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- Data Visualization (`astropy.visualization`)

# Data Visualization (`astropy.visualization`)

## Introduction

`astropy.visualization` provides functionality that can be helpful when visualizing data. At the moment, functionalities include enhanced histograms, image normalizing (including both scaling and stretching), and custom plotting styles for matplotlib.

## Using `astropy.visualization`

- Image stretching and normalization
  - Intervals and Normalization
  - Stretching
  - Combining transformations
  - Matplotlib normalization
- Choosing Histogram Bins
  - Normal Reference Rules
  - Bayesian Models

## Scripts

This module includes a command-line script, `fits2bitmap` to convert FITS images to bitmaps, including scaling and stretching of the image. To find out more about the available options and how to use it, type:

    $ fits2bitmap --help

## Reference/API

### astropy.visualization.mpl_style Module

### astropy.visualization Package

#### Functions

`hist`(x[, bins, ax]) | Enhanced histogram function
`quantity_support`([format]) | Enable support for plotting `astropy.units.Quantity` instances in matplotlib.
`scale_image`(image[, scale, power, asinh_a, ...]) | Perform scaling/stretching of an image between minimum and maximum cut levels.

#### Classes

`AsinhStretch` | An asinh stretch.
`AsymmetricPercentileInterval`(...[, n_samples]) | Interval based on a keeping a specified fraction of pixels (can be asymmetric).
`BaseInterval` | Base class for the interval classes, which, when called with an array of values, return an interval computed following different algorithms.
`BaseStretch` | Base class for the stretch classes, which, when called with an array of values in the range [0:1], return an transformed array of values, also in the range [0:1].
`BaseTransform` | A transformation object.
`CompositeTransform`(transform_1, transform_2) | A combination of two transforms.
`ContrastBiasStretch` | A stretch that takes into account contrast and bias.
`HistEqStretch` | A histogram equalization stretch.
`LinearStretch` | A linear stretch.
`LogStretch` | A log stretch.
`ManualInterval`(vmin, vmax) | Interval based on user-specified values.
`MinMaxInterval` | Interval based on the minimum and maximum values in the data.
`PercentileInterval`(percentile[, n_samples]) | Interval based on a keeping a specified fraction of pixels.
`PowerDistStretch` | An alternative power stretch.
`PowerStretch` | A power stretch.
`SinhStretch` | A sinh stretch.
`SqrtStretch` | A square root stretch.
`SquaredStretch` | A convenience class for a power stretch of 2.

#### Class Inheritance Diagram

### astropy.visualization.mpl_normalize Module

Normalization class for Matplotlib that can be used to produce colorbars.

#### Classes

`ImageNormalize`([vmin, vmax, stretch, clip]) | Normalization class to be used with Matplotlib.

#### Class Inheritance Diagram