def simulate_response(fg_recs, bg_results, amp, rtime, seed=None):
if seed is not None:
np.random.seed(seed)
dt = 1.0 / db.default_sample_rate
t = np.arange(0, 15e-3, dt)
template = Psp.psp_func(t, xoffset=0, yoffset=0, rise_time=rtime, decay_tau=15e-3, amp=1, rise_power=2)
r_amps = scipy.stats.binom.rvs(p=0.2, n=24, size=len(fg_recs)) * scipy.stats.norm.rvs(scale=0.3, loc=1, size=len(fg_recs))
r_amps *= amp / r_amps.mean()
r_latency = np.random.normal(size=len(fg_recs), scale=200e-6, loc=13e-3)
fg_results = []
traces = []
fg_recs = [RecordWrapper(rec) for rec in fg_recs] # can't modify fg_recs, so we wrap records with a mutable shell
for k,rec in enumerate(fg_recs):
rec.data = rec.data.copy()
start = int(r_latency[k] * db.default_sample_rate)
length = len(rec.data) - start
rec.data[start:] += template[:length] * r_amps[k]
fg_result = analyze_response_strength(rec, 'baseline')
fg_results.append(fg_result)
traces.append(Trace(rec.data, sample_rate=db.default_sample_rate))
traces[-1].amp = r_amps[k]
fg_results = str_analysis_result_table(fg_results, fg_recs)
conn_result = analyze_pair_connectivity({('ic', 'fg'): fg_results, ('ic', 'bg'): bg_results, ('vc', 'fg'): [], ('vc', 'bg'): []}, sign=1)
return conn_result, traces
def simulate_response(fg_recs, bg_results, amp, rtime, seed=None):
if seed is not None:
np.random.seed(seed)
dt = 1.0 / db.default_sample_rate
t = np.arange(0, 15e-3, dt)
template = Psp.psp_func(t, xoffset=0, yoffset=0, rise_time=rtime, decay_tau=15e-3, amp=1, rise_power=2)
r_amps = scipy.stats.binom.rvs(p=0.2, n=24, size=len(fg_recs)) * scipy.stats.norm.rvs(scale=0.3, loc=1, size=len(fg_recs))
r_amps *= amp / r_amps.mean()
r_latency = np.random.normal(size=len(fg_recs), scale=200e-6, loc=13e-3)
fg_results = []
traces = []
fg_recs = [RecordWrapper(rec) for rec in fg_recs] # can't modify fg_recs, so we wrap records with a mutable shell
for k,rec in enumerate(fg_recs):
rec.data = rec.data.copy()
start = int(r_latency[k] * db.default_sample_rate)
length = len(rec.data) - start
rec.data[start:] += template[:length] * r_amps[k]
fg_result = analyze_response_strength(rec, 'baseline')
fg_results.append(fg_result)
traces.append(Trace(rec.data, sample_rate=db.default_sample_rate))
traces[-1].amp = r_amps[k]
fg_results = str_analysis_result_table(fg_results, fg_recs)
conn_result = analyze_pair_connectivity({('ic', 'fg'): fg_results, ('ic', 'bg'): bg_results, ('vc', 'fg'): [], ('vc', 'bg'): []}, sign=1)
return conn_result, traces
def plot_prd_ids(self, ids, source, pen=None, trace_list=None, avg=False):
"""Plot raw or decolvolved PulseResponse data, given IDs of records in
a PulseResponseStrength table.
"""
with pg.BusyCursor():
recs = self.get_pulse_recs(ids, source)
if len(recs) == 0:
return
if source == 'fg':
traces = self.selected_fg_traces
plot = self.fg_trace_plot
else:
traces = self.selected_bg_traces
plot = self.bg_trace_plot
for i in trace_list[:]:
plot.removeItem(i)
trace_list.remove(i)
if pen is None:
alpha = np.clip(1000 / len(recs), 30, 255)
pen = (255, 255, 255, alpha)
traces = []
spike_times = []
spike_values = []
for rec in recs:
# Filter by QC unless we selected just a single record
if len(recs) > 0 and getattr(rec, self.qc_field) is False:
continue
s = {'fg': 'pulse_response', 'bg': 'baseline'}[source]
result = analyze_response_strength(rec, source=s, lpf=self.lpf_check.isChecked(),
remove_artifacts=self.ar_check.isChecked(), bsub=self.bsub_check.isChecked())
if self.deconv_check.isChecked():
trace = result['dec_trace']
else:
trace = result['raw_trace']
if self.bsub_check.isChecked():
trace = trace - np.median(trace.time_slice(0, 9e-3).data)
if self.lpf_check.isChecked():
trace = filter.bessel_filter(trace, 500)
spike_values.append(trace.value_at([result['spike_time']])[0])
if self.align_check.isChecked():
trace.t0 = -result['spike_time']
spike_times.append(0)
else:
spike_times.append(result['spike_time'])
traces.append(trace)
trace_list.append(plot.plot(trace.time_values, trace.data, pen=pen))
if avg:
mean = TraceList(traces).mean()
trace_list.append(plot.plot(mean.time_values, mean.data, pen='g'))
trace_list[-1].setZValue(10)
spike_scatter = pg.ScatterPlotItem(spike_times, spike_values, size=4, pen=None, brush=(200, 200, 0))
spike_scatter.setZValue(-100)
plot.addItem(spike_scatter)
trace_list.append(spike_scatter)
def plot_prd_ids(self, ids, source, pen=None, trace_list=None, avg=False, qc_filter=None):
"""Plot raw or decolvolved PulseResponse data, given IDs of records in
a db.PulseResponseStrength table.
"""
if qc_filter is None:
qc_filter = self.ui.qc_check.isChecked()
with pg.BusyCursor():
recs = self.get_pulse_recs(ids, source)
if len(recs) == 0:
return
if source == 'fg':
traces = self.selected_fg_traces
plot = self.fg_trace_plot
else:
traces = self.selected_bg_traces
plot = self.bg_trace_plot
for i in trace_list[:]:
plot.removeItem(i)
trace_list.remove(i)
if pen is None:
alpha = np.clip(1000 / len(recs), 30, 255)
pen = (255, 255, 255, alpha)
pen = pg.mkPen(pen)
# qc-failed traces are tinted red
fail_color = pen.color()
fail_color.setBlue(fail_color.blue() // 2)
fail_color.setGreen(fail_color.green() // 2)
qc_fail_pen = pg.mkPen(fail_color)
traces = []
spike_times = []
spike_values = []
for rec in recs:
# Filter by QC unless we selected just a single record
qc_pass = getattr(rec, self.qc_field) is True
if qc_filter is True and not qc_pass:
continue
s = {'fg': 'pulse_response', 'bg': 'baseline'}[source]
filter_opts = dict(
deconvolve=self.ui.deconv_check.isChecked(),
lpf=self.ui.lpf_check.isChecked(),
remove_artifacts=self.ui.ar_check.isChecked(),
bsub=self.ui.bsub_check.isChecked(),
)
result = analyze_response_strength(rec, source=s, **filter_opts)
trace = result['dec_trace']
spike_values.append(trace.value_at([result['spike_time']])[0])
if self.ui.align_check.isChecked():
trace.t0 = -result['spike_time']
spike_times.append(0)
else:
spike_times.append(result['spike_time'])
traces.append(trace)
trace_list.append(plot.plot(trace.time_values, trace.data, pen=(pen if qc_pass else qc_fail_pen)))
if avg and len(traces) > 0:
mean = TSeriesList(traces).mean()
trace_list.append(plot.plot(mean.time_values, mean.data, pen='g'))
trace_list[-1].setZValue(10)
spike_scatter = pg.ScatterPlotItem(spike_times, spike_values, size=4, pen=None, brush=(200, 200, 0))
spike_scatter.setZValue(-100)
plot.addItem(spike_scatter)
trace_list.append(spike_scatter)
def plot_prd_ids(self, ids, source, pen=None, trace_list=None, avg=False, qc_filter=True):
"""Plot raw or decolvolved PulseResponse data, given IDs of records in
a db.PulseResponseStrength table.
"""
with pg.BusyCursor():
recs = self.get_pulse_recs(ids, source)
if len(recs) == 0:
return
if source == 'fg':
traces = self.selected_fg_traces
plot = self.fg_trace_plot
else:
traces = self.selected_bg_traces
plot = self.bg_trace_plot
for i in trace_list[:]:
plot.removeItem(i)
trace_list.remove(i)
if pen is None:
alpha = np.clip(1000 / len(recs), 30, 255)
pen = (255, 255, 255, alpha)
traces = []
spike_times = []
spike_values = []
for rec in recs:
# Filter by QC unless we selected just a single record
if qc_filter is True and getattr(rec, self.qc_field) is False:
continue
s = {'fg': 'pulse_response', 'bg': 'baseline'}[source]
filter_opts = dict(
deconvolve=self.deconv_check.isChecked(),
lpf=self.lpf_check.isChecked(),
remove_artifacts=self.ar_check.isChecked(),
bsub=self.bsub_check.isChecked(),
)
result = analyze_response_strength(rec, source=s, **filter_opts)
trace = result['dec_trace']
spike_values.append(trace.value_at([result['spike_time']])[0])
if self.align_check.isChecked():
trace.t0 = -result['spike_time']
spike_times.append(0)
else:
spike_times.append(result['spike_time'])
traces.append(trace)
trace_list.append(plot.plot(trace.time_values, trace.data, pen=pen))
if avg and len(traces) > 0:
mean = TraceList(traces).mean()
trace_list.append(plot.plot(mean.time_values, mean.data, pen='g'))
trace_list[-1].setZValue(10)
spike_scatter = pg.ScatterPlotItem(spike_times, spike_values, size=4, pen=None, brush=(200, 200, 0))
spike_scatter.setZValue(-100)
plot.addItem(spike_scatter)
trace_list.append(spike_scatter)