#!/usr/bin/env python

""" This work is licensed under a Creative Commons Attribution 3.0 Unported
    License.
    Frank Zalkow, 2012-2013
    http://www.frank-zalkow.de/en/code-snippets/create-audio-spectrograms-with-python.html?i=1
"""
# %matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
from pysndfile import sndio as snd
from numpy.lib import stride_tricks
""" short time fourier transform of audio signal """


def stft(sig, frameSize, overlapFac=0.5, window=np.hanning):
    win = window(frameSize)
    hopSize = int(frameSize - np.floor(overlapFac * frameSize))

    # zeros at beginning (thus center of 1st window should be for sample nr. 0)
    # sig = (sig*255).astype(np.uint8)
    # import pdb;pdb.set_trace()
    count = int(np.floor(frameSize / 2.0))
    # import pdb;pdb.set_trace()
    samples = np.append(np.zeros(count), sig)
    # cols for windowing
    cols = int(np.ceil((len(samples) - frameSize) / float(hopSize)) + 1)
    # zeros at end (thus samples can be fully covered by frames)
    samples = np.append(samples, np.zeros(frameSize))

    frames = stride_tricks.as_strided(
        samples,
        shape=(cols, frameSize),
        strides=(samples.strides[0] * hopSize, samples.strides[0])).copy()
    frames *= win

    return np.fft.rfft(frames)


""" scale frequency axis logarithmically """


def logscale_spec(spec, sr=44100, factor=20.):
    timebins, freqbins = np.shape(spec)

    scale = np.linspace(0, 1, freqbins)**factor
    scale *= (freqbins - 1) / max(scale)
    scale = np.unique(np.round(scale)).astype(np.uint32)
    # import pdb;pdb.set_trace()
    # create spectrogram with new freq bins
    newspec = np.complex128(np.zeros([timebins, len(scale)]))
    for i in range(0, len(scale)):
        if i == len(scale) - 1:
            newspec[:, i] = np.sum(spec[:, scale[i]:], axis=1)
        else:
            newspec[:, i] = np.sum(spec[:, scale[i]:scale[i + 1]], axis=1)

    # list center freq of bins
    allfreqs = np.abs(np.fft.fftfreq(freqbins * 2, 1. / sr)[:freqbins + 1])
    freqs = []
    for i in range(0, len(scale)):
        if i == len(scale) - 1:
            freqs += [np.mean(allfreqs[scale[i]:])]
        else:
            freqs += [np.mean(allfreqs[scale[i]:scale[i + 1]])]

    return newspec, freqs


""" generate spectrogram for aiff audio with 150ms windows and 50ms overlap"""


def generate_aiff_spectrogram(audiopath):
    samples, samplerate, _ = snd.read(audiopath)
    # samplerate, samples = wav.read(audiopath)
    # s = stft(samples, binsize)
    s = stft(samples, samplerate * 150 // 1000, 1.0 / 3)

    sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
    ims = 20. * np.log10(np.abs(sshow) / 10e-6)
    return ims


""" plot spectrogram"""


def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
    samples, samplerate, _ = snd.read(audiopath)
    # samplerate, samples = wav.read(audiopath)
    # s = stft(samples, binsize)
    # print(samplerate*150//1000)
    s = stft(samples, samplerate * 150 // 1000, 1.0 / 3)

    sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
    ims = 20. * np.log10(np.abs(sshow) / 10e-6)  # amplitude to decibel

    timebins, freqbins = np.shape(ims)
    # import pdb;pdb.set_trace()
    plt.figure(figsize=(15, 7.5))
    plt.imshow(
        np.transpose(ims),
        origin="lower",
        aspect="auto",
        cmap=colormap,
        interpolation="none")
    plt.colorbar()

    plt.xlabel("time (s)")
    plt.ylabel("frequency (hz)")
    plt.xlim([0, timebins - 1])
    plt.ylim([0, freqbins])

    xlocs = np.float32(np.linspace(0, timebins - 1, 5))
    plt.xticks(xlocs, [
        "%.02f" % l
        for l in (
            (xlocs * len(samples) / timebins) + (0.5 * binsize)) / samplerate
    ])
    ylocs = np.int16(np.round(np.linspace(0, freqbins - 1, 10)))
    plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])

    if plotpath:
        plt.savefig(plotpath, bbox_inches="tight")
    else:
        plt.show()

    plt.clf()


snd.get_info('./outputs/sunflowers-Alex-150-normal-589.aiff')
snd_data_arr = snd.read('./outputs/sunflowers-Alex-150-normal-589.aiff')[0]
snd_data = snd_data_arr.tobytes()
snd_data_arr.dtype
len(snd_data)

if __name__ == '__main__':
    plotstft('./outputs/sunflowers-Alex-150-normal-589.aiff')
    plotstft('./outputs/sunflowers-Alex-180-normal-4763.aiff')
    plotstft('./outputs/sunflowers-Victoria-180-normal-870.aiff')
    plotstft('./outputs/sunflowers-Fred-180-phoneme-9733.aiff')
    plotstft('./outputs/sunflowers-Fred-180-normal-6515.aiff')