Fixing style in tests

.gitignore

@@ -1,3 +1,6 @@
+# version file autogenerated with pip install -e .'[all]'
+_version.py
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

mapca/tests/conftest.py

@@ -1,3 +1,5 @@
+"""Tests for mapca configuration."""
+
 import os
 from urllib.request import urlretrieve
 
@@ -6,7 +8,7 @@
 
 def fetch_file(osf_id, path, filename):
     """
-    Fetches file located on OSF and downloads to `path`/`filename`1
+    Fetch file located on OSF and downloads to `path`/`filename`1.
 
     Parameters
     ----------
@@ -25,7 +27,7 @@ def fetch_file(osf_id, path, filename):
     full_path : str
         Full path to downloaded `filename`
     """
-    url = "https://osf.io/{}/download".format(osf_id)
+    url = f"https://osf.io/{osf_id}/download"
     full_path = os.path.join(path, filename)
     if not os.path.isfile(full_path):
         urlretrieve(url, full_path)
@@ -34,35 +36,41 @@ def fetch_file(osf_id, path, filename):
 
 @pytest.fixture(scope="session")
 def testpath(tmp_path_factory):
-    """Test path that will be used to download all files"""
+    """Test path that will be used to download all files."""
     return tmp_path_factory.getbasetemp()
 
 
 @pytest.fixture
 def test_img(testpath):
+    """Fetch data file."""
     return fetch_file("jw43h", testpath, "data.nii.gz")
 
 
 @pytest.fixture
 def test_mask(testpath):
+    """Fetch mask file."""
     return fetch_file("9u2m5", testpath, "mask.nii.gz")
 
 
 @pytest.fixture
 def test_ts(testpath):
+    """Fetch comp_ts file."""
     return fetch_file("gz2hb", testpath, "comp_ts.npy")
 
 
 @pytest.fixture
 def test_varex(testpath):
+    """Fetch varex file."""
     return fetch_file("7xj5k", testpath, "varex.npy")
 
 
 @pytest.fixture
 def test_varex_norm(testpath):
+    """Fetch varex_norm file."""
     return fetch_file("jrd9c", testpath, "varex_norm.npy")
 
 
 @pytest.fixture
 def test_weights(testpath):
+    """Fetch weights file."""
     return fetch_file("t94m8", testpath, "voxel_comp_weights.npy")

mapca/tests/test_integration.py

@@ -1,6 +1,4 @@
-"""
-Integration test for mapca.
-"""
+"""Integration tests for mapca."""
 
 import shutil
 from os.path import split

mapca/tests/test_mapca.py

@@ -1,6 +1,4 @@
-"""
-Tests for mapca
-"""
+"""Tests for mapca."""
 
 import nibabel as nib
 import numpy as np
@@ -10,8 +8,7 @@
 
 
 def test_ma_pca():
-    """Check that ma_pca runs correctly with all three options"""
-
+    """Check that ma_pca runs correctly with all three options."""
     n_timepoints = 200
     n_voxels = 20
     n_vox_total = n_voxels**3
@@ -57,22 +54,21 @@ def test_ma_pca():
     assert v.shape[0] == n_timepoints
 
 
-def test_MovingAveragePCA():
+def test_moving_average_pca():
     """Check that MovingAveragePCA runs correctly with "aic" option."""
-
-    N_TIMEPOINTS = 200
-    N_VOXELS = 20  # number of voxels in each dimension
+    n_timepoints = 200
+    n_voxels = 20  # number of voxels in each dimension
 
     # Create fake data to test with
-    test_data = np.random.random((N_VOXELS, N_VOXELS, N_VOXELS, N_TIMEPOINTS))
-    time = np.linspace(0, 400, N_TIMEPOINTS)
+    test_data = np.random.random((n_voxels, n_voxels, n_voxels, n_timepoints))
+    time = np.linspace(0, 400, n_timepoints)
     freq = 1
     test_data = test_data + np.sin(2 * np.pi * freq * time)
     xform = np.eye(4) * 2
     test_img = nib.nifti1.Nifti1Image(test_data, xform)
 
     # Create mask
-    test_mask = np.zeros((N_VOXELS, N_VOXELS, N_VOXELS), dtype=int)
+    test_mask = np.zeros((n_voxels, n_voxels, n_voxels), dtype=int)
     test_mask[5:-5, 5:-5, 5:-5] = 1
     test_mask_img = nib.nifti1.Nifti1Image(test_mask, xform, dtype=np.int16)
     n_voxels_in_mask = np.sum(test_mask)
@@ -85,7 +81,7 @@ def test_MovingAveragePCA():
     assert pca.u_.shape[0] == n_voxels_in_mask
     assert pca.explained_variance_.shape[0] == 1
     assert pca.explained_variance_ratio_.shape[0] == 1
-    assert pca.components_.T.shape[0] == N_TIMEPOINTS
+    assert pca.components_.T.shape[0] == n_timepoints
 
     # Test other stuff
     pca2 = MovingAveragePCA(criterion="mdl", normalize=True)

mapca/tests/test_utils.py

@@ -10,16 +10,15 @@
 
 
 def test_autocorr():
-    """
-    Unit test on _autocorr function
-    """
+    """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_parzen_win():
+    """Test parzen gives expected output."""
     test_npoints = 3
     test_result = np.array([0.07407407, 1, 0.07407407])
     win = parzen(test_npoints)
@@ -31,9 +30,7 @@ def test_parzen_win():
 
 
 def test_ent_rate_sp():
-    """
-    Check that ent_rate_sp runs correctly, i.e. returns a float
-    """
+    """Check that ent_rate_sp runs correctly, i.e. returns a float."""
     test_data = np.random.rand(200, 10, 10)
     ent_rate = ent_rate_sp(test_data, 0)
     ent_rate = ent_rate_sp(test_data, 1)
@@ -55,9 +52,7 @@ def test_ent_rate_sp():
 
 
 def test_subsampling():
-    """
-    Unit test for subsampling function
-    """
+    """Unit test for subsampling function."""
     # 1D input
     test_data = np.array([1])
     sub_data = _subsampling(test_data, sub_depth=2)
@@ -76,18 +71,14 @@ def test_subsampling():
 
 
 def test_icatb_svd():
-    """
-    Unit test for icatb_svd function.
-    """
+    """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,)))
+    v, lambda_var = _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
-    """
+    """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, 0.0])
@@ -96,7 +87,7 @@ def test_eigensp_adj():
 
 
 def test_kurtosis():
-    # Generate data
+    """Generate data."""
     test_data = np.array([[-10, 2, 500, 0, -0.4], [-4, -200, -40, 0.1, 90]]).T
 
     # Run scipy function