Scientific Computing with Python Webinar March 19: 3D Visualization with Mayavi
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Mayavi is a cross-platform tool designed for 3D scientific data visualization, supporting scalar, vector, and tensor data in multiple dimensions. It offers features like Python scripting, custom modules, and the ability to read and save various file formats. The document provides example code snippets for using Mayavi's mlab interface to generate and display 3D visualizations.
![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()](https://image.slidesharecdn.com/scpmayavi-2010-03-19-100319161651-phpapp01/85/Scientific-Computing-with-Python-Webinar-March-19-3D-Visualization-with-Mayavi-8-320.jpg)
![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()](https://image.slidesharecdn.com/scpmayavi-2010-03-19-100319161651-phpapp01/85/Scientific-Computing-with-Python-Webinar-March-19-3D-Visualization-with-Mayavi-15-320.jpg)
Scientific Computing with Python Webinar March 19: 3D Visualization with Mayavi
- 1. 3D visualisation with Mayavi March 19, 2010
- 2. 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
- 5. Mayavi user interface DataSource, Filter and Modules Recording Shell plugin and scripting
- 6. Mayavi user interface DataSource, Filter and Modules Recording Shell plugin and scripting
- 7. Mayavi user interface DataSource, Filter and Modules Recording Shell plugin and scripting
- 8. 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()
- 9. 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
- 10. 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)
- 11. 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()
- 12. Mlab decorations mlab.title('A title') mlab.axes() mlab.colorbar() mlab.clf() mlab.figure() mlab.gcf()
- 13. 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)
- 14. 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 :
- 15. 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()
- 16. Mlab and Traits (lorenz_ui.py)
- 17. Traits, Mayavi and Chaco Vtk_commodities.py (Thanks to Travis Vaught for the example)
- 18. EPD http://www.enthought.com/products/epd.php Enthought Training: http://www.enthought.com/training/ Webinars http://www.enthought.com/training/webinars.php