Presentation Simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wood - Potato - Farmer - Bulder - Houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cvxpy as cvx\n",
    "import numpy as np\n",
    "import gurobipy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Make_Simple(Stages, Initial_state, Gradient, Costs):\n",
    "    \n",
    "    \"\"\"\n",
    "    Stages = Int\n",
    "    \n",
    "    Initial_state = [Houses, Farmers, Builders, Wood, Potato]\n",
    "    Gradient      = [buildigs/Builder, peasants/houses, supply/houses, resources/farmer]\n",
    "    Costs         = [Potato_costs, Wood_costs]\n",
    "    costs         = [wood -> Houses, Potato-> Farmers and Builders]\n",
    "                   \n",
    "    \"\"\"\n",
    "    \n",
    "    # Resources\n",
    "    Potato               = cvx.Variable(Stages+1, name = 'Potato', pos = True)\n",
    "    Wood                = cvx.Variable(Stages+1, name = 'Wood', pos = True)\n",
    "    Raw_potato_production = cvx.Variable(Stages+1, name = 'Raw Potato Production', pos = True)\n",
    "    Raw_wood_production = cvx.Variable(Stages+1, name = 'Raw Wood Production', pos = True)\n",
    "    \n",
    "    # Peasants\n",
    "    Builders    = cvx.Variable(Stages+1, name = 'Builders', pos = True)\n",
    "    Farmers  = cvx.Variable(Stages+1, name = 'Farmers', pos = True)\n",
    "    \n",
    "    # Structures\n",
    "    Houses     = cvx.Variable(Stages+1, name = 'Houses', pos = True)\n",
    "    \n",
    "    # Army\n",
    "    Army       = cvx.Variable(Stages+1, name = 'Army', pos = True)\n",
    "\n",
    "\n",
    "    B  = cvx.Variable(Stages, name = 'Builders Production')\n",
    "    F  = cvx.Variable(Stages, name = 'Farmers Production')\n",
    "    H  = cvx.Variable(Stages, name = 'Houses Production')\n",
    "    P  = cvx.Variable(Stages, name = 'Potato Production')\n",
    "    W  = cvx.Variable(Stages, name = 'Wood Production')\n",
    "\n",
    "    \n",
    "    initial_farmers    = cvx.Parameter()\n",
    "    initial_houses     = cvx.Parameter()\n",
    "    initial_builders   = cvx.Parameter()\n",
    "    initial_potato     = cvx.Parameter()\n",
    "    initial_wood       = cvx.Parameter()\n",
    "    \n",
    "    buildings_builders = cvx.Parameter()\n",
    "    peasants_houses    = cvx.Parameter()\n",
    "    supplies_houses    = cvx.Parameter()\n",
    "    resources_farmer   = cvx.Parameter()\n",
    "    \n",
    "    # Costs in Wood\n",
    "    custo_wood_houses    = cvx.Parameter()\n",
    "    \n",
    "    # Costs in Potato\n",
    "    custo_potato_farmers = cvx.Parameter()\n",
    "    custo_potato_builders   = cvx.Parameter()\n",
    "    \n",
    "    # Initial State\n",
    "    initial_houses, initial_farmers, initial_builders, initial_wood, initial_potato = Initial_state\n",
    "    \n",
    "    # Gradient\n",
    "    buildings_Builders, peasants_houses, supplies_houses, resources_farmer          = Gradient\n",
    "    \n",
    "    # Costs\n",
    "    Potato_costs, Wood_costs                    = Costs\n",
    "    custo_wood_houses                           = Wood_costs\n",
    "    custo_potato_farmers, custo_potato_builders = Potato_costs\n",
    "    \n",
    "    print('Initial Houses = ' + str(initial_houses))\n",
    "    print('Initial Farmers = ' + str(initial_farmers))\n",
    "    print('Initial Builders = ' + str(initial_builders))\n",
    "    print('Initial Potato = ' + str(initial_potato))\n",
    "    print('Initial Wood = ' + str(initial_wood) + '\\n')\n",
    "    \n",
    "    Constraints = [Farmers[0]    == initial_farmers,\n",
    "                   Houses[0]     == initial_houses,\n",
    "                   Builders[0]   == initial_builders,\n",
    "                   Army[0]       == initial_farmers + initial_builders,\n",
    "                   Wood[0]       == initial_wood,\n",
    "                   Potato[0]     == initial_potato,\n",
    "        \n",
    "                   B  >= 0,\n",
    "                   F  >= 0,\n",
    "                   H  >= 0,\n",
    "                   P  >= 0,\n",
    "                   W  >= 0]\n",
    "\n",
    "    for i in range(Stages):\n",
    "\n",
    "        Constraints.append(B[i] == Builders[i+1]  - Builders[i])\n",
    "        Constraints.append(F[i] == Farmers[i+1]   - Farmers[i])\n",
    "        Constraints.append(H[i] == Houses[i+1]    - Houses[i])\n",
    "        Constraints.append(P[i] == Potato[i+1]    - Potato[i])\n",
    "        Constraints.append(W[i] == Wood[i+1]      - Wood[i])\n",
    "\n",
    "        Constraints.append(H[i] <= buildings_Builders*Builders[i])\n",
    "        Constraints.append(B[i]  + F[i] <= peasants_houses*Houses[i])\n",
    "        Constraints.append(Raw_potato_production[i] + 2*Raw_wood_production[i] == resources_farmer*Farmers[i])\n",
    "\n",
    "        Constraints.append(Army[i+1] == Farmers[i] + Builders[i])\n",
    "        Constraints.append(Army[i+1] <= Houses[i+1]*supplies_houses)\n",
    "        \n",
    "\n",
    "        Constraints.append(P[i] == Raw_potato_production[i]\n",
    "                           - custo_potato_farmers*F[i]\n",
    "                           - custo_potato_builders*B[i])\n",
    "\n",
    "        Constraints.append(W[i] == Raw_wood_production[i]\n",
    "                           - custo_wood_houses*H[i])\n",
    "                           \n",
    "\n",
    "        Constraints.append(Potato[i]  >= custo_potato_farmers*F[i]\n",
    "                                       + custo_potato_builders*B[i])\n",
    "                           \n",
    "        Constraints.append(Wood[i]    >=  custo_wood_houses*H[i])\n",
    "\n",
    "\n",
    "    F_obj = cvx.Maximize(Army[-1])\n",
    "    \n",
    "    return cvx.Problem(F_obj, Constraints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Make_SimpleInteger(Stages, Initial_state, Gradient, Costs):\n",
    "    \n",
    "    \"\"\"\n",
    "    Stages = Int\n",
    "    \n",
    "    Initial_state = [Houses, Farmers, Builders, Wood, Potato]\n",
    "    Gradient      = [buildigs/Builder, peasants/houses, supply/houses, resources/farmer]\n",
    "    Costs         = [Potato_costs, Wood_costs]\n",
    "    costs         = [wood -> Houses, Potato-> Farmers and Builders]\n",
    "                   \n",
    "    \"\"\"\n",
    "    \n",
    "    # Resources\n",
    "    Potato               = cvx.Variable(Stages, name = 'Potato', integer = True)\n",
    "    Wood                = cvx.Variable(Stages, name = 'Wood', integer = True)\n",
    "    Raw_potato_production = cvx.Variable(Stages, name = 'Raw Potato Production', integer = True)\n",
    "    Raw_wood_production = cvx.Variable(Stages, name = 'Raw Wood Production', integer = True)\n",
    "    \n",
    "    # Peasants\n",
    "    Builders    = cvx.Variable(Stages, name = 'Builders', integer = True)\n",
    "    Farmers  = cvx.Variable(Stages, name = 'Farmers', integer = True)\n",
    "    \n",
    "    # Structures\n",
    "    Houses     = cvx.Variable(Stages, name = 'Houses', integer = True)\n",
    "\n",
    "    # Army\n",
    "    Army       = cvx.Variable(Stages, name = 'Army', integer = True)\n",
    "\n",
    "\n",
    "    B  = cvx.Variable(Stages-1, name = 'Builders Production')\n",
    "    F  = cvx.Variable(Stages-1, name = 'Farmers Production')\n",
    "    H  = cvx.Variable(Stages-1, name = 'Houses Production')\n",
    "    P  = cvx.Variable(Stages-1, name = 'Potato Production')\n",
    "    W  = cvx.Variable(Stages-1, name = 'Wood Production')\n",
    "\n",
    "    \n",
    "    initial_farmers    = cvx.Parameter()\n",
    "    initial_houses     = cvx.Parameter()\n",
    "    initial_builders   = cvx.Parameter()\n",
    "    initial_potato     = cvx.Parameter()\n",
    "    initial_wood       = cvx.Parameter()\n",
    "    \n",
    "    buildings_builders = cvx.Parameter()\n",
    "    peasants_houses    = cvx.Parameter()\n",
    "    supplies_houses    = cvx.Parameter()\n",
    "    resources_farmer   = cvx.Parameter()\n",
    "    \n",
    "    # Costs in Wood\n",
    "    custo_wood_houses    = cvx.Parameter()\n",
    "    \n",
    "    # Costs in Potato\n",
    "    custo_potato_farmers = cvx.Parameter()\n",
    "    custo_potato_builders   = cvx.Parameter()\n",
    "    \n",
    "    # Initial State\n",
    "    initial_houses, initial_farmers, initial_builders, initial_wood, initial_potato = Initial_state\n",
    "    \n",
    "    # Gradient\n",
    "    buildings_Builders, peasants_houses, supplies_houses, resources_farmer          = Gradient\n",
    "    \n",
    "    # Costs\n",
    "    Potato_costs, Wood_costs                    = Costs\n",
    "    custo_wood_houses                           = Wood_costs\n",
    "    custo_potato_farmers, custo_potato_builders = Potato_costs\n",
    "    \n",
    "    print('Initial Houses = ' + str(initial_houses))\n",
    "    print('Initial Farmers = ' + str(initial_farmers))\n",
    "    print('Initial Builders = ' + str(initial_builders))\n",
    "    print('Initial Potato = ' + str(initial_potato))\n",
    "    print('Initial Wood = ' + str(initial_wood) + '\\n')\n",
    "    \n",
    "    Constraints = [Farmers[0]    == initial_farmers,\n",
    "                   Houses[0]     == initial_houses,\n",
    "                   Builders[0]   == initial_builders,\n",
    "                   Army[0]       == initial_farmers + initial_builders,\n",
    "                   Wood[0]       == initial_wood,\n",
    "                   Potato[0]     == initial_potato,\n",
    "                    Potato >= 0,\n",
    "                    Wood >= 0,\n",
    "                    Raw_potato_production >= 0,\n",
    "                    Raw_wood_production >= 0,\n",
    "                    Builders >= 0,\n",
    "                    Farmers >= 0,\n",
    "                    Houses  >= 0,\n",
    "                    Army >= 0,\n",
    "                   B  >= 0,\n",
    "                   F  >= 0,\n",
    "                   H  >= 0,\n",
    "                   P  >= 0,\n",
    "                   W  >= 0]\n",
    "\n",
    "    for i in range(Stages-1):\n",
    "\n",
    "        Constraints.append(B[i] == Builders[i+1]  - Builders[i])\n",
    "        Constraints.append(F[i] == Farmers[i+1]   - Farmers[i])\n",
    "        Constraints.append(H[i] == Houses[i+1]    - Houses[i])\n",
    "        Constraints.append(P[i] == Potato[i+1]    - Potato[i])\n",
    "        Constraints.append(W[i] == Wood[i+1]      - Wood[i])\n",
    "\n",
    "        Constraints.append(H[i] <= buildings_Builders*Builders[i])\n",
    "        Constraints.append(B[i]  + F[i] <= peasants_houses*Houses[i])\n",
    "        Constraints.append(Raw_potato_production[i] + 2*Raw_wood_production[i] == resources_farmer*Farmers[i])\n",
    "\n",
    "        Constraints.append(Army[i+1] == Farmers[i] + Builders[i])\n",
    "        Constraints.append(Army[i+1] <= Houses[i+1]*supplies_houses)\n",
    "        \n",
    "\n",
    "        Constraints.append(P[i] == Raw_potato_production[i]\n",
    "                           - custo_potato_farmers*F[i]\n",
    "                           - custo_potato_builders*B[i])\n",
    "\n",
    "        Constraints.append(W[i] == Raw_wood_production[i]\n",
    "                           - custo_wood_houses*H[i])\n",
    "                           \n",
    "\n",
    "        Constraints.append(Potato[i]  >= custo_potato_farmers*F[i]\n",
    "                                       + custo_potato_builders*B[i])\n",
    "                           \n",
    "        Constraints.append(Wood[i]    >=  custo_wood_houses*H[i])\n",
    "\n",
    "\n",
    "    F_obj = cvx.Maximize(Army[-1])\n",
    "    \n",
    "    return cvx.Problem(F_obj, Constraints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Make_Simple_Plot(Stages, problem, save = False, Title = False, figname = False):\n",
    "\n",
    "    Army, Farmers, Houses, Builders, Wood, Potato, BuilderProd, FarmerProd, HouseProd, PotatoProd, WoodProd, RPP, RWP = problem.variables()\n",
    "\n",
    "################ Adjustments #########################################\n",
    "\n",
    "    plt.figure(figsize=(12,8))\n",
    "    plt.subplots_adjust(left=0.05, right=0.9, bottom = 0.4, top = 1.1,hspace = 0.4)\n",
    "    \n",
    "######################  First Plot  ##################################\n",
    "\n",
    "    plt.subplot(231)\n",
    "    plt.plot(Farmers.value[:Stages] + Builders.value[:Stages],'r--*', label='Army')\n",
    "    plt.plot(Wood.value[:Stages] + Potato.value[:Stages],'b--', label='Resources')\n",
    "    plt.plot(Houses.value[:Stages], label='Buildings')\n",
    "    if Title:\n",
    "        plt.title(Title)\n",
    "    else:\n",
    "        pass\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('Stages')\n",
    "    plt.legend()\n",
    "    \n",
    "######################  Second Plot  ##################################\n",
    "\n",
    "    plt.subplot(232)\n",
    "    plt.plot(Farmers.value[:Stages], 'o--', label='Farmers')\n",
    "    plt.plot(Builders.value[:Stages],'b--', label='Builders')\n",
    "    plt.plot(Houses.value[:Stages], label='Houses')\n",
    "    if Title:\n",
    "        plt.title(Title)\n",
    "    else:\n",
    "        plt.title(str(Stages - 1) + ' Estágios')\n",
    "    plt.xlabel('Stages')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "\n",
    "\n",
    "######################  Third Plot  ##################################\n",
    "\n",
    "    plt.subplot(233)\n",
    "\n",
    "    plt.plot(Wood.value[:Stages],  'o--', label='Wood')\n",
    "    plt.plot(Potato.value[:Stages],'b--', label='Potato')\n",
    "    plt.xlabel('Stages')\n",
    "\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.legend()\n",
    "    \n",
    "    \n",
    "######################  Fourth Plot  ##################################\n",
    "\n",
    "    plt.subplot(234)\n",
    "    plt.plot(Farmers.value[:Stages] + Builders.value[:Stages],'r--*', label='Army')\n",
    "    plt.plot(Wood.value[:Stages] + Potato.value[:Stages],'b--', label='Resources')\n",
    "    plt.plot(Houses.value[:Stages], label='Buildings')\n",
    "    if Title:\n",
    "        pass #plt.title(Title)\n",
    "    else:\n",
    "        pass #plt.title(str(Stages - 1) + ' Estágios')\n",
    "    plt.yscale('symlog')\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('Stages')\n",
    "\n",
    "    plt.legend()\n",
    "    \n",
    "    \n",
    "######################  Fith Plot  ##################################\n",
    "\n",
    "    plt.subplot(235)\n",
    "    \n",
    "    plt.plot(Farmers.value[:Stages], 'o--', label='Farmers')\n",
    "    plt.plot(Builders.value[:Stages],'b--', label='Builders')\n",
    "    plt.plot(Houses.value[:Stages], label='Houses')\n",
    "    if Title:\n",
    "        plt.title(Title + ' - Logscale')\n",
    "    else:\n",
    "        plt.title('Logscale')\n",
    "    plt.yscale('symlog')\n",
    "    plt.xlabel('Stages')\n",
    "\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    \n",
    "        \n",
    "######################  Sixth Plot  ##################################\n",
    "\n",
    "\n",
    "    plt.subplot(236)\n",
    "\n",
    "\n",
    "    plt.plot(RPP.value[:Stages], label=RPP.name())\n",
    "    plt.plot(RWP.value[:Stages], label=RWP.name())\n",
    "\n",
    "    plt.xlabel('Stages')\n",
    "\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.legend()\n",
    "    \n",
    "#####################################################################\n",
    "\n",
    "    if save:\n",
    "        if figname:\n",
    "            \n",
    "            plt.savefig(figname, bbox_inches = 'tight',pad_inches = 0)\n",
    "        else:\n",
    "            mnum = np.random.rand() + 10*sum(initial_state)\n",
    "            plt.savefig(str(Stages - 1)+'_Estagios_'+str(mnum), bbox_inches = 'tight', pad_inches = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Make_Integer_Simple_Plot(Stages, problem, save = False, Title = False, figname = False):\n",
    "\n",
    "    Army, Farmers, Houses, Builders, Wood, Potato,RPP,RWP, BuilderProd, FarmerProd, HouseProd, PotatoProd, WoodProd = problem.variables()\n",
    "\n",
    "################ Adjustments #########################################\n",
    "\n",
    "    plt.figure(figsize=(16,10))\n",
    "    plt.subplots_adjust(left=0.05, right=0.9, bottom = 0.4, top = 1.1,hspace = 0.4)\n",
    "    \n",
    "######################  First Plot  ##################################\n",
    "\n",
    "    plt.subplot(231)\n",
    "    plt.plot(Farmers.value[:Stages] + Builders.value[:Stages],'r--*', label='Army')\n",
    "    plt.plot(Wood.value[:Stages] + Potato.value[:Stages],'b--', label='Resources')\n",
    "    plt.plot(Houses.value[:Stages], label='Buildings')\n",
    "    if Title:\n",
    "        plt.title(Title)\n",
    "    else:\n",
    "        pass\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('Stages')\n",
    "    plt.legend()\n",
    "    \n",
    "######################  Second Plot  ##################################\n",
    "\n",
    "    plt.subplot(232)\n",
    "    plt.plot(Farmers.value[:Stages], 'o--', label='Farmers')\n",
    "    plt.plot(Builders.value[:Stages],'b--', label='Builders')\n",
    "    plt.plot(Houses.value[:Stages], label='Houses')\n",
    "    if Title:\n",
    "        plt.title(Title)\n",
    "    else:\n",
    "        plt.title(str(Stages - 1) + ' Estágios')\n",
    "    plt.xlabel('Stages')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "\n",
    "\n",
    "######################  Third Plot  ##################################\n",
    "\n",
    "    plt.subplot(233)\n",
    "\n",
    "    plt.plot(Wood.value[:Stages],  'o--', label='Wood')\n",
    "    plt.plot(Potato.value[:Stages],'b--', label='Potato')\n",
    "    plt.xlabel('Stages')\n",
    "\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.legend()\n",
    "    \n",
    "    \n",
    "######################  Fourth Plot  ##################################\n",
    "\n",
    "    plt.subplot(234)\n",
    "    plt.plot(Farmers.value[:Stages] + Builders.value[:Stages],'r--*', label='Army')\n",
    "    plt.plot(Wood.value[:Stages] + Potato.value[:Stages],'b--', label='Resources')\n",
    "    plt.plot(Houses.value[:Stages], label='Buildings')\n",
    "    if Title:\n",
    "        pass #plt.title(Title)\n",
    "    else:\n",
    "        pass #plt.title(str(Stages - 1) + ' Estágios')\n",
    "    plt.yscale('symlog')\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('Stages')\n",
    "\n",
    "    plt.legend()\n",
    "    \n",
    "    \n",
    "######################  Fith Plot  ##################################\n",
    "\n",
    "    plt.subplot(235)\n",
    "    \n",
    "    plt.plot(Farmers.value[:Stages], 'o--', label='Farmers')\n",
    "    plt.plot(Builders.value[:Stages],'b--', label='Builders')\n",
    "    plt.plot(Houses.value[:Stages], label='Houses')\n",
    "    if Title:\n",
    "        plt.title(Title + ' - Logscale')\n",
    "    else:\n",
    "        plt.title('Logscale')\n",
    "    plt.yscale('symlog')\n",
    "    plt.xlabel('Stages')\n",
    "\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    \n",
    "        \n",
    "######################  Sixth Plot  ##################################\n",
    "\n",
    "\n",
    "    plt.subplot(236)\n",
    "\n",
    "\n",
    "    plt.plot(RPP.value[:Stages], label=RPP.name())\n",
    "    plt.plot(RWP.value[:Stages], label=RWP.name())\n",
    "\n",
    "    plt.xlabel('Stages')\n",
    "\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.legend()\n",
    "    \n",
    "#####################################################################\n",
    "\n",
    "    if save:\n",
    "        if figname:\n",
    "            \n",
    "            plt.savefig(figname, bbox_inches = 'tight',pad_inches = 0)\n",
    "        else:\n",
    "            mnum = np.random.rand() + 10*sum(initial_state)\n",
    "            plt.savefig(str(Stages - 1)+'_Estagios_'+str(mnum), bbox_inches = 'tight', pad_inches = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imperio0 = [2,1,0,0,0]\n",
    "Gradi    = [1, 1, 2, 3]\n",
    "tatoco   = [3,6]\n",
    "woodco   = [5]\n",
    "Custos    = [tatoco, woodco]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Houses = 2\n",
      "Initial Farmers = 1\n",
      "Initial Builders = 0\n",
      "Initial Potato = 0\n",
      "Initial Wood = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ProblemTest = Make_Simple(31, Imperio0, Gradi, Custos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3378.376721068459"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ProblemTest.solve(solver='GUROBI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Make_Simple_Plot(31,ProblemTest, save = True, figname='simple_30_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imperio0 = [3,1,1,0,0]\n",
    "Gradi    = [1, 1, 2, 3]\n",
    "tatoco   = [6,3]\n",
    "woodco   = [5]\n",
    "Custos    = [tatoco, woodco]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Houses = 3\n",
      "Initial Farmers = 1\n",
      "Initial Builders = 1\n",
      "Initial Potato = 0\n",
      "Initial Wood = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "IntTest = Make_SimpleInteger(40, Imperio0, Gradi, Custos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntTest.solve(solver='GUROBI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 3., 4., 4., 5., 6.]),\n",
       " 'Army')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntTest.variables()[0].value, IntTest.variables()[0].name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0., -0.,  1.,  2.,  3.,  4.,  5.,  6.,  6.,  6.,  6.,  6.,  6.,\n",
       "         6.,  6.,  6.]), 'Wood')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntTest.variables()[4].value, IntTest.variables()[4].name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 12., 15., 15., 15., 18.,\n",
       "        18., 18., 21.]), 'Potato')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntTest.variables()[5].value, IntTest.variables()[5].name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 1., 1., 1., 1., 1., 1., 3., 3., 3., 3., 3., 3., 3., 3., 0.]),\n",
       " 'Raw Potato Production')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntTest.variables()[6].value, IntTest.variables()[6].name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.,  1.,  1.,  1.,  1.,  1.,  1., -0., -0., -0., -0., -0., -0.,\n",
       "        -0., -0.,  0.]), 'Raw Wood Production')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IntTest.variables()[7].value, IntTest.variables()[7].name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Army\n",
      "Farmers\n",
      "Houses\n",
      "Builders\n",
      "Wood\n",
      "Potato\n",
      "Raw Potato Production\n",
      "Raw Wood Production\n",
      "Builders Production\n",
      "Farmers Production\n",
      "Houses Production\n",
      "Potato Production\n",
      "Wood Production\n"
     ]
    }
   ],
   "source": [
    "for i in IntTest.variables():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Army\n",
      "Farmers\n",
      "Houses\n",
      "Builders\n",
      "Wood\n",
      "Potato\n",
      "Builders Production\n",
      "Farmers Production\n",
      "Houses Production\n",
      "Potato Production\n",
      "Wood Production\n",
      "Raw Potato Production\n",
      "Raw Wood Production\n"
     ]
    }
   ],
   "source": [
    "for i in ProblemTest.variables():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Make_Integer_Simple_Plot(40, IntTest, save = True, figname = 'simple302')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_next_empire(old_empire):\n",
    "    \n",
    "    Army, Farmers, Houses, Builders, Wood, Potato, BuilderProd, FarmerProd, HouseProd, PotatoProd, WoodProd, RPP, RWP = problem.variables()\n",
    "    \n",
    "    \n",
    "    new = [ ]\n",
    "    \n",
    "    new += []\n",
    "    \n",
    "    return new\n"
   ]
  }
 ],
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