Gap

A gap is a part of the observational timeseries that is missing. It is thus related to a station and an observationtype. The gaps are described by the Gap class, containing methods for manipulating the gap (i.g. filling a gap).

A regular user should not directly interact with a ``Gap`` instance. All methods for filling gaps are accessible in the Station and Dataset classes.

Constructor

Gap(gaprecords, obstype, stationname)

Represents a gap in observational data for a specific station and observation type.

Attributes

A summary of all the attributes (and properties) of the Gap class.

Gap.records	Return the records of the gap.
Gap.obstype	Return the observation type.
Gap.stationname	Return the station name.
Gap.fillsettings	Return the settings used for filling the gap.
Gap.fillstatus	Returns the fill status of the gap.
Gap.start_datetime	Return the start datetime of the gap.
Gap.end_datetime	Return the end datetime of the gap.
Gap.df	Gap DataFrame constructor.

Methods

A summary of all methods in the Gap class.

Gap.get_info([printout])	Print or return detailed information about the Gap.
Gap.flag_can_be_filled([overwrite])	Determine if the gap can be filled.
Gap.flush_fill()	Clear all fill information for this gap.
Gap.interpolate(sensordata[, method, ...])	Fill the gap using interpolation of SensorData.
Gap.raw_model_gapfill(modeltimeseries[, ...])	Fill the gap using model data without correction.
Gap.debiased_model_gapfill(sensordata, ...)	Fill the gaps using model data corrected for the bias.
Gap.diurnal_debiased_model_gapfill(...[, ...])	Fill the gaps using model data corrected for the diurnal bias.
Gap.weighted_diurnal_debiased_model_gapfill(...)	Fill the gaps using a weighted sum of model data corrected for the diurnal bias and weights with respect to the start of the gap.