def sun_inact_kin_sum_stats(data, fast=True, slow=True):
def double_exp(t, tauh, taus, Ah, As, A0):
return A0 + Ah*np.exp(-t/tauh) + As*np.exp(-t/taus)
output_tf = []
output_ts = []
for d in data.split_periodic(11000, adjust=True, closed_intervals=False):
d = d.trim_left(10000, adjust=True)
current = d['ical.i_CaL']
time = d['engine.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]
with warnings.catch_warnings():
warnings.simplefilter('error', OptimizeWarning)
warnings.simplefilter('error', RuntimeWarning)
try:
current = [c/current[0] 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=[10,200,0.5,0.5,0],
bounds=(0.,
[np.inf, np.inf, 1.0, 1.0, 1.0]),
max_nfev=1000)
fit = [double_exp(t,popt[0],popt[1],popt[2],popt[3],popt[4]) for t in time]
# Calculate r2
ss_res = np.sum((np.array(current)-np.array(fit))**2)
ss_tot = np.sum((np.array(current)-np.mean(np.array(current)))**2)
r2 = 1 - (ss_res / ss_tot)
tauf = min(popt[0],popt[1])
taus = max(popt[0],popt[1])
if r2 > fit_threshold:
if fast:
output_tf = output_tf+[tauf]
if slow:
output_ts = output_ts+[taus]
else:
raise RuntimeWarning('scipy.optimize.curve_fit found a poor fit')
except:
if fast:
output_tf = output_tf+[float('inf')]
if slow:
output_ts = output_ts+[float('inf')]
output = output_tf+output_ts
return output
def sun_rel_inact_sum_stats(data):
output = []
for d in data.split_periodic(11000, adjust=True, closed_intervals=False):
d = d.trim_left(10000, adjust=True)
current = d['ical.i_CaL']
peak = max(current, key=abs)
ss = current[-1]
try:
output = output + [1-ss/peak]
except:
output = output + [float('inf')]
return output