pyvista.DataObjectFilters.point_data_to_cell_data#
- DataObjectFilters.point_data_to_cell_data( )[source]#
Transform point data into cell data.
Point data are specified per node and cell data specified within cells. Optionally, the input point data can be passed through to the output.
- Parameters:
- pass_point_databool, default:
False
If enabled, pass the input point data through to the output.
- categoricalbool, default:
False
Control whether the source point data is to be treated as categorical. If
True
, histograming is used to assign the cell data. Specifically, a histogram is populated for each cell from the scalar values at each point, and the bin with the most elements is selected. In case of a tie, the smaller value is selected.Note
If the point data is continuous, values that are almost equal (within
1e-6
) are merged into a single bin. Otherwise, for discrete data the number of bins equals the number of unique values.- progress_barbool, default:
False
Display a progress bar to indicate progress.
- pass_point_databool, default:
- Returns:
DataSet
|MultiBlock
Dataset with the point data transformed into cell data. Return type matches input.
See also
cell_data_to_point_data
Similar transformation applied to cell data.
points_to_cells()
Re-mesh
ImageData
to a cells-based representation.
Examples
Color cells by their z coordinates. First, create point scalars based on z-coordinates of a sample sphere mesh. Then convert this point data to cell data. Use a low resolution sphere for emphasis of cell valued data.
First, plot these values as point values to show the difference between point and cell data.
>>> import pyvista as pv >>> sphere = pv.Sphere(theta_resolution=10, phi_resolution=10) >>> sphere['Z Coordinates'] = sphere.points[:, 2] >>> sphere.plot()
Now, convert these values to cell data and then plot it.
>>> import pyvista as pv >>> sphere = pv.Sphere(theta_resolution=10, phi_resolution=10) >>> sphere['Z Coordinates'] = sphere.points[:, 2] >>> sphere = sphere.point_data_to_cell_data() >>> sphere.plot()