# -*- coding: utf-8 -*-
# Copyright 2014 João Felipe Santos, jfsantos@emt.inrs.ca
#
# This file is part of the SRMRpy library, and is licensed under the
# MIT license: https://github.com/jfsantos/SRMRpy/blob/master/LICENSE

"""segmentaxis code, originally in scikits.talkbox (https://pypi.python.org/pypi/scikits.talkbox)

This code has been implemented by Anne Archibald, and has been discussed on the
ML."""
from __future__  import division
import numpy as np
import warnings

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)
            if end in ['pad','wrap']:
                b[..., :l] = a
            if end == 'pad':
                b[..., l:] = endvalue
            elif end == 'wrap':
                b[..., l:] = a[..., :roundup-l]
            a = b
        elif end == 'delay':
            s = list(a.shape)
            l_orig = l
            l += overlap
            # if l not divisible by length, pad last frame with zeros
            if l_orig % (length-overlap):
                roundup = length + (1+(l-length)//(length-overlap))*(length-overlap)
            else:
                roundup = l
            s[-1] = roundup
            b = np.empty(s,dtype=a.dtype)

            b[..., :(overlap)] = endvalue
            b[..., (overlap):(l_orig+overlap)] = a
            b[..., (l_orig+overlap):] = endvalue
            a = b
        else:
            raise ValueError("end has to be either 'cut', 'pad', 'wrap', or 'delay'.")

        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)