96 lines
125 KiB
Plaintext
96 lines
125 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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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2026-05-23T20:56:43.488066Z",
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"start_time": "2026-05-23T20:56:41.855404Z"
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}
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},
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"df = pd.read_csv(\"results.csv\")\n",
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"\n",
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"# график времени\n",
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"plt.figure(figsize=(10, 5))\n",
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"for algo in df[\"algorithm\"].unique():\n",
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" subset = df[df[\"algorithm\"] == algo]\n",
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" plt.plot(subset[\"maze\"], subset[\"time_ms\"], marker=\"o\", label=algo)\n",
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"\n",
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"plt.title(\"Время работы алгоритмов\")\n",
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"plt.ylabel(\"ms\")\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"plt.show()\n",
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"\n",
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"# график посещённых клеток\n",
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"plt.figure(figsize=(10, 5))\n",
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"for algo in df[\"algorithm\"].unique():\n",
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" subset = df[df[\"algorithm\"] == algo]\n",
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" plt.plot(subset[\"maze\"], subset[\"visited\"], marker=\"o\", label=algo)\n",
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"\n",
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"plt.title(\"Количество посещённых клеток\")\n",
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"plt.ylabel(\"cells\")\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"plt.show()"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 1000x500 with 1 Axes>"
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],
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"image/png": "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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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"jetTransient": {
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"display_id": null
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}
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1000x500 with 1 Axes>"
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],
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"image/png": "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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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"jetTransient": {
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"display_id": null
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}
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}
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],
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"execution_count": 1
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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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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}
|