Python for Scientific Computations and
Control

Course Lecturer :** **Ivo
Bukovský, (Google: ivo bukovsky) , http://users.fs.cvut.cz/ivo.bukovsky/

**Code: E37 5004**

Rozsah: 2+2

Způsob zakončení:
kz (classified
assessment, classification upon the
individually solved class projects)

Počet kreditů (**credits**): **4**

Scientific
computations and processing of online measured
data in programming environment Python, communication with connected
devices, saving and visualization of online measured dat a 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. The
analogies to Matlab will be discussed during the
course.

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.
Visualization and signal processing in Python (statistical markers,
correlation analysis, noise analysis, power spectral density)

9.
Fundamental algorithms of static function approximation (gradient
descent, Levenberg–Marquardt algorithm) and their
implementation in Python

10. Examples of the gradient
descent method for approximation of a dynamic system in Python

11. Hardware for Python, USB/Ethernet
based measurement (LabJack, Raspberry Pi, Q-Python for Android…)

12. Recording online measured
data into PC and visualization in Python

13. Example of tuning of
controller parameters for a laboratory system

14. Further potentials of
Python, summary

To pass the class, students have to actively
participate on lectures and labs. A semestral work and
presentation of results in class will be required. The students should be
familiar with Math topics of first and second year. There are no special
prerequisites for any SW or HW.

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

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