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SpectroChemPy Overview

Welcome to SpectroChemPy! This tutorial will give you a quick overview of the main features and capabilities of the library. We’ll cover:

1. Loading and displaying spectroscopic data

2. Basic data manipulation and plotting

3. Processing techniques (smoothing, baseline correction)

4. Advanced analysis methods

For more detailed information, check out:

• Gallery of Examples

• User’s Guide

• API Reference

Getting Started

First, let’s import SpectroChemPy. By convention, we use the alias scp:

import spectrochempy as scp

SpectroChemPy's API - v.0.7.2.dev7 © Copyright 2014-2025 - A.Travert & C.Fernandez @ LCS

Working with NDDataset Objects

The NDDataset is the core data structure in SpectroChemPy. It’s designed specifically for spectroscopic data and provides:

• Multi-dimensional data support

• Coordinates and units handling

• Built-in visualization

• Processing methods

Let’s load some example FTIR (Fourier Transform Infrared) data:

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

Exploring Your Data

SpectroChemPy provides multiple ways to inspect your data. Let’s look at:

1. Basic information summary

2. Detailed metadata

# Quick overview
scp.set_loglevel("INFO")  # to see information
scp.info_(ds)

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

# Detailed information
ds

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

Summary

name

:

nh4y-activation

author

:

runner@fv-az1676-495

created

:

2025-03-02 01:28:15+00:00

description

:

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

history

:

2025-03-02 01:28:15+00:00> Imported from spg file /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg.
2025-03-02 01:28:15+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)]]

Data Visualization

SpectroChemPy’s plotting capabilities are built on matplotlib but provide spectroscopy-specific features:

_ = ds.plot()

Data Selection and Manipulation

You can easily select specific regions of your spectra using intuitive slicing. Here we select wavenumbers between 4000 and 2000 cm⁻¹:

region = ds[:, 4000.0:2000.0]
_ = region.plot()

Mathematical Operations

NDDataset supports various mathematical operations. Here’s an example of baseline correction:

# Make y coordinate relative to the first point
region.y -= region.y[0]
region.y.title = "Dehydration time"

# Subtract the last spectrum from all spectra
region -= region[-1]

# Plot with colorbar to show intensity changes
_ = region.plot(colorbar=True)

Data Processing Techniques

SpectroChemPy includes numerous processing methods. Here are some common examples:

1. Spectral Smoothing

Reduce noise while preserving spectral features:

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

2. Baseline Correction

Remove baseline artifacts using various algorithms:

# Prepare data
region = ds[:, 4000.0:2000.0]
smoothed = region.smooth(size=51, window="hanning")

# Configure baseline correction
blc = scp.Baseline()
blc.ranges = [[2000.0, 2300.0], [3800.0, 3900.0]]  # Baseline regions
blc.multivariate = True
blc.model = "polynomial"
blc.order = "pchip"
blc.n_components = 5

# Apply correction
_ = blc.fit(smoothed)
_ = blc.corrected.plot()

Advanced Analysis as for instance IRIS Processing

IRIS (Iterative Regularized Inverse Solver) is an advanced technique for analyzing spectroscopic data. Here’s an example with CO adsorption data:

# Load and prepare CO adsorption data
ds = scp.read_omnic("irdata/CO@Mo_Al2O3.SPG")[:, 2250.0:1950.0]

# Define pressure coordinates
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")

# Plot the dataset
_ = ds.plot(colormap="magma")

# Perform IRIS analysis
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")