def phenotypes(m, par=None):
"""
Aggregate phenotypes for sensitivity analysis.
Wrap CellML model and adjust parameter names to conform with pacing protocol
>>> m = Model(workspace="beeler_reuter_1977", rename=dict(
... p=dict(IstimPeriod="stim_period", IstimAmplitude="stim_amplitude",
... IstimPulseDuration="stim_duration")),
... reltol=1e-10, maxsteps=1e6, chunksize=100000)
>>> m.pr.IstimStart = 0
>>> print "Result:"; phenotypes(m)
Result:...
rec.array([ (115.799..., -83.857..., 31.942..., 2.287..., 0.01384...,
127.599..., 217.0..., 256.603..., 273.144..., 0.00612..., 0.000183...,
0.00630..., 92.373..., 0.0336..., 242.2..., 268.75..., 285.98...,
302.226...)],
dtype=[('apamp', '<f8'), ('apbase', '<f8'), ('appeak', '<f8'),
('apttp', '<f8'), ('apdecayrate', '<f8'), ('apd25', '<f8'),
('apd50', '<f8'), ('apd75', '<f8'), ('apd90', '<f8'), ('ctamp', '<f8'),
('ctbase', '<f8'), ('ctpeak', '<f8'), ('ctttp', '<f8'),
('ctdecayrate', '<f8'), ('ctd25', '<f8'), ('ctd50', '<f8'),
('ctd75', '<f8'), ('ctd90', '<f8')])
"""
with m.autorestore(_p=par):
m.eq(tmax=1e4, tol=1e-3)
_t, _y, stats = m.ap()
return ap_stats_array(stats)
def phenotypes(par=None):
with m.autorestore(_p=par):
m.eq(tmax=1e7, tol=1e-3)
t, y, stats = m.ap()
return ap_stats_array(stats)
def phenotypes(par=None):
"""Aggregate phenotypes for sensitivity analysis."""
with m.autorestore(_p=par):
m.eq(tmax=1e4, tol=1e-3)
_t, _y, stats = m.ap()
return ap_stats_array(stats)
