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))
for key in idic.keys():
setattr(slm, key, idic[key])
resels_py, reselspvert_py, edg_py = compute_resels(slm)
out = {}
out["resels"] = resels_py
out["reselspvert"] = reselspvert_py
out["edg"] = edg_py
# load expected outout data
efile = open(expfile, "br")
expdic = pickle.load(efile)
efile.close()
testout = []
for key in out.keys():
if out[key] is not None and expdic[key] is not None:
comp = np.allclose(out[key],
expdic[key],
rtol=1e-05,
equal_nan=True)
testout.append(comp)
assert all(flag == True for (flag) in testout)
def generate_random_slm(rand_dict):
"""Generates a valid SLM for a surface.
Parameters
----------
surf : BSPolyData or a dictionary with key 'tri'
Brain surface.
Returns
-------
brainstat.stats.SLM
SLM object.
"""
# this is going to be the input slm
I = {}
rand_slm = SLM(FixedEffect(1), FixedEffect(1))
for key in rand_dict.keys():
setattr(rand_slm, key, rand_dict[key])
I[key] = rand_dict[key]
# this is going to be the output dict
O = {}
O["resels"], O["reselspvert"], O["edg"] = compute_resels(rand_slm)
return I, O