def dhat(x):
""" Compute the interpolatory derivative matrix D_ij associated with nodes x_j such that
^
w = D*u
- -
returns the derivative of u at the points x_i.
"""
from sem import fd_weights_full
import numpy as np
n1 = x.shape[0]
w = np.zeros((n1,2), dtype=np.float64)
Dh = np.zeros((n1,n1), dtype=np.float64);
for i in range(n1):
w = fd_weights_full(x[i],x,1)
Dh[:,i] = w[:,1]
Dh = np.transpose(Dh)
return Dh
def dhat(x):
""" Compute the interpolatory derivative matrix D_ij associated with nodes x_j such that
^
w = D*u
- -
returns the derivative of u at the points x_i.
"""
from sem import fd_weights_full
import numpy as np
n1 = x.shape[0]
w = np.zeros((n1, 2), dtype=np.float64)
Dh = np.zeros((n1, n1), dtype=np.float64)
for i in range(n1):
w = fd_weights_full(x[i], x, 1)
Dh[:, i] = w[:, 1]
Dh = np.transpose(Dh)
return Dh
def interp_mat(x, y, order = 0):
""" Compute the interpolatory derivative matrix P_ij associated with nodes x_j such that
^
w = D*u
- -
returns the order-th derivative of u at the points y_i.
"""
from sem import fd_weights_full
import numpy as np
n1 = x.shape[0]
n2 = y.shape[0]
w = np.zeros((n1,order+1))
Ph = np.zeros((n1,n2));
for i in range(n2):
w = fd_weights_full(y[i],x,order)
Ph[:,i] = w[:,order]
Ph = np.transpose(Ph)
return Ph
def interp_mat(x, y, order=0):
""" Compute the interpolatory derivative matrix P_ij associated with nodes x_j such that
^
w = D*u
- -
returns the order-th derivative of u at the points y_i.
"""
from sem import fd_weights_full
import numpy as np
n1 = x.shape[0]
n2 = y.shape[0]
w = np.zeros((n1, order + 1))
Ph = np.zeros((n1, n2))
for i in range(n2):
w = fd_weights_full(y[i], x, order)
Ph[:, i] = w[:, order]
Ph = np.transpose(Ph)
return Ph