conin_the_barbarian.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "97d327fb",
   "metadata": {},
   "source": [
    "# Load and execute the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dcb81929",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python version 3.7.6\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "print(\"python version \" + platform.python_version())\n",
    "import sys\n",
    "import json\n",
    "import cobra\n",
    "import cplex\n",
    "import re\n",
    "import os\n",
    "from os.path import exists\n",
    "import logging\n",
    "from configparser import ConfigParser\n",
    "config = ConfigParser()\n",
    "config.read(\"config.cfg\")\n",
    "paths = config.get(\"script\", \"syspaths\").split(\";\")\n",
    "for path in paths:\n",
    "    sys.path.append(path)\n",
    "\n",
    "graphs = [\n",
    "    {\n",
    "        'trial':'B4',\n",
    "        'content': 'OD',\n",
    "        'experimental_data': True\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e2c1e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mscomfit.zip\n",
      "The ['rxn name', 'rxn'] columns were dropped from the phenotypes CSV.\n",
      "The ['C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9', 'E10', 'E11', 'E12', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7', 'F8', 'F9', 'F10', 'F11', 'F12'] trials were dropped from the ecoli measurements.\n",
      "The ['B2', 'B12', 'G2', 'G12'] trials were removed from the ecoli measurements, with their deviation over time being less than the threshold of 2.\n",
      "The ['C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9', 'E10', 'E11', 'E12', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7', 'F8', 'F9', 'F10', 'F11', 'F12'] trials were dropped from the pf measurements.\n",
      "The ['B2', 'B3', 'B12', 'G12'] trials were removed from the pf measurements, with their deviation over time being less than the threshold of 2.\n",
      "The ['C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9', 'E10', 'E11', 'E12', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7', 'F8', 'F9', 'F10', 'F11', 'F12'] trials were dropped from the OD measurements.\n",
      "2022-07-16 00:19:38.217192\n",
      "Done with biomass loop: 0.3045203666666667 min\n",
      "Done with metabolites loop: 5.398467483333333 min\n",
      "Done with the dbc & diffc loop: 0.61811135 min\n",
      "Done with loading the variables, constraints, and objective: 0.2212708166666668 min\n",
      "Done exporting the content: 5.323711149999999 min\n",
      "2022-07-16 00:31:33.896290\n",
      "{'trial': 'B4', 'content': 'OD', 'experimental_data': True}\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (164,) and (1,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m~/code/ProjectNotebooks/CommunityModeling/CommFitting/mscommfitting.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, graphs, zip_name)\u001b[0m\n\u001b[1;32m    422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgraphs\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 424\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraphs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzip_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"optimal\"\u001b[0m \u001b[0;32min\u001b[0m  \u001b[0msolution\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/ProjectNotebooks/CommunityModeling/CommFitting/mscommfitting.py\u001b[0m in \u001b[0;36mgraph\u001b[0;34m(self, graphs, primal_values_filename, primal_values_zip_path, zip_name, data_timestep_hr)\u001b[0m\n\u001b[1;32m    498\u001b[0m                                     \u001b[0mexp_xs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbasename\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    499\u001b[0m                                     \u001b[0mexp_xs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexp_xs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mexp_xs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp_xs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 500\u001b[0;31m                                     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp_xs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbasename\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'experimental'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    501\u001b[0m                                     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp_xs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdata_timestep_hr\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtimestep_ratio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m                         \u001b[0;32melif\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'phenotype'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'*'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbasename\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'species'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m         \"\"\"\n\u001b[1;32m   1664\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1665\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1666\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 270\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (164,) and (1,)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "growth: 0.1 objective value: 595680.4529300063 \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "2022-07-16 00:31:56.365231\n"
     ]
    },
    {
     "data": {
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pqp9I8rwk7+5aKNCurf+r6qQk/z3JfUmePcZ45hjj5WOMH0zyPUle1r56YBEt/V9VRyW5OMnXM+v9s6bPAKcmOTezIPa3quqIZbwIYK9dl+TXkpyT5OgFa60s9zvgj3SdTvv9+ySbkjxjjHHDmvkbk5yY5JQxxl88RK2jM/t265tJnjjG+Lvd5h6V5AtJvjvJ9+w+B6xeZ+8/xPO8PLOjX24bYzxlgSUDTZbV/1X1uCS3JPmLJG9N8rE40hX2K939X1XXJjkryTljjKuXsGSgSfNn/xcl+UCSa8YYZ+9h/i+TnJTktDHGp5teAtCkqkY2cKTrqnO/g+FI1zMz+6W7Y+0v3cl7p/GcddQ6O7N/k2vX/uOOMXZm9kv5kGk/YN/q7P1v58ZpfMKCdYA+y+r/30nynUl+ZoG1AcvV1v9V9a8yC1xvFbjCAaHz/X/nOp/z/6xzP+DAsNLc72AIXU+axm1z5ret2W9VtYDlWlW/PnUa71qwDtCnvf+r6oeSnJfkbWOMzy2wNmC5Ovt/141yPjzdmOdVVXVpVf1OVf1kVf2LhVYKdOvs/+uTfC3JD1TVmbtPVNWPZnbE7J/5mwAOOivN/Q7tKLKPHTONd8yZv2PNfquqBSzXqvr1gml8/4J1gD6t/V9Vj07yriR/neTXF1sasGSd/b91Gv8hyV8mOW7N/MVV9dIxxrV7t0RgSdr6f4zx1ar6ySR/kOTaqtp1I62nZHYjrWuSnL/QaoH90Upzv4PhSNddF7aed1fx+9bst6pawHItvV+r6qeTvCDJV5O8faN1gHbd/f+WJE/K7C7FX19kYcDSdfb/kdP4c5ldv+1HMzt1+bjMruf+2CRXVdXjN7ZUoFnr+/8Y472ZnUJ8d2aXLjgvyamZXTf2o9N24OCy0tzvYAhdaxrn3RGs5mxfdi1guZbar1X1nCSXTPV/fIxx5yL1gFZt/V9VpyR5XZL/Nsb42KILA5au8/3/kGk8NMm/G2O8b4zxtTHGrWOMV2Z2+vGRSV6zsaUCzVr//q+qNyT5cJJrM7ucwBHTeF2S30zyPza2TGA/ttLc72AIXe+ZxkfPmT98Gu9dcS1guZbWr1V1YpKrkjwyyQVjjPft/fKAJWrp/6o6NMm7M7um2y/0LA1YsmX87f/FMcb/3sP870/jc9e3NGDJ2vp/OsDiP2d2aZEfG2P81RjjvjHGXyU5N8kNSV5aVT+44JqB/ctKc7+D4Zqut0/j5jnzm9fst6pawHItpV+rakuSD2V2euGbxhiXbmx5wBJ19f/mJP86sxvl/XHVP/tie9M0nlpVf5rk3jHGi/Z+qUCzzvf/26bx8w8x/7h11AKWr7P///00XjnGeHD3iTHGN6vqyiQnZ/aly56+lAEOTCvN/Q6G0PXGaXzGnPld229acS1gudr7taqekNkpRkcnuWSMcdHGlwcsUXf/Hz397MmRSZ6T2dGwwL7X2f83TON3z5l/zDQ6yw32D539vytY+X9z5ndtn/f7ATgwrTT3OxguL/DJzD4Ibamqk/cwf+40Xr2OWtckeTDJWVX1z77RrqpHJTlnmv/gxpcLNOns/VTVkZkd4fqUzE4n/PmORQJL0dL/Y4zbxhi1p58kz5t2+9C0bdO3qwWsTOf7/0cyu2HGlqp64h7mnzuN2/Z2kcBSdPb/XdN4ypz5Z03jbeteHXAgWGnud8CHrtNdhi+bHl5WVd+6LkNVvT6zC2F/Yoxx/W7bX1tVt1TVxWtqfSnJH2Z2Hcd3Tdd62+U3khyV5Ioxxl0B9qnO3q+qw5P8SZITkvxRkp8aY8y7sDawj3X2P3Bgaf7b//4klyZ5RJL/sqbWC5O8KrMbbfzusl4PsH7N7/9XTeMrq+qc3Seq6keSvCKz4MW9HeAAtL/kfgfD5QWS5C1JXpDkjCSfraqPJ3lSktOS3J3k1Wv2f2yS45I8fg+1fi7J6UlemuSWqvpMkq2ZhTE74ug32J909f5bM+v7byb5RpLfW3NtxyTJGOP8xrUDi+l87wcOLJ39f1GSs5L88FTrU5ldw/X0zA5QuXCM8ellvAhgQ7r6/6okf5zkx5L8z+lz/99mdtbbrqNfLxxj/PUyXgSwd6rqh5P8yprNj6yqP9/t8ZvHGP9r+u/9Ivc74I90TZIxxgOZnQb45iT3J3lxkicneU+Sk8cYn9uLWl/J7FSCSzNLvl+S5Lsy+0bt1Gke2A809v6R03hIZt9qv2rOD7Cf6HzvBw4szX/7P5DkB5JcmOSrSc7O7IPXx5K8aIzxttbFAwvp6v/prLbzkvxEkmuTHJvZZ/8nZ3YG3Nn6H/YrR2X25cqunySpNduOWk+hVeZ+5QxaAAAAAIA+B8WRrgAAAAAA+wuhKwAAAABAI6ErAAAAAEAjoSsAAAAAQCOhKwAAAABAI6ErAAAAAEAjoSsAAAAAQCOhKwAAAABAI6ErAAAAAEAjoSsAAAAAQCOhKwAAAABAI6ErAAAAAEAjoSsAAAAAQCOhKwAAAABAI6ErAAAAAEAjoSsAAAAAQCOhKwAAAABAI6ErAAAAAECjfwImOVAkz7yAigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1650x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "%run mscommfitting.py\n",
    "mscommfit = MSCommFitting()\n",
    "mscommfit.load_data(phenotypes_csv_path = 'PhenotypeStoichiometry.csv', \n",
    "                           signal_tsv_paths = {'RFP_pf-ec_4-29-22.txt':'ecoli', 'GFP_pf-ec_4-29-22.txt':'pf', 'OD_pf-ec_4-29-22.txt':'OD'},\n",
    "                           media_conc_path = 'media_conc.csv',\n",
    "                           species_abundance_path = 'species_abundances.csv',\n",
    "                           carbon_conc_series = {'rows': {\n",
    "                               'cpd00136': {'B':0, 'C': 0, 'D': 1, 'E': 1, 'F': 4, 'G': 4},\n",
    "                               'cpd00179': {'B':5, 'C': 5, 'D':5, 'E': 5, 'F': 5, 'G': 5},\n",
    "                               }},\n",
    "                           ignore_trials = {'rows': ['C', 'D', 'E', 'F']}, # 'wells': ['C2', 'G3', 'C12']},  # 'columns': [6,7,8,9,10,11,12]},\n",
    "                           # ignore_timesteps = [x for x in range(20,167)],\n",
    "                           zip_path='mscomfit.zip'\n",
    "                   )\n",
    "print(datetime.today())\n",
    "mscommfit.define_problem(parameters={\n",
    "    'v':0.1,\n",
    "    'cvmin': 100,\n",
    "    'bcv': 100,\n",
    "    'diffpos': 100,\n",
    "    'diffneg': 100\n",
    "    }, zip_name='simple_full_community.zip')\n",
    "print(datetime.today())\n",
    "%time mscommfit.compute(graphs, zip_name='simple_full_community.zip')\n",
    "print('growth:', mscommfit.parameters['v'], 'objective value:', mscommfit.problem.objective.value, '\\n\\n\\n\\n')\n",
    "print(datetime.today())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d11759",
   "metadata": {},
   "source": [
    "# Change the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b247f880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done loading the JSON: 0.16171875 min\n",
      "Done exporting the model: 0.9856770833333334 min\n",
      "Done loading the model: 3.76875 min\n",
      "simple_full_community.zip\n",
      "{'trial': 'B4', 'content': 'OD', 'experimental_data': True, 'phenotype': '*', 'species': '*'}\n",
      "pred b_ecoli_malt\n",
      "pred b_ecoli_acetate\n",
      "pred b_pf_acetate\n",
      "pred b_pf_4HB\n",
      "pred b_ecoli_stationary\n",
      "pred b_pf_stationary\n",
      "exp\n",
      "The solution is optimal.\n",
      "CPU times: total: 7min 50s\n",
      "Wall time: 7min 50s\n",
      "growth: 0.1 objective value: 211240.1569319003 \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "2022-07-15 21:00:51.539690\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1650x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mscommfit.change_parameters(vmax=-.4, zip_name='simple_full_community_edited.zip')  # The Vmax argument can be either a number or a dictionary that is organized by [\"time\"][\"trial\"], just as the naming scheme of the variables and constraints\n",
    "%time mscommfit.compute(graphs, 'simple_full_community_edited.zip')\n",
    "print('growth:', mscommfit.parameters['v'], 'objective value:', mscommfit.problem.objective.value, '\\n\\n\\n\\n')\n",
    "print(datetime.today())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6647e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "vmax = 3.75\n",
    "km = 2.5\n"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}