def convert_data(block):
"""return stimulus and response matrix"""
print "---------- Converting data set ----------"
fs_stim = 16000.
f_cutoff = (500., 8000.)
win_len = 0.04
fs_spec = 400. # bin with: 2 ms
filt_per_erb = 1.
n_samples = np.Inf
fb = GammatoneFilterbank(samplerate=fs_stim, f_cutoff=f_cutoff,
filt_per_erb=filt_per_erb,
spectype='magnitude', bw_factor=1.)
converter = DataConverter(fb, win_len=win_len, samplerate=fs_spec,
verbose=True, n_samples=n_samples,
scaling='dB', dynamic_range=60.,
center=True)
X, Y, rfsize, axes, stim_pos = converter.process(block)
print "Stimulus matrix: %d temp. steps x %d features" % X.shape
print "Spike matrix: %d temp. steps x %d trials" % Y.shape
print "%d spikes (%0.3f spikes per sample)" % (np.sum(Y), np.mean(Y))
return X, Y, rfsize, axes, fs_spec
def convert_data(block, t_spikefilt=0.015):
"""return stimulus and response matrix"""
print "---------- Converting data set ----------"
fs_stim = 16000.
f_cutoff = (500., 8000.)
win_len = 0.04
fs_spec = 500. # bin with: 2 ms
filt_per_erb = 1.
n_samples = np.Inf
fb = GammatoneFilterbank(samplerate=fs_stim, f_cutoff=f_cutoff,
filt_per_erb=filt_per_erb,
spectype='magnitude', bw_factor=1.)
converter = DataConverter(fb, win_len=win_len, samplerate=fs_spec,
verbose=True, n_samples=n_samples,
scaling='dB', dynamic_range=60.,
center=True)
X, Y, rfsize, axes, stim_pos = converter.process(block)
# Add post-spike filter features
n_spikefilt = int(np.ceil(t_spikefilt * fs_spec))
Y = Y[:, 0] # works only for single trials
Z = create_postfilt_features(Y, n_spikefilt)
X = np.hstack((X, Z)) # append to feature matrix
print "Stimulus matrix: %d temp. steps x %d features" % X.shape
print "%d spikes (%0.3f spikes per sample)" % (np.sum(Y), np.mean(Y))
print "Post-spike filter length: %d samples" % n_spikefilt
return X, Y, rfsize, axes, fs_spec, n_spikefilt
def convert_data(block):
"""return stimulus and response matrix"""
print "---------- Converting data set ----------"
fs_stim = 16000.
f_cutoff = (500., 8000.)
win_len = 0.04
fs_spec = 400. # bin with: 2 ms
filt_per_erb = 1.
n_samples = np.Inf
fb = GammatoneFilterbank(samplerate=fs_stim,
f_cutoff=f_cutoff,
filt_per_erb=filt_per_erb,
spectype='magnitude',
bw_factor=1.)
converter = DataConverter(fb,
win_len=win_len,
samplerate=fs_spec,
verbose=True,
n_samples=n_samples,
scaling='dB',
dynamic_range=60.,
center=True)
X, Y, rfsize, axes, stim_pos = converter.process(block)
print "Stimulus matrix: %d temp. steps x %d features" % X.shape
print "Spike matrix: %d temp. steps x %d trials" % Y.shape
print "%d spikes (%0.3f spikes per sample)" % (np.sum(Y), np.mean(Y))
return X, Y, rfsize, axes, fs_spec
