How to receive tuple as an argument to a function in python 3? - python-3.x

Question

I have some code that was working fine in python2. I need to translate it to python3.
There is one piece of it that I can't understand how to adapt.
Here is some code.
Function with error
def gauss((x, y), x0, y0, intens, sigma):
return intens*numpy.exp(-(numpy.power(x-x0, 2)+numpy.power(y-y0, 2))/(2.*sigma**2)).ravel()
Caller function
def dofwhm(psfdata):
x = numpy.arange(psfdata.shape[1])
y = numpy.arange(psfdata.shape[0])
x, y = numpy.meshgrid(x, y)
popt, pcov = opt.curve_fit(gauss, (x, y), psfdata.ravel(), p0=[psfdata.shape[1]/2, psfdata.shape[0]/2, psfdata[psfdata.shape[1]/2, psfdata.shape[0]/2], 5.0])
return 2.355*abs(popt[3])
The error that I get is
Traceback (most recent call last):
File "catalog.py", line 8, in <module>
import cutPsf
File "/Users/igor/GALPHAT/pypygalphat/preprocessingNew/cutPsf.py", line 9
def gauss((x, y), x0, y0, intens, sigma):
^
SyntaxError: invalid syntax
Can somebody help me how to adapt it for python3?
UPDATE:
Well, #hpaulj answer seems to be right. I found that there are routine to convert Python2 code to Python3 code. After running on target file 2to3 -w cutPsf.py as a result I get the suggested solution from hpaulj. Unfortunately it results in fallowing error:
Traceback (most recent call last):
File "catalog.py", line 323, in <module>
cutPsf.run(tempDir+galaxy.psffile, outDirFits+galaxy.psffile)
File "/Users/igor/GALPHAT/pypygalphat_p3/preprocessingNew/cutPsf.py", line 63, in run
coeffwhm = dofwhm(newPsf)
File "/Users/igor/GALPHAT/pypygalphat_p3/preprocessingNew/cutPsf.py", line 20, in dofwhm
psfdata.shape[1]/2, psfdata.shape[0]/2, psfdata[psfdata.shape[1]/2, psfdata.shape[0]/2], 5.0])
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
As said before, everything runs perfectly with Python2...

Answer 1

do the unpacking later
def gauss(xy, x0, y0, intens, sigma):
x, y = xy
return intens*numpy.exp(-(numpy.power(x-x0, 2)+numpy.power(y-y0, 2))/(2.*sigma**2)).ravel()
I suggested this based on the typical scipy optimized requirements, where the user defined function is called with f(x, *args), where x is the variable (possibly array) that is optimized. But curve_fit is different.
scipy.optimize.curve_fit(f, xdata, ydata, p0=None,...)
Where the f (your gauss?) satisfies:
ydata = f(xdata, *params) + eps
https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html
I guess my suggestion is still valid if xdata is the (x,y) tuple, or array made from that. And ydata is psfdata.ravel().

Answer 2

You need to make some modification with * operator.
def gauss(x, y, x0, y0, intens, sigma):
return intens*numpy.exp(-(numpy.power(x-x0, 2)+numpy.power(y-y0, 2))/(2.*sigma**2)).ravel()
def dofwhm(psfdata):
x = numpy.arange(psfdata.shape[1])
y = numpy.arange(psfdata.shape[0])
x, y = numpy.meshgrid(x, y)
popt, pcov = opt.curve_fit(gauss, *(x, y), psfdata.ravel(), p0=[psfdata.shape[1]/2, psfdata.shape[0]/2, psfdata[psfdata.shape[1]/2, psfdata.shape[0]/2], 5.0])
return 2.355*abs(popt[3])