def test_streamlines():
mesh = examples.download_carotid()
stream, src = mesh.streamlines(return_source=True, max_time=100.0,
initial_step_length=2., terminal_speed=0.1,
n_points=25, source_radius=2.0,
source_center=(133.1, 116.3, 5.0) )
assert stream.n_points > 0
assert src.n_points == 25
Plotting Glyphs (Vectors)
~~~~~~~~~~~~~~~~~~~~~~~~~
Use vectors in a dataset to plot and orient glyphs/geometric objects.
"""
# sphinx_gallery_thumbnail_number = 4
import pyvista as pv
from pyvista import examples
import numpy as np
###############################################################################
# Glyphying can be done via the :func:`pyvista.DataSetFilters.glyph` filter
mesh = examples.download_carotid().threshold(145, scalars="scalars")
# Make a geometric obhect to use as the glyph
geom = pv.Arrow() # This could be any dataset
# Perform the glyph
glyphs = mesh.glyph(orient="vectors", scale="scalars", factor=0.005, geom=geom)
# plot using the plotting class
p = pv.Plotter()
p.add_mesh(glyphs)
# Set a cool camera position
p.camera_position = [
(84.58052237950857, 77.76332116787425, 27.208569926456548),
(131.39486171068918, 99.871379394528, 20.082859824932008),
(0.13483731007732908, 0.033663777790747404, 0.9902957385932576),
def test_download_carotid():
data = examples.download_carotid()
assert data.n_cells
Estimate the gradient of a scalar or vector field in a data set.
The ordering for the output gradient tuple will be
{du/dx, du/dy, du/dz, dv/dx, dv/dy, dv/dz, dw/dx, dw/dy, dw/dz} for
an input array {u, v, w}.
Showing the :func:`pyvista.DataSetFilters.compute_gradient` filter.
"""
# sphinx_gallery_thumbnail_number = 1
import pyvista as pv
from pyvista import examples
import numpy as np
# A vtkStructuredGrid - but could be any mesh type
mesh = examples.download_carotid()
mesh
###############################################################################
# Now compute the gradients of the ``vectors`` vector field in the point data
# of that mesh. This is as simple as calling
# :func:`pyvista.DataSetFilters.compute_gradient`.
mesh_g = mesh.compute_gradient(scalars="vectors")
mesh_g["gradient"]
###############################################################################
# ``mesh_g["gradient"]`` is an ``N`` by 9 NumPy array of the gradients, so we
# could make a dictionary of NumPy arrays of the gradients like:
def gradients_to_dict(arr):
"""A helper method to label the gradients into a dictionary."""
def __init__(self):
self._example_data = examples.download_carotid()
_ExampleLoader.__init__(self)
