Changing the looks of the visual objects created¶
Adding color or size variations¶
The color of the objects created by a plotting function can be specified explicitly using the ‘color’ keyword argument of the function. This color is then applied uniformly to all the objects created.
If you want to vary the color across your visualization, you need to specify scalar information for each data point. Some functions try to guess this information: these scalars default to the norm of the vectors, for functions with vectors, or to the z elevation for functions where it is meaningful, such as
This scalar information is converted into colors using the colormap, or also called LUT, for Look Up Table. The list of possible colormaps is:
accent flag hot pubu set2 autumn gist_earth hsv pubugn set3 black-white gist_gray jet puor spectral blue-red gist_heat oranges purd spring blues gist_ncar orrd purples summer bone gist_rainbow paired rdbu winter brbg gist_stern pastel1 rdgy ylgnbu bugn gist_yarg pastel2 rdpu ylgn bupu gnbu pink rdylbu ylorbr cool gray piyg rdylgn ylorrd copper greens prgn reds dark2 greys prism set1
The easiest way to choose the colormap, most adapted to your visualization is to use the GUI (as described in the next paragraph). The dialog to set the colormap can be found in the Colors and legends node.
To use a custom-defined colormap, for the time being, you need to write specific code, as show in Custom colormap example.
- Size of the glyph
The scalar information can also be displayed in many different ways. For instance it can be used to adjust the size of glyphs positioned at the data points.
A caveat: Clamping: relative or absolute scaling Given six points positioned on a line with interpoint spacing 1:
x = [1, 2, 3, 4, 5, 6] y = [0, 0, 0, 0, 0, 0] z = y
If we represent a scalar varying from 0.5 to 1 on this dataset:
s = [.5, .6, .7, .8, .9, 1]
We represent the dataset as spheres, using
points3d(), and the scalar is mapped to diameter of the spheres:
from mayavi import mlab pts = mlab.points3d(x, y, z, s)
By default the diameter of the spheres is not ‘clamped’, in other words, the smallest value of the scalar data is represented as a null diameter, and the largest is proportional to inter-point distance. The scaling is only relative, as can be seen on the resulting figure:
This behavior gives visible points for all datasets, but may not be desired if the scalar represents the size of the glyphs in the same unit as the positions specified.
In this case, you shoud turn auto-scaling off by specifying the desired scale factor:
pts = mlab.points3d(x, y, z, s, scale_factor=1)
In earlier versions of Mayavi (up to 3.1.0 included), the glyphs are not auto-scaled, and as a result the visualization can seem empty due to the glyphs being very small. In addition the minimum diameter of the glyphs is clamped to zero, and thus the glyph are not scaled absolutely, unless you specify:
pts.glyph.glyph.clamping = False
- More representations of the attached scalars or vectors
There are many more ways to represent the scalar or vector information attached to the data. For instance, scalar data can be ‘warped’ into a displacement, e.g. using a WarpScalar filter, or the norm of scalar data can be extracted to a scalar component that can be visualized using iso-surfaces with the ExtractVectorNorm filter.
- Displaying more than one quantity
You may want to display color related to one scalar quantity while using a second for the iso-contours, or the elevation. This is possible but requires a bit of work: see Atomic orbital example.
If you simply want to display points with a size given by one quantity, and a color by a second, you can use a simple trick: add the size information using the norm of vectors, add the color information using scalars, create a
quiver3d()plot choosing the glyphs to symmetric glyphs, and use scalars to represent the color:
x, y, z, s, c = np.random.random((5, 10)) pts = mlab.quiver3d(x, y, z, s, s, s, scalars=c, mode='sphere') pts.glyph.color_mode = 'color_by_scalar' # Finally, center the glyphs on the data point pts.glyph.glyph_source.glyph_source.center = [0, 0, 0]
Changing the scale and position of objects¶
Each mlab function takes an extent keyword argument, that allows to set its (x, y, z) extents. This give both control on the scaling in the different directions and the displacement of the center. Beware that when you are using this functionality, it can be useful to pass the same extents to other modules visualizing the same data. If you don’t, they will not share the same displacement and scale.
barchart() functions, which
display 2D arrays by converting the values in height, also take a
warp_scale parameter, to control the vertical scaling.
Changing object properties interactively¶
Mayavi, and thus mlab, allows you to interactively modify your visualization.
The Mayavi pipeline tree can be displayed by clicking on the mayavi icon
in the figure’s toolbar, or by using
show_pipeline() mlab command.
One can now change the visualization using this dialog by double-clicking
on each object to edit its properties, as described in other parts of
this manual, or add new modules or filters by using this icons on the
pipeline, or through the right-click menus on the objects in the
The record feature
A very useful feature of this dialog can be found by pressing the red round button of the toolbar of the pipeline view. This opens up a recorder that tracks the changes made interactively to the visualization via the dialogs, and generates valid lines of Python code. To find out about navigating through a program in the pipeline, see Organisation of Mayavi visualizations: the pipeline.
In addition, for every object returned by a mlab function,
this_object.edit_traits() brings up a dialog that can be used to
interactively edit the object’s properties. If the dialog doesn’t show up
when you enter this command, please see Running mlab scripts.
mlab with the full Envisage UI
Sometimes it is convenient to write an mlab script but still use the full envisage application so you can click on the menus and use other modules etc. To do this you may do the following before you create an mlab figure:
from mayavi import mlab mlab.options.backend = 'envisage' f = mlab.figure() # ...
This will give you the full-fledged UI instead of the default simple window.