de-duplicated calc_apply_threshold

dap/algos/aggregation.py

@@ -1,5 +1,5 @@
 from .mask import calc_mask_pixels
-from .thresh import threshold
+from .thresh import calc_apply_threshold
 
 
 def calc_apply_aggregation(results, data, pixel_mask, aggregator):
@@ -7,7 +7,7 @@ def calc_apply_aggregation(results, data, pixel_mask, aggregator):
     if aggregator.is_ready():
         aggregator.reset()
 
-    calc_apply_threshold(results, data) # changes data in place
+    calc_apply_threshold(results, data, value=0) # changes data in place
     data = calc_aggregate(results, data, aggregator)
     calc_mask_pixels(data, pixel_mask) # changes data in place
 
@@ -18,23 +18,6 @@ def calc_apply_aggregation(results, data, pixel_mask, aggregator):
 
 
 
-#TODO: this is duplicated in calc_apply_threshold and calc_radial_integration
-def calc_apply_threshold(results, data):
-    apply_threshold = results.get("apply_threshold", False)
-    if not apply_threshold:
-        return
-
-    for k in ("threshold_min", "threshold_max"):
-        if k not in results:
-            return
-
-    threshold_min = float(results["threshold_min"])
-    threshold_max = float(results["threshold_max"])
-
-    threshold(data, threshold_min, threshold_max, 0)
-
-
-
 def calc_aggregate(results, data, aggregator):
     apply_aggregation = results.get("apply_aggregation", False)
     if not apply_aggregation:

dap/algos/radprof.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from .thresh import threshold
+from .thresh import calc_apply_threshold
 from .utils import npmemo
 
 
@@ -17,7 +17,7 @@ def calc_radial_integration(results, data, pixel_mask):
     r_min = min(rad)
     r_max = max(rad) + 1
 
-    data = calc_apply_threshold(results, data)
+    data = calc_apply_threshold(results, data, value=np.nan, copy=True)
 
     rp = radial_profile(data, rad, norm, pixel_mask)
 
@@ -59,24 +59,3 @@ def radial_profile(data, rad, norm, keep_pixels):
 
 
 
-#TODO: this is duplicated in calc_apply_threshold and calc_apply_aggregation
-def calc_apply_threshold(results, data):
-    apply_threshold = results.get("apply_threshold", False)
-    if not apply_threshold:
-        return data
-
-    for k in ("threshold_min", "threshold_max"):
-        if k not in results:
-            return data
-
-    data = data.copy() # do the following in-place changes on a copy
-
-    threshold_min = float(results["threshold_min"])
-    threshold_max = float(results["threshold_max"])
-
-    threshold(data, threshold_min, threshold_max, np.nan)
-
-    return data
-
-
-

dap/algos/thresh.py

@@ -1,23 +1,28 @@
 import numpy as np
 
 
-#TODO: this is duplicated in calc_radial_integration and calc_apply_aggregation
-def calc_apply_threshold(results, data):
+def calc_apply_threshold(results, data, value=None, copy=False):
     apply_threshold = results.get("apply_threshold", False)
     if not apply_threshold:
-        return
+        return data
 
     for k in ("threshold_min", "threshold_max"):
         if k not in results:
-            return
+            return data
 
-    threshold_value_choice = results.get("threshold_value", "NaN")
-    threshold_value = 0 if threshold_value_choice == "0" else np.nan #TODO
+    if value is None:
+        threshold_value = results.get("threshold_value", "NaN")
+        value = 0 if threshold_value == "0" else np.nan #TODO
 
     threshold_min = float(results["threshold_min"])
     threshold_max = float(results["threshold_max"])
 
-    threshold(data, threshold_min, threshold_max, threshold_value)
+    if copy:
+        data = data.copy() # do the following in-place changes on a copy
+
+    threshold(data, threshold_min, threshold_max, value)
+
+    return data