Formatting fixes

libensemble/gen_funcs/persistent_ax_multitask.py

@@ -87,6 +87,7 @@
     TaskChoiceToIntTaskChoice,
 ]
 
+
 # get_MTGP based on https://ax.dev/docs/tutorials/multi_task/
 def get_MTGP(
     experiment: Experiment,

libensemble/sim_funcs/rosenbrock.py

@@ -8,6 +8,7 @@
 
 __all__ = ["rosenbrock_eval"]
 
+
 def EvaluateFunction(x, component=None):
     """
     Evaluates the chained Rosenbrock function

scripts/aposmm_min_loc.py

@@ -19,9 +19,9 @@
 
 N = 6  # number of opt runs to show.
 
-x_name = 'x0'
-y_name = 'x1'
-z_name = 'x2'
+x_name = "x0"
+y_name = "x1"
+z_name = "x2"
 
 full_bounds = False  # For entire input space enter bounds below
 
@@ -35,7 +35,7 @@
 # Find the most recent .npy and pickle files
 try:
     H_file = max(glob.glob("*.npy"), key=os.path.getmtime)
-    persis_info_file = max(glob.iglob('*.pickle'), key=os.path.getctime)
+    persis_info_file = max(glob.iglob("*.pickle"), key=os.path.getctime)
 except Exception:
     sys.exit("Need a *.npy and a *.pickle files in run dir. Exiting...")
 
@@ -45,30 +45,30 @@
     index_sets = pickle.load(f)["run_order"]
 
 # Filter best N opt runs for clearer graph
-trimmed_index_sets = {key: indices[:-1] for key, indices in index_sets.items()}
-min_f_per_set = [(key, indices, H['f'][indices].min()) for key, indices in trimmed_index_sets.items() if len(indices) > 0]
+trim_sets = {key: indices[:-1] for key, indices in index_sets.items()}
+min_f_per_set = [(key, indices, H["f"][indices].min()) for key, indices in trim_sets.items() if len(indices) > 0]
 min_f_per_set_sorted = sorted(min_f_per_set, key=lambda x: x[2])[:N]
 
 # Plotting
 fig = plt.figure(figsize=(6, 6))
-ax = fig.add_subplot(111, projection='3d')
+ax = fig.add_subplot(111, projection="3d")
 
 for key, indices, _ in min_f_per_set_sorted:
-    min_f_index = indices[np.argmin(H['f'][indices])]
+    min_f_index = indices[np.argmin(H["f"][indices])]
 
     # Extract the corresponding 3D x position from H
     try:
-        x, y, z = H['x'][min_f_index]
+        x, y, z = H["x"][min_f_index]
     except ValueError:
         x = H[x_name][min_f_index]
         y = H[y_name][min_f_index]
         z = H[z_name][min_f_index]
 
     # Plot the 3D point
-    ax.scatter(x, y, z, marker='o', s=50, label=f'Opt run {key}')
+    ax.scatter(x, y, z, marker="o", s=50, label=f"Opt run {key}")
 
     # Draw a line from the point to the XY plane (z=0)
-    ax.plot([x, x], [y, y], [0, z], color='grey', linestyle='--')
+    ax.plot([x, x], [y, y], [0, z], color="grey", linestyle="--")
 
 if full_bounds:
     ax.set_xlim(x0_min, x0_max)
@@ -79,6 +79,6 @@
 ax.set_xlabel(x_name)
 ax.set_ylabel(y_name)
 ax.set_zlabel(z_name)
-ax.set_title('Locations of best points from each optimization run')
-ax.legend(bbox_to_anchor=(-0.1, 0.9), loc='upper left', borderaxespad=0)
+ax.set_title("Locations of best points from each optimization run")
+ax.legend(bbox_to_anchor=(-0.1, 0.9), loc="upper left", borderaxespad=0)
 plt.savefig(f"location_min_best{N}.png")

scripts/aposmm_plot_local_runs.py

@@ -21,7 +21,7 @@
 # Find the most recent .npy and pickle files
 try:
     H_file = max(glob.glob("*.npy"), key=os.path.getmtime)
-    persis_info_file = max(glob.iglob('*.pickle'), key=os.path.getctime)
+    persis_info_file = max(glob.iglob("*.pickle"), key=os.path.getctime)
 except Exception:
     sys.exit("Need a *.npy and a *.pickle files in run dir. Exiting...")
 
@@ -31,38 +31,38 @@
     index_sets = pickle.load(f)["run_order"]
 
 # Filter best N opt runs for clearer graph
-trimmed_index_sets = {key: indices[:-1] for key, indices in index_sets.items()}
-min_f_per_set = [(key, indices, H['f'][indices].min()) for key, indices in trimmed_index_sets.items() if len(indices) > 0]
+trim_sets = {key: indices[:-1] for key, indices in index_sets.items()}
+min_f_per_set = [(key, indices, H["f"][indices].min()) for key, indices in trim_sets.items() if len(indices) > 0]
 min_f_per_set_sorted = sorted(min_f_per_set, key=lambda x: x[2])[:N]
 
 # Plotting
 plt.figure(figsize=(10, 6))
-plt.scatter(range(len(H['f'])), H['f'], color='lightgrey', label='Other points', zorder=1)
+plt.scatter(range(len(H["f"])), H["f"], color="lightgrey", label="Other points", zorder=1)
 
 num_runs = len(index_sets)
 # for key, indices in index_sets.items():
 for key, indices, _ in min_f_per_set_sorted:
     # Extract the 'f' values for each index set
-    f_values = H['f'][indices]
+    f_values = H["f"][indices]
 
-    ratio = (f_values[-1] - f_values[-2])/ f_values[-2]
+    ratio = (f_values[-1] - f_values[-2]) / f_values[-2]
     # print(f"Tolerance of the last two values for {key}: {ratio}")
 
-    plt.plot(indices, f_values, marker='o', label=f'Opt run {key}', zorder=2)
+    plt.plot(indices, f_values, marker="o", label=f"Opt run {key}", zorder=2)
 
     # Identify the index and value of the minimum f
     min_index = indices[np.argmin(f_values)]
     min_f_value = np.min(f_values)
 
     # Mark the minimum f value with a distinct marker
-    plt.scatter(min_index, min_f_value, color='red', edgecolor='black', s=50, zorder=3)
+    plt.scatter(min_index, min_f_value, color="red", edgecolor="black", s=50, zorder=3)
 
 # Add a dummy point to the legend for "minima of opt run"
-plt.scatter([], [], color='red', edgecolor='black', s=50, label='Best value of opt run')
+plt.scatter([], [], color="red", edgecolor="black", s=50, label="Best value of opt run")
 
-plt.xlabel('Simulation ID')
-plt.ylabel('Objective value')
-plt.title(f'Objective values by Optimization runs. Best {N} runs from {num_runs}')
+plt.xlabel("Simulation ID")
+plt.ylabel("Objective value")
+plt.title(f"Objective values by Optimization runs. Best {N} runs from {num_runs}")
 plt.legend()
 plt.grid(True)
 plt.savefig(f"opt_runs_best{N}.png")