# Getting started¶

This page walks through Glue’s basic GUI features, using data from the W5 star forming region as an example. You can download the data files for this tutorial here

After installing Glue, open the program by either double clicking on the icon (if using a pre-built application) or from the command line:

glue


Glue’s main interface

The main window consists of 3 areas:

1. The data manager. This lists all open data sets and subsets (highlighted regions).
2. The visualization area. This is where each visualization window resides.
3. The visualization dashboard. This shows the options for the active visualization window.

## Opening Data¶

There are three ways to open data: By clicking on the red folder icon, the File->Open Data Set menu, or Ctrl+O (Cmd+O on the Mac). Find and open the file w5.fits. This is a WISE image of the W5 Star Forming Region.

## Plotting Data¶

After opening w5.fits, a new entry will appear in the data manger:

To visualize a dataset, click and drag the entry from the data manager to the visualization dashboard. A popup window asks about what kind of plot to make. Since this is an image, select Image.

## Defining Subsets¶

Work in glue revolves around “drilling down” into interesting subsets within data. Each visualization type (image, scatterplot, …) provides different ways for defining these subsets. In particular, the image window provides 4 options:

• Rectangular selection: When active, a click+drag defines rectangular regions of interest.
• Circular selection: Defines circles.
• Freeform selection: Defines arbitrary polygons.
• Contour selection: Uses the contour line that passes through the mouse.

We can highlight the west arm of W5 using the rectangle selector:

Notice that this highlights the relevant pixels in the image, adds a new subset (which we’ve named west arm) to the data manager, and adds a new visualization layer (also labeled west arm (w5)) in the visualization dashboard.

We can redefine this subset by dragging a new rectangle in the image, or we can also move around the current subset by pressing the ‘control’ key and clicking on the subset then dragging it. Alternately, we could define a second subset by clicking the New Subset button (next to the folder button).

Note

When multiple subsets are defined, only the highlighted entries in the data manager are affected when drawing new subsets. If no subsets are highlighted, then a new subset is created.

You can edit the properties of a visualization layer (color, name, etc.) By double-clicking on the entry in the visualization dashboard.

Likewise, you can re-arrange the rows in this widget to change the order in which each layer is drawn – the top entry will appear above all other entries.

## Refining Subsets and Linked Views¶

Visualizations are linked in Glue – that is, we can plot this data in many different ways, to better understand the properties of each subset. To see this, click and drag the W5 entry into the visualization area a second time, and make a histogram. Edit the settings in the histogram visualization dashboard to produce something similar to this:

This shows the distribution of intensities for the image as a whole (gray), and for the subset in red (the label PRIMARY comes from the FITS header)

Perhaps we wish to remove faint pixels from our selection. To do this, we first enable the selection mode toolbar via Toolbars->Selection Mode Toolbar, and then pick the last mode (Remove From Selection mode).:

When this mode is active, new regions defined by the mouse are subtracted from the selected subsets. Thus, I can highlight the region between x=450-500 in the histogram to remove this region from the data.

Note

Make sure you switch back to the first, default combination mode (Replace Selection mode)

Open w5_psc.vot – a catalog of Spitzer-identified point sources towards this region. You will see a new entry in the data manager.

At this point, you can visualize and drilldown into this catalog. However, Glue doesn’t know enough to intercompare the catalog and image. To do that, we must Link these two data entries. Click on the Link Data button in the data manager. This brings up a new window, showing all the pieces of information within each dataset:

The image has an attribute Right Ascension. This is the same quantity as the RAJ2000 attribute in the w5_psc catalog – they are both describing Right Ascension (the horizontal spatial coordinate on the sky). Select these entries, and click Glue to instruct the program that these quantities are equivalent. Likewise, link Declination and DEJ2000 (Declination, the other coordinate). Click OK.

Note

What does this do? This tells Glue how to derive the catalog-defined quantities DEJ2000 and RAJ2000 using data from the image, and vice versa. In this case, the derivation is simple (it aliases the quantity Declination or Right Ascension). In general, the derivation can be more complex (i.e. an arbitrary function that maps quantities in the image to a quantity in the catalog). Glue uses this information to apply subset definitions to different data sets, overplot multiple datasets, etc.

After these connections are defined, subsets that are defined via spatial constraints in the image can be used to filter rows in the catalog. Let’s see how that works.

First, make a scatter plot of the point source catalog. Then, delete the West Arm subset (by highlighting it and clicking the X button). Then, highlight a new region in the image. You should see this selection applied to both plots:

You can also overplot the catalog rows on top of the image. To do this, click the arrow next to the new subset – this shows the individual selections applied to each dataset. Click and drag the subset for the point source catalog on top of the image. To see these points more easily, you may want to disable the selection applied to the image itself by unchecking the East arm (w5) entry in the plot layer window.

Glue is able to apply this filter to both datasets because it has enough information to apply the spatial constraint in the image (fundamentally, a constraint on Right Ascension and Declination) to a constraint in the catalog (since it could derive thsoe quantities from the RAJ2000 and DEJ2000 attributes).

Tip

Glue stores subsets as sets of constraints – tracing a rectangle subset on a plot defines a set of constraints on the quantities plotted on the x and y axes (left < x < right, bottom < y < top). Copying a subset copies this definition, and pasting it applies the definition to a different subset.

As was mentioned above, the highlighted subsets in the data manager are the ones which are affected by selecting regions in the plots. Thus, instead of manually copy-pasting subsets from the image to the catalog, you can also highlight both subsets before selecting a plot region. This will update both subsets to match the selection.

Note

Careful readers will notice that we didn’t use the image subset from earlier sections when working with the catalog. This is because that selection combined spatial constraints (the original rectangle in the image) with a constraint on intensity (the histogram selection). There is no mapping from image intensity to quantities in the catalog, so it isn’t possible to filter the catalog on that subset. In situations where Glue is unable to apply a filter to a dataset, it doesn’t render the subset in the visualization.

Glue provides a number of ways to save your work, and to export your work for further analysis in other programs.

Saving The Session

You can save a Glue session for later work via the File->Save Session menu. This creates a glue session file (the preferred file extension is .glu). You can restore this session later via File->Open Session.

By default, these files store references to the files you opened, and not copies of the files themselves. Thus, you won’t be able to re-load this session if you move any of the original data. To include the data in the session file, you can select ‘Glue Session including data’ when saving:

Exporting the plots Glue can export certain kinds of plot combinations to other formats and web services.

Plot.ly is a cloud-based plot service whose features include the ability to tweak plot features (colors, annotations, etc.) through a GUI, and to easily share plots via web URLs. If your Glue session contains four or fewer scatter plots and/or histograms, these can be exported to a plotly page.

To do this, first sign up for a plotly account, and enter your user name and API key under File->Edit Settings. Then, select File->Export->Plotly. This will create a new plot, and open a browser window showing you the plot.

Exporting to D3PO

D3PO is an application created by Adrian Price Whelan, Josh Peek and others to create multi-stage “data stories”. Glue can export to the D3PO format under the following conditions:

• Only scatterplots or histograms are used.
• A single dataset is used.
• Only one subset is visible within the viewers of each Glue tab.

Saving a session via File->Export->D3PO creates a directory with thee files that convert the Glue plots to a minimal D3PO page. Glue will also start a small webserver and open a browser window to show you the exported page.

Saving Plots Static images of individual visualizations can be saved by clicking the floppy disk icon on a given visualization window.

Saving Subsets Glue is primarily an exploration environment – eventually, you may want to export subsets for further analysis. Glue currently supports saving subsets as FITS masks. Right click on the subset in the data manager, and select Save Subset to write the subset to disk. This file will have the same shape as the original data, and will contain a 1/0 indicating whether that element is part of the subset.