Add variance_median and improve test coverage.

python/lsst/meas/base/noiseReplacer.py

@@ -39,6 +39,7 @@ class NoiseReplacerConfig(lsst.pex.config.Config):
             'measure': 'Measure clipped mean and variance from the whole image',
             'meta': 'Mean = 0, variance = the "BGMEAN" metadata entry',
             'variance': "Mean = 0, variance = the image's variance",
+            'variance_median': "Mean = 0, variance = median(variance plane)"
         },
         default='measure', optional=False
     )
@@ -195,9 +196,6 @@ def __init__(self, config, exposure, footprints, noiseImage=None, exposureId=Non
         # We'll put the noise footprints in a dict heavyNoise = {id:heavyNoiseFootprint}
         self.heavyNoise = {}
         noisegen = self.getNoiseGenerator(exposure, noiseImage, noiseMeanVar, exposureId=exposureId)
-        #  The noiseGenMean and Std are used by the unit tests
-        self.noiseGenMean = noisegen.mean
-        self.noiseGenStd = noisegen.std
         if self.log:
             self.log.debug('Using noise generator: %s', str(noisegen))
         for id in self.heavies:
@@ -352,6 +350,17 @@ def getNoiseGenerator(self, exposure, noiseImage, noiseMeanVar, exposureId=None)
             var = exposure.getMaskedImage().getVariance()
             return VariancePlaneNoiseGenerator(var, mean=offset, rand=rand)
 
+        if noiseSource == 'variance_median':
+            if self.log:
+                self.log.debug('Will draw noise using the median of the variance plane.')
+            var = exposure.getMaskedImage().getVariance()
+            s = afwMath.makeStatistics(var, afwMath.MEDIAN)
+            varMedian = s.getValue(afwMath.MEDIAN)
+            if self.log:
+                self.log.debug("Measured from variance: median variance = %g",
+                               varMedian)
+            return FixedGaussianNoiseGenerator(offset, math.sqrt(varMedian), rand=rand)
+
         # Compute an image-wide clipped variance.
         im = exposure.getMaskedImage().getImage()
         s = afwMath.makeStatistics(im, afwMath.MEANCLIP | afwMath.STDEVCLIP)

tests/test_NoiseReplacer.py

@@ -29,6 +29,8 @@
 import lsst.afw.table
 import lsst.meas.base.tests
 import lsst.utils.tests
+from lsst.daf.base import PropertyList
+from lsst.utils.tests import methodParameters
 
 
 @lsst.meas.base.register("test_NoiseReplacer")
@@ -75,16 +77,22 @@ def setUp(self):
             family.addChild(140000.0, lsst.geom.Point2D(72.3, 149.1))
             family.addChild(90000.0, lsst.geom.Point2D(68.5, 156.9))
 
-    def testSingleFrameMeasurement(self):
-        """Test noise replacement in single frame measurement.
+    @methodParameters(noiseSource=['measure', 'variance', 'meta', 'variance_median'],
+                      variance=[1.0, 1.0, 2.0, 1.0])
+    def testNoiseReplacer(self, noiseSource, variance):
+        """Test noise replacement in SFM with ''measure'' mode.
 
         We compare the instFlux inside and outside source Footprints on an
         extremely high S/N image.
         """
-
         # We choose a random seed which causes the test to pass.
         task = self.makeSingleFrameMeasurementTask("test_NoiseReplacer")
-        exposure, catalog = self.dataset.realize(1.0, task.schema, randomSeed=0)
+        task.config.noiseReplacer.noiseSource = noiseSource
+        exposure, catalog = self.dataset.realize(variance, task.schema, randomSeed=0)
+        if noiseSource == 'meta':
+            md = PropertyList()
+            md['BGMEAN'] = variance
+            exposure.setMetadata(md)
         task.run(catalog, exposure)
         sumVariance = exposure.getMaskedImage().getVariance().getArray().sum()
         for record in catalog: