F4500 Python for physicists

Faculty of Science
Spring 2024
Extent and Intensity
1/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
Mgr. Filip Hroch, Ph.D. (lecturer)
Mgr. Petr Klenovský, Ph.D. (lecturer)
Mgr. Filip Münz, PhD. (lecturer)
Mgr. Adam Obrusník, Ph.D. (lecturer)
Mgr. Tomáš Plšek (lecturer)
Dr. Martin Topinka, PhD. (lecturer)
Mgr. Petr Zikán, Ph.D. (lecturer)
Guaranteed by
Mgr. Filip Hroch, Ph.D.
Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science
Contact Person: Mgr. Petr Zikán, Ph.D.
Supplier department: Department of Condensed Matter Physics – Physics Section – Faculty of Science (33,00 %), Department of Plasma Physics and Technology – Physics Section – Faculty of Science (33,00 %), Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science (34,00 %)
Mon 19. 2. to Sun 26. 5. Wed 17:00–19:50 F1 6/1014
One is recommended for students of physics and related fields.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
This course introduces Python programming language as a practical tool for physicists. Key ideas of the Python usage are demonstrated starting from simple problems up to more complex tasks. All examples are described on common physical tasks. Lectures are prepared as workshops - lectures, demonstrations, and exercises following each other. Main aim of this course is to provide the ability to prepare a wide spectrum of reports (laboratory exercises, bachelor or diploma theses) in Python including graphs, pictures and tables.
Learning outcomes
Python computer language is introduced including its applications in physics on everyday base.
We hope students will get the following skills:
To be use and install Python run-time environment,
to create programs with basic data types, data structures and flow control statements,
to apply advanced structures for data processing,
plotting scientific graphs and
to use of advanced mathematics libraries.
  • 1. Introduction (demonstration, disputation, introduction to Python).
  • 2. ... continuation of introduction ....
  • 3. ... continuation of introduction ....
  • 4. Interpretation of oscilloscope data by using of basic containers.
  • 5. Introduction to objects
  • 6. Numpy + matplotlib
  • 7. Processing of spectroscopic data - advanced numpy.
  • 8. Regression
  • 9. Particle motion in electromagnetic fields.
  • 10. Accessing data on Internet via Python, databases.
  • 11. Introduction to machine learning
  • 12. Telescope control
    recommended literature
  • MCKINNEY, Wes. Python for data analysis : [agile tools for real world data]. 1st ed. Sebastopol, Calif.: O'Reilly, 2013, xiii, 452. ISBN 9781449319793. info
Teaching methods
Lectures, presentations by professionals, demonstrations and exercises.
Assessment methods
Homework project: report in Python.
Language of instruction
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2025.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2024/F4500