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

binom_conf_interval(k, n[, ...])	Binomial proportion confidence interval given k successes, n trials.
binned_binom_proportion(x, success[, bins, ...])	Binomial proportion and confidence interval in bins of a continuous variable x.
poisson_conf_interval(n[, interval, sigma, ...])	Poisson parameter confidence interval given observed counts.
median_absolute_deviation(data[, axis, ...])	Calculate the median absolute deviation (MAD).
mad_std(data[, axis, func, ignore_nan])	Calculate a robust standard deviation using the median absolute deviation (MAD).
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.
bootstrap(data[, bootnum, samples, bootfunc])	Performs bootstrap resampling on numpy arrays.
kuiper(data[, cdf, args])	Compute the Kuiper statistic.
kuiper_two(data1, data2)	Compute the Kuiper statistic to compare two samples.
kuiper_false_positive_probability(D, N)	Compute the false positive probability for the Kuiper statistic.
cdf_from_intervals(breaks, totals)	Construct a callable piecewise-linear CDF from a pair of arrays.
interval_overlap_length(i1, i2)	Compute the length of overlap of two intervals.
histogram_intervals(n, breaks, totals)	Histogram of a piecewise-constant weight function.
fold_intervals(intervals)	Fold the weighted intervals to the interval (0,1).
biweight_location(data[, c, M, axis, ignore_nan])	Compute the biweight location.
biweight_scale(data[, c, M, axis, ...])	Compute the biweight scale.
biweight_midvariance(data[, c, M, axis, ...])	Compute the biweight midvariance.
biweight_midcovariance(data[, c, M, ...])	Compute the biweight midcovariance between pairs of multiple variables.
biweight_midcorrelation(x, y[, c, M, ...])	Compute the biweight midcorrelation between two variables.
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.
jackknife_resampling(data)	Performs jackknife resampling on numpy arrays.
jackknife_stats(data, statistic[, ...])	Performs jackknife estimation on the basis of jackknife resamples.
circmean(data[, axis, weights])	Computes the circular mean angle of an array of circular data.
circstd(data[, axis, weights, method])	Computes the circular standard deviation of an array of circular data.
circvar(data[, axis, weights])	Computes the circular variance 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.
circcorrcoef(alpha, beta[, axis, ...])	Computes the circular correlation coefficient between two array of circular data.
rayleightest(data[, axis, weights])	Performs the Rayleigh test of uniformity.
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.
vonmisesmle(data[, axis, weights])	Computes the Maximum Likelihood Estimator (MLE) for the parameters of the von Mises distribution.
bayesian_blocks(t[, x, sigma, fitness])	Compute optimal segmentation of data with Scargle's Bayesian Blocks.
histogram(a[, bins, range, weights])	Enhanced histogram function, providing adaptive binnings.
scott_bin_width(data[, return_bins])	Return the optimal histogram bin width using Scott's rule.
freedman_bin_width(data[, return_bins])	Return the optimal histogram bin width using the Freedman-Diaconis rule.
knuth_bin_width(data[, return_bins, quiet])	Return the optimal histogram bin width using Knuth's rule.
calculate_bin_edges(a[, bins, range, weights])	Calculate histogram bin edges like numpy.histogram_bin_edges.
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.
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.

Classes

SigmaClip([sigma, sigma_lower, sigma_upper, ...])	Class to perform sigma clipping.
FitnessFunc([p0, gamma, ncp_prior])	Base class for bayesian blocks fitness functions.
Events([p0, gamma, ncp_prior])	Bayesian blocks fitness for binned or unbinned events.
RegularEvents(dt[, p0, gamma, ncp_prior])	Bayesian blocks fitness for regular events.
PointMeasures([p0, gamma, ncp_prior])	Bayesian blocks fitness for point measures.
RipleysKEstimator(area[, x_max, y_max, ...])	Estimators for Ripley's K function for two-dimensional spatial data.

Class Inheritance Diagram