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)
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'