diff --git a/csaps/__init__.py b/csaps/__init__.py
index 1e507f7..650be5c 100644
--- a/csaps/__init__.py
+++ b/csaps/__init__.py
@@ -13,7 +13,6 @@
     # Shortcut
     'csaps',
     'AutoSmoothingResult',
-
     # Classes
     'ISplinePPForm',
     'ISmoothingSpline',
@@ -21,7 +20,6 @@
     'NdGridSplinePPForm',
     'CubicSmoothingSpline',
     'NdGridCubicSmoothingSpline',
-
     # Type-hints
     'UnivariateDataType',
     'MultivariateDataType',
diff --git a/csaps/_base.py b/csaps/_base.py
index d5ab3dc..b72f7e2 100644
--- a/csaps/_base.py
+++ b/csaps/_base.py
@@ -11,8 +11,7 @@
 
 
 class ISplinePPForm(abc.ABC, Generic[TData, TProps]):
-    """The interface class for spline representation in PP-form
-    """
+    """The interface class for spline representation in PP-form"""
 
     __module__ = 'csaps'
 
@@ -84,24 +83,20 @@ def shape(self) -> Tuple[int, ...]:
 
 
 class ISmoothingSpline(abc.ABC, Generic[TSpline, TSmooth, TXi, TNu, TExtrapolate]):
-    """The interface class for smooting splines
-    """
+    """The interface class for smooting splines"""
 
     __module__ = 'csaps'
 
     @property
     @abc.abstractmethod
     def smooth(self) -> TSmooth:
-        """Returns smoothing factor(s)
-        """
+        """Returns smoothing factor(s)"""
 
     @property
     @abc.abstractmethod
     def spline(self) -> TSpline:
-        """Returns spline representation in PP-form
-        """
+        """Returns spline representation in PP-form"""
 
     @abc.abstractmethod
     def __call__(self, xi: TXi, nu: Optional[TNu] = None, extrapolate: Optional[TExtrapolate] = None) -> np.ndarray:
-        """Evaluates spline on the data sites
-        """
+        """Evaluates spline on the data sites"""
diff --git a/csaps/_reshape.py b/csaps/_reshape.py
index 4479680..31ca633 100644
--- a/csaps/_reshape.py
+++ b/csaps/_reshape.py
@@ -150,7 +150,7 @@ def umv_coeffs_to_flatten(arr: np.ndarray):
         strides = arr.strides[:-3:-1]
         arr_view = as_strided(arr, shape=shape, strides=strides)
     else:  # pragma: no cover
-        raise ValueError(f"The array ndim must be 2 or 3, but given array has ndim={arr.ndim}.")
+        raise ValueError(f'The array ndim must be 2 or 3, but given array has ndim={arr.ndim}.')
 
     return arr_view
 
diff --git a/csaps/_sspndg.py b/csaps/_sspndg.py
index 32ecc8f..620eac1 100644
--- a/csaps/_sspndg.py
+++ b/csaps/_sspndg.py
@@ -62,11 +62,11 @@ def coeffs(self) -> np.ndarray:
 
     @property
     def order(self) -> Tuple[int, ...]:
-        return self.c.shape[:self.c.ndim // 2]
+        return self.c.shape[: self.c.ndim // 2]
 
     @property
     def pieces(self) -> Tuple[int, ...]:
-        return self.c.shape[self.c.ndim // 2:]
+        return self.c.shape[self.c.ndim // 2 :]
 
     @property
     def ndim(self) -> int:
@@ -112,7 +112,7 @@ def __call__(
             raise ValueError(f"'x' sequence must have length {self.ndim} according to 'breaks'")
 
         if nu is None:
-            nu = (0, ) * len(x)
+            nu = (0,) * len(x)
 
         if extrapolate is None:
             extrapolate = True
@@ -210,7 +210,6 @@ def __init__(
         smooth: Optional[Union[float, Sequence[Optional[float]]]] = None,
         normalizedsmooth: bool = False,
     ) -> None:
-
         x, y, w, s = self._prepare_data(xdata, ydata, weights, smooth)
         coeffs, smooth = self._make_spline(x, y, w, s, normalizedsmooth)
 
@@ -306,8 +305,7 @@ def _prepare_data(cls, xdata, ydata, weights, smooth):
 
         if len(smooth) != data_ndim:
             raise ValueError(
-                'Number of smoothing parameter values must '
-                f'be equal number of dimensions ({data_ndim})'
+                'Number of smoothing parameter values must ' f'be equal number of dimensions ({data_ndim})'
             )
 
         return xdata, ydata, weights, smooth
@@ -324,7 +322,7 @@ def _make_spline(xdata, ydata, weights, smooth, normalizedsmooth):
                 smooth=smooth[0],
                 normalizedsmooth=normalizedsmooth,
             )
-            return s.spline.coeffs, (s.smooth, )
+            return s.spline.coeffs, (s.smooth,)
 
         shape = ydata.shape
         coeffs = ydata
diff --git a/csaps/_sspumv.py b/csaps/_sspumv.py
index ba486aa..ea94e72 100644
--- a/csaps/_sspumv.py
+++ b/csaps/_sspumv.py
@@ -58,8 +58,7 @@ def ndim(self) -> int:
 
     @property
     def shape(self) -> Tuple[int, ...]:
-        """Returns the source data shape
-        """
+        """Returns the source data shape"""
         shape: List[int] = list(self.c.shape[2:])
         shape.insert(self.axis, self.c.shape[1] + 1)
 
@@ -124,7 +123,6 @@ def __init__(
         axis: int = -1,
         normalizedsmooth: bool = False,
     ):
-
         x, y, w, shape, axis = self._prepare_data(xdata, ydata, weights, axis)
         coeffs, smooth = self._make_spline(x, y, w, smooth, shape, normalizedsmooth)
         spline = SplinePPForm.construct_fast(coeffs, x, axis=axis)
@@ -236,7 +234,7 @@ def _compute_smooth(a, b):
         def trace(m: sp.dia_matrix):
             return m.diagonal().sum()
 
-        return 1. / (1. + trace(a) / (6. * trace(b)))
+        return 1.0 / (1.0 + trace(a) / (6.0 * trace(b)))
 
     @staticmethod
     def _normalize_smooth(x: np.ndarray, w: np.ndarray, smooth: Optional[float]):
@@ -246,8 +244,8 @@ def _normalize_smooth(x: np.ndarray, w: np.ndarray, smooth: Optional[float]):
 
         span = np.ptp(x)
 
-        eff_x = 1 + (span**2) / np.sum(np.diff(x)**2)
-        eff_w = np.sum(w)**2 / np.sum(w**2)
+        eff_x = 1 + (span**2) / np.sum(np.diff(x) ** 2)
+        eff_w = np.sum(w) ** 2 / np.sum(w**2)
         k = 80 * (span**3) * (x.size**-2) * (eff_x**-0.5) * (eff_w**-0.5)
 
         s = 0.5 if smooth is None else smooth
@@ -281,11 +279,11 @@ def _make_spline(x, y, w, smooth, shape, normalizedsmooth):
         diags_r = np.vstack((dx[1:], 2 * (dx[1:] + dx[:-1]), dx[:-1]))
         r = sp.spdiags(diags_r, [-1, 0, 1], pcount - 2, pcount - 2)
 
-        dx_recip = 1. / dx
+        dx_recip = 1.0 / dx
         diags_qtw = np.vstack((dx_recip[:-1], -(dx_recip[1:] + dx_recip[:-1]), dx_recip[1:]))
-        diags_sqrw_recip = 1. / np.sqrt(w)
+        diags_sqrw_recip = 1.0 / np.sqrt(w)
 
-        qtw = (sp.diags(diags_qtw, [0, 1, 2], (pcount - 2, pcount)) @ sp.diags(diags_sqrw_recip, 0, (pcount, pcount)))
+        qtw = sp.diags(diags_qtw, [0, 1, 2], (pcount - 2, pcount)) @ sp.diags(diags_sqrw_recip, 0, (pcount, pcount))
         qtw = qtw @ qtw.T
 
         p = smooth
@@ -295,7 +293,7 @@ def _make_spline(x, y, w, smooth, shape, normalizedsmooth):
         elif smooth is None:
             p = CubicSmoothingSpline._compute_smooth(r, qtw)
 
-        pp = (6. * (1. - p))
+        pp = 6.0 * (1.0 - p)
 
         # Solve linear system for the 2nd derivatives
         a = pp * qtw + p * r
@@ -314,15 +312,15 @@ def _make_spline(x, y, w, smooth, shape, normalizedsmooth):
         d1 = np.diff(vpad(u), axis=0) / dx
         d2 = np.diff(vpad(d1), axis=0)
 
-        diags_w_recip = 1. / w
+        diags_w_recip = 1.0 / w
         w = sp.diags(diags_w_recip, 0, (pcount, pcount))
 
         yi = y.T - (pp * w) @ d2
         pu = vpad(p * u)
 
         c1 = np.diff(pu, axis=0) / dx
-        c2 = 3. * pu[:-1, :]
-        c3 = np.diff(yi, axis=0) / dx - dx * (2. * pu[:-1, :] + pu[1:, :])
+        c2 = 3.0 * pu[:-1, :]
+        c3 = np.diff(yi, axis=0) / dx - dx * (2.0 * pu[:-1, :] + pu[1:, :])
         c4 = yi[:-1, :]
 
         c_shape = (4, pcount - 1) + shape[1:]
diff --git a/docs/conf.py b/docs/conf.py
index 393e297..3c943fb 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -25,6 +25,7 @@
 
 def _get_version():
     from csaps import __version__
+
     return __version__
 
 
@@ -54,10 +55,10 @@ def _get_version():
     'figure.autolayout': 'True',
     'figure.figsize': '5, 3.5',
     'savefig.bbox': 'tight',
-    'savefig.facecolor': "None",
+    'savefig.facecolor': 'None',
 }
 
-plot_formats = [("png", 90)]
+plot_formats = [('png', 90)]
 plot_include_source = True
 plot_html_show_source_link = False
 plot_html_show_formats = False
diff --git a/tests/conftest.py b/tests/conftest.py
index 43cd7fe..77ac540 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -18,8 +18,7 @@ class UnivariateData(NamedTuple):
 
 @pytest.fixture(scope='session')
 def univariate_data() -> UnivariateData:
-    """Univariate exponential noisy data sample
-    """
+    """Univariate exponential noisy data sample"""
 
     # yapf: disable
 
@@ -151,107 +150,218 @@ def ndgrid_2d_data() -> NdGrid2dData:
     z = np.array(
         [
             [
-                -0.153527183790132, 0.360477327564227, -0.400800187993851, -0.661751768834967, 1.39827150034968,
-                1.044246054228617, 0.069681364921588
+                -0.153527183790132,
+                0.360477327564227,
+                -0.400800187993851,
+                -0.661751768834967,
+                1.39827150034968,
+                1.044246054228617,
+                0.069681364921588,
             ],
             [
-                0.211217178485871, 0.592683030706752, 1.294599451385471, -4.924883983709012, -2.641771353280953,
-                0.245330967159293, 0.171928943618129
+                0.211217178485871,
+                0.592683030706752,
+                1.294599451385471,
+                -4.924883983709012,
+                -2.641771353280953,
+                0.245330967159293,
+                0.171928943618129,
             ],
             [
-                1.012132959440344, 0.132792505223302, -3.970096642307903, 0.702129940268655, 4.729521910567126,
-                0.208213433055832, -0.40275495492284
+                1.012132959440344,
+                0.132792505223302,
+                -3.970096642307903,
+                0.702129940268655,
+                4.729521910567126,
+                0.208213433055832,
+                -0.40275495492284,
             ],
             [
-                0.35749708646856, 2.409904780478664, 0.892801916808247, 7.563804764350773, 2.510824654279176,
-                0.317303593544217, 0.393080231785911
+                0.35749708646856,
+                2.409904780478664,
+                0.892801916808247,
+                7.563804764350773,
+                2.510824654279176,
+                0.317303593544217,
+                0.393080231785911,
             ],
             [
-                0.000706314884567, 1.009080744382149, -0.45874273220015, -0.323494125914201, -1.700362064179427,
-                -1.394209767885332, -0.645566364768713
-            ]
+                0.000706314884567,
+                1.009080744382149,
+                -0.45874273220015,
+                -0.323494125914201,
+                -1.700362064179427,
+                -1.394209767885332,
+                -0.645566364768713,
+            ],
         ]
     )
 
     zi = np.array(
         [
             [
-                -0.055377680470166, 0.195656616225213, -0.295030253111251, -0.830533929888634, 0.193176060095987,
-                0.770374649757329, 0.252865339751650
+                -0.055377680470166,
+                0.195656616225213,
+                -0.295030253111251,
+                -0.830533929888634,
+                0.193176060095987,
+                0.770374649757329,
+                0.252865339751650,
             ],
             [
-                0.471994652733459, 0.293417006151304, -0.758106516247562, -1.960431309380293, -0.781936045165379,
-                0.216341632490716, 0.180333235920312
+                0.471994652733459,
+                0.293417006151304,
+                -0.758106516247562,
+                -1.960431309380293,
+                -0.781936045165379,
+                0.216341632490716,
+                0.180333235920312,
             ],
             [
-                0.875919224697303, 0.067344259041702, -0.735889940425535, 0.882313890047783, 2.056305063365266,
-                0.896850201038262, -0.190314083560006
+                0.875919224697303,
+                0.067344259041702,
+                -0.735889940425535,
+                0.882313890047783,
+                2.056305063365266,
+                0.896850201038262,
+                -0.190314083560006,
             ],
             [
-                0.606245376082951, 0.941947682137626, 1.225331206624579, 3.379540894700002, 2.581257432070516,
-                0.581783850872262, -0.187728390603794
+                0.606245376082951,
+                0.941947682137626,
+                1.225331206624579,
+                3.379540894700002,
+                2.581257432070516,
+                0.581783850872262,
+                -0.187728390603794,
             ],
             [
-                0.183397630824828, 0.805748594104382, 0.503605325241657, 0.264260721868410, -0.874052860773297,
-                -1.188420383689933, -0.617919628357980
-            ]
+                0.183397630824828,
+                0.805748594104382,
+                0.503605325241657,
+                0.264260721868410,
+                -0.874052860773297,
+                -1.188420383689933,
+                -0.617919628357980,
+            ],
         ]
     )
 
     zi_d1 = np.array(
         [
             [
-                -0.121812472223565, -0.157326929297246, -0.828962829944406, -0.640465943383180, 0.503884652683063,
-                0.662549465069741, 0.501482011524879
+                -0.121812472223565,
+                -0.157326929297246,
+                -0.828962829944406,
+                -0.640465943383180,
+                0.503884652683063,
+                0.662549465069741,
+                0.501482011524879,
             ],
             [
-                -0.470497745190257, -0.466533613272337, 0.403062598673842, 0.767903075058928, -0.334118185393139,
-                -0.545086449738045, -0.154825057274055
+                -0.470497745190257,
+                -0.466533613272337,
+                0.403062598673842,
+                0.767903075058928,
+                -0.334118185393139,
+                -0.545086449738045,
+                -0.154825057274055,
             ],
             [
-                0.194607321700336, 0.192434581435788, 1.293210437573223, 0.562712202846881, -1.370259457802591,
-                -0.823157992133989, -0.176531696944242
+                0.194607321700336,
+                0.192434581435788,
+                1.293210437573223,
+                0.562712202846881,
+                -1.370259457802591,
+                -0.823157992133989,
+                -0.176531696944242,
             ],
             [
-                0.607431089728563, 0.510938352696447, -0.237734672001804, -1.058160086418725, -0.269494123744448,
-                0.632430110330214, 0.577443254979813
+                0.607431089728563,
+                0.510938352696447,
+                -0.237734672001804,
+                -1.058160086418725,
+                -0.269494123744448,
+                0.632430110330214,
+                0.577443254979813,
             ],
             [
-                0.006464517899505, -0.138732754726110, -1.426802191857966, -1.065472485030174, 1.029409644646945,
-                1.158453840382574, 0.696817777775121
-            ]
+                0.006464517899505,
+                -0.138732754726110,
+                -1.426802191857966,
+                -1.065472485030174,
+                1.029409644646945,
+                1.158453840382574,
+                0.696817777775121,
+            ],
         ]
     )
 
     zi_d2 = np.array(
         [
-            [0., 0., 0., 0., 0., 0., 0.],
-            [0., 0.090236774837945, 3.432579482518258, -3.029508420063717, -2.105055823406707, 1.260180219448802, 0.],
-            [0., -0.104263911255016, -2.890141730806884, -0.016560671330926, 3.251809714350141, -0.674203298932788, 0.],
-            [0., -0.111324652785139, -1.121581432777390, 3.822735995622450, -0.837099042473098, -0.929483902301833, 0.],
-            [0., 0., 0., 0., 0., 0., 0.]
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.090236774837945, 3.432579482518258, -3.029508420063717, -2.105055823406707, 1.260180219448802, 0.0],
+            [
+                0.0,
+                -0.104263911255016,
+                -2.890141730806884,
+                -0.016560671330926,
+                3.251809714350141,
+                -0.674203298932788,
+                0.0,
+            ],
+            [
+                0.0,
+                -0.111324652785139,
+                -1.121581432777390,
+                3.822735995622450,
+                -0.837099042473098,
+                -0.929483902301833,
+                0.0,
+            ],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
         ]
     )
 
     zi_ad1 = np.array(
         [
-            [0., 0., 0., 0., 0., 0., 0.],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
             [
-                0., 0.509395864624591, 0.476401125691113, -1.598603993938397, -3.760699480840437, -3.750794105947393,
-                -3.076469947082991
+                0.0,
+                0.509395864624591,
+                0.476401125691113,
+                -1.598603993938397,
+                -3.760699480840437,
+                -3.750794105947393,
+                -3.076469947082991,
             ],
             [
-                0., 1.318187655218603, 0.563946491827388, -3.255188072198998, -5.625801866969895, -4.504932326883326,
-                -3.364137445816383
+                0.0,
+                1.318187655218603,
+                0.563946491827388,
+                -3.255188072198998,
+                -5.625801866969895,
+                -4.504932326883326,
+                -3.364137445816383,
             ],
             [
-                0., 2.511070517473814, 2.161223166080562, 0.664448782633726, 3.261109214575526, 7.566477396430049,
-                9.182402413317154
+                0.0,
+                2.511070517473814,
+                2.161223166080562,
+                0.664448782633726,
+                3.261109214575526,
+                7.566477396430049,
+                9.182402413317154,
             ],
             [
-                0., 3.799550963870154, 5.127806810771760, 6.567161731071815, 12.269550101243563, 17.158134933110624,
-                18.133537415972746
-            ]
+                0.0,
+                3.799550963870154,
+                5.127806810771760,
+                6.567161731071815,
+                12.269550101243563,
+                17.158134933110624,
+                18.133537415972746,
+            ],
         ]
     )
 
@@ -268,18 +378,18 @@ class SurfaceData(NamedTuple):
 
 @pytest.fixture(scope='session')
 def surface_data() -> SurfaceData:
-    """2-d surface data sample
-    """
+    """2-d surface data sample"""
     np.random.seed(12345)
 
     xy = (np.linspace(-3.0, 3.0, 61), np.linspace(-3.5, 3.5, 51))
     i, j = np.meshgrid(*xy, indexing='ij')
 
     z = (
-        3 * (1 - j)**2. * np.exp(-(j**2) - (i + 1)**2) - 10 * (j / 5 - j**3 - i**5) * np.exp(-j**2 - i**2) -
-        1 / 3 * np.exp(-(j + 1)**2 - i**2)
+        3 * (1 - j) ** 2.0 * np.exp(-(j**2) - (i + 1) ** 2)
+        - 10 * (j / 5 - j**3 - i**5) * np.exp(-(j**2) - i**2)
+        - 1 / 3 * np.exp(-((j + 1) ** 2) - i**2)
     )
 
-    z += (np.random.randn(*z.shape) * 0.75)
+    z += np.random.randn(*z.shape) * 0.75
 
     return SurfaceData(xy, z)
diff --git a/tests/test_csaps.py b/tests/test_csaps.py
index 4808056..c33ddbb 100644
--- a/tests/test_csaps.py
+++ b/tests/test_csaps.py
@@ -8,8 +8,8 @@
 def curve():
     np.random.seed(12345)
 
-    x = np.linspace(-5., 5., 25)
-    y = np.exp(-(x / 2.5)**2) + (np.random.rand(25) - 0.2) * 0.3
+    x = np.linspace(-5.0, 5.0, 25)
+    y = np.exp(-((x / 2.5) ** 2)) + (np.random.rand(25) - 0.2) * 0.3
 
     return x, y
 
@@ -22,8 +22,9 @@ def surface():
     i, j = np.meshgrid(*x, indexing='ij')
 
     y = (
-        3 * (1 - j)**2. * np.exp(-(j**2) - (i + 1)**2) - 10 * (j / 5 - j**3 - i**5) * np.exp(-j**2 - i**2) -
-        1 / 3 * np.exp(-(j + 1)**2 - i**2)
+        3 * (1 - j) ** 2.0 * np.exp(-(j**2) - (i + 1) ** 2)
+        - 10 * (j / 5 - j**3 - i**5) * np.exp(-(j**2) - i**2)
+        - 1 / 3 * np.exp(-((j + 1) ** 2) - i**2)
     )
     y += np.random.randn(*y.shape) * 0.75
 
@@ -43,10 +44,14 @@ def data(curve, surface, request):
         return x, y, x, [0.85, 0.85], NdGridCubicSmoothingSpline
 
 
-@pytest.mark.parametrize('data', [
-    'univariate',
-    'ndgrid',
-], indirect=True)
+@pytest.mark.parametrize(
+    'data',
+    [
+        'univariate',
+        'ndgrid',
+    ],
+    indirect=True,
+)
 @pytest.mark.parametrize('tolist', [True, False])
 def test_shortcut_output(data, tolist):
     x, y, xi, smooth, sp_cls = data
@@ -76,14 +81,15 @@ def test_shortcut_output(data, tolist):
 
 
 @pytest.mark.parametrize(
-    'smooth, cls', [
+    'smooth, cls',
+    [
         (0.85, np.ndarray),
         ([0.85, 0.85], np.ndarray),
         (None, AutoSmoothingResult),
         ([None, 0.85], AutoSmoothingResult),
         ([0.85, None], AutoSmoothingResult),
         ([None, None], AutoSmoothingResult),
-    ]
+    ],
 )
 def test_shortcut_ndgrid_smooth_output(surface, smooth, cls):
     x, y = surface
@@ -99,12 +105,14 @@ def test_normalized_smooth(data, smooth, scale):
     x, y, xi, *_ = data
 
     x2 = (
-        [scale * np.array(xx, dtype=np.float64)
-         for xx in x] if isinstance(x, list) else scale * np.array(x, dtype=np.float64)
+        [scale * np.array(xx, dtype=np.float64) for xx in x]
+        if isinstance(x, list)
+        else scale * np.array(x, dtype=np.float64)
     )
     xi2 = (
-        [scale * np.array(xx, dtype=np.float64)
-         for xx in xi] if isinstance(x, list) else scale * np.array(xi, dtype=np.float64)
+        [scale * np.array(xx, dtype=np.float64) for xx in xi]
+        if isinstance(x, list)
+        else scale * np.array(xi, dtype=np.float64)
     )
 
     smoothed_data_a = csaps(x, y, xi, smooth=smooth, normalizedsmooth=True)
diff --git a/tests/test_ndg.py b/tests/test_ndg.py
index 0fab16c..3635fc5 100644
--- a/tests/test_ndg.py
+++ b/tests/test_ndg.py
@@ -6,7 +6,8 @@
 
 
 @pytest.mark.parametrize(
-    'x,y,w,p', [
+    'x,y,w,p',
+    [
         ([1, 2, 3], np.ones((10, 10)), None, None),
         ([[1], [1]], np.ones((1, 1)), None, None),
         ([[1, 2, 3], [1, 2, 3]], np.ones((4, 3)), None, None),
@@ -17,7 +18,7 @@
         ([[1, 2, 3], [1, 2, 3]], np.ones((3, 3)), None, [0.5, 0.4, 0.2]),
         (np.array([[1, 2, 3], [4, 5, 6]]), np.ones((3, 3)), None, None),
         ([np.arange(6).reshape(2, 3), np.arange(6).reshape(2, 3)], np.ones((6, 6)), None, None),
-    ]
+    ],
 )
 def test_invalid_data(x, y, w, p):
     with pytest.raises((ValueError, TypeError)):
@@ -25,12 +26,13 @@ def test_invalid_data(x, y, w, p):
 
 
 @pytest.mark.parametrize(
-    'x, xi', [
-        (([1, 2, 3], ), ([], )),
+    'x, xi',
+    [
+        (([1, 2, 3],), ([],)),
         (([1, 2, 3], [1, 2, 3]), ([1, 2], [])),
         (([1, 2, 3], [1, 2, 3], [1, 2, 3]), ([1, 2, 3], [1, 2, 3])),
         (([1, 2, 3], [1, 2, 3]), ([1, 2, 3], [1, 2, 3], [1, 2, 3])),
-    ]
+    ],
 )
 def test_invalid_evaluate_data(x, xi):
     np.random.seed(1234)
@@ -43,10 +45,11 @@ def test_invalid_evaluate_data(x, xi):
 
 
 @pytest.mark.parametrize(
-    'shape, coeffs_shape, order, pieces, ndim', [
-        ((2, ), (2, 1), (2, ), (1, ), 1),
-        ((3, ), (4, 2), (4, ), (2, ), 1),
-        ((4, ), (4, 3), (4, ), (3, ), 1),
+    'shape, coeffs_shape, order, pieces, ndim',
+    [
+        ((2,), (2, 1), (2,), (1,), 1),
+        ((3,), (4, 2), (4,), (2,), 1),
+        ((4,), (4, 3), (4,), (3,), 1),
         ((2, 4), (2, 4, 1, 3), (2, 4), (1, 3), 2),
         ((3, 4), (4, 4, 2, 3), (4, 4), (2, 3), 2),
         ((4, 4), (4, 4, 3, 3), (4, 4), (3, 3), 2),
@@ -54,7 +57,7 @@ def test_invalid_evaluate_data(x, xi):
         ((3, 4, 5), (4, 4, 4, 2, 3, 4), (4, 4, 4), (2, 3, 4), 3),
         ((2, 3, 2, 6), (2, 4, 2, 4, 1, 2, 1, 5), (2, 4, 2, 4), (1, 2, 1, 5), 4),
         ((3, 4, 5, 6), (4, 4, 4, 4, 2, 3, 4, 5), (4, 4, 4, 4), (2, 3, 4, 5), 4),
-    ]
+    ],
 )
 def test_ndsplineppform(shape, coeffs_shape, order, pieces, ndim):
     x = tuple(np.arange(s) for s in shape)
@@ -83,13 +86,14 @@ def test_surface(surface_data):
 
 
 @pytest.mark.parametrize(
-    'shape, smooth', [
-        ((4, ), 0.0),
-        ((4, ), 0.5),
-        ((4, ), 1.0),
-        ((4, ), (0.0, )),
-        ((4, ), (0.5, )),
-        ((4, ), (1.0, )),
+    'shape, smooth',
+    [
+        ((4,), 0.0),
+        ((4,), 0.5),
+        ((4,), 1.0),
+        ((4,), (0.0,)),
+        ((4,), (0.5,)),
+        ((4,), (1.0,)),
         ((4, 5), 0.0),
         ((4, 5), 0.5),
         ((4, 5), 1.0),
@@ -118,7 +122,7 @@ def test_surface(surface_data):
         ((4, 5, 6, 7), (0.0, 0.5, 0.6, 0.0)),
         ((4, 5, 6, 7), (0.0, 0.5, 0.6, 0.7)),
         ((4, 5, 6, 7), (0.4, 0.5, 0.6, 0.7)),
-    ]
+    ],
 )
 def test_smooth_factor(shape, smooth):
     x = [np.arange(s) for s in shape]
@@ -135,8 +139,9 @@ def test_smooth_factor(shape, smooth):
 
 
 @pytest.mark.parametrize(
-    'shape', [
-        (2, ),
+    'shape',
+    [
+        (2,),
         (2, 3),
         (2, 2),
         (2, 3, 4),
@@ -151,7 +156,7 @@ def test_smooth_factor(shape, smooth):
         (2, 2, 2, 3, 4),
         (2, 2, 2, 2, 3),
         (2, 2, 2, 2, 2),
-    ]
+    ],
 )
 def test_nd_2pt_array(shape: tuple):
     xdata = [np.arange(s) for s in shape]
@@ -165,9 +170,10 @@ def test_nd_2pt_array(shape: tuple):
 
 
 @pytest.mark.parametrize(
-    'shape', [
-        (2, ),
-        (3, ),
+    'shape',
+    [
+        (2,),
+        (3,),
         (2, 3),
         (3, 4),
         (3, 2, 4),
@@ -176,7 +182,7 @@ def test_nd_2pt_array(shape: tuple):
         (3, 4, 5, 6),
         (3, 2, 2, 6, 2),
         (3, 4, 5, 6, 7),
-    ]
+    ],
 )
 def test_nd_array(shape: tuple):
     xdata = [np.arange(s) for s in shape]
@@ -201,17 +207,23 @@ def test_auto_smooth_2d(ndgrid_2d_data):
     assert zi == pytest.approx(zi_expected)
 
 
-@pytest.mark.parametrize('nu', [
-    None,
-    (0, 0),
-    (1, 1),
-    (2, 2),
-])
-@pytest.mark.parametrize('extrapolate', [
-    None,
-    True,
-    False,
-])
+@pytest.mark.parametrize(
+    'nu',
+    [
+        None,
+        (0, 0),
+        (1, 1),
+        (2, 2),
+    ],
+)
+@pytest.mark.parametrize(
+    'extrapolate',
+    [
+        None,
+        True,
+        False,
+    ],
+)
 def test_evaluate_nu_extrapolate(nu: tuple, extrapolate: bool):
     x = ([1, 2, 3, 4], [1, 2, 3, 4])
     xi = ([0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5])
diff --git a/tests/test_reshape.py b/tests/test_reshape.py
index 5a1f71e..059bbfb 100644
--- a/tests/test_reshape.py
+++ b/tests/test_reshape.py
@@ -10,7 +10,8 @@
 
 
 @pytest.mark.parametrize(
-    'shape_canonical, shape_flatten, pieces', [
+    'shape_canonical, shape_flatten, pieces',
+    [
         ((2, 1), (1, 2), 1),
         ((3, 6), (1, 18), 6),
         ((4, 3), (1, 12), 3),
@@ -18,7 +19,7 @@
         ((4, 5, 2), (2, 20), 5),
         ((4, 6, 3), (3, 24), 6),
         ((4, 120, 53), (53, 480), 120),
-    ]
+    ],
 )
 def test_umv_coeffs_reshape(shape_canonical: tuple, shape_flatten: tuple, pieces: int):
     np.random.seed(1234)
@@ -35,33 +36,28 @@ def test_umv_coeffs_reshape(shape_canonical: tuple, shape_flatten: tuple, pieces
     'shape_canonical, shape_flatten, pieces',
     [
         # 1-d 2-ordered
-        ((2, 3), (2, 3), (3, )),
-        ((2, 4), (2, 4), (4, )),
-        ((2, 5), (2, 5), (5, )),
-
+        ((2, 3), (2, 3), (3,)),
+        ((2, 4), (2, 4), (4,)),
+        ((2, 5), (2, 5), (5,)),
         # 1-d 3-ordered
-        ((3, 3), (3, 3), (3, )),
-        ((3, 4), (3, 4), (4, )),
-        ((3, 5), (3, 5), (5, )),
-
+        ((3, 3), (3, 3), (3,)),
+        ((3, 4), (3, 4), (4,)),
+        ((3, 5), (3, 5), (5,)),
         # 1-d 4-ordered
-        ((4, 3), (4, 3), (3, )),
-        ((4, 4), (4, 4), (4, )),
-        ((4, 5), (4, 5), (5, )),
-
+        ((4, 3), (4, 3), (3,)),
+        ((4, 4), (4, 4), (4,)),
+        ((4, 5), (4, 5), (5,)),
         # 2-d {2,4}-ordered
         ((2, 4, 3, 4), (6, 16), (3, 4)),
         ((4, 2, 3, 3), (12, 6), (3, 3)),
         ((4, 2, 4, 3), (16, 6), (4, 3)),
         ((2, 4, 4, 4), (8, 16), (4, 4)),
-
         # 2-d {4,4}-ordered
         ((4, 4, 3, 3), (12, 12), (3, 3)),
-
         # 3-d {4,4,4}-ordered
         ((4, 4, 4, 3, 3, 3), (12, 12, 12), (3, 3, 3)),
         ((4, 4, 4, 3, 5, 7), (12, 20, 28), (3, 5, 7)),
-    ]
+    ],
 )
 def test_ndg_coeffs_reshape(shape_canonical: tuple, shape_flatten: tuple, pieces: tuple):
     np.random.seed(1234)
diff --git a/tests/test_umv.py b/tests/test_umv.py
index 07346f6..f491c99 100644
--- a/tests/test_umv.py
+++ b/tests/test_umv.py
@@ -8,14 +8,20 @@
 
 
 @pytest.mark.parametrize(
-    'x,y,w', [
-        ([1], [2], None), ([1, 2, 3], [1, 2], None), ([(1, 2, 3), (1, 2, 3)], [1, 2, 3], None),
-        ([1, 2, 3], [1, 2, 3], [1, 1]), ([1, 2, 3], [1, 2, 3], [1, 1, 1, 1]),
-        ([1, 2, 3], [1, 2, 3], [(1, 1, 1), (1, 1, 1)]), ([1, 2, 3], [(1, 2, 3, 4), (1, 2, 3, 4)], None),
-        ([1, 2, 3], np.ones((2, 4, 5)), None), ([1, 2, 3], np.ones((2, 4, 3)), np.ones((2, 4, 4))),
+    'x,y,w',
+    [
+        ([1], [2], None),
+        ([1, 2, 3], [1, 2], None),
+        ([(1, 2, 3), (1, 2, 3)], [1, 2, 3], None),
+        ([1, 2, 3], [1, 2, 3], [1, 1]),
+        ([1, 2, 3], [1, 2, 3], [1, 1, 1, 1]),
+        ([1, 2, 3], [1, 2, 3], [(1, 1, 1), (1, 1, 1)]),
+        ([1, 2, 3], [(1, 2, 3, 4), (1, 2, 3, 4)], None),
+        ([1, 2, 3], np.ones((2, 4, 5)), None),
+        ([1, 2, 3], np.ones((2, 4, 3)), np.ones((2, 4, 4))),
         ([1, 2, 3], [(1, 2, 3), (1, 2, 3)], [(1, 1, 1, 1), (1, 1, 1, 1)]),
-        ([1, 2, 3], [(1, 2, 3), (1, 2, 3)], [(1, 1, 1), (1, 1, 1), (1, 1, 1)])
-    ]
+        ([1, 2, 3], [(1, 2, 3), (1, 2, 3)], [(1, 1, 1), (1, 1, 1), (1, 1, 1)]),
+    ],
 )
 def test_invalid_data(x, y, w):
     with pytest.raises(ValueError):
@@ -27,73 +33,55 @@ def test_invalid_data(x, y, w):
     [
         # 1D (2, )
         [2, 4],
-
         # 2D (2, 2)
         [(2, 4), (3, 5)],
-
         # 2D (3, 2)
         [(2, 4), (3, 5), (4, 6)],
-
         # 3D (2, 2, 2)
         [[(1, 2), (3, 4)], [(5, 6), (7, 8)]],
-
         # 1D (3, )
         [2, 4, 6],
-
         # 2D (2, 5)
         [(1, 2, 3, 4, 5), (3, 4, 5, 6, 7)],
-
         # 2D (3, 3)
         [(2, 4, 6), (3, 5, 7), (4, 6, 8)],
-
         # 3D (2, 2, 3)
         [[(2, 4, 6), (3, 5, 7)], [(2, 4, 6), (3, 5, 7)]],
-
         # 1D (4, )
         [2, 4, 6, 8],
-
         # 2D (2, 4)
         [(2, 4, 6, 8), (3, 5, 7, 9)],
-
         # 2D (3, 4)
         [(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)],
-
         # 3D (2, 2, 4)
         [[(2, 4, 6, 8), (3, 5, 7, 9)], [(2, 4, 6, 8), (3, 5, 7, 9)]],
-
         # 3D (2, 3, 4)
         [[(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)], [(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)]],
-
         # 3D (3, 2, 4)
         [[(2, 4, 6, 8), (3, 5, 7, 9)], [(2, 4, 6, 8), (3, 5, 7, 9)], [(2, 4, 6, 8), (3, 5, 7, 9)]],
-
         # 3D (3, 3, 4)
         [
             [(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)],
             [(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)],
             [(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)],
         ],
-
         # 4D (2, 2, 2, 4)
         [
             [[(2, 4, 6, 8), (3, 5, 7, 9)], [(2, 4, 6, 8), (3, 5, 7, 9)]],
             [[(2, 4, 6, 8), (3, 5, 7, 9)], [(2, 4, 6, 8), (3, 5, 7, 9)]],
         ],
-
         # 4D (3, 2, 2, 4)
         [
             [[(2, 4, 6, 8), (3, 5, 7, 9)], [(2, 4, 6, 8), (3, 5, 7, 9)]],
             [[(2, 4, 6, 8), (3, 5, 7, 9)], [(2, 4, 6, 8), (3, 5, 7, 9)]],
             [[(2, 4, 6, 8), (3, 5, 7, 9)], [(2, 4, 6, 8), (3, 5, 7, 9)]],
         ],
-
         # 4D (3, 2, 3, 4)
         [
             [[(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)], [(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)]],
             [[(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)], [(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)]],
             [[(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)], [(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)]],
         ],
-
         # 4D (3, 3, 3, 4)
         [
             [
@@ -112,7 +100,7 @@ def test_invalid_data(x, y, w):
                 [(2, 4, 6, 8), (3, 5, 7, 9), (4, 6, 8, 10)],
             ],
         ],
-    ]
+    ],
 )
 def test_vectorize(y):
     x = np.arange(np.array(y).shape[-1])
@@ -128,7 +116,7 @@ def test_vectorize(y):
             product,
             [
                 # shape
-                [(2, ), (4, )],
+                [(2,), (4,)],
                 permutations((2, 4), 2),
                 permutations((3, 4, 5), 3),
                 permutations((3, 4, 5, 6), 4),
@@ -139,9 +127,9 @@ def test_vectorize(y):
                 range(-2, 2),
                 range(-3, 3),
                 range(-4, 4),
-            ]
+            ],
         )
-    )
+    ),
 )
 def test_axis(shape, axis):
     y = np.arange(int(np.prod(shape))).reshape(shape)
@@ -157,7 +145,7 @@ def test_axis(shape, axis):
     ndim = int(np.prod(shape)) // shape[axis]
     order = 2 if len(x) < 3 else 4
     pieces = len(x) - 1
-    coeffs_shape = (order, pieces) + shape[:axis] + shape[axis + 1:]
+    coeffs_shape = (order, pieces) + shape[:axis] + shape[axis + 1 :]
 
     assert ss.breaks == pytest.approx(x)
     assert ss.coeffs.shape == coeffs_shape
@@ -169,12 +157,12 @@ def test_axis(shape, axis):
 
 
 def test_zero_smooth():
-    x = [1., 2., 4., 6.]
-    y = [2., 4., 5., 7.]
+    x = [1.0, 2.0, 4.0, 6.0]
+    y = [2.0, 4.0, 5.0, 7.0]
 
-    sp = csaps.CubicSmoothingSpline(x, y, smooth=0.)
+    sp = csaps.CubicSmoothingSpline(x, y, smooth=0.0)
 
-    assert sp.smooth == pytest.approx(0.)
+    assert sp.smooth == pytest.approx(0.0)
 
     ys = sp(x)
 
@@ -192,16 +180,28 @@ def test_auto_smooth(univariate_data):
 
 
 @pytest.mark.parametrize(
-    'x,y,xi,yid', [
-        ([1., 2.], [3., 4.], [1., 1.5, 2.], [3., 3.5, 4.]),
-        ([1., 2., 3.], [3., 4., 5.], [1., 1.5, 2., 2.5, 3.], [3., 3.5, 4., 4.5, 5.]),
+    'x,y,xi,yid',
+    [
+        ([1.0, 2.0], [3.0, 4.0], [1.0, 1.5, 2.0], [3.0, 3.5, 4.0]),
+        ([1.0, 2.0, 3.0], [3.0, 4.0, 5.0], [1.0, 1.5, 2.0, 2.5, 3.0], [3.0, 3.5, 4.0, 4.5, 5.0]),
         (
-            [1., 2., 4., 6.], [2., 4., 5., 7.], np.linspace(1., 6., 10), [
-                2.2579392157892, 3.0231172855707, 3.6937304019483, 4.21971044584031, 4.65026761247821, 5.04804510368134,
-                5.47288175793241, 5.94265482897362, 6.44293945952166, 6.95847986982311
-            ]
+            [1.0, 2.0, 4.0, 6.0],
+            [2.0, 4.0, 5.0, 7.0],
+            np.linspace(1.0, 6.0, 10),
+            [
+                2.2579392157892,
+                3.0231172855707,
+                3.6937304019483,
+                4.21971044584031,
+                4.65026761247821,
+                5.04804510368134,
+                5.47288175793241,
+                5.94265482897362,
+                6.44293945952166,
+                6.95847986982311,
+            ],
         ),
-    ]
+    ],
 )
 def test_npoints(x, y, xi, yid):
     sp = csaps.CubicSmoothingSpline(x, y)
@@ -211,19 +211,29 @@ def test_npoints(x, y, xi, yid):
 
 
 @pytest.mark.parametrize(
-    'w,yid', [
+    'w,yid',
+    [
         (
-            [0.5, 1, 0.7, 1.2], [
-                2.39572102230177, 3.13781163365086, 3.78568993197139, 4.28992448591238, 4.7009959256016,
-                5.08290363789967, 5.49673867759808, 5.9600748344541, 6.45698622142886, 6.97068522346297
-            ]
+            [0.5, 1, 0.7, 1.2],
+            [
+                2.39572102230177,
+                3.13781163365086,
+                3.78568993197139,
+                4.28992448591238,
+                4.7009959256016,
+                5.08290363789967,
+                5.49673867759808,
+                5.9600748344541,
+                6.45698622142886,
+                6.97068522346297,
+            ],
         )
-    ]
+    ],
 )
 def test_weighted(w, yid):
-    x = [1., 2., 4., 6.]
-    y = [2., 4., 5., 7.]
-    xi = np.linspace(1., 6., 10)
+    x = [1.0, 2.0, 4.0, 6.0]
+    y = [2.0, 4.0, 5.0, 7.0]
+    xi = np.linspace(1.0, 6.0, 10)
 
     sp = csaps.CubicSmoothingSpline(x, y, weights=w)
     yi = sp(xi)