astropy
Dask
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| astropy | Dask | |
|---|---|---|
| 26 | 32 | |
| 4,210 | 11,999 | |
| 1.7% | 1.6% | |
| 9.9 | 9.6 | |
| 2 days ago | about 17 hours ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" 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.
astropy
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Julia 1.10 Released
Astropy [0] lives at the heart of most work. It has a Python interface, often backed by Fortran and C++ extension modules. If you use Astropy, you're indirectly using libraries like ERFA [6] and cfitsio [7] which are in C/Fortran.
I personally end up doing a lot of work that uses the HEALPix sky tesselation, so I use healpy [2] as well.
Openorb is perhaps a good example of a pure-Fortran package that I use quite. frequently for orbit propagation [3].
In C, there's Rebound [4] (for N-body simulations) and ASSIST [5] (which extends Rebound to use JPL's pre-calculated positions of major perturbers, and expands the force model to account for general relativity).
There are many more, these are just ones that come to mind from frequent usage in the last few months.
[0] https://www.astropy.org/
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Skyfield: Elegant Astronomy for Python
Users interested in a broader range of astronomical tools beyond coordinate transformations may be interested in https://www.astropy.org/ and its affiliated packages.
- Astropy: Common core package for Astronomy in Python
- [R] Astronomia ex machina: a history, primer and outlook on neural networks in astronomy
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License Adherence Help
I'm working on a pure Rust approximation of astropy. Up til now, I was able to recreate the intent by looking at an external API, but I'm moving on to functionality that I don't understand enough to implement without basically copying the code. Astropy uses the BSD-3 license, and it wraps the ERFA library which uses a custom license. My project currently uses the MIT license. My PR is here - my question is have I attributed everything correctly, or is there anything I need to change for everything to be above-board?
- Astro physics data analysis
- I'm a mechanical engineer with a solid background in Python and experience earlier in my career in natural science/physics. Are there any meaningful, active, open source opportunities in space science?
- OpenSource voltado à ciência
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Astronomical Calculations for Hard SF in Common Lisp
For folks who might be interested in astronomical calculations but who don't want to roll their own library, astropy (https://www.astropy.org/) is widely used by professional astronomers.
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Looking to study data from JWST's spectroscopy instruments
I agree with the other commenter. Check out their github. If you’re looking to build your skills long term (and have some experience with python) it’s worth checking out astropy and their fits file handling routines.
Dask
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
SciPy - SciPy library main repository
Numba - NumPy aware dynamic Python compiler using LLVM
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
SymPy - A computer algebra system written in pure Python
NetworkX - Network Analysis in Python
PyDy - Multibody dynamics tool kit.
pint - Laravel Pint is an opinionated PHP code style fixer for minimalists.
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python