xarray
iris
| xarray | iris | |
|---|---|---|
| 11 | 1 | |
| 3,767 | 642 | |
| 1.3% | 0.9% | |
| 9.7 | 9.4 | |
| 4 days ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | 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.
xarray
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Show HN: NumPy+Jax Except with Named Axes
If this idea sounds inderesting, you might want to look at xarray for a more established project: https://github.com/pydata/xarray
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Spectral Imaging Made Easy: A Powerful Python Library
Interesting - I'm curious whether you feel that Xarray covers these use cases already?
https://xarray.dev/
Especially as I've said before that Hyperspy shares so many features in common with Xarray that Hyperspy should just use Xarray under the hood.
https://github.com/hyperspy/hyperspy/discussions/3405
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State of Python 3.13 Performance: Free-Threading
Sadly, several python projects do not use semantic versioning, for example xarray [0] and dask. Numpy can make backward incompatible changes after a warning for two releases[1]. In general, the python packaging docs do not really read as an endorsement of semantic versioning [2]:
> A majority of Python projects use a scheme that resembles semantic versioning. However, most projects, especially larger ones, do not strictly adhere to semantic versioning, since many changes are technically breaking changes but affect only a small fraction of users...
[0] https://github.com/pydata/xarray/issues/6176
- Xarray: N-D labeled arrays and datasets in Python
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Request for Startups: Climate Tech
PyTorch and JAX are used heavily in climate science on the ML side. For more general analytics, not so much. Many of our users like to use Xarray as a high-level API. There has been some work to integrate Xarray with PyTorch (https://github.com/pydata/xarray/issues/3232) but we're not there yet.
The Python Array API standard should help align these different back-ends: https://data-apis.org/array-api/latest/
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Python for Data Analysis, 3rd Edition – The Open Access Version Online
Does polars have N-D labelled arrays, and if so can it perform computations on them quickly? I've been thinking of moving from pandas to xarray [0], but might consider poplars too if it has some of that functionality.
[0] https://xarray.dev/
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What is lacking in Julia ecosystem?
https://xarray.dev
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How we found and helped fix 24
bugs in 24 hours (in Tensorflow, Sentry, V8, PyTorch, Hue, and more)
Pydata's xarray
- Xarray awarded a support grant from NASA
- xarray: N-Dimensional labeled arrays and datasets in Python
iris
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Is the sodium and iris mod safe?
Yes, both mods are open-sourced meaning anyone can look at how the mod is made and programmed. This allows people myself included to see if there is anything malicious inside, which there isn't. Source code Github: SODIUM/IRIS
What are some alternatives?
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
MetPy - MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.
wxee - A Python interface between Earth Engine and xarray for processing time series data
gdl - GDL - GNU Data Language
dask-awkward - Native Dask collection for awkward arrays, and the library to use it.
clouddrift - CloudDrift accelerates the use of Lagrangian data for atmospheric, oceanic, and climate sciences.