Use integer literals where appropriate

examples/00_misc/05_standalone_field.py

@@ -12,11 +12,11 @@
 import gstools as gs
 
 rng = np.random.RandomState(19970221)
-x0 = rng.rand(10000) * 100.0
-x1 = rng.rand(10000) * 100.0
-x2 = rng.rand(10000) * 100.0
-x3 = rng.rand(10000) * 100.0
-values = rng.rand(10000) * 100.0
+x0 = rng.rand(10000) * 100
+x1 = rng.rand(10000) * 100
+x2 = rng.rand(10000) * 100
+x3 = rng.rand(10000) * 100
+values = rng.rand(10000) * 100
 
 ###############################################################################
 # Only thing needed to instantiate the Field is the dimension.

examples/03_variogram/00_fit_variogram.py

@@ -9,8 +9,8 @@
 ###############################################################################
 # Generate a synthetic field with an exponential model.
 
-x = np.random.RandomState(19970221).rand(1000) * 100.0
-y = np.random.RandomState(20011012).rand(1000) * 100.0
+x = np.random.RandomState(19970221).rand(1000) * 100
+y = np.random.RandomState(20011012).rand(1000) * 100
 model = gs.Exponential(dim=2, var=2, len_scale=8)
 srf = gs.SRF(model, mean=0, seed=19970221)
 field = srf((x, y))

examples/03_variogram/01_find_best_model.py

@@ -10,8 +10,8 @@
 ###############################################################################
 # Generate a synthetic field with an exponential model.
 
-x = np.random.RandomState(19970221).rand(1000) * 100.0
-y = np.random.RandomState(20011012).rand(1000) * 100.0
+x = np.random.RandomState(19970221).rand(1000) * 100
+y = np.random.RandomState(20011012).rand(1000) * 100
 model = gs.Exponential(dim=2, var=2, len_scale=8)
 srf = gs.SRF(model, mean=0, seed=19970221)
 field = srf((x, y))

examples/03_variogram/02_multi_vario.py

@@ -10,8 +10,8 @@
 
 import gstools as gs
 
-x = np.random.RandomState(19970221).rand(1000) * 100.0
-y = np.random.RandomState(20011012).rand(1000) * 100.0
+x = np.random.RandomState(19970221).rand(1000) * 100
+y = np.random.RandomState(20011012).rand(1000) * 100
 model = gs.Exponential(dim=2, var=2, len_scale=8)
 srf = gs.SRF(model, mean=0)
 

examples/03_variogram/05_auto_fit_variogram.py

@@ -9,8 +9,8 @@
 ###############################################################################
 # Generate a synthetic field with an exponential model.
 
-x = np.random.RandomState(19970221).rand(1000) * 100.0
-y = np.random.RandomState(20011012).rand(1000) * 100.0
+x = np.random.RandomState(19970221).rand(1000) * 100
+y = np.random.RandomState(20011012).rand(1000) * 100
 model = gs.Exponential(dim=2, var=2, len_scale=8)
 srf = gs.SRF(model, mean=0, seed=19970221)
 field = srf((x, y))

examples/09_spatio_temporal/01_precip_1d.py

@@ -67,14 +67,14 @@
 # the lower this value, the more will be cut off, a value of 0.2 cuts off
 # nearly everything in this example.
 cutoff = 0.55
-gs.transform.boxcox(srf, lmbda=0.5, shift=-1.0 / cutoff)
+gs.transform.boxcox(srf, lmbda=0.5, shift=-1 / cutoff)
 
 ###############################################################################
 # As a last step, the amount of precipitation is set. This should of course be
 # calibrated towards observations (the same goes for the threshold, the
 # variance, correlation length, and so on).
 
-amount = 2.0
+amount = 2
 srf.field *= amount
 P_ana = srf.field
 

examples/09_spatio_temporal/02_precip_2d.py

@@ -44,7 +44,7 @@
 
 # account for the skewness and the dry periods
 cutoff = 0.55
-gs.transform.boxcox(srf, lmbda=0.5, shift=-1.0 / cutoff)
+gs.transform.boxcox(srf, lmbda=0.5, shift=-1 / cutoff)
 
 # adjust the amount of precipitation
 amount = 4.0

examples/10_normalizer/00_lognormal_kriging.py

@@ -22,7 +22,7 @@
 cond_pos = [0.3, 1.9, 1.1, 3.3, 4.7]
 cond_val = [0.47, 0.56, 0.74, 1.47, 1.74]
 # resulting grid
-gridx = np.linspace(0.0, 15.0, 151)
+gridx = np.linspace(0, 15, 151)
 # stable covariance model
 model = gs.Stable(dim=1, var=0.5, len_scale=2.56, alpha=1.9)
 

src/gstools/covmodel/base.py

@@ -1184,12 +1184,12 @@ def name(self):
     @property
     def do_rotation(self):
         """:any:`bool`: State if a rotation is performed."""
-        return not np.all(np.isclose(self.angles, 0.0))
+        return not np.all(np.isclose(self.angles, 0))
 
     @property
     def is_isotropic(self):
         """:any:`bool`: State if a model is isotropic."""
-        return np.all(np.isclose(self.anis, 1.0))
+        return np.all(np.isclose(self.anis, 1))
 
     def __eq__(self, other):
         """Compare CovModels."""

src/gstools/covmodel/fit.py

@@ -348,13 +348,13 @@ def _check_vario(model, x_data, y_data):
 def _set_weights(model, weights, x_data, curve_fit_kwargs, is_dir_vario):
     if weights is not None:
         if callable(weights):
-            weights = 1.0 / weights(x_data)
+            weights = 1 / weights(x_data)
         elif isinstance(weights, str) and weights == "inv":
-            weights = 1.0 + x_data
+            weights = 1 + x_data
         else:
             if is_dir_vario and weights.size * model.dim == x_data.size:
                 weights = np.tile(weights, model.dim)
-            weights = 1.0 / np.asarray(weights).reshape(-1)
+            weights = 1 / np.asarray(weights).reshape(-1)
         curve_fit_kwargs["sigma"] = weights
         curve_fit_kwargs["absolute_sigma"] = True
 
@@ -500,7 +500,7 @@ def _r2_score(model, x_data, y_data, is_dir_vario):
     residuals = y_data - vario
     ss_res = np.sum(residuals**2)
     ss_tot = np.sum((y_data - np.mean(y_data)) ** 2)
-    return 1.0 - (ss_res / ss_tot)
+    return 1 - (ss_res / ss_tot)
 
 
 def logistic_weights(p=0.1, mean=0.7):  # pragma: no cover
@@ -530,6 +530,6 @@ def func(x_data):
         # logit function for growth rate
         growth = np.log(p / (1 - p)) / (p * x_range)
         x_mean = mean * x_range + np.amin(x_data)
-        return 1.0 / (1.0 + np.exp(growth * (x_mean - x_data)))
+        return 1 / (1 + np.exp(growth * (x_mean - x_data)))
 
     return func