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.
Custom Data/Subset Exporters¶
In addition to allowing you to create custom loaders and importers, glue lets you create custom exporters for datasets and subsets. These exporters can be accessed by control-clicking on specific datasets or subsets:
and selecting Export Data or Export Subsets.
A custom exporter looks like the following:
from glue.config import data_exporter @data_exporter('My exporter') def export_custom(filename, data): # write out the data here
data argument to the function can be either a
Data or a
Subset object, and
filename is a string which gives the file path. You can then write out the
file in any way you like. Note that if you get a
Subset object, you should make sure you export the
data subset, not just the mask itself. For e.g. 2-dimensional datasets, we find
that it is more intuitive to export arrays the same size as the original data
but with the values not in the subset masked or set to NaN.
Custom subset mask importers¶
When right-clicking on datasets or subsets, it is possible to select to import
subset masks from files (as well as export them). To define a new importer
format, use the
from glue.config import subset_mask_importer @subset_mask_importer(label='My Format') def my_subset_mask_importer(filename): # write code that reads in subset masks here
The function should return a dictionary where the labels are the names of the
subsets, and the values are Numpy boolean arrays. The
decorator can also take an optional
extension argument that takes a list of
Custom subset mask exporters¶
When right-clicking on datasets or subsets, it is also possible to select to
export subset masks to files. To define a new exporter format, use the
from glue.config import subset_mask_exporter @subset_mask_exporter(label='My Format') def my_subset_mask_exporter(filename, masks): # write code that writes out subset masks here
masks argument will be given a dictionary where each key is the name of
a subset, and each value is a Numpy boolean array. The
decorator can also take an optional
extension argument that takes a list of
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)
You can add menu items to run custom functions when selecting datasets, subset
groups or subsets in the data collection. To do this, you should define a
function to be called when the menu item is selected, and use the
from glue.config import layer_action @layer_action('Do something') def callback(selected_layers, data_collection): print("Called with %s, %s" % (selected_layers, data_collection))
layer_action decorator takes an optional
single keyword argument
that can be set to True or False to indicate whether the action should only
appear when a single dataset, subset group, or subset is selected. If
is True, the following keyword arguments can be used to further control when
to show the action:
data: only show the action when selecting a dataset
subset_group: only show the action when selecting a subset group
subset: only show the action when selecting a subset
These default to False, so setting e.g.:
@layer_action('Do something', single=True, data=True, subset=True) ...
means that the action will appear when a single dataset or subset is selected but not when a subset group is selected.
The callback function is called with two arguments. If
single is True, the
first argument is the selected layer, otherwise it is the list of selected
layers. The second argument is the
Custom Preference Panes¶
You can also add custom panes in the Qt preferences dialog. To do this, you
should create a Qt widget that encapsulates the preferences you want to
include, and you should make sure that this widget has a
that will get called when the preferences dialog is closed. This method should
then set any settings appropriately in the application state. The following is
an example of a custom preference pane:
from glue.config import settings, preference_panes from qtpy import QtWidgets class MyPreferences(QtWidgets.QWidget): def __init__(self, parent=None): super(MyPreferences, self).__init__(parent=parent) self.layout = QtWidgets.QFormLayout() self.option1 = QtWidgets.QLineEdit() self.option2 = QtWidgets.QCheckBox() self.layout.addRow("Option 1", self.option1) self.layout.addRow("Option 2", self.option2) self.setLayout(self.layout) self.option1.setText(settings.OPTION1) self.option2.setChecked(settings.OPTION2) def finalize(self): settings.OPTION1 = self.option1.text() settings.OPTION2 = self.option2.isChecked() settings.add('OPTION1', '') settings.add('OPTION2', False, bool) preference_panes.add('My preferences', MyPreferences)
This example then looks this the following once glue is loaded:
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