def generate_peak_clus_out(slm, I):
D = {}
D["peak"], D["clus"], D["clusid"] = peak_clus(slm,
I["thresh"],
reselspvert=I["reselspvert"],
edg=I["edg"])
return D
def dummy_test(infile, expfile):
# load input test data
ifile = open(infile, "br")
idic = pickle.load(ifile)
ifile.close()
slm = SLM(FixedEffect(1), FixedEffect(1))
slm.t = idic["t"]
slm.tri = idic["tri"]
slm.mask = idic["mask"]
slm.df = idic["df"]
slm.k = idic["k"]
slm.resl = idic["resl"]
thresh = idic["thresh"]
reselspvert = idic["reselspvert"]
edg = idic["edg"]
# call python function
P_peak, P_clus, P_clusid = peak_clus(slm, thresh, reselspvert, edg)
# load expected outout data
efile = open(expfile, "br")
expdic = pickle.load(efile)
efile.close()
O_peak = expdic["peak"]
O_clus = expdic["clus"]
O_clusid = expdic["clusid"]
testout = []
if isinstance(P_peak, dict):
for key in P_peak.keys():
comp = np.allclose(P_peak[key],
O_peak[key],
rtol=1e-05,
equal_nan=True)
testout.append(comp)
else:
comp = np.allclose(P_peak, O_peak, rtol=1e-05, equal_nan=True)
if isinstance(P_clus, dict):
for key in P_clus.keys():
comp = np.allclose(P_clus[key],
O_clus[key],
rtol=1e-05,
equal_nan=True)
else:
comp = np.allclose(P_clus, O_clus, rtol=1e-05, equal_nan=True)
testout.append(comp)
testout.append(np.allclose(P_clusid, O_clusid, rtol=1e-05, equal_nan=True))
assert all(flag == True for (flag) in testout)