diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8d9c56c..10db0e3 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -8,7 +8,7 @@ ci: repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v5.0.0 hooks: # list of supported hooks: https://pre-commit.com/hooks.html - id: trailing-whitespace @@ -26,7 +26,7 @@ repos: )$ - repo: https://github.com/python-poetry/poetry - rev: "1.5.0" + rev: "1.7.1" hooks: - id: poetry-check # too slow @@ -35,7 +35,7 @@ repos: args: ["-f", "requirements.txt", "-o", "requirements.txt"] - repo: https://github.com/asottile/pyupgrade - rev: v3.15.0 + rev: v3.20.0 hooks: - id: pyupgrade args: [--py312-plus] @@ -43,21 +43,21 @@ repos: # python formatting - repo: https://github.com/psf/black - rev: 23.3.0 + rev: 25.1.0 hooks: - id: black name: Format code args: ["--line-length=100"] - repo: https://github.com/hadialqattan/pycln - rev: v2.1.5 # Possible releases: https://github.com/hadialqattan/pycln/releases + rev: v2.5.0 # Possible releases: https://github.com/hadialqattan/pycln/releases hooks: - id: pycln args: [--all] # ref: https://github.com/microsoft/vscode-isort] - repo: https://github.com/pycqa/isort - rev: 5.12.0 + rev: 6.0.1 hooks: - id: isort name: isort (python) @@ -72,14 +72,14 @@ repos: # yaml formatting - repo: https://github.com/pre-commit/mirrors-prettier - rev: v3.0.0-alpha.9-for-vscode + rev: v4.0.0-alpha.8 hooks: - id: prettier types: [yaml] # markdown formatting - repo: https://github.com/executablebooks/mdformat - rev: 0.7.16 + rev: 0.7.22 hooks: - id: mdformat additional_dependencies: diff --git a/README.md b/README.md index b8baa59..2fda6d1 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,9 @@ Make sure to register for an API key and validate licences at: Before proceeding further, it is required to accept all the licenses in the section "Your profile" in the website of [Copernicus](https://cds.climate.copernicus.eu/profile?tab=licences). -### Passing the key as environment variable +### CDS/ADS credentials + +#### Passing the key as environment variable This is the easiest method. Use: @@ -30,7 +32,7 @@ import os os.environ["CDSADS_API_KEY"] = "YOUR_KEY" ``` -### Installing the API key in your filesytem +#### Installing the API key in your filesytem Then create the file `~/.cdsapirc` with the following content: @@ -42,6 +44,33 @@ key: Note: the URL will be dynamically managed by the script depending on the data source. The API key doesn't vary, it's the same for both ERA5 and CAMS data. +### Earth Data Hub credentials (optional) + +If you want to use the [Earth Data Hub](https://earthdatahub.destine.eu/) (EDH) as a data source instead of CDS, you need to set up your EDH personal access token. + +> **Note:** Earth Data Hub has a monthly quota of 500,000 requests per user. Data is available until the last closed month. + +#### Passing the token as environment variable + +```python +import os +os.environ["EDH_TOKEN"] = "edh_pat_..." +``` + +Or in the shell: + +```bash +export EDH_TOKEN="edh_pat_..." +``` + +#### Installing the token in your filesystem + +Create the file `~/.edh_token` with your personal access token (plain text, first line): + +``` +edh_pat_... +``` + ## Install the package ### From PyPI @@ -61,7 +90,7 @@ poetry install # Usage -> \[!NOTE\] +> [!NOTE] > When running in a Jupyter notebook, to make progress bars and interactive widgets work, make sure to install `ipywidgets` and to enable the widgets extension. ```bash @@ -82,6 +111,9 @@ poetry run era5epw_download --year 2024 --latitude 49.4 --longitude 0.1 --city-n # using installed binary, after pypi package installation era5epw_download --year 2024 --latitude 49.4 --longitude 0.1 --city-name "Le Havre" --elevation 0 --time-zone 1 + +# using Earth Data Hub as data source (faster downloads, requires EDH token) +era5epw_download --year 2024 --latitude 49.4 --longitude 0.1 --city-name "Le Havre" --elevation 0 --time-zone 1 --era5-data-source edh ``` By default, the `time-zone` argument is used only to populate the `LOCATION` header and data time is UTC. Use `--apply-time-zone-to-data` to apply it to the date and time fields (this will shift the UTC time by the provided time zone offset). @@ -105,6 +137,42 @@ download_and_make_epw( output_file="/tmp/era5epw_paris_2025.epw", apply_time_zone_to_data=True, ) + +# Or using Earth Data Hub as data source (faster downloads): +download_and_make_epw( + year=2025, + latitude=48.8, + longitude=2.4, + city_name="Paris", + time_zone=1, + elevation=0, + output_file="/tmp/era5epw_paris_2025.epw", + apply_time_zone_to_data=True, + era5_data_source="edh", +) +``` + +## Reading EPW Files into a DataFrame + +The package provides a reader to load EPW files into Pandas DataFrames for inspection, data analysis, and further processing. + +### Python API + +```python +from era5epw.reader import read_epw_file + +# Load EPW file into a DataFrame +df = read_epw_file("path/to/file.epw") + +# Inspect the data +print(df.head()) +print(df.describe()) + +# Access specific weather series +print(df["Dry Bulb Temperature"].mean()) + +# Filter by date +january_data = df[df.index.month == 1] ``` ## Visualizing EPW Files @@ -180,6 +248,7 @@ Datasets home pages: - [ERA5 Land hourly time-series data from 1950 to present](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-timeseries) (Experimental) - [ERA5 hourly data on single levels from 1940 to present](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels) - [CAMS solar radiation time-series](https://ads.atmosphere.copernicus.eu/datasets/cams-solar-radiation-timeseries) +- [Earth Data Hub - ERA5 reanalysis single levels](https://earthdatahub.destine.eu/collections/era5/datasets/reanalysis-era5-single-levels) (License: [Copernicus License](https://earthdatahub.destine.eu/collections/era5/datasets/reanalysis-era5-single-levels)) View your API requests and download responses at: diff --git a/era5epw/ads.py b/era5epw/ads.py index 3c22a85..bb5f304 100644 --- a/era5epw/ads.py +++ b/era5epw/ads.py @@ -1,4 +1,5 @@ import tempfile +from typing import Any import pandas as pd import xarray as xr @@ -19,7 +20,7 @@ def make_cams_solar_radiation_request( time_step: str = "1hour", time_reference: str = "universal_time", time_zone: int | None = None, -) -> dict[str, any] | None: +) -> dict[str, Any] | None: assert sky_type in [ "clear", "observed_cloud", diff --git a/era5epw/cds.py b/era5epw/cds.py index f7be3ff..822c49e 100644 --- a/era5epw/cds.py +++ b/era5epw/cds.py @@ -1,6 +1,7 @@ import os from multiprocessing import Pool from tempfile import TemporaryDirectory +from typing import Any import pandas as pd from tqdm.auto import tqdm @@ -30,9 +31,11 @@ "10m_u_component_of_wind", "10m_v_component_of_wind", "surface_pressure", + "total_cloud_cover", ], "reanalysis-era5-land-timeseries": [ "soil_temperature_level_1", + "snow_depth", ], "reanalysis-era5-single-levels": ["*"], } @@ -40,13 +43,13 @@ def make_cds_request( ds: str | None, - variables: [str], + variables: list[str], year: int, month: int | None, latitude: float, longitude: float, time_zone: int | None = None, -) -> list[dict[str, any]] | None: +) -> list[dict[str, Any]] | None: """Create a CDS request for the specified parameters. :param ds: The dataset to use, e.g., 'reanalysis-era5-single-levels-timeseries'. @@ -179,7 +182,7 @@ def make_cds_request( ) -def make_intermediate_file_names(tmpdir: str, cds_requests: list[dict[str, any]]) -> list[str]: +def make_intermediate_file_names(tmpdir: str, cds_requests: list[dict[str, Any]]) -> list[str]: """Generate a list of temporary file names for storing intermediate results of CDS requests. :param tmpdir: Temporary directory where intermediate files will be stored. @@ -227,7 +230,7 @@ def make_intermediate_file_names(tmpdir: str, cds_requests: list[dict[str, any]] def download_era5_data( - variables: [str], + variables: list[str], year: int, latitude: float, longitude: float, diff --git a/era5epw/edh.py b/era5epw/edh.py new file mode 100644 index 0000000..68f60df --- /dev/null +++ b/era5epw/edh.py @@ -0,0 +1,272 @@ +import logging +import os +from multiprocessing import Pool + +import pandas as pd +import xarray as xr +from tqdm.auto import tqdm + +from era5epw.utils import now_utc + +# Earth Data Hub base URL template +EDH_URL_TEMPLATE = ( + "https://edh:{token}@data.earthdatahub.destine.eu/era5/reanalysis-era5-single-levels-v0.zarr" +) + +# Mapping from CDS variable names to EDH short names +CDS_TO_EDH_VARS = { + "2m_temperature": "t2m", + "2m_dewpoint_temperature": "d2m", + "surface_pressure": "sp", + "10m_u_component_of_wind": "u10", + "10m_v_component_of_wind": "v10", + "total_cloud_cover": "tcc", + "uv_visible_albedo_for_direct_radiation": "aluvd", + "snow_depth": "sd", + "soil_temperature_level_1": "stl1", + "total_precipitation": "tp", +} + +# Variables available in the EDH dataset +EDH_AVAILABLE_VARS = list(CDS_TO_EDH_VARS.values()) + +_edh_token = None + + +def load_edh_token() -> str: + """Load the Earth Data Hub personal access token from environment variable or file. + + The token is looked up in order: + 1. Environment variable EDH_TOKEN + 2. File ~/.edh_token (plain text, first line) + """ + global _edh_token + if _edh_token is not None: + return _edh_token + + if os.getenv("EDH_TOKEN"): + _edh_token = os.getenv("EDH_TOKEN") + return _edh_token + + token_file = os.path.expanduser("~/.edh_token") + try: + with open(token_file) as f: + token = f.readline().strip() + assert token, "EDH token is empty. Please check your ~/.edh_token file." + _edh_token = token + return _edh_token + except FileNotFoundError: + raise FileNotFoundError( + "Earth Data Hub token not found. Set the EDH_TOKEN environment variable " + "or create '~/.edh_token' with your personal access token." + ) + + +def _normalize_longitude_input(longitude: float) -> float: + """Convert longitude from [-180, 180] range to [0, 360] range used by EDH. + + :param longitude: Longitude in [-180, 180] range. + :return: Longitude in [0, 360] range. + """ + if longitude < 0: + return 360.0 + longitude + return longitude + + +def _normalize_longitude_output(longitude: float) -> float: + """Convert longitude from [0, 360] range back to [-180, 180] range. + + :param longitude: Longitude in [0, 360] range. + :return: Longitude in [-180, 180] range. + """ + if longitude > 180: + return longitude - 360.0 + return longitude + + +def _fetch_edh_chunk(args: tuple[str, list[str], str, str, float, float]) -> pd.DataFrame: + """Fetch a chunk of data from EDH for a specific time range. + + This function is designed to be called in parallel via multiprocessing. + + :param args: Tuple of (url, variables, start_date, end_date, latitude, longitude_edh) + :return: DataFrame with the fetched data. + """ + url, variables, start_date, end_date, latitude, longitude_edh = args + + ds = xr.open_dataset(url, engine="zarr", chunks={}) + subset = ds[variables] + time_slice = subset.sel(valid_time=slice(start_date, end_date)) + point = time_slice.sel( + latitude=latitude, + longitude=longitude_edh, + method="nearest", + ) + + # Actual download happens here when compute() is called + df = point.compute().to_dataframe() + # Drop non-essential coordinate columns that come from the zarr dataset + cols_to_drop = [c for c in ["latitude", "longitude", "number", "surface"] if c in df.columns] + if cols_to_drop: + df = df.drop(columns=cols_to_drop) + + return df + + +def download_era5_data_edh( + variables: list[str], + year: int, + latitude: float, + longitude: float, + parallel_exec_nb: int = 4, + time_zone: int | None = None, +) -> pd.DataFrame: + """Download ERA5 data from Earth Data Hub (EDH) for a specific year and location. + + Data is lazily loaded from a zarr archive and only downloaded upon compute(). The + longitude range in EDH is [0, 360], so user inputs are normalized automatically. + + :param variables: List of CDS-style variable names (e.g., '2m_temperature'). + :param year: The year of the data. Full year will be downloaded. + :param latitude: The latitude for the data point. + :param longitude: The longitude for the data point (in [-180, 180] range). + :param parallel_exec_nb: Number of parallel executions for downloading data chunks. + :param time_zone: Time zone offset from UTC. If provided, will adjust date range to + fetch additional data needed for time zone conversion. + :return: A DataFrame containing the downloaded data, combined on the 'valid_time' + dimension. + """ + token = load_edh_token() + url = EDH_URL_TEMPLATE.format(token=token) + + # Map CDS variable names to EDH short names + edh_vars = [] + for var in variables: + if var in CDS_TO_EDH_VARS: + edh_var = CDS_TO_EDH_VARS[var] + if edh_var not in edh_vars: + edh_vars.append(edh_var) + else: + logging.warning(f"Variable '{var}' is not available in Earth Data Hub. Skipping.") + + if not edh_vars: + raise ValueError( + f"None of the requested variables are available in Earth Data Hub: {variables}" + ) + + # Normalize longitude to [0, 360] range + longitude_edh = _normalize_longitude_input(longitude) + + # Check data availability: EDH data is available until last closed month + now = now_utc() + if year > now.year: + raise ValueError( + f"Cannot download data for year {year}. " + f"Earth Data Hub data is available until the last closed month." + ) + + # Split into monthly chunks for parallel processing + chunks: list[tuple[str, list[str], str, str, float, float]] = [] + + # Determine the months to fetch + if time_zone is not None and time_zone > 0: + # Add previous year's last day + chunks.append( + ( + url, + edh_vars, + f"{year - 1}-12-31", + f"{year - 1}-12-31T23:59:59", + latitude, + longitude_edh, + ) + ) + + for month in range(1, 13): + if year == now.year and month > now.month: + break # Skip future months + month_start = f"{year}-{month:02d}-01" + if month == 12: + month_end = f"{year}-12-31T23:59:59" + else: + month_end = f"{year}-{month + 1:02d}-01" + chunks.append((url, edh_vars, month_start, month_end, latitude, longitude_edh)) + + if time_zone is not None and time_zone < 0: + # Add next year's first day + chunks.append( + ( + url, + edh_vars, + f"{year + 1}-01-01", + f"{year + 1}-01-01T23:59:59", + latitude, + longitude_edh, + ) + ) + + if not chunks: + raise ValueError( + f"No valid data chunks could be created for year {year} and variables {variables}." + ) + + tqdm.write( + f"Fetching {len(chunks)} chunks from Earth Data Hub " + f"with {parallel_exec_nb} parallel requests for {year}..." + ) + + # Create progress bar for EDH requests + edh_progress = tqdm( + total=len(chunks), desc="EDH requests", unit="chunk", position=1, leave=False + ) + + dfs: list[pd.DataFrame] = [] + with Pool(parallel_exec_nb) as pool: + result = pool.map_async(_fetch_edh_chunk, chunks) + + while not result.ready(): + result.wait(timeout=1) + + dfs = result.get() + + edh_progress.n = len(chunks) + edh_progress.refresh() + edh_progress.close() + + # Concatenate all DataFrames and sort by index + combined_df = pd.concat(dfs, axis=0, ignore_index=False).sort_index().drop_duplicates() + + # Rename 'aluvd' to 'aluvp' to match what main.py expects from CDS output + if "aluvd" in combined_df.columns: + combined_df = combined_df.rename(columns={"aluvd": "aluvp"}) + + # Rename 'sd' to 'sde' to match what main.py expects from CDS (ERA5-Land) output + if "sd" in combined_df.columns: + combined_df = combined_df.rename(columns={"sd": "sde"}) + + return combined_df + + +if __name__ == "__main__": + df = download_era5_data_edh( + variables=[ + "2m_temperature", + "2m_dewpoint_temperature", + "surface_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "total_cloud_cover", + "uv_visible_albedo_for_direct_radiation", + "snow_depth", + "total_precipitation", + "soil_temperature_level_1", + ], + year=2025, + latitude=47.2184, + longitude=-1.5533, + parallel_exec_nb=4, + time_zone=1, + ) + + print(df.head(5)) + print(df.info()) diff --git a/era5epw/main.py b/era5epw/main.py index 2e614fa..ab26747 100644 --- a/era5epw/main.py +++ b/era5epw/main.py @@ -9,6 +9,7 @@ from era5epw.ads import download_cams_solar_radiation_data from era5epw.cds import download_era5_data +from era5epw.edh import download_era5_data_edh def get_first_weekday_of_year(y: int) -> str: @@ -84,6 +85,13 @@ def create_args() -> ArgumentParser: default=f"/tmp/era5epw_{datetime.now().strftime('%Y%m%d_%H%M%S')}.epw", help="Output file path for the generated EPW file.", ) + parser.add_argument( + "--era5-data-source", + type=str, + default="cds", + choices=["cds", "edh"], + help="Data source for ERA5 data: 'cds' (Climate Data Store, default) or 'edh' (Earth Data Hub).", + ) parser.add_argument( "--verbose", action="store_true", @@ -108,6 +116,7 @@ def download_and_make_epw( parallel_exec_nb: int = 10, verbose: bool = False, apply_time_zone_to_data: bool = False, + era5_data_source: str = "cds", ) -> None: """Generate a full year EPW file from ERA5 and CAMS data. @@ -122,6 +131,8 @@ def download_and_make_epw( minute). :param verbose: If True, enable verbose logging from CDS client. :param apply_time_zone_to_data: If True, apply time zone offset to data timestamps. + :param era5_data_source: Data source for ERA5 data: 'cds' (Climate Data Store) or 'edh' + (Earth Data Hub). """ start_time = datetime.now() @@ -149,27 +160,39 @@ def download_and_make_epw( overall_progress.update(1) # CAMS download completed overall_progress.set_description("Downloading ERA5 data") - era5_df = download_era5_data( - variables=[ - "2m_temperature", - "2m_dewpoint_temperature", - "surface_pressure", - "10m_u_component_of_wind", - "10m_v_component_of_wind", - "total_cloud_cover", - "uv_visible_albedo_for_direct_radiation", - "snow_depth", - "soil_temperature_level_1", - "total_precipitation", - ], - year=year, - latitude=latitude, - longitude=longitude, - parallel_exec_nb=parallel_exec_nb, - dataset=None, # dynamic dataset selection based on variables - verbose=verbose, - time_zone=time_zone if apply_time_zone_to_data else None, - ) + era5_variables = [ + "2m_temperature", + "2m_dewpoint_temperature", + "surface_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "total_cloud_cover", + "uv_visible_albedo_for_direct_radiation", + "snow_depth", + "soil_temperature_level_1", + "total_precipitation", + ] + + if era5_data_source == "edh": + era5_df = download_era5_data_edh( + variables=era5_variables, + year=year, + latitude=latitude, + longitude=longitude, + parallel_exec_nb=parallel_exec_nb, + time_zone=time_zone if apply_time_zone_to_data else None, + ) + else: + era5_df = download_era5_data( + variables=era5_variables, + year=year, + latitude=latitude, + longitude=longitude, + parallel_exec_nb=parallel_exec_nb, + dataset=None, # dynamic dataset selection based on variables + verbose=verbose, + time_zone=time_zone if apply_time_zone_to_data else None, + ) overall_progress.update(1) # ERA5 download completed overall_progress.close() @@ -199,7 +222,10 @@ def download_and_make_epw( v10 = era5_df["v10"].values # m/s cloud = era5_df["tcc"].values * 10 # Fraction to okta (0-10 scale) uv_visible_albedo = era5_df["aluvp"].values # (0-1 scale) - snow_depth = era5_df["sd"].values * 100 # m to cm + # 'sde' variable name is used in ERA5-Land, in ERA5 it's 'sd'. + if "sde" in era5_df.columns and "sde" not in era5_df.columns: + era5_df["sd"] = era5_df["sde"] + snow_depth = era5_df["sde"].values * 100 # m to cm. total_precipitation = era5_df["tp"].values * 1000 # m to mm ghi = cams_df["GHI"].values # Global horizontal all sky irradiation in Wh/m^2 bni = cams_df["BNI"].values # Direct normal all sky irradiation in Wh/m^2 @@ -314,6 +340,7 @@ def download(): parallel_exec_nb=args.parallel_requests, verbose=args.verbose, apply_time_zone_to_data=args.apply_time_zone_to_data, + era5_data_source=args.era5_data_source, ) diff --git a/era5epw/reader.py b/era5epw/reader.py new file mode 100644 index 0000000..7d94f2e --- /dev/null +++ b/era5epw/reader.py @@ -0,0 +1,103 @@ +"""EPW file reader. + +This module provides functionality to read EnergyPlus Weather (EPW) files and return the +data as a Pandas DataFrame for inspection and data analysis. +""" + +import pandas as pd + +# EPW column names as per EnergyPlus EPW format specification +EPW_COLUMNS = [ + "Year", + "Month", + "Day", + "Hour", + "Minute", + "Data Source and Uncertainty Flags", + "Dry Bulb Temperature", + "Dew Point Temperature", + "Relative Humidity", + "Atmospheric Station Pressure", + "Extraterrestrial Horizontal Radiation", + "Extraterrestrial Direct Normal Radiation", + "Horizontal Infrared Radiation Intensity", + "Global Horizontal Radiation", + "Direct Normal Radiation", + "Diffuse Horizontal Radiation", + "Global Horizontal Illuminance", + "Direct Normal Illuminance", + "Diffuse Horizontal Illuminance", + "Zenith Luminance", + "Wind Direction", + "Wind Speed", + "Total Sky Cover", + "Opaque Sky Cover", + "Visibility", + "Ceiling Height", + "Present Weather Observation", + "Present Weather Codes", + "Precipitable Water", + "Aerosol Optical Depth", + "Snow Depth", + "Days Since Last Snowfall", + "Albedo", + "Liquid Precipitation Depth", + "Liquid Precipitation Quantity", +] + + +def read_epw_file(epw_file_path: str) -> pd.DataFrame: + """Read and parse an EPW file into a DataFrame. + + :param epw_file_path: Path to the EPW file. + :return: DataFrame with datetime index and weather data columns. + + Example usage:: + + from era5epw.reader import read_epw_file + + df = read_epw_file("path/to/file.epw") + print(df.head()) + print(df.describe()) + """ + with open(epw_file_path) as f: + # Skip the 8 header lines + lines = f.readlines() + + # Data starts from line 9 (index 8) + data_lines = lines[8:] + + # Parse data lines + data = [] + for line in data_lines: + if line.strip(): + data.append(line.strip().split(",")) + + # Create DataFrame + df = pd.DataFrame(data, columns=EPW_COLUMNS) + + # Convert numeric columns to float + for col in df.columns: + if col not in ["Year", "Month", "Day", "Hour", "Minute"]: + df[col] = pd.to_numeric(df[col], errors="coerce") + + # Convert time columns to int + df["Year"] = df["Year"].astype(int) + df["Month"] = df["Month"].astype(int) + df["Day"] = df["Day"].astype(int) + df["Hour"] = df["Hour"].astype(int) + + # Create datetime index (handle hour 24 as hour 0 of next day) + def create_datetime(row): + year, month, day, hour = row["Year"], row["Month"], row["Day"], row["Hour"] + if hour == 24: + # Hour 24 means midnight of the next day + dt = pd.Timestamp(year=year, month=month, day=day) + pd.Timedelta(days=1) + else: + dt = pd.Timestamp(year=year, month=month, day=day, hour=hour) + return dt + + df["Datetime"] = df.apply(create_datetime, axis=1) + df.set_index("Datetime", inplace=True) + + return df diff --git a/era5epw/visualize.py b/era5epw/visualize.py index 43d9a7e..5a5f61e 100644 --- a/era5epw/visualize.py +++ b/era5epw/visualize.py @@ -11,44 +11,7 @@ import pandas as pd import plotly.graph_objects as go -# EPW column names as per EnergyPlus EPW format specification -EPW_COLUMNS = [ - "Year", - "Month", - "Day", - "Hour", - "Minute", - "Data Source and Uncertainty Flags", - "Dry Bulb Temperature", - "Dew Point Temperature", - "Relative Humidity", - "Atmospheric Station Pressure", - "Extraterrestrial Horizontal Radiation", - "Extraterrestrial Direct Normal Radiation", - "Horizontal Infrared Radiation Intensity", - "Global Horizontal Radiation", - "Direct Normal Radiation", - "Diffuse Horizontal Radiation", - "Global Horizontal Illuminance", - "Direct Normal Illuminance", - "Diffuse Horizontal Illuminance", - "Zenith Luminance", - "Wind Direction", - "Wind Speed", - "Total Sky Cover", - "Opaque Sky Cover", - "Visibility", - "Ceiling Height", - "Present Weather Observation", - "Present Weather Codes", - "Precipitable Water", - "Aerosol Optical Depth", - "Snow Depth", - "Days Since Last Snowfall", - "Albedo", - "Liquid Precipitation Depth", - "Liquid Precipitation Quantity", -] +from era5epw.reader import read_epw_file # Series to hide from the dropdown menu HIDDEN_SERIES = [ @@ -94,55 +57,6 @@ } -def read_epw_file(epw_file_path: str) -> pd.DataFrame: - """Read and parse an EPW file into a DataFrame. - - :param epw_file_path: Path to the EPW file. - :return: DataFrame with datetime index and weather data columns. - """ - with open(epw_file_path) as f: - # Skip the 8 header lines - lines = f.readlines() - - # Data starts from line 9 (index 8) - data_lines = lines[8:] - - # Parse data lines - data = [] - for line in data_lines: - if line.strip(): - data.append(line.strip().split(",")) - - # Create DataFrame - df = pd.DataFrame(data, columns=EPW_COLUMNS) - - # Convert numeric columns to float - for col in df.columns: - if col not in ["Year", "Month", "Day", "Hour", "Minute"]: - df[col] = pd.to_numeric(df[col], errors="coerce") - - # Convert time columns to int - df["Year"] = df["Year"].astype(int) - df["Month"] = df["Month"].astype(int) - df["Day"] = df["Day"].astype(int) - df["Hour"] = df["Hour"].astype(int) - - # Create datetime index (handle hour 24 as hour 0 of next day) - def create_datetime(row): - year, month, day, hour = row["Year"], row["Month"], row["Day"], row["Hour"] - if hour == 24: - # Hour 24 means 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python = "~3.12" -numpy = "1.26.4" +numpy = "2.4.4" pandas = "2.2.3" -xarray = "2025.7.0" -cdsapi = "0.7.6" -netcdf4 = "1.7.2" -plotly = "^5.24.1" +xarray = "2026.4.0" +cdsapi = "0.7.7" +netcdf4 = "1.7.4" +plotly = "6.7.0" +zarr = "3.2.1" +dask = "2026.3.0" +requests = "^2.34.0" +aiohttp = "^3.13.5" [tool.poetry.scripts] era5epw_download = "era5epw.main:download" diff --git a/tests/test_edh.py b/tests/test_edh.py new file mode 100644 index 0000000..a05b171 --- /dev/null +++ b/tests/test_edh.py @@ -0,0 +1,101 @@ +import unittest + +from era5epw.edh import ( + CDS_TO_EDH_VARS, + _normalize_longitude_input, + _normalize_longitude_output, +) + + +class TestEDH(unittest.TestCase): + def test_normalize_longitude_input_positive(self): + """Positive longitude should remain unchanged.""" + self.assertAlmostEqual(_normalize_longitude_input(2.5), 2.5) + + def test_normalize_longitude_input_zero(self): + """Zero longitude should remain unchanged.""" + self.assertAlmostEqual(_normalize_longitude_input(0.0), 0.0) + + def test_normalize_longitude_input_negative(self): + """Negative longitude should be converted to [0, 360] range.""" + # Nantes: -1.5533 -> 360 - 1.5533 = 358.4467 + self.assertAlmostEqual(_normalize_longitude_input(-1.5533), 358.4467) + # -180 -> 180 + self.assertAlmostEqual(_normalize_longitude_input(-180.0), 180.0) + + def test_normalize_longitude_output_above_180(self): + """Longitude above 180 should be converted to negative.""" + self.assertAlmostEqual(_normalize_longitude_output(358.4467), -1.5533) + self.assertAlmostEqual(_normalize_longitude_output(270.0), -90.0) + + def test_normalize_longitude_output_below_180(self): + """Longitude below 180 should remain unchanged.""" + self.assertAlmostEqual(_normalize_longitude_output(2.5), 2.5) + self.assertAlmostEqual(_normalize_longitude_output(0.0), 0.0) + self.assertAlmostEqual(_normalize_longitude_output(180.0), 180.0) + + def test_cds_to_edh_vars_mapping(self): + """All expected CDS variables should have EDH mappings.""" + expected_cds_vars = [ + "2m_temperature", + "2m_dewpoint_temperature", + "surface_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "total_cloud_cover", + "uv_visible_albedo_for_direct_radiation", + "snow_depth", + "soil_temperature_level_1", + "total_precipitation", + ] + for var in expected_cds_vars: + self.assertIn(var, CDS_TO_EDH_VARS, f"Missing EDH mapping for CDS variable '{var}'") + + def test_cds_to_edh_vars_values(self): + """EDH short names should match expected values.""" + self.assertEqual(CDS_TO_EDH_VARS["2m_temperature"], "t2m") + self.assertEqual(CDS_TO_EDH_VARS["2m_dewpoint_temperature"], "d2m") + self.assertEqual(CDS_TO_EDH_VARS["surface_pressure"], "sp") + self.assertEqual(CDS_TO_EDH_VARS["10m_u_component_of_wind"], "u10") + self.assertEqual(CDS_TO_EDH_VARS["10m_v_component_of_wind"], "v10") + self.assertEqual(CDS_TO_EDH_VARS["total_cloud_cover"], "tcc") + self.assertEqual(CDS_TO_EDH_VARS["uv_visible_albedo_for_direct_radiation"], "aluvd") + self.assertEqual(CDS_TO_EDH_VARS["snow_depth"], "sd") + self.assertEqual(CDS_TO_EDH_VARS["soil_temperature_level_1"], "stl1") + self.assertEqual(CDS_TO_EDH_VARS["total_precipitation"], "tp") + + +class TestEDHTokenLoading(unittest.TestCase): + def test_load_edh_token_missing(self): + """Should raise FileNotFoundError when no token is available.""" + import os + + # Reset the cached token + import era5epw.edh + from era5epw.edh import load_edh_token + + era5epw.edh._edh_token = None + # Remove env var if set + old_env = os.environ.pop("EDH_TOKEN", None) + try: + with self.assertRaises(FileNotFoundError): + load_edh_token() + finally: + era5epw.edh._edh_token = None + if old_env is not None: + os.environ["EDH_TOKEN"] = old_env + + def test_load_edh_token_from_env(self): + """Should load token from EDH_TOKEN environment variable.""" + import os + + import era5epw.edh + + era5epw.edh._edh_token = None + os.environ["EDH_TOKEN"] = "test_token_123" + try: + token = era5epw.edh.load_edh_token() + self.assertEqual(token, "test_token_123") + finally: + era5epw.edh._edh_token = None + del os.environ["EDH_TOKEN"] diff --git a/tests/test_reader.py b/tests/test_reader.py new file mode 100644 index 0000000..8a506b1 --- /dev/null +++ b/tests/test_reader.py @@ -0,0 +1,93 @@ +"""Tests for the EPW reader module.""" + +import os +import tempfile +import unittest + +import pandas as pd + +from era5epw.reader import read_epw_file + + +class TestEPWReader(unittest.TestCase): + """Test cases for EPW reader functions.""" + + @classmethod + def setUpClass(cls): + """Create a test EPW file for all tests.""" + cls.test_epw_file = tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".epw") + cls.test_epw_path = cls.test_epw_file.name + + # Write minimal EPW header and data + cls.test_epw_file.write("LOCATION,Test City,,,ERA5,n/a,48.86,2.35,1,35\n") + cls.test_epw_file.write("DESIGN CONDITIONS,0\n") + cls.test_epw_file.write("TYPICAL/EXTREME PERIODS,0\n") + cls.test_epw_file.write("GROUND TEMPERATURES,0\n") + cls.test_epw_file.write("HOLIDAYS/DAYLIGHT SAVINGS,No,0,0,0\n") + cls.test_epw_file.write("COMMENTS 1,Test data\n") + cls.test_epw_file.write("COMMENTS 2,Test data\n") + cls.test_epw_file.write("DATA PERIODS,1,1,Data,Monday,1/1,1/2\n") + + # Write 48 hours of data (2 days) + for day in range(1, 3): + for hour in range(1, 25): + temp = 10.0 + (hour % 12) + wind = 2.0 + (hour % 6) + cls.test_epw_file.write( + f"2024,1,{day},{hour},0,9,{temp},{temp - 2},80,101325," + f"9999,9999,9999,100,50,40,11000,5250,4760,9999," + f"180,{wind},5,5,9999,77777,0,999999999,999,999,0,99,0.5,0,1\n" + ) + cls.test_epw_file.close() + + @classmethod + def tearDownClass(cls): + """Clean up test EPW file.""" + if os.path.exists(cls.test_epw_path): + os.remove(cls.test_epw_path) + + def test_read_epw_file_returns_dataframe(self): + """Test that read_epw_file returns a DataFrame.""" + df = read_epw_file(self.test_epw_path) + self.assertIsInstance(df, pd.DataFrame) + + def test_read_epw_file_row_count(self): + """Test that the DataFrame has the expected number of rows.""" + df = read_epw_file(self.test_epw_path) + self.assertEqual(len(df), 48) + + def test_read_epw_file_datetime_index(self): + """Test that the DataFrame has a DatetimeIndex.""" + df = read_epw_file(self.test_epw_path) + self.assertIsInstance(df.index, pd.DatetimeIndex) + + def test_read_epw_file_columns(self): + """Test that the DataFrame has the expected weather data columns.""" + df = read_epw_file(self.test_epw_path) + self.assertIn("Dry Bulb Temperature", df.columns) + self.assertIn("Wind Speed", df.columns) + self.assertIn("Relative Humidity", df.columns) + self.assertIn("Atmospheric Station Pressure", df.columns) + + def test_read_epw_file_numeric_values(self): + """Test that numeric columns contain proper numeric values.""" + df = read_epw_file(self.test_epw_path) + self.assertTrue(pd.api.types.is_numeric_dtype(df["Dry Bulb Temperature"])) + self.assertTrue(pd.api.types.is_numeric_dtype(df["Wind Speed"])) + + def test_read_epw_file_usable_for_analysis(self): + """Test that the returned DataFrame can be used for typical data analysis.""" + df = read_epw_file(self.test_epw_path) + + # Should be able to compute statistics + stats = df["Dry Bulb Temperature"].describe() + self.assertIn("mean", stats.index) + self.assertIn("std", stats.index) + + # Should be able to filter by date + filtered = df[df.index.day == 1] + self.assertGreater(len(filtered), 0) + + +if __name__ == "__main__": + unittest.main()