https://github.com/dhi/book-learn-mikeio-modelskill
Course material for: Python for marine modelers using MIKE IO and ModelSkill
https://github.com/dhi/book-learn-mikeio-modelskill
course dfs python validation
Last synced: 19 days ago
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Course material for: Python for marine modelers using MIKE IO and ModelSkill
- Host: GitHub
- URL: https://github.com/dhi/book-learn-mikeio-modelskill
- Owner: DHI
- License: mit
- Created: 2021-09-21T08:34:12.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-11T15:55:39.000Z (about 2 years ago)
- Last Synced: 2025-01-05T07:15:09.734Z (over 1 year ago)
- Topics: course, dfs, python, validation
- Language: Jupyter Notebook
- Homepage: https://dhi.github.io/book-learn-mikeio-modelskill/
- Size: 35.4 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Python for marine modelers using [MIKE IO](https://github.com/DHI/mikeio) and [ModelSkill](https://github.com/DHI/modelskill)
Would you like to use Python to become more efficient as a marine modeler?
## WHO SHOULD ATTEND?
This course has been designed for MIKE 21/3 modelers with some Python experience, e.g. from the course [Getting started with dfs files in Python using MIKE IO](https://dhi.github.io/getting-started-with-mikeio/intro.html) or similar, that would like to use Python for more pre- and post-processing.
## CONTENT
The course comprises typical data processing tasks in connection with MIKE 21/3 modelling, preparing input data, converting forcing data from other formats, creating roughness maps, concatenating files, extracting data from output files (e.g. vertical profiles), visualization, assessing the skill of MIKE 21/3 by comparing to station and satellite track observation data. We will use Jupyter notebooks with Python packages such as NumPy, pandas and xarray as well DHI's own MIKE IO and ModelSkill packages.
## BENEFITS
After the course, you will have improved your skills and knowledge on how to conduct typical MIKE 21/3 modeling workflows using MIKE IO and ModelSkill.