def belongs(self, img, isl):
import functions as func
# get centroid of island (as integer)
mom = func.momanalmask_gaus(isl.image, isl.mask_active, 0, 1.0, False)
cen = N.array(mom[1:3]) + isl.origin
icen = (int(round(cen[0])), int(round(cen[1])))
belong = False
# check if lies within any island of self
for i, pyrisl in enumerate(self.islands):
if N.sum([pyrisl.bbox[j].start <= cen[j] < pyrisl.bbox[j].stop for j in range(2)]) == 2:
pix = tuple([cen[j] - pyrisl.origin[j] for j in range(2)])
if not pyrisl.mask_active[pix]:
belong = True
return belong
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 fit_island(self, isl, opts, img, ngmax=None, ffimg=None, ini_gausfit=None):
"""Fit island with a set of 2D gaussians.
Parameters:
isl: island
opts: Opts structure of the image
beam: beam parameters which are used as an initial guess for
gaussian shape
Returns:
Function returns 2 lists with parameters of good and flagged
gaussians. Gaussian parameters are updated to be image-relative.
Note: "fitok" indicates whether fit converged
and one or more flagged Gaussians indicate
that significant residuals remain (peak > thr).
"""
from _cbdsm import MGFunction
import functions as func
from const import fwsig
if ffimg == None:
fit_image = isl.image-isl.islmean
else:
fit_image = isl.image-isl.islmean-ffimg
fcn = MGFunction(fit_image, isl.mask_active, 1)
# For fitting, use img.beam instead of img.pixel_beam, as we want
# to pick up the wavelet beam (img.pixel_beam is not changed for
# wavelet images, but img.beam is)
beam = N.array(img.beam2pix(img.beam))
beam = (beam[0]/fwsig, beam[1]/fwsig, beam[2]+90.0) # change angle from +y-axis to +x-axis and FWHM to sigma
if abs(beam[0]/beam[1]) < 1.1:
beam = (1.1*beam[0], beam[1], beam[2])
thr1 = isl.mean + opts.thresh_isl*isl.rms
thr2 = isl.mean + img.thresh_pix*isl.rms
thr0 = thr1
verbose = opts.verbose_fitting
g3_only = opts.fix_to_beam
peak = fcn.find_peak()[0]
dof = isl.size_active
shape = isl.shape
isl_image = isl.image - isl.islmean
size = isl.size_active/img.pixel_beamarea()*2.0
gaul = []
iter = 0
ng1 = 0
if ini_gausfit == None:
ini_gausfit = opts.ini_gausfit
if ini_gausfit not in ['default', 'simple', 'nobeam']:
ini_gausfit = 'default'
if ini_gausfit == 'simple' and ngmax == None:
ngmax = 25
if ini_gausfit == 'default' or opts.fix_to_beam:
gaul, ng1, ngmax = self.inigaus_fbdsm(isl, thr0, beam, img)
if ini_gausfit == 'nobeam' and not opts.fix_to_beam:
gaul = self.inigaus_nobeam(isl, thr0, beam, img)
ng1 = len(gaul); ngmax = ng1+2
while iter < 5:
iter += 1
fitok = self.fit_iter(gaul, ng1, fcn, dof, beam, thr0, iter, ini_gausfit, ngmax, verbose, g3_only)
gaul, fgaul = self.flag_gaussians(fcn.parameters, opts,
beam, thr0, peak, shape, isl.mask_active,
isl.image, size)
ng1 = len(gaul)
if fitok and len(fgaul) == 0:
break
if (not fitok or len(gaul) == 0) and ini_gausfit != 'simple':
# If fits using default or nobeam methods did not work,
# try using simple instead
gaul = []
iter = 0
ng1 = 0
ngmax = 25
while iter < 5:
iter += 1
fitok = self.fit_iter(gaul, ng1, fcn, dof, beam, thr0, iter, 'simple', ngmax, verbose, g3_only)
gaul, fgaul = self.flag_gaussians(fcn.parameters, opts,
beam, thr0, peak, shape, isl.mask_active,
isl.image, size)
ng1 = len(gaul)
if fitok and len(fgaul) == 0:
break
sm_isl = nd.binary_dilation(isl.mask_active)
if (not fitok or len(gaul) == 0) and N.sum(~sm_isl) >= img.minpix_isl:
# If fitting still fails, shrink the island a little and try again
fcn = MGFunction(fit_image, nd.binary_dilation(isl.mask_active), 1)
gaul = []
iter = 0
ng1 = 0
ngmax = 25
while iter < 5:
iter += 1
fitok = self.fit_iter(gaul, ng1, fcn, dof, beam, thr0, iter, 'simple', ngmax, verbose, g3_only)
gaul, fgaul = self.flag_gaussians(fcn.parameters, opts,
beam, thr0, peak, shape, isl.mask_active,
isl.image, size)
ng1 = len(gaul)
if fitok and len(fgaul) == 0:
break
lg_isl = nd.binary_erosion(isl.mask_active)
if (not fitok or len(gaul) == 0) and N.sum(~lg_isl) >= img.minpix_isl:
# If fitting still fails, expand the island a little and try again
fcn = MGFunction(fit_image, nd.binary_erosion(isl.mask_active), 1)
gaul = []
iter = 0
ng1 = 0
ngmax = 25
while iter < 5:
iter += 1
fitok = self.fit_iter(gaul, ng1, fcn, dof, beam, thr0, iter, 'simple', ngmax, verbose, g3_only)
gaul, fgaul = self.flag_gaussians(fcn.parameters, opts,
beam, thr0, peak, shape, isl.mask_active,
isl.image, size)
ng1 = len(gaul)
if fitok and len(fgaul) == 0:
break
if not fitok or len(gaul) == 0:
# If all else fails, try to use moment analysis
inisl = N.where(~isl.mask_active)
mask_id = N.zeros(isl.image.shape, dtype=N.int32) - 1
mask_id[inisl] = isl.island_id
try:
pixel_beamarea = img.pixel_beamarea()
mompara = func.momanalmask_gaus(fit_image, mask_id, isl.island_id, pixel_beamarea, True)
mompara[5] += 90.0
if not N.isnan(mompara[1]) and not N.isnan(mompara[2]):
x1 = N.int(N.floor(mompara[1]))
y1 = N.int(N.floor(mompara[2]))
xind = slice(x1, x1+2, 1); yind = slice(y1, y1+2, 1)
t=(mompara[1]-x1)/(x1+1-x1)
u=(mompara[2]-y1)/(y1+1-y1)
s_peak=(1.0-t)*(1.0-u)*fit_image[x1,y1]+t*(1.0-u)*fit_image[x1+1,y1]+ \
t*u*fit_image[x1+1,y1+1]+(1.0-t)*u*fit_image[x1,y1+1]
mompara[0] = s_peak
par = [mompara.tolist()]
par[3] /= fwsig
par[4] /= fwsig
gaul, fgaul = self.flag_gaussians(par, opts,
beam, thr0, peak, shape, isl.mask_active,
isl.image, size)
except:
pass
### return whatever we got
isl.mg_fcn = fcn
gaul = [self.fixup_gaussian(isl, g) for g in gaul]
fgaul = [(flag, self.fixup_gaussian(isl, g))
for flag, g in fgaul]
if verbose:
print 'Number of good Gaussians: %i' % (len(gaul),)
print 'Number of flagged Gaussians: %i' % (len(fgaul),)
return gaul, fgaul
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
class Op_gaul2srl(Op):
"""
Slightly modified from fortran.
"""
def __call__(self, img):
# for each island, get the gaussians into a list and then send them to process
# src_index is source number, starting from 0
mylog = mylogger.logging.getLogger("PyBDSM." + img.log + "Gaul2Srl")
mylogger.userinfo(mylog, 'Grouping Gaussians into sources')
img.aperture = img.opts.aperture
if img.aperture is not None and img.aperture <= 0.0:
mylog.warn('Specified aperture is <= 0. Skipping aperture fluxes.')
img.aperture = None
src_index = -1
dsrc_index = 0
sources = []
dsources = []
no_gaus_islands = []
for iisl, isl in enumerate(img.islands):
isl_sources = []
isl_dsources = []
g_list = []
for g in isl.gaul:
if g.flag == 0:
g_list.append(g)
if len(g_list) > 0:
if len(g_list) == 1:
src_index, source = self.process_single_gaussian(img,
g_list,
src_index,
code='S')
sources.append(source)
isl_sources.append(source)
else:
src_index, source = self.process_CM(
img, g_list, isl, src_index)
sources.extend(source)
isl_sources.extend(source)
else:
if not img.waveletimage:
dg = isl.dgaul[0]
no_gaus_islands.append(
(isl.island_id, dg.centre_pix[0], dg.centre_pix[1]))
# Put in the dummy Source as the source and use negative IDs
g_list = isl.dgaul
dsrc_index, dsource = self.process_single_gaussian(
img, g_list, dsrc_index, code='S')
dsources.append(dsource)
isl_dsources.append(dsource)
isl.sources = isl_sources
isl.dsources = isl_dsources
img.sources = sources
img.dsources = dsources
img.nsrc = src_index + 1
mylogger.userinfo(mylog, "Number of sources formed from Gaussians",
str(img.nsrc))
if not img.waveletimage and not img._pi and len(
no_gaus_islands) > 0 and not img.opts.quiet:
message = 'All Gaussians were flagged for the following island'
if len(no_gaus_islands) == 1:
message += ':\n'
else:
message += 's:\n'
for isl_id in no_gaus_islands:
message += ' Island #%i (x=%i, y=%i)\n' % isl_id
if len(no_gaus_islands) == 1:
message += 'Please check this island. If it is a valid island and\n'
else:
message += 'Please check these islands. If they are valid islands and\n'
if img.opts.atrous_do:
message += 'should be fit, try adjusting the flagging options (use\n'\
'show_fit with "ch0_flagged=True" to see the flagged Gaussians).'
else:
message += 'should be fit, try adjusting the flagging options (use\n'\
'show_fit with "ch0_flagged=True" to see the flagged Gaussians)\n'\
'or enabling the wavelet module (with "atrous_do=True").'
message += '\nTo include empty islands in output source catalogs, set\n'\
'incl_empty=True in the write_catalog task.'
mylog.warning(message)
img.completed_Ops.append('gaul2srl')
#################################################################################################
def process_single_gaussian(self, img, g_list, src_index, code):
""" Process single gaussian into a source, for both S and C type sources. g is just one
Gaussian object (not a list)."""
g = g_list[0]
total_flux = [g.total_flux, g.total_fluxE]
peak_flux_centroid = peak_flux_max = [g.peak_flux, g.peak_fluxE]
posn_sky_centroid = posn_sky_max = [g.centre_sky, g.centre_skyE]
size_sky = [g.size_sky, g.size_skyE]
size_sky_uncorr = [g.size_sky_uncorr, g.size_skyE]
deconv_size_sky = [g.deconv_size_sky, g.deconv_size_skyE]
deconv_size_sky_uncorr = [g.deconv_size_sky_uncorr, g.deconv_size_skyE]
bbox = img.islands[g.island_id].bbox
ngaus = 1
island_id = g.island_id
if g.gaus_num < 0:
gaussians = []
else:
gaussians = list([g])
aper_flux = func.ch0_aperture_flux(img, g.centre_pix, img.aperture)
source_prop = list([code, total_flux, peak_flux_centroid, peak_flux_max, aper_flux, posn_sky_centroid, \
posn_sky_max, size_sky, size_sky_uncorr, deconv_size_sky, deconv_size_sky_uncorr, bbox, ngaus, island_id, gaussians])
source = Source(img, source_prop)
if g.gaussian_idx == -1:
src_index -= 1
else:
src_index += 1
g.source_id = src_index
g.code = code
source.source_id = src_index
return src_index, source
##################################################################################################
def process_CM(self, img, g_list, isl, src_index):
"""
Bundle errors with the quantities.
ngau = number of gaussians in island
src_id = the source index array for every gaussian in island
nsrc = final number of distinct sources in the island
"""
ngau = len(g_list) # same as cisl in callgaul2srl.f
nsrc = ngau # same as islct; initially make each gaussian as a source
src_id = N.arange(nsrc) # same as islnum in callgaul2srl.f
boxx, boxy = isl.bbox
subn = boxx.stop - boxx.start
subm = boxy.stop - boxy.start
delc = [boxx.start, boxy.start]
subim = self.make_subim(subn, subm, g_list, delc)
index = [(i, j) for i in range(ngau) for j in range(ngau) if j > i]
for pair in index:
same_island = self.in_same_island(pair, img, g_list, isl, subim,
subn, subm, delc)
if same_island:
nsrc -= 1
mmax, mmin = max(src_id[pair[0]],
src_id[pair[1]]), min(src_id[pair[0]],
src_id[pair[1]])
arr = N.where(src_id == mmax)[0]
src_id[arr] = mmin
# now reorder src_id so that it is contiguous
for i in range(ngau):
ind1 = N.where(src_id == i)[0]
if len(ind1) == 0:
arr = N.where(src_id > i)[0]
if len(arr) > 0:
decr = N.min(src_id[arr]) - i
for j in arr:
src_id[j] -= decr
nsrc = N.max(src_id) + 1
# now do whats in sub_calc_para_source
source_list = []
for isrc in range(nsrc):
posn = N.where(src_id == isrc)[0]
g_sublist = []
for i in posn:
g_sublist.append(g_list[i])
ngau_insrc = len(posn)
# Do source type C
if ngau_insrc == 1:
src_index, source = self.process_single_gaussian(img,
g_sublist,
src_index,
code='C')
else:
# make mask and subim. Invalid mask value is -1 since 0 is valid srcid
mask = self.make_mask(isl, subn, subm, 1, isrc, g_sublist,
delc)
src_index, source = self.process_Multiple(img, g_sublist, mask, src_index, isrc, subim, \
isl, delc, subn, subm)
source_list.append(source)
return src_index, source_list
##################################################################################################
def in_same_island(self, pair, img, g_list, isl, subim, subn, subm, delc):
""" Whether two gaussians belong to the same source or not. """
import functions as func
def same_island_min(pair, g_list, subim, delc, tol=0.5):
""" If the minimum of the reconstructed fluxes along the line joining the peak positions
is greater than thresh_isl times the rms_clip, they belong to different islands. """
g1 = g_list[pair[0]]
g2 = g_list[pair[1]]
pix1 = N.array(g1.centre_pix)
pix2 = N.array(g2.centre_pix)
x1, y1 = N.floor(pix1) - delc
x2, y2 = N.floor(pix2) - delc
pix1 = N.array(
N.unravel_index(N.argmax(subim[x1:x1 + 2, y1:y1 + 2]),
(2, 2))) + [x1, y1]
pix2 = N.array(
N.unravel_index(N.argmax(subim[x2:x2 + 2, y2:y2 + 2]),
(2, 2))) + [x2, y2]
if pix1[1] >= subn: pix1[1] = pix1[1] - 1
if pix2[1] >= subm: pix2[1] = pix2[1] - 1
maxline = int(round(N.max(N.abs(pix1 - pix2) + 1)))
flux1 = g1.peak_flux
flux2 = g2.peak_flux
# get pix values of the line
pixdif = pix2 - pix1
same_island_min = False
same_island_cont = False
if maxline == 1:
same_island_min = True
same_island_cont = True
else:
if abs(pixdif[0]) > abs(pixdif[1]):
xline = N.round(min(pix1[0], pix2[0]) + N.arange(maxline))
yline = N.round((pix1[1]-pix2[1])/(pix1[0]-pix2[0])* \
(min(pix1[0],pix2[0])+N.arange(maxline)-pix1[0])+pix1[1])
else:
yline = N.round(min(pix1[1], pix2[1]) + N.arange(maxline))
xline = N.round((pix1[0]-pix2[0])/(pix1[1]-pix2[1])* \
(min(pix1[1],pix2[1])+N.arange(maxline)-pix1[1])+pix1[0])
rpixval = N.zeros(maxline, dtype=N.float32)
xbig = N.where(xline >= N.size(subim, 0))
xline[xbig] = N.size(subim, 0) - 1
ybig = N.where(yline >= N.size(subim, 1))
yline[ybig] = N.size(subim, 1) - 1
for i in range(maxline):
pixval = subim[xline[i], yline[i]]
rpixval[i] = pixval
min_pixval = N.min(rpixval)
minind_p = N.argmin(rpixval)
maxind_p = N.argmax(rpixval)
if minind_p in (0, maxline - 1) and maxind_p in (0,
maxline - 1):
same_island_cont = True
if min_pixval >= min(flux1, flux2):
same_island_min = True
elif abs(min_pixval - min(flux1, flux2)
) <= tol * isl.rms * img.opts.thresh_isl:
same_island_min = True
return same_island_min, same_island_cont
def same_island_dist(pair, g_list, tol=0.5):
""" If the centres are seperated by a distance less than half the sum of their
fwhms along the PA of the line joining them, they belong to the same island. """
from math import sqrt
g1 = g_list[pair[0]]
g2 = g_list[pair[1]]
pix1 = N.array(g1.centre_pix)
pix2 = N.array(g2.centre_pix)
gsize1 = g1.size_pix
gsize2 = g2.size_pix
fwhm1 = func.gdist_pa(pix1, pix2, gsize1)
fwhm2 = func.gdist_pa(pix1, pix2, gsize2)
dx = pix2[0] - pix1[0]
dy = pix2[1] - pix1[1]
dist = sqrt(dy * dy + dx * dx)
if dist <= tol * (fwhm1 + fwhm2):
same_island = True
else:
same_island = False
return same_island
if img.opts.group_by_isl:
same_isl1_min = True
same_isl1_cont = True
same_isl2 = True
else:
if img.opts.group_method == 'curvature':
subim = -1.0 * func.make_curvature_map(subim)
tol = img.opts.group_tol
same_isl1_min, same_isl1_cont = same_island_min(
pair, g_list, subim, delc, tol)
same_isl2 = same_island_dist(pair, g_list, tol / 2.0)
g1 = g_list[pair[0]]
same_island = (same_isl1_min and same_isl2) or same_isl1_cont
return same_island
##################################################################################################
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
# "deconvolve" the sizes
gaus_c = [mompara[3], mompara[4], mompara[5]]
gaus_bm = [bm_pix[0], bm_pix[1], bm_pix[2]]
gaus_dc, err = func.deconv2(gaus_bm, gaus_c)
deconv_size_sky = img.pix2gaus(
gaus_dc, [mompara[1] + delc[0], mompara[2] + delc[1]])
deconv_size_sky_uncorr = img.pix2gaus(
gaus_dc, [mompara[1] + delc[0], mompara[2] + delc[1]],
use_wcs=False)
# update all objects etc
tot = 0.0
totE_sq = 0.0
for g in g_sublist:
tot += g.total_flux
totE_sq += g.total_fluxE**2
totE = sqrt(totE_sq)
size_pix = [mompara[3], mompara[4], mompara[5]]
size_sky = img.pix2gaus(size_pix,
[mompara[1] + delc[0], mompara[2] + delc[1]])
size_sky_uncorr = img.pix2gaus(
size_pix, [mompara[1] + delc[0], mompara[2] + delc[1]],
use_wcs=False)
# Estimate uncertainties in source size and position due to
# errors in the constituent Gaussians using a Monte Carlo technique.
# Sum with Condon (1997) errors in quadrature.
plist = mompara.tolist() + [tot]
plist[0] = s_peak
plist[3] /= fwsig
plist[4] /= fwsig
errors = func.get_errors(img, plist, isl.rms)
if img.opts.do_mc_errors:
nMC = 20
mompara0_MC = N.zeros(nMC, dtype=N.float32)
mompara1_MC = N.zeros(nMC, dtype=N.float32)
mompara2_MC = N.zeros(nMC, dtype=N.float32)
mompara3_MC = N.zeros(nMC, dtype=N.float32)
mompara4_MC = N.zeros(nMC, dtype=N.float32)
mompara5_MC = N.zeros(nMC, dtype=N.float32)
for i in range(nMC):
# Reconstruct source from component Gaussians. Draw the Gaussian
# parameters from random distributions given by their errors.
subim_src_MC = self.make_subim(subn,
subm,
g_sublist,
delc,
mc=True)
try:
mompara_MC = func.momanalmask_gaus(subim_src_MC, mask,
isrc, bmar_p, True)
mompara0_MC[i] = mompara_MC[0]
mompara1_MC[i] = mompara_MC[1]
mompara2_MC[i] = mompara_MC[2]
mompara3_MC[i] = mompara_MC[3]
mompara4_MC[i] = mompara_MC[4]
mompara5_MC[i] = mompara_MC[5]
except:
mompara0_MC[i] = mompara[0]
mompara1_MC[i] = mompara[1]
mompara2_MC[i] = mompara[2]
mompara3_MC[i] = mompara[3]
mompara4_MC[i] = mompara[4]
mompara5_MC[i] = mompara[5]
mompara0E = N.std(mompara0_MC)
mompara1E = N.std(mompara1_MC)
if mompara1E > 2.0 * mompara[1]:
mompara1E = 2.0 * mompara[1] # Don't let errors get too large
mompara2E = N.std(mompara2_MC)
if mompara2E > 2.0 * mompara[2]:
mompara2E = 2.0 * mompara[2] # Don't let errors get too large
mompara3E = N.std(mompara3_MC)
if mompara3E > 2.0 * mompara[3]:
mompara3E = 2.0 * mompara[3] # Don't let errors get too large
mompara4E = N.std(mompara4_MC)
if mompara4E > 2.0 * mompara[4]:
mompara4E = 2.0 * mompara[4] # Don't let errors get too large
mompara5E = N.std(mompara5_MC)
if mompara5E > 2.0 * mompara[5]:
mompara5E = 2.0 * mompara[5] # Don't let errors get too large
else:
mompara1E = 0.0
mompara2E = 0.0
mompara3E = 0.0
mompara4E = 0.0
mompara5E = 0.0
# Now add MC errors in quadrature with Condon (1997) errors
size_skyE = [
sqrt(mompara3E**2 + errors[3]**2) * sqrt(cdeltsq),
sqrt(mompara4E**2 + errors[4]**2) * sqrt(cdeltsq),
sqrt(mompara5E**2 + errors[5]**2)
]
sraE, sdecE = (sqrt(mompara1E**2 + errors[1]**2) * sqrt(cdeltsq),
sqrt(mompara2E**2 + errors[2]**2) * sqrt(cdeltsq))
deconv_size_skyE = size_skyE # set deconvolved errors to non-deconvolved ones
# Find aperture flux
if img.opts.aperture_posn == 'centroid':
aper_pos = [mompara[1] + delc[0], mompara[2] + delc[1]]
else:
aper_pos = posn
aper_flux, aper_fluxE = func.ch0_aperture_flux(img, aper_pos,
img.aperture)
isl_id = isl.island_id
source_prop = list([
'M', [tot, totE], [s_peak, isl.rms], [maxpeak, isl.rms],
[aper_flux, aper_fluxE], [[sra, sdec], [sraE, sdecE]],
[[mra, mdec], [sraE, sdecE]], [size_sky, size_skyE],
[size_sky_uncorr, size_skyE], [deconv_size_sky, deconv_size_skyE],
[deconv_size_sky_uncorr, deconv_size_skyE], isl.bbox,
len(g_sublist), isl_id, g_sublist
])
source = Source(img, source_prop)
src_index += 1
for g in g_sublist:
g.source_id = src_index
g.code = 'M'
source.source_id = src_index
return src_index, source
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
