Git Product home page Git Product logo

raman_tl's Introduction

raman-tl.py

A Python 3 script for baseline correction, smoothing, processing and plotting of Raman spectra. Data must be in the format wavenumber [space] intensity. The baseline correction uses the asymmetrically reweighted penalized least squares smoothing algorithm (arPLS). The Whittaker filter is (by default) applied for smoothing. Optionally, the Savitzky–Golay filter can be used. Data of the processed spectra can be saved as "csv"-like data files in the format wavenumber [delimiter] intensity. An overlay spectrum (normalized and not normalized) and a normalized stacked spectrum of all processed spectra can be plotted. Plots can be saved as PNG bitmap files and as PDF.

If you use the arPLS algorithm to process your spectra, please cite:

"Baseline correction using asymmetrically reweighted penalized least squares smoothing"

Sung-June Baek, Aaron Park, Young-Jin Ahna, Jaebum Choo, Analyst 2015, 140, 250-257

DOI: https://doi.org/10.1039/C4AN01061B

The Whittaker algorithm (sometimes also referred to as Whittaker-Eilers smoother) is adapted from:

"A perfect smoother"

Paul H. C. Eilers, Anal. Chem. 2003, 75, 3631-3636

DOI https://doi.org/10.1021/ac034173t

which is based on:

"On a new method of gradutation"

E. T. Whittaker, Proceedings of the Edinburgh Mathematical Society 1922, 41, 63-75

DOI: https://doi.org/10.1017/S0013091500077853

External modules

numpy, scipy, matplotlib

Quick start

Start the script with:

python3 raman-tl.py filename

to open a single file.

Start the script with:

python3 raman-tl.py *.txt

to process all files with the extension .txt in the folder.

Under Windows, you have to open PowerShell first and start the script with:

python raman-tl.py (Get-ChildItem *.txt -Name)

to process all files with the extension .txt in the folder.

If the plot window appears empty, please resize it. Check "Known issues" for further details.

In all cases a file summary.pdf will be created which contains the following plots:

On the first page (from top to bottom):

  • raw spectrum with baseline plot (red)
  • baseline corrected spectrum
  • smoothed / filtered spectrum with peak annotation

On the following page(s):

  • smoothed / filtered spectrum with peak annotation
  • not normalized and normalized overlay spectra and normalized stacked spectra if the -o option was invoked

Command-line options

  • filename , required: filename(s), input file(s) in the format wavenumber [space] intensity
  • -l N, optional: the lambda parameter for the arPLS algorithm (default is N = 1000)
  • -p N:M, optional: invokes the Savitzky–Golay filter, N:M are the window length and polynomial order of the Savitzky–Golay filter
  • -w N, optional: the lambda parameter for the Whittaker filter (default is N = 1)
  • -xmin N , optional: start spectra at N wave numbers
  • -xmax N , optional: end spectra at N wave numbers
  • -t N , optional: threshold for peak detection, with N being the intensity (default is 5% from the maximum intensity)
  • -m N , optional: multiply intensities with N (default is N = 1)
  • -a N , optional: add or subtract N to / from wave numbers (default is N = 0)
  • -i N , optional: add or subtract N to / from intensities (default is N = 0)
  • -o , optional: show the normalized and not normalized overlay spectrum and the normalized stacked spectrum
  • -n , optional: do not save summary.pdf
  • -s p,d , optional: save P(NG) and / or D(ATA) files. The filenames are filename.png and / or filename-mod.dat for the single spectra. Data files are in the format wavenumber [delimiter] intensity. The delimiter can be set in the script. The default delimiter is [space]. Summary.png, overlay.png, overlay-normalized.png, stack-normalized.png bitmaps will be saved as well, overlay and stacked spectra only if the -o option has been invoked.

Remarks

  • The save values for the arPLS parameter lambda start from 1000. Smaller values will give sharper peaks, but broader peaks become part of the baseline. Check the red baseline curve in the summary page.
  • There is no way to turn off smoothing directly, but with two Savitzky-Golay parameters close together, e.g. -p3:2 or a Whittaker parameter -w0.01 filtering is ineffective.
  • The window length for the Savitzky–Golay filter must be an odd number and the window length must be greater than the polynomial order.
  • Polynomial based filters, such as the Savitzky–Golay filter, sometimes tend to overshoot in negative regions, especially with sharp signals in the Raman spectrum. Reduce the filtering (see above) is one way to solve this problem.
  • xmin and or xmax values outside the experimental wave number range will result in errors or strange outputs.
  • -a changes the range for xmin and xmax
  • -i and -m change the range for -t
  • The .dat file contains the data of the processed spectrum in the given range as it is shown in the plot for the single spectrum.
  • The -o option invokes the overlay plots (normalized and not normalized) and the normalized stacked plot of all processed spectra. Normalized means, that the intensities are divided by the maximium intensity in the given intensity range. The maximum intensity becomes unity. The peak detection threshold for the normalized spectrum is 0.05 (can be changed in the script: normalized_height).
  • The delimiter in the .dat file can be changed in the script: dat_delimiter = " " or dat_delimiter = " ; " for example.
  • The files summary.pdf, summary.png, overlay.png, overlay-normalized.png, stack-normalized.png will be overwritten every time the script is started (with respective options) in the same directory. Single spectra with the same filenames will be overwritten as well. Rename them if you want to keep them.

Known issues

  • Some of the peaks that are close together are not annotated. To change this, one can reduce the peak_distance in the script, which is by default peak_distance = 8.
  • Peak annotations can be overprinted by other peak annotations in the overlay spectrum. There is no workaround for this. If annotations are in the same position, one can uncomment the instruction under #no dupes in the script, then only one annotation is displayed.
  • The legend obscures part of the spectrum. If this is a problem, one can change the position of the legend in the script or prevent the legend from being printed at the spectrum (try to change head_space_y_o_s in the script for the overlay and stacked spectra).
  • Recent versions of Matplotlib and Python may encounter an issue where the plot window appears empty. As a temporary solution, resizing the window seems to solve the problem. This issue is currently unresolved: matplotlib/matplotlib#25768. However, with Matplotlib version 3.9.0, everything functions as expected.

Examples

show

Remember, under Windows you have to open PowerShell first and start the script with:

python raman-tl.py (Get-ChildItem *.txt -Name)

to open more than one file at once.

Example 1

python3 raman-tl.py s*.txt

Process all files starting with s and the extension .txt.

Summary:

Single spectra:

Example 2

python3 raman-tl.py sample-A.txt -xmin 600 -xmax 800 -spd 

Process spectrum sample-A.txt in the range from xmin = 600 to xmax = 800 cm-1 and save the PNG and DATA files (-spd).

Summary:

Single spectrum:

Example 3

python3 raman-tl.py sample-A.txt -l10000 -p7:4 -xmin 600 -xmax 800 -t50 -spd 

Process spectrum sample-A.txt with lambda = 10000 (baseline parameter), window length = 7 and polynomial order = 4 (smoothing parameters) in the range from xmin = 600 to xmax = 800 cm-1, annotate peaks with intensities equal or greater than t = 50 and save the PNG and DATA files (-spd).

Summary:

Single spectrum:

Example 4

python3 raman-tl.py sample-A.txt sample-B.txt -o -xmin 200 -xmax 1100 -sp

Process spectra sample-A.txt and sample-B.txt in the range from xmin = 200 to xmax = 1100 cm-1, plot the overlay and stacked spectra (-o) and save the PNG files (-sp).

Overlay spectrum (not normalized):

Overlay spectrum (normalized):

Stacked spectrum (normalized):

raman_tl's People

Contributors

radi0sus avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.