The astropy.stats package holds statistical functions or algorithms used in astronomy. While the scipy.stats and statsmodels packages contains a wide range of statistical tools, they are general-purpose packages and are missing some tools that are particularly useful or specific to astronomy. This package is intended to provide such functionality, but not to replace scipy.stats if its implementation satisfies astronomers' needs.
A number of different tools are contained in the stats package, and they can be accessed by importing them:
>>> from astropy import stats
A full list of the different tools are provided below. Please see the documentation for their different usages. For example, sigma clipping, which is a common way to estimate the background of an image, can be performed with the :func:`~astropy.stats.sigma_clip` function. By default, the function returns a masked array where outliers are masked.
To estimate the background of an image:
>>> data = [1, 5, 6, 8, 100, 5, 3, 2]
>>> stats.sigma_clip(data, sigma=2, maxiters=5)
masked_array(data=[1, 5, 6, 8, --, 5, 3, 2],
mask=[False, False, False, False, True, False, False, False],
fill_value=999999)
Alternatively, the :class:`~astropy.stats.SigmaClip` class provides an object-oriented interface to sigma clipping, which also returns a masked array by default:
>>> sigclip = stats.SigmaClip(sigma=2, maxiters=5)
>>> sigclip(data)
masked_array(data=[1, 5, 6, 8, --, 5, 3, 2],
mask=[False, False, False, False, True, False, False, False],
fill_value=999999)
In addition, there are also several convenience functions for making the calculation of statistics even more convenient. For example, :func:`~astropy.stats.sigma_clipped_stats` will return the mean, median, and standard deviation of a sigma-clipped array:
>>> stats.sigma_clipped_stats(data, sigma=2, maxiters=5) # doctest: +FLOAT_CMP (4.2857142857142856, 5.0, 2.2497165354319457)
There are also tools for calculating :ref:`robust statistics <stats-robust>`, sampling the data, :ref:`circular statistics <stats-circular>`, confidence limits, spatial statistics, and adaptive histograms.
Most tools are fairly self-contained, and include relevant examples in their docstrings.
More detailed information on using the package is provided on separate pages, listed below.
.. toctree:: :maxdepth: 2 robust.rst circ.rst ripley.rst ../visualization/histogram.rst
The astropy.stats package defines two constants useful for converting between Gaussian sigma and full width at half maximum (FWHM):
.. data:: gaussian_sigma_to_fwhm
Factor with which to multiply Gaussian 1-sigma standard deviation
to convert it to full width at half maximum (FWHM).
>>> from astropy.stats import gaussian_sigma_to_fwhm
>>> gaussian_sigma_to_fwhm # doctest: +FLOAT_CMP
2.3548200450309493
.. data:: gaussian_fwhm_to_sigma
Factor with which to multiply Gaussian full width at half maximum
(FWHM) to convert it to 1-sigma standard deviation.
>>> from astropy.stats import gaussian_fwhm_to_sigma
>>> gaussian_fwhm_to_sigma # doctest: +FLOAT_CMP
0.42466090014400953
- :mod:`scipy.stats`
- This SciPy package contains a variety of useful statistical functions and classes. The functionality in astropy.stats is intended to supplement this, not replace it.
- statsmodels
- The statsmodels package provides functionality for estimating different statistical models, tests, and data exploration.
- astroML
- The astroML package is a Python module for machine learning and data mining. Some of the tools from this package have been migrated here, but there are still a number of tools there that are useful for astronomy and statistical analysis.
- :func:`astropy.visualization.hist`
- The :func:`~astropy.stats.histogram` routine and related functionality defined here are used within the :func:`astropy.visualization.hist` function. For a discussion of these methods for determining histogram binnings, see :ref:`astropy-visualization-hist`.
.. automodapi:: astropy.stats