def test_invert(self):
uvfitsfile = arl_path("data/vis/ASKAP_example.fits")
nchan_ave = 32
nchan = 192
for schan in range(0, nchan, nchan_ave):
max_chan = min(nchan, schan + nchan_ave)
bv = create_blockvisibility_from_uvfits(uvfitsfile,
range(schan, max_chan))[0]
vis = convert_blockvisibility_to_visibility(bv)
from processing_components.visibility.operations import convert_visibility_to_stokesI
vis = convert_visibility_to_stokesI(vis)
model = create_image_from_visibility(
vis,
npixel=256,
polarisation_frame=PolarisationFrame('stokesI'))
dirty, sumwt = invert_2d(vis, model, context='2d')
assert (numpy.max(numpy.abs(dirty.data))) > 0.0
assert dirty.shape == (nchan_ave, 1, 256, 256)
import matplotlib.pyplot as plt
from processing_components.image.operations import show_image
show_image(dirty)
plt.show()
if self.persist:
export_image_to_fits(
dirty, '%s/test_visibility_uvfits_dirty.fits' % self.dir)
def test_create_list_spectral_average(self):
uvfitsfile = arl_path("data/vis/ASKAP_example.fits")
vis_by_channel = list()
nchan_ave = 16
nchan = 192
for schan in range(0, nchan, nchan_ave):
max_chan = min(nchan, schan + nchan_ave)
v = create_blockvisibility_from_uvfits(uvfitsfile,
range(schan, max_chan))
vis_by_channel.append(integrate_visibility_by_channel(v[0]))
assert len(vis_by_channel) == 12
for v in vis_by_channel:
assert v.vis.data.shape[-1] == 4
assert v.vis.data.shape[-2] == 1
assert v.polarisation_frame.type == "linear"