Fraction of Cooperations Viz.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Net Science - PD: Fraction of cooperators",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_Oy4CjwPBgr",
        "outputId": "b3b82cfc-6f5c-44ef-e924-b8eb604f3164"
      },
      "source": [
        "# Import Dependencies\n",
        "import numpy as np\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "random.seed(246)  \n",
        "import time\n",
        "import datetime\n",
        "import collections\n",
        "import pandas as pd\n",
        "\n",
        "print('Imports sucessful')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Imports sucessful\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A73PGs7wPEK2",
        "outputId": "afd3dccf-8ea5-49a9-c0ba-45671bdcd3ee"
      },
      "source": [
        "# Allow access to the drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VAAH_IxPFux"
      },
      "source": [
        "filepath_random=\"/content/drive/My Drive/Colab Notebooks/NS_proj2_output_files/Final outputs/random.txt\" \n",
        "filepath_regular=\"/content/drive/My Drive/Colab Notebooks/NS_proj2_output_files/Final outputs/regular.txt\"\n",
        "filepath_BAs=\"/content/drive/My Drive/Colab Notebooks/NS_proj2_output_files/Final outputs/BA_both.txt\" "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFWbBD0cQFB_"
      },
      "source": [
        "def read_file(filepath):\n",
        "  data = pd.read_csv (filepath)\n",
        "  data.to_csv (filepath, index=None)\n",
        "  return data\n",
        "#data.columns = ['first_column','second_column',...]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6sVWCuZUmfw"
      },
      "source": [
        "##READ AND CREATE ALL THE DATA SETS FOR EACH TOPOLOGY"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPVsg2yKBz_S"
      },
      "source": [
        "regular = read_file(filepath_regular)\n",
        "random = read_file(filepath_random)\n",
        "BAs = read_file(filepath_BAs)\n",
        "ba_mod = BAs[BAs.Topology ==\" BA__MOD\"]\n",
        "ba_rand = BAs[BAs.Topology ==\" BA_RAND\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbHvdEMRQHWW"
      },
      "source": [
        "data = pd.concat(objs = [regular, random, BAs])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmPWaUqPCw2P"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fej2SwWfQK97"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7vlnodkTEM4"
      },
      "source": [
        "data[[\"Topology\",\"T\", \"qt_coop\"]].groupby([\"Topology\",\"T\"]).describe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hQVn9aadUNl"
      },
      "source": [
        "#Models evaluation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "dPDjcUu3dPF9",
        "outputId": "bcb6ba2c-6166-4af5-cf51-346193cd2ae8"
      },
      "source": [
        "#MEANS\n",
        "\n",
        "fig = plt.figure()\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "\n",
        "y_mean_reg=regular[[\"T\", \"qt_coop\"]].groupby(\"T\").mean()\n",
        "y_mean_ba_rand=ba_rand[[\"T\", \"qt_coop\"]].groupby(\"T\").mean()\n",
        "y_mean_ran=random[[\"T\", \"qt_coop\"]].groupby(\"T\").mean()\n",
        "\n",
        "\n",
        "plt.plot(y_mean_reg, color='yellowgreen', marker='o', linestyle='solid', linewidth=1, markersize=8, label = 'Regular')\n",
        "plt.plot(y_mean_ran, color='tomato', marker='^', linestyle='solid', linewidth=1, markersize=8, label = 'Random')\n",
        "plt.plot(y_mean_ba_rand, color='steelblue', marker='x', linestyle='solid', linewidth=1, markersize=8, label = 'Barabasi Albert')\n",
        "\n",
        "plt.legend(loc = 'best')\n",
        "\n",
        "#plt.title('Fraction of cooperators for different topologies and values of \"b\"\\n')\n",
        "plt.xlabel(\"Temptation to defect b\")\n",
        "plt.ylabel(\"Fraction of cooperators\")\n",
        "\n",
        "\n",
        "save_results_to = '/content/drive/My Drive/Colab Notebooks/NS_proj2_output_files/Final outputs/'\n",
        "plt.savefig(save_results_to + 'all_models.png', dpi = 300)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHmqUD31ee96"
      },
      "source": [
        "##How the initial condition affects the fraction of cooperators in SFN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "-yL-tyOBTTla",
        "outputId": "9ea6fabf-c699-42a5-f1b8-c746b33e5397"
      },
      "source": [
        "#MEANS\n",
        "fig = plt.figure()\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "y_mean_ba_mod=ba_mod[[\"T\", \"qt_coop\"]].groupby(\"T\").mean()\n",
        "y_mean_ba_rand=ba_rand[[\"T\", \"qt_coop\"]].groupby(\"T\").mean()\n",
        "\n",
        "plt.plot(y_mean_ba_mod, color='orange', marker='s', linestyle='solid', linewidth=1, markersize=8, label = 'Barabasi Albert Modified')\n",
        "plt.plot(y_mean_ba_rand, color='steelblue', marker='x', linestyle='solid', linewidth=1, markersize=8, label = 'Barabasi Albert')\n",
        "\n",
        "plt.legend(loc = 'best')\n",
        "\n",
        "#plt.title('Fraction of cooperators of SFN with different starting conditions for different values of \"b\"\\n')\n",
        "plt.xlabel(\"Temptation to defect b\")\n",
        "plt.ylabel(\"Fraction of cooperators\")\n",
        "\n",
        "\n",
        "save_results_to = '/content/drive/My Drive/Colab Notebooks/NS_proj2_output_files/Final outputs/'\n",
        "plt.savefig(save_results_to + 'BA-models.png', dpi = 300)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}