autoformat

Author: jmlarson1

libensemble/gen_funcs/aposmm_localopt_support.py

@@ -2,6 +2,7 @@
 This module contains methods for APOSMM to interface with various local
 optimization routines.
 """
+
 __all__ = [
     "LocalOptInterfacer",
     "run_local_nlopt",

libensemble/gen_funcs/persistent_aposmm.py

@@ -6,6 +6,7 @@
 This implementation of APOSMM was developed by Kaushik Kulkarni and Jeffrey
 Larson in the summer of 2019.
 """
+
 __all__ = ["aposmm", "initialize_APOSMM", "decide_where_to_start_localopt", "update_history_dist"]
 
 from math import log, pi, sqrt

libensemble/gen_funcs/persistent_ax_multitask.py

@@ -8,6 +8,7 @@
 This `gen_f` is meant to be used with the `alloc_f` function
 `only_persistent_gens`
 """
+
 import os
 from copy import deepcopy
 

libensemble/gen_funcs/persistent_gpCAM.py

@@ -3,8 +3,8 @@
 import time
 
 import numpy as np
-from numpy.lib.recfunctions import repack_fields
 from gpcam import GPOptimizer as GP
+from numpy.lib.recfunctions import repack_fields
 
 from libensemble.message_numbers import EVAL_GEN_TAG, FINISHED_PERSISTENT_GEN_TAG, PERSIS_STOP, STOP_TAG
 from libensemble.tools.persistent_support import PersistentSupport
@@ -38,14 +38,14 @@ def _initialize_gpcAM(user_specs, libE_info):
 
 def _read_testpoints(U):
     """Read numpy file containing evaluated points for measuring GP error"""
-    test_points_file = U.get('test_points_file')
+    test_points_file = U.get("test_points_file")
     if test_points_file is None:
         return None
 
     test_points = np.load(test_points_file)
 
     # Remove any NaNs
-    nan_indices = [i for i, fval in enumerate(test_points['f']) if np.isnan(fval)]
+    nan_indices = [i for i, fval in enumerate(test_points["f"]) if np.isnan(fval)]
     test_points = np.delete(test_points, nan_indices, axis=0)
 
     # In case large fields we don't need
@@ -103,7 +103,7 @@ def _update_gp_and_eval_var(all_x, all_y, x_for_var, test_points, persis_info):
 
     if test_points is not None:
         f_est = my_gp2S.posterior_mean(test_points["x"])["f(x)"]
-        mse = np.mean((f_est - test_points["f"])**2)
+        mse = np.mean((f_est - test_points["f"]) ** 2)
         persis_info.setdefault("mean_squared_error", []).append(mse)
     return np.array(var_vals)
 

libensemble/gen_funcs/sampling.py

@@ -3,6 +3,7 @@
 use (and return) a random stream in ``persis_info``, given by the allocation
 function.
 """
+
 import numpy as np
 
 from libensemble.specs import output_data

libensemble/gen_funcs/surmise_calib_support.py

@@ -1,4 +1,5 @@
 """Contains supplemental methods for gen function in persistent_surmise_calib.py."""
+
 import numpy as np
 import scipy.stats as sps
 

libensemble/gen_funcs/ytopt_asktell.py

@@ -1,6 +1,7 @@
 """
 This module wraps around the ytopt generator.
 """
+
 import numpy as np
 
 from libensemble.message_numbers import EVAL_GEN_TAG, FINISHED_PERSISTENT_GEN_TAG, PERSIS_STOP, STOP_TAG

libensemble/history.py

@@ -13,7 +13,6 @@
 
 
 class History:
-
     """The History class provides methods for managing the history array.
 
     **Object Attributes:**

libensemble/resources/rset_resources.py

@@ -26,7 +26,7 @@ class RSetResources:
     rset_dtype = [
         ("group", int),  # Group ID this resource set belongs to
         ("slot", int),  # Slot ID this resource set belongs to
-        ("gpus", bool)  # Does this resource set have GPUs
+        ("gpus", bool),  # Does this resource set have GPUs
         # ('pool', int),    # Pool ID (eg. separate gen/sim resources) - not yet used.
     ]
 

libensemble/sim_funcs/branin/branin_obj.py

@@ -2,6 +2,7 @@
 Calls the branin function. Default behavior uses the python function, but
 uncommenting lines will write x.in to file, call branin.py, and then read f.out.
 """
+
 import time
 
 import numpy as np