mesa_abm_python_version2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install mesa --quiet\n",
    "import mesa\n",
    "# The library \"mesa\" is installed and imported.\n",
    "#This is the library for ABM modelization\n",
    "import numpy as np\n",
    "#We use library numpy for arrays and all that stuff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install matplotlib --quiet\n",
    "import matplotlib.pyplot as plt\n",
    "# This library is to create graphs.\n",
    "# It is used to create the model landscape.\n",
    "\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resources Classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We initiate the resources classes.\n",
    "# The resources (sugar and spice) that are traded by the agents.\n",
    "\n",
    "class Sugar(mesa.Agent):\n",
    "  \"\"\"\n",
    "  Sugar\n",
    "  - contains an amount of sugar\n",
    "  - grows one amount of sugar at each turn\n",
    "  \"\"\"\n",
    "  def __init__(self):\n",
    "    print(\"I am sugar\")\n",
    "\n",
    "class Spice(mesa.Agent):\n",
    "  \"\"\"\n",
    "  Spice\n",
    "  - contains an amount of spice\n",
    "  - grows one amount of spice at each turn\n",
    "  \"\"\"\n",
    "  def __init__(self):\n",
    "    print(\"I am spice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trader Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I believe that with this class are defined the agents that trade\n",
    "# it is repeated the same proccess with the trader agents.\n",
    "class Trader(mesa.Agent):\n",
    "  \"\"\"\n",
    "  Trader:\n",
    "  - has a metabolism of sugar and spice\n",
    "  - harvest and trade sugar and spice to survive and thrive\n",
    "  \"\"\"\n",
    "  def __init__(self):\n",
    "    print(\"I am Trader\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the declaration of the model class.\n",
    "# After the goods and the agents classes were previously defined.\n",
    "class SugarscapeG1mt(mesa.Model):\n",
    "  \"\"\"\n",
    "  Manager class to run Sugarscape with Traders\n",
    "  \"\"\"\n",
    "  # it is defined the size of the landscape.\n",
    "  def __init__(self, width=50,height=50):\n",
    "    #Initiate width and height of sugarscape\n",
    "    self.width = width\n",
    "    self.height = height\n",
    "    #initiate mesa grid class\n",
    "    self.grid = mesa.space.MultiGrid(self.width, self.height, torus=False)\n",
    "    #read in landscape file from supplementary material\n",
    "    # we use some numpy functions here\n",
    "    sugar_distribution =np.genfromtxt(\"sugar-map.txt\")\n",
    "    \n",
    "    #print(sugar_distribution.shape)\n",
    "    #print(sugar_distribution[30])\n",
    "    #With these two early commands we can observe a vector of the Sugarscape model\n",
    "    \n",
    "    spice_distribution = np.flip(sugar_distribution, 1)\n",
    "    plt.imshow(spice_distribution, origin=\"lower\")\n",
    "    #With these commands it can be seen the graph of the Sugarscape model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run Sugarscape Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = SugarscapeG1mt()"
   ]
  }
 ],
 "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": "3.10.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}