def recovery_time(v, t=None, p=0.90):
"""
Time to ``p*100%`` recovery from first local extremum to initial value.
Intended for action potential duration and the like.
:param array_like t: Time, defaults to [0, 1, 2, ...].
Examples:
>>> recovery_time([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 5, 0])
11.8...
>>> recovery_time([10, 0, 20], [100, 101, 102])
101.45
>>> recovery_time([10, 0, 20], p=0.5)
1.25
"""
if t is None:
t = np.arange(len(v))
assert 0 <= p <= 1
ex = extrema.extrema(v)[:3] # first three extrema including initial value
# We'll interpolate in a monotone slice of (v, t)
start, end = ex.index[1], 1 + ex.index[2]
vi, ti = v[start:end], t[start:end]
vp = p * ex.value[0] + (1 - p) * ex.value[1] # recovery criterion
if ex.curv[1] < 0: # recovering from a maximum
vi = vi[::-1] # interp requires vi to be increasing,
ti = ti[::-1] # so reverse vi and ti
return np.interp(vp, vi, ti) # interpolate t as function of v [sic]
def ph2agg(ph, tol=1e-3):
"""Phenotype aggregation."""
e = extrema(ph["V"], withend=False)
peak = e[e.curv < 0]
trough = e[e.curv > 0]
if len(peak.index) >= 2:
t0, t1 = ph["t"].squeeze()[peak.index[-2:]]
period = t1 - t0
amplitude = peak.value[-1] - trough.value[-1]
if amplitude < tol:
period = amplitude = 0.0
else:
period = amplitude = 0.0
return np.rec.fromrecords([(period, amplitude)],
names=["period", "amplitude"])
