kylepls/rubiks-gen.ipynb

## rubiks-gen.ipynb
{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from enum import Enum\n",
        "from labellines import labelLines\n",
        "\n",
        "\n",
        "class CircleType(Enum):\n",
        "  EMPTY = 0,\n",
        "  FILLED = 1\n",
        "  \n",
        "class Glyph:\n",
        "  def __init__(self, name, color, textOffset):\n",
        "    self.color = color\n",
        "    self.textOffset = textOffset\n",
        "    self.name = name\n",
        "    \n",
        "class Arrow(Glyph):\n",
        "  def __init__(self, name, direction, color, textOffset):\n",
        "    super().__init__(name, color, textOffset)\n",
        "    self.direction = direction\n",
        "\n",
        "class Circle(Glyph):\n",
        "  def __init__(self, name, circleType, color, textOffset):\n",
        "    super().__init__(name, color, textOffset)\n",
        "    self.circleType = circleType\n",
        "    \n",
        "mappings = {\n",
        "  \"U\": Arrow(\"U\", (0, 1), \"black\", (0.15, -0.1)),\n",
        "  \"U'\": Arrow(\"U'\", (0, -1), \"black\", (0.15, -0.1)),\n",
        "  \"U2'\": Arrow(\"U2'\", (0, -2), \"black\", (0.15, -0.1)),\n",
        "  \"U2\": Arrow(\"U2\", (0, 2), \"black\", (0.15, -0.1)),\n",
        "  \"D\": Circle(\"D\", CircleType.EMPTY, \"purple\", (0,0)),\n",
        "  \"D2\": Circle(\"D2\", CircleType.EMPTY, \"purple\", (0,0)),\n",
        "  \"D'\": Circle(\"D'\", CircleType.FILLED, \"purple\", (0,0)),\n",
        "  \"D2'\": Circle(\"D2'\", CircleType.FILLED, \"purple\", (0,0)),\n",
        "  \"L\": Arrow(\"L\", (-1, 1), \"blue\", (0.05, 0.15)),\n",
        "  \"L'\": Arrow(\"L'\", (-0.8, -1), \"blue\", (0.1, -0.2)),\n",
        "  \"R\": Arrow(\"R\", (1, 1), \"red\", (0.15,0.05)),\n",
        "  \"R2\": Arrow(\"R2\", (2, 2), \"red\", (0.15,0.05)),\n",
        "  \"R'\": Arrow(\"R'\", (0.8, -1), \"red\", (0.05,-0.3)),\n",
        "  \"F\": Arrow(\"F\", (1, 0), \"green\", (0, -0.3)),\n",
        "  \"F'\": Arrow(\"F'\", (-1, 0), \"green\", (0, -0.3))\n",
        "}\n",
        "\n",
        "def get_mapping(step):\n",
        "  if step not in mappings and (step[-1] == \"2\"):\n",
        "    str_step = step[0:-1]\n",
        "    print(step, \"=>\", mappings[str_step])\n",
        "    return [mappings[str_step]]*2\n",
        "  else:\n",
        "    return [mappings[step]]\n",
        "\n",
        "def get_steps(alg):\n",
        "  steps = alg.split(\" \")\n",
        "  steps = [m for s in steps for m in get_mapping(s)]\n",
        "  return steps\n",
        "\n",
        "\n",
        "def draw(alg):\n",
        "  alg = alg.replace(\"[\", \"\").replace(\"]\", \"\")\n",
        "  plt.figure(figsize=(10, 10))\n",
        "  x = 0\n",
        "  y = 0\n",
        "  dx = 1\n",
        "  dy = 0\n",
        "  start=plt.scatter(0,0, color='cyan', s=200)\n",
        "  start.set_zorder(100)\n",
        "  stepIndex=0\n",
        "  for step in get_steps(alg):\n",
        "    color = step.color\n",
        "    (offsetX, offsetY) = step.textOffset\n",
        "    width = 0.03\n",
        "    label = step.name\n",
        "    if isinstance(step, Arrow):\n",
        "      (dx, dy) = step.direction\n",
        "      line = plt.plot([x, x+dx], [y, y+dy], alpha=1, color=color)\n",
        "      arrow = plt.arrow(x, y, dx, dy, color=color, width=width, length_includes_head=True, overhang=0.05, head_width=5*width)\n",
        "      arrow.set_zorder(stepIndex)\n",
        "    elif isinstance(step, Circle):\n",
        "      if dx != 0:\n",
        "        dy = 0\n",
        "      else:\n",
        "        dx = 0\n",
        "\n",
        "      fill = True if step.circleType == CircleType.FILLED else False\n",
        "\n",
        "      circle = plt.Circle((x+dx/2, y+dy/2), (dx+dy)/2, fill=fill, color=color, lw=width*100)\n",
        "      circle.set_zorder(stepIndex)\n",
        "      line = plt.plot([x-1, x+1], [y-1, y+1], alpha=0, color=color) # For graph boundaries\n",
        "      plt.gca().add_artist(circle)\n",
        "\n",
        "      if fill:\n",
        "        color = 'black'\n",
        "\n",
        "    plt.annotate(label, xy=(dx/2+x+offsetX, y+dy/2+offsetY), color=color, fontsize=20, ha='center', va='center')\n",
        "    x += dx\n",
        "    y += dy\n",
        "    stepIndex += 1\n",
        "\n",
        "  plt.gca().set_aspect('equal', adjustable='box')\n",
        "\n",
        "  plt.show()\n",
        "  "
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2024-02-11T08:05:15.077Z",
          "iopub.execute_input": "2024-02-11T08:05:15.080Z",
          "iopub.status.idle": "2024-02-11T08:05:15.572Z",
          "shell.execute_reply": "2024-02-11T08:05:15.568Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alg = \"[R U' R] U R U R U' R' U' R2\"\n",
        "# alg = \"R U2 R' U' R U R' U' R U' R'\"\n",
        "draw(alg)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x720 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2024-02-11T08:05:17.425Z",
          "iopub.execute_input": "2024-02-11T08:05:17.429Z",
          "shell.execute_reply": "2024-02-11T08:05:17.615Z",
          "iopub.status.idle": "2024-02-11T08:05:17.612Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.2",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "python",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}