Customizing your Glue environment¶
config.py file as described in Configuring Glue via a startup file, you can
customize many aspects of your Glue environment, which are described in the
Before we talk about the different components of the Glue environment that you
can customize, we first need to look at registries. Glue is written so as to
allow users to easily register new data viewers, tools, exporters, and more.
Registering such components can be done via registries located in the
glue.config sub-package. Registries include for example
colormaps, and so on. As demonstrated below, some
registries can be used as decorators (see e.g. Custom Link Functions)
and for others you can add items using the
add method (see e.g. Custom
In the following sections, we show a few examples of registering new functionality, and a full list of available registries is given in Complete list of registries.
Custom Data Loaders¶
Glue lets you create custom data loader functions, to use from within the GUI.
Here’s a quick example: the default image loader in Glue reads each color in
an RGB image into 3 two-dimensional components. Perhaps you want to be able
to load these images into a single 3-dimensional component called
Here’s how you could do this:
from glue.config import data_factory from glue.core import Data from skimage.io import imread def is_jpeg(filename, **kwargs): return filename.endswith('.jpeg') @data_factory('3D image loader', is_jpeg) def read_jpeg(file_name): im = imread(file_name) return Data(cube=im)
Let’s look at this line-by-line:
- The is_jpeg function takes a filename and keywords as input, and returns True if a data factory can handle this file
@data_factorydecorator is how Glue “finds” this function. Its two arguments are a label, and the is_jpeg identifier function
- The first line in
read_jpeguses scikit-image to load an image file into a NumPy array.
- The second line constructs a Data object from this array, and returns the result.
If you put this in your
config.py file, you will see a new
file type when loading data:
If you open a file using this file type selection, Glue will pass the path of this file to your function, and use the resulting Data object.
If you are defining a data factory that may clash with an existing one, for
example if you are defining a loader for a specific type of FITS file, then
make sure that the identifier function (e.g.
is_jpeg above) returns True
only for that specific subset of FITS files. Then you can set the
keyword in the
@data_factory decorator. The value should be an integer or
floating-point number, with larger numbers indicating a higher priority.
For more examples of custom data loaders, see the example repository.
The Custom Data Loaders described above allow Glue to recognize more file formats than originally implemented, but it is also possible to write entire new ways of importing data, including new GUI dialogs. An example would be a dialog that allows the user to query and download online data.
Currently, an importer should be defined as a function that returns a list of
Data objects. In future we may relax this latter
requirement and allow existing tools in Glue to interpret the data.
An importer can be defined using the
from glue.config import importer from glue.core import Data @importer("Import from custom source") def my_importer(): # Main code here return [Data(...), Data(...)]
The label in the
@importer decorator is the text that will appear in the
Import menu in Glue.
You can add additional matplotlib colormaps to Glue’s image viewer by adding
the following code into
from glue.config import colormaps from matplotlib.cm import Paired colormaps.add('Paired', Paired)
Custom Subset Actions¶
You can add menu items to run custom functions on subsets. Use the following
from glue.config import single_subset_action def callback(subset, data_collection): print "Called with %s, %s" % (subset, data_collection) single_subset_action('Menu title', callback)
This menu item is available by right clicking on a subset when a single subset is selected in the Data Collection window. Note that you must select the subset specific to a particular Data set, and not the parent Subset Group.
Complete list of registries¶
A few registries have been demonstrated above, and a complete list of main
registries are listed below. All can be imported from
glue.config - each
registry is an instance of a class, given in the second column, and which
provides more information about what the registry is and how it can be used.
|Registry name||Registry class|
Deferring loading of plug-in functionality (advanced)¶
In some cases, you may want to defer the loading of your component/functionality until it is actually needed. To do this:
- Place the code for your plugin in a file or package that could be imported
config.py(but don’t import it directly - it just has to be importable)
- Include a function called
setupalongside the plugin, and this function should contain code to actually add your custom tools to the appropriate registries.
config.py, you can then add the plugin file or package to a registry by using the
lazy_addmethod and pass a string giving the name of the package or sub-package containing the plugin.
Imagine that you have created a data viewer
MyQtViewer. You could
directly register it using:
from glue.config import qt_client qt_client.add(MyQtViewer)
but if you want to defer the loading of the
MyQtViewer class, you can
place the definition of
MyQtViewer in a file called e.g.
my_qt_viewer.py that is located in the same directory as your
config.py file. This file should look something like:
class MyQtViewer(...): ... def setup(): from glue.config import qt_client qt_client.add(MyQtViewer)
config.py, you can do:
from glue.config import qt_client qt_client.lazy_add('my_qt_viewer')
With this in place, the
setup in your plugin will only get called if the
Qt data viewers are needed, but you will avoid unecessarily importing Qt if
you only want to access