This post is a roundup of open source scientific data processing projects that I know of. I've organized them alphabetically. I have some minimal notes on a few projects here, but mostly this is just a "roundup" page. Obviously this only scratches the surface, but I've tried to put in just the ones that I can imagine caring about.
GCPy is a Python-based toolkit containing useful functions for working specifically with the GEOS-Chem model of atmospheric chemistry and composition.
GCPy aims to build on the well-established scientific Python technical stack, leveraging tools like cartopy and xarray to simplify the task of working with model output and performing atmospheric chemistry analyses.
Here, we present a new program for decomposing mixed transient spectra into their individual component spectra and extracting the corresponding kinetic traces: KOALA (Kinetics Observed After Light Absorption). The software combines spectral target analysis with brute-force linear least squares fitting, which is computationally efficient because of the small nonlinear parameter space of most spectral features.
pycroscopy is a python package for image processing and scientific analysis of imaging modalities such as multi-frequency scanning probe microscopy, scanning tunneling spectroscopy, x-ray diffraction microscopy, and transmission electron microscopy. pycroscopy uses a data-centric model wherein the raw data collected from the microscope, results from analysis and processing routines are all written to standardized hierarchical data format (HDF5) files for traceability, reproducibility, and provenance.
Analysis of ultra-fast transient absorption spectra.
Python framework for storing, visualizing, and processing spectroscopy, imaging or any observational / experimental data.
The Universal Spectroscopic and Imaging Data (USID) model.
Skultrafast stands for scikit.ultrafast and is an python package which aims to include everything needed to analyze data from time-resolved spectroscopy experiments in the femtosecond domain.
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