How to use python to do data fitting

This article will explain in detail how to use python to do data fitting. The editor thinks it is very practical, so I share it for you as a reference. I hope you can get something after reading this article.

1. Example: fit a function Func, here is an exponential function.

Provenance:

SciPy v1.1.0 Reference Guide

# Headerimport numpy as npimport matplotlib.pyplot as pltfrom scipy.optimize import curve_fit#Define a function (here an exponential function is used) def func (x, a, b, c): return a * np.exp (- b * x) + c#Create the data to be fit with some noisexdata = np.linspace (0,4,50) y = func (xdata, 2.5,1.3,0.5) np.random.seed (1729) y_noise = 0.2 * np.random.normal (size=xdata.size) ydata = y + y_noiseplt.plot (xdata Ydata, 'bo', label='data') # Fit for the parameters aforme b, c of the function func:popt, pcov = curve_fit (func, xdata, ydata) popt # output: array ([2.55423706, 1.35190947, 0.47450618]) plt.plot (xdata, func (xdata, * popt),' r Mustang, label='fit: asides% 5.3f, baked% 5.3f, cations% 5.3f'% tuple (popt)) # In the case of parameters aforb C need be constrainted#Constrain the optimization to the region of # 0