[FIX,TST] Adds unit tests for ma_pca functions and fixes a bug in ma_…

…pca (#549)

* Adds unit tests for ma_pca functions and fixes a bug in ma_pca

* Adds trivwin false check on test_check_order

* Update tedana/tests/test_mapca.py

Co-Authored-By: Taylor Salo <tsalo006@fiu.edu>

Co-authored-by: Taylor Salo <tsalo006@fiu.edu>

tedana/decomposition/ma_pca.py

@@ -356,7 +356,7 @@ def _icatb_svd(data, n_comps=None):
     """
 
     if not n_comps:
-        n_comps = np.min(data.shape[0], data.shape[1])
+        n_comps = np.min((data.shape[0], data.shape[1]))
 
     _, Lambda, vh = svd(data, full_matrices=False)
 

tedana/tests/test_mapca.py

@@ -6,6 +6,55 @@
 import nibabel as nib
 from tedana import decomposition
 from pytest import raises
+from tedana.decomposition.ma_pca import _autocorr, _check_order, _parzen_win
+from tedana.decomposition.ma_pca import _subsampling, _kurtn, _icatb_svd, _eigensp_adj
+
+
+def test_autocorr():
+    """
+    Unit test on _autocorr function
+    """
+    test_data = np.array([1, 2, 3, 4])
+    test_result = np.array([30, 20, 11, 4])
+    autocorr = _autocorr(test_data)
+    assert np.array_equal(autocorr, test_result)
+
+
+def test_check_order():
+    """
+    Unit test on _check_order function
+    """
+    test_order = -1
+    with raises(ValueError) as errorinfo:
+        ord_out, w, trivwin = _check_order(test_order)
+    assert 'Order cannot be less than zero' in str(errorinfo.value)
+
+    test_order = 0
+    ord_out, w, trivwin = _check_order(test_order)
+    assert ord_out == test_order
+    assert trivwin
+
+    test_order = 1
+    ord_out, w, trivwin = _check_order(test_order)
+    assert ord_out == test_order
+    assert w == 1
+    assert trivwin
+
+    test_order = 4
+    ord_out, w, trivwin = _check_order(test_order)
+    assert ord_out == test_order
+    assert not trivwin
+
+
+def test_parzen_win():
+    test_npoints = 3
+    test_result = np.array([0.07407407, 1, 0.07407407])
+    win = _parzen_win(test_npoints)
+    assert np.allclose(win, test_result)
+
+    test_npoints = 1
+    win = _parzen_win(test_npoints)
+    assert win == 1
 
 
 def test_ent_rate_sp():
@@ -24,6 +73,55 @@ def test_ent_rate_sp():
         ent_rate = decomposition.ent_rate_sp(test_data, 1)
     assert 'Divide by zero encountered' in str(errorinfo.value)
 
+    # Checks ValueError with incorrect matrix dimensions
+    test_data = np.ones((200, 10, 10, 200))
+    with raises(ValueError) as errorinfo:
+        ent_rate = decomposition.ent_rate_sp(test_data, 1)
+    assert 'Incorrect matrix dimensions' in str(errorinfo.value)
+
+
+def test_subsampling():
+    """
+    Unit test for subsampling function
+    """
+    test_data = np.array([1])
+    with raises(ValueError) as errorinfo:
+        sub_data = _subsampling(test_data, 1)
+    assert 'Unrecognized matrix dimension' in str(errorinfo.value)
+
+    test_data = np.random.rand(2, 3, 4)
+    sub_data = _subsampling(test_data, sub_depth=2)
+    assert sub_data.shape == (1, 2, 2)
+
+
+def test_kurtn():
+    """
+    Unit test for _kurtn function
+    """
+    test_data = np.random.rand(2, 3, 4)
+    kurt = _kurtn(test_data)
+    assert kurt.shape == (3, 1)
+
+
+def test_icatb_svd():
+    """
+    Unit test for icatb_svd function.
+    """
+    test_data = np.diag(np.random.rand(5))
+    V, Lambda = _icatb_svd(test_data)
+    assert np.allclose(np.sum(V, axis=0), np.ones((5,)))
+
+
+def test_eigensp_adj():
+    """
+    Unit test for eigensp_adj function
+    """
+    test_eigen = np.array([0.9, 0.5, 0.2, 0.1, 0])
+    n_effective = 2
+    test_result = np.array([0.13508894, 0.11653465, 0.06727316, 0.05211424, 0.])
+    lambd_adj = _eigensp_adj(test_eigen, n_effective, p=test_eigen.shape[0])
+    assert np.allclose(lambd_adj, test_result)
+
 
 def test_ma_pca():
     """