Refactor test files

tests/test_core.py

@@ -18,42 +18,6 @@ def naive_rolling_window_dot_product(Q, T):
     return result
 
 
-def test_check_dtype_float32():
-    assert core.check_dtype(np.random.rand(10).astype(np.float32))
-
-
-def test_check_dtype_float64():
-    assert core.check_dtype(np.random.rand(10))
-
-
-def test_get_max_window_size():
-    for n in range(3, 10):
-        ref_max_m = (
-            int(
-                n
-                - math.floor(
-                    (n + (config.STUMPY_EXCL_ZONE_DENOM - 1))
-                    // (config.STUMPY_EXCL_ZONE_DENOM + 1)
-                )
-            )
-            - 1
-        )
-        cmp_max_m = core.get_max_window_size(n)
-        assert ref_max_m == cmp_max_m
-
-
-def test_check_window_size():
-    for m in range(-1, 3):
-        with pytest.raises(ValueError):
-            core.check_window_size(m)
-
-
-def test_check_max_window_size():
-    for m in range(4, 7):
-        with pytest.raises(ValueError):
-            core.check_window_size(m, max_size=3)
-
-
 def naive_compute_mean_std(T, m):
     n = T.shape[0]
 
@@ -103,6 +67,42 @@ def naive_compute_mean_std_multidimensional(T, m):
 ]
 
 
+def test_check_dtype_float32():
+    assert core.check_dtype(np.random.rand(10).astype(np.float32))
+
+
+def test_check_dtype_float64():
+    assert core.check_dtype(np.random.rand(10))
+
+
+def test_get_max_window_size():
+    for n in range(3, 10):
+        ref_max_m = (
+            int(
+                n
+                - math.floor(
+                    (n + (config.STUMPY_EXCL_ZONE_DENOM - 1))
+                    // (config.STUMPY_EXCL_ZONE_DENOM + 1)
+                )
+            )
+            - 1
+        )
+        cmp_max_m = core.get_max_window_size(n)
+        assert ref_max_m == cmp_max_m
+
+
+def test_check_window_size():
+    for m in range(-1, 3):
+        with pytest.raises(ValueError):
+            core.check_window_size(m)
+
+
+def test_check_max_window_size():
+    for m in range(4, 7):
+        with pytest.raises(ValueError):
+            core.check_window_size(m, max_size=3)
+
+
 @pytest.mark.parametrize("Q, T", test_data)
 def test_sliding_dot_product(Q, T):
     ref_mp = naive_rolling_window_dot_product(Q, T)

tests/test_motifs.py

@@ -6,18 +6,6 @@
 
 import naive
 
-test_data = [
-    (
-        np.array([0.0, 1.0, 0.0]),
-        np.array([0.0, 1.0, 0.0, -1.0, -1.0, 0.0, 1.0, 0.0, -0.5]),
-    ),
-    (
-        np.array([0.0, 1.0, 2.0]),
-        np.array([0.1, 1.0, 2.0, 3.0, -1.0, 0.1, 1.0, 2.0, -0.5]),
-    ),
-    (np.random.uniform(-1000, 1000, [8]), np.random.uniform(-1000, 1000, [64])),
-]
-
 
 def naive_match(Q, T, excl_zone, max_distance):
     m = Q.shape[0]
@@ -41,6 +29,19 @@ def naive_match(Q, T, excl_zone, max_distance):
     return np.array(result, dtype=object)
 
 
+test_data = [
+    (
+        np.array([0.0, 1.0, 0.0]),
+        np.array([0.0, 1.0, 0.0, -1.0, -1.0, 0.0, 1.0, 0.0, -0.5]),
+    ),
+    (
+        np.array([0.0, 1.0, 2.0]),
+        np.array([0.1, 1.0, 2.0, 3.0, -1.0, 0.1, 1.0, 2.0, -0.5]),
+    ),
+    (np.random.uniform(-1000, 1000, [8]), np.random.uniform(-1000, 1000, [64])),
+]
+
+
 def test_motifs_one_motif():
     # The top motif for m=3 is a [0 1 0] at indices 0, 5 and 9
     T = np.array([0.0, 1.0, 0.0, -1.0, -1.0, 0.0, 1.0, 0.0, -0.5, 2.0, 3.0, 2.0])

tests/test_scraamp.py

@@ -6,23 +6,6 @@
 import naive
 
 
-test_data = [
-    (
-        np.array([9, 8100, -60, 7], dtype=np.float64),
-        np.array([584, -11, 23, 79, 1001, 0, -19], dtype=np.float64),
-    ),
-    (
-        np.random.uniform(-1000, 1000, [8]).astype(np.float64),
-        np.random.uniform(-1000, 1000, [64]).astype(np.float64),
-    ),
-]
-
-window_size = [8, 16, 32]
-substitution_locations = [(slice(0, 0), 0, -1, slice(1, 3), [0, 3])]
-substitution_values = [np.nan, np.inf]
-percentages = [(0.01, 0.1, 1.0)]
-
-
 def naive_prescraamp(T_A, m, T_B, s, exclusion_zone=None):
     distance_matrix = naive.aamp_distance_matrix(T_A, T_B, m)
 
@@ -126,6 +109,24 @@ def naive_scraamp(T_A, m, T_B, percentage, exclusion_zone, pre_scraamp, s):
     return out
 
 
+test_data = [
+    (
+        np.array([9, 8100, -60, 7], dtype=np.float64),
+        np.array([584, -11, 23, 79, 1001, 0, -19], dtype=np.float64),
+    ),
+    (
+        np.random.uniform(-1000, 1000, [8]).astype(np.float64),
+        np.random.uniform(-1000, 1000, [64]).astype(np.float64),
+    ),
+]
+
+
+window_size = [8, 16, 32]
+substitution_locations = [(slice(0, 0), 0, -1, slice(1, 3), [0, 3])]
+substitution_values = [np.nan, np.inf]
+percentages = [(0.01, 0.1, 1.0)]
+
+
 @pytest.mark.parametrize("T_A, T_B", test_data)
 def test_prescraamp_self_join(T_A, T_B):
     m = 3

tests/test_stimp.py

@@ -8,6 +8,22 @@
 import naive
 
 
+def naive_bsf_indices(n):
+    a = np.arange(n)
+    nodes = [a.tolist()]
+    out = []
+
+    while nodes:
+        tmp = []
+        for node in nodes:
+            for n in split(node, out):
+                if n:
+                    tmp.append(n)
+        nodes = tmp
+
+    return np.array(out)
+
+
 T = [
     np.array([584, -11, 23, 79, 1001, 0, -19], dtype=np.float64),
     np.random.uniform(-1000, 1000, [64]).astype(np.float64),
@@ -29,22 +45,6 @@ def split(node, out):
     return node[:mid], node[mid + 1 :]
 
 
-def naive_bsf_indices(n):
-    a = np.arange(n)
-    nodes = [a.tolist()]
-    out = []
-
-    while nodes:
-        tmp = []
-        for node in nodes:
-            for n in split(node, out):
-                if n:
-                    tmp.append(n)
-        nodes = tmp
-
-    return np.array(out)
-
-
 @pytest.mark.parametrize("n", n)
 def test_bsf_indices(n):
     ref_bsf_indices = naive_bsf_indices(n)