MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more.

Elephant (Electrophysiology Analysis Toolkit) is an open-source, community centered library for the analysis of electrophysiological data in the Python programming language. The focus of Elephant is on generic analysis functions for spike train data and time series recordings from electrodes, such as the local field potentials (LFP) or intracellular voltages. In addition to providing a common platform for analysis code from different laboratories, the Elephant project aims to provide a consistent and homogeneous analysis framework that is built on a modular foundation. Elephant is the direct successor to Neurotools and maintains ties to complementary projects such as OpenElectrophy and spykeviewer.

This is a Python package for performing representational similarity analysis (RSA) using MNE-Python data structures. The main use-case is to perform RSA using a “searchlight” approach through time and/or a volumetric or surface source space.

bycycle is a tool for quantifying features of neural oscillations in the time domain, as opposed to the frequency domain, using a cycle-by-cycle approach.

mne-icalabel is a Python package for labeling independent components that stem from an ICA.

phy is an open-source graphical interface for visualizing and manually curating large-scale electrophysiology datasets. It is optimized for high-density probes, including Neuropixels, and provides both an interactive curation workflow and Python extension points for specialized labs.

This is an open-source tool which allows to load CURRY data into Python. It supports: raw float (.cdt), ascii (.cdt), legacy raw float (.dat) and legacy ascii (.dat).

mne-denoise brings together complementary spatial, spectral, adaptive, and source-informed denoising methods, with NumPy support and integration with MNE-Python objects.

This package provides utilities for reading the files produced by BIOPAC's AcqKnowledge software.

replay_trajectory_classification is a Python package for decoding spatial position represented by neural activity and categorizing the type of trajectory.

It has several advantages over decoders typically used to characterize hippocampal data:

• It allows for moment-by-moment estimation of position using small temporal time bins which allow for rapid movement of neural position and makes fewer assumptions about what downstream cells can integrate.

• The decoded trajectories can change direction and are not restricted to constant velocity trajectories.

• The decoder can use spikes from spike-sorted cells or use clusterless spikes and their associated waveform features to decode.

• The decoder can categorize the type of neural trajectory and give an estimate of the confidence of the model in the type of trajectory.

• Proper handling of complex 1D linearized environments.

• Ability to extract and decode 2D environments.

• Easily installable, documented code with tutorials on how to use the code.

• Fast computation using GPUs.

This package provides an electrophysiological data analysis library for Python.

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.

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.

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.

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 packge provides utilities to retrieve tile maps from the internet. It can add those tiles as basemap to matplotlib figures or write tile maps to disk into geospatial raster files. Bounding boxes can be passed in both WGS84 (EPSG:4326) and Spheric Mercator (EPSG:3857).

This package provides programmatic access to the data store catalogue of the Copernicus CDS.

NetCDF is an interface for scientific data access and a software library that provides an implementation of the interface. The netCDF library defines a machine-independent format for representing scientific data. Together, the interface, library, and format support the creation, access, and sharing of scientific data.

This package provides a three-dimensional modeller for electromagnetic (EM) diffusion as used for instance in geophysical controlled-source EM (CSEM) surveys. This includes use cases in the search for resources such as groundwater, geothermal energy, hydrocarbons, and minerals, or civil engineering and environmental applications.

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

MT Metadata is meant to be a tool to standardize both time series and transfer function metadata for magnetotelluric data.

This package provides methods for simulation and gradient-based parameter estimation in the context of geophysical applications.

The vision is to create a package for finite volume simulation with applications to geophysical imaging and subsurface flow. To enable the understanding of the many different components, this package has the following features:

• modular with respect to the spacial discretization, optimization routine, and geophysical problem

• built with the inverse problem in mind

• provides a framework for geophysical and hydrogeologic problems

• supports 1D, 2D and 3D problems

• designed for large-scale inversions

The electromagnetic modeller empymod can model electric or magnetic responses due to a three-dimensional electric or magnetic source in a layered-earth model with vertical transverse isotropic (VTI) resistivity, VTI electric permittivity, and VTI magnetic permeability, from very low frequencies (DC) to very high frequencies (GPR). The computation is carried out in the wavenumber-frequency domain, and various Hankel- and Fourier-transform methods are included to transform the responses into the space-frequency and space-time domains.

This package provides DLF as used in Geophysics for electromagnetic modelling.