Reverse Dependencies of ipywidgets

The following projects have a declared dependency on ipywidgets:

• maquinas — Formal languages and automata library
• marcopolo-pytorch — MarcoPolo: a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering
• mariqt-widgets — Contains widgets commonly used by MarIQT Notebooks.
• markers — A data labeling widget for use with Markers.ai
• market-analy — Analysis of exchange-listed financial instruments
• Marketingtool — A tool module to help you do marketing
• marketpsych — Python libraries for working with MarketPsych's feeds
• MarkovAnalyzer — Automate Markov Process Characterization
• MarsGT — MarsGT: A Python library for rare cell identification (Internal testing only)
• masspy — MASSpy is a package for dynamic modeling of biological processes.
• massspecgym — MassSpecGym: A benchmark for the discovery and identification of molecules
• mastermind — Play mastermind with python !
• matcha-tts — 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
• material-slider — Material-ui slider widgets.
• materials-visualization — Routines to visualize atomic models in jupyter
• matgraphdb — Welcome to MatGraphDB, a powerful Python package designed to interface with primary and graph databases for advanced material analysis.
• Mathics3 — A general-purpose computer algebra system.
• mathstrovehiclesim — A short description of your package
• matplotlib-radar — Radar chart for matplotlib
• mavenworks — Dashboarding for JupyterLab
• maxrf4u — Explore your X-Ray Fluorescence spectral images
• mbbefd — Univariate and bivariate MBBEFD distributions
• mbs — Utilities for MB Scientific analyzer data
• mcescher — Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Metabolic Pathways
• MCPoly — Useful tools for Computational Chemistry for polymers
• mdciao — mdciao: Accessible Analysis and Visualization of Molecular Dynamics Simulation Data
• mdml — Application of Deep learning on molecular dymanamics trajectories
• mdonatello — 2D small molecule visualization for MDAnalysis
• medcat — Concept annotation tool for Electronic Health Records
• MedicalMultitaskModeling — Multitask learning framework for medical data
• medimage-pkg — MEDimage is a Python package for processing and extracting features from medical images
• meerkat-ml — Meerkat is building new data abstractions to make machine learning easier.
• megrim — A bioinformatics tutorial framework for epi2me-labs
• membrane-curvature — MDAnalysis tool to calculate membrane curvature from MD simulations.
• memocell — Bayesian inference of stochastic cellular processes with and without memory in Python.
• meow-sim — Modeling of Eigenmodes and Overlaps in Waveguide Structures
• mesagrid — Parse grids of MESA tracks and models
• meshql — query based meshing on top of GMSH
• mess-jax — MESS: Modern Electronic Structure Simulations
• metagentorch — Set of tools used for metagenomics research project, using keras and pytorch
• metagpt — The Multi-Agent Framework
• metagpt-simple — The Multi-Agent Framework
• metanetx-sdk — Parse and clean up information from MetaNetX (https://metanetx.org).
• metapal — Experiments regarding LLM components
• metodos-numericos-dcb-fi — Biblioteca con las utilidades necesarias para ejecutar los jupyter notebooks de métodos numéricos.
• metro-sp-mdp — no summary
• mexca — Emotion expression capture from multiple modalities.
• mi-property-analyser — First package evs
• microfilm — Creating figures and animations for multi-channel images with a focus on microscopy.
• miffi — cryo-EM micrograph filtering utilizing Fourier space information
• mig-meow — MiG based manager for event oriented workflows
• mileva — Python toolkit for symbolic calculations in general relativity.
• mimicri — Masking and mixing for interactive counterfactual recombined images
• mimikit — Python package for generating audio with neural networks
• mindlab — Data science toolbox
• miniscope-io — no summary
• minitrade — A personal automated trading system
• misas — Model Interpretation through Sensitivity Analysis for Segmentation
• mitosheet2 — The Mito Spreadsheet
• mix-mavis — MAVIS 数据分析工具
• ML-Navigator — ML-Navigator is a tutorial-based Machine Learning framework. The main component of ML-Navigator is the flow. A flow is a collection of compact methods/functions that can be stuck together with guidance texts.
• ml4a — A toolkit for making art with machine learning, including an API for popular deep learning models, recipes for combining them, and a suite of educational examples
• ml4h — Machine Learning for Health python package
• mlcopilot — Assistant for data scientists and machine learning developers.
• mlworkflow — A workflow-improving library for manipulating ML experiments
• mmfutils — Small set of utilities: containers and interfaces.
• mmtfPyspark — Methods for parallel and distributed analysis and mining of the Protein Data Bank using MMTF and Apache Spark
• mobilechelonian — Turtles in the Jupyter Notebook
• mockish — A thin layer of sugar atop Python's mock.
• model-sketch-book — A package for sketching ML models
• model-validation — Model validation tools
• modelbase — A package to build metabolic models
• ModelFlowIb — A tool to solve and manage dynamic economic models
• modin-spreadsheet — An implementation for the Spreadsheet API of Modin
• modulift — A powerful search tool for discovering Python packages based on user queries
• modulo-vki — MODULO (MODal mULtiscale pOd) is a software developed at the von Karman Institute to perform Multiscale Modal Analysis of numerical and experimental data.
• molfeat — molfeat - the hub for all your molecular featurizers
• molflux — A foundational package for molecular predictive modelling
• mols2grid — Interactive 2D small molecule viewer
• monk-cls-test1 — Monk Classification's Gluoncv backend
• monk-cls-test2 — Monk Classification's Pytorch backend
• monk-cls-test3 — Monk Classification's Tf-Keras backend
• monk-cpu — Monk Classification - CPU - backends - pytorch, keras, gluon
• monk-cpu-test — Monk Classification - CPU - backends - pytorch, keras, gluon
• monk-cuda100 — Monk Classification Library - Cuda100 - backends - pytorch, keras, gluon
• monk-cuda100-test — Monk Classification Library - Cuda100 - backends - pytorch, keras, gluon
• monk-cuda101 — Monk Classification Library - Cuda101 - backends - pytorch, keras, gluon
• monk-cuda101-test — Monk Classification Library - Cuda101 - backends - pytorch, keras, gluon
• monk-cuda102 — Monk Classification Library - Cuda102 - backends - pytorch, keras, gluon
• monk-cuda102-test — Monk Classification Library - Cuda102 - backends - pytorch, keras, gluon
• monk-cuda90 — Monk Classification Library - Cuda90 - backends - pytorch, keras, gluon
• monk-cuda90-test — Monk Classification Library - Cuda90 - backends - pytorch, keras, gluon
• monk-cuda92 — Monk Classification Library - Cuda92 - backends - pytorch, keras, gluon
• monk-cuda92-test — Monk Classification Library - Cuda92 - backends - pytorch, keras, gluon
• monk-gluon-cpu — Monk Classification - CPU - backends - mxnet-gluon
• monk-gluon-cpu-test — Monk Classification - CPU - backends - mxnet-gluon
• monk-gluon-cuda100 — Monk Classification Library - Cuda100 - backends - mxnet-gluon
• monk-gluon-cuda100-test — Monk Classification Library - Cuda100 - backends - mxnet-gluon
• monk-gluon-cuda101 — Monk Classification Library - Cuda101 - backends - mxnet-gluon
• monk-gluon-cuda101-test — Monk Classification Library - Cuda101 - backends - mxnet-gluon