Astrostatistics Tools (astropy.stats)

Introduction

The astropy.stats package holds statistical functions or algorithms used in astronomy and astropy.

Getting Started

Most tools are fairly self-contained, and include relevant examples in their docstrings.

Using astropy.stats

More detailed information on using the package is provided on separate pages, listed below.

• Lomb-Scargle Periodograms
  • Basic Usage
  • Periodogram Normalizations
  • Periodogram Algorithms
  • Example
  • Literature References

See Also

• 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.

• astropy.visualization.hist()

  The histogram() routine and related functionality defined here are used within the astropy.visualization.hist() function. For a discussion of these methods for determining histogram binnings, see Choosing Histogram Bins.

Reference/API

astropy.stats Package

This subpackage contains statistical tools provided for or used by Astropy.

While the scipy.stats package contains a wide range of statistical tools, it is a general-purpose package, and is missing some that are particularly useful to astronomy or are used in an atypical way in astronomy. This package is intended to provide such functionality, but not to replace scipy.stats if its implementation satisfies astronomers’ needs.

Functions

akaike_info_criterion(log_likelihood, ...)	Computes the Akaike Information Criterion (AIC).
akaike_info_criterion_lsq(ssr, n_params, ...)	Computes the Akaike Information Criterion assuming that the observations are Gaussian distributed.
bayesian_blocks(t[, x, sigma, fitness])	Compute optimal segmentation of data with Scargle’s Bayesian Blocks
bayesian_info_criterion(log_likelihood, ...)	Computes the Bayesian Information Criterion (BIC) given the log of the likelihood function evaluated at the estimated (or analytically derived) parameters, the number of parameters, and the number of samples.
bayesian_info_criterion_lsq(ssr, n_params, ...)	Computes the Bayesian Information Criterion (BIC) assuming that the observations come from a Gaussian distribution.
binned_binom_proportion(x, success[, bins, ...])	Binomial proportion and confidence interval in bins of a continuous variable x.
binom_conf_interval(k, n[, conf, interval])	Binomial proportion confidence interval given k successes, n trials.
biweight_location(a[, c, M])	Compute the biweight location for an array.
biweight_midvariance(a[, c, M])	Compute the biweight midvariance for an array.
bootstrap(data[, bootnum, samples, bootfunc])	Performs bootstrap resampling on numpy arrays.
circcorrcoef(alpha, beta[, axis, ...])	Computes the circular correlation coefficient between two array of circular data.
circmean(data[, axis, weights])	Computes the circular mean angle of an array of circular data.
circmoment(data[, p, centered, axis, weights])	Computes the p-th trigonometric circular moment for an array of circular data.
circvar(data[, axis, weights])	Computes the circular variance of an array of circular data.
freedman_bin_width(data[, return_bins])	Return the optimal histogram bin width using the Freedman-Diaconis rule
histogram(a[, bins, range, weights])	Enhanced histogram function, providing adaptive binnings
jackknife_resampling(data)	Performs jackknife resampling on numpy arrays.
jackknife_stats(data, statistic[, conf_lvl])	Performs jackknife estimation on the basis of jackknife resamples.
knuth_bin_width(data[, return_bins, quiet])	Return the optimal histogram bin width using Knuth’s rule.
mad_std(data[, axis])	Calculate a robust standard deviation using the median absolute deviation (MAD).
median_absolute_deviation(a[, axis])	Compute the median absolute deviation.
poisson_conf_interval(n[, interval, sigma, ...])	Poisson parameter confidence interval given observed counts
rayleightest(data[, axis, weights])	Performs the Rayleigh test of uniformity.
scott_bin_width(data[, return_bins])	Return the optimal histogram bin width using Scott’s rule
sigma_clip(data[, sigma, sigma_lower, ...])	Perform sigma-clipping on the provided data.
sigma_clipped_stats(data[, mask, ...])	Calculate sigma-clipped statistics from data.
signal_to_noise_oir_ccd(t, source_eps, ...)	Computes the signal to noise ratio for source being observed in the optical/IR using a CCD.
vonmisesmle(data[, axis])	Computes the Maximum Likelihood Estimator (MLE) for the parameters of the von Mises distribution.
vtest(data[, mu, axis, weights])	Performs the Rayleigh test of uniformity where the alternative hypothesis H1 is assumed to have a known mean angle mu.

Classes

Events([p0, gamma, ncp_prior])	Bayesian blocks fitness for binned or unbinned events
FitnessFunc([p0, gamma, ncp_prior])	Base class for bayesian blocks fitness functions
LombScargle(t, y[, dy, fit_mean, ...])	Compute the Lomb-Scargle Periodogram
PointMeasures([p0, gamma, ncp_prior])	Bayesian blocks fitness for point measures
RegularEvents(dt[, p0, gamma, ncp_prior])	Bayesian blocks fitness for regular events

Class Inheritance Diagram