The documentation is available at https://djpugh.github.io/MTfit/ and can be built using sphinx from the source in MTfit/docs/, or using the build_docs.py.
The documentation includes tutorials and explanations of MTfit and the approaches used.
Please note that this code is provided as-is, and no guarantee is given that this code will perform in the desired way. Additional development and support is carried out in the developer's free time.
Restricted: For Non-Commercial Use Only This code is protected intellectual property and is available solely for teaching and non-commercially funded academic research purposes. Applications for commercial use should be made to Schlumberger or the University of Cambridge.
MTfit is available on PyPI and can be installed using:
>>pip install MTfit
Alternative this repository can be cloned and the package then installed simply by calling:
>>python setup.py install
MTfit is dependent on numpy and scipy, and for MATLAB -v7.3 support also requires h5py. Cluster support will be automatically installed via pyqsub from github MPI support requires mpi4py built against a valid MPI distribution.
To build the C extensions when compiling from source you will need cython and associated C compilers
! Known Bug - running with MPI and very large non-zero MT results can lead to an error: mpi4py SystemError: Negative size passed to PyString_FromStringAndSize - to fix, re-run with smaller sample sizes
