def scatterDiff(self, i, plot = True):
s, t = nh.getEventsFromSpikeDetector(self.sd_list[i], [self.last_sim_time,
self.sim_time])
data = np.array([np.floor(t-self.last_sim_time).astype(int), s])
data = data.transpose()
data = data.tolist()
overlap = 0
newones = 0
for item in data:
if item in self.scattDiff:
if plot:
mp.scatter(item[0], item[1], c='b')
overlap += 1
else:
if plot:
mp.scatter(item[0], item[1], c='r')
newones += 1
if plot:
for item in self.scattDiff:
if not item in data:
mp.scatter(item[0], item[1], c='r')
mp.xlim(0, 50)
if len(self.scattDiff)==0:
self.scattDiff = data
overlap = len(data)
newones = 0
return overlap, newones
def scatterDiff(self, i, plot=True):
s, t = nh.getEventsFromSpikeDetector(
self.sd_list[i], [self.last_sim_time, self.sim_time])
data = np.array([np.floor(t - self.last_sim_time).astype(int), s])
data = data.transpose()
data = data.tolist()
overlap = 0
newones = 0
for item in data:
if item in self.scattDiff:
if plot:
mp.scatter(item[0], item[1], c='b')
overlap += 1
else:
if plot:
mp.scatter(item[0], item[1], c='r')
newones += 1
if plot:
for item in self.scattDiff:
if not item in data:
mp.scatter(item[0], item[1], c='r')
mp.xlim(0, 50)
if len(self.scattDiff) == 0:
self.scattDiff = data
overlap = len(data)
newones = 0
return overlap, newones
def scatterSD(self, i, color):
""" create a scatter plot for a given spikedetector-index
for the last simulation. color refers to the color-code """
s, t = nh.getEventsFromSpikeDetector(self.sd_list[i], [self.last_sim_time,
self.sim_time])
mp.figure()
mp.scatter(t-self.last_sim_time, s, c=color)
def scatterSD(self, i, color):
""" create a scatter plot for a given spikedetector-index
for the last simulation. color refers to the color-code """
s, t = nh.getEventsFromSpikeDetector(
self.sd_list[i], [self.last_sim_time, self.sim_time])
mp.figure()
mp.scatter(t - self.last_sim_time, s, c=color)
def exportSpikes(self,t_range=[0, float("Inf")]):
""" Exports data from a list of spike detector ids for a given time range (t_range)
in CSV format into zip folder containing delay CSVs for Blender"""
with zipfile.ZipFile(self.output_prefix, 'a') as zf:
matrix = []
for index, sd in enumerate(self.sd_list):
sender, times = nh.getEventsFromSpikeDetector(sd, t_range)
for i in range(len(sender)):
matrix.append([index, sender[i] - self.ngs[index][0], times[i]])
pam2nest.csv_write_matrix(zf, 'spikes', matrix)
def getWeightsSpiking(self, sd, post = 4, target = (0,25)):
''' Returns the connection weights between neurons of the pre-synaptic
layer, that fire (given by a spike-detector) and that are connected
with specific neurons in the post-synaptic layer '''
# get spiking neurons
s, t = nh.getEventsFromSpikeDetector(sd)
# create unique spiking neuron-ids
su = np.unique(s)
connections = nest.GetConnections(su.tolist(), self.ngs[post][target[0]:target[1]])
weights = nest.GetStatus(connections, 'weight')
targets = nest.GetStatus(connections, 'target')
return weights, su, targets
def getSpikeOnsetTimes(self, spike_take = 4):
""" returns the occurence of the first spike in each area for the last
simulation
"""
data = []
for o in self.order:
s, t = nh.getEventsFromSpikeDetector(self.sd_list[o], t_range=[self.last_sim_time,
self.sim_time])
if len(t) >= spike_take:
data.append(t[spike_take-1])
else:
data.append(-1)
return np.array(data)
def getSpikeOnsetTimes(self, spike_take=4):
""" returns the occurence of the first spike in each area for the last
simulation
"""
data = []
for o in self.order:
s, t = nh.getEventsFromSpikeDetector(
self.sd_list[o], t_range=[self.last_sim_time, self.sim_time])
if len(t) >= spike_take:
data.append(t[spike_take - 1])
else:
data.append(-1)
return np.array(data)
def exportSpikes(self, t_range=[0, float("Inf")]):
""" Exports data from a list of spike detector ids for a given time range (t_range)
in CSV format into zip folder containing delay CSVs for Blender"""
with zipfile.ZipFile(self.output_prefix, 'a') as zf:
matrix = []
for index, sd in enumerate(self.sd_list):
sender, times = nh.getEventsFromSpikeDetector(sd, t_range)
for i in range(len(sender)):
matrix.append(
[index, sender[i] - self.ngs[index][0], times[i]])
pam2nest.csv_write_matrix(zf, 'spikes', matrix)
def getWeightsSpiking(self, sd, post=4, target=(0, 25)):
''' Returns the connection weights between neurons of the pre-synaptic
layer, that fire (given by a spike-detector) and that are connected
with specific neurons in the post-synaptic layer '''
# get spiking neurons
s, t = nh.getEventsFromSpikeDetector(sd)
# create unique spiking neuron-ids
su = np.unique(s)
connections = nest.GetConnections(su.tolist(),
self.ngs[post][target[0]:target[1]])
weights = nest.GetStatus(connections, 'weight')
targets = nest.GetStatus(connections, 'target')
return weights, su, targets
def getSpikeHistogramRange(self, bins, time_range, plot=True, stretch = 3):
""" Get histogram of spikes for a given time_range [x,y]"""
data = []
for o in self.order:
s, t = nh.getEventsFromSpikeDetector(self.sd_list[o], t_range=time_range)
for _ in range(stretch):
data.append(np.histogram(t, bins, range = time_range)[0])
if plot:
mp.figure()
mp.imshow(data, vmin = 0, vmax = 50, interpolation='none')
mp.title('Temporal spike evolution')
mp.xlabel('ms')
return np.array(data)
def getSpikeHistogram(self, bins, plot=True, stretch = 3):
"""Creates a histogram of spikes with bins as ms"""
data = []
for o in self.order:
s, t = nh.getEventsFromSpikeDetector(self.sd_list[o], t_range=[self.last_sim_time,
self.sim_time])
for _ in range(stretch):
data.append(np.histogram(t, bins, range = [self.last_sim_time, self.sim_time])[0])
if plot:
mp.figure()
mp.imshow(data, vmin = 0, vmax = 50, interpolation='none')
mp.title('Temporal spike evolution')
mp.xlabel('ms')
return np.array(data)
def getSpikeHistogramRange(self, bins, time_range, plot=True, stretch=3):
""" Get histogram of spikes for a given time_range [x,y]"""
data = []
for o in self.order:
s, t = nh.getEventsFromSpikeDetector(self.sd_list[o],
t_range=time_range)
for _ in range(stretch):
data.append(np.histogram(t, bins, range=time_range)[0])
if plot:
mp.figure()
mp.imshow(data, vmin=0, vmax=50, interpolation='none')
mp.title('Temporal spike evolution')
mp.xlabel('ms')
return np.array(data)
def getSpikeHistogram(self, bins, plot=True, stretch=3):
"""Creates a histogram of spikes with bins as ms"""
data = []
for o in self.order:
s, t = nh.getEventsFromSpikeDetector(
self.sd_list[o], t_range=[self.last_sim_time, self.sim_time])
for _ in range(stretch):
data.append(
np.histogram(t,
bins,
range=[self.last_sim_time, self.sim_time])[0])
if plot:
mp.figure()
mp.imshow(data, vmin=0, vmax=50, interpolation='none')
mp.title('Temporal spike evolution')
mp.xlabel('ms')
return np.array(data)