DiffMap and coDiffMap Python code. Core Python DiffMap functionality written by Robert Blum; coDiffMap adaptation written by Jared Rovny.
This code is able to replicate the analysis done in Igor Pro for two recent papers written by us (the Sean Barrett lab) and submitted to the Journal of Biomolecular NMR: "Reaching the sparse-sampling limit for reconstructing a single peak in a 2D NMR spectrum using iterated maps" and "Accelerating 2D NMR Relaxation Dispersion Experiments Using Iterated Maps" (links will be added when published). The papers of Blum et al. and Rovny et al. extend the earlier work of Frey et al. in the Journal of Magnetic Resonance in 2013 ("Accelerating multidimensional NMR and MRI experiments using iterated maps").
We have designed the code to work with nmrglue, for better cross-compatibility. It assumes that your data starts as a numpy ndarray (such as the ones created by nmrglue data load functions). If your data starts in a hypercomplex States-like format, as the data from our paper did, we have included a function to convert it into the "MRI-like" format necessary for our method.
Written in Python 3; requires numpy.
For now, you can download the source code and add the python files to your PYTHONPATH.
This code is a work in progress, and likely doesn't cover all the cases that one might want to use it with. Proper installation methods and proper dependency checks and the like aren't finished yet. We will hopefully be able to clean these things up soon, but we have to work around dissertating for now. If you want to use the code and aren't sure where to begin, or have any other questions about it, open an issue and ask! Alternatively, you can get in touch via the Barrett lab website at https://opnmr.physics.yale.edu/.