def test_adjacent_nodes_dim1():
dt = Triangulation()
pnts = [ [0,0],
[5,0],
[10,0] ]
for pnt in pnts:
dt.add_node( x=pnt )
assert np.all( dt.topo_sort_adjacent_nodes(1,ref_nbr=0)==[0,2] )
def init():
# inserting a constraint
dt = Triangulation()
pnts = [ [0,0],
[5,0],
[10,0],
[5,5],
[3,0],
[6,2],
[12,4]]
for pnt in pnts:
dt.add_node( x=pnt )
return dt
def test_constraints_dim1():
dt = Triangulation()
pnts = [ [0,0],
[5,0],
[10,0] ]
for pnt in pnts:
dt.add_node( x=pnt )
dt.add_constraint(0,1)
dt.add_constraint(1,2)
dt.remove_constraint(0,1)
dt.remove_constraint(1,2)
try:
dt.add_constraint(0,2)
assert False
except dt.ConstraintCollinearNode:
pass #
def test_test_dim_down():
dt = Triangulation()
n=dt.add_node( x=[0,0] )
for i in range(5):
other=dt.add_node( x=[10,i] )
assert dt.test_delete_node_dim_down(n)
assert not dt.test_delete_node_dim_down(other)
friend=dt.add_node( x=[0,2])
assert not dt.test_delete_node_dim_down(friend)
assert not dt.test_delete_node_dim_down(n)
def test_find_int_elts_dim1():
dt = Triangulation()
pnts = [ [0,0],
[5,0],
[10,0] ]
for pnt in pnts:
dt.add_node( x=pnt )
assert len(dt.find_intersected_elements(0,1))==2
assert len(dt.find_intersected_elements(0,2))==3
assert len(dt.find_intersected_elements(1,2))==2
def test_move1():
plot=False
dt = Triangulation()
pnts = [ [0,0],
[5,0],
[10,0],
[5,5] ]
dt.add_node( x=pnts[0] ) # This tests insert into empty
dt.add_node( x=pnts[1] ) # adjacent_vertex
dt.add_node( x=pnts[2] ) # adjacent_vertex
dt.add_node( x=pnts[3] ) # adjacent_edge
dt.add_node( x=[3,0] ) # colinear
if plot:
plt.clf()
dt.plot_nodes()
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=13,facecolor='#ddddff',edgecolor='w',zorder=-5)
n=dt.add_node( x=[6,2] ) # into cell interior
dt.modify_node(4,x=[3.01,0.25])
dt.modify_node(4,x=[3.01,-0.25])
if plot:
plt.clf()
dt.plot_nodes(labeler=lambda n,nrec: str(n) )
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=13,facecolor='#ddddff',edgecolor='w',zorder=-5)
dt.check_global_delaunay()
return dt
def test_extra1():
plot=False
dt = Triangulation()
pnts = [ [0,0],
[5,0],
[10,0],
[5,5] ]
dt.add_node( x=pnts[0] ) # This tests insert into empty
dt.add_node( x=pnts[1] ) # adjacent_vertex
dt.add_node( x=pnts[2] ) # adjacent_vertex
dt.add_node( x=pnts[3] ) # adjacent_edge
dt.add_node( x=[3,0] ) # colinear
if plot:
plt.clf()
dt.plot_nodes()
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=13,facecolor='#ddddff',edgecolor='w',zorder=-5)
n=dt.add_node( x=[6,2] ) # into cell interior
if plot:
plt.clf()
dt.plot_nodes(labeler=lambda n,nrec: str(n) )
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=13,facecolor='#ddddff',edgecolor='w',zorder=-5)
def test_fuzz1():
plot=False
# Fuzzing, regular
x=np.arange(5)
y=np.arange(5)
X,Y=np.meshgrid(x,y)
xys=np.array([X.ravel(),Y.ravel()]).T
if plot:
plt.figure(1).clf()
fig,ax=plt.subplots(num=1)
ax.plot(xys[:,0],xys[:,1],'go',alpha=0.4)
ax.axis([-1,5,-1,5])
idxs=np.zeros(len(xys),'i8')-1
dt = Triangulation()
# definitely slows down as the number of nodes gets larger.
# starting off with <1s per 100 operations, later more like 2s
for repeat in range(1):
print "Repeat: ",repeat
for step in range(1000):
if step%200==0:
print " step: ",step
toggle=np.random.randint(len(idxs))
if idxs[toggle]<0:
idxs[toggle] = dt.add_node( x=xys[toggle] )
else:
dt.delete_node(idxs[toggle])
idxs[toggle]=-1
if plot:
del ax.lines[1:]
ax.texts=[]
ax.collections=[]
dt.plot_nodes(labeler=lambda n,nrec: str(n) )
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=7,facecolor='#ddddff',edgecolor='w',zorder=-5)
plt.draw()
def test_lowdim1():
plot=False
dt = Triangulation()
dt.add_node( x=[0,0] )
dt.add_node( x=[5,0] )
dt.add_node( x=[10,0] )
dt.add_node( x=[-2,3] )
dt.add_node( x=[-2,-3] )
# and now delete the one in the middle:
dt.delete_node(1)
dt.delete_node(0)
dt.delete_node(2)
dt.delete_node(3)
dt.delete_node(4)
if plot:
plt.cla()
dt.plot_nodes(labeler=lambda n,nrec: str(n) )
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=7,facecolor='#ddddff',edgecolor='w',zorder=-5)
def test_flip2():
plot=False
# testing Delaunay flipping
dt = Triangulation() # ExactDelaunay()
dt.add_node( x=[0,0] )
for i in range(10):
dt.add_node( x=[10,i] )
dt.add_node( x=[5,1.0] )
dt.add_node( x=[10,10] )
dt.add_node( x=[5,2] )
# and test a flip when the new vertex is outside the convex hull
dt.add_node( x=[5,-1])
dt.delete_node(11)
if plot:
plt.cla()
dt.plot_nodes(labeler=lambda n,nrec: str(n) )
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=13,facecolor='#ddddff',edgecolor='w',zorder=-5)
def test_delete3():
plot=False
dt = Triangulation()
nodes=[ dt.add_node( x=[10,i] )
for i in range(10) ]
dt.delete_node( nodes[0])
dt.delete_node( nodes[4])
if plot:
plt.cla()
dt.plot_nodes()
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=13,facecolor='#ddddff',edgecolor='w',zorder=-5)
def test_delete2():
plot=False
dt = Triangulation() # ExactDelaunay()
dt.add_node( x=[0,0] )
for i in range(5):
if 0: # sparser nodes for testing
if i>0 and i<4:
continue
dt.add_node( x=[10,i] )
# This one requires a flip:
n=dt.add_node( x=[5,1] )
far=dt.add_node( x=[12,4] )
dt.delete_node(0)
if plot:
plt.cla()
dt.plot_nodes(labeler=lambda n,nr:str(n))
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=6,facecolor='#ddddff',edgecolor='w',zorder=-5)
def test_delete1():
plot=False
dt = Triangulation() # ExactDelaunay()
dt.add_node( x=[0,0] )
for i in range(5):
dt.add_node( x=[10,i] )
# This one requires a flip:
n=dt.add_node( x=[5,1] )
if plot:
plt.cla()
dt.plot_nodes(labeler=lambda n,nr:str(n))
dt.plot_edges(alpha=0.5,lw=2)
bad=(~dt.cells['deleted'])&(dt.cells_area()<=0)
good=(~dt.cells['deleted'])&(dt.cells_area()>0)
dt.plot_cells(lw=8,facecolor='#ddddff',edgecolor='w',zorder=-5,mask=good)
dt.plot_cells(lw=8,facecolor='#ffdddd',edgecolor='r',zorder=-5,mask=bad)
# deleting n work okay
dt.delete_node(n)
if plot:
plt.cla()
dt.plot_nodes(labeler=lambda n,nr:str(n))
dt.plot_edges(alpha=0.5,lw=2)
bad=(~dt.cells['deleted'])&(dt.cells_area()<=0)
good=(~dt.cells['deleted'])&(dt.cells_area()>0)
dt.plot_cells(lw=8,facecolor='#ddddff',edgecolor='w',zorder=-5,mask=good)
dt.plot_cells(lw=8,facecolor='#ffdddd',edgecolor='r',zorder=-5,mask=bad)
# delete a node which defines part of the convex hull:
dt.delete_node(5) # works, though may need to patch up edges['cells'] ?
dt.delete_node(2) # collinear portion of the convex hull
dt.delete_node(3)
# everything looks okay. Surprising that we didn't have to
# add code to deal with this case...
if plot:
plt.cla()
dt.plot_nodes(labeler=lambda n,nr:str(n))
dt.plot_edges(alpha=0.5,lw=2)
bad=(~dt.cells['deleted'])&(dt.cells_area()<=0)
good=(~dt.cells['deleted'])&(dt.cells_area()>0)
dt.plot_cells(lw=8,facecolor='#ddddff',edgecolor='w',zorder=-5,mask=good)
dt.plot_cells(lw=8,facecolor='#ffdddd',edgecolor='r',zorder=-5,mask=bad)
dt.delete_node(0)
if plot:
assert dt.dim() == 1
plt.cla()
dt.plot_nodes(labeler=lambda n,nr:str(n))
dt.plot_edges(alpha=0.5,lw=2)
bad=(~dt.cells['deleted'])&(dt.cells_area()<=0)
good=(~dt.cells['deleted'])&(dt.cells_area()>0)
dt.plot_cells(lw=8,facecolor='#ddddff',edgecolor='w',zorder=-5,mask=good)
dt.plot_cells(lw=8,facecolor='#ffdddd',edgecolor='r',zorder=-5,mask=bad)
def test_flip2():
plot=False
dt = Triangulation()
dt.add_node( x=[0,0] )
for i in range(5):
dt.add_node( x=[10,i] )
# This one requires a flip:
n=dt.add_node( x=[5,1] )
if plot:
plt.cla()
dt.plot_nodes()
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=13,facecolor='#ddddff',edgecolor='w',zorder=-5)
def test_flip1():
plot=False
dt = Triangulation()
pnts = [ [0,0],
[8,0],
[10,5],
[5,5] ]
dt.add_node( x=pnts[0] ) # This tests insert into empty
dt.add_node( x=pnts[1] ) # adjacent_vertex
dt.add_node( x=pnts[2] ) # adjacent_vertex
dt.add_node( x=pnts[3] ) # adjacent_edge
dt.add_node( x=[3,0] ) # colinear
if plot:
plt.clf()
dt.plot_nodes()
dt.plot_edges(alpha=0.5,lw=2)
dt.plot_cells(lw=13,facecolor='#ddddff',edgecolor='w',zorder=-5)
def test_find_intersected_elements():
dt = Triangulation()
pnts = [ [0,0],
[5,0],
[10,0],
[5,5] ]
nA=dt.add_node( x=pnts[0] ) # This tests insert into empty
dt.add_node( x=pnts[1] ) # adjacent_vertex
dt.add_node( x=pnts[2] ) # adjacent_vertex
dt.add_node( x=pnts[3] ) # adjacent_edge
dt.add_node( x=[3,0] ) # colinear
dt.add_node( x=[6,2] ) # into cell interior
nB=dt.add_node( x=[12,4] ) # collinear cell interior
nodes=list(dt.valid_node_iter())
for iA in range(len(nodes)):
for iB in range(iA+1,len(nodes)):
nA=nodes[iA]
nB=nodes[iB]
fwd=dt.find_intersected_elements(nA,nB)
rev=dt.find_intersected_elements(nB,nA)
assert len(fwd) == len(rev)
