Competition_Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import necessary packages:\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Competition</th>\n",
       "      <th>Blue</th>\n",
       "      <th>White</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>139</td>\n",
       "      <td>118</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>124</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>141</td>\n",
       "      <td>166</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>154</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Competition  Blue  White  notes\n",
       "0            1     0      0    NaN\n",
       "1            1     0      0    NaN\n",
       "2            2   139    118    NaN\n",
       "3            2   134    124    NaN\n",
       "4            3   141    166    NaN\n",
       "5            3   122    154    NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#input inoculum data\n",
    "df_inoc = pd.read_excel('data/competition_template.xlsx', sheetname='Initial_Water')\n",
    "df_inoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Competition</th>\n",
       "      <th>Blue</th>\n",
       "      <th>White</th>\n",
       "      <th>ratioWB_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>136.5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0.887147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>131.5</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1.219800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Competition   Blue  White  ratioWB_i\n",
       "0            1    0.0    0.0        NaN\n",
       "1            2  136.5  121.0   0.887147\n",
       "2            3  131.5  160.0   1.219800"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Process inoculum data - remove unecessary columns and calculate White/Blue ratio\n",
    "del df_inoc['notes']\n",
    "df_inoc['ratioWB_i'] = df_inoc['White']/df_inoc['Blue']\n",
    "\n",
    "#Then, average the technical replicates\n",
    "df_inoc_mean = df_inoc.groupby(['Competition'], as_index=False).mean()\n",
    "df_inoc_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Competition</th>\n",
       "      <th>Squid</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Blue</th>\n",
       "      <th>White</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>44</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>43</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Competition  Squid Dilution  Blue  White  notes\n",
       "0            1      1        A     0      0    NaN\n",
       "1            1      1        A     0      0    NaN\n",
       "2            1      2        A     0      0    NaN\n",
       "3            1      2        A     0      0    NaN\n",
       "4            1      3        A     0      0    NaN\n",
       "5            1      3        A     0      0    NaN\n",
       "6            2      1        B    44     65    NaN\n",
       "7            2      1        B    43     52    NaN\n",
       "8            2      2        B    23     25    NaN\n",
       "9            2      2        B    23     24    NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#input the squid cfu data\n",
    "df_cfu = pd.read_excel('data/competition_template.xlsx', sheetname='Final_Squid')\n",
    "df_cfu.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Delete unecessary notes column\n",
    "del df_cfu['notes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Competition</th>\n",
       "      <th>Squid</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Blue</th>\n",
       "      <th>White</th>\n",
       "      <th>Infinite</th>\n",
       "      <th>ratioWB_o</th>\n",
       "      <th>ratioWB_i</th>\n",
       "      <th>CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>43.5</td>\n",
       "      <td>58.5</td>\n",
       "      <td>n</td>\n",
       "      <td>1.344828</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.180671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>n</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>1.006247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>n</td>\n",
       "      <td>1.065217</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.079443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>n</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.353034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>20.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>n</td>\n",
       "      <td>1.925000</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.336435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>n</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.830156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>76.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>n</td>\n",
       "      <td>0.758170</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>-0.068229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>n</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.404187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>n</td>\n",
       "      <td>2.025000</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.358429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.052004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>B</td>\n",
       "      <td>48.0</td>\n",
       "      <td>50.5</td>\n",
       "      <td>n</td>\n",
       "      <td>1.052083</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.074055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>C</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>n</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.052004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Competition  Squid Dilution  Blue  White Infinite  ratioWB_o  ratioWB_i  \\\n",
       "0             1      1        A   1.0    1.0        y   1.000000        NaN   \n",
       "1             1      2        A   1.0    1.0        y   1.000000        NaN   \n",
       "2             1      3        A   1.0    1.0        y   1.000000        NaN   \n",
       "3             2      1        B  43.5   58.5        n   1.344828   0.887147   \n",
       "4             2      1        C   0.5    4.5        n   9.000000   0.887147   \n",
       "5             2      2        B  23.0   24.5        n   1.065217   0.887147   \n",
       "6             2      2        C   0.5    1.0        n   2.000000   0.887147   \n",
       "7             2      3        B  20.0   38.5        n   1.925000   0.887147   \n",
       "8             2      3        C   0.5    3.0        n   6.000000   0.887147   \n",
       "9             2      4        B  76.5   58.0        n   0.758170   0.887147   \n",
       "10            2      4        C   2.0    4.5        n   2.250000   0.887147   \n",
       "11            2      5        B  20.0   40.5        n   2.025000   0.887147   \n",
       "12            2      5        C   1.0    1.0        y   1.000000   0.887147   \n",
       "13            2      6        B  48.0   50.5        n   1.052083   0.887147   \n",
       "14            2      6        C   6.0    6.0        n   1.000000   0.887147   \n",
       "\n",
       "          CI  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3   0.180671  \n",
       "4   1.006247  \n",
       "5   0.079443  \n",
       "6   0.353034  \n",
       "7   0.336435  \n",
       "8   0.830156  \n",
       "9  -0.068229  \n",
       "10  0.404187  \n",
       "11  0.358429  \n",
       "12  0.052004  \n",
       "13  0.074055  \n",
       "14  0.052004  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correct data to account for Limit Of Detection:\n",
    "\n",
    "df_lod = df_cfu\n",
    "\n",
    "#average technical replicates\n",
    "df_lod = df_lod.groupby(['Competition','Squid','Dilution'], as_index=False).mean()\n",
    "\n",
    "#mark all infinite points\n",
    "df_lod.loc[df_lod['Blue'] != 0, 'Infinite'] = 'n'\n",
    "df_lod.loc[df_lod['White'] != 0, 'Infinite'] = 'n'\n",
    "df_lod.loc[df_lod['Blue'] == 0, 'Infinite'] = 'y'\n",
    "df_lod.loc[df_lod['White'] == 0, 'Infinite'] = 'y'\n",
    "\n",
    "#set all zeros to 1\n",
    "df_lod.loc[df_lod['Blue'] == 0, 'Blue'] = 1\n",
    "df_lod.loc[df_lod['White'] == 0, 'White'] = 1\n",
    "\n",
    "#calculate white:blue \n",
    "df_lod['ratioWB_o'] = df_lod['White']/df_lod['Blue']\n",
    "\n",
    "#merge inoculum data into table\n",
    "df_lod = pd.merge(df_lod, df_inoc_mean[['Competition', 'ratioWB_i']], on=['Competition'])\n",
    "\n",
    "#calculate competive index for each squid\n",
    "df_lod['CI'] = np.log10(df_lod['ratioWB_o'] / df_lod['ratioWB_i'])\n",
    "\n",
    "df_lod.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Competition</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Squid</th>\n",
       "      <th>Blue</th>\n",
       "      <th>White</th>\n",
       "      <th>ratioWB_o</th>\n",
       "      <th>ratioWB_i</th>\n",
       "      <th>CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>3.5</td>\n",
       "      <td>33.25</td>\n",
       "      <td>45.50</td>\n",
       "      <td>1.205022</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.130057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.378611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>3.5</td>\n",
       "      <td>36.75</td>\n",
       "      <td>120.50</td>\n",
       "      <td>3.529744</td>\n",
       "      <td>1.219800</td>\n",
       "      <td>0.460923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.330357</td>\n",
       "      <td>1.219800</td>\n",
       "      <td>0.037435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Competition Dilution  Squid   Blue   White  ratioWB_o  ratioWB_i        CI\n",
       "0            1        A    2.0   1.00    1.00   1.000000        NaN       NaN\n",
       "1            2        B    3.5  33.25   45.50   1.205022   0.887147  0.130057\n",
       "2            2        C    3.5   0.75    3.75   2.125000   0.887147  0.378611\n",
       "3            3        B    3.5  36.75  120.50   3.529744   1.219800  0.460923\n",
       "4            3        C    3.5   2.75    2.75   1.330357   1.219800  0.037435"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate medians for each competition\n",
    "df_lod_medians = df_lod.groupby(['Competition','Dilution'], as_index=False).median()\n",
    "\n",
    "df_lod_medians"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint\n",
    "### You now have two dataframes:\n",
    "1) df_lod - This has the averaged competitive index (CI) for each squid for each dilution  \n",
    "2) df_lod_medians - This has the median competitive index (CI) for each competition for each dilution\n",
    "\n",
    "Note: The following code will create a graph using the B dilution information. Still working on code to choose if C dilution should be used instead of B dilution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#B dilutions only\n",
    "df_lod_B = df_lod[df_lod.Dilution == 'B']\n",
    "df_lod_medians_B = df_lod_medians[df_lod_medians.Dilution == 'B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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TL8esjSt6b0Ssk5ln9hPf6ZSE8OXAlxq6nD0YEYdSupldV824+AjlPmLPAQ7Pfm6N0AaH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\nAAAYjKAGAAAwmP8DDKFpjL4kWo4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c605ab4208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the figure and the axes\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.set_context(\"talk\", font_scale = 1.2) #sets default bigger and scale up\n",
    "mpl.rcParams[\"figure.facecolor\"] = \"white\" #set background of plot area as white\n",
    "sns.set_style(\"whitegrid\") #set plot background as white\n",
    "\n",
    "\n",
    "# plot data points\n",
    "graph = sns.swarmplot(x='Competition', y='CI', data=df_lod_B, \n",
    "                      size=10, hue='Infinite', zorder=0)\n",
    "\n",
    "\n",
    "# plot medians\n",
    "graph = sns.pointplot(x='Competition', y='CI', data=df_lod_B, errwidth=0, markers='_', \n",
    "                      scale=2, join=False, estimator=np.median, color='red')\n",
    "\n",
    "# graph aesthetics\n",
    "graph.set(title='Competitive Index of Strains vs ES114', \n",
    "          xlabel='Strain', \n",
    "          ylabel=r'Competitive Index (Log $_{10}$)',\n",
    "          ylim=(-1.75,1.75))\n",
    "graph.set_xticklabels(['MJM1100','MJM2251'])\n",
    "\n",
    "# Legend\n",
    "#ax.legend().set_visible(False) #This command will hide legend\n",
    "graph.legend(bbox_to_anchor=(1, 1), \n",
    "             title='Limit of Detection Reached?')\n",
    "for t, l in zip(graph.legend_.texts, ['no','yes']): t.set_text(l)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Colors\n",
    "#### Don't like colors? Here's a way to create your own palette. You can also choose from pre-existing palettes.\n",
    "https://seaborn.pydata.org/tutorial/color_palettes.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABECAYAAAAiJuZQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzylYOfjuCHQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c60626f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "current_palette = sns.color_palette()\n",
    "sns.palplot(current_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAH4AAABECAYAAABHwoFDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAStJREFUeJzt3aEVgzAYReGfnhpGyBxMwDzMw04YlsBkAk4kVXUFTEva\n3vtJYh7nisg027ZtIZxb7QGqw/BQhocyPJThoe5Hh/M8X7VDH9J13cvvh+EjIoZhiJzz2wfVllKK\ncRxjmqYopdSe8xF93++enYbPOceyLG8d9E1KKbGua+0Zl/OOhzI8lOGhDA9leCjDQxkeyvBQhocy\nPJThoQwPZXgow0MZHsrwUIaHMjyU4aEMD2V4KMNDGR7K8FCGhzI8lOGhDA9leCjDQxkeyvBQhocy\nPJThoQwPZXgow0MZHsrwUIaHMjyU4aEMD2V4KMNDGR7K8FCGhzI8lOGhTt+kSSldseNyz/9q27by\nkjqao0eFfX7s9+09P3YYXv/LOx7K8FCGhzI8lOGhHrz7KlJ3fwBGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c606256898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "squid_palette = ['black','grey']\n",
    "sns.palplot(squid_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GjRqFUaNGIS8vDxcuXEBQUBDi4+OxZMkShIWFldrGu/ck8OZ+3bZtGxISEnD//n0kJSWJ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\nuBo3bqzIyEizBqxZs6a8vb2LrBWYm5urnTt3qkOHDiXuU61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R/Y4dO6agoCC1b99enTp1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c606489ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add palette to graph\n",
    "\n",
    "# Create the figure and the axes\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.set_context(\"talk\", font_scale = 1.2) #sets default bigger and scale up\n",
    "mpl.rcParams[\"figure.facecolor\"] = \"white\" #set background of plot area as white\n",
    "sns.set_style(\"whitegrid\") #set plot background as white\n",
    "\n",
    "\n",
    "# plot data points\n",
    "graph = sns.swarmplot(x='Competition', y='CI', data=df_lod_B, \n",
    "                      size=10, hue='Infinite', palette = squid_palette, zorder=0)\n",
    "\n",
    "\n",
    "# plot medians\n",
    "graph = sns.pointplot(x='Competition', y='CI', data=df_lod_B, errwidth=0, markers='_', \n",
    "                      scale=2, join=False, estimator=np.median, color='red')\n",
    "\n",
    "# graph aesthetics\n",
    "graph.set(title='Competitive Index of Strains vs ES114', \n",
    "          xlabel='Strain', \n",
    "          ylabel=r'Competitive Index (Log $_{10}$)',\n",
    "          ylim=(-1.75,1.75))\n",
    "graph.set_xticklabels(['MJM1100','MJM2251'])\n",
    "\n",
    "# Legend\n",
    "#ax.legend().set_visible(False) #This command will hide legend\n",
    "graph.legend(bbox_to_anchor=(1, 1), \n",
    "             title='Limit of Detection Reached?')\n",
    "for t, l in zip(graph.legend_.texts, ['no','yes']): t.set_text(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Finally, export your figure!\n",
    "fig.savefig('results/competition_example_graph.pdf',bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}