growth_curves_validations_v3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history saving thread hit an unexpected error (DatabaseError('database disk image is malformed')).History will not be written to the database.\n"
     ]
    }
   ],
   "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": "markdown",
   "metadata": {},
   "source": [
    "## map wells to genotype (DHFR-tagged strains)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "        vertical-align: top;\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>codon</th>\n",
       "      <th>wt</th>\n",
       "      <th>measured</th>\n",
       "      <th>CY_1</th>\n",
       "      <th>CY_1_R1</th>\n",
       "      <th>CY_1_MAD_1</th>\n",
       "      <th>CY_1_R2</th>\n",
       "      <th>CY_1_MAD_2</th>\n",
       "      <th>FC_1</th>\n",
       "      <th>FC_1_R1</th>\n",
       "      <th>...</th>\n",
       "      <th>interface</th>\n",
       "      <th>ddG_struc_HET</th>\n",
       "      <th>ddG_struc_HM</th>\n",
       "      <th>ddG_inter_HET</th>\n",
       "      <th>ddG_inter_HM</th>\n",
       "      <th>r4s_score</th>\n",
       "      <th>RSA</th>\n",
       "      <th>temp</th>\n",
       "      <th>ortholog_aa_freq</th>\n",
       "      <th>dist_act_site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>136</th>\n",
       "      <td>8</td>\n",
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       "      <td>-4.373589</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>-2.33217</td>\n",
       "      <td>-4.59949</td>\n",
       "      <td>0.00120</td>\n",
       "      <td>0.00162</td>\n",
       "      <td>1.7803</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>14.804167</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>168</th>\n",
       "      <td>10</td>\n",
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       "      <td>0.049255</td>\n",
       "      <td>0.105939</td>\n",
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       "      <th>227</th>\n",
       "      <td>12</td>\n",
       "      <td>Q</td>\n",
       "      <td>K</td>\n",
       "      <td>0.769357</td>\n",
       "      <td>1.416442</td>\n",
       "      <td>1.857860</td>\n",
       "      <td>0.122271</td>\n",
       "      <td>0.298271</td>\n",
       "      <td>-1.133104</td>\n",
       "      <td>-1.532682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.38653</td>\n",
       "      <td>0.80766</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.5190</td>\n",
       "      <td>0.563084</td>\n",
       "      <td>18.455000</td>\n",
       "      <td>0.213270</td>\n",
       "      <td>15.664775</td>\n",
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       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>14</td>\n",
       "      <td>G</td>\n",
       "      <td>W</td>\n",
       "      <td>-3.128267</td>\n",
       "      <td>-2.879006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.377528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.131018</td>\n",
       "      <td>1.272348</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>0.004717</td>\n",
       "      <td>11.052810</td>\n",
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       "      <th>263</th>\n",
       "      <td>14</td>\n",
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       "      <td>Y</td>\n",
       "      <td>-4.292014</td>\n",
       "      <td>-3.590963</td>\n",
       "      <td>1.042929</td>\n",
       "      <td>-4.993065</td>\n",
       "      <td>0.434378</td>\n",
       "      <td>-1.111130</td>\n",
       "      <td>-1.142447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>51.91922</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.0995</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.009434</td>\n",
       "      <td>11.052810</td>\n",
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       "      <td>-0.22324</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>3.428099</td>\n",
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       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>157</td>\n",
       "      <td>G</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codon wt measured      CY_1   CY_1_R1  CY_1_MAD_1   CY_1_R2  CY_1_MAD_2  \\\n",
       "136       8  S        F -1.111151 -0.922339    0.415623 -1.299963    0.276321   \n",
       "168      10  W        * -0.136679 -0.322614    0.504528  0.049255    0.105939   \n",
       "227      12  Q        K  0.769357  1.416442    1.857860  0.122271    0.298271   \n",
       "261      14  G        W -3.128267 -2.879006    0.000000 -3.377528    0.000000   \n",
       "263      14  G        Y -4.292014 -3.590963    1.042929 -4.993065    0.434378   \n",
       "...     ... ..      ...       ...       ...         ...       ...         ...   \n",
       "3233    155  D        E       NaN       NaN         NaN       NaN         NaN   \n",
       "3249    156  I        Q       NaN       NaN         NaN       NaN         NaN   \n",
       "3250    156  I        H       NaN       NaN         NaN       NaN         NaN   \n",
       "3274    157  G        D       NaN       NaN         NaN       NaN         NaN   \n",
       "3275    157  G        E       NaN       NaN         NaN       NaN         NaN   \n",
       "\n",
       "          FC_1   FC_1_R1  ...  interface  ddG_struc_HET  ddG_struc_HM  \\\n",
       "136  -4.373589 -4.495269  ...       0.00       -2.33217      -4.59949   \n",
       "168   1.184129  1.158049  ...       0.00            NaN           NaN   \n",
       "227  -1.133104 -1.532682  ...       0.00        0.38653       0.80766   \n",
       "261   1.131018  1.272348  ...       0.00       61.33926     110.37500   \n",
       "263  -1.111130 -1.142447  ...       0.00       51.91922      95.60872   \n",
       "...        ...       ...  ...        ...            ...           ...   \n",
       "3233       NaN       NaN  ...       0.75        3.98187       7.58297   \n",
       "3249       NaN       NaN  ...       1.00        1.89760       3.69370   \n",
       "3250       NaN       NaN  ...       1.00        3.51328       6.84207   \n",
       "3274       NaN       NaN  ...       1.00       17.12332      35.22290   \n",
       "3275       NaN       NaN  ...       1.00       18.43000      35.63842   \n",
       "\n",
       "      ddG_inter_HET  ddG_inter_HM  r4s_score       RSA       temp  \\\n",
       "136         0.00120       0.00162     1.7803  0.192308  14.804167   \n",
       "168             NaN           NaN     1.0380  0.196970  18.152143   \n",
       "227         0.00000       0.00000     2.5190  0.563084  18.455000   \n",
       "261         0.00000       0.00006    -0.0995  0.000000  10.426250   \n",
       "263         0.00000       0.00000    -0.0995  0.000000  10.426250   \n",
       "...             ...           ...        ...       ...        ...   \n",
       "3233       -0.01388      -0.22324    -0.9460  0.008021   8.413750   \n",
       "3249        0.24268       0.07604    -0.8920  0.082051  10.215625   \n",
       "3250        0.18452       0.36770    -0.8920  0.082051  10.215625   \n",
       "3274       11.10460      23.37869    -0.6409  0.020619  10.876250   \n",
       "3275       12.46210      23.50471    -0.6409  0.020619  10.876250   \n",
       "\n",
       "      ortholog_aa_freq  dist_act_site  \n",
       "136           0.000000      17.106971  \n",
       "168                NaN      15.447535  \n",
       "227           0.213270      15.664775  \n",
       "261           0.004717      11.052810  \n",
       "263           0.009434      11.052810  \n",
       "...                ...            ...  \n",
       "3233          0.009479       0.000000  \n",
       "3249          0.000000       1.328411  \n",
       "3250          0.004739       1.328411  \n",
       "3274          0.000000       3.428099  \n",
       "3275          0.000000       3.428099  \n",
       "\n",
       "[95 rows x 57 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df = pd.read_csv('./validation_mutants_df_v3.csv', sep=',', index_col=0)\n",
    "# DMS, ddG and evolutionnary annotations for validation variants\n",
    "# The indexes for mutants in both dataframes (DMS and validations) will match\n",
    "validations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_round = [136, 261, 263, 345, 350, 429, 439, 455, 514, 575, 582, 681, 723, 744, 745, 770, 787, 788, 1023, 1161, 1170, 1205, 1206, 1208, 1213, 1228, 1236, 1249, 1333, 1350, 1410, 1451,\n",
    "                      1460, 1527, 1551, 1560, 1563, 1565, 1568, 1570, 1642, 1770, 1778, 1830, 1854, 1877, 1897, 1910, 1915, 1921, 1980, 1983, 1990, 2004, 2020, 2189, 2298, 2434, 2435, 2445, 2467, \n",
    "                      2534, 2536, 2628, 2760, 2900, 2918, 2936, 2967, 3092, 3096, 3118, 3144, 3147, 3156, 3208, 3249, 3250, 3274, 3275]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Dropped primers positions during cloning, in 8 rows x 10 columns format:\n",
    "\n",
    "A1 --> replaced by wt\n",
    "D3\n",
    "B5\n",
    "F7 --> replaced by wt\n",
    "A10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutant_indexes = list([ x for x in validations_df.index if x in first_round])\n",
    "# list of df index for the validation mutants\n",
    "len(mutant_indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{136: 'A1',\n",
       " 261: 'B1',\n",
       " 263: 'C1',\n",
       " 345: 'D1',\n",
       " 350: 'E1',\n",
       " 429: 'F1',\n",
       " 439: 'G1',\n",
       " 455: 'H1',\n",
       " 514: 'A2',\n",
       " 575: 'B2',\n",
       " 582: 'C2',\n",
       " 681: 'D2',\n",
       " 723: 'E2',\n",
       " 744: 'F2',\n",
       " 745: 'G2',\n",
       " 770: 'H2',\n",
       " 787: 'A3',\n",
       " 788: 'B3',\n",
       " 1023: 'C3',\n",
       " 1161: 'D3',\n",
       " 1170: 'E3',\n",
       " 1205: 'F3',\n",
       " 1206: 'G3',\n",
       " 1208: 'H3',\n",
       " 1213: 'A4',\n",
       " 1228: 'B4',\n",
       " 1236: 'C4',\n",
       " 1249: 'D4',\n",
       " 1333: 'E4',\n",
       " 1350: 'F4',\n",
       " 1410: 'G4',\n",
       " 1451: 'H4',\n",
       " 1460: 'A5',\n",
       " 1527: 'B5',\n",
       " 1551: 'C5',\n",
       " 1560: 'D5',\n",
       " 1563: 'E5',\n",
       " 1565: 'F5',\n",
       " 1568: 'G5',\n",
       " 1570: 'H5',\n",
       " 1642: 'A6',\n",
       " 1770: 'B6',\n",
       " 1778: 'C6',\n",
       " 1830: 'D6',\n",
       " 1854: 'E6',\n",
       " 1877: 'F6',\n",
       " 1897: 'G6',\n",
       " 1910: 'H6',\n",
       " 1915: 'A7',\n",
       " 1921: 'B7',\n",
       " 1980: 'C7',\n",
       " 1983: 'D7',\n",
       " 1990: 'E7',\n",
       " 2004: 'F7',\n",
       " 2020: 'G7',\n",
       " 2189: 'H7',\n",
       " 2298: 'A8',\n",
       " 2434: 'B8',\n",
       " 2435: 'C8',\n",
       " 2445: 'D8',\n",
       " 2467: 'E8',\n",
       " 2534: 'F8',\n",
       " 2536: 'G8',\n",
       " 2628: 'H8',\n",
       " 2760: 'A9',\n",
       " 2900: 'B9',\n",
       " 2918: 'C9',\n",
       " 2936: 'D9',\n",
       " 2967: 'E9',\n",
       " 3092: 'F9',\n",
       " 3096: 'G9',\n",
       " 3118: 'H9',\n",
       " 3144: 'A10',\n",
       " 3147: 'B10',\n",
       " 3156: 'C10',\n",
       " 3208: 'D10',\n",
       " 3249: 'E10',\n",
       " 3250: 'F10',\n",
       " 3274: 'G10',\n",
       " 3275: 'H10'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "primer_position_dict = {}\n",
    "\n",
    "index_pos = 0\n",
    "\n",
    "for col in range(1,11):\n",
    "    \n",
    "    for row in ['A','B', 'C', 'D', 'E', 'F', 'G', 'H']:\n",
    "        \n",
    "        primer_position_dict[mutant_indexes[index_pos]] = row+str(col)\n",
    "        \n",
    "        index_pos+=1\n",
    "        \n",
    "primer_position_dict\n",
    "        \n",
    "# variants are ordered according to ther DMS dataframe index, so we can directly\n",
    "# map their position to the fusion PCR oligo plates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rearray_96_dict = {}\n",
    "\n",
    "well_index = 0\n",
    "\n",
    "for col in range(1,12):\n",
    "    \n",
    "    for row in ['A','B', 'C', 'D', 'E', 'F', 'G']:\n",
    "        \n",
    "        rearray_96_dict[well_index] = row+str(col)\n",
    "        \n",
    "        well_index+=1\n",
    "        \n",
    "#rearray_96_dict\n",
    "# this dictionnary converts the coordinates of oligos in a 96 well plate to the corresponding position in a 7x11\n",
    "# array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{136: nan,\n",
       " 261: 'B1',\n",
       " 263: 'C1',\n",
       " 345: 'D1',\n",
       " 350: 'E1',\n",
       " 429: 'F1',\n",
       " 439: 'G1',\n",
       " 455: 'A2',\n",
       " 514: 'B2',\n",
       " 575: 'C2',\n",
       " 582: 'D2',\n",
       " 681: 'E2',\n",
       " 723: 'F2',\n",
       " 744: 'G2',\n",
       " 745: 'A3',\n",
       " 770: 'B3',\n",
       " 787: 'C3',\n",
       " 788: 'D3',\n",
       " 1023: 'E3',\n",
       " 1161: nan,\n",
       " 1170: 'F3',\n",
       " 1205: 'G3',\n",
       " 1206: 'A4',\n",
       " 1208: 'B4',\n",
       " 1213: 'C4',\n",
       " 1228: 'D4',\n",
       " 1236: 'E4',\n",
       " 1249: 'F4',\n",
       " 1333: 'G4',\n",
       " 1350: 'A5',\n",
       " 1410: 'B5',\n",
       " 1451: 'C5',\n",
       " 1460: 'D5',\n",
       " 1527: nan,\n",
       " 1551: 'E5',\n",
       " 1560: 'F5',\n",
       " 1563: 'G5',\n",
       " 1565: 'A6',\n",
       " 1568: 'B6',\n",
       " 1570: 'C6',\n",
       " 1642: 'D6',\n",
       " 1770: 'E6',\n",
       " 1778: 'F6',\n",
       " 1830: 'G6',\n",
       " 1854: 'A7',\n",
       " 1877: 'B7',\n",
       " 1897: 'C7',\n",
       " 1910: 'D7',\n",
       " 1915: 'E7',\n",
       " 1921: 'F7',\n",
       " 1980: 'G7',\n",
       " 1983: 'A8',\n",
       " 1990: 'B8',\n",
       " 2004: nan,\n",
       " 2020: 'D8',\n",
       " 2189: 'E8',\n",
       " 2298: 'F8',\n",
       " 2434: 'G8',\n",
       " 2435: 'A9',\n",
       " 2445: 'B9',\n",
       " 2467: 'C9',\n",
       " 2534: 'D9',\n",
       " 2536: 'E9',\n",
       " 2628: 'F9',\n",
       " 2760: 'G9',\n",
       " 2900: 'A10',\n",
       " 2918: 'B10',\n",
       " 2936: 'C10',\n",
       " 2967: 'D10',\n",
       " 3092: 'E10',\n",
       " 3096: 'F10',\n",
       " 3118: 'G10',\n",
       " 3144: nan,\n",
       " 3147: 'A11',\n",
       " 3156: 'B11',\n",
       " 3208: 'C11',\n",
       " 3249: 'D11',\n",
       " 3250: 'E11',\n",
       " 3274: 'F11',\n",
       " 3275: 'G11'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plate_96w_position_dict = {}\n",
    "\n",
    "\n",
    "used_for_wt = ['A1', 'F7']\n",
    "dropped = ['D3', 'B5', 'A10']\n",
    "\n",
    "pos_mod = 0\n",
    "#ajusts plate position for dropped strains\n",
    "\n",
    "for mutant in mutant_indexes:\n",
    "    \n",
    "    mutant_no = mutant_indexes.index(mutant)\n",
    "    \n",
    "    if primer_position_dict[mutant] in used_for_wt:\n",
    "        plate_96w_position_dict[mutant] = np.nan\n",
    "    \n",
    "    \n",
    "    elif primer_position_dict[mutant] in dropped:\n",
    "        plate_96w_position_dict[mutant] = np.nan\n",
    "        pos_mod +=1\n",
    "        \n",
    "    else:\n",
    "        plate_96w_position_dict[mutant] = rearray_96_dict[mutant_no-pos_mod]\n",
    "        \n",
    "plate_96w_position_dict\n",
    "# looks good based on printed values        \n",
    "# adds the information of which strains could successfully be constructed to map mutants to the 7x11 array\n",
    "        \n",
    "        \n",
    "#for mut in mutant_indexes:\n",
    "#    print(primer_position_dict[mut], plate_96w_position_dict[mut])\n",
    "    # printed values match the plan in my lab notebook      \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mtx_row_dict = {'A':'B',\n",
    "                'B':'D',\n",
    "                'C':'F',\n",
    "                'D':'H',\n",
    "                'E':'J',\n",
    "                'F':'L',\n",
    "                'G':'N'}\n",
    "dmso_row_dict = {'A':'C',\n",
    "                 'B':'E',\n",
    "                 'C':'G',\n",
    "                 'D':'I',\n",
    "                 'E':'K',\n",
    "                 'F':'M',\n",
    "                 'G':'O'}\n",
    "# converts the 7x11 rows of the variant array to the corresponding rows in the 384. The positions are shifted to\n",
    "# allow for a water border on the edge of the plate, to limit the effect of evaporation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "plate_384_mtx_1_position_dict = {}\n",
    "\n",
    "for mutant in mutant_indexes:   \n",
    "    if isinstance(plate_96w_position_dict[mutant], str) == False:\n",
    "        plate_384_mtx_1_position_dict[mutant] = np.NaN\n",
    "        \n",
    "    else:\n",
    "        mutant_row = plate_96w_position_dict[mutant][0]\n",
    "        mutant_col = int(plate_96w_position_dict[mutant][1:])\n",
    "        \n",
    "        plate_384_mtx_1_position_dict[mutant] = mtx_row_dict[mutant_row]+str((mutant_col*2))       \n",
    "        \n",
    "#plate_384_mtx_1_position_dict\n",
    "#positions of variants for replicate 1, MTX media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "plate_384_mtx_2_position_dict = {}\n",
    "\n",
    "for mutant in mutant_indexes:   \n",
    "    if isinstance(plate_96w_position_dict[mutant], str) == False:\n",
    "        plate_384_mtx_2_position_dict[mutant] = np.NaN\n",
    "        \n",
    "    else:\n",
    "        mutant_row = plate_96w_position_dict[mutant][0]\n",
    "        mutant_col = int(plate_96w_position_dict[mutant][1:])\n",
    "        \n",
    "        plate_384_mtx_2_position_dict[mutant] = mtx_row_dict[mutant_row]+str(((mutant_col*2)+1))\n",
    "#plate_384_mtx_2_position_dict\n",
    "#positions of variants for replicate 2, MTX media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "plate_384_dmso_1_position_dict = {}\n",
    "\n",
    "for mutant in mutant_indexes:   \n",
    "    if isinstance(plate_96w_position_dict[mutant], str) == False:\n",
    "        plate_384_dmso_1_position_dict[mutant] = np.NaN\n",
    "        \n",
    "    else:\n",
    "        mutant_row = plate_96w_position_dict[mutant][0]\n",
    "        mutant_col = int(plate_96w_position_dict[mutant][1:])\n",
    "        \n",
    "        plate_384_dmso_1_position_dict[mutant] = dmso_row_dict[mutant_row]+str(((mutant_col*2)))\n",
    "#plate_384_dmso_1_position_dict\n",
    "#positions of variants for replicate 1, DMSO media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "plate_384_dmso_2_position_dict = {}\n",
    "\n",
    "for mutant in mutant_indexes:   \n",
    "    if isinstance(plate_96w_position_dict[mutant], str) == False:\n",
    "        plate_384_dmso_2_position_dict[mutant] = np.NaN\n",
    "        \n",
    "    else:\n",
    "        mutant_row = plate_96w_position_dict[mutant][0]\n",
    "        mutant_col = int(plate_96w_position_dict[mutant][1:])\n",
    "        \n",
    "        plate_384_dmso_2_position_dict[mutant] = dmso_row_dict[mutant_row]+str(((mutant_col*2)+1))\n",
    "#plate_384_dmso_2_position_dict\n",
    "#positions of variants for replicate 2, DMSO media"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add growth curve plate position in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>CY_1</th>\n",
       "      <th>CY_1_R1</th>\n",
       "      <th>CY_1_MAD_1</th>\n",
       "      <th>CY_1_R2</th>\n",
       "      <th>CY_1_MAD_2</th>\n",
       "      <th>FC_1</th>\n",
       "      <th>FC_1_R1</th>\n",
       "      <th>...</th>\n",
       "      <th>RSA</th>\n",
       "      <th>temp</th>\n",
       "      <th>ortholog_aa_freq</th>\n",
       "      <th>dist_act_site</th>\n",
       "      <th>primer_position</th>\n",
       "      <th>plate_96w_position</th>\n",
       "      <th>mtx1_position</th>\n",
       "      <th>mtx2_position</th>\n",
       "      <th>dmso1_position</th>\n",
       "      <th>dmso2_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>-1.299963</td>\n",
       "      <td>0.276321</td>\n",
       "      <td>-4.373589</td>\n",
       "      <td>-4.495269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>14.804167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.106971</td>\n",
       "      <td>A1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>10</td>\n",
       "      <td>W</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.136679</td>\n",
       "      <td>-0.322614</td>\n",
       "      <td>0.504528</td>\n",
       "      <td>0.049255</td>\n",
       "      <td>0.105939</td>\n",
       "      <td>1.184129</td>\n",
       "      <td>1.158049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196970</td>\n",
       "      <td>18.152143</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>12</td>\n",
       "      <td>Q</td>\n",
       "      <td>K</td>\n",
       "      <td>0.769357</td>\n",
       "      <td>1.416442</td>\n",
       "      <td>1.857860</td>\n",
       "      <td>0.122271</td>\n",
       "      <td>0.298271</td>\n",
       "      <td>-1.133104</td>\n",
       "      <td>-1.532682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.563084</td>\n",
       "      <td>18.455000</td>\n",
       "      <td>0.213270</td>\n",
       "      <td>15.664775</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>14</td>\n",
       "      <td>G</td>\n",
       "      <td>W</td>\n",
       "      <td>-3.128267</td>\n",
       "      <td>-2.879006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.377528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.131018</td>\n",
       "      <td>1.272348</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.426250</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>11.052810</td>\n",
       "      <td>B1</td>\n",
       "      <td>B1</td>\n",
       "      <td>D2</td>\n",
       "      <td>D3</td>\n",
       "      <td>E2</td>\n",
       "      <td>E3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>14</td>\n",
       "      <td>G</td>\n",
       "      <td>Y</td>\n",
       "      <td>-4.292014</td>\n",
       "      <td>-3.590963</td>\n",
       "      <td>1.042929</td>\n",
       "      <td>-4.993065</td>\n",
       "      <td>0.434378</td>\n",
       "      <td>-1.111130</td>\n",
       "      <td>-1.142447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.426250</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>11.052810</td>\n",
       "      <td>C1</td>\n",
       "      <td>C1</td>\n",
       "      <td>F2</td>\n",
       "      <td>F3</td>\n",
       "      <td>G2</td>\n",
       "      <td>G3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>155</td>\n",
       "      <td>D</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>NaN</td>\n",
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       "      <td>0.008021</td>\n",
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       "      <td>0.009479</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3249</th>\n",
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       "      <td>I</td>\n",
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       "      <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>0.082051</td>\n",
       "      <td>10.215625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.328411</td>\n",
       "      <td>E10</td>\n",
       "      <td>D11</td>\n",
       "      <td>H22</td>\n",
       "      <td>H23</td>\n",
       "      <td>I22</td>\n",
       "      <td>I23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>156</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.004739</td>\n",
       "      <td>1.328411</td>\n",
       "      <td>F10</td>\n",
       "      <td>E11</td>\n",
       "      <td>J22</td>\n",
       "      <td>J23</td>\n",
       "      <td>K22</td>\n",
       "      <td>K23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3274</th>\n",
       "      <td>157</td>\n",
       "      <td>G</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020619</td>\n",
       "      <td>10.876250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.428099</td>\n",
       "      <td>G10</td>\n",
       "      <td>F11</td>\n",
       "      <td>L22</td>\n",
       "      <td>L23</td>\n",
       "      <td>M22</td>\n",
       "      <td>M23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>157</td>\n",
       "      <td>G</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020619</td>\n",
       "      <td>10.876250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.428099</td>\n",
       "      <td>H10</td>\n",
       "      <td>G11</td>\n",
       "      <td>N22</td>\n",
       "      <td>N23</td>\n",
       "      <td>O22</td>\n",
       "      <td>O23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codon wt measured      CY_1   CY_1_R1  CY_1_MAD_1   CY_1_R2  CY_1_MAD_2  \\\n",
       "136       8  S        F -1.111151 -0.922339    0.415623 -1.299963    0.276321   \n",
       "168      10  W        * -0.136679 -0.322614    0.504528  0.049255    0.105939   \n",
       "227      12  Q        K  0.769357  1.416442    1.857860  0.122271    0.298271   \n",
       "261      14  G        W -3.128267 -2.879006    0.000000 -3.377528    0.000000   \n",
       "263      14  G        Y -4.292014 -3.590963    1.042929 -4.993065    0.434378   \n",
       "...     ... ..      ...       ...       ...         ...       ...         ...   \n",
       "3233    155  D        E       NaN       NaN         NaN       NaN         NaN   \n",
       "3249    156  I        Q       NaN       NaN         NaN       NaN         NaN   \n",
       "3250    156  I        H       NaN       NaN         NaN       NaN         NaN   \n",
       "3274    157  G        D       NaN       NaN         NaN       NaN         NaN   \n",
       "3275    157  G        E       NaN       NaN         NaN       NaN         NaN   \n",
       "\n",
       "          FC_1   FC_1_R1  ...       RSA       temp  ortholog_aa_freq  \\\n",
       "136  -4.373589 -4.495269  ...  0.192308  14.804167          0.000000   \n",
       "168   1.184129  1.158049  ...  0.196970  18.152143               NaN   \n",
       "227  -1.133104 -1.532682  ...  0.563084  18.455000          0.213270   \n",
       "261   1.131018  1.272348  ...  0.000000  10.426250          0.004717   \n",
       "263  -1.111130 -1.142447  ...  0.000000  10.426250          0.009434   \n",
       "...        ...       ...  ...       ...        ...               ...   \n",
       "3233       NaN       NaN  ...  0.008021   8.413750          0.009479   \n",
       "3249       NaN       NaN  ...  0.082051  10.215625          0.000000   \n",
       "3250       NaN       NaN  ...  0.082051  10.215625          0.004739   \n",
       "3274       NaN       NaN  ...  0.020619  10.876250          0.000000   \n",
       "3275       NaN       NaN  ...  0.020619  10.876250          0.000000   \n",
       "\n",
       "      dist_act_site  primer_position  plate_96w_position  mtx1_position  \\\n",
       "136       17.106971               A1                 NaN            NaN   \n",
       "168       15.447535              NaN                 NaN            NaN   \n",
       "227       15.664775              NaN                 NaN            NaN   \n",
       "261       11.052810               B1                  B1             D2   \n",
       "263       11.052810               C1                  C1             F2   \n",
       "...             ...              ...                 ...            ...   \n",
       "3233       0.000000              NaN                 NaN            NaN   \n",
       "3249       1.328411              E10                 D11            H22   \n",
       "3250       1.328411              F10                 E11            J22   \n",
       "3274       3.428099              G10                 F11            L22   \n",
       "3275       3.428099              H10                 G11            N22   \n",
       "\n",
       "      mtx2_position  dmso1_position  dmso2_position  \n",
       "136             NaN             NaN             NaN  \n",
       "168             NaN             NaN             NaN  \n",
       "227             NaN             NaN             NaN  \n",
       "261              D3              E2              E3  \n",
       "263              F3              G2              G3  \n",
       "...             ...             ...             ...  \n",
       "3233            NaN             NaN             NaN  \n",
       "3249            H23             I22             I23  \n",
       "3250            J23             K22             K23  \n",
       "3274            L23             M22             M23  \n",
       "3275            N23             O22             O23  \n",
       "\n",
       "[95 rows x 63 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df['primer_position'] = pd.Series(primer_position_dict)\n",
    "validations_df['plate_96w_position'] = pd.Series(plate_96w_position_dict)\n",
    "validations_df['mtx1_position'] = pd.Series(plate_384_mtx_1_position_dict)\n",
    "validations_df['mtx2_position'] = pd.Series(plate_384_mtx_2_position_dict)\n",
    "validations_df['dmso1_position'] = pd.Series(plate_384_dmso_1_position_dict)\n",
    "validations_df['dmso2_position'] = pd.Series(plate_384_dmso_2_position_dict)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "validations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['codon', 'wt', 'measured', 'CY_1', 'CY_1_R1', 'CY_1_MAD_1', 'CY_1_R2',\n",
       "       'CY_1_MAD_2', 'FC_1', 'FC_1_R1', 'FC_1_MAD_1', 'FC_1_R2', 'FC_1_MAD_2',\n",
       "       'CY_2', 'CY_2_R1', 'CY_2_MAD_1', 'CY_2_R2', 'CY_2_MAD_2', 'FC_2',\n",
       "       'FC_2_R1', 'FC_2_MAD_1', 'FC_2_R2', 'FC_2_MAD_2', 'FC_CY_2',\n",
       "       'FC_CY_2_R1', 'FC_CY_2_MAD_1', 'FC_CY_2_R2', 'FC_CY_2_MAD_2', 'CY_3',\n",
       "       'CY_3_R1', 'CY_3_MAD_1', 'CY_3_R2', 'CY_3_MAD_2', 'FC_3', 'FC_3_R1',\n",
       "       'FC_3_MAD_1', 'FC_3_R2', 'FC_3_MAD_2', 'CY_1_to_2', 'CY_3_to_2',\n",
       "       'FC_1_to_2', 'FC_3_to_2', 'CY_123', 'FC_123', 'CY_score', 'FC_score',\n",
       "       'FC_CY_score', 'interface', 'ddG_struc_HET', 'ddG_struc_HM',\n",
       "       'ddG_inter_HET', 'ddG_inter_HM', 'r4s_score', 'RSA', 'temp',\n",
       "       'ortholog_aa_freq', 'dist_act_site', 'primer_position',\n",
       "       'plate_96w_position', 'mtx1_position', 'mtx2_position',\n",
       "       'dmso1_position', 'dmso2_position'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df.columns\n",
    "# check if new columns are all there"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define function to measure derivative growth rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_derivative_growth_rate(well, df, tmax=25):\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": "markdown",
   "metadata": {},
   "source": [
    "## get PPI growth rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "MTX_source_table = './validationsmtx384FCY1.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0s</th>\n",
       "      <th>900s</th>\n",
       "      <th>1800s</th>\n",
       "      <th>2699s</th>\n",
       "      <th>3599s</th>\n",
       "      <th>4499s</th>\n",
       "      <th>5399s</th>\n",
       "      <th>6299s</th>\n",
       "      <th>7199s</th>\n",
       "      <th>8099s</th>\n",
       "      <th>...</th>\n",
       "      <th>250185s</th>\n",
       "      <th>251085s</th>\n",
       "      <th>251985s</th>\n",
       "      <th>252885s</th>\n",
       "      <th>253785s</th>\n",
       "      <th>254685s</th>\n",
       "      <th>255585s</th>\n",
       "      <th>256485s</th>\n",
       "      <th>257385s</th>\n",
       "      <th>258285s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1</th>\n",
       "      <td>0.095589</td>\n",
       "      <td>0.095128</td>\n",
       "      <td>0.094868</td>\n",
       "      <td>0.094764</td>\n",
       "      <td>0.094723</td>\n",
       "      <td>0.094714</td>\n",
       "      <td>0.094529</td>\n",
       "      <td>0.094543</td>\n",
       "      <td>0.094411</td>\n",
       "      <td>0.094230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093680</td>\n",
       "      <td>0.093899</td>\n",
       "      <td>0.093791</td>\n",
       "      <td>0.093803</td>\n",
       "      <td>0.093867</td>\n",
       "      <td>0.093662</td>\n",
       "      <td>0.093698</td>\n",
       "      <td>0.093661</td>\n",
       "      <td>0.093745</td>\n",
       "      <td>0.093646</td>\n",
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       "    <tr>\n",
       "      <th>A2</th>\n",
       "      <td>0.096706</td>\n",
       "      <td>0.096245</td>\n",
       "      <td>0.095912</td>\n",
       "      <td>0.095766</td>\n",
       "      <td>0.095713</td>\n",
       "      <td>0.095656</td>\n",
       "      <td>0.095506</td>\n",
       "      <td>0.095525</td>\n",
       "      <td>0.095387</td>\n",
       "      <td>0.095213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094875</td>\n",
       "      <td>0.094980</td>\n",
       "      <td>0.094861</td>\n",
       "      <td>0.094935</td>\n",
       "      <td>0.095068</td>\n",
       "      <td>0.094833</td>\n",
       "      <td>0.094903</td>\n",
       "      <td>0.094910</td>\n",
       "      <td>0.094971</td>\n",
       "      <td>0.094854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>0.098366</td>\n",
       "      <td>0.097993</td>\n",
       "      <td>0.097509</td>\n",
       "      <td>0.097363</td>\n",
       "      <td>0.097303</td>\n",
       "      <td>0.097168</td>\n",
       "      <td>0.096952</td>\n",
       "      <td>0.097047</td>\n",
       "      <td>0.096810</td>\n",
       "      <td>0.096694</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096354</td>\n",
       "      <td>0.096500</td>\n",
       "      <td>0.096349</td>\n",
       "      <td>0.096456</td>\n",
       "      <td>0.096443</td>\n",
       "      <td>0.096330</td>\n",
       "      <td>0.096442</td>\n",
       "      <td>0.096425</td>\n",
       "      <td>0.096475</td>\n",
       "      <td>0.096399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <td>0.098503</td>\n",
       "      <td>0.098177</td>\n",
       "      <td>0.097600</td>\n",
       "      <td>0.097784</td>\n",
       "      <td>0.097661</td>\n",
       "      <td>0.097324</td>\n",
       "      <td>0.097425</td>\n",
       "      <td>0.097531</td>\n",
       "      <td>0.097285</td>\n",
       "      <td>0.097152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096906</td>\n",
       "      <td>0.096977</td>\n",
       "      <td>0.096885</td>\n",
       "      <td>0.096887</td>\n",
       "      <td>0.096840</td>\n",
       "      <td>0.096757</td>\n",
       "      <td>0.096838</td>\n",
       "      <td>0.096824</td>\n",
       "      <td>0.096918</td>\n",
       "      <td>0.096790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>0.096659</td>\n",
       "      <td>0.096284</td>\n",
       "      <td>0.095846</td>\n",
       "      <td>0.095650</td>\n",
       "      <td>0.095631</td>\n",
       "      <td>0.095505</td>\n",
       "      <td>0.095305</td>\n",
       "      <td>0.095388</td>\n",
       "      <td>0.095131</td>\n",
       "      <td>0.095008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094509</td>\n",
       "      <td>0.094718</td>\n",
       "      <td>0.094633</td>\n",
       "      <td>0.094681</td>\n",
       "      <td>0.094594</td>\n",
       "      <td>0.094618</td>\n",
       "      <td>0.094672</td>\n",
       "      <td>0.094693</td>\n",
       "      <td>0.094698</td>\n",
       "      <td>0.094482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 288 columns</p>\n",
       "</div>"
      ],
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       "          0s      900s     1800s     2699s     3599s     4499s     5399s  \\\n",
       "A1  0.095589  0.095128  0.094868  0.094764  0.094723  0.094714  0.094529   \n",
       "A2  0.096706  0.096245  0.095912  0.095766  0.095713  0.095656  0.095506   \n",
       "A3  0.098366  0.097993  0.097509  0.097363  0.097303  0.097168  0.096952   \n",
       "A4  0.098503  0.098177  0.097600  0.097784  0.097661  0.097324  0.097425   \n",
       "A5  0.096659  0.096284  0.095846  0.095650  0.095631  0.095505  0.095305   \n",
       "\n",
       "       6299s     7199s     8099s  ...   250185s   251085s   251985s   252885s  \\\n",
       "A1  0.094543  0.094411  0.094230  ...  0.093680  0.093899  0.093791  0.093803   \n",
       "A2  0.095525  0.095387  0.095213  ...  0.094875  0.094980  0.094861  0.094935   \n",
       "A3  0.097047  0.096810  0.096694  ...  0.096354  0.096500  0.096349  0.096456   \n",
       "A4  0.097531  0.097285  0.097152  ...  0.096906  0.096977  0.096885  0.096887   \n",
       "A5  0.095388  0.095131  0.095008  ...  0.094509  0.094718  0.094633  0.094681   \n",
       "\n",
       "     253785s   254685s   255585s   256485s   257385s   258285s  \n",
       "A1  0.093867  0.093662  0.093698  0.093661  0.093745  0.093646  \n",
       "A2  0.095068  0.094833  0.094903  0.094910  0.094971  0.094854  \n",
       "A3  0.096443  0.096330  0.096442  0.096425  0.096475  0.096399  \n",
       "A4  0.096840  0.096757  0.096838  0.096824  0.096918  0.096790  \n",
       "A5  0.094594  0.094618  0.094672  0.094693  0.094698  0.094482  \n",
       "\n",
       "[5 rows x 288 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MTX_source_df = pd.read_excel(MTX_source_table, header=0, nrows=384, index_col=0)\n",
    "\n",
    "MTX_source_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.094723</td>\n",
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       "      <td>0.093698</td>\n",
       "      <td>0.093661</td>\n",
       "      <td>0.093745</td>\n",
       "      <td>0.093646</td>\n",
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       "    <tr>\n",
       "      <th>A2</th>\n",
       "      <td>0.096706</td>\n",
       "      <td>0.096245</td>\n",
       "      <td>0.095912</td>\n",
       "      <td>0.095766</td>\n",
       "      <td>0.095713</td>\n",
       "      <td>0.095656</td>\n",
       "      <td>0.095506</td>\n",
       "      <td>0.095525</td>\n",
       "      <td>0.095387</td>\n",
       "      <td>0.095213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094875</td>\n",
       "      <td>0.094980</td>\n",
       "      <td>0.094861</td>\n",
       "      <td>0.094935</td>\n",
       "      <td>0.095068</td>\n",
       "      <td>0.094833</td>\n",
       "      <td>0.094903</td>\n",
       "      <td>0.094910</td>\n",
       "      <td>0.094971</td>\n",
       "      <td>0.094854</td>\n",
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       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>0.098366</td>\n",
       "      <td>0.097993</td>\n",
       "      <td>0.097509</td>\n",
       "      <td>0.097363</td>\n",
       "      <td>0.097303</td>\n",
       "      <td>0.097168</td>\n",
       "      <td>0.096952</td>\n",
       "      <td>0.097047</td>\n",
       "      <td>0.096810</td>\n",
       "      <td>0.096694</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096354</td>\n",
       "      <td>0.096500</td>\n",
       "      <td>0.096349</td>\n",
       "      <td>0.096456</td>\n",
       "      <td>0.096443</td>\n",
       "      <td>0.096330</td>\n",
       "      <td>0.096442</td>\n",
       "      <td>0.096425</td>\n",
       "      <td>0.096475</td>\n",
       "      <td>0.096399</td>\n",
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       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <td>0.098503</td>\n",
       "      <td>0.098177</td>\n",
       "      <td>0.097600</td>\n",
       "      <td>0.097784</td>\n",
       "      <td>0.097661</td>\n",
       "      <td>0.097324</td>\n",
       "      <td>0.097425</td>\n",
       "      <td>0.097531</td>\n",
       "      <td>0.097285</td>\n",
       "      <td>0.097152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096906</td>\n",
       "      <td>0.096977</td>\n",
       "      <td>0.096885</td>\n",
       "      <td>0.096887</td>\n",
       "      <td>0.096840</td>\n",
       "      <td>0.096757</td>\n",
       "      <td>0.096838</td>\n",
       "      <td>0.096824</td>\n",
       "      <td>0.096918</td>\n",
       "      <td>0.096790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>0.096659</td>\n",
       "      <td>0.096284</td>\n",
       "      <td>0.095846</td>\n",
       "      <td>0.095650</td>\n",
       "      <td>0.095631</td>\n",
       "      <td>0.095505</td>\n",
       "      <td>0.095305</td>\n",
       "      <td>0.095388</td>\n",
       "      <td>0.095131</td>\n",
       "      <td>0.095008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094509</td>\n",
       "      <td>0.094718</td>\n",
       "      <td>0.094633</td>\n",
       "      <td>0.094681</td>\n",
       "      <td>0.094594</td>\n",
       "      <td>0.094618</td>\n",
       "      <td>0.094672</td>\n",
       "      <td>0.094693</td>\n",
       "      <td>0.094698</td>\n",
       "      <td>0.094482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 288 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0.000000   0.250000   0.500000   0.749722   0.999722   1.249722   \\\n",
       "A1   0.095589   0.095128   0.094868   0.094764   0.094723   0.094714   \n",
       "A2   0.096706   0.096245   0.095912   0.095766   0.095713   0.095656   \n",
       "A3   0.098366   0.097993   0.097509   0.097363   0.097303   0.097168   \n",
       "A4   0.098503   0.098177   0.097600   0.097784   0.097661   0.097324   \n",
       "A5   0.096659   0.096284   0.095846   0.095650   0.095631   0.095505   \n",
       "\n",
       "    1.499722   1.749722   1.999722   2.249722   ...  69.495833  69.745833  \\\n",
       "A1   0.094529   0.094543   0.094411   0.094230  ...   0.093680   0.093899   \n",
       "A2   0.095506   0.095525   0.095387   0.095213  ...   0.094875   0.094980   \n",
       "A3   0.096952   0.097047   0.096810   0.096694  ...   0.096354   0.096500   \n",
       "A4   0.097425   0.097531   0.097285   0.097152  ...   0.096906   0.096977   \n",
       "A5   0.095305   0.095388   0.095131   0.095008  ...   0.094509   0.094718   \n",
       "\n",
       "    69.995833  70.245833  70.495833  70.745833  70.995833  71.245833  \\\n",
       "A1   0.093791   0.093803   0.093867   0.093662   0.093698   0.093661   \n",
       "A2   0.094861   0.094935   0.095068   0.094833   0.094903   0.094910   \n",
       "A3   0.096349   0.096456   0.096443   0.096330   0.096442   0.096425   \n",
       "A4   0.096885   0.096887   0.096840   0.096757   0.096838   0.096824   \n",
       "A5   0.094633   0.094681   0.094594   0.094618   0.094672   0.094693   \n",
       "\n",
       "    71.495833  71.745833  \n",
       "A1   0.093745   0.093646  \n",
       "A2   0.094971   0.094854  \n",
       "A3   0.096475   0.096399  \n",
       "A4   0.096918   0.096790  \n",
       "A5   0.094698   0.094482  \n",
       "\n",
       "[5 rows x 288 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rename_dict = {}\n",
    "\n",
    "\n",
    "for col_name in MTX_source_df.columns:\n",
    "    to_hours = np.float64(col_name.strip('s'))/(60*60)\n",
    "    rename_dict[col_name] = to_hours\n",
    "    \n",
    "MTX_source_df.rename(columns = rename_dict, inplace=True)\n",
    "\n",
    "MTX_source_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0.999722</th>\n",
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       "      <td>0.095046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>0.096466</td>\n",
       "      <td>0.096656</td>\n",
       "      <td>0.097392</td>\n",
       "      <td>0.097633</td>\n",
       "      <td>0.097392</td>\n",
       "      <td>0.097809</td>\n",
       "      <td>0.096591</td>\n",
       "      <td>0.096272</td>\n",
       "      <td>0.096727</td>\n",
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       "    <tr>\n",
       "      <th>70.995833</th>\n",
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       "      <td>0.095027</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.095848</td>\n",
       "      <td>0.096649</td>\n",
       "      <td>0.094239</td>\n",
       "      <td>0.097638</td>\n",
       "      <td>0.095034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096964</td>\n",
       "      <td>0.096518</td>\n",
       "      <td>0.096680</td>\n",
       "      <td>0.097457</td>\n",
       "      <td>0.097622</td>\n",
       "      <td>0.097259</td>\n",
       "      <td>0.097840</td>\n",
       "      <td>0.096465</td>\n",
       "      <td>0.096216</td>\n",
       "      <td>0.096707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.495833</th>\n",
       "      <td>0.093745</td>\n",
       "      <td>0.094971</td>\n",
       "      <td>0.096475</td>\n",
       "      <td>0.096918</td>\n",
       "      <td>0.094698</td>\n",
       "      <td>0.095973</td>\n",
       "      <td>0.096783</td>\n",
       "      <td>0.094361</td>\n",
       "      <td>0.097852</td>\n",
       "      <td>0.095187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096989</td>\n",
       "      <td>0.096494</td>\n",
       "      <td>0.096703</td>\n",
       "      <td>0.097399</td>\n",
       "      <td>0.097690</td>\n",
       "      <td>0.097298</td>\n",
       "      <td>0.097772</td>\n",
       "      <td>0.096431</td>\n",
       "      <td>0.096255</td>\n",
       "      <td>0.110920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.745833</th>\n",
       "      <td>0.093646</td>\n",
       "      <td>0.094854</td>\n",
       "      <td>0.096399</td>\n",
       "      <td>0.096790</td>\n",
       "      <td>0.094482</td>\n",
       "      <td>0.095726</td>\n",
       "      <td>0.096573</td>\n",
       "      <td>0.094121</td>\n",
       "      <td>0.097616</td>\n",
       "      <td>0.094946</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.096453</td>\n",
       "      <td>0.096711</td>\n",
       "      <td>0.097465</td>\n",
       "      <td>0.097726</td>\n",
       "      <td>0.097337</td>\n",
       "      <td>0.097760</td>\n",
       "      <td>0.096390</td>\n",
       "      <td>0.096199</td>\n",
       "      <td>0.109910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 384 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A1        A2        A3        A4        A5        A6  \\\n",
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       "0.250000   0.095128  0.096245  0.097993  0.098177  0.096284  0.097336   \n",
       "0.500000   0.094868  0.095912  0.097509  0.097600  0.095846  0.096934   \n",
       "0.749722   0.094764  0.095766  0.097363  0.097784  0.095650  0.096745   \n",
       "0.999722   0.094723  0.095713  0.097303  0.097661  0.095631  0.096805   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "70.745833  0.093662  0.094833  0.096330  0.096757  0.094618  0.095818   \n",
       "70.995833  0.093698  0.094903  0.096442  0.096838  0.094672  0.095802   \n",
       "71.245833  0.093661  0.094910  0.096425  0.096824  0.094693  0.095848   \n",
       "71.495833  0.093745  0.094971  0.096475  0.096918  0.094698  0.095973   \n",
       "71.745833  0.093646  0.094854  0.096399  0.096790  0.094482  0.095726   \n",
       "\n",
       "                 A7        A8        A9       A10  ...       P15       P16  \\\n",
       "0.000000   0.097852  0.095522  0.099376  0.096738  ...  0.097820  0.096801   \n",
       "0.250000   0.097765  0.095395  0.099204  0.096586  ...  0.098260  0.097286   \n",
       "0.500000   0.097460  0.095050  0.098704  0.096207  ...  0.098067  0.097185   \n",
       "0.749722   0.097277  0.094822  0.098460  0.095992  ...  0.097937  0.097016   \n",
       "0.999722   0.097297  0.094838  0.098510  0.096033  ...  0.097910  0.097027   \n",
       "...             ...       ...       ...       ...  ...       ...       ...   \n",
       "70.745833  0.096673  0.094283  0.097649  0.095046  ...  0.097049  0.096466   \n",
       "70.995833  0.096678  0.094247  0.097666  0.095027  ...  0.096922  0.096414   \n",
       "71.245833  0.096649  0.094239  0.097638  0.095034  ...  0.096964  0.096518   \n",
       "71.495833  0.096783  0.094361  0.097852  0.095187  ...  0.096989  0.096494   \n",
       "71.745833  0.096573  0.094121  0.097616  0.094946  ...  0.096939  0.096453   \n",
       "\n",
       "                P17       P18       P19       P20       P21       P22  \\\n",
       "0.000000   0.096886  0.098186  0.097967  0.098217  0.098597  0.097094   \n",
       "0.250000   0.097560  0.098834  0.098593  0.098459  0.099281  0.097815   \n",
       "0.500000   0.097567  0.098736  0.098657  0.098443  0.099274  0.097645   \n",
       "0.749722   0.097410  0.098469  0.098405  0.098423  0.098973  0.097513   \n",
       "0.999722   0.097332  0.098604  0.098404  0.098434  0.098942  0.097502   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "70.745833  0.096656  0.097392  0.097633  0.097392  0.097809  0.096591   \n",
       "70.995833  0.096599  0.097295  0.097604  0.097290  0.097723  0.096507   \n",
       "71.245833  0.096680  0.097457  0.097622  0.097259  0.097840  0.096465   \n",
       "71.495833  0.096703  0.097399  0.097690  0.097298  0.097772  0.096431   \n",
       "71.745833  0.096711  0.097465  0.097726  0.097337  0.097760  0.096390   \n",
       "\n",
       "                P23       P24  \n",
       "0.000000   0.097017  0.097519  \n",
       "0.250000   0.097606  0.097942  \n",
       "0.500000   0.097562  0.098115  \n",
       "0.749722   0.097408  0.098013  \n",
       "0.999722   0.097411  0.097875  \n",
       "...             ...       ...  \n",
       "70.745833  0.096272  0.096727  \n",
       "70.995833  0.096237  0.096592  \n",
       "71.245833  0.096216  0.096707  \n",
       "71.495833  0.096255  0.110920  \n",
       "71.745833  0.096199  0.109910  \n",
       "\n",
       "[288 rows x 384 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MTX_source_df=MTX_source_df.T\n",
    "\n",
    "MTX_source_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10',\n",
       "       ...\n",
       "       'P15', 'P16', 'P17', 'P18', 'P19', 'P20', 'P21', 'P22', 'P23', 'P24'],\n",
       "      dtype='object', length=384)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MTX_source_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mtx_growth_rates = {}\n",
    "\n",
    "for well in list(MTX_source_df.columns):\n",
    "    \n",
    "    row = well[0]\n",
    "    col = int(well[1:])\n",
    "    \n",
    "    if row not in ['A', 'P'] and col not in [1,24]:\n",
    "        \n",
    "        if col not in [18,19,20,21,22,23]:\n",
    "    \n",
    "            growth_rate = get_derivative_growth_rate((row, col), MTX_source_df)\n",
    "\n",
    "            mtx_growth_rates[well] = growth_rate[0]\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            mtx_growth_rates[well] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mtx1_growth_series = {}\n",
    "mtx2_growth_series = {}\n",
    "dmso1_growth_series = {}\n",
    "dmso2_growth_series = {}\n",
    "\n",
    "\n",
    "for mutant in mutant_indexes:\n",
    "    \n",
    "    index_96 = plate_96w_position_dict[mutant]\n",
    "    \n",
    "    if isinstance(index_96, str) == True:\n",
    "    \n",
    "        index_mtx1 = plate_384_mtx_1_position_dict[mutant]\n",
    "        index_mtx2 = plate_384_mtx_2_position_dict[mutant]\n",
    "\n",
    "        index_dmso1 = plate_384_dmso_1_position_dict[mutant]\n",
    "        index_dmso2 = plate_384_dmso_2_position_dict[mutant]\n",
    "        \n",
    "        mtx1_growth_series[mutant] = mtx_growth_rates[index_mtx1]\n",
    "        mtx2_growth_series[mutant] = mtx_growth_rates[index_mtx2]\n",
    "        dmso1_growth_series[mutant] = mtx_growth_rates[index_dmso1]\n",
    "        dmso2_growth_series[mutant] = mtx_growth_rates[index_dmso2]\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        mtx1_growth_series[mutant] = np.nan\n",
    "        mtx2_growth_series[mutant] = np.nan\n",
    "        dmso1_growth_series[mutant] = np.nan\n",
    "        dmso2_growth_series[mutant] = np.nan\n",
    "\n",
    "\n",
    "\n",
    "validations_df['mtx1'] = pd.Series(mtx1_growth_series)\n",
    "validations_df['mtx2'] = pd.Series(mtx2_growth_series)\n",
    "validations_df['dmso1'] = pd.Series(dmso1_growth_series)\n",
    "validations_df['dmso2'] = pd.Series(dmso2_growth_series)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>codon</th>\n",
       "      <th>wt</th>\n",
       "      <th>measured</th>\n",
       "      <th>CY_1</th>\n",
       "      <th>CY_1_R1</th>\n",
       "      <th>CY_1_MAD_1</th>\n",
       "      <th>CY_1_R2</th>\n",
       "      <th>CY_1_MAD_2</th>\n",
       "      <th>FC_1</th>\n",
       "      <th>FC_1_R1</th>\n",
       "      <th>...</th>\n",
       "      <th>primer_position</th>\n",
       "      <th>plate_96w_position</th>\n",
       "      <th>mtx1_position</th>\n",
       "      <th>mtx2_position</th>\n",
       "      <th>dmso1_position</th>\n",
       "      <th>dmso2_position</th>\n",
       "      <th>mtx1</th>\n",
       "      <th>mtx2</th>\n",
       "      <th>dmso1</th>\n",
       "      <th>dmso2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>8</td>\n",
       "      <td>S</td>\n",
       "      <td>F</td>\n",
       "      <td>-1.111151</td>\n",
       "      <td>-0.922339</td>\n",
       "      <td>0.415623</td>\n",
       "      <td>-1.299963</td>\n",
       "      <td>0.276321</td>\n",
       "      <td>-4.373589</td>\n",
       "      <td>-4.495269</td>\n",
       "      <td>...</td>\n",
       "      <td>A1</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>10</td>\n",
       "      <td>W</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.136679</td>\n",
       "      <td>-0.322614</td>\n",
       "      <td>0.504528</td>\n",
       "      <td>0.049255</td>\n",
       "      <td>0.105939</td>\n",
       "      <td>1.184129</td>\n",
       "      <td>1.158049</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>12</td>\n",
       "      <td>Q</td>\n",
       "      <td>K</td>\n",
       "      <td>0.769357</td>\n",
       "      <td>1.416442</td>\n",
       "      <td>1.857860</td>\n",
       "      <td>0.122271</td>\n",
       "      <td>0.298271</td>\n",
       "      <td>-1.133104</td>\n",
       "      <td>-1.532682</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>14</td>\n",
       "      <td>G</td>\n",
       "      <td>W</td>\n",
       "      <td>-3.128267</td>\n",
       "      <td>-2.879006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.377528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.131018</td>\n",
       "      <td>1.272348</td>\n",
       "      <td>...</td>\n",
       "      <td>B1</td>\n",
       "      <td>B1</td>\n",
       "      <td>D2</td>\n",
       "      <td>D3</td>\n",
       "      <td>E2</td>\n",
       "      <td>E3</td>\n",
       "      <td>0.01380</td>\n",
       "      <td>0.01360</td>\n",
       "      <td>0.21628</td>\n",
       "      <td>0.21208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>14</td>\n",
       "      <td>G</td>\n",
       "      <td>Y</td>\n",
       "      <td>-4.292014</td>\n",
       "      <td>-3.590963</td>\n",
       "      <td>1.042929</td>\n",
       "      <td>-4.993065</td>\n",
       "      <td>0.434378</td>\n",
       "      <td>-1.111130</td>\n",
       "      <td>-1.142447</td>\n",
       "      <td>...</td>\n",
       "      <td>C1</td>\n",
       "      <td>C1</td>\n",
       "      <td>F2</td>\n",
       "      <td>F3</td>\n",
       "      <td>G2</td>\n",
       "      <td>G3</td>\n",
       "      <td>0.01388</td>\n",
       "      <td>0.01552</td>\n",
       "      <td>0.20184</td>\n",
       "      <td>0.20676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>155</td>\n",
       "      <td>D</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>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3249</th>\n",
       "      <td>156</td>\n",
       "      <td>I</td>\n",
       "      <td>Q</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>E10</td>\n",
       "      <td>D11</td>\n",
       "      <td>H22</td>\n",
       "      <td>H23</td>\n",
       "      <td>I22</td>\n",
       "      <td>I23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3250</th>\n",
       "      <td>156</td>\n",
       "      <td>I</td>\n",
       "      <td>H</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>F10</td>\n",
       "      <td>E11</td>\n",
       "      <td>J22</td>\n",
       "      <td>J23</td>\n",
       "      <td>K22</td>\n",
       "      <td>K23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3274</th>\n",
       "      <td>157</td>\n",
       "      <td>G</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>G10</td>\n",
       "      <td>F11</td>\n",
       "      <td>L22</td>\n",
       "      <td>L23</td>\n",
       "      <td>M22</td>\n",
       "      <td>M23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>157</td>\n",
       "      <td>G</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>H10</td>\n",
       "      <td>G11</td>\n",
       "      <td>N22</td>\n",
       "      <td>N23</td>\n",
       "      <td>O22</td>\n",
       "      <td>O23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codon wt measured      CY_1   CY_1_R1  CY_1_MAD_1   CY_1_R2  CY_1_MAD_2  \\\n",
       "136       8  S        F -1.111151 -0.922339    0.415623 -1.299963    0.276321   \n",
       "168      10  W        * -0.136679 -0.322614    0.504528  0.049255    0.105939   \n",
       "227      12  Q        K  0.769357  1.416442    1.857860  0.122271    0.298271   \n",
       "261      14  G        W -3.128267 -2.879006    0.000000 -3.377528    0.000000   \n",
       "263      14  G        Y -4.292014 -3.590963    1.042929 -4.993065    0.434378   \n",
       "...     ... ..      ...       ...       ...         ...       ...         ...   \n",
       "3233    155  D        E       NaN       NaN         NaN       NaN         NaN   \n",
       "3249    156  I        Q       NaN       NaN         NaN       NaN         NaN   \n",
       "3250    156  I        H       NaN       NaN         NaN       NaN         NaN   \n",
       "3274    157  G        D       NaN       NaN         NaN       NaN         NaN   \n",
       "3275    157  G        E       NaN       NaN         NaN       NaN         NaN   \n",
       "\n",
       "          FC_1   FC_1_R1  ...  primer_position  plate_96w_position  \\\n",
       "136  -4.373589 -4.495269  ...               A1                 NaN   \n",
       "168   1.184129  1.158049  ...              NaN                 NaN   \n",
       "227  -1.133104 -1.532682  ...              NaN                 NaN   \n",
       "261   1.131018  1.272348  ...               B1                  B1   \n",
       "263  -1.111130 -1.142447  ...               C1                  C1   \n",
       "...        ...       ...  ...              ...                 ...   \n",
       "3233       NaN       NaN  ...              NaN                 NaN   \n",
       "3249       NaN       NaN  ...              E10                 D11   \n",
       "3250       NaN       NaN  ...              F10                 E11   \n",
       "3274       NaN       NaN  ...              G10                 F11   \n",
       "3275       NaN       NaN  ...              H10                 G11   \n",
       "\n",
       "      mtx1_position  mtx2_position  dmso1_position  dmso2_position     mtx1  \\\n",
       "136             NaN            NaN             NaN             NaN      NaN   \n",
       "168             NaN            NaN             NaN             NaN      NaN   \n",
       "227             NaN            NaN             NaN             NaN      NaN   \n",
       "261              D2             D3              E2              E3  0.01380   \n",
       "263              F2             F3              G2              G3  0.01388   \n",
       "...             ...            ...             ...             ...      ...   \n",
       "3233            NaN            NaN             NaN             NaN      NaN   \n",
       "3249            H22            H23             I22             I23      NaN   \n",
       "3250            J22            J23             K22             K23      NaN   \n",
       "3274            L22            L23             M22             M23      NaN   \n",
       "3275            N22            N23             O22             O23      NaN   \n",
       "\n",
       "         mtx2    dmso1    dmso2  \n",
       "136       NaN      NaN      NaN  \n",
       "168       NaN      NaN      NaN  \n",
       "227       NaN      NaN      NaN  \n",
       "261   0.01360  0.21628  0.21208  \n",
       "263   0.01552  0.20184  0.20676  \n",
       "...       ...      ...      ...  \n",
       "3233      NaN      NaN      NaN  \n",
       "3249      NaN      NaN      NaN  \n",
       "3250      NaN      NaN      NaN  \n",
       "3274      NaN      NaN      NaN  \n",
       "3275      NaN      NaN      NaN  \n",
       "\n",
       "[95 rows x 67 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['codon', 'wt', 'measured', 'CY_1', 'CY_1_R1', 'CY_1_MAD_1', 'CY_1_R2',\n",
       "       'CY_1_MAD_2', 'FC_1', 'FC_1_R1', 'FC_1_MAD_1', 'FC_1_R2', 'FC_1_MAD_2',\n",
       "       'CY_2', 'CY_2_R1', 'CY_2_MAD_1', 'CY_2_R2', 'CY_2_MAD_2', 'FC_2',\n",
       "       'FC_2_R1', 'FC_2_MAD_1', 'FC_2_R2', 'FC_2_MAD_2', 'FC_CY_2',\n",
       "       'FC_CY_2_R1', 'FC_CY_2_MAD_1', 'FC_CY_2_R2', 'FC_CY_2_MAD_2', 'CY_3',\n",
       "       'CY_3_R1', 'CY_3_MAD_1', 'CY_3_R2', 'CY_3_MAD_2', 'FC_3', 'FC_3_R1',\n",
       "       'FC_3_MAD_1', 'FC_3_R2', 'FC_3_MAD_2', 'CY_1_to_2', 'CY_3_to_2',\n",
       "       'FC_1_to_2', 'FC_3_to_2', 'CY_123', 'FC_123', 'CY_score', 'FC_score',\n",
       "       'FC_CY_score', 'interface', 'ddG_struc_HET', 'ddG_struc_HM',\n",
       "       'ddG_inter_HET', 'ddG_inter_HM', 'r4s_score', 'RSA', 'temp',\n",
       "       'ortholog_aa_freq', 'dist_act_site', 'primer_position',\n",
       "       'plate_96w_position', 'mtx1_position', 'mtx2_position',\n",
       "       'dmso1_position', 'dmso2_position', 'mtx1', 'mtx2', 'dmso1', 'dmso2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.14)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=validations_df, x='mtx1', y='mtx2')\n",
    "\n",
    "plt.plot(get_derivative_growth_rate(('B', 2), MTX_source_df)[0], get_derivative_growth_rate(('B', 3), MTX_source_df)[0], 'ko')\n",
    "plt.plot(get_derivative_growth_rate(('F', 16), MTX_source_df)[0], get_derivative_growth_rate(('F', 17), MTX_source_df)[0], 'ro')\n",
    "\n",
    "plt.xlim(0,0.14)\n",
    "plt.ylim(0,0.14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1, 0.25)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=validations_df, x='dmso1', y='dmso2')\n",
    "\n",
    "plt.plot(get_derivative_growth_rate(('C', 2), MTX_source_df)[0], get_derivative_growth_rate(('C', 3), MTX_source_df)[0], 'ko')\n",
    "plt.plot(get_derivative_growth_rate(('G', 16), MTX_source_df)[0], get_derivative_growth_rate(('G', 17), MTX_source_df)[0], 'ro')\n",
    "\n",
    "plt.xlim(0.10,0.25)\n",
    "plt.ylim(0.10,0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136             Elbow\n",
       "168     Nonsense-like\n",
       "227             Elbow\n",
       "261     Nonsense-like\n",
       "263             Elbow\n",
       "            ...      \n",
       "3233            other\n",
       "3249            Elbow\n",
       "3250            Elbow\n",
       "3274            other\n",
       "3275            other\n",
       "Name: mut_type, Length: 95, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f_type(x, y, thresholds=[(0.4, 0.5), (0.5,0.5), (0.1,0.1)]):\n",
    "    \n",
    "    if x >=thresholds[0][0] and y<thresholds[0][1]:\n",
    "        mut_type = 'Wt-like'\n",
    "\n",
    "    elif x <thresholds[1][0] and y>=thresholds[1][1]:\n",
    "        mut_type = 'Nonsense-like'\n",
    "\n",
    "    elif x <thresholds[2][0] and y<thresholds[2][1]:\n",
    "        mut_type = 'Elbow'\n",
    "        \n",
    "    else:\n",
    "        mut_type='other'\n",
    "    \n",
    "    return mut_type\n",
    "\n",
    "validations_df['mut_type'] = validations_df.apply(lambda x: f_type(x['CY_123'], x['FC_123']), axis=1)\n",
    "\n",
    "validations_df['mut_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors=sns.color_palette(\"hls\", 3)\n",
    "mut_colors = {'Wt-like':colors[1], 'Nonsense-like':colors[0], 'Elbow':colors[2], 'other':'lightgrey'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/phil/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.1, 0.25)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=validations_df, x='dmso1', y='dmso2', hue='mut_type',\n",
    "            palette = mut_colors)\n",
    "\n",
    "plt.xlim(0.10,0.25)\n",
    "plt.ylim(0.10,0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "validations_df['mtx_average'] = (validations_df['mtx1']+validations_df['mtx2'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136        NaN\n",
       "168        NaN\n",
       "227        NaN\n",
       "261     0.0137\n",
       "263     0.0147\n",
       "         ...  \n",
       "3233       NaN\n",
       "3249       NaN\n",
       "3250       NaN\n",
       "3274       NaN\n",
       "3275       NaN\n",
       "Name: mtx_average, Length: 95, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df['mtx_average']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Haploid validations"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Dropped primers positions during cloning, in 8 rows x 10 columns format:\n",
    "\n",
    "A1 --> replaced by wt\n",
    "E1\n",
    "A2\n",
    "E2\n",
    "F2\n",
    "D3\n",
    "H6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "plate_map_haploids = {'A1':'WT', 'A2':'B2', 'A3':'C3', 'A4':'B4', 'A5':'A5', 'A6':'H5', 'A7':'F6', 'A8':'F7', 'A9':'E8', 'A10':'C9', 'A11':'A10',\n",
    "                      'B1':'B1', 'B2':'C2', 'B3':'E3', 'B4':'C4', 'B5':'B5', 'B6':'WT', 'B7':'G6', 'B8':'G7', 'B9':'F8', 'B10':'D9', 'B11':'B10',\n",
    "                      'C1':'C1', 'C2':'D2', 'C3':'F3', 'C4':'D4', 'C5':'C5', 'C6':'A6', 'C7':'A7', 'C8':'H7', 'C9':'G8', 'C10':'E9', 'C11':'C10',\n",
    "                      'D1':'D1', 'D2':'G2', 'D3':'G3', 'D4':'E4', 'D5':'D5', 'D6':'B6', 'D7':'B7', 'D8':'A8', 'D9':'H8', 'D10':'F9', 'D11':'D10',\n",
    "                      'E1':'F1', 'E2':'H2', 'E3':'H3', 'E4':'F4', 'E5':'E5', 'E6':'C6', 'E7':'C7', 'E8':'B8', 'E9':'WT', 'E10':'G9', 'E11':'E10',\n",
    "                      'F1':'G1', 'F2':'A3', 'F3':'WT', 'F4':'G4', 'F5':'F5', 'F6':'D6', 'F7':'D7', 'F8':'C8', 'F9':'A9', 'F10':'H9', 'F11':'F10',\n",
    "                      'G1':'H1', 'G2':'B3', 'G3':'A4', 'G4':'H4', 'G5':'G5', 'G6':'E6', 'G7':'E7', 'G8':'D8', 'G9':'B9', 'G10':'H10', 'G11':'G10'}\n",
    "inv_map_haps = {v: k for k, v in plate_map_haploids.items()}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'G1'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_map_haps['H1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{136: nan,\n",
       " 261: 'B1',\n",
       " 263: 'C1',\n",
       " 345: 'D1',\n",
       " 350: nan,\n",
       " 429: 'E1',\n",
       " 439: 'F1',\n",
       " 455: 'G1',\n",
       " 514: nan,\n",
       " 575: 'A2',\n",
       " 582: 'B2',\n",
       " 681: 'C2',\n",
       " 723: nan,\n",
       " 744: nan,\n",
       " 745: 'D2',\n",
       " 770: 'E2',\n",
       " 787: 'F2',\n",
       " 788: 'G2',\n",
       " 1023: 'A3',\n",
       " 1161: nan,\n",
       " 1170: 'B3',\n",
       " 1205: 'C3',\n",
       " 1206: 'D3',\n",
       " 1208: 'E3',\n",
       " 1213: 'G3',\n",
       " 1228: 'A4',\n",
       " 1236: 'B4',\n",
       " 1249: 'C4',\n",
       " 1333: 'D4',\n",
       " 1350: 'E4',\n",
       " 1410: 'F4',\n",
       " 1451: 'G4',\n",
       " 1460: 'A5',\n",
       " 1527: 'B5',\n",
       " 1551: 'C5',\n",
       " 1560: 'D5',\n",
       " 1563: 'E5',\n",
       " 1565: 'F5',\n",
       " 1568: 'G5',\n",
       " 1570: 'A6',\n",
       " 1642: 'C6',\n",
       " 1770: 'D6',\n",
       " 1778: 'E6',\n",
       " 1830: 'F6',\n",
       " 1854: 'G6',\n",
       " 1877: 'A7',\n",
       " 1897: 'B7',\n",
       " 1910: nan,\n",
       " 1915: 'C7',\n",
       " 1921: 'D7',\n",
       " 1980: 'E7',\n",
       " 1983: 'F7',\n",
       " 1990: 'G7',\n",
       " 2004: 'A8',\n",
       " 2020: 'B8',\n",
       " 2189: 'C8',\n",
       " 2298: 'D8',\n",
       " 2434: 'E8',\n",
       " 2435: 'F8',\n",
       " 2445: 'G8',\n",
       " 2467: 'A9',\n",
       " 2534: 'B9',\n",
       " 2536: 'C9',\n",
       " 2628: 'D9',\n",
       " 2760: 'F9',\n",
       " 2900: 'G9',\n",
       " 2918: 'A10',\n",
       " 2936: 'B10',\n",
       " 2967: 'C10',\n",
       " 3092: 'D10',\n",
       " 3096: 'E10',\n",
       " 3118: 'F10',\n",
       " 3144: 'A11',\n",
       " 3147: 'B11',\n",
       " 3156: 'C11',\n",
       " 3208: 'D11',\n",
       " 3249: 'E11',\n",
       " 3250: 'F11',\n",
       " 3274: 'G11',\n",
       " 3275: 'G10'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plate_96w_position_dict_haploids = {}\n",
    "\n",
    "\n",
    "for mutant in mutant_indexes:\n",
    "    \n",
    "    mutant_no = mutant_indexes.index(mutant)\n",
    "    \n",
    "    if primer_position_dict[mutant] in plate_map_haploids.values():\n",
    "        \n",
    "        \n",
    "        plate_96w_position_dict_haploids[mutant] = inv_map_haps[primer_position_dict[mutant]]\n",
    "         \n",
    "    else:\n",
    "        plate_96w_position_dict_haploids[mutant] = np.nan\n",
    "        \n",
    "plate_96w_position_dict_haploids\n",
    "#looks good based on printed values        \n",
    "        \n",
    "        \n",
    "#for mut in mutant_indexes:\n",
    "#    print(primer_position_dict[mut], plate_96w_position_dict_haploids[mut])\n",
    "    # printed values match the plan in my lab notebook, looks good\n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "345 \t D1\n",
      "455 \t G1\n",
      "575 \t A2\n",
      "582 \t B2\n",
      "745 \t D2\n",
      "787 \t F2\n",
      "788 \t G2\n",
      "1023 \t A3\n",
      "1410 \t F4\n",
      "1527 \t B5\n",
      "1770 \t D6\n",
      "1778 \t E6\n",
      "1830 \t F6\n",
      "1854 \t G6\n",
      "1877 \t A7\n",
      "1915 \t C7\n",
      "1921 \t D7\n",
      "2298 \t D8\n",
      "2445 \t G8\n",
      "2467 \t A9\n",
      "2534 \t B9\n",
      "2536 \t C9\n",
      "2967 \t C10\n",
      "3156 \t C11\n",
      "3208 \t D11\n",
      "3249 \t E11\n",
      "3250 \t F11\n",
      "3274 \t G11\n",
      "3275 \t G10\n"
     ]
    }
   ],
   "source": [
    "c_neo_list = [345, 455, 575, 582, 745, 787, 788, 1023, 1410, 1527, 1770, 1778, 1830, 1854, 1877, 1915, 1921, 2298, 2445, 2467,\n",
    "                 2534, 2536, 2967, 3156, 3208, 3249, 3250, 3274, 3275]\n",
    "\n",
    "for x in c_neo_list:\n",
    "    print(x,'\\t' ,plate_96w_position_dict_haploids[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "FC_row_dict = {'A':'B',\n",
    "                'B':'D',\n",
    "                'C':'F',\n",
    "                'D':'H',\n",
    "                'E':'J',\n",
    "                'F':'L',\n",
    "                'G':'N'}\n",
    "CY_row_dict = {'A':'C',\n",
    "                 'B':'E',\n",
    "                 'C':'G',\n",
    "                 'D':'I',\n",
    "                 'E':'K',\n",
    "                 'F':'M',\n",
    "                 'G':'O'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type('A1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add haploid CY/FC growth curve plate position in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "plate_384_hap_FC_1_position_dict = {}\n",
    "\n",
    "for mutant in mutant_indexes:   \n",
    "    if isinstance(plate_96w_position_dict_haploids[mutant], str) == False:\n",
    "        plate_384_hap_FC_1_position_dict[mutant] = np.NaN\n",
    "        \n",
    "    else:\n",
    "        mutant_row = plate_96w_position_dict_haploids[mutant][0]\n",
    "        mutant_col = int(plate_96w_position_dict_haploids[mutant][1:])\n",
    "        \n",
    "        plate_384_hap_FC_1_position_dict[mutant] = FC_row_dict[mutant_row]+str((mutant_col*2))       \n",
    "        \n",
    "#plate_384_hap_FC_1_position_dict\n",
    "#looks good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "plate_384_hap_FC_2_position_dict = {}\n",
    "\n",
    "for mutant in mutant_indexes:   \n",
    "    if isinstance(plate_96w_position_dict_haploids[mutant], str) == False:\n",
    "        plate_384_hap_FC_2_position_dict[mutant] = np.NaN\n",
    "        \n",
    "    else:\n",
    "        mutant_row = plate_96w_position_dict_haploids[mutant][0]\n",
    "        mutant_col = int(plate_96w_position_dict_haploids[mutant][1:])\n",
    "        \n",
    "        plate_384_hap_FC_2_position_dict[mutant] = FC_row_dict[mutant_row]+str(((mutant_col*2)+1))\n",
    "#plate_384_hap_FC_2_position_dict\n",
    "#looks good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "plate_384_hap_CY_1_position_dict = {}\n",
    "\n",
    "for mutant in mutant_indexes:   \n",
    "    if isinstance(plate_96w_position_dict_haploids[mutant], str) == False:\n",
    "        plate_384_hap_CY_1_position_dict[mutant] = np.NaN\n",
    "        \n",
    "    else:\n",
    "        mutant_row = plate_96w_position_dict_haploids[mutant][0]\n",
    "        mutant_col = int(plate_96w_position_dict_haploids[mutant][1:])\n",
    "        \n",
    "        plate_384_hap_CY_1_position_dict[mutant] = CY_row_dict[mutant_row]+str(((mutant_col*2)))\n",
    "#plate_384_hap_CY_1_position_dict\n",
    "#looks good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "plate_384_hap_CY_2_position_dict = {}\n",
    "\n",
    "for mutant in mutant_indexes:   \n",
    "    if isinstance(plate_96w_position_dict_haploids[mutant], str) == False:\n",
    "        plate_384_hap_CY_2_position_dict[mutant] = np.NaN\n",
    "        \n",
    "    else:\n",
    "        mutant_row = plate_96w_position_dict_haploids[mutant][0]\n",
    "        mutant_col = int(plate_96w_position_dict_haploids[mutant][1:])\n",
    "        \n",
    "        plate_384_hap_CY_2_position_dict[mutant] = CY_row_dict[mutant_row]+str(((mutant_col*2)+1))\n",
    "#plate_384_hap_CY_2_position_dict\n",
    "#looks good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>codon</th>\n",
       "      <th>wt</th>\n",
       "      <th>measured</th>\n",
       "      <th>CY_1</th>\n",
       "      <th>CY_1_R1</th>\n",
       "      <th>CY_1_MAD_1</th>\n",
       "      <th>CY_1_R2</th>\n",
       "      <th>CY_1_MAD_2</th>\n",
       "      <th>FC_1</th>\n",
       "      <th>FC_1_R1</th>\n",
       "      <th>...</th>\n",
       "      <th>mtx2</th>\n",
       "      <th>dmso1</th>\n",
       "      <th>dmso2</th>\n",
       "      <th>mut_type</th>\n",
       "      <th>mtx_average</th>\n",
       "      <th>plate_96w_position_haploid</th>\n",
       "      <th>fc_hap_1_position</th>\n",
       "      <th>fc_hap_2_position</th>\n",
       "      <th>cy_hap_1_position</th>\n",
       "      <th>cy_hap_2_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>8</td>\n",
       "      <td>S</td>\n",
       "      <td>F</td>\n",
       "      <td>-1.111151</td>\n",
       "      <td>-0.922339</td>\n",
       "      <td>0.415623</td>\n",
       "      <td>-1.299963</td>\n",
       "      <td>0.276321</td>\n",
       "      <td>-4.373589</td>\n",
       "      <td>-4.495269</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elbow</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>10</td>\n",
       "      <td>W</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.136679</td>\n",
       "      <td>-0.322614</td>\n",
       "      <td>0.504528</td>\n",
       "      <td>0.049255</td>\n",
       "      <td>0.105939</td>\n",
       "      <td>1.184129</td>\n",
       "      <td>1.158049</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nonsense-like</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>12</td>\n",
       "      <td>Q</td>\n",
       "      <td>K</td>\n",
       "      <td>0.769357</td>\n",
       "      <td>1.416442</td>\n",
       "      <td>1.857860</td>\n",
       "      <td>0.122271</td>\n",
       "      <td>0.298271</td>\n",
       "      <td>-1.133104</td>\n",
       "      <td>-1.532682</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elbow</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>14</td>\n",
       "      <td>G</td>\n",
       "      <td>W</td>\n",
       "      <td>-3.128267</td>\n",
       "      <td>-2.879006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.377528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.131018</td>\n",
       "      <td>1.272348</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01360</td>\n",
       "      <td>0.21628</td>\n",
       "      <td>0.21208</td>\n",
       "      <td>Nonsense-like</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>B1</td>\n",
       "      <td>D2</td>\n",
       "      <td>D3</td>\n",
       "      <td>E2</td>\n",
       "      <td>E3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>14</td>\n",
       "      <td>G</td>\n",
       "      <td>Y</td>\n",
       "      <td>-4.292014</td>\n",
       "      <td>-3.590963</td>\n",
       "      <td>1.042929</td>\n",
       "      <td>-4.993065</td>\n",
       "      <td>0.434378</td>\n",
       "      <td>-1.111130</td>\n",
       "      <td>-1.142447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01552</td>\n",
       "      <td>0.20184</td>\n",
       "      <td>0.20676</td>\n",
       "      <td>Elbow</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>C1</td>\n",
       "      <td>F2</td>\n",
       "      <td>F3</td>\n",
       "      <td>G2</td>\n",
       "      <td>G3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>155</td>\n",
       "      <td>D</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3249</th>\n",
       "      <td>156</td>\n",
       "      <td>I</td>\n",
       "      <td>Q</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elbow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E11</td>\n",
       "      <td>J22</td>\n",
       "      <td>J23</td>\n",
       "      <td>K22</td>\n",
       "      <td>K23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3250</th>\n",
       "      <td>156</td>\n",
       "      <td>I</td>\n",
       "      <td>H</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elbow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F11</td>\n",
       "      <td>L22</td>\n",
       "      <td>L23</td>\n",
       "      <td>M22</td>\n",
       "      <td>M23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3274</th>\n",
       "      <td>157</td>\n",
       "      <td>G</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G11</td>\n",
       "      <td>N22</td>\n",
       "      <td>N23</td>\n",
       "      <td>O22</td>\n",
       "      <td>O23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>157</td>\n",
       "      <td>G</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G10</td>\n",
       "      <td>N20</td>\n",
       "      <td>N21</td>\n",
       "      <td>O20</td>\n",
       "      <td>O21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codon wt measured      CY_1   CY_1_R1  CY_1_MAD_1   CY_1_R2  CY_1_MAD_2  \\\n",
       "136       8  S        F -1.111151 -0.922339    0.415623 -1.299963    0.276321   \n",
       "168      10  W        * -0.136679 -0.322614    0.504528  0.049255    0.105939   \n",
       "227      12  Q        K  0.769357  1.416442    1.857860  0.122271    0.298271   \n",
       "261      14  G        W -3.128267 -2.879006    0.000000 -3.377528    0.000000   \n",
       "263      14  G        Y -4.292014 -3.590963    1.042929 -4.993065    0.434378   \n",
       "...     ... ..      ...       ...       ...         ...       ...         ...   \n",
       "3233    155  D        E       NaN       NaN         NaN       NaN         NaN   \n",
       "3249    156  I        Q       NaN       NaN         NaN       NaN         NaN   \n",
       "3250    156  I        H       NaN       NaN         NaN       NaN         NaN   \n",
       "3274    157  G        D       NaN       NaN         NaN       NaN         NaN   \n",
       "3275    157  G        E       NaN       NaN         NaN       NaN         NaN   \n",
       "\n",
       "          FC_1   FC_1_R1  ...     mtx2    dmso1    dmso2       mut_type  \\\n",
       "136  -4.373589 -4.495269  ...      NaN      NaN      NaN          Elbow   \n",
       "168   1.184129  1.158049  ...      NaN      NaN      NaN  Nonsense-like   \n",
       "227  -1.133104 -1.532682  ...      NaN      NaN      NaN          Elbow   \n",
       "261   1.131018  1.272348  ...  0.01360  0.21628  0.21208  Nonsense-like   \n",
       "263  -1.111130 -1.142447  ...  0.01552  0.20184  0.20676          Elbow   \n",
       "...        ...       ...  ...      ...      ...      ...            ...   \n",
       "3233       NaN       NaN  ...      NaN      NaN      NaN          other   \n",
       "3249       NaN       NaN  ...      NaN      NaN      NaN          Elbow   \n",
       "3250       NaN       NaN  ...      NaN      NaN      NaN          Elbow   \n",
       "3274       NaN       NaN  ...      NaN      NaN      NaN          other   \n",
       "3275       NaN       NaN  ...      NaN      NaN      NaN          other   \n",
       "\n",
       "      mtx_average  plate_96w_position_haploid  fc_hap_1_position  \\\n",
       "136           NaN                         NaN                NaN   \n",
       "168           NaN                         NaN                NaN   \n",
       "227           NaN                         NaN                NaN   \n",
       "261        0.0137                          B1                 D2   \n",
       "263        0.0147                          C1                 F2   \n",
       "...           ...                         ...                ...   \n",
       "3233          NaN                         NaN                NaN   \n",
       "3249          NaN                         E11                J22   \n",
       "3250          NaN                         F11                L22   \n",
       "3274          NaN                         G11                N22   \n",
       "3275          NaN                         G10                N20   \n",
       "\n",
       "      fc_hap_2_position  cy_hap_1_position  cy_hap_2_position  \n",
       "136                 NaN                NaN                NaN  \n",
       "168                 NaN                NaN                NaN  \n",
       "227                 NaN                NaN                NaN  \n",
       "261                  D3                 E2                 E3  \n",
       "263                  F3                 G2                 G3  \n",
       "...                 ...                ...                ...  \n",
       "3233                NaN                NaN                NaN  \n",
       "3249                J23                K22                K23  \n",
       "3250                L23                M22                M23  \n",
       "3274                N23                O22                O23  \n",
       "3275                N21                O20                O21  \n",
       "\n",
       "[95 rows x 74 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df['plate_96w_position_haploid'] = pd.Series(plate_96w_position_dict_haploids)\n",
    "validations_df['fc_hap_1_position'] = pd.Series(plate_384_hap_FC_1_position_dict)\n",
    "validations_df['fc_hap_2_position'] = pd.Series(plate_384_hap_FC_2_position_dict)\n",
    "validations_df['cy_hap_1_position'] = pd.Series(plate_384_hap_CY_1_position_dict)\n",
    "validations_df['cy_hap_2_position'] = pd.Series(plate_384_hap_CY_2_position_dict)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "validations_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get haploid growth rates fc/cy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "hap_fccy_source_table = './validationsmtx3845FCCY.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0s</th>\n",
       "      <th>900s</th>\n",
       "      <th>1800s</th>\n",
       "      <th>2699s</th>\n",
       "      <th>3599s</th>\n",
       "      <th>4499s</th>\n",
       "      <th>5399s</th>\n",
       "      <th>6299s</th>\n",
       "      <th>7199s</th>\n",
       "      <th>8099s</th>\n",
       "      <th>...</th>\n",
       "      <th>181789s</th>\n",
       "      <th>182689s</th>\n",
       "      <th>183589s</th>\n",
       "      <th>184489s</th>\n",
       "      <th>185389s</th>\n",
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       "      <th>188089s</th>\n",
       "      <th>188989s</th>\n",
       "      <th>189889s</th>\n",
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       "      <td>0.093836</td>\n",
       "      <td>0.093565</td>\n",
       "      <td>0.093299</td>\n",
       "      <td>0.093380</td>\n",
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       "      <td>0.093201</td>\n",
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       "      <td>0.093104</td>\n",
       "      <td>0.093073</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092610</td>\n",
       "      <td>0.092871</td>\n",
       "      <td>0.092788</td>\n",
       "      <td>0.092778</td>\n",
       "      <td>0.092799</td>\n",
       "      <td>0.092769</td>\n",
       "      <td>0.092839</td>\n",
       "      <td>0.092833</td>\n",
       "      <td>0.092944</td>\n",
       "      <td>0.093085</td>\n",
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       "      <th>A2</th>\n",
       "      <td>0.094874</td>\n",
       "      <td>0.094589</td>\n",
       "      <td>0.094351</td>\n",
       "      <td>0.094107</td>\n",
       "      <td>0.094149</td>\n",
       "      <td>0.094059</td>\n",
       "      <td>0.094048</td>\n",
       "      <td>0.094012</td>\n",
       "      <td>0.093942</td>\n",
       "      <td>0.093899</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093704</td>\n",
       "      <td>0.093825</td>\n",
       "      <td>0.093844</td>\n",
       "      <td>0.093719</td>\n",
       "      <td>0.093741</td>\n",
       "      <td>0.093712</td>\n",
       "      <td>0.093682</td>\n",
       "      <td>0.093690</td>\n",
       "      <td>0.093638</td>\n",
       "      <td>0.093824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>0.096128</td>\n",
       "      <td>0.096049</td>\n",
       "      <td>0.095701</td>\n",
       "      <td>0.095448</td>\n",
       "      <td>0.095497</td>\n",
       "      <td>0.095356</td>\n",
       "      <td>0.095368</td>\n",
       "      <td>0.095280</td>\n",
       "      <td>0.095212</td>\n",
       "      <td>0.095123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094900</td>\n",
       "      <td>0.095019</td>\n",
       "      <td>0.095058</td>\n",
       "      <td>0.094953</td>\n",
       "      <td>0.095014</td>\n",
       "      <td>0.094976</td>\n",
       "      <td>0.094960</td>\n",
       "      <td>0.094911</td>\n",
       "      <td>0.094914</td>\n",
       "      <td>0.095034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <td>0.096040</td>\n",
       "      <td>0.095852</td>\n",
       "      <td>0.095531</td>\n",
       "      <td>0.095280</td>\n",
       "      <td>0.095295</td>\n",
       "      <td>0.095151</td>\n",
       "      <td>0.095198</td>\n",
       "      <td>0.095064</td>\n",
       "      <td>0.095082</td>\n",
       "      <td>0.094998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094920</td>\n",
       "      <td>0.095033</td>\n",
       "      <td>0.095014</td>\n",
       "      <td>0.094954</td>\n",
       "      <td>0.095047</td>\n",
       "      <td>0.094997</td>\n",
       "      <td>0.095000</td>\n",
       "      <td>0.094885</td>\n",
       "      <td>0.094955</td>\n",
       "      <td>0.095081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>0.094631</td>\n",
       "      <td>0.094482</td>\n",
       "      <td>0.094168</td>\n",
       "      <td>0.093955</td>\n",
       "      <td>0.093977</td>\n",
       "      <td>0.093889</td>\n",
       "      <td>0.093823</td>\n",
       "      <td>0.093735</td>\n",
       "      <td>0.093694</td>\n",
       "      <td>0.093612</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093606</td>\n",
       "      <td>0.093738</td>\n",
       "      <td>0.093757</td>\n",
       "      <td>0.093677</td>\n",
       "      <td>0.093733</td>\n",
       "      <td>0.093672</td>\n",
       "      <td>0.093645</td>\n",
       "      <td>0.093550</td>\n",
       "      <td>0.093630</td>\n",
       "      <td>0.093706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 212 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0s      900s     1800s     2699s     3599s     4499s     5399s  \\\n",
       "A1  0.093999  0.093836  0.093565  0.093299  0.093380  0.093251  0.093201   \n",
       "A2  0.094874  0.094589  0.094351  0.094107  0.094149  0.094059  0.094048   \n",
       "A3  0.096128  0.096049  0.095701  0.095448  0.095497  0.095356  0.095368   \n",
       "A4  0.096040  0.095852  0.095531  0.095280  0.095295  0.095151  0.095198   \n",
       "A5  0.094631  0.094482  0.094168  0.093955  0.093977  0.093889  0.093823   \n",
       "\n",
       "       6299s     7199s     8099s  ...   181789s   182689s   183589s   184489s  \\\n",
       "A1  0.093185  0.093104  0.093073  ...  0.092610  0.092871  0.092788  0.092778   \n",
       "A2  0.094012  0.093942  0.093899  ...  0.093704  0.093825  0.093844  0.093719   \n",
       "A3  0.095280  0.095212  0.095123  ...  0.094900  0.095019  0.095058  0.094953   \n",
       "A4  0.095064  0.095082  0.094998  ...  0.094920  0.095033  0.095014  0.094954   \n",
       "A5  0.093735  0.093694  0.093612  ...  0.093606  0.093738  0.093757  0.093677   \n",
       "\n",
       "     185389s   186289s   187189s   188089s   188989s   189889s  \n",
       "A1  0.092799  0.092769  0.092839  0.092833  0.092944  0.093085  \n",
       "A2  0.093741  0.093712  0.093682  0.093690  0.093638  0.093824  \n",
       "A3  0.095014  0.094976  0.094960  0.094911  0.094914  0.095034  \n",
       "A4  0.095047  0.094997  0.095000  0.094885  0.094955  0.095081  \n",
       "A5  0.093733  0.093672  0.093645  0.093550  0.093630  0.093706  \n",
       "\n",
       "[5 rows x 212 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hap_fccy_source_df = pd.read_excel(hap_fccy_source_table, header=0, nrows=384, index_col=0)\n",
    "\n",
    "hap_fccy_source_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.093299</td>\n",
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       "      <td>0.092944</td>\n",
       "      <td>0.093085</td>\n",
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       "      <td>0.094874</td>\n",
       "      <td>0.094589</td>\n",
       "      <td>0.094351</td>\n",
       "      <td>0.094107</td>\n",
       "      <td>0.094149</td>\n",
       "      <td>0.094059</td>\n",
       "      <td>0.094048</td>\n",
       "      <td>0.094012</td>\n",
       "      <td>0.093942</td>\n",
       "      <td>0.093899</td>\n",
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       "      <td>0.093704</td>\n",
       "      <td>0.093825</td>\n",
       "      <td>0.093844</td>\n",
       "      <td>0.093719</td>\n",
       "      <td>0.093741</td>\n",
       "      <td>0.093712</td>\n",
       "      <td>0.093682</td>\n",
       "      <td>0.093690</td>\n",
       "      <td>0.093638</td>\n",
       "      <td>0.093824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>0.096128</td>\n",
       "      <td>0.096049</td>\n",
       "      <td>0.095701</td>\n",
       "      <td>0.095448</td>\n",
       "      <td>0.095497</td>\n",
       "      <td>0.095356</td>\n",
       "      <td>0.095368</td>\n",
       "      <td>0.095280</td>\n",
       "      <td>0.095212</td>\n",
       "      <td>0.095123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094900</td>\n",
       "      <td>0.095019</td>\n",
       "      <td>0.095058</td>\n",
       "      <td>0.094953</td>\n",
       "      <td>0.095014</td>\n",
       "      <td>0.094976</td>\n",
       "      <td>0.094960</td>\n",
       "      <td>0.094911</td>\n",
       "      <td>0.094914</td>\n",
       "      <td>0.095034</td>\n",
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       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <td>0.096040</td>\n",
       "      <td>0.095852</td>\n",
       "      <td>0.095531</td>\n",
       "      <td>0.095280</td>\n",
       "      <td>0.095295</td>\n",
       "      <td>0.095151</td>\n",
       "      <td>0.095198</td>\n",
       "      <td>0.095064</td>\n",
       "      <td>0.095082</td>\n",
       "      <td>0.094998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094920</td>\n",
       "      <td>0.095033</td>\n",
       "      <td>0.095014</td>\n",
       "      <td>0.094954</td>\n",
       "      <td>0.095047</td>\n",
       "      <td>0.094997</td>\n",
       "      <td>0.095000</td>\n",
       "      <td>0.094885</td>\n",
       "      <td>0.094955</td>\n",
       "      <td>0.095081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>0.094631</td>\n",
       "      <td>0.094482</td>\n",
       "      <td>0.094168</td>\n",
       "      <td>0.093955</td>\n",
       "      <td>0.093977</td>\n",
       "      <td>0.093889</td>\n",
       "      <td>0.093823</td>\n",
       "      <td>0.093735</td>\n",
       "      <td>0.093694</td>\n",
       "      <td>0.093612</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093606</td>\n",
       "      <td>0.093738</td>\n",
       "      <td>0.093757</td>\n",
       "      <td>0.093677</td>\n",
       "      <td>0.093733</td>\n",
       "      <td>0.093672</td>\n",
       "      <td>0.093645</td>\n",
       "      <td>0.093550</td>\n",
       "      <td>0.093630</td>\n",
       "      <td>0.093706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 212 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0.000000   0.250000   0.500000   0.749722   0.999722   1.249722   \\\n",
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       "A2   0.094874   0.094589   0.094351   0.094107   0.094149   0.094059   \n",
       "A3   0.096128   0.096049   0.095701   0.095448   0.095497   0.095356   \n",
       "A4   0.096040   0.095852   0.095531   0.095280   0.095295   0.095151   \n",
       "A5   0.094631   0.094482   0.094168   0.093955   0.093977   0.093889   \n",
       "\n",
       "    1.499722   1.749722   1.999722   2.249722   ...  50.496944  50.746944  \\\n",
       "A1   0.093201   0.093185   0.093104   0.093073  ...   0.092610   0.092871   \n",
       "A2   0.094048   0.094012   0.093942   0.093899  ...   0.093704   0.093825   \n",
       "A3   0.095368   0.095280   0.095212   0.095123  ...   0.094900   0.095019   \n",
       "A4   0.095198   0.095064   0.095082   0.094998  ...   0.094920   0.095033   \n",
       "A5   0.093823   0.093735   0.093694   0.093612  ...   0.093606   0.093738   \n",
       "\n",
       "    50.996944  51.246944  51.496944  51.746944  51.996944  52.246944  \\\n",
       "A1   0.092788   0.092778   0.092799   0.092769   0.092839   0.092833   \n",
       "A2   0.093844   0.093719   0.093741   0.093712   0.093682   0.093690   \n",
       "A3   0.095058   0.094953   0.095014   0.094976   0.094960   0.094911   \n",
       "A4   0.095014   0.094954   0.095047   0.094997   0.095000   0.094885   \n",
       "A5   0.093757   0.093677   0.093733   0.093672   0.093645   0.093550   \n",
       "\n",
       "    52.496944  52.746944  \n",
       "A1   0.092944   0.093085  \n",
       "A2   0.093638   0.093824  \n",
       "A3   0.094914   0.095034  \n",
       "A4   0.094955   0.095081  \n",
       "A5   0.093630   0.093706  \n",
       "\n",
       "[5 rows x 212 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rename_dict = {}\n",
    "\n",
    "\n",
    "for col_name in hap_fccy_source_df.columns:\n",
    "    to_hours = np.float64(col_name.strip('s'))/(60*60)\n",
    "    rename_dict[col_name] = to_hours\n",
    "    \n",
    "hap_fccy_source_df.rename(columns = rename_dict, inplace=True)\n",
    "\n",
    "hap_fccy_source_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.100140</td>\n",
       "      <td>0.093761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094124</td>\n",
       "      <td>0.093897</td>\n",
       "      <td>0.093698</td>\n",
       "      <td>0.094332</td>\n",
       "      <td>0.094803</td>\n",
       "      <td>0.094127</td>\n",
       "      <td>0.094495</td>\n",
       "      <td>0.093732</td>\n",
       "      <td>0.093519</td>\n",
       "      <td>0.093556</td>\n",
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       "    <tr>\n",
       "      <th>51.996944</th>\n",
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       "      <td>0.093761</td>\n",
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       "      <td>0.094911</td>\n",
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       "      <td>0.093550</td>\n",
       "      <td>0.094402</td>\n",
       "      <td>0.095596</td>\n",
       "      <td>0.093021</td>\n",
       "      <td>0.100190</td>\n",
       "      <td>0.093789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094033</td>\n",
       "      <td>0.093843</td>\n",
       "      <td>0.093592</td>\n",
       "      <td>0.094314</td>\n",
       "      <td>0.094707</td>\n",
       "      <td>0.094037</td>\n",
       "      <td>0.094383</td>\n",
       "      <td>0.093649</td>\n",
       "      <td>0.093309</td>\n",
       "      <td>0.093544</td>\n",
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       "    <tr>\n",
       "      <th>52.496944</th>\n",
       "      <td>0.092944</td>\n",
       "      <td>0.093638</td>\n",
       "      <td>0.094914</td>\n",
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       "      <td>0.093630</td>\n",
       "      <td>0.094408</td>\n",
       "      <td>0.095529</td>\n",
       "      <td>0.092952</td>\n",
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       "      <td>0.093741</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094275</td>\n",
       "      <td>0.094013</td>\n",
       "      <td>0.093753</td>\n",
       "      <td>0.094467</td>\n",
       "      <td>0.094858</td>\n",
       "      <td>0.094230</td>\n",
       "      <td>0.094597</td>\n",
       "      <td>0.093884</td>\n",
       "      <td>0.093544</td>\n",
       "      <td>0.093741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52.746944</th>\n",
       "      <td>0.093085</td>\n",
       "      <td>0.093824</td>\n",
       "      <td>0.095034</td>\n",
       "      <td>0.095081</td>\n",
       "      <td>0.093706</td>\n",
       "      <td>0.094513</td>\n",
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       "      <td>0.093068</td>\n",
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       "      <td>0.093817</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094165</td>\n",
       "      <td>0.093865</td>\n",
       "      <td>0.093622</td>\n",
       "      <td>0.094447</td>\n",
       "      <td>0.094750</td>\n",
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       "      <td>0.094455</td>\n",
       "      <td>0.093735</td>\n",
       "      <td>0.093409</td>\n",
       "      <td>0.093668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>212 rows × 384 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A1        A2        A3        A4        A5        A6  \\\n",
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       "0.250000   0.093836  0.094589  0.096049  0.095852  0.094482  0.095046   \n",
       "0.500000   0.093565  0.094351  0.095701  0.095531  0.094168  0.094749   \n",
       "0.749722   0.093299  0.094107  0.095448  0.095280  0.093955  0.094518   \n",
       "0.999722   0.093380  0.094149  0.095497  0.095295  0.093977  0.094583   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "51.746944  0.092769  0.093712  0.094976  0.094997  0.093672  0.094359   \n",
       "51.996944  0.092839  0.093682  0.094960  0.095000  0.093645  0.094379   \n",
       "52.246944  0.092833  0.093690  0.094911  0.094885  0.093550  0.094402   \n",
       "52.496944  0.092944  0.093638  0.094914  0.094955  0.093630  0.094408   \n",
       "52.746944  0.093085  0.093824  0.095034  0.095081  0.093706  0.094513   \n",
       "\n",
       "                 A7        A8        A9       A10  ...       P15       P16  \\\n",
       "0.000000   0.095889  0.093355  0.096407  0.094229  ...  0.094504  0.093788   \n",
       "0.250000   0.095907  0.093367  0.096491  0.094400  ...  0.094541  0.094049   \n",
       "0.500000   0.095720  0.093155  0.096349  0.094107  ...  0.094254  0.093754   \n",
       "0.749722   0.095568  0.092936  0.096312  0.093860  ...  0.094024  0.093584   \n",
       "0.999722   0.095565  0.092986  0.096605  0.093946  ...  0.093864  0.093494   \n",
       "...             ...       ...       ...       ...  ...       ...       ...   \n",
       "51.746944  0.095553  0.093027  0.100140  0.093761  ...  0.094124  0.093897   \n",
       "51.996944  0.095597  0.093036  0.100150  0.093761  ...  0.094174  0.093977   \n",
       "52.246944  0.095596  0.093021  0.100190  0.093789  ...  0.094033  0.093843   \n",
       "52.496944  0.095529  0.092952  0.100180  0.093741  ...  0.094275  0.094013   \n",
       "52.746944  0.095575  0.093068  0.100210  0.093817  ...  0.094165  0.093865   \n",
       "\n",
       "                P17       P18       P19       P20       P21       P22  \\\n",
       "0.000000   0.093808  0.094550  0.094756  0.093980  0.094620  0.093618   \n",
       "0.250000   0.094194  0.094994  0.095173  0.094373  0.094952  0.093955   \n",
       "0.500000   0.093926  0.094611  0.094859  0.094093  0.094698  0.093747   \n",
       "0.749722   0.093761  0.094574  0.094787  0.094090  0.094702  0.093781   \n",
       "0.999722   0.093683  0.094470  0.094745  0.093885  0.094611  0.093694   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "51.746944  0.093698  0.094332  0.094803  0.094127  0.094495  0.093732   \n",
       "51.996944  0.093723  0.094405  0.094794  0.094069  0.094488  0.093715   \n",
       "52.246944  0.093592  0.094314  0.094707  0.094037  0.094383  0.093649   \n",
       "52.496944  0.093753  0.094467  0.094858  0.094230  0.094597  0.093884   \n",
       "52.746944  0.093622  0.094447  0.094750  0.094115  0.094455  0.093735   \n",
       "\n",
       "                P23       P24  \n",
       "0.000000   0.093362  0.093628  \n",
       "0.250000   0.093812  0.094167  \n",
       "0.500000   0.093562  0.094155  \n",
       "0.749722   0.093580  0.094122  \n",
       "0.999722   0.093572  0.094065  \n",
       "...             ...       ...  \n",
       "51.746944  0.093519  0.093556  \n",
       "51.996944  0.093401  0.093411  \n",
       "52.246944  0.093309  0.093544  \n",
       "52.496944  0.093544  0.093741  \n",
       "52.746944  0.093409  0.093668  \n",
       "\n",
       "[212 rows x 384 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hap_fccy_source_df=hap_fccy_source_df.T\n",
    "\n",
    "hap_fccy_source_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10',\n",
       "       ...\n",
       "       'P15', 'P16', 'P17', 'P18', 'P19', 'P20', 'P21', 'P22', 'P23', 'P24'],\n",
       "      dtype='object', length=384)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hap_fccy_source_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fccy_growth_rates = {}\n",
    "\n",
    "for well in list(hap_fccy_source_df.columns):\n",
    "    \n",
    "    row = well[0]\n",
    "    col = int(well[1:])\n",
    "    \n",
    "    if row not in ['A', 'P'] and col not in [1,24]:\n",
    "    \n",
    "        growth_rate = get_derivative_growth_rate((row, col), hap_fccy_source_df, tmax=48)\n",
    "    \n",
    "        fccy_growth_rates[well] = growth_rate[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "hap_fc_1_growth_series = {}\n",
    "hap_fc_2_growth_series = {}\n",
    "hap_cy_1_growth_series = {}\n",
    "hap_cy_2_growth_series = {}\n",
    "\n",
    "\n",
    "for mutant in mutant_indexes:\n",
    "    \n",
    "    index_96 = plate_96w_position_dict_haploids[mutant]\n",
    "    \n",
    "    if isinstance(index_96, str) == True:\n",
    "    \n",
    "        index_hap_fc_1 = plate_384_hap_FC_1_position_dict[mutant]\n",
    "        index_hap_fc_2 = plate_384_hap_FC_2_position_dict[mutant]\n",
    "\n",
    "        index_hap_cy_1 = plate_384_hap_CY_1_position_dict[mutant]\n",
    "        index_hap_cy_2 = plate_384_hap_CY_2_position_dict[mutant]\n",
    "        \n",
    "        hap_fc_1_growth_series[mutant] = fccy_growth_rates[index_hap_fc_1]\n",
    "        hap_fc_2_growth_series[mutant] = fccy_growth_rates[index_hap_fc_2]\n",
    "        hap_cy_1_growth_series[mutant] = fccy_growth_rates[index_hap_cy_1]\n",
    "        hap_cy_2_growth_series[mutant] = fccy_growth_rates[index_hap_cy_2]\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        \n",
    "        hap_fc_1_growth_series[mutant] = np.nan\n",
    "        hap_fc_2_growth_series[mutant] = np.nan\n",
    "        hap_cy_1_growth_series[mutant] = np.nan\n",
    "        hap_cy_2_growth_series[mutant] = np.nan\n",
    "\n",
    "    \n",
    "    \n",
    "#sc_growth_series \n",
    "#looks good\n",
    "\n",
    "validations_df['hap_fc_1'] = pd.Series(hap_fc_1_growth_series)\n",
    "validations_df['hap_fc_2'] = pd.Series(hap_fc_2_growth_series)\n",
    "validations_df['hap_cy_1'] = pd.Series(hap_cy_1_growth_series)\n",
    "validations_df['hap_cy_2'] = pd.Series(hap_cy_2_growth_series)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "validations_df['hap_fc_average'] = (validations_df['hap_fc_1']+validations_df['hap_fc_2'])/2\n",
    "validations_df['hap_cy_average'] = (validations_df['hap_cy_1']+validations_df['hap_cy_2'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "validations_df['hap_fc_average'] = (validations_df['hap_fc_1']+validations_df['hap_fc_2'])/2\n",
    "validations_df['hap_cy_average'] = (validations_df['hap_cy_1']+validations_df['hap_cy_2'])/2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f30b04f3610>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAaIElEQVR4nO3dX4xc5Znn8e/PbTuh8Fgk3R2Fsd1VZOUd1vsX0kuYyWo0ChMJmyjOxV4AjYdJtGoZwoyZXRSx2zezFy2ttNEIkMCohzACu2asiKAVithlFG1WmovAuk1mmTgOUY9TbRqQaHqHQGgU2/SzF1XtLVefqjrVfaqr6vTvI5XUdc771nmq3PX06/e85zmKCMzMLL+29ToAMzPrLid6M7Occ6I3M8s5J3ozs5xzojczy7ntvQ4gycjISJRKpV6HYWY2MM6cOfNuRIwm7evLRF8qlZidne11GGZmA0PSfLN9nroxM8s5J3ozs5xzojczyzknejOznHOiNzPLuVSJXtLtkl6XNCfp4YT9N0r6kaRfS3ooYf+QpB9L+n4WQZuZ5Um5XKZUKrFt2zZKpRLlcjnT12+7vFLSEPA48GVgATgt6YWI+Glds/8L/DHwtSYvcww4B+zeULRmZjlTLpeZnJxkeXkZgPn5eSYnJwGYmJjI5BhpRvS3AHMRcT4iLgKngMP1DSLinYg4DVxq7CxpL3AH8FQG8ZqZ5crU1NSVJL9qeXmZqampzI6RJtHvAd6oe75Q25bWI8C3gJVWjSRNSpqVNLu4uNjBy5uZDa4LFy50tH090iR6JWxLdbcSSV8B3omIM+3aRsRMRIxHxPjoaOJVvGZmuTM2NtbR9vVIk+gXgH11z/cCb6V8/S8CX5VUoTrl8yVJJzuK0Mwsx6anpykUCldtKxQKTE9PZ3aMNIn+NLBf0g2SdgJ3Ai+kefGI+I8RsTciSrV+/zMi7ll3tGZmOTMxMcHMzAzFYhFJFItFZmZmMjsRCylW3UTEZUkPAC8BQ8DTEXFW0tHa/iclfRaYpbqqZkXSg8CBiHg/s0jNzHJqYmIi08TeSP14c/Dx8fFw9Uozs/QknYmI8aR9vjLWzCznnOjNzHLOid7MLOec6M3Mcs6J3sws55zozWzLKpfLjIyMIAlJjIyMZF45sh/05c3Bzcy6rVwuc++99/Lxxx9f2ba0tMQ3vvENILvKkf3AI3oz25KOHTt2VZJfdfHixUwrR/YDJ3oz25KWlpaa7suycmQ/cKI3s9zZ6B2bsqwc2Q+c6M0sV1bv2DQ/P09EXLljU2OyHx4ebvoanVSO7PZtADMREX33+PznPx9mZutRLBaD6j0zrnoUi8Wr2p08eTJ27Nixpt19992X+lgnT56MQqFwVf9CoRAnT57M+F21B8xGk5zqomZmlivbtm0jKa9JYmXl6hvdlctlpqamuHDhAmNjY0xPT3e02qZUKjE/P79me7FYpFKpdBz7RriomZltGa3u2NQ4zQJQqVQ4ceIEAEeOHGk5/dLYPynJQx+ezG021O/lw1M3ZtbMyZMno1gshqQoFotrpkmSplN27NgR11577ZppGklx2223pZp+SXpdSammiTYDLaZuep7Ukx5O9GaWJO2ceP0fg+Hh4di5c2diQm71aEzWzeb+G5O95+hT8hy9mSVZz5x4qymWVhrn9JvN/a8ef73z/FlpNUfvEghmNjCazX23mhNf73x541z/2NhY35x47ZRPxprZwGh1orXTPvUkXfW8UCisWUs/PT1NoVBo264fOdGb2cBYT7JN6tPY/+jRoxSLRSRRLBaZmZlZM/0yMTHBzMxM23Z9qdnkff0DuB14HZgDHk7YfyPwI+DXwEN12/cBPwTOAWeBY2mO55OxZtZMu1U37foMDw/H8PBwR/0HARs5GStpCPg58GVgATgN3BURP61r8xmgCHwN+IeI+HZt+/XA9RHxqqTfAM4AX6vvm8QnY83MOrPRC6ZuAeYi4nxEXAROAYfrG0TEOxFxGrjUsP3tiHi19vMHVEf2e9bxHszMgAGpLdNn0qy62QO8Ufd8AfhCpweSVAJuAl5psn8SmIT8VY4zs2ysFixbXl4GuFKwDPJ1o5CspRnRK2FbR4vvJe0Cvgc8GBHvJ7WJiJmIGI+I8dHR0U5e3sy2iKmpqStJftXy8nLubhSStTSJfoHqSdVVe4G30h5A0g6qSb4cEc93Fp51g//ra4NiYGvL9Jk0Uzengf2SbgDeBO4E7k7z4qouTv0OcC4i/mzdUVpm/F9fGxRJv6uSEq9O9XRva6lKIEg6BDwCDAFPR8S0pKMAEfGkpM8Cs8BuYAX4FXAA+BfA3wB/V9sO8J8i4sVWx/Oqm+7pp7KqZq2kLV1QKBQGZz17F7VadeNaN1tMJ7W6zXqpVW2ZVcPDwzz66KNbPsmD69FbnfVcQm7WC2l+J3ft2uUkn4IT/RYzyPU6bGtpV7oAfBI2LSf6LWag63XYllL/u9qM/yeajufozazvNa7AAZ+EbeQ5ejMbaP6f6MZ4RG9mlgMe0ZuZbWFO9GZmOedEb2aWc070ZmY550RvZpZzTvRm1pLLWg++NGWKzWyLclnrfPCI3sya8h2d8sGJ3syaalY0zMXEBosTvZk15bLW+eBEb2ZNuax1PjjRm1lTLiaWDy5qZmaWAy5qZmZteb18fqVK9JJul/S6pDlJDyfsv1HSjyT9WtJDnfQ1s95bXS8/Pz9PRFxZL+9knw9tp24kDQE/B74MLACngbsi4qd1bT4DFIGvAf8QEd9O2zeJp27MNlepVGJ+fn7N9mKxSKVS2fyArGMbnbq5BZiLiPMRcRE4BRyubxAR70TEaeBSp33NrPe8Xj7f0iT6PcAbdc8XatvSSN1X0qSkWUmzi4uLKV/ezLLg9fL5libRK2Fb2qU6qftGxExEjEfE+OjoaMqXN7MseL18vqVJ9AvAvrrne4G3Ur7+Rvqa2Sbxevl8S1O98jSwX9INwJvAncDdKV9/I33NbBNNTEw4sedU20QfEZclPQC8BAwBT0fEWUlHa/uflPRZYBbYDaxIehA4EBHvJ/Xt0nsxM7MEvjLWzCwHfGWsmdkW5kRvZpZzTvRmZjnnRG9mlnNO9GZmOedEb2aWc070ZmY550RvZpZzTvRmZjnnRG9mlnNO9GZmOedEb2aWc070ZmY550RvZpZzTvRmZjnnRG9mlnNO9GZmOedEb2aWc070ZmY550RvZpZzTvRmZjmXKtFLul3S65LmJD2csF+SHqvtf03SzXX7/kTSWUk/kfRXkj6Z5RswM7PW2iZ6SUPA48BB4ABwl6QDDc0OAvtrj0ngeK3vHuCPgfGI+GfAEHBnZtGbmVlbaUb0twBzEXE+Ii4Cp4DDDW0OA89G1cvAdZKur+3bDlwjaTtQAN7KKHYzM0shTaLfA7xR93yhtq1tm4h4E/g2cAF4G/hlRPx10kEkTUqalTS7uLiYNn4zM2sjTaJXwrZI00bSp6iO9m8AfhO4VtI9SQeJiJmIGI+I8dHR0RRhmZlZGmkS/QKwr+75XtZOvzRr8/vALyJiMSIuAc8Dv7P+cM3MrFNpEv1pYL+kGyTtpHoy9YWGNi8Af1BbfXMr1Smat6lO2dwqqSBJwG3AuQzjNzOzNra3axARlyU9ALxEddXM0xFxVtLR2v4ngReBQ8AcsAx8vbbvFUnPAa8Cl4EfAzPdeCNmZpZMEY3T7b03Pj4es7OzvQ7DzGxgSDoTEeNJ+3xlrJlZzjnRm5nlnBO9mVnOOdGb9UC5XKZUKrFt2zZKpRLlcrnXIVmOOdHblrfZSbdcLjM5Ocn8/DwRwfz8PJOTk0721jVedWNbUrlcZmpqivn5eSRR/z0oFArMzMwwMTHRlWOXSiXm5+fXbC8Wi1Qqla4c0/LPq27M6tSPqAEaBzvLy8tMTU2t6ZPVqP/ChQsdbTfbKCd623KmpqZYXl5u2aY+6WY91TI2NtbRdrONcqK3LSfNyLk+6Sb9YUga9ac1PT1NoVC4aluhUGB6enpdr2fWjhO9bTntRs6NSTfrqZaJiQlmZmYoFotIolgsdvWcgJkTvW05SSPqas09EpNuN6ZaJiYmqFQqrKysUKlUnOStq5zobctJGlGfOHGCiEhMup5qsUHn5ZVmKawux7xw4QJjY2NMT097FG59pdXySid6M7Mc8Dp6M7MtzInezCznnOjNzHLOid7MLOec6M3Mcs6J3vqWa7abZSNVopd0u6TXJc1JejhhvyQ9Vtv/mqSb6/ZdJ+k5ST+TdE7Sb2f5BiyfXLPdLDttE72kIeBx4CBwALhL0oGGZgeB/bXHJHC8bt+jwP+IiBuBfwmcyyBuy7msC4mZbWVpRvS3AHMRcT4iLgKngMMNbQ4Dz0bVy8B1kq6XtBv4XeA7ABFxMSLeyy58yyvXbDfLTppEvwd4o+75Qm1bmjafAxaBv5D0Y0lPSbo26SCSJiXNSppdXFxM/QYsn1yz3Sw7aRK9ErY11k1o1mY7cDNwPCJuAj4E1szxA0TETESMR8T46OhoirAsz1xIzCw7aRL9ArCv7vle4K2UbRaAhYh4pbb9OaqJ36wl12w3y872FG1OA/sl3QC8CdwJ3N3Q5gXgAUmngC8Av4yItwEkvSHptyLideA24KeZRW+5NjEx4cRuloG2iT4iLkt6AHgJGAKejoizko7W9j8JvAgcAuaAZeDrdS/xR0BZ0k7gfMM+MzPrMpcpNjPLAZcpNjPbwpzozcxyzoneBpLr4Jill2bVjVlfWa2Ds1oiYbUODuBVOmYJPKK3geM6OGadcaK3geM6OGadcaK3geM6OGadcaK3geM6OGadcaK3geM6OGad8ZWxZmY54Ctjzcy2MCd6M7Occ6K3lnwFqtngc6K3plavQJ2fnycirlyBWp/s/YfArP/5ZKw1VSqVmJ+fX7O9WCxSqVTWlCKA6jJHr4Ax23ytTsY60VtT27ZtI+n3QxIrKytt/xCY2ebxqhtbl3ZXoLoUgdlgcKK3ptpdgepSBGaDwYneWrrmmmuu/Dw8PHzV/LtLEZgNBid6S7R6onVpaenKto8++uiqNmlLEXhljllvpToZK+l24FFgCHgqIv5Lw37V9h8CloE/jIhX6/YPAbPAmxHxlXbH88nY3svqRKtX5phtjg2djK0l6ceBg8AB4C5JBxqaHQT21x6TwPGG/ceAcx3GbT2U1YlW3yTErPfSTN3cAsxFxPmIuAicAg43tDkMPBtVLwPXSboeQNJe4A7gqQzjti7L6kSrV+aY9V6aRL8HeKPu+UJtW9o2jwDfAlZaHUTSpKRZSbOLi4spwrJuWJ1Pn5+fpzoj9/+t50SrV+aY9V6aRK+EbY0T+4ltJH0FeCcizrQ7SETMRMR4RIyPjo6mCMuyVl/yACAiriT79dZ898ocs97bnqLNArCv7vle4K2Ubf4t8FVJh4BPArslnYyIe9YfsnVL0nx6RGzoStfVPwxTU1NcuHCBsbExpqenfSLWbBO1XXUjaTvwc+A24E3gNHB3RJyta3MH8ADVVTdfAB6LiFsaXuf3gIe86qZ/tSt5YGb9a0OrbiLiMtUk/hLVlTPfjYizko5KOlpr9iJwHpgD/hy4P5PIbVN5Pt0sn1JdMBURL0bEP46IfxQR07VtT0bEk7WfIyK+Wdv/zyNizXA8Iv5XmtG89U6r+XRf9GQ2uHxlrF3R7EpXoG1dejPrXy5TbG25HLFZ/3OZYtsQX/RkNtic6AdML+bKfZLWbLA50Q+QNPdw7QZf9GQ22JzoB0ivCoSlLUdsZv3JJ2MHiC9oMrNmfDI2JzxXbmbr4UQ/QDxXbmbr4UQ/QDxXbmbr4Tl6M7Mc8By9mdkW5kRvZpZzTvRmZjnnRJ8jLiVsZkmc6LtoMxNvr8ojmFn/c6LvknaJN+s/Ar0qj2Bm/c+JvktaJd5ORt9p/yC4lLCZNeN19F3Sqi7N2NhYqht5rP5BqP+DUSgUEi+S8s1BzLY2r6PvgU9/+tOJ28fGxlKPvjuZjnF5BDNrxom+C8rlMh988MGa7Tt27GB6ejp1cbJOpmNcHsHMmkmV6CXdLul1SXOSHk7YL0mP1fa/Junm2vZ9kn4o6Zyks5KOZf0G+tHU1BQXL15cs3337t1MTEykHn13Wq1yYmKCSqXCysoKlUrFSd7MgBSJXtIQ8DhwEDgA3CXpQEOzg8D+2mMSOF7bfhn4DxHxT4BbgW8m9M2dZiPxpaUlSqUSR44c4ZprrmF4eLjl6NvTMWaWhTQj+luAuYg4HxEXgVPA4YY2h4Fno+pl4DpJ10fE2xHxKkBEfACcA/ZkGH9fajbilnRlpc3S0hIfffQRJ06caDr69nSMmWUhTaLfA7xR93yBtcm6bRtJJeAm4JWkg0ialDQraXZxcTFFWP0raSQOrFmFs7y8zLFjx1oun/R0jJltVJpEr4RtjesGW7aRtAv4HvBgRLyfdJCImImI8YgYHx0dTRHW5qlfyz4yMsLIyEjLde2NI/Hh4eGmr720tOSrWc2sq9Ik+gVgX93zvcBbadtI2kE1yZcj4vn1h7r5yuUyIyMj3HPPPVdNuSwtLV1JzEeOHOH+++9f07d+JL5r167Ux/TVrGaWtTSJ/jSwX9INknYCdwIvNLR5AfiD2uqbW4FfRsTbkgR8BzgXEX+WaeRdtnqx0tLSUst2EcHx48cZGRnp+KrVZnw1q5llqW2ij4jLwAPAS1RPpn43Is5KOirpaK3Zi8B5YA74c2B1iPtF4AjwJUl/W3scyvpNdEPSxUqtLC0tNR3dNzs5u21b8sfvm32bWZZcAqGJZiUM2pHEiRMnrjpp2qyUwb333sszzzyTqsSBmVkrLoHQRlLhsPWOqiNizRx7s2WSTzzxhJdPmlnXbfkRfdJoe8eOHXziE5/gV7/61bpeUxIrKytZhWhm1larEf32zQ6m3yTNxV+6dIlLly6t+zU9x25m/WTLT91kvcJl9epX38rPzPrFlk/0SVewpjU0NMRtt91GsVgEqkl+dSrMFz+ZWb/Ykol+9eSrJD788MOO+0vi5MmTXL58mR/84AdUKhWKxWJiiQNf/GRmvbblTsYmnXxdj8bPrdUdpXxi1sy6zcsr63R6IVSS1amaep3Wjjcz2yxbKtGXy+XE+6p2olk9eNeON7N+ldtE33gR1P3338/k5OSGXrPVBU2uHW9m/SqXc/TrnYcfHh7mvffe4+OPP75q+86dO3n66aedtM2sb22pOfpyucy9997bUZIfHh4mInj33Xd55plnrqofPzw87CRvZgMtVyP69Y7kvTLGzAbdliiBsDqSb5x2ScMrY8wsz3IxdbM6kl9Pkge8MsbMci0XiX4ja+OHh4c9/25muZaLRL/ewmSFQoFHH30042jMzPpLLhL9eubYvc7dzLaKXCT6pKtSq/clh127dl35eWhoiPvuu4+IoFKpOMmb2ZaQi0SfdFXqiRMniAg++OADVlZWiAguX77ME0880etwzcw2VapEL+l2Sa9LmpP0cMJ+SXqstv81STen7ZuViYkJKpUKKysrHq2bmdVpm+glDQGPAweBA8Bdkg40NDsI7K89JoHjHfQ1M7MuSjOivwWYi4jzEXEROAUcbmhzGHg2ql4GrpN0fcq+ZmbWRWkS/R7gjbrnC7Vtadqk6QuApElJs5JmFxcXU4RlZmZppEn0StjWWCCnWZs0fasbI2YiYjwixkdHR1OEZWZmaaSpdbMA7Kt7vhd4K2WbnSn6mplZF6VJ9KeB/ZJuAN4E7gTubmjzAvCApFPAF4BfRsTbkhZT9F3jzJkz70pqvBXUCPBuinj7hePtLsfbXY63u7oR79p7nNa0TfQRcVnSA8BLwBDwdESclXS0tv9J4EXgEDAHLANfb9U3xTHXzN1Imm1WgrMfOd7ucrzd5Xi7a7PjTVWmOCJepJrM67c9WfdzAN9M29fMzDZPLq6MNTOz5gYp0c/0OoAOOd7ucrzd5Xi7a1Pj7ctbCZqZWXYGaURvZmbr4ERvZpZzPU/0g1AZM4t4Je2T9ENJ5ySdlXSsn+Ot2z8k6ceSvt/v8Uq6TtJzkn5W+5x/u8/j/ZPa78JPJP2VpE/2Qbw3SvqRpF9LeqiTvv0Wcx9/55p+xrX92X/nIqJnD6pr6/8e+BzVq2j/D3Cgoc0h4L9TLadwK/BK2r59Fu/1wM21n38D+Hk/x1u3/98Dfwl8v59/H2r7ngH+Xe3nncB1/Rov1ZpPvwCuqT3/LvCHfRDvZ4B/DUwDD3XStw9j7tfvXGK8dfsz/871ekQ/aJUx1x1vRLwdEa8CRMQHwDmaFHjrh3gBJO0F7gCe6nKcG45X0m7gd4HvAETExYh4r1/jre3bDlwjaTtQoPvlQdrGGxHvRMRp4FKnffst5n79zrX4jLv2net1ot+UypgZ2ki8V0gqATcBr2QfYmextGnzCPAtYKVL8TXaSLyfAxaBv6j9t/cpSdd2M9gWsbRtExFvAt8GLgBvUy0b8tddjLVpLJvQdyMyOW6ffedaeYQufOd6neg3pTJmhjYSb3WntAv4HvBgRLyfYWxJ1h2vpK8A70TEmezDamojn+924GbgeETcBHwIdHseeSOf76eojvRuAH4TuFbSPRnH12gj35lefN8yOW4ffueSO3bxO9frRL+Ryphp+mZtI/EiaQfVX7hyRDzfxTjbxpKizReBr0qqUP3v55cknexeqC1jSdNmAViIiNUR23NUE383bSTe3wd+ERGLEXEJeB74nS7G2iqWbvfdiA0dt0+/c8107zvXzRMTKU5cbAfOUx3VrJ64+KcNbe7g6pNZ/ztt3z6LV8CzwCOD8Pk2tPk9Nudk7IbiBf4G+K3az38K/Nd+jZdqldezVOfmRfVE8h/1Ot66tn/K1Sc2N/37lkHMffmdaxZvw75Mv3Ob8ubbfDCHqJ4N/3tgqrbtKHC07h/r8dr+vwPGW/Xt13iBf0P1v3CvAX9bexzq13i7+UvXxd+HfwXM1j7j/wZ8qs/j/c/Az4CfACeAT/RBvJ+lOip9H3iv9vPuZn375HciMeY+/s41/YzrXiPT75xLIJiZ5Vyv5+jNzKzLnOjNzHLOid7MLOec6M3Mcs6J3sws55zozcxyzonezCzn/h/3mupBK/ybvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(validations_df['hap_cy_1'], validations_df['hap_cy_2'], color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1770   -0.0776\n",
       "3118   -0.0060\n",
       "1451   -0.0048\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(validations_df['hap_cy_1']-validations_df['hap_cy_2']).nsmallest(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I13'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df.loc[1770]['cy_hap_2_position']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#validations_df.loc[1770]\n",
    "# is outlier, caused by problem with plate. will exclude from analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f30b05c8390>"
      ]
     },
     "execution_count": 56,
     "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": [
    "plt.scatter(validations_df['hap_fc_1'], validations_df['hap_fc_2'], color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers_list = [1770]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df = validations_df.loc[[x for x in list(validations_df.index) if x not in outliers_list]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f30b04de1d0>"
      ]
     },
     "execution_count": 59,
     "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": [
    "plt.scatter(plot_df['hap_cy_1'], plot_df['hap_cy_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "validations_df['dmso_average'] = (validations_df['dmso1'] + validations_df['dmso2'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136        NaN\n",
       "168        NaN\n",
       "227        NaN\n",
       "261     0.0137\n",
       "263     0.0147\n",
       "         ...  \n",
       "3233       NaN\n",
       "3249       NaN\n",
       "3250       NaN\n",
       "3274       NaN\n",
       "3275       NaN\n",
       "Name: mtx_average, Length: 95, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df['mtx_average']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "validations_df.to_csv('validations_table_v3.csv', columns=['codon', 'wt', 'measured', 'mtx1', 'mtx2', 'dmso1', 'dmso2', 'mtx_average', 'dmso_average', 'hap_fc_1', 'hap_fc_2', 'hap_cy_1', 'hap_cy_2', 'hap_fc_average', 'hap_cy_average'], sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_batch_outliers = [168, 227, 416, 418, 567, 574, 578, 583, 645, 766, 773, 1391, 1678, 1876, 3233]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A5': 168,\n",
       " 'A6': 645,\n",
       " 'A7': 455,\n",
       " 'A8': 3275,\n",
       " 'B5': 227,\n",
       " 'B6': 766,\n",
       " 'B7': 575,\n",
       " 'B8': 1410,\n",
       " 'C5': 416,\n",
       " 'C6': 773,\n",
       " 'C7': 582,\n",
       " 'C8': 1830,\n",
       " 'D5': 418,\n",
       " 'D6': 1391,\n",
       " 'D7': 745,\n",
       " 'D8': 1854,\n",
       " 'E5': 567,\n",
       " 'E6': 1678,\n",
       " 'E7': 770,\n",
       " 'E8': 1915,\n",
       " 'F5': 574,\n",
       " 'F6': 1876,\n",
       " 'F7': 1023,\n",
       " 'F8': 2445,\n",
       " 'G5': 578,\n",
       " 'G6': 3233,\n",
       " 'G7': 1778,\n",
       " 'G8': 2967,\n",
       " 'H5': 583,\n",
       " 'H6': 439,\n",
       " 'H7': 3118,\n",
       " 'H8': 3208}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_plate_map_R1 = {'A1':168, 'A2':645,  'A3':455,  'A4':3275,\n",
    "                         'B1':227, 'B2':766,  'B3':575,  'B4':1410,\n",
    "                         'C1':416, 'C2':773,  'C3':582,  'C4':1830,\n",
    "                         'D1':418, 'D2':1391, 'D3':745,  'D4':1854,\n",
    "                         'E1':567, 'E2':1678, 'E3':770,  'E4':1915,\n",
    "                         'F1':574, 'F2':1876, 'F3':1023, 'F4':2445,\n",
    "                         'G1':578, 'G2':3233, 'G3':1778, 'G4':2967,\n",
    "                         'H1':583, 'H2':439,  'H3':3118, 'H4':3208 }\n",
    "\n",
    "\n",
    "outliers_plate_map_R2 = {}\n",
    "outliers_plate_map_R3 = {}\n",
    "\n",
    "\n",
    "for key in list(outliers_plate_map_R1.keys()):\n",
    "    \n",
    "    row = key[0]\n",
    "    col = int(key[1:])\n",
    "    \n",
    "    variant = outliers_plate_map_R1[key]\n",
    "    \n",
    "    R2_col = col+4\n",
    "    outliers_plate_map_R2[(row+str(R2_col))] = variant\n",
    "    \n",
    "    R3_col = col+8\n",
    "    outliers_plate_map_R3[(row+str(R3_col))] = variant\n",
    "\n",
    "outliers_plate_map_R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "FC_outlierv2_table = './outliersv3_FC.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.093800</td>\n",
       "      <td>0.094175</td>\n",
       "      <td>0.094675</td>\n",
       "      <td>0.095275</td>\n",
       "      <td>0.09600</td>\n",
       "      <td>0.096850</td>\n",
       "      <td>0.097800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.93900</td>\n",
       "      <td>0.93885</td>\n",
       "      <td>0.93870</td>\n",
       "      <td>0.93847</td>\n",
       "      <td>0.93877</td>\n",
       "      <td>0.93870</td>\n",
       "      <td>0.93837</td>\n",
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       "      <td>0.98778</td>\n",
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       "          0s      900s     1800s     2700s     3600s     4501s     5401s  \\\n",
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       "\n",
       "      6301s     7201s     8101s  ...  172822s  173722s  174622s  175522s  \\\n",
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       "A5  0.09605  0.097575  0.099450  ...  0.98072  0.98040  0.98072  0.97995   \n",
       "\n",
       "    176422s  177323s  178223s  179123s  180023s  180923s  \n",
       "A1  0.94865  0.94865  0.94862  0.94842  0.94952  0.94925  \n",
       "A2  0.97507  0.97488  0.97457  0.97515  0.97580  0.97565  \n",
       "A3  0.93877  0.93870  0.93837  0.93845  0.93872  0.93845  \n",
       "A4  0.98840  0.98860  0.98805  0.98875  0.98898  0.98880  \n",
       "A5  0.98072  0.98102  0.98000  0.98072  0.98090  0.98057  \n",
       "\n",
       "[5 rows x 202 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FC_outlierv2_df = pd.read_excel(FC_outlierv2_table, header=0, nrows=96, index_col=0)\n",
    "\n",
    "FC_outlierv2_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.93877</td>\n",
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       "      <td>0.98880</td>\n",
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       "      <td>0.98057</td>\n",
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       "<p>5 rows × 202 columns</p>\n",
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       "    0.000000   0.250000   0.500000   0.750000   1.000000   1.250278   \\\n",
       "A1   0.092275   0.092475   0.093025   0.093325   0.093800   0.094100   \n",
       "A2   0.092325   0.094425   0.094700   0.094600   0.094600   0.094975   \n",
       "A3   0.092775   0.093525   0.093675   0.093800   0.094175   0.094675   \n",
       "A4   0.093150   0.094300   0.095050   0.095875   0.096375   0.097200   \n",
       "A5   0.090700   0.093100   0.093225   0.093025   0.093150   0.093800   \n",
       "\n",
       "    1.500278   1.750278   2.000278   2.250278   ...  48.006111  48.256111  \\\n",
       "A1   0.094725    0.09580   0.097325   0.098900  ...    0.94917    0.94875   \n",
       "A2   0.095700    0.09675   0.098125   0.099825  ...    0.97490    0.97483   \n",
       "A3   0.095275    0.09600   0.096850   0.097800  ...    0.93900    0.93885   \n",
       "A4   0.098425    0.10002   0.102200   0.104500  ...    0.98752    0.98778   \n",
       "A5   0.094750    0.09605   0.097575   0.099450  ...    0.98072    0.98040   \n",
       "\n",
       "    48.506111  48.756111  49.006111  49.256389  49.506389  49.756389  \\\n",
       "A1    0.94883    0.94830    0.94865    0.94865    0.94862    0.94842   \n",
       "A2    0.97473    0.97460    0.97507    0.97488    0.97457    0.97515   \n",
       "A3    0.93870    0.93847    0.93877    0.93870    0.93837    0.93845   \n",
       "A4    0.98788    0.98740    0.98840    0.98860    0.98805    0.98875   \n",
       "A5    0.98072    0.97995    0.98072    0.98102    0.98000    0.98072   \n",
       "\n",
       "    50.006389  50.256389  \n",
       "A1    0.94952    0.94925  \n",
       "A2    0.97580    0.97565  \n",
       "A3    0.93872    0.93845  \n",
       "A4    0.98898    0.98880  \n",
       "A5    0.98090    0.98057  \n",
       "\n",
       "[5 rows x 202 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rename_dict = {}\n",
    "\n",
    "\n",
    "for col_name in FC_outlierv2_df.columns:\n",
    "    to_hours = np.float64(col_name.strip('s'))/(60*60)\n",
    "    rename_dict[col_name] = to_hours\n",
    "    \n",
    "FC_outlierv2_df.rename(columns = rename_dict, inplace=True)\n",
    "\n",
    "FC_outlierv2_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A1        A2        A3        A4        A5        A6  \\\n",
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       "...             ...       ...       ...       ...       ...       ...   \n",
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       "\n",
       "                A7        A8        A9       A10  ...        H3        H4  \\\n",
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       "0.500000   0.09280  0.092525  0.092375  0.093450  ...  0.092475  0.100200   \n",
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       "...            ...       ...       ...       ...  ...       ...       ...   \n",
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       "49.756389  0.92930  0.991950  0.982800  1.002400  ...  0.957070  0.966280   \n",
       "50.006389  0.92965  0.992520  0.982900  1.002700  ...  0.957570  0.966550   \n",
       "50.256389  0.92905  0.992100  0.982470  1.002200  ...  0.956800  0.965720   \n",
       "\n",
       "                 H5       H6        H7        H8        H9       H10  \\\n",
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       "0.250000   0.098775  0.09910  0.089825  0.098875  0.097125  0.098675   \n",
       "0.500000   0.098875  0.09850  0.090000  0.099750  0.097000  0.101150   \n",
       "0.750000   0.098775  0.09830  0.089850  0.101200  0.098050  0.101370   \n",
       "1.000000   0.099475  0.09885  0.090200  0.102180  0.099075  0.102250   \n",
       "...             ...      ...       ...       ...       ...       ...   \n",
       "49.256389  0.964220  0.99720  0.942120  0.976330  0.963570  1.008000   \n",
       "49.506389  0.964050  0.99715  0.942420  0.976300  0.963250  1.008000   \n",
       "49.756389  0.964670  0.99738  0.942300  0.976370  0.963420  1.007800   \n",
       "50.006389  0.964780  0.99815  0.942750  0.976630  0.963650  1.008500   \n",
       "50.256389  0.964100  0.99737  0.942050  0.975850  0.962830  1.008000   \n",
       "\n",
       "                H11       H12  \n",
       "0.000000   0.085975  0.095075  \n",
       "0.250000   0.087475  0.097100  \n",
       "0.500000   0.087450  0.097375  \n",
       "0.750000   0.087250  0.097800  \n",
       "1.000000   0.087275  0.098500  \n",
       "...             ...       ...  \n",
       "49.256389  0.947950  0.969800  \n",
       "49.506389  0.947930  0.969320  \n",
       "49.756389  0.947350  0.969130  \n",
       "50.006389  0.947730  0.969450  \n",
       "50.256389  0.948130  0.969450  \n",
       "\n",
       "[202 rows x 96 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FC_outlierv2_df=FC_outlierv2_df.T\n",
    "FC_outlierv2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "FC_R1_dict = {}\n",
    "FC_R2_dict = {}\n",
    "FC_R3_dict = {}\n",
    "\n",
    "\n",
    "for well in list(FC_outlierv2_df.columns):\n",
    "    \n",
    "    row = well[0]\n",
    "    col = int(well[1:])\n",
    "\n",
    "    growth_rate = get_derivative_growth_rate((row, col), FC_outlierv2_df, tmax=48)\n",
    "    \n",
    "    if col in [1,2,3,4]:\n",
    "        mutant = outliers_plate_map_R1[well]\n",
    "        FC_R1_dict[mutant] = growth_rate[0]\n",
    "        \n",
    "    elif col in [5,6,7,8]:\n",
    "        mutant = outliers_plate_map_R2[well]\n",
    "        FC_R2_dict[mutant] = growth_rate[0]\n",
    "        \n",
    "    else:\n",
    "        mutant = outliers_plate_map_R3[well]\n",
    "        FC_R3_dict[mutant] = growth_rate[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{168: 0.11216000000000026,\n",
       " 645: 0.11163999999999996,\n",
       " 455: 0.04844000000000004,\n",
       " 3275: 0.11047999999999991,\n",
       " 227: 0.04899999999999993,\n",
       " 766: 0.11339999999999995,\n",
       " 575: 0.11439999999999984,\n",
       " 1410: 0.09972000000000003,\n",
       " 416: 0.11431999999999998,\n",
       " 773: 0.11451999999999996,\n",
       " 582: 0.05591999999999997,\n",
       " 1830: 0.0505199999999999,\n",
       " 418: 0.11007999999999996,\n",
       " 1391: 0.049880000000000146,\n",
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       " 1854: 0.09379999999999988,\n",
       " 567: 0.11399999999999988,\n",
       " 1678: 0.04811999999999994,\n",
       " 770: 0.11407999999999996,\n",
       " 1915: 0.05339999999999989,\n",
       " 574: 0.06303999999999998,\n",
       " 1876: 0.04919999999999991,\n",
       " 1023: 0.11396000000000006,\n",
       " 2445: 0.11448000000000014,\n",
       " 578: 0.07431999999999994,\n",
       " 3233: 0.10036,\n",
       " 1778: 0.10939999999999994,\n",
       " 2967: 0.04952000000000001,\n",
       " 583: 0.053279999999999994,\n",
       " 439: 0.11279999999999979,\n",
       " 3118: 0.04812000000000016,\n",
       " 3208: 0.05903999999999998}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FC_R2_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "CY_outlierv2_table = './outliersv3_CY.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
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       "      <td>0.90553</td>\n",
       "      <td>0.90600</td>\n",
       "      <td>0.90630</td>\n",
       "      <td>0.90635</td>\n",
       "      <td>0.90687</td>\n",
       "      <td>0.90695</td>\n",
       "      <td>0.90705</td>\n",
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       "      <td>0.094225</td>\n",
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       "      <td>0.13900</td>\n",
       "      <td>0.13930</td>\n",
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       "\n",
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       "A2  0.13847  0.13885  0.13870  0.13873  0.13900  0.13910  \n",
       "A3  0.90635  0.90687  0.90695  0.90705  0.90748  0.90785  \n",
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       "\n",
       "[5 rows x 203 columns]"
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     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CY_outlierv2_df = pd.read_excel(CY_outlierv2_table, header=0, nrows=96, index_col=0)\n",
    "\n",
    "CY_outlierv2_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
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       "A5   0.093725   0.094125   0.094575   0.094975  ...    0.13085    0.13100   \n",
       "\n",
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       "\n",
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       "A4    0.13982    0.13993  \n",
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       "\n",
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     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rename_dict = {}\n",
    "\n",
    "\n",
    "for col_name in CY_outlierv2_df.columns:\n",
    "    to_hours = np.float64(col_name.strip('s'))/(60*60)\n",
    "    rename_dict[col_name] = to_hours\n",
    "    \n",
    "CY_outlierv2_df.rename(columns = rename_dict, inplace=True)\n",
    "\n",
    "CY_outlierv2_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
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       "      <td>0.093950</td>\n",
       "      <td>0.092425</td>\n",
       "      <td>0.093675</td>\n",
       "      <td>0.094125</td>\n",
       "      <td>0.095450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087800</td>\n",
       "      <td>0.095400</td>\n",
       "      <td>0.094050</td>\n",
       "      <td>0.095950</td>\n",
       "      <td>0.086550</td>\n",
       "      <td>0.095125</td>\n",
       "      <td>0.094500</td>\n",
       "      <td>0.096475</td>\n",
       "      <td>0.086025</td>\n",
       "      <td>0.09520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.250000</th>\n",
       "      <td>0.091625</td>\n",
       "      <td>0.094875</td>\n",
       "      <td>0.093550</td>\n",
       "      <td>0.093175</td>\n",
       "      <td>0.093325</td>\n",
       "      <td>0.095525</td>\n",
       "      <td>0.093325</td>\n",
       "      <td>0.094875</td>\n",
       "      <td>0.095050</td>\n",
       "      <td>0.096500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>0.096675</td>\n",
       "      <td>0.095200</td>\n",
       "      <td>0.097325</td>\n",
       "      <td>0.087775</td>\n",
       "      <td>0.096950</td>\n",
       "      <td>0.095550</td>\n",
       "      <td>0.097300</td>\n",
       "      <td>0.087100</td>\n",
       "      <td>0.09690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500000</th>\n",
       "      <td>0.091975</td>\n",
       "      <td>0.095350</td>\n",
       "      <td>0.093625</td>\n",
       "      <td>0.093425</td>\n",
       "      <td>0.093300</td>\n",
       "      <td>0.095025</td>\n",
       "      <td>0.093350</td>\n",
       "      <td>0.095000</td>\n",
       "      <td>0.094925</td>\n",
       "      <td>0.096050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088875</td>\n",
       "      <td>0.096700</td>\n",
       "      <td>0.094500</td>\n",
       "      <td>0.096550</td>\n",
       "      <td>0.087525</td>\n",
       "      <td>0.096975</td>\n",
       "      <td>0.094825</td>\n",
       "      <td>0.096825</td>\n",
       "      <td>0.087100</td>\n",
       "      <td>0.09720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.750000</th>\n",
       "      <td>0.091850</td>\n",
       "      <td>0.095450</td>\n",
       "      <td>0.093475</td>\n",
       "      <td>0.093925</td>\n",
       "      <td>0.092900</td>\n",
       "      <td>0.095025</td>\n",
       "      <td>0.092850</td>\n",
       "      <td>0.095400</td>\n",
       "      <td>0.094700</td>\n",
       "      <td>0.095725</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088850</td>\n",
       "      <td>0.096675</td>\n",
       "      <td>0.093925</td>\n",
       "      <td>0.096250</td>\n",
       "      <td>0.087475</td>\n",
       "      <td>0.097375</td>\n",
       "      <td>0.094500</td>\n",
       "      <td>0.096725</td>\n",
       "      <td>0.087025</td>\n",
       "      <td>0.09785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000</th>\n",
       "      <td>0.091950</td>\n",
       "      <td>0.095800</td>\n",
       "      <td>0.093950</td>\n",
       "      <td>0.094225</td>\n",
       "      <td>0.093025</td>\n",
       "      <td>0.095475</td>\n",
       "      <td>0.093125</td>\n",
       "      <td>0.095925</td>\n",
       "      <td>0.094825</td>\n",
       "      <td>0.095925</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088900</td>\n",
       "      <td>0.097350</td>\n",
       "      <td>0.094100</td>\n",
       "      <td>0.096425</td>\n",
       "      <td>0.087700</td>\n",
       "      <td>0.098100</td>\n",
       "      <td>0.094600</td>\n",
       "      <td>0.096950</td>\n",
       "      <td>0.087125</td>\n",
       "      <td>0.09850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.506389</th>\n",
       "      <td>0.130950</td>\n",
       "      <td>0.138850</td>\n",
       "      <td>0.906870</td>\n",
       "      <td>0.139450</td>\n",
       "      <td>0.131220</td>\n",
       "      <td>0.135980</td>\n",
       "      <td>0.901650</td>\n",
       "      <td>0.142930</td>\n",
       "      <td>0.136000</td>\n",
       "      <td>0.138220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.882150</td>\n",
       "      <td>0.265800</td>\n",
       "      <td>0.878850</td>\n",
       "      <td>0.141800</td>\n",
       "      <td>0.877500</td>\n",
       "      <td>0.265650</td>\n",
       "      <td>0.875670</td>\n",
       "      <td>0.146550</td>\n",
       "      <td>0.883700</td>\n",
       "      <td>0.27075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.756389</th>\n",
       "      <td>0.131270</td>\n",
       "      <td>0.138700</td>\n",
       "      <td>0.906950</td>\n",
       "      <td>0.139530</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.135900</td>\n",
       "      <td>0.901800</td>\n",
       "      <td>0.142880</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>0.138080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.882770</td>\n",
       "      <td>0.266820</td>\n",
       "      <td>0.879900</td>\n",
       "      <td>0.141950</td>\n",
       "      <td>0.878000</td>\n",
       "      <td>0.266170</td>\n",
       "      <td>0.876100</td>\n",
       "      <td>0.147150</td>\n",
       "      <td>0.884380</td>\n",
       "      <td>0.27150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.006667</th>\n",
       "      <td>0.131330</td>\n",
       "      <td>0.138730</td>\n",
       "      <td>0.907050</td>\n",
       "      <td>0.139420</td>\n",
       "      <td>0.131380</td>\n",
       "      <td>0.136050</td>\n",
       "      <td>0.901970</td>\n",
       "      <td>0.143080</td>\n",
       "      <td>0.136230</td>\n",
       "      <td>0.137970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.883250</td>\n",
       "      <td>0.267600</td>\n",
       "      <td>0.880730</td>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.878600</td>\n",
       "      <td>0.267580</td>\n",
       "      <td>0.877270</td>\n",
       "      <td>0.147820</td>\n",
       "      <td>0.884580</td>\n",
       "      <td>0.27263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.256667</th>\n",
       "      <td>0.131030</td>\n",
       "      <td>0.139000</td>\n",
       "      <td>0.907480</td>\n",
       "      <td>0.139820</td>\n",
       "      <td>0.131600</td>\n",
       "      <td>0.136300</td>\n",
       "      <td>0.902050</td>\n",
       "      <td>0.143200</td>\n",
       "      <td>0.136330</td>\n",
       "      <td>0.138250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.883620</td>\n",
       "      <td>0.267970</td>\n",
       "      <td>0.881430</td>\n",
       "      <td>0.141900</td>\n",
       "      <td>0.879000</td>\n",
       "      <td>0.267120</td>\n",
       "      <td>0.877600</td>\n",
       "      <td>0.147050</td>\n",
       "      <td>0.885050</td>\n",
       "      <td>0.27270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.506667</th>\n",
       "      <td>0.131430</td>\n",
       "      <td>0.139100</td>\n",
       "      <td>0.907850</td>\n",
       "      <td>0.139930</td>\n",
       "      <td>0.131870</td>\n",
       "      <td>0.136400</td>\n",
       "      <td>0.902430</td>\n",
       "      <td>0.143180</td>\n",
       "      <td>0.136480</td>\n",
       "      <td>0.138480</td>\n",
       "      <td>...</td>\n",
       "      <td>0.884120</td>\n",
       "      <td>0.268370</td>\n",
       "      <td>0.881870</td>\n",
       "      <td>0.142000</td>\n",
       "      <td>0.878920</td>\n",
       "      <td>0.268050</td>\n",
       "      <td>0.877800</td>\n",
       "      <td>0.147280</td>\n",
       "      <td>0.885000</td>\n",
       "      <td>0.27318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A1        A2        A3        A4        A5        A6  \\\n",
       "0.000000   0.091075  0.094125  0.092725  0.092500  0.092675  0.093950   \n",
       "0.250000   0.091625  0.094875  0.093550  0.093175  0.093325  0.095525   \n",
       "0.500000   0.091975  0.095350  0.093625  0.093425  0.093300  0.095025   \n",
       "0.750000   0.091850  0.095450  0.093475  0.093925  0.092900  0.095025   \n",
       "1.000000   0.091950  0.095800  0.093950  0.094225  0.093025  0.095475   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "49.506389  0.130950  0.138850  0.906870  0.139450  0.131220  0.135980   \n",
       "49.756389  0.131270  0.138700  0.906950  0.139530  0.131300  0.135900   \n",
       "50.006667  0.131330  0.138730  0.907050  0.139420  0.131380  0.136050   \n",
       "50.256667  0.131030  0.139000  0.907480  0.139820  0.131600  0.136300   \n",
       "50.506667  0.131430  0.139100  0.907850  0.139930  0.131870  0.136400   \n",
       "\n",
       "                 A7        A8        A9       A10  ...        H3        H4  \\\n",
       "0.000000   0.092425  0.093675  0.094125  0.095450  ...  0.087800  0.095400   \n",
       "0.250000   0.093325  0.094875  0.095050  0.096500  ...  0.089000  0.096675   \n",
       "0.500000   0.093350  0.095000  0.094925  0.096050  ...  0.088875  0.096700   \n",
       "0.750000   0.092850  0.095400  0.094700  0.095725  ...  0.088850  0.096675   \n",
       "1.000000   0.093125  0.095925  0.094825  0.095925  ...  0.088900  0.097350   \n",
       "...             ...       ...       ...       ...  ...       ...       ...   \n",
       "49.506389  0.901650  0.142930  0.136000  0.138220  ...  0.882150  0.265800   \n",
       "49.756389  0.901800  0.142880  0.136150  0.138080  ...  0.882770  0.266820   \n",
       "50.006667  0.901970  0.143080  0.136230  0.137970  ...  0.883250  0.267600   \n",
       "50.256667  0.902050  0.143200  0.136330  0.138250  ...  0.883620  0.267970   \n",
       "50.506667  0.902430  0.143180  0.136480  0.138480  ...  0.884120  0.268370   \n",
       "\n",
       "                 H5        H6        H7        H8        H9       H10  \\\n",
       "0.000000   0.094050  0.095950  0.086550  0.095125  0.094500  0.096475   \n",
       "0.250000   0.095200  0.097325  0.087775  0.096950  0.095550  0.097300   \n",
       "0.500000   0.094500  0.096550  0.087525  0.096975  0.094825  0.096825   \n",
       "0.750000   0.093925  0.096250  0.087475  0.097375  0.094500  0.096725   \n",
       "1.000000   0.094100  0.096425  0.087700  0.098100  0.094600  0.096950   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "49.506389  0.878850  0.141800  0.877500  0.265650  0.875670  0.146550   \n",
       "49.756389  0.879900  0.141950  0.878000  0.266170  0.876100  0.147150   \n",
       "50.006667  0.880730  0.142500  0.878600  0.267580  0.877270  0.147820   \n",
       "50.256667  0.881430  0.141900  0.879000  0.267120  0.877600  0.147050   \n",
       "50.506667  0.881870  0.142000  0.878920  0.268050  0.877800  0.147280   \n",
       "\n",
       "                H11      H12  \n",
       "0.000000   0.086025  0.09520  \n",
       "0.250000   0.087100  0.09690  \n",
       "0.500000   0.087100  0.09720  \n",
       "0.750000   0.087025  0.09785  \n",
       "1.000000   0.087125  0.09850  \n",
       "...             ...      ...  \n",
       "49.506389  0.883700  0.27075  \n",
       "49.756389  0.884380  0.27150  \n",
       "50.006667  0.884580  0.27263  \n",
       "50.256667  0.885050  0.27270  \n",
       "50.506667  0.885000  0.27318  \n",
       "\n",
       "[203 rows x 96 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CY_outlierv2_df=CY_outlierv2_df.T\n",
    "CY_outlierv2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f30b0526190>]"
      ]
     },
     "execution_count": 75,
     "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": [
    "plt.plot(CY_outlierv2_df['C4'])\n",
    "plt.plot(CY_outlierv2_df['E4'])\n",
    "plt.plot(CY_outlierv2_df['G4'])\n",
    "plt.plot(CY_outlierv2_df['H4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "CY_R1_dict = {}\n",
    "CY_R2_dict = {}\n",
    "CY_R3_dict = {}\n",
    "\n",
    "\n",
    "for well in list(CY_outlierv2_df.columns):\n",
    "    \n",
    "    row = well[0]\n",
    "    col = int(well[1:])\n",
    "\n",
    "    growth_rate = get_derivative_growth_rate((row, col), CY_outlierv2_df, tmax=48)\n",
    "    \n",
    "    if col in [1,2,3,4]:\n",
    "        mutant = outliers_plate_map_R1[well]\n",
    "        CY_R1_dict[mutant] = growth_rate[0]\n",
    "        \n",
    "    elif col in [5,6,7,8]:\n",
    "        mutant = outliers_plate_map_R2[well]\n",
    "        CY_R2_dict[mutant] = growth_rate[0]\n",
    "        \n",
    "    else:\n",
    "        mutant = outliers_plate_map_R3[well]\n",
    "        CY_R3_dict[mutant] = growth_rate[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{168: 0.0025200000000000222,\n",
       " 645: 0.0028799999999999937,\n",
       " 455: 0.07891999999999966,\n",
       " 3275: 0.0028000000000000247,\n",
       " 227: 0.08607999999999993,\n",
       " 766: 0.003599999999999992,\n",
       " 575: 0.0030999999999999917,\n",
       " 1410: 0.0031999999999999806,\n",
       " 416: 0.0033999999999999586,\n",
       " 773: 0.0034800000000000386,\n",
       " 582: 0.04492000000000029,\n",
       " 1830: 0.07859999999999978,\n",
       " 418: 0.003920000000000035,\n",
       " 1391: 0.08148,\n",
       " 745: 0.0036800000000000166,\n",
       " 1854: 0.003720000000000001,\n",
       " 567: 0.0032799999999999496,\n",
       " 1678: 0.07999999999999963,\n",
       " 770: 0.0036800000000000166,\n",
       " 1915: 0.03332000000000024,\n",
       " 574: 0.010999999999999899,\n",
       " 1876: 0.0798000000000001,\n",
       " 1023: 0.0038000000000000256,\n",
       " 2445: 0.003599999999999992,\n",
       " 578: 0.006500000000000006,\n",
       " 3233: 0.0050000000000000044,\n",
       " 1778: 0.0041199999999999015,\n",
       " 2967: 0.08036000000000021,\n",
       " 583: 0.06040000000000001,\n",
       " 439: 0.004200000000000037,\n",
       " 3118: 0.07767999999999997,\n",
       " 3208: 0.006759999999999988}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CY_R1_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "FC_R1_series = pd.Series(FC_R1_dict, name='FC_out_R1')\n",
    "FC_R2_series = pd.Series(FC_R2_dict, name='FC_out_R2')\n",
    "FC_R3_series = pd.Series(FC_R3_dict, name='FC_out_R3')\n",
    "\n",
    "CY_R1_series = pd.Series(CY_R1_dict, name='CY_out_R1')\n",
    "CY_R2_series = pd.Series(CY_R2_dict, name='CY_out_R2')\n",
    "CY_R3_series = pd.Series(CY_R3_dict, name='CY_out_R3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_df = pd.DataFrame([FC_R1_series, FC_R2_series, FC_R3_series, CY_R1_series, CY_R2_series, CY_R3_series]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>FC_out_R1</th>\n",
       "      <th>FC_out_R2</th>\n",
       "      <th>FC_out_R3</th>\n",
       "      <th>CY_out_R1</th>\n",
       "      <th>CY_out_R2</th>\n",
       "      <th>CY_out_R3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.11012</td>\n",
       "      <td>0.11216</td>\n",
       "      <td>0.11232</td>\n",
       "      <td>0.00252</td>\n",
       "      <td>0.00240</td>\n",
       "      <td>0.00272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>0.11008</td>\n",
       "      <td>0.11164</td>\n",
       "      <td>0.11532</td>\n",
       "      <td>0.00288</td>\n",
       "      <td>0.00268</td>\n",
       "      <td>0.00248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>0.04868</td>\n",
       "      <td>0.04844</td>\n",
       "      <td>0.04852</td>\n",
       "      <td>0.07892</td>\n",
       "      <td>0.07960</td>\n",
       "      <td>0.08128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>0.11084</td>\n",
       "      <td>0.11048</td>\n",
       "      <td>0.11092</td>\n",
       "      <td>0.00280</td>\n",
       "      <td>0.00292</td>\n",
       "      <td>0.00348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.04860</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.08608</td>\n",
       "      <td>0.08560</td>\n",
       "      <td>0.08852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>0.11208</td>\n",
       "      <td>0.11340</td>\n",
       "      <td>0.11460</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00330</td>\n",
       "      <td>0.00388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>0.11288</td>\n",
       "      <td>0.11440</td>\n",
       "      <td>0.11556</td>\n",
       "      <td>0.00310</td>\n",
       "      <td>0.00290</td>\n",
       "      <td>0.00360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>0.09552</td>\n",
       "      <td>0.09972</td>\n",
       "      <td>0.09920</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0.11220</td>\n",
       "      <td>0.11432</td>\n",
       "      <td>0.11552</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>0.11252</td>\n",
       "      <td>0.11452</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>0.05628</td>\n",
       "      <td>0.05592</td>\n",
       "      <td>0.05512</td>\n",
       "      <td>0.04492</td>\n",
       "      <td>0.04240</td>\n",
       "      <td>0.04384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>0.05072</td>\n",
       "      <td>0.05052</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07860</td>\n",
       "      <td>0.08004</td>\n",
       "      <td>0.07972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>0.10560</td>\n",
       "      <td>0.11008</td>\n",
       "      <td>0.11028</td>\n",
       "      <td>0.00392</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1391</th>\n",
       "      <td>0.05080</td>\n",
       "      <td>0.04988</td>\n",
       "      <td>0.04968</td>\n",
       "      <td>0.08148</td>\n",
       "      <td>0.08300</td>\n",
       "      <td>0.08304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>0.10780</td>\n",
       "      <td>0.10828</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.00432</td>\n",
       "      <td>0.00380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>0.09052</td>\n",
       "      <td>0.09380</td>\n",
       "      <td>0.09540</td>\n",
       "      <td>0.00372</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>0.10840</td>\n",
       "      <td>0.11400</td>\n",
       "      <td>0.11560</td>\n",
       "      <td>0.00328</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.00332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>0.04952</td>\n",
       "      <td>0.04812</td>\n",
       "      <td>0.04848</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.08220</td>\n",
       "      <td>0.08248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>0.11460</td>\n",
       "      <td>0.11408</td>\n",
       "      <td>0.11452</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.00312</td>\n",
       "      <td>0.00360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>0.05240</td>\n",
       "      <td>0.05340</td>\n",
       "      <td>0.05544</td>\n",
       "      <td>0.03332</td>\n",
       "      <td>0.03232</td>\n",
       "      <td>0.02932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>0.06100</td>\n",
       "      <td>0.06304</td>\n",
       "      <td>0.06360</td>\n",
       "      <td>0.01100</td>\n",
       "      <td>0.01052</td>\n",
       "      <td>0.00992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.04920</td>\n",
       "      <td>0.05012</td>\n",
       "      <td>0.07980</td>\n",
       "      <td>0.08112</td>\n",
       "      <td>0.08212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>0.11392</td>\n",
       "      <td>0.11396</td>\n",
       "      <td>0.11280</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00344</td>\n",
       "      <td>0.00380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2445</th>\n",
       "      <td>0.11380</td>\n",
       "      <td>0.11448</td>\n",
       "      <td>0.11472</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00372</td>\n",
       "      <td>0.00400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>0.07400</td>\n",
       "      <td>0.07432</td>\n",
       "      <td>0.07624</td>\n",
       "      <td>0.00650</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.00580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>0.10040</td>\n",
       "      <td>0.10036</td>\n",
       "      <td>0.10220</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>0.11120</td>\n",
       "      <td>0.10940</td>\n",
       "      <td>0.10928</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>0.04940</td>\n",
       "      <td>0.04952</td>\n",
       "      <td>0.05052</td>\n",
       "      <td>0.08036</td>\n",
       "      <td>0.08088</td>\n",
       "      <td>0.08388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>0.05252</td>\n",
       "      <td>0.05328</td>\n",
       "      <td>0.05276</td>\n",
       "      <td>0.06040</td>\n",
       "      <td>0.05940</td>\n",
       "      <td>0.06068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.11128</td>\n",
       "      <td>0.11280</td>\n",
       "      <td>0.11460</td>\n",
       "      <td>0.00420</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3118</th>\n",
       "      <td>0.04760</td>\n",
       "      <td>0.04812</td>\n",
       "      <td>0.04860</td>\n",
       "      <td>0.07768</td>\n",
       "      <td>0.07884</td>\n",
       "      <td>0.07808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3208</th>\n",
       "      <td>0.05860</td>\n",
       "      <td>0.05904</td>\n",
       "      <td>0.05940</td>\n",
       "      <td>0.00676</td>\n",
       "      <td>0.00668</td>\n",
       "      <td>0.00680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      FC_out_R1  FC_out_R2  FC_out_R3  CY_out_R1  CY_out_R2  CY_out_R3\n",
       "168     0.11012    0.11216    0.11232    0.00252    0.00240    0.00272\n",
       "645     0.11008    0.11164    0.11532    0.00288    0.00268    0.00248\n",
       "455     0.04868    0.04844    0.04852    0.07892    0.07960    0.08128\n",
       "3275    0.11084    0.11048    0.11092    0.00280    0.00292    0.00348\n",
       "227     0.04860    0.04900    0.05000    0.08608    0.08560    0.08852\n",
       "766     0.11208    0.11340    0.11460    0.00360    0.00330    0.00388\n",
       "575     0.11288    0.11440    0.11556    0.00310    0.00290    0.00360\n",
       "1410    0.09552    0.09972    0.09920    0.00320    0.00360    0.00420\n",
       "416     0.11220    0.11432    0.11552    0.00340    0.00340    0.00320\n",
       "773     0.11252    0.11452    0.11660    0.00348    0.00320    0.00320\n",
       "582     0.05628    0.05592    0.05512    0.04492    0.04240    0.04384\n",
       "1830    0.05072    0.05052    0.05000    0.07860    0.08004    0.07972\n",
       "418     0.10560    0.11008    0.11028    0.00392    0.00340    0.00320\n",
       "1391    0.05080    0.04988    0.04968    0.08148    0.08300    0.08304\n",
       "745     0.10780    0.10828    0.11100    0.00368    0.00432    0.00380\n",
       "1854    0.09052    0.09380    0.09540    0.00372    0.00340    0.00432\n",
       "567     0.10840    0.11400    0.11560    0.00328    0.00368    0.00332\n",
       "1678    0.04952    0.04812    0.04848    0.08000    0.08220    0.08248\n",
       "770     0.11460    0.11408    0.11452    0.00368    0.00312    0.00360\n",
       "1915    0.05240    0.05340    0.05544    0.03332    0.03232    0.02932\n",
       "574     0.06100    0.06304    0.06360    0.01100    0.01052    0.00992\n",
       "1876    0.05000    0.04920    0.05012    0.07980    0.08112    0.08212\n",
       "1023    0.11392    0.11396    0.11280    0.00380    0.00344    0.00380\n",
       "2445    0.11380    0.11448    0.11472    0.00360    0.00372    0.00400\n",
       "578     0.07400    0.07432    0.07624    0.00650    0.00600    0.00580\n",
       "3233    0.10040    0.10036    0.10220    0.00500    0.00380    0.00368\n",
       "1778    0.11120    0.10940    0.10928    0.00412    0.00348    0.00372\n",
       "2967    0.04940    0.04952    0.05052    0.08036    0.08088    0.08388\n",
       "583     0.05252    0.05328    0.05276    0.06040    0.05940    0.06068\n",
       "439     0.11128    0.11280    0.11460    0.00420    0.00380    0.00360\n",
       "3118    0.04760    0.04812    0.04860    0.07768    0.07884    0.07808\n",
       "3208    0.05860    0.05904    0.05940    0.00676    0.00668    0.00680"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FC_out_R1 FC_out_R1 SpearmanrResult(correlation=1.0, pvalue=0.0)\n",
      "FC_out_R1 FC_out_R2 SpearmanrResult(correlation=0.9670087976539589, pvalue=2.2722708902448884e-19)\n",
      "FC_out_R1 FC_out_R3 SpearmanrResult(correlation=0.9166895831736401, pvalue=1.7529147581194088e-13)\n",
      "FC_out_R1 CY_out_R1 SpearmanrResult(correlation=-0.8126489595759148, pvalue=1.6131329733983397e-08)\n",
      "FC_out_R1 CY_out_R2 SpearmanrResult(correlation=-0.8470351057479287, pvalue=9.832505800416814e-10)\n",
      "FC_out_R1 CY_out_R3 SpearmanrResult(correlation=-0.8232167026125642, pvalue=7.281058182649505e-09)\n",
      "FC_out_R2 FC_out_R1 SpearmanrResult(correlation=0.9670087976539589, pvalue=2.2722708902448884e-19)\n",
      "FC_out_R2 FC_out_R2 SpearmanrResult(correlation=1.0, pvalue=0.0)\n",
      "FC_out_R2 FC_out_R3 SpearmanrResult(correlation=0.9744294789344273, pvalue=5.22407944996434e-21)\n",
      "FC_out_R2 CY_out_R1 SpearmanrResult(correlation=-0.8509624340968637, pvalue=6.842531886042174e-10)\n",
      "FC_out_R2 CY_out_R2 SpearmanrResult(correlation=-0.8536339509777329, pvalue=5.315342775021208e-10)\n",
      "FC_out_R2 CY_out_R3 SpearmanrResult(correlation=-0.8641116290288265, pvalue=1.8771916962015726e-10)\n",
      "FC_out_R3 FC_out_R1 SpearmanrResult(correlation=0.9166895831736401, pvalue=1.7529147581194088e-13)\n",
      "FC_out_R3 FC_out_R2 SpearmanrResult(correlation=0.9744294789344273, pvalue=5.22407944996434e-21)\n",
      "FC_out_R3 FC_out_R3 SpearmanrResult(correlation=1.0, pvalue=0.0)\n",
      "FC_out_R3 CY_out_R1 SpearmanrResult(correlation=-0.8680905710686423, pvalue=1.2358493714691033e-10)\n",
      "FC_out_R3 CY_out_R2 SpearmanrResult(correlation=-0.8474793767186068, pvalue=9.442217639990246e-10)\n",
      "FC_out_R3 CY_out_R3 SpearmanrResult(correlation=-0.8880331057239529, pvalue=1.2140893351876472e-11)\n",
      "CY_out_R1 FC_out_R1 SpearmanrResult(correlation=-0.8126489595759148, pvalue=1.6131329733983397e-08)\n",
      "CY_out_R1 FC_out_R2 SpearmanrResult(correlation=-0.8509624340968636, pvalue=6.842531886042222e-10)\n",
      "CY_out_R1 FC_out_R3 SpearmanrResult(correlation=-0.8680905710686422, pvalue=1.235849371469121e-10)\n",
      "CY_out_R1 CY_out_R1 SpearmanrResult(correlation=1.0, pvalue=0.0)\n",
      "CY_out_R1 CY_out_R2 SpearmanrResult(correlation=0.9359244699694654, pvalue=3.893381063243716e-15)\n",
      "CY_out_R1 CY_out_R3 SpearmanrResult(correlation=0.8960015276389017, pvalue=4.238001160487123e-12)\n",
      "CY_out_R2 FC_out_R1 SpearmanrResult(correlation=-0.8470351057479286, pvalue=9.832505800416882e-10)\n",
      "CY_out_R2 FC_out_R2 SpearmanrResult(correlation=-0.853633950977733, pvalue=5.315342775021131e-10)\n",
      "CY_out_R2 FC_out_R3 SpearmanrResult(correlation=-0.8474793767186068, pvalue=9.442217639990246e-10)\n",
      "CY_out_R2 CY_out_R1 SpearmanrResult(correlation=0.9359244699694653, pvalue=3.893381063243786e-15)\n",
      "CY_out_R2 CY_out_R2 SpearmanrResult(correlation=1.0, pvalue=0.0)\n",
      "CY_out_R2 CY_out_R3 SpearmanrResult(correlation=0.9226961079919883, pvalue=5.943501794059406e-14)\n",
      "CY_out_R3 FC_out_R1 SpearmanrResult(correlation=-0.8232167026125641, pvalue=7.281058182649558e-09)\n",
      "CY_out_R3 FC_out_R2 SpearmanrResult(correlation=-0.8641116290288264, pvalue=1.8771916962015995e-10)\n",
      "CY_out_R3 FC_out_R3 SpearmanrResult(correlation=-0.888033105723953, pvalue=1.2140893351876296e-11)\n",
      "CY_out_R3 CY_out_R1 SpearmanrResult(correlation=0.8960015276389015, pvalue=4.238001160487259e-12)\n",
      "CY_out_R3 CY_out_R2 SpearmanrResult(correlation=0.9226961079919882, pvalue=5.943501794059511e-14)\n",
      "CY_out_R3 CY_out_R3 SpearmanrResult(correlation=1.0, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "\n",
    "index=1\n",
    "\n",
    "for column1 in out_df.columns:\n",
    "    \n",
    "    for column2 in out_df.columns:\n",
    "        print(column1, column2, stats.spearmanr(out_df[column1], out_df[column2]))\n",
    "        plt.subplot(6,6,index)\n",
    "        plt.scatter(out_df[column1], out_df[column2])\n",
    "        index+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_df['FC_out_average'] = (out_df['FC_out_R1'] + out_df['FC_out_R2'] + out_df['FC_out_R3'])/3\n",
    "out_df['CY_out_average'] = (out_df['CY_out_R1'] + out_df['CY_out_R2'] + out_df['CY_out_R3'])/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.1)"
      ]
     },
     "execution_count": 83,
     "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": [
    "plt.scatter(out_df['FC_out_average'], out_df['CY_out_average'])\n",
    "plt.xlim(0.02, 0.12)\n",
    "plt.ylim(0, 0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>FC_out_R1</th>\n",
       "      <th>FC_out_R2</th>\n",
       "      <th>FC_out_R3</th>\n",
       "      <th>CY_out_R1</th>\n",
       "      <th>CY_out_R2</th>\n",
       "      <th>CY_out_R3</th>\n",
       "      <th>FC_out_average</th>\n",
       "      <th>CY_out_average</th>\n",
       "      <th>hap_cy</th>\n",
       "      <th>hap_fc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.11012</td>\n",
       "      <td>0.11216</td>\n",
       "      <td>0.11232</td>\n",
       "      <td>0.00252</td>\n",
       "      <td>0.00240</td>\n",
       "      <td>0.00272</td>\n",
       "      <td>0.111533</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>0.11008</td>\n",
       "      <td>0.11164</td>\n",
       "      <td>0.11532</td>\n",
       "      <td>0.00288</td>\n",
       "      <td>0.00268</td>\n",
       "      <td>0.00248</td>\n",
       "      <td>0.112347</td>\n",
       "      <td>0.002680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>0.04868</td>\n",
       "      <td>0.04844</td>\n",
       "      <td>0.04852</td>\n",
       "      <td>0.07892</td>\n",
       "      <td>0.07960</td>\n",
       "      <td>0.08128</td>\n",
       "      <td>0.048547</td>\n",
       "      <td>0.079933</td>\n",
       "      <td>0.12496</td>\n",
       "      <td>0.10310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>0.11084</td>\n",
       "      <td>0.11048</td>\n",
       "      <td>0.11092</td>\n",
       "      <td>0.00280</td>\n",
       "      <td>0.00292</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.110747</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>0.01578</td>\n",
       "      <td>0.20662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.04860</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.08608</td>\n",
       "      <td>0.08560</td>\n",
       "      <td>0.08852</td>\n",
       "      <td>0.049200</td>\n",
       "      <td>0.086733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>0.11208</td>\n",
       "      <td>0.11340</td>\n",
       "      <td>0.11460</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00330</td>\n",
       "      <td>0.00388</td>\n",
       "      <td>0.113360</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>0.11288</td>\n",
       "      <td>0.11440</td>\n",
       "      <td>0.11556</td>\n",
       "      <td>0.00310</td>\n",
       "      <td>0.00290</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.114280</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.01524</td>\n",
       "      <td>0.21842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>0.09552</td>\n",
       "      <td>0.09972</td>\n",
       "      <td>0.09920</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00420</td>\n",
       "      <td>0.098147</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.01450</td>\n",
       "      <td>0.20546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0.11220</td>\n",
       "      <td>0.11432</td>\n",
       "      <td>0.11552</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.114013</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>0.11252</td>\n",
       "      <td>0.11452</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.114547</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>0.05628</td>\n",
       "      <td>0.05592</td>\n",
       "      <td>0.05512</td>\n",
       "      <td>0.04492</td>\n",
       "      <td>0.04240</td>\n",
       "      <td>0.04384</td>\n",
       "      <td>0.055773</td>\n",
       "      <td>0.043720</td>\n",
       "      <td>0.10442</td>\n",
       "      <td>0.12586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>0.05072</td>\n",
       "      <td>0.05052</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07860</td>\n",
       "      <td>0.08004</td>\n",
       "      <td>0.07972</td>\n",
       "      <td>0.050413</td>\n",
       "      <td>0.079453</td>\n",
       "      <td>0.11874</td>\n",
       "      <td>0.12182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>0.10560</td>\n",
       "      <td>0.11008</td>\n",
       "      <td>0.11028</td>\n",
       "      <td>0.00392</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.108653</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1391</th>\n",
       "      <td>0.05080</td>\n",
       "      <td>0.04988</td>\n",
       "      <td>0.04968</td>\n",
       "      <td>0.08148</td>\n",
       "      <td>0.08300</td>\n",
       "      <td>0.08304</td>\n",
       "      <td>0.050120</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>0.10780</td>\n",
       "      <td>0.10828</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.00432</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.109027</td>\n",
       "      <td>0.003933</td>\n",
       "      <td>0.01634</td>\n",
       "      <td>0.22164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>0.09052</td>\n",
       "      <td>0.09380</td>\n",
       "      <td>0.09540</td>\n",
       "      <td>0.00372</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00432</td>\n",
       "      <td>0.093240</td>\n",
       "      <td>0.003813</td>\n",
       "      <td>0.01498</td>\n",
       "      <td>0.17446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>0.10840</td>\n",
       "      <td>0.11400</td>\n",
       "      <td>0.11560</td>\n",
       "      <td>0.00328</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>0.04952</td>\n",
       "      <td>0.04812</td>\n",
       "      <td>0.04848</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.08220</td>\n",
       "      <td>0.08248</td>\n",
       "      <td>0.048707</td>\n",
       "      <td>0.081560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>0.11460</td>\n",
       "      <td>0.11408</td>\n",
       "      <td>0.11452</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.00312</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.114400</td>\n",
       "      <td>0.003467</td>\n",
       "      <td>0.01660</td>\n",
       "      <td>0.23132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>0.05240</td>\n",
       "      <td>0.05340</td>\n",
       "      <td>0.05544</td>\n",
       "      <td>0.03332</td>\n",
       "      <td>0.03232</td>\n",
       "      <td>0.02932</td>\n",
       "      <td>0.053747</td>\n",
       "      <td>0.031653</td>\n",
       "      <td>0.07736</td>\n",
       "      <td>0.13704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>0.06100</td>\n",
       "      <td>0.06304</td>\n",
       "      <td>0.06360</td>\n",
       "      <td>0.01100</td>\n",
       "      <td>0.01052</td>\n",
       "      <td>0.00992</td>\n",
       "      <td>0.062547</td>\n",
       "      <td>0.010480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.04920</td>\n",
       "      <td>0.05012</td>\n",
       "      <td>0.07980</td>\n",
       "      <td>0.08112</td>\n",
       "      <td>0.08212</td>\n",
       "      <td>0.049773</td>\n",
       "      <td>0.081013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>0.11392</td>\n",
       "      <td>0.11396</td>\n",
       "      <td>0.11280</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00344</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.113560</td>\n",
       "      <td>0.003680</td>\n",
       "      <td>0.00642</td>\n",
       "      <td>0.22108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2445</th>\n",
       "      <td>0.11380</td>\n",
       "      <td>0.11448</td>\n",
       "      <td>0.11472</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00372</td>\n",
       "      <td>0.00400</td>\n",
       "      <td>0.114333</td>\n",
       "      <td>0.003773</td>\n",
       "      <td>0.01660</td>\n",
       "      <td>0.23052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>0.07400</td>\n",
       "      <td>0.07432</td>\n",
       "      <td>0.07624</td>\n",
       "      <td>0.00650</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.074853</td>\n",
       "      <td>0.006100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>0.10040</td>\n",
       "      <td>0.10036</td>\n",
       "      <td>0.10220</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.100987</td>\n",
       "      <td>0.004160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>0.11120</td>\n",
       "      <td>0.10940</td>\n",
       "      <td>0.10928</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00372</td>\n",
       "      <td>0.109960</td>\n",
       "      <td>0.003773</td>\n",
       "      <td>0.01576</td>\n",
       "      <td>0.23300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>0.04940</td>\n",
       "      <td>0.04952</td>\n",
       "      <td>0.05052</td>\n",
       "      <td>0.08036</td>\n",
       "      <td>0.08088</td>\n",
       "      <td>0.08388</td>\n",
       "      <td>0.049813</td>\n",
       "      <td>0.081707</td>\n",
       "      <td>0.12886</td>\n",
       "      <td>0.14082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>0.05252</td>\n",
       "      <td>0.05328</td>\n",
       "      <td>0.05276</td>\n",
       "      <td>0.06040</td>\n",
       "      <td>0.05940</td>\n",
       "      <td>0.06068</td>\n",
       "      <td>0.052853</td>\n",
       "      <td>0.060160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.11128</td>\n",
       "      <td>0.11280</td>\n",
       "      <td>0.11460</td>\n",
       "      <td>0.00420</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.112893</td>\n",
       "      <td>0.003867</td>\n",
       "      <td>0.01240</td>\n",
       "      <td>0.20690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3118</th>\n",
       "      <td>0.04760</td>\n",
       "      <td>0.04812</td>\n",
       "      <td>0.04860</td>\n",
       "      <td>0.07768</td>\n",
       "      <td>0.07884</td>\n",
       "      <td>0.07808</td>\n",
       "      <td>0.048107</td>\n",
       "      <td>0.078200</td>\n",
       "      <td>0.12140</td>\n",
       "      <td>0.14056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3208</th>\n",
       "      <td>0.05860</td>\n",
       "      <td>0.05904</td>\n",
       "      <td>0.05940</td>\n",
       "      <td>0.00676</td>\n",
       "      <td>0.00668</td>\n",
       "      <td>0.00680</td>\n",
       "      <td>0.059013</td>\n",
       "      <td>0.006747</td>\n",
       "      <td>0.02936</td>\n",
       "      <td>0.14894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      FC_out_R1  FC_out_R2  FC_out_R3  CY_out_R1  CY_out_R2  CY_out_R3  \\\n",
       "168     0.11012    0.11216    0.11232    0.00252    0.00240    0.00272   \n",
       "645     0.11008    0.11164    0.11532    0.00288    0.00268    0.00248   \n",
       "455     0.04868    0.04844    0.04852    0.07892    0.07960    0.08128   \n",
       "3275    0.11084    0.11048    0.11092    0.00280    0.00292    0.00348   \n",
       "227     0.04860    0.04900    0.05000    0.08608    0.08560    0.08852   \n",
       "766     0.11208    0.11340    0.11460    0.00360    0.00330    0.00388   \n",
       "575     0.11288    0.11440    0.11556    0.00310    0.00290    0.00360   \n",
       "1410    0.09552    0.09972    0.09920    0.00320    0.00360    0.00420   \n",
       "416     0.11220    0.11432    0.11552    0.00340    0.00340    0.00320   \n",
       "773     0.11252    0.11452    0.11660    0.00348    0.00320    0.00320   \n",
       "582     0.05628    0.05592    0.05512    0.04492    0.04240    0.04384   \n",
       "1830    0.05072    0.05052    0.05000    0.07860    0.08004    0.07972   \n",
       "418     0.10560    0.11008    0.11028    0.00392    0.00340    0.00320   \n",
       "1391    0.05080    0.04988    0.04968    0.08148    0.08300    0.08304   \n",
       "745     0.10780    0.10828    0.11100    0.00368    0.00432    0.00380   \n",
       "1854    0.09052    0.09380    0.09540    0.00372    0.00340    0.00432   \n",
       "567     0.10840    0.11400    0.11560    0.00328    0.00368    0.00332   \n",
       "1678    0.04952    0.04812    0.04848    0.08000    0.08220    0.08248   \n",
       "770     0.11460    0.11408    0.11452    0.00368    0.00312    0.00360   \n",
       "1915    0.05240    0.05340    0.05544    0.03332    0.03232    0.02932   \n",
       "574     0.06100    0.06304    0.06360    0.01100    0.01052    0.00992   \n",
       "1876    0.05000    0.04920    0.05012    0.07980    0.08112    0.08212   \n",
       "1023    0.11392    0.11396    0.11280    0.00380    0.00344    0.00380   \n",
       "2445    0.11380    0.11448    0.11472    0.00360    0.00372    0.00400   \n",
       "578     0.07400    0.07432    0.07624    0.00650    0.00600    0.00580   \n",
       "3233    0.10040    0.10036    0.10220    0.00500    0.00380    0.00368   \n",
       "1778    0.11120    0.10940    0.10928    0.00412    0.00348    0.00372   \n",
       "2967    0.04940    0.04952    0.05052    0.08036    0.08088    0.08388   \n",
       "583     0.05252    0.05328    0.05276    0.06040    0.05940    0.06068   \n",
       "439     0.11128    0.11280    0.11460    0.00420    0.00380    0.00360   \n",
       "3118    0.04760    0.04812    0.04860    0.07768    0.07884    0.07808   \n",
       "3208    0.05860    0.05904    0.05940    0.00676    0.00668    0.00680   \n",
       "\n",
       "      FC_out_average  CY_out_average   hap_cy   hap_fc  \n",
       "168         0.111533        0.002547      NaN      NaN  \n",
       "645         0.112347        0.002680      NaN      NaN  \n",
       "455         0.048547        0.079933  0.12496  0.10310  \n",
       "3275        0.110747        0.003067  0.01578  0.20662  \n",
       "227         0.049200        0.086733      NaN      NaN  \n",
       "766         0.113360        0.003593      NaN      NaN  \n",
       "575         0.114280        0.003200  0.01524  0.21842  \n",
       "1410        0.098147        0.003667  0.01450  0.20546  \n",
       "416         0.114013        0.003333      NaN      NaN  \n",
       "773         0.114547        0.003293      NaN      NaN  \n",
       "582         0.055773        0.043720  0.10442  0.12586  \n",
       "1830        0.050413        0.079453  0.11874  0.12182  \n",
       "418         0.108653        0.003507      NaN      NaN  \n",
       "1391        0.050120        0.082507      NaN      NaN  \n",
       "745         0.109027        0.003933  0.01634  0.22164  \n",
       "1854        0.093240        0.003813  0.01498  0.17446  \n",
       "567         0.112667        0.003427      NaN      NaN  \n",
       "1678        0.048707        0.081560      NaN      NaN  \n",
       "770         0.114400        0.003467  0.01660  0.23132  \n",
       "1915        0.053747        0.031653  0.07736  0.13704  \n",
       "574         0.062547        0.010480      NaN      NaN  \n",
       "1876        0.049773        0.081013      NaN      NaN  \n",
       "1023        0.113560        0.003680  0.00642  0.22108  \n",
       "2445        0.114333        0.003773  0.01660  0.23052  \n",
       "578         0.074853        0.006100      NaN      NaN  \n",
       "3233        0.100987        0.004160      NaN      NaN  \n",
       "1778        0.109960        0.003773  0.01576  0.23300  \n",
       "2967        0.049813        0.081707  0.12886  0.14082  \n",
       "583         0.052853        0.060160      NaN      NaN  \n",
       "439         0.112893        0.003867  0.01240  0.20690  \n",
       "3118        0.048107        0.078200  0.12140  0.14056  \n",
       "3208        0.059013        0.006747  0.02936  0.14894  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_df['hap_cy'] = validations_df['hap_cy_average']\n",
    "out_df['hap_fc'] = validations_df['hap_fc_average']\n",
    "out_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.8627450980392157, pvalue=8.317125318327623e-06)"
      ]
     },
     "execution_count": 85,
     "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": [
    "plt.scatter(out_df['hap_fc'], out_df['FC_out_average'])\n",
    "stats.spearmanr(out_df['hap_fc'], out_df['FC_out_average'], nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.7720588235294118, pvalue=0.00028201264704692656)"
      ]
     },
     "execution_count": 86,
     "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": [
    "plt.scatter(out_df['hap_cy'], out_df['CY_out_average'])\n",
    "stats.spearmanr(out_df['hap_cy'], out_df['CY_out_average'], nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = ~np.isnan(out_df['FC_out_average']) & ~np.isnan(out_df['hap_fc'])\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(out_df['FC_out_average'][mask], y=out_df['hap_fc'][mask])\n",
    "\n",
    "out_df['adjusted_2nd_batch_FC'] = (out_df['FC_out_average']*slope)+intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = ~np.isnan(out_df['CY_out_average']) & ~np.isnan(out_df['hap_cy'])\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(out_df['CY_out_average'][mask], y=out_df['hap_cy'][mask])\n",
    "\n",
    "out_df['adjusted_2nd_batch_CY'] = (out_df['CY_out_average']*slope)+intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f30aefedcd0>"
      ]
     },
     "execution_count": 89,
     "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": [
    "plt.scatter(out_df['hap_fc'], out_df['adjusted_2nd_batch_FC'])\n",
    "plt.scatter(out_df['hap_cy'], out_df['adjusted_2nd_batch_CY'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>FC_out_R1</th>\n",
       "      <th>FC_out_R2</th>\n",
       "      <th>FC_out_R3</th>\n",
       "      <th>CY_out_R1</th>\n",
       "      <th>CY_out_R2</th>\n",
       "      <th>CY_out_R3</th>\n",
       "      <th>FC_out_average</th>\n",
       "      <th>CY_out_average</th>\n",
       "      <th>hap_cy</th>\n",
       "      <th>hap_fc</th>\n",
       "      <th>adjusted_2nd_batch_FC</th>\n",
       "      <th>adjusted_2nd_batch_CY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.11012</td>\n",
       "      <td>0.11216</td>\n",
       "      <td>0.11232</td>\n",
       "      <td>0.00252</td>\n",
       "      <td>0.00240</td>\n",
       "      <td>0.00272</td>\n",
       "      <td>0.111533</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.219011</td>\n",
       "      <td>0.016087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>0.11008</td>\n",
       "      <td>0.11164</td>\n",
       "      <td>0.11532</td>\n",
       "      <td>0.00288</td>\n",
       "      <td>0.00268</td>\n",
       "      <td>0.00248</td>\n",
       "      <td>0.112347</td>\n",
       "      <td>0.002680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.220223</td>\n",
       "      <td>0.016281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>0.04868</td>\n",
       "      <td>0.04844</td>\n",
       "      <td>0.04852</td>\n",
       "      <td>0.07892</td>\n",
       "      <td>0.07960</td>\n",
       "      <td>0.08128</td>\n",
       "      <td>0.048547</td>\n",
       "      <td>0.079933</td>\n",
       "      <td>0.12496</td>\n",
       "      <td>0.10310</td>\n",
       "      <td>0.125171</td>\n",
       "      <td>0.129166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>0.11084</td>\n",
       "      <td>0.11048</td>\n",
       "      <td>0.11092</td>\n",
       "      <td>0.00280</td>\n",
       "      <td>0.00292</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.110747</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>0.01578</td>\n",
       "      <td>0.20662</td>\n",
       "      <td>0.217839</td>\n",
       "      <td>0.016846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.04860</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.08608</td>\n",
       "      <td>0.08560</td>\n",
       "      <td>0.08852</td>\n",
       "      <td>0.049200</td>\n",
       "      <td>0.086733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126145</td>\n",
       "      <td>0.139102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>0.11208</td>\n",
       "      <td>0.11340</td>\n",
       "      <td>0.11460</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00330</td>\n",
       "      <td>0.00388</td>\n",
       "      <td>0.113360</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.221732</td>\n",
       "      <td>0.017616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>0.11288</td>\n",
       "      <td>0.11440</td>\n",
       "      <td>0.11556</td>\n",
       "      <td>0.00310</td>\n",
       "      <td>0.00290</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.114280</td>\n",
       "      <td>0.003200</td>\n",
       "      <td>0.01524</td>\n",
       "      <td>0.21842</td>\n",
       "      <td>0.223103</td>\n",
       "      <td>0.017041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>0.09552</td>\n",
       "      <td>0.09972</td>\n",
       "      <td>0.09920</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00420</td>\n",
       "      <td>0.098147</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.01450</td>\n",
       "      <td>0.20546</td>\n",
       "      <td>0.199067</td>\n",
       "      <td>0.017723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0.11220</td>\n",
       "      <td>0.11432</td>\n",
       "      <td>0.11552</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.114013</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.222706</td>\n",
       "      <td>0.017236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>0.11252</td>\n",
       "      <td>0.11452</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.114547</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.223500</td>\n",
       "      <td>0.017178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>0.05628</td>\n",
       "      <td>0.05592</td>\n",
       "      <td>0.05512</td>\n",
       "      <td>0.04492</td>\n",
       "      <td>0.04240</td>\n",
       "      <td>0.04384</td>\n",
       "      <td>0.055773</td>\n",
       "      <td>0.043720</td>\n",
       "      <td>0.10442</td>\n",
       "      <td>0.12586</td>\n",
       "      <td>0.135938</td>\n",
       "      <td>0.076250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>0.05072</td>\n",
       "      <td>0.05052</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.07860</td>\n",
       "      <td>0.08004</td>\n",
       "      <td>0.07972</td>\n",
       "      <td>0.050413</td>\n",
       "      <td>0.079453</td>\n",
       "      <td>0.11874</td>\n",
       "      <td>0.12182</td>\n",
       "      <td>0.127952</td>\n",
       "      <td>0.128465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>0.10560</td>\n",
       "      <td>0.11008</td>\n",
       "      <td>0.11028</td>\n",
       "      <td>0.00392</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>0.108653</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214720</td>\n",
       "      <td>0.017489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1391</th>\n",
       "      <td>0.05080</td>\n",
       "      <td>0.04988</td>\n",
       "      <td>0.04968</td>\n",
       "      <td>0.08148</td>\n",
       "      <td>0.08300</td>\n",
       "      <td>0.08304</td>\n",
       "      <td>0.050120</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.127515</td>\n",
       "      <td>0.132926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>0.10780</td>\n",
       "      <td>0.10828</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.00432</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.109027</td>\n",
       "      <td>0.003933</td>\n",
       "      <td>0.01634</td>\n",
       "      <td>0.22164</td>\n",
       "      <td>0.215276</td>\n",
       "      <td>0.018113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>0.09052</td>\n",
       "      <td>0.09380</td>\n",
       "      <td>0.09540</td>\n",
       "      <td>0.00372</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>0.00432</td>\n",
       "      <td>0.093240</td>\n",
       "      <td>0.003813</td>\n",
       "      <td>0.01498</td>\n",
       "      <td>0.17446</td>\n",
       "      <td>0.191757</td>\n",
       "      <td>0.017937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>0.10840</td>\n",
       "      <td>0.11400</td>\n",
       "      <td>0.11560</td>\n",
       "      <td>0.00328</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.00332</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.220699</td>\n",
       "      <td>0.017372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>0.04952</td>\n",
       "      <td>0.04812</td>\n",
       "      <td>0.04848</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.08220</td>\n",
       "      <td>0.08248</td>\n",
       "      <td>0.048707</td>\n",
       "      <td>0.081560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.125410</td>\n",
       "      <td>0.131543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>0.11460</td>\n",
       "      <td>0.11408</td>\n",
       "      <td>0.11452</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.00312</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.114400</td>\n",
       "      <td>0.003467</td>\n",
       "      <td>0.01660</td>\n",
       "      <td>0.23132</td>\n",
       "      <td>0.223282</td>\n",
       "      <td>0.017431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>0.05240</td>\n",
       "      <td>0.05340</td>\n",
       "      <td>0.05544</td>\n",
       "      <td>0.03332</td>\n",
       "      <td>0.03232</td>\n",
       "      <td>0.02932</td>\n",
       "      <td>0.053747</td>\n",
       "      <td>0.031653</td>\n",
       "      <td>0.07736</td>\n",
       "      <td>0.13704</td>\n",
       "      <td>0.132918</td>\n",
       "      <td>0.058618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>0.06100</td>\n",
       "      <td>0.06304</td>\n",
       "      <td>0.06360</td>\n",
       "      <td>0.01100</td>\n",
       "      <td>0.01052</td>\n",
       "      <td>0.00992</td>\n",
       "      <td>0.062547</td>\n",
       "      <td>0.010480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.146029</td>\n",
       "      <td>0.027679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.04920</td>\n",
       "      <td>0.05012</td>\n",
       "      <td>0.07980</td>\n",
       "      <td>0.08112</td>\n",
       "      <td>0.08212</td>\n",
       "      <td>0.049773</td>\n",
       "      <td>0.081013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126999</td>\n",
       "      <td>0.130744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>0.11392</td>\n",
       "      <td>0.11396</td>\n",
       "      <td>0.11280</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00344</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.113560</td>\n",
       "      <td>0.003680</td>\n",
       "      <td>0.00642</td>\n",
       "      <td>0.22108</td>\n",
       "      <td>0.222030</td>\n",
       "      <td>0.017743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2445</th>\n",
       "      <td>0.11380</td>\n",
       "      <td>0.11448</td>\n",
       "      <td>0.11472</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.00372</td>\n",
       "      <td>0.00400</td>\n",
       "      <td>0.114333</td>\n",
       "      <td>0.003773</td>\n",
       "      <td>0.01660</td>\n",
       "      <td>0.23052</td>\n",
       "      <td>0.223182</td>\n",
       "      <td>0.017879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>0.07400</td>\n",
       "      <td>0.07432</td>\n",
       "      <td>0.07624</td>\n",
       "      <td>0.00650</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.00580</td>\n",
       "      <td>0.074853</td>\n",
       "      <td>0.006100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.164364</td>\n",
       "      <td>0.021279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>0.10040</td>\n",
       "      <td>0.10036</td>\n",
       "      <td>0.10220</td>\n",
       "      <td>0.00500</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00368</td>\n",
       "      <td>0.100987</td>\n",
       "      <td>0.004160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.203298</td>\n",
       "      <td>0.018444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>0.11120</td>\n",
       "      <td>0.10940</td>\n",
       "      <td>0.10928</td>\n",
       "      <td>0.00412</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.00372</td>\n",
       "      <td>0.109960</td>\n",
       "      <td>0.003773</td>\n",
       "      <td>0.01576</td>\n",
       "      <td>0.23300</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>0.017879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2967</th>\n",
       "      <td>0.04940</td>\n",
       "      <td>0.04952</td>\n",
       "      <td>0.05052</td>\n",
       "      <td>0.08036</td>\n",
       "      <td>0.08088</td>\n",
       "      <td>0.08388</td>\n",
       "      <td>0.049813</td>\n",
       "      <td>0.081707</td>\n",
       "      <td>0.12886</td>\n",
       "      <td>0.14082</td>\n",
       "      <td>0.127058</td>\n",
       "      <td>0.131757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>0.05252</td>\n",
       "      <td>0.05328</td>\n",
       "      <td>0.05276</td>\n",
       "      <td>0.06040</td>\n",
       "      <td>0.05940</td>\n",
       "      <td>0.06068</td>\n",
       "      <td>0.052853</td>\n",
       "      <td>0.060160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.131587</td>\n",
       "      <td>0.100273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.11128</td>\n",
       "      <td>0.11280</td>\n",
       "      <td>0.11460</td>\n",
       "      <td>0.00420</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.112893</td>\n",
       "      <td>0.003867</td>\n",
       "      <td>0.01240</td>\n",
       "      <td>0.20690</td>\n",
       "      <td>0.221037</td>\n",
       "      <td>0.018015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3118</th>\n",
       "      <td>0.04760</td>\n",
       "      <td>0.04812</td>\n",
       "      <td>0.04860</td>\n",
       "      <td>0.07768</td>\n",
       "      <td>0.07884</td>\n",
       "      <td>0.07808</td>\n",
       "      <td>0.048107</td>\n",
       "      <td>0.078200</td>\n",
       "      <td>0.12140</td>\n",
       "      <td>0.14056</td>\n",
       "      <td>0.124516</td>\n",
       "      <td>0.126633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3208</th>\n",
       "      <td>0.05860</td>\n",
       "      <td>0.05904</td>\n",
       "      <td>0.05940</td>\n",
       "      <td>0.00676</td>\n",
       "      <td>0.00668</td>\n",
       "      <td>0.00680</td>\n",
       "      <td>0.059013</td>\n",
       "      <td>0.006747</td>\n",
       "      <td>0.02936</td>\n",
       "      <td>0.14894</td>\n",
       "      <td>0.140765</td>\n",
       "      <td>0.022224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      FC_out_R1  FC_out_R2  FC_out_R3  CY_out_R1  CY_out_R2  CY_out_R3  \\\n",
       "168     0.11012    0.11216    0.11232    0.00252    0.00240    0.00272   \n",
       "645     0.11008    0.11164    0.11532    0.00288    0.00268    0.00248   \n",
       "455     0.04868    0.04844    0.04852    0.07892    0.07960    0.08128   \n",
       "3275    0.11084    0.11048    0.11092    0.00280    0.00292    0.00348   \n",
       "227     0.04860    0.04900    0.05000    0.08608    0.08560    0.08852   \n",
       "766     0.11208    0.11340    0.11460    0.00360    0.00330    0.00388   \n",
       "575     0.11288    0.11440    0.11556    0.00310    0.00290    0.00360   \n",
       "1410    0.09552    0.09972    0.09920    0.00320    0.00360    0.00420   \n",
       "416     0.11220    0.11432    0.11552    0.00340    0.00340    0.00320   \n",
       "773     0.11252    0.11452    0.11660    0.00348    0.00320    0.00320   \n",
       "582     0.05628    0.05592    0.05512    0.04492    0.04240    0.04384   \n",
       "1830    0.05072    0.05052    0.05000    0.07860    0.08004    0.07972   \n",
       "418     0.10560    0.11008    0.11028    0.00392    0.00340    0.00320   \n",
       "1391    0.05080    0.04988    0.04968    0.08148    0.08300    0.08304   \n",
       "745     0.10780    0.10828    0.11100    0.00368    0.00432    0.00380   \n",
       "1854    0.09052    0.09380    0.09540    0.00372    0.00340    0.00432   \n",
       "567     0.10840    0.11400    0.11560    0.00328    0.00368    0.00332   \n",
       "1678    0.04952    0.04812    0.04848    0.08000    0.08220    0.08248   \n",
       "770     0.11460    0.11408    0.11452    0.00368    0.00312    0.00360   \n",
       "1915    0.05240    0.05340    0.05544    0.03332    0.03232    0.02932   \n",
       "574     0.06100    0.06304    0.06360    0.01100    0.01052    0.00992   \n",
       "1876    0.05000    0.04920    0.05012    0.07980    0.08112    0.08212   \n",
       "1023    0.11392    0.11396    0.11280    0.00380    0.00344    0.00380   \n",
       "2445    0.11380    0.11448    0.11472    0.00360    0.00372    0.00400   \n",
       "578     0.07400    0.07432    0.07624    0.00650    0.00600    0.00580   \n",
       "3233    0.10040    0.10036    0.10220    0.00500    0.00380    0.00368   \n",
       "1778    0.11120    0.10940    0.10928    0.00412    0.00348    0.00372   \n",
       "2967    0.04940    0.04952    0.05052    0.08036    0.08088    0.08388   \n",
       "583     0.05252    0.05328    0.05276    0.06040    0.05940    0.06068   \n",
       "439     0.11128    0.11280    0.11460    0.00420    0.00380    0.00360   \n",
       "3118    0.04760    0.04812    0.04860    0.07768    0.07884    0.07808   \n",
       "3208    0.05860    0.05904    0.05940    0.00676    0.00668    0.00680   \n",
       "\n",
       "      FC_out_average  CY_out_average   hap_cy   hap_fc  adjusted_2nd_batch_FC  \\\n",
       "168         0.111533        0.002547      NaN      NaN               0.219011   \n",
       "645         0.112347        0.002680      NaN      NaN               0.220223   \n",
       "455         0.048547        0.079933  0.12496  0.10310               0.125171   \n",
       "3275        0.110747        0.003067  0.01578  0.20662               0.217839   \n",
       "227         0.049200        0.086733      NaN      NaN               0.126145   \n",
       "766         0.113360        0.003593      NaN      NaN               0.221732   \n",
       "575         0.114280        0.003200  0.01524  0.21842               0.223103   \n",
       "1410        0.098147        0.003667  0.01450  0.20546               0.199067   \n",
       "416         0.114013        0.003333      NaN      NaN               0.222706   \n",
       "773         0.114547        0.003293      NaN      NaN               0.223500   \n",
       "582         0.055773        0.043720  0.10442  0.12586               0.135938   \n",
       "1830        0.050413        0.079453  0.11874  0.12182               0.127952   \n",
       "418         0.108653        0.003507      NaN      NaN               0.214720   \n",
       "1391        0.050120        0.082507      NaN      NaN               0.127515   \n",
       "745         0.109027        0.003933  0.01634  0.22164               0.215276   \n",
       "1854        0.093240        0.003813  0.01498  0.17446               0.191757   \n",
       "567         0.112667        0.003427      NaN      NaN               0.220699   \n",
       "1678        0.048707        0.081560      NaN      NaN               0.125410   \n",
       "770         0.114400        0.003467  0.01660  0.23132               0.223282   \n",
       "1915        0.053747        0.031653  0.07736  0.13704               0.132918   \n",
       "574         0.062547        0.010480      NaN      NaN               0.146029   \n",
       "1876        0.049773        0.081013      NaN      NaN               0.126999   \n",
       "1023        0.113560        0.003680  0.00642  0.22108               0.222030   \n",
       "2445        0.114333        0.003773  0.01660  0.23052               0.223182   \n",
       "578         0.074853        0.006100      NaN      NaN               0.164364   \n",
       "3233        0.100987        0.004160      NaN      NaN               0.203298   \n",
       "1778        0.109960        0.003773  0.01576  0.23300               0.216667   \n",
       "2967        0.049813        0.081707  0.12886  0.14082               0.127058   \n",
       "583         0.052853        0.060160      NaN      NaN               0.131587   \n",
       "439         0.112893        0.003867  0.01240  0.20690               0.221037   \n",
       "3118        0.048107        0.078200  0.12140  0.14056               0.124516   \n",
       "3208        0.059013        0.006747  0.02936  0.14894               0.140765   \n",
       "\n",
       "      adjusted_2nd_batch_CY  \n",
       "168                0.016087  \n",
       "645                0.016281  \n",
       "455                0.129166  \n",
       "3275               0.016846  \n",
       "227                0.139102  \n",
       "766                0.017616  \n",
       "575                0.017041  \n",
       "1410               0.017723  \n",
       "416                0.017236  \n",
       "773                0.017178  \n",
       "582                0.076250  \n",
       "1830               0.128465  \n",
       "418                0.017489  \n",
       "1391               0.132926  \n",
       "745                0.018113  \n",
       "1854               0.017937  \n",
       "567                0.017372  \n",
       "1678               0.131543  \n",
       "770                0.017431  \n",
       "1915               0.058618  \n",
       "574                0.027679  \n",
       "1876               0.130744  \n",
       "1023               0.017743  \n",
       "2445               0.017879  \n",
       "578                0.021279  \n",
       "3233               0.018444  \n",
       "1778               0.017879  \n",
       "2967               0.131757  \n",
       "583                0.100273  \n",
       "439                0.018015  \n",
       "3118               0.126633  \n",
       "3208               0.022224  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "validations_df['2nd_batch_FC'] = out_df['FC_out_average']\n",
    "validations_df['2nd_batch_CY'] = out_df['CY_out_average']\n",
    "\n",
    "validations_df['adjusted_2nd_batch_FC'] = out_df['adjusted_2nd_batch_FC']\n",
    "validations_df['adjusted_2nd_batch_CY'] = out_df['adjusted_2nd_batch_CY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "validations_df['combined_hap_fc']  = validations_df[[ 'hap_fc_average', 'adjusted_2nd_batch_FC']].mean(axis=1)\n",
    "validations_df['combined_hap_cy']  = validations_df[[ 'hap_cy_average', 'adjusted_2nd_batch_CY']].mean(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "codon                         86\n",
       "wt                             T\n",
       "measured                       M\n",
       "CY_1                         NaN\n",
       "CY_1_R1                      NaN\n",
       "                          ...   \n",
       "2nd_batch_CY                 NaN\n",
       "adjusted_2nd_batch_FC        NaN\n",
       "adjusted_2nd_batch_CY        NaN\n",
       "combined_hap_fc          0.16594\n",
       "combined_hap_cy          0.07756\n",
       "Name: 1770, Length: 87, dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df.loc[1770]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f30aefd4350>"
      ]
     },
     "execution_count": 94,
     "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": [
    "plt.scatter(validations_df['combined_hap_fc'], validations_df['combined_hap_cy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f30aecd2950>"
      ]
     },
     "execution_count": 100,
     "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": [
    "plt.scatter(validations_df['CY_score'], validations_df[\"FC_score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers_first_round = [439, 455, 514, 575, 582, 723, 744, 745, 770, 1023, 1778, 3118, 3275]\n",
    "second_round_outliers = [168, 227, 416, 418, 567, 574, 578, 583, 645, 766, 773, 1391, 1678, 1876, 3233]\n",
    "\n",
    "escape_outlier_dict = {}\n",
    "\n",
    "for index in list(validations_df.index):\n",
    "    \n",
    "    if validations_df.loc[index]['CY_score'] >0.35 and validations_df.loc[index]['FC_score'] >0.1:\n",
    "        escape_outlier_dict[index] = 'Yes'\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        escape_outlier_dict[index] = 'No'\n",
    "        \n",
    "validations_df['escape_outlier'] = pd.Series(escape_outlier_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "validations_df.at[1770, 'combined_hap_cy'] =np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "validations_df.to_csv('validations_table_v4.csv', columns=['codon', 'wt', 'measured', 'mtx1', 'mtx2', 'dmso1', 'dmso2', 'mtx_average', 'dmso_average', 'hap_fc_1', 'hap_fc_2', 'hap_cy_1', 'hap_cy_2', 'hap_fc_average', 'hap_cy_average', '2nd_batch_FC', '2nd_batch_CY','adjusted_2nd_batch_FC',\t'adjusted_2nd_batch_CY', 'combined_hap_fc',\t'combined_hap_cy', 'escape_outlier'], sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>codon</th>\n",
       "      <th>wt</th>\n",
       "      <th>measured</th>\n",
       "      <th>CY_1</th>\n",
       "      <th>CY_1_R1</th>\n",
       "      <th>CY_1_MAD_1</th>\n",
       "      <th>CY_1_R2</th>\n",
       "      <th>CY_1_MAD_2</th>\n",
       "      <th>FC_1</th>\n",
       "      <th>FC_1_R1</th>\n",
       "      <th>...</th>\n",
       "      <th>hap_fc_average</th>\n",
       "      <th>hap_cy_average</th>\n",
       "      <th>dmso_average</th>\n",
       "      <th>2nd_batch_FC</th>\n",
       "      <th>2nd_batch_CY</th>\n",
       "      <th>adjusted_2nd_batch_FC</th>\n",
       "      <th>adjusted_2nd_batch_CY</th>\n",
       "      <th>combined_hap_fc</th>\n",
       "      <th>combined_hap_cy</th>\n",
       "      <th>escape_outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>8</td>\n",
       "      <td>S</td>\n",
       "      <td>F</td>\n",
       "      <td>-1.111151</td>\n",
       "      <td>-0.922339</td>\n",
       "      <td>0.415623</td>\n",
       "      <td>-1.299963</td>\n",
       "      <td>0.276321</td>\n",
       "      <td>-4.373589</td>\n",
       "      <td>-4.495269</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>10</td>\n",
       "      <td>W</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.136679</td>\n",
       "      <td>-0.322614</td>\n",
       "      <td>0.504528</td>\n",
       "      <td>0.049255</td>\n",
       "      <td>0.105939</td>\n",
       "      <td>1.184129</td>\n",
       "      <td>1.158049</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.111533</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>0.219011</td>\n",
       "      <td>0.016087</td>\n",
       "      <td>0.219011</td>\n",
       "      <td>0.016087</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>12</td>\n",
       "      <td>Q</td>\n",
       "      <td>K</td>\n",
       "      <td>0.769357</td>\n",
       "      <td>1.416442</td>\n",
       "      <td>1.857860</td>\n",
       "      <td>0.122271</td>\n",
       "      <td>0.298271</td>\n",
       "      <td>-1.133104</td>\n",
       "      <td>-1.532682</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.049200</td>\n",
       "      <td>0.086733</td>\n",
       "      <td>0.126145</td>\n",
       "      <td>0.139102</td>\n",
       "      <td>0.126145</td>\n",
       "      <td>0.139102</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>14</td>\n",
       "      <td>G</td>\n",
       "      <td>W</td>\n",
       "      <td>-3.128267</td>\n",
       "      <td>-2.879006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.377528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.131018</td>\n",
       "      <td>1.272348</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20750</td>\n",
       "      <td>0.01094</td>\n",
       "      <td>0.21418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>0.010940</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>14</td>\n",
       "      <td>G</td>\n",
       "      <td>Y</td>\n",
       "      <td>-4.292014</td>\n",
       "      <td>-3.590963</td>\n",
       "      <td>1.042929</td>\n",
       "      <td>-4.993065</td>\n",
       "      <td>0.434378</td>\n",
       "      <td>-1.111130</td>\n",
       "      <td>-1.142447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.16960</td>\n",
       "      <td>0.03144</td>\n",
       "      <td>0.20430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.169600</td>\n",
       "      <td>0.031440</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>155</td>\n",
       "      <td>D</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.100987</td>\n",
       "      <td>0.004160</td>\n",
       "      <td>0.203298</td>\n",
       "      <td>0.018444</td>\n",
       "      <td>0.203298</td>\n",
       "      <td>0.018444</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3249</th>\n",
       "      <td>156</td>\n",
       "      <td>I</td>\n",
       "      <td>Q</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.15884</td>\n",
       "      <td>0.01588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.158840</td>\n",
       "      <td>0.015880</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3250</th>\n",
       "      <td>156</td>\n",
       "      <td>I</td>\n",
       "      <td>H</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.15546</td>\n",
       "      <td>0.02130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.155460</td>\n",
       "      <td>0.021300</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3274</th>\n",
       "      <td>157</td>\n",
       "      <td>G</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20648</td>\n",
       "      <td>0.01174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.206480</td>\n",
       "      <td>0.011740</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>157</td>\n",
       "      <td>G</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>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20662</td>\n",
       "      <td>0.01578</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.110747</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>0.217839</td>\n",
       "      <td>0.016846</td>\n",
       "      <td>0.212229</td>\n",
       "      <td>0.016313</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codon wt measured      CY_1   CY_1_R1  CY_1_MAD_1   CY_1_R2  CY_1_MAD_2  \\\n",
       "136       8  S        F -1.111151 -0.922339    0.415623 -1.299963    0.276321   \n",
       "168      10  W        * -0.136679 -0.322614    0.504528  0.049255    0.105939   \n",
       "227      12  Q        K  0.769357  1.416442    1.857860  0.122271    0.298271   \n",
       "261      14  G        W -3.128267 -2.879006    0.000000 -3.377528    0.000000   \n",
       "263      14  G        Y -4.292014 -3.590963    1.042929 -4.993065    0.434378   \n",
       "...     ... ..      ...       ...       ...         ...       ...         ...   \n",
       "3233    155  D        E       NaN       NaN         NaN       NaN         NaN   \n",
       "3249    156  I        Q       NaN       NaN         NaN       NaN         NaN   \n",
       "3250    156  I        H       NaN       NaN         NaN       NaN         NaN   \n",
       "3274    157  G        D       NaN       NaN         NaN       NaN         NaN   \n",
       "3275    157  G        E       NaN       NaN         NaN       NaN         NaN   \n",
       "\n",
       "          FC_1   FC_1_R1  ...  hap_fc_average  hap_cy_average  dmso_average  \\\n",
       "136  -4.373589 -4.495269  ...             NaN             NaN           NaN   \n",
       "168   1.184129  1.158049  ...             NaN             NaN           NaN   \n",
       "227  -1.133104 -1.532682  ...             NaN             NaN           NaN   \n",
       "261   1.131018  1.272348  ...         0.20750         0.01094       0.21418   \n",
       "263  -1.111130 -1.142447  ...         0.16960         0.03144       0.20430   \n",
       "...        ...       ...  ...             ...             ...           ...   \n",
       "3233       NaN       NaN  ...             NaN             NaN           NaN   \n",
       "3249       NaN       NaN  ...         0.15884         0.01588           NaN   \n",
       "3250       NaN       NaN  ...         0.15546         0.02130           NaN   \n",
       "3274       NaN       NaN  ...         0.20648         0.01174           NaN   \n",
       "3275       NaN       NaN  ...         0.20662         0.01578           NaN   \n",
       "\n",
       "      2nd_batch_FC  2nd_batch_CY  adjusted_2nd_batch_FC  \\\n",
       "136            NaN           NaN                    NaN   \n",
       "168       0.111533      0.002547               0.219011   \n",
       "227       0.049200      0.086733               0.126145   \n",
       "261            NaN           NaN                    NaN   \n",
       "263            NaN           NaN                    NaN   \n",
       "...            ...           ...                    ...   \n",
       "3233      0.100987      0.004160               0.203298   \n",
       "3249           NaN           NaN                    NaN   \n",
       "3250           NaN           NaN                    NaN   \n",
       "3274           NaN           NaN                    NaN   \n",
       "3275      0.110747      0.003067               0.217839   \n",
       "\n",
       "      adjusted_2nd_batch_CY  combined_hap_fc  combined_hap_cy  escape_outlier  \n",
       "136                     NaN              NaN              NaN              No  \n",
       "168                0.016087         0.219011         0.016087             Yes  \n",
       "227                0.139102         0.126145         0.139102             Yes  \n",
       "261                     NaN         0.207500         0.010940              No  \n",
       "263                     NaN         0.169600         0.031440              No  \n",
       "...                     ...              ...              ...             ...  \n",
       "3233               0.018444         0.203298         0.018444             Yes  \n",
       "3249                    NaN         0.158840         0.015880              No  \n",
       "3250                    NaN         0.155460         0.021300              No  \n",
       "3274                    NaN         0.206480         0.011740             Yes  \n",
       "3275               0.016846         0.212229         0.016313              No  \n",
       "\n",
       "[95 rows x 88 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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