nmr-analysis

To process raw NMR FID data, you apply apodization, zero-filling, Fourier transform, and phase correction using nmrglue and scipy. This deterministic pipeline converts the time-domain free-induction decay into an interpretable phase-corrected frequency-domain spectrum.

Yes, you can extract J-coupling constants from a specific ppm window by applying Lorentzian-based multiplet line-shape fitting. The process converts the ppm fitting results into Hz coupling constants using the known spectrometer frequency.

Automated peak picking detects spectral peaks and exports them to a structured pandas DataFrame, while chemical shift referencing aligns the ppm axis to internal standards like TMS or DSS. This eliminates manual tuning and referencing errors in NMR workflows.

Yes, the NMR processing pipeline supports both 1D and 2D NMR workflows. It handles raw data conversion from Bruker, Varian, and Agilent formats, applying necessary Fourier transforms and phase corrections to generate interpretable frequency-domain spectra.

The best way to convert Bruker NMR data into structured peak lists is through an automated deterministic pipeline. It processes the FID data with nmrglue, applies phase correction, and uses scipy for automated peak detection, outputting results directly into a pandas DataFrame.

Yes, you need nmrglue to perform apodization, zero-filling, and Fourier transforms on NMR FID data. The processing pipeline relies on nmrglue for these specialized NMR operations, alongside scipy and numpy for peak detection and line-shape fitting.