SuppFigS4.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",
    "from matplotlib.patches import Patch\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import time\n",
    "\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/fig4a/\" "
   ]
  },
  {
   "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": "d6b6d98e-963b-4af4-bb50-f8a64356d212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "len(mets2plot2_sel3met)\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": 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].replace(\"_\",\" \")\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": 10,
   "id": "c48bbd9e-6774-48ac-8847-43c3706b18eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "startid = 180\n",
    "endid = int(input_df['time_s'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "865c4ecf-4fef-4179-818d-04afa6c51136",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = input_df.loc[:,mets2plot2_sel3met+['condition','time_s']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4bbee516-8214-43b0-93ea-7bedbc2cd4f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp = input_df.loc[(input_df['time_s']>=startid),:].copy()  \n",
    "input_df_mp = input_df_mp.drop(columns='time_s')\n",
    "startid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5cddaa52-55d8-407f-be7c-4f62a67829c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ccv(x):\n",
    "    if np.mean(x) !=0:\n",
    "        return np.std(x)/np.mean(x)\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "input_df_data2 = input_df_mp.groupby('condition').agg([np.mean,np.median,np.max,np.min,ccv]) #.melt(ignore_index=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a6459726-a017-43fd-b8e2-8cf35a0fb2bb",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Mg_x_n</th>\n",
       "      <th colspan=\"5\" halign=\"left\">NADHmito_n</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"5\" halign=\"left\">GSH_a</th>\n",
       "      <th colspan=\"5\" halign=\"left\">cAMP_a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "      <th>...</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</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>1 blood glc ini</th>\n",
       "      <td>0.632960</td>\n",
       "      <td>0.636766</td>\n",
       "      <td>0.640518</td>\n",
       "      <td>0.617168</td>\n",
       "      <td>0.012840</td>\n",
       "      <td>0.316774</td>\n",
       "      <td>0.315259</td>\n",
       "      <td>0.354367</td>\n",
       "      <td>0.272225</td>\n",
       "      <td>0.055510</td>\n",
       "      <td>...</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299999</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>1.219881e-07</td>\n",
       "      <td>0.038962</td>\n",
       "      <td>0.039058</td>\n",
       "      <td>0.039097</td>\n",
       "      <td>0.038631</td>\n",
       "      <td>0.003985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 lac pls</th>\n",
       "      <td>0.631750</td>\n",
       "      <td>0.636420</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.614536</td>\n",
       "      <td>0.014159</td>\n",
       "      <td>0.316967</td>\n",
       "      <td>0.315169</td>\n",
       "      <td>0.354783</td>\n",
       "      <td>0.271986</td>\n",
       "      <td>0.055791</td>\n",
       "      <td>...</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>6.258639e-08</td>\n",
       "      <td>0.038978</td>\n",
       "      <td>0.039073</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.038651</td>\n",
       "      <td>0.003943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11 bhb pls</th>\n",
       "      <td>0.631742</td>\n",
       "      <td>0.636452</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.614408</td>\n",
       "      <td>0.014239</td>\n",
       "      <td>0.316918</td>\n",
       "      <td>0.315407</td>\n",
       "      <td>0.354614</td>\n",
       "      <td>0.271986</td>\n",
       "      <td>0.055922</td>\n",
       "      <td>...</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>1.293971e-07</td>\n",
       "      <td>0.038979</td>\n",
       "      <td>0.039077</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.038652</td>\n",
       "      <td>0.003946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 470 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Mg_x_n                                         NADHmito_n  \\\n",
       "                     mean    median      amax      amin       ccv       mean   \n",
       "condition                                                                      \n",
       "1 blood glc ini  0.632960  0.636766  0.640518  0.617168  0.012840   0.316774   \n",
       "10 lac pls       0.631750  0.636420  0.639592  0.614536  0.014159   0.316967   \n",
       "11 bhb pls       0.631742  0.636452  0.639592  0.614408  0.014239   0.316918   \n",
       "\n",
       "                                                         ...     GSH_a  \\\n",
       "                   median      amax      amin       ccv  ...      mean   \n",
       "condition                                                ...             \n",
       "1 blood glc ini  0.315259  0.354367  0.272225  0.055510  ...  4.299998   \n",
       "10 lac pls       0.315169  0.354783  0.271986  0.055791  ...  4.299998   \n",
       "11 bhb pls       0.315407  0.354614  0.271986  0.055922  ...  4.299997   \n",
       "\n",
       "                                                               cAMP_a  \\\n",
       "                   median      amax      amin           ccv      mean   \n",
       "condition                                                               \n",
       "1 blood glc ini  4.299998  4.299999  4.299997  1.219881e-07  0.038962   \n",
       "10 lac pls       4.299998  4.299998  4.299997  6.258639e-08  0.038978   \n",
       "11 bhb pls       4.299997  4.299998  4.299997  1.293971e-07  0.038979   \n",
       "\n",
       "                                                         \n",
       "                   median      amax      amin       ccv  \n",
       "condition                                                \n",
       "1 blood glc ini  0.039058  0.039097  0.038631  0.003985  \n",
       "10 lac pls       0.039073  0.039110  0.038651  0.003943  \n",
       "11 bhb pls       0.039077  0.039110  0.038652  0.003946  \n",
       "\n",
       "[3 rows x 470 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_data2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc5078f6-c978-47d5-aa8b-7ad0033f6424",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_data2 = input_df_data2.reset_index(drop=False)\n",
    "input_df_mp_cvAmpl_l = pd.melt(input_df_data2,id_vars=['condition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d347e7c7-49fb-4fb1-9f76-18248808a679",
   "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>condition</th>\n",
       "      <th>variable_0</th>\n",
       "      <th>variable_1</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 blood glc ini</td>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.632960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10 lac pls</td>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.631750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11 bhb pls</td>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.631742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         condition variable_0 variable_1     value\n",
       "0  1 blood glc ini     Mg_x_n       mean  0.632960\n",
       "1       10 lac pls     Mg_x_n       mean  0.631750\n",
       "2       11 bhb pls     Mg_x_n       mean  0.631742"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa00273f-5ec5-4ae1-85ce-94063c133117",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6597fc98-16f6-4605-a632-02f587ec4d76",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_cmps_index(df):\n",
    "    cmps1 = []\n",
    "    for i in df['variable_0'].to_list():\n",
    "       \n",
    "        if i in ['ddHb','O2cap']:\n",
    "            cmps1.append(\"b_ecs\")\n",
    "                \n",
    "        elif len(i.split(\"_\")) >1:\n",
    "            if i.split(\"_\")[-1] == \"ecsAN\":\n",
    "                cmps1.append(\"b_ecs\")\n",
    "                \n",
    "            elif i.split(\"_\")[-1] == \"ecsBA\":\n",
    "                cmps1.append(\"b_ecs\")\n",
    "                \n",
    "            elif i.split(\"_\")[-1] == \"ecs\":\n",
    "                cmps1.append(\"b_ecs\")\n",
    "                \n",
    "            elif i.split(\"_\")[-1] == \"t\":\n",
    "                cmps1.append(\"b_ecs\")\n",
    "                \n",
    "            elif i.split(\"_\")[-1] == \"b\":\n",
    "                cmps1.append(\"b_ecs\")\n",
    "                \n",
    "            elif i.split(\"_\")[-1] == \"out\":\n",
    "                cmps1.append(\"b_ecs\")\n",
    "                \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": 18,
   "id": "47f36487-f052-45c7-ad7e-522b95173ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'b_ecs', 'n'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(cmps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a75f113-f9d9-4090-8420-7298d2c20dda",
   "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>condition</th>\n",
       "      <th>variable_0</th>\n",
       "      <th>variable_1</th>\n",
       "      <th>value</th>\n",
       "      <th>cmps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 blood glc ini</td>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.632960</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10 lac pls</td>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.631750</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11 bhb pls</td>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.631742</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         condition variable_0 variable_1     value cmps\n",
       "0  1 blood glc ini     Mg_x_n       mean  0.632960    n\n",
       "1       10 lac pls     Mg_x_n       mean  0.631750    n\n",
       "2       11 bhb pls     Mg_x_n       mean  0.631742    n"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "id": "228bdbff-c868-461d-b0d9-2612a037c141",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmps_d = dict(zip(input_df_mp_cvAmpl_l['variable_0'],input_df_mp_cvAmpl_l['cmps']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e160540-40ab-4720-a8b2-dd5265b96eb8",
   "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'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "354e2546-20b6-4206-ae88-2c91b2c08e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mp_cvAmpl_l['fns'] = input_df_mp_cvAmpl_l['variable_0'].map(v2function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f612a1d8-ed96-4a2f-883f-9ca1e87618c9",
   "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>condition</th>\n",
       "      <th>variable_0</th>\n",
       "      <th>variable_1</th>\n",
       "      <th>value</th>\n",
       "      <th>cmps</th>\n",
       "      <th>fns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 blood glc ini</td>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.632960</td>\n",
       "      <td>n</td>\n",
       "      <td>ETC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10 lac pls</td>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.631750</td>\n",
       "      <td>n</td>\n",
       "      <td>ETC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11 bhb pls</td>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.631742</td>\n",
       "      <td>n</td>\n",
       "      <td>ETC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         condition variable_0 variable_1     value cmps  fns\n",
       "0  1 blood glc ini     Mg_x_n       mean  0.632960    n  ETC\n",
       "1       10 lac pls     Mg_x_n       mean  0.631750    n  ETC\n",
       "2       11 bhb pls     Mg_x_n       mean  0.631742    n  ETC"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a1c9fa1d-8dc4-4a4e-8635-bc1e001d79f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mp_cvAmpl_l = input_df_mp_cvAmpl_l.sort_values(by=['cmps','fns'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "39d548b6-ed8c-4bf7-8d37-43efd7632e42",
   "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>condition</th>\n",
       "      <th>variable_0</th>\n",
       "      <th>variable_1</th>\n",
       "      <th>value</th>\n",
       "      <th>cmps</th>\n",
       "      <th>fns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>1 blood glc ini</td>\n",
       "      <td>NADHmito_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.316774</td>\n",
       "      <td>n</td>\n",
       "      <td>redox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>10 lac pls</td>\n",
       "      <td>NADHmito_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.316967</td>\n",
       "      <td>n</td>\n",
       "      <td>redox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>11 bhb pls</td>\n",
       "      <td>NADHmito_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.316918</td>\n",
       "      <td>n</td>\n",
       "      <td>redox</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           condition  variable_0 variable_1     value cmps    fns\n",
       "950  1 blood glc ini  NADHmito_n       mean  0.316774    n  redox\n",
       "951       10 lac pls  NADHmito_n       mean  0.316967    n  redox\n",
       "952       11 bhb pls  NADHmito_n       mean  0.316918    n  redox"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mp_cvAmpl_l.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4da696fe-3bf1-41df-9e6d-ccbc2347cc84",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mp_cvAmpl_l[[\"variable_id\",\"variable_cmp\"]] = input_df_mp_cvAmpl_l['variable_0'].str.split(\"_\",1, expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d7d6a9b-655f-4475-8dcb-74aa4530e438",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2a598f5-c130-473d-940d-cdb9700e4024",
   "metadata": {},
   "outputs": [],
   "source": [
    "mypal_cmps = ['#00b7ff','#1f58a3','#8206a1']  \n",
    "lut_cmps = dict(zip(['n','a','b_ecs'], mypal_cmps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b5cba51-226b-468f-9594-b87703d451d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bde4272d-954d-400d-a76c-d5cbfa5fd8ac",
   "metadata": {},
   "source": [
    "### Clustermaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "189524be-258b-4f9d-bdac-05cdb100baba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gpfs/bbp.cscs.ch/ssd/apps/bsd/2022-01-10/stage_applications/install_gcc-11.2.0-skylake/py-pandas-1.3.4-exvllw/lib/python3.9/site-packages/pandas/core/generic.py:4150: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "  obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n"
     ]
    }
   ],
   "source": [
    "input_df_data2.index = input_df_data2['condition']\n",
    "input_df_data2 = input_df_data2.drop(columns='condition')\n",
    "\n",
    "input_df_data2.index = input_df_data2.index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "54145068-28fe-41b2-a97f-f1b08d80d101",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Mg_x_n</th>\n",
       "      <th colspan=\"5\" halign=\"left\">NADHmito_n</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"5\" halign=\"left\">GSH_a</th>\n",
       "      <th colspan=\"5\" halign=\"left\">cAMP_a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "      <th>...</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</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>1 blood glc ini</th>\n",
       "      <td>0.632960</td>\n",
       "      <td>0.636766</td>\n",
       "      <td>0.640518</td>\n",
       "      <td>0.617168</td>\n",
       "      <td>0.012840</td>\n",
       "      <td>0.316774</td>\n",
       "      <td>0.315259</td>\n",
       "      <td>0.354367</td>\n",
       "      <td>0.272225</td>\n",
       "      <td>0.055510</td>\n",
       "      <td>...</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299999</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>1.219881e-07</td>\n",
       "      <td>0.038962</td>\n",
       "      <td>0.039058</td>\n",
       "      <td>0.039097</td>\n",
       "      <td>0.038631</td>\n",
       "      <td>0.003985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 lac pls</th>\n",
       "      <td>0.631750</td>\n",
       "      <td>0.636420</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.614536</td>\n",
       "      <td>0.014159</td>\n",
       "      <td>0.316967</td>\n",
       "      <td>0.315169</td>\n",
       "      <td>0.354783</td>\n",
       "      <td>0.271986</td>\n",
       "      <td>0.055791</td>\n",
       "      <td>...</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>6.258639e-08</td>\n",
       "      <td>0.038978</td>\n",
       "      <td>0.039073</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.038651</td>\n",
       "      <td>0.003943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11 bhb pls</th>\n",
       "      <td>0.631742</td>\n",
       "      <td>0.636452</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.614408</td>\n",
       "      <td>0.014239</td>\n",
       "      <td>0.316918</td>\n",
       "      <td>0.315407</td>\n",
       "      <td>0.354614</td>\n",
       "      <td>0.271986</td>\n",
       "      <td>0.055922</td>\n",
       "      <td>...</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>1.293971e-07</td>\n",
       "      <td>0.038979</td>\n",
       "      <td>0.039077</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.038652</td>\n",
       "      <td>0.003946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 470 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Mg_x_n                                         NADHmito_n  \\\n",
       "                     mean    median      amax      amin       ccv       mean   \n",
       "condition                                                                      \n",
       "1 blood glc ini  0.632960  0.636766  0.640518  0.617168  0.012840   0.316774   \n",
       "10 lac pls       0.631750  0.636420  0.639592  0.614536  0.014159   0.316967   \n",
       "11 bhb pls       0.631742  0.636452  0.639592  0.614408  0.014239   0.316918   \n",
       "\n",
       "                                                         ...     GSH_a  \\\n",
       "                   median      amax      amin       ccv  ...      mean   \n",
       "condition                                                ...             \n",
       "1 blood glc ini  0.315259  0.354367  0.272225  0.055510  ...  4.299998   \n",
       "10 lac pls       0.315169  0.354783  0.271986  0.055791  ...  4.299998   \n",
       "11 bhb pls       0.315407  0.354614  0.271986  0.055922  ...  4.299997   \n",
       "\n",
       "                                                               cAMP_a  \\\n",
       "                   median      amax      amin           ccv      mean   \n",
       "condition                                                               \n",
       "1 blood glc ini  4.299998  4.299999  4.299997  1.219881e-07  0.038962   \n",
       "10 lac pls       4.299998  4.299998  4.299997  6.258639e-08  0.038978   \n",
       "11 bhb pls       4.299997  4.299998  4.299997  1.293971e-07  0.038979   \n",
       "\n",
       "                                                         \n",
       "                   median      amax      amin       ccv  \n",
       "condition                                                \n",
       "1 blood glc ini  0.039058  0.039097  0.038631  0.003985  \n",
       "10 lac pls       0.039073  0.039110  0.038651  0.003943  \n",
       "11 bhb pls       0.039077  0.039110  0.038652  0.003946  \n",
       "\n",
       "[3 rows x 470 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_data2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "deb0ac8b-62ec-49be-bfed-fed805058c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(190, 470)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1506ff81-e17a-43a3-9320-d5e468ff80de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "af2634fc-1713-498f-aeef-2bb6bd32cb17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Mg_x_n</th>\n",
       "      <th colspan=\"5\" halign=\"left\">NADHmito_n</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"5\" halign=\"left\">GSH_a</th>\n",
       "      <th colspan=\"5\" halign=\"left\">cAMP_a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "      <th>...</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>amax</th>\n",
       "      <th>amin</th>\n",
       "      <th>ccv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</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>1 blood glc ini</th>\n",
       "      <td>0.632960</td>\n",
       "      <td>0.636766</td>\n",
       "      <td>0.640518</td>\n",
       "      <td>0.617168</td>\n",
       "      <td>0.012840</td>\n",
       "      <td>0.316774</td>\n",
       "      <td>0.315259</td>\n",
       "      <td>0.354367</td>\n",
       "      <td>0.272225</td>\n",
       "      <td>0.055510</td>\n",
       "      <td>...</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299999</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>1.219881e-07</td>\n",
       "      <td>0.038962</td>\n",
       "      <td>0.039058</td>\n",
       "      <td>0.039097</td>\n",
       "      <td>0.038631</td>\n",
       "      <td>0.003985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 lac pls</th>\n",
       "      <td>0.631750</td>\n",
       "      <td>0.636420</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.614536</td>\n",
       "      <td>0.014159</td>\n",
       "      <td>0.316967</td>\n",
       "      <td>0.315169</td>\n",
       "      <td>0.354783</td>\n",
       "      <td>0.271986</td>\n",
       "      <td>0.055791</td>\n",
       "      <td>...</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>6.258639e-08</td>\n",
       "      <td>0.038978</td>\n",
       "      <td>0.039073</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.038651</td>\n",
       "      <td>0.003943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11 bhb pls</th>\n",
       "      <td>0.631742</td>\n",
       "      <td>0.636452</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.614408</td>\n",
       "      <td>0.014239</td>\n",
       "      <td>0.316918</td>\n",
       "      <td>0.315407</td>\n",
       "      <td>0.354614</td>\n",
       "      <td>0.271986</td>\n",
       "      <td>0.055922</td>\n",
       "      <td>...</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>4.299998</td>\n",
       "      <td>4.299997</td>\n",
       "      <td>1.293971e-07</td>\n",
       "      <td>0.038979</td>\n",
       "      <td>0.039077</td>\n",
       "      <td>0.039110</td>\n",
       "      <td>0.038652</td>\n",
       "      <td>0.003946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 470 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Mg_x_n                                         NADHmito_n  \\\n",
       "                     mean    median      amax      amin       ccv       mean   \n",
       "condition                                                                      \n",
       "1 blood glc ini  0.632960  0.636766  0.640518  0.617168  0.012840   0.316774   \n",
       "10 lac pls       0.631750  0.636420  0.639592  0.614536  0.014159   0.316967   \n",
       "11 bhb pls       0.631742  0.636452  0.639592  0.614408  0.014239   0.316918   \n",
       "\n",
       "                                                         ...     GSH_a  \\\n",
       "                   median      amax      amin       ccv  ...      mean   \n",
       "condition                                                ...             \n",
       "1 blood glc ini  0.315259  0.354367  0.272225  0.055510  ...  4.299998   \n",
       "10 lac pls       0.315169  0.354783  0.271986  0.055791  ...  4.299998   \n",
       "11 bhb pls       0.315407  0.354614  0.271986  0.055922  ...  4.299997   \n",
       "\n",
       "                                                               cAMP_a  \\\n",
       "                   median      amax      amin           ccv      mean   \n",
       "condition                                                               \n",
       "1 blood glc ini  4.299998  4.299999  4.299997  1.219881e-07  0.038962   \n",
       "10 lac pls       4.299998  4.299998  4.299997  6.258639e-08  0.038978   \n",
       "11 bhb pls       4.299997  4.299998  4.299997  1.293971e-07  0.038979   \n",
       "\n",
       "                                                         \n",
       "                   median      amax      amin       ccv  \n",
       "condition                                                \n",
       "1 blood glc ini  0.039058  0.039097  0.038631  0.003985  \n",
       "10 lac pls       0.039073  0.039110  0.038651  0.003943  \n",
       "11 bhb pls       0.039077  0.039110  0.038652  0.003946  \n",
       "\n",
       "[3 rows x 470 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_data2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6686fca0-d7e5-40bf-b2c3-6555633a3d3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>condition</th>\n",
       "      <th>1 blood glc ini</th>\n",
       "      <th>10 lac pls</th>\n",
       "      <th>11 bhb pls</th>\n",
       "      <th>12 akg pls</th>\n",
       "      <th>121 default</th>\n",
       "      <th>13 succoa pls</th>\n",
       "      <th>137 blood glc ini dose1</th>\n",
       "      <th>138 blood glc ini dose2</th>\n",
       "      <th>139 blood glc ini dose3</th>\n",
       "      <th>14 fum pls</th>\n",
       "      <th>...</th>\n",
       "      <th>th 182 SUCCOAmito na ini dose1</th>\n",
       "      <th>th 183 SUCCOAmito na ini dose2</th>\n",
       "      <th>th 184 SUCCOAmito na ini dose3</th>\n",
       "      <th>th 185 SUCCOAmito na ini dose4</th>\n",
       "      <th>th 186 SUCCOAmito na ini dose5</th>\n",
       "      <th>th 187 ATP dose1</th>\n",
       "      <th>th 188 ATP dose2</th>\n",
       "      <th>th 189 ATP dose3</th>\n",
       "      <th>th 190 ATP dose4</th>\n",
       "      <th>th 191 ATP dose5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mg_x_n</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.632960</td>\n",
       "      <td>0.631750</td>\n",
       "      <td>0.631742</td>\n",
       "      <td>0.631736</td>\n",
       "      <td>0.631735</td>\n",
       "      <td>0.631092</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>0.632398</td>\n",
       "      <td>0.633464</td>\n",
       "      <td>0.631459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.630508</td>\n",
       "      <td>0.630493</td>\n",
       "      <td>0.630478</td>\n",
       "      <td>0.630463</td>\n",
       "      <td>0.630449</td>\n",
       "      <td>0.626970</td>\n",
       "      <td>0.627190</td>\n",
       "      <td>0.627352</td>\n",
       "      <td>0.627253</td>\n",
       "      <td>0.627069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.636766</td>\n",
       "      <td>0.636420</td>\n",
       "      <td>0.636452</td>\n",
       "      <td>0.636436</td>\n",
       "      <td>0.636435</td>\n",
       "      <td>0.636053</td>\n",
       "      <td>0.636399</td>\n",
       "      <td>0.636631</td>\n",
       "      <td>0.636938</td>\n",
       "      <td>0.636165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.634492</td>\n",
       "      <td>0.634485</td>\n",
       "      <td>0.634476</td>\n",
       "      <td>0.634469</td>\n",
       "      <td>0.634462</td>\n",
       "      <td>0.632158</td>\n",
       "      <td>0.632933</td>\n",
       "      <td>0.634091</td>\n",
       "      <td>0.634181</td>\n",
       "      <td>0.634122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amax</th>\n",
       "      <td>0.640518</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.639038</td>\n",
       "      <td>0.640087</td>\n",
       "      <td>0.640898</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.641968</td>\n",
       "      <td>0.641952</td>\n",
       "      <td>0.641936</td>\n",
       "      <td>0.641921</td>\n",
       "      <td>0.641906</td>\n",
       "      <td>0.636410</td>\n",
       "      <td>0.636110</td>\n",
       "      <td>0.635808</td>\n",
       "      <td>0.635509</td>\n",
       "      <td>0.635217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 190 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "condition      1 blood glc ini  10 lac pls  11 bhb pls  12 akg pls  \\\n",
       "Mg_x_n mean           0.632960    0.631750    0.631742    0.631736   \n",
       "       median         0.636766    0.636420    0.636452    0.636436   \n",
       "       amax           0.640518    0.639592    0.639592    0.639592   \n",
       "\n",
       "condition      121 default  13 succoa pls  137 blood glc ini dose1  \\\n",
       "Mg_x_n mean       0.631735       0.631092                 0.630952   \n",
       "       median     0.636435       0.636053                 0.636399   \n",
       "       amax       0.639592       0.639592                 0.639038   \n",
       "\n",
       "condition      138 blood glc ini dose2  139 blood glc ini dose3  14 fum pls  \\\n",
       "Mg_x_n mean                   0.632398                 0.633464    0.631459   \n",
       "       median                 0.636631                 0.636938    0.636165   \n",
       "       amax                   0.640087                 0.640898    0.639592   \n",
       "\n",
       "condition      ...  th 182 SUCCOAmito na ini dose1  \\\n",
       "Mg_x_n mean    ...                        0.630508   \n",
       "       median  ...                        0.634492   \n",
       "       amax    ...                        0.641968   \n",
       "\n",
       "condition      th 183 SUCCOAmito na ini dose2  th 184 SUCCOAmito na ini dose3  \\\n",
       "Mg_x_n mean                          0.630493                        0.630478   \n",
       "       median                        0.634485                        0.634476   \n",
       "       amax                          0.641952                        0.641936   \n",
       "\n",
       "condition      th 185 SUCCOAmito na ini dose4  th 186 SUCCOAmito na ini dose5  \\\n",
       "Mg_x_n mean                          0.630463                        0.630449   \n",
       "       median                        0.634469                        0.634462   \n",
       "       amax                          0.641921                        0.641906   \n",
       "\n",
       "condition      th 187 ATP dose1  th 188 ATP dose2  th 189 ATP dose3  \\\n",
       "Mg_x_n mean            0.626970          0.627190          0.627352   \n",
       "       median          0.632158          0.632933          0.634091   \n",
       "       amax            0.636410          0.636110          0.635808   \n",
       "\n",
       "condition      th 190 ATP dose4  th 191 ATP dose5  \n",
       "Mg_x_n mean            0.627253          0.627069  \n",
       "       median          0.634181          0.634122  \n",
       "       amax            0.635509          0.635217  \n",
       "\n",
       "[3 rows x 190 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_data2_T = input_df_data2.T\n",
    "input_df_data2_T.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dd914eee-4fce-4b84-816e-bc967d5d3943",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "39\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  ('dose1' not in i) and \n",
    "                                             ('dose2' not in i) and \n",
    "                                             ('dose3' not in i) and \n",
    "                                             ('dose4' not in i) and \n",
    "                                             \n",
    "                                             ('dose6' not in i) and \n",
    "                                             ('dose7' not in i) and \n",
    "                                             ('dose8' not in i) and \n",
    "                                             (' pls' not in i) and (' PDH' not in i) ]\n",
    "                  \n",
    "                  \n",
    "                  \n",
    "\n",
    "print(len(group1_control))\n",
    "\n",
    "\n",
    "group2_GLUT1 = [i for i in all_cnds_l if (i.startswith('g1 ') or i.startswith('th ')) and ('dose1' not in i) and \n",
    "                                             ('dose2' not in i) and \n",
    "                                             ('dose3' not in i) and \n",
    "                                             ('dose4' not in i) and \n",
    "                                             \n",
    "                                             ('dose6' not in i) and \n",
    "                                             ('dose7' not in i) and \n",
    "                                             ('dose8' not in i) and \n",
    "                                             (' pls' not in i) and (' PDH' not in i) \n",
    "               ]\n",
    "\n",
    "print(len(group2_GLUT1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "679560e5-007d-4c6b-ab74-a8082209ae27",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnds_groups = {\n",
    "    \n",
    "    'group1_control':group1_control,\n",
    "    \n",
    "    'group2_GLUT1d':group2_GLUT1 \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "945fac7c-2533-45d2-bdef-d51fea98fadc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnds_groups_r = { v: k for k, l in cnds_groups.items() for v in l }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f10d949a-fa15-451f-9de9-bb8db5342232",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(470, 190)\n",
      "(470, 64)\n"
     ]
    }
   ],
   "source": [
    "print(input_df_data2_T.shape)\n",
    "input_df_data2_T = input_df_data2_T.loc[:,cnds_groups_r.keys()]\n",
    "print(input_df_data2_T.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b31bed84-e501-4da1-a388-34a8c39408fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['group1_control', 'group2_GLUT1d'])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnds_groups.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e409c0ff-a039-49cd-8e88-3e3f751123c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mypal_cnds = ['#069e1d','#fa6f0c' ]  \n",
    "lut_cnds = dict(zip(['group1_control', 'group2_GLUT1d' ], mypal_cnds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "698a0fff-ec39-456c-81f3-595ba2e2b0ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_colors2funs = pd.Series(input_df_data2_T.columns,index=input_df_data2_T.columns).map(cnds_groups_r).map(lut_cnds)\n",
    "\n",
    "color_df_cols_2 = pd.DataFrame({\"cnd_group\": col_colors2funs}, index=input_df_data2_T.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2175aad5-6399-4543-9b9a-81d64973dc0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_data2_T = input_df_data2_T.reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4e9a0e3e-1927-434a-8020-30e3dcff9d30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>condition</th>\n",
       "      <th>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>121 default</th>\n",
       "      <th>141 blood glc ini dose5</th>\n",
       "      <th>146 blood lac ini dose5</th>\n",
       "      <th>151 blood bhb ini dose5</th>\n",
       "      <th>156 AcCoAmito na ini dose5</th>\n",
       "      <th>161 SUCCOAmito na ini dose5</th>\n",
       "      <th>17 glclac ini</th>\n",
       "      <th>18 glcbhb ini</th>\n",
       "      <th>...</th>\n",
       "      <th>g1 72 LDH</th>\n",
       "      <th>g1 76 SUCmito</th>\n",
       "      <th>g1 90 AcAcCoa lac</th>\n",
       "      <th>g1 96 blood glc ini dose5</th>\n",
       "      <th>th 166 blood glc ini dose5</th>\n",
       "      <th>th 171 blood lac ini dose5</th>\n",
       "      <th>th 176 blood bhb ini dose5</th>\n",
       "      <th>th 181 AcCoAmito na ini dose5</th>\n",
       "      <th>th 186 SUCCOAmito na ini dose5</th>\n",
       "      <th>th 191 ATP dose5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>mean</td>\n",
       "      <td>0.631735</td>\n",
       "      <td>0.634951</td>\n",
       "      <td>0.631886</td>\n",
       "      <td>0.631949</td>\n",
       "      <td>0.631529</td>\n",
       "      <td>0.631713</td>\n",
       "      <td>0.633087</td>\n",
       "      <td>0.633110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626174</td>\n",
       "      <td>0.626173</td>\n",
       "      <td>0.626447</td>\n",
       "      <td>0.631935</td>\n",
       "      <td>0.636276</td>\n",
       "      <td>0.630583</td>\n",
       "      <td>0.630634</td>\n",
       "      <td>0.629987</td>\n",
       "      <td>0.630449</td>\n",
       "      <td>0.627069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>median</td>\n",
       "      <td>0.636435</td>\n",
       "      <td>0.637597</td>\n",
       "      <td>0.636920</td>\n",
       "      <td>0.636760</td>\n",
       "      <td>0.636390</td>\n",
       "      <td>0.636429</td>\n",
       "      <td>0.637176</td>\n",
       "      <td>0.636994</td>\n",
       "      <td>...</td>\n",
       "      <td>0.630039</td>\n",
       "      <td>0.630059</td>\n",
       "      <td>0.630562</td>\n",
       "      <td>0.637067</td>\n",
       "      <td>0.640164</td>\n",
       "      <td>0.634544</td>\n",
       "      <td>0.634687</td>\n",
       "      <td>0.634035</td>\n",
       "      <td>0.634462</td>\n",
       "      <td>0.634122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mg_x_n</td>\n",
       "      <td>amax</td>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.642049</td>\n",
       "      <td>0.639754</td>\n",
       "      <td>0.639812</td>\n",
       "      <td>0.639293</td>\n",
       "      <td>0.639561</td>\n",
       "      <td>0.640644</td>\n",
       "      <td>0.640671</td>\n",
       "      <td>...</td>\n",
       "      <td>0.637038</td>\n",
       "      <td>0.637049</td>\n",
       "      <td>0.637200</td>\n",
       "      <td>0.639878</td>\n",
       "      <td>0.644850</td>\n",
       "      <td>0.642004</td>\n",
       "      <td>0.642090</td>\n",
       "      <td>0.641311</td>\n",
       "      <td>0.641906</td>\n",
       "      <td>0.635217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "condition level_0 level_1  121 default  141 blood glc ini dose5  \\\n",
       "0          Mg_x_n    mean     0.631735                 0.634951   \n",
       "1          Mg_x_n  median     0.636435                 0.637597   \n",
       "2          Mg_x_n    amax     0.639592                 0.642049   \n",
       "\n",
       "condition  146 blood lac ini dose5  151 blood bhb ini dose5  \\\n",
       "0                         0.631886                 0.631949   \n",
       "1                         0.636920                 0.636760   \n",
       "2                         0.639754                 0.639812   \n",
       "\n",
       "condition  156 AcCoAmito na ini dose5  161 SUCCOAmito na ini dose5  \\\n",
       "0                            0.631529                     0.631713   \n",
       "1                            0.636390                     0.636429   \n",
       "2                            0.639293                     0.639561   \n",
       "\n",
       "condition  17 glclac ini  18 glcbhb ini  ...  g1 72 LDH  g1 76 SUCmito  \\\n",
       "0               0.633087       0.633110  ...   0.626174       0.626173   \n",
       "1               0.637176       0.636994  ...   0.630039       0.630059   \n",
       "2               0.640644       0.640671  ...   0.637038       0.637049   \n",
       "\n",
       "condition  g1 90 AcAcCoa lac  g1 96 blood glc ini dose5  \\\n",
       "0                   0.626447                   0.631935   \n",
       "1                   0.630562                   0.637067   \n",
       "2                   0.637200                   0.639878   \n",
       "\n",
       "condition  th 166 blood glc ini dose5  th 171 blood lac ini dose5  \\\n",
       "0                            0.636276                    0.630583   \n",
       "1                            0.640164                    0.634544   \n",
       "2                            0.644850                    0.642004   \n",
       "\n",
       "condition  th 176 blood bhb ini dose5  th 181 AcCoAmito na ini dose5  \\\n",
       "0                            0.630634                       0.629987   \n",
       "1                            0.634687                       0.634035   \n",
       "2                            0.642090                       0.641311   \n",
       "\n",
       "condition  th 186 SUCCOAmito na ini dose5  th 191 ATP dose5  \n",
       "0                                0.630449          0.627069  \n",
       "1                                0.634462          0.634122  \n",
       "2                                0.641906          0.635217  \n",
       "\n",
       "[3 rows x 66 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_data2_T.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c1bad526-88e1-4aa6-9f6f-92dfc927cbe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_data2_T = input_df_data2_T.drop(columns = \"level_1\")\n",
    "input_df_data2_T.index = input_df_data2_T['level_0']\n",
    "\n",
    "input_df_data2_T = input_df_data2_T.drop(columns = \"level_0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6c69758e-8635-417f-8d6e-af6404568f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>condition</th>\n",
       "      <th>121 default</th>\n",
       "      <th>141 blood glc ini dose5</th>\n",
       "      <th>146 blood lac ini dose5</th>\n",
       "      <th>151 blood bhb ini dose5</th>\n",
       "      <th>156 AcCoAmito na ini dose5</th>\n",
       "      <th>161 SUCCOAmito na ini dose5</th>\n",
       "      <th>17 glclac ini</th>\n",
       "      <th>18 glcbhb ini</th>\n",
       "      <th>19 lacbhb ini</th>\n",
       "      <th>1 blood glc ini</th>\n",
       "      <th>...</th>\n",
       "      <th>g1 72 LDH</th>\n",
       "      <th>g1 76 SUCmito</th>\n",
       "      <th>g1 90 AcAcCoa lac</th>\n",
       "      <th>g1 96 blood glc ini dose5</th>\n",
       "      <th>th 166 blood glc ini dose5</th>\n",
       "      <th>th 171 blood lac ini dose5</th>\n",
       "      <th>th 176 blood bhb ini dose5</th>\n",
       "      <th>th 181 AcCoAmito na ini dose5</th>\n",
       "      <th>th 186 SUCCOAmito na ini dose5</th>\n",
       "      <th>th 191 ATP dose5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level_0</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>Mg_x_n</th>\n",
       "      <td>0.631735</td>\n",
       "      <td>0.634951</td>\n",
       "      <td>0.631886</td>\n",
       "      <td>0.631949</td>\n",
       "      <td>0.631529</td>\n",
       "      <td>0.631713</td>\n",
       "      <td>0.633087</td>\n",
       "      <td>0.633110</td>\n",
       "      <td>0.632013</td>\n",
       "      <td>0.632960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626174</td>\n",
       "      <td>0.626173</td>\n",
       "      <td>0.626447</td>\n",
       "      <td>0.631935</td>\n",
       "      <td>0.636276</td>\n",
       "      <td>0.630583</td>\n",
       "      <td>0.630634</td>\n",
       "      <td>0.629987</td>\n",
       "      <td>0.630449</td>\n",
       "      <td>0.627069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mg_x_n</th>\n",
       "      <td>0.636435</td>\n",
       "      <td>0.637597</td>\n",
       "      <td>0.636920</td>\n",
       "      <td>0.636760</td>\n",
       "      <td>0.636390</td>\n",
       "      <td>0.636429</td>\n",
       "      <td>0.637176</td>\n",
       "      <td>0.636994</td>\n",
       "      <td>0.637076</td>\n",
       "      <td>0.636766</td>\n",
       "      <td>...</td>\n",
       "      <td>0.630039</td>\n",
       "      <td>0.630059</td>\n",
       "      <td>0.630562</td>\n",
       "      <td>0.637067</td>\n",
       "      <td>0.640164</td>\n",
       "      <td>0.634544</td>\n",
       "      <td>0.634687</td>\n",
       "      <td>0.634035</td>\n",
       "      <td>0.634462</td>\n",
       "      <td>0.634122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mg_x_n</th>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.642049</td>\n",
       "      <td>0.639754</td>\n",
       "      <td>0.639812</td>\n",
       "      <td>0.639293</td>\n",
       "      <td>0.639561</td>\n",
       "      <td>0.640644</td>\n",
       "      <td>0.640671</td>\n",
       "      <td>0.639875</td>\n",
       "      <td>0.640518</td>\n",
       "      <td>...</td>\n",
       "      <td>0.637038</td>\n",
       "      <td>0.637049</td>\n",
       "      <td>0.637200</td>\n",
       "      <td>0.639878</td>\n",
       "      <td>0.644850</td>\n",
       "      <td>0.642004</td>\n",
       "      <td>0.642090</td>\n",
       "      <td>0.641311</td>\n",
       "      <td>0.641906</td>\n",
       "      <td>0.635217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mg_x_n</th>\n",
       "      <td>0.614404</td>\n",
       "      <td>0.621198</td>\n",
       "      <td>0.614413</td>\n",
       "      <td>0.614533</td>\n",
       "      <td>0.614374</td>\n",
       "      <td>0.614396</td>\n",
       "      <td>0.617168</td>\n",
       "      <td>0.617262</td>\n",
       "      <td>0.614498</td>\n",
       "      <td>0.617168</td>\n",
       "      <td>...</td>\n",
       "      <td>0.606431</td>\n",
       "      <td>0.606400</td>\n",
       "      <td>0.606451</td>\n",
       "      <td>0.613943</td>\n",
       "      <td>0.618622</td>\n",
       "      <td>0.610902</td>\n",
       "      <td>0.610979</td>\n",
       "      <td>0.610504</td>\n",
       "      <td>0.610833</td>\n",
       "      <td>0.607100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mg_x_n</th>\n",
       "      <td>0.014235</td>\n",
       "      <td>0.011024</td>\n",
       "      <td>0.014403</td>\n",
       "      <td>0.014314</td>\n",
       "      <td>0.014175</td>\n",
       "      <td>0.014231</td>\n",
       "      <td>0.012981</td>\n",
       "      <td>0.012892</td>\n",
       "      <td>0.014436</td>\n",
       "      <td>0.012840</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016800</td>\n",
       "      <td>0.016818</td>\n",
       "      <td>0.017050</td>\n",
       "      <td>0.014949</td>\n",
       "      <td>0.014063</td>\n",
       "      <td>0.015714</td>\n",
       "      <td>0.015703</td>\n",
       "      <td>0.015504</td>\n",
       "      <td>0.015659</td>\n",
       "      <td>0.016973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "condition  121 default  141 blood glc ini dose5  146 blood lac ini dose5  \\\n",
       "level_0                                                                    \n",
       "Mg_x_n        0.631735                 0.634951                 0.631886   \n",
       "Mg_x_n        0.636435                 0.637597                 0.636920   \n",
       "Mg_x_n        0.639592                 0.642049                 0.639754   \n",
       "Mg_x_n        0.614404                 0.621198                 0.614413   \n",
       "Mg_x_n        0.014235                 0.011024                 0.014403   \n",
       "\n",
       "condition  151 blood bhb ini dose5  156 AcCoAmito na ini dose5  \\\n",
       "level_0                                                          \n",
       "Mg_x_n                    0.631949                    0.631529   \n",
       "Mg_x_n                    0.636760                    0.636390   \n",
       "Mg_x_n                    0.639812                    0.639293   \n",
       "Mg_x_n                    0.614533                    0.614374   \n",
       "Mg_x_n                    0.014314                    0.014175   \n",
       "\n",
       "condition  161 SUCCOAmito na ini dose5  17 glclac ini  18 glcbhb ini  \\\n",
       "level_0                                                                \n",
       "Mg_x_n                        0.631713       0.633087       0.633110   \n",
       "Mg_x_n                        0.636429       0.637176       0.636994   \n",
       "Mg_x_n                        0.639561       0.640644       0.640671   \n",
       "Mg_x_n                        0.614396       0.617168       0.617262   \n",
       "Mg_x_n                        0.014231       0.012981       0.012892   \n",
       "\n",
       "condition  19 lacbhb ini  1 blood glc ini  ...  g1 72 LDH  g1 76 SUCmito  \\\n",
       "level_0                                    ...                             \n",
       "Mg_x_n          0.632013         0.632960  ...   0.626174       0.626173   \n",
       "Mg_x_n          0.637076         0.636766  ...   0.630039       0.630059   \n",
       "Mg_x_n          0.639875         0.640518  ...   0.637038       0.637049   \n",
       "Mg_x_n          0.614498         0.617168  ...   0.606431       0.606400   \n",
       "Mg_x_n          0.014436         0.012840  ...   0.016800       0.016818   \n",
       "\n",
       "condition  g1 90 AcAcCoa lac  g1 96 blood glc ini dose5  \\\n",
       "level_0                                                   \n",
       "Mg_x_n              0.626447                   0.631935   \n",
       "Mg_x_n              0.630562                   0.637067   \n",
       "Mg_x_n              0.637200                   0.639878   \n",
       "Mg_x_n              0.606451                   0.613943   \n",
       "Mg_x_n              0.017050                   0.014949   \n",
       "\n",
       "condition  th 166 blood glc ini dose5  th 171 blood lac ini dose5  \\\n",
       "level_0                                                             \n",
       "Mg_x_n                       0.636276                    0.630583   \n",
       "Mg_x_n                       0.640164                    0.634544   \n",
       "Mg_x_n                       0.644850                    0.642004   \n",
       "Mg_x_n                       0.618622                    0.610902   \n",
       "Mg_x_n                       0.014063                    0.015714   \n",
       "\n",
       "condition  th 176 blood bhb ini dose5  th 181 AcCoAmito na ini dose5  \\\n",
       "level_0                                                                \n",
       "Mg_x_n                       0.630634                       0.629987   \n",
       "Mg_x_n                       0.634687                       0.634035   \n",
       "Mg_x_n                       0.642090                       0.641311   \n",
       "Mg_x_n                       0.610979                       0.610504   \n",
       "Mg_x_n                       0.015703                       0.015504   \n",
       "\n",
       "condition  th 186 SUCCOAmito na ini dose5  th 191 ATP dose5  \n",
       "level_0                                                      \n",
       "Mg_x_n                           0.630449          0.627069  \n",
       "Mg_x_n                           0.634462          0.634122  \n",
       "Mg_x_n                           0.641906          0.635217  \n",
       "Mg_x_n                           0.610833          0.607100  \n",
       "Mg_x_n                           0.015659          0.016973  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_data2_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0bd1c1b2-da87-4e6f-97fd-24c07cd5dff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "row_colors2cmps = pd.Series(input_df_data2_T.index,index=input_df_data2_T.index).map(cmps_d).map(lut_cmps)\n",
    "row_colors2funs = pd.Series(input_df_data2_T.index,index=input_df_data2_T.index).map(v2function).map(lut_funs)\n",
    "\n",
    "color_df_rows_2 = pd.DataFrame({\"function\": row_colors2funs, \"cmps\": row_colors2cmps}, index=input_df_data2_T.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b42a3cb-8270-4567-b675-4338b944e30c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a62862c2-9ead-4d85-9b59-0f1efff4be82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average, cosine\n"
     ]
    }
   ],
   "source": [
    "cm_method = [\"single\",\"complete\",\"average\",\"weighted\",\"centroid\",\"median\",\"ward\" ][2]  \n",
    "cm_metric =  \"cosine\" \n",
    "\n",
    "print(f\"{cm_method}, {cm_metric}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "28de58d4-73db-444f-970c-5fc2b989eb75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#009395', '#5eb9bb', '#bce0e1', '#f5c9ce', '#fa7785', '#ff253d']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#009395;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#5eb9bb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bce0e1;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f5c9ce;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fa7785;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff253d;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "['#009395', '#5eb9bb', '#bce0e1', '#f5c9ce', '#fa7785', '#ff253d']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  matplotlib.colors import LinearSegmentedColormap\n",
    "c = [\"#08e8dd\",\"white\",\"#ff253d\"]\n",
    "v = [0,.5,1.]\n",
    "l = list(zip(v,c))\n",
    "\n",
    "cmap2=LinearSegmentedColormap.from_list('vlag',l, N=256)\n",
    "\n",
    "custompalette = sns.diverging_palette(195, 10, 100, 55,1, as_cmap=True)\n",
    "\n",
    "print(sns.diverging_palette(195, 10, 100, 55,1, as_cmap=False).as_hex())\n",
    "\n",
    "sns.diverging_palette(195, 10, 100, 55,1, as_cmap=False).as_hex()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b8f99f0a-17e5-4a1e-bd4b-36035d2e75f8",
   "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>condition</th>\n",
       "      <th>121 default</th>\n",
       "      <th>141 blood glc ini dose5</th>\n",
       "      <th>146 blood lac ini dose5</th>\n",
       "      <th>151 blood bhb ini dose5</th>\n",
       "      <th>156 AcCoAmito na ini dose5</th>\n",
       "      <th>161 SUCCOAmito na ini dose5</th>\n",
       "      <th>17 glclac ini</th>\n",
       "      <th>18 glcbhb ini</th>\n",
       "      <th>19 lacbhb ini</th>\n",
       "      <th>1 blood glc ini</th>\n",
       "      <th>...</th>\n",
       "      <th>g1 72 LDH</th>\n",
       "      <th>g1 76 SUCmito</th>\n",
       "      <th>g1 90 AcAcCoa lac</th>\n",
       "      <th>g1 96 blood glc ini dose5</th>\n",
       "      <th>th 166 blood glc ini dose5</th>\n",
       "      <th>th 171 blood lac ini dose5</th>\n",
       "      <th>th 176 blood bhb ini dose5</th>\n",
       "      <th>th 181 AcCoAmito na ini dose5</th>\n",
       "      <th>th 186 SUCCOAmito na ini dose5</th>\n",
       "      <th>th 191 ATP dose5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level_0</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>Mg_x_n</th>\n",
       "      <td>0.631735</td>\n",
       "      <td>0.634951</td>\n",
       "      <td>0.631886</td>\n",
       "      <td>0.631949</td>\n",
       "      <td>0.631529</td>\n",
       "      <td>0.631713</td>\n",
       "      <td>0.633087</td>\n",
       "      <td>0.633110</td>\n",
       "      <td>0.632013</td>\n",
       "      <td>0.632960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626174</td>\n",
       "      <td>0.626173</td>\n",
       "      <td>0.626447</td>\n",
       "      <td>0.631935</td>\n",
       "      <td>0.636276</td>\n",
       "      <td>0.630583</td>\n",
       "      <td>0.630634</td>\n",
       "      <td>0.629987</td>\n",
       "      <td>0.630449</td>\n",
       "      <td>0.627069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mg_x_n</th>\n",
       "      <td>0.636435</td>\n",
       "      <td>0.637597</td>\n",
       "      <td>0.636920</td>\n",
       "      <td>0.636760</td>\n",
       "      <td>0.636390</td>\n",
       "      <td>0.636429</td>\n",
       "      <td>0.637176</td>\n",
       "      <td>0.636994</td>\n",
       "      <td>0.637076</td>\n",
       "      <td>0.636766</td>\n",
       "      <td>...</td>\n",
       "      <td>0.630039</td>\n",
       "      <td>0.630059</td>\n",
       "      <td>0.630562</td>\n",
       "      <td>0.637067</td>\n",
       "      <td>0.640164</td>\n",
       "      <td>0.634544</td>\n",
       "      <td>0.634687</td>\n",
       "      <td>0.634035</td>\n",
       "      <td>0.634462</td>\n",
       "      <td>0.634122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mg_x_n</th>\n",
       "      <td>0.639592</td>\n",
       "      <td>0.642049</td>\n",
       "      <td>0.639754</td>\n",
       "      <td>0.639812</td>\n",
       "      <td>0.639293</td>\n",
       "      <td>0.639561</td>\n",
       "      <td>0.640644</td>\n",
       "      <td>0.640671</td>\n",
       "      <td>0.639875</td>\n",
       "      <td>0.640518</td>\n",
       "      <td>...</td>\n",
       "      <td>0.637038</td>\n",
       "      <td>0.637049</td>\n",
       "      <td>0.637200</td>\n",
       "      <td>0.639878</td>\n",
       "      <td>0.644850</td>\n",
       "      <td>0.642004</td>\n",
       "      <td>0.642090</td>\n",
       "      <td>0.641311</td>\n",
       "      <td>0.641906</td>\n",
       "      <td>0.635217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "condition  121 default  141 blood glc ini dose5  146 blood lac ini dose5  \\\n",
       "level_0                                                                    \n",
       "Mg_x_n        0.631735                 0.634951                 0.631886   \n",
       "Mg_x_n        0.636435                 0.637597                 0.636920   \n",
       "Mg_x_n        0.639592                 0.642049                 0.639754   \n",
       "\n",
       "condition  151 blood bhb ini dose5  156 AcCoAmito na ini dose5  \\\n",
       "level_0                                                          \n",
       "Mg_x_n                    0.631949                    0.631529   \n",
       "Mg_x_n                    0.636760                    0.636390   \n",
       "Mg_x_n                    0.639812                    0.639293   \n",
       "\n",
       "condition  161 SUCCOAmito na ini dose5  17 glclac ini  18 glcbhb ini  \\\n",
       "level_0                                                                \n",
       "Mg_x_n                        0.631713       0.633087       0.633110   \n",
       "Mg_x_n                        0.636429       0.637176       0.636994   \n",
       "Mg_x_n                        0.639561       0.640644       0.640671   \n",
       "\n",
       "condition  19 lacbhb ini  1 blood glc ini  ...  g1 72 LDH  g1 76 SUCmito  \\\n",
       "level_0                                    ...                             \n",
       "Mg_x_n          0.632013         0.632960  ...   0.626174       0.626173   \n",
       "Mg_x_n          0.637076         0.636766  ...   0.630039       0.630059   \n",
       "Mg_x_n          0.639875         0.640518  ...   0.637038       0.637049   \n",
       "\n",
       "condition  g1 90 AcAcCoa lac  g1 96 blood glc ini dose5  \\\n",
       "level_0                                                   \n",
       "Mg_x_n              0.626447                   0.631935   \n",
       "Mg_x_n              0.630562                   0.637067   \n",
       "Mg_x_n              0.637200                   0.639878   \n",
       "\n",
       "condition  th 166 blood glc ini dose5  th 171 blood lac ini dose5  \\\n",
       "level_0                                                             \n",
       "Mg_x_n                       0.636276                    0.630583   \n",
       "Mg_x_n                       0.640164                    0.634544   \n",
       "Mg_x_n                       0.644850                    0.642004   \n",
       "\n",
       "condition  th 176 blood bhb ini dose5  th 181 AcCoAmito na ini dose5  \\\n",
       "level_0                                                                \n",
       "Mg_x_n                       0.630634                       0.629987   \n",
       "Mg_x_n                       0.634687                       0.634035   \n",
       "Mg_x_n                       0.642090                       0.641311   \n",
       "\n",
       "condition  th 186 SUCCOAmito na ini dose5  th 191 ATP dose5  \n",
       "level_0                                                      \n",
       "Mg_x_n                           0.630449          0.627069  \n",
       "Mg_x_n                           0.634462          0.634122  \n",
       "Mg_x_n                           0.641906          0.635217  \n",
       "\n",
       "[3 rows x 64 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_data2_T.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "dd25fcea-4d11-4b09-b1a1-7bb701578d31",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c1 = sns.clustermap(input_df_data2_T, \n",
    "                     robust=True, #False,# True,\n",
    "                    \n",
    "                    annot=False,\n",
    "                    \n",
    "                     row_cluster=True,\n",
    "                     col_cluster=True,\n",
    "                    \n",
    "                    method=cm_method,\n",
    "                    metric=cm_metric,\n",
    "                    \n",
    "                    z_score=0,\n",
    "                    linewidths=0,  \n",
    "                    center=0,\n",
    "                    #vmin=-3,vmax=3,\n",
    "                    dendrogram_ratio=0.05,\n",
    "                    \n",
    "                    figsize=(16,8),\n",
    "                    cbar_pos=(1, 0.8, 0.05, 0.18),\n",
    "                    \n",
    "                    cmap=cmap2,\n",
    "                    \n",
    "                    row_colors=color_df_rows_2,\n",
    "                    col_colors=color_df_cols_2,\n",
    "                    \n",
    "                    xticklabels=True, yticklabels=False\n",
    "                    \n",
    "           )\n",
    "\n",
    "# Variables functions legend\n",
    "for label in lut_funs.keys():\n",
    "    c1.ax_col_dendrogram.bar(0, 0, color=lut_funs[str(label)], label=label, linewidth=0)\n",
    "l1 = c1.ax_col_dendrogram.legend(title='Functions', loc=\"upper right\", ncol=1, bbox_to_anchor=(1.05,0.55), frameon=False, bbox_transform=plt.gcf().transFigure)\n",
    "\n",
    "# Variables cmps legend\n",
    "for label in lut_cmps.keys():\n",
    "    c1.ax_col_dendrogram.bar(0, 0, color=lut_cmps[label], label=label, linewidth=0)\n",
    "l2 = c1.ax_col_dendrogram.legend(title='Functions', loc='upper right', ncol=1, bbox_to_anchor=(1.05, 1), frameon=False, bbox_transform=plt.gcf().transFigure)\n",
    "\n",
    "# Parameters cnds legend\n",
    "for label in lut_cnds.keys():\n",
    "    c1.ax_row_dendrogram.bar(0, 0, color=lut_cnds[label], label=label, linewidth=0)\n",
    "l3 = c1.ax_row_dendrogram.legend(title='Conditions', loc='upper right', ncol=1, bbox_to_anchor=(0.95, 1),frameon=False,  bbox_transform=plt.gcf().transFigure)\n",
    "\n",
    "c1.fig.subplots_adjust(right=0.7)\n",
    "c1.ax_cbar.set_position((0.8, .2, .03, .4))\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    c1.savefig(outpath + \"fig4a_clustermap_\" + timestr +  \".pdf\",transparent=True)\n",
    "    c1.savefig(outpath + \"fig4a_clustermap_\" + timestr +  \".png\",dpi=300, bbox_inches=\"tight\",transparent=False)\n",
    "else:\n",
    "    print(\"savefigures == False\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27b1d2ef-26e4-4b3f-81cf-5a81f06f2997",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "391c20c3-f73f-48b7-96a0-78eada536a13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d50ff007-399c-44ef-9e79-e8651d94c4b7",
   "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
}