#!/usr/bin/env python
#coding: utf-8
""" 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()

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')