black

Author: jmlarson1

libensemble/tests/unit_tests/test_aposmm_starting_point_finder.py

@@ -2,7 +2,7 @@
 from libensemble.gen_funcs.persistent_aposmm import (
     initialize_APOSMM,
     update_history_dist,
-    decide_where_to_start_localopt
+    decide_where_to_start_localopt,
 )
 from libensemble.sim_funcs.six_hump_camel import six_hump_camel_func
 
@@ -11,14 +11,14 @@ def setup_history_and_find_rk(n_s, num_to_start, lb, ub, f_vals, x_points):
     """
     Populate the history array H with n_s points and bisect over r_k to find a value
     producing num_to_start local optimization start points using decide_where_to_start_localopt.
-    
+
     Parameters:
     - n_s (int): Number of initial sample points.
     - num_to_start (int): Desired number of starting points for local optimization.
     - lb, ub (np.ndarray): Lower and upper bounds of the domain.
     - f_vals (np.ndarray): Function values at each x_point.
     - x_points (np.ndarray): n_s x d array of sample points.
-    
+
     Returns:
     - H (np structured array): Updated history array.
     - rk_final (float): Value of r_k yielding num_to_start local opt starts.
@@ -30,21 +30,31 @@ def setup_history_and_find_rk(n_s, num_to_start, lb, ub, f_vals, x_points):
 
     n = x_points.shape[1]
 
-    H = np.zeros(n_s,dtype=[("sim_id",int), ("x",float,n),("x_on_cube",float,n),("f",float),("local_pt",bool),("sim_ended",bool)])
-    
+    H = np.zeros(
+        n_s,
+        dtype=[
+            ("sim_id", int),
+            ("x", float, n),
+            ("x_on_cube", float, n),
+            ("f", float),
+            ("local_pt", bool),
+            ("sim_ended", bool),
+        ],
+    )
+
     # Setup history
     for i in range(n_s):
-        H[i]['x'] = x_points[i]
-        H[i]['sim_id'] = i
-        H[i]['x_on_cube'] = (x_points[i] - lb) / (ub - lb)
-        H[i]['f'] = f_vals[i]
-        H[i]['local_pt'] = False
-        H[i]['sim_ended'] = True  # Ensure point is considered by distance function
-
-    local_H = initialize_APOSMM(H,{"lb": lb, "ub": ub,"initial_sample_size": n_s, "localopt_method":None},{"comm": None})[-1]
+        H[i]["x"] = x_points[i]
+        H[i]["sim_id"] = i
+        H[i]["x_on_cube"] = (x_points[i] - lb) / (ub - lb)
+        H[i]["f"] = f_vals[i]
+        H[i]["local_pt"] = False
+        H[i]["sim_ended"] = True  # Ensure point is considered by distance function
+
+    local_H = initialize_APOSMM(H, {"lb": lb, "ub": ub, "initial_sample_size": n_s, "localopt_method": None}, {"comm": None})[-1]
     local_H = local_H[:n_s]  # Use only the required number of entries
 
-    update_history_dist(local_H,n)
+    update_history_dist(local_H, n)
 
     # Search to find r_k that yields exactly num_to_start start points
     r_low, r_high = 1e-5, 2.0  # Conservative initial bounds