def testPositions(self):
# The pixel position x, y of the source should be the same as the
# position of the maximum in the generated result map.
x_pos = 40
y_pos = 60
empty_data = numpy.zeros((100, 100))
SrcMeasurement = namedtuple("SrcMeasurement",
['peak', 'x', 'y', 'smaj', 'smin', 'theta']
)
src_measurement = SrcMeasurement(
peak=Uncertain(10), x=Uncertain(x_pos), y=Uncertain(y_pos),
smaj=Uncertain(10), smin=Uncertain(10), theta=Uncertain(0)
)
gaussian_map, residual_map = generate_result_maps(empty_data, [src_measurement])
gaussian_max_x = numpy.where(gaussian_map==gaussian_map.max())[0][0]
gaussian_max_y = numpy.where(gaussian_map==gaussian_map.max())[1][0]
self.assertEqual(gaussian_max_x, x_pos)
self.assertEqual(gaussian_max_y, y_pos)
def testPositions(self):
# The pixel position x, y of the source should be the same as the
# position of the maximum in the generated result map.
x_pos = 40
y_pos = 60
empty_data = numpy.zeros((100, 100))
SrcMeasurement = namedtuple(
"SrcMeasurement", ['peak', 'x', 'y', 'smaj', 'smin', 'theta'])
src_measurement = SrcMeasurement(peak=Uncertain(10),
x=Uncertain(x_pos),
y=Uncertain(y_pos),
smaj=Uncertain(10),
smin=Uncertain(10),
theta=Uncertain(0))
gaussian_map, residual_map = generate_result_maps(
empty_data, [src_measurement])
gaussian_max_x = numpy.where(gaussian_map == gaussian_map.max())[0][0]
gaussian_max_y = numpy.where(gaussian_map == gaussian_map.max())[1][0]
self.assertEqual(gaussian_max_x, x_pos)
self.assertEqual(gaussian_max_y, y_pos)
def run_sourcefinder(files, options):
"""
Iterate over the list of files, running a sourcefinding step on each in
turn. If specified, a DS9-compatible region file and/or a FITS file
showing the residuals after Gaussian fitting are dumped for each file.
A string containing a human readable list of sources is returned.
"""
output = StringIO()
beam = get_beam(options.bmaj, options.bmin, options.bpa)
configuration = get_sourcefinder_configuration(options)
if options.mode == "detimage":
labels, labelled_data = get_detection_labels(options.detection_image,
options.detection,
options.analysis, beam,
configuration)
else:
labels, labelled_data = [], None
for counter, filename in enumerate(files):
print "Processing %s (file %d of %d)." % (filename, counter + 1,
len(files))
imagename = os.path.splitext(os.path.basename(filename))[0]
ff = open_accessor(filename, beam=beam, plane=0)
imagedata = sourcefinder_image_from_accessor(ff, **configuration)
if options.mode == "fixed":
sr = imagedata.fit_fixed_positions(
options.fixed_coords, options.ffbox * max(imagedata.beam[0:2]))
else:
if options.mode == "fdr":
print "Using False Detection Rate algorithm with alpha = %f" % (
options.alpha, )
sr = imagedata.fd_extract(
alpha=options.alpha,
deblend_nthresh=options.deblend_thresholds,
force_beam=options.force_beam)
else:
if labelled_data is None:
print "Thresholding with det = %f sigma, analysis = %f sigma" % (
options.detection, options.analysis)
sr = imagedata.extract(
det=options.detection,
anl=options.analysis,
labelled_data=labelled_data,
labels=labels,
deblend_nthresh=options.deblend_thresholds,
force_beam=options.force_beam)
if options.regions:
regionfile = imagename + ".reg"
regionfile = open(regionfile, 'w')
regionfile.write(regions(sr))
regionfile.close()
if options.residuals or options.islands:
gaussian_map, residual_map = generate_result_maps(
imagedata.data, sr)
if options.residuals:
residualfile = imagename + ".residuals.fits"
writefits(residualfile, residual_map, pyfits.getheader(filename))
if options.islands:
islandfile = imagename + ".islands.fits"
writefits(islandfile, gaussian_map, pyfits.getheader(filename))
if options.rmsmap:
rmsfile = imagename + ".rms.fits"
writefits(rmsfile, numpy.array(imagedata.rmsmap),
pyfits.getheader(filename))
if options.sigmap:
sigfile = imagename + ".sig.fits"
writefits(sigfile,
numpy.array(imagedata.data_bgsubbed / imagedata.rmsmap),
pyfits.getheader(filename))
if options.skymodel:
with open(imagename + ".skymodel", 'w') as skymodelfile:
if ff.freq_eff:
skymodelfile.write(skymodel(sr, ff.freq_eff))
else:
print "WARNING: Using default reference frequency for %s" % (
skymodelfile.name, )
skymodelfile.write(skymodel(sr))
if options.csv:
with open(imagename + ".csv", 'w') as csvfile:
csvfile.write(csv(sr))
print >> output, summary(filename, sr),
return output.getvalue()
def run_sourcefinder(files, options):
"""
Iterate over the list of files, running a sourcefinding step on each in
turn. If specified, a DS9-compatible region file and/or a FITS file
showing the residuals after Gaussian fitting are dumped for each file.
A string containing a human readable list of sources is returned.
"""
output = StringIO()
beam = get_beam(options.bmaj, options.bmin, options.bpa)
configuration = get_sourcefinder_configuration(options)
if options.mode == "detimage":
labels, labelled_data = get_detection_labels(
options.detection_image, options.detection, options.analysis, beam, configuration
)
else:
labels, labelled_data = [], None
for counter, filename in enumerate(files):
print "Processing %s (file %d of %d)." % (filename, counter + 1, len(files))
imagename = os.path.splitext(os.path.basename(filename))[0]
ff = open_accessor(filename, beam=beam, plane=0)
imagedata = sourcefinder_image_from_accessor(ff, **configuration)
if options.mode == "fixed":
sr = imagedata.fit_fixed_positions(options.fixed_coords, options.ffbox * max(imagedata.beam[0:2]))
else:
if options.mode == "fdr":
print "Using False Detection Rate algorithm with alpha = %f" % (options.alpha,)
sr = imagedata.fd_extract(
alpha=options.alpha, deblend_nthresh=options.deblend_thresholds, force_beam=options.force_beam
)
else:
if labelled_data is None:
print "Thresholding with det = %f sigma, analysis = %f sigma" % (
options.detection,
options.analysis,
)
sr = imagedata.extract(
det=options.detection,
anl=options.analysis,
labelled_data=labelled_data,
labels=labels,
deblend_nthresh=options.deblend_thresholds,
force_beam=options.force_beam,
)
if options.regions:
regionfile = imagename + ".reg"
regionfile = open(regionfile, "w")
regionfile.write(regions(sr))
regionfile.close()
if options.residuals or options.islands:
gaussian_map, residual_map = generate_result_maps(imagedata.data, sr)
if options.residuals:
residualfile = imagename + ".residuals.fits"
writefits(residualfile, residual_map, pyfits.getheader(filename))
if options.islands:
islandfile = imagename + ".islands.fits"
writefits(islandfile, gaussian_map, pyfits.getheader(filename))
if options.rmsmap:
rmsfile = imagename + ".rms.fits"
writefits(rmsfile, numpy.array(imagedata.rmsmap), pyfits.getheader(filename))
if options.sigmap:
sigfile = imagename + ".sig.fits"
writefits(sigfile, numpy.array(imagedata.data_bgsubbed / imagedata.rmsmap), pyfits.getheader(filename))
if options.skymodel:
with open(imagename + ".skymodel", "w") as skymodelfile:
if ff.freq_eff:
skymodelfile.write(skymodel(sr, ff.freq_eff))
else:
print "WARNING: Using default reference frequency for %s" % (skymodelfile.name,)
skymodelfile.write(skymodel(sr))
if options.csv:
with open(imagename + ".csv", "w") as csvfile:
csvfile.write(csv(sr))
print >> output, summary(filename, sr),
return output.getvalue()
def run_sourcefinder(files, options):
"""
Iterate over the list of files, running a sourcefinding step on each in
turn. If specified, a DS9-compatible region file and/or a FITS file
showing the residuals after Gaussian fitting are dumped for each file.
A string containing a human readable list of sources is returned.
"""
output = StringIO()
configuration = {
"back_sizex": options.grid,
"back_sizey": options.grid,
"margin": options.margin,
"radius": options.radius,
"deblend": bool(options.deblend),
"deblend_nthresh": options.deblend_thresholds,
"force_beam": options.force_beam
}
if options.residuals or options.islands:
configuration['residuals'] = True
if options.detection_image:
if options.fdr:
print "WARNING: --detection-image not supported with --fdr; ignored"
else:
print "Detecting islands in %s" % (options.detection_image)
print "Thresholding with det = %f sigma, analysis = %f sigma" % (options.detection, options.analysis)
if (
isinstance(options.bmaj, float)
and isinstance(options.bmin, float)
and isinstance(options.bpa, float)
):
ff = FitsImage(options.detection_image, beam=(options.bmaj, options.bmin, options.bpa), plane=0)
else:
ff = FitsImage(options.detection_image, plane=0)
imagedata = sourcefinder_image_from_accessor(ff, **configuration)
labels, labelled_data = imagedata.label_islands(
options.detection * imagedata.rmsmap, options.analysis * imagedata.rmsmap
)
else:
labels, labelled_data = [], None
for counter, filename in enumerate(files):
print "Processing %s (file %d of %d)." % (filename, counter+1, len(files))
if (
isinstance(options.bmaj, float)
and isinstance(options.bmin, float)
and isinstance(options.bpa, float)
):
ff = FitsImage(filename, beam=(options.bmaj, options.bmin, options.bpa), plane=0)
else:
ff = FitsImage(filename, plane=0)
imagedata = sourcefinder_image_from_accessor(ff, **configuration)
if options.fdr:
print "Using False Detection Rate algorithm with alpha = %f" % (options.alpha,)
sr = imagedata.fd_extract(options.alpha)
else:
if labelled_data is None:
print "Thresholding with det = %f sigma, analysis = %f sigma" % (options.detection, options.analysis)
sr = imagedata.extract(options.detection, options.analysis, labelled_data=labelled_data, labels=labels)
if options.regions:
regionfile = os.path.splitext(os.path.basename(filename))[0] + ".reg"
regionfile = open(regionfile, 'w')
regionfile.write(regions(sr))
regionfile.close()
if options.residuals or options.islands:
gaussian_map, residual_map = generate_result_maps(imagedata.data, sr)
if options.residuals:
residualfile = os.path.splitext(os.path.basename(filename))[0] + ".residuals.fits"
writefits(residualfile, residual_map, pyfits.getheader(filename))
if options.islands:
islandfile = os.path.splitext(os.path.basename(filename))[0] + ".islands.fits"
writefits(islandfile, gaussian_map, pyfits.getheader(filename))
if options.rmsmap:
rmsfile = os.path.splitext(os.path.basename(filename))[0] + ".rms.fits"
writefits(rmsfile, numpy.array(imagedata.rmsmap), pyfits.getheader(filename))
if options.sigmap:
sigfile = os.path.splitext(os.path.basename(filename))[0] + ".sig.fits"
writefits(sigfile, numpy.array(imagedata.data_bgsubbed/imagedata.rmsmap), pyfits.getheader(filename))
if options.skymodel:
skymodelfile = os.path.splitext(os.path.basename(filename))[0] + ".skymodel"
skymodelfile = open(skymodelfile, 'w')
if ff.freq_eff:
skymodelfile.write(skymodel(sr, ff.freq_eff))
else:
print "WARNING: Using default reference frequency for %s" % (skymodelfile.name,)
skymodelfile.write(skymodel(sr))
skymodelfile.close()
if options.csv:
csvfile = os.path.splitext(os.path.basename(filename))[0] + ".csv"
csvfile = open(csvfile, 'w')
csvfile.write(csv(sr))
csvfile.close()
print >>output, summary(filename, sr),
return output.getvalue()
