icenet.data.processors package

icenet.data.processors package#

Submodules#

icenet.data.processors.cmip module#

class icenet.data.processors.cmip.IceNetCMIPPreProcessor(source: str, member: str, *args, **kwargs)[source]#

Bases: IceNetPreProcessor

Parameters:

  • source

  • member

pre_normalisation(var_name: str, da: object)[source]#
Parameters:

  • var_name

  • da

Returns:

icenet.data.processors.cmip.main()[source]#

icenet.data.processors.era5 module#

class icenet.data.processors.era5.IceNetERA5PreProcessor(*args, **kwargs)[source]#

Bases: IceNetPreProcessor

icenet.data.processors.era5.main()[source]#

icenet.data.processors.hres module#

class icenet.data.processors.hres.IceNetHRESPreProcessor(*args, **kwargs)[source]#

Bases: IceNetPreProcessor

icenet.data.processors.hres.main()[source]#

icenet.data.processors.meta module#

class icenet.data.processors.meta.IceNetMetaPreProcessor(name: str, include_circday: bool = True, include_land: bool = True, **kwargs)[source]#

Bases: IceNetPreProcessor

Parameters:

  • name

  • include_circday

  • include_land

init_source_data(lag_days: object = None, lead_days: object = None)[source]#
Parameters:

  • lag_days

  • lead_days

process()[source]#
icenet.data.processors.meta.main()[source]#

icenet.data.processors.osi module#

class icenet.data.processors.osi.IceNetOSIPreProcessor(*args, missing_dates: object = None, **kwargs)[source]#

Bases: IceNetPreProcessor

Parameters:

missing_dates

pre_normalisation(var_name: str, da: object)[source]#
Parameters:

  • var_name

  • da

Returns:

icenet.data.processors.osi.main()[source]#

icenet.data.processors.utils module#

icenet.data.processors.utils.condense_data(identifier: str, hemisphere: str, variable: str)[source]#

Takes existing daily files and creates yearly files

Previous early versions of the pipeline were storing files day by day, which is pretty wasteful. This allows us to create the yearly files and avoid all that nasty re-downloading business

Parameters:

  • identifier

  • hemisphere

  • variable

icenet.data.processors.utils.condense_main()[source]#
icenet.data.processors.utils.sic_interpolate(da: object, masks: object) → object[source]#
Parameters:

  • da

  • masks

Returns:

Module contents#