im2dhist
Numba
im2dhist | Numba | |
---|---|---|
1 | 124 | |
6 | 9,471 | |
- | 1.1% | |
4.8 | 9.9 | |
3 months ago | 3 days ago | |
Python | Python | |
GNU General Public License v3.0 only | BSD 2-clause "Simplified" License |
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im2dhist
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Using numba my code runs faster by about 20 times 😲
I recently added im2dhisteq to my repository and because it was very slow, I searched for a new way to make my code run faster, as I was already using numpy built-in functions and there were no (at least easy) other way to optimize the code using just numpy. I recently found out about Numba, which is advertised to run my codes 1000 times faster, but as I later found out the degree of that is actually very dependant on your code. After reading through their website and through a long series of trials and erros I learned how to write a code that is Numba-friendly and is satisfyingly faster than my base code. website of Numba: Numba Proof: My code: https://github.com/Mamdasn/im2dhisteq In my repository, in addition to the Numba-flavord versions, I released the Numba-less versions, which are accessible here: im2dhisteq and imhist. You can check them out and compare them to come to a base understanding of how Numba-friendly codes looks like.
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
im2dhisteq - This module attempts to enhance contrast of a given image by equalizing its two dimensional histogram.
NetworkX - Network Analysis in Python
imhblpce - This module attempts to enhance contrast of a given image by employing a method called HBLPCE.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
hist - Histogramming for analysis powered by boost-histogram
Dask - Parallel computing with task scheduling
color-matcher - automatic color-grading
cupy - NumPy & SciPy for GPU
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
SymPy - A computer algebra system written in pure Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python
julia - The Julia Programming Language