القائمة
Spectral data analysis for NMR, IR, mass spectrometry, and UV-Vis
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npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill spectroscopy-analysis-guideيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
استنادا إلى تصنيف SOC المهني
| name | spectroscopy-analysis-guide |
| description | Spectral data analysis for NMR, IR, mass spectrometry, and UV-Vis |
| metadata | {"openclaw":{"emoji":"🔬","category":"domains","subcategory":"chemistry","keywords":["spectroscopy","nmr","mass-spectrometry","infrared","uv-vis","analytical-chemistry"],"source":"wentor"}} |
A skill for processing and interpreting spectroscopic data in chemistry research. Covers NMR, IR, mass spectrometry, and UV-Vis spectroscopy including data formats, baseline correction, peak detection, spectral matching, and structure elucidation workflows.
| Format | Spectroscopy | Description |
|---|---|---|
| JCAMP-DX (.jdx, .dx) | All types | IUPAC standard exchange format |
| Bruker (1r, fid, acqu) | NMR | Raw and processed Bruker data |
| mzML / mzXML | MS | Open mass spectrometry format |
| SPC (.spc) | IR, UV-Vis | Galactic/Thermo spectral format |
| CSV / TXT | All | Simple x,y pairs (wavelength/wavenumber, intensity) |
import numpy as np
from scipy.signal import find_peaks, savgol_filter
def read_jcamp() -> :
x_data, y_data = [], []
metadata = {}
(filepath, ) f:
line f:
line = line.strip()
line.startswith():
key_val = line[:].split(, )
(key_val) == :
metadata[key_val[].strip()] = key_val[].strip()
line line.startswith():
parts = line.split()
:
values = [(v) v parts]
(values) >= :
x_data.append(values[])
y_data.extend(values[:])
ValueError:
{
: np.array(x_data),
: np.array(y_data[:(x_data)]),
: metadata,
}
import nmrglue as ng
def process_1h_nmr(bruker_dir: str) -> dict:
"""
Process 1H NMR data from Bruker format using nmrglue.
bruker_dir: path to Bruker experiment directory
"""
# Read raw data
dic, data = ng.bruker.read(bruker_dir)
# Apply processing
data = ng.bruker.remove_digital_filter(dic, data)
data = ng.proc_base.zf_size(data, 65536) # zero-fill
data = ng.proc_base.fft(data) # Fourier transform
data = ng.proc_autophase.autops(data, "acme") # automatic phasing
data = ng.proc_base.rev(data) # reverse spectrum
data = ng.proc_base.di(data) # discard imaginary
# Generate chemical shift axis (ppm)
udic = ng.bruker.guess_udic(dic, data)
uc = ng.fileiobase.uc_from_udic(udic)
ppm = uc.ppm_scale()
return {
"ppm": ppm,
"spectrum": data.real,
"sf": dic["acqus"]["SFO1"], # spectrometer frequency (MHz)
"sw_ppm": dic["acqus"]["SW"], # sweep width (ppm)
}
def pick_nmr_peaks(ppm: np.ndarray, spectrum: np.ndarray,
threshold: float = 0.05) -> list[dict]:
"""
Automatic peak picking for 1H NMR.
threshold: minimum peak height as fraction of max intensity.
"""
min_height = threshold * np.max(spectrum)
indices, properties = find_peaks(
spectrum, height=min_height, distance=10, prominence=min_height * 0.5
)
peaks = []
for idx in indices:
peaks.append({
"ppm": round(float(ppm[idx]), 3),
"intensity": float(spectrum[idx]),
})
# Sort by chemical shift (high to low, NMR convention)
peaks.sort(key=lambda p: p["ppm"], reverse=True)
return peaks
| Chemical Shift (ppm) | Functional Group |
|---|---|
| 0.8-1.0 | CH3 (methyl, alkyl) |
| 1.2-1.4 | CH2 (methylene, alkyl chain) |
| 2.0-2.5 | CH next to C=O |
| 3.3-3.9 | CH next to O or N (ethers, amines) |
| 4.5-5.5 | Vinyl C=CH2, OCH |
| 6.5-8.5 | Aromatic H |
| 9.0-10.0 | Aldehyde CHO |
| 10.0-12.0 | Carboxylic acid OH |
from pyteomics import mzml
import numpy as np
def read_mzml_spectra(filepath: str, ms_level: int = 1) -> list[dict]:
"""
Read mass spectra from an mzML file.
ms_level: 1 for MS1 (survey scans), 2 for MS/MS
"""
spectra = []
with mzml.read(filepath) as reader:
for spectrum in reader:
if spectrum.get("ms level") == ms_level:
spectra.append({
"scan": spectrum["index"],
"rt": spectrum["scanList"]["scan"][0].get(
"scan start time", 0
),
"mz": spectrum["m/z array"],
"intensity": spectrum["intensity array"],
"tic": np.sum(spectrum["intensity array"]),
})
return spectra
def find_molecular_ion(mz: np.ndarray, intensity: np.ndarray,
expected_mw: float = None,
tolerance_da: float = 0.5) -> list[dict]:
"""
Identify molecular ion peaks ([M+H]+, [M+Na]+, [M-H]-).
"""
# Find top peaks
top_indices = np.argsort(intensity)[::-1][:20]
candidates = []
adducts = {
"[M+H]+": 1.00728,
"[M+Na]+": 22.98922,
"[M+K]+": 38.96316,
"[M-H]-": -1.00728,
"[M+NH4]+": 18.03437,
}
for idx in top_indices:
peak_mz = mz[idx]
peak_int = intensity[idx]
if expected_mw:
for adduct_name, adduct_mass in adducts.items():
calc_mw = peak_mz - adduct_mass
if abs(calc_mw - expected_mw) < tolerance_da:
candidates.append({
"mz": round(float(peak_mz), 4),
"intensity": float(peak_int),
"adduct": adduct_name,
"calc_mw": round(calc_mw, 4),
"error_da": round(abs(calc_mw - expected_mw), 4),
})
else:
candidates.append({
"mz": round(float(peak_mz), 4),
"intensity": float(peak_int),
})
return candidates
# Standard IR functional group frequency table
IR_ASSIGNMENTS = {
(3200, 3600): "O-H stretch (broad: alcohol, acid; sharp: free OH)",
(3300, 3500): "N-H stretch (primary amine: 2 bands; secondary: 1 band)",
(2850, 3000): "C-H stretch (sp3: 2850-2960; sp2: 3000-3100)",
(2100, 2260): "Triple bond stretch (C-triple-N: 2210-2260; C-triple-C: 2100-2150)",
(1680, 1750): "C=O stretch (ketone ~1715; ester ~1735; acid ~1710; amide ~1650)",
(1600, 1680): "C=C stretch (alkene ~1640; aromatic ~1600, 1500)",
(1000, 1300): "C-O stretch (ether, ester, alcohol)",
}
def assign_ir_peaks(wavenumber: np.ndarray, absorbance: np.ndarray,
threshold: float = 0.1) -> list[dict]:
"""Detect and assign IR absorption peaks to functional groups."""
# Invert for peak detection (absorbance peaks are positive)
peaks, properties = find_peaks(absorbance, height=threshold, prominence=0.05)
assignments = []
for idx in peaks:
wn = float(wavenumber[idx])
assignment = "unassigned"
for (low, high), group in IR_ASSIGNMENTS.items():
if low <= wn <= high:
assignment = group
break
assignments.append({
"wavenumber_cm-1": round(wn, 1),
"absorbance": round(float(absorbance[idx]), 4),
"assignment": assignment,
})
return sorted(assignments, key=lambda x: x["wavenumber_cm-1"], reverse=True)
def baseline_correction(y: np.ndarray, lam: float = 1e6,
p: float = 0.001, n_iter: int = 10) -> np.ndarray:
"""
Asymmetric least squares baseline correction (Eilers and Boelens, 2005).
lam: smoothness parameter (larger = smoother baseline)
p: asymmetry parameter (smaller = more emphasis on fitting below peaks)
"""
from scipy.sparse import diags, csc_matrix
from scipy.sparse.linalg import spsolve
L = len(y)
D = diags([1, -2, 1], [0, -1, -2], shape=(L, L - 2)).toarray()
H = lam * D.dot(D.T)
w = np.ones(L)
for _ in range(n_iter):
W = diags(w, 0, shape=(L, L))
Z = csc_matrix(W + H)
baseline = spsolve(Z, w * y)
w = p * (y > baseline) + (1 - p) * (y < baseline)
return y - baseline
def smooth_spectrum(y: np.ndarray, window: int = 11,
polyorder: int = 3) -> np.ndarray:
"""Apply Savitzky-Golay smoothing to a spectrum."""
return savgol_filter(y, window, polyorder)