squid_competition_plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/markmandel/anaconda/lib/python3.5/site-packages/pandas/computation/__init__.py:19: UserWarning: The installed version of numexpr 2.4.4 is not supported in pandas and will be not be used\n",
      "\n",
      "  UserWarning)\n",
      "/Users/markmandel/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>competition</th>\n",
       "      <th>animal</th>\n",
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      "text/plain": [
       "   competition  animal  blue  white\n",
       "0            2       1   175    121\n",
       "1            2       1   176    118\n",
       "2            2       2   420     37\n",
       "3            2       2   504     45\n",
       "4            2       3   298     12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inoculum = pd.read_excel('data/squid_competition_1.xlsx', sheetname='inoculum')\n",
    "df = pd.read_excel('data/squid_competition_1.xlsx', sheetname='squid')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not what we are looking for obviously, but a reminder that `pandas` has some built-in plotting functionality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average the inoculum counts and normalize the squid counts to the inoculum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "   competition  blue  white   ratioWB\n",
       "0            1     0      0       NaN\n",
       "1            1     0      0       NaN\n",
       "2            2   154    121  0.785714\n",
       "3            2   129    109  0.844961\n",
       "4            4   148    102  0.689189\n",
       "5            4   163     96  0.588957\n",
       "6            5   172    100  0.581395\n",
       "7            5   200    129  0.645000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inoculum['ratioWB'] = df_inoculum['white']/df_inoculum['blue']\n",
    "df_inoculum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "              blue  white   ratioWB\n",
       "competition                        \n",
       "1              0.0    0.0       NaN\n",
       "2            141.5  115.0  0.815338\n",
       "4            155.5   99.0  0.639073\n",
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   "source": [
    "df_inoculum.groupby(['competition']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>competition</th>\n",
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      ],
      "text/plain": [
       "   competition  animal  blue  white   ratioWB\n",
       "0            2       1   175    121  0.691429\n",
       "1            2       1   176    118  0.670455\n",
       "2            2       2   420     37  0.088095\n",
       "3            2       2   504     45  0.089286\n",
       "4            2       3   298     12  0.040268"
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     "execution_count": 21,
     "metadata": {},
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   "source": [
    "df['ratioWB'] = df['white']/df['blue']\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>1</th>\n",
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       "      <td>413.5</td>\n",
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       "      <td>0.039891</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>261.5</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>449.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.001279</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
       "      <th>1</th>\n",
       "      <td>96.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.041707</td>\n",
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       "      <th>2</th>\n",
       "      <td>328.0</td>\n",
       "      <td>174.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>436.0</td>\n",
       "      <td>188.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.068179</td>\n",
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       "      <th>5</th>\n",
       "      <td>610.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.036115</td>\n",
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      ],
      "text/plain": [
       "                     blue  white   ratioWB\n",
       "competition animal                        \n",
       "2           1       175.5  119.5  0.680942\n",
       "            2       462.0   41.0  0.088690\n",
       "            3       300.0   13.0  0.043313\n",
       "            4       180.0   77.5  0.431370\n",
       "            5       724.5   12.5  0.017284\n",
       "4           1       413.5   16.5  0.039891\n",
       "            2       261.5    8.5  0.032536\n",
       "            3       286.5    1.5  0.005169\n",
       "            4       136.5    0.0  0.000000\n",
       "            5       449.0    0.5  0.001279\n",
       "5           1        96.0  100.0  1.041707\n",
       "            2       328.0  174.5  0.530348\n",
       "            3       436.0  188.0  0.431748\n",
       "            4       455.5   31.0  0.068179\n",
       "            5       610.5   22.0  0.036115"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['competition', 'animal']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>competition</th>\n",
       "      <th>blue</th>\n",
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      "text/plain": [
       "   competition   blue  white   ratioWB\n",
       "0            1    0.0    0.0       NaN\n",
       "1            2  141.5  115.0  0.815338\n",
       "2            4  155.5   99.0  0.639073\n",
       "3            5  186.0  114.5  0.613198"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inoculum_summary = df_inoculum.groupby(['competition'], as_index=False).mean()\n",
    "df_inoculum_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>2</td>\n",
       "      <td>420</td>\n",
       "      <td>37</td>\n",
       "      <td>0.088095</td>\n",
       "      <td>0.815338</td>\n",
       "      <td>-0.966385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>504</td>\n",
       "      <td>45</td>\n",
       "      <td>0.089286</td>\n",
       "      <td>0.815338</td>\n",
       "      <td>-0.960556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>298</td>\n",
       "      <td>12</td>\n",
       "      <td>0.040268</td>\n",
       "      <td>0.815338</td>\n",
       "      <td>-1.306373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   competition  animal  blue  white  ratioWB_o  ratioWB_i        CI\n",
       "0            2       1   175    121   0.691429   0.815338 -0.071590\n",
       "1            2       1   176    118   0.670455   0.815338 -0.084968\n",
       "2            2       2   420     37   0.088095   0.815338 -0.966385\n",
       "3            2       2   504     45   0.089286   0.815338 -0.960556\n",
       "4            2       3   298     12   0.040268   0.815338 -1.306373"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary = pd.merge(df, df_inoculum_summary[['competition', 'ratioWB']], on=['competition'])\n",
    "df_summary.rename(columns={'ratioWB_x': 'ratioWB_o', 'ratioWB_y': 'ratioWB_i'}, inplace=True)\n",
    "df_summary['CI'] = np.log10(df_summary['ratioWB_o'] / df_summary['ratioWB_i'])\n",
    "df_summary.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>animal</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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>175.5</td>\n",
       "      <td>119.5</td>\n",
       "      <td>0.680942</td>\n",
       "      <td>0.815338</td>\n",
       "      <td>-0.078279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>462.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.088690</td>\n",
       "      <td>0.815338</td>\n",
       "      <td>-0.963470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.043313</td>\n",
       "      <td>0.815338</td>\n",
       "      <td>-1.275795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>180.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>0.431370</td>\n",
       "      <td>0.815338</td>\n",
       "      <td>-0.279291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>724.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.017284</td>\n",
       "      <td>0.815338</td>\n",
       "      <td>-1.692186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   competition  animal   blue  white  ratioWB_o  ratioWB_i        CI\n",
       "0            2       1  175.5  119.5   0.680942   0.815338 -0.078279\n",
       "1            2       2  462.0   41.0   0.088690   0.815338 -0.963470\n",
       "2            2       3  300.0   13.0   0.043313   0.815338 -1.275795\n",
       "3            2       4  180.0   77.5   0.431370   0.815338 -0.279291\n",
       "4            2       5  724.5   12.5   0.017284   0.815338 -1.692186"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary = df_summary.groupby(['competition', 'animal'], as_index=False).mean()\n",
    "df_summary.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot using seaborn\n",
    "## https://stanford.edu/~mwaskom/software/seaborn/examples/scatterplot_categorical.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ce477f0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9dtptxhgdOHBACxcuVH5+vjZs2BDc4QAAiFJd+ky/vLxca9asaXdfSUmJxo8fr507d572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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce984e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df_summary.plot.scatter('competition', 'CI')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d842a20>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d756208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_summary.groupby('competition', as_index=False).median().plot.scatter('competition', 'CI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9ZcsWbdq0qei97u5uXXPNNXrvvfeO+5lEIqEpU6YUXns8Hg0ODo6xVHN4PB61tLRwLyIA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\nAQAwCMENAIBBCG4AAAzy/7Cv0ZgqwVsEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e7d3278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new figure of size 8x6 points, using 100 dots per inch\n",
    "plt.figure(figsize=(8,6), dpi=100)\n",
    "\n",
    "# Create a new subplot from a grid of 1x1\n",
    "plt.subplot(111)\n",
    "\n",
    "plt.scatter(df_summary['competition'], df_summary['CI'], marker='o', s=75,\n",
    "            color='gray', linewidths=2, alpha=0.7)\n",
    "\n",
    "plt.scatter(df_summary.groupby('competition', as_index=False).median()['competition'], \n",
    "            df_summary.groupby('competition', as_index=False).median()['CI'], marker='_', s=500,\n",
    "            color='black', linewidths=2, alpha=1.0)\n",
    "\n",
    "# Show result on screen\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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