Metadata-Version: 2.1
Name: numexpr
Version: 2.8.4
Summary: Fast numerical expression evaluator for NumPy
Home-page: https://github.com/pydata/numexpr
Author: David M. Cooke, Francesc Alted, and others
Maintainer: Robert A. McLeod
Maintainer-email: robbmcleod@gmail.com
License: MIT
Classifier: Development Status :: 6 - Mature
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
License-File: LICENSE.txt
License-File: AUTHORS.txt
Requires-Dist: numpy (>=1.13.3)

======================================================
NumExpr: Fast numerical expression evaluator for NumPy
======================================================

:Author: David M. Cooke, Francesc Alted, and others.
:Maintainer: Robert A. McLeod
:Contact: robbmcleod@gmail.com
:URL: https://github.com/pydata/numexpr
:Documentation: http://numexpr.readthedocs.io/en/latest/
:Travis CI: |travis|
:GitHub Actions: |actions|
:PyPi: |version|
:DOI: |doi|
:readthedocs: |docs|

.. |actions| image:: https://github.com/pydata/numexpr/workflows/Build/badge.svg
        :target: https://github.com/pydata/numexpr/actions
.. |travis| image:: https://travis-ci.org/pydata/numexpr.png?branch=master
        :target: https://travis-ci.org/pydata/numexpr
.. |docs| image:: https://readthedocs.org/projects/numexpr/badge/?version=latest
        :target: http://numexpr.readthedocs.io/en/latest
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.. |version| image:: https://img.shields.io/pypi/v/numexpr.png
        :target: https://pypi.python.org/pypi/numexpr

What is NumExpr?
----------------

NumExpr is a fast numerical expression evaluator for NumPy.  With it,
expressions that operate on arrays (like :code:`'3*a+4*b'`) are accelerated
and use less memory than doing the same calculation in Python.

In addition, its multi-threaded capabilities can make use of all your
cores -- which generally results in substantial performance scaling compared
to NumPy.

Last but not least, numexpr can make use of Intel's VML (Vector Math
Library, normally integrated in its Math Kernel Library, or MKL).
This allows further acceleration of transcendent expressions.


How NumExpr achieves high performance
-------------------------------------

The main reason why NumExpr achieves better performance than NumPy is
that it avoids allocating memory for intermediate results. This
results in better cache utilization and reduces memory access in
general. Due to this, NumExpr works best with large arrays.

NumExpr parses expressions into its own op-codes that are then used by
an integrated computing virtual machine. The array operands are split
into small chunks that easily fit in the cache of the CPU and passed
to the virtual machine. The virtual machine then applies the
operations on each chunk. It's worth noting that all temporaries and
constants in the expression are also chunked. Chunks are distributed among 
the available cores of the CPU, resulting in highly parallelized code 
execution.

The result is that NumExpr can get the most of your machine computing
capabilities for array-wise computations. Common speed-ups with regard
to NumPy are usually between 0.95x (for very simple expressions like
:code:`'a + 1'`) and 4x (for relatively complex ones like :code:`'a*b-4.1*a > 2.5*b'`), 
although much higher speed-ups can be achieved for some functions  and complex 
math operations (up to 15x in some cases).

NumExpr performs best on matrices that are too large to fit in L1 CPU cache. 
In order to get a better idea on the different speed-ups that can be achieved 
on your platform, run the provided benchmarks.

Installation
------------

From wheels
^^^^^^^^^^^

NumExpr is available for install via `pip` for a wide range of platforms and 
Python versions (which may be browsed at: https://pypi.org/project/numexpr/#files). 
Installation can be performed as::

    pip install numexpr

If you are using the Anaconda or Miniconda distribution of Python you may prefer 
to use the `conda` package manager in this case::

    conda install numexpr

From Source
^^^^^^^^^^^

On most \*nix systems your compilers will already be present. However if you 
are using a virtual environment with a substantially newer version of Python than
your system Python you may be prompted to install a new version of `gcc` or `clang`.

For Windows, you will need to install the Microsoft Visual C++ Build Tools 
(which are free) first. The version depends on which version of Python you have 
installed:

https://wiki.python.org/moin/WindowsCompilers

For Python 3.6+ simply installing the latest version of MSVC build tools should 
be sufficient. Note that wheels found via pip do not include MKL support. Wheels 
available via `conda` will have MKL, if the MKL backend is used for NumPy.

See `requirements.txt` for the required version of NumPy.

NumExpr is built in the standard Python way::

  python setup.py build install

You can test `numexpr` with::

  python -c "import numexpr; numexpr.test()"

Do not test NumExpr in the source directory or you will generate import errors.

Enable Intel® MKL support
^^^^^^^^^^^^^^^^^^^^^^^^^

NumExpr includes support for Intel's MKL library. This may provide better 
performance on Intel architectures, mainly when evaluating transcendental 
functions (trigonometrical, exponential, ...). 

If you have Intel's MKL, copy the `site.cfg.example` that comes with the 
distribution to `site.cfg` and edit the latter file to provide correct paths to 
the MKL libraries in your system.  After doing this, you can proceed with the 
usual building instructions listed above.

Pay attention to the messages during the building process in order to know 
whether MKL has been detected or not.  Finally, you can check the speed-ups on 
your machine by running the `bench/vml_timing.py` script (you can play with 
different parameters to the `set_vml_accuracy_mode()` and `set_vml_num_threads()` 
functions in the script so as to see how it would affect performance).

Usage
-----

::

  >>> import numpy as np
  >>> import numexpr as ne

  >>> a = np.arange(1e6)   # Choose large arrays for better speedups
  >>> b = np.arange(1e6)

  >>> ne.evaluate("a + 1")   # a simple expression
  array([  1.00000000e+00,   2.00000000e+00,   3.00000000e+00, ...,
           9.99998000e+05,   9.99999000e+05,   1.00000000e+06])

  >>> ne.evaluate('a*b-4.1*a > 2.5*b')   # a more complex one
  array([False, False, False, ...,  True,  True,  True], dtype=bool)

  >>> ne.evaluate("sin(a) + arcsinh(a/b)")   # you can also use functions
  array([        NaN,  1.72284457,  1.79067101, ...,  1.09567006,
          0.17523598, -0.09597844])

  >>> s = np.array([b'abba', b'abbb', b'abbcdef'])
  >>> ne.evaluate("b'abba' == s")   # string arrays are supported too
  array([ True, False, False], dtype=bool)


Documentation
-------------

Please see the official documentation at `numexpr.readthedocs.io <https://numexpr.readthedocs.io>`_.
Included is a user guide, benchmark results, and the reference API.


Authors
-------

Please see `AUTHORS.txt <https://github.com/pydata/numexpr/blob/master/AUTHORS.txt>`_.


License
-------

NumExpr is distributed under the `MIT <http://www.opensource.org/licenses/mit-license.php>`_ license.


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