def test_deconvolve_msclean_subpsf(self):
self.comp, self.residual = deconvolve_cube(self.dirty,
psf=self.psf,
psf_support=200,
window_shape='quarter',
niter=1000,
gain=0.7,
algorithm='msclean',
scales=[0, 3, 10, 30],
threshold=0.01)
if self.persist:
export_image_to_fits(
self.comp,
"%s/test_deconvolve_msclean_subpsf-comp.fits" % (self.dir))
if self.persist:
export_image_to_fits(
self.residual,
"%s/test_deconvolve_msclean_subpsf-residual.fits" % (self.dir))
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
if self.persist:
export_image_to_fits(
self.cmodel,
"%s/test_deconvolve_msclean_subpsf-clean.fits" % (self.dir))
assert numpy.max(self.residual.data[..., 56:456, 56:456]) < 1.0
def test_deconvolve_mmclean_no_taylor(self):
self.comp, self.residual = deconvolve_cube(self.dirty,
self.psf,
niter=self.niter,
gain=0.1,
algorithm='mmclean',
scales=[0, 3, 10],
threshold=0.01,
nmoment=1,
findpeak='RASCIL',
fractional_threshold=0.01,
window=self.innerquarter)
if self.persist:
export_image_to_fits(
self.comp,
"%s/test_deconvolve_mmclean_notaylor-comp.fits" % self.dir)
if self.persist:
export_image_to_fits(
self.residual,
"%s/test_deconvolve_mmclean_notaylor-residual.fits" % self.dir)
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
if self.persist:
export_image_to_fits(
self.cmodel,
"%s/test_deconvolve_mmclean_notaylor-clean.fits" % self.dir)
assert numpy.max(self.residual.data) < 3.0
def test_deconvolve_hogbom(self):
self.comp, self.residual = deconvolve_cube(self.dirty,
self.psf,
niter=10000,
gain=0.1,
algorithm='hogbom',
threshold=0.01)
export_image_to_fits(
self.residual,
"%s/test_deconvolve_hogbom-residual.fits" % (self.dir))
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
export_image_to_fits(
self.cmodel, "%s/test_deconvolve_hogbom-clean.fits" % (self.dir))
assert numpy.max(self.residual.data) < 1.2
def test_deconvolve_msclean(self):
self.comp, self.residual = deconvolve_cube(self.dirty,
self.psf,
niter=1000,
gain=0.7,
algorithm='msclean',
scales=[0, 3, 10, 30],
threshold=0.01)
export_image_to_fits(
self.comp, "%s/test_deconvolve_msclean-comp.fits" % (self.dir))
export_image_to_fits(
self.residual,
"%s/test_deconvolve_msclean-residual.fits" % (self.dir))
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
export_image_to_fits(
self.cmodel, "%s/test_deconvolve_msclean-clean.fits" % (self.dir))
assert numpy.max(self.residual.data) < 1.2
def test_deconvolve_hogbom_subpsf(self):
self.comp, self.residual = deconvolve_cube(self.dirty,
psf=self.psf,
psf_support=200,
window_shape='quarter',
niter=10000,
gain=0.1,
algorithm='hogbom',
threshold=0.01)
export_image_to_fits(
self.residual,
"%s/test_deconvolve_hogbom_subpsf-residual.fits" % (self.dir))
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
export_image_to_fits(
self.cmodel,
"%s/test_deconvolve_hogbom_subpsf-clean.fits" % (self.dir))
assert numpy.max(self.residual.data[..., 56:456, 56:456]) < 1.2
def test_deconvolve_hogbom_no_edge(self):
self.comp, self.residual = deconvolve_cube(self.dirty,
self.psf,
window_shape='no_edge',
niter=10000,
gain=0.1,
algorithm='hogbom',
threshold=0.01)
if self.persist:
export_image_to_fits(
self.residual,
"%s/test_deconvolve_hogbom_noedge-residual.fits" % (self.dir))
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
if self.persist:
export_image_to_fits(
self.cmodel,
"%s/test_deconvolve_hogbom_no_edge-clean.fits" % (self.dir))
assert numpy.max(self.residual.data) < 1.2
polarisation_frame=PolarisationFrame('stokesIQUV'))
dirty, sumwt = invert_list_serial_workflow([vt], [model],
context='2d')[0]
psf, sumwt = invert_list_serial_workflow([vt], [model],
context='2d',
dopsf=True)[0]
export_image_to_fits(
dirty, '%s/compare_imaging_sim2_dirty.fits' % (results_dir))
export_image_to_fits(
psf, '%s/compare_imaging_sim2_psf.fits' % (results_dir))
# Deconvolve using clean
comp, residual = deconvolve_cube(dirty,
psf,
niter=10000,
threshold=0.001,
fractional_threshold=0.001,
window_shape='quarter',
gain=0.7,
scales=[0, 3, 10, 30])
restored = restore_cube(comp, psf, residual)
export_image_to_fits(
restored, '%s/compare_imaging_sim2_restored.fits' % (results_dir))
export_image_to_fits(
residual, '%s/compare_imaging_sim2_residual.fits' % (results_dir))
qa = qa_image(restored)
assert numpy.abs(qa.data['max'] - 1.006140596404203) < 1e-7, qa
assert numpy.abs(qa.data['maxabs'] - 1.006140596404203) < 1e-7, qa
assert numpy.abs(qa.data['min'] + 0.23890808520954754) < 1e-7, qa
