def restore_list_serial_workflow_nosumwt(model_imagelist,
psf_imagelist,
residual_imagelist=None,
**kwargs):
""" Create a graph to calculate the restored image
:param model_imagelist: Model list
:param psf_imagelist: PSF list (without the sumwt term)
:param residual_imagelist: Residual list (without the sumwt term)
:param kwargs: Parameters for functions in components
:return:
"""
if residual_imagelist is None:
residual_imagelist = []
if len(residual_imagelist) > 0:
return [
restore_cube(model_imagelist[i], psf_imagelist[i],
residual_imagelist[i], **kwargs)
for i, _ in enumerate(model_imagelist)
]
else:
return [
restore_cube(model_imagelist[i], psf_imagelist[i], **kwargs)
for i, _ in enumerate(model_imagelist)
]
def restore_list_serial_workflow(model_imagelist, psf_imagelist,
residual_imagelist, **kwargs):
""" Create a graph to calculate the restored image
:param model_imagelist: Model list
:param psf_imagelist: PSF list
:param residual_imagelist: Residual list
:param kwargs: Parameters for functions in components
:return:
"""
return [
restore_cube(model_imagelist[i], psf_imagelist[i][0],
residual_imagelist[i][0], **kwargs)
for i, _ in enumerate(model_imagelist)
]
def load_invert_and_deconvolve(c):
v1 = create_visibility_from_ms(input_vis[0], channum=[c])[0]
v2 = create_visibility_from_ms(input_vis[1], channum=[c])[0]
vf = append_visibility(v1, v2)
vf = convert_visibility_to_stokes(vf)
vf.configuration.diameter[...] = 35.0
rows = vis_select_uvrange(vf, 0.0, uvmax=uvmax)
v = create_visibility_from_rows(vf, rows)
pol_frame = PolarisationFrame("stokesIQUV")
m = create_image_from_visibility(v,
npixel=npixel,
cellsize=cellsize,
polarisation_frame=pol_frame)
if context == '2d':
d, sumwt = invert_2d(v, m, dopsf=False)
p, sumwt = invert_2d(v, m, dopsf=True)
else:
d, sumwt = invert_list_serial_workflow([v], [m],
context=context,
dopsf=False,
vis_slices=vis_slices)[0]
p, sumwt = invert_list_serial_workflow([v], [m],
context=context,
dopsf=True,
vis_slices=vis_slices)[0]
c, resid = deconvolve_cube(d,
p,
m,
threshold=0.01,
fracthresh=0.01,
window_shape='quarter',
niter=100,
gain=0.1,
algorithm='hogbom-complex')
r = restore_cube(c, p, resid, psfwidth=psfwidth)
return r
vt = convert_blockvisibility_to_visibility(bvt)
vt = convert_visibility_to_stokes(vt)
cellsize = 20.0 * numpy.pi / (180.0 * 3600.0)
npixel = 512
model = create_image_from_visibility(vt, cellsize=cellsize, npixel=npixel,
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
assert numpy.abs(qa.data['rms'] - 0.007366519782047875) < 1e-7, qa
assert numpy.abs(qa.data['medianabs'] - 0.0005590537883509844) < 1e-7, qa
except ModuleNotFoundError as error:
print(error.__class__.__name__ + ": " + error.msg)
pass