def test_opacity_transfer_functions():
n = 256
mapping = pyvista.opacity_transfer_function('linear', n)
assert len(mapping) == n
mapping = pyvista.opacity_transfer_function('sigmoid_10', n)
assert len(mapping) == n
with pytest.raises(KeyError):
mapping = pyvista.opacity_transfer_function('foo', n)
with pytest.raises(RuntimeError):
mapping = pyvista.opacity_transfer_function(np.linspace(0, 1, 2*n), n)
foo = np.linspace(0, n, n)
mapping = pyvista.opacity_transfer_function(foo, n)
assert np.allclose(foo, mapping)
foo = [0,0.2,0.9,0.2,0.1]
mapping = pyvista.opacity_transfer_function(foo, n, interpolate=False)
assert len(mapping) == n
foo = [3, 5, 6, 10]
mapping = pyvista.opacity_transfer_function(foo, n)
assert len(mapping) == n
###############################################################################
# It's also possible to use your own transfer function that will be linearly
# mapped to the scalar array plotted. For example, we can create an opacity
# mapping as:
opacity = [0, 0.2, 0.9, 0.6, 0.3]
###############################################################################
# When given a minimized opacity mapping like that above, PyVista interpolates
# it across a range of how many colors are shown when mapping the scalars.
# If ``scipy`` is available, then a quadratic interpolation is used -
# otherwise, a simple linear interpolation is used.
# Curious what that opacity transfer function looks like? You can fetch it:
# Have PyVista interpolate the transfer function
tf = pv.opacity_transfer_function(opacity, 256).astype(float) / 255.
import matplotlib.pyplot as plt
plt.plot(tf)
plt.title('My Interpolated Opacity Transfer Function')
plt.ylabel('Opacity')
plt.xlabel('Index along scalar mapping')
plt.show()
###############################################################################
# That opacity mapping will have an opacity of 0.0 at the minimum scalar range,
# a value or 0.9 at the middle of the scalar range, and a value of 0.3 at the
# maximum of the scalar range:
mesh.plot(opacity=opacity)
grid = pv.StructuredGrid(x, y, z)
print(grid.points.shape)
# let's define array d which will contain count values
# for each point of the grid. Now array d is filled with random
# integer values
d = np.random.randint(low=1, high=100, size=grid.points.shape)
# or you can fill the scalar values with zeros
# d = np.zeros_like(grid.points)
grid.plot(show_edges=True, scalars=d[:, 1], stitle='Counts (CPM)')
# next example: opacity transfer function (opacity of each cell depends
# on the scalar value). Example from pyvista project page:
# https://docs.pyvista.org/examples/02-plot/opacity.html
opacity = [0, 0.2, 0.9, 0.6, 0.3]
# Have PyVista interpolate the transfer function
tf = pv.opacity_transfer_function(opacity, 100).astype(float) / 100.
plt.plot(tf)
plt.title('My Interpolated Opacity Transfer Function')
plt.ylabel('Opacity')
plt.xlabel('Index along scalar mapping')
plt.show()
grid.plot(show_edges=True,
scalars=d[:, 1],
opacity=opacity,
stitle='Counts (CPM) with Opacity')
