def perform_analysis(self):
for sheet in self.datastore.sheets():
# Load up spike trains for the right sheet and the corresponding stimuli, and
# transform spike trains into psth
dsv = select_result_sheet_query(self.datastore,sheet)
assert equal_ads_except(dsv,['stimulus_id'])
assert ads_with_equal_stimulus_type(dsv)
assert equal_stimulus_type(dsv)
psths = [psth(seg.spiketrains,self.parameters.bin_length) for seg in dsv.get_segments()]
st = [StimulusID(s) for s in dsv.get_stimuli()]
# average across trials
psths,stids = colapse(psths,st,parameter_list=['trial'],func=neo_mean,allow_non_identical_stimuli=True)
# retrieve the computed orientation preferences
pnvs = self.datastore.get_analysis_result(identifier='PerNeuronValue',sheet_name=sheet,value_name='orientation preference')
if len(pnvs) != 1:
logger.error('ERROR: Expected only one PerNeuronValue per sheet with value_name \'orientation preference\' in datastore, got: ' + str(len(pnvs)))
return None
else:
or_pref = pnvs[0]
# find closest orientation of grating to a given orientation preference of a neuron
# first find all the different presented stimuli:
ps = {}
for s in st:
ps[StimulusID(s).params['orientation']] = True
ps = ps.keys()
# now find the closest presented orientations
closest_presented_orientation = []
for i in xrange(0,len(or_pref.values)):
circ_d = 100000
idx = 0
for j in xrange(0,len(ps)):
if circ_d > circular_dist(or_pref.values[i],ps[j],numpy.pi):
circ_d = circular_dist(or_pref.values[i],ps[j],numpy.pi)
idx = j
closest_presented_orientation.append(ps[idx])
closest_presented_orientation = numpy.array(closest_presented_orientation)
# colapse along orientation - we will calculate MR for each parameter combination other than orientation
d = colapse_to_dictionary(psths,stids,"orientation")
for (st,vl) in d.items():
# here we will store the modulation ratios, one per each neuron
modulation_ratio = numpy.zeros((numpy.shape(psths[0])[1],))
frequency = StimulusID(st).params['temporal_frequency'] * StimulusID(st).units['temporal_frequency']
for (orr,ppsth) in zip(vl[0],vl[1]):
for j in numpy.nonzero(orr == closest_presented_orientation)[0]:
modulation_ratio[j] = self.calculate_MR(ppsth[:,j],frequency)
self.datastore.full_datastore.add_analysis_result(PerNeuronValue(modulation_ratio,qt.dimensionless,value_name = 'Modulation ratio',sheet_name=sheet,tags=self.tags,period=None,analysis_algorithm=self.__class__.__name__,stimulus_id=str(st)))
import pylab
pylab.figure()
pylab.hist(modulation_ratio)
def perform_analysis(self):
for sheet in self.datastore.sheets():
# Load up spike trains for the right sheet and the corresponding stimuli, and
# transform spike trains into psth
dsv = select_result_sheet_query(self.datastore,sheet)
psths = [psth(seg.spiketrains,self.parameters.bin_length) for seg in dsv.get_segments()]
st = [StimulusID(s) for s in dsv.get_stimuli()]
# average across trials
psths,stids = colapse(psths,st,parameter_list=['trial'],func=neo_mean,allow_non_identical_stimuli=True)
for ppsth,stid in zip(psths,stids):
t_start = ppsth[0].t_start
duration = ppsth[0].t_stop-ppsth[0].t_start
al = []
for n in self.parameters.neurons:
ac = numpy.correlate(numpy.array(ppsth[:,n]), numpy.array(ppsth[:,n]), mode='full')
div = numpy.sum(numpy.power(numpy.array(ppsth[:,n]),2))
if div != 0:
ac = ac / div
al.append(AnalogSignal(ac, t_start=-duration,t_stop=duration-self.parameters.bin_length*t_start.units,sampling_period=self.parameters.bin_length*qt.ms,units=qt.dimensionless))
logger.debug('Adding AnalogSignalList:' + str(sheet))
self.datastore.full_datastore.add_analysis_result(AnalogSignalList(al,self.parameters.neurons,qt.ms,qt.dimensionless,x_axis_name='time',y_axis_name='autocorrelation',sheet_name=sheet,tags=self.tags,analysis_algorithm=self.__class__.__name__,stimulus_id=str(stid)))
