LARMOR is a desktop application for fitting solid-state NMR spectra, written as an open successor to dmfit. It keeps dmfit's fitting workflow and adds two things dmfit does not have: an uncertainty on every fitted number, and fits that can be reproduced later from a plain text file.
The physics comes from mrsimulator and the fitting from lmfit; LARMOR adds the interactive fitting UI, file readers, batch workflows and figure export on top. Spectra are read directly from instrument folders (Bruker, Varian/Agilent, legacy dmfit files); the acquired files are never modified. A fit is saved as a JSON "recipe" that references the data by path and hash, so it can be re-run, diffed, and shared — typically right next to the spectrum it fits.
The figure above is rendered by LARMOR itself from the example data in examples/ — a real ¹¹B and ²⁷Al dataset of one glass that all the tutorials run against.
On Windows, double-click install.bat in this folder. It creates the Python
environment and puts a LARMOR.bat launcher on your Desktop; update.bat updates
an existing install. Full instructions for all platforms, including the usual
troubleshooting, are in INSTALL.md.
Manual install, from inside the repository:
conda env create -f environment.yml # or: pip install -r requirements.txt
conda activate larmor
larmor desktop
Use Python 3.10–3.12 (3.11 recommended). On 3.13 mrsimulator currently has no wheels and the install fails; see INSTALL.md if that happens to you.
Fitting. Drag components directly on the spectrum (position, amplitude, width handles), or edit them in a parameter table with per-parameter fix/bounds/links. Fifteen lineshape models are available, from plain Gauss/Lorentz, Voigt and J-multiplets through Czjzek, extended Czjzek, dmfit's "Amorphous" Gaussian disorder, second-order quadrupolar, quad+CSA and CSA powder patterns, to external spectra used as backgrounds. The quadrupolar models take the nucleus and field from the data, so there is nothing special to do for a new isotope. Fit windows can be a union of regions, dmfit-style, and a multi-start "Auto fit" helps with awkward starting points.
Uncertainties. Every fit reports errors, three ways: from the covariance matrix, by Monte-Carlo resampling, or from χ² profiling (proper 1σ/2σ confidence intervals). The latter two run on all CPU cores. Parameters that finish pinned at a bound are flagged rather than reported with a meaningless error bar, and the fit warns when two parameters are so correlated the data cannot separate them.
Series of spectra. A batch fit applies one shared model to many spectra at once, with amplitudes free per spectrum and any parameter optionally "released" to drift between samples; components can be excluded per sample. A sequential fit walks a composition or temperature series, each spectrum starting from its neighbour's result. Multi-field datasets can be fit simultaneously, which is often the only way to separate Cq from δiso. Results export to a long-format CSV with errors and populations.
2D and MQMAS. Interactive 2D fitting (click to place a site, the fitted model drawn as contours over the data), hypercomplex phasing, shearing, and contour figures with projections and computed CS/QIS reference lines.
Processing. The usual TopSpin/ssNake operations: apodization windows, zero-filling, Fourier transform, phasing (manual or automatic), baseline correction, linear prediction, Hilbert reconstruction, whole-echo processing, region extraction, spectrum algebra and alignment. Processing steps are stored in the recipe and replayed whenever the data is reloaded.
Relaxation and dipolar experiments. T1/T2 extraction from arrayed experiments (saturation/inversion recovery, CPMG, T1ρ), including per-site decomposition; REDOR curves to dipolar couplings and distances; QCPMG echo trains to spikelet or sum-echo spectra with a guided, step-by-step processing dialog.
Figures. A plotting studio produces publication figures (overlays, stacked plots, deconvolution grids from a batch fit, species distributions, relaxation series) from a JSON spec that can be saved and re-rendered identically. Journal style presets, PNG/SVG/PDF output.
Other. DFT tensor import from CASTEP/Quantum ESPRESSO .magres files, an
optional bridge to SIMPSON for exact recoupling simulations, and a larmor CLI
(info, import, fit, batchfit, seqfit, multifit, satrec, redor,
magres) for scripted use — the whole thing is an ordinary Python package
underneath.
dmfit is the reference point throughout, and LARMOR reads its .fxml/.fxmla
fit files directly. The practical differences: uncertainties are computed for
everything rather than being a separate tool, fits are plain JSON instead of an
opaque format, batch and series fitting are built in, everything is scriptable
from Python, and it runs on any platform. If you have years of dmfit fits, they
import — that was one of the design goals, and 20 fits from a published
¹¹B study import with no warnings and match the paper's parameters to its own
rounding (validation report, §5.7).
A fitting program should not be trusted on faith. The physics is checked
against analytic theory, direct ensemble simulations, and real published
data: see docs/validation.md. The figures in that report
are generated by a script in the repo, not drawn. The test suite (about 680
tests, pytest tests/) guards the physics constants and conventions and, on
machines that have them, fits real Bruker datasets; the published-paper
reproductions above are documented in the validation report.
The desktop app is the primary interface and where all development happens. About 30k lines of Python, of which the fitting core is Qt-free and usable as a library. Known gaps are tracked in docs/roadmap.md — the honest summary is that the code is well tested but there is no CI or signed release pipeline yet, so installing from source is the reliable route. A FastAPI web variant exists in the tree but is unmaintained; don't use it.
Anyone modifying the code should start with docs/development-notes.md: the conventions the numbers depend on, the validation anchors a change must not break, and an index of the bugs already fixed here. docs/architecture.md maps the modules.
Tutorials live in docs/tutorials/. Raw instrument data is
never committed to this repository and never written to — data/ holds only
notes about where data lives.
MIT. If you use LARMOR in published work, a citation of the repository is appreciated, alongside dmfit (Massiot et al., Magn. Reson. Chem. 2002) whose design this follows and mrsimulator which does the quantum mechanics.
Sam Soudani — McCloy group, Washington State University.