PbI2 Crystallization, TESCAN1- Gel beads coated with a RuC13 coatings, Magdalena Parlinska, University of Rzeszow, Poland Coral, TESCAN
2- Diatom, Magdalena Parlinska, University of Rzeszow, Poland 3- Diatom, Magdalena Parlinska, University of Rzeszow, Poland Plasma coating crossection, TESCAN
Orchid root stained with Acridine orange, S. R. Senthilkumar, St. Joseph´s College, India Energy filtered TEM micrograph of yttria (in green) - zirconia (in red) multilayers, Chanchal Ghosh,  IGCAR, Kalpakkam, India  Scenedesmus quadricauda - Viktor Sykora, Charles University , Věda je krásná
Procapsid and nucleocapsid of dsRNA bacteriophage phi6, D. Nemecek, CryoEM Research Group CEITEC, Czech Republic Rust fungus spore,Adriana Dominguez and Eduardo Favret, CNEA - INTA, Argentina.jpg Salt, TESCAN
Pollen grain of Hyoseris radiata, TESCANOrchid root showing with idioblastic cells, S. R. Senthilkumar, St. Joseph´s College, India Powder metallurgy substrate, TESCAN
Eudorina - Pavel Skaloud, Charles University, Věda je krásná2- CVD grown diamond film, Magdalena Parlinska, University of Rzeszow, Poland Scabiosa columbaria - Viktor Sykora, Charles University, Věda je krásná
Rotaviruses, Electronmicroscopy, Elisabeth M. Schraner, Institutes of Vet. Anatomy and Virology, Switzerland Butterfly Wings, Benedykt R. Jany, Marian SmoluchowskiInstitute of Physics - Jagiellonian University, Poland Polymer fibers, TESCAN

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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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