# Path: scikits/talkbox/tools/segmentaxis.py
# def segment_axis(a, length, overlap=0, axis=None, end='cut', endvalue=0):
#     """Generate a new array that chops the given array along the given axis
#     into overlapping frames.
# 
#     example:
#     >>> segment_axis(arange(10), 4, 2)
#     array([[0, 1, 2, 3],
#            [2, 3, 4, 5],
#            [4, 5, 6, 7],
#            [6, 7, 8, 9]])
# 
#     arguments:
#     a       The array to segment
#     length  The length of each frame
#     overlap The number of array elements by which the frames should overlap
#     axis    The axis to operate on; if None, act on the flattened array
#     end     What to do with the last frame, if the array is not evenly
#             divisible into pieces. Options are:
# 
#             'cut'   Simply discard the extra values
#             'wrap'  Copy values from the beginning of the array
#             'pad'   Pad with a constant value
# 
#     endvalue    The value to use for end='pad'
# 
#     The array is not copied unless necessary (either because it is unevenly
#     strided and being flattened or because end is set to 'pad' or 'wrap').
#     """
# 
#     if axis is None:
#         a = np.ravel(a) # may copy
#         axis = 0
# 
#     l = a.shape[axis]
# 
#     if overlap >= length:
#         raise ValueError, "frames cannot overlap by more than 100%"
#     if overlap < 0 or length <= 0:
#         raise ValueError, "overlap must be nonnegative and length must "\
#                           "be positive"
# 
#     if l < length or (l-length) % (length-overlap):
#         if l>length:
#             roundup = length + (1+(l-length)//(length-overlap))*(length-overlap)
#             rounddown = length + ((l-length)//(length-overlap))*(length-overlap)
#         else:
#             roundup = length
#             rounddown = 0
#         assert rounddown < l < roundup
#         assert roundup == rounddown + (length-overlap) \
#                or (roundup == length and rounddown == 0)
#         a = a.swapaxes(-1,axis)
# 
#         if end == 'cut':
#             a = a[..., :rounddown]
#         elif end in ['pad','wrap']: # copying will be necessary
#             s = list(a.shape)
#             s[-1] = roundup
#             b = np.empty(s,dtype=a.dtype)
#             b[..., :l] = a
#             if end == 'pad':
#                 b[..., l:] = endvalue
#             elif end == 'wrap':
#                 b[..., l:] = a[..., :roundup-l]
#             a = b
# 
#         a = a.swapaxes(-1,axis)
# 
# 
#     l = a.shape[axis]
#     if l == 0:
#         raise ValueError, \
#               "Not enough data points to segment array in 'cut' mode; "\
#               "try 'pad' or 'wrap'"
#     assert l >= length
#     assert (l-length) % (length-overlap) == 0
#     n = 1 + (l-length) // (length-overlap)
#     s = a.strides[axis]
#     newshape = a.shape[:axis] + (n,length) + a.shape[axis+1:]
#     newstrides = a.strides[:axis] + ((length-overlap)*s,s) + a.strides[axis+1:]
# 
#     try:
#         return np.ndarray.__new__(np.ndarray, strides=newstrides,
#                                   shape=newshape, buffer=a, dtype=a.dtype)
#     except TypeError:
#         warnings.warn("Problem with ndarray creation forces copy.")
#         a = a.copy()
#         # Shape doesn't change but strides does
#         newstrides = a.strides[:axis] + ((length-overlap)*s,s) \
#                      + a.strides[axis+1:]
#         return np.ndarray.__new__(np.ndarray, strides=newstrides,
#                                   shape=newshape, buffer=a, dtype=a.dtype)


import numpy as np
from numpy.testing import TestCase, assert_array_almost_equal, assert_equal
from scikits.talkbox.tools.segmentaxis import segment_axis


class TestSegmentAxis(TestCase):
    def test1(self):
        x = np.linspace(0, 9, 10)

        y = segment_axis(x, 4)
        assert_array_almost_equal(y, np.array([[0, 1, 2, 3], [4, 5, 6, 7]]))

        y = segment_axis(x, 4, 2)
        assert_array_almost_equal(y, np.array([[0, 1, 2, 3], [2, 3, 4, 5], 
                                               [4, 5, 6, 7], [6, 7, 8, 9]]))

        y = segment_axis(x, 3, 2)
        assert_array_almost_equal(y, np.array([[0, 1, 2], [1, 2, 3], 
                                               [2, 3, 4], [3, 4, 5],
                                               [4, 5, 6], [5, 6, 7],
                                               [6, 7, 8], [7, 8, 9]]))


# Path: scikits/talkbox/tools/segmentaxis.py
# def segment_axis(a, length, overlap=0, axis=None, end='cut', endvalue=0):
#     """Generate a new array that chops the given array along the given axis
#     into overlapping frames.
# 
#     example:
#     >>> segment_axis(arange(10), 4, 2)
#     array([[0, 1, 2, 3],
#            [2, 3, 4, 5],
#            [4, 5, 6, 7],
#            [6, 7, 8, 9]])
# 
#     arguments:
#     a       The array to segment
#     length  The length of each frame
#     overlap The number of array elements by which the frames should overlap
#     axis    The axis to operate on; if None, act on the flattened array
#     end     What to do with the last frame, if the array is not evenly
#             divisible into pieces. Options are:
# 
#             'cut'   Simply discard the extra values
#             'wrap'  Copy values from the beginning of the array
#             'pad'   Pad with a constant value
# 
#     endvalue    The value to use for end='pad'
# 
#     The array is not copied unless necessary (either because it is unevenly
#     strided and being flattened or because end is set to 'pad' or 'wrap').
#     """
# 
#     if axis is None:
#         a = np.ravel(a) # may copy
#         axis = 0
# 
#     l = a.shape[axis]
# 
#     if overlap >= length:
#         raise ValueError, "frames cannot overlap by more than 100%"
#     if overlap < 0 or length <= 0:
#         raise ValueError, "overlap must be nonnegative and length must "\
#                           "be positive"
# 
#     if l < length or (l-length) % (length-overlap):
#         if l>length:
#             roundup = length + (1+(l-length)//(length-overlap))*(length-overlap)
#             rounddown = length + ((l-length)//(length-overlap))*(length-overlap)
#         else:
#             roundup = length
#             rounddown = 0
#         assert rounddown < l < roundup
#         assert roundup == rounddown + (length-overlap) \
#                or (roundup == length and rounddown == 0)
#         a = a.swapaxes(-1,axis)
# 
#         if end == 'cut':
#             a = a[..., :rounddown]
#         elif end in ['pad','wrap']: # copying will be necessary
#             s = list(a.shape)
#             s[-1] = roundup
#             b = np.empty(s,dtype=a.dtype)
#             b[..., :l] = a
#             if end == 'pad':
#                 b[..., l:] = endvalue
#             elif end == 'wrap':
#                 b[..., l:] = a[..., :roundup-l]
#             a = b
# 
#         a = a.swapaxes(-1,axis)
# 
# 
#     l = a.shape[axis]
#     if l == 0:
#         raise ValueError, \
#               "Not enough data points to segment array in 'cut' mode; "\
#               "try 'pad' or 'wrap'"
#     assert l >= length
#     assert (l-length) % (length-overlap) == 0
#     n = 1 + (l-length) // (length-overlap)
#     s = a.strides[axis]
#     newshape = a.shape[:axis] + (n,length) + a.shape[axis+1:]
#     newstrides = a.strides[:axis] + ((length-overlap)*s,s) + a.strides[axis+1:]
# 
#     try:
#         return np.ndarray.__new__(np.ndarray, strides=newstrides,
#                                   shape=newshape, buffer=a, dtype=a.dtype)
#     except TypeError:
#         warnings.warn("Problem with ndarray creation forces copy.")
#         a = a.copy()
#         # Shape doesn't change but strides does
#         newstrides = a.strides[:axis] + ((length-overlap)*s,s) \
#                      + a.strides[axis+1:]
#         return np.ndarray.__new__(np.ndarray, strides=newstrides,
#                                   shape=newshape, buffer=a, dtype=a.dtype)


# Path: scikits/talkbox/tools/tests/test_segmentaxis.py
import numpy as np
from numpy.testing import TestCase, assert_array_almost_equal, assert_equal
from scikits.talkbox.tools.segmentaxis import segment_axis



class TestSegmentAxis(TestCase):
    def test1(self):
        x = np.linspace(0, 9, 10)

        y = segment_axis(x, 4)
        assert_array_almost_equal(y, np.array([[0, 1, 2, 3], [4, 5, 6, 7]]))

        y = segment_axis(x, 4, 2)
        assert_array_almost_equal(y, np.array([[0, 1, 2, 3], [2, 3, 4, 5], 
                                               [4, 5, 6, 7], [6, 7, 8, 9]]))

        y = segment_axis(x, 3, 2)
        assert_array_almost_equal(y, np.array([[0, 1, 2], [1, 2, 3], 
                                               [2, 3, 4], [3, 4, 5],
                                               [4, 5, 6], [5, 6, 7],
                                               [6, 7, 8], [7, 8, 9]]))


        y = segment_axis(x, 4, 3)