update black

Author: MuellerSeb

examples/02_cov_model/00_intro.py

@@ -19,7 +19,7 @@
 # use CovModel as the base-class
 class Gau(gs.CovModel):
     def cor(self, h):
-        return np.exp(-(h ** 2))
+        return np.exp(-(h**2))
 
 
 ###############################################################################

examples/02_cov_model/05_additional_para.py

@@ -20,7 +20,7 @@ def default_opt_arg(self):
         return {"alpha": 1.5}
 
     def cor(self, h):
-        return np.exp(-(h ** self.alpha))
+        return np.exp(-(h**self.alpha))
 
 
 ###############################################################################

examples/02_cov_model/06_fitting_para_ranges.py

@@ -16,7 +16,7 @@ def default_opt_arg(self):
         return {"alpha": 1.5}
 
     def cor(self, h):
-        return np.exp(-(h ** self.alpha))
+        return np.exp(-(h**self.alpha))
 
 
 # Exemplary variogram data (e.g. estimated from field observations)

gstools/covmodel/fit.py

@@ -499,7 +499,7 @@ def _r2_score(model, x_data, y_data, is_dir_vario):
     else:
         vario = model.variogram(x_data)
     residuals = y_data - vario
-    ss_res = np.sum(residuals ** 2)
+    ss_res = np.sum(residuals**2)
     ss_tot = np.sum((y_data - np.mean(y_data)) ** 2)
     return 1.0 - (ss_res / ss_tot)
 

gstools/covmodel/models.py

@@ -68,7 +68,7 @@ class Gaussian(CovModel):
 
     def cor(self, h):
         """Gaussian normalized correlation function."""
-        return np.exp(-(h ** 2))
+        return np.exp(-(h**2))
 
     def default_rescale(self):
         """Gaussian rescaling factor to result in integral scale."""
@@ -147,7 +147,7 @@ def cor(self, h):
     def spectral_density(self, k):  # noqa: D102
         k = np.asarray(k, dtype=np.double)
         return (
-            self.len_rescaled ** self.dim
+            self.len_rescaled**self.dim
             * sps.gamma((self.dim + 1) / 2.0)
             / (np.pi * (1.0 + (k * self.len_rescaled) ** 2))
             ** ((self.dim + 1) / 2.0)
@@ -186,7 +186,7 @@ def spectral_rad_ppf(self, u):
         if self.dim == 2:
             u_power = np.divide(
                 1,
-                u ** 2,
+                u**2,
                 out=np.full_like(u, np.inf),
                 where=np.logical_not(np.isclose(u, 0)),
             )
@@ -454,7 +454,7 @@ def default_opt_arg_bounds(self):
 
     def cor(self, h):
         """Rational normalized correlation function."""
-        return np.power(1 + h ** 2 / self.alpha, -self.alpha)
+        return np.power(1 + h**2 / self.alpha, -self.alpha)
 
     def calc_integral_scale(self):  # noqa: D102
         return (
@@ -499,7 +499,7 @@ class Cubic(CovModel):
     def cor(self, h):
         """Spherical normalized correlation function."""
         h = np.minimum(np.abs(h, dtype=np.double), 1.0)
-        return 1.0 - 7 * h ** 2 + 8.75 * h ** 3 - 3.5 * h ** 5 + 0.75 * h ** 7
+        return 1.0 - 7 * h**2 + 8.75 * h**3 - 3.5 * h**5 + 0.75 * h**7
 
 
 class Linear(CovModel):
@@ -578,7 +578,7 @@ def cor(self, h):
         h_l1 = h < 1.0
         h_low = h[h_l1]
         res[h_l1] = (
-            2 / np.pi * (np.arccos(h_low) - h_low * np.sqrt(1 - h_low ** 2))
+            2 / np.pi * (np.arccos(h_low) - h_low * np.sqrt(1 - h_low**2))
         )
         return res
 
@@ -619,7 +619,7 @@ class Spherical(CovModel):
     def cor(self, h):
         """Spherical normalized correlation function."""
         h = np.minimum(np.abs(h, dtype=np.double), 1.0)
-        return 1.0 - 1.5 * h + 0.5 * h ** 3
+        return 1.0 - 1.5 * h + 0.5 * h**3
 
     def check_dim(self, dim):
         """Spherical model is only valid in 1D, 2D and 3D."""

gstools/covmodel/tools.py

@@ -128,7 +128,7 @@ def rad_fac(dim, r):
     elif dim == 2:
         fac = 2 * np.pi * r
     elif dim == 3:
-        fac = 4 * np.pi * r ** 2
+        fac = 4 * np.pi * r**2
     else:  # pragma: no cover
         fac = (
             dim

gstools/field/upscaling.py

@@ -74,7 +74,7 @@ def var_coarse_graining(model, point_volumes=0.0):
     # interprete volume as a hypercube and calculate the edge length
     edge = point_volumes ** (1.0 / model.dim)
     var_factor = (
-        model.len_scale ** 2 / (model.len_scale ** 2 + edge ** 2 / 4)
+        model.len_scale**2 / (model.len_scale**2 + edge**2 / 4)
     ) ** (model.dim / 2.0)
 
     return model.sill * var_factor

gstools/random/rng.py

@@ -170,8 +170,8 @@ def sample_sphere(self, dim, size=None):
         elif dim == 3:
             ang1 = self.random.uniform(0.0, 2 * np.pi, size)
             ang2 = self.random.uniform(-1.0, 1.0, size)
-            coord[0] = np.sqrt(1.0 - ang2 ** 2) * np.cos(ang1)
-            coord[1] = np.sqrt(1.0 - ang2 ** 2) * np.sin(ang1)
+            coord[0] = np.sqrt(1.0 - ang2**2) * np.cos(ang1)
+            coord[1] = np.sqrt(1.0 - ang2**2) * np.sin(ang1)
             coord[2] = ang2
         else:  # pragma: no cover
             # http://corysimon.github.io/articles/uniformdistn-on-sphere/

gstools/random/tools.py

@@ -31,7 +31,7 @@ class MasterRNG:
     def __init__(self, seed):
         self._seed = seed
         self._master_rng_fct = rand.RandomState(seed)
-        self._master_rng = lambda: self._master_rng_fct.randint(1, 2 ** 16)
+        self._master_rng = lambda: self._master_rng_fct.randint(1, 2**16)
 
     def __call__(self):
         """Return a random seed."""

gstools/tools/special.py

@@ -66,9 +66,9 @@ def inc_gamma(s, x):
     if np.isclose(s, 0):
         return sps.exp1(x)
     if np.isclose(s, np.around(s)) and s < -0.5:
-        return x ** s * sps.expn(int(1 - np.around(s)), x)
+        return x**s * sps.expn(int(1 - np.around(s)), x)
     if s < 0:
-        return (inc_gamma(s + 1, x) - x ** s * np.exp(-x)) / s
+        return (inc_gamma(s + 1, x) - x**s * np.exp(-x)) / s
     return sps.gamma(s) * sps.gammaincc(s, x)
 
 
@@ -87,7 +87,7 @@ def inc_gamma_low(s, x):
     if np.isclose(s, np.around(s)) and s < 0.5:
         return np.full_like(x, np.inf, dtype=np.double)
     if s < 0:
-        return (inc_gamma_low(s + 1, x) + x ** s * np.exp(-x)) / s
+        return (inc_gamma_low(s + 1, x) + x**s * np.exp(-x)) / s
     return sps.gamma(s) * sps.gammainc(s, x)
 
 
@@ -206,7 +206,7 @@ def tpl_exp_spec_dens(k, dim, len_scale, hurst, len_low=0.0):
         b = hurst + 0.5
         c = hurst + dim / 2.0 + 1.0
         d = dim / 2.0 + 0.5
-        fac = len_scale ** dim * hurst * sps.gamma(d) / (np.pi ** d * a)
+        fac = len_scale**dim * hurst * sps.gamma(d) / (np.pi**d * a)
         return fac / (1.0 + z) ** a * sps.hyp2f1(a, b, c, z / (1.0 + z))
     fac_up = (len_scale + len_low) ** (2 * hurst)
     spec_up = tpl_exp_spec_dens(k, dim, len_scale + len_low, hurst)