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Lecturer(s)
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Březina Jan, doc. Mgr. Ph.D.
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Course content
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Lectures: 1) IPython - basic calculation, combining with text and equations 2) Python - basic syntax, data types 3) matplotlib - 2D graphs 4) Sympy - symbolic calculation, Numpy - multi-dimensional arrays, vectorization 5) SciPy - overview, linear algebra, interpolation, quadrature, ODE 6) SciPy - algebraic equations, optimization, statistics 7) Parallel programming, threads 8) Parallel programming, MPI 9) Paraview (basics, example of embeded Python) 10) MayaVi (3D vizualiztion) Tutorials: 1) Python, IPython - work with basic data types, files 2) Introduction of project topics, modules 3) Matplotlib 4) SymPy, NumPy 5) SciPy 6) Metacentrum/Hydra - get access, job execution 7) Parallel programming, threads. 8) Distributed computing, MPI, PBS 9) Presentation of selected works: Matplotlib 10) Presentation of selected works: SymPy, SciPy
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing), Self-study (text study, reading, problematic tasks, practical tasks, experiments, research, written assignments), Laboratory work
- Class attendance
- 40 hours per semester
- Preparation for laboratory testing; outcome analysis
- 26 hours per semester
- Preparation for formative assessments
- 18 hours per semester
- Preparation for credit
- 25 hours per semester
- Home preparation for classes
- 20 hours per semester
- Presentation preparation (report)
- 20 hours per semester
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Learning outcomes
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The course provides an introduction into the Python language emphesizing its usage in the scientific computing as a glue tool and scripting language for the specialized numerical software. The closing part of the course is an introduction to the paralell programing and high-performance computing.
After the course the student should be able to program its own specialized scripts, apply basic paralellization techniques, and executing jobs on paralellel computers.
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Prerequisites
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Basic knowledge of any programming language.
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Assessment methods and criteria
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Oral presentation of self-study
The graded credit is granted according to the activity at the tutorials and the quality of the seminal work.
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Recommended literature
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Jupyter and the future of IPython ? IPython [online]. [cit. 2016-01-08]. Dostupné z: http://ipython.org/.
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ParaView/Python Scripting - KitwarePublic [online]. [cit. 2016-01-08]. Dostupné z: http://www.paraview.org/Wiki/ParaView/Python_Scripting.
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Ponořme se do Pythonu 3 [online]. [cit. 2016-01-08]. Dostupné z: http://diveintopython3.py.cz/index.html.
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Cyrille Rossant. IPython Interactive Computing and Visualization Cookbook.
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J. Elkner, A. B. Downey, Ch. Meyers. Učíme se programovat v jazyce Python 3 [online]. 2008, 2015 [cit. 2016-01-08]. Dostupné z: http://howto.py.cz/.
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Mark Pilgrim. Ponořme se do Pythonu 3 [online]. [cit. 2016-01-08]. Dostupné z: http://diveintopython3.py.cz/index.html.
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Švec. Seriál Létající cirkus [online]. Root.cz, 2015 [cit. 2016-01-08]. Dostupné z: http://www.root.cz/serialy/letajici-cirkus/.
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