86 lines
11 KiB
Plaintext
86 lines
11 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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"# Grid\n",
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"\n",
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"Displaying a grid on the axes in matploblib.\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 matplotlib.pyplot as plt\n",
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"from matplotlib import ticker\n",
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"\n",
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"ax = plt.axes((0.025, 0.025, 0.95, 0.95))\n",
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"\n",
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"ax.set_xlim(0, 4)\n",
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"ax.set_ylim(0, 3)\n",
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"ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))\n",
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"ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.1))\n",
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"ax.yaxis.set_major_locator(ticker.MultipleLocator(1.0))\n",
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"ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.1))\n",
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"ax.grid(which=\"major\", axis=\"x\", linewidth=0.75, linestyle=\"-\", color=\"0.75\")\n",
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"ax.grid(which=\"minor\", axis=\"x\", linewidth=0.25, linestyle=\"-\", color=\"0.75\")\n",
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"ax.grid(which=\"major\", axis=\"y\", linewidth=0.75, linestyle=\"-\", color=\"0.75\")\n",
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"ax.grid(which=\"minor\", axis=\"y\", linewidth=0.25, linestyle=\"-\", color=\"0.75\")\n",
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"ax.set_xticklabels([])\n",
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"ax.set_yticklabels([])\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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