See also

SpectroChemPy is built on top of the broader scientific Python ecosystem. Depending on your needs, the following projects may also be of interest.

Graphical data analysis

If you are looking for a graphical user interface for exploratory data analysis, chemometrics, and spectroscopy, consider:

Orange provides an interactive workflow-based environment, while Orange-Spectroscopy adds tools dedicated to spectral data analysis.

Labeled scientific arrays

SpectroChemPy shares some concepts with projects that combine numerical arrays with coordinate metadata:

These libraries provide labeled data structures for multidimensional and tabular data respectively. SpectroChemPy extends similar ideas to spectroscopic datasets by combining numerical data, coordinates, units, metadata, provenance information, and domain-specific processing tools.

Scientific computing foundations

Many SpectroChemPy features rely on the scientific Python ecosystem:

Users interested in the underlying numerical and scientific computing tools may wish to consult these projects directly.

Machine learning and tensor methods

For machine learning workflows, multivariate analysis, and tensor decompositions, the following projects are commonly used alongside SpectroChemPy:

TensorLy is also used by the official spectrochempy-tensor plugin for tensor decompositions such as CP/PARAFAC.

Visualization

For advanced statistical graphics and interactive visualization:

These libraries complement SpectroChemPy’s built-in plotting capabilities.

Scientific data models

Users interested in interoperable scientific data models and formats may also wish to explore:

CSDM (Core Scientific Dataset Model)

CSDM is a community-driven data model designed for scientific spectroscopy, magnetic resonance, and multidimensional experimental datasets.