# TODO : Doc, Tests, PEP8 check

import numpy as np

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

	is0 = image.shape[0]
	is1 = image.shape[1]
	f0 = factors[0]
	f1 = factors[1]

	if (f0 - int(f0) != 0) or (f1 - int(f1) != 0):
		return "Use resample for non-integer downsampling"
	cropped = image[: is0 - (is0 % f0), : is1 - (is1 % f1)]
	out = np.zeros((cropped.shape[0] / f0, cropped.shape[1] / f1))

	if how == 'sum':
		for i in range(cropped.shape[0]):
			for j in range(cropped.shape[1]):
				out[int(i / f0)][int(j / f1)] += cropped[i][j]
		return out

	if how == 'mean':
		for i in range(cropped.shape[0]):
			for j in range(cropped.shape[1]):
				out[int(i / f0)][int(j / f1)] += cropped[i][j] / float(f0 * f1)
		return out


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

	is0 = image.shape[0]
	is1 = image.shape[1]
	f0 = factors[0]
	f1 = factors[1]

	if (f0 - int(f0) != 0) or (f1 - int(f1) != 0):
		return "Use resample for non-integer upsampling"
	out = np.zeros((f0 * image.shape[0], f1 * image.shape[1]))

	if how == 'divide':
		for i in range(out.shape[0]):
			for j in range(out.shape[1]):
				out[i][j] = (image[i / f0][j / f1]) / float(f0 * f1)
		return out

	if how == 'uniform':
		for i in range(out.shape[0]):
			for j in range(out.shape[1]):
				out[i][j] = (image[i / f0][j / f1])
		return out