def test_x_intersection_2d(self):
"""Check that no error messages are created in the process of creating a
split_fracture.
"""
f_1 = np.array([[0, 2], [1, 1]])
f_2 = np.array([[1, 1], [0, 2]])
f_set = [f_1, f_2]
nx = [3, 3]
grids = structured._cart_grid_2d(f_set, nx, physdims=nx)
num_grids = [1, 2, 1]
for i, g in enumerate(grids):
self.assertTrue(len(g) == num_grids[i])
g_2d = grids[0][0]
g_1d_1 = grids[1][0]
g_1d_2 = grids[1][1]
g_0d = grids[2][0]
f_nodes_1 = [4, 5, 6]
f_nodes_2 = [1, 5, 9]
f_nodes_0 = [5]
glob_1 = np.sort(g_1d_1.global_point_ind)
glob_2 = np.sort(g_1d_2.global_point_ind)
glob_0 = np.sort(np.atleast_1d(g_0d.global_point_ind))
self.assertTrue(np.all(f_nodes_1 == glob_1))
self.assertTrue(np.all(f_nodes_2 == glob_2))
self.assertTrue(np.all(f_nodes_0 == glob_0))
def cart_grid(fracs: List[np.ndarray], nx: np.ndarray,
**kwargs) -> pp.GridBucket:
"""
Creates a cartesian fractured GridBucket in 2- or 3-dimensions.
Parameters
----------
fracs (list of np.ndarray): One list item for each fracture. Each item
consist of a (nd x npt) array describing fracture vertices, where npt is 2
for 2d domains, 4 for 3d domains. The fractures has to be rectangles(3D) or
straight lines(2D) that alignes with the axis. The fractures may be intersecting.
The fractures will snap to closest grid faces.
nx (np.ndarray): Number of cells in each direction. Should be 2D or 3D
**kwargs:
physdims (np.ndarray): Physical dimensions in each direction.
Defaults to same as nx, that is, cells of unit size.
May also contain fracture tags, options for gridding, etc.
Returns:
-------
GridBucket: A complete bucket where all fractures are represented as
lower dim grids. The higher dim fracture faces are split in two,
and on the edges of the GridBucket graph the mapping from lower dim
cells to higher dim faces are stored as 'face_cells'. Each face is
given boolean tags depending on the type:
domain_boundary_faces: All faces that lie on the domain boundary
(i.e. should be given a boundary condition).
fracture_faces: All faces that are split (i.e. has a connection to a
lower dim grid).
tip_faces: A boundary face that is not on the domain boundary, nor
coupled to a lower domentional domain.
The union of the above three is the tag boundary_faces.
Examples
--------
frac1 = np.array([[1, 4], [2, 2]])
frac2 = np.array([[2, 2], [1, 4]])
fracs = [frac1, frac2]
gb = cart_grid(fracs, [5, 5])
"""
ndim = np.asarray(nx).size
physdims = kwargs.get("physdims", None)
if physdims is None:
physdims = nx
elif np.asarray(physdims).size != ndim:
raise ValueError("Physical dimension must equal grid dimension")
# Call relevant method, depending on grid dimensions
if ndim == 2:
grids = structured._cart_grid_2d(fracs, nx, physdims=physdims)
elif ndim == 3:
grids = structured._cart_grid_3d(fracs, nx, physdims=physdims)
else:
raise ValueError("Only support for 2 and 3 dimensions")
return grid_list_to_grid_bucket(grids, **kwargs)