notebook/TruePCR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TruePCR workflow\n",
    "=============\n",
    "TruePCR main workflow in a single, hopefully simple, notebook.\n",
    "\n",
    "Dependencies\n",
    "------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# standard library modules\n",
    "import os\n",
    "\n",
    "\n",
    "# external modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Global settings\n",
    "----------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "DATA_LOC = '../data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parsing the data\n",
    "-----------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Well,Dye Name,Reading 1,Reading 2,Reading 3,Reading 4,Reading 5,Reading 6,Reading 7,Reading 8,Reading 9,Reading 10,Reading 11,Reading 12,Reading 13,Reading 14,Reading 15,Reading 16,Reading 17,Reading 18,Reading 19,Reading 20,Reading 21,Reading 22,Reading 23,Reading 24,Reading 25,Reading 26,Reading 27,Reading 28,Reading 29,Reading 30,Reading 31,Reading 32,Reading 33,Reading 34,Reading 35,Reading 36,Reading 37,Reading 38,Reading 39,Reading 40,Reading 41,Reading 42,Reading 43,Reading 44,Reading 45,Reading 46,Reading 47,Reading 48,Reading 49,Reading 50,Reading 51,Reading 52,Reading 53,Reading 54,Reading 55,Reading 56,Reading 57,Reading 58,Reading 59,Reading 60,Reading 61,Reading 62,Reading 63,Reading 64,Reading 65,Reading 66,Reading 67,Reading 68,Reading 69,Reading 70,Reading 71,Reading 72,Reading 73,Reading 74,Reading 75,Reading 76,Reading 77,Reading 78,Reading 79,Reading 80,Reading 81,Reading 82,Reading 83,Reading 84,Reading 85,Reading 86,Reading 87,Reading 88,Reading 89,Reading 90,Reading 91,Reading 92,Reading 93,Reading 94,Reading 95,Reading 96,Reading 97,Reading 98,Reading 99,Reading 100,Reading 101,Reading 102,Reading 103,Reading 104,Reading 105,Reading 106,Reading 107,Reading 108,Reading 109,Reading 110,Reading 111,Reading 112,Reading 113,Reading 114,Reading 115,Reading 116,Reading 117,Reading 118,Reading 119,Reading 120,Reading 121,Reading 122,Reading 123,Reading 124,Reading 125,Reading 126,Reading 127,Reading 128,Reading 129,Reading 130,Reading 131\r\n",
      "15,ROX,50718.367188,51313.871094,51656.718750,51801.609375,51964.367188,52088.664063,52213.367188,52264.246094,52264.546875,52250.972656,52343.453125,52403.007813,52419.457031,52434.531250,52497.355469,52484.078125,52474.640625,52519.722656,52547.921875,52588.187500,52618.753906,52680.734375,52673.285156,52710.964844,52652.429688,52659.933594,52625.558594,52567.683594,52330.593750,51994.089844,51384.007813,50367.035156,48770.246094,46610.628906,44422.312500,42954.742188,42378.191406,42318.671875,42345.890625,42342.964844,47517.992188,47786.585938,47971.414063,48111.957031,48268.515625,48459.867188,48708.667969,48918.175781,49053.371094,49158.894531,49318.035156,49418.472656,49520.726563,49572.292969,49729.207031,49808.144531,49977.953125,50130.437500,50280.453125,50314.738281,50394.378906,50545.136719,50673.468750,50841.605469,50934.183594,50983.902344,51073.003906,51238.609375,51293.742188,51254.230469,51290.539063,51346.171875,51413.761719,51515.968750,51693.625000,51811.292969,51784.828125,51759.769531,51825.937500,51912.945313,51948.449219,52041.906250,52141.761719,52195.843750,52139.929688,52173.796875,52300.710938,52387.058594,52441.617188,52514.050781,52615.500000,52624.250000,52615.273438,52686.109375,52762.171875,52857.566406,52838.382813,52890.621094,52904.984375,53006.515625,53066.734375,53182.621094,53171.777344,53109.015625,53079.191406,53074.910156,53261.542969,53410.660156,53650.906250,53794.828125,54032.378906,54120.375000,54139.437500,53888.085938,53587.546875,53301.628906,53132.218750,52991.824219,52950.242188,52963.441406,52938.824219,52931.292969,52927.093750,52930.968750,52896.195313,52863.261719,52788.839844,52738.621094,52701.269531,52650.851563,52537.011719\r\n"
     ]
    }
   ],
   "source": [
    "filepath = os.path.join(DATA_LOC, 'raw_input.csv')\n",
    "! head -n 2 $filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Well</th>\n",
       "      <th colspan=\"2\" halign=\"left\">15</th>\n",
       "      <th colspan=\"2\" halign=\"left\">16</th>\n",
       "      <th colspan=\"2\" halign=\"left\">17</th>\n",
       "      <th colspan=\"2\" halign=\"left\">18</th>\n",
       "      <th colspan=\"2\" halign=\"left\">19</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">44</th>\n",
       "      <th colspan=\"2\" halign=\"left\">45</th>\n",
       "      <th colspan=\"2\" halign=\"left\">46</th>\n",
       "      <th colspan=\"2\" halign=\"left\">47</th>\n",
       "      <th colspan=\"2\" halign=\"left\">48</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dye Name</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "      <th>...</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "      <th>ROX</th>\n",
       "      <th>SYBR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cycle</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",
       "      <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>50718.367188</td>\n",
       "      <td>5318.887207</td>\n",
       "      <td>57887.613281</td>\n",
       "      <td>33920.878906</td>\n",
       "      <td>53284.023438</td>\n",
       "      <td>30377.804688</td>\n",
       "      <td>49185.601563</td>\n",
       "      <td>19894.083984</td>\n",
       "      <td>58374.417969</td>\n",
       "      <td>23573.378906</td>\n",
       "      <td>...</td>\n",
       "      <td>94933.070313</td>\n",
       "      <td>35734.375000</td>\n",
       "      <td>93904.476563</td>\n",
       "      <td>30905.425781</td>\n",
       "      <td>105897.640625</td>\n",
       "      <td>24347.509766</td>\n",
       "      <td>147489.656250</td>\n",
       "      <td>37397.796875</td>\n",
       "      <td>27118.876953</td>\n",
       "      <td>133027.968750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51313.871094</td>\n",
       "      <td>5319.723145</td>\n",
       "      <td>58529.898438</td>\n",
       "      <td>34382.972656</td>\n",
       "      <td>53991.269531</td>\n",
       "      <td>30802.255859</td>\n",
       "      <td>47991.695313</td>\n",
       "      <td>21463.738281</td>\n",
       "      <td>58777.265625</td>\n",
       "      <td>26820.228516</td>\n",
       "      <td>...</td>\n",
       "      <td>95864.281250</td>\n",
       "      <td>36351.441406</td>\n",
       "      <td>95067.859375</td>\n",
       "      <td>31462.052734</td>\n",
       "      <td>107160.507813</td>\n",
       "      <td>24351.603516</td>\n",
       "      <td>149480.093750</td>\n",
       "      <td>38126.917969</td>\n",
       "      <td>27046.447266</td>\n",
       "      <td>132011.218750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51656.718750</td>\n",
       "      <td>5502.428711</td>\n",
       "      <td>58904.281250</td>\n",
       "      <td>34775.125000</td>\n",
       "      <td>54442.910156</td>\n",
       "      <td>31073.466797</td>\n",
       "      <td>47495.050781</td>\n",
       "      <td>22455.042969</td>\n",
       "      <td>59029.562500</td>\n",
       "      <td>28510.072266</td>\n",
       "      <td>...</td>\n",
       "      <td>96518.070313</td>\n",
       "      <td>36885.535156</td>\n",
       "      <td>95763.320313</td>\n",
       "      <td>31842.537109</td>\n",
       "      <td>107968.500000</td>\n",
       "      <td>24459.154297</td>\n",
       "      <td>150645.390625</td>\n",
       "      <td>38287.609375</td>\n",
       "      <td>26924.074219</td>\n",
       "      <td>131143.328125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51801.609375</td>\n",
       "      <td>5654.279297</td>\n",
       "      <td>59058.332031</td>\n",
       "      <td>34852.347656</td>\n",
       "      <td>54659.476563</td>\n",
       "      <td>31155.664063</td>\n",
       "      <td>47424.453125</td>\n",
       "      <td>22938.957031</td>\n",
       "      <td>59083.628906</td>\n",
       "      <td>29043.996094</td>\n",
       "      <td>...</td>\n",
       "      <td>96841.328125</td>\n",
       "      <td>37157.957031</td>\n",
       "      <td>96063.695313</td>\n",
       "      <td>31980.916016</td>\n",
       "      <td>108297.554688</td>\n",
       "      <td>24571.220703</td>\n",
       "      <td>151119.484375</td>\n",
       "      <td>38157.128906</td>\n",
       "      <td>26859.259766</td>\n",
       "      <td>130266.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51964.367188</td>\n",
       "      <td>5661.381836</td>\n",
       "      <td>59207.218750</td>\n",
       "      <td>34564.160156</td>\n",
       "      <td>54847.093750</td>\n",
       "      <td>31097.007813</td>\n",
       "      <td>47456.546875</td>\n",
       "      <td>23155.125000</td>\n",
       "      <td>59162.906250</td>\n",
       "      <td>29435.638672</td>\n",
       "      <td>...</td>\n",
       "      <td>97093.429688</td>\n",
       "      <td>37162.527344</td>\n",
       "      <td>96271.257813</td>\n",
       "      <td>32102.951172</td>\n",
       "      <td>108553.015625</td>\n",
       "      <td>24484.470703</td>\n",
       "      <td>151471.281250</td>\n",
       "      <td>38177.941406</td>\n",
       "      <td>26733.949219</td>\n",
       "      <td>129527.695313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Well                15                         16                          17  \\\n",
       "Dye Name           ROX         SYBR           ROX          SYBR           ROX   \n",
       "cycle                                                                           \n",
       "1         50718.367188  5318.887207  57887.613281  33920.878906  53284.023438   \n",
       "2         51313.871094  5319.723145  58529.898438  34382.972656  53991.269531   \n",
       "3         51656.718750  5502.428711  58904.281250  34775.125000  54442.910156   \n",
       "4         51801.609375  5654.279297  59058.332031  34852.347656  54659.476563   \n",
       "5         51964.367188  5661.381836  59207.218750  34564.160156  54847.093750   \n",
       "\n",
       "Well                              18                          19  \\\n",
       "Dye Name          SYBR           ROX          SYBR           ROX   \n",
       "cycle                                                              \n",
       "1         30377.804688  49185.601563  19894.083984  58374.417969   \n",
       "2         30802.255859  47991.695313  21463.738281  58777.265625   \n",
       "3         31073.466797  47495.050781  22455.042969  59029.562500   \n",
       "4         31155.664063  47424.453125  22938.957031  59083.628906   \n",
       "5         31097.007813  47456.546875  23155.125000  59162.906250   \n",
       "\n",
       "Well                        ...                  44                \\\n",
       "Dye Name          SYBR      ...                 ROX          SYBR   \n",
       "cycle                       ...                                     \n",
       "1         23573.378906      ...        94933.070313  35734.375000   \n",
       "2         26820.228516      ...        95864.281250  36351.441406   \n",
       "3         28510.072266      ...        96518.070313  36885.535156   \n",
       "4         29043.996094      ...        96841.328125  37157.957031   \n",
       "5         29435.638672      ...        97093.429688  37162.527344   \n",
       "\n",
       "Well                45                           46                \\\n",
       "Dye Name           ROX          SYBR            ROX          SYBR   \n",
       "cycle                                                               \n",
       "1         93904.476563  30905.425781  105897.640625  24347.509766   \n",
       "2         95067.859375  31462.052734  107160.507813  24351.603516   \n",
       "3         95763.320313  31842.537109  107968.500000  24459.154297   \n",
       "4         96063.695313  31980.916016  108297.554688  24571.220703   \n",
       "5         96271.257813  32102.951172  108553.015625  24484.470703   \n",
       "\n",
       "Well                 47                          48                 \n",
       "Dye Name            ROX          SYBR           ROX           SYBR  \n",
       "cycle                                                               \n",
       "1         147489.656250  37397.796875  27118.876953  133027.968750  \n",
       "2         149480.093750  38126.917969  27046.447266  132011.218750  \n",
       "3         150645.390625  38287.609375  26924.074219  131143.328125  \n",
       "4         151119.484375  38157.128906  26859.259766  130266.812500  \n",
       "5         151471.281250  38177.941406  26733.949219  129527.695313  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dna = pd.read_csv(filepath, index_col=[0,1])\n",
    "dna = dna.transpose()\n",
    "dna.index = [int(s.split(' ')[1]) for s in dna.index]\n",
    "dna.index.name = 'cycle'\n",
    "#dna.columns\n",
    "dna.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting\n",
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some examples of selecting data. [loc](http://pandas.pydata.org/pandas-docs/stable/indexing.html#different-choices-for-indexing-loc-iloc-and-ix) is a label-based selection method.\n",
    "\n",
    "All rows, single column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v+deqfxExM4ItR7e4HZYp4fIz5/C4iGwSkalen+hrAwe86sQ4ZbWBaK/yaKcs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\nYvSCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc49710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dna.loc[:, 15].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First 5 rows, 2 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X/8V0NTl86cSJE0lPT2fhwoWMHTuWZs2aMWXKFIYPHx4t88QTT9ChQwemTp1a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\nSIiISCgFCRERCfX/Aet1/hK9v2efAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc49390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dna.loc[:5, 15:16].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Single dye only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0MbNBUfkoYLu7dwYmAzpCvIaoS0kkOPxwePDB0L20a1fc0cQn1eRgZbzHxcD0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd8f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fGCUiBe7uLbueiGMR1ItPcesmpT//hEuX/qn4ZMvmwiOW0iJnTnj1VZ0kLlyA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\ncBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10de612e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dna.loc[:,(15, 'SYBR')].plot()\n",
    "plt.show()\n",
    "# cross-section in columns: axis=1\n",
    "# two levels: 0 (cycle) and 1 (dye)\n",
    "dna.xs('SYBR', level=1, axis=1).loc[:,15:17].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modeling\n",
    "--------\n",
    "Automatically slice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h859Pw8F33TXvaEqa7xyKTadOcOWVeUdhVpo+8pE0mGPMmLwjKWu+c2hrEWmJ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e13e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#concentrations = dna.loc[:,(31, 'SYBR')][:40].values\n",
    "concentrations_raw = dna.loc[:,(31, 'SYBR')]\n",
    "concentrations_raw.plot(style='r--')\n",
    "conc_diff = concentrations_raw.diff()\n",
    "decrease_start_index = conc_diff[conc_diff<0].index[0]\n",
    "concentrations_series = concentrations_raw[: decrease_start_index - 1]\n",
    "concentrations_series.plot(style='r-')\n",
    "concentrations = concentrations_series.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b9t8/+3nqqdl+375w0EHubjIrB01NGgE8JGkbcFtE3AHsExFrASJijaSe6dje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\n+/tjaHo9uAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cd91780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "concentrations_series.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Older model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2.49657656e+05   3.41212406e+05   1.01000000e+00   3.41222406e+05\n",
      "   3.00000000e+00]\n",
      "[  4.82588558e+04   2.19956974e+01   5.50689842e-01   3.51287979e+05\n",
      "   1.68330183e+00]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "from scipy.optimize import curve_fit\n",
    "import ipdb\n",
    "\n",
    "k = 1\n",
    "n = 5\n",
    "nx = 1\n",
    "nc = 5\n",
    "mode = 0\n",
    "maxit = 100000\n",
    "iprt = 0\n",
    "\n",
    "# input_file_name = 'inp.txt'\n",
    "\n",
    "# concentrations = np.loadtxt(input_file_name)\n",
    "\n",
    "# offset\n",
    "offset = np.mean(concentrations[5:8])\n",
    "\n",
    "# expression\n",
    "last_reading = concentrations[-1]\n",
    "index = np.where((concentrations - offset) > 0.1 * last_reading)[0][0]\n",
    "expression = ((concentrations[index] - offset) /\n",
    "              math.exp(math.log(2.0)*index*1.0))\n",
    "\n",
    "# rate\n",
    "rate = 1.0\n",
    "\n",
    "# reactants\n",
    "reactants = last_reading/2.0\n",
    "\n",
    "# power\n",
    "power = 1.0\n",
    "\n",
    "sy = concentrations.copy()\n",
    "for i, elm in enumerate(concentrations):\n",
    "    if (elm > 1.5 * offset) and (elm < 0.5 * last_reading):\n",
    "        sy[i] = 0.5\n",
    "    elif (i < index - 10):\n",
    "        sy[i] = 2.5\n",
    "    elif (elm > last_reading/1.5):\n",
    "        sy[i] = 1.2\n",
    "\n",
    "# bounds\n",
    "al = 0.0\n",
    "variables = np.array([5*offset, last_reading, 1.01, last_reading + 10.0, 3.0])\n",
    "print(variables)\n",
    "\n",
    "def f(meas, offset, n0, gama, nn, alfa):\n",
    "    meas_calc = np.zeros(len(meas))\n",
    "    for i in meas:\n",
    "        # ipdb.set_trace()\n",
    "        broj = np.zeros(i)\n",
    "        if n0 < 0 or nn < 0:\n",
    "            return np.repeat(1e10, len(meas))\n",
    "        broj[0] = n0\n",
    "        suma = n0\n",
    "        for idx in range(1, i):\n",
    "            broj[idx] = broj[idx - 1] * (1.0 + gama /\n",
    "                                         (1.0 +\n",
    "                                          math.exp(alfa * math.log(suma / nn))))\n",
    "            suma = suma + broj[idx]\n",
    "\n",
    "        meas_calc[i - 1] = broj[-1] + offset\n",
    "    return meas_calc\n",
    "\n",
    "times = list(range(1, len(concentrations) + 1))\n",
    "fitted_params = curve_fit(f, times, concentrations, p0=variables)\n",
    "params = fitted_params[0]\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  46291.660156,   47628.90625 ,   48538.496094,   49069.910156,\n",
       "         49409.421875,   49651.507813,   49918.171875,   50224.914063,\n",
       "         50366.925781,   50520.902344,   50773.109375,   51177.683594,\n",
       "         51961.804688,   53454.484375,   56117.375   ,   60834.640625,\n",
       "         69399.929688,   83715.734375,  104588.578125,  130064.46875 ,\n",
       "        157435.71875 ,  184299.59375 ,  209869.171875,  233132.03125 ,\n",
       "        253379.796875,  270186.34375 ,  283986.75    ,  295564.625   ,\n",
       "        305173.65625 ,  312926.59375 ,  319316.8125  ,  324381.90625 ,\n",
       "        328349.5     ,  331837.25    ,  334953.1875  ,  337200.78125 ,\n",
       "        338646.375   ,  339733.9375  ,  340569.21875 ,  341212.40625 ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concentrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1989.19132887,   664.05804097,  -226.74860425,  -729.0357793 ,\n",
       "       -1023.38083874, -1195.42768078, -1353.48414554, -1491.81406017,\n",
       "       -1372.68524834, -1121.7621101 ,  -746.25324759,  -177.94991851,\n",
       "         544.88538075,  1383.59836774,  2318.45679674,  3124.13418795,\n",
       "        2946.81120648,  1100.78931409, -1972.34424953, -3773.8524297 ,\n",
       "       -2820.79128873,   132.74733638,  2430.04731956,  3104.8976848 ,\n",
       "        2504.63009253,  1553.72804806,   545.98305598,  -626.87109113,\n",
       "       -1674.64196474, -2294.69483395, -2668.71605155, -2600.75699534,\n",
       "       -2142.34881109, -1777.05058493, -1509.60833716,  -762.9959074 ,\n",
       "         459.90727025,  1765.72836868,  3089.47998486,  4404.13987871])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(times, *params) - concentrations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K2BXhxpRiU6ZM4eGHH7aCYVxlPQ1jAsD3339PkyZN2Lp1K7Vq1Sq8gTE+KE5P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\nmRUNY4wxPrOiYYwxxmdWNIwxxvjs/wO/vBqFu4+hnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dddec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitted_vals = f(times, *params)\n",
    "\n",
    "plt.plot(times, concentrations, 'k-', label='measurements')\n",
    "plt.plot(times, fitted_vals, 'r--', label='model')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Newer model\n",
    "\n",
    "Add the TruePCR package to the system path, so that we can include the module from there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/kermit/projekti/git/TruePCR\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '/Users/kermit/.virtualenvs/s3/lib/python35.zip',\n",
       " '/Users/kermit/.virtualenvs/s3/lib/python3.5',\n",
       " '/Users/kermit/.virtualenvs/s3/lib/python3.5/plat-darwin',\n",
       " '/Users/kermit/.virtualenvs/s3/lib/python3.5/lib-dynload',\n",
       " '/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/lib/python3.5',\n",
       " '/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/lib/python3.5/plat-darwin',\n",
       " '/Users/kermit/.virtualenvs/s3/lib/python3.5/site-packages',\n",
       " '/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages',\n",
       " '/Users/kermit/.virtualenvs/s3/lib/python3.5/site-packages/IPython/extensions',\n",
       " '/Users/kermit/.ipython',\n",
       " '/Users/kermit/projekti/git/TruePCR']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "TruePCR_path = os.path.abspath(os.path.join('..'))\n",
    "print(TruePCR_path)\n",
    "if TruePCR_path not in sys.path:\n",
    "    sys.path.append(TruePCR_path)\n",
    "sys.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.49657656e+05,   3.41212406e+05,   1.01000000e+00,\n",
       "         3.41222406e+05,   3.00000000e+00])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  4.85642748e+04   8.73408643e+00   6.49551324e-01   3.38866798e+05\n",
      "   9.35599025e-01]\n"
     ]
    },
    {
     "data": {
      "image/png": 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RRx8lPT2dN9980+1UTBFSkKJht0Y3xs8d3LqVhA8/5OvNm91OxRjraRjj72a0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\n/rvvkp79QZyeTkZaGgfCwni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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e270160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from TruePCR.lib.fitter import f\n",
    "\n",
    "# older model function – check ../TruePCR/lib/fitter.py (imported above) for the newest version\n",
    "# def f(meas, offset, n0, gama, nn, alfa):\n",
    "#     meas_calc = np.zeros(len(meas))\n",
    "#     for i in meas:\n",
    "#         num = np.zeros(i)\n",
    "#         prim = np.zeros(i)\n",
    "#         if n0 < 0 or nn < 0:\n",
    "#             return np.repeat(1e10, len(meas))\n",
    "#         num[0] = n0\n",
    "#         summ = n0\n",
    "#         prim[0] = nn\n",
    "#         for idx in range(1, i):\n",
    "#             num[idx] = num[idx - 1] * (\n",
    "#                 1.0 + gama / (1.0 + alfa * (summ / prim[idx - 1]))\n",
    "#             )\n",
    "#             summ = summ + num[idx]\n",
    "#             prim[idx] = prim[idx - 1] - (num[idx] - num[idx - 1])\n",
    "\n",
    "#         meas_calc[i - 1] = num[-1] + offset\n",
    "#     return meas_calc\n",
    "\n",
    "# obtained by randomly tweaking the output params and passing them into the curve_fit again\n",
    "good_variables = [1.76050783e+05, 5.78e+02, +1.13766225e+00, 7.42308865e+05, 8.11068606e+00]\n",
    "times = list(range(1, len(concentrations) + 1))\n",
    "fitted_params = curve_fit(f, times, concentrations, p0=good_variables)\n",
    "params = fitted_params[0]\n",
    "print(params)\n",
    "\n",
    "fitted_vals = f(times, *params)\n",
    "\n",
    "plt.plot(times, concentrations, 'k-', label='measurements')\n",
    "plt.plot(times, fitted_vals, 'r--', label='model')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}