PointModelResult

PointModelResult(
    data,
    *,
    name=None,
    x=None,
    y=None,
    z=None,
    item=None,
    quantity=None,
    aux_items=None,
)

Model result for a single point location.

Construct a PointModelResult from a 0d data source: dfs0 file, mikeio.Dataset/DataArray, pandas.DataFrame/Series or xarray.Dataset/DataArray

Parameters

Name	Type	Description	Default
data	(str, Path, mikeio.Dataset, mikeio.DataArray, pd.DataFrame, pd.Series, xr.Dataset or xr.DataArray)	filename (.dfs0 or .nc) or object with the data	required
name	str | None	The name of the model result, by default None (will be set to file name or item name)	None
x	float	first coordinate of point position, inferred from data if not given, else None	None
y	float	second coordinate of point position, inferred from data if not given, else None	None
z	float	third coordinate of point position, inferred from data if not given, else None	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
quantity	Quantity	Model quantity, for MIKE files this is inferred from the EUM information	None
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

Methods

Name	Description
copy	Create a deep copy of the TimeSeries.
equals	Check if two TimeSeries are equal
interp_time	Interpolate model result to the time of the observation
sel	Select data by label
to_dataframe	Convert matched data to pandas DataFrame
trim	Trim observation data to a given time interval

copy

PointModelResult.copy()

Create a deep copy of the TimeSeries.

Returns

Name	Type	Description
	TimeSeries	Deep copy of the TimeSeries object

equals

PointModelResult.equals(other)

Check if two TimeSeries are equal

interp_time

PointModelResult.interp_time(observation, **kwargs)

Interpolate model result to the time of the observation

wrapper around xarray.Dataset.interp()

Parameters

Name	Type	Description	Default
observation	Observation	The observation to interpolate to	required
**kwargs	Any	Additional keyword arguments passed to xarray.interp	{}

Returns

Name	Type	Description
	PointModelResult	Interpolated model result

sel

PointModelResult.sel(**kwargs)

Select data by label

to_dataframe

PointModelResult.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

PointModelResult.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'