{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import scikit_posthocs as sp\n",
    "plt.rcParams['pdf.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>strain</th>\n",
       "      <th>genotype</th>\n",
       "      <th>stage</th>\n",
       "      <th>strain_num</th>\n",
       "      <th>spindle app</th>\n",
       "      <th>nuc div</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HS306</td>\n",
       "      <td>WT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HS306</td>\n",
       "      <td>WT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HS306</td>\n",
       "      <td>WT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HS306</td>\n",
       "      <td>WT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>53</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HS306</td>\n",
       "      <td>WT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>43</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>HS309</td>\n",
       "      <td>wee1op</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>HS309</td>\n",
       "      <td>wee1op</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>HS309</td>\n",
       "      <td>wee1op</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>58</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>HS309</td>\n",
       "      <td>wee1op</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>HS309</td>\n",
       "      <td>wee1op</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>61</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   strain genotype  stage  strain_num  spindle app  nuc div  duration\n",
       "0   HS306       WT      2           1            5       23        18\n",
       "1   HS306       WT      2           1           13       31        18\n",
       "2   HS306       WT      2           1           23       37        14\n",
       "3   HS306       WT      2           1           27       53        26\n",
       "4   HS306       WT      2           1           23       43        20\n",
       "..    ...      ...    ...         ...          ...      ...       ...\n",
       "74  HS309   wee1op      4           3           17       31        14\n",
       "75  HS309   wee1op      4           3           15       32        17\n",
       "76  HS309   wee1op      4           3           48       58        10\n",
       "77  HS309   wee1op      4           3           29       45        16\n",
       "78  HS309   wee1op      4           3           47       61        14\n",
       "\n",
       "[79 rows x 7 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('summary.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT: n=29\n",
      "cdc25op: n=24\n",
      "wee1op: n=26\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(3,5))\n",
    "sns.boxplot(data=data, x='genotype', y='duration', color='lightgray')\n",
    "sns.swarmplot(data=data, x='genotype', y='duration',alpha=0.8)\n",
    "plt.savefig('duration.pdf')\n",
    "plt.show()\n",
    "print('WT: n=' + str(len(data.loc[data['genotype'] == 'WT'])))\n",
    "print('cdc25op: n=' + str(len(data.loc[data['genotype'] == 'cdc25op'])))\n",
    "print('wee1op: n=' + str(len(data.loc[data['genotype'] == 'wee1op'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean\n",
      "genotype\n",
      "WT         18.517241\n",
      "cdc25op    19.791667\n",
      "wee1op     16.076923\n",
      "Name: duration, dtype: float64\n",
      " \n",
      "Median\n",
      "genotype\n",
      "WT         18.0\n",
      "cdc25op    20.0\n",
      "wee1op     17.0\n",
      "Name: duration, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Mean')\n",
    "print(data.groupby('genotype').mean()['duration'])\n",
    "\n",
    "print(' ')\n",
    "print('Median')\n",
    "print(data.groupby('genotype').median()['duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='duration', ylabel='Count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=data, x='duration', hue='genotype', kde=True, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GeBhrLU3/kStSubm7mZndb2YvRMtdgdNij0xEsi6VSpeVlbBjh0VcZ1dTs1UnVtx3P/z+96GI2hVXwPrrp3ysdM4ra8DdG3wRbuieCEyMltcCJjW2X3O+dtttNxeRzBo2zL2kxD3cnQ2vkpLQXqvygaV+Qau7/XPauYM/wonebd0PV9km1WM1ZVtpGFDl9eTURh/gMrP/ufvuZjbe3XeN2ia4+y5x/kBKpAe4RDKvrCyUNa6rtBRmfezwxBN8dHI/tl32IaM5gL4M4n/ssXKbWSkea9aqbelsKw1bkwe4vjOzTYlG8pjZXsA3zRyfiOSYZBUtS6vfhL37wpgx1LAjv+I5nucIEp/XrLtvOlUzVWEzfqmM6vkT8AzQ0cz+AzwEnB9rVCKSdXUrWu7AVJ6mJ29wQJjU/P776dlhIs/zK+o+pF9333SqZqrCZvxSGdUzDjgA2Ac4G9jR3d+LOzARya7aSpdb8BlDOItJ/JwDeIMJJ94QiqmdcQYDbmiZUjXMdKpmqsJmBtTX8Z/4An5b36ux/ZrzpZu7IlmwcKFP6tnPa2w9X0Irv7/1BT7ir/NX22zYMPfSUnez8J7sJmyq26W7rSTHGtzcvTNhcV3gYGCcux8fxw+i+ujmrkgGLV0K994bxuB/+WWY3Pz662HbbbMdmaSpyTd33X2V/nwz2xh4pPlCE5GcsGIFjBgB/fqFp20POggGDYLddst2ZNLMmjIRy3eEQm0iUihefx323BN+85tQD/+FF+CVV5T0C1SjV/xm9iwri7K1ALoCj8UZlIhkyKRJcNll8Pzz0L49PPAAnHoqtGyZ7cgkRqmM4/9zwudlQLW7z44pHhHJhE8/DZOfPPggbLRR6NI5/3xYd91sRyYZkEof/xuZCEREMmDhQrjpJrj99tCnf9FFcPnlsMkm2Y5MMihp4jezRdRfd98Ad/cNY4tKRJrXkiVw991hMPzXX4funAEDQh0EKTpJb+66e2t337CeV+tUkr6ZtTez181sqplNMbMLovZNzGyUmX0Yvf+sOf9AIrJS5cMruKBNJbPW7QIXXcRnW+0O48fDQw9R+e9SVcAsUimP6jGzdmbWofaVwi7LgIvcvSuwF3BeVNL5MuBVd+8MvBoti0gze/WyUex4Wjm3LziVr9iEQxhF55kvUjl5Zyorwxzn1dWh/mV1dVhW8i8OqTzA1RO4BdgSmAeUAtPcfce0TmT2NHBX9Orh7nPNbAtgtLt3aWhfPcAlkoYJE+DSS+Hll/mYMq7geoZzMh5d59X27qgCZuFL9gBXKlf8AwhX7NPdfRvCk7vvpnnyMmBXYAywmbvPjVZ9DmyWZJ8+ZlZlZlXz589P53Qixam6Gnr1gu7doaqKP3Er2/M+/6Tip6QPocqlKmAWt1QS/4/uvgBoYWYt3P11YLWfIMmY2QbAE8CF7r7KXL1RLYl6f+Vw9yHuXu7u5W3btk31dCLF56uv4OKLYbvt4PHHoW9fmDmTkaV/ZCnrrLZ5hw6qgFnsUkn8C6Pk/SZQaWa3E57ebZSZtSIk/Up3Hxk1fxF18RC9z0s/bBHhhx9g8GDo2BFuvTXU1Jk+PQzX3HjjBqtcqgJmcUua+M3sBDNbFzgaqAH+CLwIzASOauzAZmbA/YT7AbcmrHqGlXP2ngY83bTQRYrU8uXw0EPhCr9vX9hnH5g4MTx12779T5tVVMCQIaHf3iy8DxkS2htaJ4Uv6c1dM3sS2Bd4CRgOvOTuy1M+sNl+wFvAJGBF1NyP0M//GNABqAZOdPevGjqWbu6KEIbfvPRSuHH73ntQXh6euD3wwGxHJjkq7eqc7n6smW0IHEuYcev+aGTO8FSe5nX3f1N3Wp6VDk4tbBEBYOzYcHX/2muhPPIjj8AJJ4RB+CJpavBfjbt/6+4PuvvhQDdgPHCHmX2akehEit3HH8Mpp4Sr+/feC6UWpk2Dk05S0pcmS6VIG9HTtccBJwGbAI/HGZRI0fvyy3Cn9e67Ya21oH//cMW/oSqlyJprqFbPBoRunpMJY/CfIYzpH+2NPfUlIk1TUxOu6m+6CRYvhjPOgGuuga22ynZkUkAauuKfRRjFcw/hxu6PGYlIpBgtXx5KJF91FcyZA0cdFZJ/167ZjkwKUEOJv727f5+xSESKkXuYBOXSS2HKlDAL1vDhsP/+2Y5MClhD1TmV9EWaoLKSpFUvE9cdvcV/+aLrgXDkkXy7YCnnthlBizHvUNZr/6T7NHQ8VdiUVKV0c1dEUlNb9bKmJizXVr2s1acPbFEzg0fox4mfj2De5+14ZP+7ObvqLL79vlXSfRo6Xn3r9CCWNKTR6py5QA9wSb4oK0te9XLT5fPoPXsA53AvS1iHW7iIP3Mx37dszfJ6Ho1srIpmQ+tUYVOgCQ9w1ZlkfTXu3rOZYhMpGPVVtyzhO3pV38YlDKKEGv7OWVzL1XzB5mGDJM/DN1Qps6nrRKDhrp7aSdaPAzYHhkXLJwNfxBmUSL7q0GHlVXhLlnE6/+BarmZL5vLiesdywfc3Mp1Vp59o2ZJ6r/hrK2XWd1WfyjqRZBq6uftGVJphX3c/yd2fjV6nABpyIFKPgQOhZD3nKJ5hEj/n7/RhVotteemq/7Dg7yOZXbJq0i8pCf3yTamiqQqb0mTu3uALmAZsm7C8DaHiZqP7Ntdrt912c5G88M47/sV2+7mDT6OLn9X2SR/28IqfVg8b5l5a6m4W3ocNa7h9TdaJAFVeT05NZerFw4AhwEeEomulwNnu/lKMP49WoZu7kvOmT4fLL4eRI2HzzcPTtmeeGcotiGRJ2jd3a7n7i2bWGdg+anrf3Zc0d4AieemLL+Daa0Mx+/XWg+uugz/9CdZfP9uRiSTVaOI3sxLgT0Cpu59lZp3NrIu7Pxd/eCI5avFi+POfw2vJEjjnnFBuoV27bEcm0qhU6rr+A1gK7B0tzwGujy0ikVz244/w179Cp07hSv/ww2HqVLjrLiV9yRupJP6O7j4I+BHA3WtIPsGKSGFyD/333brB738PXbrAu+/CiBHQuXO2oxNJSyqJf6mZrUf0MJeZdQTUxy/F49//hn33hV//OtysffZZGD06FFQTyUOpJP6rCeWZ25tZJfAq0DfWqERywbRpcMwxoVJmdTXcd1+Y1PzII8MM5SJ5qsHEb2YtgNrZt3oTJl0vd/fRjR3YzIaa2Twzm5zQtouZvWtmE8ysysz2WKPoJS81VlFyTdYnW5fOPiPv+iw8VdWtG0tffp1BGw9k/c8+pGzAmVQ+ulZKMYrktPoG9ye+SPIAQAr7/QLoDkxOaHsZODz6fARhNi89wFVEhg1zLylxD53m4VVSsuqDTE1dn2zdueemtk9rvvHruMIXU+LLWrbyab+8wNuvNz+t44nkkmT5O5UHuG4CvgQeBb5L+IHxVWM/VMysDHjO3btFyy8BQ939UTM7GTjKQwmIBukBrsLRUPXKWbPWbD3Uvy5ZLZzafT6rXsrZ/I2ruI62fMlwfsM9Ww7k01bbpn08VcWUXJLsAa5UEv/H9TS7u2+bwknLWDXx7wC8RBgV1ALYx93r+a8FZtYH6APQoUOH3arr+x8oeadFi3CNXJcZrFixZuuh/nXJGM4JjGAg/ejETF7jQPoyiLGUN+14UYwiuSJZ4m/05q67b1PPq9Gkn8S5wB/dvT3wR+D+Bs47xN3L3b28bdu2TTyd5JpklSNr29dkfbJ1LVuu3nYAoxm39p48yknUUMLhPM/BvMpYypt0vIZiE8k1jSZ+MysxsyvMbEi03NnMjmzi+U4DRkafRwC6uVtkGqsouSbrk61LrH65I5N5liMZzYF03mAu7/T5B/uuN54XOZzax1PSOV59MYrkvPo6/hNfhL79vkQ3aYESYEJj+0XblrHqzd1pQI/o88HA2FSOo5u7haWxipJrsj7Zuidu/9QfXf90X0YL/8Y28nG/udm9pqbJx1NVTMkHrMHN3Sp3Lzez8e6+a9Q20d13bmS/4UAPoA1h4pargQ+A2wk1gn4Afu/uYxv74aSbu9JkCxfCzTfDX/4SOuD/8Afo1w823TTbkYnErsnVOWnik7vufnKSVbulcE6RNbNkSaipM2AAfPUVnHpq+FxWlu3IRLIulcRf98ndfQkPc4nknhUr4JFHoH//MLbykENg0CDYdddsRyaSM1Kpxz/KzMYBexHufl3g7l/GHplIul59Ffr2hXHjYOed4aWX4NBDsx2VSM5JmvjNrHudprnRewcz6+Du4+ILSyQNEyfCpZeGRN+hAzz8MJxyShj0LyKraeiK/5bofV2gHJhIuOLfCahiZX1+kez45BO48sqQ6DfeGAYPDjdv110325GJ5LSkid/dDwQws5FAd3efFC13A67JSHQi9fn6a7jxRrjjjrB88cVhvtuf/Sy7cYnkiVR+F+5Sm/QB3H0ysEN8IUm+i61y5Q8/hKkOO3YM77/5TZjkfNAgJX2RNKQyqmeSmd0HDIuWK4D34gtJ8lllZXiytaYmLFdXh2WAioomHnTFinDgK64I3TuHHQY33RRu4IpI2lK54u8NTAEuiF5TgdNjjEnyWP/+K5N+rZqa0N4kL78Mu+0Gv/0ttGkDr7wCL7ygpC+yBhq84jezlsALUX//bZkJSfLZJ5+k157U+PFhaOYrr8A228A//wknnaSROiLNoMH/Re6+HFhhZhtlKB7Jc41V12zUxx+HPqHu3UPy/8tfwhSIJ5+spC/STFLp419M6OcfxaoTsfxfbFFJ3ho4cNU+fkixcuWCBWGju+8OCf7yy8PY/I10zSHS3FJJ/CNZWUpZpEG1N3D79w/dOx06hHye9Mbu99+HYZk33giLFkHv3nDttbD11pkKWaTopFKdc12gU7Q4w91/iD2qOlSdswAtXx4evLrySpg9G448MiT/bt2yHZlIwUh7Bi4zW8vMBgGzgQeBh4BPzWyQmbWKL1QpaO7w/POwyy5w+umw5ZYwejQ8+6ySvkiGNHS3bDCwCbCNu+/m7t2BjsDGwJ8zEJsUmv/9Dw46CH71q/Aw1mOPwbvvwgEHZDsykaLSUOI/EjjL3RfVNrj7t4R5c4+IOzApIDNnhqds99gDpkyBO+8M7yecsHKWdBHJmIZu7tZO3VW3cbmZNXxjQARg/vww+cm990KrVuHJ20sugQ03zHZkIkWtoSv+qWb227qNZnYq8H58IUne++67MJSnY8cwPPP002HGjPBDQElfJOsauuI/DxhpZmcAtfPilgPrAcfGHZjkoWXL4IEH4KqrYO5c6Nkz1NTZQTX9RHJJ0it+d5/j7nsC1wGzotd17r6Hu89p7MBmNtTM5pnZ5Drt55vZ+2Y2JRo1JHmk3sqb7vDMM7DTTnDWWVBaCm+9BU8/raQvkoNSmXrxNeC1Jhz7AeAuwjBQAMzsQOBoYGd3X2Jm7ZpwXMmS+ipv/v3Md/l/A/rS7oO3YLvt4Ikn4NhjddNWJIfFVvzE3d8EvqrTfC5wk7svibaZF9f5pfklVt7szHRGcDyjl+yNfTgd/vpXmDwZjjtOSV8kx2W66tV2wP5mNsbM3jCz3ZNtaGZ9zKzKzKrmz5+fwRAlmU8+gXZ8wV2cxxR25DBe5GquYdsVM+Ccc8LIHRHJeanU6mnu820C7AXsDjxmZtsmGTY6BBgCoWRDRqOU1S1ezJ83vJWzvhnMenzPEPpwLVczj80oLc12cCKSjkxf8c8GRnrwX2AF0CbDMUg6fvwxjMPv1Ik/fXM1r7T8JV2Zynncwzw2S63ypojklEwn/qeA2knctwPWBr7McAySCnd48kn4+c/h3HOhc2d4+21qHnycpaXbYRYG7wwZsgZTKopIVsTW1WNmw4EeQBszmw1cDQwFhkZDPJcCp9XXzSNZ9vbb4Qnbt9+G7bcPwzKPOgrMqNhbiV4k38WW+N395CSrTo3rnLKG3n8/TIDy1FOwxRbhcv7002GtTN8KEpE4aS47CU/ZnnNOKIv86qtw/fXw4YfhYSwlfZGCo//VxWzRIhg8GG65BZYuhd//PhRSa6fn6kQKmRJ/Mfrxx9CNc+21oYLmiSeGoTmdOjW+r4jkPXX1FBN3GDECunaFP/whvI8ZA48+qqQvUkSU+IvFm2/C3nuHq/t11oHnnoPXXw+To4hIUVHiL3RTpoShmAccECY1HzoUJk4M0x+qpo5IUVLiL1Aj75zDI61/x/JuO/Htv95k/Ik3wvTpYXhmy5arbV9vuWURKUhK/IXmm2+Y3LMfh/1fZ45b/BB38H9s6zPZ77nLqHyypN5dasstV1eH2wDV1WFZyV+kMFk+PDhbXl7uVVVV2Q4jty1dGkojDxgACxZQySlcwfXMYpufNikthVmzVt+1rCwk+7qSbS8i+cHMxrp7ed12XfHnuxUr4JFHwkxXF14Iu+xCOVWcSuUqSR9CWeX6pNsuIvlNiT+fvfZaGJVz8snQujW8+CKMGsWXpbvVu3mHDvUfJt12EclvSvz5aNIkOOIIOPhgmDcPHnoIxo2DX/4SzBg4EErqdOc3VD453e1FJL8p8eeTTz8No3J23hneeSeUW5g+HXr1CsNxIhUV4cHc0lJSKp+c7vYikt90czcfLFwIN90Et98eht2cf36oornJJtmOTERyWLKbu6rVk8uWLIG77w59Ll9/Ha7sr7sOzXUoImtCXT25aMWKMIi+Sxe46CLYfXcYPx4efFBJX0TWmBJ/rhk1CsrL4dRTQ1fOqFFhtM7OO2c7MhEpEEr8uWLChDAq59BDQ7dOZSVUVcEhh2Q7MhEpMEr82VZdHfruu3cPif7WW8MUiKecsspIHRGR5qKbu9ny1Vdwww1w550hwV96aXhtvHG2IxORAhfbJaWZDTWzeWY2uZ51F5mZm1mbuM6fTQ1WuvzhhzD+vmPHcHVfURHmt73xxnqTvqpmikhzi/OK/wHgLuChxEYzaw8cChRkJZjaSpc1NWG5ttKlrVjOKSuGwZVXhgexjjgijM3/+c/TPhbo4SoRabrYrvjd/U3gq3pW3Qb0BXL/ybEm6N9/ZaIOnP1rXmTXM3eF3r1h883DzFf/+leDSb/+Y4Xl/v2bO2oRKSYZvXtoZkcDc9x9Ygrb9jGzKjOrmj9/fgaiax6JFS27M5ZXOIQXOZy1f/wuzG07Zgz06JH2sVJpFxFJRcYSv5mVAP2Aq1LZ3t2HuHu5u5e3bds23uCaUYcOUMbHVHIKYylnJ97jfO7gsA7Twny3aUx3qKqZIhKHTF7xdwS2ASaa2Sxga2CcmW2ewRji9eWXvLDDH/mALhzDU1xPfzoyk6El53PNDWunfThVzRSROGQs8bv7JHdv5+5l7l4GzAa6u/vnmYohNjU1YVROx47s8PIdVB/Ymx5bzeAqu55NSjdscqVLVc0UkTjENqrHzIYDPYA2ZjYbuNrd74/rfFmxfHmon3PVVTBnDvTsCTfeSOeuXflvM52iokKJXkSaV2yJ391PbmR9WVznjp17GJVz2WUwZQrsuScMHw7775/tyEREGqWaAOn673/DqJyjjgoTnD/+eJgURUlfRPKEEn+qZswIo3L23DPU0rnnnnC1/+tfpzVSR0Qk21SrpzHz5sGAAXDvvbDOOnD11aFGfuvW2Y5MRKRJlPiT+e47uO02uPlm+P57OOuskPQ3L5zRpyJSnJT461q2DIYOhWuugblz4bjjQhXNLl2yHZmISLNQ4q/lDs88E0bqvP8+7LtvuHG7zz7ZjkxEpFnp5i6sHJVzzDHhB8BTT8Fbbynpi0hBKu7E/8EHYVTOPvvAzJnwt7/B5Mlw9NEaqSMiBas4E//nn8O558KOO8LLL8N114Xhmn36wFrq/RKRwlZcWW7RIrjlFvjzn2HJkpD8r7wS2rXLdmQiIhlTHIn/xx/hvvvCSJ158+CEE8JInU6dsh2ZiEjGFXbid4eRI6FfP5g+HX7xizByZ889sx2ZiEjWFHYf/9lnw/HHQ6tW8OyzMHq0kr6IFL3CvuLv1Qv22gtOOw1atsx2NCIiOaGwE//++6tqpohIHYXd1SMiIqtR4hcRKTJK/CIiRUaJX0SkyMSW+M1sqJnNM7PJCW2Dzex9M3vPzJ40s43jOn9lJZSVQYsW4b2yMq4ziYjklziv+B8ADqvTNgro5u47AdOBy+M4cWVlKLtTXR2e4aquDstK/iIiMSZ+d38T+KpO28vuvixafBfYOo5z9+8PNTWrttXUhHYRkWKXzT7+M4AXkq00sz5mVmVmVfPnz0/rwJ98kl67iEgxyUriN7P+wDIgaeeLuw9x93J3L2/btm1ax+/QIb12EZFikvHEb2a9gSOBCnf3OM4xcCCUlKzaVlIS2kVEil1GE7+ZHQb0BXq6e01j2zdVRQUMGQKlpWEirdLSsFxREdcZRUTyh8V00Y2ZDQd6AG2AL4CrCaN41gEWRJu96+7nNHas8vJyr6qqiiVOEZFCZWZj3b28bntsRdrc/eR6mu+P63wiIpIaPbkrIlJklPhFRIqMEr+ISJFR4hcRKTKxjeppTmY2H6huwq5tgC+bOZzmkKtxQe7GprjSl6uxKa70NTW2Undf7QnYvEj8TWVmVfUNZcq2XI0Lcjc2xZW+XI1NcaWvuWNTV4+ISJFR4hcRKTKFnviHZDuAJHI1Lsjd2BRX+nI1NsWVvmaNraD7+EVEZHWFfsUvIiJ1KPGLiBSZgkr8qU7mbmaHmdkHZjbDzC7LQFwnmNkUM1thZkmHZJnZLDObZGYTzCwj5UjTiC3T39kmZjbKzD6M3n+WZLvl0fc1wcyeiTGeBv/8ZraOmT0arR9jZmVxxZJmXL3NbH7Cd/S7DMU11MzmmdnkJOvNzO6I4n7PzLpnIq4UY+thZt8kfGdXZSiu9mb2uplNjf5PXlDPNs3zvbl7wbyAQ4G1os83AzfXs01LYCawLbA2MBHoGnNcOwBdgNFAeQPbzQLaZPg7azS2LH1ng4DLos+X1fd3Ga1bnIHvqNE/P/B74N7o82+AR3Mkrt7AXZn8NxWd9xdAd2BykvVHEKZeNWAvYEwOxdYDeC4L39kWQPfoc2tgej1/n83yvRXUFb+nNpn7HsAMd//I3ZcCjwBHxxzXNHf/IM5zNFWKsWX8O4uO/2D0+UHgmJjP15BU/vyJ8T4OHGxmlgNxZYW7vwl81cAmRwMPefAusLGZbZEjsWWFu89193HR50XANGCrOps1y/dWUIm/jmSTuW8FfJqwPJvVv9xsceBlMxtrZn2yHUyCbHxnm7n73Ojz58BmSbZb18yqzOxdMzsmplhS+fP/tE108fENsGlM8aQTF8Cvo26Bx82sfcwxpSqX/x8C7G1mE83sBTPbMdMnj7oKdwXG1FnVLN9bbBOxxMXMXgE2r2dVf3d/Otqm0cncsxFXCvZz9zlm1g4YZWbvR1cnuRBbs2sorsQFd3czSzbuuDT6zrYFXjOzSe4+s7ljzWPPAsPdfYmZnU34reSgLMeU68YR/l0tNrMjgKeAzpk6uZltADwBXOju38ZxjrxL/O5+SEPrbeVk7gd71ClWxxwg8apn66gt1rhSPMac6H2emT1J+FV+jRN/M8SW8e/MzL4wsy3cfW70q+y8JMeo/c4+MrPRhKuk5k78qfz5a7eZbWZrARuxcorRuDQal7snxnAf4d5JLojl31RzSEy27v68md1jZm3cPfYCbmbWipD0K919ZD2bNMv3VlBdPZbaZO7/Azqb2TZmtjbhRlxso0FSZWbrm1nr2s+EG9X1jjrIgmx8Z88Ap0WfTwNW+83EzH5mZutEn9sA+wJTY4gllT9/YrzHA68lufDIaFx1+n97EvqNc8EzwG+jUSp7Ad8kdO1llZltXnt/xsz2IOTJuH+IE53zfmCau9+aZLPm+d4yfec6zhcwg9D/NSF61Y6y2BJ4PmG7Iwh3zGcSujvijutYQl/cEsLE8y/VjYswMmNi9JqSibhSjS1L39mmwKvAh8ArwCZRezlwX/R5H2BS9J1NAs6MMZ7V/vzAdYSLDIB1gRHRv8H/Attm6O+vsbhujP49TQReB7bPUFzDgbnAj9G/rzOBc4BzovUG3B3FPYkGRrtlIbY/JHxn7wL7ZCiu/Qj3+d5LyGFHxPG9qWSDiEiRKaiuHhERaZwSv4hIkVHiFxEpMkr8IiJFRolfRKTIKPFLRpnZpglVDz83sznR54VmFscY/IZiOcbMuiYsX2dmaT/sZmZlySo9ZoKZ9auz/Hb0ntW4JHcp8UtGufsCd9/F3XcB7gVuiz7vAqxo7vNFT9EmcwzwU+J396vc/ZXmjiEDVkn87r5PtgKR/KDEL7mkpZn9PapF/rKZrQdgZh3N7MWoeN1bZrZ91F5mZq9FBcheNbMOUfsDZnavmY0BBtW3v5ntQ3iSdXD0G0fHaL/jo2PsbmZvR4W6/mtmraPzvWVm46JXgwk2erryLgv18l8xs+cTjj8retoYMyuPyk1gZnuY2TtmNj46f5eovbeZjYz+HB+a2aCo/SZgvejPUBm1La4nlpYW5qv4X/R9nR21b2Fmb0b7Tzaz/dfw71DyQaaeltNLr7ov4Brg4uhzGaGw3i7R8mPAqdHnV4HO0ec9CeUQIBQgOy36fAbwVPT5AeA5oGUj+z8AHJ8QzwOEcgtrAx8Bu0ftGxLqWpUA60ZtnYGqhNhXq+0OHAeMItTN3xJYWHs+EuZeIDyNPDrxXNHnQ4Anos+9o5g2IjwlXA20j9YtrnPexXXjAvoAV0Sf1wGqgG2Ai1j5xG9LoHW2/13oFf8r74q0SUH72N0nRJ/HAmUWKhXuA4ywleXt14ne9yYkV4CHWbUA2Qh3X97I/sl0Aea6+/9gZdEuCzWU7jKzXYDlwHaNHOcXhMqYy4HPzOy1RraHkNgfNLPOhMf3WyWse9Xdv4limQqUsmqJ3oYcCuxU+xtHdJ7OhHo/Qy0UB3sq4fuXAqbEL7lkScLn5cB6hO7IhR7uA6Tju+i9qfvX54+EekY7R8f9YQ2OtYyVXa3rJrQPAF5392Mt1GQfnbCu7veTzv9fA85395dWW2H2C+BXwANmdqu7P5TGcSUPqY9fclp0tf2xmZ0AP/Wb7xytfptQkRKgAngrzf0XEaa4q+sDYAsz2z3ap7WtLLU8191XAL0IXSMNeRM4Kepf3wI4MGHdLGC36POvE9o3YmWZ3d6NHL/Wj9EVe0NeAs6t3c7MtrNQEbYU+MLd/04o25yxuW8le5T4JR9UAGeaWW3l0trpBc8HTjez9wiJeLXJqRvZ/xHgkuhGasfajT1MY3gScGe0zyjCVfk9wGlR2/as/K0imScJ1UWnAg8B7ySsuxa43cyqCFfvtQYBN5rZeFK/oh8CvFd7czeJ+6I4xkVDPP8WHb8HMDE630nA7SmeU/KYqnOKZIiZPUCYxPvxbMcixU1X/CIiRUZX/CIiRUZX/CIiRUaJX0SkyCjxi4gUGSV+EZEio8QvIlJk/j9OYeno6bDJqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Q-Q plot\n",
    "stats.probplot(data.loc[data['genotype'] == 'WT', 'duration'], plot=plt)\n",
    "plt.show()\n",
    "\n",
    "stats.probplot(data.loc[data['genotype'] == 'cdc25op', 'duration'], plot=plt)\n",
    "plt.show()\n",
    "\n",
    "stats.probplot(data.loc[data['genotype'] == 'wee1op', 'duration'], plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4042559862136841\n",
      "0.9189369082450867\n",
      "0.049983616918325424\n"
     ]
    }
   ],
   "source": [
    "# Shapiro test\n",
    "print(stats.shapiro(data.loc[data['genotype'] == 'WT', 'duration'])[1])\n",
    "print(stats.shapiro(data.loc[data['genotype'] == 'cdc25op', 'duration'])[1])\n",
    "print(stats.shapiro(data.loc[data['genotype'] == 'wee1op', 'duration'])[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT vs cdc25op\n",
      "MannwhitneyuResult(statistic=256.0, pvalue=0.09946091741820945)\n",
      " \n",
      "cdc25op vs wee1op\n",
      "MannwhitneyuResult(statistic=506.5, pvalue=0.0001489871612317012)\n",
      " \n",
      "wee1op vs WT\n",
      "MannwhitneyuResult(statistic=197.0, pvalue=0.002289998289268508)\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# U test\n",
    "print('WT vs cdc25op')\n",
    "print(stats.mannwhitneyu(data.loc[data['genotype'] == 'WT', 'duration'], \n",
    "                         data.loc[data['genotype'] == 'cdc25op', 'duration'], alternative='two-sided'))\n",
    "print(' ')\n",
    "\n",
    "print('cdc25op vs wee1op')\n",
    "print(stats.mannwhitneyu(data.loc[data['genotype'] == 'cdc25op', 'duration'], \n",
    "                         data.loc[data['genotype'] == 'wee1op', 'duration'], alternative='two-sided'))\n",
    "print(' ')\n",
    "\n",
    "print('wee1op vs WT')\n",
    "print(stats.mannwhitneyu(data.loc[data['genotype'] == 'wee1op', 'duration'], \n",
    "                         data.loc[data['genotype'] == 'WT', 'duration'], alternative='two-sided'))\n",
    "print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>WT</th>\n",
       "      <th>cdc25op</th>\n",
       "      <th>wee1op</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WT</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.222459</td>\n",
       "      <td>0.006302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cdc25op</th>\n",
       "      <td>0.222459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wee1op</th>\n",
       "      <td>0.006302</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               WT   cdc25op    wee1op\n",
       "WT       1.000000  0.222459  0.006302\n",
       "cdc25op  0.222459  1.000000  0.001000\n",
       "wee1op   0.006302  0.001000  1.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Steel-Dwass test\n",
    "sp.posthoc_dscf(data, val_col='duration', group_col='genotype')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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