Overview

The purpose of this page is to give you some quick examples of what can be done with SpectroChemPy.

See the gallery of examples and consult the user’s guide for more information on using SpectroChemPy

Before using the package, we must load the API (Application Programming Interface)

[1]:

import spectrochempy as scp
  SpectroChemPy's API - v.0.6.8
© Copyright 2014-2024 - A.Travert & C.Fernandez @ LCS

NDDataset, the main object

NDDataset is a python object, actually a container, which can represent most of your multidimensional spectroscopic data.

For instance, in the following we read data from a series of FTIR experiments, provided by the OMNIC software:

[2]:

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

Display dataset information

Short information:

[3]:

print(ds)

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

Detailed information on the main metadata:

[4]:

ds

[4]:
name nh4y-activation
author runner@fv-az1152-754
created 2024-03-08 01:01:24+00:00
description
Omnic title: NH4Y-activation.SPG
Omnic filename: /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg
history
2024-03-08 01:01:24+00:00> Imported from spg file /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg.
2024-03-08 01:01:24+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)]]

Plotting a dataset

[5]:

_ = ds.plot()
../_images/gettingstarted_overview_12_0.png

Slicing a dataset

[6]:

region = ds[:, 4000.0:2000.0]
_ = region.plot()
../_images/gettingstarted_overview_14_0.png

Maths on datasets

[7]:

region.y -= region.y[0]  # make y coordinate relative to the first point
region.y.title = "time of dehydratation"
region -= region[-1]  # suppress the last spectra to all
_ = region.plot(colorbar=True)
../_images/gettingstarted_overview_16_0.png

Processing a dataset

We just give here few examples

Smoothing

[8]:

smoothed = region.smooth(window_length=51, window="hanning")
_ = smoothed.plot(colormap="magma")

 WARNING | (DeprecationWarning)  The `window_length` attribute is now deprecated. Use `size` instead. `window_length` attribute will be removed in version 0.8.
../_images/gettingstarted_overview_20_1.png

Baseline correction

[9]:

region = ds[:, 4000.0:2000.0]
smoothed = region.smooth(window_length=51, window="hanning")
blc = scp.Baseline()
blc.ranges = [[2000.0, 2300.0], [3800.0, 3900.0]]
blc.multivariate = True
blc.model = "polynomial"
blc.order = "pchip"
blc.n_components = 5

_ = blc.fit(smoothed)

 WARNING | (DeprecationWarning)  The `window_length` attribute is now deprecated. Use `size` instead. `window_length` attribute will be removed in version 0.8.

[10]:

_ = blc.corrected.plot()
../_images/gettingstarted_overview_23_0.png

Analysis

IRIS processing

[11]:

ds = scp.read_omnic("irdata/CO@Mo_Al2O3.SPG")[:, 2250.0:1950.0]
pressure = [
    0.00300,
    0.00400,
    0.00900,
    0.01400,
    0.02100,
    0.02600,
    0.03600,
    0.05100,
    0.09300,
    0.15000,
    0.20300,
    0.30000,
    0.40400,
    0.50300,
    0.60200,
    0.70200,
    0.80100,
    0.90500,
    1.00400,
]
ds.y = scp.Coord(pressure, title="Pressure", units="torr")
_ = ds.plot(colormap="magma")
../_images/gettingstarted_overview_25_0.png 
[12]:


iris = scp.IRIS(reg_par=[-10, 1, 12])
K = scp.IrisKernel(ds, "langmuir", q=[-8, -1, 50])
iris.fit(ds, K)
_ = iris.plotdistribution(-7, colormap="magma")
../_images/gettingstarted_overview_26_0.png