本文整理汇总了Python中sparkle.stim.stimulus_model.StimulusModel.signalFromDoc方法的典型用法代码示例。如果您正苦于以下问题：Python StimulusModel.signalFromDoc方法的具体用法？Python StimulusModel.signalFromDoc怎么用？Python StimulusModel.signalFromDoc使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sparkle.stim.stimulus_model.StimulusModel的用法示例。
在下文中一共展示了StimulusModel.signalFromDoc方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: displayOldData
# 需要导入模块: from sparkle.stim.stimulus_model import StimulusModel [as 别名]
# 或者: from sparkle.stim.stimulus_model.StimulusModel import signalFromDoc [as 别名]
def displayOldData(self, path, tracenum, repnum=0):
if self.activeOperation is None:
# requires use of AcquisitionData API
path = str(path)
if '/' in path:
group_path = os.path.dirname(path)
else:
group_path = path
group_info = dict(self.acqmodel.datafile.get_info(group_path))
aifs = group_info['samplerate_ad']
if repnum == -1:
showall = True
response = self.acqmodel.datafile.get_data(path, (tracenum,))
repnum = response.shape[0] -1
if len(response.shape) == 2:
# backwards compatibility: reshape old data to have channel dimension
response = response.reshape((response.shape[0], 1, response.shape[1]))
else:
showall = False
response = self.acqmodel.datafile.get_data(path, (tracenum, repnum))
if len(response.shape) == 1:
# backwards compatibility: reshape old data to have channel dimension
response = response.reshape((1, response.shape[0]))
npoints = response.shape[-1]
nchans = response.shape[-2]
winsz = float(npoints)/aifs
times = np.linspace(0, winsz, npoints)
# plot response signal
self.ui.plotDock.switchDisplay('standard')
self.display.setXlimits((0,winsz))
if len(self._aichans) != nchans:
cnames = get_ai_chans(self.advanced_options['device_name'])
self.setNewChannels(cnames[:nchans])
for chan, name in enumerate(self._aichans):
if len(response.shape) == 3:
# overlay plot
self.display.updateSpiketrace(times, response[:,chan,:], name)
else:
self.display.updateSpiketrace(times, response[chan,:], name)
stimuli = self.acqmodel.datafile.get_trace_stim(path)
stimulus = stimuli[tracenum]
# show the stimulus details
self.reportProgress(-1, tracenum, stimulus)
self.reportRep(repnum)
# need to also recreate the stim
if repnum == 0:
# assume user must first access the first presentation
# before being able to browse through reps
# recreate stim signal
stim_signal = StimulusModel.signalFromDoc(stimulus, self.calvals['calv'], self.calvals['caldb'])
fs = stimulus['samplerate_da']
timevals = np.arange(len(stim_signal)).astype(float)/fs
freq, spectrum = calc_spectrum(stim_signal, fs)
spectrum = calc_db(spectrum, self.calvals['calv']) + self.calvals['caldb']
self.display.updateSignal(timevals, stim_signal)
self.display.updateFft(freq, spectrum)
self.display.updateSpec(stim_signal, fs)
self.ui.psth.clearData()
self.display.clearRaster()
# recreate PSTH for current threshold and current rep
tracedata = self.acqmodel.datafile.get_data(path, (tracenum,))
if len(tracedata.shape) == 2:
# backwards compatibility: reshape old data to have channel dimension
tracedata = tracedata.reshape((tracedata.shape[0], 1, tracedata.shape[1]))
self.display.setNreps(tracedata.shape[0])
binsz = float(self.ui.binszSpnbx.value())
winsz = float(tracedata.shape[-1])/aifs
# set the max of the PSTH subwindow to the size of this data
self.ui.psthStopField.setMaximum(winsz)
self.ui.psthStartField.setMaximum(winsz)
nbins = np.ceil(winsz/binsz)
bin_centers = (np.arange(nbins)*binsz)+(binsz/2)
self.ui.psth.setBins(bin_centers)
# because we can scroll forwards or backwards, re-do entire plot every time
spike_counts = []
spike_latencies = []
spike_rates = []
for irep in range(repnum+1):
count, latency, rate, response_bins = self.do_spike_stats(tracedata[irep], aifs)
spike_counts.extend(count)
spike_latencies.extend(latency)
spike_rates.extend(rate)
for chan, name in enumerate(self._aichans):
#.........这里部分代码省略.........