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.

Constants

The astropy.stats package defines two constants useful for converting between Gaussian sigma and full width at half maximum (FWHM):

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
2.3548200450309493
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
0.42466090014400953

See Also

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(data[, c, M, axis]) Compute the biweight location.
biweight_midcorrelation(x, y[, c, M, ...]) Compute the biweight midcorrelation between two variables.
biweight_midcovariance(data[, c, M, ...]) Compute the biweight midcovariance between pairs of multiple variables.
biweight_midvariance(data[, c, M, axis, ...]) Compute the biweight midvariance.
biweight_scale(data[, c, M, axis, ...]) Compute the biweight scale.
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, func, ignore_nan]) Calculate a robust standard deviation using the median absolute deviation (MAD).
median_absolute_deviation(data[, axis, ...]) Calculate the median absolute deviation (MAD).
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 on the provided 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

Inheritance diagram of astropy.stats.bayesian_blocks.Events, astropy.stats.bayesian_blocks.FitnessFunc, astropy.stats.lombscargle.core.LombScargle, astropy.stats.bayesian_blocks.PointMeasures, astropy.stats.bayesian_blocks.RegularEvents