如何根据测量数据拟合图？ [英] How can I fit my plots from measured data?

问题描述

如何拟合图？

到目前为止，我有以下代码，该代码可以绘制数组中的各种图形（实验中的数据）放入循环中

Up to now, I've got the following code, which plots a variety of graphs from an array (data from an experiment) as it is placed in a loop:

import matplotlib as plt
plt.figure(6)
plt.semilogx(Tau_Array, Correlation_Array, '+-')
plt.ylabel('Correlation')
plt.xlabel('Tau')
plt.title("APD" + str(detector) + "_Correlations_log_graph")
plt.savefig(DataFolder + "/APD" + str(detector) + "_Correlations_log_graph.png")

到目前为止，这种方法可以使用对数图，但是我想知道拟合过程在这里如何工作。最后，我希望有一个最能描述我所测量数据的公式或/和图表。

This works so far with a logarithmic plot, but I am wondering how the fitting process could work right here. In the end I would like to have some kind of a formula or/and a graph which best describes the data I measured.

如果有人可以帮助我，我将非常高兴！

I would be pleased if someone could help me!

推荐答案

您可以在 scipy.optimize <中使用 curve_fit / code>。这是一个示例

You can use curve_fit from scipy.optimize for this. Here is an example

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

def func(x,a):
   return np.exp(a*x)

x,y,z = np.loadtxt("fit3.dat",unpack=True)

popt,pcov = curve_fit(func,x,y)
y_fit = np.exp(popt[0]*x)


plt.plot(x,y,'o')
plt.errorbar(x,y,yerr=z)
plt.plot(x,y_fit)
plt.savefig("fit3_plot.png")
plt.show()

在您的情况下，可以相应地定义 func 。 popt 是一个包含拟合参数值的数组。

In yourcase, you can define the func accordingly. popt is an array containing the value of your fitting parameters.

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