Fix False masks (#103)

* Fix False masks

* Deliver True masks instead of negate False one

echopype/clean/signal_attenuation.py

@@ -65,9 +65,12 @@ def _ryan(source_Sv: xr.DataArray, desired_channel: str, parameters=DEFAULT_RYAN
     if r0 > r1:
         raise Exception("Minimum range has to be shorter than maximum range")
 
-    # return empty mask if searching range is outside the echosounder range
+    # give empty mask if searching range is outside the echosounder range
     if (r0 > r[-1]) or (r1 < r[0]):
-        warnings.warn("Searching range is outside the echosounder range. Returning empty mask.")
+        warnings.warn(
+            "Searching range is outside the echosounder range."
+            "Returning a default mask with all True values."
+        )
         mask = np.zeros_like(Sv, dtype=bool)
         mask_ = np.zeros_like(Sv, dtype=bool)
 

echopype/clean/transient_noise.py

@@ -14,7 +14,6 @@
     "Mihai Boldeanu",  # adapted the mask transient noise algorithms to echopype
 ]
 
-
 import warnings
 
 import numpy as np
@@ -54,6 +53,7 @@ def _ryan(source_Sv: xr.DataArray, desired_channel: str, parameters: dict = RYAN
     transient noise. Sv values are removed if exceed a threshold. Masking is
     excluded above 250 m by default to avoid the removal of aggregated biota.
 
+    Args:
     Args:
         source_Sv (xr.DataArray): Sv array
         selected_channel (str): name of the channel to process
@@ -115,7 +115,6 @@ def _ryan(source_Sv: xr.DataArray, desired_channel: str, parameters: dict = RYAN
                         )
                     )
                 mask[i, j] = sample - block > thr
-
     mask = np.logical_not(mask)
     return_mask = xr.DataArray(
         mask,
@@ -189,21 +188,19 @@ def _fielding(
     if r0 > r1:
         raise Exception("Minimum range has to be shorter than maximum range")
 
-    # In the case where the searching range is outside the echosounder range:
+    # return a default mask with all True values
+    # if searching range is outside the echosounder range
     if (r0 > r[-1]) or (r1 < r[0]):
-        mask = np.zeros_like(Sv, dtype=bool)
-        mask_ = np.zeros_like(Sv, dtype=bool)
-
         # Raise a warning to inform the user
         warnings.warn(
             "The searching range is outside the echosounder range. "
-            "A default mask with all False values is returned, "
+            "A default mask with all True values is returned, "
             "which won't mask any data points in the dataset."
         )
-
-        combined_mask = mask
+        mask = np.ones_like(Sv, dtype=bool)
+        mask_ = np.ones_like(Sv, dtype=bool)
         return xr.DataArray(
-            combined_mask,
+            mask,
             dims=("ping_time", "range_sample"),
             coords={"ping_time": source_Sv.ping_time, "range_sample": source_Sv.range_sample},
         )

echopype/mask/seabed.py

@@ -331,9 +331,12 @@ def _blackwell(Sv_ds: xr.DataArray, desired_channel: str, parameters: dict = MAX
         below = np.zeros((len(r) - r1, maskchunk.shape[1]), dtype=bool)
         mask = np.r_[above, maskchunk, below]
 
-    # return empty mask if aliased-seabed was not detected in Theta & Phi
+    # give empty mask if aliased-seabed was not detected in Theta & Phi
     else:
-        warnings.warn("No aliased seabed detected in Theta & Phi. Returning empty mask.")
+        warnings.warn(
+            "No aliased seabed detected in Theta & Phi. "
+            "A default mask with all True values is returned."
+        )
         mask = np.zeros_like(Sv, dtype=bool)
 
     mask = np.logical_not(mask.T)
@@ -423,9 +426,12 @@ def _blackwell_mod(
     if r0 > r1:
         raise Exception("Minimum range has to be shorter than maximum range")
 
-    # return empty mask if searching range is outside the echosounder range
+    # give empty mask if searching range is outside the echosounder range
     if (r0 > r[-1]) or (r1 < r[0]):
-        warnings.warn("Search range is outside the echosounder range. Returning empty mask.")
+        warnings.warn(
+            "Search range is outside the echosounder range."
+            "A default mask with all True values is returned."
+        )
         mask = np.zeros_like(Sv, dtype=bool)
 
     # delimit the analysis within user-defined range limits
@@ -485,9 +491,12 @@ def _blackwell_mod(
         below = np.zeros((len(r) - i1, maskchunk.shape[1]), dtype=bool)
         mask = np.r_[above, maskchunk, below]
 
-    # return empty mask if aliased-seabed was not detected in Theta & Phi
+    # give empty mask if aliased-seabed was not detected in Theta & Phi
     else:
-        warnings.warn("Aliased seabed not detected in Theta & Phi. Returning empty mask.")
+        warnings.warn(
+            "Aliased seabed not detected in Theta & Phi."
+            "A default mask with all True values is returned."
+        )
         mask = np.zeros_like(Sv, dtype=bool)
 
     mask = np.logical_not(mask.T)
@@ -708,9 +717,12 @@ def _ariza(Sv_ds: xr.DataArray, desired_channel: str, parameters: dict = MAX_SV_
     if r0 > r1:
         raise Exception("Minimum range has to be shorter than maximum range")
 
-    # return empty mask if searching range is outside the echosounder range
+    # give empty mask if searching range is outside the echosounder range
     if (r0 > r[-1]) or (r1 < r[0]):
-        warnings.warn("Search range is outside the echosounder range. Returning empty mask.")
+        warnings.warn(
+            "Search range is outside the echosounder range. "
+            "A default mask with all True values is returned."
+        )
         mask = np.zeros_like(Sv, dtype=bool)
 
     # get indexes for range offset and range limits
@@ -723,9 +735,11 @@ def _ariza(Sv_ds: xr.DataArray, desired_channel: str, parameters: dict = MAX_SV_
     Sv_[0:r0, :] = -999
     Sv_[r1:, :] = -999
 
-    # return empty mask if there is nothing above threshold
+    # give empty mask if there is nothing above threshold
     if not (Sv_ > thr).any():
-        warnings.warn("Nothing found above the threshold. Returning empty mask.")
+        warnings.warn(
+            "Nothing found above the threshold. " "A default mask with all True values is returned."
+        )
         mask = np.zeros_like(Sv_, dtype=bool)
 
     # search for seabed otherwise