yohanesnuwara/snowflake.ipynb

## snowflake.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "snowflake.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOICVrjWIusJs04K1FRLw77",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/yohanesnuwara/2544cb5c3a7df6fc7c26b04957f1556f/snowflake.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "QqV0bqNPa2TK"
      },
      "outputs": [],
      "source": [
        "# import libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "\n",
        "# ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def koch_line(start, end, factor):\n",
        "    \"\"\"\n",
        "    Segments a line to Koch line, creating fractals.\n",
        "    \n",
        "    \n",
        "    :param tuple start:  (x, y) coordinates of the starting point\n",
        "    :param tuple end: (x, y) coordinates of the end point\n",
        "    :param float factor: the multiple of sixty degrees to rotate\n",
        "    :returns tuple: tuple of all points of segmentation\n",
        "    \"\"\"\n",
        "    \n",
        "    # coordinates of the start\n",
        "    x1, y1 = start[0], start[1]\n",
        "    \n",
        "    # coordinates of the end\n",
        "    x2, y2 = end[0], end[1]\n",
        "    \n",
        "    # the length of the line\n",
        "    l = np.sqrt((x2 - x1)**2 + (y2 - y1)**2)\n",
        "    \n",
        "    # first point: same as the start \n",
        "    a = (x1, y1)\n",
        "    \n",
        "    # second point: one third in each direction from the first point\n",
        "    b = (x1 + (x2 - x1)/3., y1 + (y2 - y1)/3.)\n",
        "    \n",
        "    # third point: rotation for multiple of 60 degrees\n",
        "    c = (b[0] + l/3. * np.cos(factor * np.pi/3.), b[1] + l/3. * np.sin(factor * np.pi/3.))\n",
        "    \n",
        "    # fourth point: two thirds in each direction from the first point\n",
        "    d = (x1 + 2. * (x2 - x1)/3., y1 + 2. * (y2 - y1)/3.)\n",
        "    \n",
        "    # the last point\n",
        "    e = end\n",
        "    \n",
        "    return {'a': a, 'b': b, 'c': c, 'd' : d, 'e' : e, 'factor' : factor}"
      ],
      "metadata": {
        "id": "XGERUC-Za61s"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def koch_snowflake(degree, s=5.0):\n",
        "    \"\"\"Generates all lines for a Koch Snowflake with a given degree.\n",
        "    \n",
        "    :param int degree: how deep to go in the branching process\n",
        "    :param float s: the length of the initial equilateral triangle\n",
        "    :returns list: list of all lines that form the snowflake\n",
        "    \"\"\"\n",
        "    # all lines of the snowflake\n",
        "    lines = []\n",
        "    \n",
        "    # we rotate in multiples of 60 degrees\n",
        "    sixty_degrees = np.pi / 3.\n",
        "    \n",
        "    # vertices of the initial equilateral triangle\n",
        "    A = (0., 0.)\n",
        "    B = (s, 0.)\n",
        "    C = (s * np.cos(sixty_degrees), s * np.sin(sixty_degrees))\n",
        "    \n",
        "    # set the initial lines\n",
        "    if degree == 0:\n",
        "        lines.append(koch_line(A, B, 0))\n",
        "        lines.append(koch_line(B, C, 2))\n",
        "        lines.append(koch_line(C, A, 4))\n",
        "    else:\n",
        "        lines.append(koch_line(A, B, 5))\n",
        "        lines.append(koch_line(B, C, 1))\n",
        "        lines.append(koch_line(C, A, 3))\n",
        "    \n",
        "    for i in range(1, degree):\n",
        "        # every lines produce 4 more lines\n",
        "        for _ in range(3*4**(i - 1)):\n",
        "            line = lines.pop(0)\n",
        "            factor = line['factor']\n",
        "\n",
        "            lines.append(koch_line(line['a'], line['b'], factor % 6))  # a to b\n",
        "            lines.append(koch_line(line['b'], line['c'], (factor - 1) % 6))  # b to c\n",
        "            lines.append(koch_line(line['c'], line['d'], (factor + 1) % 6))  # d to c\n",
        "            lines.append(koch_line(line['d'], line['e'], factor % 6))  # d to e\n",
        "\n",
        "    return lines"
      ],
      "metadata": {
        "id": "Wc_M2fZJa-yE"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def line_function(a, b, num_points):\n",
        "    \"\"\"Determining the function of the line that passes through the points a, b\n",
        "    with coordinates (x1, y1) and (x2, y2) respectively. The equation is\n",
        "    y = m*x + b where m = (y2 - y1)/(x2 - x1). Then, b = y2 - m*x2 = y1 - m*x1.\n",
        "    \n",
        "    :param tuple a: (x, y) coordinates of the first point\n",
        "    :param tuple b: (x, y) coordinates of the second point\n",
        "    :param int num_points: number of points to generate\n",
        "    :returns tuple: x and f(x) values of the function\n",
        "    \"\"\"\n",
        "    x1, y1 = a[0], a[1]\n",
        "    x2, y2 = b[0], b[1]\n",
        "    \n",
        "    m = 1.0*(y2 - y1)/(x2 - x1)\n",
        "    b = y2 - m*x2\n",
        "    \n",
        "    x = np.linspace(x1, x2, num_points)\n",
        "    y = m*x + b\n",
        "    \n",
        "    return list(x), list(y)"
      ],
      "metadata": {
        "id": "0q756Et4bBZu"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# how much to branch\n",
        "degree = 5\n",
        "\n",
        "# number of segments\n",
        "num_init_lines = 3\n",
        "\n",
        "# keep the lines to draw in levels related to their degree\n",
        "lines_draw = {d: {s: {'x': [], 'y': []} for s in range(num_init_lines)} for d in range(degree)}\n",
        "\n",
        "# angles of the initial equilateral triangle\n",
        "sixty_degrees = np.pi / 3.\n",
        "\n",
        "# side length of the initial equilateral triangle\n",
        "s = 5.\n",
        "\n",
        "# vertices of the initial three lines of the triangle\n",
        "A = (0., 0.)\n",
        "B = (5, 0.)\n",
        "C = (s * np.cos(sixty_degrees), s * np.sin(sixty_degrees))\n",
        "\n",
        "# add initial lines\n",
        "init_line1 = line_function(A, B, 4**degree)\n",
        "lines_draw[0][0]['x'].extend(init_line1[0])\n",
        "lines_draw[0][0]['y'].extend(init_line1[1])\n",
        "\n",
        "init_line2 = line_function(B, C, 4**degree)\n",
        "lines_draw[0][1]['x'].extend(init_line2[0])\n",
        "lines_draw[0][1]['y'].extend(init_line2[1])\n",
        "\n",
        "init_line3 = line_function(C, A, 4**degree)\n",
        "lines_draw[0][2]['x'].extend(init_line3[0])\n",
        "lines_draw[0][2]['y'].extend(init_line3[1])\n",
        "\n",
        "for i in range(1, degree):\n",
        "    # generate koch lines for the current degree\n",
        "    koch_lines = koch_snowflake(i)\n",
        "    # how many lines per segment\n",
        "    num_lines_segment = len(koch_lines) // num_init_lines\n",
        "    for j in range(num_init_lines):\n",
        "        for k in range(num_lines_segment):\n",
        "            line = koch_lines[j * num_lines_segment + k]\n",
        "        \n",
        "            # generate functions for the lines\n",
        "            l1 = line_function(line['a'], line['b'], 4**(degree - i))\n",
        "            l2 = line_function(line['b'], line['c'], 4**(degree - i))\n",
        "            l3 = line_function(line['c'], line['d'], 4**(degree - i))\n",
        "            l4 = line_function(line['d'], line['e'], 4**(degree - i))\n",
        "\n",
        "            # level, segment, coordinate\n",
        "            lines_draw[i][j]['x'].extend(l1[0])\n",
        "            lines_draw[i][j]['y'].extend(l1[1])\n",
        "\n",
        "            lines_draw[i][j]['x'].extend(l2[0])\n",
        "            lines_draw[i][j]['y'].extend(l2[1])\n",
        "\n",
        "            lines_draw[i][j]['x'].extend(l3[0])\n",
        "            lines_draw[i][j]['y'].extend(l3[1])\n",
        "\n",
        "            lines_draw[i][j]['x'].extend(l4[0])\n",
        "            lines_draw[i][j]['y'].extend(l4[1])"
      ],
      "metadata": {
        "id": "NIP5x6RhbEaV"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# determine the min and max to set the limits in the animation\n",
        "min_x, max_x = float('inf'), float('-inf')\n",
        "min_y, max_y = float('inf'), float('-inf')\n",
        "for i in range(degree):\n",
        "    for j in range(num_init_lines):\n",
        "        min_x = min(min_x, np.min(lines_draw[i][j]['x']))\n",
        "        max_x = max(max_x, np.max(lines_draw[i][j]['x']))\n",
        "        \n",
        "        min_y = min(min_y, np.min(lines_draw[i][j]['y']))\n",
        "        max_y = max(max_y, np.max(lines_draw[i][j]['y']))"
      ],
      "metadata": {
        "id": "811oaCxnbHT1"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "from matplotlib.animation import FuncAnimation, PillowWriter "
      ],
      "metadata": {
        "id": "sX_ijhPCbeu-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!apt install imagemagick"
      ],
      "metadata": {
        "id": "L2eFYpkJboZs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# how many animation frames per level\n",
        "frames_per_level = 32\n",
        "# how many points will be drawn each frame\n",
        "points_per_frame = 1024 // frames_per_level\n",
        "total_frames = degree * frames_per_level\n",
        "\n",
        "# color for each level\n",
        "colors = ['#661D98', '#2CBDFE', '#47DBCD', '#F5B14C', '#960019']\n",
        "\n",
        "fig = plt.figure(figsize=(10, 10))\n",
        "\n",
        "# formatting options\n",
        "ax = plt.axes(xlim=(round(min_x) - 0.5, round(max_x) + 0.5), ylim=(round(min_y) - 0.5, round(max_y) + 0.5))\n",
        "ax.set_xticks([], [])\n",
        "ax.set_yticks([], [])\n",
        "text = ax.text(round(min_x) - 0.2, round(min_y) - 0.2, 'Merry Christmas! \\nWith Joy, \\nYohanes Nuwara', fontsize=18, fontfamily='fantasy')\n",
        "\n",
        "# initialize the three line segments\n",
        "line1, = ax.plot([], [], color=colors[0], alpha=1., lw=2)\n",
        "line2, = ax.plot([], [], color=colors[0], alpha=1., lw=2)\n",
        "line3, = ax.plot([], [], color=colors[0], alpha=1., lw=2)\n",
        "\n",
        "# initialze the leadinng points\n",
        "point1, = ax.plot([], [], color=colors[0], marker='o', ms=5)\n",
        "point2, = ax.plot([], [], color=colors[0], marker='o', ms=5)\n",
        "point3, = ax.plot([], [], color=colors[0], marker='o', ms=5)\n",
        "\n",
        "def animate(i):\n",
        "    global frames_per_level\n",
        "    global points_per_frame\n",
        "    global total_frames\n",
        "    \n",
        "    if i >= 160:\n",
        "        return line1, line2, line3, point1, point2, point3,\n",
        "    \n",
        "    # determine the current level\n",
        "    level = i // 32\n",
        "    \n",
        "    # level change\n",
        "    if i % 32 == 0:\n",
        "        # empty the data in the line segments\n",
        "        line1.set_data([], [])\n",
        "        line2.set_data([], [])\n",
        "        line3.set_data([], [])\n",
        "        \n",
        "        # change the color of the lines\n",
        "        line1.set_color(colors[level])\n",
        "        line2.set_color(colors[level])\n",
        "        line3.set_color(colors[level])\n",
        "        \n",
        "        # empty the data in the leading points\n",
        "        point1.set_data([], [])\n",
        "        point2.set_data([], [])\n",
        "        point3.set_data([], [])\n",
        "        \n",
        "        # change color of the leading points\n",
        "        point1.set_color(colors[level])\n",
        "        point2.set_color(colors[level])\n",
        "        point3.set_color(colors[level])\n",
        "    \n",
        "    # set data\n",
        "    line1.set_data(lines_draw[level][0]['x'][:((i % 32 + 1)*32)], lines_draw[level][0]['y'][:((i % 32 + 1)*32)])\n",
        "    line2.set_data(lines_draw[level][1]['x'][:((i % 32 + 1)*32)], lines_draw[level][1]['y'][:((i % 32 + 1)*32)])\n",
        "    line3.set_data(lines_draw[level][2]['x'][:((i % 32 + 1)*32)], lines_draw[level][2]['y'][:((i % 32 + 1)*32)])\n",
        "    \n",
        "    # set data\n",
        "    point1.set_data(lines_draw[level][0]['x'][((i % 32 + 1)*32) - 1], lines_draw[level][0]['y'][((i % 32 + 1)*32) - 1])\n",
        "    point2.set_data(lines_draw[level][1]['x'][((i % 32 + 1)*32) - 1], lines_draw[level][1]['y'][((i % 32 + 1)*32) - 1])\n",
        "    point3.set_data(lines_draw[level][2]['x'][((i % 32 + 1)*32) - 1], lines_draw[level][2]['y'][((i % 32 + 1)*32) - 1])\n",
        "    \n",
        "    return line1, line2, line3, point1, point2, point3,\n",
        "\n",
        "# call the animator\t \n",
        "anim = animation.FuncAnimation(fig, animate, frames=total_frames + 64, interval=80, blit=True)\n",
        "# save the animation as mp4 video file \n",
        "anim.save('snowflake.gif',writer='imagemagick') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "LbJBHKpZbKOA",
        "outputId": "fce5d829-9fef-4e2d-f609-54e602ae1442"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}