haploid_validations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b54bd9c-aee4-4ffc-bc07-b57331656fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import scipy.stats as stats\n",
    "\n",
    "#import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c5edb74-b93f-47a5-894c-f941de360a89",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_derivative_growth_rate(well, df, tmax=30):\n",
    "    # well : a tuple of the format (row, col)\n",
    "    # source : DataFrame where well was measured\n",
    "    # tmax : defines the interval in which the function will measure the growth rate, from t=0 to t=tmax\n",
    "    \n",
    "    df_cols = [ x for x in list(df.index) if x<=tmax]\n",
    "    # define time range\n",
    "    \n",
    "    df_data = df.loc[df_cols]\n",
    "    # select the subset of the DataFrame in the selected timerange\n",
    "    data = df_data[(well[0]+str(well[1]))]\n",
    "    # \n",
    "\n",
    "    data_diff = data.diff()*4\n",
    "    \n",
    "    get_top_5 = data_diff.nlargest(5)\n",
    "    \n",
    "    growth_rate = np.median(get_top_5)\n",
    "    std_err = np.std(get_top_5)\n",
    "    \n",
    "    return growth_rate, std_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6dbbbc4d-80cc-429b-aa71-bf653399a5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_table(imported_df):\n",
    "    \n",
    "    rename_dict = {}\n",
    "\n",
    "\n",
    "    for col_name in imported_df.columns:\n",
    "        to_hours = np.float64(col_name.strip('s'))/(60*60)\n",
    "        rename_dict[col_name] = to_hours\n",
    "    \n",
    "    imported_df.rename(columns = rename_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ad6c09c-55a7-4e1d-9b77-73492b20c760",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_growth_rate_dict(df):\n",
    "    \n",
    "    growth_rate_dict = {}\n",
    "    \n",
    "    for well in list(df.columns):\n",
    "    \n",
    "        row = well[0]\n",
    "        col = int(well[1:])\n",
    "\n",
    "        growth_rate = get_derivative_growth_rate((row, col), df, tmax=24)\n",
    "\n",
    "        growth_rate_dict[well] = growth_rate[0]\n",
    "        \n",
    "    return growth_rate_dict\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27524d1b-92a3-478c-9435-abfb2868b251",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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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>codon</th>\n",
       "      <th>wt</th>\n",
       "      <th>measured</th>\n",
       "      <th>mat_a_1_1</th>\n",
       "      <th>mat_a_1_2</th>\n",
       "      <th>mat_a_1_3</th>\n",
       "      <th>mat_a_2_1</th>\n",
       "      <th>mat_a_2_2</th>\n",
       "      <th>mat_a_2_3</th>\n",
       "      <th>mat_a_3_1</th>\n",
       "      <th>...</th>\n",
       "      <th>mat_alpha_3_3_norm</th>\n",
       "      <th>mat_a_1_norm_median</th>\n",
       "      <th>mat_a_2_norm_median</th>\n",
       "      <th>mat_a_3_norm_median</th>\n",
       "      <th>mat_alpha_1_norm_median</th>\n",
       "      <th>mat_alpha_2_norm_median</th>\n",
       "      <th>mat_alpha_3_norm_median</th>\n",
       "      <th>FC_score</th>\n",
       "      <th>overall_median</th>\n",
       "      <th>crossed_pool_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>*</td>\n",
       "      <td>-4.627119</td>\n",
       "      <td>-3.826984</td>\n",
       "      <td>-4.577480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.022385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.197819</td>\n",
       "      <td>0.001265</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>G</td>\n",
       "      <td>-5.157296</td>\n",
       "      <td>-4.373192</td>\n",
       "      <td>-5.123687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-0.163936</td>\n",
       "      <td>-0.087447</td>\n",
       "      <td>-0.102027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>A</td>\n",
       "      <td>-4.801714</td>\n",
       "      <td>-4.001580</td>\n",
       "      <td>-4.752075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.067115</td>\n",
       "      <td>-0.010461</td>\n",
       "      <td>-0.029696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>V</td>\n",
       "      <td>-5.277176</td>\n",
       "      <td>-3.477042</td>\n",
       "      <td>-3.642575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.072400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.083743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.182365</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.087058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>L</td>\n",
       "      <td>-4.899342</td>\n",
       "      <td>-4.099208</td>\n",
       "      <td>-4.831206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-0.063405</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006310</td>\n",
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       "      <td>-0.176187</td>\n",
       "      <td>-0.028548</td>\n",
       "      <td>-0.048699</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-0.618938</td>\n",
       "      <td>-0.166884</td>\n",
       "      <td>-0.179591</td>\n",
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       "    <tr>\n",
       "      <th>3293</th>\n",
       "      <td>158</td>\n",
       "      <td>E</td>\n",
       "      <td>K</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.077485</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.093036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019404</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>158</td>\n",
       "      <td>E</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "      <td>158</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.088172</td>\n",
       "      <td>...</td>\n",
       "      <td>0.440530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.410763</td>\n",
       "      <td>-0.613846</td>\n",
       "      <td>0.253239</td>\n",
       "      <td>0.152679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3296</th>\n",
       "      <td>158</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.190908</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.199397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.213075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.196512</td>\n",
       "      <td>-0.192505</td>\n",
       "      <td>-0.204793</td>\n",
       "      <td>-0.196512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3297 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codon wt measured  mat_a_1_1  mat_a_1_2  mat_a_1_3  mat_a_2_1  \\\n",
       "0         2  V        *  -4.627119  -3.826984  -4.577480        NaN   \n",
       "1         2  V        G  -5.157296  -4.373192  -5.123687        NaN   \n",
       "2         2  V        A  -4.801714  -4.001580  -4.752075        NaN   \n",
       "3         2  V        V  -5.277176  -3.477042  -3.642575        NaN   \n",
       "4         2  V        L  -4.899342  -4.099208  -4.831206        NaN   \n",
       "...     ... ..      ...        ...        ...        ...        ...   \n",
       "3292    158  E        H        NaN        NaN        NaN        NaN   \n",
       "3293    158  E        K        NaN        NaN        NaN        NaN   \n",
       "3294    158  E        R        NaN        NaN        NaN        NaN   \n",
       "3295    158  E        D        NaN        NaN        NaN        NaN   \n",
       "3296    158  E        E        NaN        NaN        NaN        NaN   \n",
       "\n",
       "      mat_a_2_2  mat_a_2_3  mat_a_3_1  ...  mat_alpha_3_3_norm  \\\n",
       "0           NaN        NaN        NaN  ...                 NaN   \n",
       "1           NaN        NaN        NaN  ...                 NaN   \n",
       "2           NaN        NaN        NaN  ...                 NaN   \n",
       "3           NaN        NaN        NaN  ...                 NaN   \n",
       "4           NaN        NaN        NaN  ...                 NaN   \n",
       "...         ...        ...        ...  ...                 ...   \n",
       "3292        NaN        NaN  -5.074609  ...           -0.180542   \n",
       "3293        NaN        NaN  -4.077485  ...           -0.093036   \n",
       "3294        NaN        NaN  -4.705405  ...           -0.113147   \n",
       "3295        NaN        NaN  -3.088172  ...            0.440530   \n",
       "3296        NaN        NaN  -5.190908  ...           -0.199397   \n",
       "\n",
       "      mat_a_1_norm_median  mat_a_2_norm_median  mat_a_3_norm_median  \\\n",
       "0               -0.019855                  NaN                  NaN   \n",
       "1               -0.113607                  NaN                  NaN   \n",
       "2               -0.049823                  NaN                  NaN   \n",
       "3                0.072400                  NaN                  NaN   \n",
       "4               -0.063405                  NaN                  NaN   \n",
       "...                   ...                  ...                  ...   \n",
       "3292                  NaN                  NaN            -0.192846   \n",
       "3293                  NaN                  NaN            -0.019404   \n",
       "3294                  NaN                  NaN            -0.128626   \n",
       "3295                  NaN                  NaN             0.095715   \n",
       "3296                  NaN                  NaN            -0.213075   \n",
       "\n",
       "      mat_alpha_1_norm_median  mat_alpha_2_norm_median  \\\n",
       "0                    0.022385                      NaN   \n",
       "1                   -0.061287                      NaN   \n",
       "2                    0.028901                      NaN   \n",
       "3                   -0.083743                      NaN   \n",
       "4                    0.006310                      NaN   \n",
       "...                       ...                      ...   \n",
       "3292                      NaN                      NaN   \n",
       "3293                      NaN                      NaN   \n",
       "3294                      NaN                      NaN   \n",
       "3295                      NaN                      NaN   \n",
       "3296                      NaN                      NaN   \n",
       "\n",
       "      mat_alpha_3_norm_median  FC_score  overall_median  crossed_pool_median  \n",
       "0                         NaN -0.197819        0.001265             0.004287  \n",
       "1                         NaN -0.163936       -0.087447            -0.102027  \n",
       "2                         NaN -0.067115       -0.010461            -0.029696  \n",
       "3                         NaN -0.182365       -0.005671            -0.087058  \n",
       "4                         NaN -0.176187       -0.028548            -0.048699  \n",
       "...                       ...       ...             ...                  ...  \n",
       "3292                -0.140922 -0.618938       -0.166884            -0.179591  \n",
       "3293                -0.088460 -0.255429       -0.053932            -0.019404  \n",
       "3294                -0.108890 -0.553013       -0.118758            -0.108890  \n",
       "3295                 0.410763 -0.613846        0.253239             0.152679  \n",
       "3296                -0.196512 -0.192505       -0.204793            -0.196512  \n",
       "\n",
       "[3297 rows x 48 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DMS_v2_score = pd.read_csv('./miseq_single_col/DMSv2_scores.csv', sep=',', index_col=0)\n",
    "\n",
    "DMS_v2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6781c316-c1dc-4e17-8941-8991ad2bf154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.613846036"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_mutant_DMS_score(mutant, score_source):\n",
    "    # mutant format example: A23N\n",
    "    # score_source is the column af the DMS df\n",
    "    \n",
    "    if mutant in ['NaN', 'wt', 'delta']:\n",
    "        score = np.nan\n",
    "        \n",
    "    else:\n",
    "    \n",
    "        codon = int(mutant[1:-1])\n",
    "        measured = mutant[-1]\n",
    "\n",
    "        DMS_slice = DMS_v2_score[(DMS_v2_score['codon']==codon) & (DMS_v2_score['measured'] == measured)]\n",
    "\n",
    "        score = float(DMS_slice[score_source])\n",
    "    \n",
    "    \n",
    "    return score\n",
    "\n",
    "\n",
    "get_mutant_DMS_score('E158D', 'FC_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a93e8df-4714-4307-827b-a8c2fa25001b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat_a_plan = {'A1':'NaN',   'A2':'G72P', 'A3':'C94D',  'A4':'D155A', 'A5':'A59H', 'A6':'T60F',\n",
    "              'B1':'R53I',  'B2':'R73G', 'B3':'A97F',  'B4':'D155S', 'B5':'T60F', 'B6':'L74Q',\n",
    "              'C1':'R53D',  'C2':'NaN',  'C3':'M100W', 'C4':'I156P', 'C5':'T60R', 'C6':'E75R',\n",
    "              'D1':'H62C',  'D2':'Y79S', 'D3':'Y101H', 'D4':'A23N',  'D5':'L61F', 'D6':'NaN',\n",
    "              'E1':'E64V',  'E2':'Y79H', 'E3':'T124Y', 'E4':'G29Q',  'E5':'NaN',  'E6':'delta',\n",
    "              'F1':'E64C',  'F2':'Y79K', 'F3':'E135D', 'F4':'N39F',  'F5':'S66M', 'F6':'NaN',\n",
    "              'G1':'E64S',  'G2':'Y79E', 'G3':'R136I', 'G4':'S89V',  'G5':'E69V', 'G6':'wt',\n",
    "              'H1':'R136I', 'H2':'M93G', 'H3':'F153*', 'H4':'I98L',  'H5':'C71A', 'H6':'NaN'}\n",
    "#D4 dubious\n",
    "\n",
    "mat_alpha_plan = {'A1':'G14*', 'A2':'G72P', 'A3':'A97F',  'A4':'D155A', 'A5':'A59H', 'A6':'C71Y',\n",
    "              'B1':'R53I', 'B2':'R73G', 'B3':'M100W', 'B4':'D155S', 'B5':'T60F', 'B6':'L74Q',\n",
    "              'C1':'R53D', 'C2':'L74P', 'C3':'C94D',  'C4':'I156P', 'C5':'T60R', 'C6':'E75R',\n",
    "              'D1':'H62C', 'D2':'Y79S', 'D3':'Y101H', 'D4':'A23N',  'D5':'L61F', 'D6':'G76M',\n",
    "              'E1':'E64V', 'E2':'Y79H', 'E3':'T124Y', 'E4':'G29Q',  'E5':'I65F', 'E6':'delta',\n",
    "              'F1':'E64C', 'F2':'Y79K', 'F3':'D134C', 'F4':'N39F',  'F5':'S66M', 'F6':'G76Y',\n",
    "              'G1':'E64S', 'G2':'Y79E', 'G3':'R136I', 'G4':'S89V',  'G5':'E69V', 'G6':'wt',\n",
    "              'H1':'E69L', 'H2':'M93G', 'H3':'F153*', 'H4':'I98L',  'H5':'C71A', 'H6':'G76H'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9305f109-b767-455b-9788-ca293d52c3fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "FC_100 = pd.read_excel('./growth_curves/lofxlof_validations_hap_FC_100.xlsx', index_col=0)\n",
    "CY_200 = pd.read_excel('./growth_curves/lofxlof_validations_hap_CY_200.xlsx', index_col=0)\n",
    "CY_20 = pd.read_excel('./growth_curves/lofxlof_validations_hap_CY_20.xlsx', index_col=0)\n",
    "\n",
    "for df in [FC_100, CY_200, CY_20]:\n",
    "    \n",
    "    df = format_table(df)\n",
    "\n",
    "FC_100 = FC_100.T\n",
    "CY_200 = CY_200.T\n",
    "CY_20 =  CY_20.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a257a310-a958-496b-91af-fc9a833699df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f918b593590>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for well in CY_20.columns:\n",
    "    plt.plot(CY_20[well])\n",
    "    \n",
    "plt.axvline(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7388471-6d78-4455-ba79-758ab5809b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f918a9909d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for well in CY_200.columns:\n",
    "    plt.plot(CY_200[well])\n",
    "    \n",
    "plt.axvline(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "843f5775-f650-4657-865a-e3b66bbafaf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f918a7cfbd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for well in FC_100.columns:\n",
    "    plt.plot(FC_100[well])\n",
    "    \n",
    "plt.axvline(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4820a5fd-7beb-429d-b21a-d3b82d0713b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "growth_rate_FC_100 = get_growth_rate_dict(FC_100)\n",
    "growth_rate_CY_200 = get_growth_rate_dict(CY_200)\n",
    "growth_rate_CY_20 = get_growth_rate_dict(CY_20)\n",
    "\n",
    "\n",
    "#print(growth_rate_FC_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dcbfd484-1fbd-4c64-8072-d3780a88b67a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>mat_a_pos</th>\n",
       "      <th>mutant_a</th>\n",
       "      <th>FC_100_a</th>\n",
       "      <th>CY_200_a</th>\n",
       "      <th>CY_20_a</th>\n",
       "      <th>mutant_alpha</th>\n",
       "      <th>FC_100_alpha</th>\n",
       "      <th>CY_200_alpha</th>\n",
       "      <th>CY_20_alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00080</td>\n",
       "      <td>0.00150</td>\n",
       "      <td>0.00648</td>\n",
       "      <td>G14*</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.00352</td>\n",
       "      <td>0.00932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B1</td>\n",
       "      <td>B1</td>\n",
       "      <td>R53I</td>\n",
       "      <td>0.09772</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>R53I</td>\n",
       "      <td>0.09212</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C1</td>\n",
       "      <td>C1</td>\n",
       "      <td>R53D</td>\n",
       "      <td>0.10252</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00988</td>\n",
       "      <td>R53D</td>\n",
       "      <td>0.10448</td>\n",
       "      <td>0.00388</td>\n",
       "      <td>0.00912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>H62C</td>\n",
       "      <td>0.10312</td>\n",
       "      <td>0.08272</td>\n",
       "      <td>0.03908</td>\n",
       "      <td>H62C</td>\n",
       "      <td>0.10400</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E1</td>\n",
       "      <td>E1</td>\n",
       "      <td>E64V</td>\n",
       "      <td>0.10260</td>\n",
       "      <td>0.00324</td>\n",
       "      <td>0.00960</td>\n",
       "      <td>E64V</td>\n",
       "      <td>0.10440</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  well mat_a_pos mutant_a  FC_100_a  CY_200_a  CY_20_a mutant_alpha  \\\n",
       "0   A1        A1      NaN   0.00080   0.00150  0.00648         G14*   \n",
       "1   B1        B1     R53I   0.09772   0.00380  0.00660         R53I   \n",
       "2   C1        C1     R53D   0.10252   0.00348  0.00988         R53D   \n",
       "3   D1        D1     H62C   0.10312   0.08272  0.03908         H62C   \n",
       "4   E1        E1     E64V   0.10260   0.00324  0.00960         E64V   \n",
       "\n",
       "   FC_100_alpha  CY_200_alpha  CY_20_alpha  \n",
       "0       0.10480       0.00352      0.00932  \n",
       "1       0.09212       0.00320      0.00740  \n",
       "2       0.10448       0.00388      0.00912  \n",
       "3       0.10400       0.00332      0.00740  \n",
       "4       0.10440       0.00340      0.00628  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_well_dict = {}\n",
    "mutant_array_a_dict = {}\n",
    "mutant_dict_a = {}\n",
    "\n",
    "mutant_array_alpha_dict = {}\n",
    "mutant_dict_alpha = {}\n",
    "\n",
    "FC_100_dict_a = {}\n",
    "CY_200_dict_a = {}\n",
    "CY_20_dict_a = {}\n",
    "\n",
    "FC_100_dict_alpha = {}\n",
    "CY_200_dict_alpha = {}\n",
    "CY_20_dict_alpha = {}\n",
    "\n",
    "index_pos = 0\n",
    "\n",
    "for col in range(1,7):\n",
    "    \n",
    "    for row in ['A','B', 'C', 'D', 'E', 'F', 'G', 'H']:\n",
    "        \n",
    "        well = row+str(col)\n",
    "        \n",
    "        index_well_dict[index_pos] = well\n",
    "        # store well information\n",
    "        \n",
    "        \n",
    "        mutant_array_a_dict[index_pos] = well\n",
    "        mutant_dict_a[index_pos] = mat_a_plan[well]\n",
    "        FC_100_dict_a[index_pos] = growth_rate_FC_100[well]\n",
    "        CY_200_dict_a[index_pos] = growth_rate_CY_200[well]\n",
    "        CY_20_dict_a[index_pos] = growth_rate_CY_20[well]\n",
    "        \n",
    "        \n",
    "        # add growth rates\n",
    "        \n",
    "        mutant_array_alpha_dict[index_pos] = well\n",
    "        mutant_dict_alpha[index_pos] = mat_alpha_plan[well]\n",
    "        FC_100_dict_alpha[index_pos] = growth_rate_FC_100[row+str(col+6)]\n",
    "        CY_200_dict_alpha[index_pos] = growth_rate_CY_200[row+str(col+6)]\n",
    "        CY_20_dict_alpha[index_pos] = growth_rate_CY_20[row+str(col+6)]\n",
    "            \n",
    "            \n",
    "        \n",
    "        \n",
    "        \n",
    "                \n",
    "        index_pos+=1\n",
    "        \n",
    "\n",
    "hap_growth_rate_df = pd.concat([pd.Series(index_well_dict, name='well'), pd.Series(mutant_array_a_dict, name='mat_a_pos')], axis=1)\n",
    "hap_growth_rate_df['mutant_a'] = pd.Series(mutant_dict_a, dtype=str)\n",
    "hap_growth_rate_df['FC_100_a'] = pd.Series(FC_100_dict_a)\n",
    "hap_growth_rate_df['CY_200_a'] = pd.Series(CY_200_dict_a)\n",
    "hap_growth_rate_df['CY_20_a'] = pd.Series(CY_20_dict_a)\n",
    "\n",
    "\n",
    "hap_growth_rate_df['mutant_alpha'] = pd.Series(mutant_dict_alpha, dtype=str)\n",
    "hap_growth_rate_df['FC_100_alpha'] = pd.Series(FC_100_dict_alpha)\n",
    "hap_growth_rate_df['CY_200_alpha'] = pd.Series(CY_200_dict_alpha)\n",
    "hap_growth_rate_df['CY_20_alpha'] = pd.Series(CY_20_dict_alpha)\n",
    "\n",
    "\n",
    "\n",
    "hap_growth_rate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9a37d94b-1e58-4d50-9118-fa3565b880ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>mat_a_pos</th>\n",
       "      <th>mutant_a</th>\n",
       "      <th>FC_100_a</th>\n",
       "      <th>CY_200_a</th>\n",
       "      <th>CY_20_a</th>\n",
       "      <th>mutant_alpha</th>\n",
       "      <th>FC_100_alpha</th>\n",
       "      <th>CY_200_alpha</th>\n",
       "      <th>CY_20_alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G14*</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.00352</td>\n",
       "      <td>0.00932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B1</td>\n",
       "      <td>B1</td>\n",
       "      <td>R53I</td>\n",
       "      <td>0.09772</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>R53I</td>\n",
       "      <td>0.09212</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C1</td>\n",
       "      <td>C1</td>\n",
       "      <td>R53D</td>\n",
       "      <td>0.10252</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00988</td>\n",
       "      <td>R53D</td>\n",
       "      <td>0.10448</td>\n",
       "      <td>0.00388</td>\n",
       "      <td>0.00912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>H62C</td>\n",
       "      <td>0.10312</td>\n",
       "      <td>0.08272</td>\n",
       "      <td>0.03908</td>\n",
       "      <td>H62C</td>\n",
       "      <td>0.10400</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E1</td>\n",
       "      <td>E1</td>\n",
       "      <td>E64V</td>\n",
       "      <td>0.10260</td>\n",
       "      <td>0.00324</td>\n",
       "      <td>0.00960</td>\n",
       "      <td>E64V</td>\n",
       "      <td>0.10440</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F1</td>\n",
       "      <td>F1</td>\n",
       "      <td>E64C</td>\n",
       "      <td>0.10200</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>E64C</td>\n",
       "      <td>0.10320</td>\n",
       "      <td>0.00300</td>\n",
       "      <td>0.00772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G1</td>\n",
       "      <td>G1</td>\n",
       "      <td>E64S</td>\n",
       "      <td>0.10028</td>\n",
       "      <td>0.00448</td>\n",
       "      <td>0.00948</td>\n",
       "      <td>E64S</td>\n",
       "      <td>0.10140</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H1</td>\n",
       "      <td>H1</td>\n",
       "      <td>R136I</td>\n",
       "      <td>0.08312</td>\n",
       "      <td>0.02112</td>\n",
       "      <td>0.01072</td>\n",
       "      <td>E69L</td>\n",
       "      <td>0.07860</td>\n",
       "      <td>0.00532</td>\n",
       "      <td>0.00660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A2</td>\n",
       "      <td>A2</td>\n",
       "      <td>G72P</td>\n",
       "      <td>0.09968</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>0.00552</td>\n",
       "      <td>G72P</td>\n",
       "      <td>0.09060</td>\n",
       "      <td>0.00408</td>\n",
       "      <td>0.00728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B2</td>\n",
       "      <td>B2</td>\n",
       "      <td>R73G</td>\n",
       "      <td>0.08532</td>\n",
       "      <td>0.00444</td>\n",
       "      <td>0.00788</td>\n",
       "      <td>R73G</td>\n",
       "      <td>0.07404</td>\n",
       "      <td>0.00420</td>\n",
       "      <td>0.00784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C2</td>\n",
       "      <td>C2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L74P</td>\n",
       "      <td>0.09480</td>\n",
       "      <td>0.00488</td>\n",
       "      <td>0.00812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>D2</td>\n",
       "      <td>D2</td>\n",
       "      <td>Y79S</td>\n",
       "      <td>0.09200</td>\n",
       "      <td>0.00408</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>Y79S</td>\n",
       "      <td>0.08712</td>\n",
       "      <td>0.00480</td>\n",
       "      <td>0.00708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>E2</td>\n",
       "      <td>E2</td>\n",
       "      <td>Y79H</td>\n",
       "      <td>0.08648</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>Y79H</td>\n",
       "      <td>0.07280</td>\n",
       "      <td>0.00512</td>\n",
       "      <td>0.00660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>F2</td>\n",
       "      <td>F2</td>\n",
       "      <td>Y79K</td>\n",
       "      <td>0.08500</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>0.00700</td>\n",
       "      <td>Y79K</td>\n",
       "      <td>0.06812</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.00740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>G2</td>\n",
       "      <td>G2</td>\n",
       "      <td>Y79E</td>\n",
       "      <td>0.10088</td>\n",
       "      <td>0.00408</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>Y79E</td>\n",
       "      <td>0.10268</td>\n",
       "      <td>0.00428</td>\n",
       "      <td>0.00660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>H2</td>\n",
       "      <td>H2</td>\n",
       "      <td>M93G</td>\n",
       "      <td>0.10092</td>\n",
       "      <td>0.00528</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>M93G</td>\n",
       "      <td>0.09984</td>\n",
       "      <td>0.00760</td>\n",
       "      <td>0.00800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A3</td>\n",
       "      <td>A3</td>\n",
       "      <td>C94D</td>\n",
       "      <td>0.10628</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>A97F</td>\n",
       "      <td>0.10440</td>\n",
       "      <td>0.00344</td>\n",
       "      <td>0.00676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>B3</td>\n",
       "      <td>B3</td>\n",
       "      <td>A97F</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00888</td>\n",
       "      <td>M100W</td>\n",
       "      <td>0.07528</td>\n",
       "      <td>0.00468</td>\n",
       "      <td>0.00620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>C3</td>\n",
       "      <td>C3</td>\n",
       "      <td>M100W</td>\n",
       "      <td>0.08316</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00912</td>\n",
       "      <td>C94D</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.00280</td>\n",
       "      <td>0.00528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>D3</td>\n",
       "      <td>D3</td>\n",
       "      <td>Y101H</td>\n",
       "      <td>0.02148</td>\n",
       "      <td>0.10292</td>\n",
       "      <td>0.06388</td>\n",
       "      <td>Y101H</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.09560</td>\n",
       "      <td>0.07912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>E3</td>\n",
       "      <td>E3</td>\n",
       "      <td>T124Y</td>\n",
       "      <td>0.00640</td>\n",
       "      <td>0.12092</td>\n",
       "      <td>0.11252</td>\n",
       "      <td>T124Y</td>\n",
       "      <td>0.00524</td>\n",
       "      <td>0.11468</td>\n",
       "      <td>0.11092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>F3</td>\n",
       "      <td>F3</td>\n",
       "      <td>E135D</td>\n",
       "      <td>0.00700</td>\n",
       "      <td>0.11604</td>\n",
       "      <td>0.11020</td>\n",
       "      <td>D134C</td>\n",
       "      <td>0.00540</td>\n",
       "      <td>0.11300</td>\n",
       "      <td>0.11156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>G3</td>\n",
       "      <td>G3</td>\n",
       "      <td>R136I</td>\n",
       "      <td>0.08560</td>\n",
       "      <td>0.01688</td>\n",
       "      <td>0.01072</td>\n",
       "      <td>R136I</td>\n",
       "      <td>0.07692</td>\n",
       "      <td>0.01868</td>\n",
       "      <td>0.00748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>H3</td>\n",
       "      <td>H3</td>\n",
       "      <td>F153*</td>\n",
       "      <td>0.10180</td>\n",
       "      <td>0.00608</td>\n",
       "      <td>0.00880</td>\n",
       "      <td>F153*</td>\n",
       "      <td>0.10028</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.00752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A4</td>\n",
       "      <td>A4</td>\n",
       "      <td>D155A</td>\n",
       "      <td>0.10408</td>\n",
       "      <td>0.00328</td>\n",
       "      <td>0.00884</td>\n",
       "      <td>D155A</td>\n",
       "      <td>0.10452</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>B4</td>\n",
       "      <td>B4</td>\n",
       "      <td>D155S</td>\n",
       "      <td>0.10588</td>\n",
       "      <td>0.00392</td>\n",
       "      <td>0.00792</td>\n",
       "      <td>D155S</td>\n",
       "      <td>0.10128</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>I156P</td>\n",
       "      <td>0.10532</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00872</td>\n",
       "      <td>I156P</td>\n",
       "      <td>0.10168</td>\n",
       "      <td>0.00260</td>\n",
       "      <td>0.00520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>D4</td>\n",
       "      <td>D4</td>\n",
       "      <td>A23N</td>\n",
       "      <td>0.00560</td>\n",
       "      <td>0.11932</td>\n",
       "      <td>0.11108</td>\n",
       "      <td>A23N</td>\n",
       "      <td>0.00488</td>\n",
       "      <td>0.11472</td>\n",
       "      <td>0.10820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>E4</td>\n",
       "      <td>E4</td>\n",
       "      <td>G29Q</td>\n",
       "      <td>0.00608</td>\n",
       "      <td>0.11260</td>\n",
       "      <td>0.11040</td>\n",
       "      <td>G29Q</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.10912</td>\n",
       "      <td>0.11132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>F4</td>\n",
       "      <td>F4</td>\n",
       "      <td>N39F</td>\n",
       "      <td>0.00596</td>\n",
       "      <td>0.11500</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>N39F</td>\n",
       "      <td>0.00512</td>\n",
       "      <td>0.11140</td>\n",
       "      <td>0.10928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>G4</td>\n",
       "      <td>G4</td>\n",
       "      <td>S89V</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.11412</td>\n",
       "      <td>0.11040</td>\n",
       "      <td>S89V</td>\n",
       "      <td>0.00624</td>\n",
       "      <td>0.11312</td>\n",
       "      <td>0.10860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>H4</td>\n",
       "      <td>H4</td>\n",
       "      <td>I98L</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.11400</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>I98L</td>\n",
       "      <td>0.00744</td>\n",
       "      <td>0.11048</td>\n",
       "      <td>0.10768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>A5</td>\n",
       "      <td>A5</td>\n",
       "      <td>A59H</td>\n",
       "      <td>0.08360</td>\n",
       "      <td>0.00448</td>\n",
       "      <td>0.00744</td>\n",
       "      <td>A59H</td>\n",
       "      <td>0.07472</td>\n",
       "      <td>0.00556</td>\n",
       "      <td>0.00832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>B5</td>\n",
       "      <td>B5</td>\n",
       "      <td>T60F</td>\n",
       "      <td>0.04852</td>\n",
       "      <td>0.00920</td>\n",
       "      <td>0.00552</td>\n",
       "      <td>T60F</td>\n",
       "      <td>0.04048</td>\n",
       "      <td>0.01200</td>\n",
       "      <td>0.00800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>C5</td>\n",
       "      <td>C5</td>\n",
       "      <td>T60R</td>\n",
       "      <td>0.05764</td>\n",
       "      <td>0.01244</td>\n",
       "      <td>0.00668</td>\n",
       "      <td>T60R</td>\n",
       "      <td>0.04472</td>\n",
       "      <td>0.01352</td>\n",
       "      <td>0.00780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>D5</td>\n",
       "      <td>D5</td>\n",
       "      <td>L61F</td>\n",
       "      <td>0.00592</td>\n",
       "      <td>0.11232</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>L61F</td>\n",
       "      <td>0.00620</td>\n",
       "      <td>0.10748</td>\n",
       "      <td>0.11280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>E5</td>\n",
       "      <td>E5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I65F</td>\n",
       "      <td>0.03680</td>\n",
       "      <td>0.09272</td>\n",
       "      <td>0.01248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>F5</td>\n",
       "      <td>F5</td>\n",
       "      <td>S66M</td>\n",
       "      <td>0.00620</td>\n",
       "      <td>0.11272</td>\n",
       "      <td>0.11288</td>\n",
       "      <td>S66M</td>\n",
       "      <td>0.00572</td>\n",
       "      <td>0.10944</td>\n",
       "      <td>0.11192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>G5</td>\n",
       "      <td>G5</td>\n",
       "      <td>E69V</td>\n",
       "      <td>0.05556</td>\n",
       "      <td>0.01640</td>\n",
       "      <td>0.00720</td>\n",
       "      <td>E69V</td>\n",
       "      <td>0.04400</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>H5</td>\n",
       "      <td>H5</td>\n",
       "      <td>C71A</td>\n",
       "      <td>0.00712</td>\n",
       "      <td>0.11672</td>\n",
       "      <td>0.10868</td>\n",
       "      <td>C71A</td>\n",
       "      <td>0.00704</td>\n",
       "      <td>0.11372</td>\n",
       "      <td>0.10988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>A6</td>\n",
       "      <td>A6</td>\n",
       "      <td>T60F</td>\n",
       "      <td>0.04932</td>\n",
       "      <td>0.00760</td>\n",
       "      <td>0.00760</td>\n",
       "      <td>C71Y</td>\n",
       "      <td>0.09900</td>\n",
       "      <td>0.00440</td>\n",
       "      <td>0.00860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>B6</td>\n",
       "      <td>B6</td>\n",
       "      <td>L74Q</td>\n",
       "      <td>0.00492</td>\n",
       "      <td>0.12200</td>\n",
       "      <td>0.11340</td>\n",
       "      <td>L74Q</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.11292</td>\n",
       "      <td>0.11040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>C6</td>\n",
       "      <td>C6</td>\n",
       "      <td>E75R</td>\n",
       "      <td>0.00532</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>0.11300</td>\n",
       "      <td>E75R</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>0.11060</td>\n",
       "      <td>0.10508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>D6</td>\n",
       "      <td>D6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G76M</td>\n",
       "      <td>0.00728</td>\n",
       "      <td>0.08720</td>\n",
       "      <td>0.07672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>E6</td>\n",
       "      <td>E6</td>\n",
       "      <td>delta</td>\n",
       "      <td>0.10396</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00680</td>\n",
       "      <td>delta</td>\n",
       "      <td>0.09920</td>\n",
       "      <td>0.00452</td>\n",
       "      <td>0.00892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>F6</td>\n",
       "      <td>F6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G76Y</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.10068</td>\n",
       "      <td>0.10056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>G6</td>\n",
       "      <td>G6</td>\n",
       "      <td>wt</td>\n",
       "      <td>0.00628</td>\n",
       "      <td>0.12368</td>\n",
       "      <td>0.11080</td>\n",
       "      <td>wt</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.11080</td>\n",
       "      <td>0.10628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>H6</td>\n",
       "      <td>H6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G76H</td>\n",
       "      <td>0.00716</td>\n",
       "      <td>0.10248</td>\n",
       "      <td>0.10540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   well mat_a_pos mutant_a  FC_100_a  CY_200_a  CY_20_a mutant_alpha  \\\n",
       "0    A1        A1      NaN       NaN       NaN      NaN         G14*   \n",
       "1    B1        B1     R53I   0.09772   0.00380  0.00660         R53I   \n",
       "2    C1        C1     R53D   0.10252   0.00348  0.00988         R53D   \n",
       "3    D1        D1     H62C   0.10312   0.08272  0.03908         H62C   \n",
       "4    E1        E1     E64V   0.10260   0.00324  0.00960         E64V   \n",
       "5    F1        F1     E64C   0.10200   0.00412  0.00800         E64C   \n",
       "6    G1        G1     E64S   0.10028   0.00448  0.00948         E64S   \n",
       "7    H1        H1    R136I   0.08312   0.02112  0.01072         E69L   \n",
       "8    A2        A2     G72P   0.09968   0.00412  0.00552         G72P   \n",
       "9    B2        B2     R73G   0.08532   0.00444  0.00788         R73G   \n",
       "10   C2        C2      NaN       NaN       NaN      NaN         L74P   \n",
       "11   D2        D2     Y79S   0.09200   0.00408  0.00748         Y79S   \n",
       "12   E2        E2     Y79H   0.08648   0.00520  0.00748         Y79H   \n",
       "13   F2        F2     Y79K   0.08500   0.00520  0.00700         Y79K   \n",
       "14   G2        G2     Y79E   0.10088   0.00408  0.00800         Y79E   \n",
       "15   H2        H2     M93G   0.10092   0.00528  0.00748         M93G   \n",
       "16   A3        A3     C94D   0.10628   0.00320  0.00908         A97F   \n",
       "17   B3        B3     A97F   0.10600   0.00332  0.00888        M100W   \n",
       "18   C3        C3    M100W   0.08316   0.00360  0.00912         C94D   \n",
       "19   D3        D3    Y101H   0.02148   0.10292  0.06388        Y101H   \n",
       "20   E3        E3    T124Y   0.00640   0.12092  0.11252        T124Y   \n",
       "21   F3        F3    E135D   0.00700   0.11604  0.11020        D134C   \n",
       "22   G3        G3    R136I   0.08560   0.01688  0.01072        R136I   \n",
       "23   H3        H3    F153*   0.10180   0.00608  0.00880        F153*   \n",
       "24   A4        A4    D155A   0.10408   0.00328  0.00884        D155A   \n",
       "25   B4        B4    D155S   0.10588   0.00392  0.00792        D155S   \n",
       "26   C4        C4    I156P   0.10532   0.00360  0.00872        I156P   \n",
       "27   D4        D4     A23N   0.00560   0.11932  0.11108         A23N   \n",
       "28   E4        E4     G29Q   0.00608   0.11260  0.11040         G29Q   \n",
       "29   F4        F4     N39F   0.00596   0.11500  0.10900         N39F   \n",
       "30   G4        G4     S89V   0.00660   0.11412  0.11040         S89V   \n",
       "31   H4        H4     I98L   0.00660   0.11400  0.10820         I98L   \n",
       "32   A5        A5     A59H   0.08360   0.00448  0.00744         A59H   \n",
       "33   B5        B5     T60F   0.04852   0.00920  0.00552         T60F   \n",
       "34   C5        C5     T60R   0.05764   0.01244  0.00668         T60R   \n",
       "35   D5        D5     L61F   0.00592   0.11232  0.11000         L61F   \n",
       "36   E5        E5      NaN       NaN       NaN      NaN         I65F   \n",
       "37   F5        F5     S66M   0.00620   0.11272  0.11288         S66M   \n",
       "38   G5        G5     E69V   0.05556   0.01640  0.00720         E69V   \n",
       "39   H5        H5     C71A   0.00712   0.11672  0.10868         C71A   \n",
       "40   A6        A6     T60F   0.04932   0.00760  0.00760         C71Y   \n",
       "41   B6        B6     L74Q   0.00492   0.12200  0.11340         L74Q   \n",
       "42   C6        C6     E75R   0.00532   0.12300  0.11300         E75R   \n",
       "43   D6        D6      NaN       NaN       NaN      NaN         G76M   \n",
       "44   E6        E6    delta   0.10396   0.00380  0.00680        delta   \n",
       "45   F6        F6      NaN       NaN       NaN      NaN         G76Y   \n",
       "46   G6        G6       wt   0.00628   0.12368  0.11080           wt   \n",
       "47   H6        H6      NaN       NaN       NaN      NaN         G76H   \n",
       "\n",
       "    FC_100_alpha  CY_200_alpha  CY_20_alpha  \n",
       "0        0.10480       0.00352      0.00932  \n",
       "1        0.09212       0.00320      0.00740  \n",
       "2        0.10448       0.00388      0.00912  \n",
       "3        0.10400       0.00332      0.00740  \n",
       "4        0.10440       0.00340      0.00628  \n",
       "5        0.10320       0.00300      0.00772  \n",
       "6        0.10140       0.00348      0.00728  \n",
       "7        0.07860       0.00532      0.00660  \n",
       "8        0.09060       0.00408      0.00728  \n",
       "9        0.07404       0.00420      0.00784  \n",
       "10       0.09480       0.00488      0.00812  \n",
       "11       0.08712       0.00480      0.00708  \n",
       "12       0.07280       0.00512      0.00660  \n",
       "13       0.06812       0.00580      0.00740  \n",
       "14       0.10268       0.00428      0.00660  \n",
       "15       0.09984       0.00760      0.00800  \n",
       "16       0.10440       0.00344      0.00676  \n",
       "17       0.07528       0.00468      0.00620  \n",
       "18       0.09780       0.00280      0.00528  \n",
       "19       0.01340       0.09560      0.07912  \n",
       "20       0.00524       0.11468      0.11092  \n",
       "21       0.00540       0.11300      0.11156  \n",
       "22       0.07692       0.01868      0.00748  \n",
       "23       0.10028       0.00600      0.00752  \n",
       "24       0.10452       0.00340      0.00628  \n",
       "25       0.10128       0.00332      0.00540  \n",
       "26       0.10168       0.00260      0.00520  \n",
       "27       0.00488       0.11472      0.10820  \n",
       "28       0.00580       0.10912      0.11132  \n",
       "29       0.00512       0.11140      0.10928  \n",
       "30       0.00624       0.11312      0.10860  \n",
       "31       0.00744       0.11048      0.10768  \n",
       "32       0.07472       0.00556      0.00832  \n",
       "33       0.04048       0.01200      0.00800  \n",
       "34       0.04472       0.01352      0.00780  \n",
       "35       0.00620       0.10748      0.11280  \n",
       "36       0.03680       0.09272      0.01248  \n",
       "37       0.00572       0.10944      0.11192  \n",
       "38       0.04400       0.02000      0.00952  \n",
       "39       0.00704       0.11372      0.10988  \n",
       "40       0.09900       0.00440      0.00860  \n",
       "41       0.00380       0.11292      0.11040  \n",
       "42       0.00520       0.11060      0.10508  \n",
       "43       0.00728       0.08720      0.07672  \n",
       "44       0.09920       0.00452      0.00892  \n",
       "45       0.00580       0.10068      0.10056  \n",
       "46       0.00600       0.11080      0.10628  \n",
       "47       0.00716       0.10248      0.10540  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_wells = [0, 10, 36, 43, 45, 47]\n",
    "\n",
    "for ind in empty_wells:\n",
    "    for col in ['FC_100_a', 'CY_200_a', 'CY_20_a']:\n",
    "        hap_growth_rate_df.at[ind, col] = np.NaN\n",
    "\n",
    "hap_growth_rate_df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "68f8bdd4-79fb-4777-bb93-d84ba82ab996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>mat_a_pos</th>\n",
       "      <th>mutant_a</th>\n",
       "      <th>FC_100_a</th>\n",
       "      <th>CY_200_a</th>\n",
       "      <th>CY_20_a</th>\n",
       "      <th>mutant_alpha</th>\n",
       "      <th>FC_100_alpha</th>\n",
       "      <th>CY_200_alpha</th>\n",
       "      <th>CY_20_alpha</th>\n",
       "      <th>FC_score_IC50_a</th>\n",
       "      <th>FC_score_IC50_alpha</th>\n",
       "      <th>FC_score_100_a</th>\n",
       "      <th>FC_score_100_alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G14*</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.00352</td>\n",
       "      <td>0.00932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.949734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.935475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B1</td>\n",
       "      <td>B1</td>\n",
       "      <td>R53I</td>\n",
       "      <td>0.09772</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>R53I</td>\n",
       "      <td>0.09212</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00740</td>\n",
       "      <td>0.966067</td>\n",
       "      <td>0.966067</td>\n",
       "      <td>0.928732</td>\n",
       "      <td>0.928732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C1</td>\n",
       "      <td>C1</td>\n",
       "      <td>R53D</td>\n",
       "      <td>0.10252</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00988</td>\n",
       "      <td>R53D</td>\n",
       "      <td>0.10448</td>\n",
       "      <td>0.00388</td>\n",
       "      <td>0.00912</td>\n",
       "      <td>1.032346</td>\n",
       "      <td>1.032346</td>\n",
       "      <td>1.108824</td>\n",
       "      <td>1.108824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>H62C</td>\n",
       "      <td>0.10312</td>\n",
       "      <td>0.08272</td>\n",
       "      <td>0.03908</td>\n",
       "      <td>H62C</td>\n",
       "      <td>0.10400</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00740</td>\n",
       "      <td>0.995737</td>\n",
       "      <td>0.995737</td>\n",
       "      <td>1.125737</td>\n",
       "      <td>1.125737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E1</td>\n",
       "      <td>E1</td>\n",
       "      <td>E64V</td>\n",
       "      <td>0.10260</td>\n",
       "      <td>0.00324</td>\n",
       "      <td>0.00960</td>\n",
       "      <td>E64V</td>\n",
       "      <td>0.10440</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00628</td>\n",
       "      <td>1.007457</td>\n",
       "      <td>1.007457</td>\n",
       "      <td>1.047183</td>\n",
       "      <td>1.047183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F1</td>\n",
       "      <td>F1</td>\n",
       "      <td>E64C</td>\n",
       "      <td>0.10200</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>E64C</td>\n",
       "      <td>0.10320</td>\n",
       "      <td>0.00300</td>\n",
       "      <td>0.00772</td>\n",
       "      <td>0.981935</td>\n",
       "      <td>0.981935</td>\n",
       "      <td>1.028532</td>\n",
       "      <td>1.028532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G1</td>\n",
       "      <td>G1</td>\n",
       "      <td>E64S</td>\n",
       "      <td>0.10028</td>\n",
       "      <td>0.00448</td>\n",
       "      <td>0.00948</td>\n",
       "      <td>E64S</td>\n",
       "      <td>0.10140</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00728</td>\n",
       "      <td>0.997316</td>\n",
       "      <td>0.997316</td>\n",
       "      <td>1.111080</td>\n",
       "      <td>1.111080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H1</td>\n",
       "      <td>H1</td>\n",
       "      <td>R136I</td>\n",
       "      <td>0.08312</td>\n",
       "      <td>0.02112</td>\n",
       "      <td>0.01072</td>\n",
       "      <td>E69L</td>\n",
       "      <td>0.07860</td>\n",
       "      <td>0.00532</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.926609</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.535743</td>\n",
       "      <td>0.598206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A2</td>\n",
       "      <td>A2</td>\n",
       "      <td>G72P</td>\n",
       "      <td>0.09968</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>0.00552</td>\n",
       "      <td>G72P</td>\n",
       "      <td>0.09060</td>\n",
       "      <td>0.00408</td>\n",
       "      <td>0.00728</td>\n",
       "      <td>0.957109</td>\n",
       "      <td>0.957109</td>\n",
       "      <td>0.614988</td>\n",
       "      <td>0.614988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B2</td>\n",
       "      <td>B2</td>\n",
       "      <td>R73G</td>\n",
       "      <td>0.08532</td>\n",
       "      <td>0.00444</td>\n",
       "      <td>0.00788</td>\n",
       "      <td>R73G</td>\n",
       "      <td>0.07404</td>\n",
       "      <td>0.00420</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.902137</td>\n",
       "      <td>0.902137</td>\n",
       "      <td>0.344910</td>\n",
       "      <td>0.344910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C2</td>\n",
       "      <td>C2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L74P</td>\n",
       "      <td>0.09480</td>\n",
       "      <td>0.00488</td>\n",
       "      <td>0.00812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.017153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.725383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>D2</td>\n",
       "      <td>D2</td>\n",
       "      <td>Y79S</td>\n",
       "      <td>0.09200</td>\n",
       "      <td>0.00408</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>Y79S</td>\n",
       "      <td>0.08712</td>\n",
       "      <td>0.00480</td>\n",
       "      <td>0.00708</td>\n",
       "      <td>0.950865</td>\n",
       "      <td>0.950865</td>\n",
       "      <td>0.420369</td>\n",
       "      <td>0.420369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>E2</td>\n",
       "      <td>E2</td>\n",
       "      <td>Y79H</td>\n",
       "      <td>0.08648</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>Y79H</td>\n",
       "      <td>0.07280</td>\n",
       "      <td>0.00512</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.898874</td>\n",
       "      <td>0.898874</td>\n",
       "      <td>0.486318</td>\n",
       "      <td>0.486318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>F2</td>\n",
       "      <td>F2</td>\n",
       "      <td>Y79K</td>\n",
       "      <td>0.08500</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>0.00700</td>\n",
       "      <td>Y79K</td>\n",
       "      <td>0.06812</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.00740</td>\n",
       "      <td>0.976096</td>\n",
       "      <td>0.976096</td>\n",
       "      <td>0.269649</td>\n",
       "      <td>0.269649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>G2</td>\n",
       "      <td>G2</td>\n",
       "      <td>Y79E</td>\n",
       "      <td>0.10088</td>\n",
       "      <td>0.00408</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>Y79E</td>\n",
       "      <td>0.10268</td>\n",
       "      <td>0.00428</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>1.016476</td>\n",
       "      <td>1.016476</td>\n",
       "      <td>0.792861</td>\n",
       "      <td>0.792861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>H2</td>\n",
       "      <td>H2</td>\n",
       "      <td>M93G</td>\n",
       "      <td>0.10092</td>\n",
       "      <td>0.00528</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>M93G</td>\n",
       "      <td>0.09984</td>\n",
       "      <td>0.00760</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>0.997973</td>\n",
       "      <td>0.997973</td>\n",
       "      <td>1.016545</td>\n",
       "      <td>1.016545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A3</td>\n",
       "      <td>A3</td>\n",
       "      <td>C94D</td>\n",
       "      <td>0.10628</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>A97F</td>\n",
       "      <td>0.10440</td>\n",
       "      <td>0.00344</td>\n",
       "      <td>0.00676</td>\n",
       "      <td>0.988644</td>\n",
       "      <td>1.059536</td>\n",
       "      <td>0.922998</td>\n",
       "      <td>1.201365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>B3</td>\n",
       "      <td>B3</td>\n",
       "      <td>A97F</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00888</td>\n",
       "      <td>M100W</td>\n",
       "      <td>0.07528</td>\n",
       "      <td>0.00468</td>\n",
       "      <td>0.00620</td>\n",
       "      <td>1.059536</td>\n",
       "      <td>0.550655</td>\n",
       "      <td>1.201365</td>\n",
       "      <td>0.334764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>C3</td>\n",
       "      <td>C3</td>\n",
       "      <td>M100W</td>\n",
       "      <td>0.08316</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00912</td>\n",
       "      <td>C94D</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.00280</td>\n",
       "      <td>0.00528</td>\n",
       "      <td>0.550655</td>\n",
       "      <td>0.988644</td>\n",
       "      <td>0.334764</td>\n",
       "      <td>0.922998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>D3</td>\n",
       "      <td>D3</td>\n",
       "      <td>Y101H</td>\n",
       "      <td>0.02148</td>\n",
       "      <td>0.10292</td>\n",
       "      <td>0.06388</td>\n",
       "      <td>Y101H</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.09560</td>\n",
       "      <td>0.07912</td>\n",
       "      <td>-0.089358</td>\n",
       "      <td>-0.089358</td>\n",
       "      <td>0.258212</td>\n",
       "      <td>0.258212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>E3</td>\n",
       "      <td>E3</td>\n",
       "      <td>T124Y</td>\n",
       "      <td>0.00640</td>\n",
       "      <td>0.12092</td>\n",
       "      <td>0.11252</td>\n",
       "      <td>T124Y</td>\n",
       "      <td>0.00524</td>\n",
       "      <td>0.11468</td>\n",
       "      <td>0.11092</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>-0.033971</td>\n",
       "      <td>-0.033971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>F3</td>\n",
       "      <td>F3</td>\n",
       "      <td>E135D</td>\n",
       "      <td>0.00700</td>\n",
       "      <td>0.11604</td>\n",
       "      <td>0.11020</td>\n",
       "      <td>D134C</td>\n",
       "      <td>0.00540</td>\n",
       "      <td>0.11300</td>\n",
       "      <td>0.11156</td>\n",
       "      <td>-0.234954</td>\n",
       "      <td>-0.321626</td>\n",
       "      <td>-0.078780</td>\n",
       "      <td>0.213638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>G3</td>\n",
       "      <td>G3</td>\n",
       "      <td>R136I</td>\n",
       "      <td>0.08560</td>\n",
       "      <td>0.01688</td>\n",
       "      <td>0.01072</td>\n",
       "      <td>R136I</td>\n",
       "      <td>0.07692</td>\n",
       "      <td>0.01868</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>0.926609</td>\n",
       "      <td>0.926609</td>\n",
       "      <td>0.535743</td>\n",
       "      <td>0.535743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>H3</td>\n",
       "      <td>H3</td>\n",
       "      <td>F153*</td>\n",
       "      <td>0.10180</td>\n",
       "      <td>0.00608</td>\n",
       "      <td>0.00880</td>\n",
       "      <td>F153*</td>\n",
       "      <td>0.10028</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.00752</td>\n",
       "      <td>0.992665</td>\n",
       "      <td>0.992665</td>\n",
       "      <td>1.035836</td>\n",
       "      <td>1.035836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A4</td>\n",
       "      <td>A4</td>\n",
       "      <td>D155A</td>\n",
       "      <td>0.10408</td>\n",
       "      <td>0.00328</td>\n",
       "      <td>0.00884</td>\n",
       "      <td>D155A</td>\n",
       "      <td>0.10452</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00628</td>\n",
       "      <td>1.028667</td>\n",
       "      <td>1.028667</td>\n",
       "      <td>1.081790</td>\n",
       "      <td>1.081790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>B4</td>\n",
       "      <td>B4</td>\n",
       "      <td>D155S</td>\n",
       "      <td>0.10588</td>\n",
       "      <td>0.00392</td>\n",
       "      <td>0.00792</td>\n",
       "      <td>D155S</td>\n",
       "      <td>0.10128</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00540</td>\n",
       "      <td>1.017568</td>\n",
       "      <td>1.017568</td>\n",
       "      <td>0.951485</td>\n",
       "      <td>0.951485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>I156P</td>\n",
       "      <td>0.10532</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00872</td>\n",
       "      <td>I156P</td>\n",
       "      <td>0.10168</td>\n",
       "      <td>0.00260</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>1.020008</td>\n",
       "      <td>1.020008</td>\n",
       "      <td>1.085426</td>\n",
       "      <td>1.085426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>D4</td>\n",
       "      <td>D4</td>\n",
       "      <td>A23N</td>\n",
       "      <td>0.00560</td>\n",
       "      <td>0.11932</td>\n",
       "      <td>0.11108</td>\n",
       "      <td>A23N</td>\n",
       "      <td>0.00488</td>\n",
       "      <td>0.11472</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>0.232684</td>\n",
       "      <td>0.232684</td>\n",
       "      <td>-0.006390</td>\n",
       "      <td>-0.006390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>E4</td>\n",
       "      <td>E4</td>\n",
       "      <td>G29Q</td>\n",
       "      <td>0.00608</td>\n",
       "      <td>0.11260</td>\n",
       "      <td>0.11040</td>\n",
       "      <td>G29Q</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.10912</td>\n",
       "      <td>0.11132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.177857</td>\n",
       "      <td>0.177857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>F4</td>\n",
       "      <td>F4</td>\n",
       "      <td>N39F</td>\n",
       "      <td>0.00596</td>\n",
       "      <td>0.11500</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>N39F</td>\n",
       "      <td>0.00512</td>\n",
       "      <td>0.11140</td>\n",
       "      <td>0.10928</td>\n",
       "      <td>-0.205059</td>\n",
       "      <td>-0.205059</td>\n",
       "      <td>0.030598</td>\n",
       "      <td>0.030598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>G4</td>\n",
       "      <td>G4</td>\n",
       "      <td>S89V</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.11412</td>\n",
       "      <td>0.11040</td>\n",
       "      <td>S89V</td>\n",
       "      <td>0.00624</td>\n",
       "      <td>0.11312</td>\n",
       "      <td>0.10860</td>\n",
       "      <td>-0.039452</td>\n",
       "      <td>-0.039452</td>\n",
       "      <td>-0.206310</td>\n",
       "      <td>-0.206310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>H4</td>\n",
       "      <td>H4</td>\n",
       "      <td>I98L</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.11400</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>I98L</td>\n",
       "      <td>0.00744</td>\n",
       "      <td>0.11048</td>\n",
       "      <td>0.10768</td>\n",
       "      <td>-0.109324</td>\n",
       "      <td>-0.109324</td>\n",
       "      <td>-0.119486</td>\n",
       "      <td>-0.119486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>A5</td>\n",
       "      <td>A5</td>\n",
       "      <td>A59H</td>\n",
       "      <td>0.08360</td>\n",
       "      <td>0.00448</td>\n",
       "      <td>0.00744</td>\n",
       "      <td>A59H</td>\n",
       "      <td>0.07472</td>\n",
       "      <td>0.00556</td>\n",
       "      <td>0.00832</td>\n",
       "      <td>0.948449</td>\n",
       "      <td>0.948449</td>\n",
       "      <td>0.765197</td>\n",
       "      <td>0.765197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>B5</td>\n",
       "      <td>B5</td>\n",
       "      <td>T60F</td>\n",
       "      <td>0.04852</td>\n",
       "      <td>0.00920</td>\n",
       "      <td>0.00552</td>\n",
       "      <td>T60F</td>\n",
       "      <td>0.04048</td>\n",
       "      <td>0.01200</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>0.492590</td>\n",
       "      <td>0.492590</td>\n",
       "      <td>0.044409</td>\n",
       "      <td>0.044409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>C5</td>\n",
       "      <td>C5</td>\n",
       "      <td>T60R</td>\n",
       "      <td>0.05764</td>\n",
       "      <td>0.01244</td>\n",
       "      <td>0.00668</td>\n",
       "      <td>T60R</td>\n",
       "      <td>0.04472</td>\n",
       "      <td>0.01352</td>\n",
       "      <td>0.00780</td>\n",
       "      <td>0.542476</td>\n",
       "      <td>0.542476</td>\n",
       "      <td>-0.014413</td>\n",
       "      <td>-0.014413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>D5</td>\n",
       "      <td>D5</td>\n",
       "      <td>L61F</td>\n",
       "      <td>0.00592</td>\n",
       "      <td>0.11232</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>L61F</td>\n",
       "      <td>0.00620</td>\n",
       "      <td>0.10748</td>\n",
       "      <td>0.11280</td>\n",
       "      <td>-0.206614</td>\n",
       "      <td>-0.206614</td>\n",
       "      <td>-0.206626</td>\n",
       "      <td>-0.206626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>E5</td>\n",
       "      <td>E5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I65F</td>\n",
       "      <td>0.03680</td>\n",
       "      <td>0.09272</td>\n",
       "      <td>0.01248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.545507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>F5</td>\n",
       "      <td>F5</td>\n",
       "      <td>S66M</td>\n",
       "      <td>0.00620</td>\n",
       "      <td>0.11272</td>\n",
       "      <td>0.11288</td>\n",
       "      <td>S66M</td>\n",
       "      <td>0.00572</td>\n",
       "      <td>0.10944</td>\n",
       "      <td>0.11192</td>\n",
       "      <td>-0.328789</td>\n",
       "      <td>-0.328789</td>\n",
       "      <td>-0.036887</td>\n",
       "      <td>-0.036887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>G5</td>\n",
       "      <td>G5</td>\n",
       "      <td>E69V</td>\n",
       "      <td>0.05556</td>\n",
       "      <td>0.01640</td>\n",
       "      <td>0.00720</td>\n",
       "      <td>E69V</td>\n",
       "      <td>0.04400</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00952</td>\n",
       "      <td>0.405725</td>\n",
       "      <td>0.405725</td>\n",
       "      <td>0.123648</td>\n",
       "      <td>0.123648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>H5</td>\n",
       "      <td>H5</td>\n",
       "      <td>C71A</td>\n",
       "      <td>0.00712</td>\n",
       "      <td>0.11672</td>\n",
       "      <td>0.10868</td>\n",
       "      <td>C71A</td>\n",
       "      <td>0.00704</td>\n",
       "      <td>0.11372</td>\n",
       "      <td>0.10988</td>\n",
       "      <td>0.081624</td>\n",
       "      <td>0.081624</td>\n",
       "      <td>-0.144559</td>\n",
       "      <td>-0.144559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>A6</td>\n",
       "      <td>A6</td>\n",
       "      <td>T60F</td>\n",
       "      <td>0.04932</td>\n",
       "      <td>0.00760</td>\n",
       "      <td>0.00760</td>\n",
       "      <td>C71Y</td>\n",
       "      <td>0.09900</td>\n",
       "      <td>0.00440</td>\n",
       "      <td>0.00860</td>\n",
       "      <td>0.492590</td>\n",
       "      <td>0.902766</td>\n",
       "      <td>0.044409</td>\n",
       "      <td>0.944570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>B6</td>\n",
       "      <td>B6</td>\n",
       "      <td>L74Q</td>\n",
       "      <td>0.00492</td>\n",
       "      <td>0.12200</td>\n",
       "      <td>0.11340</td>\n",
       "      <td>L74Q</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.11292</td>\n",
       "      <td>0.11040</td>\n",
       "      <td>0.130441</td>\n",
       "      <td>0.130441</td>\n",
       "      <td>-0.049838</td>\n",
       "      <td>-0.049838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>C6</td>\n",
       "      <td>C6</td>\n",
       "      <td>E75R</td>\n",
       "      <td>0.00532</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>0.11300</td>\n",
       "      <td>E75R</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>0.11060</td>\n",
       "      <td>0.10508</td>\n",
       "      <td>0.059889</td>\n",
       "      <td>0.059889</td>\n",
       "      <td>-0.077131</td>\n",
       "      <td>-0.077131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>D6</td>\n",
       "      <td>D6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G76M</td>\n",
       "      <td>0.00728</td>\n",
       "      <td>0.08720</td>\n",
       "      <td>0.07672</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.691908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.104187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>E6</td>\n",
       "      <td>E6</td>\n",
       "      <td>delta</td>\n",
       "      <td>0.10396</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00680</td>\n",
       "      <td>delta</td>\n",
       "      <td>0.09920</td>\n",
       "      <td>0.00452</td>\n",
       "      <td>0.00892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>F6</td>\n",
       "      <td>F6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G76Y</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.10068</td>\n",
       "      <td>0.10056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.398586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.062457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>G6</td>\n",
       "      <td>G6</td>\n",
       "      <td>wt</td>\n",
       "      <td>0.00628</td>\n",
       "      <td>0.12368</td>\n",
       "      <td>0.11080</td>\n",
       "      <td>wt</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.11080</td>\n",
       "      <td>0.10628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>H6</td>\n",
       "      <td>H6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G76H</td>\n",
       "      <td>0.00716</td>\n",
       "      <td>0.10248</td>\n",
       "      <td>0.10540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.507562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.071739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   well mat_a_pos mutant_a  FC_100_a  CY_200_a  CY_20_a mutant_alpha  \\\n",
       "0    A1        A1      NaN       NaN       NaN      NaN         G14*   \n",
       "1    B1        B1     R53I   0.09772   0.00380  0.00660         R53I   \n",
       "2    C1        C1     R53D   0.10252   0.00348  0.00988         R53D   \n",
       "3    D1        D1     H62C   0.10312   0.08272  0.03908         H62C   \n",
       "4    E1        E1     E64V   0.10260   0.00324  0.00960         E64V   \n",
       "5    F1        F1     E64C   0.10200   0.00412  0.00800         E64C   \n",
       "6    G1        G1     E64S   0.10028   0.00448  0.00948         E64S   \n",
       "7    H1        H1    R136I   0.08312   0.02112  0.01072         E69L   \n",
       "8    A2        A2     G72P   0.09968   0.00412  0.00552         G72P   \n",
       "9    B2        B2     R73G   0.08532   0.00444  0.00788         R73G   \n",
       "10   C2        C2      NaN       NaN       NaN      NaN         L74P   \n",
       "11   D2        D2     Y79S   0.09200   0.00408  0.00748         Y79S   \n",
       "12   E2        E2     Y79H   0.08648   0.00520  0.00748         Y79H   \n",
       "13   F2        F2     Y79K   0.08500   0.00520  0.00700         Y79K   \n",
       "14   G2        G2     Y79E   0.10088   0.00408  0.00800         Y79E   \n",
       "15   H2        H2     M93G   0.10092   0.00528  0.00748         M93G   \n",
       "16   A3        A3     C94D   0.10628   0.00320  0.00908         A97F   \n",
       "17   B3        B3     A97F   0.10600   0.00332  0.00888        M100W   \n",
       "18   C3        C3    M100W   0.08316   0.00360  0.00912         C94D   \n",
       "19   D3        D3    Y101H   0.02148   0.10292  0.06388        Y101H   \n",
       "20   E3        E3    T124Y   0.00640   0.12092  0.11252        T124Y   \n",
       "21   F3        F3    E135D   0.00700   0.11604  0.11020        D134C   \n",
       "22   G3        G3    R136I   0.08560   0.01688  0.01072        R136I   \n",
       "23   H3        H3    F153*   0.10180   0.00608  0.00880        F153*   \n",
       "24   A4        A4    D155A   0.10408   0.00328  0.00884        D155A   \n",
       "25   B4        B4    D155S   0.10588   0.00392  0.00792        D155S   \n",
       "26   C4        C4    I156P   0.10532   0.00360  0.00872        I156P   \n",
       "27   D4        D4     A23N   0.00560   0.11932  0.11108         A23N   \n",
       "28   E4        E4     G29Q   0.00608   0.11260  0.11040         G29Q   \n",
       "29   F4        F4     N39F   0.00596   0.11500  0.10900         N39F   \n",
       "30   G4        G4     S89V   0.00660   0.11412  0.11040         S89V   \n",
       "31   H4        H4     I98L   0.00660   0.11400  0.10820         I98L   \n",
       "32   A5        A5     A59H   0.08360   0.00448  0.00744         A59H   \n",
       "33   B5        B5     T60F   0.04852   0.00920  0.00552         T60F   \n",
       "34   C5        C5     T60R   0.05764   0.01244  0.00668         T60R   \n",
       "35   D5        D5     L61F   0.00592   0.11232  0.11000         L61F   \n",
       "36   E5        E5      NaN       NaN       NaN      NaN         I65F   \n",
       "37   F5        F5     S66M   0.00620   0.11272  0.11288         S66M   \n",
       "38   G5        G5     E69V   0.05556   0.01640  0.00720         E69V   \n",
       "39   H5        H5     C71A   0.00712   0.11672  0.10868         C71A   \n",
       "40   A6        A6     T60F   0.04932   0.00760  0.00760         C71Y   \n",
       "41   B6        B6     L74Q   0.00492   0.12200  0.11340         L74Q   \n",
       "42   C6        C6     E75R   0.00532   0.12300  0.11300         E75R   \n",
       "43   D6        D6      NaN       NaN       NaN      NaN         G76M   \n",
       "44   E6        E6    delta   0.10396   0.00380  0.00680        delta   \n",
       "45   F6        F6      NaN       NaN       NaN      NaN         G76Y   \n",
       "46   G6        G6       wt   0.00628   0.12368  0.11080           wt   \n",
       "47   H6        H6      NaN       NaN       NaN      NaN         G76H   \n",
       "\n",
       "    FC_100_alpha  CY_200_alpha  CY_20_alpha  FC_score_IC50_a  \\\n",
       "0        0.10480       0.00352      0.00932              NaN   \n",
       "1        0.09212       0.00320      0.00740         0.966067   \n",
       "2        0.10448       0.00388      0.00912         1.032346   \n",
       "3        0.10400       0.00332      0.00740         0.995737   \n",
       "4        0.10440       0.00340      0.00628         1.007457   \n",
       "5        0.10320       0.00300      0.00772         0.981935   \n",
       "6        0.10140       0.00348      0.00728         0.997316   \n",
       "7        0.07860       0.00532      0.00660         0.926609   \n",
       "8        0.09060       0.00408      0.00728         0.957109   \n",
       "9        0.07404       0.00420      0.00784         0.902137   \n",
       "10       0.09480       0.00488      0.00812              NaN   \n",
       "11       0.08712       0.00480      0.00708         0.950865   \n",
       "12       0.07280       0.00512      0.00660         0.898874   \n",
       "13       0.06812       0.00580      0.00740         0.976096   \n",
       "14       0.10268       0.00428      0.00660         1.016476   \n",
       "15       0.09984       0.00760      0.00800         0.997973   \n",
       "16       0.10440       0.00344      0.00676         0.988644   \n",
       "17       0.07528       0.00468      0.00620         1.059536   \n",
       "18       0.09780       0.00280      0.00528         0.550655   \n",
       "19       0.01340       0.09560      0.07912        -0.089358   \n",
       "20       0.00524       0.11468      0.11092        -0.290105   \n",
       "21       0.00540       0.11300      0.11156        -0.234954   \n",
       "22       0.07692       0.01868      0.00748         0.926609   \n",
       "23       0.10028       0.00600      0.00752         0.992665   \n",
       "24       0.10452       0.00340      0.00628         1.028667   \n",
       "25       0.10128       0.00332      0.00540         1.017568   \n",
       "26       0.10168       0.00260      0.00520         1.020008   \n",
       "27       0.00488       0.11472      0.10820         0.232684   \n",
       "28       0.00580       0.10912      0.11132              NaN   \n",
       "29       0.00512       0.11140      0.10928        -0.205059   \n",
       "30       0.00624       0.11312      0.10860        -0.039452   \n",
       "31       0.00744       0.11048      0.10768        -0.109324   \n",
       "32       0.07472       0.00556      0.00832         0.948449   \n",
       "33       0.04048       0.01200      0.00800         0.492590   \n",
       "34       0.04472       0.01352      0.00780         0.542476   \n",
       "35       0.00620       0.10748      0.11280        -0.206614   \n",
       "36       0.03680       0.09272      0.01248              NaN   \n",
       "37       0.00572       0.10944      0.11192        -0.328789   \n",
       "38       0.04400       0.02000      0.00952         0.405725   \n",
       "39       0.00704       0.11372      0.10988         0.081624   \n",
       "40       0.09900       0.00440      0.00860         0.492590   \n",
       "41       0.00380       0.11292      0.11040         0.130441   \n",
       "42       0.00520       0.11060      0.10508         0.059889   \n",
       "43       0.00728       0.08720      0.07672              NaN   \n",
       "44       0.09920       0.00452      0.00892              NaN   \n",
       "45       0.00580       0.10068      0.10056              NaN   \n",
       "46       0.00600       0.11080      0.10628              NaN   \n",
       "47       0.00716       0.10248      0.10540              NaN   \n",
       "\n",
       "    FC_score_IC50_alpha  FC_score_100_a  FC_score_100_alpha  \n",
       "0              0.949734             NaN            0.935475  \n",
       "1              0.966067        0.928732            0.928732  \n",
       "2              1.032346        1.108824            1.108824  \n",
       "3              0.995737        1.125737            1.125737  \n",
       "4              1.007457        1.047183            1.047183  \n",
       "5              0.981935        1.028532            1.028532  \n",
       "6              0.997316        1.111080            1.111080  \n",
       "7              0.946809        0.535743            0.598206  \n",
       "8              0.957109        0.614988            0.614988  \n",
       "9              0.902137        0.344910            0.344910  \n",
       "10             1.017153             NaN            0.725383  \n",
       "11             0.950865        0.420369            0.420369  \n",
       "12             0.898874        0.486318            0.486318  \n",
       "13             0.976096        0.269649            0.269649  \n",
       "14             1.016476        0.792861            0.792861  \n",
       "15             0.997973        1.016545            1.016545  \n",
       "16             1.059536        0.922998            1.201365  \n",
       "17             0.550655        1.201365            0.334764  \n",
       "18             0.988644        0.334764            0.922998  \n",
       "19            -0.089358        0.258212            0.258212  \n",
       "20            -0.290105       -0.033971           -0.033971  \n",
       "21            -0.321626       -0.078780            0.213638  \n",
       "22             0.926609        0.535743            0.535743  \n",
       "23             0.992665        1.035836            1.035836  \n",
       "24             1.028667        1.081790            1.081790  \n",
       "25             1.017568        0.951485            0.951485  \n",
       "26             1.020008        1.085426            1.085426  \n",
       "27             0.232684       -0.006390           -0.006390  \n",
       "28                  NaN        0.177857            0.177857  \n",
       "29            -0.205059        0.030598            0.030598  \n",
       "30            -0.039452       -0.206310           -0.206310  \n",
       "31            -0.109324       -0.119486           -0.119486  \n",
       "32             0.948449        0.765197            0.765197  \n",
       "33             0.492590        0.044409            0.044409  \n",
       "34             0.542476       -0.014413           -0.014413  \n",
       "35            -0.206614       -0.206626           -0.206626  \n",
       "36             0.545507             NaN            0.095401  \n",
       "37            -0.328789       -0.036887           -0.036887  \n",
       "38             0.405725        0.123648            0.123648  \n",
       "39             0.081624       -0.144559           -0.144559  \n",
       "40             0.902766        0.044409            0.944570  \n",
       "41             0.130441       -0.049838           -0.049838  \n",
       "42             0.059889       -0.077131           -0.077131  \n",
       "43            -0.691908             NaN           -0.104187  \n",
       "44                  NaN             NaN                 NaN  \n",
       "45            -0.398586             NaN           -0.062457  \n",
       "46                  NaN             NaN                 NaN  \n",
       "47            -0.507562             NaN           -0.071739  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hap_growth_rate_df['FC_score_IC50_a'] = hap_growth_rate_df.apply(lambda x: get_mutant_DMS_score(x.mutant_a, 'FC_score'), axis=1)\n",
    "hap_growth_rate_df['FC_score_IC50_alpha'] = hap_growth_rate_df.apply(lambda x: get_mutant_DMS_score(x.mutant_alpha, 'FC_score'), axis=1)\n",
    "\n",
    "hap_growth_rate_df['FC_score_100_a'] = hap_growth_rate_df.apply(lambda x: get_mutant_DMS_score(x.mutant_a, 'overall_median'), axis=1)\n",
    "hap_growth_rate_df['FC_score_100_alpha'] = hap_growth_rate_df.apply(lambda x: get_mutant_DMS_score(x.mutant_alpha, 'overall_median'), axis=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "hap_growth_rate_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea515205-4ae9-4229-9d65-91b7a1ce3d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "hap_growth_rate_df.at[44,'FC_score_IC50_a'] = np.nanmedian(DMS_v2_score[DMS_v2_score['measured']=='*']['FC_score'])\n",
    "hap_growth_rate_df.at[44,'FC_score_IC50_alpha'] = np.nanmedian(DMS_v2_score[DMS_v2_score['measured']=='*']['FC_score'])\n",
    "\n",
    "hap_growth_rate_df.at[44,'FC_score_100_a'] = np.nanmedian(DMS_v2_score[DMS_v2_score['measured']=='*']['overall_median'])\n",
    "hap_growth_rate_df.at[44,'FC_score_100_alpha'] = np.nanmedian(DMS_v2_score[DMS_v2_score['measured']=='*']['overall_median'])\n",
    "\n",
    "\n",
    "hap_growth_rate_df.at[46,'FC_score_IC50_a'] = np.nanmedian(DMS_v2_score[DMS_v2_score['measured']==DMS_v2_score['wt']]['FC_score'])\n",
    "hap_growth_rate_df.at[46,'FC_score_IC50_alpha'] = np.nanmedian(DMS_v2_score[DMS_v2_score['measured']==DMS_v2_score['wt']]['FC_score'])\n",
    "\n",
    "hap_growth_rate_df.at[46,'FC_score_100_a'] = np.nanmedian(DMS_v2_score[DMS_v2_score['measured']==DMS_v2_score['wt']]['FC_score'])\n",
    "hap_growth_rate_df.at[46,'FC_score_100_alpha'] = np.nanmedian(DMS_v2_score[DMS_v2_score['measured']==DMS_v2_score['wt']]['FC_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c03db21d-168e-4e91-ac58-51798edc2e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# as H62C mat a is an outlier. we exclude it from further analysis\n",
    "hap_growth_rate_df.at[3,'FC_100_a'] = np.NaN\n",
    "hap_growth_rate_df.at[3,'CY_200_a'] = np.NaN\n",
    "hap_growth_rate_df.at[3,'CY_20_a'] = np.NaN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d18629c7-577e-4928-b9e6-749807172a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "same_mutants = hap_growth_rate_df[hap_growth_rate_df['mutant_a'] == hap_growth_rate_df['mutant_alpha']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8f4e048e-9600-4ecf-93d5-4d5504cee03d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(same_mutants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "750d84a9-a5ff-4437-823a-08992aa9ef9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35 36\n",
      "35 36\n",
      "35 36\n",
      "42 41\n",
      "48 48\n",
      "47 48\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18,12))\n",
    "\n",
    "\n",
    "####################################\n",
    "plt.subplot(231)\n",
    "#plt.title('5-FC 100 ug/ml', fontsize=14)\n",
    "plt.xlabel('MATa 5-FC 100 ug/ml growth rate (OD/h)', fontsize=14)\n",
    "plt.ylabel(r'MAT$\\alpha$ 5-FC 100 ug/ml growth rate (OD/h)', fontsize=14)\n",
    "plt.xlim(0,0.13)\n",
    "plt.ylim(0,0.13)\n",
    "\n",
    "ax = sns.scatterplot(data=same_mutants, x='FC_100_a', y='FC_100_alpha', color='k', alpha=0.5)\n",
    "vals = stats.spearmanr(same_mutants['FC_100_a'], same_mutants['FC_100_alpha'], nan_policy='omit')\n",
    "plt.text(0.065,0.04, r'$\\rho$='+str(np.round(vals[0], 3)), fontsize=14)\n",
    "plt.text(0.065,0.03, 'pval='+str(np.format_float_scientific(vals[1],3)), fontsize=14)\n",
    "\n",
    "plt.text(-0.17, 1.03, 'a', transform=ax.transAxes, fontsize=22)\n",
    "\n",
    "\n",
    "print(len(same_mutants['FC_100_a'].dropna()), len(same_mutants['FC_100_alpha'].dropna()))\n",
    "\n",
    "\n",
    "\n",
    "####################################\n",
    "plt.subplot(232)\n",
    "#plt.title('Cyto 200 ug/ml', fontsize=14)\n",
    "plt.xlabel('MATa CY 200 ug/ml growth rate (OD/h)', fontsize=14)\n",
    "plt.ylabel(r'MAT$\\alpha$ CY 200 ug/ml growth rate (OD/h)', fontsize=14)\n",
    "plt.xlim(0,0.13)\n",
    "plt.ylim(0,0.13)\n",
    "\n",
    "ax = sns.scatterplot(data=same_mutants, x='CY_200_a', y='CY_200_alpha', color='k', alpha=0.5)\n",
    "vals = stats.spearmanr(same_mutants['CY_200_a'], same_mutants['CY_200_alpha'], nan_policy='omit')\n",
    "plt.text(0.065,0.04, r'$\\rho$='+str(np.round(vals[0], 3)), fontsize=14)\n",
    "plt.text(0.065,0.03, 'pval='+str(np.format_float_scientific(vals[1],3)), fontsize=14)\n",
    "\n",
    "plt.text(-0.17, 1.03, 'b', transform=ax.transAxes, fontsize=22)\n",
    "\n",
    "\n",
    "print(len(same_mutants['CY_200_a'].dropna()), len(same_mutants['CY_200_alpha'].dropna()))\n",
    "\n",
    "\n",
    "####################################\n",
    "plt.subplot(233)\n",
    "#plt.title('Cyto 20 ug/ml', fontsize=14)\n",
    "plt.xlabel('MATa CY 20 ug/ml growth rate (OD/h)', fontsize=14)\n",
    "plt.ylabel(r'MAT$\\alpha$ CY 20 ug/ml growth rate (OD/h)', fontsize=14)\n",
    "plt.xlim(0,0.13)\n",
    "plt.ylim(0,0.13)\n",
    "\n",
    "\n",
    "ax = sns.scatterplot(data=same_mutants, x='CY_20_a', y='CY_20_alpha', color='k', alpha=0.5)\n",
    "vals = stats.spearmanr(same_mutants['CY_20_a'], same_mutants['CY_20_alpha'], nan_policy='omit')\n",
    "plt.text(0.065,0.04, r'$\\rho$='+str(np.round(vals[0], 3)), fontsize=14)\n",
    "plt.text(0.065,0.03, 'pval='+str(np.format_float_scientific(vals[1],3)), fontsize=14)\n",
    "\n",
    "plt.text(-0.17, 1.03, 'c', transform=ax.transAxes, fontsize=22)\n",
    "\n",
    "\n",
    "print(len(same_mutants['CY_20_a'].dropna()), len(same_mutants['CY_20_alpha'].dropna()))\n",
    "\n",
    "\n",
    "####################################\n",
    "\n",
    "plt.subplot(234)\n",
    "\n",
    "\n",
    "ax= sns.scatterplot(data=hap_growth_rate_df, x='FC_score_100_a', y='FC_100_a', color='k', alpha=0.5)\n",
    "plt.xlabel('Fitness$_{IC>100}$ score', fontsize=14)\n",
    "plt.ylabel('MATa 5-FC 100 ug/ml growth rate (OD/h)', fontsize=14)\n",
    "\n",
    "vals = stats.spearmanr(hap_growth_rate_df['FC_score_100_a'], hap_growth_rate_df['FC_100_a'], nan_policy='omit')\n",
    "plt.text(0.4,0.04, r'$\\rho$='+str(np.round(vals[0], 3)), fontsize=14)\n",
    "plt.text(0.4,0.03, 'pval='+str(np.format_float_scientific(vals[1],3)), fontsize=14)\n",
    "print(len(hap_growth_rate_df['FC_score_100_a'].dropna()), len(hap_growth_rate_df['FC_100_a'].dropna()))\n",
    "\n",
    "plt.text(-0.17, 1.03, 'd', transform=ax.transAxes, fontsize=22)\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(235)\n",
    "\n",
    "####################################\n",
    "ax = sns.scatterplot(data=hap_growth_rate_df, x='FC_score_100_alpha', y='FC_100_alpha', color='k', alpha=0.5)\n",
    "plt.xlabel('Fitness$_{IC>100}$ score', fontsize=14)\n",
    "plt.ylabel(r'MAT$\\alpha$ 5-FC 100 ug/ml growth rate (OD/h)', fontsize=14)\n",
    "\n",
    "plt.text(-0.17, 1.03, 'e', transform=ax.transAxes, fontsize=22)\n",
    "\n",
    "\n",
    "vals = stats.spearmanr(hap_growth_rate_df['FC_score_100_alpha'], hap_growth_rate_df['FC_100_alpha'], nan_policy='omit')\n",
    "plt.text(0.4,0.04, r'$\\rho$='+str(np.round(vals[0], 3)), fontsize=14)\n",
    "plt.text(0.4,0.03, 'pval='+str(np.format_float_scientific(vals[1],3)), fontsize=14)\n",
    "print(len(hap_growth_rate_df['FC_score_100_alpha'].dropna()), len(hap_growth_rate_df['FC_100_alpha'].dropna()))\n",
    "\n",
    "\n",
    "####################################\n",
    "plt.subplot(236)\n",
    "\n",
    "ax = sns.scatterplot(data=hap_growth_rate_df, x='FC_score_IC50_alpha', y='FC_score_100_alpha', color='k', alpha=0.5)\n",
    "plt.xlabel('Fitness$_{IC50}$ score', fontsize=14)\n",
    "plt.ylabel('Fitness$_{IC>100}$ score', fontsize=14)\n",
    "\n",
    "plt.text(-0.17, 1.02, 'f', transform=ax.transAxes, fontsize=22)\n",
    "\n",
    "vals = stats.spearmanr(hap_growth_rate_df['FC_score_IC50_alpha'], hap_growth_rate_df['FC_score_100_alpha'], nan_policy='omit')\n",
    "plt.text(-0.35,0.6, r'$\\rho$='+str(np.round(vals[0], 3)), fontsize=14)\n",
    "plt.text(-0.35,0.45, 'pval='+str(np.format_float_scientific(vals[1],3)), fontsize=14)\n",
    "plt.xlim(-0.5,1.3)\n",
    "plt.ylim(-0.5,1.3)\n",
    "\n",
    "print(len(hap_growth_rate_df['FC_score_IC50_alpha'].dropna()), len(hap_growth_rate_df['FC_score_100_alpha'].dropna()))\n",
    "\n",
    "\n",
    "#plt.savefig('./figures/haploid_KI_phenotypes.svg', format='svg', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0c3e196-d69b-45f6-8750-ce536619b87e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "590ca9dd-d4ac-47d2-a52c-a3788589b09f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>mat_a_pos</th>\n",
       "      <th>mutant_a</th>\n",
       "      <th>FC_100_a</th>\n",
       "      <th>CY_200_a</th>\n",
       "      <th>CY_20_a</th>\n",
       "      <th>mutant_alpha</th>\n",
       "      <th>FC_100_alpha</th>\n",
       "      <th>CY_200_alpha</th>\n",
       "      <th>CY_20_alpha</th>\n",
       "      <th>FC_score_IC50_a</th>\n",
       "      <th>FC_score_IC50_alpha</th>\n",
       "      <th>FC_score_100_a</th>\n",
       "      <th>FC_score_100_alpha</th>\n",
       "      <th>type_pos_a</th>\n",
       "      <th>type_pos_alpha</th>\n",
       "      <th>residue_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>A1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G14*</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.00352</td>\n",
       "      <td>0.00932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.949734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.935475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B1</td>\n",
       "      <td>B1</td>\n",
       "      <td>R53I</td>\n",
       "      <td>0.09772</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>R53I</td>\n",
       "      <td>0.09212</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00740</td>\n",
       "      <td>0.966067</td>\n",
       "      <td>0.966067</td>\n",
       "      <td>0.928732</td>\n",
       "      <td>0.928732</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C1</td>\n",
       "      <td>C1</td>\n",
       "      <td>R53D</td>\n",
       "      <td>0.10252</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00988</td>\n",
       "      <td>R53D</td>\n",
       "      <td>0.10448</td>\n",
       "      <td>0.00388</td>\n",
       "      <td>0.00912</td>\n",
       "      <td>1.032346</td>\n",
       "      <td>1.032346</td>\n",
       "      <td>1.108824</td>\n",
       "      <td>1.108824</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D1</td>\n",
       "      <td>D1</td>\n",
       "      <td>H62C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>H62C</td>\n",
       "      <td>0.10400</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00740</td>\n",
       "      <td>0.995737</td>\n",
       "      <td>0.995737</td>\n",
       "      <td>1.125737</td>\n",
       "      <td>1.125737</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E1</td>\n",
       "      <td>E1</td>\n",
       "      <td>E64V</td>\n",
       "      <td>0.10260</td>\n",
       "      <td>0.00324</td>\n",
       "      <td>0.00960</td>\n",
       "      <td>E64V</td>\n",
       "      <td>0.10440</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00628</td>\n",
       "      <td>1.007457</td>\n",
       "      <td>1.007457</td>\n",
       "      <td>1.047183</td>\n",
       "      <td>1.047183</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F1</td>\n",
       "      <td>F1</td>\n",
       "      <td>E64C</td>\n",
       "      <td>0.10200</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>E64C</td>\n",
       "      <td>0.10320</td>\n",
       "      <td>0.00300</td>\n",
       "      <td>0.00772</td>\n",
       "      <td>0.981935</td>\n",
       "      <td>0.981935</td>\n",
       "      <td>1.028532</td>\n",
       "      <td>1.028532</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G1</td>\n",
       "      <td>G1</td>\n",
       "      <td>E64S</td>\n",
       "      <td>0.10028</td>\n",
       "      <td>0.00448</td>\n",
       "      <td>0.00948</td>\n",
       "      <td>E64S</td>\n",
       "      <td>0.10140</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00728</td>\n",
       "      <td>0.997316</td>\n",
       "      <td>0.997316</td>\n",
       "      <td>1.111080</td>\n",
       "      <td>1.111080</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H1</td>\n",
       "      <td>H1</td>\n",
       "      <td>R136I</td>\n",
       "      <td>0.08312</td>\n",
       "      <td>0.02112</td>\n",
       "      <td>0.01072</td>\n",
       "      <td>E69L</td>\n",
       "      <td>0.07860</td>\n",
       "      <td>0.00532</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.926609</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.535743</td>\n",
       "      <td>0.598206</td>\n",
       "      <td>unk</td>\n",
       "      <td>interface</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A2</td>\n",
       "      <td>A2</td>\n",
       "      <td>G72P</td>\n",
       "      <td>0.09968</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>0.00552</td>\n",
       "      <td>G72P</td>\n",
       "      <td>0.09060</td>\n",
       "      <td>0.00408</td>\n",
       "      <td>0.00728</td>\n",
       "      <td>0.957109</td>\n",
       "      <td>0.957109</td>\n",
       "      <td>0.614988</td>\n",
       "      <td>0.614988</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B2</td>\n",
       "      <td>B2</td>\n",
       "      <td>R73G</td>\n",
       "      <td>0.08532</td>\n",
       "      <td>0.00444</td>\n",
       "      <td>0.00788</td>\n",
       "      <td>R73G</td>\n",
       "      <td>0.07404</td>\n",
       "      <td>0.00420</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.902137</td>\n",
       "      <td>0.902137</td>\n",
       "      <td>0.344910</td>\n",
       "      <td>0.344910</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C2</td>\n",
       "      <td>C2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L74P</td>\n",
       "      <td>0.09480</td>\n",
       "      <td>0.00488</td>\n",
       "      <td>0.00812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.017153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.725383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>interface</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>D2</td>\n",
       "      <td>D2</td>\n",
       "      <td>Y79S</td>\n",
       "      <td>0.09200</td>\n",
       "      <td>0.00408</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>Y79S</td>\n",
       "      <td>0.08712</td>\n",
       "      <td>0.00480</td>\n",
       "      <td>0.00708</td>\n",
       "      <td>0.950865</td>\n",
       "      <td>0.950865</td>\n",
       "      <td>0.420369</td>\n",
       "      <td>0.420369</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>E2</td>\n",
       "      <td>E2</td>\n",
       "      <td>Y79H</td>\n",
       "      <td>0.08648</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>Y79H</td>\n",
       "      <td>0.07280</td>\n",
       "      <td>0.00512</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.898874</td>\n",
       "      <td>0.898874</td>\n",
       "      <td>0.486318</td>\n",
       "      <td>0.486318</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>F2</td>\n",
       "      <td>F2</td>\n",
       "      <td>Y79K</td>\n",
       "      <td>0.08500</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>0.00700</td>\n",
       "      <td>Y79K</td>\n",
       "      <td>0.06812</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.00740</td>\n",
       "      <td>0.976096</td>\n",
       "      <td>0.976096</td>\n",
       "      <td>0.269649</td>\n",
       "      <td>0.269649</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>G2</td>\n",
       "      <td>G2</td>\n",
       "      <td>Y79E</td>\n",
       "      <td>0.10088</td>\n",
       "      <td>0.00408</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>Y79E</td>\n",
       "      <td>0.10268</td>\n",
       "      <td>0.00428</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>1.016476</td>\n",
       "      <td>1.016476</td>\n",
       "      <td>0.792861</td>\n",
       "      <td>0.792861</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>H2</td>\n",
       "      <td>H2</td>\n",
       "      <td>M93G</td>\n",
       "      <td>0.10092</td>\n",
       "      <td>0.00528</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>M93G</td>\n",
       "      <td>0.09984</td>\n",
       "      <td>0.00760</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>0.997973</td>\n",
       "      <td>0.997973</td>\n",
       "      <td>1.016545</td>\n",
       "      <td>1.016545</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A3</td>\n",
       "      <td>A3</td>\n",
       "      <td>C94D</td>\n",
       "      <td>0.10628</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>A97F</td>\n",
       "      <td>0.10440</td>\n",
       "      <td>0.00344</td>\n",
       "      <td>0.00676</td>\n",
       "      <td>0.988644</td>\n",
       "      <td>1.059536</td>\n",
       "      <td>0.922998</td>\n",
       "      <td>1.201365</td>\n",
       "      <td>unk</td>\n",
       "      <td>interface</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>B3</td>\n",
       "      <td>B3</td>\n",
       "      <td>A97F</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00888</td>\n",
       "      <td>M100W</td>\n",
       "      <td>0.07528</td>\n",
       "      <td>0.00468</td>\n",
       "      <td>0.00620</td>\n",
       "      <td>1.059536</td>\n",
       "      <td>0.550655</td>\n",
       "      <td>1.201365</td>\n",
       "      <td>0.334764</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>C3</td>\n",
       "      <td>C3</td>\n",
       "      <td>M100W</td>\n",
       "      <td>0.08316</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00912</td>\n",
       "      <td>C94D</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.00280</td>\n",
       "      <td>0.00528</td>\n",
       "      <td>0.550655</td>\n",
       "      <td>0.988644</td>\n",
       "      <td>0.334764</td>\n",
       "      <td>0.922998</td>\n",
       "      <td>interface</td>\n",
       "      <td>unk</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>D3</td>\n",
       "      <td>D3</td>\n",
       "      <td>Y101H</td>\n",
       "      <td>0.02148</td>\n",
       "      <td>0.10292</td>\n",
       "      <td>0.06388</td>\n",
       "      <td>Y101H</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.09560</td>\n",
       "      <td>0.07912</td>\n",
       "      <td>-0.089358</td>\n",
       "      <td>-0.089358</td>\n",
       "      <td>0.258212</td>\n",
       "      <td>0.258212</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>E3</td>\n",
       "      <td>E3</td>\n",
       "      <td>T124Y</td>\n",
       "      <td>0.00640</td>\n",
       "      <td>0.12092</td>\n",
       "      <td>0.11252</td>\n",
       "      <td>T124Y</td>\n",
       "      <td>0.00524</td>\n",
       "      <td>0.11468</td>\n",
       "      <td>0.11092</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>-0.033971</td>\n",
       "      <td>-0.033971</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>F3</td>\n",
       "      <td>F3</td>\n",
       "      <td>E135D</td>\n",
       "      <td>0.00700</td>\n",
       "      <td>0.11604</td>\n",
       "      <td>0.11020</td>\n",
       "      <td>D134C</td>\n",
       "      <td>0.00540</td>\n",
       "      <td>0.11300</td>\n",
       "      <td>0.11156</td>\n",
       "      <td>-0.234954</td>\n",
       "      <td>-0.321626</td>\n",
       "      <td>-0.078780</td>\n",
       "      <td>0.213638</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>G3</td>\n",
       "      <td>G3</td>\n",
       "      <td>R136I</td>\n",
       "      <td>0.08560</td>\n",
       "      <td>0.01688</td>\n",
       "      <td>0.01072</td>\n",
       "      <td>R136I</td>\n",
       "      <td>0.07692</td>\n",
       "      <td>0.01868</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>0.926609</td>\n",
       "      <td>0.926609</td>\n",
       "      <td>0.535743</td>\n",
       "      <td>0.535743</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>H3</td>\n",
       "      <td>H3</td>\n",
       "      <td>F153*</td>\n",
       "      <td>0.10180</td>\n",
       "      <td>0.00608</td>\n",
       "      <td>0.00880</td>\n",
       "      <td>F153*</td>\n",
       "      <td>0.10028</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.00752</td>\n",
       "      <td>0.992665</td>\n",
       "      <td>0.992665</td>\n",
       "      <td>1.035836</td>\n",
       "      <td>1.035836</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A4</td>\n",
       "      <td>A4</td>\n",
       "      <td>D155A</td>\n",
       "      <td>0.10408</td>\n",
       "      <td>0.00328</td>\n",
       "      <td>0.00884</td>\n",
       "      <td>D155A</td>\n",
       "      <td>0.10452</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00628</td>\n",
       "      <td>1.028667</td>\n",
       "      <td>1.028667</td>\n",
       "      <td>1.081790</td>\n",
       "      <td>1.081790</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>B4</td>\n",
       "      <td>B4</td>\n",
       "      <td>D155S</td>\n",
       "      <td>0.10588</td>\n",
       "      <td>0.00392</td>\n",
       "      <td>0.00792</td>\n",
       "      <td>D155S</td>\n",
       "      <td>0.10128</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.00540</td>\n",
       "      <td>1.017568</td>\n",
       "      <td>1.017568</td>\n",
       "      <td>0.951485</td>\n",
       "      <td>0.951485</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>C4</td>\n",
       "      <td>C4</td>\n",
       "      <td>I156P</td>\n",
       "      <td>0.10532</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00872</td>\n",
       "      <td>I156P</td>\n",
       "      <td>0.10168</td>\n",
       "      <td>0.00260</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>1.020008</td>\n",
       "      <td>1.020008</td>\n",
       "      <td>1.085426</td>\n",
       "      <td>1.085426</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "      <td>catalytic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>D4</td>\n",
       "      <td>D4</td>\n",
       "      <td>A23N</td>\n",
       "      <td>0.00560</td>\n",
       "      <td>0.11932</td>\n",
       "      <td>0.11108</td>\n",
       "      <td>A23N</td>\n",
       "      <td>0.00488</td>\n",
       "      <td>0.11472</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>0.232684</td>\n",
       "      <td>0.232684</td>\n",
       "      <td>-0.006390</td>\n",
       "      <td>-0.006390</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>E4</td>\n",
       "      <td>E4</td>\n",
       "      <td>G29Q</td>\n",
       "      <td>0.00608</td>\n",
       "      <td>0.11260</td>\n",
       "      <td>0.11040</td>\n",
       "      <td>G29Q</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.10912</td>\n",
       "      <td>0.11132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.177857</td>\n",
       "      <td>0.177857</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>F4</td>\n",
       "      <td>F4</td>\n",
       "      <td>N39F</td>\n",
       "      <td>0.00596</td>\n",
       "      <td>0.11500</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>N39F</td>\n",
       "      <td>0.00512</td>\n",
       "      <td>0.11140</td>\n",
       "      <td>0.10928</td>\n",
       "      <td>-0.205059</td>\n",
       "      <td>-0.205059</td>\n",
       "      <td>0.030598</td>\n",
       "      <td>0.030598</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>G4</td>\n",
       "      <td>G4</td>\n",
       "      <td>S89V</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.11412</td>\n",
       "      <td>0.11040</td>\n",
       "      <td>S89V</td>\n",
       "      <td>0.00624</td>\n",
       "      <td>0.11312</td>\n",
       "      <td>0.10860</td>\n",
       "      <td>-0.039452</td>\n",
       "      <td>-0.039452</td>\n",
       "      <td>-0.206310</td>\n",
       "      <td>-0.206310</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>H4</td>\n",
       "      <td>H4</td>\n",
       "      <td>I98L</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.11400</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>I98L</td>\n",
       "      <td>0.00744</td>\n",
       "      <td>0.11048</td>\n",
       "      <td>0.10768</td>\n",
       "      <td>-0.109324</td>\n",
       "      <td>-0.109324</td>\n",
       "      <td>-0.119486</td>\n",
       "      <td>-0.119486</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>A5</td>\n",
       "      <td>A5</td>\n",
       "      <td>A59H</td>\n",
       "      <td>0.08360</td>\n",
       "      <td>0.00448</td>\n",
       "      <td>0.00744</td>\n",
       "      <td>A59H</td>\n",
       "      <td>0.07472</td>\n",
       "      <td>0.00556</td>\n",
       "      <td>0.00832</td>\n",
       "      <td>0.948449</td>\n",
       "      <td>0.948449</td>\n",
       "      <td>0.765197</td>\n",
       "      <td>0.765197</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>B5</td>\n",
       "      <td>B5</td>\n",
       "      <td>T60F</td>\n",
       "      <td>0.04852</td>\n",
       "      <td>0.00920</td>\n",
       "      <td>0.00552</td>\n",
       "      <td>T60F</td>\n",
       "      <td>0.04048</td>\n",
       "      <td>0.01200</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>0.492590</td>\n",
       "      <td>0.492590</td>\n",
       "      <td>0.044409</td>\n",
       "      <td>0.044409</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>C5</td>\n",
       "      <td>C5</td>\n",
       "      <td>T60R</td>\n",
       "      <td>0.05764</td>\n",
       "      <td>0.01244</td>\n",
       "      <td>0.00668</td>\n",
       "      <td>T60R</td>\n",
       "      <td>0.04472</td>\n",
       "      <td>0.01352</td>\n",
       "      <td>0.00780</td>\n",
       "      <td>0.542476</td>\n",
       "      <td>0.542476</td>\n",
       "      <td>-0.014413</td>\n",
       "      <td>-0.014413</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>D5</td>\n",
       "      <td>D5</td>\n",
       "      <td>L61F</td>\n",
       "      <td>0.00592</td>\n",
       "      <td>0.11232</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>L61F</td>\n",
       "      <td>0.00620</td>\n",
       "      <td>0.10748</td>\n",
       "      <td>0.11280</td>\n",
       "      <td>-0.206614</td>\n",
       "      <td>-0.206614</td>\n",
       "      <td>-0.206626</td>\n",
       "      <td>-0.206626</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>E5</td>\n",
       "      <td>E5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I65F</td>\n",
       "      <td>0.03680</td>\n",
       "      <td>0.09272</td>\n",
       "      <td>0.01248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.545507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>F5</td>\n",
       "      <td>F5</td>\n",
       "      <td>S66M</td>\n",
       "      <td>0.00620</td>\n",
       "      <td>0.11272</td>\n",
       "      <td>0.11288</td>\n",
       "      <td>S66M</td>\n",
       "      <td>0.00572</td>\n",
       "      <td>0.10944</td>\n",
       "      <td>0.11192</td>\n",
       "      <td>-0.328789</td>\n",
       "      <td>-0.328789</td>\n",
       "      <td>-0.036887</td>\n",
       "      <td>-0.036887</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>G5</td>\n",
       "      <td>G5</td>\n",
       "      <td>E69V</td>\n",
       "      <td>0.05556</td>\n",
       "      <td>0.01640</td>\n",
       "      <td>0.00720</td>\n",
       "      <td>E69V</td>\n",
       "      <td>0.04400</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.00952</td>\n",
       "      <td>0.405725</td>\n",
       "      <td>0.405725</td>\n",
       "      <td>0.123648</td>\n",
       "      <td>0.123648</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>H5</td>\n",
       "      <td>H5</td>\n",
       "      <td>C71A</td>\n",
       "      <td>0.00712</td>\n",
       "      <td>0.11672</td>\n",
       "      <td>0.10868</td>\n",
       "      <td>C71A</td>\n",
       "      <td>0.00704</td>\n",
       "      <td>0.11372</td>\n",
       "      <td>0.10988</td>\n",
       "      <td>0.081624</td>\n",
       "      <td>0.081624</td>\n",
       "      <td>-0.144559</td>\n",
       "      <td>-0.144559</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>A6</td>\n",
       "      <td>A6</td>\n",
       "      <td>T60F</td>\n",
       "      <td>0.04932</td>\n",
       "      <td>0.00760</td>\n",
       "      <td>0.00760</td>\n",
       "      <td>C71Y</td>\n",
       "      <td>0.09900</td>\n",
       "      <td>0.00440</td>\n",
       "      <td>0.00860</td>\n",
       "      <td>0.492590</td>\n",
       "      <td>0.902766</td>\n",
       "      <td>0.044409</td>\n",
       "      <td>0.944570</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>B6</td>\n",
       "      <td>B6</td>\n",
       "      <td>L74Q</td>\n",
       "      <td>0.00492</td>\n",
       "      <td>0.12200</td>\n",
       "      <td>0.11340</td>\n",
       "      <td>L74Q</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.11292</td>\n",
       "      <td>0.11040</td>\n",
       "      <td>0.130441</td>\n",
       "      <td>0.130441</td>\n",
       "      <td>-0.049838</td>\n",
       "      <td>-0.049838</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "      <td>interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>C6</td>\n",
       "      <td>C6</td>\n",
       "      <td>E75R</td>\n",
       "      <td>0.00532</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>0.11300</td>\n",
       "      <td>E75R</td>\n",
       "      <td>0.00520</td>\n",
       "      <td>0.11060</td>\n",
       "      <td>0.10508</td>\n",
       "      <td>0.059889</td>\n",
       "      <td>0.059889</td>\n",
       "      <td>-0.077131</td>\n",
       "      <td>-0.077131</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>D6</td>\n",
       "      <td>D6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G76M</td>\n",
       "      <td>0.00728</td>\n",
       "      <td>0.08720</td>\n",
       "      <td>0.07672</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.691908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.104187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>E6</td>\n",
       "      <td>E6</td>\n",
       "      <td>delta</td>\n",
       "      <td>0.10396</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00680</td>\n",
       "      <td>delta</td>\n",
       "      <td>0.09920</td>\n",
       "      <td>0.00452</td>\n",
       "      <td>0.00892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996685</td>\n",
       "      <td>0.996685</td>\n",
       "      <td>ctrl</td>\n",
       "      <td>ctrl</td>\n",
       "      <td>ctrl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>F6</td>\n",
       "      <td>F6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G76Y</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.10068</td>\n",
       "      <td>0.10056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.398586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.062457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>G6</td>\n",
       "      <td>G6</td>\n",
       "      <td>wt</td>\n",
       "      <td>0.00628</td>\n",
       "      <td>0.12368</td>\n",
       "      <td>0.11080</td>\n",
       "      <td>wt</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.11080</td>\n",
       "      <td>0.10628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>ctrl</td>\n",
       "      <td>ctrl</td>\n",
       "      <td>ctrl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>H6</td>\n",
       "      <td>H6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G76H</td>\n",
       "      <td>0.00716</td>\n",
       "      <td>0.10248</td>\n",
       "      <td>0.10540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.507562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.071739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   well mat_a_pos mutant_a  FC_100_a  CY_200_a  CY_20_a mutant_alpha  \\\n",
       "0    A1        A1      NaN       NaN       NaN      NaN         G14*   \n",
       "1    B1        B1     R53I   0.09772   0.00380  0.00660         R53I   \n",
       "2    C1        C1     R53D   0.10252   0.00348  0.00988         R53D   \n",
       "3    D1        D1     H62C       NaN       NaN      NaN         H62C   \n",
       "4    E1        E1     E64V   0.10260   0.00324  0.00960         E64V   \n",
       "5    F1        F1     E64C   0.10200   0.00412  0.00800         E64C   \n",
       "6    G1        G1     E64S   0.10028   0.00448  0.00948         E64S   \n",
       "7    H1        H1    R136I   0.08312   0.02112  0.01072         E69L   \n",
       "8    A2        A2     G72P   0.09968   0.00412  0.00552         G72P   \n",
       "9    B2        B2     R73G   0.08532   0.00444  0.00788         R73G   \n",
       "10   C2        C2      NaN       NaN       NaN      NaN         L74P   \n",
       "11   D2        D2     Y79S   0.09200   0.00408  0.00748         Y79S   \n",
       "12   E2        E2     Y79H   0.08648   0.00520  0.00748         Y79H   \n",
       "13   F2        F2     Y79K   0.08500   0.00520  0.00700         Y79K   \n",
       "14   G2        G2     Y79E   0.10088   0.00408  0.00800         Y79E   \n",
       "15   H2        H2     M93G   0.10092   0.00528  0.00748         M93G   \n",
       "16   A3        A3     C94D   0.10628   0.00320  0.00908         A97F   \n",
       "17   B3        B3     A97F   0.10600   0.00332  0.00888        M100W   \n",
       "18   C3        C3    M100W   0.08316   0.00360  0.00912         C94D   \n",
       "19   D3        D3    Y101H   0.02148   0.10292  0.06388        Y101H   \n",
       "20   E3        E3    T124Y   0.00640   0.12092  0.11252        T124Y   \n",
       "21   F3        F3    E135D   0.00700   0.11604  0.11020        D134C   \n",
       "22   G3        G3    R136I   0.08560   0.01688  0.01072        R136I   \n",
       "23   H3        H3    F153*   0.10180   0.00608  0.00880        F153*   \n",
       "24   A4        A4    D155A   0.10408   0.00328  0.00884        D155A   \n",
       "25   B4        B4    D155S   0.10588   0.00392  0.00792        D155S   \n",
       "26   C4        C4    I156P   0.10532   0.00360  0.00872        I156P   \n",
       "27   D4        D4     A23N   0.00560   0.11932  0.11108         A23N   \n",
       "28   E4        E4     G29Q   0.00608   0.11260  0.11040         G29Q   \n",
       "29   F4        F4     N39F   0.00596   0.11500  0.10900         N39F   \n",
       "30   G4        G4     S89V   0.00660   0.11412  0.11040         S89V   \n",
       "31   H4        H4     I98L   0.00660   0.11400  0.10820         I98L   \n",
       "32   A5        A5     A59H   0.08360   0.00448  0.00744         A59H   \n",
       "33   B5        B5     T60F   0.04852   0.00920  0.00552         T60F   \n",
       "34   C5        C5     T60R   0.05764   0.01244  0.00668         T60R   \n",
       "35   D5        D5     L61F   0.00592   0.11232  0.11000         L61F   \n",
       "36   E5        E5      NaN       NaN       NaN      NaN         I65F   \n",
       "37   F5        F5     S66M   0.00620   0.11272  0.11288         S66M   \n",
       "38   G5        G5     E69V   0.05556   0.01640  0.00720         E69V   \n",
       "39   H5        H5     C71A   0.00712   0.11672  0.10868         C71A   \n",
       "40   A6        A6     T60F   0.04932   0.00760  0.00760         C71Y   \n",
       "41   B6        B6     L74Q   0.00492   0.12200  0.11340         L74Q   \n",
       "42   C6        C6     E75R   0.00532   0.12300  0.11300         E75R   \n",
       "43   D6        D6      NaN       NaN       NaN      NaN         G76M   \n",
       "44   E6        E6    delta   0.10396   0.00380  0.00680        delta   \n",
       "45   F6        F6      NaN       NaN       NaN      NaN         G76Y   \n",
       "46   G6        G6       wt   0.00628   0.12368  0.11080           wt   \n",
       "47   H6        H6      NaN       NaN       NaN      NaN         G76H   \n",
       "\n",
       "    FC_100_alpha  CY_200_alpha  CY_20_alpha  FC_score_IC50_a  \\\n",
       "0        0.10480       0.00352      0.00932              NaN   \n",
       "1        0.09212       0.00320      0.00740         0.966067   \n",
       "2        0.10448       0.00388      0.00912         1.032346   \n",
       "3        0.10400       0.00332      0.00740         0.995737   \n",
       "4        0.10440       0.00340      0.00628         1.007457   \n",
       "5        0.10320       0.00300      0.00772         0.981935   \n",
       "6        0.10140       0.00348      0.00728         0.997316   \n",
       "7        0.07860       0.00532      0.00660         0.926609   \n",
       "8        0.09060       0.00408      0.00728         0.957109   \n",
       "9        0.07404       0.00420      0.00784         0.902137   \n",
       "10       0.09480       0.00488      0.00812              NaN   \n",
       "11       0.08712       0.00480      0.00708         0.950865   \n",
       "12       0.07280       0.00512      0.00660         0.898874   \n",
       "13       0.06812       0.00580      0.00740         0.976096   \n",
       "14       0.10268       0.00428      0.00660         1.016476   \n",
       "15       0.09984       0.00760      0.00800         0.997973   \n",
       "16       0.10440       0.00344      0.00676         0.988644   \n",
       "17       0.07528       0.00468      0.00620         1.059536   \n",
       "18       0.09780       0.00280      0.00528         0.550655   \n",
       "19       0.01340       0.09560      0.07912        -0.089358   \n",
       "20       0.00524       0.11468      0.11092        -0.290105   \n",
       "21       0.00540       0.11300      0.11156        -0.234954   \n",
       "22       0.07692       0.01868      0.00748         0.926609   \n",
       "23       0.10028       0.00600      0.00752         0.992665   \n",
       "24       0.10452       0.00340      0.00628         1.028667   \n",
       "25       0.10128       0.00332      0.00540         1.017568   \n",
       "26       0.10168       0.00260      0.00520         1.020008   \n",
       "27       0.00488       0.11472      0.10820         0.232684   \n",
       "28       0.00580       0.10912      0.11132              NaN   \n",
       "29       0.00512       0.11140      0.10928        -0.205059   \n",
       "30       0.00624       0.11312      0.10860        -0.039452   \n",
       "31       0.00744       0.11048      0.10768        -0.109324   \n",
       "32       0.07472       0.00556      0.00832         0.948449   \n",
       "33       0.04048       0.01200      0.00800         0.492590   \n",
       "34       0.04472       0.01352      0.00780         0.542476   \n",
       "35       0.00620       0.10748      0.11280        -0.206614   \n",
       "36       0.03680       0.09272      0.01248              NaN   \n",
       "37       0.00572       0.10944      0.11192        -0.328789   \n",
       "38       0.04400       0.02000      0.00952         0.405725   \n",
       "39       0.00704       0.11372      0.10988         0.081624   \n",
       "40       0.09900       0.00440      0.00860         0.492590   \n",
       "41       0.00380       0.11292      0.11040         0.130441   \n",
       "42       0.00520       0.11060      0.10508         0.059889   \n",
       "43       0.00728       0.08720      0.07672              NaN   \n",
       "44       0.09920       0.00452      0.00892         1.000000   \n",
       "45       0.00580       0.10068      0.10056              NaN   \n",
       "46       0.00600       0.11080      0.10628         0.000000   \n",
       "47       0.00716       0.10248      0.10540              NaN   \n",
       "\n",
       "    FC_score_IC50_alpha  FC_score_100_a  FC_score_100_alpha type_pos_a  \\\n",
       "0              0.949734             NaN            0.935475        NaN   \n",
       "1              0.966067        0.928732            0.928732  catalytic   \n",
       "2              1.032346        1.108824            1.108824  catalytic   \n",
       "3              0.995737        1.125737            1.125737  catalytic   \n",
       "4              1.007457        1.047183            1.047183  catalytic   \n",
       "5              0.981935        1.028532            1.028532  catalytic   \n",
       "6              0.997316        1.111080            1.111080  catalytic   \n",
       "7              0.946809        0.535743            0.598206        unk   \n",
       "8              0.957109        0.614988            0.614988  interface   \n",
       "9              0.902137        0.344910            0.344910  interface   \n",
       "10             1.017153             NaN            0.725383        NaN   \n",
       "11             0.950865        0.420369            0.420369  interface   \n",
       "12             0.898874        0.486318            0.486318  interface   \n",
       "13             0.976096        0.269649            0.269649  interface   \n",
       "14             1.016476        0.792861            0.792861  interface   \n",
       "15             0.997973        1.016545            1.016545  interface   \n",
       "16             1.059536        0.922998            1.201365        unk   \n",
       "17             0.550655        1.201365            0.334764  interface   \n",
       "18             0.988644        0.334764            0.922998  interface   \n",
       "19            -0.089358        0.258212            0.258212  interface   \n",
       "20            -0.290105       -0.033971           -0.033971        unk   \n",
       "21            -0.321626       -0.078780            0.213638        unk   \n",
       "22             0.926609        0.535743            0.535743        unk   \n",
       "23             0.992665        1.035836            1.035836  catalytic   \n",
       "24             1.028667        1.081790            1.081790  catalytic   \n",
       "25             1.017568        0.951485            0.951485  catalytic   \n",
       "26             1.020008        1.085426            1.085426  catalytic   \n",
       "27             0.232684       -0.006390           -0.006390        unk   \n",
       "28                  NaN        0.177857            0.177857        unk   \n",
       "29            -0.205059        0.030598            0.030598        unk   \n",
       "30            -0.039452       -0.206310           -0.206310        unk   \n",
       "31            -0.109324       -0.119486           -0.119486        unk   \n",
       "32             0.948449        0.765197            0.765197        unk   \n",
       "33             0.492590        0.044409            0.044409        unk   \n",
       "34             0.542476       -0.014413           -0.014413        unk   \n",
       "35            -0.206614       -0.206626           -0.206626        unk   \n",
       "36             0.545507             NaN            0.095401        NaN   \n",
       "37            -0.328789       -0.036887           -0.036887        unk   \n",
       "38             0.405725        0.123648            0.123648  interface   \n",
       "39             0.081624       -0.144559           -0.144559        unk   \n",
       "40             0.902766        0.044409            0.944570        unk   \n",
       "41             0.130441       -0.049838           -0.049838  interface   \n",
       "42             0.059889       -0.077131           -0.077131        unk   \n",
       "43            -0.691908             NaN           -0.104187        NaN   \n",
       "44             1.000000        0.996685            0.996685       ctrl   \n",
       "45            -0.398586             NaN           -0.062457        NaN   \n",
       "46             0.000000        0.000000            0.000000       ctrl   \n",
       "47            -0.507562             NaN           -0.071739        NaN   \n",
       "\n",
       "   type_pos_alpha residue_type  \n",
       "0             unk          NaN  \n",
       "1       catalytic    catalytic  \n",
       "2       catalytic    catalytic  \n",
       "3       catalytic    catalytic  \n",
       "4       catalytic    catalytic  \n",
       "5       catalytic    catalytic  \n",
       "6       catalytic    catalytic  \n",
       "7       interface          unk  \n",
       "8       interface    interface  \n",
       "9       interface    interface  \n",
       "10      interface          NaN  \n",
       "11      interface    interface  \n",
       "12      interface    interface  \n",
       "13      interface    interface  \n",
       "14      interface    interface  \n",
       "15      interface    interface  \n",
       "16      interface          unk  \n",
       "17      interface    interface  \n",
       "18            unk    interface  \n",
       "19      interface    interface  \n",
       "20            unk          unk  \n",
       "21            unk          unk  \n",
       "22            unk          unk  \n",
       "23      catalytic    catalytic  \n",
       "24      catalytic    catalytic  \n",
       "25      catalytic    catalytic  \n",
       "26      catalytic    catalytic  \n",
       "27            unk          unk  \n",
       "28            unk          unk  \n",
       "29            unk          unk  \n",
       "30            unk          unk  \n",
       "31            unk          unk  \n",
       "32            unk          unk  \n",
       "33            unk          unk  \n",
       "34            unk          unk  \n",
       "35            unk          unk  \n",
       "36            unk          NaN  \n",
       "37            unk          unk  \n",
       "38      interface    interface  \n",
       "39            unk          unk  \n",
       "40            unk          unk  \n",
       "41      interface    interface  \n",
       "42            unk          unk  \n",
       "43            unk          NaN  \n",
       "44           ctrl         ctrl  \n",
       "45            unk          NaN  \n",
       "46           ctrl         ctrl  \n",
       "47            unk          NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hap_growth_rate_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f96ec218-b579-42ef-bf7f-de0e96048971",
   "metadata": {},
   "outputs": [],
   "source": [
    "#hap_growth_rate_df.to_csv('./hap_validation_growth_rates.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ee064b0-2f71-4f8c-82e9-02bae0d22515",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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