pycif.plugins.datastreams.backgrounds.tm5_background — API reference

Configuration reference: tm5_background plugin

pycif.plugins.datastreams.backgrounds.tm5_background.fetch.fetch(ref_dir, ref_file, date_interval, target_dir, tracer=None, **kwargs)

Locate TM5-4DVAR annual background files and link them to the working directory.

The requested date interval is expanded to cover full calendar years, and one yearly-formatted file name (ref_file formatted with the year’s start date) is checked per year. Each existing file is symlinked into target_dir and opened to read its time stamps (tracer.time_varname), from which hourly [t, t + 1h] date pairs are built.

Parameters:

• ref_dir – directory holding the TM5-4DVAR background files.

• ref_file – strftime-style file name pattern (relative to ref_dir), formatted once per calendar year.

• date_interval – 2-element sequence (datei, datef) giving the requested date range.

• target_dir – directory where matching files are symlinked.

• tracer – the background datastream Plugin, used for tracer.time_varname.

Returns:

(list_files, list_dates), dicts keyed by each year’s start date. list_files maps each key to a list of the (single, repeated) linked file path, one entry per time stamp found in the file; list_dates maps each key to the corresponding list of [t, t + 1h] date pairs. Years without an existing file are simply absent from both dicts.

Return type:

tuple

pycif.plugins.datastreams.backgrounds.tm5_background.read.read(self, name, varnames, dates, files, interpol_flx=False, tracer=None, model=None, **kwargs)

Read TM5-4DVAR background concentrations into a pyCIF datastore.

Iterates over the unique files referenced in files; for each new file, decodes the station-ID variable (tracer.id_varname) into station/network/agl (above-ground-level) components, reads the time stamps (tracer.time_varname) and the simulated-concentration array (self.varname), meshes stations against time stamps, and packs the result into an empty pyCIF datastore.

Note

Only the datastore built from the last (in iteration order) distinct file in files is returned: ds is reassigned on each new file rather than accumulated/concatenated across files.

Parameters:

• self – the background datastream Plugin.

• name – name of the observed parameter/component.

• varnames – unused (kept for interface consistency with other datastream read functions).

• dates – list of [start, end] date pairs, aligned with files.

• files – list of TM5-4DVAR annual background files, aligned with dates. Consecutive duplicate entries are only read once.

• interpol_flx (bool) – unused here (kept for interface consistency).

• tracer – the background tracer, used for id_varname, time_varname and the numscale scaling factor.

• model – unused here (kept for interface consistency).

Returns:

pyCIF datastore (as built by init_empty) for the last file processed, with date, station, network, parameter and duration metadata columns, and obserror/ spec main-data columns.

Return type:

pandas.DataFrame

pycif.plugins.datastreams.backgrounds.tm5_background.write.write(self, prescr_file, prescr, typefile, mode='a', **kwargs)

Write prescribed species background fields for LMDZ.

Parameters:

• self (Fluxes) – the Fluxes plugin.

• prescr_file (str) – the file where to write the prescribed field.

• prescr (xarray.Dataset) – prescribed species data to write. For typefile == "bin", must expose "fwd" and "tl" data variables.

• typefile (str) – output format, either "bin" for a raw binary file (fwd/tl arrays transposed and dumped with tofile) or "nc" for a NETCDF3_CLASSIC file.

• mode (str) – unused; kept for interface consistency. NetCDF output is always merged into any existing file, since xarray’s append mode is not supported here.