pyvista.DataSetFilters.contour#

DataSetFilters.contour(isosurfaces=10, scalars=None, compute_normals=False, compute_gradients=False, compute_scalars=True, rng=None, preference='point', method='contour', progress_bar=False)[source]#

Contour an input self by an array.

isosurfaces can be an integer specifying the number of isosurfaces in the data range or a sequence of values for explicitly setting the isosurfaces.

Parameters:
isosurfacesint | sequence[float], optional

Number of isosurfaces to compute across valid data range or a sequence of float values to explicitly use as the isosurfaces.

scalarsstr | array_like[float], optional

Name or array of scalars to threshold on. If this is an array, the output of this filter will save them as "Contour Data". Defaults to currently active scalars.

compute_normalsbool, default: False

Compute normals for the dataset.

compute_gradientsbool, default: False

Compute gradients for the dataset.

compute_scalarsbool, default: True

Preserves the scalar values that are being contoured.

rngsequence[float], optional

If an integer number of isosurfaces is specified, this is the range over which to generate contours. Default is the scalars array’s full data range.

preferencestr, default: “point”

When scalars is specified, this is the preferred array type to search for in the dataset. Must be either 'point' or 'cell'.

methodstr, default: “contour”

Specify to choose which vtk filter is used to create the contour. Must be one of 'contour', 'marching_cubes' and 'flying_edges'.

progress_barbool, default: False

Display a progress bar to indicate progress.

Returns:
pyvista.PolyData

Contoured surface.

Examples

Generate contours for the random hills dataset.

>>> from pyvista import examples
>>> hills = examples.load_random_hills()
>>> contours = hills.contour()
>>> contours.plot(line_width=5)
https://d33wubrfki0l68.cloudfront.net/dc8bc0f0f915005e42787b35eb17d68059dc032a/f589d/_images/pyvista-datasetfilters-contour-1_00_00.png

Generate the surface of a mobius strip using flying edges.

>>> import pyvista as pv
>>> a = 0.4
>>> b = 0.1
>>> def f(x, y, z):
...     xx = x * x
...     yy = y * y
...     zz = z * z
...     xyz = x * y * z
...     xx_yy = xx + yy
...     a_xx = a * xx
...     b_yy = b * yy
...     return (
...         (xx_yy + 1) * (a_xx + b_yy)
...         + zz * (b * xx + a * yy)
...         - 2 * (a - b) * xyz
...         - a * b * xx_yy
...     ) ** 2 - 4 * (xx + yy) * (a_xx + b_yy - xyz * (a - b)) ** 2
...
>>> n = 100
>>> x_min, y_min, z_min = -1.35, -1.7, -0.65
>>> grid = pv.ImageData(
...     dimensions=(n, n, n),
...     spacing=(
...         abs(x_min) / n * 2,
...         abs(y_min) / n * 2,
...         abs(z_min) / n * 2,
...     ),
...     origin=(x_min, y_min, z_min),
... )
>>> x, y, z = grid.points.T
>>> values = f(x, y, z)
>>> out = grid.contour(
...     1,
...     scalars=values,
...     rng=[0, 0],
...     method='flying_edges',
... )
>>> out.plot(color='lightblue', smooth_shading=True)
https://d33wubrfki0l68.cloudfront.net/8b77a6f9da6fc931443e13f587514894106dc191/151f0/_images/pyvista-datasetfilters-contour-1_01_00.png

See Using Common Filters or Marching Cubes for more examples using this filter.