def _FV_utils(self, lGIDs, verbose=False):
"""
This function constructs the Finite Volume discretisation for each local
triangularised grid.
Parameters
----------
lGIDs
Numpy integer-type array filled with the global vertex IDs for each local grid located
within the partition (including those on the edges).
inIDs
Numpy integer-type array filled with the global vertex IDs for each local grid located
within the partition (not those on the edges).
"""
# Build the voronoi diagram (dual of the delaunay)
walltime = time.clock()
Vor_pts, Vor_edges = triangle.voronoi(self.node_coords)
if verbose:
print(" - build the voronoi diagram ", time.clock() - walltime)
# Call the finite volume frame construction function from libUtils
walltime = time.clock()
self.control_volumes, self.neighbours, self.vor_edges, \
self.edge_length, maxNgbhs = FVframe.discretisation.build( \
lGIDs+1, self.localIDs+1, self.node_coords[:,0], self.node_coords[:,1],
self.edges[:,:2]+1, self.cells[:,:3]+1, Vor_pts[:,0], Vor_pts[:,1], \
Vor_edges[:,:2]+1)
if verbose:
print(" - construct Finite Volume representation ",
time.clock() - walltime)
# Maximum number of neighbours for each partition
self.maxNgbh = numpy.array(maxNgbhs)
def test_voronoi():
pts = [[0, 0], [0, 1], [0.5, 0.5], [1, 1], [1, 0]]
points, edges, ray_origin, ray_direct = voronoi(pts)
assert np.allclose(
points,
[[0.0, 0.5], [1.0, 0.5], [0.5, 0.0], [0.5, 1.0]],
)
assert np.allclose(edges, [[0, 2], [0, 3], [1, 2], [1, 3]])
assert np.allclose(ray_origin, [0, 1, 2, 3])
assert np.allclose(
ray_direct,
[[-1.0, 0.0], [1.0, 0.0], [0.0, -1.0], [0.0, 1.0]],
)
def _FV_utils(self, lGIDs, verbose=False):
"""
This function constructs the Finite Volume discretisation for each local
triangularised grid.
Parameters
----------
variable : lGIDs
Numpy integer-type array filled with the global vertex IDs for each local grid located
within the partition (including those on the edges).
variable: inIDs
Numpy integer-type array filled with the global vertex IDs for each local grid located
within the partition (not those on the edges).
"""
comm = mpi.COMM_WORLD
rank = comm.Get_rank()
# Build the voronoi diagram (dual of the delaunay)
walltime = time.clock()
Vor_pts, Vor_edges = triangle.voronoi(self.node_coords)
if rank == 0 and verbose:
print " - build the voronoi diagram ", time.clock() - walltime
# Call the finite volume frame construction function from libUtils
walltime = time.clock()
self.control_volumes, self.neighbours, self.vor_edges, self.edge_length, maxNgbhs = FVframe.discretisation.build(
lGIDs + 1,
self.localIDs + 1,
self.node_coords[:, 0],
self.node_coords[:, 1],
self.edges[:, :2] + 1,
self.cells[:, :3] + 1,
Vor_pts[:, 0],
Vor_pts[:, 1],
Vor_edges[:, :2] + 1,
)
if rank == 0 and verbose:
print " - construct Finite Volume representation ", time.clock() - walltime
# Maximum number of neighbours for each partition
maxNgbh = numpy.array(maxNgbhs)
comm.Allreduce(mpi.IN_PLACE, maxNgbh, op=mpi.MAX)
self.maxNgbh = maxNgbh
def crust(points: list, filename = None)-> list:
points = np.array(points)
ax = plt.axes()
drawing.plot_and_save(os.path.join(images_folder, filename+"_original.png"), False, ax, vertices=points, vertices_color="g")
lim = ax.axis()
vertices, edges, ray_origins, ray_directions = triangle.voronoi(points)
try:
drawing.plot_and_save(os.path.join(images_folder, os.path.join("voronoi", filename+"_voronoi.png")),
False, ax, vertices=vertices, edges=edges, ray_origins=ray_origins, ray_directions=ray_directions)
ax.axis(lim)
except Exception as e:
print("Could not draw voronoi diagram. Please, check if you have the correct directory")
extended_points = np.concatenate((points, vertices))
triangles = triangle.delaunay(extended_points)
delaunay_edges = []
for t in triangles:
delaunay_edges.extend(edges_from_triangle(t))
rec_edges = [e for e in delaunay_edges if ((e[0] < len(points)) and (e[1] < len(points)))]
try:
drawing.plot(ax, vertices=extended_points, edges=delaunay_edges, segments=rec_edges)
drawing.plot_and_save(os.path.join(images_folder, os.path.join("delaunay",filename+"delaunay.png")),
True, ax, vertices=points, vertices_color="b")
ax.axis(lim)
except Exception as e:
print("Could not draw delaunay diagram. Please, check if you have the correct directory")
#final reconstruction
ax = plt.axes()
drawing.plot_and_save(os.path.join(images_folder, filename+"_reconstruction.png"),
True, ax, vertices=points, segments=rec_edges, draw_vertices=False)
# print(points)
# print(rec_edges)
ordered_points = []
return ordered_points
def _FV_utils(self, allIDs, verbose=False):
"""
This function constructs the Finite Volume discretisation for each local
triangularised grid.
Parameters
----------
variable : allIDs
Numpy integer-type array filled with the global vertex IDs for each local grid located
within the partition (including those on the edges).
variable: inIDs
Numpy integer-type array filled with the global vertex IDs for each local grid located
within the partition (not those on the edges).
"""
comm = mpi.COMM_WORLD
rank = comm.Get_rank()
# Build the voronoi diagram (dual of the delaunay)
walltime = time.clock()
Vor_pts, Vor_edges = triangle.voronoi(self.node_coords)
if rank == 0 and verbose:
print " - build the voronoi diagram ", time.clock() - walltime
# Call the finite volume frame construction function from libUtils
walltime = time.clock()
self.control_volumes, self.neighbours, self.vor_edges, \
self.edge_length, maxNgbhs = FVframe.discretisation.build( \
allIDs+1, self.localIDs+1, self.node_coords[:,0], self.node_coords[:,1],
self.edges[:,:2]+1, self.cells[:,:3]+1, Vor_pts[:,0], Vor_pts[:,1], \
Vor_edges[:,:2]+1)
if rank == 0 and verbose:
print " - construct Finite Volume representation ", time.clock(
) - walltime
# Maximum number of neighbours for each partition
maxNgbh = numpy.array(maxNgbhs)
comm.Allreduce(mpi.IN_PLACE, maxNgbh, op=mpi.MAX)
self.maxNgbh = maxNgbh
import matplotlib.pyplot as plt
import triangle as tr
pts = tr.get_data('dots')['vertices']
A = dict(vertices=pts)
points, edges, ray_origin, ray_direct = tr.voronoi(pts)
B = dict(vertices=points,
edges=edges,
ray_origins=ray_origin,
ray_directions=ray_direct)
tr.compare(plt, A, B)
plt.show()
import triangle
import triangle.plot
import matplotlib.pyplot as plt
pts = [[0, 0], [0, 1], [0.5, 0.5], [1, 1], [1, 0]]
import numpy as np
pts = np.array(pts)
vertices, edges, ray_origins, ray_directions = triangle.voronoi(pts)
ax = plt.axes()
triangle.plot.plot(ax, vertices=pts)
lim = ax.axis()
triangle.plot.plot(ax,
vertices=vertices,
edges=edges,
ray_origins=ray_origins,
ray_directions=ray_directions)
ax.axis(lim)
plt.show()
import triangle
import triangle.plot
import matplotlib.pyplot as plt
pts = triangle.get_data('dots')['vertices']
ax1 = plt.subplot(121, aspect='equal')
triangle.plot.plot(ax1, vertices=pts)
lim = ax1.axis()
points, edges, ray_origin, ray_direct = triangle.voronoi(pts)
d = dict(vertices=points, edges=edges, ray_origins=ray_origin, ray_directions=ray_direct)
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
triangle.plot.plot(ax2, **d)
ax2.axis(lim)
plt.show()
import triangle
import triangle.plot
import matplotlib.pyplot as plt
pts = [[0, 0], [0, 1], [0.5, 0.5], [1, 1], [1, 0]]
import numpy as np
pts = np.array(pts)
vertices, edges, ray_origins, ray_directions = triangle.voronoi(pts)
ax = plt.axes()
triangle.plot.plot(ax, vertices=pts)
lim = ax.axis()
triangle.plot.plot(ax, vertices=vertices, edges=edges,
ray_origins=ray_origins, ray_directions=ray_directions)
ax.axis(lim)
plt.show()