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Using PyPy, which is a real compiler, might help. That's doing spatial data processing by exaustive search, which is inherently slow. There are better algorithms. If the number of items to be searched is large, the spatial indices of MySQL could help.
Using PyPy, which is a real compiler, might help. That's doing spatial data processing by exaustive search, which is inherently slow. There are better algorithms. If the number of items to be searched is large, the spatial indices of MySQL could help.
PyPy is a JIT compiler not a "real compiler", it requires warm up time to start optimizing code on runtime.
Using PyPy, which is a real compiler, might help. That's doing spatial data processing by exaustive search, which is inherently slow. There are better algorithms. If the number of items to be searched is large, the spatial indices of MySQL could help.
PyPy is a JIT compiler not a "real compiler", it requires warm up time to start optimizing code on runtime.
I had a similar problem, when I was working as a PhD student a few years ago, where I needed to match the voxel representation of a 3D printer with the tetrahedral mesh of our rendering application. My first attempt in Python was both prohibitively slow and more complicated than necessary, because I tried to use vectorized numpy, where possible. Since this was only a small standalone script, I rewrote it in Julia in…
Using PyPy, which is a real compiler, might help. That's doing spatial data processing by exaustive search, which is inherently slow. There are better algorithms. If the number of items to be searched is large, the spatial indices of MySQL could help.
The most important part of the article seems to be that this Python code is taking "an avg of 293.41ms per iteration": def find_close_polygons( polygon_subset: List[Polygon], point: np.array, max_dist: float ) -> List[Polygon]: close_polygons = [] for poly in polygon_subset: if np.linalg.norm(poly.center - point) And after replacing it with this Rust code, it is taking "an avg of 23.44ms per iteration": use pyo3::pre…
Python's for loop implementation is slow, also. You can use built in utils like map() which are "native" and can be a lot faster than a for loop with a push: https://levelup.gitconnected.com/python-performance-showdown...
Earlier quoted context omitted.
Today, there is a Python package for everything . The ecosystem is possibly best in class for having a library available that will do X. You cannot separate the language from the ecosystem. Being better, faster, and stronger means little if I have to write all of my own supporting libraries. Also, few scientific programmers have any notion of what C or Fortran is under the hood. Most are happy to stand on the shoulde…
>Today, there is a Python package for everything . The same could be said about CPAN and NPM. Yet Perl is basically dead and JavaScript isn't used for any machine learning tasks as far as I'm aware. WebAssembly did help bring a niche array of audio and video codecs to the ecosystem[1][2], something I'm yet to see from Python. I don't use Python, but with what little exposure I've had to it at work, its overall sluggi…