Promoting open practices in research, data, and scientific computing.

NumFOCUS is a 501(c)(3) nonprofit organization that promotes open practices in research, data, and scientific computing. We accomplish this mission by serving as a fiscal sponsor for open source projects and organizing community-driven educational programs. NumFOCUS currently sponsors over two dozen open source projects including well-known and widely used tools such as Project Jupyter, NumPy, Pandas, Matplotlib, and Julia. (https://numfocus.org/sponsored-projects/) We also organize the popular PyData network of educational events and meetups. (https://pydata.org)

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NumFOCUS 2019 Projects

  • Aniruddha Banerjea
    Apply Numba Project Wide
    Numba is a JIT(just in time) compiler that compiles a given section of the code(specified by the user) at a given time instead of compiling the...
  • Juan Martín Loyola
    Bayesian Additive Regression Trees in PyMC3
    Bayesian Additive Regression Trees (BART) is a Bayesian nonparametric approach to estimating functions using regression trees. A BART model consist...
  • Ninad Bhat
    Better Periodic Boundary Handling
    Molecular simulations are predominantly ran under periodic boundary conditions, i.e., upon leaving one face of the simulation volume, you re-enter in...
  • Piyush Raikwar
    CuPy automatic fallback to NumPy
    NumPy is the fundamental and most widely used library in Python for scientific computation. But it is executed over CPU only. So, we have CuPy with...
  • harshitbansal05
    Data Retriever: Extract Scripts into Separate System
    The Data Retriever is a package manager for data. The Data Retriever automatically finds, downloads and pre-processes publicly available datasets and...
  • Saumya Biswas
    Development of Qutip Functions for analysis of Bosonic/Fermionic Lattices
    The following functionality is aimed at: Choice of Model (lattice dimensionality, Boson/Fermion, Hubbard, XXZ, Boundary conditions etc.)....
  • Arpit Bhatia
    Ecosystem Improvements and a JuMPTutorials.jl Package
    Ecosystem issues such as improvements to tutorials and examples are an important milestone on the road map to JuMP 1.0. This project aims to improve...
  • Naresh Bachwani
    Effect Plot and PCA Visualizer
    YellowBricks is an open source python data visualization library aiding both exploratory data analysis and machine learning tasks. As my Google...
  • Ivan Yashchuk
    Expand ChainerX Ops: Differentiable Linear Algebra
    ChainerX is a versatile ndarray implementation with special support of deep learning-specific operations. Therefore, it is important to support many...
  • Rishav Chourasia
    Extending Elichika & ch2o: Parsing jump statements and more
    Elichika and ch2o are python to ONNX experimental compilers for Chainer ML framework (Elichika would eventually replace ch2o). Given a Chainer model,...
  • Abhinav Gupta
    GMsh/XDMF/DOLFIN mesh processing pipeline
    Finite element methods require a discretization of a domain into small elements, called a mesh. Typically, users of DOLFIN use an external mesh...
  • Oriol Abril Pla
    Information criteria and convergence assessment tools for ArviZ
    ArviZ is a Python package for exploratory analysis of Bayesian models, from diagnostics to visualization. It is designed as a backend-agnostic tool...
  • Igor Almeida Baratta
    Interface to KaHIP partitioner
    One of the main ingredients in DOLFIN’s native support of parallel computations is the mesh partitioner. The mesh partitioner seeks to ensure a load...
  • Guilherme Bodin
    JuMP Automatic Dualization
    We will implement a MathOptInterface feature that allows JuMP to take an optimization problem in its primal form and return the dual form in terms of...
  • Demetri Pananos
    Ordinary Differential Equations
    Parameter estimation and uncertainty propagation are salient aspects of applied dynamical systems of practical interest. Both parameter estimation...
  • Boxi Li
    QuTiP Project: Noise Models in QIP Module
    QuTiP is best known for solving open quantum system dynamics. At the same time, it also has a Quantum Information Processing (QIP) submodule...
  • Aniket Didolkar
    Recurrent Neural Networks for ChainerX
    In this project, I propose to add Recurrent Neural Networks to ChainerX. RNNs are a very integral part of deep learning research. ChainerX is faster...
  • Apoorva Pandey
    Retriever Provenance
    The Data Retriever is a package manager for data. It downloads, cleans, and stores publicly available data, so that analysts spend less time cleaning...
  • Joseph Willard
    Symbolic PyMC and PyMC4 Integration
    The pymc project is an open source collaboration that focuses on providing Bayesian modeling as well as probabilistic machine learning. The current...
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2019