def _simulate_with_casadi_no_inputs(self, initcon, tsim, integrator,
integrator_options):
# Old way (10 times faster, but can't incorporate time
# varying parameters/controls)
xalltemp = [self._templatemap[i] for i in self._diffvars]
xall = casadi.vertcat(*xalltemp)
odealltemp = [convert_pyomo2casadi(self._rhsdict[i])
for i in self._derivlist]
odeall = casadi.vertcat(*odealltemp)
dae = {'x': xall, 'ode': odeall}
if len(self._algvars) != 0:
zalltemp = [self._templatemap[i] for i in self._simalgvars]
zall = casadi.vertcat(*zalltemp)
algalltemp = [convert_pyomo2casadi(i) for i in self._alglist]
algall = casadi.vertcat(*algalltemp)
dae['z'] = zall
dae['alg'] = algall
integrator_options['grid'] = tsim
integrator_options['output_t0'] = True
F = casadi.integrator('F', integrator, dae, integrator_options)
sol = F(x0=initcon)
profile = sol['xf'].full().T
if len(self._algvars) != 0:
algprofile = sol['zf'].full().T
profile = np.concatenate((profile, algprofile), axis=1)
return [tsim, profile]
def test_create_objective_from_numpy(self):
# Test issue #87
model = ConcreteModel()
nsample = 3
nvariables = 2
X0 = np.array(range(nsample)).reshape([nsample, 1])
model.X = 1 + np.array(range(nsample * nvariables)).reshape(
(nsample, nvariables))
X = np.concatenate([X0, model.X], axis=1)
model.I = RangeSet(1, nsample)
model.J = RangeSet(1, nvariables)
error = np.ones((nsample, 1))
beta = np.ones((nvariables + 1, 1))
model.Y = np.dot(X, beta) + error
model.beta = Var(model.J)
model.beta0 = Var()
def obj_fun(model):
return sum(
abs(model.Y[i - 1] -
(model.beta0 + sum(model.X[i - 1, j - 1] * model.beta[j]
for j in model.J))) for i in model.I)
model.OBJ = Objective(rule=obj_fun)
def obj_fun_quad(model):
return sum(
(model.Y[i - 1] -
(model.beta0 + sum(model.X[i - 1, j - 1] * model.beta[j]
for j in model.J)))**2 for i in model.I)
model.OBJ_QUAD = Objective(rule=obj_fun_quad)
self.assertEqual(
str(model.OBJ.expr), "abs(4.0 - (beta[1] + 2*beta[2] + beta0)) + "
"abs(9.0 - (3*beta[1] + 4*beta[2] + beta0)) + "
"abs(14.0 - (5*beta[1] + 6*beta[2] + beta0))")
self.assertEqual(model.OBJ.expr.polynomial_degree(), None)
self.assertEqual(model.OBJ_QUAD.expr.polynomial_degree(), 2)
def _simulate_with_casadi_with_inputs(self, initcon, tsim, varying_inputs,
integrator, integrator_options):
xalltemp = [self._templatemap[i] for i in self._diffvars]
xall = casadi.vertcat(*xalltemp)
time = casadi.SX.sym('time')
odealltemp = [time * convert_pyomo2casadi(self._rhsdict[i])
for i in self._derivlist]
odeall = casadi.vertcat(*odealltemp)
# Time-varying inputs
ptemp = [self._templatemap[i]
for i in self._siminputvars.values()]
pall = casadi.vertcat(time, *ptemp)
dae = {'x': xall, 'p': pall, 'ode': odeall}
if len(self._algvars) != 0:
zalltemp = [self._templatemap[i] for i in self._simalgvars]
zall = casadi.vertcat(*zalltemp)
# Need to do anything special with time scaling??
algalltemp = [convert_pyomo2casadi(i) for i in self._alglist]
algall = casadi.vertcat(*algalltemp)
dae['z'] = zall
dae['alg'] = algall
integrator_options['tf'] = 1.0
F = casadi.integrator('F', integrator, dae, integrator_options)
N = len(tsim)
# This approach removes the time scaling from tsim so must
# create an array with the time step between consecutive
# time points
tsimtemp = np.hstack([0, tsim[1:] - tsim[0:-1]])
tsimtemp.shape = (1, len(tsimtemp))
palltemp = [casadi.DM(tsimtemp)]
# Need a similar np array for each time-varying input
for p in self._siminputvars.keys():
profile = varying_inputs[p]
tswitch = list(profile.keys())
tswitch.sort()
tidx = [tsim.searchsorted(i) for i in tswitch] + \
[len(tsim) - 1]
ptemp = [profile[0]] + \
[casadi.repmat(profile[tswitch[i]], 1,
tidx[i + 1] - tidx[i])
for i in range(len(tswitch))]
temp = casadi.horzcat(*ptemp)
palltemp.append(temp)
I = F.mapaccum('simulator', N)
sol = I(x0=initcon, p=casadi.vertcat(*palltemp))
profile = sol['xf'].full().T
if len(self._algvars) != 0:
algprofile = sol['zf'].full().T
profile = np.concatenate((profile, algprofile), axis=1)
return [tsim, profile]
def packXYZ(x, y, z):
"""
This function concatenate x, y, x to one vector and return it.
"""
t = np.concatenate([x, y, z])
return t
def concatenate(arrays):
return np.concatenate([a.flatten() for a in arrays])
