def _pre_pos(self, pos, mesh_type="unstructured", make_unstruct=False):
"""
Preprocessing positions and mesh_type.
Parameters
----------
pos : :any:`iterable`
the position tuple, containing main direction and transversal
directions
mesh_type : :class:`str`
'structured' / 'unstructured'
make_unstruct: :class:`bool`
State if mesh_type should be made unstructured.
Returns
-------
x : :class:`numpy.ndarray`
first components of unrotated and isotropic position vectors
y : :class:`numpy.ndarray` or None
analog to x
z : :class:`numpy.ndarray` or None
analog to x
pos : :class:`tuple` of :class:`numpy.ndarray`
the normalized position tuple
mesh_type_gen : :class:`str`
'structured' / 'unstructured' for the generator
mesh_type_changed : :class:`bool`
State if the mesh_type was changed.
axis_lens : :class:`tuple` or :any:`None`
axis lengths of the structured mesh if mesh type was changed.
"""
x, y, z = pos2xyz(pos, max_dim=self.model.dim)
pos = xyz2pos(x, y, z)
mesh_type_gen = mesh_type
# format the positional arguments of the mesh
check_mesh(self.model.dim, x, y, z, mesh_type)
mesh_type_changed = False
axis_lens = None
if (self.model.do_rotation
or make_unstruct) and mesh_type == "structured":
mesh_type_changed = True
mesh_type_gen = "unstructured"
x, y, z, axis_lens = reshape_axis_from_struct_to_unstruct(
self.model.dim, x, y, z)
if self.model.do_rotation:
x, y, z = unrotate_mesh(self.model.dim, self.model.angles, x, y, z)
if not self.model.is_isotropic:
y, z = make_isotropic(self.model.dim, self.model.anis, y, z)
return x, y, z, pos, mesh_type_gen, mesh_type_changed, axis_lens
def __call__(
self, pos, seed=np.nan, point_volumes=0.0, mesh_type="unstructured"
):
"""Generate the 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
point_volumes : :class:`float` or :class:`numpy.ndarray`
If your evaluation points for the field are coming from a mesh,
they are probably representing a certain element volume.
This volume can be passed by `point_volumes` to apply the
given variance upscaling. If `point_volumes` is ``0`` nothing
is changed. Default: ``0``
mesh_type : :class:`str`
'structured' / 'unstructured'
Returns
-------
field : :class:`numpy.ndarray`
the SRF
"""
# internal conversation
x, y, z = pos2xyz(pos, max_dim=self.model.dim)
self.pos = xyz2pos(x, y, z)
self.mesh_type = mesh_type
# update the model/seed in the generator if any changes were made
self.generator.update(self.model, seed)
# format the positional arguments of the mesh
check_mesh(self.model.dim, x, y, z, mesh_type)
mesh_type_changed = False
if self.model.do_rotation:
if mesh_type == "structured":
mesh_type_changed = True
mesh_type_old = mesh_type
mesh_type = "unstructured"
x, y, z, axis_lens = reshape_axis_from_struct_to_unstruct(
self.model.dim, x, y, z
)
x, y, z = unrotate_mesh(self.model.dim, self.model.angles, x, y, z)
y, z = make_isotropic(self.model.dim, self.model.anis, y, z)
# generate the field
self.raw_field = self.generator.__call__(x, y, z, mesh_type)
# reshape field if we got an unstructured mesh
if mesh_type_changed:
mesh_type = mesh_type_old
self.raw_field = reshape_field_from_unstruct_to_struct(
self.model.dim, self.raw_field, axis_lens
)
# apply given conditions to the field
if self.condition:
(
cond_field,
krige_field,
err_field,
krigevar,
info,
) = self.cond_func(self)
# store everything in the class
self.field = cond_field
self.krige_field = krige_field
self.err_field = err_field
self.krige_var = krigevar
if "mean" in info: # ordinary krging estimates mean
self.mean = info["mean"]
else:
self.field = self.raw_field + self.mean
# upscaled variance
if not np.isscalar(point_volumes) or not np.isclose(point_volumes, 0):
scaled_var = self.upscaling_func(self.model, point_volumes)
self.field -= self.mean
self.field *= np.sqrt(scaled_var / self.model.sill)
self.field += self.mean
return self.field
def __call__(self, pos, mesh_type="unstructured"):
"""
Generate the simple kriging 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 (x, [y, z])
mesh_type : :class:`str`
'structured' / 'unstructured'
Returns
-------
field : :class:`numpy.ndarray`
the kriged field
krige_var : :class:`numpy.ndarray`
the kriging error variance
"""
# internal conversation
x, y, z = pos2xyz(pos, dtype=np.double, max_dim=self.model.dim)
c_x, c_y, c_z = pos2xyz(self.cond_pos,
dtype=np.double,
max_dim=self.model.dim)
self.pos = xyz2pos(x, y, z)
self.mesh_type = mesh_type
# format the positional arguments of the mesh
check_mesh(self.model.dim, x, y, z, mesh_type)
mesh_type_changed = False
if mesh_type == "structured":
mesh_type_changed = True
mesh_type_old = mesh_type
mesh_type = "unstructured"
x, y, z, axis_lens = reshape_axis_from_struct_to_unstruct(
self.model.dim, x, y, z)
if self.model.do_rotation:
x, y, z = unrotate_mesh(self.model.dim, self.model.angles, x, y, z)
c_x, c_y, c_z = unrotate_mesh(self.model.dim, self.model.angles,
c_x, c_y, c_z)
y, z = make_isotropic(self.model.dim, self.model.anis, y, z)
c_y, c_z = make_isotropic(self.model.dim, self.model.anis, c_y, c_z)
# set condtions to zero mean
cond = self.cond_val - self.mean
krig_mat = inv(self._get_cov_mat((c_x, c_y, c_z), (c_x, c_y, c_z)))
krig_vecs = self._get_cov_mat((c_x, c_y, c_z), (x, y, z))
# generate the kriged field
field, krige_var = krigesum(krig_mat, krig_vecs, cond)
# reshape field if we got an unstructured mesh
if mesh_type_changed:
mesh_type = mesh_type_old
field = reshape_field_from_unstruct_to_struct(
self.model.dim, field, axis_lens)
krige_var = reshape_field_from_unstruct_to_struct(
self.model.dim, krige_var, axis_lens)
# calculate the kriging error
self.krige_var = self.model.sill - krige_var
# add the given mean
self.field = field + self.mean
return self.field, self.krige_var
def __call__(self, pos, mesh_type="unstructured"):
"""
Generate the ordinary kriging 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 (x, [y, z])
mesh_type : :class:`str`
'structured' / 'unstructured'
Returns
-------
field : :class:`numpy.ndarray`
the kriged field
krige_var : :class:`numpy.ndarray`
the kriging error variance
"""
# internal conversation
x, y, z = pos2xyz(pos, dtype=np.double, max_dim=self.model.dim)
c_x, c_y, c_z = pos2xyz(self.cond_pos,
dtype=np.double,
max_dim=self.model.dim)
self.pos = xyz2pos(x, y, z)
self.mesh_type = mesh_type
# format the positional arguments of the mesh
check_mesh(self.model.dim, x, y, z, mesh_type)
mesh_type_changed = False
if mesh_type == "structured":
mesh_type_changed = True
mesh_type_old = mesh_type
mesh_type = "unstructured"
x, y, z, axis_lens = reshape_axis_from_struct_to_unstruct(
self.model.dim, x, y, z)
if self.model.do_rotation:
x, y, z = unrotate_mesh(self.model.dim, self.model.angles, x, y, z)
c_x, c_y, c_z = unrotate_mesh(self.model.dim, self.model.angles,
c_x, c_y, c_z)
y, z = make_isotropic(self.model.dim, self.model.anis, y, z)
c_y, c_z = make_isotropic(self.model.dim, self.model.anis, c_y, c_z)
# set condtions
cond = np.concatenate((self.cond_val, [0]))
krig_mat = inv(self._get_krig_mat((c_x, c_y, c_z), (c_x, c_y, c_z)))
krig_vecs = self._get_vario_mat((c_x, c_y, c_z), (x, y, z), add=True)
# generate the kriged field
field, krige_var = krigesum(krig_mat, krig_vecs, cond)
# calculate the estimated mean (kriging field at infinity)
mean_est = np.concatenate((np.full_like(self.cond_val,
self.model.sill), [1]))
self.mean = np.einsum("i,ij,j", cond, krig_mat, mean_est)
# reshape field if we got an unstructured mesh
if mesh_type_changed:
mesh_type = mesh_type_old
field = reshape_field_from_unstruct_to_struct(
self.model.dim, field, axis_lens)
krige_var = reshape_field_from_unstruct_to_struct(
self.model.dim, krige_var, axis_lens)
# save the field
self.krige_var = krige_var
self.field = field
return self.field, self.krige_var
def __call__(
self,
pos,
seed=np.nan,
force_moments=False,
point_volumes=0.0,
mesh_type="unstructured",
):
"""Generate the spatial random field.
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
force_moments : :class:`bool`
Force the generator to exactly match mean and variance.
Default: ``False``
point_volumes : :class:`float` or :class:`numpy.ndarray`
If your evaluation points for the field are coming from a mesh,
they are probably representing a certain element volume.
This volumes can be passed by `point_volumes` to apply the
given variance upscaling. If `point_volumes` is ``0`` nothing
is changed. Default: ``0``
mesh_type : :class:`str`
'structured' / 'unstructured'
Returns
-------
field : :class:`numpy.ndarray`
the SRF
"""
# internal conversation
x, y, z = pos2xyz(pos)
# update the model/seed in the generator if any changes were made
self.generator.update(self.model, seed)
# format the positional arguments of the mesh
check_mesh(self.model.dim, x, y, z, mesh_type)
mesh_type_changed = False
if self.do_rotation:
if mesh_type == "structured":
mesh_type_changed = True
mesh_type_old = mesh_type
mesh_type = "unstructured"
x, y, z, axis_lens = reshape_axis_from_struct_to_unstruct(
self.model.dim, x, y, z)
x, y, z = unrotate_mesh(self.model.dim, self.model.angles, x, y, z)
y, z = make_isotropic(self.model.dim, self.model.anis, y, z)
x, y, z = reshape_input(x, y, z, mesh_type)
# generate the field
field = self.generator.__call__(x, y, z)
# reshape field if we got an unstructured mesh
if mesh_type_changed:
mesh_type = mesh_type_old
field = reshape_field_from_unstruct_to_struct(
self.model.dim, field, axis_lens)
# force variance and mean to be exactly as given (if wanted)
if force_moments:
var_in = np.var(field)
mean_in = np.mean(field)
rescale = np.sqrt(self.model.sill / var_in)
field = rescale * (field - mean_in)
# upscaled variance
scaled_var = self.upscaling_func(self.model, point_volumes)
# rescale and shift the field to the mean
self.field = np.sqrt(scaled_var / self.model.sill) * field + self.mean
return self.field