L04_Shelling_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "L04: Shelling Model.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ojmakinde/coding.13/blob/main/L04_Shelling_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ws6rXa1xv_gv"
      },
      "source": [
        "# [L04: Implementing the Shelling Model](https://docs.google.com/document/d/1NtGAKnsfwLRYMYrL74aMVMtTuQRJ1oDjvSFWlmMjpzs/edit?usp=sharing)\n",
        "Written by Dr. Jan Pearce, Berea College\n",
        "\n",
        "Complete the code by meeting all linked requirements and fixing all FIXMEs\n",
        "\n",
        "## Your Name: FIXME"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvEpt0HPKq9X"
      },
      "source": [
        "**SUMMARY**: A brief summary description of the design and implementation, including how much your initial design plan evolved, the final result you achieved and the amount of time you spent as a programmer or programmers in accomplishing these results. This should be no more than two paragraphs.\n",
        "\n",
        "It was a great learning experience and one that I found to be rather difficult at certain points. I was able to better understand the intricacies of the schelling model and it was also very interesting seeing the agents transform in behavior over time.\n",
        "\n",
        "**PERSONNEL**: A description of who you worked with and on what sections.  It is expected that all collaboration is done as pair programming together. Also, note these collaborations in the code section itself.\n",
        "\n",
        "I worked on this alone.\n",
        "\n",
        "**CHALLENGES**: Descriptions of the largest challenges you overcame and what made them challenging.\n",
        "\n",
        "The Object Oriented Design Structure was a bit difficult to follow. I initially had a design that consisted of using 0's, 1's and a null value for all the different states. However, I was able to modify it to the OOP paradigm.\n",
        "\n",
        "**INNOVATIONS**: Any innovations that were not specifically required by the assignment. These are not required, but should be highlighted if included.\n",
        "\n",
        "None.\n",
        "\n",
        "**TESTING**: Describe how you tested this work.\n",
        "\n",
        "I was able to run it with different values and ensure that it worked the way I wanted it to. Through the testing I was able to see that certain parts of the logic needed work, and I was able improve them.\n",
        "\n",
        "**ERRORS**: A list in bulleted form of all known errors and deficiencies.\n",
        "\n",
        "Some values don't seem to work. If I use 1.0 for instance, the initial and final states are empty.\n",
        "\n",
        "**COMMENTS**: A paragraph or so of your own comments on and reactions to the Lab.\n",
        "\n",
        "Working on this lab provided an amazing learning experience into understanding how the model works. It was also very interesting to see and think up more optimized solutions than the one I implemented, interestingly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lRXe-Atyv30H"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6koAGG2mhix"
      },
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "from urllib.request import urlopen\n",
        "import random\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qGcB5022KlHL"
      },
      "source": [
        "## The Schelling Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIzVOICElISX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0074d92f-2894-4f3d-a8e7-1a4ab691cfaa"
      },
      "source": [
        "class Resident:\n",
        "    def __init__(self, community_type):\n",
        "        self.community_type = community_type\n",
        "        self.satisfied = False\n",
        "        self.days_in_location = 0\n",
        "\n",
        "class Neighborhood:\n",
        "    def __init__(self, length, breadth, density, community_a_ratio, satisfaction_threshold):\n",
        "        self.length = length\n",
        "        self.breadth = breadth\n",
        "        self.density = density\n",
        "        self.community_a_ratio = community_a_ratio\n",
        "        self.satisfaction_threshold = satisfaction_threshold\n",
        "        self.neighborhood_map = []\n",
        "        self.available_locations = []\n",
        "        self.relocation_count = 0\n",
        "        self._create_neighborhood()\n",
        "\n",
        "    def _create_neighborhood(self):\n",
        "        total_spaces = self.length * self.breadth\n",
        "        occupied_spaces = int(total_spaces * self.density)\n",
        "        community_a_count = int(occupied_spaces * self.community_a_ratio)\n",
        "        community_b_count = occupied_spaces - community_a_count\n",
        "\n",
        "        residents = (\n",
        "            [Resident('Community_A') for _ in range(community_a_count)] +\n",
        "            [Resident('Community_B') for _ in range(community_b_count)]\n",
        "        )\n",
        "        random.shuffle(residents)\n",
        "\n",
        "        self.neighborhood_map = [[None for _ in range(self.length)]\n",
        "                               for _ in range(self.breadth)]\n",
        "\n",
        "        all_locations = [(x, y) for x in range(self.breadth)\n",
        "                        for y in range(self.length)]\n",
        "        random.shuffle(all_locations)\n",
        "\n",
        "        for i, loc in enumerate(all_locations[:occupied_spaces]):\n",
        "            x, y = loc\n",
        "            self.neighborhood_map[x][y] = residents[i]\n",
        "\n",
        "        self.available_locations = all_locations[occupied_spaces:]\n",
        "\n",
        "    def _check_surroundings(self, x, y):\n",
        "        similar_neighbors = 0\n",
        "        total_neighbors = 0\n",
        "        resident = self.neighborhood_map[x][y]\n",
        "\n",
        "        for dx in [-2, -1, 0, 1, 2]:\n",
        "            for dy in [-2, -1, 0, 1, 2]:\n",
        "                if dx == 0 and dy == 0:\n",
        "                    continue\n",
        "\n",
        "                new_x, new_y = x + dx, y + dy\n",
        "                if (0 <= new_x < self.breadth and\n",
        "                    0 <= new_y < self.length and\n",
        "                    self.neighborhood_map[new_x][new_y]):\n",
        "\n",
        "                    total_neighbors += 1\n",
        "                    if (self.neighborhood_map[new_x][new_y].community_type ==\n",
        "                        resident.community_type):\n",
        "                        similar_neighbors += 1\n",
        "\n",
        "        return (similar_neighbors / total_neighbors >= self.satisfaction_threshold\n",
        "                if total_neighbors > 0 else False)\n",
        "\n",
        "    def _relocate_resident(self, x, y):\n",
        "        if not self.available_locations:\n",
        "            return False\n",
        "\n",
        "        new_location = random.choice(self.available_locations)\n",
        "        self.available_locations.remove(new_location)\n",
        "        self.available_locations.append((x, y))\n",
        "\n",
        "        new_x, new_y = new_location\n",
        "        self.neighborhood_map[new_x][new_y] = self.neighborhood_map[x][y]\n",
        "        self.neighborhood_map[x][y] = None\n",
        "        self.relocation_count += 1\n",
        "        return True\n",
        "\n",
        "    def simulate_movement(self, max_iterations=100):\n",
        "        for iteration in range(max_iterations):\n",
        "            relocations = 0\n",
        "\n",
        "            for x in range(self.breadth):\n",
        "                for y in range(self.length):\n",
        "                    if (self.neighborhood_map[x][y] and\n",
        "                        not self._check_surroundings(x, y)):\n",
        "                        if self._relocate_resident(x, y):\n",
        "                            relocations += 1\n",
        "\n",
        "            if relocations == 0:\n",
        "                break\n",
        "\n",
        "    def visualize_neighborhood(self, title=\"Neighborhood Map\"):\n",
        "        display_map = np.zeros((self.breadth, self.length))\n",
        "\n",
        "        for x in range(self.breadth):\n",
        "            for y in range(self.length):\n",
        "                if self.neighborhood_map[x][y]:\n",
        "                    display_map[x][y] = (1 if self.neighborhood_map[x][y].community_type\n",
        "                                       == 'Community_A' else -1)\n",
        "\n",
        "        plt.figure(figsize=(10, 10))\n",
        "        img = plt.imshow(display_map, cmap='RdBu')\n",
        "        plt.title(title, pad=20)\n",
        "        plt.axis('off')\n",
        "        plt.colorbar(img).remove()\n",
        "        plt.show()\n",
        "\n",
        "def run_simulation():\n",
        "    print(\"\\nNeighborhood Simulation Setup!\")\n",
        "    length = int(input(\"Enter grid length: \"))\n",
        "    breadth = int(input(\"Enter grid breadth: \"))\n",
        "    print(\"\\n The higher the density, the less space there is for migration.\")\n",
        "    density = float(input(\"Enter density (0-1): \"))\n",
        "    community_a_ratio = float(input(\"Enter community A ratio (0-1): \"))\n",
        "    satisfaction_threshold = float(input(\"Enter satisfaction threshold (0-1): \"))\n",
        "\n",
        "    neighborhood = Neighborhood(length, breadth, density, community_a_ratio, satisfaction_threshold)\n",
        "\n",
        "    print(\"\\nInitial State:\")\n",
        "    neighborhood.visualize_neighborhood(\"Initial State\")\n",
        "\n",
        "    neighborhood.simulate_movement()\n",
        "\n",
        "    print(\"\\nFinal State:\")\n",
        "    neighborhood.visualize_neighborhood(\"Final State\")\n",
        "    print(f\"Total Relocations: {neighborhood.relocation_count}\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    run_simulation()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Neighborhood Simulation Setup!\n",
            "Enter grid length: 200\n",
            "Enter grid breadth: 200\n",
            "\n",
            " The higher the density, the less space there is for migration.\n",
            "Enter density (0-1): 0.3\n",
            "Enter community A ratio (0-1): 0.5\n",
            "Enter satisfaction threshold (0-1): 0.4\n",
            "\n",
            "Initial State:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Final State:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Relocations: 7164\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6HarsXemeKP"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hix-ipzHHnxp"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVTdKeGVH0Im"
      },
      "source": [
        "## Integrity statement\n",
        "\n",
        "Please briefly describe all references you used, all help you received and all help you gave to others in completing this assignment. Be sure to say that you got no help if you got none.\n",
        "\n",
        "I got no help."
      ]
    }
  ]
}