blackened examples [skip ci]

examples/00_gaussian.py

@@ -1,4 +1,5 @@
 from gstools import SRF, Gaussian
+
 # structured field with a size of 100x100 and a grid-size of 1x1
 x = y = range(100)
 model = Gaussian(dim=2, var=1, len_scale=10)

examples/01_tpl_stable.py

@@ -1,16 +1,17 @@
 import numpy as np
 from gstools import SRF, TPLStable
+
 x = y = np.linspace(0, 100, 100)
 model = TPLStable(
-    dim=2,           # spatial dimension
-    var=1,           # variance (C is calculated internally, so that the variance is actually 1)
-    len_low=0,       # lower truncation of the power law
-    len_scale=10,    # length scale (a.k.a. range), len_up = len_low + len_scale
-    nugget=0.1,      # nugget
-    anis=0.5,        # anisotropy between main direction and transversal ones
-    angles=np.pi/4,  # rotation angles
-    alpha=1.5,       # shape parameter from the stable model
-    hurst=0.7,       # hurst coefficient from the power law
+    dim=2,  # spatial dimension
+    var=1,  # variance (C is calculated internally, so that the variance is actually 1)
+    len_low=0,  # lower truncation of the power law
+    len_scale=10,  # length scale (a.k.a. range), len_up = len_low + len_scale
+    nugget=0.1,  # nugget
+    anis=0.5,  # anisotropy between main direction and transversal ones
+    angles=np.pi / 4,  # rotation angles
+    alpha=1.5,  # shape parameter from the stable model
+    hurst=0.7,  # hurst coefficient from the power law
 )
 srf = SRF(model, mean=1, mode_no=1000, seed=19970221, verbose=True)
 srf.structured([x, y])

examples/02_fit_variogram.py

@@ -1,8 +1,9 @@
 import numpy as np
 from gstools import SRF, Exponential, Stable, vario_estimate_unstructured
+
 # generate a synthetic field with an exponential model
-x = np.random.RandomState(19970221).rand(1000) * 100.
-y = np.random.RandomState(20011012).rand(1000) * 100.
+x = np.random.RandomState(19970221).rand(1000) * 100.0
+y = np.random.RandomState(20011012).rand(1000) * 100.0
 model = Exponential(dim=2, var=2, len_scale=8)
 srf = SRF(model, mean=0, seed=19970221)
 field = srf((x, y))

examples/03_cov_model.py

@@ -1,13 +1,16 @@
 from gstools import CovModel
 import numpy as np
+
 # use CovModel as the base-class
 class Gau(CovModel):
     def correlation(self, r):
-        return np.exp(-(r/self.len_scale)**2)
+        return np.exp(-(r / self.len_scale) ** 2)
+
 
-model = Gau(dim=2, var=2., len_scale=10)
+model = Gau(dim=2, var=2.0, len_scale=10)
 
 from gstools.covmodel.plot import plot_variogram
+
 plot_variogram(model)
 
 print(model.dim, model.var, model.len_scale, model.nugget, model.sill)
@@ -17,49 +20,53 @@ def correlation(self, r):
 model.nugget = 0.1
 print(model.dim, model.var, model.len_scale, model.nugget, model.sill)
 
-model = Gau(dim=3, var=2., len_scale=10, anis=0.5)
+model = Gau(dim=3, var=2.0, len_scale=10, anis=0.5)
 print(model.anis)
 print(model.len_scale_vec)
 
-model = Gau(dim=3, var=2., len_scale=[10, 5, 4])
+model = Gau(dim=3, var=2.0, len_scale=[10, 5, 4])
 print(model.anis)
 print(model.len_scale)
 print(model.len_scale_vec)
 
-model = Gau(dim=3, var=2., len_scale=10, angles=2.5)
+model = Gau(dim=3, var=2.0, len_scale=10, angles=2.5)
 print(model.angles)
 
-model = Gau(dim=3, var=2., len_scale=10, nugget=0.5)
-print(model.variogram(10.))
-print(model.variogram_normed(10.))
-print(model.covariance(10.))
-print(model.correlation(10.))
+model = Gau(dim=3, var=2.0, len_scale=10, nugget=0.5)
+print(model.variogram(10.0))
+print(model.variogram_normed(10.0))
+print(model.covariance(10.0))
+print(model.correlation(10.0))
 
-model = Gau(dim=3, var=2., len_scale=10)
+model = Gau(dim=3, var=2.0, len_scale=10)
 print(model.spectrum(0.1))
 print(model.spectral_density(0.1))
 
-model = Gau(dim=3, var=2., len_scale=10)
+model = Gau(dim=3, var=2.0, len_scale=10)
 print(model.integral_scale)
 print(model.integral_scale_vec)
 
-model = Gau(dim=3, var=2., integral_scale=[10, 4, 2])
+model = Gau(dim=3, var=2.0, integral_scale=[10, 4, 2])
 print(model.anis)
 print(model.len_scale)
 print(model.len_scale_vec)
 print(model.integral_scale)
 print(model.integral_scale_vec)
 
-model = Gau(dim=3, var=2., len_scale=10)
+model = Gau(dim=3, var=2.0, len_scale=10)
 print(model.percentile_scale(0.9))
 
+
 class Stab(CovModel):
     def default_opt_arg(self):
         return {"alpha": 1.5}
+
     def correlation(self, r):
-        return np.exp(-(r/self.len_scale)**self.alpha)
-model1 = Stab(dim=2, var=2., len_scale=10)
-model2 = Stab(dim=2, var=2., len_scale=10, alpha=0.5)
+        return np.exp(-(r / self.len_scale) ** self.alpha)
+
+
+model1 = Stab(dim=2, var=2.0, len_scale=10)
+model2 = Stab(dim=2, var=2.0, len_scale=10, alpha=0.5)
 print(model1)
 print(model2)
 
@@ -73,6 +80,7 @@ def correlation(self, r):
 results, pcov = model.fit_variogram(x, y, nugget=False)
 print(results)
 from matplotlib import pyplot as plt
+
 plt.cla()
 plt.scatter(x, y, color="k")
 plot_variogram(model)

examples/04_export.py

@@ -1,6 +1,7 @@
 from gstools import SRF, Gaussian
+
 x = y = range(100)
 model = Gaussian(dim=2, var=1, len_scale=10)
 srf = SRF(model)
-field = srf((x, y), mesh_type='structured')
+field = srf((x, y), mesh_type="structured")
 srf.vtk_export("field")

examples/05_srf_ensemble.py

@@ -12,11 +12,11 @@
 field = []
 seed = MasterRNG(20170519)
 for i in range(ens_no):
-    field.append(srf((x, y), seed=seed(), mesh_type='structured'))
+    field.append(srf((x, y), seed=seed(), mesh_type="structured"))
 
 fig, ax = pt.subplots(2, 2, sharex=True, sharey=True)
 ax = ax.flatten()
 for i in range(ens_no):
-    ax[i].imshow(field[i].T, origin='lower')
+    ax[i].imshow(field[i].T, origin="lower")
 
 pt.show()

examples/06_unstr_srf_export.py

@@ -9,13 +9,13 @@
 x = rng.randint(0, 100, size=10000)
 y = rng.randint(0, 100, size=10000)
 
-model = Exponential(dim=2, var=1, len_scale=[12., 3.], angles=np.pi / 8.)
+model = Exponential(dim=2, var=1, len_scale=[12.0, 3.0], angles=np.pi / 8.0)
 
 srf = SRF(model, seed=20170519)
 
 field = srf((x, y))
 srf.vtk_export("field")
 
 pt.tricontourf(x, y, field.T)
-pt.axes().set_aspect('equal')
+pt.axes().set_aspect("equal")
 pt.show()

examples/07_srf_merge.py

@@ -9,7 +9,7 @@
 x = rng.randint(0, 100, size=10000)
 y = rng.randint(0, 100, size=10000)
 
-model = Exponential(dim=2, var=1, len_scale=[12., 3.], angles=np.pi/8.)
+model = Exponential(dim=2, var=1, len_scale=[12.0, 3.0], angles=np.pi / 8.0)
 
 srf = SRF(model, seed=20170519)
 
@@ -25,5 +25,5 @@
 
 pt.tricontourf(x, y, field.T)
 pt.tricontourf(x2, y2, field2.T)
-pt.axes().set_aspect('equal')
+pt.axes().set_aspect("equal")
 pt.show()