Assignment3 in dsp coursera course. Efficient matrix implementation

assignment3.py

import numpy as np

def subband_filtering(x, h):
    """ ASSIGNMENT 3

        Write a routine to implement the efficient version of the subband filter
        as specified by the MP3 standard

        Arguments:
        x:  a new 512-point data buffer, in time-reversed order [x[n],x[n-1],...,x[n-511]].
        h:  The prototype filter of the filter bank you found in the previous assignment

        Returns:
        s: 32 new output samples
    """
    # Time reverse input and muliply by filter response
    r = np.multiply(h,x)
    # Reshape for further processing into 64 sub
    r = r.reshape(-1,64)
    # The weights, in this case only of size 8 : -1 1 -1 1 ...
    weights = np.array([1,-1]*4)
    #  do the sum(weights * r[:,q] for q in range(8)) with a matrix multiplication
    c = np.dot(np.transpose(r),weights)
    # generate two coordintes, ii and qq, which just loop over the array 
    ii , qq = np.ogrid[:32,:c.shape[0]]
    # Do \sum cos(....) * c[q], but with .dot
    s = np.squeeze(np.dot(np.cos(np.pi / 64 * (2*ii +1)*(qq -16)),c[qq].transpose()))
    return s