Finished up the TVE scan.

hiddenSymmetries · Dec 18, 2024 · 220c0d6 · 220c0d6
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Showing 3 changed files with 24 additions and 22 deletions.
diff --git a/examples/3_Advanced/coil_force_optimization/analysis_tools.py b/examples/3_Advanced/coil_force_optimization/analysis_tools.py
@@ -70,9 +70,9 @@ def get_dfs(INPUT_DIR='./output/QA/with-force-penalty/1/optimizations/', OUTPUT_
         f"and max_arclength_variance < 1e-2"
         f"and coil_surface_distance < 0.375"
         f"and coil_coil_distance < 0.15"
-        f"and max_length > 3.5"
+        f"and max_length > 3.0"
         f"and normalized_BdotN < {4e-2}"
-        f"and max_max_force<60000"
+        f"and max_max_force<50000"
     )
 
     ### STEP 3: Generate Pareto front and export UUIDs as .txt

diff --git a/examples/3_Advanced/coil_force_optimization/coil_force_pareto_scans.py b/examples/3_Advanced/coil_force_optimization/coil_force_pareto_scans.py
@@ -8,32 +8,34 @@
 # import imageio
 import matplotlib.pyplot as plt
 
-INPUT_DIR = "./output/QA/TVE_new/"
+INPUT_DIR = "./output/QA/TVE_fix/"
 df, df_filtered, df_pareto = get_dfs(INPUT_DIR=INPUT_DIR)
 success_plt(df, df_filtered).show()
 df, df_filtered, df_pareto = get_dfs(INPUT_DIR=INPUT_DIR)
 
-#df_filtered = df
-#df_pareto = df
 y_axes = ["tve"]  #, "mean_RMS_force", "max_max_torque", "mean_RMS_torque", "net_forces", "net_torques"]
 labels = ["Total Vacuum Magnetic Energy [J]"]  #, "mean force [N/m]", "max torque [N]", "mean torque [N]", "net force [N]", "net torque [N-m]"]
-y_lims = [(7500, 30000)]  #, (5500, 10000), (5000, 30000), (2000, 6200), (4000, 13000), (5e2, 7000)]
-#keys = ["coil_coil_distance"]
+y_lims = [(20000, 60000)]  #, (5500, 10000), (5000, 30000), (2000, 6200), (4000, 13000), (5e2, 7000)]
+#keys = ["mean_RMS_force"]
+#keys = ["mean_RMS_torque"]
+#keys = ["max_max_torque"]
+#keys = ["max_max_force"]
+#keys = ["max_MSC"]
 #keys = ["max_length"]
-#keys = ["max_max_κ"]
+keys = ["max_max_κ"]
 #keys = ["force_threshold"]
 #['max_max_torque']
-keys = ['length_target', 'length_weight', 'max_κ_threshold', 'max_κ_weight',
-       'msc_threshold', 'msc_weight', 'cc_threshold', 'cc_weight',
-       'cs_threshold', 'cs_weight', 'force_threshold', 'force_weight',
-       'arclength_weight', 'JF', 'Jf', 'gradient_norm', 
-       'max_length', 'max_max_κ','max_MSC', 
-       'max_max_force', 'min_min_force',
-       'mean_RMS_force', 
-       'max_max_torque', 'min_min_torque', 
-       'mean_RMS_torque', 'max_arclength_variance',
-       'BdotN', 'mean_AbsB', 'normalized_BdotN', 'coil_coil_distance',
-       'coil_surface_distance']
+#keys = ['length_target', 'length_weight', 'max_κ_threshold', 'max_κ_weight',
+#       'msc_threshold', 'msc_weight', 'cc_threshold', 'cc_weight',
+#       'cs_threshold', 'cs_weight', 'force_threshold', 'force_weight',
+#      'arclength_weight', 'JF', 'Jf', 'gradient_norm', 
+#       'max_length', 'max_max_κ','max_MSC', 
+#       'max_max_force', 'min_min_force',
+#       'mean_RMS_force', 
+#       'max_max_torque', 'min_min_torque', 
+#       'mean_RMS_torque', 'max_arclength_variance',
+#       'BdotN', 'mean_AbsB', 'normalized_BdotN', 'coil_coil_distance',
+#       'coil_surface_distance']
 for color in keys:
     print(color)
     for y_axis, label, y_lim in zip(y_axes, labels, y_lims):
@@ -102,13 +104,13 @@
         )
         plt.xlabel(r'$\langle|\mathbf{B}\cdot\mathbf{n}|\rangle/\langle B \rangle$ [unitless]')
         plt.ylabel(label)
-        plt.xlim(0.5 * min(df_filtered["normalized_BdotN"]), max(df_filtered["normalized_BdotN"]))
+        plt.xlim(0.7 * min(df_filtered["normalized_BdotN"]), max(df_filtered["normalized_BdotN"]))
         plt.ylim(y_lim)
         plt.xscale("log")
         #if y_lim[0] < 1:
         #    plt.yscale("log")
         plt.colorbar(label=color_label)
-        #plt.clim(0.17, 0.31)
+        plt.clim(3.0, 6.0)
         # plt.clim(3.5, 6.5)
         # plt.clim(6e3, 2e4)
         #plt.clim(3.5, 4.8)

diff --git a/examples/3_Advanced/coil_force_optimization/optimization_tools.py b/examples/3_Advanced/coil_force_optimization/optimization_tools.py
@@ -128,7 +128,7 @@ def initial_optimizations(N=10000, with_force=True, MAXITER=14000,
         if with_force:  
             # (1e-20, 1e-8) for Squared forces
             # (1e-14, 1e-5) for Lpforces or TVE
-            FORCE_WEIGHT = 10.0 ** rand(-11, -6)
+            FORCE_WEIGHT = 10.0 ** rand(-13, -8)
         else:
             FORCE_WEIGHT = 0