def v06_psf_2d_lmfit_profile(pars,distmatrix,bgrid,r500,instrument, theta, energy,
xcen_obj,ycen_obj,data_profile=None,
data_profile_err=None):
"""
Fits the surface brightness profile by creating a 2D model of the
image - v06 model (without the central beta model) x psf
No bg.
Also allows to return directly residuals.
"""
USE_ERROR=True # debug option
# make first a 2D image
model_image = make_2d_v06_psf(pars, distmatrix, bgrid, r500,
instrument, theta, energy)
# FIXME: is this necessary for each step?
# trim distmatrix size to image post convolution
distmatrix = roll(roll(distmatrix,2,axis=0),2,axis=1)
distmatrix = trim_fftconvolve(distmatrix)
# profile extraction
r_length = r500 # factor out r500
r = arange(1.0, r_length+1, 1.0)
(profile, geometric_area) = extract_profile_fast2(model_image, distmatrix, bgrid)
model_profile = profile[0:r_length] / geometric_area[0:r_length] # trim the corners
# print '*'*30
# print len(r), len(model_profile)
# print len(profile[0:r_length]), r_length
# # print len(data_profile[0:r_length]), len(model_profile), r500, r_length
# print '*'*30
if data_profile == None:
# return (r, model_profile, geometric_area)
return (r, model_profile)
else:
residuals = data_profile - model_profile
# is this biasing?
if USE_ERROR: residuals = residuals / data_profile_err
print "full resid :: ", sum(residuals)
# return residuals
return residuals
def v06_psf_2d_lmfit_profile_joint(pars,distmatrix,bgrid, rfit,
instruments, psf_dict,
xcen_obj,ycen_obj,
data_r=None,
data_profile=None,
data_profile_err=None):
"""
Fits the surface brightness profile by creating a 2D model of the
image - v06 model (without the central beta model) x psf for a
combinaiton of instruments
No bg. Also allows to return directly
residuals.
"""
USE_ERROR=True # debug option
# setup dictionaries
model_image = {}
profile = {}
geometric_area = {}
model_profile = {}
# profile extraction
r = arange(1.0, rfit+1, 1.0)
# make first a 2D image
for instrument in instruments:
model_image[instrument] = make_2d_v06_psf(pars, distmatrix, bgrid, rfit,
instrument, psf_dict[instrument])
# FIXME: is this necessary for each step? - would require to
# have 2 distance matrices! trim distmatrix size to image
# post convolution
distmatrix = roll(roll(distmatrix,2,axis=0),2,axis=1)
distmatrix = trim_fftconvolve(distmatrix)
(profile[instrument], geometric_area[instrument]) = \
extract_profile_fast2(model_image[instrument], distmatrix, bgrid)
# trim the corners
model_profile[instrument] = profile[instrument][0:rfit] / geometric_area[instrument][0:rfit]
# FIXME: bin here
if data_profile == None:
# return (r, model_profile, geometric_area)
return (r, model_profile)
else:
# combine the likelihoods
residuals = 0.0
for instrument in instruments:
residuals_inst = abs(data_profile[instrument] - model_profile[instrument])
# is this biasing?
if USE_ERROR: residuals_inst = residuals_inst / data_profile_err[instrument]
print instrument, "resid :: ", sum(residuals_inst)
residuals = residuals + residuals_inst
print pars['n0_'+instruments[0]].value, pars['alpha'].value, pars['beta'].value, pars['epsilon'].value, pars['rc'].value, pars['rs'].value
print "full resid :: ", sum(residuals), "Chisqr :: ", sum(residuals**2)
print '='*35
return residuals