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branning/matplotlib_jupyterlite_loop.ipynb

Last active October 22, 2023 23:25
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Matplotlib nbagg example that updates in a loop
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matplotlib_jupyterlite_loop.ipynb
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matplotlib_notebook_loop.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"%matplotlib widget\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def pltsin(ax, colors=['b']):\n",
" x = np.linspace(0,1,100)\n",
" if ax.lines:\n",
" for line in ax.lines:\n",
" line.set_xdata(x)\n",
" y = np.random.random(size=(100,1))\n",
" line.set_ydata(y)\n",
" else:\n",
" for color in colors:\n",
" y = np.random.random(size=(100,1))\n",
" ax.plot(x, y, color)\n",
" fig.canvas.draw()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7430fad9469e432aac8cc107167258fe",
"version_major": 2,
"version_minor": 0
},
"image/png": 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",
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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' width=640.0/>\n",
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax = plt.subplots(1,1)\n",
"ax.set_xlabel('X')\n",
"ax.set_ylabel('Y')\n",
"ax.set_xlim(0,1)\n",
"ax.set_ylim(0,1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"# run this cell to dynamically update plot\n",
"for f in range(5):\n",
" pltsin(ax, ['b', 'r'])\n",
" time.sleep(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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