SKILL·F2C5BF

nmrglue

K-Dense-AI
Updated 7 days ago
2 views
48,288
4,343
48,288
View on GitHub
Othergeneral

About

This skill processes raw 1D NMR free-induction decay (FID) data into phased spectra, identified peaks, and integration regions using the Python nmrglue library. It handles the full processing pipeline including Fourier transformation, manual phasing, baseline correction, and ppm-axis verification. Developers can use it for reproducible, local NMR spectral analysis without external credentials.

Quick Install

Claude Code

Recommended
Primary
npx skills add K-Dense-AI/claude-scientific-skills -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/K-Dense-AI/claude-scientific-skills
Git CloneAlternative
git clone https://github.com/K-Dense-AI/claude-scientific-skills.git ~/.claude/skills/nmrglue

Copy and paste this command in Claude Code to install this skill

Documentation

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.

GitHub Repository

K-Dense-AI/claude-scientific-skills
Path: skills/nmrglue
0
agent-skillsai-scientistbioinformaticschemoinformaticsclaudeclaude-skills
FAQ

Frequently asked questions

What is the nmrglue skill?

nmrglue is a Claude Skill by K-Dense-AI. Skills package instructions and resources that Claude loads on demand, so Claude can perform nmrglue-related tasks without extra prompting.

How do I install nmrglue?

Use the install commands on this page: add nmrglue to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.

What category does nmrglue belong to?

nmrglue is in the Other category.

Is nmrglue free to use?

Yes. nmrglue is listed on AIMCP and free to install.

Related Skills

sports-betting-analyzer
Other

This Claude Skill analyzes sports betting markets including spreads, over/unders, and prop bets by examining historical trends and situational statistics to identify value bets. It provides structured markdown output with actionable recommendations for educational purposes. Developers should use this for sports betting analysis tools while noting it's designed for entertainment/education only.

View skill
llamaguard
Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

View skill
cost-optimization
Other

This Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.

View skill
quantizing-models-bitsandbytes
Other

This skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.

View skill