def test_unit_square(self):
p = np.array([[0, 0], [1, 0], [1, 1], [0, 1]]).T
d = cg.dist_pointset(p)
s2 = np.sqrt(2)
known = np.array([[0, 1, s2, 1], [1, 0, 1, s2], [s2, 1, 0, 1],
[1, s2, 1, 0]])
assert np.allclose(d, known)
def test_3d(self):
p = np.array([[0, 0, 0], [0, 1, 0], [1, 0, 0]]).T
d = cg.dist_pointset(p)
known = np.array([[0, 1, 1],
[1, 0, np.sqrt(2)],
[1, np.sqrt(2), 0]])
assert np.allclose(d, known)
def merge_1d_grids(g, h, global_ind_offset=0, tol=1e-4):
""" Merge two 1d grids with non-matching nodes to a single grid.
The grids should have common start and endpoints. They can be into 3d space
in a genreal way.
The function is primarily intended for merging non-conforming DFN grids.
Parameters:
g: 1d tensor grid.
h: 1d tensor grid
glob_ind_offset (int, defaults to 0): Off set for the global point
index of the new grid.
tol (double, defaults to 1e-4): Tolerance for when two nodes are merged
into one.
Returns:
TensorGrid: New tensor grid, containing the combined node definition.
int: New global ind offset, increased by the number of cells in the
combined grid.
np.array (int): Indices of common nodes (after sorting) of g and the
new grid.
np.array (int): Indices of common nodes (after sorting) of h and the
new grid.
np.array (int): Permutation indices that sort the node coordinates of
g. The common indices between g and the new grid are found as
new_grid.nodes[:, g_in_combined] = g.nodes[:, sorted]
np.array (int): Permutation indices that sort the node coordinates of
h. The common indices between h and the new grid are found as
new_grid.nodes[:, h_in_combined] = h.nodes[:, sorted]
"""
# Nodes of the two 1d grids, combine them
gp = g.nodes
hp = h.nodes
combined = np.hstack((gp, hp))
num_g = gp.shape[1]
num_h = hp.shape[1]
# Keep track of where we put the indices of the original grids
g_in_full = np.arange(num_g)
h_in_full = num_g + np.arange(num_h)
# The tolerance should not be larger than the smallest distance between
# two points on any of the grids.
diff_gp = np.min(cg.dist_pointset(gp, True))
diff_hp = np.min(cg.dist_pointset(hp, True))
min_diff = np.minimum(tol, 0.5 * np.minimum(diff_gp, diff_hp))
# Uniquify points
combined_unique, _, new_2_old = unique_columns_tol(combined, tol=min_diff)
# Follow locations of the original grid points
g_in_unique = new_2_old[g_in_full]
h_in_unique = new_2_old[h_in_full]
# The combined nodes must be sorted along their natural line.
# Find the dimension with the largest spatial extension, and sort those
# coordinates
max_coord = combined_unique.max(axis=1)
min_coord = combined_unique.min(axis=1)
dx = max_coord - min_coord
sort_dim = np.argmax(dx)
sort_ind = np.argsort(combined_unique[sort_dim])
combined_sorted = combined_unique[:, sort_ind]
# Follow the position of the orginial nodes through sorting
_, g_sorted = ismember_rows(g_in_unique, sort_ind)
_, h_sorted = ismember_rows(h_in_unique, sort_ind)
num_new_grid = combined_sorted.shape[1]
# Create a new 1d grid.
# First use a 1d coordinate to initialize topology
new_grid = TensorGrid(np.arange(num_new_grid))
# Then set the right, 3d coordinates
new_grid.nodes = cg.make_collinear(combined_sorted)
# Set global point indices
new_grid.global_point_ind = global_ind_offset + np.arange(num_new_grid)
global_ind_offset += num_new_grid
return new_grid, global_ind_offset, g_sorted, h_sorted, np.arange(num_g),\
np.arange(num_h)
def test_zero_diagonal(self):
sz = 5
p = np.random.rand(3, sz)
d = cg.dist_pointset(p)
self.assertTrue(np.allclose(np.diagonal(d), np.zeros(sz)))
def test_single_point(self):
p = np.random.rand(2)
d = cg.dist_pointset(p)
self.assertTrue(d.shape == (1, 1))
self.assertTrue(d[0, 0] == 0)
def test_symmetry(self):
p = np.random.rand(3, 7)
d = cg.dist_pointset(p)
self.assertTrue(np.allclose(d, d.T))
def test_single_point(self):
p = np.random.rand(2)
d = cg.dist_pointset(p)
assert d.shape == (1, 1)
assert d[0, 0] == 0
