def test_fftim(self):
self.m31image = create_test_image(cellsize=0.001,
frequency=[1e8],
canonical=True)
m31_fft = fft_image(self.m31image)
m31_fft_ifft = fft_image(m31_fft, self.m31image)
numpy.testing.assert_array_almost_equal(self.m31image.data,
m31_fft_ifft.data.real, 12)
m31_fft.data = numpy.abs(m31_fft.data)
export_image_to_fits(m31_fft,
fitsfile='%s/test_m31_fft.fits' % (self.dir))
def test_tapering_Gaussian(self):
self.actualSetUp()
size_required = 0.01
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) < 0.001 * size_required, \
"Fit should be %f, actually is %f" % (size_required, size)
def test_fftim_factors(self):
for i in [3, 5, 7]:
npixel = 256 * i
m31image = create_test_image(cellsize=0.001,
frequency=[1e8],
canonical=True)
padded = pad_image(m31image, [1, 1, npixel, npixel])
assert padded.shape == (1, 1, npixel, npixel)
padded_fft = fft_image(padded)
padded_fft_ifft = fft_image(padded_fft, m31image)
numpy.testing.assert_array_almost_equal(padded.data,
padded_fft_ifft.data.real,
12)
padded_fft.data = numpy.abs(padded_fft.data)
export_image_to_fits(padded_fft,
fitsfile='%s/test_m31_fft_%d.fits' %
(self.dir, npixel))
def test_convert_image_to_kernel(self):
m31image = create_test_image(cellsize=0.001,
frequency=[1e8],
canonical=True)
screen = create_w_term_like(m31image, w=20000.0, remove_shift=True)
screen_fft = fft_image(screen)
converted = convert_image_to_kernel(screen_fft, 8, 8)
assert converted.shape == (1, 1, 8, 8, 8, 8)
with self.assertRaises(AssertionError):
converted = convert_image_to_kernel(m31image, 15, 1)
with self.assertRaises(AssertionError):
converted = convert_image_to_kernel(m31image, 15, 1000)
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)