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90 lines
19 KiB
Plaintext
90 lines
19 KiB
Plaintext
3 years ago
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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": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import pandas as pd\n",
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"%matplotlib inline"
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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": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.array([5, 10, 20, 30, 40, 50, 60, 70])\n",
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"y_tmp = np.array([124524, 40162, 12673, 5561, 2796, 1365, 774, 449])\n",
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"y = y_tmp/720"
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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": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert to pandas dataframe\n",
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"d = {'x' : x, 'y' :y}\n",
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"data = pd.DataFrame(d)"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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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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"# plot using lineplot\n",
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"sns.set(style='darkgrid')\n",
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"plot = sns.lineplot(x='x', y='y', data=data)\n",
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"plot.set_xlabel('MIN_ACTORS value')\n",
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"plot.set_ylabel('Time (minutes)')\n",
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"plt.savefig('actors_time.png', dpi=300)\n"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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},
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"kernelspec": {
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"display_name": "Python 3.9.7 64-bit",
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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": "3.9.7"
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},
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"orig_nbformat": 4
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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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