DataArray

DataArray(
    self,
    data,
    *,
    time=None,
    name=None,
    type=None,
    unit=None,
    item=None,
    geometry=None,
    zn=None,
    dims=None,
    dt=1.0,
)

DataArray with data and metadata for a single item in a dfs file.

Parameters

Name	Type	Description	Default
data	ArrayLike	a numpy array containing the data	required
time	pd.DatetimeIndex | str | None	a pandas.DatetimeIndex with the time instances of the data	None
name	str | None	Name of the array	None
type	EUMType | None	EUM type	None
unit	EUMUnit | None	EUM unit	None
item	ItemInfo | None	an ItemInfo with name, type and unit, as an alternative to name, type and unit	None
geometry	GeometryType | None	a geometry object e.g. Grid2D or GeometryFM2D	None
zn	np.ndarray | None	only relevant for Dfsu3d	None
dims	Sequence[str] | None	named dimensions	None
dt	float	placeholder timestep	1.0

Examples

import pandas as pd
import mikeio

da = mikeio.DataArray([0.0, 1.0],
    time=pd.date_range("2020-01-01", periods=2),
    item=mikeio.ItemInfo("Water level", mikeio.EUMType.Water_Level))
da

<mikeio.DataArray>
name: Water level
dims: (time:2)
time: 2020-01-01 00:00:00 - 2020-01-02 00:00:00 (2 records)
geometry: GeometryUndefined()
values: [0, 1]

Attributes

Name	Description
dtype	Data-type of the array elements.
end_time	Last time instance (as datetime).
is_equidistant	Is DataArray equidistant in time?
n_timesteps	Number of time steps.
name	Name of this DataArray (=da.item.name).
ndim	Number of array dimensions.
shape	Tuple of array dimensions.
start_time	First time instance (as datetime).
timestep	Time step in seconds if equidistant (and at
type	EUMType.
unit	EUMUnit.
values	Values as a np.ndarray (equivalent to to_numpy()).

Methods

Name	Description
aggregate	Aggregate along an axis.
average	Compute the weighted average along the specified axis.
concat	Concatenate DataArrays along the time axis.
copy	Make copy of DataArray.
describe	Generate descriptive statistics by wrapping pandas.DataFrame.describe.
dropna	Remove time steps where values are NaN.
extract_track	Extract data along a moving track.
fillna	Fill NA/NaN value.
flipud	Flip upside down (on first non-time axis).
interp	Interpolate data in time and space.
interp_like	Interpolate in space (and in time) to other geometry (and time axis).
interp_na	Fill in NaNs by interpolating according to different methods.
interp_time	Temporal interpolation.
isel	Return a new DataArray whose data is given by
max	Max value along an axis.
mean	Mean value along an axis.
min	Min value along an axis.
nanmax	Max value along an axis (NaN removed).
nanmean	Mean value along an axis (NaN removed).
nanmin	Min value along an axis (NaN removed).
nanquantile	Compute the q-th quantile of the data along the specified axis, while ignoring nan values.
nanstd	Standard deviation value along an axis (NaN removed).
ptp	Range (max - min) a.k.a Peak to Peak along an axis.
quantile	Compute the q-th quantile of the data along the specified axis.
sel	Return a new DataArray whose data is given by
squeeze	Remove axes of length 1.
std	Standard deviation values along an axis.
to_dataframe	Convert to DataFrame.
to_dfs	Write data to a new dfs file.
to_numpy	Values as a np.ndarray (equivalent to values).
to_pandas	Convert to Pandas Series.
to_xarray	Export to xarray.DataArray.

aggregate

DataArray.aggregate(axis=0, func=np.nanmean, **kwargs)

Aggregate along an axis.

Parameters

Name	Type	Description	Default
axis	int | str | None	axis number or “time” or “space”, by default 0	0
func	Callable[…, Any]	default np.nanmean	np.nanmean
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	dataarray with aggregated values

See Also

max : Max values
nanmax : Max values with NaN values removed

average

DataArray.average(weights, axis=0, **kwargs)

Compute the weighted average along the specified axis.

Parameters

Name	Type	Description	Default
axis	int | str	axis number or “time” or “space”, by default	0
weights	np.ndarray	weights to apply to the values	required
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	DataArray with weighted average values

See Also

aggregate : Weighted average

Examples

import mikeio
da= mikeio.read("../data/HD2D.dfsu")["Current speed"]
area = da.geometry.get_element_area()
da.average(axis="space", weights=area)

<mikeio.DataArray>
name: Current speed
dims: (time:9)
time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)
geometry: GeometryUndefined()
values: [0.05017, 0.05321, ..., 0.06071]

concat

DataArray.concat(dataarrays, keep='last')

Concatenate DataArrays along the time axis.

Parameters

Name	Type	Description	Default
dataarrays	Sequence[DataArray]	DataArrays to concatenate	required
keep	Literal['last', 'first']	default: last	'last'

Returns

Name	Type	Description
	DataArray	The concatenated DataArray

Examples

da1 = mikeio.read("../data/HD2D.dfsu", time=[0,1])[0]
da2 = mikeio.read("../data/HD2D.dfsu", time=[2,3])[0]
da1.time

DatetimeIndex(['1985-08-06 07:00:00', '1985-08-06 09:30:00'], dtype='datetime64[ns]', freq=None)

da3 = mikeio.DataArray.concat([da1,da2])
da3

<mikeio.DataArray>
name: Surface elevation
dims: (time:4, element:884)
time: 1985-08-06 07:00:00 - 1985-08-06 14:30:00 (4 records)
geometry: Dfsu2D (884 elements, 529 nodes)

copy

DataArray.copy()

Make copy of DataArray.

describe

DataArray.describe(percentiles=None, include=None, exclude=None)

Generate descriptive statistics by wrapping pandas.DataFrame.describe.

Parameters

Name	Type	Description	Default
percentiles	list-like of numbers	The percentiles to include in the output. All should fall between 0 and 1.	None
include	'all', list-like of dtypes or None (default)	A white list of data types to include in the result.	None
exclude	list-like of dtypes or None (default)	A black list of data types to omit from the result.	None

Returns

Name	Type	Description
	pd.DataFrame

dropna

DataArray.dropna()

Remove time steps where values are NaN.

extract_track

DataArray.extract_track(track, method='nearest', dtype=np.float32)

Extract data along a moving track.

Parameters

Name	Type	Description	Default
track	str | Path | Dataset | pd.DataFrame	with DatetimeIndex and (x, y) of track points as first two columns x,y coordinates must be in same coordinate system	required
method	Literal['nearest', 'inverse_distance']	Spatial interpolation method (‘nearest’ or ‘inverse_distance’) default=‘nearest’	'nearest'
dtype	Any	Data type of the output data, default=np.float32	np.float32

Returns

Name	Type	Description
	Dataset	A dataset with data dimension t The first two items will be x- and y- coordinates of track

fillna

DataArray.fillna(value=0.0)

Fill NA/NaN value.

Parameters

Name	Type	Description	Default
value	float	Value used to fill missing values. Default is 0.0.	0.0

Examples

import numpy as np
import mikeio

da = mikeio.DataArray([np.nan, 1.0])
da

<mikeio.DataArray>
name: NoName
dims: (x:2)
time: 2018-01-01 00:00:00 (time-invariant)
geometry: GeometryUndefined()
values: [nan, 1]

da.fillna(0.0)

<mikeio.DataArray>
name: NoName
dims: (x:2)
time: 2018-01-01 00:00:00 (time-invariant)
geometry: GeometryUndefined()
values: [0, 1]

flipud

DataArray.flipud()

Flip upside down (on first non-time axis).

interp

DataArray.interp(
    time=None,
    x=None,
    y=None,
    z=None,
    n_nearest=3,
    interpolant=None,
    **kwargs,
)

Interpolate data in time and space.

This method currently has limited functionality for spatial interpolation. It will be extended in the future.

The spatial parameters available depend on the geometry of the Dataset:

• Grid1D: x
• Grid2D: x, y
• Grid3D: [not yet implemented!]
• GeometryFM: (x,y)
• GeometryFMLayered: (x,y) [surface point will be returned!]

Parameters

Name	Type	Description	Default
time	(float, pd.DatetimeIndex or DataArray)	timestep in seconds or discrete time instances given by pd.DatetimeIndex (typically from another DataArray da2.time), by default None (=don’t interp in time)	None
x	float	x-coordinate of point to be interpolated to, by default None	None
y	float	y-coordinate of point to be interpolated to, by default None	None
z	float	z-coordinate of point to be interpolated to, by default None	None
n_nearest	int	When using IDW interpolation, how many nearest points should be used, by default: 3	3
interpolant	tuple	Precomputed interpolant, by default None	None
**kwargs	Any	Additional keyword arguments to be passed to the interpolation	{}

Returns

Name	Type	Description
	DataArray	new DataArray with interped data

See Also

sel : Select data using label indexing interp_like : Interp to another time/space of another DataArray interp_time : Interp in the time direction only

Examples

import mikeio

da = mikeio.read("../data/HD2D.dfsu")["Current speed"]
da.interp(x=340000, y=6160000)

<mikeio.DataArray>
name: Current speed
dims: (time:9)
time: 1985-08-06 07:00:00 - 1985-08-07 03:00:00 (9 records)
geometry: GeometryPoint2D(x=340000, y=6160000)
values: [nan, nan, ..., nan]

interp_like

DataArray.interp_like(other, interpolant=None, **kwargs)

Interpolate in space (and in time) to other geometry (and time axis).

Note: currently only supports interpolation from dfsu-2d to dfs2 or other dfsu-2d DataArrays

Parameters

Name	Type	Description	Default
other	DataArray | Grid2D | GeometryFM2D | pd.DatetimeIndex	The target geometry (and time axis) to interpolate to	required
interpolant	tuple[Any, Any] | None	Reuse pre-calculated index and weights	None
**kwargs	Any	additional kwargs are passed to interpolation method	{}

Returns

Name	Type	Description
	DataArray	Interpolated DataArray

interp_na

DataArray.interp_na(axis='time', **kwargs)

Fill in NaNs by interpolating according to different methods.

Wrapper of xarray.DataArray.interpolate_na

Examples

import numpy as np
import pandas as pd
time = pd.date_range("2000", periods=3, freq="D")
da = mikeio.DataArray(data=np.array([0.0, np.nan, 2.0]), time=time)
da

<mikeio.DataArray>
name: NoName
dims: (time:3)
time: 2000-01-01 00:00:00 - 2000-01-03 00:00:00 (3 records)
geometry: GeometryUndefined()
values: [0, nan, 2]

da.interp_na()

<mikeio.DataArray>
name: NoName
dims: (time:3)
time: 2000-01-01 00:00:00 - 2000-01-03 00:00:00 (3 records)
geometry: GeometryUndefined()
values: [0, 1, 2]

interp_time

DataArray.interp_time(
    dt,
    *,
    method='linear',
    extrapolate=True,
    fill_value=np.nan,
)

Temporal interpolation.

Wrapper of scipy.interpolate.interp1d

Parameters

Name	Type	Description	Default
dt	pd.DatetimeIndex | DataArray | Dataset | int | float	output timestep in seconds or new time axis	required
method	str	Specifies the kind of interpolation as a string (‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, ‘next’, where ‘zero’, ‘slinear’, ‘quadratic’ and ‘cubic’ refer to a spline interpolation of zeroth, first, second or third order; ‘previous’ and ‘next’ simply return the previous or next value of the point) or as an integer specifying the order of the spline interpolator to use. Default is ‘linear’.	'linear'
extrapolate	bool	Default True. If False, a ValueError is raised any time interpolation is attempted on a value outside of the range of x (where extrapolation is necessary). If True, out of bounds values are assigned fill_value	True
fill_value	float	Default NaN. this value will be used to fill in for points outside of the time range.	np.nan

Returns

Name	Type	Description
	DataArray

isel

DataArray.isel(
    idx=None,
    *,
    time=None,
    x=None,
    y=None,
    z=None,
    element=None,
    node=None,
    layer=None,
    frequency=None,
    direction=None,
    axis=0,
)

Return a new DataArray whose data is given by integer indexing along the specified dimension(s).

Note that the data will be a view of the original data if possible (single index or slice), otherwise a copy (fancy indexing) following NumPy convention.

The spatial parameters available depend on the dims (i.e. geometry) of the DataArray:

• Grid1D: x
• Grid2D: x, y
• Grid3D: x, y, z
• GeometryFM: element

Parameters

Name	Type	Description	Default
idx	IndexType	Index, or indices, along the specified dimension(s)	None
axis	Any	axis number or “time”, by default 0	0
time	int	time index,by default None	None
x	int	x index, by default None	None
y	int	y index, by default None	None
z	int	z index, by default None	None
layer	IndexType	layer index, only used in dfsu 3d	None
direction	IndexType	direction index, only used in sprectra	None
frequency	IndexType	frequencey index, only used in spectra	None
node	IndexType	node index, only used in spectra	None
element	int	Bounding box of coordinates (left lower and right upper) to be selected, by default None	None

Returns

Name	Type	Description
	DataArray	new DataArray with selected data

See Also

dims : Get axis names sel : Select data using labels

Examples

da = mikeio.read("../data/europe_wind_long_lat.dfs2")[0]
da

<mikeio.DataArray>
name: Mean Sea Level Pressure
dims: (time:1, y:101, x:221)
time: 2012-01-01 00:00:00 (time-invariant)
geometry: Grid2D (ny=101, nx=221)

da.isel(time=-1)

<mikeio.DataArray>
name: Mean Sea Level Pressure
dims: (y:101, x:221)
time: 2012-01-01 00:00:00 (time-invariant)
geometry: Grid2D (ny=101, nx=221)

da.isel(x=slice(10,20), y=slice(40,60))

<mikeio.DataArray>
name: Mean Sea Level Pressure
dims: (time:1, y:20, x:10)
time: 2012-01-01 00:00:00 (time-invariant)
geometry: Grid2D (ny=20, nx=10)

da = mikeio.read("../data/oresund_sigma_z.dfsu").Temperature
da.isel(element=range(200))

<mikeio.DataArray>
name: Temperature
dims: (time:3, element:200)
time: 1997-09-15 21:00:00 - 1997-09-16 03:00:00 (3 records)
geometry: Dfsu3DSigmaZ (200 elements, 638 nodes)

max

DataArray.max(axis=0, **kwargs)

Max value along an axis.

Parameters

Name	Type	Description	Default
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	array with max values

See Also

nanmax : Max values with NaN values removed

mean

DataArray.mean(axis=0, **kwargs)

Mean value along an axis.

Parameters

Name	Type	Description	Default
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	array with mean values

See Also

nanmean : Mean values with NaN values removed

min

DataArray.min(axis=0, **kwargs)

Min value along an axis.

Parameters

Name	Type	Description	Default
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	array with min values

See Also

nanmin : Min values with NaN values removed

nanmax

DataArray.nanmax(axis=0, **kwargs)

Max value along an axis (NaN removed).

Parameters

Name	Type	Description	Default
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	array with max values

See Also

nanmax : Max values with NaN values removed

nanmean

DataArray.nanmean(axis=0, **kwargs)

Mean value along an axis (NaN removed).

Parameters

Name	Type	Description	Default
axis	int | str | None	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	array with mean values

See Also

mean : Mean values

nanmin

DataArray.nanmin(axis=0, **kwargs)

Min value along an axis (NaN removed).

Parameters

Name	Type	Description	Default
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	array with min values

See Also

nanmin : Min values with NaN values removed

nanquantile

DataArray.nanquantile(q, *, axis=0, **kwargs)

Compute the q-th quantile of the data along the specified axis, while ignoring nan values.

Wrapping np.nanquantile

Parameters

Name	Type	Description	Default
q	float | Sequence[float]	Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive.	required
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	data with quantile values

Examples

>>> da.nanquantile(q=[0.25,0.75])
>>> da.nanquantile(q=0.5)
>>> da.nanquantile(q=[0.01,0.5,0.99], axis="space")

See Also

quantile : Quantile with NaN values

nanstd

DataArray.nanstd(axis=0, **kwargs)

Standard deviation value along an axis (NaN removed).

Parameters

Name	Type	Description	Default
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	array with standard deviation values

See Also

std : Standard deviation

ptp

DataArray.ptp(axis=0, **kwargs)

Range (max - min) a.k.a Peak to Peak along an axis.

Parameters

Name	Type	Description	Default
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	array with peak to peak values

quantile

DataArray.quantile(q, *, axis=0, **kwargs)

Compute the q-th quantile of the data along the specified axis.

Wrapping np.quantile

Parameters

Name	Type	Description	Default
q	float | Sequence[float]	Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive.	required
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	data with quantile values

Examples

>>> da.quantile(q=[0.25,0.75])
>>> da.quantile(q=0.5)
>>> da.quantile(q=[0.01,0.5,0.99], axis="space")

See Also

nanquantile : quantile with NaN values ignored

sel

DataArray.sel(
    time=None,
    x=None,
    y=None,
    z=None,
    coords=None,
    area=None,
    layers=None,
)

Return a new DataArray whose data is given by selecting index labels along the specified dimension(s).

In contrast to DataArray.isel, indexers for this method should use labels instead of integers.

The spatial parameters available depend on the geometry of the DataArray:

• Grid1D: x
• Grid2D: x, y, coords, area
• Grid3D: [not yet implemented! use isel instead]
• GeometryFM: (x,y), coords, area
• GeometryFMLayered: (x,y,z), coords, area, layers

Parameters

Name	Type	Description	Default
time	(str, pd.DatetimeIndex, DataArray)	time labels e.g. “2018-01” or slice(“2018-1-1”,“2019-1-1”), by default None	None
x	float	x-coordinate of point to be selected, by default None	None
y	float	y-coordinate of point to be selected, by default None	None
z	float	z-coordinate of point to be selected, by default None	None
coords	np.array(float, float)	As an alternative to specifying x, y and z individually, the argument coords can be used instead. (x,y)- or (x,y,z)-coordinates of point to be selected, by default None	None
area	(float, float, float, float)	Bounding box of coordinates (left lower and right upper) to be selected, by default None	None
layers	int or str or list	layer(s) to be selected: “top”, “bottom” or layer number from bottom 0,1,2,… or from the top -1,-2,… or as list of these; only for layered dfsu, by default None	None

Returns

Name	Type	Description
	DataArray	new DataArray with selected data

See Also

isel : Select data using integer indexing interp : Interp data in time and space

Examples

da = mikeio.read("../data/random.dfs1")[0]
da

<mikeio.DataArray>
name: testing water level
dims: (time:100, x:3)
time: 2012-01-01 00:00:00 - 2012-01-01 00:19:48 (100 records)
geometry: Grid1D (n=3, dx=100)

da.sel(time=slice(None, "2012-1-1 00:02"))

<mikeio.DataArray>
name: testing water level
dims: (time:15, x:3)
time: 2012-01-01 00:00:00 - 2012-01-01 00:02:48 (15 records)
geometry: Grid1D (n=3, dx=100)

da.sel(x=100)

<mikeio.DataArray>
name: testing water level
dims: (time:100)
time: 2012-01-01 00:00:00 - 2012-01-01 00:19:48 (100 records)
geometry: GeometryUndefined()
values: [0.3231, 0.6315, ..., 0.7506]

da = mikeio.read("../data/oresund_sigma_z.dfsu").Temperature
da

<mikeio.DataArray>
name: Temperature
dims: (time:3, element:17118)
time: 1997-09-15 21:00:00 - 1997-09-16 03:00:00 (3 records)
geometry: Dfsu3DSigmaZ (17118 elements, 12042 nodes)

da.sel(time="1997-09-15")

<mikeio.DataArray>
name: Temperature
dims: (element:17118)
time: 1997-09-15 21:00:00 (time-invariant)
geometry: Dfsu3DSigmaZ (17118 elements, 12042 nodes)
values: [16.31, 16.43, ..., 16.69]

da.sel(x=340000, y=6160000, z=-3)

<mikeio.DataArray>
name: Temperature
dims: (time:3)
time: 1997-09-15 21:00:00 - 1997-09-16 03:00:00 (3 records)
geometry: GeometryPoint3D(x=340028.1116933554, y=6159980.070243686, z=-3.0)
values: [17.54, 17.31, 17.08]

da.sel(layers="bottom")

<mikeio.DataArray>
name: Temperature
dims: (time:3, element:3700)
time: 1997-09-15 21:00:00 - 1997-09-16 03:00:00 (3 records)
geometry: Dfsu2D (3700 elements, 2090 nodes)

squeeze

DataArray.squeeze()

Remove axes of length 1.

Returns

Name	Type	Description
	DataArray

std

DataArray.std(axis=0, **kwargs)

Standard deviation values along an axis.

Parameters

Name	Type	Description	Default
axis	int | str	axis number or “time” or “space”, by default 0	0
**kwargs	Any	Additional keyword arguments	{}

Returns

Name	Type	Description
	DataArray	array with standard deviation values

See Also

nanstd : Standard deviation values with NaN values removed

to_dataframe

DataArray.to_dataframe(unit_in_name=False, round_time='ms')

Convert to DataFrame.

Parameters

Name	Type	Description	Default
unit_in_name	bool	include unit in column name, default False,	False
round_time	str | bool	round time to, by default “ms”, use False to avoid rounding	'ms'

Returns

Name	Type	Description
	pd.DataFrame

to_dfs

DataArray.to_dfs(filename, **kwargs)

Write data to a new dfs file.

Parameters

Name	Type	Description	Default
filename	str | Path	full path to the new dfs file	required
**kwargs	Any	additional arguments passed to the writing function, e.g. dtype for dfs0	{}

to_numpy

DataArray.to_numpy()

Values as a np.ndarray (equivalent to values).

to_pandas

DataArray.to_pandas()

Convert to Pandas Series.

Returns

Name	Type	Description
	pd.Series

to_xarray

DataArray.to_xarray()

Export to xarray.DataArray.