View difference between Paste ID: NCyqGiWd and xxDUs1rs

 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib import cm
 from tqdm import tqdm
 import os
 from scipy.integrate import solve_ivp
 
 # Parameters
 SM = 0.0
-INT = 5.0
+INT = 10.0
 PROD = -2.0
 
 TS1_values = np.linspace(5, 30, 40)
-TS2_values = np.linspace(5, 40, 40)
+TS2_values = np.linspace(5, 30, 40)
 
 R = 8.314e-3
 T = 298.0
 RT = R * T
 A = 1.0
 
 # ODE system
 def reaction_odes(t, y, k1f, k1r, k2f, k2r):
     SM_conc, INT_conc, PROD_conc = y
     dSMdt = -k1f * SM_conc + k1r * INT_conc
     dINTdt = k1f * SM_conc - k1r * INT_conc - k2f * INT_conc + k2r * PROD_conc
     dPRODdt = k2f * INT_conc - k2r * PROD_conc
     return [dSMdt, dINTdt, dPRODdt]
 
 y0 = [1.0, 0.0, 0.0]
 t_span = (0, 1000.0)
 t_eval = [1000.0]
 
 TS1_grid, TS2_grid = np.meshgrid(TS1_values, TS2_values)
 rate_grid = np.zeros_like(TS1_grid)
 
 # Calculate rates
 for i in tqdm(range(len(TS2_values)), desc="Rows"):
     for j in range(len(TS1_values)):
         TS1_val = TS1_grid[i, j]
         TS2_val = TS2_grid[i, j]
 
-        if (TS1_val >= 5) and (TS2_val >= 5) and ((TS2_val - INT) < (TS1_val - SM)):
+        if (TS1_val >= 10) and (TS2_val >= 10)  and ((TS1_val - SM) > (TS2_val - INT)) and (TS2_val > TS1_val):
             k1f = A * np.exp(-(TS1_val - SM) / RT)
             k1r = A * np.exp(-(TS1_val - INT) / RT)
             k2f = A * np.exp(-(TS2_val - INT) / RT)
             k2r = A * np.exp(-(TS2_val - PROD) / RT)
 
             sol = solve_ivp(reaction_odes, t_span, y0, t_eval=t_eval, args=(k1f, k1r, k2f, k2r))
             if sol.success:
                 SM_final, INT_final, PROD_final = sol.y[:, -1]
                 rate = (k2f * INT_final) - (k2r * PROD_final)
             else:
                 rate = np.nan
         else:
             rate = np.nan
 
         rate_grid[i, j] = rate
 
 # Stats
 valid_rates = rate_grid[~np.isnan(rate_grid)]
 mean_rate = np.mean(valid_rates) if valid_rates.size > 0 else np.nan
 std_rate = np.std(valid_rates) if valid_rates.size > 0 else np.nan
 max_rate = np.nanmax(rate_grid)
 min_rate = np.nanmin(rate_grid)
 
 print("Summary Statistics:")
 print(f"Mean rate: {mean_rate:.4e}")
 print(f"Std dev of rate: {std_rate:.4e}")
 print(f"Max rate: {max_rate:.4e}")
 print(f"Min rate: {min_rate:.4e}")
 
 # Calculate intersection line where TS1 = TS2
 intersection_mask = np.isclose(TS1_grid, TS2_grid)
 intersection_TS1 = TS1_grid[intersection_mask]
 intersection_TS2 = TS2_grid[intersection_mask]
 intersection_rates = rate_grid[intersection_mask]
 
 # Plot
 fig = plt.figure(figsize=(10, 7))
 ax = fig.add_subplot(111, projection='3d')
 
 masked_rate = np.ma.masked_invalid(rate_grid)
 
 # Plot the main surface
 surf = ax.plot_surface(TS1_grid, TS2_grid, masked_rate, cmap=cm.viridis, edgecolor='none')
 
 # Add color bar
 fig.colorbar(surf, shrink=0.5, aspect=5)
 
 # Labels and title
-output_path = "3d_rate_plot_odes_reversible.png"
+ax.set_zlim(0, max_rate)
 ax.set_xlabel('TS1 Free Energy')
 ax.set_ylabel('TS2 Free Energy')
 ax.set_zlabel('Net Rate')
 ax.set_title('Rate Dependence (Assuming A=1)')
 
 # Add legend
 ax.legend()
 
 # Invert axes
 ax.invert_xaxis()
 ax.invert_yaxis()
 
 # Set view angle
 ax.view_init(elev=30, azim=210)
 
 plt.tight_layout()
 
 # Save and show plot
 output_path = "3d_rate_plot_odes_intersection_line.png"
 plt.savefig(output_path, dpi=300)
 print(f"Plot saved as {os.path.abspath(output_path)}")
 
 plt.show()