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.
Installing MTfit
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