def gradient(narray, dataset=None):
"Computes the gradient of a point-centered scalar array over a given dataset."
if not dataset:
dataset = narray.DataSet()
if not dataset:
raise RuntimeError, 'Need a dataset to compute gradients'
ncomp = narray.shape[1]
if ncomp != 1 and ncomp != 3:
raise RuntimeError, 'Gradient only works with scalars (1 component) and vectors (3 component)'
cd = vtk.vtkCellDerivatives()
if ncomp == 1:
attribute_type = 'scalars'
else:
attribute_type = 'vectors'
dsa = _cell_derivatives(narray, dataset, attribute_type, cd)
if ncomp == 1:
retVal = dsa.GetVectors()
else:
retVal = dsa.GetTensors()
try:
if narray.GetName():
retVal.SetName("gradient of " + narray.GetName())
else:
retVal.SetName("gradient")
except AttributeError:
retVal.SetName("gradient")
return dataset_adapter.vtkDataArrayToVTKArray(retVal, dataset)
def gradient(narray, dataset=None):
"Returns the gradient of an array of scalars/vectors."
if not dataset: dataset = narray.DataSet()
if not dataset: raise RuntimeError, 'Need a dataset to compute gradient'
ncomp = narray.shape[1]
if ncomp != 1 and ncomp != 3:
raise RuntimeError, 'Gradient only works with scalars (1 component) and vectors (3 component)'\
'Input shape ' + narray.shape
cd = vtk.vtkCellDerivatives()
if ncomp == 1 : attribute_type = 'scalars'
else : attribute_type = 'vectors'
dsa = _cell_derivatives(narray, dataset, attribute_type, cd)
if ncomp == 1 : retVal = dsa.GetVectors()
else : retVal = dsa.GetTensors()
try:
if narray.GetName() : retVal.SetName("gradient of " + narray.GetName())
else : retVal.SetName("gradient")
except AttributeError : retVal.SetName("gradient")
ans = dataset_adapter.vtkDataArrayToVTKArray(retVal, dataset)
# The association information has been lost over the vtk filter
# we must reconstruct it otherwise lower pipeline will be broken.
ans.Association = narray.Association
return ans
def strain(narray, dataset=None):
if not dataset:
dataset = narray.DataSet()
cd = vtk.vtkCellDerivatives()
cd.SetTensorModeToComputeStrain()
dsa = _cell_derivatives(narray, dataset, 'vectors', cd)
retVal = dsa.GetTensors()
retVal.SetName("strain")
return dataset_adapter.vtkDataArrayToVTKArray(retVal, dataset)
def curl(narray, dataset=None):
if not dataset:
dataset = narray.DataSet()
cd = vtk.vtkCellDerivatives()
cd.SetVectorModeToComputeVorticity()
dsa = _cell_derivatives(narray, dataset, 'vectors', cd)
retVal = dsa.GetVectors()
retVal.SetName("vorticity")
return dataset_adapter.vtkDataArrayToVTKArray(retVal, dataset)
def gradient(narray, dataset=None):
"Computes the gradient of a point-centered scalar array over a given dataset."
if not dataset:
dataset = narray.DataSet
if not dataset:
raise RuntimeError, 'Need a dataset to compute gradients'
if len(narray.shape) == 1:
narray = narray.reshape((narray.shape[0], 1))
if narray.shape[0] != narray.GetNumberOfTuples():
raise RuntimeError, 'The number of points does not match the number of tuples in the array'
if narray.shape[1] > 1:
raise RuntimeError, 'Gradient only works with 1 component arrays'
ds = dataset.NewInstance()
ds.CopyStructure(dataset.VTKObject)
# numpy_to_vtk converts only contiguous arrays
if not narray.flags.contiguous:
narray = narray.copy()
varray = numpy_support.numpy_to_vtk(narray)
varray.SetName('scalars')
ds.GetPointData().SetScalars(varray)
cd = vtk.vtkCellDerivatives()
cd.SetInput(ds)
ds.UnRegister(None)
c2p = vtk.vtkCellDataToPointData()
c2p.SetInputConnection(cd.GetOutputPort())
c2p.Update()
retVal = c2p.GetOutput().GetPointData().GetVectors()
try:
if narray.GetName():
retVal.SetName("gradient of " + narray.GetName())
else:
retVal.SetName("gradient")
except AttributeError:
retVal.SetName("gradient")
return dataset_adapter.vtkDataArrayToVTKArray(retVal, dataset)
def strain (narray, dataset=None) :
"Returns the strain of an array of 3D vectors."
if not dataset : dataset = narray.DataSet()
if not dataset : raise RuntimeError, 'Need a dataset to compute strain'
if 2 != narray.ndim or 3 != narray.shape[1] :
raise RuntimeError, 'strain only works with an array of 3D vectors'\
'Input shape ' + narray.shape
cd = vtk.vtkCellDerivatives()
cd.SetTensorModeToComputeStrain()
dsa = _cell_derivatives(narray, dataset, 'vectors', cd)
retVal = dsa.GetTensors()
retVal.SetName("strain")
ans = dataset_adapter.vtkDataArrayToVTKArray(retVal, dataset)
# The association information has been lost over the vtk filter
# we must reconstruct it otherwise lower pipeline will be broken.
ans.Association = narray.Association
return ans
