def __new__(cls, array: Union[Iterable, vtkAbstractArray], dataset=None,
association=FieldAssociation.NONE):
"""Allocate the array."""
if isinstance(array, Iterable):
obj = np.asarray(array).view(cls)
elif isinstance(array, vtkAbstractArray):
obj = convert_array(array).view(cls)
obj.VTKObject = array
obj.association = association
obj.dataset = vtkWeakReference()
if isinstance(dataset, VTKObjectWrapper):
obj.dataset.Set(dataset.VTKObject)
else:
obj.dataset.Set(dataset)
return obj
def __new__(cls,
array: Union[Iterable, _vtk.vtkAbstractArray],
dataset=None,
association=FieldAssociation.NONE):
"""Allocate the array."""
if isinstance(array, Iterable):
obj = np.asarray(array).view(cls)
elif isinstance(array, _vtk.vtkAbstractArray):
obj = convert_array(array).view(cls)
obj.VTKObject = array
else:
raise TypeError(
f'pyvista_ndarray got an invalid type {type(array)}. '
'Expected an Iterable or vtk.vtkAbstractArray')
obj.association = association
obj.dataset = _vtk.vtkWeakReference()
if isinstance(dataset, _vtk.VTKObjectWrapper):
obj.dataset.Set(dataset.VTKObject)
else:
obj.dataset.Set(dataset)
return obj
def _prepare_array(self, data: Union[Sequence[Number], Number, np.ndarray],
name: str, deep_copy: bool) -> _vtk.vtkDataSet:
"""Prepare an array to be added to this dataset."""
if data is None:
raise TypeError('``data`` cannot be None.')
if isinstance(data, Iterable):
data = pyvista_ndarray(data)
if self.association == FieldAssociation.POINT:
array_len = self.dataset.GetNumberOfPoints()
elif self.association == FieldAssociation.CELL:
array_len = self.dataset.GetNumberOfCells()
else:
array_len = data.shape[0] if isinstance(data, np.ndarray) else 1
# Fixup input array length for scalar input
if not isinstance(data, np.ndarray) or np.ndim(data) == 0:
tmparray = np.empty(array_len)
tmparray.fill(data)
data = tmparray
if data.shape[0] != array_len:
raise ValueError(
f'data length of ({data.shape[0]}) != required length ({array_len})'
)
if data.dtype == np.bool_:
self.dataset.association_bitarray_names[self.association.name].add(
name)
data = data.view(np.uint8)
shape = data.shape
if data.ndim == 3:
# Array of matrices. We need to make sure the order in
# memory is right. If row major (C/C++),
# transpose. VTK wants column major (Fortran order). The deep
# copy later will make sure that the array is contiguous.
# If column order but not contiguous, transpose so that the
# deep copy below does not happen.
size = data.dtype.itemsize
if ((data.strides[1] / size == 3 and data.strides[2] / size == 1)
or
(data.strides[1] / size == 1 and data.strides[2] / size == 3
and not data.flags.contiguous)):
data = data.transpose(0, 2, 1)
# If array is not contiguous, make a deep copy that is contiguous
if not data.flags.contiguous:
data = np.ascontiguousarray(data)
# Flatten array of matrices to array of vectors
if len(shape) == 3:
data = data.reshape(shape[0], shape[1] * shape[2])
# Swap bytes from big to little endian.
if data.dtype.byteorder == '>':
data = data.byteswap(inplace=True)
# this handles the case when an input array is directly added to the
# output. We want to make sure that the array added to the output is not
# referring to the input dataset.
copy = pyvista_ndarray(data)
return helpers.convert_array(copy, name, deep=deep_copy)
def append(self,
narray: Union[Sequence[Number], Number, np.ndarray],
name: str,
deep_copy=False,
active_vectors=True,
active_scalars=True) -> None:
"""Add an array to this object.
Parameters
----------
narray : array_like, scalar value
A pyvista_ndarray, numpy.ndarray, list, tuple or scalar value.
name : str
Name of the array to add.
deep_copy : bool
When True makes a full copy of the array.
active_vectors : bool
If True, make this the active vector array.
active_scalars : bool:
If True, make this the active scalar array.
"""
if narray is None:
raise TypeError('narray cannot be None.')
if isinstance(narray, Iterable):
narray = pyvista_ndarray(narray)
if self.association == FieldAssociation.POINT:
array_len = self.dataset.GetNumberOfPoints()
elif self.association == FieldAssociation.CELL:
array_len = self.dataset.GetNumberOfCells()
else:
array_len = narray.shape[0] if isinstance(narray,
np.ndarray) else 1
# Fixup input array length for scalar input:
if not isinstance(narray, np.ndarray) or np.ndim(narray) == 0:
tmparray = np.empty(array_len)
tmparray.fill(narray)
narray = tmparray
if narray.shape[0] != array_len:
raise ValueError(
f'narray length of ({narray.shape[0]}) != required length ({array_len})'
)
if narray.dtype == np.bool_:
self.dataset.association_bitarray_names[self.association].add(name)
narray = narray.view(np.uint8)
shape = narray.shape
if len(shape) == 3:
# Array of matrices. We need to make sure the order in memory is right.
# If column order (c order), transpose. VTK wants row order (fortran
# order). The deep copy later will make sure that the array is contiguous.
# If row order but not contiguous, transpose so that the deep copy below
# does not happen.
size = narray.dtype.itemsize
if (narray.strides[1] / size == 3 and narray.strides[2] / size == 1) or \
(narray.strides[1] / size == 1 and narray.strides[2] / size == 3 and \
not narray.flags.contiguous):
narray = narray.transpose(0, 2, 1)
# If array is not contiguous, make a deep copy that is contiguous
if not narray.flags.contiguous:
narray = np.ascontiguousarray(narray)
# Flatten array of matrices to array of vectors
if len(shape) == 3:
narray = narray.reshape(shape[0], shape[1] * shape[2])
# Swap bytes from big to little endian.
if narray.dtype.byteorder == '>':
narray = narray.byteswap(inplace=True)
# this handles the case when an input array is directly appended on the
# output. We want to make sure that the array added to the output is not
# referring to the input dataset.
copy = pyvista_ndarray(narray)
vtk_arr = helpers.convert_array(copy, name, deep=deep_copy)
self.VTKObject.AddArray(vtk_arr)
try:
if active_scalars or self.active_scalars is None:
self.active_scalars = name # type: ignore
if active_vectors or self.active_vectors is None:
# verify this is actually vector data
if len(shape) == 2 and shape[1] == 3:
self.active_vectors = name # type: ignore
except TypeError:
pass
self.VTKObject.Modified()
