Blog

Astropy visualization

Bases: object. Helper class to control one of the coordinates in the WCSAxes. The CoordinatesMap object this coordinate belongs to. The index of this coordinate in the CoordinatesMap. The type of this coordinate, which is used to determine the wrapping and boundary behavior of coordinates.

The frame of the WCSAxes. The format to use - by default the formatting will be adjusted depending on whether Matplotlib is using LaTeX or MathTex. True if default labels will be rendered. Whether to plot the contours by determining the grid lines in world coordinates and then plotting them in world coordinates 'lines' or by determining the world coordinates at many positions in the image and then drawing contours 'contours'.

The first is recommended for 2-d images, while for 3-d or higher dimensional cubes, the 'contours' option is recommended. Keywords are passed to matplotlib. These can include keywords to set the colorsizeweightand other text properties. The axes on which the axis label for this coordinate should appear.

Should be a string containing zero or more of 'b''t''l''r'. For example, 'lb' will lead the axis label to be shown on the left and bottom axis.

Whether to use decimal formatting. By default this is False for degrees or hours which therefore use sexagesimal formatting and True for all other units.

The separator between numbers in sexagesimal representation. Can be either a string or a tuple or None for default. Other keyword arguments are passed to matplotlib. The axes on which the tick labels for this coordinate should appear.

For example, 'lb' will lead the tick labels to be shown on the left and bottom axis.

astropy visualization

The visibility of ticks. At most one of the options from valuesspacingor number can be specified.This includes a framework for plotting Astronomical images with coordinates with Matplotlib previously the standalone wcsaxes packagefunctionality related to image normalization including both scaling and stretchingsmart histogram plotting, RGB color image creation from separate images, and custom plotting styles for Matplotlib.

This module includes a command-line script, fits2bitmap to convert FITS images to bitmaps, including scaling and stretching of the image.

astropy visualization

To find out more about the available options and how to use it, type:. Enable support for plotting astropy. Quantity instances in matplotlib. Time instances in matplotlib.

AsinhStretch [a]. Base class for the interval classes, which, when called with an array of values, return an interval computed following different algorithms. Base class for the stretch classes, which, when called with an array of values in the range [], return an transformed array of values, also in the range []. LogStretch [a]. PowerDistStretch [a]. PowerStretch a.

Astropy development visualization (Nov 2013)

SinhStretch [a]. Inheritance diagram of astropy. AsinhStretch, astropy. AsymmetricPercentileInterval, astropy. BaseInterval, astropy. BaseStretch, astropy.

astropy visualization

BaseTransform, astropy. CompositeStretch, astropy.

Source code for astropy.visualization.wcsaxes.core

CompositeTransform, astropy. ContrastBiasStretch, astropy. HistEqStretch, astropy. ImageNormalize, astropy.

LinearStretch, astropy. LogStretch, astropy. ManualInterval, astropy. MinMaxInterval, astropy. PercentileInterval, astropy. PowerDistStretch, astropy.Bases: object. Helper class to control one of the coordinates in the WCSAxes. The CoordinatesMap object this coordinate belongs to. The index of this coordinate in the CoordinatesMap. The type of this coordinate, which is used to determine the wrapping and boundary behavior of coordinates.

The frame of the WCSAxes. Whether to plot the contours by determining the grid lines in world coordinates and then plotting them in world coordinates 'lines' or by determining the world coordinates at many positions in the image and then drawing contours 'contours'. The first is recommended for 2-d images, while for 3-d or higher dimensional cubes, the 'contours' option is recommended. Keywords are passed to matplotlib. These can include keywords to set the colorsizeweightand other text properties.

The axes on which the axis label for this coordinate should appear. Should be a string containing zero or more of 'b''t''l''r'. For example, 'lb' will lead the axis label to be shown on the left and bottom axis. Keyword arguments are passed to matplotlib. The axes on which the tick labels for this coordinate should appear. For example, 'lb' will lead the tick labels to be shown on the left and bottom axis.

The visibility of ticks. At most one of the options from valuesspacingor number can be specified. The axes on which the ticks for this coordinate should appear. For example, 'lb' will lead the ticks to be shown on the left and bottom axis. Setting as False will hide ticks along this coordinate. Page Contents CoordinateHelper. Can be either a string or a tuple.The astropy. Two main types of transformations are provided:. In addition, classes are provided in order to identify lower and upper limits for a dataset based on specific algorithms such as using percentiles.

Identifying lower and upper limits, as well as re-normalizing, is described in the Intervals and Normalization section, while stretching is described in the Stretching section.

astropy visualization

Several classes are provided for determining intervals and for normalizing values in this interval to the [] range.

One of the simplest examples is the MinMaxInterval which determines the limits of the values based on the minimum and maximum values in the array. The class is instantiated with no arguments:. The interval instance can also be called like a function to actually normalize values to the range:. For these three, values in the array can fall outside of the limits given by the interval. A clip argument is provided to control the behavior of the normalization when values fall outside the limits:.

These map a [] range onto a transformed [] range. A simple example is the SqrtStretch class:. As for the intervals, values outside the [] range can be treated differently depending on the clip argument. By default, output values are clipped to the [] range:. The stretch functions are similar but not always strictly identical to those used in e.

DS9 although they should have the same behavior. The equations for the DS9 stretches can be found here and can be compared to the equations for our stretches provided in the astropy. The main difference between our stretches and DS9 is that we have adjusted them so that the [] range always maps exactly to the [] range.

For example, to apply normalization based on a percentile value, followed by a square root stretch, you can do:. As before, the combined transformation can also accept a clip argument which is True by default.

Matplotlib allows a custom normalization and stretch to be used when showing data, and requires a Normalize object to be passed to e.The astropy package contains key functionality and common tools needed for performing astronomy and astrophysics with Python. It is at the core of the Astropy Projectwhich aims to enable the community to develop a robust ecosystem of Affiliated Packages covering a broad range of needs for astronomical research, data processing, and data analysis.

If you use Astropy for work presented in a publication or talk please help the project via proper citation or acknowledgement.

This also applies to use of software or affliated packages that depend on the astropy core package. The developer documentation contains instructions for how to contribute to Astropy or affiliated packages, as well as coding, documentation, and testing guidelines.

For the guiding vision of this process and the project as a whole, see Vision for a Common Astronomy Python Package. Module Index. Search Page.

Important If you use Astropy for work presented in a publication or talk please help the project via proper citation or acknowledgement.The astropy. For computing bins without the accompanying plot, see astropy. Source codepnghires. Upon visual inspection, it is clear that each of these choices is suboptimal: with 10 bins, the fine structure of the data distribution is lost, while with bins, heights of individual bins are affected by sampling error.

The tried-and-true method employed by most scientists is a trial and error approach that attempts to find a suitable midpoint between these. These methods are implemented in astropy.

These rules proceed by assuming the data is close to normally-distributed, and applying a rule-of-thumb intended to minimize the difference between the histogram and the underlying distribution of data. As we can see, both of these rules of thumb choose an intermediate number of bins which provide a good tradeoff between data representation and noise suppression.

Other methods of bin selection use fitness functions computed on the actual data to choose an optimal binning.

Because both of these require the minimization of a cost function across the dataset, they are more computationally intensive than the rules-of-thumb mentioned above. Here are the results of these procedures for the above dataset:. Compared to standard defaults, these Bayesian optimization methods provide a much more principled means of choosing histogram binning. The following figure shows the results of these two rules on the above dataset: Source codepnghires.

Here are the results of these procedures for the above dataset: Source codepnghires.At the moment, the main functionality is image normalizing including both scaling and stretching. Another feature included here is a plotting style for matplotlib.

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:. Data Visualization astropy.

Using astropy. 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 [], return an transformed array of values, also in the range []. BaseTransform A transformation object. ContrastBiasStretch A stretch that takes into account contrast and bias.

HistEqStretch A histogram equalization stretch. LinearStretch A linear stretch. LogStretch A log stretch. MinMaxInterval Interval based on the minimum and maximum values in the data. 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. Page Contents Data Visualization astropy. Base class for the interval classes, which, when called with an array of values, return an interval computed following different algorithms.

Base class for the stretch classes, which, when called with an array of values in the range [], return an transformed array of values, also in the range [].


thoughts on “Astropy visualization

Leave a Reply