indian_flag_matplotlib.ipynb

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    "colab": {
      "name": "Republic_Day_2020.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PIKQyjbmGy9c",
        "colab_type": "text"
      },
      "source": [
        "# Lets celebrate this Republic Day and Independence with some Python !"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Fetching our dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21qaCDWtGuWu",
        "colab_type": "code",
        "outputId": "4bd4917a-cb94-4eec-8707-760bcac3ee27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "tags": []
      },
      "source": [
        "#### First lets make the tricolor of Indian National Flag\n",
        "#The green colour depicting the fertility, growth and auspiciousness of the land\n",
        "green = patch.Rectangle((0,0), width=10, height=2, facecolor='#008000')\n",
        "#The white color depicting peace and truth \n",
        "white = patch.Rectangle((0,2), width=10, height=2, facecolor='#ffffff') \n",
        "#The kesari color depicting the strength and courage of the country\n",
        "kesari = patch.Rectangle((0,4), width=10, height=2, facecolor='#FF9933') \n",
        "fig,ax = plt.subplots()\n",
        "ax.add_patch(green)\n",
        "ax.add_patch(white)\n",
        "ax.add_patch(kesari)\n",
        "#### Now lets make the Ashok Chakra Chakra depicting that there is life in movement and death in stagnation\n",
        "r=0.8\n",
        "plt.plot(5,3, marker = 'o', markerfacecolor = '#000088ff', markersize = 10)\n",
        "ashoka_chakra = plt.Circle((5, 3), r, color='#000088ff', fill=False, linewidth=7)\n",
        "ax.add_artist(ashoka_chakra)\n",
        "\n",
        "#### Continuing with adding small circles inside the edge of Chakra\n",
        "\n",
        "chakra_circles_x = []\n",
        "chakra_circles_y = []\n",
        "\n",
        "\n",
        "for i in range(1,25):\n",
        "    chakra_circles_x.append(5+(r-0.05) * np.cos(np.pi/24 + np.pi*i/12))\n",
        "    chakra_circles_y.append(3+(r-0.05) * np.sin(np.pi/24 + np.pi*i/12))\n",
        "\n",
        "\n",
        "plt.plot(chakra_circles_x, chakra_circles_y, 'o',\n",
        "         markersize=4,\n",
        "         markerfacecolor='#000088ff',\n",
        "         markeredgecolor = '#000088ff')\n",
        "\n",
        "#### And finally lets make the 24 spokes within the chakra\n",
        "for i in range(0,24):\n",
        "    x1 =  5 + r/2 * np.cos(np.pi*i/12 + np.pi/48)\n",
        "    x2 =  5 + r/2 * np.cos(np.pi*i/12 - np.pi/48)\n",
        "    y1 =  3 + r/2 * np.sin(np.pi*i/12 + np.pi/48)\n",
        "    y2 =  3 + r/2 * np.sin(np.pi*i/12 - np.pi/48)\n",
        "    x3 =  5 + r * np.cos(np.pi*i/12)\n",
        "    y3 =  3 + r * np.sin(np.pi*i/12)\n",
        "    ax.add_patch(patch.Polygon([[5,3], [x1,y1], [x3,y3],[x2,y2]], fill=True, closed=True, color='#000088ff'))\n",
        "\n",
        "plt.axis('equal')\n",
        "\n",
        "#### Kaboom!\n",
        "plt.show()\n",
        "print(\"Vande Mataram\")\n",
        "\n",
        "#And a quote by B. R. Ambedkar that till date resides in my heart \n",
        "print(\"\\nWe are Indians, firstly and lastly.\")"
      ],
      "execution_count": 6,
      "outputs": [
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Vande Mataram\n\nWe are Indians, firstly and lastly.\n"
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}