Depreciated Xarray Accessor modules

Warning

These methods have been depreciated in favour of a new segysak Xarray accessor. Examples in this documentation have been updated to use the new accessor or consult the reference.

Accessor modules are accessed as namespaces within the Xarray.Dataset objects created by SEGY-SAK. When segysak is imported, all xarray.Dataset objects will contain the .seis and .seisio namespaces.

segysak Xarray accessor modules

SeisIO

to_netcdf(file_path, **kwargs)

Output to netcdf4 with specs for seisnc.

Parameters:

Name	Type	Description	Default
file_path	Union[str, PathLike]	The output file path.	required
**kwargs	Dict[str, Any]	As per xarray function to_netcdf.	{}

to_segy(segy_file, use_text=True, coord_scalar=None, data_var='data', vert_dimension='samples', write_dead_traces=False, dead_trace_var=None, trace_header_map=None, **dim_kwargs)

Output Xarray Dataset to SEG-Y format.

Parameters:

Name	Type	Description	Default
segy_file	Union[str, PathLike]	The output file to write to.	required
use_text	bool	Write the text attribute from the dataset to the text header.	True
coord_scalar	Union[float, None]	A SEG-Y compatible coordinate scalar.	None
data_var	str	The variable name of the trace data in the Dataset.	'data'
vert_dimension	str	The vertical (samples) dimension of the data_var.	'samples'
write_dead_traces	bool	Write dead traces as zeros to the SEG-Y file.	False
dead_trace_var	Union[str, None]	A dataset variable containing boolean values that identifies dead traces on the non-vertical dimension.	None
trace_header_map	Dict[str, int]	Defaults to None. A dictionary of Dataset variables and byte locations. The variable will be written to the trace headers in the assigned byte location. By default CMP=23, cdp_x=181, cdp_y=185, iline=189, xline=193.	None
dim_kwargs	int	The dimension/byte location pairs to output dimensions to. The number of dim_kwargs should be equal to the number of dimensions on the output data_array. The trace sort order will be as per the order passed to the function.	{}

Example

Python

ds3d.seisio.to_segy(
    "output_file.segy",
    use_text = True,
    vert_dimension = "samples",
    trace_header_map = {'cdp_x':181, 'cdp_y':185}
    iline = 189,
    xline = 193,
)

to_subsurface()

Convert seismic data to a subsurface StructuredData Object

Raises:

Type	Description
err	[description]
NotImplementedError	[description]

Returns:

Type	Description
	subsurface.structs.base_structures.StructuredData: subsurface struct

SeisGeom

Bases: TemplateAccessor

calc_corner_points()

Calculate the corner points of the geometry or end points of a 2D line.

This puts two properties in the seisnc attrs with the calculated il/xl and cdp_x and cdp_y if available.

Attr

ds.attrs['corner_points'] ds.attrs['corner_points_xy']

fill_cdpna()

Fills NaN cdp locations by fitting known cdp x and y values to the local grid using a planar surface relationshipt.

get_affine_transform()

Calculate the forward iline/xline -> cdp_x, cdp_y Affine transform for Matplotlib using corner point geometry.

Returns:

Type	Description
	matplotlib.transforms.Affine2D:

Raises:

Type	Description
ValueError	If Dataset is not 3D

get_dead_trace_map(scan=None, zeros_as_nan=False)

Scan the vertical axis of a volume to find traces that are all NaN and return an DataArray which maps the all dead traces.

Faster scans can be performed by setting scan to an int or list of int representing horizontal slice indexes to use for the scan.

Parameters:

Name	Type	Description	Default
scan	int/list of int	Horizontal indexes to scan. Defaults to None (scan full volume).	None
zeros_as_nan	bool	Treat zeros as NaN during scan.	False

Returns:

Type	Description
	xarray.DataArray: boolean dead trace map.

get_measurement_system()

Return measurement_system if present, else None

grid_rotation()

Calculate the rotation of the grid using the sum under the curve method.

(x2 − x1)(y2 + y1)

Returns a value >0 if the rotation of points along inline is clockwise.

interp_line(cdp_x, cdp_y, extra=None, bin_spacing_hint=10, line_method='slinear', xysel_method='linear')

Select data at x and y coordinates

Parameters:

Name	Type	Description	Default
bin_spacing_hint	number	a bin spacing to stay close to, in cdp world units. Default: 10	10
line_method	string	valid values for the kind argument in scipy.interpolate.interp1d	'slinear'
xysel_method	string	valid values for DataArray.interp	'linear'

Returns:

Type	Description
	xarray.Dataset: Interpolated traces along the arbitrary line

is_2d()

Returns True if the dataset is 2D peformant else False

is_2dgath()

Returns True if the dataset is 2D peformant and has offset or angle else False

is_3d()

Returns True if the dataset is 3D peformant else False

is_3dgath()

Returns True if the dataset is 3D peformant and has offset or angle else False

is_depth()

Check if seisnc volume is in depth

is_empty()

Check if empty

is_twt()

Check if seisnc volume is in twt

plot_bounds(ax=None)

Plot survey bbounding box to a new or existing axis

Parameters:

Name	Type	Description	Default
ax		(optional) axis to plot to	None

Returns:

Type	Description
	matplotlib axis used

subsample_dims(**dim_kwargs)

Return a dictionary of subsampled dims suitable for xarray.interp.

This tool halves

Parameters:

Name	Type	Description	Default
dim_kwargs		dimension names as keyword arguments with values of how many times we should divide the dimension by 2.	{}

surface_from_points(points, attr, left=('cdp_x', 'cdp_y'), right=None, key=None, method='linear')

Sample a 2D point set with an attribute (like Z) to the seisnc geometry using interpolation.

Parameters:

Name	Type	Description	Default
points	array - like	Nx2 array-like of points with coordinates corresponding to coord1 and coord2.	required
attr	(array - like, str)	If str points should be a DataFrame where attr is the column name of the attribute to be interpolated to the seisnc geometry. Else attr is a 1D array of length N.	required
left	tuple	Length 2 tuple of coordinate dimensions to interpolate to.	('cdp_x', 'cdp_y')
right	tuple	If points is DataFrame right is a length 2 tuple of column keys corresponding to coordinates in argument left.	None
method	str	The interpolation method to use for griddata from scipy. Defaults to 'linear'	'linear'

Returns:

Type	Description
	xr.Dataset: Surface with geometry specified in left.

xysel(cdp_x, cdp_y, method='nearest', sample_dim_name='cdp')

Select data at x and y coordinates

Parameters:

Name	Type	Description	Default
method	str	Same as methods for xarray.Dataset.interp	'nearest'
sample_dim_name	str	The name to give the output sampling dimension.	'cdp'

Returns:

Type	Description
	xarray.Dataset: At selected coordinates.

zeros_like()

Create a new dataset with the same attributes and coordinates and dimensions but with data filled by zeros.