def isolation_score(sp, spt, sp_win, spike_type='positive', lam=10., max_spikes=None):
"calculate spike isolation score from raw data and spike times"
spike_waves = extract.extract_spikes(sp, spt, sp_win)
spt_noise = detect_noise(sp, spt, sp_win, spike_type)
noise_waves = extract.extract_spikes(sp, spt_noise, sp_win)
iso_score = calc_isolation_score(spike_waves, noise_waves,
spike_type, lam=lam, max_spikes=max_spikes)
return iso_score
def detect_noise(sp,
spt,
sp_win,
type="positive",
max_spikes=None,
resample=1):
"""Find noisy spikes"""
spike_waves = extract.extract_spikes(sp, spt, sp_win)
if type == "positive":
threshold = calc_noise_threshold(spike_waves, 1)
spt_noise = extract.detect_spikes(sp, threshold, 'rising')
spt_noise = rand_sample_spt(spt_noise, max_spikes)
spt_noise = extract.remove_spikes(spt_noise, spt, sp_win)
spt_noise = extract.align_spikes(sp,
spt_noise,
sp_win,
'max',
resample=resample)
else:
threshold = calc_noise_threshold(spike_waves, -1)
spt_noise = extract.detect_spikes(sp, threshold, 'falling')
spt_noise = rand_sample_spt(spt_noise, max_spikes)
spt_noise = extract.remove_spikes(spt_noise, spt, sp_win)
spt_noise = extract.align_spikes(sp,
spt_noise,
sp_win,
'min',
resample=resample)
return spt_noise
def extract_noise_cluster(sp, spt, sp_win, type="positive"):
deprecation("extract_noise_cluster deprecated. Use"
" detect_noise and extract_spikes instead.")
spt_noise = detect_noise(sp, spt, sp_win, type)
sp_waves = extract.extract_spikes(sp, spt_noise, sp_win)
return sp_waves
def extract_noise_cluster(sp, spt, sp_win, type="positive"):
deprecation("extract_noise_cluster deprecated. Use"
" detect_noise and extract_spikes instead.")
spt_noise = detect_noise(sp, spt, sp_win, type)
sp_waves = extract.extract_spikes(sp, spt_noise, sp_win)
return sp_waves
def isolation_score(sp,
spt,
sp_win,
spike_type='positive',
lam=10.,
max_spikes=None):
"calculate spike isolation score from raw data and spike times"
spike_waves = extract.extract_spikes(sp, spt, sp_win)
spt_noise = detect_noise(sp, spt, sp_win, spike_type)
noise_waves = extract.extract_spikes(sp, spt_noise, sp_win)
iso_score = calc_isolation_score(spike_waves,
noise_waves,
spike_type,
lam=lam,
max_spikes=max_spikes)
return iso_score
def detect_noise(sp, spt, sp_win, type="positive", max_spikes=None,
resample=1):
"""Find noisy spikes"""
spike_waves = extract.extract_spikes(sp, spt, sp_win)
if type == "positive":
threshold = calc_noise_threshold(spike_waves, 1)
spt_noise = extract.detect_spikes(sp, threshold, 'rising')
spt_noise = rand_sample_spt(spt_noise, max_spikes)
spt_noise = extract.remove_spikes(spt_noise, spt, sp_win)
spt_noise = extract.align_spikes(sp, spt_noise, sp_win, 'max',
resample=resample)
else:
threshold = calc_noise_threshold(spike_waves, -1)
spt_noise = extract.detect_spikes(sp, threshold, 'falling')
spt_noise = rand_sample_spt(spt_noise, max_spikes)
spt_noise = extract.remove_spikes(spt_noise, spt, sp_win)
spt_noise = extract.align_spikes(sp, spt_noise, sp_win, 'min',
resample=resample)
return spt_noise
