158 lines
3.5 KiB
Plaintext
158 lines
3.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import networkx as nx\n",
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"\n",
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"with open('input', 'r') as f:\n",
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" data=f.readlines()\n",
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"\n",
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"read_rules = True\n",
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"sequences =[]\n",
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"#was expecting fancier things\n",
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"rules = nx.DiGraph()\n",
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"\n",
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"for ln,line in enumerate([line.strip() for line in data]):\n",
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" if read_rules:\n",
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" if len(line) == 0:\n",
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" read_rules = False\n",
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" continue\n",
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" left,right = line.split('|')\n",
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" rules.add_edge(left,right)\n",
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" else:\n",
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" sequences.append(line.split(','))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def test_sequence(sequence):\n",
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" for lidx,left in enumerate(sequence):\n",
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" if rules.has_node(left):\n",
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" for right in sequence[lidx:]:\n",
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" if rules.has_node(right):\n",
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" if rules.has_edge(right,left):\n",
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" return False\n",
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" return True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"5087"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sum([int(sequence[len(sequence)//2]) for sequence in sequences if test_sequence(sequence)])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Part 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"87"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"incorrect_sequences=[sequence for sequence in sequences if not test_sequence(sequence)]\n",
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"len(incorrect_sequences)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"from functools import cmp_to_key\n",
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"\n",
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"def comparator(x, y):\n",
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" # If both elements are in the graph\n",
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" if x in rules.nodes and y in rules.nodes:\n",
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" if rules.has_edge(x, y): # x must come before y\n",
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" return -1\n",
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" elif rules.has_edge(y, x): # y must come before x\n",
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" return 1\n",
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" # If one or both elements are missing, treat them as equal\n",
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" return 0\n",
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"\n",
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"sorted_incorrect = [sorted(sequence, key=cmp_to_key(comparator)) for sequence in incorrect_sequences]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"4971"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sum([int(sequence[len(sequence)//2]) for sequence in sorted_incorrect if test_sequence(sequence)])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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