def test_regression_spiky():
# standard example
st1 = SpikeTrain(np.arange(100, 1201, 100), 1300)
st2 = SpikeTrain(np.arange(100, 1201, 110), 1300)
isi_dist = spk.isi_distance(st1, st2)
assert_almost_equal(isi_dist, 9.0909090909090939e-02, decimal=15)
isi_profile = spk.isi_profile(st1, st2)
assert_equal(isi_profile.y, 0.1/1.1 * np.ones_like(isi_profile.y))
spike_dist = spk.spike_distance(st1, st2)
assert_equal(spike_dist, 2.1105878248735391e-01)
spike_sync = spk.spike_sync(st1, st2)
assert_equal(spike_sync, 8.6956521739130432e-01)
# multivariate check
spike_trains = spk.load_spike_trains_from_txt("test/PySpike_testdata.txt",
(0.0, 4000.0))
isi_dist = spk.isi_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(isi_dist, 0.17051816816999129656, decimal=15)
spike_profile = spk.spike_profile_multi(spike_trains)
assert_equal(len(spike_profile.y1)+len(spike_profile.y2), 1252)
spike_dist = spk.spike_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(spike_dist, 2.4432433330596512e-01, decimal=15)
spike_sync = spk.spike_sync_multi(spike_trains)
# get the full precision from SPIKY
assert_equal(spike_sync, 0.7183531505298066)
def test_regression_spiky():
# standard example
st1 = SpikeTrain(np.arange(100, 1201, 100), 1300)
st2 = SpikeTrain(np.arange(100, 1201, 110), 1300)
isi_dist = spk.isi_distance(st1, st2)
assert_almost_equal(isi_dist, 9.0909090909090939e-02, decimal=15)
isi_profile = spk.isi_profile(st1, st2)
assert_equal(isi_profile.y, 0.1 / 1.1 * np.ones_like(isi_profile.y))
spike_dist = spk.spike_distance(st1, st2)
assert_equal(spike_dist, 0.211058782487353908)
spike_sync = spk.spike_sync(st1, st2)
assert_equal(spike_sync, 8.6956521739130432e-01)
# multivariate check
spike_trains = spk.load_spike_trains_from_txt(
os.path.join(TEST_PATH, "PySpike_testdata.txt"), (0.0, 4000.0))
isi_dist = spk.isi_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(isi_dist, 0.17051816816999129656, decimal=15)
spike_profile = spk.spike_profile_multi(spike_trains)
assert_equal(len(spike_profile.y1) + len(spike_profile.y2), 1252)
spike_dist = spk.spike_distance_multi(spike_trains)
# get the full precision from SPIKY
assert_almost_equal(spike_dist, 0.25188056475463755, decimal=15)
spike_sync = spk.spike_sync_multi(spike_trains)
# get the full precision from SPIKY
assert_equal(spike_sync, 0.7183531505298066)
# Eero's edge correction example
st1 = SpikeTrain([0.5, 1.5, 2.5], 6.0)
st2 = SpikeTrain([3.5, 4.5, 5.5], 6.0)
f = spk.spike_profile(st1, st2)
expected_times = np.array([0.0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.0])
y_all = np.array([
0.271604938271605, 0.271604938271605, 0.271604938271605,
0.617283950617284, 0.617283950617284, 0.444444444444444,
0.285714285714286, 0.285714285714286, 0.444444444444444,
0.617283950617284, 0.617283950617284, 0.271604938271605,
0.271604938271605, 0.271604938271605
])
expected_y1 = y_all[::2]
expected_y2 = y_all[1::2]
assert_equal(f.x, expected_times)
assert_array_almost_equal(f.y1, expected_y1, decimal=14)
assert_array_almost_equal(f.y2, expected_y2, decimal=14)
def test_regression_random():
spike_file = "test/numeric/regression_random_spikes.mat"
spikes_name = "spikes"
result_name = "Distances"
result_file = "test/numeric/regression_random_results_cSPIKY.mat"
spike_train_sets = loadmat(spike_file)[spikes_name][0]
results_cSPIKY = loadmat(result_file)[result_name]
for i, spike_train_data in enumerate(spike_train_sets):
spike_trains = []
for spikes in spike_train_data[0]:
spike_trains.append(spk.SpikeTrain(spikes.flatten(), 100.0))
isi = spk.isi_distance_multi(spike_trains)
isi_prof = spk.isi_profile_multi(spike_trains).avrg()
spike = spk.spike_distance_multi(spike_trains)
spike_prof = spk.spike_profile_multi(spike_trains).avrg()
spike_sync = spk.spike_sync_multi(spike_trains)
spike_sync_prof = spk.spike_sync_profile_multi(spike_trains).avrg()
assert_almost_equal(isi,
results_cSPIKY[i][0],
decimal=14,
err_msg="Index: %d, ISI" % i)
assert_almost_equal(isi_prof,
results_cSPIKY[i][0],
decimal=14,
err_msg="Index: %d, ISI" % i)
assert_almost_equal(spike,
results_cSPIKY[i][1],
decimal=14,
err_msg="Index: %d, SPIKE" % i)
assert_almost_equal(spike_prof,
results_cSPIKY[i][1],
decimal=14,
err_msg="Index: %d, SPIKE" % i)
assert_almost_equal(spike_sync,
spike_sync_prof,
decimal=14,
err_msg="Index: %d, SPIKE-Sync" % i)
spiketrains = []
print("")
print("Constructing {} spike trains ...".format(N))
for n in range(N):
spiketrains.append(sl.tools.poisson_spikes(end, 10))
print("Running pairwise_mp ...")
tstart = time.time()
# _ = sl.metrics.kreuz.pairwise_mp(spiketrains, start, end, 2000)
tend = time.time()
sla.append(tend-tstart)
print("Running multivariate ...")
tstart = time.time()
_ = sl.metrics.kreuz.multivariate(spiketrains, start, end, 2000)
tend = time.time()
slm.append(tend-tstart)
for idx in range(len(spiketrains)):
spiketrains[idx] = spk.add_auxiliary_spikes(spiketrains[idx], end)
print("Running PySpike ...")
tstart = time.time()
_ = spk.spike_profile_multi(spiketrains)
tend = time.time()
spa.append(tend-tstart)
print("Finished {}".format(N))
plt.figure()
plt.plot(Nlist, sla, label="PW-MP")
plt.plot(Nlist, slm, label="Multi")
plt.plot(Nlist, spa, label="PySpk")
plt.legend()
plt.show()
sim_dir = '../simulations/' + sim_ref
time = np.loadtxt(sim_dir + '/time.dat')
spike_trains = []
for cell in range(n_cells):
spikes = np.loadtxt(
'{}/Golgi_network_reduced_{}.SPIKES_min20.spike'.format(
sim_dir, cell))
spike_trains.append(pyspike.add_auxiliary_spikes(spikes, sim_duration))
if trial == 0:
ax[0].scatter(spikes,
np.zeros_like(spikes) + cell,
marker='|',
s=2,
c=color)
distances.append(
pyspike.spike_profile_multi(spike_trains[n_excluded_cells:]))
# average synchrony index across trials
average_distance = distances[0]
for distance in distances[1:]:
average_distance.add(distance)
average_distance.mul_scalar(1. / n_trials)
xmin = 50
xmax = 1900
x, y = average_distance.get_plottable_data()
ximin = np.searchsorted(x, xmin)
ximax = np.searchsorted(x, xmax)
lines.append(ax[1].plot(x[ximin:ximax + 1],
1 - y[ximin:ximax + 1],
lw=2,
def Synchronization_analysis(sim_duration,specify_targets,no_of_groups,exp_specify,spike_plot_parameters,general_plot_parameters):
cell_no_array=[]
for exp_id in range(0,len(exp_specify[0]) ):
if exp_specify[1][1]==True:
for cell_group in range(0,no_of_groups):
cell_group_positions=np.loadtxt('simulations/%s/Golgi_pop%d.txt'%(exp_specify[0][exp_id],cell_group))
dim_array=np.shape(cell_group_positions)
cell_no_array.append(dim_array[0])
else:
for cell_group in range(0,no_of_groups):
cell_group_positions=np.loadtxt('simulations/%s/sim0/Golgi_pop%d.txt'%(exp_specify[0][exp_id],cell_group))
dim_array=np.shape(cell_group_positions)
cell_no_array.append(dim_array[0])
n_trials=exp_specify[2]
lines = []
lines_sep=[]
experiment_seed=random.sample(range(0,15000),1)[0]
for exp_id in range(0,len(exp_specify[0])):
#get target ids
experiment_parameters=[]
experiment_parameters.append(exp_specify[0][exp_id])
experiment_parameters.append(exp_specify[1])
experiment_parameters.append(exp_specify[2])
target_cell_array=get_cell_ids_for_sync_analysis(specify_targets,no_of_groups,experiment_parameters,experiment_seed)
test_array=target_cell_array
if exp_id==0:
if "save sync plot to a separate file" in spike_plot_parameters:
fig_sync, ax_sync=plt.subplots(figsize=(2,1.5),ncols=1,nrows=1)
if spike_plot_parameters[0]=="2D raster plots":
trial_indicator_target=False
if n_trials >1:
target_cell_array_target_trial=target_cell_array[spike_plot_parameters[1]]
no_of_rasters=0
if target_cell_array_target_trial != []:
test_non_empty_target_array=True
trial_indicator_target=True
no_of_rasters=len(target_cell_array_target_trial)
rows=1+no_of_rasters
columns=1
fig_stack, ax_stack = plt.subplots(figsize=(4,rows+1),ncols=columns,nrows=rows, sharex=True)
ax_stack=ax_stack.ravel()
else:
no_of_rasters=0
if target_cell_array != []:
target_cell_array_target_trial=target_cell_array[0]
trial_indicator_target=True
no_of_rasters=len(target_cell_array)
rows=1+no_of_rasters
columns=1
fig_stack, ax_stack = plt.subplots(figsize=(4,rows),ncols=columns,nrows=rows, sharex=True)
ax_stack=ax_stack.ravel()
if "save all trials to separate files" in spike_plot_parameters:
raster_fig_array=[]
raster_ax_array=[]
pop_no_array=[]
trial_indicator_target=False
if n_trials >1:
non_empty_trial_indices=[]
for trial in range(0,n_trials):
if target_cell_array[trial] !=[]:
non_empty_trial_indices.append(trial)
test_indices=non_empty_trial_indices
for trial in range(0,len(non_empty_trial_indices)):
target_trial=target_cell_array[non_empty_trial_indices[trial]]
columns=1
rows=len(target_trial)
pop_no_array.append(rows)
plot_rows=rows+1
fig, ax = plt.subplots(figsize=(4,plot_rows),ncols=columns,nrows=rows, sharex=True)
if len(target_trial) >1 :
ax=ax.ravel()
raster_fig_array.append(fig)
raster_ax_array.append(ax)
else:
no_of_rasters=0
if target_cell_array != []:
trial_indicator_target=True
no_of_rasters=len(target_cell_array)
rows=no_of_rasters
pop_no_array.append(rows)
columns=1
plot_rows=rows+1
fig_stack_one_trial, ax_stack_one_trial= plt.subplots(figsize=(4,plot_rows),ncols=columns,nrows=rows, sharex=True)
if no_of_rasters > 1:
ax_stack_one_trial=ax_stack.ravel()
if "save all trials to one separate file" in spike_plot_parameters:
pop_no_array=[]
if n_trials >1:
non_empty_trial_indices=[]
for trial in range(0,n_trials):
if target_cell_array[trial] !=[]:
non_empty_trial_indices.append(trial)
total_no_of_rows=0
for trial in range(0,len(non_empty_trial_indices)):
target_trial=target_cell_array[non_empty_trial_indices[trial]]
rows=len(target_trial)
pop_no_array.append(rows)
total_no_of_rows=total_no_of_rows + rows
fig_all_trials, ax_all_trials = plt.subplots(figsize=(4,total_no_of_rows),ncols=1,nrows=total_no_of_rows, sharex=True)
if total_no_of_rows >1 :
ax_all_trials=ax_all_trials.ravel()
non_empty_non_unitary_trial_counter=0
distances = []
color = sns.color_palette()[exp_id+1]
row_counter=0
target_pop_index_array=[]
if spike_plot_parameters[0]=="2D raster plots":
if "save all trials to one separate file" in spike_plot_parameters:
row_array=range(0,total_no_of_rows)
if spike_plot_parameters[0]=="2D raster plots":
if "save all trials to separate files" in spike_plot_parameters:
raster_ax_row_counter=0
for trial in range(0,n_trials):
sim_dir = 'simulations/' + exp_specify[0][exp_id]+'/sim%d'%trial+'/txt'
######
if n_trials > 1:
target_cell_array_per_trial=target_cell_array[trial]
else:
target_cell_array_per_trial=target_cell_array[0]
if target_cell_array_per_trial !=[]:
spike_trains = []
target_pop_index_array_per_trial=[]
print(" Trial %d is not empty"%(trial))
for target_pop in range(0,len(target_cell_array_per_trial)):
for pop in range(0,no_of_groups):
if ('pop%d'%(pop)) in target_cell_array_per_trial[target_pop]:
target_pop_index_array_per_trial.append(pop)
target_cells = [x for x in target_cell_array_per_trial[target_pop] if isinstance(x,int)]
print target_cells
for cell in range(0,len(target_cells)):
#create target txt file containing spike times
#if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cells[cell])):
get_spike_times('Golgi_pop%d_cell%d'%(pop,target_cells[cell]),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cells[cell]))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
print spike_trains
if spike_plot_parameters[0]=="2D raster plots":
if spike_plot_parameters[1]==trial:
ax_stack[target_pop].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
if "save all trials to separate files" in spike_plot_parameters:
if n_trials >1:
if len(target_cell_array_per_trial) > 1:
raster_ax_array[raster_ax_row_counter][target_pop].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
else:
raster_ax_array[raster_ax_row_counter].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
else:
if len(target_cell_array_per_trial) >1:
ax_stack_one_trial[target_pop].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
else:
ax_stack_one_trial.scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
if "save all trials to one separate file" in spike_plot_parameters:
if n_trials >1:
if total_no_of_rows >1 :
ax_all_trials[row_array[row_counter]].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
else:
ax_all_trials.scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
if spike_plot_parameters[0]=="2D raster plots":
if "save all trials to one separate file" in spike_plot_parameters:
row_counter=row_counter+1
if spike_plot_parameters[0]=="2D raster plots":
if "save all trials to separate files" in spike_plot_parameters:
raster_ax_row_counter+=1
target_pop_index_array.append(target_pop_index_array_per_trial)
if spike_plot_parameters[1]==trial:
target_trial_index=target_pop_index_array.index(target_pop_index_array_per_trial)
#target_pop_index_array.append(target_pop_index_array_per_trial)
########
print("Length of spike_trains is %d"%len(spike_trains))
if len(spike_trains) >1:
print("Trial %d contains more than one cell; Synchrony metric will be computed for this trial"%(trial))
non_empty_non_unitary_trial_counter+=1
print non_empty_non_unitary_trial_counter
distances.append(pyspike.spike_profile_multi(spike_trains))
else:
print("Trial %d contains one cell; Synchrony metric will not be computed for this trial"%(trial))
########
# average synchrony index across "non empty" trials
average_distance = distances[0]
for distance in distances[1:]:
average_distance.add(distance)
average_distance.mul_scalar(1./non_empty_non_unitary_trial_counter)
# below blocks for saving synchrony and spike raster plots
mark_steps=sim_duration/50
marks=[]
for mark in range(0,mark_steps+1):
Mark=50*mark
marks.append(Mark)
xmin = 50
right_shaded_region=50
if sim_duration >=1000:
right_shaded_region=100
xmax = sim_duration-right_shaded_region
x, y = average_distance.get_plottable_data()
ximin = np.searchsorted(x, xmin)
ximax = np.searchsorted(x, xmax)
if spike_plot_parameters[0]=="2D raster plots":
if target_cell_array_target_trial != []:
target_cell_array_target_trial_indicator=True
lines.append(ax_stack[no_of_rasters].plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
ax_stack[no_of_rasters].plot(x[:ximin+1], 1-y[:ximin+1], lw=2, c=color, alpha=0.4)
ax_stack[no_of_rasters].plot(x[ximax:], 1-y[ximax:], lw=2, c=color, alpha=0.4)
if "save sync plot to a separate file" in spike_plot_parameters:
lines_sep.append(ax_sync.plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
ax_sync.plot(x[:ximin+1], 1-y[:ximin+1], lw=2, c=color, alpha=0.4)
ax_sync.plot(x[ximax:], 1-y[ximax:], lw=2, c=color, alpha=0.4)
if "save sync plot to a separate file" in spike_plot_parameters:
print("save sync plot to a separate file is specified")
for tick in ax_sync.xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_sync.yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
ax_sync.locator_params(axis='y', tight=True, nbins=10)
ax_sync.set_xlabel('Time (ms)',fontsize=4)
ax_sync.set_ylabel('Synchrony index',size=4)
ax_sync.set_ylim(0,1)
ax_sync.set_xticks(marks)
ax_sync.set_title('Synchronization between %s'%general_plot_parameters[1],size=4)
fig_sync.tight_layout()
fig_sync.subplots_adjust(top=0.90)
fig_sync.subplots_adjust(bottom=0.55)
l=fig_sync.legend(lines_sep,general_plot_parameters[3],title=general_plot_parameters[2],loc='center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.52, 0.25),prop={'size':4})
plt.setp(l.get_title(),fontsize=4)
fig_sync.savefig('simulations/sync_only_%s.%s'%(general_plot_parameters[0],spike_plot_parameters[-1]))
print("saved %s in simulations"%'sync_only_%s.%s'%(general_plot_parameters[0],spike_plot_parameters[-1]))
if spike_plot_parameters[0]=="2D raster plots":
print("Intend to plot a main figure with representative 2D raster plots")
#create label array
if trial_indicator_target:
print("2D raster plot procedures started")
for pop in range(0,no_of_rasters):
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[target_trial_index][pop]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[target_trial_index][pop]]-1)
left_value=cell_no_array[target_pop_index_array[target_trial_index][pop]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[target_trial_index][pop]])
left_value=left_value+2+cell_no_array[target_pop_index_array[target_trial_index][pop]]
print label_array
print ytick_array
ax_stack[pop].set_yticks(ytick_array)
fig_stack.canvas.draw()
ax_stack[pop].set_ylim(0,(cell_no_array[target_pop_index_array[target_trial_index][pop]]+1)*len(general_plot_parameters[3]) )
ax_stack[pop].set_ylabel('Cell ids, population %d'%pop,size=4)
ax_stack[pop].set_yticks([cell_no_array[target_pop_index_array[target_trial_index][pop]]+(cell_no_array[target_pop_index_array[target_trial_index][pop]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_stack[pop].yaxis.grid(False, which='major')
ax_stack[pop].yaxis.grid(True, which='minor')
labels = [x.get_text() for x in ax_stack[pop].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_stack[pop].set_yticklabels(labels)
if pop==0:
ax_stack[pop].set_title('Raster plots for target Golgi cell populations (trial id=%d)'%spike_plot_parameters[1],size=6)
for pop in range(0,no_of_rasters+1):
for tick in ax_stack[pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_stack[pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
ax_stack[no_of_rasters].locator_params(axis='y', tight=True, nbins=10)
ax_stack[no_of_rasters].set_xlabel('Time (ms)',fontsize=6)
ax_stack[no_of_rasters].set_ylabel('Synchrony index',size=4)
ax_stack[no_of_rasters].set_ylim(0,1)
ax_stack[no_of_rasters].set_xticks(marks)
ax_stack[no_of_rasters].set_title('Synchronization between %s'%general_plot_parameters[1],size=6)
fig_stack.tight_layout()
fig_stack.subplots_adjust(top=0.80)
fig_stack.subplots_adjust(bottom=0.15)
fig_stack.subplots_adjust(hspace=.4)
l=fig_stack.legend(lines,general_plot_parameters[3],title=general_plot_parameters[2], loc='upper center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.55, 1.2))
max_xticks = 10
xloc = plt.MaxNLocator(max_xticks)
ax_stack[no_of_rasters].xaxis.set_major_locator(xloc)
plt.setp(l.get_title(),fontsize=6)
plt.setp(l.get_texts(), fontsize=6)
fig_stack.savefig('simulations/%s.%s'%(general_plot_parameters[0],spike_plot_parameters[-1]),bbox_extra_artists=(l,),bbox_inches='tight')
else:
print("Intended to plot raster plots for trial %d, but specified region-specific selection of cells produces an empty target array.\nThus a main figure with a representative raster will not be produced.\nHowever, synchrony plot can be saved in a separate file.\nAlternatively, plot a non-empty trial"%(spike_plot_parameters[1]))
if "save all trials to one separate file" in spike_plot_parameters:
print target_pop_index_array
if total_no_of_rows >1:
row_counter=0
for trial in range(0,len(non_empty_trial_indices)):
if len(target_pop_index_array[trial]) >1:
for pop in range(0,len(target_pop_index_array[trial])):
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[trial][pop] ]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[trial][pop]]-1)
left_value=cell_no_array[target_pop_index_array[trial][pop]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[trial][pop]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[trial][pop]]
ax_all_trials[row_counter].set_yticks(ytick_array)
#ax_all_trials[row_counter].canvas.draw()
ax_all_trials[row_counter].set_ylim(0,(cell_no_array[target_pop_index_array[trial][pop]]+1)*len(general_plot_parameters[3]) )
ax_all_trials[row_counter].set_ylabel('Cell ids for Golgi pop %d\ntrial %d'%(target_pop_index_array[trial][pop],non_empty_trial_indices[trial]),size=4)
ax_all_trials[row_counter].set_yticks([cell_no_array[target_pop_index_array[trial][pop]]+( cell_no_array[target_pop_index_array[trial][pop]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_all_trials[row_counter].yaxis.grid(False, which='major')
ax_all_trials[row_counter].yaxis.grid(True, which='minor')
if row_counter==total_no_of_rows-1:
ax_all_trials[row_counter].set_xlabel('Time (ms)',fontsize=4)
labels = [x.get_text() for x in ax_all_trials[row_counter].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_all_trials[row_counter].set_yticklabels(labels)
for tick in ax_all_trials[row_counter].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_all_trials[row_counter].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
row_counter+=1
else:
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[trial][0]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[trial][0]]-1)
left_value=cell_no_array[target_pop_index_array[trial][0]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[trial][0]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[trial][0]]
ax_all_trials[row_counter].set_yticks(ytick_array)
ax_all_trials[row_counter].set_ylim(0,(cell_no_array[target_pop_index_array[trial][0]]+1)*len(general_plot_parameters[3]) )
ax_all_trials[row_counter].set_ylabel('Cell ids for pop %d\ntrial %d'%(target_pop_index_array[trial][0],non_empty_trial_indices[trial]),size=4)
ax_all_trials[row_counter].set_yticks([cell_no_array[target_pop_index_array[trial][0]]+( cell_no_array[target_pop_index_array[trial][0]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_all_trials[row_counter].yaxis.grid(False, which='major')
ax_all_trials[row_counter].yaxis.grid(True, which='minor')
if row_counter==total_no_of_rows-1:
ax_all_trials[row_counter].set_xlabel('Time (ms)',fontsize=4)
labels = [x.get_text() for x in ax_all_trials[row_counter].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_all_trials[row_counter].set_yticklabels(labels)
for tick in ax_all_trials[row_counter].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_all_trials[row_counter].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
row_counter+=1
else:
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[0][0]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[0][pop]]-1)
left_value=cell_no_array[target_pop_index_array[0][0]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[0][0]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[0][0]]
ax_all_trials.set_yticks(ytick_array)
ax_all_trials.canvas.draw()
ax_all_trials.set_ylim(0,(cell_no_array[target_pop_index_array[0][0]]+1)*len(general_plot_parameters[3]) )
ax_all_trials.set_ylabel('Cell ids for pop %d\ntrial %d'%(target_pop_index_array[trial][pop],non_empty_trial_indices[trial]),size=4)
ax_all_trials.set_yticks([cell_no_array[target_pop_index_array[0][0]]+( cell_no_array[target_pop_index_array[0][0]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_all_trials.yaxis.grid(False, which='major')
ax_all_trials.yaxis.grid(True, which='minor')
ax_all_trials.set_xlabel('Time (ms)',fontsize=4)
labels = [x.get_text() for x in ax_all_trials.get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_all_trials.set_yticklabels(labels)
for tick in ax_all_trials.xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_all_trials.yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
fig_all_trials.subplots_adjust(hspace=0.1)
fig_all_trials.subplots_adjust(top=0.95)
fig_all_trials.subplots_adjust(bottom=0.1)
l=fig_all_trials.legend(lines,general_plot_parameters[3],title=general_plot_parameters[2],loc='center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.52, 0.98),prop={'size':4})
plt.setp(l.get_title(),fontsize=4)
fig_all_trials.savefig('simulations/all_trials_%s.%s'%(general_plot_parameters[0],spike_plot_parameters[-1]))
plt.clf()
if "save all trials to separate files" in spike_plot_parameters:
if n_trials >1:
for trial in range(0,len(non_empty_trial_indices)):
print("started ploting non-empty trials")
if pop_no_array[trial] >1:
for pop in range(0,pop_no_array[trial]):
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[trial][pop]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[trial][pop]]-1)
left_value=cell_no_array[target_pop_index_array[trial][pop]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[trial][pop]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[trial][pop]]
raster_ax_array[trial][pop].set_yticks(ytick_array)
raster_fig_array[trial].canvas.draw()
raster_ax_array[trial][pop].set_ylim(0,(cell_no_array[target_pop_index_array[trial][pop]]+1)*len(general_plot_parameters[3]) )
raster_ax_array[trial][pop].set_ylabel('Cell ids, population %d'%target_pop_index_array[trial][pop],size=4)
raster_ax_array[trial][pop].set_yticks([cell_no_array[target_pop_index_array[trial][pop]]+( cell_no_array[target_pop_index_array[trial][pop]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
raster_ax_array[trial][pop].yaxis.grid(False, which='major')
raster_ax_array[trial][pop].yaxis.grid(True, which='minor')
labels = [x.get_text() for x in raster_ax_array[trial][pop].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
raster_ax_array[trial][pop].set_yticklabels(labels)
if pop==0:
raster_ax_array[trial][pop].set_title('Raster plot for Golgi cell populations (trial id=%d)'%non_empty_trial_indices[trial],size=6)
if pop==pop_no_array[trial]-1:
raster_ax_array[trial][pop].set_xlabel('Time (ms)',fontsize=6)
for tick in raster_ax_array[trial][pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in raster_ax_array[trial][pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
else:
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[trial][0]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[trial][0]]-1)
left_value=cell_no_array[target_pop_index_array[trial][0]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[trial][0]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[trial][0]]
raster_ax_array[trial].set_yticks(ytick_array)
raster_fig_array[trial].canvas.draw()
raster_ax_array[trial].set_ylim(0,(cell_no_array[target_pop_index_array[trial][0]]+1)*len(general_plot_parameters[3]) )
raster_ax_array[trial].set_ylabel('Cell ids, population %d'% target_pop_index_array[trial][0],size=4)
raster_ax_array[trial].set_yticks([cell_no_array[target_pop_index_array[trial][0]]+( cell_no_array[target_pop_index_array[trial][0]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
raster_ax_array[trial].yaxis.grid(False, which='major')
raster_ax_array[trial].yaxis.grid(True, which='minor')
labels = [x.get_text() for x in raster_ax_array[trial].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
raster_ax_array[trial].set_yticklabels(labels)
raster_ax_array[trial].set_title('Raster plot for Golgi cell populations (trial id=%d)'%non_empty_trial_indices[trial],size=6)
raster_ax_array[trial].set_xlabel('Time (ms)',fontsize=6)
for tick in raster_ax_array[trial].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in raster_ax_array[trial].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
raster_fig_array[trial].subplots_adjust(top=0.90)
raster_fig_array[trial].subplots_adjust(bottom=0.3)
l=raster_fig_array[trial].legend(lines,general_plot_parameters[3],title=general_plot_parameters[2],loc='center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.52, 0.1),prop={'size':4})
plt.setp(l.get_title(),fontsize=4)
raster_fig_array[trial].savefig('simulations/sim%d_rasters_%s.%s'%(non_empty_trial_indices[trial],general_plot_parameters[0],spike_plot_parameters[-1]))
else:
if trial_indicator:
if pop_no_array[0] >1:
for pop in range(0,pop_no_array[0]):
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[0][pop]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[0][pop]]-1)
left_value=cell_no_array[target_pop_index_array[0][pop]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[0][pop]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[0][pop]]
ax_stack_one_trial[pop].set_yticks(ytick_array)
fig_stack_one_trial.canvas.draw()
ax_stack_one_trial[pop].set_ylim(0,(cell_no_array[target_pop_index_array[0][pop]]+1)*len(general_plot_parameters[3]) )
ax_stack_one_trial[pop].set_ylabel('Cell ids, population %d'%target_pop_index_array[0][pop],size=4)
ax_stack_one_trial[pop].set_yticks([cell_no_array[target_pop_index_array[0][pop]]+( cell_no_array[target_pop_index_array[0][pop]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_stack_one_trial[pop].yaxis.grid(False, which='major')
ax_stack_one_trial[pop].yaxis.grid(True, which='minor')
labels = [x.get_text() for x in ax_stack_one_trial[pop].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_stack_one_trial[pop].set_yticklabels(labels)
if pop==0:
ax_stack_one_trial[pop].set_title('Raster plot for Golgi cell populations (trial id=%d)'%spike_plot_parameters[1],size=6)
if pop==pop_no_array[0]-1:
ax_stack_one_trial[pop].set_xlabel('Time (ms)',fontsize=6)
for tick in ax_stack_one_trial[pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_stack_one_trial[pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
else:
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[0][0]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[0][0]]-1)
left_value=cell_no_array[target_pop_index_array[0][0]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[0][0]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[0][0]]
ax_stack_one_trial.set_yticks(ytick_array)
fig_stack_one_trial.canvas.draw()
ax_stack_one_trial.set_ylim(0,(cell_no_array[target_pop_index_array[0][0]]+1)*len(general_plot_parameters[3]) )
ax_stack_one_trial.set_ylabel('Cell ids, population %d'%target_pop_index_array[0][0],size=4)
ax_stack_one_trial.set_yticks([cell_no_array[target_pop_index_array[0][0]]+( cell_no_array[target_pop_index_array[0][0]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_stack_one_trial.yaxis.grid(False, which='major')
ax_stack_one_trial.yaxis.grid(True, which='minor')
labels = [x.get_text() for x in ax_stack_one_trial.get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_stack_one_trial.set_yticklabels(labels)
ax_stack_one_trial.set_title('Raster plot for Golgi cell populations (trial id=%d)'%spike_plot_parameters[1],size=6)
ax_stack_one_trial.set_xlabel('Time (ms)',fontsize=6)
for tick in ax_stack_one_trial.xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_stack_one_trial.yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
fig_stack_one_trial.subplots_adjust(top=0.90)
fig_stack_one_trial.subplots_adjust(bottom=0.3)
l=fig_stack_one_trial.legend(lines,general_plot_parameters[3],title=general_plot_parameters[2],loc='center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.52, 0.1),prop={'size':4})
plt.setp(l.get_title(),fontsize=4)
fig_stack_one_trial.savefig('simulations/sim%d_rasters_%s.%s'%(spike_plot_parameters[1],general_plot_parameters[0],spike_plot_parameters[-1]))
plt.clf()
for trial in range(n_trials):
sim_ref = utils.desynchronisation_random_graph(timestamp,
trial)
sim_dir = '../simulations/' + sim_ref
time = np.loadtxt(sim_dir + '/time.dat')
spike_trains = []
for cell in range(n_cells):
spikes = np.loadtxt('{}/Golgi_network_reduced_{}.SPIKES_min20.spike'.format(sim_dir, cell))
spike_trains.append(pyspike.add_auxiliary_spikes(spikes, sim_duration))
if trial==0:
ax[0].scatter(spikes,
np.zeros_like(spikes)+cell,
marker='|',
s=2,
c=color)
distances.append(pyspike.spike_profile_multi(spike_trains[n_excluded_cells:]))
# average synchrony index across trials
average_distance = distances[0]
for distance in distances[1:]:
average_distance.add(distance)
average_distance.mul_scalar(1./n_trials)
xmin = 50
xmax = 1900
x, y = average_distance.get_plottable_data()
ximin = np.searchsorted(x, xmin)
ximax = np.searchsorted(x, xmax)
lines.append(ax[1].plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
#labels.append('{:g}'.format(variance_scaling))
for cell in range(n_cells):
spikes = np.loadtxt(
'{}/Golgi_network_reduced_large_{}.SPIKES_min20.spike'.format(
sim_dir, cell))
spike_trains.append(
pyspike.add_auxiliary_spikes(spikes, sim_duration))
if trial == 0 and cell in cells_for_raster:
ax[0].scatter(spikes,
np.zeros_like(spikes) +
(raster_index + i * n_cells_in_raster),
marker='|',
s=2,
c=color)
raster_index += 1
distances.append(
pyspike.spike_profile_multi(
[spike_trains[c] for c in cells_for_distance]))
# average synchrony index across trials
average_distance = distances[0]
for distance in distances[1:]:
average_distance.add(distance)
average_distance.mul_scalar(1. / n_trials)
synchrony_indices.append(1 - average_distance.avrg())
xmin = 50
xmax = 1900
x, y = average_distance.get_plottable_data()
ximin = np.searchsorted(x, xmin)
ximax = np.searchsorted(x, xmax)
lines.append(ax[1].plot(x[ximin:ximax + 1],
1 - y[ximin:ximax + 1],
lw=2,
def Synchronization_analysis(sim_duration,specify_targets,no_of_groups,exp_specify,spike_plot_parameters,general_plot_parameters):
cell_no_array=[]
for exp_id in range(0,len(exp_specify[0]) ):
if exp_specify[1][1]==True:
for cell_group in range(0,no_of_groups):
cell_group_positions=np.loadtxt('simulations/%s/Golgi_pop%d.txt'%(exp_specify[0][exp_id],cell_group))
dim_array=np.shape(cell_group_positions)
cell_no_array.append(dim_array[0])
else:
for cell_group in range(0,no_of_groups):
cell_group_positions=np.loadtxt('simulations/%s/sim0/Golgi_pop%d.txt'%(exp_specify[0][exp_id],cell_group))
dim_array=np.shape(cell_group_positions)
cell_no_array.append(dim_array[0])
n_trials=exp_specify[2]
lines = []
lines_sep=[]
experiment_seed=random.sample(range(0,15000),1)[0]
for exp_id in range(0,len(exp_specify[0])):
#get target ids
experiment_parameters=[]
experiment_parameters.append(exp_specify[0][exp_id])
experiment_parameters.append(exp_specify[1])
experiment_parameters.append(exp_specify[2])
target_cell_array=get_cell_ids_for_sync_analysis(specify_targets,no_of_groups,experiment_parameters,experiment_seed)
test_array=target_cell_array
if exp_id==0:
if "save sync plot to a separate file" in spike_plot_parameters:
fig_sync, ax_sync=plt.subplots(figsize=(2,1.5),ncols=1,nrows=1)
if spike_plot_parameters[0]=="2D raster plots":
trial_indicator_target=False
if n_trials >1:
target_cell_array_target_trial=target_cell_array[spike_plot_parameters[1]]
no_of_rasters=0
if target_cell_array_target_trial != []:
test_non_empty_target_array=True
trial_indicator_target=True
no_of_rasters=len(target_cell_array_target_trial)
rows=1+no_of_rasters
columns=1
fig_stack, ax_stack = plt.subplots(figsize=(4,rows+1),ncols=columns,nrows=rows, sharex=True)
ax_stack=ax_stack.ravel()
else:
no_of_rasters=0
if target_cell_array != []:
trial_indicator=True
no_of_rasters=len(target_cell_array)
rows=1+no_of_rasters
columns=1
fig_stack, ax_stack = plt.subplots(figsize=(4,rows),ncols=columns,nrows=rows, sharex=True)
ax_stack=ax_stack.ravel()
if "save all trials to separate files" in spike_plot_parameters:
raster_fig_array=[]
raster_ax_array=[]
pop_no_array=[]
trial_indicator=False
if n_trials >1:
non_empty_trial_indices=[]
for trial in range(0,n_trials):
if target_cell_array[trial] !=[]:
non_empty_trial_indices.append(trial)
test_indices=non_empty_trial_indices
for trial in range(0,len(non_empty_trial_indices)):
target_trial=target_cell_array[non_empty_trial_indices[trial]]
columns=1
rows=len(target_trial)
pop_no_array.append(rows)
plot_rows=rows+1
fig, ax = plt.subplots(figsize=(4,plot_rows),ncols=columns,nrows=rows, sharex=True)
if len(target_trial) >1 :
ax=ax.ravel()
raster_fig_array.append(fig)
raster_ax_array.append(ax)
else:
no_of_rasters=0
if target_cell_array != []:
trial_indicator=True
no_of_rasters=len(target_cell_array)
rows=no_of_rasters
pop_no_array.append(rows)
columns=1
plot_rows=rows+1
fig_stack_one_trial, ax_stack_one_trial= plt.subplots(figsize=(4,plot_rows),ncols=columns,nrows=rows, sharex=True)
if no_of_rasters > 1:
ax_stack_one_trial=ax_stack.ravel()
if "save all trials to one separate file" in spike_plot_parameters:
pop_no_array=[]
if n_trials >1:
non_empty_trial_indices=[]
for trial in range(0,n_trials):
if target_cell_array[trial] !=[]:
non_empty_trial_indices.append(trial)
total_no_of_rows=0
for trial in range(0,len(non_empty_trial_indices)):
target_trial=target_cell_array[non_empty_trial_indices[trial]]
rows=len(target_trial)
pop_no_array.append(rows)
total_no_of_rows=total_no_of_rows + rows
fig_all_trials, ax_all_trials = plt.subplots(figsize=(4,total_no_of_rows),ncols=1,nrows=total_no_of_rows, sharex=True)
if total_no_of_rows >1 :
ax_all_trials=ax_all_trials.ravel()
non_empty_non_unitary_trial_counter=0
distances = []
color = sns.color_palette()[exp_id+1]
row_counter=0
target_pop_index_array=[]
if spike_plot_parameters[0]=="2D raster plots":
if "save all trials to one separate file" in spike_plot_parameters:
row_array=range(0,total_no_of_rows)
if spike_plot_parameters[0]=="2D raster plots":
if "save all trials to separate files" in spike_plot_parameters:
raster_ax_row_counter=0
for trial in range(0,n_trials):
sim_dir = 'simulations/' + exp_specify[0][exp_id]+'/sim%d'%trial+'/txt'
######
if n_trials > 1:
target_cell_array_per_trial=target_cell_array[trial]
else:
target_cell_array_per_trial=target_cell_array
if target_cell_array_per_trial !=[]:
spike_trains = []
target_pop_index_array_per_trial=[]
print(" Trial %d is not empty"%(trial))
for target_pop in range(0,len(target_cell_array_per_trial)):
for pop in range(0,no_of_groups):
if ('pop%d'%(pop)) in target_cell_array_per_trial[target_pop]:
target_pop_index_array_per_trial.append(pop)
target_cells = [x for x in target_cell_array_per_trial[target_pop] if isinstance(x,int)]
print target_cells
for cell in range(0,len(target_cells)):
#create target txt file containing spike times
if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cells[cell])):
get_spike_times('Golgi_pop%d_cell%d'%(pop,target_cells[cell]),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cells[cell]))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
print spike_trains
if spike_plot_parameters[0]=="2D raster plots":
if spike_plot_parameters[1]==trial:
ax_stack[target_pop].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
if "save all trials to separate files" in spike_plot_parameters:
if n_trials >1:
if len(target_cell_array_per_trial) > 1:
raster_ax_array[raster_ax_row_counter][target_pop].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
else:
raster_ax_array[raster_ax_row_counter].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
else:
if len(target_cell_array_per_trial) >1:
ax_stack_one_trial[target_pop].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
else:
ax_stack_one_trial.scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
if "save all trials to one separate file" in spike_plot_parameters:
if n_trials >1:
if total_no_of_rows >1 :
ax_all_trials[row_array[row_counter]].scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
else:
ax_all_trials.scatter(spikes,np.zeros_like(spikes)+target_cells[cell]+exp_id*(cell_no_array[pop]+1) ,marker='|',s=2,c=color)
if spike_plot_parameters[0]=="2D raster plots":
if "save all trials to one separate file" in spike_plot_parameters:
row_counter=row_counter+1
if spike_plot_parameters[0]=="2D raster plots":
if "save all trials to separate files" in spike_plot_parameters:
raster_ax_row_counter+=1
if spike_plot_parameters[1]==trial:
target_trial_index=target_pop_index_array.index(target_pop_index_array_per_trial)
target_pop_index_array.append(target_pop_index_array_per_trial)
########
print("Length of spike_trains is %d"%len(spike_trains))
if len(spike_trains) >1:
print("Trial %d contains more than one cell; Synchrony metric will be computed for this trial"%(trial))
non_empty_non_unitary_trial_counter+=1
print non_empty_non_unitary_trial_counter
distances.append(pyspike.spike_profile_multi(spike_trains))
else:
print("Trial %d contains one cell; Synchrony metric will not be computed for this trial"%(trial))
########
# average synchrony index across "non empty" trials
average_distance = distances[0]
for distance in distances[1:]:
average_distance.add(distance)
average_distance.mul_scalar(1./non_empty_non_unitary_trial_counter)
# below blocks for saving synchrony and spike raster plots
mark_steps=sim_duration/50
marks=[]
for mark in range(0,mark_steps+1):
Mark=50*mark
marks.append(Mark)
xmin = 50
right_shaded_region=50
if sim_duration >=1000:
right_shaded_region=100
xmax = sim_duration-right_shaded_region
x, y = average_distance.get_plottable_data()
ximin = np.searchsorted(x, xmin)
ximax = np.searchsorted(x, xmax)
if spike_plot_parameters[0]=="2D raster plots":
if target_cell_array_target_trial != []:
target_cell_array_target_trial_indicator=True
lines.append(ax_stack[no_of_rasters].plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
ax_stack[no_of_rasters].plot(x[:ximin+1], 1-y[:ximin+1], lw=2, c=color, alpha=0.4)
ax_stack[no_of_rasters].plot(x[ximax:], 1-y[ximax:], lw=2, c=color, alpha=0.4)
if "save sync plot to a separate file" in spike_plot_parameters:
lines_sep.append(ax_sync.plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
ax_sync.plot(x[:ximin+1], 1-y[:ximin+1], lw=2, c=color, alpha=0.4)
ax_sync.plot(x[ximax:], 1-y[ximax:], lw=2, c=color, alpha=0.4)
if "save sync plot to a separate file" in spike_plot_parameters:
print("save sync plot to a separate file is specified")
for tick in ax_sync.xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_sync.yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
ax_sync.locator_params(axis='y', tight=True, nbins=10)
ax_sync.set_xlabel('Time (ms)',fontsize=4)
ax_sync.set_ylabel('Synchrony index',size=4)
ax_sync.set_ylim(0,1)
ax_sync.set_xticks(marks)
ax_sync.set_title('Synchronization between %s'%general_plot_parameters[1],size=4)
fig_sync.tight_layout()
fig_sync.subplots_adjust(top=0.90)
fig_sync.subplots_adjust(bottom=0.55)
l=fig_sync.legend(lines_sep,general_plot_parameters[3],title=general_plot_parameters[2],loc='center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.52, 0.25),prop={'size':4})
plt.setp(l.get_title(),fontsize=4)
fig_sync.savefig('simulations/sync_only_%s.%s'%(general_plot_parameters[0],spike_plot_parameters[-1]))
print("saved %s in simulations"%'sync_only_%s.%s'%(general_plot_parameters[0],spike_plot_parameters[-1]))
if spike_plot_parameters[0]=="2D raster plots":
print("Intend to plot a main figure with representative 2D raster plots")
#create label array
if trial_indicator_target:
print("2D raster plot procedures started")
for pop in range(0,no_of_rasters):
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[target_trial_index][pop]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[target_trial_index][pop]]-1)
left_value=cell_no_array[target_pop_index_array[target_trial_index][pop]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[target_trial_index][pop]])
left_value=left_value+2+cell_no_array[target_pop_index_array[target_trial_index][pop]]
print label_array
print ytick_array
ax_stack[pop].set_yticks(ytick_array)
fig_stack.canvas.draw()
ax_stack[pop].set_ylim(0,(cell_no_array[target_pop_index_array[target_trial_index][pop]]+1)*len(general_plot_parameters[3]) )
ax_stack[pop].set_ylabel('Cell ids, population %d'%pop,size=4)
ax_stack[pop].set_yticks([cell_no_array[target_pop_index_array[target_trial_index][pop]]+(cell_no_array[target_pop_index_array[target_trial_index][pop]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_stack[pop].yaxis.grid(False, which='major')
ax_stack[pop].yaxis.grid(True, which='minor')
labels = [x.get_text() for x in ax_stack[pop].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_stack[pop].set_yticklabels(labels)
if pop==0:
ax_stack[pop].set_title('Raster plots for target Golgi cell populations (trial id=%d)'%spike_plot_parameters[1],size=6)
for pop in range(0,no_of_rasters+1):
for tick in ax_stack[pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_stack[pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
ax_stack[no_of_rasters].locator_params(axis='y', tight=True, nbins=10)
ax_stack[no_of_rasters].set_xlabel('Time (ms)',fontsize=6)
ax_stack[no_of_rasters].set_ylabel('Synchrony index',size=4)
ax_stack[no_of_rasters].set_ylim(0,1)
ax_stack[no_of_rasters].set_xticks(marks)
ax_stack[no_of_rasters].set_title('Synchronization between %s'%general_plot_parameters[1],size=6)
fig_stack.tight_layout()
fig_stack.subplots_adjust(top=0.80)
fig_stack.subplots_adjust(bottom=0.15)
fig_stack.subplots_adjust(hspace=.4)
l=fig_stack.legend(lines,general_plot_parameters[3],title=general_plot_parameters[2], loc='upper center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.55, 1.0))
plt.setp(l.get_title(),fontsize=6)
plt.setp(l.get_texts(), fontsize=6)
fig_stack.savefig('simulations/%s.%s'%(general_plot_parameters[0],spike_plot_parameters[-1]))
else:
print("Intended to plot raster plots for trial %d, but specified region-specific selection of cells produces an empty target array.\nThus a main figure with a representative raster will not be produced.\nHowever, synchrony plot can be saved in a separate file.\nAlternatively, plot a non-empty trial"%(spike_plot_parameters[1]))
if "save all trials to one separate file" in spike_plot_parameters:
print target_pop_index_array
if total_no_of_rows >1:
row_counter=0
for trial in range(0,len(non_empty_trial_indices)):
if len(target_pop_index_array[trial]) >1:
for pop in range(0,len(target_pop_index_array[trial])):
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[trial][pop] ]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[trial][pop]]-1)
left_value=cell_no_array[target_pop_index_array[trial][pop]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[trial][pop]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[trial][pop]]
ax_all_trials[row_counter].set_yticks(ytick_array)
#ax_all_trials[row_counter].canvas.draw()
ax_all_trials[row_counter].set_ylim(0,(cell_no_array[target_pop_index_array[trial][pop]]+1)*len(general_plot_parameters[3]) )
ax_all_trials[row_counter].set_ylabel('Cell ids for pop %d\ntrial %d'%(target_pop_index_array[trial][pop],non_empty_trial_indices[trial]),size=4)
ax_all_trials[row_counter].set_yticks([cell_no_array[target_pop_index_array[trial][pop]]+( cell_no_array[target_pop_index_array[trial][pop]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_all_trials[row_counter].yaxis.grid(False, which='major')
ax_all_trials[row_counter].yaxis.grid(True, which='minor')
if row_counter==total_no_of_rows-1:
ax_all_trials[row_counter].set_xlabel('Time (ms)',fontsize=4)
labels = [x.get_text() for x in ax_all_trials[row_counter].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_all_trials[row_counter].set_yticklabels(labels)
for tick in ax_all_trials[row_counter].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_all_trials[row_counter].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
row_counter+=1
else:
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[trial][0]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[trial][0]]-1)
left_value=cell_no_array[target_pop_index_array[trial][0]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[trial][0]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[trial][0]]
ax_all_trials[row_counter].set_yticks(ytick_array)
ax_all_trials[row_counter].set_ylim(0,(cell_no_array[target_pop_index_array[trial][0]]+1)*len(general_plot_parameters[3]) )
ax_all_trials[row_counter].set_ylabel('Cell ids for pop %d\ntrial %d'%(target_pop_index_array[trial][0],non_empty_trial_indices[trial]),size=4)
ax_all_trials[row_counter].set_yticks([cell_no_array[target_pop_index_array[trial][0]]+( cell_no_array[target_pop_index_array[trial][0]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_all_trials[row_counter].yaxis.grid(False, which='major')
ax_all_trials[row_counter].yaxis.grid(True, which='minor')
if row_counter==total_no_of_rows-1:
ax_all_trials[row_counter].set_xlabel('Time (ms)',fontsize=4)
labels = [x.get_text() for x in ax_all_trials[row_counter].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_all_trials[row_counter].set_yticklabels(labels)
for tick in ax_all_trials[row_counter].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_all_trials[row_counter].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
row_counter+=1
else:
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[0][0]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[0][pop]]-1)
left_value=cell_no_array[target_pop_index_array[0][0]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[0][0]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[0][0]]
ax_all_trials.set_yticks(ytick_array)
ax_all_trials.canvas.draw()
ax_all_trials.set_ylim(0,(cell_no_array[target_pop_index_array[0][0]]+1)*len(general_plot_parameters[3]) )
ax_all_trials.set_ylabel('Cell ids for pop %d\ntrial %d'%(target_pop_index_array[trial][pop],non_empty_trial_indices[trial]),size=4)
ax_all_trials.set_yticks([cell_no_array[target_pop_index_array[0][0]]+( cell_no_array[target_pop_index_array[0][0]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_all_trials.yaxis.grid(False, which='major')
ax_all_trials.yaxis.grid(True, which='minor')
ax_all_trials.set_xlabel('Time (ms)',fontsize=4)
labels = [x.get_text() for x in ax_all_trials.get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_all_trials.set_yticklabels(labels)
for tick in ax_all_trials.xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_all_trials.yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
fig_all_trials.subplots_adjust(hspace=0.1)
fig_all_trials.subplots_adjust(top=0.95)
fig_all_trials.subplots_adjust(bottom=0.1)
l=fig_all_trials.legend(lines,general_plot_parameters[3],title=general_plot_parameters[2],loc='center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.52, 0.98),prop={'size':4})
plt.setp(l.get_title(),fontsize=4)
fig_all_trials.savefig('simulations/all_trials_%s.%s'%(general_plot_parameters[0],spike_plot_parameters[-1]))
plt.clf()
if "save all trials to separate files" in spike_plot_parameters:
if n_trials >1:
for trial in range(0,len(non_empty_trial_indices)):
print("started ploting non-empty trials")
if pop_no_array[trial] >1:
for pop in range(0,pop_no_array[trial]):
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[trial][pop]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[trial][pop]]-1)
left_value=cell_no_array[target_pop_index_array[trial][pop]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[trial][pop]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[trial][pop]]
raster_ax_array[trial][pop].set_yticks(ytick_array)
raster_fig_array[trial].canvas.draw()
raster_ax_array[trial][pop].set_ylim(0,(cell_no_array[target_pop_index_array[trial][pop]]+1)*len(general_plot_parameters[3]) )
raster_ax_array[trial][pop].set_ylabel('Cell ids, population %d'%target_pop_index_array[trial][pop],size=4)
raster_ax_array[trial][pop].set_yticks([cell_no_array[target_pop_index_array[trial][pop]]+( cell_no_array[target_pop_index_array[trial][pop]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
raster_ax_array[trial][pop].yaxis.grid(False, which='major')
raster_ax_array[trial][pop].yaxis.grid(True, which='minor')
labels = [x.get_text() for x in raster_ax_array[trial][pop].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
raster_ax_array[trial][pop].set_yticklabels(labels)
if pop==0:
raster_ax_array[trial][pop].set_title('Raster plot for Golgi cell populations (trial id=%d)'%non_empty_trial_indices[trial],size=6)
if pop==pop_no_array[trial]-1:
raster_ax_array[trial][pop].set_xlabel('Time (ms)',fontsize=6)
for tick in raster_ax_array[trial][pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in raster_ax_array[trial][pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
else:
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[trial][0]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[trial][0]]-1)
left_value=cell_no_array[target_pop_index_array[trial][0]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[trial][0]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[trial][0]]
raster_ax_array[trial].set_yticks(ytick_array)
raster_fig_array[trial].canvas.draw()
raster_ax_array[trial].set_ylim(0,(cell_no_array[target_pop_index_array[trial][0]]+1)*len(general_plot_parameters[3]) )
raster_ax_array[trial].set_ylabel('Cell ids, population %d'% target_pop_index_array[trial][0],size=4)
raster_ax_array[trial].set_yticks([cell_no_array[target_pop_index_array[trial][0]]+( cell_no_array[target_pop_index_array[trial][0]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
raster_ax_array[trial].yaxis.grid(False, which='major')
raster_ax_array[trial].yaxis.grid(True, which='minor')
labels = [x.get_text() for x in raster_ax_array[trial].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
raster_ax_array[trial].set_yticklabels(labels)
raster_ax_array[trial].set_title('Raster plot for Golgi cell populations (trial id=%d)'%non_empty_trial_indices[trial],size=6)
raster_ax_array[trial].set_xlabel('Time (ms)',fontsize=6)
for tick in raster_ax_array[trial].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in raster_ax_array[trial].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
raster_fig_array[trial].subplots_adjust(top=0.90)
raster_fig_array[trial].subplots_adjust(bottom=0.3)
l=raster_fig_array[trial].legend(lines,general_plot_parameters[3],title=general_plot_parameters[2],loc='center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.52, 0.1),prop={'size':4})
plt.setp(l.get_title(),fontsize=4)
raster_fig_array[trial].savefig('simulations/sim%d_rasters_%s.%s'%(non_empty_trial_indices[trial],general_plot_parameters[0],spike_plot_parameters[-1]))
else:
if trial_indicator:
if pop_no_array[0] >1:
for pop in range(0,pop_no_array[0]):
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[0][pop]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[0][pop]]-1)
left_value=cell_no_array[target_pop_index_array[0][pop]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[0][pop]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[0][pop]]
ax_stack_one_trial[pop].set_yticks(ytick_array)
fig_stack_one_trial.canvas.draw()
ax_stack_one_trial[pop].set_ylim(0,(cell_no_array[target_pop_index_array[0][pop]]+1)*len(general_plot_parameters[3]) )
ax_stack_one_trial[pop].set_ylabel('Cell ids, population %d'%target_pop_index_array[0][pop],size=4)
ax_stack_one_trial[pop].set_yticks([cell_no_array[target_pop_index_array[0][pop]]+( cell_no_array[target_pop_index_array[0][pop]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_stack_one_trial[pop].yaxis.grid(False, which='major')
ax_stack_one_trial[pop].yaxis.grid(True, which='minor')
labels = [x.get_text() for x in ax_stack_one_trial[pop].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_stack_one_trial[pop].set_yticklabels(labels)
if pop==0:
ax_stack_one_trial[pop].set_title('Raster plot for Golgi cell populations (trial id=%d)'%spike_plot_parameters[1],size=6)
if pop==pop_no_array[0]-1:
ax_stack_one_trial[pop].set_xlabel('Time (ms)',fontsize=6)
for tick in ax_stack_one_trial[pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_stack_one_trial[pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
else:
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[target_pop_index_array[0][0]]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[target_pop_index_array[0][0]]-1)
left_value=cell_no_array[target_pop_index_array[0][0]]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+1+cell_no_array[target_pop_index_array[0][0]] )
left_value=left_value+2+cell_no_array[target_pop_index_array[0][0]]
ax_stack_one_trial.set_yticks(ytick_array)
fig_stack_one_trial.canvas.draw()
ax_stack_one_trial.set_ylim(0,(cell_no_array[target_pop_index_array[0][0]]+1)*len(general_plot_parameters[3]) )
ax_stack_one_trial.set_ylabel('Cell ids, population %d'%target_pop_index_array[0][0],size=4)
ax_stack_one_trial.set_yticks([cell_no_array[target_pop_index_array[0][0]]+( cell_no_array[target_pop_index_array[0][0]]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_stack_one_trial.yaxis.grid(False, which='major')
ax_stack_one_trial.yaxis.grid(True, which='minor')
labels = [x.get_text() for x in ax_stack_one_trial.get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_stack_one_trial.set_yticklabels(labels)
ax_stack_one_trial.set_title('Raster plot for Golgi cell populations (trial id=%d)'%spike_plot_parameters[1],size=6)
ax_stack_one_trial.set_xlabel('Time (ms)',fontsize=6)
for tick in ax_stack_one_trial.xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_stack_one_trial.yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
fig_stack_one_trial.subplots_adjust(top=0.90)
fig_stack_one_trial.subplots_adjust(bottom=0.3)
l=fig_stack_one_trial.legend(lines,general_plot_parameters[3],title=general_plot_parameters[2],loc='center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.52, 0.1),prop={'size':4})
plt.setp(l.get_title(),fontsize=4)
fig_stack_one_trial.savefig('simulations/sim%d_rasters_%s.%s'%(spike_plot_parameters[1],general_plot_parameters[0],spike_plot_parameters[-1]))
plt.clf()
sim_dir = '../simulations/' + sim_ref
time = np.loadtxt(sim_dir + '/time.dat')
spike_trains = []
raster_index = 0
for cell in range(n_cells):
spikes = np.loadtxt('{}/Golgi_network_reduced_large_{}.SPIKES_min20.spike'.format(sim_dir, cell))
spike_trains.append(pyspike.add_auxiliary_spikes(spikes, sim_duration))
if trial==0 and cell in cells_for_raster:
ax[0].scatter(spikes,
np.zeros_like(spikes)+(raster_index+i*n_cells_in_raster),
marker='|',
s=2,
c=color)
raster_index += 1
distances.append(pyspike.spike_profile_multi([spike_trains[c] for c in cells_for_distance]))
# average synchrony index across trials
average_distance = distances[0]
for distance in distances[1:]:
average_distance.add(distance)
average_distance.mul_scalar(1./n_trials)
synchrony_indices.append(1-average_distance.avrg())
xmin = 50
xmax = 1900
x, y = average_distance.get_plottable_data()
ximin = np.searchsorted(x, xmin)
ximax = np.searchsorted(x, xmax)
lines.append(ax[1].plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
labels.append('{:g}'.format(rewiring_p))
ax[1].plot(x[:ximin+1], 1-y[:ximin+1], lw=2, c=color, alpha=0.4)
def Synchronization_analysis(sim_duration,specify_targets,no_of_groups,exp_specify,spike_plot_parameters,general_plot_parameters):
cell_no_array=[]
for exp_id in range(0,len(exp_specify[0]) ):
if exp_specify[1][1]==True:
for cell_group in range(0,no_of_groups):
cell_group_positions=np.loadtxt('simulations/%s/Golgi_pop%d.txt'%(exp_specify[0][exp_id],cell_group))
dim_array=np.shape(cell_group_positions)
cell_no_array.append(dim_array[0])
else:
for cell_group in range(0,no_of_groups):
cell_group_positions=np.loadtxt('simulations/%s/sim0/Golgi_pop%d.txt'%(exp_specify[0][exp_id],cell_group))
dim_array=np.shape(cell_group_positions)
cell_no_array.append(dim_array[0])
n_trials=exp_specify[2]
if spike_plot_parameters[0]=="2D raster plots":
columns=1
rows=1+no_of_groups
fig_stack, ax_stack = plt.subplots(figsize=(4,rows),ncols=columns,nrows=rows, sharex=True)
ax_stack=ax_stack.ravel()
if spike_plot_parameters[2]=="save all simulations to separate files":
raster_fig_array=[]
raster_ax_array=[]
fig_sync, ax_sync=plt.subplots(figsize=(2,1),ncols=1,nrows=1)
for trial in range(0,n_trials):
columns=1
rows=no_of_groups
fig, ax = plt.subplots(figsize=(4,rows),ncols=columns,nrows=rows, sharex=True)
if no_of_groups >1:
ax=ax.ravel()
raster_fig_array.append(fig)
raster_ax_array.append(ax)
if spike_plot_parameters[0]=="3D scatter plot":
nrows_3D = 1+no_of_groups
ncols_3D = 1
fig_with_3D_rasters= plt.figure(figsize=(4*ncols_3D,4*nrows_3D))
ax_3D=[]
for pop in range(0,no_of_groups):
ax_3D.append(fig_with_3D_rasters.add_subplot(nrows_3D,ncols_3D,pop+1,projection='3d'))
ax_3D.append(fig_with_3D_rasters.add_subplot(nrows_3D,ncols_3D,no_of_groups+1))
if spike_plot_parameters[2]=="save 3D scatter plots separately":
fig_sync, ax_sync=plt.subplots(figsize=(2,1),ncols=1,nrows=1)
rows_3D_minus_sync = max(1,math.ceil(no_of_groups/3))
cols_3D_minus_sync = min(3,no_of_groups)
rasters3D_only=plt.figure(figsize=(4*cols_3D_minus_sync,4*rows_3D_minus_sync))
ax_3D_rasters_only=[]
for pop in range(0,no_of_groups):
ax_3D_rasters_only.append(rasters3D_only.add_subplot(nrows_3D,ncols_3D,pop+1,projection='3d'))
lines = []
lines_sep=[]
for exp_id in range(0,len(exp_specify[0])):
#get target ids
experiment_parameters=[]
experiment_parameters.append(exp_specify[0][exp_id])
experiment_parameters.append(exp_specify[1])
experiment_parameters.append(exp_specify[2])
target_cell_array=methods.get_cell_ids_for_sync_analysis(specify_targets,no_of_groups,experiment_parameters)
#plot_params=["2D raster plot",[0],"save all simulations to separate files","pdf",#"jpeg"or"png",....]
#plot_params=["3D raster plot",[0], # the same options !!!!!
#plot_params=["3D scatter plot", # one plot for each population include all sims; then the same options !!!!!
distances = []
color = sns.color_palette()[exp_id]
if spike_plot_parameters[0]=="3D scatter plot":
spikes_3D=[]
spike_times_3D=[]
sim_axis_array=[]
population_wise_3D_spikes=[]
population_wise_3D_spike_times=[]
population_wise_sim_axis_array=[]
for trial in range(0,n_trials):
sim_dir = 'simulations/' + exp_specify[0][exp_id]+'/sim%d'%trial+'/txt'
spike_trains = []
if spike_plot_parameters[0]=="3D scatter plot":
spikes_array_per_trial_3D=[]
spike_times_array_per_trial_3D=[]
sim_axis_array_per_trial=[]
######
if (specify_targets[0]=="3D region specific") and (not("subtype specific" in specify_targets )):
if exp_specify[1][1]==True:
for pop in range(0,len(target_cell_array)):
if spike_plot_parameters[0]=="3D scatter plot":
spikes_per_pop_3D=[]
spike_times_per_pop_3D=[]
sim_axis_per_pop=[]
for cell in range(0,len(target_cell_array[pop])):
#create target txt file containing spike times
if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[pop][cell])):
methods.get_spike_times('Golgi_pop%d_cell%d'%(pop,target_cell_array[pop][cell]),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[pop][cell]))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
if spike_plot_parameters[0]=="2D raster plots":
if trial in spike_plot_parameters[1]:
ax_stack[pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*(cell_no_array[pop]+1)),marker='|',s=2,c=color)
if spike_plot_parameters[2]=="save all simulations to separate files":
raster_ax_array[trial][pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*(cell_no_array[pop]+1)),marker='|',s=2,c=color)
if spike_plot_parameters[0]=="3D scatter plot":
if cell==0:
spikes_per_pop_3D=np.zeros_like(spikes)+(target_cell_array[pop][cell]+ exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spikes
sim_axis_per_pop=np.zeros_like(spikes)+trial+1 # for display start numbering trials from the index of 1
else:
spikes_per_pop_3D=spikes_per_pop_3D+np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spike_times_per_pop_3D +spikes
sim_axis_per_pop=sim_axis_per_pop+np.zeros_like(spikes)+trial+1
if spike_plot_parameters[0]=="3D scatter plot":
spikes_array_per_trial_3D.append(spikes_per_pop_3D)
spike_times_array_per_trial_3D.append(spike_times_per_pop_3D)
sim_axis_array_per_trial.append(sim_axis_per_pop)
else:
for pop in range(0,len(target_cell_array[trial])):
if spike_plot_parameters[0]=="3D scatter plot":
spikes_per_pop_3D=[]
spike_times_per_pop_3D=[]
sim_axis_per_pop=[]
if len(target_cell_array[trial][pop]) > 0:
for cell in range(0,len(target_cell_array[trial][pop])):
if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[trial][pop][cell])):
methods.get_spike_times('Golgi_pop%d_cell%d'%(pop,target_cell_array[trial][pop][cell]),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[trial][pop][cell]))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
if spike_plot_parameters[0]=="2D raster plots":
if trial in spike_plot_parameters[1]:
ax_stack[pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*(cell_no_array[pop]+1)),marker='|',s=2,c=color)
if spike_plot_parameters[2]=="save all simulations to separate files":
raster_ax_array[trial][pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*(cell_no_array[pop]+1)),marker='|',s=2,c=color)
if spike_plot_parameters[0]=="3D scatter plot":
if cell==0:
spikes_per_pop_3D=np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+ exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spikes
sim_axis_per_pop=np.zeros_like(spikes)+trial+1 # for display start numbering trials from the index of 1
else:
spikes_per_pop_3D=spikes_per_pop_3D+np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spike_times_per_pop_3D +spikes
sim_axis_per_pop=sim_axis_per_pop+np.zeros_like(spikes)+trial+1
if spike_plot_parameters[0]=="3D scatter plot":
spikes_array_per_trial_3D.append(spikes_per_pop_3D)
spike_times_array_per_trial_3D.append(spike_times_per_pop_3D)
sim_axis_array_per_trial.append(sim_axis_per_pop)
######
if string.lower(specify_targets[0])=="all":
for pop in range(0,len(target_cell_array)):
if spike_plot_parameters[0]=="3D scatter plot":
spikes_per_pop_3D=[]
spike_times_per_pop_3D=[]
sim_axis_per_pop=[]
for cell in range(0,len(target_cell_array[pop])):
#create target txt file containing spike times
if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,cell)):
methods.get_spike_times('Golgi_pop%d_cell%d'%(pop,cell),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop, cell))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
if spike_plot_parameters[0]=="2D raster plots":
if trial in spike_plot_parameters[1]:
ax_stack[pop].scatter(spikes,np.zeros_like(spikes)+(cell+exp_id*cell_no_array[pop]),marker='|',s=2,c=color)
if spike_plot_parameters[2]=="save all simulations to separate files":
raster_ax_array[trial][pop].scatter(spikes,np.zeros_like(spikes)+(cell+exp_id*cell_no_array[pop]),marker='|',s=2,c=color)
if spike_plot_parameters[0]=="3D scatter plot":
if cell==0:
spikes_per_pop_3D=np.zeros_like(spikes)+(cell+exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spikes
sim_axis_per_pop=np.zeros_like(spikes)+trial+1 # for display start numbering trials from the index of 1
else:
spikes_per_pop_3D=spikes_per_pop_3D+np.zeros_like(spikes)+(cell+exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spike_times_per_pop_3D+spikes
sim_axis_per_pop=sim_axis_per_pop+np.zeros_like(spikes)+trial+1
if spike_plot_parameters[0]=="3D scatter plot":
spikes_array_per_trial_3D.append(spikes_per_pop_3D)
spike_times_array_per_trial_3D.append(spike_times_per_pop_3D)
sim_axis_array_per_trial.append(sim_axis_per_pop)
########
if (specify_targets[0]=="3D region specific") and ("subtype specific" in specify_targets):
if exp_specify[1][1]==True:
if ("randomly set target ids only once" in specify_targets ) or ("explicit list" in specify_targets):
for pop in range(0,len(target_cell_array)):
for cell in range(0,len(target_cell_array[pop])):
#create target txt file containing spike times
if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[pop][cell])):
methods.get_spike_times('Golgi_pop%d_cell%d'%(pop,target_cell_array[pop][cell]),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[pop][cell]))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
if spike_plot_parameters[0]=="2D raster plots":
if trial in spike_plot_parameters[1]:
ax_stack[pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*cell_no_array[pop]),marker='|',s=2,c=color)
if spike_plot_parameters[2]=="save all simulations to separate files":
raster_ax_array[trial][pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*cell_no_array[pop]),marker='|',s=2,c=color)
if spike_plot_parameters[0]=="3D scatter plot":
if cell==0:
spikes_per_pop_3D=np.zeros_like(spikes)+(target_cell_array[pop][cell]+ exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spikes
sim_axis_per_pop=np.zeros_like(spikes)+trial+1 # for display start numbering trials from the index of 1
else:
spikes_per_pop_3D=spikes_per_pop_3D+np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spike_times_per_pop_3D+spikes
sim_axis_per_pop=sim_axis_per_pop+np.zeros_like(spikes)+trial+1
if spike_plot_parameters[0]=="3D scatter plot":
spikes_array_per_trial_3D.append(spikes_per_pop_3D)
spike_times_array_per_trial_3D.append(spike_times_per_pop_3D)
sim_axis_array_per_trial.append(sim_axis_per_pop)
else:
for pop in range(0,len(target_cell_array[trial])):
for cell in range(0,len(target_cell_array[trial][pop])):
if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[trial][pop][cell])):
methods.get_spike_times('Golgi_pop%d_cell%d'%(pop,target_cell_array[trial][pop][cell]),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[trial][pop][cell]))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
if spike_plot_parameters[0]=="2D raster plots":
if trial in spike_plot_parameters[1]:
ax_stack[pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*cell_no_array[pop]),marker='|',s=2,c=color)
if spike_plot_parameters[2]=="save all simulations to separate files":
raster_ax_array[trial][pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*cell_no_array[pop]),marker='|',s=2,c=color)
if spike_plot_parameters[0]=="3D scatter plot":
if cell==0:
spikes_per_pop_3D=np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+ exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spikes
sim_axis_per_pop=np.zeros_like(spikes)+trial+1 # for display start numbering trials from the index of 1
else:
spikes_per_pop_3D=spikes_per_pop_3D+np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spike_times_per_pop_3D+spikes
sim_axis_per_pop=sim_axis_per_pop+np.zeros_like(spikes)+trial+1
if spike_plot_parameters[0]=="3D scatter plot":
spikes_array_per_trial_3D.append(spikes_per_pop_3D)
spike_times_array_per_trial_3D.append(spike_times_per_pop_3D)
sim_axis_array_per_trial.append(sim_axis_per_pop)
else:
for pop in range(0,len(target_cell_array[trial])):
for cell in range(0,len(target_cell_array[trial][pop])):
if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[trial][pop][cell])):
methods.get_spike_times('Golgi_pop%d_cell%d'%(pop,target_cell_array[trial][pop][cell]),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[trial][pop][cell]))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
if spike_plot_parameters[0]=="2D raster plots":
if trial in spike_plot_parameters[1]:
ax_stack[pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*cell_no_array[pop]),marker='|',s=2,c=color)
if spike_plot_parameters[2]=="save all simulations to separate files":
raster_ax_array[trial][pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*cell_no_array[pop]),marker='|',s=2,c=color)
if spike_plot_parameters[0]=="3D scatter plot":
if cell==0:
spikes_per_pop_3D=np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+ exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spikes
sim_axis_per_pop=np.zeros_like(spikes)+trial+1 # for display start numbering trials from the index of 1
else:
spikes_per_pop_3D=spikes_per_pop_3D+np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*cell_no_array[pop])
spike_times_per_pop_3D=spike_times_per_pop_3D+spikes
sim_axis_per_pop=sim_axis_per_pop+np.zeros_like(spikes)+trial+1
if spike_plot_parameters[0]=="3D scatter plot":
spikes_array_per_trial_3D.append(spikes_per_pop_3D)
spike_times_array_per_trial_3D.append(spike_times_per_pop_3D)
sim_axis_array_per_trial.append(sim_axis_per_pop)
########
if string.lower(specify_targets[0])=="subtype specific":
if specify_targets[2]=="randomly set target ids only once" or specify_targets[1]=="explicit list":
for pop in range(0,len(target_cell_array)):
for cell in range(0,len(target_cell_array[pop])):
#create target txt file containing spike times
if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[pop][cell])):
methods.get_spike_times('Golgi_pop%d_cell%d'%(pop,target_cell_array[pop][cell]),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[pop][cell]))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
if spike_plot_parameters[0]=="2D raster plots":
if trial in spike_plot_parameters[1]:
ax_stack[pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*(cell_no_array[pop]+1)),marker='|',s=2,c=color)
if spike_plot_parameters[2]=="save all simulations to separate files":
raster_ax_array[trial][pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*(cell_no_array[pop]+1)),marker='|',s=2,c=color)
if spike_plot_parameters[0]=="3D scatter plot":
if cell==0:
spikes_per_pop_3D=np.zeros_like(spikes)+(target_cell_array[pop][cell]+ exp_id*(cell_no_array[pop]+1))
spike_times_per_pop_3D=spikes
sim_axis_per_pop=np.zeros_like(spikes)+trial+1 # for display start numbering trials from the index of 1
else:
spikes_per_pop_3D=np.append(spikes_per_pop_3D,np.zeros_like(spikes)+(target_cell_array[pop][cell]+exp_id*(cell_no_array[pop]+1)))
spike_times_per_pop_3D=np.append(spike_times_per_pop_3D,spikes)
sim_axis_per_pop=np.append(sim_axis_per_pop,np.zeros_like(spikes)+trial+1)
if spike_plot_parameters[0]=="3D scatter plot":
spikes_array_per_trial_3D.append(spikes_per_pop_3D)
spike_times_array_per_trial_3D.append(spike_times_per_pop_3D)
sim_axis_array_per_trial.append(sim_axis_per_pop)
else:
for pop in range(0,len(target_cell_array[trial])):
for cell in range(0,len(target_cell_array[trial][pop]) ):
#create target txt file containing spike times
if not os.path.isfile('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[trial][pop][cell])):
methods.get_spike_times('Golgi_pop%d_cell%d'%(pop,target_cell_array[trial][pop][cell]),exp_specify[0][exp_id],trial)
spikes = np.loadtxt('%s/Golgi_pop%d_cell%d.txt'%(sim_dir,pop,target_cell_array[trial][pop][cell]))
spike_train=pyspike.SpikeTrain(spikes,[0,sim_duration])
spike_trains.append(spike_train)
if spike_plot_parameters[0]=="2D raster plots":
if trial in spike_plot_parameters[1]:
ax_stack[pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*(cell_no_array[pop]+1)),marker='|',s=2,c=color)
if spike_plot_parameters[2]=="save all simulations to separate files":
raster_ax_array[trial][pop].scatter(spikes,np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*(cell_no_array[pop]+1)),marker='|',s=2,c=color)
if spike_plot_parameters[0]=="3D scatter plot":
if cell==0:
spikes_per_pop_3D=np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+ exp_id*(cell_no_array[pop]+1))
spike_times_per_pop_3D=spikes
sim_axis_per_pop=np.zeros_like(spikes)+trial+1 # for display start numbering trials from the index of 1
else:
spikes_per_pop_3D=np.append(spikes_per_pop_3D,np.zeros_like(spikes)+(target_cell_array[trial][pop][cell]+exp_id*cell_no_array[pop]))
spike_times_per_pop_3D=np.append(spike_times_per_pop_3D,spikes)
sim_axis_per_pop=np.append(sim_axis_per_pop,np.zeros_like(spikes)+trial+1)
if spike_plot_parameters[0]=="3D scatter plot":
spikes_array_per_trial_3D.append(spikes_per_pop_3D)
spike_times_array_per_trial_3D.append(spike_times_per_pop_3D)
sim_axis_array_per_trial.append(sim_axis_per_pop)
########
if spike_plot_parameters[0]=="3D scatter plot":
spikes_3D.append(spikes_array_per_trial_3D)
spike_times_3D.append(spike_times_array_per_trial_3D)
sim_axis_array.append(sim_axis_array_per_trial)
distances.append(pyspike.spike_profile_multi(spike_trains))
########
######
if spike_plot_parameters[0]=="3D scatter plot":
for pop in range(0,no_of_groups):
population_wise_3D_spikes.append(spikes_3D[0][pop])
population_wise_3D_spike_times.append(spike_times_3D[0][pop])
population_wise_sim_axis_array.append(sim_axis_array[0][pop])
for pop in range(0,no_of_groups):
for trial in range(1,n_trials):
population_wise_3D_spikes[pop]=np.append(population_wise_3D_spikes[pop],spikes_3D[trial][pop])
population_wise_3D_spike_times[pop]=np.append(population_wise_3D_spike_times[pop],spike_times_3D[trial][pop])
population_wise_sim_axis_array[pop]=np.append(population_wise_sim_axis_array[pop],sim_axis_array[trial][pop])
for pop in range(0,no_of_groups):
ax_3D[pop].scatter(population_wise_3D_spike_times[pop],population_wise_sim_axis_array[pop],population_wise_3D_spikes[pop],marker='|',s=2,c=color)
if spike_plot_parameters[2]=="save 3D scatter plots separately":
for pop in range(0,no_of_groups):
ax_3D_rasters_only[pop].scatter(population_wise_3D_spike_times[pop],population_wise_sim_axis_array[pop],population_wise_3D_spikes[pop],marker='|',s=2,c=color)
######
# average synchrony index across trials
average_distance = distances[0]
for distance in distances[1:]:
average_distance.add(distance)
average_distance.mul_scalar(1./exp_specify[2])
# below blocks for saving synchrony and spike raster plots
mark_steps=sim_duration/50
marks=[]
for mark in range(0,mark_steps+1):
Mark=50*mark
marks.append(Mark)
xmin = 50
right_shaded_region=50
if sim_duration >=1000:
right_shaded_region=100
xmax = sim_duration-right_shaded_region
x, y = average_distance.get_plottable_data()
ximin = np.searchsorted(x, xmin)
ximax = np.searchsorted(x, xmax)
if spike_plot_parameters[0]=="2D raster plots":
lines.append(ax_stack[no_of_groups].plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
ax_stack[no_of_groups].plot(x[:ximin+1], 1-y[:ximin+1], lw=2, c=color, alpha=0.4)
ax_stack[no_of_groups].plot(x[ximax:], 1-y[ximax:], lw=2, c=color, alpha=0.4)
if spike_plot_parameters[2]=="save all simulations to separate files":
lines_sep.append(ax_sync.plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
ax_sync.plot(x[:ximin+1], 1-y[:ximin+1], lw=2, c=color, alpha=0.4)
ax_sync.plot(x[ximax:], 1-y[ximax:], lw=2, c=color, alpha=0.4)
if spike_plot_parameters[0]=="3D scatter plot":
lines.append(ax_3D[no_of_groups].plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
ax_3D[no_of_groups].plot(x[:ximin+1], 1-y[:ximin+1], lw=2, c=color, alpha=0.4)
ax_3D[no_of_groups].plot(x[ximax:], 1-y[ximax:], lw=2, c=color, alpha=0.4)
#ax_3D[no_of_groups].locator_params(axis='y', tight=True, nbins=mark_steps+1)
if spike_plot_parameters[2]=="save 3D scatter plots separately":
lines_sep.append(ax_sync.plot(x[ximin:ximax+1], 1-y[ximin:ximax+1], lw=2, c=color)[0])
ax_sync.plot(x[:ximin+1], 1-y[:ximin+1], lw=2, c=color, alpha=0.4)
ax_sync.plot(x[ximax:], 1-y[ximax:], lw=2, c=color, alpha=0.4)
#ax_sync.locator_params(axis='y', tight=True, nbins=mark_steps+1)
if spike_plot_parameters[0]=="3D scatter plot":
for pop in range(0,no_of_groups):
label_array=[]
ztick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[pop]-1))
if exp==0:
ztick_array.append(exp)
ztick_array.append(cell_no_array[pop]-1)
left_value=cell_no_array[pop]-1
else:
ztick_array.append(left_value+2)
ztick_array.append(left_value+2+cell_no_array[pop])
left_value=left_value+2+cell_no_array[pop]
print label_array
print ztick_array
ax_3D[pop].set_zticks(ztick_array)
fig_with_3D_rasters.canvas.draw()
ax_3D[pop].set_zlim(0,(cell_no_array[pop]+1)*len(general_plot_parameters[3]) )
ax_3D[pop].set_zlabel('Cell id, population %d'%pop,size=4)
ax_3D[pop].set_zticks([cell_no_array[pop]+(cell_no_array[pop]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_3D[pop].zaxis.grid(False, which='major')
ax_3D[pop].zaxis.grid(True, which='minor')
labels = [x.get_text() for x in ax_3D[pop].get_zticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_3D[pop].set_zticklabels(labels)
if pop==0:
ax_3D[pop].set_title('3D spike plots for Golgi cell populations across trials',size=6)
ax_3D[pop].set_xticks(marks)
ax_3D[pop].set_xlabel('Time (ms)')
ax_3D[pop].set_yticks(range(0,n_trials+1))
ax_3D[pop].set_ylabel('Simulation number')
for pop in range(0,no_of_groups+1):
for tick in ax_3D[pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_3D[pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
ax_3D[no_of_groups].locator_params(axis='y', tight=True, nbins=10)
ax_3D[no_of_groups].set_xlabel('Time (ms)',fontsize=6)
ax_3D[no_of_groups].set_ylabel('Synchrony index',size=4)
ax_3D[no_of_groups].set_xticks(marks)
ax_3D[no_of_groups].set_title('Synchronization between %s'%general_plot_parameters[1],size=6)
fig_with_3D_rasters.tight_layout()
fig_with_3D_rasters.subplots_adjust(top=0.80)
fig_with_3D_rasters.subplots_adjust(bottom=0.15)
fig_with_3D_rasters.subplots_adjust(hspace=.4)
l=fig_with_3D_rasters.legend(lines,general_plot_parameters[3],title=general_plot_parameters[2], loc='upper center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.55, 1.0))
plt.setp(l.get_title(),fontsize=6)
plt.setp(l.get_texts(), fontsize=6)
fig_with_3D_rasters.savefig('simulations/%s.%s'%(general_plot_parameters[0],spike_plot_parameters[3]))
if spike_plot_parameters[2]=="save 3D scatter plots separately":
for tick in ax_sync.xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_sync.yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
#ax_sync.locator_params(axis='y', tight=True, nbins=10)
ax_sync.set_xlabel('Time (ms)')
ax_sync.set_ylabel('Synchrony index: %s'%general_plot_parameters[1],size=5)
ax_sync.set_xticks(marks)
plt.tight_layout()
fig_sync.legend(lines_sep,general_plot_parameters[3],title=general_plot_parameters[2], loc='upper center',ncol=len(general_plot_parameters[3]))
fig_sync.savefig('simulations/sync_only_3D_%s.%s'%(general_plot_parameters[0],spike_plot_parameters[3]))
plt.clf()
for pop in range(0,no_of_groups):
label_array=[]
ztick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[pop]-1))
if exp==0:
ztick_array.append(exp)
ztick_array.append(cell_no_array[pop]-1)
left_value=cell_no_array[pop]-1
else:
ztick_array.append(left_value+2)
ztick_array.append(left_value+2+cell_no_array[pop])
left_value=left_value+2+cell_no_array[pop]
ax_3D_rasters_only[pop].set_zticks(ztick_array)
rasters3D_only.canvas.draw()
ax_3D_rasters_only[pop].set_zlim(0,(cell_no_array[pop]+1)*len(general_plot_parameters[3]) )
ax_3D_rasters_only[pop].set_zlabel('Cell id, population %d'%pop,size=4)
ax_3D_rasters_only[pop].set_zticks([cell_no_array[pop]+(cell_no_array[pop]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_3D_rasters_only[pop].zaxis.grid(False, which='major')
ax_3D_rasters_only[pop].zaxis.grid(True, which='minor')
labels = [x.get_text() for x in ax_3D_rasters_only[pop].get_zticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_3D_rasters_only[pop].set_zticklabels(labels)
if pop==0:
ax_3D_rasters_only[pop].set_title('3D spike plots for Golgi cell populations across trials',size=6)
ax_3D_rasters_only[pop].set_xticks(marks)
ax_3D_rasters_only[pop].set_xlabel('Time (ms)')
ax_3D_rasters_only[pop].set_yticks(range(0,n_trials+1))
ax_3D_rasters_only[pop].set_ylabel('Simulation number')
for pop in range(0,no_of_groups):
for tick in ax_3D_rasters_only[pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_3D_rasters_only[pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
rasters3D_only.subplots_adjust(top=0.80)
rasters3D_only.subplots_adjust(bottom=0.15)
rasters3D_only.subplots_adjust(hspace=.4)
l=rasters3D_only.legend(lines_sep,general_plot_parameters[3],title=general_plot_parameters[2], loc='upper center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.55, 1.0))
plt.setp(l.get_title(),fontsize=6)
plt.setp(l.get_texts(), fontsize=6)
rasters3D_only.savefig('simulations/scatter3D_only_%s.%s'%(general_plot_parameters[0],spike_plot_parameters[3]))
plt.clf()
if spike_plot_parameters[0]=="2D raster plots":
#create label array
for pop in range(0,no_of_groups):
label_array=[]
ytick_array=[]
for exp in range(0,len(general_plot_parameters[3])):
label_array.append("%d"%0)
label_array.append("%d"%(cell_no_array[pop]-1))
if exp==0:
ytick_array.append(exp)
ytick_array.append(cell_no_array[pop]-1)
left_value=cell_no_array[pop]-1
else:
ytick_array.append(left_value+2)
ytick_array.append(left_value+2+cell_no_array[pop])
left_value=left_value+2+cell_no_array[pop]
print label_array
print ytick_array
ax_stack[pop].set_yticks(ytick_array)
fig_stack.canvas.draw()
ax_stack[pop].set_ylim(0,(cell_no_array[pop]+1)*len(general_plot_parameters[3]) )
ax_stack[pop].set_ylabel('Cell id, population %d'%pop,size=4)
ax_stack[pop].set_yticks([cell_no_array[pop]+(cell_no_array[pop]+2)*k for k in range(0,len(general_plot_parameters[3]))],minor=True)
ax_stack[pop].yaxis.grid(False, which='major')
ax_stack[pop].yaxis.grid(True, which='minor')
labels = [x.get_text() for x in ax_stack[pop].get_yticklabels()]
for label in range(0,len(labels)):
labels[label] =label_array[label]
ax_stack[pop].set_yticklabels(labels)
if pop==0:
ax_stack[pop].set_title('Raster plots for Golgi cell populations (trial id=%d)'%spike_plot_parameters[1][0],size=6)
for pop in range(0,no_of_groups+1):
for tick in ax_stack[pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_stack[pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
ax_stack[no_of_groups].locator_params(axis='y', tight=True, nbins=10)
ax_stack[no_of_groups].set_xlabel('Time (ms)',fontsize=6)
ax_stack[no_of_groups].set_ylabel('Synchrony index',size=4)
ax_stack[no_of_groups].set_xticks(marks)
ax_stack[no_of_groups].set_title('Synchronization between %s'%general_plot_parameters[1],size=6)
fig_stack.tight_layout()
fig_stack.subplots_adjust(top=0.80)
fig_stack.subplots_adjust(bottom=0.15)
fig_stack.subplots_adjust(hspace=.4)
l=fig_stack.legend(lines,general_plot_parameters[3],title=general_plot_parameters[2], loc='upper center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(0.55, 1.0))
plt.setp(l.get_title(),fontsize=6)
plt.setp(l.get_texts(), fontsize=6)
fig_stack.savefig('simulations/%s.%s'%(general_plot_parameters[0],spike_plot_parameters[3]))
if spike_plot_parameters[2]=="save all simulations to separate files":
for tick in ax_sync.xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in ax_sync.yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
ax_sync.locator_params(axis='y', tight=True, nbins=10)
ax_sync.set_xlabel('Time (ms)',fontsize=6)
ax_sync.set_ylabel('Synchrony index',size=4)
ax_sync.set_xticks(marks)
ax_sync.set_title('Synchronization between %s'%general_plot_parameters[1],size=6)
plt.tight_layout()
fig_sync.legend(lines_sep,general_plot_parameters[3],title=general_plot_parameters[2], loc='upper center',ncol=len(general_plot_parameters[3]),bbox_to_anchor=(1.05, 0.55))
fig_sync.savefig('simulations/sync_only_%s.%s'%(general_plot_parameters[0],spike_plot_parameters[3]))
for trial in range(0,n_trials):
if no_of_groups >1:
for pop in range(0,no_of_groups):
raster_ax_array[trial][pop].set_xticks(marks)
raster_ax_array[trial][pop].set_yticks([cell_no_array[pop] * k for k in range(len(general_plot_parameters[3]))])
raster_ax_array[trial][pop].set_ylim(((cell_no_array[pop]*len(general_plot_parameters[3]))-1, 0))
#raster_ax_array[trial][pop].locator_params(axis='y',tight=True, nbins=len(general_plot_parameters[3]))
raster_ax_array[trial][pop].set_ylabel('Cell number, population %d'%pop,size=5)
for pop in range(0,no_of_groups):
for tick in raster_ax_array[trial][pop].xaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[4])
for tick in raster_ax_array[trial][pop].yaxis.get_major_ticks():
tick.label.set_fontsize(general_plot_parameters[5])
#plt.tight_layout()
else:
raster_ax_array[trial].set_xticks(marks)
raster_ax_array[trial].set_yticks([cell_no_array[0] * k for k in range(len(general_plot_parameters[3]))])
raster_ax_array[trial].set_ylim(((cell_no_array[0]*len(general_plot_parameters[3]))-1, 0))
#raster_ax_array[trial].locator_params(axis='y',tight=True, nbins=len(general_plot_parameters[3]))
raster_ax_array[trial].set_ylabel('Cell number, population %d'%pop,size=5)
raster_fig_array[trial].legend(lines_sep,general_plot_parameters[3],title=general_plot_parameters[2], loc='upper center',ncol=len(general_plot_parameters[3]))
raster_fig_array[trial].savefig('simulations/sim%d_rasters_%s.%s'%(trial,general_plot_parameters[0],spike_plot_parameters[3]))
plt.clf()
