ph = np.concatenate([phenotypes(i) for i in mat2par(mat)])
result = ph["apd90"]
result[np.isnan(result)] = 0
return py2ri(result)
@ri.rternalize
def appeak(mat):
ph = np.concatenate([phenotypes(i) for i in mat2par(mat)])
result = ph["appeak"]
result[np.isnan(result)] = 0
return py2ri(result)
# mu mu.star sigma
#Cm -0.4348831 0.6603604 0.9338907
#Vmyo 0.1264869 0.1264869 0.1657938
# mu mu.star sigma
#Cm 0.6716927 0.6716927 0.3328038
#Vmyo -0.3135712 0.3135712 0.3033346
if __name__ == "__main__":
binf = 0.5 * unstruct(m.pr[factors])
bsup = 1.5 * unstruct(m.pr[factors])
r.pdf("morrisplot.pdf")
r.plot(r.morris(apd90, factors=factors, r=2, design={"type": "oat", "levels": 10, "grid.jump": 5}, binf=binf, bsup=bsup))
r.dev_off()
# saved = np.load("/tmp/saveme.npy")
# ph = [phenotypes(i) for i in saved]
# fail = [np.isnan(i).all() for i in ph]
# result = np.logical_not(fail).astype(float)
"""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