VerticalModelResult

VerticalModelResult(
    data,
    *,
    name=None,
    item=None,
    quantity=None,
    z_item=0,
    x=None,
    y=None,
    aux_items=None,
)

Model result for a vertical profile at a fixed (x, y) location.

Construct a VerticalModelResult 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 modelled 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
name	str	Name of the model result, by default the file or item name.	None
item	str or int	Index or name of the value item. Required if the input has more than two items.	None
quantity	Quantity	Model quantity, for MIKE files this is inferred from the EUM information	None
z_item	str or int	Index or name of the item holding the vertical coordinate, by default 0.	0
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
aux_items	list[int | str]	Auxiliary items to keep alongside the value item, by default None.	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": [30.1, 30.3, 30.4, 30.5, 30.3, 30.3],
    },
    index=times,
)
ms.VerticalModelResult(
    df,
    item="Salinity",
    z_item="z",
    x=12.0,
    y=55.0,
    quantity=ms.Quantity("Salinity", "PSU"),
)

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

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

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

<VerticalModelResult>: VerticalModel_at_obs
Time: 2022-06-12 00:00:00 - 2022-06-20 22:00:00
Quantity: Salinity [PSU]

Attributes

Name	Description
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
x	x-coordinate
y	y-coordinate
z	z-coordinate

Methods

Name	Description
align	Align model result to observation by matching nearest times and interpolating to observation depths.
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

align

VerticalModelResult.align(vo, temporal_tolerance=None)

Align model result to observation by matching nearest times and interpolating to observation depths.

Observation depths outside the model depth range are assigned NaN values; no extrapolation is performed.

Parameters

Name	Type	Description	Default
vo	VerticalObservation	Vertical observation to align with	required
temporal_tolerance	pd.Timedelta	Maximum allowed time difference for matching, by default None	None

Returns

Name	Type	Description
	xr.Dataset	Aligned model result

copy

VerticalModelResult.copy()

Create a deep copy of the TimeSeries.

Returns

Name	Type	Description
	TimeSeries	Deep copy of the TimeSeries object

equals

VerticalModelResult.equals(other)

Check if two TimeSeries are equal

sel

VerticalModelResult.sel(**kwargs)

Select data by label

to_dataframe

VerticalModelResult.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

VerticalModelResult.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'