def fit_island_iteratively(self, img, isl, iter_ngmax=5, opts=None):
"""Fits an island iteratively.
For large islands, which can require many Gaussians to fit well,
it is much faster to fit a small number of Gaussians simultaneously
and iterate. However, this does usually result in larger residuals.
"""
import functions as func
sgaul = []; sfgaul = []
gaul = []; fgaul = []
if opts == None:
opts = img.opts
thresh_isl = opts.thresh_isl
thresh_pix = opts.thresh_pix
thresh = opts.fittedimage_clip
thr = isl.mean + thresh_isl * isl.rms
rms = isl.rms
if opts.verbose_fitting:
print 'Iteratively fitting island ', isl.island_id
gaul = []; fgaul = []
ffimg_tot = N.zeros(isl.shape, dtype=N.float32)
peak_val = N.max(isl.image - isl.islmean)
while peak_val >= thr:
sgaul, sfgaul = self.fit_island(isl, opts, img, ffimg=ffimg_tot, ngmax=iter_ngmax, ini_gausfit='simple')
gaul = gaul + sgaul; fgaul = fgaul + sfgaul
# Calculate residual image
if len(sgaul) > 0:
for g in sgaul:
gcopy = g[:]
gcopy[1] -= isl.origin[0]
gcopy[2] -= isl.origin[1]
S1, S2, Th = func.corrected_size(gcopy[3:6])
gcopy[3] = S1
gcopy[4] = S2
gcopy[5] = Th
A, C1, C2, S1, S2, Th = gcopy
shape = isl.shape
b = find_bbox(thresh*isl.rms, gcopy)
bbox = N.s_[max(0, int(C1-b)):min(shape[0], int(C1+b+1)),
max(0, int(C2-b)):min(shape[1], int(C2+b+1))]
x_ax, y_ax = N.mgrid[bbox]
ffimg = func.gaussian_fcn(gcopy, x_ax, y_ax)
ffimg_tot[bbox] += ffimg
peak_val_prev = peak_val
peak_val = N.max(isl.image - isl.islmean - ffimg_tot)
if func.approx_equal(peak_val, peak_val_prev):
break
else:
break
if len(gaul) == 0:
# Fitting iteratively did not work -- try normal fit
gaul, fgaul = self.fit_island(isl, opts, img, ini_gausfit='default')
return gaul, fgaul
def process_island(self, isl, img, opts=None):
"""Processes a single island.
Returns shapelet parameters.
"""
if opts is None:
opts = img.opts
if opts.shapelet_gresid:
shape = img.shape
thresh = opts.fittedimage_clip
model_gaus = N.zeros(shape, dtype=N.float32)
for g in isl.gaul:
C1, C2 = g.centre_pix
b = find_bbox(thresh * isl.rms, g)
bbox = N.s_[max(0, int(C1 - b)):min(shape[0], int(C1 + b + 1)),
max(0, int(C2 - b)):min(shape[1], int(C2 + b + 1))]
x_ax, y_ax = N.mgrid[bbox]
ffimg = func.gaussian_fcn(g, x_ax, y_ax)
model_gaus[bbox] = model_gaus[bbox] + ffimg
arr = isl.image - isl.islmean - model_gaus[isl.bbox]
if N.std(arr) < thresh * isl.rms:
return [
beta,
tuple(N.array(centre) + N.array(isl.origin)), nmax, basis,
cf
]
else:
arr = isl.image - isl.islmean
mask = isl.mask_active
basis = opts.shapelet_basis
beam_pix = img.pixel_beam()
mode = opts.shapelet_fitmode
if mode != 'fit':
mode = ''
fixed = (0, 0, 0)
(beta, centre,
nmax) = self.get_shapelet_params(arr, mask, basis, beam_pix, fixed,
N.array(isl.origin), mode)
cf = decompose_shapelets(arr, mask, basis, beta, centre, nmax, mode)
return [
beta,
tuple(N.array(centre) + N.array(isl.origin)), nmax, basis, cf
]
def subtract_wvgaus(self, opts, residim, gaussians, islands):
import functions as func
from make_residimage import Op_make_residimage as opp
dummy = opp()
shape = residim.shape
thresh = opts.fittedimage_clip
for g in gaussians:
if g.valid:
C1, C2 = g.centre_pix
if hasattr(g, 'wisland_id'):
isl = islands[g.wisland_id]
else:
isl = islands[g.island_id]
b = opp.find_bbox(dummy, thresh * isl.rms, g)
bbox = N.s_[max(0, int(C1 - b)):min(shape[0], int(C1 + b + 1)),
max(0, int(C2 - b)):min(shape[1], int(C2 + b + 1))]
x_ax, y_ax = N.mgrid[bbox]
ffimg = func.gaussian_fcn(g, x_ax, y_ax)
residim[bbox] = residim[bbox] - ffimg
return residim
def subtract_wvgaus(self, opts, residim, gaussians, islands):
import functions as func
from make_residimage import Op_make_residimage as opp
dummy = opp()
shape = residim.shape
thresh = opts.fittedimage_clip
for g in gaussians:
if g.valid:
C1, C2 = g.centre_pix
if hasattr(g, 'wisland_id'):
isl = islands[g.wisland_id]
else:
isl = islands[g.island_id]
b = opp.find_bbox(dummy, thresh * isl.rms, g)
bbox = N.s_[max(0, int(C1 - b)):min(shape[0], int(C1 + b + 1)),
max(0, int(C2 - b)):min(shape[1], int(C2 + b + 1))]
x_ax, y_ax = N.mgrid[bbox]
ffimg = func.gaussian_fcn(g, x_ax, y_ax)
residim[bbox] = residim[bbox] - ffimg
return residim
def process_island(self, isl, img, opts=None):
"""Processes a single island.
Returns shapelet parameters.
"""
if opts == None:
opts = img.opts
if opts.shapelet_gresid:
shape = img.shape
thresh= opts.fittedimage_clip
model_gaus = N.zeros(shape, dtype=N.float32)
for g in isl.gaul:
C1, C2 = g.centre_pix
b = find_bbox(thresh*isl.rms, g)
bbox = N.s_[max(0, int(C1-b)):min(shape[0], int(C1+b+1)),
max(0, int(C2-b)):min(shape[1], int(C2+b+1))]
x_ax, y_ax = N.mgrid[bbox]
ffimg = func.gaussian_fcn(g, x_ax, y_ax)
model_gaus[bbox] = model_gaus[bbox] + ffimg
arr = isl.image - isl.islmean - model_gaus[isl.bbox]
if N.std(arr) < thresh * isl.rms:
return [beta, tuple(N.array(centre) + N.array(isl.origin)), nmax, basis, cf]
else:
arr = isl.image - isl.islmean
mask = isl.mask_active
basis = opts.shapelet_basis
beam_pix = img.pixel_beam()
mode = opts.shapelet_fitmode
if mode != 'fit':
mode = ''
fixed = (0,0,0)
(beta, centre, nmax) = self.get_shapelet_params(arr, mask, basis, beam_pix, fixed, N.array(isl.origin), mode)
cf = decompose_shapelets(arr, mask, basis, beta, centre, nmax, mode)
return [beta, tuple(N.array(centre) + N.array(isl.origin)), nmax, basis, cf]
def __call__(self, img):
import functions as func
from copy import deepcopy as cp
import os
mylog = mylogger.logging.getLogger("PyBDSM." + img.log + "ResidImage")
mylog.info(
"Calculating residual image after subtracting reconstructed gaussians"
)
shape = img.ch0_arr.shape
thresh = img.opts.fittedimage_clip
resid_gaus = cp(img.ch0_arr)
model_gaus = N.zeros(shape, dtype=N.float32)
for g in img.gaussians:
C1, C2 = g.centre_pix
if hasattr(g, 'wisland_id') and img.waveletimage:
isl = img.islands[g.wisland_id]
else:
isl = img.islands[g.island_id]
b = self.find_bbox(thresh * isl.rms, g)
bbox = N.s_[max(0, int(C1 - b)):min(shape[0], int(C1 + b + 1)),
max(0, int(C2 - b)):min(shape[1], int(C2 + b + 1))]
x_ax, y_ax = N.mgrid[bbox]
ffimg = func.gaussian_fcn(g, x_ax, y_ax)
resid_gaus[bbox] = resid_gaus[bbox] - ffimg
model_gaus[bbox] = model_gaus[bbox] + ffimg
# Apply mask to model and resid images
if hasattr(img, 'rms_mask'):
mask = img.rms_mask
else:
mask = img.mask_arr
if isinstance(img.mask_arr, N.ndarray):
pix_masked = N.where(img.mask_arr == True)
model_gaus[pix_masked] = N.nan
resid_gaus[pix_masked] = N.nan
img.model_gaus_arr = model_gaus
img.resid_gaus_arr = resid_gaus
if img.opts.output_all:
if img.waveletimage:
resdir = img.basedir + '/wavelet/residual/'
moddir = img.basedir + '/wavelet/model/'
else:
resdir = img.basedir + '/residual/'
moddir = img.basedir + '/model/'
if not os.path.exists(resdir): os.makedirs(resdir)
if not os.path.exists(moddir): os.makedirs(moddir)
func.write_image_to_file(img.use_io,
img.imagename + '.resid_gaus.fits',
resid_gaus, img, resdir)
mylog.info(
'%s %s' %
('Writing', resdir + img.imagename + '.resid_gaus.fits'))
func.write_image_to_file(img.use_io, img.imagename + '.model.fits',
(img.ch0_arr - resid_gaus), img, moddir)
mylog.info(
'%s %s' %
('Writing', moddir + img.imagename + '.model_gaus.fits'))
### residual rms and mean per island
for isl in img.islands:
resid = resid_gaus[isl.bbox]
self.calc_resid_mean_rms(isl, resid, type='gaus')
# Calculate some statistics for the Gaussian residual image
non_masked = N.where(~N.isnan(img.ch0_arr))
mean = N.mean(resid_gaus[non_masked], axis=None)
std_dev = N.std(resid_gaus[non_masked], axis=None)
skew = stats.skew(resid_gaus[non_masked], axis=None)
kurt = stats.kurtosis(resid_gaus[non_masked], axis=None)
stat_msg = "Statistics of the Gaussian residual image:\n"
stat_msg += " mean: %.3e (Jy/beam)\n" % mean
stat_msg += " std. dev: %.3e (Jy/beam)\n" % std_dev
stat_msg += " skew: %.3f\n" % skew
stat_msg += " kurtosis: %.3f" % kurt
mylog.info(stat_msg)
# Now residual image for shapelets
if img.opts.shapelet_do:
mylog.info(
"Calculating residual image after subtracting reconstructed shapelets"
)
shape = img.ch0_arr.shape
fimg = N.zeros(shape, dtype=N.float32)
for isl in img.islands:
if isl.shapelet_beta > 0: # make sure shapelet has nonzero scale for this island
mask = isl.mask_active
cen = isl.shapelet_centre - N.array(isl.origin)
basis, beta, nmax, cf = isl.shapelet_basis, isl.shapelet_beta, \
isl.shapelet_nmax, isl.shapelet_cf
image_recons = reconstruct_shapelets(
isl.shape, mask, basis, beta, cen, nmax, cf)
fimg[isl.bbox] += image_recons
model_shap = fimg
resid_shap = img.ch0_arr - fimg
# Apply mask to model and resid images
if hasattr(img, 'rms_mask'):
mask = img.rms_mask
else:
mask = img.mask_arr
if isinstance(mask, N.ndarray):
pix_masked = N.where(mask == True)
model_shap[pix_masked] = N.nan
resid_shap[pix_masked] = N.nan
img.model_shap_arr = model_shap
img.resid_shap_arr = resid_shap
if img.opts.output_all:
func.write_image_to_file(img.use_io,
img.imagename + '.resid_shap.fits',
resid_shap, img, resdir)
mylog.info(
'%s %s' %
('Writing ', resdir + img.imagename + '.resid_shap.fits'))
### shapelet residual rms and mean per island
for isl in img.islands:
resid = resid_shap[isl.bbox]
self.calc_resid_mean_rms(isl, resid, type='shap')
# Calculate some statistics for the Shapelet residual image
non_masked = N.where(~N.isnan(img.ch0_arr))
mean = N.mean(resid_shap[non_masked], axis=None)
std_dev = N.std(resid_shap[non_masked], axis=None)
skew = stats.skew(resid_shap[non_masked], axis=None)
kurt = stats.kurtosis(resid_shap[non_masked], axis=None)
mylog.info("Statistics of the Shapelet residual image:")
mylog.info(" mean: %.3e (Jy/beam)" % mean)
mylog.info(" std. dev: %.3e (Jy/beam)" % std_dev)
mylog.info(" skew: %.3f" % skew)
mylog.info(" kurtosis: %.3f" % kurt)
img.completed_Ops.append('make_residimage')
return img
def __call__(self, img):
import functions as func
from copy import deepcopy as cp
import os
mylog = mylogger.logging.getLogger("PyBDSM."+img.log+"ResidImage")
mylog.info("Calculating residual image after subtracting reconstructed gaussians")
shape = img.ch0_arr.shape
thresh= img.opts.fittedimage_clip
resid_gaus = cp(img.ch0_arr)
model_gaus = N.zeros(shape, dtype=N.float32)
for g in img.gaussians:
C1, C2 = g.centre_pix
if hasattr(g, 'wisland_id') and img.waveletimage:
isl = img.islands[g.wisland_id]
else:
isl = img.islands[g.island_id]
b = self.find_bbox(thresh*isl.rms, g)
bbox = N.s_[max(0, int(C1-b)):min(shape[0], int(C1+b+1)),
max(0, int(C2-b)):min(shape[1], int(C2+b+1))]
x_ax, y_ax = N.mgrid[bbox]
ffimg = func.gaussian_fcn(g, x_ax, y_ax)
resid_gaus[bbox] = resid_gaus[bbox] - ffimg
model_gaus[bbox] = model_gaus[bbox] + ffimg
# Apply mask to model and resid images
if hasattr(img, 'rms_mask'):
mask = img.rms_mask
else:
mask = img.mask_arr
if isinstance(img.mask_arr, N.ndarray):
pix_masked = N.where(img.mask_arr == True)
model_gaus[pix_masked] = N.nan
resid_gaus[pix_masked] = N.nan
img.model_gaus_arr = model_gaus
img.resid_gaus_arr = resid_gaus
if img.opts.output_all:
if img.waveletimage:
resdir = img.basedir + '/wavelet/residual/'
moddir = img.basedir + '/wavelet/model/'
else:
resdir = img.basedir + '/residual/'
moddir = img.basedir + '/model/'
if not os.path.exists(resdir): os.makedirs(resdir)
if not os.path.exists(moddir): os.makedirs(moddir)
func.write_image_to_file(img.use_io, img.imagename + '.resid_gaus.fits', resid_gaus, img, resdir)
mylog.info('%s %s' % ('Writing', resdir+img.imagename+'.resid_gaus.fits'))
func.write_image_to_file(img.use_io, img.imagename + '.model.fits', (img.ch0_arr - resid_gaus), img, moddir)
mylog.info('%s %s' % ('Writing', moddir+img.imagename+'.model_gaus.fits'))
### residual rms and mean per island
for isl in img.islands:
resid = resid_gaus[isl.bbox]
self.calc_resid_mean_rms(isl, resid, type='gaus')
# Calculate some statistics for the Gaussian residual image
non_masked = N.where(~N.isnan(img.ch0_arr))
mean = N.mean(resid_gaus[non_masked], axis=None)
std_dev = N.std(resid_gaus[non_masked], axis=None)
skew = stats.skew(resid_gaus[non_masked], axis=None)
kurt = stats.kurtosis(resid_gaus[non_masked], axis=None)
stat_msg = "Statistics of the Gaussian residual image:\n"
stat_msg += " mean: %.3e (Jy/beam)\n" % mean
stat_msg += " std. dev: %.3e (Jy/beam)\n" % std_dev
stat_msg += " skew: %.3f\n" % skew
stat_msg += " kurtosis: %.3f" % kurt
mylog.info(stat_msg)
# Now residual image for shapelets
if img.opts.shapelet_do:
mylog.info("Calculating residual image after subtracting reconstructed shapelets")
shape = img.ch0_arr.shape
fimg = N.zeros(shape, dtype=N.float32)
for isl in img.islands:
if isl.shapelet_beta > 0: # make sure shapelet has nonzero scale for this island
mask=isl.mask_active
cen=isl.shapelet_centre-N.array(isl.origin)
basis, beta, nmax, cf = isl.shapelet_basis, isl.shapelet_beta, \
isl.shapelet_nmax, isl.shapelet_cf
image_recons=reconstruct_shapelets(isl.shape, mask, basis, beta, cen, nmax, cf)
fimg[isl.bbox] += image_recons
model_shap = fimg
resid_shap = img.ch0_arr - fimg
# Apply mask to model and resid images
if hasattr(img, 'rms_mask'):
mask = img.rms_mask
else:
mask = img.mask_arr
if isinstance(mask, N.ndarray):
pix_masked = N.where(mask == True)
model_shap[pix_masked] = N.nan
resid_shap[pix_masked] = N.nan
img.model_shap_arr = model_shap
img.resid_shap_arr = resid_shap
if img.opts.output_all:
func.write_image_to_file(img.use_io, img.imagename + '.resid_shap.fits', resid_shap, img, resdir)
mylog.info('%s %s' % ('Writing ', resdir+img.imagename+'.resid_shap.fits'))
### shapelet residual rms and mean per island
for isl in img.islands:
resid = resid_shap[isl.bbox]
self.calc_resid_mean_rms(isl, resid, type='shap')
# Calculate some statistics for the Shapelet residual image
non_masked = N.where(~N.isnan(img.ch0_arr))
mean = N.mean(resid_shap[non_masked], axis=None)
std_dev = N.std(resid_shap[non_masked], axis=None)
skew = stats.skew(resid_shap[non_masked], axis=None)
kurt = stats.kurtosis(resid_shap[non_masked], axis=None)
mylog.info("Statistics of the Shapelet residual image:")
mylog.info(" mean: %.3e (Jy/beam)" % mean)
mylog.info(" std. dev: %.3e (Jy/beam)" % std_dev)
mylog.info(" skew: %.3f" % skew)
mylog.info(" kurtosis: %.3f" % kurt)
img.completed_Ops.append('make_residimage')
return img
