from . import rfft

import numpy as np
import scipy.signal as sig

def magnitudes_window_setup(s, size=8192):
    L = s.shape[0]
    overlap = 0.661
    step = np.ceil(size*(1 - overlap))
    segs = np.ceil(L/step)
    return step, segs

def magnitudes(s, size=8192):
    import scipy.linalg as linalg

    step, segs = magnitudes_window_setup(s, size)

    L = s.shape[0]

    # blindly pad with zeros for friendlier ffts and overlapping
    z = np.zeros(size)
    s = np.hstack((s, z))

    win_size = size

    win = sig.blackmanharris(win_size)
    win /= linalg.norm(win)

    count = 0
    for i in range(0, L - 1, int(step)):
        windowed = s[i:i+win_size]*win
        power = np.abs(rfft(windowed, size))**2
        # this scraps the nyquist value to get exactly size outputs
        yield power[0:size]
        count += 1

    #assert(segs == count)

def averfft(s, size=8192):
    """calculates frequency magnitudes by fft and averages them together."""
    step, segs = magnitudes_window_setup(s, size)

    avg = np.zeros(size)
    for power in magnitudes(s, size):
        avg += power/segs

    avg_db = 10*np.log10(avg)
    return avg_db