adjust to PEP8

Author: SvenKlaassen

doubleml/double_ml_data.py

@@ -725,10 +725,9 @@ def from_arrays(cls, x, y, d, cluster_vars, z=None, use_other_treat_as_covariate
 
         data = pd.concat((pd.DataFrame(cluster_vars, columns=cluster_cols), dml_data.data), axis=1)
 
-        return(cls(data, dml_data.y_col, dml_data.d_cols,
-                   cluster_cols,
-                   dml_data.x_cols, dml_data.z_cols,
-                   dml_data.use_other_treat_as_covariate, dml_data.force_all_x_finite))
+        return (cls(data, dml_data.y_col, dml_data.d_cols, cluster_cols,
+                    dml_data.x_cols, dml_data.z_cols,
+                    dml_data.use_other_treat_as_covariate, dml_data.force_all_x_finite))
 
     @property
     def cluster_cols(self):

doubleml/double_ml_iivm.py

@@ -206,7 +206,7 @@ def _check_data(self, obj_dml_data):
         one_treat = (obj_dml_data.n_treat == 1)
         binary_treat = (type_of_target(obj_dml_data.d) == 'binary')
         zero_one_treat = np.all((np.power(obj_dml_data.d, 2) - obj_dml_data.d) == 0)
-        if not(one_treat & binary_treat & zero_one_treat):
+        if not (one_treat & binary_treat & zero_one_treat):
             raise ValueError('Incompatible data. '
                              'To fit an IIVM model with DML '
                              'exactly one binary variable with values 0 and 1 '
@@ -219,7 +219,7 @@ def _check_data(self, obj_dml_data):
         if one_instr:
             binary_instr = (type_of_target(obj_dml_data.z) == 'binary')
             zero_one_instr = np.all((np.power(obj_dml_data.z, 2) - obj_dml_data.z) == 0)
-            if not(one_instr & binary_instr & zero_one_instr):
+            if not (one_instr & binary_instr & zero_one_instr):
                 raise ValueError(err_msg)
         else:
             raise ValueError(err_msg)

doubleml/double_ml_irm.py

@@ -176,7 +176,7 @@ def _check_data(self, obj_dml_data):
         one_treat = (obj_dml_data.n_treat == 1)
         binary_treat = (type_of_target(obj_dml_data.d) == 'binary')
         zero_one_treat = np.all((np.power(obj_dml_data.d, 2) - obj_dml_data.d) == 0)
-        if not(one_treat & binary_treat & zero_one_treat):
+        if not (one_treat & binary_treat & zero_one_treat):
             raise ValueError('Incompatible data. '
                              'To fit an IRM model with DML '
                              'exactly one binary variable with values 0 and 1 '