def test_gen_gains(self):
fqs = np.linspace(0.12, 0.18, 1024, endpoint=False)
g = sigchain.gen_gains(fqs, [1, 2], gain_spread=0, dly_rng=(10, 20))
np.testing.assert_allclose(np.abs(g[1]), np.abs(g[2]), 1e-5)
for i in g:
p = np.polyfit(fqs, np.unwrap(np.angle(g[i])), deg=1)
self.assertAlmostEqual(p[-1] % (2 * np.pi), 0.0, -2)
self.assertLessEqual(p[0], 20 * 2 * np.pi)
self.assertGreaterEqual(p[0], 10 * 2 * np.pi)
def __init__(self):
self.fqs = np.linspace(.1, .2, 64, endpoint=False)
self.lsts = np.linspace(0, 2 * np.pi, 256, endpoint=False)
self.times = self.lsts / (2 * np.pi) * aipy.const.sidereal_day
self.bl_len_ns_list = [5, 10, 20, 100, 500]
self.n_sources_list = [1, 10, 50, 200, 1000]
self.MX, self.DRNG = 2.5, 3
self.Tsky_mdl = noise.HERA_Tsky_mdl['xx']
self.g = sigchain.gen_gains(self.fqs, [100, 2, 3])
r = np.random.randint(0, 5)
self.bl_len_ns = self.bl_len_ns_list[r]
#self.bl_len_ns = self.bl_len_ns_list[3]
#self.n_sources = self.n_sources_list[np.random.randint(0,5)]
self.n_sources = self.n_sources_list[3]
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 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
## Generate visibilities for all redundant baselines in array.
ants = np.loadtxt('antenna_positions_37.dat')
idxs = np.arange(37)
antpos = {}
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])
import datetime
import random
import pickle
from tqdm import tqdm
import pandas as pd
from preprocessor import preprocessor
fqs = np.linspace(.1, .2, 64, endpoint=False)
lsts = np.linspace(0, 2 * np.pi, 256, endpoint=False)
times = lsts / (2 * np.pi) * aipy.const.sidereal_day
bl_len_ns_list = [
1, 2, 5, 10, 20, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000
]
MX, DRNG = 2.5, 3
Tsky_mdl = noise.HERA_Tsky_mdl['xx']
g = sigchain.gen_gains(fqs, [1, 2, 3])
class HERA_generator():
def __init__(self):
self.bl_len_ns = bl_len_ns_list[random.randint(0,
len(bl_len_ns_list) -
1)]
self.nsrcs = random.randint(1, 1001)
def generate_diffuse_foreground(self):
return foregrounds.diffuse_foreground(
lsts, fqs, self.bl_len_ns, Tsky_mdl=Tsky_mdl) / 40
def generate_point_source(self):
return foregrounds.pntsrc_foreground(lsts,