def _ploter(self, idx, gs):
pair = self.pairs[idx]
# Let's figure out the varying parameters
p1 = varying_parameters(pair)
if pair[0].stimulus_id == None or pair[1].stimulus_id == None:
p2 = []
elif MozaikParametrized.idd(pair[0].stimulus_id).name != MozaikParametrized.idd(pair[1].stimulus_id).name:
p2 = ['name']
else:
p2 = varying_parameters([MozaikParametrized.idd(p.stimulus_id) for p in pair])
p1 = [x for x in p1 if ((x != 'value_name') and (x != 'stimulus_id'))]
x_label = pair[0].value_name + '(' + pair[0].value_units.dimensionality.latex + ')'
y_label = pair[1].value_name + '(' + pair[1].value_units.dimensionality.latex + ')'
for p in p1:
x_label += '\n' + str(p) + " = " + str(getattr(pair[0],p))
y_label += '\n' + str(p) + " = " + str(getattr(pair[1],p))
for p in p2:
x_label += '\n' + str(p) + " = " + str(getattr(MozaikParametrized.idd(pair[0].stimulus_id),p))
y_label += '\n' + str(p) + " = " + str(getattr(MozaikParametrized.idd(pair[1].stimulus_id),p))
params = {}
params["x_label"] = x_label
params["y_label"] = y_label
params["title"] = self.sheets[idx]
if pair[0].value_units != pair[1].value_units or pair[1].value_units == pq.dimensionless:
params["equal_aspect_ratio"] = False
ids = list(set(pair[0].ids) & set(pair[1].ids))
return [("ScatterPlot",ScatterPlot(pair[0].get_value_by_id(ids), pair[1].get_value_by_id(ids)),gs,params)]
def __init__(self, datastore, single_trial=False, **params):
self.single_trial = single_trial
PerDSVPlot.__init__(self, datastore, **params)
ss = self._get_stimulus_ids()
assert ss != [], "Error, empty datastore!"
if self.title_style == "Clever":
stimulus = MozaikParametrized.idd(ss[0])
for s in ss:
s = MozaikParametrized.idd(s)
if s.name != stimulus.name:
logger.warning(
'Datastore does not contain same type of stimuli: changing title_style from Clever to Standard'
)
self.title_style = "Standard"
break
# lets find parameter indexes that vary if we need 'Clever' title style
if self.title_style == "Clever":
self.varied = varying_parameters(
[MozaikParametrized.idd(s) for s in ss])
if not self.single_trial:
self.varied = [x for x in self.varied if x != 'trial']
if self.title_style == "Standard":
self.extra_space_top = 0.07
if self.title_style == "Clever":
self.extra_space_top = len(self.varied) * 0.005
def __init__(self, datastore,single_trial=False, **params):
self.single_trial = single_trial
PerDSVPlot.__init__(self, datastore, **params)
ss = self._get_stimulus_ids()
assert ss != [], "Error, empty datastore!"
if self.title_style == "Clever":
stimulus = MozaikParametrized.idd(ss[0])
for s in ss:
s = MozaikParametrized.idd(s)
if s.name != stimulus.name:
logger.warning('Datastore does not contain same type of stimuli: changing title_style from Clever to Standard')
self.title_style = "Standard"
break
# lets find parameter indexes that vary if we need 'Clever' title style
if self.title_style == "Clever":
self.varied = varying_parameters([MozaikParametrized.idd(s) for s in ss])
if not self.single_trial:
self.varied = [x for x in self.varied if x != 'trial']
if self.title_style == "Standard":
self.extra_space_top = 0.07
if self.title_style == "Clever":
self.extra_space_top = len(self.varied)*0.005
def exportToElphy(data_store_location,elphy_export_location,sheets=None,threshold=None):
import os.path
if not os.path.isdir(elphy_export_location):
if os.path.exists(elphy_export_location):
raise ValueError("The elphy export path is not a directory")
else:
os.makedirs(elphy_export_location)
setup_logging()
data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':data_store_location, 'store_stimuli' : False}))
ps = MP.parameter_value_list([MP.MozaikParametrized.idd(s) for s in data_store.get_stimuli()],'name')
for i,sn in enumerate(ps):
if sheets == None: sheets = data_store.sheets()
for shn in sheets:
dsv = param_filter_query(data_store,st_name = sn,sheet_name = shn)
if dsv.get_stimuli() == []: continue
varying_parameters = MP.varying_parameters([MP.MozaikParametrized.idd(s) for s in dsv.get_stimuli()])
segments,stimuli = MP.colapse(dsv.get_segments(),[MP.MozaikParametrized.idd(s) for s in dsv.get_stimuli()],parameter_list=['trial'],allow_non_identical_objects=True)
j = 0
for segs,st in zip(segments,stimuli):
# just make sure all segments are fully loaded, in future this should probably soreted out such that this line can be deleted
for s in segs: s.load_full()
# create file name:
filename = "name=" + sn + "#" + "sheet_name=" + shn
for pn in varying_parameters:
if pn != "trial":
filename += "#" + str(pn) + "=" + str(getattr(MP.MozaikParametrized.idd(st),pn))
path = os.path.join(elphy_export_location,filename+".dat")
# if the threshold is defined add spikes into Vms
if threshold != None:
for seg in segs : addSpikes(seg,threshold)
createFileFromSegmentList( segs, path)
print "Finished saving file %d/%d for sheet %s and %d-th stimulus" % (j+1,len(segments),shn,i)
# release segments from memory
for s in segs: s.release()
j = j + 1
print "Finished saving %d/%d stimulus" % (i+1,len(ps))
def exportToElphy(data_store_location,
elphy_export_location,
sheets=None,
threshold=None):
import os.path
if not os.path.isdir(elphy_export_location):
if os.path.exists(elphy_export_location):
raise ValueError("The elphy export path is not a directory")
else:
os.makedirs(elphy_export_location)
setup_logging()
data_store = PickledDataStore(load=True,
parameters=ParameterSet({
'root_directory':
data_store_location,
'store_stimuli':
False
}))
ps = MP.parameter_value_list(
[MP.MozaikParametrized.idd(s) for s in data_store.get_stimuli()],
'name')
for i, sn in enumerate(ps):
if sheets == None: sheets = data_store.sheets()
for shn in sheets:
dsv = param_filter_query(data_store, st_name=sn, sheet_name=shn)
if dsv.get_stimuli() == []: continue
varying_parameters = MP.varying_parameters(
[MP.MozaikParametrized.idd(s) for s in dsv.get_stimuli()])
segments, stimuli = MP.colapse(
dsv.get_segments(),
[MP.MozaikParametrized.idd(s) for s in dsv.get_stimuli()],
parameter_list=['trial'],
allow_non_identical_objects=True)
j = 0
for segs, st in zip(segments, stimuli):
# just make sure all segments are fully loaded, in future this should probably soreted out such that this line can be deleted
for s in segs:
s.load_full()
# create file name:
filename = "name=" + sn + "#" + "sheet_name=" + shn
for pn in varying_parameters:
if pn != "trial":
filename += "#" + str(pn) + "=" + str(
getattr(MP.MozaikParametrized.idd(st), pn))
path = os.path.join(elphy_export_location, filename + ".dat")
# if the threshold is defined add spikes into Vms
if threshold != None:
for seg in segs:
addSpikes(seg, threshold)
createFileFromSegmentList(segs, path)
print "Finished saving file %d/%d for sheet %s and %d-th stimulus" % (
j + 1, len(segments), shn, i)
# release segments from memory
for s in segs:
s.release()
j = j + 1
print "Finished saving %d/%d stimulus" % (i + 1, len(ps))
def _ploter(self, idx, gs):
plots = []
gs = gridspec.GridSpecFromSubplotSpec(len(self.st), 1, subplot_spec=gs)
for i,(dic, st, pnvs) in enumerate(zip(self.tc_dict,self.st,self.pnvs)):
period = st[0].params()[self.parameters.parameter_name].period
if self.parameters.centered:
assert period != None, "ERROR: You asked for centering of tuning curves even though the domain over which it is measured is not periodic."
xs = []
ys = []
labels = []
for k in dic.keys():
(par, val) = dic[k]
if self.parameters.mean:
v = 0
for idx in xrange(0,len(self.parameters.neurons)):
vv,p = self.center_tc(val[:,idx],par,period,self.max_mean_response_indexes[i][idx])
v = v + vv
val = v / len(self.parameters.neurons)
par = p
else:
val,par = self.center_tc(val[:,idx],par,period,self.max_mean_response_indexes[i][idx])
if period != None:
par = list(par)
val = list(val)
par.append(par[0] + period)
val.append(val[0])
if par != sorted(par):
print "BBBBBBB"
print par
xs.append(numpy.array(par))
ys.append(numpy.array(val))
l = ""
for p in varying_parameters([MozaikParametrized.idd(e) for e in dic.keys()]):
l = l + str(p) + " : " + str(getattr(MozaikParametrized.idd(k),p))
labels.append(l)
params={}
params["x_label"] = self.parameters.parameter_name
params["y_label"] = pnvs[0].value_name
params['labels']=None
params['linewidth'] = 2
params['colors'] = [cm.jet(j/float(len(xs))) for j in xrange(0,len(xs))]
if pnvs == self.pnvs[0]:
params["title"] = 'Neuron ID: %d' % self.parameters.neurons[idx]
if self.parameters.centered:
if period == pi:
params["x_ticks"] = [-pi/2, 0, pi/2]
params["x_lim"] = (-pi/2, pi/2)
params["x_tick_style"] = "Custom"
params["x_tick_labels"] = ["-$\\frac{\\pi}{2}$", "0", "$\\frac{\\pi}{2}$"]
if period == 2*pi:
params["x_ticks"] = [-pi, 0, pi]
params["x_lim"] = (-pi, pi)
params["x_tick_style"] = "Custom"
params["x_tick_labels"] = ["-$\\pi$","0", "$\\pi$"]
else:
if period == pi:
params["x_ticks"] = [0, pi/2, pi]
params["x_lim"] = (0, pi)
params["x_tick_style"] = "Custom"
params["x_tick_labels"] = ["0", "$\\frac{\\pi}{2}$", "$\\pi$"]
if period == 2*pi:
params["x_ticks"] = [0, pi, 2*pi]
params["x_lim"] = (0, 2*pi)
params["x_tick_style"] = "Custom"
params["x_tick_labels"] = ["0", "$\\pi$", "$2\\pi$"]
if pnvs != self.pnvs[-1]:
params["x_axis"] = None
plots.append(("TuningCurve_" + pnvs[0].value_name,StandardStyleLinePlot(xs, ys),gs[i],params))
return plots
