pyrtid.utils#

pyRTID submodule providing tools and utilities for other submodules.

Working with dataclasses#

Utilities for python dataclasses.

Helper to set a default value for a dataclass field.

Provide utilities to work with regular grids.

`indices_to_node_number`(ix[, nx, iy, ny, iz, ...])	Convert indices (ix, iy, iz) to a node-number.
`node_number_to_indices`(node_number[, nx, ...])	Convert a node-number to indices (ix, iy, iz) for a regular grid.
`span_to_node_numbers_2d`(span, nx, ny)	Convert the given span to an array of node indices.
`span_to_node_numbers_3d`(span, nx, ny, nz)	Convert the given span to an array of node indices.
`get_array_borders_selection_2d`(nx, ny)	Get a selection of the array border as a bool array.
`get_array_borders_selection_3d`(nx, ny, nz)	Get a selection of the array border as a bool array.
`get_a_not_in_b_1d`(a, b)	Return the elements of a not found in b sorted by ascending order.
`get_pts_coords_regular_grid`(mesh_dim, shape)	Create an array of points coordinates for regular grids.
`create_selections_array_2d`(polygons, ...)	Return a grid array containing the sel_ids as values.
`RectilinearGrid`([x0, y0, z0, dx, dy, dz, ...])	Represent a rectilinear 3D grid.
`get_polygon_selection_with_dilation_2d`(...)	Extend the selections using binary dilation.
`get_extended_grid_shape`(grid, axis, extend)

Utilities to create wellfields.

gen_wells_coordinates(startx, starty, endx, ...)

Generate sequences of injectors and producers coordinates.

Provide utilities to generate random fields. This is based on the GStools package.

`gen_random_ensemble`(model, n_ensemble, var, ...)	Generate a (ne, nx, ny, nz) ensemble of 3D random fields.
`get_normalized_mean_from_lognormal_params`(...)	Get the mean of the normalized log-normal distribution.
`get_normalized_std_from_lognormal_params`(...)	Get the standard deviation of the normalized log-normal distribution.
`get_log_normalized_mean_from_normal_params`(...)	Get the mean of the log-normalized normal distribution.
`get_log_normalized_std_from_normal_params`(...)	Get the mean of the log-normalized normal distribution.

Provide a str enum class.u

StrEnum(value[, names, module, qualname, ...])

Hashable string Enum.

Provide functions to compute the gradient of a function by finite difference numerical approximation.

`finite_jacobian`(x, fm[, fm_args, fm_kwargs, ...])	Compute the Jacobian by finite difference.
`finite_gradient`(x, fm[, fm_args, fm_kwargs, ...])	Compute the gradient by finite difference.
`is_all_close`(v1, v2[, eps])	Return whether the two vectors are approximately equal.
`is_jacobian_correct`(x, fm, jac[, fm_args, ...])	Check by finite difference if the Jacobian matrix is correct.
`is_gradient_correct`(x, fm, grad[, fm_args, ...])	Check by finite difference if the gradient is correct.

Provide functions for spatial differentiation.

`gradient_ffd`(param, dx[, axis])	Compute the gradient using the first order forward differences.
`gradient_bfd`(param, dx[, axis])	Compute the gradient using the first order forward differences.
`hessian_cfd`(param, dx[, axis])	Compute the hessian matching gradient_ffd.

Provide functions to perform mean and their first derivative.

`arithmetic_mean`(xi, xj)	Return the arithmetic mean of xi and xj.
`dxi_arithmetic_mean`(xi, xj)	Return the first derivative of xi and xj arithmetic mean with respect to xi.
`harmonic_mean`(xi, xj)	Return the harmonic mean of xi and xj.
`dxi_harmonic_mean`(xi, xj)	Return the first derivative of xi and xj arithmetic mean with respect to xi.
`MeanType`(value[, names, module, qualname, ...])
`get_mean_values_for_last_axis`(arr, mean_type)	Get the mean values for the last axis of the input array.
`amean_gradient`(values[, weights])	Return the gradient of the weighted arithmetic mean.
`gmean_gradient`(values[, weights])	Return the gradient of the weighted geometric mean.
`hmean_gradient`(values[, weights])	Return the gradient of the harmonic arithmetic mean.
`get_mean_values_gradient_for_last_axis`(arr, ...)	Get the mean values for the last axis of the input array.

Provide some spatial filters

`Filter`()	A gradient filter.
`GaussianFilter`(sigmas[, order, mode, cval, ...])	Apply gaussian filter filtering.

Other functions

`object_or_object_sequence_to_list`(_input)	Convert a singleton or an iterable of this object to a list of object.
`get_super_ilu_preconditioner`(mat, **kwargs)	Get an incomplete LU preconditioner for the given sparse matrix.
`show_versions`(logger)	Show the versions of all packages used by pyrtid.
`check_random_state`(seed)	Turn seed into a np.random.RandomState instance.

Other functions

`NDArrayFloat`	ndarray(shape, dtype=float, buffer=None, offset=0,
`NDArrayInt`	ndarray(shape, dtype=float, buffer=None, offset=0,
`NDArrayBool`	ndarray(shape, dtype=float, buffer=None, offset=0,
`Int`	alias of `Union`[`int`, `ndarray`[`Any`, `dtype`[`int64`]], `Sequence`[`int`]]

Helpers to work with sparse matrices and sparse arrays.

`sparse_cholesky`(arr)
`assert_allclose_sparse`(A, B[, atol, rtol])	Assert that two sparse matrices or arrays are almost equal.