AntroPy is a Python package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to extract features from EEG signals.

mne-icalabel is a Python package for labeling independent components that stem from an Independent Component Analysis (ICA).

This package provides support for reading and writing EEGLAB files in Python.

This inspector is meant as a companion to the PyNWB validator, which checks for strict schema compliance. This tool attempts to apply some common sense to find components of the file that are technically compliant, but possibly incorrect, suboptimal in their representation, or deviate from best practices.

A simple python package for fitting L2- and smoothing-penalized generalized linear models. Built primarily because the statsmodels GLM fit_regularized method is built to do elastic net (combination of L1 and L2 penalities), but if you just want to do an L2 or a smoothing penalty (like in generalized additive models), using a penalized iteratively reweighted least squares (p-IRLS) is much faster.

edfio is a Python package for reading and writing EDF and EDF+C files.

pybv is a lightweight I/O utility for the BrainVision data format. The BrainVision data format is a recommended data format for use in the Brain Imaging Data Structure.

spike sorting pipeline.

MNE-LSL (Documentation website) provides a real-time brain signal streaming framework. MNE-LSL contains an improved python-binding for the Lab Streaming Layer C++ library, mne_lsl.lsl, replacing pylsl. This low-level binding is used in high-level objects to interact with LSL streams.

Tools to analyze and simulate neural time series, using digital signal processing.

PyNWB is a Python package for working with NWB files. It provides a high-level API for efficiently working with Neurodata stored in the NWB format.

A Python package to handle the layout, geometry, and wiring of silicon probes for extracellular electrophysiology experiments.

deviceXlib is a library that wraps device-oriented routines and utilities, such as device data allocation, host-device data transfers. It supports CUDA language, together with OpenACC and OpenMP programming paradigms. It wraps a subset of functions from Nvidia cuBLAS, Intel oneMKL BLAS and AMD rocBLAS libraries.

Fortran Package Manager (fpm) is a package manager and build system for Fortran. Its key goal is to improve the user experience of Fortran programmers. It does so by making it easier to build your Fortran program or library, run the executables, tests, and examples, and distribute it as a dependency to other Fortran projects. Fpm's user interface is modeled after Rust's Cargo, so if you're familiar with that tool, you will feel at home with fpm. Fpm's long term vision is to nurture and grow the ecosystem of modern Fortran applications and libraries.

Fypp is a Python powered preprocessor. It can be used for any programming languages but its primary aim is to offer a Fortran preprocessor, which helps to extend Fortran with condititional compiling and template metaprogramming capabilities. Instead of introducing its own expression syntax, it uses Python expressions in its preprocessor directives, offering the consistency and versatility of Python when formulating metaprogramming tasks.

deviceXlib is a library that wraps device-oriented routines and utilities, such as device data allocation, host-device data transfers. It supports CUDA language, together with OpenACC and OpenMP programming paradigms. It wraps a subset of functions from Nvidia cuBLAS, Intel oneMKL BLAS and AMD rocBLAS libraries.

This package provides discretization tools for finite volume and inverse problems.

The vision is to create a package for finite volume simulation with a focus on large scale inverse problems. This package has the following features:

• modular with respect to the spacial discretization

• built with the inverse problem in mind

• supports 1D, 2D and 3D problems

• access to sparse matrix operators

• access to derivatives to mesh variables

Currently, discretize supports:

• Tensor Meshes (1D, 2D and 3D)

• Cylindrically Symmetric Meshes

• QuadTree and OcTree Meshes (2D and 3D)

• Logically Rectangular Meshes (2D and 3D)

• Triangular (2D) and Tetrahedral (3D) Meshes

This package provides a collection of (mostly) analytic functions in geophysics. We take an object oriented approach with the aim of having users be able to readily interact with the functions using Jupyter.

Provides a high-level interface to the ECWMF ECCODES C library for reading GRIB files. There are limited capabilities for writing GRIB files (you can modify the contents of an existing file, but you can't create one from scratch).

This package provides a Python library for manipulation and storage of a wide range of geoscientific data (points, curve, surface, 2D and 3D grids) in *.geoh5 file format.

Harmonica is a Python library for processing and modeling gravity and magnetic data. It includes common processing steps, like calculation of Bouguer and terrain corrections, reduction to the pole, upward continuation, equivalent sources, and more. There are forward modeling functions for basic geometric shapes, like point sources, prisms and tesseroids.

xgcm is a Python package for working with the datasets produced by numerical General Circulation Models (GCMs) and similar gridded datasets that are amenable to finite volume analysis. In these datasets, different variables are located at different positions with respect to a volume or area element (e.g. cell center, cell face, etc.) xgcm solves the problem of how to interpolate and difference these variables from one position to another.

This package provides powerful tools for geospatial data manipulation in Python, including working with coordinate reference systems, grid definitions, and spatial transformations.

Choclo is a Python library that hosts optimized kernel functions for running geophysical forward and inverse models, intended to be used by other libraries as the underlying layer of their computation.