"""Internal array utilities."""
from __future__ import annotations
from collections import UserDict
import enum
from itertools import product
import json
from typing import TYPE_CHECKING
from typing import Sequence
from typing import Union
import numpy as np
import pyvista
from pyvista.core import _vtk_core as _vtk
from pyvista.core.errors import AmbiguousDataError
from pyvista.core.errors import MissingDataError
if TYPE_CHECKING: # pragma: no cover
from pyvista.core._typing_core import MatrixLike
from pyvista.core._typing_core import NumpyArray
from pyvista.core._typing_core import TransformLike
from pyvista.core._typing_core import VectorLike
class FieldAssociation(enum.Enum):
"""Represents which type of vtk field a scalar or vector array is associated with."""
POINT = _vtk.vtkDataObject.FIELD_ASSOCIATION_POINTS
CELL = _vtk.vtkDataObject.FIELD_ASSOCIATION_CELLS
NONE = _vtk.vtkDataObject.FIELD_ASSOCIATION_NONE
ROW = _vtk.vtkDataObject.FIELD_ASSOCIATION_ROWS
def parse_field_choice(field):
"""Return a field association object for a given field type string.
Parameters
----------
field : str, FieldAssociation
Name of the field (e.g, ``'cell'``, ``'field'``, ``'point'``,
``'row'``).
Returns
-------
pyvista.FieldAssociation
Field association.
"""
if isinstance(field, str):
field = field.strip().lower()
if field in ['cell', 'c', 'cells']:
field = FieldAssociation.CELL
elif field in ['point', 'p', 'points']:
field = FieldAssociation.POINT
elif field in ['field', 'f', 'fields']:
field = FieldAssociation.NONE
elif field in ['row', 'r']:
field = FieldAssociation.ROW
else:
raise ValueError(f'Data field ({field}) not supported.')
elif isinstance(field, FieldAssociation):
pass
else:
raise TypeError(f'Data field ({field}) not supported.')
return field
def _coerce_pointslike_arg(
points: MatrixLike[float] | VectorLike[float],
copy: bool = False,
) -> tuple[NumpyArray[float], bool]:
"""Check and coerce arg to (n, 3) np.ndarray.
Parameters
----------
points : MatrixLike[float] | VectorLike[float]
Argument to coerce into (n, 3) :class:`numpy.ndarray`.
copy : bool, default: False
Whether to copy the ``points`` array. Copying always occurs if ``points``
is not :class:`numpy.ndarray`.
Returns
-------
numpy.ndarray
Size ``(n, 3)`` array.
bool
Whether the input was a single point in an array-like with shape ``(3,)``.
"""
if isinstance(points, Sequence):
points = np.asarray(points)
if not isinstance(points, np.ndarray):
raise TypeError("Given points must be convertible to a numerical array.")
if points.ndim > 2:
raise ValueError("Array of points must be 1D or 2D")
if points.ndim == 2:
if points.shape[1] != 3:
raise ValueError("Array of points must have three values per point (shape (n, 3))")
singular = False
else:
if points.size != 3:
raise ValueError("Given point must have three values")
singular = True
points = np.reshape(points, [1, 3])
if copy:
return points.copy(), singular
return points, singular
def copy_vtk_array(array, deep=True):
"""Create a deep or shallow copy of a VTK array.
Parameters
----------
array : vtk.vtkDataArray | vtk.vtkAbstractArray
VTK array.
deep : bool, optional
When ``True``, create a deep copy of the array. When ``False``, returns
a shallow copy.
Returns
-------
vtk.vtkDataArray or vtk.vtkAbstractArray
Copy of the original VTK array.
Examples
--------
Perform a deep copy of a vtk array.
>>> import vtk
>>> import pyvista as pv
>>> arr = vtk.vtkFloatArray()
>>> _ = arr.SetNumberOfValues(10)
>>> arr.SetValue(0, 1)
>>> arr_copy = pv.core.utilities.arrays.copy_vtk_array(arr)
>>> arr_copy.GetValue(0)
1.0
"""
if not isinstance(array, (_vtk.vtkDataArray, _vtk.vtkAbstractArray)):
raise TypeError(f"Invalid type {type(array)}.")
new_array = type(array)()
if deep:
new_array.DeepCopy(array)
else:
new_array.ShallowCopy(array)
return new_array
def has_duplicates(arr):
"""Return if an array has any duplicates.
Parameters
----------
arr : numpy.ndarray
Array to be checked for duplicates.
Returns
-------
bool
``True`` if the array has any duplicates, otherwise ``False``.
"""
s = np.sort(arr, axis=None)
return (s[1:] == s[:-1]).any()
def raise_has_duplicates(arr):
"""Raise a ValueError if an array is not unique.
Parameters
----------
arr : numpy.ndarray
Array to be checked for duplicates.
Raises
------
ValueError
If the array contains duplicate values.
"""
if has_duplicates(arr):
raise ValueError("Array contains duplicate values.")
def convert_array(arr, name=None, deep=False, array_type=None):
"""Convert a NumPy array to a vtkDataArray or vice versa.
Parameters
----------
arr : np.ndarray | vtkDataArray
A numpy array or vtkDataArry to convert.
name : str, optional
The name of the data array for VTK.
deep : bool, default: False
If input is numpy array then deep copy values.
array_type : int, optional
VTK array type ID as specified in ``vtkType.h``.
Returns
-------
vtkDataArray or numpy.ndarray
The converted array. If input is a :class:`numpy.ndarray` then
returns ``vtkDataArray`` or is input is ``vtkDataArray`` then
returns NumPy ``ndarray``.
"""
if arr is None:
return None
if isinstance(arr, (list, tuple, str)):
arr = np.array(arr)
if isinstance(arr, np.ndarray):
if arr.dtype == np.dtype('O'):
arr = arr.astype('|S')
if arr.dtype.type in (np.str_, np.bytes_):
# This handles strings
if arr.ndim > 0:
# Do not call ascontiguousarray for scalar strings since this will reshape to 1D
# and scalars are already contiguous anyway
arr = np.ascontiguousarray(arr)
vtk_data = convert_string_array(arr)
else:
# This will handle numerical data
arr = np.ascontiguousarray(arr)
vtk_data = _vtk.numpy_to_vtk(num_array=arr, deep=deep, array_type=array_type)
if isinstance(name, str):
vtk_data.SetName(name)
return vtk_data
# Otherwise input must be a vtkDataArray
if not isinstance(arr, (_vtk.vtkDataArray, _vtk.vtkBitArray, _vtk.vtkStringArray)):
raise TypeError(f'Invalid input array type ({type(arr)}).')
# Handle booleans
if isinstance(arr, _vtk.vtkBitArray):
arr = vtk_bit_array_to_char(arr)
# Handle string arrays
if isinstance(arr, _vtk.vtkStringArray):
return convert_string_array(arr)
# Convert from vtkDataArry to NumPy
return _vtk.vtk_to_numpy(arr)
def get_array(mesh, name, preference='cell', err=False) -> pyvista.ndarray | None:
"""Search point, cell and field data for an array.
Parameters
----------
mesh : pyvista.DataSet
Dataset to get the array from.
name : str
The name of the array to get the range.
preference : str, default: "cell"
When scalars is specified, this is the preferred array type to
search for in the dataset. Must be either ``'point'``,
``'cell'``, or ``'field'``.
err : bool, default: False
Whether to throw an error if array is not present.
Returns
-------
pyvista.pyvista_ndarray or None
Requested array. Return ``None`` if there is no array
matching the ``name`` and ``err=False``.
"""
if isinstance(mesh, _vtk.vtkTable):
arr = row_array(mesh, name)
if arr is None and err:
raise KeyError(f'Data array ({name}) not present in this dataset.')
return arr
if not isinstance(preference, str):
raise TypeError('`preference` must be a string')
if preference not in ['cell', 'point', 'field']:
raise ValueError(
f'`preference` must be either "cell", "point", "field" for a '
f'{type(mesh)}, not "{preference}".',
)
parr = point_array(mesh, name)
carr = cell_array(mesh, name)
farr = field_array(mesh, name)
preference = parse_field_choice(preference)
if sum([array is not None for array in (parr, carr, farr)]) > 1:
if preference == FieldAssociation.CELL:
return carr
elif preference == FieldAssociation.POINT:
return parr
else: # must be field
return farr
if parr is not None:
return parr
elif carr is not None:
return carr
elif farr is not None:
return farr
elif err:
raise KeyError(f'Data array ({name}) not present in this dataset.')
return None
def get_array_association(mesh, name, preference='cell', err=False) -> FieldAssociation:
"""Return the array association.
Parameters
----------
mesh : Dataset
Dataset to get the array association from.
name : str
The name of the array.
preference : str, default: "cell"
When scalars is specified, this is the preferred array type to
search for in the dataset. Must be either ``'point'``,
``'cell'``, or ``'field'``.
err : bool, default: False
Boolean to control whether to throw an error if array is not
present.
Returns
-------
pyvista.core.utilities.arrays.FieldAssociation
Association of the array. If array is not present and ``err`` is
``False``, ``FieldAssociation.NONE`` is returned.
"""
if isinstance(mesh, _vtk.vtkTable):
arr = row_array(mesh, name)
if arr is None and err:
raise KeyError(f'Data array ({name}) not present in this dataset.')
return FieldAssociation.ROW
# with multiple arrays, return the array preference if possible
parr = point_array(mesh, name)
carr = cell_array(mesh, name)
farr = field_array(mesh, name)
arrays = [parr, carr, farr]
preferences = [FieldAssociation.POINT, FieldAssociation.CELL, FieldAssociation.NONE]
preference = parse_field_choice(preference)
if preference not in preferences:
raise ValueError(f'Data field ({preference}) not supported.')
matches = [pref for pref, array in zip(preferences, arrays) if array is not None]
# optionally raise if no match
if not matches:
if err:
raise KeyError(f'Data array ({name}) not present in this dataset.')
return FieldAssociation.NONE
# use preference if it applies
if preference in matches:
return preference
# otherwise return first in order of point -> cell -> field
return matches[0]
def raise_not_matching(scalars, dataset):
"""Raise exception about inconsistencies.
Parameters
----------
scalars : numpy.ndarray
Array of scalars.
dataset : pyvista.DataSet
Dataset to check against.
Raises
------
ValueError
Raises a ValueError if the size of scalars does not the dataset.
"""
if isinstance(dataset, _vtk.vtkTable):
raise ValueError(
f'Number of scalars ({scalars.shape[0]}) must match number of rows ({dataset.n_rows}).',
)
raise ValueError(
f'Number of scalars ({scalars.shape[0]}) '
f'must match either the number of points ({dataset.n_points}) '
f'or the number of cells ({dataset.n_cells}).',
)
def _assoc_array(obj, name, association='point'):
"""Return a point, cell, or field array from a pyvista.DataSet or VTK object.
If the array or index doesn't exist, return nothing. This matches VTK's
behavior when using ``GetAbstractArray`` with an invalid key or index.
"""
vtk_attr = f'Get{association.title()}Data'
python_attr = f'{association.lower()}_data'
if isinstance(obj, pyvista.DataSet):
try:
return getattr(obj, python_attr).get_array(name)
except KeyError: # pragma: no cover
return None
abstract_array = getattr(obj, vtk_attr)().GetAbstractArray(name)
if abstract_array is not None:
return pyvista.pyvista_ndarray(abstract_array)
return None
def point_array(obj, name):
"""Return point array of a pyvista or vtk object.
Parameters
----------
obj : pyvista.DataSet | vtk.vtkDataSet
PyVista or VTK dataset.
name : str | int
Name or index of the array.
Returns
-------
pyvista.pyvista_ndarray or None
Wrapped array if the index or name is valid. Otherwise, ``None``.
"""
return _assoc_array(obj, name, 'point')
def field_array(obj, name):
"""Return field data of a pyvista or vtk object.
Parameters
----------
obj : pyvista.DataSet or vtk.vtkDataSet
PyVista or VTK dataset.
name : str | int
Name or index of the array.
Returns
-------
pyvista.pyvista_ndarray or None
Wrapped array if the index or name is valid. Otherwise, ``None``.
"""
return _assoc_array(obj, name, 'field')
def cell_array(obj, name):
"""Return cell array of a pyvista or vtk object.
Parameters
----------
obj : pyvista.DataSet or vtk.vtkDataSet
PyVista or VTK dataset.
name : str | int
Name or index of the array.
Returns
-------
pyvista.pyvista_ndarray or None
Wrapped array if the index or name is valid. Otherwise, ``None``.
"""
return _assoc_array(obj, name, 'cell')
def row_array(obj, name):
"""Return row array of a vtk object.
Parameters
----------
obj : vtk.vtkDataSet
PyVista or VTK dataset.
name : str
Name of the array.
Returns
-------
numpy.ndarray
Wrapped array.
"""
vtkarr = obj.GetRowData().GetAbstractArray(name)
return convert_array(vtkarr)
[docs]
def get_vtk_type(typ):
"""Look up the VTK type for a given numpy data type.
Corrects for string type mapping issues.
Parameters
----------
typ : numpy.dtype
Numpy data type.
Returns
-------
int
Integer type id specified in ``vtkType.h``.
"""
typ = _vtk.get_vtk_array_type(typ)
# This handles a silly string type bug
if typ == 3:
return 13
return typ
def vtk_bit_array_to_char(vtkarr_bint):
"""Cast vtk bit array to a char array.
Parameters
----------
vtkarr_bint : vtk.vtkBitArray
VTK binary array.
Returns
-------
vtk.vtkCharArray
VTK char array.
Notes
-----
This performs a copy.
"""
vtkarr = _vtk.vtkCharArray()
vtkarr.DeepCopy(vtkarr_bint)
return vtkarr
def vtk_id_list_to_array(vtk_id_list):
"""Convert a vtkIdList to a NumPy array.
Parameters
----------
vtk_id_list : vtk.vtkIdList
VTK ID list.
Returns
-------
numpy.ndarray
Array of IDs.
"""
return np.array([vtk_id_list.GetId(i) for i in range(vtk_id_list.GetNumberOfIds())])
def _set_string_scalar_object_name(vtkarr: _vtk.vtkStringArray):
"""Set object name for scalar string arrays."""
# This is used as a flag so that scalar arrays can be reshaped later.
try:
vtkarr.SetObjectName('scalar')
except AttributeError:
vtkarr.GetObjectName = lambda: 'scalar'
[docs]
def convert_string_array(arr, name=None):
"""Convert a numpy array of strings to a vtkStringArray or vice versa.
If a scalar string is provided, it is converted to a vtkCharArray
Parameters
----------
arr : numpy.ndarray | str
Numpy string array to convert.
name : str, optional
Name to set the vtkStringArray to.
Returns
-------
vtkStringArray
VTK string array.
Notes
-----
Note that this is terribly inefficient. If you have ideas on how
to make this faster, please consider opening a pull request.
"""
arr = np.array(arr) if isinstance(arr, str) else arr
if isinstance(arr, np.ndarray):
# VTK default fonts only support ASCII. See https://gitlab.kitware.com/vtk/vtk/-/issues/16904
if (
np.issubdtype(arr.dtype, np.str_) and not ''.join(arr.tolist()).isascii()
): # avoids segfault
raise ValueError(
'String array contains non-ASCII characters that are not supported by VTK.',
)
vtkarr = _vtk.vtkStringArray()
if arr.ndim == 0:
arr = arr.reshape((1,))
# distinguish scalar inputs from array inputs by
# setting the object name
_set_string_scalar_object_name(vtkarr)
########### OPTIMIZE ###########
for val in arr:
vtkarr.InsertNextValue(val)
################################
if isinstance(name, str):
vtkarr.SetName(name)
return vtkarr
# Otherwise it is a vtk array and needs to be converted back to numpy
############### OPTIMIZE ###############
nvalues = arr.GetNumberOfValues()
arr_out = np.array([arr.GetValue(i) for i in range(nvalues)], dtype='|U')
try:
if arr.GetObjectName() == 'scalar':
return np.array("".join(arr_out))
except AttributeError:
pass
return arr_out
########################################
def array_from_vtkmatrix(matrix) -> NumpyArray[float]:
"""Convert a vtk matrix to an array.
Parameters
----------
matrix : vtk.vtkMatrix3x3 | vtk.vtkMatrix4x4
The vtk matrix to be converted to a ``numpy.ndarray``.
Returned ndarray has shape (3, 3) or (4, 4) as appropriate.
Returns
-------
numpy.ndarray
Numpy array containing the data from ``matrix``.
"""
if isinstance(matrix, _vtk.vtkMatrix3x3):
shape = (3, 3)
elif isinstance(matrix, _vtk.vtkMatrix4x4):
shape = (4, 4)
else:
raise TypeError(
'Expected vtk.vtkMatrix3x3 or vtk.vtkMatrix4x4 input,'
f' got {type(matrix).__name__} instead.',
)
array = np.zeros(shape)
for i, j in product(range(shape[0]), range(shape[1])):
array[i, j] = matrix.GetElement(i, j)
return array
def vtkmatrix_from_array(array):
"""Convert a ``numpy.ndarray`` or array-like to a vtk matrix.
Parameters
----------
array : array_like[float]
The array or array-like to be converted to a vtk matrix.
Shape (3, 3) gets converted to a ``vtk.vtkMatrix3x3``, shape (4, 4)
gets converted to a ``vtk.vtkMatrix4x4``. No other shapes are valid.
Returns
-------
vtk.vtkMatrix3x3 or vtk.vtkMatrix4x4
VTK matrix.
"""
array = np.asarray(array)
if array.shape == (3, 3):
matrix = _vtk.vtkMatrix3x3()
elif array.shape == (4, 4):
matrix = _vtk.vtkMatrix4x4()
else:
raise ValueError(f'Invalid shape {array.shape}, must be (3, 3) or (4, 4).')
m, n = array.shape
for i, j in product(range(m), range(n)):
matrix.SetElement(i, j, array[i, j])
return matrix
def set_default_active_vectors(mesh: pyvista.DataSet) -> None:
"""Set a default vectors array on mesh, if not already set.
If an active vector already exists, no changes are made.
If an active vectors does not exist, it checks for possibly cell
or point arrays with shape ``(n, 3)``. If only one exists, then
it is set as the active vectors. Otherwise, an error is raised.
Parameters
----------
mesh : pyvista.DataSet
Dataset to set default active vectors.
Raises
------
MissingDataError
If no vector-like arrays exist.
AmbiguousDataError
If more than one vector-like arrays exist.
"""
if mesh.active_vectors_name is not None:
return
point_data = mesh.point_data
cell_data = mesh.cell_data
possible_vectors_point = [
name for name, value in point_data.items() if value.ndim == 2 and value.shape[1] == 3
]
possible_vectors_cell = [
name for name, value in cell_data.items() if value.ndim == 2 and value.shape[1] == 3
]
possible_vectors = possible_vectors_point + possible_vectors_cell
n_possible_vectors = len(possible_vectors)
if n_possible_vectors == 1:
preference = 'point' if len(possible_vectors_point) == 1 else 'cell'
mesh.set_active_vectors(possible_vectors[0], preference=preference)
elif n_possible_vectors < 1:
raise MissingDataError("No vector-like data available.")
elif n_possible_vectors > 1:
raise AmbiguousDataError(
"Multiple vector-like data available\n"
f"cell data: {possible_vectors_cell}.\n"
f"point data: {possible_vectors_point}.\n"
"Set one as active using DataSet.set_active_vectors(name, preference=type)",
)
def set_default_active_scalars(mesh: pyvista.DataSet) -> None:
"""Set a default scalars array on mesh, if not already set.
If an active scalars already exists, no changes are made.
If an active scalars does not exist, it checks for point or cell
arrays. If only one exists, then it is set as the active scalars.
Otherwise, an error is raised.
Parameters
----------
mesh : pyvista.DataSet
Dataset to set default active scalars.
Raises
------
MissingDataError
If no arrays exist.
AmbiguousDataError
If more than one array exists.
"""
if mesh.active_scalars_name is not None:
return
point_data = mesh.point_data
cell_data = mesh.cell_data
possible_scalars_point = point_data.keys()
possible_scalars_cell = cell_data.keys()
possible_scalars = possible_scalars_point + possible_scalars_cell
n_possible_scalars = len(possible_scalars)
if n_possible_scalars == 1:
preference = 'point' if len(possible_scalars_point) == 1 else 'cell'
mesh.set_active_scalars(possible_scalars[0], preference=preference)
elif n_possible_scalars < 1:
raise MissingDataError("No data available.")
elif n_possible_scalars > 1:
raise AmbiguousDataError(
"Multiple data available\n"
f"cell data: {possible_scalars_cell}.\n"
f"point data: {possible_scalars_point}.\n"
"Set one as active using DataSet.set_active_scalars(name, preference=type)",
)
def _coerce_transformlike_arg(transform_like: TransformLike) -> NumpyArray[float]:
"""Check and coerce transform-like arg to a 4x4 numpy array.
Parameters
----------
transform_like : np.ndarray | vtkMatrix3x3 | vtkMatrix4x4 | vtkTransform
Transformation matrix as a 3x3 or 4x4 numpy array, vtkMatrix, or
from a vtkTransform.
Returns
-------
np.ndarray
4x4 transformation matrix.
"""
transform_array: NumpyArray[float] = np.eye(4)
if isinstance(transform_like, _vtk.vtkMatrix4x4):
transform_array = array_from_vtkmatrix(transform_like)
elif isinstance(transform_like, _vtk.vtkMatrix3x3):
transform_array[:3, :3] = array_from_vtkmatrix(transform_like)
elif isinstance(transform_like, _vtk.vtkTransform):
transform_array = array_from_vtkmatrix(transform_like.GetMatrix())
elif isinstance(transform_like, np.ndarray):
if transform_like.shape == (3, 3):
transform_array[:3, :3] = transform_like
elif transform_like.shape == (4, 4):
transform_array = transform_like
else:
raise ValueError('Transformation array must be 3x3 or 4x4.')
else:
raise TypeError(
'Input transform must be one of:\n'
'\tvtk.vtkMatrix4x4\n'
'\tvtk.vtkMatrix3x3\n'
'\tvtk.vtkTransform\n'
'\t4x4 np.ndarray\n'
'\t3x3 np.ndarray\n',
)
return transform_array
_JSONValueType = Union[
dict, # type: ignore[type-arg]
list, # type: ignore[type-arg]
tuple, # type: ignore[type-arg]
str,
int,
float,
bool,
None,
]
# TODO: add generic type annotations 'UserDict[str, _JSONValueType]'
# once support for Python 3.8 is dropped
class _SerializedDictArray(UserDict, _vtk.vtkStringArray): # type: ignore[type-arg]
"""Dict-like object with a JSON-serialized string array representation.
This class behaves just like a regular dict, except its contents
are represented internally as a JSON-formatted vtkStringArray.
The string array is updated dynamically any time the dict is
modified, such that modifying the dict will also implicitly modify
its JSON string representation.
Notes
-----
This class is intended for use as a dict with a small number of keys and
relatively small values, e.g. for storing metadata. It should not be
used to store frequently accessed array data with hundreds of entries.
"""
@property
def _string(self) -> str:
"""Get the vtkStringArray string."""
return ''.join([self.GetValue(i) for i in range(self.GetNumberOfValues())])
@_string.setter
def _string(self, str_: str):
"""Set the vtkStringArray to a specified string."""
self.SetNumberOfValues(0) # Clear string
for char in str_: # Populate string
self.InsertNextValue(char)
def _update_string(self):
"""Format dict data as JSON and update the vtkStringArray."""
data_str = json.dumps(self.data)
if data_str != self._string:
self._string = data_str
def __repr__(self):
"""Return JSON-formatted dict representation."""
return self._string
def __init__(self, dict_=None, /, **kwargs):
# Init from JSON string
if isinstance(dict_, str):
dict_ = json.loads(dict_)
# Init UserDict
super().__init__(dict_, **kwargs)
self._update_string()
# Flag self as a scalar string
# This is only needed so that the Field DatasetAttributes repr
# shows this array as `str`
_set_string_scalar_object_name(self)
# Override any/all `UserDict` or `MutableMapping` methods which mutate
# the dictionary. This ensures the serialized string is also updated
# and synced with the dict
def __setitem__(self, key, item):
super().__setitem__(key, item)
self._update_string()
def __delitem__(self, key):
super().__delitem__(key)
self._update_string()
def __setattr__(self, key, value):
object.__setattr__(self, key, value)
self._update_string() if key != '_string' else None
def update(self, *args, **kwargs):
super().update(*args, **kwargs)
self._update_string()
def popitem(self):
item = super().popitem()
self._update_string()
return item
def pop(self, __key):
item = super().pop(__key)
self._update_string()
return item
def clear(self):
super().clear()
self._update_string()
def setdefault(self, *args, **kwargs):
super().setdefault(*args, **kwargs)
self._update_string()