Upcoming

[22, 23 Nov 2019] NumPy developer sprint

Several NumPy core developers, the BIDS NumPy staff, and possibly some of the documentation team will be meeting to:

  • refresh the NumPy roadmap

  • discuss DuckArray protocol/dispatching

  • review the dtype refactoring

  • revamp the MacPython/numpy-wheels repo

  • work on reviewing/closing PRs and issues.

and more.

Notes: https://github.com/numpy/archive/blob/master/sprints/2019-11-22.md

Location: Berkeley Institute for Data Science

[15 Oct 2019] NumPy “Spring Cleaning” sprint

At this virtual sprint, we will close as many PRs and issues as we can.

Past Sprints

[10, 11 May 2019] NumPy developer sprint

Meeting notes

Several NumPy core developers will be meeting to review the new random number generation system API, plan the dtype refactoring, and work on reviewing/closing PRs and issues.

Agenda: https://hackmd.io/OtUbEI_4T06noPYtlLp-4w

Location: Berkeley Institute for Data Science

[30 Nov, 1 Dec 2018] NumPy developer sprint

Meeting notes

We met and discussed NumPy’s roadmap, low-level NumPy-like libraries, data type refactoring, and type annotations.

[28 May–2 June 2018] Joint scikit-learn, scikit-image, dask sprint

scikit-learn and scikit-image are two of the major scientific Python toolbox, enabling data-driven discoveries. The first one proposes simple yet efficient tools for data mining and data analysis, while the latter focuses on image processing algorithms. With the flow of data being processed and analysed, these two libraries face unprecedent scalability challenges.

One currently under-utilized avenue for solving such scalability challenge is to leverage the Python library Dask, which provides flexible parallelized NumPy and Pandas DataFrame, the core numerical objects used in Scientific Python. Our goal is thus to organize a sprint bringing together a small number of developers from scikit-learn, scikit-image, and Dask to experiment and improve the three libraries.

Repository for ideas: https://github.com/scisprints/2018_05_sklearn_skimage_dask

Dates: May, 28th to June 2nd, 2018

Location: Monday: Evans, Tue–Fri: Berkeley Institute for Data Science

[24, 25 May 2018] NumPy developer sprint

NumPy is the fundamental numerical package for scientific computing in Python. It is a Python library that provides a multidimensional array object, and an assortment of routines for fast operations on arrays. While useful on its own, the array object is the core data structure for many packages in the Python landscape, including Pandas, OpenCV’s python bindings, and deep learning frameworks such as TensorFlow and PyTorch.

NumPy is managed by a steering committee, and run by a group of developers who rarely meet in person. The stars have aligned, and a group of the steering committee/core developers will be in Berkeley for two days.

We will discuss and maybe even resolve some of the thornier open pull requests and issues, set some short term goals, and better define deeper issues that need more community input.

Dates: May 24-25, 2018 Location: Berkeley Institute for Data Science

[29 - 30 March, 2018] Matplotlib/GraphXD sprint

Visualizing the structure of graphs is informative when doing network analysis, but currently is not well supported by scientific Python tools. NetworkX is the community standard for representing and analyzing graphs and, while capable of simple visualization, historically has not emphasized this feature in order to avoid additional maintenance burden. Matplotlib, on the other hand, is the predominant plotting library in the Python ecosystem, but has no official support for graph structures.

At GraphXD we have brought together core members of the NetworkX and Matplotlib communities. At the event sprints, we will work together to improve the state of graph visualization in Python. Specifically, we aim to:

  • build a small library in Python that utilizes Matplotlib and NetworkX for visualizing graph structures, and

  • factor out the visualization components of NetworkX into this new library, such that the analytics features of NetworkX remain separate.

We plan for this package to continue growing beyond the GraphXD sprint, and to become a community standard in visualizing graphs with Python.

Dates: March, 28th and 29th, 2018 Location: Berkeley Institute for Data Science