I recently needed to do some simple 3D plotting in python. The strongest choice as discussed on the web is Mayavi, which is part of the Enthought Python distribution. However, I have a previous python distribution already installed (via scisoft) with all of my favorite packages updated to the latest and greatest versions (e.g. matplotlib, PyEphem, slalib for python, etc.). I wanted to install Mayavi into my existing pythong distribution. In theory this should have been straightforward as there are eggs (a python package zip-type file). However, no matter how hard I tried, I could not get Mayavi, and all its required software, installed properly. Part of this may have been that I was doing a local install without admin privileges.

After spending an entire day with Mayavi issues, just in my attempt to make a very simple 3D scatter plot, I ran across a much easier option. The latest edition of matplotlib (0.99.1) comes with a toolkit called mplot3d. After looking over examples here and here, it took me about 5 minutes to download and install the latest matplotlib version and I was ready to go! Simple 3D figures are a snap to put together and you can interact in order to zoom and rotate around. The toolkit uses matplotlib on the backend, so if you are familiar with all of the plotting options there, then mplot3d follows on logically.

My plot from matplotlib.mplot3d

There are still a few bugs/features that I haven’t quite figured out. Most notably, in wire or surface plots, I can’t seem to set the rstride and cstride (row and column segment step sizes) to anything less than 1. But one of my favorite things about python is that I should be able to go into the source code for mplot3d and figure out what the issue is.

If you have experience with either Mayavi or mplot3d, let us know in the comments when you would need to use Mayavi over the simpler functionality of mplot3d or if you have run into other issues with either.

Do you use the tool “convert” to manipulate plots and images?  It’s simple, powerful and downright great!  Quick examples:

Modify a plot for presentation use, by swapping to a black background with white lines, so it’s easier to read on a screen:

> convert  -negate frompaper.ps  forscreen.jpg

Make a thumbnail:

> convert -geometry 50×50 big.jpg thumbnail.gif

Blur an image w a Gaussian (for easier Journal Club reading of little datapoints):

>convert -gaussian 2×2  hard2read.ps  easy2read.gif

Other tools can do all these tasks, of course.  The advantage of Convert is that it’s very simple, fast to type, and scriptable.  Convert is part of the open-source ImageMagick package, which is easy to install in linux or OS X.   Quick, what are some other astronomy-relevant uses of Convert?

Today APLpy 0.9.3 is out! It is a Python plotting package made to generate publication-quality plots in multiple formats such as EPS, PDF, PS, PNG, and SVG.

APLpy was created by Thomas Robitaille and Eli Bressert, who come from a Fortran and IDL background. With Python’s ease of use, portability, and programming they decided to make a plotting package for astronomers. APLpy’s objective is easy usage with great looking publication plots. In other words, more bang for the buck. Here’s an example of what an APLpy plot looks like:

A color generated APLpy plot from FITS files with grid lines.

To see more examples and how to make the plots in Python check out this link.

APLpy has quite a few features (some listed below – from the APLpy site) and is continuing to expand. Fortunately, there’s good documentation to accompany the features.

• Make plots interactively or using scripts
• Show grayscale, colorscale, and 3-color RGB images of FITS files
• Generate co-aligned FITS cubes to make 3-color RGB images
• Overlay any number of contour sets
• Overlay markers with fully customizable symbol
• Plot customizable shapes like circles, ellipses, and rectangles
• Overlay coordinate grids
• Customize the appearance of labels and ticks
• Hide, show, and remove different contour and marker layers
• Pan, zoom, and save any view as a full publication-quality plot
• Save plots as EPS, PDF, PS, PNG, and SVG

The plotting package is under active development and there has been extensive interaction between the users and developers. We should be seeing some exciting features added to APLpy in the next few releases.