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The tvtk module (also called TVTK) provides a traits enabled version of VTK. TVTK objects wrap around VTK objects but additionally support traits, and provide a convenient Pythonic API. TVTK is implemented mostly in pure Python (except for a small extension module). Here is a list of current features.
All VTK classes are wrapped.
Classes are generated at install time on the installed platform.
Support for traits.
Elementary pickle support.
Handles numpy arrays/Python lists transparently.
Support for a pipeline browser, ivtk and a high-level mlab like module.
Envisage plugins for a tvtk scene and the pipeline browser.
tvtk is free software with a BSD style license.
The following is a list of requirements for you to be able to run tvtk.
Python-2.3 or greater.
Python should be built with zlib support and must support ZIP file imports.
VTK-5.x, VTK-4.4 or VTK-4.2. It is unlikely to work with VTK-3.x.
numpy – Any recent version should be fine.
To use ivtk.py, mlab you need to have pyface installed.
To use the plugins you need Envisage installed.
TVTK is meant to be installed as part of the mayavi package. Please visit the installation guide on the Mayavi documentation:
This document only covers building and using TVTK from inside a checkout of the the mayavi repository. The tvtk module lives inside tvtk. To build the tvtk module and use them from inside the mayavi sources do the following from the base of the mayavi source tree (we assume here that this is in /home/user/src/lib/mayavi):
$ pwd /home/user/src/lib/mayavi $ python setup.py build
The code generation will take a bit of time. On a PentiumIII machine at 450Mhz, generating the code takes about a minute. If the code generation was successful you should see a ZIP file called tvtk_classes.zip in the tvtk directory along with an extension module.
This completes the installation. The build can be tested by running the tests in the tests/ directory. This tests the built code:
$ pwd /home/user/src/lib/mayavi $ python -m nose.core -v tvtk/tests [...]
If the tests run fine, the build is good. There are other tests in the tests directory that can also be run.
Documentation is available in the ‘docs/’ directory. The ‘examples/’ directory contains a few simple examples demonstrating tvtk in action.
An example of how tvtk can be used follows:
>>> from tvtk.api import tvtk >>> cs = tvtk.ConeSource() >>> cs.resolution = 36 >>> m = tvtk.PolyDataMapper() >>> m.set_input_data(cs.output) >>> a = tvtk.Actor() >>> a.mapper = m >>> p = a.property >>> p.representation = 'w' >>> print(p.representation) 'wireframe'
>>> from tvtk.api import tvtk >>> cs = tvtk.ConeSource(resolution=36) >>> m = tvtk.PolyDataMapper(input=cs.output) >>> p = tvtk.Property(representation='w') >>> a = tvtk.Actor(mapper=m, property=p)
Note that the properties of the object can be set during the instantiation.
To import tvtk please use:
from tvtk.api import tvtk
While this is perhaps a little inconvenient, note that tvtk provides access to all the VTK classes. This is the same way that the vtk package also behaves.
If you are familiar with VTK-Python it is clear from the above example that tvtk “feels” like VTK but is more Pythonic. The most important differences are.
tvtk class names are essentially similar to VTK classes except there is no annoying ‘vtk’ at the front. The only difficulty is with classes that start with a digit. For example ‘vtk3DSImporter’ becomes ‘3DSImporter’. This is illegal in Python and therefore the class name used is ‘ThreeDSImporter’. So, if the first character is a digit, it is replaced by an equivalent non-digit string. There are very few classes like this so this is not a big deal.
tvtk method names are enthought style names and not CamelCase. That is, if a VTK method is called AddItem, the equivalent tvtk name is add_item. This is done for the sake of consistency with names used in the enthought package.
Many VTK methods are replaced by handy properties. In the above example, we used m.input = cs.output and p.representation = ‘w’ instead of what would have been m.SetInput(cs.GetOutput()) and p.SetRepresentationToWireframe() etc. Some of these properties are really traits.
Unlike VTK objects, one can set the properties of a tvtk object when the object is initialized by passing the properties (traits) of the object as keyword arguments at the time of class instantiation. For example cs = tvtk.ConeSource(radius=0.1, height=0.5).
If you are used to VTK, this might take a little getting used to. However, these changes are consistent across all of tvtk. If they aren’t, its a bug. Please let me know if you see inconsistencies.
If the underlying VTK object returns another VTK object, this is suitably wrapped as a tvtk object. Similarly, all relevant parameters for a tvtk method should be tvtk objects, these are transparently converted to VTK objects.
There are several important new features that tvtk provides in addition to the above. A tvtk object basically wraps around a VTK-Python object and provides a trait enabled API for the VTK object. Before we discuss these new features it is important to understand the notion of what we mean by the “basic state” or “state” of a tvtk object. This is defined and subsequently the new features are discussed in some detail.
In tvtk the set of all properties of the VTK object that are represented as traits and have for their value a simple Python type (int/float/string) or a special value (like a tuple specifying color) define the state.
In terms of the implementation of tvtk, any property of a VTK object that can be set by using methods having the form <Property>On or <Property>Off, Set<Property>To<Value> and Set/Get<Property> (where the return type is an int/float/string/tuple) are represented as traits. These properties are said to represent the “basic state” of the tvtk object.
Note that the complete state of the underlying C++ object is impossible to represent in the Python world since this usually involves various pointers to other C++ objects.
It is also important to consider that the identity of objects is preserved according to the VTK behavior. For example, in the following code, the default object created by the VTK implementation of GetLines() is the same for any vtkPolyData:
>>> data1 = vtk.vtkPolyData() >>> data2 = vtk.vtkPolyData() >>> data1.GetLines() (vtkCellArray)0x103b52e30 >>> data2.GetLines() (vtkCellArray)0x103b52e30
The equivalent tvtk code behaves in the same way:
>>> data1 = tvtk.PolyData() >>> data2 = tvtk.PolyData() >>> data1.lines <tvtk.tvtk_classes.cell_array.CellArray at 0xe11e570> >>> data2.lines <tvtk.tvtk_classes.cell_array.CellArray at 0xe11e570>
The user should not ordinarily know this (or rely on this!) but it sometimes helps to know how to access the underlying VTK object that the tvtk object has wrapped. The recommended way to do this is by using the to_vtk function. For example:
>>> pd = tvtk.PolyData() >>> pd_vtk = tvtk.to_vtk(pd)
The inverse process of creating a tvtk object from a VTK object is to use the to_tvtk function like so:
>>> pd1 = tvtk.to_tvtk(pd_vtk) >>> print(pd1 == pd) True
Notice that pd1 == pd. TVTK maintains an internal cache of existing tvtk objects and when the to_tvtk method is given a VTK object it returns the cached object for the particular VTK object. This is particularly useful in situations like this:
>>> cs = tvtk.ConeSource() >>> o = cs.output >>> m = tvtk.PolyDataMapper() >>> m.input = o >>> # ... >>> print(m.input == o) True
It must be noted that if a tvtk object’s goes out of scope in Python, it is garbage collected. However, its underlying VTK object may still exist inside the VTK pipeline. When one accesses this object, a new tvtk wrapper object is created. The following illustrates this:
>>> cs = tvtk.ConeSource() >>> o = cs.output >>> m = tvtk.PolyDataMapper() >>> m.input = o >>> print(hash(o)) 1109012188 >>> print(hash(m.input)) 1109012188 >>> del o >>> print(hash(m.input)) 1119694156
Thus, after o is garbage collected m.input no longer refers to the original tvtk object and a new one is created. This is very similar to VTK’s behaviour. Changing this behaviour is tricky and there are no plans currently to change this.
All tvtk objects are derived from traits.HasStrictTraits. As discussed above, all the basic state related methods are represented as traits in tvtk. This is why we are able to do:
>>> p = a.property >>> p.representation = 'w' >>> print(p.representation) 'wireframe' >>> # OR do this: >>> p = tvtk.Property(opacity=0.5, color=(1,0,0), representation='w')
Also note that it is possible to set many properties of a tvtk object in one go using the set method. For example:
>>> p = tvtk.Property() >>> p.trait_set(opacity=0.5, color=(1,0,0), representation='w')
Any tvtk object will automatically provide the basic functionality of a traited class. Thus, one can also pop up a standard GUI editor for any tvtk object trivially. For example if one is using pycrust or is using gui_thread (this module should be available if SciPy is installed) or ipython -wthread one could easily do this:
>>> p = tvtk.Property() >>> p.edit_traits() >>> # OR >>> p.configure_traits() # This should work even without gui_thread
A GUI editor should pop-up at this point. Note, that changes made to the trait will automagically be propagated to the underlying VTK object. Most importantly, the reverse is also true. That is, if some other object changes the basic state of the wrapped VTK object, then the trait will be automagically updated. For example:
>>> p = tvtk.Property() >>> print(p.representation) 'surface' >>> p_vtk = tvtk.to_tvtk(p) >>> p_vtk.SetRepresentationToWireframe() >>> print(p.representation) 'wireframe'
This also means that if you change properties of the object on the interpreter and at the same time are using a GUI editor, if the object changes, the GUI editor will update automatically.
It is important to note that tvtk objects have strict traits. It is therefore an error to set an attribute that is not already defined in the class. This is illustrated in the following example:
>>> cs = tvtk.ConeSource() >>> cs.foo = 1 Traceback (most recent call last): File "<stdin>", line 1, in ? TraitError: Cannot set the undefined 'foo' attribute of a 'ConeSource' object.
You may subclass tvtk classes but please keep in mind the following:
All tvtk classes derive from HasStrictTraits.
You must make sure to call the super class’s __init__ correctly.
If you have to override the __del__ method, you must make sure you call the super class’s __del__ method.
tvtk objects support a simple form of pickling. The state of the tvtk object maybe pickled. The references to other VTK objects held by the object are NOT picklable. For example:
>>> import cPickle >>> p = tvtk.Property() >>> p.representation = 'w' >>> s = cPickle.dumps(p) >>> del p >>> p = cPickle.load(s) >>> print(p.representation) 'wireframe'
Once again, only the state of the object is pickled. Internal references are not. So the construction of the VTK pipeline will not be pickled. For example if we pickled the actor from the example given in the Basic Usage section, then the ConeSource, PolyDataMapper etc. will not be pickled. Some of the tvtk classes like Matrix4x4 also have special code that enables better pickling.
It is also possible to set the state of a live object. Normally, pickle.load will create a new object. However, by using __setstate__ directly it is possible to just update the state of the object. For example:
>>> p = tvtk.Property() >>> p.interpolation = 'flat' >>> d = p.__getstate__() >>> del p >>> p = Prop() >>> p.__setstate__(d)
Here, after __setstate__ is called, the object’s state alone will be updated. No new object is created.
All tvtk methods and traits are documented using the VTK docstrings. The class names and method names are changed suitably as mentioned in the Basic Usage section. These docstrings are generated automatically and there might be small mistakes in them. All methods of a tvtk object also provide method signature information in the docstring.
All the tvtk wrapper classes are generated automatically. Sometimes one would like to customize a particular class and use that instead of the default. The easiest way to do this would be to copy out the relevant class from the tvtk_classes.zip file, modify it suitably (without changing the name of the file or class name of course) and then add this file into the tvtk/custom directory. Any file here will override the default inside the ZIP file.
Any object derived from Collection (i.e. vtkCollection) will behave like a proper Python sequence. Here is an example:
>>> ac = tvtk.ActorCollection() >>> print(len(ac)) 0 >>> ac.append(tvtk.Actor()) >>> print(len(ac)) 1 >>> for i in ac: ... print(i) ... [...] >>> ac[-1] = tvtk.Actor() >>> del ac >>> print(len(ac)) 0
Currently, only subclasses of Collection behave this way.
All the DataArray subclasses behave like Pythonic arrays and support the iteration protocol in addition to __getitem__, __setitem__, __repr__, append, extend etc. Further, it is possible to set the value of the array using either a numpy array or a Python list (using the from_array method). One can also get the data stored in the array into a numpy array (using the to_array method). Similarly, the Points and IdList classes also support these features. The CellArray class only provides the from_array and to_array methods and does not provide a sequence like protocol. This is because of the peculiarity of the wrapped vtkCellArray class.
One extremely useful feature is that almost any tvtk method/property that accepts a DataArray, Points, IdList or CellArray instance, will transparently accept a numpy array or a Python list. Here is a simple example demonstrating these:
>>> ######################################## >>> from tvtk.api import tvtk >>> import numpy as np >>> data = np.array([[0,0,0,10], [1,0,0,20], ... [0,1,0,20], [0,0,1,30]], 'f') >>> triangles = np.array([[0,1,3], [0,3,2], ... [1,2,3], [0,2,1]]) >>> points = data[:,:3] >>> temperature = data[:,-1] >>> mesh = tvtk.PolyData() >>> mesh.points = points >>> mesh.polys = triangles >>> mesh.point_data.scalars = temperature >>> ######################################## >>> # Array's are Pythonic. >>> import operator >>> reduce(operator.add, mesh.point_data.scalars, 0.0) 80.0 >>> print(mesh.point_data.scalars) [10.0, 20.0, 20.0, 30.0] >>> print(mesh.points) [(0.0, 0.0, 0.0), (1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)] >>> ######################################## >>> # Demo of from_array/to_array >>> pts = tvtk.Points() >>> pts.from_array(points) >>> print(pts.to_array()) [[ 0. 0. 0.] [ 1. 0. 0.] [ 0. 1. 0.] [ 0. 0. 1.]]
As can be seen, no DataArray, Points or CellArray instances need to be created. Note that Python tuples are not converted implicitly. The conversion from the passed arrays to the VTK arrays is handled transparently and very efficiently. The only exception to this is the IdList class where the conversion is inefficient. However, the IdList class is not used commonly.
The CellArray is used to specify the connectivity list for polygonal data and has some peculiarities. The CellArray needs to be initialized using the cell connectivity list. This can be specified in one of several ways.
A Python list of 1D lists. Each 1D list can contain one cell connectivity list. This is very slow and is to be used only when efficiency is of no consequence.
A 2D numpy array with the cell connectivity list.
A Python list of 2D numpy arrays. Each numpy array can have a different shape. This makes it easy to generate a cell array having cells of different kinds.
This conversion is most efficient if the passed numpy arrays have a typecode of tvtk.array_handler.ID_TYPE_CODE. Otherwise a typecast is necessary and this involves an extra copy. The input data is always copied during the conversion. Here is an example illustrating these different approaches:
>>> a = [, [1, 2], [3, 4, 5], [6, 7, 8, 9]] >>> cells = tvtk.CellArray() >>> cells.from_array(a) >>> a = np.array([[0,1,2], [3,4,5], [6,7,8]], int) >>> cells.from_array(a) >>> l_a = [a[:,:1], a[:2,:2], a] >>> cells.from_array(a)
An alternative way to use an arbitrary connectivity list having different numbers of points per cell is to use the following approach:
>>> ids = np.array([3, 0,1,3, ... 3, 0,3,2, ... 3, 1,2,3, ... 3, 0,2,1]) >>> # The list is of form [npts,p0,p1,...p(npts-1), ...] >>> n_cell = 4 >>> cells = tvtk.CellArray() >>> cells.set_cells(n_cell, ids) >>> print(cells.data) [3.0, ..., 1.0], length = 16
This is done very efficiently and does not copy the input data. More details on this are provided in the next sub-section.
Also note that DataArray objects can still be passed to these methods as before. For example we could have just as easily done this:
>>> points = tvtk.Points() >>> points.from_array(data[:,:3]) >>> temperature = tvtk.FloatArray() >>> temperature.from_array(data[:,-1]) >>> cells = tvtk.CellArray() >>> cells.set_cells(n_cell, ids) >>> mesh = tvtk.PolyData() >>> mesh.points = points >>> mesh.polys = cells >>> mesh.point_data.scalars = temperature
To clarify the ensuing discussion we make a distinction between two different forms of array handling.
Explicit conversion – These happen when the user is creating a tvtk array object and initializes this from a numpy array or Python list. Like so:>>> f = tvtk.FloatArray() >>> a = np.array([1,2,3], int) >>> f.from_array(a)
Implicit conversion – These happen when the user passes an array or list to a tvtk method that expects a DataArray, Points, IdList or CellArray instance.
There are a few issues to keep in mind when using tvtk’s array handling features. When possible, tvtk uses a view of the passed numpy array and does not make a copy of the data stored in it. This means that changes to the VTK data array or to the numpy array are visible in the other. For example:
>>> f = tvtk.FloatArray() >>> a = np.array([1,2,3], 'f') >>> f.from_array(a) >>> a = 10.0 >>> print(f) [10.0, 2.0, 3.0] >>> f = -1.0 >>> print(a) [-1. 2. 3.]
It is important to note that it is perfectly safe to delete the reference to the numpy array since this array is actually cached safely to eliminate nasty problems. This memory is freed when the VTK array is garbage collected. Saving a reference to the numpy array also ensures that the numpy array cannot be resized (this could have disastrous effects).
However, there are exceptions to this behaviour of using “views” of the numpy array. The DataArray class and its subclasses and the Points class only make copies of the given data in the following situations.
A Python list is given as the data.
A non-contiguous numpy array is given.
The method requiring the conversion of the array to a VTK array expects a vtkBitArray instance.
The types of the expected VTK array and the passed numpy array are not equivalent to each other. For example if the array passed has typecode ‘i’ but the tvtk method expects a FloatArray.
The cases 3 and 4 occur very rarely in implicit conversions because most methods accept DataArray instances rather than specific subclasses. However, these cases are likely to occur in explicit conversions.
CellArray always makes a copy of the data on assignment. For example:
>>> ca = tvtk.CellArray() >>> triangles = np.array([[0,1,3], [0,3,2], ... [1,2,3], [0,2,1]]) >>> ca.from_array(triangles)
This always makes a copy. However, if one uses the set_cells method a copy is made in the same circumstances as specified above for DataArray and Points classes. If no copy is made, the cell data is a “view” of the numpy array. Thus, the following example does not make a copy:
>>> ids = np.array([3, 0,1,3, ... 3, 0,3,2, ... 3, 1,2,3, ... 3, 0,2,1], int) >>> ca.set_cells(4, ids)
Changing the values of the ids or changing the number of cells is not recommended and will lead to undefined behaviour. It should also be noted that it is best to pass cell connectivity data in arrays having typecode tvtk.array_handler.ID_TYPE_CODE (this is actually computed dynamically depending on your VTK build and platform).
The IdList also always makes a copy of the data passed to it.
Another issue to keep in mind is that VTK’s data arrays always re-allocate memory if they are resized. This is illustrated in the following example:
>>> d = tvtk.DoubleArray() >>> a = np.array([1,2,3], 'd') >>> d.from_array(a) >>> a = 10 >>> d.append(4.0) >>> a = 1 >>> print(a) [ 1. 2. 3.] >>> print(d) [10.0, 2.0, 3.0, 4.0] >>> # Notice that d == 10.0
In this case, a is not resized but d is. Here, d actually makes a copy of a and any further changes to d or a will not be reflected in the other. This case also illustrates a small problem. d will hold a reference to a internally even though it uses none of a’s memory. Fortunately, when d is garbage collected the memory occupied by a will be freed. Thus the problem is not serious but probably worth keeping in mind.
To summarize the considerations of the previous sub-section, the following are to be noted.
Most often DataArray and Points objects do not make copies of the numpy data. The exceptions are listed above. This means changes to either the tvtk object or the array are reflected in the other.
It is safe to delete references to the array object converted. You cannot resize the numpy array though.
CellArray always copies data on assignment. However, when using set_cells, the behaviour is similar to what happens for DataArray objects. Note that it is not advisable to change the connectivity ids and number of cells when this is done. Also note that for the CellArray it is best to pass data in the form of numpy arrays having a typecode of tvtk.array_handler.ID_TYPE_CODE). Otherwise one incurs an extra copy due to a typecast.
IdList always makes a copy of the data. This class is very rarely used.
tvtk array objects always copy the data when resized. This could lead to increased memory usage in some circumstances. However, this is not a memory leak.
The upshot of these features is that array conversion can be extremely efficient in terms of speed and memory considerations.
The tvtk package ships with several other utility modules. These are briefly described in the following sections.
If you need to write out VTK data files given a TVTK dataset. The
tvtk.api.write_data function should be useful. For example:
>>> from tvtk.api import tvtk, write_data >>> pd = tvtk.PolyData() >>> # ... >>> write_data(pd, 'file_name')
This will write out an XML file with basename
file_name. If one specifies
.vtk extension, like say:
>>> write_data(pd, 'file_name.vtk')
It will write out an old-style ASCII file. See the docstring for more details.
VTK-Python defines several handy colors and these are made available in TVTK. For example:
>>> from tvtk.api import colors >>> colors.alice_blue (0.9412, 0.9725, 1.0)
This allows you to refer to color by name easily.
The PipelineBrowser class presents the view of the VTK pipeline as a tree. Double-clicking any node will let you edit the properties of the object with a trait sheet editor. The TreeEditor from the traits package is used to represent the view. This pipeline browser is similar to but more sophisticated than MayaVi’s (1.x) pipeline browser. The browser will most often automatically update itself as you change the VTK pipeline. When it does not you can right click on any node and click refresh.
The algorithm to generate the objects in the tree can be changed. The user may subclass TreeGenerator and use that instead. Please read the code and docstrings for more details on the implementation.
A utility module that makes VTK/TVTK easier to use from the Python interpreter. The module uses the tvtk.scene module to provide a wxPython widget. ivtk basically provides this scene along with an optional Python interpreter (via PyCrust) and an optional pipeline browser view.
For a stand-alone application one may simply run the module. To use this under IPython (with -wthread) use the viewer() helper function. For example:
>>> from tvtk.tools import ivtk >>> from tvtk.api import tvtk >>> # Create your actor ... >>> a = tvtk.Actor() >>> # Now create the viewer. >>> v = ivtk.viewer() >>> v.scene.add_actors(a) # or v.scene.add_actor(a)
ivtk provides several useful classes that you may use from either PyFace or wxPython – IVTK, IVTKWithCrust, IVTKWithBrowser and IVTKWithCrustAndBrowser. Again read the code and docstrings to learn more. An example using ivtk is also available in examples/ivtk_example.py.
A module that provides Matlab-like 3d visualization functionality. The general idea is shamelessly stolen from the high-level API provided by Octaviz. Some of the test cases and demos are also translated from there!
The implementation provided here is object oriented and each visualization capability is implemented as a class that has traits. So each of these may be configured. Each visualization class derives (ultimately) from MLabBase which is responsible for adding/removing its actors into the render window. The classes all require that the RenderWindow be a tvtk.scene.Scene instance (this constraint can be relaxed if necessary later on).
This module offers the following broad class of functionality:
This basically manages all of the objects rendered. Just like figure in any Matlab like environment. A convenience function called figure may be used to create a nice Figure instance.
This and its subclasses let one place glyphs at points specified as inputs. The subclasses are: Arrows, Cones, Cubes, Cylinders, Spheres, and Points.
Draws lines between the points specified at initialization time.
Draws an outline for the contained objects.
Draws a title for the entire figure.
Manages a lookup table and a scalar bar (legend) for it. This is subclassed by all classes that need a LUT.
MayaVi1’s imv.surf like functionality that plots surfaces given x (1D), y(1D) and z (or a callable) arrays.
Also plots contour lines.
Given triangle connectivity and points, plots a mesh of them.
Plots the mesh using tubes and spheres so its fancier.
Given x, y generated from numpy.mgrid, and a z to go with it. Along with optional scalars. This class builds the triangle connectivity (assuming that x, y are from numpy.mgrid) and builds a mesh and shows it.
Like mesh but shows the mesh using tubes and spheres.
This generates a surface mesh just like Mesh but renders the mesh as a surface.
Shows contour for a mesh.
Allows one to view large numpy arrays as image data using an image actor. This is just like MayaVi1’s mayavi.tools.imv.viewi.
To see nice examples of all of these look at the test_* functions at the end of the mlab.py file. Here is a quick example that uses some of these test functions:
>>> from tvtk.tools import mlab >>> f = mlab.figure() >>> mlab.test_surf(f) # Create a spherical harmonic. >>> f.pop() # Remove it. >>> mlab.test_molecule(f) # Show a caffeine molecule. >>> f.renwin.reset_zoom() # Scale the view. >>> f.pop() # Remove this. >>> mlab.test_lines(f) # Show pretty lines. >>> f.clear() # Remove all the stuff on screen.
Here is the test_surf function just to show you how easy it is to use mlab:
>>> # Create the spherical harmonic. >>> from numpy import pi, cos, sin, 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, m1, m2, m3, m4, m5, m6, m7 = 4, 3, 2, 3, 6, 2, 6, 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) >>> # Now show the surface. >>> from tvtk.tools import mlab >>> fig = mlab.figure() >>> s = Surf(x, y, z, z) >>> fig.add(s)
As you may notice, this example has also been translated from the Octaviz site.
TVTK ships with two Envisage plugins. One for a TVTK scene and another for the pipeline browser.
The scene plugin allows one to create a new TVTK scene on the work area. Any number of these may be created. It provides useful menu’s to set the view of the scene (like the ivtk menus). It also allows the user to save the view to an image. The plugin also provides a few preferences to set the background color of the window etc.
The browser plugin places a pipeline browser on the left of the Envisage window. This browser is hooked up to listen to scene additions to the work area. Each time a scene is added it will show up as a top-level node in the browser.
These plugins may be used from any Envisage plugin. To see a fully functional example of this please look at the examples/plugin/ directory. The example demonstrates how to use the plugins in Envisage and make an application.