def Li_recov_40_sum_stats(data):
output = []
sub_loop = 0
d_split = data.split_periodic(tperiod_recov_40, adjust=True)
for d in d_split[:
-1]: # for no reason the last split contains no information
dcond = d.trim(tpreMeasuringList1_recov_40[sub_loop],
tpreMeasuringList1_recov_40[sub_loop] +
tMeasuring1_recov_40,
adjust=True)
dtest = d.trim_left(tpreMeasuring2_recov_40, adjust=True)
current_cond = dcond['i_caL.i_Ca_L'][:-1]
current_test = dtest['i_caL.i_Ca_L'][:-1]
index_cond = np.argmax(np.abs(current_cond))
index_test = np.argmax(np.abs(current_test))
try:
output = output + [
current_test[index_test] / current_cond[index_cond]
] # should I still normalize ?
sub_loop += 1
except:
output = output + [float('inf')]
sub_loop += 1
return output
def Li_iv_80_sum_stats(data):
output = []
for d in data.split_periodic(tperiod_iv_Li, adjust=True):
d = d.trim_left(tpreMeasuring_iv_Li, adjust=True)
current = d['i_caL.i_Ca_L'][:-1] # the last value is sometimes a nan
# (because V =0 at the end of the simulation and that I in nygren model is not defined for V = 0)
output = output + [max(current, key=abs) - current[-1]]
return output
def Li_inact_kin_80_sum_stats(data):
def double_exp(t, tauh, taus, Ah, As):
return Ah * np.exp(-t / tauh) + As * np.exp(-t / taus)
output = []
ss_list = []
for d in data.split_periodic(tperiod_kin_80_Li, adjust=True):
d = d.trim_left(tpreMeasuring_kin_80_Li, adjust=True)
#d = d.trim(tpreMeasuring_kin_80_Li,tpreMeasuring_kin_80_Li+2, adjust = True)
current = d[
'i_caL.i_Ca_L'][:
-1] # sometimes, the last value is nan and crashes the following,
# so getting rid of the last value is perhaps the solution
time = d['environment.time'][:-1]
index = np.argmax(np.abs(current))
# Set time zero to peak current
current = current[index:]
time = time[index:]
t0 = time[0]
time = [t - t0 for t in time]
with warnings.catch_warnings():
warnings.simplefilter('error', OptimizeWarning)
warnings.simplefilter('error', RuntimeWarning)
try:
imax = max(current, key=abs)
current = [c_ / imax for c_ in current]
if len(time) <= 1 or len(current) <= 1:
raise Exception('Failed simulation')
popt, _ = so.curve_fit(double_exp,
time,
current,
p0=[5, 1, 1, 1],
bounds=([0.01, 0.01, 0.01,
0.01], [100.0, 10, 10, 10]))
tauh = min(popt[0], popt[1])
taus = max(popt[0], popt[1])
output = output + [tauh]
ss_list = ss_list + [taus]
#debug
# plt.plot(time,current,time,double_exp(np.asarray(time), popt[0], popt[1], popt[2], popt[3]))
# plt.show()
except:
output = output + [float('inf')]
ss_list = ss_list + [float('inf')]
output = output + ss_list
return output
def Li_inact_150_sum_stats(data):
output = []
for d in data.split_periodic(tperiod_inact_Li_150, adjust=True):
d = d.trim_left(tpreMeasuring_inact_Li_150, adjust=True)
inact_gate = d['i_caL.G_Na_norm']
index = np.argmax(np.abs(inact_gate))
output = output + [np.abs(inact_gate[index])]
Norm = output[0]
for i in range(len(output)):
output[i] /= Norm
return output