NumFOCUS promotes open source scientific software.

NumFOCUS supports and promotes world-class, innovative, open source scientific software. Most individual projects, even the wildly successful ones, find the overhead of a non-profit to be too large for their community to bear. NumFOCUS provides a critical service as an umbrella organization for these projects.

The following projects will be participating under the NumFOCUS umbrella:

  • conda-forge - A community led collection of recipes for the conda package manager.
  • Data Retriever - The Data Retriever is a package manager for data. It downloads, cleans, and stores publicly available data, so that analysts spend less time cleaning and managing data, and more time analyzing it.
  • FEniCS Project - The FEniCS Project is a collection of tools with extensive features for the automated and efficient solution of differential equations. Partial differential equations can be specified in near-mathematical notation (as finite element variational problems) and solved automatically.
  • Gensim - Gensim is an open-source Python library for topic modelling, document indexing and similarity retrieval with large corpora.
  • matplotlib - matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
  • MDAnalysis - MDAnalysis is an object-oriented python toolkit to analyze molecular dynamics trajectories generated by CHARMM, Gromacs, Amber, NAMD, or LAMMPS.
  • nteract - nteract allows users to edit and share interactive notebook documents that contain explanatory text, executive code, and interactive visualizations.
  • PyMC3 - PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.
  • Stan - Stan is a probabilistic programming language for data analysis, enabling automatic inference for a large class of statistical models.

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  • python
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  • r
  • c/c++

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NumFOCUS 2017 Projects

  • Harshit Patni
    2d Color Maps
    All of the color mapping in Matplotlib is currently derived fromScalerMappable which as the name suggests maps scalers from R^1 ->R^4 RGBA color...
  • Ivan Yashchuk
    Develop assembly of finite element forms on quadrilateral and hexahedral meshes [FEniCS]
    One of the first steps in the finite element method (FEM) is splitting the domain on which the partial differential equation (PDE) is solved into...
  • Michal Habera
    Develop XDMF format for visualisation and checkpointing
    XDMF is a file format which is designed for very large simulation datasets. The main file is XML, but there is provision for the "heavy data" to be...
  • Maxim Kochurov
    Extend Variational Inference Methods in PyMC3
    Variational inference is a great approach for doing really complex, often intractable Bayesian inference in approximate form. Common methods (e.g....
  • Chinmaya Pancholi
    Gensim : Gensim integration with scikit-learn and Keras
    Gensim[1] is a topic-modeling package in Python for unsupervised learning. This implies that to be able to usefully apply it to a real business...
  • katiec1029
    Matplotlib Serialization & PythreeJS
    Serialization is the process in which a data structure or object is translated and stored in the format of a file compatible in memory then...
  • Utkarsh Bansal
    Port to pytest
    Testing is crucial to the development of any software and MDAnalysis currently uses nose to test their code. Unfortunately, nose is no longer under...
  • Shivam Negi
    Python and Julia Interface for Data Retriever
    Data Retriever automates the tasks of finding, downloading, and cleaning up publicly available data, and then stores them in a local database or as...
  • Bill Engels
    Single Precision Support and Gaussian Process Functionality
    PyMC3 contains a rich suite of building blocks for probabilistic modeling and inference. This proposal contains two parts: first is finishing...
  • Parul Sethi
    Training and Topic Visualizations
    Knowing about the progress and performance of a model, as we train them, could be very helpful in understanding it’s learning process and makes it...
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2017