This blog introduces PyTrees — nested Python data structures (such as lists, dicts, and tuples) with numerical leaf values — designed to simplify working with complex, hierarchically organized data.
While such structures are often cumbersome to manipulate, PyTrees make them more manageable by allowing them to be flattened into a list of leaves along with a reusable structure blueprint in a generic way.
This enables flexible, generic operations like mapping and reducing from functional programming.
By bringing those functional paradigms to structured data, PyTrees let you focus on what transformations to apply, not how to traverse the structure — no matter how deeply nested or complex it is.
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PyPalettes is a new Python library designed to simplify the use of color palettes in Python charts.
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The 2024 Scientific Python Developer summit was held 3–5 June in Seattle. Here’s a summary of what we did.
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TL;DR: If you have GPU code in your project, setup a GitHub hosted GPU runner today. It is fairly quick to do and will free you from having to run tests manually.
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We are happy to announce that we have organized the necessary infrastructure and processes to allow volunteers to start translating multiple project websites. In this blog post, we will discuss how we set up manage translations for Scientific Python projects, and how you can participate in the translation and localization effort.
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Eighteen years since the release of NumPy 1.0, we are thrilled to announce the launch of NumPy 2.0! This major release marks a significant milestone in the evolution of NumPy, bringing a wealth of enhancements and improvements to users, and setting the stage for future feature development.
Read more...Given the practical challenges of achieving true randomness, deterministic algorithms, known as Pseudo Random Number Generators (RNGs), are employed in science to create sequences that mimic randomness. These generators are used for simulations, experiments, and analysis where it is essential to have numbers that appear unpredictable. I want to share here what I have learned about best practices with pseudo RNGs and especially the ones available in NumPy.
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One outcome of the 2023 Scientific Python Developer Summit was the Scientific Python Development Guide, a comprehensive guide to modern Python package development, complete with a new project template supporting 10+ build backends and a WebAssembly-powered checker with checks linked to the guide. The guide covers topics like modern, compiled, and classic packaging, style checks, type checking, docs, task runners, CI, tests, and much more! There also are sections of tutorials, principles, and some common patterns.
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