PyGMI
Overview
PyGMI stands for Python Geoscience Modelling and Interpretation. It is a modelling and interpretation suite aimed at magnetic, gravity, remote sensing and other datasets. PyGMI has a graphical user interface, and is meant to be run as such.
PyGMI is developed at the Council for Geoscience (Geological Survey of South Africa).
It includes:
Magnetic and Gravity 3D forward modelling.
Cluster Analysis, including use of scikit-learn libraries.
Routines for cutting, reprojecting and doing simple modifications to data.
Convenient display of data using pseudo-color, ternary and sunshaded representation.
MT processing and 1D inversion using MTpy.
Gravity processing.
Seismological functions for SEISAN data.
Remote sensing ratios and improved imports.
It is released under the Gnu General Public License version 3.0
The PyGMI Wiki pages, include installation and full usage! Contributors can check this link for ways to contribute.
The latest release version (including windows installers) can be found here.
You may need to install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019.
If you have any comments or queries, you can contact the author either through GitHub or via email at pcole@geoscience.org.za
Installation
The simplest installation of PyGMI is on Windows, using a pre-built installer at 64-bit.
If you prefer building from source, you can use PyPi or Conda.
Once built using PyPi, running pygmi can be done at the command prompt as follows:
pygmi
If you are in python, you can run PyGMI by using the following commands:
from pygmi.main import main
main()
If you prefer not to install pygmi as a library, download the source code and execute the following command to run it manually:
python quickstart.py
Requirements
PyGMI will run on both Windows and Linux. It should be noted that the main development is done in Python 3.13 on Windows.
PyGMI should still work with Python 3.10.
PyGMI is developed and has been tested with the following libraries in order to function:
geopandas>=1.2.0
h5netcdf>=1.8.1
matplotlib>=3.11.2
natsort>=8.4.0
numba>=0.68.0
numexpr>=2.14.2
openpyxl>=3.1.5
psutil>=7.2.2
pyside6>=6.12.0
pytest>=9.1.1
pyvista>=0.49.1
pyvistaqt>=0.13.1
rasterio>=1.5.2
rioxarray>=0.23.0
scikit-learn>=1.9.1
scikit-image>=0.26.0
shapelysmooth>=0.2.1
simpeg>=0.25.2
beautifulsoup4>=4.15.0
pyyaml>=6.0.3
pwlf>=2.7.0
contextily>=1.7.1
matplotlib-map-utils>=4.1.1
h5py>=3.16.0
PyPi - Windows
Windows users can use the WinPython distribution as an alternative to Anaconda. It comes with most libraries preinstalled, so using pip should be sufficient.
Install with the following command.
pip install pygmi
Should you wish to manually install binaries, related binaries can be obtained at the website by Christoph Gohlke.
If you wish to update GDAL, you will need to download and install:
fiona
GDAL
pyproj
rasterio
Rtree
shapely
All these binaries should be downloaded since they have internal co-dependencies.
PyPi - Linux
Linux normally comes with python installed, but the additional libraries will still need to be installed.
The process is as follows:
sudo apt-get install pipx
pipx ensurepath
pipx install pygmi
Once installed, running pygmi can be done at the command prompt as follows:
pygmi
If you get the following error: qt.qpa.plugin: Could not load the Qt platform plugin “xcb” in “” even though it was found., then you can try the following command, since this is Linux issue:
sudo apt-get install libxcb-xinerama0
Anaconda
Anaconda users are advised not to use pip since it can break PySide6. However, one package is installed only by pip, so a Conda environment should be created.
The process to install is as follows:
conda create -n pygmi python=3.14
conda activate pygmi
conda config –env –add channels conda-forge
conda install pyside6
conda install fiona
conda install matplotlib
conda install psutil
conda install numexpr
conda install rasterio
conda install geopandas
conda install natsort
conda install numba
conda install scikit-learn
conda install scikit-image
conda install pyvista
conda install pyvistaqt
conda install simpeg
conda install shapelysmooth
conda install openpyxl
conda install h5netcdf
conda install rioxarray
conda install pytest
conda install beautifulsoup4
conda install pyyaml
conda install pwlf
pip install matplotlib-map-utils
conda update –all
Once this is done, download pygmi, extract (unzip) it to a directory, and run it from its root directory with the following command:
python quickstart.py
References
Cole, P. 2012, Development of a 3D Potential Field Forward Modelling System in Python, AGU fall meeting, 3-7 December, San Francisco, USA
Cole, P. 2013, PyGMI – The use of Python in geophysical modelling and interpretation. South African Geophysical Association, 13th Biennial Conference, Skukuza Rest Camp, Kruger National Park (7-9 October)
Cole, P. 2014, The history and design behind the Python Geophysical Modelling and Interpretation (PyGMI) package, SciPy 2014, Austin, Texas (6-12 July)
Cole, P. 2016, The continued evolution of the open source PyGMI project. 35th IGC, Cape Town.
Cole, P. 2025, PyGMI - a python package for geoscience modelling and interpretation. Journal of Open Source Software, 10(111), 7019, https://doi.org/10.21105/joss.07019
1 maintainer
Metadata
1 maintainer
Release files for pygmi 3.3.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| pygmi-3.3.1.0.tar.gz | 19.1 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pygmi-3.3.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 38.6 MB
Release files / pygmi-3.3.1.0.tar.gz
| Download URL | pygmi-3.3.1.0.tar.gz |
|---|---|
| Size | 19.1 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a3313e7cc464e0fc50a3fd43dba6e4dc9d626945bb0cc106cc3948d8e3e2ea84
|
|
BLAKE2b-256 checksum How to use checksums |
43a191a7ac50138552934e4dccb2dfeab57f880924345a9be63c396b8913250f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.14.7
|
Release files / pygmi-3.3.1.0-py3-none-any.whl
| Download URL | pygmi-3.3.1.0-py3-none-any.whl |
|---|---|
| Size | 19.5 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
589df2916d98599b81ae940531da38f76a23ea5ceea6a34ec6b27869104c7a94
|
|
BLAKE2b-256 checksum How to use checksums |
7531ec88812b42401c82174ce6bf7a3b3d4f8b582a4fba20ecee70b3ffccd24f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.14.7
|
Security for pygmi 3.3.1.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
