pct_diagram.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM2sEa7BRTps1gAaAFMiGYX",
      "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/github/ruperty/colab/blob/master/pct_diagram.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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "-u97UABp9r_f",
        "outputId": "dc3b0539-d7cb-4974-8713-5d8050134746"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "import numpy as np\n",
        "\n",
        "class IsoscelesTriangleDiagram:\n",
        "    def __init__(self, base_length=1, position=(0, 0), circle_colors=('lightblue', 'red', 'green'),\n",
        "                 line_style='--', circle_relative_size=0.1):\n",
        "        self.base_length = base_length\n",
        "        self.position = position\n",
        "        self.height = np.sqrt((self.base_length ** 2) - (self.base_length / 2) ** 2)\n",
        "        self.circle_radius = self.base_length * circle_relative_size\n",
        "        self.dash_pattern = (10, 5) if line_style == '--' else None\n",
        "        self.circle_colors = circle_colors\n",
        "        self.line_style = line_style\n",
        "\n",
        "    def create_patches(self):\n",
        "        patches_list = []\n",
        "\n",
        "        # Calculate vertices based on base length and position\n",
        "        x0, y0 = self.position\n",
        "        vertices = [\n",
        "            (x0, y0),\n",
        "            (x0 + self.base_length, y0),\n",
        "            (x0 + self.base_length / 2, y0 + self.height)\n",
        "        ]\n",
        "\n",
        "        # Define the circle properties\n",
        "        circle_properties = [\n",
        "            (vertices[0], self.circle_colors[0]),  # Bottom-left\n",
        "            (vertices[2], self.circle_colors[1]),  # Top\n",
        "            (vertices[1], self.circle_colors[2])   # Bottom-right\n",
        "        ]\n",
        "\n",
        "        # Add circles to the vertices\n",
        "        for (vertex, color) in circle_properties:\n",
        "            circle = patches.Circle(vertex, radius=self.circle_radius, color=color, zorder=1)\n",
        "            patches_list.append(circle)\n",
        "\n",
        "        # Create the isosceles triangle\n",
        "        triangle = patches.Polygon(\n",
        "            vertices, closed=True, fill=True, edgecolor='darkblue', linestyle=self.line_style, linewidth=2, facecolor='white', zorder=2\n",
        "        )\n",
        "        if self.dash_pattern:\n",
        "            triangle.set_linestyle((0, self.dash_pattern))  # Set custom dash pattern if needed\n",
        "        patches_list.append(triangle)\n",
        "\n",
        "        return patches_list\n",
        "\n",
        "# Function to display multiple objects\n",
        "def display_diagrams(diagrams, save_path='isosceles_triangle_diagram.png'):\n",
        "    fig, ax = plt.subplots()\n",
        "\n",
        "    for diagram in diagrams:\n",
        "        patches_list = diagram.create_patches()\n",
        "        for patch in patches_list:\n",
        "            ax.add_patch(patch)\n",
        "\n",
        "    # Set the aspect ratio and limits\n",
        "    ax.set_aspect('equal')\n",
        "    buffer = max(d.base_length for d in diagrams) * 0.1\n",
        "    min_x = min(d.position[0] for d in diagrams) - buffer\n",
        "    max_x = max(d.position[0] + d.base_length for d in diagrams) + buffer\n",
        "    min_y = min(d.position[1] for d in diagrams) - buffer\n",
        "    max_y = max(d.position[1] + d.height for d in diagrams) + buffer\n",
        "    ax.set_xlim(min_x, max_x)\n",
        "    ax.set_ylim(min_y, max_y)\n",
        "\n",
        "    # Remove axis\n",
        "    ax.axis('off')\n",
        "\n",
        "    # Save the diagram\n",
        "    plt.savefig(save_path, bbox_inches='tight')\n",
        "    plt.show()\n",
        "\n",
        "# Example usage:\n",
        "triangle_diagram1 = IsoscelesTriangleDiagram(\n",
        "    base_length=2,\n",
        "    position=(0, 0),\n",
        "    circle_colors=('cyan', 'magenta', 'yellow'),\n",
        "    line_style='-',\n",
        "    circle_relative_size=0.05\n",
        ")\n",
        "triangle_diagram2 = IsoscelesTriangleDiagram(\n",
        "    base_length=1,\n",
        "    position=(3, 1),\n",
        "    circle_colors=('blue', 'orange', 'purple'),\n",
        "    line_style='--',\n",
        "    circle_relative_size=0.1\n",
        ")\n",
        "display_diagrams([triangle_diagram1, triangle_diagram2])\n"
      ]
    }
  ]
}