diff --git a/src/AoC_2024/Dazbo's_Advent_of_Code_2024.ipynb b/src/AoC_2024/Dazbo's_Advent_of_Code_2024.ipynb index dfe7bd4..e1e6ea7 100644 --- a/src/AoC_2024/Dazbo's_Advent_of_Code_2024.ipynb +++ b/src/AoC_2024/Dazbo's_Advent_of_Code_2024.ipynb @@ -48,12 +48,138 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 170, "metadata": { "id": "9Dgeww1rMrkQ", "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "34313.21s - pydevd: Sending message related to process being replaced timed-out after 5 seconds\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: jupyterlab-lsp in /home/darren/miniconda3/envs/.aoc-conda-env/lib/python3.13/site-packages (5.1.0)\n", + "Requirement already satisfied: ipykernel in /home/darren/miniconda3/envs/.aoc-conda-env/lib/python3.13/site-packages (6.29.5)\n", + "Requirement already satisfied: ipywidgets in 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"Requirement already satisfied: types-python-dateutil>=2.8.10 in /home/darren/miniconda3/envs/.aoc-conda-env/lib/python3.13/site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server>=1.1.2->jupyter-lsp>=2.0.0->jupyterlab-lsp) (2.9.0.20241003)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "%pip install --upgrade --no-cache-dir \\\n", " jupyterlab-lsp ipykernel ipywidgets \\\n", @@ -64,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 171, "metadata": { "id": "p5Ki_HvOJUWk", "tags": [] @@ -124,12 +250,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 172, "metadata": { "id": "SwzjfUFCKhXe", "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m18:04:26.026:aoc2024 - INF: Logger initialised.\u001b[39m\n", + "\u001b[34m18:04:26.027:aoc2024 - DBG: Debugging enabled\u001b[39m\n" + ] + } + ], "source": [ "# Setup logger\n", "YEAR = 2024\n", @@ -152,9 +287,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m18:04:26.054:aoc2024 - DBG: Checking if ffmpeg is installed\u001b[39m\n", + "\u001b[34m18:04:26.086:aoc2024 - DBG: ffmpeg version: ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\n", + "built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\n", + "configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/aarch64-linux-gnu --incdir=/usr/include/aarch64-linux-gnu --arch=arm64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\n", + "libavutil 58. 29.100 / 58. 29.100\n", + "libavcodec 60. 31.102 / 60. 31.102\n", + "libavformat 60. 16.100 / 60. 16.100\n", + "libavdevice 60. 3.100 / 60. 3.100\n", + "libavfilter 9. 12.100 / 9. 12.100\n", + "libswscale 7. 5.100 / 7. 5.100\n", + "libswresample 4. 12.100 / 4. 12.100\n", + "libpostproc 57. 3.100 / 57. 3.100\u001b[39m\n", + "\u001b[34m18:04:26.087:aoc2024 - DBG: ffmpeg is already installed.\u001b[39m\n", + "\u001b[34m18:04:26.088:aoc2024 - DBG: Checking if dot is installed\u001b[39m\n", + "\u001b[34m18:04:26.092:aoc2024 - DBG: dot is not installed or absent from path.\u001b[39m\n", + "\u001b[32m18:04:26.093:aoc2024 - INF: Installing graphviz on Linux...\u001b[39m\n" + ] + } + ], "source": [ "import subprocess\n", "\n", @@ -258,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 174, "metadata": { "id": "lwP0r3BAaxjt", "tags": [] @@ -331,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 175, "metadata": { "id": "Y6nbd6WMryWi", "tags": [] @@ -359,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 176, "metadata": { "id": "A8sU4Ez_bBKl", "tags": [] @@ -529,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 177, "metadata": { "id": "DT5FSYliC9wp", "tags": [] @@ -8132,9 +8290,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 178, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "#### See [Day 22](https://adventofcode.com/2024/day/22)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "DAY = \"22\" # replace with actual number (without leading digit)\n", "show_day_link(DAY)" @@ -8142,9 +8313,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m18:04:26.245:aoc2024 - DBG: input.txt already exists\u001b[39m\n", + "\u001b[32m18:04:26.249:aoc2024 - INF: Input data:\n", + " 1: 4688826\n", + " 2: 16200140\n", + " 3: 16105529\n", + " 4: 14489781\n", + " 5: 6567477\n", + "...\n", + "2004: 11798329\n", + "2005: 16268496\n", + "2006: 599307\n", + "2007: 1916773\n", + "2008: 666746\u001b[39m\n" + ] + } + ], "source": [ "d_name = \"d\" + str(DAY).zfill(2) # e.g. d01\n", "script_name = \"aoc\" + str(YEAR) + d_name # e.g. aoc2024d01\n", @@ -8199,7 +8390,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 180, "metadata": {}, "outputs": [], "source": [ @@ -8243,9 +8434,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 181, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m18:04:31.772:aoc2024 - DBG: initial_secrets=[1, 10, 100, 2024]\u001b[39m\n", + "\u001b[34m18:04:31.774:aoc2024 - DBG: secret=1\u001b[39m\n", + "\u001b[34m18:04:31.775:aoc2024 - DBG: secret=10\u001b[39m\n", + "\u001b[34m18:04:31.776:aoc2024 - DBG: secret=100\u001b[39m\n", + "\u001b[34m18:04:31.777:aoc2024 - DBG: secret=2024\u001b[39m\n", + "\u001b[32m18:04:31.778:aoc2024 - INF: Test passed\u001b[39m\n", + "\u001b[32m18:04:31.780:aoc2024 - INF: All tests passed!\u001b[39m\n", + "\u001b[32m18:04:32.916:aoc2024 - INF: Part 1 soln=16953639210\u001b[39m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.2 s, sys: 12.6 ms, total: 1.21 s\n", + "Wall time: 1.15 s\n" + ] + } + ], "source": [ "%%time\n", "sample_inputs = []\n", @@ -8293,179 +8507,110 @@ " 7753432: 2 (-2)\n", " ```\n", "\n", + "E.g. the prices offered are initially 3 bananas, then 0 (change of `-3`), then 6 (change of `6`), etc.\n", + "\n", "So: \n", "\n", - "- We need to determine the changes in prices with each iteration.\n", - "- We need to establish a set of four changes that, if applied across all of our buyers, will result in the greatest cumulative acquisition of bananas. There will only be ONE sequence of four price changes, for all buyers.\n", - "- There will be 2000 price changes per buyer. \n", + "- We need to determine the changes in prices with each iteration. There will be 2000 price changes per buyer.\n", + "- We need to establish a set of _four changes_ that, if applied across all of our buyers, will result in the greatest cumulative acquisition of bananas. There will only be ONE single sequence of four price changes that we must use across all buyers. E.g. in the sample above, the sequence `[-1,-1,0,2]` finishes by pointing at the price 6. This is the highest number of bananas that we can retrieve from this list of prices. (The previous 6 cannot occur before the end of a four-change sequence.)\n", + " \n", + "We're asked to: determine the best sequence that, when applied to every buyer's list of prices (bananas), results in the greatest number of total bananas acquired. **What is the most bananas you can get?**\n", "\n", "#### Solution Approach\n", "\n", - "Can we brute force this?\n", + "Can we brute-force this? The approach would be:\n", + "\n", + "- Retrieve our initial list of buyers. (Our input data).\n", + "- For each buyer:\n", + " - Determine all prices. (We require 2001.)\n", + " - Determine all price changes, i.e. the difference between prices. There will be 2000.\n", + " - Determine all possible sequences of four price changes. Add to an overall dict of sequences, called `bananas_for_seq`. They key is the sequence, and the value is the accumulation of the price (bananas) across all buyers.\n", + " - The first time we see any given sequence, retrieve the price from the corresponding index in the prices list. (We only get the first price where the sequence is matched.)\n", + "- Finally, return the maximum value of bananas for any sequence.\n", "\n", - "- Determine all possible sequences of four price changes.\n", - "- Then apply this sequence to all buyers, to determine the total bananas?\n", - "\n" + "This works pretty quickly!" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 216, "metadata": {}, "outputs": [], "source": [ - "### OLD ###\n", - "\n", - "CHANGES_SEQ_LEN = 4\n", - "SECRETS = 2000\n", - "\n", "def solve_part2(data):\n", + " CHANGES_SEQ_LEN = 4\n", + " SECRETS = 2000\n", + " \n", + " # As with part 1, retrieve initial secret value for each \"buyer\"\n", " initial_secrets = list(map(int, data))\n", " logger.debug(f\"{initial_secrets=}\")\n", " \n", - " prices_for_buyer: dict[int, list[int]] = defaultdict(list)\n", - " diffs_for_buyer: dict[int, list[int]] = {}\n", - " all_change_sequences: set[tuple[int]] = set()\n", - " \n", + " # Store sequences and how many bananas each accumulates\n", + " bananas_for_seq = defaultdict(int) # { (2, 0, 6, -4): 5, (0, 6, -4, 4): 9, ... }\n", + "\n", " # for buyer, secret in enumerate([123]): # Test buyer\n", " for buyer, secret in enumerate(initial_secrets):\n", " logger.debug(f\"{buyer=},{secret=}\")\n", " \n", - " # list the first n prices for this buyer\n", - " for _ in range(SECRETS+1):\n", - " prices_for_buyer[buyer].append(secret % 10) # Get units column\n", - " secret = generate_next_secret(secret)\n", - " \n", - " # logger.debug(f\"For {buyer=}, prices_for_buyer: {prices_for_buyer[buyer]}\")\n", - " \n", - " # determine the diffs between each price for this buyer\n", - " diffs_for_buyer[buyer] = tuple(j-i for i, j in zip(prices_for_buyer[buyer], prices_for_buyer[buyer][1:]))\n", - " assert len(diffs_for_buyer[buyer]) == SECRETS, f\"Should be {SECRETS} diffs\"\n", - " \n", - " # Add sequences into set\n", - " all_change_sequences.update(diffs_for_buyer[buyer][i:i+CHANGES_SEQ_LEN] \n", - " for i in range(len(diffs_for_buyer[buyer])-CHANGES_SEQ_LEN+1))\n", - " \n", - " bananas_for_seq = defaultdict(int)\n", - " # Check if / where this seq appears in changes for each buyer\n", - " for seq in tqdm(all_change_sequences):\n", - " for buyer, diffs in diffs_for_buyer.items():\n", - " if chg_idx := index_seq_complete(seq, diffs):\n", - " bananas_for_seq[seq] += prices_for_buyer[buyer][chg_idx+1]\n", - " \n", - " # logger.debug(f\"{bananas_for_seq=}\")\n", - " most_bananas = max(bananas_for_seq.items(), key=lambda x: x[1])\n", - " logger.debug(f\"{most_bananas=}\")\n", - " \n", - " return most_bananas[1]\n", - " \n", - "def index_seq_complete(change_seq: Iterable[int], diffs_for_this_buyer):\n", - " \"\"\" Determines if this sequence is found in the diffs list.\n", - " If so, it returns the index where the index COMPLETES. (Which must be > 0.)\n", - " \n", - " Return 0 if the sequence is not found.\n", - " \"\"\"\n", - " for i in range(SECRETS - CHANGES_SEQ_LEN + 1):\n", - " if diffs_for_this_buyer[i:i+CHANGES_SEQ_LEN] == change_seq:\n", - " return i+CHANGES_SEQ_LEN-1 # the index where this seq completed\n", - " \n", - " return 0\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "### OPTIMISING ###\n", - "CHANGES_SEQ_LEN = 4\n", - "SECRETS = 2000\n", - "\n", - "# def solve_part2(data):\n", - "# initial_secrets = list(map(int, data))\n", - "# logger.debug(f\"{initial_secrets=}\")\n", - " \n", - "# prices_for_buyer: dict[int, list[int]] = defaultdict(list)\n", - "# diffs_for_buyer: dict[int, list[int]] = {}\n", - "# all_change_sequences: set[tuple[int]] = set()\n", - " \n", - "# # for buyer, secret in enumerate([123]): # Test buyer\n", - "# for buyer, secret in enumerate(initial_secrets):\n", - "# logger.debug(f\"{buyer=},{secret=}\")\n", - " \n", - "# # list the first n prices for this buyer\n", - "# for _ in range(SECRETS+1):\n", - "# prices_for_buyer[buyer].append(secret % 10) # Get units column\n", - "# secret = generate_next_secret(secret)\n", - " \n", - "# # logger.debug(f\"For {buyer=}, prices_for_buyer: {prices_for_buyer[buyer]}\")\n", - " \n", - "# # determine the diffs between each price for this buyer\n", - "# diffs_for_buyer[buyer] = tuple(j-i for i, j in zip(prices_for_buyer[buyer], prices_for_buyer[buyer][1:]))\n", - "# assert len(diffs_for_buyer[buyer]) == SECRETS, f\"Should be {SECRETS} diffs\"\n", - " \n", - "# # Add sequences into set\n", - "# all_change_sequences.update(diffs_for_buyer[buyer][i:i+CHANGES_SEQ_LEN] \n", - "# for i in range(len(diffs_for_buyer[buyer])-CHANGES_SEQ_LEN+1))\n", - " \n", - "# bananas_for_seq = defaultdict(int)\n", - "# # Check if / where this seq appears in changes for each buyer\n", - "# for seq in tqdm(all_change_sequences):\n", - "# for buyer, diffs in diffs_for_buyer.items():\n", - "# if chg_idx := index_seq_complete(seq, diffs):\n", - "# bananas_for_seq[seq] += prices_for_buyer[buyer][chg_idx+1]\n", - " \n", - "# # logger.debug(f\"{bananas_for_seq=}\")\n", - "# most_bananas = max(bananas_for_seq.items(), key=lambda x: x[1])\n", - "# logger.debug(f\"{most_bananas=}\")\n", - " \n", - "# return most_bananas[1]\n", - " \n", - "# def index_seq_complete(change_seq: Iterable[int], diffs_for_this_buyer):\n", - "# \"\"\" Determines if this sequence is found in the diffs list.\n", - "# If so, it returns the index where the index COMPLETES. (Which must be > 0.)\n", - " \n", - "# Return 0 if the sequence is not found.\n", - "# \"\"\"\n", - "# for i in range(SECRETS - CHANGES_SEQ_LEN + 1):\n", - "# if diffs_for_this_buyer[i:i+CHANGES_SEQ_LEN] == change_seq:\n", - "# return i+CHANGES_SEQ_LEN-1 # the index where this seq completed\n", - " \n", - "# return 0\n", + " prices = [] # list of future prices for each buyer (n prices)\n", + " diffs = [] # list of differences between prices (n-1 diffs)\n", "\n", - "def solve_part2(data):\n", - " initial_secrets = list(map(int, data))\n", - " \n", - " # Store sequences and their banana contributions\n", - " bananas_for_seq = defaultdict(int)\n", - "\n", - " for buyer, secret in enumerate(initial_secrets):\n", - " prices = []\n", - " diffs = []\n", - "\n", - " # Generate prices and calculate diffs in a single loop\n", + " # Generate prices\n", " for _ in range(SECRETS + 1):\n", " prices.append(secret % 10)\n", " secret = generate_next_secret(secret)\n", " \n", + " # Calculate diffs\n", " diffs = [j - i for i, j in zip(prices, prices[1:])]\n", + " assert len(diffs) == SECRETS, f\"Should be {SECRETS} diffs\"\n", " \n", " # Extract sequences and calculate bananas for each sequence\n", - " for i in range(len(diffs) - CHANGES_SEQ_LEN + 1):\n", - " seq = tuple(diffs[i:i+CHANGES_SEQ_LEN])\n", - " bananas_for_seq[seq] += prices[i + CHANGES_SEQ_LEN]\n", + " seen = set() # We retrieve the bananas the FIRST time we see the sequence\n", + " for i in range(len(diffs) - CHANGES_SEQ_LEN + 1): # We need to stop 4 from the end\n", + " seq = tuple(diffs[i:i+CHANGES_SEQ_LEN]) # We need a tuple, as lists are unhashable\n", + " if seq not in seen: # Get banana price the first time we see the sequence\n", + " # We will accumulate the bananas with each buyer (outer loop)\n", + " bananas_for_seq[seq] += prices[i + CHANGES_SEQ_LEN]\n", + " seen.add(seq)\n", + " \n", + " logger.debug(f\"{bananas_for_seq=}\")\n", "\n", " # Find the sequence yielding the most bananas\n", - " best_seq = max(bananas_for_seq.items(), key=lambda x: x[1])\n", + " best_seq = max(bananas_for_seq.items(), key=lambda x: x[1]) # E.g. ((-2, 1, -1, 3), 23)\n", + " logger.debug(f\"{best_seq=}\")\n", " return best_seq[1]" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m18:53:02.078:aoc2024 - DBG: initial_secrets=[1, 2, 3, 2024]\u001b[39m\n", + "\u001b[34m18:53:02.079:aoc2024 - DBG: buyer=0,secret=1\u001b[39m\n", + "\u001b[34m18:53:02.083:aoc2024 - DBG: buyer=1,secret=2\u001b[39m\n", + "\u001b[34m18:53:02.086:aoc2024 - DBG: buyer=2,secret=3\u001b[39m\n", + "\u001b[34m18:53:02.091:aoc2024 - DBG: buyer=3,secret=2024\u001b[39m\n", + "\u001b[34m18:53:02.095:aoc2024 - DBG: bananas_for_seq=defaultdict(, {(2, 0, 6, -4): 5, (0, 6, -4, 4): 9, (6, -4, 4, -9): 0, (-4, 4, -9, 5): 5, (4, -9, 5, 4): 9, (-9, 5, 4, -2): 7, (5, 4, -2, -5): 2, (4, -2, -5, -2): 0, (-2, -5, -2, 1): 1, (-5, -2, 1, 2): 3, (-2, 1, 2, 6): 9, (1, 2, 6, -1): 8, (2, 6, -1, -3): 5, (6, -1, -3, 2): 7, (-1, -3, 2, -1): 6, (-3, 2, -1, 0): 7, (2, -1, 0, 3): 9, (-1, 0, 3, -6): 3, (0, 3, -6, 4): 7, (3, -6, 4, -2): 5, (-6, 4, -2, -3): 2, (4, -2, -3, -1): 1, (-2, -3, -1, 2): 3, (-3, -1, 2, 5): 8, (-1, 2, 5, -3): 5, (2, 5, -3, -2): 3, (5, -3, -2, 2): 5, (-3, -2, 2, -3): 2, (-2, 2, -3, 3): 12, (2, -3, 3, 3): 14, (-3, 3, 3, 0): 8, (3, 3, 0, -1): 12, (3, 0, -1, -3): 4, (0, -1, -3, -4): 0, (-1, -3, -4, 7): 7, (-3, -4, 7, -4): 3, (-4, 7, -4, 2): 5, (7, -4, 2, 1): 6, (-4, 2, 1, -3): 3, (2, 1, -3, 4): 7, (1, -3, 4, -7): 0, (-3, 4, -7, 1): 1, (4, -7, 1, 4): 5, (-7, 1, 4, 0): 5, (1, 4, 0, 3): 8, (4, 0, 3, -2): 6, (0, 3, -2, 2): 8, (3, -2, 2, 1): 9, (-2, 2, 1, -5): 4, (2, 1, -5, 3): 13, (1, -5, 3, 0): 7, (-5, 3, 0, 2): 9, (3, 0, 2, -8): 1, (0, 2, -8, 7): 8, (2, -8, 7, 0): 8, (-8, 7, 0, -8): 0, (7, 0, -8, 0): 0, (0, -8, 0, 7): 7, (-8, 0, 7, -3): 4, (0, 7, -3, -3): 2, (7, -3, -3, 7): 8, (-3, -3, 7, -4): 4, (-3, 7, -4, 5): 9, (7, -4, 5, -5): 4, (-4, 5, -5, -2): 2, (5, -5, -2, 1): 3, (-5, -2, 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6, -5, 8): 9, (6, -5, 8, -9): 0, (-5, 8, -9, 7): 7, (8, -9, 7, -6): 1, (-9, 7, -6, 7): 8})\u001b[39m\n", + "\u001b[34m18:53:02.097:aoc2024 - DBG: best_seq=((-2, 1, -1, 3), 23)\u001b[39m\n", + "\u001b[32m18:53:02.097:aoc2024 - INF: Test passed\u001b[39m\n", + "\u001b[32m18:53:02.100:aoc2024 - INF: Tests passed!\u001b[39m\n", + "\u001b[32m18:53:05.093:aoc2024 - INF: Part 2 soln=1863\u001b[39m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3.21 s, sys: 17.8 ms, total: 3.23 s\n", + "Wall time: 3.02 s\n" + ] + } + ], "source": [ "%%time\n", "sample_inputs = []\n",