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 __init__(self, imagename, psfname, pmodel, nmodel, local_step=10,
snr_thresh=40, high_corr_thresh=0.5, negdetec_region=10,
negatives_thresh=5, phasecenter_excl_radius=None,
prefix=None, loglevel=0):
""" Determines sources that require direction-dependent (DD)
calibration solutions.
psfname: PSF fits data
pmodel: Model of the positive image.
nmodel: Model of the negative image
header: The header of the input image
snr_thresh: float, optional. Default is 40.
Any source with 40 x the minimum SNR.
high_corr_thresh: float, optional. Default is 0.5.
Sources of high PSF correlation have correlation above 0.5.
negdetec_region: float, optional. Default is 10.
Region to lookup for negative detections around. In beam size.
negative_thresh: float, optional. Default is 5.
The number of nearby negative detections. Sources
with number > 5 require direction
dependent (DD) calibration solutions.
phasecenter_excl_region: float (in degrees), optional.
A radius from the phase center (in beam sizes) to exclude the sources
from the evaluation.
prefix: str, optional. Sets a prefix to the output directory.
loglevel: int, optional. Default 0. Python logging.
0, 1, 2, 3 for info, debug, error and
critical, respectively.
"""
self.loglevel = loglevel
self.prefix = prefix
self.log = utils.logger(self.loglevel, prefix=self.prefix)
#image and psf image
self.pmodel = pmodel
self.nmodel = nmodel
self.psfname = psfname
self.log.info(" Loading image data")
# tags
self.dd_tag = "dE"
# thresholds
self.snr_factor = snr_thresh
#self.localthresh = local_thresh
self.corrthresh = high_corr_thresh
self.negthresh = negatives_thresh
with pyfits.open(imagename) as hdu:
self.hdr = hdu[0].header
self.data = utils.image_data(hdu[0].data)
self.wcs = WCS(self.hdr, mode="pyfits")
self.locstep = local_step
#regions
self.phaserad = phasecenter_excl_radius # radius from the phase center
self.negregion = negdetec_region # region to look for negatives
# conversion
self.bmaj = self.hdr["BMAJ"] # in degrees
self.ra0 = numpy.deg2rad(self.hdr["CRVAL1"])
self.dec0 = numpy.deg2rad(self.hdr["CRVAL2"])
def __init__(self, imagename, psfname=None, sourcefinder_name='pybdsm',
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,
**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.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
# reading imagename data
imagedata, self.wcs, self.header, self.pixelsize =\
utils.reshape_data(self.imagename, prefix=self.prefix)
self.imagedata = numpy.array(imagedata, dtype=numpy.float32)
self.image2by2 = numpy.array(utils.image_data(imagedata, prefix),
dtype=numpy.float32)
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
if not self.psfname:
self.log.info(" No psf provided, do_psf_corr is set to False.")
self.do_psf_corr = False
if self.psfname:
psfdata, self.psfhdr = utils.open_psf_image(self.psfname)
self.psfdata = utils.image_data(psfdata, prefix)
# computing negative noise
self.noise, self.mean = utils.negative_noise(self.imagedata, self.prefix) #here is 2X2 data here
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"
negativedata = utils.invert_image(
self.imagename, self.imagedata,
self.header, self.negimage, prefix)
self.negimage2by2 = numpy.array(utils.image_data(negativedata, prefix),
dtype=numpy.float32)
self.negativedata = numpy.array(negativedata, numpy.float32)
# 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.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 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 __init__(self,
imagename,
psfname,
pmodel,
nmodel,
local_step=10,
snr_thresh=40,
high_corr_thresh=0.5,
negdetec_region=10,
negatives_thresh=5,
phasecenter_excl_radius=None,
prefix=None,
loglevel=0):
""" Determines sources that require direction-dependent (DD)
calibration solutions.
psfname: PSF fits data
pmodel: Model of the positive image.
nmodel: Model of the negative image
header: The header of the input image
snr_thresh: float, optional. Default is 40.
Any source with 40 x the minimum SNR.
high_corr_thresh: float, optional. Default is 0.5.
Sources of high PSF correlation have correlation above 0.5.
negdetec_region: float, optional. Default is 10.
Region to lookup for negative detections around. In beam size.
negative_thresh: float, optional. Default is 5.
The number of nearby negative detections. Sources
with number > 5 require direction
dependent (DD) calibration solutions.
phasecenter_excl_region: float (in degrees), optional.
A radius from the phase center (in beam sizes) to exclude the sources
from the evaluation.
prefix: str, optional. Sets a prefix to the output directory.
loglevel: int, optional. Default 0. Python logging.
0, 1, 2, 3 for info, debug, error and
critical, respectively.
"""
self.loglevel = loglevel
self.prefix = prefix
self.log = utils.logger(self.loglevel, prefix=self.prefix)
#image and psf image
self.pmodel = pmodel
self.nmodel = nmodel
self.psfname = psfname
self.log.info(" Loading image data")
# tags
self.dd_tag = "dE"
# thresholds
self.snr_factor = snr_thresh
#self.localthresh = local_thresh
self.corrthresh = high_corr_thresh
self.negthresh = negatives_thresh
with pyfits.open(imagename) as hdu:
self.hdr = hdu[0].header
self.data = utils.image_data(hdu[0].data)
self.wcs = WCS(self.hdr, mode="pyfits")
self.locstep = local_step
#regions
self.phaserad = phasecenter_excl_radius # radius from the phase center
self.negregion = negdetec_region # region to look for negatives
# conversion
self.bmaj = self.hdr["BMAJ"] # in degrees
self.ra0 = numpy.deg2rad(self.hdr["CRVAL1"])
self.dec0 = numpy.deg2rad(self.hdr["CRVAL2"])
