Getting started with dfs files in Python
Using MIKE IO
An instructor-led online course | November 2022
Learn how to efficiently read, write and perform data analysis on data stored in dfs files using Python.
Course details
What is MIKE IO?
Is this course right for me?
What are my expected outcomes?
How will I learn?
What is MIKE IO?
MIKE IO is a free, open-source Python library for MIKE data file processing and automation.
With MIKE IO for Python, you can:
- Read, write and analyse dfs0, dfs1, dfs2, dfs3, dfsu and mesh files
- Rely on flexible data processing and visualisation via Python data science ecosystem
- Convert data to / from 3rd party data formats such as csv, Excel and NetCDF
- Facilitate scripting and automation of water modelling workflows
- Read and write dfs files using a simple syntax with the powerful MIKE Core SDK backend
Is this course right for me?
This course is right for you if:
✔️ You work with MIKE dfs files and would like to be more efficient and do better analysis
✔️ You use or would like to use Python for programming or data analysis
✔️ You are prepared to complete a four-module instructor-led online course over four weeks with group work included in each module
What are my expected outcomes?
Upon completion of the course, you will be able to:
✔️ Save time by becoming more efficient at MIKE dfs file processing
✔️ Conduct better data analysis and thereby provide new insights faster
✔️ Read, write and analyse dfs0, dfs1, dfs2, dfs3, dfsu and mesh files
✔️ Convert data to/from 3rd party data formats such as csv, Excel and NetCDF
✔️ Facilitate scripting and automation of water modelling workflows
How will I learn?
This course includes four modules conducted via online live sessions by our qualified trainers. Additionally, if you are new to Python, an optional getting-started-with-Python module will be offered 2 days prior to the first course module. You will learn through a combination of instructor-led lectures and quizzes, hands-on exercises and reading materials.
Throughout the course, you will have ample time to discuss tips & tricks, best practices and examples with your instructor and fellow participants.
A part of the online sessions is allocated to group work, which is based on home assignments set by your trainer. For maximum benefits, you are strongly advised to allocate a minimum of two hours of self-study for each module.
Modules
This online course is divided into four 1.5-hour training sessions hosted Thursdays from 08:00-09:30 UTC and an optional 1.5-hour session on introduction to Python for participants with little or no experience with Python.
Tuesday 15 November 2022
Optional Module 0 | Getting started with Python
Topics: Python, NumPy, Pandas and Jupyter notebook
Thursday 17 November 2022
Module 1 | Timeseries dfs0
Topics: Installation, Dataset, timeseries (dfs0)
Thursday 24 November 2022
Module 2 | Generic and dfs1
Topics: The DFS files format, Generic dfs processing, line series (dfs1)
Thursday 1 December 2022
Module 3 | dfs2
Topics: Gridded 2D data (dfs2)
Thursday 8 December 2022
Module 4 | dfsu 2D
Topics: Flexible mesh data
Can’t make it to this course? Let us know if you’d like additional future offerings.
Instructors
Henrik is a Senior Innovation Engineer with experience in biogeochemical modeling, statistical analysis, scientific programming, web development, and biogeochemical sampling. He has been involved in several international research projects as well as commercial projects related to data assimilation and marine forecasting.
PhD Utrecht University, MSc Gothenburg University
Jesper is a data & modelling specialist at DHI with a PhD background in applied mathematics, hydrodynamics and scientific computing. He has been with DHI since 2009 and has worked with data assimilation, data science, machine learning, scientific software development, marine forecasting, metocean data and wave model development.
PhD, MSc Technical University of Denmark
Pricing tiers
*Customers with an active SMA or subscription