Linked Visualizations for Astrophysical Data Chris Beaumont (U. - - PowerPoint PPT Presentation

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Linked Visualizations for Astrophysical Data Chris Beaumont (U. - - PowerPoint PPT Presentation

Nov 03, 2022 378 likes •804 views

Linked Visualizations for Astrophysical Data Chris Beaumont (U. Hawaii, Harvard) with Alyssa Goodman, Michelle Borkin, Thomas Robitaille Wednesday, October 12, 11 Motivation Wednesday, October 12, 11 Links Across Data Lada, Lombardi,

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SLIDE 1

Linked Visualizations

for

Astrophysical Data

Chris Beaumont (U. Hawaii, Harvard) with Alyssa Goodman, Michelle Borkin, Thomas Robitaille

Wednesday, October 12, 11

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SLIDE 2

Motivation

Wednesday, October 12, 11

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SLIDE 3

Links Across Data

Lada, Lombardi, Alves 2010

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SLIDE 4

Links Across Data

Kirk et al. 2010

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SLIDE 5

Links Across Data

Beaumont et al. in prep

Wednesday, October 12, 11

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SLIDE 6

Two Challenges

Conceptually Easy Conceptually Hard Computationally Easy Computationally Hard

Basic reduction and analysis of small data (data << RAM) Uncovering relationships within: several data sets high-dimensional data Basic reduction and analysis of large data Feature Extraction Automatic data calibration/analysis

Wednesday, October 12, 11

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A case for computationally easy, conceptually hard problems

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SLIDE 8

A case for computationally easy, conceptually hard problems

  • The MB-GB realm is still relevant

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SLIDE 9

A case for computationally easy, conceptually hard problems

  • The MB-GB realm is still relevant
  • A wealth of computational resources

Wednesday, October 12, 11

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SLIDE 10

A case for computationally easy, conceptually hard problems

  • The MB-GB realm is still relevant
  • A wealth of computational resources
  • Relevant for the resources most

researchers already have

Wednesday, October 12, 11

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SLIDE 11

A case for computationally easy, conceptually hard problems

  • The MB-GB realm is still relevant
  • A wealth of computational resources
  • Relevant for the resources most

researchers already have

  • Computers get faster -- brains don’t

Wednesday, October 12, 11

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SLIDE 12

A case for computationally easy, conceptually hard problems

  • The MB-GB realm is still relevant
  • A wealth of computational resources
  • Relevant for the resources most

researchers already have

  • Computers get faster -- brains don’t
  • Not incompatible with the computationally

hard domain

Wednesday, October 12, 11

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SLIDE 13

Requirements

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SLIDE 14

Tukey’s “Four Essentials” (c.1972)

Watch the PRIM-9 video at: http://stat-graphics.org/movies/prim9.html

Wednesday, October 12, 11

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SLIDE 15

Tukey’s “Four Essentials” (c.1972)

Picturing

Watch the PRIM-9 video at: http://stat-graphics.org/movies/prim9.html

Wednesday, October 12, 11

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SLIDE 16

Tukey’s “Four Essentials” (c.1972)

Rotation Picturing

Watch the PRIM-9 video at: http://stat-graphics.org/movies/prim9.html

Wednesday, October 12, 11

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SLIDE 17

Tukey’s “Four Essentials” (c.1972)

Rotation Picturing Isolation

Watch the PRIM-9 video at: http://stat-graphics.org/movies/prim9.html

Wednesday, October 12, 11

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SLIDE 18

Tukey’s “Four Essentials” (c.1972)

Masking Rotation Picturing Isolation

Watch the PRIM-9 video at: http://stat-graphics.org/movies/prim9.html

Wednesday, October 12, 11

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SLIDE 19

Selection

Tukey’s “Four Essentials” (c.1972)

Masking Rotation Picturing Isolation

Watch the PRIM-9 video at: http://stat-graphics.org/movies/prim9.html

Wednesday, October 12, 11

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SLIDE 20

Selection

Tukey’s “Four Essentials” (c.1972)

Brushing Linking Masking Rotation Picturing Isolation

and these “need to work together” in a “dynamic display”

Watch the PRIM-9 video at: http://stat-graphics.org/movies/prim9.html

Wednesday, October 12, 11

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SLIDE 21

Selection

Tukey’s “Four Essentials” (c.1972)

Brushing Linking Masking Rotation

Results...

  • 1. for immediate insight
  • 2. as visual source of ideas for statistical algorithms

Picturing Isolation

and these “need to work together” in a “dynamic display”

Watch the PRIM-9 video at: http://stat-graphics.org/movies/prim9.html

Wednesday, October 12, 11

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SLIDE 22

DataDesk (est. 1986)

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SLIDE 23

DataDesk (est. 1986)

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Practical Issues

  • Visualization and connection of several data

products

  • catalogs, images, spectra, data cubes
  • Support for common file formats and coordinates
  • WCS, FITS,

VOTable, CSV, ...

  • Ability to script and extend
  • Preferably in a language astronomers use (IDL,

Python)

Wednesday, October 12, 11

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SLIDE 25

Implementation

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SLIDE 26

Pre-existing tools?

SAMP SPLAT ds9 TOPCAT

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SLIDE 27

First Attempt: CloudViz

http://code.google.com/p/cloud-viz/

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SLIDE 28

First Attempt: CloudViz

http://code.google.com/p/cloud-viz/

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SLIDE 29

Second Attempt (python)

Data Hub Visualization Client Visualization Client Visualization Client Subsets

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SLIDE 30

Single dataset linking

Data Hub Subsets

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SLIDE 31

Data Hub Subsets

Single dataset linking

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SLIDE 32

Data Hub Subsets

Single dataset linking

Subsets

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SLIDE 33

Data Hub Subsets

Single dataset linking

Subsets

Wednesday, October 12, 11

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SLIDE 34

Data Hub Subsets

Single dataset linking

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SLIDE 35

Data Hub Subsets

Single dataset linking

Subsets

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SLIDE 36

Data Hub Subsets

Single dataset linking

Subsets

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SLIDE 37

Multi-Data Linking

(Image) Data Hub Subsets (Catalog) Data Subsets Data Bridge

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SLIDE 38

(Image) Data Hub Subsets (Catalog) Data Subsets Data Bridge

Multi-Data Linking

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SLIDE 39

(Image) Data Hub Subsets (Catalog) Data Subsets Data Bridge

Multi-Data Linking

Subsets Subsets

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SLIDE 40

(Image) Data Hub Subsets (Catalog) Data Subsets Data Bridge

Multi-Data Linking

Subsets Subsets

Wednesday, October 12, 11

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SLIDE 41

Next Steps

  • UI design
  • 3D selection (Borkin PhD Thesis)
  • Topcat/ds9/etc clients via SAMP
  • Extension to big data

Wednesday, October 12, 11