Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

feat(MaskBaselines): mask baselines under an average weight threshold #195

Merged
merged 2 commits into from
Sep 16, 2022
Merged

feat(MaskBaselines): mask baselines under an average weight threshold #195

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
d636d59
feat(MaskBaselines): mask baselines under an average weight threshold
jrs65 Sep 15, 2022
070f8b7
refactor(MaskBaselines): change convention to make mask=True for bad …
jrs65 Sep 16, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
45 changes: 32 additions & 13 deletions draco/analysis/flagging.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -199,6 +199,7 @@ class MaskBaselines(task.SingleTask):
mask_short_ew = config.Property(proptype=float, default=None)
mask_short_ns = config.Property(proptype=float, default=None)

weight_threshold = config.Property(proptype=float, default=None)
missing_threshold = config.Property(proptype=float, default=None)

zero_data = config.Property(proptype=bool, default=False)
Expand Down Expand Up @@ -233,23 +234,36 @@ def process(self, ss):
ss.redistribute("freq")

baselines = self.telescope.baselines
mask = np.ones_like(ss.weight[:], dtype=bool)

# The masking array. True will *retain* a sample
ljgray marked this conversation as resolved.
Show resolved Hide resolved
mask = np.zeros_like(ss.weight[:], dtype=bool)

if self.mask_long_ns is not None:
long_ns_mask = np.abs(baselines[:, 1]) < self.mask_long_ns
mask *= long_ns_mask[np.newaxis, :, np.newaxis]
long_ns_mask = np.abs(baselines[:, 1]) > self.mask_long_ns
mask |= long_ns_mask[np.newaxis, :, np.newaxis]

if self.mask_short is not None:
short_mask = np.sum(baselines**2, axis=1) ** 0.5 > self.mask_short
mask *= short_mask[np.newaxis, :, np.newaxis]
short_mask = np.sum(baselines**2, axis=1) ** 0.5 < self.mask_short
mask |= short_mask[np.newaxis, :, np.newaxis]

if self.mask_short_ew is not None:
short_ew_mask = np.abs(baselines[:, 0]) > self.mask_short_ew
mask *= short_ew_mask[np.newaxis, :, np.newaxis]
short_ew_mask = np.abs(baselines[:, 0]) < self.mask_short_ew
mask |= short_ew_mask[np.newaxis, :, np.newaxis]

if self.mask_short_ns is not None:
short_ns_mask = np.abs(baselines[:, 1]) > self.mask_short_ns
mask *= short_ns_mask[np.newaxis, :, np.newaxis]
short_ns_mask = np.abs(baselines[:, 1]) < self.mask_short_ns
mask |= short_ns_mask[np.newaxis, :, np.newaxis]

if self.weight_threshold is not None:
# Get the sum of the weights over frequencies
weight_sum_local = ss.weight[:].local_array.sum(axis=0)
weight_sum_tot = np.zeros_like(weight_sum_local)
self.comm.Allreduce(weight_sum_local, weight_sum_tot, op=MPI.SUM)

# Retain only baselines with average weights larger than the threshold
mask |= weight_sum_tot[np.newaxis, :, :] < self.weight_threshold * len(
ss.freq
)

if self.missing_threshold is not None:
# Get the total number of samples for each baseline accumulated onto each
Expand All @@ -260,9 +274,9 @@ def process(self, ss):

# Mask out baselines with more that `missing_threshold` samples missing
baseline_missing_ratio = 1 - nsamp_tot / nsamp_tot.max()
mask &= (
mask |= (
baseline_missing_ratio[np.newaxis, :, np.newaxis]
< self.missing_threshold
> self.missing_threshold
)

if self.share == "all":
Expand All @@ -273,10 +287,15 @@ def process(self, ss):
ssc = ss.copy()

# Apply the mask to the weight
ssc.weight[:] *= mask
np.multiply(
ssc.weight[:].local_array, 0.0, where=mask, out=ssc.weight[:].local_array
)

# Apply the mask to the data
if self.zero_data:
ssc.vis[:] *= mask
np.multiply(
ssc.vis[:].local_array, 0.0, where=mask, out=ssc.vis[:].local_array
)

return ssc

Expand Down