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.. _book_fig_root:

Textbook Figures
----------------

This section makes available the source code used to generate every figure
in the book `Statistics, Data Mining, and Machine Learning in Astronomy`.
Many of the figures are fairly self-explanatory, though some will be less
so without the book as a reference.  The table of contents of the book
can be seen :download:`here(pdf) <../documents/DMbookTOC.pdf>`.

Figure Contents
~~~~~~~~~~~~~~~
Each chapter links to a page with thumbnails of the figures from the chapter.




.. toctree::
   :maxdepth: 2

   chapter1/index
   chapter2/index
   chapter3/index
   chapter4/index
   chapter5/index
   chapter6/index
   chapter7/index
   chapter8/index
   chapter9/index
   chapter10/index
   appendix/index





.. toctree::
   :hidden:







Getting Started/Frequently Asked Questions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
There is so much here: where to begin?

1) Getting SDSS and other data, and quick analysis and plotting:

   - `How do I access SDSS imaging data and plot various color-color diagrams?` **Chapter 1**.
   - `How do I access an SDSS spectrum and plot it?` **Chapter 1**.
   - `How do plot data in pixelated sky projections?` **Chapter 1**.
   - `How can I visualize a four-dimensional data set and its intrinsic correlations?` **Chapter 1**.

2) Basic statistical tools:

   - `How do I use python to evaluate and plot various statistical distributions, such as Cauchy, Laplace, etc.` **Chapter 3**.
   - `How do I robustly estimate location and scale parameters of a one-dimensional data set?` **Chapter 3**.
   - `How do I robustly estimate parameters of a two-dimensional Gaussian?` **Chapter 3**.
   - `How do I account for selection effects (e.g. luminosity functions)?` **Chapter 4**.
   - `How do I generate a simulated sample drawn from an arbitrary distribution?` **Chapter 4**.
   - `How do I choose optimal bin width for a histogram? Do bins need to be same size?` **Chapters 4 and 5**.
   - `How do I fit y(x) when y has non-Gaussian uncertainties?` **Chapter 8**.
   - `How do I fit y(x) when both x and y have non-negligible uncertainties?` **Chapter 8**.


3) Non-trivial data mining and other tools:

   - `How do I run PCA on many SDSS spectra?` **Chapter 7**.
   - `How do I fit a multi-component Gaussian (or any other function) to my histogram?` **Chapter 5**.
   - `How do I decide if I have "detection"?` **Chapters 4, 5, 8**.
   - `How do I fit a multi-component Gaussian (while accounting for errors) to my multi-dimensional data?` **Chapter 6**.
   - `How do I justify the use of,  for example, a parabola instead of a straight line to fit my data?` **Chapter 5**.
   - `How do I use Markov Chain Monte Carlo to fit a complex function to my multi-dimensional data?` **Chapter 5**.
   - `How do I estimate underlying density traced by a finite-size sample of points?` **Chapter 6**.
   - `How do I find clusters (over-densities, classes, features) in my data set?` **Chapters 6 and 9**.
   - `How do I estimate a light curve period (Lomb-Scargle)?` **Chapter 10**.
   - `How do I analyze a non-periodic light curve?` **Chapter 10**.
   - `How do I estimate power spectrum for unevenly sampled data with large heteroscedastic uncertainties?` **Chapter 10**.
   - `How do I use detection times for individual photons to estimate exponential decay time?` **Chapter 10**.


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