NodeObservation

NodeObservation(
    data,
    node,
    *,
    item=None,
    name=None,
    weight=1.0,
    quantity=None,
    aux_items=None,
    attrs=None,
)

Class for observations at network nodes.

Create a NodeObservation from a DataFrame or other data source. The node ID is specified as an integer.

To create multiple NodeObservation objects from a single data source, use :method:from_multiple.

Parameters

Name	Type	Description	Default
data	(str, Path, mikeio.Dataset, mikeio.DataArray, pd.DataFrame, pd.Series, xr.Dataset or xr.DataArray)	data source with time series for the node	required
item	(int, str)	index or name of the wanted item/column, by default None if data contains more than one item, item must be given	None
node	int	node ID (integer), by default None	required
name	str	user-defined name for easy identification in plots etc, by default derived from data	None
weight	float	weighting factor for skill scores, by default 1.0	1.0
quantity	Quantity	The quantity of the observation, for validation with model results	None
aux_items	list	list of names or indices of auxiliary items, by default None	None
attrs	dict	additional attributes to be added to the data, by default None	None

Examples

>>> import modelskill as ms
>>> o1 = ms.NodeObservation(data, node=123, name="123")
>>> o2 = ms.NodeObservation(df, item="Water Level", node=456)
>>>
>>> # Multiple node observations from separate data sources
>>> obs = ms.NodeObservation.from_multiple(nodes={123: df1, 456: df2})

Attributes

Name	Description
attrs	Attributes of the observation
gtype	Geometry type
n_points	Number of data points
name	Name of time series (value item name)
node	Node ID of observation
plot	Plot using the ComparerPlotter
quantity	Quantity of time series
time	Time index
values	Values as numpy array
weight	Weighting factor for skill scores
x	x-coordinate
y	y-coordinate

Methods

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

copy

NodeObservation.copy()

Create a deep copy of the TimeSeries.

Returns

Name	Type	Description
	TimeSeries	Deep copy of the TimeSeries object

equals

NodeObservation.equals(other)

Check if two TimeSeries are equal

from_multiple

NodeObservation.from_multiple(
    data=None,
    nodes=None,
    quantity=None,
    aux_items=None,
    attrs=None,
)

Create multiple NodeObservation objects.

Two calling conventions are supported:

1. Separate data sources — pass only nodes as a dict mapping each node ID to its own data source (file path, DataFrame, etc.)::

   obs = NodeObservation.from_multiple(nodes={123: df1, 456: "sensor.csv"})

2. Shared data source — pass a single data object together with nodes as a dict mapping each node ID to the column name or index to select from data::

   obs = NodeObservation.from_multiple(data=df, nodes={123: "col_a", 456: "col_b"})

Parameters

Name	Type	Description	Default
data	PointType	Shared data source (required when nodes values are column selectors).	None
nodes	dict[int, PointType | str | int]	Mapping of node_id -> data source or column selector.	None
quantity	Quantity | None	Physical quantity metadata, by default None.	None
aux_items	list[int | str] | None	Auxiliary items, by default None.	None
attrs	dict | None	Additional attributes, by default None.	None

Returns

Name	Type	Description
	list[NodeObservation]	List of NodeObservation objects.

sel

NodeObservation.sel(**kwargs)

Select data by label

to_dataframe

NodeObservation.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

NodeObservation.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'