NodeObservation

NodeObservation(
    data,
    *,
    at,
    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 at parameter accepts three forms:

int — internal network ID, used directly.
str — original node alias (e.g. Res1D node name), resolved to an integer ID automatically when matched against a :class:~modelskill.model.network.NetworkModelResult.
tuple[str, float] — breakpoint location as (reach_id, distance) along a reach, resolved via the alias map at match time.

.. note:: “Node” in this API follows the broad graph sense: it covers both junctions (named connection points) and chainage points (breakpoints along a reach). MIKE 1D users who distinguish node (junction) from gridpoint sharply can use the (reach_id, distance) tuple form to target a specific chainage point, or :class:ReachObservation when the quantity is uniform across the whole reach and any breakpoint will do.

To create multiple NodeObservation objects from a single data source, use :meth: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
at	int, str, or tuple[str, float]	Observation location. Accepted forms: * int — internal network ID. * str — original node alias (e.g. Res1D node name). * tuple[str, float] — breakpoint as `(reach_id, distance)`.	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`
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, at=123, name="123")
>>> o2 = ms.NodeObservation(df, item="Water Level", at=456)
>>>
>>> # String alias resolved at match time
>>> o3 = ms.NodeObservation(data, at="node_A")
>>>
>>> # Breakpoint as (reach_id, distance) tuple
>>> o4 = ms.NodeObservation(data, at=("reach_1", 24.5))
>>>
>>> # Multiple node observations from separate data sources
>>> obs = ms.NodeObservation.from_multiple(nodes={123: df1, 456: df2})

Attributes

Name	Description
at	Observation location: node ID (int/str) or breakpoint `(reach_id, distance)` tuple.
attrs	Attributes of the observation
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
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:

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"})
```
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'`