def precompute_attributes(self, model_arr, flags_arr, noise):
"""
Set the initial weights to 1 and set the weights of the flags data points to 0
Args:
model_arr (np.ndarray):
Shape (n_dir, n_mod, n_tim, n_fre, n_ant, n_ant, n_cor, n_cor) array containing
model visibilities.
flags_arr (np.ndarray):
Shape (n_tim, n_fre, n_ant, n_ant) array containing flags
"""
PerIntervalGains.precompute_attributes(self, model_arr, flags_arr,
noise)
self.weights_shape = [
self.n_mod, self.n_tim, self.n_fre, self.n_ant, self.n_ant, 1
]
self.weights = np.ones(self.weights_shape, dtype=self.dtype)
self.weights[:, flags_arr != 0] = 0
self._init_flags = flags_arr
unflagged = flags_arr == 0
self.num_init_unflaged_eqs = np.sum(unflagged)
self.v = 2. #t-distribution number of degrees of freedom
def precompute_attributes(self, data_arr, model_arr, flags_arr, noise):
"""
"""
PerIntervalGains.precompute_attributes(self, data_arr, model_arr,
flags_arr, noise)
if self._estimate_pzd:
marr = model_arr[..., (0, 1), (1, 0)][:, 0].sum(0)
darr = data_arr[..., (0, 1), (1, 0)][0]
mask = (flags_arr[..., np.newaxis] != 0) | (marr == 0)
dm = darr * (np.conj(marr) / abs(marr))
dabs = np.abs(darr)
dm[mask] = 0
dabs[mask] = 0
# collapse time/freq axis into intervals and sum antenna axes
dm_sum = self.interval_sum(dm).sum(axis=(2, 3))
dabs_sum = self.interval_sum(dabs).sum(axis=(2, 3))
# sum off-diagonal terms
dm_sum = dm_sum[..., 0] + np.conj(dm_sum[..., 1])
dabs_sum = dabs_sum[..., 0] + np.conj(dabs_sum[..., 1])
pzd = np.angle(dm_sum / dabs_sum)
pzd[dabs_sum == 0] = 0
print("{}: PZD estimate {}".format(self.chunk_label, pzd),
file=log(2))
self.gains[:, :, :, :, 1, 1] = np.exp(-1j * pzd)[np.newaxis, :, :,
np.newaxis]
def precompute_attributes(self, data_arr, model_arr, flags_arr, noise):
"""
Precompute (J\ :sup:`H`\J)\ :sup:`-1`, which does not vary with iteration.
Args:
model_arr (np.ndarray):
Shape (n_dir, n_mod, n_tim, n_fre, n_ant, n_ant, n_cor, n_cor) array containing
model visibilities.
"""
PerIntervalGains.precompute_attributes(self, data_arr, model_arr, flags_arr, noise)
self.jhjinv = np.zeros_like(self.gains)
self.kernel_solve.compute_jhj(model_arr, self.jhjinv, self.t_int, self.f_int)
self.kernel_solve.compute_jhjinv(self.jhjinv, self.jhjinv, self.gflags, self.eps, FL.ILLCOND)
self.jhjinv = self.jhjinv.real