astroML 0.4

Core

To install the core astroML package in your home directory, use:

pip install astroML

The core package is pure python, so installation should be straightforward on most systems. To install from source, use:

python setup.py install

You can specify an arbitrary directory for installation using:

python setup.py install --prefix='/some/path'

To install system-wide on Linux/Unix systems:

python setup.py build
sudo python setup.py install

Core Dependencies

The core astroML package requires the following:

• Python version 2.7 and 3.3+

• Numpy >= 1.4

• Scipy >= 0.11

• Scikit-learn >= 0.18

• Matplotlib >= 0.99

• AstroPy >= 1.1 AstroPy is required to read Flexible Image Transport System (FITS) files, which are used by several datasets.

This configuration matches the Ubuntu 10.04 LTS release from April 2010, with the addition of scikit-learn.

To run unit tests, you will also need nose >= 0.10

Optional Dependencies

Several of the example scripts require specialized or upgraded packages. These requirements are listed at the top of the particular scripts

• PyMC provides a nice interface for Markov-Chain Monte Carlo. Several astroML examples use pyMC for exploration of high-dimensional spaces. The examples were written with pymc version 2.2

• HEALPy provides an interface to the HEALPix pixelization scheme, as well as fast spherical harmonic transforms.

Contribution

We strongly encourage contributions of useful astronomy-related code: for astroML to be a relevant tool for the python/astronomy community, it will need to grow with the field of research. There are a few guidelines for contribution:

Package Author

• Jake Vanderplas <vanderplas@astro.washington.edu> http://jakevdp.github.com

Code Contribution

• Morgan Fouesneau https://github.com/mfouesneau

• Julian Taylor http://github.com/juliantaylor