def _global_func(impl, array, axis, controller): if type(array) == dsa.VTKCompositeDataArray: if axis is None or axis == 0: res = impl.serial_composite(array, axis) else: res = apply_ufunc(impl.op(), array, (axis, )) else: res = impl.op()(array, axis) if res is not dsa.NoneArray: res = res.astype(numpy.float64) if axis is None or axis == 0: if controller is None and vtkMultiProcessController is not None: controller = vtkMultiProcessController.GetGlobalController() if controller and controller.IsA("vtkMPIController"): from mpi4py import MPI comm = vtkMPI4PyCommunicator.ConvertToPython( controller.GetCommunicator()) max_dims, size = _reduce_dims(res, comm) # All NoneArrays if size == 0: return dsa.NoneArray if res is dsa.NoneArray: if max_dims is 1: # Weird trick to make the array look like a scalar max_dims = () res = numpy.empty(max_dims) res.fill(impl.default()) res_recv = numpy.array(res) mpi_type = _lookup_mpi_type(res.dtype) comm.Allreduce([res, mpi_type], [res_recv, mpi_type], impl.mpi_op()) if array is dsa.NoneArray: return dsa.NoneArray res = res_recv return res
def _array_count(array, axis, controller): if array is dsa.NoneArray: size = numpy.int64(0) elif axis is None: size = numpy.int64(array.size) else: size = numpy.int64(shape(array)[0]) if controller is None and vtkMultiProcessController is not None: controller = vtkMultiProcessController.GetGlobalController() if controller and controller.IsA("vtkMPIController"): from mpi4py import MPI comm = vtkMPI4PyCommunicator.ConvertToPython(controller.GetCommunicator()) total_size = numpy.array(size, dtype=numpy.int64) mpitype = _lookup_mpi_type(numpy.int64) comm.Allreduce([size, mpitype], [total_size, mpitype], MPI.SUM) size = total_size return size
def unstructured_from_composite_arrays(points, arrays, controller=None): """Given a set of VTKCompositeDataArrays, creates a vtkUnstructuredGrid. The main goal of this function is to transform the output of XXX_per_block() methods to a single dataset that can be visualized and further processed. Here arrays is an iterable (e.g. list) of (array, name) pairs. Here is an example: centroid = mean_per_block(composite_data.Points) T = mean_per_block(composite_data.PointData['Temperature']) ug = unstructured_from_composite_arrays(centroid, (T, 'Temperature')) When called in parallel, this function makes sure that each array in the input dataset is represented only on 1 process. This is important because methods like mean_per_block() return the same value for blocks that are partitioned on all of the participating processes. If the same point were to be created across multiple processes in the output, filters like histogram would report duplicate values erroneously. """ try: dataset = points.DataSet except AttributeError: dataset = None if dataset is None and points is not dsa.NoneArray: raise ValueError( "Expecting a points arrays with an associated dataset.") if points is dsa.NoneArray: cpts = [] else: cpts = points.Arrays ownership = numpy.zeros(len(cpts), dtype=numpy.int32) rank = 0 # Let's first create a map of array index to composite ids. if dataset is None: ids = [] else: it = dataset.NewIterator() it.UnRegister(None) itr = cpts.__iter__() ids = numpy.empty(len(cpts), dtype=numpy.int32) counter = 0 while not it.IsDoneWithTraversal(): _id = it.GetCurrentFlatIndex() ids[counter] = _id counter += 1 it.GoToNextItem() if controller is None and vtkMultiProcessController is not None: controller = vtkMultiProcessController.GetGlobalController() if controller and controller.IsA("vtkMPIController"): from mpi4py import MPI comm = vtkMPI4PyCommunicator.ConvertToPython( controller.GetCommunicator()) rank = comm.Get_rank() # Determine the max id to use for reduction # operations # Get all ids from dataset, including empty ones. lmax_id = numpy.int32(0) if dataset is not None: it = dataset.NewIterator() it.UnRegister(None) it.SetSkipEmptyNodes(False) while not it.IsDoneWithTraversal(): _id = it.GetCurrentFlatIndex() lmax_id = numpy.max((lmax_id, _id)).astype(numpy.int32) it.GoToNextItem() max_id = numpy.array(0, dtype=numpy.int32) mpitype = _lookup_mpi_type(numpy.int32) comm.Allreduce([lmax_id, mpitype], [max_id, mpitype], MPI.MAX) # Now we figure out which processes have which ids lownership = numpy.empty(max_id, dtype=numpy.int32) lownership.fill(numpy.iinfo(numpy.int32).max) ownership = numpy.empty(max_id, dtype=numpy.int32) if dataset is not None: it = dataset.NewIterator() it.UnRegister(None) it.InitTraversal() itr = cpts.__iter__() while not it.IsDoneWithTraversal(): _id = it.GetCurrentFlatIndex() if itr.next() is not dsa.NoneArray: lownership[_id] = rank it.GoToNextItem() mpitype = _lookup_mpi_type(numpy.int32) # The process with the lowest id containing a block will # produce the output for that block. comm.Allreduce([lownership, mpitype], [ownership, mpitype], MPI.MIN) # Iterate over blocks to produce points and arrays from vtk.vtkCommonDataModel import vtkUnstructuredGrid from vtk.vtkCommonCore import vtkDoubleArray, vtkPoints ugrid = vtkUnstructuredGrid() da = vtkDoubleArray() da.SetNumberOfComponents(3) pts = vtkPoints() pts.SetData(da) counter = 0 for pt in cpts: if ownership[ids[counter]] == rank: pts.InsertNextPoint(tuple(pt)) counter += 1 ugrid.SetPoints(pts) for ca, name in arrays: if ca is not dsa.NoneArray: da = vtkDoubleArray() ncomps = ca.Arrays[0].flatten().shape[0] da.SetNumberOfComponents(ncomps) counter = 0 for a in ca.Arrays: if ownership[ids[counter]] == rank: a = a.flatten() for i in range(ncomps): da.InsertNextValue(a[i]) counter += 1 if len(a) > 0: da.SetName(name) ugrid.GetPointData().AddArray(da) return ugrid
def _global_per_block(impl, array, axis=None, controller=None): if axis > 0: return impl.op()(array, axis=axis, controller=controller) try: dataset = array.DataSet except AttributeError: dataset = None t = type(array) if t == dsa.VTKArray or t == numpy.ndarray: from vtk.vtkCommonDataModel import vtkMultiBlockDataSet array = dsa.VTKCompositeDataArray([array]) ds = vtkMultiBlockDataSet() ds.SetBlock(0, dataset.VTKObject) dataset = ds results = _apply_func2(impl.op2(), array, (axis, )) if controller is None and vtkMultiProcessController is not None: controller = vtkMultiProcessController.GetGlobalController() if controller and controller.IsA("vtkMPIController"): from mpi4py import MPI comm = vtkMPI4PyCommunicator.ConvertToPython( controller.GetCommunicator()) # First determine the number of components to use # for reduction res = dsa.NoneArray for res in results: if res is not dsa.NoneArray: break max_dims, size = _reduce_dims(res, comm) # All NoneArrays if size == 0: return dsa.NoneArray # Next determine the max id to use for reduction # operations # Get all ids from dataset, including empty ones. ids = [] lmax_id = numpy.int32(0) if dataset is not None: it = dataset.NewIterator() it.UnRegister(None) it.SetSkipEmptyNodes(False) while not it.IsDoneWithTraversal(): _id = it.GetCurrentFlatIndex() lmax_id = numpy.max((lmax_id, _id)).astype(numpy.int32) if it.GetCurrentDataObject() is not None: ids.append(_id) it.GoToNextItem() max_id = numpy.array(0, dtype=numpy.int32) mpitype = _lookup_mpi_type(numpy.int32) comm.Allreduce([lmax_id, mpitype], [max_id, mpitype], MPI.MAX) has_ids = numpy.zeros(max_id + 1, dtype=numpy.int32) for _id in ids: has_ids[_id] = 1 id_count = numpy.array(has_ids) comm.Allreduce([has_ids, mpitype], [id_count, mpitype], MPI.SUM) if numpy.all(id_count <= 1): return dsa.VTKCompositeDataArray(results, dataset=dataset) # Now that we know which blocks are shared by more than # 1 rank. The ones that have a count of 2 or more. reduce_ids = [] for _id in xrange(len(id_count)): if id_count[_id] > 1: reduce_ids.append(_id) to_reduce = len(reduce_ids) # If not block is shared, short circuit. No need to # communicate any more. if to_reduce == 0: return dsa.VTKCompositeDataArray(results, dataset=dataset) # Create the local array that will be used for # reduction. Set it to a value that won't effect # the reduction. lresults = numpy.empty(size * to_reduce) lresults.fill(impl.default()) # Just get non-empty ids. Doing this again in case # the traversal above results in a different order. # We need the same order since we'll use izip below. if dataset is not None: it = dataset.NewIterator() it.UnRegister(None) ids = [] while not it.IsDoneWithTraversal(): ids.append(it.GetCurrentFlatIndex()) it.GoToNextItem() # Fill the local array with available values. for _id, _res in izip(ids, results): success = True try: loc = reduce_ids.index(_id) except ValueError: success = False if success: if _res is not dsa.NoneArray: lresults[loc * size:(loc + 1) * size] = _res.flatten() # Now do the MPI reduction. rresults = numpy.array(lresults) mpitype = _lookup_mpi_type(numpy.double) comm.Allreduce([lresults, mpitype], [rresults, mpitype], impl.mpi_op()) if array is dsa.NoneArray: return dsa.NoneArray # Fill in the reduced values. for i in xrange(to_reduce): _id = reduce_ids[i] success = True try: loc = ids.index(_id) except ValueError: success = False if success: if size == 1: results[loc] = dsa.VTKArray(rresults[i]) else: results[loc] = rresults[i * size:(i + 1) * size].reshape(max_dims) return dsa.VTKCompositeDataArray(results, dataset=dataset)
def RequestData(): import math import numpy import paraview import vtk.numpy_interface.dataset_adapter import vtk.numpy_interface.algorithms #from mpi4py import MPI try: from vtk.vtkParallelCore import vtkMultiProcessController from vtk.vtkParallelMPI4Py import vtkMPI4PyCommunicator except ImportError: vtkMultiProcessController = None vtkMPI4PyCommunicator = None # -- this will import vtkMultiProcessController and vtkMPI4PyCommunicator #if controller is None and vtkMultiProcessController is not None: # controller = vtkMultiProcessController.GetGlobalController() controller = vtkMultiProcessController.GetGlobalController() nProcs = controller.GetNumberOfProcesses() print ' nProcs: ', nProcs if controller and controller.IsA( "vtkMPIController") and controller.GetNumberOfProcesses() > 1: from mpi4py import MPI comm = vtkMPI4PyCommunicator.ConvertToPython( controller.GetCommunicator()) rank = comm.Get_rank() else: rank = 0 # This script computes the particle distribution function. Missing: Selection of # spacial coordinate and velocity deltaX = float(xMax - xMin) / float(NumberOfSpaceBins) deltaV = float(maxVelo - minVelo) / float(NumberOfVeloBins) # input input = self.GetInputDataObject(0, 0) if input.IsA("vtkMultiBlockDataSet"): # here: new format with vtk-multiblock print(" vtkMultiBlockDataSet") iter = input.NewIterator() iter.UnRegister(None) iter.InitTraversal() pdi = iter.GetCurrentDataObject() else: # old format without multiblock pdi = input.GetInput() nParts = pdi.GetNumberOfPoints() if nProcs > 1: #nTotalParts = numpy.array(0, 'i') #nTotalParts=[] #comm.Allreduce([nParts, MPI.INT], [nTotalParts, MPI.INT], op=MPI.SUM) nTotalParts = comm.allreduce(nParts, op=MPI.SUM) else: nTotalParts = nParts if rank == 0: print ' nTotalParts:', nTotalParts # output pdo = self.GetOutputDataObject(0) pdo = self.GetOutput() # generate 2d grid pdo.SetDimensions(NumberOfSpaceBins + 1, NumberOfVeloBins + 1, 0) deltaXplot = 1. / float(NumberOfSpaceBins) deltaVplot = 1. / float(NumberOfVeloBins) if (maxVelo == -minVelo): pdo.SetOrigin(0, -0.5, 0.) else: pdo.SetOrigin(0, 0.0, 0.) pdo.SetSpacing(deltaXplot, deltaVplot, 1.) # On ParaView 3.98, 4.0 and 4.1 pdo.SetExtent(0, NumberOfSpaceBins, 0, NumberOfVeloBins, 0, 1) PDF = numpy.zeros((NumberOfSpaceBins, NumberOfVeloBins), dtype='float') # generate array # loop over all particles nPartsMin = 0 nPartsMax = 0 nPartsIn = 0 nXmin = 0 nXmax = 0 for i in range(0, nParts): coord = pdi.GetPoint(i) pos = coord[iDirect] if (iVelocity == 3): # vabs vx = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i) vy = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i + 1) vz = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i + 2) velo = math.sqrt(vx**2 + vy**2 + vz**2) elif (iVelocity == 4): # v-tang to x vy = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i + 1) vz = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i + 2) velo = math.sqrt(vy**2 + vz**2) elif (iVelocity == 5): # vabv-tang to y vx = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i) vz = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i + 2) velo = math.sqrt(vx**2 + vz**2) elif (iVelocity == 6): # vabv-tang to z vx = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i) vy = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i + 1) velo = math.sqrt(vx**2 + vy**2) else: # velocity in x,y or z velo = pdi.GetPointData().GetArray("Velocity").GetValue(3 * i + iVelocity) #if (iVelocity!=3): # velo = pdi.GetPointData().GetArray("Velocity").GetValue(3*i + iVelocity) #else: # vx =pdi.GetPointData().GetArray("Velocity").GetValue(3*i ) # vy =pdi.GetPointData().GetArray("Velocity").GetValue(3*i + 1) # vz =pdi.GetPointData().GetArray("Velocity").GetValue(3*i + 2) # velo=math.sqrt(vx**2+vy**2+vz**2) # check x range if (xMin > pos): nXmin = nXmin + 1 elif (xMax < pos): nMax = nXmax + 1 else: # particle in x-range if (minVelo > velo): nPartsMin = nPartsMin + 1 elif (maxVelo < velo): nPartsMax = nPartsMax + 1 else: # compute position in 2d-space pos-velo array ipos = int((pos - xMin) / deltaX) #+1 ivelo = int((velo - minVelo) / deltaV) #+1 nPartsIn = nPartsIn + 1 PDF[ipos, ivelo] = PDF[ipos, ivelo] + 1.0 #/float(nParts) totalPDF = numpy.zeros((NumberOfSpaceBins, NumberOfVeloBins), dtype='float') if nProcs > 1: # mpi stuff nTotalPartsIn = comm.allreduce(nPartsIn, op=MPI.SUM) totalPDF = comm.allreduce(PDF, op=MPI.SUM) for j in range(0, NumberOfVeloBins): for i in range(0, NumberOfSpaceBins): totalPDF[i, j] = totalPDF[i, j] / float(nTotalPartsIn) else: nTotalPartsIn = nPartsIn for j in range(0, NumberOfVeloBins): for i in range(0, NumberOfSpaceBins): totalPDF[i, j] = PDF[i, j] / float(nTotalPartsIn) array = vtk.vtkFloatArray() if nProcs > 1: # sum up to total nTotalXmin = comm.reduce(nXmin, op=MPI.SUM) nTotalXmax = comm.reduce(nXmax, op=MPI.SUM) nTotalPartsMin = comm.reduce(nPartsMin, op=MPI.SUM) nTotalPartsMax = comm.reduce(nPartsMax, op=MPI.SUM) else: nTotalXmin = nXmin nTotalXmax = nXmax nTotalPartsMin = nPartsMin nTotalPartsMax = nPartsMax nTotalPartsOut = nTotalParts - nTotalPartsIn if rank == 0: # output if (nTotalXmin > 0) or (nTotalXmax > 0): print " Particles out of coordinate range." print " nMinOut: ", nTotalXmin print " nMaxOut: ", nTotalXmax print " Percent coord out: ", float( nTotalXmin + nTotalXmax) / float(nTotalParts) * 100.0 if (nTotalPartsMin > 0) or (nTotalPartsMax > 0): print " Particles out of velocity range. Velocity truncated!!!" print " nPartsMin: ", nTotalPartsMin print " nPartsMax: ", nTotalPartsMax print " Percent velo out of nPartsIn: ", float( nTotalPartsMin + nTotalPartsMax) / float(nTotalPartsIn) * 100.0 print " Percent velo out of nParts: ", float( nTotalPartsMin + nTotalPartsMax) / float(nTotalParts) * 100.0 if (nTotalPartsOut > 0): print " nPartsIn: ", nTotalPartsIn print " total out: ", nTotalPartsOut print " Percent nPartIn: ", float(nTotalPartsIn) / float( nTotalParts) * 100.0 print " Percent nPartOut: ", float(nTotalPartsOut) / float( nTotalParts) * 100.0 array.SetName("PDF") array.SetNumberOfComponents(1) ncells = NumberOfSpaceBins * NumberOfVeloBins array.SetNumberOfTuples(ncells) pdo.GetCellData().AddArray(array) ipos = 0 for j in range(0, NumberOfVeloBins): for i in range(0, NumberOfSpaceBins): # caution: transpoesed index because of storage #array.SetValue(ipos,totalPDF[i,j]/float(nPartsIn)) array.SetValue(ipos, totalPDF[i, j]) ipos = ipos + 1