loleg/pelican_on_skis.ipynb

## pelican_on_skis.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56b70c78-9b51-4900-97d8-4b29776c38d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPython 3.13.11 interpreter at: \u001b[36m/usr/bin/python\u001b[39m\n",
      "Creating virtual environment at: \u001b[36m.venv\u001b[39m\n",
      "\u001b[2K\u001b[2mResolved \u001b[1m12 packages\u001b[0m \u001b[2min 625ms\u001b[0m\u001b[0m                                        \u001b[0m\n",
      "\u001b[2K\u001b[2mPrepared \u001b[1m3 packages\u001b[0m \u001b[2min 4.63s\u001b[0m\u001b[0m                                             \n",
      "\u001b[2K\u001b[2mInstalled \u001b[1m11 packages\u001b[0m \u001b[2min 224ms\u001b[0m\u001b[0m                              \u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mcontourpy\u001b[0m\u001b[2m==1.3.3\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mcycler\u001b[0m\u001b[2m==0.12.1\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mfonttools\u001b[0m\u001b[2m==4.61.1\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mkiwisolver\u001b[0m\u001b[2m==1.4.9\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mmatplotlib\u001b[0m\u001b[2m==3.10.8\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mnumpy\u001b[0m\u001b[2m==2.4.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mpackaging\u001b[0m\u001b[2m==25.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mpillow\u001b[0m\u001b[2m==12.0.0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mpyparsing\u001b[0m\u001b[2m==3.3.1\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1mpython-dateutil\u001b[0m\u001b[2m==2.9.0.post0\u001b[0m\n",
      " \u001b[32m+\u001b[39m \u001b[1msix\u001b[0m\u001b[2m==1.17.0\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%uv add matplotlib numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "203094b4-f403-492c-8fd4-57377a86d7f7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.patches import Polygon, Circle, Rectangle\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "\"\"\"Create a beautiful Swiss Alpine winter scene with New Year celebration\"\"\"\n",
    "\n",
    "# Set up the figure with a winter sky background\n",
    "fig, ax = plt.subplots(figsize=(12, 8), facecolor='#B0C4DE')\n",
    "ax.set_xlim(0, 10)\n",
    "ax.set_ylim(0, 8)\n",
    "ax.set_aspect('equal')\n",
    "ax.axis('off')\n",
    "\n",
    "# Winter sky gradient\n",
    "sky_colors = ['#B0C4DE', '#E6E6FA', '#F0F8FF']\n",
    "for i, color in enumerate(sky_colors):\n",
    "    y_start = 8 - i * 2.5\n",
    "    rect = Rectangle((0, y_start), 10, 2.5, facecolor=color, alpha=0.8)\n",
    "    ax.add_patch(rect)\n",
    "\n",
    "# Winter sun\n",
    "sun = Circle((8.5, 6.5), 0.4, facecolor='#FFFACD', edgecolor='#F0E68C', linewidth=2)\n",
    "ax.add_patch(sun)\n",
    "\n",
    "# Mountains (Matterhorn-inspired with more snow)\n",
    "# Main mountain\n",
    "mountain_points = [\n",
    "    (2, 2), (3.5, 5.5), (4, 5.2), (5, 2)\n",
    "]\n",
    "mountain = Polygon(mountain_points, facecolor='#8B7D6B', edgecolor='#696969', linewidth=1)\n",
    "ax.add_patch(mountain)\n",
    "\n",
    "# Extended snow cap on main mountain\n",
    "snow_points = [\n",
    "    (3.0, 4.0), (3.5, 5.5), (4.0, 4.0)\n",
    "]\n",
    "snow_cap = Polygon(snow_points, facecolor='#FFFFFF', edgecolor='#E0E0E0', linewidth=0.5)\n",
    "ax.add_patch(snow_cap)\n",
    "\n",
    "# Second mountain\n",
    "mountain2_points = [\n",
    "    (5, 2), (6, 4.5), (7, 2)\n",
    "]\n",
    "mountain2 = Polygon(mountain2_points, facecolor='#A0522D', edgecolor='#8B4513', linewidth=1)\n",
    "ax.add_patch(mountain2)\n",
    "\n",
    "# Extended snow on second mountain\n",
    "snow2_points = [\n",
    "    (5.5, 3.5), (6, 4.5), (6.5, 3.5)\n",
    "]\n",
    "snow2_cap = Polygon(snow2_points, facecolor='#FFFFFF', edgecolor='#E0E0E0', linewidth=0.5)\n",
    "ax.add_patch(snow2_cap)\n",
    "\n",
    "# Frozen lake (changed to white)\n",
    "lake = Rectangle((0, 0), 10, 2, facecolor='#FFFFFF', alpha=0.6, edgecolor='#E0E0E0', linewidth=1)\n",
    "ax.add_patch(lake)\n",
    "\n",
    "# Ice reflection patterns on frozen lake\n",
    "for i in range(8):\n",
    "    reflection = Rectangle((i*1.2 + 0.3, 0.2), 0.8, 0.15, \n",
    "                         facecolor='#F0F8FF', alpha=0.5)\n",
    "    ax.add_patch(reflection)\n",
    "\n",
    "# Ground (brown thin rectangle overlapping house and trees)\n",
    "ground = Rectangle((0, 2), 10, 0.3, facecolor='#8B4513', alpha=0.8)\n",
    "ax.add_patch(ground)\n",
    "\n",
    "# Snow covering the ground\n",
    "snow_ground = Rectangle((0, 2.15), 10, 0.15, facecolor='#FFFFFF', alpha=0.9)\n",
    "ax.add_patch(snow_ground)\n",
    "\n",
    "# Swiss chalet\n",
    "chalet_x, chalet_y = 1.5, 2.1\n",
    "\n",
    "# Chalet base\n",
    "chalet_base = Rectangle((chalet_x, chalet_y), 1.2, 0.8, \n",
    "                       facecolor='#8B4513', edgecolor='#654321', linewidth=1)\n",
    "ax.add_patch(chalet_base)\n",
    "\n",
    "# Snow on chalet roof edges\n",
    "roof_snow = Rectangle((chalet_x + 0.1, chalet_y + 0.75), 1.0, 0.05, \n",
    "                     facecolor='#FFFFFF', alpha=0.9)\n",
    "ax.add_patch(roof_snow)\n",
    "\n",
    "# Chalet roof\n",
    "roof_points = [\n",
    "    (chalet_x - 0.1, chalet_y + 0.8),\n",
    "    (chalet_x + 0.6, chalet_y + 1.4),\n",
    "    (chalet_x + 1.3, chalet_y + 0.8)\n",
    "]\n",
    "roof = Polygon(roof_points, facecolor='#8B0000', edgecolor='#660000', linewidth=1)\n",
    "ax.add_patch(roof)\n",
    "\n",
    "# Chalet windows\n",
    "window1 = Rectangle((chalet_x + 0.2, chalet_y + 0.3), 0.2, 0.2, \n",
    "                   facecolor='#87CEEB', edgecolor='#4682B4', linewidth=1)\n",
    "window2 = Rectangle((chalet_x + 0.8, chalet_y + 0.3), 0.2, 0.2, \n",
    "                   facecolor='#87CEEB', edgecolor='#4682B4', linewidth=1)\n",
    "ax.add_patch(window1)\n",
    "ax.add_patch(window2)\n",
    "\n",
    "# Chalet door\n",
    "door = Rectangle((chalet_x + 0.45, chalet_y), 0.3, 0.5, \n",
    "                facecolor='#654321', edgecolor='#4A3018', linewidth=1)\n",
    "ax.add_patch(door)\n",
    "\n",
    "# Swiss flag on chalet\n",
    "flag_pole = Rectangle((chalet_x + 1.3, chalet_y + 0.8), 0.05, 0.6, \n",
    "                     facecolor='#696969')\n",
    "ax.add_patch(flag_pole)\n",
    "\n",
    "# Flag background (red)\n",
    "flag_bg = Rectangle((chalet_x + 1.35, chalet_y + 1.2), 0.3, 0.2, \n",
    "                   facecolor='#FF0000', edgecolor='#CC0000', linewidth=1)\n",
    "ax.add_patch(flag_bg)\n",
    "\n",
    "# White cross on flag\n",
    "cross_vertical = Rectangle((chalet_x + 1.47, chalet_y + 1.22), 0.06, 0.16, \n",
    "                          facecolor='#FFFFFF')\n",
    "cross_horizontal = Rectangle((chalet_x + 1.38, chalet_y + 1.28), 0.24, 0.04, \n",
    "                            facecolor='#FFFFFF')\n",
    "ax.add_patch(cross_vertical)\n",
    "ax.add_patch(cross_horizontal)\n",
    "\n",
    "# Pine trees\n",
    "def draw_tree(x, y, scale=1.0):\n",
    "    # Tree trunk\n",
    "    trunk = Rectangle((x - 0.05*scale, y), 0.1*scale, 0.3*scale, \n",
    "                     facecolor='#8B4513', edgecolor='#654321')\n",
    "    ax.add_patch(trunk)\n",
    "    \n",
    "    # Tree layers (triangle shape) with snow\n",
    "    for i, height_factor in enumerate([0.4, 0.3, 0.2]):\n",
    "        tree_y = y + 0.3*scale + i*0.25*scale\n",
    "        tree_points = [\n",
    "            (x - 0.3*scale + i*0.05*scale, tree_y),\n",
    "            (x, tree_y + height_factor*scale),\n",
    "            (x + 0.3*scale - i*0.05*scale, tree_y)\n",
    "        ]\n",
    "        tree = Polygon(tree_points, facecolor='#228B22', edgecolor='#006400', linewidth=0.5)\n",
    "        ax.add_patch(tree)\n",
    "        \n",
    "        # Snow on tree branches\n",
    "        if i == 0:  # Snow on top layer\n",
    "            snow_points = [\n",
    "                (x - 0.2*scale + i*0.05*scale, tree_y + height_factor*scale - 0.05*scale),\n",
    "                (x, tree_y + height_factor*scale),\n",
    "                (x + 0.2*scale - i*0.05*scale, tree_y + height_factor*scale - 0.05*scale)\n",
    "            ]\n",
    "            snow_on_tree = Polygon(snow_points, facecolor='#FFFFFF', alpha=0.8)\n",
    "            ax.add_patch(snow_on_tree)\n",
    "\n",
    "# Add several pine trees\n",
    "draw_tree(6.5, 2.1, 0.8)\n",
    "draw_tree(7.2, 2.1, 0.6)\n",
    "draw_tree(3.5, 2.1, 0.7)\n",
    "draw_tree(0.8, 2.1, 0.5)\n",
    "\n",
    "# Pelican on skis\n",
    "pelican_x, pelican_y = 5, 1\n",
    "\n",
    "# Pelican head (black beak and eye)\n",
    "beak = Polygon([(pelican_x - 0.7, pelican_y - 0.2), \n",
    "                (pelican_x + 0.5, pelican_y - 0.2), \n",
    "                (pelican_x + 0.2, pelican_y)], \n",
    "               facecolor='#000000', edgecolor='#000000', alpha=0.8)\n",
    "ax.add_patch(beak)\n",
    "\n",
    "eye = Circle((pelican_x, pelican_y - 0.4), 0.2, \n",
    "             facecolor='black', edgecolor='black', alpha=0.8)\n",
    "ax.add_patch(eye)\n",
    "\n",
    "# Cross-country skis (blue and white)\n",
    "ski1_points = [(pelican_x - 0.5, pelican_y - 0.55), \n",
    "               (pelican_x + 0.5, pelican_y - 0.58), \n",
    "               (pelican_x + 0.55, pelican_y - 0.55), \n",
    "               (pelican_x - 0.5, pelican_y - 0.58)]\n",
    "ski1 = Polygon(ski1_points, facecolor='#307087', \n",
    "               edgecolor='#000000', linewidth=0.5, alpha=0.5)\n",
    "ax.add_patch(ski1)\n",
    "\n",
    "ski2_points = [(pelican_x - 0.5, pelican_y - 0.65), \n",
    "               (pelican_x + 0.5, pelican_y - 0.68), \n",
    "               (pelican_x + 0.55, pelican_y - 0.65), \n",
    "               (pelican_x - 0.5, pelican_y - 0.68)]\n",
    "ski2 = Polygon(ski2_points, facecolor='#307087', \n",
    "               edgecolor='#000000', linewidth=0.5, alpha=0.5)\n",
    "ax.add_patch(ski2)\n",
    "\n",
    "\n",
    "# Snowflakes falling\n",
    "np.random.seed(42)  # For consistent snowflake positions\n",
    "num_snowflakes = 50\n",
    "for _ in range(num_snowflakes):\n",
    "    x = np.random.uniform(0, 10)\n",
    "    y = np.random.uniform(0.5, 7.5)\n",
    "    size = np.random.uniform(0.02, 0.08)\n",
    "    snowflake = Circle((x, y), size, facecolor='#FFFFFF', alpha=0.8)\n",
    "    ax.add_patch(snowflake)\n",
    "\n",
    "# Add some larger decorative snowflakes\n",
    "special_snowflake_positions = [(1.5, 4), (4, 3.5), (5, 5), (7, 4.5), (8.5, 3)]\n",
    "for x, y in special_snowflake_positions:\n",
    "    # Create a 6-pointed snowflake\n",
    "    for angle in range(0, 360, 60):\n",
    "        rad = np.radians(angle)\n",
    "        x_end = x + 0.15 * np.cos(rad)\n",
    "        y_end = y + 0.15 * np.sin(rad)\n",
    "        ax.plot([x, x_end], [y, y_end], 'white', linewidth=2, alpha=0.9)\n",
    "    # Center dot\n",
    "    center = Circle((x, y), 0.03, facecolor='#FFFFFF')\n",
    "    ax.add_patch(center)\n",
    "\n",
    "# New Year title\n",
    "ax.text(5, 7, 'Happy New Year!', fontsize=60, fontweight='bold', \n",
    "       ha='center', color='white')\n",
    "\n",
    "# Add some winter clouds\n",
    "for i in range(3):\n",
    "    cloud_x = 2 + i * 2.5\n",
    "    cloud_y = 6 + i * 0.3\n",
    "    for j in range(3):\n",
    "        cloud = Circle((cloud_x + j*0.2, cloud_y), 0.15, \n",
    "                     facecolor='white', alpha=0.8)\n",
    "        ax.add_patch(cloud)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
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