Often it isn’t sufficient to just plot the data. You may also want to change the data of the plot and update the plot without having to recreate the entire visualization, for instance to do animations, or in an interactive application. Indeed, recreating the entire visualization is very inefficient and leads to very jerky looking animations. To do this, mlab provides a very convenient way to change the data of an existing mlab visualization. Consider a very simple example. The mlab.test_simple_surf_anim function has this code:
import numpy as np from mayavi import mlab x, y = np.mgrid[0:3:1,0:3:1] s = mlab.surf(x, y, np.asarray(x*0.1, 'd')) for i in range(10): s.mlab_source.scalars = np.asarray(x*0.1*(i+1), 'd')
The first two lines define a simple plane and view that. The next three lines animate that data by changing the scalars producing a plane that rotates about the origin. The key here is that the s object above has a special attribute called mlab_source. This sub-object allows us to manipulate the points and scalars. If we wanted to change the x values we could set that too by:
s.mlab_source.x = new_x
The only thing to keep in mind here is that the shape of x should not be changed.
If multiple values have to be changed, you can use the set method of the mlab_source to set them as shown in the more complicated example below:
# Produce some nice data. n_mer, n_long = 6, 11 pi = np.pi dphi = pi/1000.0 phi = np.arange(0.0, 2*pi + 0.5*dphi, dphi, 'd') mu = phi*n_mer x = np.cos(mu)*(1+np.cos(n_long*mu/n_mer)*0.5) y = np.sin(mu)*(1+np.cos(n_long*mu/n_mer)*0.5) z = np.sin(n_long*mu/n_mer)*0.5 # View it. l = plot3d(x, y, z, np.sin(mu), tube_radius=0.025, colormap='Spectral') # Now animate the data. ms = l.mlab_source for i in range(10): x = np.cos(mu)*(1+np.cos(n_long*mu/n_mer + np.pi*(i+1)/5.)*0.5) scalars = np.sin(mu + np.pi*(i+1)/5) ms.set(x=x, scalars=scalars)
Notice the use of the set method above. With this method, the visualization is recomputed only once. In this case, the shape of the new arrays has not changed, only their values have. If the shape of the array changes then one should use the reset method as shown below:
x, y = np.mgrid[0:3:1,0:3:1] s = mlab.surf(x, y, np.asarray(x*0.1, 'd'), representation='wireframe') # Animate the data. fig = mlab.gcf() ms = s.mlab_source for i in range(5): x, y = np.mgrid[0:3:1.0/(i+2),0:3:1.0/(i+2)] sc = np.asarray(x*x*0.05*(i+1), 'd') ms.reset(x=x, y=y, scalars=sc) fig.scene.reset_zoom()
Many standard examples for animating data are provided with mlab. Try the examples with the name mlab.test_<name>_anim, i.e. where the name ends with an _anim to see how these work and run.
It is important to remember distinction between set and reset. Use set or directly set the attributes (x, y, scalars etc.) when you are not changing the shape of the data but only the values. Use reset when the arrays are changing shape and size. Reset usually regenerates all the data and can be inefficient when compared to set or directly setting the traits.
When creating a Mayavi pipeline, as explained in the following subsection, instead of using ready-made plotting function, the mlab_source attribute is created only on sources created via mlab. Pipeline created entirely using mlab will present this attribute.
If you are animating several plot objects, each time you modify the data with there mlab_source attribute, Mayavi will trigger a refresh of the scene. This operation might take time, and thus slow your animation. In this case, the tip Accelerating a Mayavi script may come in handy.