def test_plot_data():
dp = DataPack(
'/home/albert/data/gains_screen/data/L342938_DDS5_full_merged.h5',
readonly=True)
with dp:
select = dict(pol=slice(0, 1, 1), ant=[0, 50], time=slice(0, 100, 1))
dp.current_solset = 'sol000'
dp.select(**select)
tec_mean, axes = dp.tec
tec_mean = tec_mean[0, ...]
const_mean, axes = dp.const
const_mean = const_mean[0, ...]
tec_std, axes = dp.weights_tec
tec_std = tec_std[0, ...]
patch_names, directions = dp.get_directions(axes['dir'])
antenna_labels, antennas = dp.get_antennas(axes['ant'])
timestamps, times = dp.get_times(axes['time'])
antennas = ac.ITRS(*antennas.cartesian.xyz, obstime=times[0])
ref_ant = antennas[0]
for i, time in enumerate(times):
frame = ENU(obstime=time, location=ref_ant.earth_location)
antennas = antennas.transform_to(frame)
directions = directions.transform_to(frame)
x = antennas.cartesian.xyz.to(au.km).value.T
k = directions.cartesian.xyz.value.T
dtec = tec_mean[:, 1, i]
ax = plot_vornoi_map(k[:, 0:2], dtec)
ax.set_title(time)
plt.show()
def main(data_dir, working_dir, obs_num):
logger.info("Merging slow solutions into screen and smoothed.")
smoothed_h5parm = os.path.join(data_dir,
'L{}_DDS5_full_merged.h5'.format(obs_num))
screen_h5parm = os.path.join(data_dir,
'L{}_DDS6_full_merged.h5'.format(obs_num))
slow_h5parm = os.path.join(data_dir,
'L{}_DDS7_full_slow_merged.h5'.format(obs_num))
merged_h5parm = os.path.join(working_dir,
'L{}_DDS8_full_merged.h5'.format(obs_num))
linked_merged_h5parm = os.path.join(data_dir,
os.path.basename(merged_h5parm))
link_overwrite(merged_h5parm, linked_merged_h5parm)
select = dict(pol=slice(0, 1, 1))
###
# get slow phase and amplitude
datapack_slow = DataPack(slow_h5parm, readonly=True)
logger.info("Getting slow000/phase000+amplitude000")
datapack_slow.current_solset = 'sol000'
datapack_slow.select(**select)
axes = datapack_slow.axes_phase
patch_names, directions = datapack_slow.get_directions(axes['dir'])
directions_slow = np.stack([directions.ra.rad, directions.dec.rad], axis=1)
timestamps, times = datapack_slow.get_times(axes['time'])
time_slow = times.mjd * 86400.
phase_slow, axes = datapack_slow.phase
amplitude_slow, axes = datapack_slow.amplitude
###
# get smoothed phase and amplitude
datapack_smoothed = DataPack(smoothed_h5parm, readonly=True)
logger.info("Getting directionally_referenced/const000")
datapack_smoothed.current_solset = 'sol000'
datapack_smoothed.select(**select)
axes = datapack_smoothed.axes_phase
patch_names, directions = datapack_smoothed.get_directions(axes['dir'])
directions_smoothed = np.stack([directions.ra.rad, directions.dec.rad],
axis=1)
timestamps, times = datapack_smoothed.get_times(axes['time'])
time_smoothed = times.mjd * 86400.
phase_smoothed, axes = datapack_smoothed.phase
amplitude_smoothed, axes = datapack_smoothed.amplitude
Ncal = directions_smoothed.shape[0]
###
# get screen phase and amplitude
datapack_screen = DataPack(screen_h5parm, readonly=False)
logger.info("Getting screen_posterior/phase000+amplitude000")
datapack_screen.current_solset = 'sol000'
datapack_screen.select(**select)
axes = datapack_screen.axes_phase
patch_names, directions = datapack_screen.get_directions(axes['dir'])
directions_screen = np.stack([directions.ra.rad, directions.dec.rad],
axis=1)
timestamps, times = datapack_screen.get_times(axes['time'])
time_screen = times.mjd * 86400.
phase_screen, axes = datapack_screen.phase
amplitude_screen, axes = datapack_screen.amplitude
###
# Create and set screen_slow000
logger.info("Creating screen_slow/phase000+amplitude000")
make_soltab(screen_h5parm,
from_solset='sol000',
to_solset='screen_slow',
from_soltab='phase000',
to_soltab=['phase000', 'amplitude000'],
remake_solset=True,
to_datapack=merged_h5parm)
logger.info("Creating smoothed_slow/phase000+amplitude000")
make_soltab(smoothed_h5parm,
from_solset='sol000',
to_solset='smoothed_slow',
from_soltab='phase000',
to_soltab=['phase000', 'amplitude000'],
remake_solset=True,
to_datapack=merged_h5parm)
logger.info("Creating time mapping")
time_map = np.asarray(
[np.argmin(np.abs(time_slow - t)) for t in time_screen])
logger.info("Creating direction mapping")
dir_map = np.asarray([
jnp.argmin(
great_circle_sep(directions_slow[:, 0], directions_slow[:, 1], ra,
dec))
for (ra, dec) in zip(directions_screen[:, 0], directions_screen[:, 1])
])
#TODO: see if only applying slow on calibrators and screen elsewhere gets rid of artefacts.
#TODO: see if only applying tec screen gets rid of artefacts (include slow if the above experiment doesn't work)
#TODO: visibility flagging based on tec outliers (update imaging command)
phase_smooth_slow = phase_slow[..., time_map] + phase_smoothed
amplitude_smooth_slow = amplitude_slow[..., time_map] * amplitude_smoothed
phase_screen_slow = phase_screen + phase_slow[..., time_map][:, dir_map,
...]
amplitude_screen_slow = amplitude_screen * amplitude_slow[
..., time_map][:, dir_map, ...]
logger.info("Phase screen+slow contains {} nans.".format(
np.isnan(phase_screen_slow).sum()))
phase_screen_slow = np.where(np.isnan(phase_screen_slow), 0.,
phase_screen_slow)
logger.info("Amplitude screen+slow contains {} nans.".format(
np.isnan(amplitude_screen_slow).sum()))
amplitude_screen_slow = np.where(np.isnan(amplitude_screen_slow), 1.,
amplitude_screen_slow)
logger.info("Saving results to {}".format(merged_h5parm))
datapack = DataPack(merged_h5parm, readonly=False)
datapack.current_solset = 'screen_slow'
datapack.select(**select)
datapack.phase = phase_screen_slow
datapack.amplitude = amplitude_screen_slow
datapack.current_solset = 'smoothed_slow'
datapack.select(**select)
datapack.phase = phase_smooth_slow
datapack.amplitude = amplitude_smooth_slow
return mean[..., Nt - extra:], uncert[..., Nt - extra:], mean_lengthscale[
..., Nt - extra:], mean_sigma[..., Nt - extra:], ESS[
..., Nt - extra:], logZ, likelihood_evals[..., Nt - extra:]
if __name__ == '__main__':
from jax.config import config
config.update("jax_enable_x64", True)
dp = DataPack(
'/home/albert/data/gains_screen/data/L342938_DDS5_full_merged.h5',
readonly=True)
with dp:
select = dict(pol=slice(0, 1, 1), ant=[50], time=slice(0, 9, 1))
dp.current_solset = 'sol000'
dp.select(**select)
tec_mean, axes = dp.tec
dtec = jnp.asarray(tec_mean[0, :, :, :])
tec_std, axes = dp.weights_tec
dtec_uncert = jnp.asarray(tec_std[0, :, :, :])
patch_names, directions = dp.get_directions(axes['dir'])
antenna_labels, antennas = dp.get_antennas(axes['ant'])
timestamps, times = dp.get_times(axes['time'])
# antennas = ac.ITRS(*antennas.cartesian.xyz, obstime=times[0])
# ref_ant = antennas[0]
# frame = ENU(obstime=times[0], location=ref_ant.earth_location)
# antennas = antennas.transform_to(frame)
# ref_ant = antennas[0]
# directions = directions.transform_to(frame)
def train_neural_network(datapack: DataPack, batch_size, learning_rate,
num_batches):
with datapack:
select = dict(pol=slice(0, 1, 1), ant=None, time=slice(0, 1, 1))
datapack.current_solset = 'sol000'
datapack.select(**select)
axes = datapack.axes_tec
patch_names, directions = datapack.get_directions(axes['dir'])
antenna_labels, antennas = datapack.get_antennas(axes['ant'])
timestamps, times = datapack.get_times(axes['time'])
antennas = ac.ITRS(*antennas.cartesian.xyz, obstime=times[0])
ref_ant = antennas[0]
frame = ENU(obstime=times[0], location=ref_ant.earth_location)
antennas = antennas.transform_to(frame)
ref_ant = antennas[0]
directions = directions.transform_to(frame)
x = antennas.cartesian.xyz.to(au.km).value.T
k = directions.cartesian.xyz.value.T
t = times.mjd
t -= t[len(t) // 2]
t *= 86400.
n_screen = 250
kstar = random.uniform(random.PRNGKey(29428942), (n_screen, 3),
minval=jnp.min(k, axis=0),
maxval=jnp.max(k, axis=0))
kstar /= jnp.linalg.norm(kstar, axis=-1, keepdims=True)
X = jnp.asarray(
make_coord_array(x, jnp.concatenate([k, kstar], axis=0), t[:, None]))
x0 = jnp.asarray(antennas.cartesian.xyz.to(au.km).value.T[0, :])
ref_ant = x0
kernel = TomographicKernel(x0, ref_ant, RBF(), S_marg=100)
neural_kernel = NeuralTomographicKernel(x0, ref_ant)
def loss(params, key):
keys = random.split(key, 5)
indices = random.permutation(keys[0],
jnp.arange(X.shape[0]))[:batch_size]
X_batch = X[indices, :]
wind_velocity = random.uniform(keys[1],
shape=(3, ),
minval=jnp.asarray([-200., -200., 0.]),
maxval=jnp.asarray([200., 200., 0.
])) / 1000.
bottom = random.uniform(keys[2], minval=50., maxval=500.)
width = random.uniform(keys[3], minval=40., maxval=300.)
l = random.uniform(keys[4], minval=1., maxval=30.)
sigma = 1.
K = kernel(X_batch,
X_batch,
bottom,
width,
l,
sigma,
wind_velocity=wind_velocity)
neural_kernel.set_params(params)
neural_K = neural_kernel(X_batch,
X_batch,
bottom,
width,
l,
sigma,
wind_velocity=wind_velocity)
return jnp.mean((K - neural_K)**2) / width**2
init_params = neural_kernel.init_params(random.PRNGKey(42))
def train_one_batch(params, key):
l, g = value_and_grad(lambda params: loss(params, key))(params)
params = tree_multimap(lambda p, g: p - learning_rate * g, params, g)
return params, l
final_params, losses = jit(lambda key: scan(
train_one_batch, init_params, random.split(key, num_batches)))(
random.PRNGKey(42))
plt.plot(losses)
plt.yscale('log')
plt.show()
