Example #1
0
def motograms_from_file(file_name, wav_file_dir):

    hf = h5py.File(file_name, 'r')

    motograms = list()

    for md5, stim_group in hf['motograms'].iteritems():

        wav_file_name = os.path.join(wav_file_dir, '%s.wav' % md5)
        if not os.path.exists(wav_file_name):
            print 'No such file: %s' % wav_file_name
            continue

        intercept = stim_group.attrs['intercept']
        slope = stim_group.attrs['slope']

        wf = WavFile(wav_file_name, log_spectrogram=False)
        wf.analyze(min_freq=300,
                   max_freq=8000,
                   spec_sample_rate=1000.0,
                   freq_spacing=125.0)

        for split_name, split_group in stim_group.iteritems():

            mgram = np.array(split_group['motogram'])
            time = mgram[:, 0]
            alpha = mgram[:, 1]
            beta = -1.0 * (
                slope * alpha + intercept
            )  # sign inversion from difference between Hedi's and Mike's models
            mu = mgram[:, 2]
            sigma1 = mgram[:, 3]
            sigma2 = mgram[:, 4]
            fitvals = mgram[:, 5]

            motogram = Motogram()
            motogram.alpha = alpha
            motogram.beta = beta
            motogram.mu = mu
            motogram.sigma1 = sigma1
            motogram.sigma2 = sigma2

            motogram.fitvals = fitvals
            motogram.hedi_spectrogram = np.array(split_group['spectrogram'])
            motogram.hedi_spectrogram_freq = np.array(
                split_group['frequencies'])
            motogram.start_time = split_group.attrs['start_time'] / 1000.0
            motogram.end_time = split_group.attrs['end_time'] / 1000.0
            motogram.md5 = md5
            motogram.wav_file = wf
            motogram.wav_file_name = wav_file_name

            motograms.append(motogram)

    hf.close()

    return motograms
Example #2
0
    def __init__(self, output, duration, dt):
        self.output = output
        self.duration = duration
        self.dt = dt

        wf = WavFile()
        wf.sample_rate = 1.0 / dt
        wf.data = self.output[:, 0]
        wf.analyze()

        self.wav_file = wf
Example #3
0
def motograms_from_file(file_name, wav_file_dir):

    hf = h5py.File(file_name, 'r')

    motograms = list()

    for md5,stim_group in hf['motograms'].iteritems():

        wav_file_name = os.path.join(wav_file_dir, '%s.wav' % md5)
        if not os.path.exists(wav_file_name):
            print 'No such file: %s' % wav_file_name
            continue

        intercept = stim_group.attrs['intercept']
        slope = stim_group.attrs['slope']

        wf = WavFile(wav_file_name, log_spectrogram=False)
        wf.analyze(min_freq=300, max_freq=8000, spec_sample_rate=1000.0, freq_spacing=125.0)

        for split_name,split_group in stim_group.iteritems():

            mgram = np.array(split_group['motogram'])
            time = mgram[:, 0]
            alpha = mgram[:, 1]
            beta = -1.0 * (slope*alpha + intercept)  # sign inversion from difference between Hedi's and Mike's models
            mu = mgram[:, 2]
            sigma1 = mgram[:, 3]
            sigma2 = mgram[:, 4]
            fitvals = mgram[:, 5]

            motogram = Motogram()
            motogram.alpha = alpha
            motogram.beta = beta
            motogram.mu = mu
            motogram.sigma1 = sigma1
            motogram.sigma2 = sigma2

            motogram.fitvals = fitvals
            motogram.hedi_spectrogram = np.array(split_group['spectrogram'])
            motogram.hedi_spectrogram_freq = np.array(split_group['frequencies'])
            motogram.start_time = split_group.attrs['start_time'] / 1000.0
            motogram.end_time = split_group.attrs['end_time'] / 1000.0
            motogram.md5 = md5
            motogram.wav_file = wf
            motogram.wav_file_name = wav_file_name

            motograms.append(motogram)

    hf.close()

    return motograms