VerticalObservation

VerticalObservation(
    data,
    *,
    item=None,
    x=None,
    y=None,
    z_item=0,
    name=None,
    weight=1.0,
    keep_duplicates='first',
    quantity=None,
    aux_items=None,
    attrs=None,
)

Class for observations of vertical profiles.

Create a VerticalObservation from a dfs0/nc file or tabular data containing time, vertical coordinate, and observed values.

Parameters

Name	Type	Description	Default
data	(str, Path, pd.DataFrame, mikeio.Dfs0, mikeio.Dataset, xr.Dataset)	Input data with vertical profile observations.	required
item	int or str	Index or name of the primary observation item. If the input contains more than one candidate value item, this argument must be provided.	None
x	float	x-coordinate of the observation location. If not provided, it is inferred from data when possible.	None
y	float	y-coordinate of the observation location. If not provided, it is inferred from data when possible.	None
z_item	int or str	Index or name of the vertical coordinate item, by default 0.	0
name	str	User-defined name for identification in plots and summaries.	None
weight	float	Weighting factor for skill scores, by default 1.0.	1.0
keep_duplicates	('first', 'last', False)	Strategy for handling duplicate timestamps/z pairs.	"first"
quantity	Quantity	Physical quantity metadata used for validation against model results.	None
aux_items	list[int | str]	List of auxiliary item names or indices to keep in the dataset.	None
attrs	dict	Additional attributes to be added to the underlying dataset.	None

Examples

>>> import modelskill as ms
>>> import pandas as pd
>>> df = pd.DataFrame(
...     {
...         "z": [0.0, -5.0, -10.0, 0.0, -5.0, -10.0],
...         "value": [0.1, 0.3, 0.4, 0.5, 0.3, 0.3],
...     },
...     index=pd.to_datetime(
...         [
...             "2010-01-01 01:00:00",
...             "2010-01-01 01:00:00",
...             "2010-01-01 01:00:00",
...             "2010-01-01 02:00:00",
...             "2010-01-01 02:00:00",
...             "2010-01-01 02:00:00",
...         ]
...     ),
... )
>>> df.index.name = "t"
>>> print(df.to_string())
                       z  value
t
2010-01-01 01:00:00   0.0    0.1
2010-01-01 01:00:00  -5.0    0.3
2010-01-01 01:00:00 -10.0    0.4
2010-01-01 02:00:00   0.0    0.5
2010-01-01 02:00:00  -5.0    0.3
2010-01-01 02:00:00 -10.0    0.3

>>> o = ms.VerticalObservation(df, item="value", z_item="z", x=12.0, y=55.0)

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-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
sel	Select data by label
to_dataframe	Convert matched data to pandas DataFrame
trim	Trim observation data to a given time interval

copy

VerticalObservation.copy()

Create a deep copy of the TimeSeries.

Returns

Name	Type	Description
	TimeSeries	Deep copy of the TimeSeries object

equals

VerticalObservation.equals(other)

Check if two TimeSeries are equal

sel

VerticalObservation.sel(**kwargs)

Select data by label

to_dataframe

VerticalObservation.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

VerticalObservation.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'