TrackModelResult

TrackModelResult(
    data,
    *,
    name=None,
    item=None,
    quantity=None,
    x_item=0,
    y_item=1,
    keep_duplicates='first',
    aux_items=None,
)

Model result for a track.

Construct a TrackModelResult from a dfs0 file, mikeio.Dataset, pandas.DataFrame or a xarray.Datasets

Parameters

Name	Type	Description	Default
data	types.TrackType	The input data or file path	required
name	Optional[str]	The name of the model result, by default None (will be set to file name or item name)	None
item	str | int | None	If multiple items/arrays are present in the input an item must be given (as either an index or a string), by default None	None
x_item	str | int | None	Item of the first coordinate of positions, by default None	0
y_item	str | int | None	Item of the second coordinate of positions, by default None	1
quantity	Quantity	Model quantity, for MIKE files this is inferred from the EUM information	None
keep_duplicates	(str, bool)	Strategy for handling duplicate timestamps (wraps xarray.Dataset.drop_duplicates) “first” to keep first occurrence, “last” to keep last occurrence, False to drop all duplicates, “offset” to add milliseconds to consecutive duplicates, by default “first”	'first'
aux_items	Optional[list[int | str]]	Auxiliary items, by default None	None

Attributes

Name	Description
gtype	Geometry type
n_points	Number of data points
name	Name of time series (value item name)
plot	Plot using the ComparerPlotter
quantity	Quantity of time series
time	Time index
values	Values as numpy array
x	x-coordinate
y	y-coordinate

Methods

Name	Description
equals	Check if two TimeSeries are equal
sel	Select data by label
to_dataframe	Convert matched data to pandas DataFrame
trim	Trim observation data to a given time interval

equals

TrackModelResult.equals(other)

Check if two TimeSeries are equal

sel

TrackModelResult.sel(**kwargs)

Select data by label

to_dataframe

TrackModelResult.to_dataframe()

Convert matched data to pandas DataFrame

Include x, y coordinates only if gtype=track

Returns

Name	Type	Description
	pd.DataFrame	data as a pandas DataFrame

trim

TrackModelResult.trim(
    start_time=None,
    end_time=None,
    buffer='1s',
    no_overlap='error',
)

Trim observation data to a given time interval

Parameters

Name	Type	Description	Default
start_time	pd.Timestamp	start time	None
end_time	pd.Timestamp	end time	None
buffer	str	buffer time around start and end time, by default “1s”	'1s'
no_overlap	Literal['ignore', 'error', 'warn']	Empty data handling.	'error'