Merge pull request enthought#41 from joonro/patch-1

added numpy (as np), mayavi.mlab import statements and changed numpy to ...

docs/source/mayavi/mlab_animating.rst

@@ -1,3 +1,4 @@
+
 .. _mlab-animating-data:
 
 Animating the data
@@ -12,11 +13,13 @@ 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::
 
-    x, y = numpy.mgrid[0:3:1,0:3:1]
-    s = mlab.surf(x, y, numpy.asarray(x*0.1, 'd'))
+    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 = numpy.asarray(x*0.1*(i+1), 'd')
+        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
@@ -36,39 +39,39 @@ below::
 
     # Produce some nice data.
     n_mer, n_long = 6, 11
-    pi = numpy.pi
+    pi = np.pi
     dphi = pi/1000.0
-    phi = numpy.arange(0.0, 2*pi + 0.5*dphi, dphi, 'd')
+    phi = np.arange(0.0, 2*pi + 0.5*dphi, dphi, 'd')
     mu = phi*n_mer
-    x = numpy.cos(mu)*(1+numpy.cos(n_long*mu/n_mer)*0.5)
-    y = numpy.sin(mu)*(1+numpy.cos(n_long*mu/n_mer)*0.5)
-    z = numpy.sin(n_long*mu/n_mer)*0.5
+    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, numpy.sin(mu), tube_radius=0.025, colormap='Spectral')
+    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 = numpy.cos(mu)*(1+numpy.cos(n_long*mu/n_mer +
-                                          numpy.pi*(i+1)/5.)*0.5)
-        scalars = numpy.sin(mu + numpy.pi*(i+1)/5)
+        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 = numpy.mgrid[0:3:1,0:3:1]
-    s = mlab.surf(x, y, numpy.asarray(x*0.1, 'd'),
+    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 = numpy.mgrid[0:3:1.0/(i+2),0:3:1.0/(i+2)]
-        sc = numpy.asarray(x*x*0.05*(i+1), 'd')
+        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()