def do_sensitivity(exposure, workspace, protocol, statistic, par=None,
seed=None, model=None, changeset=None, variant=None):
"""
Sensitivity analysis.
Callback from R.
>>> r("fun <- function(func, x) func(x)")
RClosure with name <return value from eval>:
<R function>
>>> r.fun(r.sum, range(5))
array([10])
(R-style, sealed)
>>> m = Model(workspace="beeler_reuter_1977", rename=dict(
... p=dict(IstimPeriod="stim_period", IstimAmplitude="stim_amplitude",
... IstimPulseDuration="stim_duration", IstimStart="stim_start")),
... reltol=1e-10, maxsteps=1e6, chunksize=100000)
>>> m.pr.IstimStart = 0
>>> print "Result:", do_sensitivity("", "", "protocol", "apbase", ("C", "g_Na"), seed=1, model=m)
Result:...
Model runs: 6
mu mu.star sigma
C 0.03672701 0.03672701 0.05130616
g_Na -0.41200934 0.41200934 0.04219007
TODO: Make optional arguments of exposure, lower, upper, etc.
TODO: Accept json dict of model_kwargs, morris_kwargs
"""
if model is None:
m = Model(workspace, exposure, changeset, variant,
maxsteps=1e6, chunksize=1e5, reltol=1e-8)
m.pr.stim_start = 0
else:
m = model
phenotypes(m) # initialize and cache default
factors = [str(i) for i in par] # Numpy cannot handle Unicode names
if not factors:
# Default: sample within plus/minus 50% of all nonzero parameters
factors = [k for k in m.dtype.p.names if m.pr[k] != 0]
baseline = unstruct(m.pr[factors]).squeeze()
lower = 0.5 * baseline
upper = 1.5 * baseline
if seed is not None:
r.set_seed(seed)
fun = scalar_pheno(statistic, m, factors)
design = {"type": "oat", "levels": 10, "grid.jump": 5}
return r.morris(fun, factors=factors, r=2, design=design,
binf=lower, bsup=upper)
# This requires that an ipcluster is already started, see
# http://ipython.org/ipython-doc/stable/parallel/parallel_task.html#parallel-function-decorator
# rc = Client()
# lview = rc.load_balanced_view()
try:
r.library("sensitivity")
except RRuntimeError:
r.install_packages("sensitivity", repos="http://cran.us.r-project.org")
r.library("sensitivity")
class Model(ex.Bond, AttractorMixin):
pass
logging.basicConfig()
r.set_seed(20120221)
m = Model(reltol=1e-10, maxsteps=1e6, chunksize=100000)
mem = Memory("/tmp/sensitivity")
factors = [k for k in m.dtype.p.names if m.pr[k] != 0]
@mem.cache
@failwithnanlikefirst
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)
phenotypes() # initialize and cache default
"""Make a function to return a named field of the phenotype array."""
@ri.rternalize
def fun(rmatrix):
"""Scalar function for use with R's sensitivity::morris()."""
ph = np.concatenate([phenotypes(i) for i in mat2par(rmatrix)])
return py2ri(ph[field])
return fun
if __name__ == "__main__":
# Sensitivity analysis
baseline = unstruct(m.pr[factors])
lower = 0.5 * baseline
upper = 1.5 * baseline
result = dict()
for field in "appeak", "apd90", "ctpeak", "ctbase", "ctd90":
r.set_seed(20120221) # repeatable random sampling
result[field] = r.morris(scalar_pheno(field), factors=factors, r=2,
design={"type": "oat", "levels": 10, "grid.jump": 5},
binf=lower, bsup=upper)
# Print and visualize results
r.png("sensitivity.png", width=1024, height=768, pointsize=24)
r.par(mfrow=(2, 3)) # multiple figures in two rows, three columns
for k, v in result.items():
print "===================================================="
print k
print v
r.plot(v, log="y", main=k)
r.dev_off() # close image file
