def test_resample_Tsky(self):
fqs = np.linspace(0.1, 0.2, 100)
lsts = np.linspace(0, 2 * np.pi, 200)
tsky = noise.resample_Tsky(fqs, lsts)
self.assertEqual(tsky.shape, (200, 100))
self.assertTrue(np.all(tsky[0] == tsky[1]))
self.assertFalse(np.all(tsky[:, 0] == tsky[:, 1]))
# import uvtools, pylab as plt
# uvtools.plot.waterfall(tsky); plt.show()
tsky = noise.resample_Tsky(fqs, lsts, Tsky_mdl=noise.HERA_Tsky_mdl["xx"])
self.assertEqual(tsky.shape, (200, 100))
self.assertFalse(np.all(tsky[0] == tsky[1]))
self.assertFalse(np.all(tsky[:, 0] == tsky[:, 1]))
def test_resample_Tsky(self):
fqs = np.linspace(0.1, 0.2, 100)
lsts = np.linspace(0, 2 * np.pi, 200)
tsky = noise.resample_Tsky(fqs, lsts)
self.assertEqual(tsky.shape, (200, 100))
self.assertTrue(np.all(tsky[0] == tsky[1]))
self.assertFalse(np.all(tsky[:, 0] == tsky[:, 1]))
tsky = noise.resample_Tsky(fqs,
lsts,
Tsky_mdl=noise.HERA_Tsky_mdl["xx"])
self.assertEqual(tsky.shape, (200, 100))
self.assertFalse(np.all(tsky[0] == tsky[1]))
self.assertFalse(np.all(tsky[:, 0] == tsky[:, 1]))
def test_sky_noise_jy(self):
fqs = np.linspace(0.1, 0.2, 100)
lsts = np.linspace(0, 2 * np.pi, 500)
omp = noise.bm_poly_to_omega_p(fqs)
tsky = noise.resample_Tsky(fqs, lsts)
jy2T = noise.jy2T(fqs, omega_p=omp) / 1e3
jy2T.shape = (1, -1)
nos_jy = noise.sky_noise_jy(tsky, fqs, lsts, inttime=10.7, omega_p=omp)
self.assertEqual(nos_jy.shape, (500, 100))
np.testing.assert_allclose(np.average(nos_jy, axis=0), 0, atol=0.7)
scaling = np.average(tsky, axis=0) / jy2T
np.testing.assert_allclose(np.std(nos_jy, axis=0) / scaling *
np.sqrt(1e6 * 10.7),
1.0,
atol=0.1)
np.random.seed(0)
nos_jy = noise.sky_noise_jy(tsky, fqs, lsts, inttime=None, omega_p=omp)
np.testing.assert_allclose(
np.std(nos_jy, axis=0) / scaling *
np.sqrt(1e6 * aipy.const.sidereal_day / 500),
1.0,
atol=0.1)
np.random.seed(0)
nos_jy = noise.sky_noise_jy(tsky,
fqs,
lsts,
B=.1,
inttime=10.7,
omega_p=omp)
np.testing.assert_allclose(np.std(nos_jy, axis=0) / scaling *
np.sqrt(1e8 * 10.7),
1.0,
atol=0.1)
def generate_noise(self):
omega_p = noise.bm_poly_to_omega_p(self.fqs)
tsky = noise.resample_Tsky(self.fqs,
self.lsts,
Tsky_mdl=noise.HERA_Tsky_mdl['xx'])
t_rx = 150.
return noise.sky_noise_jy(tsky + t_rx, self.fqs, self.lsts, omega_p)
def gen_HERA_vis(self,
tsamples,
fsamples,
bl_len_ns=400.,
add_rfi=False,
inject_frb=False):
#### Convert time samples to appropriate LST hours where 60 time samples = 10 min
#### !!!! LST is in rads not hrs
sph = 60. / .1667
lst_add = tsamples / sph
fqs = np.linspace(.1, .2, fsamples, endpoint=False)
lsts = np.linspace(np.pi / 2., np.pi / 2. + lst_add, tsamples)
times = lsts / (2. * np.pi) * a.const.sidereal_day
#### FOREGROUNDS ####
# Diffuse
Tsky_mdl = noise.HERA_Tsky_mdl['xx']
vis_fg_diffuse = foregrounds.diffuse_foreground(
Tsky_mdl, lsts, fqs, bl_len_ns)
# Point Sources
vis_fg_pntsrc = foregrounds.pntsrc_foreground(lsts,
fqs,
bl_len_ns,
nsrcs=1000)
# FRBs
vis_fg_frb = np.asarray(gen_simulated_frb(NFREQ=1024,
NTIME=61,
width=1.,
sim=True,
delta_t=10.,
freq=(200, 100),
FREQ_REF=150.,
fluence=(60., 600.),
scintillate=True,
dm=(300., 1800.))[0].T,
dtype=np.complex128)
vis_fg_frb *= np.exp(
1j * (lsts[30] + .1 * np.pi * np.random.randn(61, 1024)))
# Combined
if inject_frb:
vis_fg = vis_fg_diffuse + vis_fg_pntsrc + vis_fg_frb
else:
vis_fg = vis_fg_diffuse + vis_fg_pntsrc
#### Noise ####
tsky = noise.resample_Tsky(fqs,
lsts,
Tsky_mdl=noise.HERA_Tsky_mdl['xx'])
t_rx = 150.
nos_jy = noise.sky_noise_jy(tsky + t_rx, fqs, lsts)
# Add Noise
vis_fg_nos = vis_fg + nos_jy
#### RFI ####
if add_rfi:
g = sigchain.gen_gains(fqs, [1, 2, 3])
with open(add_rfi, 'r') as infile:
RFIdict = yaml.load(infile)
rfi = RFI_Sim(RFIdict)
rfi.applyRFI()
self.rfi_true = rfi.getFlags()
vis_fg_nos_rfi = np.copy(vis_fg_nos) + rfi.getRFI()
vis_total_rfi = sigchain.apply_gains(vis_fg_nos_rfi, g, (1, 2))
# add cross-talk
xtalk = sigchain.gen_xtalk(np.linspace(.1, .2, 1024),
amplitude=.001)
vis_total_rfi = sigchain.apply_xtalk(vis_total_rfi, xtalk)
self.data_rfi = vis_total_rfi
else:
g = sigchain.gen_gains(fqs, [1, 2, 3])
vis_total_norfi = sigchain.apply_gains(vis_fg_nos, g, (1, 2))
self.data = vis_total_norfi
for k, v in zip(idxs, ants):
antpos[k] = v
reds = redcal.get_pos_reds(antpos)
# Extract all ants
ants = list(set([ant for bls in reds for bl in bls for ant in bl]))
# Generate gains
gains = sigchain.gen_gains(fqs, ants, dly_rng=(-1, 1))
true_vis, data = {}, {}
# Generate sky model--common for all ants
Tsky_mdl = noise.HERA_Tsky_mdl['xx']
tsky = noise.resample_Tsky(fqs, lsts, Tsky_mdl=noise.HERA_Tsky_mdl['xx'])
fp = h5py.File('fake_vis.hdf5', 'a')
fp.attrs.create('Nants', 37)
fp.create_dataset('fqs', data=fqs)
fp.create_dataset('lsts', data=lsts)
for a in ants:
fp.create_dataset('g%d' % a, data=gains[a])
for bls in reds:
for (i, j) in bls:
data[(i, j)] = fp.create_dataset('%d-%d' % (i, j),
(len(lsts), len(fqs)),
chunks=True,