def qa_image(im, mask=None, context="") -> QA:
"""Assess the quality of an image
:param params:
:param im:
:return: QA
"""
assert isinstance(im, Image)
if mask is None:
data = {
'shape': str(im.data.shape),
'max': numpy.max(im.data),
'min': numpy.min(im.data),
'rms': numpy.std(im.data),
'sum': numpy.sum(im.data),
'medianabs': numpy.median(numpy.abs(im.data)),
'median': numpy.median(im.data)
}
else:
mdata = im.data[mask.data > 0.0]
data = {
'shape': str(im.data.shape),
'max': numpy.max(mdata),
'min': numpy.min(mdata),
'rms': numpy.std(mdata),
'sum': numpy.sum(mdata),
'medianabs': numpy.median(numpy.abs(mdata)),
'median': numpy.median(mdata)
}
qa = QA(origin="qa_image", data=data, context=context)
return qa
def qa_image(im, mask=None, **kwargs) -> QA:
"""Assess the quality of an image
:param params:
:param im:
:return: QA
"""
assert type(im) == Image
if mask is None:
data = {'shape': str(im.data.shape),
'max': numpy.max(im.data),
'min': numpy.min(im.data),
'rms': numpy.std(im.data),
'sum': numpy.sum(im.data),
'medianabs': numpy.median(numpy.abs(im.data)),
'median': numpy.median(im.data)}
else:
mdata = im.data[mask.data > 0.0]
data = {'shape': str(im.data.shape),
'max': numpy.max(mdata),
'min': numpy.min(mdata),
'rms': numpy.std(mdata),
'sum': numpy.sum(mdata),
'medianabs': numpy.median(numpy.abs(mdata)),
'median': numpy.median(mdata)}
qa = QA(origin="qa_image",
data=data,
context=get_parameter(kwargs, '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_gaintable(gt, context=None):
"""Assess the quality of a gaintable
:param gt:
:returns: AQ
"""
agt = numpy.abs(gt.gain)
data = {
'maxabs': numpy.max(agt),
'minabs': numpy.min(agt),
'rms': numpy.std(agt),
'medianabs': numpy.median(agt),
'residual': numpy.max(gt.residual)
}
return QA(origin='qa_gaintable', data=data, context=context)
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
"""
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=None, data=data, context=context)
return qa
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),
'rms': numpy.std(im.data),
'sum': numpy.sum(im.data),
'medianabs': numpy.median(numpy.abs(im.data)),
'median': numpy.median(im.data)}
qa = QA(origin="qa_image", data=data, context=context)
return qa
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 = {
'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)