python多维数组拟合_python – 将3D数据数组拟合到具有numpy或scipy的1D函数

使用

np.apply_along_axis()可以解决您的问题.这样做：

func1d = lambda y, *args: optimize.curve_fit(f, xdata=x, ydata=y, *args)[0] #

param = np.apply_along_axis( func1d, axis=2, arr=data )

请参阅以下示例：

from scipy import optimize

import numpy as np

def f(x,p1,p2,p3,p4):

return p1 + p2*np.sin(2*np.pi*p3*x + p4)

sx = 50 # size x

sy = 200 # size y

sz = 100 # size z

# creating the reference parameters

tmp = np.empty((4,sy,sz))

tmp[0,:,:] = (1.2-0.8) * np.random.random_sample((sy,sz)) + 0.8

tmp[1,:,:] = (1.2-0.8) * np.random.random_sample((sy,sz)) + 0.8

tmp[2,:,:] = np.ones((sy,sz))

tmp[3,:,:] = np.ones((sy,sz))*np.pi/4

param_ref = np.empty((4,sy,sz,sx)) # param_ref in this shape will allow an

for i in range(sx): # one-shot evaluation of f() to create

param_ref[:,:,:,i] = tmp # the data sample

# creating the data sample

x = np.linspace(0,2*np.pi)

factor = (1.1-0.9)*np.random.random_sample((sy,sz,sx))+0.9

data = f(x, *param_ref) * factor # the one-shot evalution is here

# finding the adjusted parameters

func1d = lambda y, *args: optimize.curve_fit(f, xdata=x, ydata=y, *args)[0] #

param = np.apply_along_axis( func1d, axis=2, arr=data )

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