def test_tapering_Gaussian(self):
self.actualSetUp()
size_required = 0.010
self.componentvis = weight_visibility(self.componentvis,
self.model,
algoritm='uniform')
self.componentvis = taper_visibility_gaussian(self.componentvis,
beam=size_required)
psf, sumwt = invert_2d(self.componentvis, self.model, dopsf=True)
export_image_to_fits(
psf, '%s/test_weighting_gaussian_taper_psf.fits' % self.dir)
xfr = fft_image(psf)
xfr.data = xfr.data.real.astype('float')
export_image_to_fits(
xfr, '%s/test_weighting_gaussian_taper_xfr.fits' % self.dir)
npixel = psf.data.shape[3]
sl = slice(npixel // 2 - 7, npixel // 2 + 8)
fit = fit_2dgaussian(psf.data[0, 0, sl, sl])
# if fit.x_stddev <= 0.0 or fit.y_stddev <= 0.0:
# raise ValueError('Error in fitting to psf')
# fit_2dgaussian returns sqrt of variance. We need to convert that to FWHM.
# https://en.wikipedia.org/wiki/Full_width_at_half_maximum
scale_factor = numpy.sqrt(8 * numpy.log(2.0))
size = numpy.sqrt(fit.x_stddev * fit.y_stddev) * scale_factor
# Now we need to convert to radians
size *= numpy.pi * self.model.wcs.wcs.cdelt[1] / 180.0
# Very impressive! Desired 0.01 Acheived 0.0100006250829
assert numpy.abs(size - size_required) < 0.01 * size_required, \
"Fit should be %f, actually is %f" % (size_required, size)
def test_tapering_Tukey(self):
self.actualSetUp()
self.componentvis = weight_visibility(self.componentvis,
self.model,
algoritm='uniform')
self.componentvis = taper_visibility_tukey(self.componentvis,
tukey=1.0)
psf, sumwt = invert_2d(self.componentvis, self.model, dopsf=True)
export_image_to_fits(
psf, '%s/test_weighting_tukey_taper_psf.fits' % self.dir)
xfr = fft_image(psf)
xfr.data = xfr.data.real.astype('float')
export_image_to_fits(
xfr, '%s/test_weighting_tukey_taper_xfr.fits' % self.dir)
def test_convert_weight(self):
cvis = convert_blockvisibility_to_visibility(self.blockvis)
model = create_image_from_visibility(
vis=cvis,
npixel=256,
cellsize=0.001,
phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame('stokesI'))
cvis = weight_visibility(cvis, model)
assert numpy.mean(cvis.data['imaging_weight']) < 1.0
assert numpy.std(cvis.data['imaging_weight']) > 0.0
dvis = decoalesce_visibility(cvis)
assert numpy.mean(dvis.data['imaging_weight']) < 1.0
assert numpy.std(dvis.data['imaging_weight']) > 0.0
assert dvis.nvis == self.blockvis.nvis