the sunglint restoral seems to be fixed now, although there are still some...

the sunglint restoral seems to be fixed now, although there are still some minor differences that might need to be polished

mvcm/restoral.py

@@ -116,18 +116,18 @@ class Restoral(object):
     # chk_sunglint
     def sunglint(self,
                  bit: np.ndarray,
-                 variability_threshold: float,
                  confidence: np.ndarray) -> np.ndarray:
 
         threshold = self.thresholds['Sun_Glint']
+        variability_threshold = self.thresholds['Daytime_Ocean_Spatial_Variability']['var_11um']
         m02 = self.data.M07.values
         m09 = self.data.M02.values
         m20 = self.data.M12.values
         m31 = self.data.M15.values
 
-        reg_var_mean = local_nxn_mean(m02)
-        reg_std = local_nxn_standard_deviation(m02)
-        bt_var = spatial_var(m31, variability_threshold)
+        reg_var_mean = utils.local_nxn_mean(m02)
+        reg_std = utils.local_nxn_standard_deviation(m02)
+        bt_var = utils.spatial_var(m31, variability_threshold)
         diff37_11 = m20 - m31
 
         idx = np.nonzero((bt_var == 1) & (bit == 1) & (m09 < threshold['m09']) &
@@ -150,8 +150,8 @@ class Restoral(object):
 
         threshold = self.thresholds['Ocean_NDVI_and_Shallow_Water']
 
-        ndvi = compute_ndvi(self.data.M05.values, self.data.M07.values)
-        std_m05 = local_nxn_standard_deviation(self.data.M08.values)
+        ndvi = utils.compute_ndvi(self.data.M05.values, self.data.M07.values)
+        std_m05 = utils.local_nxn_standard_deviation(self.data.M08.values)
 
         idx = np.nonzero((bits['ir'] == 1) & (bits['nir_1'] == 1) & (bits['nir_2'] == 1) &
                          (bits['sst'] == 0) & (self.scene_flag['sh_ocean'] == 0) &
@@ -220,8 +220,9 @@ class Restoral(object):
         ndvi = utils.compute_ndvi(self.data.M05.values, self.data.M07.values)
         threshold = self.thresholds['Coastal_NDVI_Thresholds']
 
-        idx = np.nonzero((self.scene_flag['day'] == 1) & (self.scene_flag['snow'] == 0) &
-                         (self.scene_flag['ice'] == 0) & (self.scene_flag['coast'] == 1) &
+        idx = np.nonzero((self.scene_flag['day'] == 1) & (self.scene_flag['land'] == 1) &
+                         (self.scene_flag['snow'] == 0) & (self.scene_flag['ice'] == 0) &
+                         (self.scene_flag['coast'] == 1) &
                          (bit == 1) & ((ndvi < threshold['coast_ndvi'][0]) | (ndvi >= threshold['coast_ndvi'][1])))
         confidence[idx] = 1
 
@@ -259,6 +260,7 @@ class Restoral(object):
         return confidence
 
 
+'''
 # eventually move this function to utility_functions
 def local_nxn_mean(arr: np.ndarray,
                    x: int = 3,
@@ -298,8 +300,10 @@ def compute_ndvi(b01: np.ndarray,
     """Compute Normalized Difference Vegetation Index (NDVI) """
     ndvi = (b02-b01)/(b01+b02)
     return ndvi
+'''
 
 
+'''
 def sunglint(viirs_data, threshold, bit, conf):
 
     m09 = viirs_data.M02.values
@@ -409,3 +413,4 @@ def coast(viirs_data, threshold, scene, conf):
         conf[idx] = 1
 
     return conf
+'''