ifm_contrib is an Open-Source Extension of the IFM, the Python API of the groundwater modellierung software FEFLOW by DHI.
It extents the namespace of the classic IFM to facilitate using FEFLOW in combination with the popular Python datascience libraries pandas, geopandas and matplotlib.
The ifm_contrib namespace is a superset of the classic ifm namespace, thus scripts written for the latter will be back-wards compatible and only one library import statement is required.
The library has been developed since 2019, and reached maturity around 2021 with most features for (geo-)pandas as matplotlib integration in place.
ifm_contrib is published under the MIT license. It is free to use, share and modify. Users are encouraged to contribute their own code back to the project.
Please note that the maintainer of this project is not associated with DHI.
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Clone this repo
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Inside the repo:
python -m venv .venv source .venv/bin/activate pip install ".[test]"
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Run unit test with
pytest
The recommended way of installing ifm_contrib is by copying or cloning the repository directly into the FEFLOW program director next to the classic IFM.
Installation via pip should be done by:
pip install https://github.com/red5alex/ifm_contrib/archive/refs/heads/master.zipAn installation of FEFLOW (version 7.2 or higher) is required.
For a detailed introduction and installation notes, see ifm_contrib: An Introduction
Its not a pretty one, but you can find an (unregularly updated) help system including API reference here](https://red5alex.github.io/ifm_contrib/ifm_contrib.contrib_lib.html).
If you run into issues please open an issue. Please note that the maintainer may not dedicate significant time to the project at this time but will try its best to respond.
Create inline plots with the look-and-feel of FEFLOWs directly within Jupyter. The plots can be exported as GeoDataFrames and saved to shape-files easily.
ifm_contrib adds light support for coordinate systems to FEFLOW.
Time Series (aka Power functions) and History charts can be easily exported to pandas DataFrames. Automatic conversion to DateTime based on FEFLOWs Reference Time. In-Built Synchronization to observation point reference data.
Read more in ifm_contrib: An Introduction