Python for Scientific Computations and Control (E375004)
| Departments: | ústav přístrojové a řídící techniky (12110) |
| Abbreviation: | | Approved: | 10.09.2012 |
| Valid until: | ?? | Range: | 2P+2C |
| Semestr: | * | Credits: | 4 |
| Completion: | KZ | Language: | EN |
Annotation
Scientific computations and processing of online measured data in programming environment Python, communication with connected devices, saving and visualization of online measured data into PC using Python in real time, libraries, programming the common tasks of numerical mathematics in Python, programming graphic user interfaces, visualization, demonstration of solved problems. Classification upon the individually solved class projects. The analogies to Matlab will be discussed during the course.
Teacher's
Ing. Adam Peichl
Letní 2022/2023
Ing. Matouš Cejnek Ph.D.
Zimní 2022/2023
Ing. Michal Kuchař
Zimní 2022/2023
Ing. Matouš Cejnek Ph.D.
Letní 2021/2022
Ing. Michal Kuchař
Letní 2021/2022
Ing. Matouš Cejnek Ph.D.
Zimní 2021/2022
Ing. Michal Kuchař
Zimní 2021/2022
Ing. Matouš Cejnek Ph.D.
Letní 2020/2021
Ing. Matouš Cejnek Ph.D.
Zimní 2020/2021
Ing. Michal Kuchař
Zimní 2020/2021
Structure
1.Programming environment Python and its potentials
2.Programming language Python for scientific computations and data processing (NumPy, SciPy)
3.Working with vectors and matrices - matrix operations, solving sets of linear equations in Python
4.Eigenvalues and eigenvectors in Python, data compression by PCA in Python
5.Data visualization (MatplotLib)
6.A simple ODE solver for simulation of a set of differential equations and their sets; computing of a discrete time (difference) equation in Python
7.Graphic User Interface (GUI) designs in Python
8.Recording online measured data into PC and visualization in Python
9.Vizualization and signal processing in Python (statistical markers, correlation analysis, noise analysis, power spectral density)
10.Fundamental algorithms of static function approximation (gradient descent, Levenberg-Marquardt algorithm) and their implementation in Python
11.Examples of the gradient descent method for approximation of a dynamic system in Python
12.Example of tuning of controller parameters for a (real) laboratory system
13.Demonstration - options for design of artificial neural network and fuzzy system in Python
14.Further potentials of Python, summary
Literarture
see http://users.fs.cvut.cz/ivo.bukovsky/
Keywords
Python, scientific computations, matrix operations, ODE solver, data compression PCA, online recordings of measured data to PC, data processing and visualization, approximation of a static function, gradient descent adaptation, Levenberg-Marquardt algorithm, example of controller optimization in Python, Windows, Linux
čs
en