shape = None
source = SkyModel.Source(name, pos, flux, shape=shape)
# Adding source peak flux (error) as extra flux attributes for sources,
# and to avoid null values for point sources I_peak = src["Total_flux"]
if shape:
source.setAttribute("I_peak", float(src["peak_flux"]))
source.setAttribute("I_peak_err", float(src["err_peak_flux"]))
else:
source.setAttribute("I_peak", float(src["int_flux"]))
source.setAttribute("I_peak_err", float(src["err_int_flux"]))
return source
data = Table.read('{0}_comp.{1}'.format(
outfile.split('.')[0],
outfile.split('.')[-1]),
format=outfile.split('.')[-1])
for i, src in enumerate(data):
model.sources.append(tigger_src(src, i))
wcs = WCS(image)
centre = wcs.getCentreWCSCoords()
model.ra0, model.dec0 = map(numpy.deg2rad, centre)
model.save(tname_lsm)
# Rename using CORPAT
utils.xrun("tigger-convert", [tname_lsm, "--rename -f"])
class load(object):
def __init__(self, imagename, psfname=None, sourcefinder_name='pybdsm',
saveformat="gaul", makeplots=True, do_psf_corr=True,
do_local_var=True, psf_corr_region=5, local_var_region=10,
rel_excl_src=None, pos_smooth=2, neg_smooth=2, loglevel=0,
thresh_pix=5, thresh_isl=3, neg_thresh_isl=3,
neg_thresh_pix=5, reset_rel=None, prefix=None,
do_nearsources=False, savefits=False,
increase_beam_cluster=False, savemask_pos=False,
savemask_neg=False, no_smooth=True, **kw):
""" Takes in image and extracts sources and makes
reliability estimations..
imagename: Fits image
psfname: PSF fits image, optional.
sourcefinder_name: str, optional. Default 'pybdsm'.
Uses source finder specified.
makeplots: bool, optional. Default is True.
Make reliability plots.
do_psf_corr : bool, optional. Default True.
If True, PSF correlation will be added
as an extra parameter for density estimations.
NB: the PSF fits image must be provided.
do_local_var : bool, optional. Default is True.
If True, adds local variance as an extra parameter,
for density estimations.
do_nearsources: boolean. Default is False.
If true it adds number of nearest neighnours as an extra
parameter. It looks for sources around 5 beam sizes.
psf_corr_region : int, optional. Default value is 5.
Data size to correlate around a source, in beam sizes.
local_var_region: int, optional. Default 10.
Data size to compute the local variance in beam sizes.
rel_excl_src : floats, optional. Default is None.
Excludes sources in a specified region
e.g ra, dec, radius in degrees. For
2 regions: ra1, dec1, radius1: ra2, dec2, radius2, etc.
pos_smooth : float, optional. Default 2.
Masking threshold for the positive image.
For default value 2, data peaks < 2 * image noise
are masked.
neg_smooth : float, optional. Default 2.
Similar to pos_smooth but applied to the negative image.
thresh_isl : float, optional. Default is 3.
Threshold for forming islands in the positive image
thresh_pix : float, optional. Default is 5.
Threshold for model fitting, in positive image.
neg_thresh_isl : float, optional. Default is 3.
Simialr to thresh_isl but for negative image.
neg_thresh_pix : float, optional. Default is 5.
Similar to thresh_pix but for negative image.
savefits: boolean. Default is False.
If True a negative image is saved.
reset_rel: boolean. Default is False. If true then
sources with correlation < 0.002 and rel >0.60
have their reliabilities set to 0.
increase_beam_cluster: boolean, optional. If True, sources
groupings will be increase by 20% the beam size. If False,
the actual beam size will be used. Default is False.
savemask_pos: boolean, optional. If true the mask applied on
the positive side of an image after smoothing is saved.
savemask_neg: Similar to savemask_pos but for the negative
side of an image.
loglevel : int, optional. Default is 0.
Provides Pythonlogging options, 0, 1, 2 and 3 are for info, debug,
error and critial respectively.
kw : kward for source extractions. Should be a mapping e.g
kw['thresh_isl'] = 2.0 or kw['do_polarization'] = True
"""
#
self.smoothing = not no_smooth
self.prefix = prefix
# log level
self.loglevel = loglevel
self.log = utils.logger(self.loglevel, prefix=self.prefix)
# image, psf image
self.imagename = imagename
self.psfname = psfname
with pyfits.open(imagename) as hdu:
self.header = hdu[0].header
self.wcs = WCS(self.header, mode="pyfits")
self.pixelsize = abs(self.header["cdelt1"])
self.bmaj = numpy.deg2rad(self.header["BMAJ"])
# boolean optionals
self.makeplots = makeplots
self.do_local_var = do_local_var
self.nearsources = do_nearsources
self.do_psf_corr = do_psf_corr
self.savemaskpos = savemask_pos
self.savemaskneg = savemask_neg
self.savefits = savefits
self.derel = reset_rel
self.log.info("Catalogues will be saved as %s, where srl is source "
" and gaul is Gaussians. "%saveformat)
self.catalogue_format = "." + saveformat
if not self.psfname:
self.log.info(" No psf provided, do_psf_corr is set to False.")
self.do_psf_corr = False
# computing negative noise
self.noise, self.mean = utils.negative_noise(self.imagename, self.prefix)
self.log.info(" The negative noise is %e Jy/beam"%self.noise)
if self.noise == 0:
self.log.debug(" The negative noise is 0, check image")
# source finder initialization
self.sourcefinder_name = sourcefinder_name
self.log.info(" Using %s source finder to extract the sources."%
self.sourcefinder_name)
self.negimage = self.prefix + "_negative.fits"
utils.invert_image(self.imagename, self.negimage)
# smoothing factors
self.pos_smooth = pos_smooth
self.neg_smooth = neg_smooth
# region to evaluate
self.corrstep = psf_corr_region
self.localstep = local_var_region
self.radiusrm = rel_excl_src
self.do_beam = increase_beam_cluster
beam_pix = int(round(numpy.rad2deg(self.bmaj)/self.pixelsize))
self.locstep = self.localstep * beam_pix
self.cfstep = self.corrstep * beam_pix
self.bmin, self.bpa = self.header["BMIN"], self.header["BPA"]
self.opts_pos = {}
if self.do_beam:
bmaj = self.header["BMAJ"]
self.opts_pos["beam"] = (1.2*bmaj, 1.2*self.bmin, self.bpa)
# Pybdsm or source finder fitting thresholds
self.thresh_isl = thresh_isl
self.thresh_pix = thresh_pix
self.opts_pos = dict(thresh_pix=self.thresh_pix,
thresh_isl=self.thresh_isl)
self.opts_pos.update(kw)
self.opts_neg = {}
self.opts_neg.update(kw)
self.neg_thresh_isl = neg_thresh_isl
self.neg_thresh_pix = neg_thresh_pix
self.opts_neg["thresh_isl"] = self.neg_thresh_isl
self.opts_neg["thresh_pix"] = self.neg_thresh_pix
def source_finder(self, image=None, thresh=None, prefix=None,
noise=None, output=None, savemask=None, **kw):
#kw.update(kwards)
tpos = None
naxis = self.header["NAXIS1"]
boundary = numpy.array([self.locstep, self.cfstep])
#trim_box = (boundary.max(), naxis - boundary.max(),
# boundary.max(), naxis - boundary.max())
trim_box = None
# data smoothing
if self.smoothing:
ext = utils.fits_ext(image)
tpos = tempfile.NamedTemporaryFile(suffix="."+ext, dir=".")
tpos.flush()
mask, noise = utils.thresh_mask(image, tpos.name,
thresh=thresh, noise=self.noise,
sigma=True, smooth=True, prefix=prefix,
savemask=savemask)
# using the masked image for forming islands
kw["detection_image"] = tpos.name
kw["blank_limit"] = self.noise/1.0e5
# source extraction
utils.sources_extraction(
image=image, output=output,
sourcefinder_name=self.sourcefinder_name,
trim_box=trim_box,
prefix=self.prefix, **kw)
if tpos:
tpos.close()
def remove_sources_within(self, model):
sources = model.sources
rel_remove = self.radiusrm[0].split(":")
for i in range(len(rel_remove)):
ra, dec, tolerance = rel_remove[i].split(",")
ra_r = numpy.deg2rad(float(ra))
dec_r = numpy.deg2rad(float(dec))
tolerance_r = numpy.deg2rad(float(tolerance))
within = model.getSourcesNear(ra_r, dec_r, tolerance_r)
for src in sorted(sources):
if src in within:
model.sources.remove(src)
return model
def params(self, modelfits):
# reads in source finder output
with pyfits.open(modelfits) as hdu:
data = hdu[1].data
tfile = tempfile.NamedTemporaryFile(suffix=".txt")
tfile.flush()
# writes a catalogue in a temporaty txt file
with open(tfile.name, "w") as std:
std.write("#format:name ra_rad dec_rad i emaj_r emin_r pa_r\n")
model = Tigger.load(tfile.name) # open a tmp. file
peak, total, area, loc, corr = [], [], [], [], []
for i in range(len(data)):
flux = data["Total_flux"][i]
dc_emaj, dc_emin = data["DC_Maj"][i], data["DC_Min"][i]
ra, dec = data["RA"][i], data["DEC"][i]
pa = data["DC_PA"][i]
name = "SRC%d"%i
peak_flux = data["Peak_flux"][i]
posrd = ModelClasses.Position(numpy.deg2rad(ra), numpy.deg2rad(dec))
flux_I = ModelClasses.Polarization(flux, 0, 0, 0)
if dc_emaj == 0 and dc_emin == 0:
shape = None
else:
shape = ModelClasses.Gaussian(numpy.deg2rad(dc_emaj), numpy.deg2rad(dc_emin),
numpy.deg2rad(pa))
srs = SkyModel.Source(name, posrd, flux_I, shape=shape)
# using convolved maj and min for reliability estimate
emaj, emin = data["Maj"][i], data["Min"][i]
# area: find ex and ey if are 0 assign beam size
if emaj or emin == 0:
srcarea = math.pi * (numpy.rad2deg(self.bmaj)) * pow(3600.0, 2) *\
(numpy.rad2deg(self.bmin))
if emaj and emin > 0:
srcarea = emaj * emin * math.pi * pow(3600.0, 2) # arcsecond
# only accepts sources with flux > 0 and not nan RA and DEC
# and local variance
pos = [self.wcs.wcs2pix(*(ra, dec))][0] #positions from deg to pixel
with pyfits.open(self.negimage) as hdu:
negdata = utils.image_data( hdu[0].data )
if flux > 0 and peak_flux > 0 and not math.isnan(float(ra))\
and not math.isnan(float(dec)):
local = utils.compute_local_variance(negdata,
pos, self.locstep)
srs.setAttribute("local_variance", local)
if not math.isnan(float(local)) or local > 0:
if self.psfname:
pdata, psf = utils.compute_psf_correlation(self.imagename,
self.psfname, pos, self.cfstep)
if len(pdata) == len(psf):
c_region = numpy.corrcoef((pdata, psf))
cf = (numpy.diag((numpy.rot90(c_region))**2)
.sum())**0.5/2**0.5
srs.setAttribute("correlation_factor", cf)
corr.append(cf)
model.sources.append(srs)
peak.append(peak_flux)
total.append(flux)
area.append(srcarea)
loc.append(local)
else:
model.sources.append(srs)
peak.append(peak_flux)
total.append(flux)
area.append(srcarea)
loc.append(local)
labels = dict(size=(0, "Log$_{10}$(Source area)"),
peak=(1, "Log$_{10}$( Peak flux [Jy] )"),
tot=(2, "Log$_{10}$( Total flux [Jy] )"))
if self.do_psf_corr:
labels.update( {"coeff":(len(labels),
"Log$_{10}$ (CF)")})
if self.do_local_var:
labels.update( {"local": (len(labels),
"Log$_{10}$(Local Variance)")})
if self.nearsources:
labels.update( {"near": (len(labels),
"Log$_{10}$(Near Sources)")})
nsrc = len(model.sources)
out = numpy.zeros([nsrc, len(labels)])
# returning parameters
for i, src in enumerate(model.sources):
ra, dec = src.pos.ra, src.pos.dec
near = model.getSourcesNear(ra, dec, 5 * self.bmaj)
nonear = len(near)
if self.nearsources:
src.setAttribute("neibours", nonear)
if self.do_psf_corr and self.do_local_var and self.nearsources:
out[i,...] = area[i], peak[i], total[i], corr[i], loc[i], nonear
elif self.do_psf_corr and self.do_local_var and not self.nearsources:
out[i,...] = area[i], peak[i], total[i] , corr[i], loc[i]
elif self.do_psf_corr and self.nearsources and not self.do_local_var:
out[i,...] = area[i], peak[i], total[i] , corr[i], nonear
elif not self.do_psf_corr and self.do_local_var and self.nearsources:
out[i,...] = area[i], peak[i], total[i] , loc[i], nonear
elif self.do_psf_corr and not self.do_local_var and not self.nearsources:
out[i,...] = area[i], peak[i], total[i] , corr[i]
elif not self.do_psf_corr and self.do_local_var and not self.nearsources:
out[i,...] = area[i], peak[i], total[i] , loc[i]
elif not self.do_psf_corr and not self.do_local_var and self.nearsources:
out[i,...] = area[i], peak[i], total[i] , nonear
else:
out[i,...] = area[i], peak[i], total[i]
# removes the rows with 0s
removezeros = (out == 0).sum(1)
output = out[removezeros <= 0, :]
return model, numpy.log10(output), labels
def get_reliability(self):
# finding sources
self.log.info(" Extracting the sources on both sides ")
pfile = self.prefix + self.catalogue_format + ".fits"
nfile = self.prefix + "_negative" + self.catalogue_format + ".fits"
# i need to catch mmap.mmap error here
# running a source finder
self.source_finder(image=self.negimage,
output=nfile, thresh=self.neg_smooth,
savemask=self.savemaskneg,
prefix=self.prefix, **self.opts_neg)
self.source_finder(image=self.imagename,
output=pfile, thresh=self.pos_smooth,
savemask=self.savemaskpos,
prefix=self.prefix, **self.opts_pos)
self.log.info(" Source Finder completed successfully ")
pmodel, positive, labels = self.params(pfile)
nmodel, negative, labels = self.params(nfile)
# setting up a kernel, Gaussian kernel
bandwidth = []
for plane in negative.T:
bandwidth.append(plane.std())
nplanes = len(labels)
cov = numpy.zeros([nplanes, nplanes])
nnsrc = len(negative)
npsrc = len(positive)
self.log.info(" There are %d positive and %d negtive detections "%(npsrc, nnsrc))
if nnsrc == 0 or npsrc ==0:
self.log.error("The resulting array has length of 0 thus cannot compute"
" the reliability. Aborting.")
self.log.info(" Computing the reliabilities ")
for i in range(nplanes):
for j in range(nplanes):
if i == j:
cov[i, j] = bandwidth[i]*((4.0/((nplanes+2)*
nnsrc))**(1.0/(nplanes+4.0)))
self.log.info("The resulting covariance matrix is %r"%cov)
pcov = utils.gaussian_kde_set_covariance(positive.T, cov)
ncov = utils.gaussian_kde_set_covariance(negative.T, cov)
# get number densities
nps = pcov(positive.T) * npsrc
nns = ncov(positive.T) * nnsrc
# define reliability of positive catalog
rel = (nps-nns)/nps
for src, rf in zip(pmodel.sources, rel):
src.setAttribute("rel", rf)
self.log.info(" Saved the reliabilities values.")
# remove sources with poor correlation and high reliability,
# the values are currently arbitrary
if self.do_psf_corr and self.derel:
for s in pmodel.sources:
cf, r = s.correlation_factor, s.rel
if cf < 0.006 and r > 0.60:
s.rel = 0.0
if self.makeplots:
savefig = self.prefix + "_planes.png"
utils.plot(positive, negative, rel=rel, labels=labels,
savefig=savefig, prefix=self.prefix)
# removes sources in a given radius from the phase center
if self.radiusrm:
self.log.info(" Remove sources ra, dec, radius of %r"
" from the phase center" %self.radiusrm)
pmodel = self.remove_sources_within(pmodel)
if not self.savefits:
self.log.info(" Deleting the negative image.")
os.system("rm -r %s"%self.negimage)
# Set field Center
pmodel.ra0, pmodel.dec0 = map(numpy.deg2rad, self.wcs.getCentreWCSCoords())
return pmodel, nmodel, self.locstep
class load(object):
def __init__(self,
imagename,
psfname=None,
sourcefinder_name='pybdsm',
saveformat="gaul",
makeplots=True,
do_psf_corr=True,
do_local_var=True,
psf_corr_region=5,
local_var_region=10,
rel_excl_src=None,
pos_smooth=2,
neg_smooth=2,
loglevel=0,
thresh_pix=5,
thresh_isl=3,
neg_thresh_isl=3,
neg_thresh_pix=5,
reset_rel=None,
prefix=None,
do_nearsources=False,
savefits=False,
increase_beam_cluster=False,
savemask_pos=False,
savemask_neg=False,
no_smooth=True,
**kw):
""" Takes in image and extracts sources and makes
reliability estimations..
imagename: Fits image
psfname: PSF fits image, optional.
sourcefinder_name: str, optional. Default 'pybdsm'.
Uses source finder specified.
makeplots: bool, optional. Default is True.
Make reliability plots.
do_psf_corr : bool, optional. Default True.
If True, PSF correlation will be added
as an extra parameter for density estimations.
NB: the PSF fits image must be provided.
do_local_var : bool, optional. Default is True.
If True, adds local variance as an extra parameter,
for density estimations.
do_nearsources: boolean. Default is False.
If true it adds number of nearest neighnours as an extra
parameter. It looks for sources around 5 beam sizes.
psf_corr_region : int, optional. Default value is 5.
Data size to correlate around a source, in beam sizes.
local_var_region: int, optional. Default 10.
Data size to compute the local variance in beam sizes.
rel_excl_src : floats, optional. Default is None.
Excludes sources in a specified region
e.g ra, dec, radius in degrees. For
2 regions: ra1, dec1, radius1: ra2, dec2, radius2, etc.
pos_smooth : float, optional. Default 2.
Masking threshold for the positive image.
For default value 2, data peaks < 2 * image noise
are masked.
neg_smooth : float, optional. Default 2.
Similar to pos_smooth but applied to the negative image.
thresh_isl : float, optional. Default is 3.
Threshold for forming islands in the positive image
thresh_pix : float, optional. Default is 5.
Threshold for model fitting, in positive image.
neg_thresh_isl : float, optional. Default is 3.
Simialr to thresh_isl but for negative image.
neg_thresh_pix : float, optional. Default is 5.
Similar to thresh_pix but for negative image.
savefits: boolean. Default is False.
If True a negative image is saved.
reset_rel: boolean. Default is False. If true then
sources with correlation < 0.002 and rel >0.60
have their reliabilities set to 0.
increase_beam_cluster: boolean, optional. If True, sources
groupings will be increase by 20% the beam size. If False,
the actual beam size will be used. Default is False.
savemask_pos: boolean, optional. If true the mask applied on
the positive side of an image after smoothing is saved.
savemask_neg: Similar to savemask_pos but for the negative
side of an image.
loglevel : int, optional. Default is 0.
Provides Pythonlogging options, 0, 1, 2 and 3 are for info, debug,
error and critial respectively.
kw : kward for source extractions. Should be a mapping e.g
kw['thresh_isl'] = 2.0 or kw['do_polarization'] = True
"""
#
self.smoothing = not no_smooth
self.prefix = prefix
# log level
self.loglevel = loglevel
self.log = utils.logger(self.loglevel, prefix=self.prefix)
# image, psf image
self.imagename = imagename
self.psfname = psfname
with pyfits.open(imagename) as hdu:
self.header = hdu[0].header
self.wcs = WCS(self.header, mode="pyfits")
self.pixelsize = abs(self.header["cdelt1"])
self.bmaj = numpy.deg2rad(self.header["BMAJ"])
# boolean optionals
self.makeplots = makeplots
self.do_local_var = do_local_var
self.nearsources = do_nearsources
self.do_psf_corr = do_psf_corr
self.savemaskpos = savemask_pos
self.savemaskneg = savemask_neg
self.savefits = savefits
self.derel = reset_rel
self.log.info("Catalogues will be saved as %s, where srl is source "
" and gaul is Gaussians. " % saveformat)
self.catalogue_format = "." + saveformat
if not self.psfname:
self.log.info(" No psf provided, do_psf_corr is set to False.")
self.do_psf_corr = False
# computing negative noise
self.noise, self.mean = utils.negative_noise(self.imagename,
self.prefix)
self.log.info(" The negative noise is %e Jy/beam" % self.noise)
if self.noise == 0:
self.log.debug(" The negative noise is 0, check image")
# source finder initialization
self.sourcefinder_name = sourcefinder_name
self.log.info(" Using %s source finder to extract the sources." %
self.sourcefinder_name)
self.negimage = self.prefix + "_negative.fits"
utils.invert_image(self.imagename, self.negimage)
# smoothing factors
self.pos_smooth = pos_smooth
self.neg_smooth = neg_smooth
# region to evaluate
self.corrstep = psf_corr_region
self.localstep = local_var_region
self.radiusrm = rel_excl_src
self.do_beam = increase_beam_cluster
beam_pix = int(round(numpy.rad2deg(self.bmaj) / self.pixelsize))
self.locstep = self.localstep * beam_pix
self.cfstep = self.corrstep * beam_pix
self.bmin, self.bpa = self.header["BMIN"], self.header["BPA"]
self.opts_pos = {}
if self.do_beam:
bmaj = self.header["BMAJ"]
self.opts_pos["beam"] = (1.2 * bmaj, 1.2 * self.bmin, self.bpa)
# Pybdsm or source finder fitting thresholds
self.thresh_isl = thresh_isl
self.thresh_pix = thresh_pix
self.opts_pos = dict(thresh_pix=self.thresh_pix,
thresh_isl=self.thresh_isl)
self.opts_pos.update(kw)
self.opts_neg = {}
self.opts_neg.update(kw)
self.neg_thresh_isl = neg_thresh_isl
self.neg_thresh_pix = neg_thresh_pix
self.opts_neg["thresh_isl"] = self.neg_thresh_isl
self.opts_neg["thresh_pix"] = self.neg_thresh_pix
def source_finder(self,
image=None,
thresh=None,
prefix=None,
noise=None,
output=None,
savemask=None,
**kw):
#kw.update(kwards)
tpos = None
naxis = self.header["NAXIS1"]
boundary = numpy.array([self.locstep, self.cfstep])
#trim_box = (boundary.max(), naxis - boundary.max(),
# boundary.max(), naxis - boundary.max())
trim_box = None
# data smoothing
if self.smoothing:
ext = utils.fits_ext(image)
tpos = tempfile.NamedTemporaryFile(suffix="." + ext, dir=".")
tpos.flush()
mask, noise = utils.thresh_mask(image,
tpos.name,
thresh=thresh,
noise=self.noise,
sigma=True,
smooth=True,
prefix=prefix,
savemask=savemask)
# using the masked image for forming islands
kw["detection_image"] = tpos.name
kw["blank_limit"] = self.noise / 1.0e5
# source extraction
utils.sources_extraction(image=image,
output=output,
sourcefinder_name=self.sourcefinder_name,
trim_box=trim_box,
prefix=self.prefix,
**kw)
if tpos:
tpos.close()
def remove_sources_within(self, model):
sources = model.sources
rel_remove = self.radiusrm[0].split(":")
for i in range(len(rel_remove)):
ra, dec, tolerance = rel_remove[i].split(",")
ra_r = numpy.deg2rad(float(ra))
dec_r = numpy.deg2rad(float(dec))
tolerance_r = numpy.deg2rad(float(tolerance))
within = model.getSourcesNear(ra_r, dec_r, tolerance_r)
for src in sorted(sources):
if src in within:
model.sources.remove(src)
return model
def params(self, modelfits):
# reads in source finder output
with pyfits.open(modelfits) as hdu:
data = hdu[1].data
tfile = tempfile.NamedTemporaryFile(suffix=".txt")
tfile.flush()
# writes a catalogue in a temporaty txt file
with open(tfile.name, "w") as std:
std.write("#format:name ra_rad dec_rad i emaj_r emin_r pa_r\n")
model = Tigger.load(tfile.name) # open a tmp. file
peak, total, area, loc, corr = [], [], [], [], []
for i in range(len(data)):
flux = data["Total_flux"][i]
dc_emaj, dc_emin = data["DC_Maj"][i], data["DC_Min"][i]
ra, dec = data["RA"][i], data["DEC"][i]
pa = data["DC_PA"][i]
name = "SRC%d" % i
peak_flux = data["Peak_flux"][i]
posrd = ModelClasses.Position(numpy.deg2rad(ra),
numpy.deg2rad(dec))
flux_I = ModelClasses.Polarization(flux, 0, 0, 0)
if dc_emaj == 0 and dc_emin == 0:
shape = None
else:
shape = ModelClasses.Gaussian(numpy.deg2rad(dc_emaj),
numpy.deg2rad(dc_emin),
numpy.deg2rad(pa))
srs = SkyModel.Source(name, posrd, flux_I, shape=shape)
# using convolved maj and min for reliability estimate
emaj, emin = data["Maj"][i], data["Min"][i]
# area: find ex and ey if are 0 assign beam size
if emaj or emin == 0:
srcarea = math.pi * (numpy.rad2deg(self.bmaj)) * pow(3600.0, 2) *\
(numpy.rad2deg(self.bmin))
if emaj and emin > 0:
srcarea = emaj * emin * math.pi * pow(3600.0, 2) # arcsecond
# only accepts sources with flux > 0 and not nan RA and DEC
# and local variance
pos = [self.wcs.wcs2pix(*(ra, dec))
][0] #positions from deg to pixel
with pyfits.open(self.negimage) as hdu:
negdata = utils.image_data(hdu[0].data)
if flux > 0 and peak_flux > 0 and not math.isnan(float(ra))\
and not math.isnan(float(dec)):
local = utils.compute_local_variance(negdata, pos,
self.locstep)
srs.setAttribute("local_variance", local)
if not math.isnan(float(local)) or local > 0:
if self.psfname:
pdata, psf = utils.compute_psf_correlation(
self.imagename, self.psfname, pos, self.cfstep)
if len(pdata) == len(psf):
c_region = numpy.corrcoef((pdata, psf))
cf = (numpy.diag((numpy.rot90(c_region))**
2).sum())**0.5 / 2**0.5
srs.setAttribute("correlation_factor", cf)
corr.append(cf)
model.sources.append(srs)
peak.append(peak_flux)
total.append(flux)
area.append(srcarea)
loc.append(local)
else:
model.sources.append(srs)
peak.append(peak_flux)
total.append(flux)
area.append(srcarea)
loc.append(local)
labels = dict(size=(0, "Log$_{10}$(Source area)"),
peak=(1, "Log$_{10}$( Peak flux [Jy] )"),
tot=(2, "Log$_{10}$( Total flux [Jy] )"))
if self.do_psf_corr:
labels.update({"coeff": (len(labels), "Log$_{10}$ (CF)")})
if self.do_local_var:
labels.update(
{"local": (len(labels), "Log$_{10}$(Local Variance)")})
if self.nearsources:
labels.update({"near": (len(labels), "Log$_{10}$(Near Sources)")})
nsrc = len(model.sources)
out = numpy.zeros([nsrc, len(labels)])
# returning parameters
for i, src in enumerate(model.sources):
ra, dec = src.pos.ra, src.pos.dec
near = model.getSourcesNear(ra, dec, 5 * self.bmaj)
nonear = len(near)
if self.nearsources:
src.setAttribute("neibours", nonear)
if self.do_psf_corr and self.do_local_var and self.nearsources:
out[i,
...] = area[i], peak[i], total[i], corr[i], loc[i], nonear
elif self.do_psf_corr and self.do_local_var and not self.nearsources:
out[i, ...] = area[i], peak[i], total[i], corr[i], loc[i]
elif self.do_psf_corr and self.nearsources and not self.do_local_var:
out[i, ...] = area[i], peak[i], total[i], corr[i], nonear
elif not self.do_psf_corr and self.do_local_var and self.nearsources:
out[i, ...] = area[i], peak[i], total[i], loc[i], nonear
elif self.do_psf_corr and not self.do_local_var and not self.nearsources:
out[i, ...] = area[i], peak[i], total[i], corr[i]
elif not self.do_psf_corr and self.do_local_var and not self.nearsources:
out[i, ...] = area[i], peak[i], total[i], loc[i]
elif not self.do_psf_corr and not self.do_local_var and self.nearsources:
out[i, ...] = area[i], peak[i], total[i], nonear
else:
out[i, ...] = area[i], peak[i], total[i]
# removes the rows with 0s
removezeros = (out == 0).sum(1)
output = out[removezeros <= 0, :]
return model, numpy.log10(output), labels
def get_reliability(self):
# finding sources
self.log.info(" Extracting the sources on both sides ")
pfile = self.prefix + self.catalogue_format + ".fits"
nfile = self.prefix + "_negative" + self.catalogue_format + ".fits"
# i need to catch mmap.mmap error here
# running a source finder
self.source_finder(image=self.negimage,
output=nfile,
thresh=self.neg_smooth,
savemask=self.savemaskneg,
prefix=self.prefix,
**self.opts_neg)
self.source_finder(image=self.imagename,
output=pfile,
thresh=self.pos_smooth,
savemask=self.savemaskpos,
prefix=self.prefix,
**self.opts_pos)
self.log.info(" Source Finder completed successfully ")
pmodel, positive, labels = self.params(pfile)
nmodel, negative, labels = self.params(nfile)
# setting up a kernel, Gaussian kernel
bandwidth = []
for plane in negative.T:
bandwidth.append(plane.std())
nplanes = len(labels)
cov = numpy.zeros([nplanes, nplanes])
nnsrc = len(negative)
npsrc = len(positive)
self.log.info(" There are %d positive and %d negtive detections " %
(npsrc, nnsrc))
if nnsrc == 0 or npsrc == 0:
self.log.error(
"The resulting array has length of 0 thus cannot compute"
" the reliability. Aborting.")
self.log.info(" Computing the reliabilities ")
for i in range(nplanes):
for j in range(nplanes):
if i == j:
cov[i, j] = bandwidth[i] * ((4.0 / (
(nplanes + 2) * nnsrc))**(1.0 / (nplanes + 4.0)))
self.log.info("The resulting covariance matrix is %r" % cov)
pcov = utils.gaussian_kde_set_covariance(positive.T, cov)
ncov = utils.gaussian_kde_set_covariance(negative.T, cov)
# get number densities
nps = pcov(positive.T) * npsrc
nns = ncov(positive.T) * nnsrc
# define reliability of positive catalog
rel = (nps - nns) / nps
for src, rf in zip(pmodel.sources, rel):
src.setAttribute("rel", rf)
self.log.info(" Saved the reliabilities values.")
# remove sources with poor correlation and high reliability,
# the values are currently arbitrary
if self.do_psf_corr and self.derel:
for s in pmodel.sources:
cf, r = s.correlation_factor, s.rel
if cf < 0.006 and r > 0.60:
s.rel = 0.0
if self.makeplots:
savefig = self.prefix + "_planes.png"
utils.plot(positive,
negative,
rel=rel,
labels=labels,
savefig=savefig,
prefix=self.prefix)
# removes sources in a given radius from the phase center
if self.radiusrm:
self.log.info(" Remove sources ra, dec, radius of %r"
" from the phase center" % self.radiusrm)
pmodel = self.remove_sources_within(pmodel)
if not self.savefits:
self.log.info(" Deleting the negative image.")
os.system("rm -r %s" % self.negimage)
# Set field Center
pmodel.ra0, pmodel.dec0 = map(numpy.deg2rad,
self.wcs.getCentreWCSCoords())
return pmodel, nmodel, self.locstep
class FITSWCSpix (_Projector):
"""FITS WCS projection, as determined by a FITS header. lm is in pixels (0-based)."""
def __init__ (self,header):
"""Constructor. Create from filename (treated as FITS file), or a FITS header object""";
# attach to FITS file or header
if isinstance(header,str):
header = pyfits.open(header)[0].header;
else:
self.wcs = WCS(header,mode="pyfits");
try:
ra0,dec0 = self.wcs.getCentreWCSCoords();
self.xpix0,self.ypix0 = self.wcs.wcs2pix(*self.wcs.getCentreWCSCoords());
self.xscale = self.wcs.getXPixelSizeDeg()*DEG;
self.yscale = self.wcs.getYPixelSizeDeg()*DEG;
has_projection = True;
except:
print "No WCS in FITS file, falling back to pixel coordinates.";
ra0 = dec0 = self.xpix0 = self.ypix0 = 0;
self.xscale = self.yscale = DEG/3600;
has_projection = False;
_Projector.__init__(self,ra0*DEG,dec0*DEG,has_projection=has_projection);
def lm (self,ra,dec):
if not self.has_projection():
return numpy.sin(ra)/self.xscale,numpy.sin(dec)/self.yscale;
if numpy.isscalar(ra) and numpy.isscalar(dec):
if ra - self.ra0 > math.pi:
ra -= 2*math.pi;
if ra - self.ra0 < -math.pi:
ra += 2*math.pi;
return self.wcs.wcs2pix(ra/DEG,dec/DEG);
else:
if numpy.isscalar(ra):
ra = numpy.array(ra);
ra[ra - self.ra0 > math.pi] -= 2*math.pi;
ra[ra - self.ra0 < -math.pi] += 2*math.pi;
## when fed in arrays of ra/dec, wcs.wcs2pix will return a nested list of
## [[l1,m1],[l2,m2],,...]. Convert this to an array and extract columns.
lm = numpy.array(self.wcs.wcs2pix(ra/DEG,dec/DEG));
return lm[...,0],lm[...,1];
def radec (self,l,m):
if not self.has_projection():
return numpy.arcsin(l*self.xscale),numpy.arcsin(m*self.yscale);
if numpy.isscalar(l) and numpy.isscalar(m):
ra,dec = self.wcs.pix2wcs(l,m);
else:
## this is slow as molasses because of the way astLib.WCS implements the loop. ~120 seconds for 4M pixels
## when fed in arrays of ra/dec, wcs.wcs2pix will return a nested list of
## [[l1,m1],[l2,m2],,...]. Convert this to an array and extract columns.
# radec = numpy.array(self.wcs.pix2wcs(l,m));
# ra = radec[...,0];
# dec = radec[...,1];
### try a faster implementation -- oh well, only a bit faster, ~95 seconds for the same
### can also replace list comprehension with map(), but that doesn't improve things.
### Note also that the final array constructor takes ~10 secs!
radec = numpy.array([ PyWCSTools.wcs.pix2wcs(self.wcs.WCSStructure,x,y) for x,y in zip(l+1,m+1) ]);
ra = radec[...,0];
dec = radec[...,1];
return ra*DEG,dec*DEG;
def offset (self,dra,ddec):
return self.xpix0 - dra/self.xscale,self.ypix0 + ddec/self.xscale;
def __eq__ (self,other):
"""By default, two projections are the same if their classes match, and their ra0/dec0 match."""
return type(self) is type(other) and (self.ra0,self.dec0,self.xpix0,self.ypix0,self.xscale,self.yscale) == (other.ra0,other.dec0,other.xpix0,other.ypix0,other.xscale,other.yscale);
class FITSWCSpix (_Projector):
"""FITS WCS projection, as determined by a FITS header. lm is in pixels (0-based)."""
def __init__ (self,header):
"""Constructor. Create from filename (treated as FITS file), or a FITS header object""";
# attach to FITS file or header
if isinstance(header,str):
header = pyfits.open(header)[0].header;
else:
self.wcs = WCS(header,mode="pyfits");
try:
ra0,dec0 = self.wcs.getCentreWCSCoords();
self.xpix0,self.ypix0 = self.wcs.wcs2pix(*self.wcs.getCentreWCSCoords());
self.xscale = self.wcs.getXPixelSizeDeg()*DEG;
self.yscale = self.wcs.getYPixelSizeDeg()*DEG;
has_projection = True;
except:
print "No WCS in FITS file, falling back to pixel coordinates.";
ra0 = dec0 = self.xpix0 = self.ypix0 = 0;
self.xscale = self.yscale = DEG/3600;
has_projection = False;
_Projector.__init__(self,ra0*DEG,dec0*DEG,has_projection=has_projection);
def lm (self,ra,dec):
if not self.has_projection():
return numpy.sin(ra)/self.xscale,numpy.sin(dec)/self.yscale;
if numpy.isscalar(ra) and numpy.isscalar(dec):
if ra - self.ra0 > math.pi:
ra -= 2*math.pi;
if ra - self.ra0 < -math.pi:
ra += 2*math.pi;
return self.wcs.wcs2pix(ra/DEG,dec/DEG);
else:
if numpy.isscalar(ra):
ra = numpy.array(ra);
ra[ra - self.ra0 > math.pi] -= 2*math.pi;
ra[ra - self.ra0 < -math.pi] += 2*math.pi;
## when fed in arrays of ra/dec, wcs.wcs2pix will return a nested list of
## [[l1,m1],[l2,m2],,...]. Convert this to an array and extract columns.
lm = numpy.array(self.wcs.wcs2pix(ra/DEG,dec/DEG));
return lm[...,0],lm[...,1];
def radec (self,l,m):
if not self.has_projection():
return numpy.arcsin(l*self.xscale),numpy.arcsin(m*self.yscale);
if numpy.isscalar(l) and numpy.isscalar(m):
ra,dec = self.wcs.pix2wcs(l,m);
else:
## this is slow as molasses because of the way astLib.WCS implements the loop. ~120 seconds for 4M pixels
## when fed in arrays of ra/dec, wcs.wcs2pix will return a nested list of
## [[l1,m1],[l2,m2],,...]. Convert this to an array and extract columns.
# radec = numpy.array(self.wcs.pix2wcs(l,m));
# ra = radec[...,0];
# dec = radec[...,1];
### try a faster implementation -- oh well, only a bit faster, ~95 seconds for the same
### can also replace list comprehension with map(), but that doesn't improve things.
### Note also that the final array constructor takes ~10 secs!
radec = numpy.array([ PyWCSTools.wcs.pix2wcs(self.wcs.WCSStructure,x,y) for x,y in zip(l+1,m+1) ]);
ra = radec[...,0];
dec = radec[...,1];
return ra*DEG,dec*DEG;
def offset (self,dra,ddec):
return self.xpix0 - dra/self.xscale,self.ypix0 + ddec/self.xscale;
def __eq__ (self,other):
"""By default, two projections are the same if their classes match, and their ra0/dec0 match."""
return type(self) is type(other) and (self.ra0,self.dec0,self.xpix0,self.ypix0,self.xscale,self.yscale) == (other.ra0,other.dec0,other.xpix0,other.ypix0,other.xscale,other.yscale);
