SMG-bud-count-culture-conditions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: ../jupyter_figures: File exists\n"
     ]
    }
   ],
   "source": [
    "import sys, os, glob, warnings, logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sw_plotting import change_bar_width, plotCountBar\n",
    "from sw_utilities import tukeyTest, getCountDFbyPrefix\n",
    "\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.DEBUG)\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)\n",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.ERROR)\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# plotting configuration\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 7}\n",
    "matplotlib.rc('font', **font)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "# Make a folder if it is not already there to store exported figures\n",
    "!mkdir ../jupyter_figures\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>file name</th>\n",
       "      <th>bud_number</th>\n",
       "      <th>culture_condition</th>\n",
       "      <th>replicate_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1_1_48h.JPG</td>\n",
       "      <td>14</td>\n",
       "      <td>FGF7_NRG1_ITS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_2_48h.JPG</td>\n",
       "      <td>8</td>\n",
       "      <td>FGF7_NRG1_ITS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1_3_48h.JPG</td>\n",
       "      <td>10</td>\n",
       "      <td>FGF7_NRG1_ITS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1_4_48h.JPG</td>\n",
       "      <td>8</td>\n",
       "      <td>FGF7_NRG1_ITS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1_6_48h.JPG</td>\n",
       "      <td>8</td>\n",
       "      <td>FGF7_NRG1_ITS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     file name  bud_number culture_condition  replicate_id\n",
       "0  1_1_48h.JPG          14     FGF7_NRG1_ITS             1\n",
       "1  1_2_48h.JPG           8     FGF7_NRG1_ITS             1\n",
       "2  1_3_48h.JPG          10     FGF7_NRG1_ITS             1\n",
       "3  1_4_48h.JPG           8     FGF7_NRG1_ITS             1\n",
       "4  1_6_48h.JPG           8     FGF7_NRG1_ITS             1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the raw data sets to plot\n",
    "\n",
    "df = pd.read_csv('../data/culture-conditions-bud-count-and-images/culture-conditions-bud-count-Rei.csv')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ChcnrReCUyLFXEgyqyQEeMbMjqjZSEZGaIdXmuf0eODTycx7BAJPvSeFJ3GbWBHiC+JuqFq/fGfh15OEM4HR3L1xdZbGZPU9w3o2A24B+VRqwiEgNkGrJbQRBiy0XOMHdf0xyPHGZ2VDgLoL5eRAMhimrNfy/kTp5wG+KJTYA3P0/ZnYTMAXoa2ZHuPt7VRu5SMU0bdqULVu2lHq++HM5OTklynJycli9enW1xyYSTaolt5aR+z+kQWJ7EhgVeejA/wP6UHZL65TI/SJ3XxGjzsMEyQ3gVCCtk9vPP//MG2+8QXZ2Nv379yc7u/Rlyc8//5ydO3fGfZ233nqLH3/8kY4dO3LIIRpzk0hbtmyJmtx2ryOSKlLqmhu7JnH/kMwgyunoyP03wCB3v76sA8ysLdA88nBRrHruvhr4NPIwbbsl8/Pz+c1vfkPLli055ZRTOPHEE2nVqhUPPPBAUZ3Vq1czZMgQDjnkEHbsCMYEbd26lSVLlhTVeeedd+jYsSO9evVixIgRtG/fnhNPPJH//ve/CT+nmionJyfqraw6Iknj7ilzA54B8oFLkx1LOWJ9E7gKyC723KsErbiVMY4ZGil34KwyXv+FSL2fk3R+nYrF6rm5uV5RV155pRd/jeK3WbNmeUFBgR955JFRyxs2bOjLly/3FStWeKNGjaLWOfzww72goKDCcUnVycnJccBzcnKSHYqkmNzc3N3/z3byBP4NS7Vuyf8DhgO/NbMZ7l6u+WJJ0tvdCyp4TItiP5e1xNh3kft9zCzb3fd4UI2ZNQOaVvCwSu2Nt379eu66666Y5TfffDN169blnXfeiXt8ZmYm69ati1rn/fffZ/78+Zx44omVCVVEQiilkpu7LzazKwhGCb5rZv8HvELQ9bcRiHtRZg+SzR7bw/fau9jPm8qou7nYzw2BVXvwfoXGAzdW4vgKe++99+Jeg1myZAn/+te/4r7GokWLyMyM/0900aJFSm4iUkpKJTczWxD5cS3QDPhTBQ53Uux8oig+kmJbGXW3xjguLdStWzduee3atalXr17cOjk5OWRlZZVZR0Rkd6k2oGQgwXyxfSKP02ISdwUUb+2VtXN48fNJWIu0qhx55JEceOCBMctPO+00Ro8ejVnsj2306NGMGjUqZrmZxS0XkZor1Vo6D1P2H/10Vrwrsk4ZdYuXV3YS+13A0xU8ph0we0/fMCMjg6lTpzJy5Ejy8/NLlDVt2pRJkybRtm1brrjiCqZMmVLq+J49e3LuuediZjzwwAO88cYbpepceumlmhIgItElcvRK2G+UPVryAnaNHDqmjNeaHqlXAGQm4VwqPVrS3X3hwoU+ePBgr127tjdo0MAvuOAC//LLL0vUefDBB7179+5F75WZmembNm0qKt+8ebPfcMMN3rp1a8/MzPRu3br59OnT9ygeqVoaLSmxJHu0pLmHuaGUWGb2KsG8tO/cvVWU8gHAS5GHo9w9ZmvKzF4ATgRWufu+1RBuXGbWiWClGAByc3Pp1KlTtb5n3bp12bJlCzk5OWzevLnsAyTp9JlJLMuWLaNz587Fn+rs7ssS9f6pds0t7P5d7Oeyhtq3jXKMiIiUQ0pdczOzMZU53t0frqpYqoO7fx/Z660VcGysembWFCi8mPRmImKras8991yFjym8Npefn1+h44cNG1bh9xKRcEup5Eawf9ue9pM6wYCUVDeTYFeAQWbWwt2/j1LnvGI/z0pIVCIiIZKK3ZIVHf5fOOgiXS4e3kewxFg2cI+Z1SpeaGbtgWsjD5e4+9sJjk9EJO2lWsttUBnlGUADgm69QcAQYDkw1N3/U82xVQl3zzWzuwhabycBiyIrsfwA9ASuJ9jLLY9gexwREamglEpu7v5yBarfYWYjCXbAnmdm3d19XfVEVuWuIFhnciTBDty77+C9HTjP3d9NdGAiImGQit2S5ebuM4C/EWwWemWSwyk3d89z99OAMwimBvxM0FJbSXDd8XB3fzJ5ESZeXl4empaSOBs3bmTFihXk5eXFrLN69Wq+/z7aJeGAu1NQEH/xnO3bt7NixQpNE5CES+vkFvFM5P7UpEYBuHt/d7doc9xi1H/S3Qe5exN3r+3urd39fHf/pLpjTRU7d+7kH//4B2PHji3azy0vL481a9YkObJwWrFiBaNHj6Zx48a0adOGVq1aMWnSpBKryCxatIjevXvTrFkzWrZsSefOnXnyyZLfte6//34OPvhgtm0Llkjdtm0b77//flH59u3bufrqq9lvv/1o06YNTZo0YezYsfz000+JOVGp8cKQ3AqXtNo/qVHIHpk6dSpPPfUUGzZsKHquoKCA3/3ud/q2X8V+/vln+vTpw1NPPVXUYlu1ahUTJ05k3LhxALzxxhsMGjSoxHJny5Yt44wzzuChhx4CYMqUKVx88cV8+eWXRXUKCgro168fubnBvP+RI0dy6623snbtWiBIfg899BD9+/fXjt2SEGFIbsdF7vWXMM18/fXXLFoUfUPyH374gQULFkQtkz1z9913s3z58qhlDz30EJ9++ik33nhjUQt6d9dffz0bNmzgpptuilq+adMmJk+ezOuvv87zzz8ftc6yZct45JFH9uwERCogrZObmY0CfkcwDUBD5tPMkiVLKlUuFTNnzpyYZe7OjBkzeOWVV2LWWbFiBX/7299Yv3593PeI9z5lxSFSVVJqtKSZRf8av1s1YC+C6QBN2TXX7e5qDE2qQVkDSMoarCAVU9bvc+fOnWV+Jjt3xt0vmPz8/DLfZ/ddIkSqQ6q13HoTLEsV73YMcBjBZqaFm4Hd6e7R+0EkZR1xxBFxy3v06JGgSGqGwYMHxy0fMWIEvXv3jlnevHlzxo4dG3cj2iFDhpT5PkOGDIkfqEgVSLXk9m05bsuBL4F3CYbN/8LdL0lGsFI5hxxyCEceeWTUsiZNmnDCCSckOKJwmzBhAs2bN49adtppp9GlSxduvPFGatWqFbXOddddR+PGjbnyyuizburUqcM111zDgAED6N+/f9Q6bdu25bzzzotaJlKVUiq5ufsB7n5gGbe27n6Iux/l7he4+0tlv7Kkqt/+9rcMHTqUOnV27c2akZHBzTffTMOGDZMYWfg0b96cV199lUGDBhXtgF6/fn0uu+yyokEeAwcOZPbs2SW2KmndujX33HMPEyZMAGDixInccsst7Lvvrp2YMjIymD9/flFrfM6cOYwbN4699toLgFq1anHKKaewcOFCGjRokJDzlZpN+7lJVJXdz62iuwJs2bKFc845h7y8PLKzs3nmmWfKPihCuwJU3Pfff8/PP//MgQceSL169aLW+fzzz9mxYwcdOnSI2prLy8ujQYMGbNu2LeZ+buvXr2f58uU0b96cZs2aVfl5SOpK9n5uKTWgRGqunJwcMjJSqiMh1Fq0aEGLFi3i1jn44IPjlmdlZZX5mTVs2JCuXbtWOD6RykrZ5GZmdQn2NKtHEKfFPwLcPfY4ZhERqTFSLrmZ2X7AbcBwoHYFDnVS8HxERCTxUioZmFk94HXgAMrRUhMREYkmpZIb8BvgQIJW2ErgKeBrgqW1NPJFRETKJdWS28jI/SfAMe6+MZnBiNQkFR3hCrtWG8nPz6/Q8RrhKtUt1YantSNoof1JiU1ERPZUqrXcCheu+yypUUi5/Pjjj0yZMoVnnnmGbdu20b9/f6666qoSy2p9+eWXPPvss3z00UdkZWVx7LHHMmLECPbZZ5+iOosXL2bu3Lls374dCNYvzMvLIysrCwjmU/3zn//kpZdeYu3atbRu3ZqTTjqJY47ZfQNzKcvWrVv5y1/+wkMPPcTq1avp1q0bl156aYmW1E8//cSzzz7L4sWLyc/Pp1u3bowcOZIDDjigqM6nn37KzJkzS3xm69evLzHx/rXXXuO5557ju+++Y5999mHQoEEMGTIk5gooifDCCy8wdepUPvzwQ5o0acKYMWO45JJLyMnJAYIW6L333sv06dNZuXIlhxxyCOPHj+ess85KWsyyh9w9ZW7AEiAfGJHsWGr6DehE0Ip2wHNzc724lStXeps2bbx4HcBr167t8+bN87lz5/of//hHz8rKKlWnSZMm/uCDD/rcuXP9nHPOKVUOePfu3X3mzJk+a9YsP/zww6PWOfPMM33u3Lku5bNt2zbv06dP1N/lrbfe6nPnzvX777/f995776if68033+xz58716667zmvVqlWqTvPmzf3hhx/2uXPn+siRI6O+z9FHH+2zZs1KyvlPnTo1akzHHnusb9261fPz8/3UU0+NWufyyy9PSszpLDc3d/ffYydP5N+wRL5ZmcEEA0oKgOeSHUtNv5WV3C666KKofwQAP+igg3z27NnesmXLmHUGDRrkDz74oGdkZMSs85vf/MavuOKKmOUZGRk+ffp0l/K55557Yv4ua9eu7Q8//LD37ds3Zp0DDzzQZ82a5U2aNIlZ56STTvK77rorZjng11xzTcLPfdWqVZ6dnR0zpjvvvNPnzJkTN+7d/w9IfMlObql2ze2vwAfAYDObbGZZyQ5Ionv88cdjln3xxRe8/PLLfPfddzHrLFy4kEWLFsXdHmXhwoUsXLgwZnlBQUHMzU6ltMceeyxm2Y4dO3jttdd48803Y9b5+uuveeWVV/jpp59i1in8XOOJ95lWlxkzZhR1oUbz6KOP8uijj8Z9jXi/P0k9qXbNrRfwR+Au4LfAuMgeb8spx07b7n5D9YYnEFyX2LRpU9w68Ta0hOCPaVmvsXnz5jKvz2zZsiVuueyyYcOGMsvL2q+trM91y5YtZX4m0dagrG5lnfvu1wtj1ZH0kWrJ7VVKzmdrDIyowPFKbglQq1YtevTowbvvvhu1vHbt2vTs2ZPHH3+cHTt2RK3Ttm1bOnbsGPd92rdvT61atfjkk0/i1pHyOfroo/nwww9jlnfp0oU33niDlStXRi2vU6cOPXv25B//+EfMFnf79u3L/EyS8Zn17NmzzPI2bdrwwgsv7PFrSGpJtW5JCFYm2ZObJNBVV10Vs2zs2LG0atWKAQMGxKxz6qmncsQRR7D//vtHLc/OzmbYsGEMGTKkxHY4xbVu3TrmfnBS2iWXXEJ2dnbUsiOOOIJu3boxYkTs75InnngirVq1om/fvjHrjBgxgmOOOSbmvnE5OTllbmZaHfr27cvRRx8dtax27dpceumlXHTRRTFbbwcccACnn356dYYoVSzVktuBlbi1TUK8NdaoUaOYOnVqie1SMjIyOPfcc5k2bRoAF110EQMGDCixcnydOnW44IIL6NevHxkZGUycODHqN/nrr7+e/fbbj+bNm3PDDTfQpEmTEuWHHHIIEydOTOqw8nTTsWNHZs6cWSrx9OnTh7lz5wLwi1/8gnPOOYfatXct65qRkcEJJ5xQtMnohAkT6NOnT9GecIV+9atfcfTRR5OZmckf/vAH2rYt+V+yWbNm3HjjjTRu3Lg6Tq9Ms2fPpl+/fiWe23fffZkxYwZdunRhv/32Y968eSWmPAB069aNF198MeYXA0lN2s9Noirvfm4bNmxg3rx5bN26lX79+hX9YSi+WsWqVav4+OOPqV27NkcccUTRnKLiPvvsM37729+yc+dOateuzYwZM0qU5+fn8+GHH7J27VratGnDIYccUlSm1S4qJi8vj/nz5/PTTz/RtWtXDj/8cKDkZ7Zp0ybef/99du7cSdeuXUt9uQD44YcfGD9+fMzPDOA///kPK1euZJ999qF79+5FX3SS+Zl98MEHLF26tGi398L5lIUKCgp4+eWXi+a5HXvssUmKNL1pPzdJaw0aNGDUqFFx6zRr1ixuFyUELbFatWqxc+fOUi0CCK7zFZ8cLnsuKyurzORSr169uN2PAPvtt1/czwygQ4cOdOjQYY9jrQ6HHXYYhx12WMzyjIwMBg0alMCIpDqkWrekiIhIpSm5iYhI6Ci5iYhI6Ci5iYhI6GhAiSTc2WefHXUppMLntm/fzmmnnVaiLDs7u8zlkURECim5ScJt37497jp/hXVERPaUkptUq02bNvHiiy8W7ed2zDHHlJggDMHOFPn5+UW7OgOlJsxqAq1UheXLl3PPPfcU7ed23nnnMXDgwBJ1lixZUmI/t1/96lcceuihSYpY9pSSm1Sb7777jmuvvZaff/656LnFixfTsWNHJk2aRJ06dVi8eDG33HJLicQGwVJP48aNS3TIEmLz589nxIgRbN26tei5Rx55hOzs7KKVbvLy8sjLyysqnzdvHtOmTaN27dpkZu76c5mTk8Pq1asTF7xUmAaUSLWZNm1aicRW6JNPPuHJJ59k8+bN3HbbbVFXop89ezbvv/9+IsKUGmDr1q2cddZZJRJboe3btxftZlA8sRW3Y8eOojrl2flAki8pLTcze6UaXtbdPf4yGJIwK1asiLua/4IFC2jWrBnbtm2LWWf+/PlFS0OJVMazzz7LmjVrYpZnZGRgZqV6EIrLysoqWqor2hJyklqS1S3Zn2Brm1ir+e++4OXu9aKVa5HMFBKtxVbchg0b+O9//1up1xApr1jb+BTq0KEDbdq0Yd68eTHrXHTRRdx5551VHZpUk2Qlt0XETkYZwLHs6jJdDrwH/BfIA/YGugFdI+WbgUeA+LssSkK1aNECMyPWwtxNmzalTZs2cV+jZcuW1RGa1EDFF9qOpn379rRu3TpuctPegeklKcnN3fvHKjOzRwkS26fA/7j7qzHqdQYeAHoA+wNDqzxQ2WPNmjWjR48evPPOO1HLhw4dyrHHHsuDDz4YdYdjM2PIkCHVHabUECeddBJt2rTh22+/jVo+YcIEWrRowZ133hm1a7J+/fqMGTOmusOUKpRSA0rM7FzgTGAFcGysxAbg7rnA8cBnwAnArxMRo5TfJZdcUmpPLwg2jhw+fDjZ2dlcd9111K9fv0R5RkYGF110kb4pS5XJzMxk1qxZ7LvvviWeNzMmT57MgAEDOPTQQ3nwwQdLbYFTv359nn32WRo1apTAiKWyUm0qwK8IuitvdvfYV38j3H2zmf0ZeBA4H5hWzfFJBTRq1IjbbruNt99+u2ie27HHHlsiaXXo0IHp06fz6quvsnz5cvbee2+OP/54mjVrlsTIJYwOO+wwvvrqKx577LGieW7nnnsu7dq1K6ozZswYBg4cyAEHHEBeXh5ZWVlF/y4lvaRacivc2a4iY8ALN787qIpjkSpQq1YtevXqRa9evWLWycnJURekJEROTk6Z8ydbtGhBVlZWUXJTYktPKdUtCRSuuVR629/YCkclbKziWEREJE2lWnL7LHJ/QQWOuYSgKzO36sMREZF0lGrdkn8HjgFGmNlNwA3uXhCtopnVAe4G+hAkt7sTFaRITaOdHCTdpFpyexgYD3QHfg+cYWYzgKVA4QCTpgTD/08HCoc+Pe/uMxMbqkjNoZ0cJN2kVHJz9x1mdgLwIsEk7bbAlTGqF65a8hwwOgHhidRYsXZlKJ7QtJODpJKUSm4A7r7azA4nmBYwjmA1kt2X3yoA3gD+4u5PJzhEkRpH3YuSblIuuQFErrPdBdxlZvsC7YHCiU8/Av9x95+SFZ9ITefuLF26lNzcXOrUqUPv3r1p3rx5ssMq086dO5k9ezYffPABTZs25cwzzyw1p3L9+vVFuwPs3LmTbdu2UadOnWSEK5WQksmtOHf/L8G6kiKSAtavX8+kSZP4/PPPi557+OGHGT58OBdcUJGBzon16aefMmTIEL766qui566++mpuv/12/ud//geAmTNncu655xYltx07drD//vsza9asuHM1JfWk2lQAEUlxU6dOLZHYIGjJzZw5k5deeilJUcVXUFDASSedVCKxQZC8JkyYwBtvvMGXX37JGWecwebNm0vUWbVqFcOGDWPjRk2lTScp1XIzsxsqc7y7/6GqYhGR0r7//nvee++9mOVz585l4MCBCYyofF544YVSCbmQuzNt2jTatGnDjh07otZZs2YNjzzySFELT1JfSiU3YCKV25dNyU2kGi1fvrxS5cmSmxt/jYfc3Fw2bNhQqdeQ1JJqyQ1ib2AajQPr2DUHTkSqUVnrLKbqOoy77wawu+bNm5erjqSPVLvmdnAZt/bAkcAIgtGUeQQJ7gJ3PzgZAYvUJB06dKBVq1YxywcMGJDAaMpv5MiRpbZWKu7888/n/PPPj1mekZGh/dzSTEq13Nz9y3JWfQ+YbWaPAS8BM82sq7t/X33RiQjAZZddxo033lhq4EX79u1LLcGVKhYuXMiECROYMmUKO3fuLFHWu3dvGjRowMaNGznllFOYPXt2iXIzY9y4cXz88cd8/PHHZb7XsGHDqjR22TMpldwqyt3fNLO7gMuB3wKXJjkkkdBr374906ZN45///CfLli0jOzubPn36cNxxx1G7du1khxfTscceS6tWrXj++ef56quvaNCgAQMGDKBXr15kZASdWOPGjePwww9n0qRJFBQUkJGRwS233KKNc9NQWie3iBcIkttQlNxEEqJZs2aMHTs22WFU2P7778/48ePj1jn88MPJyspi+/btZGVlKbGlqVS75rYnCvsYWiY1ChERSRlhSG6FHdzrkhlERZnZOWbm5bxpl3ERkQpI225JM6tHsD3OZQQjJl9LakAV1y3ZAYiIhFVKJTcz+7Y81YC9gEaRn40gud1ebYFVj8Lk9hrBbuLxlOf3IiIiESmV3IDYE2hiKwB+5+5vVnUw1awwuS129w+TGYiISNikWnJbRNnLbznBIJJ1QC7wpLt/Ws1xVSkza86uLXyWJjMWEZEwSqnk5u79kx1DghS/3vZhsoIQqYy1a9fy2WefkZ2dTefOncnMTP6fk3Xr1vHaa6+RlZVFv3792GuvvUrV+fHHH/n6669p0KABHTt2xKz0in8FBQUl7ne3c+dOli1bxtatW2nfvn3KLjtWkyX/X2PNVJjctgFp1eoUycvL47777uOll14qWu2jUaNGXHjhhfTv3z8pMbk71157LXfccQdbtmwBgnUuJ02axK9//WsANm3axB133MGSJUtwDzqI9ttvP37961/TpUsXAL755htuu+22ov3c8vLy+P3vf88VV1xBkyZNAHjttdeYPn06a9YES9pmZmYyYMAAfvnLX5KVlZXQ85bYwjAVIB11j9znAkeY2UNm9q2Z7TCzVWY2x8yGJjE+kZjuu+8+5s2bV2IZq3Xr1jF16lQ++OCDpMT0xz/+kZtvvrkosUHQsrzkkkt45JFHALj55pt56623ihIbwA8//MCkSZP47rvvWL9+Pddddx1ff/11idfOzc3l+uuvJz8/n48++ogpU6YUJTYIWnHz58/nnnvuqeazlIpIm5abmY0CzgEOAjYTJIZ/uPsrSQ1szxS23NoDb1FyJ4SmwEnASWb2CHChu0ffZKqczKxZ5HUrol1l3lPCae3atTE3JC0oKODZZ5/lsMMOS2hMW7du5fbbb49Z/qc//YmxY8fy0UcfRS3fvn07c+bMoUmTJqxfvz5qnZUrV7J48WJefPHFmF2VL7/8MmeffXaF45fqkdTkZkFn9wjgLIJV/7cA7wJ3u/snkToZwBPAyN0OPxwYY2ZzgLPdfQtpwMzqAIdEHtYHVgJ3AG9HnutNMHevKUEy3wFcWMm3HQ/cWMnXEOGzzz4rtfBwccnY8yw3N5e1a9fGLF+2bBnvv/9+3NdYtmwZTZvG//63bNkyli1bFrM8Pz+ff//73/GDlYRJWnKLjBh8Gjhmt6KjgIvN7DJ3vxv4MxBvqfGTCZLfydUSaNXrxK7f+5vAMHcv/j9zkZn9nWDkaDvgAjN73N2jf10WSaDs7OxKlVeHnJycuOUZGRll1snOzi7XuWVnZ7N9+/a4dSQ1JOWam5nVAmYTJLbCidjrCTYdNSAL+KuZnQP8D7tWIDkOaADsTdCS+3ek/lAzS82NpEr7kKBrdRAwfLfEBkBk657irbWyJnmLJETnzp1p1KhRzPI+ffokLpiITp060alTp5jlQ4cOpW/fvtSqVStmnT59+tC7d++471NWnYYNG9KtmxYeShXJGlByDsGmowD/AA52933cvSnQGniYIGk9BOQQdNkNcPeF7r7J3de7+0yCLrzC1TvOTOgZ7CF3z3f3L939JXdfHafeQqBwf7v+Fm28cvndBXSu4O2USryfhFRmZiYXXnhh0RYxxe2zzz6MGjUqCVHB1KlTo26306hRIyZPnsw+++zD6NGjox7btm1bTjjhBHr16kX37t2j1hk0aBDt2rVj1KhRRaMmizMzLrjgAo2WTCHJ6pY8NXI/393PK17g7t8BY83sAKAvQattiruX6uh397VmdidB1+UR1RtyUnxM0DVZn6C1uiZ+9ejcfRWwqiLHVC6XSpj179+fhg0b8uyzz5Kbm1u0n9uoUaPKvG5VXQYNGsSiRYuYPHkyCxYsIDMzk+HDh3PttdfSoUMHvvnmG84880xatGjB7Nmzi+a5HXfccZx++ulF8+FuuOEGnn322aIRlmbGL3/5S4YMGQJA48aNufXWW3nqqad47bXX2LZtGx07dmTkyJEcfvjhSTl3iS5Zya0LQdJ6NE6d6QTJDXYNtohmSeS+dRXElWqKD5JJ3V0gpcY57LDDEj4qsixHH310qV20d9evXz/69esXszwrK4vRo0fz9NNPs337dmrXrs3QoSVn5TRp0oTx48eXuS+cJFeyklvh17tv4tT5stjPP8WptzFyX68yASWKmXUjuObWFLjXi0+6Ka3w95TPHrbaRERqomQlt7oELbfNceoUtVrcfWuceoWTTtKls/tyYEzk538RY4USM8sCekQeflzZuW4iIjVJsidxR58NGW6L2JXcziL2/LNzCa6zATxV3UGJ1FRnn312qeH9hY+3b9/OaaeVnomUnZ3No4/Gu6oiyabltxLvGXZ1MV4e6aYswcy6AlMiD1cD9yYoNpEaZ/v27aVuZZXHm+smqSHZLbcax93Xm9llBNMd6gGvmdkU4GWgFvALghVK6hJcaxvn7rreJlJNok28Lp68opVrsnbqS3ZyK2vvtlBy93+YWUNgKsEw/0mRW3EbgYvdfU6i4xOpSdS9GE7JTm4vmFmsgRJFQ9/N7Ks4r5GWQ+Td/a9m9hLB6iMDCXYhzweWA88Df3X3FUkMUSSU8vPzWbNmDXXr1o25LNe2bdvYuHEjjRo10sTsNJXs5NaijPLClt0B5aiTdtz9PwSLGotINcvPz+eZZ57hueeeY926ddSqVYuePXtywQUX0KxZMwA2bNjA3//+dxYuXMiOHTuoW7cugwYN4txzz426AoqkrmQlt29J46QkIunn7rvvZv78+UWP8/PzeeONN/j000+5/fbbqVOnDtdeey3ffPNNUZ3Nmzcza9Ysvv32WyZN2v3KgaSypCQ3dz8gGe8rIjXT999/z4IFC6KW/fTTTzz//PM0adKkRGIr7v333+fjjz8u2rFbUp+mAohI6L399tvEWwxoyZIlvP12vFX+gjqSPpTcRCT04q9yF+wiHmuH7eJ1JH0ouYlI6B1xRPxNQ3r06FGuOpI+lNxEJPTatGlD3759o5Y1atSIYcOGcfzxx9OiRfQB3B07dky5XRAkPiU3EakRLrvsMoYPH160d5uZ0b17dyZPnkzjxo3Za6+9mDx5Mr169SrajLV27doMGjSIG2+8UXscpplkz3MTEUmIrKwsLrzwQs4++2x+/PFH6tevT+PGjUvUady4Mddccw3r1q1j7dq1NG3alHr10mI3LdmNkpuI1Ch16tThgAMOiFunUaNGNGrUKCHxSPVQt6SIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiISOkpuIiIROZrIDEEkFs2bN4vzzz2fLli3su+++vPTSS/znP//hnXfeYe+99+aMM86gRYsWJY7ZuHEjTz75JN988w3t2rVj1KhR1K1bt0SdFStW8OSTT7J+/Xp69erFiSeeSEZGye+U7777Ls8//zxmxsknn0z37t1LlOfn5/P888+XiOXGG2/kkUcewd3p27cvCxYsqJbfiyTH008/zfXXX8+WLVsYOHAgd9xxBzNmzOCLL75g//3354wzzqB+/foljvnhhx944oknWLNmDUcccQQnnXQStWrVKlFn6dKlzJkzh4KCAgYPHsxRRx1VorygoID58+fz5ptv0qBBA0aPHk2bNm1K1NmyZQtPPfVUiVgeeOABZs6cSZ06dbj88ss54YQTqucXUwHm7smOQVKQmXUCcgsf5+bm0qlTp3If/9xzz1VHWFENGzasUse3adOGFStWxK2TmZnJzTffzJVXXgnAvHnzGD16NBs2bCiqs/feezNjxgyOO+44AG666SYmTpxIfn5+UZ3OnTvzwgsv0KpVK7Zt28bo0aOZM2dOifc6/fTTefTRR8nKyuLrr79m8ODBfPrpp2Wex+LFi+nZs2e5z3t36fSZVVQ6nVurVq347rvv4tapX78+jz/+OEOHDgVg6tSpXH311ezcubOozsEHH8wLL7xAu3btyMvLY8yYMTzxxBOlYn3qqafYa6+9+O677xgyZAgfffRRUXmtWrW47rrrmDhxIgALFy7k1FNPZc2aNXHj69KlC48++ihdu3Yt/nRnd19W9m+gaqhbUmq0Cy64oMzEBrBz506uuuoq5s+fz/fff8+pp55aIrEBrF27llNOOYWffvqJmTNncv3115dIbBB8STj99NMB+N3vflcqsUHwrf2GG24AYMSIEeVKbADHHHNMuepJ6ho0aFCZiQ2CXoPTTjuNFStW8NJLL3H55ZeXSGwAn3/+OcOHD8fd+cMf/lAqsUGQ9K+66ioAzjjjjBKJDYJeg0mTJvHMM8+wZs0aTj755DITG8DHH39c9LrJom5JqdEeeuihCtWfNm0aRx11FFu3bo1avnHjRv72t7/xz3/+M+ZrvPXWWyxatIgHHnggZp377ruP448/nqVLl5Y7NndnxowZjBw5stzHSGp55ZVXyl1327Zt3H///Xz44Ycx6+Tm5rJgwQLuueeemHX+/ve/M3r0aF5//fWYdaZNm8a3335b6gtdPC+//HK561YHJTep0QoKCipUPzc3t9R1td1dffXVZb5Ov3794pavWbOGxYsXVyg2gOnTpyu5pbE9+fe4bFn8nr4TTzwxbvnmzZvp27dvme9z8MEHVyi23VuSiaZuSanRzKxC9Zs3bx6z1VaVMjMzWbduXYWPO/roo6s+GElZ27Zto3bt2tX+PvXq1WPjxo0VOqai/7eqmpKb1GhdunSpUP2xY8cyYMCAaopml549e9KnTx+aNGlSoeMKL/xLemrbtm2F6g8cOJCBAwdWUzS7DBgwgAEDBlQoYR166KHVGFHZlNykRlu6dGmpofmxDB06lIsuuoiDDjqI0aNHR60zZswY5s6dy7PPPltqSD9AdnY2N910E3PnzmX69OlRk1fz5s0ZN24cWVlZPPTQQ2RnZ5crvgkTJpSrnqSuRYsWlRq+H8vvf/97DjnkEAYOHBi1xZ6VlcX111/P3Llzeeihh9hvv/1K1WnSpAn3338/c+fOZfLkydSpU6dUna5du3LyySfTsmVL/vSnP5Urtr322os77rijXHWri6YCSFQ1aSoAQKdOnfjkk0+AoDtl4MCBdOjQoWhu2TnnnMOoUaPIzMwsOrePPvqIefPmsWrVKvbdd1+GDBlS4ne0c+dOFi1axMKFC9m8eTPt27dn2LBhJf7IbNiwgfnz5/Puu+9iZhx11FH84he/oF69ekXn9umnn3LnnXcWxdKjRw9uv/32om6iOnXqcM8993DeeedV6neQbp9ZRaTTua1evZoRI0awZMkSCgoKaNq0KePHj+ezzz4rmlt28cUXM2DAgKLzys/P54033uBf//oXGzdu5KCDDmLo0KG0bt266HU3b97MggULil63R48enHDCCTRs2LCozo8//sjzzz/Pv//9b3JycujXrx99+/YlKyur6Nxef/117rnnnqJY+vfvz6OPPsrSpUvJysrihBNO4C9/+Qv//e9/6dy5c/FTS+hUACU3iaqmJbeKCOu5hfW8ILznlsjzgoqd27Jly5Ka3NQtKSIioaPklkRmtpeZ/c7M3jezTWa20cw+MrMbzKxRsuMTEUlXmueWJGbWAngR6LhbUZfI7XwzG+runyQ8OBGRNKeWWxKYWSYwmyCxOXA3cDzQH5gGFAAHAHPMrEFyohQRSV9KbslxEdAj8vOl7j7e3f/l7gvd/VLg7EhZO+CKpEQoIpLGlNyS45LI/b+BO3cvdPcngMJhUL+OtPRERKSclNwSzMw6AB0iDx9z91iLyRWu6Ls3cFy1ByYiEiJKbolXfF+SRXHqFV+iO/4quyIiUoKSW+IVX3Dti1iV3P1HoHCF3uQu0iYikmZ0LSfxWkTudwI/lFH3e4JBJS3KqBeXmTUDmlbwsA7FH3zxRcw8HNXy5csr+HZ7rqwtP6paWM8trOcF4T23RJ4XVOzcovzNqP7tC4pRcku8vSP3m73stc82R+4bxq1VtvHAjZV5geHDh1cyBBGp4VoDHyTqzdQtmXiFS7xvK0fdwm7J8i0LLyIigJJbMhSOjizPitWFmydVbHteEZEaTt2Sibcpcl9646TSCutsr+R73gU8XcFj6hFMNN8ArAdWADsqGUc87QhWbSl0CvBlNb5fooT1vCC85xbW84LEnlttgq7IQgur6X2iUnJLvMK92nPKUbdu5H5tZd7Q3VcBq/bg0CWVed+KiLLD75eJ3B6juoT1vCC85xbW84KknFvCrrHtTt2SiVc4vKm2mZU1grFwlOT31RiPiEjoKLkl3r+L/dwuViUz2w/YK8oxIiJSBiW3xHu72M/HxqnXu9jPb1ZTLCIioaTklmDu/gXwceThGIvSCR5xXuR+LQm+ECsiku6U3JLj7sh9V+Cq3QvNbDQwNPLwPnev7GhJEZEaRaMlk+M+gj3dDgP+bGadgH8QDLUfAfw6Uu8b4OZkBCgiks6U3JLA3fPNbBjwMsEajmMit+K+A4a5+/pExyciku7ULZkk7v49Qcvtt8D7BPPfthOMjLwZ6BqWuTUiIommllsSufs24JbITUREqohabiIiEjpKbiIiEjrqlpRUsRqYtNvjMAjreUF4zy2s5wXhPrcSrOz9MkVERNKLuiVFRCR0lNxERCR0lNxERCR0lNxERCR0lNxERCR0lNxERCR0lNxERCR0lNxERCR0lNxERCR0lNxERCR0lNxERCR0lNxERCR0lNxERCR0lNwkpZhZezPbZmYrkx1LZZnZQWZ2h5nlmtnGyHl9bWYPm9mRyY5vT5lZRzO738y+MrPtZrbKzBaZ2S/NrHay46tqZna1mXnklnbbhJnZOcXiL+t2ULLjrSra8kZShpllA68AxwDfuXurJIe0x8zsYuAvQLw/9pPd/doEhVQlzOwi4K/EPq+PgOHu/nXioqo+ZtYO+BjYK/JUlrvvTGJIFWZmtwJXlrP6we7+RXXGkyhp9y1EwinyjfhJgsSW1szsFOAewIA1wFRgEZAHHAlcBbQCrjGzNe7+f8mKtSLMbAhwL8F5rQf+BLwFNABGAGOBrsBcMzvK3bckKdSqdC+7Elu66ha5fw24pIy631ZzLAmjlpsknZk1AZ4ABhR7Oi1bbmZWC/gCOAD4Cejp7l/uVqcJ8CZwMLAFONDdVyU41AoxMwM+JYh5M3C4u3+2W50rgVsjD//X3e9MbJRVy8wuAB7Y7el0bLn9F2gG3OLuv012PImia26SVGY2FHiPXYmtIInhVIV+BIkNYOLuiQ3A3X9iVzdRDkGrJ9X1JkhsEPyR/CxKndsIWqoAIxMSVTUxs32BKZGHPyUzlsows+YEiQ1gaTJjSTQlN0kaM3sSeA5oAzhwE0HXSTor3q06N069V4r93KWaYqlKGQSf1QpinJe7FxC0WiHodk1nfwH2Jvicnk9yLJXRrdjPHyYriGTQNTdJpqMj998A49z9ZTN7NXnhVInFBNeiWgHfxalX/ItldrVGVAXcfSGwMF6dSNdl68jDH6s9qGpiZicBpwPbgF8BaTXoZzeFyW0bQbdyjaHkJsn0PXAnMM3dtyc7mKrg7i8DL5ejar9iP6+opnASbTywX+Tnp5MZyJ4ys/rAXZGHN7n750HOTlvdI/e5wBFmNgE4DmgOrCMYEHSvu6dz6zQqJTdJpt6Rrqya6KpiPy9IWhSVEGmpNQI6EyS20ZGiN4D7khRWZf2ZoNW9DLglybFUhcKWW3uCRFY8UzcFTgJOMrNHgAvdfUeC46s2Sm6SNDU1sUW+PfeJPHzb3d9KZjyVcA3BddLiHgCuSMeWuJkdS9AN6cDF7p6X5JAqxczqAIdEHtYHVgJ3AG9HnusNXEaQ5M4BdgAXJjbK6qMBJSIJZGa/IJj3BrATuDSJ4VRW6yjPDQTOS3QglRVZWeV+gpbNve7+ZpJDqgqd2NWAeRPo6u5T3H1R5DaZoNuycETvBWY2MAlxVgslN5EEMbP+wLNAVuSpa9O41QYwA+hPMEL0coJrqPsDd5jZX5MY1564FjgU+AH4XZJjqSofAgcBgwhWjVm7ewV3/56SrbWyJnmnDU3ilpQSGS3ZjzSdxB1LZHWPZ9i12sXd7j4+iSFVOTNrRnC9rXB9whPdfX4SQyoXM+sIfECwpNjp7v7MbuV/Z1drNO0mcZeHmX0BtAM2Ag09BIlBLTeRamZmY4HZ7Eps9wMTkhZQNYmsslL8vMYkK5byMrMMYDpBYntu98RWg3wcua9PML8v7WlAiUg1MrNrgP9X7KlpwGVh+GYcw8sES4rlkB6T038J9ALygfvNrHuUOvsU+7mbmeUDO9z9kwTElyjF1wENxc4OSm4i1cTM/gxcXeypie4+KVnxVIaZNSJYfquxu8+LVc/d881sPUFyS4c/koULCdQiaF2X5d3I/XJ2LbOWksysG0EXcVOCQTLxvlA1jdzns2sJtbSm5CZSDczsRnYltgJgvLvfm8SQKms6wXqRO8xsH3ffHK2SmdVl1x/KeCu0SPW7nF1dw/8ixgolZpYF9Ig8/Dgsc910zU2kipnZcGBi5OFO4Ow0T2wAr0fuawNnxql3Fru+NJdnpZakcvex7m7xbsBDxQ7Jijx/QJJCrohFxX4+K069c9l1ne2p6gsnsZTcRKqQmTWm5OocE9z9iWTFU4UeJRhJB/BHM2uze4VIN1jhljcbSN9VSsLiGXZ1MV4e+XxKMLOu7Nr9YDXB/nWhoG5Jkap1Kbu65ZYAb8cYpFDcplTf/djdV5vZ1cDdBOsSLo1cUyycp3cC8GugLrtW+EjbrWLCwN3Xm9llwMNAPeA1M5tC0KKuBfyCYIWSugTX2sa5eyiut4HmuUmKSfd5bma2EmhZwcMWunv/aginykU2JP0TwR/HaLYAF7n7Y4mLqnql+zw3M/tfglVxYjVmNhJ8GQlDD0MRdUuKVJHIDtsVTWxpxd2nECzZNB34CtgObAI+Ilh0+KAwJbYwcPe/EkzLuBv4HNhK8JkVLg7dKWyJDdRyExGREFLLTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQkfJTUREQuf/AxREYtbBYsDPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 420x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 1.4, 1.2 # inches\n",
    "line_width = .8\n",
    "output_prefix = 'SMG-single-bud-culture-conditions'\n",
    "outputFigPath = '../jupyter_figures/' + output_prefix + '.svg'\n",
    "# outputFigPath = '../jupyter_figures/' + output_prefix + '.tif'\n",
    "\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.3, 0.14, 0.65, 0.81])\n",
    "\n",
    "# palette_dict = {'sg-Control': 'black', 'sg1-Itgb1': 'blue', 'sg2-Itgb1': 'red'}\n",
    "plotting_order = ['FGF7_NRG1_ITS', 'NRG1_ITS', 'FGF7_ITS', 'FGF7', 'medium_only']\n",
    "\n",
    "sns.swarmplot(data = df,\n",
    "              x = 'culture_condition', y = 'bud_number',\n",
    "              size = 2,\n",
    "              # hue = 'replicate_id',\n",
    "              color = 'k',\n",
    "              order = plotting_order,\n",
    "#               alpha = .5,\n",
    "#               palette = palette_dict,\n",
    "             )\n",
    "sns.barplot(data = df,\n",
    "            x = 'culture_condition', y = 'bud_number',\n",
    "            order = plotting_order,\n",
    "            color=\".7\", # This a nicer looking lighter gray to my own taste\n",
    "            errwidth=.7, errcolor=\"k\", capsize=.2, ci='sd')\n",
    "\n",
    "change_bar_width(ax, .5)\n",
    "\n",
    "y_max = 18\n",
    "plt.ylim([0,y_max])\n",
    "\n",
    "# rotate and customize x tick labels if necessary\n",
    "x_labels = [1, 2, 3, 4, 5]\n",
    "ax.set_xticklabels(labels=x_labels)\n",
    "# x_labels = ax.get_xticklabels()\n",
    "# ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Bud number\")\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "plt.savefig(outputFigPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      "         Multiple Comparison of Means - Tukey HSD, FWER=0.05        \n",
      "===================================================================\n",
      "    group1        group2    meandiff p-adj   lower    upper  reject\n",
      "-------------------------------------------------------------------\n",
      "         FGF7      FGF7_ITS   2.5303 0.0018   0.7425  4.3181   True\n",
      "         FGF7 FGF7_NRG1_ITS   3.7821  0.001   2.0675  5.4966   True\n",
      "         FGF7      NRG1_ITS  -4.8333  0.001  -6.4921 -3.1746   True\n",
      "         FGF7   medium_only  -4.8333  0.001  -6.6671 -2.9995   True\n",
      "     FGF7_ITS FGF7_NRG1_ITS   1.2517 0.2744  -0.5028  3.0063  False\n",
      "     FGF7_ITS      NRG1_ITS  -7.3636  0.001  -9.0637 -5.6635   True\n",
      "     FGF7_ITS   medium_only  -7.3636  0.001  -9.2349 -5.4923   True\n",
      "FGF7_NRG1_ITS      NRG1_ITS  -8.6154  0.001 -10.2383 -6.9925   True\n",
      "FGF7_NRG1_ITS   medium_only  -8.6154  0.001 -10.4168 -6.8139   True\n",
      "     NRG1_ITS   medium_only      0.0    0.9  -1.7485  1.7485  False\n",
      "-------------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00175691 0.001      0.001      0.001      0.27436529 0.001\n",
      " 0.001      0.001      0.001      0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fdc580f3b20>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df.bud_number, df.culture_condition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FGF7_NRG1_ITS n= 13\n",
      "FGF7_ITS n= 11\n",
      "NRG1_ITS n= 15\n",
      "FGF7 n= 12\n",
      "medium_only n= 10\n"
     ]
    }
   ],
   "source": [
    "for i in df.culture_condition.unique():\n",
    "    print(i, 'n=', len(df[df.culture_condition==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}