pycontrails.datalib.ecmwf.IFS#

class pycontrails.datalib.ecmwf.IFS(time, variables, pressure_levels=-1, paths=None, grid=None, forecast_path=None, forecast_date=None)#

Bases: MetDataSource

ECMWF Integrated Forecasting System (IFS) data source.

Warning

This data source is not fully implemented.

Parameters:
  • time (datalib.TimeInput | None) – The time range for data retrieval, either a single datetime or (start, end) datetime range. Input must be a single datetime-like or tuple of datetime-like (datetime, pandas.Timestamp, numpy.datetime64) specifying the (start, end) of the date range, inclusive. If None, all time coordinates will be loaded.

  • variables (datalib.VariableInput) – Variable name (i.e. “air_temperature”, [“air_temperature, relative_humidity”]) See pressure_level_variables for the list of available variables.

  • pressure_levels (datalib.PressureLevelInput, optional) – Pressure level bounds for data (min, max), in hPa (mbar) Set to -1 for to download surface level parameters. Defaults to -1.

  • paths (str | list[str] | pathlib.Path | list[pathlib.Path] | None, optional) – UNSUPPORTED FOR IFS

  • forecast_path (str | pathlib.Path | None, optional) – Path to local forecast files. Defaults to None

  • forecast_date (DatetimeLike, optional) – Forecast date to load specific netcdf files. Defaults to None

Notes

This takes an average pressure of the model level to create pressure level dimensions.

__init__(time, variables, pressure_levels=-1, paths=None, grid=None, forecast_path=None, forecast_date=None)#

Methods

__init__(time, variables[, pressure_levels, ...])

cache_dataset(dataset)

Cache data from data source.

create_cachepath(t)

Return cachepath to local data file based on datetime.

download(**xr_kwargs)

Confirm all data files are downloaded and available locally in the cachestore.

download_dataset(times)

Download data from data source for input times.

is_datafile_cached(t, **xr_kwargs)

Check datafile defined by datetime for variables and pressure levels in class.

list_timesteps_cached(**xr_kwargs)

Get a list of data files available locally in the cachestore.

list_timesteps_not_cached(**xr_kwargs)

Get a list of data files not available locally in the cachestore.

open_dataset(disk_paths, **xr_kwargs)

Open multi-file dataset in xarray.

open_metdataset([dataset, xr_kwargs])

Open MetDataset from data source.

set_metadata(ds)

Set met source metadata on ds.attrs.

Attributes

forecast_date

Forecast datetime of IFS forecast

forecast_path

Root path of IFS data

grid

Lat / Lon grid spacing

hash

Generate a unique hash for this datasource.

paths

Path to local source files to load.

pressure_level_variables

Parameters available from data source.

pressure_levels

List of pressure levels.

single_level_variables

Parameters available from data source.

supported_pressure_levels

IFS does not provide constant pressure levels and instead uses model levels.

supported_variables

IFS parameters available.

timesteps

List of individual timesteps from data source derived from time Use parse_time() to handle TimeInput.

variable_shortnames

Return a list of variable short names.

variable_standardnames

Return a list of variable standard names.

variables

Variables requested from data source Use parse_variables() to handle VariableInput.

cachestore

Cache store for intermediates while processing data source If None, cache is turned off.

cache_dataset(dataset)#

Cache data from data source.

Parameters:

dataset (xarray.Dataset) – Dataset loaded from remote API or local files. The dataset must have the same format as the original data source API or files.

create_cachepath(t)#

Return cachepath to local data file based on datetime.

Parameters:

t (datetime) – Datetime of datafile

Returns:

str – Path to cached data file

download_dataset(times)#

Download data from data source for input times.

Parameters:

times (list[datetime]) – List of datetimes to download a store in cache

forecast_date#

Forecast datetime of IFS forecast

forecast_path#

Root path of IFS data

open_metdataset(dataset=None, xr_kwargs=None, **kwargs)#

Open MetDataset from data source.

This method should download / load any required datafiles and returns a MetDataset of the multi-file dataset opened by xarray.

Parameters:
  • dataset (xr.Dataset | None, optional) – Input xr.Dataset loaded manually. The dataset must have the same format as the original data source API or files.

  • xr_kwargs (dict[str, int] | None, optional) – Dictionary of keyword arguments passed into xarray.open_mfdataset() when opening files. Examples include “chunks”, “engine”, “parallel”, etc. Ignored if dataset is input.

  • **kwargs (Any) – Keyword arguments passed through directly into MetDataset constructor.

Returns:

MetDataset – Meteorology dataset

set_metadata(ds)#

Set met source metadata on ds.attrs.

This is called within the open_metdataset() method to set metadata on the returned MetDataset instance.

Parameters:

ds (xr.Dataset | MetDataset) – Dataset to set metadata on. Mutated in place.

property supported_pressure_levels#

IFS does not provide constant pressure levels and instead uses model levels.

Returns:

list[int]

property supported_variables#

IFS parameters available.

Returns:

list[MetVariable] | None – List of MetVariable available in datasource