def threshold_list(imagelist,
threshold,
fractional_threshold,
use_moment0=True,
prefix=''):
""" Find actual threshold for list of results, optionally using moment 0
:param imagelist:
:param threshold: Absolute threshold
:param fractional_threshold: Fractional threshold
:param use_moment0: Use moment 0 for threshold
:return:
"""
peak = 0.0
for result in imagelist:
if use_moment0:
moments = calculate_image_frequency_moments(result)
peak = max(
peak,
numpy.max(numpy.abs(moments.data[0, ...] / result.shape[0])))
else:
peak = max(peak, numpy.max(numpy.abs(result.data)))
actual = max(peak * fractional_threshold, threshold)
if use_moment0:
log.info(
"threshold_list %s: peak in moment 0 = %.6f, threshold will be %.6f"
% (prefix, peak, actual))
else:
log.info("threshold_list %s: peak = %.6f, threshold will be %.6f" %
(prefix, peak, actual))
return actual
def test_calculate_image_frequency_moments(self):
frequency = numpy.linspace(0.9e8, 1.1e8, 9)
cube = create_low_test_image_from_gleam(npixel=512,
cellsize=0.0001,
frequency=frequency,
flux_limit=1.0)
log.debug(
export_image_to_fits(cube,
fitsfile='%s/test_moments_cube.fits' %
(self.dir)))
original_cube = copy_image(cube)
moment_cube = calculate_image_frequency_moments(cube, nmoment=3)
log.debug(
export_image_to_fits(moment_cube,
fitsfile='%s/test_moments_moment_cube.fits' %
(self.dir)))
reconstructed_cube = calculate_image_from_frequency_moments(
cube, moment_cube)
log.debug(
export_image_to_fits(
reconstructed_cube,
fitsfile='%s/test_moments_reconstructed_cube.fits' %
(self.dir)))
error = numpy.std(reconstructed_cube.data - original_cube.data)
assert error < 0.2
def threshold_list(imagelist, threshold, fractional_threshold, use_moment0=True, prefix=''):
""" Find actual threshold for list of results, optionally using moment 0
:param imagelist:
:param threshold: Absolute threshold
:param fractional_threshold: Fractional threshold
:param use_moment0: Use moment 0 for threshold
:return:
"""
peak = 0.0
for i, result in enumerate(imagelist):
if use_moment0:
moments = calculate_image_frequency_moments(result)
this_peak = numpy.max(numpy.abs(moments.data[0, ...] / result.shape[0]))
peak = max(peak, this_peak)
log.info("threshold_list: using moment 0, sub_image %d, peak = %f," % (i, this_peak))
else:
ref_chan = result.data.shape[0] // 2
this_peak = numpy.max(numpy.abs(result.data[ref_chan]))
peak = max(peak, this_peak)
log.info("threshold_list: using refchan %d , sub_image %d, peak = %f," % (ref_chan, i, this_peak))
actual = max(peak * fractional_threshold, threshold)
if use_moment0:
log.info("threshold_list %s: Global peak in moment 0 = %.6f, sub-image clean threshold will be %.6f" % (prefix,
peak,
actual))
else:
log.info("threshold_list %s: Global peak = %.6f, sub-image clean threshold will be %.6f" % (prefix, peak,
actual))
return actual
def deconvolve(dirty, psf, model, facet, gthreshold):
import time
starttime = time.time()
if prefix == '':
lprefix = "facet %d" % facet
else:
lprefix = "%s, facet %d" % (prefix, facet)
nmoments = get_parameter(kwargs, "nmoments", 0)
if nmoments > 0:
moment0 = calculate_image_frequency_moments(dirty)
this_peak = numpy.max(numpy.abs(
moment0.data[0, ...])) / dirty.data.shape[0]
else:
this_peak = numpy.max(numpy.abs(dirty.data[0, ...]))
if this_peak > 1.1 * gthreshold:
log.info(
"deconvolve_arlexecute %s: cleaning - peak %.6f > 1.1 * threshold %.6f"
% (lprefix, this_peak, gthreshold))
kwargs['threshold'] = gthreshold
result, _ = deconvolve_cube(dirty, psf, prefix=lprefix, **kwargs)
if result.data.shape[0] == model.data.shape[0]:
result.data += model.data
else:
log.warning(
"deconvolve_arlexecute %s: Initial model %s and clean result %s do not have the same shape"
% (lprefix, str(
model.data.shape[0]), str(result.data.shape[0])))
flux = numpy.sum(result.data[0, 0, ...])
log.info(
'### %s, %.6f, %.6f, True, %.3f # cycle, facet, peak, cleaned flux, clean, time?'
% (lprefix, this_peak, flux, time.time() - starttime))
return result
else:
log.info(
"deconvolve_arlexecute %s: Not cleaning - peak %.6f <= 1.1 * threshold %.6f"
% (lprefix, this_peak, gthreshold))
log.info(
'### %s, %.6f, %.6f, False, %.3f # cycle, facet, peak, cleaned flux, clean, time?'
% (lprefix, this_peak, 0.0, time.time() - starttime))
return copy_image(model)
