def __init__(self, x, y):
self.x = np.asarray(x, dtype=np.float64)
self.y = np.asarray(y, dtype=np.float64)
if self.x.shape != self.y.shape or len(self.x.shape) != 1:
raise ValueError("x,y must be equal-length 1-D arrays")
self.old_shape = self.x.shape
duplicates = self._get_duplicate_point_indices()
if len(duplicates) > 0:
warnings.warn(
"Input data contains duplicate x,y points; some values are "
"ignored.",
DuplicatePointWarning,
)
# self.j_unique is the array of non-duplicate indices, in
# increasing order.
self.j_unique = np.delete(np.arange(len(self.x)), duplicates)
self.x = self.x[self.j_unique]
self.y = self.y[self.j_unique]
else:
self.j_unique = None
# If there are duplicate points, need a map of point indices used
# by delaunay to those used by client. If there are no duplicate
# points then the map is not needed. Either way, the map is
# conveniently the same as j_unique, so share it.
self._client_point_index_map = self.j_unique
self.circumcenters, self.edge_db, self.triangle_nodes, \
self.triangle_neighbors = delaunay(self.x, self.y)
self.hull = self._compute_convex_hull()
def __init__(self, x, y):
self.x = np.asarray(x, dtype=np.float64)
self.y = np.asarray(y, dtype=np.float64)
if self.x.shape != self.y.shape or len(self.x.shape) != 1:
raise ValueError("x,y must be equal-length 1-D arrays")
self.old_shape = self.x.shape
j_unique = self._collapse_duplicate_points()
if j_unique.shape != self.x.shape:
warnings.warn(
"Input data contains duplicate x,y points; some values are ignored.",
DuplicatePointWarning,
)
self.j_unique = j_unique
self.x = self.x[self.j_unique]
self.y = self.y[self.j_unique]
else:
self.j_unique = None
self.circumcenters, self.edge_db, self.triangle_nodes, \
self.triangle_neighbors = delaunay(self.x, self.y)
self.hull = self._compute_convex_hull()
def __init__(self, x, y):
self.x = np.asarray(x, dtype=np.float64)
self.y = np.asarray(y, dtype=np.float64)
if self.x.shape != self.y.shape or len(self.x.shape) != 1:
raise ValueError("x,y must be equal-length 1-D arrays")
self.old_shape = self.x.shape
duplicates = self._get_duplicate_point_indices()
if len(duplicates) > 0:
warnings.warn(
"Input data contains duplicate x,y points; some values are ignored.",
DuplicatePointWarning,
)
# self.j_unique is the array of non-duplicate indices, in
# increasing order.
self.j_unique = np.delete(np.arange(len(self.x)), duplicates)
self.x = self.x[self.j_unique]
self.y = self.y[self.j_unique]
else:
self.j_unique = None
# If there are duplicate points, need a map of point indices used
# by delaunay to those used by client. If there are no duplicate
# points then the map is not needed. Either way, the map is
# conveniently the same as j_unique, so share it.
self._client_point_index_map = self.j_unique
self.circumcenters, self.edge_db, self.triangle_nodes, \
self.triangle_neighbors = delaunay(self.x, self.y)
self.hull = self._compute_convex_hull()
def __init__(self, x, y):
self.x = np.asarray(x, dtype=np.float64)
self.y = np.asarray(y, dtype=np.float64)
if self.x.shape != self.y.shape or len(self.x.shape) != 1:
raise ValueError("x,y must be equal-length 1-D arrays")
self.old_shape = self.x.shape
j_unique = self._collapse_duplicate_points()
if j_unique.shape != self.x.shape:
warnings.warn(
"Input data contains duplicate x,y points; some values are ignored.",
DuplicatePointWarning,
)
self.j_unique = j_unique
self.x = self.x[self.j_unique]
self.y = self.y[self.j_unique]
else:
self.j_unique = None
self.circumcenters, self.edge_db, self.triangle_nodes, \
self.triangle_neighbors = delaunay(self.x, self.y)
self.hull = self._compute_convex_hull()
def vor(pnts, plot=True):
"""
: D delaunay triangulation
: tr triangles
: C
"""
x = pnts[:, 0]
y = pnts[:, 1]
lbl = np.arange(len(x))
#D = tri.Triangulation(x, y)
#tr = D.triangles
#n = tr.shape[0]
#C = circumcircle2(pnts[tr])
Dd = dy.delaunay(x, y) # for version 3.5
c_cents, edges, tri_pnts, tri_nbrs = Dd
if plot:
# Mask off unwanted triangles.
min_radius = 0.25
xmid = x[tri_pnts].mean(axis=1)
ymid = y[tri_pnts].mean(axis=1)
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1,
0)
# Plot the triangulation.
plt.figure()
plt.gca().set_aspect('equal')
#plt.triplot(D, 'bo-')
for label, xpt, ypt in zip(lbl, x, y):
plt.text(xpt, ypt, label)
plt.triplot(x, y, tri_pnts, mask=mask)
plt.scatter(x, y, s=20, c='b', marker='o')
plt.scatter(c_cents[:, 0], c_cents[:, 1], s=40, c='r', marker='x')
plt.title('triplot of Delaunay triangulation')
plt.show()
plt.close()
return Dd, c_cents, edges, tri_pnts, tri_nbrs #D, tr, n, Dd
