gstools.field.CondSRF

class gstools.field.CondSRF(krige, generator='RandMeth', **generator_kwargs)

Bases: Field

A class to generate conditioned spatial random fields (SRF).

Parameters:

• krige (Krige) – Kriging setup to condition the spatial random field.

• generator (str or Generator, optional) – Name or class of the field generator to be used. At the moment, only the following generator is provided:

  • “RandMeth” : The Randomization Method. See: RandMeth

  Default: “RandMeth”

• **generator_kwargs – Keyword arguments that are forwarded to the generator in use. Have a look at the provided generators for further information.

Attributes:

all_fields

list: All fields as stacked list.

dim

int: Dimension of the field.

field_names

list: Names of present fields.

field_shape

tuple: The shape of the field.

generator

callable: The generator of the field.

krige

Krige: The underlying kriging class.

latlon

bool: Whether the field depends on geographical coords.

mean

float or callable: The mean of the field.

mesh_type

str: The mesh type of the field.

model

CovModel: The covariance model of the field.

name

str: The name of the class.

normalizer

Normalizer: Normalizer of the field.

pos

tuple: The position tuple of the field.

temporal

bool: Whether the field depends on time.

trend

float or callable: The trend of the field.

value_type

str: Type of the field values (scalar, vector).

Methods

__call__([pos, seed, mesh_type, ...])	Generate the conditioned spatial random field.
delete_fields([select])	Delete selected fields.
get_scaling(krige_var, shape)	Get scaling coefficients for the random field.
get_store_config(store[, default, fld_cnt])	Get storage configuration from given selection.
mesh(mesh[, points, direction, name])	Generate a field on a given meshio, ogs5py or PyVista mesh.
plot([field, fig, ax])	Plot the spatial random field.
post_field(field[, name, process, save])	Postprocessing field values.
pre_pos([pos, mesh_type, info])	Preprocessing positions and mesh_type.
set_generator(generator, **generator_kwargs)	Set the generator for the field.
set_pos(pos[, mesh_type, info])	Set positions and mesh_type.
structured(*args, **kwargs)	Generate a field on a structured mesh.
to_pyvista([field_select, fieldname])	Create a VTK/PyVista grid of the stored field.
transform(method[, field, store, process])	Apply field transformation.
unstructured(*args, **kwargs)	Generate a field on an unstructured mesh.
vtk_export(filename[, field_select, fieldname])	Export the stored field to vtk.

__call__(pos=None, seed=nan, mesh_type='unstructured', post_process=True, store=True, krige_store=True, **kwargs)

Generate the conditioned spatial random field.

The field is saved as self.field and is also returned.

Parameters:

• pos (list, optional) – the position tuple, containing main direction and transversal directions

• seed (int, optional) – seed for RNG for resetting. Default: keep seed from generator

• mesh_type (str) – ‘structured’ / ‘unstructured’

• post_process (bool, optional) – Whether to apply mean, normalizer and trend to the field. Default: True

• store (str or bool or list, optional) – Whether to store fields (True/False) with default names or with specified names. The default is True for default names [“field”, “raw_field”, “raw_krige”].

• krige_store (str or bool or list, optional) – Whether to store kriging fields (True/False) with default name or with specified names. The default is True for default names [“field”, “krige_var”].

• **kwargs – keyword arguments that are forwarded to the kriging routine in use.

Returns:

field – the conditioned SRF

Return type:

numpy.ndarray

delete_fields(select=None)

Delete selected fields.

get_scaling(krige_var, shape)

Get scaling coefficients for the random field.

Parameters:

• krige_var (numpy.ndarray) – Kriging variance.

• shape (tuple of int) – Field shape.

Returns:

• var_scale (numpy.ndarray) – Variance scaling factor for the random field.

• nugget (numpy.ndarray or int) – Nugget to be added to the field.

get_store_config(store, default=None, fld_cnt=None)

Get storage configuration from given selection.

Parameters:

• store (str or bool or list, optional) – Whether to store fields (True/False) with default names or with specified names. The default is True for default names.

• default (str or list, optional) – Default field names. The default is “field”.

• fld_cnt (None or int, optional) – Number of fields when using lists. The default is None.

Returns:

• name (str or list) – Name(s) of field.

• save (bool or list) – Whether to save field(s).

mesh(mesh, points='centroids', direction='all', name='field', **kwargs)

Generate a field on a given meshio, ogs5py or PyVista mesh.

Parameters:

• mesh (meshio.Mesh or ogs5py.MSH or PyVista mesh) – The given mesh

• points (str, optional) – The points to evaluate the field at. Either the “centroids” of the mesh cells (calculated as mean of the cell vertices) or the “points” of the given mesh. Default: “centroids”

• direction (str or list, optional) – Here you can state which direction should be chosen for lower dimension. For example, if you got a 2D mesh in xz direction, you have to pass “xz”. By default, all directions are used. One can also pass a list of indices. Default: “all”

• name (str or list of str, optional) – Name(s) to store the field(s) in the given mesh as point_data or cell_data. If to few names are given, digits will be appended. Default: “field”

• **kwargs – Keyword arguments forwarded to __call__.

Notes

This will store the field in the given mesh under the given name, if a meshio or PyVista mesh was given.

See:

• meshio: https://github.com/nschloe/meshio

• ogs5py: https://github.com/GeoStat-Framework/ogs5py

• PyVista: https://github.com/pyvista/pyvista

plot(field='field', fig=None, ax=None, **kwargs)

Plot the spatial random field.

Parameters:

• field (str, optional) – Field that should be plotted. Default: “field”

• fig (Figure or None) – Figure to plot the axes on. If None, a new one will be created. Default: None

• ax (Axes or None) – Axes to plot on. If None, a new one will be added to the figure. Default: None

• **kwargs – Forwarded to the plotting routine.

post_field(field, name='field', process=True, save=True)

Postprocessing field values.

Parameters:

• field (numpy.ndarray) – Field values.

• name (str, optional) – Name. to store the field. The default is “field”.

• process (bool, optional) – Whether to process field to apply mean, normalizer and trend. The default is True.

• save (bool, optional) – Whether to store the field under the given name. The default is True.

Returns:

field – Processed field values.

Return type:

numpy.ndarray

pre_pos(pos=None, mesh_type='unstructured', info=False)

Preprocessing positions and mesh_type.

Parameters:

• pos (iterable) – the position tuple, containing main direction and transversal directions

• mesh_type (str, optional) – ‘structured’ / ‘unstructured’ Default: “unstructured”

• info (bool, optional) – Whether to return information

Returns:

• iso_pos ((d, n), numpy.ndarray) – Isometrized position tuple.

• shape (tuple) – Shape of the resulting field.

• info (dict, optional) – Information about settings.

Warning

When setting a new position tuple that differs from the present one, all stored fields will be deleted.

set_generator(generator, **generator_kwargs)

Set the generator for the field.

Parameters:

• generator (str or Generator, optional) – Name or class of the generator to use for field generation. Default: “RandMeth”

• **generator_kwargs – keyword arguments that are forwarded to the generator in use.

set_pos(pos, mesh_type='unstructured', info=False)

Set positions and mesh_type.

Parameters:

• pos (iterable) – the position tuple, containing main direction and transversal directions

• mesh_type (str, optional) – ‘structured’ / ‘unstructured’ Default: “unstructured”

• info (bool, optional) – Whether to return information

Returns:

info – Information about settings.

Return type:

dict, optional

Warning

When setting a new position tuple that differs from the present one, all stored fields will be deleted.

structured(*args, **kwargs)

Generate a field on a structured mesh.

See __call__

to_pyvista(field_select='field', fieldname='field')

Create a VTK/PyVista grid of the stored field.

Parameters:

• field_select (str, optional) – Field that should be stored. Can be: “field”, “raw_field”, “krige_field”, “err_field” or “krige_var”. Default: “field”

• fieldname (str, optional) – Name of the field in the VTK file. Default: “field”

transform(method, field='field', store=True, process=False, **kwargs)

Apply field transformation.

Parameters:

• method (str) – Method to use. See gstools.transform for available transformations.

• field (str, optional) – Name of field to be transformed. The default is “field”.

• store (str or bool, optional) – Whether to store field inplace (True/False) or under a given name. The default is True.

• process (bool, optional) – Whether to process in/out fields with trend, normalizer and mean of given Field instance. The default is False.

• **kwargs – Keyword arguments forwarded to selected method.

Raises:

ValueError – When method is unknown.

Returns:

Transformed field.

Return type:

numpy.ndarray

unstructured(*args, **kwargs)

Generate a field on an unstructured mesh.

See __call__

vtk_export(filename, field_select='field', fieldname='field')

Export the stored field to vtk.

Parameters:

• filename (str) – Filename of the file to be saved, including the path. Note that an ending (.vtr or .vtu) will be added to the name.

• field_select (str, optional) – Field that should be stored. Can be: “field”, “raw_field”, “krige_field”, “err_field” or “krige_var”. Default: “field”

• fieldname (str, optional) – Name of the field in the VTK file. Default: “field”

property all_fields

All fields as stacked list.

Type:

list

default_field_names = ['field', 'raw_field', 'raw_krige']

Default field names.

Type:

list

property dim

Dimension of the field.

Type:

int

property field_names

Names of present fields.

Type:

list

property field_shape

The shape of the field.

Type:

tuple

property generator

The generator of the field.

Type:

callable

property krige

The underlying kriging class.

Type:

Krige

property latlon

Whether the field depends on geographical coords.

Type:

bool

property mean

The mean of the field.

Type:

float or callable

property mesh_type

The mesh type of the field.

Type:

str

property model

The covariance model of the field.

Type:

CovModel

property name

The name of the class.

Type:

str

property normalizer

Normalizer of the field.

Type:

Normalizer

property pos

The position tuple of the field.

Type:

tuple

property temporal

Whether the field depends on time.

Type:

bool

property trend

The trend of the field.

Type:

float or callable

valid_value_types = ['scalar']

valid field value types.

Type:

list of str

property value_type

Type of the field values (scalar, vector).

Type:

str