def schneider_taum_sum_stats(data):
output = []
for d in data.split_periodic(600, adjust=True):
d = d.trim(500, 600, adjust=True)
current = d['ina.i_Na']
time = d['environment.time']
# Remove constant
c0 = d['ina.i_Na'][0]
current = [(c_ - c0) for c_ in current]
def sum_of_exp(t, taum, tauh):
return ((1 - np.exp(-t / taum))**3 * np.exp(-t / tauh))
with warnings.catch_warnings():
warnings.simplefilter('error', so.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(sum_of_exp,
time,
current,
p0=[0.5, 1.],
bounds=([0., 0.], [1., 100.]))
output = output + [popt[0] / max_tm]
except:
output = output + [float('inf')]
return output
def sakakibara_iv_sum_stats(data):
output = []
for d in data.split_periodic(10000, adjust=True):
d = d.trim(9900, 10000, adjust=True)
current = d['ina.i_Na']
index = np.argmax(np.abs(current))
output = output + [current[index] / sakakibara_iv_max_peak]
return output
def schneider_iv_sum_stats(data):
output = []
for d in data.split_periodic(512, adjust=True):
d = d.trim(500, 512, adjust=True)
current = d['ina.i_Na']
index = np.argmax(np.abs(current))
output = output + [current[index] / schneider_iv_max_peak]
return output
def sakakibara_act_sum_stats(data):
output = []
for d in data.split_periodic(10000, adjust=True):
d = d.trim(9900, 10000, adjust=True)
gate = d['ina.g']
output = output + [max(gate)]
for i in range(len(output)):
output[i] = output[i] / output[-1]
return output
def sakakibara_inact_sum_stats(data):
output = []
for d in data.split_periodic(11030, adjust=True):
d = d.trim(11000, 11030, adjust=True).npview()
gate = d['ina.g']
output = output + [max(gate)]
for i in range(1, len(output)):
output[i] = output[i] / output[0]
output[0] = 1.
return output
def sakakibara_tauh_sum_stats(data):
output = []
def simple_exp(t, tauh):
return np.exp(-t / tauh)
for d in data.split_periodic(1100, adjust=True):
d = d.trim(1000, 1100, adjust=True)
current = d['ina.i_Na']
time = d['environment.time']
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]
# Keep only decay phase (i.e. not beyond when current=0)
index = np.argwhere(np.isclose(current, 0.0))
if len(index) != 0:
current = current[:index[0][0]]
time = time[:index[0][0]]
with warnings.catch_warnings():
warnings.simplefilter('error', so.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(simple_exp,
time,
current,
p0=[5],
bounds=([0.01], [20.0]))
tauh = popt[0]
output = output + [tauh / max_th]
except:
output = output + [float('inf')]
return output
def sakakibara_tauh_depol_sum_stats(data):
output = []
def simple_exp(t, tauh):
return np.exp(-t / tauh)
for d in data.split_periodic(sum(twaits) + len(twaits) * 3000,
adjust=True):
with warnings.catch_warnings():
warnings.simplefilter('error', so.OptimizeWarning)
warnings.simplefilter('error', RuntimeWarning)
try:
# Get recovery curve
rec = []
trim1, trim2, trim3 = 1000, 2000, 3000
for i, t in enumerate(twaits_split):
trace, data = d.split(t)
peak1 = max(trace.trim(trim1, trim2)['ina.i_Na'], key=abs)
peak2 = max(trace.trim(trim2 + twaits[i],
trim3 + twaits[i])['ina.i_Na'],
key=abs)
rec.append(peak2 / peak1)
# Update trim times for next iteration (not adjusting time)
trim1 += twaits[i] + 3000
trim2 += twaits[i] + 3000
trim3 += twaits[i] + 3000
# Fit double exponential to recovery curve
popt, _ = so.curve_fit(simple_exp,
twaits,
1. - np.asarray(rec),
p0=[1],
bounds=([0], [100]))
tauh = popt[0]
output = output + [tauh / max_th_depol]
except:
output = output + [float('inf')]
return output