nmrglue is a module for working with NMR data in Python. When used with the numpy, scipy, and matplotlib packages nmrglue provides a robust interpreted environment for processing, analyzing, and inspecting NMR data.
nmrglue has the ability to read, write and convert between a number of common NMR file formats including Varian, Bruker, NMRPipe, and Sparky files. The files, which are represented in python as dictionaries of spectral parameters and Numpy array objects, can be easily examined, modified and processed as desired.
nmrglue provides a number of common functions for processing NMR data such as apodization, spectral shifting, Fourier and other transformations, baseline smoothing and flattening, and linear prediction. In addition new processing schemes can be implemented easily using the nmrglue provided functions and the multitude of numerical routines provided by the Numpy and Scipy packages.
When used in conjunction with the matplotlib (or other) python plotting library nmrglue can be used to create publication quality figures of NMR spectrum or examine data interactively.
nmrglue can be used to analyze NMR data, with routines to perform peak picking, multidimensional lineshape fitting (peak fitting), and peak integration. New analysis methods can be rapidly developed and tested in Python or by integrating Fortran and C/C++ code.
To get started, see our online documentation: http://nmrglue.readthedocs.org/en/latest/index.html
If you find nmrglue useful in your research please cite the package as:
J.J. Helmus, C.P. Jaroniec, Nmrglue: An open source Python package for the analysis of multidimensional NMR data, J. Biomol. NMR 2013, 55, 355-367, http://dx.doi.org/10.1007/s10858-013-9718-x.
FAQs
A module for working with NMR data in Python
We found that nmrglue demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
