# TODO : Doc, Tests, PEP8 check

import numpy as np
from skimage.util.shape import view_as_blocks


def _pad_asymmetric_zeros(arr, pad_amt, axis=-1):
    """Pads `arr` by `pad_amt` along specified `axis`"""
    if axis == -1:
        axis = arr.ndim - 1

    zeroshape = tuple([x if i != axis else pad_amt
                       for (i, x) in enumerate(arr.shape)])

    return np.concatenate((arr, np.zeros(zeroshape, dtype=arr.dtype)),
                          axis=axis)


def downsample(image, factors, method='sum'):

    pad_size = []
    if len(factors) != image.ndim:
        raise ValueError("'factors' must have the same length "
                         "as 'image.shape'")
    else:
        for i in range(len(factors)):
            if image.shape[i] % factors[i] != 0:
                pad_size.append(factors[i] - (image.shape[i] % factors[i]))
            else:
                pad_size.append(0)

    for i in range(len(pad_size)):
        image = _pad_asymmetric_zeros(image, pad_size[i], i)

    out = view_as_blocks(image, factors)
    block_shape = out.shape

    if method == 'sum':
        for i in range(len(block_shape)/2):
            out = out.sum(-1)
    else:
        for i in range(len(block_shape)/2):
            out = out.mean(-1)
    return out

def upsample(image, factors, method='divide'):

    f = factors

    if (f[0] - int(f[0]) != 0) or (f[1] - int(f[1]) != 0):
        raise ValueError('Use resample() for non-integer upsampling')
    out = np.zeros((f[0] * image.shape[0], f[1] * image.shape[1]))

    for i in range(out.shape[0]):
        for j in range(out.shape[1]):
            out[i][j] = (image[i / f[0]][j / f[1]])
    if method == 'divide':
        return out / float(f[0] * f[1])
    else:
        return out