NMR Data Handling with Python 0: Series Introduction

by Scott Anthony Robson Ph. D. | March 8, 2019, 7:58 p.m.

Series Introduction:

In this series of articles, we will explore how Nuclear Magnetic Resonance (NMR) data is stored and processed. To do this, we will use the Python (Python 3) programming language to manipulate data from Bruker (and eventually Varian/Agilent) spectrometers. Python is an easy to read programming language which will allow us to not only describe the operations involved in data processing but actually work with real data and see the results.

Unlike some other biophysical techniques, the data from the NMR spectrometer:

• is recorded during a time period (time domain) which needs to be converted into frequency information (frequency domain) by a transformation: The Fourier Transform

• can have high dimensionality, these higher dimensions need to be acquired in 'quadrature' (more on that later)

• has dimensions that are often truncated (time constraints cuts down amount of data we can collect)

• has strong signals from molecules we don't care so much about - like a concentration of 110 M ¹H atoms present in the water required to dissolve some of our molecules.

All these problems need to be mitigated to some extent. We will visit these problems and how we mitigate them as we move from raw NMR data to spectra we can interpret. 

Topics:

• Getting data from the spectrometer and converting it to a numpy (python) array

• Introduction to Fourier Transforms

• Post Acquisition suppression of a solvent signal

• Introduction to 2-dimensional data

• Indirect Acquisition Modes: States-TPPI and Echo-Antiecho processing