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- Astrostatistics Tools (`astropy.stats`)

# Astrostatistics Tools (`astropy.stats`)

## Introduction

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

## Getting Started

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

## 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

`bayesian_blocks`(t[, x, sigma, fitness]) | Compute optimal segmentation of data with Scargle’s Bayesian Blocks
`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.
`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
`knuth_bin_width`(data[, return_bins, quiet]) | Return the optimal histogram bin width using Knuth’s rule.
`mad_std`(data) | 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
`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.

#### 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
`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