def rao_inact_sum_stats(data):
output = []
for d in data.split_periodic(6400, adjust=True):
d = d.trim(6000, 6400, adjust=True)
current = d['ical.i_CaL']
output = output + [max(current, key=abs)]
for i in range(1, len(output)):
output[i] = output[i] / output[0]
output[0] = 1.
return output
def dias_iv_sum_stats(data):
output = []
for d in data.split_periodic(5450, adjust=True):
d = d.trim(5200, 5450, adjust=True)
# ical should peak within the 250ms step
curr = d['ical.i_CaL']
index = np.argmax(np.abs(curr))
if index == len(curr) - 1:
output = output + [float('inf')]
else:
output = output + [curr[index]]
return output
def rao_taui_sum_stats(data):
out = []
def single_exp(t, tau, A):
return A * np.exp(-t / tau)
for d in data.split_periodic(5100, adjust=True):
d = d.trim(5000, 5100, adjust=True)
curr = d['ical.i_CaL']
index = np.argmax(np.abs(curr))
time = d['engine.time']
# separate decay portion
decay = curr[index:]
time = time[index:]
t0 = time[0]
time = [t - t0 for t in time]
with warnings.catch_warnings():
warnings.simplefilter('error', so.OptimizeWarning)
warnings.simplefilter('error', RuntimeWarning)
try:
# fit to single exponential
popt, _ = so.curve_fit(single_exp,
time,
decay,
p0=[50., 1.],
bounds=([0., -np.inf], np.inf))
fit = [single_exp(t, popt[0], popt[1]) for t in time]
# calculate r squared for fit
ss_res = np.sum((np.array(decay) - np.array(fit))**2)
ss_tot = np.sum(
(np.array(decay) - np.mean(np.array(decay)))**2)
r2 = 1 - (ss_res / ss_tot)
taui = popt[0]
if r2 > 0.99:
out = out + [taui]
else:
out = out + [float('inf')]
except:
out = out + [float('inf')]
return out
def dias_iv_tau_sum_stats(data):
out1 = []
out2 = []
def single_exp(t, tau, A, A0):
return A * (1 - np.exp(-t / tau)) + A0
for d in data.split_periodic(5450, adjust=True):
d = d.trim(5200, 5450, adjust=True)
curr = d['ical.i_CaL']
index = np.argmax(np.abs(curr))
out1 = out1 + [curr[index]]
if (d['membrane.V'][0] >= vsteps_tau[0]
and d['membrane.V'][0] <= vsteps_tau[-1]):
# Separate decay portion
time = d['engine.time']
decay = curr[index:]
time = time[index:]
t0 = time[0]
time = [t - t0 for t in time]
# fit to single exponential
with warnings.catch_warnings():
warnings.simplefilter('error', so.OptimizeWarning)
warnings.simplefilter('error', RuntimeWarning)
try:
popt, _ = so.curve_fit(single_exp,
time,
decay,
p0=[5., 1., 0.],
bounds=([0., -np.inf,
-np.inf], np.inf))
taui = popt[0]
out2 = out2 + [taui]
except:
out2 = out2 + [float('inf')]
return out1 + out2
def dias_iv_sum_stats(data):
output = []
for d in data.split_periodic(5450, adjust=True):
d = d.trim(5200, 5450, adjust=True)
output = output + [max(d['ical.i_CaL'], key=abs)]
return output