| Sub organizations | IDEAS LIST | Student guides |
NumFOCUS will be applying again as an umbrella mentoring organization for Google Summer of Code 2021. NumFOCUS supports and promotes world-class, innovative, open source scientific software.
NumFOCUS is committed to promoting and sustaining a professional and ethical community. Our Code of Conduct is our effort to uphold these values and it provides a guideline and some of the tools and resources necessary to achieve this.
Google Summer of Code is an annual open source internship program sponsored by Google. This repository contains information specific to NumFOCUS' participation in GSoC. For general information about the competition, including this year's application timeline and key phases involved, please see the GSoC website
This Git repository stores information about NumFOCUS' application for Google Summer of Code in the current and previous years.
Table of Contents
- Students
- Sub Organizations
- Organizations Confirmed Under NumFOCUS Umbrella
- NumFOCUS Projects
- About GSoC
NumFOCUS is participating as a umbrella organization. This means that you will need to identify a specific project to apply to under the NumFOCUS umbrella. (Projects are listed below.)
Read this document to learn how to apply for the GSoC program with NumFOCUS. Please also check out our ideas list.
For any questions, please open an issue in our issue tracker or send a email to [email protected], our mailing list address. Please also consider subscribing to the mailing list at https://groups.google.com/a/numfocus.org/forum/#!forum/gsoc.
If you want to participate as a sub organization of NumFOCUS please read this guide.
In alphabetic order.
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AiiDA is a python framework for managing computational science workflows, with roots in computational materials science. It helps researchers manage large numbers of simulations (1k, 10k, 100k, ...) and complex workflows involving multiple executables. At the same time, it records the provenance of the entire simulation pipeline with the aim to make it fully reproducible. Website | Ideas List | Contact | Source Code |
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ArviZ is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, sample diagnostics, model checking, and comparison. The goal is to provide backend-agnostic tools for diagnostics and visualizations of Bayesian inference in Python, by first converting inference data into xarray objects. |
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CB-Geo MPM is an HPC-enabled Material Point Method solver for large-deformation modeling. It supports isoparametric elements to model complex geometries and creates photo-realistic rendering. |
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Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. It is freely available under the New BSD License terms. Website | Ideas List | Contact | Source Code |
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CuPy is an open-source matrix library accelerated with NVIDIA CUDA. It also uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture. Website | Ideas List | Contact | Source Code |
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Dask is a flexible parallel computing library for analytics. Website | Ideas List | Contact | Source Code |
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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. |
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equadratures is an open-source library for uncertainty quantification, machine learning, optimisation, numerical integration and dimension reduction – all using orthogonal polynomials. |
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GeoPandas is an open source project to make working with geospatial data in Python easier, focusing on tabular vector data. |
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Gridap is a new generation, open-source, finite element (FE) library implemented in the Julia programming language. Gridap aims at adopting a more modern programming style than existing FE applications written in C/C++ or Fortran. |
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JuMP is a modeling language and supporting packages for mathematical optimization in Julia. JuMP makes it easy to formulate and solve linear programming, semidefinite programming, integer programming, convex optimization, constrained nonlinear optimization, and related classes of optimization problems. Website | Developers chat on Gitter | Ideas Page | Source Code |
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LFortran is a modern open-source (BSD licensed) interactive Fortran compiler built on top of LLVM. It can execute user’s code interactively to allow exploratory work (much like Python, MATLAB or Julia) as well as compile to binaries with the goal to run user’s code on modern architectures such as multi-core CPUs and GPUs. Website | Developers chat on Zulip | Ideas Page | Source Code |
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Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
Website | Gitter | Discourse | Ideas Page | Source Code |
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NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Website | Ideas Page | Contact (GitHub Discussions) | Source Code |
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Optuna is an open source hyperparameter optimization framework to automate hyperparameter search. Optuna provides eager search spaces for automated search for optimal hyperparameters using Python conditionals, loops, and syntax, state-of-the-art algorithms to efficiently search large spaces and prune unpromising trials for faster results, and easy parallelization for hyperparameter searches over multiple threads or processes without modifying code. Website | Developers chat on Gitter | Ideas Page | Source Code |
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pvlib python provides a set of functions and classes for simulating the performance of photovoltaic energy systems. |
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PyBaMM (Python Battery Mathematical Modelling) solves physics-based electrochemical DAE models by using state-of-the-art automatic differentiation and numerical solvers. Website | Contact | Ideas Page | Source Code |
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PyMC3 is a python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems. |
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PySAL is an open source cross-platform library for geospatial data science. It supports many different areas of statistics and geographical analyses, such as the detection of spatial clusters, hotspots, and outliers; the construction of graphs from geographic data; Bayesian and Maximum Likelihood spatial regression and statistical modelling for geographical networks; spatial econometrics; space-time Markov modelling; and distribution dynamics for segregation and inequality. Website | Contact (Gitter chat room) | Ideas Page | Source Code |
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PyTorch-Ignite is a high-level library to help with training neural networks in PyTorch Website | Discord | GitHub Discussions | Ideas Page | Source Code |
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QuTiP is a software for simulating quantum systems. QuTiP aims to provide tools for user-friendly and efficient numerical simulations of open quantum systems. It can be used to simulate a wide range of physical phenomenon in areas such as quantum optics, trapped ions, superconducting circuits and quantum nanomechanical resonators. In addition, it contains a number of other modules to simplify the numerical simulation and study of many topics in quantum physics such as quantum optimal control, quantum information, and computing. Website | Contact | Ideas Page | Source Code |
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SciML is an open source software organization created to unify the packages for scientific machine learning. This includes the development of modular scientific simulation support software, such as differential equation solvers, along with the methodologies for inverse problems and automated model discovery. By providing a diverse set of tools with a common interface, we provide a modular, easily-extendable, and highly performant ecosystem for handling a wide variety of scientific simulations. |
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Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business. Users specify log density functions in Stan’s probabilistic programming language and get: 1) full Bayesian statistical inference with MCMC sampling (NUTS, HMC). 2) approximate Bayesian inference with variational inference (ADVI) 3) penalized maximum likelihood estimation with optimization (L-BFGS). Stan’s math library provides differentiable probability functions & linear algebra (C++ autodiff). Additional R packages provide expression-based linear modeling, posterior visualization, and leave-one-out cross-validation. |
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