debugged
This commit is contained in:
Michael Stumpf
parent
2b1d859a7e
commit
d485e40ec4
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@ -25,8 +25,9 @@ class ibmppp:
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# Declare variables which hold fields
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self.field = {'u': None, 'v': None, 'w': None, 'p': None, 's1': None}
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# Declare variables which hold statistics
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self.spatialMean = {'u': None, 'v': None, 'w': None, 'p': None, 's1': None}
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self.spatialMsqr = {'u': None, 'v': None, 'w': None, 'p': None, 's1': None}
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self.spatialMean = {'u': None, 'v': None, 'w': None, 'p': None, 's1': None}
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self.spatialMsqr = {'u': None, 'v': None, 'w': None, 'p': None, 's1': None}
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self.spatialNsample = {'u': None, 'v': None, 'w': None, 'p': None, 's1': None}
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# Declare variables for grid (to be set by "loadGrid()")
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self.grid = {'u': [None,None,None], 'v':[None,None,None], 'w': [None,None,None], 'p': [None,None,None], 's1': [None,None,None]}
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self.domainBounds = (None,None,None,None,None,None)
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@ -598,10 +599,13 @@ class ibmppp:
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gid = fid.create_group('/'+key)
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did = gid.create_dataset('mean',data=self.spatialMean[key].ravel('C'))
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did.attrs['F_CONTIGUOUS'] = 0
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did.attrs['DIM'] = spatialMean[key].shape
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did.attrs['DIM'] = self.spatialMean[key].shape
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did = gid.create_dataset('meansquare',data=self.spatialMsqr[key].ravel('C'))
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did.attrs['F_CONTIGUOUS'] = 0
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did.attrs['DIM'] = spatialMsqr[key].shape
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did.attrs['DIM'] = self.spatialMsqr[key].shape
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did = gid.create_dataset('nsample',data=self.spatialNsample[key].ravel('C'))
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did.attrs['F_CONTIGUOUS'] = 0
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did.attrs['DIM'] = self.spatialNsample[key].shape
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# Close the file
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fid.close()
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@ -930,8 +934,11 @@ class ibmppp:
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if collocate:
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self.shiftField(keyout,self.__getShiftDirection(keyout,'p'))
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def spatialStatistics(self,key,homogeneous=(self.__xperiodic,self.__yperiodic,self.__zperiodic)):
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def spatialStatistics(self,key,homogeneous=None):
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'''Computes spatial mean and meansquare over homogeneous directions.'''
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# If no homogeneous directions are provided, use periodicity
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if homogeneous is None:
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homogeneous = (self.__xperiodic,self.__yperiodic,self.__zperiodic)
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# Check if there is any homogeneous direction
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if not np.any(homogeneous):
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raise ValueError('Cannot compute statistics, since there is no homogeneous direction.')
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@ -949,32 +956,43 @@ class ibmppp:
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tmpsum = np.nansum(tmpsum,axis=idir,keepdims=True)
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tmpssq = np.nansum(tmpssq,axis=idir,keepdims=True)
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# Reduce the statistics to processors with col/row/pln=0 in homogeneous directions
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proc_layout = np.array(range(0,self.__nproc)).reshape(self.__nxp,self.__nyp,self.__nzp)
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for idir in range(0,3):
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if homogeneous[idir]:
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pos_dst = [self.__icol,self.__jrow,self.__kpln]
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pos_dst[idir] = 0
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rank_dst = self.__rankFromPosition(pos_dst[0],pos_dst[1],pos_dst[2],self.__nxp,self.__nyp,self.__nzp)
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# Create groups for collective communication: get rank of all procs in current direction
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proc_slice = [slice(self.__icol,self.__icol+1),slice(self.__jrow,self.__jrow+1),slice(self.__kpln,self.__kpln+1)]
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proc_slice[idir] = slice(0,None)
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rank_list = proc_layout[proc_slice[0],proc_slice[1],proc_slice[2]].ravel()
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comm = self.__comm.Create(MPI.Group.Incl(self.__comm.Get_group(),rank_list))
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rank = comm.Get_rank()
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# Communicate counter
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if self.__rank==rank_dst:
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#print('Receiving rank',self.__rank,'is known in this comminucator as',rank,'for idir =',idir)
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recvbuf = np.zeros_like(counter)
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else:
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recvbuf = counter
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self.__comm.Reduce(counter,recvbuf,op=MPI.SUM,root=rank_dst)
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#print(self.__rank,'Reduce 1: destination',rank_dst,pos_dst)
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comm.Reduce(counter,recvbuf,op=MPI.SUM,root=0)
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counter = recvbuf
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# Communicate tmpsum
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if self.__rank==rank_dst:
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recvbuf = np.zeros_like(tmpsum)
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else:
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recvbuf = tmpsum
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self.__comm.Reduce(tmpsum,recvbuf,op=MPI.SUM,root=rank_dst)
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#print(self.__rank,'Reduce 2')
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comm.Reduce(tmpsum,recvbuf,op=MPI.SUM,root=0)
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tmpsum = recvbuf
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# Communicate tmpssq
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if self.__rank==rank_dst:
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recvbuf = np.zeros_like(tmpssq)
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else:
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recvbuf = tmpssq
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self.__comm.Reduce(tmpssq,recvbuf,op=MPI.SUM,root=rank_dst)
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comm.Reduce(tmpssq,recvbuf,op=MPI.SUM,root=0)
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tmpssq = recvbuf
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comm.Free()
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# All processors with col/row/pln!=0 in homogeneous direction are done.
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pos = [self.__icol,self.__jrow,self.__kpln]
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for idir in range(0,3):
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@ -984,18 +1002,17 @@ class ibmppp:
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# the full line or plane. Rank 0 allocates the final array
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if self.__rank==0:
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# Determine size of final array
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nxg = self.__procGrid[key][1][-1]-self.__procGrid[key][0][0]
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nyg = self.__procGrid[key][3][-1]-self.__procGrid[key][2][0]
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nzg = self.__procGrid[key][5][-1]-self.__procGrid[key][4][0]
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nxg = self.__procGrid[key][1][-1]-self.__procGrid[key][0][0]+1
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nyg = self.__procGrid[key][3][-1]-self.__procGrid[key][2][0]+1
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nzg = self.__procGrid[key][5][-1]-self.__procGrid[key][4][0]+1
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dim = [nxg,nyg,nzg]
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for idir in range(0,3):
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if homogeneous[idir]:
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dim[idir] = 1
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# Allocate a nice spot of memory
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self.spatialMean[key] = np.zeros(dim,dtype=self.__precision)
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self.spatialMsqr[key] = np.zeros(dim,dtype=self.__precision)
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# Also allocate one helper array
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counter_full = np.zeros(dim,dtype=counter.dtype)
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self.spatialMean[key] = np.zeros(dim,dtype=self.__precision)
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self.spatialMsqr[key] = np.zeros(dim,dtype=self.__precision)
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self.spatialNsample[key] = np.zeros(dim,dtype='i')
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# Create iterators for receive request
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ipend = 0 if homogeneous[0] else self.__nxp-1
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jpend = 0 if homogeneous[1] else self.__nyp-1
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@ -1017,21 +1034,24 @@ class ibmppp:
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if rank_src==0:
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recvbuf = counter
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else:
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recvbuf = self.__comm.Recv(source=rank_src,tag=1)
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counter_full[ib:ie+1,jb:je+1,kb:ke+1] = recvbuf
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recvbuf = np.empty_like(counter)
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self.__comm.Recv(recvbuf,source=rank_src,tag=1)
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self.spatialNsample[key][ib:ie+1,jb:je+1,kb:ke+1] = recvbuf
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if rank_src==0:
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recvbuf = tmpsum
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else:
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recvbuf = self.__comm.Recv(source=rank_src,tag=2)
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recvbuf = np.empty_like(tmpsum)
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self.__comm.Recv(recvbuf,source=rank_src,tag=2)
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self.spatialMean[key][ib:ie+1,jb:je+1,kb:ke+1] = recvbuf
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if rank_src==0:
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recvbuf = tmpssq
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else:
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recvbuf = self.__comm.Recv(source=rank_src,tag=3)
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recvbuf = np.empty_like(tmpssq)
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self.__comm.Recv(recvbuf,source=rank_src,tag=3)
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self.spatialMsqr[key][ib:ie+1,jb:je+1,kb:ke+1] = recvbuf
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# Rank 0 got all the data now. Finalize it!
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self.spatialMean[key] /= counter_full
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self.spatialMsqr[key] /= counter_full
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self.spatialMean[key] /= self.spatialNsample[key]
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self.spatialMsqr[key] /= self.spatialNsample[key]
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else:
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# All tanks but rank 0 send their data
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self.__comm.Send(counter,dest=0,tag=1)
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