def extractMesh(t,
extractionMesh,
order=2,
extrapOrder=1,
constraint=40.,
tol=1.e-6,
mode='robust',
hook=None,
graph=None):
if graph is None:
tb = Cmpi.createBBoxTree(t)
tb2 = Cmpi.createBBoxTree(extractionMesh)
graph = Cmpi.computeGraph(tb, type='bbox3', t2=tb2)
tl = Cmpi.addXZones(t, graph)
tl = Cmpi.convert2PartialTree(tl)
ext = Cmpi.convert2PartialTree(extractionMesh)
# print info
zones = Internal.getZones(tl)
print('Rank %d has %d source zones.' % (Cmpi.rank, len(zones)))
ext = P.extractMesh(tl,
ext,
order=order,
extrapOrder=extrapOrder,
constraint=constraint,
tol=tol,
mode=mode,
hook=hook)
return ext
def optimizeOverlap(t, double_wall=0, priorities=[], graph=None,
intersectionsDict=None):
if graph is None:
tb = Cmpi.createBBoxTree(t)
graph = Cmpi.computeGraph(tb, type='bbox2',
intersectionsDict=intersectionsDict)
tl = Cmpi.addXZones(t, graph)
tl = Cmpi.convert2PartialTree(tl)
# print info
zones = Internal.getZones(tl)
#print 'Rank %d has %d zones.'%(Cmpi.rank, len(zones))
tl = X.optimizeOverlap(tl, double_wall, priorities, intersectionsDict)
tl = Cmpi.rmXZones(tl)
return tl
def _setInterpTransfers(aR, aD, variables=[],
variablesIBC=['Density','MomentumX','MomentumY','MomentumZ','EnergyStagnationDensity'],
bcType=0, varType=1, compact=0, graph=None,
procDict=None, type='ALLD',
Gamma=1.4, Cv=1.7857142857142865, MuS=1.e-08,
Cs=0.3831337844872463, Ts=1.0):
if (procDict is None): procDict = Cmpi.getProcDict(aD)
if (graph is None): graph = Cmpi.computeGraph(aD, type=type)
# Transferts locaux/globaux
# Calcul des solutions interpolees par arbre donneur
# On envoie aussi les indices receveurs pour l'instant
datas = {}
zonesD = Internal.getZones(aD)
for zD in zonesD:
infos = X.setInterpTransfersD(zD, variables, variablesIBC, bcType, varType, compact, Gamma, Cv, MuS, Cs, Ts)
for n in infos:
rcvName = n[0]
proc = procDict[rcvName]
if (proc == Cmpi.rank):
field = n[1]
#print 'direct', Cmpi.rank, rcvName
if field != []:
listIndices = n[2]
z = Internal.getNodesFromName2(aR, rcvName)[0]
C._setPartialFields(z, [field], [listIndices], loc=n[3])
else:
rcvNode = procDict[rcvName]
#print Cmpi.rank, 'envoie a ',rcvNode
if not datas.has_key(rcvNode): datas[rcvNode] = [n]
else: datas[rcvNode] += [n]
#print datas
# Envoie des numpys suivant le graph
rcvDatas = Cmpi.sendRecv(datas, graph)
# Remise des champs interpoles dans l'arbre receveur
for i in rcvDatas.keys():
#print Cmpi.rank, 'recoit de',i, '->', len(rcvDatas[i])
for n in rcvDatas[i]:
rcvName = n[0]
#print 'reception', Cmpi.rank, rcvName
field = n[1]
if field != []:
listIndices = n[2]
z = Internal.getNodesFromName2(aR, rcvName)[0]
C._setPartialFields(z, [field], [listIndices], loc=n[3])
return None
def redispatch(t, graph=None):
if graph is None:
graph = Cmpi.computeGraph(t, type='proc')
procs = D2.getProcDict(t)
t = Cmpi.addXZones(t, graph)
# Enleve les zones envoyees
zones = Internal.getZones(t)
for z in zones:
tag = Internal.getNodesFromName1(z, 'XZone')
if len(tag) == 0:
if procs[z[0]] != Cmpi.rank:
(p, c) = Internal.getParentOfNode(t, z)
del p[2][c]
else: # enleve le noeud tag XZone
(p, c) = Internal.getParentOfNode(z, tag[0])
del p[2][c]
return t
# - computeGraph (pyTree) -
import Converter.PyTree as C
import Converter.Mpi as Cmpi
import Distributor2.PyTree as Distributor2
import Generator.PyTree as G
import Connector.PyTree as X
import KCore.test as test
# Through BBox
a = G.cart((0, 0, 0), (1, 1, 1), (10, 10, 10))
b = G.cart((8, 0, 0), (1, 1, 1), (10, 10, 10))
t = C.newPyTree(['Base', a, b])
(t, dic) = Distributor2.distribute(t, NProc=2, algorithm='fast')
tb = Cmpi.createBBoxTree(t)
graph = Cmpi.computeGraph(tb, type='bbox')
if Cmpi.rank == 0: test.testO(graph, 1)
# Through match
a = G.cart((0, 0, 0), (1, 1, 1), (10, 10, 10))
b = G.cart((9, 0, 0), (1, 1, 1), (10, 10, 10))
t = C.newPyTree(['Base'])
t[2][1][2] += [a, b]
t = X.connectMatch(t)
(t, dic) = Distributor2.distribute(t, NProc=2, algorithm='fast')
graph = Cmpi.computeGraph(t, type='match')
if Cmpi.rank == 0:
test.testO(graph, 2)
# - addXZones (pyTree) -
import Converter.PyTree as C
import Converter.Mpi as Cmpi
import Distributor2.PyTree as Distributor2
import Generator.PyTree as G
import KCore.test as test
# Cree le fichier test
if (Cmpi.rank == 0):
a = G.cart( (0,0,0), (1,1,1), (10,10,10) )
b = G.cart( (9,0,0), (1,1,1), (10,10,10) )
t = C.newPyTree(['Base',a,b])
C.convertPyTree2File(t, 'test.cgns')
Cmpi.barrier()
# Relit des zones par procs
t = Cmpi.convertFile2SkeletonTree('test.cgns')
(t, dic) = Distributor2.distribute(t, NProc=Cmpi.size, algorithm='fast')
t = Cmpi.readZones(t, 'test.cgns', proc=Cmpi.rank)
# Cree le bbox tree
tb = Cmpi.createBBoxTree(t)
# Cree le graph
graph = Cmpi.computeGraph(tb)
# Add X Zones
t = Cmpi.addXZones(t, graph)
if (Cmpi.rank == 0): test.testT(t, 1)
N = 11
# Cas test
t = C.newPyTree(['Base'])
off = 0
for i in range(N):
a = G.cart((off, 0, 0), (1, 1, 1), (10 + i, 10, 10))
off += 9 + i
t[2][1][2].append(a)
t = X.connectMatch(t)
t, stats = D2.distribute(t, NProc=5, algorithm='fast', useCom=0)
if Cmpi.rank == 0: C.convertPyTree2File(t, LOCAL + '/in.cgns')
Cmpi.barrier()
# full
graph = Cmpi.computeGraph(t, type='match')
if Cmpi.rank == 0: test.testO(graph, 1)
# skel (doit etre identique)
t = Cmpi.convertFile2SkeletonTree(LOCAL + '/in.cgns')
graph = Cmpi.computeGraph(t, type='match')
if Cmpi.rank == 0: test.testO(graph, 2)
# load skel (doit etre identique)
t = Cmpi.convertFile2SkeletonTree(LOCAL + '/in.cgns')
t = Cmpi.readZones(t, LOCAL + '/in.cgns', rank=Cmpi.rank)
graph = Cmpi.computeGraph(t, type='match')
if Cmpi.rank == 0: test.testO(graph, 3)
# partial (doit etre identique)
t = Cmpi.convertFile2SkeletonTree(LOCAL + '/in.cgns')
