`vtkmodules.numpy_interface.vtk_indexed_array`#

VTKIndexedArray — numpy-compatible mixin for vtkIndexedArray.

This module provides the VTKIndexedArray mixin class and registers overrides for all vtkIndexedArray instantiations so that indexed arrays coming from VTK automatically have numpy-compatible operations.

A vtkIndexedArray provides reindexed access to a base array via an index list: result[i] = base_array[index_array[i]]. It is used in HyperTree filters and probe filters for zero-copy subset views.

Materialization uses efficient numpy fancy indexing: numpy.asarray(base)[numpy.asarray(indexes)]. Reductions delegate to the materialized array since there is no closed-form shortcut for arbitrary index subsets.

Module Contents#

Classes#

VTKIndexedArray

A numpy-compatible mixin for indexed (reindexed subset) implicit arrays.

Functions#

`_indexed_sum`
`_indexed_mean`
`_indexed_min`
`_indexed_max`
`_indexed_std`
`_indexed_var`
`_indexed_any`
`_indexed_all`
`_indexed_prod`
`_indexed_concatenate`
`_register_indexed_overrides`	Register VTKIndexedArray mixin as override for all vtkIndexedArray template types.

API#

class vtkmodules.numpy_interface.vtk_indexed_array.VTKIndexedArray(indexes=None, array=None, **kwargs)#

Bases: vtkmodules.numpy_interface._vtk_array_mixin.VTKDataArrayMixin

A numpy-compatible mixin for indexed (reindexed subset) implicit arrays.

vtkIndexedArray stores a base array and an index array, presenting base[indexes[i]] as a virtual array. This Python mixin adds numpy integration: arithmetic, reductions, indexing, and to_numpy().

Initialization

Create an indexed array.

Parameters

indexes : vtkDataArray, optional Index array for indirection. array : vtkDataArray, optional Base array to index into. **kwargs Forwarded to the VTK base-class initializer.

property base_array#: Return the original base array used for value lookup.

property index_array#: Return the original index array used for indirection.

property dtype#

property nbytes#

to_numpy(dtype=None)#: Materialize the full array as a numpy ndarray.

__array__(dtype=None, copy=None)#: Materialize the full array via numpy fancy indexing.

__buffer__(flags)#: Override C-level buffer protocol to materialize via array.

_new_indexed(new_base_np)#

Create a new VTKIndexedArray with the same indexes but a new base.

Parameters

new_base_np : numpy.ndarray The transformed base array (flat values).

Returns

VTKIndexedArray or None A new indexed array, or None if the dtype is unsupported.

__array_ufunc__(ufunc, method, *inputs, **kwargs)#

Handle numpy ufuncs, staying lazy when possible.

For unary ufuncs and binary ufuncs with a scalar operand, the ufunc is applied to the base array and a new indexed array is returned with the same index mapping — no materialization needed.

For binary ufuncs with another array operand, the indexed array is materialized since the other array’s elements don’t align with the base.

__array_function__(func, types, args, kwargs)#: Dispatch numpy functions via override registry.

_sub_index_array(tuple_indices)#

Build a new value-level index array for the given tuple indices.

Parameters

tuple_indices : array-like of int Which tuples to select.

Returns

numpy.ndarray Flat value-level indices into the base array.

_new_from_sub_indexes(new_idx_np)#

Create a new VTKIndexedArray with sub-selected indexes, same base.

Parameters

new_idx_np : numpy.ndarray New value-level index array (flat).

Returns

VTKIndexedArray or None

__getitem__(key)#

Index into the indexed array.

Scalar index: direct lookup, no materialization. Slice / fancy int / boolean mask on axis 0: compose a new index array pointing at the same base — stays lazy. Tuple indexing (row, col) or other: materialize.

__setitem__(key, value)#

__add__(other)#

__radd__(other)#

__sub__(other)#

__rsub__(other)#

__mul__(other)#

__rmul__(other)#

__truediv__(other)#

__rtruediv__(other)#

__floordiv__(other)#

__rfloordiv__(other)#

__pow__(other)#

__rpow__(other)#

__mod__(other)#

__rmod__(other)#

__lt__(other)#

__le__(other)#

__eq__(other)#

__ne__(other)#

__ge__(other)#

__gt__(other)#

__neg__()#

__pos__()#

__abs__()#

sum(axis=None, **kwargs)#

min(axis=None, **kwargs)#

max(axis=None, **kwargs)#

mean(axis=None, **kwargs)#

std(axis=None, **kwargs)#

var(axis=None, **kwargs)#

any(axis=None, **kwargs)#

all(axis=None, **kwargs)#

prod(axis=None, **kwargs)#

__iter__()#

__repr__()#

__str__()#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_sum(a, axis=None, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_mean(a, axis=None, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_min(a, axis=None, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_max(a, axis=None, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_std(a, axis=None, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_var(a, axis=None, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_any(a, axis=None, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_all(a, axis=None, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_prod(a, axis=None, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._indexed_concatenate(arrays, axis=0, **kwargs)#

vtkmodules.numpy_interface.vtk_indexed_array._register_indexed_overrides()#: Register VTKIndexedArray mixin as override for all vtkIndexedArray template types.

vtkmodules.numpy_interface.vtk_indexed_array#

Module Contents#

Classes#

Functions#

API#

Parameters

Parameters

Returns

Parameters

Returns

Parameters

Returns

`vtkmodules.numpy_interface.vtk_indexed_array`#