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# Changing the looks of the visual objects created

## Adding color or size variations

Color: | 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 than 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 is meaningful, such as `surf()` or `barchart()`. 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.
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 positionned 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 enthought.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)
Warning 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 specifie:
    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 extract to a scalar component that can be visualized using iso-surfaces with the ExtractVectorNorm filter.

## 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.

The `surf()`, `contour_surf()`, and bar_chart 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 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.

Using `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 enthought.mayavi import mlab
    mlab.options.backend = 'envisage'
    f = mlab.figure()
    # ...

This will give you the full-fledged UI instead of the default simple window.

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