Eudorina - Pavel Skaloud, Charles University, Věda je krásnáPollen grain of Hyoseris radiata, TESCANOrchid root with Mycorrhiza, S. R. Senthilkumar, St. Joseph´s College, India
Leaf Fract, TESCAN1- CVD grown diamond film, Magdalena Parlinska, University of Rzeszow, Poland SEM image of ink-bottle silica nanopores, A. Sterczynska,NanoBioMediacl Centre (CNBM), Poznan, Poland
Powder metallurgy substrate, TESCANScabiosa columbaria - Viktor Sykora, Charles University, Věda je krásnáRust fungus spore,Adriana Dominguez and Eduardo Favret, CNEA - INTA, Argentina.jpg
Plasma coating crossection, TESCAN3- Diatom, Magdalena Parlinska, University of Rzeszow, Poland Cross section of an Abutilon leaf, Adriana Dominguez and Eduardo Favret, CNEA - INTA, Argentina
Coral, TESCANUniform core shell Fe nanoparticles, S. Bandyopadhyay, NTNU, Trondheim Chroococcus giganteus - Jan Stastny, Charles University, Věda je krásná
2- Gel beads coated with a RuC13 coatings, Magdalena Parlinska, University of Rzeszow, Poland 1- Diatom, Magdalena Parlinska, University of Rzeszow, Poland PbI2 Crystallization, TESCAN
3- Gel beads coated with a RuC13 coatings, Magdalena Parlinska, University of Rzeszow, Poland Pollen of Lavatera arborea, TESCAN2- Diatom, Magdalena Parlinska, University of Rzeszow, Poland

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Interactive Data Analysis with Python/Hyperspy  

0rganizer: Francisco de la Peña, Michael Walls

Contact: fjd29@cam.ac.uk

Abstract: In all forms of microscopy, as in many other fields, several technological breakthroughs have dramatically increased the amount, rate and precision of experimental data that can be recorded. In parallel, there have been important advances in the field of data analysis which can greatly assist in the task of extracting information from multi-dimensional datasets. In this context, the Python programming language is rapidly establishing itself as the *lingua franca* in most areas of scientific computing, including microscopy.
In this workshop, brief introductory lectures will be followed by hands-on tutorials on microscopy data analysis using Hyperspy (a Python package for interactive multidimensional data analysis) and Swift (a Python package for live data acquisition and analysis). The topics covered will range from multidimensional data visualization to blind source separation methods and will have a strong emphasis on spectroscopic data analysis. Most examples will be taken from the field of electron microscopy but the same methodology can be applied to other domains. No previous knowledge of the Python programming language is required.

Date: one day workshop, September 7, 2014 from 9AM to 4:30PM

Venue: Faculty of Science, Charles University in Prague, Viničná 7, Prague 2 (computer classroom B5, 1st floor)

Minimal number of participants: 20

Registration fee: 60 EUR (included coffee breaks, one lunch and workshop materials)

 

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