def qa_image(im, context="") -> QA:
"""Assess the quality of an image
:param im:
:return: QA
"""
assert isinstance(im, Image), im
data = {
'shape':
str(im.data.shape),
'max':
numpy.max(im.data),
'min':
numpy.min(im.data),
'maxabs':
numpy.max(numpy.abs(im.data)),
'rms':
numpy.std(im.data),
'sum':
numpy.sum(im.data),
'medianabs':
numpy.median(numpy.abs(im.data)),
'medianabsdevmedian':
numpy.median(numpy.abs(im.data - numpy.median(im.data))),
'median':
numpy.median(im.data)
}
qa = QA(origin="qa_image", data=data, context=context)
return qa
def test_qa(self):
qa = QA(origin='foo',
data={
'rms': 100.0,
'median': 10.0
},
context='test of qa')
log.debug(str(qa))
def qa_convolutionfunction(cf, context="") -> QA:
"""Assess the quality of a convolutionfunction
:param cf:
:return: QA
"""
assert isinstance(cf, ConvolutionFunction), cf
data = {'shape': str(cf.data.shape),
'max': numpy.max(cf.data),
'min': numpy.min(cf.data),
'rms': numpy.std(cf.data),
'sum': numpy.sum(cf.data),
'medianabs': numpy.median(numpy.abs(cf.data)),
'median': numpy.median(cf.data)}
qa = QA(origin="qa_image", data=data, context=context)
return qa
def qa_visibility(vis: Union[Visibility, BlockVisibility], context=None) -> QA:
"""Assess the quality of Visibility
:param context:
:param vis: Visibility to be assessed
:return: QA
"""
assert isinstance(vis, Visibility) or isinstance(vis, BlockVisibility), vis
avis = numpy.abs(vis.vis)
data = {
'maxabs': numpy.max(avis),
'minabs': numpy.min(avis),
'rms': numpy.std(avis),
'medianabs': numpy.median(avis)
}
qa = QA(origin='qa_visibility', data=data, context=context)
return qa
def qa_griddata(gd, context="") -> QA:
"""Assess the quality of a griddata
:param gd:
:return: QA
"""
assert isinstance(gd, GridData), gd
data = {
'shape': str(gd.data.shape),
'max': numpy.max(gd.data),
'min': numpy.min(gd.data),
'rms': numpy.std(gd.data),
'sum': numpy.sum(gd.data),
'medianabs': numpy.median(numpy.abs(gd.data)),
'median': numpy.median(gd.data)
}
qa = QA(origin="qa_image", data=data, context=context)
return qa
def qa_pointingtable(pt: PointingTable, context=None) -> QA:
"""Assess the quality of a pointingtable
:param pt:
:return: AQ
"""
apt = numpy.abs(pt.pointing[pt.weight > 0.0])
ppt = numpy.angle(pt.pointing[pt.weight > 0.0])
data = {
'shape': pt.pointing.shape,
'maxabs-amp': numpy.max(apt),
'minabs-amp': numpy.min(apt),
'rms-amp': numpy.std(apt),
'medianabs-amp': numpy.median(apt),
'maxabs-phase': numpy.max(ppt),
'minabs-phase': numpy.min(ppt),
'rms-phase': numpy.std(ppt),
'medianabs-phase': numpy.median(ppt),
'residual': numpy.max(pt.residual)
}
return QA(origin='qa_pointingtable', data=data, context=context)
def qa_gaintable(gt: GainTable, context=None) -> QA:
"""Assess the quality of a gaintable
:param gt:
:return: AQ
"""
agt = numpy.abs(gt.gain)
pgt = numpy.angle(gt.gain)
data = {
'shape': gt.gain.shape,
'maxabs-amp': numpy.max(agt),
'minabs-amp': numpy.min(agt),
'rms-amp': numpy.std(agt),
'medianabs-amp': numpy.median(agt),
'maxabs-phase': numpy.max(pgt),
'minabs-phase': numpy.min(pgt),
'rms-phase': numpy.std(pgt),
'medianabs-phase': numpy.median(pgt),
'residual': numpy.max(gt.residual)
}
return QA(origin='qa_gaintable', data=data, context=context)
