scipipe
ipyflow
scipipe | ipyflow | |
---|---|---|
1 | 20 | |
1,054 | 1,079 | |
0.2% | 0.5% | |
3.0 | 9.5 | |
10 months ago | about 17 hours ago | |
Go | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
scipipe
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Ask HN: What have you created that deserves a second chance on HN?
https://scipipe.org - A pipeline tool for shell commands by a declarative flow-based API in Go
Github link: https://github.com/scipipe/scipipe
There are many pipeline tools for shell commands, but a majority has one or more limitations in their API which makes certain complex pipelines impossible or really hard to write.
We were pushing the limits of all the tools we tried, so developed our own, and implemented it in Go, with a declarative API for defining the data flow dependencies, instead of inventing yet another DSL. This has allowed us great flexibility in developing also complex pipelines, e.g. combining parameter sweeps nested with cross-validation implemented as workflow constructs.
SciPipe is also unique in providing an audit report for every single output of the workflow, in a structured JSON format. A helper tool allows converting these reports to either an HTML report, a PDF, or a Bash script that will generate the one accompanying output file from scratch.
An extra cool things is that, because the audit reports live alongside output files, if you run a scipipe workflow that uses files generated by another scipipe workflow, it will pick up also all the history for the input files generated by this earlier workflow, meaning that you get a 100% complete audit report, even if your analysis spans multiple workflows!
ipyflow
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Show HN: Marimo – an open-source reactive notebook for Python
You're probably referring to nbgather (https://github.com/microsoft/gather), which shipped with VSCode for a while.
nbgather used static slicing to get all the code necessary to reconstruct some cell. I actually worked with Andrew Head (original nbgather author) and Shreya Shankar to implement something similar in ipyflow (but with dynamic slicing and a not-as-nice interface): https://github.com/ipyflow/ipyflow?tab=readme-ov-file#state-...
I have no doubt something like this will make its way into marimo's roadmap at some point :)
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React Jam just started, making a game in 13 days with React
Np.
From https://news.ycombinator.com/context?id=35887168 re: ipyflow I learned about ReactiveX for Python (RxPY) https://rxpy.readthedocs.io/en/latest/ .
https://github.com/ipyflow/ipyflow :
> IPyflow is a next-generation Python kernel for Jupyter and other notebook interfaces that tracks dataflow relationships between symbols and cells during a given interactive session, thereby making it easier to reason about notebook state.
FWIU e.g. panda3d does not have a react or rxpy-like API, but probably does have a component tree model?
https://news.ycombinator.com/item?id=38527552 :
>> It actually looks like pygame-web (pygbag) supports panda3d and harfang in WASM
> Harfang and panda3d do 3D with WebGL, but FWIU not yet agents in SSBO/VBO/GPUBuffer
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The GitHub Black Market That Helps Coders Cheat the Popularity Contest
> Another giveaway is the ratio of stars to watchers / forks. I remember one project with thousands of stars but only 10 users "watching" it. They went on to raise a sizable seed round too.
Not necessarily indicative of foul play. I have two projects like this (https://github.com/smacke/ffsubsync and https://github.com/ipyflow/ipyflow) and I attribute it to not having great developer documentation.
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Python 3.12
It's not in the highlights, but one of the things that excites me most is this: https://docs.python.org/dev/whatsnew/3.12.html#pep-669-low-i...
> PEP 669 defines a new API for profilers, debuggers, and other tools to monitor events in CPython. It covers a wide range of events, including calls, returns, lines, exceptions, jumps, and more. This means that you only pay for what you use, providing support for near-zero overhead debuggers and coverage tools. See sys.monitoring for details.
Low-overhead instrumentation opens up a whole bunch of interesting interactive use cases (i.e. Jupyter etc.), and as the author of one library that relies heavily on instrumentation (https://github.com/ipyflow/ipyflow), I'm very keen to explore the possibilities here.
- Excel Labs, a Microsoft Garage Project
- GitHub - ipyflow/ipyflow: A reactive Python kernel for Jupyter notebooks
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IPython kernel alternatives
You’re looking for reactive kernels: https://github.com/ipyflow/ipyflow
- IPyflow: Reactive Python Notebooks in Jupyter(Lab)
What are some alternatives?
codebase-visualizer-action - Visualize your codebase during CI.
elyra - Elyra extends JupyterLab with an AI centric approach.
pytkml - Write tests for machine learning models
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
tripods-web - A puzzle game.
osxphotos - Python app to work with pictures and associated metadata from Apple Photos on macOS. Also includes a package to provide programmatic access to the Photos library, pictures, and metadata.
UrlChecker - Android app by TrianguloY: URLCheck
nopdb - NoPdb: Non-interactive Python Debugger
dotfile - Simple version control made for tracking single files
subtls - A proof-of-concept TypeScript TLS 1.3 client
quarto-cli - Open-source scientific and technical publishing system built on Pandoc.