accounting for data units

romancal/source_detection/source_detection_step.py

@@ -33,7 +33,7 @@ class SourceDetectionStep(RomanStep):
 
     spec = """
         save_catalogs = boolean(default=True) # Save source catalog to file? Will overwrite an existing catalog of the same name.
-        coverage_mask = boolean(default=True) # Mask DO_NOT_USE pixels in image?
+        coverage_mask = boolean(default=False) # Mask DO_NOT_USE pixels in image?
         kernel_fwhm = float(default=1.5)  # DAOStarFinder: size of Gaussian kernel, in pixels.
         sharplo = float(default=0.5)  # DAOStarFinder: lower bound for sharpness.
         sharphi = float(default=1.0)  # DAOStarFinder: upper bound for sharpness.
@@ -57,11 +57,18 @@ def process(self, input):
         # This will need to be changed later to deal with associations
         with rdd.open(input) as input_model:
 
-            self.data = input_model.data
+            # strip units from data - 'threshold' must be a scalar and cannot be compared to 
+            # data with units. 
+            if hasattr(input_model.data, 'unit'):
+                self.data = input_model.data.value
+            else:
+                self.data = input_model.data
 
             if self.coverage_mask:  # mask DO_NOT_USE pixels
                 self.coverage_mask = ((dqflags.pixel['DO_NOT_USE']) &
                                       input_model.dq).astype(bool)
+            else:
+                self.coverage_mask = None
 
             # first, determine threshold for source detection
             # check if at least and only one of the threshold options is present
@@ -75,6 +82,7 @@ def process(self, input):
 
             detect_sources_in_segments = False  # always False unless using calc_threshold_2d
 
+
             # if a pre-determined threshold value for detection for the whole
             # image is provided, use this
             if self.scalar_threshold:
@@ -102,7 +110,7 @@ def process(self, input):
                 detect_sources_in_segments = True
                 bkg = self._calc_2D_background()
                 threshold_img = bkg.background + (self.snr_threshold * bkg.background_rms)
-                detect_sources_in_segments = True
+                # detect_sources_in_segments = True
 
             # run DAOStar finder in segments, combine outputs and cut table if
             # max_sources is larger than the total final table