def test_get_afni_design_matrix():
contrast_info = [{
"entities": {
"space": "MNI152NLin2009cAsym",
"subject": "01",
"task": "rhymejudgment",
},
"name":
"a_test",
"type":
"F",
"weights": [
{
"trial_type.pseudoword": 2,
"trial_type.word": 5
},
{
"trial_type.pseudoword": 1,
"trial_type.word": -5
},
],
}]
design = pd.DataFrame({
"trial_type.pseudoword": [11.2, 1],
"trial_type.word": [20, -1],
"noise": [7, 7],
"drift": [1, 2],
})
contrasts = prepare_contrasts(contrast_info, design.columns.tolist())
t_r = 2
stim_labels = ["trial_type.pseudoword", "trial_type.word"]
stim_labels_with_tag = ['stim_' + sl for sl in stim_labels]
test_info = create_glt_test_info(design, contrasts)
design_vals = design.to_csv(sep=" ", index=False, header=False)
cols = "; ".join(design.columns)
expected = f"""\
# <matrix
# ni_type = "{design.shape[1]}*double"
# ni_dimen = "{design.shape[0]}"
# RowTR = "{t_r}"
# GoodList = "0..{design.shape[0] - 1}"
# NRowFull = "{design.shape[0]}"
# CommandLine = "{' '.join(sys.argv)}"
# ColumnLabels = "{cols}"
# {test_info}
# Nstim = 2
# StimBots = "0; 1"
# StimTops = "0; 1"
# StimLabels = "{'; '.join(stim_labels_with_tag)}"
# >
{design_vals}
# </matrix>
"""
expected = "\n".join([x.lstrip() for x in expected.splitlines()])
assert expected == get_afni_design_matrix(design, contrasts, stim_labels,
t_r)
def test_prepare_contrasts():
contrasts = [
{
"name": "a",
"conditions": ["x", "y"],
"weights": [1, -1],
"test": "t",
"entities": {},
},
{
"name": "b",
"conditions": ["y", "z"],
"weights": [1, -1],
"test": "t",
"entities": {},
},
{
"name": "c",
"conditions": ["x", "z"],
"weights": [[1, 0], [0, 1]],
"test": "F",
"entities": {},
},
{
"name": "d",
"conditions": ["z", "y", "x"],
"weights": [-0.5, -0.5, 1],
"test": "t",
"entities": {},
},
]
all_regressors = ["x", "y", "z"]
ret = prepare_contrasts(contrasts, all_regressors)
assert len(ret) == 4
assert ret[0][::3] == ("a", "t")
assert ret[1][::3] == ("b", "t")
assert ret[2][::3] == ("c", "F")
assert ret[3][::3] == ("d", "t")
# Adding columns
assert np.array_equal(ret[0][1], np.array([[1, -1, 0]]))
assert np.array_equal(ret[1][1], np.array([[0, 1, -1]]))
assert np.array_equal(ret[2][1], np.array([[1, 0, 0], [0, 0, 1]]))
# Reordering columns
assert np.array_equal(ret[3][1], np.array([[1, -0.5, -0.5]]))
def test_create_glt_test_info():
contrast_info = [{
"entities": {
"space": "MNI152NLin2009cAsym",
"subject": "01",
"task": "rhymejudgment",
},
"name":
"a_test",
"type":
"F",
"weights": [
{
"trial_type.pseudoword": 2,
"trial_type.word": 5
},
{
"trial_type.pseudoword": 1,
"trial_type.word": -5
},
],
}]
design = pd.DataFrame({
"trial_type.pseudoword": [11.2, 1],
"trial_type.word": [20, -1],
"noise": [7, 7],
"drift": [1, 2],
})
contrasts = prepare_contrasts(contrast_info, design.columns.tolist())
expected = """
# Nglt = "1"
# GltLabels = "a_test"
# GltMatrix_000000 = "2; 4; 2; 5; 0; 0; 1; -5; 0; 0; "\
"""
assert expected == create_glt_test_info(design, contrasts)
def test_create_glt_test_info():
entities = {
"space": "MNI152NLin2009cAsym",
"subject": "01",
"task": "rhymejudgment",
}
contrast_info = [
ContrastInfo(
'a_test',
['trial_type.pseudoword', 'trial_type.word'],
[[2, 5], [1, -5]],
'F',
entities,
)
]
design = pd.DataFrame(
{
"trial_type.pseudoword": [11.2, 1],
"trial_type.word": [20, -1],
"noise": [7, 7],
"drift": [1, 2],
}
)
contrast_info = [c._asdict().copy() for c in contrast_info]
contrasts = prepare_contrasts(contrast_info, design.columns.tolist())
expected = """
# Nglt = "1"
# GltLabels = "a_test"
# GltMatrix_000000 = "2; 4; 2; 5; 0; 0; 1; -5; 0; 0; "\
"""
assert expected == create_glt_test_info(design, contrasts)