def dummy_test(infile, expfile):
ifile = open(infile, "br")
Din = pickle.load(ifile)
ifile.close()
Y = Din["Y"]
M = Din["M"]
# Convert M to a true BrainStat model
fixed_effects = FixedEffect(M[:, Din["n_random"]:])
if Din["n_random"] != 0:
mixed_effects = MixedEffect(
M[:, :Din["n_random"]],
name_ran=["f" + str(x) for x in range(Din["n_random"])],
)
M = fixed_effects + mixed_effects
else:
M = fixed_effects
# assign slm params
slm = SLM(M, FixedEffect(1), surf=Din["surf"])
# here we go --> run the linear model
slm._linear_model(Y)
ofile = open(expfile, "br")
Dout = pickle.load(ofile)
ofile.close()
# compare...
testout = []
for k, v in Dout.items():
if k == "surf":
# Surface data is only stored for reconstruction in MATLAB.
continue
a = getattr(slm, k)
comp = np.allclose(a, v, rtol=1e-05, equal_nan=True)
testout.append(comp)
assert all(testout)
def dummy_test(infile, expfile):
# load input test data
ifile = open(infile, "br")
idic = pickle.load(ifile)
ifile.close()
model = array2effect(idic["M"], idic["n_random"])
contrast = -idic["M"][:, -1]
# run _t_test
slm = SLM(model, contrast, idic["surf"])
slm._linear_model(idic["Y"])
slm._t_test()
# load expected outout data
efile = open(expfile, "br")
expdic = pickle.load(efile)
efile.close()
testout = []
for key in expdic.keys():
if isinstance(expdic[key], dict):
slm_sub_dict = getattr(slm, key)
exp_sub_dict = expdic[key]
comp = np.all([
np.allclose(slm_sub_dict[x], exp_sub_dict[x])
for x in exp_sub_dict.keys()
])
else:
comp = np.allclose(getattr(slm, key),
expdic[key],
rtol=1e-05,
equal_nan=True)
testout.append(comp)
assert all(flag == True for (flag) in testout)