def analyze_response_strength(rec,
source,
remove_artifacts=False,
deconvolve=True,
lpf=True,
bsub=True,
lowpass=1000):
"""Perform a standardized strength analysis on a record selected by response_query or baseline_query.
1. Determine timing of presynaptic stimulus pulse edges and spike
2. Measure peak deflection on raw trace
3. Apply deconvolution / artifact removal / lpf
4. Measure peak deflection on deconvolved trace
"""
data = TSeries(rec.data, sample_rate=db.default_sample_rate)
if source == 'pulse_response':
# Find stimulus pulse edges for artifact removal
start = rec.pulse_start - rec.rec_start
pulse_times = [start, start + rec.pulse_dur]
if rec.spike_time is None:
# these pulses failed QC, but we analyze them anyway to make all data visible
spike_time = 11e-3
else:
spike_time = rec.spike_time - rec.rec_start
elif source == 'baseline':
# Fake stimulus information to ensure that background data receives
# the same filtering / windowing treatment
pulse_times = [10e-3, 12e-3]
spike_time = 11e-3
else:
raise ValueError("Invalid source %s" % source)
results = {}
results['raw_trace'] = data
results['pulse_times'] = pulse_times
results['spike_time'] = spike_time
# Measure crosstalk from pulse onset
p1 = data.time_slice(pulse_times[0] - 200e-6, pulse_times[0]).median()
p2 = data.time_slice(pulse_times[0], pulse_times[0] + 200e-6).median()
results['crosstalk'] = p2 - p1
# crosstalk artifacts in VC are removed before deconvolution
if rec.clamp_mode == 'vc' and remove_artifacts is True:
data = remove_crosstalk_artifacts(data, pulse_times)
remove_artifacts = False
# Measure deflection on raw data
results['pos_amp'], _ = measure_peak(data, '+', spike_time, pulse_times)
results['neg_amp'], _ = measure_peak(data, '-', spike_time, pulse_times)
# Deconvolution / artifact removal / filtering
if deconvolve:
tau = 15e-3 if rec.clamp_mode == 'ic' else 5e-3
else:
tau = None
dec_data = deconv_filter(data,
pulse_times,
tau=tau,
lpf=lpf,
remove_artifacts=remove_artifacts,
bsub=bsub,
lowpass=lowpass)
results['dec_trace'] = dec_data
# Measure deflection on deconvolved data
results['pos_dec_amp'], results['pos_dec_latency'] = measure_peak(
dec_data, '+', spike_time, pulse_times)
results['neg_dec_amp'], results['neg_dec_latency'] = measure_peak(
dec_data, '-', spike_time, pulse_times)
return results
def set_data(self, data, show_spikes=False, subtract_baseline=True):
self.spike_plot.setVisible(show_spikes)
self.spike_plot.enableAutoRange(False, False)
self.data_plot.enableAutoRange(False, False)
psp = StackedPsp()
for recording in data:
pulses = sorted(list(data[recording].keys()))
for pulse_n in pulses:
rec = data[recording][pulse_n]
# spike-align pulse + offset for pulse number
spike_t = rec.StimPulse.first_spike_time
if spike_t is None:
spike_t = rec.StimPulse.onset_time + 1e-3
qc_pass = rec.PulseResponse.in_qc_pass if rec.Synapse.synapse_type == 'in' else rec.PulseResponse.ex_qc_pass
pen = (255, 255, 255, 100) if qc_pass else (200, 50, 0, 100)
t0 = rec.PulseResponse.data_start_time - spike_t
ts = TSeries(data=rec.data,
t0=t0,
sample_rate=db.default_sample_rate)
c = self.data_plot.plot(ts.time_values, ts.data, pen=pen)
# arrange plots nicely
y0 = 0 if not subtract_baseline else ts.time_slice(None,
0).median()
shift = (pulse_n * 35e-3 + (30e-3 if pulse_n > 8 else 0), -y0)
zval = 0 if qc_pass else -10
c.setPos(*shift)
c.setZValue(zval)
if show_spikes:
t0 = rec.spike_data_start_time - spike_t
spike_ts = TSeries(data=rec.spike_data,
t0=t0,
sample_rate=db.default_sample_rate)
c = self.spike_plot.plot(spike_ts.time_values,
spike_ts.data,
pen=pen)
c.setPos(*shift)
c.setZValue(zval)
# evaluate recorded fit for this response
fit_par = rec.PulseResponseFit
if fit_par.fit_amp is None:
continue
fit = psp.eval(
x=ts.time_values,
exp_amp=fit_par.fit_exp_amp,
exp_tau=fit_par.fit_decay_tau,
amp=fit_par.fit_amp,
rise_time=fit_par.fit_rise_time,
decay_tau=fit_par.fit_decay_tau,
xoffset=fit_par.fit_latency,
yoffset=fit_par.fit_yoffset,
rise_power=2,
)
pen = (0, 255, 0, 100) if qc_pass else (50, 150, 0, 100)
c = self.data_plot.plot(ts.time_values, fit, pen=pen)
c.setZValue(10)
c.setPos(*shift)
if not qc_pass:
print(
"qc fail: ",
rec.PulseResponse.meta.get('qc_failures',
'no qc failures recorded'))
self.spike_plot.enableAutoRange(True, True)
self.data_plot.enableAutoRange(True, True)
