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glue & the Webb Telescope ❤ a match made for the heavens!

10QVizTeam July 11, 2022

How was this great match made?

On April 23, 2012 in an email, then-Space Telescope Science Institute Director Matt Mountain asked Harvard Professor Alyssa Goodman:

“Presume through Alberto [Conti] you will touch base with the other JWST folks looking at IFU data visualization like Tracy Beck, Massimo (our Acting Head of the JWST[?].”

Three days later, on a trip to Baltimore from Boston, Goodman was in Mountain’s office at STScI, where he showed her his copy of her “Principles of High-Dimensional Data Visualization in Astronomy.” Conti, then a NASA “Innovation Scientist,” had shared Goodman’s draft with Mountain, knowing how relevant the “principles” in the paper could be for JWST data in the future. To Goodman’s complete surprise, about 5 minutes into the conversation, Mountain offered Goodman (who was not actively seeking funding at the time) “something like a million dollars” to make the “glue” software described in the draft “real enough to use.”

Note: Astronomers often still call the “Webb” or “James Webb” space telescope by its NASA acronym, “JWST.”

Why were these astronomers so interested in glue + JWST?

The Webb telescope doesn’t just take images. It can take a spectrum, breaking up light into constituent colors, at many many positions within an image at once, using a device called an “Integral Field Unit.” The resulting data format, which has “x-y” positions on the sky, plus a “z” axis that corresponds to wavelength, is called a “spectral line image cube.” Astronomers trained to use radio telescopes, including Goodman, have used such cubes for decades. Goodman and her colleagues designed glue to exploit both high-dimensional data (e.g. cubes) and the principles of “exploratory data analysis” shown in glue’s logo. (The red-highlighted points and regions in the glue logo are all coordinated, in that salient values selected in any open display of data are also selected, live, in others.)

What’s glue done in a decade?

Now, ten years later, the glue software environment is a robust open-source ecosystem that underlies all of Jdaviz, the web-based analysis tools being provided to scientists as the way to analyze JWST data. Thanks to initial and ongoing support from the NASA-JWST program, as well as from the National Science Foundation and the Moore Foundation, the glue exploratory data analysis tools are now are now used in many astronomical investigations, in genomics, and in many other contexts.

Recent astronomy-related discoveries made using glue include the discovery of the Radcliffe Wave and the Perseus-Taurus Supershell, and the star-forming significance of the Local Bubble around the Sun. glue has also been used to produce the first augmented reality figures published in a major astronomy journal.

glue is also used to teach data science. At the high-school/community college/college level, it’s a key element of the infrastructure powering the “Cosmic Data Stories” project of NASA’s Science Activation Program. And, for more advanced data scientists, glue is being used to train data scientists in astronomy, for example in the “Seeing More of the Universe” YouTube series created by Alyssa Goodman for NSFs Rubin Data Science Fellows program.

About Jdaviz

Jdaviz offers four special packages intended for different specific purposes. All of the packages use JupyterNotebook, JupyterLab, glue, and many Astropy functions to accomplish their goals. The “Glupyter Framework Overview” page on the Jdaviz website gives a good summary of how glue-jupyter (also called “glupyter”) is used, and can be extended, within the jdaviz environment.

The four packages that comprise Jdaviz are called “Imviz,” “Cubeviz,” “Mosviz,” and “Specviz,” and super-short descriptions of each, from the Jdaviz website, are shown below. For power users’ reference, glue outside of Jdaviz can integrate functionality across all of the specific tasks accomplished in these four tools, simultaneously. See the glue website or these online demo and training videos for more on how to use glue in its most flexible forms.

Imviz
Imviz is a tool for visualization and analysis of 2D astronomical images. It incorporates visualization tools with analysis capabilities, such as Astropy regions and photutils packages.

Cubeviz
Cubeviz is a visualization and analysis toolbox for data cubes from integral field units (IFUs). It is built as part of the Glue visualization tool. Cubeviz is designed to work with data cubes from the NIRSpec and MIRI instruments on JWST, and will work with IFU data cubes. It uses the specutils package from Astropy.

Mosviz
Mosviz is a quick-look analysis and visualization tool for multi-object spectroscopy (MOS). It is designed to work with pipeline output: spectra and associated images, or just with spectra.

Specviz
Specviz is a tool for visualization and quick-look analysis of 1D astronomical spectra. It incorporates visualization tools with analysis capabilities, such as Astropy regions and specutils packages. Specviz … supports flexible spectral unit conversions, custom plotting attributes, interactive selections, multiple plots, and other features. Specviz notably includes a measurement tool for spectral lines which enables the user, with a few mouse actions, to perform and record measurements. It has a model fitting capability that enables the user to create simple (e.g., single Gaussian) or multi-component models (e.g., multiple Gaussians for emission and absorption lines in addition to regions of flat continua).

IHME COVID-19 Model Uncertainty Visualization

10QVizTeam May 17, 2020

Why this post?

This post is motivated by a new interactive visualization tool provided by glue solutions, inc. that allows for visual exploration of the evolution of the IHME models of the COVID-19 pandemic, over time.

In the body of the post, we take a look at how uncertainty is represented in the original Institute for Health Metrics and Evaluation (IHME) deaths per day graphs for COVID-19, and then at the graphical features of the new interactive tool for exploring the IHME models’ predictive history. An essay online at the Prediction Project site offers context on why exploring the history of the IHME models graphically is so interesting.

➡ At this link, you can create your own graphical comparisons of the IHME deaths/day model by clicking on the three dots at the top-right of each graphic there and downloading a PNG

➡ To join the discussion, please comment using the Disqus tool at the bottom of this post, which permits you to upload graphics with your comment.

The IHME Daily Deaths Display

Here’s an annotated sample of a typical IHME plot for the whole United States, with a very clean design, in which a solid red curve shows the record of past deaths, and a dashed red curve shows the models forecast of deaths/day, going in to the future. The shaded red band shows a 95% confidence interval, illustrating the uncertainty in the model prediction. On the IHME web site, users can hover-over the plot to read off the forecast, with uncertainty information, dates in the future, or actual recorded deaths for dates in the past.

The glue solutions interactive tool for creating historical comparisons of IHME forecasts

Here is an annotated sample of output from the glue solutions interactive tool, for the United States. In the glue solutions visualizations, in order to distinguish data from model, actual deaths/day are shown as red points, and all model information is shown in blue. Shading for the (95%) confidence model works exactly as in the original IHME visualizations, only is shown in blue. The average forecasts are shown as solid blue lines within the blue shaded uncertainty bands.

A graphical choice has been made to not assign different dates different colors. A multi-color option, for a small number of overlain models, does make it easier to distinguish which model is which, but as more and more models are overlain, the color in overlap regions turns to mud. Choosing instead just one color (blue) for every model allows user to see a region of ever-darker blue, corresponding to the region of the graph where past models agree, as more and more models are added.

Choices have also been made on the glue solutions site presenting the interactive tool about how users can interact with these graphics. A time slider has been added outside of the graphic, and a date selector has been added on the right. Constraints on these graphical choices were imposed by the Vega visualization grammar. (For example, the developer of the tool, Jonathan Foster, would have liked to add: color in the graph title, a slider within the graph, and more responsive features for mobile, but Vega could not allow those features when this graphic was made).

Timelines, Revisited

Matthew Brehmer June 14, 2019

TL;DR: Throughout history, people have recorded events on timelines. This post is about the remarkably varied design space of timelines as explanation tools.

We begin our timeline adventure with a primer on spatial metaphors for time.

Joseph Priestley's "Chart of Biography" (PD, 1765) — Joseph Priestley’s “*Chart of Biography*” (1765)

Perhaps the most common representation for time is linear: the “time as an arrow” metaphor. Consider Joseph Priestley’s “Chart of Biography”, published over 250 years ago. In Priestley’s design, time is mapped from left to right (the dates are BCE). You can see the lifespans of a number historical figures, offset vertically to avoid overdrawing. Otherwise there’s no meaning to the vertical placement aside from faceting the data with statesmen at the bottom and philosophers at the top. This left-to-right linear representation of time remains popular today, but it is certainly not the only way to explain a set of events.

A timeline design space. — A timeline design space with three dimensions: representation, scale, and layout. Different combinations of these serve different communicative intents.

Alternative representations of time

Radial representations are especially effective when explaining and highlighting natural cycles and events that repeat, such as biological life cycles or the seasons of the year. However, time is both linear and cyclic, something that repeats and yet coils upwards or forwards. Thus, spirals are certainly another way to represent time, though one that is less common than the line or the circle. Yet another representation for time is the grid, often manifesting as a calendar. Like radial representations, calendars are good for showing repeating events and deviations from patterns of events, especially when these patterns correspond to conventions of weeks and months.

One of Mark Twain's whimsical curved timelines (1914). — One of Mark Twain’s whimsical curved timelines (1914).

The last category of representation doesn’t conform to any specific shape. Mark Twain drew whimsical curved timelines for remembering dates along annotated curves. Many timeline infographics that we see today still have this freeform board-game-like appearance.

What is a timeline for? Timelines as explanation tools

These different spatial metaphors for time are one dimension of a timeline design space. Before introducing the second dimension, consider the following question: What is a timeline for? Why do we draw these in the first place?

Basically, a timeline can be used to explain “what happened when?”. If you unpack this question, a timeline can answer a number of more detailed questions. In what sequence did events occur? How long were they? Did event A and event B co-occur? And when did they occur relative to some baseline event? These questions relate to the second dimension of a timeline design space, and that pertains to time scale.

Frames of reference: Alternative time scales

As an example, consider Visualising Painters’ Lives by Accurat, depicting the lives of notable 20th century painters. In these timelines, each lifespan, artistic period, travel, and romantic conquest for several famous painters appear along a common chronological scale.

This video shows a simplified timeline representing the career of Salvador Dalí, inspired by Accurat’s project. You can see his artistic periods like Surrealism and Dada and his travel to places like Paris. As the video progresses, the career of Matisse now shown alongside Dalí’s. Using a common chronological scale, you will notice that were born at different times. You will see when their respective Cubist and Abstract periods took place and how they might have influenced one another. When the scale changes to one relative their birth dates, you can now compare the age at which they started their art career and how long they lived. You can also spot similarities like how they both traveled to Paris in their early thirties. Finally, the aspect of chronological duration disappears altogether, and what remains is simply a sequential ordering of their artistic periods. Ultimately, each of these transformations resulted in a different timeline, telling a different story.

A third dimension of the timeline design space is layout, or how to draw one or more timelines within a page or display. You could draw a single timeline. You could facet the events into multiple timelines, such as by drawing one timeline per famous painter. Or you could wrap a single timeline into meaningful segments of time, like a decade or a century.

The origin of this timeline design space

This design space arose from a research project led by Matthew Brehmer in collaboration with Bongshin Lee, Nathalie Henry Riche, Benjamin Bach, and Tamara Munzner. The team collected and categorized 145 timelines and timeline visualization tools from various sources, which helped them identify the dimensions of the design space. Next, they verified that the design space could be used to label 118 additional timelines from different sources. They also implemented points in the design space with 28 event datasets. These datasets varied in a number of ways: the number of events, the temporal extent of the events, and the rate of event co-ocurrence.

This process of categorization and implementation also led the researchers to identify 20 viable points in the design space. These points are combinations of representation, scale, and layout that are purposeful in terms of their communicative intent, interpretable in terms of which perceptual task the viewer is expected to make, and generalizable across a range of timeline datasets. This thumbnail gallery acts as a visual index for these points in the design space, and at timelinesrevisited.github.io, you will find each of these example designs in detail along with a description of what narrative or communicative intent they serve.

Considerations for storytelling with timelines

So now that you have all of these design choices, how do you use these design choices to craft explanations with timelines? How do you combine different points in this design space? This is important because despite the variety of ways that we visually represent and scale time, existing timeline presentation tools limit us to linear representations and chronological time scales. Existing tools also tend toward a chronological narrative. Some tools show the entire timeline as a static image, and viewers are therefore likely to begin at the start of the timeline. Alternatively, other tools reveal events one at a time in chronological order.

For some stories, a chronological introduction of events makes sense, while for others it does not. Consider the painters example, in which the career of Matisse adds context to the career of Dalí. Additionally, to achieve expressive narrative design, you can make use of animation, highlighting, and annotation to incrementally reveal parts of a narrative, and allow the viewer to make new comparisons.

Creative routine timelines

A second example attempts to tie all of these design considerations together. This video presents the daily routines of famous creative people, one inspired by infographics by Podio and RJ Andrews (a.k.a. Info We Trust), which in turn drew from Mason Currey‘s 2013 book Daily Rituals: How Artists Work.

In this video, you will encounter a set of radial timelines depicting a typical 24 hours in the lives of 26 writers, artists, composers, and the like. When they work, eat, sleep, exercise, and do other activities. A good starting point is to ask: when do creative people create? Are people similar in this regard? You can also ask about the relationship between sleep and creativity. What about variation and creativity? A chronological scale isn’t the best way to convey the number or heterogeneity of activities. Instead, it’s better to use a sequential scale to highlight these aspects. And to determine who varied the most and least, a linear representation is perhaps better than a radial one. Toward the end of the video, a chronological scale returns. This scale allows you to compare timelines just by scanning up and down, to spot synchronocities such as who works or sleeps at the same time of day. It also invites you to compare your own daily rhythm to those of these creative people.

Conclusion

Despite the apparent simplicity of the question of “What happened when?”, this post hopefully relayed the richness of the timeline design space. You have different visual representations and different time scales at your disposal that serve different communicative purposes. This design space grows even richer when you use dynamic storytelling elements like incremental reveal, selective highlighting, animated transitions, and an annotation layer comprised of labels and captions (metadata for events or for the timeline itself, respectively).

Timeline Storyteller

The tool that generated the video examples in this post is an interactive visualization authoring and presentation tool called Timeline Storyteller, a realization of the timeline design space and the considerations for storytelling described above. This tool is open-source and free to use in your browser at timelinestoryteller.com. It was released by Microsoft Research as an open-source web application in January 2017, and later as a free add-on to Microsoft Power BI, which allows you to publish timeline stories such as iFrames (here’s an example). You can also see Timeline Storyteller featured in the opening keynote of the 2017 Microsoft Data Insights Summit and in a 2017 OpenVisConf presentation, or you can read about it in a 2019 Computation + Journalism symposium paper.

The Sciviz Continuum: Figurative to Abstract

10QVizTeam May 12, 2019

An excerpt from “Visualizing Science: Illustration and Beyond”
Guest post by
Jen Christiansen

Jen is a senior graphics editor with the Scientific American.

The Sciviz Continuum: Figurative to Abstract

I tend to think of information graphics as a continuum, with figurative representations at one end and abstract representations on the other (see the first Figure below). In the world of science visualization, you could argue that the full continuum can also be referred to as data visualizations. After all, essentially all of our work is rooted in data collection at some stage in the process: from bone length measurements in dinosaur reconstructions, to meticulously documented laboratory experiments that build up to a more complete understanding of processes like photosynthesis, to representations of mathematical expressions (like Feynman diagrams), to straight-up plotting of the raw data itself, in chart form.

Outside of the world of science visualization, it may be more useful to think of the continuum like this:

When I flip through old issues of Scientific American, it strikes me that many artists worked across the full spectrum. But as a graphics editor at the magazine now, I find myself maintaining discrete freelance pools for each of the different points along the continuum.

Perhaps this is an artifact of my own biases, but it occurs to me that this increased specialization may also be in part due to the shifting tools of each of these areas. When the primary tool for developing representative illustrations, explanatory diagrams, and data visualizations for print magazines was pen and ink, an artist could become a master at pen and ink, then explore different methods of problem solving in each of these areas.

Since desktop publishing became ubiquitous and digital rendering tools diversified and became more widely available, it seems to me that the simple act of choosing a primary tool starts to define the edges of the artist’s scope. As an art director, I find myself specifically looking for 3D artists to build physical objects; folks who hone in on composition and the flow of information by iterating with tools like Adobe Illustrator for explanatory diagrams; and data designers that build solutions with code for visualizing large datasets.

Each of these tools, mediums, styles and genres take lots of time to master, and tend to favor certain portions of the continuum. Many of the conferences I attend and communities that I engage with seem to reinforce these divisions, by focusing on the tools. And it seems that artists that span more than one of these orbs are harder and harder to find.

Perhaps that way of thinking about things is a bit overly dramatic. The reality is probably much more like this.

And perhaps this is a completely natural and fine state to be in—particularly since the primary tools for these different sub-disciplines have bifurcated over time. And perhaps there isn’t value in trying to force discrete clusters to reconnect.

That said, I argue that even if you don’t have the desire to work across the full continuum—or the time to dedicate to becoming proficient across the full continuum—there is lots to be learned from each of these clusters, and I’d love to see more cross pollination of ideas between them. I think we’d all benefit, if things looked more like this:

And even better, if things looked like this:

I’m not arguing that everyone along this continuum should learn how to code. Or that everyone along the continuum should build clay models and paint from life. I’m arguing that we can—and should—learn how science visualizers from cross the full spectrum think through and solve problems.

For the full post, see Visualizing Science: Illustration and Beyond.

“Save the pies for dessert”?

Arzu Coltekin July 13, 2018

Included in Stephen Few’s very interesting visualization blog (perceptual edge) is the provocatively titled “Save the Pies for Dessert” post. Pie charts are notoriously bad for perceptually judging magnitude. Here is an annotated excerpt from Few’s post, giving just one example of how hard it can be to judge scale using pie charts…

Not everyone hates pie charts, though…for example, here is a blog post from “Junk Charts” on the downside of discouraging pie charts.

Bonus: An amusing pie chart which shows the shadow illusion featured in the Categories question is this fascinating little image. Once you see the pyramid, you cannot unsee it:

Pyramid pie chart art.

I did not manage to identify the original maker of this -sort of- meme at this point, if you know, please tell me in the comments. Image above is copied from Rebecca Barter here: http://www.rebeccabarter.com/blog/2015-07-23-pie/

Another case made against the rainbow colors

Arzu Coltekin July 13, 2018

Rainbow colors are pretty, and many of us like them. However, go to any visualization-related conference, and you’ll hear a lot of ‘rainbow-hate’. Where does that come from? Below is an excellent example that shows how rainbow color tables might mislead and make us see categories, or patterns, that might not be there. The image below is featured in a blog post titled “How The Rainbow Color Map Misleads” by Robert Kosara, in his wonderful visualization and visual communication blog eagereyes:

There is more to the art and science of choosing colors. Here is another informative post by Lisa Charlotte Rost: https://blog.datawrapper.de/colors/

Maybe a treemap would be better?

Alyssa Goodman July 12, 2018

Consider this infographic about imprisonment, from this article on the American Legislative Exchange Council blog. Most people would look at it and find it very engaging and attractive, which it is. But, as a visualization expert, one wonders if the odd coloring variations in the outer ring of the main figure and in the “Juvenile” block at right, which just show how the larger wedges (categories) divide up more finely (into sub-categories) wouldn’t be better shown in a Tree Map, using the ideas about showing hierarchical categories proposed by Ben Shneiderman in the 199os. A Tree Map version of these data would almost certainly show the area of sub-categories and categories relative to each other (context) better than the snazzy graphic shown here.

The Periodic Table of Visualization

Alyssa Goodman July 12, 2018

In 2007, for their paper entitled Towards a Periodic Table of Visualization Methods for Management, Lengler & Eppler created the static graphic shown here. Later, Chris Wallace created an interactive javascript version, now hosted at the excellent visual-literacy.org web site.

This fun table has SO much to teach us about visualization–just think about all the 10QViz “Questions” to which it directly relates…

Primarily–the highly successful table is about “explaining” what’s meant by different kinds of visualization, but it (especially the interactive display mode) also let’s a user “explore” how those visualizations relate to each other. The table’s two dimensional layout, mimics the periodic table of the elements, familiar to most viewers (cf. “Who?”), and suggests (and labels) categorical groupings (shown in patterns of color and position). The interactivity adds hugely rich “metadata,” by way of examples for each cell in the table.

Reference for the Original Table: “Towards A Periodic Table of Visualization Methods for Management”
Lengler R., Eppler M. (2007). Towards A Periodic Table of Visualization Methods for Management. IASTED Proceedings of the Conference on Graphics and Visualization in Engineering (GVE 2007), Clearwater, Florida, USA. More on the history of the interactive version of the table can be found in this blog.

Why (and what is) a “3D PDF”?

Alyssa Goodman March 9, 2017

For hundreds of years, scientists have published their results in scientific journals that were printed on paper. Today, though, most journals have gone entirely online. Articles less and less frequently printed out and read on paper, so why should they still look and funciton exactly the way they did in the 1600s?

Josh Peek and I, and our colleagues wrote a fully online paper presenting The ‘Paper’ of the Future back in 2014, which highlights (with embedded demonstrations) many of the technologies available to scientists publishing today, and in the near future. One particularly important technology–“3DPDF”– discussed in that paper of the “future” was actually first deployed in a Nature article by my “Astronomical Medicine” collaborators and me, way back in 2009.

Our challenge was to show the difference between two “segmentation” techniques used to define salient structures inside of star-forming regions. The science isn’t important here (sorry). What’s important is that we wanted to offer the “reader” multiple, interactive, views of high-dimensional data, inside of a journal article.

To see the PDF in action, take a look at this video, or download the “nature_demo” file and open it, on any Mac or PC, with an Adobe PDF viewer of any kind (not Preview).

Other authors (e.g Peek 2012) have since published methods for creating these 3D PDFs using free software, and a (perhaps too small!) number of authors have now embedded these 3D images inside of the scholarly articles. Even though interactive images are clearly seen to add value to articles, they are not (yet) widely used. 3D PDF as a format may be short-lived, as articles move more and more to a fully online environment, where other (e.g. javascript-based) technologies can offer superior options. BUT, the general idea of embedding data and interactive views of it, be they “3D” or not, is extremely valuable, and we will return to it in future posts–for now go have a look at The ‘Paper’ of the Future (Goodman at al. 2014).

But what about software!?

All 10QViz Posts

10QVizTeam March 9, 2017

We knew this might be the first question you’d ask! One of the 10VizQ founders (Alyssa Goodman) is quite involved in making new visualization software, and in the scientific software world in general, so she promised to make posts about software from time to time. At present, Alyssa’s pet project is “glue,” which is a python-based (but GUI enabled) tool for exploring diverse data sets using linked views. She’ll make a separate post on glue soon–it’s utility applies across all 10 Questions. Ok… Alyssa finally made that new glue post, in honor of the release of the first color images from the James Webb Space Telescope. Here it is!