Avoid numpy warnings of invalid values. Prevent correction factor fro…

…m being nan.

python/lsst/pipe/tasks/scaleVariance.py

@@ -121,11 +121,11 @@ def run(self, maskedImage):
         """
         with self.subtractedBackground(maskedImage):
             factor = self.pixelBased(maskedImage)
-            if np.isnan(factor) or factor > self.config.limit:
+            if factor > self.config.limit:
                 self.log.warn("Pixel-based variance rescaling factor (%f) exceeds configured limit (%f); "
                               "trying image-based method", factor, self.config.limit)
                 factor = self.imageBased(maskedImage)
-                if np.isnan(factor) or factor > self.config.limit:
+                if factor > self.config.limit:
                     raise RuntimeError("Variance rescaling factor (%f) exceeds configured limit (%f)" %
                                        (factor, self.config.limit))
             self.log.info("Renormalizing variance by %f" % (factor,))
@@ -143,10 +143,9 @@ def calculateBothFactors(self, maskedImage):
         Returns
         -------
         R : `lsst.pipe.base.Struct`
-          - ``pixFactor : `float` The pixel based variance rescaling factor
-            or `nan` if all pixels are masked or invalid.
+          - ``pixFactor : `float` The pixel based variance rescaling factor.
           - ``imageFactor`` : `float` The image based variance rescaling factor
-            or `nan` if all pixels are masked or invalid.
+            or 1 if all pixels are masked or invalid.
         """
         with self.subtractedBackground(maskedImage):
             pixFactor = self.pixelBased(maskedImage)
@@ -174,24 +173,24 @@ def pixelBased(self, maskedImage):
         Returns
         -------
         factor : `float`
-            Variance rescaling factor.
+            Variance rescaling factor or 1 if all pixels are masked or non-finite.
+
         """
         maskVal = maskedImage.mask.getPlaneBitMask(self.config.maskPlanes)
-        isGood = ((maskedImage.mask.array & maskVal) == 0) & (maskedImage.variance.array > 0)
-        self.log.debugf("Number of selected background pixels: {} of {}.",
-                        np.sum(isGood), isGood.size)
-
-        variance = maskedImage.variance
-        snr = maskedImage.image.array/np.sqrt(variance.array)
-        # Robust measurement of stdev using inter-quartile range
-        try:
-            q1, q3 = np.percentile(snr[isGood], (25, 75))
-        except IndexError:
-            # This error is raised when all data is nan. Catch error so that it does not
-            # propagate any higher. Set the stdev to one will not fix the bad data, but
-            # it will allow the task to complete. It should be the job of higher level
-            # tasks to handle missing or bad data
+        isGood = (((maskedImage.mask.array & maskVal) == 0)
+                  & np.isfinite(maskedImage.image.array)
+                  & np.isfinite(maskedImage.variance.array)
+                  & (maskedImage.variance.array > 0))
+
+        nGood = np.sum(isGood)
+        self.log.debugf("Number of selected background pixels: {} of {}.", nGood, isGood.size)
+        if nGood < 2:
+            # Not enough good data, np.percentile needs at least 2 points
+            # to estimate a range
             return 1.0
+        # Robust measurement of stdev using inter-quartile range
+        snr = maskedImage.image.array[isGood]/np.sqrt(maskedImage.variance.array[isGood])
+        q1, q3 = np.percentile(snr, (25, 75))
         stdev = 0.74*(q3 - q1)
         return stdev**2
 
@@ -216,12 +215,19 @@ def imageBased(self, maskedImage):
         Returns
         -------
         factor : `float`
-            Variance rescaling factor.
+            Variance rescaling factor or 1 if all pixels are masked or non-finite.
         """
         maskVal = maskedImage.mask.getPlaneBitMask(self.config.maskPlanes)
-        isGood = ((maskedImage.mask.array & maskVal) == 0) & (maskedImage.variance.array > 0)
-        self.log.debugf("Number of selected background pixels: {} of {}.",
-                        np.sum(isGood), isGood.size)
+        isGood = (((maskedImage.mask.array & maskVal) == 0)
+                  & np.isfinite(maskedImage.image.array)
+                  & np.isfinite(maskedImage.variance.array)
+                  & (maskedImage.variance.array > 0))
+        nGood = np.sum(isGood)
+        self.log.debugf("Number of selected background pixels: {} of {}.", nGood, isGood.size)
+        if nGood < 2:
+            # Not enough good data, np.percentile needs at least 2 points
+            # to estimate a range
+            return 1.0
         # Robust measurement of stdev
         q1, q3 = np.percentile(maskedImage.image.array[isGood], (25, 75))
         ratio = 0.74*(q3 - q1)/np.sqrt(np.median(maskedImage.variance.array[isGood]))