carm_contrib

opengate/contrib/carm/anode_heel_effect_model.py

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+import numpy as np
+import pandas as pd
+import pathlib
+import joblib
+from scipy.interpolate import interp1d
+
+
+# Function to generate phi_weights and phi_angles based on kvp (energy)
+def get_phi_distribution(kvp):
+    # Load the scaler and model
+    current_path = pathlib.Path(__file__).parent.resolve()
+    scaler = joblib.load(current_path / "gbr_scaler.pkl")
+    gbr_model = joblib.load(current_path / "gbr_model.pkl")
+
+    distances = np.arange(-9, 10, 1)
+    phi_angles = np.arctan(distances / 70)
+
+    # Create a DataFrame for prediction
+    input_data = pd.DataFrame(
+        {
+            "Distance": distances,
+            "Energy": kvp,
+            "Theta": phi_angles,
+            "ThetaDegrees": np.degrees(phi_angles),
+        }
+    )
+
+    # Scale the input data
+    input_data_scaled = scaler.transform(input_data)
+
+    # Predict the weights
+    phi_weights = gbr_model.predict(input_data_scaled)
+
+    # Now interpolate between the predicted values with finer steps
+    fine_distances = np.arange(-9, 9.01, 0.1)
+    fine_phi_angles = np.arctan(fine_distances / 70)
+
+    # Interpolate the predicted weights using linear interpolation
+    interp_function = interp1d(distances, phi_weights, kind="linear")
+
+    # Interpolate predictions for the finer distances
+    fine_phi_weights = interp_function(fine_distances)
+    fine_phi_weights = fine_phi_weights[1:]
+
+    # Adjust angles (shift them by 90 degrees in radians)
+    for i in range(len(fine_phi_angles)):
+        fine_phi_angles[i] = fine_phi_angles[i] + 180 * np.pi / 180
+
+    return fine_phi_weights, fine_phi_angles