107 lines
17 KiB
Plaintext
107 lines
17 KiB
Plaintext
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{
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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": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqSElEQVR4nO3de1yUZf7/8dc9B2Y4DSignCxUTETF0kzKSsy2jMq0PG3abrXllv462HZwtdOaltvuZrubZtth9SdWuqSVZnkotczwmEdEIw+JCgKKMMAMM3PP9w9iFEVUmKN8no9Hj0fe9z23nyF7e8811/W5FKfTiRBCCO/Q+LoAIYRoSSR0hRDCiyR0hRDCiyR0hRDCiyR0hRDCi3SNnYyOjnYmJSV5qRQhhLg0bN68ucTpdMY0dK7R0E1KSmLTpk2eqUoIIS5RiqIcPNc5GV4QQggvktAVQggvktAVQggvktAVQggvktAVQggvktAVQggvktAVQggvktAVQggvanRxhHCfErOV7M0F5BWWU26xYzLqSIk1MaxXIlFhBl+XJ4TwEgldD9t2qIwZq/NZs7cYAKtddZ0z6gqZvnIvGZ1jGNsvmR7tIn1UpRDCWyR0PSgr5wBTl+ZhsTtoaIMOy68BvDy3iG/3ljApM4XR6UneLVII4VUSuh5SG7i7qbap573W6YRqm4OpS3cDSPAKcQmTL9I8YNuhMqYuzbugwD1dtU1l6tI8theUeaYwIYTPSeh6wIzV+VjsjnrHCudNoGLbMuxlRRycdgdO1dHgay12BzNX53ujTCGED0joulmJ2cqavcUNjuFeCKcTVu0pptRsdW9hQgi/IKHrZtmbC5p9DwXI3tL8+wgh/I+ErpvlFZbXmxbWFBa7St7RCjdVJITwJxK6blZusbvpPja33EcI4V8kdN3MZHTPLDyTUe+W+wgh/IuErpulxJow6Jr3YzXqNKTEhbupIiGEP5HQdbOhvRKbfQ8nMLRn8+8jhPA/ErpuFh1moN8VMShKQ2cbPFj/CgX6d46RJjhCXKIkdD1gXEYyRp223jFnTTXa4HDUmioUQyiKRtvga406LWMzkr1RphDCByR0PaBHu0gmZaYQrK/98dYUH8RWeoigth2pzFuLIbbhUA3Wa5iUmUJaYqQXqxVCeJM0vPGQuqY1Tzz1DCd3fENkxv0UZj2DNrQVUZlP1rtWUWqfcKXLmBCXPsXZyHrVq6++2rlp0yYvlnPp2V5QxszV+azMLURRFE7vgWPUaXBSO4Y7NiNZnnCFuEQoirLZ6XRe3dA5edL1sLTESN4e1Yv49ldw4/3PEt+lN+UWGyajnpS4cIb2lJ0jhGhJJHS94J133qHwYD7hBeuZ/vLDvi5HCOFD8kWah23evJknn3wSgC1btvi2GCGEz0noetDx48fJzMzEaq1t07hr1y7MZrOPqxJC+JKErgetW7eOkydPotVq0Wg0qKrK6tWrfV2WEMKHZEzXg+644w4qKytJSEige/fudOnShbi4OF+XJYTwIQldD9NqtZSWljJ+/HgyMzN9XY4QwsdkeMHD9u/fj91u5+abb/Z1KUIIPyCh62GLFi0iPDycoKAgX5cihPADEroetmrVKjp06ODrMoQQfkJC18N27NjB1Vc3uBpQCNECyRdpHvLxxx8TGRnJkSNHuO2223xdjhDCT0jDGw+JjY3l5MmTWCwWIiMjGTRoEHPmzPF1WUIIL2is4Y0ML3jIwIEDsVgsAFgsFkwmk48rEkL4AwldD7nrrrvQ6WpHb1JTU3njjTd8XJEQwh9I6HrITTfdhN1uJygoiCVLlqDXy5bqQgj5Is1jIiIiMBgMvPDCC7L0VwjhIqHrRiVmK9mbC8grLKfcYqfDqMlEpN9FqdkqjcqFEIDMXnCLbYfKmLE6nzV7iwGw2k/tyVO3JU9G5xjG9kumR7tI3xQphPAa2a7Hg7JyDjB1aR4Wu4OG/v6y/BrAy3OL+HZviWw+KUQLJ6HbDLWBu5vq03ebPAenE6ptDqYu3Q0gwStECyWzF5po26Eypi7Nqxe4BTMfpPrAVszbV1KY9WyDr6u2qUxdmsf2gjIvVSqE8CcSuk00Y3U+FrujSa+12B3MXJ3v5oqEEIFAQrcJSsxW1uwtbnAM90I4nbBqTzGlZqt7CxNC+D0J3SbI3lzQ7HsoQPaW5t9HCBFYJHSbIK+wvN60sKaw2FXyjla4qSIhRKCQ0G2CcovdTfexueU+QojAIaHbBCaje2bamYzSj0GIlkZCtwlSYk0YdM370Rl1GlLiwt1UkRAiUEjoNsHQXonNvocTGNqz+fcRQgQWWZHWBNFhBvpdEcOK3UX1po0ljv3A9e9haefecl1RoH/nGGmCI0QLJE+6TTQuIxmjTtuk1xp1WsZmJLu5IiFEIJDQbaIe7SKZlJlCsP7ifoTBeg2TMlNIS4z0TGFCCL8mwwvNUNe0prEuY3UUpfYJV7qMCdGySeg20+j0JNISI5m5Op9Ve4qxWiygC3Kdr+un279zDGMzkuUJV4gWTkLXDdISI5k1+mreenc2z3+whAeenES5xY7JqCclLpyhPRPlSzMhBCCh6zaVlZVM/NPjVFRU8IeuL9Ot21W+LkkI4YfkizQ3efTRR6msrARgwYIFPq5GCOGvJHTd4LPPPmPBggWoam0TnKysLB9XJITwVxK6brBr1y7Cw2uX9AYFBbF//35++eUXH1clhPBHshuwG2m1WubMmUNERAS33HILBoN8eSZESyS7AXtBQUEBqqoycuRIdDr5sQohGibDC27y9ddfExwcLIErhGiUhK6b5OTkEB0d7esyhBB+TkLXTXbu3EmHDh18XYYQws9J6LrJ/v376d69u6/LEEL4OQldNyktLSU9Pd3XZQgh/JyErhvY7XYsFgsDBgzwdSlCCD8noesGOTk5aLVaYmNjfV2KEMLPSei6wZo1azCZTL4uQwgRACR03WDz5s0kJCT4ugwhRACQ0HWDvXv3csUVV/i6DCFEAJDQdYMjR47Qs2dPX5chhAgAErpucPLkSfr16+frMoQQAUAaBTSR0+lk//79KIqCqqoyR1cIcUHkSbeJioqK6NixI8nJyQDceOONvPvuuz6uSgjh7yR0myg2NpbLLrvMtVvEpk2bKC4u9nFVQgh/J6HbDKNHj0ZRFADS0tJ47rnnfFyREMLfSeg2w/Dhw3E6neh0OhYuXIhWq/V1SUIIPyeh2wxpaWkoisIjjzzCZZdd5utyhBABQGYvXKQSs5XszQXkFZZTbrGTMHQiyXf8llKzlagw2RNNCNE42ZjyAm07VMaM1fms2Vv7ZZnVrrrOGXUanEBG5xjG9kumR7tI3xQphPALsjFlM2XlHGDq0jwsdgcN/R1l+TWAl+cW8e3eEiZlpjA6Pcm7RQohAoKE7nnUBu5uqm3qea91OqHa5mDq0t0AErxCiLPIF2mN2HaojKlL8y4ocE9XbVOZujSP7QVlnilMCBGwJHQbMWN1Pha7A4DCeRM4NH0ETrvtrOtsZYUcnHYnpctmuI5Z7A5mrs73Wq1CiMAgoXsOJWYra/YW43SCvawIa0EuKApV+evPurZy5zdojGFU7f7OFcpOJ6zaU0yp2ert0oUQfkxC9xyyNxe4/t288xsM8Z0J7T6Ayh1f17vO6XRSufMbIm+8DzS6eqGsANlbChBCiDoSuueQV1jumhZWufMbQrtmENq1P9X7t+CoPOG6zlqwC3tFCSGpNxKacn29ULbYVfKOVni9diGE/5LQPYdyix0Ay6Fd2MuPEZJyPYbYZHSRcVTuWuO6rnLHNwR3uBqtMYzQrv1+DeWy0+5z9hiwEKLlktA9B5OxdjZd5c6vCW5/FdqQCABCU/th3ln7NKvarFTuWUto1wwADAld0JliqMxdc9p99N4tXAjh12SeLmCxWNBqtej1pwKyc9tw9E4blXlrQVU59O/RtSfsNlRrJTVF+7CV/ILTWsXxZTM5vmIWAKqlEvOOrzH1vgujTkNKXLgv3pIQwk9J6AI33XQTP/zwAyEhIYSGhlJVVYUmJILga3+LomiIe+gtFO2pQC7+dBrmnd9gKzlIaNp
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"nx.draw_networkx(G, with_labels=True)\n",
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"plt.draw()"
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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 (ipykernel)",
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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": "3.9.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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