lsst.meas.base.tests.CentroidTransformTestCase Class Reference

Inheritance diagram for lsst.meas.base.tests.CentroidTransformTestCase:

Protected Member Functions
	_setFieldsInRecords (self, records, name)
	_compareFieldsInRecords (self, inSrc, outSrc, name)

Detailed Description

Definition at line 965 of file tests.py.

Member Function Documentation

_compareFieldsInRecords()

lsst.meas.base.tests.CentroidTransformTestCase._compareFieldsInRecords ( self, inSrc, outSrc, name )

protected

Definition at line 978 of file tests.py.

    def _compareFieldsInRecords(self, inSrc, outSrc, name):
        centroidResultKey = CentroidResultKey(inSrc.schema[self.name])
        centroidResult = centroidResultKey.get(inSrc)
 
        coord = lsst.afw.table.CoordKey(outSrc.schema[self.name]).get(outSrc)
        coordTruth = self.calexp.getWcs().pixelToSky(centroidResult.getCentroid())
        self.assertEqual(coordTruth, coord)
 
        # If the centroid has an associated uncertainty matrix, the coordinate
        # must have one too, and vice versa.
        try:
            coordErr = lsst.afw.table.CovarianceMatrix2fKey(outSrc.schema[self.name],
                                                            ["ra", "dec"]).get(outSrc)
        except lsst.pex.exceptions.NotFoundError:
            self.assertFalse(centroidResultKey.getCentroidErr().isValid())
        else:
            transform = self.calexp.getWcs().linearizePixelToSky(coordTruth, lsst.geom.radians)
            coordErrTruth = np.dot(np.dot(transform.getLinear().getMatrix(),
                                          centroidResult.getCentroidErr()),
                                   transform.getLinear().getMatrix().transpose())
            np.testing.assert_array_almost_equal(np.array(coordErrTruth), coordErr)

_setFieldsInRecords()

lsst.meas.base.tests.CentroidTransformTestCase._setFieldsInRecords ( self, records, name )

protected

Definition at line 967 of file tests.py.

    def _setFieldsInRecords(self, records, name):
        for record in records:
            record[record.schema.join(name, 'x')] = np.random.random()
            record[record.schema.join(name, 'y')] = np.random.random()
            # Some algorithms set no errors; some set only sigma on x & y; some provide
            # a full covariance matrix. Set only those which exist in the schema.
            for fieldSuffix in ('xErr', 'yErr', 'x_y_Cov'):
                fieldName = record.schema.join(name, fieldSuffix)
                if fieldName in record.schema:
                    record[fieldName] = np.random.random()
 

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The documentation for this class was generated from the following file:

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-8.0.0/Linux64/meas_base/g212a7c68fe+04a9158687/python/lsst/meas/base/tests.py