#Analysis of level of smoothness of the ML yield function using the g…

…radient of the yield function

examples/data_6d/Data_6D.py

@@ -257,84 +257,6 @@ def rgb_to_hex(rgb):
 ax_leg.legend(handles=[handle1], loc="center")
 plt.show()
 
-ngrid = 100
-xx, yy = np.meshgrid(np.linspace(-1, 1, ngrid), np.linspace(0, 2, ngrid))
-yy *= mat_ml.scale_seq
-xx *= np.pi
-hh = np.c_[yy.ravel(), xx.ravel()]
-Cart_hh = FE.sp_cart(hh)
-zeros_array = np.zeros((ngrid*ngrid, 3))
-Cart_hh_6D = np.hstack((Cart_hh, zeros_array))
-grad_hh = mat_ml.calc_fgrad(Cart_hh_6D)
-# 1. Compute gradient magnitudes for each grid point
-grad_magnitudes = np.linalg.norm(grad_hh, axis=1)
-
-# 2. Compute mean and standard deviation of gradient magnitudes
-mean_grad_magnitude = np.mean(grad_magnitudes)
-std_grad_magnitude = np.std(grad_magnitudes)
-print(f"Mean Gradient Magnitude: {mean_grad_magnitude}")
-print(f"Standard Deviation of Gradient Magnitudes: {std_grad_magnitude}")
-
-# 3. Compute the change in gradient magnitudes between neighboring points
-# For simplicity, let's calculate the change in magnitude between consecutive grid points
-diff_grad_magnitudes = np.diff(grad_magnitudes)
-
-# 4. Plotting
-fig, ax = plt.subplots(2, 1, figsize=(10, 10))
-
-# Plot of gradient magnitudes
-ax[0].plot(grad_magnitudes, color='blue', label='Gradient Magnitude')
-ax[0].axhline(mean_grad_magnitude, color='red', linestyle='--', label='Mean Magnitude')
-ax[0].set_title("Gradient Magnitudes across Grid Points")
-ax[0].legend()
-
-# Plot of changes in gradient magnitudes
-ax[1].plot(diff_grad_magnitudes, color='green', label='Change in Gradient Magnitude')
-ax[1].axhline(0, color='red', linestyle='--')
-ax[1].set_title("Change in Gradient Magnitudes between Neighboring Grid Points")
-ax[1].legend()
-
-plt.tight_layout()
-plt.show()
-fixed_yy = mat_ml.scale_seq  # or any other constant value you want to use
-ngrid = 1000
-xx = np.linspace(-np.pi, np.pi, ngrid)
-yy = np.full_like(xx, fixed_yy).reshape(-1, 1)
-hh = np.c_[yy, xx]
-Cart_hh = FE.sp_cart(hh)
-zeros_array = np.zeros((ngrid, 3))
-Cart_hh_6D = np.hstack((Cart_hh, zeros_array))
-grad_hh = mat_ml.calc_fgrad(Cart_hh_6D)
-# 1. Compute gradient magnitudes for each grid point
-grad_magnitudes = np.linalg.norm(grad_hh, axis=1)
-
-# 2. Compute mean and standard deviation of gradient magnitudes
-mean_grad_magnitude = np.mean(grad_magnitudes)
-std_grad_magnitude = np.std(grad_magnitudes)
-print(f"Mean Gradient Magnitude: {mean_grad_magnitude}")
-print(f"Standard Deviation of Gradient Magnitudes: {std_grad_magnitude}")
-
-# 3. Compute the change in gradient magnitudes between neighboring points
-# For simplicity, let's calculate the change in magnitude between consecutive grid points
-diff_grad_magnitudes = np.diff(grad_magnitudes)
-
-# 4. Plotting
-fig, ax = plt.subplots(2, 1, figsize=(10, 10))
-
-# Plot of gradient magnitudes
-ax[0].plot(grad_magnitudes, color='blue', label='Gradient Magnitude')
-ax[0].axhline(mean_grad_magnitude, color='red', linestyle='--', label='Mean Magnitude')
-ax[0].set_title("Gradient Magnitudes across Grid Points")
-ax[0].legend()
-
-# Plot of changes in gradient magnitudes
-ax[1].plot(diff_grad_magnitudes, color='green', label='Change in Gradient Magnitude')
-ax[1].axhline(0, color='red', linestyle='--')
-ax[1].set_title("Change in Gradient Magnitudes between Neighboring Grid Points")
-ax[1].legend()
-
-plt.tight_layout()
-plt.show()
 
 #Analysis of level of smoothness of the ML yield function using the gradient of the yield function
 colors = ['blue', 'green', 'red', 'purple']