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Peak Integration

This tutorial shows how to find peak maxima and determine peak areas with SpectroChemPy. As a prerequisite, the user is expected to have read the Import, Import IR, Slicing, and Baseline Correction tutorials.

First, let’s import the SpectroChemPy API.

import spectrochempy as scp

SpectroChemPy's API - v.0.8.2.dev7 ©Copyright 2014-2025 - A.Travert & C.Fernandez @ LCS

Now, import some 2D data into an NDDataset object.

ds = scp.read_omnic("irdata/nh4y-activation.spg")
ds

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MPL Configuration directory: /home/runner/.config/matplotlib
Stylelib directory: /home/runner/.config/matplotlib/stylelib

NDDataset: [float64] a.u. (shape: (y:55, x:5549))[nh4y-activation]

Summary

name

:

nh4y-activation

author

:

runner@fv-az2211-104

created

:

2025-04-27 01:45:18+00:00

description

:

Omnic title: NH4Y-activation.SPG
Omnic filename: /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg

history

:

2025-04-27 01:45:18+00:00> Imported from spg file /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg.
2025-04-27 01:45:18+00:00> Sorted by date

Data

title

:

absorbance

values

:

...

[[ 2.057 2.061 ... 2.013 2.012]
[ 2.033 2.037 ... 1.913 1.911]
...
[ 1.794 1.791 ... 1.198 1.198]
[ 1.816 1.815 ... 1.24 1.238]] a.u.

shape

:

(y:55, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

55

title

:

acquisition timestamp (GMT)

coordinates

:

[1.468e+09 1.468e+09 ... 1.468e+09 1.468e+09] s

labels

:

...

[[ 2016-07-06 19:03:14+00:00 2016-07-06 19:13:14+00:00 ... 2016-07-07 04:03:17+00:00 2016-07-07 04:13:17+00:00]
[ vz0466.spa, Wed Jul 06 21:00:38 2016 (GMT+02:00) vz0467.spa, Wed Jul 06 21:10:38 2016 (GMT+02:00) ...
vz0520.spa, Thu Jul 07 06:00:41 2016 (GMT+02:00) vz0521.spa, Thu Jul 07 06:10:41 2016 (GMT+02:00)]]

It’s a series of 55 spectra.

For the demonstration, select only the first 20 on a limited region from 1250 to 1800 cm\(^{-1}\) (Do not forget to use floating numbers for slicing).

X = ds[:20, 1250.0:1800.0]

We can also eventually remove the offset on the acquisition time dimension (y).

X.y -= X.y[0]
X.y.ito("min")
X.y.title = "acquisition time"

We set some plotting preferences and then plot the raw data.

prefs = scp.preferences
prefs.figure.figsize = (6, 3)
prefs.colormap = "Dark2"
prefs.colorbar = True
X.plot()

Now we can perform some baseline correction.

blc = scp.Baseline()
blc.ranges = (
    [1740.0, 1800.0],
    [1550.0, 1570.0],
    [1250.0, 1300.0],
)  # define 3 regions where we want the baseline to reach zero.
blc.model = "polynomial"
blc.order = 3

blc.fit(X)  # fit the baseline

Xcorr = blc.corrected  # get the corrected dataset
Xcorr.plot()

To integrate each row on the full range, we can use the sum or trapz method of an NDDataset.

inttrapz = Xcorr.trapezoid(dim="x")
intsimps = Xcorr.simpson(dim="x")

As you can see, both methods give almost the same results in this case.

scp.plot_multiple(
    method="scatter",
    ms=5,
    datasets=[inttrapz, intsimps],
    labels=["trapezoidal rule", "Simpson's rule"],
    legend="best",
)

The difference between the trapezoidal and Simpson integration methods is visualized below. In this case, they are extremely close.

diff = (inttrapz - intsimps) * 100.0 / intsimps
diff.title = "difference"
diff.units = "percent"
diff.plot(scatter=True, ms=5)