Fig2abGLUT1def.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",
    "\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": 3,
   "id": "3eb24459-423a-4ca3-94be-c3e8e5ed9a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################\n",
    "# set to True to save fig\n",
    "\n",
    "savefigures = True #False #True \n",
    "\n",
    "##############################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1_control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group2_GLUT1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "id": "a433d92d-30fe-4cd1-9838-16050dcb583e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>31179</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>54.885139</td>\n",
       "      <td>0.636269</td>\n",
       "      <td>0.311712</td>\n",
       "      <td>0.016209</td>\n",
       "      <td>0.132212</td>\n",
       "      <td>0.032215</td>\n",
       "      <td>0.723906</td>\n",
       "      <td>1.876094</td>\n",
       "      <td>0.200565</td>\n",
       "      <td>...</td>\n",
       "      <td>5.075794</td>\n",
       "      <td>4.926586</td>\n",
       "      <td>0.039093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>0.068591</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31180</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>54.885139</td>\n",
       "      <td>0.636248</td>\n",
       "      <td>0.311703</td>\n",
       "      <td>0.016214</td>\n",
       "      <td>0.132224</td>\n",
       "      <td>0.032215</td>\n",
       "      <td>0.723789</td>\n",
       "      <td>1.876211</td>\n",
       "      <td>0.200528</td>\n",
       "      <td>...</td>\n",
       "      <td>5.075794</td>\n",
       "      <td>4.926579</td>\n",
       "      <td>0.039093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>0.068591</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31181</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>54.885139</td>\n",
       "      <td>0.636228</td>\n",
       "      <td>0.311694</td>\n",
       "      <td>0.016220</td>\n",
       "      <td>0.132237</td>\n",
       "      <td>0.032214</td>\n",
       "      <td>0.723670</td>\n",
       "      <td>1.876330</td>\n",
       "      <td>0.200491</td>\n",
       "      <td>...</td>\n",
       "      <td>5.075794</td>\n",
       "      <td>4.926572</td>\n",
       "      <td>0.039093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>0.068591</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  \\\n",
       "31179        0.000018  54.885139  0.636269    0.311712   0.016209   \n",
       "31180        0.000018  54.885139  0.636248    0.311703   0.016214   \n",
       "31181        0.000018  54.885139  0.636228    0.311694   0.016220   \n",
       "\n",
       "       CytCredmito_n      O2_n  ATPmito_n  ADPmito_n  ATP_mx_n  ...      Cr_a  \\\n",
       "31179       0.132212  0.032215   0.723906   1.876094  0.200565  ...  5.075794   \n",
       "31180       0.132224  0.032215   0.723789   1.876211  0.200528  ...  5.075794   \n",
       "31181       0.132237  0.032214   0.723670   1.876330  0.200491  ...  5.075794   \n",
       "\n",
       "          PCr_a    cAMP_a  NE_neuromod  UDPgluco_a  UTP_a   GS_a     GPa_a  \\\n",
       "31179  4.926586  0.039093          0.0       0.109   0.23  0.003  0.001409   \n",
       "31180  4.926579  0.039093          0.0       0.109   0.23  0.003  0.001409   \n",
       "31181  4.926572  0.039093          0.0       0.109   0.23  0.003  0.001409   \n",
       "\n",
       "          GPb_a  time_s  \n",
       "31179  0.068591   180.0  \n",
       "31180  0.068591   181.0  \n",
       "31181  0.068591   182.0  \n",
       "\n",
       "[3 rows x 184 columns]"
      ]
     },
     "execution_count": 14,
     "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(group2_GLUT1)),:].copy() \n",
    "\n",
    "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": {
      "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_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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180.0</th>\n",
       "      <td>5.582234e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001838</td>\n",
       "      <td>0.005177</td>\n",
       "      <td>0.016793</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.008520</td>\n",
       "      <td>0.009308</td>\n",
       "      <td>0.003643</td>\n",
       "      <td>0.010669</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001264</td>\n",
       "      <td>5.249494e-16</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.273192e-16</td>\n",
       "      <td>9.654113e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012042</td>\n",
       "      <td>0.000251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181.0</th>\n",
       "      <td>5.582234e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001841</td>\n",
       "      <td>0.005178</td>\n",
       "      <td>0.016835</td>\n",
       "      <td>0.003927</td>\n",
       "      <td>0.008522</td>\n",
       "      <td>0.009322</td>\n",
       "      <td>0.003648</td>\n",
       "      <td>0.010684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001270</td>\n",
       "      <td>5.249494e-16</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.273192e-16</td>\n",
       "      <td>9.654113e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012057</td>\n",
       "      <td>0.000251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182.0</th>\n",
       "      <td>5.582234e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>0.005178</td>\n",
       "      <td>0.016878</td>\n",
       "      <td>0.003948</td>\n",
       "      <td>0.008525</td>\n",
       "      <td>0.009336</td>\n",
       "      <td>0.003653</td>\n",
       "      <td>0.010701</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>5.249494e-16</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.273192e-16</td>\n",
       "      <td>9.654113e-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012071</td>\n",
       "      <td>0.000252</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  CytCredmito_n  \\\n",
       "time_s                                                                          \n",
       "180.0     5.582234e-16    0.0  0.001838    0.005177   0.016793       0.003908   \n",
       "181.0     5.582234e-16    0.0  0.001841    0.005178   0.016835       0.003927   \n",
       "182.0     5.582234e-16    0.0  0.001843    0.005178   0.016878       0.003948   \n",
       "\n",
       "            O2_n  ATPmito_n  ADPmito_n  ATP_mx_n  ...     PCr_n          Cr_a  \\\n",
       "time_s                                            ...                           \n",
       "180.0   0.008520   0.009308   0.003643  0.010669  ...  0.001264  5.249494e-16   \n",
       "181.0   0.008522   0.009322   0.003648  0.010684  ...  0.001270  5.249494e-16   \n",
       "182.0   0.008525   0.009336   0.003653  0.010701  ...  0.001276  5.249494e-16   \n",
       "\n",
       "           PCr_a    cAMP_a  NE_neuromod    UDPgluco_a         UTP_a  GS_a  \\\n",
       "time_s                                                                      \n",
       "180.0   0.000179  0.000525          NaN  1.273192e-16  9.654113e-16   0.0   \n",
       "181.0   0.000181  0.000526          NaN  1.273192e-16  9.654113e-16   0.0   \n",
       "182.0   0.000182  0.000528          NaN  1.273192e-16  9.654113e-16   0.0   \n",
       "\n",
       "           GPa_a     GPb_a  \n",
       "time_s                      \n",
       "180.0   0.012042  0.000251  \n",
       "181.0   0.012057  0.000251  \n",
       "182.0   0.012071  0.000252  \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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       "\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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>180.0</td>\n",
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       "      <td>5.582234e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>181.0</td>\n",
       "      <td>C_H_mitomatr_n</td>\n",
       "      <td>5.582234e-16</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>182.0</td>\n",
       "      <td>C_H_mitomatr_n</td>\n",
       "      <td>5.582234e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time_s        variable         value\n",
       "0   180.0  C_H_mitomatr_n  5.582234e-16\n",
       "1   181.0  C_H_mitomatr_n  5.582234e-16\n",
       "2   182.0  C_H_mitomatr_n  5.582234e-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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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>time_s</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   time_s        variable         value\n",
       "0   180.0  C_H_mitomatr_n  5.582234e-16\n",
       "1   181.0  C_H_mitomatr_n  5.582234e-16\n",
       "2   182.0  C_H_mitomatr_n  5.582234e-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": [
    {
     "data": {
      "text/html": [
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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",
       "    </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>5.582234e-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>5.582234e-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>5.582234e-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  5.582234e-16    n\n",
       "1   181.0  C_H_mitomatr_n  5.582234e-16    n\n",
       "2   182.0  C_H_mitomatr_n  5.582234e-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": 21,
   "id": "dc9c0a70-47a0-470d-a8e8-712783250d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "      <th>time_s</th>\n",
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       "      <th>fns</th>\n",
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       "      <th>1</th>\n",
       "      <td>181.0</td>\n",
       "      <td>C_H_mitomatr_n</td>\n",
       "      <td>5.582234e-16</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>182.0</td>\n",
       "      <td>C_H_mitomatr_n</td>\n",
       "      <td>5.582234e-16</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time_s        variable         value cmps  fns\n",
       "0   180.0  C_H_mitomatr_n  5.582234e-16    n  NaN\n",
       "1   181.0  C_H_mitomatr_n  5.582234e-16    n  NaN\n",
       "2   182.0  C_H_mitomatr_n  5.582234e-16    n  NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": [
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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",
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       "    <tr>\n",
       "      <th>15065</th>\n",
       "      <td>180.0</td>\n",
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       "      <td>0.074184</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>0.074413</td>\n",
       "      <td>t</td>\n",
       "      <td>GLCLS</td>\n",
       "    </tr>\n",
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       "      <th>15067</th>\n",
       "      <td>182.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>0.074641</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  0.074184    t  GLCLS\n",
       "15066   181.0  Glc_t_t  0.074413    t  GLCLS\n",
       "15067   182.0  Glc_t_t  0.074641    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 = ['#fc8f53','#d92f00','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",
       "    .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>0.074184</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>0.074413</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>0.074641</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>0.074870</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>0.075099</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  0.074184    t  GLCLS         Glc          t_t\n",
       "15066   181.0  Glc_t_t  0.074413    t  GLCLS         Glc          t_t\n",
       "15067   182.0  Glc_t_t  0.074641    t  GLCLS         Glc          t_t\n",
       "15068   183.0  Glc_t_t  0.074870    t  GLCLS         Glc          t_t\n",
       "15069   184.0  Glc_t_t  0.075099    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": 31,
   "id": "285a8301-c11c-4ed1-9e92-79fc919554a3",
   "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": 31,
     "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": 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_b_CV_overCNDS_perT_GLUT1_\"\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 =  0.08874549381671706 \n",
      " a =  0.18197933775321262\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": "6cbd8e80-0465-41f3-a429-942be39db47d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('dotted,','color=red',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=darkred',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=red',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=darkred',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",
    "# dotted, color=red -2.4219826258117956\n",
    "# dotted, color=darkred -1.703862127178729\n",
    "# solid line, color=red -4.38752324980128\n",
    "# solid line, color=darkred -3.3356663771048494"
   ]
  },
  {
   "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='red', 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='darkred', 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='red', 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='darkred', linestyle='-', lw=1) # \n",
    "\n",
    "\n",
    "ax.legend(bbox_to_anchor=(0.7,1.15),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",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"fig2g1_CVlog_hist_cmps_GLUT1_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"fig2g1_CVlog_hist_cmps_GLUT1_\" + 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 + \"fig2g2_CV_cmps_ci95_GLUT1_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"fig2g2_CV_cmps_ci95_GLUT1_\" + 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>0.181979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n</td>\n",
       "      <td>0.088745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cmps     value\n",
       "0    a  0.181979\n",
       "1    n  0.088745"
      ]
     },
     "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>0.035591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n</td>\n",
       "      <td>0.012431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cmps     value\n",
       "0    a  0.035591\n",
       "1    n  0.012431"
      ]
     },
     "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>0.000756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>ETC</td>\n",
       "      <td>0.008262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>GLCLS</td>\n",
       "      <td>0.030512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cmps    fns     value\n",
       "0    a  ATDMP  0.000756\n",
       "1    a    ETC  0.008262\n",
       "2    a  GLCLS  0.030512"
      ]
     },
     "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>0.074184</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>0.074413</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>0.074641</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  0.074184    t  GLCLS         Glc          t_t\n",
       "15066   181.0  Glc_t_t  0.074413    t  GLCLS         Glc          t_t\n",
       "15067   182.0  Glc_t_t  0.074641    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 + \"fig2g3_CV_fns_ci95_GLUT1_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"fig2g3_CV_fns_ci95_GLUT1_\" + 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
}