def perform_analysis(self):
dsv = queries.param_filter_query(self.datastore,identifier='PerNeuronValue',sheet_name=self.parameters.sheet_name,st_name='DriftingSinusoidalGratingCenterSurroundStimulus')
if len(dsv.get_analysis_result()) == 0: return
assert queries.ads_with_equal_stimulus_type(dsv)
assert queries.equal_ads(dsv,except_params=['stimulus_id'])
self.pnvs = dsv.get_analysis_result()
# get stimuli
self.st = [MozaikParametrized.idd(s.stimulus_id) for s in self.pnvs]
# transform the pnvs into a dictionary of tuning curves according along the 'surround_orientation' parameter
# also make sure they are ordered according to the first pnv's idds
self.tc_dict = colapse_to_dictionary([z.get_value_by_id(self.parameters.neurons) for z in self.pnvs],self.st,"surround_orientation")
for k in self.tc_dict.keys():
sis = []
surround_tuning=[]
# we will do the calculation neuron by neuron
for i in xrange(0,len(self.parameters.neurons)):
ors = self.tc_dict[k][0]
values = numpy.array([a[i] for a in self.tc_dict[k][1]])
d={}
for o,v in zip(ors,values):
d[o] = v
sis.append(d[0] / d[numpy.pi/2])
self.datastore.full_datastore.add_analysis_result(PerNeuronValue(sis,self.parameters.neurons,None,value_name = 'Suppression index of ' + self.pnvs[0].value_name ,sheet_name=self.parameters.sheet_name,tags=self.tags,period=None,analysis_algorithm=self.__class__.__name__,stimulus_id=str(k)))
def __init__(self, datastore, parameters, plot_file_name=None,fig_param=None):
Plotting.__init__(self, datastore, parameters, plot_file_name, fig_param)
self.st = []
self.tc_dict = []
self.pnvs = []
self.max_mean_response_indexes = []
assert queries.ads_with_equal_stimulus_type(datastore)
assert len(self.parameters.neurons) > 0 , "ERROR, empty list of neurons specified"
dsvs = queries.partition_analysis_results_by_parameters_query(self.datastore,parameter_list=['value_name'],excpt=True)
for dsv in dsvs:
dsv = queries.param_filter_query(dsv,identifier='PerNeuronValue',sheet_name=self.parameters.sheet_name)
assert matching_parametrized_object_params(dsv.get_analysis_result(), params=['value_name'])
self.pnvs.append(dsv.get_analysis_result())
# get stimuli
st = [MozaikParametrized.idd(s.stimulus_id) for s in self.pnvs[-1]]
self.st.append(st)
# transform the pnvs into a dictionary of tuning curves along the parameter_name
# also make sure the values are ordered according to ids in the first pnv
dic = colapse_to_dictionary([z.get_value_by_id(self.parameters.neurons) for z in self.pnvs[-1]],st,self.parameters.parameter_name)
#sort the entries in dict according to the parameter parameter_name values
for k in dic:
(b, a) = dic[k]
par, val = zip(
*sorted(
zip(b,
numpy.array(a))))
dic[k] = (par,numpy.array(val))
self.tc_dict.append(dic)
if self.parameters.centered:
self.max_mean_response_indexes.append(numpy.argmax(sum([a[1] for a in dic.values()]),axis=0))
def perform_analysis(self):
dsv = queries.param_filter_query(self.datastore,identifier='PerNeuronValue',sheet_name=self.parameters.sheet_name,st_name='DriftingSinusoidalGratingDisk')
if len(dsv.get_analysis_result()) == 0: return
assert queries.ads_with_equal_stimulus_type(dsv)
assert queries.equal_ads(dsv,except_params=['stimulus_id'])
self.pnvs = dsv.get_analysis_result()
# get stimuli
self.st = [MozaikParametrized.idd(s.stimulus_id) for s in self.pnvs]
# transform the pnvs into a dictionary of tuning curves according along the 'radius' parameter
# also make sure they are ordered according to the first pnv's idds
self.tc_dict = colapse_to_dictionary([z.get_value_by_id(self.parameters.neurons) for z in self.pnvs],self.st,"radius")
for k in self.tc_dict.keys():
crf_sizes = []
supp_sizes= []
sis = []
max_responses=[]
# we will do the calculation neuron by neuron
for i in xrange(0,len(self.parameters.neurons)):
rads = self.tc_dict[k][0]
values = numpy.array([a[i] for a in self.tc_dict[k][1]])
# sort them based on radiuses
rads , values = zip(*sorted(zip(rads,values)))
max_response = numpy.max(values)
crf_index = numpy.argmax(values)
crf_size = rads[crf_index]
if crf_index < len(values)-1:
supp_index = crf_index+numpy.argmax(values[crf_index+1:])+1
else:
supp_index = len(values)-1
supp_size = rads[supp_index]
if values[crf_index] != 0:
si = (values[crf_index]-values[supp_index])/values[crf_index]
else:
si = 0
crf_sizes.append(crf_size)
supp_sizes.append(supp_size)
sis.append(si)
max_responses.append(max_response)
self.datastore.full_datastore.add_analysis_result(PerNeuronValue(max_responses,self.parameters.neurons,self.st[0].params()["radius"].units,value_name = 'Max. response of ' + self.pnvs[0].value_name ,sheet_name=self.parameters.sheet_name,tags=self.tags,period=None,analysis_algorithm=self.__class__.__name__,stimulus_id=str(k)))
self.datastore.full_datastore.add_analysis_result(PerNeuronValue(crf_sizes,self.parameters.neurons,self.st[0].params()["radius"].units,value_name = 'Max. facilitation radius of ' + self.pnvs[0].value_name ,sheet_name=self.parameters.sheet_name,tags=self.tags,period=None,analysis_algorithm=self.__class__.__name__,stimulus_id=str(k)))
self.datastore.full_datastore.add_analysis_result(PerNeuronValue(supp_sizes,self.parameters.neurons,self.st[0].params()["radius"].units,value_name = 'Max. suppressive radius of ' + self.pnvs[0].value_name ,sheet_name=self.parameters.sheet_name,tags=self.tags,period=None,analysis_algorithm=self.__class__.__name__,stimulus_id=str(k)))
self.datastore.full_datastore.add_analysis_result(PerNeuronValue(sis,self.parameters.neurons,None,value_name = 'Suppression index of ' + self.pnvs[0].value_name ,sheet_name=self.parameters.sheet_name,tags=self.tags,period=None,analysis_algorithm=self.__class__.__name__,stimulus_id=str(k)))
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(self):
dsv = queries.param_filter_query(
self.datastore,
identifier='PerNeuronValue',
sheet_name=self.parameters.sheet_name,
st_name='DriftingSinusoidalGratingDisk')
if len(dsv.get_analysis_result()) == 0: return
assert queries.ads_with_equal_stimulus_type(dsv)
assert queries.equal_ads(dsv, except_params=['stimulus_id'])
self.pnvs = dsv.get_analysis_result()
# get stimuli
self.st = [MozaikParametrized.idd(s.stimulus_id) for s in self.pnvs]
# transform the pnvs into a dictionary of tuning curves according along the 'radius' parameter
# also make sure they are ordered according to the first pnv's idds
self.tc_dict = colapse_to_dictionary(
[z.get_value_by_id(self.parameters.neurons) for z in self.pnvs],
self.st, "radius")
for k in self.tc_dict.keys():
crf_sizes = []
supp_sizes = []
sis = []
max_responses = []
csis = []
# we will do the calculation neuron by neuron
for i in range(0, len(self.parameters.neurons)):
rads = self.tc_dict[k][0]
values = numpy.array([a[i] for a in self.tc_dict[k][1]])
# sort them based on radiuses
rads, values = zip(*sorted(zip(rads, values)))
max_response = numpy.max(values)
crf_index = numpy.argmax(values)
crf_size = rads[crf_index]
if crf_index < len(values) - 1:
supp_index = crf_index + numpy.argmin(
values[crf_index + 1:]) + 1
else:
supp_index = len(values) - 1
supp_size = rads[supp_index]
if supp_index < len(values) - 1:
cs_index = supp_index + numpy.argmax(
values[supp_index + 1:]) + 1
else:
cs_index = len(values) - 1
if values[crf_index] != 0:
si = (values[crf_index] -
values[supp_index]) / values[crf_index]
else:
si = 0
if values[cs_index] != 0:
csi = (values[cs_index] -
values[supp_index]) / values[crf_index]
else:
csi = 0
crf_sizes.append(crf_size)
supp_sizes.append(supp_size)
sis.append(si)
max_responses.append(max_response)
csis.append(csi)
self.datastore.full_datastore.add_analysis_result(
PerNeuronValue(max_responses,
self.parameters.neurons,
self.st[0].getParams()["radius"].units,
value_name='Max. response of ' +
self.pnvs[0].value_name,
sheet_name=self.parameters.sheet_name,
tags=self.tags,
period=None,
analysis_algorithm=self.__class__.__name__,
stimulus_id=str(k)))
self.datastore.full_datastore.add_analysis_result(
PerNeuronValue(crf_sizes,
self.parameters.neurons,
self.st[0].getParams()["radius"].units,
value_name='Max. facilitation radius of ' +
self.pnvs[0].value_name,
sheet_name=self.parameters.sheet_name,
tags=self.tags,
period=None,
analysis_algorithm=self.__class__.__name__,
stimulus_id=str(k)))
self.datastore.full_datastore.add_analysis_result(
PerNeuronValue(supp_sizes,
self.parameters.neurons,
self.st[0].getParams()["radius"].units,
value_name='Max. suppressive radius of ' +
self.pnvs[0].value_name,
sheet_name=self.parameters.sheet_name,
tags=self.tags,
period=None,
analysis_algorithm=self.__class__.__name__,
stimulus_id=str(k)))
self.datastore.full_datastore.add_analysis_result(
PerNeuronValue(sis,
self.parameters.neurons,
None,
value_name='Suppression index of ' +
self.pnvs[0].value_name,
sheet_name=self.parameters.sheet_name,
tags=self.tags,
period=None,
analysis_algorithm=self.__class__.__name__,
stimulus_id=str(k)))
self.datastore.full_datastore.add_analysis_result(
PerNeuronValue(csis,
self.parameters.neurons,
None,
value_name='Counter-suppression index of ' +
self.pnvs[0].value_name,
sheet_name=self.parameters.sheet_name,
tags=self.tags,
period=None,
analysis_algorithm=self.__class__.__name__,
stimulus_id=str(k)))
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 = list(ps.keys())
# now find the closest presented orientations
closest_presented_orientation = []
for i in range(0, len(or_pref.values)):
circ_d = 100000
idx = 0
for j in range(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)