ValueError: did not find a match in any of xarray's currently installed IO backends ['netcdf4']. Consider explicitly selecting one of the installed engines via the ``engine`` parameter, or installing additional IO dependencies, see:
https://docs.xarray.dev/en/stable/getting-started-guide/installing.html
https://docs.xarray.dev/en/stable/user-guide/io.html
collection_concept_id = 'C2499940517-POCLOUD', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2499940517-POCLOUD', 'concept-id': 'G2529918741-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': 'C2499940517-POCLOUD'}]}, 'meta': {'association-details': {'collecti..._TRACK_DIMENSION'}, {'Name': 'ni', 'Size': 120, 'Type': 'CROSS_TRACK_DIMENSION'}], 'LongName': 'longitude', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C2499940510')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_spatial = set()

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_spatial):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_spatial:
            pytest.skip(f"Known collection to skip for spatial testing {collection_concept_id}")
    
        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)

verify_collection.py:471: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/api.py:592: in open_dataset
    engine = plugins.guess_engine(filename_or_obj)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

store_spec = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C2499940510/87008307_20160223042056-OSISAF-L2P_GHRSST-SSTskin-IASI_SST_METOP_A-sstiasi_metop02_20160223_042056_48494-v02.0-fv01.0_subsetted.nc4')

    def guess_engine(
        store_spec: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore,
    ) -> str | type[BackendEntrypoint]:
        engines = list_engines()
    
        for engine, backend in engines.items():
            try:
                if backend.guess_can_open(store_spec):
                    return engine
            except PermissionError:
                raise
            except Exception:
                warnings.warn(f"{engine!r} fails while guessing", RuntimeWarning)
    
        compatible_engines = []
        for engine, (_, backend_cls) in BACKEND_ENTRYPOINTS.items():
            try:
                backend = backend_cls()
                if backend.guess_can_open(store_spec):
                    compatible_engines.append(engine)
            except Exception:
                warnings.warn(f"{engine!r} fails while guessing", RuntimeWarning)
    
        installed_engines = [k for k in engines if k != "store"]
        if not compatible_engines:
            if installed_engines:
                error_msg = (
                    "did not find a match in any of xarray's currently installed IO "
                    f"backends {installed_engines}. Consider explicitly selecting one of the "
                    "installed engines via the ``engine`` parameter, or installing "
                    "additional IO dependencies, see:\n"
                    "https://docs.xarray.dev/en/stable/getting-started-guide/installing.html\n"
                    "https://docs.xarray.dev/en/stable/user-guide/io.html"
                )
            else:
                error_msg = (
                    "xarray is unable to open this file because it has no currently "
                    "installed IO backends. Xarray's read/write support requires "
                    "installing optional IO dependencies, see:\n"
                    "https://docs.xarray.dev/en/stable/getting-started-guide/installing.html\n"
                    "https://docs.xarray.dev/en/stable/user-guide/io"
                )
        else:
            error_msg = (
                "found the following matches with the input file in xarray's IO "
                f"backends: {compatible_engines}. But their dependencies may not be installed, see:\n"
                "https://docs.xarray.dev/en/stable/user-guide/io.html \n"
                "https://docs.xarray.dev/en/stable/getting-started-guide/installing.html"
            )
    
>       raise ValueError(error_msg)
E       ValueError: did not find a match in any of xarray's currently installed IO backends ['netcdf4']. Consider explicitly selecting one of the installed engines via the ``engine`` parameter, or installing additional IO dependencies, see:
E       https://docs.xarray.dev/en/stable/getting-started-guide/installing.html
E       https://docs.xarray.dev/en/stable/user-guide/io.html

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/plugins.py:187: ValueError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:441 Using granule G2529918741-POCLOUD for test
INFO     root:verify_collection.py:457 Sending harmony request https://harmony.earthdata.nasa.gov/C2499940517-POCLOUD/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat%2865.69915%3A83.22285%29&subset=lon%2870.985875%3A146.221125%29&granuleId=G2529918741-POCLOUD&variable=all
INFO     root:verify_collection.py:461 Submitted harmony job dff19fd5-9ec5-4e30-a1b8-f3f065eec325
INFO     root:verify_collection.py:467 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw4/test_spatial_subset_C2499940510/87008307_20160223042056-OSISAF-L2P_GHRSST-SSTskin-IASI_SST_METOP_A-sstiasi_metop02_20160223_042056_48494-v02.0-fv01.0_subsetted.nc4

Failed: No data in lon and lat
collection_concept_id = 'C1693440798-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C1693440798-GES_DISC', 'concept-id': 'G2031458814-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': 'C1693440798-GES_DISC'}]}, 'meta': {'association-details': {'collect...RL': 'https://cdn.earthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, 'Name': 'aux/fg_surf_temp', ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1693440790')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_spatial = set()

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_spatial):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_spatial:
            pytest.skip(f"Known collection to skip for spatial testing {collection_concept_id}")
    
        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]
    
        subsetted_ds_new = walk_netcdf_groups(subsetted_filepath, lat_var_name)
    
        assert lat_var_name and lon_var_name
    
        var_ds = None
        msk = None
    
        science_vars = get_science_vars(collection_variables)
        if science_vars:
            for var in science_vars:
                science_var_name = var['umm']['Name']
                var_ds = find_variable(subsetted_ds_new, science_var_name)
                if var_ds is not None:
                    try:
                        msk = np.logical_not(np.isnan(var_ds.data.squeeze()))
                        break
                    except Exception:
                        continue
            else:
                var_ds, msk = None, None
        else:
            for science_var_name in subsetted_ds_new.variables:
                if (str(science_var_name) not in lat_var_name and
                    str(science_var_name) not in lon_var_name and
                    'time' not in str(science_var_name)):
    
                    var_ds = find_variable(subsetted_ds_new, science_var_name)
                    if var_ds is not None:
                        try:
                            msk = np.logical_not(np.isnan(var_ds.data.squeeze()))
                            break
                        except Exception:
                            continue
            else:
                var_ds, msk = None, None
    
        if var_ds is None or msk is None:
            pytest.fail("Unable to find variable from umm-v to use as science variable.")
    
        try:
            msk = np.logical_not(np.isnan(var_ds.data.squeeze()))
            llat = subsetted_ds_new[lat_var_name].where(msk)
            llon = subsetted_ds_new[lon_var_name].where(msk)
        except ValueError:
    
            llat = subsetted_ds_new[lat_var_name]
            llon = subsetted_ds_new[lon_var_name]
    
        lat_max = llat.max()
        lat_min = llat.min()
    
        lon_min = llon.min()
        lon_max = llon.max()
    
        lon_min = (lon_min + 180) % 360 - 180
        lon_max = (lon_max + 180) % 360 - 180
    
        lat_var_fill_value = subsetted_ds_new[lat_var_name].encoding.get('_FillValue')
        lon_var_fill_value = subsetted_ds_new[lon_var_name].encoding.get('_FillValue')
    
        partial_pass = False
        if lat_var_fill_value:
            if (lat_max <= north or np.isclose(lat_max, north)) and (lat_min >= south or np.isclose(lat_min, south)):
                logging.info("Successful Latitude subsetting")
            elif np.isnan(lat_max) and np.isnan(lat_min):
                logging.info("Partial Lat Success - no Data")
                partial_pass = True
            else:
                assert False
    
        if lon_var_fill_value:
            if (lon_max <= east or np.isclose(lon_max, east)) and (lon_min >= west or np.isclose(lon_min, west)):
                logging.info("Successful Longitude subsetting")
            elif np.isnan(lon_max) and np.isnan(lon_min):
                logging.info("Partial Lon Success - no Data")
                partial_pass = True
            else:
                assert False
    
        if partial_pass:
            valid_lon = np.isfinite(llon) & (llon != lon_var_fill_value)
            valid_lat = np.isfinite(llat) & (llat != lat_var_fill_value)
    
            if not np.any(valid_lon) or not np.any(valid_lat):
>               pytest.fail("No data in lon and lat")
E               Failed: No data in lon and lat

verify_collection.py:562: Failed
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:441 Using granule G2031458814-GES_DISC for test
INFO     root:verify_collection.py:457 Sending harmony request https://harmony.earthdata.nasa.gov/C1693440798-GES_DISC/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat%28-83.56275%3A-58.96725%29&subset=lon%28-95.82525000000001%3A-25.344750000000005%29&granuleId=G2031458814-GES_DISC&variable=all
INFO     root:verify_collection.py:461 Submitted harmony job 2baf847f-84ea-42b1-9cfa-e253b392e1b4
INFO     root:verify_collection.py:467 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw9/test_spatial_subset_C1693440790/87008698_SNDR.AQUA.AIRS_IM.20160925T0317.m06.g033.L2_CLIMCAPS_RET.std.v02_39.G.210408174412_subsetted.nc4
WARNING  root:verify_collection.py:314 Unable to find lat/lon vars in UMM-Var
INFO     root:verify_collection.py:543 Partial Lat Success - no Data
INFO     root:verify_collection.py:552 Partial Lon Success - no Data

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-gw3/test_spatial_subset_C2936721440')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_spatial = set()

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_spatial):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_spatial:
            pytest.skip(f"Known collection to skip for spatial testing {collection_concept_id}")
    
        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:474: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

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-gw3/test_spatial_subset_C2936721440/87008784_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:371: Failed
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:441 Using granule G3062447313-POCLOUD for test
INFO     root:verify_collection.py:457 Sending harmony request https://harmony.earthdata.nasa.gov/C2936721448-POCLOUD/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat%28-82.29044999999999%3A82.08044999999998%29&subset=lon%284.51755%3A175.50045%29&granuleId=G3062447313-POCLOUD&variable=all
INFO     root:verify_collection.py:461 Submitted harmony job f345c88a-e3ba-4fd7-a65f-1a3fc9a1061b
INFO     root:verify_collection.py:467 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw3/test_spatial_subset_C2936721440/87008784_RSS_SMAP_SSS_L2C_r47700_20240106T014035_2024006_FNL_V05.3.nc4
WARNING  root:verify_collection.py:314 Unable to find lat/lon vars in UMM-Var
WARNING  root:verify_collection.py:324 Unable to find lat/lon vars using cf_xarray
WARNING  root:verify_collection.py:355 Unable to find lat/lon vars using l2ss-py
WARNING  root:verify_collection.py:368 Unable to find lat/lon vars using 'units' attribute

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': 'G3331454048-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-gw1/test_spatial_subset_C2832224410')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_spatial = set()

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_spatial):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_spatial:
            pytest.skip(f"Known collection to skip for spatial testing {collection_concept_id}")
    
        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:474: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

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-gw1/test_spatial_subset_C2832224410/87008813_RSS_SMAP_SSS_L2C_r52746_20241216T021714_2024351_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:371: Failed
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:441 Using granule G3331454048-POCLOUD for test
INFO     root:verify_collection.py:457 Sending harmony request https://harmony.earthdata.nasa.gov/C2832224417-POCLOUD/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat%28-83.163825%3A47.448825%29&subset=lon%28-146.28109999999998%3A-37.870900000000006%29&granuleId=G3331454048-POCLOUD&variable=all
INFO     root:verify_collection.py:461 Submitted harmony job 3eed76b3-0741-4dee-8524-f71d61933867
INFO     root:verify_collection.py:467 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw1/test_spatial_subset_C2832224410/87008813_RSS_SMAP_SSS_L2C_r52746_20241216T021714_2024351_NRT_V06.0_001.nc4
WARNING  root:verify_collection.py:314 Unable to find lat/lon vars in UMM-Var
WARNING  root:verify_collection.py:324 Unable to find lat/lon vars using cf_xarray
WARNING  root:verify_collection.py:355 Unable to find lat/lon vars using l2ss-py
WARNING  root:verify_collection.py:368 Unable to find lat/lon vars using 'units' attribute

Failed: No data in lon and lat
collection_concept_id = 'C2208421887-POCLOUD', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2208421887-POCLOUD', 'concept-id': 'G2904605619-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': 'C2208421887-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-gw0/test_spatial_subset_C2208421880')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_spatial = set()

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_spatial):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_spatial:
            pytest.skip(f"Known collection to skip for spatial testing {collection_concept_id}")
    
        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]
    
        subsetted_ds_new = walk_netcdf_groups(subsetted_filepath, lat_var_name)
    
        assert lat_var_name and lon_var_name
    
        var_ds = None
        msk = None
    
        science_vars = get_science_vars(collection_variables)
        if science_vars:
            for var in science_vars:
                science_var_name = var['umm']['Name']
                var_ds = find_variable(subsetted_ds_new, science_var_name)
                if var_ds is not None:
                    try:
                        msk = np.logical_not(np.isnan(var_ds.data.squeeze()))
                        break
                    except Exception:
                        continue
            else:
                var_ds, msk = None, None
        else:
            for science_var_name in subsetted_ds_new.variables:
                if (str(science_var_name) not in lat_var_name and
                    str(science_var_name) not in lon_var_name and
                    'time' not in str(science_var_name)):
    
                    var_ds = find_variable(subsetted_ds_new, science_var_name)
                    if var_ds is not None:
                        try:
                            msk = np.logical_not(np.isnan(var_ds.data.squeeze()))
                            break
                        except Exception:
                            continue
            else:
                var_ds, msk = None, None
    
        if var_ds is None or msk is None:
            pytest.fail("Unable to find variable from umm-v to use as science variable.")
    
        try:
            msk = np.logical_not(np.isnan(var_ds.data.squeeze()))
            llat = subsetted_ds_new[lat_var_name].where(msk)
            llon = subsetted_ds_new[lon_var_name].where(msk)
        except ValueError:
    
            llat = subsetted_ds_new[lat_var_name]
            llon = subsetted_ds_new[lon_var_name]
    
        lat_max = llat.max()
        lat_min = llat.min()
    
        lon_min = llon.min()
        lon_max = llon.max()
    
        lon_min = (lon_min + 180) % 360 - 180
        lon_max = (lon_max + 180) % 360 - 180
    
        lat_var_fill_value = subsetted_ds_new[lat_var_name].encoding.get('_FillValue')
        lon_var_fill_value = subsetted_ds_new[lon_var_name].encoding.get('_FillValue')
    
        partial_pass = False
        if lat_var_fill_value:
            if (lat_max <= north or np.isclose(lat_max, north)) and (lat_min >= south or np.isclose(lat_min, south)):
                logging.info("Successful Latitude subsetting")
            elif np.isnan(lat_max) and np.isnan(lat_min):
                logging.info("Partial Lat Success - no Data")
                partial_pass = True
            else:
                assert False
    
        if lon_var_fill_value:
            if (lon_max <= east or np.isclose(lon_max, east)) and (lon_min >= west or np.isclose(lon_min, west)):
                logging.info("Successful Longitude subsetting")
            elif np.isnan(lon_max) and np.isnan(lon_min):
                logging.info("Partial Lon Success - no Data")
                partial_pass = True
            else:
                assert False
    
        if partial_pass:
            valid_lon = np.isfinite(llon) & (llon != lon_var_fill_value)
            valid_lat = np.isfinite(llat) & (llat != lat_var_fill_value)
    
            if not np.any(valid_lon) or not np.any(valid_lat):
>               pytest.fail("No data in lon and lat")
E               Failed: No data in lon and lat

verify_collection.py:562: Failed
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:441 Using granule G2904605619-POCLOUD for test
INFO     root:verify_collection.py:457 Sending harmony request https://harmony.earthdata.nasa.gov/C2208421887-POCLOUD/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat%28-82.28791065216065%3A82.04254169464112%29&subset=lon%28-170.9522521972656%3A170.9765441894531%29&granuleId=G2904605619-POCLOUD&variable=all
INFO     root:verify_collection.py:461 Submitted harmony job 0bb958a6-8372-4161-9a60-31e4a855c0ab
INFO     root:verify_collection.py:467 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw0/test_spatial_subset_C2208421880/87008847_RSS_SMAP_SSS_L2C_r48736_20240316T214722_2024076_FNL_V05.0.nc4
INFO     root:verify_collection.py:543 Partial Lat Success - no Data
INFO     root:verify_collection.py:552 Partial Lon Success - no Data

harmony.harmony.ProcessingFailedException: WorkItem failed: podaac/l2ss-py:2.12.0: Error in file '/home/dockeruser/.local/lib/python3.10/site-packages/xarray/core/variable.py', line 2335, in function '_binary_op': '>=' not supported between instances of 'Array' and 'datetime.datetime'
collection_concept_id = 'C2082387249-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2082387249-GES_DISC', 'concept-id': 'G2808192207-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': 'C2082387249-GES_DISC'}]}, 'meta': {'association-details': {'collect...ion': {'Name': 'UMM-Var', 'URL': 'https://cdn.earthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw6/test_temporal_subset_C208238720')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_temporal = {'C2036882456-POCLOUD', 'C2205121315-POCLOUD'}

    @pytest.mark.timeout(600)
    def test_temporal_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_temporal):
        test_spatial_subset.__doc__ = f"Verify temporal subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_temporal:
            pytest.skip(f"Known collection to skip for temporal testing {collection_concept_id}")
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
        temporal_subset = get_half_temporal_extent(start_time, end_time)
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection,
                                          granule_id=[granule_json['meta']['concept-id']],
                                          temporal=temporal_subset)
    
        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:591: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <harmony.harmony.Client object at 0x7fbf44a96ad0>
job_id = '175f008a-ab53-462e-834f-6520d24a3f64', 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:2.12.0: Error in file '/home/dockeruser/.local/lib/python3.10/site-packages/xarray/core/variable.py', line 2335, in function '_binary_op': '>=' not supported between instances of 'Array' and 'datetime.datetime'

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/harmony/harmony.py:1085: ProcessingFailedException
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:572 Using granule G2808192207-GES_DISC for test
INFO     root:verify_collection.py:585 Sending harmony request https://harmony.earthdata.nasa.gov/C2082387249-GES_DISC/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=time%28%222023-11-12T06%3A00%3A00%22%3A%222023-11-12T18%3A00%3A00%22%29&granuleId=G2808192207-GES_DISC&variable=all
INFO     root:verify_collection.py:589 Submitted harmony job 175f008a-ab53-462e-834f-6520d24a3f64

ValueError: did not find a match in any of xarray's currently installed IO backends ['netcdf4']. Consider explicitly selecting one of the installed engines via the ``engine`` parameter, or installing additional IO dependencies, see:
https://docs.xarray.dev/en/stable/getting-started-guide/installing.html
https://docs.xarray.dev/en/stable/user-guide/io.html
collection_concept_id = 'C2036882072-POCLOUD', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2036882072-POCLOUD', 'concept-id': 'G2283376704-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': 'C2036882072-POCLOUD'}]}, 'meta': {'association-details': {'collecti...to derive wind_speed, degrees North.', 'Dimensions': [{'Name': 'sample', 'Size': 192730, 'Type': 'OTHER'}], ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw2/test_spatial_subset_C2036882070')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_spatial = set()

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_spatial):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_spatial:
            pytest.skip(f"Known collection to skip for spatial testing {collection_concept_id}")
    
        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)

verify_collection.py:471: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/api.py:592: in open_dataset
    engine = plugins.guess_engine(filename_or_obj)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

store_spec = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw2/test_spatial_subset_C2036882070/87009247_cyg.ddmi.s20220528-000000-e20220528-235959.l2.wind_trackgridsize25km_NOAAv1.1_L1a21.d21_subsetted.nc4')

    def guess_engine(
        store_spec: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore,
    ) -> str | type[BackendEntrypoint]:
        engines = list_engines()
    
        for engine, backend in engines.items():
            try:
                if backend.guess_can_open(store_spec):
                    return engine
            except PermissionError:
                raise
            except Exception:
                warnings.warn(f"{engine!r} fails while guessing", RuntimeWarning)
    
        compatible_engines = []
        for engine, (_, backend_cls) in BACKEND_ENTRYPOINTS.items():
            try:
                backend = backend_cls()
                if backend.guess_can_open(store_spec):
                    compatible_engines.append(engine)
            except Exception:
                warnings.warn(f"{engine!r} fails while guessing", RuntimeWarning)
    
        installed_engines = [k for k in engines if k != "store"]
        if not compatible_engines:
            if installed_engines:
                error_msg = (
                    "did not find a match in any of xarray's currently installed IO "
                    f"backends {installed_engines}. Consider explicitly selecting one of the "
                    "installed engines via the ``engine`` parameter, or installing "
                    "additional IO dependencies, see:\n"
                    "https://docs.xarray.dev/en/stable/getting-started-guide/installing.html\n"
                    "https://docs.xarray.dev/en/stable/user-guide/io.html"
                )
            else:
                error_msg = (
                    "xarray is unable to open this file because it has no currently "
                    "installed IO backends. Xarray's read/write support requires "
                    "installing optional IO dependencies, see:\n"
                    "https://docs.xarray.dev/en/stable/getting-started-guide/installing.html\n"
                    "https://docs.xarray.dev/en/stable/user-guide/io"
                )
        else:
            error_msg = (
                "found the following matches with the input file in xarray's IO "
                f"backends: {compatible_engines}. But their dependencies may not be installed, see:\n"
                "https://docs.xarray.dev/en/stable/user-guide/io.html \n"
                "https://docs.xarray.dev/en/stable/getting-started-guide/installing.html"
            )
    
>       raise ValueError(error_msg)
E       ValueError: did not find a match in any of xarray's currently installed IO backends ['netcdf4']. Consider explicitly selecting one of the installed engines via the ``engine`` parameter, or installing additional IO dependencies, see:
E       https://docs.xarray.dev/en/stable/getting-started-guide/installing.html
E       https://docs.xarray.dev/en/stable/user-guide/io.html

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/xarray/backends/plugins.py:187: ValueError
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:441 Using granule G2283376704-POCLOUD for test
INFO     root:verify_collection.py:457 Sending harmony request https://harmony.earthdata.nasa.gov/C2036882072-POCLOUD/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat%28-35.72367499999999%3A35.766675%29&subset=lon%284.500975000000011%3A175.500025%29&granuleId=G2283376704-POCLOUD&variable=all
INFO     root:verify_collection.py:461 Submitted harmony job 2435be56-5a40-4e1d-a033-962e0683a2f3
INFO     root:verify_collection.py:467 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw2/test_spatial_subset_C2036882070/87009247_cyg.ddmi.s20220528-000000-e20220528-235959.l2.wind_trackgridsize25km_NOAAv1.1_L1a21.d21_subsetted.nc4

Failed: No data in lon and lat
collection_concept_id = 'C2832221740-POCLOUD', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2832221740-POCLOUD', 'concept-id': 'G3328644538-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-gw8/test_spatial_subset_C2832221740')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_spatial = set()

    @pytest.mark.timeout(600)
    def test_spatial_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_spatial):
        test_spatial_subset.__doc__ = f"Verify spatial subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_spatial:
            pytest.skip(f"Known collection to skip for spatial testing {collection_concept_id}")
    
        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]
    
        subsetted_ds_new = walk_netcdf_groups(subsetted_filepath, lat_var_name)
    
        assert lat_var_name and lon_var_name
    
        var_ds = None
        msk = None
    
        science_vars = get_science_vars(collection_variables)
        if science_vars:
            for var in science_vars:
                science_var_name = var['umm']['Name']
                var_ds = find_variable(subsetted_ds_new, science_var_name)
                if var_ds is not None:
                    try:
                        msk = np.logical_not(np.isnan(var_ds.data.squeeze()))
                        break
                    except Exception:
                        continue
            else:
                var_ds, msk = None, None
        else:
            for science_var_name in subsetted_ds_new.variables:
                if (str(science_var_name) not in lat_var_name and
                    str(science_var_name) not in lon_var_name and
                    'time' not in str(science_var_name)):
    
                    var_ds = find_variable(subsetted_ds_new, science_var_name)
                    if var_ds is not None:
                        try:
                            msk = np.logical_not(np.isnan(var_ds.data.squeeze()))
                            break
                        except Exception:
                            continue
            else:
                var_ds, msk = None, None
    
        if var_ds is None or msk is None:
            pytest.fail("Unable to find variable from umm-v to use as science variable.")
    
        try:
            msk = np.logical_not(np.isnan(var_ds.data.squeeze()))
            llat = subsetted_ds_new[lat_var_name].where(msk)
            llon = subsetted_ds_new[lon_var_name].where(msk)
        except ValueError:
    
            llat = subsetted_ds_new[lat_var_name]
            llon = subsetted_ds_new[lon_var_name]
    
        lat_max = llat.max()
        lat_min = llat.min()
    
        lon_min = llon.min()
        lon_max = llon.max()
    
        lon_min = (lon_min + 180) % 360 - 180
        lon_max = (lon_max + 180) % 360 - 180
    
        lat_var_fill_value = subsetted_ds_new[lat_var_name].encoding.get('_FillValue')
        lon_var_fill_value = subsetted_ds_new[lon_var_name].encoding.get('_FillValue')
    
        partial_pass = False
        if lat_var_fill_value:
            if (lat_max <= north or np.isclose(lat_max, north)) and (lat_min >= south or np.isclose(lat_min, south)):
                logging.info("Successful Latitude subsetting")
            elif np.isnan(lat_max) and np.isnan(lat_min):
                logging.info("Partial Lat Success - no Data")
                partial_pass = True
            else:
                assert False
    
        if lon_var_fill_value:
            if (lon_max <= east or np.isclose(lon_max, east)) and (lon_min >= west or np.isclose(lon_min, west)):
                logging.info("Successful Longitude subsetting")
            elif np.isnan(lon_max) and np.isnan(lon_min):
                logging.info("Partial Lon Success - no Data")
                partial_pass = True
            else:
                assert False
    
        if partial_pass:
            valid_lon = np.isfinite(llon) & (llon != lon_var_fill_value)
            valid_lat = np.isfinite(llat) & (llat != lat_var_fill_value)
    
            if not np.any(valid_lon) or not np.any(valid_lat):
>               pytest.fail("No data in lon and lat")
E               Failed: No data in lon and lat

verify_collection.py:562: Failed
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:441 Using granule G3328644538-POCLOUD for test
INFO     root:verify_collection.py:457 Sending harmony request https://harmony.earthdata.nasa.gov/C2832221740-POCLOUD/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=lat%28-82.28625373840332%3A82.07150154113769%29&subset=lon%28-170.99963607788087%3A170.98983993530274%29&granuleId=G3328644538-POCLOUD&variable=all
INFO     root:verify_collection.py:461 Submitted harmony job fd3a5ec1-4347-4ea4-963e-c9c48a6c7c34
INFO     root:verify_collection.py:467 Downloaded: /tmp/pytest-of-runner/pytest-0/popen-gw8/test_spatial_subset_C2832221740/87009349_RSS_SMAP_SSS_L2C_r52641_20241208T215904_2024343_FNL_V06.0.nc4
INFO     root:verify_collection.py:543 Partial Lat Success - no Data
INFO     root:verify_collection.py:552 Partial Lon Success - no Data

harmony.harmony.ProcessingFailedException: WorkItem failed: podaac/l2ss-py:2.12.0: Error in file '/home/dockeruser/.local/lib/python3.10/site-packages/xarray/core/variable.py', line 2335, in function '_binary_op': '>=' not supported between instances of 'Array' and 'datetime.datetime'
collection_concept_id = 'C2248652649-GES_DISC', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2248652649-GES_DISC', 'concept-id': 'G2966978055-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': 'C2248652649-GES_DISC'}]}, 'meta': {'association-details': {'collect...ion': {'Name': 'UMM-Var', 'URL': 'https://cdn.earthdata.nasa.gov/umm/variable/v1.9.0', 'Version': '1.9.0'}, ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw5/test_temporal_subset_C224865260')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_temporal = {'C2036882456-POCLOUD', 'C2205121315-POCLOUD'}

    @pytest.mark.timeout(600)
    def test_temporal_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_temporal):
        test_spatial_subset.__doc__ = f"Verify temporal subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_temporal:
            pytest.skip(f"Known collection to skip for temporal testing {collection_concept_id}")
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
        temporal_subset = get_half_temporal_extent(start_time, end_time)
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection,
                                          granule_id=[granule_json['meta']['concept-id']],
                                          temporal=temporal_subset)
    
        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:591: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <harmony.harmony.Client object at 0x7efe734c8880>
job_id = '1f85ddd2-ca15-46a1-a210-058436590062', 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:2.12.0: Error in file '/home/dockeruser/.local/lib/python3.10/site-packages/xarray/core/variable.py', line 2335, in function '_binary_op': '>=' not supported between instances of 'Array' and 'datetime.datetime'

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/harmony/harmony.py:1085: ProcessingFailedException
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:572 Using granule G2966978055-GES_DISC for test
INFO     root:verify_collection.py:585 Sending harmony request https://harmony.earthdata.nasa.gov/C2248652649-GES_DISC/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=time%28%222024-03-30T06%3A00%3A00%22%3A%222024-03-30T18%3A00%3A00%22%29&granuleId=G2966978055-GES_DISC&variable=all
INFO     root:verify_collection.py:589 Submitted harmony job 1f85ddd2-ca15-46a1-a210-058436590062

harmony.harmony.ProcessingFailedException: WorkItem failed: podaac/l2ss-py:2.12.0: Error in file '/home/dockeruser/.local/lib/python3.10/site-packages/xarray/core/variable.py', line 2335, in function '_binary_op': '>=' not supported between instances of 'float' and 'datetime.datetime'
collection_concept_id = 'C2208418228-POCLOUD', env = 'ops'
granule_json = {'meta': {'collection-concept-id': 'C2208418228-POCLOUD', 'concept-id': 'G3331549467-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': 'C2208418228-POCLOUD'}]}, 'meta': {'association-details': {'collecti...ny_dim_1', 'Size': 812, 'Type': 'OTHER'}], 'FillValues': [{'Type': 'SCIENCE_FILLVALUE', 'Value': -9999.0}], ...}}, ...]
harmony_env = <Environment.PROD: 4>
tmp_path = PosixPath('/tmp/pytest-of-runner/pytest-0/popen-gw7/test_temporal_subset_C220841820')
bearer_token = 'eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIj...0nlL-K596RCI-k0gbQsTkkjAtWl2o7dAugcj3VuU0Wx3jDeAYyzGA-P2mt852xfkroyDelavsXnCL4W8iyrZudHxVJ4wLhdsH-xJn6npOrQ_-SuR9UtewA'
skip_temporal = {'C2036882456-POCLOUD', 'C2205121315-POCLOUD'}

    @pytest.mark.timeout(600)
    def test_temporal_subset(collection_concept_id, env, granule_json, collection_variables,
                            harmony_env, tmp_path: pathlib.Path, bearer_token, skip_temporal):
        test_spatial_subset.__doc__ = f"Verify temporal subset for {collection_concept_id} in {env}"
    
        if collection_concept_id in skip_temporal:
            pytest.skip(f"Known collection to skip for temporal testing {collection_concept_id}")
    
        logging.info("Using granule %s for test", granule_json['meta']['concept-id'])
    
        start_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["BeginningDateTime"]
        end_time = granule_json['umm']["TemporalExtent"]["RangeDateTime"]["EndingDateTime"]
        temporal_subset = get_half_temporal_extent(start_time, end_time)
    
        # Build harmony request
        harmony_client = harmony.Client(env=harmony_env, token=bearer_token)
        request_collection = harmony.Collection(id=collection_concept_id)
        harmony_request = harmony.Request(collection=request_collection,
                                          granule_id=[granule_json['meta']['concept-id']],
                                          temporal=temporal_subset)
    
        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:591: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <harmony.harmony.Client object at 0x7fabdfa79300>
job_id = 'c303c2ce-b46e-4f87-b4d6-88a481c8ff6a', 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:2.12.0: Error in file '/home/dockeruser/.local/lib/python3.10/site-packages/xarray/core/variable.py', line 2335, in function '_binary_op': '>=' not supported between instances of 'float' and 'datetime.datetime'

../../../../.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/lib/python3.10/site-packages/harmony/harmony.py:1085: ProcessingFailedException
--------------------------------- Captured Log ---------------------------------
INFO     root:verify_collection.py:572 Using granule G3331549467-POCLOUD for test
INFO     root:verify_collection.py:585 Sending harmony request https://harmony.earthdata.nasa.gov/C2208418228-POCLOUD/ogc-api-coverages/1.0.0/collections/parameter_vars/coverage/rangeset?forceAsync=true&subset=time%28%222024-12-16T04%3A21%3A07.750000%22%3A%222024-12-16T05%3A10%3A21.250000%22%29&granuleId=G3331549467-POCLOUD&variable=all
INFO     root:verify_collection.py:589 Submitted harmony job c303c2ce-b46e-4f87-b4d6-88a481c8ff6a