Zinser_Fig2a.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exper time</th>\n",
       "      <th>cells A</th>\n",
       "      <th>cells B</th>\n",
       "      <th>FALS</th>\n",
       "      <th>time of day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6.346000e+07</td>\n",
       "      <td>6.503348e+07</td>\n",
       "      <td>0.1838</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6.821390e+07</td>\n",
       "      <td>6.512750e+07</td>\n",
       "      <td>0.2147</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>6.871540e+07</td>\n",
       "      <td>6.523250e+07</td>\n",
       "      <td>0.2640</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>6.129510e+07</td>\n",
       "      <td>6.298554e+07</td>\n",
       "      <td>0.3314</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6.547930e+07</td>\n",
       "      <td>6.597570e+07</td>\n",
       "      <td>0.3950</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>6.468750e+07</td>\n",
       "      <td>6.559250e+07</td>\n",
       "      <td>0.4416</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>7.017000e+07</td>\n",
       "      <td>7.009750e+07</td>\n",
       "      <td>0.4033</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>7.907500e+07</td>\n",
       "      <td>8.327500e+07</td>\n",
       "      <td>0.3108</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>8.929500e+07</td>\n",
       "      <td>9.382750e+07</td>\n",
       "      <td>0.2448</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18</td>\n",
       "      <td>1.049125e+08</td>\n",
       "      <td>1.045200e+08</td>\n",
       "      <td>0.2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>1.136461e+08</td>\n",
       "      <td>1.139425e+08</td>\n",
       "      <td>0.1774</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>22</td>\n",
       "      <td>1.154868e+08</td>\n",
       "      <td>1.171877e+08</td>\n",
       "      <td>0.1649</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>24</td>\n",
       "      <td>1.239525e+08</td>\n",
       "      <td>1.258125e+08</td>\n",
       "      <td>0.1736</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>26</td>\n",
       "      <td>1.261110e+08</td>\n",
       "      <td>1.255850e+08</td>\n",
       "      <td>0.2042</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>28</td>\n",
       "      <td>1.282216e+08</td>\n",
       "      <td>1.243662e+08</td>\n",
       "      <td>0.2489</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30</td>\n",
       "      <td>1.332250e+08</td>\n",
       "      <td>1.268950e+08</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32</td>\n",
       "      <td>1.308725e+08</td>\n",
       "      <td>1.278550e+08</td>\n",
       "      <td>0.3853</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>34</td>\n",
       "      <td>1.288325e+08</td>\n",
       "      <td>1.312525e+08</td>\n",
       "      <td>0.4247</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36</td>\n",
       "      <td>1.301725e+08</td>\n",
       "      <td>1.322150e+08</td>\n",
       "      <td>0.3566</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>38</td>\n",
       "      <td>1.352691e+08</td>\n",
       "      <td>1.517837e+08</td>\n",
       "      <td>0.3033</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>1.669651e+08</td>\n",
       "      <td>1.489675e+08</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>42</td>\n",
       "      <td>1.865975e+08</td>\n",
       "      <td>1.861050e+08</td>\n",
       "      <td>0.1977</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>44</td>\n",
       "      <td>1.960775e+08</td>\n",
       "      <td>2.044880e+08</td>\n",
       "      <td>0.1753</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>46</td>\n",
       "      <td>2.185850e+08</td>\n",
       "      <td>2.179350e+08</td>\n",
       "      <td>0.1632</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>48</td>\n",
       "      <td>2.230925e+08</td>\n",
       "      <td>2.320675e+08</td>\n",
       "      <td>0.1697</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    exper time       cells A       cells B    FALS  time of day\n",
       "0            0  6.346000e+07  6.503348e+07  0.1838            8\n",
       "1            2  6.821390e+07  6.512750e+07  0.2147           10\n",
       "2            4  6.871540e+07  6.523250e+07  0.2640           12\n",
       "3            6  6.129510e+07  6.298554e+07  0.3314           14\n",
       "4            8  6.547930e+07  6.597570e+07  0.3950           16\n",
       "5           10  6.468750e+07  6.559250e+07  0.4416           18\n",
       "6           12  7.017000e+07  7.009750e+07  0.4033           20\n",
       "7           14  7.907500e+07  8.327500e+07  0.3108           22\n",
       "8           16  8.929500e+07  9.382750e+07  0.2448            0\n",
       "9           18  1.049125e+08  1.045200e+08  0.2020            2\n",
       "10          20  1.136461e+08  1.139425e+08  0.1774            4\n",
       "11          22  1.154868e+08  1.171877e+08  0.1649            6\n",
       "12          24  1.239525e+08  1.258125e+08  0.1736            8\n",
       "13          26  1.261110e+08  1.255850e+08  0.2042           10\n",
       "14          28  1.282216e+08  1.243662e+08  0.2489           12\n",
       "15          30  1.332250e+08  1.268950e+08  0.3215           14\n",
       "16          32  1.308725e+08  1.278550e+08  0.3853           16\n",
       "17          34  1.288325e+08  1.312525e+08  0.4247           18\n",
       "18          36  1.301725e+08  1.322150e+08  0.3566           20\n",
       "19          38  1.352691e+08  1.517837e+08  0.3033           22\n",
       "20          40  1.669651e+08  1.489675e+08  0.2350            0\n",
       "21          42  1.865975e+08  1.861050e+08  0.1977            2\n",
       "22          44  1.960775e+08  2.044880e+08  0.1753            4\n",
       "23          46  2.185850e+08  2.179350e+08  0.1632            6\n",
       "24          48  2.230925e+08  2.320675e+08  0.1697            8"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "\n",
    "data_fig2a = pandas.read_csv('data/Zinser_Figure2A.csv')\n",
    "\n",
    "t = data_fig2a['exper time'] * 60\n",
    "a_norm = data_fig2a['cells A'].copy()\n",
    "a_norm /= a_norm[0]\n",
    "b_norm = data_fig2a['cells B'].copy()\n",
    "b_norm /= b_norm[0]\n",
    "\n",
    "data_fig2a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55f9f7f320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(12,10))\n",
    "ax.plot(t, a_norm, color='black', marker='s', label='normalized \"cells A\"')\n",
    "ax.plot(t, b_norm, color='red', marker='^', label='normalized \"cells B\"')\n",
    "for iday in range(2):\n",
    "    ax.axvspan(iday*24*60+12*60, iday*24*60+22*60, color='0.7', zorder=0)\n",
    "ax.legend()\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}