Getting started#

Starting up the application#

To start using glue in the Jupyter notebook or Jupyter lab, you will need to call the jglue() function:

>>> from glue_jupyter import jglue
>>> app = jglue()

This will automatically set up a data container (called a data collection in glue) and the app object can then be used to load data, link data, and create visualizations.

Loading data#

For instance, suppose that you have a CSV file that you want to visualize. Start off by loading it with:

>>> table = app.load_data('mytable.csv')

The table variable points to a glue Data object. You can print out a description of the dataset using:

>>> print(table)
Data Set: w5_psc
Number of dimensions: 1
Shape: 17771
Main components:
 - ID
 - RAJ2000 [deg]
 - DEJ2000 [deg]
 - Jmag [mag]
 - Hmag [mag]
 - Ksmag [mag]
 - Type
Coordinate components:
 - Pixel Axis 0 [x]
 - World 0

This shows that the table has seven main columns (or ‘components’). The ‘Coordinate components’ are essentially indexes for the table and can be ignored for now. To access a particular columns, you can use the indexing notation:

>>> table['RAJ2000']
array([41.081526, 41.09856 , 41.100737, ..., 46.015912, 46.022295,

You can also find out more about how to work with and extract values from glue Data objects, as well as creating selections/subsets of data programmatically in this tutorial in the main glue documentation.

Creating visualizations#

You can then create visualizations using methods on app - for example, to create a histogram visualization, use histogram1d:

>>> histogram = app.histogram1d(data=table)

for a 2-d scatter plot, use scatter2d:

>>> scatter2d = app.scatter2d(data=table)

and for a 3-d scatter plot, use scatter3d:

>>> scatter3d = app.scatter3d(data=table)

Other available visualizations include :profile1d for collapsing n-dimensional datasets down to one dimension, imshow for images (including image slices through n-dimensional datasets), and volshow for volume renderings.

Creating and accessing subsets#

To create subsets, click on one of the icons above the image viewer (typically these icons are blue shapes). You will then be able to click and drag in the image viewer to defined a subset. The drop-down menu to the right of the selection tools can be used to determine whether to create a new subset or change an existing one.

Once you have created one or more subsets, you can access them programmatically with:

>>> subset = table.subsets[0]  # access the first subset

and you can e.g. convert this to a boolean mask:

>>> subset.to_mask()
array([ True,  True,  True, ...,  True,  True,  True])

or you can also retrieve the subset of values for a given column/attribute:

>>> subset['RAJ2000']
array([41.081526, 41.09856 , 41.100737, ..., 46.015912, 46.022295,

Modifying viewers and layers programmatically#

Each viewer has an associated ‘state’ which is an object with properties that can be used to control the appearance and contents of the viewer. The state is accessible via the .state attribute:

>>> histogram.state
<glue.viewers.histogram.state.HistogramViewerState object at 0x7f9714b25c50>

For example, if you are using a 2D scatter viewer, you can change the attribute shown on the x-axis using:

>>> histogram.state.x_att =['RAJ2000']

You will notice that we specified['RAJ2000'] rather than['RAJ2000'] as the former is the identifier for the column rather than the actual values (which the former will return).

Each viewer/visualization contains ‘layers’, where a layer is for example the points corresponding to a given dataset or a subset. We can find a complete list of layers with:

>>> histogram.state.layers
[HistogramLayerState for w5_psc, HistogramLayerState for Jmag > 5]

In this example, there are two layers - one for the main dataset, and one for a subset of the data. We can access the first layer with:

>>> layer_state = histogram.state.layers[0]

and the layer itself then has properties that can be changed, such as the color of the points (this is a property that is specific to the layer, not an overall property of the viewer):

>>> layer_state.color = 'blue'
>>> layer_state.alpha = 0.5

In the following table, you can click on the name of one of the state classes to find out the complete list of viewer properties that can be changed for the viewer state objects and the layer state objects. Note that in some viewers, the subset state is different from the main data state:


Viewer state

Data layer state

Subset layer state