Bridging the Gap: Providing Public Science Dissemination...

Department of Science and Technology Institutionen för teknik och naturvetenskap Linköping University Linköpings universitet

gnipökrroN 47 106 nedewS ,gnipökrroN 47 106-ES

LiU-ITN-TEK-A-16/054--SE

Bridging the Gap: ProvidingPublic Science Dissemination

through Expert ToolsMichael Nilsson

Sebastian Piwell

2016-11-18

LiU-ITN-TEK-A-16/054--SE

Bridging the Gap: ProvidingPublic Science Dissemination

through Expert ToolsExamensarbete utfört i Medieteknik

vid Tekniska högskolan vidLinköpings universitet

Michael NilssonSebastian Piwell

Handledare Alexander BockExaminator Anders Ynnerman

Norrköping 2016-11-18

Upphovsrätt

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© Michael Nilsson, Sebastian Piwell

Institutionen för teknik ochnaturvetenskap

Department of Science and Technology

Examensarbete

Bridging the Gap: Providing PublicScience Dissemination Through Expert

Tools

by

Michael Nilsson & Sebastian Piwell

LIU-IDA/LITH-EX-A--16/001--SE

2016-11-14

Linköpings universitetSE-581 83 Linköping, Sweden

Linköpings universitet581 83 Linköping

Linköpings universitet

Institutionen för teknik och naturvetenskap

Examensarbete

Bridging the Gap: Providing PublicScience Dissemination Through

Expert Tools

by


LIU-IDA/LITH-EX-A--16/001--SE

2016-11-14

Examinator: Anders YnnermanHandledare: Alexander Bock

Abstract

This thesis aims to provide public science dissemination of space weather data by inte-

grating a space weather analysis system used by experts in the field into an interactive

visualization software called OpenSpace; designed to visualize the entire known Universe.

Data and images from complex space weather models were processed and used as textures

on different surface geometries, which are then positioned, oriented and scaled correctly

relative other planets in the solar system. The obtained results were within the goals of the

thesis and has successfully incorporated several features that will help understanding of

space weather phenomena.

Acknowledgments

We would like to thank professor Anders Ynnerman for the opportunity to work on thisproject and our supervisor Alexander Bock for the guidance we received through the dura-tion of this thesis. Together with Emil Axelsson they have supported us with valuable inputand suggestions every week. This thesis was supported by our friends at NASA’s GoddardSpace Flight Center where we were offered an office and funding for travel expenses. Aspecial thanks to Asher Pembroke, for assisting us in moving forward; Masha Kuznetsova,for allowing us to come and work at NASA; Justin Boblitt and Richard Mullinix, for answer-ing questions concerning iSWA; Leila Mays, for her general helpfulness; Lutz Rastaetter, forproviding us with model output data and images.

We would also like to thank Carter Emmart for having us come to the American Mu-seum of Natural History and test our work in the Hayden Planetarium. Last but not least,we would like to thank the rest of the OpenSpace team, and send our best wishes. Keep upthe good work.

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Contents

Abstract iii

Acknowledgments iv

Contents v

List of Figures vii

1 Introduction 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Background 3

2.1 OpenSpace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.3 Space Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.4 Integrated Space Weather Analysis System (iSWA) . . . . . . . . . . . . . . . . . 52.5 Reference Frames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.6 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 Theory 11

3.1 Standard Score Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 Histogram Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.3 Pseudo-Color Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Method 15

4.1 Current Cygnets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.2 Visualization Cygnets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.3 CDF Cygnets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.4 Software Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5 Results 24

5.1 Screen Space Cygnets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245.2 Visualization Cygnets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.3 CDF File Cygnets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.4 Standard Deviation Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.5 Histogram Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

6 Discussion 31

6.1 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316.2 Data Format Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

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6.3 Comparison with iSWA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.4 Rainbow Color Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.5 Standard Deviation Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.6 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

7 Conclusion 35

Bibliography 36

List of Figures

2.1 A Cygnet on iSWA that is generated from the ENLIL model output data. Imagetaken from the iSWA web app . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 A Cygnet on iSWA that is generated from the BATS-R-US model output data. Im-age taken from the iSWA web app . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 An overview of the iSWA system showing different data sources and cygnets. Im-age taken from CCMCs iSWA wiki page . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.4 Example of an iSWA dashboard with different Cygnets (graphs, images, illustra-tions). Image created and taken from the iSWA web app . . . . . . . . . . . . . . . 7

2.5 The magnetic axis is tilted at an angle of about 12 degrees with respect to theEarth’s rotational axis. Image taken from Tufts University web page . . . . . . . . . 8

2.6 The the celestial equator and ecliptic plane and their relation to Earth. Image takenfrom the earthsky web page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.7 An overview of Kameleons architecture from model input to the interface layer.Image taken from slides by David Hyon Berrios . . . . . . . . . . . . . . . . . . . . 9

3.1 Comparison between two standard deviation intervals. The red line is the stan-dard deviation curve. The red highlighted area represents the interval. σ is thestandard deviation and E is the entropy of the histogram. (The data set is thepressure variable of the global magnetosphere) . . . . . . . . . . . . . . . . . . . . . 12

3.2 Comparison between a histogram equalized image and histogram and original.Image taken from the tutorialspoint web page . . . . . . . . . . . . . . . . . . . . . 13

3.3 The rainbow color scale. The minimum value is mapped to the color red and themaximum is mapped to blue, the intermediate values are interpolated in between 14

4.1 An example of two current cygnets in OpenSpace. . . . . . . . . . . . . . . . . . . . . 164.2 Fisheye configuration with six viewports (shown in blue) for dome environments.

Each Cygnet is positioned and rendered in the center of each viewport’s screenspace. Red lines represents the blend areas/blend centers. . . . . . . . . . . . . . . 17

4.3 An example of the Visualization cygnet. . . . . . . . . . . . . . . . . . . . . . . . . . . 194.4 An example of the CDF cygnet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.5 A class diagram of the Visualization Cygnet component. . . . . . . . . . . . . . . . 214.6 A class diagram of the Data Processor component. . . . . . . . . . . . . . . . . . . . 224.7 A class diagram of the Cygnet Group component. . . . . . . . . . . . . . . . . . . . 224.8 A class diagram of the iSWAManager. . . . . . . . . . . . . . . . . . . . . . . . . . . 234.9 A class diagram of the Screen Space Renderable component. . . . . . . . . . . . . . 23

5.1 Example of two Cygnets (Earth Connectivity and Fok Ring Current) being dis-played in Screen Space for both fisheye and flat screen projection. . . . . . . . . . . 24

5.2 Three perpendicular cut planes visualizing pressure in the magnetosphere, pre-sented as a timeseries with approximately 10 hours between each time step. . . . . 25

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5.3 Visualization of the Magnetosphere, presented with three different data variables:N (pressure), Vx (x-component of the velocity) and Bz (Z-component of the mag-netic field). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.4 Visualization of the Ionosphere, presented with four different data variables: Eave(average energy), eflux (energy flux), ep (electric potential) and jr (radial currentcomponent) respectively from left to right. . . . . . . . . . . . . . . . . . . . . . . . 26

5.5 Before (left) and after (right) applying histogram equalization on the Z-componentof the magnetic field in the Magnetosphere. . . . . . . . . . . . . . . . . . . . . . . . 26

5.6 Before (left) and after (right) applying auto-filter on the x-component of the veloc-ity in the Magnetosphere. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5.7 Different color maps can be used for the same data. The first and second imageportrays the same data variable with color maps of different nuances. The thirdimage, three different data variables are rendered on the same planes but withdifferent color maps: red, green and blue. All are contrast enhanced and auto-filtered. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.8 Three cut-planes interpolated from a CDF-file (with auto-filter), one of which isdragged through the volume. The data variable being visualized is rho. . . . . . . 27

5.9 CDF file Cygnets can be visualized together with field-lines (here representing themagnetic field) for added value. The planes have increased transparency to makethe field-lines easier to view. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.10 Comparison between different standard deviation intervals. On the top the in-terval is set to 1 and on the bottom it is equal to the original histograms entropy.From left to right the figure shows the histogram of the raw data with the standarddistribution (in red), histogram from the clamped values, the equalized histogramof the clamped values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.11 Visual comparison of the result form the histograms in figure 5.10 . . . . . . . . . . 295.12 Visual comparison of the result from the histograms between standard deviation

intervals for the eave component of the Ionosphere. . . . . . . . . . . . . . . . . . . 295.13 Visual comparison of histogram equalization methods on the eave variable of the

Ionosphere data set. The images to the left is the result without equalization. Themiddle image is the equalized data with considering the highest bin of the his-togram. The right image show the result when equalizing the data without thehighest bin considered. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.14 Visual comparison of the different histogram equalization methods on the pres-sure variable of the global magnetosphere. The left image shows a histogramequalization with the highest bin considered and the to the right shows the equal-ization without the highest bin considered. . . . . . . . . . . . . . . . . . . . . . . . 30

6.1 Examples of images of the sun in different wavelength available on iSWA. . . . . . 33

1 Introduction

1.1 Motivation

The Community Coordinated Modeling Center (CCMC) at NASA provides public spaceweather data in the form of images through a web based analysis system called iSWA (inte-grated Space Weather Analysis). This system is primarily used by specialists in the field toforecast space weather and it can be hard to understand in this context for anyone who arenot conversant with this subject. The drawback of this is that the wide array of public spaceweather data never reaches the public.

Space weather consist mostly of our Sun’s activity and the effect it has on the Earth andour solar system. It is important to predict for missions in space but it can also disrupttechnology we use daily (e.g. satellites). It is also the cause of the northern and southernlights. By visualizing space weather in OpenSpace it can show the public that even thougheverything seems quiet when looking up at the stars, a lot more is going on.

1.2 Aim

The aim is to make the information on iSWA available to a broader audience by visualizingthe streams of images (Cygnets) and data in a more suitable context and in bigger arenas. Thiswill be accomplished by positioning, scaling, orienting and mapping the images in an inter-active 3D environment of our solar system, where the extra spatial dimension and realisticsurroundings will help more people understand the content. This will also include processingdata from different sources and different aspects (variables) of space whether and visualizethem, sometimes at the same time. The implementation is to be integrated as a feature inOpenSpace, a program made for visualizing what we know about the universe and adaptedto work in dome theaters. This will benefit the dissemination of the public data on iSWA byreaching out to more people and exposing it to an immersive and intuitive environment.

1.3 Research Questions

• How do we integrate as many Cygnets as possible in an intuitive and qualitative way?

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1.4. Delimitations

• Is there any way to enhance the cygnet visualization and make it more interactive?

• How can we work with User Experience to make cygnets easy to use?

• Can this be achieved in real time? Seeing space weather as it is happening?

1.4 Delimitations

This thesis will primarily focus on visualizing 2-dimensional Cygnets and 2-dimensional sim-ulation output data. Only a subset of Cygnets and model output will be chosen for testingbecause what is currently available is not suitable for integration in any meaningful way as is(see section 6.3). We will also limit our work to uniformly sampled data (equally spaced sam-ples). We will only deal with space weather within the solar system (around the earth/fromthe sun to the earth) and concentrate on visualizing Cygnets and data on planes and spheres,but still make the software design easily extensible for other geometries (e.g. cylinders).

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2 Background

2.1 OpenSpace

OpenSpace [26] is a open-source software application currently being developed to accu-rately visualize the known universe by using public space-related data sources. The project isa collaboration between Linköping University (LiU), the Community Coordinated ModelingCenter (CCMC) at NASA and the American Museum of Natural History (AMNH) and wasinitiated in the end of 2013. The purpose of the program is for dissemination of scienceby letting an audience interactively travel through space and time experiencing the vastscale, details and phenomena of the universe. The application can be used either in homeenvironments, using desktop computers, or immersive environments, such as the HaydenPlanetarium at AMNH in New York. This thesis will solely work on integrating the spaceweather data from iSWA to OpenSpace.

The current state of the OpenSpace software can visualize our solar system accuratelywhich includes the planets and the Sun in their right size and position for a given time. Itcan also render some specifically chosen space-crafts and satellites with accurate models andposition. One module recreates the New Horizons fly-by of Pluto. OpenSpace has the abilityto render magnetic field lines based on CCMC models of space weather. It has the abilityfor interactive volumetric rendering in both space and time for different models of spaceweather.

2.2 Definitions

Plasma

One of the four states of matter that are observable in everyday life where the othersare, solid, liquid and gas. Plasma can be said to be a gas of charged particles like ions,electrons and protons [33].

Solar Wind

An outward flowing, weakly magnetized plasma at a temperature around 100 000kelvin from the Sun’s upper atmosphere that reaches far beyond Pluto’s orbit fillinga bubble-like region in space called the Heliosphere [3, 34].

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2.3. Space Weather

Magnetosphere

A region around the Earth where the geomagnetic field determines the motion ofcharged particles, producing a cavity in the solar wind [17].

Ionosphere

Part of the Earth’s atmosphere that is ionized primarily through ultraviolet radiationfrom the Sun but also through particle precipitation from the magnetosphere. It is ahighly variable region which roughly extends from 60 km to 1000 km altitude [17].

Magnetopause

The boundary between Earth’s magnetic field and the solar wind.

Solar Flares

A huge magnetic energy release process on the Sun (approximately 1025 J within thefull duration of 10 minutes), which accelerates particles and consequently emits electro-magnetic radiation throughout the spectrum from radio waves to X- and gamma-rays[17].

Coronal Mass Ejection (CME)

Large plasma and magnetic clouds ejected from the sun that travel at a speed roughlybetween 280 to 750 km/s when reaching Earth. CMEs are often observed in conjunctionwith solar flares [17].

Celestial Equator

An imaginary infinitely big circle with its center at Earth and coplanar with Earth’sequator.

Ecliptic plane

The plane in which the Earth orbits the sun, 23.4 degrees from the celestial equator.

First Point of Aries

The point defined by the intersection between the Celestial Equator and the Eclipticplane. [32]. Also called vernal equinox and spring equinox.

Astronomical Unit (AU)

A unit of measurement equal to 149.6 million kilometers, the mean distance from thecenter of the Earth to the center of the Sun [24].

2.3 Space Weather

Definition

Space weather is a field within solar-terrestrial physics that grew predominantly during the1990s that deals with solar activity which may affect technological systems in space and onthe ground and endanger human health. The topics of interest include the conditions in theSun, solar wind, magnetosphere and ionosphere, where space storms (e.g. Solar Flares andCoronal Mass Ejections) are the most harmful appearances of space weather [17].

Space Weather Models

In order to analyze and forecast space weather there is an active development of models thatcan simulate the environment of interest. Space weather models use sets of mathematicalequations to describe a complicated physical process from a limited amount of input [17].CCMC at NASA hosts, develops and evaluates models that cover the entire domain from thesolar corona to the Earth’s upper atmosphere, providing services that are targeted towardspace weather research and operational communities [4, 5]. The Space Weather Modeling

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2.4. Integrated Space Weather Analysis System (iSWA)

Framework (SWMF) is a collection of modeling components with a common operating en-vironment. Each of the components models particular aspects of space weather (e.g. Sun,heliosphere and magnetosphere) [13].

ENLIL model

The ENLIL model is a physics based model of the heliosphere. It solves equations for plasmamass, magnetic fields, momentum density and energy density to give a 1-4 day forecast ofsolar winds and CMEs. Its inner radial boundary is located at 21.5 to 30 solar radii and itsouter boundary can stretch out to 10 AU. It covers 60 degrees north to 60 degrees south inlatitude and 360 degrees in azimuth. The information from the ENLIL model can be used topredict geomagnetic storms and when CMEs will hit Earth (see Figure 2.1) [6].

Figure 2.1: A Cygnet on iSWA that is generated from the ENLIL model output data. Imagetaken from the iSWA web app [8].

BATS-R-US

The Block-Adaptive-Tree-Solarwind-Roe-Upwind-Scheme focuses on the global magneto-sphere and space weather around the Earth. Its inputs are the solar wind plasma and mag-netic field measurements propagated from solar wind monitoring satellites. Its outputs in-clude the magnetospheric plasma parameters and ionospheric parameters. It is in the GSMcoordinate system (2.5) which is aligned with the sun-earth line. It boundaries usually includeabout 30 Earth radii on the day side and stretches further back on the night side with a gaparound the Earth about 2-5 Earth radii wide. The model can be used to predict space weathereffects on the magnetosphere, ionosphere and magnetopause (e.g. reconnection events thatcan give cause to auroras). (see Figure 2.2) [12].

2.4 Integrated Space Weather Analysis System (iSWA)

iSWA is a web-based dissemination system for NASA related space weather information.The system provides a combination of forecasts from advanced space weather models with

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2.5. Reference Frames

Figure 2.2: A Cygnet on iSWA that is generated from the BATS-R-US model output data.Image taken from the iSWA web app [8].

concurrent observational data through a flexible and configurable user interface. The spaceweather information is presented from a large and highly diverse set of sources in a form thatis accessible and useful for NASA customers. iSWA is used as a decision-making tool whenanalyzing the present and expected future of NASA’s human and robotic missions (see Figure2.3) [7]. The system input is space weather data that can come from many different sources,including physics based models. The system then sorts, characterizes and processes the datainto Cygnets that can be more easily read by humans. These Cygnets can be plots, graphs,images or raw data depending on the data input. There are more than 500 different Cygnetswhich displays different values and different domains of space weather. The Cygnets can besaved in a layout to give an overview of the space weather at a specific time (see Figure 2.4)[9].

2.5 Reference Frames

Definition

A reference frame is an ordered set of (possible time-dependent) reference points with anassociated center that locates and orients a coordinate system. In simplified terms, this canbe described as the physical object to which we attach a coordinate system. There are twoclassifications of reference frames that describe their characteristics: Inertial Frames are thosein which Newton’s law of motion hold, typically reference frames with a constant velocityand no rotation. Non-inertial reference frames are accelerating (including by rotation). [14,22].

The space weather models output data in a certain reference frame, and needs to be trans-formed in order to be render it correctly in OpenSpace. NASA uses a number of well definedreference frames to facilitate and clarify the analysis [23].

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Figure 2.3: An overview of the iSWA system showing different data sources and cygnets.Image taken from CCMCs iSWA wiki page [9].

Figure 2.4: Example of an iSWA dashboard with different Cygnets (graphs, images, illustra-tions). Image created and taken from the iSWA web app [8].

Geocentric Systems

These reference frames have their coordinate system at the center of the Earth. In this cate-gory are systems based on the Earth’s rotation axis, systems based on the Earth-Sun line and

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systems based on the dipole axis (see Figure 2.5) of the Earth’s magnetic field [15]. Figure2.6 shows some key features for these coordinate systems including the ecliptic plane and thecelestial equator.

Figure 2.5: The magnetic axis is tilted at an angle of about 12 degrees with respect to theEarth’s rotational axis. Image taken from Tufts University web page [18].

Figure 2.6: The the celestial equator and ecliptic plane and their relation to Earth. Image takenfrom the earthsky web page [21].

Geocentric solar ecliptic (GSE)

This system has its X axis towards the Sun and its Z axis perpendicular to the eclipticplane. (positive North). This frame is widely used when representing vector quantitiesand convenient for specifying boundaries in the magnetosphere [15].

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2.6. Software

Geocentric solar magnetospheric (GSM)

This system has its X axis towards the Sun and its Z axis is the projection of the Earth’smagnetic dipole axis (positive North) on to the plane perpendicular to the X axis. It isconsidered the best system to use when studying the effects of interplanetary magneticfield components on magnetospheric and ionospheric phenomena [15].

Geocentric equatorial inertial (GEI)

This system has its Z axis parallel to the Earth’s rotation axis (positive to the North) andits X axis towards the First Point of Aries (or spring equinox). It is convenient for speci-fying the location of Earth-orbiting spacecrafts. However, the earth orbiting spacecraftsare often specified in a separate but similar coordinate system GEI2000 which is knownas J2000. This reference frame is the same as GEI but for a standardized fixed epoch[15].

Heliocentric Systems

The coordinate frames in this category have their origin in the center of the Sun. Amongthose are frames that are based on the Sun’s rotation axis and others that are based on theplane formed from Earth’s orbit around the sun. One example of this in the HeliocentricEarth equatorial (HEEQ), which has its Z axis parallel to the Sun’s rotation axis (positive tothe North) [16].

2.6 Software

Kameleon

Kameleon is a software suite that addresses the complication of analyzing the various outputdata formats of space weather model simulations. By employing a format standardizationprocedure, Kameleon reads data directly from the model simulation outputs and convertsit to a common science format, Common Data Format (CDF). The new data files consistsof additional metadata properties to make them self-contained and platform independent.Other than that, a high-level interface for access and interpolation of these converted datafiles is provided by Kameleon to facilitate data analysis and maximize code reuse. In otherwords, Kameleon works as a software layer between the model output and data dissemina-tion by abstracting away the reading and interaction with a easy-to-use interface (see Figure2.7) [11]. Kameleon also offers transformations between commonly used coordinate frames.

Figure 2.7: An overview of Kameleons architecture from model input to the interface layer.Image taken from slides by David Hyon Berrios [2]).

The space weather community has defined many different coordinate frames for differentdomains of space. The frames that Kameleon supports are mostly dynamic frames. Thismeans that they depend on multiple celestial bodies and the relation to each other. [10].

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2.6. Software

OpenSpace uses the Kameleon tool to render field lines of the CDF files. Given a set ofpoints in 3D space (seed points) the application traces how these points would move in theworld based on the data in the CDF file read by the Kameleon tool.

The SPICE Toolkit

SPICE is an information system that support scientists in planning and interpreting scientificobservations from space-borne instruments and assist NASA engineers to prepare planetaryexploration missions. The SPICE software toolkit uses "kernel" files with ancillary data1tocompute observation geometry parameters (also known as derived data of interests) at se-lected times. This can be information such as two spacecrafts distance from each other, ifa comet is in viewing angle of a certain instrument or when an object is in shadow of theSun [27, 28]. In OpenSpace the SPICE software toolkit is used for calculating the positions ofplanetary bodies and transforming between reference frames.

Simple Graphics Cluster Toolkit

SGCT is a windowing system for synchronizing clusters of computers together to create animage. It can synchronize multiple screens and computers to run in different configurations.The setup is read from XML files and can, without rebuilding, render the application in adome theater, a VR headset or a power wall [1]. OpenSpace uses this as their windowingsystem.

1Data that describes properties like spacecraft, planet or comet positions as a function of time, instrument de-scriptions such as shape, field of view and orientation, transformation matrices providing spacecraft orientationangles for a specific time, events and reference frames etc [22].

10

3 Theory

This chapter will introduce techniques used for processing and visualizing data. How thesetechniques are used in the project will be presented in chapter 4. The techniques to be in-troduced are: Standard score normalization, Histogram Equalization and Pseudo-Color processing.The Standard score normalization are used to process the data to be able to show multiple datavariables on the same plane. This technique is also used to process the data so it can be trans-formed into a texture. The Histogram equalization is used to process the data to give morecontrast in the final visualization. The last technique introduced, the Pseudo-Color process isused to visualize the data as colors on the screen.

3.1 Standard Score Normalization

To visualize the data it is first necessary to process it into a required format. To create a textureof the data it needs to be normalized into values between one and zero. But, space weatherdata comes in many different units of measurement and in drastically different scales. To beable to present multiple values together at the same time a normalization method is usedbring them to the same unit of measure and to the same scale (otherwise values in a smallscale could get lost in values with a big scale). When the values are at the same scale thevalues are normalized into the required format with values between one and zero.

The normalization method used to eliminate the unit is called Standard score normaliza-tion[35][20]. The method transforms the data into a new score (value) with a mean of zeroand a standard deviation of one. The absolute value of the new score represents the distancebetween the old value and the population mean of the data in units of standard deviations.The standard score (z) of the old value (x) is calculated as:

z =x ´ µ

σ(3.1)

Where µ is the population mean of the data and σ is its standard deviation. The standarddeviation of a data set is calculated as:

σ =

g

f

f

e

1

N

Nÿ

i=1

(xi ´ µ)2 (3.2)

11

3.1. Standard Score Normalization

Where N is the number of data values in the data set population.

When the old values has been normalized to a standard score they will be normalizedonce again to fit the required format for the texture (values between one and zero). This isdone by considering a standard deviation interval ([ń, n]) around the mean (z = 0), thenclamp values outside this interval and normalize values inside it to values between one andzero as shown in equation 3.3.

z1

=

$

’

’

&

’

’

%

1 if z ą n

0 if z ă ń

z1

+n2n otherwise

(3.3)

Where z1

is the normalized value for the texture and n is the standard deviation distanceconsidered in the standard deviation interval.

To get the best visualization result of a data set different standard deviation intervals arerequired for different sets. The optimal interval depends on how the data is distributed. Forspread out data sets the interval is better if it is large and for compact sets it is better if theinterval is small. A value representing this well is the entropy of a histogram. The entropy iscalculated as:

E = ´

Nÿ

i=1

xi

Nlog

( xi

N

)

(3.4)

Where E is the entropy of the histogram. This project uses the entropy as a heuristic whenchoosing the standard deviation interval. Figure 3.1 shows two identical histograms gen-erated by a non-normalized data set. The red line is the standard deviation curve and thehighlighted area represents the standard deviation interval. These graphs represents whatvalues are inside and outside the interval in the original data set. The first graph shows astandard deviation interval of one (n = 1) and the second graph shows the standard devia-tion interval set to the entropy of the histogram (n = E). The comparison shows that whenthe interval is one, it will clamp a lot more values then when it is set to the entropy and thisis something that will effect the visual result. Chapter 5 will show visual comparisons of this.(The interval [´1, 1] for the standard score values will be µ + [´σ, σ] in the original data set.)

(a) µ + [´σ, σ] (b) µ + [Éσ, Eσ]

Figure 3.1: Comparison between two standard deviation intervals. The red line is the stan-dard deviation curve. The red highlighted area represents the interval. σ is the standarddeviation and E is the entropy of the histogram. (The data set is the pressure variable of theglobal magnetosphere)

12

3.2. Histogram Equalization

3.2 Histogram Equalization

Contrast makes it easier to distinguish between objects in an image. Similarly, it makes iteasier to see features in the visualization of space weather data. The normalization methoddescribed in the previous section may cause visualizations to have low contrast. To increasethe contrast in the visualization Histogram Equalization is introduced.

Histogram Equalization is a technique that improves the contrast in an image. The tech-nique can also be used to the normalized scores to get a more even spread of values betweenone and zero. The technique accomplishes this by spreading out the most frequent intensityvalues in areas of low contrast to gain a higher contrast [30]. Figure 3.2 shows an example ofthis.

Figure 3.2: Comparison between a histogram equalized image and histogram and original.Image taken from the tutorialspoint web page [30].

The technique first requires a histogram to be created from the data. An image has adefined number of values (typically 256) but for a data set the number of levels must bechosen. The constant of available value levels is L and an equal number of bins will be usedin the histogram. The mapping of a data value to an available level is:

li =yi ´ ymin

ymax ´ ymin¨ L (3.5)

The probability of an occurrence of a level in the data set is:

px(i) =liN

(3.6)

Where N is the number of sampled data values. The cumulative distribution function corre-sponding to px is:

cd fx(i) =i

ÿ

j=0

px(j) (3.7)

The equalized bins/levels of the histogram is then defined as:

l1

i = li ¨ cd fx(i) (3.8)

13

3.3. Pseudo-Color Processing

After calculating the cumulative distribution function of a data set with equation 3.7 an equal-ized value can be calculated by combining equation 3.5 and 3.8. By converting the normalizedvalues from the previous section into equalized values, the contrast of the visualization willincrease.

3.3 Pseudo-Color Processing

Pseudo-color processing is a technique that maps each value in a grey level image into anassigned color from a color lookup table or function. This pseudo-colored presentationcan increase the visible difference between pixels with small or gradual changes by takingadvantage of the human ability to distinguish more colors than gray-scale values [31].

Pseudo-colors is often used in scientific visualization to represent properties like veloc-ity, density, temperature or composition in order to make identification of certain featureseasier for the observer. These mappings are computationally simple and fast and can givecontrast enhancement effects if they are used right. CCMC uses different color maps whengenerating the Cygnets on iSWA, and among those is the Rainbow color map, which is basedon the order of colors in the spectrum of visible light.

This project uses the pseudo-color process on multiple color maps (chosen by the user)to relate the values generated by the previous two techniques into a color for the visual-ization. Figure 3.3 shows the rainbow color map and how the generated values relate toit.

Figure 3.3: The rainbow color scale. The minimum value is mapped to the color red and themaximum is mapped to blue, the intermediate values are interpolated in between [25].

14

4 Method

In order to integrate the iSWA Cygnets and the information generating them into OpenSpace,a new type of Cygnet was created. These Cygnets were produced in association with CCMCduring the project and contain the necessary metadata to scale, orient and position the vi-sualizations in the correct location. The new Cygnets provide either rendered images of theraw data or the raw data itself. These new Cygnets are called Visualization Cygnets and havehigher resolution and the images have a more intuitive and less misleading color scale thanwhat the current Cygnets use (see section 6.4). This limits the amount of cygnets that couldbe integrated into OpenSpace (because CCMC cannot recreate all the current Cygnets) butit did raise the quality of the ones recreated. The current Cygnets were also integrated intoOpenSpace but because of the lack of metadata these are rendered in screen space. Data frommodel output contained in CDF files were also integrated into OpenSpace. The CDF file con-tains all the necessary metadata and 3D data of a region and 2D cut planes from this regionwere visualized. These create three different parts of the project, the visualization Cygnetintegration, the current Cygnet integration and the CDF file integration.

4.1 Current Cygnets

The current Cygnets are only images and will not be rendered in 3D space, but instead inscreen space. At the start of the program the information for all active Cygnets are pulledfrom the iSWA web API. A checklist with name and description is created in the GUI allowinga user to choose which Cygnet to visualize. When requesting a Cygnet the image is loadedinto memory and rendered on a rectangular plane. This plane is rendered on the screenwith either Cartesian coordinates or polar coordinates. This allows a user to naturally moveit when using a normal monitor or in a dome theater. More parameters like size, order andtransparency are also controlled by the user through the GUI. The current Cygnets also have atimestamp and when that time has passed in OpenSpace a new image is downloaded. Figure4.1 shows an example of two cygnets from iSWA in screen space.

iSWA API

The iSWA system exposes several access points allowing users to stream Cygnets directlyfrom the system. This allows for integrating specific instances of Cygnets in screen space

15

4.1. Current Cygnets

Figure 4.1: An example of two current cygnets in OpenSpace.

using a single URL. The base URL for the CygnetStreamingServlet is:

http://iswa.gsfc.nasa.gov/IswaSystemWebApp/iSWACygnetStreamer

To specify the Cygnet to stream there are three required parameters that must be speci-fied:

Timestamp

Specifies the point in date and time (UTC) that you want to request a cygnet from.

Window

This argument takes a number that will provide three different behaviors of the datesearch method.

• If passed a negative number (<0), the response will be the most recent availableCygnet for a specific date and time.

• If passed zero, the date and time have to match the exact timestamp for the Cygnet.

• If passed a number greater than zero (>0), then the most recent cygnet within therange timestamp-window and timestamp+window will be returned.

cygnetId

each cygnet is given a unique identifier, which is used to select a specific iSWA Cygnet.

These arguments must be specified as a URL GET request:

http://iswa.gsfc.nasa.gov/IswaSystemWebApp/iSWACygnetStreamer?timestamp=2012-03-0913:01:00&window=-1&cygnetId=205

For the purposes of this project the window parameter was fixed at -1 and a variablecygnetId depending on what the user wants to look at. The OpenSpace time was format-ted and passed as the timestamp. All Cygnets on iSWA has a fixed update interval that isretrieved in JSON format together with the Cygnet description and cygnetId through the

16

4.1. Current Cygnets

systems CygnetHealthService:

http://iswa3.ccmc.gsfc.nasa.gov/IswaSystemWebApp/CygnetHealthServlet

The update interval is then used to calculate how often a Cygnet needs to be updatedwith a new request.

Projection

The difficult part with screen space projection in OpenSpace is the underlying configurationof SGCT. OpenSpace is meant to run on not only single monitors but in large displays suchas domes with multiple projectors run by a computer cluster. This means that each projectorwill have its own viewport and screen space coordinate system, which complicates the posi-tioning of the images. When each computer in the cluster renders a Cygnet withing its ownviewport’s screen space they will not appear in the same position across the full display. Thisis shown in figure 4.2.

Figure 4.2: Fisheye configuration with six viewports (shown in blue) for dome environments.Each Cygnet is positioned and rendered in the center of each viewport’s screen space. Redlines represents the blend areas/blend centers.

By using the images as textures on a plane, that is then given a world space position witha fixed distance in front of the camera, the state of the plane is synchronized and renderedcorrectly over each node in the computer cluster by SGCT. As a result, making it look like theimages are rendered directly in screen space.

In order to achieve this, a plane is created with the same size ratio as the image and ro-

17

4.2. Visualization Cygnets

tated to face positive Z at a fixed distance of -1 from the camera (within the viewing frustum).Subsequently the plane is textured with the Cygnet image and only translated in the xy-plane. A third coordinate is used for occlusion with the other screen space images. Finally,the view and projection matrix of the camera is applied.

The description of a position of an image in screen space differs from a flat screen (e.g.a computer monitor) and a spherical screen (e.g. a dome theater). For the flat screen it isintuitive to describe the position in Euclidean coordinates, with x and y relating to the heightand width of the screen. For a spherical screen it facilitates to describe the position withspherical coordinates with the polar angle (θ), azimuthal angle (φ) and radial distance (r).Where r, the radius is used for occlusion.

One requirement was that the user should be able to move a screen space image on aEuclidean xy-plane in front of the camera and on spherical displays as well. Therefore,conversion between the two coordinate systems was necessary. Given that the screen spaceimages is described in euclidean coordinates, they were converted to spherical coordinateswith equation 4.1

r =b

x2 + y2 + z2

φ = arccos(y

r)

θ = arctan(y

x)

(4.1)

For conversion in the opposite direction, equation 4.2 is used.

x = r ¨ sin(φ) cos(θ)

y = r ¨ sin(φ) sin(θ)

z = r ¨ cos(φ)

(4.2)

4.2 Visualization Cygnets

The Visualization Cygnets are the new Cygnets that provide higher resolution visualizationsthen what is currently on iSWA, an example is be shown in figure 4.3. The visualizationCygnet pipeline begins with a request from a user for a specific Cygnet through the GUI.Then metadata for that Cygnet is downloaded into memory. At this point, the most impor-tant information is what data type the Cygnet contains and what geometry it has. This willdetermine what Cygnet subclass will be created in the scene. The data type could either beimage or raw data and the geometry could be plane or sphere. The metadata provided isparsed and the necessary data for size and position is calculated.

An object with the right geometry and size is created and placed in the correct location.If the data is an image, the image can directly be used as a texture. If the data type is rawdata, it needs some more processing before a texture can be made from it.

The first process is the normalization described in section 3.1. What standard deviationinterval to use depends on the user, they can use the GUI to set it themselves or use theentropy heuristic described in the theory. Since the data set is time varying, one problem isto keep the color mapping consistent. If the normalization method were to individually beapplied to each timestep, the standard deviation, mean and entropy would be different. Thiswould result in that the color mapping would also differ and the same value would changecolor each timestep. To avoid this the standard deviation, mean and entropy for the dataset is stored for its first normalization, then the same values are used later to keep the colormapping consistent.

18


Figure 4.3: An example of the Visualization cygnet.

The next process is the histogram equalization described in section 3.2. It is an optionalprocess that the user can turn on or off. For some data sets there is one very dominantvalue, such as data sets with large regions of uniform data that could be considered back-ground. This dominant value in the data set will reduce the quality of the visualization whenperforming histogram equalization. Because of this it is not considered in the equalizationprocess. More on this and visual comparison is discussed in 5.5.

After these processes each value in the data set has been transformed into a vale between oneand zero and a texture can be made with the Pseudo-Color process described in section 3.3.Multiple variables of each Cygnet can be processed and visualized at the same time. Eachvariable is made into a 1-dimensional texture sent to the shader where the Pseudo-Colorprocess is performed. Since multiple variables can be visualized two different method canbe used to get the final color. Method 1, uses one color map and the average of all values asthe color value as shown in equation 4.3. Method 2, performs the color mapping first usingone color map for each value, then adds the colors together for one final color. This methodis show in equation 4.4. In the equations v is the values from the normalized data sets, n isthe number of variables, colormap(x) is the function that generates a color from a value andc is the final color.

colormap(v1 + v2 + ... + vn

n) = c (4.3)

colormap1(v1) + colormap2(v2) + ... + colormapn(vn) = c (4.4)

Metadata

To get the right position, orientation and scaling for the geometry, metadata about theCygnets are needed. To assess what was needed Kameleon and CDF files was used. Thefollowing list is the conclusion of the research of the preferred metadata needed to visualizethe geometry in the right place in the virtual universe.

19

4.3. CDF Cygnets

• Coordinate frame - To instruct Spice and Kameleon to transform between different co-ordinate frames and rotate the geometry to the right orientation.

• Parent - The scene graph parent, in this case the planet that the coordinate frame iscentered around. It could be deduced from the coordinate frame itself but facilitates ifit is present.

• Coordinate type - If the coordinates are in spherical or euclidean coordinates. This isneeded to choose the right geometry.

• Minimum and maximum coordinate of sampled grid - Used to calculate the offsetfrom the center of the system, so that the geometry can be translated to the right locationfrom the parent.

• Spatial scale - The unit of measurement. Used to know how far to translate the geome-try (if needed) and what size it is.

• Update time - The time interval between two consecutive Cygnet images/data.

Demo Server API

The visualization Cygnets and data files were not available through the iSWA API, thereforea server that mimics the current iSWA API was created. This allows for a a smooth transitionbetween the two in the future.

The main difference is how the parameters are appended to the url, whereas for iSWAthey are added as query string parameters, on the demo server they are added to the end ofthe path.

http://iswa.gsfc.nasa.gov/IswaSystemWebApp/iSWACygnetStreamer?timestamp=2012-03-0913:01:00&window=-1&cygnetId=205

compared to

http://localhost/:id/:datetime

The window parameter was also removed form the demo API and set to -1 by default.The new Cygnet IDs are chosen arbitrarily starting as 1 and increasing incrementally as moretest files were added.

4.3 CDF Cygnets

The CDF Cygnets are Cygnets visualized with information from CDF files, an example of theCygnet and field lines can be seen in figure 4.4. The CDF files are stored on the disc becauseof their size. Information about available CDF files are loaded into OpenSpace with a smallscript and some configuration files. The user will chose what CDF file to load through achecklist of the available files. When the CDF file is chosen it is opened with Kameleon.Kameleon extracts all the necessary metadata to place the information in the right place.Kameleon provides an interpolator interface that is used to uniformly extract slices of 2Dinformation from the 3D volumetric data that the CDF file contains. The slices’ location canbe chosen dynamically by the user and will be processed and visualized in the same way asthe Visualization cygnet data sets.

The CDF files integration also comes with the option to render field lines. A list of seedpoints must be provided to start the field line tracing. Using the information in the CDF files,

20

4.4. Software Structure

colored lines are rendered. The lines are colored differently depending on if the field linesare open, closed or not connected to earth.

Figure 4.4: An example of the CDF cygnet.

4.4 Software Structure

The project is structured around a few class trees that are separated by function. The classesare explained in the following sections.

The Visualization Cygnet Class Hierarchy

Figure 4.5: A class diagram of the Visualization Cygnet component.

This is the main tree for the Visualization Cygnets in world space. Its function is to renderand update the Cygnets. It contains information about the size, position and update time forthe Cygnet. The tree is split at two different levels. The first level separates what kind of datato visualize. The source could be an image (TextureCygnet) or a data stream (DataCygnet).The DataCygnet owns a DataProcessor to process the data into a texture that can be visualized.The DataProcessor is another class tree explained later. The Cygnet fetches 2D data or imagesfrom a server (except the KameleonPlane) at a regular interval based on the application timeand visualizes it in the world.

21


At the second level the tree separates the geometry. The implementation can handle twotypes of simple geometries, planes and spheres. The Kameleon data source needs to behandled differently and adds some extra functionality because of this it is its own class. TheKameleonPlane gets its data from disk and reads it from CDF files, therefore the plane canchange resolution and slice the volume at different positions using the Kameleon application.

The Data Processor Class Hierarchy

Figure 4.6: A class diagram of the Data Processor component.

The data processors process data for the DataCygnets. It transforms data from one formatto one that can easily be transformed into a texture. The data is time varying and the scalecan vary greatly. The data processor keeps the visual output consistent, meaning one valueat one time step should correspond to the same color in another (this is not always the casedepending on how the normalization works). It achieves this by storing some key values inthe beginning and with the normalization and histogram equalization explained earlier.

As of now the DataProcessor deals with three different kinds of data formats. The firstone is unstandardized and is defined in regular text files, which are handled by the DataPro-cessorText. The second format is CDF, that is handled by the DataProcessorKameleon that usesthe Kameleon library to read the CDF files. The last data format is JSON and is handled bythe DataProcessorJson with a 3rd party JSON parser [19].

The Cygnet Group Class Hierarchy

Figure 4.7: A class diagram of the Cygnet Group component.

iSWACygnets displays 2D data from a 3D data source. To better understand the 3D structuremultiple 2D planes can be used. The iSWABaseGroup adds the functionality to group theiSWACygnets together. This allows the parameters to be updated simultaneously for everyCygnet in the group. The base group has the same type of data processor as the Cygnets andthe Cygnets will use the groups processor instead of their own. Each child of the base groupcontrols more parameters. The IswaBaseGroup handles variables every IswaCygnet uses (e.g.transparency). The IswaDataGroup handles parameters for the DataCygnets (e.g. color maps

22


and normalization variables). The IswaKameleonGroup handles KameleonCygnets variables (e.gresolution).

The iSWAManager

Figure 4.8: A class diagram of the iSWAManager.

The iSWAManager controls everything in this module that are outside of the Cygnets respon-sibility. When OpenSpace is started and the iSWA module enabled, the iSWAManager willquery the iSWA web app for healthy Cygnets and gather information about them, such asdescription, name and cygnet id number. These Cygnets will be the Current cygnet discussedin section 4.1 and will be displayed in screen space if the user choses to display them. SomeCygnets on the iSWA web app will be considered not healthy if they are down or hasn’t beenupdated for some time.

The iSWAManager downloads and reads metadata from different sources (as text, JSONand Kameleon). It will format this metadata into a script with the necessary information tocreate a new Cygnet (screen space, plane, sphere or Kameleon plane).

The iSWAManager keeps and reads information about the available CDF files discussedin section 4.3. It will also create, update crate and destroy iSWAGroups.

Screen Space Renderables

Figure 4.9: A class diagram of the Screen Space Renderable component.

The ScreenSpaceRenderable controls the screen space information in OpenSpace. Not all infor-mation is suitable in the 3D world and is better displayed in screen space. The informationon the screen can be images, graphs or videos. The class renders a rectangular plane withinformation on the screen and can control its positions, size and order (for occlusion). TheScreenSpaceImage class renders an image from disk or from the web. The ScreenSpaceCygnetsis a special case focused on streaming Cygnet images from the iSWA API. The ScreenSpace-Framebuffer is able to render dynamic information like videos or other rendering objects.

23

5 Results

The obtained results are well within the goals of the thesis and has produced a number ofdifferent approaches to visualize 2-dimensional space weather data. The results are groupedinto four sections where the first three corresponds to the different sub-tasks of the thesis(Screen Space Cygnets, Visualization Cygnets and CDF file interpolated Cygnets) and endswith normalization and equalization results. The Visualization and Screen Space Cygnets canbe updated both by going forward and backwards in time and thus achieving an animationof their content.

5.1 Screen Space Cygnets

All the active Cygnets on iSWA are available for streaming in OpenSpace by selecting themfrom a list with their names and descriptions in the menu. Once loaded, the Cygnets willappear in the center of the screen and their properties becomes available for adjusting. Theproperties of screen space Cygnets that can be adjusted are: Position in screen space, trans-parency and size (where the side ratio is fixed). This feature works just as well on a singlecomputer as with a computer cluster with multiple projectors (Figure 5.1).

Figure 5.1: Example of two Cygnets (Earth Connectivity and Fok Ring Current) being dis-played in Screen Space for both fisheye and flat screen projection.

24


5.2 Visualization Cygnets

The new Visualization Cygnets (including the raw data) were successfully retrieved from alocal server, processed, positioned and rendered correctly in OpenSpace with a few interac-tive adjustments and options to manipulate them.

The Visualization Cygnets below are demonstrated with three perpendicular cut planesto give a better sense of their volumetric nature. The ready-made Cygnets are presented witha fixed data variable leaving a user with the possibility to enable/disable cut-planes andadjust transparency. Figure 5.2 shows the Cygnet during different times. Visualization of

Figure 5.2: Three perpendicular cut planes visualizing pressure in the magnetosphere, pre-sented as a timeseries with approximately 10 hours between each time step.

Cygnet data has been realized on plane and sphere geometries. The Cygnets demonstratedbelow have the option to visualize different data variables for model outputs by themselvesor in conjunction with others. The user has the possibility to change the data variable to bevisualized (Figure 5.3), change color map for each data variable (Figure 5.7), increase contrastthrough histogram equalization (Figure 5.5) and filter values automatically or manually(Figure 5.6). Just like the other Cygnets, these also give the possibility to adjust transparency.

Figure 5.3: Visualization of the Magnetosphere, presented with three different data variables:N (pressure), Vx (x-component of the velocity) and Bz (Z-component of the magnetic field).

25

5.3. CDF File Cygnets

Figure 5.4: Visualization of the Ionosphere, presented with four different data variables: Eave(average energy), eflux (energy flux), ep (electric potential) and jr (radial current component)respectively from left to right.

Figure 5.5: Before (left) and after (right) applying histogram equalization on the Z-componentof the magnetic field in the Magnetosphere.

Figure 5.6: Before (left) and after (right) applying auto-filter on the x-component of the veloc-ity in the Magnetosphere.

5.3 CDF File Cygnets

Cut-plane interpolation of CDF files is accomplished with good results. Most of the featuresare the same as for the Visualization cygnets (auto-filter, different color maps and histogram

26

5.4. Standard Deviation Interval

Figure 5.7: Different color maps can be used for the same data. The first and second imageportrays the same data variable with color maps of different nuances. The third image, threedifferent data variables are rendered on the same planes but with different color maps: red,green and blue. All are contrast enhanced and auto-filtered.

equalization). Since these Cygnets represents a cut plane of a 3D region the planes can bedragged through the volumetric data generating new images each time step (Figure 5.8).

Figure 5.8: Three cut-planes interpolated from a CDF-file (with auto-filter), one of which isdragged through the volume. The data variable being visualized is rho.

Field-lines

Using an existing module of OpenSpace, field-lines can be visualized from the same CDF filesthat are used to create the cut-planes (Figure 5.9). To achieve this, a file with seed-points whichdefines positions of where a field-line should be interpolated from. These files can be loadedin to the GUI as a check-box list so that the user can select the seed-points during run-time.

5.4 Standard Deviation Interval

Higher contrast visualization can be made with the normalization method and histogramequalization. Depending on the standard deviation interval the effect will differ. In fig-ure 5.10 is histograms representing the pressure variable of the global magnetosphere anddifferent stages of the normalization method. The process goes from left to right. To the leftis the original histogram of the raw data values with its normal distribution and a cut-offrepresenting the standard deviation interval (represented as a red line and highlighted area).In the middle is a new histogram made with the clamped values of the original. To the rightis the histogram equalization of the middle histogram. The top row of figure 5.10 represents astandard deviation interval of 1 and the bottom row represents a standard deviation intervalequal to the original histogram entropy. Figure 5.11 shows the difference in the visual result.The left image represents the case with the standard deviation interval to 1 and the right im-age has the interval set to the entropy. Another, more noticeable visual result is shown infigure 5.12.

27


Figure 5.9: CDF file Cygnets can be visualized together with field-lines (here representingthe magnetic field) for added value. The planes have increased transparency to make thefield-lines easier to view.

Figure 5.10: Comparison between different standard deviation intervals. On the top the in-terval is set to 1 and on the bottom it is equal to the original histograms entropy. From left toright the figure shows the histogram of the raw data with the standard distribution (in red),histogram from the clamped values, the equalized histogram of the clamped values.

5.5 Histogram Equalization

Histogram equalization on data sets with one dominant value can decrease the contrast andvisual result of the final image. Figure 5.13 represents the eave component of the Ionosphere.The values from this data set is zero almost anywhere except on the poles of the earth. Thismakes the zero value dominant in the histogram. The image to the left in the figure representsthe data set without any histogram equalization. The middle image represents the data setwith histogram equalization where the highest bin is considered and the right image is wherethe highest bin is not considered. All the images has the standard deviation interval set toone. Not all data sets benefits as much with this alteration of the histogram equalization. In

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Figure 5.11: Visual comparison of the result form the histograms in figure 5.10

Figure 5.12: Visual comparison of the result from the histograms between standard deviationintervals for the eave component of the Ionosphere.

Figure 5.13: Visual comparison of histogram equalization methods on the eave variable ofthe Ionosphere data set. The images to the left is the result without equalization. The middleimage is the equalized data with considering the highest bin of the histogram. The rightimage show the result when equalizing the data without the highest bin considered.

Figure 5.14 is the comparison of the pressure variable of the global magnetosphere with and

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without considering the highest bin. The visual result between the two images is differentbut harder to tell which one is more favorable and without a defined metric it is hard to test.We can only rely on the visual result. The left image in the figure shows the method withthe highest bin considered and in the right one it is not. Arguably this data set would bebetter of with the highest value considered because of the noticeable clamped values in thebrightest areas. Because of this, the alteration may not favor every data set or variable and

Figure 5.14: Visual comparison of the different histogram equalization methods on the pres-sure variable of the global magnetosphere. The left image shows a histogram equalizationwith the highest bin considered and the to the right shows the equalization without the high-est bin considered.

could therefore be improved. By defining a metric of when the data set contains a clearlydominant value or not, a test could be made to decide if the highest bin should be consideredin the equalization method or not. Such a metric could be if the highest bin contains morevalues then the rest of the values together or a height comparison between the highest andnext to highest bin.

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6 Discussion

6.1 Performance

To achieve a fluid animation of the Cygnets, new requests for data had to be performed veryoften and the download time can not be too extensive. Since we are dealing with relativelysmall 2D data files and images, this will unlikely be a problem. However, choosing a rea-sonable file size that is not too large and will not affect the network performance has beendifficult since we have mainly tested against the demo server. This server can serve fileswithout much delay. A delay problem would probably occur if these Cygnets were servedtrough the iSWA API (which is not tested). The data Cygnets are bigger than the imageCygnets because they provide multiple data variables in the same file and are uncompressed.This does however mean that there is room for improvement if necessary by splitting thedata variables into separate files and only request the ones that you want to visualize.

It is likely that the network will not be the bottleneck even if we request the Cygnetsfrom iSWA. Reading and parsing the data is a very performance demanding process whichat the moment is performed on a single thread on the CPU. If the sample resolution washigher (to achieve better quality), then this process would eventually have to be parallelizedor put some of the workload on the GPU to maintain support to low performing PCs.

6.2 Data Format Standards

One challenge in this project has been to work with different data formats. Since iSWA cur-rently does not serve the data content we need, a standard for the data does not exist. Todecide this, one must answer the question of who this service is for. If this is a service whichexists only for the purpose of space weather visualization in OpenSpace, then the question isquite easy. We could specify an appropriate resolution of the samples, what data variables weneed, what cut-planes are of interest, exactly what should be included in the metadata, andthat it should conform to some universal standard like JSON. If however this service is moregeneral purpose, then this question becomes more difficult to answer. To make this serviceas general purpose as possible would require a lot of work. The code has been designed toprepare for changes in the data format by separating the parsing functionality from the dataprocessing.

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6.3. Comparison with iSWA

6.3 Comparison with iSWA

One of the main goal of this thesis is the dissemination of public data, and to achieve this itneeds to be more available and easier to understand. The thesis is based on a few assump-tions on how to accomplish this goal, which is yet to be tested on its users. By integratingthe data from iSWA into OpenSpace we are extending the user and viewer group, whichmeans that more people will come in contact with this information. OpenSpace also hasthe benefit of being an interactive 3D environment of space where everything is positionedcorrectly in relation to each other. While iSWA leaves it up to its users to visualize a mentalpicture of a Cygnets position and orientation in space, OpenSpace does this for you. Enhanc-ing the Cygnets was necessary for a number of reasons: Increasing the resolution to avoidpixelated images on large displays, facilitation the integration into OpenSpace, choosing bet-ter suited color maps and removing annotations in the images that would confuse the viewer.

iSWA allows the users to interact by choosing a time interval for a Cygnet and displaythe images that were generated in a sequence. Our work extends the interactiveness signif-icantly by allowing the users to view the images from any direction, filter values, changingcolor maps and changing what variable(s) to visualize. CDF files gives an even greateradvantage because it allows the user to interact by dragging the planes themselves anddisplaying 3D field-lines. For these reasons we believe that we have achieved a greater un-derstanding and availability of the public data. Despite this, space weather visualization maynot be self-explanatory for someone that has not been in contact with it before. Therefore,OpenSpace is a great platform because it is used for public presentations, where someonecan guide and explain what the audience is seeing.

6.4 Rainbow Color Map

Rainbow color maps are widely used within data visualization in many different scientificand engineering communities today and among them is NASA. The color map has the ad-vantage of being familiar to these communities where they have become accustomed to whatthe colors mean. There is however a high degree of agreement about the disadvantages of therainbow color map. The lack of natural ordering of the colors is visually confusing and canslow down tasks because viewers frequently has to look at the reference color bar to assurethemselves of the meaning of a color. Details in the data can also be obscured because thegreen and cyan ranges are perceptually indistinct, making an area with corresponding colorslook uniform. This problem is even more apparent among color-impaired viewers. Otherthat that there are experimental studies suggesting that the color yellow is special becauseour eyes are the most sensitive to it, making it inappropriate to place it in the middle of ageneral purpose color map. The color map can also cause unwanted segmentation of intervaldata, making it appear like there are borders where there are none [29].

iSWA uses the rainbow color map for a lot of Cygnets and because of these disadvantages itgives us a greater incentive to create new Cygnets that are better suited for a non-scientificcommunity. When it comes to visualizing the data, we chose to add the option of changingthe color map so that each Cygnet can be presented with the map the user prefers.

6.5 Standard Deviation Interval

The standard deviation interval in the standard score method is used to clamp values that area certain distance away from the mean of the data set. For the best result, few values shouldbe clamped but the distance interval should be small. The user can specify their own intervalbut a way to find the optimal interval was explored. The heuristic used for this project was

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6.6. Future Work

the entropy of the histogram created from the data set. The entropy represents how spreadout the values are in a data set and was considered to be a good heuristic because of this. Noother methods were tested and without a clearly defined metric of how good the result is noreal comparisons can be made. The visual result acted as the metric for this project but it isnot clearly defined and can be hard to compare.

The standard deviation interval defined is the distance from the mean, but dependingon how the values are spread out in the data set, a possible improvement to consider is tohave the interval at different distances above and under the mean. This would allow for atighter interval and decrease the number of values clamped by the method.

6.6 Future Work

Some features were discussed for this project but were ultimately left out. One of these wasto visualize the ENLIL model in OpenSpace. The pipeline for this new feature would bethe same as for the BATS-R-US model. The positioning of the model and visualization ofthe data values would be the same. What is currently lacking in OpenSpace is the abilityto create cylinder shaped geometry and textures in spherical coordinates. Additionally, nonew improved Visualization Cygnets has been made for the model. The original idea was toreplace and visualize every Cygnet in iSWA and with the ENLIL model a lot more would becovered.

Sun Cygnets was another feature that was discussed. iSWA have plenty of images ofthe Sun in different wavelengths. With a bit more metadata about these images they couldeasily be incorporated in OpenSpace. The metadata would have to contain information aboutwhere the images were taken. Then the images could be placed in front of the sun. It wouldstill be a 2D representation of the sun but oriented in the right direction and from the rightposition it would replace the view of the sun. A bit more advanced idea was to wrap theimages over half the sun sphere. The problem would be to identify the edge of the sun andwhat part of the image is outside that edge. Figure 6.1 shows examples of images of the sunin different wavelength available on iSWA.

Figure 6.1: Examples of images of the sun in different wavelength available on iSWA.

In addition to images, iSWA also contains graphs of one dimensional data. The raw data usedto generate the graphs can be accessed through the iSWA API. Currently, only the graphsfrom iSWA are incorporated in to OpenSpace as screen space Cygnets. One improvementwould be to dynamically create our own graphs from this data. This would allow to showmultiple data variables in one graph and in better resolution. With more information aboutwhat the data is, more advanced visualizations could be made. One example would be if

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6.6. Future Work

the data is measurements from a certain satellite over time. The data could then be renderedwith the trail of the satellite with the values corresponding to the distance over/under theactual trail. This would give the user a better understanding of where the data is collected,what it represents and maybe why the measurements change over time.

So far, the data integrated has only been uniformly sampled. This allows for simplificationswhen creating textures to render on. Not all model data is necessarily sampled uniformlyand one improvement for this project would be to consider them too. If the method of howthe data is sampled is known, the texture coordinates could be computed. If the method isnot known, the position of the values must be read and transformed into texture coordinatesalong with the values.

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7 Conclusion

The obtained results are well within the goals of the thesis and has produced a numberof different approaches to visualize 2-dimensional space weather data. This will not onlybenefit dissemination by extending the user group, but also by making the information easierto understand.

We have worked quantitatively by presenting all Cygnets from iSWA flat on the screen,and qualitatively by integrating new Cygnet types with high resolution that are positionedcorrectly in OpenSpace, which consequently can be used in Immersive environments. TheCygnet visualizations are enhanced with respect to iSWA by introducing interactiveness,utilizing a 3D environment and taking advantage of existing functionality in OpenSpace.This includes features like grouping multiple Cygnets together, enabling different cut-planeCygnets to be controlled as one; dragging cut-planes through volumetric data; visualizingfield lines from volumetric data together with the Cygnets, using existing functionality; andrendering different data variables on top of each other, using different color maps for eachchannel.

The new Visualization Cygnets could be generated and served to the OpenSpace clientin near real time, providing the users the possibility to witness space weather as it is hap-pening. The end product in contained in a loosely coupled module of OpenSpace that isextendable with new data parsers and surface geometry types.

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