gstools.krige.Krige

class gstools.krige.Krige(model, cond_pos, cond_val, drift_functions=None, ext_drift=None, mean=None, normalizer=None, trend=None, unbiased=True, exact=False, cond_err='nugget', pseudo_inv=True, pseudo_inv_type='pinv', fit_normalizer=False, fit_variogram=False)[source]

Bases: Field

A Swiss Army knife for kriging.

A Kriging class enabling the basic kriging routines: Simple-, Ordinary-, Universal-, External Drift- and detrended/regression-Kriging as well as Kriging the Mean [Wackernagel2003].

Parameters:

  • model (CovModel) – Covariance Model used for kriging.

  • cond_pos (list) – tuple, containing the given condition positions (x, [y, z])

  • cond_val (numpy.ndarray) – the values of the conditions (nan values will be ignored)

  • drift_functions (list of callable, str or int) – Either a list of callable functions, an integer representing the polynomial order of the drift or one of the following strings:

    • “linear” : regional linear drift (equals order=1)

    • “quadratic” : regional quadratic drift (equals order=2)

  • ext_drift (numpy.ndarray or None, optional) – the external drift values at the given cond. positions.

  • mean (float, optional) – mean value used to shift normalized conditioning data. Could also be a callable. The default is None.

  • normalizer (None or Normalizer, optional) – Normalizer to be applied to the input data to gain normality. The default is None.

  • trend (None or float or callable, optional) – A callable trend function. Should have the signature: f(x, [y, z, …]) This is used for detrended kriging, where the trended is subtracted from the conditions before kriging is applied. This can be used for regression kriging, where the trend function is determined by an external regression algorithm. If no normalizer is applied, this behaves equal to ‘mean’. The default is None.

  • unbiased (bool, optional) – Whether the kriging weights should sum up to 1, so the estimator is unbiased. If unbiased is False and no drifts are given, this results in simple kriging. Default: True

  • exact (bool, optional) – Whether the interpolator should reproduce the exact input values. If False, cond_err is interpreted as measurement error at the conditioning points and the result will be more smooth. Default: False

  • cond_err (str, :class float or list, optional) – The measurement error at the conditioning points. Either “nugget” to apply the model-nugget, a single value applied to all points or an array with individual values for each point. The “exact=True” variant only works with “cond_err=’nugget’”. Default: “nugget”

  • pseudo_inv (bool, optional) – Whether the kriging system is solved with the pseudo inverted kriging matrix. If True, this leads to more numerical stability and redundant points are averaged. But it can take more time. Default: True

  • pseudo_inv_type (str or callable, optional) –

    Here you can select the algorithm to compute the pseudo-inverse matrix:

    • “pinv”: use pinv from scipy which uses SVD

    • “pinvh”: use pinvh from scipy which uses eigen-values

    If you want to use another routine to invert the kriging matrix, you can pass a callable which takes a matrix and returns the inverse. Default: “pinv”

  • fit_normalizer (bool, optional) – Whether to fit the data-normalizer to the given conditioning data. Default: False

  • fit_variogram (bool, optional) – Whether to fit the given variogram model to the data. Directional variogram fitting is triggered by setting any anisotropy factor of the model to anything unequal 1 but the main axes of correlation are taken from the model rotation angles. If the model is a spatio-temporal latlon model, this will raise an error. This assumes the sill to be the data variance and with standard bins provided by the standard_bins routine. Default: False

Notes

If you have changed any properties in the class, you can update the kriging setup by calling Krige.set_condition without any arguments.

References

[Wackernagel2003]

Wackernagel, H., “Multivariate geostatistics”, Springer, Berlin, Heidelberg (2003)

Attributes:
all_fields

list: All fields as stacked list.

cond_err

list: The measurement errors at the condition points.

cond_ext_drift

numpy.ndarray: The ext. drift at the conditions.

cond_mean

numpy.ndarray: Trend at the conditions.

cond_no

int: The number of the conditions.

cond_pos

list: The position tuple of the conditions.

cond_trend

numpy.ndarray: Trend at the conditions.

cond_val

list: The values of the conditions.

dim

int: Dimension of the field.

drift_functions

list of callable: The drift functions.

drift_no

int: Number of drift values per point.

exact

bool: Whether the interpolator is exact.

ext_drift_no

int: Number of external drift values per point.

field_names

list: Names of present fields.

field_shape

tuple: The shape of the field.

has_const_mean

bool: Whether the field has a constant mean or not.

int_drift_no

int: Number of internal drift values per point.

krige_size

int: Size of the kriging system.

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.

pseudo_inv

bool: Whether pseudo inverse matrix is used.

pseudo_inv_type

str: Method selector for pseudo inverse calculation.

temporal

bool: Whether the field depends on time.

trend

float or callable: The trend of the field.

unbiased

bool: Whether the kriging is unbiased or not.

value_type

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

Methods

__call__([pos, mesh_type, ext_drift, ...])

Generate the kriging field.

delete_fields([select])

Delete selected fields.

get_mean([post_process])

Calculate the estimated mean of the detrended 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_condition([cond_pos, cond_val, ...])

Set the conditions for kriging.

set_drift_functions([drift_functions])

Set the drift functions for universal kriging.

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, mesh_type='unstructured', ext_drift=None, chunk_size=None, only_mean=False, return_var=True, post_process=True, store=True)[source]

Generate the kriging field.

The field is saved as self.field and is also returned. The error variance is saved as self.krige_var and is also returned.

Parameters:

  • pos (list, optional) – the position tuple, containing main direction and transversal directions (x, [y, z])

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

  • ext_drift (numpy.ndarray or None, optional) – the external drift values at the given positions (only for EDK)

  • chunk_size (int, optional) – Chunk size to cut down the size of the kriging system to prevent memory errors. Default: None

  • only_mean (bool, optional) – Whether to only calculate the mean of the kriging field. Default: False

  • return_var (bool, optional) – Whether to return the variance along with the field. Default: True

  • 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 kriging fields (True/False) with default name or with specified names. The default is True for default names [“field”, “krige_var”] or “mean_field” if only_mean=True.

Returns:

  • field (numpy.ndarray) – the kriged field or mean_field

  • krige_var (numpy.ndarray, optional) – the kriging error variance (if return_var is True and only_mean is False)

delete_fields(select=None)

Delete selected fields.

get_mean(post_process=True)[source]

Calculate the estimated mean of the detrended field.

Parameters:

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

Returns:

mean – Mean of the Kriging System.

Return type:

float or None

Notes

Only not None if the Kriging System has a constant mean. This means, no drift is given and the given field-mean is constant. The result is neglecting a potential given trend.

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:
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_condition(cond_pos=None, cond_val=None, ext_drift=None, cond_err=None, fit_normalizer=False, fit_variogram=False)[source]

Set the conditions for kriging.

This method could also be used to update the kriging setup, when properties were changed. Then you can call it without arguments.

Parameters:

  • cond_pos (list, optional) – the position tuple of the conditions (x, [y, z]). Default: current.

  • cond_val (numpy.ndarray, optional) – the values of the conditions (nan values will be ignored). Default: current.

  • ext_drift (numpy.ndarray or None, optional) – the external drift values at the given conditions (only for EDK) For multiple external drifts, the first dimension should be the index of the drift term. When passing None, the extisting external drift will be used.

  • cond_err (str, :class float, list, optional) – The measurement error at the conditioning points. Either “nugget” to apply the model-nugget, a single value applied to all points or an array with individual values for each point. The measurement error has to be <= nugget. The “exact=True” variant only works with “cond_err=’nugget’”. Default: “nugget”

  • fit_normalizer (bool, optional) – Whether to fit the data-normalizer to the given conditioning data. Default: False

  • fit_variogram (bool, optional) – Whether to fit the given variogram model to the data. Directional variogram fitting is triggered by setting any anisotropy factor of the model to anything unequal 1 but the main axes of correlation are taken from the model rotation angles. If the model is a spatio-temporal latlon model, this will raise an error. This assumes the sill to be the data variance and with standard bins provided by the standard_bins routine. Default: False

set_drift_functions(drift_functions=None)[source]

Set the drift functions for universal kriging.

Parameters:

drift_functions (list of callable, str or int) – Either a list of callable functions, an integer representing the polynomial order of the drift or one of the following strings:

  • “linear” : regional linear drift (equals order=1)

  • “quadratic” : regional quadratic drift (equals order=2)

Raises:

ValueError – If the given drift functions are not callable.

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

property cond_err

The measurement errors at the condition points.

Type:

list

property cond_ext_drift

The ext. drift at the conditions.

Type:

numpy.ndarray

property cond_mean

Trend at the conditions.

Type:

numpy.ndarray

property cond_no

The number of the conditions.

Type:

int

property cond_pos

The position tuple of the conditions.

Type:

list

property cond_trend

Trend at the conditions.

Type:

numpy.ndarray

property cond_val

The values of the conditions.

Type:

list

default_field_names = ['field', 'krige_var', 'mean_field']

Default field names.

Type:

list

property dim

Dimension of the field.

Type:

int

property drift_functions

The drift functions.

Type:

list of callable

property drift_no

Number of drift values per point.

Type:

int

property exact

Whether the interpolator is exact.

Type:

bool

property ext_drift_no

Number of external drift values per point.

Type:

int

property field_names

Names of present fields.

Type:

list

property field_shape

The shape of the field.

Type:

tuple

property has_const_mean

Whether the field has a constant mean or not.

Type:

bool

property int_drift_no

Number of internal drift values per point.

Type:

int

property krige_size

Size of the kriging system.

Type:

int

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 pseudo_inv

Whether pseudo inverse matrix is used.

Type:

bool

property pseudo_inv_type

Method selector for pseudo inverse calculation.

Type:

str

property temporal

Whether the field depends on time.

Type:

bool

property trend

The trend of the field.

Type:

float or callable

property unbiased

Whether the kriging is unbiased or not.

Type:

bool

valid_value_types = ['scalar']

valid field value types.

Type:

list of str

property value_type

Type of the field values (scalar, vector).

Type:

str