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我正在尝试编写一个程序，将 .csv 文件中的两组数据读取到数组中，然后将其拟合为分段函数。对我来说最重要的是这些拟合是同时完成的，因为它们具有相同的参数。这个分段函数是我尝试这样做的，但如果您知道同时适合它们的更好方法，我也会非常感谢有关此的建议。

为了避免上传 csv 文件，我将数据直接添加到数组中。

import numpy
import csv
import matplotlib
from scipy import optimize

xdata = [2.0, 10.0, 30.0, 50.0, 70.0, 90.0, 110.0, 130.0, 150.0, 250.0, 400.0, 1002.0, 1010.0, 1030.0, 1050.0, 1070.0, 1090.0, 1110.0, 1130.0, 1150.0, 1250.0, 1400.0]
ydata = [0.013833958803215633, 0.024273268442992078, 0.08792766000711709, 0.23477725658012044, 0.31997367288103884, 0.3822895295625711, 0.46037063893452784, 0.5531831477605121, 0.559757863748663, 0.6443036770720387, 0.7344601382896991, 2.6773979205076136e-09, 9.297289736857164e-10, 0.10915332214935693, 0.1345307163724643, 0.1230161681870127, 0.11286094974672768, 0.09186485171688986, 0.06609131137369342, 0.052616358869021135, 0.034629686697483314, 0.03993853791147095]

我想拟合标记为“SSdecay”的函数的前 11 个点，以及我想拟合标记为“SUdecay”的函数的后 11 个点。我同时这样做的尝试是制作标记为“fitfunciton”的分段函数。

#defines functions to be used in fitting

#to fit the first half of data
def SSdecay(x, lam1, lam2, norm, xoff):
    return norm*(1 + lam2/(lam1 - lam2)*numpy.exp(-lam1*(x - xoff)) -
        lam1/(lam1 - lam2)*numpy.exp(-lam2*(x - xoff)))

#to fit the second half of data
def SUdecay(x, lam1, lam2, norm, xoff):
    return norm*(lam1/(lam1 - lam2))*(-numpy.exp(-lam1*(x - xoff)) +
        numpy.exp(-lam2*(x - xoff)))

#piecewise function combining SS and SU functions to fit the whole data set
def fitfunction(x, lam1, lam2, norm, xoff):
    y = numpy.piecewise(x,[x < 1000, x >= 1000],[SSdecay(x, lam1, lam2, norm, xoff),SUdecay(x, lam1, lam2, norm, xoff)])
    return y

#fits the piecewise function with initial guesses for parameters
p0=[0.01,0.02,1,0]
popt, pcov = optimize.curve_fit(fitfunction, xdata, ydata, p0)

print(popt)
print(pcov)

运行后我得到错误：

ValueError: NumPy boolean array indexing assignment cannot assign 22 input values to the 11 output values where the mask is true

似乎curve_fit不喜欢我使用分段函数，但我不确定为什么或者它是否是一个可修复的问题。

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1 回答 1

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这是我使用标准化数据分别拟合这两个函数的结果。这些看起来不太可能作为单个分段方程工作，请参阅下面的绘图图像和源代码。对于这两个方程，我也有非常不同的拟合参数：

SS参数：[0.0110936, 0.09560932, 0.72929264, 6.82520026]

SU 参数：[ 3.46853883e-02, 9.54208972e-03, 1.99877873e-01, 1.00465563e+03]

阴谋

import numpy
import matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

xdata = [2.0, 10.0, 30.0, 50.0, 70.0, 90.0, 110.0, 130.0, 150.0, 250.0, 400.0, 1002.0, 1010.0, 1030.0, 1050.0, 1070.0, 1090.0, 1110.0, 1130.0, 1150.0, 1250.0, 1400.0]
ydata = [0.013833958803215633, 0.024273268442992078, 0.08792766000711709, 0.23477725658012044, 0.31997367288103884, 0.3822895295625711, 0.46037063893452784, 0.5531831477605121, 0.559757863748663, 0.6443036770720387, 0.7344601382896991, 2.6773979205076136e-09, 9.297289736857164e-10, 0.10915332214935693, 0.1345307163724643, 0.1230161681870127, 0.11286094974672768, 0.09186485171688986, 0.06609131137369342, 0.052616358869021135, 0.034629686697483314, 0.03993853791147095]

#to fit the first half of data
def SSdecay(x, lam1, lam2, norm, xoff):
    return norm*(1 + lam2/(lam1 - lam2)*numpy.exp(-lam1*(x - xoff)) -
        lam1/(lam1 - lam2)*numpy.exp(-lam2*(x - xoff)))

#to fit the second half of data
def SUdecay(x, lam1, lam2, norm, xoff):
    return norm*(lam1/(lam1 - lam2))*(-numpy.exp(-lam1*(x - xoff)) +
        numpy.exp(-lam2*(x - xoff)))

# some initial parameter values
initialParameters_ss = numpy.array([0.01, 0.02, 1.0, 0.0])
initialParameters_su = initialParameters_ss # same values for this example

# curve fit the equations individually to their respective data
ssParameters, pcov = curve_fit(SSdecay, xdata[:11], ydata[:11], initialParameters_ss)
suParameters, pcov = curve_fit(SUdecay, xdata[11:], ydata[11:], initialParameters_su)

# values for display of fitted function
lam1_ss, lam2_ss, norm_ss, xoff_ss = ssParameters
lam1_su, lam2_su, norm_su, xoff_su = suParameters

# for plotting the fitting results
y_fit_ss = SSdecay(xdata[:11], lam1_ss, lam2_ss, norm_ss, xoff_ss) # first data set, first equation
y_fit_su = SUdecay(xdata[11:], lam1_su, lam2_su, norm_su, xoff_su) # second data set, second equation

plt.plot(xdata, ydata, 'D') # plot the raw data as a scatterplot
plt.plot(xdata[:11], y_fit_ss) # plot the SS equation using the fitted parameters
plt.plot(xdata[11:], y_fit_su) # plot the SU equation using the fitted parameters
plt.show()

print('SS parameters:', ssParameters)
print('SU parameters:', suParameters)
于 2018-06-13T22:22:27.603 回答