community_model_Exchange_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": {
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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": {
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       "    }\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": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "exchange_SA = json.loads(open(\"exchange_SA.json\").read())\n",
    "exchange_DP = json.loads(open(\"exchange_DP.json\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "exchange = dict()\n",
    "for key, val in exchange_SA.items():\n",
    "    exchange[key] = val \n",
    "for key, val in exchange_DP.items():\n",
    "    exchange[key] = val "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "model.set_medium(exchange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA growth:  2.2982805284808725\n",
      "DP growth:  -0.0\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": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA growth:  1.2087958233205274\n",
      "DP growth:  0.28236539253378184\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, 0.1)\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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       "    <tr>\n",
       "      <th></th>\n",
       "      <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>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>6.403268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.403268</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>6.463947</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>1.133794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.133794</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>0.561931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.561931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.002840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002840</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.002485</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001065</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.000710</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000710</td>\n",
       "      <td>EX_ni2_e</td>\n",
       "      <td>-0.000355</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.008716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008716</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.007747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.007747</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.006779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002905</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.001937</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001937</td>\n",
       "      <td>EX_26dap__M_e</td>\n",
       "      <td>-0.000968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000968</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 -0.000000                 0.0                  0.000000   \n",
       "109       EX_ni2_e -0.003195                 0.0                 -0.003195   \n",
       "110  EX_26dap__M_e -0.008716                 0.0                 -0.008716   \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  6.403268                 0.0                  6.403268   \n",
       "109       EX_ni2_e -0.002840                 0.0                 -0.002840   \n",
       "110  EX_26dap__M_e -0.007747                 0.0                 -0.007747   \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  6.463947  ...                 0.0   \n",
       "109       EX_ni2_e -0.002485  ...                 0.0   \n",
       "110  EX_26dap__M_e -0.006779  ...                 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                  0.000000         EX_s_e  1.133794                 0.0   \n",
       "109                 -0.001065       EX_ni2_e -0.000710                 0.0   \n",
       "110                 -0.002905  EX_26dap__M_e -0.001937                 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                  1.133794         EX_s_e  0.561931                 0.0   \n",
       "109                 -0.000710       EX_ni2_e -0.000355                 0.0   \n",
       "110                 -0.001937  EX_26dap__M_e -0.000968                 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                  0.561931  \n",
       "109                 -0.000355  \n",
       "110                 -0.000968  \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_exchange_RM.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"4\" halign=\"left\">0.9</th>\n",
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       "    <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",
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       "      <th>Exchanges</th>\n",
       "      <th>Flux</th>\n",
       "      <th>iYS854 Shuttel Flux</th>\n",
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       "  </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",
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       "      <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",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <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>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.116975</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>9.970520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.970520</td>\n",
       "      <td>EX_s_e</td>\n",
       "      <td>9.970361</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.970361</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 -0.000000                 0.0                  0.000000   \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 -0.000000                 0.0                  0.000000   \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 -0.000000  ...                 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                  5.116975         EX_s_e  9.970520                 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                  9.970520         EX_s_e  9.970361                 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                  9.970361  \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_exchange_RM.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_font = {'fontname':'Arial', 'size':'12', 'color':'black', 'weight':'normal'} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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": [
       "[-9.997020068237367,\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_exchange_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_exchange_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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\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 exchange media\", **title_font)\n",
    "plt.savefig(\"community_glucose_dependence_weights10_1_exchange_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[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 exchange media\", **title_font)\n",
    "plt.savefig(\"community_glucose_dependence_weights1_1_exchange_RM.pdf\", bbox_inches='tight', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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_exchange_RM.pdf\", bbox_inches='tight', dpi=1000) \n",
    "plt.show()"
   ]
  },
  {
   "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_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_exchange_RM.pdf\", bbox_inches='tight', dpi=1000) \n",
    "plt.show()     "
   ]
  }
 ],
 "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
}