[ENH] Consolidate stats unit tests and fix linting errors in tests

tedana/tests/test_stats_get_coeffs.py → tedana/tests/test_stats.py

@@ -1,13 +1,80 @@
 """
-Tests for tedana.stats.get_coeffs
+Tests for the tedana stats module
 """
-
 import numpy as np
 import pytest
+import random
 
+from tedana.stats import computefeats2
 from tedana.stats import get_coeffs
+from tedana.stats import getfbounds
+
+
+"""
+Tests for computefeats2
+"""
+def test_break_computefeats2():
+    """
+    Ensure that computefeats2 fails when input data do not have the right
+    shapes.
+    """
+    n_samples, n_vols, n_comps = 10000, 100, 50
+    data = np.empty((n_samples, n_vols))
+    mmix = np.empty((n_vols, n_comps))
+    mask = np.empty((n_samples))
 
+    data = np.empty((n_samples))
+    with pytest.raises(ValueError) as e_info:
+        computefeats2(data, mmix, mask, normalize=True)
+    assert str(e_info.value) == ('Parameter data should be 2d, not {0}d'.format(data.ndim))
+
+    data = np.empty((n_samples, n_vols))
+    mmix = np.empty((n_vols))
+    with pytest.raises(ValueError) as e_info:
+        computefeats2(data, mmix, mask, normalize=True)
+    assert str(e_info.value) == ('Parameter mmix should be 2d, not {0}d'.format(mmix.ndim))
+
+    mmix = np.empty((n_vols, n_comps))
+    mask = np.empty((n_samples, n_vols))
+    with pytest.raises(ValueError) as e_info:
+        computefeats2(data, mmix, mask, normalize=True)
+    assert str(e_info.value) == ('Parameter mask should be 1d, not {0}d'.format(mask.ndim))
+
+    mask = np.empty((n_samples+1))
+    with pytest.raises(ValueError) as e_info:
+        computefeats2(data, mmix, mask, normalize=True)
+    assert str(e_info.value) == ('First dimensions (number of samples) of data ({0}) '
+                                 'and mask ({1}) do not match.'.format(data.shape[0],
+                                                                       mask.shape[0]))
+    data.shape[1] != mmix.shape[0]
+    mask = np.empty((n_samples))
+    mmix = np.empty((n_vols+1, n_comps))
+    with pytest.raises(ValueError) as e_info:
+        computefeats2(data, mmix, mask, normalize=True)
+    assert str(e_info.value) == ('Second dimensions (number of volumes) of data ({0}) '
+                                 'and mmix ({1}) do not match.'.format(data.shape[0],
+                                                                       mmix.shape[0]))
 
+
+# SMOKE TEST
+
+def test_smoke_computefeats2():
+    """
+    Ensures that computefeats2 works with random inputs and different optional parameters
+    """
+    n_samples, n_times, n_components = 100, 20, 6
+    data = np.random.random((n_samples, n_times))
+    mmix = np.random.random((n_times, n_components))  
+    mask = np.random.randint(2, size=n_samples)     
+
+    assert computefeats2(data, mmix) is not None
+    assert computefeats2(data, mmix, mask=mask) is not None
+    assert computefeats2(data, mmix, normalize=False) is not None     
+
+
+"""
+Tests for tedana.stats.get_coeffs
+"""
 def test_get_coeffs():
     """
     Check least squares coefficients.
@@ -94,3 +161,27 @@ def test_smoke_get_coeffs():
     # assert get_coeffs(data_3d, x) is not None TODO: submit an issue for the bug 
     assert get_coeffs(data_2d, x, mask=mask) is not None
     assert get_coeffs(data_2d, x, add_const=True) is not None
+
+
+"""
+Tests for tedana.stats.getfbounds
+"""
+def test_getfbounds():
+    good_inputs = range(1, 12)
+
+    for n_echos in good_inputs:
+        getfbounds(n_echos)
+
+
+# SMOKE TEST 
+
+def test_smoke_getfbounds():
+    """ 
+    Ensures that getfbounds returns outputs when fed in a random number of echos
+    """
+    n_echos = random.randint(3, 10) # At least two echos!
+    f05, f025, f01 = getfbounds(n_echos)
+
+    assert f05 is not None
+    assert f025 is not None
+    assert f01 is not None