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我有一张图像，我想将其拟合到 2D 方程以提取 nx 和 ny 参数。首先，我从 fit 中定义了 2D 函数和残差，然后我读取了图像文件，然后我尝试使用 leastsq 方法对其进行拟合，这是我的代码： 

#!/usr/bin/python

import pyfits
import numpy as np
import numpy.random as npr
import matplotlib.pyplot as plt
import scipy.optimize


nx=870
ny=901

# define 2D function
def fun(nx,ny):

   n=(1+((nx**2+ny**2)**0.5/150)**2)**-3.7
   return n
vfun=np.vectorize(fun)
nxlist=np.linspace(-nx,nx,870)
nylist=np.linspace(-ny,ny,901)
X,Y=np.meshgrid(nxlist,nylist)
Z=vfun(X,Y)


def residuals(p,y,nx,ny):
    nx,ny = p
    err = y-fun(nx,ny)
    return err

def peval (nx,ny,p):
    nx,ny=p
    return fun(nx,ny)


# read image file

def image():
    h = pyfits.open('image.fits')
    IM = h[0].data      # copy the image data into a numpy (numerical python) array
    return IM
y_true = image()
y_meas = y_true+0.1*np.random.randn(ny,nx)         # add noise
colmap = plt.get_cmap('CMRmap') # load CMRmap colormap
plt.imshow(y_meas, cmap=colmap, origin='lower') # plot image using gray colorbar
plt.show()


# initial values
p0=[300,500]

plsq = scipy.optimize.leastsq(residuals,p0,args=(y_meas,nx,ny))
print plsq

但是，我收到此错误消息

File "image_fit_test.py", line 51, in <module>    
plsq =   scipy.optimize.leastsq(residuals,p0,args=(y_meas,nx,ny))
File "/.../anaconda/lib/python2.7/site-packages/scipy/optimize/minpack.py", line 364,
in leastsq
gtol, maxfev, epsfcn, factor, diag)
minpack.error: Result from function call is not a proper array of floats.

请有人可以提出任何解决方案，这哪里出错了？

先感谢您。

4

1 回答 1

1

只需替换residuals()为以下内容即可解决您的问题：

def residuals(p,y,nx,ny):
    nx,ny = p
    err = y-fun(nx,ny)
    return err.flatten()

基本上我怀疑函数调用residuals(p0, meas, nx, ny)会返回2d array的形状(nx, ny)，这会导致minpack.error异常。您需要将 a 1d array（或 a float）传递给leastsq().

于 2014-03-08T17:52:24.837 回答