def test_diffuse_foreground_orientation(self):
fqs = np.linspace(.1, .2, 100, endpoint=False)
omega_p = noise.bm_poly_to_omega_p(fqs)
lsts = np.linspace(0, 2 * np.pi, 1000)
Tsky_mdl = noise.HERA_Tsky_mdl['xx']
bl_vec = (0, 30.0)
vis = foregrounds.diffuse_foreground(lsts,
fqs,
bl_vec,
Tsky_mdl=Tsky_mdl,
fringe_filter_type='tophat',
omega_p=omega_p)
self.assertEqual(vis.shape, (lsts.size, fqs.size))
# assert foregrounds show up at positive fringe-rates for FFT
bl_vec = (100.0, 0.0)
vis = foregrounds.diffuse_foreground(lsts,
fqs,
bl_vec,
Tsky_mdl=Tsky_mdl,
fringe_filter_type='gauss',
fr_width=1e-5,
omega_p=omega_p)
dfft = np.fft.fftshift(np.fft.fft(
vis * dspec.gen_window('blackmanharris', len(vis))[:, None],
axis=0),
axes=0)
frates = np.fft.fftshift(
np.fft.fftfreq(len(lsts),
np.diff(lsts)[0] * 12 * 3600 / np.pi))
max_frate = frates[np.argmax(np.abs(dfft[:, 0]))]
nt.assert_true(max_frate > 0)
bl_vec = (-100.0, 0.0)
vis = foregrounds.diffuse_foreground(lsts,
fqs,
bl_vec,
Tsky_mdl=Tsky_mdl,
fringe_filter_type='gauss',
fr_width=1e-5,
omega_p=omega_p)
dfft = np.fft.fftshift(np.fft.fft(
vis * dspec.gen_window('blackmanharris', len(vis))[:, None],
axis=0),
axes=0)
max_frate = frates[np.argmax(np.abs(dfft[:, 0]))]
nt.assert_true(max_frate < 0)
def test_diffuse_foreground(self):
fqs = np.linspace(.1, .2, 100, endpoint=False)
lsts = np.linspace(0, 2 * np.pi, 1000)
times = lsts / (2 * np.pi) * aipy.const.sidereal_day
Tsky_mdl = noise.HERA_Tsky_mdl['xx']
#Tsky = Tsky_mdl(lsts,fqs)
bl_len_ns = 30.
vis = foregrounds.diffuse_foreground(lsts,
fqs, [bl_len_ns, 0, 0],
Tsky_mdl=Tsky_mdl,
delay_filter_type='tophat',
fringe_filter_type='tophat')
self.assertEqual(vis.shape, (lsts.size, fqs.size))
# XXX check more substantial things
#import uvtools, pylab as plt
#uvtools.plot.waterfall(vis, mode='log'); plt.colorbar(); plt.show()
nt.assert_raises(TypeError, foregrounds.diffuse_foreground, lsts, fqs,
[bl_len_ns])
def setUp(self):
# setup simulation parameters
np.random.seed(0)
fqs = np.linspace(0.1, 0.2, 100, endpoint=False)
lsts = np.linspace(0, 2 * np.pi, 200)
times = lsts / (2 * np.pi) * aipy.const.sidereal_day
Tsky_mdl = noise.HERA_Tsky_mdl["xx"]
Tsky = Tsky_mdl(lsts, fqs)
bl_vec = np.array([50.0, 0, 0])
# + 20 is to boost k=0 mode
vis = foregrounds.diffuse_foreground(lsts, fqs, bl_vec, Tsky_mdl=Tsky_mdl, delay_filter_type='gauss') + 20
self.freqs = fqs
self.lsts = lsts
self.Tsky = Tsky
self.bl_vec = bl_vec
self.vis = vis
self.vfft = np.fft.fft(vis, axis=1)
self.dlys = np.fft.fftfreq(len(fqs), d=np.median(np.diff(fqs)))
def generate_diffuse_foreground(self):
#GENERATE FOREGROUNDS
return foregrounds.diffuse_foreground(
self.lsts, self.fqs, self.bl_len_ns_list,
Tsky_mdl=self.Tsky_mdl) / 40
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
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,
dtype='complex64')
for bls in reds:
bl_len = get_distance(bls[0], antpos)
bl_len_ns = bl_len / aipy.const.c * 1e11
vis_fg_pntsrc = foregrounds.pntsrc_foreground(lsts,
fqs,
bl_len_ns,
nsrcs=200)
vis_fg_diffuse = foregrounds.diffuse_foreground(Tsky_mdl, lsts, fqs,
bl_len_ns)
true_vis[bls[0]] = vis_fg_pntsrc + vis_fg_diffuse
for (i, j) in bls:
print(i, j)
nos_jy = noise.sky_noise_jy(tsky + 150., fqs, lsts)
vis_tot = nos_jy + true_vis[bls[0]]
data[(i, j)][:, :] = sigchain.apply_gains(vis_tot, gains, (i, j))
fp.close()
def generate_diffuse_foreground(self):
return foregrounds.diffuse_foreground(
lsts, fqs, self.bl_len_ns, Tsky_mdl=Tsky_mdl) / 40