Warning

You are reading the documentation related to the development version. Go here if you are looking for the documentation of the stable release.

Introduction

The SpectroChemPy project was developed to provide advanced tools for processing and analyzing spectroscopic data, initially for internal purposes within the LCS (https://www.lcs.ensicaen.fr).

The SpectroChemPy project is essentially a library written in python language and provides objects, NDDataset and Project, to hold data, equipped with methods to analyze, transform or display these data in a simple way through a python type interface.

The processed data are mainly spectroscopic data from techniques such as IR, Raman or NMR, but they are not limited to this type of application, as any type of data can be used.

About this user’s guide

The User's guide and tutorials section is designed to give you a quick overview of the main features of SpectroChemPy. It does not cover all features, but should help you to get started quickly, and to find your way around. For more details on the various features, check out the Public API reference section which gives a more detailed description of the API. You can also refer to the Gallery of example

What to do if questions arise

If, despite this documentation, which we’re constantly trying to improve, you still have unresolved questions, don’t hesitate to post your question on the discussion forum. You can also post on StackOverflow, if you prefer, but don’t forget to put “Spectrochempy” in the title (as there are no tags yet to enable us to find new questions easily.

How to get started

We assume that the SpectroChemPy package has been correctly installed. if is not the case, please go to SpectroChemPy installation procedure.

Writing and executing SpectroChemPy scripts

If you are already an experienced python user, you can certainly use your favorite IDE to run your scripts, debug them and display the results. But if you want an easier way, especially if you are a beginner, we recommend you to use Jupyter Lab to do it. To get started, you can follow this link : Jupyter Lab interface.

Loading the API

Before using SpectroChemPy, we need to load the API (Application Programming Interface): it exposes many objects and functions.

To load the API, you must import it using one of the following syntax.

Syntax 1 (recommended)

In the first syntax we load the library into a namespace called scp (we recommend this name, but you can choose whatever you want - except something already in use):

import spectrochempy as scp

nd = scp.NDDataset()

SpectroChemPy's API - v.0.6.9.dev9 © Copyright 2014-2024 - A.Travert & C.Fernandez @ LCS

Syntax 2 (discouraged)

With a wild * import. In this second syntax, the access to objects/functions can be greatly simplified. For example, we can use directly NDDataset without a prefix instead of scp.NDDataset but there is always a risk of overwriting some variables or functions already present in the namespace. Therefore, the first syntax is generally highly recommended.

from spectrochempy import *  # noqa

nd = NDDataset()

Alternatively, you can also load only the objects and function required by your application:

from spectrochempy import NDDataset

nd = NDDataset()

nd

name	NDDataset_54952594
author	runner@fv-az1535-633
created	2024-04-25 03:08:30+02:00
values	empty
size	0

Where to go next?

The User's guide & tutorial section is divided into several sections describing the main features of spectrochempy.

• Core objects : This is really the starting point for understanding how SpectroChemPy works. In fact, virtually all the functions offered by the API use one of these objects: NDDataset (the most important!) , Project or Script.

• Import & export : This part shows how to import spectroscopic data and transform it into an NDDataset, the object on which processsing and analysis procedures will then be applied.

• Processing : This section explains how to prepare datasets for future analysis. By processing, we mean methods which generally return the same dataset transformed by mathematical operations (for example, this may be a sqrt or log operation, or a fft operation or many others), or part of a dataset transformed by a slicing or concatenation operation.

• Analysis : This section presents the analysis methods implemented in SpectroChemPy. Generally speaking, analysis methods are any methods used to extract properties or characteristics from one or more NDDatasets. Analysis methods include statistical methods (mean, standard deviation, etc.), peaks, integration, etc. But perhaps the most important is the implementation of various chemometrics methods (PCA, PLS-R, EFA, MCR-ALS, …), data fit methods, and baseline extraction.

• Plotting : Finally, this section attempts to give the basics for using the plot methods included in SpectroChemPy. These methods do not claim to cover all needs, and so it may be worthwhile for the user to learn how to use packages such as matplotlib, on which the methods described in this section are based.