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airpls's Introduction

1. Introduction

adaptive iteratively reweighted Penalized Least Squares (airPLS) algorithm doesn’t require any user intervention and prior information, such as detected peaks. It iteratively changes weights of sum squares errors (SSE) between the fitted baseline and original signals, and the weights of SSE are obtained adaptively using between previously fitted baseline and original signals. This baseline estimator is fast and flexible in fitting baseline.

graphical abstract of airPLS

2. Installation

2.1 MATLAB version

  • Install MATLAB 6.5 or higher in you computer.
  • download, unzip and enjoy it from this url.

2.2 R version

By taking the advantage of sparse matrix in R package "Matrix", we implemented the sparse version of whittaker smoother and airPLS alogrithm. Now the speed of airPLS 2.0 is faster than airPLS 1.0 by 100 times or more.

  • install airPLS R version from github with following R scripts

     install.packages('devtools')
     library(devtools)
     httr::set_config( httr::config( ssl_verifypeer = 0L ) )
     install_github("zmzhang/airPLS_R")
  • load airPLS package and run example with following R scripts

     library(airPLS)
     ?simulate

2.3 Python version

Python version of airPLS using the scipy framework by Renato Lombardo of University of Palermo.

  • Install Python Python 3 is recommended https://www.python.org/download

  • Install Numpy, Scipy, Matplotlib with following commands

     pip install numpy
     pip install scipy
     pip install matplotlib
  • clone this project and run airPLS.py

2.4 C++ version

We have already noticed the parameter optimizing problem in R and Matlab version of airPLS. So we have rewritten this airPLS algorithm in C++ and MFC (Visual Studio 2010) to provide a better user interface for baseline-correction. One can tune the lambda parameter by dragging the slider easily.

  • It can be downloaded from this url
  • Click the downloaded exe file, install it in your computer and run the installed program.

3. Contact

For any questions, please contact:

[email protected]

4. Manuscript and citation

Z.-M. Zhang, S. Chen, and Y.-Z. Liang, Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst 135 (5), 1138-1146 (2010).

Download pdf and endnote citation here if you have subscription of RSC

Download manuscript of airPLS here if you doesn't have subscription of RSC

airpls's People

Contributors

irene-moreno avatar lesliewongcv avatar zmzhang avatar

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airpls's Issues

weighting equation discrepancy

The R & Python code for computing the weight vector (e.g., https://github.com/zmzhang/airPLS/blob/a92b37a7bcc4d22eac02db2ebdb0a1b9ce1a1a55/airPLS.py#L81-L83C19) appear to differ from the equation given in the paper:

  • The code uses e^(t * abs(x_i - z_i) / abs_d), which is e^(t * (z_i - x_i) / abs_d), not e^(t * (x_i - z_i) / abs_d) that the paper says.
  • The code uses a special value for w_1 and w_m based on the max negative deviation, which is by definition negative.

The code thus weights points with a lower value more highly, whereas the paper does the opposite. I don't know why the code uses a value for the first and last weights that raises e to a negative power.

基线问题

关于airPLS,我有几个问题想要请教一下:
(1)如果谱图中出现一个下降曲线,在去除基线后会形成为一个峰,这种情况该如何解决?
image
(2)有些平缓区域的基线消除不够,该如何调整?
image

Question about Python implementation

The array edge value is determined by line 82 in the Python implementation.

It occurred to me that this expression is taking the maximum of negative values d[d<0].max() which will likely be close to 0 in most cases... resulting in the exponential evaluating to 1.

If this is not the intention, should the algorithm be updated to the max of the absolute of the negative values (i.e. d[d<0].abs().max())?

If this is the intention, can we just save the effort and set the value to 1? The difference between 1 and very close to 1 doesn't seem worth the computation here...

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