mikeio 2.0.1

A package that uses the DHI dfs libraries to create, write and read dfs and mesh files.

MIKE IO: input/output of MIKE files in Python

[!NOTE]

Instructor-led course

Getting started with MIKE IO for Python processing of dfs files

From 4th September 2024 to 2nd October 2024

Read, write and manipulate dfs0, dfs1, dfs2, dfs3, dfsu and mesh files.

MIKE IO facilitates common data processing workflows for MIKE files in Python.

Requirements

• Windows or Linux operating system
• Python x64 3.9 - 3.12
• (Windows) VC++ redistributables (already installed if you have MIKE)

More info about dependencies

Installation

From PyPI:

pip install mikeio

Or development version:

pip install https://github.com/DHI/mikeio/archive/main.zip

:warning: Don't use conda to install MIKE IO!, the version on conda is outdated.

Getting started

The material from the last Academy by DHI course is available here: Getting started with Dfs files in Python using MIKE IO

Where can I get help?

• Documentation - https://dhi.github.io/mikeio/
• General help, new ideas and feature requests - GitHub Discussions
• Bugs - GitHub Issues

Tested

MIKE IO is tested extensively.

See detailed test coverage report below:

---------- coverage: platform linux, python 3.12.4-final-0 -----------
Name                                      Stmts   Miss  Cover
-------------------------------------------------------------
mikeio/__init__.py                           31      3    90%
mikeio/_interpolation.py                     68      6    91%
mikeio/_spectral.py                          97      7    93%
mikeio/_time.py                              29      1    97%
mikeio/_track.py                            103     14    86%
mikeio/dataset/__init__.py                    3      0   100%
mikeio/dataset/_data_plot.py                358     38    89%
mikeio/dataset/_data_utils.py                20      0   100%
mikeio/dataset/_dataarray.py                730     52    93%
mikeio/dataset/_dataset.py                  734     57    92%
mikeio/dfs/__init__.py                        5      0   100%
mikeio/dfs/_dfs0.py                         198     13    93%
mikeio/dfs/_dfs1.py                          58      2    97%
mikeio/dfs/_dfs2.py                         132      3    98%
mikeio/dfs/_dfs3.py                         147      9    94%
mikeio/dfs/_dfs.py                          290     18    94%
mikeio/dfsu/__init__.py                       6      0   100%
mikeio/dfsu/_common.py                       36      1    97%
mikeio/dfsu/_dfsu.py                        223      7    97%
mikeio/dfsu/_factory.py                      20      1    95%
mikeio/dfsu/_layered.py                     190      7    96%
mikeio/dfsu/_mesh.py                         54      8    85%
mikeio/dfsu/_spectral.py                    214     36    83%
mikeio/eum/__init__.py                        2      0   100%
mikeio/eum/_eum.py                         1334      9    99%
mikeio/exceptions.py                         24      4    83%
mikeio/generic.py                           451     17    96%
mikeio/pfs/__init__.py                        8      0   100%
mikeio/pfs/_pfsdocument.py                  248     13    95%
mikeio/pfs/_pfssection.py                   223      9    96%
mikeio/spatial/_FM_geometry.py              521     24    95%
mikeio/spatial/_FM_geometry_layered.py      415     30    93%
mikeio/spatial/_FM_geometry_spectral.py      94      9    90%
mikeio/spatial/_FM_utils.py                 275     22    92%
mikeio/spatial/__init__.py                    6      0   100%
mikeio/spatial/_geometry.py                  78      8    90%
mikeio/spatial/_grid_geometry.py            639     45    93%
mikeio/spatial/_utils.py                     39      0   100%
mikeio/spatial/crs.py                        51      5    90%
mikeio/xyz.py                                14      0   100%
-------------------------------------------------------------
TOTAL                                      8168    478    94%

Cloud enabled

It is possible to run MIKE IO in your favorite cloud notebook environment e.g. Deepnote, Google Colab, etc...