def spherical_beam(self, coordinate, bls, thetamax=10 * u.deg):
if self.ordering.upper() == 'RING':
nest = False
else:
nest = True
v1 = coord2vec(coordinate)
nside2 = 4 * self.nside
npix2 = hp.nside2npix(nside2)
beam_map = np.zeros(npix2)
listpix = hp.query_disc(nside2,
v1,
thetamax.to(u.rad).value,
inclusive=True,
nest=nest)
for pixel in listpix:
v2 = hp.pix2vec(nside2, pixel, nest=nest)
d = hp.rotator.angdist(v1, v2) * u.rad
d = d.value
beam_map[pixel] = hp.bl2beam(bls, d)
beam_map = hp.ud_grade(beam_map, self.nside, order_in=self.ordering)
beam_map = beam_map / beam_map.max()
return Fitsmap(beam_map, self.mask, self.header, self.ordering)
def test_bl2beam(self):
""" Test bl2beam against analytical transform of Gaussian beam. """
theta = np.linspace(0, np.radians(3.), 1000)
sigma = np.radians(1.) / np.sqrt(8. * np.log(2.))
gaussian_beam = np.exp(-.5 * (theta / sigma) ** 2) / (2 * np.pi * sigma ** 2)
ell = np.arange(2048 + 1.)
gaussian_window = np.exp(-.5 * ell * (ell + 1) * sigma ** 2)
beam = hp.bl2beam(gaussian_window, theta)
np.testing.assert_allclose(gaussian_beam, beam, rtol=1e-3)
def test_bl2beam(self):
""" Test bl2beam against analytical transform of Gaussian beam. """
theta = np.linspace(0, np.radians(3.0), 1000)
sigma = np.radians(1.0) / np.sqrt(8.0 * np.log(2.0))
gaussian_beam = np.exp(-0.5 * (theta / sigma) ** 2) / (2 * np.pi * sigma ** 2)
ell = np.arange(2048 + 1.0)
gaussian_window = np.exp(-0.5 * ell * (ell + 1) * sigma ** 2)
beam = hp.bl2beam(gaussian_window, theta)
np.testing.assert_allclose(gaussian_beam, beam, rtol=1e-3)
def flat_beam(self, coordinate, bls, npix=512, deltatheta_deg=14.658):
p0 = self.patch(coordinate, npix * 4, deltatheta_deg=deltatheta_deg)
dmap = np.zeros(p0.datos.shape)
cent = [x // 2 for x in p0.size]
for i in range(npix * 4):
for j in range(i, npix * 4):
d = p0.angular_distance(cent[0], cent[1], i,
j)[0].to(u.rad).value
dmap[i, j] = d
dmap[j, i] = d
p1 = Imagen(hp.bl2beam(bls, dmap), p0.centro, p0.size, p0.pixsize)
p2 = p1.downsample(factor=4, func=np.mean)
return p2
