pycif.plugins.datastreams.fluxes.gridded_NetCDF — API reference#
Configuration reference: gridded_NetCDF plugin
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.fetch.fetch(ref_dir, ref_file, input_interval, target_dir, tracer=None, component=None, **kwargs)[source]#
Fetch files and corresponding dates in Gridded NetCDF files
- Parameters:
ref_dir (str) – Path to the data
ref_file (str) – File format of the data
input_interval (list[datetime.datetime]) – Date range
target_dir (str) – Where to link the data
tracer (_type_, optional) – _description_. Defaults to None.
- Returns:
_description_
- Return type:
_type_
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.get_domain.get_domain(ref_dir, ref_file, input_interval=None, target_dir=None, tracer=None)[source]#
Read information from the reference file to define the data horizontal and, if relevant, vertical domain.
- Parameters:
ref_dir (str) – the path to the input files
ref_file (str) – format of the input files
input_interval (list) – simulation interval (start and end dates)
target_dir (str) – where to copy
tracer – the tracer Plugin
- Returns:
a domain class object, with the definition of the center grid cells coordinates, as well as corners
- Return type:
domain (Domain)
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.read.read(self, name, varnames, dates, files, interpol_flx=False, tracer=None, model=None, ddi=None, debug_read=False, **kwargs)[source]#
Get fluxes from raw files and load them into a pyCIF variables.
- Parameters:
name (str) – name of the component
varnames (list[str]) – original names of variables to read; use name if varnames is empty
dates (list) – list of the date intervals to extract
files (list) – list of the files matching dates
- Returns:
- the actual data with dimension:
time, levels, latitudes, longitudes
- Return type:
xr.DataArray
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.time_coord.find_time_coord(ds: Dataset, tracer, ref_date)[source]#
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.time_coord.get_calendar(ds: Dataset, time_varname: str)[source]#
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.time_coord.preprocess_time_coord(ds: Dataset, tracer, time_varname: str, ref_date, time_midpoint: bool = False, time_endpoint: bool = False, file_freq: str = '', is_climatology: bool = False) Dataset[source]#
Preprocesses the time coordinate
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.time_coord.decode_datetimes(ds: Dataset, tracer, var_name: str, ref_date, file_freq: str = '', is_climatology: bool = False) Dataset[source]#
Decode datetimes from variable ‘var_name’ in the dataset ‘ds’
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.time_coord.decode_datetimes_with_units(ds: Dataset, var_name: str, units: str) Dataset[source]#
Decode datetimes from variable ‘var_name’ in the dataset ‘ds’ using its ‘units’ attribute
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.time_coord.decode_datetimes_with_format(ds: Dataset, var_name: str, time_format: str) Dataset[source]#
Decode datetimes from variable ‘var_name’ in the dataset ‘ds’ using a time format string
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.time_coord.shift_years(ds: Dataset, var_name: str, year_offset: int) Dataset[source]#
Offset years in files if necessary then converts to standard calendar
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.time_coord.convert_calendar(ds: Dataset, tracer, var_name: str, calendar: str) Dataset[source]#
Converts to standard calendar
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.time_coord.expand_leap_years(ds: Dataset, tracer, var_name: str) Dataset[source]#
- pycif.plugins.datastreams.fluxes.gridded_NetCDF.utils.get_time_intervals(ds, tracer, time_varname, date_i, date_f, calendar, time_midpoint=False, time_endpoint=False)[source]#