SpectroChemPy is a framework for processing, analyzing and modeling spectroscopic data for chemistry with Python
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Updated
Dec 13, 2024 - Python
SpectroChemPy is a framework for processing, analyzing and modeling spectroscopic data for chemistry with Python
A Python library of algorithms for the baseline correction of experimental data.
🌈 A collaborative list of awesome tools for spectroscopy. Also, check:
Python package for reading Bruker OPUS files.
A python package for communicating with Bruker OPUS spectroscopy software and reading its binary file format.
JASCO file to text file converter
Read Nicolet/Thermo Omnic files on python (.spa).
Jupyter Notebooks for spectroscopic data processing
Python package for working with spectral data. !!The project has been moved!!
Explainable Detection of Microplastics using Transformer Neural Networks. Includes codes for a classification transformer aimed at classifying microplastic FTIR data.
QUIDDIT is a software tool for automated processing of diamond IR spectra
Jupyter notebook fitting reflectance and transmittance curves of unknown thin film on substrate obtained by Fourier Transform Infrared Spectroscopy to Lorentz and Brendel-Bormann oscillator models respectively
Convert many binary format OPUS files to .dpt (Data Point Table) file format
The scripts contained in this repository relate directly to the work conducted by the Tree Root Microbiome Project (TRMP) led by Dr Steve Wakelin.
A simple GUI tool to plot FTIR (Fourier transform infrared) spectroscopy data.
Productivity tools for FTIR
Code to accompany the "Metasurface-enhanced infrared spectroscopy in multiwell format for real-time assaying of live cells" (Labs on a chip, 2023) 10.1039/d3lc00017f
From the Lower Crust to the mantle: elastic properties, anisotropy, and water content of the Cabo Ortegal complex
Example codes for photonic simulations, mostly in the mid-IR region (2 um-20 um), using various open source packages.
This repository showcases a database of 10,000 generated IR spectrum samples of leukemia cases (N=5000) and healthy controls (N=5000). Only the fingerprint region (1800 cm^-1 to 850 cm^-1 at 4 cm^-1 resolution) was considered.
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