tidy3d.TriangularGridDataset

tidy3d.TriangularGridDataset#

class TriangularGridDataset[source]#

Bases: UnstructuredGridDataset

Dataset for storing triangular grid data. Data values are associated with the nodes of the grid.

Parameters:

  • attrs (dict = {}) – Dictionary storing arbitrary metadata for a Tidy3D object. This dictionary can be freely used by the user for storing data without affecting the operation of Tidy3D as it is not used internally. Note that, unlike regular Tidy3D fields, attrs are mutable. For example, the following is allowed for setting an attr obj.attrs['foo'] = bar. Also note that Tidy3D` will raise a TypeError if attrs contain objects that can not be serialized. One can check if attrs are serializable by calling obj.json().

  • points (PointDataArray) – Coordinates of points composing the unstructured grid.

  • values (IndexedDataArray) – Values stored at the grid points.

  • cells (CellDataArray) – Cells composing the unstructured grid specified as connections between grid points.

  • normal_axis (Literal[0, 1, 2]) – Orientation of the grid.

  • normal_pos (float) – Coordinate of the grid along the normal direction.

Note

To use full functionality of unstructured datasets one must install vtk package (pip install tidy3d[vtk] or pip install vtk). Otherwise the functionality of unstructured datasets is limited to creation, writing to/loading from a file, and arithmetic manipulations.

Example

>>> tri_grid_points = PointDataArray(
...     [[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]],
...     coords=dict(index=np.arange(4), axis=np.arange(2)),
... )
>>>
>>> tri_grid_cells = CellDataArray(
...     [[0, 1, 2], [1, 2, 3]],
...     coords=dict(cell_index=np.arange(2), vertex_index=np.arange(3)),
... )
>>>
>>> tri_grid_values = IndexedDataArray(
...     [1.0, 2.0, 3.0, 4.0], coords=dict(index=np.arange(4)),
... )
>>>
>>> tri_grid = TriangularGridDataset(
...     normal_axis=1,
...     normal_pos=0,
...     points=tri_grid_points,
...     cells=tri_grid_cells,
...     values=tri_grid_values,
... )

Attributes

bounds

Grid bounds.

attrs

Methods

does_cover(bounds)

Check whether data fully covers specified by bounds spatial region.

interp(x, y, z[, fill_value, use_vtk, ...])

Interpolate data at provided x, y, and z.

plane_slice(axis, pos)

Slice data with a plane and return the resulting line as a SpatialDataArray.

plot([ax, field, grid, cbar, cmap, vmin, ...])

Plot the data field and/or the unstructured grid.

reflect(axis, center[, reflection_only])

Reflect unstructured data across the plane define by parameters axis and center.

sel([x, y, z])

Extract/interpolate data along one or more Cartesian directions.

sel_inside(bounds)

Return a new TriangularGridDataset that contains the minimal amount data necessary to cover a spatial region defined by bounds.

Inherited Common Usage

normal_axis#
normal_pos#
property bounds#

Grid bounds.

plane_slice(axis, pos)[source]#

Slice data with a plane and return the resulting line as a SpatialDataArray.

Parameters:

  • axis (Axis) – The normal direction of the slicing plane.

  • pos (float) – Position of the slicing plane along its normal direction.

Returns:

The resulting slice.

Return type:

SpatialDataArray

plot(ax=None, field=True, grid=True, cbar=True, cmap='viridis', vmin=None, vmax=None, shading='gouraud', cbar_kwargs=None, pcolor_kwargs=None)[source]#

Plot the data field and/or the unstructured grid.

Parameters:

  • ax (matplotlib.axes._subplots.Axes = None) – matplotlib axes to plot on, if not specified, one is created.

  • field (bool = True) – Whether to plot the data field.

  • grid (bool = True) – Whether to plot the unstructured grid.

  • cbar (bool = True) – Display colorbar (only if field == True).

  • cmap (str = "viridis") – Color map to use for plotting.

  • vmin (float = None) – The lower bound of data range that the colormap covers. If None, they are inferred from the data and other keyword arguments.

  • vmax (float = None) – The upper bound of data range that the colormap covers. If None, they are inferred from the data and other keyword arguments.

  • shading (Literal["gourand", "flat"] = "gourand") – Type of shading to use when plotting the data field.

  • cbar_kwargs (Dict = {}) – Additional parameters passed to colorbar object.

Returns:

The supplied or created matplotlib axes.

Return type:

matplotlib.axes._subplots.Axes

interp(x, y, z, fill_value=None, use_vtk=False, method='linear', ignore_normal_pos=True, max_samples_per_step=10000, max_cells_per_step=10000, rel_tol=1e-06)[source]#

Interpolate data at provided x, y, and z. Note that data is assumed to be invariant along the dataset’s normal direction.

Parameters:

  • x (Union[float, ArrayLike]) – x-coordinates of sampling points.

  • y (Union[float, ArrayLike]) – y-coordinates of sampling points.

  • z (Union[float, ArrayLike]) – z-coordinates of sampling points.

  • fill_value (Union[float, Literal["extrapolate"]] = 0) – Value to use when filling points without interpolated values. If "extrapolate" then nearest values are used. Note: in a future version the default value will be changed to "extrapolate".

  • use_vtk (bool = False) – Use vtk’s interpolation functionality or Tidy3D’s own implementation. Note: this option will be removed in a future version.

  • method (Literal["linear", "nearest"] = "linear") – Interpolation method to use.

  • ignore_normal_pos (bool = True) – (Depreciated) Assume data is invariant along the normal direction to the grid plane.

  • max_samples_per_step (int = 1e4) – Max number of points to interpolate at per iteration (used only if use_vtk=False). Using a higher number may speed up calculations but, at the same time, it increases RAM usage.

  • max_cells_per_step (int = 1e4) – Max number of cells to interpolate from per iteration (used only if use_vtk=False). Using a higher number may speed up calculations but, at the same time, it increases RAM usage.

  • rel_tol (float = 1e-6) – Relative tolerance when determining whether a point belongs to a cell.

Returns:

Interpolated data.

Return type:

SpatialDataArray

sel(x=None, y=None, z=None)[source]#

Extract/interpolate data along one or more Cartesian directions. At least of x, y, and z must be provided.

Parameters:

  • x (Union[float, ArrayLike] = None) – x-coordinate of the slice.

  • y (Union[float, ArrayLike] = None) – y-coordinate of the slice.

  • z (Union[float, ArrayLike] = None) – z-coordinate of the slice.

Returns:

Extracted data.

Return type:

SpatialDataArray

reflect(axis, center, reflection_only=False)[source]#

Reflect unstructured data across the plane define by parameters axis and center. By default the original data is preserved, setting reflection_only to True will produce only deflected data.

Parameters:

  • axis (Literal[0, 1, 2]) – Normal direction of the reflection plane.

  • center (float) – Location of the reflection plane along its normal direction.

  • reflection_only (bool = False) – Return only reflected data.

Returns:

Data after reflextion is performed.

Return type:

UnstructuredGridDataset

sel_inside(bounds)[source]#

Return a new TriangularGridDataset that contains the minimal amount data necessary to cover a spatial region defined by bounds.

Parameters:

bounds (Tuple[float, float, float], Tuple[float, float float]) – Min and max bounds packaged as (minx, miny, minz), (maxx, maxy, maxz).

Returns:

Extracted spatial data array.

Return type:

TriangularGridDataset

does_cover(bounds)[source]#

Check whether data fully covers specified by bounds spatial region. If data contains only one point along a given direction, then it is assumed the data is constant along that direction and coverage is not checked.

Parameters:

bounds (Tuple[float, float, float], Tuple[float, float float]) – Min and max bounds packaged as (minx, miny, minz), (maxx, maxy, maxz).

Returns:

Full cover check outcome.

Return type:

bool

__hash__()#

Hash method.