Source code for pipeline.hif.tasks.lowgainflag.qa

import collections

import pipeline.h.tasks.exportdata.aqua as aqua
import pipeline.infrastructure.logging as logging
import pipeline.infrastructure.pipelineqa as pqa
import pipeline.infrastructure.utils as utils
import pipeline.qa.scorecalculator as qacalc
from . import resultobjects

LOG = logging.get_logger(__name__)


[docs]class LowgainflagQAHandler(pqa.QAPlugin): result_cls = resultobjects.LowgainflagResults child_cls = None
[docs] def handle(self, context, result): vis = result.inputs['vis'] ms = context.observing_run.get_ms(vis) # Calculate QA score from presence of flagging views and from the # flagging summary in the result, adopting the minimum score as the # representative score for this task. score1 = qacalc.score_fraction_newly_flagged(ms.basename, result.summaries, ms.basename) new_origin = pqa.QAOrigin(metric_name='%HighLowGainFlags', metric_score=score1.origin.metric_score, metric_units='Percentage of high or low gain flag data newly flagged') score1.origin = new_origin score2 = qacalc.score_flagging_view_exists(ms.basename, result) new_origin = pqa.QAOrigin(metric_name='ValidFlaggingView', metric_score=score2.origin.metric_score, metric_units='Valid flagging view') score2.origin = new_origin scores = [score1, score2] result.qa.pool[:] = scores
[docs]class LowgainflagListQAHandler(pqa.QAPlugin): result_cls = collections.Iterable child_cls = resultobjects.LowgainflagResults
[docs] def handle(self, context, result): # collate the QAScores from each child result, pulling them into our # own QAscore list collated = utils.flatten([r.qa.pool for r in result]) result.qa.pool[:] = collated
aqua_exporter = aqua.xml_generator_for_metric('%HighLowGainFlags', '{:0.3%}') aqua.register_aqua_metric(aqua_exporter)