from . import tau

import numpy as np

def sweep(amp, length, begin=20, end=20480, method='linear'):
    method = method or 'linear'
    xs = np.arange(length)/length
    if method in ('linear', 'quadratic', 'logarithmic', 'hyperbolic'):
        ys = amp*sig.chirp(xs, begin, 1, end, method=method)
    elif method is 'sinesweep':
        ang = lambda f: tau*f
        # because xs ranges from 0:1, length is 1 and has been simplified out
        domain = np.log(ang(end)/ang(begin))
        ys = amp*np.sin(ang(begin)/domain*(np.exp(xs*domain) - 1))
    return ys

def tsp(N, m=0.5):
    """
        OATSP(Optimized Aoshima's Time-Stretched Pulse) generator
        x = tsp( N, m )
        N : length of the time-stretched pulse
        m : ratio of the swept sine  (0 < m < 1)

        Author(s): Seigo UTO 8-23-95

        Reference:
           Yoiti SUZUKI, Futoshi ASANO, Hack-Yoon KIM and Toshio SONE,
           "Considerations on the Design of Time-Stretched Pulses,"
               Techical Report of IEICE, EA92-86(1992-12)
    """
    # http://www.sound.sie.dendai.ac.jp/dsp/e-21.html

    if m < 0 or m > 1:
        raise Exception("sdfgsdfgsdg")

    if N < 0:
        raise Exception("The number of length must be the positive number")

    NN = 2**np.floor(np.log2(N)) # nearest
    NN2 = NN//2
    M = np.round(NN2*m)

    nn2 = np.arange(NN2 + 1)**2

    j = np.complex(0, 1)

    H = np.exp(j*4*M*np.pi*nn2/NN**2)
    H2 = np.hstack([H, np.conj(H[1:NN2][::-1])])

    x = np.fft.ifft(H2)
    x = np.hstack([x[NN2 - M:NN + 1], x[0:NN2 - M + 1]])
    x = np.hstack([x.real, np.zeros(1, N - NN)])

    return x