86 lines
2.3 KiB
Plaintext
86 lines
2.3 KiB
Plaintext
{
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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# Curve fitting\n\nDemos a simple curve fitting\n"
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]
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},
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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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"First generate some data\n\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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import numpy as np\n\n# Seed the random number generator for reproducibility\nrng = np.random.default_rng(27446968)\n\nx_data = np.linspace(-5, 5, num=50)\nnoise = 0.01 * np.cos(100 * x_data)\na, b = 2.9, 1.5\ny_data = a * np.cos(b * x_data) + noise\n\n# And plot it\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(6, 4))\nplt.scatter(x_data, y_data)"
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]
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},
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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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"Now fit a simple sine function to the data\n\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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import scipy as sp\n\n\ndef test_func(x, a, b, c):\n return a * np.sin(b * x + c)\n\n\nparams, params_covariance = sp.optimize.curve_fit(\n test_func, x_data, y_data, p0=[2, 1, 3]\n)\n\nprint(params)"
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]
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},
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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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"And plot the resulting curve on the data\n\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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(6, 4))\nplt.scatter(x_data, y_data, label=\"Data\")\nplt.plot(x_data, test_func(x_data, *params), label=\"Fitted function\")\n\nplt.legend(loc=\"best\")\n\nplt.show()"
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]
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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",
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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": 0
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} |