math/py-numexpr 2.10.2_1 MidnightBSD
Fast numerical array expression evaluator for Python and NumPy
- System
- MidnightBSD
- Repository
- mports
- Maintainer
- [email protected]
- License
- mit
- Categories
- math, python
Description
numexpr - Fast numerical array expression evaluator for Python and NumPy.
The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime.
Also, numexpr has support for the Intel VML (Vector Math Library) -- integrated in Intel MKL (Math Kernel Library) --, allowing nice speed-ups when computing transcendental functions (like trigonometrical, exponentials...) on top of Intel-compatible platforms. This support also allows to use multiple cores in your computations.
In the other systems
| System | Port | Version |
|---|---|---|
| MidnightBSD | math/py-numexpr | 2.10.2_1 |
| FreeBSD | math/py-numexpr | 2.14.2 |
| OpenBSD | math/py-numexpr | 2.10.2p1 |
| NetBSD | math/py-numexpr | 2.14.2 |
| DragonFly | math/py-numexpr | 2.10.2 |
Install
Binary package:
mport install ${PYTHON_PKGNAMEPREFIX}numexpr
From mports:
cd /usr/mports/math/py-numexpr && make install clean
To depend on this port in another port's Makefile:
RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}numexpr>0:math/py-numexpr
Dependencies
As this port's Makefile names them.
Build 1
${PYTHON_PKGNAMEPREFIX}numpy>=1.13.3,1: math/py-numpy
Runtime 1
${PYTHON_PKGNAMEPREFIX}numpy>=1.13.3,1: math/py-numpy
Build dependencies 1
Run dependencies 1
Required by 1
Configuration
No options to configure.
- USES
- dos2unix python
- Service scripts
- none
Files
distinfo 1
| File | Size | SHA256 |
|---|---|---|
| numexpr-2.10.2.tar.gz | 106787 | b0aff6b48ebc99d2f54f27b5f73a58cb92fde650aeff1b397c71c8788b4fff1a |
Master sites 2
- PYPI
- https://github.com/pydata/numexpr/releases/download/v2.10.2/