pyvista.DataSetFilters.plot_over_circular_arc#

DataSetFilters.plot_over_circular_arc(pointa, pointb, center, resolution=None, scalars=None, title=None, ylabel=None, figsize=None, figure=True, show=True, tolerance=None, fname=None, progress_bar=False)[source]#

Sample a dataset along a circular arc and plot it.

Plot the variables of interest in 2D where the X-axis is distance from Point A and the Y-axis is the variable of interest. Note that this filter returns None.

Parameters:

pointasequence[float]: Location in [x, y, z].
pointbsequence[float]: Location in [x, y, z].
centersequence[float]: Location in [x, y, z].
resolutionint, optional: Number of pieces to divide the circular arc into. Defaults to number of cells in the input mesh. Must be a positive integer.
scalarsstr, optional: The string name of the variable in the input dataset to probe. The active scalar is used by default.
titlestr, optional: The string title of the matplotlib figure.
ylabelstr, optional: The string label of the Y-axis. Defaults to the variable name.
figsizetuple(int), optional: The size of the new figure.
figurebool, default: True: Flag on whether or not to create a new figure.
showbool, default: True: Shows the matplotlib figure when True.
tolerancefloat, optional: Tolerance used to compute whether a point in the source is in a cell of the input. If not given, tolerance is automatically generated.
fnamestr, optional: Save the figure this file name when set.
progress_barbool, default: False: Display a progress bar to indicate progress.

Examples

Sample a dataset along a high resolution circular arc and plot.

>>> from pyvista import examples
>>> mesh = examples.load_uniform()
>>> a = [mesh.bounds[0], mesh.bounds[2], mesh.bounds[5]]
>>> b = [mesh.bounds[1], mesh.bounds[2], mesh.bounds[4]]
>>> center = [mesh.bounds[0], mesh.bounds[2], mesh.bounds[4]]
>>> mesh.plot_over_circular_arc(
...     a, b, center, resolution=1000, show=False
... )  