2024 Sponsored Project Updates

Python Software Foundation

October

New release of the Python Language

Python 3.13.0 is now available. This is the stable release of Python 3.13.0.

Python 3.13.0 is the newest major release of the Python programming language, and it contains many new features and optimizations compared to Python 3.12. (Compared to the last release candidate, 3.13.0rc3, 3.13.0 contains two small bug fixes and some documentation and testing changes.)

Major new features of the 3.13 series, compared to 3.12

Some of the new major new features and changes in Python 3.13 are:

New features

Typing

Removals and new deprecations

  • PEP 594 (Removing dead batteries from the standard library)

scheduled removals of many deprecated modules: aifc, audioop, chunk, cgi, cgitb, crypt, imghdr, mailcap, msilib, nis, nntplib, ossaudiodev, pipes, sndhdr, spwd, sunau, telnetlib, uu, xdrlib, lib2to3.

  • Many other removals of deprecated classes, functions and methods in various standard library modules.

  • C API removals and deprecations: Some removals present in alpha 1 were reverted in alpha 2, as the removals were deemed too disruptive at this time.

  • New deprecations, most of which are scheduled for removal from Python 3.15 or 3.16.

For more details on the changes to Python 3.13, see What’s new in Python 3.13.

More resources

We hope you enjoy the new releases!

Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself or through organization contributions to the Python Software Foundation.

Sponsored Projects

Astropy

November

Astropy released version 7.0, in addition to new features. This version also includes numerous enhancements and bug fixes. Some of the new features improve compatibility within astropy sub packages and with other packages in the ecosystem, such as storing coordinate frames in tables, making astropy Quantity compatible with NumPy constructor functions, or improving compatibility with dask. Other features improve speeds or ease of use for existing astropy functionality.

Over 50 people have contributed code to this release; 20 of them for the first time. Thanks for all the community support!

Blosc

February

  • First public release of Caterva2, a new distributed system written in Python meant for sharing Blosc2 datasets among different hosts by using a publish–subscribe messaging pattern. Docs are over here: https://www.blosc.org/Caterva2/index.html. Feedback is welcome!

  • New release of blosc2-grok plugin allowing to encode images with JPEG 2000. High-quality lossy compression arrives at Blosc2. We have blogged about it: https://www.blosc.org/posts/blosc2-grok-release/

  • Maintenance releases for C-Blosc2 2.13.2 and Python-Blosc2 2.5.1. Among other things, the new versions allow the loading of Blosc2 plugins dynamically, without user intervention (other than installing the plugins.

June

Here are some announcements that we want to share with the community (we have been quite busy with some of them lately):

  • The Blosc development team is pleased to announce the first beta release of Python-Blosc2 3.0.0. We have been working hard to provide a new evaluation engine (based on numexpr) for NDArray instances, and we would like to get feedback from the community before the final release. More info at: https://github.com/Blosc/python-blosc2/blob/main/ANNOUNCE.rst

  • C-Blosc2 2.15.0 has been released. This time we added a new `io` mode for memory map files. This brings even more I/O performance, especially for reading compressed data on disk. More info: https://github.com/Blosc/c-blosc2/releases

  • numexpr 2.10.1 is out. Here, we are consolidating NumPy 2 support, fixing some bugs, preliminary support for Python 3.13, and modernizing the installation procedure. Even if small, numexpr has become an important cornerstone for reaching great performance in packages like pandas, PyTables, Blosc2 and many others. See: https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst

Enjoy data!

November

Announcing C-Blosc2 2.15.2: A fast, compressed and persistent binary data store library for C.

What is new?

This is a maintenance release in which we are fixing some issues that have been reported by the community. The most relevant changes are:

  • Support wasm32 by disabling the ZLIB WITH_OPTIM option. Thanks to Miles Granger.

  • Added support for nvcc (NVidia Cuda Compiler) in CMake. Thanks to @dqwu.

  • Fix public include directories for blosc2 targets. Thanks to Dmitry Mikushin.

For more info, please see the release notes at: https://github.com/Blosc/c-blosc2/blob/main/RELEASE_NOTES.md

What is it?

Blosc2 is a high-performance data container optimized for binary data. It builds on the shoulders of Blosc, the high-performance meta-compressor. Blosc2 is the next generation of Blosc, an `award-winning` library that has been around for more than a decade. Blosc2 expands the capabilities of Blosc by providing a higher lever container that is able to store many chunks on it (hence the super-block name). It supports storing data on both memory and disk using the same API. Also, it adds more compressors and filters.

Download sources

The GitHub repository is over here: https://github.com/Blosc/c-blosc2Blosc is distributed using the BSD license; see LICENSE.txt for details.

Mailing list

There is an official Blosc mailing list at: blosc@googlegroups.com

Twitter feed

Please follow @Blosc2 to get informed about the latest developments.

Enjoy Data!- The Blosc Development Team

Bokeh

May

conda

October

We’re excited to share updates on the latest conda releases and an important upcoming changes in channel management.

Conda September 2024 Releases: conda 24.9.0 and 24.9.1 have been released to both main and conda-forge channels.

Announcement: Following feedback from conda users about the pre-configuration of the conda code base to favor channels from Anaconda Inc., we’ve started the process to deprecate hardcoding Anaconda’s channels as the default set of channels in the conda source code, which is a remnant of conda’s incubation at the company.

In the future, we will rely on providers of conda distributions, such as miniforge or Anaconda (including miniconda), to pre-configure their preferred channels, e.g. by running the necessary conda config — set channels command.

We’ll also continue to work on improving channel management in the foreseeable future and would love your feedback.

Learn more in our release blog post.

CuPy

June

We have released CuPy v13.2.0!

Please refer to the release note:

https://github.com/cupy/cupy/releases/tag/v13.2.0

August/September

We have just released CuPy v13.3.0! Refer to the release note for details:

https://github.com/cupy/cupy/releases/tag/v13.3.0

We opened a new RFC issue on removing NumPy Fallback Mode in CuPy v14. If you have any comments or use cases using this feature, please react/join the discussion at https://github.com/cupy/cupy/issues/8497.

Dask

February

  • Dask support for pandas 2.2. Pandas 2.2 includes improvements that rely on the Apache Arrow ecosystem, like better PyArrow support. Dask is compatible with versions >= 2024.1.1. See the release notes.

  • Dask with query optimization covers most of the Dask DataFrame API. Logical query planning for core Dask DataFrame is expected soon. More details in the project GitHub repo

  • Partial rechunks for P2P rechunking. This benefits workloads requiring “local” rechunks, like rechunking squares to different squares. See the release notes.

New Blog Posts

March

  • Query planning for Dask DataFrame. Dask DataFrame is now more performant and reliable since optimizations like predicate pushdown and column filtering are applied automatically. Query planning is enabled by default for Dask >= 2024.3.0. See the GitHub issue.

May

Efficient joins for Dask DataFrame. Dask DataFrame avoids unnecessary data shuffling, an expensive operation, for merges and groupby aggregations for Dask >= 2024.4.2. See the release notes.

New Blog Posts

  • Example data pipeline with Prefect, Delta Lake, and Dask. Runnable example of a lightweight, scalable data pipeline that runs large Python jobs on a schedule. Read the blog post.

  • How does Dask compare to Spark? In the latest TPC-H benchmark results, Dask is often faster than PySpark both locally and when processing a 10 TB dataset on the cloud. Read the blog post.

June

New machine learning examples. Coiled enhances ML workflows by making it easy to get cloud GPU machines that are automatically configured like your current machine. We’ve added new examples on common use cases like model training, experiment tracking, and interactive development. Learn more.

See more here.

DataFrames at Scale Comparison: TPC-H. We run TPC-H benchmarks on a variety of scales, hardware architectures, and DataFrame projects like Spark, Dask, DuckDB, and Polars. No project wins. Read the blog post.

Dask DataFrame is Fast Now. Dask DataFrame scales out pandas, making it easy to work on hundreds of GBs to TB-scale datasets. Due to a number of performance-focused engineering improvements, Dask is 20x faster. Read the blog post.

See more here.

August/September

It’s easier to get big clusters. Thanks to improvements in adaptive scaling and cross-zone AWS clusters, it’s now much easier to get clusters with 1000s of virtual machines on AWS. See the release notes.

Faster software environment builds. Package sync now uses uv, a faster Rust-powered package resolver, instead of pip for environment builds. This is part of a larger effort to have clusters up and running faster. Learn more about package sync.

Coiled + Arraylake for big geoscience. Arraylake, a cloud data lake platform for managing multidimensional arrays, integrates well with Coiled+Dask. Learn more.

Automatic shutdown of stuck clusters. Sometimes Dask clusters get stuck. But that doesn’t mean your VMs have to stay on. If your cluster is stuck and the scheduler is unresponsive for at least 20 minutes, Coiled will now automatically shut down your VMs for you. Learn more in the release notes.

November

Coiled referral program. Know someone who might have a good use case for Coiled? Receive up to $200 in Amazon gift cards when you refer someone to Coiled by Dec. 31st, 2024. Learn more.

SLURM-style job arrays on the cloud. HPC job scripts are simple and accessible to almost anyone. Oddly, they’re pretty hard to replicate on the cloud, so we replicated the API with Coiled. More in our blog post.

Better performance for Xarray GroupBy.Map. Running GroupBy-Map patterns backed by Dask arrays is common in large-scale geospatial workloads. The latest version of Dask uses a new algorithm for selecting data that’s more robust. Learn more in our blog post.

Legacy Dask DataFrame is being deprecated. Now is a great time to switch to the new DataFrame implementation which includes optimizations like column projection and filter pushdown. Please report any issues that come up. See the release notes.

Dynare

October

Dynare is an open source software platform offering computational tools to handle a wide range of economic models. With its intuitive interface and well-documented features, Dynare simplifies the process of describing economic models and provides academic, professional, and student social scientists with reliable routines to solve, simulate, and estimate them.

FEniCS

November

The FEniCS project has released v0.9.0, with many new features, see:

https://fenicsproject.org/blog/v0.9.0/ for details.

GeoPandas

June

We released GeoPandas 1.0 on Jun 24, 2024

HoloViz

May

What is HoloViz? It is a suite of tools designed to simplify the process of creating interactive visualizations and dashboards, even for large and complex datasets.

Updates:

- Panel 1.4 was released in late March, followed by a few patch releases. Highlights include the addition of a dashboard builder interface that allows building a dashboard layout entirely using a drag-and-drop interface and the creation of tutorials. Check out the release blog post for more!

hvPlot 0.10 was released in early May. Check out the release blog post, which also highlights the features released in version 0.9, including integration with Polars, enhancements to the Explorer low-code interface, enhancements and documentation for large time series analysis, and an improved contributor experience.

August/September

Panel 1.5.0 is finally released. You can get it from PyPI and shortly from conda-forge.

For an overview of the changes in this release see: https://blog.holoviz.org/posts/panel_release_1.5/

And for a full changelog take a look at https://github.com/holoviz/panel/releases/tag/v1.5.0

Many, many thanks to everyone who contributed whether by making PRs or by reporting issues!

October

Please find below the news from the HoloViz project.

We have released Panel 1.5! Check out the announcement blog post to find out more about these new features:

  • Easily create new components: It is now trivially easy to build new JavaScript, React, or AnyWidget-based components with hot-reload, built-in compilation, and bundling. Likewise for Python based widgets, panes and layouts.

  • Native FastAPI integration: We’ve added native support for running Panel apps on a FastAPI server.

  • PY.CAFE support: You can now share your Panel apps online for free, thanks to PY.CAFE

  • New components, improved Chat interface, improved contributor experience, etc.

And we have also released hvPlot 0.11! Together with the addition of a new integration for DuckDB, this version exposes more features from HoloViews like support for displaying subcoordinate y-axis, and new hover options; find out more in the blog post.

Andrew published a blog post on how to incorporate LLMs with data visualizations using HoloViz packages.

ITK

May

  • The Insight Toolkit (ITK) has introduced substantial enhancements in version 5.4 Release Candidate 4: ALL THE DICOMs, focusing on improved DICOM capabilities which are crucial for handling a wide range of medical imaging data. This update includes expanded support for various imaging modalities, particularly in processing and interpreting medical images, which is a significant step forward for ITK’s DICOM applications. With contributions from key community members and the implementation of modern C++ features, ITK continues to advance in usability and performance.

Key highlights from the ITK release:

  • Enhanced DICOM support with expanded modality features and crucial spatial metadata for Secondary Capture images.

  • Introduction of modern C++ support, enabling more efficient coding practices through features like structured bindings for multidimensional data.

  • Improved Python support through Stable ABI Python wheels, facilitating better compatibility and future-proofing for newer Python versions.

  • Encouragement for module developers to migrate to a scikit-build-core *pyproject.toml* file, enhancing the sustainability of ITK modules and support for mac ARM/Apple Silicon Python wheels.

The impact of spatial metadata handling on the NLM Visible Human cryomacrotome anatomic secondary capture images, available in the NIH Imaging Data Commons, when visualized in 3D Slicer.

July

The third Get Your Brain Together Hackathon, held from July 26th to July 28th, 2024, was a dynamic event that brought together over 30 participants from academic, industry, and private research institutions. The hackathon, hosted in a hybrid format at the University of North Carolina-Chapel Hill and online, focused on advancing OME-Zarr spatial transformations. The event featured tutorial sessions on the first day, collaborative review and proposal sessions on the second day, and hands-on implementation activities on the third day. Participants engaged in discussions and activities aimed at enhancing the current coordinate transformations draft and incorporating relevant neuroimaging additions.

One of the notable outcomes of the hackathon was the development of an OME-Zarr Request for Comments (RFC) on Coordinate Transformations and Axis Anatomical Orientation. This RFC aims to standardize spatial transformations in OME-Zarr, promoting reproducibility, integration with analysis workflows, and efficiency in handling large-scale bioimages. The collaborative efforts during the event underscored the importance of having standardized formats and tools to ensure consistency and accuracy across different platforms and applications in the neuroimaging community.

The hackathon’s tutorial sessions, recordings, and materials are now available on the event’s website, providing valuable resources for ongoing and future projects. The hackathon not only fostered innovation and collaboration but also highlighted the community’s commitment to advancing open-source resources and standards that facilitate the discovery of brain structure and function.

Julia

May

JuliaCon 2024 is coming to the PSV Stadium in Eindhoven, Netherlands, between July 9th and 13th. It will be a day of workshops followed by three days of talks and poster presentations. The draft schedule of talks and workshops is now out. Tickets are still available; get yours today!

June

The Julia developers are pleased to announce that the first release candidate for Julia v1.11.0 is now available. You can download binaries using JuliaUp or from https://julialang.org/downloads/ in the “upcoming release” section. Binaries are available for macOS (M-series and Intel), Windows (32- and 64-bit), glibc Linux (x86, x86_64, AArch64, PowerPC), musl Linux (x86_64), and FreeBSD (x86_64).

As a release candidate, 1.11.0-rc1 should not be considered production-ready. Rather, it’s intended to give users, especially package developers, a chance to try out their code with 1.11.0 prior to a full release. Check out the NEWS file to see what will be new in 1.11.0.

Note that 1.11 on Travis, AppVeyor, and Cirrus now refers to 1.11.0-beta2. On GitHub Actions, use ~1.11.0–0.

August/September

We are happy to announce JuliaCon Global 2025 will be held in Pittsburgh, Pennsylvania at the end of July 2025! julialang.org

October

Julia version 1.11.1, the first patch release in the 1.11 series of releases, is now available. Binaries are available via JuliaUp and at https://julialang.org/downloads/ for macOS (Intel and M-series processors), Windows (x86 and x86–64), glibc Linux (x86, x86–64, AArch64, and PowerPC), and FreeBSD (x86–64). Unfortunately, musl Linux binaries are not currently available for this release.

As a patch release, 1.11.1 contains no new features or breaking changes, only bug fixes, documentation improvements, and performance improvements. You can see the list of commits included since 1.11.0 here. We recommend that anyone currently using 1.11.0 upgrade to 1.11.1.

Note that 1.11 on GitHub Actions, Cirrus, Travis, and AppVeyor now refers to 1.11.1.

MDAnalysis

February

Releases

MDAnalysis v2.7.0 is now available on conda-forge and PyPi. A special thanks goes out to our community, especially to our 5 new contributors, as well as to NumFOCUS and the Chan Zuckerberg Initiative for their support!

Additional Announcements

March

Additional Announcements

May

June

  • From June 24 to 25th, MDAnalysis partnered with the Molecular Sciences Software Institute (MolSSI) to offer a free, hybrid workshop titled Moving from User to Developer: Analyzing Molecular Simulations and Building New Tools. All workshop materials are publicly available on a GitHub repository.

  • Registration for the MDAnalysis UGM, taking place August 21–23, 2024, in London, UK, is now open! To stay up-to-date on the event, check the UGM event page, the MDAnalysis blog, and the MDAnalysis socials (LinkedIn, X, and Bluesky).

October

  • Two Google Summer of Code and one Outreachy contributor wrapped up their projects with MDAnalysis; follow the links to read more about the projects on the MDAnalysis blog.

  • The 2024 MDAnalysis UGM (User Group Meeting) was held from August 21st — 23rd in London, United Kingdom. Sixty-five users and developers convened for talks on materials science applications, drug discovery and therapeutics, machine learning and multiscale modeling, and tools for molecular dynamics simulation analysis, as well as to participate in a hackathon event.

  • MDAnalysis hosted a workshop in the CCPBioSim training week taking place at the University of Sheffield during the first week of September. For more information on MDAnalysis workshops hosted throughout 2024, check out this recap blog post.

mlpack

May

  • mlpack was accepted into Google Summer of Code 2024 and has selected 6 students from 68 competitive proposals.

  • mlpack won a NumFOCUS Small Development Grant to continue our work overhauling our documentation, creating application studies, and use case tutorials, and revamping our website.

  • Work is ongoing and making great progress to integrate Bandicoot, a GPU linear algebra library, into mlpack.

July

Great month for mlpack… we won a NASA ROSES grant to fund two mlpack contributors to work on improving the library for spaceflight machine learning applications!

napari

February

  • napari just released version 0.4.19! This is primarily a bug fix release but it was a hard-fought one and fixes many long-standing bugs in addition to featuring some performance, API, and documentation improvements. See the Mastodon thread here and the full release notes here.

  • napari received a targeted grant from the Chan Zuckerberg Imaging Institute to implement instanced rendering. We are looking for a paid short-term contractor to help with that effort, guided by napari and VisPy core developer Lorenzo Gaifas. If you think you might be interested, please get in touch by emailing napari-core-devs@googlegroups.com or by joining us on our Zulip chat room.

  • Finally, if you have experience with polygon triangulation, we can use your help! Napari can’t currently display polygons with holes in them, which severely limits its utility for displaying geodata. That’s been a thorn in our side for some time now so if you have ideas about implementing this correctly we would super-appreciate the help!

July

napari recently released versions 0.5.0 and 0.5.1! 0.5.0 is a *huge* milestone for the project because it involved a significant architectural overhaul and so it was the first release from the main branch in 18 months!

But the dividends paid off immediately with an easy follow-up bugfix release in 0.5.1. See the release notes for 0.5.0 here and those for 0.5.1 here. The napari team is excited to return to a fast-release cadence in the coming weeks and months! Related: Lucy Liu led most of the architecture efforts over that time, with help from team member Draga Doncila Pop, and has now joined the core team!

napari also has received a small grant from the Chan Zuckerberg Imaging Institute to implement instanced mesh rendering for fast rendering of repeating units in a 3D volume. If you have experience with VisPy and/or OpenGL and some room to earn a bit of extra cash in the coming months, please get in touch by writing to napari-core-devs@googlegroups.com! Or just say hello in our Zulip chat room.

OpenMBEE

October

OpenMBEE has officially clicked off the development of the SysML version 2 API.

pandas

August/September

We are pleased to announce the release of pandas v2.2.3.

This is a patch release for the 2.2.x series, which includes some regression fixes and bug fixes. We recommend that all users upgrade to this version.

See the release notes for a list of all the changes.

The release can be installed from PyPI

python -m pip install — upgrade pandas==2.2.3

Or from conda-forge

conda install -c conda-forge pandas==2.2.3

Please report any issues with the release on the pandas issue tracker.

Thanks to all the contributors who made this release possible.

Parsl

August/September

Welcome to our new Sponsored Project: Parsl!

Parsl is a flexible and scalable parallel programming library for Python that enables concurrent execution by annotating functions as tasks, managing dependencies, and efficiently running programs across diverse computing environments from, laptops to supercomputers.

PyBaMM

February

  • We have just released a new version: 24.1. We will publish the release notes on our website shortly.

  • We are taking part in GSoC 2024 under the NumFOCUS umbrella.

May

  • We held a training event at Oxford (UK) with over 50 attendees.

  • We have 3 slots for the upcoming GSoC

  • We got funding approved by The Faraday Institution to support the first PyBaMM conference. It will take place in the UK early next year, more details will follow in the next few weeks.

  • Our website has now been improved, with a better and more accessible theme.

October

Here are some updates from PyBaMM:

The PyBaMM job board is now live (PyBaMM — PyBaMM Jobs Board), where organizations can advertise their battery modeling positions (in exchange for a small fee that will help fund PyBaMM). Thanks to Nolan for assisting us in setting up the payment portal!

November

The first PyBaMM conference taking place next February in London: PyBaMM Battery Modelling Conference.

rOpenSci

July

  • Announcing New Software Peer Review Editors: Beatriz Milz and Margaret Siple

  • A fresh new look for R-universe

  • Resources from the rOpenSci community at useR! 2024

  • Upcoming coworking sessions: “Building your first R package” in August, TBA in September

  • New package {osmapiR}

  • Package news

  • 3 use cases

  • Blog posts

  • Calls for contributions

  • Package Development corner

  • rOpenSci News Digest, July 2024

  • New editors, new look for R-universe, useR! resources, coworking

Recent releases

A fresh new look for R-universe!

You might have noticed that R-universe got a big refresh. Read all about this big overhaul of the interface.

October

Recent release:

Community call recording: Navigating the R ecosystem using R-universe (Video and resources). Learn more about R-Universe and how you can use it to improve your R package development workflow. In this community call, Jeroen Ooms provided details on what R-Universe is and an update on what you can do with it today. He also discussed the future of R-Universe and how it can be used to navigate the R ecosystem.

R-Universe list pages

R-Universe now features a list of all datasets in all packages and a list of all vignettes from CRAN, Bioconductor, and others.

Another important global table is the package scoreboard that lets you explore the scores used by R-Universe search engine for ranking, which is based on:

  • Stars: Number of stars on GitHub;

  • Downloads per month from CRAN or Bioconductor mirrors;

  • Scripts: (NEW) number of files on GitHub that mention ’library(pkgname)’;

  • Dependents: recursive reverse dependencies;

  • Unique contributors;

  • Yearly commits

Resources from rOpenSci training sessions

We’ve added a searchable table of past training sessions to our resources hosted on the rOpenSci website. Explore the full list of our resources.

Materials: Screen Reader Accessible Tools and Resources for Learning and Working with R

  • We have now published the recording of the webinar that walks through learning and using R with screen readers—and it’s bilingual (English and Turkish)! with subtitles in English.

  • Webinar video featuring Liz Hare, PhD, and Alican Cagri Gokcek

  • Also, Liz Hare’s detailed technical note with all the necessary resources to get started is worth reading.

Scientific Python

August/September

We are happy to announce that we are setting up infrastructure for multiple project websites to be translated.

You can read more in this blog post: https://blog.scientific-python.org/scientific-python/translations/

We also have some documentation at https://scientific-python-translations.github.io/

If you are interested in joining this effort, feel free to engage in ⁠the `#translation` channel of the Scientific Python Discord server, where we can provide more information.

See you there! Check out our blog.

Translations for Scientific Python projects: Setting up and managing translations for Scientific Python projects. Scientific Python Translations

Mission: To develop and publish translations of the websites for the Scientific Python Core Projects. Funding is supported by the CZI Scientific Python Community & Communications Infrastructure grant. See Translations into Brazilian Portuguese and Japanese are already published for https://numpy.org/!

Join the Scientific Python Discord Server!

Check out the Scientific Python community on Discord — hang out with 465 other members and enjoy free voice and text chat.

Scientific PythonReady to grow your accessibility awareness this fall? Join the Scientific Python project and Quansight Labs on October 1 and October 15 for two open-to-all events describing digital accessibility basics and actions you can take to improve the accessibility of your own work. Learn more and register on the event announcement blog post!

Ready to grow your accessibility awareness this fall? Join the Scientific Python project and Quansight Labs on October 1 and October 15 for two open-to-all events describing digital accessibility basics and actions you can take to improve the accessibility of your own work. Learn more and register on the event announcement blog post!

SciML

August/September

Check out our recent blog posts: https://sciml.ai/news/2024/08/25/rootfinding_enzyme/

Spyder

February

  • Spyder 5.5.1 is released, with new native M1 installers for macOS, support for formatting selections with Black, and a number of bug fixes.

  • Spyder 6.0.0 Alpha 4 is now out, with a number of UI improvements, plotting enhancements, performance tweaks, and more over Alpha 3.

March

  • Spyder 5.5.2 was released, which future-proofs and improves the updater, resolves console crashes, and fixes hangs while plotting

May

  • The first beta of Spyder 6 was released, which adds support for automatically connecting to and running code on remote hosts and more robust lockfile-based updates with our installers, along with all the new features introduced in previous alphas.

July

  • Released 5.5.6, which allows Spyder to run without QtWebEngine

  • Also released 6.0 Beta 3, which is the last beta before the next major stable release and very close to what users will see in Spyder 6.0

  • Presented a tools plenary talk, a poster, and a Birds of a Feather session at SciPy 2024

August/September

We’re pleased to announce that Spyder 6.0.1 has been released and is available for Windows, GNU/Linux, and MacOS X: https://github.com/spyder-ide/spyder/releases

This release comes three weeks after version 6.0.0 and it contains the following important fixes:

  • Fix Spyder hanging at startup on Linux when started in a terminal in background mode.

  • Fix appeal/sponsor Spyder message being shown at every startup.

  • Fix the error that prevented mouse clicks in Spyder from working on the Windows Subsystem for Linux.

  • Avoid crashes at startup from faulty/outdated external plugins.

  • Fix Spyder installer not being able to finish installation due to Start Menu entry error in some Conda installations.

  • Fix the Spyder installer not installing the right Spyder version (6.0.0 vs 6.0.0rc2)

  • Fix Binder instance with example workshop project from being non-responsive.

  • Fix errors related to unmaximizing panes and layout changes.

In this release, we fixed 14 issues and merged 20 pull requests. For a full list of fixes, please see our Changelog.

Don’t forget to follow Spyder updates/news on the project’s website.

Last but not least, we welcome any contribution that helps make Spyder an efficient scientific development and computing environment. Join us to help create your favorite environment!

TARDIS

March

  • We are excited to announce that TARDIS has been accepted as a mentoring organization in Google Summer of Code 2024! Check out our ideas page at https://tardis-sn.github.io/summer_of_code/ideas/ and connect with us on Gitter at https://gitter.im/tardis-sn/gsoc if you are interested!

  • We have been modularising the components further to make it easier to customize TARDIS, unlocking the potential for more science cases.

May

We’re excited to announce that the TARDIS team will be mentoring three new Google Summer of Code 2024 students this summer-

  • Asish Kumar will be adding benchmarks to TARDIS.

  • Sumit Gupta will be working on the packet tracking framework which will allow us to better investigate and process TARDIS simulation data.

  • Sarthak Srivastava will implement the velocity packet tracker visualization tool, which will allow us to better understand the regions of the ejecta material that are most important for producing different SN features!

July

We have successfully wrapped up another TARDIS Con! TARDIS Con 2024 was a unique two-week un-conference where we worked on improving the code’s efficiency, implementing new features, and discussing the latest scientific insights related to supernovae. We built samplers for the new TARDIS physics processes along with improving old ones and also built a new logger widget for TARDIS Simulation runs! For more information please visit- https://tardis-sn.github.io/news/

  • We are also excited to announce that all three of our GSoC students have passed the midterm evaluations. So far, we have a visualization tool that plots photon packet interactions in the ejecta, improved benchmarks, and newly restructured trackers, all of which will significantly aid in the study and interpretation of supernova spectra.

Affiliated Projects

aeon

July

Some of the developers at the aeon project will be giving tutorials on time series machine learning at the KDD 2024 (https://aeon-tutorials.github.io/KDD-2024/) and ECML 2024 (https://github.com/aeon-tutorials/ECML-2024) conferences in August and September.

Crystal

May

The Crystal programming language is proud to announce the release of Crystal 1.12. The past two releases brought significant improvements, in particular, a great leap forward to supporting Windows. Also of relevance, we are working hard to improve the support for muli-threaded applications. Lastly, we have a new website! It includes great improvements in its visuals, accessibility, and access to information.

July

The Crystal programming language is proud to announce the release of Crystal 1.13. This release follows the previous one, improving support for multi-threaded applications.

CVXPY

May

We released CVXPY 1.5, with many new features and other improvements:

  • Switched the default solver for LPs and SOCPs from ECOS to Clarabel.

  • Major updates to the documentation, adding a number of new sections to the User Guide and breaking up the monolithic Advanced features page

  • Added .curvatures containing all curvatures an expression is compatible with

  • Variable bounds can be specified with cp.Variable(bound=(lower, upper)) and are directly passed to the solver when helpful. lower and upper can be either a NumPy array or floating point number.

  • Constants can be named by writing cp.Constant(name=’…’)

  • Added a new atom, vdot, that has the same behavior as scalar_product

  • CVXPY runs in the next PyOdide release via wasm

  • Added or-tools 9.9 support

  • Major rewrite to the PDLP interface

  • Dropped MOSEK <= 9 support and upgraded the MOSEK integration code

Folium

August/September

Folium builds on the data-wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in a Leaflet map via Folium.

GeomScale

March

GeomScale is a research and development project that delivers open-source code for state-of-the-art algorithms at the intersection of data science, optimization, geometric, and statistical computing.

Our news:

  • GeomScale organization has been accepted as a mentoring organization for the 2024 Google Summer of Code. We are searching for contributors with strong programming skills and background in computer science and/or applied mathematics. For more details: https://www.linkedin.com/feed/update/urn:li:activity:7167873127702511616/

  • A new version 1.2.0 of the R interface to volesti library is released and hosted to a new repository: https://github.com/GeomScale/Rvolesti

  • Many new functions for statistical tests are available as well as new samplers for logconcave distributions. More details: https://github.com/GeomScale/Rvolesti/blob/develop/NEWS.md#volesti-120

  • A new preprint on “Randomized Control in Performance Analysis and Empirical Asset Pricing” is available on Arxiv: https://arxiv.org/pdf/2403.00009.pdf. The article explores the application of randomized control techniques in empirical asset pricing and performance evaluation.

GNU Radio

May

  • GNU Radio saw release 3.10.10.0 on the 22nd of April, and it *is* quite exciting! It ships with the new, Qt-based GNU Radio Companion (run `gnuradio-companion — qt` to use it), which is slated to be our graphical signal processing flow graph design tool for years to come. Find the detailed release notes in the Changelog.md, on the Github release page here, or in Josh’s project update video!

GPJax

October

We released v0.9 of GPJax. In this release, we migrated the backend of GPJax to Flax. The release information can be found here: https://github.com/JaxGaussianProcesses/GPJax/releases/tag/v0.9.0

igraph

July

Magpylib

February

Version 4.4 and 4.5 are out and focus mostly on interface and documentation improvements, field computation of triangular mesh bodies, and bugfixes

March

Magpylib v5.0.0 was just released.

https://magpylib.readthedocs.io/en/latest/_pages/reso_changelog.html

Mesa

March

Mesa (Agent-Based Models with Python)

Mesa is proud to announce it was selected for Google Summer of Code (GSoC)!

This is Mesa’s first time in GSoC, and we have four projects to help Mesa become even more capable.

MesaFrames

Description: Contributors develop a DataFrame way to conduct vectorized operations as part of agent-based modeling to cause significant speed-up in Mesa processes. Initial attempts have shown Polars provides significant speed-up to Mesa models and can help dramatically improve ABMs in Python.

Mesa RL

Description: One of the first reinforcement learning models was the ABM El Farol Bar model. Multi-Agent RL (MARL) is a fundamental way to develop agent behaviors to uncover the dynamics of complex systems. Mesa would like an extension that allows users to easily integrate Python’s reinforcement learning libraries (e.g. KerasRL, OpenAIbaselines, Open AI Gym, TFAgents etc) into agent evolution.

Cacheable Mesa

Description: As ABMs are simulations and often have phase transitions (periods of rapid change to new stable states), being able to go back in time and replay key results would be a great addition to Mesa. Critically, no computation would be needed as the results are stored.

NetworkScheduler

Description: Create an activation regime based on a network or hypergraph approach with potential inputs from set, group or topology that allows for multiple levels of agent processing. The idea is dynamic agent groups get , created, destroyed, activated,

or go dormant based on interactions with the environment or other agents. A substantive start is multi-level mesa which needs to refactored, with a detailed description of this idea is available through arxiv — Multi-Level Mesa

GSoC proposals open March 18th, and based on the discussion so far, we are looking forward to some exciting proposals.

May

Mesa is pleased to announce the release of Mesa 2.3.0.

Release Overview:

Mesa 2.3.0 is a big feature release and the last feature release before 3.0.

There are two main new features:

The experimental cell-centric discrete spaces, added in #1994, allow cells with not only properties but also active behaviors: the CellAgent. It's inspired by NetLogo’s patches but extends and this concept further.

We now have full support for discrete event scheduling, as added in #2066. It allows scheduling events (like Agent actions) at any time, including non-integer timesteps.

There are many other features: The Jupyter visualization now supports an easier way to specify sliders, NetworkGrid.get_neighbors() supports a radius, AgentSet.get() can retrieve multiple attributes, and there are now benchmarks to track Mesa performance during development.

Finally, 2.3.0 stabilizes the AgentSet (including model.agents), making it the first experimental Mesa feature that is taken out of its experimental phase.

Three Google Summer of Code (GSoC) contributors will join Mesa in Summer 2024

We are humbled by the overwhelming response to our first year being selected for GSoC, with an impressive 51 submissions to Mesa. It was a challenging task to review so many well-written proposals and code contributions.

We are excited to announce that three contributors will join us through Google Summer of Code (GSoC) to advance three efforts for Mesa.

These efforts include increasing its performance and scalability, enabling better functionality and decision-making, and better integrating demand machine learning functionality of reinforcement learning into the Mesa ecosystem.

The individuals joining us and the projects they will be working on are listed below.

Adam Amer is working on mesa-frames: Vectorized Operations for Performance and Scalability.

Dong Jun is working on Cacheable Mesa.

Harsh Mahesheka is working on Mesa RL.

We eagerly anticipate sharing the progress and updates as development unfolds. We welcome and encourage all those who submitted proposals to stay involved with Mesa, we certainly wish we were allowed to accept more.

July

Mesa Google Summer of Code

Mesa’s currently has three contributors through Google Summer of Code and they are performing exceptionally. Our contributors are working on Mesa Frames to vectorize operations, Mesa RL to link Mesa to reinforcement learning libraries and Cacheable Mesa so Users can replay critical replay their simulation without requiring the compute overhead.

Mesa Builds to 3.0

Mesa continues to build to our 3.0 release pre-release. At this point there are two major breaking changes at this point:

  • The old visualization is removed in favor of the new, Solara-based Jupyter Viz. This was already available in the 2.3.x release series but is now stabilized. Check out our new Visualization Tutorial. More examples and a migration guide will follow later in the Mesa 3.0 development.

  • The `mesa.flat` namespace is removed since it was not used very often.

  • Mesa 3.0 will require Python 3.10+.

Mesa-Geo Builds to 1.0

We are doing some major upgrades to Mesa-Geo to ensure it is compatible with the current version of Mesa, particularly the visualization.

November

Mesa is proud to announce the release of Mesa 3.0!

After our most extensive pre-release program ever. Mesa 3.0 brings major improvements to agent-based modeling, making it more intuitive and powerful while reducing complexity. This release modernizes core functionalities and introduces new capabilities for both beginners and advanced users.

TLDR: Check out a model | Get Started | See Examples

Streamlined agent management

The centerpiece of Mesa 3.0 is its new agent management system. Agents are now automatically tracked and assigned unique IDs, eliminating common boilerplate code. The new AgentSet functionality provides an elegant and flexible way to work with agents

The AgentSet provides powerful methods for filtering, grouping, and analyzing agents, making it easier to express complex model logic. Each model automatically maintains an AgentSet containing all agents (model.agents) and separate AgentSets for each agent type (model.agents_by_type). See AgentSet Examples

Modern Visualization with SolaraViz

Mesa 3.0’s new experimental visualization system, SolaraViz, provides a modern, interactive interface for model exploration:

Try the new interface at py.cafe

Note: SolaraViz is in active development. We might make API breaking changes between Mesa 3.0 and 3.1.

Enhanced data collection

The DataCollector now supports collecting different metrics for different agent types. Check it out

Experimental features

Mesa 3.0 introduces several experimental features for advanced modeling:

  • Cell Space with integrated PropertyLayers and improved agent movement capabilities

  • Voronoi grid implementation

  • Event-scheduling simulation capabilities

These experimental features are in active development and might break API between releases.

Transitioning from Mesa 2?

See our Mesa 3.0 migration guide for a full overview.

For questions or support, join our GitHub Discussions or Matrix Chat.

We would love to hear what you think about Mesa 3.0! Say hello here and leave any feedback on 3.0 here.

An incredible thanks to Ewout and Jan for leading this effort and doing the majority of the work!

ObsPy

May

We just did a bugfix release for ObsPy a few days ago, corresponding info:

ObsPy 1.4.1 was released. Anyone can find more information here.

Join our mailing list

Check out our user forum: https://discourse.obspy.org/

Follow us on X: https://twitter.com/obspy

Open2C

March

  • Our NumFOCUS-affiliated project, Bioframe, has been published as an application note in Bioinformatics, “Bioframe: operations on genomic intervals in Pandas dataframes”

  • We are hiring! Abdennur Lab, a core contributor to Open2C at UMass Chan Medical School, is looking for a #Postdoc in #ComputationalBiology and #Genomics. Projects focus on investigating 3D and functional genomics and on cutting-edge genomic data science and data vis. Successful candidates will contribute to the NumFOCUS-affiliated projects in Open2C. https://www.ummsjobs.com/job/10102/

optimagic

August/September

The estimagic project has re-branded to optimagic.

Optuna

February

We are working on the next minor release (v3.6) planned for March. We are planning to include several new features to Optuna v3.6, including:

  • Wilcoxon pruner, a pruner based on Wilcoxon signed-rank test

  • Native GP implementation, which supports mixed (with continuous, discrete and categorical) search spaces and is faster than BoTorchSampler

  • PED-ANOVA importance evaluator

We are going to move the entire “optuna.integration” module to “optuna-integration” package. Please run “pip install optuna-integration” to use features under “optuna.integration”.

We are implementing a subset of Optuna features with Rust. This is useful for building Optuna bindings for other languages, such as C++ and Typescript.

March

The next minor release (v3.6) is coming soon! We are planning to include several new features to Optuna v3.6, including:

  • Wilcoxon pruner, a pruner based on Wilcoxon signed-rank test

  • Native GP implementation faster than BoTorchSampler, which supports mixed (with continuous, discrete, and categorical) search spaces

  • PED-ANOVA importance evaluator much quicker than the original f-ANOVA

  • Migrating the entire `optuna.integration` module to `optuna-integration` package while retaining the backward compatibility with `pip install optuna-integration`,

Furthermore, we are implementing a subset of Optuna features with Rust to provide a faster Optuna binding for other languages such as C++ and TypeScript.

Last but not least, we are recruiting GSoC 2024 participants! Please check out the program description here.

May

Optuna v3.6 is out with a lot of new features! Check out the release blog for more information. New features include:

  • Wilcoxon pruner, a pruner based on Wilcoxon signed-rank test,

  • Native GP implementation, which supports mixed (with continuous, discrete, and categorical) search spaces and is faster than BoTorchSampler

  • PED-ANOVA importance evaluator is much quicker than the original f-ANOVA, and more.

  • We have migrated the entire `optuna.integration` module to `optuna-integration` package. Please run `pip install optuna-integration` if you encounter an `ImportError`.

  • We implemented a prototype of Rust version of Optuna. This significantly improves speed and is useful for building Optuna bindings for other languages, such as C++ and Typescript.

  • We are working on the next major release (Optuna v4). We plan to stabilize many experimental features, remove deprecated ones, and implement new functionality.

June

Optuna has gained 10k stars! Yay!
The next release will be a major update (Optuna v4.0). We are planning to include the following items:

  • Stabilization of experimental features, including Artifact and JournalStorage. Optuna will enhance support for these experiment management functions.

  • Removal of deprecated features. Note that it will be a breaking change, and we are planning to provide a migration guide for important changes.

- Hideaki Imamura, one of our core developers, will give an invited talk at AutoML Conference in September. Stay tuned!

July

  • We released beta version of Optuna v4.0/OptunaHub.

  • With OptunaHub, you can get/share state-of-the-art algorithms/useful visualizations very easily. Check out the blog post for more information.

  • The new release also includes the stabilization of artifacts and JournalStorage and the removal of old multi-objective optimization interfaces, CLI interfaces, etc. If you use MOTPESampler, please migrate to TPESampler.

  • Hideaki Imamura and Shuhei Watanabe, our core developers, will give presentations at the AutoML Conference in September. Stay tuned! Presentation 1 Link | Presentation 2 Link

October

Here are our updates for Optuna.

Optuna 4.0 & OptunaHub is released! Check out the release notes for details.

You can easily use/share any sampler/pruner/visualization from https://hub.optuna.org/. Check out this blog post.

Artifact management features and JournalStorage (including file-based storage) are stabilized. Check out these resources:

Large speedup for multi-objective TPE sampler is made.

A new terminator algorithm (used for early termination of optimization) is added.

We have new algorithms in OptunaHub!

CatCMA: https://medium.com/optuna/introduction-to-catcma-in-optunahub-ffa316309cb8

INGO: https://medium.com/optuna/a-natural-gradient-based-optimization-algorithm-registered-on-optunahub-0dbe17cb0f7d

The OptunaHub has a lot more. Check them out!

Imamura Hideaki & Shuhei Watanabe gave a presentation in AutoML Conference! Slides are available here: https://speakerdeck.com/pfn/20240909-automl-conference-optuna

November

Here are our updates for Optuna.

We posted articles about new algorithms! Check them out:

Polars

July

Below are links to recently published updates from Polars.

pvlib

February

A lot has happened at pvlib since our last NumFOCUS update. The latest version, pvlib-0.10.3, was released in 2023–12–20. It had 19 contributors. Here are the highlights:

  • Functions in pvlib.iotools for fetching solar resource data from SolarAnywhere and Solcast

  • Forward and reverse irradiance transposition using the continuous Perez-Driesse models

  • Parameter fitting and converter functions for popular IAM models

For the full list of what’s new and for highlights from past releases, please see the documentation.

Releases are available from PyPI and the conda-forge channel:

NOTE: new pvlib releases are no longer uploaded to the “pvlib” conda channel. Please install from PyPI or the conda-forge channel instead.

Read the Documentation: https://pvlib-python.readthedocs.io/en/stable/index.html

Report issues & contribute: https://github.com/pvlib/pvlib-python

May

The pvlib python maintainers are happy to release v0.10.5. This release contains contributions from 9 people!

v0.10.5 is a bugfix release. Notably, it should be compatible with the upcoming numpy 2.0 release. It also drops support for Python 3.7. For the full list of what’s new, see the documentation: https://pvlib-python.readthedocs.io/en/stable/whatsnew.html

Google Summer of Code:

pvlib is proud to participate in GSoC under the NumFOCUS umbrella for the second time. This year pvlib will be mentoring three students, Echedey Luis Alvarez, Ioannis Sifnaios, and Rajiv Daxini under mentorship from maintainers Kevin Anderson and Adam R. Jensen. The students will contribute to a wide range of areas including spectral and electrical mismatch, shading, floating PV, and agrivoltaic irradiance models.

Releases are available from PyPI and the conda-forge channel:

Read the Documentation:

Report issues & contribute:

Thank you for using pvlib python!

July

The 11th major version of pvlib python v0.11.0 was released on June 21, 2024. This release is the product of contributions from 16 people, including three participants in the Google Summer of Code program!

v0.11.0 Highlights:

  • Two new spectral correction factor models

  • A water albedo model for floating PV applications

  • Functions to calculate shaded fraction and electrical loss due to row-to-row shading

  • A simple transformer efficiency model

v0.11.0 also contains several breaking changes relative to the 0.10.* series. Please check the documentation for details: https://pvlib-python.readthedocs.io/en/stable/whatsnew.html

Releases are available from PyPI and the conda-forge channel:

Report issues & contribute:

pyhf

February

We’re happy to announce that pyhf v0.7.6 is out on PyPI and Conda-forge! This is a small patch release with the following highlights:

Please see the release notes for a full list of changes.

PyVista

October

PyVista is a library for creating stunning visuals of 3D scientific data.

PyVista’s latest release was in July 2024, which focused on ensuring compatibility with NumPy 2.0, upgrades for future compatibility with VTK, enhanced documentation hosting, and indexing by search engines. For a complete list of changes, visit the release notes and subsequent patch releases.

Need help getting the most out of PyVista? Reach out to info@pyvista.org and we can help connect you with community experts or organizations with deep expertise in scientific computing.

For more detailed updates and to stay connected with the PyVista community:

Slack: https://slack.pyvista.org/

November

PyVista is a library for creating stunning visuals of 3D scientific data.

PyVista’s latest release was in July 2024 which focused on ensuring compatibility with NumPy 2.0, upgrades for future compatibility with VTK, enhanced documentation hosting and indexing by search engines. For a complete list of changes, visit the release notes and subsequent patch releases.

In upcoming work, we plan to enhance PyVista’s volume rendering capabilities by supporting dynamic visualization pipelines, unifying the volume rendering API with the robust mesh rendering capabilities, and providing comprehensive documentation. The improvements will optimize the performance and usability of volume rendering in PyVista, making it a more robust tool for scientific 3D visualization while addressing a backlog of bugs and feature requests.

A preview of PyVista’s latest developments (not yet released) include:

  • Using operators for boolean operations (see pull request)

  • result = sphere_a | sphere_b

Need help getting the most out of PyVista? Contact info@pyvista.org, and we can connect you with community experts or organizations with deep expertise in scientific computing.

For more detailed updates and to stay connected with the PyVista community:

signac

March

  • More caching in the signac framework makes common code paths up to 200 times faster on slow file systems, making daily work with large projects more fluid.

Releases with these improvements:

Skforecast

May

skforecast just released the new version 0.12.0

What’s new?

Dive into the full details: https://skforecast.org

July

Skforecast new release 0.13.0

  • The Global Forecasters ForecasterAutoregMultiSeries and ForecasterAutoregMultiSeriesCustom can forecast series not seen during training. This is useful when new products are introduced but no historical data is available yet.

  • A new create_predict_X method has been added to all recursive and direct forecasters, allowing the user to inspect the matrices passed to the regressor’s predict method.

  • New metrics module with functions to compute time series prediction metrics such as mean_absolute_scaled_error and root_mean_squared_scaled_error.

  • New argument add_aggregated_metric in backtesting_forecaster_multiseries to include, in addition to the metrics for each level, the aggregated metric of all levels using the average (arithmetic mean), weighted average (weighted by the number of predicted values of each level), or pooling (the values of all levels are pooled and then the metric is calculated).

  • Added the skip_folds argument to the model_selection and model_selection_multiseries functions. It allows the user to skip some folds during backtesting, which can be useful to speed up the backtesting process and thus the hyperparameter search.

November

Skforecast 0.14.0 has just been released!

skforecast has undergone a major refactoring, improving both usability and speed, so you can forecast faster than ever!

Unified Forecaster Types

We’ve simplified the structure by unifying multiple 𝘧𝘰𝘳𝘦𝘤𝘢𝘴𝘵𝘦𝘳 types, reducing the complexity of classes while retaining all key functionalities.

Accelerated Multi-Series Forecasting

The 𝘍𝘰𝘳𝘦𝘤𝘢𝘴𝘵𝘦𝘳𝘙𝘦𝘤𝘶𝘳𝘴𝘪𝘷𝘦𝘔𝘶𝘭𝘵𝘪𝘚𝘦𝘳𝘪𝘦𝘴 is now considerably faster, making it possible to forecast at scale — even across thousands of series.

Window Features for All Forecasters

Now, all forecasters support window features, giving you more flexibility in feature engineering.

New cv Argument for Model Selection

The 𝘮𝘰𝘥𝘦𝘭_𝘴𝘦𝘭𝘦𝘤𝘵𝘪𝘰𝘯 functions now include a new 𝘤𝘷 argument. You can use 𝘛𝘪𝘮𝘦𝘚𝘦𝘳𝘪𝘦𝘴𝘍𝘰𝘭𝘥 or 𝘖𝘯𝘦𝘚𝘵𝘦𝘱𝘈𝘩𝘦𝘢𝘥𝘍𝘰𝘭𝘥 to define custom validation strategies

Seamless Migration with Simple Code Changes

Take advantage of these new features with just a few tweaks to your code! Check out our migration guide to get started smoothly.

With all these updates, we aim to make skforecast more user-friendly and aligned with the standards of scikit-learn, ensuring an intuitive experience for every user.

Release details: https://skforecast.org/0.14.0/releases/releases#0.14.0

Skforecast docs: https://skforecast.org

Thank you very much for helping to make the project’s development visible.

STUMPY

July

STUMPY 1.13.0 has been released!

Highlights:

  • Easier-to-Use Matrix Profile (Array) Data Structure

  • NumPy 2.0 Support

  • pyproject.toml Adoption

  • Improved Documentation and Testing

  • Python 3.12 Support

  • And Much More!

TNL

October

We have started a YouTube channel where we explain how to develop parallel algorithms with TNL — https://www.youtube.com/@GPUProgrammingWithTNL. Currently, there are only a few episodes but we want to add more soon. If you find this interesting, feel free to add it to the newsletter.

toqito

August/September

Welcome toqito as a new Affiliated Projects!

The toqito package is an open-source library for studying various objects in quantum information, namely, states, channels, and measurements. toqito provides numerical tools to study problems about entanglement theory, nonlocal games, and other aspects of quantum information often associated with computer science.

October

toqito 1.1.0 has been released: https://pypi.org/project/toqito/

Release notes for this particular release can be found here: https://github.com/vprusso/toqito/releases/tag/v1.1.0

Trixi.jl

March

We released Trixi.Jl v0.7.0, which since the last minor release, v0.6.0, in December, has a ton of nice improvements and updates. The biggest change is certainly that support for advanced simulations of shallow water-type equations has been moved into a new downstream package TrixiShallowWater.jl, where we will bundle all our efforts in this area.

June

From Trixi.jl we have the following news:

We will be present with three talks at the upcoming JuliaCon 2024 conference in Eindhoven, NL next month:

Visual Python

May

  • Visual Python is a GUI-based Python code generator developed on the Jupyter Lab, Jupyter Notebook, and Google Colab as an extension.

  • Visual Python is an open-source project started for students who struggle with coding during Python classes for data science.

  • If you would like to manage data with minimal coding skills, overcome learning barriers for Python, or eliminate repeated work using codes, try Visual Python.

  • Supported features: Basic data analysis / Data pre-processing / Visualization / Machine learning / Statistics

Updates and Future issues

June

Recent Release

  • Visual Python released version 3.0.2 with significant documentation updates and a bug fix.

Current Updates

  • A new manual has arrived. Descriptions for all applications and functions are supported now.

  • Bug fixed on Subset, Machine Learning, and Pipeline applications.

Yellowbrick

May

Yellowbrick plans to resolve two open issues that we have regarding our Cook distance visualizer and to fix a bug with our classification report visualizer. We also plan to work on our open PRs and issues.