def auto_plot_8(data1, data2):
g0 = data2.data_extra[2]
g1 = data2.data_extra[3]
g2 = data2.data_extra[4]
g3 = data2.data_extra[5]
f, axarr = plt.subplots(3, sharex=True, figsize=(8.5, 6.1))
time_a, res_a, min_a, max_a = analysis.periodo(data1.v_live)
time_b, res_b, min_b, max_b = analysis.periodo(data1.v_model_scaled)
res_times = []
#Recorremos elementos
for i in range(len(time_a)):
#Para cada elementos recorremos su entorno
tmp_min = 99999
for j in range(50):
if i - j >= 0 and i - j < len(time_b):
tmp = abs(time_a[i] - time_b[i - j])
if tmp < tmp_min:
tmp_min = tmp
if i + j < len(time_b):
tmp = abs(time_a[i] - time_b[i + j])
if tmp < tmp_min:
tmp_min = tmp
res_times.append(tmp_min)
axarr[0].plot(data1.time,
data1.v_model_scaled,
label="Model",
linewidth=0.4)
axarr[0].plot(data1.time, data1.v_live, label="Live", linewidth=0.4)
axarr[0].plot(time_a, res_a, 'o', linewidth=0.4)
axarr[0].plot(time_b, res_b, 'o', linewidth=0.4)
axarr[0].axhline(y=max_a, color='C1', linestyle='--', linewidth=0.2)
axarr[0].axhline(y=min_a, color='C1', linestyle='--', linewidth=0.2)
axarr[0].axhline(y=max_b, color='C2', linestyle='--', linewidth=0.2)
axarr[0].axhline(y=min_b, color='C2', linestyle='--', linewidth=0.2)
axarr[0].set_title("Voltage")
axarr[0].legend()
axarr[1].plot(data2.time, data2.data_extra[0])
axarr[1].plot(data2.time, data2.data_extra[1])
axarr[1].set_title("Conductances")
axarr[1].legend()
t2 = np.linspace(0, t[len(t) - 1], num=len(res_times))
axarr[2].plot(t2, res_times)
axarr[2].set_title("Phase")
axarr[2].legend()
plt.xlabel("Time (s)")
plt.tight_layout()
plt.show()
def save_events(data1, data2, args):
times = pa.periodo(data1.time,
data1.time_ms,
data1.v_model_scaled,
args.freq,
all_events=True)[0]
pa.events_to_file(times, 'extra/' + args.file + '_1_events_model.txt')
times = pa.periodo(data1.time,
data1.time_ms,
data1.data_in[0],
args.freq,
all_events=True)[0]
pa.events_to_file(times, 'extra/' + args.file + '_1_events_live.txt')
def plot_regularity(data1, data2, args):
t_exp = (5 + 480) * args.freq
t_post = (5 + 480 + 480) * args.freq
times = pa.periodo(data1.time[:t_exp], data1.time_ms[:t_exp],
data1.data_in[0][:t_exp], args.freq)[0]
pa.cal_coef_var(times, True)
times = pa.periodo(data1.time[t_exp:t_post], data1.time_ms[t_exp:t_post],
data1.data_in[0][t_exp:t_post], args.freq)[0]
pa.cal_coef_var(times, True)
times = pa.periodo(data1.time[t_post:], data1.time_ms[t_post:],
data1.data_in[0][t_post:], args.freq)[0]
pa.cal_coef_var(times, True)
##############################
##############################
times = pa.periodo(data1.time, data1.time_ms, data1.v_model_scaled,
args.freq)[0]
times1, res1 = pa.regularity2(times)
times = pa.periodo(data1.time, data1.time_ms, data1.data_in[0],
args.freq)[0]
times2, res2 = pa.regularity2(times)
#Crete and size
plt.figure(figsize=(12, 8))
#Plots
ax1 = plt.subplot(2, 1, 1)
plot_line_voltage(data1, data2, args)
ax2 = plt.subplot(2, 1, 2, sharex=ax1)
plt.plot(times1,
res1,
label="Model neuron period variance (%)",
linewidth=0.8)
plt.plot(times2,
res2,
label="Living neuron period variance (%)",
linewidth=0.8)
plt.ylim([0, 100])
plt.ylabel("Variance percentage")
plt.legend(loc=1, framealpha=1.0)
#Details
plt.xlabel("Time (s)")
plt.tight_layout()
plt.show()
########
# EXTRAER EVENTOS
########
'''
data1.v_model_scaled[t_ignore:t_ignore_fin] = fil.butter_filter(data1.v_model_scaled[t_ignore:t_ignore_fin], 150, args.freq)
data1.data_in[0][t_ignore:t_ignore_fin] = fil.butter_filter(data1.data_in[0][t_ignore:t_ignore_fin], 150, args.freq)
fil.threshold_filter(data1.v_model_scaled[t_ignore:t_ignore_fin], 0)
fil.threshold_filter(data1.data_in[0][t_ignore:t_ignore_fin], 0)
'''
times, model_times_ms, events, minis, maxis = pa.periodo(
data1.time[t_ignore:t_ignore_fin],
data1.time_ms[t_ignore:t_ignore_fin],
data1.v_model_scaled[t_ignore:t_ignore_fin],
args.freq,
all_events=True,
plot_on=verbose)
t_model_first, t_model_last = pa.clean_all_events(times,
events,
plot_on=verbose)
times, living_times_ms, events, minis, maxis = pa.periodo(
data1.time[t_ignore:t_ignore_fin],
data1.time_ms[t_ignore:t_ignore_fin],
data1.data_in[0][t_ignore:t_ignore_fin],
args.freq,
all_events=True,
plot_on=verbose)
t_living_first, t_living_last = pa.clean_all_events(times,