[Numpy-discussion] Array interface and builtin array.array example

[Filip Wasilewski]

Hi,

the way of accessing data with __array_interface__, as shown by Travis
in [1], also works nicely when used with builtin array.array (if someone
here is still using it;).

Time to convert array.array to ndarray is O(N) but can be made O(1) just
by simple subclassing.

[1] http://aspn.activestate.com/ASPN/Mail/Message/numpy-discussion/3191164

cheers,
fw

-----------------------------------------------------------------------

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import array as _array
import sys

if sys.byteorder == 'little':
     _ENDIAN = '<'
else:
     _ENDIAN = '>'

_TYPES_CONV ={
        'c': '|u%%d',              #character          1
        'b': '|i%%d',              #signed integer     1
        'B': '|u%%d',              #unsigned integer   1
        'u': '%su%%d' % _ENDIAN,   #Unicode character  2
        'h': '%si%%d' % _ENDIAN,   #signed integer     2
        'H': '%su%%d' % _ENDIAN,   #unsigned integer   2
        'i': '%si%%d' % _ENDIAN,   #signed integer     2 (4?)
        'I': '%su%%d' % _ENDIAN,   #unsigned integer   2 (4?)
        'l': '%si%%d' % _ENDIAN,   #signed integer     4
        'L': '%su%%d' % _ENDIAN,   #unsigned integer   4
        'f': '%sf%%d' % _ENDIAN,   #floating point     4
        'd': '%sf%%d' % _ENDIAN,   #floating point     8
}        

class array(_array.array):
    def __get_array_interface__(self):
        new = {}
        shape, typestr = (self.__len__(),), (_TYPES_CONV[self.typecode] % self.itemsize)
        new['shape'] = shape
        new['typestr'] = typestr
        new['data'] = (self.buffer_info()[0], False) # writable
        return new

    __array_interface__ = property(__get_array_interface__, None, doc="array interface")    

if __name__ == '__main__':

    size = 1000000
    typecode = 'f'

    new = array(typecode, xrange(size))
    old = _array.array(typecode, xrange(size))

    import numpy
    from time import clock as time

    t1 = time()
    nd = numpy.asarray(new)
    t1 = time() - t1
    #print nd
    
    t2 = time()
    nd = numpy.asarray(old)
    t2 = time() - t2
    #print nd

    print "new:", t1
    print "old:", t2
    
#EOF