blacken tests

GeoStat-Framework · Mar 27, 2020 · f4cbfa2 · f4cbfa2
1 parent 2c54ca5
commit f4cbfa2
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Showing 3 changed files with 1,611 additions and 1,027 deletions.
diff --git a/tests/test_api.py b/tests/test_api.py
@@ -8,9 +8,8 @@
 
 
 def _method_and_vergiogram():
-    method = ['ordinary', 'universal', 'ordinary3d', 'universal3d']
-    variogram_model = ['linear', 'power', 'gaussian', 'spherical',
-                       'exponential']
+    method = ["ordinary", "universal", "ordinary3d", "universal3d"]
+    variogram_model = ["linear", "power", "gaussian", "spherical", "exponential"]
     return product(method, variogram_model)
 
 
@@ -21,25 +20,26 @@ def test_krige():
     y = 5 * np.random.rand(20)
 
     for m, v in _method_and_vergiogram():
-        param_dict = {'method': [m], 'variogram_model': [v]}
-
-        estimator = GridSearchCV(Krige(),
-                                 param_dict,
-                                 n_jobs=-1,
-                                 iid=False,
-                                 pre_dispatch='2*n_jobs',
-                                 verbose=False,
-                                 cv=5,
-                                 )
+        param_dict = {"method": [m], "variogram_model": [v]}
+
+        estimator = GridSearchCV(
+            Krige(),
+            param_dict,
+            n_jobs=-1,
+            iid=False,
+            pre_dispatch="2*n_jobs",
+            verbose=False,
+            cv=5,
+        )
         # run the gridsearch
-        if m in ['ordinary', 'universal']:
+        if m in ["ordinary", "universal"]:
             estimator.fit(X=X[:, :2], y=y)
         else:
             estimator.fit(X=X, y=y)
-        if hasattr(estimator, 'best_score_'):
+        if hasattr(estimator, "best_score_"):
             if m in threed_krige:
                 assert estimator.best_score_ > -10.0
             else:
                 assert estimator.best_score_ > -3.0
-        if hasattr(estimator, 'cv_results_'):
-            assert estimator.cv_results_['mean_train_score'] > 0
+        if hasattr(estimator, "cv_results_"):
+            assert estimator.cv_results_["mean_train_score"] > 0