robotpics_analysis_manuscript_edition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13375c76-26e2-4356-b2f1-9358bf4fd23b",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62c22e4a-bc23-497b-a2cb-029d3a7caad9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, os.path\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7483d872-8000-4d31-a395-bf6df38b4937",
   "metadata": {},
   "source": [
    "# Specify filepaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d53c2340-fa13-4bb1-a0dd-b1ac29b670e1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rhop30_path = 'data/20210729_edit/'\n",
    "rhom30_path = 'data/20210923_edit/'\n",
    "rhop37_path = 'data/20210802_edit/'\n",
    "rhom37_path = 'data/20210921_edit/'\n",
    "rhomYPG_path = 'data/20211001_edit/'\n",
    "rhodamine_data = 'data/20230405_rhodamine_df.xlsx'\n",
    "tecan384_data = 'data/20230418_tecan384wide.xlsx'\n",
    "layout_path = 'data/20220517_mutant_plates_rearray.xlsx'\n",
    "graph_outpath = 'graphs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e8d57e3-fc8d-4334-b829-945080797405",
   "metadata": {},
   "source": [
    "# S & P data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156ba512-a21b-4d7c-9ec2-505e848f320e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29d13381-c76e-4fa3-8ff2-a6cf99da7ae9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def annotate_df(df, plate_ID_path, layout_path):\n",
    "    plateID_df = pd.read_excel(plate_ID_path, header=0)\n",
    "    layout_df = pd.read_excel(layout_path, header=0)\n",
    "    annot_plate_df = df.merge(right=plateID_df, on=['plate_number'])\n",
    "    annot_layout_df = annot_plate_df.merge(right=layout_df, on=['rearray_plate_nb','RA_well'])\n",
    "    return annot_layout_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bba75737-cc94-42a4-baf5-a7962f8c9a16",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import math\n",
    "from datetime import datetime\n",
    "\n",
    "def pyphe_to_df(p, layout):\n",
    "    \n",
    "    list_of_plate_df = []\n",
    "\n",
    "    for f in glob.glob(p + 'pyphe_quant/*.csv'):  \n",
    "        plate_df = pd.read_csv(f, header=0, index_col=0)\n",
    "    \n",
    "        #For every picture in the folder, the file name is read and parsed ('_') to retrieve the plate number and timestamp\n",
    "        plate_df['plate_number']=int(os.path.basename(f).split('_')[2])\n",
    "        plate_df['timestamp'] = datetime.strptime('_'.join(os.path.basename(f).replace('.JPG.csv','').split('_')[3:]), '%y-%m-%d_%H-%M-%S')\n",
    "        \n",
    "        list_of_plate_df.append(plate_df)\n",
    "\n",
    "    #Above we created one dataframe per plate, which we now concatenate into a single dataframe\n",
    "    pic_df = pd.concat(list_of_plate_df, axis=0)\n",
    "    \n",
    "    #t0 is extracted by sorting by timestamp\n",
    "    timestamps_list = pic_df['timestamp'].to_list()\n",
    "    timestamps_list.sort()\n",
    "    t0 = timestamps_list[0]\n",
    "    \n",
    "    #Timestamps are converted into time (s), then time (h)\n",
    "    pic_df['time (s)'] = pic_df['timestamp'].apply(lambda x: (x - t0).total_seconds())\n",
    "    pic_df['time (h)'] = round(pic_df['time (s)'] / 3600, 1)\n",
    "\n",
    "    #This is useful if the plate has been expanded to feature 4 replicates per samples\n",
    "    #'spot_well' would designate a single spot (= one replicate)\n",
    "    #'RA_well' is short for 'rearray_well' and designates a sample on the original plate (before expansion)\n",
    "    #For example B1 if I'm not mistaken, corresponds to the third replicate on a 384-array of sample in A1 on the corresponding 96-array pre-expansion\n",
    "    pic_df['spot_well'] = pic_df.apply(lambda row: chr(64 + row.row) + str(row.column), axis=1)\n",
    "    pic_df['RA_well'] = pic_df.apply(lambda row: chr(64 + math.ceil(row.row / 2)) + str(math.ceil(row.column / 2)), axis=1)\n",
    "\n",
    "    pic_df = pic_df.sort_values(by=['plate_number','time (h)'])\n",
    "    \n",
    "    #Annotate dataframe\n",
    "    annot_layout_df = annotate_df(pic_df, p+p[5:-6]+'_plates_identity.xlsx', layout)\n",
    "\n",
    "    return annot_layout_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19621926-bbe1-4ee7-8fa4-d066311bb854",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_robot_auc(g):\n",
    "    return np.trapz(g.area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc8e0a3c-0c30-4866-87de-ecf6e993a30a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_normalized_value(df, pn, strain, well):\n",
    "    control = df.loc[(df['genotype']=='wt') & (df['plate_number']==pn) & (df['strain']==strain), 'auc'].values[0]\n",
    "    val = df.loc[(df['plate_number']==pn) & (df['RA_well']==well), 'auc'].values[0]\n",
    "    return val/control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cd81b86-ba42-4e59-8b12-da178d163f35",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def generate_relative_fitness(path, incubation_time, layout):\n",
    "    df = pyphe_to_df(path, layout)\n",
    "    aucdf = df[df['time (h)'] <= incubation_time].groupby(['plate_number','RA_well','spot_well'])[['area']].apply(func=get_robot_auc).reset_index(name='auc')\n",
    "    meandf = aucdf.groupby(['plate_number','RA_well'])[['auc']].mean().reset_index()\n",
    "    rho_df = annotate_df(meandf, path+path[5:-6]+'_plates_identity.xlsx', layout)\n",
    "    rho_df['rel_fit'] = rho_df.apply(lambda row: get_normalized_value(rho_df, row.plate_number, row.strain, row.RA_well), axis=1)\n",
    "    return rho_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5691f91b-767b-46b3-8dc2-4611b35f1aa4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_Spearman(df, strain_name, rho_status, condition1, condition2):\n",
    "    \n",
    "    from scipy import stats\n",
    "    \n",
    "    fit1 = df[(df.strain==strain_name) & (df.respiration==rho_status)][condition1].values\n",
    "    fit2 = df[(df.strain==strain_name) & (df.respiration==rho_status)][condition2].values\n",
    "    \n",
    "    return stats.spearmanr(fit1, fit2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca030e5-8385-4392-ad88-854c4c03ac5d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Rho+ 37°C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df3cf2d0-0c45-4aa6-8cc4-4873556e3873",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>plate_number</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>auc</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>fluc_mutant</th>\n",
       "      <th>genotype</th>\n",
       "      <th>FCY1_genotype</th>\n",
       "      <th>FUR1_genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>key</th>\n",
       "      <th>Genomix_plate_nb</th>\n",
       "      <th>Genomix_plate_well</th>\n",
       "      <th>rel_fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B10</td>\n",
       "      <td>19964.125</td>\n",
       "      <td>1</td>\n",
       "      <td>YPD</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.926485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B10</td>\n",
       "      <td>13022.375</td>\n",
       "      <td>1</td>\n",
       "      <td>SD</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.993439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B10</td>\n",
       "      <td>18286.875</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>2.226848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B10</td>\n",
       "      <td>7054.500</td>\n",
       "      <td>1</td>\n",
       "      <td>Mica_low</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.822359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B10</td>\n",
       "      <td>4436.375</td>\n",
       "      <td>1</td>\n",
       "      <td>Mica_mid</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.852268</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>4615</th>\n",
       "      <td>11</td>\n",
       "      <td>O9</td>\n",
       "      <td>2543.125</td>\n",
       "      <td>1</td>\n",
       "      <td>Nystatin_mid</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>0.795161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4616</th>\n",
       "      <td>12</td>\n",
       "      <td>O9</td>\n",
       "      <td>2168.625</td>\n",
       "      <td>1</td>\n",
       "      <td>Nystatin_high</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>0.857037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4617</th>\n",
       "      <td>13</td>\n",
       "      <td>O9</td>\n",
       "      <td>7997.375</td>\n",
       "      <td>1</td>\n",
       "      <td>Fluco_low</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>0.748249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4618</th>\n",
       "      <td>14</td>\n",
       "      <td>O9</td>\n",
       "      <td>6127.375</td>\n",
       "      <td>1</td>\n",
       "      <td>Fluco_mid</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>0.766149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4619</th>\n",
       "      <td>15</td>\n",
       "      <td>O9</td>\n",
       "      <td>6395.875</td>\n",
       "      <td>1</td>\n",
       "      <td>Fluco_high</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>0.888810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4620 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      plate_number RA_well        auc  rearray_plate_nb      condition  \\\n",
       "0                1     B10  19964.125                 1            YPD   \n",
       "1                2     B10  13022.375                 1             SD   \n",
       "2                3     B10  18286.875                 1           5-FC   \n",
       "3                4     B10   7054.500                 1       Mica_low   \n",
       "4                5     B10   4436.375                 1       Mica_mid   \n",
       "...            ...     ...        ...               ...            ...   \n",
       "4615            11      O9   2543.125                 1   Nystatin_mid   \n",
       "4616            12      O9   2168.625                 1  Nystatin_high   \n",
       "4617            13      O9   7997.375                 1      Fluco_low   \n",
       "4618            14      O9   6127.375                 1      Fluco_mid   \n",
       "4619            15      O9   6395.875                 1     Fluco_high   \n",
       "\n",
       "      mutant_plate 96_well    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "1                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "2                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "3                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "4                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "...            ...     ...       ...         ...          ...          ...   \n",
       "4615             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "4616             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "4617             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "4618             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "4619             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "\n",
       "     genotype FCY1_genotype  FUR1_genotype respiration    key  \\\n",
       "0      mutant           NaN            NaN        True  113.0   \n",
       "1      mutant           NaN            NaN        True  113.0   \n",
       "2      mutant           NaN            NaN        True  113.0   \n",
       "3      mutant           NaN            NaN        True  113.0   \n",
       "4      mutant           NaN            NaN        True  113.0   \n",
       "...       ...           ...            ...         ...    ...   \n",
       "4615   mutant           NaN            NaN        True  112.0   \n",
       "4616   mutant           NaN            NaN        True  112.0   \n",
       "4617   mutant           NaN            NaN        True  112.0   \n",
       "4618   mutant           NaN            NaN        True  112.0   \n",
       "4619   mutant           NaN            NaN        True  112.0   \n",
       "\n",
       "      Genomix_plate_nb Genomix_plate_well   rel_fit  \n",
       "0                  1.0                A11  0.926485  \n",
       "1                  1.0                A11  0.993439  \n",
       "2                  1.0                A11  2.226848  \n",
       "3                  1.0                A11  0.822359  \n",
       "4                  1.0                A11  0.852268  \n",
       "...                ...                ...       ...  \n",
       "4615               1.0                D10  0.795161  \n",
       "4616               1.0                D10  0.857037  \n",
       "4617               1.0                D10  0.748249  \n",
       "4618               1.0                D10  0.766149  \n",
       "4619               1.0                D10  0.888810  \n",
       "\n",
       "[4620 rows x 19 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rhop_df = generate_relative_fitness(rhop37_path, incubation_time = 22, layout = layout_path)\n",
    "rhop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8eaf00a3-3330-4585-a4fd-191ac1f82801",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rhop_df.to_excel('data/rhop37_22h.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3e625b-8955-4910-9a0e-346ea431b820",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Rho- 37°C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "46153cdd-4eff-4365-b3a7-6b1de7c7a2c9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plate_number</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>auc</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>fluc_mutant</th>\n",
       "      <th>genotype</th>\n",
       "      <th>FCY1_genotype</th>\n",
       "      <th>FUR1_genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>key</th>\n",
       "      <th>Genomix_plate_nb</th>\n",
       "      <th>Genomix_plate_well</th>\n",
       "      <th>rel_fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B10</td>\n",
       "      <td>10944.250</td>\n",
       "      <td>2</td>\n",
       "      <td>YPD</td>\n",
       "      <td>7</td>\n",
       "      <td>D3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.465901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B10</td>\n",
       "      <td>7147.125</td>\n",
       "      <td>2</td>\n",
       "      <td>SD</td>\n",
       "      <td>7</td>\n",
       "      <td>D3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.636828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B10</td>\n",
       "      <td>9443.375</td>\n",
       "      <td>2</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>7</td>\n",
       "      <td>D3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.459532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B10</td>\n",
       "      <td>11702.500</td>\n",
       "      <td>2</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>7</td>\n",
       "      <td>D3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.244277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B10</td>\n",
       "      <td>6108.500</td>\n",
       "      <td>2</td>\n",
       "      <td>Fluco_low</td>\n",
       "      <td>7</td>\n",
       "      <td>D3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.845233</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>1900</th>\n",
       "      <td>29</td>\n",
       "      <td>G9</td>\n",
       "      <td>5166.875</td>\n",
       "      <td>3</td>\n",
       "      <td>Caspo_high</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.147493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>30</td>\n",
       "      <td>G9</td>\n",
       "      <td>9393.625</td>\n",
       "      <td>3</td>\n",
       "      <td>Mica_low</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.099361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>31</td>\n",
       "      <td>G9</td>\n",
       "      <td>5692.625</td>\n",
       "      <td>3</td>\n",
       "      <td>Mica_mid</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.899664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>32</td>\n",
       "      <td>G9</td>\n",
       "      <td>4455.250</td>\n",
       "      <td>3</td>\n",
       "      <td>Mica_high</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.996951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>28</td>\n",
       "      <td>E4</td>\n",
       "      <td>239.750</td>\n",
       "      <td>3</td>\n",
       "      <td>Caspo_mid</td>\n",
       "      <td>3</td>\n",
       "      <td>B1</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.062186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1905 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      plate_number RA_well        auc  rearray_plate_nb   condition  \\\n",
       "0                1     B10  10944.250                 2         YPD   \n",
       "1                2     B10   7147.125                 2          SD   \n",
       "2                3     B10   9443.375                 2        5-FC   \n",
       "3                4     B10  11702.500                 2        5-FU   \n",
       "4                5     B10   6108.500                 2   Fluco_low   \n",
       "...            ...     ...        ...               ...         ...   \n",
       "1900            29      G9   5166.875                 3  Caspo_high   \n",
       "1901            30      G9   9393.625                 3    Mica_low   \n",
       "1902            31      G9   5692.625                 3    Mica_mid   \n",
       "1903            32      G9   4455.250                 3   Mica_high   \n",
       "1904            28      E4    239.750                 3   Caspo_mid   \n",
       "\n",
       "      mutant_plate 96_well    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "1                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "2                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "3                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "4                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "...            ...     ...       ...         ...          ...          ...   \n",
       "1900             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1901             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1902             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1903             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1904             3      B1     NC-02         1.0          1.0         12.0   \n",
       "\n",
       "     genotype FCY1_genotype  FUR1_genotype respiration   key  \\\n",
       "0      mutant           NaN            NaN       False  49.0   \n",
       "1      mutant           NaN            NaN       False  49.0   \n",
       "2      mutant           NaN            NaN       False  49.0   \n",
       "3      mutant           NaN            NaN       False  49.0   \n",
       "4      mutant           NaN            NaN       False  49.0   \n",
       "...       ...           ...            ...         ...   ...   \n",
       "1900   mutant           NaN            NaN       False  48.0   \n",
       "1901   mutant           NaN            NaN       False  48.0   \n",
       "1902   mutant           NaN            NaN       False  48.0   \n",
       "1903   mutant           NaN            NaN       False  48.0   \n",
       "1904   mutant           NaN            NaN       False  16.0   \n",
       "\n",
       "      Genomix_plate_nb Genomix_plate_well   rel_fit  \n",
       "0                  NaN                NaN  0.465901  \n",
       "1                  NaN                NaN  0.636828  \n",
       "2                  NaN                NaN  2.459532  \n",
       "3                  NaN                NaN  2.244277  \n",
       "4                  NaN                NaN  0.845233  \n",
       "...                ...                ...       ...  \n",
       "1900               NaN                NaN  1.147493  \n",
       "1901               NaN                NaN  1.099361  \n",
       "1902               NaN                NaN  0.899664  \n",
       "1903               NaN                NaN  0.996951  \n",
       "1904               NaN                NaN  0.062186  \n",
       "\n",
       "[1905 rows x 19 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rhom_df = generate_relative_fitness(rhom37_path, incubation_time = 22, layout = layout_path)\n",
    "rhom_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2b1c33cc-caad-463a-9392-b0f737a0140b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Filtering out misannotated rho+ mutants from rho- plates\n",
    "rhom_df.drop(rhom_df[(rhom_df.genotype=='mutant') & (rhom_df.respiration==True)].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "16b7a3e0-1c27-4483-93b8-307c7b7954a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rhom_df.to_excel('data/rhom37_22h.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fdf81f12-aa0e-49d3-ac79-fb81b3d67e02",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plate_number</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>auc</th>\n",
       "      <th>rearray_plate_nb</th>\n",
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       "      <th>FUR1_genotype</th>\n",
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       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.459532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B10</td>\n",
       "      <td>11702.500</td>\n",
       "      <td>2</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>7</td>\n",
       "      <td>D3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.244277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B10</td>\n",
       "      <td>6108.500</td>\n",
       "      <td>2</td>\n",
       "      <td>Fluco_low</td>\n",
       "      <td>7</td>\n",
       "      <td>D3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.845233</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>1900</th>\n",
       "      <td>29</td>\n",
       "      <td>G9</td>\n",
       "      <td>5166.875</td>\n",
       "      <td>3</td>\n",
       "      <td>Caspo_high</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.147493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>30</td>\n",
       "      <td>G9</td>\n",
       "      <td>9393.625</td>\n",
       "      <td>3</td>\n",
       "      <td>Mica_low</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.099361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>31</td>\n",
       "      <td>G9</td>\n",
       "      <td>5692.625</td>\n",
       "      <td>3</td>\n",
       "      <td>Mica_mid</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.899664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>32</td>\n",
       "      <td>G9</td>\n",
       "      <td>4455.250</td>\n",
       "      <td>3</td>\n",
       "      <td>Mica_high</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.996951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>28</td>\n",
       "      <td>E4</td>\n",
       "      <td>239.750</td>\n",
       "      <td>3</td>\n",
       "      <td>Caspo_mid</td>\n",
       "      <td>3</td>\n",
       "      <td>B1</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.062186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1841 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      plate_number RA_well        auc  rearray_plate_nb   condition  \\\n",
       "0                1     B10  10944.250                 2         YPD   \n",
       "1                2     B10   7147.125                 2          SD   \n",
       "2                3     B10   9443.375                 2        5-FC   \n",
       "3                4     B10  11702.500                 2        5-FU   \n",
       "4                5     B10   6108.500                 2   Fluco_low   \n",
       "...            ...     ...        ...               ...         ...   \n",
       "1900            29      G9   5166.875                 3  Caspo_high   \n",
       "1901            30      G9   9393.625                 3    Mica_low   \n",
       "1902            31      G9   5692.625                 3    Mica_mid   \n",
       "1903            32      G9   4455.250                 3   Mica_high   \n",
       "1904            28      E4    239.750                 3   Caspo_mid   \n",
       "\n",
       "      mutant_plate 96_well    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "1                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "2                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "3                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "4                7      D3  LL13-040         7.0          3.0          2.0   \n",
       "...            ...     ...       ...         ...          ...          ...   \n",
       "1900             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1901             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1902             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1903             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1904             3      B1     NC-02         1.0          1.0         12.0   \n",
       "\n",
       "     genotype FCY1_genotype  FUR1_genotype  respiration  key  \\\n",
       "0      mutant           NaN            NaN        False   49   \n",
       "1      mutant           NaN            NaN        False   49   \n",
       "2      mutant           NaN            NaN        False   49   \n",
       "3      mutant           NaN            NaN        False   49   \n",
       "4      mutant           NaN            NaN        False   49   \n",
       "...       ...           ...            ...          ...  ...   \n",
       "1900   mutant           NaN            NaN        False   48   \n",
       "1901   mutant           NaN            NaN        False   48   \n",
       "1902   mutant           NaN            NaN        False   48   \n",
       "1903   mutant           NaN            NaN        False   48   \n",
       "1904   mutant           NaN            NaN        False   16   \n",
       "\n",
       "      Genomix_plate_nb  Genomix_plate_well   rel_fit  \n",
       "0                  NaN                 NaN  0.465901  \n",
       "1                  NaN                 NaN  0.636828  \n",
       "2                  NaN                 NaN  2.459532  \n",
       "3                  NaN                 NaN  2.244277  \n",
       "4                  NaN                 NaN  0.845233  \n",
       "...                ...                 ...       ...  \n",
       "1900               NaN                 NaN  1.147493  \n",
       "1901               NaN                 NaN  1.099361  \n",
       "1902               NaN                 NaN  0.899664  \n",
       "1903               NaN                 NaN  0.996951  \n",
       "1904               NaN                 NaN  0.062186  \n",
       "\n",
       "[1841 rows x 19 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rhom_df = pd.read_excel('data/rhom37_22h.xlsx', index_col=0)\n",
    "rhom_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b13b10bb-380c-4836-a11d-f9c682241c5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>5-FC</th>\n",
       "      <th>Caspo_high</th>\n",
       "      <th>Fluco_high</th>\n",
       "      <th>Mica_high</th>\n",
       "      <th>Nystatin_high</th>\n",
       "      <th>SD</th>\n",
       "      <th>YPD</th>\n",
       "      <th>type</th>\n",
       "      <th>well</th>\n",
       "      <th>perc_pos</th>\n",
       "      <th>perc_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>B10</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>2.459532</td>\n",
       "      <td>0.490954</td>\n",
       "      <td>1.221070</td>\n",
       "      <td>1.526173</td>\n",
       "      <td>0.346933</td>\n",
       "      <td>0.636828</td>\n",
       "      <td>0.465901</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B10</td>\n",
       "      <td>7.40</td>\n",
       "      <td>0.632479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B11</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.932511</td>\n",
       "      <td>0.112269</td>\n",
       "      <td>0.939837</td>\n",
       "      <td>1.223133</td>\n",
       "      <td>0.142565</td>\n",
       "      <td>0.594282</td>\n",
       "      <td>0.410868</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B11</td>\n",
       "      <td>9.25</td>\n",
       "      <td>0.790598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>0.805020</td>\n",
       "      <td>0.147903</td>\n",
       "      <td>0.437949</td>\n",
       "      <td>0.591763</td>\n",
       "      <td>0.215291</td>\n",
       "      <td>0.261539</td>\n",
       "      <td>0.475016</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B02</td>\n",
       "      <td>6.05</td>\n",
       "      <td>0.517094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>B3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>2.646666</td>\n",
       "      <td>0.285013</td>\n",
       "      <td>1.006576</td>\n",
       "      <td>1.716826</td>\n",
       "      <td>0.408580</td>\n",
       "      <td>0.630558</td>\n",
       "      <td>0.564787</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B03</td>\n",
       "      <td>6.60</td>\n",
       "      <td>0.564103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>B4</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>3.071559</td>\n",
       "      <td>0.517232</td>\n",
       "      <td>1.231443</td>\n",
       "      <td>1.939455</td>\n",
       "      <td>0.661468</td>\n",
       "      <td>0.610643</td>\n",
       "      <td>0.559545</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B04</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.747863</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>110</th>\n",
       "      <td>3</td>\n",
       "      <td>G5</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.885240</td>\n",
       "      <td>0.857420</td>\n",
       "      <td>1.648419</td>\n",
       "      <td>1.065928</td>\n",
       "      <td>0.864654</td>\n",
       "      <td>0.881574</td>\n",
       "      <td>0.798866</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G05</td>\n",
       "      <td>6.15</td>\n",
       "      <td>0.280182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>3</td>\n",
       "      <td>G6</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.713849</td>\n",
       "      <td>0.928266</td>\n",
       "      <td>1.793975</td>\n",
       "      <td>1.095242</td>\n",
       "      <td>0.964798</td>\n",
       "      <td>0.936484</td>\n",
       "      <td>0.788152</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G06</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.273349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>3</td>\n",
       "      <td>G7</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.542052</td>\n",
       "      <td>1.076231</td>\n",
       "      <td>1.807980</td>\n",
       "      <td>0.963414</td>\n",
       "      <td>1.025095</td>\n",
       "      <td>0.841602</td>\n",
       "      <td>0.834562</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G07</td>\n",
       "      <td>6.15</td>\n",
       "      <td>0.280182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>3</td>\n",
       "      <td>G8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.907561</td>\n",
       "      <td>0.731525</td>\n",
       "      <td>1.804633</td>\n",
       "      <td>1.177198</td>\n",
       "      <td>0.968706</td>\n",
       "      <td>0.889176</td>\n",
       "      <td>0.793636</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G08</td>\n",
       "      <td>3.95</td>\n",
       "      <td>0.179954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>3</td>\n",
       "      <td>G9</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.809128</td>\n",
       "      <td>1.147493</td>\n",
       "      <td>1.633225</td>\n",
       "      <td>0.996951</td>\n",
       "      <td>1.047773</td>\n",
       "      <td>0.765923</td>\n",
       "      <td>0.771201</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G09</td>\n",
       "      <td>8.05</td>\n",
       "      <td>0.366743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rearray_plate_nb RA_well    strain  fluc_assay  pre_culture genotype  \\\n",
       "0                   2     B10  LL13-040         7.0          3.0   mutant   \n",
       "1                   2     B11  LL13-040         7.0          3.0   mutant   \n",
       "2                   2      B2  LL13-040         1.0          1.0   mutant   \n",
       "3                   2      B3  LL13-040         3.0          3.0   mutant   \n",
       "4                   2      B4  LL13-040         4.0          2.0   mutant   \n",
       "..                ...     ...       ...         ...          ...      ...   \n",
       "110                 3      G5     NC-02         4.0          2.0   mutant   \n",
       "111                 3      G6     NC-02         4.0          1.0   mutant   \n",
       "112                 3      G7     NC-02         4.0          1.0   mutant   \n",
       "113                 3      G8     NC-02         4.0          4.0   mutant   \n",
       "114                 3      G9     NC-02         5.0          3.0   mutant   \n",
       "\n",
       "     respiration      5-FC  Caspo_high  Fluco_high  Mica_high  Nystatin_high  \\\n",
       "0          False  2.459532    0.490954    1.221070   1.526173       0.346933   \n",
       "1          False  1.932511    0.112269    0.939837   1.223133       0.142565   \n",
       "2          False  0.805020    0.147903    0.437949   0.591763       0.215291   \n",
       "3          False  2.646666    0.285013    1.006576   1.716826       0.408580   \n",
       "4          False  3.071559    0.517232    1.231443   1.939455       0.661468   \n",
       "..           ...       ...         ...         ...        ...            ...   \n",
       "110        False  1.885240    0.857420    1.648419   1.065928       0.864654   \n",
       "111        False  1.713849    0.928266    1.793975   1.095242       0.964798   \n",
       "112        False  1.542052    1.076231    1.807980   0.963414       1.025095   \n",
       "113        False  1.907561    0.731525    1.804633   1.177198       0.968706   \n",
       "114        False  1.809128    1.147493    1.633225   0.996951       1.047773   \n",
       "\n",
       "           SD       YPD  type well  perc_pos  perc_norm  \n",
       "0    0.636828  0.465901  rho0  B10      7.40   0.632479  \n",
       "1    0.594282  0.410868  rho0  B11      9.25   0.790598  \n",
       "2    0.261539  0.475016  rho0  B02      6.05   0.517094  \n",
       "3    0.630558  0.564787  rho0  B03      6.60   0.564103  \n",
       "4    0.610643  0.559545  rho0  B04      8.75   0.747863  \n",
       "..        ...       ...   ...  ...       ...        ...  \n",
       "110  0.881574  0.798866  rho0  G05      6.15   0.280182  \n",
       "111  0.936484  0.788152  rho0  G06      6.00   0.273349  \n",
       "112  0.841602  0.834562  rho0  G07      6.15   0.280182  \n",
       "113  0.889176  0.793636  rho0  G08      3.95   0.179954  \n",
       "114  0.765923  0.771201  rho0  G09      8.05   0.366743  \n",
       "\n",
       "[115 rows x 18 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing rhodamine data\n",
    "selrhom37_df = rhom_df[rhom_df.condition.isin(['YPD','SD','5-FC','Mica_high','Caspo_high','Nystatin_high','Fluco_high'])]\n",
    "widerhom37_df = selrhom37_df.pivot(index=['rearray_plate_nb','RA_well','strain','fluc_assay','pre_culture','genotype','respiration'],columns='condition', values='rel_fit').reset_index()\n",
    "rhod_df = pd.read_excel(rhodamine_data, header=0, index_col=0)\n",
    "widerhom37_df = widerhom37_df.merge(right=rhod_df, on=['rearray_plate_nb','strain','RA_well'])\n",
    "widerhom37_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e3f8e73-ad11-457e-9b26-b4f59dbc0afb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "widerhom37_df.to_excel('data/widerhom37_22h.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db39dc9-5a55-4769-8828-d1bdfb3f3437",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Pooling rho+ and rho- 37°C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ebde7d50-e902-42d4-b085-a4ad8a75b532",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rhop_df = pd.read_excel('data/rhop37_22h.xlsx', header=0, index_col=0)\n",
    "rhom_df = pd.read_excel('data/rhom37_22h.xlsx', header=0, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3619d92f-0d45-466b-84e7-c451cd153131",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditions missing in one of the dataframes: ['5-FU']\n"
     ]
    }
   ],
   "source": [
    "missing_conditions = [x for x in max(rhop_df.condition.unique().tolist(),rhom_df.condition.unique().tolist(), key=len) if x not in min(rhop_df.condition.unique().tolist(),rhom_df.condition.unique().tolist(), key=len)]\n",
    "print(\"Conditions missing in one of the dataframes:\", missing_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd920439-da2e-4980-a91c-54801fce0271",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>plate_number</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>auc</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>fluc_mutant</th>\n",
       "      <th>genotype</th>\n",
       "      <th>FCY1_genotype</th>\n",
       "      <th>FUR1_genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>key</th>\n",
       "      <th>Genomix_plate_nb</th>\n",
       "      <th>Genomix_plate_well</th>\n",
       "      <th>rel_fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B10</td>\n",
       "      <td>19964.125</td>\n",
       "      <td>1</td>\n",
       "      <td>YPD</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.926485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B10</td>\n",
       "      <td>13022.375</td>\n",
       "      <td>1</td>\n",
       "      <td>SD</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.993439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B10</td>\n",
       "      <td>18286.875</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>2.226848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B10</td>\n",
       "      <td>7054.500</td>\n",
       "      <td>1</td>\n",
       "      <td>Mica_low</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.822359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B10</td>\n",
       "      <td>4436.375</td>\n",
       "      <td>1</td>\n",
       "      <td>Mica_mid</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.852268</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>1900</th>\n",
       "      <td>29</td>\n",
       "      <td>G9</td>\n",
       "      <td>5166.875</td>\n",
       "      <td>3</td>\n",
       "      <td>Caspo_high</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.147493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>30</td>\n",
       "      <td>G9</td>\n",
       "      <td>9393.625</td>\n",
       "      <td>3</td>\n",
       "      <td>Mica_low</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.099361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>31</td>\n",
       "      <td>G9</td>\n",
       "      <td>5692.625</td>\n",
       "      <td>3</td>\n",
       "      <td>Mica_mid</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.899664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>32</td>\n",
       "      <td>G9</td>\n",
       "      <td>4455.250</td>\n",
       "      <td>3</td>\n",
       "      <td>Mica_high</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.996951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>28</td>\n",
       "      <td>E4</td>\n",
       "      <td>239.750</td>\n",
       "      <td>3</td>\n",
       "      <td>Caspo_mid</td>\n",
       "      <td>3</td>\n",
       "      <td>B1</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.062186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6461 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      plate_number RA_well        auc  rearray_plate_nb   condition  \\\n",
       "0                1     B10  19964.125                 1         YPD   \n",
       "1                2     B10  13022.375                 1          SD   \n",
       "2                3     B10  18286.875                 1        5-FC   \n",
       "3                4     B10   7054.500                 1    Mica_low   \n",
       "4                5     B10   4436.375                 1    Mica_mid   \n",
       "...            ...     ...        ...               ...         ...   \n",
       "1900            29      G9   5166.875                 3  Caspo_high   \n",
       "1901            30      G9   9393.625                 3    Mica_low   \n",
       "1902            31      G9   5692.625                 3    Mica_mid   \n",
       "1903            32      G9   4455.250                 3   Mica_high   \n",
       "1904            28      E4    239.750                 3   Caspo_mid   \n",
       "\n",
       "      mutant_plate 96_well    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "1                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "2                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "3                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "4                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "...            ...     ...       ...         ...          ...          ...   \n",
       "1900             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1901             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1902             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1903             4      A8     NC-02         5.0          3.0          7.0   \n",
       "1904             3      B1     NC-02         1.0          1.0         12.0   \n",
       "\n",
       "     genotype FCY1_genotype  FUR1_genotype  respiration  key  \\\n",
       "0      mutant           NaN            NaN         True  113   \n",
       "1      mutant           NaN            NaN         True  113   \n",
       "2      mutant           NaN            NaN         True  113   \n",
       "3      mutant           NaN            NaN         True  113   \n",
       "4      mutant           NaN            NaN         True  113   \n",
       "...       ...           ...            ...          ...  ...   \n",
       "1900   mutant           NaN            NaN        False   48   \n",
       "1901   mutant           NaN            NaN        False   48   \n",
       "1902   mutant           NaN            NaN        False   48   \n",
       "1903   mutant           NaN            NaN        False   48   \n",
       "1904   mutant           NaN            NaN        False   16   \n",
       "\n",
       "      Genomix_plate_nb Genomix_plate_well   rel_fit  \n",
       "0                  1.0                A11  0.926485  \n",
       "1                  1.0                A11  0.993439  \n",
       "2                  1.0                A11  2.226848  \n",
       "3                  1.0                A11  0.822359  \n",
       "4                  1.0                A11  0.852268  \n",
       "...                ...                ...       ...  \n",
       "1900               NaN                NaN  1.147493  \n",
       "1901               NaN                NaN  1.099361  \n",
       "1902               NaN                NaN  0.899664  \n",
       "1903               NaN                NaN  0.996951  \n",
       "1904               NaN                NaN  0.062186  \n",
       "\n",
       "[6461 rows x 19 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pool37_df = pd.concat([rhop_df, rhom_df])\n",
    "pool37_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8eb728b9-662c-40a5-837d-69e0a4904e1c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strain</th>\n",
       "      <th>condition</th>\n",
       "      <th>auc</th>\n",
       "      <th>rel_fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Caspo_high</td>\n",
       "      <td>3633.030878</td>\n",
       "      <td>0.819523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Caspo_low</td>\n",
       "      <td>7912.038504</td>\n",
       "      <td>0.841839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Caspo_mid</td>\n",
       "      <td>4398.615513</td>\n",
       "      <td>0.817245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Fluco_high</td>\n",
       "      <td>6864.108259</td>\n",
       "      <td>1.004135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Fluco_low</td>\n",
       "      <td>8487.426339</td>\n",
       "      <td>0.866589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Fluco_mid</td>\n",
       "      <td>6687.703125</td>\n",
       "      <td>0.908459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Mica_high</td>\n",
       "      <td>3469.802455</td>\n",
       "      <td>1.057932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Mica_low</td>\n",
       "      <td>6299.497396</td>\n",
       "      <td>0.927621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Mica_mid</td>\n",
       "      <td>4278.814918</td>\n",
       "      <td>1.187795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Nystatin_high</td>\n",
       "      <td>2054.582775</td>\n",
       "      <td>0.822450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Nystatin_low</td>\n",
       "      <td>6034.957589</td>\n",
       "      <td>0.857075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>Nystatin_mid</td>\n",
       "      <td>2511.928943</td>\n",
       "      <td>0.811335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Caspo_high</td>\n",
       "      <td>4542.796482</td>\n",
       "      <td>0.893661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Caspo_low</td>\n",
       "      <td>8824.179020</td>\n",
       "      <td>0.919498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Caspo_mid</td>\n",
       "      <td>5071.311250</td>\n",
       "      <td>0.966119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Fluco_high</td>\n",
       "      <td>8168.196608</td>\n",
       "      <td>1.213132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Fluco_low</td>\n",
       "      <td>8898.745603</td>\n",
       "      <td>1.061514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Fluco_mid</td>\n",
       "      <td>8337.921482</td>\n",
       "      <td>1.054913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Mica_high</td>\n",
       "      <td>5544.746859</td>\n",
       "      <td>1.003336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Mica_low</td>\n",
       "      <td>11447.513191</td>\n",
       "      <td>1.050722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Mica_mid</td>\n",
       "      <td>7285.624372</td>\n",
       "      <td>0.966831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Nystatin_high</td>\n",
       "      <td>4040.494137</td>\n",
       "      <td>1.006326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Nystatin_low</td>\n",
       "      <td>7424.957915</td>\n",
       "      <td>0.923888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>Nystatin_mid</td>\n",
       "      <td>4171.886725</td>\n",
       "      <td>0.857093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      strain      condition           auc   rel_fit\n",
       "2   LL13-040     Caspo_high   3633.030878  0.819523\n",
       "3   LL13-040      Caspo_low   7912.038504  0.841839\n",
       "4   LL13-040      Caspo_mid   4398.615513  0.817245\n",
       "5   LL13-040     Fluco_high   6864.108259  1.004135\n",
       "6   LL13-040      Fluco_low   8487.426339  0.866589\n",
       "7   LL13-040      Fluco_mid   6687.703125  0.908459\n",
       "8   LL13-040      Mica_high   3469.802455  1.057932\n",
       "9   LL13-040       Mica_low   6299.497396  0.927621\n",
       "10  LL13-040       Mica_mid   4278.814918  1.187795\n",
       "11  LL13-040  Nystatin_high   2054.582775  0.822450\n",
       "12  LL13-040   Nystatin_low   6034.957589  0.857075\n",
       "13  LL13-040   Nystatin_mid   2511.928943  0.811335\n",
       "18     NC-02     Caspo_high   4542.796482  0.893661\n",
       "19     NC-02      Caspo_low   8824.179020  0.919498\n",
       "20     NC-02      Caspo_mid   5071.311250  0.966119\n",
       "21     NC-02     Fluco_high   8168.196608  1.213132\n",
       "22     NC-02      Fluco_low   8898.745603  1.061514\n",
       "23     NC-02      Fluco_mid   8337.921482  1.054913\n",
       "24     NC-02      Mica_high   5544.746859  1.003336\n",
       "25     NC-02       Mica_low  11447.513191  1.050722\n",
       "26     NC-02       Mica_mid   7285.624372  0.966831\n",
       "27     NC-02  Nystatin_high   4040.494137  1.006326\n",
       "28     NC-02   Nystatin_low   7424.957915  0.923888\n",
       "29     NC-02   Nystatin_mid   4171.886725  0.857093"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gby37 = pool37_df.groupby(['strain','condition'])[['auc','rel_fit']].mean().reset_index()\n",
    "gby37[(~gby37.condition.isin(['YPD', 'SD', '5-FC', '5-FU']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "06a10037-593b-4377-8ff0-16ec6989e881",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>condition</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>5-FC</th>\n",
       "      <th>Caspo_low</th>\n",
       "      <th>Fluco_high</th>\n",
       "      <th>Mica_low</th>\n",
       "      <th>Nystatin_low</th>\n",
       "      <th>SD</th>\n",
       "      <th>YPD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B10</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>True</td>\n",
       "      <td>2.226848</td>\n",
       "      <td>0.925959</td>\n",
       "      <td>0.938230</td>\n",
       "      <td>0.822359</td>\n",
       "      <td>1.031657</td>\n",
       "      <td>0.993439</td>\n",
       "      <td>0.926485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>B11</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>True</td>\n",
       "      <td>1.891424</td>\n",
       "      <td>0.933826</td>\n",
       "      <td>0.859766</td>\n",
       "      <td>0.847902</td>\n",
       "      <td>0.875719</td>\n",
       "      <td>0.909714</td>\n",
       "      <td>0.911814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>B12</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>True</td>\n",
       "      <td>1.847464</td>\n",
       "      <td>0.953826</td>\n",
       "      <td>0.908647</td>\n",
       "      <td>0.865549</td>\n",
       "      <td>0.946033</td>\n",
       "      <td>0.896583</td>\n",
       "      <td>0.881957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>B13</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>True</td>\n",
       "      <td>1.935734</td>\n",
       "      <td>0.922960</td>\n",
       "      <td>1.001824</td>\n",
       "      <td>0.922815</td>\n",
       "      <td>0.930109</td>\n",
       "      <td>0.941059</td>\n",
       "      <td>0.914813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>B14</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>True</td>\n",
       "      <td>2.330562</td>\n",
       "      <td>0.964526</td>\n",
       "      <td>1.037661</td>\n",
       "      <td>1.111597</td>\n",
       "      <td>0.940065</td>\n",
       "      <td>1.023869</td>\n",
       "      <td>0.941790</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",
       "      <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>418</th>\n",
       "      <td>3</td>\n",
       "      <td>G5</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.885240</td>\n",
       "      <td>0.808299</td>\n",
       "      <td>1.648419</td>\n",
       "      <td>0.892301</td>\n",
       "      <td>0.932186</td>\n",
       "      <td>0.881574</td>\n",
       "      <td>0.798866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>3</td>\n",
       "      <td>G6</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.713849</td>\n",
       "      <td>0.824582</td>\n",
       "      <td>1.793975</td>\n",
       "      <td>1.037172</td>\n",
       "      <td>1.008687</td>\n",
       "      <td>0.936484</td>\n",
       "      <td>0.788152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>3</td>\n",
       "      <td>G7</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.542052</td>\n",
       "      <td>0.819145</td>\n",
       "      <td>1.807980</td>\n",
       "      <td>1.030458</td>\n",
       "      <td>0.884778</td>\n",
       "      <td>0.841602</td>\n",
       "      <td>0.834562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>3</td>\n",
       "      <td>G8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.907561</td>\n",
       "      <td>0.971211</td>\n",
       "      <td>1.804633</td>\n",
       "      <td>0.982474</td>\n",
       "      <td>0.961764</td>\n",
       "      <td>0.889176</td>\n",
       "      <td>0.793636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>3</td>\n",
       "      <td>G9</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.809128</td>\n",
       "      <td>0.964354</td>\n",
       "      <td>1.633225</td>\n",
       "      <td>1.099361</td>\n",
       "      <td>0.859415</td>\n",
       "      <td>0.765923</td>\n",
       "      <td>0.771201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>423 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "condition  rearray_plate_nb RA_well    strain  fluc_assay  pre_culture  \\\n",
       "0                         1     B10  LL13-040         5.0          3.0   \n",
       "1                         1     B11  LL13-040         5.0          4.0   \n",
       "2                         1     B12  LL13-040         6.0          3.0   \n",
       "3                         1     B13  LL13-040         6.0          5.0   \n",
       "4                         1     B14     NC-02         2.0          1.0   \n",
       "..                      ...     ...       ...         ...          ...   \n",
       "418                       3      G5     NC-02         4.0          2.0   \n",
       "419                       3      G6     NC-02         4.0          1.0   \n",
       "420                       3      G7     NC-02         4.0          1.0   \n",
       "421                       3      G8     NC-02         4.0          4.0   \n",
       "422                       3      G9     NC-02         5.0          3.0   \n",
       "\n",
       "condition genotype  respiration      5-FC  Caspo_low  Fluco_high  Mica_low  \\\n",
       "0           mutant         True  2.226848   0.925959    0.938230  0.822359   \n",
       "1           mutant         True  1.891424   0.933826    0.859766  0.847902   \n",
       "2           mutant         True  1.847464   0.953826    0.908647  0.865549   \n",
       "3           mutant         True  1.935734   0.922960    1.001824  0.922815   \n",
       "4           mutant         True  2.330562   0.964526    1.037661  1.111597   \n",
       "..             ...          ...       ...        ...         ...       ...   \n",
       "418         mutant        False  1.885240   0.808299    1.648419  0.892301   \n",
       "419         mutant        False  1.713849   0.824582    1.793975  1.037172   \n",
       "420         mutant        False  1.542052   0.819145    1.807980  1.030458   \n",
       "421         mutant        False  1.907561   0.971211    1.804633  0.982474   \n",
       "422         mutant        False  1.809128   0.964354    1.633225  1.099361   \n",
       "\n",
       "condition  Nystatin_low        SD       YPD  \n",
       "0              1.031657  0.993439  0.926485  \n",
       "1              0.875719  0.909714  0.911814  \n",
       "2              0.946033  0.896583  0.881957  \n",
       "3              0.930109  0.941059  0.914813  \n",
       "4              0.940065  1.023869  0.941790  \n",
       "..                  ...       ...       ...  \n",
       "418            0.932186  0.881574  0.798866  \n",
       "419            1.008687  0.936484  0.788152  \n",
       "420            0.884778  0.841602  0.834562  \n",
       "421            0.961764  0.889176  0.793636  \n",
       "422            0.859415  0.765923  0.771201  \n",
       "\n",
       "[423 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel37_df = pool37_df[pool37_df.condition.isin(['YPD','SD','5-FC','Mica_low','Caspo_low','Nystatin_low','Fluco_high'])]\n",
    "wide37_df = sel37_df.pivot(index=['rearray_plate_nb','RA_well','strain','fluc_assay','pre_culture','genotype','respiration'],columns='condition', values='rel_fit').reset_index()\n",
    "wide37_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8868ba21-f237-4f93-b30e-8b33ed9852ce",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Rho+ 30°C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a14f2223-5124-43f5-880f-0daf504571ed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plate_number</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>auc</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>fluc_mutant</th>\n",
       "      <th>genotype</th>\n",
       "      <th>FCY1_genotype</th>\n",
       "      <th>FUR1_genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>key</th>\n",
       "      <th>Genomix_plate_nb</th>\n",
       "      <th>Genomix_plate_well</th>\n",
       "      <th>rel_fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B10</td>\n",
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       "      <td>1</td>\n",
       "      <td>YPD</td>\n",
       "      <td>5</td>\n",
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       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.974173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B10</td>\n",
       "      <td>10836.625</td>\n",
       "      <td>1</td>\n",
       "      <td>SD</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.958061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B10</td>\n",
       "      <td>13931.625</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>1.954321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B10</td>\n",
       "      <td>9752.000</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FU_high</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>1.870618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B10</td>\n",
       "      <td>11265.875</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FU_low</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>1.602913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>1</td>\n",
       "      <td>O9</td>\n",
       "      <td>13071.000</td>\n",
       "      <td>1</td>\n",
       "      <td>YPD</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>0.866153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>2</td>\n",
       "      <td>O9</td>\n",
       "      <td>10250.375</td>\n",
       "      <td>1</td>\n",
       "      <td>SD</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>0.906231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>3</td>\n",
       "      <td>O9</td>\n",
       "      <td>11339.000</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>1.590629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>4</td>\n",
       "      <td>O9</td>\n",
       "      <td>7949.875</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FU_high</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>1.524936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1539</th>\n",
       "      <td>5</td>\n",
       "      <td>O9</td>\n",
       "      <td>9720.500</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FU_low</td>\n",
       "      <td>5</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>D10</td>\n",
       "      <td>1.383037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1540 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      plate_number RA_well        auc  rearray_plate_nb  condition  \\\n",
       "0                1     B10  14701.125                 1        YPD   \n",
       "1                2     B10  10836.625                 1         SD   \n",
       "2                3     B10  13931.625                 1       5-FC   \n",
       "3                4     B10   9752.000                 1  5-FU_high   \n",
       "4                5     B10  11265.875                 1   5-FU_low   \n",
       "...            ...     ...        ...               ...        ...   \n",
       "1535             1      O9  13071.000                 1        YPD   \n",
       "1536             2      O9  10250.375                 1         SD   \n",
       "1537             3      O9  11339.000                 1       5-FC   \n",
       "1538             4      O9   7949.875                 1  5-FU_high   \n",
       "1539             5      O9   9720.500                 1   5-FU_low   \n",
       "\n",
       "      mutant_plate 96_well    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "1                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "2                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "3                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "4                5      A3  LL13-040         5.0          3.0          2.0   \n",
       "...            ...     ...       ...         ...          ...          ...   \n",
       "1535             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "1536             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "1537             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "1538             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "1539             5      B2  LL13-040         5.0          2.0          2.0   \n",
       "\n",
       "     genotype FCY1_genotype  FUR1_genotype respiration    key  \\\n",
       "0      mutant           NaN            NaN        True  113.0   \n",
       "1      mutant           NaN            NaN        True  113.0   \n",
       "2      mutant           NaN            NaN        True  113.0   \n",
       "3      mutant           NaN            NaN        True  113.0   \n",
       "4      mutant           NaN            NaN        True  113.0   \n",
       "...       ...           ...            ...         ...    ...   \n",
       "1535   mutant           NaN            NaN        True  112.0   \n",
       "1536   mutant           NaN            NaN        True  112.0   \n",
       "1537   mutant           NaN            NaN        True  112.0   \n",
       "1538   mutant           NaN            NaN        True  112.0   \n",
       "1539   mutant           NaN            NaN        True  112.0   \n",
       "\n",
       "      Genomix_plate_nb Genomix_plate_well   rel_fit  \n",
       "0                  1.0                A11  0.974173  \n",
       "1                  1.0                A11  0.958061  \n",
       "2                  1.0                A11  1.954321  \n",
       "3                  1.0                A11  1.870618  \n",
       "4                  1.0                A11  1.602913  \n",
       "...                ...                ...       ...  \n",
       "1535               1.0                D10  0.866153  \n",
       "1536               1.0                D10  0.906231  \n",
       "1537               1.0                D10  1.590629  \n",
       "1538               1.0                D10  1.524936  \n",
       "1539               1.0                D10  1.383037  \n",
       "\n",
       "[1540 rows x 19 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rhop_df = generate_relative_fitness(rhop30_path, incubation_time = 22, layout = layout_path)\n",
    "rhop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "87e56500-4705-479f-a90e-f8ff0edabecf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Dropping 5-FU_low condition\n",
    "rhop_df.drop(rhop_df[rhop_df.condition=='5-FU_low'].index, inplace=True)\n",
    "rhop_df.replace('5-FU_high','5-FU', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "48f582c4-a73e-4abf-b953-c8666f921d4d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rhop_df.to_excel('data/rhop30.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d66fcc-e62a-43c8-97ca-5a033d5009b5",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Rho- 30°C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "47569619-f6ab-47bd-9591-efdbd865e312",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plate_number</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>auc</th>\n",
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       "      <th>genotype</th>\n",
       "      <th>FCY1_genotype</th>\n",
       "      <th>FUR1_genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>key</th>\n",
       "      <th>Genomix_plate_nb</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <td>5914.875</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B10</td>\n",
       "      <td>7893.375</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.619278</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B10</td>\n",
       "      <td>8580.250</td>\n",
       "      <td>2</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>7</td>\n",
       "      <td>D3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.518561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>B11</td>\n",
       "      <td>8665.750</td>\n",
       "      <td>2</td>\n",
       "      <td>YPD</td>\n",
       "      <td>7</td>\n",
       "      <td>E4</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.472992</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>471</th>\n",
       "      <td>8</td>\n",
       "      <td>G8</td>\n",
       "      <td>8472.875</td>\n",
       "      <td>3</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>3</td>\n",
       "      <td>H10</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.830242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>5</td>\n",
       "      <td>G9</td>\n",
       "      <td>13367.500</td>\n",
       "      <td>3</td>\n",
       "      <td>YPD</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.817153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>6</td>\n",
       "      <td>G9</td>\n",
       "      <td>9779.125</td>\n",
       "      <td>3</td>\n",
       "      <td>SD</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>7</td>\n",
       "      <td>G9</td>\n",
       "      <td>7290.625</td>\n",
       "      <td>3</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.987765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>8</td>\n",
       "      <td>G9</td>\n",
       "      <td>7130.625</td>\n",
       "      <td>3</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.540300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>476 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     plate_number RA_well        auc  rearray_plate_nb condition  \\\n",
       "0               1     B10  11451.750                 2       YPD   \n",
       "1               2     B10   5914.875                 2        SD   \n",
       "2               3     B10   7893.375                 2      5-FC   \n",
       "3               4     B10   8580.250                 2      5-FU   \n",
       "4               1     B11   8665.750                 2       YPD   \n",
       "..            ...     ...        ...               ...       ...   \n",
       "471             8      G8   8472.875                 3      5-FU   \n",
       "472             5      G9  13367.500                 3       YPD   \n",
       "473             6      G9   9779.125                 3        SD   \n",
       "474             7      G9   7290.625                 3      5-FC   \n",
       "475             8      G9   7130.625                 3      5-FU   \n",
       "\n",
       "     mutant_plate 96_well    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0               7      D3  LL13-040         7.0          3.0          2.0   \n",
       "1               7      D3  LL13-040         7.0          3.0          2.0   \n",
       "2               7      D3  LL13-040         7.0          3.0          2.0   \n",
       "3               7      D3  LL13-040         7.0          3.0          2.0   \n",
       "4               7      E4  LL13-040         7.0          3.0         11.0   \n",
       "..            ...     ...       ...         ...          ...          ...   \n",
       "471             3     H10     NC-02         4.0          4.0          2.0   \n",
       "472             4      A8     NC-02         5.0          3.0          7.0   \n",
       "473             4      A8     NC-02         5.0          3.0          7.0   \n",
       "474             4      A8     NC-02         5.0          3.0          7.0   \n",
       "475             4      A8     NC-02         5.0          3.0          7.0   \n",
       "\n",
       "    genotype FCY1_genotype  FUR1_genotype respiration   key  Genomix_plate_nb  \\\n",
       "0     mutant           NaN            NaN       False  49.0               NaN   \n",
       "1     mutant           NaN            NaN       False  49.0               NaN   \n",
       "2     mutant           NaN            NaN       False  49.0               NaN   \n",
       "3     mutant           NaN            NaN       False  49.0               NaN   \n",
       "4     mutant           NaN            NaN       False  55.0               NaN   \n",
       "..       ...           ...            ...         ...   ...               ...   \n",
       "471   mutant           NaN            NaN       False  42.0               NaN   \n",
       "472   mutant           NaN            NaN       False  48.0               NaN   \n",
       "473   mutant           NaN            NaN       False  48.0               NaN   \n",
       "474   mutant           NaN            NaN       False  48.0               NaN   \n",
       "475   mutant           NaN            NaN       False  48.0               NaN   \n",
       "\n",
       "    Genomix_plate_well   rel_fit  \n",
       "0                  NaN  0.625057  \n",
       "1                  NaN  0.620357  \n",
       "2                  NaN  1.619278  \n",
       "3                  NaN  1.518561  \n",
       "4                  NaN  0.472992  \n",
       "..                 ...       ...  \n",
       "471                NaN  1.830242  \n",
       "472                NaN  0.817153  \n",
       "473                NaN  1.000281  \n",
       "474                NaN  1.987765  \n",
       "475                NaN  1.540300  \n",
       "\n",
       "[476 rows x 19 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rhom_df = generate_relative_fitness(rhom30_path, incubation_time = 22, layout = layout_path)\n",
    "rhom_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ca973000-f5ea-4464-ae61-9a616a050a6a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Filtering out misannotated rho+ mutants from rho- plates\n",
    "rhom_df.drop(rhom_df[(rhom_df.genotype=='mutant') & (rhom_df.respiration==True)].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "89a76a22-2202-40fc-bb9d-5005a88a427e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rhom_df.to_excel('data/rhom30.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2929a03a-6a44-4c20-af90-88d28bac8685",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Pooling rho+ and rho- 30°C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45e3f557-1383-4d5f-aac5-5ce3855da1c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rhop_df = pd.read_excel('data/rhop30.xlsx', header=0, index_col=0)\n",
    "rhom_df = pd.read_excel('data/rhom30.xlsx', header=0, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d161f03-7273-4957-a9bc-4ee477bee914",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditions missing in one of the dataframes: []\n"
     ]
    }
   ],
   "source": [
    "missing_conditions = [x for x in max(rhop_df.condition.unique().tolist(),rhom_df.condition.unique().tolist(), key=len) if x not in min(rhop_df.condition.unique().tolist(),rhom_df.condition.unique().tolist(), key=len)]\n",
    "print(\"Conditions missing in one of the dataframes:\", missing_conditions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ea7c3aa-2a65-4900-b733-51704c595799",
   "metadata": {
    "tags": []
   },
   "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>plate_number</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>auc</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>fluc_mutant</th>\n",
       "      <th>genotype</th>\n",
       "      <th>FCY1_genotype</th>\n",
       "      <th>FUR1_genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>key</th>\n",
       "      <th>Genomix_plate_nb</th>\n",
       "      <th>Genomix_plate_well</th>\n",
       "      <th>rel_fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B10</td>\n",
       "      <td>14701.125</td>\n",
       "      <td>1</td>\n",
       "      <td>YPD</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.974173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B10</td>\n",
       "      <td>10836.625</td>\n",
       "      <td>1</td>\n",
       "      <td>SD</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>0.958061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B10</td>\n",
       "      <td>13931.625</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>1.954321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B10</td>\n",
       "      <td>9752.000</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>5</td>\n",
       "      <td>A3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A11</td>\n",
       "      <td>1.870618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>B11</td>\n",
       "      <td>12602.375</td>\n",
       "      <td>1</td>\n",
       "      <td>YPD</td>\n",
       "      <td>5</td>\n",
       "      <td>E5</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>127</td>\n",
       "      <td>2.0</td>\n",
       "      <td>A7</td>\n",
       "      <td>0.835099</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>471</th>\n",
       "      <td>8</td>\n",
       "      <td>G8</td>\n",
       "      <td>8472.875</td>\n",
       "      <td>3</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>3</td>\n",
       "      <td>H10</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.830242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>5</td>\n",
       "      <td>G9</td>\n",
       "      <td>13367.500</td>\n",
       "      <td>3</td>\n",
       "      <td>YPD</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.817153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>6</td>\n",
       "      <td>G9</td>\n",
       "      <td>9779.125</td>\n",
       "      <td>3</td>\n",
       "      <td>SD</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>7</td>\n",
       "      <td>G9</td>\n",
       "      <td>7290.625</td>\n",
       "      <td>3</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.987765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>8</td>\n",
       "      <td>G9</td>\n",
       "      <td>7130.625</td>\n",
       "      <td>3</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.540300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1692 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     plate_number RA_well        auc  rearray_plate_nb condition  \\\n",
       "0               1     B10  14701.125                 1       YPD   \n",
       "1               2     B10  10836.625                 1        SD   \n",
       "2               3     B10  13931.625                 1      5-FC   \n",
       "3               4     B10   9752.000                 1      5-FU   \n",
       "5               1     B11  12602.375                 1       YPD   \n",
       "..            ...     ...        ...               ...       ...   \n",
       "471             8      G8   8472.875                 3      5-FU   \n",
       "472             5      G9  13367.500                 3       YPD   \n",
       "473             6      G9   9779.125                 3        SD   \n",
       "474             7      G9   7290.625                 3      5-FC   \n",
       "475             8      G9   7130.625                 3      5-FU   \n",
       "\n",
       "     mutant_plate 96_well    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0               5      A3  LL13-040         5.0          3.0          2.0   \n",
       "1               5      A3  LL13-040         5.0          3.0          2.0   \n",
       "2               5      A3  LL13-040         5.0          3.0          2.0   \n",
       "3               5      A3  LL13-040         5.0          3.0          2.0   \n",
       "5               5      E5  LL13-040         5.0          4.0         12.0   \n",
       "..            ...     ...       ...         ...          ...          ...   \n",
       "471             3     H10     NC-02         4.0          4.0          2.0   \n",
       "472             4      A8     NC-02         5.0          3.0          7.0   \n",
       "473             4      A8     NC-02         5.0          3.0          7.0   \n",
       "474             4      A8     NC-02         5.0          3.0          7.0   \n",
       "475             4      A8     NC-02         5.0          3.0          7.0   \n",
       "\n",
       "    genotype FCY1_genotype  FUR1_genotype  respiration  key  Genomix_plate_nb  \\\n",
       "0     mutant           NaN            NaN         True  113               1.0   \n",
       "1     mutant           NaN            NaN         True  113               1.0   \n",
       "2     mutant           NaN            NaN         True  113               1.0   \n",
       "3     mutant           NaN            NaN         True  113               1.0   \n",
       "5     mutant           NaN            NaN         True  127               2.0   \n",
       "..       ...           ...            ...          ...  ...               ...   \n",
       "471   mutant           NaN            NaN        False   42               NaN   \n",
       "472   mutant           NaN            NaN        False   48               NaN   \n",
       "473   mutant           NaN            NaN        False   48               NaN   \n",
       "474   mutant           NaN            NaN        False   48               NaN   \n",
       "475   mutant           NaN            NaN        False   48               NaN   \n",
       "\n",
       "    Genomix_plate_well   rel_fit  \n",
       "0                  A11  0.974173  \n",
       "1                  A11  0.958061  \n",
       "2                  A11  1.954321  \n",
       "3                  A11  1.870618  \n",
       "5                   A7  0.835099  \n",
       "..                 ...       ...  \n",
       "471                NaN  1.830242  \n",
       "472                NaN  0.817153  \n",
       "473                NaN  1.000281  \n",
       "474                NaN  1.987765  \n",
       "475                NaN  1.540300  \n",
       "\n",
       "[1692 rows x 19 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pool30_df = pd.concat([rhop_df, rhom_df])\n",
    "pool30_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b58abb7-76dc-4093-9d1c-9219d2f7b7f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>condition</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>strain</th>\n",
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       "      <td>0.964426</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>B13</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.735410</td>\n",
       "      <td>0.914298</td>\n",
       "      <td>0.920457</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1.743459</td>\n",
       "      <td>0.843219</td>\n",
       "      <td>0.691845</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>1.970725</td>\n",
       "      <td>1.562927</td>\n",
       "      <td>0.939254</td>\n",
       "      <td>0.825092</td>\n",
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       "<p>423 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "condition  rearray_plate_nb RA_well    strain  fluc_assay  pre_culture  \\\n",
       "0                         1     B10  LL13-040         5.0          3.0   \n",
       "1                         1     B11  LL13-040         5.0          4.0   \n",
       "2                         1     B12  LL13-040         6.0          3.0   \n",
       "3                         1     B13  LL13-040         6.0          5.0   \n",
       "4                         1     B14     NC-02         2.0          1.0   \n",
       "..                      ...     ...       ...         ...          ...   \n",
       "418                       3      G5     NC-02         4.0          2.0   \n",
       "419                       3      G6     NC-02         4.0          1.0   \n",
       "420                       3      G7     NC-02         4.0          1.0   \n",
       "421                       3      G8     NC-02         4.0          4.0   \n",
       "422                       3      G9     NC-02         5.0          3.0   \n",
       "\n",
       "condition genotype  respiration      5-FC      5-FU        SD       YPD  \n",
       "0           mutant         True  1.954321  1.870618  0.958061  0.974173  \n",
       "1           mutant         True  1.673482  1.542248  0.829005  0.835099  \n",
       "2           mutant         True  1.682267  1.625977  0.964426  0.882454  \n",
       "3           mutant         True  1.777464  1.735410  0.914298  0.920457  \n",
       "4           mutant         True  2.067802  1.880870  1.032883  0.924059  \n",
       "..             ...          ...       ...       ...       ...       ...  \n",
       "418         mutant        False  2.108173  1.455434  0.835816  0.763091  \n",
       "419         mutant        False  1.961659  1.743459  0.843219  0.691845  \n",
       "420         mutant        False  1.970725  1.562927  0.939254  0.825092  \n",
       "421         mutant        False  2.322473  1.830242  0.877281  0.681720  \n",
       "422         mutant        False  1.987765  1.540300  1.000281  0.817153  \n",
       "\n",
       "[423 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel30_df = pool30_df[pool30_df.condition.isin(['YPD','SD','5-FC','5-FU','Mica_low','Caspo_low','Nystatin_low','Fluco_high'])]\n",
    "wide30_df = sel30_df.pivot(index=['rearray_plate_nb','RA_well','strain','fluc_assay','pre_culture','genotype','respiration'],columns='condition', values='rel_fit').reset_index()\n",
    "wide30_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e00e7ea0-a2b7-4b41-af38-55dd0433baaf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <td>3.0</td>\n",
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       "      <td>0.891466</td>\n",
       "      <td>0.905299</td>\n",
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       "      <td>1.682382</td>\n",
       "      <td>1.000115</td>\n",
       "      <td>0.819896</td>\n",
       "      <td>1.118174</td>\n",
       "      <td>0.616176</td>\n",
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       "      <th>92</th>\n",
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       "      <td>F3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>2.368550</td>\n",
       "      <td>1.845606</td>\n",
       "      <td>1.143369</td>\n",
       "      <td>0.837899</td>\n",
       "      <td>0.907390</td>\n",
       "      <td>0.547253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>3</td>\n",
       "      <td>G2</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.813373</td>\n",
       "      <td>1.406831</td>\n",
       "      <td>0.624171</td>\n",
       "      <td>0.624288</td>\n",
       "      <td>1.292205</td>\n",
       "      <td>0.844786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>3</td>\n",
       "      <td>G3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>2.203292</td>\n",
       "      <td>1.658296</td>\n",
       "      <td>0.964928</td>\n",
       "      <td>0.849644</td>\n",
       "      <td>1.354301</td>\n",
       "      <td>0.615235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    rearray_plate_nb RA_well    strain  fluc_assay  pre_culture genotype  \\\n",
       "0                  1     B14     NC-02         2.0          1.0   mutant   \n",
       "1                  1     B15     NC-02         3.0          2.0   mutant   \n",
       "2                  1      B2  LL13-040         1.0          1.0   mutant   \n",
       "3                  1      B3  LL13-040         3.0          1.0   mutant   \n",
       "4                  1      B4  LL13-040         3.0          3.0   mutant   \n",
       "..               ...     ...       ...         ...          ...      ...   \n",
       "90                 3      E3     NC-02         2.0          1.0   mutant   \n",
       "91                 3      F2     NC-02         3.0          1.0   mutant   \n",
       "92                 3      F3     NC-02         3.0          1.0   mutant   \n",
       "93                 3      G2     NC-02         3.0          3.0   mutant   \n",
       "94                 3      G3     NC-02         3.0          1.0   mutant   \n",
       "\n",
       "    respiration      5-FC      5-FU        SD       YPD  5-FC_liq    SD_liq  \n",
       "0          True  2.067802  1.880870  1.032883  0.924059  2.757345  1.269227  \n",
       "1          True  2.114228  1.966782  1.168387  0.934091  2.806210  1.096943  \n",
       "2          True  1.576461  1.649475  0.833138  0.928525  2.866106  1.279732  \n",
       "3          True  1.599975  1.633146  0.855064  0.903501  2.683896  1.283241  \n",
       "4          True  1.591699  1.623411  0.891466  0.905299  2.715165  1.475373  \n",
       "..          ...       ...       ...       ...       ...       ...       ...  \n",
       "90        False  2.091098  2.073903  0.812558  0.807785  0.816232  0.508661  \n",
       "91        False  2.249370  1.682382  1.000115  0.819896  1.118174  0.616176  \n",
       "92        False  2.368550  1.845606  1.143369  0.837899  0.907390  0.547253  \n",
       "93        False  1.813373  1.406831  0.624171  0.624288  1.292205  0.844786  \n",
       "94        False  2.203292  1.658296  0.964928  0.849644  1.354301  0.615235  \n",
       "\n",
       "[95 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tecan384df = pd.read_excel(tecan384_data, index_col=0)\n",
    "wide_sol_liq = wide30_df.merge(right=tecan384df, on=['rearray_plate_nb','RA_well','strain','genotype','respiration'])\n",
    "wide_sol_liq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "587a1b0b-a9a8-452c-b186-0a6f58e32a67",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>5-FC</th>\n",
       "      <th>5-FU</th>\n",
       "      <th>SD</th>\n",
       "      <th>YPD</th>\n",
       "      <th>5-FC_liq</th>\n",
       "      <th>SD_liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>E23</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>True</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>F23</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>True</td>\n",
       "      <td>1.176065</td>\n",
       "      <td>0.878850</td>\n",
       "      <td>1.061738</td>\n",
       "      <td>0.928241</td>\n",
       "      <td>2.965905</td>\n",
       "      <td>1.153566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>I13</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>True</td>\n",
       "      <td>1.205825</td>\n",
       "      <td>0.991344</td>\n",
       "      <td>1.039099</td>\n",
       "      <td>0.994243</td>\n",
       "      <td>2.179410</td>\n",
       "      <td>0.969056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2</td>\n",
       "      <td>G11</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>True</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rearray_plate_nb RA_well    strain  fluc_assay  pre_culture genotype  \\\n",
       "18                 1     E23     NC-02         NaN          NaN       wt   \n",
       "24                 1     F23     NC-02         NaN          NaN     FCY1   \n",
       "37                 1     I13  LL13-040         NaN          NaN     FCY1   \n",
       "82                 2     G11  LL13-040         NaN          NaN       wt   \n",
       "\n",
       "    respiration      5-FC      5-FU        SD       YPD  5-FC_liq    SD_liq  \n",
       "18         True  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "24         True  1.176065  0.878850  1.061738  0.928241  2.965905  1.153566  \n",
       "37         True  1.205825  0.991344  1.039099  0.994243  2.179410  0.969056  \n",
       "82         True  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wide_sol_liq[wide_sol_liq.genotype!='mutant']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9465c39f-cf66-4139-a8b5-0d3d62087f12",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Rho- YPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8daefe69-d73b-4689-9790-498bde410bd1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plate_number</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>auc</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>fluc_mutant</th>\n",
       "      <th>genotype</th>\n",
       "      <th>FCY1_genotype</th>\n",
       "      <th>FUR1_genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>key</th>\n",
       "      <th>Genomix_plate_nb</th>\n",
       "      <th>Genomix_plate_well</th>\n",
       "      <th>rel_fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B10</td>\n",
       "      <td>2011.875000</td>\n",
       "      <td>2</td>\n",
       "      <td>YPG</td>\n",
       "      <td>7</td>\n",
       "      <td>D3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.340239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>B11</td>\n",
       "      <td>2150.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>YPG</td>\n",
       "      <td>7</td>\n",
       "      <td>E4</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.363682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>B2</td>\n",
       "      <td>1263.833333</td>\n",
       "      <td>2</td>\n",
       "      <td>YPG</td>\n",
       "      <td>1</td>\n",
       "      <td>B1</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.213734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>B3</td>\n",
       "      <td>1495.666667</td>\n",
       "      <td>2</td>\n",
       "      <td>YPG</td>\n",
       "      <td>2</td>\n",
       "      <td>B1</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.252940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>B4</td>\n",
       "      <td>2462.375000</td>\n",
       "      <td>2</td>\n",
       "      <td>YPG</td>\n",
       "      <td>2</td>\n",
       "      <td>E8</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.416425</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>113</th>\n",
       "      <td>2</td>\n",
       "      <td>G5</td>\n",
       "      <td>3069.375000</td>\n",
       "      <td>3</td>\n",
       "      <td>YPG</td>\n",
       "      <td>3</td>\n",
       "      <td>C7</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.377595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2</td>\n",
       "      <td>G6</td>\n",
       "      <td>3127.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>YPG</td>\n",
       "      <td>3</td>\n",
       "      <td>E4</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.384746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2</td>\n",
       "      <td>G7</td>\n",
       "      <td>3348.125000</td>\n",
       "      <td>3</td>\n",
       "      <td>YPG</td>\n",
       "      <td>3</td>\n",
       "      <td>F3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.411887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2</td>\n",
       "      <td>G8</td>\n",
       "      <td>3193.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>YPG</td>\n",
       "      <td>3</td>\n",
       "      <td>H10</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.392865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2</td>\n",
       "      <td>G9</td>\n",
       "      <td>3402.375000</td>\n",
       "      <td>3</td>\n",
       "      <td>YPG</td>\n",
       "      <td>4</td>\n",
       "      <td>A8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.418561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     plate_number RA_well          auc  rearray_plate_nb condition  \\\n",
       "0               1     B10  2011.875000                 2       YPG   \n",
       "1               1     B11  2150.500000                 2       YPG   \n",
       "2               1      B2  1263.833333                 2       YPG   \n",
       "3               1      B3  1495.666667                 2       YPG   \n",
       "4               1      B4  2462.375000                 2       YPG   \n",
       "..            ...     ...          ...               ...       ...   \n",
       "113             2      G5  3069.375000                 3       YPG   \n",
       "114             2      G6  3127.500000                 3       YPG   \n",
       "115             2      G7  3348.125000                 3       YPG   \n",
       "116             2      G8  3193.500000                 3       YPG   \n",
       "117             2      G9  3402.375000                 3       YPG   \n",
       "\n",
       "     mutant_plate 96_well    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0               7      D3  LL13-040         7.0          3.0          2.0   \n",
       "1               7      E4  LL13-040         7.0          3.0         11.0   \n",
       "2               1      B1  LL13-040         1.0          1.0          3.0   \n",
       "3               2      B1  LL13-040         3.0          3.0         22.0   \n",
       "4               2      E8  LL13-040         4.0          2.0         16.0   \n",
       "..            ...     ...       ...         ...          ...          ...   \n",
       "113             3      C7     NC-02         4.0          2.0         19.0   \n",
       "114             3      E4     NC-02         4.0          1.0         18.0   \n",
       "115             3      F3     NC-02         4.0          1.0         11.0   \n",
       "116             3     H10     NC-02         4.0          4.0          2.0   \n",
       "117             4      A8     NC-02         5.0          3.0          7.0   \n",
       "\n",
       "    genotype FCY1_genotype  FUR1_genotype respiration   key  Genomix_plate_nb  \\\n",
       "0     mutant           NaN            NaN       False  49.0               NaN   \n",
       "1     mutant           NaN            NaN       False  55.0               NaN   \n",
       "2     mutant            wt            NaN       False   1.0               NaN   \n",
       "3     mutant           NaN            NaN       False   7.0               NaN   \n",
       "4     mutant           NaN            NaN       False  13.0               NaN   \n",
       "..       ...           ...            ...         ...   ...               ...   \n",
       "113   mutant           NaN            NaN       False  24.0               NaN   \n",
       "114   mutant           NaN            NaN       False  30.0               NaN   \n",
       "115   mutant           NaN            NaN       False  36.0               NaN   \n",
       "116   mutant           NaN            NaN       False  42.0               NaN   \n",
       "117   mutant           NaN            NaN       False  48.0               NaN   \n",
       "\n",
       "    Genomix_plate_well   rel_fit  \n",
       "0                  NaN  0.340239  \n",
       "1                  NaN  0.363682  \n",
       "2                  NaN  0.213734  \n",
       "3                  NaN  0.252940  \n",
       "4                  NaN  0.416425  \n",
       "..                 ...       ...  \n",
       "113                NaN  0.377595  \n",
       "114                NaN  0.384746  \n",
       "115                NaN  0.411887  \n",
       "116                NaN  0.392865  \n",
       "117                NaN  0.418561  \n",
       "\n",
       "[118 rows x 19 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rhomYPG = generate_relative_fitness(rhomYPG_path, incubation_time = 22, layout = layout_path)\n",
    "rhomYPG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92b5f66-39f0-4aeb-856f-1cdf68ef9a92",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Scatterplots 37°C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c458c39b-c134-4395-ab74-ae3dc2149f39",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Two-way ANOVA with Tukey's multiple comparison test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f87174bf-92ea-41f5-8981-d81143267a10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n"
     ]
    },
    {
     "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>condition1</th>\n",
       "      <th>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>reject</th>\n",
       "      <th>stat_significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>False / 5-FC</td>\n",
       "      <td>True / 5-FC</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>ns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Caspo_low</td>\n",
       "      <td>False / Caspo_low</td>\n",
       "      <td>True / Caspo_low</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>ns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Fluco_high</td>\n",
       "      <td>False / Fluco_high</td>\n",
       "      <td>True / Fluco_high</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Mica_low</td>\n",
       "      <td>False / Mica_low</td>\n",
       "      <td>True / Mica_low</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>True</td>\n",
       "      <td>***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Nystatin_low</td>\n",
       "      <td>False / Nystatin_low</td>\n",
       "      <td>True / Nystatin_low</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>True</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>SD</td>\n",
       "      <td>False / SD</td>\n",
       "      <td>True / SD</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>ns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>YPD</td>\n",
       "      <td>False / YPD</td>\n",
       "      <td>True / YPD</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      condition1                group1               group2   p-adj  reject  \\\n",
       "6           5-FC          False / 5-FC          True / 5-FC  1.0000   False   \n",
       "19     Caspo_low     False / Caspo_low     True / Caspo_low  1.0000   False   \n",
       "31    Fluco_high    False / Fluco_high    True / Fluco_high  0.0000    True   \n",
       "42      Mica_low      False / Mica_low      True / Mica_low  0.0007    True   \n",
       "52  Nystatin_low  False / Nystatin_low  True / Nystatin_low  0.0287    True   \n",
       "61            SD            False / SD            True / SD  1.0000   False   \n",
       "69           YPD           False / YPD           True / YPD  0.0000    True   \n",
       "\n",
       "   stat_significance  \n",
       "6                 ns  \n",
       "19                ns  \n",
       "31              ****  \n",
       "42               ***  \n",
       "52                 *  \n",
       "61                ns  \n",
       "69              ****  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from flucytosine_functions import get_Tukey\n",
    "get_Tukey(sel37_df[(sel37_df.genotype.isin(['wt','mutant'])) & (sel37_df.strain=='NC-02')], 'respiration', 'condition', 'rel_fit')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "172326b9-5eb3-4c51-acbf-1b9139a5f103",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\seaborn\\axisgrid.py:712: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "C:\\Users\\duran\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\seaborn\\utils.py:482: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  new_legend = legend_func(handles, labels, loc=loc, **props)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 833.706x402 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# V1 with lowest concentration of drugs for caspo-mica-nystatin and medium for fluconazole\n",
    "fig = sns.catplot(data=sel37_df[sel37_df.genotype.isin(['wt','mutant'])],\n",
    "                  col='strain', col_order=['LL13-040','NC-02'],\n",
    "                  x='condition', y='rel_fit',\n",
    "                  hue='respiration', hue_order=[True, False], palette=['grey','red'], alpha=.3,\n",
    "                  kind='strip', dodge=True, legend_out=False,\n",
    "                  height=4.02, aspect=0.93 # height*aspect = width of EACH facet\n",
    "                 )\n",
    "\n",
    "fig.map(sns.boxplot,\n",
    "        data=sel37_df[sel37_df.genotype.isin(['wt','mutant'])],\n",
    "        x='condition', y='rel_fit',\n",
    "        hue='respiration', hue_order=[True, False], palette=['grey','red'],\n",
    "        fliersize=0\n",
    "       )\n",
    "\n",
    "fig.map(plt.axhline, y=1, ls='--', c='grey')\n",
    "plt.yscale('log',base=2)\n",
    "fig.set(ylim=(2**-3,2**2))\n",
    "fig.set_xticklabels(['YPD','SD','5-FC','Micafungin','Caspofungin','Nystatin','Fluconazole'], rotation=45)\n",
    "fig.set_axis_labels('', 'Relative growth')\n",
    "fig.set_titles(col_template='{col_name}')\n",
    "\n",
    "handles, labels = fig.axes[0][1].get_legend_handles_labels()\n",
    "sns.move_legend(fig, bbox_to_anchor=(0.08, 0.23), loc='lower left', title=None, frameon=True,\n",
    "                handles=handles, labels=['$\\it{rho}$⁺','$\\it{rho}$⁻'], markerscale=0.75,\n",
    "                handletextpad=0.1, borderpad=0.3)\n",
    "\n",
    "# Because move_legend function does not accept formatted labels, we need to set them manually\n",
    "#for i, v in enumerate():\n",
    "#    fig._legend.texts[i].set_text(v)\n",
    "\n",
    "for i, s in enumerate(sel37_df.strain.unique()):\n",
    "    for j, x in enumerate(sel37_df.condition.unique()):\n",
    "        maxv = sel37_df[(sel37_df.genotype.isin(['wt','mutant'])) & (sel37_df.strain==s) & (sel37_df.condition==x)].rel_fit.max()\n",
    "        tukey_df = get_Tukey(sel37_df[(sel37_df.genotype.isin(['wt','mutant'])) & (sel37_df.strain==s)], 'respiration','condition','rel_fit')[1]\n",
    "        tukey_stat = tukey_df.loc[tukey_df.condition1==x, 'stat_significance'].values[0]\n",
    "        fig.axes[0][i].text(j, maxv+0.2, tukey_stat, ha='center')\n",
    "\n",
    "#fig.fig.subplots_adjust(top=0.92)\n",
    "#fig.fig.suptitle('Relative fitness at 37°C')\n",
    "fig.tight_layout()\n",
    "plt.savefig(graph_outpath+'20230724_rel_fit_pooled_respi_37_22h_stats.svg', format='svg', dpi=300)\n",
    "plt.savefig(graph_outpath+'20230724_rel_fit_pooled_respi_37_22h_stats.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fa849569-a810-43e7-b390-d56296c10784",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\seaborn\\utils.py:482: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  new_legend = legend_func(handles, labels, loc=loc, **props)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 747.72x402 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(data=sel37_df[sel37_df.genotype == 'mutant'],\n",
    "                  col='strain', col_order=['LL13-040','NC-02'],\n",
    "                  x='condition', y='auc',\n",
    "                  hue='respiration', hue_order=[True, False], palette=['grey','red'], alpha=.5,\n",
    "                  kind='violin', cut=0, split=True, legend_out=False,\n",
    "                  height=4.02, aspect=0.93 # height*aspect = width of EACH facet\n",
    "                 )\n",
    "\n",
    "cdic = {1: 'grey', 2: 'red', 3: 'red'}\n",
    "for i, s in enumerate(sel37_df.strain.unique()):\n",
    "    fig.axes[0][i].scatter(data=sel37_df[(sel37_df.genotype == 'wt') & (sel37_df.strain == s)],\n",
    "                           x='condition', y='auc',\n",
    "                           c=sel37_df[(sel37_df.genotype == 'wt') & (sel37_df.strain == s)].rearray_plate_nb.map(cdic),\n",
    "                           marker='D', zorder=1000, edgecolor='black', linewidth=2\n",
    "                          )\n",
    "\n",
    "fig.set(xlim=(-0.5, len(sel37_df.condition.unique())-0.5))\n",
    "fig.set_xticklabels(['YPD','SD','5-FC','Micafungin','Caspofungin','Nystatin','Fluconazole'], rotation=45)\n",
    "fig.set_axis_labels('', 'Growth (AUC)')\n",
    "fig.set_titles(col_template='{col_name}')\n",
    "\n",
    "handles, labels = fig.axes[0][1].get_legend_handles_labels()\n",
    "sns.move_legend(fig, bbox_to_anchor=(0.41, 0.78), loc='lower left', title=None, frameon=True,\n",
    "                handles=handles, labels=['$\\it{rho}$⁺','$\\it{rho}$⁻'], markerscale=0.75,\n",
    "                handletextpad=0.25, borderpad=0.3)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig(graph_outpath+'20230724_AUC_pooled_respi_37_22h_stats.svg', format='svg', dpi=300)\n",
    "plt.savefig(graph_outpath+'20230724_AUC_pooled_respi_37_22h_stats.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede03740-4b87-4190-8030-aae674a71d59",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Rhodamine plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6fe039c6-9777-4770-a089-bb68f26c68ca",
   "metadata": {
    "tags": []
   },
   "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>rearray_plate_nb</th>\n",
       "      <th>strain</th>\n",
       "      <th>type</th>\n",
       "      <th>well</th>\n",
       "      <th>perc_pos</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>perc_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>negative_control</td>\n",
       "      <td>A02</td>\n",
       "      <td>1.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>positive_control</td>\n",
       "      <td>A01</td>\n",
       "      <td>97.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B02</td>\n",
       "      <td>6.05</td>\n",
       "      <td>B2</td>\n",
       "      <td>0.517094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B03</td>\n",
       "      <td>6.60</td>\n",
       "      <td>B3</td>\n",
       "      <td>0.564103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B04</td>\n",
       "      <td>8.75</td>\n",
       "      <td>B4</td>\n",
       "      <td>0.747863</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>118</th>\n",
       "      <td>3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G08</td>\n",
       "      <td>3.95</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.179954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G09</td>\n",
       "      <td>8.05</td>\n",
       "      <td>G9</td>\n",
       "      <td>0.366743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G10</td>\n",
       "      <td>7.10</td>\n",
       "      <td>G10</td>\n",
       "      <td>0.323462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>rho_plus</td>\n",
       "      <td>D08</td>\n",
       "      <td>16.00</td>\n",
       "      <td>D8</td>\n",
       "      <td>0.728929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>wt</td>\n",
       "      <td>G11</td>\n",
       "      <td>21.95</td>\n",
       "      <td>G11</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rearray_plate_nb    strain              type well  perc_pos RA_well  \\\n",
       "0                   2  LL13-040  negative_control  A02      1.05     NaN   \n",
       "1                   2  LL13-040  positive_control  A01     97.50     NaN   \n",
       "2                   2  LL13-040              rho0  B02      6.05      B2   \n",
       "3                   2  LL13-040              rho0  B03      6.60      B3   \n",
       "4                   2  LL13-040              rho0  B04      8.75      B4   \n",
       "..                ...       ...               ...  ...       ...     ...   \n",
       "118                 3     NC-02              rho0  G08      3.95      G8   \n",
       "119                 3     NC-02              rho0  G09      8.05      G9   \n",
       "120                 3     NC-02              rho0  G10      7.10     G10   \n",
       "121                 3     NC-02          rho_plus  D08     16.00      D8   \n",
       "122                 3     NC-02                wt  G11     21.95     G11   \n",
       "\n",
       "     perc_norm  \n",
       "0     0.089744  \n",
       "1     8.333333  \n",
       "2     0.517094  \n",
       "3     0.564103  \n",
       "4     0.747863  \n",
       "..         ...  \n",
       "118   0.179954  \n",
       "119   0.366743  \n",
       "120   0.323462  \n",
       "121   0.728929  \n",
       "122   1.000000  \n",
       "\n",
       "[123 rows x 7 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percdf = pd.read_excel(rhodamine_data, header=0, index_col=0)\n",
    "percdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57419f65-f9da-4361-819f-89e91cd749ff",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Relative % rhodamine accumulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2950be-a15a-4de1-80ee-61814c719c9f",
   "metadata": {},
   "source": [
    "This version of the figure has not been kept in the final version of the manuscript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e6d8153-f1ca-4b85-ac1b-c0c86a2ee7ba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Lbaseline = percdf.loc[(percdf.strain=='LL13-040') & (percdf.type=='negative_control'), 'perc_norm'].values[0]\n",
    "Nbaseline = percdf.loc[(percdf.strain=='NC-02') & (percdf.type=='negative_control'), 'perc_norm'].values[0]\n",
    "Lpos = percdf.loc[(percdf.strain=='LL13-040') & (percdf.type=='positive_control'), 'perc_norm'].values[0]\n",
    "Npos = percdf.loc[(percdf.strain=='NC-02') & (percdf.type=='positive_control'), 'perc_norm'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c09a0d9-898c-4b77-be5d-900f4fe45a60",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/utils.py:413: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  new_legend = legend_func(handles, labels, loc=loc, **props)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "f, ax = plt.subplots(figsize=(4, 5))\n",
    "fig = sns.stripplot(data=percdf[percdf.type.isin(['wt','rho_plus','rho0'])], x='strain', y='perc_norm', hue='type', hue_order=['wt','rho_plus','rho0'], palette=['grey','grey','red'], alpha=.5)\n",
    "sns.stripplot(data=percdf[percdf.type=='positive_control'], x='strain', y='perc_norm', color='#1e6a12', alpha=.5, jitter=False)\n",
    "sns.stripplot(data=percdf[percdf.type=='negative_control'], x='strain', y='perc_norm', color='#0555b5', alpha=.5, jitter=False)\n",
    "sns.barplot(data=percdf[percdf.type=='negative_control'], x='strain', y='perc_norm', color='#0555b5', alpha=.2) # display detection threshold\n",
    "\n",
    "plt.yscale('log', base=2)\n",
    "fig.set(ylim=(0.05,10))\n",
    "fig.axes.axhline(y=1, ls='--', c='grey')\n",
    "\n",
    "handles, labels = fig.get_legend_handles_labels()\n",
    "handles_rho = handles[-2:]\n",
    "sns.move_legend(fig, bbox_to_anchor=(1, 1.02), loc='upper right', title=None, frameon=True, handles=handles_rho, labels=['rho+','rho-'], markerscale=0.5, handletextpad=0.1, borderpad=0.3)\n",
    "\n",
    "ax.text(0.5, 6, u'Δ$\\itABC$', horizontalalignment='center')\n",
    "plt.plot([0, 0.3], [Lpos, 6.5], color='k', linestyle='-', linewidth=0.5, zorder=0)\n",
    "plt.plot([0.7, 1], [6.5, Npos], color='k', linestyle='-', linewidth=0.5, zorder=0)\n",
    "\n",
    "fig.axes.set(xlabel='', ylabel='Relative % rhodamine-fluorescent cells')\n",
    "plt.suptitle('Rhodamine retention of 5-FC mutants')\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "plt.savefig(graph_outpath+'20221201_rhodamine_retention_normalized.png', format='png', dpi=300)\n",
    "plt.savefig(graph_outpath+'20221201_rhodamine_retention_normalized.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9519823-c53e-4171-b3d0-f1a9d001c710",
   "metadata": {
    "tags": []
   },
   "source": [
    "### % rhodamine accumulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e801f225-55aa-4840-b836-4bd87e14daec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Lpos = percdf.loc[(percdf.strain=='LL13-040') & (percdf.type=='positive_control'), 'perc_pos'].values[0]\n",
    "Npos = percdf.loc[(percdf.strain=='NC-02') & (percdf.type=='positive_control'), 'perc_pos'].values[0]\n",
    "Lwt = percdf.loc[(percdf.strain=='LL13-040') & (percdf.type=='wt'), 'perc_pos'].values[0]\n",
    "Nwt = percdf.loc[(percdf.strain=='NC-02') & (percdf.type=='wt'), 'perc_pos'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "48bedbc3-be7d-4e1e-81c4-a3ae8dd74dc7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\seaborn\\utils.py:482: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  new_legend = legend_func(handles, labels, loc=loc, **props)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 280x455 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "f, ax = plt.subplots(figsize=(2.8, 4.55))\n",
    "fig = sns.stripplot(data=percdf[percdf.type.isin(['rho_plus','rho0'])], x='strain', y='perc_pos', hue='type', hue_order=['rho_plus','rho0'], palette=['grey','red'], alpha=.5)\n",
    "sns.stripplot(data=percdf[percdf.type=='wt'], x='strain', y='perc_pos', color='black', alpha=.5, jitter=False)\n",
    "sns.stripplot(data=percdf[percdf.type=='positive_control'], x='strain', y='perc_pos', color='#1e6a12', alpha=.5, jitter=False)\n",
    "sns.stripplot(data=percdf[percdf.type=='negative_control'], x='strain', y='perc_pos', color='#0555b5', alpha=.5, jitter=False)\n",
    "sns.barplot(data=percdf[percdf.type=='negative_control'], x='strain', y='perc_pos', color='#0555b5', alpha=.2) # display detection threshold\n",
    "\n",
    "plt.yscale('log', base=10)\n",
    "fig.set(ylim=(None,100))\n",
    "\n",
    "handles, labels = fig.get_legend_handles_labels()\n",
    "handles_rho = handles[-2:]\n",
    "sns.move_legend(fig, bbox_to_anchor=(0.5, 0.85), loc='upper right', title=None, frameon=True, handles=handles_rho, labels=['$\\it{rho}$⁺','$\\it{rho}$⁻'], markerscale=0.75, handletextpad=0.1, borderpad=0.3)\n",
    "\n",
    "ax.text(0.5, (Lpos+Npos)/2, u'Δ$\\it{ABC}$', horizontalalignment='center')\n",
    "plt.plot([0, 0.3], [Lpos, (Lpos+Npos)/2], color='k', linestyle='-', linewidth=0.5, zorder=0)\n",
    "plt.plot([0.7, 1], [(Lpos+Npos)/2, Npos], color='k', linestyle='-', linewidth=0.5, zorder=0)\n",
    "\n",
    "ax.text(0, Lwt+2, 'WT', horizontalalignment='center')\n",
    "ax.text(1, Nwt+2, 'WT', horizontalalignment='center')\n",
    "\n",
    "fig.axes.set(xlabel='', ylabel='% rhodamine-fluorescent cells')\n",
    "#plt.suptitle('Rhodamine retention of 5-FC mutants')\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "plt.savefig(graph_outpath+'20230413_rhodamine_retention.png', format='png', dpi=300)\n",
    "plt.savefig(graph_outpath+'20230413_rhodamine_retention.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2e49ee7-0917-4012-84a4-7c8a0bfa941e",
   "metadata": {},
   "source": [
    "### Correlation rhodamine - fluconazole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6db3ca11-d596-421e-992e-50b7f6e4f241",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
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       "        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>rearray_plate_nb</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>5-FC</th>\n",
       "      <th>Caspo_high</th>\n",
       "      <th>Fluco_high</th>\n",
       "      <th>Mica_high</th>\n",
       "      <th>Nystatin_high</th>\n",
       "      <th>SD</th>\n",
       "      <th>YPD</th>\n",
       "      <th>type</th>\n",
       "      <th>well</th>\n",
       "      <th>perc_pos</th>\n",
       "      <th>perc_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>B10</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>2.459532</td>\n",
       "      <td>0.490954</td>\n",
       "      <td>1.221070</td>\n",
       "      <td>1.526173</td>\n",
       "      <td>0.346933</td>\n",
       "      <td>0.636828</td>\n",
       "      <td>0.465901</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B10</td>\n",
       "      <td>7.40</td>\n",
       "      <td>0.632479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B11</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.932511</td>\n",
       "      <td>0.112269</td>\n",
       "      <td>0.939837</td>\n",
       "      <td>1.223133</td>\n",
       "      <td>0.142565</td>\n",
       "      <td>0.594282</td>\n",
       "      <td>0.410868</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B11</td>\n",
       "      <td>9.25</td>\n",
       "      <td>0.790598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>B2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>0.805020</td>\n",
       "      <td>0.147903</td>\n",
       "      <td>0.437949</td>\n",
       "      <td>0.591763</td>\n",
       "      <td>0.215291</td>\n",
       "      <td>0.261539</td>\n",
       "      <td>0.475016</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B02</td>\n",
       "      <td>6.05</td>\n",
       "      <td>0.517094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>B3</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>2.646666</td>\n",
       "      <td>0.285013</td>\n",
       "      <td>1.006576</td>\n",
       "      <td>1.716826</td>\n",
       "      <td>0.408580</td>\n",
       "      <td>0.630558</td>\n",
       "      <td>0.564787</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B03</td>\n",
       "      <td>6.60</td>\n",
       "      <td>0.564103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>B4</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>3.071559</td>\n",
       "      <td>0.517232</td>\n",
       "      <td>1.231443</td>\n",
       "      <td>1.939455</td>\n",
       "      <td>0.661468</td>\n",
       "      <td>0.610643</td>\n",
       "      <td>0.559545</td>\n",
       "      <td>rho0</td>\n",
       "      <td>B04</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.747863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <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>110</th>\n",
       "      <td>3</td>\n",
       "      <td>G5</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.885240</td>\n",
       "      <td>0.857420</td>\n",
       "      <td>1.648419</td>\n",
       "      <td>1.065928</td>\n",
       "      <td>0.864654</td>\n",
       "      <td>0.881574</td>\n",
       "      <td>0.798866</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G05</td>\n",
       "      <td>6.15</td>\n",
       "      <td>0.280182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>3</td>\n",
       "      <td>G6</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.713849</td>\n",
       "      <td>0.928266</td>\n",
       "      <td>1.793975</td>\n",
       "      <td>1.095242</td>\n",
       "      <td>0.964798</td>\n",
       "      <td>0.936484</td>\n",
       "      <td>0.788152</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G06</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.273349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>3</td>\n",
       "      <td>G7</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.542052</td>\n",
       "      <td>1.076231</td>\n",
       "      <td>1.807980</td>\n",
       "      <td>0.963414</td>\n",
       "      <td>1.025095</td>\n",
       "      <td>0.841602</td>\n",
       "      <td>0.834562</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G07</td>\n",
       "      <td>6.15</td>\n",
       "      <td>0.280182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>3</td>\n",
       "      <td>G8</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.907561</td>\n",
       "      <td>0.731525</td>\n",
       "      <td>1.804633</td>\n",
       "      <td>1.177198</td>\n",
       "      <td>0.968706</td>\n",
       "      <td>0.889176</td>\n",
       "      <td>0.793636</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G08</td>\n",
       "      <td>3.95</td>\n",
       "      <td>0.179954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>3</td>\n",
       "      <td>G9</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>False</td>\n",
       "      <td>1.809128</td>\n",
       "      <td>1.147493</td>\n",
       "      <td>1.633225</td>\n",
       "      <td>0.996951</td>\n",
       "      <td>1.047773</td>\n",
       "      <td>0.765923</td>\n",
       "      <td>0.771201</td>\n",
       "      <td>rho0</td>\n",
       "      <td>G09</td>\n",
       "      <td>8.05</td>\n",
       "      <td>0.366743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rearray_plate_nb RA_well    strain  fluc_assay  pre_culture genotype  \\\n",
       "0                   2     B10  LL13-040         7.0          3.0   mutant   \n",
       "1                   2     B11  LL13-040         7.0          3.0   mutant   \n",
       "2                   2      B2  LL13-040         1.0          1.0   mutant   \n",
       "3                   2      B3  LL13-040         3.0          3.0   mutant   \n",
       "4                   2      B4  LL13-040         4.0          2.0   mutant   \n",
       "..                ...     ...       ...         ...          ...      ...   \n",
       "110                 3      G5     NC-02         4.0          2.0   mutant   \n",
       "111                 3      G6     NC-02         4.0          1.0   mutant   \n",
       "112                 3      G7     NC-02         4.0          1.0   mutant   \n",
       "113                 3      G8     NC-02         4.0          4.0   mutant   \n",
       "114                 3      G9     NC-02         5.0          3.0   mutant   \n",
       "\n",
       "     respiration      5-FC  Caspo_high  Fluco_high  Mica_high  Nystatin_high  \\\n",
       "0          False  2.459532    0.490954    1.221070   1.526173       0.346933   \n",
       "1          False  1.932511    0.112269    0.939837   1.223133       0.142565   \n",
       "2          False  0.805020    0.147903    0.437949   0.591763       0.215291   \n",
       "3          False  2.646666    0.285013    1.006576   1.716826       0.408580   \n",
       "4          False  3.071559    0.517232    1.231443   1.939455       0.661468   \n",
       "..           ...       ...         ...         ...        ...            ...   \n",
       "110        False  1.885240    0.857420    1.648419   1.065928       0.864654   \n",
       "111        False  1.713849    0.928266    1.793975   1.095242       0.964798   \n",
       "112        False  1.542052    1.076231    1.807980   0.963414       1.025095   \n",
       "113        False  1.907561    0.731525    1.804633   1.177198       0.968706   \n",
       "114        False  1.809128    1.147493    1.633225   0.996951       1.047773   \n",
       "\n",
       "           SD       YPD  type well  perc_pos  perc_norm  \n",
       "0    0.636828  0.465901  rho0  B10      7.40   0.632479  \n",
       "1    0.594282  0.410868  rho0  B11      9.25   0.790598  \n",
       "2    0.261539  0.475016  rho0  B02      6.05   0.517094  \n",
       "3    0.630558  0.564787  rho0  B03      6.60   0.564103  \n",
       "4    0.610643  0.559545  rho0  B04      8.75   0.747863  \n",
       "..        ...       ...   ...  ...       ...        ...  \n",
       "110  0.881574  0.798866  rho0  G05      6.15   0.280182  \n",
       "111  0.936484  0.788152  rho0  G06      6.00   0.273349  \n",
       "112  0.841602  0.834562  rho0  G07      6.15   0.280182  \n",
       "113  0.889176  0.793636  rho0  G08      3.95   0.179954  \n",
       "114  0.765923  0.771201  rho0  G09      8.05   0.366743  \n",
       "\n",
       "[115 rows x 18 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widerhom37_df = pd.read_excel('data/widerhom37_22h.xlsx', header=0, index_col=0)\n",
    "widerhom37_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a35dea85-3b5d-4bdd-bb62-1b3fa0ac2438",
   "metadata": {},
   "source": [
    "#### Relative % rhodamine accumulation vs fluconazole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fee0086b-ed0d-4683-be56-78e7aa76f7c8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---LL13-040---\n",
      "rho = -0.1591083997011304, p-value = 0.24148503188835507\n",
      "---NC-02---\n",
      "rho = -0.4637305548215473, p-value = 0.0002800848487115466\n"
     ]
    }
   ],
   "source": [
    "rhoL, pvalL = get_Spearman(widerhom37_df, 'LL13-040', False, 'Fluco_high', 'perc_norm')\n",
    "rhoN, pvalN = get_Spearman(widerhom37_df, 'NC-02', False, 'Fluco_high', 'perc_norm')\n",
    "\n",
    "print('---LL13-040---')\n",
    "print('rho = {}, p-value = {}'.format(rhoL, pvalL))\n",
    "print('---NC-02---')\n",
    "print('rho = {}, p-value = {}'.format(rhoN, pvalN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22855e0c-2153-4e2f-926e-0160d99df270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---LL13-040---\n",
      "rho = -0.24587615183724315, p-value = 0.06775658012562376\n",
      "---NC-02---\n",
      "rho = -0.7007829749037029, p-value = 1.2855598808050278e-09\n"
     ]
    }
   ],
   "source": [
    "# Old version\n",
    "rhoL, pvalL = get_Spearman(widerhom37_df, 'LL13-040', False, 'Fluco_high', 'perc_norm')\n",
    "rhoN, pvalN = get_Spearman(widerhom37_df, 'NC-02', False, 'Fluco_high', 'perc_norm')\n",
    "\n",
    "print('---LL13-040---')\n",
    "print('rho = {}, p-value = {}'.format(rhoL, pvalL))\n",
    "print('---NC-02---')\n",
    "print('rho = {}, p-value = {}'.format(rhoN, pvalN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2153c384-3a75-4626-87e8-c55cee82e4c9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450.84x221 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.relplot(data=widerhom37_df, col='strain', col_order = ['LL13-040', 'NC-02'],\n",
    "                  x='Fluco_high', y='perc_norm',\n",
    "                  hue='respiration', hue_order=[True, False], palette=['black','red'], alpha=.5,\n",
    "                  legend=False,\n",
    "                  height=2.21, aspect=1.02 # height*aspect = width of EACH facet\n",
    "                 )\n",
    "plt.xscale('log', base=2)\n",
    "plt.yscale('log', base=2)\n",
    "fig.set_axis_labels('fluconazole', 'rhodamine')\n",
    "fig.set_titles(col_template='{col_name}')\n",
    "\n",
    "d = {'LL13-040':['S. rho', round(rhoL,2), 'p-val', pvalL], 'NC-02':['S. rho', round(rhoN,2), 'p-val', pvalN]}\n",
    "for i, s in enumerate(d.keys()):\n",
    "    fig.axes[0][i].text(0.43, 0.1, f'{d[s][0]} {d[s][1]}\\n'+\n",
    "                       f'{d[s][2]} {d[s][3]:.2e}')\n",
    "\n",
    "#fig.axes[0][0].text(1.02,1,'WT')\n",
    "#fig.axes[0][1].text(1.02,1,'WT')\n",
    "\n",
    "#handles, labels = fig.axes[0][1].get_legend_handles_labels()\n",
    "#sns.move_legend(fig, bbox_to_anchor=(0.07, 0.88), loc='upper left', title=None, frameon=True, handles=handles, labels=['$\\it{rho}$⁺','$\\it{rho}$⁻'], markerscale=0.75, handletextpad=0.1, borderpad=0.3)\n",
    "\n",
    "#fig.fig.subplots_adjust(top=0.92)\n",
    "#fig.fig.suptitle('Rhodamine accumulation and resistance to fluconazole of rho- mutants')\n",
    "fig.tight_layout()\n",
    "plt.savefig(graph_outpath+'20230413_2D_fluco_rhodamine_combined.svg', format='svg', dpi=300)\n",
    "plt.savefig(graph_outpath+'20230413_2D_fluco_rhodamine_combined.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25391928-f521-4569-a1e3-2e3913a7a7cb",
   "metadata": {},
   "source": [
    "#### Relative % rhodamine accumulation vs 5-FC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5f972bfb-9311-4dc6-bb42-42a02da4f35e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---LL13-040---\n",
      "rho = 0.18584298684472383, p-value = 0.17027063422224734\n",
      "---NC-02---\n",
      "rho = 0.08263766668345386, p-value = 0.5411167494669101\n"
     ]
    }
   ],
   "source": [
    "rhoL, pvalL = get_Spearman(widerhom37_df, 'LL13-040', False, '5-FC', 'perc_norm')\n",
    "rhoN, pvalN = get_Spearman(widerhom37_df, 'NC-02', False, '5-FC', 'perc_norm')\n",
    "\n",
    "print('---LL13-040---')\n",
    "print('rho = {}, p-value = {}'.format(rhoL, pvalL))\n",
    "print('---NC-02---')\n",
    "print('rho = {}, p-value = {}'.format(rhoN, pvalN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "40604b2c-af01-45c0-8f1a-93bdcff13f4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---LL13-040---\n",
      "rho = 0.12741663207694737, p-value = 0.3493684548834671\n",
      "---NC-02---\n",
      "rho = 0.27650712402635463, p-value = 0.037331708754831966\n"
     ]
    }
   ],
   "source": [
    "# Old version\n",
    "rhoL, pvalL = get_Spearman(widerhom37_df, 'LL13-040', False, '5-FC', 'perc_norm')\n",
    "rhoN, pvalN = get_Spearman(widerhom37_df, 'NC-02', False, '5-FC', 'perc_norm')\n",
    "\n",
    "print('---LL13-040---')\n",
    "print('rho = {}, p-value = {}'.format(rhoL, pvalL))\n",
    "print('---NC-02---')\n",
    "print('rho = {}, p-value = {}'.format(rhoN, pvalN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b7430ace-d56e-453d-933a-6de894912014",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450.84x221 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.relplot(data=widerhom37_df, col='strain', col_order = ['LL13-040', 'NC-02'],\n",
    "                  x='5-FC', y='perc_norm',\n",
    "                  hue='respiration', hue_order=[True, False], palette=['black','red'], alpha=.5,\n",
    "                  legend=False,\n",
    "                  height=2.21, aspect=1.02 # height*aspect = width of EACH facet\n",
    "                 )\n",
    "plt.xscale('log', base=2)\n",
    "plt.yscale('log', base=2)\n",
    "fig.set_axis_labels('5-FC', 'rhodamine')\n",
    "fig.set_titles(col_template='{col_name}')\n",
    "\n",
    "d = {'LL13-040':['S. rho', round(rhoL,2), 'p-val', pvalL], 'NC-02':['S. rho', round(rhoN,2), 'p-val', pvalN]}\n",
    "for i, s in enumerate(d.keys()):\n",
    "    fig.axes[0][i].text(0.43, 0.1, f'{d[s][0]} {d[s][1]}\\n'+\n",
    "                       f'{d[s][2]} {d[s][3]:.2e}')\n",
    "\n",
    "#fig.axes[0][0].text(1.02,1,'WT')\n",
    "#fig.axes[0][1].text(1.02,1,'WT')\n",
    "\n",
    "#handles, labels = fig.axes[0][1].get_legend_handles_labels()\n",
    "#sns.move_legend(fig, bbox_to_anchor=(0.07, 0.88), loc='upper left', title=None, frameon=True, handles=handles, labels=['$\\it{rho}$⁺','$\\it{rho}$⁻'], markerscale=0.75, handletextpad=0.1, borderpad=0.3)\n",
    "\n",
    "#fig.fig.subplots_adjust(top=0.92)\n",
    "#fig.fig.suptitle('Rhodamine accumulation and resistance to fluconazole of rho- mutants')\n",
    "fig.tight_layout()\n",
    "plt.savefig(graph_outpath+'20230413_2D_5FC_rhodamine_combined.svg', format='svg', dpi=300)\n",
    "plt.savefig(graph_outpath+'20230413_2D_5FC_rhodamine_combined.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71969abf-eceb-4124-9fc5-7c4ff80caecc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Scatterplots 30°C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b58579-545a-41be-a9e4-ff7fed826a18",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Two-way ANOVA with Tukey's multiple comparison test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "89d78bd2-5afd-4cbf-9a63-d756faffe390",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n"
     ]
    },
    {
     "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>condition1</th>\n",
       "      <th>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>p-adj</th>\n",
       "      <th>reject</th>\n",
       "      <th>stat_significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>False / 5-FC</td>\n",
       "      <td>True / 5-FC</td>\n",
       "      <td>0.2904</td>\n",
       "      <td>False</td>\n",
       "      <td>ns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5-FU</td>\n",
       "      <td>False / 5-FU</td>\n",
       "      <td>True / 5-FU</td>\n",
       "      <td>0.2880</td>\n",
       "      <td>False</td>\n",
       "      <td>ns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SD</td>\n",
       "      <td>False / SD</td>\n",
       "      <td>True / SD</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>YPD</td>\n",
       "      <td>False / YPD</td>\n",
       "      <td>True / YPD</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>True</td>\n",
       "      <td>****</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condition1        group1       group2   p-adj  reject stat_significance\n",
       "3        5-FC  False / 5-FC  True / 5-FC  0.2904   False                ns\n",
       "10       5-FU  False / 5-FU  True / 5-FU  0.2880   False                ns\n",
       "16         SD    False / SD    True / SD  0.0000    True              ****\n",
       "21        YPD   False / YPD   True / YPD  0.0000    True              ****"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from flucytosine_functions import get_Tukey\n",
    "get_Tukey(sel30_df[(sel30_df.genotype.isin(['wt','mutant'])) & (sel30_df.strain=='LL13-040')], 'respiration','condition','rel_fit')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0733f3bf-6d41-4f48-97f3-bd2c5c4db7c7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\seaborn\\axisgrid.py:712: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "C:\\Users\\duran\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\seaborn\\utils.py:482: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  new_legend = legend_func(handles, labels, loc=loc, **props)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\OneDrive - Université Laval\\Notebooks\\ASUS_notebooks\\Durand_et_al_2023\\flucytosine_functions.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  anova_df['combination'] = anova_df.apply(lambda row: str(row[factor1])+' / '+str(row[factor2]), axis=1)\n",
      "C:\\Users\\duran\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 833.706x402 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "fig = sns.catplot(data=sel30_df[sel30_df.genotype.isin(['wt','mutant'])], x='condition', y='rel_fit',\n",
    "                  col='strain', col_order=['LL13-040','NC-02'],\n",
    "                  hue='respiration', hue_order=[True, False], palette=['grey','red'], alpha=.5,\n",
    "                  kind='strip', dodge=True, legend_out=False,\n",
    "                  height=4.02, aspect=0.93 # height*aspect = width of EACH facet\n",
    "                 )\n",
    "\n",
    "fig.map(sns.boxplot,\n",
    "        data=sel30_df[sel30_df.genotype.isin(['wt','mutant'])],\n",
    "        x='condition', y='rel_fit',\n",
    "        hue='respiration', hue_order=[True, False], palette=['grey','red'],\n",
    "        fliersize=0\n",
    "       )\n",
    "\n",
    "fig.map(plt.axhline, y=1, ls='--', c='grey')\n",
    "plt.yscale('log',base=2)\n",
    "fig.set(ylim=(2**-4,2**2))\n",
    "fig.set_xticklabels(['YPD','SD','5-FC','5-FU'], rotation=45)\n",
    "fig.set_axis_labels('', 'Relative growth')\n",
    "fig.set_titles(col_template='{col_name}')\n",
    "\n",
    "handles, labels = fig.axes[0][1].get_legend_handles_labels()\n",
    "sns.move_legend(fig, bbox_to_anchor=(0.08, 0.32), loc='upper left', title=None, frameon=True, handles=handles, labels=['$\\it{rho}$⁺','$\\it{rho}$⁻'], markerscale=0.75, handletextpad=0.1, borderpad=0.3)\n",
    "\n",
    "for i, s in enumerate(wide30_df.strain.unique()):\n",
    "    for j, x in enumerate(sel30_df.condition.unique()):\n",
    "        maxv = sel30_df[(sel30_df.genotype.isin(['wt','mutant'])) & (sel30_df.strain==s) & (sel30_df.condition==x)].rel_fit.max()\n",
    "        tukey_df = get_Tukey(sel30_df[(sel30_df.genotype.isin(['wt','mutant'])) & (sel30_df.strain==s)], 'respiration','condition','rel_fit')[1]\n",
    "        tukey_stat = tukey_df.loc[tukey_df.condition1==x, 'stat_significance'].values[0]\n",
    "        fig.axes[0][i].text(j, maxv+0.2, tukey_stat, ha='center')\n",
    "    \n",
    "    FU = wide30_df[(wide30_df.strain==s) & (wide30_df.genotype.isin(['wt','mutant']))]['5-FU'].values\n",
    "    FC = wide30_df[(wide30_df.strain==s) & (wide30_df.genotype.isin(['wt','mutant']))]['5-FC'].values\n",
    "    rho, pval = stats.spearmanr(FU,FC)\n",
    "    \n",
    "    ins = inset_axes(fig.axes[0][i], \"100%\", \"100%\",\n",
    "                   bbox_to_anchor=(.55, .1, .4, .4), # Coordinates of inset in units relative to axes of full panel - left bottom width height\n",
    "                   bbox_transform=fig.axes[0][i].transAxes,\n",
    "                   borderpad=0)\n",
    "    ins.scatter(FC, FU, c=wide30_df[(wide30_df.strain==s) & (wide30_df.genotype.isin(['wt','mutant']))]['respiration'].map({True:'grey', False:'red'}), alpha=.5, s=4)\n",
    "    plt.xscale('log',base=2)\n",
    "    plt.yscale('log',base=2)\n",
    "    ins.set(xlim=(0.55,3), ylim=(0.55,3))\n",
    "    ins.set_xlabel('5-FC')\n",
    "    ins.xaxis.set_label_coords(.1, -.02)\n",
    "    ins.set_ylabel('5-FU')\n",
    "    ins.yaxis.set_label_coords(-.02, .1)\n",
    "    ins.text(0.6,2.3, f'S. rho {round(rho,2)}')\n",
    "    ins.text(0.75,0.6, f'p-val {pval:.2e}')\n",
    "\n",
    "#fig.fig.subplots_adjust(top=0.92)\n",
    "#fig.fig.suptitle('Relative fitness at 30°C')\n",
    "fig.tight_layout()\n",
    "plt.savefig(graph_outpath+'20230724_rel_fit_pooled_respi_30_22h_stats.svg', format='svg', dpi=300)\n",
    "plt.savefig(graph_outpath+'20230724_rel_fit_pooled_respi_30_22h_stats.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ab88f849-65eb-48a1-a9d4-f7d785a9dd65",
   "metadata": {
    "tags": []
   },
   "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>plate_number</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>auc</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>fluc_mutant</th>\n",
       "      <th>genotype</th>\n",
       "      <th>FCY1_genotype</th>\n",
       "      <th>FUR1_genotype</th>\n",
       "      <th>respiration</th>\n",
       "      <th>key</th>\n",
       "      <th>Genomix_plate_nb</th>\n",
       "      <th>Genomix_plate_well</th>\n",
       "      <th>rel_fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1</td>\n",
       "      <td>E23</td>\n",
       "      <td>14764.750</td>\n",
       "      <td>1</td>\n",
       "      <td>YPD</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>298</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B12</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>2</td>\n",
       "      <td>E23</td>\n",
       "      <td>10704.500</td>\n",
       "      <td>1</td>\n",
       "      <td>SD</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>298</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B12</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>3</td>\n",
       "      <td>E23</td>\n",
       "      <td>6496.875</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>298</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B12</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>4</td>\n",
       "      <td>E23</td>\n",
       "      <td>5012.375</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>298</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B12</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>5</td>\n",
       "      <td>G11</td>\n",
       "      <td>16358.625</td>\n",
       "      <td>3</td>\n",
       "      <td>YPD</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>6</td>\n",
       "      <td>G11</td>\n",
       "      <td>9776.375</td>\n",
       "      <td>3</td>\n",
       "      <td>SD</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>7</td>\n",
       "      <td>G11</td>\n",
       "      <td>3667.750</td>\n",
       "      <td>3</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>8</td>\n",
       "      <td>G11</td>\n",
       "      <td>4629.375</td>\n",
       "      <td>3</td>\n",
       "      <td>5-FU</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     plate_number RA_well        auc  rearray_plate_nb condition  \\\n",
       "400             1     E23  14764.750                 1       YPD   \n",
       "401             2     E23  10704.500                 1        SD   \n",
       "402             3     E23   6496.875                 1      5-FC   \n",
       "403             4     E23   5012.375                 1      5-FU   \n",
       "440             5     G11  16358.625                 3       YPD   \n",
       "441             6     G11   9776.375                 3        SD   \n",
       "442             7     G11   3667.750                 3      5-FC   \n",
       "443             8     G11   4629.375                 3      5-FU   \n",
       "\n",
       "     mutant_plate 96_well strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "400             3     E12  NC-02         NaN          NaN          NaN   \n",
       "401             3     E12  NC-02         NaN          NaN          NaN   \n",
       "402             3     E12  NC-02         NaN          NaN          NaN   \n",
       "403             3     E12  NC-02         NaN          NaN          NaN   \n",
       "440             3     E12  NC-02         NaN          NaN          NaN   \n",
       "441             3     E12  NC-02         NaN          NaN          NaN   \n",
       "442             3     E12  NC-02         NaN          NaN          NaN   \n",
       "443             3     E12  NC-02         NaN          NaN          NaN   \n",
       "\n",
       "    genotype FCY1_genotype  FUR1_genotype  respiration  key  Genomix_plate_nb  \\\n",
       "400       wt           NaN            NaN         True  298               3.0   \n",
       "401       wt           NaN            NaN         True  298               3.0   \n",
       "402       wt           NaN            NaN         True  298               3.0   \n",
       "403       wt           NaN            NaN         True  298               3.0   \n",
       "440       wt           NaN            NaN         True   60               NaN   \n",
       "441       wt           NaN            NaN         True   60               NaN   \n",
       "442       wt           NaN            NaN         True   60               NaN   \n",
       "443       wt           NaN            NaN         True   60               NaN   \n",
       "\n",
       "    Genomix_plate_well  rel_fit  \n",
       "400                B12      1.0  \n",
       "401                B12      1.0  \n",
       "402                B12      1.0  \n",
       "403                B12      1.0  \n",
       "440                NaN      1.0  \n",
       "441                NaN      1.0  \n",
       "442                NaN      1.0  \n",
       "443                NaN      1.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel30_df[(sel30_df.genotype == 'wt') & (sel30_df.strain == 'NC-02')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "74f30cfe-b79c-4568-85f1-2b653d52cbf8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\seaborn\\utils.py:482: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  new_legend = legend_func(handles, labels, loc=loc, **props)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 747.72x402 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(data=sel30_df[sel30_df.genotype == 'mutant'], x='condition', y='auc',\n",
    "                  col='strain', col_order=['LL13-040','NC-02'],\n",
    "                  hue='respiration', hue_order=[True, False], palette=['grey','red'], alpha=.5,\n",
    "                  kind='violin', cut=0, split=True, legend_out=False,\n",
    "                  height=4.02, aspect=0.93 # height*aspect = width of EACH facet\n",
    "                 )\n",
    "\n",
    "cdic = {1: 'grey', 2: 'red', 3: 'red'}\n",
    "for i, s in enumerate(sel30_df.strain.unique()):\n",
    "    fig.axes[0][i].scatter(data=sel30_df[(sel30_df.genotype == 'wt') & (sel30_df.strain == s)],\n",
    "                           x='condition', y='auc',\n",
    "                           c=sel30_df[(sel30_df.genotype == 'wt') & (sel30_df.strain == s)].rearray_plate_nb.map(cdic),\n",
    "                           marker='D', zorder=1000, edgecolor='black', linewidth=2\n",
    "                          )\n",
    "\n",
    "fig.set(xlim=(-0.5, len(sel30_df.condition.unique())-0.5))\n",
    "fig.set_xticklabels(['YPD','SD','5-FC','5-FU'], rotation=45)\n",
    "fig.set_axis_labels('', 'Growth (AUC)')\n",
    "fig.set_titles(col_template='{col_name}')\n",
    "\n",
    "handles, labels = fig.axes[0][1].get_legend_handles_labels()\n",
    "sns.move_legend(fig, bbox_to_anchor=(0.41, 0.92), loc='upper left', title=None, frameon=True,\n",
    "                handles=handles, labels=['$\\it{rho}$⁺','$\\it{rho}$⁻'],\n",
    "                markerscale=0.75, handletextpad=0.25, borderpad=0.3)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig(graph_outpath+'20230425_AUC_pooled_respi_30_22h_stats.svg', format='svg', dpi=300)\n",
    "plt.savefig(graph_outpath+'20220425_AUC_pooled_respi_30_22h_stats.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fd61bf5e-4f8d-47ed-bba8-407c7373177f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\AppData\\Roaming\\jupyterlab-desktop\\jlab_server\\lib\\site-packages\\seaborn\\utils.py:482: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  new_legend = legend_func(handles, labels, loc=loc, **props)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 479.025x430 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "strains = ['LL13-040','NC-02']\n",
    "fig = sns.relplot(data=wide_sol_liq,\n",
    "                  x='5-FC', y='5-FC_liq',\n",
    "                  col='strain', col_order=strains,\n",
    "                  hue='respiration', hue_order=[True,False], palette=['grey','red'], alpha=.5,\n",
    "                  style='genotype', markers=['o','o','D'],\n",
    "                  height=4.3, aspect=0.44 # height*aspect = width of EACH facet\n",
    "                 )\n",
    "plt.xscale('log', base=2)\n",
    "plt.yscale('log', base=2)\n",
    "fig.set(ylim=(None,2**2))\n",
    "fig.set_axis_labels('solid medium', 'liquid medium')\n",
    "fig.set_titles(col_template='{col_name}')\n",
    "\n",
    "for i, s in enumerate(strains): # for each panel (1 per strain)\n",
    "    # Label WT\n",
    "    fig.axes[0][i].text(1,1.04,'WT',horizontalalignment='center')\n",
    "    \n",
    "    # Get Spearman correlation coefficient\n",
    "    sol = wide_sol_liq[wide_sol_liq.strain == s]['5-FC'].values\n",
    "    liq = wide_sol_liq[wide_sol_liq.strain == s]['5-FC_liq'].values\n",
    "    rho, pval = stats.spearmanr(sol,liq)\n",
    "    fig.axes[0][i].text(.8,3.5,f'S. rho {round(rho,2)}\\np-val {pval:.2e}',horizontalalignment='left')\n",
    "\n",
    "handles, labels = fig.axes[0][0].get_legend_handles_labels()\n",
    "new_handles = [handles[1], handles[2], handles[6]]\n",
    "sns.move_legend(fig, bbox_to_anchor=(0.06, 0.5), loc='upper left', title=None, frameon=True,\n",
    "                handles=new_handles, labels=['$\\it{rho}$⁺','$\\it{rho}$⁻',u'$\\it{fcy1}$Δ'],\n",
    "                markerscale=0.75, handletextpad=0.1, borderpad=0.3)\n",
    "\n",
    "#fig.fig.subplots_adjust(top=0.92)\n",
    "#fig.fig.suptitle('Relative fitness in liquid vs solid medium + 5-FC')\n",
    "fig.tight_layout()\n",
    "plt.savefig(graph_outpath+'/20230914_2D_5-FC_sol_liq.svg', format='svg', dpi=300)\n",
    "plt.savefig(graph_outpath+'/20230914_2D_5-FC_sol_liq.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f6ac54-17e3-42aa-bc70-8368942f25ef",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Scatterplots YPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca4ac328-d70a-4eb4-bd5b-c8693c4815b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1B/d3//rXvyzWOHYDgYHA7NnAt9/SXMbAgUBbGxAZCQQE0O2hQ23dSteQlASkp9PKlq6ICJqsdjKiV1UmTJhg+skkEhw4cOCmG9VVHH5VRU0QaBnW3R0ICbF1a1zXtWvArl3AlSu0ktK/PzB1KuDvb+uWWRzvVXGGwMHsS0sLBQ7deSYnc1MJYMXFxZBIJIiMjLRUexhzfC5Qp0b05KhKpcJrr70GmUyG6Oho9O7dG0FBQVwBnTEXIrrH8dJLL+GTTz7BmjVrcOutt0IQBPz000945ZVX0NLSgjfeeMMa7WSM2RHRcxwRERH44IMPMG3aNL3rO3bswGOPPYarDrRmzXMcjHWO6KFKVVUV+vfvb3C9f//+DlXhnDHWeaIDx5AhQ7B+/XqD6+vXr8eQIUMs0ijGmH0TPcfxj3/8A3fffTf27duHUaNGQSKR4Oeff0ZRURF++OEHa7SR6Wpqok1rggDExztljoDDKC8HTp2ivUC9e1OynZeXrVvVJTqVx1FSUoL33nsP58+fhyAISExMxGOPPYYIB0ttdrg5jrNngbQ0QKGg21IpcM89nB1qC7m5wJdfUjkDtdBQYOFCl1iO5QQwRwkcTU3Av/6lDRpqbm7A4sW0Z6VbN6670RUEAXjnHaC62vC+228Hxo7t+jZ1Ma4A5ijOnzcMGkolcO4c8PLL2v0pEycCPNdkXdXVxoMGAFy65BKBw/lKEzkrYx3D3FzaH6FOvKuvB7ZvBwoKurRpLsfb23RVL1/frm2LjXDgcBQJCfrDkLY24Pp1+rNukRhBAE6c6Nq2uRpfX3o/jElJ6dq22AgHDkfh7w/cfbf2m66tjYJEv35Uf0NXQ0PXt8/VTJsGxMbSn6uraYjS1gYcOAB88QVw6JBTvw88OWrPk6MtLcDx4/Sh9PGhb7PQUKpqnp9PxYllMsMlwHHjgA7KHzAL2rUL2LOHgviZMzQPFRwMDB5MwX7hQqB7d1u30uJET45eu3YNzzzzjKbKefu4o9RdnmKd19oKbNxIcxhq589TwZiLF4HmZvqw/vorEBMD9OpFj+nWDRgxwjZtdjVNTZTH4eVFRXzKy2k42dBABXwkEup5/PGPtm6pxYkOHAsWLEBhYSFWrFjBVc6t6fRp/aAB0CToRx8BqalUtCcsjCbqrl6lgJGUREFDtxYps56CAgoeGRlUSEkQqMchl1OQHzNGv6C0ExEdOI4ePYojR44gOTnZCs1hGoWFhtdqaqin0dCgrWMpk9HPgAE8POlqvr50/EFrK8096fa2q6ooiLSff3ISoidHe/XqZTA8YVYQEGB4Tf26G6ssxUPErte7N/UumpoMA4SPD11PTbVN26xMdOBYt24dnn/+eVy5csUKzWEaqamGWaBBQTQ8MZYrMGBAlzSL/U6ppJwZhYJ6gvX1dM3NjYaNAHDrrcDIkbZspdWYNVTp1q2b3lxGY2Mj+vXrB19fX4Mq57y13kJCQoBZs4Ddu6nbK5HQUQf33gt8/71+D+OWW4A+fWzWVJeiVNL8xY8/0mbD3r2BykpaAWttpXkoT0+gZ08qWpyXB8TF2brVFmdW4Fi3bp2Vm8GMio+nD111NU2CqnsasbG09NfaSo9xsM2FDqulhY55LCmhpfDmZlpRiY+nCepz5+hxI0fSSld9PW2Ee+IJOsrCiZgVOObPn2/tdtyUe++9F4cOHcIdd9yBrVu32ro5liUIFDB0d1wGBgLmnKanVAI//wz89hslJ8XFAePH81b8zjpyhIIGoN03JJdTBm/fvnS2jVSqTQxTPy4ri1ZYnIjoVRWpVIrS0lKEhobqXa+srERoaKhN8jieeOIJLFy4EJ999lmX/91WIwiUA3D8OH2z9egB3HGHuLmM7duB7Gzt7ZMnKXHskUecunS/1agP+AYoqau4mGpxXLtG75FCQQl67TU1dV0bu4joyVFTKypyuRyeNvowTpgwAQHGViEc2cGDlFTU3Ey3KyqAr76icbM5KitpOGPsum4wYebTnawOC6OeRl0dvUf19ZQA1qMHBZCKChpiqrcFOBmzexzvvPMOADqt7eOPP4a/TndXqVTi8OHDRmuR3sjq1auxbds2nD9/Hj4+Phg9ejTefPNNJJjaROQKFArqabSnzhQ1ZyL02jXjO2rV9zHxBg/WvnZlZUB4OOXUqDe99elD8xzu7trJ6+hop9wxa3bgWLt2LQDqcXzwwQeQ6kRfT09P9OnTBx988IHoBqSnp2Pp0qUYPnw4FAoFXnrpJUyePBk5OTnw8/NDamoq5HK5we/t2bNHdMUxuVyu91x1dXWi29slWlrox5iaGvOeo6P9EcHBopvk8lpbKdGuooL2DpWWUoZuZKT28O/GRlpFCQ+noN2jB62OffUVTZA6UZa12YEjPz8fAA0Ltm3bhm7qteqbtHv3br3bGzduRGhoKE6dOoWxY8fi1KlTFvl7AOrdvPrqqxZ7Pqvx86MPaW2t/vXaWsoTOHKEvv06OgU9LIw+wGfOUDKSOg3d358L/YhRUkJL4gcP0h6hqCjqXTQ2Uq9iwAAKGgD1QgICDOehqqspPd2JlsxFz3Fs2LDBYkHDmNrf/2cJtsK34gsvvIDa2lrNT1FRkcX/DouQSAzTx3NzaW6ioQHYvx94913jcxgAfaDT0miJsLqa9lKcPUtj7QULnDYN2uLq62lvUFoarU6VldGmttOntQFD9zOkVJpeGm9t7Zo2dxHRqyoJCQno2bMnxo0bh3HjxmH8+PEWm48QBAFPP/00xowZg0GDBpn9e1OmTEFGRgYaGxsRFRWFtLQ0DB8+3OBxXl5e8HKUKtTJyTQ2PnaMusZKJRUlVo+XlUpg507KIWg/Ka1egpVKaQk2Lo66ziEh+kV/WMdOn6afmhrtnIUgUO8hPp56EOqho7s7BfviYsPn8fKiuQ4nIjpwlJaW4sCBA0hPT8fatWvx6KOPIiwsTBNElixZ0unGPP7448jKysLRo0dF/d6PP/7Y6b/TrsXH08+uXcbvb2mhVZb4eP3rmZmGj5VI6Pqdd1q4kU6sqIgCg5sb/eiejVxcTBm7sbHApEmUW+PtDWzbpr9qJZHQa+4oX1hmEh04wsLCMGfOHMyZMwcAkJeXh7///e/YtGkTvv76604HjmXLluHbb7/F4cOHERUV1anncFodVS53N/IWmuoWO1l32erUE8wSCc1d6M45eXjQ9TFjaD5J7b77aA4pN5d6gklJxnM7HJzowNHQ0ICjR4/i0KFDSE9PR2ZmJgYMGIBly5Zh3LhxohsgCAKWLVuGtLQ0HDp0CH379hX9HE5v8GAafrQXGGh8wi0ujuY12mvfM2EdGz8e2LCB9gqpJ6Lr62miOT4emDnT8PWXSKgXops96oRElw708PBAcHAw5s2bhwkTJmDMmDGQyWSdbsBjjz2GzZs3Y8eOHXpzJTKZDD5WnsSz+9KBuo4doxJ16rG2vz8wezbN8rdXVwd8+qn+0q2/P02M8hyHOKdPA+vWUXKXunhScjKVBHThSWbRgWPGjBk4evQopFIpxo8fr/kZ0Mlt3aYqiG3cuBELFizo1HOay6ECB0BLgJcuURc4Ntb4MEVNLqcJ0mvXKFgMGeKUiUhdorJSW5GtsJDmlqRSWpYdPZqGJVVVtCM2JYXmM06coFUvQaAVmJEjO36/HEynixVnZWUhPT0d6enpOHLkCCQSCcaPH48tW7ZYuo1W43CBg9mOQgG8957+QUx1dZTwNXSoNrlLJqN6HO23BsTEAPPmOU0SWKdDYFJSEpRKJdra2iCXy7F7925s27bNkm1jzH6cO2d4etulSxQ8qqq0E6nFxbRdoH2KwuXLtMEwJqZr2mtlogPH2rVrcejQIRw5cgT19fVITk7GuHHj8Mgjj2CsCxx9ZxNtbTS3Ye5hxidP0pxIXR3NgYwfr62CzjqnfdBQqbSrLOqNiAC95qaOhywudt3AsWnTJowfPx6LFy/G2LFjuYtvTU1NwA8/0LedUknVpu66i8bSpvz0E7B3r/b2pUuUsPTww5SCzjqnfUaoRELzHEqlfn1YLy/Tcxk3sYhgb0QHjpMnT1qjHcyYLVv0q50XFlIFqscfN34EglJJgaM9hQL45RcqO8g6p18/yv5Un8srkVAAb2rSDwjduxtfbQkMBBITu6atXaBTcxw1NTX45JNPcO7cOUgkEgwYMACLFi26qWVZ1k5JifEjEpqbabXEWAWwxkbTRWPKyy3bPlcjkQAPPED5NDk5dHv0aJocLSykYaRUStmkI0ZQXdi8PFpV6duXju9sV5/XkYne5Hby5En069cPa9euRVVVFSoqKrB27Vr069cPGcaSjljndLR93tR9fn6ml1w5f6Nz1KX/Dh2iyc1x44DHHqNl11OntAd/BwUBjz4KTJ5Mf37gAfoBaFPia6/RDlsnIbrHsXz5ckybNg0fffQR3H8fyykUCjz88MN46qmncPjwYYs30iWpjxA0tloeGWn8d6RS+hbct4/SywsKKAfBw4M+6CqV9tBqdmM1NcBnn+lPdkZF0Wusu3/I25seu38/VaYHaJ/L00/TZClAvZLsbNph+/t2DUfWqR7HX//6V03QAAB3d3c899xzPP9hSUFBxg/zCQ8HBg40/XtjxgBTplA3ubSUPtSxsZSCvn27tVrrnHbv1gaNlhZKqisuptPojTl/noaLAM1FtS8UJQhU1Ed3FcZBie5xBAYGorCw0KBMYFFRkfPV/bS1u++mCbiff6alv+RkYOLEG2cgensbn4jLzgbGjuVhizmUSsoIrauj/zY00PWgIMrcNfb6CgIFGD8/+h1jqqup1+Hge7JE9zhmzZqFRYsW4csvv0RRURGKi4uxZcsWPPzww5ods8xClEoaV6vPIc3IoO7wjZJ9y8rog56bS2nPxcX0XILA9UbNJZFQ/kxWljZoADQkqagw/h4EBWnLMpoqROXp2XHlNgchusfx1ltvQSKR4MEHH4Ti97MlPDw88Oijj2LNmjUWb6BLO3BAv8qXSkV7IIKDgf79adzs50dJRbpzF7W1NHGn/nBXVFAwSU7WHk/IOubmRsuqCgUF3bo6Gqq4u1NWqFSqX59DKqW6G3I5vWfdulGQlkppwtrHh4LRyJFO8R6IChxKpRK//PILVq5cidWrV+PSpUsQBAGxsbHw5Q1Ulnf6tPHrmzZRNS91YAgOBv70J/qvSkWToh4e+vU31N+afOqb+QYNoiMq8vK0u5J9fCgQJybSRGlBASWApabS671uHSXdXbxIwae6mrbiy2SUR/P88zb9J1mKqMAhlUoxZcoUnDt3DsHBwRg8eLC12sXU4+X2ysvpQ6k7T1FVRZWnHn6YPtTNzdS7yMuj+9zcaDu4k6Q7d5n4eHqda2po2OLlpa3kdfkycP/92kOlBQFYv56CRG6uttxgUBD1UPr3p01uTrIVX/RQZfDgwbh8+TIX3LE2iYSyFfPy9K9fu2Z8/FxcTN9u6i6xry9Vn1L3SiQSp0p57hLx8TQ08fHR/x8+MpIS7RoatMdpXr9OS9/Xr2vnlNQuXKDfyctzmgI/oidH33jjDTzzzDPYuXMnSktLUVdXp/fDLGjwYPpmy8qibdptbbRiYqrMvkJB3Wbd09ElEu1W7pQUa7fYubi5AbffTv+zqyc+Bwyg2x4e+nVE1XNM16/rBw21y5edqu6o6B7Hnb8Xu502bZpeER5BECCRSGxydqxTOncO2LGDvqlKS2n40dAAPPQQnauiq7qausabN1Oex4gR9OE9f56u+/lRt5p7ieKNHEkrW+pKa01NdNSEjw+wdi0NCSdMoDmnsDAK7u7u2kOpAer9NTU51VGQogPHQSdKm7VbgkBlAlUq+pbS7WEoldSjuHiRbldWUpAZNIgCSHU13TdwID2Ppyc9x6+/UuAICbHJP8lhJSQAU6dSynltLfX+goLotWxqohybqioq43jffRTUQ0Op56HuAfr7085mc8siOIBOVwBzBnZZAUwup630H35It0NDaVemutJ5r15U7/LSJRq+7N1L9+merVJbS7P9SUn6zx0aSvssmHgKBfDjjxQojG1We/xxmkg9exZ4/31aYXFzo0OxCgroPQoOpoD++OOGhX4cjOgeR1ZWltHrEokE3t7e6N27t+McemRvBAH473+payyX0+3CQgoEQ4fSxGhxMa2sRERQgZ6ffjJMRrp2jWb327t+nYY9HdXzYMa5u9PrbGqHa0UFBY6BA2n1JD2deoKFhfR7vr70+hcVUZ7HG28Y3+HsIEQHjuTkZJMFhgFKBps1axY+/PBDeDtR16xLXLpEHyx3d+odqLM8a2tpZr60lHoRLS002XblCn1gq6upxxEWRsMSpdL0sh+frdJ5ps5HkUj07xs5Ehg2DHjlFfoSUCjovVMnjBUXA++8Q0OY9r1CByF6VSUtLQ1xcXHYsGEDMjMzcfr0aWzYsAEJCQnYvHkzPvnkExw4cAB/+9vfrNFe56abDh4fTx9G9Q7ZoiKa21AvxapUlCB29ix9EC9fplqXNTVUTMZYqUA/P+PHKTDzDBminy4uCPQzaJDhErl6glQioaxT3SxTpZImuw8cuPH2ATslusfxxhtv4O2338aUKVM015KSkhAVFYUVK1bg+PHj8PPzw1/+8he89dZbFm2s09P98EmllJ3Y2kqTcG1t+hObZWXavI3ISG3v4/hxSgRzc6MejHpuRCoF7rmn41PhmCFBoPqtJ05QcG5spP/pCwvpvYmKooDS0mI4+TlgAKX+y+X61318aNK0poZWyhxwc6jowJGdnY1oIwfoRkdHI/v3MzOTk5NRWlp6861zNQkJFBx0q3V5etKHs7lZf96iooL+q64Foc7zaGig7fMqFS3NenhQt/m++5xij0SXO3SI5iuuXKEfpZISudzdqQfn6Ukn2peVGU48T55MQefqVe0Q0c2N3uOoKBpWOmgmqeihSv/+/bFmzRq06oyV29rasGbNGs1W+6tXryJM9zxNZh43N+DBBynxSyqlD2dSEl0bM8bwseodnBcuaLvFjY10uHRVFfVIwsJomMO7YsVrbaVlbHVRJIAmOJubqRfR0kK9weZmKp5UUqL/+z17AqtW0funzvNITaXygt7eFNAd9JAm0a1+7733MG3aNERFRSEpKQkSiQRZWVlQKpXYuXMnAODy5ct4jJf9OicgAPjjH6mHAGizPm+5hT5kv/xCASE5mb7Jzp/XrzOqVNI3WV0dfRsKAj3Hzz/TfglmvtpaChC1tdq5CHWhHvWchbr+RlUVBY72mwiDgmhj2x/+QCURqqro/Rk+nLJSHZTowDF69GhcuXIF//3vf5GbmwtBEHD//fdj7ty5mkI+8+bNs3hDXY6xlavUVP2qYF9+SYHDzY0Chru7Nq1ZqaRei/p5TJ31wUyTyej1lEi0u4vVqeXq/0qlFEzq6ymnpqYGGDXKsAr9wIE0Z9XUpC1s7MA61U/y9/fHkiVLLN0WJtZ991GuQGamtkxgSYl2J6fuOSqcuyGepyfNC+3ZQz241lbtXIU6oCiVFCxCQqh3cvQovSeLFxtOlkokxo+1cECOOcBixMODiscIAn2Qr12jD3pVFaU4q7fRu7tTyUAmjrrMX79+1EMoKaFgoFDQOSnqpK6QEP3XV31I9ahRtmu7lXHgcHTqilInT9KHOCCAdm8WF9MHOCwMuO0205XRmWnq6mtRUdr8l+JibfatuzsNUWJiDIcexcVd29YuxoHDnimV9OG9coWyDIcONV6LIyGBfsrLaekwI4MeP3Ys/Q7rHN2kLYByN/Ly6HW+do16H9eu0RBl4ED9eSl72ftkJRw47FVjI7BiBQUOlYp6FQEBFAxGjKAVEt06o9XVwKefakvv19XRtvyGBupxMPEGDNAeoqRQ0JJsdTWtpPToQb0MDw9KtAsMpOEhQIFELqcckMRE06nqDqzTR0Bu3boVly5dwrPPPovg4GBkZGQgLCwMkdwlvjlKJWV/rlpF+Rm+vvTNlp9P32jFxfTnXr1oM5V6FeXYMePndfz0Ew1nnOj4wS4TGkq1Ng4epECuVNJ/g4O1Q5MePWjCtLSUAkdTE91Wn2p46BAdadE+D8fBdWp37MSJEyGTyXDlyhUsXrwYwcHBSEtLQ0FBAT7//HNrtNN1fPMNBYG8PO0sfksLBQj1blk/P8rh6N2bshMBmsQzpqVFO+vPxBs3jnoeJ05QL069zA3Qe1NVRasvgYEUUFpaDI8/2L+feh6mjkxwQKIzR59++mksWLAAFy9e1Nv9etddd/HxjzertJQONG5s1I6X1UWLW1tpIq66msbYeXmU6qw+R7Z7d+PP6eHh9ONtqwsNpcOxJk7UDjtUKu38hr8/9Tyys+lA8PYb1wSBeo9ORHTgOHHiBB555BGD65GRkSgz9a3HzHP1Kv3Xz89w41NTE30AfXy0acrqcTSgzSxtb9gwp6p1aVP33UcZoF5e9H64uVEgkcmoSptEQsPFqirD33XwhK/2RAcOb29vo0WJL1y4gBDuDt8cdRc3IIBSl7t1ow+nhwd9w3l66i+rhoVpjxoMDaU5D/UEnZ8fjc8nTerSf4JT8/ICFiwAtmyhg6OHD6edscOG0XsTEkLvV/tjLdQ7nZ2I6DmO6dOn47XXXsNXX30FgCp/FRYW4vnnn8cf//hHizfQpfTrp61XOWgQdYFLSmiY0thIE6XqXkV4OAUX3d5EdDSVFeRT6a2rWzdgxgzDKmseHjQfottb9PAApk/XHqPgJETXHK2rq8PUqVNx9uxZ1NfXIyIiAmVlZRg1ahR++OEH+DlQSq1d1hytr6eaoxcu0NAkJoaK5WZkAN9/T99m6qxFdRGZ1FTqaTjoTkuHJAjAf/5DNTp0DRwITJtGBaMFgYovOWElvE4XKz5w4AAyMjKgUqmQkpKCiRMnWrptVmeXgUOttVWbSg5QHkFaGk2eCgLlZ1RX09DFzY0CybRpvAO2K1RXa4sk1dVpj3scNIiGLS7Q2xMdOK5cuYI+pg4EcjB2HThMUZ8WtnUrBQ/1AUDBwbSy8uSTvIpiTQcPAocPa1dOfH1pvsNYqUYnJjo0xsTEYMyYMfjwww9RZWz2mFlX9+70jVZaSoli+fmU25GZSb2R36uwMSu4coVS+nW/a5uaKIi3T093cqIDx8mTJzFq1Cj8/e9/R0REBKZPn46vv/4a8vZ1FZn1qA82bt9ZLC01HHMzy1FvegNo6FhcTPVQsrKcLk/jRkQHjpSUFPzzn/9EYWEhdu3ahdDQUDzyyCMIDQ3FwoULrdFG1p5Uavx8UkD/6EFmWepALZfTbuS8PMrYzcsD/v534N//pont69dpjqqwkIaWTsgiJ7llZGRg0aJFmhKCjsKu5zjq66lbnJdHE6RDh1KSl0RCw5PXXzfsXYSH09myOhXomQXl5dGBWbm52vqiSiUFivBwmmcaMoSCSUCAtmhP3750dq8DrTjeSKfX74qKivDFF19g8+bNyM7OxqhRo7B+/XpLts11tbTQTlfdcn+7d1Nl83vuoXyNwYMpY/H6dRpf9+hBH1wHPeDHIcTGUtLXr79qr9XXa4solZTQFoDyckrmmzCBeof5+bQi9qc/2arlFic6cGzYsAGbNm3CTz/9hISEBDzwwAPYvn2706y02IXMTOM1QjMyaFt9YCAwcyZlMMpkdJ9UShve1CUClUqa/c/MpK51XBwVx+UjEm7O3XfTBPSFC5Q3k5NDWwUEgdLNa2pouFhbSzuTU1Io+evSJbqmfr8cnOjA8frrr2P27Nl4++23kZycbIUmMZM7XdUbqwIDKX/jqafoAymXU6KYbld4+3b9FZbsbKon8eijDnuWh90YP556hYKg7X2oa5GqT3drbaUAcuECJeipA4urBo7CwsIOz45lFtB++7VSqd1a3/60t/h4w9+vqtJfAVCrq6MeiBPXwrQqpZICgUpF8xa5uZQVWl9Pc08+PtqAIZXSsnl9Pb13ISFOVdDHrMCRlZWFQYMGwc3NTXNamylJPMa+eSkpdH5KczP1EgoL6UMbHk7DlYkTjR+foFZRYfpMUt1T4pj5qqqAzz/XljEAaG/R2LG0DP7bb7SfyNOT3hvdnoWbGxWVdqKMUrMCR3JyMsrKyhAaGqo5rV53MUZ9WyKRONSqit3y96ddmB9+SBNrUikNTWJiaNzs7d1xOcAePbSHVRu7j4n37bf6QQOggB4To11BOXNG29OIiKBgHxEBPPGE/lEVTsCswJGfn6/ZMp+fn2/VBrHfhYVR13bcOLqt28M4frzjwBEcTJut2g9XAgK4eHFnNDZS1qgxPj405/HrrzQUrK+nYUxICPU65s1zymBtVuDQPWS6oKAAo0ePhnu7nZgKhQI///yz0QOpWSepx87tNTRQbyIvj7IXZTLaYOXpqX3MjBkUQE6f1q6q3HEHT4xamkRCgWPsWDoIq7qahpcBAfSaNzTQ6+9kxZREJ4BJpVKUlpYitN1ET2VlJUJDQx1qqGLXCWAA8NVXtNzXXlQU1XnQ7f35+dHwhospWcfGjdqDp3XddRcl5rV3+jRw4AAFf3d36undeafTVAITPVujnstor7Ky0qFqcTiE8eMNazl4eFAuRn4+ze5fukTHDn7/PfDii6aXctnN+cMfDHcdx8fTNvr2jh2jWh21tXRboaBix3v2WL+dXcTsHsd9v5+evmPHDtx5553w0ul6KZVKZGVlISEhAbt377ZOS63A7nscAM3mHztG+Rvdu9NRB9u3U9JRTg5ljuq6/XbK77DXf48jUyjoXNjaWtpG335YXllJO2X37KE/e3pScFHPcXh4AM895xRHVZidxyH7fXlJEAQEBATAR2es7OnpiZEjR2Lx4sWWb6GrCw6m7rAudV3L9kEDoDyCU6co3ZlZlrs7pfobIwjAF1/QUrh6p3hrK3D2LB2g5eNDcyAtLa4VODZu3AgA6NOnD5555hkeltjS4MG0lbu94GD6cDvpjky7VlhIQQOgyeqGBvqzINDwsW9fGmI6Se1R0XMcK1eu5KBha8OG0WYr3bkmX19tFqmT5Qw4hKYm7Z979dJf4VIo6L26UeKeA+nU7titW7fiq6++QmFhIVrVOfq/y1Affcesx82NdlpKJLT1Xp2KLpHQ3EZKiq1b6Hqio6m3p1DQhHZKCi2V19VRkL/vPu3RFU5AdI/jnXfewUMPPYTQ0FCcPn0aI0aMQPfu3XH58mXc1X4szm5eeTkty65ZA7z9NnDkiLZM3dy5dBxCYiJ1j4cOpdu+vrZtsyvy9dWfV/L2pm34s2dT5qgTBQ2gE3kc/fv3x8qVKzFnzhwEBATgt99+Q0xMDF5++WVUVVU5VE0Ou19Vqa+nqlK63WCAvsHuvlv/WnU1TcSpVFTp3Ik2VDmUggLat9LaSkPHQYOcao+KmujA4evri3PnziE6OhqhoaHYu3cvhgwZgosXL2LkyJGodKCJObsPHAcP0lCkPakUWL5cO9GWkQHs3KlfMHfCBG26OmMWJjoUhoeHa4JDdHQ0fv29HkF+fj4sUIWQ6TK23ArQ5il1hfnGRqpzqVJpaz60tlLQMfX77Oa1tmprcBhTUABs20aJYEeOGB4L6eBET47efvvt+O6775CSkoJFixZh+fLl2Lp1K06ePKlJEmMWEhJCCUftSaXauhwXL9KEXGUl/Vn9Ae3Rg2pvTJ7cZc11CTU1FKgvXqTbcXF00p763F+AeoDffafdnXzpEi2fL1rkNKe6dap0oOr3LvGSJUsQHByMo0eP4g9/+AOWLFli8Qa6tGHDqJp2+zmOoUMpmejCBep5lJbSTljdxKKKCvqm48BhOUol1eTQPU8oN5de66VLKaArFMC+fYYlDcrLKTHv1lu7ts1WIjpwuLm5wU1nsmfmzJmYOXOmRRvFfhcYSFXL9++niuY+PhQ0KiuBd96hNPQLF2iXLEDfZsHB9AF3c6MMxqoqw4pirHPUgbq9qioKIAMGUIBoH+jVrlxxrcCRZSxL0QSuAGZhISG0pKd25AjVD21spGGMIFBPo6GBeiGVlTRp2r07dY8vX+bAYSnGCki3v8/Pz3QRJSfJGgVEVABrX/XLGK4A1gUyM+m/ZWXaD2dgoLbWpacnLcX6+lIgSU+nYrlOkrFoU5GRhtcUCur5nTlD70FKCi3Dtj/ZTSIxvpPWQZldAYzZCfVMflsbfWjr62lI0thIwUIq1QaJPn3o/oIC+jO7OX36UJ3RS5fodmsrBXJvbzpTpaSEasX+z//QUPH8eQruAQE012Qs8Dgo0RXAmI3FxlKRGB8f+qCqczd8fLTpzpGR9NO9O93X0bIhE2fOHAoOZ89SWYOwMP2T6uVy6uUtWkTDx6YmWuFysiSwTv1r/vOf/+DWW29FREQECn6virRu3Trs2LHDoo1jRkyYQEt/9fXajVRSKQ1PvLwoYCQlaYOGp6dh3QjWee7uVO91yRLK0I2ONgwKRUW0LO7vT++LkwUNoBOB49///jeefvppTJ06FTU1NZo5jaCgIKxbt87S7WPtBQbSoUo9ewLJyfSTlKRNb9ady5BIqJaHk9W7tBumXld3d/pxYqL/de+++y4++ugjzJgxA2vWrNFcHzZsGJ555hmLNo6Z4OUFJCRoexVq6hPEhg+nKlVSKU3cFRXpd6eZZQwdSq9te4MHO33gEN3jyM/Px1AjJfa9vLzQ2NhokUYxMxiboZdIqCCury/lFZw7R2X7P/mECucyyxo6lE7F0y1AHBdH74GTEx0W+/bti8zMTIMJ0127diExMdFiDWM3cMstNM9x7Jj2yMHUVNpi//77ho8/coSGNE54xofNSCTAlCmU1FVWRnNPcjkFa29vGjo6adEr0YHj2WefxdKlS9HS0gJBEHD8+HF88cUXWL16NT7++GNrtJHpamigWf3CQlrm++Mfad7Dy4uGKTk5xpOPBIH2V3DgsDx/f1rt+u47SitX27cPmDWL7nMyogPHQw89BIVCgeeeew5NTU2YO3cuIiMj8fbbb2O2boYjs7yGBuCjj7Rl9wEKFN270+YrpZJ2xNbX09GE7ZO+dMvZMcu6eFE/aACUa7N9O5VAcJLzVNQ6tU60ePFiFBQU4Pr16ygrK0NRUREWLVqEq1evWrp9TNevv+oHDYD2P3z3HQ1XAEovLymh4xN0eXrSMIZZh7FdzAAFe2MTqA7uphaYe/TogdDQUJSVlWHZsmWIdcIumV0x9gEsLaUhSnMz3XZ3p3Nj1RW3AZosnTmTj3+0po5yNVw5j6OmpgYPPPAAQkJCEBERgXfeeQcqlQovv/wyYmJi8Ouvv+LTTz+1ZluZsU1Sra00JNEdhgQH0+Tpn/4EPPAA8PTTTjnOtiumzlsJCnLKpXCz5zhefPFFHD58GPPnz8fu3buxfPly7N69Gy0tLdi1axfGcZk668nPp+XUs2fpJzKSPowSCZ3V4elpmDcQG8vBoitFR1OpxsOHtZPTfn7A/fc75QZDs2uORkdH45NPPsHEiRNx+fJlxMbG4oknnnDobFG7rzkK0HzFJ5/QxCdACV2XL1PmaGwsHUBdXKyd4wBoSLJwIR9AbQs1NbQJztubsnmd4NQ2Y8wOHB4eHigoKEBERAQAKlp8/PhxDBo0yKoNtCaHCBzffEP1N3Sp37LnnqNvtepqOtS4ooL2RowYwWfHMqsye6iiUqngoRM9pVIpn+jWFYxVjVd3fdW9jG7duEQg61JmBw5BELBgwQLNKfUtLS1YsmSJQfDYtm2bZVvo6sLCaLjSnp+fU1WUYo7F7MAxf/58vdt/+tOfLN4YZsTo0TQh2r6mxm23OV1SkcO4fp2Wwbt1c7oT2swl+kAmZ+IQcxwAfUjT02kSNDCQllqHDLF1q1yPSgWkpenPOUVF0VGcLnbspnPv/XV0KhUFC0GgBC4nTCRyKMeOGU5UFxcDu3bRniEXwoHDXhUW0oqKOsU8MBC4916gb1/btsuVmar2n5MDTJ/u9DU4dPFXmD1qbQW++EJ/X0pdHbBli9MdJehQdHNldKmP33QhHDjs0blz2r0nuuRymihltpGQYPx6TIzTJnqZwoHDHnXUq+Aeh+2MGUMZu7r8/V2i4ld7rjMocyT9+pm+j/ef2I63N/Dww9QjLCmh5dikJJcsBs2Bwx716EHl6H76Sf/6LbdQQhizHamUSgI68FYLS+DAYa8mTaKex9mzNPGWmMi9DWY3OHDYs5gY+mHMzvDkKGNMNA4cjDHReKjiSMrLacK0rIzKA44c6bKbrJhtceBwFNeuUSUw9S7ZsjLg/Hk6PT0uzrZtYy6HhyqO4vBhw631KhWwf79t2sNcGgcOR1FcbPx6WZm2HiljXYQDh6OQyYxf9/fngj6sy3HgcBQjR4q7zpgVceBwFImJwB/+oK1e7uMDjB9PqemMdTEuHegIpQN1qVRAUxMFDh6iMBvh5VhH4+bG1c2ZzXHgYMyUykoqEn3lCgXr4cOBoUNt3Sq7wIGDMWNqaynhrqmJbtfVATt20H/5nGSeHGXMqGPHtEFD188/GybiuSAOHIwZU1Zm/LpcTmf1ujgOHIwZExxs/Lq7u+lkPBfCgYMxY265xXjl8pQUqj3q4jhwMGZMSAgwb562bIGvL02KumBFc2M4AczREsBMqaoC2tqA0FBAIrF1a5yLSsXHb7bDy7GOrroa2LYNKCqi2926UWo61yq1HA4aBvgVcWSCAGzerA0aAAWSL76gfAPGrIQDhyMrKKBygu21tQG//db17WEugwOHo7pyhap/FRVRbkF7jY1d3iTmOniOw9EIAs1pZGdTwLh8GcjPp5PFdHMP+vSxWROZ8+PA4QiqqylA+PgACgUFDYDOLO3dm4Ys588Do0bRikq/fkB8vG3bzJwaBw57t28fHYmgXjXPz6ezZQMC6HbfvlTc59o1yj0YNQpITuaVAGZV/OmyZ5cuAUePaoMGQBuscnL0r3XvThXC7rsPSE3lAj/M6jhw2DP1kERXaCjQ3Aw0NOhf79ED6Nmza9rlqtragIsXqdenUtm6NTbFQxV7ZuzD2a0bTXzqZocGBgL3388Zo9Z09izw3XdASwvdlsmAmTOByEjbtstGOHDYswEDgKwsw+tDhgAPPURLsj4+QGwsD0+sqboa+OYb/UBeWwts2QI89ZRLvvY8VLFn/fvTRKcuT09gxgwgKIjuS0hwyQ9ul8rONt77q6+neSgXxD0OeyaRUJBITaUPqI8PMHgw7dRkXaejil/Gku9cAAcOR9CrF/0w24iLo9Wt9qRSl91MyEMVxm4kOtp4dfNJkwA/v65vjx3gHgdj5pg+ndL6z5+n8oFJSS69/M2BgzFz9etHP4yHKowx8ThwMMZE48DBGBONAwdjTDQOHIwx0ThwMMZE48DBGBONAwdjTDQOHIwx0ThwMMZE48DBGBONA4craG01rFHK2E3gTW7OTC4Hdu2iClZKJRU6vvNOl60hwSzH4XscRUVFGD9+PBITE5GUlISvv/7a1k2yH2lpQGYmBQ0AuH6dDqmuqLBps5jjc/jA4e7ujnXr1iEnJwf79u3D8uXL0cjnpgI1NcCFC4bXFQrg5Mkubw5zLg4/VOnZsyd6/l5QJTQ0FMHBwaiqqoKfi1Zm0qir0z+0qf19jN0Em/c4Vq9ejeHDhyMgIAChoaGYMWMGLhj7pjTDyZMnoVKp0Ivrc9J8hqen8fuiorq2Lczp2LzHkZ6ejqVLl2L48OFQKBR46aWXMHnyZOTk5MDPzw+pqamQG6kkvWfPHkRERGhuV1ZW4sEHH8THH39s8u+Sy+V6z1XnzN+83t7AbbcB+/frXw8OBlJSbNMm5jQkgmCqP2sb5eXlCA0NRXp6OsaOHWvW78jlckyaNAmLFy/GvHnzTD7ulVdewauvvmpwvba2FoGBgZ1us13LyQFOnaJjI2Ni6FBqVx/GsZtmd4EjLy8PcXFxyM7OxqBBg274eEEQMHfuXCQkJOCVV17p8LHGehy9evVy7sDBuk5FBU0+h4U5/XGcdhU4BEHA9OnTUV1djSNHjpj1O0ePHsXYsWORlJSkufaf//wHgwcPvuHv1tXVQSaTceBgN6eigo6ILC2l2926AdOmAX372rZdVmRXgWPp0qX4/vvvcfToUUR1wQQeBw5201Qq4N136XxZXZ6ewBNPAP7+tmmXldl8VUVt2bJl+Pbbb3Hw4MEuCRqMWcTly4ZBA6A0f2MHhjsJm6+qCIKAZcuWIS0tDYcOHUJfJ+7eMSfU1GT6PidORLR54Fi6dCk2b96MHTt2ICAgAGVlZQAAmUwGHx8fG7eOsRuIjgbc3IyfZu/EX4I2n+OQmJh93rhxIxYsWGDVv5vnOJhF7N8PtJ/MT0gAZs922tUVm/c47GhulrHOueMOoFcv2oWsUFDQSEpy2qAB2EHgYMwpxMfTj4uwm1UVxpjj4MDBGBONAwdjTDQOHIwx0ThwMMZE48DBGBONAwdjTDQOHIwx0ThwMMZE48DBGBONAwdjTDQOHIwx0ThwMMZE48DBGBONAwdjTDQOHIwx0ThwMMZE48DBGBONAwdjTDQOHIwx0ThwMMZE48DBGBONj0dwdDU1QF4e4OVF53l4etq6RcwFcOBwZIcOAenpgPpQKx8fOj0sOtqmzWLOj4cqjqqwkAKH7kl4zc3A1q3GzzFlzII4cDiqM2eMX6+vBwoKurYtzOVw4HBUHZ25yz0OZmUcOBzVgAHGr/v58RwHszoOHI4qJga45Rb9ax4ewIwZgDvPeTPrkghCR31e51ZXVweZTIba2loEBgbaujmdU1oKXLxIy7GDBlGPgzEr468mR9ezJ/0w1oV4qMIYE40DB2NMNA4cjDHROHAwxkTjwMEYE40DB2NMNA4cjDHROHAwxkTjwMEYE40DB2NMNA4cjDHROHAwxkRz6U1u6o3BdXV1Nm4JY/YjICAAEomkw8e4dOCor68HAPTq1cvGLWHMfphTZsKl63GoVCqUlJSYFWHtVV1dHXr16oWioiLHrSniBJzpfeAexw24ubkhKirK1s2wiMDAQIf/wDoDV3kfeHKUMSYaBw7GmGgcOBycl5cXVq5cCS8vL1s3xaW52vvg0pOjjLHO4R4HY0w0DhyMMdE4cDDGROPAwRgTjQMHY0w0DhwWsmDBAsyYMcPofX369MG6detM/u6TTz6J1NRUeHl5ITk52eD+CxcuYMKECQgLC4O3tzdiYmLwt7/9DW1tbTds1zfffIPExER4eXkhMTERaWlpJh+7evVqSCQSPPXUU3rXBUHAK6+8goiICPj4+GD8+PE4e/bsDf9ue7dgwQJIJBKsWbNG7/r27dv1Uq4FQcCGDRtwyy23wN/fH0FBQRg2bBjWrVuHpqamDv+O999/H3379oW3tzdSU1Nx5MgRzX1tbW3461//isGDB8PPzw8RERF48MEHUVJSYtl/qBVw4LADgiBg4cKFmDVrltH7PTw88OCDD2LPnj24cOEC1q1bh48++ggrV67s8Hl/+eUXzJo1C/PmzcNvv/2GefPmYebMmTh27JjBY0+cOIENGzYgKSnJ4L5//OMf+Ne//oX169fjxIkTCA8Px6RJkzSbBB2Zt7c33nzzTVRXV5t8zLx58/DUU09h+vTpOHjwIDIzM7FixQrs2LEDe/bsMfl7X375JZ566im89NJLOH36NG677TbcddddKCwsBAA0NTUhIyMDK1asQEZGBrZt24bc3FxMmzbN4v9OixOYRcyfP1+YPn260fuio6OFtWvX3vA5Vq5cKQwZMsSsv2/58uXCmDFjOnzMzJkzhTvvvFPv2pQpU4TZs2frXauvrxfi4uKEvXv3CuPGjROefPJJzX0qlUoIDw8X1qxZo7nW0tIiyGQy4YMPPjCrrfZq/vz5wj333CP0799fePbZZzXX09LSBPX/Gl9++aUAQNi+fbvB76tUKqGmpsbk848YMUJYsmSJ3rX+/fsLzz//vMnfOX78uABAKCgoEPvP6VLc43BAeXl52L17N8aNG9fh43755RdMnjxZ79qUKVPw888/611bunQp7r77bkycONHgOfLz81FWVqb3PF5eXhg3bpzB8zgiqVSKVatW4d1330VxcbHB/Zs2bUJCQgKmT59ucJ9EIoFMJjP6vK2trTh16pTB6z958uQOX7fa2lpIJBIEBQWJ+4d0MQ4cDmT06NHw9vZGXFwcbrvtNrz22msdPr6srAxhYWF618LCwlBWVqa5vWXLFmRkZGD16tUmn0P9ex09jyO79957kZycbHTod/HiRSQkJIh+zoqKCiiVSlGvW0tLC55//nnMnTvX7nfYcuBwIF9++SUyMjKwefNmfP/993jrrbcAAIWFhfD399f8rFq1SvM77esqCIKguVZUVIQnn3wS//3vf+Ht7d3h393R8ziDN998E5999hlycnL0rpvz7zxy5Ije679p0ybNfea+bm1tbZg9ezZUKhXef//9m/iXdA2XrsfhaNSVyhITE6FUKvHnP/8Zf/nLXxAREYHMzEzN44KDgwEA4eHhBt9u169f13wLnjp1CtevX0dqaqrmfqVSicOHD2P9+vWQy+UIDw8HQD2Pnj17Gn0eZzB27FhMmTIFL774IhYsWKC5Hh8fj3PnznX4u8OGDdN7/cPCwuDl5QWpVNrh66/W1taGmTNnIj8/HwcOHLD73gbAPQ6HJQgC2traIAgC3N3dERsbq/lRB45Ro0Zh7969er+3Z88ejB49GgBwxx13IDs7G5mZmZqfYcOG4YEHHkBmZiakUin69u2L8PBwvedpbW1Fenq65nmcxZo1a/Ddd9/pzUHMnTsXubm52LFjh8HjBUFAbW0tfHx89F7/gIAAeHp6IjU11eD137t3r97rpg4aFy9exL59+9C9e3fr/QMtiHscFlRbW6v3zQNov/2vXr1qcF/v3r0RHByMvLw8NDQ0oKysDM3NzZrHJSYmwtPTE5s2bYKHhwcGDx4MLy8vnDp1Ci+88AJmzZoFd3fTb+GTTz6JsWPH4s0338T06dOxY8cO7Nu3D0ePHgVAJeIGDRqk9zt+fn7o3r275ro6r2PVqlWIi4tDXFwcVq1aBV9fX8ydO/cmXi37M3jwYDzwwAN49913NddmzpyJtLQ0zJkzBytWrMCkSZMQEhKC7OxsrF27FsuWLTOZv/P0009j3rx5GDZsGEaNGoUNGzagsLAQS5YsAQAoFArcf//9yMjIwM6dO6FUKjU9lODgYHh6elr939xpNlzRcSrz588XABj8zJ8/X4iOjjZ638aNGwVBEIRx48YZvT8/P18QBEHYsmWLkJKSIvj7+wt+fn5CYmKisGrVKqG5ufmG7fr666+FhIQEwcPDQ+jfv7/wzTffdPj49suxgkDLjitXrhTCw8MFLy8vYezYsUJ2dnZnXia7YmwJ/cqVK4KXl5eg+7+GUqkU/v3vfwvDhw8XfH19hcDAQCE1NVV4++23haampg7/jvfee0+Ijo4WPD09hZSUFCE9PV1zX35+vtH3HYBw8OBBS/5TLY7rcTDGROM5DsaYaBw4GGOiceBgjInGgYMxJhoHDsaYaBw4GGOiceBgjInGgYMxJhoHDsaYaBw4GGOiceBgjIn2/1QKxiQiEoO4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 280x455 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(2.8, 4.55))\n",
    "fig = sns.stripplot(data=rhomYPG, x='strain', y='rel_fit',\n",
    "                    hue='respiration', hue_order=[True,False], palette=['grey','red'], alpha=.5,\n",
    "                    legend=None\n",
    "                   )\n",
    "plt.axhline(y=1, ls='--', c='grey')\n",
    "plt.yscale('log', base=2)\n",
    "fig.axes.set(xlabel='', ylabel='Relative growth on YPG')\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.savefig(graph_outpath+'/20230419_YPG.svg', format='svg', dpi=300)\n",
    "plt.savefig(graph_outpath+'/20230419_YPG.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39d8a7ed-69ea-4e9d-8291-4531a0115568",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}