954751  2.048233\n",
       "28     2.803096 -1.060387  1.415775\n",
       "29     1.557137 -0.937651  1.780598\n",
       "...         ...       ...       ...\n",
       "13343 -0.023649 -2.053885  2.349242\n",
       "13344 -0.096931 -1.959386  2.210831\n",
       "13345 -0.145467 -1.361705  1.142086\n",
       "13346  0.078348 -1.477327  2.253168\n",
       "13347 -0.741128 -2.192901  2.914381\n",
       "13348 -0.534840 -2.141206  2.858298\n",
       "13349  1.179843  0.191758  2.854454\n",
       "13350  0.184662 -1.652853  2.476905\n",
       "13351 -0.010600 -1.319169  2.427523\n",
       "13352 -0.024472 -1.635905  1.815091\n",
       "13353 -0.579322 -0.916727  1.031197\n",
       "13354  0.479878 -2.350614  2.619806\n",
       "13355  0.151900 -1.849754  2.196189\n",
       "13356  0.249665 -1.980036  2.394748\n",
       "13357  0.068630 -1.922177  2.133150\n",
       "13358  0.273144 -2.067154  2.432444\n",
       "13359 -0.261155 -1.289115  1.311853\n",
       "13360  0.135078 -1.212672  1.505061\n",
       "13361  0.902746 -0.476329  2.201073\n",
       "13362  0.862971 -3.604417  0.606099\n",
       "13363  1.387338 -1.326220  1.562776\n",
       "13364  1.401712 -1.112762  1.710803\n",
       "13365  0.945322 -3.376436  0.436639\n",
       "13366  0.580914 -3.405534  0.482366\n",
       "13367  2.803096 -1.060387  1.415775\n",
       "13368  1.193006 -1.211506  2.009974\n",
       "13369  1.327766 -0.830434  2.577443\n",
       "13370  2.722209 -1.042559  1.311239\n",
       "13371  1.223868 -1.283525  2.154937\n",
       "13372  1.327765 -0.830435  2.577443\n",
       "\n",
       "[13373 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, show, output_notebook\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Environment (conda_deeplearning)",
   "language": "python",
   "name": "conda_deeplearning"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
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}