match

match(
    obs,
    mod,
    *,
    obs_item=None,
    mod_item=None,
    gtype=None,
    max_model_gap=None,
    spatial_method=None,
    spatial_tolerance=0.001,
    obs_no_overlap='error',
)

Match observation and model result data in space and time

NOTE: In case of multiple model results with different time coverage, only the overlapping time period will be used! (intersection)

NOTE: In case of multiple observations, multiple models can only be matched if they are all of SpatialField type, e.g. DfsuModelResult or GridModelResult.

Parameters

Name	Type	Description	Default
obs	(str, Path, pd.DataFrame, Observation, Sequence[Observation])	Observation(s) to be compared	required
mod	(str, Path, pd.DataFrame, ModelResult, Sequence[ModelResult])	Model result(s) to be compared	required
obs_item	int or str	observation item if obs is a file/dataframe, by default None	None
mod_item	(int, str)	model item if mod is a file/dataframe, by default None	None
gtype	(str, optional)	Geometry type of the model result (if mod is a file/dataframe). If not specified, it will be guessed.	None
max_model_gap	(float, optional)	Maximum time gap (s) in the model result (e.g. for event-based model results), by default None	None
spatial_method	str	For Dfsu- and GridModelResult, spatial interpolation/selection method. - For DfsuModelResult, one of: ‘contained’ (=isel), ‘nearest’, ‘inverse_distance’ (with 5 nearest points), by default “inverse_distance”. - For GridModelResult, passed to xarray.interp() as method argument, by default ‘linear’.	None
spatial_tolerance	float	Spatial tolerance (in the units of the coordinate system) for matching model track points to observation track points. Model points outside this tolerance will be discarded. Only relevant for TrackModelResult and TrackObservation, by default 1e-3.	0.001
obs_no_overlap	Literal['ignore', 'error', 'warn']	How to handle observations with no overlap with model results. One of: ‘ignore’, ‘error’, ‘warn’, by default ‘error’.	'error'

Returns

Name	Type	Description
	Comparer	In case of a single observation
	ComparerCollection	In case of multiple observations

See Also

from_matched - Create a Comparer from observation and model results that are already matched