Metadata-Version: 1.0
Name: numexpr
Version: 2.7.3
Summary: Fast numerical expression evaluator for NumPy
Home-page: https://github.com/pydata/numexpr
Author: David M. Cooke, Francesc Alted and others
Author-email: david.m.cooke@gmail.com, faltet@gmail.com
License: MIT
Description: ======================================================
        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|
        
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                :target: https://github.com/pydata/numexpr/actions
        .. |travis| image:: https://travis-ci.org/pydata/numexpr.png?branch=master
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                :target: http://numexpr.readthedocs.io/en/latest
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                :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 installating 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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