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 = 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 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 = 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) :
"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 = 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
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 = 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