def evaluate(self, theta, u_P, n, t):
"""
"""
self.matrix[-1, 0:2] = self._sign * np.dot(A_theta_matrix(theta), t)
# body i
if self.id == 0:
_theta_i = theta
# second partial derivative of t_i with respect to s_i
_dt_i_ds_i = d_ds(t)
_theta_j = self._parent.body_list[1].theta[2]
_n_j = self._parent._n_list[1]
self.matrix[-1, -1] = self._sign * np.dot(
np.dot(A_2theta_matrix(_theta_i), _dt_i_ds_i),
np.dot(A_matrix(_theta_j), _n_j))
# body j
elif self.id == 1:
_theta_i = self._parent.body_list[0].theta[2]
_t_i = self._parent._t_list[0]
_theta_j = theta
# second partial derivative of n_j with respect to s_j
_dn_j_ds_j = d_ds(n)
self.matrix[-1, -1] = self._sign * np.dot(
np.dot(A_matrix(_theta_i), _t_i),
np.dot(A_2theta_matrix(_theta_j), _dn_j_ds_j))
def d2_ds2(node):
"""
Function numerically calculates a second derivative d2/ds2 of node that defines a vector (line)
to calculate derivative
"""
_d_ds = d_ds(node)
_d2_ds2 = d_ds(_d_ds)
return _d2_ds2
def d2_ds2(node):
"""
Function numerically calculates a second derivative d2/ds2 of node that defines a vector (line)
to calculate derivative
"""
_d_ds = d_ds(node)
_d2_ds2 = d_ds(_d_ds)
return _d2_ds2
def evaluate(self, theta, u_P, n,
t): # manjka se tretja komponenta od produkta n.t
"""
Function calculate values in predefined zero matrix
"""
dt_ds = d_ds(t)
self.matrix[0:2] = self._sign * np.dot(A_matrix(theta), dt_ds)
# body i
if self.id == 0:
_theta_i = theta
_t_i = d2_ds2(t)
_theta_j = self._parent.body_list[1].theta[2]
_n_j = self._parent._n_list[1]
self.matrix[-1] = self._sign * np.dot(
np.dot(A_2theta_matrix(_theta_i), _t_i),
np.dot(A_matrix(_theta_j), _n_j))
# body j
elif self.id == 1:
_theta_i = self._parent.body_list[0].theta[2]
_t_i = self._parent._t_list[0]
_theta_j = theta
_n_j = d2_ds2(n)
self.matrix[-1] = self._sign * np.dot(
np.dot(A_matrix(_theta_i), _t_i),
np.dot(A_2theta_matrix(_theta_j), _n_j))
def evaluate(self, theta, u_P, n, t):
"""
"""
self.matrix[-1, 0:2] = self._sign * np.dot(A_theta_matrix(theta), t)
# body i
if self.id == 0:
_theta_i = theta
# second partial derivative of t_i with respect to s_i
_dt_i_ds_i = d_ds(t)
_theta_j = self._parent.body_list[1].theta[2]
_n_j = self._parent._n_list[1]
self.matrix[-1, -1] = self._sign * np.dot(np.dot(A_2theta_matrix(_theta_i), _dt_i_ds_i), np.dot(A_matrix(_theta_j), _n_j))
# body j
elif self.id == 1:
_theta_i = self._parent.body_list[0].theta[2]
_t_i = self._parent._t_list[0]
_theta_j = theta
# second partial derivative of n_j with respect to s_j
_dn_j_ds_j = d_ds(n)
self.matrix[-1, -1] = self._sign * np.dot(np.dot(A_matrix(_theta_i), _t_i), np.dot(A_2theta_matrix(_theta_j), _dn_j_ds_j))
def evaluate(self, theta, u_P, n, t): # manjka se tretja komponenta od produkta n.t
"""
Function calculate values in predefined zero matrix
"""
dt_ds = d_ds(t)
self.matrix[0:2] = self._sign * np.dot(A_matrix(theta), dt_ds)
# body i
if self.id == 0:
_theta_i = theta
_t_i = d2_ds2(t)
_theta_j = self._parent.body_list[1].theta[2]
_n_j = self._parent._n_list[1]
self.matrix[-1] = self._sign * np.dot(np.dot(A_2theta_matrix(_theta_i), _t_i), np.dot(A_matrix(_theta_j), _n_j))
# body j
elif self.id == 1:
_theta_i = self._parent.body_list[0].theta[2]
_t_i = self._parent._t_list[0]
_theta_j = theta
_n_j = d2_ds2(n)
self.matrix[-1] = self._sign * np.dot(np.dot(A_matrix(_theta_i), _t_i), np.dot(A_2theta_matrix(_theta_j), _n_j))
