def test_triu_to_mat(self):
for n in range(2, 15):
mat_src = rand_symm(n)
triu1vec = mat_utils.get_triu_off_diag_flat(mat_src)
triu1 = mat_utils.triu_off_diag_to_mat(triu1vec)
assert_true(np.all(np.triu(mat_src, k=1) == triu1))
def test_calc_model_rdm_values(self):
fp_list = [
self.fpath_in1, self.fpath_in2, self.fpath_in3, self.fpath_in4
]
mrdm = ModelRDMScaled(fp_list).apply(do_disable_tqdm=True)
mrdm = triu_off_diag_to_mat(mrdm)
mrdm += mrdm.T
for r, fp_r in enumerate(fp_list):
rdm_r = np.load(fp_r)
self.assert_rdm_shape(rdm_r)
rdm_r = get_triu_off_diag_flat(rdm_r) if rdm_r.ndim > 1 else rdm_r
for c, fp_c in enumerate(fp_list):
rdm_c = np.load(fp_c)
self.assert_rdm_shape(rdm_c)
rdm_c = get_triu_off_diag_flat(
rdm_c) if rdm_c.ndim > 1 else rdm_c
assert_equal(mrdm[r, c],
100 * (1 - spearmanr(rdm_r, rdm_c).correlation))
def test_calc_model_rdm_identical(self):
fp_list = [
self.fpath_in1, self.fpath_in2, self.fpath_in3, self.fpath_in4
]
m = ModelRDM(fp_list)
mrdm = m.apply(do_disable_tqdm=True)
mrdm = triu_off_diag_to_mat(mrdm)
for r, fp_r in enumerate(fp_list):
for c, fp_c in enumerate(fp_list):
if fp_r == fp_c:
assert_equal(mrdm[r, c], 0)
assert_equal(mrdm[c, r], 0)
elif np.all(np.load(fp_r) == np.load(fp_c)):
# print(r,c)
assert_equal(mrdm[r, c], 0)
assert_equal(mrdm[c, r], 0)
else:
# print(mrdm[r, c], r, c)
assert_true(0 <= mrdm[r, c] <= 2)
def triu_off_diag_vec_to_rdm(triu1vec):
in_rdm = triu_off_diag_to_mat(triu1vec)
in_rdm += in_rdm.T
return in_rdm