Fig2abControl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9dc0a0e4-cec0-453a-84ca-4b477fc54918",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "from itertools import chain\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "import time\n",
    "import glob\n",
    "\n",
    "params = {\n",
    "   'axes.labelsize': 14,\n",
    "   'font.size': 14,\n",
    "   'legend.fontsize': 14,\n",
    "   'xtick.labelsize': 14,\n",
    "   'ytick.labelsize': 14,\n",
    "   'text.usetex': False,\n",
    "   'figure.figsize': [4.5, 4.5],\n",
    "    'savefig.facecolor':'white'\n",
    "   }\n",
    "\n",
    "mpl.rcParams.update(params)\n",
    "\n",
    "timestr = time.strftime(\"%Y%m%d%H%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9bb67f27-e03e-4a0e-add9-33db97a7388f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "sol_saveat_dt = [1,1e-4][0]\n",
    "\n",
    "print(sol_saveat_dt)\n",
    "\n",
    "inpath = \"output/\"\n",
    "outpath = \"figures/fig2/\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3eb24459-423a-4ca3-94be-c3e8e5ed9a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################\n",
    "# set to True to save fig\n",
    "\n",
    "savefigures = False #False #True \n",
    "\n",
    "##############################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b338d80-de58-4cf5-a78e-964bd16fc620",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190\n"
     ]
    }
   ],
   "source": [
    "print(len(glob.glob(inpath+\"*_u.csv\"))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff2b52ae-46a5-4b95-9c76-01c3ab482c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_ids = [i.rstrip(\"_u.csv\") for i in glob.glob(inpath+\"*_u.csv\")]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39a27e61-10f7-4738-9e6d-55de20dc5f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_ids = sorted(input_file_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c92fe670-7b11-4e57-9c27-10e9e3a81341",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60166afa-3a76-4383-885e-81cf53826faf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "input_dfs = []\n",
    "\n",
    "for fid in input_file_ids:\n",
    "\n",
    "    dfn = fid + \"_u.csv\"\n",
    "    tfn = fid + \"_t.csv\"\n",
    "    nfn = fid + \"_unames.csv\"\n",
    "    \n",
    "    df = pd.read_csv(dfn,header=None)\n",
    "    tf = pd.read_csv(tfn,header=None)\n",
    "    nf = pd.read_csv(nfn,header=None)\n",
    "    \n",
    "    df.columns = [i[:-1] for i in nf[0].values.tolist() ]\n",
    "    \n",
    "    df['time_s'] = tf[0].copy()\n",
    "    df['condition'] = fid.replace(\"output/sim_\",\"\").split(\"_extraTag\",1)[0]\n",
    "    \n",
    "    input_dfs.append(df)\n",
    "    \n",
    "input_df = pd.concat(input_dfs, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9fbc2867-d604-4c06-9603-f5279f9b35c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58900, 185)\n"
     ]
    }
   ],
   "source": [
    "print(input_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "988bf959-b7f3-47ee-9f40-827559a0aa29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e9f8740-9851-4e01-af4d-81d41bd1c0cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94\n"
     ]
    }
   ],
   "source": [
    "mets2plot2_sel3met = [i[0:-1] for i in [\"Mg_x_n0\",\"NADHmito_n0\", \"QH2mito_n0\", \"CytCredmito_n0\",  \n",
    "            \"ATPmito_n0\", \"ATP_mx_n0\", \"ATP_i_n0\", \"ATP_mi_n0\", \n",
    "             \n",
    "            \"ATP_n0\", \"FUMmito_n0\", \"MALmito_n0\", \"OXAmito_n0\", \"SUCmito_n0\", \"SUCCOAmito_n0\", \"CoAmito_n0\", \n",
    "            \"AKGmito_n0\",  \"ISOCITmito_n0\", \"CITmito_n0\", \"AcCoAmito_n0\", \"AcAc_n0\", \"AcAcCoA_n0\", \"PYRmito_n0\", \"bHB_n0\", \"bHB_ecs0\", \"bHB_b0\",  \"GLU_n0\", \n",
    "            \"NADH_n0\", \"Mg_x_a0\",   \"NADHmito_a0\", \"QH2mito_a0\", \"CytCredmito_a0\",  \n",
    "            \"ATPmito_a0\",   \"ATP_mx_a0\",  \"ATP_i_a0\",   \"ATP_mi_a0\",  \n",
    "             \n",
    "            \"ATP_a0\",  \"FUMmito_a0\", \"MALmito_a0\", \"OXAmito_a0\", \"SUCmito_a0\", \"SUCCOAmito_a0\", \"CoAmito_a0\", \n",
    "            \"AKGmito_a0\", \"ISOCITmito_a0\", \"CITmito_a0\", \"AcCoAmito_a0\", \"PYRmito_a0\", \n",
    "            \"GLN_a0\", \"GLUT_a0\", \n",
    "             \n",
    "            \"ddHb0\", \"O2cap0\", \n",
    "              \"Glc_b0\", \"Glc_t_t0\", \"Glc_ecsBA0\", \"Glc_a0\", \"Glc_ecsAN0\", \"Glc_n0\", \"G6P_n0\", \"G6P_a0\", \"F6P_n0\", \"F6P_a0\", \"FBP_n0\", \"FBP_a0\", \"f26bp_a0\", \"GLY_a0\", \"G1P_a0\", \"GAP_n0\", \"GAP_a0\", \n",
    "              \"DHAP_n0\", \"DHAP_a0\", \"BPG13_n0\", \"BPG13_a0\", \"NADH_a0\", \"PG3_n0\", \"PG3_a0\", \"PG2_n0\", \"PG2_a0\", \"PEP_n0\", \"PEP_a0\", \"Pyr_n0\", \"Pyr_a0\", \"Lac_b0\", \"Lac_ecs0\", \"Lac_a0\", \"Lac_n0\", \n",
    "              \"NADPH_n0\", \"NADPH_a0\", \"R5P_n0\", \"R5P_a0\",  \"E4P_n0\", \"E4P_a0\", \n",
    "              \"GSH_n0\", \"GSH_a0\", \"cAMP_a0\"\n",
    "]]\n",
    "\n",
    "print(len(mets2plot2_sel3met))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb87347b-69d6-4975-81b5-0e40a265d595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n",
      "38\n"
     ]
    }
   ],
   "source": [
    "all_cnds_l = input_df['condition'].unique()\n",
    "\n",
    "group1_control = [i for i in all_cnds_l if (not i.startswith('g')) and (not i.startswith('th')) and (\"_dose\" not in i) \n",
    "                 ]\n",
    "\n",
    "print(len(group1_control))\n",
    "\n",
    "\n",
    "\n",
    "group2_GLUT1 = [i for i in all_cnds_l if ((i.startswith('g1_')) or (i.startswith('th_')))  and (\"_dose\" not in i) \n",
    "               ]\n",
    "\n",
    "print(len(group2_GLUT1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d3616c12-9672-490f-a09c-163337ade2a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['10_lac_pls',\n",
       " '11_bhb_pls',\n",
       " '121_default',\n",
       " '12_akg_pls',\n",
       " '13_succoa_pls',\n",
       " '14_fum_pls',\n",
       " '15_mal_pls',\n",
       " '16_oxa_pls',\n",
       " '17_glclac_ini',\n",
       " '18_glcbhb_ini',\n",
       " '19_lacbhb_ini',\n",
       " '1_blood_glc_ini',\n",
       " '20_glclacbhb_ini',\n",
       " '2_blood_lac_ini',\n",
       " '3_blood_bhb_ini',\n",
       " '4_aKG_ini',\n",
       " '5_succoa_ini',\n",
       " '63_AcAcCoA_ini',\n",
       " '64_Acon',\n",
       " '6_fum_ini',\n",
       " '7_mal_ini',\n",
       " '82_ISOCIT',\n",
       " '83_LDH',\n",
       " '84_PCm_bloodglc',\n",
       " '85_PCm',\n",
       " '86_PDHdef_bloodglc',\n",
       " '87_PDHdef_bloodlac',\n",
       " '88_PDHdef',\n",
       " '89_SUCmito',\n",
       " '8_oxa_ini',\n",
       " '9_glc_pls']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1_control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "42955bd0-b95f-4d07-9434-f2c53eefbc8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['g1_118_keto_lac_nad',\n",
       " 'g1_119_keto_lac_nad_Qtot_n',\n",
       " 'g1_120_keto_lac_nad_Qtot_na',\n",
       " 'g1_21_blood_glc_ini',\n",
       " 'g1_22_blood_lac_ini',\n",
       " 'g1_23_blood_bhb_ini',\n",
       " 'g1_24_aKG_ini',\n",
       " 'g1_25_succoa_ini',\n",
       " 'g1_26_fum_ini',\n",
       " 'g1_27_mal_ini',\n",
       " 'g1_28_oxa_ini',\n",
       " 'g1_29_glc_pls',\n",
       " 'g1_30_lac_pls',\n",
       " 'g1_31_bhb_pls',\n",
       " 'g1_32_akg_pls',\n",
       " 'g1_33_succoa_pls',\n",
       " 'g1_34_fum_pls',\n",
       " 'g1_35_mal_pls',\n",
       " 'g1_36_oxa_pls',\n",
       " 'g1_37_glclac_ini',\n",
       " 'g1_38_glcbhb_ini',\n",
       " 'g1_39_lacbhb_ini',\n",
       " 'g1_40_glclacbhb_ini',\n",
       " 'g1_61_def',\n",
       " 'g1_65_PCm_keto',\n",
       " 'g1_66_PCm_lac',\n",
       " 'g1_67_PCm',\n",
       " 'g1_68_AcAcCoA',\n",
       " 'g1_69_Acon',\n",
       " 'g1_70_ISOCIT',\n",
       " 'g1_71_LDH_keto',\n",
       " 'g1_72_LDH',\n",
       " 'g1_73_PDH_keto',\n",
       " 'g1_74_PDH_lac',\n",
       " 'g1_75_PDH',\n",
       " 'g1_76_SUCmito',\n",
       " 'g1_90_AcAcCoa_lac',\n",
       " 'g1_91_PDH_Lac_keto']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group2_GLUT1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "679560e5-007d-4c6b-ab74-a8082209ae27",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cnds_groups = {\n",
    "    'group1_control':group1_control,\n",
    "    'group2_GLUT1d':group2_GLUT1\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25129a63-b126-4869-a6e6-3313363354b1",
   "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>C_H_mitomatr_n</th>\n",
       "      <th>K_x_n</th>\n",
       "      <th>Mg_x_n</th>\n",
       "      <th>NADHmito_n</th>\n",
       "      <th>QH2mito_n</th>\n",
       "      <th>CytCredmito_n</th>\n",
       "      <th>O2_n</th>\n",
       "      <th>ATPmito_n</th>\n",
       "      <th>ADPmito_n</th>\n",
       "      <th>ATP_mx_n</th>\n",
       "      <th>...</th>\n",
       "      <th>PCr_a</th>\n",
       "      <th>cAMP_a</th>\n",
       "      <th>NE_neuromod</th>\n",
       "      <th>UDPgluco_a</th>\n",
       "      <th>UTP_a</th>\n",
       "      <th>GS_a</th>\n",
       "      <th>GPa_a</th>\n",
       "      <th>GPb_a</th>\n",
       "      <th>time_s</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>54.885139</td>\n",
       "      <td>0.63959</td>\n",
       "      <td>0.314409</td>\n",
       "      <td>0.015754</td>\n",
       "      <td>0.132538</td>\n",
       "      <td>0.031431</td>\n",
       "      <td>0.743227</td>\n",
       "      <td>1.856773</td>\n",
       "      <td>0.206694</td>\n",
       "      <td>...</td>\n",
       "      <td>4.927256</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.068653</td>\n",
       "      <td>180.0</td>\n",
       "      <td>10_lac_pls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>54.885139</td>\n",
       "      <td>0.63959</td>\n",
       "      <td>0.314409</td>\n",
       "      <td>0.015753</td>\n",
       "      <td>0.132538</td>\n",
       "      <td>0.031431</td>\n",
       "      <td>0.743228</td>\n",
       "      <td>1.856772</td>\n",
       "      <td>0.206695</td>\n",
       "      <td>...</td>\n",
       "      <td>4.927248</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.068653</td>\n",
       "      <td>181.0</td>\n",
       "      <td>10_lac_pls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>54.885139</td>\n",
       "      <td>0.63959</td>\n",
       "      <td>0.314409</td>\n",
       "      <td>0.015753</td>\n",
       "      <td>0.132538</td>\n",
       "      <td>0.031431</td>\n",
       "      <td>0.743228</td>\n",
       "      <td>1.856772</td>\n",
       "      <td>0.206695</td>\n",
       "      <td>...</td>\n",
       "      <td>4.927240</td>\n",
       "      <td>0.039109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.068653</td>\n",
       "      <td>182.0</td>\n",
       "      <td>10_lac_pls</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 185 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     C_H_mitomatr_n      K_x_n   Mg_x_n  NADHmito_n  QH2mito_n  CytCredmito_n  \\\n",
       "179        0.000018  54.885139  0.63959    0.314409   0.015754       0.132538   \n",
       "180        0.000018  54.885139  0.63959    0.314409   0.015753       0.132538   \n",
       "181        0.000018  54.885139  0.63959    0.314409   0.015753       0.132538   \n",
       "\n",
       "         O2_n  ATPmito_n  ADPmito_n  ATP_mx_n  ...     PCr_a    cAMP_a  \\\n",
       "179  0.031431   0.743227   1.856773  0.206694  ...  4.927256  0.039110   \n",
       "180  0.031431   0.743228   1.856772  0.206695  ...  4.927248  0.039110   \n",
       "181  0.031431   0.743228   1.856772  0.206695  ...  4.927240  0.039109   \n",
       "\n",
       "     NE_neuromod  UDPgluco_a  UTP_a   GS_a     GPa_a     GPb_a  time_s  \\\n",
       "179          0.0       0.109   0.23  0.003  0.001347  0.068653   180.0   \n",
       "180          0.0       0.109   0.23  0.003  0.001347  0.068653   181.0   \n",
       "181          0.0       0.109   0.23  0.003  0.001347  0.068653   182.0   \n",
       "\n",
       "      condition  \n",
       "179  10_lac_pls  \n",
       "180  10_lac_pls  \n",
       "181  10_lac_pls  \n",
       "\n",
       "[3 rows x 185 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp = input_df.loc[(input_df['time_s']>=180),:].copy()  \n",
    "\n",
    "input_df_mp = input_df_mp.loc[(input_df_mp['condition'].isin(group1_control)),:].copy() \n",
    "input_df_mp.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7dca03b6-1621-403b-9e79-05e72d09a973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, (4061, 185))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp['condition'].nunique(), input_df_mp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a433d92d-30fe-4cd1-9838-16050dcb583e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>C_H_mitomatr_n</th>\n",
       "      <th>K_x_n</th>\n",
       "      <th>Mg_x_n</th>\n",
       "      <th>NADHmito_n</th>\n",
       "      <th>QH2mito_n</th>\n",
       "      <th>CytCredmito_n</th>\n",
       "      <th>O2_n</th>\n",
       "      <th>ATPmito_n</th>\n",
       "      <th>ADPmito_n</th>\n",
       "      <th>ATP_mx_n</th>\n",
       "      <th>...</th>\n",
       "      <th>Cr_a</th>\n",
       "      <th>PCr_a</th>\n",
       "      <th>cAMP_a</th>\n",
       "      <th>NE_neuromod</th>\n",
       "      <th>UDPgluco_a</th>\n",
       "      <th>UTP_a</th>\n",
       "      <th>GS_a</th>\n",
       "      <th>GPa_a</th>\n",
       "      <th>GPb_a</th>\n",
       "      <th>time_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>54.885139</td>\n",
       "      <td>0.63959</td>\n",
       "      <td>0.314409</td>\n",
       "      <td>0.015754</td>\n",
       "      <td>0.132538</td>\n",
       "      <td>0.031431</td>\n",
       "      <td>0.743227</td>\n",
       "      <td>1.856773</td>\n",
       "      <td>0.206694</td>\n",
       "      <td>...</td>\n",
       "      <td>5.075794</td>\n",
       "      <td>4.927256</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.068653</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>54.885139</td>\n",
       "      <td>0.63959</td>\n",
       "      <td>0.314409</td>\n",
       "      <td>0.015753</td>\n",
       "      <td>0.132538</td>\n",
       "      <td>0.031431</td>\n",
       "      <td>0.743228</td>\n",
       "      <td>1.856772</td>\n",
       "      <td>0.206695</td>\n",
       "      <td>...</td>\n",
       "      <td>5.075794</td>\n",
       "      <td>4.927248</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.068653</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>54.885139</td>\n",
       "      <td>0.63959</td>\n",
       "      <td>0.314409</td>\n",
       "      <td>0.015753</td>\n",
       "      <td>0.132538</td>\n",
       "      <td>0.031431</td>\n",
       "      <td>0.743228</td>\n",
       "      <td>1.856772</td>\n",
       "      <td>0.206695</td>\n",
       "      <td>...</td>\n",
       "      <td>5.075794</td>\n",
       "      <td>4.927240</td>\n",
       "      <td>0.039109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.068653</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 184 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     C_H_mitomatr_n      K_x_n   Mg_x_n  NADHmito_n  QH2mito_n  CytCredmito_n  \\\n",
       "179        0.000018  54.885139  0.63959    0.314409   0.015754       0.132538   \n",
       "180        0.000018  54.885139  0.63959    0.314409   0.015753       0.132538   \n",
       "181        0.000018  54.885139  0.63959    0.314409   0.015753       0.132538   \n",
       "\n",
       "         O2_n  ATPmito_n  ADPmito_n  ATP_mx_n  ...      Cr_a     PCr_a  \\\n",
       "179  0.031431   0.743227   1.856773  0.206694  ...  5.075794  4.927256   \n",
       "180  0.031431   0.743228   1.856772  0.206695  ...  5.075794  4.927248   \n",
       "181  0.031431   0.743228   1.856772  0.206695  ...  5.075794  4.927240   \n",
       "\n",
       "       cAMP_a  NE_neuromod  UDPgluco_a  UTP_a   GS_a     GPa_a     GPb_a  \\\n",
       "179  0.039110          0.0       0.109   0.23  0.003  0.001347  0.068653   \n",
       "180  0.039110          0.0       0.109   0.23  0.003  0.001347  0.068653   \n",
       "181  0.039109          0.0       0.109   0.23  0.003  0.001347  0.068653   \n",
       "\n",
       "     time_s  \n",
       "179   180.0  \n",
       "180   181.0  \n",
       "181   182.0  \n",
       "\n",
       "[3 rows x 184 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp = input_df_mp.drop(columns='condition')\n",
    "\n",
    "\n",
    "input_df_mp.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de471cda-4073-4851-9eb6-db398813c316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(131, 184)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C_H_mitomatr_n</th>\n",
       "      <th>K_x_n</th>\n",
       "      <th>Mg_x_n</th>\n",
       "      <th>NADHmito_n</th>\n",
       "      <th>QH2mito_n</th>\n",
       "      <th>CytCredmito_n</th>\n",
       "      <th>O2_n</th>\n",
       "      <th>ATPmito_n</th>\n",
       "      <th>ADPmito_n</th>\n",
       "      <th>ATP_mx_n</th>\n",
       "      <th>...</th>\n",
       "      <th>PCr_n</th>\n",
       "      <th>Cr_a</th>\n",
       "      <th>PCr_a</th>\n",
       "      <th>cAMP_a</th>\n",
       "      <th>NE_neuromod</th>\n",
       "      <th>UDPgluco_a</th>\n",
       "      <th>UTP_a</th>\n",
       "      <th>GS_a</th>\n",
       "      <th>GPa_a</th>\n",
       "      <th>GPb_a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_s</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180.0</th>\n",
       "      <td>1.860745e-16</td>\n",
       "      <td>1.294599e-16</td>\n",
       "      <td>0.000725</td>\n",
       "      <td>0.003804</td>\n",
       "      <td>0.006408</td>\n",
       "      <td>0.002057</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>0.003613</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.004139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>5.249494e-16</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.447349e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009603</td>\n",
       "      <td>0.000187</td>\n",
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       "    <tr>\n",
       "      <th>181.0</th>\n",
       "      <td>1.860745e-16</td>\n",
       "      <td>1.294599e-16</td>\n",
       "      <td>0.000726</td>\n",
       "      <td>0.003800</td>\n",
       "      <td>0.006424</td>\n",
       "      <td>0.002062</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.003618</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>5.249494e-16</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.447349e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.000188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182.0</th>\n",
       "      <td>1.860745e-16</td>\n",
       "      <td>1.294599e-16</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.003795</td>\n",
       "      <td>0.006439</td>\n",
       "      <td>0.002066</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.003623</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>0.004150</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>5.249494e-16</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.447349e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009628</td>\n",
       "      <td>0.000188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 183 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        C_H_mitomatr_n         K_x_n    Mg_x_n  NADHmito_n  QH2mito_n  \\\n",
       "time_s                                                                  \n",
       "180.0     1.860745e-16  1.294599e-16  0.000725    0.003804   0.006408   \n",
       "181.0     1.860745e-16  1.294599e-16  0.000726    0.003800   0.006424   \n",
       "182.0     1.860745e-16  1.294599e-16  0.000727    0.003795   0.006439   \n",
       "\n",
       "        CytCredmito_n      O2_n  ATPmito_n  ADPmito_n  ATP_mx_n  ...  \\\n",
       "time_s                                                           ...   \n",
       "180.0        0.002057  0.000901   0.003613   0.001451  0.004139  ...   \n",
       "181.0        0.002062  0.000900   0.003618   0.001453  0.004144  ...   \n",
       "182.0        0.002066  0.000900   0.003623   0.001455  0.004150  ...   \n",
       "\n",
       "           PCr_n          Cr_a     PCr_a    cAMP_a  NE_neuromod  UDPgluco_a  \\\n",
       "time_s                                                                        \n",
       "180.0   0.000887  5.249494e-16  0.000139  0.000381          NaN         0.0   \n",
       "181.0   0.000891  5.249494e-16  0.000140  0.000381          NaN         0.0   \n",
       "182.0   0.000895  5.249494e-16  0.000141  0.000382          NaN         0.0   \n",
       "\n",
       "               UTP_a  GS_a     GPa_a     GPb_a  \n",
       "time_s                                          \n",
       "180.0   8.447349e-16   0.0  0.009603  0.000187  \n",
       "181.0   8.447349e-16   0.0  0.009615  0.000188  \n",
       "182.0   8.447349e-16   0.0  0.009628  0.000188  \n",
       "\n",
       "[3 rows x 183 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl = pd.DataFrame(input_df_mp.groupby(\"time_s\").apply(lambda x: np.std(np.abs(x))/np.mean(np.abs(x)) ))\n",
    "\n",
    "print(input_df_mp_cvAmpl.shape)\n",
    "\n",
    "input_df_mp_cvAmpl = input_df_mp_cvAmpl.drop(columns='time_s')\n",
    "\n",
    "input_df_mp_cvAmpl.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cf604ea6-8443-4fbb-8761-3734e7f3766a",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mp_cvAmpl = input_df_mp_cvAmpl.reset_index(drop=False)\n",
    "\n",
    "input_df_mp_cvAmpl_l = pd.melt(input_df_mp_cvAmpl,id_vars=['time_s'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b175ec09-e66f-472c-ad8d-73ae86d5225a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>time_s</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>181.0</td>\n",
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       "      <td>1.860745e-16</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>182.0</td>\n",
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       "      <td>1.860745e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time_s        variable         value\n",
       "0   180.0  C_H_mitomatr_n  1.860745e-16\n",
       "1   181.0  C_H_mitomatr_n  1.860745e-16\n",
       "2   182.0  C_H_mitomatr_n  1.860745e-16"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "58a36985-ad12-4af2-b0bf-e3fb23b56423",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   time_s        variable         value\n",
       "0   180.0  C_H_mitomatr_n  1.860745e-16\n",
       "1   181.0  C_H_mitomatr_n  1.860745e-16\n",
       "2   182.0  C_H_mitomatr_n  1.860745e-16"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6597fc98-16f6-4605-a632-02f587ec4d76",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_cmps_index(df):\n",
    "    cmps1 = []\n",
    "    for i in df['variable'].to_list():\n",
    "        if len(i.split(\"_\")) >1:\n",
    "            if i.split(\"_\")[-1] == \"ecsAN0\":\n",
    "                cmps1.append(\"ecs\")\n",
    "            elif i.split(\"_\")[-1] == \"ecsBA0\":\n",
    "                cmps1.append(\"ecs\")\n",
    "            elif i.split(\"_\")[-1] == \"out0\":\n",
    "                cmps1.append(\"ecs\")\n",
    "            else:\n",
    "                cmps1.append(i.split(\"_\")[-1].rstrip(\"0\"))\n",
    "        else:\n",
    "            #print(i)\n",
    "            cmps1.append(\"other\")\n",
    "            \n",
    "    return cmps1\n",
    "\n",
    "cmps = get_cmps_index(input_df_mp_cvAmpl_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a75f113-f9d9-4090-8420-7298d2c20dda",
   "metadata": {},
   "outputs": [
    {
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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>time_s</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>cmps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>180.0</td>\n",
       "      <td>C_H_mitomatr_n</td>\n",
       "      <td>1.860745e-16</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>181.0</td>\n",
       "      <td>C_H_mitomatr_n</td>\n",
       "      <td>1.860745e-16</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>182.0</td>\n",
       "      <td>C_H_mitomatr_n</td>\n",
       "      <td>1.860745e-16</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time_s        variable         value cmps\n",
       "0   180.0  C_H_mitomatr_n  1.860745e-16    n\n",
       "1   181.0  C_H_mitomatr_n  1.860745e-16    n\n",
       "2   182.0  C_H_mitomatr_n  1.860745e-16    n"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l['cmps'] = cmps\n",
    "input_df_mp_cvAmpl_l.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4094734-f2c1-43f4-85d2-50d4ca054870",
   "metadata": {},
   "outputs": [],
   "source": [
    "fid = '202303211313'\n",
    "with open(f\"data/v2function_{fid}.json\",\"r\") as f:\n",
    "    v2function = json.load(f)\n",
    "\n",
    "lut_funs = {'keto': '#a6cee3',\n",
    " 'ETC': '#1f78b4',\n",
    " 'TCA': '#b2df8a',\n",
    " 'redox': '#33a02c',\n",
    " 'ATDMP': '#fb9a99',\n",
    " 'GLTGLN': '#e31a1c',\n",
    " 'resp': '#fdbf6f',\n",
    " 'GLCLS': '#ff7f00',\n",
    " 'PPP': '#cab2d6'}\n",
    "\n",
    "input_df_mp_cvAmpl_l['fns'] = input_df_mp_cvAmpl_l['variable'].map(v2function)\n",
    "\n",
    "input_df_mp_cvAmpl_l.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "165eafec-4f14-45c0-9d19-e9c1f4e6d4fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_s</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>cmps</th>\n",
       "      <th>fns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15065</th>\n",
       "      <td>180.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.077604</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15066</th>\n",
       "      <td>181.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.077803</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15067</th>\n",
       "      <td>182.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.078001</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time_s variable     value cmps    fns\n",
       "15065   180.0  Glc_t_t  0.077604    t  GLCLS\n",
       "15066   181.0  Glc_t_t  0.077803    t  GLCLS\n",
       "15067   182.0  Glc_t_t  0.078001    t  GLCLS"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l = input_df_mp_cvAmpl_l.sort_values(by=['cmps','fns'],ascending=False)\n",
    "\n",
    "input_df_mp_cvAmpl_l.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4da696fe-3bf1-41df-9e6d-ccbc2347cc84",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mp_cvAmpl_l[[\"variable_id\",\"variable_cmp\"]] = input_df_mp_cvAmpl_l['variable'].str.split(\"_\",1, expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2819c1fc-6b63-44f7-baa8-f158082181fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a588016-e059-4e0c-9343-a82775c7b890",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e88c92ac-ffbf-4c6b-9f38-e7d711d75b0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e2a598f5-c130-473d-940d-cdb9700e4024",
   "metadata": {},
   "outputs": [],
   "source": [
    "mypal_cmps = ['#32db70','#06916a','red'] \n",
    "lut_cmps = dict(zip(['n','a','b_ecs'], mypal_cmps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "451a978d-9826-484e-aebd-8da4cc39bc15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0022e1b9-45d3-4e58-96f1-7722f19c4166",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mp_cvAmpl_l_sel = input_df_mp_cvAmpl_l.loc[input_df_mp_cvAmpl_l['variable'].isin(mets2plot2_sel3met),:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f704577-0e99-498f-9893-cc88e48355f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_s</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>cmps</th>\n",
       "      <th>fns</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>variable_cmp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15065</th>\n",
       "      <td>180.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.077604</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>Glc</td>\n",
       "      <td>t_t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15066</th>\n",
       "      <td>181.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.077803</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>Glc</td>\n",
       "      <td>t_t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15067</th>\n",
       "      <td>182.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.078001</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>Glc</td>\n",
       "      <td>t_t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15068</th>\n",
       "      <td>183.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.078199</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>Glc</td>\n",
       "      <td>t_t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15069</th>\n",
       "      <td>184.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.078397</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>Glc</td>\n",
       "      <td>t_t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time_s variable     value cmps    fns variable_id variable_cmp\n",
       "15065   180.0  Glc_t_t  0.077604    t  GLCLS         Glc          t_t\n",
       "15066   181.0  Glc_t_t  0.077803    t  GLCLS         Glc          t_t\n",
       "15067   182.0  Glc_t_t  0.078001    t  GLCLS         Glc          t_t\n",
       "15068   183.0  Glc_t_t  0.078199    t  GLCLS         Glc          t_t\n",
       "15069   184.0  Glc_t_t  0.078397    t  GLCLS         Glc          t_t"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l_sel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "da56ed86-bd91-4f56-9913-f52b784b8126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove met that are present only in one of the cell types\n",
    "\n",
    "met_n = []\n",
    "met_a = []\n",
    "\n",
    "for i,row in input_df_mp_cvAmpl_l_sel.iterrows():\n",
    "    if row['cmps'] == 'n':\n",
    "        met_n.append(row['variable_id'])\n",
    "    elif row['cmps'] == 'a':\n",
    "        met_a.append(row['variable_id'])\n",
    "        \n",
    "met_na = list(set(set(met_n).intersection(met_a)))\n",
    "\n",
    "len(met_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "548eb07d-1ed8-4839-971c-d064026621df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AKGmito',\n",
       " 'ATP',\n",
       " 'ATPmito',\n",
       " 'AcCoAmito',\n",
       " 'BPG13',\n",
       " 'CITmito',\n",
       " 'CoAmito',\n",
       " 'CytCredmito',\n",
       " 'DHAP',\n",
       " 'E4P',\n",
       " 'F6P',\n",
       " 'FBP',\n",
       " 'FUMmito',\n",
       " 'G6P',\n",
       " 'GAP',\n",
       " 'GSH',\n",
       " 'Glc',\n",
       " 'ISOCITmito',\n",
       " 'Lac',\n",
       " 'MALmito',\n",
       " 'Mg',\n",
       " 'NADH',\n",
       " 'NADHmito',\n",
       " 'NADPH',\n",
       " 'OXAmito',\n",
       " 'PEP',\n",
       " 'PG2',\n",
       " 'PG3',\n",
       " 'PYRmito',\n",
       " 'Pyr',\n",
       " 'QH2mito',\n",
       " 'R5P',\n",
       " 'SUCCOAmito',\n",
       " 'SUCmito']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(met_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "adf12191-5ebf-4563-a47a-992c926a4b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mp_cvAmpl_l_sel = input_df_mp_cvAmpl_l.loc[input_df_mp_cvAmpl_l['variable_id'].isin(met_na),:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "216fbfe9-765d-4497-af10-a6db1e551be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10480, 7)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l_sel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285a8301-c11c-4ed1-9e92-79fc919554a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ca02ea3-ca7c-47cb-98a3-9a79855a7ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,10))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n','a'])], \n",
    "            x='value', y='variable_id', \n",
    "            hue=\"cmps\",palette=lut_cmps,orient='h',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlabel(\"Coefficient of variation, a.u.\")\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.15, 1.0),frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"suppFig_CV_overCNDS_perT_control_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f022585-6c3e-40a5-8bb3-163379c95c68",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "88ea9368-eebb-42b7-9c88-c250cbfbc5ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n =  0.06678604647283767 \n",
      " a =  0.16106161548004652\n"
     ]
    }
   ],
   "source": [
    "print(\"n = \",input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n']),'value'].mean(), \"\\n\",\n",
    "\"a = \",input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['a']),'value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57e0fd6c-76fd-4682-9c5d-0d5317701f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('dotted,','color=#208c48',np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n']),'value'].mean())) #, color='#208c48', linestyle='dotted', lw=1) # \n",
    "print('dotted,','color=#03402e',np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['a']),'value'].mean())) #, color='#03402e', linestyle='dotted', lw=1) # \n",
    "\n",
    "print('solid line,','color=#208c48',np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n']),'value'].median())) #, color='#208c48', linestyle='-', lw=1) # \n",
    "print('solid line,','color=#03402e',np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['a']),'value'].median())) #, color='#03402e', linestyle='-', lw=1) # \n",
    "\n",
    "# dotted, color=#208c48 -2.706261105396236\n",
    "# dotted, color=#03402e -1.825968282365485\n",
    "# solid line, color=#208c48 -5.156003957815724\n",
    "# solid line, color=#03402e -3.598420598810139"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32d915e5-a803-4d30-b928-fcd5628f7b4f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5efd6678-f92f-45d5-9d03-67d0d606c6d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (4,2.5))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "nbins=20\n",
    "\n",
    "bins_shared = np.linspace( -0.1 + np.min([np.min(np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n']),'value'].values)),\n",
    "                                   np.min(np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['a']),'value'].values))]),\n",
    "                          \n",
    "                          0.1 + np.max([np.max(np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n']),'value'].values)),\n",
    "                                   np.max(np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['a']),'value'].values))]),\n",
    "                          nbins\n",
    "                         )\n",
    "\n",
    "\n",
    "\n",
    "plt.hist( np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n']),'value'].values),bins=bins_shared,color=lut_cmps['n'],label='n')\n",
    "\n",
    "plt.hist( np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['a']),'value'].values),bins=bins_shared,color=lut_cmps['a'],label='a', alpha=0.85)\n",
    "\n",
    "\n",
    "ax.axvline(x=np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n']),'value'].mean()), color='#208c48', linestyle='dotted', lw=1) # \n",
    "ax.axvline(x=np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['a']),'value'].mean()), color='#03402e', linestyle='dotted', lw=1) # \n",
    "\n",
    "ax.axvline(x=np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n']),'value'].median()), color='#208c48', linestyle='-', lw=1) # \n",
    "ax.axvline(x=np.log(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['a']),'value'].median()), color='#03402e', linestyle='-', lw=1) # \n",
    "\n",
    "\n",
    "ax.legend(bbox_to_anchor=(0.7,1.15),frameon=False, ncol=2)\n",
    "#ax.legend(loc='upper left',frameon=False, ncol=2)\n",
    "\n",
    "ax.set_xlabel(\"log(CV)\")\n",
    "ax.set_ylabel(\"counts\")\n",
    "\n",
    "\n",
    "ax.set_title(\"log(CV)\", pad=20)\n",
    "\n",
    "#ax.set_xlim((-16,6))\n",
    "\n",
    "#ax.set_ylim((0,490))\n",
    "\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "#plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"fig2c1_CVlog_hist_cmps_control_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"fig2c1_CVlog_hist_cmps_control_\" + timestr + \".png\",transparent=False, bbox_inches=\"tight\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0075f148-d3a4-4656-ad98-5e447f4cc153",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n','a']),['value','cmps']], \n",
    "            x='value', y='cmps', \n",
    "            hue=\"cmps\",palette=lut_cmps,orient='h', ci=95)\n",
    "\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlabel(\"Coefficient of variation, a.u.\")\n",
    "\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.05, 0.6),frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"fig2c2_CV_cmps_ci95_control_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"fig2c2_CV_cmps_ci95_control_\" + timestr + \".png\",transparent=False, bbox_inches=\"tight\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bee298e-32bb-4c2e-938a-056b07ce86b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "04caf35f-5150-4d8c-abee-48b8cb18f591",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_all_cond = pd.DataFrame(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n','a']),['value','cmps']].groupby('cmps')['value'].apply(np.mean)).reset_index(drop=False)\n",
    "agg_all_cond.columns = ['cmps','value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1ba81fe2-dbed-4c37-a5f4-b47468c25fa1",
   "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>cmps</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0.161062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n</td>\n",
       "      <td>0.066786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cmps     value\n",
       "0    a  0.161062\n",
       "1    n  0.066786"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_all_cond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d6ea875d-81de-48e0-b50d-6de771b56724",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "agg_all_cond = pd.DataFrame(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n','a']),['value','cmps']].groupby('cmps').apply(np.nanmedian)).reset_index(drop=False)\n",
    "agg_all_cond.columns = ['cmps','value']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e478017c-c8c9-488c-85ca-3bb6a91f0a27",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmps</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0.027367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n</td>\n",
       "      <td>0.005765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cmps     value\n",
       "0    a  0.027367\n",
       "1    n  0.005765"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_all_cond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7a26b81e-7372-4445-bed5-c953e601ad20",
   "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>cmps</th>\n",
       "      <th>fns</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>ATDMP</td>\n",
       "      <td>0.000529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>ETC</td>\n",
       "      <td>0.007208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>0.021252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cmps    fns     value\n",
       "0    a  ATDMP  0.000529\n",
       "1    a    ETC  0.007208\n",
       "2    a  GLCLS  0.021252"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_all_cond_fns = pd.DataFrame(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n','a']),['value','cmps','fns']].groupby(['cmps','fns']).apply(np.nanmedian)).reset_index(drop=False)\n",
    "agg_all_cond_fns.columns = ['cmps','fns','value']\n",
    "\n",
    "agg_all_cond_fns.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "caeabad7-24e9-47c3-9257-f4ecefd4b74d",
   "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>time_s</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>cmps</th>\n",
       "      <th>fns</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>variable_cmp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15065</th>\n",
       "      <td>180.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.077604</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>Glc</td>\n",
       "      <td>t_t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15066</th>\n",
       "      <td>181.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.077803</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>Glc</td>\n",
       "      <td>t_t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15067</th>\n",
       "      <td>182.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.078001</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>Glc</td>\n",
       "      <td>t_t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time_s variable     value cmps    fns variable_id variable_cmp\n",
       "15065   180.0  Glc_t_t  0.077604    t  GLCLS         Glc          t_t\n",
       "15066   181.0  Glc_t_t  0.077803    t  GLCLS         Glc          t_t\n",
       "15067   182.0  Glc_t_t  0.078001    t  GLCLS         Glc          t_t"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l_sel.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9bd8be95-ff40-433a-9a2e-8f742df7cf68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NADHmito_n', 'NADH_n', 'NADPH_n', 'GSH_n', 'NADHmito_a', 'NADH_a',\n",
       "       'NADPH_a', 'GSH_a'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['fns']=='redox','variable'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b13ed743-e550-4989-bea6-785deb91139f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,5))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n','a']),['value','cmps','fns']], \n",
    "            x='value', y='fns', \n",
    "            hue=\"cmps\",palette=lut_cmps,orient='h',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_xlabel(\"Coefficient of variation, a.u.\")\n",
    "\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.05, 1.0),frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"fig2c3_CV_fns_ci95_control_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"fig2c3_CV_fns_ci95_control_\" + timestr + \".png\",transparent=False, bbox_inches=\"tight\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a86fd79-030b-47ec-8e39-1cc8d8f45f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n','a']),['value','cmps','fns']].groupby(['fns','cmps'])['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e252fb75-9f3f-48bc-857a-bc221d2a76ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mp_cvAmpl_l_sel.loc[input_df_mp_cvAmpl_l_sel['cmps'].isin(['n','a']),['value','cmps','fns']].groupby(['fns','cmps'])['value'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e497aaf6-6ddd-45eb-926e-315ec7c8fc56",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}