def shape_function_partial(self):
nn = self.nn
ng = self.ng
G, W = Gauss_Points(ng)
pN_value = np.zeros((ng, 1, nn))
for i in range(ng):
pN_value[i] = Diff_Shape_Function(nn, G[i])
return pN_value
def shape_function_value(self):
nn = self.nn
ng = self.ng
G, W = Gauss_Points(ng)
N_value = np.zeros((ng, 1, nn))
for i in range(ng):
N_value[i] = Shape_Function(2, G[i])
return N_value
def gaussian_weights(self):
ng = self.ng
G, W = Gauss_Points(ng)
w_xi = np.array([W[0], W[1], W[0], W[1]])
w_eta = np.array([W[0], W[0], W[1], W[1]])
W = np.multiply(w_xi, w_eta)
W = W.reshape(ng**2)
return W
def shape_function_value(self):
nn = self.nn
ng = self.ng
G, W = Gauss_Points(ng)
N_value = np.zeros((ng ** 2, 1, nn))
xi = np.array([G[0], G[1], G[1], G[0]])
eta = np.array([G[0], G[0], G[1], G[1]])
# w_xi = np.array([W[0], W[1], W[0], W[1]])
# w_eta = np.array([W[0], W[0], W[1], W[1]])
for i in range(ng ** 2):
for j in range(nn):
N_xi_j = Shape_Function(ng, xi[i])[self.coord_position[j][0]]
N_eta_j = Shape_Function(ng, eta[i])[self.coord_position[j][1]]
N_value[i][0][j] = N_xi_j * N_eta_j
return N_value
def shape_function_partial(self):
nn = self.nn
ng = self.ng
G, W = Gauss_Points(ng)
pN_value = np.zeros((ng**2, 2, nn))
xi = np.array([G[0], G[1], G[1], G[0]])
eta = np.array([G[0], G[0], G[1], G[1]])
# w_xi = np.array([W[0], W[1], W[0], W[1]])
# w_eta = np.array([W[0], W[0], W[1], W[1]])
for i in range(ng**2):
for j in range(nn):
N_xi_j = Shape_Function(self.nn_per_dim,
xi[i])[self.coord_position[j][0]]
N_eta_j = Shape_Function(self.nn_per_dim,
eta[i])[self.coord_position[j][1]]
pN_xi_j = Diff_Shape_Function(self.nn_per_dim,
xi[i])[self.coord_position[j][0]]
pN_eta_j = Diff_Shape_Function(
self.nn_per_dim, eta[i])[self.coord_position[j][1]]
pN_value[i][0][j] = pN_xi_j * N_eta_j
pN_value[i][1][j] = pN_eta_j * N_xi_j
return pN_value