astroml / text_errata

A Catalog of errors and typos in the book "Statistics, Data Mining, and Machine Learning in Astronomy

AstroML: Machine Learning for Astronomy

AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, and matplotlib, and distributed under the BSD license. It contains a growing library of statistical and machine learning routines for analyzing astronomical data in python, loaders for several open astronomical datasets, and a large suite of examples of analyzing and visualizing astronomical datasets.

This project was started in 2012 by Jake VanderPlas to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray.

Important Links

• HTML documentation: https://www.astroML.org
• Core source-code repository: https://github.com/astroML/astroML
• Figure source-code repository: https://github.com/astroML/astroML-figures
• Issue Tracker: https://github.com/astroML/astroML/issues
• Mailing List: https://groups.google.com/forum/#!forum/astroml-general

Installation

Before installation, make sure your system meets the prerequisites listed in Dependencies, listed below.

Core

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

pip install astroML

A conda package for astroML is also available either on the conda-forge or on the astropy conda channels:

conda install -c astropy 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

Dependencies

There are two levels of dependencies in astroML. Core dependencies are required for the core astroML package. Optional dependencies are required to run some (but not all) of the example scripts. Individual example scripts will list their optional dependencies at the top of the file.

Core Dependencies

The core astroML package requires the following (some of the functionality might work with older versions):

• Python version 3.6+
• Numpy >= 1.13
• Scipy >= 0.19
• Scikit-learn >= 0.18
• Matplotlib >= 3.0
• AstroPy >= 3.0

Optional Dependencies

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

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

Development

This package is designed to be a repository for well-written astronomy code, and submissions of new routines are encouraged. After installing the version-control system Git, you can check out the latest sources from GitHub using:

git clone git://github.com/astroML/astroML.git

or if you have write privileges:

git clone [email protected]:astroML/astroML.git

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:

General

Any contribution should be done through the github pull request system (for more information, see the help page Code submitted to astroML should conform to a BSD-style license, and follow the PEP8 style guide.

Documentation and Examples

All submitted code should be documented following the Numpy Documentation Guide. This is a unified documentation style used by many packages in the scipy universe.

In addition, it is highly recommended to create example scripts that show the usefulness of the method on an astronomical dataset (preferably making use of the loaders in astroML.datasets). These example scripts are in the examples subdirectory of the main source repository.

Authors

Package Author

• Jake Vanderplas https://github.com/jakevdp http://jakevdp.github.com

Maintainer

• Brigitta Sipocz https://github.com/bsipocz

Contributors

• Alex Conley
• Andreas Kopecky
• Andrew Connolly
• Asif Imran
• Benjamin Alan Weaver
• Brigitta Sipőcz
• Chris Desira
• Daniel Andreasen
• Dino Bektešević
• Edward Betts
• Hans Moritz Günther
• Hugo van Kemenade
• Jake Vanderplas
• Jeremy Blow
• Jonathan Sick
• Joris van Vugt
• Juanjo Bazán
• Julian Taylor
• Lars Buitinck
• Michael Radigan
• Morgan Fouesneau
• Nicholas Hunt-Walker
• Ole Streicher
• Pey Lian Lim
• Rodrigo Nemmen
• Ross Fadely
• Vlad Skripniuk
• Zlatan Vasović
• Engineero
• stonebig

text_errata's Issues

Eq 8.11

Shouldn't eqaution 8.11 for the $\sigma^2$ be divided by $(N-2)$ ?

Chapter 7 (v2), Page 296, Figure 7.6

Page 296 in Chapter 7 refers to Figure 7.6 (which got moved from v1), but this is actually Figure 7.7 and it should be the right panel, not the middle panel. But really, the figure should be changed such that NMF is the middle and ICA is the right. Note that the text was apparently wrong in v1 as well.

Typo on Page 427

On page 427, in the first paragraph of section 10.3.1, it says omega = 2 pi f = 2 pi P. Should be 2 pi / P.

Dubious units in Equation 8.77 and 8.78?

I'm going out on a limb here, but...

Equation 8.77 and 8.78 seem to have strange units- y_units^(0.5).
Should the argument of the summation over i be squared?
I am using the digital online edition of the book accessed through EBL at my library, and have not checked the paper copy of the book.

Eq. 9.21 incorrect?

Not positive, but I think the argument of the argmax should read:

\ln \pi_k - \frac{1}{2}\sum \left( \ln(2\pi (\sigma_k^i)^2) + \frac{(x^i - \mu_k^i)^2}{(\sigma_k^i)^2} \right)

since it is just the logarithm of a Gaussian?

Eqns 8.67 and 8.68

Hi,

I wonder if eqns 8.67 and 8.68 contain a similar error. Should sigma_i^2 not appear in the second term of the likelihood function? The second term reflects the background component, so why should the statistics of it have anything to do with sigma_i?

Cheers,
Joel Roediger

Figure 3.3 might be fine as printed, despite erratum

Am I wrong? See my comment on the original commit to the README.md file.

Book erratum: Different distances

I am preparing a class where I am going to use the Statistics Data Mining, and Machine Learning in Astronomy book. I think I run into something strange in section 2.5.2. I hope this is the right place to address this.

The distances calculated by the easy_nn and vectorized_nnare not the same. I think it lacks a square root at equations 2.4, 2.5 and 2.6.

I set up a notebook showing this.