def parse_spikes(self, to_parse):
spikes = []
if isinstance(to_parse, list):
spikes = to_parse
elif 'file' in to_parse:
if not self.f.exists():
inform('ERROR! Datafile %s does not exist (relative to %s)!'%
(self.f.filename, getcwd()), indent=2, verbosity=0, underline='-')
spikes = None
elif not self.f.has_changed():
inform('ERROR! Datafile %s has not changed!'
% self.f.filename, indent=2, verbosity=0, underline='-')
spikes = None
elif self.f.has_changed():
tv = self.f.get_timeseries()
inform('Reading timeseries from: ', self.f,
indent=1, verbosity=1)
if 'spike detection' in to_parse:
sd = to_parse['spike detection']
method = sd.get('method', 'threshold')
threshold = float(sd.get('threshold', 0))
inform('Detecting spikes with method: ',
method, indent=2, verbosity=1)
spikes = ts.spikes_from_timeseries(tv, method=method,
threshold=threshold)
else: # file contains spike times
spikes = tv
else:
inform('ERROR! Preexistent datafile %s has not been updated!'
% self.f.filename, indent=2, verbosity=0, underline='-')
spikes = []
return spikes
def parse_spikes(self, to_parse):
if isinstance(to_parse, list):
spikes = to_parse
elif 'file' in to_parse:
if self.f.has_changed():
tv = self.f.get_timeseries()
inform('Reading timeseries from: ',
self.f,
indent=1,
verbosity=1)
if 'spike detection' in to_parse:
sd = to_parse['spike detection']
method = sd.get('method', 'threshold')
threshold = float(sd.get('threshold', 0))
inform('Detecting spikes with method: ',
method,
indent=2,
verbosity=1)
spikes = ts.spikes_from_timeseries(tv,
method=method,
threshold=threshold)
else:
inform('ERROR! Preexistent datafile %s has not been updated!' %
self.f.filename,
indent=2,
verbosity=0,
underline='-')
spikes = []
return spikes
def parse_spikes(self, to_parse):
if isinstance(to_parse, list):
spikes = to_parse
elif 'file' in to_parse:
f = fn.FileNodeHelper(to_parse['file'])
tv = f.get_timeseries()
inform('Reading timeseries from:', f, indent=1)
if 'spike detection' in to_parse:
sd = to_parse['spike detection']
method = sd.get('method', 'threshold')
threshold = float(sd.get('threshold', 0))
inform('Detecting spikes with method:', method, indent=2)
spikes = ts.spikes_from_timeseries(tv, method=method, threshold=threshold)
return spikes