rename util.metrics to dynonet.metrics

Author: temcdrm

Diff for: examples/BW/BW_test.py

@@ -5,7 +5,7 @@
 import matplotlib
 import matplotlib.pyplot as plt
 from dynonet.lti import MimoLinearDynamicalOperator
-import util.metrics
+import dynonet.metrics
 from dynonet.static import MimoStaticNonLinearity
 
 if __name__ == '__main__':
@@ -99,9 +99,9 @@ def model(u_in):
 
     # In[Metrics]
     n_skip = 300
-    e_rms = util.metrics.error_rmse(scaler_y*y[n_skip:], scaler_y*y_hat[n_skip:])[0]
-    fit_idx = util.metrics.fit_index(y[n_skip:], y_hat[n_skip:])[0]
-    r_sq = util.metrics.r_squared(y[n_skip:], y_hat[n_skip:])[0]
+    e_rms = dynonet.metrics.error_rmse(scaler_y*y[n_skip:], scaler_y*y_hat[n_skip:])[0]
+    fit_idx = dynonet.metrics.fit_index(y[n_skip:], y_hat[n_skip:])[0]
+    r_sq = dynonet.metrics.r_squared(y[n_skip:], y_hat[n_skip:])[0]
 
     print(f"RMSE: {e_rms:.2E} mm\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.2f}")
 

Diff for: examples/EMPS/EMPS_test.py

@@ -8,7 +8,7 @@
 import matplotlib.pyplot as plt
 from dynonet.lti import MimoLinearDynamicalOperator, SisoLinearDynamicalOperator
 from dynonet.static import MimoStaticNonLinearity, MimoStaticNonLinearity
-import util.metrics
+import dynonet.metrics
 
 if __name__ == '__main__':
 
@@ -96,9 +96,9 @@ def model(u_in):
     plt.show()
 
     # In[Metrics]
-    e_rms = util.metrics.error_rmse(y_meas, y_hat)[0]
-    fit_idx = util.metrics.fit_index(y_meas, y_hat)[0]
-    r_sq = util.metrics.r_squared(y_meas, y_hat)[0]
+    e_rms = dynonet.metrics.error_rmse(y_meas, y_hat)[0]
+    fit_idx = dynonet.metrics.fit_index(y_meas, y_hat)[0]
+    r_sq = dynonet.metrics.r_squared(y_meas, y_hat)[0]
 
     print(f"RMSE: {e_rms:.2E} mm\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.2f}")
 

Diff for: examples/ParWH/parWH_test.py

@@ -7,7 +7,7 @@
 import control
 from dynonet.lti import MimoLinearDynamicalOperator
 from dynonet.static import MimoStaticNonLinearity
-import util.metrics
+import dynonet.metrics
 
 
 if __name__ == '__main__':
@@ -160,10 +160,10 @@
 
     idx_test = range(n_skip, N)
 
-    e_rms = 1000*util.metrics.error_rmse(y_meas[idx_test], y_hat[idx_test])
-    mae = 1000 * util.metrics.error_mae(y_meas[idx_test], y_hat[idx_test])
-    fit_idx = util.metrics.fit_index(y_meas[idx_test], y_hat[idx_test])
-    r_sq = util.metrics.r_squared(y_meas[idx_test], y_hat[idx_test])
-    u_rms = 1000*util.metrics.error_rmse(u, 0)
+    e_rms = 1000*dynonet.metrics.error_rmse(y_meas[idx_test], y_hat[idx_test])
+    mae = 1000 * dynonet.metrics.error_mae(y_meas[idx_test], y_hat[idx_test])
+    fit_idx = dynonet.metrics.fit_index(y_meas[idx_test], y_hat[idx_test])
+    r_sq = dynonet.metrics.r_squared(y_meas[idx_test], y_hat[idx_test])
+    u_rms = 1000*dynonet.metrics.error_rmse(u, 0)
 
     print(f"RMSE: {e_rms:.2f}mV\nMAE: {mae:.2f}mV\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.1f}\nRMSU: {u_rms:.2f}mV")

Diff for: examples/ParWH/parWH_train.py

@@ -9,7 +9,7 @@
 
 import matplotlib.pyplot as plt
 import time
-import util.metrics
+import dynonet.metrics
 
 if __name__ == '__main__':
 
@@ -211,8 +211,8 @@
     # In[Metrics]
 
 #    idx_metric = range(0, N_per_period)
-#    e_rms = util.metrics.error_rmse(y[idx_metric], y_hat_np[idx_metric])
-#    fit_idx = util.metrics.fit_index(y[idx_metric], y_hat_np[idx_metric])
-#    r_sq = util.metrics.r_squared(y[idx_metric], y_hat_np[idx_metric])
+#    e_rms = dynonet.metrics.error_rmse(y[idx_metric], y_hat_np[idx_metric])
+#    fit_idx = dynonet.metrics.fit_index(y[idx_metric], y_hat_np[idx_metric])
+#    r_sq = dynonet.metrics.r_squared(y[idx_metric], y_hat_np[idx_metric])
 
 #    print(f"RMSE: {e_rms:.4f}V\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.1f}")

Diff for: examples/ParWH/parWH_train_refine.py

@@ -9,7 +9,7 @@
 
 import matplotlib.pyplot as plt
 import time
-import util.metrics
+import dynonet.metrics
 
 if __name__ == '__main__':
 
@@ -217,8 +217,8 @@
     # In[Metrics]
 
 #    idx_metric = range(0, N_per_period)
-#    e_rms = util.metrics.error_rmse(y[idx_metric], y_hat_np[idx_metric])
-#    fit_idx = util.metrics.fit_index(y[idx_metric], y_hat_np[idx_metric])
-#    r_sq = util.metrics.r_squared(y[idx_metric], y_hat_np[idx_metric])
+#    e_rms = dynonet.metrics.error_rmse(y[idx_metric], y_hat_np[idx_metric])
+#    fit_idx = dynonet.metrics.fit_index(y[idx_metric], y_hat_np[idx_metric])
+#    r_sq = dynonet.metrics.r_squared(y[idx_metric], y_hat_np[idx_metric])
 
 #    print(f"RMSE: {e_rms:.4f}V\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.1f}")

Diff for: examples/ParWH/parWH_train_single_dataset.py

@@ -7,7 +7,7 @@
 
 import matplotlib.pyplot as plt
 import time
-import util.metrics
+import dynonet.metrics
 
 class StaticNonLin(nn.Module):
 
@@ -172,8 +172,8 @@ def forward(self, u_lin):
     # In[Metrics]
 
     idx_metric = range(0, N)
-    e_rms = util.metrics.error_rmse(y[idx_metric], y_hat_np[idx_metric])
-    fit_idx = util.metrics.fit_index(y[idx_metric], y_hat_np[idx_metric])
-    r_sq = util.metrics.r_squared(y[idx_metric], y_hat_np[idx_metric])
+    e_rms = dynonet.metrics.error_rmse(y[idx_metric], y_hat_np[idx_metric])
+    fit_idx = dynonet.metrics.fit_index(y[idx_metric], y_hat_np[idx_metric])
+    r_sq = dynonet.metrics.r_squared(y[idx_metric], y_hat_np[idx_metric])
 
     print(f"RMSE: {e_rms:.4f}V\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.1f}")

Diff for: examples/Silverbox/silverbox_train_W.py

@@ -7,7 +7,7 @@
 import time
 import torch.nn as nn
 
-import util.metrics
+import dynonet.metrics
 
 
 class StaticNonLin(nn.Module):
@@ -157,9 +157,9 @@ def forward(self, y_lin):
     plt.show()
 
     # In[Plot]
-    e_rms = util.metrics.error_rmse(y_fit, y_hat)[0]
-    fit_idx = util.metrics.fit_index(y_fit, y_hat)[0]
-    r_sq = util.metrics.r_squared(y_fit, y_hat)[0]
+    e_rms = dynonet.metrics.error_rmse(y_fit, y_hat)[0]
+    fit_idx = dynonet.metrics.fit_index(y_fit, y_hat)[0]
+    r_sq = dynonet.metrics.r_squared(y_fit, y_hat)[0]
     print(f"RMSE: {e_rms:.4f}V\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.1f}")
 
 

Diff for: examples/Silverbox/silverbox_train_WH.py

@@ -7,7 +7,7 @@
 import time
 import torch.nn as nn
 
-import util.metrics
+import dynonet.metrics
 
 
 class StaticNonLin(nn.Module):
@@ -164,7 +164,7 @@ def forward(self, y_lin):
     plt.show()
 
     # In[Plot]
-    e_rms = util.metrics.error_rmse(y_hat, y_fit)[0]
+    e_rms = dynonet.metrics.error_rmse(y_hat, y_fit)[0]
     print(f"RMSE: {e_rms:.2f}")
 
 

Diff for: examples/Silverbox/silverbox_train_feedback.py

@@ -6,7 +6,7 @@
 import time
 from dynonet.lti import SisoLinearDynamicalOperator
 from dynonet.static import SisoStaticNonLinearity
-import util.metrics
+import dynonet.metrics
 
 if __name__ == '__main__':
 
@@ -172,9 +172,9 @@
     plt.show()
 
 # In[Metrics]
-    e_rms = util.metrics.error_rmse(y_fit, y_lin)[0]
-    fit_idx = util.metrics.fit_index(y_fit, y_lin)[0]
-    r_sq = util.metrics.r_squared(y_fit, y_lin)[0]
+    e_rms = dynonet.metrics.error_rmse(y_fit, y_lin)[0]
+    fit_idx = dynonet.metrics.fit_index(y_fit, y_lin)[0]
+    r_sq = dynonet.metrics.r_squared(y_fit, y_lin)[0]
     print(f"RMSE: {e_rms:.4f}V\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.1f}")
 
 

Diff for: examples/WH2009/WH2009_test.py

@@ -8,7 +8,7 @@
 import matplotlib
 import matplotlib.pyplot as plt
 import control
-import util.metrics
+import dynonet.metrics
 
 
 # In[Main]
@@ -135,9 +135,9 @@
 
     # In[Metrics]
     idx_test = range(t_test_start + t_skip, t_test_end)
-    e_rms = 1000*util.metrics.error_rmse(y_meas[idx_test], y_hat[idx_test])[0]
-    fit_idx = util.metrics.fit_index(y_meas[idx_test], y_hat[idx_test])[0]
-    r_sq = util.metrics.r_squared(y_meas[idx_test], y_hat[idx_test])[0]
+    e_rms = 1000*dynonet.metrics.error_rmse(y_meas[idx_test], y_hat[idx_test])[0]
+    fit_idx = dynonet.metrics.fit_index(y_meas[idx_test], y_hat[idx_test])[0]
+    r_sq = dynonet.metrics.r_squared(y_meas[idx_test], y_hat[idx_test])[0]
 
     print(f"RMSE: {e_rms:.1f}V\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.4f}")
 

Diff for: examples/WH2009/WH2009_test_FIR.py

@@ -9,7 +9,7 @@
 import matplotlib.pyplot as plt
 import torch.nn as nn
 import control
-import util.metrics
+import dynonet.metrics
 
 
 if __name__ == '__main__':
@@ -127,9 +127,9 @@
 
     # In[Metrics]
     idx_test = range(t_test_start + t_skip, t_test_end)
-    e_rms = 1000*util.metrics.error_rmse(y_meas[idx_test], y_hat[idx_test])[0]
-    fit_idx = util.metrics.fit_index(y_meas[idx_test], y_hat[idx_test])[0]
-    r_sq = util.metrics.r_squared(y_meas[idx_test], y_hat[idx_test])[0]
+    e_rms = 1000*dynonet.metrics.error_rmse(y_meas[idx_test], y_hat[idx_test])[0]
+    fit_idx = dynonet.metrics.fit_index(y_meas[idx_test], y_hat[idx_test])[0]
+    r_sq = dynonet.metrics.r_squared(y_meas[idx_test], y_hat[idx_test])[0]
 
     print(f"RMSE: {e_rms:.1f}V\nFIT:  {fit_idx:.1f}%\nR_sq: {r_sq:.2f}")
 

Diff for: examples/WH2009/WH2009_train.py

@@ -9,7 +9,7 @@
 import torch.nn as nn
 
 
-import util.metrics
+import dynonet.metrics
 
 # In[Main]
 if __name__ == '__main__':
@@ -168,7 +168,7 @@ def closure():
     plt.show()
 
     # In[Plot]
-    e_rms = util.metrics.error_rmse(y_hat, y_fit)[0]
+    e_rms = dynonet.metrics.error_rmse(y_hat, y_fit)[0]
     print(f"RMSE: {e_rms:.2f}") # target: 1mv
 
 

Diff for: examples/WH2009/WH2009_train_FIR.py

@@ -8,7 +8,7 @@
 import time
 import torch.nn as nn
 
-import util.metrics
+import dynonet.metrics
 
 # In[Main]
 if __name__ == '__main__':
@@ -166,5 +166,5 @@ def closure():
     plt.show()
 
     # In[Plot]
-    e_rms = util.metrics.error_rmse(y_hat, y_fit)[0]
+    e_rms = dynonet.metrics.error_rmse(y_hat, y_fit)[0]
     print(f"RMSE: {e_rms:.2f}")

Diff for: examples/WH2009/WH2009_train_comparisons.py

@@ -7,7 +7,7 @@
 import matplotlib.pyplot as plt
 import time
 import torch.nn as nn
-import util.metrics
+import dynonet.metrics
 
 
 def normal_standard_cdf(val):
@@ -172,7 +172,7 @@ def model(u_in):
     plt.show()
 
     # In[RMSE]
-    e_rms = util.metrics.error_rmse(y_hat, y_fit)[0]
+    e_rms = dynonet.metrics.error_rmse(y_hat, y_fit)[0]
     print(f"RMSE: {e_rms:.2f}") # target: 1mv
 
     # In[v_hat]

Diff for: examples/WH2009/WH2009_train_process_noise.py

@@ -9,7 +9,7 @@
 import time
 
 
-import util.metrics
+import dynonet.metrics
 
 # In[Main]
 if __name__ == '__main__':
@@ -197,7 +197,7 @@ def closure():
     plt.show()
 
     # In[Plot]
-    e_rms = util.metrics.error_rmse(y_hat, y_fit)[0]
+    e_rms = dynonet.metrics.error_rmse(y_hat, y_fit)[0]
     print(f"RMSE: {e_rms:.2f}") # target: 1mv
 
 

Diff for: examples/WH2009/WH2009_train_process_noise_PEM.py

@@ -9,7 +9,7 @@
 import time
 
 
-import util.metrics
+import dynonet.metrics
 
 # In[Main]
 if __name__ == '__main__':

Diff for: examples/WH2009/WH2009_train_quantized.py

@@ -6,7 +6,7 @@
 from dynonet.static import SisoStaticNonLinearity
 import matplotlib.pyplot as plt
 import time
-import util.metrics
+import dynonet.metrics
 
 
 def normal_standard_cdf(val):
@@ -180,7 +180,7 @@ def model(u_in):
     plt.show()
 
     # In[Plot]
-    e_rms = util.metrics.error_rmse(y_hat, y_fit)[0]
+    e_rms = dynonet.metrics.error_rmse(y_hat, y_fit)[0]
     print(f"RMSE: {e_rms:.2f}") # target: 1mv
 
 

Diff for: examples/coupled_drive/drive_train_W.py

@@ -5,7 +5,7 @@
 from dynonet.lti import SisoLinearDynamicalOperator
 from dynonet.static import SisoStaticNonLinearity
 import time
-import util.metrics
+import dynonet.metrics
 import matplotlib.pyplot as plt
 
 if __name__ == '__main__':
@@ -130,7 +130,7 @@
     plt.show()
 
     # In[Plot]
-    e_rms = util.metrics.error_rmse(y_hat, y_fit)[0]
+    e_rms = dynonet.metrics.error_rmse(y_hat, y_fit)[0]
     print(f"RMSE: {e_rms:.2f}")