def test_reflection_gains(self):
# introduce a cable reflection into the autocorrelation
gains = sigchain.gen_reflection_gains(self.freqs, [0], amp=[1e-1], dly=[300], phs=[1])
outvis = sigchain.apply_gains(self.vis, gains, [0, 0])
ovfft = np.fft.fft(
outvis * windows.blackmanharris(len(self.freqs))[None, :], axis=1
)
# assert reflection is at +300 ns and check its amplitude
select = self.dlys > 200
nt.assert_almost_equal(
self.dlys[select][np.argmax(np.mean(np.abs(ovfft), axis=0)[select])], 300
)
select = np.argmin(np.abs(self.dlys - 300))
m = np.mean(np.abs(ovfft), axis=0)
nt.assert_true(np.isclose(m[select] / m[0], 1e-1, atol=1e-2))
# assert also reflection at -300 ns
select = self.dlys < -200
nt.assert_almost_equal(
self.dlys[select][np.argmax(np.mean(np.abs(ovfft), axis=0)[select])], -300
)
select = np.argmin(np.abs(self.dlys - -300))
m = np.mean(np.abs(ovfft), axis=0)
nt.assert_true(np.isclose(m[select] / m[0], 1e-1, atol=1e-2))
def test_reflection_gains(self):
# introduce a cable reflection into the autocorrelation
gains = sigchain.gen_reflection_gains(self.freqs, [0],
amp=[1e-1],
dly=[300],
phs=[1])
outvis = sigchain.apply_gains(self.vis, gains, [0, 0])
ovfft = np.fft.fft(outvis *
windows.blackmanharris(len(self.freqs))[None, :],
axis=1)
# assert reflection is at +300 ns and check its amplitude
select = self.dlys > 200
nt.assert_almost_equal(
self.dlys[select][np.argmax(
np.mean(np.abs(ovfft), axis=0)[select])], 300)
select = np.argmin(np.abs(self.dlys - 300))
m = np.mean(np.abs(ovfft), axis=0)
nt.assert_true(np.isclose(m[select] / m[0], 1e-1, atol=1e-2))
# assert also reflection at -300 ns
select = self.dlys < -200
nt.assert_almost_equal(
self.dlys[select][np.argmax(
np.mean(np.abs(ovfft), axis=0)[select])], -300)
select = np.argmin(np.abs(self.dlys - -300))
m = np.mean(np.abs(ovfft), axis=0)
nt.assert_true(np.isclose(m[select] / m[0], 1e-1, atol=1e-2))
# test reshaping into Ntimes
amp = np.linspace(1e-2, 1e-3, 3)
gains = sigchain.gen_reflection_gains(self.freqs, [0],
amp=[amp],
dly=[300],
phs=[1])
nt.assert_equal(gains[0].shape, (3, 100))
# test frequency evolution with one time
amp = np.linspace(1e-2, 1e-3, 100).reshape(1, -1)
gains = sigchain.gen_reflection_gains(self.freqs, [0],
amp=[amp],
dly=[300],
phs=[1])
nt.assert_equal(gains[0].shape, (1, 100))
# now test with multiple times
amp = np.repeat(np.linspace(1e-2, 1e-3, 100).reshape(1, -1),
10,
axis=0)
gains = sigchain.gen_reflection_gains(self.freqs, [0],
amp=[amp],
dly=[300],
phs=[1])
nt.assert_equal(gains[0].shape, (10, 100))
# exception
amp = np.linspace(1e-2, 1e-3, 2).reshape(1, -1)
nt.assert_raises(AssertionError,
sigchain.gen_reflection_gains,
self.freqs, [0],
amp=[amp],
dly=[300],
phs=[1])
def test_apply_gains(self):
fqs = np.linspace(0.12, 0.18, 1024, endpoint=False)
vis = np.ones((100, fqs.size), dtype=np.complex)
g = sigchain.gen_gains(fqs, [1, 2], gain_spread=0, dly_rng=(10, 10))
gvis = sigchain.apply_gains(vis, g, (1, 2))
np.testing.assert_allclose(np.angle(gvis), 0, 1e-5)
def generate_gains(self, vis):
return sigchain.apply_gains(vis, self.g, (1, 2))
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_gains(self, vis):
gainscale = np.average([np.median(np.abs(g[i])) for i in g])
MXG = MX + np.log10(gainscale)
return sigchain.apply_gains(vis, g, (1, 2))