def test_split_segment_partly_overlapping(self):
p = np.array([[0, 1, 2, 3], [0, 0, 0, 0]])
lines = np.array([[0, 2], [1, 3]]).T
box = np.array([[1], [1]])
new_pts, new_lines = cg.remove_edge_crossings(p, lines, box=box)
p_known = cg.snap_to_grid(p, box=box)
lines_known = np.array([[0, 1], [1, 2], [2, 3]]).T
self.assertTrue(np.allclose(new_pts, p_known))
self.assertTrue(np.allclose(new_lines, lines_known))
def test_three_lines_one_crossing(self):
# This test gave an error at some point
p = np.array([[0., 0.5, 0.3, 1., 0.3, 0.5], [2 / 3, 0.3, 0.3, 2 / 3, 0.5, 0.5]])
lines = np.array([[0, 3], [2, 5], [1, 4]]).T
box = np.array([[1], [2]])
new_pts, new_lines = cg.remove_edge_crossings(p, lines, box=box)
p_known = np.hstack((p, np.array([[0.4], [0.4]])))
p_known = cg.snap_to_grid(p_known, box=box)
lines_known = np.array([[0, 3], [2, 6], [6, 5], [1, 6], [6, 4]]).T
self.assertTrue(np.allclose(new_pts, p_known))
self.assertTrue(np.allclose(new_lines, lines_known))
def test_three_lines_no_crossing(self):
# This test gave an error at some point
p = np.array(
[[0., 0., 0.3, 1., 1., 0.5], [2 / 3, 1 / .7, 0.3, 2 / 3, 1 / .7, 0.5]]
)
lines = np.array([[0, 3], [1, 4], [2, 5]]).T
box = np.array([[1], [2]])
new_pts, new_lines = cg.remove_edge_crossings(p, lines, box=box)
p_known = cg.snap_to_grid(p, box=box)
self.assertTrue(np.allclose(new_pts, p_known))
self.assertTrue(np.allclose(new_lines, lines))
def test_split_segment_fully_overlapping_switched_order(self):
p = np.array([[0, 1, 2, 3], [0, 0, 0, 0]])
lines = np.array([[0, 3], [2, 1]]).T
box = np.array([[1], [1]])
new_pts, new_lines = cg.remove_edge_crossings(p, lines, box=box)
new_lines = np.sort(new_lines, axis=0)
p_known = cg.snap_to_grid(p, box=box)
lines_known = np.array([[0, 1], [1, 2], [2, 3]]).T
self.assertTrue(np.allclose(new_pts, p_known))
self.assertTrue(np.allclose(new_lines, lines_known))
def test_lines_crossing_origin(self):
p = np.array([[-1, 1, 0, 0], [0, 0, -1, 1]])
lines = np.array([[0, 2], [1, 3], [1, 2], [3, 4]])
box = np.array([[2], [2]])
new_pts, new_lines = cg.remove_edge_crossings(p, lines, box=box)
p_known = np.hstack((p, np.array([[0], [0]])))
p_known = cg.snap_to_grid(p_known, box=box)
lines_known = np.array([[0, 4, 2, 4], [4, 1, 4, 3], [1, 1, 2, 2], [3, 3, 4, 4]])
self.assertTrue(np.allclose(new_pts, p_known))
self.assertTrue(np.allclose(new_lines, lines_known))
def test_split_segment_partly_overlapping_switched_order(self):
# Same partly overlapping test, but switch order of edge-point
# connection. Should make no difference
p = np.array([[0, 1, 2, 3], [0, 0, 0, 0]])
lines = np.array([[0, 2], [3, 1]]).T
box = np.array([[1], [1]])
new_pts, new_lines = cg.remove_edge_crossings(p, lines, box=box)
new_lines = np.sort(new_lines, axis=0)
p_known = cg.snap_to_grid(p, box=box)
lines_known = np.array([[0, 1], [1, 2], [2, 3]]).T
self.assertTrue(np.allclose(new_pts, p_known))
self.assertTrue(np.allclose(new_lines, lines_known))
def triangle_grid(fracs, domain, do_snap_to_grid=False, **kwargs):
"""
Generate a gmsh grid in a 2D domain with fractures.
The function uses modified versions of pygmsh and mesh_io,
both downloaded from github.
To be added:
Functionality for tuning gmsh, including grid size, refinements, etc.
Parameters
----------
fracs: (dictionary) Two fields: points (2 x num_points) np.ndarray,
edges (2 x num_lines) connections between points, defines fractures.
box: (dictionary) keys xmin, xmax, ymin, ymax, [together bounding box
for the domain]
do_snap_to_grid (boolean, optional): If true, points are snapped to an
underlying Cartesian grid with resolution tol before geometry
computations are carried out. This used to be the standard, but
indications are it is better not to do this. This keyword construct is
a stop-gap measure to invoke the old functionality if desired. This
option will most likely dissapear in the future.
**kwargs: To be explored.
Returns
-------
list (length 3): For each dimension (2 -> 0), a list of all grids in
that dimension.
Examples
p = np.array([[-1, 1, 0, 0], [0, 0, -1, 1]])
lines = np.array([[0, 2], [1, 3]])
char_h = 0.5 * np.ones(p.shape[1])
tags = np.array([1, 3])
fracs = {'points': p, 'edges': lines}
box = {'xmin': -2, 'xmax': 2, 'ymin': -2, 'ymax': 2}
g = triangle_grid(fracs, box)
"""
logger.info("Create 2d mesh")
# Verbosity level
verbose = kwargs.get("verbose", 1)
# File name for communication with gmsh
file_name = kwargs.get("file_name", "gmsh_frac_file")
kwargs.pop("file_name", str())
tol = kwargs.get("tol", 1e-4)
in_file = file_name + ".geo"
out_file = file_name + ".msh"
# Pick out fracture points, and their connections
frac_pts = fracs["points"]
frac_con = fracs["edges"]
# Unified description of points and lines for domain, and fractures
pts_all, lines, domain_pts = __merge_domain_fracs_2d(
domain, frac_pts, frac_con)
# Snap to underlying grid before comparing points
if do_snap_to_grid:
pts_all = cg.snap_to_grid(pts_all, tol)
assert np.all(np.diff(lines[:2], axis=0) != 0)
# Ensure unique description of points
pts_all, _, old_2_new = unique_columns_tol(pts_all, tol=tol)
lines[:2] = old_2_new[lines[:2]]
to_remove = np.where(lines[0, :] == lines[1, :])[0]
lines = np.delete(lines, to_remove, axis=1)
# In some cases the fractures and boundaries impose the same constraint
# twice, although it is not clear why. Avoid this by uniquifying the lines.
# This may disturb the line tags in lines[2], but we should not be
# dependent on those.
li = np.sort(lines[:2], axis=0)
_, new_2_old, old_2_new = unique_columns_tol(li, tol=tol)
lines = lines[:, new_2_old]
assert np.all(np.diff(lines[:2], axis=0) != 0)
# We split all fracture intersections so that the new lines do not
# intersect, except possible at the end points
logger.info("Remove edge crossings")
tm = time.time()
pts_split, lines_split = cg.remove_edge_crossings(pts_all,
lines,
tol=tol,
snap=do_snap_to_grid)
logger.info("Done. Elapsed time " + str(time.time() - tm))
# Ensure unique description of points
if do_snap_to_grid:
pts_split = cg.snap_to_grid(pts_split, tol)
pts_split, _, old_2_new = unique_columns_tol(pts_split, tol=tol)
lines_split[:2] = old_2_new[lines_split[:2]]
to_remove = np.where(lines[0, :] == lines[1, :])[0]
lines = np.delete(lines, to_remove, axis=1)
# Remove lines with the same start and end-point.
# This can be caused by L-intersections, or possibly also if the two
# endpoints are considered equal under tolerance tol.
remove_line_ind = np.where(np.diff(lines_split[:2], axis=0)[0] == 0)[0]
lines_split = np.delete(lines_split, remove_line_ind, axis=1)
# TODO: This operation may leave points that are not referenced by any
# lines. We should probably delete these.
# We find the end points that are shared by more than one intersection
intersections = __find_intersection_points(lines_split)
# Gridding size
if "mesh_size_frac" in kwargs.keys():
# Tag points at the domain corners
logger.info("Determine mesh size")
tm = time.time()
boundary_pt_ind = ismember_rows(pts_split, domain_pts, sort=False)[0]
mesh_size, pts_split, lines_split = tools.determine_mesh_size(
pts_split, boundary_pt_ind, lines_split, **kwargs)
logger.info("Done. Elapsed time " + str(time.time() - tm))
else:
mesh_size = None
# gmsh options
meshing_algorithm = kwargs.get("meshing_algorithm")
# Create a writer of gmsh .geo-files
gw = gmsh_interface.GmshWriter(
pts_split,
lines_split,
domain=domain,
mesh_size=mesh_size,
intersection_points=intersections,
meshing_algorithm=meshing_algorithm,
)
gw.write_geo(in_file)
triangle_grid_run_gmsh(file_name, **kwargs)
return triangle_grid_from_gmsh(file_name, **kwargs)
def triangle_grid(fracs, domain, **kwargs):
"""
Generate a gmsh grid in a 2D domain with fractures.
The function uses modified versions of pygmsh and mesh_io,
both downloaded from github.
To be added:
Functionality for tuning gmsh, including grid size, refinements, etc.
Parameters
----------
fracs: (dictionary) Two fields: points (2 x num_points) np.ndarray,
edges (2 x num_lines) connections between points, defines fractures.
box: (dictionary) keys xmin, xmax, ymin, ymax, [together bounding box
for the domain]
**kwargs: To be explored.
Returns
-------
list (length 3): For each dimension (2 -> 0), a list of all grids in
that dimension.
Examples
p = np.array([[-1, 1, 0, 0], [0, 0, -1, 1]])
lines = np.array([[0, 2], [1, 3]])
char_h = 0.5 * np.ones(p.shape[1])
tags = np.array([1, 3])
fracs = {'points': p, 'edges': lines}
box = {'xmin': -2, 'xmax': 2, 'ymin': -2, 'ymax': 2}
g = triangle_grid(fracs, box)
"""
# Verbosity level
verbose = kwargs.get('verbose', 1)
# File name for communication with gmsh
file_name = kwargs.get('file_name', 'gmsh_frac_file')
kwargs.pop('file_name', str())
tol = kwargs.get('tol', 1e-4)
in_file = file_name + '.geo'
out_file = file_name + '.msh'
# Pick out fracture points, and their connections
frac_pts = fracs['points']
frac_con = fracs['edges']
# Unified description of points and lines for domain, and fractures
pts_all, lines = __merge_domain_fracs_2d(domain, frac_pts, frac_con)
# Snap to underlying grid before comparing points
pts_all = cg.snap_to_grid(pts_all, tol)
assert np.all(np.diff(lines[:2], axis=0) != 0)
# Ensure unique description of points
pts_all, _, old_2_new = unique_columns_tol(pts_all, tol=tol)
lines[:2] = old_2_new[lines[:2]]
to_remove = np.where(lines[0, :] == lines[1, :])[0]
lines = np.delete(lines, to_remove, axis=1)
# In some cases the fractures and boundaries impose the same constraint
# twice, although it is not clear why. Avoid this by uniquifying the lines.
# This may disturb the line tags in lines[2], but we should not be
# dependent on those.
li = np.sort(lines[:2], axis=0)
li_unique, new_2_old, old_2_new = unique_columns_tol(li)
lines = lines[:, new_2_old]
assert np.all(np.diff(lines[:2], axis=0) != 0)
# We split all fracture intersections so that the new lines do not
# intersect, except possible at the end points
pts_split, lines_split = cg.remove_edge_crossings(pts_all, lines, tol=tol)
# Ensure unique description of points
pts_split = cg.snap_to_grid(pts_split, tol)
pts_split, _, old_2_new = unique_columns_tol(pts_split, tol=tol)
lines_split[:2] = old_2_new[lines_split[:2]]
to_remove = np.where(lines[0, :] == lines[1, :])[0]
lines = np.delete(lines, to_remove, axis=1)
# Remove lines with the same start and end-point.
# This can be caused by L-intersections, or possibly also if the two
# endpoints are considered equal under tolerance tol.
remove_line_ind = np.where(np.diff(lines_split[:2], axis=0)[0] == 0)[0]
lines_split = np.delete(lines_split, remove_line_ind, axis=1)
# TODO: This operation may leave points that are not referenced by any
# lines. We should probably delete these.
# We find the end points that is shared by more than one intersection
intersections = __find_intersection_points(lines_split)
# Gridding size
if 'mesh_size' in kwargs.keys():
mesh_size, mesh_size_bound, pts_split, lines_split = \
utils.determine_mesh_size(pts_split, lines_split,
**kwargs['mesh_size'])
else:
mesh_size = None
mesh_size_bound = None
# gmsh options
meshing_algorithm = kwargs.get('meshing_algorithm')
# Create a writer of gmsh .geo-files
gw = gmsh_interface.GmshWriter(pts_split,
lines_split,
domain=domain,
mesh_size=mesh_size,
mesh_size_bound=mesh_size_bound,
intersection_points=intersections,
meshing_algorithm=meshing_algorithm)
gw.write_geo(in_file)
triangle_grid_run_gmsh(file_name, **kwargs)
return triangle_grid_from_gmsh(file_name, **kwargs)
def test_aniso_snapping(self):
p_snapped = cg.snap_to_grid(self.p, box=self.anisobox, tol=1)
self.assertTrue(np.allclose(p_snapped, np.array([0, 1])))
