[ENH] Allow string expressions for second_level_contrast of non_parametric_inference

nilearn/glm/second_level/second_level.py

@@ -274,9 +274,13 @@ def _get_con_val(second_level_contrast, design_matrix):
         else:
             raise ValueError("No second-level contrast is specified.")
     if not isinstance(second_level_contrast, str):
-        con_val = second_level_contrast
-        if np.all(con_val == 0):
-            raise ValueError("Contrast is null")
+        con_val = np.array(second_level_contrast)
+        if np.all(con_val == 0) or len(con_val) == 0:
+            raise ValueError(
+                "Contrast is null. Second_level_contrast must be a valid "
+                "contrast vector, a list/array of 0s and 1s, a string, or a "
+                "string expression."
+            )
     else:
         design_columns = design_matrix.columns.tolist()
         con_val = expression_to_contrast_vector(
@@ -285,31 +289,6 @@ def _get_con_val(second_level_contrast, design_matrix):
     return con_val
 
 
-def _get_contrast(second_level_contrast, design_matrix):
-    """Check and return contrast when testing one contrast at the time."""
-    if isinstance(second_level_contrast, str):
-        if second_level_contrast in design_matrix.columns.tolist():
-            contrast = second_level_contrast
-        else:
-            raise ValueError(
-                f'"{second_level_contrast}" is not a valid contrast name'
-            )
-    else:
-        # Check contrast definition
-        if second_level_contrast is None:
-            if design_matrix.shape[1] == 1:
-                second_level_contrast = np.ones([1])
-            else:
-                raise ValueError("No second-level contrast is specified.")
-        elif (np.nonzero(second_level_contrast)[0]).size != 1:
-            raise ValueError(
-                "second_level_contrast must be a list of 0s and 1s."
-            )
-        con_val = np.asarray(second_level_contrast, dtype=bool)
-        contrast = np.asarray(design_matrix.columns.tolist())[con_val][0]
-    return contrast
-
-
 def _infer_effect_maps(second_level_input, contrast_def):
     """Deal with the different possibilities of second_level_input."""
     # Build the design matrix X and list of imgs Y for GLM fit
@@ -970,7 +949,7 @@ def non_parametric_inference(
         )
 
     # Check and obtain the contrast
-    contrast = _get_contrast(second_level_contrast, design_matrix)
+    contrast = _get_con_val(second_level_contrast, design_matrix)
     # Get first-level effect_maps
     effect_maps = _infer_effect_maps(second_level_input, first_level_contrast)
 
@@ -981,9 +960,11 @@ def non_parametric_inference(
     var_names = design_matrix.columns.tolist()
 
     # Obtain tested_var
-    tested_var = np.asarray(design_matrix[contrast])
+    column_mask = [bool(val) for val in contrast]
+    tested_var = np.dot(design_matrix, contrast)
+
     # Remove tested var from remaining var names
-    var_names.remove(contrast)
+    var_names = [var for var, mask in zip(var_names, column_mask) if not mask]
 
     # Obtain confounding vars
     if len(var_names) == 0:

nilearn/glm/tests/test_second_level.py

@@ -29,7 +29,6 @@
     _check_input_as_first_level_model,
     _check_output_type,
     _check_second_level_input,
-    _get_contrast,
     _infer_effect_maps,
     _process_second_level_input_as_dataframe,
     _process_second_level_input_as_firstlevelmodels,
@@ -420,32 +419,6 @@ def test_check_effect_maps():
         _check_effect_maps([1, 2], np.array([[1, 2], [3, 4], [5, 6]]))
 
 
-def test_get_contrast():
-    design_matrix = pd.DataFrame([1, 2, 3], columns=["conf"])
-    assert _get_contrast("conf", design_matrix) == "conf"
-
-    design_matrix = pd.DataFrame({"conf1": [1, 2, 3], "conf2": [4, 5, 6]})
-    assert _get_contrast([0, 1], design_matrix) == "conf2"
-    assert _get_contrast([1, 0], design_matrix) == "conf1"
-
-
-def test_get_contrast_errors():
-    design_matrix = pd.DataFrame([1, 2, 3], columns=["conf"])
-    with pytest.raises(ValueError, match='"foo" is not a valid contrast name'):
-        _get_contrast("foo", design_matrix)
-
-    design_matrix = pd.DataFrame({"conf1": [1, 2, 3], "conf2": [4, 5, 6]})
-    with pytest.raises(
-        ValueError, match="No second-level contrast is specified."
-    ):
-        _get_contrast(None, design_matrix)
-    with pytest.raises(
-        ValueError,
-        match="second_level_contrast must be a list of 0s and 1s",
-    ):
-        _get_contrast([0, 0], design_matrix)
-
-
 def test_infer_effect_maps(tmp_path):
     shapes, rk = (SHAPE, (7, 8, 7, 16)), 3
     mask, fmri_data, design_matrices = write_fake_fmri_data_and_design(
@@ -1217,7 +1190,9 @@ def test_non_parametric_inference_contrast_computation(tmp_path):
     )
 
 
-@pytest.mark.parametrize("second_level_contrast", [[1, 0], "r1"])
+@pytest.mark.parametrize(
+    "second_level_contrast", [[1, 0], "r1", "r1-r2", [-1, 1]]
+)
 def test_non_parametric_inference_contrast_formula(
     tmp_path, second_level_contrast, rng
 ):
@@ -1253,7 +1228,7 @@ def test_non_parametric_inference_contrast_computation_errors(tmp_path, rng):
     # passing null contrast should give back a value error
     with pytest.raises(
         ValueError,
-        match=("second_level_contrast must be a list of 0s and 1s."),
+        match=("Second_level_contrast must be a valid"),
     ):
         non_parametric_inference(
             second_level_input=Y,
@@ -1263,7 +1238,7 @@ def test_non_parametric_inference_contrast_computation_errors(tmp_path, rng):
         )
     with pytest.raises(
         ValueError,
-        match=("second_level_contrast must be a list of 0s and 1s."),
+        match=("Second_level_contrast must be a valid"),
     ):
         non_parametric_inference(
             second_level_input=Y,