Merge branch 'google:main' into main

.github/workflows/ci.yml

@@ -53,8 +53,8 @@ jobs:
       uses: conda-incubator/setup-miniconda@v2
       with:
         python-version: ${{ matrix.python-version }}
+        channels: conda-forge
         environment-file: ci${{ matrix.python-version}}.yml
-        use-only-tar-bz2: true # IMPORTANT: This needs to be set for caching to work properly!
         activate-environment: weather-tools
     - name: Check MetView's installation
       shell: bash -l {0}

ci3.8.yml

@@ -15,10 +15,11 @@ dependencies:
   - eccodes=2.27.0
   - requests=2.28.1
   - netcdf4=1.6.1
-  - rioxarray=0.12.2
+  - rioxarray=0.13.4
   - xarray-beam=0.3.1
   - ecmwf-api-client=1.6.3
-  - fsspec=2022.10.0
+  - fsspec=2022.11.0
+  - gcsfs=2022.11.0
   - gdal=3.5.1
   - pyproj=3.4.0
   - geojson=2.5.0=py_0
@@ -28,8 +29,10 @@ dependencies:
   - pandas=1.5.1
   - pip=22.3
   - pygrib=2.1.4
+  - xarray==2023.1.0
+  - ruff==0.0.260
+  - google-cloud-sdk=410.0.0
+  - aria2=1.36.0
   - pip:
     - earthengine-api==0.1.329
-    - xarray==2023.1.0
-    - ruff==0.0.260
     - .[test]

ci3.9.yml

@@ -15,10 +15,11 @@ dependencies:
   - eccodes=2.27.0
   - requests=2.28.1
   - netcdf4=1.6.1
-  - rioxarray=0.12.2
+  - rioxarray=0.13.4
   - xarray-beam=0.3.1
   - ecmwf-api-client=1.6.3
-  - fsspec=2022.10.0
+  - fsspec=2022.11.0
+  - gcsfs=2022.11.0
   - gdal=3.5.1
   - pyproj=3.4.0
   - geojson=2.5.0=py_0
@@ -28,8 +29,10 @@ dependencies:
   - pandas=1.5.1
   - pip=22.3
   - pygrib=2.1.4
+  - google-cloud-sdk=410.0.0
+  - aria2=1.36.0
+  - xarray==2023.1.0
+  - ruff==0.0.260
   - pip:
     - earthengine-api==0.1.329
-    - xarray==2023.1.0
-    - ruff==0.0.260
     - .[test]

environment.yml

@@ -8,7 +8,7 @@ dependencies:
   - xarray=2023.1.0
   - fsspec=2022.11.0
   - gcsfs=2022.11.0
-  - rioxarray=0.12.2
+  - rioxarray=0.13.4
   - gdal=3.5.1
   - pyproj=3.4.0
   - cdsapi=0.5.1

weather_dl/download_pipeline/fetcher.py

@@ -48,6 +48,7 @@ class Fetcher(beam.DoFn):
     client_name: str
     manifest: Manifest = NoOpManifest(Location('noop://in-memory'))
     store: t.Optional[Store] = None
+    log_level: t.Optional[int] = logging.INFO
 
     def __post_init__(self):
         if self.store is None:
@@ -66,7 +67,7 @@ def fetch_data(self, config: Config, *, worker_name: str = 'default') -> None:
         if skip_partition(config, self.store, self.manifest):
             return
 
-        client = CLIENTS[self.client_name](config)
+        client = CLIENTS[self.client_name](config, self.log_level)
         target = prepare_target_name(config)
 
         with tempfile.NamedTemporaryFile() as temp:

weather_dl/download_pipeline/pipeline.py

@@ -125,7 +125,10 @@ def subsection_and_request(it: Config) -> t.Tuple[str, int]:
             (
                 partitions
                 | 'GroupBy Request Limits' >> beam.GroupBy(subsection_and_request)
-                | 'Fetch Data' >> beam.ParDo(Fetcher(args.client_name, args.manifest, args.store))
+                | 'Fetch Data' >> beam.ParDo(Fetcher(args.client_name,
+                                                     args.manifest,
+                                                     args.store,
+                                                     args.known_args.log_level))
             )
 
 
@@ -172,10 +175,12 @@ def run(argv: t.List[str], save_main_session: bool = True) -> PipelineArgs:
                         help="To enable file skipping logic in dry-run mode. Default: 'false'.")
     parser.add_argument('-u', '--update-manifest', action='store_true', default=False,
                         help="Update the manifest for the already downloaded shards and exit. Default: 'false'.")
+    parser.add_argument('--log-level', type=int, default=2,
+                      help='An integer to configure log level. Default: 2(INFO)')
 
     known_args, pipeline_args = parser.parse_known_args(argv[1:])
 
-    configure_logger(3)  # 0 = error, 1 = warn, 2 = info, 3 = debug
+    configure_logger(known_args.log_level)  # 0 = error, 1 = warn, 2 = info, 3 = debug
 
     configs = []
     for cfg in known_args.config:
@@ -223,7 +228,8 @@ def run(argv: t.List[str], save_main_session: bool = True) -> PipelineArgs:
         manifest = LocalManifest(Location(local_dir))
 
     num_requesters_per_key = known_args.num_requests_per_key
-    client = CLIENTS[client_name](configs[0])
+    known_args.log_level = 40 - known_args.log_level * 10
+    client = CLIENTS[client_name](configs[0], known_args.log_level)
     if num_requesters_per_key == -1:
         num_requesters_per_key = client.num_requests_per_key(config.dataset)
 

weather_dl/download_pipeline/pipeline_test.py

@@ -57,7 +57,8 @@
                                   partition_chunks=None,
                                   schedule='in-order',
                                   check_skip_in_dry_run=False,
-                                  update_manifest=False),
+                                  update_manifest=False,
+                                  log_level=20),
     pipeline_options=PipelineOptions('--save_main_session True'.split()),
     configs=[Config.from_dict(CONFIG)],
     client_name='cds',

weather_dl/download_pipeline/util.py

@@ -203,6 +203,8 @@ def download_with_aria2(url: str, path: str) -> None:
 
 def generate_hdate(date: str, subtract_year: str) -> str:
     """Generate a historical date by subtracting a specified number of years from the given date.
+    If input date is leap day (Feb 29), return Feb 28 even if target hdate is also a leap year.
+    This is expected in ECMWF API.
 
     Args:
         date (str): The input date in the format 'YYYY-MM-DD'.
@@ -213,6 +215,9 @@ def generate_hdate(date: str, subtract_year: str) -> str:
     """
     try:
         input_date = datetime.datetime.strptime(date, "%Y-%m-%d")
+        # Check for leap day
+        if input_date.month == 2 and input_date.day == 29:
+            input_date = input_date - datetime.timedelta(days=1)
         subtract_year = int(subtract_year)
     except (ValueError, TypeError):
         logger.error("Invalid input.")

weather_dl/download_pipeline/util_test.py

@@ -89,3 +89,13 @@ def test_valid_hdate(self):
         substract_year = '4'
         expected_result = '2016-01-02'
         self.assertEqual(generate_hdate(date, substract_year), expected_result)
+
+        # Also test for leap day correctness
+        date = '2020-02-29'
+        substract_year = '3'
+        expected_result = '2017-02-28'
+        self.assertEqual(generate_hdate(date, substract_year), expected_result)
+
+        substract_year = '4'
+        expected_result = '2016-02-28'
+        self.assertEqual(generate_hdate(date, substract_year), expected_result)

weather_mv/loader_pipeline/bq.py

@@ -39,7 +39,6 @@
 )
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
 
 DEFAULT_IMPORT_TIME = datetime.datetime.utcfromtimestamp(0).replace(tzinfo=datetime.timezone.utc).isoformat()
 DATA_IMPORT_TIME_COLUMN = 'data_import_time'

weather_mv/loader_pipeline/ee.py

@@ -40,7 +40,6 @@
 from .util import make_attrs_ee_compatible, RateLimit, validate_region
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
 
 COMPUTE_ENGINE_STR = 'Metadata-Flavor: Google'
 # For EE ingestion retry logic.

weather_mv/loader_pipeline/pipeline.py

@@ -111,6 +111,8 @@ def run(argv: t.List[str]) -> t.Tuple[argparse.Namespace, t.List[str]]:
                            'Default: `{"chunks": null, "consolidated": true}`.')
     base.add_argument('-d', '--dry-run', action='store_true', default=False,
                       help='Preview the weather-mv job. Default: off')
+    base.add_argument('--log-level', type=int, default=2,
+                      help='An integer to configure log level. Default: 2(INFO)')
 
     subparsers = parser.add_subparsers(help='help for subcommand', dest='subcommand')
 
@@ -131,7 +133,7 @@ def run(argv: t.List[str]) -> t.Tuple[argparse.Namespace, t.List[str]]:
 
     known_args, pipeline_args = parser.parse_known_args(argv[1:])
 
-    configure_logger(2)  # 0 = error, 1 = warn, 2 = info, 3 = debug
+    configure_logger(known_args.log_level)  # 0 = error, 1 = warn, 2 = info, 3 = debug
 
     # Validate Zarr arguments
     if known_args.uris.endswith('.zarr'):

weather_mv/loader_pipeline/pipeline_test.py

@@ -65,6 +65,7 @@ def setUp(self) -> None:
             'tif_metadata_for_datetime': None,
             'zarr': False,
             'zarr_kwargs': {},
+            'log_level': 2,
         }
 
 
@@ -74,6 +75,10 @@ def test_dry_runs_are_allowed(self):
         known_args, _ = run(self.base_cli_args + '--dry-run'.split())
         self.assertEqual(known_args.dry_run, True)
 
+    def test_log_level_arg(self):
+        known_args, _ = run(self.base_cli_args + '--log-level 3'.split())
+        self.assertEqual(known_args.log_level, 3)
+
     def test_tif_metadata_for_datetime_raise_error_for_non_tif_file(self):
         with self.assertRaisesRegex(RuntimeError, 'can be specified only for tif files.'):
             run(self.base_cli_args + '--tif_metadata_for_datetime start_time'.split())

weather_mv/loader_pipeline/regrid.py

@@ -31,7 +31,6 @@
 from .sinks import ToDataSink, open_local, copy
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
 
 try:
     import metview as mv

weather_mv/loader_pipeline/sinks.py

@@ -30,6 +30,7 @@
 import cfgrib
 import numpy as np
 import rasterio
+import rioxarray
 import xarray as xr
 from apache_beam.io.filesystem import CompressionTypes, FileSystem, CompressedFile, DEFAULT_READ_BUFFER_SIZE
 from pyproj import Transformer
@@ -40,7 +41,6 @@
 DEFAULT_TIME_ORDER_LIST = ['%Y', '%m', '%d', '%H', '%M', '%S']
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
 
 
 class KwargsFactoryMixin:
@@ -145,14 +145,9 @@ def _preprocess_tif(ds: xr.Dataset, filename: str, tif_metadata_for_datetime: st
     This also retrieves datetime from tif's metadata and stores it into dataset.
     """
 
-    def _get_band_data(i):
-        if not band_names_dict:
-            band = ds.band_data[i]
-            band.name = ds.band_data.attrs['long_name'][i]
-        else:
-            band = ds.band_data
-            band.name = band_names_dict.get(band.name)
-        return band
+    def _replace_dataarray_names_with_long_names(ds: xr.Dataset):
+        rename_dict = {var_name: ds[var_name].attrs.get('long_name', var_name) for var_name in ds.variables}
+        return ds.rename(rename_dict)
 
     y, x = np.meshgrid(ds['y'], ds['x'])
     transformer = Transformer.from_crs(ds.spatial_ref.crs_wkt, TIF_TRANSFORM_CRS_TO, always_xy=True)
@@ -162,14 +157,9 @@ def _get_band_data(i):
     ds['x'] = lon[:, 0]
     ds = ds.rename({'y': 'latitude', 'x': 'longitude'})
 
-    band_length = len(ds.band)
-    ds = ds.squeeze().drop_vars('band').drop_vars('spatial_ref')
-
-    band_data_list = [_get_band_data(i) for i in range(band_length)]
+    ds = ds.squeeze().drop_vars('spatial_ref')
 
-    ds_is_normalized_attr = ds.attrs['is_normalized']
-    ds = xr.merge(band_data_list)
-    ds.attrs['is_normalized'] = ds_is_normalized_attr
+    ds = _replace_dataarray_names_with_long_names(ds)
 
     end_time = None
     if initialization_time_regex and forecast_time_regex:
@@ -184,17 +174,15 @@ def _get_band_data(i):
         ds.attrs['start_time'] = start_time
         ds.attrs['end_time'] = end_time
 
-    # TODO(#159): Explore ways to capture required metadata using xarray.
-    with rasterio.open(filename) as f:
-        datetime_value_ms = None
-        try:
-            datetime_value_s = (int(end_time.timestamp()) if end_time is not None
-                                else int(f.tags()[tif_metadata_for_datetime]) / 1000.0)
-            ds = ds.assign_coords({'time': datetime.datetime.utcfromtimestamp(datetime_value_s)})
-        except KeyError:
-            raise RuntimeError(f"Invalid datetime metadata of tif: {tif_metadata_for_datetime}.")
-        except ValueError:
-            raise RuntimeError(f"Invalid datetime value in tif's metadata: {datetime_value_ms}.")
+    datetime_value_ms = None
+    try:
+        datetime_value_s = (int(end_time.timestamp()) if end_time is not None
+                        else int(ds.attrs[tif_metadata_for_datetime]) / 1000.0)
+        ds = ds.assign_coords({'time': datetime.datetime.utcfromtimestamp(datetime_value_s)})
+    except KeyError:
+        raise RuntimeError(f"Invalid datetime metadata of tif: {tif_metadata_for_datetime}.")
+    except ValueError:
+        raise RuntimeError(f"Invalid datetime value in tif's metadata: {datetime_value_ms}.")
 
     return ds
 
@@ -306,7 +294,7 @@ def __open_dataset_file(filename: str,
 
     # If URI extension is .tif, try opening file by specifying engine="rasterio".
     if uri_extension in ['.tif', '.tiff']:
-        return _add_is_normalized_attr(xr.open_dataset(filename, engine='rasterio'), False)
+        return _add_is_normalized_attr(rioxarray.open_rasterio(filename, band_as_variable=True), False)
 
     # If no open kwargs are available and URI extension is other than tif, make educated guesses about the dataset.
     try:
@@ -408,9 +396,12 @@ def open_dataset(uri: str,
                                              forecast_time_regex)
 
             # Extracting dtype, crs and transform from the dataset & storing them as attributes.
-            with rasterio.open(local_path, 'r') as f:
-                dtype, crs, transform = (f.profile.get(key) for key in ['dtype', 'crs', 'transform'])
-                xr_dataset.attrs.update({'dtype': dtype, 'crs': crs, 'transform': transform})
+            try:
+                with rasterio.open(local_path, 'r') as f:
+                    dtype, crs, transform = (f.profile.get(key) for key in ['dtype', 'crs', 'transform'])
+                    xr_dataset.attrs.update({'dtype': dtype, 'crs': crs, 'transform': transform})
+            except rasterio.errors.RasterioIOError:
+                logger.warning('Cannot parse projection and data type information for Dataset %r.', uri)
 
             logger.info(f'opened dataset size: {xr_dataset.nbytes}')
 

weather_mv/loader_pipeline/streaming.py

@@ -28,9 +28,7 @@
 import apache_beam as beam
 from apache_beam.transforms.window import FixedWindows
 
-logging.getLogger().setLevel(logging.INFO)
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
 
 
 class GroupMessagesByFixedWindows(beam.PTransform):

weather_mv/loader_pipeline/util.py

@@ -41,7 +41,6 @@
 from .sinks import DEFAULT_COORD_KEYS
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
 
 CANARY_BUCKET_NAME = 'anthromet_canary_bucket'
 CANARY_RECORD = {'foo': 'bar'}
@@ -118,6 +117,8 @@ def to_json_serializable_type(value: t.Any) -> t.Any:
 
         # We assume here that naive timestamps are in UTC timezone.
         return value.replace(tzinfo=datetime.timezone.utc).isoformat()
+    elif isinstance(value, datetime.timedelta):
+        return value.total_seconds()
     elif isinstance(value, np.timedelta64):
         # Return time delta in seconds.
         return float(value / np.timedelta64(1, 's'))

weather_mv/loader_pipeline/util_test.py

@@ -14,7 +14,7 @@
 import itertools
 import unittest
 from collections import Counter
-from datetime import datetime, timezone
+from datetime import datetime, timezone, timedelta
 
 import xarray
 import xarray as xr
@@ -240,3 +240,6 @@ def test_to_json_serializable_type_datetime(self):
         self.assertEqual(self._convert(np.datetime64(1, 'Y')), '1971-01-01T00:00:00+00:00')
         self.assertEqual(self._convert(np.datetime64(30, 'Y')), input_date)
         self.assertEqual(self._convert(np.timedelta64(1, 'm')), float(60))
+        self.assertEqual(self._convert(timedelta(seconds=1)), float(1))
+        self.assertEqual(self._convert(timedelta(minutes=1)), float(60))
+        self.assertEqual(self._convert(timedelta(days=1)), float(86400))

weather_mv/setup.py

@@ -49,7 +49,7 @@
     "netcdf4==1.6.1",
     "geojson==2.5.0",
     "simplejson==3.17.6",
-    "rioxarray==0.12.2",
+    "rioxarray==0.13.4",
     "metview==1.13.1",
     "rasterio==1.3.1",
     "earthengine-api>=0.1.263",
@@ -62,7 +62,7 @@
     packages=find_packages(),
     author='Anthromets',
     author_email='anthromets-ecmwf@google.com',
-    version='0.2.14',
+    version='0.2.15',
     url='https://weather-tools.readthedocs.io/en/latest/weather_mv/',
     description='A tool to load weather data into BigQuery.',
     install_requires=beam_gcp_requirements + base_requirements,