Reverse Dependencies of ipywidgets

The following projects have a declared dependency on ipywidgets:

• sleap — SLEAP (Social LEAP Estimates Animal Poses) is a deep learning framework for animal pose tracking.
• sleepeegpy — Sleep EEG preprocessing, analysis and visualization
• slicevis — A package for interactive slice visualization.
• slitflow — A Python framework for single-molecule dynamics and localization analysis
• SLKB — Analysis pipeline for Synthetic Lethality Knowledge Base
• slmat — ServerLess Materials Design
• smart-progress — Smart progressbar with multiple backends selected depending on the environment
• SmartAnno — A smart snippet annotation tool with deep learning backbone.
• smilei-slider — A package for showing sliders for smilei
• smpl-animation — SiMPLe plotting and fitting
• smpr3d — smpr3d is a toolkit for 3D reconstruction from scanning diffraction data
• snippetlib — Jupyter Notebook Menu Extension.
• Snowballing — Provides tools for literature snowballing
• soft-comp — Soft-Computing algorithms and helpers for lecture and exercises - Intelligent Embedded Systems (IES), University of Kassel
• softlab — Toolkit to build software-defined laboratory
• sokort — CEMC Graphics Script Tool.
• solara-ipyantd — React for ipywidgets that just works
• solara-ui — no summary
• somap — Self-Organizing Maps in python
• soniscope-jupyter — jupyter notebook widget with a scatter plot and an interactive lens to enable interactive sonification
• sonivis-lens-widget — jupyter notebook widget with a scatter plot and an interactive lens
• sound-analyzer-encoder — Sound analyzer and encoder
• soundata — Python library for loading and working with sound datasets.
• soupernova — Make viewing stars a blast
• sovai — python for asset management
• spacr — Spatial phenotype analysis of crisp screens (SpaCr)
• spacy-annotator — SpaCy annotator for Named Entity Recognition (NER) using ipywidgets.
• spadsim — This is a 2D Monte Carlo simulator written in Python to model the operation of single-photon avalanche detectors. It simulates the stochastic avalanche multiplication process of charge carriers following the absorption of an input photon; a successful detection event is defined as the avalanche current exceeding a pre-defined threshold. The simulator output can be used to analyse the photon detection efficiency and its timing characteristics. This program does not directly simulate dark noise.
• spamdfba — no summary
• spanner-graph-notebook — Visually query Spanner Graph data in notebooks.
• sparclet — A python library to generate and interact with flatmaps in a jupyter notebook using the ipyleaflet library.
• spark-datax-tools — spark_datax_tools
• sparseml — Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
• sparseml-nightly — Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
• sparsezoo — Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
• sparsezoo-nightly — Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
• spateo-release — Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics
• spaTrack — an algorithm that combine both gene expression and spot location to inference cell trajectory
• specidentify — Specidentify is for identifying spectral lines and calibrating spectroscopic observations
• specification-curve — Specification_Curve
• specsanalyzer — Python package for loading and converting SPECS Phoibos analyzer data.
• spectHR — HRApp: An Interactive Heart Rate Variability (HRV) Analysis Tool
• spectrafit — Fast fitting of 2D- and 3D-Spectra with established routines
• speechtotext-python — Python package to benchmark speech2text models.
• speechwidgets — A library with Jupyter widgets for speech processing
• speedy-utils — Fast and easy-to-use package for data science
• spgrep-modulation — Collective atomic modulation analysis with irreducible space-group representation
• spherpro — Tool to analize tumor spheroid data
• sphinx-book-theme — A clean book theme for scientific explanations and documentation with Sphinx
• sphynxml — no summary
• spikeinterface — Python toolkit for analysis, visualization, and comparison of spike sorting output
• splot — Visual analytics for spatial analysis with PySAL.
• splotch-st — Splotch is a hierarchical generative probabilistic model for analyzing Spatial Transcriptomics (ST) data
• spotify-confidence — Package for calculating and visualising confidence intervals, e.g. for A/B test analysis.
• spotify-to-sqlite — Convert a Spotify export zip to a SQLite database
• spotter — Forward models of fluxes and spectra time-series of non-uniform stars.
• spsdk — Open Source Secure Provisioning SDK for NXP MCU/MPU
• sqlmesh — no summary
• sqlmesh-cube — SQLMesh extension for generating Cube semantic layer configurations
• sqltrack — SQL-based experiment tracking
• squidpy — Spatial Single Cell Analysis in Python
• ssb-fagfunksjoner — Fellesfunksjoner for ssb i Python
• ssb-ipython-kernels — Jupyter kernels for working with dapla services
• ssb-klass-python — A Python package built on top of KLASS's API for retrieving classifications, codes, correspondences etc.
• ssbio-edbr — Tools to enable structural systems biology, forked from SBRG/ssbio
• sssm — a wrapper of sssm
• sst-crispy — A Python package for using data from the Swedish 1 m Solar Telescope's CRisp Imaging SpectroPolarimeter instrument.
• st-speckmol — Streamlit component for for Speck molecule visualization.
• stablepy — A tool for easy use of stable diffusion
• stac-nb — stac-nb exposes STAC in Jupyter Notebooks
• stackstac — Load a STAC collection into xarray with dask
• stackview — Interactive image stack viewing in jupyter notebooks
• stadiffuser — A versatile simulation framework for spatial transcriptomics data
• stapesai-ssi — This project builds upon Whisper and VAD systems to provide plug and play solutions (FastAPI router) that can be easily included in any AI Assistant type project to have Streaming ASR in their application.
• star3d — STAR-3D (STardist-based network for AnisotRopic 3D images) is a trained StarDist-3D-based network for 3D nucleus segmentation in highly anisotropic images.
• starspotter — Stellar contamination estimates from rotational light curves
• stats-n-plots — Computation and visualization of statistical analyses common in the Life sciences made easy
• StatWrap — A package for people new to statistics and Python.
• stellarphot — A package for performing stellar photometry.
• stimupy — Stimuli for vision science, as image arrays
• stmol — Streamlit component for molecular visualization
• stochasticx — Stochastic client library
• stomatadetector — Stomata detection from FLEX/TIFF fluorescent images
• stonkgs — Sophisticated Transformers for Biomedical Text and Knowledge Graph Data
• stormvogel — User-friendly frontend to the Storm model checker
• stratmc — Bayesian statistical framework for reconstructing past Earth system change from the stratigraphic record
• stratosphere — A lightweight experimentation toolkit for data scientists.
• streaming-jupyter-integrations — JupyterNotebook Flink magics
• streamlit-jupyter — Simple Python package to preview and develop streamlit apps in jupyter notebooks
• streamlit-tfx — TensorFlow Extended visualizers for Streamlit apps
• streamui — no summary
• streetviewdownloader — A Python library and utility to download Google StreetView images
• stripnet — STriP Net: Semantic Similarity of Scientific Papers (S3P) Network
• student-success-tool — School-agnostic lib for implementing Student Success Tool workflows.
• stuned — Utility code from STAI (https://scalabletrustworthyai.github.io/)
• sugardata — Generates synthetic datasets tailored for transformer-based models
• summony — Collection of tools and UIs for chatting and experimenting with multiple LLMs (via their APIs) at the same time.
• super-rubik-cube — Library for simulating rubik's cubes of different sizes (N x N), for solving 2 x 2 and 3 x 3 cubes with kociemba algorithm, and for 3D visualization of cubes.
• supergraph — Supergraph compiler
• superintendent — Interactive machine learning supervision.