def test_tgrid():
nfft = 256
shift = 10
ntapers = 5
D = libtfr.mfft_dpss(nfft, 3, ntapers, nfft)
Z = D.mtstft(sig, shift)
tgrid1 = libtfr.tgrid(sig.size, 1, shift)
tgrid2 = libtfr.tgrid(Z, 1, shift)
def test_tgrid():
nfft = 256
shift = 10
ntapers = 5
D = libtfr.mfft_dpss(nfft, 3, ntapers, nfft)
Z = D.mtstft(sig, shift)
tgrid1 = libtfr.tgrid(sig.size, 1, shift)
tgrid2 = libtfr.tgrid(Z, 1, shift)
def matched_spectrogram(self, signal, Fs):
"""
Calculate a spectrogram of a signal using the same parameters
as the template. Returns spectrogram, time grid, and freq grid.
"""
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)
return spec, libtfr.tgrid(spec, Fs, options['shift']), self.template.fgrid * Fs
def specgram(x, NFFT=256, shift=128, Fs=1.0, drange=60,
ax=None, cmap=mplt.cm.gray_r, **kwargs):
from numpy import hanning
from libtfr import stft, fgrid, tgrid, dynamic_range
w = hanning(NFFT)
S = stft(x, w, shift)
F,ind = fgrid(Fs,NFFT,(0,Fs/2))
T = tgrid(x.size, Fs, shift)
S = nx.log10(dynamic_range(S, drange))
if ax is None: ax = mplt.gca()
ax.imshow(S, extent = (T[0],T[-1],F[0]-0.01,F[-1]), cmap=cmap, **kwargs)
return S
def specgram(x, NFFT=256, shift=128, Fs=1.0, drange=60,
ax=None, cmap=mplt.cm.gray_r, **kwargs):
from numpy import hanning
from libtfr import stft, fgrid, tgrid, dynamic_range
w = hanning(NFFT)
S = stft(x, w, shift)
F,ind = fgrid(Fs,NFFT,(0,Fs/2))
T = tgrid(x.size, Fs, shift)
S = nx.log10(dynamic_range(S, drange))
if ax is None: ax = mplt.gca()
ax.imshow(S, extent = (T[0],T[-1],F[0]-0.01,F[-1]), cmap=cmap, **kwargs)
return S
def specgram(signal, sampling_rate, nfft=256, shift=128, compress=1):
"""
Calculate a spectrogram of signal.
Parameters:
signal - the input signal (needs to be a 1D array)
nfft - the window size, in points
shift - how much to shift the window in each frame, in points
sampling_rate - the number of samples per second in the signal
compress - how much to compress the spectrogram. Effectively sets the floor of the
spectrogram at log10(compress)
Returns: the spectrogram, the frequency grid, and the time grid
"""
from numpy import log10
import libtfr
# generate a transform object
D = libtfr.mfft_dpss(nfft, 3, 5, nfft)
# calculate the power spectrogram
P = D.mtspec(signal, shift)
freq, find = libtfr.fgrid(sampling_rate, nfft)
bins = libtfr.tgrid(P, sampling_rate, shift)
return (log10(P + compress) - log10(compress), freq, bins)
