85 lines
310 KiB
Plaintext
85 lines
310 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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"# Plotting a scatter of points\n",
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"\n",
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"A simple example showing how to plot a scatter of points 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 = 1024\n",
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"rng = np.random.default_rng()\n",
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"X = rng.normal(0, 1, n)\n",
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"Y = rng.normal(0, 1, n)\n",
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"T = np.arctan2(Y, 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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"plt.scatter(X, Y, s=75, c=T, alpha=0.5)\n",
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"\n",
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"plt.xlim(-1.5, 1.5)\n",
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"plt.xticks([])\n",
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"plt.ylim(-1.5, 1.5)\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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