def detect_epochs(self):
"""
Detect epochs if they are not provided in the constructor
"""
if "test" not in self.epochs:
self.epochs["test"] = ep.get_test_epoch(self.stimulus,
self.sampling_rate)
if self.epochs["test"]:
test_pulse = True
else:
test_pulse = False
epoch_detectors = {
"sweep":
ep.get_sweep_epoch(self.response),
"recording":
ep.get_recording_epoch(self.response),
"experiment":
ep.get_experiment_epoch(self._i, self.sampling_rate, test_pulse),
"stim":
ep.get_stim_epoch(self.stimulus, test_pulse),
}
for epoch_name, epoch_detector in epoch_detectors.items():
if epoch_name not in self.epochs:
self.epochs[epoch_name] = epoch_detector
def detect_epochs(self):
"""
Detect epochs if they are not provided in the constructor
"""
if "test" not in self.epochs:
self.epochs["test"] = ep.get_test_epoch(self._stimulus, self.sampling_rate)
if self.epochs["test"]:
test_pulse = True
else:
test_pulse = False
if "sweep" not in self.epochs:
self.epochs["sweep"] = ep.get_sweep_epoch(self._i)
if "recording" not in self.epochs:
self.epochs["recording"] = ep.get_recording_epoch(self._response)
# get valid recording by selecting epoch and using i/v prop before detecting stim
self.select_epoch("recording")
stim = self.i if self.clamp_mode == "CurrentClamp" else self.v
if "stim" not in self.epochs:
self.epochs["stim"] = ep.get_stim_epoch(stim, test_pulse)
if "experiment" not in self.epochs:
self.epochs["experiment"] = ep.get_experiment_epoch(stim, self.sampling_rate, test_pulse)
# Drop failed sweeps: sweeps with incomplete recording or failing QC criteria
drop_failed_sweeps(data_set)
short_square_table = data_set.filtered_sweep_table(
stimuli=data_set.ontology.short_square_names
)
short_square_sweeps = data_set.sweep_set(short_square_table.sweep_number)
# Select epoch corresponding to the actual recording from the sweeps
# and align sweeps so that the experiment would start at the same time
short_square_sweeps.select_epoch("recording")
short_square_sweeps.align_to_start_of_epoch("experiment")
# find the start and end time of the stimulus
# (treating the first sweep as representative)
stim_start_index, stim_end_index = get_stim_epoch(short_square_sweeps.i[0])
stim_start_time = short_square_sweeps.t[0][stim_start_index]
stim_end_time = short_square_sweeps.t[0][stim_end_index]
# Estimate the dv cutoff and threshold fraction
dv_cutoff, thresh_frac = estimate_adjusted_detection_parameters(
short_square_sweeps.v,
short_square_sweeps.t,
stim_start_time,
stim_start_time + 0.001
)
# Build the extractors
spfx = SpikeFeatureExtractor(
start=stim_start_time, dv_cutoff=dv_cutoff, thresh_frac=thresh_frac
)
def test_get_stim_epoch(i, stim_epoch):
assert stim_epoch == ep.get_stim_epoch(i)
stimuli=data_set.ontology.ramp_names)
ramp_sweeps = data_set.sweep_set(ramp_table.sweep_number)
# Select epoch corresponding to the actual recording from the sweeps
# and align sweeps so that the experiment would start at the same time
ramp_sweeps.select_epoch("recording")
ramp_sweeps.align_to_start_of_epoch("experiment")
# Select epoch corresponding to the actual recording from the sweeps
# and align sweeps so that the experiment would start at the same time
ramp_sweeps.select_epoch("recording")
ramp_sweeps.align_to_start_of_epoch("experiment")
# find the start and end time of the stimulus
# (treating the first sweep as representative)
stim_start_index, stim_end_index = get_stim_epoch(ramp_sweeps.i[0])
stim_start_time = ramp_sweeps.t[0][stim_start_index]
stim_end_time = ramp_sweeps.t[0][stim_end_index]
spx = SpikeFeatureExtractor(start=stim_start_time, end=None)
sptfx = SpikeTrainFeatureExtractor(start=stim_start_time, end=None)
# Run the analysis
ramp_analysis = RampAnalysis(spx, sptfx)
results = ramp_analysis.analyze(ramp_sweeps)
# Plot the sweeps and the latency to the first spike of each
sns.set_style("white")
for swp in ramp_sweeps.sweeps:
plt.plot(swp.t, swp.v, linewidth=0.5)
sns.rugplot(results["spiking_sweeps"]["latency"].values + stim_start_time)