89 lines
36 KiB
Plaintext
89 lines
36 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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"# Plot and filled plots\n",
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"\n",
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"Simple example of plots and filling between them with 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 640x480 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.pyplot as plt\n",
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"\n",
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"n = 256\n",
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"X = np.linspace(-np.pi, np.pi, n)\n",
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"Y = np.sin(2 * X)\n",
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"\n",
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"plt.axes((0.025, 0.025, 0.95, 0.95))\n",
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"\n",
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"plt.plot(X, Y + 1, color=\"blue\", alpha=1.00)\n",
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"plt.fill_between(X, 1, Y + 1, color=\"blue\", alpha=0.25)\n",
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"\n",
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"plt.plot(X, Y - 1, color=\"blue\", alpha=1.00)\n",
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"plt.fill_between(X, -1, Y - 1, (Y - 1) > -1, color=\"blue\", alpha=0.25)\n",
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"plt.fill_between(X, -1, Y - 1, (Y - 1) < -1, color=\"red\", alpha=0.25)\n",
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"\n",
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"plt.xlim(-np.pi, np.pi)\n",
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"plt.xticks([])\n",
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"plt.ylim(-2.5, 2.5)\n",
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"plt.yticks([])\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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