88 lines
18 KiB
Plaintext
88 lines
18 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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"# Imshow elaborate\n",
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"\n",
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"An example demoing imshow and styling the figure.\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 2 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",
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"def f(x, y):\n",
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" return (1 - x / 2 + x**5 + y**3) * np.exp(-(x**2) - y**2)\n",
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"\n",
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"\n",
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"n = 10\n",
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"x = np.linspace(-3, 3, int(3.5 * n))\n",
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"y = np.linspace(-3, 3, int(3.0 * n))\n",
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"X, Y = np.meshgrid(x, y)\n",
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"Z = f(X, Y)\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.imshow(Z, interpolation=\"nearest\", cmap=\"bone\", origin=\"lower\")\n",
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"plt.colorbar(shrink=0.92)\n",
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"\n",
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"plt.xticks([])\n",
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"plt.yticks([])\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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