From 52399f80ec843886ea5eb22e0ca0125602f59b8b Mon Sep 17 00:00:00 2001
From: Peter Norvig <peter.norvig@gmail.com>
Date: Fri, 12 Dec 2025 00:02:12 -0800
Subject: [PATCH] Add files via upload

---
 ipynb/Advent-2025.ipynb | 739 +++++++++++++++++++++++++++++-----------
 ipynb/tiles2025.png     | Bin 0 -> 53625 bytes
 2 files changed, 531 insertions(+), 208 deletions(-)
 create mode 100644 ipynb/tiles2025.png

diff --git a/ipynb/Advent-2025.ipynb b/ipynb/Advent-2025.ipynb
index 6d0d997..b637e67 100644
--- a/ipynb/Advent-2025.ipynb
+++ b/ipynb/Advent-2025.ipynb
@@ -184,7 +184,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  1.2:   .1417 seconds, answer 6907            correct"
+       "Puzzle  1.2:   .1463 seconds, answer 6907            correct"
       ]
      },
      "execution_count": 6,
@@ -234,7 +234,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  1.2:   .0009 seconds, answer 6907            correct"
+       "Puzzle  1.2:   .0010 seconds, answer 6907            correct"
       ]
      },
      "execution_count": 8,
@@ -379,7 +379,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  2.1:   .0033 seconds, answer 23560874270     correct"
+       "Puzzle  2.1:   .0029 seconds, answer 23560874270     correct"
       ]
      },
      "execution_count": 12,
@@ -463,7 +463,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  2.1:   .0027 seconds, answer 23560874270     correct"
+       "Puzzle  2.1:   .0029 seconds, answer 23560874270     correct"
       ]
      },
      "execution_count": 15,
@@ -485,7 +485,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  2.2:   .0036 seconds, answer 44143124633     correct"
+       "Puzzle  2.2:   .0038 seconds, answer 44143124633     correct"
       ]
      },
      "execution_count": 16,
@@ -693,7 +693,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  3.2:   .0019 seconds, answer 169408143086082 correct"
+       "Puzzle  3.2:   .0024 seconds, answer 169408143086082 correct"
       ]
      },
      "execution_count": 24,
@@ -792,7 +792,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  4.1:   .0538 seconds, answer 1569            correct"
+       "Puzzle  4.1:   .0553 seconds, answer 1569            correct"
       ]
      },
      "execution_count": 27,
@@ -843,7 +843,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  4.2:  1.2313 seconds, answer 9280            correct"
+       "Puzzle  4.2:  1.2531 seconds, answer 9280            correct"
       ]
      },
      "execution_count": 29,
@@ -872,7 +872,7 @@
    "outputs": [],
    "source": [
     "def removable_rolls(grid: Grid) -> Iterable[Point]:\n",
-    "    \"\"\"The positions of paper rolls that can be removed, in any nuber of iterations.\"\"\"\n",
+    "    \"\"\"The positions of paper rolls that can be removed, in any number of iterations.\"\"\"\n",
     "    grid2 = grid.copy() # To avoid mutating the original input grid\n",
     "    Q = list(grid)      # A queue of possibly removable positions in the grid\n",
     "    while Q:\n",
@@ -892,7 +892,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  4.2:   .1409 seconds, answer 9280            correct"
+       "Puzzle  4.2:   .1394 seconds, answer 9280            correct"
       ]
      },
      "execution_count": 31,
@@ -905,6 +905,49 @@
     "       quantify(removable_rolls(paper_grid)))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4aae3157-9c06-40d1-b0cd-c6f5515b0064",
+   "metadata": {},
+   "source": [
+    "Let's visualize the paper rolls before after removal:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "402e78e0-cf05-4285-bd9c-07b6ebd9b602",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "before = set(paper_grid.findall('@'))\n",
+    "after  = before - set(removable_rolls(paper_grid))\n",
+    "\n",
+    "naked_plot(before, 'o', markersize=2)\n",
+    "naked_plot(after,  'o', markersize=2)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1f9a1e40-192a-4386-8cfb-bc5f69c88a9b",
@@ -917,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "id": "4b508db5-aeae-410e-a062-1b2d4ce41253",
    "metadata": {},
    "outputs": [
@@ -967,7 +1010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "id": "ae5b33e4-92f5-4fe2-bd5a-6b22095724fa",
    "metadata": {},
    "outputs": [],
@@ -980,17 +1023,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "id": "112a84f7-9bb8-45f0-9d7e-f600f37f0fdf",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  5.1:   .0073 seconds, answer 635             correct"
+       "Puzzle  5.1:   .0123 seconds, answer 635             correct"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1014,7 +1057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "id": "47a660d2-746c-433f-b4ae-9d01f70bc504",
    "metadata": {},
    "outputs": [
@@ -1024,7 +1067,7 @@
        "476036797138761"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1043,7 +1086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "id": "f52e8ecd-325e-4ed4-8928-de9365b5b7d4",
    "metadata": {},
    "outputs": [],
@@ -1061,17 +1104,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "id": "fab6f1ed-543f-4f42-b1d5-2551742e3a4f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  5.2:   .0001 seconds, answer 369761800782619 correct"
+       "Puzzle  5.2:   .0002 seconds, answer 369761800782619 correct"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1095,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "id": "bca852a9-e4c5-4706-abfc-afc6d4a4eeb5",
    "metadata": {},
    "outputs": [
@@ -1130,7 +1173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "id": "92f320f4-c7a3-4dfd-ae61-6b1b180bdc93",
    "metadata": {},
    "outputs": [],
@@ -1145,17 +1188,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "id": "92a47f3a-127f-41e8-bee1-6276885bd36b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  6.1:   .0015 seconds, answer 5877594983578   correct"
+       "Puzzle  6.1:   .0022 seconds, answer 5877594983578   correct"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1192,7 +1235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "id": "5866583b-245d-4a43-a037-229bf35f1be2",
    "metadata": {},
    "outputs": [],
@@ -1228,17 +1271,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "id": "39a8fa78-946f-45aa-8d36-76883f4aeaff",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  6.2:   .0039 seconds, answer 11159825706149  correct"
+       "Puzzle  6.2:   .0063 seconds, answer 11159825706149  correct"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1258,7 +1301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "id": "a13dbe89-3178-433f-9a50-28ed9a2f8358",
    "metadata": {},
    "outputs": [
@@ -1268,7 +1311,7 @@
        "3263827"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1285,7 +1328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "id": "3aa0b661-367a-4316-905a-60f1d9e6df01",
    "metadata": {},
    "outputs": [
@@ -1298,7 +1341,7 @@
        " ['*  ', '+  ', '*  ', '+']]"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1309,7 +1352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "id": "13392d9c-fef8-4946-b945-48ad6bc7eca9",
    "metadata": {},
    "outputs": [
@@ -1322,7 +1365,7 @@
        " ('64 ', '23 ', '314', '+')]"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1333,7 +1376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "id": "e29df77c-7385-4ba9-a27b-4074a78a0d74",
    "metadata": {},
    "outputs": [
@@ -1343,7 +1386,7 @@
        "[1, 24, 356]"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1356,7 +1399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "id": "9a25ff50-f465-4de3-95ca-71b7a3d9569b",
    "metadata": {},
    "outputs": [],
@@ -1378,7 +1421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "id": "e85af831-b6cf-4073-a131-ce68debaea75",
    "metadata": {},
    "outputs": [
@@ -1419,7 +1462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "id": "d4d3bdbb-d8b2-4e22-adb4-c99a212a2ca7",
    "metadata": {},
    "outputs": [],
@@ -1440,17 +1483,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "id": "0e14ad5e-6871-4a9c-bd94-5a863c2341e0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  7.1:   .0007 seconds, answer 1681            correct"
+       "Puzzle  7.1:   .0010 seconds, answer 1681            correct"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1474,17 +1517,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "id": "c231059d-edad-4f1c-b584-e811fa8fad46",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  7.2:   .0014 seconds, answer 422102272495018 correct"
+       "Puzzle  7.2:   .0021 seconds, answer 422102272495018 correct"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1518,7 +1561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "id": "a5cc43b2-3c5d-4d28-8c94-112bbb5739e7",
    "metadata": {},
    "outputs": [
@@ -1575,12 +1618,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "id": "fb7744c9-105b-439b-aa57-caf93b8117b8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def greedy_connect(boxes, n=1000) -> Dict[Point, Tuple[Point, ...]]:\n",
+    "Circuit = Tuple[Point, ...]\n",
+    "\n",
+    "def greedy_connect(boxes, n=1000) -> Dict[Point, Circuit]:\n",
     "    \"\"\"Go through the `n` closest pairs of boxes, shortest first. \n",
     "    If two boxes can be connected to form a new circuit, do it.\"\"\"\n",
     "    circuits = {B: (B,) for B in boxes} # A dict of {box: circuit}\n",
@@ -1608,17 +1653,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "id": "337ab7d6-d142-4c56-a0a3-2b6cd06cd895",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  8.1:   .6008 seconds, answer 24360           correct"
+       "Puzzle  8.1:   .5834 seconds, answer 24360           correct"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1640,7 +1685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "id": "1096ded6-749e-4787-9b90-2de917b147c4",
    "metadata": {},
    "outputs": [],
@@ -1660,17 +1705,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "id": "58f06244-ca67-47c9-8ca5-3346a249e1fc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  8.2:   .6149 seconds, answer 2185817796      correct"
+       "Puzzle  8.2:   .6182 seconds, answer 2185817796      correct"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1700,7 +1745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "id": "2dba6fa4-a09b-4ab3-a163-0f94f98e82f2",
    "metadata": {},
    "outputs": [
@@ -1751,19 +1796,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "id": "75f2742e-9dbd-4005-a882-3d3931fb1b36",
    "metadata": {},
    "outputs": [],
    "source": [
-    "Corners = Tuple[Point, Point] # Type representing a rectangle as specifyied by two corners\n",
+    "Corners = Tuple[Point, Point] # Type representing a rectangle as specified by two corners\n",
     "\n",
     "def tile_area(corners: Corners):\n",
     "    \"\"\"Area, in tiles, of a rectangle formed by tiles at these corner positions.\"\"\"\n",
     "    (x1, y1), (x2, y2) = corners\n",
-    "    return (abs(x1 - x2) + 1) * (abs(y1 - y2) + 1)\n",
-    "\n",
-    "assert tile_area(((0, 0), (1, 1))) == 4"
+    "    return (abs(x1 - x2) + 1) * (abs(y1 - y2) + 1)"
    ]
   },
   {
@@ -1771,22 +1814,22 @@
    "id": "271f2f81-dc40-41db-9912-0c45f4d75dbb",
    "metadata": {},
    "source": [
-    "That's all there is to Part 1; just maximize the area over all combinations of two corners:"
+    "Now we just maximize the area over all combinations of two corners:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "id": "27f350c5-7866-4b7b-8d32-91512fcec5b9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  9.1:   .0264 seconds, answer 4772103936      correct"
+       "Puzzle  9.1:   .0370 seconds, answer 4772103936      correct"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1803,16 +1846,18 @@
    "source": [
     "### Part 2: What is the largest area of any rectangle that has only red and green tiles?\n",
     "\n",
-    "In Part 2 we pay attention to the **green** tiles on the floor. Every red tile is connected to the red tile before and after it (in the input list order) by a straight line of green tiles. (It is guaranteed this will always be a straight horizontal or vertical line.) The first red tile is also connected to the last red tile. This forms a closed polygon, and the interior of the polygon is also all green. (The color of the tiles outside of the polygon is not stated, but I'm saying  white.) The elves want to know: What is the largest area of any rectangle that consists of only red and green tiles?\n",
+    "In Part 2 we pay attention to the **green** tiles on the floor. Every red tile is connected to the red tile before and after it (in the input list order) by a straight line of green tiles. (It is guaranteed this will always be a straight horizontal or vertical line.) The first red tile is also connected to the last red tile. This forms a closed polygon, and the interior of the polygon is also all green. (The color of the tiles outside of the polygon is not stated, but I'm going to say  \"white.\") The elves want to know: What is the largest area of any rectangle that consists of only red and green tiles?\n",
     "\n",
-    "**This is a tough one!** More difficult than all the previous puzzles. There are only 496 red tiles, so enumerating all pairs of them in Part 1 was easy. But there are roughly 100,000<sup>2</sup>or 10 billion total tiles, so filling in all the green tiles and checking them for each pair of corners would be too slow. \n",
+    "**This is a tough one!** More difficult than all the previous puzzles. There are only 496 red tiles, so enumerating all pairs of them in Part 1 was easy. But there are roughly 100,000<sup>2</sup>or 10 billion total tiles, so doing something like a [flood fill](https://en.wikipedia.org/wiki/Flood_fill) for all the green tiles and checking against them for each pair of corners would be too slow. \n",
     "\n",
-    "To get some ideas for what to try, I really want to see what the red and green tiles look like. I'll plot the border tiles, but not the interior tiles:"
+    "I really want to see what the red and green tiles look like! \n",
+    "\n",
+    "I'll plot the border tiles, but not the interior tiles:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "id": "b5967a65-338d-4198-9f59-986ff74132e6",
    "metadata": {},
    "outputs": [
@@ -1844,31 +1889,33 @@
    "source": [
     "**Very Interesting!** Here's what I'm thinking:\n",
     "- Most of the lines of green tiles are very short, except for the two long lines across the \"equator.\"\n",
-    "- A red-and-green rectangle can't cross the two equator lines, because there are white tiles between them.\n",
-    "- Therefore it seems that one of the corners of the maximal rectangle has to be one of the two points on the east end of the equator lines, and the other corner has to be somewhere on the left side of the circle, in the same semi-circle as the first corner.\n",
+    "- A maximal-area red-and-green rectangle can't cross the two equator lines, because there are white tiles between them.\n",
+    "- Therefore one of the corners of the maximal rectangle has to be one of the two points on the east end of the equator lines, and the other corner has to be somewhere on the left side of the circle.\n",
     "- The points are all roughly in a circle, so we're  looking for a rectangle roughly inscribed in the circle.\n",
-    "- A roughly correct way to check if a candidate inscribed rectangle is all red-and-green is to see if it contains a red tile in the interior.\n",
-    "- To be more precise, it would be ok if a red tile is on the border of the rectangle. It would also be ok if the red tile is part of a line that only goes one square in from the border. That would just mean more green on the inside. But if the red tile is two squares in from the border, then there must be a white square on at least one side of it, and thus a white square insiude the rectange. \n",
-    "- This red-tile-in-interior heuristic by itself isn't enough to stop a candidate rectangle from crossing over the long equator lines. Or detect a line that starts on the border or one square in and then crosses all the way across the rectangle. We can fix that by estimating the side-length of the rectangle we are looking for and then altering the set of red tiles by inserting some more red tiles on any lines that are longer than that. Then, any rectangle of sufficient size that crosses a long line will contain a red tile in the interior. \n",
+    "- A roughly correct way to check if a candidate rectangle is all red-and-green is to see if **the rectangle contains a red tile** in its interior.\n",
+    "- To be more precise, consider the diagram below, in which the two large red circles mark the corners of a rectangle depicted with small purple squares. I have filled in the green tiles that connect the red tiles to form a polygon, and used light green for the interior of the polygon. Is the purple rectangle valid? It is ok that there are two other red tiles on the bottom border; they don't let a white square in. It is ok that there are two red tiles at the top that extend one square into the rectangle; they still don't let a white square in. But the red tile near the bottom right corner is two squares in from the border, and any red tiles in that position let in white squares (here three of them in the bottom right of the rectangle. The only exception is when there are two adjacent red tiles that form a 180 degree turn (as in the left of the rectangle); they do intrude into the rectangle, but because they are adjacent there are no white squares between them. \n",
+    "- <img src=\"tiles2025.png\" width=300>\n",
+    "- To deal with the two-adjacent-red-tile problem, I will verify that there are no adjacent red tiles in *my* input. I bet Eric Wastl designed it so that nobody gets two adjacent tiles, but they aren't explicitly forbidden in the rules.\n",
+    "- This red-tile-in-interior heuristic by itself isn't enough to stop a candidate rectangle from crossing over the long equator lines.  But if we assume that any maximal-area rectangle has sides at least *d* units long, we can fix the problem by inserting an extra red tile every *d* steps along the path. Then, any rectangle with sides greater than *d* that crosses the equator (or any other long line) will contain a red tile in the interior.\n",
+    "- Normally in problems like this we have to check if the rectangle we are considering is inside the polygon or outside of it. But for polygons that are anything like miine, only very small rectangles can be on the outside. Any sufficiently large polygon must be on the inside, so I didn't bother checking this.\n",
     "\n",
-    "I'm ready to start coding.\n",
+    "I'm ready to start coding! I'll start with this:\n",
     "- `find_possible_corners` will return a list of the two candidate corner points at the east end of the equator lines.\n",
     "- `breadcrumbs` will leave \"breadcrumbs\" (that is, red tiles) along the trail at least every `d` spaces."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "id": "e08d6c65-5b4e-456e-b4de-38339d209372",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def find_possible_corners(red_tiles) -> List[Point]:\n",
-    "    \"\"\"Split the tiles into top and bottom halves, and also return the two right-side corners.\"\"\"\n",
-    "    # Find the index, i, of the tile with the biggest gap to the next tile\n",
-    "    i = max(range(-1, len(red_tiles) - 1), key=lambda i: distance(red_tiles[i], red_tiles[i + 1]))\n",
-    "    return red_tiles[i+1:i+3]\n",
-    "    \n",
+    "def find_possible_corners(red_tiles, d=10000) -> Optional[List[Point]]:\n",
+    "    \"\"\"Find two adjacent corners, separated on each side by a gap of at least `d`.\"\"\"\n",
+    "    return first([B, C] for [A, B, C, D] in sliding_window(red_tiles, 4)\n",
+    "                 if distance(A, B) > d and distance(C, D) > d)\n",
+    "\n",
     "def breadcrumbs(points, d=10000) -> List[Point]:\n",
     "    \"\"\"Leave extra points along the trail on long lines, every `d` spaces.\"\"\"\n",
     "    trail = [points[-1]]\n",
@@ -1891,7 +1938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "id": "9dabf8bd-bdac-4b9a-941f-9de68480ca5d",
    "metadata": {},
    "outputs": [
@@ -1916,26 +1963,9 @@
    "id": "d8039a9d-1af7-4a74-80c9-18bba48aeb7b",
    "metadata": {},
    "source": [
-    "Now I'll define `biggest_rectangle` to find the largest possible all-red-and-green rectangle. I'll do that by considering pairs of corner points, where one of the corners can be any red tile, and the other corner by default will be one of the two `find_possible_corners` points. We then sort the possible rectangles by area, biggest first, and go through them one at a time. When we find one that does not have `any_intrusions`, we return it; it must be the biggest.\n",
+    "Now I'll define `biggest_rectangle` to find the maximal-area all-red-and-green rectangle. I'll do that by considering pairs of corner points, where the first corner can be any red tile, and for the second corner there are three cases: you can pass in a list of candidate corners, but if you don't, it will call `find_possible_corners` to try to find the equator-end points, and failing that, it will fall back to all the red tiles. We then sort the possible rectangles by area, biggest first, and go through them one at a time. When we find one that does not have `any_intrusions`, we return it; it must be the biggest. (Note that we verify that there are no adjacent red tiles.)\n",
     "\n",
-    "The function `any_intrusions` checks to see if a red tile is completely inside the rectangle defined by the corners. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "b699b2aa-17c2-416c-bc1c-90110b64dab2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def biggest_rectangle(red_tiles, d=10000) -> Corners:\n",
-    "    \"\"\"Find the biggest rectangle that stays within the interior-and-border of the tiles.\n",
-    "    If no list of candidates for `corner1_list` are given, use find_possible_corners(red_tiles).\"\"\"\n",
-    "    tiles = breadcrumbs(red_tiles, d)\n",
-    "    corners_list = combinations(red_tiles, 2)\n",
-    "    for corners in sorted(corners_list, key=tile_area, reverse=True):\n",
-    "        if not any_intrusions(tiles, corners):\n",
-    "            return corners"
+    "The function `any_intrusions` checks to see if a red tile is completely inside the rectangle defined by the corners, by checking the x and y coordinates."
    ]
   },
   {
@@ -1945,24 +1975,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def biggest_rectangle(red_tiles, corner1_list=None, d=10000) -> Corners:\n",
+    "def biggest_rectangle(red_tiles, second_corner_list=None, d=10000) -> Corners:\n",
     "    \"\"\"Find the biggest rectangle that stays within the interior-and-border of the tiles.\n",
-    "    By default, try `find_possible_corners(red_tiles)` to narrow down choices for first corner.\"\"\"\n",
+    "    By default, tries`find_possible_corners(red_tiles)` to narrow down choices for second corner.\"\"\"\n",
+    "    assert not any(distance(p, q) == 1 # This is only guaranteed if no red tiles are adjacent\n",
+    "                   for (p, q) in sliding_window(red_tiles, 2))\n",
     "    tiles = breadcrumbs(red_tiles, d)\n",
-    "    # Use a given list for first corner, or try to find 2 on equator, or just use all the red tiles\n",
-    "    corner1_list = corner1_list or find_possible_corners(red_tiles) or red_tiles\n",
-    "    corners_list = list(cross_product(red_tiles, corner1_list))\n",
-    "    for corners in sorted(corners_list, key=tile_area, reverse=True):\n",
+    "    # Pass in a list of possible second corners, or try to find 2 on equator, or use all the red tiles\n",
+    "    second_corner_list = second_corner_list or find_possible_corners(red_tiles) or red_tiles\n",
+    "    both_corners_list = cross_product(second_corner_list, red_tiles)\n",
+    "    for corners in sorted(both_corners_list, key=tile_area, reverse=True):\n",
     "        if not any_intrusions(tiles, corners):\n",
     "            return corners\n",
+    "    raise ValueError('No rectangle') # Shouldn't get here unless there are no corners\n",
     "\n",
     "def any_intrusions(red_tiles: List[Point], corners: Corners) -> bool:\n",
-    "    \"\"\"Does any point p in tiles intrude inside the rectangle defined by the corners?\"\"\"\n",
+    "    \"\"\"Does any red tile intrude inside the rectangle defined by the corners?\"\"\"\n",
     "    # OK for a red tile to be on border or just one square in, but not 2 squares in\n",
-    "    xrange = range(min(Xs(corners)) + 2, max(Xs(corners)) - 2 + 1)\n",
-    "    yrange = range(min(Ys(corners)) + 2, max(Ys(corners)) - 2 + 1)\n",
-    "    return any(X_(p) in xrange and Y_(p) in yrange\n",
-    "               for p in red_tiles)"
+    "    xlo, xhi = min(Xs(corners)) + 2, max(Xs(corners)) - 2\n",
+    "    ylo, yhi = min(Ys(corners)) + 2, max(Ys(corners)) - 2\n",
+    "    return any(xlo <= x <= xhi and ylo <= y <= yhi\n",
+    "               for (x, y) in red_tiles)"
    ]
   },
   {
@@ -1982,7 +2015,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  9.2:   .0296 seconds, answer 1529675217      correct"
+       "Puzzle  9.2:   .0164 seconds, answer 1529675217      correct"
       ]
      },
      "execution_count": 65,
@@ -2000,7 +2033,7 @@
    "id": "53ef7a00-aef7-4c20-8a2b-d735df8f28dd",
    "metadata": {},
    "source": [
-    "Let's see what the biggest rectangle looks like, a little bit larger to see more detail:"
+    "Let's see what the biggest rectangle looks like:"
    ]
   },
   {
@@ -2011,7 +2044,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
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\n",
       "text/plain": [
        "<Figure size 1200x1200 with 1 Axes>"
       ]
@@ -2032,34 +2065,14 @@
    "id": "d7016370-a8b6-4d7a-9787-70a50b3207b9",
    "metadata": {},
    "source": [
-    "We see that if the upper-left corner of the blue rectangle were any higher, then there would be red (and white) tiles in the upper-right corner of the rectangle. If the upper-left corner of the blue rectangle were any further west that would be ok, but would result in a slightly smaller area. You'll just have to take it for granted that all the possible rectangles formed below the equater lines are also a little bit smaller in area.\n",
+    "We see that if the upper-left corner of the blue rectangle were any higher, then there would be red (and white) tiles in the upper-right corner of the rectangle. If the upper-left corner of the blue rectangle were any further southwest that would be ok, but would result in a slightly smaller area. You'll just have to take it for granted that all the possible rectangles formed below the equater lines are also a little bit smaller in area.\n",
     "\n",
-    "Here's **one thing that bothers me**: suppose the two candidate corners on the east end of the equator were just one space apart from each other. Then there would be *no* white space between them, and a rectangle would be free to cross the equator. (If points were real-valued there would always be some space between them, but on a grid, it is possible to have no empty squares between two lines.) So if it is possible for two red tiles to be adjacent, then my `any_intrusions` algorithm could reject a valid rectangle. Now, it turns out there are no instances of adjacent red tiles:"
+    "How long would the run time be if we didn't rely on the second corner being an equatorial point? Would the answer be the same? Let's  check:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "ff79f008-3ef4-43f0-a5fc-386fa5160d13",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert not any(distance(p, q) == 1 for (p, q) in sliding_window(red_tiles, 2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ff570de-917a-488f-8b76-a814bfef4b20",
-   "metadata": {},
-   "source": [
-    "But the instructions do not explicitly state that this is impossible, so my algorithm might fail on some inputs.\n",
-    "\n",
-    "Another thing that bothers me: if `find_possible_corners` doesn't find corners, will it still work? Let's check, by passing in all red tiles as the possible corners:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
    "id": "d3b44691-da52-4794-ab77-bc4326aa6ca2",
    "metadata": {},
    "outputs": [
@@ -2067,13 +2080,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3.69 s, sys: 83.8 ms, total: 3.77 s\n",
-      "Wall time: 3.18 s\n"
+      "CPU times: user 2.33 s, sys: 90.5 ms, total: 2.42 s\n",
+      "Wall time: 1.56 s\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "%time assert biggest_rectangle(red_tiles, red_tiles) == biggest_rectangle(red_tiles)"
+    "%time tile_area(biggest_rectangle(red_tiles, red_tiles)) == tile_area(biggest_rectangle(red_tiles))"
    ]
   },
   {
@@ -2081,7 +2104,7 @@
    "id": "b4e35203-687a-434e-88f5-0afea9f31c57",
    "metadata": {},
    "source": [
-    "Yes, it finds the same maximal rectangle, but it takes a lot longer to find it.\n",
+    "Yes, it finds the same maximal-area rectangle, but it takes a lot longer to find it.\n",
     "\n",
     "**Two final remarks**: One, this was the first puzzle of the year that was **difficult**. Two, my solution is **unsatisfying** in that it works for *my* input, and I strongly suspect that it would work for *your* input, because Eric Wastl probably created them all to be similar. But it may not work on every possible input allowed by the rules. "
    ]
@@ -2097,12 +2120,12 @@
     "\n",
     "    [.##.] (3) (1,3) (2) (2,3) (0,2) (0,1) {3,5,4,7}\n",
     "\n",
-    "has four lights, and the goal configuration `.##.` means that the second and third light should be on and the others off. There are six buttons (each delimited by parentheses): the first button toggles light number 3, the second toggles lights 1 and 3, and so on. (The machine uses 0-based indexing.) Finally, the joltage requirements for the four lights are `3,5,4,7`. We can parse the input lines into machine descriptions like this:"
+    "has four lights, and the goal configuration `.##.` means that the second and third light should be on and the others off. There are six buttons (each delimited by parentheses): the first button toggles light number 3, the second toggles lights 1 and 3, and so on. (The machine uses 0-based indexing, so light number 3 is the fourth light.) Finally, the joltage requirements for the four lights are `3,5,4,7`. We can parse the input lines into machine descriptions like this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "id": "84cd63a7-4d50-4f14-9807-3cb439f80c88",
    "metadata": {},
    "outputs": [
@@ -2158,14 +2181,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "id": "179e62b1-e4cb-43e4-8dc7-b87a28d21e8d",
    "metadata": {},
    "outputs": [],
    "source": [
     "def minimal_button_presses(machine) -> int:\n",
     "    \"\"\"How many button presses to configure lights on this machine?\n",
-    "    First try all ways of pressing 1 button, then all ways of pressing 2, ...\n",
+    "    First try all ways of pressing 1 button, then all ways of pressing 2 buttons, ...\n",
     "    Return as soon as one way matches the goal configuration of lights.\"\"\"\n",
     "    lights, buttons, joltage = machine\n",
     "    goal = [\".#\".index(ch) for ch in lights] # i.e., lights = \".##.\" ⇒ goal = (0, 1, 1, 0)\n",
@@ -2179,17 +2202,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "id": "d1368c2f-d792-4353-82e7-b0161ece784f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 10.1:   .0533 seconds, answer 441             correct"
+       "Puzzle 10.1:   .0531 seconds, answer 441             correct"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2208,14 +2231,14 @@
     "\n",
     "In Part 2 we move a lever, and now the function of the buttons changes: they control the joltage levels of the lights, not the lights themselves. The joltage levels all start at zero. Pressing the button `(2, 3)` increments the joltage level of lights numbered 2 and 3 by one unit each. Our task is to get the joltage levels all exactly to the target levels in the minimum number of presses.\n",
     "\n",
-    "My first thought when reading the puzzle description was \"*This is an [integer linear programming](https://en.wikipedia.org/wiki/Integer_programming) problem.*\" My thought was confirmed by the instructions that said \"*You have to push each button an integer number of times; there's no such thing as 0.5 presses (nor can you push a button a negative number of times).*\" because having fractional or negative results is exactly what you might get from linear programming; you have to take extra steps to constrain the results to be non-negative and to be integers.\n",
+    "My first thought when reading the puzzle description was \"*This is an [**integer linear programming**](https://en.wikipedia.org/wiki/Integer_programming) problem.*\" My thought was confirmed by the instructions that said \"*You have to push each button an integer number of times; there's no such thing as 0.5 presses (nor can you push a button a negative number of times),*\" because having fractional or negative results is exactly what you might get from linear programming (you have to take extra steps to constrain the results to be non-negative and to be integers).\n",
     "\n",
-    "Still, I was reluctant to use an integer linear programming package; that would mean that someone else is writing most of the code for the solution. This could also be seen as a search problem; I started programming an A* search solution, but it was way too slow. Why is it slow? Let's investigate. First, the number of buttons per machine is not too bad:"
+    "Still, I was reluctant to use an integer linear programming package; that would mean that someone else is writing most of the code. This could also be seen as a search problem; I started programming an A* search solution, but it was way too slow. Why is it slow? Let's investigate. First, the number of buttons per machine is not too bad:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "id": "1d2683fa-5126-4d2a-9dad-6a0e155f3449",
    "metadata": {},
    "outputs": [
@@ -2225,7 +2248,7 @@
        "7.181818181818182"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2244,7 +2267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "id": "a714c2c2-d433-41d0-8d1e-01832b35a1a9",
    "metadata": {},
    "outputs": [
@@ -2254,7 +2277,7 @@
        "114.81498043610085"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2275,15 +2298,15 @@
     "4) Some actions will be forced: if there is only one button that increments a given light, we *must* press it until the light hits its goal.\n",
     "5) If there are only two buttons that increment a given light, then the total presses of those two buttons must equal the joltage requirement for that light.\n",
     "6) And if there are three buttons that increment a given light, maybe we can somehow eliminate one button to get to two, and then to one.\n",
-    "7) That process of button elimination is called [Gaussian elimination](https://en.wikipedia.org/wiki/Gaussian_elimination). Reluctantly, I will give in to the power of linear programming.\n",
+    "7) Come to think of it, that process of button elimination already has a name: [Gaussian elimination](https://en.wikipedia.org/wiki/Gaussian_elimination). Reluctantly, I will give in to the power of linear programming.\n",
     "\n",
-    "A linear programming solver finds a solution **x** to the equation **A** **x** = **b** that minimizes **c** **x**, where **A** is a two-dimensional matrix and the other variables are one-dimensional vectors.\n",
+    "A linear programming solver finds a solution **x** to the equation **A** **x** = **b** that minimizes the dot product **c** · **x**, where **A** is a two-dimensional matrix and the other variables are one-dimensional vectors.\n",
     "\n",
     "For our problem we have:\n",
     "- **b** is the vector of joltage requirements for each light,\n",
-    "- **c** says how much it costs to press each button, which is one press each so it is a vector of all ones,\n",
-    "- **A** is a matrix where **A**<sub>*i,j*</sub> says how much button *i* increments joltage *j* (each is either 0 or 1),\n",
-    "- **x** will be the solution: a vector of number-of-pushes for each button.\n",
+    "- **c** says how much it costs to press each button, which is one press each so **c** is a vector of all ones,\n",
+    "- **A** is a matrix where **A**<sub>*i,j*</sub> says how much button *j* increments joltage *i* (either 0 or 1),\n",
+    "- **x** will be the solution returned by the solver: a vector of number-of-pushes for each button.\n",
     "\n",
     "\n",
     "\n",
@@ -2292,16 +2315,16 @@
     "I started researching integer programming packages that run in Python. [Z3](https://github.com/Z3Prover/z3) seems to be the most popular, but it is a separate step to install it. I know that I (and many other people) already have **scipy** installed, and the [**scipy.optimize**](https://docs.scipy.org/doc/scipy/tutorial/optimize.html) package contains the function [**milp**](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.milp.html#scipy.optimize.milp), for \"mixed integer linear programming.\"  The \"mixed\" part means that we can declare that some of the variables must be integers, while others can be continuous. (For our problem they will all be integers.)\n",
     "\n",
     "The arguments to **milp** are:\n",
-    "- **c**: the cost vector (a 1 for every button in our problem).\n",
-    "- **integrality**: indicates which variables must be integers; a 1 (True) for every button.\n",
-    "- **constraints**: a linear constraint. I want to say **A** **x** = **b**, but in this package I have to say  **lb** ≤ **A** **x** ≤ **ub**, where **lb** and **ub** are the lower and upper bounds on **b**. \n",
+    "- **c**: the cost vector (a 1 for every button in our problem),\n",
+    "- **integrality**: indicates which variables must be integers (a 1 (True) for every button in our problem),\n",
+    "- **constraints**: a linear constraint. I want to say **A** **x** = **b**, but in this package I have to say  **lb** ≤ **A** **x** ≤ **ub**, where **lb** and **ub** are the lower and upper bounds on **b**. In our problem both **lb** and **ub** are the joltage requirements.\n",
     "\n",
-    "If we give it the right inputs, **milp** will magically return an optimal result for **x**. Here's how we get the data out of a `machine` and feed it to **milp**:"
+    "If we give it the right inputs, **milp** will magically return a minimal-cost result for **x**. Here's how we get the data out of a `machine` and feed it to **milp**:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "id": "713a1503-ae14-445f-91ea-714fcd618ad0",
    "metadata": {},
    "outputs": [],
@@ -2310,11 +2333,11 @@
     "import numpy as np\n",
     "\n",
     "def minimal_joltage_presses(machine) -> int:\n",
-    "    \"\"\"The minimal number of button presses to set the joltage on this machine.\"\"\"\n",
+    "    \"\"\"The minimal number of button presses to set the joltages on this machine.\"\"\"\n",
     "    lights, buttons, joltage = machine\n",
     "    A = T([[int(i in button) for i in range(len(lights))]\n",
     "           for button in buttons])\n",
-    "    ones = [1]*len(buttons)\n",
+    "    ones = [1] * len(buttons)\n",
     "    result = milp(c=ones, \n",
     "                  integrality=ones,\n",
     "                  constraints=LinearConstraint(A, lb=joltage, ub=joltage))\n",
@@ -2331,17 +2354,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "id": "46d0d274-bd4c-44af-83e9-35c791a8e96b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 10.2:   .1198 seconds, answer 18559           correct"
+       "Puzzle 10.2:   .1123 seconds, answer 18559           correct"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2368,7 +2391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "id": "b9f82fef-612b-48a5-9de1-6ff0b137f7fb",
    "metadata": {},
    "outputs": [
@@ -2378,7 +2401,7 @@
        "Counter({-1: 68, 1: 65, 0: 32})"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2397,7 +2420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 76,
    "id": "e536441d-64d4-410d-bb40-2343f6e01d88",
    "metadata": {},
    "outputs": [
@@ -2407,7 +2430,7 @@
        "Counter({-1: 32, -2: 31, 1: 34, 2: 31, 0: 32, -3: 5})"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2434,7 +2457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 77,
    "id": "b72544c8-6069-4310-a6dc-b4acd77981b4",
    "metadata": {},
    "outputs": [],
@@ -2461,7 +2484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 78,
    "id": "11e17f6a-acba-44c4-b704-7e4ff7471e7e",
    "metadata": {},
    "outputs": [
@@ -2514,7 +2537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 79,
    "id": "7540a982-988a-4822-af0d-6581f6f848c6",
    "metadata": {},
    "outputs": [],
@@ -2541,17 +2564,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 80,
    "id": "0c2d68a5-843b-49d6-aff6-23045968207f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 11.1:   .0003 seconds, answer 574             correct"
+       "Puzzle 11.1:   .0009 seconds, answer 574             correct"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2573,7 +2596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "id": "0294e044-c7ef-418a-9c02-71615a453002",
    "metadata": {},
    "outputs": [],
@@ -2593,17 +2616,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "id": "677a97b3-183b-474e-87ba-7db0d6b763d8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 11.2:   .0031 seconds, answer 306594217920240 correct"
+       "Puzzle 11.2:   .0017 seconds, answer 306594217920240 correct"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2613,6 +2636,297 @@
     "       count_constrained_paths(devices))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "dc115ac2-5357-499d-8917-3fff29f4063b",
+   "metadata": {},
+   "source": [
+    "# [Day 12](https://adventofcode.com/2025/day/12): Christmas Tree Farm \n",
+    "\n",
+    "On the twelfth day, we're in a cavern full of Christmas trees and the elves would like help arranging presents under the trees. The day's input is in two sections. The first section is a list of 6 shapes, each annotated with their shape number. The second section is a list of regions, which has a width and length, and a desired number of presents of each shape, in shape-number order. Each shape is a separate paragraph, but the regions are all in one paragraph, so I can parse them like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "a1b304e8-339e-4e32-a462-3ba88103c415",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "Puzzle input ➜ 1030 strs of size 0 to 24:\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "0:\n",
+      "###\n",
+      ".##\n",
+      "##.\n",
+      "\n",
+      "1:\n",
+      "##.\n",
+      "##.\n",
+      "###\n",
+      "\n",
+      "2:\n",
+      "#..\n",
+      "##.\n",
+      "###\n",
+      "\n",
+      "3:\n",
+      "###\n",
+      "#.#\n",
+      "#.#\n",
+      "\n",
+      "4:\n",
+      "#.#\n",
+      "###\n",
+      "#.#\n",
+      "\n",
+      "5:\n",
+      "##.\n",
+      ".##\n",
+      "..#\n",
+      "\n",
+      "45x41: 52 43 45 41 47 59\n",
+      "45x41: 29 27 34 34 34 36\n",
+      "41x37: 44 34 38 35 40 44\n",
+      "...\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "Parsed representation ➜ 7 tuples of size 2 to 1000:\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "(0, ['###', '.##', '##.'])\n",
+      "(1, ['##.', '##.', '###'])\n",
+      "(2, ['#..', '##.', '###'])\n",
+      "(3, ['###', '#.#', '#.#'])\n",
+      "(4, ['#.#', '###', '#.#'])\n",
+      "(5, ['##.', '.##', '..#'])\n",
+      "((45, 41, [52, 43, 45, 41, 47, 59]), (45, 41, [29, 27, 34, 34, 34, 36]), (41, 37, [44, 34, 38, 3 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "def parse_presents(text: str):\n",
+    "    \"\"\"Parse either a single present (e.g. \"5: ###...\") or list of regions (e.g. \"12x5: 1 0 1 0 2 2\\n...\").\"\"\"\n",
+    "    if 'x' in text:\n",
+    "        return tuple((x, y, quantities) for (x, y, *quantities) in map(ints, text.splitlines()))\n",
+    "    else:\n",
+    "        id, *shape = text.splitlines()\n",
+    "        return (int(id[:-1]), shape)\n",
+    "    \n",
+    "*shapes, regions = parse(12, parse_presents, paragraphs, show=33)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e735589a-634d-4ef9-b3b1-db9019aacf9d",
+   "metadata": {},
+   "source": [
+    "### Part 1: How many of the regions can fit all of the presents listed?\n",
+    "\n",
+    "There have been Tetris-like puzzles in past AoC years. Is this another search problem? If so, will the searches be fast or slow? I want to get a feel for it. First, how many regions?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "194cbece-0104-4934-b335-13a4e7c720e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(regions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f579c5f1-f44c-4f9e-b6a9-5c75dbf20cfa",
+   "metadata": {},
+   "source": [
+    "What's the average size of the regions?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "388d13ab-b5db-47e4-9f33-50f7062424b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1822.223"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean(x * y for (x, y, _) in regions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cefca7b5-a9a2-4fb9-bd4b-071f7ecd5db0",
+   "metadata": {},
+   "source": [
+    "And average total quantity of presents in a region?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "5d982737-cbca-4739-be1c-fa1dce319ef4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "240.488"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean(sum(quantities) for (_, _, quantities) in regions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af3fe830-aa75-469b-9046-3f36ff3a03e2",
+   "metadata": {},
+   "source": [
+    "Next I want to get a feel for the variation in how tight the packing is. Each present can definitely fit into a 3x3 square, so it would be trivially easy if we just put down one present in each 3x3 square, without trying to make them overlap.  The number of full 3x3 squares in a region is `(x // 3) * (y // 3)`, which discards any leftover 1 or 2 units of width or length. So we have:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "b8cf45b6-5513-426a-86c2-425d0d74d781",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def squares(x, y) -> int: \"Number of full 3x3 squares in a region.\"; return (x // 3) * (y // 3)\n",
+    "    \n",
+    "occupancy_ratios = [sum(quantities) / squares(x, y) \n",
+    "                    for (x, y, quantities) in regions]\n",
+    "\n",
+    "plt.hist(occupancy_ratios, bins=100);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54f2eaa0-b8b6-4a60-bcde-28eeefa26e8c",
+   "metadata": {},
+   "source": [
+    "**Very interesting!** There's a real split. A lot of regions have an occupabncy ratio below 1.0 and thus are trivially easy to fit, and the rest of the regions with an occupancy ratio of around 1.35 or more look like they are impossible to fit. I can do triage on the regions to classify each one:  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "58b879f2-19ad-4e3f-a804-8c22151e5865",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def triage(region, shape_area=[cat(s).count('#') for s in shapes]) -> str:\n",
+    "    \"\"\"Decide if a region's presents trivially fit, or are impossible to fit, or it is uncertain.\"\"\"\n",
+    "    x, y, quantities = region\n",
+    "    presents_area = sum(q * shape_area[i] for (i, q) in enumerate(quantities))\n",
+    "    if sum(quantities) <= squares(x, y):\n",
+    "        return 'fit'        # The number of presents is no more than the number of 3x3 squares\n",
+    "    elif presents_area > x * y:\n",
+    "        return 'impossible' # The area of all the presents is greater than the area of the region\n",
+    "    else:\n",
+    "        return 'uncertain'  # We would need to do a search to see if the presents fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e85a2de-8e75-4be0-8324-de816df889fb",
+   "metadata": {},
+   "source": [
+    "Here goes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "6768fa83-2af7-4aab-a930-9106da3859bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'impossible': 546, 'fit': 454})"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Counter(map(triage, regions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbedea21-3bed-4c99-9b57-fc1f1e03f76b",
+   "metadata": {},
+   "source": [
+    "**There are no uncertain regions!** The problem is solved, and I didn't have to rotate a single present!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "0ec553c4-85eb-40a6-8bed-7adddf75e512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Puzzle 12.1:   .0019 seconds, answer 454             correct"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "answer(12.1, 454, lambda:\n",
+    "       Counter(map(triage, regions))['fit'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7f31ae9b-6606-40b0-9bb1-ed9b3fe3cbf0",
@@ -2625,7 +2939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 91,
    "id": "4d512a50-c6ae-4803-a787-b8f6e0103e31",
    "metadata": {},
    "outputs": [
@@ -2634,35 +2948,44 @@
      "output_type": "stream",
      "text": [
       "Puzzle  1.1:   .0005 seconds, answer 1182            correct\n",
-      "Puzzle  1.2:   .0009 seconds, answer 6907            correct\n",
-      "Puzzle  2.1:   .0027 seconds, answer 23560874270     correct\n",
-      "Puzzle  2.2:   .0036 seconds, answer 44143124633     correct\n",
+      "Puzzle  1.2:   .0010 seconds, answer 6907            correct\n",
+      "Puzzle  2.1:   .0029 seconds, answer 23560874270     correct\n",
+      "Puzzle  2.2:   .0038 seconds, answer 44143124633     correct\n",
       "Puzzle  3.1:   .0006 seconds, answer 17085           correct\n",
-      "Puzzle  3.2:   .0019 seconds, answer 169408143086082 correct\n",
-      "Puzzle  4.1:   .0538 seconds, answer 1569            correct\n",
-      "Puzzle  4.2:   .1409 seconds, answer 9280            correct\n",
-      "Puzzle  5.1:   .0073 seconds, answer 635             correct\n",
-      "Puzzle  5.2:   .0001 seconds, answer 369761800782619 correct\n",
-      "Puzzle  6.1:   .0015 seconds, answer 5877594983578   correct\n",
-      "Puzzle  6.2:   .0039 seconds, answer 11159825706149  correct\n",
-      "Puzzle  7.1:   .0007 seconds, answer 1681            correct\n",
-      "Puzzle  7.2:   .0014 seconds, answer 422102272495018 correct\n",
-      "Puzzle  8.1:   .6008 seconds, answer 24360           correct\n",
-      "Puzzle  8.2:   .6149 seconds, answer 2185817796      correct\n",
-      "Puzzle  9.1:   .0264 seconds, answer 4772103936      correct\n",
-      "Puzzle  9.2:   .0296 seconds, answer 1529675217      correct\n",
-      "Puzzle 10.1:   .0533 seconds, answer 441             correct\n",
-      "Puzzle 10.2:   .1198 seconds, answer 18559           correct\n",
-      "Puzzle 11.1:   .0003 seconds, answer 574             correct\n",
-      "Puzzle 11.2:   .0031 seconds, answer 306594217920240 correct\n",
+      "Puzzle  3.2:   .0024 seconds, answer 169408143086082 correct\n",
+      "Puzzle  4.1:   .0553 seconds, answer 1569            correct\n",
+      "Puzzle  4.2:   .1394 seconds, answer 9280            correct\n",
+      "Puzzle  5.1:   .0123 seconds, answer 635             correct\n",
+      "Puzzle  5.2:   .0002 seconds, answer 369761800782619 correct\n",
+      "Puzzle  6.1:   .0022 seconds, answer 5877594983578   correct\n",
+      "Puzzle  6.2:   .0063 seconds, answer 11159825706149  correct\n",
+      "Puzzle  7.1:   .0010 seconds, answer 1681            correct\n",
+      "Puzzle  7.2:   .0021 seconds, answer 422102272495018 correct\n",
+      "Puzzle  8.1:   .5834 seconds, answer 24360           correct\n",
+      "Puzzle  8.2:   .6182 seconds, answer 2185817796      correct\n",
+      "Puzzle  9.1:   .0370 seconds, answer 4772103936      correct\n",
+      "Puzzle  9.2:   .0164 seconds, answer 1529675217      correct\n",
+      "Puzzle 10.1:   .0531 seconds, answer 441             correct\n",
+      "Puzzle 10.2:   .1123 seconds, answer 18559           correct\n",
+      "Puzzle 11.1:   .0009 seconds, answer 574             correct\n",
+      "Puzzle 11.2:   .0017 seconds, answer 306594217920240 correct\n",
+      "Puzzle 12.1:   .0019 seconds, answer 454             correct\n",
       "\n",
-      "Time in seconds: sum = 1.668, mean =  .076, median =  .003, max =  .615\n"
+      "Time in seconds: sum = 1.655, mean =  .072, median =  .003, max =  .618\n"
      ]
     }
    ],
    "source": [
     "summary(answers)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1098c017-2746-403c-bfb0-1a08cacc835d",
+   "metadata": {},
+   "source": [
+    "I got them all done in under 2 seconds of run time. Happy Advent everyone, and thank you Eric for the interesting puzzles!"
+   ]
   }
  ],
  "metadata": {
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