Getting Started

Install Metbit from PyPI and run your first analysis.

Requirements: Python 3.9+ (3.10/3.11 recommended), pip or conda/mamba with pip.

Install

pip install metbit
Use a virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install metbit

Minimal Example

  1. Load your data (X = features, y = classes)
  2. Fit OPLS‑DA and compute VIP scores
  3. Visualize important features
import pandas as pd
from metbit.metbit import opls_da

df = pd.read_csv('your_data.csv')
y = df['Group']
X = df.drop(columns=['Group'])

model = opls_da(
  X, y,
  features_name=list(X.columns), n_components=2,
  scaling_method='pareto', kfold=3,
  estimator='opls', random_state=94, auto_ncomp=True
)
model.fit()
fig = model.vip_plot(threshold=1.0)
fig.show()

Preprocessing (optional)

Use Metbit utilities to prepare spectra before modeling.

from metbit.nmr_preprocess import nmr_preprocessing
from metbit.utility import Normalise

prep = nmr_preprocessing(...)
X = prep.X  # DataFrame

Next Steps

Browse Full API →Explore Overview