Scientific Computing with Python Webinar March 19: 3D Visualization with Mayavi

3D visualisation with Mayavi
March 19, 2010

What is Mayavi ?
Mayavi is a general purpose, cross-platform tool for 3-D scientific data visualization
Visualization of scalar, vector and tensor data in 2 and 3 dimensions.
Easy scriptability using Python.
Easy extendability via custom sources, modules, and data filters.
Reading several file formats: VTK, PLOT3D, etc.
Saving of visualizations.
Saving rendered visualization in a variety of image formats.
Convenient functionality for rapid scientific plotting via mlab
A very example of what you can build on top of ETS

Enthought Tool Suite

Mayavi user interface

Mayavi user interface
DataSource, Filter and Modules
Recording
Shell plugin and scripting

Mayavi “API” or scripting with mlab # Create the data. from numpy import pi, sin, cos, mgrid dphi, dtheta = pi/250.0, pi/250.0 [phi,theta] = mgrid[0:pi+dphi*1.5:dphi,0:2*pi+dtheta*1.5:dtheta] m0 = 4; m1 = 3; m2 = 2; m3 = 3; m4 = 6; m5 = 2; m6 = 6; m7 = 4; r = sin(m0*phi)**m1 + cos(m2*phi)**m3 + sin(m4*theta)**m5 + cos(m6*theta)**m7 x = r*sin(phi)*cos(theta) y = r*cos(phi) z = r*sin(phi)*sin(theta) # View it. from enthought.mayavi import mlab s = mlab.mesh(x, y, z) mlab.show()

Running mlab within ipython C: ipython –wthread >>> from enthought.mayavi import mlab matplotlib also has an mlab namespace. Be sure you are using the one from enthought.mayavi

Plotting commands
0D data
mlab.points3d(x, y, z)
1D data mlab.plot3d(x, y, z) 3D data mlab.contour3d(x, y, z) Vector field mlab.quiver(x, y, z, u, v, w) 2D data mlab.surf(x, y, z)

Example with points in 3D mlab.points3d(x, y, z, color=(1.0,0.0,1.0), mode=‘sphere’, scale_factor=0.1) { x.shape == y.shape == z.shape color = (R, G, B) 0.0 <= R, G, B <= 1.0 default is (1.0, 1.0, 1.0) mode = ‘sphere’ , ‘cone’, ‘cube’, ‘arrow’, ‘cylinder’, ‘point’, ‘2darrow’, ‘2dcircle’, ‘2dcross’, ‘2ddash’, ‘2ddiamond’, ‘2dhooked_arrow’, ‘2dsquare’, ‘2dthick_arrow’, ‘2dthick_cross’, ‘2dtriangle’, ‘2dvertex’ scaling applied from numpy.random import rand x,y,z = rand(30),rand(30),rand(30) mlab.axes()

Mlab decorations
mlab.title('A title')
mlab.axes()
mlab.colorbar()
mlab.clf()
mlab.figure()
mlab.gcf()

Mlab helper functions and the engine pipeline >>> mlab.figure() >>> mlab.surf(call_values) >>> mlab.axes() Array2DSource _ WarpScalar _ PolyDataNormals _ Colors and legends _ Surface Array2DSource _ WarpScalar _ PolyDataNormals _ Colors and leg _ Surface def complete_pipeline_call(data_array): src = mlab.pipeline.array2d_source(data_array) warp = mlab.pipeline.warp_scalar(src) normals = mlab.pipeline.poly_data_normals(warp) return mlab.pipeline.surface(normals)

Looking for help and demo’s
mlab.test_points3d()
mlab.test_plot3d()
mlab.test_surf()
mlab.test_contour3d()
mlab.test_quiver3d()
mlab.test_molecule()
mlab.test_flow()
mlab.test_mesh()
Use ?? in IPython to look at the source code of these examples.
Documentation
Mayavi examples coming with EPD
enthought-dev mailing list
Mlab test functions :

Mlab and Traits (mlab_traits_ui.py) class ActorViewer(HasTraits): scene = Instance(MlabSceneModel, ()) view = View(Item(name='scene‘, editor=SceneEditor(scene_class=MayaviScene), show_label=False, resizable=True, height=500, width=500), resizable=True) def __init__(self, **traits): HasTraits.__init__(self, **traits) self.generate_data() def generate_data(self): X, Y = mgrid[-2:2:100j, -2:2:100j] R = 10*sqrt(X**2 + Y**2) Z = sin(R)/R self.scene.mlab.surf(X, Y, Z, colormap='gist_earth') if __name__ == '__main__': a = ActorViewer() a.configure_traits()

Mlab and Traits (lorenz_ui.py)

Traits, Mayavi and Chaco Vtk_commodities.py (Thanks to Travis Vaught for the example)

EPD http://www.enthought.com/products/epd.php Enthought Training: http://www.enthought.com/training/ Webinars http://www.enthought.com/training/webinars.php

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Scientific Computing with Python Webinar March 19: 3D Visualization with Mayavi

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