This project has moved to GitLab: https://gitlab.com/madphysicist/is_integer_ufunc. Please download the latest code and submit issues there rather than GitHub, which will no longer be monitored until this stale clone is deleted.
This is a sample implementation of a ufunc that checks if a floating point value is an integer. It is inspired by my Stack Overflow question https://stackoverflow.com/q/35042128/2988730, and others, which has garnered a bit of mild interest.
To build this package, run
$ python setup.py build_ext --inplace
$ cd is_integer_ufunc
Due to the small and experimental nature of this library, cleaning is largely a manual process:
$ rm -rf build/ is_integer_ufunc.* __pycache__/
Fairly comprehensive tests are available, currently in test_is_integer.py.
Some preliminary benchmarks show that the double version of the function is
5x to 15x faster than using (x % 1) == 0. Here is a simple timing test:
In [0]: import numpy as np
In [1]: from is_integer import is_integer
In [2]: np.random.seed(0xBEEF)
In [3]: x = np.random.rand(10000, 10000)
In [4]: x[5000:, :] = np.random.randint(-1000, 1000, size=(5000, 10000))
In [5]: np.random.shuffle(x)
In [6]: np.array_equal(((x % 1) == 0), is_integer(x))
Out[6]: True
In [7]: %timeit is_integer(x)
135 ms ± 1.02 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [8]: %timeit (x % 1) == 0
2.08 s ± 28.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Setting the size to (1000, 1000) yields a 5x rather than a 15x improvement,
likely due to the smaller overhead imposed by the intermediate arrays.
A larger variety of benchmarks are available in benchmark_is_integer.py.
The script compares the runtimes of the modulo approach for different datatypes
and array sizes. Here is a sample output:
========================================================================
Speedup of is_integer vs (x % 1) == 0 ========================================================================
Integers -------------+--------------+--------------+--------------+-------------
dtype \ N | 100 | 10000 | 1000000 | 100000000
-------------+--------------+--------------+--------------+-------------
uint8 | 5.1x | 11.6x | 13.6x | 9.7x
uint16 | 4.5x | 11.4x | 12.0x | 10.6x
uint32 | 4.0x | 11.2x | 13.8x | 11.8x
uint64 | 4.1x | 24.4x | 29.2x | 23.5x
int8 | 5.5x | 19.0x | 20.1x | 16.2x
int16 | 4.7x | 19.5x | 21.0x | 16.8x
int32 | 4.1x | 23.8x | 27.9x | 22.6x
int64 | 3.8x | 40.8x | 50.5x | 40.7x
========================================================================
Floats -------------+--------------+--------------+--------------+-------------
dtype \ N | 100 | 10000 | 1000000 | 100000000
-------------+--------------+--------------+--------------+-------------
float16 | 5.7x | 31.2x | 33.5x | 31.1x
float32 | 4.0x | 16.7x | 5.9x | 6.5x
float64 | 4.0x | 25.6x | 26.2x | 25.1x
float128 | 4.2x | 9.0x | 11.2x | 13.7x
========================================================================
- https://standards.ieee.org/standard/754-2019.html: IEEE 754-2019 - IEEE Standard for Floating-Point Arithmetic
- https://en.wikipedia.org/wiki/IEEE_754
- https://en.wikipedia.org/wiki/Half-precision_floating-point_format
- https://en.wikipedia.org/wiki/Single-precision_floating-point_format
- https://en.wikipedia.org/wiki/Double-precision_floating-point_format
- https://en.wikipedia.org/wiki/Extended_precision
- https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format
- https://stackoverflow.com/q/35042128/2988730: Numpy: Check if float array contains whole numbers
- https://stackoverflow.com/q/64044147/2988730: How to check if all elements of a numpy array have integer values?
- https://stackoverflow.com/q/36505879/2988730: Convert float64 to int64 using "safe"
- https://stackoverflow.com/q/21583758/2988730: How to check if a float value is a whole number
- https://stackoverflow.com/q/934616/2988730: How do I find out if a numpy array contains integers?
- https://stackoverflow.com/q/6209008/2988730: Checking if float is equivalent to an integer value in python
- https://stackoverflow.com/q/52094533/2988730: Filter integers in numpy float array