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- Astropy v5.2.1 »
- Data Visualization (`astropy.visualization`)

# Data Visualization (`astropy.visualization`)

## Introduction

`astropy.visualization` provides functionality that can be helpful when visualizing data. This includes a framework for plotting Astronomical images with coordinates with Matplotlib (previously the standalone wcsaxes package), functionality related to image normalization (including both scaling and stretching), smart histogram plotting, RGB color image creation from separate images, and custom plotting styles for Matplotlib.

## Using `astropy.visualization`

- Plotting Astropy objects in Matplotlib
  - Plotting quantities
  - Plotting times
- Making plots with world coordinates (WCSAxes)
  - Getting started
  - Using WCSAxes
  - Reference/API
- Image stretching and normalization
  - Intervals and Normalization
  - Stretching
  - Combining transformations
  - Matplotlib normalization
  - Combining stretches and Matplotlib normalization
- Choosing Histogram Bins
  - Normal Reference Rules
  - Bayesian Models
- Creating color RGB images
- Creating color RGB images using the Lupton et al (2004) scheme

## 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 Package

#### Functions

`hist`(x[, bins, ax, max_bins]) | Enhanced histogram function
`imshow_norm`(data[, ax]) | A convenience function to call matplotlib's `matplotlib.pyplot.imshow` function, using an `ImageNormalize` object as the normalization.
`make_lupton_rgb`(image_r, image_g, image_b[, ...]) | Return a Red/Green/Blue color image from up to 3 images using an asinh stretch.
`quantity_support`([format]) | Enable support for plotting `astropy.units.Quantity` instances in matplotlib.
`simple_norm`(data[, stretch, power, asinh_a, ...]) | Return a Normalization class that can be used for displaying images with Matplotlib.
`time_support`(*[, scale, format, simplify]) | Enable support for plotting `astropy.time.Time` instances in matplotlib.

#### Classes

`AsinhStretch`([a]) | 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.
`CompositeStretch`(transform_1, transform_2) | A combination of two stretches.
`CompositeTransform`(transform_1, transform_2) | A combination of two transforms.
`ContrastBiasStretch`(contrast, bias) | A stretch that takes into account contrast and bias.
`HistEqStretch`(data[, values]) | A histogram equalization stretch.
`ImageNormalize`([data, interval, vmin, vmax, ...]) | Normalization class to be used with Matplotlib.
`LinearStretch`([slope, intercept]) | A linear stretch with a slope and offset.
`LogStretch`([a]) | 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`([a]) | An alternative power stretch.
`PowerStretch`(a) | A power stretch.
`SinhStretch`([a]) | A sinh stretch.
`SqrtStretch`() | A square root stretch.
`SquaredStretch`() | A convenience class for a power stretch of 2.
`ZScaleInterval`([n_samples, contrast, ...]) | Interval based on IRAF's zscale.

#### Class Inheritance Diagram

### astropy.visualization.mpl_normalize Module

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

#### Functions

`simple_norm`(data[, stretch, power, asinh_a, ...]) | Return a Normalization class that can be used for displaying images with Matplotlib.
`imshow_norm`(data[, ax]) | A convenience function to call matplotlib's `matplotlib.pyplot.imshow` function, using an `ImageNormalize` object as the normalization.

#### Classes

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

#### Class Inheritance Diagram