def perform_analysis_and_visualization(data_store):
if True:
analog_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
#find neuron with preference closet to 0
NeuronAnnotationsToPerNeuronValues(data_store,ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].get_value_by_id(analog_ids),l4_exc_phase[0].get_value_by_id(analog_ids))])]
l4_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_inh_or[0].get_value_by_id(analog_ids_inh),l4_inh_phase[0].get_value_by_id(analog_ids_inh))])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
print "Prefered orientation of plotted exc neurons:"
print 'index ' + str(l4_exc)
print "Prefered phase of plotted exc neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
print "Prefered orientation of plotted inh neurons:"
print l4_inh_phase[0].get_value_by_id(l4_inh)
print 'index ' + str(l4_inh)
print "Prefered phase of plotted inh neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
#dsv = param_filter_query(data_store,sheet_name='V1_Exc_L4')
#TrialAveragedFiringRate(param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],st_name="FullfieldDriftingSinusoidalGrating"),ParameterSet({})).analyse()
dsv = param_filter_query(data_store,st_name=['InternalStimulus'])
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[0], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name='SSExcAnalog.png').plot()
RetinalInputMovie(dsv, ParameterSet({}), fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name='video').plot()
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 __init__(self, network, source, target, parameters,name):
MozaikComponent.__init__(self,network,parameters)
self.name = name
self.source = source
self.target = target
weights = []
for (i,neuron1) in enumerate(self.target.pop.all()):
for (j,neuron2) in enumerate(self.source.pop.all()):
or_dist = circular_dist(self.target.get_neuron_annotation(i,'LGNAfferentOrientation'),self.source.get_neuron_annotation(j,'LGNAfferentOrientation'),numpy.pi) / (numpy.pi/2)
if self.parameters.specific_arborization.target_synapses == 'excitatory':
phase_dist = circular_dist(self.target.get_neuron_annotation(i,'LGNAfferentPhase'),self.source.get_neuron_annotation(j,'LGNAfferentPhase'),2*numpy.pi) / numpy.pi
elif self.parameters.specific_arborization.target_synapses == 'inhibitory':
phase_dist = (numpy.pi - circular_dist(self.target.get_neuron_annotation(i,'LGNAfferentPhase'),self.source.get_neuron_annotation(j,'LGNAfferentPhase'),2*numpy.pi)) / numpy.pi
else:
logger.error('Unknown type of synapse!')
return
or_gauss = normal_function(or_dist,mean=0,sigma=self.parameters.or_sigma)
phase_gauss = normal_function(phase_dist,mean=0,sigma=self.parameters.or_sigma)
w = phase_gauss*or_gauss*or_gauss
weights.append((j,i,w,self.parameters.propagation_constant))
we = numpy.zeros((len(self.source.pop),len(self.target.pop)))
pnv_source = []
pnv_target = []
for (i,neuron1) in enumerate(self.target.pop.all()):
pnv_target.append(self.target.get_neuron_annotation(i,'LGNAfferentOrientation'))
for (i,neuron1) in enumerate(self.source.pop.all()):
pnv_source.append(self.source.get_neuron_annotation(i,'LGNAfferentOrientation'))
for (j,i,w,xsj) in weights:
we[j,i] = w
(angle,mag) = circ_mean(numpy.array([numpy.array(pnv_target).T for i in xrange(0,numpy.shape(we)[0])]),weights=numpy.array(we),low=0,high=numpy.pi,axis=1)
if self.parameters.probabilistic:
SpecificProbabilisticArborization(network, self.source, self.target, weights,self.parameters.specific_arborization,self.name).connect()
else:
SpecificArborization(network, self.source, self.target, weights,self.parameters.specific_arborization,self.name).connect()
def pick_close_to_annotation(self):
picked = []
# z = self.sheet.pop.all_cells.astype(int)
if isinstance(self.sheet.pop.all_cells, list):
if hasattr(self.sheet.pop.all_cells[0], 'id'):
z = numpy.asarray([idm.id for idm in self.sheet.pop.all_cells
]) # for IDMixin
else:
z = numpy.asarray(self.sheet.pop.all_cells)
else:
z = self.sheet.pop.all_cells.astype(int)
vals = [
self.sheet.get_neuron_annotation(i, self.parameters.annotation)
for i in range(0, len(z))
]
if self.parameters.period != 0:
picked = numpy.array([
i for i in range(0, len(z))
if abs(vals[i] -
self.parameters.value) < self.parameters.distance
])
else:
picked = numpy.array([
i for i in range(0, len(z))
if circular_dist(vals[i], self.parameters.value, self.
parameters.period) < self.parameters.distance
])
return picked
def generate_idd_list_of_neurons(self):
picked = []
z = self.sheet.pop.all_cells.astype(int)
vals = [self.sheet.get_neuron_annotation(i,self.parameters.annotation) for i in xrange(0,len(z))]
if self.parameters.period != 0:
pikced = [i for i in xrange(0,len(z)) if abs(vals[i]-self.parameters.value) < self.parameters.distance]
else:
pikced = [i for i in xrange(0,len(z)) if circular_dist(vals[i],self.parameters.value,self.parameters.period) < self.parameters.distance]
return z[mozaik.rng.shuffle(picked)[:self.parameters.num_of_cells]]
def pick_close_to_annotation(self):
picked = []
z = self.sheet.pop.all_cells.astype(int)
vals = [self.sheet.get_neuron_annotation(i,self.parameters.annotation) for i in xrange(0,len(z))]
if self.parameters.period != 0:
picked = numpy.array([i for i in xrange(0,len(z)) if abs(vals[i]-self.parameters.value) < self.parameters.distance])
else:
picked = numpy.array([i for i in xrange(0,len(z)) if circular_dist(vals[i],self.parameters.value,self.parameters.period) < self.parameters.distance])
return picked
def pick_close_to_annotation(self):
picked = []
z = self.sheet.pop.all_cells.astype(int)
vals = [self.sheet.get_neuron_annotation(i,self.parameters.annotation) for i in xrange(0,len(z))]
if self.parameters.period != 0:
picked = numpy.array([i for i in xrange(0,len(z)) if abs(vals[i]-self.parameters.value) < self.parameters.distance])
else:
picked = numpy.array([i for i in xrange(0,len(z)) if circular_dist(vals[i],self.parameters.value,self.parameters.period) < self.parameters.distance])
return picked
def test_stimulating_function(sheet, coor_x, coor_y, current_update_interval,
parameters):
z = sheet.pop.all_cells.astype(int)
vals = numpy.array([
sheet.get_neuron_annotation(i, 'LGNAfferentOrientation')
for i in xrange(0, len(z))
])
mean_orientations = []
px, py = sheet.vf_2_cs(sheet.pop.positions[0], sheet.pop.positions[1])
pylab.subplot(151)
pylab.gca().set_aspect('equal')
pylab.title('Orientatin preference (neurons)')
pylab.scatter(px, py, c=vals / numpy.pi, cmap='hsv')
pylab.hold(True)
#pylab.scatter(coor_x.flatten(),coor_y.flatten(),c='k',cmap='hsv')
ors = scipy.interpolate.griddata(zip(px, py),
vals, (coor_x, coor_y),
method='nearest')
pylab.subplot(152)
pylab.title('Orientatin preference (stimulators)')
pylab.gca().set_aspect('equal')
pylab.scatter(coor_x.flatten(),
coor_y.flatten(),
c=ors.flatten(),
cmap='hsv')
signals = numpy.zeros((numpy.shape(coor_x)[0], numpy.shape(coor_x)[1],
int(parameters.duration / current_update_interval)))
for i in xrange(0, numpy.shape(coor_x)[0]):
for j in xrange(0, numpy.shape(coor_x)[0]):
signals[i, j,
int(
numpy.floor(parameters.onset_time /
current_update_interval)
):int(
numpy.floor(parameters.offset_time /
current_update_interval)
)] = parameters.scale.value * numpy.exp(-numpy.power(
circular_dist(parameters.orientation.value, ors[i][j],
numpy.pi), 2) / parameters.sharpness)
pylab.subplot(153)
pylab.gca().set_aspect('equal')
pylab.title('Activation magnitude (stimulators)')
pylab.scatter(coor_x.flatten(),
coor_y.flatten(),
c=numpy.squeeze(numpy.mean(signals, axis=2)).flatten(),
cmap='gray')
pylab.title(str(parameters.orientation.value))
#pylab.colorbar()
return numpy.array(signals), parameters.scale.value
def subplot(self, subplotspec):
plots = {}
gs = gridspec.GridSpecFromSubplotSpec(1,2, subplot_spec=subplotspec,hspace=1.0, wspace=1.0)
var_gr = 0
var_ni = 0
std_gr = 0
std_ni = 0
orr = list(set([MozaikParametrized.idd(s).orientation for s in queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',st_contrast=100).get_stimuli()]))
l4_exc_or = self.datastore.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = self.parameters.sheet_name)
# lets calculate spont. activity trial to trial variability
# we assume that the spontaneous activity had already the spikes removed
dsv = queries.param_filter_query(self.datastore,st_name='InternalStimulus',st_direct_stimulation_name='None',sheet_name=self.parameters.sheet_name,analysis_algorithm='ActionPotentialRemoval',ads_unique=True)
ids = dsv.get_analysis_result()[0].ids
sp = {}
for idd in ids:
assert len(dsv.get_analysis_result()) == 1
s = dsv.get_analysis_result()[0].get_asl_by_id(idd).magnitude
sp[idd] = 1/numpy.mean(numpy.std([s[i*int(len(s)/10):(i+1)*int(len(s)/10)] for i in xrange(0,10)],axis=0,ddof=1))
#sp[idd] = 1/numpy.std(s,ddof=1)
print sp[ids[1]]
#lets calculate the mean of trial-to-trial variances across the neurons in the datastore for gratings
dsv = queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=self.parameters.sheet_name,st_contrast=100,analysis_algorithm='TrialVariability',y_axis_name='Vm (no AP) trial-to-trial variance')
assert queries.equal_ads(dsv, except_params=['stimulus_id'])
ids = dsv.get_analysis_result()[0].ids
var_gr_ind = []
logger.info("AA")
logger.info(str([sp[i] for i in ids]))
for i in ids:
# find the or pereference of the neuron
o = orr[numpy.argmin([circular_dist(o,l4_exc_or[0].get_value_by_id(i),numpy.pi) for o in orr])]
assert len(queries.param_filter_query(dsv,st_orientation=o,ads_unique=True).get_analysis_result())==1
a = 1/numpy.mean(numpy.sqrt(queries.param_filter_query(dsv,st_orientation=o,ads_unique=True).get_analysis_result()[0].get_asl_by_id(i).magnitude))
var_gr = var_gr + a / sp[i]
var_gr_ind.append(a / sp[i])
std_gr = std_gr + a
var_gr = var_gr / len(ids)
std_gr = std_gr / len(ids)
logger.info(str(var_gr_ind))
#lets calculate the mean of trial-to-trial variances across the neurons in the datastore for natural images
dsv = queries.param_filter_query(self.datastore,st_name='NaturalImageWithEyeMovement',sheet_name=self.parameters.sheet_name,y_axis_name='Vm (no AP) trial-to-trial variance',ads_unique=True)
var_ni_ind = [1/numpy.mean(numpy.sqrt(dsv.get_analysis_result()[0].get_asl_by_id(i).magnitude)) / sp[i] for i in ids]
var_ni = numpy.mean(var_ni_ind)
plots['Bar'] = (BarComparisonPlot({"NI" : var_ni*100.0, "GR" : var_gr*100.0}),gs[0,0],{})
plots['Scatter'] = (ScatterPlot(var_gr_ind*100, var_ni_ind*100),gs[0,1],{'x_label' : 'GR', 'y_label' : 'NI','identity_line' : True})
return plots
def generate_idd_list_of_neurons(self):
grid_ids = []
picked = []
assert math.fmod(self.parameters.size,self.parameters.spacing) < 0.000000001 , "Error the size has to be multiple of spacing!"
z = self.sheet.pop.all_cells.astype(int)
for x in self.parameters.offset_x + numpy.arange(0,self.parameters.size,self.parameters.spacing) - self.parameters.size/2.0:
for y in self.parameters.offset_y + numpy.arange(0,self.parameters.size,self.parameters.spacing) - self.parameters.size/2.0:
xx,yy = self.sheet.cs_2_vf(x,y)
grid_ids.append(numpy.argmin(numpy.power(self.sheet.pop.positions[0] - xx,2) + numpy.power(self.sheet.pop.positions[1] - yy,2)))
g = list(set(grid_ids))
vals = [self.sheet.get_neuron_annotation(idx, self.parameters.annotation) for idx in g]
if self.parameters.period != 0:
picked = numpy.array([i for i in xrange(0,len(g)) if abs(vals[i]-self.parameters.value) < self.parameters.distance])
else:
picked = numpy.array([i for i in xrange(0,len(g)) if circular_dist(vals[i],self.parameters.value,self.parameters.period) < self.parameters.distance])
return z[picked[:]]
def perform_analysis_and_visualization(data_store):
if True:
analog_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
#find neuron with preference closet to 0
NeuronAnnotationsToPerNeuronValues(data_store,ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].get_value_by_id(analog_ids),l4_exc_phase[0].get_value_by_id(analog_ids))])]
l4_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_inh_or[0].get_value_by_id(analog_ids_inh),l4_inh_phase[0].get_value_by_id(analog_ids_inh))])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
print "Prefered orientation of plotted exc neurons:"
print 'index ' + str(l4_exc)
print "Prefered phase of plotted exc neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
print "Prefered orientation of plotted inh neurons:"
print l4_inh_phase[0].get_value_by_id(l4_inh)
print 'index ' + str(l4_inh)
print "Prefered phase of plotted inh neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
if False:
#data_store.remove_ads_from_datastore()
dsv = param_filter_query(data_store,sheet_name='V1_Exc_L4')
ActionPotentialRemoval(dsv,ParameterSet({'window_length' : 10.0})).analyse()
TrialAveragedFiringRate(param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],st_name="FullfieldDriftingSinusoidalGrating"),ParameterSet({})).analyse()
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='TrialAveragedFiringRate',sheet_name=['V1_Exc_L4','V1_Inh_L4'])
GaussianTuningCurveFit(dsv,ParameterSet({'parameter_name' : 'orientation'})).analyse()
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4'])
Analog_F0andF1(dsv,ParameterSet({})).analyse()
data_store.save()
Analog_MeanSTDAndFanoFactor(data_store,ParameterSet({})).analyse()
#data_store.save()
PSTH(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'bin_length' : 2.0 })).analyse()
GSTA(param_filter_query(data_store,sheet_name='V1_Exc_L4',st_name='InternalStimulus',direct_stimulation_name='None'),ParameterSet({'neurons' : [l4_exc], 'length' : 250.0 }),tags=['GSTA']).analyse()
GSTA(param_filter_query(data_store,sheet_name='V1_Exc_L4',st_name='FullfieldDriftingSinusoidalGrating',st_orientation=[0,numpy.pi/2]),ParameterSet({'neurons' : [l4_exc], 'length' : 250.0 }),tags=['GSTA']).analyse()
GSTA(param_filter_query(data_store,sheet_name='V1_Inh_L4',st_name='FullfieldDriftingSinusoidalGrating',st_orientation=[0,numpy.pi/2]),ParameterSet({'neurons' : [l4_inh], 'length' : 250.0 }),tags=['GSTA']).analyse()
GSTA(param_filter_query(data_store,sheet_name='V1_Exc_L4',st_name='NaturalImageWithEyeMovement'),ParameterSet({'neurons' : [l4_exc], 'length' : 250.0 }),tags=['GSTA']).analyse()
#data_store.print_content(full_ADS=True)
#Precision(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'bin_length' : 10.0 })).analyse()
#dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement',sheet_name='V1_Exc_L4',analysis_algorithm='ActionPotentialRemoval')
#TrialVariability(dsv,ParameterSet({'vm': False, 'cond_exc': False, 'cond_inh': False})).analyse()
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name='V1_Exc_L4',st_contrast=100,analysis_algorithm='ActionPotentialRemoval')
TrialVariability(dsv,ParameterSet({'vm': False, 'cond_exc': False, 'cond_inh': False})).analyse()
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='TrialAveragedFiringRate',sheet_name=['V1_Exc_L4','V1_Inh_L4'])
PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(dsv,ParameterSet({'parameter_name' : 'orientation'})).analyse()
pnv = param_filter_query(data_store,st_name='InternalStimulus',sheet_name='V1_Exc_L4',analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],value_name='Mean(ECond)',st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name='V1_Exc_L4',analysis_algorithm=['Analog_F0andF1'],value_name='F0_Exc_Cond')
SubtractPNVfromPNVS(pnv,dsv,ParameterSet({})).analyse()
pnv = param_filter_query(data_store,st_name='InternalStimulus',sheet_name='V1_Exc_L4',analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],value_name='Mean(ICond)',st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name='V1_Exc_L4',analysis_algorithm=['Analog_F0andF1'],value_name='F0_Inh_Cond')
SubtractPNVfromPNVS(pnv,dsv,ParameterSet({})).analyse()
pnv = param_filter_query(data_store,st_name='InternalStimulus',sheet_name='V1_Exc_L4',analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],value_name='Mean(VM)',st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name='V1_Exc_L4',analysis_algorithm=['Analog_F0andF1'],value_name='F0_Vm')
SubtractPNVfromPNVS(pnv,dsv,ParameterSet({})).analyse()
TrialToTrialCrossCorrelationOfAnalogSignalList(param_filter_query(data_store,sheet_name='V1_Exc_L4',st_name="NaturalImageWithEyeMovement",analysis_algorithm='ActionPotentialRemoval'),ParameterSet({'neurons' : list(analog_ids)})).analyse()
TrialToTrialCrossCorrelationOfAnalogSignalList(param_filter_query(data_store,sheet_name='V1_Exc_L4',st_name="NaturalImageWithEyeMovement",analysis_algorithm='PSTH'),ParameterSet({'neurons' : list(analog_ids)})).analyse()
data_store.save()
dsv = param_filter_query(data_store,analysis_algorithm='TrialToTrialCrossCorrelationOfAnalogSignalList')
dsv.print_content(full_ADS=True)
if True: # PLOTTING
#TrialCrossCorrelationAnalysis(data_store,ParameterSet({'neurons' : list(analog_ids)}),fig_param={"dpi" : 100,"figsize": (25,12)},plot_file_name="trial-to-trial-cross-correlation.png").plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[0]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR1.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[1]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR2.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[2]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR3.png').plot()
dsv = param_filter_query(data_store,st_name=['InternalStimulus'])
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[0], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name='SSExcAnalog.png').plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : analog_ids_inh[0], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (28,12)},plot_file_name='SSInhAnalog.png').plot()
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name='FullfieldDriftingSinusoidalGrating',st_trial=0)
#VmPlot(dsv,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'})).plot({'Vm_plot.y_lim' : (-67,-56)})
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='ActionPotentialRemoval',st_trial=0)
#AnalogSignalListPlot(dsv,ParameterSet({'neurons' : [l4_exc],'sheet_name' : 'V1_Exc_L4'})).plot({'AnalogSignalPlot.y_lim' : (-67,-56)})
#RetinalInputMovie(data_store,ParameterSet({'frame_rate' : 10})).plot()
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],value_name='LGNAfferentOrientation')
#PerNeuronValuePlot(dsv,ParameterSet({}),plot_file_name="LGNAfferentOrientation.png").plot()
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],value_name='orientation preference',analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',st_contrast=100)
#PerNeuronValuePlot(dsv,ParameterSet({})).plot()
#dsv = param_filter_query(data_store,value_name=['orientation HWHH'],sheet_name=['V1_Exc_L4','V1_Inh_L4'])
#PerNeuronValueScatterPlot(dsv,ParameterSet({})).plot({ 'ScatterPlot.x_lim' : (0,90), 'ScatterPlot.y_lim' : (0,90), 'ScatterPlot.identity_line' : True})
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[0]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR1.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[1]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR2.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[2]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR3.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[0],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison1.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[1],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison2.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[2],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison3.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[3],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison4.png').plot()
#TrialToTrialVariabilityComparison(data_store,ParameterSet({}),plot_file_name='TtTVar.png').plot()
#ConductanceAndVmTuningSummary(data_store,ParameterSet({'many' : True,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (25,16)},plot_file_name='Cond&VMTuning.png').plot()
#dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
#KremkowOverviewFigure(dsv,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='NMOverview.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='StimulusResponseComparison.png').plot()
#OrientationTuningSummary(data_store,ParameterSet({}),fig_param={'dpi' : 100,'figsize': (20,12)},plot_file_name='OrientationTuningSummary.png').plot()
if True:
#OverviewPlot(data_store,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="All.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',st_orientation=[0,numpy.pi/2])
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc1.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',st_contrast=100)
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc2.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[0], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc3.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[1], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc4.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[2], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc5.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[3], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc6.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[4], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc7.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[5], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc8.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[6], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc9.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[7], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc10.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="Inh.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'X_ON', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids())[0], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN0On.png").plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'X_OFF', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_OFF").get_segments()[0].get_stored_esyn_ids())[0], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN0Off.png").plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'X_ON', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids())[1], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN1On.png").plot()
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'X_OFF', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_OFF").get_segments()[0].get_stored_esyn_ids())[1], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN1Off.png").plot()
#TrialAveragedFiringRate(param_filter_query(data_store,sheet_name=['X_ON'],st_name="DriftingSinusoidalGratingDisk"),ParameterSet({})).analyse()
##dsv = param_filter_query(data_store,st_name='DriftingSinusoidalGratingDisk',analysis_algorithm=['TrialAveragedFiringRate'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'spatial_frequency', 'neurons': list(sorted(param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids())), 'sheet_name' : 'X_ON','centered' : False,'mean' : False})).plot({'TuningCurve F0_Inh_Cond.y_lim' : (0,180) , 'TuningCurve F0_Exc_Cond.y_lim' : (0,80)})
#import pylab
#pylab.show()
if True:
dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="NatExc2.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[0], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="NatExc3.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[1], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="NatExc4.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[2], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="NatExc5.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[3], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="NatExc6.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[4], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="NatExc7.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[5], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="NatExc8.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[6], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="NatExc9.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[7], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="NatExc10.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'X_ON', 'neuron' : sorted(param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids())[0], 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="LGN0On.png").plot()
import pylab
pylab.show()
if False:
dsv = param_filter_query(data_store,st_name='DriftingSineGratingWithEyeMovement')
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="DGExc2.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[0], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="DGExc3.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[1], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="DGExc4.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[2], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="DGExc5.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[3], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="DGExc6.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[4], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="DGExc7.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[5], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="DGExc8.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[6], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="DGExc9.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : analog_ids[7], 'sheet_activity' : {},'spontaneous' : True}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="DGExc10.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
# tuninc curves
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids), 'sheet_name' : 'V1_Exc_L4','centered' : True,'mean' : False,'polar' : False, 'pool' : False}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name="TCExc.png").plot({'TuningCurve F0_Inh_Cond.y_lim' : (0,180) , 'TuningCurve F0_Exc_Cond.y_lim' : (0,80)})
PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids_inh), 'sheet_name' : 'V1_Inh_L4','centered' : True,'mean' : False,'polar' : False, 'pool' : False}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name="TCInh.png").plot({'TuningCurve F0_Inh_Cond.y_lim' : (0,180) , 'TuningCurve F0_Exc_Cond.y_lim' : (0,80)})
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['Analog_MeanSTDAndFanoFactor'])
PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids), 'sheet_name' : 'V1_Exc_L4','centered' : True,'mean' : False,'polar' : False, 'pool' : False}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name="TCExcA.png").plot()
PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids_inh), 'sheet_name' : 'V1_Inh_L4','centered' : True,'mean' : False,'polar' : False, 'pool' : False}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name="TCInhA.png").plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids), 'sheet_name' : 'V1_Exc_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids_inh), 'sheet_name' : 'V1_Inh_L4'})).plot()
import pylab
pylab.show()
def plot(self):
self.fig = pylab.figure(facecolor='w', **self.fig_param)
gs = gridspec.GridSpec(1, 1)
gs.update(left=0.07, right=0.97, top=0.9, bottom=0.1)
gs = gs[0,0]
gs = gridspec.GridSpecFromSubplotSpec(4, 5,subplot_spec=gs)
orr = list(set([MozaikParametrized.idd(s).orientation for s in queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',st_contrast=100).get_stimuli()]))
l4_exc_or = self.datastore.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
col = orr[numpy.argmin([circular_dist(o,l4_exc_or[0].get_value_by_id(self.parameters.neuron),numpy.pi) for o in orr])]
#segs = queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',st_contrast=100,st_orientation=col,sheet_name='V1_Exc_L4').get_segments()
#signals = [seg.get_vm(self.parameters.neuron) for seg in segs]
dsv = queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',sheet_name='V1_Exc_L4',st_contrast=100,analysis_algorithm='ActionPotentialRemoval',st_orientation=col)
assert queries.ads_with_equal_stimuli(dsv,except_params=["trial"])
adss = dsv.get_analysis_result()
signals = [ads.get_asl_by_id(self.parameters.neuron) for ads in adss]
(signal,noise,snr) = self.wavelet_decomposition(signals)
ax = pylab.subplot(gs[0,0:2])
for s in signals:
ax.plot(s,c='k')
pylab.ylabel('Vm')
pylab.title("Gratings",fontsize=20)
pylab.xlim(0,len(signals[0]))
pylab.ylim(-80,-50)
ax = pylab.subplot(gs[1,0:2])
ax.imshow(signal,aspect='auto',origin='lower')
pylab.ylabel('Signal')
ax = pylab.subplot(gs[2,0:2])
ax.imshow(noise,aspect='auto',origin='lower')
pylab.ylabel('Noise')
ax = pylab.subplot(gs[3,0:2])
ax.imshow(snr,aspect='auto',origin='lower')
pylab.ylabel('SNR')
pylab.xlabel('time')
#segs = queries.param_filter_query(self.datastore,st_name='NaturalImageWithEyeMovement',sheet_name='V1_Exc_L4').get_segments()
#signals = [seg.get_vm(self.parameters.neuron) for seg in segs]
dsv = queries.param_filter_query(self.datastore,st_name='NaturalImageWithEyeMovement',sheet_name='V1_Exc_L4',analysis_algorithm='ActionPotentialRemoval')
assert queries.ads_with_equal_stimuli(dsv,except_params=["trial"])
adss = dsv.get_analysis_result()
signals = [ads.get_asl_by_id(self.parameters.neuron) for ads in adss]
(signal_ni,noise_ni,snr_ni) = self.wavelet_decomposition(signals)
ax = pylab.subplot(gs[0,2:4])
for s in signals:
ax.plot(s,c='k')
pylab.xlim(0,len(signals[0]))
pylab.ylim(-80,-50)
pylab.title("NI",fontsize=20)
ax = pylab.subplot(gs[1,2:4])
ax.imshow(signal_ni,aspect='auto',origin='lower')
ax = pylab.subplot(gs[2,2:4])
ax.imshow(noise_ni,aspect='auto',origin='lower')
ax = pylab.subplot(gs[3,2:4])
ax.imshow(snr_ni,aspect='auto',origin='lower')
pylab.xlabel('time')
ax = pylab.subplot(gs[1,4])
ax.plot(numpy.mean(signal,axis=1),label="GR")
ax.plot(numpy.mean(signal_ni,axis=1),label="NI")
ax.set_xscale('log')
ax.set_yscale('log')
pylab.legend()
ax = pylab.subplot(gs[2,4])
ax.plot(numpy.mean(noise,axis=1))
ax.plot(numpy.mean(noise_ni,axis=1))
ax.set_xscale('log')
ax.set_yscale('log')
ax = pylab.subplot(gs[3,4])
ax.plot(numpy.mean(snr,axis=1))
ax.plot(numpy.mean(snr_ni,axis=1))
ax.set_xscale('log')
ax.set_yscale('log')
pylab.xlabel("frequency")
if self.plot_file_name:
pylab.savefig(Global.root_directory+self.plot_file_name)
def plot(self):
self.fig = pylab.figure(facecolor='w', **self.fig_param)
gs = gridspec.GridSpec(1, 1)
gs.update(left=0.1, right=0.9, top=0.9, bottom=0.1)
gs = gs[0,0]
gs = gridspec.GridSpecFromSubplotSpec(2, 1,subplot_spec=gs)
orr = list(set([MozaikParametrized.idd(s).orientation for s in queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',st_contrast=100).get_stimuli()]))
oor = self.datastore.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = self.parameters.sheet_name)
if True:
for neuron_idd in self.parameters.neurons:
col = orr[numpy.argmin([circular_dist(o,oor[0].get_value_by_id(neuron_idd),numpy.pi) for o in orr])]
dsv = queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',st_contrast=100,st_orientation=col,sheet_name=self.parameters.sheet_name,analysis_algorithm='ActionPotentialRemoval')
TrialToTrialCrossCorrelationOfAnalogSignalList(dsv,ParameterSet({'neurons' : [neuron_idd]}),tags=['helper']).analyse()
dsv = queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',st_contrast=100,st_orientation=col,sheet_name=self.parameters.sheet_name,analysis_algorithm='PSTH')
TrialToTrialCrossCorrelationOfAnalogSignalList(dsv,ParameterSet({'neurons' : [neuron_idd]}),tags=['helper']).analyse()
dsv = queries.tag_based_query(self.datastore,['helper'])
dsv1 = queries.param_filter_query(dsv,y_axis_name='trial-trial cross-correlation of Vm (no AP)',st_name='FullfieldDriftingSinusoidalGrating',sheet_name=self.parameters.sheet_name)
vm_cc_gr = numpy.mean(numpy.array([asl.asl[0] for asl in dsv1.get_analysis_result()]),axis=0)
dsv1 = queries.param_filter_query(dsv,y_axis_name='trial-trial cross-correlation of psth (bin=2.0)',st_name='FullfieldDriftingSinusoidalGrating',sheet_name=self.parameters.sheet_name)
psth_cc_gr = numpy.mean(numpy.array([asl.asl[0] for asl in dsv1.get_analysis_result()]),axis=0)
#queries.param_filter_query(self.datastore,analysis_algorithm='TrialToTrialCrossCorrelationOfAnalogSignalList').print_content(full_ADS=True)
dsv = queries.param_filter_query(self.datastore,y_axis_name='trial-trial cross-correlation of Vm (no AP)',st_name="NaturalImageWithEyeMovement",sheet_name=self.parameters.sheet_name,ads_unique=True)
vm_cc_ni = numpy.mean(numpy.array(dsv.get_analysis_result()[0].asl),axis=0)
dsv = queries.param_filter_query(self.datastore,y_axis_name='trial-trial cross-correlation of psth (bin=2.0)',st_name="NaturalImageWithEyeMovement",sheet_name=self.parameters.sheet_name,ads_unique=True)
psth_cc_ni = numpy.mean(numpy.array(dsv.get_analysis_result()[0].asl),axis=0)
logger.info(str(vm_cc_gr))
logger.info(str(vm_cc_ni))
z = int(min(self.parameters.window_length,len(vm_cc_gr-1)/2,len(vm_cc_ni-1)/2)/2)*2
logger.info(str(psth_cc_ni))
logger.info(str(psth_cc_gr))
fontsize = 30
pylab.rcParams['xtick.major.pad'] = fontsize-5
pylab.rcParams['ytick.major.pad'] = 10
pylab.rc('axes', linewidth=5)
logger.info(len(vm_cc_gr[int(len(vm_cc_gr)/2)-z:int(len(vm_cc_gr)/2)+z+1]))
logger.info(len(numpy.linspace(-z,z,2*z+1)))
ax = pylab.subplot(gs[0,0])
ax.plot(numpy.linspace(-z,z,2*z+1),vm_cc_gr[int(len(vm_cc_gr)/2)-z:int(len(vm_cc_gr)/2)+z+1],label="Gratings")
ax.plot(numpy.linspace(-z,z,2*z+1),vm_cc_ni[int(len(vm_cc_ni)/2)-z:int(len(vm_cc_ni)/2)+z+1],label="Natural images")
pylab.legend()
pylab.title("VM")
pylab.xlabel("time (ms)")
#pylab.ylabel("corr coef")
ax = pylab.subplot(gs[1,0])
ax.plot(numpy.linspace(-z,z,z+1),psth_cc_gr[int(len(psth_cc_gr)/2)-z/2:int(len(psth_cc_gr)/2)+z/2+1],label="Gratings")
ax.plot(numpy.linspace(-z,z,z+1),psth_cc_ni[int(len(psth_cc_ni)/2)-z/2:int(len(psth_cc_ni)/2)+z/2+1],label="Natural images")
pylab.xlim(-z,z)
pylab.xticks([-z,0,z],[-250,0,250])#[-2*z,0,2*z])
pylab.yticks([-1.0,0.0,1.0])
#pylab.legend()
#pylab.title("Spikes")
#pylab.xlabel("time (ms)",fontsize=fontsize)
#pylab.ylabel("corr. coef.",fontsize=fontsize)
#three_tick_axis(pylab.gca().xaxis)
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(fontsize)
if self.plot_file_name:
pylab.savefig(Global.root_directory+self.plot_file_name)
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 = queries.param_filter_query(self.datastore,identifier='AnalogSignalList',sheet_name=sheet,analysis_algorithm='PSTH',st_name='FullfieldDriftingSinusoidalGrating')
assert queries.equal_ads(dsv,except_params=['stimulus_id']) , "It seems PSTH computed in different ways are present in datastore, ModulationRatio can accept only one"
psths = dsv.get_analysis_result()
st = [MozaikParametrized.idd(p.stimulus_id) for p in psths]
# average across trials
psths, stids = colapse(psths,st,parameter_list=['trial'],func=neo_sum,allow_non_identical_objects=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
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 = OrderedDict()
for s in st:
ps[MozaikParametrized.idd(s).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)
# collapse 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 = []
f0 = []
f1 = []
ids = []
frequency = MozaikParametrized.idd(st).temporal_frequency * MozaikParametrized.idd(st).getParams()['temporal_frequency'].units
for (orr, ppsth) in zip(vl[0], vl[1]):
for j in numpy.nonzero(orr == closest_presented_orientation)[0]:
if or_pref.ids[j] in ppsth.ids:
a = or_pref.ids[j]
mr,F0,F1 = self._calculate_MR(ppsth.get_asl_by_id(or_pref.ids[j]).flatten(),frequency)
modulation_ratio.append(mr)
f0.append(F0)
f1.append(F1)
ids.append(or_pref.ids[j])
logger.debug('Adding PerNeuronValue:' + str(sheet))
self.datastore.full_datastore.add_analysis_result(
PerNeuronValue(modulation_ratio,
ids,
qt.dimensionless,
value_name='Modulation ratio' + '(' + psths[0].x_axis_name + ')',
sheet_name=sheet,
tags=self.tags,
period=None,
analysis_algorithm=self.__class__.__name__,
stimulus_id=str(st)))
self.datastore.full_datastore.add_analysis_result(
PerNeuronValue(f0,
ids,
qt.dimensionless,
value_name='F0' + '(' + psths[0].x_axis_name + ')',
sheet_name=sheet,
tags=self.tags,
period=None,
analysis_algorithm=self.__class__.__name__,
stimulus_id=str(st)))
self.datastore.full_datastore.add_analysis_result(
PerNeuronValue(f1,
ids,
qt.dimensionless,
value_name='F1' + '(' + psths[0].x_axis_name + ')',
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)
setup_logging()
data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'ST'}))
logger.info('Loaded data store')
NeuronAnnotationsToPerNeuronValues(data_store,ParameterSet({})).analyse()
# find neuron with preference closet to 0
analog_indexes = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_isyn_ids()
analog_indexes_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_isyn_ids()
#find neuron with preference closet to 0
NeuronAnnotationsToPerNeuronValues(data_store,ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Exc_L4')
l4_exc = analog_indexes[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].get_value_by_id(analog_indexes),l4_exc_phase[0].get_value_by_id(analog_indexes))])]
l4_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentPhase', sheet_name = 'V1_Inh_L4')
l4_inh = analog_indexes_inh[numpy.argmin([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_inh_or[0].get_value_by_id(analog_indexes_inh),l4_inh_phase[0].get_value_by_id(analog_indexes_inh))])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,numpy.pi/2,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
os.mkdir('REPORT')
l4_exc_data = param_filter_query(data_store,sheet_name='V1_Exc_L4')
l4_inh_data = param_filter_query(data_store,sheet_name='V1_Inh_L4')
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',contrast=100)
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),plot_file_name='REPORT/GratingsExc.png',fig_param={'dpi' : 100,'figsize': (14,8)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),plot_file_name='REPORT/GratingsInh.png',fig_param={'dpi' : 100,'figsize': (14,8)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
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
print sheet
self.datastore.print_content()
dsv = queries.param_filter_query(self.datastore,identifier='AnalogSignalList',sheet_name=sheet,analysis_algorithm='PSTH',st_name='FullfieldDriftingSinusoidalGrating')
dsv.print_content()
assert queries.equal_ads(dsv,except_params=['stimulus_id']) , "It seems PSTH computed in different ways are present in datastore, ModulationRatio can accept only one"
psths = dsv.get_analysis_result()
st = [MozaikParametrized.idd(p.stimulus_id) for p in psths]
# average across trials
psths, stids = colapse(psths,st,parameter_list=['trial'],func=neo_sum,allow_non_identical_objects=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
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[MozaikParametrized.idd(s).orientation] = True
ps = ps.keys()
print ps
# 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)
# collapse 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 = []
ids = []
frequency = MozaikParametrized.idd(st).temporal_frequency * MozaikParametrized.idd(st).params()['temporal_frequency'].units
for (orr, ppsth) in zip(vl[0], vl[1]):
for j in numpy.nonzero(orr == closest_presented_orientation)[0]:
if or_pref.ids[j] in ppsth.ids:
modulation_ratio.append(self._calculate_MR(ppsth.get_asl_by_id(or_pref.ids[j]),frequency))
ids.append(or_pref.ids[j])
logger.debug('Adding PerNeuronValue:' + str(sheet))
self.datastore.full_datastore.add_analysis_result(
PerNeuronValue(modulation_ratio,
ids,
qt.dimensionless,
value_name='Modulation ratio' + '(' + psths[0].x_axis_name + ')',
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_and_visualization(data_store):
if True:
analog_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
#find neuron with preference closet to 0
NeuronAnnotationsToPerNeuronValues(data_store,
ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L4')
l4_exc_phase = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentPhase',
sheet_name='V1_Exc_L4')
l4_exc = analog_ids[numpy.argmin([
circular_dist(o, numpy.pi / 2, numpy.pi)
for (o, p) in zip(l4_exc_or[0].get_value_by_id(analog_ids),
l4_exc_phase[0].get_value_by_id(analog_ids))
])]
l4_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Inh_L4')
l4_inh_phase = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentPhase',
sheet_name='V1_Inh_L4')
l4_inh = analog_ids_inh[numpy.argmin([
circular_dist(o, numpy.pi / 2, numpy.pi)
for (o, p) in zip(l4_inh_or[0].get_value_by_id(analog_ids_inh),
l4_inh_phase[0].get_value_by_id(analog_ids_inh))
])]
l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(
numpy.array([
circular_dist(o, numpy.pi / 2, numpy.pi)
for (o, p) in zip(l4_exc_or[0].values, l4_exc_phase[0].values)
]) < 0.1)[0]]
print "Prefered orientation of plotted exc neurons:"
print 'index ' + str(l4_exc)
print "Prefered phase of plotted exc neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
print "Prefered orientation of plotted inh neurons:"
print l4_inh_phase[0].get_value_by_id(l4_inh)
print 'index ' + str(l4_inh)
print "Prefered phase of plotted inh neurons:"
print l4_exc_phase[0].get_value_by_id(l4_exc)
if False:
#data_store.remove_ads_from_datastore()
dsv = param_filter_query(data_store, sheet_name='V1_Exc_L4')
ActionPotentialRemoval(dsv, ParameterSet({'window_length':
10.0})).analyse()
TrialAveragedFiringRate(
param_filter_query(data_store,
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'],
st_name="FullfieldDriftingSinusoidalGrating"),
ParameterSet({})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm='TrialAveragedFiringRate',
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
GaussianTuningCurveFit(dsv,
ParameterSet({'parameter_name':
'orientation'})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
Analog_F0andF1(dsv, ParameterSet({})).analyse()
data_store.save()
Analog_MeanSTDAndFanoFactor(data_store, ParameterSet({})).analyse()
#data_store.save()
PSTH(param_filter_query(data_store, sheet_name='V1_Exc_L4'),
ParameterSet({'bin_length': 2.0})).analyse()
GSTA(param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name='InternalStimulus',
direct_stimulation_name='None'),
ParameterSet({
'neurons': [l4_exc],
'length': 250.0
}),
tags=['GSTA']).analyse()
GSTA(param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=[0, numpy.pi / 2]),
ParameterSet({
'neurons': [l4_exc],
'length': 250.0
}),
tags=['GSTA']).analyse()
GSTA(param_filter_query(data_store,
sheet_name='V1_Inh_L4',
st_name='FullfieldDriftingSinusoidalGrating',
st_orientation=[0, numpy.pi / 2]),
ParameterSet({
'neurons': [l4_inh],
'length': 250.0
}),
tags=['GSTA']).analyse()
GSTA(param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name='NaturalImageWithEyeMovement'),
ParameterSet({
'neurons': [l4_exc],
'length': 250.0
}),
tags=['GSTA']).analyse()
#data_store.print_content(full_ADS=True)
#Precision(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'bin_length' : 10.0 })).analyse()
#dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement',sheet_name='V1_Exc_L4',analysis_algorithm='ActionPotentialRemoval')
#TrialVariability(dsv,ParameterSet({'vm': False, 'cond_exc': False, 'cond_inh': False})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
st_contrast=100,
analysis_algorithm='ActionPotentialRemoval')
TrialVariability(
dsv,
ParameterSet({
'vm': False,
'cond_exc': False,
'cond_inh': False
})).analyse()
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm='TrialAveragedFiringRate',
sheet_name=['V1_Exc_L4', 'V1_Inh_L4'])
PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(
dsv, ParameterSet({'parameter_name': 'orientation'})).analyse()
pnv = param_filter_query(
data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],
value_name='Mean(ECond)',
st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_F0andF1'],
value_name='F0_Exc_Cond')
SubtractPNVfromPNVS(pnv, dsv, ParameterSet({})).analyse()
pnv = param_filter_query(
data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],
value_name='Mean(ICond)',
st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_F0andF1'],
value_name='F0_Inh_Cond')
SubtractPNVfromPNVS(pnv, dsv, ParameterSet({})).analyse()
pnv = param_filter_query(
data_store,
st_name='InternalStimulus',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'],
value_name='Mean(VM)',
st_direct_stimulation_name='None').get_analysis_result()[0]
dsv = param_filter_query(data_store,
st_name='FullfieldDriftingSinusoidalGrating',
sheet_name='V1_Exc_L4',
analysis_algorithm=['Analog_F0andF1'],
value_name='F0_Vm')
SubtractPNVfromPNVS(pnv, dsv, ParameterSet({})).analyse()
TrialToTrialCrossCorrelationOfAnalogSignalList(
param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name="NaturalImageWithEyeMovement",
analysis_algorithm='ActionPotentialRemoval'),
ParameterSet({'neurons': list(analog_ids)})).analyse()
TrialToTrialCrossCorrelationOfAnalogSignalList(
param_filter_query(data_store,
sheet_name='V1_Exc_L4',
st_name="NaturalImageWithEyeMovement",
analysis_algorithm='PSTH'),
ParameterSet({'neurons': list(analog_ids)})).analyse()
data_store.save()
dsv = param_filter_query(
data_store,
analysis_algorithm='TrialToTrialCrossCorrelationOfAnalogSignalList')
dsv.print_content(full_ADS=True)
if True: # PLOTTING
#TrialCrossCorrelationAnalysis(data_store,ParameterSet({'neurons' : list(analog_ids)}),fig_param={"dpi" : 100,"figsize": (25,12)},plot_file_name="trial-to-trial-cross-correlation.png").plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[0]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR1.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[1]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR2.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[2]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR3.png').plot()
dsv = param_filter_query(data_store, st_name=['InternalStimulus'])
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSExcAnalog.png').plot()
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Inh_L4',
'neuron': analog_ids_inh[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (28, 12)
},
plot_file_name='SSInhAnalog.png').plot()
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name='FullfieldDriftingSinusoidalGrating',st_trial=0)
#VmPlot(dsv,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'})).plot({'Vm_plot.y_lim' : (-67,-56)})
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='ActionPotentialRemoval',st_trial=0)
#AnalogSignalListPlot(dsv,ParameterSet({'neurons' : [l4_exc],'sheet_name' : 'V1_Exc_L4'})).plot({'AnalogSignalPlot.y_lim' : (-67,-56)})
#RetinalInputMovie(data_store,ParameterSet({'frame_rate' : 10})).plot()
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],value_name='LGNAfferentOrientation')
#PerNeuronValuePlot(dsv,ParameterSet({}),plot_file_name="LGNAfferentOrientation.png").plot()
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],value_name='orientation preference',analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',st_contrast=100)
#PerNeuronValuePlot(dsv,ParameterSet({})).plot()
#dsv = param_filter_query(data_store,value_name=['orientation HWHH'],sheet_name=['V1_Exc_L4','V1_Inh_L4'])
#PerNeuronValueScatterPlot(dsv,ParameterSet({})).plot({ 'ScatterPlot.x_lim' : (0,90), 'ScatterPlot.y_lim' : (0,90), 'ScatterPlot.identity_line' : True})
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[0]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR1.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[1]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR2.png').plot()
#SNRAnalysis(data_store,ParameterSet({"neuron" : analog_ids[2]}),fig_param={'dpi' : 100,'figsize': (25,12)},plot_file_name='SNR3.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[0],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison1.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[1],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison2.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[2],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison3.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : analog_ids[3],'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (10,12)},plot_file_name='StimulusResponseComparison4.png').plot()
#TrialToTrialVariabilityComparison(data_store,ParameterSet({}),plot_file_name='TtTVar.png').plot()
#ConductanceAndVmTuningSummary(data_store,ParameterSet({'many' : True,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (25,16)},plot_file_name='Cond&VMTuning.png').plot()
#dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
#KremkowOverviewFigure(dsv,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='NMOverview.png').plot()
#StimulusResponseComparison(data_store,ParameterSet({'neuron' : l4_exc,'sheet_name' : 'V1_Exc_L4'}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name='StimulusResponseComparison.png').plot()
#OrientationTuningSummary(data_store,ParameterSet({}),fig_param={'dpi' : 100,'figsize': (20,12)},plot_file_name='OrientationTuningSummary.png').plot()
if True:
#OverviewPlot(data_store,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="All.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',st_orientation=[0,numpy.pi/2])
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (19,12)},plot_file_name="Exc1.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
st_contrast=100)
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': l4_exc,
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc2.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc3.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[1],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc4.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[2],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc5.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[3],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc6.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[4],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc7.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[5],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc8.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[6],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc9.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[7],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="Exc10.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Inh_L4',
'neuron': l4_inh,
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="Inh.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_ON',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_ON").get_segments()
[0].get_stored_esyn_ids())[0],
'sheet_activity': {},
'spontaneous':
True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN0On.png").plot()
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_OFF',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_OFF").get_segments()
[0].get_stored_esyn_ids())[0],
'sheet_activity': {},
'spontaneous':
True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN0Off.png").plot()
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_ON',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_ON").get_segments()
[0].get_stored_esyn_ids())[1],
'sheet_activity': {},
'spontaneous':
True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN1On.png").plot()
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_OFF',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_OFF").get_segments()
[0].get_stored_esyn_ids())[1],
'sheet_activity': {},
'spontaneous':
True
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN1Off.png").plot()
#TrialAveragedFiringRate(param_filter_query(data_store,sheet_name=['X_ON'],st_name="DriftingSinusoidalGratingDisk"),ParameterSet({})).analyse()
##dsv = param_filter_query(data_store,st_name='DriftingSinusoidalGratingDisk',analysis_algorithm=['TrialAveragedFiringRate'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'spatial_frequency', 'neurons': list(sorted(param_filter_query(data_store,sheet_name="X_ON").get_segments()[0].get_stored_esyn_ids())), 'sheet_name' : 'X_ON','centered' : False,'mean' : False})).plot({'TuningCurve F0_Inh_Cond.y_lim' : (0,180) , 'TuningCurve F0_Exc_Cond.y_lim' : (0,80)})
#import pylab
#pylab.show()
if True:
dsv = param_filter_query(data_store,
st_name='NaturalImageWithEyeMovement')
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': l4_exc,
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc2.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc3.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[1],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc4.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[2],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc5.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[3],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc6.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[4],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc7.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[5],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc8.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[6],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc9.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[7],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="NatExc10.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(
dsv,
ParameterSet({
'sheet_name':
'X_ON',
'neuron':
sorted(
param_filter_query(data_store,
sheet_name="X_ON").get_segments()
[0].get_stored_esyn_ids())[0],
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="LGN0On.png").plot()
import pylab
pylab.show()
if False:
dsv = param_filter_query(
data_store, st_name='DriftingSineGratingWithEyeMovement')
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': l4_exc,
'sheet_activity': {}
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc2.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[0],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc3.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[1],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc4.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[2],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc5.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[3],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc6.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[4],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc7.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[5],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc8.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[6],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc9.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
OverviewPlot(dsv,
ParameterSet({
'sheet_name': 'V1_Exc_L4',
'neuron': analog_ids[7],
'sheet_activity': {},
'spontaneous': True
}),
fig_param={
'dpi': 100,
'figsize': (19, 12)
},
plot_file_name="DGExc10.png").plot({
'Vm_plot.y_lim': (-67, -56),
'Conductance_plot.y_lim': (0, 35.0)
})
# tuninc curves
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm=['TrialAveragedFiringRate', 'Analog_F0andF1'])
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids),
'sheet_name': 'V1_Exc_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name="TCExc.png").plot({
'TuningCurve F0_Inh_Cond.y_lim': (0, 180),
'TuningCurve F0_Exc_Cond.y_lim': (0, 80)
})
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids_inh),
'sheet_name': 'V1_Inh_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name="TCInh.png").plot({
'TuningCurve F0_Inh_Cond.y_lim': (0, 180),
'TuningCurve F0_Exc_Cond.y_lim': (0, 80)
})
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm=['Analog_MeanSTDAndFanoFactor'])
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids),
'sheet_name': 'V1_Exc_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name="TCExcA.png").plot()
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'orientation',
'neurons': list(analog_ids_inh),
'sheet_name': 'V1_Inh_L4',
'centered': True,
'mean': False,
'polar': False,
'pool': False
}),
fig_param={
'dpi': 100,
'figsize': (25, 12)
},
plot_file_name="TCInhA.png").plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids), 'sheet_name' : 'V1_Exc_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(analog_ids_inh), 'sheet_name' : 'V1_Inh_L4'})).plot()
import pylab
pylab.show()
def plot(self):
self.fig = pylab.figure(facecolor='w', **self.fig_param)
gs = gridspec.GridSpec(1, 1)
gs.update(left=0.07, right=0.97, top=0.9, bottom=0.1)
gs = gs[0,0]
gs = gridspec.GridSpecFromSubplotSpec(4, 5,subplot_spec=gs)
orr = list(set([MozaikParametrized.idd(s).orientation for s in queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',st_contrast=100).get_stimuli()]))
l4_exc_or = self.datastore.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
col = orr[numpy.argmin([circular_dist(o,l4_exc_or[0].get_value_by_id(self.parameters.neuron),numpy.pi) for o in orr])]
#segs = queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',st_contrast=100,st_orientation=col,sheet_name='V1_Exc_L4').get_segments()
#signals = [seg.get_vm(self.parameters.neuron) for seg in segs]
dsv = queries.param_filter_query(self.datastore,st_name='FullfieldDriftingSinusoidalGrating',sheet_name='V1_Exc_L4',st_contrast=100,analysis_algorithm='ActionPotentialRemoval',st_orientation=col)
assert queries.ads_with_equal_stimuli(dsv,except_params=["trial"])
adss = dsv.get_analysis_result()
signals = [ads.get_asl_by_id(self.parameters.neuron) for ads in adss]
(signal,noise,snr) = self.wavelet_decomposition(signals)
ax = pylab.subplot(gs[0,0:2])
for s in signals:
ax.plot(s,c='k')
pylab.ylabel('Vm')
pylab.title("Gratings",fontsize=20)
pylab.xlim(0,len(signals[0]))
pylab.ylim(-80,-50)
ax = pylab.subplot(gs[1,0:2])
ax.imshow(signal,aspect='auto',origin='lower')
pylab.ylabel('Signal')
ax = pylab.subplot(gs[2,0:2])
ax.imshow(noise,aspect='auto',origin='lower')
pylab.ylabel('Noise')
ax = pylab.subplot(gs[3,0:2])
ax.imshow(snr,aspect='auto',origin='lower')
pylab.ylabel('SNR')
pylab.xlabel('time')
#segs = queries.param_filter_query(self.datastore,st_name='NaturalImageWithEyeMovement',sheet_name='V1_Exc_L4').get_segments()
#signals = [seg.get_vm(self.parameters.neuron) for seg in segs]
dsv = queries.param_filter_query(self.datastore,st_name='NaturalImageWithEyeMovement',sheet_name='V1_Exc_L4',analysis_algorithm='ActionPotentialRemoval')
assert queries.ads_with_equal_stimuli(dsv,except_params=["trial"])
adss = dsv.get_analysis_result()
signals = [ads.get_asl_by_id(self.parameters.neuron) for ads in adss]
(signal_ni,noise_ni,snr_ni) = self.wavelet_decomposition(signals)
ax = pylab.subplot(gs[0,2:4])
for s in signals:
ax.plot(s,c='k')
pylab.xlim(0,len(signals[0]))
pylab.ylim(-80,-50)
pylab.title("NI",fontsize=20)
ax = pylab.subplot(gs[1,2:4])
ax.imshow(signal_ni,aspect='auto',origin='lower')
ax = pylab.subplot(gs[2,2:4])
ax.imshow(noise_ni,aspect='auto',origin='lower')
ax = pylab.subplot(gs[3,2:4])
ax.imshow(snr_ni,aspect='auto',origin='lower')
pylab.xlabel('time')
ax = pylab.subplot(gs[1,4])
ax.plot(numpy.mean(signal,axis=1),label="GR")
ax.plot(numpy.mean(signal_ni,axis=1),label="NI")
ax.set_xscale('log')
ax.set_yscale('log')
pylab.legend()
ax = pylab.subplot(gs[2,4])
ax.plot(numpy.mean(noise,axis=1))
ax.plot(numpy.mean(noise_ni,axis=1))
ax.set_xscale('log')
ax.set_yscale('log')
ax = pylab.subplot(gs[3,4])
ax.plot(numpy.mean(snr,axis=1))
ax.plot(numpy.mean(snr_ni,axis=1))
ax.set_xscale('log')
ax.set_yscale('log')
pylab.xlabel("frequency")
if self.plot_file_name:
pylab.savefig(Global.root_directory+self.plot_file_name)
def perform_analysis_and_visualization(data_store):
pref_or = numpy.pi / 2
#find neuron with preference closet to pref_or
l4_analog_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
l4_analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_esyn_ids()
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L4')
l4_exc = l4_analog_ids[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l4_exc_or[0].get_value_by_id(l4_analog_ids)
])]
l4_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Inh_L4')
l4_inh = l4_analog_ids_inh[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l4_inh_or[0].get_value_by_id(l4_analog_ids_inh)
])]
l23_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L2/3')
l23_exc = l23_analog_ids[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l23_exc_or[0].get_value_by_id(l23_analog_ids)
])]
l23_inh_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Inh_L2/3')
l23_inh = l23_analog_ids_inh[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l23_inh_or[0].get_value_by_id(l23_analog_ids_inh)
])]
#l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,pref_or,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
# Find neurons for which spikes were recorded and whose orientation is close to pref_or
l4_spike_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_spike_train_ids()
l4_spike_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_spike_train_ids()
l23_spike_ids = param_filter_query(data_store,
sheet_name="V1_Exc_L2/3").get_segments(
)[0].get_stored_spike_train_ids()
l23_spike_ids_inh = param_filter_query(
data_store, sheet_name="V1_Inh_L2/3").get_segments(
)[0].get_stored_spike_train_ids()
l4_spike_exc_or_close_to_pi_half = numpy.array(l4_spike_ids)[numpy.nonzero(
numpy.array([
circular_dist(o, pref_or, numpy.pi)
for o in l4_exc_or[0].get_value_by_id(l4_spike_ids)
]) < 0.1)[0]].tolist()
l4_spike_inh_or_close_to_pi_half = numpy.array(l4_spike_ids_inh)[
numpy.nonzero(
numpy.array([
circular_dist(o, pref_or, numpy.pi)
for o in l4_inh_or[0].get_value_by_id(l4_spike_ids_inh)
]) < 0.1)[0]].tolist()
l23_spike_exc_or_close_to_pi_half = numpy.array(l23_spike_ids)[
numpy.nonzero(
numpy.array([
circular_dist(o, pref_or, numpy.pi)
for o in l23_exc_or[0].get_value_by_id(l23_spike_ids)
]) < 0.1)[0]].tolist()
l23_spike_inh_or_close_to_pi_half = numpy.array(l23_spike_ids_inh)[
numpy.nonzero(
numpy.array([
circular_dist(o, pref_or, numpy.pi)
for o in l23_inh_or[0].get_value_by_id(l23_spike_ids_inh)
]) < 0.1)[0]].tolist()
print "Prefered orientation of plotted exc neurons:"
print 'id: ' + str(l4_exc)
print "Prefered orientation of plotted inh neurons:"
print 'id: ' + str(l4_inh)
if True: #ANALYSIS
#TrialAveragedFiringRate(data_store,ParameterSet({'stimulus_type':"DriftingSinusoidalGratingCenterSurroundStimulus"})).analyse()
TrialAveragedFiringRate(data_store, ParameterSet({})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='TrialAveragedFiringRate')
#GaussianTuningCurveFit(dsv,ParameterSet({'parameter_name' : 'orientation'})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#Analog_F0andF1(dsv,ParameterSet({})).analyse()
#TrialVariability(dsv,ParameterSet({'vm': True, 'cond_exc': False, 'cond_inh': False})).analyse()
#TrialMean(dsv,ParameterSet({'vm': True, 'cond_exc': False, 'cond_inh': False})).analyse()
#GSTA(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'length' : 250.0 }),tags=['GSTA']).analyse()
#Precision(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'bin_length' : 10.0 })).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#PSTH(dsv,ParameterSet({'bin_length' : 10.0})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],y_axis_name='psth (bin=10.0)',st_contrast=100,analysis_algorithm='PSTH')
#dsv1 = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],value_name='orientation preference',st_contrast=100,analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage')
#ModulationRatio(dsv+dsv1,ParameterSet({})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='TrialAveragedFiringRate',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(dsv,ParameterSet({'parameter_name' : 'orientation'})).analyse()
#PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(dsv,ParameterSet({'parameter_name' : 'phase'})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4'],value_name='orientation preference',st_contrast=100,analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage')
#LocalHomogeneityIndex(dsv,ParameterSet({'sigma' : 0.1})).analyse()
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],value_name='LGNAfferentOrientation')
#LocalHomogeneityIndex(dsv,ParameterSet({'sigma' : 100.0})).analyse()
data_store.save()
if True: # PLOTTING
#verify_connectivity(data_store)
activity_plot_param = {
'frame_rate': 5,
'bin_width': 20.0,
'scatter': True,
'resolution': 0
}
#dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,st_name='DriftingGratingWithEyeMovement')
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],value_name='LocalHomogeneityIndex' + '(0.1:orientation preference)')
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],value_name='LocalHomogeneityIndex' + '(100.0:LGNAfferentOrientation)')
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],value_name='LGNAfferentOrientation')
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],value_name='orientation preference',analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',st_contrast=100)
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False, '*.x_axis' : None, '*.y_axis' : None})
#TuningComparison(data_store,ParameterSet({}),fig_param={'dpi' : 100,'figsize': (17,7.5)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/TuningComparison.png").plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],analysis_algorithm='TrialVariability',st_orientation = 0)
#PerNeuronValueScatterPlot(dsv,ParameterSet({})).plot({'ScatterPlot.identity_line' : True, 'ScatterPlot.mark_means' : True})
#dsv = param_filter_query(data_store,value_name=['orientation HWHH'],sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#PerNeuronValueScatterPlot(dsv,ParameterSet({}),plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/MR.png").plot({ 'ScatterPlot.x_lim' : (0,90), 'ScatterPlot.y_lim' : (0,90), 'ScatterPlot.identity_line' : True})
#HWHH(data_store,ParameterSet({}),fig_param={'dpi' : 100,'figsize': (8,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/HWHH.png").plot({ '*.x_lim' : (0,90), '*.y_lim' : (0,90), '*.identity_line' : True, '*.title' : None})
# LHI orientation selectivity correlation
#dsv1 = param_filter_query(data_store,value_name='orientation selectivity',sheet_name=['V1_Exc_L4'],analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',st_contrast=100)
#dsv2 = param_filter_query(data_store,value_name='LocalHomogeneityIndex' + '(0.1:orientation preference)',sheet_name=['V1_Exc_L4'])
#PerNeuronValueScatterPlot(dsv1+dsv2,ParameterSet({}),plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/LHIvsSelectivity.png").plot()
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name='DriftingSinusoidalGratingDisk',st_contrast=100)
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name=['FullfieldDriftingSinusoidalGrating'],st_contrast=100)
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewEXCL4.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewEXCL23.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewINHL4.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewINHL23.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,st_name='Null')
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
# PLOT ORIENTATION TUNING
dsv = param_filter_query(
data_store,
st_name='FullfieldDriftingSinusoidalGrating',
analysis_algorithm=['TrialAveragedFiringRate'])
PlotTuningCurve(dsv,
ParameterSet({
'parameter_name': 'contrast',
'neurons': l4_spike_exc_or_close_to_pi_half,
'sheet_name': 'V1_Exc_L4'
}),
plot_file_name="CTC.png",
fig_param={
'dpi': 200,
'figsize': (14, 3)
}).plot({'TuningCurve_firing_rate.y_lim': (0, 50)})
CTC(dsv,
ParameterSet({
'l4_exc_neurons':
numpy.array(l4_spike_exc_or_close_to_pi_half)[:4].tolist(),
'l4_inh_neurons':
numpy.array(l4_spike_inh_or_close_to_pi_half)[:4].tolist(),
'l23_exc_neurons':
numpy.array(l23_spike_exc_or_close_to_pi_half)[:4].tolist(),
'l23_inh_neurons':
numpy.array(l23_spike_inh_or_close_to_pi_half)[:4].tolist()
}),
fig_param={
'dpi': 100,
'figsize': (14, 12)
},
plot_file_name="CTC.svg").plot({'*.title': None})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l4_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L4'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l23_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l23_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
# PLOT SIZETUNING
#dsv = param_filter_query(data_store,st_name='DriftingSinusoidalGratingDisk',analysis_algorithm=['TrialAveragedFiringRate'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l4_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L4'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l4_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L4'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l23_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l23_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#STC(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:6].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:6].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half)[:6].tolist(),'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:6].tolist()}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/STC.png").plot({'*.title' : None})
# PLOT OCTC
#dsv = param_filter_query(data_store,st_name='DriftingSinusoidalGratingCenterSurroundStimulus',analysis_algorithm=['TrialAveragedFiringRate'])
#OCTC(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:5].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:5].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half).tolist()[:5],'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:5].tolist()}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/OCTC.png").plot({'*.title' : None})
# tuninc curves
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids), 'sheet_name' : 'V1_Exc_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l23_analog_ids), 'sheet_name' : 'V1_Exc_L2/3'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids_inh), 'sheet_name' : 'V1_Inh_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l23_analog_ids_inh), 'sheet_name' : 'V1_Inh_L2/3'})).plot()
#OR(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:4].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:4].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half)[:4].tolist(),'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:4].tolist()}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/OR.png").plot({'*.title' : None})
#MRfig(data_store,ParameterSet({'SimpleSheetName' : 'V1_Exc_L4', 'ComplexSheetName' : 'V1_Exc_L2/3',}),fig_param={'dpi' : 100,'figsize': (8,6)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/MR.png").plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids), 'sheet_name' : 'V1_Exc_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids_inh), 'sheet_name' : 'V1_Inh_L4'})).plot()
import pylab
pylab.show()
def perform_analysis_and_visualization(data_store):
pref_or = numpy.pi/2
#find neuron with preference closet to pref_or
l4_analog_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
l4_analog_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids = param_filter_query(data_store,sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_esyn_ids()
NeuronAnnotationsToPerNeuronValues(data_store,ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
l4_exc = l4_analog_ids[numpy.argmin([circular_dist(o,pref_or,numpy.pi) for o in l4_exc_or[0].get_value_by_id(l4_analog_ids)])]
l4_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L4')
l4_inh = l4_analog_ids_inh[numpy.argmin([circular_dist(o,pref_or,numpy.pi) for o in l4_inh_or[0].get_value_by_id(l4_analog_ids_inh)])]
l23_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L2/3')
l23_exc = l23_analog_ids[numpy.argmin([circular_dist(o,pref_or,numpy.pi) for o in l23_exc_or[0].get_value_by_id(l23_analog_ids)])]
l23_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L2/3')
l23_inh = l23_analog_ids_inh[numpy.argmin([circular_dist(o,pref_or,numpy.pi) for o in l23_inh_or[0].get_value_by_id(l23_analog_ids_inh)])]
#l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,pref_or,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
# Find neurons for which spikes were recorded and whose orientation is close to pref_or
l4_spike_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_spike_train_ids()
l4_spike_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_spike_train_ids()
l23_spike_ids = param_filter_query(data_store,sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
l23_spike_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_spike_train_ids()
l4_spike_exc_or_close_to_pi_half = numpy.array(l4_spike_ids)[numpy.nonzero(numpy.array([circular_dist(o,pref_or,numpy.pi) for o in l4_exc_or[0].get_value_by_id(l4_spike_ids)]) < 0.1)[0]].tolist()
l4_spike_inh_or_close_to_pi_half = numpy.array(l4_spike_ids_inh)[numpy.nonzero(numpy.array([circular_dist(o,pref_or,numpy.pi) for o in l4_inh_or[0].get_value_by_id(l4_spike_ids_inh)]) < 0.1)[0]].tolist()
l23_spike_exc_or_close_to_pi_half = numpy.array(l23_spike_ids)[numpy.nonzero(numpy.array([circular_dist(o,pref_or,numpy.pi) for o in l23_exc_or[0].get_value_by_id(l23_spike_ids)]) < 0.1)[0]].tolist()
l23_spike_inh_or_close_to_pi_half = numpy.array(l23_spike_ids_inh)[numpy.nonzero(numpy.array([circular_dist(o,pref_or,numpy.pi) for o in l23_inh_or[0].get_value_by_id(l23_spike_ids_inh)]) < 0.1)[0]].tolist()
print "Prefered orientation of plotted exc neurons:"
print 'id: ' + str(l4_exc)
print "Prefered orientation of plotted inh neurons:"
print 'id: ' + str(l4_inh)
if True: #ANALYSIS
#TrialAveragedFiringRate(data_store,ParameterSet({'stimulus_type':"DriftingSinusoidalGratingCenterSurroundStimulus"})).analyse()
TrialAveragedFiringRate(data_store,ParameterSet({})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='TrialAveragedFiringRate')
#GaussianTuningCurveFit(dsv,ParameterSet({'parameter_name' : 'orientation'})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#Analog_F0andF1(dsv,ParameterSet({})).analyse()
#TrialVariability(dsv,ParameterSet({'vm': True, 'cond_exc': False, 'cond_inh': False})).analyse()
#TrialMean(dsv,ParameterSet({'vm': True, 'cond_exc': False, 'cond_inh': False})).analyse()
#GSTA(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'length' : 250.0 }),tags=['GSTA']).analyse()
#Precision(param_filter_query(data_store,sheet_name='V1_Exc_L4'),ParameterSet({'neurons' : [l4_exc], 'bin_length' : 10.0 })).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#PSTH(dsv,ParameterSet({'bin_length' : 10.0})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],y_axis_name='psth (bin=10.0)',st_contrast=100,analysis_algorithm='PSTH')
#dsv1 = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],value_name='orientation preference',st_contrast=100,analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage')
#ModulationRatio(dsv+dsv1,ParameterSet({})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm='TrialAveragedFiringRate',sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(dsv,ParameterSet({'parameter_name' : 'orientation'})).analyse()
#PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage(dsv,ParameterSet({'parameter_name' : 'phase'})).analyse()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',sheet_name=['V1_Exc_L4'],value_name='orientation preference',st_contrast=100,analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage')
#LocalHomogeneityIndex(dsv,ParameterSet({'sigma' : 0.1})).analyse()
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],value_name='LGNAfferentOrientation')
#LocalHomogeneityIndex(dsv,ParameterSet({'sigma' : 100.0})).analyse()
data_store.save()
if True: # PLOTTING
#verify_connectivity(data_store)
activity_plot_param = {
'frame_rate' : 5,
'bin_width' : 20.0,
'scatter' : True,
'resolution' : 0
}
#dsv = param_filter_query(data_store,st_name='NaturalImageWithEyeMovement')
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,st_name='DriftingGratingWithEyeMovement')
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],value_name='LocalHomogeneityIndex' + '(0.1:orientation preference)')
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4'],value_name='LocalHomogeneityIndex' + '(100.0:LGNAfferentOrientation)')
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],value_name='LGNAfferentOrientation')
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'],value_name='orientation preference',analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',st_contrast=100)
#PerNeuronValuePlot(dsv,ParameterSet({})).plot({'*.colorbar' : False, '*.x_axis' : None, '*.y_axis' : None})
#TuningComparison(data_store,ParameterSet({}),fig_param={'dpi' : 100,'figsize': (17,7.5)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/TuningComparison.png").plot({'*.colorbar' : False,'*.x_axis' : None, '*.y_axis' : None})
#dsv = param_filter_query(data_store,sheet_name=['V1_Exc_L4','V1_Inh_L4'],analysis_algorithm='TrialVariability',st_orientation = 0)
#PerNeuronValueScatterPlot(dsv,ParameterSet({})).plot({'ScatterPlot.identity_line' : True, 'ScatterPlot.mark_means' : True})
#dsv = param_filter_query(data_store,value_name=['orientation HWHH'],sheet_name=['V1_Exc_L4','V1_Inh_L4','V1_Exc_L2/3','V1_Inh_L2/3'])
#PerNeuronValueScatterPlot(dsv,ParameterSet({}),plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/MR.png").plot({ 'ScatterPlot.x_lim' : (0,90), 'ScatterPlot.y_lim' : (0,90), 'ScatterPlot.identity_line' : True})
#HWHH(data_store,ParameterSet({}),fig_param={'dpi' : 100,'figsize': (8,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/HWHH.png").plot({ '*.x_lim' : (0,90), '*.y_lim' : (0,90), '*.identity_line' : True, '*.title' : None})
# LHI orientation selectivity correlation
#dsv1 = param_filter_query(data_store,value_name='orientation selectivity',sheet_name=['V1_Exc_L4'],analysis_algorithm='PeriodicTuningCurvePreferenceAndSelectivity_VectorAverage',st_contrast=100)
#dsv2 = param_filter_query(data_store,value_name='LocalHomogeneityIndex' + '(0.1:orientation preference)',sheet_name=['V1_Exc_L4'])
#PerNeuronValueScatterPlot(dsv1+dsv2,ParameterSet({}),plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/LHIvsSelectivity.png").plot()
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name='DriftingSinusoidalGratingDisk',st_contrast=100)
#dsv = param_filter_query(data_store,st_orientation=[0,numpy.pi/2],st_name=['FullfieldDriftingSinusoidalGrating'],st_contrast=100)
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewEXCL4.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewEXCL23.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewINHL4.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="OverviewINHL23.png").plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#dsv = param_filter_query(data_store,st_name='Null')
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L4', 'neuron' : l4_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L4', 'neuron' : l4_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Exc_L2/3', 'neuron' : l23_exc, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
#OverviewPlot(dsv,ParameterSet({'sheet_name' : 'V1_Inh_L2/3', 'neuron' : l23_inh, 'sheet_activity' : {}}),fig_param={'dpi' : 100,'figsize': (14,12)}).plot({'Vm_plot.y_lim' : (-67,-56),'Conductance_plot.y_lim' : (0,35.0)})
# PLOT ORIENTATION TUNING
dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate'])
PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l4_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L4'}),plot_file_name="CTC.png",fig_param={'dpi' : 200,'figsize': (14,3)}).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
CTC(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:4].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:4].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half)[:4].tolist(),'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:4].tolist()}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="CTC.svg").plot({'*.title' : None})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l4_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L4'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l23_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'contrast', 'neurons': l23_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
# PLOT SIZETUNING
#dsv = param_filter_query(data_store,st_name='DriftingSinusoidalGratingDisk',analysis_algorithm=['TrialAveragedFiringRate'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l4_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L4'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l4_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L4'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l23_spike_exc_or_close_to_pi_half, 'sheet_name' : 'V1_Exc_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'radius', 'neurons': l23_spike_inh_or_close_to_pi_half, 'sheet_name' : 'V1_Inh_L2/3'})).plot({'TuningCurve_firing_rate.y_lim' : (0,50)})
#STC(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:6].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:6].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half)[:6].tolist(),'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:6].tolist()}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/STC.png").plot({'*.title' : None})
# PLOT OCTC
#dsv = param_filter_query(data_store,st_name='DriftingSinusoidalGratingCenterSurroundStimulus',analysis_algorithm=['TrialAveragedFiringRate'])
#OCTC(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:5].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:5].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half).tolist()[:5],'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:5].tolist()}),fig_param={'dpi' : 100,'figsize': (14,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/OCTC.png").plot({'*.title' : None})
# tuninc curves
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids), 'sheet_name' : 'V1_Exc_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l23_analog_ids), 'sheet_name' : 'V1_Exc_L2/3'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids_inh), 'sheet_name' : 'V1_Inh_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialAveragedFiringRate','Analog_F0andF1'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l23_analog_ids_inh), 'sheet_name' : 'V1_Inh_L2/3'})).plot()
#OR(dsv,ParameterSet({'l4_exc_neurons' : numpy.array(l4_spike_exc_or_close_to_pi_half)[:4].tolist(),'l4_inh_neurons' : numpy.array(l4_spike_inh_or_close_to_pi_half)[:4].tolist(),'l23_exc_neurons' : numpy.array(l23_spike_exc_or_close_to_pi_half)[:4].tolist(),'l23_inh_neurons' : numpy.array(l23_spike_inh_or_close_to_pi_half)[:4].tolist()}),fig_param={'dpi' : 100,'figsize': (16,12)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/OR.png").plot({'*.title' : None})
#MRfig(data_store,ParameterSet({'SimpleSheetName' : 'V1_Exc_L4', 'ComplexSheetName' : 'V1_Exc_L2/3',}),fig_param={'dpi' : 100,'figsize': (8,6)},plot_file_name="/home/antolikjan/Doc/Talks/JCUnicMar2013/MR.png").plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids), 'sheet_name' : 'V1_Exc_L4'})).plot()
#dsv = param_filter_query(data_store,st_name='FullfieldDriftingSinusoidalGrating',analysis_algorithm=['TrialVariability'])
#PlotTuningCurve(dsv,ParameterSet({'parameter_name' : 'orientation', 'neurons': list(l4_analog_ids_inh), 'sheet_name' : 'V1_Inh_L4'})).plot()
import pylab
pylab.show()
sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
l4_analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids = param_filter_query(
data_store,
sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids_inh = param_filter_query(
data_store,
sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_esyn_ids()
NeuronAnnotationsToPerNeuronValues(data_store, ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L4')
l4_exc = l4_analog_ids[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l4_exc_or[0].get_value_by_id(l4_analog_ids)
])]
l4_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Inh_L4')
l4_inh = l4_analog_ids_inh[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
for o in l4_inh_or[0].get_value_by_id(l4_analog_ids_inh)
])]
l23_exc_or = data_store.get_analysis_result(
identifier='PerNeuronValue',
value_name='LGNAfferentOrientation',
sheet_name='V1_Exc_L2/3')
l23_exc = l23_analog_ids[numpy.argmin([
circular_dist(o, pref_or, numpy.pi)
def test_stimulating_function_Naka(sheet, coor_x, coor_y,
current_update_interval, parameters):
z = sheet.pop.all_cells.astype(int)
vals = numpy.array([
sheet.get_neuron_annotation(i, 'LGNAfferentOrientation')
for i in range(0, len(z))
])
mean_orientations = []
# x = []
# y = []
# for (i, neuron2) in enumerate(sheet.pop.all()):
# x.append(sheet.pop.positions[i][0])
# y.append(sheet.pop.positions[i][1])
# px, py = sheet.vf_2_cs(sheet.pop.positions[0], sheet.pop.positions[1])
px, py = sheet.vf_2_cs(sheet.pop.positions[:, 0], sheet.pop.positions[:,
1])
# px, py = sheet.vf_2_cs(x, y)
pylab.subplot(151)
pylab.gca().set_aspect('equal')
pylab.title('Orientatin preference (neurons)')
pylab.scatter(px, py, c=vals / numpy.pi, cmap='hsv')
pylab.hold(True)
ors = scipy.interpolate.griddata(zip(px, py),
vals, (coor_x, coor_y),
method='nearest')
pylab.subplot(152)
pylab.title('Orientatin preference (stimulators)')
pylab.gca().set_aspect('equal')
pylab.scatter(coor_x.flatten(),
coor_y.flatten(),
c=ors.flatten(),
cmap='hsv')
signals = numpy.zeros((numpy.shape(coor_x)[0], numpy.shape(coor_x)[1],
int(parameters.duration / current_update_interval)))
# figure out the light scale
rate = parameters.nv_r_max * numpy.power(
parameters.contrast.value, parameters.nv_exponent) / (
numpy.power(parameters.contrast.value, parameters.nv_exponent) +
parameters.nv_c50)
scale = numpy.power(
rate * parameters.cs_c50 / (parameters.cs_r_max - rate),
1 / parameters.cs_exponent)
for i in range(0, numpy.shape(coor_x)[0]):
for j in range(0, numpy.shape(coor_x)[0]):
signals[i, j,
int(
numpy.floor(parameters.onset_time /
current_update_interval)
):int(
numpy.floor(parameters.offset_time /
current_update_interval)
)] = scale * numpy.exp(-numpy.power(
circular_dist(parameters.orientation.value, ors[i][j],
numpy.pi), 2) / parameters.sharpness)
pylab.subplot(153)
pylab.gca().set_aspect('equal')
pylab.title('Activation magnitude (stimulators)')
pylab.scatter(coor_x.flatten(),
coor_y.flatten(),
c=numpy.squeeze(numpy.mean(signals, axis=2)).flatten(),
cmap='gray')
pylab.title(str(parameters.orientation.value))
return numpy.array(signals), scale
data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'E'}),replace=True)
logger.info('Loaded data store')
import resource
print "Current memory usage: %iMB" % (resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/(1024))
pref_or = numpy.pi/2
#find neuron with preference closet to pref_or
l4_analog_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_esyn_ids()
l4_analog_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids = param_filter_query(data_store,sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_esyn_ids()
l23_analog_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_esyn_ids()
NeuronAnnotationsToPerNeuronValues(data_store,ParameterSet({})).analyse()
l4_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L4')
l4_exc = l4_analog_ids[numpy.argmin([circular_dist(o,pref_or,numpy.pi) for o in l4_exc_or[0].get_value_by_id(l4_analog_ids)])]
l4_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L4')
l4_inh = l4_analog_ids_inh[numpy.argmin([circular_dist(o,pref_or,numpy.pi) for o in l4_inh_or[0].get_value_by_id(l4_analog_ids_inh)])]
l23_exc_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Exc_L2/3')
l23_exc = l23_analog_ids[numpy.argmin([circular_dist(o,pref_or,numpy.pi) for o in l23_exc_or[0].get_value_by_id(l23_analog_ids)])]
l23_inh_or = data_store.get_analysis_result(identifier='PerNeuronValue',value_name = 'LGNAfferentOrientation', sheet_name = 'V1_Inh_L2/3')
l23_inh = l23_analog_ids_inh[numpy.argmin([circular_dist(o,pref_or,numpy.pi) for o in l23_inh_or[0].get_value_by_id(l23_analog_ids_inh)])]
#l4_exc_or_many = numpy.array(l4_exc_or[0].ids)[numpy.nonzero(numpy.array([circular_dist(o,pref_or,numpy.pi) for (o,p) in zip(l4_exc_or[0].values,l4_exc_phase[0].values)]) < 0.1)[0]]
# Find neurons for which spikes were recorded and whose orientation is close to pref_or
l4_spike_ids = param_filter_query(data_store,sheet_name="V1_Exc_L4").get_segments()[0].get_stored_spike_train_ids()
l4_spike_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L4").get_segments()[0].get_stored_spike_train_ids()
l23_spike_ids = param_filter_query(data_store,sheet_name="V1_Exc_L2/3").get_segments()[0].get_stored_spike_train_ids()
l23_spike_ids_inh = param_filter_query(data_store,sheet_name="V1_Inh_L2/3").get_segments()[0].get_stored_spike_train_ids()
l4_spike_exc_or_close_to_pi_half = numpy.array(l4_spike_ids)[numpy.nonzero(numpy.array([circular_dist(o,pref_or,numpy.pi) for o in l4_exc_or[0].get_value_by_id(l4_spike_ids)]) < 0.1)[0]].tolist()
