[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

notebooks/2021-08/2021-08-25/video.py

@@ -68,7 +68,6 @@
 channel_indexes = [1, 8, 9]
 satellite_data = []
 for channel_index in channel_indexes:
-
     # renormalize
     satellite_data.append(
         data["sat_data"][batch_index, :, :, :, channel_index] * SAT_STD.values[channel_index]

notebooks/2021-08/2021-08-26/video.py

@@ -88,7 +88,6 @@
 channel_indexes = [1, 9, 8]
 satellite_data = []
 for channel_index in channel_indexes:
-
     # renormalize
     satellite_data.append(
         data["sat_data"][batch_index, :, :, :, channel_index] * SAT_STD.values[channel_index]

notebooks/2021-09/2021-09-13/remove_hash.py

@@ -20,7 +20,6 @@
 
 for filenames in [train_filenames, validation_filenames]:
     for file in train_filenames:
-
         print(file)
 
         filename = file.split("/")[-1]

notebooks/2021-09/2021-09-14/gsp_centroid.py

@@ -15,7 +15,6 @@
 
 # for index in range(0, len(shape_data_raw)):
 for index in range(140, 150):
-
     # just select the first one
     shape_data = shape_data_raw.iloc[index : index + 1]
     shapes_dict = json.loads(shape_data["geometry"].to_json())

notebooks/2021-09/2021-09-14/gsp_duplicated.py

@@ -12,7 +12,6 @@
 duplicated_raw["Amount"] = range(0, len(duplicated_raw))
 
 for i in range(0, 8, 2):
-
     # just select the first one
     duplicated = duplicated_raw.iloc[i : i + 2]
     shapes_dict = json.loads(duplicated["geometry"].to_json())

notebooks/2021-09/2021-09-29/gsp_duplicated.py

@@ -14,7 +14,6 @@
 duplicated_raw["Amount"] = range(0, len(duplicated_raw))
 
 for i in range(0, 8, 2):
-
     # just select the first one
     duplicated = duplicated_raw.iloc[i : i + 2]
     shapes_dict = json.loads(duplicated["geometry"].to_json())

notebooks/2021-09/2021-09-29/video.py

@@ -41,7 +41,6 @@
 
 
 def get_trace(dt):
-
     # plot to check it looks right
     return go.Choroplethmapbox(
         geojson=shapes_dict,
@@ -54,7 +53,6 @@ def get_trace(dt):
 
 
 def get_frame(dt):
-
     # plot to check it looks right
     return go.Choroplethmapbox(
         z=gps_data[dt],

notebooks/2021-10/2021-10-01/pydantic.py

@@ -11,7 +11,6 @@
 
 
 class Satellite(BaseModel):
-
     # width: int = Field(..., g=0, description="The width of the satellite image")
     # height: int = Field(..., g=0, description="The width of the satellite image")
     # num_channels: int = Field(..., g=0, description="The width of the satellite image")
@@ -49,7 +48,6 @@ class Config:
 
 
 class Batch(BaseModel):
-
     batch_size: int = Field(
         ...,
         g=0,

notebooks/2021-10/2021-10-08/xr_compression.py

@@ -9,7 +9,6 @@
 def get_satellite_xrarray_data_array(
     batch_size, seq_length_5, satellite_image_size_pixels, number_sat_channels=10
 ):
-
     r = np.random.randn(
         # self.batch_size,
         seq_length_5,

notebooks/2021-10/2021-10-08/xr_pydantic.py

@@ -25,7 +25,6 @@ def v_image_data(cls, v):
 
 
 class Batch(BaseModel):
-
     batch_size: int = 0
     satellite: Satellite
 

nowcasting_dataset/data_sources/data_source.py

@@ -82,7 +82,6 @@ def __post_init__(self):
     def _get_start_dt(
         self, t0_datetime_utc: Union[pd.Timestamp, pd.DatetimeIndex]
     ) -> Union[pd.Timestamp, pd.DatetimeIndex]:
-
         return t0_datetime_utc - self.history_duration
 
     def _get_end_dt(

nowcasting_dataset/data_sources/fake/batch.py

@@ -504,7 +504,6 @@ def topographic_fake(
     # make batch of arrays
     xr_arrays = []
     for i in range(batch_size):
-
         x, y = make_image_coords_osgb(
             size_x=image_size_pixels_width,
             size_y=image_size_pixels_height,

nowcasting_dataset/data_sources/gsp/eso.py

@@ -164,7 +164,6 @@ def get_gsp_shape_from_eso(
             shape_gpd["RegionID"] = range(1, len(shape_gpd) + 1)
 
     if save_local_file:
-
         # rename the columns to less than 10 characters
         shape_gpd_to_save = shape_gpd.copy()
         shape_gpd_to_save.rename(columns=rename_save_columns, inplace=True)

nowcasting_dataset/data_sources/gsp/gsp_data_source.py

@@ -173,7 +173,6 @@ def get_all_locations(self, t0_datetimes_utc: pd.DatetimeIndex) -> List[SpaceTim
         if total_gsp_nan_count > 0:
             assert Exception("There are nans in the GSP data. Can't get locations for all GSPs")
         else:
-
             t0_datetimes_utc.name = "t0_datetime_utc"
 
             # get all locations
@@ -236,7 +235,6 @@ def get_locations(self, t0_datetimes_utc: pd.DatetimeIndex) -> List[SpaceTimeLoc
 
         total_gsp_nan_count = self.gsp_power.isna().sum().sum()
         if total_gsp_nan_count == 0:
-
             # get random GSP metadata
             indexes = sorted(
                 list(self.rng.integers(low=0, high=len(self.metadata), size=len(t0_datetimes_utc)))
@@ -249,7 +247,6 @@ def get_locations(self, t0_datetimes_utc: pd.DatetimeIndex) -> List[SpaceTimeLoc
             ids = list(metadata.index)
 
         else:
-
             logger.warning(
                 "There are some nans in the gsp data, "
                 "so to get x,y locations we have to do a big loop"
@@ -262,7 +259,6 @@ def get_locations(self, t0_datetimes_utc: pd.DatetimeIndex) -> List[SpaceTimeLoc
             ids = []
 
             for t0_dt in t0_datetimes_utc:
-
                 # Choose start and end times
                 start_dt = self._get_start_dt(t0_dt)
                 end_dt = self._get_end_dt(t0_dt)
@@ -290,7 +286,6 @@ def get_locations(self, t0_datetimes_utc: pd.DatetimeIndex) -> List[SpaceTimeLoc
 
         locations = []
         for i in range(len(x_centers_osgb)):
-
             locations.append(
                 SpaceTimeLocation(
                     t0_datetime_utc=t0_datetimes_utc[i],

nowcasting_dataset/data_sources/gsp/pvlive.py

@@ -89,7 +89,6 @@ def load_pv_gsp_raw_data_from_pvlive(
     future_tasks = []
     with futures.ThreadPoolExecutor(max_workers=1) as executor:
         for gsp_id in gsp_ids:
-
             # set the first chunk start and end times
             start_chunk = first_start_chunk
             end_chunk = first_end_chunk

nowcasting_dataset/data_sources/metadata/metadata_model.py

@@ -114,7 +114,6 @@ def save_to_csv(self, path):
             metadata_df = pd.DataFrame(metadata_dict)
 
         else:
-
             metadata_df = pd.read_csv(filename)
 
             metadata_df_extra = pd.DataFrame(metadata_dict)

nowcasting_dataset/data_sources/pv/live.py

@@ -43,7 +43,6 @@ def get_metadata_from_database(providers: List[str] = None) -> pd.DataFrame:
 
     pv_system_all_df = []
     for provider in providers:
-
         logger.debug(f"Get PV systems from database for {provider}")
 
         with db_connection.get_session() as session:
@@ -136,7 +135,6 @@ def get_pv_power_from_database(
         logger.debug(f"Found {len(pv_yields_df)} pv yields")
 
     if len(pv_yields_df) == 0:
-
         data = create_empty_pv_data(end_utc=now, providers=providers, start_utc=start_utc)
 
         return data

nowcasting_dataset/data_sources/pv/pv_data_source.py

@@ -98,7 +98,6 @@ def get_data_model_for_batch():
         return PV
 
     def _load_metadata(self):
-
         logger.debug(f"Loading PV metadata from {self.files_groups}")
 
         # collect all metadata together
@@ -155,7 +154,6 @@ def _load_metadata(self):
         logger.debug(f"Found {len(pv_metadata)} pv systems")
 
     def _load_pv_power(self):
-
         logger.debug(f"Loading PV Power data from {self.files_groups}")
 
         if not self.is_live:
@@ -453,6 +451,7 @@ def get_locations(self, t0_datetimes_utc: pd.DatetimeIndex) -> List[SpaceTimeLoc
         Returns:  x_locations, y_locations. Each has one entry per t0_datetime.
             Locations are in OSGB coordinates.
         """
+
         # Set this up as a separate function, so we can cache the result!
         @functools.cache  # functools.cache requires Python >= 3.9
         def _get_pv_system_ids(t0_datetime: pd.Timestamp) -> pd.Int64Dtype:

nowcasting_dataset/data_sources/sun/raw_data_load_save.py

@@ -49,16 +49,13 @@ def get_azimuth_and_elevation(
     names = []
     # loop over locations and find azimuth and elevation angles,
     with futures.ThreadPoolExecutor() as executor:
-
         logger.debug("Setting up jobs")
 
         # Submit tasks to the executor.
         future_azimuth_and_elevation_per_location = []
         for i in tqdm(range(len(x_centers))):
-
             name = x_y_to_name(x_centers[i], y_centers[i])
             if name not in names:
-
                 lat, lon = geospatial.osgb_to_lat_lon(x=x_centers[i], y=y_centers[i])
 
                 future_azimuth_and_elevation = executor.submit(

nowcasting_dataset/data_sources/sun/sun_data_source.py

@@ -69,7 +69,6 @@ def get_example(self, location: SpaceTimeLocation) -> xr.Dataset:
         end_dt = self._get_end_dt(t0_datetime_utc)
 
         if not self.load_live:
-
             # The names of the columns get truncated when saving, therefore we need to look for the
             # name of the columns near the location we are looking for
             locations = np.array(
@@ -96,7 +95,6 @@ def get_example(self, location: SpaceTimeLocation) -> xr.Dataset:
             elevation = self.elevation.loc[start_dt:end_dt][name]
 
         else:
-
             latitude, longitude = osgb_to_lat_lon(x=x_center_osgb, y=y_center_osgb)
 
             datestamps = pd.date_range(start=start_dt, end=end_dt, freq="5T").tolist()
@@ -115,7 +113,6 @@ def get_example(self, location: SpaceTimeLocation) -> xr.Dataset:
         return sun
 
     def _load(self):
-
         logger.info(f"Loading Sun data from {self.zarr_path}")
 
         if not self.load_live:

nowcasting_dataset/dataset/batch.py

@@ -137,7 +137,6 @@ def load_netcdf(
 
             # loop over data sources
             for data_source_name in data_sources_names:
-
                 local_netcdf_filename = os.path.join(
                     local_netcdf_path, data_source_name, get_netcdf_filename(batch_idx)
                 )
@@ -193,7 +192,6 @@ def load_netcdf(
 
             # legacy NWP
             if "nwp" in batch_dict.keys():
-
                 nwp_rename_dict = {
                     "x_index": "x_osgb_index",
                     "y_index": "y_osgb_index",

nowcasting_dataset/dataset/split/method.py

@@ -85,7 +85,6 @@ def split_method(
         test_periods = unique_periods[unique_periods["modulo"].isin(test_indexes)]["period"]
 
     elif method == "random":
-
         # randomly sort indexes
         rng = np.random.default_rng(seed)
         unique_periods_in_dataset = rng.permutation(unique_periods_in_dataset)
@@ -108,7 +107,6 @@ def split_method(
         test_periods = pd.to_datetime(unique_periods_in_dataset[validation_test_split:])
 
     elif method == "specific":
-
         train_periods = unique_periods_in_dataset[
             unique_periods_in_dataset.isin(train_test_validation_specific.train)
         ]

nowcasting_dataset/filesystem/utils.py

@@ -90,7 +90,6 @@ def delete_all_files_in_temp_path(path: Union[Path, str], delete_dirs: bool = Fa
     else:
         # loop over folder structure, but only delete files
         for root, dirs, files in filesystem.walk(path):
-
             for f in files:
                 filesystem.rm(f"{root}/{f}")
 

nowcasting_dataset/manager/manager.py

@@ -273,15 +273,13 @@ def sample_spatial_and_temporal_locations_for_examples(
         shuffled_t0_datetimes = pd.DatetimeIndex(shuffled_t0_datetimes)
 
         if get_all_locations:
-
             # note that the returned 'shuffled_t0_datetimes'
             # has duplicate datetimes for each location
             locations = self.data_source_which_defines_geospatial_locations.get_all_locations(
                 t0_datetimes_utc=shuffled_t0_datetimes
             )
 
         else:
-
             locations = self.data_source_which_defines_geospatial_locations.get_locations(
                 shuffled_t0_datetimes
             )
@@ -404,7 +402,6 @@ def create_batches(self, overwrite_batches: bool) -> None:
                 for worker_id, (data_source_name, data_source) in enumerate(
                     self.data_sources.items()
                 ):
-
                     # Get indexes of first batch and example. And subset locations_for_split.
                     idx_of_first_batch = first_batches_to_create[split_name][data_source_name]
                     idx_of_first_example = idx_of_first_batch * self.config.process.batch_size

nowcasting_dataset/manager/manager_live.py

@@ -186,7 +186,6 @@ def create_batches(self, use_async: Optional[bool] = True) -> None:
             async_results_from_create_batches = []
             an_error_has_occured = multiprocessing.Event()
             for worker_id, (data_source_name, data_source) in enumerate(self.data_sources.items()):
-
                 # Get indexes of first batch and example. And subset locations_for_split.
                 idx_of_first_batch = 0
                 locations = locations_for_each_example
@@ -226,7 +225,6 @@ def create_batches(self, use_async: Optional[bool] = True) -> None:
                     # Sometimes when debuggin it is easy to use non async
                     data_source.create_batches(**kwargs_for_create_batches)
                 else:
-
                     async_result = pool.apply_async(
                         data_source.create_batches,
                         kwds=kwargs_for_create_batches,

nowcasting_dataset/utils.py

@@ -180,6 +180,7 @@ def shutdown(self, wait=True):
 
 def arg_logger(func):
     """A function decorator to log all the args and kwargs passed into a function."""
+
     # Adapted from https://stackoverflow.com/a/23983263/732596
     @wraps(func)
     def inner_func(*args, **kwargs):
@@ -191,6 +192,7 @@ def inner_func(*args, **kwargs):
 
 def exception_logger(func):
     """A function decorator to log exceptions thrown by the inner function."""
+
     # Adapted from
     # www.blog.pythonlibrary.org/2016/06/09/python-how-to-create-an-exception-logging-decorator
     @wraps(func)

scripts/generate_raw_data/get_raw_pv_gsp_data.py

@@ -48,21 +48,24 @@ def fetch_data():
     data_df = load_pv_gsp_raw_data_from_pvlive(start=start, end=end, normalize_data=False)
 
     # pivot to index as datetime_gmt, and columns as gsp_id
-    data_generation_df = data_df.pivot(index="datetime_gmt", columns="gsp_id", values="generation_mw")
-    data_installedcapacity_df = data_df.pivot(index="datetime_gmt", columns="gsp_id", values="installedcapacity_mwp")
+    data_generation_df = data_df.pivot(
+        index="datetime_gmt", columns="gsp_id", values="generation_mw"
+    )
+    data_installedcapacity_df = data_df.pivot(
+        index="datetime_gmt", columns="gsp_id", values="installedcapacity_mwp"
+    )
     data_capacity_df = data_df.pivot(index="datetime_gmt", columns="gsp_id", values="capacity_mwp")
-    data_updated_gmt_df = data_df.pivot(index="datetime_gmt", columns="gsp_id", values="updated_gmt")
+    data_updated_gmt_df = data_df.pivot(
+        index="datetime_gmt", columns="gsp_id", values="updated_gmt"
+    )
     data_xarray = xr.Dataset(
         data_vars={
             "generation_mw": (("datetime_gmt", "gsp_id"), data_generation_df),
             "installedcapacity_mwp": (("datetime_gmt", "gsp_id"), data_installedcapacity_df),
             "capacity_mwp": (("datetime_gmt", "gsp_id"), data_capacity_df),
             "updated_gmt": (("datetime_gmt", "gsp_id"), data_updated_gmt_df),
         },
-        coords={
-            "datetime_gmt": data_generation_df.index,
-            "gsp_id": data_generation_df.columns
-        },
+        coords={"datetime_gmt": data_generation_df.index, "gsp_id": data_generation_df.columns},
     )
 
     # save config to file
@@ -71,7 +74,8 @@ def fetch_data():
 
     # Make encoding
     encoding = {
-        var: {"compressor": numcodecs.Blosc(cname="zstd", clevel=5)} for var in data_xarray.data_vars
+        var: {"compressor": numcodecs.Blosc(cname="zstd", clevel=5)}
+        for var in data_xarray.data_vars
     }
 
     # save data to file