wolfhece.irm_alaro

Module Contents

wolfhece.irm_alaro.OPENDATA_FORECASTS = 'forecasts'

wolfhece.irm_alaro.OPENDATA_FTP_SERVER = 'opendata.meteo.be'

wolfhece.irm_alaro.OPENDATA_ALARO_40L = 'alaro_40l'

wolfhece.irm_alaro.FILE_PREFIX = 'alaro40l'

wolfhece.irm_alaro._convert_col2date_str(col: str) → str

Create a string representation of the date from the column name.

wolfhece.irm_alaro._extract_dates_from_columnstr(col: str) → list[datetime.datetime]

Extract the run date and forecast date from the column name.

wolfhece.irm_alaro.ArrayBelgium() → wolfhece.wolf_array.WolfArray

Create a WolfArray for the Belgium domain.

class wolfhece.irm_alaro.GribFiles(*args, **kwds)

Bases: enum.Enum

Enum for Grib files used in IRM Alaro data.

FILE_10U = '10U.grb'

FILE_10V = '10V.grb'

FILE_MaxT2 = 'MaxT2.grb'

FILE_MinT2 = 'MinT2.grb'

FILE_2D = '2D.grb'

FILE_2T = '2T.grb'

FILE_RH2M = 'RH2M.grb'

FILE_CR = 'CR.grb'

FILE_CS = 'CS.grb'

FILE_Z = 'Z.grb'

FILE_IFCCC = 'IFCCC.grb'

FILE_IFHCC = 'IFHCC.grb'

FILE_IFLCC = 'IFLCC.grb'

FILE_IFMCC = 'IFMCC.grb'

FILE_IFTCC = 'IFTCC.grb'

FILE_LSR = 'LSR.grb'

FILE_LSS = 'LSS.grb'

FILE_MSLP = 'MSLP.grb'

FILE_R = 'R.grb'

FILE_Q = 'Q.grb'

FILE_MerGust = 'MerGust.grb'

FILE_ZonGust = 'ZonGust.grb'

FILE_SurfSWrad = 'SurfSWrad.grb'

FILE_SurfLWrad = 'SurfLWrad.grb'

FILE_SurfCape = 'SurfCape.grb'

FILE_ST = 'ST.grb'

FILE_ORO = 'ORO.grb'

FILE_T = 'T.grb'

FILE_TotPrecip = 'TotPrecip.grb'

FILE_U = 'U.grb'

FILE_V = 'V.grb'

FILE_W = 'W.grb'

FILE_WBPT = 'WBPT.grb'

FILE_fzht = 'fzht.grb'

class wolfhece.irm_alaro.IRM_Alaro(ftp_server: str = OPENDATA_FTP_SERVER, ftp_path: str = OPENDATA_FORECASTS)

Class for handling IRM Alaro forecasts.

ftp_server = 'opendata.meteo.be'

ftp_path = 'forecasts'

_gdf = None

_gdf_cache = None

_gdf_diff = None

_zones = None

_array

_available_run_dates = []

_colormap = 'Blues'

_cities

_ftp_init()

Initialize the FTP connection.

_ftp_close()

Close the FTP connection.

list_run_dates_cached() → list

Return the cached list of available run dates.

list_run_dates() → list

List available data files on the FTP server.

property run_dates: list

Return the available forecasts run dates.

property run_dates_str: str

Return the available forecasts run dates as a string.

list_files_for_forecast(run_date: str) → list

List files for a specific forecast.

Parameters:

run_date – The forecast time.

_get_filename(file: GribFiles, run_date: str) → str

Generate the filename for a given Grib file and forecast.

Parameters:

• file – The Grib file enum.

• run_date – The forecast time.

property data_directory: pathlib.Path

Return the data directory path.

download_data(filename: GribFiles | str, run_date: str) → pathlib.Path

Download data from the FTP server.

Parameters:

• filename – The Grib file to download or a specific filename.

• run_date – The forecast time.

download_TotalPrecipitations_available_files() → list[pathlib.Path]

Download Cumulated rain, Temperature et 2m - files available on the FTP server.

download_all_available_files() → list[pathlib.Path]

Download all files available on the FTP server.

_get_center_coordinates(filename: GribFiles, run_date: str, EPSG: str = 'EPSG:31370') → tuple[numpy.ndarray, numpy.ndarray]

Load GRIB data and compute coordinates.

Parameters:

• filename – The GRIB file to process.

• forecast – The forecast time.

• download – Whether to download the file if it doesn’t exist.

• EPSG – The target EPSG code for the output coordinates.

Returns:

The center coordinates (x, y) for the given GRIB file and forecast.

_get_corners_coordinates(filename: GribFiles, run_date: str, EPSG: str = 'EPSG:31370') → tuple[tuple[numpy.ndarray, numpy.ndarray]]

Load GRIB data and compute coordinates.

Parameters:

• filename – The GRIB file to process.

• forecast – The forecast time.

• download – Whether to download the file if it doesn’t exist.

• EPSG – The target EPSG code for the output coordinates.

Returns:

The coordinates (x, y) for (center, lower-left, lower-right, upper-right, upper-left).

_prepare_gdf_from_grib(filename: GribFiles, run_date: str, EPSG: str = 'EPSG:31370') → geopandas.GeoDataFrame

Prepare a GeoDataFrame from grib file.

Parameters:

• filename – The GRIB file to process.

• forecast – The forecast time.

• EPSG – The target EPSG code for the output coordinates.

Returns:

The GeoDataFrame with polygons for each grid cell.

_prepare_Zones_from_grib(filename: GribFiles, run_date: str, EPSG: str = 'EPSG:31370') → wolfhece.PyVertexvectors.Zones

Prepare a Zones from brib file.

Parameters:

• filename – The GRIB file to process.

• forecast – The forecast time.

• EPSG – The target EPSG code for the output coordinates.

Returns:

The Zones with polygons for each grid cell.

_load_grib_data(filename: GribFiles, run_dates: str | list[str]) → geopandas.GeoDataFrame

Load GRIB data from a file and add it to the GeoDataFrame.

Parameters:

• filename – The GRIB file to process.

• run_dates – The forecast run dates.

Returns:

The GeoDataFrame with added data.

reset_gdf()

Reset the GeoDataFrame.

load_grib_data_to_gdf(filename: GribFiles, run_dates: str | list[str]) → geopandas.GeoDataFrame

Load GRIB data from a file and add it to the GeoDataFrame.

Parameters:

• filename – The GRIB file to process.

• run_dates – The forecast run dates.

Returns:

The GeoDataFrame with added data.

load_grib_data_to_Zones(filename: GribFiles, run_dates: str | list[str]) → geopandas.GeoDataFrame

Load GRIB data from a file and add it to the Zones.

Parameters:

• filename – The GRIB file to process.

• run_dates – The forecast run dates.

Returns:

The Zones with added data.

forecasts_to_arrays(forecasts: str | list[str] = None) → list[wolfhece.wolf_array.WolfArray]

Set the forecasts to the WolfArray.

Parameters:

forecasts – List of forecast columns to convert to WolfArray. If None, all forecast columns are used.

get_forecast_columns(diff: bool = False) → list[str]

Get the list of forecast columns in the GeoDataFrame.

Exclude ‘geometry’, ‘centroid_x’, ‘centroid_y’ columns.

Parameters:

diff – If True, get columns from the diff GeoDataFrame.

_compute_diff()

Compute local rain by difference in total cumulated rainfall

_load_grib_metadata(filename: GribFiles, run_date: str) → numpy.ndarray

Load GRIB metadata from a file.

Parameters:

• filename – The GRIB file to process.

• run_date – The forecast run date.

_create_animation(filename: GribFiles, run_date: str, bounds: list[list[float], list[float]] | str = 'Belgium', vmin: float = None, vmax: float = None, factor: float = 1.0) → matplotlib.animation.FuncAnimation

Create a video from the GeoDataFrame data.

Parameters:

• filename – The GRIB file to process.

• run_date – The forecast run date to visualize.

• bounds – Bounds for the plot. Can be ‘Belgium’ or a list of [xlim, ylim].

• vmin – Minimum value for color scaling.

• vmax – Maximum value for color scaling.

• factor – Factor to multiply the data values for scaling.

Returns:

The animation object.

_create_comparison_animation(filename: GribFiles, run_dates: list[str], size=10, bounds: list[list[float], list[float]] | str = 'Belgium', vmin: float = None, vmax: float = None, factor: float = 1.0) → matplotlib.animation.FuncAnimation

Create a video from the GeoDataFrame data.

Parameters:

• filename – The GRIB file to process.

• run_dates – List of forecast run dates to compare.

• size – Size of each subplot.

• bounds – Bounds for the plot. Can be ‘Belgium’ or a list of [xlim, ylim].

• vmin – Minimum value for color scaling.

• vmax – Maximum value for color scaling.

• factor – Factor to multiply the data values for scaling.

Returns:

The animation object.

_create_comparison_animation_diff(filename: GribFiles, run_dates: list[str], size=10, bounds: list[list[float], list[float]] | str = 'Belgium', vmin: float = None, vmax: float = None, factor: float = 1.0) → matplotlib.animation.FuncAnimation

Create a video from the GeoDataFrame data.

Parameters:

• filename – The GRIB file to process.

• run_dates – List of forecast run dates to compare.

• size – Size of each subplot.

• bounds – Bounds for the plot. Can be ‘Belgium’ or a list of [xlim, ylim].

• vmin – Minimum value for color scaling.

• vmax – Maximum value for color scaling.

• factor – Factor to multiply the data values for scaling.

Returns:

The animation object.

video_cumulated_rain(run_date: str, output_file: pathlib.Path, fps: int = 2)

Create a MP4 video comparison of cumulated rain forecasts.

Parameters:

• forecast – The forecast date string.

• output_file – The output MP4 file path.

• fps – Frames per second for the video.

videos_cumulated_rain_allforecasts(output_dir: pathlib.Path, fps: int = 2, run_dates: str | list[str] = None) → list[pathlib.Path] | None

Create a MP4 video comparison of cumulated rain forecasts.

Parameters:

• output_dir – The output directory for the MP4 files.

• fps – Frames per second for the video.

video_gradient_cumulated_rain_compare(output_file: pathlib.Path, fps: int = 2, run_dates: str | list[str] = None) → pathlib.Path | None

Create a MP4 video comparison of cumulated rain forecasts.

Parameters:

• output_file – The output MP4 file path.

• fps – Frames per second for the video.

convert_gdf2dataframe(X: float, Y: float, use_diff: bool = False) → pandas.DataFrame

Convert the GeoDataFrame to a Pandas DataFrame for a given point (X, Y).

Parameters:

• X – The X coordinate.

• Y – The Y coordinate.

Returns:

The Pandas DataFrame with the data for the nearest grid cell.

_plot4XY(X: float, Y: float, factor: float = 1.0, size: tuple[int, int] = (10, 5), use_diff: bool = False, figax: tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes] = None) → matplotlib.pyplot.Figure

Plot the data for a given point (X, Y).

Parameters:

• X – The X coordinate.

• Y – The Y coordinate.

• factor – The factor to multiply the data values for scaling.

• size – The size of the plot.

Returns:

The Matplotlib Figure object.

_bar4XY(X: float, Y: float, factor: float = 1.0, size: tuple[int, int] = (10, 5), use_diff: bool = False, figax: tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes] = None) → matplotlib.pyplot.Figure

Plot the data for a given point (X, Y).

Parameters:

• X – The X coordinate.

• Y – The Y coordinate.

• factor – The factor to multiply the data values for scaling.

• size – The size of the plot.

Returns:

The Matplotlib Figure object.

plot_TotPrec4XY(X: float, Y: float, size: tuple[int, int] = (10, 5)) → matplotlib.pyplot.Figure

Plot the total precipitation data for a given point (X, Y).

Parameters:

• X – The X coordinate.

• Y – The Y coordinate.

• size – The size of the plot.

Returns:

The Matplotlib Figure object.

plot_RainIntensity4XY(X: float, Y: float, size: tuple[int, int] = (10, 5)) → matplotlib.pyplot.Figure

Plot the rain intensity data for a given point (X, Y).

Parameters:

• X – The X coordinate.

• Y – The Y coordinate.

• size – The size of the plot.

Returns:

The Matplotlib Figure object.

plot_Rain_and_TotPrecip4XY(X: float, Y: float, size: tuple[int, int] = (10, 10)) → matplotlib.pyplot.Figure

Plot the rain intensity and total precipitation data for a given point (X, Y).

Parameters:

• X – The X coordinate.

• Y – The Y coordinate.

• size – The size of the plot.

Returns:

The Matplotlib Figure object.