community_model_COMPM_weights_16.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import cobra\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.optimize import linprog\n",
    "import scipy\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Community modeling\n",
    "\n",
    "In this notebook we will implement a method to create community models of two or more species specific metabolic models using cobrapy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growth:  0.2823653925322476\n",
      "Growth:  2.558694612613393\n"
     ]
    }
   ],
   "source": [
    "model_DP = cobra.io.read_sbml_model(\"models/consistent_DP_SNM.xml\")\n",
    "model_SA = cobra.io.read_sbml_model(\"models/consistent_iYS854_SNM.xml\")\n",
    "print(\"Growth: \", model_DP.slim_optimize())\n",
    "print(\"Growth: \", model_SA.slim_optimize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for rec in model_SA.reactions:\n",
    "    rec.lower_bound = max(rec.lower_bound, -1000)\n",
    "    rec.upper_bound = min(rec.upper_bound, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound</th>\n",
       "      <th>BiGG</th>\n",
       "      <th>ModelSeed</th>\n",
       "      <th>KEGG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alanine</td>\n",
       "      <td>ala__L</td>\n",
       "      <td>cpd00035</td>\n",
       "      <td>C00041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arginine</td>\n",
       "      <td>arg__L</td>\n",
       "      <td>cpd00051</td>\n",
       "      <td>C00062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cysteine</td>\n",
       "      <td>cys__L</td>\n",
       "      <td>cpd00084</td>\n",
       "      <td>C00097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Glutamic acid</td>\n",
       "      <td>glu__L</td>\n",
       "      <td>cpd00023</td>\n",
       "      <td>C00025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Glycine</td>\n",
       "      <td>gly</td>\n",
       "      <td>cpd00033</td>\n",
       "      <td>C00037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Compound    BiGG ModelSeed    KEGG\n",
       "0        Alanine  ala__L  cpd00035  C00041\n",
       "1       Arginine  arg__L  cpd00051  C00062\n",
       "2       Cysteine  cys__L  cpd00084  C00097\n",
       "3  Glutamic acid  glu__L  cpd00023  C00025\n",
       "4        Glycine     gly  cpd00033  C00037"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snm3 = pd.read_csv(\"SNM3.csv\", sep =\";\")\n",
    "snm3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "BIOMASS_DP = \"Growth\" \n",
    "BIOMASS_SA = \"BIOMASS_iYS_wild_type\"\n",
    "models = [model_DP.copy(), model_SA.copy()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Constructing of community model explicitely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_stoichiometry_matrix(model):\n",
    "    metabolites = model.metabolites \n",
    "    reactions = model.reactions \n",
    "    S = np.zeros((len(metabolites), len(reactions)))\n",
    "    \n",
    "    met_id = dict()\n",
    "    rec_id = dict()\n",
    "    for i,reaction in enumerate(model.reactions):\n",
    "        rec_id[reaction.id] = i\n",
    "        for metabolite, stoich in reaction.metabolites.items():\n",
    "            met_id[metabolite.id] = int(metabolites.index(metabolite))\n",
    "            S[metabolites.index(metabolite), i] = stoich\n",
    "    return S, met_id, rec_id "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model():\n",
    "    def __init__(self, model, biomass_function):\n",
    "        \"\"\" This is a new class of metabolic model, capable of flux balance analysis\n",
    "        Attributes:\n",
    "        models (list): CobraPy models of single organisms which will be used in construction\n",
    "        biomass_reactions (list): List of strings containing the ids for the growth reactions\n",
    "        \"\"\"\n",
    "        self.biomass_function = biomass_function\n",
    "        self.model = model\n",
    "        self.id = model.id\n",
    "        # Compute stoichimetry_matrix\n",
    "        S, met_id, rec_id = create_stoichiometry_matrix(model)\n",
    "        self.num_reactions = S.shape[1]\n",
    "        self.num_metabolites = S.shape[0]\n",
    "        self.stoichiometry_matrix = scipy.sparse.csr_matrix(S)\n",
    "        self.met_id = met_id\n",
    "        self.rec_id = rec_id \n",
    "        # Set objective\n",
    "        idx = self.rec_id[biomass_function]\n",
    "        c = np.zeros(self.num_reactions)\n",
    "        c[idx] = 1\n",
    "        self.objective_c = c\n",
    "        self.medium = model.medium\n",
    "        # Set bounds\n",
    "        self._reset_bounds()\n",
    "    \n",
    "    @property\n",
    "    def reactions(self):\n",
    "        return self.model.reactions\n",
    "    @property\n",
    "    def exchanges(self):\n",
    "        return self.model.exchanges\n",
    "    @property\n",
    "    def metabolites(self):\n",
    "        return self.model.metabolites\n",
    "\n",
    "\n",
    "    def set_medium(self, medium):\n",
    "        ex_ids = [ex.id for ex in self.exchanges]\n",
    "        new_med = {}\n",
    "        for key,val in medium.items():\n",
    "            if key in ex_ids:\n",
    "                new_med[key] = val\n",
    "        self.model.medium = new_med\n",
    "        self._reset_bounds()\n",
    "        \n",
    "    def optimize(self, disp=False):\n",
    "        sol = linprog(-self.objective_c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), bounds=self.bounds, method=\"highs\", options={\"disp\":disp})\n",
    "        sol[\"fun\"] = -sol[\"fun\"] # As we have to minimize\n",
    "        return sol \n",
    "    \n",
    "    def slim_optimize(self, disp=False):\n",
    "        sol = self.optimize(disp=disp)\n",
    "        return sol[\"fun\"]\n",
    "\n",
    "    def summary(self):\n",
    "        sol = self.optimize()\n",
    "        flux = sol[\"x\"]\n",
    "        ex_ids = [ex.id for ex in self.exchanges]\n",
    "        fluxes = []\n",
    "        for ex in ex_ids:\n",
    "            idx = self.rec_id[ex]\n",
    "            fluxes.append(flux[idx])\n",
    "        summary_df = pd.DataFrame({\"Exchange reaction\": ex_ids, \"Flux\": fluxes})\n",
    "        summary_df.sort_values([\"Flux\"], inplace=True)\n",
    "        return summary_df\n",
    "\n",
    "    def _reset_bounds(self):\n",
    "        self.bounds = []\n",
    "        for rec in self.model.reactions:\n",
    "            self.bounds.append((rec.lower_bound, rec.upper_bound))\n",
    "\n",
    "    def __add__(self, model2):\n",
    "        \"\"\" Adding another model creates a community model \"\"\"\n",
    "        return CommunityModel([self,model2], [1.,1.])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CommunityModel(Model):\n",
    "    def __init__(self, models, weight):\n",
    "        self.models = models\n",
    "        self.id = \"|\".join([model.id for model in models])\n",
    "        self.shared_exchanges = []\n",
    "        self.weight = weight\n",
    "        for model in models:\n",
    "            for rec in model.exchanges:\n",
    "                if rec.id not in self.shared_exchanges:\n",
    "                    self.shared_exchanges.append(rec.id)\n",
    "        # Create community stoichimetry matrix with shuttel reactions!\n",
    "        self._shifts = [len(self.shared_exchanges)]\n",
    "        for i,model in enumerate(models):\n",
    "            self._shifts.append(self._shifts[i] + model.num_reactions)\n",
    "        S_EX = -np.eye(self._shifts[0])\n",
    "        matrices = [S_EX] + [m.stoichiometry_matrix.todense() for m in models]\n",
    "        S = scipy.linalg.block_diag(*matrices)\n",
    "        self.num_reactions = S.shape[1]\n",
    "        self.num_metabolites = S.shape[0]\n",
    "        for i, id in enumerate(self.shared_exchanges):\n",
    "            for j,model in enumerate(models):\n",
    "                if id in model.rec_id:\n",
    "                    S[i,self._shifts[j] + model.rec_id[id]] = 1\n",
    "        self.stoichiometry_matrix = scipy.sparse.csr_matrix(S)\n",
    "        # Cretae objective:\n",
    "        self._weighted_objective(weight)\n",
    "        # Create bounds\n",
    "        self._reset_bounds()\n",
    "    \n",
    "    @property\n",
    "    def reactions(self):\n",
    "        return [model.reactions for model in self.models]\n",
    "    @property\n",
    "    def exchanges(self):\n",
    "        return self.shared_exchanges\n",
    "    @property\n",
    "    def metabolites(self):\n",
    "        return [model.metabolites for model in self.models]\n",
    "    @property\n",
    "    def medium(self):\n",
    "        medium = {}\n",
    "        for model in self.models:\n",
    "            for key,val in model.medium.items():\n",
    "                medium[key] = val\n",
    "        return medium\n",
    "\n",
    "    def _weighted_objective(self, weight):\n",
    "        self.weight = weight\n",
    "        self.objective_c = np.zeros(self._shifts[0])\n",
    "        for i,model in enumerate(self.models):\n",
    "            self.objective_c = np.append(self.objective_c, weight[i]*model.objective_c)\n",
    "    def _reset_bounds(self):\n",
    "        self.bounds = []\n",
    "        for id in self.shared_exchanges:\n",
    "            min_lower_bound = 0\n",
    "            for model in self.models:\n",
    "                if id in model.rec_id:\n",
    "                    rec = model.reactions.get_by_id(id)\n",
    "                    if rec.lower_bound < min_lower_bound:\n",
    "                        min_lower_bound = rec.lower_bound \n",
    "            self.bounds.append((min_lower_bound, 1000))\n",
    "        for model in self.models:\n",
    "            self.bounds += model.bounds\n",
    "\n",
    "    def compute_alpha_weightings(self, alpha, maxbiomass=0.1):\n",
    "        assert alpha <= 1 and alpha >= 0\n",
    "        assert len(self.models) == 2\n",
    "        alphas = np.array([alpha,1-alpha])\n",
    "        # Alpha objective...\n",
    "        c = np.zeros(self._shifts[0])\n",
    "        for i, model in enumerate(self.models):\n",
    "            c = np.append(c, model.objective_c)\n",
    "        # Biomoss constraints\n",
    "        c_mask = (c > 0)\n",
    "        A_ub = np.zeros((2,len(c)))\n",
    "        A_ub[0,c_mask] = np.array([1,0])\n",
    "        A_ub[1,c_mask] = np.array([0,1])\n",
    "        b_ub = alphas*maxbiomass\n",
    "        sol = linprog(-c, A_eq=self.stoichiometry_matrix, b_eq=np.zeros(self.num_metabolites), A_ub = A_ub, b_ub=b_ub, bounds=self.bounds, method=\"highs\")\n",
    "        fluxes = sol[\"x\"]\n",
    "        growths = fluxes[c > 0]\n",
    "        summary = self.summary(sol)\n",
    "        return growths, fluxes,summary\n",
    "\n",
    "    def set_medium(self, medium):\n",
    "        for model in self.models:\n",
    "            model.set_medium(medium)\n",
    "        self._reset_bounds()\n",
    "\n",
    "    def summary(self, sol=None):\n",
    "        if sol == None:\n",
    "            sol = self.optimize()\n",
    "        flux = sol[\"x\"]\n",
    "        ex_ids = self.shared_exchanges\n",
    "        ex_flux = flux[:len(ex_ids)]\n",
    "        df_ex = pd.DataFrame({\"Exchanges\": ex_ids, \"Flux\": ex_flux})\n",
    "        df_ex.sort_values([\"Flux\"], inplace=True)\n",
    "        for i,model in enumerate(self.models):\n",
    "            shuttel_ids = [ex.id for ex in model.exchanges]\n",
    "            id = str(model.id) + \" Shuttel Flux\"\n",
    "            df_ex[id] = 0.\n",
    "            for sh in shuttel_ids:\n",
    "                idx = model.rec_id[sh]\n",
    "                df_ex[id][ex_ids.index(sh)] = flux[self._shifts[i] +idx]\n",
    "        \n",
    "        return df_ex\n",
    "    \n",
    "    def set_weights(self, weight):\n",
    "        self._weighted_objective(weight)\n",
    "    \n",
    "    def get_model_growths(self):\n",
    "        mask = self.objective_c != 0\n",
    "        sol = self.optimize()\n",
    "        flux = sol[\"x\"]\n",
    "        return flux[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growth:  0.28236539253224774\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Exchange reaction</th>\n",
       "      <th>Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>EX_o2_e</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>EX_cys__L_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EX_h2o_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>EX_arg__L_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>EX_fum_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Exchange reaction  Flux\n",
       "32           EX_o2_e -20.0\n",
       "57       EX_cys__L_e -10.0\n",
       "1        EX_glc__D_e -10.0\n",
       "2           EX_h2o_e -10.0\n",
       "16       EX_arg__L_e -10.0\n",
       "39          EX_fum_e -10.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_DP = Model(model_DP, BIOMASS_DP)\n",
    "print(\"Growth: \",model_DP.slim_optimize())\n",
    "model_DP.summary().head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growth:  2.5586946126118373\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Exchange reaction</th>\n",
       "      <th>Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>EX_o2_e</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>EX_orn_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>EX_pyr_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>EX_glu__L_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>EX_ser__L_e</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Exchange reaction  Flux\n",
       "42           EX_o2_e -20.0\n",
       "51       EX_glc__D_e -10.0\n",
       "43          EX_orn_e -10.0\n",
       "52          EX_pyr_e -10.0\n",
       "54       EX_glu__L_e -10.0\n",
       "56       EX_ser__L_e -10.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_SA = Model(model_SA, BIOMASS_SA)\n",
    "print(\"Growth: \",model_SA.slim_optimize())\n",
    "model_SA.summary().head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights 1:1:  2.5586946126136296\n",
      "SA growth:  2.5586946126136296\n",
      "DP growth:  -0.0\n",
      "weights 1:10  4.294950548440156\n",
      "SA growth:  1.4712966231176634\n",
      "DP growth:  0.28236539253224924\n"
     ]
    }
   ],
   "source": [
    "# Community model\n",
    "model = model_SA + model_DP\n",
    "print(\"Weights 1:1: \", model.slim_optimize())\n",
    "growths = model.get_model_growths()\n",
    "print(\"SA growth: \", growths[0])\n",
    "print(\"DP growth: \", growths[1])\n",
    "model.set_weights([1,10])\n",
    "print(\"weights 1:10 \", model.slim_optimize())\n",
    "growths = model.get_model_growths()\n",
    "print(\"SA growth: \", growths[0])\n",
    "print(\"DP growth: \", growths[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "compm_SA = json.loads(open(\"compm_SA.json\").read())\n",
    "compm_DP = json.loads(open(\"compm_DP.json\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "compm = dict()\n",
    "for key, val in compm_SA.items():\n",
    "    compm[key] = val \n",
    "for key, val in compm_DP.items():\n",
    "    compm[key] = val "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.set_medium(compm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA growth:  2.558694612613441\n",
      "DP growth:  -9.732222301975288e-13\n"
     ]
    }
   ],
   "source": [
    "model.set_weights([1,1])\n",
    "growths = model.get_model_growths()\n",
    "print(\"SA growth: \", growths[0])\n",
    "print(\"DP growth: \", growths[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA growth:  2.175506751508334\n",
      "DP growth:  0.07290151281852739\n"
     ]
    }
   ],
   "source": [
    "model.set_weights([1,10])\n",
    "growths = model.get_model_growths()\n",
    "print(\"SA growth: \", growths[0])\n",
    "print(\"DP growth: \", growths[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigate weighting...\n",
    "\n",
    "We will fix $\\nu_{comm} = 0.1$. That is the total biomass will be fixed to 0.1. Now we will compute for several $\\alpha \\in (0,1)$ following optimization problem:\n",
    "$$ \\max \\  \\nu_{DP} + \\nu_{SA} \\text{ subject to usuall constraints and }  \\nu_{SA} \\leq \\alpha \\cdot 0.1 \\quad  \\nu_{DP} \\leq (1-\\alpha) \\cdot 0.1$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "summaries = dict()\n",
    "model.set_weights([1,1])\n",
    "for alpha in [i/10 for i in range(1,10)]:\n",
    "    growths, fluxes, summary = model.compute_alpha_weightings(alpha, 0.3)\n",
    "    summaries[alpha] = summary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full = pd.concat(summaries, join=\"outer\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th colspan=\"4\" halign=\"left\">0.1</th>\n",
       "      <th colspan=\"4\" halign=\"left\">0.2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0.3</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0.7</th>\n",
       "      <th colspan=\"4\" halign=\"left\">0.8</th>\n",
       "      <th colspan=\"4\" halign=\"left\">0.9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Exchanges</th>\n",
       "      <th>Flux</th>\n",
       "      <th>iYS854 Shuttel Flux</th>\n",
       "      <th>DP_83VPs_KB5 Shuttel Flux</th>\n",
       "      <th>Exchanges</th>\n",
       "      <th>Flux</th>\n",
       "      <th>iYS854 Shuttel Flux</th>\n",
       "      <th>DP_83VPs_KB5 Shuttel Flux</th>\n",
       "      <th>Exchanges</th>\n",
       "      <th>Flux</th>\n",
       "      <th>...</th>\n",
       "      <th>iYS854 Shuttel Flux</th>\n",
       "      <th>DP_83VPs_KB5 Shuttel Flux</th>\n",
       "      <th>Exchanges</th>\n",
       "      <th>Flux</th>\n",
       "      <th>iYS854 Shuttel Flux</th>\n",
       "      <th>DP_83VPs_KB5 Shuttel Flux</th>\n",
       "      <th>Exchanges</th>\n",
       "      <th>Flux</th>\n",
       "      <th>iYS854 Shuttel Flux</th>\n",
       "      <th>DP_83VPs_KB5 Shuttel Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>8.231034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.231034</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>7.870420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.870420</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>7.506030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.855337</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>8.461639</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.461639</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>7.529321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.529321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.009584</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.009584</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.008519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008519</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.007454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.002130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002130</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.001065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.026147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.026147</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.023242</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.023242</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.020337</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008716</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.005811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.005811</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.002905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 36 columns</p>\n",
       "</div>"
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      "text/plain": [
       "               0.1                                                          \\\n",
       "         Exchanges      Flux iYS854 Shuttel Flux DP_83VPs_KB5 Shuttel Flux   \n",
       "0     EX_istfrnA_e  0.000000                -0.0                  0.000000   \n",
       "1     EX_istfrnB_e  0.000000                -0.0                  0.000000   \n",
       "2      EX_stfrnA_e  0.000000                -0.0                  0.000000   \n",
       "3      EX_stfrnB_e  0.000000                -0.0                  0.000000   \n",
       "4          EX_ac_e -0.000000                 0.0                  0.000000   \n",
       "..             ...       ...                 ...                       ...   \n",
       "106      EX_lipa_e -0.000000                 0.0                 -0.000000   \n",
       "107     EX_pdima_e -0.000000                 0.0                 -0.000000   \n",
       "108         EX_s_e  8.231034                 0.0                  8.231034   \n",
       "109       EX_ni2_e -0.009584                 0.0                 -0.009584   \n",
       "110  EX_26dap__M_e -0.026147                 0.0                 -0.026147   \n",
       "\n",
       "               0.2                                                          \\\n",
       "         Exchanges      Flux iYS854 Shuttel Flux DP_83VPs_KB5 Shuttel Flux   \n",
       "0     EX_istfrnA_e  0.000000                -0.0                  0.000000   \n",
       "1     EX_istfrnB_e  0.000000                -0.0                  0.000000   \n",
       "2      EX_stfrnA_e  0.000000                -0.0                  0.000000   \n",
       "3      EX_stfrnB_e  0.000000                -0.0                  0.000000   \n",
       "4          EX_ac_e -0.000000                 0.0                  0.000000   \n",
       "..             ...       ...                 ...                       ...   \n",
       "106      EX_lipa_e -0.000000                 0.0                  0.000000   \n",
       "107     EX_pdima_e -0.000000                 0.0                 -0.000000   \n",
       "108         EX_s_e  7.870420                 0.0                  7.870420   \n",
       "109       EX_ni2_e -0.008519                 0.0                 -0.008519   \n",
       "110  EX_26dap__M_e -0.023242                 0.0                 -0.023242   \n",
       "\n",
       "               0.3            ...                 0.7  \\\n",
       "         Exchanges      Flux  ... iYS854 Shuttel Flux   \n",
       "0     EX_istfrnA_e  0.000000  ...                -0.0   \n",
       "1     EX_istfrnB_e  0.000000  ...                -0.0   \n",
       "2      EX_stfrnA_e  0.000000  ...                -0.0   \n",
       "3      EX_stfrnB_e  0.000000  ...                -0.0   \n",
       "4          EX_ac_e -0.000000  ...                 0.0   \n",
       "..             ...       ...  ...                 ...   \n",
       "106      EX_lipa_e -0.000000  ...                 0.0   \n",
       "107     EX_pdima_e -0.000000  ...                 0.0   \n",
       "108         EX_s_e  7.506030  ...                 0.0   \n",
       "109       EX_ni2_e -0.007454  ...                 0.0   \n",
       "110  EX_26dap__M_e -0.020337  ...                 0.0   \n",
       "\n",
       "                                         0.8                                \\\n",
       "    DP_83VPs_KB5 Shuttel Flux      Exchanges      Flux iYS854 Shuttel Flux   \n",
       "0                    0.000000   EX_istfrnA_e  0.000000                -0.0   \n",
       "1                    0.000000   EX_istfrnB_e  0.000000                -0.0   \n",
       "2                    0.000000    EX_stfrnA_e  0.000000                -0.0   \n",
       "3                    0.000000    EX_stfrnB_e  0.000000                -0.0   \n",
       "4                    0.000000        EX_ac_e -0.000000                 0.0   \n",
       "..                        ...            ...       ...                 ...   \n",
       "106                  0.000000      EX_lipa_e -0.000000                 0.0   \n",
       "107                 -0.000000     EX_pdima_e -0.000000                 0.0   \n",
       "108                  8.855337         EX_s_e  8.461639                 0.0   \n",
       "109                 -0.003195       EX_ni2_e -0.002130                 0.0   \n",
       "110                 -0.008716  EX_26dap__M_e -0.005811                 0.0   \n",
       "\n",
       "                                         0.9                                \\\n",
       "    DP_83VPs_KB5 Shuttel Flux      Exchanges      Flux iYS854 Shuttel Flux   \n",
       "0                    0.000000   EX_istfrnA_e  0.000000                -0.0   \n",
       "1                    0.000000   EX_istfrnB_e  0.000000                -0.0   \n",
       "2                    0.000000    EX_stfrnA_e  0.000000                -0.0   \n",
       "3                    0.000000    EX_stfrnB_e  0.000000                -0.0   \n",
       "4                    0.000000        EX_ac_e -0.000000                 0.0   \n",
       "..                        ...            ...       ...                 ...   \n",
       "106                  0.000000      EX_lipa_e -0.000000                 0.0   \n",
       "107                 -0.000000     EX_pdima_e -0.000000                 0.0   \n",
       "108                  8.461639         EX_s_e  7.529321                 0.0   \n",
       "109                 -0.002130       EX_ni2_e -0.001065                 0.0   \n",
       "110                 -0.005811  EX_26dap__M_e -0.002905                 0.0   \n",
       "\n",
       "                               \n",
       "    DP_83VPs_KB5 Shuttel Flux  \n",
       "0                    0.000000  \n",
       "1                    0.000000  \n",
       "2                    0.000000  \n",
       "3                    0.000000  \n",
       "4                    0.000000  \n",
       "..                        ...  \n",
       "106                  0.000000  \n",
       "107                 -0.000000  \n",
       "108                  7.529321  \n",
       "109                 -0.001065  \n",
       "110                 -0.002905  \n",
       "\n",
       "[111 rows x 36 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full.to_csv(\"summaries_all_alpha_weight_1_1_compm_RM.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Exchanges</th>\n",
       "      <th>Flux</th>\n",
       "      <th>iYS854 Shuttel Flux</th>\n",
       "      <th>DP_83VPs_KB5 Shuttel Flux</th>\n",
       "      <th>Exchanges</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_istfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnA_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_stfrnB_e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>4</th>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_ac_e</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>-0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_ac_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>106</th>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_lipa_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>EX_pdima_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>8.231034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.231034</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>7.870420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.870420</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>7.506030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.855337</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>8.461639</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.461639</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>7.529321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.529321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.009584</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.009584</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.008519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008519</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.007454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.002130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002130</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.001065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.026147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.026147</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.023242</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.023242</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.020337</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008716</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.005811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.005811</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.002905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0.1                                                          \\\n",
       "         Exchanges      Flux iYS854 Shuttel Flux DP_83VPs_KB5 Shuttel Flux   \n",
       "0     EX_istfrnA_e  0.000000                -0.0                  0.000000   \n",
       "1     EX_istfrnB_e  0.000000                -0.0                  0.000000   \n",
       "2      EX_stfrnA_e  0.000000                -0.0                  0.000000   \n",
       "3      EX_stfrnB_e  0.000000                -0.0                  0.000000   \n",
       "4          EX_ac_e -0.000000                 0.0                  0.000000   \n",
       "..             ...       ...                 ...                       ...   \n",
       "106      EX_lipa_e -0.000000                 0.0                 -0.000000   \n",
       "107     EX_pdima_e -0.000000                 0.0                 -0.000000   \n",
       "108         EX_s_e  8.231034                 0.0                  8.231034   \n",
       "109       EX_ni2_e -0.009584                 0.0                 -0.009584   \n",
       "110  EX_26dap__M_e -0.026147                 0.0                 -0.026147   \n",
       "\n",
       "               0.2                                                          \\\n",
       "         Exchanges      Flux iYS854 Shuttel Flux DP_83VPs_KB5 Shuttel Flux   \n",
       "0     EX_istfrnA_e  0.000000                -0.0                  0.000000   \n",
       "1     EX_istfrnB_e  0.000000                -0.0                  0.000000   \n",
       "2      EX_stfrnA_e  0.000000                -0.0                  0.000000   \n",
       "3      EX_stfrnB_e  0.000000                -0.0                  0.000000   \n",
       "4          EX_ac_e -0.000000                 0.0                  0.000000   \n",
       "..             ...       ...                 ...                       ...   \n",
       "106      EX_lipa_e -0.000000                 0.0                  0.000000   \n",
       "107     EX_pdima_e -0.000000                 0.0                 -0.000000   \n",
       "108         EX_s_e  7.870420                 0.0                  7.870420   \n",
       "109       EX_ni2_e -0.008519                 0.0                 -0.008519   \n",
       "110  EX_26dap__M_e -0.023242                 0.0                 -0.023242   \n",
       "\n",
       "               0.3            ...                 0.7  \\\n",
       "         Exchanges      Flux  ... iYS854 Shuttel Flux   \n",
       "0     EX_istfrnA_e  0.000000  ...                -0.0   \n",
       "1     EX_istfrnB_e  0.000000  ...                -0.0   \n",
       "2      EX_stfrnA_e  0.000000  ...                -0.0   \n",
       "3      EX_stfrnB_e  0.000000  ...                -0.0   \n",
       "4          EX_ac_e -0.000000  ...                 0.0   \n",
       "..             ...       ...  ...                 ...   \n",
       "106      EX_lipa_e -0.000000  ...                 0.0   \n",
       "107     EX_pdima_e -0.000000  ...                 0.0   \n",
       "108         EX_s_e  7.506030  ...                 0.0   \n",
       "109       EX_ni2_e -0.007454  ...                 0.0   \n",
       "110  EX_26dap__M_e -0.020337  ...                 0.0   \n",
       "\n",
       "                                         0.8                                \\\n",
       "    DP_83VPs_KB5 Shuttel Flux      Exchanges      Flux iYS854 Shuttel Flux   \n",
       "0                    0.000000   EX_istfrnA_e  0.000000                -0.0   \n",
       "1                    0.000000   EX_istfrnB_e  0.000000                -0.0   \n",
       "2                    0.000000    EX_stfrnA_e  0.000000                -0.0   \n",
       "3                    0.000000    EX_stfrnB_e  0.000000                -0.0   \n",
       "4                    0.000000        EX_ac_e -0.000000                 0.0   \n",
       "..                        ...            ...       ...                 ...   \n",
       "106                  0.000000      EX_lipa_e -0.000000                 0.0   \n",
       "107                 -0.000000     EX_pdima_e -0.000000                 0.0   \n",
       "108                  8.855337         EX_s_e  8.461639                 0.0   \n",
       "109                 -0.003195       EX_ni2_e -0.002130                 0.0   \n",
       "110                 -0.008716  EX_26dap__M_e -0.005811                 0.0   \n",
       "\n",
       "                                         0.9                                \\\n",
       "    DP_83VPs_KB5 Shuttel Flux      Exchanges      Flux iYS854 Shuttel Flux   \n",
       "0                    0.000000   EX_istfrnA_e  0.000000                -0.0   \n",
       "1                    0.000000   EX_istfrnB_e  0.000000                -0.0   \n",
       "2                    0.000000    EX_stfrnA_e  0.000000                -0.0   \n",
       "3                    0.000000    EX_stfrnB_e  0.000000                -0.0   \n",
       "4                    0.000000        EX_ac_e -0.000000                 0.0   \n",
       "..                        ...            ...       ...                 ...   \n",
       "106                  0.000000      EX_lipa_e -0.000000                 0.0   \n",
       "107                 -0.000000     EX_pdima_e -0.000000                 0.0   \n",
       "108                  8.461639         EX_s_e  7.529321                 0.0   \n",
       "109                 -0.002130       EX_ni2_e -0.001065                 0.0   \n",
       "110                 -0.005811  EX_26dap__M_e -0.002905                 0.0   \n",
       "\n",
       "                               \n",
       "    DP_83VPs_KB5 Shuttel Flux  \n",
       "0                    0.000000  \n",
       "1                    0.000000  \n",
       "2                    0.000000  \n",
       "3                    0.000000  \n",
       "4                    0.000000  \n",
       "..                        ...  \n",
       "106                  0.000000  \n",
       "107                 -0.000000  \n",
       "108                  7.529321  \n",
       "109                 -0.001065  \n",
       "110                 -0.002905  \n",
       "\n",
       "[111 rows x 36 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summaries = dict()\n",
    "model.set_weights([1,10])\n",
    "for alpha in [i/10 for i in range(1,10)]:\n",
    "    growths, fluxes, summary = model.compute_alpha_weightings(alpha, 0.3)\n",
    "    summaries[alpha] = summary \n",
    "df_full = pd.concat(summaries, join=\"outer\", axis=1)\n",
    "df_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full.to_csv(\"summaries_all_alpha_weight_1_10_compm_RM.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_font = {'fontname':'Arial', 'size':'12', 'color':'black', 'weight':'normal'} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We investigate serine and cysteine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "alphas = [i/10 for i in range(1,10)]\n",
    "serine_DP = []\n",
    "serine_SA = []\n",
    "cysteine_DP = []\n",
    "cysteine_SA = []\n",
    "for i,row in df_full.iterrows():\n",
    "    for a in alphas:\n",
    "        row_a = row[a]\n",
    "        if row_a[\"Exchanges\"] == \"EX_cys__L_e\":\n",
    "            cysteine_DP.append(row_a[\"DP_83VPs_KB5 Shuttel Flux\"])\n",
    "            cysteine_SA.append(row_a[\"iYS854 Shuttel Flux\"])\n",
    "        if row_a[\"Exchanges\"] == \"EX_ser__D_e\":\n",
    "            serine_DP.append(row_a[\"DP_83VPs_KB5 Shuttel Flux\"])\n",
    "            serine_SA.append(row_a[\"iYS854 Shuttel Flux\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-9.996433705799296,\n",
       " -9.994040136474734,\n",
       " -9.9910602047121,\n",
       " -9.988080272949468,\n",
       " -9.985100341186834,\n",
       " -9.982120409424201,\n",
       " -9.979140477661568,\n",
       " -9.976160545898935,\n",
       " -9.973180614136302]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cysteine_DP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.title(\"Cysteine\", **title_font)\n",
    "plt.plot(alphas, cysteine_DP, 'g--' )\n",
    "plt.plot(alphas, cysteine_SA, 'darkgoldenrod')\n",
    "plt.xlabel(r'$\\alpha \\in (0,1)$')\n",
    "plt.ylabel(r'$Cysteine\\; concentration\\;(\\it{mmol})$')\n",
    "plt.legend([\"$\\it{D. pigrum}$\", \"$\\it{S. aureus}$\"])\n",
    "plt.savefig(\"cysteine_plot_COMPM_RM.pdf\" , bbox_inches='tight', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plttitle(\"Serine D\", **title_font)\n",
    "plt.plot(alphas, serine_DP, 'g--')\n",
    "plt.plot(alphas, serine_SA, 'darkgoldenrod')\n",
    "plt.xlabel(r'$\\alpha \\in (0,1)$')\n",
    "plt.ylabel(r'$D-Serine \\;concentration\\;(\\it{mmol})$')\n",
    "plt.legend([\"$\\it{D. pigrum}$\", \"$\\it{S. aureus}$\"])\n",
    "plt.savefig(\"Serien_plot_COMPM_RM.pdf\" , bbox_inches='tight', dpi=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Glucose dependence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For community \n",
    "model.set_weights([1,10])\n",
    "model.slim_optimize()\n",
    "medium = model.medium\n",
    "glc = np.linspace(0,100,100) \n",
    "growth = []\n",
    "for g in glc:\n",
    "    medium[\"EX_glc__D_e\"] = g \n",
    "    model.set_medium(medium)\n",
    "    growth.append(model.slim_optimize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wn9evAz8C/itpFUmX1B119Y+I8yV9GbgzTf46As8CI6LgB0IF235L0lRgQUTMKyfeEnXIpJyC8koe54CS560CRV9rSW3qfTFYg4r3fFhLlSaaTwDfiIhnMix3JPD7iGjyReEse5KGA3tHxP+l02eTDD7+dL6RrU7S1cCMiPjNOqx7OnB4RBTrIjCrGJXyeW0tfGXkCiLpYJIm8fuyTHKsIswA9lPyE9aJJANUz845pmKGsOb4nqIk7SVpKyU/G/0EyS+TLm5sPbMKUCmf11bBLTpmlhklvwjauX43V4llzyD5+XB7khPDjyPi3vUaoJm1Ok50zMzMrGq568rMzMyqVlm/urLK06tXr+jfv3/eYZiZVYznnnvu3Yio/2sfq3BOdKpU//79GTt2bN5hmJlVDElr/FzbKp+7rszMzKxqOdExMzOzquVEx8zMzKqWEx0zMzOrWk50zMzMrGo50TEzM7Oq5UTHzMzMqpavo2NmmZm9YCmjxs5kZU1t3qFYK/adT+yApLzDsBbCiY6ZZeKx6XP4zj/G8f6SlfgcY3n6zid2yDsEa0Gc6JhZk6yqqeWKh17k94++xI6bdmP0GXuzTe8N8g7LzAxwomNmTTBn4TK+fcs4nn7lPT49vB8XHjWIzh3a5h2WmdmHnOiY2ToZ88p7fPOWF/hg2UouP2EoJw7vl3dIZmZrcKJjZmslIrjmiVe47N/T2bJnF2788h7suGn3vMMyMyvKiY6ZlW3B0pV879bxPDjlHQ4bsimXHj+Ubp3a5x2WmVlJTnTMrCyT3lrA1296ntkLlvKTIwfyxb37+ye8ZtbiOdExswZFBLc8+yYX3j2ZXl078I+vjWDXLTfKOywzs7I40TGzkpasWMUPbp/E7S+8xcd26M0Vn96Znl075B2WmVnZnOiYWVEvzVnE1296jhfnLOLsT+zAmftvR5s27qoys8riRMfM1nDX+FmcN3oCndu35a+n7sm+2/fKOyQzs3XiRMfMPrR8VQ2/+NdUbnj6dYZvtRG//9yubLphp7zDMjNbZ050zAyAme8v4Rs3Pc/4mQv4yr5bc+6hO9K+bZu8wzIzaxInOmbGo9PmcNY/xlFbG/zh5F05ZPBmeYdkZpYJJzpmrdiqmlp+89AMrnz0ZXbarDtXn7Qr/Xt1zTssM7PMONExa6XmLlzOt2554cMbcv706EF0au8bcppZdXGiY9YKPfvqPM68+XnfkNPMqp4THbNWJCL44xOvcLlvyGlmrYQTHbNWwjfkNLPWyImOWSsw6a0FnHHTc8yev4wfHzGQL+3jG3KaWevgi2S0IJKukzRH0qSCeZdLmiZpgqTbJfXIMUSrMBHBzWPe4Lir/8uqmuAfXxvBqftu7STHzFoNJzoty/XAIfXmPQgMjoihwAzg/OYOyirTkhWrOPuf47ng9onsuXVP/vWt/dhtK9913MxaF3ddtSAR8YSk/vXmPVAw+QxwQrMGZRXJN+Q0M0s40akspwL/yDsIa9kmvbWAz1zzDB3ateHGU/dgv+175x2SmVlunOhUCEk/AFYBNzWwzGnAaQBbbrllM0VmLcms+Us59fr/sWHn9tx6+gj69uicd0hmZrnyGJ0KIOkU4AjgpIiIUstFxDURMTwihvfu7W/xrc3CZSs59fr/sXRFDdd9cXcnOWZmuEWnxZN0CHAu8PGIWJJ3PNYy1dQGZ978Ai/OWcRfvrg7AzbtlndIZmYtglt0WhBJtwBPAwMkzZT0ZeD3QDfgQUnjJP0h1yCtRbruqVd5fMZcLjp6EB/bwa15ZmZ13KLTgkTEZ4vMvrbZA7GK8tq7i/l/D07noJ368Lk9PDbLzKyQW3TMKlhEcN5tE2jfpg0/P2awLwRoZlaPEx2zCvb3/73JM6/M4weH78SmG3bKOxwzsxbHiY5ZhXp7wTJ+8a+pjNhmYz69e7+8wzEza5Gc6JhVoIjgh3dMZGVtLZccP8RdVmZmJTjRMatA90yYzUNT5/C9Tw5gq4275h2OmVmL5UTHrMLMW7yCC++azLB+PfjSPlvnHY6ZWYvmRMeswlx092Q+WLaSy44fSlvfqNPMrEFOdMwqyKPT5nDHuFl8feR2vvqxmVkZnOiYVYiFy1Zywe0T2aHPBnxj/+3yDsfMrCI40TGrEJfeP413PljGZScMo0M7f3TNzMrho6VZBRjzynv87Zk3+NI+W7Nzvx55h2NmVjGc6Ji1cMtW1nDebRPp17Mz3/3kDnmHY2ZWUZzorAeSukpqm3ccVh2ueOhFXn13MZccN5QuHXwfXjOzteFEJwOS2kj6nKR/SZoDTANmS5os6XJJ2+cdo1WmiTMX8KcnX+HTw/uxz3a98g7HzKziONHJxqPAtsD5wKYR0S8iNgH2A54BLpF0cp4BWuVZWVPLOaMnsHHXDlxw+E55h2NmVpHcDp6NgyJiZf2ZETEPGA2MltS++cOySnbNE68wdfYH/PHzu7FhZ799zMzWhVt0MlA/ySk2RqdYImRWyktzFvHbh17k8CGbcfCgTfMOx8ysYjnRyYDH6FiWamuDc0dPoHOHtlx41KC8wzEzq2hOdLLhMTqWmRuffo3nXn+fnxw5kN7dOuYdjplZRfMYnWx4jI5l4s15S7js39P5+A69OXaXzfMOx8ys4jnRyUBdkiPpAOAkYD4wCZgATIqI5R6jY42JCC64fSICLj52MJLvTG5m1lROdLL1N+AbJPt1KHAMMAjwHRitUaOff4snX3yXi44exBYbdck7HDOzquBEJ1svRcTt6eNbc43EKsqchcv42T1T2L3/Rpy851Z5h2NmVjU8GDkDkm6UdBbwtKTv5h2PVZ4L75rM0pU1XHL8UNq0cZeVmVlWnOhk4waSfbkp8HlJr0u6S9LPJJ2Yc2zWwt0/aTb3Tnybsw7anm17b5B3OGZmVcVdVxmIiIeBh+umJbUDBgLDgD0osxtL0nXAEcCciBiczusJ/APoD7wGfCoi3s8wfMvRgiUr+dGdkxm4WXe+ut82eYdjZlZ13KKzfvQFJkbEXyPi+2ux3vXAIfXmnQc8HBHbkyRT52UTorUEF987hXmLV3DZCUNp39YfRzOzrPnImjFJnYExwCZru25EPAHMqzf7aJKuMdL/xzQlPms5nnrxXf45dianfWwbBm++Yd7hmJlVJSc6GZD04W+BI2JpRGwWEe9kVHyfiJidlj2bBhIoSadJGitp7Ny5czPavK0PS1as4rzbJrBNr658+0DfIcTMbH1xopONGZKulDQkzyAi4pqIGB4Rw3v37p1nKNaIy/89nZnvL+XSE4bSqX3bxlcwM7N14kQnGwOAccC1kp6S9HlJWd2k6B1JmwGk/+dkVK7l5LnX3+f6/77GF0Zsxe79e+YdjplZVXOik4GIWBwRf4qIPYAzgb2BqZJ+lcGdy+8CTkkfnwLc2cTyLEfLV9Vw3ugJbNa9E+ccsmPe4ZiZVT3/vDwDkrYDugPd0v9PAS8DXwG+A5TVNyHpFmAk0EvSTOAnwCXAPyV9GXgD8HV5KtiVj7zEi3MW8Zcv7c4GHf3xMzNb33ykzcYM4C3gduB9YBGwEPhZ+r8sEfHZEk8d2NQALX9TZ3/AVY+9zHG7bM7+A9b6R3lmZrYOnOhkY1fga8D+wN+BGzP81ZVVgVU1tZw7egIbdm7Pj44YmHc4ZmathsfoZCAixkXEGcBeJIOF75D0T0kH5ByatRDX/edVJsxcwE+PHsRGXTvkHY6ZWavhFp1s1ZIMFn4UOAC4ShIR4VGnrdhr7y7m/z0wg4N26sPhQzbLOxwzs1bFiU4GJNXde2ox8EH6txCYDCzIKy7LX21tcO7oCXRo14aLjx2M5DuTm5k1Jyc62egZEZF3ENby/P1/bzLm1XlcctwQ+nTvlHc4ZmatjsfoZKAuyZG0haQhkrrmHZPlb/aCpfzy3qmM2GZjPr17v7zDMTNrlZzoZEBSf0nPA88CdwBzJd0taUC+kVleIoIf3TGJlbW1XHL8EHdZmZnlxIlONi4F/hgRfSNiW5KLBt4N3JvBlZGtAt01fhYPTZ3D9z45gK02dgOfmVlenOhkY4eI+GPdRESsiohrgDOAH+cXluXhvUXL+endUxjWrwdf2mfrvMMxM2vVnOhko+hA5Ih4ANipmWOxnF10zxQWLlvJZccPpW0bd1mZmeXJiU42NpX0ZUl7Stqg3nP+NVYr8vDUd7hz3Cy+sf92DNi0W97hmJm1ev55eTYuBHYBvgAMlvQByTV0JgGb5hiXNaOFy1bywzsmMaBPN74+cru8wzEzM5zoZCIdj/MhSVsAQ4EhwOO5BGXN7pL7pvHOB8u46qRd6dDOjaVmZi2BE50MSXoRmAiMB8YB/4iIS3MNyprFM6+8x01j3uAr+27NLltulHc4ZmaW8tfObP0ReBt4DzgUmCRpoqSLJLXPNzRbX5atrOG80RPYsmcXzv7kDnmHY2ZmBdyik62TI2LnuglJfwC+RHLvq18D38wpLluPfvPQDF57bwk3f2VPunTwR8rMrCVxi062FkgaWjcREeOAvSLiV8A+uUVl683EmQv485Ov8unh/dh7u155h2NmZvX462e2vgbcJGkcyRidAUBt+lyHnGKy9WRlTS3fHzWejbt24ILDfbkkM7OWyC06GYqIacAewP3AJsBLwBHpTT7/nmdslr0/Pv4y095eyM+PGcyGnT0Ey8ysJXKLTgYkqe4O5hFRA9ya/hUuc3Eesdn68dKchfzfwy9xxNDN+OQgXyrJzKylcotONh6V9E1JWxbOlNRB0gGSbgBOySk2y1hNbXDOqAl06diWC48alHc4ZmbWALfoZOMQ4FTgFklbA/OBziSJ5APAb9KByVYFbnz6NZ5/Yz6//tQwem3QMe9wzMysAU50MhARy4CrgKvS6+X0ApZGxPxcA7PMvTlvCZfdP52RA3pz7C6b5x2OmZk1wl1XGZA0QpIAImJlRMx2klN9IoILbp9IG8HFxw4hfcnNzKwFc6KTjVOA5yX9XdIXJWU+OlXSdyRNljRJ0i2SOmW9DWvYqOdm8uSL73LeoTuyeY/OeYdjZmZlcKKTgYg4PSJ2IbmL+UbA9ZKelvQLSR+T1LYp5UvaHPgWMDwiBgNtgc80NW4r35yFy/jZPVPYvf9GnLTnVnmHY2ZmZXKikwFJN0g6G+gL3BgRhwAHAE8BJwJjMthMO6CzpHZAF2BWBmVamX5y52SWrarlkuOH0qaNu6zMzCqFE51s3Jj+PwV4WNLLJNfR2Rt4IiKGN6XwiHgL+BXwBjAbWBARD9RfTtJpksZKGjt37tymbNIK3DdxNvdNepuzDtqebXtvkHc4Zma2FpzoZCAiHo6IX0fEKelNPQcAFwDTSa6U3CSSNgKOBrYmaTXqKunkInFcExHDI2J47969m7pZAxYsWcmP7pzMoL7d+ep+2+QdjpmZrSX/vHw9iIhVwIT0LwsHAa9GxFwASbeRtBb9LaPyrYSf/WsK7y9ZwQ2n7k77tv5eYGZWaZzoZEhST+A7JPe5mkIyXuf9DIp+A9hLUhdgKXAgMDaDcq0BT8yYy6jnZvL1kdsyqO+GeYdjZmbrwF9Rs/V3YCFwN8mA4ackNbnrKiLGAKOA54GJJK/bNU0t10pbvHwV5982kW16d+VbB26fdzhmZraO3KKTrc0i4rL08T2S/gHcDOzV1IIj4ifAT5pajpXn8n9PZ9aCpdz6tRF0at+kqwOYmVmO3KKTrXmShtZNRMQrJC07VkGee30eNzz9Gl/YayuG9++ZdzhmZtYEbtHJ1teAUZKeJOliGgS8nG9ItjaWr6rhnFET6LthZ75/yI55h2NmZk3kFp0MRcQ0YFfgUaA38AK+gnFF+f0jL/Hy3MVcfOxgNujo7wFmZpXOR/IMSfozcCzJL6NmAeOBzsDv8ozLyjNl1gdc/djLHLfr5owcsEne4ZiZWQac6GRrP2DTiFiZ3p9qGDC0kXWsBVhVU8u5oyfQo0t7fnT4wLzDMTOzjDjRydYzJDf1nJPetuEt4N58Q7JyXPvUq0x8awFXfm5XNuraIe9wzMwsIx6jk61rgMclfU/SfpJ8lbkK8Oq7i/n1gzM4eFAfDhuyad7hmJlZhpzoZOtvwD9JWsq+Dvw3vcGntVC1tcF5oyfQoV0bLjp6MJLvTG5mVk3cdZWtmemF/T4kqWNewVjjbvnfG4x5dR6XHT+UPt075R2OmZllzC062Ron6duFMyJieV7BWMNmL1jKL++dxj7bbcyJw7fIOxwzM1sP3KKTrT7AQZLOJbkv1XhgXETcmm9YVl9E8IPbJ1FTG/zy2KHusjIzq1JOdDIUEZ+CD7urBgFDgD0BJzotzF3jZ/HItDn86IiBbLmx79JhZlatnOhkTFL7tLvq+fTPWpj3Fi3np3dPYed+Pfji3v3zDsfMzNYjJzoZSq+MfJykxSRXRp4ATIgIXxm5BbnoniksXLaSy04YSts27rIyM6tmTnSytR/Qx1dGbrkenvoOd46bxXcO2oEd+nTLOxwzM1vPnOhky1dGbsE+WLaSH9w+iQF9unHGyG3zDsfMzJqBf16eLV8ZuQW75L5pzFm4jEtPGEqHdn7rm5m1Bj7aZ8tXRm6hnn75PW4e8wan7rM1O/frkXc4ZmbWTNx1lS1fGbkFWrqihvNvm8CWPbvw3U8OyDscMzNrRm7RyZavjNwCXfHQDF57bwmXHD+Ezh3a5h2OmZk1I7foZMtXRm5hJsycz5+efIXP7tGPvbftlXc4ZmbWzJzoZMhXRm5ZVqyq5ZxRE+jdrSPnH7ZT3uGYmVkOnOhkQJIiIuqmi10Zuf4ytv798fGXmfb2Qv70heF079Q+73DMzCwHHqOTjUclfVPSloUzJXWQdICkG4BTcoqtVXrxnYX87pGXOHJYXz4xsE/e4ZiZWU6c6GTjEKAGuEXSLElTJL0CvAh8FvhNRFzflA1I6iFplKRpkqZKGtH0sKtTTW1w7ugJdO3Ylp8cOTDvcMzMLEfuuspARCwDrgKuktQe6AUsjYj5GW7mt8D9EXGCpA6Ab7ldwo1Pv8bzb8znik/vTK8N/Ot+M7PWzIlOxiJiJTA7yzIldQc+Bnwx3cYKYEWW26gWb85bwmX3T2fkgN4cvXPfvMMxM7OcueuqMmwDzAX+IukFSX+W1DXvoFqaiOCC2yfSRvCLY4cg+c7kZmatnROdytAO2BW4OiJ2ARYD59VfSNJpksZKGjt37tzmjjF3o56byZMvvst5h+1E3x6d8w7HzMxaAHddZSi9fs7xQH8K9m1EXNTEomeS3F5iTDo9iiKJTkRcQ3JjUYYPH96qfso+Z+EyfnbPFPbo35OT9tiy8RXMzKxVcItOtu4EjgZWkbS61P01SUS8Dbwpqe5GTQcCU5pabjX58R2TWbaqlkuOH0KbNu6yMjOzhFt0srVFRByynsr+JnBT+ourV4AvraftVJz7Js7m/slvc84hA9im9wZ5h2NmZi2IE51s/VfSkIiYmHXBETEOGJ51uZVu/pIV/OjOyQzevDun7bdN3uGYmVkL40QnA5ImAkGyP7+UXixwOSAgImJonvFVs5//ayrzl6zghlN3p11b98SamdnqnOhk44i8A2iNnpgxl1HPzeTM/bdjUN8N8w7HzMxaIH8FzkBEvB4RrwNfr3tcOC/v+KrR4uWrOP+2iWzbuytnHrBd3uGYmVkL5UQnW58oMu/QZo+iFbj839OZtWAplx4/lE7t2+YdjpmZtVDuusqApDNIWm62kTSh4KluwH/yiap6jX1tHjc8/RqnjOjP8P498w7HzMxaMCc62bgZuA/4JatfyG9hRMzLJ6TqtGxlDeeOnkDfDTvz/YMHNL6CmZm1ak50MhARC4AFwGfzjqXa/f6Rl3h57mJuOHUPunb029fMzBrmMToZknSjpK9K2jHvWKrR5FkL+MPjL3Pcrpvz8R165x2OmZlVACc62boe2Az4naSXJY2W9O2cY6oKq2pqOXf0BHp0ac+PjxiYdzhmZlYh3PafoYh4RNLjwO7A/sDpwCDgt7kGVgX+/NSrTHrrA646aVd6dOmQdzhmZlYhnOhkSNLDQFfgaeBJYPeImJNvVJXvlbmL+M2DMzh4UB8OHbxp3uGYmVkFcddVtiYAK4DBwFBgsKTO+YZU2Wprg/Num0jHdm342dGDkXxncjMzK59bdDIUEd8BkLQByd3F/wJsCnTMM65KdvOzb/Dsq/O47PihbNK9U97hmJlZhXGikyFJZwL7AbsBrwPXkXRh2TqYNX8pl9w3jX2368WJw7fIOxwzM6tATnSy1Rn4NfBcRKzKO5hKFhH88I5J1NQGvzxuiLuszMxsnXiMToYi4nJgGXC6pDMlDcs7pkp11/hZPDJtDt8/eAD9enbJOxwzM6tQTnQyJOlbwE3AJunf3yR9M9+oKs+7i5Zz4V2T2WXLHpyyd/+8wzEzswrmrqtsfQXYMyIWA0i6lOSn5r/LNaoK89O7p7Bo+SouPX4obdu4y8rMzNadW3SyJaCmYLomnWdlenDKO9w9fhbfPGB7dujTLe9wzMyswrlFJ1t/AcZIuj2dPga4Nr9wKssHy1bywzsmsuOm3Tj949vmHY6ZmVUBJzoZUfKzoFuBx4B9SVpyvhQRL+QZVyX55b3TmLtwOdd8fjgd2rmx0czMms6JTkYiIiTdERG7Ac/nHU+l+e/L73LLs2/w1f22Zli/HnmHY2ZmVcJfm7P1jKTd8w6i0ixdUcP5t01kq427cPYnBuQdjpmZVRG36GRrf+Brkl4HFpN0X0VEDM03rJbt1w9O5/X3lnDzV/ekc4e2eYdjZmZVxIlOtg7NO4BKM+7N+Vz71Kt8do8t2XvbXnmHY2ZmVcaJTgYkHQ1sERFXptNjgN7p0+eQ3Pcqi+20BcYCb0XEEVmUmacVq2o5d9QEenfryPmH7Zh3OGZmVoU8Ricb5wB3FUx3BHYHRgJnZLidbwNTMywvV1c/9jLT31nIz48ZQvdO7fMOx8zMqpATnWx0iIg3C6afioj3IuINoGsWG5C0BXA48OcsysvbjHcW8vtHX+TIYX35xMA+eYdjZmZVyolONjYqnIiIMwsme5ONK0hajmpLLSDpNEljJY2dO3duRpvNXk1tcM6oCWzQsR0XHjkw73DMzKyKOdHJxhhJX60/U9LXgGebWrikI4A5EfFcQ8tFxDURMTwihvfunVV+lb0b/vsa496cz4VHDWLjDTrmHY6ZmVUxD0bOxneAOyR9jo8uFrgbyVidYzIofx/gKEmHAZ2A7pL+FhEnZ1B2s3pz3hIu//d0DthxE44a1jfvcMzMrMo50clARMwB9pZ0ADAonf2viHgko/LPB84HkDQS+F4lJjkRwfm3TaRtG/HzYwaT3DXDzMxs/XGik6E0sckkualGtz43k6deepeLjx1M3x6d8w7HzMxaASc6FSYiHiO5cWhFmfPBMn5+zxT23Lonn919y7zDMTOzVsKDka1Z/PjOySxfVcslxw+lTRt3WZmZWfNwomPr3X0TZ3P/5Lc5+xM7sHWvTC4rZGZmVhYnOrZezV+ygh/dOZkhm2/Il/fdOu9wzMyslfEYHVuvfnbPVOYvWcGNp+5Bu7bOq83MrHn5zGPrzeMz5jL6+ZmcMXJbBvbtnnc4ZmbWCjnRsfVi0fJVXHDbRLbt3ZUzD9gu73DMzKyVcteVrRe/+vd0Zi1YyqjTR9CxXdu8wzEzs1bKLTqWubGvzeOGp1/jlBH92W2rnnmHY2ZmrZgTHcvUspU1nDN6An037Mz3Dx6QdzhmZtbKuevKMvW7R17klbmLufHUPeja0W8vMzPLl1t0LDOTZy3gD4+/wgm7bcHHduiddzhmZmZOdCwbq2pqOWfUBDbq0oEfHr5T3uGYmZkB7rqyjPzpyVeZPOsDrj5pV3p06ZB3OGZmZoBbdCwDr8xdxG8emsEhgzbl0CGb5R2OmZnZh5zoWJPU1gbnjZ5Ip3ZtuOjoQXmHY2ZmthonOtYkNz37Bs++No8fHjGQTbp3yjscMzOz1TjRsXX21vylXHLvVPbdrhcn7rZF3uGYmZmtwYmOrZOI4Ae3T6Q24JfHDUFS3iGZmZmtwYmOrZM7x83iselzOeeQAfTr2SXvcMzMzIpyomNr7d1Fy/np3ZPZdcsefGFE/7zDMTMzK8mJjq21C++azOLlNVx6/FDatnGXlZmZtVxOdGytPDD5be6ZMJszD9iO7ft0yzscMzOzBjnRsbItWLqSH905iR037cbpH98273DMzMwa5VtAWNkuuW8qcxcu509fGE6Hds6Rzcys5fPZqgJI6ifpUUlTJU2W9O3mjuG/L73LLc++yVf324ahW/Ro7s2bmZmtE7foVIZVwHcj4nlJ3YDnJD0YEVOaY+NLV9Rw/u0T6b9xF846aIfm2KSZmVkm3KJTASJidkQ8nz5eCEwFNm+u7f/6wem8/t4SfnncUDp3aNtcmzUzM2syJzoVRlJ/YBdgTJHnTpM0VtLYuXPnZrK9cW/O59qnXuVze27JiG03zqRMMzOz5uJEp4JI2gAYDZwVER/Ufz4iromI4RExvHfv3k3e3opVtZw7agKbdOvEeYfu2OTyzMzMmpvH6FQISe1JkpybIuK25tjm1Y+9zPR3FvLnLwyne6f2zbFJMzOzTLlFpwIouWPmtcDUiPh1c2xzxjsL+f2jL3LksL4cNLBPc2zSzMwsc050KsM+wOeBAySNS/8OW18bq6kNzhk1gW6d2nPhkQPX12bMzMzWO3ddVYCIeApotptKXf/f1xj35nx++5md2XiDjs21WTMzs8y5RcdW88Z7S/jVv6dz4I6bcNSwvnmHY2Zm1iROdOxDEcF5t02gbRvx82MHkwwNMjMzq1zuurIPrawJdtqsO0cO68tmG3bOOxwzM7Mmc6JjH+rQrg0/OsKDj83MrHq468rMzMyqlhMdMzMzq1pOdMzMzKxqOdExMzOzquVEx8zMzKqWEx0zMzOrWk50zMzMrGo50TEzM7OqpYjIOwZbDyTNBV5fx9V7Ae9mGE4lcJ1bh9ZW59ZWX2hanbeKiN5ZBmP5c6Jja5A0NiKG5x1Hc3KdW4fWVufWVl9onXW2hrnryszMzKqWEx0zMzOrWk50rJhr8g4gB65z69Da6tza6guts87WAI/RMTMzs6rlFh0zMzOrWk50zMzMrGo50bEPSTpE0nRJL0k6L+941gdJ/SQ9KmmqpMmSvp3O7ynpQUkvpv83yjvWrElqK+kFSfek01VdZ0k9JI2SNC19vUe0gjp/J31fT5J0i6RO1VZnSddJmiNpUsG8knWUdH56TJsu6eB8orY8OdExIDkJAlcChwIDgc9KGphvVOvFKuC7EbETsBfwjbSe5wEPR8T2wMPpdLX5NjC1YLra6/xb4P6I2BEYRlL3qq2zpM2BbwHDI2Iw0Bb4DNVX5+uBQ+rNK1rH9LP9GWBQus5V6bHOWhEnOlZnD+CliHglIlYAfweOzjmmzEXE7Ih4Pn28kOTktzlJXW9IF7sBOCaXANcTSVsAhwN/LphdtXWW1B34GHAtQESsiIj5VHGdU+2AzpLaAV2AWVRZnSPiCWBevdml6ng08PeIWB4RrwIvkRzrrBVxomN1NgfeLJiemc6rWpL6A7sAY4A+ETEbkmQI2CTH0NaHK4BzgNqCedVc522AucBf0u66P0vqShXXOSLeAn4FvAHMBhZExANUcZ0LlKpjqzuu2Zqc6FgdFZlXtdcekLQBMBo4KyI+yDue9UnSEcCciHgu71iaUTtgV+DqiNgFWEzld9k0KB2XcjSwNdAX6Crp5Hyjyl2rOq5ZcU50rM5MoF/B9BYkzd5VR1J7kiTnpoi4LZ39jqTN0uc3A+bkFd96sA9wlKTXSLokD5D0N6q7zjOBmRExJp0eRZL4VHOdDwJejYi5EbESuA3Ym+quc51SdWw1xzUrzYmO1fkfsL2krSV1IBnAd1fOMWVOkkjGbUyNiF8XPHUXcEr6+BTgzuaObX2JiPMjYouI6E/yuj4SESdT3XV+G3hT0oB01oHAFKq4ziRdVntJ6pK+zw8kGYNWzXWuU6qOdwGfkdRR0tbA9sCzOcRnOfKVke1Dkg4jGcvRFrguIi7ON6LsSdoXeBKYyEfjVS4gGafzT2BLkhPGiRFRf8BjxZM0EvheRBwhaWOquM6SdiYZfN0BeAX4EsmXu2qu80+BT5P8uvAF4CvABlRRnSXdAowEegHvAD8B7qBEHSX9ADiVZJ+cFRH3NX/UlicnOmZmZla13HVlZmZmVcuJjpmZmVUtJzpmZmZWtZzomJmZWdVyomNmZmZVy4mOmZmZVS0nOmZmZla1nOiYNZGkPpJulvSKpOckPS3p2PS5RXnH1xJJ6iHp61mtK+m/2US2WpmdJT0uqW3WZTewzYMk/VVSB0lPpHchN7MmcKJj1gTppfbvAJ6IiG0iYjeS2yxskWtgLV8PoGiio0RDx6Y11o2IvTOL7COnArdFRM16KLuUYcD4iFgBPExylWMzawInOmZNcwCwIiL+UDcjIl6PiN8VLiSpv6RJBdPfk3Rh+vgLkiZIGi/prwXLnC1pUvp3Vjqvq6R/pctOkvTpdP7Jkp6VNE7SH0u1QhTbVont9Jc0VdKfJE2W9ICkzo3Eu0YMDZRzCbBtuuzlBctdBTwP9JN0R9pCNlnSaQXVWG3ddNuL1qUujTiJgvtCSbpV0u8lPSXpdUn7SrpR0gxJ15a7TLrcjmmLzWRJD0nqlT41DBiXPr4jjcHMmiIi/Oc//63jH/At4DcNPL8o/d8fmFQw/3vAhcAgYDrQK53fM/2/G8n9uLqS3KtoMrALcDzwp4JyNgR2Au4G2qfzrgK+UCSWNbbVwHb6k9wbaOd02X8CJzcQb9EYGiin/v7oT3Lvsb0K5tWV3RmYBGxcbF/W7ee1rUsjr2sH4O1686YBZ6ePL0r3w2bpsu8DHctcpmNdbOly5wIXp4/HA73Tx22BuXm/x/3nv0r/c4uOWYYkXZm2dPyvzFUOAEZFxLsA8dHNFvcFbo+IxRGxCLgN2I/kRH6QpEsl7RcRC0juUr0b8D9J49LpbcrcVqntALwaEePSx8+RJAyl4m0ohmLlFPN6RDxTMP0tSeOBZ4B+JHeebsja1qUhvYD5dROSOpF0mV2RzloKXBsRsyPpZlpC0kLe2DIrgGOApyLihXS5KcAmktoD3SNiLkAkXWYrJHVrJFYza4AHupk1zWSSVhYAIuIbaTfE2HrLrWL1ruJO6X8Bxe6sq2Ibi4gZknYDDgN+KekBkpaCGyLi/EZiLbatottJLS94XEPSstJQvGvEIKl/iXKKWVyw3kjgIGBERCyR9Bgf7bNS1rYuDVlab3uDgOcjou6O98OAq9NYtwBmAQMbWyYiQtJAkoS1zhCSZGcgMLVeHB2BZY3EamYNcIuOWdM8AnSSdEbBvC5FlnuH5Fv7xpI6Akek8x8GPiVpYwBJPdP5TwDHSOoiqStwLPCkpL7Akoj4G/ArYNe0jBMkbVJXhqStisRQbFtFt9NAfUvFW24MdRYCDbVUbAi8nyY5OwJ7lbHu2talpIh4H2ibtuRAkoyML1hkKDAhfTwsfVzOMgBvkSQ1SNoG+DxwI6uPzyHdx3MjYuW61MHMEm7RMWuC9Bv6McBvJJ0DzCVpmTi33nIrJV0EjAFeJRnLQURMlnQx8LikGuAF4IsR8byk64Fn0yL+HBEvSDoYuFxSLbASOCMipkj6IfCAkl8rrQS+AbxeL4Y1thURXyyxnf4l6lsq3lIxvF2inPck/UfJAO37gCvrLXI/cLqkCSTjXJ4ptW5EfD+dX2qfFa1LGR4g6Q57iCSJeRY+7MbqnCZD8FFCU84yAH8FDpM0kaTl6NS0TsMKYgfYH7h3HWM3s5QiirVCm5m1bpJ2IRlY/Pmctn8bcH5ETM9j+2bVwl1XZmZFpIOFH1UzXjCwjqQOwB1Ocsyazi06ZmZmVrXcomNmZmZVy4mOmZmZVS0nOmZmZla1nOiYmZlZ1XKiY2ZmZlXLiY6ZmZlVrf8PSN9DDpcN/6AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(glc, growth)\n",
    "plt.xlabel(\"Glucose concentration $\\;(\\it{mmol})$\")    \n",
    "plt.ylabel(\"Growth $(\\it{mmol/(gDW⋅h)})$\")\n",
    "plt.title(\"Glucose community dependences with weights $ \\it{D. pigrum}:\\it{S.aureus} = 10:1 $ as using COMPM media\", **title_font)\n",
    "plt.savefig(\"community_glucose_dependence_weights10_1_COMPM_RM.pdf\", bbox_inches='tight', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5457511759833413"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Growth without glucose\n",
    "growth[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For community \n",
    "model.set_weights([1,1])\n",
    "model.slim_optimize()\n",
    "medium = model.medium\n",
    "glc = np.linspace(0,100,100) \n",
    "growth = []\n",
    "for g in glc:\n",
    "    medium[\"EX_glc__D_e\"] = g \n",
    "    model.set_medium(medium)\n",
    "    growth.append(model.slim_optimize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(glc, growth)\n",
    "plt.xlabel(\"Glucose concentration $\\;(\\it{mmol})$\")    \n",
    "plt.ylabel(\"Growth $(\\it{mmol/(gDW⋅h)})$\")\n",
    "plt.title(\"Glucose community dependences with weights $ \\it{D. pigrum}:\\it{S.aureus} = 1:1$ as using COMPM media\", **title_font)\n",
    "plt.savefig(\"community_glucose_dependence_weights1_1_COMPM_RM.pdf\", bbox_inches='tight', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5457511759833893"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Growth without glucose\n",
    "growth[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growth:  0.2823653925322476\n",
      "Growth:  2.558694612613393\n"
     ]
    }
   ],
   "source": [
    "# For single models\n",
    "model_DP = cobra.io.read_sbml_model(\"models/consistent_DP_SNM.xml\")\n",
    "model_SA = cobra.io.read_sbml_model(\"models/consistent_iYS854_SNM.xml\")\n",
    "print(\"Growth: \", model_DP.slim_optimize())\n",
    "print(\"Growth: \", model_SA.slim_optimize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "growths = []\n",
    "x = np.linspace(0,100,100)\n",
    "for c in x:\n",
    "    with model_DP as model:\n",
    "        medium = model.medium \n",
    "        medium[\"EX_glc__D_e\"] = c \n",
    "        model.medium = medium \n",
    "        growths.append(model.slim_optimize())\n",
    "plt.plot(x,growths, 'g')\n",
    "plt.xlabel(\"Glucose concentration $\\;(\\it{mmol})$\")    \n",
    "plt.ylabel(\"Growth $(\\it{mmol/(gDW⋅h)})$\")      \n",
    "plt.savefig(\"glc_concentration_single_DP_COMPM_RM.pdf\", bbox_inches='tight', dpi=1000) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "growths = []\n",
    "x = np.linspace(0,100,100)\n",
    "for c in x:\n",
    "    with model_SA as model:\n",
    "        medium = model.medium \n",
    "        medium[\"EX_glc__D_e\"] = c \n",
    "        model.medium = medium \n",
    "        growths.append(model.slim_optimize())\n",
    "plt.plot(x,growths, 'darkgoldenrod')\n",
    "plt.xlabel(\"Glucose concentration $\\;(\\it{mmol})$\")    \n",
    "plt.ylabel(\"Growth $\\;(\\it{mmol/(gDW⋅h)})$\")\n",
    "plt.savefig(\"glc_concentration_single_SA_COMPM_RM.pdf\", bbox_inches='tight', dpi=1000)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  },
  "metadata": {
   "interpreter": {
    "hash": "a8f61be024eba58adef938c9aa1e29e02cb3dece83a5348b1a2dafd16a070453"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}