prelimenary exploration for fitter

Multi_fitter_test_file.py

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+import numpy as np
+from scipy.optimize import minimize
+
+
+# example model
+def model(params, x):
+    return params[0] * x + params[1]
+
+
+# Cost function
+def cost_function(params, x_data, y_data):
+    return np.sum((model(params, x_data) - y_data) ** 2)
+
+
+# Global cost function
+def global_cost_function(params, experiments):
+    total_cost = 0
+    for x_data, y_data in experiments:
+        total_cost += cost_function(params, x_data, y_data)
+    return total_cost
+
+
+# Example data for two experiments
+x_data_1 = np.array([0, 1, 2, 3])
+y_data_1 = np.array([1, 3, 5, 7])
+
+x_data_2 = np.array([0, 1, 2, 3])
+y_data_2 = np.array([2, 4, 6, 8])
+
+experiments = [(x_data_1, y_data_1), (x_data_2, y_data_2)]
+
+# Initial guess/parameters
+initial_params = [1, 0]
+
+# Run the optimization
+result = minimize(global_cost_function, initial_params, args=(experiments,))
+
+# Optimized parameters
+print("Optimized parameters:", result.x)