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90 lines
14 KiB
Plaintext
90 lines
14 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": 1,
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.array([500, 1000, 5000, 10000, 25000, 50000, 75000, 100000])\n",
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"y_tmp = np.array([34933, 18122, 3916, 2125, 940, 533, 366, 269])\n",
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"y = y_tmp/720 # Dividing by 60 (results in minutes) x 12 (number of threads)"
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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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"# 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": 4,
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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(' value')\n",
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"plot.set_ylabel('Time (minutes)')\n",
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"plt.savefig('movies_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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