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+python-versions = ">=3.8,<3.12" +content-hash = "f98e0e3d8c4e16a1c467ca9c581bd27783682f52fa5a37a8a4fb520c549cf495" diff --git a/pyproject.toml b/pyproject.toml index 48667a8..08f5528 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "weather_provider_api" -version = "2.41.0" +version = "2.43" description = "Weather Provider Libraries and API" authors = ["Verbindingsteam", "Raoul Linnenbank <58594297+rflinnenbank@users.noreply.github.com>"] license = "MPL-2.0" @@ -11,13 +11,13 @@ include = [ ] [tool.poetry.dependencies] -python = ">=3.8,<3.11" -fastapi = "^0.95.1" +python = ">=3.8,<3.12" +fastapi = "^0.103.1" requests = "^2.28.1" geopy = "^2.3.0" -numpy = "^1.23.5" +numpy = "^1.24.4" structlog = "^23.1.0" -gunicorn = "^20.1.0" +gunicorn = "^21.2.0" eccodes = "^1.5.0" accept-types = "^0.4.1" lxml = "^4.9.1" @@ -25,13 +25,13 @@ starlette-prometheus = "^0.9.0" beautifulsoup4 = "^4.11.1" netcdf4 = "^1.6.2" tomli = "^2.0.1" -pandas = "1.5.3" -xarray = "^2022.12.0" +pandas = "2.0.3" +xarray = "^2022.8.0" cfgrib = "^0.9.10.3" uvicorn = "^0.21.1" slowapi = "^0.1.7" ecmwflibs = "0.5.1" -starlette = "^0.26.1" +starlette = "^0.27.0" [tool.poetry.group.dev.dependencies] pytest = "^7.2.0" @@ -47,9 +47,11 @@ pylint = "^2.15.10" wpla_update_era5sl = "weather_provider_api.scripts.update_era5sl_repository:main" wpla_update_era5land = "weather_provider_api.scripts.update_era5land_repository:main" wpla_update_arome = "weather_provider_api.scripts.update_arome_repository:main" +wpla_update_waarnemingen = "weather_provider_api.scripts.update_waarnemingen_register:main" wpla_clear_era5sl = "weather_provider_api.scripts.erase_era5sl_repository:main" wpla_clear_era5land = "weather_provider_api.scripts.erase_era5land_repository:main" wpla_clear_arome = "weather_provider_api.scripts.erase_arome_repository:main" +wpla_clear_waarnemingen = "weather_provider_api.scripts.erase_waarnemingen_register:main" wpla_run_api = "weather_provider_api.main:main" [tool.pylint] diff --git a/tests/test_knmi_actuele_waarnemingen.py b/tests/test_knmi_actuele_waarnemingen.py index a5a24f5..5c05c5a 100644 --- a/tests/test_knmi_actuele_waarnemingen.py +++ b/tests/test_knmi_actuele_waarnemingen.py @@ -13,6 +13,8 @@ import xarray as xr from weather_provider_api.routers.weather.sources.knmi.models.actuele_waarnemingen import ActueleWaarnemingenModel +from weather_provider_api.routers.weather.sources.knmi.utils import download_actuele_waarnemingen_weather, \ + _retrieve_observation_moment from weather_provider_api.routers.weather.utils.geo_position import GeoPosition @@ -42,20 +44,16 @@ def test_get_weather(mock_coordinates, start, end): @pytest.mark.skip(reason="Test currently not working via Tox on GitHub Actions") def test__retrieve_observation_date(): # Test to verify error handling - aw_model = ActueleWaarnemingenModel() - current_locale = locale.getlocale(locale.LC_TIME) locale.setlocale(locale.LC_TIME, "dutch") - assert aw_model.retrieve_observation_moment(None).date() == datetime.now().date() # System now + assert _retrieve_observation_moment(None).date() == datetime.now().date() # System now locale.setlocale(locale.LC_TIME, current_locale) def test__download_weather(monkeypatch): # Test to verify error handling - aw_model = ActueleWaarnemingenModel() - def mock_request_get(_, *args, **kwargs): raise requests.exceptions.BaseHTTPError("Fake BaseHTTP Error!") monkeypatch.setattr(requests, "get", mock_request_get) - assert aw_model._download_weather() is None + assert download_actuele_waarnemingen_weather() is None diff --git a/weather_provider_api/app_config.py b/weather_provider_api/app_config.py index 0ef7a1f..c29a733 100644 --- a/weather_provider_api/app_config.py +++ b/weather_provider_api/app_config.py @@ -20,9 +20,13 @@ class BaseConfig(object): """ APP_NAME = os.environ.get("APP_NAME", __name__) - APP_DESCRIPTION = os.environ.get("APP_DESCRIPTION", """Alliander Weather Provider API""") + APP_DESCRIPTION = os.environ.get( + "APP_DESCRIPTION", """Alliander Weather Provider API""" + ) APP_MAINTAINER = os.environ.get("APP_MAINTAINER", "DNB/ST Innovatieteam") - APP_MAINTAINER_EMAIL = os.environ.get("APP_MAINTAINER_EMAIL", "weather.provider@alliander.com") + APP_MAINTAINER_EMAIL = os.environ.get( + "APP_MAINTAINER_EMAIL", "weather.provider@alliander.com" + ) SHOW_MAINTAINER = os.environ.get("SHOW_MAINTAINER", False) default_version = None @@ -51,7 +55,9 @@ class BaseConfig(object): DEPLOYED = os.environ.get("DEPLOYED", False) DEBUG = os.environ.get("DEBUG", True) - REPO_FOLDER = os.environ.get("REPO_FOLDER", f"{tempfile.gettempdir()}/Weather_Repository") + REPO_FOLDER = os.environ.get( + "REPO_FOLDER", f"{tempfile.gettempdir()}/Weather_Repository" + ) class LocalConfig(BaseConfig): diff --git a/weather_provider_api/main.py b/weather_provider_api/main.py index 8579e2a..c258d9e 100644 --- a/weather_provider_api/main.py +++ b/weather_provider_api/main.py @@ -62,9 +62,9 @@ def redirect_to_docs(): return RedirectResponse(url=redirect_url) -logger.info(f"--------------------------------------", datetime=datetime.utcnow()) -logger.info(f"Finished booting; starting uvicorn...", datetime=datetime.utcnow()) -logger.info(f"--------------------------------------", datetime=datetime.utcnow()) +logger.info("--------------------------------------", datetime=datetime.utcnow()) +logger.info("Finished booting; starting uvicorn...", datetime=datetime.utcnow()) +logger.info("--------------------------------------", datetime=datetime.utcnow()) def main(): diff --git a/weather_provider_api/routers/weather/sources/knmi/client/actuele_waarnemingen_register_repository.py b/weather_provider_api/routers/weather/sources/knmi/client/actuele_waarnemingen_register_repository.py new file mode 100644 index 0000000..463dc15 --- /dev/null +++ b/weather_provider_api/routers/weather/sources/knmi/client/actuele_waarnemingen_register_repository.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# SPDX-FileCopyrightText: 2019-2023 Alliander N.V. +# SPDX-License-Identifier: MPL-2.0 + +""" This module houses the repository class for the Actuele Waarnemingen Register. """ +from datetime import datetime +from typing import List + +import structlog +import xarray as xr +from dateutil import tz +from dateutil.relativedelta import relativedelta + +from weather_provider_api.routers.weather.repository.repository import ( + WeatherRepositoryBase, +) +from weather_provider_api.routers.weather.sources.knmi.utils import ( + download_actuele_waarnemingen_weather, +) +from weather_provider_api.routers.weather.utils.geo_position import GeoPosition +from weather_provider_api.routers.weather.utils.grid_helpers import ( + round_coordinates_to_wgs84_grid, +) + + +class ActueleWaarnemingenRegisterRepository(WeatherRepositoryBase): + """ """ + + def _delete_files_outside_of_scope(self): + if self.filename.exists(): + current_data = xr.load_dataset(self.filename, engine="netcdf4") + current_data = current_data.sel( + time=slice(self.first_day_of_repo, self.last_day_of_repo) + ) + current_data.to_netcdf(self.filename, format="NETCDF4") + + def _get_file_list_for_period(self, start: datetime, end: datetime): + return self.filename + + def __init__(self): + # Pre-work + super().__init__() + + self.logger = structlog.get_logger(__name__) + self.repository_name = "KNMI Actuele Waarnemingen - 48 uur register" + + # Repository settings + self.file_prefix = "ACTUEEL48" + self.runtime_limit = 3 * 60 # Three minutes maximum runtime + self.permanent_suffixes = [] + self.filename = self.repository_folder / f"{self.file_prefix}_register.nc" + self.file_identifier_length = 0 + + self.logger.debug( + f"Initialized the [{self.repository_name}] repository", + datetime=datetime.utcnow(), + ) + + @property + def repository_sub_folder(self): + return "ACTUEEL48" # not using self.file_prefix, because that may not exist the first time using this + + def _get_repo_sub_folder(self): + return self.repository_sub_folder + + @property + def first_day_of_repo(self) -> datetime: + # Property to get the first moment of the repository as translated to the Dutch timezone. + from_zone = tz.gettz("UTC") + to_zone = tz.gettz("Europe/Amsterdam") + + first_day_of_repo = datetime.utcnow() - relativedelta(hours=48) # 48 hours + first_day_of_repo = first_day_of_repo.replace(tzinfo=from_zone) + first_day_of_repo = first_day_of_repo.astimezone(to_zone) + return first_day_of_repo.replace(tzinfo=None) + + @property + def last_day_of_repo(self) -> datetime: + # Property to get the last moment of the repository as translated to the Dutch timezone. + from_zone = tz.gettz("UTC") + to_zone = tz.gettz("Europe/Amsterdam") + + last_day_of_repo = datetime.utcnow() # Right now + last_day_of_repo = last_day_of_repo.replace(tzinfo=from_zone) + last_day_of_repo = last_day_of_repo.astimezone(to_zone) + + return last_day_of_repo.replace(tzinfo=None) + + def update(self): + """Implementation of the WeatherRepository update method + + Attempts to update the repository with the current data. For this repository, that means getting the current + Actuele Waarnemingen output and storing it, while removing any data not within the scope of the repository. + + Returns: + Nothing. + + """ + raw_weather_ds = download_actuele_waarnemingen_weather() + time = datetime.utcnow().replace(second=0, microsecond=0) + + # Cleanup any old data + self.cleanup() + + # Update the file + self._update_file_with_new_data(new_data_ds=raw_weather_ds, update_moment=time) + + def _update_file_with_new_data( + self, new_data_ds: xr.Dataset, update_moment: datetime + ): + """This method updates any existing data file with new data or creates a new from scratch if needed, using the + given dataset. + + Args: + new_data_ds (xr.Dataset): A Xarray Dataset holding the data to append / create the data file with. + update_moment (datetime): A datetime holding the moment of update. Note that this doesn't need to be the + moment that the data is from. + + Returns: + Nothing. The file is just updated. + + """ + # Opening the file: + if self.filename.exists(): + stored_data_ds = xr.load_dataset(self.filename, engine="netcdf4") + else: + stored_data_ds = None + + self.logger.info( + f"Storing data for: {update_moment.strftime('%m-%d-%Y %H:%M:%S')} " + f"[{new_data_ds.isel(STN=0, time=0)['time'].values}]" + ) + if stored_data_ds is None: + new_data_ds.to_netcdf(self.filename, format="NETCDF4") + else: + # Check if time not already in system + try: + new_stored_data_ds = xr.merge([new_data_ds, stored_data_ds]) + + new_stored_data_ds.to_netcdf(self.filename, format="NETCDF4") + except ValueError as value_error: + self.logger.warning(f"Could not update file: {value_error}") + + def get_24_hour_registry_for_station(self, station: int) -> xr.Dataset: + """This method obtains the last 24 hours of data of Actuele Waarnemingen and returns it for single station. + + Args: + station (int): An integer representing the station to gather data for + + Returns: + xr.Dataset: A Xarray Dataset holding last 24 hours of data for the requested station + + """ + stored_data_ds = xr.load_dataset(self.filename, engine="netcdf4") + return stored_data_ds.sel( + STN=station, + time=slice( + self.first_day_of_repo + relativedelta(days=1), self.last_day_of_repo + ), + ) + + def get_48_hour_registry_for_station(self, station: int) -> xr.Dataset: + """This method obtains the last 48 hours of data of Actuele Waarnemingen and returns it for single station. + + Args: + station (int): An integer representing the station to gather data for + + Returns: + xr.Dataset: A Xarray Dataset holding last 48 hours of data for the requested station + + """ + stored_data_ds = xr.load_dataset(self.filename, engine="netcdf4") + stored_data_ds = stored_data_ds.sel( + STN=station, + time=slice(self.first_day_of_repo, self.last_day_of_repo), + ) + return stored_data_ds + + def get_grid_coordinates(self, coordinates: List[GeoPosition]) -> List[GeoPosition]: + """Rounds a list of GeoPositions to the resolution set through grid_resolution""" + return round_coordinates_to_wgs84_grid(coordinates, (0.023, 0.037), (49, 0)) diff --git a/weather_provider_api/routers/weather/sources/knmi/client/arome_repository.py b/weather_provider_api/routers/weather/sources/knmi/client/arome_repository.py index 91cb2c4..36c7234 100644 --- a/weather_provider_api/routers/weather/sources/knmi/client/arome_repository.py +++ b/weather_provider_api/routers/weather/sources/knmi/client/arome_repository.py @@ -20,11 +20,18 @@ import xarray as xr from dateutil.relativedelta import relativedelta -from weather_provider_api.routers.weather.repository.repository import RepositoryUpdateResult, WeatherRepositoryBase -from weather_provider_api.routers.weather.sources.knmi.client.knmi_downloader import KNMIDownloader +from weather_provider_api.routers.weather.repository.repository import ( + RepositoryUpdateResult, + WeatherRepositoryBase, +) +from weather_provider_api.routers.weather.sources.knmi.client.knmi_downloader import ( + KNMIDownloader, +) from weather_provider_api.routers.weather.sources.knmi.knmi_factors import arome_factors from weather_provider_api.routers.weather.utils.geo_position import GeoPosition -from weather_provider_api.routers.weather.utils.grid_helpers import round_coordinates_to_wgs84_grid +from weather_provider_api.routers.weather.utils.grid_helpers import ( + round_coordinates_to_wgs84_grid, +) class HarmonieAromeRepository(WeatherRepositoryBase): @@ -36,7 +43,9 @@ def __init__(self): self.logger = structlog.get_logger(__name__) self.repository_name = "KNMI Harmonie (Arome)" - self.logger.debug(f"Initialized {self.repository_name} repository", datetime=datetime.utcnow()) + self.logger.debug( + f"Initialized {self.repository_name} repository", datetime=datetime.utcnow() + ) # Repository settings self.file_prefix = "AROME" @@ -66,13 +75,19 @@ def _get_repo_sub_folder(self): @property def first_day_of_repo(self): - first_day_of_repo = datetime.utcnow() - relativedelta(years=3) # Three years back - first_day_of_repo = first_day_of_repo.replace(day=1, hour=0, minute=0, second=0, microsecond=0) # Start of day + first_day_of_repo = datetime.utcnow() - relativedelta( + years=3 + ) # Three years back + first_day_of_repo = first_day_of_repo.replace( + day=1, hour=0, minute=0, second=0, microsecond=0 + ) # Start of day return first_day_of_repo @property def last_day_of_repo(self): - last_day_of_repo = datetime.utcnow().replace(minute=0, second=0, microsecond=0) # Start of today + last_day_of_repo = datetime.utcnow().replace( + minute=0, second=0, microsecond=0 + ) # Start of today return last_day_of_repo def update(self): @@ -84,8 +99,12 @@ def update(self): """ # Stop if cfgrib wasn't added into the system modules during boot. if "cfgrib" not in sys.modules: - self.logger.error('CANNOT PERFORM UPDATE: No valid "cfgrib" installation available.') - self.logger.info('Please properly install "cfgrib" and restart the system to enable updates.') + self.logger.error( + 'CANNOT PERFORM UPDATE: No valid "cfgrib" installation available.' + ) + self.logger.info( + 'Please properly install "cfgrib" and restart the system to enable updates.' + ) quit() # Cleanup the repository @@ -96,30 +115,51 @@ def update(self): # Configure initial settings for the update start_of_update = datetime.utcnow() no_of_items_processed = 0 - average_seconds_per_item = 1500 # Assuming 25 minutes of processing time per month for the first item - forced_end_of_update = start_of_update + relativedelta(seconds=self.runtime_limit) + average_seconds_per_item = ( + 1500 # Assuming 25 minutes of processing time per month for the first item + ) + forced_end_of_update = start_of_update + relativedelta( + seconds=self.runtime_limit + ) - self.logger.info(f"Update of [{self.repository_name}] started", datetime=datetime.utcnow()) + self.logger.info( + f"Update of [{self.repository_name}] started", datetime=datetime.utcnow() + ) self.logger.info(f"- A forced end time of [{forced_end_of_update}] was set.") prediction_to_evaluate = self.get_most_recent_prediction_moment download_folder = Path(tempfile.gettempdir()).joinpath(self.dataset_name) - knmi_downloader = KNMIDownloader(self.dataset_name, self.dataset_version, str(download_folder), True) - files_in_dataset = {item["filename"]: item["size"] for item in knmi_downloader.get_all_available_files()} + knmi_downloader = KNMIDownloader( + self.dataset_name, self.dataset_version, str(download_folder), True + ) + files_in_dataset = { + item["filename"]: item["size"] + for item in knmi_downloader.get_all_available_files() + } while prediction_to_evaluate >= self.first_day_of_repo: if no_of_items_processed != 0: - average_seconds_per_item = (datetime.utcnow() - start_of_update).total_seconds() / no_of_items_processed + average_seconds_per_item = ( + datetime.utcnow() - start_of_update + ).total_seconds() / no_of_items_processed - if forced_end_of_update < (datetime.utcnow() + relativedelta(seconds=average_seconds_per_item)): + if forced_end_of_update < ( + datetime.utcnow() + relativedelta(seconds=average_seconds_per_item) + ): self.logger.info( - f"- Not enough time left to update before [{forced_end_of_update}]", datetime=datetime.utcnow() + f"- Not enough time left to update before [{forced_end_of_update}]", + datetime=datetime.utcnow(), + ) + self.logger.info( + f"Update of [{self.repository_name}] ended...", + datetimee=datetime.utcnow(), ) - self.logger.info(f"Update of [{self.repository_name}] ended...", datetimee=datetime.utcnow()) return RepositoryUpdateResult.timed_out if not self._prediction_already_available(prediction_to_evaluate): - self.logger.debug(f"- Gathering the prediction for: {prediction_to_evaluate}") + self.logger.debug( + f"- Gathering the prediction for: {prediction_to_evaluate}" + ) file_to_download = ( f"harm40_v1_p1_{prediction_to_evaluate.year}" f"{str(prediction_to_evaluate.month).zfill(2)}" @@ -129,8 +169,12 @@ def update(self): if file_to_download in files_in_dataset.keys(): # File exists - knmi_downloader.download_specific_file(file_to_download, files_in_dataset[file_to_download]) - self._process_downloaded_file(download_folder, file_to_download, prediction_to_evaluate) + knmi_downloader.download_specific_file( + file_to_download, files_in_dataset[file_to_download] + ) + self._process_downloaded_file( + download_folder, file_to_download, prediction_to_evaluate + ) no_of_items_processed += 1 else: self.logger.info( @@ -138,11 +182,15 @@ def update(self): f"Moving on to the next file!" ) else: - self.logger.debug(f"The prediction for [{prediction_to_evaluate}] is already stored in the repository.") + self.logger.debug( + f"The prediction for [{prediction_to_evaluate}] is already stored in the repository." + ) prediction_to_evaluate -= relativedelta(hours=6) - self.logger.info(f"Update of [{self.repository_name}] ended...", datetime=datetime.utcnow()) + self.logger.info( + f"Update of [{self.repository_name}] ended...", datetime=datetime.utcnow() + ) return RepositoryUpdateResult.completed def _prediction_already_available(self, prediction_moment: datetime): @@ -155,35 +203,49 @@ def _prediction_already_available(self, prediction_moment: datetime): return False - def _process_downloaded_file(self, download_folder: Path, filename: str, prediction_time: datetime): + def _process_downloaded_file( + self, download_folder: Path, filename: str, prediction_time: datetime + ): """The function that processes a number of downloaded files into repository files.""" stage = "Started unpacking files.." try: self._unpack_downloaded_file(download_folder, filename) stage = "Files were unpacked" - self._convert_unpacked_data_to_netcdf4_files(download_folder, prediction_time) + self._convert_unpacked_data_to_netcdf4_files( + download_folder, prediction_time + ) stage = "Data was converted to NetCDF4" self._fuse_hourly_netcdf4_files(download_folder, prediction_time) stage = "NetCDF4 files were properly fused together" self._clear_temp_folder(download_folder) except Exception as e: - self.logger.warning(f"Processing did not get past stage: {stage}", datetime=datetime.utcnow()) self.logger.warning( - f"The downloaded data could not be properly downloaded: {e}", datetime=datetime.utcnow() + f"Processing did not get past stage: {stage}", + datetime=datetime.utcnow(), + ) + self.logger.warning( + f"The downloaded data could not be properly downloaded: {e}", + datetime=datetime.utcnow(), ) def _clear_temp_folder(self, download_folder: Path): """A function that cleans up the temporary download folder to prevent issues with partially written files.""" - self.logger.debug(f"Emptying the download folder: {download_folder}", datetime=datetime.utcnow()) + self.logger.debug( + f"Emptying the download folder: {download_folder}", + datetime=datetime.utcnow(), + ) for existing_file in glob.glob(f"{download_folder}*.*"): try: # Try to delete: Path(existing_file).unlink() except Exception as e: self.logger.info( - f"An error occurred while deleting the file [{existing_file}]: {e}", datetime=datetime.utcnow() + f"An error occurred while deleting the file [{existing_file}]: {e}", + datetime=datetime.utcnow(), + ) + self.logger.warning( + "There may be issues while updating using a file with an identical name..." ) - self.logger.warning("There may be issues while updating using a file with an identical name...") def _unpack_downloaded_file(self, download_folder: Path, file_name: str): """The function that unpacks downloaded files to prediction files.""" @@ -193,21 +255,32 @@ def _unpack_downloaded_file(self, download_folder: Path, file_name: str): tar_file.extractall(path=download_folder) tar_file.close() except Exception as e: - self.logger.error(f"The tarfile [{file_name}] could not be unpacked!", datetime=datetime.utcnow()) + self.logger.error( + f"The tarfile [{file_name}] could not be unpacked!", + datetime=datetime.utcnow(), + ) raise e - def _convert_unpacked_data_to_netcdf4_files(self, download_folder: Path, prediction_time: datetime): + def _convert_unpacked_data_to_netcdf4_files( + self, download_folder: Path, prediction_time: datetime + ): """This function converts any unpacked data files into NetCDF4 files""" try: import cfgrib - self.logger.debug(f"Import of cfgrib was successful") + self.logger.debug("Import of cfgrib was successful") except RuntimeError as e: - self.logger.error("CFGRIB was not properly installed. Cannot access GRIB files.") + self.logger.error( + "CFGRIB was not properly installed. Cannot access GRIB files." + ) raise e grib_files_available = glob.glob( - str(download_folder.joinpath(f'HA40_N25_{prediction_time.strftime("%Y%m%d%H")}00_*_GB')) + str( + download_folder.joinpath( + f'HA40_N25_{prediction_time.strftime("%Y%m%d%H")}00_*_GB' + ) + ) ) for grib_file in grib_files_available: @@ -226,7 +299,9 @@ def _convert_grib_file_to_NetCDF(self, grib_file: Path): Nothing """ - prediction_moment, predicted_hour = self._get_times_from_filename(grib_file.name) + prediction_moment, predicted_hour = self._get_times_from_filename( + grib_file.name + ) grib_filestream = cfgrib.FileStream(str(grib_file)) file_dataset = xr.Dataset() @@ -234,8 +309,13 @@ def _convert_grib_file_to_NetCDF(self, grib_file: Path): grib_message = item[1] # We skip the rotated grid data of the first file in each file-set and only process the regular_ll grids. if grib_message["gridType"] == "regular_ll": - field_name, message_dataset = self._process_grib_message_to_message_dataset( - grib_message=grib_message, prediction_moment=prediction_moment, predicted_hour=predicted_hour + ( + field_name, + message_dataset, + ) = self._process_grib_message_to_message_dataset( + grib_message=grib_message, + prediction_moment=prediction_moment, + predicted_hour=predicted_hour, ) if message_dataset: if not file_dataset: @@ -256,7 +336,10 @@ def _convert_grib_file_to_NetCDF(self, grib_file: Path): self.logger.info(f"Saved partial dataset as: {filename_to_save_to}") def _process_grib_message_to_message_dataset( - self, grib_message: cfgrib.Message, prediction_moment: datetime, predicted_hour: int + self, + grib_message: cfgrib.Message, + prediction_moment: datetime, + predicted_hour: int, ) -> (str, xr.Dataset): """ @@ -271,7 +354,9 @@ def _process_grib_message_to_message_dataset( """ # Gather the message's level type and the level it was set to - msg_level_type = "_".join(re.findall("[A-Z][^A-Z]*", grib_message["typeOfLevel"])).lower() + msg_level_type = "_".join( + re.findall("[A-Z][^A-Z]*", grib_message["typeOfLevel"]) + ).lower() msg_level = grib_message["level"] # Process the supported factors @@ -293,7 +378,9 @@ def _process_grib_message_to_message_dataset( # Prepare data for dataset creation field_name = field_name.strip() # Strip any excess spaces - lats, lons = self._build_lat_lon_grid(grib_message) # Get the dimensions of the message + lats, lons = self._build_lat_lon_grid( + grib_message + ) # Get the dimensions of the message field_values = np.reshape(grib_message["values"], len(lats) * len(lons)) data_dict = {field_name: (["time", "coord"], [field_values])} @@ -318,19 +405,27 @@ def get_most_recent_prediction_moment(self): A datetime holding the most recent available prediction moment """ - knmi_lag_time = 5 # There is a known 5-hour calculation lag to take into account + knmi_lag_time = ( + 5 # There is a known 5-hour calculation lag to take into account + ) # Determine the current CET time (the KNMI works from the Netherlands, and this project from UTC) time_cet = self.last_day_of_repo.astimezone(pytz.timezone("Europe/Amsterdam")) - time_cet += time_cet.utcoffset() # We add the UTC offset to change the actual values to those hours + time_cet += ( + time_cet.utcoffset() + ) # We add the UTC offset to change the actual values to those hours time_cet = time_cet.replace(tzinfo=None, microsecond=0) # Finish formatting time_cet -= relativedelta(hours=knmi_lag_time) # Subtract the KMNI lag - rounded_hour = (time_cet.hour // 6) * 6 # Get the nearest preceding hours divisible by six + rounded_hour = ( + time_cet.hour // 6 + ) * 6 # Get the nearest preceding hours divisible by six time_cet = time_cet.replace(hour=rounded_hour, minute=0, second=0) return time_cet - def _fuse_hourly_netcdf4_files(self, download_folder: Path, prediction_moment: datetime): + def _fuse_hourly_netcdf4_files( + self, download_folder: Path, prediction_moment: datetime + ): """ Args: @@ -341,7 +436,9 @@ def _fuse_hourly_netcdf4_files(self, download_folder: Path, prediction_moment: d """ self.logger.info(f"Starting merge of all files for: {prediction_moment}") - existing_prediction_files = sorted(glob.glob(str(Path(download_folder).joinpath(f"{self.file_prefix}*.nc")))) + existing_prediction_files = sorted( + glob.glob(str(Path(download_folder).joinpath(f"{self.file_prefix}*.nc"))) + ) fused_dataset = None @@ -359,12 +456,18 @@ def _fuse_hourly_netcdf4_files(self, download_folder: Path, prediction_moment: d f"{str(prediction_moment.day).zfill(2)}_{str(prediction_moment.hour).zfill(2)}00.nc" ) - self.logger.debug(f"- Saving prediction [{prediction_moment}] to: {filename_to_save_to}") + self.logger.debug( + f"- Saving prediction [{prediction_moment}] to: {filename_to_save_to}" + ) encoding = {v: {"zlib": True, "complevel": 4} for v in fused_dataset.variables} - fused_dataset.to_netcdf(filename_to_save_to, format="NETCDF4", engine="netcdf4", encoding=encoding) + fused_dataset.to_netcdf( + filename_to_save_to, format="NETCDF4", engine="netcdf4", encoding=encoding + ) - def _build_lat_lon_grid(self, grib_message: cfgrib.Message) -> Tuple[List[float], List[float]]: + def _build_lat_lon_grid( + self, grib_message: cfgrib.Message + ) -> Tuple[List[float], List[float]]: """This function uses an existing GRIB file to extract the dimensions of the 'regular_ll' grid and format those into a list of latitudes and a list of longitudes that together make up the grid. @@ -391,12 +494,16 @@ def _build_lat_lon_grid(self, grib_message: cfgrib.Message) -> Tuple[List[float] # Build the lists latitudes = list( range( # We add the step-value to the max-value to include it itself. - int(minimum_latitude * 1_000), int(maximum_latitude * 1_000 + step_for_latitude), step_for_latitude + int(minimum_latitude * 1_000), + int(maximum_latitude * 1_000 + step_for_latitude), + step_for_latitude, ) ) longitudes = list( range( # We add the step-value to the max-value to include it itself. - int(minimum_longitude * 1_000), int(maximum_longitude * 1_000 + step_for_longitude), step_for_longitude + int(minimum_longitude * 1_000), + int(maximum_longitude * 1_000 + step_for_longitude), + step_for_longitude, ) ) @@ -424,13 +531,19 @@ def _get_times_from_filename(self, filename: str) -> Tuple[datetime, int]: predicted_hour = int(filename[-7:-5]) except Exception as e: - raise ValueError(f"Could not properly obtain the prediction from the title of the file: {filename} [{e}]") + raise ValueError( + f"Could not properly obtain the prediction from the title of the file: {filename} [{e}]" + ) prediction_moment = datetime( - year=prediction_year, month=prediction_month, day=prediction_day, hour=prediction_hour + year=prediction_year, + month=prediction_month, + day=prediction_day, + hour=prediction_hour, ) self.logger.debug( - f"File [{filename}] was parsed to prediction moment and hour:" f" [{prediction_moment}],[{predicted_hour}]" + f"File [{filename}] was parsed to prediction moment and hour:" + f" [{prediction_moment}],[{predicted_hour}]" ) return prediction_moment, predicted_hour @@ -447,25 +560,51 @@ def _delete_files_outside_of_scope(self): """ - counter_until_date_in_filename = len(str(self.repository_folder.joinpath(self.file_prefix))) + 1 + counter_until_date_in_filename = ( + len(str(self.repository_folder.joinpath(self.file_prefix))) + 1 + ) - for file_name in glob.glob(f"{self.repository_folder.joinpath(self.file_prefix)}*.nc"): + for file_name in glob.glob( + f"{self.repository_folder.joinpath(self.file_prefix)}*.nc" + ): file_date = datetime( - year=int(file_name[counter_until_date_in_filename : counter_until_date_in_filename + 4]), - month=int(file_name[counter_until_date_in_filename + 4 : counter_until_date_in_filename + 6]), - day=int(file_name[counter_until_date_in_filename + 6 : counter_until_date_in_filename + 8]), + year=int( + file_name[ + counter_until_date_in_filename : counter_until_date_in_filename + + 4 + ] + ), + month=int( + file_name[ + counter_until_date_in_filename + + 4 : counter_until_date_in_filename + + 6 + ] + ), + day=int( + file_name[ + counter_until_date_in_filename + + 6 : counter_until_date_in_filename + + 8 + ] + ), hour=0, minute=0, second=0, microsecond=0, ).date() - if file_date < self.first_day_of_repo.date() or file_date > self.last_day_of_repo.date(): + if ( + file_date < self.first_day_of_repo.date() + or file_date > self.last_day_of_repo.date() + ): self.logger.debug( f"The current scope is [{self.first_day_of_repo.date()}-{self.last_day_of_repo.date()}]", datetime=datetime.utcnow(), ) - self.logger.debug(f"The file [{file_name}] resolved to [{file_date}], which lies out of scope.") + self.logger.debug( + f"The file [{file_name}] resolved to [{file_date}], which lies out of scope." + ) self.logger.debug(f"The file [{file_name}] will therefor be deleted.") self._safely_delete_file(file_name) @@ -483,16 +622,37 @@ def _get_file_list_for_period(self, start: datetime, end: datetime) -> List[Path """ self.logger.debug(f"Finding files for the timeframe: [({start})-({end})]") self.logger.debug(f"Searching folder: [{self.repository_folder}]") - counter_until_date_in_filename = len(str(self.repository_folder.joinpath(self.file_prefix))) + 1 + counter_until_date_in_filename = ( + len(str(self.repository_folder.joinpath(self.file_prefix))) + 1 + ) - list_of_all_netcdf4_files_in_repo = glob.glob(str(self.repository_folder.joinpath(f"{self.file_prefix}*.nc"))) + list_of_all_netcdf4_files_in_repo = glob.glob( + str(self.repository_folder.joinpath(f"{self.file_prefix}*.nc")) + ) list_of_files_in_timeframe = [] for file_name in list_of_all_netcdf4_files_in_repo: file_date = datetime( - year=int(file_name[counter_until_date_in_filename : counter_until_date_in_filename + 4]), - month=int(file_name[counter_until_date_in_filename + 4 : counter_until_date_in_filename + 6]), - day=int(file_name[counter_until_date_in_filename + 6 : counter_until_date_in_filename + 8]), + year=int( + file_name[ + counter_until_date_in_filename : counter_until_date_in_filename + + 4 + ] + ), + month=int( + file_name[ + counter_until_date_in_filename + + 4 : counter_until_date_in_filename + + 6 + ] + ), + day=int( + file_name[ + counter_until_date_in_filename + + 6 : counter_until_date_in_filename + + 8 + ] + ), hour=0, minute=0, second=0, diff --git a/weather_provider_api/routers/weather/sources/knmi/knmi.py b/weather_provider_api/routers/weather/sources/knmi/knmi.py index 137b42a..f3fd8fd 100644 --- a/weather_provider_api/routers/weather/sources/knmi/knmi.py +++ b/weather_provider_api/routers/weather/sources/knmi/knmi.py @@ -5,11 +5,22 @@ # SPDX-License-Identifier: MPL-2.0 from weather_provider_api.routers.weather.base_models.source import WeatherSourceBase -from weather_provider_api.routers.weather.sources.knmi.models.actuele_waarnemingen import ActueleWaarnemingenModel -from weather_provider_api.routers.weather.sources.knmi.models.daggegevens import DagGegevensModel -from weather_provider_api.routers.weather.sources.knmi.models.harmonie_arome import HarmonieAromeModel +from weather_provider_api.routers.weather.sources.knmi.models.actuele_waarnemingen import ( + ActueleWaarnemingenModel, +) +from weather_provider_api.routers.weather.sources.knmi.models.actuele_waarnemingen_register import ( + ActueleWaarnemingenRegisterModel, +) +from weather_provider_api.routers.weather.sources.knmi.models.daggegevens import ( + DagGegevensModel, +) +from weather_provider_api.routers.weather.sources.knmi.models.harmonie_arome import ( + HarmonieAromeModel, +) from weather_provider_api.routers.weather.sources.knmi.models.pluim import PluimModel -from weather_provider_api.routers.weather.sources.knmi.models.uurgegevens import UurgegevensModel +from weather_provider_api.routers.weather.sources.knmi.models.uurgegevens import ( + UurgegevensModel, +) class KNMI(WeatherSourceBase): @@ -20,6 +31,7 @@ def __init__(self): HarmonieAromeModel(), PluimModel(), ActueleWaarnemingenModel(), + ActueleWaarnemingenRegisterModel(), ] self.id = "knmi" diff --git a/weather_provider_api/routers/weather/sources/knmi/models/actuele_waarnemingen.py b/weather_provider_api/routers/weather/sources/knmi/models/actuele_waarnemingen.py index c67e6a6..c2cf763 100644 --- a/weather_provider_api/routers/weather/sources/knmi/models/actuele_waarnemingen.py +++ b/weather_provider_api/routers/weather/sources/knmi/models/actuele_waarnemingen.py @@ -7,24 +7,19 @@ """ KNMI current weather data fetcher. """ import copy -import locale -import re -from datetime import datetime from typing import List, Optional import numpy as np -import pandas as pd -import requests import structlog import xarray as xr from weather_provider_api.routers.weather.base_models.model import WeatherModelBase from weather_provider_api.routers.weather.sources.knmi.stations import ( stations_actual, - stations_actual_reversed, ) from weather_provider_api.routers.weather.sources.knmi.utils import ( find_closest_stn_list, + download_actuele_waarnemingen_weather, ) from weather_provider_api.routers.weather.utils.geo_position import GeoPosition from weather_provider_api.routers.weather.utils.pandas_helpers import coords_to_pd_index @@ -95,12 +90,11 @@ def get_weather( updated_weather_factors = self._request_weather_factors(weather_factors) # Download the current weather data - raw_ds = self._download_weather() + raw_ds = download_actuele_waarnemingen_weather() + print(raw_ds) # Get a list of the relevant STNs and choose the closest STN for each coordinate - coords_stn, stns, coords_stn_ind = find_closest_stn_list( - stations_actual, coords - ) + coords_stn, _, _ = find_closest_stn_list(stations_actual, coords) # Select the data for the found closest STNs ds = raw_ds.sel(STN=coords_stn) @@ -120,131 +114,6 @@ def get_weather( ds = ds.unstack("coord") return ds - def _download_weather(self) -> Optional[xr.Dataset]: - """ - A function that downloads the Actuele Waarnemingen site and parses it into a Weather Provider API compatible - format. - - Returns: - An Xarray Dataset containing the parsed weather data. - """ - raw_ds = None - - try: - knmi_site_response = requests.get(self.url) - - if knmi_site_response.ok: - knmi_site_df = pd.read_html(knmi_site_response.text)[0] - - column_translations = { - "Station": "station", - "Weer": "weather_description", - "Temp (°C)": "temperature", - "Chill (°C)": "wind_chill", - "RV (%)": "relative_humidity", - "Wind (bft)": "wind_direction", - "Wind (m/s)": "wind_speed", - "Wind (km/uur)": "wind_speed_kmu", - "Zicht (m)": "visibility", - "Druk (hPa)": "air_pressure", - } - - # Rename to the conventional naming system used for the Weather Provider API - for dictionary_item in knmi_site_df.columns.copy(deep=True): - if dictionary_item in column_translations.keys(): - knmi_site_df = knmi_site_df.rename( - columns={ - dictionary_item: column_translations[dictionary_item] - } - ) - else: - knmi_site_df = knmi_site_df.drop( - dictionary_item, axis="columns" - ) - - current_observation_moment = self.retrieve_observation_moment( - knmi_site_response.text - ) - knmi_site_df["time"] = current_observation_moment - if "wind_direction" in knmi_site_df: - knmi_site_df["wind_direction"] = knmi_site_df[ - "wind_direction" - ].str.strip("\n") - - # Add a field for the station with its (lat, lon)-coordinates and remove the original station code - knmi_site_df["STN"] = knmi_site_df["station"].apply( - lambda x: stations_actual_reversed[x.upper()] - ) - - stations_actual_indexed = stations_actual.set_index("STN") - - knmi_site_df["lat"] = knmi_site_df["STN"].apply( - lambda x: stations_actual_indexed.loc[x, "lat"] - ) - knmi_site_df["lon"] = knmi_site_df["STN"].apply( - lambda x: stations_actual_indexed.loc[x, "lon"] - ) - knmi_site_df = knmi_site_df.drop("station", axis="columns") - - # Rebuild the index - knmi_site_df = knmi_site_df.set_index(["time", "STN"]) - - # The following fields get removed because they aren't currently used in this API - if "weather_description" in knmi_site_df: - knmi_site_df = knmi_site_df.drop( - "weather_description", axis="columns" - ) - if "wind_chill" in knmi_site_df: - knmi_site_df = knmi_site_df.drop("wind_chill", axis="columns") - - # Generate a Xarray DataSet - raw_ds = knmi_site_df.to_xarray() - - except (requests.exceptions.BaseHTTPError, IndexError) as e: - logger.exception(str(e)) - - return raw_ds - - @staticmethod - def retrieve_observation_moment(html_body: str): - """ - A function that extracts the observation date from the supplied html body of the Actuele Waarnemingen site. - - Args: - html_body: The text from the body downloaded from the Actuele Waarnemingen Site. - - Returns: - Either the matching datetime if it can be extracted, or the current datetime if it cannot. - """ - try: - re_match = re.search( - r"Waarnemingen\s(\d+\s\w+\s\d{4}\s\d{2}:\d{2})\suur", - html_body, - ) - if re_match: - dt_str = re_match.group(1) - current_locale = locale.getlocale(locale.LC_TIME) - locale.setlocale(locale.LC_TIME, "dutch") - dt = datetime.strptime(dt_str, "%d %B %Y %H:%M") - locale.setlocale(locale.LC_TIME, current_locale) - return dt - except locale.Error as e: - logger.warning('No locale could be determined for KNMI Waarnemingen datetime transformation. ' - 'Using local datetime instead.', datetime=datetime.utcnow()) - return datetime.now() - except Exception as e: - logger.exception( - 'An unknown exception occurred while retrieving the KNMI Waarnemingen datetime for use in the response. ' - f'Using local datetime instead. Error: {e}', datetime=datetime.utcnow() - ) - return datetime.now() - - current_locale = locale.getlocale(locale.LC_TIME) - locale.setlocale(locale.LC_TIME, "dutch") - dt = datetime.now() - locale.setlocale(locale.LC_TIME, current_locale) - return dt - def is_async(self): # pragma: no cover return self.async_model diff --git a/weather_provider_api/routers/weather/sources/knmi/models/actuele_waarnemingen_register.py b/weather_provider_api/routers/weather/sources/knmi/models/actuele_waarnemingen_register.py new file mode 100644 index 0000000..4d3ab7f --- /dev/null +++ b/weather_provider_api/routers/weather/sources/knmi/models/actuele_waarnemingen_register.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# SPDX-FileCopyrightText: 2019-2023 Alliander N.V. +# SPDX-License-Identifier: MPL-2.0 + +""" KNMI current weather data aggregate fetcher. +""" +import copy +from datetime import datetime +from typing import List, Optional + +import numpy as np +import structlog +import xarray as xr +from dateutil.relativedelta import relativedelta + +from weather_provider_api.routers.weather.base_models.model import WeatherModelBase +from weather_provider_api.routers.weather.sources.knmi.client.actuele_waarnemingen_register_repository import ( + ActueleWaarnemingenRegisterRepository, +) +from weather_provider_api.routers.weather.sources.knmi.stations import stations_actual +from weather_provider_api.routers.weather.sources.knmi.utils import ( + find_closest_stn_list, +) +from weather_provider_api.routers.weather.utils.geo_position import GeoPosition +from weather_provider_api.routers.weather.utils.pandas_helpers import coords_to_pd_index + +logger = structlog.get_logger(__name__) + + +class ActueleWaarnemingenRegisterModel(WeatherModelBase): + """ + A Weather model aimed at accessing a 24-hour register for the "KNMi Actuele Waarnemingen" dataset. + """ + + def is_async(self): + return self.async_model + + def __init__(self): + super().__init__() + self.id = "waarnemingen_register" + self.name = "KNMI Actuele Waarnemingen - 48 uur register" + self.version = None + self.url = "https://www.knmi.nl/nederland-nu/weer/waarnemingen" + self.predictive = False + self.time_step_size_minutes = 10 + self.num_time_steps = 12 + self.description = "48 Hour register for current weather observations. Updated every 10 minutes." + self.async_model = False + self.repository = ActueleWaarnemingenRegisterRepository() + + self.to_si = { + "weather_description": { + "name": "weather_description", + "convert": self.no_conversion, + }, + "temperature": {"convert": self.celsius_to_kelvin}, + "humidity": {"convert": self.percentage_to_frac}, + "wind_direction": {"convert": self.dutch_wind_direction_to_degrees}, + "wind_speed": {"convert": self.no_conversion}, # m/s + "visibility": {"convert": self.no_conversion}, # m + "air_pressure": {"convert": lambda x: x * 100}, # hPa to Pa + } + self.to_human = copy.deepcopy(self.to_si) + self.to_human["temperature"]["convert"] = self.no_conversion # C + + self.human_to_model_specific = self._create_reverse_lookup(self.to_si) + + def get_weather( + self, + coords: List[GeoPosition], + begin: Optional[np.datetime64], + end: Optional[np.datetime64], + weather_factors: List[str] = None, + ) -> xr.Dataset: + """ + The function that gathers and processes the requested Actuele Waarnemingen Register weather data from the + 48-hour register and returns it as a Xarray Dataset. + (The register for this model interprets directly from an HTML page, but the information is also available from + the data platform. Due to it being rather impractically handled, we stick to the site for now.) + + Args: + coords: A list of GeoPositions containing the locations the data is requested for. + begin: A datetime containing the start of the period to request data for. + end: A datetime containing the end of the period to request data for. + weather_factors: A list of weather factors to request data for (in string format) + + Returns: + An Xarray Dataset containing the weather data for the requested period, locations and factors. + + NOTES: + As this model only return the current weather data the 'begin' and 'end' values are not actually used. + """ + updated_weather_factors = self._request_weather_factors(weather_factors) + coords_stn, _, _ = find_closest_stn_list(stations_actual, coords) + + now = datetime.utcnow() + if now - relativedelta(days=1) > begin: + raw_ds = self.repository.get_48_hour_registry_for_station( + station=coords_stn + ) + else: + raw_ds = self.repository.get_24_hour_registry_for_station( + station=coords_stn + ) + + data_dictionary = { + var_name: (["time", "coord"], var.values) + for var_name, var in raw_ds.data_vars.items() + if var_name in updated_weather_factors and var_name not in ["lat", "lon"] + } + + timeline = raw_ds.coords["time"].values + + output_ds = xr.Dataset( + data_vars=data_dictionary, + coords={"time": timeline, "coord": coords_to_pd_index(coords)}, + ) + output_ds = output_ds.unstack("coord") + return output_ds + + def _request_weather_factors(self, factors: Optional[List[str]]) -> List[str]: + # Implementation of the Base Weather Model function that returns a list of known weather factors for the model. + if factors is None: + return list(self.to_si.keys()) + + new_factors = [] + + for f in factors: + f_low = f.lower() + if f_low in self.to_si: + new_factors.append(f_low) + + return list(set(new_factors)) # Cleanup any duplicate values and return diff --git a/weather_provider_api/routers/weather/sources/knmi/models/daggegevens.py b/weather_provider_api/routers/weather/sources/knmi/models/daggegevens.py index b9b5746..0bda5ce 100644 --- a/weather_provider_api/routers/weather/sources/knmi/models/daggegevens.py +++ b/weather_provider_api/routers/weather/sources/knmi/models/daggegevens.py @@ -22,8 +22,12 @@ from weather_provider_api.routers.weather.base_models.model import WeatherModelBase from weather_provider_api.routers.weather.sources.knmi.stations import stations_history -from weather_provider_api.routers.weather.sources.knmi.utils import find_closest_stn_list -from weather_provider_api.routers.weather.utils.date_helpers import validate_begin_and_end +from weather_provider_api.routers.weather.sources.knmi.utils import ( + find_closest_stn_list, +) +from weather_provider_api.routers.weather.utils.date_helpers import ( + validate_begin_and_end, +) from weather_provider_api.routers.weather.utils.geo_position import GeoPosition from weather_provider_api.routers.weather.utils.pandas_helpers import coords_to_pd_index @@ -201,9 +205,13 @@ def get_weather( An Xarray Dataset containing the weather data for the requested period, locations and factors. """ # Test and account for invalid datetime timeframes or input - begin, end = validate_begin_and_end(begin, end, None, datetime.utcnow() - relativedelta(days=1)) + begin, end = validate_begin_and_end( + begin, end, None, datetime.utcnow() - relativedelta(days=1) + ) # Get a list of the relevant STNs and choose the closest STN for each coordinate - station_id, stns, coords_stn_ind = find_closest_stn_list(stations_history, coords) + station_id, stns, coords_stn_ind = find_closest_stn_list( + stations_history, coords + ) # Download the weather data for the relevant STNs raw_data = self._download_weather( @@ -247,7 +255,9 @@ def _download_weather( A field containing the full response of the made download-request (text-based) """ # fetch data - params = self._create_request_params(start, end, inseason, stations, weather_factors) + params = self._create_request_params( + start, end, inseason, stations, weather_factors + ) r = requests.post(url=self.download_url, data=params) if r.status_code != 200: @@ -291,9 +301,9 @@ def _create_request_params(self, start, end, inseason, stations, weather_factors def _parse_raw_weather_data(self, raw_data: str) -> xr.Dataset: json_data = json.loads(raw_data) dataframe_data = pd.DataFrame.from_dict(json_data, orient="columns") + dataframe_data["date"] = pd.to_datetime(dataframe_data["date"]) conversion_dict = { - "date": "datetime64[ns]", "station_code": int, } for weather_factor in self.to_si.keys(): @@ -314,8 +324,11 @@ def _prepare_weather_data(coordinates: List[GeoPosition], station_id, raw_ds): ds = raw_ds.sel(station_code=station_id) # dict of data - data_dict = {var_name: (["coord", "time"], var.values) for var_name, var in ds.data_vars.items()} - timeline = ds.coords["date"].values + data_dict = { + var_name: (["coord", "time"], var.values) + for var_name, var in ds.data_vars.items() + } + timeline = pd.DatetimeIndex(ds.coords["date"].values) ds = xr.Dataset( data_vars=data_dict, diff --git a/weather_provider_api/routers/weather/sources/knmi/models/uurgegevens.py b/weather_provider_api/routers/weather/sources/knmi/models/uurgegevens.py index 6b25078..22e7087 100644 --- a/weather_provider_api/routers/weather/sources/knmi/models/uurgegevens.py +++ b/weather_provider_api/routers/weather/sources/knmi/models/uurgegevens.py @@ -251,7 +251,6 @@ def _parse_raw_weather_data(self, raw_data: str) -> xr.Dataset: dataframe_data = pd.DataFrame.from_dict(json_data, orient="columns") conversion_dict = { - "date": "datetime64[ns]", "hour": str, "station_code": int, } @@ -262,6 +261,7 @@ def _parse_raw_weather_data(self, raw_data: str) -> xr.Dataset: # KNMI measures the -th hour. (The 24th hour is from 23:00 to 00:00 the next day) We use 23:00 to indicate that. dataframe_data["hour"] = dataframe_data["hour"] - 1 dataframe_data = dataframe_data.astype(conversion_dict) + dataframe_data["date"] = pd.to_datetime(dataframe_data["date"]) # Convert hours from time to timestamp dataframe_data["timestamp"] = pd.to_timedelta(dataframe_data["hour"] + ":00:00") @@ -284,7 +284,7 @@ def _prepare_weather_data(coordinates: List[GeoPosition], station_id, raw_ds): # dict of data data_dict = {var_name: (["coord", "time"], var.values) for var_name, var in ds.data_vars.items()} - timeline = ds.coords["date"].values + timeline = pd.DatetimeIndex(ds.coords["date"].values) ds = xr.Dataset( data_vars=data_dict, diff --git a/weather_provider_api/routers/weather/sources/knmi/utils.py b/weather_provider_api/routers/weather/sources/knmi/utils.py index 1cd135b..c671501 100644 --- a/weather_provider_api/routers/weather/sources/knmi/utils.py +++ b/weather_provider_api/routers/weather/sources/knmi/utils.py @@ -6,25 +6,37 @@ """Utilities for handling KNMI datasets. """ +import locale +import re +from datetime import datetime from typing import List import numpy as np import pandas as pd +import requests +import structlog +import xarray as xr from geopy.distance import great_circle +from weather_provider_api.routers.weather.sources.knmi.stations import ( + stations_actual, + stations_actual_reversed, +) from weather_provider_api.routers.weather.utils.geo_position import GeoPosition +logger = structlog.get_logger(__name__) + def find_closest_stn_list(stn_stations: pd.DataFrame, coords: List[GeoPosition]): """ - A function that finds the closest stations to the locations in the given list of GeoPositions + A function that finds the closest stations to the locations in the given list of GeoPositions Args: - stn_stations: A Pandas Dataframe containing all the station data for the KNMI stations - coords: A list of GeoPositions with locations to find the nearest stations to. - Returns: - A list of coordinates for found stations - A list of found stations in the same order - A list of index numbers for the found in the same order + stn_stations: A Pandas Dataframe containing all the station data for the KNMI stations + coords: A list of GeoPositions with locations to find the nearest stations to. + Returns: + A list of coordinates for found stations + A list of found stations in the same order + A list of index numbers for the found in the same order """ _stn_stations = stn_stations.copy(deep=True) @@ -52,3 +64,123 @@ def _find_closest_stn_single(stn_stations: pd.DataFrame, coord: GeoPosition): min_ind = np.argmin(stn_stations["distance"].values) # Return the found stn return stn_stations.loc[min_ind, "STN"] + + +def download_actuele_waarnemingen_weather() -> xr.Dataset: + """ """ + raw_ds = None + + try: + knmi_site_response = requests.get( + "https://www.knmi.nl/nederland-nu/weer/waarnemingen" + ) + + if knmi_site_response.ok: + knmi_site_df = pd.read_html(knmi_site_response.text)[0] + + column_translations = { + "Station": "station", + "Weer": "weather_description", + "Temp (°C)": "temperature", + "Chill (°C)": "wind_chill", + "RV (%)": "relative_humidity", + "Wind (bft)": "wind_direction", + "Wind (m/s)": "wind_speed", + "Wind (km/uur)": "wind_speed_kmu", + "Zicht (m)": "visibility", + "Druk (hPa)": "air_pressure", + } + + # Rename to the conventional naming system used for the Weather Provider API + for dictionary_item in knmi_site_df.columns.copy(deep=True): + if dictionary_item in column_translations.keys(): + knmi_site_df = knmi_site_df.rename( + columns={dictionary_item: column_translations[dictionary_item]} + ) + else: + knmi_site_df = knmi_site_df.drop(dictionary_item, axis="columns") + + current_observation_moment = _retrieve_observation_moment( + knmi_site_response.text + ) + knmi_site_df["time"] = current_observation_moment + if "wind_direction" in knmi_site_df: + knmi_site_df["wind_direction"] = knmi_site_df[ + "wind_direction" + ].str.strip("\n") + + # Add a field for the station with its (lat, lon)-coordinates and remove the original station code + knmi_site_df["STN"] = knmi_site_df["station"].apply( + lambda x: stations_actual_reversed[x.upper()] + ) + + stations_actual_indexed = stations_actual.set_index("STN") + + knmi_site_df["lat"] = knmi_site_df["STN"].apply( + lambda x: stations_actual_indexed.loc[x, "lat"] + ) + knmi_site_df["lon"] = knmi_site_df["STN"].apply( + lambda x: stations_actual_indexed.loc[x, "lon"] + ) + knmi_site_df = knmi_site_df.drop("station", axis="columns") + + # Rebuild the index + knmi_site_df = knmi_site_df.set_index(["time", "STN"]) + + # The following fields get removed because they aren't currently used in this API + if "weather_description" in knmi_site_df: + knmi_site_df = knmi_site_df.drop("weather_description", axis="columns") + if "wind_chill" in knmi_site_df: + knmi_site_df = knmi_site_df.drop("wind_chill", axis="columns") + + # Generate a Xarray DataSet + raw_ds = knmi_site_df.to_xarray() + + except (requests.exceptions.BaseHTTPError, IndexError) as expected_error: + logger.exception(str(expected_error)) + + return raw_ds + + +def _retrieve_observation_moment(html_body: str): + """ + A function that extracts the observation date from the supplied html body of the Actuele Waarnemingen site. + + Args: + html_body: The text from the body downloaded from the Actuele Waarnemingen Site. + + Returns: + Either the matching datetime if it can be extracted, or the current datetime if it cannot. + """ + try: + re_match = re.search( + r"Waarnemingen\s(\d+\s\w+\s\d{4}\s\d{2}:\d{2})\suur", + html_body, + ) + if re_match: + dt_str = re_match.group(1) + current_locale = locale.getlocale(locale.LC_TIME) + locale.setlocale(locale.LC_TIME, "dutch") + dt = datetime.strptime(dt_str, "%d %B %Y %H:%M") + locale.setlocale(locale.LC_TIME, current_locale) + return dt + except locale.Error: + logger.warning( + "No locale could be determined for KNMI Waarnemingen datetime transformation. " + "Using local datetime instead.", + datetime=datetime.utcnow(), + ) + return datetime.now() + except Exception as e: + logger.exception( + "An unknown exception occurred while retrieving the KNMI Waarnemingen datetime for use in the response." + f" Using local datetime instead. Error: {e}", + datetime=datetime.utcnow(), + ) + return datetime.now() + + current_locale = locale.getlocale(locale.LC_TIME) + locale.setlocale(locale.LC_TIME, "dutch") + dt = datetime.now() + locale.setlocale(locale.LC_TIME, current_locale) + return dt diff --git a/weather_provider_api/routers/weather/utils/date_helpers.py b/weather_provider_api/routers/weather/utils/date_helpers.py index d0c3b6f..3c047c1 100644 --- a/weather_provider_api/routers/weather/utils/date_helpers.py +++ b/weather_provider_api/routers/weather/utils/date_helpers.py @@ -25,7 +25,7 @@ def parse_datetime( if datetime_string is None: return None - dt = pd.to_datetime(datetime_string, dayfirst=True, errors="coerce") + dt = pd.to_datetime(datetime_string, dayfirst=False, errors="coerce") if pd.isnull(dt): logger.exception("Error while parsing datetime string", input=datetime_string) diff --git a/weather_provider_api/scripts/erase_era5sl_repository.py b/weather_provider_api/scripts/erase_era5sl_repository.py index 7f77453..22948a0 100644 --- a/weather_provider_api/scripts/erase_era5sl_repository.py +++ b/weather_provider_api/scripts/erase_era5sl_repository.py @@ -4,10 +4,13 @@ # SPDX-FileCopyrightText: 2019-2022 Alliander N.V. # SPDX-License-Identifier: MPL-2.0 -from weather_provider_api.routers.weather.sources.cds.client.era5sl_repository import ERA5SLRepository +from weather_provider_api.routers.weather.sources.cds.client.era5sl_repository import ( + ERA5SLRepository, +) def main(): + # Simple method wrapper for purging data era5sl_repo = ERA5SLRepository() era5sl_repo.purge_repository() diff --git a/weather_provider_api/scripts/erase_waarnemingen_register.py b/weather_provider_api/scripts/erase_waarnemingen_register.py new file mode 100644 index 0000000..c3d0846 --- /dev/null +++ b/weather_provider_api/scripts/erase_waarnemingen_register.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# SPDX-FileCopyrightText: 2019-2023 Alliander N.V. +# SPDX-License-Identifier: MPL-2.0 + +from weather_provider_api.routers.weather.sources.knmi.client.actuele_waarnemingen_register_repository import ( + ActueleWaarnemingenRegisterRepository, +) + + +def main(): + # Simple method wrapper for purging data + waarnemingen_repo = ActueleWaarnemingenRegisterRepository() + waarnemingen_repo.purge_repository() + + +if __name__ == "__main__": + main() diff --git a/weather_provider_api/scripts/update_waarnemingen_register.py b/weather_provider_api/scripts/update_waarnemingen_register.py new file mode 100644 index 0000000..5eddb2a --- /dev/null +++ b/weather_provider_api/scripts/update_waarnemingen_register.py @@ -0,0 +1,18 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + + +# SPDX-FileCopyrightText: 2019-2023 Alliander N.V. +# SPDX-License-Identifier: MPL-2.0 +from weather_provider_api.routers.weather.sources.knmi.client.actuele_waarnemingen_register_repository import ( + ActueleWaarnemingenRegisterRepository, +) + + +def main(): + waarnemingen_repo = ActueleWaarnemingenRegisterRepository() + waarnemingen_repo.update() + + +if __name__ == "__main__": # pragma: no cover + main()