def beta_2d_lmfit_profile(pars, imsize=None, data_profile=None, data_profile_err=None):
"""
Fits the surface brightness profile by creating a 2D model of the
image.
No psf, or bg.
Also allows to return directly residuals.
Arguments:
"""
# unpack parameters
norm = pars['norm'].value
rcore = pars['rcore'].value
beta = pars['beta'].value
xcen = pars['xcen'].value
ycen = pars['ycen'].value
# model in 2d and extract profile
t1 = time.clock()
model_image = make_2d_beta(imsize, xcen, ycen, norm, rcore, beta)
t2 = time.clock()
print "beta inside minimize took: ", t2-t1, " s"
######################################################################
# original extraction
# t1 = time.clock()
# (r, profile, geometric_area) = extract_profile_generic(model_image, xcen, ycen)
# model_profile = profile / geometric_area
# t2 = time.clock()
# print "extract inside minimize took: ", t2-t1, " s"
######################################################################
#ADDED SPEED#####################################################################
# setup data for the profile extraction - for speedup
t1 = time.clock()
distmatrix = distance_matrix(data, xcen_obj, ycen_obj).astype(int) # need int for bincount
r_length = data.shape[0]/2 + 1
r = arange(0, r_length, 1.0)
(profile, geometric_area) = extract_profile_fast(data, distmatrix, xcen_obj, ycen_obj)
model_profile = profile[0:r_length] / geometric_area[0:r_length] # trim the corners
t2 = time.clock()
print "extract inside minimize took: ", t2-t1, " s"
#ADDED SPEED#####################################################################
if data_profile == None:
return (r, model_profile)
else:
residuals = data_profile - model_profile
# is this biasing?
residuals = residuals / data_profile_err
# print xcen, ycen, rcore
return residuals
def make_2d_beta_psf(pars, imsize, xsize_obj, ysize_obj, instrument, im_psf, APPLY_PSF, DO_ZERO_PAD):
"""
Creates a 2D image of a beta model convolved with psf
Arguments:
"""
# FIXME: the _object coordinates are not needed anymore
# hdr = pyfits.getheader('pn-test.fits')
# FIXME: would be better to create a fits header from scratch
xcen = pars['xcen'].value
ycen = pars['ycen'].value
norm = pars['norm_'+instrument].value
rcore = pars['rcore'].value
beta = pars['beta'].value
im_beta = make_2d_beta(imsize, xcen, ycen, norm, rcore, beta)
# hdu = pyfits.PrimaryHDU(im_beta, hdr) # extension - array, header
# hdulist = pyfits.HDUList([hdu]) # list all extensions here
# hdulist.writeto('tmpa.fits', clobber=True)
# if DO_ZERO_PAD: im_beta = zero_pad_image(im_beta, xsize_obj)
# hdu = pyfits.PrimaryHDU(im_beta, hdr) # extension - array, header
# hdulist = pyfits.HDUList([hdu]) # list all extensions here
# hdulist.writeto('tmp0.fits', clobber=True)
im_output = im_beta
if APPLY_PSF:
# create PSF
# im_psf = make_2d_king_old(imsize, xcen, ycen, instrument, theta, energy)
# im_psf = psf[instrument]
# convolve
im_output = fftconvolve(im_beta.astype(float), im_psf.astype(float), mode = 'same')
im_output = trim_fftconvolve(im_output)
return im_output
