""" Cast Copy Tranpose is used in numpy LinearAlgebra.py to convert
    C ordered arrays to Fortran order arrays before calling Fortran
    functions.  A couple of C implementations are provided here that
    show modest speed improvements.  One is an "inplace" transpose that
    does an in memory transpose of an arrays elements.  This is the
    fastest approach and is beneficial if you don't need to keep the
    original array.
"""
# C:\home\ej\wrk\scipy\compiler\examples>python cast_copy_transpose.py
# Cast/Copy/Transposing (150,150)array 1 times
#  speed in python: 0.870999932289
#  speed in c: 0.25
#  speed up: 3.48
#  inplace transpose c: 0.129999995232
#  speed up: 6.70
from __future__ import absolute_import, print_function

import numpy
from numpy import *
import sys
sys.path.insert(0,'..')
import scipy.weave.inline_tools as inline_tools
import scipy.weave.c_spec as c_spec
from scipy.weave.converters import blitz as cblitz


def _cast_copy_transpose(type,a_2d):
    assert(len(shape(a_2d)) == 2)
    new_array = zeros(shape(a_2d),type)
    code = """
           for(int i = 0; i < Na_2d[0]; i++)
               for(int j = 0; j < Na_2d[1]; j++)
                   new_array(i,j) = a_2d(j,i);
           """
    inline_tools.inline(code,['new_array','a_2d'],
                        type_converters=cblitz,
                        compiler='gcc',
                        verbose=1)
    return new_array


def _cast_copy_transpose2(type,a_2d):
    assert(len(shape(a_2d)) == 2)
    new_array = zeros(shape(a_2d),type)
    code = """
           const int I = Na_2d[0];
           const int J = Na_2d[1];
           for(int i = 0; i < I; i++)
           {
               int new_off = i*J;
               int old_off = i;
               for(int j = 0; j < J; j++)
               {
                   new_array[new_off++] = a_2d[old_off];
                   old_off += I;
               }
           }
           """
    inline_tools.inline(code,['new_array','a_2d'],compiler='gcc',verbose=1)
    return new_array


def _inplace_transpose(a_2d):
    assert(len(shape(a_2d)) == 2)
    numeric_type = c_spec.num_to_c_types[a_2d.dtype.char]
    code = """
           %s temp;
           for(int i = 0; i < Na_2d[0]; i++)
               for(int j = 0; j < Na_2d[1]; j++)
               {
                   temp = a_2d(i,j);
                   a_2d(i,j) = a_2d(j,i);
                   a_2d(j,i) = temp;
               }
           """ % numeric_type
    inline_tools.inline(code,['a_2d'],
                        type_converters=cblitz,
                        compiler='gcc',
                        extra_compile_args=['-funroll-all-loops'],
                        verbose=2)
    return a_2d
    #assert(len(shape(a_2d)) == 2)
    #type = a_2d.typecode()
    #new_array = zeros(shape(a_2d),type)
    ##trans_a_2d = transpose(a_2d)
    #numeric_type = c_spec.num_to_c_types[type]
    #code = """
    #       for(int i = 0; i < Na_2d[0]; i++)
    #           for(int j = 0; j < Na_2d[1]; j++)
    #               new_array(i,j) = (%s) a_2d(j,i);
    #       """ % numeric_type
    #inline_tools.inline(code,['new_array','a_2d'],
    #                    type_converters = cblitz,
    #                    compiler='gcc',
    #                    verbose = 1)
    #return new_array


def cast_copy_transpose(type,*arrays):
    results = []
    for a in arrays:
        results.append(_cast_copy_transpose(type,a))
    if len(results) == 1:
        return results[0]
    else:
        return results


def cast_copy_transpose2(type,*arrays):
    results = []
    for a in arrays:
        results.append(_cast_copy_transpose2(type,a))
    if len(results) == 1:
        return results[0]
    else:
        return results


def inplace_cast_copy_transpose(*arrays):
    results = []
    for a in arrays:
        results.append(_inplace_transpose(a))
    if len(results) == 1:
        return results[0]
    else:
        return results


def _castCopyAndTranspose(type, *arrays):
    cast_arrays = ()
    import copy
    for a in arrays:
        if a.dtype == numpy.dtype(type):
            cast_arrays = cast_arrays + (copy.copy(numpy.transpose(a)),)
        else:
            cast_arrays = cast_arrays + (copy.copy(
                                       numpy.transpose(a).astype(type)),)
    if len(cast_arrays) == 1:
        return cast_arrays[0]
    else:
        return cast_arrays

import time


def compare(m,n):
    a = ones((n,n),float64)
    type = float32
    print('Cast/Copy/Transposing (%d,%d)array %d times' % (n,n,m))
    t1 = time.time()
    for i in range(m):
        for i in range(n):
            b = _castCopyAndTranspose(type,a)
    t2 = time.time()
    py = (t2-t1)
    print(' speed in python:', (t2 - t1)/m)

    # load into cache
    b = cast_copy_transpose(type,a)
    t1 = time.time()
    for i in range(m):
        for i in range(n):
            b = cast_copy_transpose(type,a)
    t2 = time.time()
    print(' speed in c (blitz):',(t2 - t1) / m)
    print(' speed up   (blitz): %3.2f' % (py/(t2-t1)))

    # load into cache
    b = cast_copy_transpose2(type,a)
    t1 = time.time()
    for i in range(m):
        for i in range(n):
            b = cast_copy_transpose2(type,a)
    t2 = time.time()
    print(' speed in c (pointers):',(t2 - t1) / m)
    print(' speed up   (pointers): %3.2f' % (py/(t2-t1)))

    # inplace tranpose
    b = _inplace_transpose(a)
    t1 = time.time()
    for i in range(m):
        for i in range(n):
            b = _inplace_transpose(a)
    t2 = time.time()
    print(' inplace transpose c:',(t2 - t1) / m)
    print(' speed up: %3.2f' % (py/(t2-t1)))

if __name__ == "__main__":
    m,n = 1,500
    compare(m,n)
