Failed: Unable to find latitude and longitude variables.
collection_concept_id = 'C2832224417-POCLOUD', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2832224417-POCLOUD', 'concept-id': 'G3226579291-POCLOUD', 'concept-type': 'granul...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C2832224417-POCLOUD'}]}, 'meta': {'association-details': {'collecti...me': 'look', 'Size': 2, 'Type': 'OTHER'}], 'FillValues': [{'Type': 'SCIENCE_FILLVALUE', 'Value': -9999.0}], ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C2832224410')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
>       lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)

verify_collection.py:406: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

dataset = <xarray.Dataset> Size: 232B
Dimensions:                 (ydim_grid: 1, xdim_grid: 1, look: 1,
                        ...                                 -0.43
    history_json:                                       [{"date_time": "2024-...
file_to_subset = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C2832224410/76611599_RSS_SMAP_SSS_L2C_r51241_20240904T043343_2024248_NRT_V06.0_001.nc4')
collection_variable_list = [{'associations': {'collections': [{'concept-id': 'C2832224417-POCLOUD'}]}, 'meta': {'association-details': {'collecti...me': 'look', 'Size': 2, 'Type': 'OTHER'}], 'FillValues': [{'Type': 'SCIENCE_FILLVALUE', 'Value': -9999.0}], ...}}, ...]
collection_concept_id = 'C2832224417-POCLOUD'

    def get_lat_lon_var_names(dataset: xarray.Dataset, file_to_subset: str, collection_variable_list: List[Dict], collection_concept_id: str):
        # Try getting it from UMM-Var first
        lat_var_json, lon_var_json, _ = get_coordinate_vars_from_umm(collection_variable_list)
        lat_var_name = get_variable_name_from_umm_json(lat_var_json)
        lon_var_name = get_variable_name_from_umm_json(lon_var_json)
    
        if lat_var_name and lon_var_name:
            return lat_var_name, lon_var_name
    
        logging.warning("Unable to find lat/lon vars in UMM-Var")
    
        # If that doesn't work, try using cf-xarray to infer lat/lon variable names
        try:
            latitude = [lat for lat in dataset.cf.coordinates['latitude']
                             if lat.lower() in VALID_LATITUDE_VARIABLE_NAMES][0]
            longitude = [lon for lon in dataset.cf.coordinates['longitude']
                             if lon.lower() in VALID_LONGITUDE_VARIABLE_NAMES][0]
            return latitude, longitude
        except:
            logging.warning("Unable to find lat/lon vars using cf_xarray")
    
        # If that still doesn't work, try using l2ss-py directly
        try:
            # file not able to be flattened unless locally downloaded
            filename = f'my_copy_file_{collection_concept_id}.nc'
            shutil.copy(file_to_subset, filename)
            nc_dataset = netCDF4.Dataset(filename, mode='r+')
            # flatten the dataset
            nc_dataset_flattened = podaac.subsetter.group_handling.transform_grouped_dataset(nc_dataset, filename)
    
            args = {
                    'decode_coords': False,
                    'mask_and_scale': False,
                    'decode_times': False
                    }
    
            with xarray.open_dataset(
                xarray.backends.NetCDF4DataStore(nc_dataset_flattened),
                **args
                ) as flat_dataset:
                    # use l2ss-py to find lat and lon names
                    lat_var_names, lon_var_names = podaac.subsetter.subset.compute_coordinate_variable_names(flat_dataset)
    
            os.remove(filename)
            if lat_var_names and lon_var_names:
                lat_var_name = lat_var_names.split('__')[-1] if isinstance(lat_var_names, str) else lat_var_names[0].split('__')[-1]
                lon_var_name = lon_var_names.split('__')[-1] if isinstance(lon_var_names, str) else lon_var_names[0].split('__')[-1]
                return lat_var_name, lon_var_name
    
        except ValueError:
            logging.warning("Unable to find lat/lon vars using l2ss-py")
    
        # Still no dice, try using the 'units' variable attribute
        for coord_name, coord in dataset.coords.items():
            if 'units' not in coord.attrs:
                continue
            if coord.attrs['units'] == 'degrees_north' and lat_var_name is None:
                lat_var_name = coord_name
            if coord.attrs['units'] == 'degrees_east' and lon_var_name is None:
                lon_var_name = coord_name
        if lat_var_name and lon_var_name:
            return lat_var_name, lon_var_name
        else:
            logging.warning("Unable to find lat/lon vars using 'units' attribute")
    
        # Out of options, fail the test because we couldn't determine lat/lon variables
>       pytest.fail(f"Unable to find latitude and longitude variables.")
E       Failed: Unable to find latitude and longitude variables.

verify_collection.py:359: Failed
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3226579291-POCLOUD for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C2832224417-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.32055%3A80.33655000000002%29&subset=lon%28108.23122500000001%3A178.159775%29&granuleId=G3226579291-POCLOUD
INFO     root:verify_collection.py:393 Submitted harmony job 5b0d836f-e0b9-4f4b-847a-13199600d2b0
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C2832224410/76611599_RSS_SMAP_SSS_L2C_r51241_20240904T043343_2024248_NRT_V06.0_001.nc4
WARNING  root:verify_collection.py:302 Unable to find lat/lon vars in UMM-Var
WARNING  root:verify_collection.py:312 Unable to find lat/lon vars using cf_xarray
WARNING  root:verify_collection.py:343 Unable to find lat/lon vars using l2ss-py
WARNING  root:verify_collection.py:356 Unable to find lat/lon vars using 'units' attribute

IndexError: list index out of range
collection_concept_id = 'C1918210023-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1918210023-GES_DISC', 'concept-id': 'G3226541794-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1918210023-GES_DISC'}]}, 'meta': {'association-details': {'collect...RL': 'https://cdn.earthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/qa_value', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1918210020')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7ffb8491ab40>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7ffb8491a440>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7ffb8491a340>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3226541794-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1918210023-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-84.474975%3A-68.17202499999999%29&subset=lon%28-116.4801%3A92.47810000000001%29&granuleId=G3226541794-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 611c8a4c-ce8f-4527-9a9a-7a755c6d059e
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1918210020/76611601_S5P_OFFL_L2_HCHO_20240902T130431_20240902T144600_35698_03_020601_20240904T051909_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1627516298-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1627516298-GES_DISC', 'concept-id': 'G2087797426-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1627516298-GES_DISC'}]}, 'meta': {'association-details': {'collect..., 'Version': '1.9.0'}, 'Name': 'METADATA/QA_STATISTICS/nitrogendioxide_tropospheric_column_histogram_axis', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw8/test_spatial_subset_C1627516290')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd16e1fdb40>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd16e1fd640>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7fd16e1fd540>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G2087797426-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1627516298-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-76.82889999999999%3A-59.7251%29&subset=lon%28-77.22455%3A-1.6634499999999974%29&granuleId=G2087797426-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job f65a9ae8-bdba-4e20-8b85-9330d0794a7f
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw8/test_spatial_subset_C1627516290/76611615_S5P_OFFL_L2_NO2_20210701T170324_20210701T184453_19257_01_010400_20210703T102341_subsetted.nc4

Failed: Unable to find latitude and longitude variables.
collection_concept_id = 'C2936721448-POCLOUD', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2936721448-POCLOUD', 'concept-id': 'G3062447313-POCLOUD', 'concept-type': 'granul...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C2936721448-POCLOUD'}]}, 'meta': {'association-details': {'collecti...rization_2', 'Size': 2, 'Type': 'OTHER'}], 'FillValues': [{'Type': 'SCIENCE_FILLVALUE', 'Value': -9999.0}], ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C2936721440')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
>       lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)

verify_collection.py:406: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

dataset = <xarray.Dataset> Size: 240B
Dimensions:                 (ydim_grid: 1, xdim_grid: 1, look: 1,
                        ...                                 -0.43
    history_json:                                           [{"date_time": "2...
file_to_subset = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C2936721440/76611625_RSS_SMAP_SSS_L2C_r47700_20240106T014035_2024006_FNL_V05.3.nc4')
collection_variable_list = [{'associations': {'collections': [{'concept-id': 'C2936721448-POCLOUD'}]}, 'meta': {'association-details': {'collecti...rization_2', 'Size': 2, 'Type': 'OTHER'}], 'FillValues': [{'Type': 'SCIENCE_FILLVALUE', 'Value': -9999.0}], ...}}, ...]
collection_concept_id = 'C2936721448-POCLOUD'

    def get_lat_lon_var_names(dataset: xarray.Dataset, file_to_subset: str, collection_variable_list: List[Dict], collection_concept_id: str):
        # Try getting it from UMM-Var first
        lat_var_json, lon_var_json, _ = get_coordinate_vars_from_umm(collection_variable_list)
        lat_var_name = get_variable_name_from_umm_json(lat_var_json)
        lon_var_name = get_variable_name_from_umm_json(lon_var_json)
    
        if lat_var_name and lon_var_name:
            return lat_var_name, lon_var_name
    
        logging.warning("Unable to find lat/lon vars in UMM-Var")
    
        # If that doesn't work, try using cf-xarray to infer lat/lon variable names
        try:
            latitude = [lat for lat in dataset.cf.coordinates['latitude']
                             if lat.lower() in VALID_LATITUDE_VARIABLE_NAMES][0]
            longitude = [lon for lon in dataset.cf.coordinates['longitude']
                             if lon.lower() in VALID_LONGITUDE_VARIABLE_NAMES][0]
            return latitude, longitude
        except:
            logging.warning("Unable to find lat/lon vars using cf_xarray")
    
        # If that still doesn't work, try using l2ss-py directly
        try:
            # file not able to be flattened unless locally downloaded
            filename = f'my_copy_file_{collection_concept_id}.nc'
            shutil.copy(file_to_subset, filename)
            nc_dataset = netCDF4.Dataset(filename, mode='r+')
            # flatten the dataset
            nc_dataset_flattened = podaac.subsetter.group_handling.transform_grouped_dataset(nc_dataset, filename)
    
            args = {
                    'decode_coords': False,
                    'mask_and_scale': False,
                    'decode_times': False
                    }
    
            with xarray.open_dataset(
                xarray.backends.NetCDF4DataStore(nc_dataset_flattened),
                **args
                ) as flat_dataset:
                    # use l2ss-py to find lat and lon names
                    lat_var_names, lon_var_names = podaac.subsetter.subset.compute_coordinate_variable_names(flat_dataset)
    
            os.remove(filename)
            if lat_var_names and lon_var_names:
                lat_var_name = lat_var_names.split('__')[-1] if isinstance(lat_var_names, str) else lat_var_names[0].split('__')[-1]
                lon_var_name = lon_var_names.split('__')[-1] if isinstance(lon_var_names, str) else lon_var_names[0].split('__')[-1]
                return lat_var_name, lon_var_name
    
        except ValueError:
            logging.warning("Unable to find lat/lon vars using l2ss-py")
    
        # Still no dice, try using the 'units' variable attribute
        for coord_name, coord in dataset.coords.items():
            if 'units' not in coord.attrs:
                continue
            if coord.attrs['units'] == 'degrees_north' and lat_var_name is None:
                lat_var_name = coord_name
            if coord.attrs['units'] == 'degrees_east' and lon_var_name is None:
                lon_var_name = coord_name
        if lat_var_name and lon_var_name:
            return lat_var_name, lon_var_name
        else:
            logging.warning("Unable to find lat/lon vars using 'units' attribute")
    
        # Out of options, fail the test because we couldn't determine lat/lon variables
>       pytest.fail(f"Unable to find latitude and longitude variables.")
E       Failed: Unable to find latitude and longitude variables.

verify_collection.py:359: Failed
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3062447313-POCLOUD for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C2936721448-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.29044999999999%3A82.08044999999998%29&subset=lon%284.51755%3A175.50045%29&granuleId=G3062447313-POCLOUD
INFO     root:verify_collection.py:393 Submitted harmony job 8994493d-d345-492b-a5cc-76e21aaf691a
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C2936721440/76611625_RSS_SMAP_SSS_L2C_r47700_20240106T014035_2024006_FNL_V05.3.nc4
WARNING  root:verify_collection.py:302 Unable to find lat/lon vars in UMM-Var
WARNING  root:verify_collection.py:312 Unable to find lat/lon vars using cf_xarray
WARNING  root:verify_collection.py:343 Unable to find lat/lon vars using l2ss-py
WARNING  root:verify_collection.py:356 Unable to find lat/lon vars using 'units' attribute

IndexError: list index out of range
collection_concept_id = 'C1627516285-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1627516285-GES_DISC', 'concept-id': 'G2084435970-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1627516285-GES_DISC'}]}, 'meta': {'association-details': {'collect.../variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'METADATA/QA_STATISTICS/aerosol_index_354_388_histogram_bounds'}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw7/test_spatial_subset_C1627516280')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f23cfbc8740>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f23cfbc8b40>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7f23cfbc9c40>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G2084435970-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1627516285-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-76.82889999999999%3A-59.7251%29&subset=lon%28-77.22455%3A-1.6634499999999974%29&granuleId=G2084435970-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 397d54be-86c9-4515-84d6-b47232af7ae8
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw7/test_spatial_subset_C1627516280/76611639_S5P_OFFL_L2_AER_AI_20210701T170324_20210701T184453_19257_01_010400_20210703T065109_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1627516292-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1627516292-GES_DISC', 'concept-id': 'G1898261144-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1627516292-GES_DISC'}]}, 'meta': {'association-details': {'collect... 'URL': 'https://cdn.earthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/layer', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C1627516290')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f4432cd9440>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f4432cd8d40>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7f4432cd8c40>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1898261144-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1627516292-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-78.0453%3A-60.6907%29&subset=lon%28-164.82465%3A-84.66935000000001%29&granuleId=G1898261144-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 630e79c9-7820-4c66-b042-515493f77fd5
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C1627516290/76611657_S5P_OFFL_L2_HCHO_20200712T224601_20200713T002730_14238_01_010108_20200715T122623_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1442068493-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1442068493-GES_DISC', 'concept-id': 'G1628685465-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1442068493-GES_DISC'}]}, 'meta': {'association-details': {'collect...arthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/carbonmonoxide_total_column', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw6/test_spatial_subset_C1442068490')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fb26ee20240>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fb26ee21240>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7fb2705bf940>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1628685465-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1442068493-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.468625%3A-64.100375%29&subset=lon%28-112.00605%3A163.26405%29&granuleId=G1628685465-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 8b00ca69-c652-4c48-9140-0f80bd9fdb80
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw6/test_spatial_subset_C1442068490/76611663_S5P_OFFL_L2_CO_20190806T003836_20190806T022006_09387_01_010302_20190811T235959_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1442068508-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1442068508-GES_DISC', 'concept-id': 'G1628710396-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1442068508-GES_DISC'}]}, 'meta': {'association-details': {'collect...v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/fitted_radiance_squeeze_win3', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw8/test_spatial_subset_C1442068500')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd16e1ff540>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd16e1fc440>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7fd16e1fc340>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1628710396-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1442068508-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.265975%3A-63.873025000000005%29&subset=lon%28-112.057275%3A162.74827499999998%29&granuleId=G1628710396-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 3dccbaea-d39a-4913-ab6e-c74535fd70e6
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw8/test_spatial_subset_C1442068500/76611668_S5P_OFFL_L2_SO2_20190806T003836_20190806T022006_09387_01_010107_20190812T085130_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C2087131083-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2087131083-GES_DISC', 'concept-id': 'G3226579487-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C2087131083-GES_DISC'}]}, 'meta': {'association-details': {'collect.../variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'METADATA/QA_STATISTICS/aerosol_index_354_388_histogram_bounds'}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw3/test_spatial_subset_C2087131080')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f866a312140>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f866a311c40>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7f866a311b40>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3226579487-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C2087131083-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-84.425025%3A-67.971975%29&subset=lon%28-167.9476%3A41.8656%29&granuleId=G3226579487-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 28dc4390-199e-4340-bbec-5b29d32f5ba8
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw3/test_spatial_subset_C2087131080/76611669_S5P_OFFL_L2_AER_AI_20240902T162729_20240902T180859_35700_03_020600_20240904T061423_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C2179081549-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2179081549-GES_DISC', 'concept-id': 'G3226315092-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C2179081549-GES_DISC'}]}, 'meta': {'association-details': {'collect...escription': 'Extracted from _FillValue metadata attribute', 'Type': 'SCIENCE_FILLVALUE', 'Value': -9999}], ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw2/test_spatial_subset_C2179081540')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'Swath': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd1666a8840>}
nc_d = <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7fd1666a8740>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
>                   data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
E                   IndexError: list index out of range

verify_collection.py:435: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3226315092-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C2179081549-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-66.2540835%3A-60.4736565%29&subset=lon%28-74.46766675%3A-44.20282325%29&granuleId=G3226315092-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job bd47439c-004e-4d23-afbb-351034ca12da
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw2/test_spatial_subset_C2179081540/76611705_2A.GPM.DPR.GPM-SLH.20240902-S222126-E235439.059710.V07C_subsetted.nc4
WARNING  root:verify_collection.py:302 Unable to find lat/lon vars in UMM-Var
WARNING  root:verify_collection.py:312 Unable to find lat/lon vars using cf_xarray

IndexError: list index out of range
collection_concept_id = 'C1918209846-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1918209846-GES_DISC', 'concept-id': 'G3226541675-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1918209846-GES_DISC'}]}, 'meta': {'association-details': {'collect...tracted from _FillValue metadata attribute', 'Type': 'SCIENCE_FILLVALUE', 'Value': 9.969209968386869e+36}], ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1918209840')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7ffb81922640>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7ffb81923440>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7ffb81923540>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3226541675-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1918209846-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-84.493375%3A-67.939625%29&subset=lon%28-39.902225%3A168.405225%29&granuleId=G3226541675-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job faaa791c-9fe8-4b58-8728-8256fc73f595
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1918209840/76611709_S5P_OFFL_L2_O3_20240902T080003_20240902T094132_35695_03_020601_20240904T001603_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1918209669-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1918209669-GES_DISC', 'concept-id': 'G3226579903-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1918209669-GES_DISC'}]}, 'meta': {'association-details': {'collect...tracted from _FillValue metadata attribute', 'Type': 'SCIENCE_FILLVALUE', 'Value': 9.969209968386869e+36}], ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw6/test_spatial_subset_C1918209660')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fb26ec47940>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fb26ec47240>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7fb26ec47040>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3226579903-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1918209669-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-84.4375%3A-67.9645%29&subset=lon%28-142.39475%3A67.18475000000001%29&granuleId=G3226579903-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 3bc4ed27-b156-411b-a583-a555af6855ce
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw6/test_spatial_subset_C1918209660/76611736_S5P_OFFL_L2_CLOUD_20240902T144600_20240902T162729_35699_03_020601_20240904T061741_subsetted.nc4

harmony.harmony.ProcessingFailedException: WorkItem failed: podaac/l2ss-py:2.11.0: Service request failed with an unknown error
collection_concept_id = 'C2832221740-POCLOUD', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2832221740-POCLOUD', 'concept-id': 'G3215246926-POCLOUD', 'concept-type': 'granul...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C2832221740-POCLOUD'}]}, 'meta': {'association-details': {'collecti...rization_2', 'Size': 2, 'Type': 'OTHER'}], 'FillValues': [{'Type': 'SCIENCE_FILLVALUE', 'Value': -9999.0}], ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C2832221740')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
>       harmony_client.wait_for_processing(job_id, show_progress=True)

verify_collection.py:394: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <harmony.harmony.Client object at 0x7ffb8188bac0>
job_id = '61ee4d76-731f-498c-8de0-5b6a77b88bb5', show_progress = True

    def wait_for_processing(self, job_id: str, show_progress: bool = False) -> None:
        """Retrieve a submitted job's completion status in percent.
    
        Args:
            job_id: UUID string for the job you wish to interrogate.
    
        Returns:
            The job's processing progress as a percentage.
    
        :raises
            Exception: This can happen if an invalid job_id is provided or Harmony services
            can't be reached.
        """
        # How often to refresh the screen for progress updates and animating spinners.
        ui_update_interval = 0.33  # in seconds
        running_w_errors_logged = False
    
        intervals = round(self.check_interval / ui_update_interval)
        if show_progress:
            with progressbar.ProgressBar(max_value=100, widgets=progressbar_widgets) as bar:
                progress = 0
                while progress < 100:
                    progress, status, message = self.progress(job_id)
                    if status == 'failed':
>                       raise ProcessingFailedException(job_id, message)
E                       harmony.harmony.ProcessingFailedException: WorkItem failed: podaac/l2ss-py:2.11.0: Service request failed with an unknown error

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/harmony/harmony.py:986: ProcessingFailedException
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3215246926-POCLOUD for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C2832221740-POCLOUD/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.29166699999999%3A82.04666699999999%29&subset=lon%28-171.0%3A171.0%29&granuleId=G3215246926-POCLOUD
INFO     root:verify_collection.py:393 Submitted harmony job 61ee4d76-731f-498c-8de0-5b6a77b88bb5

IndexError: list index out of range
collection_concept_id = 'C1442068505-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1442068505-GES_DISC', 'concept-id': 'G1628685470-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1442068505-GES_DISC'}]}, 'meta': {'association-details': {'collect...hdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/methane_mixing_ratio_precision', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw2/test_spatial_subset_C1442068500')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd1666abc40>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd1666ab740>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7fd1666ab640>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1628685470-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1442068505-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.468625%3A-64.100375%29&subset=lon%28-112.00605%3A163.26405%29&granuleId=G1628685470-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 150735eb-1b21-4670-bf0b-68a52969fea3
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw2/test_spatial_subset_C1442068500/76611768_S5P_OFFL_L2_CH4_20190806T003836_20190806T022006_09387_01_010302_20190812T015759_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1442068491-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1442068491-GES_DISC', 'concept-id': 'G1642673899-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1442068491-GES_DISC'}]}, 'meta': {'association-details': {'collect...s://cdn.earthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/aerosol_mid_height', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1442068490')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7ffb817f9d40>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7ffb817f9a40>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7ffb817f9340>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1642673899-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1442068491-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.32457500000001%3A-63.840424999999996%29&subset=lon%28-112.0001%3A163.1541%29&granuleId=G1642673899-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job a90f5764-c6fe-49cf-ae2b-34750021cc07
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1442068490/76611775_S5P_OFFL_L2_AER_LH_20190806T003836_20190806T022006_09387_01_010302_20190812T015801_subsetted.nc4

OSError: [Errno group not found: PRODUCT] 'PRODUCT'
ds = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd16df71340>
group = '/METADATA/PRODUCT', mode = 'r'
create_group = <function _netcdf4_create_group at 0x7fd172a6f880>

    def _nc4_require_group(ds, group, mode, create_group=_netcdf4_create_group):
        if group in {None, "", "/"}:
            # use the root group
            return ds
        else:
            # make sure it's a string
            if not isinstance(group, str):
                raise ValueError("group must be a string or None")
            # support path-like syntax
            path = group.strip("/").split("/")
            for key in path:
                try:
>                   ds = ds.groups[key]
E                   KeyError: 'PRODUCT'

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:190: KeyError

During handling of the above exception, another exception occurred:

collection_concept_id = 'C1627516287-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1627516287-GES_DISC', 'concept-id': 'G2084463561-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1627516287-GES_DISC'}]}, 'meta': {'association-details': {'collect...'URL': 'https://cdn.earthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/corner', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw8/test_spatial_subset_C1627516280')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
verify_collection.py:431: in group_walk
    subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/api.py:571: in open_dataset
    backend_ds = backend.open_dataset(
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:646: in open_dataset
    store = NetCDF4DataStore.open(
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:409: in open
    return cls(manager, group=group, mode=mode, lock=lock, autoclose=autoclose)
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:356: in __init__
    self.format = self.ds.data_model
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:418: in ds
    return self._acquire()
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:413: in _acquire
    ds = _nc4_require_group(root, self._group, self._mode)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

ds = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd16df71340>
group = '/METADATA/PRODUCT', mode = 'r'
create_group = <function _netcdf4_create_group at 0x7fd172a6f880>

    def _nc4_require_group(ds, group, mode, create_group=_netcdf4_create_group):
        if group in {None, "", "/"}:
            # use the root group
            return ds
        else:
            # make sure it's a string
            if not isinstance(group, str):
                raise ValueError("group must be a string or None")
            # support path-like syntax
            path = group.strip("/").split("/")
            for key in path:
                try:
                    ds = ds.groups[key]
                except KeyError as e:
                    if mode != "r":
                        ds = create_group(ds, key)
                    else:
                        # wrap error to provide slightly more helpful message
>                       raise OSError(f"group not found: {key}", e)
E                       OSError: [Errno group not found: PRODUCT] 'PRODUCT'

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:196: OSError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G2084463561-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1627516287-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-76.99937499999999%3A-59.951625%29&subset=lon%28-76.6214%3A-1.5866000000000042%29&granuleId=G2084463561-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 42f4634e-9e20-4fb8-9366-7f5c8c671727
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw8/test_spatial_subset_C1627516280/76611799_S5P_OFFL_L2_CO_20210701T170324_20210701T184453_19257_01_010400_20210703T065107_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1627516300-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1627516300-GES_DISC', 'concept-id': 'G1902371249-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1627516300-GES_DISC'}]}, 'meta': {'association-details': {'collect...asa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/ozone_total_vertical_column_precision', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw8/test_spatial_subset_C1627516300')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd16dfcc340>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fd16dfcdf40>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7fd16dfce040>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1902371249-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1627516300-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-78.0453%3A-60.6907%29&subset=lon%28-164.82465%3A-84.66935000000001%29&granuleId=G1902371249-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 13cb93df-0cef-418b-b585-6c2fd67c45da
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw8/test_spatial_subset_C1627516300/76611815_S5P_OFFL_L2_O3_20200712T224601_20200713T002730_14238_01_010108_20200715T122623_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1442068510-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1442068510-GES_DISC', 'concept-id': 'G1628685468-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1442068510-GES_DISC'}]}, 'meta': {'association-details': {'collect...mm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/averaging_kernel', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C1442068510')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f4432765e40>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f4432765740>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7f4432765640>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1628685468-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1442068510-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.265975%3A-63.873025000000005%29&subset=lon%28-112.057275%3A162.74827499999998%29&granuleId=G1628685468-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job a7c2034e-f59a-41a9-946a-803e9257efe3
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C1442068510/76611833_S5P_OFFL_L2_HCHO_20190806T003836_20190806T022006_09387_01_010107_20190812T015759_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1442068509-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1442068509-GES_DISC', 'concept-id': 'G1628706233-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1442068509-GES_DISC'}]}, 'meta': {'association-details': {'collect... 'URL': 'https://cdn.earthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/level', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw5/test_spatial_subset_C1442068500')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f947bd14840>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f947bd14240>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7f947bd14140>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1628706233-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1442068509-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.265975%3A-63.873025000000005%29&subset=lon%28-112.057275%3A162.74827499999998%29&granuleId=G1628706233-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 6449ebcc-90a9-43cf-8d3f-ec1a9acac329
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw5/test_spatial_subset_C1442068500/76611851_S5P_OFFL_L2_O3_20190806T003836_20190806T022006_09387_01_010107_20190812T015759_subsetted.nc4

OSError: [Errno group not found: PRODUCT] 'PRODUCT'
ds = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f23cfb22a40>
group = '/METADATA/PRODUCT', mode = 'r'
create_group = <function _netcdf4_create_group at 0x7f23d838e5f0>

    def _nc4_require_group(ds, group, mode, create_group=_netcdf4_create_group):
        if group in {None, "", "/"}:
            # use the root group
            return ds
        else:
            # make sure it's a string
            if not isinstance(group, str):
                raise ValueError("group must be a string or None")
            # support path-like syntax
            path = group.strip("/").split("/")
            for key in path:
                try:
>                   ds = ds.groups[key]
E                   KeyError: 'PRODUCT'

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:190: KeyError

During handling of the above exception, another exception occurred:

collection_concept_id = 'C1442068490-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1442068490-GES_DISC', 'concept-id': 'G1628672811-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1442068490-GES_DISC'}]}, 'meta': {'association-details': {'collect...ov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'METADATA/QA_STATISTICS/aerosol_index_354_388_pdf_bounds'}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw7/test_spatial_subset_C1442068490')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
verify_collection.py:431: in group_walk
    subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/api.py:571: in open_dataset
    backend_ds = backend.open_dataset(
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:646: in open_dataset
    store = NetCDF4DataStore.open(
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:409: in open
    return cls(manager, group=group, mode=mode, lock=lock, autoclose=autoclose)
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:356: in __init__
    self.format = self.ds.data_model
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:418: in ds
    return self._acquire()
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:413: in _acquire
    ds = _nc4_require_group(root, self._group, self._mode)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

ds = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f23cfb22a40>
group = '/METADATA/PRODUCT', mode = 'r'
create_group = <function _netcdf4_create_group at 0x7f23d838e5f0>

    def _nc4_require_group(ds, group, mode, create_group=_netcdf4_create_group):
        if group in {None, "", "/"}:
            # use the root group
            return ds
        else:
            # make sure it's a string
            if not isinstance(group, str):
                raise ValueError("group must be a string or None")
            # support path-like syntax
            path = group.strip("/").split("/")
            for key in path:
                try:
                    ds = ds.groups[key]
                except KeyError as e:
                    if mode != "r":
                        ds = create_group(ds, key)
                    else:
                        # wrap error to provide slightly more helpful message
>                       raise OSError(f"group not found: {key}", e)
E                       OSError: [Errno group not found: PRODUCT] 'PRODUCT'

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:196: OSError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1628672811-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1442068490-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.265975%3A-63.873025000000005%29&subset=lon%28-112.057275%3A162.74827499999998%29&granuleId=G1628672811-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 97828a0f-e142-4aa2-9d2f-24c79d03e9f0
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw7/test_spatial_subset_C1442068490/76611873_S5P_OFFL_L2_AER_AI_20190806T003836_20190806T022006_09387_01_010302_20190812T000004_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C2089270961-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2089270961-GES_DISC', 'concept-id': 'G3225943329-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C2089270961-GES_DISC'}]}, 'meta': {'association-details': {'collect....0', 'Version': '1.9.0'}, 'Name': 'METADATA/QA_STATISTICS/nitrogendioxide_tropospheric_column_histogram_bounds'}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C2089270960')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f443065b640>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f443065af40>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7f443065ae40>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3225943329-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C2089270961-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-85.214825%3A-67.234175%29&subset=lon%28-154.44475%3A120.53275000000001%29&granuleId=G3225943329-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job c287007d-229e-4ca2-8d36-53122ef4be13
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C2089270960/76611918_S5P_OFFL_L2_NO2_20240826T220433_20240826T234603_35604_03_020600_20240828T140255_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C2087216100-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2087216100-GES_DISC', 'concept-id': 'G3226541955-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C2087216100-GES_DISC'}]}, 'meta': {'association-details': {'collect...m/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'METADATA/QA_STATISTICS/aerosol_mid_pressure_histogram_bounds'}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw6/test_spatial_subset_C2087216100')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fb26d9fbb40>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7fb26d9f9840>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7fb26d9f8940>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3226541955-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C2087216100-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-84.548475%3A-67.92252500000001%29&subset=lon%28-92.0782%3A117.82619999999999%29&granuleId=G3226541955-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 39a53a0e-0220-4bc6-8223-7956cb1ce439
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw6/test_spatial_subset_C2087216100/76611929_S5P_OFFL_L2_AER_LH_20240902T112301_20240902T130431_35697_03_020600_20240904T034143_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1442068511-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1442068511-GES_DISC', 'concept-id': 'G1629705055-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1442068511-GES_DISC'}]}, 'meta': {'association-details': {'collect...e/v1.9.0', 'Version': '1.9.0'}, 'Name': 'METADATA/QA_STATISTICS/nitrogendioxide_tropospheric_column_pdf_bounds'}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw3/test_spatial_subset_C1442068510')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f866a041e40>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f866a041540>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7f866a041640>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G1629705055-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1442068511-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-82.265975%3A-63.873025000000005%29&subset=lon%28-112.057275%3A162.74827499999998%29&granuleId=G1629705055-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 5a9da395-8e41-4b46-a1e5-8d7e575aed97
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw3/test_spatial_subset_C1442068510/76612059_S5P_OFFL_L2_NO2_20190806T003836_20190806T022006_09387_01_010302_20190812T015802_subsetted.nc4

OSError: [Errno group not found: PRODUCT] 'PRODUCT'
ds = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f947baa8a40>
group = '/METADATA/PRODUCT', mode = 'r'
create_group = <function _netcdf4_create_group at 0x7f948867ec20>

    def _nc4_require_group(ds, group, mode, create_group=_netcdf4_create_group):
        if group in {None, "", "/"}:
            # use the root group
            return ds
        else:
            # make sure it's a string
            if not isinstance(group, str):
                raise ValueError("group must be a string or None")
            # support path-like syntax
            path = group.strip("/").split("/")
            for key in path:
                try:
>                   ds = ds.groups[key]
E                   KeyError: 'PRODUCT'

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:190: KeyError

During handling of the above exception, another exception occurred:

collection_concept_id = 'C1627516288-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1627516288-GES_DISC', 'concept-id': 'G2085128317-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1627516288-GES_DISC'}]}, 'meta': {'association-details': {'collect... 'https://cdn.earthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/ground_pixel', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw5/test_spatial_subset_C1627516280')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
verify_collection.py:431: in group_walk
    subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/api.py:571: in open_dataset
    backend_ds = backend.open_dataset(
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:646: in open_dataset
    store = NetCDF4DataStore.open(
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:409: in open
    return cls(manager, group=group, mode=mode, lock=lock, autoclose=autoclose)
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:356: in __init__
    self.format = self.ds.data_model
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:418: in ds
    return self._acquire()
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:413: in _acquire
    ds = _nc4_require_group(root, self._group, self._mode)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

ds = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7f947baa8a40>
group = '/METADATA/PRODUCT', mode = 'r'
create_group = <function _netcdf4_create_group at 0x7f948867ec20>

    def _nc4_require_group(ds, group, mode, create_group=_netcdf4_create_group):
        if group in {None, "", "/"}:
            # use the root group
            return ds
        else:
            # make sure it's a string
            if not isinstance(group, str):
                raise ValueError("group must be a string or None")
            # support path-like syntax
            path = group.strip("/").split("/")
            for key in path:
                try:
                    ds = ds.groups[key]
                except KeyError as e:
                    if mode != "r":
                        ds = create_group(ds, key)
                    else:
                        # wrap error to provide slightly more helpful message
>                       raise OSError(f"group not found: {key}", e)
E                       OSError: [Errno group not found: PRODUCT] 'PRODUCT'

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:196: OSError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G2085128317-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1627516288-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-76.99937499999999%3A-59.951625%29&subset=lon%28-76.6214%3A-1.5866000000000042%29&granuleId=G2085128317-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 7e508dbb-c929-47cb-9b89-7b2eeff65ee0
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw5/test_spatial_subset_C1627516280/76612073_S5P_OFFL_L2_CH4_20210701T170324_20210701T184453_19257_01_010400_20210703T102338_subsetted.nc4

IndexError: list index out of range
collection_concept_id = 'C1918210292-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1918210292-GES_DISC', 'concept-id': 'G3226580915-GES_DISC', 'concept-type': 'gran...pecification': {'Name': 'UMM-G', 'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6', 'Version': '1.6.6'}, ...}}
collection_variables = [{'associations': {'collections': [{'concept-id': 'C1918210292-GES_DISC'}]}, 'meta': {'association-details': {'collect...v1.9.0', 'Version': '1.9.0'}, 'Name': 'PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/number_of_slant_columns_win2', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1918210290')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...Jb1BnM7KDdEqo_3EoDdVhQCL6YyPeFO5phn_VtkdRRgce7fIpgz79Xenaj8C_tgpF1XCp4JT2t834vHhVXrf_lRaldMpl7WxjRzMfWMdoxY5ZFahb_B-Yw'

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        # Compute a box that is smaller than the granule extent bounding box
        north, south, east, west = get_bounding_box(granule_json)
        east, west, north, south = create_smaller_bounding_box(east, west, north, south, .95)
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_bbox = harmony.BBox(w=west, s=south, e=east, n=north)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection, spatial=request_bbox,
                                          granule_id=[granule_json['meta']['concept-id']])
    
        logging.info("Sending harmony request %s", harmony_client.request_as_url(harmony_request))
    
        # Submit harmony request and download result
        job_id = harmony_client.submit(harmony_request)
        logging.info("Submitted harmony job %s", job_id)
        harmony_client.wait_for_processing(job_id, show_progress=True)
        subsetted_filepath = None
        for filename in [file_future.result()
                         for file_future
                         in harmony_client.download_all(job_id, directory=f'{tmp_path}', overwrite=True)]:
            logging.info(f'Downloaded: %s', filename)
            subsetted_filepath = pathlib.Path(filename)
    
        # Verify spatial subset worked
        subsetted_ds = xarray.open_dataset(subsetted_filepath, decode_times=False)
        group = None
        # Try to read group in file
        lat_var_name, lon_var_name = get_lat_lon_var_names(subsetted_ds, subsetted_filepath, collection_variables, collection_concept_id)
        lat_var_name = lat_var_name.split('/')[-1]
        lon_var_name = lon_var_name.split('/')[-1]
    
        with netCDF4.Dataset(subsetted_filepath) as f:
            group_list = []
            def group_walk(groups, nc_d, current_group):
                global subsetted_ds_new
                subsetted_ds_new = None
                # check if the top group has lat or lon variable
                if lat_var_name in list(nc_d.variables.keys()):
                    subsetted_ds_new = subsetted_ds
                else:
                    # if not then we'll need to keep track of the group layers
                    group_list.append(current_group)
    
                # loop through the groups in the current layer
                for g in groups:
                    # end the loop if we've already found latitude
                    if subsetted_ds_new:
                        break
                    # check if the groups have latitude, define the dataset and end the loop if found
                    if lat_var_name in list(nc_d.groups[g].variables.keys()):
                        group_list.append(g)
                        lat_group = '/'.join(group_list)
                        subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                        # add a science variable to the dataset if other groups are in the lat/lon group
                        # some GPM collections won't have any other variables in the same group as lat/lon
                        if len(list(nc_d.groups[g].groups.keys())) > 0:
                            data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                            g_data = lat_group+'/'+data_group
                            subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
                            sci_var = list(subsetted_ds_data.variables.keys())[0]
                            subsetted_ds_new['science_test'] = subsetted_ds_data[sci_var]
                        break
                    # recall the function on a group that has groups in it and didn't find latitude
                    # this is going 'deeper' into the groups
                    if len(list(nc_d.groups[g].groups.keys())) > 0:
                        group_walk(nc_d.groups[g].groups, nc_d.groups[g], g)
                    else:
                        continue
    
>           group_walk(f.groups, f, '')

verify_collection.py:448: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

groups = {'METADATA': <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7ffb815e0b40>, 'PRODUCT': <[RuntimeError('NetCDF: Not a valid ID') raised in repr()] Group object at 0x7ffb815e0440>}
nc_d = <[AttributeError('NetCDF: Not a valid ID') raised in repr()] Dataset object at 0x7ffb815e0340>
current_group = ''

    def group_walk(groups, nc_d, current_group):
        global subsetted_ds_new
        subsetted_ds_new = None
        # check if the top group has lat or lon variable
        if lat_var_name in list(nc_d.variables.keys()):
            subsetted_ds_new = subsetted_ds
        else:
            # if not then we'll need to keep track of the group layers
            group_list.append(current_group)
    
        # loop through the groups in the current layer
        for g in groups:
            # end the loop if we've already found latitude
            if subsetted_ds_new:
                break
            # check if the groups have latitude, define the dataset and end the loop if found
            if lat_var_name in list(nc_d.groups[g].variables.keys()):
                group_list.append(g)
                lat_group = '/'.join(group_list)
                subsetted_ds_new = xarray.open_dataset(subsetted_filepath, group=lat_group, decode_times=False)
                # add a science variable to the dataset if other groups are in the lat/lon group
                # some GPM collections won't have any other variables in the same group as lat/lon
                if len(list(nc_d.groups[g].groups.keys())) > 0:
                    data_group = [v for v in list(nc_d.groups[g].groups.keys()) if 'time' not in str(v).lower()][0]
                    g_data = lat_group+'/'+data_group
                    subsetted_ds_data = xarray.open_dataset(subsetted_filepath, group=g_data, decode_times=False)
>                   sci_var = list(subsetted_ds_data.variables.keys())[0]
E                   IndexError: list index out of range

verify_collection.py:438: IndexError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:373 Using granule G3226580915-GES_DISC for test
INFO     root:verify_collection.py:389 Sending harmony request https://harmony.earthdata.nasa.gov/C1918210292-GES_DISC/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?forceAsync=true&subset=lat%28-84.493375%3A-67.939625%29&subset=lon%28-39.902225%3A168.405225%29&granuleId=G3226580915-GES_DISC
INFO     root:verify_collection.py:393 Submitted harmony job 0bf27e58-0ea7-4cf0-ab0e-df3ae5ba7939
INFO     root:verify_collection.py:399 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1918210290/76612085_S5P_OFFL_L2_SO2_20240902T080003_20240902T094132_35695_03_020601_20240904T062045_subsetted.nc4