import matplotlib.pyplot as plt
# Set quantity of interest and input labels
QofI = 'QoI'
in_labels = ['rho', 'mu', 'dpdz', 'c_p', 'k', 'Pr_t']
# Set the number of parameter (m) and active subspace dimension (n)
m, n = 6, 2
# Load random sampling data
data = np.loadtxt('params_and_results.dat')
X_phys, f, df_phys = data[:, :m], data[:, m].reshape((data.shape[0], 1)), data[:, m+1:]
M = f.shape[0]
# Scale data so parameters are in [-1, 1]
X_norm, df_norm = utils_data.physical_to_normalized(X=X_phys, df=df_phys)
# Compute the active/inactive subspaces
sub = active_subspaces.subspaces.Subspaces()
sub.compute(df_norm/np.linalg.norm(df_norm, axis=1).reshape((df_norm.shape[0], 1)))
# Rewrite the active subspace to be 1-dimensional
sub.W1, sub.W2 = sub.eigenvectors[:, :n], sub.eigenvectors[:, n:]
sub.W1 = sub.W1.reshape(m, n)
sub.W2 = sub.W2.reshape(m, m-n)
# Redefine inputs in terms of active/inactive subspaces
Y, Z = np.dot(X_norm, sub.W1), np.dot(X_norm, sub.W2)
Z = active_subspaces.domains.hit_and_run_z(20, Y[0, :].reshape((n, 1)), sub.W1, sub.W2)
import active_subspaces
# Set quantity of interest and input labels
QofI = 'QoI'
in_labels = ['rho', 'mu', 'dpdz', 'c_p', 'k', 'Pr_t']
# Set the number of parameter (m) and active subspace dimension (n)
m, n = 6, 1
# Load random sampling data
data = np.loadtxt('params_and_results.dat')
X_phys, f, df_phys = data[:, :m], data[:, m].reshape((data.shape[0], 1)), data[:, m+1:]
M = f.shape[0]
# Scale data so parameters are in [-1, 1]
X_norm, df_norm = utils_data.physical_to_normalized(X=X_phys, df=df_phys)
###############################################################################
##### Local Linear Approximation Gradients
###############################################################################
# Estimate gradients using local linear approximation
df_local_linear = active_subspaces.gradients.local_linear_gradients(X_phys, f)
# Scale data so parameters are in [-1, 1]
df_local_linear = utils_data.physical_to_normalized(df=df_local_linear)
# Compute the active/inactive subspaces
# NOTE: The gradient is normalized for this computation
sub = active_subspaces.subspaces.Subspaces()
sub.compute(df_local_linear/np.linalg.norm(df_local_linear, axis=1).reshape((df_local_linear.shape[0], 1)))
