dfsu.Dfsu2DH

dfsu.Dfsu2DH(self, filename)

Attributes

Name	Description
deletevalue	File delete value.
end_time	File end time.
items	List of items.
n_items	Number of items.
n_timesteps	Number of time steps.
start_time	File start time.
timestep	Time step size in seconds.

Methods

Name	Description
append	Append data to an existing dfsu file.
extract_track	Extract track data from a dfsu file.
get_overset_grid	get a 2d grid that covers the domain by specifying spacing or shape.
read	Read data from a dfsu file.

append

dfsu.Dfsu2DH.append(ds, validate=True)

Append data to an existing dfsu file.

Parameters

Name	Type	Description	Default
ds	Dataset	Dataset to be appended	required
validate	bool	Validate that the items and geometry match, by default True	True

extract_track

dfsu.Dfsu2DH.extract_track(
    track,
    items=None,
    method='nearest',
    dtype=np.float32,
)

Extract track data from a dfsu file.

Parameters

Name	Type	Description	Default
track	pd.DataFrame	with DatetimeIndex and (x, y) of track points as first two columns x,y coordinates must be in same coordinate system as dfsu	required
track	pd.DataFrame	filename of csv or dfs0 file containing t,x,y	required
items	int | str | Sequence[int | str] | None	Extract only selected items, by number (0-based), or by name	None
method	Literal['nearest', 'inverse_distance']	Spatial interpolation method (‘nearest’ or ‘inverse_distance’) default=‘nearest’	'nearest'
dtype	Any	Data type to read, by 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

Examples

>>> dfsu = mikeio.open("tests/testdata/NorthSea_HD_and_windspeed.dfsu")
>>> ds = dfsu.extract_track("tests/testdata/altimetry_NorthSea_20171027.csv")
>>> ds
<mikeio.Dataset>
dims: (time:1115)
time: 2017-10-26 04:37:37 - 2017-10-30 20:54:47 (1115 non-equidistant records)
geometry: GeometryUndefined()
items:
  0:  Longitude <Undefined> (undefined)
  1:  Latitude <Undefined> (undefined)
  2:  Surface elevation <Surface Elevation> (meter)
  3:  Wind speed <Wind speed> (meter per sec)

get_overset_grid

dfsu.Dfsu2DH.get_overset_grid(dx=None, dy=None, nx=None, ny=None, buffer=0.0)

get a 2d grid that covers the domain by specifying spacing or shape.

Parameters

Name	Type	Description	Default
dx	float	grid resolution in x-direction (or in x- and y-direction)	None
dy	float	grid resolution in y-direction	None
nx	int	number of points in x-direction, by default None (the value will be inferred)	None
ny	int	number of points in y-direction, by default None (the value will be inferred)	None
buffer	float	positive to make the area larger, default=0 can be set to a small negative value to avoid NaN values all around the domain.	0.0

Returns

Name	Type	Description
	<mikeio.Grid2D>	2d grid

read

dfsu.Dfsu2DH.read(
    items=None,
    time=None,
    elements=None,
    area=None,
    x=None,
    y=None,
    keepdims=False,
    dtype=np.float32,
    error_bad_data=True,
    fill_bad_data_value=np.nan,
)

Read data from a dfsu file.

Parameters

Name	Type	Description	Default
items	str | int | Sequence[str | int] | None	Read only selected items, by number (0-based), or by name	None
time	int | str | slice | None	Read only selected time steps, by default None (=all)	None
keepdims	bool	When reading a single time step only, should the time-dimension be kept in the returned Dataset? by default: False	False
area	tuple[float, float, float, float] | None	Read only data inside (horizontal) area given as a bounding box (tuple with left, lower, right, upper) or as list of coordinates for a polygon, by default None	None
x	float | None	Read only data for elements containing the (x,y) points(s), by default None	None
y	float | None	Read only data for elements containing the (x,y) points(s), by default None	None
elements	Sequence[int] | np.ndarray | None	Read only selected element ids, by default None	None
error_bad_data	bool	raise error if data is corrupt, by default True,	True
fill_bad_data_value	float	fill value for to impute corrupt data, used in conjunction with error_bad_data=False default np.nan	np.nan
dtype	Any	Data type to read, by default np.float32	np.float32

Returns

Name	Type	Description
	Dataset	A Dataset with data dimensions [t,elements]