def pause_metrics(self):
"""Estimate average number of pauses and average fraction of time spent in a pause
Attempts to detect pauses with a variety of conditions and averages results together.
Pauses that are consistently detected contribute more to estimates.
Returns
-------
avg_n_pauses : average number of pauses detected across conditions
avg_pause_frac : average fraction of interval (between start and end) spent in a pause
max_reliability : max fraction of times most reliable pause was detected given weights tested
n_max_rel_pauses : number of pauses detected with `max_reliability`
"""
thresholds = self._spikes_df["threshold_index"].values.astype(int)
isis = ft.get_isis(self.t, thresholds)
weight = 1.0
pause_list = self._process_pauses(weight)
if len(pause_list) == 0:
return 0, 0.
n_pauses = len(pause_list)
pause_frac = isis[pause_list].sum()
pause_frac /= self.end - self.start
return n_pauses, pause_frac
def _process_spike_related_features(self):
t = self.t
if len(self._spikes_df) == 0:
self._sweep_features["avg_rate"] = 0
return
thresholds = self._spikes_df["threshold_index"].values.astype(int)
isis = ft.get_isis(t, thresholds)
with warnings.catch_warnings():
# ignore mean of empty slice warnings here
warnings.filterwarnings("ignore", category=RuntimeWarning, module="numpy")
sweep_level_features = {
"adapt": ft.adaptation_index(isis),
"latency": ft.latency(t, thresholds, self.start),
"isi_cv": (isis.std() / isis.mean()) if len(isis) >= 1 else np.nan,
"mean_isi": isis.mean(),
"median_isi": np.median(isis),
"first_isi": isis[0] if len(isis) >= 1 else np.nan,
"avg_rate": ft.average_rate(t, thresholds, self.start, self.end),
}
for k, v in sweep_level_features.iteritems():
self._sweep_features[k] = v
def _process_pauses(self, cost_weight=1.0):
# Pauses are unusually long ISIs with a "detour reset" among delay resets
thresholds = self._spikes_df["threshold_index"].values.astype(int)
isis = ft.get_isis(self.t, thresholds)
isi_types = self._spikes_df["isi_type"][:-1].values
return ft.detect_pauses(isis, isi_types, cost_weight)
def pause_metrics(self):
"""Estimate average number of pauses and average fraction of time spent in a pause
Attempts to detect pauses with a variety of conditions and averages results together.
Pauses that are consistently detected contribute more to estimates.
Returns
-------
avg_n_pauses : average number of pauses detected across conditions
avg_pause_frac : average fraction of interval (between start and end) spent in a pause
max_reliability : max fraction of times most reliable pause was detected given weights tested
n_max_rel_pauses : number of pauses detected with `max_reliability`
"""
thresholds = self._spikes_df["threshold_index"].values.astype(int)
isis = ft.get_isis(self.t, thresholds)
weight = 1.0
pause_list = self._process_pauses(weight)
if len(pause_list) == 0:
return 0, 0.
n_pauses = len(pause_list)
pause_frac = isis[pause_list].sum()
pause_frac /= self.end - self.start
return n_pauses, pause_frac
def _process_pauses(self, cost_weight=1.0):
# Pauses are unusually long ISIs with a "detour reset" among delay resets
thresholds = self._spikes_df["threshold_index"].values.astype(int)
isis = ft.get_isis(self.t, thresholds)
isi_types = self._spikes_df["isi_type"][:-1].values
return ft.detect_pauses(isis, isi_types, cost_weight)
def _process_spike_related_features(self):
t = self.t
if len(self._spikes_df) == 0:
self._sweep_features["avg_rate"] = 0
return
thresholds = self._spikes_df["threshold_index"].values.astype(int)
isis = ft.get_isis(t, thresholds)
with warnings.catch_warnings():
# ignore mean of empty slice warnings here
warnings.filterwarnings("ignore",
category=RuntimeWarning,
module="numpy")
sweep_level_features = {
"adapt": ft.adaptation_index(isis),
"latency": ft.latency(t, thresholds, self.start),
"isi_cv":
(isis.std() / isis.mean()) if len(isis) >= 1 else np.nan,
"mean_isi": isis.mean(),
"median_isi": np.median(isis),
"first_isi": isis[0] if len(isis) >= 1 else np.nan,
"avg_rate": ft.average_rate(t, thresholds, self.start,
self.end),
}
for k, v in sweep_level_features.iteritems():
self._sweep_features[k] = v
def _process_spike_related_features(self):
t = self.t
if len(self._spikes_df) == 0:
self._sweep_features["avg_rate"] = 0
return
# Start recently added
peak_heights = None
if not self._spikes_df.empty:
peak_heights = self._spikes_df['peak_v'].values - self._spikes_df['threshold_v'].values
# End recently added
thresholds = self._spikes_df["threshold_index"].values.astype(int)
isis = ft.get_isis(t, thresholds)
with warnings.catch_warnings():
# ignore mean of empty slice warnings here
warnings.filterwarnings("ignore", category=RuntimeWarning, module="numpy")
sweep_level_features = {
"adapt": ft.adaptation_index(isis),
"latency": ft.latency(t, thresholds, self.start),
"isi_cv": (isis.std() / isis.mean()) if len(isis) >= 1 else np.nan,
"mean_isi": isis.mean() if len(isis) > 0 else np.nan,
"median_isi": np.median(isis),
"first_isi": isis[0] if len(isis) >= 1 else np.nan,
# We want at least 3 peaks (i.e. 2 isis) to calculate the adaptation index (given in percentage)
"isi_adapt": (isis[1]/isis[0]) if len(isis) >= 2 else np.nan,
# Start recently added
#"AP_amp_adapt": self._spikes_df['peak_height'][1]/self._spikes_df['peak_height'][0] if self._spikes_df.shape[1] >= 2 else np.nan,
"AP_amp_adapt": (peak_heights[1]/peak_heights[0]) if peak_heights.size >= 2 else np.nan,
#"AP_amp_change": ft.ap_amp_change(self._spikes_df['peak_height'].values) if self._spikes_df.shape.shape[1] >= 2 else np.nan,
"AP_amp_adapt_average": ft.ap_amp_adaptation(peak_heights) if peak_heights.size >= 2 else np.nan,
# End recently added
"AP_fano_factor": ((peak_heights.std()**2)/peak_heights.mean()) if peak_heights.size >=2 else np.nan,
"AP_cv": ((peak_heights.std())/peak_heights.mean()) if peak_heights.size >=2 else np.nan,
"isi_adapt_average": ft.isi_adaptation(isis) if len(isis) >= 2 else np.nan,
#"norm_sq_isis": ft.norm_sq_diff(isis) if len(isis) >= 2 else np.nan,
# You could in principle make the Fano factor and cv 0 for n = 1 ISI, but we choose to make them Nan, i.e. they
# are not that informative here
"fano_factor": ((isis.std()**2) / isis.mean()) if len(isis) > 1 else np.nan,
"cv": (isis.std() / isis.mean()) if len(isis) > 1 else np.nan,
"avg_rate": ft.average_rate(t, thresholds, self.start, self.end)
}
for k, v in six.iteritems(sweep_level_features):
self._sweep_features[k] = v
def _process_bursts(self, tol=0.5, pause_cost=1.0):
thresholds = self._spikes_df["threshold_index"].values.astype(int)
isis = ft.get_isis(self.t, thresholds)
isi_types = self._spikes_df["isi_type"][:-1].values
fast_tr_v = self._spikes_df["fast_trough_v"].values
fast_tr_t = self._spikes_df["fast_trough_t"].values
slow_tr_v = self._spikes_df["slow_trough_v"].values
slow_tr_t = self._spikes_df["slow_trough_t"].values
thr_v = self._spikes_df["threshold_v"].values
bursts = ft.detect_bursts(isis, isi_types, fast_tr_v, fast_tr_t, slow_tr_v, slow_tr_t,
thr_v, tol, pause_cost)
return np.array(bursts)
def _process_bursts(self, tol=0.5, pause_cost=1.0):
thresholds = self._spikes_df["threshold_index"].values.astype(int)
isis = ft.get_isis(self.t, thresholds)
isi_types = self._spikes_df["isi_type"][:-1].values
fast_tr_v = self._spikes_df["fast_trough_v"].values
fast_tr_t = self._spikes_df["fast_trough_t"].values
slow_tr_v = self._spikes_df["slow_trough_v"].values
slow_tr_t = self._spikes_df["slow_trough_t"].values
thr_v = self._spikes_df["threshold_v"].values
bursts = ft.detect_bursts(isis, isi_types, fast_tr_v, fast_tr_t, slow_tr_v, slow_tr_t,
thr_v, tol, pause_cost)
return np.array(bursts)
