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src/c/h/chirp-1.2.2/chirp/common/signal.py   chirp(Download)
    def linspect(self, signal, Fs, nfft=None):
        """ Calculate the spectrogram on a linear power scale.  """
        import numpy as nx
        from libtfr import stft, tfr_spec, tgrid
        shift = int(self.options['window_shift'] * Fs)
            Np = nfft
        if self.options['spec_method'] == 'tfr':
            S = tfr_spec(signal, nfft, shift, Np,
                         K=self.options['tfr_order'], tm=self.options['tfr_tm'],
                         flock=self.options['tfr_flock'], tlock=self.options['tfr_tlock'])

src/c/h/chirp-1.2.2/chirp/pitch/tracker.py   chirp(Download)
        """
        options = self.options
        spec = libtfr.tfr_spec(signal, options['nfft'], options['shift'], options['winsize'],
                               options['tfr_order'], options['tfr_tm'], options['tfr_flock'],
                               options['tfr_tlock'], fgrid=self.template.fgrid)

src/c/h/chirp-1.2.2/chirp/pitch/template.py   chirp(Download)
        N = int(1 / self.pgrid[0] * 50)
        pt = pulse_train(self.pgrid[0], N)
        A = libtfr.tfr_spec(pt, N, 1, N, fgrid=self.fgrid)[:, 0]
        A, p = normalize_lobes(A, lobes, decay)
 
        # negative lobes generated by double frequency spacing
        # and then keeping every other peak.
        B = libtfr.tfr_spec(pt, N, 1, N, fgrid=self.fgrid * 2)[:, 0]