def rebin_data(self, grid, use_psf=True):
"""Calculates the center of mass of the grid and then
rebins so that the center pixel really is the center of the array
For this we do a 2-d interpolation on the grid
"""
a = psf_fitter.psffit(abs(grid), circle=False, rotate=1)
xcen = a[2]
ycen = a[2]
xlen, ylen = grid.shape
xval = arange(xlen)
yval = arange(ylen)
xint = interp1d(xval, self.xpos_abs)
yint = interp1d(yval, self.ypos_abs)
xintcen = self.xmax_pos-xint(xcen)
yintcen = self.ymax_pos-yint(ycen)
print self.xmax_pos, xintcen, self.ymax_pos, yintcen
f_real = interp2d(self.xpos_rel, self.ypos_rel, real(grid))
f_imag = interp2d(self.xpos_rel, self.ypos_rel, imag(grid))
xnew = self.xpos_rel - xintcen
ynew = self.ypos_rel - yintcen
recen_grid = f_real(xnew, ynew) + 1j*f_imag(xnew, ynew)
print nd.center_of_mass(abs(recen_grid))
return recen_grid
def calculate_grid_center(self, grid, fit_type="psf"):
if fit_type is "psf":
a = psf_fitter.psffit(abs(grid), circle=False, rotate=1)
print a
elif fit_type is "cm":
print "cm"
else:
print "nope"
figure(1)
clf()
x = linspace(0,5,1000)
plot(x,agpy.psf_fitter._airy_func(x),label='Airy')
plot(x,agpy.psf_fitter._gaussian_func(x),label='Gaussian')
plot(x,agpy.psf_fitter._gaussian_func(x,sigma=airy_fwhm/gaussian_fwhm),label='Gaussian (sigma=%5.3f)' % (airy_fwhm/gaussian_fwhm))
figure(2)
clf()
test_airy = psf_fitter.airy([1,50,50,5],vheight=False,shape=[100,100])
imshow((test_airy),norm=asinh_norm.AsinhNorm())
colorbar()
airyfitp,airyfitimg = psf_fitter.psffit(test_airy,returnfitimage=True,vheight=False)
gaussfitp,gaussfitimg = psf_fitter.psffit(test_airy,psffunction=psf_fitter.twodgaussian,returnfitimage=True,vheight=False)
print "When fitting, the Gaussian FWHM will be smaller than the Airy FWHM by %f (inverted, this is %f)" % (gaussfitp[4]*gaussian_fwhm/(airyfitp[4]*airy_fwhm),airyfitp[4]*airy_fwhm/(gaussfitp[4]*gaussian_fwhm))
print ""
print "Fitted Airy parameters: amp %10f xcen %10f ycen %10f width %10f fwhm %10f. Residual: %10g Res^2: %10g" % (tuple(airyfitp[1:].tolist())+
(airyfitp[4]*airy_fwhm,(test_airy-airyfitimg).sum(),((test_airy-airyfitimg)**2).sum()))
print "Fitted Gaussian parameters: amp %10f xcen %10f ycen %10f width %10f fwhm %10f. Residual: %10g Res^2: %10g" % (tuple(gaussfitp[1:].tolist())+
(gaussfitp[4]*gaussian_fwhm,(test_airy-gaussfitimg).sum(),((test_airy-gaussfitimg)**2).sum()))
figure(3)
clf()
subplot(121)
imshow((test_airy-airyfitimg), norm=asinh_norm.AsinhNorm())
colorbar()
plot(x, agpy.psf_fitter._airy_func(x), label='Airy')
plot(x, agpy.psf_fitter._gaussian_func(x), label='Gaussian')
plot(x,
agpy.psf_fitter._gaussian_func(x, sigma=airy_fwhm / gaussian_fwhm),
label='Gaussian (sigma=%5.3f)' % (airy_fwhm / gaussian_fwhm))
figure(2)
clf()
test_airy = psf_fitter.airy([1, 50, 50, 5],
vheight=False,
shape=[100, 100])
imshow((test_airy), norm=asinh_norm.AsinhNorm())
colorbar()
airyfitp, airyfitimg = psf_fitter.psffit(test_airy,
returnfitimage=True,
vheight=False)
gaussfitp, gaussfitimg = psf_fitter.psffit(
test_airy,
psffunction=psf_fitter.twodgaussian,
returnfitimage=True,
vheight=False)
print "When fitting, the Gaussian FWHM will be smaller than the Airy FWHM by %f (inverted, this is %f)" % (
gaussfitp[4] * gaussian_fwhm /
(airyfitp[4] * airy_fwhm), airyfitp[4] * airy_fwhm /
(gaussfitp[4] * gaussian_fwhm))
print ""
print "Fitted Airy parameters: amp %10f xcen %10f ycen %10f width %10f fwhm %10f. Residual: %10g Res^2: %10g" % (
tuple(airyfitp[1:].tolist()) + (airyfitp[4] * airy_fwhm,
(test_airy - airyfitimg).sum(),