pybaselines

import numpy as np from pybaselines.api import Baseline # 1. Generate some example data x = np.linspace(0, 100, 500) y_peak = 10 * np.exp(-((x - 50)**2) / 20) # A simple peak y_baseline = 0.1 * x + 5 # A sloping baseline y_data = y_peak + y_baseline + np.random.normal(0, 0.5, x.shape) # 2. Initialize the Baseline object (using the class-based API, recommended for v1.0.0+) baseline_fitter = Baseline() # 3. Apply a baseline correction algorithm, e.g., Asymmetric Least Squares (ASLS) # The first return value is the estimated baseline, the second is a dictionary of parameters estimated_baseline, params = baseline_fitter.asls(y_data, lam=1e6, p=0.01) # 4. The corrected signal is the original data minus the estimated baseline corrected_signal = y_data - estimated_baseline print("Baseline estimation complete.") print(f"First 5 original data values: {y_data[:5].round(2)}") print(f"First 5 estimated baseline values: {estimated_baseline[:5].round(2)}") print(f"First 5 corrected signal values: {corrected_signal[:5].round(2)}")