VerticalObservation

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

Observation of a vertical profile at a fixed (x, y) location.

Create a VerticalObservation from a dfs0 file, mikeio.Dataset, pandas.DataFrame or xarray.Dataset in long format: one row per (time, z) pair, with one item/column holding the vertical coordinate and another holding the observed value. At least two items are required (z + value); if more are present, item must be given.

Parameters

Name	Type	Description	Default
data	(str, Path, pd.DataFrame, mikeio.Dfs0, mikeio.Dataset, xr.Dataset)	Input data or path to a dfs0 file.	required
item	int or str	Index or name of the value item. Required if the input has more than two items.	None
x	float	x-coordinate of the profile location, inferred from data when possible.	None
y	float	y-coordinate of the profile location, inferred from data when possible.	None
z_item	int or str	Index or name of the item holding the vertical coordinate, by default 0.	0
name	str	Name of the observation, by default the file or item name.	None
weight	float	Weighting factor for skill scores in a ComparerCollection, by default 1.0.	1.0
quantity	Quantity	Observed quantity, for MIKE files this is inferred from the EUM information	None
aux_items	list[int | str]	Auxiliary items to keep alongside the value item, by default None.	None
attrs	dict	Additional attributes to be added to the underlying dataset.	None

Notes

A dfs0 with N depth levels has its profile timestamps repeated N times on a non-equidistant time axis. Duplicate (time, z) pairs are not allowed and will raise a ValueError.

Examples

From a pandas.DataFrame in long format:

import modelskill as ms
import pandas as pd

times = pd.to_datetime(["2010-01-01 01:00"] * 3 + ["2010-01-01 02:00"] * 3)
df = pd.DataFrame(
    {
        "z": [0.0, -5.0, -10.0, 0.0, -5.0, -10.0],
        "Salinity": [30.0, 30.2, 30.3, 30.4, 30.2, 30.2],
    },
    index=times,
)
ms.VerticalObservation(
    df,
    item="Salinity",
    z_item="z",
    x=12.0,
    y=55.0,
    quantity=ms.Quantity("Salinity", "PSU"),
)

<VerticalObservation>: Salinity
Time: 2010-01-01 01:00:00 - 2010-01-01 02:00:00
Quantity: Salinity [PSU]

From a dfs0 file (with z and Salinity items):

ms.VerticalObservation(
    "../data/vertical/VerticalProfile_obs1.dfs0",
    item="Salinity",
    z_item="z",
    x=657500,
    y=6553600,
)

<VerticalObservation>: VerticalProfile_obs1
Time: 2022-06-12 11:43:00 - 2022-06-20 11:06:00
Quantity: Salinity [PSU]

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'