F4500 Python for physicists

Faculty of Science
Spring 2019
Extent and Intensity
1/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
Teacher(s)
Mgr. Filip Hroch, Ph.D. (lecturer), Mgr. Petr Zikán, Ph.D. (deputy)
Mgr. Petr Klenovský, Ph.D. (lecturer)
Mgr. Filip Münz, PhD. (lecturer)
Mgr. Adam Obrusník, Ph.D. (lecturer)
Mgr. Petr Synek, Ph.D. (lecturer)
Mgr. Jan Voráč, Ph.D. (lecturer)
Mgr. Petr Zikán, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Mirko Černák, CSc.
Department of Physical Electronics - Physics Section - Faculty of Science
Contact Person: Mgr. Petr Zikán, Ph.D.
Supplier department: Department of Physical Electronics - Physics Section - Faculty of Science
Timetable
Mon 18. 2. to Fri 17. 5. Thu 17:00–19:50 Fcom,01034
Prerequisites
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.
Syllabus
  • 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
Literature
    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
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2017, spring 2018, Spring 2020, Spring 2021.
  • Enrolment Statistics (Spring 2019, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2019/F4500