82 lines
27 KiB
Plaintext
82 lines
27 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"# A simple, good-looking plot\n",
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"\n",
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"Demoing some simple features of matplotlib\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": 1,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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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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"text/plain": [
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"<Figure size 360x288 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 numpy as np\n",
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"import matplotlib\n",
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"\n",
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"#matplotlib.use(\"Agg\")\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"fig = plt.figure(figsize=(5, 4), dpi=72)\n",
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"axes = fig.add_axes((0.01, 0.01, 0.98, 0.98))\n",
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"X = np.linspace(0, 2, 200)\n",
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"Y = np.sin(2 * np.pi * X)\n",
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"plt.plot(X, Y, lw=2)\n",
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"plt.ylim(-1.1, 1.1)\n",
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"plt.grid()\n",
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"\n",
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"plt.show()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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