Metadata-Version: 1.1
Name: py_find_1st
Version: 1.0.4
Summary: Numpy extension module for efficient search of first array index that compares true
Home-page: http://github.com/roebel/py_find_1st
Author: A. Roebel
Author-email: axel.dot.roebel@ircam.dot.fr
License: GPL
Download-URL: https://github.com/roebel/py_find_1st/archive/v1.0.4.tar.gz
Description: py\_find\_1st
        =============
        
        py\_find\_1st is a numpy extension that allows to find the first index
        into an array that validates a boolean condition that can consist of a
        comparison operator and a limit value.
        
        Functionality
        -------------
        
        This extension solves the very frequent problem of finding first indices
        without requiring to read the full array.
        
        The call sequence
        
        ::
        
            import numpy as np
            import utils_find_1st as utf1st
        
            limit = 0.
            rr= np.random.randn(100)
            ind = utf1st.find_1st(rr < limit, True, utf1st.cmp_equal)
        
        and more efficiently
        
        ::
        
            ind = utf1st.find_1st(rr, limit, utf1st.cmp_smaller)
        
        is equivalent to
        
        ::
        
            import numpy as np
            limit = 0.
            rr= np.random.randn(100)
            ind = np.flatnonzero(rr < limit)
            if len(ind) :
                ret = ind[0]
            else:
                ret = -1
        
        Implementation details
        ----------------------
        
        py\_find\_1st is written as a numpy extension making use of a templated
        implementation of the find\_1st function that currently supports
        operating on arrays of dtypes:
        
        ::
        
            [np.float64, np.float32, np.int64, np.int32, np.bool]
        
        Comparison operators are selected using integer opcodes with the
        following meaning:
        
        ::
        
            opcode == utils_find_1st.cmp_smaller    ->  comp op: <
            opcode == utils_find_1st.cmp_smaller_eq ->  comp op: <=
            opcode == utils_find_1st.cmp_equal      ->  comp op: ==
            opcode == utils_find_1st.cmp_not_equal  ->  comp op: !=
            opcode == utils_find_1st.cmp_larger     ->  comp op: <
            opcode == utils_find_1st.cmp_larger_eq  ->  comp op: <=
        
        Performance
        -----------
        
        The runtime difference is strongly depending on the number of true cases
        in the array. If the condition is never valid runtime is the same - both
        implementations do not produce a valid index and need to compare the
        full array - but on case that there are matches np.flatnonzero needs to
        run through the full array and needs to create a result array with size
        that depends o the number of matches while find\_1st only produces a
        scalar result and only needs to compare the array until the first match
        is found.
        
        Depending on the size of the array and the number of matches the speed
        difference can be very significant (easily > factor 10)
        
        Benchmarking
        ~~~~~~~~~~~~
        
        We can easily compare the runtime using the three lines
        
        ::
        
            In [6]: timeit ind = np.flatnonzero(rr < limit)[0]
            1.69 µs ± 24.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        
            In [4]: timeit ind = utf1st.find_1st(rr < limit, True, utf1st.cmp_equal)
            1.13 µs ± 18.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        
            In [5]: timeit ind = utf1st.find_1st(rr, limit, utf1st.cmp_smaller)
            270 ns ± 5.57 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        
        Which shows the rather significant improvement obtained by the last
        version that does not require to perform all comparisons of the 100
        elements. In the above case the second element is tested positive. In
        the worst case, where no valid element is present all comparisons have
        to be performed and flatnonzero does not need to create a results array,
        and therefore performance should be similar. For the small array sizes
        we used so far the overhead of np.flanonzero is dominating the costs as
        can be seen in the following.
        
        ::
        
            In [9]: limit = -1000.
            In [10]: timeit ind = np.flatnonzero(rr < limit)
            1.56 µs ± 13.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        
            In [11]: timeit ind = utf1st.find_1st(rr<limit, True, utf1st.cmp_equal)
            1.16 µs ± 7.07 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        
            In [12]: timeit ind = utf1st.find_1st(rr, limit, utf1st.cmp_smaller)
            314 ns ± 3.36 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        
        For a significantly larger array size costs become more comparable
        
        ::
        
            rr= np.random.randn(10000)
            In [13]: timeit ind = np.flatnonzero(rr < limit)
            4.87 µs ± 101 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
        
            In [14]: timeit ind = utf1st.find_1st(rr<limit, True, utf1st.cmp_equal)
            8.95 µs ± 497 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
        
            In [15]: timeit ind = utf1st.find_1st(rr, limit, utf1st.cmp_smaller)
            4.4 µs ± 47.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
        
        Which demonstrates that even in this case the find\_1st extension is
        more efficient besides if the boolean intermediate array is used in line
        14.
        
        This result is a bit astonishing as the overhead involved in passing the
        boolean intermediate array into the find\_1st extension seems rather
        large compared to the simple boolean comparison
        
        ::
        
            In [35]: timeit ind = rr < limit
            3.31 µs ± 47.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
        
        The clarification of this remaining issue needs further investigation.
        Any comments a re welcome.
        
        Changes
        -------
        
        Version 1.0.4 (2017-05-31)
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        -  Fixed setup.py problems on the fly generation of LONG\_DESCRPTION
           file.
        
        Version 1.0.3 (2017-05-31)
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        -  Moved to github
        
        Version 1.0.2 (2017-05-31)
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        -  Force using c++ compiler
        
        Version 1.0.1 (2017-05-31)
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        -  initial release
        
        Copyright
        ---------
        
        Copyright (C) 2017 IRCAM
        
        License
        -------
        
        GPL see file Copying.txt
        
        Author
        ------
        
        Axel Roebel
        
Keywords: numpy,extension,find
Platform: UNKNOWN
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
