def plotPSTHs(ax,
sts,
t=None,
bs=100,
c='b',
show_single=False,
show_ticks=False,
ylim=None,
show_sd=True,
lw=2):
single_neurons = []
if len(sts.shape) > 1:
for st in sts:
xs, ys = spk.psth(st, bs).get_plottable_data()
single_neurons.append(ys)
if show_single:
ax.plot(xs, ys, c='grey', lw=0.3)
else:
show_sd = False
xy, ys = spk.psth(sts.flatten(), bs).get_plottable_data()
ys = ys / sts.shape[0]
if show_sd:
ax.fill_between(xy,
ys - np.std(single_neurons, 0),
ys + np.std(single_neurons, 0),
color=c)
ax.plot(xy, ys, c='k', lw=2)
else:
ax.plot(xy, ys, c=c, lw=lw)
if t is not None:
ax.set_title(t)
if ylim is not None:
ax.set_ylim(ylim)
if show_ticks == False:
ax.set_xticks(())
ax.set_yticks(())
f = spk.spike_profile(spike_trains[0], spike_trains[1])
x, y = f.get_plottable_data()
plt.plot(x, y, '-b', label="SPIKE-profile")
plt.axis([0, 4000, -0.1, 1.1])
plt.legend(loc="center right")
plt.figure()
plt.subplot(211)
f = spk.spike_sync_profile_multi(spike_trains)
x, y = f.get_plottable_data()
plt.plot(x, y, '-b', alpha=0.7, label="SPIKE-Sync profile")
x1, y1 = f.get_plottable_data(averaging_window_size=50)
plt.plot(x1, y1, '-k', lw=2.5, label="averaged SPIKE-Sync profile")
plt.subplot(212)
f_psth = spk.psth(spike_trains, bin_size=50.0)
x, y = f_psth.get_plottable_data()
plt.plot(x, y, '-k', alpha=1.0, label="PSTH")
print("Average:", f.avrg())
plt.show()
f = spk.spike_sync_profile(spike_trains)
x, y = f.get_plottable_data()
with open('../export/spike_sync_profile.csv', 'w') as fw:
writer = csv.writer(fw)
writer.writerows(t([x, y]))
plt.plot(x, y, '-b', alpha=0.7, label="SPIKE-Sync profile")
x1, y1 = f.get_plottable_data(averaging_window_size=50)
plt.plot(x1, y1, '-k', lw=2.5, label="averaged SPIKE-Sync profile")
with open('../export/spike_sync_averaged.csv', 'w') as fw:
writer = csv.writer(fw)
writer.writerows(t([x1, y1]))
plt.subplot(212)
f_psth = spk.psth(spike_trains, bin_size=50.0)
x, y = f_psth.get_plottable_data()
plt.plot(x, y, '-k', alpha=1.0, label="PSTH")
with open('../export/spike_sync_averaged_last.csv', 'w') as fw:
writer = csv.writer(fw)
writer.writerows(t([x, y]))
print("Average:", f.avrg())
plt.show()
def iter_plot0(md):
import seaborn as sns
import pickle
with open('cell_indexs.p', 'rb') as f:
returned_list = pickle.load(f)
index_exc = returned_list[0]
index_inh = returned_list[1]
index, mdf1 = md
#wgf = {0.025:None,0.05:None,0.125:None,0.25:None,0.3:None,0.4:None,0.5:None,1.0:None,1.5:None,2.0:None,2.5:None,3.0:None}
wgf = {
0.0025: None,
0.0125: None,
0.025: None,
0.05: None,
0.125: None,
0.25: None,
0.3: None,
0.4: None,
0.5: None,
1.0: None,
1.5: None,
2.0: None,
2.5: None,
3.0: None
}
weight_gain_factors = {k: v for k, v in enumerate(wgf.keys())}
print(len(weight_gain_factors))
print(weight_gain_factors.keys())
#weight_gain_factors = {0:0.5,1:1.0,2:1.5,3:2.0,4:2.5,5:3}
#weight_gain_factors = {:None,1.0:None,1.5:None,2.0:None,2.5:None}
k = weight_gain_factors[index]
#print(len(mdf1.segments),'length of block')
ass = mdf1.analogsignals[0]
time_points = ass.times
avg = np.mean(ass, axis=0) # Average over signals of Segment
#maxx = np.max(ass, axis=0) # Average over signals of Segment
std = np.std(ass, axis=0) # Average over signals of Segment
#avg_minus =
plt.figure()
plt.plot([i for i in range(0, len(avg))], avg)
plt.plot([i for i in range(0, len(std))], std)
plt.title("Mean and Standard Dev of $V_{m}$ amplitude per neuron ")
plt.xlabel('time $(ms)$')
plt.xlabel('Voltage $(mV)$')
plt.savefig(str(index) + 'prs.png')
vm_spiking = []
vm_not_spiking = []
spike_trains = []
binary_trains = []
max_spikes = 0
vms = np.array(mdf1.analogsignals[0].as_array().T)
#print(data)
#for i,vm in enumerate(data):
cnt = 0
for spiketrain in mdf1.spiketrains:
#spiketrain = mdf1.spiketrains[index]
y = np.ones_like(spiketrain) * spiketrain.annotations['source_id']
#import sklearn
#sklearn.decomposition.NMF(y)
# argument edges is the time interval you want to be considered.
pspikes = pyspike.SpikeTrain(spiketrain, edges=(0, len(ass)))
spike_trains.append(pspikes)
if len(spiketrain) > max_spikes:
max_spikes = len(spiketrain)
if np.max(ass[spiketrain.annotations['source_id']]) > 0.0:
vm_spiking.append(vms[spiketrain.annotations['source_id']])
else:
vm_not_spiking.append(vms[spiketrain.annotations['source_id']])
cnt += 1
for spiketrain in mdf1.spiketrains:
x = conv.BinnedSpikeTrain(spiketrain,
binsize=1 * pq.ms,
t_start=0 * pq.s)
binary_trains.append(x)
end_floor = np.floor(float(mdf1.t_stop))
dt = float(mdf1.t_stop) % end_floor
mdf1.t_start
#v = mdf1.take_slice_of_analogsignalarray_by_unit()
t_axis = np.arange(float(mdf1.t_start), float(mdf1.t_stop), dt)
plt.figure()
plt.clf()
plt.figure()
plt.clf()
cleaned = []
data = np.array(mdf1.analogsignals[0].as_array().T)
#print(data)
for i, vm in enumerate(data):
if np.max(vm) > 900.0 or np.min(vm) < -900.0:
pass
else:
plt.plot(ass.times, vm) #,label='neuron identifier '+str(i)))
cleaned.append(vm)
#vm = s#.as_array()[:,i]
assert len(cleaned) < len(ass)
print(len(cleaned))
plt.title('neuron $V_{m}$')
#plt.legend(loc="upper left")
plt.savefig(str('weight_') + str(k) + 'analogsignals' + '.png')
plt.xlabel('Time $(ms)$')
plt.ylabel('Voltage $(mV)$')
plt.close()
#pass
plt.figure()
plt.clf()
plt.title('Single Neuron $V_{m}$ trace')
plt.plot(ass.times[0:int(len(ass.times) / 10)],
vm_not_spiking[index_exc[0]][0:int(len(ass.times) / 10)])
plt.xlabel('$ms$')
plt.ylabel('$mV$')
plt.xlabel('Time $(ms)$')
plt.ylabel('Voltage $(mV)$')
plt.savefig(str('weight_') + str(k) + 'eespecific_analogsignals' + '.png')
plt.close()
plt.figure()
plt.clf()
plt.title('Single Neuron $V_{m}$ trace')
plt.plot(ass.times[0:int(len(ass.times) / 10)],
vm_not_spiking[index_inh[0]][0:int(len(ass.times) / 10)])
plt.xlabel('$ms$')
plt.ylabel('$mV$')
plt.savefig(str('weight_') + str(k) + 'inhibitory_analogsignals' + '.png')
plt.close()
cvs = [0 for i in range(0, len(spike_trains))]
cvsd = {}
cvs = []
cvsi = []
rates = [] # firing rates per cell. in spikes a second.
for i, j in enumerate(spike_trains):
rates.append(float(len(j) / 2.0))
cva = cv(j)
if np.isnan(cva) or cva == 0:
pass
#cvs[i] = 0
#cvsd[i] = 0
else:
pass
#cvs[i] = cva
#cvsd[i] = cva
cvs.append(cva)
#import pickle
#with open(str('weight_')+str(k)+'coefficients_of_variation.p','wb') as f:
# pickle.dump([cvs,cvsd],f)
import numpy
a = numpy.asarray(cvs)
numpy.savetxt('pickles/' + str('weight_') + str(k) +
'coefficients_of_variation.csv',
a,
delimiter=",")
import numpy
a = numpy.asarray(rates)
numpy.savetxt('pickles/' + str('weight_') + str(k) + 'firing_of_rate.csv',
a,
delimiter=",")
cvs = [i for i in cvs if i != 0]
cells = [i for i in range(0, len(cvs))]
plt.clf()
fig, axes = plt.subplots()
axes.set_title('Coefficient of Variation Versus Neuron')
axes.set_xlabel('Neuron number')
axes.set_ylabel('CV estimate')
mcv = np.mean(cvs)
#plt.scatter(cells,cvs)
cvs = np.array(cvs)
plt.scatter(index_inh, cvs[index_inh], label="inhibitory cells")
plt.scatter(index_exc, cvs[index_exc], label="excitatory cells")
plt.legend(loc="upper left")
fig.tight_layout()
plt.savefig(str('weight_') + str(k) + 'cvs_mean_' + str(mcv) + '.png')
plt.close()
plt.clf()
#frequencies, power = elephant.spectral.welch_psd(ass)
#mfreq = frequencies[np.where(power==np.max(power))[0][0]]
#fig, axes = plt.subplots()
axes.set_title('Firing Rate Versus Neuron Number at mean f=' +
str(np.mean(rates)) + str('(Spike Per Second)'))
axes.set_xlabel('Neuron number')
axes.set_ylabel('Spikes per second')
rates = np.array(rates)
plt.scatter(index_inh, rates[index_inh], label="inhibitory cells")
plt.scatter(index_exc, rates[index_exc], label="excitatory cells")
plt.legend(loc="upper left")
fig.tight_layout()
plt.savefig(str('firing_rates_per_cell_') + str(k) + str(mcv) + '.png')
plt.close()
'''
import pandas as pd
d = {'coefficent_of_variation': cvs, 'cells': cells}
df = pd.DataFrame(data=d)
ax = sns.regplot(x='cells', y='coefficent_of_variation', data=df)#, fit_reg=False)
plt.savefig(str('weight_')+str(k)+'cvs_regexp_'+str(mcv)+'.png');
plt.close()
'''
spike_trains = []
ass = mdf1.analogsignals[0]
tstop = mdf1.t_stop
np.max(ass.times) == mdf1.t_stop
#assert tstop == 2000
tstop = 2000
vm_spiking = []
for spiketrain in mdf1.spiketrains:
vm_spiking.append(
mdf1.analogsignals[0][spiketrain.annotations['source_id']])
y = np.ones_like(spiketrain) * spiketrain.annotations['source_id']
# argument edges is the time interval you want to be considered.
pspikes = pyspike.SpikeTrain(spiketrain, edges=(0, tstop))
spike_trains.append(pspikes)
# plot the spike times
plt.clf()
for (i, spike_train) in enumerate(spike_trains):
plt.scatter(spike_train, i * np.ones_like(spike_train), marker='.')
plt.xlabel('Time (ms)')
plt.ylabel('Cell identifier')
plt.title('Raster Plot for weight strength:' + str(k))
plt.savefig(str('weight_') + str(k) + 'raster_plot' + '.png')
plt.close()
f = spk.isi_profile(spike_trains, indices=[0, 1])
x, y = f.get_plottable_data()
#text_file.close()
text_file = open(str('weight_') + str(index) + 'net_out.txt', 'w')
plt.figure()
plt.plot(x, np.abs(y), '--k', label="ISI-profile")
print("ISI-distance: %.8f" % f.avrg())
f = spk.spike_profile(spike_trains, indices=[0, 1])
x, y = f.get_plottable_data()
plt.plot(x, y, '-b', label="SPIKE-profile")
#print("SPIKE-distance: %.8f" % f.avrg())
string_to_write = str("ISI-distance:") + str(f.avrg()) + str("\n\n")
plt.title(string_to_write)
plt.xlabel('Time $(ms)$')
plt.ylabel('ISI distance')
plt.legend(loc="upper left")
plt.savefig(str('weight_') + str(k) + 'ISI_distance_bivariate' + '.png')
plt.close()
text_file.write(string_to_write)
#text_file.write("SPIKE-distance: %.8f" % f.avrg())
#text_file.write("\n\n")
plt.figure()
f = spk.spike_sync_profile(spike_trains[0], spike_trains[1])
x, y = f.get_plottable_data()
plt.plot(x, y, '--ok', label="SPIKE-SYNC profile")
print(f, f.avrg())
print("Average:" + str(f.avrg()))
#print(len(f.avrg()),f.avrg())
string_to_write = str("instantaneous synchrony:") + str(
f.avrg()) + 'weight: ' + str(index)
plt.title(string_to_write)
plt.xlabel('Time $(ms)$')
plt.ylabel('instantaneous synchrony')
text_file.write(string_to_write)
#text_file.write(list())
f = spk.spike_profile(spike_trains[0], spike_trains[1])
x, y = f.get_plottable_data()
plt.plot(x, y, '-b', label="SPIKE-profile")
plt.axis([0, 4000, -0.1, 1.1])
plt.legend(loc="center right")
plt.clf()
plt.figure()
plt.subplot(211)
f = spk.spike_sync_profile(spike_trains)
x, y = f.get_plottable_data()
plt.plot(x, y, '-b', alpha=0.7, label="SPIKE-Sync profile")
x1, y1 = f.get_plottable_data(averaging_window_size=50)
plt.plot(x1, y1, '-k', lw=2.5, label="averaged SPIKE-Sync profile")
plt.subplot(212)
f_psth = spk.psth(spike_trains, bin_size=50.0)
x, y = f_psth.get_plottable_data()
plt.plot(x, y, '-k', alpha=1.0, label="PSTH")
plt.savefig(str('weight_') + str(k) + 'multivariate_PSTH' + '.png')
plt.close()
plt.xlabel('Time $(ms)$')
plt.ylabel('Spikes per bin')
plt.clf()
plt.figure()
f_psth = spk.psth(spike_trains, bin_size=50.0)
x, y = f_psth.get_plottable_data()
plt.plot(x, y, '-k', alpha=1.0, label="PSTH")
plt.savefig(str('weight_') + str(k) + 'exclusively_PSTH' + '.png')
plt.close()
plt.figure()
isi_distance = spk.isi_distance_matrix(spike_trains)
plt.imshow(isi_distance, interpolation='none')
plt.title('Pairwise ISI distance, T=0-2000')
plt.xlabel('post-synaptic neuron number')
plt.ylabel('pre-synaptic neuron number')
plt.title("ISI-distance")
plt.savefig(str('weight_') + str(k) + 'ISI_distance' + '.png')
plt.close()
#plt.show()
plt.figure()
plt.clf()
import seaborn as sns
sns.set()
sns.clustermap(isi_distance) #,vmin=-,vmax=1);
plt.savefig(str('weight_') + str(k) + 'cluster_isi_distance' + '.png')
plt.close()
plt.figure()
spike_distance = spk.spike_distance_matrix(spike_trains,
interval=(0, float(tstop)))
import pickle
with open('spike_distance_matrix.p', 'wb') as f:
pickle.dump(spike_distance, f)
plt.imshow(spike_distance, interpolation='none')
plt.title("Pairwise SPIKE-distance, T=0-2000")
plt.xlabel('post-synaptic neuron number')
plt.ylabel('pre-synaptic neuron number')
plt.savefig(str('weight_') + str(k) + 'spike_distance_matrix' + '.png')
plt.close()
plt.figure()
plt.clf()
sns.set()
sns.clustermap(spike_distance)
plt.savefig(str('weight_') + str(k) + 'cluster_spike_distance' + '.png')
plt.close()
plt.figure()
spike_sync = spk.spike_sync_matrix(spike_trains,
interval=(0, float(tstop)))
plt.imshow(spike_sync, interpolation='none')
plt.title('Pairwise Spike Synchony, T=0-2000')
plt.xlabel('post-synaptic neuron number')
plt.ylabel('pre-synaptic neuron number')
import numpy
a = numpy.asarray(spike_sync)
numpy.savetxt("spike_sync_matrix.csv", a, delimiter=",")
plt.figure()
plt.clf()
sns.clustermap(spike_sync)
plt.savefig(
str('weight_') + str(k) + 'cluster_spike_sync_distance' + '.png')
plt.close()
def medmaxSpikeRate(st_s, ms=200):
mxs = []
for st in st_s:
n = spk.psth(st, ms).y
mxs.append((np.max(n) - np.median(n)) / (ms / 1000))
return np.asarray(mxs)
def getPSTHs(sts, bs=100):
single_neurons = []
for st in sts:
xs, ys = spk.psth(st, bs).get_plottable_data()
single_neurons.append(ys)
return xs, single_neurons
