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 beta_psf_2d_lmfit_profile(pars, imsize, xsize_obj, ysize_obj,
distmatrix,
instrument, theta, energy, APPLY_PSF,
DO_ZERO_PAD, data_profile=None,
data_profile_err=None):
"""
Fits the surface brightness profile by creating a 2D model of the
image - beta model x psf
No bg.
Also allows to return directly residuals.
"""
USE_ERROR=True # debug option
# model in 2d image beta x PSF = and extract profile
model_image = make_2d_beta_psf(pars, imsize, xsize_obj, ysize_obj,
instrument, theta, energy,
APPLY_PSF, DO_ZERO_PAD)
# this is the new version
xcen_obj = xsize_obj / 2
ycen_obj = ysize_obj / 2
# 2013-05-22 not needed anymore
# we want just the relevant part of the image
# data = model_image[ycen-ysize_obj/2:ycen+ysize_obj/2, xcen-xsize_obj/2:xcen+xsize_obj/2]
# data = model_image
# setup data for the profile extraction - for speedup
# was refactored - pass it for speed not
# distmatrix = distance_matrix(model_image, xcen_obj, ycen_obj).astype(int) # need int for bincount
r_length = model_image.shape[0]/2 + 1
# r = arange(0, r_length, 1.0) # original line: before 2013-03-13
r = arange(1.0, r_length+1, 1.0)
# FIXME: can be geoemtric area removed from here and instead
# passes? or is there some edge effect
(profile, geometric_area) = extract_profile_fast(model_image, distmatrix, xcen_obj, ycen_obj)
model_profile = profile[0:r_length] / geometric_area[0:r_length] # trim the corners
if data_profile == None:
return (r, model_profile)
else:
residuals = data_profile - model_profile
# is this biasing?
if USE_ERROR: residuals = residuals / data_profile_err
return residuals
def beta_psf_2d_lmfit_profile_joint(pars, imsize, xsize_obj, ysize_obj,
distmatrix,
instruments, psf_dict,
APPLY_PSF, DO_ZERO_PAD,
data_r=None,
data_profile=None,
data_profile_err=None):
"""
Fits the surface brightness profile by creating a 2D model of the
image - beta model x psf for a combination of instruments
No bg.
Also allows to return directly residuals.
"""
USE_ERROR=True # debug option
# setup dictionaries
model_image = {}
profile = {}
geometric_area = {}
model_profile = {}
model_profile_bin = {}
# this is the new version
xcen_obj = xsize_obj / 2
ycen_obj = ysize_obj / 2
# we want just the relevant part of the image
# data = model_image[ycen-ysize_obj/2:ycen+ysize_obj/2, xcen-xsize_obj/2:xcen+xsize_obj/2]
for instrument in instruments:
# model in 2d image beta x PSF = and extract profile
model_image[instrument] = make_2d_beta_psf(pars, imsize, xsize_obj, ysize_obj,
instrument, psf_dict[instrument],
APPLY_PSF, DO_ZERO_PAD)
r_length = model_image[instrument].shape[0]/2 + 1
r = arange(1.0, r_length+1, 1.0)
# FIXME: can be geometric areas removed from here and instead
# pass it? or is there some edge effect
(profile[instrument], geometric_area[instrument]) = \
extract_profile_fast(model_image[instrument],
distmatrix, xcen_obj, ycen_obj)
# trim the corners
model_profile[instrument] = profile[instrument][0:r_length] / geometric_area[instrument][0:r_length]
######################################################################
# binning
######################################################################
if data_profile == None:
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 "full resid :: ", sum(residuals)
print '='*35
return residuals
