VerticalModelResult

VerticalModelResult(
    data,
    *,
    name=None,
    item=None,
    quantity=None,
    z_item=0,
    x=None,
    y=None,
    keep_duplicates='first',
    aux_items=None,
)

Model result for a vertical column.

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

Parameters

Name	Type	Description	Default
data	(str, Path, pd.DataFrame, mikeio.Dfs0, mikeio.Dfs0, xr.Dataset)	The input data or file path	required
name	str | None	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
z_item	str | int | None	Item of the first coordinate of positions, by default None	0
x	float	lateral coordinate of point position, inferred from data if not given, else None	None
y	float	zonal coordinate of point position, inferred from data if not given, else None	None
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	list[int | str] | None	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)
node	node-coordinate
plot	Plot using the ComparerPlotter
quantity	Quantity of time series
time	Time index
values	Values as numpy array
x	x-coordinate
y	y-coordinate
z	z-coordinate

Methods

Name	Description
align	Align model result to observation by matching nearest times and interpolating to observation depths.
copy	Create a deep copy of the TimeSeries.
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

align

VerticalModelResult.align(vo, temporal_tolerance=None)

Align model result to observation by matching nearest times and interpolating to observation depths.

Observation depths outside the model depth range are assigned NaN values; no extrapolation is performed.

Parameters

Name	Type	Description	Default
vo	VerticalObservation	Vertical observation to align with	required
temporal_tolerance	pd.Timedelta	Maximum allowed time difference for matching, by default None	None

Returns

Name	Type	Description
	xr.Dataset	Aligned model result

copy

VerticalModelResult.copy()

Create a deep copy of the TimeSeries.

Returns

Name	Type	Description
	TimeSeries	Deep copy of the TimeSeries object

equals

VerticalModelResult.equals(other)

Check if two TimeSeries are equal

sel

VerticalModelResult.sel(**kwargs)

Select data by label

to_dataframe

VerticalModelResult.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

VerticalModelResult.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'