downsample code

gistfile1.py

def downsample2d(inputArray, kernelSize):
    """This function downsamples a 2d numpy array by convolving with a flat
    kernel and then sub-sampling the resulting array.

    A kernel size of 2 means convolution with a 2x2 array [[1, 1], [1, 1]] and
    a resulting downsampling of 2-fold.

    :param: inputArray: 2d numpy array
    :param: kernelSize: integer
    """
    average_kernel = np.ones((kernelSize,kernelSize))

    blurred_array = sig.convolve2d(inputArray, average_kernel, mode='same')
    downsampled_array = blurred_array[::kernelSize,::kernelSize]
    return downsampled_array

def downsample3d(inputArray, kernelSize):
    """This function downsamples a 3d numpy array (an image stack)
    by convolving each frame with a flat kernel and then sub-sampling the resulting array,
    re-building a smaller 3d numpy array.

    A kernel size of 2 means convolution with a 2x2 array [[1, 1], [1, 1]] and
    a resulting downsampling of 2-fold.

    The array will be downsampled in the first 2 dimensions, as shown below.

    import numpy as np
    >>> A = np.random.random((100,100,20))
    >>> B = downsample3d(A, 2)
    >>> A.shape
    (100, 100, 20)
    >>> B.shape
    (50, 50, 20)

    :param: inputArray: 2d numpy array
    :param: kernelSize: integer
    """
    first_smaller = downsample2d(inputArray[:,:,0], kernelSize)
    smaller = np.zeros((first_smaller.shape[0], first_smaller.shape[1], inputArray.shape[2]))
    smaller[:,:,0] = first_smaller

    for i in range(1, inputArray.shape[2]):
        smaller[:,:,i] = downsample2d(inputArray[:,:,i], kernelSize)
    return smaller