Feature/empty dataset (#283)

* update get time functions

* update to fix pylint flake8

* update project dependencies

* fix tests

* update libraries for snyk

* poetry update

* update pytoml

* update test retry time

* add harmony deployment into github actions

* update harmony deployment script

* update pytoml version to develop

* update tests delays

* fix harmony deploy url

* removed debug print statement

* update copy of empty data, and check bounds for empty datasets

* update tests for empty datasets

* update to fix temporal subsetting

* fix ghrsst dataset temporal subsetting

* add try except for getting long name

* optimize mask and scale code

* poetry update

* update changelog

CHANGELOG.md

@@ -18,6 +18,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - [issue/267](https://github.com/podaac/l2ss-py/pull/261): Add xtrack and atrack dimension options for get_nd_indexers when bounding box subsetting is performed on SNDR.
 - Fix temporal subsetting ghrsst dataset by adding time delta to time variable.
 - Add a function to test ghrsst dataset ability to access variables when mask_and_scale is true. 
+- Update l2ss-py to allow the return of empty granules.
 ### Changed
 ### Deprecated 
 ### Removed

podaac/subsetter/subset.py

@@ -1099,7 +1099,6 @@ def open_as_nc_dataset(filepath: str) -> Tuple[nc.Dataset, bool]:
     try:
         nc_dataset = nc.Dataset(filepath, mode='r')
         has_groups = bool(nc_dataset.groups)
-
         # If dataset has groups, transform to work with xarray
         if has_groups:
             nc_dataset = transform_grouped_dataset(nc_dataset, filepath)
@@ -1249,7 +1248,6 @@ def subset(file_to_subset: str, bbox: np.ndarray, output_file: str,
         if 'ScanTime' in [var.split('__')[-2] for var in list(nc_dataset.variables.keys())]:
             gc.change_var_dims(nc_dataset, variables)
             hdf_type = 'GPM'
-
     args = {
         'decode_coords': False,
         'mask_and_scale': False,

podaac/subsetter/subset_harmony.py

@@ -199,8 +199,10 @@ def filter_by_subtype(variables, subtype):
             if result_bbox is not None:
                 if message.subset:
                     message.subset.process('bbox')
-                result.bbox = podaac_to_harmony_bbox(result_bbox)
-                result.geometry = bbox_to_geometry(result.bbox)
+                    bounding_box_array = np.array(podaac_to_harmony_bbox(result_bbox))
+                    if not np.all(np.isnan(bounding_box_array)):
+                        result.bbox = podaac_to_harmony_bbox(result_bbox)
+                        result.geometry = bbox_to_geometry(result.bbox)
 
             # Return the STAC record
             return result

podaac/subsetter/xarray_enhancements.py

@@ -145,8 +145,8 @@ def copy_empty_dataset(dataset: xr.Dataset) -> xr.Dataset:
     # Create a dict object where each key is a variable in the dataset and the value is an
     # array initialized to the fill value for that variable or NaN if there is no fill value
     # attribute for the variable
-    empty_data = {k: np.full(v.shape, dataset.variables[k].attrs.get('_FillValue', np.nan)) for k, v in
-                  dataset.items()}
+
+    empty_data = {k: np.full(v.shape, dataset.variables[k].attrs.get('_FillValue', np.nan), dtype=v.dtype) for k, v in dataset.items()}
 
     # Create a copy of the dataset filled with the empty data. Then select the first index along each
     # dimension and return the result

tests/test_subset.py

@@ -350,7 +350,15 @@ def test_subset_empty_bbox(test_file, data_dir, subset_output_dir, request):
 
     # Ensure all variables are present but empty.
     for _, variable in empty_dataset.data_vars.items():
-        assert np.all(variable.data == variable.attrs.get('_FillValue', np.nan) or np.isnan(variable.data))
+        fill_value = variable.attrs.get('_FillValue', np.nan)
+        data = variable.data
+
+        # Perform the main check
+        condition = np.all(data == fill_value) or np.all(np.isnan(data))
+
+        # Handle the specific integer dtype case
+        if not condition and not (np.isnan(fill_value) and np.issubdtype(variable.dtype, np.integer)):
+            assert condition, f"Data does not match fill value for variable: {variable}"
 
     assert test_input_dataset.dims.keys() == empty_dataset.dims.keys()