3- Gel beads coated with a RuC13 coatings, Magdalena Parlinska, University of Rzeszow, Poland Phragmites communis - Jan Martinek, Charles University, Věda je krásná2- Gel beads coated with a RuC13 coatings, Magdalena Parlinska, University of Rzeszow, Poland
Scabiosa columbaria - Viktor Sykora, Charles University, Věda je krásná2- CVD grown diamond film, Magdalena Parlinska, University of Rzeszow, Poland Pollen grain of Hyoseris radiata, TESCAN
Scenedesmus quadricauda - Viktor Sykora, Charles University , Věda je krásnáCoral, TESCANEnergy filtered TEM micrograph of yttria (in green) - zirconia (in red) multilayers, Chanchal Ghosh,  IGCAR, Kalpakkam, India
Polymer fibers, TESCANLeaf Fract, TESCANPlasma coating crossection, TESCAN
Offretite Scagno, TESCANRust fungus spore,Adriana Dominguez and Eduardo Favret, CNEA - INTA, Argentina.jpg Uniform core shell Fe nanoparticles, S. Bandyopadhyay, NTNU, Trondheim
3- Diatom, Magdalena Parlinska, University of Rzeszow, Poland Diatoms World, Mostafa Moonir Shawrav, Institute of Solid State Electronics, Austria Powder metallurgy substrate, TESCAN
Rotaviruses, Electronmicroscopy, Elisabeth M. Schraner, Institutes of Vet. Anatomy and Virology, Switzerland Gold on Germanium, Benedykt R. Jany, Marian SmoluchowskiInstitute of Physics - Jagiellonian University, Poland Chroococcus giganteus - Jan Stastny, Charles University, Věda je krásná

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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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