87 lines
12 KiB
Plaintext
87 lines
12 KiB
Plaintext
{
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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 example of plotting not quite right\n",
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"\n",
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"An \"ugly\" example of plotting.\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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",
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"text/plain": [
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"<Figure size 360x288 with 1 Axes>"
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import matplotlib\n",
|
|
"\n",
|
|
"#matplotlib.use(\"Agg\")\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"matplotlib.rc(\"grid\", color=\"black\", linestyle=\"-\", linewidth=1)\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(5, 4), dpi=72)\n",
|
|
"axes = fig.add_axes((0.01, 0.01, 0.98, 0.98), facecolor=\".75\")\n",
|
|
"X = np.linspace(0, 2, 40)\n",
|
|
"Y = np.sin(2 * np.pi * X)\n",
|
|
"plt.plot(X, Y, lw=0.05, c=\"b\", antialiased=False)\n",
|
|
"\n",
|
|
"plt.xticks([])\n",
|
|
"plt.yticks(np.arange(-1.0, 1.0, 0.2))\n",
|
|
"plt.grid()\n",
|
|
"ax = plt.gca()\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.11"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|