def get_sparsity_in(self, i):
n = cas.nlpsol_out(i)
if n == 'f':
return cas.Sparsity.scalar()
elif n in ('x', 'lam_x'):
return cas.Sparsity.dense(self.nx)
elif n in ('g', 'lam_g'):
return cas.Sparsity.dense(self.ng)
elif n in ('p', 'lam_p'):
return cas.Sparsity.dense(self.np)
else:
return cas.Sparsity(0, 0)
def eval(self, arg):
print('TEST')
darg = {}
for (i, s) in enumerate(cas.nlpsol_out()):
darg[s] = arg[i]
sol = darg['x']
V = self.V_callback(sol)
model = self.model
#plot_trajectory(V)
plot_states(V, model)
plot_controls(V, model)
plot_algebraic_vars(V, model)
plot_invariants(V, self.p_fix_num(0.), self.nlp)
plt.show(block=True)
# time.sleep(1)
# plt.close('all')
return [0]
def get_name_in(self, i):
return cas.nlpsol_out(i)