ProcessGantt.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "HO4uHSp9Vf5g"
      },
      "outputs": [],
      "source": [
        "from gantt import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "4qnMPP6jVxsv"
      },
      "outputs": [],
      "source": [
        "category_names = ['CPU1', 'CPU2', 'I/O Printer', 'I/O disk']\n",
        "\n",
        "# results dict is scheduling data\n",
        "# take 'P1' as example:\n",
        "# since there are 4 categories: 'CPU1', 'CPU2', 'I/O Printer', 'I/O disk'\n",
        "# so 'P1': [[(0, 80)], [(180, 40)], [(80, 100)], []] means:\n",
        "# 'CPU1' from 0 to 0+80=80\n",
        "# 'CPU2' from 180 to 180+40=220\n",
        "# 'I/O Printer' from 80 to 80+100=180\n",
        "# no 'I/O disk'\n",
        "results = {\n",
        "    'P1': [[(0, 80)], [(180, 40)], [(80, 100)], []],\n",
        "    'P2': [[(80, 180), (330, 20)], [], [(260, 70)], []],\n",
        "    'P3': [[], [(0, 130), (220, 50)], [], [(130, 50)]]\n",
        "}\n",
        "\n",
        "# program arrival time\n",
        "arrival_t = [0, 10, 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "GRvCOvdvV2BI",
        "outputId": "96d7ef12-6d24-4324-ab0e-6f7de1bbb9a2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 841.68x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "gantt(category_names, results, arrival_t).show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Untitled3.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
    },
    "kernelspec": {
      "display_name": "Python 3.9.7 64-bit",
      "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": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}