107 lines
17 KiB
Plaintext
107 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 39,
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"id": "6b0b44f6-bcbf-4092-9201-d1049430e165",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"sample = \"\"\"\\\n",
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"Valve AA has flow rate=0; tunnels lead to valves DD, II, BB\n",
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"Valve BB has flow rate=13; tunnels lead to valves CC, AA\n",
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"Valve CC has flow rate=2; tunnels lead to valves DD, BB\n",
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"Valve DD has flow rate=20; tunnels lead to valves CC, AA, EE\n",
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"Valve EE has flow rate=3; tunnels lead to valves FF, DD\n",
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"Valve FF has flow rate=0; tunnels lead to valves EE, GG\n",
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"Valve GG has flow rate=0; tunnels lead to valves FF, HH\n",
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"Valve HH has flow rate=22; tunnel leads to valve GG\n",
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"Valve II has flow rate=0; tunnels lead to valves AA, JJ\n",
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"Valve JJ has flow rate=21; tunnel leads to valve II\n",
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"\"\"\""
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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": 40,
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"id": "3c34673d-f03a-43ee-89b7-0ee8e953c827",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import re\n",
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"pat = re.compile('Valve ([A-Z]{2}) has flow rate=([0-9]+); tunnels? leads? to valves? ([ ,A-Z]+)')\n",
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"P = dict()\n",
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"for i,l in enumerate(sample.splitlines()):\n",
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" g = pat.search(l)\n",
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" P[g[1]] = (i, int(g[2]), g[3].split(\", \"))\n"
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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": 42,
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"id": "8c46fa33-d751-444a-9a41-1bb2145df583",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import networkx as nx\n",
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"G = nx.DiGraph()\n",
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"for (k,(i,o,V)) in P.items():\n",
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" for v in V:\n",
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" G.add_edge(k,v) #,label=str(i))"
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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": 43,
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"id": "82fdac2e-de6d-44bf-b117-a00addbd8d49",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"nx.draw_networkx(G, with_labels=True)\n",
|
|
"plt.draw()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|