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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| # -*- coding: utf-8 -*- | ||
| """ | ||
| GStools subpackage providing a class for conditioned spatial random fields. | ||
| .. currentmodule:: gstools.field.cond_srf | ||
| The following classes are provided | ||
| .. autosummary:: | ||
| CondSRF | ||
| """ | ||
| # pylint: disable=C0103 | ||
|
|
||
| import numpy as np | ||
| from gstools.field.generator import RandMeth | ||
| from gstools.field.base import Field | ||
| from gstools.krige import Krige | ||
|
|
||
| __all__ = ["CondSRF"] | ||
|
|
||
| GENERATOR = { | ||
| "RandMeth": RandMeth, | ||
| } | ||
|
|
||
|
|
||
| class CondSRF(Field): | ||
| """A class to generate conditioned spatial random fields (SRF). | ||
| Parameters | ||
| ---------- | ||
| model : :any:`CovModel` | ||
| Covariance Model of the spatial random field. | ||
| generator : :class:`str`, optional | ||
| Name of the field generator to be used. | ||
| At the moment, the following generators are provided: | ||
| * "RandMeth" : The Randomization Method. | ||
| See: :any:`RandMeth` | ||
| * "IncomprRandMeth" : The incompressible Randomization Method. | ||
| This is the original algorithm proposed by Kraichnan 1970 | ||
| See: :any:`IncomprRandMeth` | ||
| * "VectorField" : an alias for "IncomprRandMeth" | ||
| * "VelocityField" : an alias for "IncomprRandMeth" | ||
| Default: "RandMeth" | ||
| **generator_kwargs | ||
| Keyword arguments that are forwarded to the generator in use. | ||
| Have a look at the provided generators for further information. | ||
| """ | ||
|
|
||
| def __init__(self, krige, generator="RandMeth", **generator_kwargs): | ||
| if not isinstance(krige, Krige): | ||
| raise ValueError("CondSRF: krige should be an instance of Krige.") | ||
| self.krige = krige | ||
| # initialize attributes | ||
| self.pos = None | ||
| self.mesh_type = None | ||
| self.field = None | ||
| self.raw_field = None | ||
| # initialize private attributes | ||
| self._value_type = None | ||
| self._generator = None | ||
| # initialize attributes | ||
| self.set_generator(generator, **generator_kwargs) | ||
|
|
||
| def __call__(self, pos, seed=np.nan, mesh_type="unstructured", **kwargs): | ||
| """Generate the conditioned spatial random field. | ||
| The field is saved as `self.field` and is also returned. | ||
| Parameters | ||
| ---------- | ||
| pos : :class:`list` | ||
| the position tuple, containing main direction and transversal | ||
| directions | ||
| seed : :class:`int`, optional | ||
| seed for RNG for reseting. Default: keep seed from generator | ||
| mesh_type : :class:`str` | ||
| 'structured' / 'unstructured' | ||
| **kwargs | ||
| keyword arguments that are forwarded to the kriging routine in use. | ||
| Returns | ||
| ------- | ||
| field : :class:`numpy.ndarray` | ||
| the conditioned SRF | ||
| """ | ||
| kwargs["mesh_type"] = mesh_type | ||
| kwargs["only_mean"] = False # overwrite if given | ||
| kwargs["return_var"] = True # overwrite if given | ||
| # update the model/seed in the generator if any changes were made | ||
| self.generator.update(self.model, seed) | ||
| # get isometrized positions and the resulting field-shape | ||
| iso_pos, shape = self.pre_pos(pos, mesh_type) | ||
| # generate the field | ||
| self.raw_field = np.reshape(self.generator(iso_pos), shape) | ||
| field, krige_var = self.krige(pos, **kwargs) | ||
| var_scale, nugget = self.get_scaling(krige_var, shape) | ||
| self.field = field + var_scale * self.raw_field + nugget | ||
| return self.field | ||
|
|
||
| def get_scaling(self, krige_var, shape): | ||
| """ | ||
| Get scaling coefficients for the random field. | ||
| Parameters | ||
| ---------- | ||
| krige_var : :class:`numpy.ndarray` | ||
| Kriging variance. | ||
| shape : :class:`tuple` of :class:`int` | ||
| Field shape. | ||
| Returns | ||
| ------- | ||
| var_scale : :class:`numpy.ndarray` | ||
| Variance scaling factor for the random field. | ||
| nugget : :class:`numpy.ndarray` or `class:`int | ||
| Nugget to be added to the field. | ||
| """ | ||
| if self.model.nugget > 0: | ||
| var_scale = np.maximum(krige_var - self.model.nugget, 0) | ||
| nug_scale = np.sqrt((krige_var - var_scale) / self.model.nugget) | ||
| var_scale = np.sqrt(var_scale / self.model.var) | ||
| nugget = nug_scale * self.generator.get_nugget(shape) | ||
| else: | ||
| var_scale = np.sqrt(krige_var / self.model.var) | ||
| nugget = 0 | ||
| return var_scale, nugget | ||
|
|
||
| def set_generator(self, generator, **generator_kwargs): | ||
| """Set the generator for the field. | ||
| Parameters | ||
| ---------- | ||
| generator : :class:`str`, optional | ||
| Name of the generator to use for field generation. | ||
| Default: "RandMeth" | ||
| **generator_kwargs | ||
| keyword arguments that are forwarded to the generator in use. | ||
| """ | ||
| if generator in GENERATOR: | ||
| gen = GENERATOR[generator] | ||
| self._generator = gen(self.model, **generator_kwargs) | ||
| self.value_type = self._generator.value_type | ||
| else: | ||
| raise ValueError("gstools.CondSRF: Unknown generator " + generator) | ||
| if self.value_type != "scalar": | ||
| raise ValueError("CondSRF: only scalar field value type allowed.") | ||
|
|
||
| @property | ||
| def generator(self): | ||
| """:any:`callable`: The generator of the field.""" | ||
| return self._generator | ||
|
|
||
| @property | ||
| def model(self): | ||
| """:any:`CovModel`: The covariance model of the field.""" | ||
| return self.krige.model | ||
|
|
||
| @model.setter | ||
| def model(self, model): | ||
| pass | ||
|
|
||
| def __repr__(self): | ||
| """Return String representation.""" | ||
| return "CondSRF(krige={0}, generator={1})".format( | ||
| self.krige, self.generator | ||
| ) |
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