It provides a graphical means of exploring relationships within large datasets. It works best for data that is stored in a spreadsheet or table rather than 3D simulation data.
GlueViz enables you to:
- Explore data via statistical graphics such as scatter plots, histograms and images.
- Linkage between graphics allows subsets selected in one view to be propagated to the others.
- Python scripting: additional features can be added using Python, without having to learn too much programming.
This is the pilot version of a general hands-on course that introduces some of the basic visualization techniques before some more advanced Python techniques. It covers:
- Overview of user interface, drag and drop, subsets.
- How to load data, visualize, define subsets, and save subsets; input & output file formats.
- How to use the built-in visualisation tools: histogram .... table
- Generate multiple related views to present data subsets, unions intersections etc.
- Comparing and merging multiple data sets
- Interactive programming via GluViz's interactive IPython terminal
- Export Python code for generating plots.
- Develop your own visualisation tools in Python.
- Q&A on issues around your own data.
Experience with Python programming would help for the later parts of the course, but in not essential.
- Anacond3 with GlueViz and Spyder installed.
- MS Excel
Joanna is a senior research software engineer (RSE) at the University of Leeds. Before becoming an RSE Joanna worked in medical testing. Her first role as an RSE saw her spend three years work on medical visualization, which included cochlea implants, virtual endoscopy, aneurisms and virtual archaeology.
Since then she has worked on a variety of medical based projects including but not limited to heart modelling, epidemiology and the design of the artificial hip.
Jonathan graduated from Coventry Polytechnic with a BSc in Materials Science, and worked as a research metallurgist, before taking a computer science conversion MSc at the University of Manchester. After that he took a research MPhil in computer science at Aberystwyth University followed by a PhD, specializing in the application of artificial intelligence to computer graphics at the University of York.
After York Pickering was employed in various posts as research assistant or a teaching assistant at the Universities of Huddersfield, York, Salford. He then worked in industry developing a web-based database for a safety testing company. After which joined the University of Leeds and became a research assistant working on the deployment of molecular mechanics research software to industry, before taking up his present post.