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Split up VTKHDF classes into seperate files

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nmannall
2024-11-08 13:03:09 +00:00
父节点 24863a1b9f
当前提交 237c3bae4a
共有 4 个文件被更改，包括 360 次插入 和 339 次删除
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4
gprMax/geometry_outputs.py
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@@ -29,7 +29,9 @@ from tqdm import tqdm
import gprMax.config as config
from gprMax.grid.fdtd_grid import FDTDGrid
from gprMax.vtkhdf import VtkCellType, VtkImageData, VtkUnstructuredGrid
from gprMax.vtkhdf_filehandlers.vtk_image_data import VtkImageData
from gprMax.vtkhdf_filehandlers.vtk_unstructured_grid import VtkUnstructuredGrid
from gprMax.vtkhdf_filehandlers.vtkhdf import VtkCellType
from ._version import __version__
from .cython.geometry_outputs import get_line_properties

184
gprMax/vtkhdf_filehandlers/vtk_image_data.py 普通文件
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@@ -0,0 +1,184 @@
from os import PathLike
from typing import Literal, Optional, Union
import numpy as np
import numpy.typing as npt
from mpi4py import MPI
from gprMax.vtkhdf_filehandlers.vtkhdf import VtkHdfFile
class VtkImageData(VtkHdfFile):
"""File handler for creating a VTKHDF Image Data file.
File format information is available here:
https://docs.vtk.org/en/latest/design_documents/VTKFileFormats.html#image-data
"""
DIRECTION_ATTR = "Direction"
ORIGIN_ATTR = "Origin"
SPACING_ATTR = "Spacing"
WHOLE_EXTENT_ATTR = "WholeExtent"
DIMENSIONS = 3
@property
def TYPE(self) -> Literal["ImageData"]:
return "ImageData"
def __init__(
self,
filename: Union[str, PathLike],
shape: npt.NDArray[np.intc],
origin: Optional[npt.NDArray[np.single]] = None,
spacing: Optional[npt.NDArray[np.single]] = None,
direction: Optional[npt.NDArray[np.single]] = None,
comm: Optional[MPI.Comm] = None,
):
"""Create a new VtkImageData file.
If the file already exists, it will be overriden. Required
attributes (Type and Version) will be written to the file.
The file will be opened using the 'mpio' h5py driver if an MPI
communicator is provided.
Args:
filename: Name of the file (can be a file path). The file
extension will be set to '.vtkhdf'.
shape: Shape of the image data to be stored in the file.
This specifies the number of cells. Image data can be
1D, 2D, or 3D.
origin (optional): Origin of the image data. Default
[0, 0, 0].
spacing (optional): Discritisation of the image data.
Default [1, 1, 1].
direction (optional): Array of direction vectors for each
dimension of the image data. Can be a flattened array.
I.e. [[1, 0, 0], [0, 1, 0], [0, 0, 1]] and
[1, 0, 0, 0, 1, 0, 0, 0, 1] are equivalent. Default
[[1, 0, 0], [0, 1, 0], [0, 0, 1]].
comm (optional): MPI communicator containing all ranks that
want to write to the file.
"""
super().__init__(filename, comm)
if len(shape) == 0:
raise ValueError(f"Shape must not be empty.")
if len(shape) > self.DIMENSIONS:
raise ValueError(f"Shape must not have more than {self.DIMENSIONS} dimensions.")
elif len(shape) < self.DIMENSIONS:
shape = np.concatenate((shape, np.ones(self.DIMENSIONS - len(shape), dtype=np.intc)))
self.shape = shape
whole_extent = np.zeros(2 * self.DIMENSIONS, dtype=np.intc)
whole_extent[1::2] = self.shape
self._set_root_attribute(self.WHOLE_EXTENT_ATTR, whole_extent)
if origin is None:
origin = np.zeros(self.DIMENSIONS, dtype=np.single)
self.set_origin(origin)
if spacing is None:
spacing = np.ones(self.DIMENSIONS, dtype=np.single)
self.set_spacing(spacing)
if direction is None:
direction = np.diag(np.ones(self.DIMENSIONS, dtype=np.single))
self.set_direction(direction)
@property
def whole_extent(self) -> npt.NDArray[np.intc]:
return self._get_root_attribute(self.WHOLE_EXTENT_ATTR)
@property
def origin(self) -> npt.NDArray[np.single]:
return self._get_root_attribute(self.ORIGIN_ATTR)
@property
def spacing(self) -> npt.NDArray[np.single]:
return self._get_root_attribute(self.SPACING_ATTR)
@property
def direction(self) -> npt.NDArray[np.single]:
return self._get_root_attribute(self.DIRECTION_ATTR)
def set_origin(self, origin: npt.NDArray[np.single]):
"""Set the origin coordinate of the image data.
Args:
origin: x, y, z coordinates to set as the origin.
"""
if len(origin) != self.DIMENSIONS:
raise ValueError(f"Origin attribute must have {self.DIMENSIONS} dimensions.")
self._set_root_attribute(self.ORIGIN_ATTR, origin)
def set_spacing(self, spacing: npt.NDArray[np.single]):
"""Set the discritisation of the image data.
Args:
spacing: Discritisation of the x, y, and z dimensions.
"""
if len(spacing) != self.DIMENSIONS:
raise ValueError(f"Spacing attribute must have {self.DIMENSIONS} dimensions.")
self._set_root_attribute(self.SPACING_ATTR, spacing)
def set_direction(self, direction: npt.NDArray[np.single]):
"""Set the coordinate system of the image data.
Args:
direction: Array of direction vectors for each dimension of
the image data. Can be a flattened array. I.e.
[[1, 0, 0], [0, 1, 0], [0, 0, 1]] and
[1, 0, 0, 0, 1, 0, 0, 0, 1] are equivalent.
"""
direction = direction.flatten()
if len(direction) != self.DIMENSIONS * self.DIMENSIONS:
raise ValueError(
f"Direction array must contain {self.DIMENSIONS * self.DIMENSIONS} elements."
)
self._set_root_attribute(self.DIRECTION_ATTR, direction)
def add_point_data(
self, name: str, data: npt.NDArray, offset: Optional[npt.NDArray[np.intc]] = None
):
"""Add point data to the VTKHDF file.
Args:
name: Name of the dataset.
data: Data to be saved.
offset (optional): Offset to store the provided data at. Can
be omitted if data provides the full dataset.
Raises:
ValueError: Raised if data has invalid dimensions.
"""
points_shape = self.shape + 1
if offset is None and any(data.shape != points_shape): # type: ignore
raise ValueError(
"If no offset is specified, data.shape must be one larger in each dimension than"
f" this vtkImageData object. {data.shape} != {points_shape}"
)
return super().add_point_data(name, data, points_shape, offset)
def add_cell_data(
self, name: str, data: npt.NDArray, offset: Optional[npt.NDArray[np.intc]] = None
):
"""Add cell data to the VTKHDF file.
Args:
name: Name of the dataset.
data: Data to be saved.
offset (optional): Offset to store the provided data at. Can
be omitted if data provides the full dataset.
Raises:
ValueError: Raised if data has invalid dimensions.
"""
if offset is None and any(data.shape != self.shape): # type: ignore
raise ValueError(
"If no offset is specified, data.shape must match the dimensions of this"
f" VtkImageData object. {data.shape} != {self.shape}"
)
return super().add_cell_data(name, data, self.shape, offset)

172
gprMax/vtkhdf_filehandlers/vtk_unstructured_grid.py 普通文件
查看文件

@@ -0,0 +1,172 @@
import logging
from os import PathLike
from typing import Literal, Optional, Union
import numpy as np
import numpy.typing as npt
from mpi4py import MPI
from gprMax.vtkhdf_filehandlers.vtkhdf import VtkCellType, VtkHdfFile
logger = logging.getLogger(__name__)
class VtkUnstructuredGrid(VtkHdfFile):
"""File handler for creating a VTKHDF Unstructured Grid file.
File format information is available here:
https://docs.vtk.org/en/latest/design_documents/VTKFileFormats.html#unstructured-grid
"""
class Dataset(VtkHdfFile.Dataset):
CONNECTIVITY = "Connectivity"
NUMBER_OF_CELLS = "NumberOfCells"
NUMBER_OF_CONNECTIVITY_IDS = "NumberOfConnectivityIds"
NUMBER_OF_POINTS = "NumberOfPoints"
OFFSETS = "Offsets"
POINTS = "Points"
TYPES = "Types"
@property
def TYPE(self) -> Literal["UnstructuredGrid"]:
return "UnstructuredGrid"
def __init__(
self,
filename: Union[str, PathLike],
points: npt.NDArray,
cell_types: npt.NDArray[VtkCellType],
connectivity: npt.NDArray,
cell_offsets: npt.NDArray,
comm: Optional[MPI.Comm] = None,
) -> None:
"""Create a new VtkUnstructuredGrid file.
An unstructured grid has N points and C cells. A cell is defined
as a collection of points which is specified by the connectivity
and cell_offsets arguments along with the list of cell_types.
If the file already exists, it will be overriden. Required
attributes (Type and Version) will be written to the file.
The file will be opened using the 'mpio' h5py driver if an MPI
communicator is provided.
Args:
filename: Name of the file (can be a file path). The file
extension will be set to '.vtkhdf'.
points: Array of point coordinates of shape (N, 3).
cell_types: Array of VTK cell types of shape (C,).
connectivity: Array of point IDs that together with
cell_offsets, defines the points that make up each cell.
Each point ID has a value between 0 and (N-1) inclusive
and corresponds to a point in the points array.
cell_offsets: Array listing where each cell starts and ends
in the connectivity array. It has shape (C + 1,).
comm (optional): MPI communicator containing all ranks that
want to write to the file.
Raises:
Value Error: Raised if argument dimensions are invalid.
"""
super().__init__(filename, comm)
if len(cell_offsets) != len(cell_types) + 1:
raise ValueError(
"cell_offsets should be one longer than cell_types."
" I.e. one longer than the number of cells"
)
is_sorted = lambda a: np.all(a[:-1] <= a[1:])
if not is_sorted(cell_offsets):
raise ValueError("cell_offsets should be sorted in ascending order")
if len(connectivity) < cell_offsets[-1]:
raise ValueError("Connectivity array is shorter than final cell_offsets value")
elif len(connectivity) > cell_offsets[-1]:
raise logger.warning(
"Connectivity array longer than final cell_offsets value."
" Some connectivity data will be ignored"
)
self._write_root_dataset(self.Dataset.CONNECTIVITY, connectivity)
self._write_root_dataset(self.Dataset.NUMBER_OF_CELLS, len(cell_types))
self._write_root_dataset(self.Dataset.NUMBER_OF_CONNECTIVITY_IDS, len(connectivity))
self._write_root_dataset(self.Dataset.NUMBER_OF_POINTS, len(points))
self._write_root_dataset(self.Dataset.OFFSETS, cell_offsets)
self._write_root_dataset(self.Dataset.POINTS, points, xyz_data_ordering=False)
self._write_root_dataset(self.Dataset.TYPES, cell_types)
@property
def number_of_cells(self) -> int:
number_of_cells = self._get_root_dataset(self.Dataset.NUMBER_OF_CELLS)
return np.sum(number_of_cells, dtype=np.intc)
@property
def number_of_connectivity_ids(self) -> int:
number_of_connectivity_ids = self._get_root_dataset(self.Dataset.NUMBER_OF_CONNECTIVITY_IDS)
return np.sum(number_of_connectivity_ids, dtype=np.intc)
@property
def number_of_points(self) -> int:
number_of_points = self._get_root_dataset(self.Dataset.NUMBER_OF_POINTS)
return np.sum(number_of_points, dtype=np.intc)
def add_point_data(
self, name: str, data: npt.NDArray, offset: Optional[npt.NDArray[np.intc]] = None
):
"""Add point data to the VTKHDF file.
Args:
name: Name of the dataset.
data: Data to be saved.
offset (optional): Offset to store the provided data at. Can
be omitted if data provides the full dataset.
Raises:
ValueError: Raised if data has invalid dimensions.
"""
shape = np.array(data.shape)
number_of_dimensions = len(shape)
if number_of_dimensions < 1 or number_of_dimensions > 2:
raise ValueError(f"Data must have 1 or 2 dimensions, not {number_of_dimensions}")
elif len(data) != self.number_of_points:
raise ValueError(
"Length of data must match the number of points in the vtkUnstructuredGrid."
f" {len(data)} != {self.number_of_points}"
)
elif number_of_dimensions == 2 and shape[1] != 1 and shape[1] != 3:
raise ValueError(f"The second dimension should have shape 1 or 3, not {shape[1]}")
return super().add_point_data(name, data, shape, offset)
def add_cell_data(
self, name: str, data: npt.NDArray, offset: Optional[npt.NDArray[np.intc]] = None
):
"""Add cell data to the VTKHDF file.
Args:
name: Name of the dataset.
data: Data to be saved.
offset (optional): Offset to store the provided data at. Can
be omitted if data provides the full dataset.
Raises:
ValueError: Raised if data has invalid dimensions.
"""
shape = np.array(data.shape)
number_of_dimensions = len(shape)
if number_of_dimensions < 1 or number_of_dimensions > 2:
raise ValueError(f"Data must have 1 or 2 dimensions, not {number_of_dimensions}.")
elif len(data) != self.number_of_cells:
raise ValueError(
"Length of data must match the number of cells in the vtkUnstructuredGrid."
f" {len(data)} != {self.number_of_cells}"
)
elif number_of_dimensions == 2 and shape[1] != 1 and shape[1] != 3:
raise ValueError(f"The second dimension should have shape 1 or 3, not {shape[1]}")
return super().add_cell_data(name, data, shape, offset)

339
gprMax/vtkhdf_filehandlers/vtkhdf.py
查看文件

@@ -5,7 +5,7 @@ from enum import Enum
from os import PathLike
from pathlib import Path
from types import TracebackType
from typing import Literal, Optional, Union
from typing import Optional, Union
import h5py
import numpy as np
@@ -345,182 +345,6 @@ class VtkHdfFile(AbstractContextManager):
self._write_dataset(dataset_path, data, shape, offset)
class VtkImageData(VtkHdfFile):
"""File handler for creating a VTKHDF Image Data file.
File format information is available here:
https://docs.vtk.org/en/latest/design_documents/VTKFileFormats.html#image-data
"""
DIRECTION_ATTR = "Direction"
ORIGIN_ATTR = "Origin"
SPACING_ATTR = "Spacing"
WHOLE_EXTENT_ATTR = "WholeExtent"
DIMENSIONS = 3
@property
def TYPE(self) -> Literal["ImageData"]:
return "ImageData"
def __init__(
self,
filename: Union[str, PathLike],
shape: npt.NDArray[np.intc],
origin: Optional[npt.NDArray[np.single]] = None,
spacing: Optional[npt.NDArray[np.single]] = None,
direction: Optional[npt.NDArray[np.single]] = None,
comm: Optional[MPI.Comm] = None,
):
"""Create a new VtkImageData file.
If the file already exists, it will be overriden. Required
attributes (Type and Version) will be written to the file.
The file will be opened using the 'mpio' h5py driver if an MPI
communicator is provided.
Args:
filename: Name of the file (can be a file path). The file
extension will be set to '.vtkhdf'.
shape: Shape of the image data to be stored in the file.
This specifies the number of cells. Image data can be
1D, 2D, or 3D.
origin (optional): Origin of the image data. Default
[0, 0, 0].
spacing (optional): Discritisation of the image data.
Default [1, 1, 1].
direction (optional): Array of direction vectors for each
dimension of the image data. Can be a flattened array.
I.e. [[1, 0, 0], [0, 1, 0], [0, 0, 1]] and
[1, 0, 0, 0, 1, 0, 0, 0, 1] are equivalent. Default
[[1, 0, 0], [0, 1, 0], [0, 0, 1]].
comm (optional): MPI communicator containing all ranks that
want to write to the file.
"""
super().__init__(filename, comm)
if len(shape) == 0:
raise ValueError(f"Shape must not be empty.")
if len(shape) > self.DIMENSIONS:
raise ValueError(f"Shape must not have more than {self.DIMENSIONS} dimensions.")
elif len(shape) < self.DIMENSIONS:
shape = np.concatenate((shape, np.ones(self.DIMENSIONS - len(shape), dtype=np.intc)))
self.shape = shape
whole_extent = np.zeros(2 * self.DIMENSIONS, dtype=np.intc)
whole_extent[1::2] = self.shape
self._set_root_attribute(self.WHOLE_EXTENT_ATTR, whole_extent)
if origin is None:
origin = np.zeros(self.DIMENSIONS, dtype=np.single)
self.set_origin(origin)
if spacing is None:
spacing = np.ones(self.DIMENSIONS, dtype=np.single)
self.set_spacing(spacing)
if direction is None:
direction = np.diag(np.ones(self.DIMENSIONS, dtype=np.single))
self.set_direction(direction)
@property
def whole_extent(self) -> npt.NDArray[np.intc]:
return self._get_root_attribute(self.WHOLE_EXTENT_ATTR)
@property
def origin(self) -> npt.NDArray[np.single]:
return self._get_root_attribute(self.ORIGIN_ATTR)
@property
def spacing(self) -> npt.NDArray[np.single]:
return self._get_root_attribute(self.SPACING_ATTR)
@property
def direction(self) -> npt.NDArray[np.single]:
return self._get_root_attribute(self.DIRECTION_ATTR)
def set_origin(self, origin: npt.NDArray[np.single]):
"""Set the origin coordinate of the image data.
Args:
origin: x, y, z coordinates to set as the origin.
"""
if len(origin) != self.DIMENSIONS:
raise ValueError(f"Origin attribute must have {self.DIMENSIONS} dimensions.")
self._set_root_attribute(self.ORIGIN_ATTR, origin)
def set_spacing(self, spacing: npt.NDArray[np.single]):
"""Set the discritisation of the image data.
Args:
spacing: Discritisation of the x, y, and z dimensions.
"""
if len(spacing) != self.DIMENSIONS:
raise ValueError(f"Spacing attribute must have {self.DIMENSIONS} dimensions.")
self._set_root_attribute(self.SPACING_ATTR, spacing)
def set_direction(self, direction: npt.NDArray[np.single]):
"""Set the coordinate system of the image data.
Args:
direction: Array of direction vectors for each dimension of
the image data. Can be a flattened array. I.e.
[[1, 0, 0], [0, 1, 0], [0, 0, 1]] and
[1, 0, 0, 0, 1, 0, 0, 0, 1] are equivalent.
"""
direction = direction.flatten()
if len(direction) != self.DIMENSIONS * self.DIMENSIONS:
raise ValueError(
f"Direction array must contain {self.DIMENSIONS * self.DIMENSIONS} elements."
)
self._set_root_attribute(self.DIRECTION_ATTR, direction)
def add_point_data(
self, name: str, data: npt.NDArray, offset: Optional[npt.NDArray[np.intc]] = None
):
"""Add point data to the VTKHDF file.
Args:
name: Name of the dataset.
data: Data to be saved.
offset (optional): Offset to store the provided data at. Can
be omitted if data provides the full dataset.
Raises:
ValueError: Raised if data has invalid dimensions.
"""
points_shape = self.shape + 1
if offset is None and any(data.shape != points_shape): # type: ignore
raise ValueError(
"If no offset is specified, data.shape must be one larger in each dimension than"
f" this vtkImageData object. {data.shape} != {points_shape}"
)
return super().add_point_data(name, data, points_shape, offset)
def add_cell_data(
self, name: str, data: npt.NDArray, offset: Optional[npt.NDArray[np.intc]] = None
):
"""Add cell data to the VTKHDF file.
Args:
name: Name of the dataset.
data: Data to be saved.
offset (optional): Offset to store the provided data at. Can
be omitted if data provides the full dataset.
Raises:
ValueError: Raised if data has invalid dimensions.
"""
if offset is None and any(data.shape != self.shape): # type: ignore
raise ValueError(
"If no offset is specified, data.shape must match the dimensions of this"
f" VtkImageData object. {data.shape} != {self.shape}"
)
return super().add_cell_data(name, data, self.shape, offset)
class VtkCellType(np.uint8, Enum):
"""VTK cell types as defined here:
https://vtk.org/doc/nightly/html/vtkCellType_8h_source.html#l00019
@@ -544,164 +368,3 @@ class VtkCellType(np.uint8, Enum):
PYRAMID = 14
PENTAGONAL_PRISM = 15
HEXAGONAL_PRISM = 16
class VtkUnstructuredGrid(VtkHdfFile):
"""File handler for creating a VTKHDF Unstructured Grid file.
File format information is available here:
https://docs.vtk.org/en/latest/design_documents/VTKFileFormats.html#unstructured-grid
"""
class Dataset(VtkHdfFile.Dataset):
CONNECTIVITY = "Connectivity"
NUMBER_OF_CELLS = "NumberOfCells"
NUMBER_OF_CONNECTIVITY_IDS = "NumberOfConnectivityIds"
NUMBER_OF_POINTS = "NumberOfPoints"
OFFSETS = "Offsets"
POINTS = "Points"
TYPES = "Types"
@property
def TYPE(self) -> Literal["UnstructuredGrid"]:
return "UnstructuredGrid"
def __init__(
self,
filename: Union[str, PathLike],
points: npt.NDArray,
cell_types: npt.NDArray[VtkCellType],
connectivity: npt.NDArray,
cell_offsets: npt.NDArray,
comm: Optional[MPI.Comm] = None,
) -> None:
"""Create a new VtkUnstructuredGrid file.
An unstructured grid has N points and C cells. A cell is defined
as a collection of points which is specified by the connectivity
and cell_offsets arguments along with the list of cell_types.
If the file already exists, it will be overriden. Required
attributes (Type and Version) will be written to the file.
The file will be opened using the 'mpio' h5py driver if an MPI
communicator is provided.
Args:
filename: Name of the file (can be a file path). The file
extension will be set to '.vtkhdf'.
points: Array of point coordinates of shape (N, 3).
cell_types: Array of VTK cell types of shape (C,).
connectivity: Array of point IDs that together with
cell_offsets, defines the points that make up each cell.
Each point ID has a value between 0 and (N-1) inclusive
and corresponds to a point in the points array.
cell_offsets: Array listing where each cell starts and ends
in the connectivity array. It has shape (C + 1,).
comm (optional): MPI communicator containing all ranks that
want to write to the file.
Raises:
Value Error: Raised if argument dimensions are invalid.
"""
super().__init__(filename, comm)
if len(cell_offsets) != len(cell_types) + 1:
raise ValueError(
"cell_offsets should be one longer than cell_types."
" I.e. one longer than the number of cells"
)
is_sorted = lambda a: np.all(a[:-1] <= a[1:])
if not is_sorted(cell_offsets):
raise ValueError("cell_offsets should be sorted in ascending order")
if len(connectivity) < cell_offsets[-1]:
raise ValueError("Connectivity array is shorter than final cell_offsets value")
elif len(connectivity) > cell_offsets[-1]:
raise logger.warning(
"Connectivity array longer than final cell_offsets value."
" Some connectivity data will be ignored"
)
self._write_root_dataset(self.Dataset.CONNECTIVITY, connectivity)
self._write_root_dataset(self.Dataset.NUMBER_OF_CELLS, len(cell_types))
self._write_root_dataset(self.Dataset.NUMBER_OF_CONNECTIVITY_IDS, len(connectivity))
self._write_root_dataset(self.Dataset.NUMBER_OF_POINTS, len(points))
self._write_root_dataset(self.Dataset.OFFSETS, cell_offsets)
self._write_root_dataset(self.Dataset.POINTS, points, xyz_data_ordering=False)
self._write_root_dataset(self.Dataset.TYPES, cell_types)
@property
def number_of_cells(self) -> int:
number_of_cells = self._get_root_dataset(self.Dataset.NUMBER_OF_CELLS)
return np.sum(number_of_cells, dtype=np.intc)
@property
def number_of_connectivity_ids(self) -> int:
number_of_connectivity_ids = self._get_root_dataset(self.Dataset.NUMBER_OF_CONNECTIVITY_IDS)
return np.sum(number_of_connectivity_ids, dtype=np.intc)
@property
def number_of_points(self) -> int:
number_of_points = self._get_root_dataset(self.Dataset.NUMBER_OF_POINTS)
return np.sum(number_of_points, dtype=np.intc)
def add_point_data(
self, name: str, data: npt.NDArray, offset: Optional[npt.NDArray[np.intc]] = None
):
"""Add point data to the VTKHDF file.
Args:
name: Name of the dataset.
data: Data to be saved.
offset (optional): Offset to store the provided data at. Can
be omitted if data provides the full dataset.
Raises:
ValueError: Raised if data has invalid dimensions.
"""
shape = np.array(data.shape)
number_of_dimensions = len(shape)
if number_of_dimensions < 1 or number_of_dimensions > 2:
raise ValueError(f"Data must have 1 or 2 dimensions, not {number_of_dimensions}")
elif len(data) != self.number_of_points:
raise ValueError(
"Length of data must match the number of points in the vtkUnstructuredGrid."
f" {len(data)} != {self.number_of_points}"
)
elif number_of_dimensions == 2 and shape[1] != 1 and shape[1] != 3:
raise ValueError(f"The second dimension should have shape 1 or 3, not {shape[1]}")
return super().add_point_data(name, data, shape, offset)
def add_cell_data(
self, name: str, data: npt.NDArray, offset: Optional[npt.NDArray[np.intc]] = None
):
"""Add cell data to the VTKHDF file.
Args:
name: Name of the dataset.
data: Data to be saved.
offset (optional): Offset to store the provided data at. Can
be omitted if data provides the full dataset.
Raises:
ValueError: Raised if data has invalid dimensions.
"""
shape = np.array(data.shape)
number_of_dimensions = len(shape)
if number_of_dimensions < 1 or number_of_dimensions > 2:
raise ValueError(f"Data must have 1 or 2 dimensions, not {number_of_dimensions}.")
elif len(data) != self.number_of_cells:
raise ValueError(
"Length of data must match the number of cells in the vtkUnstructuredGrid."
f" {len(data)} != {self.number_of_cells}"
)
elif number_of_dimensions == 2 and shape[1] != 1 and shape[1] != 3:
raise ValueError(f"The second dimension should have shape 1 or 3, not {shape[1]}")
return super().add_cell_data(name, data, shape, offset)