Add Solution for 14

14/14.ipynb

@@ -0,0 +1,268 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "inputname = \"input\"\n",
+    "xdim = 101\n",
+    "ydim = 103\n",
+    "\n",
+    "def read_vectors():\n",
+    "    with open(inputname , 'r' ) as infile:\n",
+    "        for line in infile.readlines():\n",
+    "            p,v = (line.strip().split(\" \"))\n",
+    "            _,p = p.split('=')\n",
+    "            _,v = v.split('=')\n",
+    "            p = np.array(p.split(',')).astype(int)\n",
+    "            v = np.array(v.split(',')).astype(int)\n",
+    "            yield p,v\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_positions(vectors,time,xdim,ydim):\n",
+    "    for p,v in vectors:\n",
+    "        # new position = Postion plus velocity * time\n",
+    "        x,y = p+(time*v)\n",
+    "        # wrapping around\n",
+    "        x = (x % xdim + xdim) % xdim\n",
+    "        y = (y % ydim + ydim) % ydim\n",
+    "        yield x,y\n",
+    "\n",
+    "\n",
+    "def count_quadrants(vectors,xdim,ydim):\n",
+    "    vectors=np.array(vectors,dtype=int)\n",
+    "    xmid=xdim//2\n",
+    "    ymid=ydim//2\n",
+    "    top_right = (vectors[:,0]>xmid) & (vectors[:,1]>ymid)\n",
+    "    top_left = (vectors[:,0]<xmid) & (vectors[:,1]>ymid)\n",
+    "    bottom_left = (vectors[:,0]<xmid) & (vectors[:,1]<ymid)\n",
+    "    bottom_right = (vectors[:,0]>xmid) & (vectors[:,1]<ymid)\n",
+    "    counts = {\n",
+    "        \"top_right\": np.sum(top_right),\n",
+    "        \"top_left\": np.sum(top_left),\n",
+    "        \"bottom_left\": np.sum(bottom_left),\n",
+    "        \"bottom_right\": np.sum(bottom_right),\n",
+    "    }\n",
+    "    \n",
+    "    return counts\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "positions = [ (x,y) for x,y in calc_positions(read_vectors(),100,xdim,ydim)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(231221760)"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import math\n",
+    "math.prod(count_quadrants(positions,xdim,ydim).values())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Part 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.ndimage import label\n",
+    "\n",
+    "def pos_to_binmap(positions,xdim,ydim):\n",
+    "    temp_map = np.full((xdim,ydim),False)\n",
+    "    for x,y in positions:\n",
+    "        temp_map[x,y]=True\n",
+    "    return temp_map\n",
+    " \n",
+    "\n",
+    "def biggest_cluster(positions,xdim,ydim):\n",
+    "    grid = np.array(positions) # Convert to numpy array if not already\n",
+    "    binary_map = pos_to_binmap(grid,xdim,ydim)\n",
+    "    labeled_map, num_features = label(binary_map)\n",
+    "    maxsize = 0\n",
+    "    for cluster_id in range(1, num_features + 1):\n",
+    "        cluster_mask = (labeled_map == cluster_id) # Mask for current cluster\n",
+    "        size = cluster_mask.sum() # Size of the cluster\n",
+    "        if size > maxsize:\n",
+    "            maxsize = size\n",
+    "    \n",
+    "    return maxsize\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def draw_bitmap_map(points, xdim, ydim, title=None):\n",
+    "    # Initialize a bitmap with all zeros\n",
+    "    bitmap = np.zeros((ydim, xdim), dtype=int)\n",
+    "    \n",
+    "    # Mark positions based on the points\n",
+    "    for point in points:\n",
+    "        # Ensure the point is within the grid bounds\n",
+    "        x, y = point\n",
+    "        if 0 <= x < xdim and 0 <= y < ydim:\n",
+    "            bitmap[y, x] = 1  # Mark the position with a 1\n",
+    "    \n",
+    "    # Plot the bitmap as an image\n",
+    "    plt.imshow(bitmap, cmap='Greys', interpolation='nearest')\n",
+    "    if title:\n",
+    "        plt.title(title)\n",
+    "    plt.show()\n",
+    "\n",
+    "positions = [ (x,y) for x,y in calc_positions(read_vectors(),100,xdim,ydim)]\n",
+    "draw_bitmap_map(positions, xdim, ydim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i=0\n",
+    "oldmax=0\n",
+    "while oldmax<100:\n",
+    "    i+=1\n",
+    "    positions = [ (x,y) for x,y in calc_positions(read_vectors(),i,xdim,ydim)]\n",
+    "    max=biggest_cluster(positions,xdim,ydim)\n",
+    "    if max>oldmax:\n",
+    "        draw_bitmap_map(positions,xdim,ydim,f\"cluster of {max} pixels found in step {i}\")\n",
+    "        oldmax=max\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pdf",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

14/input

@@ -0,0 +1,500 @@
+p=81,85 v=-35,88
+p=89,76 v=-33,35
+p=79,21 v=92,-25
+p=64,67 v=-30,42
+p=18,21 v=31,52
+p=1,23 v=10,-13
+p=36,24 v=60,13
+p=89,34 v=-38,-51
+p=70,91 v=-82,96
+p=28,52 v=31,-5
+p=96,102 v=-39,86
+p=29,32 v=-71,52
+p=72,71 v=-5,-65
+p=93,26 v=-64,-55
+p=40,97 v=73,-50
+p=39,2 v=39,21
+p=45,49 v=-80,72
+p=82,29 v=-9,37
+p=94,70 v=-67,95
+p=31,77 v=5,84
+p=23,19 v=47,23
+p=85,0 v=91,97
+p=72,4 v=42,47
+p=89,89 v=-25,29
+p=50,51 v=-86,40
+p=1,93 v=35,-22
+p=39,70 v=-74,-34
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+p=67,48 v=71,-16
+p=97,8 v=-41,-75
+p=96,64 v=-96,-93
+p=52,101 v=-79,9

14/testinput

@@ -0,0 +1,12 @@
+p=0,4 v=3,-3
+p=6,3 v=-1,-3
+p=10,3 v=-1,2
+p=2,0 v=2,-1
+p=0,0 v=1,3
+p=3,0 v=-2,-2
+p=7,6 v=-1,-3
+p=3,0 v=-1,-2
+p=9,3 v=2,3
+p=7,3 v=-1,2
+p=2,4 v=2,-3
+p=9,5 v=-3,-3