def lu_iv_sum_stats(data):
output = []
for d in data.split_periodic(5330, adjust=True):
d = d.trim(5030, 5330, adjust=True)
peak_curr = max(d['ito.i_to'], key=abs)
output = output + [peak_curr/max_peak]
return output
def yang_iv_sum_stats(data):
out_pk = []
for i, d in enumerate(data.split_periodic(5450, adjust=True)):
d = d.trim(5000, 5450, adjust=True)
peak_curr = np.max(d['ito.i_to'])
out_pk = out_pk + [peak_curr] #/max_peak]
return out_pk
def xu_ta_sum_stats(data):
output = []
def single_exp(t, tau, A):
return A*(1-np.exp(-t/tau))
for d in data.split_periodic(19500, adjust=True):
d = d.trim(15000, 19500, adjust=True)
curr = d['ito.i_to']
time = d['engine.time']
# Get rising phase of current
index = np.argmax(np.abs(curr))
curr = curr[:index+1]
time = time[:index+1]
with warnings.catch_warnings():
warnings.simplefilter('error', so.OptimizeWarning)
warnings.simplefilter('error', RuntimeWarning)
try:
if len(time) <= 1 or len(curr) <= 1:
raise Exception('failed simulation')
imax = max(curr, key=abs)
curr = [c_/imax for c_ in curr]
popt, _ = so.curve_fit(single_exp, time, curr,
p0=[10., 1.],
bounds=([0., -np.inf],
[np.inf, np.inf]))
taua = popt[0]
output = output + [taua]#/max_taua]
except:
output = output + [float('inf')]
return output
def xu_inact_sum_stats(data):
output = []
for d in data.split_periodic(25000., adjust=True):
d = d.trim(20000., 25000., adjust=True)
current = d['ito.i_to']
output = output + [max(current, key=abs)]
for i in range(1, len(output)):
output[i] = output[i]/output[0]
output[0] = 1.
return output