Science Presentation

Had the great privileged to be one of the judges at the Manchester Grammar School, Science Fair Projects involving about 200 very enthusiastic future scientists. Each group, of three, had to design and evaluate a scientific hypothesis applied to sport, which was the theme for this year; e.g. do balls roll faster with change in temperature, does sugar consumption relate to concentration performance analysis, …


All did very well, but there was a good debate and points raised on how to present their work results- should they use bar charts, line graphs,and what scale ranges are important and if error outliers should be included. Interesting discussion also occurred on the quality of the graphs, produced automatically by computer software programs and how to change these.

It was good at this age that they could grasp some of the concepts that seem illusive to certain older more mature researchers when they present their work. A key lesson learnt was not to spend all your effort creating wonderful results, if the presentation and visualisation story is not given a proportional amount of time to be created.



Virtual Reality- is this just for engineers?

Visited EON Reality – UK headquarters are just up the road from Manchester city centre – very convenient as only a couple of tram stops away now.

They have always been involved with various CAVE technologies; including a fun training system shown on their portable (dismantles into two crates) system, wp_20170303_002_cw

One feature would like to consider is how their 3D software works with volume and engineering visualisation – and as discussed with the guys – how the VR headset systems produce as good an experience (if not better). An important part was the ability for the sw to display on all types of platform from tablet to large scale VR system.

EON Reality had a whole set of objects including standard curved walls and  a reverse curved perspex type screen for exhibition type spaces. To complement this there was a training academy for 30+ students each year who could understand not just the sw but the whole production life-cycle.



Vis with HoloLens and VisDish

Thanks to Ros at Digilab for inviting us over and showing some of her new interactive teaching and research equipment. Couple of VR/AR items and a discussion on how they would solve understanding scale and context issues for visualisation of large volume data sets.

HoloLens One of the early UK released versions of Miicrosoft HoloLens (top right image) was available and impressed due to its response and quality. The demonstrator was a game allowing you to blast holes in the walls of the room, where large robot insects would emerge and then attack you. Is a very immersive experience of Augmented Reality and worked extremely well with rapid head movements as well as correctly obscuring the right parts of the real world – so was absorbing.  Discussed science based planning exercises where;

  • Items on the floor – could be marked up as discovered; geology pointcloud mark-ups.
  • Volumes could co-locate with real objects and be cropped/clipped on demand (“shot at” metaphor)

One key failure in this version was the small field of view – as had to move your head often and setting up the glasses on your head was an issue as took a few attempts to see most of this small FOV. This in future versions will improve – just needs more compute power and resolution!

ROVR An add on movement system (top left image) – relatively cheap one – was shown by the ROVR. A simple idea so while you are standing in a slippery bowl with slippery (low friction) shoes you can use standard VR headset and slide your feet (like walking on skies without lifting your feet). This feeds back via USB forward speed and rotational direction for very intuitive movement. Played a immersive pacman running game and again experience was great – not needing the main protection bars once sussed out balance. Highly impressed as allows:

  • You can travel distances and appreciate the distance; say along a feature in a visualisation.
  • Comprehend scale and distance – for example travel to a location and see scale of fature in 3D volume set – eg cavity.

Will be tricky to move from first or third person mode for viewing  as not directly connected to the framework for context but worth watching and programming for.


“Redundancy Visualisation”

This was a wonderful little term mentioned in passing at a synchrotron vis meeting. It sort of means what can be thrown away and still produce the message or story that the visualisation wishes to convey.

There are two areas where data can be thrown away – from the original or derived data set so you select parts that are appropriate; or from the items you wish to show in the visualisation itself – cropping isosurfaces or streamlines for example.

A tomography pipeline operation should be mentioned that addresses the 100GB problem – and could be said to be the first half.

100GB Problem

I have a scanned 3D data set that is about 4k x 4k x 4k in size, with 16 bits per voxel grey scale we have, 128 GB or raw data. How do we visualise this.

We can just use lots of CPUs and GPUs and this is fine – although not necessarily straightforward. See video from (TO Upload)

Do a simple dataflow so steps:

  • Load the complete data set into a fat memory workstation – you have to find one of these but there are ‘many’ 1/2 TB RAM systems out there.
  • Volume visualise the complete data set that works on simple GPU parallel code.
  • Select volume of interest
  • Crop this volume – aiming for about 1-2 GB
  • Extract this sub-volume and then possibly scale to 8 bits per volxel
  • You have a data volume about 1/2 – 1 GB that can go into your laptop for normal visualisation and hand editing / markup. Simple.

Not always practical but then there are lots of cool code that only works on <2GB volume due to meshing , level-set analysis and your heart and CPU are freed.

Important to go back to the raw volume and check you are have the right conclusions.

Visualisation needs RSEs

Imaging and visualisation has at its heart coding so the emergence of the Research Software Engineer as a career by being supported by the university, industrial and Research Council sectors is very welcome.

Vis From Manchester Central to being On the Top of The World – Still making the user via visualisation be deep within the HPC-Loop

Had the opportunity to present ideas of integrating human visualisation within the HPC (high-performance computation) loop. On 14 December 2016, Computing Insight UK 2016 launched with over 250 delegates and suppliers present; it was a great session to review the use of Visualisation within the Hartree Centre and describe how it has been important to keep the human in the visualisation/computational loop. This included the use of multi-use vis and discussion spaces as well as incorporating fat-memory GPU nodes at strategic locations; and then defined a future proposal to have an infinite job submission system that would stop only under human-visualisation control.

Needs for RSE to be integrated and employed

This talk was then modified and semi-repeated for a synchrotron (x-ray imaging) related workshop, an EU COST / PSI event on 9-12 January 2017 in Switzerland, which focused on the visualisation of complex imaging for a specific audience. For this we need software developers who can look at specific problems for users and have the dedicated time to create these solutions.


We submitting an EPSRC proposal with Manchester Research IT Services, for X-ray Tomographic Imaging, which is soon to successfully launch a new Flagship grant program in April 2017 – this will fund two RSEs from Manchester; Daniil Kazantsev (included in photo above 5th from right) and Jakob Sauer Jørgensen to revive the reconstruction codes within the reconstruction library; specifically these are for complex multi-channel data. Starting for three years both will be employed by the University of Manchester, but Daniil will be permanently based at the RAL – Harwell Campus next to the Diamond Light Source.


Is VR ready for Vis

Was at a show session in London – hosted by jigsaw24 – with a range of the new headset VR  (all except Sony [IMO one of the easiest and most comfortable to wear – possibly because it is well balanced]) creators and some hardware suppliers. 100+ attendees, with a mix of commercial and academic interest.


After a techy roadmap

  • EditShare (showing mainly 360VR data capture and pipeline use) emphasising on data storage issues and;
  • HTC Vive with the new Nvidia cards for automatic optic distortion was good – as always nvidia themselves create great prototypes (see their circus games in VR – with PhysX and some tactile feedback);


An interesting sales pitch was as a full system can use up to 4mx3m in space with multiple tracking nodes – there is an opportunity for nvidia to sell a new high-end pc in a new location at home. This space is too big for many living rooms; but the garage is an ideal (when empty) location to host a VR studio to achieve the best experience – so you will need a new PC (with high-end Pascal architecture quadro card!) for the garage.

Couple of interesting add-ons were shown but not demonstrated live from HTC – the new wireless modes which add only a few ms of latency, but allow you to wear a headset and do back-flips and other gymnastic maneuvers, a common occurrence in the future! The other was the HTC Vive Tracker a small attachment you can fix to a physical device, say a baseball bat, model gun etc… Six attached to a body created a simple but effective motion tracking system.


A developer panel was formed from; Alchemy VR, Rewind, Halo and the Mill – discussing use cases after a few years of practice and experience. Key questions were what did not work that included mainly thinking that you are creating a standard “framed” 2D/3D movie and old ideas fade to start and focus on specific POV object have to be rewritten. You can move but in sharper steps, and can cut when in context, although should contact some fixed reference point, and can experiment with exploration but may need to guide users for example turning down certain lights to allow the user to change their POV to the required place.


  • Cool examples to search and download include the Everest exploration viewer – this takes the traditional vertigo effect seen in caves within immersive environments to a new cool location being half way up everest.
  • ‘Home’ is the spacewalk that has parts of this that break good immersive experience (loosing a frame of reference) but that is what ‘…being in space is like’.
  • For 360 video the one of the Great Barrier Reef is now a classic for its beauty and simplicity.
  • HTC claimed over 1000 available just from last year and this will expand.


So can you do visualisation within this medium – will consider in detail later – but as an experience I would say there is scope to add this scale effect and immersion to help understand data. Couple of quick examples I would include;

  • Understanding astronomy as is fully 3D and huge (see below from Manchester)
  • Seeing the ATLAS detector and large underground building type structure showed its scale and shape



Human in the viz-loop

On 14 December 2016 at Computing Insight UK with over 250 delegates and suppliers present I had the opportunity to discuss areas of human-vis within HPC – few tweets on the vis presentation (so far). The specific example of the IMAT beamline was used, but the past four years usage were considered. Thanks to Srikanth Nagella, Erica Yang and Martin Turner; Peter Oliver, Callum Williams, Joe Kelleher, Genoveva Burca, Triestino Minniti, Winfried Kockelmann

Abstract for CIUK Presentation

Energy selective imaging detectors with over 10MP sensors have been incorporated within large science laboratories allowing high-quality materials at the micro- (10-6 m) or nano- (10-9 m) scales analysis but creating a large data problem. A service for neutron analysis is being offered by the IMAT (Imaging and Materials Science & Engineering) instrument, at the ISIS pulsed neutron source in the UK. ULTRA is a compute intensive HPC platform enabling high-throughput neutron tomographic image data analysis so that images can be scrutinised during an experiment rather than as a batch-mode post-process operation.

Dataflow Problem:

Unlike normal computed tomography (CT) scans used in hospitals where one 2D image is acquired for each ‘shot’ (a rotation angle), in energy selective neutron imaging, an image stack comprising of potentially thousands of 2D images are collected at each ‘shot’. So for the MCP camera , capable of collecting 3,000 images per angle where each image uniquely corresponds to one of the 3,000 energy bands; this results in 0.3 million images during a 100 angle experiment.

New Materials Science Analysis:

The reason this is carried out is that neutron interactions can vary drastically with neutron energy for certain materials, allowing for chemistry discrimination [2]. The amount of neutrons that is able to penetrate through a material and reach the image detectors, namely, neutron intensity, is strongly affected by the crystalline structures and microstructures of a material, exhibiting Bragg edges [1]. The computational reconstruction needs to be near interactive as each peak represents a potential energy band region suitable for HPC reconstruction.

Creating a bespoke HPC engine:

A specific HPC-based analysis and visualisation technology is being employed to enable this new mode of operation. Traditionally, a typical 3D reconstruction takes mins, using the Filtered Back Projection algorithm (FBP) [3], one of the most common and fast algorithm. However, in energy selective imaging, the reconstructions need to be performed repetitively across selected energy ranges and as signal to noise levels are lower, iterative algorithms, which are much slower than the FBP algorithm are required and can take >100s of minutes to run.

ULTRA has been constructed to receive the data to a STFC HPC cluster, a distance of about a mile, on demand from the experimental facility and process the data directly – this then allows for different interaction modes gives instantaneous feedback for example through small mpeg movie clips, as well as final results transmitted by remote visualisation from the login node via paraview. Using the Savu pipeline , a python based dataflow mechanism, different options of filtering, reconstruction and presentation can be incorporated . We will explain the specific cluster based HPC hardware setup that includes GPU based login nodes designed to minimise data movement.

For the scientists the insights obtained through this analysis process is then used to steer the next experiment step, for example, to adjust sample positions and beam alignment, or to decide whether to use different reconstruction algorithms or parameters, or image filters.

Video Outreach:

We created a video that will be presented and described to help others develop a better understanding to create this kind of hardware/software dataflow experiment. A dataset is transferred (using the open source test date – SophiaBeads dataset [4], a microCT dataset), captured, reconstructed using the FBP algorithm from the TomoPy image reconstruction toolkit [6] running on a single node with 128GB RAM, 12 CPU cores using the STFC’s large HPC cluster, SCARF and then segmented using the algorithms available in the commercial software package Avizo [5].

[1] T. Minniti et. al., “Material analysis opportunities on the new neutron imaging facility IMAT@ISIS”, Journal of Instrumentation, Volume 11, March 2016, IOP Publishing Ltd and Sissa Medialab srl.
[2] J. Santisteban et. al., “Time-of-Flight neutron transmission diffraction”, J. Appl. Cryst. 34 (2001) 289.
[3] Peter Toft, “The Radon Transform – Theory and Implementation”, Ph.D. thesis. Department of Mathematical Modelling, Technical University of Denmark, June 1996.
[4] Sophia Bethany Coban, “SophiaBeads Datasets Project Documentation and Tutorials”, April 2015, MIMS EPrint: 2015.26.
[5] Avizo 9. “Avizo User’s Guide”, FEI Visualisation Sciences Group.
[6] Doǧa Gürsoy, Francesco De Carlo, Xianghui Xiao, and Chris Jacobsen, “TomoPy: a framework for the analysis of synchrotron tomographic data”, J Synchrotron Radiat. 2014 Sep 1; 21(Pt 5): 1188–1193. DOI: 10.1107/S1600577514013939.