# fitting an image with 2D equation in python

I have an image and I want to fit it to 2D equation in order to extract nx and ny parameters. First I defined 2D function and residuals from fit then I read the image file and then I tried to fit it using leastsq method, this is my code:

```#!/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)

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
```

However, I got this error message

```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.
```

Please can someone suggest any solution and where is this going wrong?

Simply replace residuals() with the following should solve your problem:

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

Basically I suspect the function call of residuals(p0, meas, nx, ny) would return a 2d array of the shape of (nx, ny), which results in the minpack.error exception. You need to pass a 1d array (or a float) to leastsq().