import pipeline.h.cli.utils as utils
# docstring and type hints: inherits from hifa.tasks.wvrgcalflag.wvrgcalflag.WvrgcalflagInputs.__init__
[docs]
@utils.cli_wrapper
def hifa_wvrgcalflag(vis=None, caltable=None, offsetstable=None, hm_toffset=None, toffset=None, segsource=None,
sourceflag=None, hm_tie=None, tie=None, nsol=None, disperse=None, wvrflag=None, hm_smooth=None,
smooth=None, scale=None, maxdistm=None, minnumants=None, mingoodfrac=None, refant=None,
flag_intent=None, qa_intent=None, qa_bandpass_intent=None, accept_threshold=None, flag_hi=None,
fhi_limit=None, fhi_minsample=None, ants_with_wvr_thresh=None, parallel=None):
"""Flag bad WVR calibration in gain table and interpolate over antennas with bad radiometers.
This task will first identify for each vis whether it includes at least 3
antennas with Water Vapor Radiometer (WVR) data, and that the fraction of
WVR antennas / all antennas exceeds the minimum threshold
(ants_with_wvr_thresh).
If there are not enough WVR antennas by number and/or fraction, then no WVR
caltable is created and no WVR calibration will be applied to the corresponding
vis. If there are enough WVR antennas, then the task proceeds as follows for
each valid vis:
First, generate a gain table based on the Water Vapor Radiometer data for
each vis.
Second, apply the WVR calibration to the data specified by 'flag_intent',
calculate flagging 'views' showing the ratio
'phase-rms with WVR / phase-rms without WVR' for each scan. A ratio < 1
implies that the phase noise is improved, a ratio > 1 implies that it
is made worse.
Third, search the flagging views for antennas with anomalous high values.
If any are found then recalculate the WVR calibration with the 'wvrflag'
parameter set to ignore their data and interpolate results from other
antennas according to 'maxdistm' and 'minnumants'.
Fourth, after flagging, if the remaining unflagged antennas with WVR number
fewer than 3, or represent a smaller fraction of antennas than the minimum
threshold (ants_with_wvr_thresh), then the WVR calibration file is rejected
and will not be merged into the context, i.e. not be used in subsequent
calibration.
Fifth, if the overall QA score for the final WVR correction of a vis file
is greater than the value in 'accept_threshold' then make available the
wvr calibration file for merging into the context and use in the
subsequent reduction.
Returns:
The results object for the pipeline task is returned.
Examples:
1. Compute the WVR calibration for all the MeasurementSets:
>>> hifa_wvrgcalflag(hm_tie='automatic')
"""