1. SylabUZ
2. Wydział Fizyki i Astronomii
3. semestr zimowy 2017/2018
4. Fizyka - Program Erasmus pierwszego stopnia
5. Python language in numerical calculations

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Python language in numerical calculations - opis przedmiotu

Cel przedmiotu

The course aim is to introduce the Python as the scientific programming tool. Python is a general purpose, high-level and modern programming language and the capabilities of its standard library as well as the external modules to handle the numerical analysis in physics and related fields will be presented.

Wymagania wstępne

Zapisz zmiany

Basic knowledge in programming and object oriented programming.

Zakres tematyczny

Metody kształcenia

1) General Python introduction

- Language syntex and data types

- Flow-control and exceptions

- Interactive shell

- Scripts

- Functions

- Modules

2) File I/O operations

- Writing to and saving files

- Data serialization

- Typical I/O operations errors

3) Object Oriented Programming

- Classes and objects

- Inheritance and polymorphism

- Abstractions

4) Introduction to software engineering

- Version control systems

- Linux as IDE

- Introduction to unit-tests

- Software efficiency and profiling

5) Numerical analysis and computer simulations introduction

- The math module

- NuPy's arrays

- Random numbers

- Basic linear algebra operations in NumPy

- Differential equations solvers in NumPy

- Data visualisations in the matplotlib module

- Introduction to parallel computing with mpi4py

6) Visualization, animations and image processing

- The canvas and graphical primitives

- Plots

- Animations

- Image processing with openCV (computer vision) module

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

Lecture:

To pass the exam the student will be asked to numerically solve a certain problem of the classical physics or data analysis. The examined knowledge fields and the final exam grade will be evaluated using the following aspects: the problem analysis, presentation of the algorithms used in the problem solution, the presentation of the source code and the validity of the results.

Laboratory:

30% - tests ad activity during laboratories

70% - final project

Before taking the exam the student must obtain a pass from the laboratory.

Score: weighted average rating of the exam (60%) and exercise (40%).

 

Literatura podstawowa

[1] Mark Lutz, Python. Wprowadzenie, Wydanie IV, Helion, Gliwice 2010.
[2] http://python.org
[3] http://python-ebook.blogspot.com/
[4] http://numpy.scipy.org
[5] Hans Petter Langtangen, A primer on scientific programming with Python, Springer, Berlin 2009.

Literatura uzupełniająca

[1] Internet

Uwagi

Zmodyfikowane przez dr hab. Maria Przybylska, prof. UZ (ostatnia modyfikacja: 09-07-2018 23:01)