def __call__(self, img):
if img.opts.psf_vary_do:
mylog = mylogger.logging.getLogger("PyBDSM."+img.log+"Psf_Vary")
mylogger.userinfo(mylog, '\nEstimating PSF variations')
opts = img.opts
dir = img.basedir + '/misc/'
plot = False # debug figures
image = img.ch0_arr
try:
from astropy.io import fits as pyfits
old_pyfits = False
except ImportError, err:
from distutils.version import StrictVersion
import pyfits
if StrictVersion(pyfits.__version__) < StrictVersion('2.2'):
old_pyfits = True
else:
old_pyfits = False
if old_pyfits:
mylog.warning('PyFITS version is too old: psf_vary module skipped')
return
over = 2
generators = opts.psf_generators; nsig = opts.psf_nsig; kappa2 = opts.psf_kappa2
snrtop = opts.psf_snrtop; snrbot = opts.psf_snrbot; snrcutstack = opts.psf_snrcutstack
gencode = opts.psf_gencode; primarygen = opts.psf_primarygen; itess_method = opts.psf_itess_method
tess_sc = opts.psf_tess_sc; tess_fuzzy= opts.psf_tess_fuzzy
bright_snr_cut = opts.psf_high_snr
s_only = opts.psf_stype_only
if opts.psf_snrcut < 5.0:
mylogger.userinfo(mylog, "Value of psf_snrcut too low; increasing to 5")
snrcut = 5.0
else:
snrcut = opts.psf_snrcut
img.psf_snrcut = snrcut
if opts.psf_high_snr != None:
if opts.psf_high_snr < 10.0:
mylogger.userinfo(mylog, "Value of psf_high_snr too low; increasing to 10")
high_snrcut = 10.0
else:
high_snrcut = opts.psf_high_snr
else:
high_snrcut = opts.psf_high_snr
img.psf_high_snr = high_snrcut
wtfns=['unity', 'roundness', 'log10', 'sqrtlog10']
if 0 <= itess_method < 4: tess_method=wtfns[itess_method]
else: tess_method='unity'
### now put all relevant gaussian parameters into a list
ngaus = img.ngaus
nsrc = img.nsrc
num = N.zeros(nsrc, dtype=N.int32)
peak = N.zeros(nsrc)
xc = N.zeros(nsrc)
yc = N.zeros(nsrc)
bmaj = N.zeros(nsrc)
bmin = N.zeros(nsrc)
bpa = N.zeros(nsrc)
code = N.array(['']*nsrc);
rms = N.zeros(nsrc)
src_id_list = []
for i, src in enumerate(img.sources):
src_max = 0.0
for gmax in src.gaussians:
# Take only brightest Gaussian per source
if gmax.peak_flux > src_max:
src_max = gmax.peak_flux
g = gmax
num[i] = i
peak[i] = g.peak_flux
xc[i] = g.centre_pix[0]
yc[i] = g.centre_pix[1]
bmaj[i] = g.size_pix[0]
bmin[i] = g.size_pix[1]
bpa[i] = g.size_pix[2]
code[i] = img.sources[g.source_id].code
rms[i] = img.islands[g.island_id].rms
gauls = (num, peak, xc, yc, bmaj, bmin, bpa, code, rms)
tr_gauls = self.trans_gaul(gauls)
# takes gaussians with code=S and snr > snrcut.
if s_only:
tr = [n for n in tr_gauls if n[1]/n[8]>snrcut and n[7] == 'S']
else:
tr = [n for n in tr_gauls if n[1]/n[8]>snrcut]
g_gauls = self.trans_gaul(tr)
# computes statistics of fitted sizes. Same as psfvary_fullstat.f in fBDSM.
bmaj_a, bmaj_r, bmaj_ca, bmaj_cr, ni = _cbdsm.bstat(bmaj, None, nsig)
bmin_a, bmin_r, bmin_ca, bmin_cr, ni = _cbdsm.bstat(bmin, None, nsig)
bpa_a, bpa_r, bpa_ca, bpa_cr, ni = _cbdsm.bstat(bpa, None, nsig)
# get subset of sources deemed to be unresolved. Same as size_ksclip_wenss.f in fBDSM.
flag_unresolved = self.get_unresolved(g_gauls, img.beam, nsig, kappa2, over, img.psf_high_snr, plot)
# see how much the SNR-weighted sizes of unresolved sources differ from the synthesized beam.
wtsize_beam_snr = self.av_psf(g_gauls, img.beam, flag_unresolved)
# filter out resolved sources
tr_gaul = self.trans_gaul(g_gauls)
tr = [n for i, n in enumerate(tr_gaul) if flag_unresolved[i]]
g_gauls = self.trans_gaul(tr)
mylogger.userinfo(mylog, 'Number of unresolved sources', str(len(g_gauls[0])))
# get a list of voronoi generators. vorogenS has values (and not None) if generators='field'.
vorogenP, vorogenS = self.get_voronoi_generators(g_gauls, generators, gencode, snrcut, snrtop, snrbot, snrcutstack)
mylogger.userinfo(mylog, 'Number of generators for PSF variation', str(len(vorogenP[0])))
if len(vorogenP[0]) < 3:
mylog.warning('Insufficient number of generators')
return
mylogger.userinfo(mylog, 'Tesselating image')
# group generators into tiles
tile_prop = self.edit_vorogenlist(vorogenP, frac=0.9)
# tesselate the image
volrank, vorowts = self.tesselate(vorogenP, vorogenS, tile_prop, tess_method, tess_sc, tess_fuzzy, \
generators, gencode, image.shape)
if opts.output_all:
func.write_image_to_file(img.use_io, img.imagename + '.volrank.fits', volrank, img, dir)
tile_list, tile_coord, tile_snr = tile_prop
ntile = len(tile_list)
bar = statusbar.StatusBar('Determining PSF variation ............... : ', 0, ntile)
mylogger.userinfo(mylog, 'Number of tiles for PSF variation', str(ntile))
# For each tile, calculate the weighted averaged psf image. Also for all the sources in the image.
cdelt = list(img.wcs_obj.acdelt[0:2])
factor=3.
psfimages, psfcoords, totpsfimage, psfratio, psfratio_aper = self.psf_in_tile(image, img.beam, g_gauls, \
cdelt, factor, snrcutstack, volrank, tile_prop, plot, img)
npsf = len(psfimages)
if opts.psf_use_shap:
# use totpsfimage to get beta, centre and nmax for shapelet decomposition. Use nmax=5 or 6
mask=N.zeros(totpsfimage.shape, dtype=bool)
(m1, m2, m3)=func.moment(totpsfimage, mask)
betainit=sqrt(m3[0]*m3[1])*2.0 * 1.4
tshape = totpsfimage.shape
cen = N.array(N.unravel_index(N.argmax(totpsfimage), tshape))+[1,1]
cen = tuple(cen)
nmax = 12
basis = 'cartesian'
betarange = [0.5,sqrt(betainit*max(tshape))]
beta, error = sh.shape_varybeta(totpsfimage, mask, basis, betainit, cen, nmax, betarange, plot)
if error == 1: print ' Unable to find minimum in beta'
# decompose all the psf images using the beta from above
nmax=12; psf_cf=[]
for i in range(npsf):
psfim = psfimages[i]
cf = sh.decompose_shapelets(psfim, mask, basis, beta, cen, nmax, mode='')
psf_cf.append(cf)
if img.opts.quiet == False:
bar.increment()
bar.stop()
# transpose the psf image list
xt, yt = N.transpose(tile_coord)
tr_psf_cf = N.transpose(N.array(psf_cf))
# interpolate the coefficients across the image. Ok, interpolate in scipy for
# irregular grids is crap. doesnt even pass through some of the points.
# for now, fit polynomial.
compress = 100.0
x, y = N.transpose(psfcoords)
if len(x) < 3:
mylog.warning('Insufficient number of tiles to do interpolation of PSF variation')
return
psf_coeff_interp, xgrid, ygrid = self.interp_shapcoefs(nmax, tr_psf_cf, psfcoords, image.shape, \
compress, plot)
psfshape = psfimages[0].shape
skip = 5
aa = self.create_psf_grid(psf_coeff_interp, image.shape, xgrid, ygrid, skip, nmax, psfshape, \
basis, beta, cen, totpsfimage, plot)
img.psf_images = aa
else:
if ntile < 4:
mylog.warning('Insufficient number of tiles to do interpolation of PSF variation')
return
else:
# Fit stacked PSFs with Gaussians and measure aperture fluxes
bm_pix = N.array([img.pixel_beam()[0]*fwsig, img.pixel_beam()[1]*fwsig, img.pixel_beam()[2]])
psf_maj = N.zeros(npsf)
psf_min = N.zeros(npsf)
psf_pa = N.zeros(npsf)
if img.opts.quiet == False:
bar.start()
for i in range(ntile):
psfim = psfimages[i]
mask = N.zeros(psfim.shape, dtype=bool)
x_ax, y_ax = N.indices(psfim.shape)
maxv = N.max(psfim)
p_ini = [maxv, (psfim.shape[0]-1)/2.0*1.1, (psfim.shape[1]-1)/2.0*1.1, bm_pix[0]/fwsig*1.3,
bm_pix[1]/fwsig*1.1, bm_pix[2]*2]
para, ierr = func.fit_gaus2d(psfim, p_ini, x_ax, y_ax, mask)
### first extent is major
if para[3] < para[4]:
para[3:5] = para[4:2:-1]
para[5] += 90
### clip position angle
para[5] = divmod(para[5], 180)[1]
psf_maj[i] = para[3]
psf_min[i] = para[4]
posang = para[5]
while posang >= 180.0:
posang -= 180.0
psf_pa[i] = posang
if img.opts.quiet == False:
bar.increment()
bar.stop()
# Interpolate Gaussian parameters
if img.aperture == None:
psf_maps = [psf_maj, psf_min, psf_pa, psfratio]
else:
psf_maps = [psf_maj, psf_min, psf_pa, psfratio, psfratio_aper]
nimgs = len(psf_maps)
bar = statusbar.StatusBar('Interpolating PSF images ................ : ', 0, nimgs)
if img.opts.quiet == False:
bar.start()
map_list = mp.parallel_map(func.eval_func_tuple,
itertools.izip(itertools.repeat(self.interp_prop),
psf_maps, itertools.repeat(psfcoords),
itertools.repeat(image.shape)), numcores=opts.ncores,
bar=bar)
if img.aperture == None:
psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int = map_list
else:
psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int, psf_ratio_aper_int = map_list
# Smooth if desired
if img.opts.psf_smooth != None:
sm_scale = img.opts.psf_smooth / img.pix2beam([1.0, 1.0, 0.0])[0] / 3600.0 # pixels
if img.opts.aperture == None:
psf_maps = [psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int]
else:
psf_maps = [psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int, psf_ratio_aper_int]
nimgs = len(psf_maps)
bar = statusbar.StatusBar('Smoothing PSF images .................... : ', 0, nimgs)
if img.opts.quiet == False:
bar.start()
map_list = mp.parallel_map(func.eval_func_tuple,
itertools.izip(itertools.repeat(self.blur_image),
psf_maps, itertools.repeat(sm_scale)), numcores=opts.ncores,
bar=bar)
if img.aperture == None:
psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int = map_list
else:
psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int, psf_ratio_aper_int = map_list
# Make sure all smoothed, interpolated images are ndarrays
psf_maj_int = N.array(psf_maj_int)
psf_min_int = N.array(psf_min_int)
psf_pa_int = N.array(psf_pa_int)
psf_ratio_int = N.array(psf_ratio_int)
if img.aperture == None:
psf_ratio_aper_int = N.zeros(psf_maj_int.shape, dtype=N.float32)
else:
psf_ratio_aper_int = N.array(psf_ratio_aper_int, dtype=N.float32)
# Blank with NaNs if needed
mask = img.mask_arr
if isinstance(mask, N.ndarray):
pix_masked = N.where(mask == True)
psf_maj_int[pix_masked] = N.nan
psf_min_int[pix_masked] = N.nan
psf_pa_int[pix_masked] = N.nan
psf_ratio_int[pix_masked] = N.nan
psf_ratio_aper_int[pix_masked] = N.nan
# Store interpolated images. The major and minor axis images are
# the sigma in units of arcsec, the PA image in units of degrees east of
# north, the ratio images in units of 1/beam.
img.psf_vary_maj_arr = psf_maj_int * img.pix2beam([1.0, 1.0, 0.0])[0] * 3600.0 # sigma in arcsec
img.psf_vary_min_arr = psf_min_int * img.pix2beam([1.0, 1.0, 0.0])[0] * 3600.0 # sigma in arcsec
img.psf_vary_pa_arr = psf_pa_int
img.psf_vary_ratio_arr = psf_ratio_int # in 1/beam
img.psf_vary_ratio_aper_arr = psf_ratio_aper_int # in 1/beam
if opts.output_all:
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_maj.fits', img.psf_vary_maj_arr*fwsig, img, dir)
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_min.fits', img.psf_vary_min_arr*fwsig, img, dir)
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_pa.fits', img.psf_vary_pa_arr, img, dir)
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_ratio.fits', img.psf_vary_ratio_arr, img, dir)
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_ratio_aper.fits', img.psf_vary_ratio_aper_arr, img, dir)
# Loop through source and Gaussian lists and deconvolve the sizes using appropriate beam
bar2 = statusbar.StatusBar('Correcting deconvolved source sizes ..... : ', 0, img.nsrc)
if img.opts.quiet == False:
bar2.start()
for src in img.sources:
src_pos = img.sky2pix(src.posn_sky_centroid)
src_pos_int = (int(src_pos[0]), int(src_pos[1]))
gaus_c = img.gaus2pix(src.size_sky, src.posn_sky_centroid)
gaus_bm = [psf_maj_int[src_pos_int]*fwsig, psf_min_int[src_pos_int]*fwsig, psf_pa_int[src_pos_int]*fwsig]
gaus_dc, err = func.deconv2(gaus_bm, gaus_c)
src.deconv_size_sky = img.pix2gaus(gaus_dc, src_pos)
src.deconv_size_skyE = [0.0, 0.0, 0.0]
for g in src.gaussians:
gaus_c = img.gaus2pix(g.size_sky, src.posn_sky_centroid)
gaus_dc, err = func.deconv2(gaus_bm, gaus_c)
g.deconv_size_sky = img.pix2gaus(gaus_dc, g.centre_pix)
g.deconv_size_skyE = [0.0, 0.0, 0.0]
if img.opts.quiet == False:
bar2.spin()
if img.opts.quiet == False:
bar2.increment()
bar2.stop()
img.completed_Ops.append('psf_vary')
def inigaus_nobeam(self, isl, thr, beam, img):
""" To get initial guesses when the source sizes are very different
from the beam, and can also be elongated. Mainly in the context of
a-trous transform images. Need to arrive at a good guess of the sizes
and hence need to partition the image around the maxima first. Tried the
IFT watershed algo but with markers, it segments the island only around
the minima and not the whole island. Cant find a good weighting scheme
for tesselation either. Hence will try this :
Calculate number of maxima. If one, then take moment as initial
guess. If more than one, then moment of whole island is one of the
guesses if mom1 is within n pixels of one of the maxima. Else dont take
whole island moment. Instead, find minima on lines connecting all maxima
and use geometric mean of all minima of a peak as the size of that peak.
"""
from math import sqrt
from const import fwsig
import scipy.ndimage as nd
import functions as func
im = isl.image-isl.islmean
if img.opts.ini_method == 'curvature':
im_pos = -1.0 * func.make_curvature_map(isl.image-isl.islmean)
thr_pos = 0.0
else:
im_pos = im
thr_pos = -1e9
mask = isl.mask_active
av = img.clipped_mean
inipeak, iniposn, im1 = func.get_maxima(im, mask, thr_pos, isl.shape, beam, im_pos=im_pos)
npeak = len(iniposn)
gaul = []
av, stdnew, maxv, maxp, minv, minp = func.arrstatmask(im, mask)
mom = func.momanalmask_gaus(isl.image-isl.islmean, isl.mask_active, 0, 1.0, True)
if npeak <= 1:
g = (float(maxv), int(round(mom[1])), int(round(mom[2])), mom[3]/fwsig, \
mom[4]/fwsig, mom[5])
gaul.append(g)
if npeak > 1: # markers start from 1=background, watershed starts from 1=background
watershed, markers = func.watershed(im, mask=isl.mask_active)
nshed = N.max(markers)-1 # excluding background
xm, ym = N.transpose([N.where(markers==i) for i in range(1,nshed+2)])[0]
coords = [c for c in N.transpose([xm,ym])[1:]]
alldists = [func.dist_2pt(c1, c2) for c1 in coords for c2 in coords if N.any(c1!=c2)] # has double
meandist = N.mean(alldists) # mean dist between all pairs of markers
compact = []; invmask = []
for ished in range(nshed):
shedmask = N.where(watershed==ished+2, False, True) + isl.mask_active # good unmasked pixels = 0
imm = nd.binary_dilation(~shedmask, N.ones((3,3), int))
xbad, ybad = N.where((imm==1)*(im>im[xm[ished+1], ym[ished+1]]))
imm[xbad, ybad] = 0
invmask.append(imm); x, y = N.where(imm); xcen, ycen = N.mean(x), N.mean(y) # good pixels are now = 1
dist = func.dist_2pt([xcen, ycen], [xm[ished+1], ym[ished+1]])
if dist < max(3.0, meandist/4.0):
compact.append(True) # if not compact, break source + diffuse
else:
compact.append(False)
if not N.all(compact):
avsize = []
ind = N.where(compact)[0]
for i in ind: avsize.append(N.sum(invmask[i]))
avsize = sqrt(N.mean(N.array(avsize)))
for i in range(len(compact)):
if not compact[i]: # make them all compact
newmask = N.zeros(imm.shape, bool)
newmask[max(0,xm[i+1]-avsize/2):min(im.shape[0],xm[i+1]+avsize/2), \
max(0,ym[i+1]-avsize/2):min(im.shape[1],ym[i+1]+avsize/2)] = True
invmask[i] = invmask[i]*newmask
resid = N.zeros(im.shape, dtype=N.float32) # approx fit all compact ones
for i in range(nshed):
mask1 = ~invmask[i]
size = sqrt(N.sum(invmask))/fwsig
xf, yf = coords[i][0], coords[i][1]
p_ini = [im[xf, yf], xf, yf, size, size, 0.0]
x, y = N.indices(im.shape)
p, success = func.fit_gaus2d(im*invmask[i], p_ini, x, y)
resid = resid + func.gaus_2d(p, x, y)
gaul.append(p)
resid = im - resid
if not N.all(compact): # just add one gaussian to fit whole unmasked island
maxv = N.max(resid) # assuming resid has only diffuse emission. can be false
x, y = N.where(~isl.mask_active); xcen = N.mean(x); ycen = N.mean(y)
invm = ~isl.mask_active
#bound = invm - nd.grey_erosion(invm, footprint = N.ones((3,3), int)) # better to use bound for ellipse fitting
mom = func.momanalmask_gaus(invm, N.zeros(invm.shape, dtype=N.int16), 0, 1.0, True)
g = (maxv, xcen, ycen, mom[3]/fwsig, mom[4]/fwsig, mom[5]-90.)
gaul.append(g)
coords.append([xcen, ycen])
return gaul
def __call__(self, img):
if img.opts.psf_vary_do:
mylog = mylogger.logging.getLogger("PyBDSM."+img.log+"Psf_Vary")
mylogger.userinfo(mylog, '\nEstimating PSF variations')
opts = img.opts
dir = img.basedir + '/misc/'
plot = False # debug figures
image = img.ch0_arr
try:
from astropy.io import fits as pyfits
old_pyfits = False
except ImportError, err:
from distutils.version import StrictVersion
import pyfits
if StrictVersion(pyfits.__version__) < StrictVersion('2.2'):
old_pyfits = True
else:
old_pyfits = False
if old_pyfits:
mylog.warning('PyFITS version is too old: psf_vary module skipped')
return
if opts.psf_fwhm is not None:
# User has specified a constant PSF to use, so skip PSF fitting/etc.
psf_maj = opts.psf_fwhm[0] # FWHM in deg
psf_min = opts.psf_fwhm[1] # FWHM in deg
psf_pa = opts.psf_fwhm[2] # PA in deg
mylogger.userinfo(mylog, 'Using constant PSF (major, minor, pos angle)',
'(%.5e, %.5e, %s) degrees' % (psf_maj, psf_maj,
round(psf_pa, 1)))
else:
# Use did not specify a constant PSF to use, so estimate it
over = 2
generators = opts.psf_generators; nsig = opts.psf_nsig; kappa2 = opts.psf_kappa2
snrtop = opts.psf_snrtop; snrbot = opts.psf_snrbot; snrcutstack = opts.psf_snrcutstack
gencode = opts.psf_gencode; primarygen = opts.psf_primarygen; itess_method = opts.psf_itess_method
tess_sc = opts.psf_tess_sc; tess_fuzzy= opts.psf_tess_fuzzy
bright_snr_cut = opts.psf_high_snr
s_only = opts.psf_stype_only
if opts.psf_snrcut < 5.0:
mylogger.userinfo(mylog, "Value of psf_snrcut too low; increasing to 5")
snrcut = 5.0
else:
snrcut = opts.psf_snrcut
img.psf_snrcut = snrcut
if opts.psf_high_snr is not None:
if opts.psf_high_snr < 10.0:
mylogger.userinfo(mylog, "Value of psf_high_snr too low; increasing to 10")
high_snrcut = 10.0
else:
high_snrcut = opts.psf_high_snr
else:
high_snrcut = opts.psf_high_snr
img.psf_high_snr = high_snrcut
wtfns=['unity', 'roundness', 'log10', 'sqrtlog10']
if 0 <= itess_method < 4: tess_method=wtfns[itess_method]
else: tess_method='unity'
### now put all relevant gaussian parameters into a list
ngaus = img.ngaus
nsrc = img.nsrc
num = N.zeros(nsrc, dtype=N.int32)
peak = N.zeros(nsrc)
xc = N.zeros(nsrc)
yc = N.zeros(nsrc)
bmaj = N.zeros(nsrc)
bmin = N.zeros(nsrc)
bpa = N.zeros(nsrc)
code = N.array(['']*nsrc);
rms = N.zeros(nsrc)
src_id_list = []
for i, src in enumerate(img.sources):
src_max = 0.0
for gmax in src.gaussians:
# Take only brightest Gaussian per source
if gmax.peak_flux > src_max:
src_max = gmax.peak_flux
g = gmax
num[i] = i
peak[i] = g.peak_flux
xc[i] = g.centre_pix[0]
yc[i] = g.centre_pix[1]
bmaj[i] = g.size_pix[0]
bmin[i] = g.size_pix[1]
bpa[i] = g.size_pix[2]
code[i] = img.sources[g.source_id].code
rms[i] = img.islands[g.island_id].rms
gauls = (num, peak, xc, yc, bmaj, bmin, bpa, code, rms)
tr_gauls = self.trans_gaul(gauls)
# takes gaussians with code=S and snr > snrcut.
if s_only:
tr = [n for n in tr_gauls if n[1]/n[8]>snrcut and n[7] == 'S']
else:
tr = [n for n in tr_gauls if n[1]/n[8]>snrcut]
g_gauls = self.trans_gaul(tr)
# computes statistics of fitted sizes. Same as psfvary_fullstat.f in fBDSM.
bmaj_a, bmaj_r, bmaj_ca, bmaj_cr, ni = _cbdsm.bstat(bmaj, None, nsig)
bmin_a, bmin_r, bmin_ca, bmin_cr, ni = _cbdsm.bstat(bmin, None, nsig)
bpa_a, bpa_r, bpa_ca, bpa_cr, ni = _cbdsm.bstat(bpa, None, nsig)
# get subset of sources deemed to be unresolved. Same as size_ksclip_wenss.f in fBDSM.
flag_unresolved = self.get_unresolved(g_gauls, img.beam, nsig, kappa2, over, img.psf_high_snr, plot)
if len(flag_unresolved) == 0:
mylog.warning('Insufficient number of sources to determine PSF variation.\nTry changing the PSF options or specify a (constant) PSF with the "psf_fwhm" option')
return
# see how much the SNR-weighted sizes of unresolved sources differ from the synthesized beam.
wtsize_beam_snr = self.av_psf(g_gauls, img.beam, flag_unresolved)
# filter out resolved sources
tr_gaul = self.trans_gaul(g_gauls)
tr = [n for i, n in enumerate(tr_gaul) if flag_unresolved[i]]
g_gauls = self.trans_gaul(tr)
mylogger.userinfo(mylog, 'Number of unresolved sources', str(len(g_gauls[0])))
# get a list of voronoi generators. vorogenS has values (and not None) if generators='field'.
vorogenP, vorogenS = self.get_voronoi_generators(g_gauls, generators, gencode, snrcut, snrtop, snrbot, snrcutstack)
mylogger.userinfo(mylog, 'Number of generators for PSF variation', str(len(vorogenP[0])))
if len(vorogenP[0]) < 3:
mylog.warning('Insufficient number of generators')
return
mylogger.userinfo(mylog, 'Tesselating image')
# group generators into tiles
tile_prop = self.edit_vorogenlist(vorogenP, frac=0.9)
# tesselate the image
volrank, vorowts = self.tesselate(vorogenP, vorogenS, tile_prop, tess_method, tess_sc, tess_fuzzy, \
generators, gencode, image.shape)
if opts.output_all:
func.write_image_to_file(img.use_io, img.imagename + '.volrank.fits', volrank, img, dir)
tile_list, tile_coord, tile_snr = tile_prop
ntile = len(tile_list)
bar = statusbar.StatusBar('Determining PSF variation ............... : ', 0, ntile)
mylogger.userinfo(mylog, 'Number of tiles for PSF variation', str(ntile))
# For each tile, calculate the weighted averaged psf image. Also for all the sources in the image.
cdelt = list(img.wcs_obj.acdelt[0:2])
factor=3.
psfimages, psfcoords, totpsfimage, psfratio, psfratio_aper = self.psf_in_tile(image, img.beam, g_gauls, \
cdelt, factor, snrcutstack, volrank, tile_prop, plot, img)
npsf = len(psfimages)
if opts.psf_use_shap:
if opts.psf_fwhm is None:
# use totpsfimage to get beta, centre and nmax for shapelet decomposition. Use nmax=5 or 6
mask=N.zeros(totpsfimage.shape, dtype=bool)
(m1, m2, m3)=func.moment(totpsfimage, mask)
betainit=sqrt(m3[0]*m3[1])*2.0 * 1.4
tshape = totpsfimage.shape
cen = N.array(N.unravel_index(N.argmax(totpsfimage), tshape))+[1,1]
cen = tuple(cen)
nmax = 12
basis = 'cartesian'
betarange = [0.5,sqrt(betainit*max(tshape))]
beta, error = sh.shape_varybeta(totpsfimage, mask, basis, betainit, cen, nmax, betarange, plot)
if error == 1: print ' Unable to find minimum in beta'
# decompose all the psf images using the beta from above
nmax=12; psf_cf=[]
for i in range(npsf):
psfim = psfimages[i]
cf = sh.decompose_shapelets(psfim, mask, basis, beta, cen, nmax, mode='')
psf_cf.append(cf)
if img.opts.quiet == False:
bar.increment()
bar.stop()
# transpose the psf image list
xt, yt = N.transpose(tile_coord)
tr_psf_cf = N.transpose(N.array(psf_cf))
# interpolate the coefficients across the image. Ok, interpolate in scipy for
# irregular grids is crap. doesnt even pass through some of the points.
# for now, fit polynomial.
compress = 100.0
x, y = N.transpose(psfcoords)
if len(x) < 3:
mylog.warning('Insufficient number of tiles to do interpolation of PSF variation')
return
psf_coeff_interp, xgrid, ygrid = self.interp_shapcoefs(nmax, tr_psf_cf, psfcoords, image.shape, \
compress, plot)
psfshape = psfimages[0].shape
skip = 5
aa = self.create_psf_grid(psf_coeff_interp, image.shape, xgrid, ygrid, skip, nmax, psfshape, \
basis, beta, cen, totpsfimage, plot)
img.psf_images = aa
else:
if opts.psf_fwhm is None:
if ntile < 4:
mylog.warning('Insufficient number of tiles to do interpolation of PSF variation')
return
else:
# Fit stacked PSFs with Gaussians and measure aperture fluxes
bm_pix = N.array([img.pixel_beam()[0]*fwsig, img.pixel_beam()[1]*fwsig, img.pixel_beam()[2]])
psf_maj = N.zeros(npsf)
psf_min = N.zeros(npsf)
psf_pa = N.zeros(npsf)
if img.opts.quiet == False:
bar.start()
for i in range(ntile):
psfim = psfimages[i]
mask = N.zeros(psfim.shape, dtype=bool)
x_ax, y_ax = N.indices(psfim.shape)
maxv = N.max(psfim)
p_ini = [maxv, (psfim.shape[0]-1)/2.0*1.1, (psfim.shape[1]-1)/2.0*1.1, bm_pix[0]/fwsig*1.3,
bm_pix[1]/fwsig*1.1, bm_pix[2]*2]
para, ierr = func.fit_gaus2d(psfim, p_ini, x_ax, y_ax, mask)
### first extent is major
if para[3] < para[4]:
para[3:5] = para[4:2:-1]
para[5] += 90
### clip position angle
para[5] = divmod(para[5], 180)[1]
psf_maj[i] = para[3]
psf_min[i] = para[4]
posang = para[5]
while posang >= 180.0:
posang -= 180.0
psf_pa[i] = posang
if img.opts.quiet == False:
bar.increment()
bar.stop()
# Interpolate Gaussian parameters
if img.aperture is None:
psf_maps = [psf_maj, psf_min, psf_pa, psfratio]
else:
psf_maps = [psf_maj, psf_min, psf_pa, psfratio, psfratio_aper]
nimgs = len(psf_maps)
bar = statusbar.StatusBar('Interpolating PSF images ................ : ', 0, nimgs)
if img.opts.quiet == False:
bar.start()
map_list = mp.parallel_map(func.eval_func_tuple,
itertools.izip(itertools.repeat(self.interp_prop),
psf_maps, itertools.repeat(psfcoords),
itertools.repeat(image.shape)), numcores=opts.ncores,
bar=bar)
if img.aperture is None:
psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int = map_list
else:
psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int, psf_ratio_aper_int = map_list
# Smooth if desired
if img.opts.psf_smooth is not None:
sm_scale = img.opts.psf_smooth / img.pix2beam([1.0, 1.0, 0.0])[0] / 3600.0 # pixels
if img.opts.aperture is None:
psf_maps = [psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int]
else:
psf_maps = [psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int, psf_ratio_aper_int]
nimgs = len(psf_maps)
bar = statusbar.StatusBar('Smoothing PSF images .................... : ', 0, nimgs)
if img.opts.quiet == False:
bar.start()
map_list = mp.parallel_map(func.eval_func_tuple,
itertools.izip(itertools.repeat(self.blur_image),
psf_maps, itertools.repeat(sm_scale)), numcores=opts.ncores,
bar=bar)
if img.aperture is None:
psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int = map_list
else:
psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int, psf_ratio_aper_int = map_list
# Make sure all smoothed, interpolated images are ndarrays
psf_maj_int = N.array(psf_maj_int)
psf_min_int = N.array(psf_min_int)
psf_pa_int = N.array(psf_pa_int)
psf_ratio_int = N.array(psf_ratio_int)
if img.aperture is None:
psf_ratio_aper_int = N.zeros(psf_maj_int.shape, dtype=N.float32)
else:
psf_ratio_aper_int = N.array(psf_ratio_aper_int, dtype=N.float32)
# Blank with NaNs if needed
mask = img.mask_arr
if isinstance(mask, N.ndarray):
pix_masked = N.where(mask == True)
psf_maj_int[pix_masked] = N.nan
psf_min_int[pix_masked] = N.nan
psf_pa_int[pix_masked] = N.nan
psf_ratio_int[pix_masked] = N.nan
psf_ratio_aper_int[pix_masked] = N.nan
# Store interpolated images. The major and minor axis images are
# the sigma in units of arcsec, the PA image in units of degrees east of
# north, the ratio images in units of 1/beam.
img.psf_vary_maj_arr = psf_maj_int * img.pix2beam([1.0, 1.0, 0.0])[0] * 3600.0 # sigma in arcsec
img.psf_vary_min_arr = psf_min_int * img.pix2beam([1.0, 1.0, 0.0])[0] * 3600.0 # sigma in arcsec
img.psf_vary_pa_arr = psf_pa_int
img.psf_vary_ratio_arr = psf_ratio_int # in 1/beam
img.psf_vary_ratio_aper_arr = psf_ratio_aper_int # in 1/beam
if opts.output_all:
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_maj.fits', img.psf_vary_maj_arr*fwsig, img, dir)
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_min.fits', img.psf_vary_min_arr*fwsig, img, dir)
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_pa.fits', img.psf_vary_pa_arr, img, dir)
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_ratio.fits', img.psf_vary_ratio_arr, img, dir)
func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_ratio_aper.fits', img.psf_vary_ratio_aper_arr, img, dir)
# Loop through source and Gaussian lists and deconvolve the sizes using appropriate beam
bar2 = statusbar.StatusBar('Correcting deconvolved source sizes ..... : ', 0, img.nsrc)
if img.opts.quiet == False:
bar2.start()
for src in img.sources:
src_pos = img.sky2pix(src.posn_sky_centroid)
src_pos_int = (int(src_pos[0]), int(src_pos[1]))
gaus_c = img.gaus2pix(src.size_sky, src.posn_sky_centroid)
if opts.psf_fwhm is None:
gaus_bm = [psf_maj_int[src_pos_int]*fwsig, psf_min_int[src_pos_int]*fwsig, psf_pa_int[src_pos_int]]
else:
# Use user-specified constant PSF instead
gaus_bm = img.beam2pix(opts.psf_fwhm)
gaus_dc, err = func.deconv2(gaus_bm, gaus_c)
src.deconv_size_sky = img.pix2gaus(gaus_dc, src_pos)
src.deconv_size_skyE = [0.0, 0.0, 0.0]
for g in src.gaussians:
gaus_c = img.gaus2pix(g.size_sky, src.posn_sky_centroid)
gaus_dc, err = func.deconv2(gaus_bm, gaus_c)
g.deconv_size_sky = img.pix2gaus(gaus_dc, g.centre_pix)
g.deconv_size_skyE = [0.0, 0.0, 0.0]
if img.opts.quiet == False:
bar2.spin()
if img.opts.quiet == False:
bar2.increment()
bar2.stop()
img.completed_Ops.append('psf_vary')
def process_Multiple(self, img, g_sublist, mask, src_index, isrc, subim,
isl, delc, subn, subm):
""" Same as gaul_to_source.f. isrc is same as k in the fortran version. """
from math import pi, sqrt
from const import fwsig
from scipy import ndimage
import functions as func
mylog = mylogger.logging.getLogger("PyBDSM." + img.log + "Gaul2Srl ")
dum = img.beam[0] * img.beam[1]
cdeltsq = img.wcs_obj.acdelt[0] * img.wcs_obj.acdelt[1]
bmar_p = 2.0 * pi * dum / (cdeltsq * fwsig * fwsig)
# try
subim_src = self.make_subim(subn, subm, g_sublist, delc)
mompara = func.momanalmask_gaus(subim_src, mask, isrc, bmar_p, True)
# initial peak posn and value
maxv = N.max(subim_src)
maxx, maxy = N.unravel_index(N.argmax(subim_src), subim_src.shape)
# fit gaussian around this posn
blc = N.zeros(2)
trc = N.zeros(2)
n, m = subim_src.shape[0:2]
bm_pix = N.array([
img.pixel_beam()[0] * fwsig,
img.pixel_beam()[1] * fwsig,
img.pixel_beam()[2]
])
ssubimsize = max(N.int(N.round(N.max(bm_pix[0:2]) * 2)) + 1, 5)
blc[0] = max(0, maxx - (ssubimsize - 1) / 2)
blc[1] = max(0, maxy - (ssubimsize - 1) / 2)
trc[0] = min(n, maxx + (ssubimsize - 1) / 2)
trc[1] = min(m, maxy + (ssubimsize - 1) / 2)
s_imsize = trc - blc + 1
p_ini = [maxv, (s_imsize[0]-1)/2.0*1.1, (s_imsize[1]-1)/2.0*1.1, bm_pix[0]/fwsig*1.3, \
bm_pix[1]/fwsig*1.1, bm_pix[2]*2]
data = subim_src[blc[0]:blc[0] + s_imsize[0],
blc[1]:blc[1] + s_imsize[1]]
smask = mask[blc[0]:blc[0] + s_imsize[0], blc[1]:blc[1] + s_imsize[1]]
rmask = N.where(smask == isrc, False, True)
x_ax, y_ax = N.indices(data.shape)
if N.sum(~rmask) >= 6:
para, ierr = func.fit_gaus2d(data, p_ini, x_ax, y_ax, rmask)
if (0.0<para[1]<s_imsize[0]) and (0.0<para[2]<s_imsize[1]) and \
para[3]<s_imsize[0] and para[4]<s_imsize[1]:
maxpeak = para[0]
else:
maxpeak = maxv
posn = para[1:3] - (0.5 * N.sum(s_imsize) - 1) / 2.0 + N.array(
[maxx, maxy]) - 1 + delc
else:
maxpeak = maxv
posn = N.unravel_index(N.argmax(data * ~rmask),
data.shape) + N.array(delc) + blc
# calculate peak by bilinear interpolation around centroid
# First check that moment analysis gave a valid position. If not, use
# posn from gaussian fit instead.
if N.isnan(mompara[1]):
mompara[1] = posn[0] - delc[0]
x1 = N.int(N.floor(mompara[1]))
if N.isnan(mompara[2]):
mompara[2] = posn[1] - delc[1]
y1 = N.int(N.floor(mompara[2]))
xind = slice(x1, x1 + 2, 1)
yind = slice(y1, y1 + 2, 1)
if img.opts.flag_smallsrc and (
N.sum(mask[xind, yind] == N.ones((2, 2)) * isrc) != 4):
mylog.debug('Island = ' + str(isl.island_id))
mylog.debug('Mask = ' + repr(mask[xind, yind]) +
'xind, yind, x1, y1 = ' + repr(xind) + ' ' +
repr(yind) + ' ' + repr(x1) + ' ' + repr(y1))
t = (mompara[1] - x1) / (x1 + 1 - x1) # in case u change it later
u = (mompara[2] - y1) / (y1 + 1 - y1)
s_peak=(1.0-t)*(1.0-u)*subim_src[x1,y1]+t*(1.0-u)*subim_src[x1+1,y1]+ \
t*u*subim_src[x1+1,y1+1]+(1.0-t)*u*subim_src[x1,y1+1]
if (not img.opts.flag_smallsrc) and (
N.sum(mask[xind, yind] == N.ones((2, 2)) * isrc) != 4):
mylog.debug('Speak ' + repr(s_peak) + 'Mompara = ' + repr(mompara))
mylog.debug('x1, y1 : ' + repr(x1) + ', ' + repr(y1))
# import pylab as pl
# pl.imshow(N.transpose(subim_src), origin='lower', interpolation='nearest')
# pl.suptitle('Image of bad M source '+str(isl.island_id))
# convert pixels to coords
try:
sra, sdec = img.pix2sky(
[mompara[1] + delc[0], mompara[2] + delc[1]])
mra, mdec = img.pix2sky(posn)
except RuntimeError, err:
# Invalid pixel wcs coordinate
sra, sdec = 0.0, 0.0
mra, mdec = 0.0, 0.0
def process_Multiple(self, img, g_sublist, mask, src_index, isrc, subim, isl, delc, subn, subm):
""" Same as gaul_to_source.f. isrc is same as k in the fortran version. """
from math import pi, sqrt
from const import fwsig
from scipy import ndimage
import functions as func
mylog = mylogger.logging.getLogger("PyBDSM."+img.log+"Gaul2Srl ")
dum = img.beam[0]*img.beam[1]
cdeltsq = img.wcs_obj.acdelt[0]*img.wcs_obj.acdelt[1]
bmar_p = 2.0*pi*dum/(cdeltsq*fwsig*fwsig)
# try
subim_src = self.make_subim(subn, subm, g_sublist, delc)
mompara = func.momanalmask_gaus(subim_src, mask, isrc, bmar_p, True)
# initial peak posn and value
maxv = N.max(subim_src)
maxx, maxy = N.unravel_index(N.argmax(subim_src), subim_src.shape)
# fit gaussian around this posn
blc = N.zeros(2); trc = N.zeros(2)
n, m = subim_src.shape[0:2]
bm_pix = N.array([img.pixel_beam()[0]*fwsig, img.pixel_beam()[1]*fwsig, img.pixel_beam()[2]])
ssubimsize = max(N.int(N.round(N.max(bm_pix[0:2])*2))+1, 5)
blc[0] = max(0, maxx-(ssubimsize-1)/2); blc[1] = max(0, maxy-(ssubimsize-1)/2)
trc[0] = min(n, maxx+(ssubimsize-1)/2); trc[1] = min(m, maxy+(ssubimsize-1)/2)
s_imsize = trc - blc + 1
p_ini = [maxv, (s_imsize[0]-1)/2.0*1.1, (s_imsize[1]-1)/2.0*1.1, bm_pix[0]/fwsig*1.3, \
bm_pix[1]/fwsig*1.1, bm_pix[2]*2]
data = subim_src[blc[0]:blc[0]+s_imsize[0], blc[1]:blc[1]+s_imsize[1]]
smask = mask[blc[0]:blc[0]+s_imsize[0], blc[1]:blc[1]+s_imsize[1]]
rmask = N.where(smask==isrc, False, True)
x_ax, y_ax = N.indices(data.shape)
if N.sum(~rmask) >=6:
para, ierr = func.fit_gaus2d(data, p_ini, x_ax, y_ax, rmask)
if (0.0<para[1]<s_imsize[0]) and (0.0<para[2]<s_imsize[1]) and \
para[3]<s_imsize[0] and para[4]<s_imsize[1]:
maxpeak = para[0]
else:
maxpeak = maxv
posn = para[1:3]-(0.5*N.sum(s_imsize)-1)/2.0+N.array([maxx, maxy])-1+delc
else:
maxpeak = maxv
posn = N.unravel_index(N.argmax(data*~rmask), data.shape)+N.array(delc) +blc
# calculate peak by bilinear interpolation around centroid
# First check that moment analysis gave a valid position. If not, use
# posn from gaussian fit instead.
if N.isnan(mompara[1]):
mompara[1] = posn[0] - delc[0]
x1 = N.int(N.floor(mompara[1]))
if N.isnan(mompara[2]):
mompara[2] = posn[1] - delc[1]
y1 = N.int(N.floor(mompara[2]))
xind = slice(x1, x1+2, 1); yind = slice(y1, y1+2, 1)
if img.opts.flag_smallsrc and (N.sum(mask[xind, yind]==N.ones((2,2))*isrc) != 4):
mylog.debug('Island = '+str(isl.island_id))
mylog.debug('Mask = '+repr(mask[xind, yind])+'xind, yind, x1, y1 = '+repr(xind)+' '+repr(yind)+' '+repr(x1)+' '+repr(y1))
t=(mompara[1]-x1)/(x1+1-x1) # in case u change it later
u=(mompara[2]-y1)/(y1+1-y1)
s_peak=(1.0-t)*(1.0-u)*subim_src[x1,y1]+t*(1.0-u)*subim_src[x1+1,y1]+ \
t*u*subim_src[x1+1,y1+1]+(1.0-t)*u*subim_src[x1,y1+1]
if (not img.opts.flag_smallsrc) and (N.sum(mask[xind, yind]==N.ones((2,2))*isrc) != 4):
mylog.debug('Speak '+repr(s_peak)+'Mompara = '+repr(mompara))
mylog.debug('x1, y1 : '+repr(x1)+', '+repr(y1))
# import pylab as pl
# pl.imshow(N.transpose(subim_src), origin='lower', interpolation='nearest')
# pl.suptitle('Image of bad M source '+str(isl.island_id))
# convert pixels to coords
try:
sra, sdec = img.pix2sky([mompara[1]+delc[0], mompara[2]+delc[1]])
mra, mdec = img.pix2sky(posn)
except RuntimeError, err:
# Invalid pixel wcs coordinate
sra, sdec = 0.0, 0.0
mra, mdec = 0.0, 0.0