Simple deconvolution of NMR peaks for extraction of intensities. Provided an NMRPipe format spectrum (2D or Pseudo 3D) and a peak list (NMRPipe, Sparky or Analysis2), overlapped peaks are automatically/interactively clustered and groups of overlapped peaks are fitted together using Gaussian, Lorentzian or Pseudo-Voigt (Gaussian + Lorentzian) lineshape.
The easiest way to install peakipy is with poetry...
git clone https://github.com/j-brady/peakipy.git
cd peakipy; poetry install
If you don't have poetry please refer to the poetry documentation for more details
At this point the package should be installed and the main scripts (peakipy read
, peakipy edit
, peakipy fit
and peakipy check
)
should have been added to your path.
There are four main commands:
peakipy read
converts your peak list and selects clusters of peaks.peakipy edit
is used to check and adjust fit parameters interactively (i.e clusters and mask radii) if initial clustering is not satisfactory.peakipy fit
fits clusters of peaks.peakipy check
is used to check individual fits or groups of fits and make plots.You can use the -h
or --help
flags for instructions on how to run the programs (e.g. peakipy read -h)
,fit_prefix,assignment,amp,amp_err,center_x,center_y,sigma_x,sigma_y,fraction,clustid,plane,x_radius,y_radius,x_radius_ppm,y_radius_ppm,lineshape,fwhm_x,fwhm_y,center_x_ppm,center_y_ppm,sigma_x_ppm,sigma_y_ppm,fwhm_x_ppm,fwhm_y_ppm,fwhm_x_hz,fwhm_y_hz
0,_None_,None,291803398.52980924,5502183.185104156,158.44747896487527,9.264911100915297,1.1610674220702277,1.160506074898704,0.0,1,0,4.773,3.734,0.035,0.35,G,2.3221348441404555,2.321012149797408,9.336283145411077,129.6698850201278,0.008514304888101518,0.10878688239041588,0.017028609776203036,0.21757376478083176,13.628064792721176,17.645884354478063
1,_None_,None,197443035.67109975,3671708.463467884,158.44747896487527,9.264911100915297,1.1610674220702277,1.160506074898704,0.0,1,1,4.773,3.734,0.035,0.35,G,2.3221348441404555,2.321012149797408,9.336283145411077,129.6698850201278,0.008514304888101518,0.10878688239041588,0.017028609776203036,0.21757376478083176,13.628064792721176,17.645884354478063
etc...
If --plot=<path>
option selected the first plane of each fit will be plotted in --show
option calls plt.show()
on each fit so you can see what it looks like. However, using peakipy check
should be preferable since plotting the fits during fitting
slows down the process a lot.
To plot fits for all planes or interactively check them you can run peakipy check
peakipy check fits.csv test.ft2 --dims=0,1,2 --clusters=1,10,20 --show --outname=plot.pdf
Will plot clusters 1,10 and 20 showing each plane in an interactive matplotlib window and save the plots to a multipage pdf called plot.pdf. Calling peakipy check
with the --first
flag results in only the first plane of each fit being plotted.
Run peakipy check -h
for more options.
You can explore the output data conveniently with pandas
.
In [1]: import pandas as pd
In [2]: import matplotlib.pyplot as plt
In [3]: data = pd.read_csv("fits.csv")
In [4]: groups = data.groupby("assignment")
In [5]: for ind, group in groups:
...: plt.errorbar(group.vclist,group.amp,yerr=group.amp_err,fmt="o",label=group.assignment.iloc[0])
...: plt.legend()
...: plt.show()
Where Gaussian lineshape is
And Lorentzian is
The fit minimises the residuals of the functions in each dimension
Fraction parameter is fraction of Lorentzian lineshape.
The linewidth for the G lineshape is
The linewidth for PV and L lineshapes is
Download from git repo. To test the program for yourself cd
into the test
directory . I wrote some tests for the code itself which should be run from the top directory like so python test/test_core.py
.
A sanity check... Peak intensities were fit using the nlinLS program from NMRPipe and compared with the output from peakipy for the same dataset.
If you would rather use FuDA then try running peakipy read
with the --fuda
flag to create a FuDA parameter file
(params.fuda) and peak list (peaks.fuda).
This should hopefully save you some time on configuration.
Thanks to Jonathan Helmus for writing the wonderful nmrglue
package.
The lmfit
team for their awesome work.
bokeh
and matplotlib
for beautiful plotting.
scikit-image
!
My colleagues, Rui Huang, Alex Conicella, Enrico Rennella, Rob Harkness and Tae Hun Kim for their extremely helpful input.