def _unique_baselines(self):
"""Calculate the unique baseline pairs.
Pairs are considered identical if they have the same baseline
separation,
Parameters
----------
fpairs : np.ndarray
An array of all the feed pairs, packed as [[i1, i2, ...], [j1, j2, ...] ].
Returns
-------
baselines : np.ndarray
An array of all the unique pairs. Packed as [ [i1, i2, ...], [j1, j2, ...]].
redundancy : np.ndarray
For each unique pair, give the number of equivalent pairs.
"""
base_map, base_mask = super(CylinderTelescope, self)._unique_baselines()
if not self.in_cylinder:
# Construct array of indices
fshape = [self.nfeed, self.nfeed]
f_ind = np.indices(fshape)
# Construct array of baseline separations in complex representation
bl1 = (self.feedpositions[f_ind[0]] - self.feedpositions[f_ind[1]])
ic_mask = np.where(bl1[..., 0] != 0.0, np.ones(fshape, dtype=np.bool), np.zeros(fshape, dtype=np.bool))
base_mask = np.logical_and(base_mask, ic_mask)
base_map = telescope._remap_keyarray(base_map, base_mask)
return base_map, base_mask
def _unique_beams(self):
beam_mask = np.identity(self.nfeed, dtype=np.bool)
beam_map = telescope._remap_keyarray(np.diag(np.arange(self.nfeed)),
mask=beam_mask)
return beam_map, beam_mask
def _unique_baselines(self):
"""
Ensures baselines from different pointings are treated as unique
"""
fmap, mask = self.single_pointing_telescope._unique_baselines()
block_fmap = linalg.block_diag(*[
fmap + i * self.single_pointing_telescope.nfeed
for i, _ in enumerate(self.pointings)
])
block_mask = linalg.block_diag(*[mask for _ in self.pointings])
return _remap_keyarray(block_fmap, block_mask), block_mask
def _unique_baselines(self):
"""Calculate the unique baseline pairs.
Pairs are considered identical if they have the same baseline
separation,
Parameters
----------
fpairs : np.ndarray
An array of all the feed pairs, packed as [[i1, i2, ...], [j1, j2, ...] ].
Returns
-------
baselines : np.ndarray
An array of all the unique pairs. Packed as [ [i1, i2, ...], [j1, j2, ...]].
redundancy : np.ndarray
For each unique pair, give the number of equivalent pairs.
"""
base_map, base_mask = super(CylinderTelescope,
self)._unique_baselines()
if not self.in_cylinder:
# Construct array of indices
fshape = [self.nfeed, self.nfeed]
f_ind = np.indices(fshape)
# Construct array of baseline separations in complex representation
bl1 = self.feedpositions[f_ind[0]] - self.feedpositions[f_ind[1]]
ic_mask = np.where(
bl1[..., 0] != 0.0,
np.ones(fshape, dtype=bool),
np.zeros(fshape, dtype=bool),
)
base_mask = np.logical_and(base_mask, ic_mask)
base_map = telescope._remap_keyarray(base_map, base_mask)
return base_map, base_mask
def _unique_baselines(self):
# Reimplement unique baselines in order to mask out either according to total
# baseline length or maximum North-South and East-West baseline seperation.
from drift.core import telescope
# Construct array of indices
fshape = [self.nfeed, self.nfeed]
f_ind = np.indices(fshape)
# Construct array of baseline separations
bl1 = self.feedpositions[f_ind[0]] - self.feedpositions[f_ind[1]]
bl2 = np.around(bl1[..., 0] + 1.0j * bl1[..., 1], self._bl_tol)
# Construct array of baseline lengths
blen = np.sum(bl1**2, axis=-1)**0.5
if self.baseline_masking_type == "total_length":
# Create mask of included baselines
mask = np.logical_and(blen >= self.minlength,
blen <= self.maxlength)
else:
mask_ew = np.logical_and(
abs(bl1[..., 0]) >= self.minlength_ew,
abs(bl1[..., 0]) <= self.maxlength_ew,
)
mask_ns = np.logical_and(
abs(bl1[..., 1]) >= self.minlength_ns,
abs(bl1[..., 1]) <= self.maxlength_ns,
)
mask = np.logical_and(mask_ew, mask_ns)
# Remove the auto correlated baselines between all polarisations
if not self.auto_correlations:
mask = np.logical_and(blen > 0.0, mask)
return telescope._remap_keyarray(bl2, mask), mask
def _unique_beams(self):
beam_mask = np.identity(self.nfeed, dtype=np.bool)
beam_map = telescope._remap_keyarray(np.diag(np.arange(self.nfeed)), mask=beam_mask)
return beam_map, beam_mask
