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nmrglue

Processes calibrated one-dimensional complex NMR free-induction decays with nmrglue into phased spectra, peak candidates, and signed integration regions.

Install / Use

npx skills add K-Dense-AI/scientific-agent-skills --skill nmrglue

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

95/100

Supported Platforms

Universal

Our assessment of nmrglue

nmrglue scores 95/100 on our quality scale, 319th of 4,585 Development & Engineering skills we index (top 7%).

Its SKILL.md is 8.3 KB long, split into 6 sections with 2 code examples: a thorough specification that gives an agent plenty to work with.

With 46,441 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
16/20
Description
15/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 16 days ago, so nmrglue is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

nmrglue compared with similar skills

All 4 of these similar skills score higher than nmrglue; compare them before choosing.

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Frequently asked questions

How do I install nmrglue?
Run npx skills add K-Dense-AI/scientific-agent-skills --skill nmrglue. The install tabs above show the steps for each supported agent.
Which AI agents does nmrglue work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is nmrglue safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is nmrglue still maintained?
The repository was last updated 16 days ago, so nmrglue is actively maintained.

name: nmrglue description: Processes calibrated one-dimensional complex NMR free-induction decays with nmrglue into phased spectra, peak candidates, and signed integration regions. Use for raw 1D NMR processing, ppm-axis verification, apodization, Fourier transformation, manual phasing, baseline correction, or reproducible spectral integration. license: MIT compatibility: Requires Python 3.12+, nmrglue, NumPy 2+, and SciPy. Installation needs network access; processing is local and needs no credentials. metadata: version: "1.1" skill-author: K-Dense Inc. tested-package-version: "0.12" last-reviewed: "2026-10-01"

nmrglue: calibrated 1D FID processing

When to use

Use for a uniformly sampled complex 1D FID whose acquisition parameters and complex frequency convention are available. The helper produces a descending ppm spectrum, positive peak candidates, signed region integrals, and a reproducible processing report. It does not identify compounds or assign resonances.

The executable accepts a NumPy .npz containing exactly one complex fid array or a canonical 1D complex time-domain NMRPipe file. NMRPipe reading is tested with a synthetic write/read round trip, known-spectrum recovery, and a small upstream NMRPipe-generated binary fixture. Experimental Bruker, Varian, and JEOL imports are not verified by this suite. For those formats, first inspect the relevant nmrglue reader and acquisition metadata. Opening a converted file does not validate the original acquisition decoding. Read references/acquisition-and-validation.md for conversion boundaries, axis calibration, and quantitative limits.

Workflow

  1. Preserve the raw FID. Establish spectral width in Hz, positive observation frequency in MHz, carrier in ppm, observed nucleus, and the sign convention from the acquisition or a known reference. Determine whether digital-filter/group-delay removal has already occurred. Do not infer these from array length or typical instrument defaults.
  2. Copy assets/processing.json and replace its synthetic example values with the measured parameters and explicit processing choices. Its sign -i means a resonance at offset f = (ppm - carrier_ppm) * observation_mhz has time dependence exp(-2*pi*i*f*t). Select +i only for the opposite convention; the helper conjugates it before processing. Validate with a known reference peak.
  3. Choose nonnegative exponential line broadening (Hz), an even zero-filled size at least as large as the acquired FID, first-point scaling, and phase angles. Zero filling improves interpolation, not acquired spectral resolution. First-point scaling 0.5 is suitable for the supplied causal synthetic example; acquisition and prior preprocessing may require another value.
  4. Run the helper, inspect the real and imaginary spectra, and revise manual phase if needed. phase0_deg + phase1_deg * index / zero_fill_points is applied after FT; index zero is the high-ppm edge. There is no implicit pivot or automatic phase estimate.
  5. Only fit a linear baseline when explicitly supplied ppm regions are signal-free. Set baseline to linear and add baseline_regions_ppm containing at least two regions. Inspect residuals and broad peaks; fitting through signals biases integrals.
  6. Compare peak positions with references, inspect peak candidates for artifacts, and integrate specified regions. Report overlapped peaks as overlapped. Preserve negative areas as diagnostic evidence of phase/baseline problems instead of taking absolute values.

Execute

Tested with Python 3.12, nmrglue 0.12, NumPy 2.5.3, and SciPy 1.18.1:

uv run --no-project --python 3.12 --with nmrglue==0.12 --with numpy==2.5.3 --with scipy==1.18.1 \
  python skills/nmrglue/scripts/process_1d.py fid.npz processing.json nmr-result

Paths assume the collection root. Adjust them when installed elsewhere. The output directory must be new, so repeated processing keeps previous results reviewable.

For an existing 1D NMRPipe FID, add --input-format nmrpipe and supply its path in place of fid.npz. The helper requires the canonical FDF2 direct dimension, complex quadrature, a time-domain flag, and agreement between header and JSON spectral width, observation frequency, and carrier. JSON settings remain explicit; a mismatch fails instead of silently recalibrating. FDF2TDSIZE must equal the stored complex-point count, and FDF2CENTER / FDF2ORIG must describe a canonical centered axis. Previously zero-filled, truncated, or recentered files need a separate acquisition-aware workflow. The nucleus/complex sign and previous digital-filter corrections still need acquisition evidence. A time-domain flag alone does not establish an unprocessed FID.

This executable synthetic example matches the supplied settings, generates resonances at 3 and 7 ppm in a 1:2 amplitude ratio, and does not represent an experimental sample:

import numpy as np

t = np.arange(8192) / 4000.0
fid = sum(a * np.exp(-np.pi * 2.0 * t)
          * np.exp(-2j * np.pi * (ppm - 5.0) * 400.0 * t)
          for ppm, a in [(3.0, 1.0), (7.0, 2.0)])
np.savez("fid.npz", fid=fid)

Run it with assets/processing.json as the settings argument. The repository suite executes this signal and the CLI, checks both peak locations within 0.001 ppm, checks integral ratio and analytic area, and checks phase and baseline recovery. The NMRPipe round-trip test writes this FID using ng.pipe.create_dic/ng.pipe.write, reads it through the CLI, and verifies the recovered peaks and integral ratio. Processed frequency-domain files and conflicting calibration metadata are rejected.

The 2,176-byte upstream fixture checks complex sample order and header calibration using a file generated by NMRPipe's simTimeND / SET tools. Those native tools were not run in this review; this is fixture compatibility, not a live NMRPipe processing comparison.

Deliverables and interpretation

  • spectrum.csv: descending ppm, real signal after baseline correction, phased imaginary signal, and the fitted real baseline. Plot NMR with the high-ppm end on the left.
  • report.json: input/settings SHA-256, package versions, all settings, acquired duration, zero-filled digital spacing, positive peak candidates, and signed region areas.

Integrals use endpoint interpolation and trapezoidal integration along increasing ppm; area units are arbitrary signal times ppm, independent of display direction. Regions outside the sampled ppm axis fail rather than being silently clipped. Peak prominence is a fraction of the largest positive real intensity; it is not a noise-derived detection limit. Strong solvent signals can obscure weak candidates at the default threshold.

For quantitative NMR, additionally establish relaxation delay, pulse angle, saturation, receiver behavior, internal/external reference amount, and integration uncertainty. The helper does not calculate concentrations or correct unequal relaxation. Preserve these limits with the result rather than converting arbitrary areas to molecule counts.

Upstream contracts

The hosted latest documentation identified itself as 0.9-dev when checked; the actual 0.12 package APIs and numerical behavior were tested. Its proc_base.fft uses the negative-exponent NumPy FFT followed by fftshift; NMRPipe's FT convention corresponds to fft_positive, so do not substitute it without revisiting the FID sign and phase. See the v0.12 processing source. Multidimensional processing, nonuniform sampling, automated assignment, and experimental vendor imports remain outside this helper's validated scope.

Related Skills

View on GitHub
GitHub Stars46.4k
CategoryDevelopment
Updated16d ago
Forks4.2k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions