pybaselines 0.5.0

Introduction

pybaselines provides many different baseline correction algorithms for fitting baselines to data from experimental techniques such as Raman, FTIR, NMR, XRD, PIXE, etc. The aim of the project is to provide a semi-unified API to allow quickly testing and comparing multiple baseline correction algorithms to find the best one for a set of data.

pybaselines has 30+ baseline correction algorithms. The algorithms are grouped accordingly (note: when a method is labelled as ‘improved’, that is the method’s name, not editorialization):

1. Polynomial methods (pybaselines.polynomial)

   1. poly (Regular Polynomial)

   2. modpoly (Modified Polynomial)

   3. imodpoly (Improved Modified Polynomial)

   4. penalized_poly (Penalized Polynomial)

   5. loess (Locally Estimated Scatterplot Smoothing)

   6. quant_reg (Quantile Regression)

2. Whittaker-smoothing-based methods (pybaselines.whittaker)

   1. asls (Asymmetric Least Squares)

   2. iasls (Improved Asymmetric Least Squares)

   3. airpls (Adaptive Iteratively Reweighted Penalized Least Squares)

   4. arpls (Asymmetrically Reweighted Penalized Least Squares)

   5. drpls (Doubly Reweighted Penalized Least Squares)

   6. iarpls (Improved Asymmetrically Reweighted Penalized Least Squares)

   7. aspls (Adaptive Smoothness Penalized Least Squares)

   8. psalsa (Peaked Signal’s Asymmetric Least Squares Algorithm)

3. Morphological methods (pybaselines.morphological)

   1. mpls (Morphological Penalized Least Squares)

   2. mor (Morphological)

   3. imor (Improved Morphological)

   4. mormol (Morphological and Mollified Baseline)

   5. amormol (Averaging Morphological and Mollified Baseline)

   6. rolling_ball (Rolling Ball Baseline)

   7. mwmv (Moving Window Minimum Value)

   8. tophat (Top-hat Transformation)

4. Window-based methods (pybaselines.window)

   1. noise_median (Noise Median method)

   2. snip (Statistics-sensitive Non-linear Iterative Peak-clipping)

   3. swima (Small-Window Moving Average)

5. Baseline/Peak Classification methods (pybaselines.classification)

   1. dietrich (Dietrich’s Classification Method)

   2. golotvin (Golotvin’s Classification Method)

   3. std_distribution (Standard Deviation Distribution)

6. Optimizers (pybaselines.optimizers)

   1. collab_pls (Collaborative Penalized Least Squares)

   2. optimize_extended_range

   3. adaptive_minmax (Adaptive MinMax)

7. Miscellaneous methods (pybaselines.misc)

   1. interp_pts (Interpolation between points)

   2. beads (Baseline Estimation And Denoising with Sparsity)

Installation

pip install pybaselines

pip install pybaselines[full]

git clone https://github.com/derb12/pybaselines.git

cd pybaselines
pip install .

Quick Start

To use the various functions in pybaselines, simply input the measured data and any required parameters. All baseline correction functions in pybaselines will output two items: a numpy array of the calculated baseline and a dictionary of potentially useful parameters.

For more details on each baseline algorithm, refer to the algorithms section of pybaselines’s documentation.

A simple example is shown below.

import matplotlib.pyplot as plt
import numpy as np
import pybaselines
from pybaselines import utils

x = np.linspace(1, 1000, 1000)
# a measured signal containing several Gaussian peaks
signal = (
    utils.gaussian(x, 4, 120, 5)
    + utils.gaussian(x, 5, 220, 12)
    + utils.gaussian(x, 5, 350, 10)
    + utils.gaussian(x, 7, 400, 8)
    + utils.gaussian(x, 4, 550, 6)
    + utils.gaussian(x, 5, 680, 14)
    + utils.gaussian(x, 4, 750, 12)
    + utils.gaussian(x, 5, 880, 8)
)
# exponentially decaying baseline
true_baseline = 2 + 10 * np.exp(-x / 400)
np.random.seed(1)  # set random seed
noise = np.random.normal(0, 0.2, x.size)

y = signal + true_baseline + noise

bkg_1 = pybaselines.polynomial.modpoly(y, x, poly_order=3)[0]
bkg_2 = pybaselines.whittaker.asls(y, lam=1e7, p=0.02)[0]
bkg_3 = pybaselines.morphological.mor(y, half_window=30)[0]
bkg_4 = pybaselines.window.snip(
    y, max_half_window=40, decreasing=True, smooth_half_window=3
)[0]

plt.plot(x, y, label='raw data', lw=1.5)
plt.plot(x, true_baseline, lw=3, label='true baseline')
plt.plot(x, bkg_1, '--', label='modpoly')
plt.plot(x, bkg_2, '--', label='asls')
plt.plot(x, bkg_3, '--', label='mor')
plt.plot(x, bkg_4, '--', label='snip')

plt.legend()
plt.show()

The above code will produce the image shown below.

Contributing

Contributions are welcomed and greatly appreciated. For information on submitting bug reports, pull requests, or general feedback, please refer to the contributing guide.

Changelog

Refer to the changelog for information on pybaselines’s changes.

License

pybaselines is open source and freely available under the BSD 3-clause license. For more information, refer to the license.

Author

• Donald Erb <donnie.erb@gmail.com>