Python中具有折断幂定律的曲线拟合 [英] Curve fitting with broken power law in Python
问题描述
我试图遵循并重复使用@ThePredator某人建议的一段代码(带有我自己的数据)(由于我目前不具备50的声誉，所以我无法对此线程发表评论).完整的代码如下:
Im trying to follow and re-use a piece of code (with my own data) suggested by someone named @ThePredator (I couldn't comment on that thread since I don't currently have the required reputation of 50). The full code is as follows:
import numpy as np # This is the Numpy module
from scipy.optimize import curve_fit # The module that contains the curve_fit routine
import matplotlib.pyplot as plt # This is the matplotlib module which we use for plotting the result
""" Below is the function that returns the final y according to the conditions """
def fitfunc(x,a1,a2):
y1 = (x**(a1) )[x<xc]
y2 = (x**(a1-a2) )[x>xc]
y3 = (0)[x==xc]
y = np.concatenate((y1,y2,y3))
return y
x = array([0.001, 0.524, 0.625, 0.670, 0.790, 0.910, 1.240, 1.640, 2.180, 35460])
y = array([7.435e-13, 3.374e-14, 1.953e-14, 3.848e-14, 4.510e-14, 5.702e-14, 5.176e-14, 6.0e-14,3.049e-14,1.12e-17])
""" In the above code, we have imported 3 modules, namely Numpy, Scipy and matplotlib """
popt,pcov = curve_fit(fitfunc,x,y,p0=(10.0,1.0)) #here we provide random initial parameters a1,a2
a1 = popt[0]
a2 = popt[1]
residuals = y - fitfunc(x,a1,a2)
chi-sq = sum( (residuals**2)/fitfunc(x,a1,a2) ) # This is the chi-square for your fitted curve
""" Now if you need to plot, perform the code below """
curvey = fitfunc(x,a1,a2) # This is your y axis fit-line
plt.plot(x, curvey, 'red', label='The best-fit line')
plt.scatter(x,y, c='b',label='The data points')
plt.legend(loc='best')
plt.show()
我在运行此代码时遇到问题，我得到的错误如下:
Im having some problem running this code and the errors I get are as follows:
y3 =(0)[x == xc]
y3 = (0)[x==xc]
TypeError:"int"对象没有属性" getitem "
TypeError: 'int' object has no attribute 'getitem'
还有:
xc未定义
我没有看到代码中缺少任何内容(不必定义xc吗?).
I don't see anything missing in the code (xc shouldn't have to be defined?).
作者(@ThePredator)或其他对此有所了解的人可以帮助我确定我没有看到的内容.
Could the author (@ThePredator) or someone else having knowledge about this please help me identify what i haven't seen.
-
新版本的代码:
New version of code:
import numpy as np # This is the Numpy module
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
def fitfunc(x, a1, a2, xc):
if x.all() < xc:
y = x**a1
elif x.all() > xc:
y = x**(a1 - a2) * x**a2
else:
y = 0
return y
xc = 2
x = np.array([0.001, 0.524, 0.625, 0.670, 0.790, 0.910, 1.240, 1.640, 2.180, 35460])
y = np.array([7.435e-13, 3.374e-14, 1.953e-14, 3.848e-14, 4.510e-14, 5.702e-14, 5.176e-14, 6.0e-14,3.049e-14,1.12e-17])
popt,pcov = curve_fit(fitfunc,x,y,p0=(1.0,1.0))
a1 = popt[0]
a2 = popt[1]
residuals = y - fitfunc(x, a1, a2, xc)
chisq = sum((residuals**2)/fitfunc(x, a1, a2, xc))
curvey = [fitfunc(val, a1, a2, xc) for val in x] # y-axis fit-line
plt.plot(x, curvey, 'red', label='The best-fit line')
plt.scatter(x,y, c='b',label='The data points')
plt.legend(loc='best')
plt.show()
推荐答案
Hi执行以下操作定义您的函数，它将解决.x是一个数组(或列表)，并且应返回y作为数组(或列表).然后可以在curvefit中使用它.
Hi Do the following to define your function, and it will solve. x is an array (or list) and it should return y as an array (or list). And then you can use it in curvefit.
def fit_function(x, a1, a2, xc):
y = []
for xx in x:
if xx<xc:
y.append(x**a1)
elif xx>xc:
y.append(x**(a1 - a2) * x**a2)
else:
y.append(0.0)
return y
这篇关于Python中具有折断幂定律的曲线拟合的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
