#BUG DEMONSTRATION

import numpy as np
from scipy.optimize import curve_fit

def swap_model(x_in, X_eff, Y_eff):
    denominator = X_eff + x_in
    # Avoid division by zero, though bounds should help
    if np.isscalar(denominator):
        return (Y_eff * x_in) / (denominator if not np.isclose(denominator, 0) else 1e-9)
    else: # It's an array
        denominator_copy = np.copy(denominator) # Work on a copy to avoid modifying input array if x_in is array-like
        denominator_copy[np.isclose(denominator_copy, 0)] = 1e-9
        return (Y_eff * x_in) / denominator_copy

bounds = ([1e-6, 1e-6], [np.inf, np.inf]) # Parameters must be positive

# Test input for prediction
x_test_value = 6507.3125

print("--- Case 1: Poor Fit ---")
x_data_bad = np.array([1, 2, 39000])
y_data_bad = np.array([8423.724335, 16846.531751, 319765062.090722])
# Actual value for x_test_value corresponding to this "bad" scenario's universe
y_actual_for_bad_case_test = 54311422.15982

try:
    popt_bad, _ = curve_fit(swap_model, x_data_bad, y_data_bad, bounds=bounds, maxfev=5000)
    X_eff_bad, Y_eff_bad = popt_bad
    print(f"Fitted (Bad): X_eff = {X_eff_bad:.4f}, Y_eff = {Y_eff_bad:.4f}")
    y_pred_bad = swap_model(x_test_value, X_eff_bad, Y_eff_bad)
    error_bad = abs(y_actual_for_bad_case_test - y_pred_bad) / y_actual_for_bad_case_test * 100
    print(f"Predicted for {x_test_value:.4f}: {y_pred_bad:.4f}, Actual: {y_actual_for_bad_case_test:.4f}, Error: {error_bad:.2f}%")
except RuntimeError as e: print(f"RuntimeError: {e}")
except Exception as e: print(f"Other exception in bad fit: {e}")


print("\n--- Case 2: Better Fit ---")
x_data_good = np.array([1, 2, 32000])
y_data_good = np.array([8430.169107, 16859.464909, 264727159.854379])
# Actual value for x_test_value corresponding to this "good" scenario's universe
y_actual_for_good_case_test = 54549399.392518

try:
    popt_good, _ = curve_fit(swap_model, x_data_good, y_data_good, bounds=bounds, maxfev=5000)
    X_eff_good, Y_eff_good = popt_good
    print(f"Fitted (Good): X_eff = {X_eff_good:,.4f}, Y_eff = {Y_eff_good:,.4f}") # Added comma for readability
    y_pred_good = swap_model(x_test_value, X_eff_good, Y_eff_good)
    error_good = abs(y_actual_for_good_case_test - y_pred_good) / y_actual_for_good_case_test * 100
    print(f"Predicted for {x_test_value:.4f}: {y_pred_good:.4f}, Actual: {y_actual_for_good_case_test:.4f}, Error: {error_good:.2f}%")
except RuntimeError as e: print(f"RuntimeError: {e}")
except Exception as e: print(f"Other exception in good fit: {e}")