def spectral_line_imaging_list_serial_workflow(vis_list,
model_imagelist,
context,
continuum_model_imagelist=None,
vis_slices=1,
facets=1,
gcfcf=None,
**kwargs):
"""Create graph for spectral line imaging pipeline
Uses the continuum imaging rsexecute pipeline after subtraction of a continuum model
:param vis_list: List of visibility components
:param model_imagelist: Spectral line model graph
:param continuum_model_imagelist: Continuum model list
:param context: Imaging context
:param kwargs: Parameters for functions in components
:return: (deconvolved model, residual, restored)
"""
if continuum_model_imagelist is not None:
vis_list = predict_list_serial_workflow(vis_list,
continuum_model_imagelist,
context=context,
gcfcf=gcfcf,
vis_slices=vis_slices,
facets=facets,
**kwargs)
return continuum_imaging_list_serial_workflow(vis_list,
model_imagelist,
context=context,
gcfcf=gcfcf,
vis_slices=vis_slices,
facets=facets,
**kwargs)
def ft_ift_sm(ov, sm, g):
assert isinstance(ov, Visibility), ov
assert isinstance(sm, SkyModel), sm
if g is not None:
assert len(g) == 2, g
assert isinstance(g[0], Image), g[0]
assert isinstance(g[1], ConvolutionFunction), g[1]
v = copy_visibility(ov)
v.data['vis'][...] = 0.0 + 0.0j
if len(sm.components) > 0:
if isinstance(sm.mask, Image):
comps = copy_skycomponent(sm.components)
comps = apply_beam_to_skycomponent(comps, sm.mask)
v = predict_skycomponent_visibility(v, comps)
else:
v = predict_skycomponent_visibility(v, sm.components)
if isinstance(sm.image, Image):
if numpy.max(numpy.abs(sm.image.data)) > 0.0:
if isinstance(sm.mask, Image):
model = copy_image(sm.image)
model.data *= sm.mask.data
else:
model = sm.image
v = predict_list_serial_workflow([v], [model],
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=[g],
**kwargs)[0]
assert isinstance(sm.image, Image), sm.image
result = invert_list_serial_workflow([v], [sm.image],
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf,
**kwargs)[0]
if isinstance(sm.mask, Image):
result[0].data *= sm.mask.data
return result
def _predict_base(self, context='2d', extra='', fluxthreshold=1.0, facets=1, vis_slices=1,
gcfcf=None, **kwargs):
centre = self.freqwin // 2
vis_list = zero_list_serial_workflow(self.vis_list)
vis_list = predict_list_serial_workflow(vis_list, self.model_list, context=context,
vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs)
vis_list = subtract_list_serial_workflow(self.vis_list, vis_list)
dirty = invert_list_serial_workflow(vis_list, self.model_list, context=context, dopsf=False,
gcfcf=gcfcf, normalize=True, vis_slices=vis_slices)[centre]
assert numpy.max(numpy.abs(dirty[0].data)), "Residual image is empty"
if self.persist: export_image_to_fits(dirty[0], '%s/test_imaging_predict_%s%s_serial_dirty.fits' %
(self.dir, context, extra))
maxabs = numpy.max(numpy.abs(dirty[0].data))
assert maxabs < fluxthreshold, "Error %.3f greater than fluxthreshold %.3f " % (maxabs, fluxthreshold)
def ft_cal_sm(ov, sm, g):
assert isinstance(ov, Visibility), ov
assert isinstance(sm, SkyModel), sm
if g is not None:
assert len(g) == 2, g
assert isinstance(g[0], Image), g[0]
assert isinstance(g[1], ConvolutionFunction), g[1]
v = copy_visibility(ov)
v.data['vis'][...] = 0.0 + 0.0j
if len(sm.components) > 0:
if isinstance(sm.mask, Image):
comps = copy_skycomponent(sm.components)
comps = apply_beam_to_skycomponent(comps, sm.mask)
v = predict_skycomponent_visibility(v, comps)
else:
v = predict_skycomponent_visibility(v, sm.components)
if isinstance(sm.image, Image):
if numpy.max(numpy.abs(sm.image.data)) > 0.0:
if isinstance(sm.mask, Image):
model = copy_image(sm.image)
model.data *= sm.mask.data
else:
model = sm.image
v = predict_list_serial_workflow([v], [model],
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=[g],
**kwargs)[0]
if docal and isinstance(sm.gaintable, GainTable):
bv = convert_visibility_to_blockvisibility(v)
bv = apply_gaintable(bv, sm.gaintable, inverse=True)
v = convert_blockvisibility_to_visibility(bv)
return v
def ical_list_serial_workflow(vis_list,
model_imagelist,
context,
vis_slices=1,
facets=1,
gcfcf=None,
calibration_context='TG',
do_selfcal=True,
**kwargs):
"""Run ICAL pipeline
:param vis_list:
:param model_imagelist:
:param context: imaging context e.g. '2d'
:param calibration_context: Sequence of calibration steps e.g. TGB
:param do_selfcal: Do the selfcalibration?
:param kwargs: Parameters for functions in components
:return:
"""
gt_list = list()
if gcfcf is None:
gcfcf = [create_pswf_convolutionfunction(model_imagelist[0])]
psf_imagelist = invert_list_serial_workflow(vis_list,
model_imagelist,
dopsf=True,
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf,
**kwargs)
model_vislist = [copy_visibility(v, zero=True) for v in vis_list]
if do_selfcal:
cal_vis_list = [copy_visibility(v) for v in vis_list]
else:
cal_vis_list = vis_list
if do_selfcal:
# Make the predicted visibilities, selfcalibrate against it correcting the gains, then
# form the residual visibility, then make the residual image
model_vislist = predict_list_serial_workflow(model_vislist,
model_imagelist,
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf,
**kwargs)
cal_vis_list, gt_list = calibrate_list_serial_workflow(
cal_vis_list,
model_vislist,
calibration_context=calibration_context,
**kwargs)
residual_vislist = subtract_list_serial_workflow(
cal_vis_list, model_vislist)
residual_imagelist = invert_list_serial_workflow(residual_vislist,
model_imagelist,
context=context,
dopsf=False,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf,
iteration=0,
**kwargs)
else:
# If we are not selfcalibrating it's much easier and we can avoid an unnecessary round of gather/scatter
# for visibility partitioning such as timeslices and wstack.
residual_imagelist = residual_list_serial_workflow(
cal_vis_list,
model_imagelist,
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf,
**kwargs)
deconvolve_model_imagelist = deconvolve_list_serial_workflow(
residual_imagelist,
psf_imagelist,
model_imagelist,
prefix='cycle 0',
**kwargs)
nmajor = get_parameter(kwargs, "nmajor", 5)
if nmajor > 1:
for cycle in range(nmajor):
if do_selfcal:
model_vislist = predict_list_serial_workflow(
model_vislist,
deconvolve_model_imagelist,
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf,
**kwargs)
cal_vis_list = [copy_visibility(v) for v in vis_list]
cal_vis_list, gt_list = calibrate_list_serial_workflow(
cal_vis_list,
model_vislist,
calibration_context=calibration_context,
iteration=cycle,
**kwargs)
residual_vislist = subtract_list_serial_workflow(
cal_vis_list, model_vislist)
residual_imagelist = invert_list_serial_workflow(
residual_vislist,
model_imagelist,
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf,
**kwargs)
else:
residual_imagelist = residual_list_serial_workflow(
cal_vis_list,
deconvolve_model_imagelist,
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf,
**kwargs)
prefix = "cycle %d" % (cycle + 1)
deconvolve_model_imagelist = deconvolve_list_serial_workflow(
residual_imagelist,
psf_imagelist,
deconvolve_model_imagelist,
prefix=prefix,
**kwargs)
residual_imagelist = residual_list_serial_workflow(
cal_vis_list,
deconvolve_model_imagelist,
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=gcfcf,
**kwargs)
restore_imagelist = restore_list_serial_workflow(
deconvolve_model_imagelist, psf_imagelist, residual_imagelist)
return deconvolve_model_imagelist, residual_imagelist, restore_imagelist, gt_list
def test_export_ms(self):
if run_ms_tests == False:
return
msoutfile = rascil_path("test_results/test_export_ms_ASKAP_output.ms")
from astropy.coordinates import SkyCoord
from astropy import units as u
from rascil.processing_components.image.operations import show_image, export_image_to_fits
from rascil.processing_components.simulation import create_named_configuration
from rascil.processing_components.simulation import create_test_image
from rascil.processing_components.imaging.base import create_image_from_visibility
from rascil.processing_components.imaging.base import advise_wide_field
from rascil.workflows.serial.imaging.imaging_serial import invert_list_serial_workflow, predict_list_serial_workflow
from rascil.data_models.polarisation import PolarisationFrame
lowr3 = create_named_configuration('LOWBD2', rmax=750.0)
times = numpy.zeros([1])
frequency = numpy.array([1e8])
channelbandwidth = numpy.array([1e6])
phasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-45.0 * u.deg,
frame='icrs',
equinox='J2000')
bvis = create_blockvisibility(
lowr3,
times,
frequency,
phasecentre=phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'),
channel_bandwidth=channelbandwidth)
vt = convert_blockvisibility_to_visibility(bvis)
advice = advise_wide_field(vt,
guard_band_image=3.0,
delA=0.1,
facets=1,
wprojection_planes=1,
oversampling_synthesised_beam=4.0)
cellsize = advice['cellsize']
m31image = create_test_image(frequency=frequency, cellsize=cellsize)
nchan, npol, ny, nx = m31image.data.shape
m31image.wcs.wcs.crval[0] = vt.phasecentre.ra.deg
m31image.wcs.wcs.crval[1] = vt.phasecentre.dec.deg
m31image.wcs.wcs.crpix[0] = float(nx // 2)
m31image.wcs.wcs.crpix[1] = float(ny // 2)
vt = predict_list_serial_workflow([vt], [m31image], context='2d')[0]
# uvdist = numpy.sqrt(vt.data['uvw'][:, 0] ** 2 + vt.data['uvw'][:, 1] ** 2)
#
# model = create_image_from_visibility(vt, cellsize=cellsize, npixel=512)
# dirty, sumwt = invert_list_serial_workflow([vt], [model], context='2d')[0]
# psf, sumwt = invert_list_serial_workflow([vt], [model], context='2d', dopsf=True)[0]
#
# show_image(dirty)
# print("Max, min in dirty image = %.6f, %.6f, sumwt = %f" % (dirty.data.max(), dirty.data.min(), sumwt))
#
# print("Max, min in PSF = %.6f, %.6f, sumwt = %f" % (psf.data.max(), psf.data.min(), sumwt))
# results_dir="/Users/f.wang"
# export_image_to_fits(dirty, '%s/imaging_dirty.fits' % (results_dir))
# export_image_to_fits(psf, '%s/imaging_psf.fits' % (results_dir))
v = convert_visibility_to_blockvisibility(vt)
vis_list = []
vis_list.append(v)
export_blockvisibility_to_ms(msoutfile, vis_list, source_name='M31')