def sphere_intersect_sphere( c1 , r1 , c2 , r2 ):
"""
returns the average intersection point if the two spheres centred in *c1* and *c2* and radius *r1*, *r2* are intersecting, else it returns *None*
"""
d = dist.distance( c1 , c2 )
if r1 + r2 >= d :
u = ( c2 - c1 ) / LA.norm( c2 - c1 )
p = ( ( c1 + u*r1 ) + ( c2 - u*r2 ) ) / 2.0
return p
else :
return None
def search_neighbour( self , cand_queue , res_list , pset , X , r ):
"""
Search the elements included in the volume centred in *X* with the radius *r* and append the results in the list *res_list*.
*res_list* contains the indicies of the particles included in the the sphere.
"""
while len(cand_queue) :
tree = cand_queue.pop()
if distance( pset.X[tree.particle,:] , X ) <= r :
res_list.append( tree.particle )
if tree.__tree == None :
continue
for t in tree.__tree:
if t.particle != None and box_intersects_sphere( t.min_vertex , t.max_vertex , X , r ) :
cand_queue.append( t )
def search_neighbour( self , cand_queue , res_list , pset , X , r ):
"""
Search the elements included in the volume centred in *X* with the radius *r* and append the results in the list *res_list*.
*res_list* contains the indicies of the particles included in the the sphere.
"""
while len(cand_queue) :
tree = cand_queue.pop()
if distance( pset.X[tree.particle,:] , X ) <= r :
res_list.append( tree.particle )
if tree.__tree == None :
continue
for t in tree.__tree:
if t.particle != None and box_intersects_sphere( t.min_vertex , t.max_vertex , X , r ) :
cand_queue.append( t )
def __find_second_particle(x0, x1, x2, i):
# if self.__particle_is_in_cell(particle_cell[i,:], (x, y, z)): # Reference particle
particle_index.append(i)
j = i
# Scans particles_set
for j in range(i, part_nbr):
scnd_part_found = False
if (j not in particle_index):
# Checks in 9 cells if there are particles
if scnd_part_found == False:
if self.__particle_is_in_cell(particle_cell[j,:], (x0, x1, x2 )):
x = np.array([pset.X[i,0],pset.X[i,1],pset.X[i,2]], dtype=np.float64)
y = np.array([pset.X[j,0],pset.X[j,1],pset.X[j,2]], dtype=np.float64)
scnd_part_found = True
if scnd_part_found == False:
if self.__particle_is_in_cell(particle_cell[j,:], (x0+1,x1, x2 )):
x = np.array([pset.X[i,0],pset.X[i,1],pset.X[i,2]], dtype=np.float64)
y = np.array([pset.X[j,0],pset.X[j,1],pset.X[j,2]], dtype=np.float64)
scnd_part_found = True
if scnd_part_found == False:
if self.__particle_is_in_cell(particle_cell[j,:], (x0+1,x1+1,x2 )):
x = np.array([pset.X[i,0],pset.X[i,1],pset.X[i,2]], dtype=np.float64)
y = np.array([pset.X[j,0],pset.X[j,1],pset.X[j,2]], dtype=np.float64)
scnd_part_found = True
if scnd_part_found == False:
if self.__particle_is_in_cell(particle_cell[j,:], (x0+1,x1+1,x2+1)):
x = np.array([pset.X[i,0],pset.X[i,1],pset.X[i,2]], dtype=np.float64)
y = np.array([pset.X[j,0],pset.X[j,1],pset.X[j,2]], dtype=np.float64)
scnd_part_found = True
if scnd_part_found == False:
if self.__particle_is_in_cell(particle_cell[j,:], (x0, x1+1,x2 )):
x = np.array([pset.X[i,0],pset.X[i,1],pset.X[i,2]], dtype=np.float64)
y = np.array([pset.X[j,0],pset.X[j,1],pset.X[j,2]], dtype=np.float64)
scnd_part_found = True
if scnd_part_found == False:
if self.__particle_is_in_cell(particle_cell[j,:], (x0, x1+1,x2+1)):
x = np.array([pset.X[i,0],pset.X[i,1],pset.X[i,2]], dtype=np.float64)
y = np.array([pset.X[j,0],pset.X[j,1],pset.X[j,2]], dtype=np.float64)
scnd_part_found = True
if scnd_part_found == False:
if self.__particle_is_in_cell(particle_cell[j,:], (x0, x1, x2+1)):
x = np.array([pset.X[i,0],pset.X[i,1],pset.X[i,2]], dtype=np.float64)
y = np.array([pset.X[j,0],pset.X[j,1],pset.X[j,2]], dtype=np.float64)
scnd_part_found = True
if scnd_part_found == False:
if self.__particle_is_in_cell(particle_cell[j,:], (x0+1,x1, x2+1)):
x = np.array([pset.X[i,0],pset.X[i,1],pset.X[i,2]], dtype=np.float64)
y = np.array([pset.X[j,0],pset.X[j,1],pset.X[j,2]], dtype=np.float64)
scnd_part_found = True
if scnd_part_found:
dist = self.__INI_FLOAT
dist = distance(x, y)
if dist <= h:
conn = [self.__INI_INT, self.__INI_INT]
conn = [i,j]
return conn
