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Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


python-mne-rsa 0.10
Propagated dependencies: python-mne@1.10.2 python-nibabel@5.3.2 python-pyside-6@6.9.2 python-pyvista@0.44.2 python-pyvistaqt@0.11.3 python-scikit-learn@1.7.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://mne.tools/mne-rsa
Licenses: Modified BSD
Synopsis: Representational Similarity Analysis on MEG and EEG data
Description:

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.

python-neurodsp 2.3.0
Propagated dependencies: python-matplotlib@3.8.2 python-numpy@1.26.4 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://neurodsp-tools.github.io/neurodsp
Licenses: ASL 2.0
Synopsis: Digital signal processing for neural time series
Description:

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

python-nwbinspector 0.6.5
Propagated dependencies: python-aiohttp@3.11.11 python-click@8.1.8 python-fsspec@2025.9.0 python-hdmf-zarr@0.12.0 python-isodate@0.7.2 python-jsonschema@4.23.0 python-natsort@8.4.0 python-packaging@25.0 python-pynwb@3.1.2 python-pyyaml@6.0.2 python-requests@2.32.5 python-tqdm@4.67.1
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://nwbinspector.readthedocs.io/
Licenses: Modified BSD
Synopsis: Inspect NWB files for compliance with NWB Best Practices
Description:

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.

python-mne-features 0.3
Propagated dependencies: python-mne@1.10.2 python-numba@0.61.0 python-numpy@1.26.4 python-pandas@2.2.3 python-pywavelets@1.8.0 python-scikit-learn@1.7.0 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://mne.tools/mne-features
Licenses: Modified BSD
Synopsis: Software for extracting features from multivariate time series
Description:

This package provides code for feature extraction with M/EEG data.

python-pyedflib 0.1.42
Propagated dependencies: python-numpy@1.26.4
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://pyedflib.readthedocs.io
Licenses: Modified BSD
Synopsis: Library to read/write EDF+/BDF+ files
Description:

pyEDFlib is a Python library to read/write EDF+/BDF+ files based on EDFlib. EDF means European Data Format

python-autoreject 0.4.3
Propagated dependencies: python-h5io@0.2.5 python-joblib@1.5.1 python-matplotlib@3.8.2 python-mne@1.10.2 python-numpy@1.26.4 python-pymatreader@1.1.0 python-scikit-learn@1.7.0 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: http://autoreject.github.io
Licenses: Modified BSD
Synopsis: Automated rejection and repair of epochs in M/EEG
Description:

This is a library to automatically reject bad trials and repair bad sensors in magneto-/electroencephalography (M/EEG) data.

python-antropy 0.1.9
Propagated dependencies: python-numba@0.61.0 python-numpy@1.26.4 python-scikit-learn@1.7.0 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://raphaelvallat.com/antropy
Licenses: Modified BSD
Synopsis: Entropy and complexity of (EEG) time-series in Python
Description:

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.

python-nixio 1.5.4
Propagated dependencies: python-h5py@3.13.0 python-numpy@1.26.4 python-six@1.17.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://github.com/G-Node/nixpy
Licenses: Modified BSD
Synopsis: Python implementation of the NIX data model
Description:

The NIX data model allows to store fully annotated scientific dataset, i.e. the data together with its metadata within the same container. The current implementations store the actual data using the HDF5 file format as a storage backend.

python-neo 0.14.2
Propagated dependencies: python-numpy@1.26.4 python-packaging@25.0 python-quantities@0.16.2
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: http://neo.readthedocs.io
Licenses: Modified BSD
Synopsis: Electrophysiology data in Python
Description:

Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats.

meggie 1.10.0
Propagated dependencies: python-appdirs@1.4.4 python-colorama@0.4.6 python-h5io@0.2.5 python-json-logger@2.0.7 python-matplotlib@3.8.2 python-mne@1.10.2 python-mne-qt-browser@0.7.2 python-numpy@1.26.4 python-pandas@2.2.3 python-pyqt@5.15.11 python-scikit-learn@1.7.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://cibr-jyu.github.io/meggie
Licenses: Modified BSD
Synopsis: User-friendly graphical user interface to do M/EEG analysis
Description:

Meggie is an open-source software designed for intuitive MEG and EEG analysis. With its user-friendly graphical interface, Meggie brings the powerful analysis methods of MNE-Python to researchers without requiring programming skills.

python-alphacsc 0.4.1
Propagated dependencies: python-joblib@1.5.1 python-matplotlib@3.8.2 python-mne@1.10.2 python-numba@0.61.0 python-numpy@1.26.4 python-scikit-learn@1.7.0 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://alphacsc.github.io/
Licenses: Modified BSD
Synopsis: Convolutional dictionary learning for noisy signals
Description:

This is a library to perform shift-invariant sparse dictionary learning, also known as convolutional sparse coding (CSC), on time-series data.

python-klusta 3.0.16
Propagated dependencies: python-click@8.1.8 python-h5py@3.13.0 python-numpy@1.26.4 python-scipy@1.12.0 python-six@1.17.0 python-tqdm@4.67.1
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://klusta.cortexlab.net
Licenses: Modified BSD
Synopsis: Spike detection and automatic clustering for spike sorting
Description:

klusta is an open source package for automatic spike sorting of multielectrode neurophysiological recordings made with probes containing up to a few dozens of sites.

python-fooof 1.1.1
Propagated dependencies: python-numpy@1.26.4 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://github.com/fooof-tools/fooof
Licenses: ASL 2.0
Synopsis: Fitting oscillations & one over f (FOOOF)
Description:

Fast, efficient, and physiologically-informed tool to parameterize neural power spectra

python-mne-faster 1.2.2
Propagated dependencies: python-mne@1.10.2 python-numpy@1.26.4 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://github.com/wmvanvliet/mne-faster
Licenses: Modified BSD
Synopsis: Automatic EEG bad channel/epoch/ICA-component detection using FASTER
Description:

FASTER is a fully automated, unsupervised method for processing of high density EEG data.

python-lspopt 1.4.0
Propagated dependencies: python-numpy@1.26.4 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://github.com/hbldh/lspopt
Licenses: Expat
Synopsis: Multitaper window method for estimating Wigner spectra for certain locally stationary processes
Description:

This package provides a Python implementation of a multitaper window method for estimating Wigner spectra for certain locally stationary processes.

python-mne-bids 0.17.0
Propagated dependencies: python-mne@1.10.2 python-numpy@1.26.4 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://mne.tools/mne-bids
Licenses: Modified BSD
Synopsis: Organize MEG, EEG, and iEEG data according to the BIDS specification
Description:

MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python.

python-mne-lsl 1.11.0
Propagated dependencies: python-click@8.1.8 python-mne@1.10.2 python-numpy@1.26.4 python-packaging@25.0 python-pooch@1.8.1 python-psutil@7.0.0 python-pyqtgraph@0.13.7 python-qtpy@2.4.3 python-scipy@1.12.0 python-tomli@2.2.1
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://mne.tools/mne-lsl
Licenses: Modified BSD
Synopsis: Real-time framework integrated with MNE-Python for online neuroscience research through LSL-compatible devices
Description:

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.

python-mne 1.10.2
Propagated dependencies: python-decorator@5.2.1 python-jinja2@3.1.2 python-lazy-loader@0.4 python-matplotlib@3.8.2 python-numpy@1.26.4 python-packaging@25.0 python-pooch@1.8.1 python-scipy@1.12.0 python-tqdm@4.67.1
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://mne.tools/stable/index.html
Licenses: Modified BSD
Synopsis: MEG and EEG analysis and visualization
Description:

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.

mnelab 1.0.5
Propagated dependencies: python-edfio@0.4.10 python-matplotlib@3.8.2 python-mne@1.10.2 python-numpy@1.26.4 python-pybv@0.7.6 python-pyside-6@6.9.2 python-pyxdf@1.17.1 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://github.com/cbrnr/mnelab
Licenses: Modified BSD
Synopsis: A graphical user interface for MNE
Description:

MNELAB is a GUI for MNE-Python, a Python package for EEG/MEG analysis.

python-snirf 0.8.0
Propagated dependencies: python-colorama@0.4.6 python-h5py@3.13.0 python-numpy@1.26.4 python-termcolor@2.5.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://github.com/BUNPC/pysnirf2
Licenses: GPL 3
Synopsis: Interface and validator for SNIRF files
Description:

Python library for reading, writing, and validating SNIRF files

python-pybv 0.7.6
Propagated dependencies: python-numpy@1.26.4
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://pybv.readthedocs.io
Licenses: Modified BSD
Synopsis: I/O utility for the BrainVision data format
Description:

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.

python-igor2 0.5.12
Propagated dependencies: python-matplotlib@3.8.2 python-numpy@1.26.4
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://github.com/AFM-analysis/igor2
Licenses: LGPL 3
Synopsis: Interface for reading binary IGOR files
Description:

Python parser for Igor Binary Waves (.ibw) and Packed Experiment (.pxp) files written by WaveMetrics' IGOR Pro software.

python-mne-qt-browser 0.7.2
Propagated dependencies: python-darkdetect@0.8.0 python-matplotlib@3.8.2 python-mne@1.10.2 python-numpy@1.26.4 python-pyopengl@3.1.9 python-pyqtgraph@0.13.7 python-qdarkstyle@3.2.3 python-qtpy@2.4.3 python-scipy@1.12.0 python-scooby@0.5.12
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://mne.tools
Licenses: Modified BSD
Synopsis: Backend based on pyqtgraph for the 2D-Data-Browser in MNE-Python
Description:

This package provides a new backend based on pyqtgraph for the 2D-Data-Browser in MNE-Python.

python-bycycle 1.2.0
Propagated dependencies: python-matplotlib@3.8.2 python-neurodsp@2.3.0 python-numpy@1.26.4 python-pandas@2.2.3 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/electrophysiology.scm (guix-science packages electrophysiology)
Home page: https://bycycle-tools.github.io/
Licenses: ASL 2.0
Synopsis: Cycle-by-cycle analyses of neural oscillations
Description:

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.

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