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 setUp(self):
from data_models.parameters import arl_path
self.dir = arl_path('test_results')
self.niter = 1000
self.lowcore = create_named_configuration('LOWBD2-CORE')
self.nchan = 5
self.times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 7)
self.frequency = numpy.linspace(0.9e8, 1.1e8, self.nchan)
self.channel_bandwidth = numpy.array(
self.nchan * [self.frequency[1] - self.frequency[0]])
self.phasecentre = SkyCoord(ra=+0.0 * u.deg,
dec=-45.0 * u.deg,
frame='icrs',
equinox='J2000')
self.vis = create_visibility(
self.lowcore,
self.times,
self.frequency,
self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'),
zerow=True)
self.vis.data['vis'] *= 0.0
# Create model
self.test_model = create_low_test_image_from_gleam(
npixel=512,
cellsize=0.001,
phasecentre=self.vis.phasecentre,
frequency=self.frequency,
channel_bandwidth=self.channel_bandwidth,
flux_limit=1.0)
beam = create_low_test_beam(self.test_model)
export_image_to_fits(beam,
"%s/test_deconvolve_mmclean_beam.fits" % self.dir)
self.test_model.data *= beam.data
export_image_to_fits(
self.test_model,
"%s/test_deconvolve_mmclean_model.fits" % self.dir)
self.vis = predict_2d(self.vis, self.test_model)
assert numpy.max(numpy.abs(self.vis.vis)) > 0.0
self.model = create_image_from_visibility(
self.vis,
npixel=512,
cellsize=0.001,
polarisation_frame=PolarisationFrame('stokesI'))
self.dirty, sumwt = invert_2d(self.vis, self.model)
self.psf, sumwt = invert_2d(self.vis, self.model, dopsf=True)
export_image_to_fits(
self.dirty, "%s/test_deconvolve_mmclean-dirty.fits" % self.dir)
export_image_to_fits(self.psf,
"%s/test_deconvolve_mmclean-psf.fits" % self.dir)
window = numpy.ones(shape=self.model.shape, dtype=numpy.bool)
window[..., 129:384, 129:384] = True
self.innerquarter = create_image_from_array(
window,
self.model.wcs,
polarisation_frame=PolarisationFrame('stokesI'))
def test_get_frequency_map_gleam(self):
self.model = create_low_test_image_from_gleam(
npixel=128,
cellsize=0.001,
frequency=self.frequency,
channel_bandwidth=self.channel_bandwidth,
flux_limit=10.0)
spectral_mode, vfrequency_map = get_frequency_map(self.vis, self.model)
assert numpy.max(vfrequency_map) == self.model.nchan - 1
assert spectral_mode == 'channel'
def test_create_low_test_image_from_gleam(self):
im = create_low_test_image_from_gleam(npixel=256, cellsize=0.001,
channel_bandwidth=self.channel_bandwidth,
frequency=self.frequency,
phasecentre=self.phasecentre,
kind='cubic', flux_limit=0.3)
assert im.data.shape[0] == 5
assert im.data.shape[1] == 1
assert im.data.shape[2] == 256
assert im.data.shape[3] == 256
export_image_to_fits(im, '%s/test_test_support_low_gleam.fits' % (self.dir))
polarisation_frame=PolarisationFrame("stokesI"))
wprojection_planes = 1
advice = advise_wide_field(block_vis,
guard_band_image=4.0,
delA=0.02,
wprojection_planes=wprojection_planes)
vis_slices = advice['vis_slices']
npixel = advice['npixels2']
cellsize = advice['cellsize']
gleam_model = create_low_test_image_from_gleam(
npixel=npixel,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
cellsize=cellsize,
phasecentre=phasecentre,
flux_limit=1.0,
applybeam=True)
predicted_vis = predict_function(block_vis,
gleam_model,
vis_slices=51,
context='wstack')
#print("np.sum(predicted_vis.data): ", numpy.sum(predicted_vis.data['vis']))
block_vis = convert_visibility_to_blockvisibility(predicted_vis)
#print("np.sum(block_vis.data): ", numpy.sum(block_vis.data['vis']))
#print("nchan npol nants ", block_vis.nchan, block_vis.npol, block_vis.nants)
#print("uvw", block_vis.uvw, numpy.sum(block_vis.uvw))
#print("vis", block_vis.vis, numpy.sum(block_vis.vis))
