Merge pull request #97 from opera-adt/dswx-s1-final

Main branch update from DSWx-S1-final

src/dswx_sar/defaults/algorithm_parameter_ni.yaml

@@ -76,7 +76,7 @@ runconfig:
             # using bimodality strategy.
             # One values are required for bimodality method
             tile_selection_bimodality: 0.7
-            # Stratey to interpolate the tile-based thresholds.
+            # Strategy to interpolate the tile-based thresholds.
             # Currently, only 'smoothed' is available.
             extending_method: 'gdal_grid'
             # Thresholding algorithm for initial thresholds.
@@ -161,7 +161,7 @@ runconfig:
             # The elements will be masked out during this step.
             land_cover_darkland_list: ['Bare sparse vegetation', 'Urban', 'Moss and lichen']
             # The elements is considered as the dark land candidates
-            # where these elemtns are spatially connected to the dark land.
+            # where these elements are spatially connected to the dark land.
             land_cover_darkland_extension_list: ['Grassland', 'Shrubs']
             land_cover_water_label: ['Permanent water bodies']
             # VV and VH threshold values for dark land candidates

src/dswx_sar/defaults/algorithm_parameter_s1.yaml

@@ -93,7 +93,7 @@ runconfig:
             # using bimodality strategy.
             # One values are required for bimodality method
             tile_selection_bimodality: 0.7
-            # Stratey to interpolate the tile-based thresholds.
+            # Strategy to interpolate the tile-based thresholds.
             # Currently, only 'smoothed' is available.
             extending_method: 'gdal_grid'
             # Thresholding algorithm for initial thresholds.
@@ -178,7 +178,7 @@ runconfig:
             # The elements will be masked out during this step.
             land_cover_darkland_list: ['Bare sparse vegetation', 'Urban', 'Moss and lichen']
             # The elements is considered as the dark land candidates
-            # where these elemtns are spatially connected to the dark land.
+            # where these elements are spatially connected to the dark land.
             land_cover_darkland_extension_list: ['Grassland', 'Shrubs']
             land_cover_water_label: ['Permanent water bodies']
             # VV and VH threshold values for dark land candidates

src/dswx_sar/defaults/dswx_ni.yaml

@@ -50,7 +50,7 @@ runconfig:
             # algorithm parameter
             algorithm_parameters:
 
-            # placeholder for inundated vegettion 
+            # placeholder for inundated vegetation 
             mean_backscattering:
             standard_deviation_backscattering:
 

src/dswx_sar/dswx_comparison.py

@@ -82,7 +82,7 @@ def _compare_dswx_sar_metadata(metadata_1, metadata_2):
         metadata_error_message = (
             f'* input 1 metadata has {len(metadata_1.keys())} entries'
             f' whereas input 2 metadata has {len(metadata_2.keys())} entries.')
-        
+
         set_1_m_2 = set(metadata_1.keys()) - set(metadata_2.keys())
         if len(set_1_m_2) > 0:
             metadata_error_message += (' Input 1 metadata has extra entries'

src/dswx_sar/dswx_geogrid.py

@@ -144,4 +144,3 @@ def update_geogrid(self, geotiff_path):
 
         self.epsg = new_geogrid.epsg \
             if not np.isnan(new_geogrid.epsg) else self.epsg
-

src/dswx_sar/dswx_ni_runconfig.py

@@ -307,7 +307,7 @@ def custom_sort(pol):
             pol_mode = pol_mode_name
 
     if pol_mode is None:
-        err_msg = 'unable to identify polarzation mode.'
+        err_msg = 'unable to identify polarization mode.'
         logger.warning(err_msg)
     return co_pol_list, cross_pol_list, sorted_pol_list, pol_mode
 

src/dswx_sar/dswx_runconfig.py

@@ -19,10 +19,10 @@
 
 # Potential polarization scenarios for DSWx-S1
 # NOTE: DO NOT CHANGE THE ORDER of the items in the dictionary below.
-# There are more modes such as 'MIX_DUAL_V_SINGLE_V' and 
+# There are more modes such as 'MIX_DUAL_V_SINGLE_V' and
 # 'MIX_DUAL_H_SINGLE_H' describing the dual_polarization and
-# single polarization with same polarization. This mode will be 
-# deteremine by counting the available bursts. 
+# single polarization with same polarization. This mode will be
+# determined by counting the available bursts.
 DSWX_S1_POL_DICT = {
     'CO_POL': ['HH', 'VV'],
     'CROSS_POL': ['HV', 'VH'],
@@ -305,7 +305,7 @@ def custom_sort(pol):
             pol_mode = pol_mode_name
 
     if pol_mode is None:
-        err_msg = 'unable to identify polarzation mode.'
+        err_msg = 'unable to identify polarization mode.'
         logger.warning(err_msg)
     return co_pol_list, cross_pol_list, sorted_pol_list, pol_mode
 

src/dswx_sar/dswx_sar_util.py

@@ -51,7 +51,7 @@
 Internally, DSWx-S1 has 2 water classes;
     1. low-backscattering water
     2. high-backscattering water
-There classes are collapesed into water class when
+There classes are collapsed into water class when
 WTR layers converts to BWTR.
 
 Low-backscattering land (dark land) is captured from
@@ -477,15 +477,15 @@ def change_epsg_tif(input_tif, output_tif, epsg_output,
 
     # Get pixel dimensions
     pixel_x_spacing = metadata['geotransform'][1]
-    pixel_y_sapcing = metadata['geotransform'][5]
+    pixel_y_spacing = metadata['geotransform'][5]
 
     # Get the number of rows and columns
     cols = metadata['width']
     rows = metadata['length']
 
     # Calculate coordinates of the lower right corner
     x_max = x_min + (cols * pixel_x_spacing)
-    y_min = y_max + (rows * pixel_y_sapcing)
+    y_min = y_max + (rows * pixel_y_spacing)
 
     corners = [
         (x_min, y_max),  # Top-left
@@ -499,7 +499,7 @@ def change_epsg_tif(input_tif, output_tif, epsg_output,
         metadata['epsg'],
         epsg_output,
         x_snap=pixel_x_spacing,
-        y_snap=pixel_y_sapcing)
+        y_snap=pixel_y_spacing)
 
     x_coords, y_coords = zip(*corner_output)
     x_min_output, x_max_output = min(x_coords), max(x_coords)
@@ -1120,7 +1120,7 @@ def merge_binary_layers(layer_list, value_list, merged_layer_path,
     cog_flag : bool, optional
         Write to COG if True. Defaults to True.
     scratch_dir : str, optional
-        Path to scrath dir. Defaults to '.'.
+        Path to scratch dir. Defaults to '.'.
 
     Returns
     -------
@@ -1182,7 +1182,7 @@ def intensity_display(intensity, outputdir, pol, immin=-30, immax=0):
     pol: str
         specific polarization added to the file name
     immin: float
-        mininum dB value for displaying intensity
+        minimum dB value for displaying intensity
     immax: float
         maximum dB value for displaying intensity
     """

src/dswx_sar/filter_SAR.py

@@ -57,7 +57,7 @@ def compute_window_mean_std(arr, winsize):
     mean = masked_convolve2d(arr_masked, window, mode='same')
     c2 = masked_convolve2d(arr_masked*arr_masked, window, mode='same')
 
-    var = (c2 - mean * mean)
+    var = c2 - mean * mean
 
     # The negative number in sqrt is replaced
     # with the negligibly small number to avoid numpy warning message.

src/dswx_sar/fuzzy_value_computation.py

@@ -252,7 +252,7 @@ def compute_fuzzy_value(intensity,
         workflows i.e.twele or opera_dswx_s1
     fuzzy_option : dict
         fuzzy options to compute the fuzzy values containing
-        fllowing key:value parameters
+        following key:value parameters
             'hand_threshold': HAND value to mask out
             'slope_min': minimum value for z membership function for slope
             'slope_max': maximum value for z membership function for slope
@@ -291,7 +291,7 @@ def compute_fuzzy_value(intensity,
         s-membership for reference water
     copol_only : numpy.ndarray
        binary image showing the area where
-       ony co-polization is used.
+       ony co-polarization is used.
     '''
     _, rows, cols = intensity.shape
 
@@ -377,7 +377,7 @@ def compute_fuzzy_value(intensity,
             change_ind = co_pol_ind
 
         # Cross-polarization intensity is replaced with co- (or span-) pol
-        # where water varation is high and areas are dark/flat.
+        # where water variation is high and areas are dark/flat.
         intensity_z_set[cross_pol_ind][high_frequent_water] = \
             intensity_z_set[change_ind][high_frequent_water]
         intensity_z_set[cross_pol_ind][landcover_flat_area] = \

src/dswx_sar/initial_threshold.py

@@ -117,7 +117,7 @@ def select_tile_bimodality(self,
                              numstep+1),
             density=True)
 
-        bincenter = ((intensity_bins[:-1] + intensity_bins[1:]) / 2)
+        bincenter = (intensity_bins[:-1] + intensity_bins[1:]) / 2
         intensity_db_variance = np.nanstd(intensity_db)**2
 
         intensity_bins_step = intensity_bins[2] - intensity_bins[1]
@@ -268,7 +268,7 @@ def tile_selection_wbd(self,
                            water_mask,
                            win_size=200,
                            selection_methods=['combined'],
-                           mininum_tile=20,
+                           minimum_tile=20,
                            minimum_pixel_number=40):
         '''Select the tile candidates containing water and non-water
         from aid of water body layer based on the selection method
@@ -369,7 +369,7 @@ def tile_selection_wbd(self,
             # both water bodies and lands from the reference water map.
             # if 0.0 < water_coverage < 1 and 0.0 < land_coverage < 1:
             if water_area_flag:
-                while (num_detected_box_sum <= mininum_tile) and \
+                while (num_detected_box_sum <= minimum_tile) and \
                         (win_size >= minimum_pixel_number):
 
                     subrun += 1
@@ -489,7 +489,7 @@ def tile_selection_wbd(self,
                                 selected_tile_chini.append(False)
                                 selected_tile_bimodality.append(False)
 
-                            # keep coordiates for the searching window.
+                            # keep coordinates for the searching window.
                             coordinate.append(
                                 [ind_subtile,
                                  x_start, x_end,
@@ -524,7 +524,7 @@ def tile_selection_wbd(self,
                                 selected_tile_bimodality)
                         num_detected_box = np.sum(detected_box_array)
 
-                    if num_detected_box_sum <= mininum_tile:
+                    if num_detected_box_sum <= minimum_tile:
                         # try tile-selection with smaller win size
                         win_size = int(win_size * 0.5)
                         num_pixel_max = win_size * win_size / 3
@@ -1581,7 +1581,7 @@ def fill_threshold_with_distance(threshold_array,
         for block in range(0, nblocks):
             row_start = block * lines_per_block
 
-            if (row_start + lines_per_block > rows):
+            if row_start + lines_per_block > rows:
                 block_rows = rows - row_start
             else:
                 block_rows = lines_per_block

src/dswx_sar/masking_with_ancillary.py

@@ -131,7 +131,7 @@ def extract_bbox_with_buffer(
     sizes :  np.ndarray
         Sizes of the connected components.
     label_image : np.ndarray
-        2 dimensional lebel array for each binary object
+        2 dimensional label array for each binary object
     """
     rows, cols = binary.shape
 
@@ -515,7 +515,7 @@ def split_extended_water_parallel(
 
 
 def compute_spatial_coverage_from_ancillary_parallel(
-        flase_water_binary_path: str,
+        false_water_binary_path: str,
         reference_water_path: str,
         mask_landcover_path: str,
         output_file_path: str,
@@ -530,7 +530,7 @@ def compute_spatial_coverage_from_ancillary_parallel(
 
     Parameters
     ----------
-    flase_water_binary_path : str
+    false_water_binary_path : str
         Path to the binary water mask GeoTIFF file.
     reference_water_path : str
         Path to the reference water GeoTIFF file.
@@ -555,8 +555,8 @@ def compute_spatial_coverage_from_ancillary_parallel(
         Saves the computed water mask indicating water areas in the
         specified output file path.
     """
-    water_mask = dswx_sar_util.read_geotiff(flase_water_binary_path)
-    meta_info = dswx_sar_util.get_meta_from_tif(flase_water_binary_path)
+    water_mask = dswx_sar_util.read_geotiff(false_water_binary_path)
+    meta_info = dswx_sar_util.get_meta_from_tif(false_water_binary_path)
 
     # Extract bounding boxes with buffer
     coord_list, sizes, label_image = extract_bbox_with_buffer(
@@ -611,7 +611,7 @@ def compute_spatial_coverage_from_ancillary_parallel(
                   water_label_str, ref_land_tif_str)
                  for i in range(len(sizes))]
 
-    # Output consists of index and 2D image consisting of True/Flase.
+    # Output consists of index and 2D image consisting of True/False.
     # True represents the land and False represents not-land.
     results = Parallel(n_jobs=number_workers)(
         delayed(compute_spatial_coverage)(args)
@@ -997,7 +997,7 @@ def hand_filter_along_boundary(
             scratch_dir=scratch_dir,
             datatype='float32')
         hand_binary_path = os.path.join(
-            scratch_dir, "landcover_hand_bindary.tif")
+            scratch_dir, "landcover_hand_binary.tif")
         dswx_sar_util.save_dswx_product(
             hand_filtered_binary,
             hand_binary_path,
@@ -1164,7 +1164,7 @@ def run(cfg):
     # spatially connected with `mask_excluded_landcover` are added.
     if extended_landcover_flag:
         logger.info('Extending landcover enabled.')
-        rg_excluded_area = (interp_wbd > dry_water_area_threshold)
+        rg_excluded_area = interp_wbd > dry_water_area_threshold
 
         mask_excluded_landcover = extend_land_cover(
             landcover_path=landcover_path,
@@ -1187,7 +1187,7 @@ def run(cfg):
         projection=water_meta['projection'],
         scratch_dir=outputdir)
 
-    # 2) Create intial mask binary
+    # 2) Create initial mask binary
     # mask_excluded indicates the areas satisfying all conditions which are
     # 1: `dry_water_area_threshold` of water occurrence over 37 year (Pekel)
     # 2: specified landcovers (bare ground, sparse vegetation, urban, moss...)
@@ -1259,14 +1259,14 @@ def run(cfg):
         cog_flag=True,
         scratch_dir=outputdir)
 
-    adjacent_false_positive_bindary_path = \
+    adjacent_false_positive_binary_path = \
         os.path.join(outputdir, 'false_positive_connected_water.tif')
 
     compute_spatial_coverage_from_ancillary_parallel(
-            flase_water_binary_path=false_water_candidate_path,
+            false_water_binary_path=false_water_candidate_path,
             reference_water_path=interp_wbd_str,
             mask_landcover_path=mask_excluded_landcover_path,
-            output_file_path=adjacent_false_positive_bindary_path,
+            output_file_path=adjacent_false_positive_binary_path,
             outputdir=outputdir,
             water_max_value=ref_water_max,
             number_workers=number_workers,
@@ -1282,7 +1282,7 @@ def run(cfg):
              os.path.join(outputdir, f"refine_landcover_binary_{pol_str}.tif")
 
     dswx_sar_util.merge_binary_layers(
-        layer_list=[adjacent_false_positive_bindary_path,
+        layer_list=[adjacent_false_positive_binary_path,
                     darkland_cand_path,
                     water_map_tif_str],
         value_list=[0, 0, 1],

src/dswx_sar/metadata.py

@@ -559,4 +559,4 @@ def create_dswx_ni_metadata(cfg,
     # Merge extra_meta_data with dswx_metadata_dict if provided
     if extra_meta_data is not None:
         dswx_metadata_dict.update(extra_meta_data)
-    return dswx_metadata_dict
+    return dswx_metadata_dict