ran black v20 (#61)

tommyod · Aug 31, 2020 · 0f90bd8 · 0f90bd8
1 parent 966d13c
commit 0f90bd8
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Showing 7 changed files with 128 additions and 21 deletions.
diff --git a/KDEpy/FFTKDE.py b/KDEpy/FFTKDE.py
@@ -64,7 +64,7 @@ class FFTKDE(BaseKDE):
       London ; New York: Chapman and Hall/CRC, 1995. Pages 182-192.
     - Statsmodels implementation, at
       ``statsmodels.nonparametric.kde.KDEUnivariate``.
-      
+
     """
 
     def __init__(self, kernel="gaussian", bw=1, norm=2):

diff --git a/KDEpy/binning.py b/KDEpy/binning.py
@@ -268,7 +268,10 @@ def linbin_Ndim_python(data, grid_points, weights=None):
         # Compute integer part and fractional part for every x_i
         # Compute relation to previous grid point, and next grid point
         int_frac = (
-            ((int(coordinate), 1 - (coordinate % 1)), (int(coordinate) + 1, (coordinate % 1)),)
+            (
+                (int(coordinate), 1 - (coordinate % 1)),
+                (int(coordinate) + 1, (coordinate % 1)),
+            )
             for coordinate in observation
         )
 

diff --git a/KDEpy/tests/test_NaiveKDE.py b/KDEpy/tests/test_NaiveKDE.py
@@ -67,12 +67,41 @@ def test_additivity_with_weights(data, split_index):
 @pytest.mark.parametrize(
     "kernel, bw, n, expected_result",
     [
-        ("box", 0.1, 5, np.array([2.101278e-19, 3.469447e-18, 1.924501e00, 0.000000e00, 9.622504e-01]),),
-        ("box", 0.2, 5, np.array([3.854941e-18, 2.929755e-17, 9.622504e-01, 0.000000e00, 4.811252e-01]),),
+        (
+            "box",
+            0.1,
+            5,
+            np.array([2.101278e-19, 3.469447e-18, 1.924501e00, 0.000000e00, 9.622504e-01]),
+        ),
+        (
+            "box",
+            0.2,
+            5,
+            np.array([3.854941e-18, 2.929755e-17, 9.622504e-01, 0.000000e00, 4.811252e-01]),
+        ),
         ("box", 0.6, 3, np.array([0.1603751, 0.4811252, 0.4811252])),
         ("tri", 0.6, 3, np.array([0.1298519, 0.5098009, 0.3865535])),
-        ("epa", 0.1, 6, np.array([0.000000e00, 7.285839e-17, 2.251871e-01, 1.119926e00, 0.000000e00, 1.118034e00,]),),
-        ("biweight", 2, 5, np.array([0.1524078, 0.1655184, 0.1729870, 0.1743973, 0.1696706]),),
+        (
+            "epa",
+            0.1,
+            6,
+            np.array(
+                [
+                    0.000000e00,
+                    7.285839e-17,
+                    2.251871e-01,
+                    1.119926e00,
+                    0.000000e00,
+                    1.118034e00,
+                ]
+            ),
+        ),
+        (
+            "biweight",
+            2,
+            5,
+            np.array([0.1524078, 0.1655184, 0.1729870, 0.1743973, 0.1696706]),
+        ),
     ],
 )
 def test_against_R_density(kernel, bw, n, expected_result):
@@ -95,7 +124,19 @@ def test_against_R_density(kernel, bw, n, expected_result):
     "bw, n, expected_result",
     [
         (1, 3, np.array([0.17127129, 0.34595518, 0.30233275])),
-        (0.1, 5, np.array([2.56493684e-22, 4.97598466e-06, 2.13637668e00, 4.56012216e-04, 1.32980760e00,]),),
+        (
+            0.1,
+            5,
+            np.array(
+                [
+                    2.56493684e-22,
+                    4.97598466e-06,
+                    2.13637668e00,
+                    4.56012216e-04,
+                    1.32980760e00,
+                ]
+            ),
+        ),
         (0.01, 3, np.array([0.0, 13.29807601, 13.29807601])),
     ],
 )
@@ -124,7 +165,7 @@ def test_constant_values_silverman():
     Test that a data set with constant values does not fail when using silverman's rule.
     Tests with "almost" constant values should also get a bw assigned automatically,
     although silverman's rule technically does not do this.
-    
+
     https://github.com/tommyod/KDEpy/issues/9
     """
 

diff --git a/KDEpy/tests/test_TreeKDE.py b/KDEpy/tests/test_TreeKDE.py
@@ -66,12 +66,41 @@ def test_additivity_with_weights(data, split_index):
 @pytest.mark.parametrize(
     "kernel, bw, n, expected_result",
     [
-        ("box", 0.1, 5, np.array([2.101278e-19, 3.469447e-18, 1.924501e00, 0.000000e00, 9.622504e-01]),),
-        ("box", 0.2, 5, np.array([3.854941e-18, 2.929755e-17, 9.622504e-01, 0.000000e00, 4.811252e-01]),),
+        (
+            "box",
+            0.1,
+            5,
+            np.array([2.101278e-19, 3.469447e-18, 1.924501e00, 0.000000e00, 9.622504e-01]),
+        ),
+        (
+            "box",
+            0.2,
+            5,
+            np.array([3.854941e-18, 2.929755e-17, 9.622504e-01, 0.000000e00, 4.811252e-01]),
+        ),
         ("box", 0.6, 3, np.array([0.1603751, 0.4811252, 0.4811252])),
         ("tri", 0.6, 3, np.array([0.1298519, 0.5098009, 0.3865535])),
-        ("epa", 0.1, 6, np.array([0.000000e00, 7.285839e-17, 2.251871e-01, 1.119926e00, 0.000000e00, 1.118034e00,]),),
-        ("biweight", 2, 5, np.array([0.1524078, 0.1655184, 0.1729870, 0.1743973, 0.1696706]),),
+        (
+            "epa",
+            0.1,
+            6,
+            np.array(
+                [
+                    0.000000e00,
+                    7.285839e-17,
+                    2.251871e-01,
+                    1.119926e00,
+                    0.000000e00,
+                    1.118034e00,
+                ]
+            ),
+        ),
+        (
+            "biweight",
+            2,
+            5,
+            np.array([0.1524078, 0.1655184, 0.1729870, 0.1743973, 0.1696706]),
+        ),
     ],
 )
 def test_against_R_density(kernel, bw, n, expected_result):
@@ -92,7 +121,19 @@ def test_against_R_density(kernel, bw, n, expected_result):
     "bw, n, expected_result",
     [
         (1, 3, np.array([0.17127129, 0.34595518, 0.30233275])),
-        (0.1, 5, np.array([2.56493684e-22, 4.97598466e-06, 2.13637668e00, 4.56012216e-04, 1.32980760e00,]),),
+        (
+            0.1,
+            5,
+            np.array(
+                [
+                    2.56493684e-22,
+                    4.97598466e-06,
+                    2.13637668e00,
+                    4.56012216e-04,
+                    1.32980760e00,
+                ]
+            ),
+        ),
         (0.01, 3, np.array([0.0, 13.29807601, 13.29807601])),
     ],
 )

diff --git a/KDEpy/tests/test_binning.py b/KDEpy/tests/test_binning.py
@@ -69,7 +69,12 @@ class TestBinningFunctions:
     @pytest.mark.parametrize(
         "data, func",
         itertools.product(
-            [[1, 2, 3, 4, 5, 6], [0.04, 0.54, 0.33, 0.85, 0.16], [-4.12, 0.98, -4.3, -1.85], [0, 0, 1],],
+            [
+                [1, 2, 3, 4, 5, 6],
+                [0.04, 0.54, 0.33, 0.85, 0.16],
+                [-4.12, 0.98, -4.3, -1.85],
+                [0, 0, 1],
+            ],
             [linear_binning, linbin_Ndim_python],
         ),
     )
@@ -105,7 +110,8 @@ def test_binning_simple_examples(self, data, weights, ans):
             assert np.allclose(y, ans)
 
     @pytest.mark.parametrize(
-        "dims, use_weights, eq_grid", itertools.product([1, 2, 3, 4], [True, False], [True, False]),
+        "dims, use_weights, eq_grid",
+        itertools.product([1, 2, 3, 4], [True, False], [True, False]),
     )
     def test_cython_binning(self, dims, use_weights, eq_grid):
         """

diff --git a/KDEpy/tests/test_kernel_funcs.py b/KDEpy/tests/test_kernel_funcs.py
@@ -15,7 +15,16 @@
 
 class TestKernelHelperFunctions:
     @pytest.mark.parametrize(
-        "dim, expected", [(0, 1.25331), (1, 1), (2, 1.25331), (3, 2), (4, 3.75994), (5, 8), (6, 18.7997),],
+        "dim, expected",
+        [
+            (0, 1.25331),
+            (1, 1),
+            (2, 1.25331),
+            (3, 2),
+            (4, 3.75994),
+            (5, 8),
+            (6, 18.7997),
+        ],
     )
     def test_gauss_integral(self, dim, expected):
         """
@@ -49,7 +58,8 @@ def test_integral_unity(self, fname, function):
         assert np.isclose(integral, 1)
 
     @pytest.mark.parametrize(
-        "p, kernel_name", itertools.product([0.5, 1, 1.5, 2, 13, np.inf], ["triweight", "gaussian"]),
+        "p, kernel_name",
+        itertools.product([0.5, 1, 1.5, 2, 13, np.inf], ["triweight", "gaussian"]),
     )
     def test_integral_unity_2D_many_p_norms(self, p, kernel_name):
         """

diff --git a/KDEpy/tests/test_sorted_grid.py b/KDEpy/tests/test_sorted_grid.py
@@ -12,7 +12,7 @@ class TestGridSorted:
     def test_regression_issue_grids(self):
         """
         This test is based on Issue 15, raised by @blasern.
-        
+
         https://github.com/tommyod/KDEpy/issues/15
         """
 
@@ -42,7 +42,7 @@ def test_regression_issue_grids(self):
     def test_regression_issue_code(self):
         """
         This test is based on Issue 15, raised by @blasern. Tests the full code.
-        
+
         https://github.com/tommyod/KDEpy/issues/15
         """
         # imports
@@ -71,7 +71,10 @@ def test_on_good_grids(self):
         grid = np.array([[0], [0], [0], [1], [1], [1], [2], [2], [2]], dtype=float)
         assert grid_is_sorted(grid)
 
-        grid = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1], [2, 2]], dtype=float,)
+        grid = np.array(
+            [[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1], [2, 2]],
+            dtype=float,
+        )
         assert grid_is_sorted(grid)
 
         grid = np.array([[1, 1], [2, 2], [3, 3]], dtype=float)
@@ -300,7 +303,10 @@ def test_on_bad_grids(self):
         grid = np.array([[0], [0], [2], [1], [1], [1], [2], [2], [2]], dtype=float)
         assert not grid_is_sorted(grid)
 
-        grid = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 2], [1, 1], [2, 0], [2, 1], [2, 2]], dtype=float,)
+        grid = np.array(
+            [[0, 0], [0, 1], [0, 2], [1, 0], [1, 2], [1, 1], [2, 0], [2, 1], [2, 2]],
+            dtype=float,
+        )
         assert not grid_is_sorted(grid)
 
         grid = np.array([[1, 1], [3, 3], [2, 2]], dtype=float)