def initialize(self, tau=1.0, scale=1.0):
'''
Build the dae system of transient CSTR model
:param tau: space time of CSTR model
'''
Ptot = cas.sumRows(self._partP_in) # Total Pressure
for i in range(self.ngas):
self._partP[i] = self._flow[i]/self._Flowtot * Ptot
self.build_kinetic()
self.build_rate(scale=1)
for j in range(self.ngas):
self._d_flow[j] = self._partP_in[j] / Ptot * self._Flowtot \
- self._flow[j]
for i in range(self.nrxn):
self._d_flow[j] += self.stoimat[i][j] * self._rate[i]
# Scale the flow rate
self._d_flow[j] /= tau
for j in range(self.nsurf):
self._d_cover[j] = 0
for i in range(self.nrxn):
self._d_cover[j] += self.stoimat[i][j + self.ngas] * self._rate[i]
self._x = cas.vertcat([self._flow, self._cover])
# ???
self._p = cas.vertcat([self._dEa, self._dBE])
self._u = cas.vertcat([self._partP_in, self._Tem, self._Flowtot])
self._xdot = cas.vertcat([self._d_flow, self._d_cover])
#self._dae_ = dict(x=self._x, p=self._p, ode=self._xdot)
self._dae_ = cas.SXFunction('dae', cas.controldaeIn(x=self._x, p=self._p, u=self._u, t=self._t),
cas.daeOut(ode=self._xdot))
def get_dependency(expression):
sym = symvar(expression)
f = MXFunction('f', sym, [expression])
dep = {}
for index, sym in enumerate(sym):
J = f.jacSparsity(index, 0)
dep[sym] = sorted(sumRows(J).find())
return dep
def set_data(self, data):
""" Attach experimental measurement data.
data : a pd.DataFrame object
Data should have columns corresponding to the state labels in
self.state_names, with an index corresponding to the measurement
times.
"""
# Should raise an error if no state name is present
df = data.loc[:, self.state_names]
# Rename columns with state indicies
df.columns = np.arange(self.NEQ)
# Remove empty (nonmeasured) states
self.data = df.loc[:, ~pd.isnull(df).all(0)]
obj_list = []
for ((ti, state), xi) in self.data.stack().iteritems():
obj_list += [
(self._get_interp(ti, [state]) - xi) / self.data[state].max()
]
obj_resid = cs.sum_square(cs.vertcat(obj_list))
# ts = data.index
# Define objective function
# obj_resid = cs.sum_square(cs.vertcat(
# [(self._get_interp(ti, self._states_indicies) - xi)/ xs.max(0)
# for ti, xi in zip(ts, xs)]))
alpha = cs.SX.sym('alpha')
obj_lasso = alpha * cs.sumRows(cs.fabs(self._P))
self._obj = obj_resid + obj_lasso
# Create the solver object
self._nlp = cs.SXFunction('nlp', cs.nlpIn(x=self._V, p=alpha),
cs.nlpOut(f=self._obj, g=self._g))
def set_data(self, data):
""" Attach experimental measurement data.
data : a pd.DataFrame object
Data should have columns corresponding to the state labels in
self.state_names, with an index corresponding to the measurement
times.
"""
# Should raise an error if no state name is present
df = data.loc[:, self.state_names]
# Rename columns with state indicies
df.columns = np.arange(self.NEQ)
# Remove empty (nonmeasured) states
self.data = df.loc[:, ~pd.isnull(df).all(0)]
obj_list = []
for ((ti, state), xi) in self.data.stack().iteritems():
obj_list += [(self._get_interp(ti, [state]) - xi) / self.data[state].max()]
obj_resid = cs.sum_square(cs.vertcat(obj_list))
# ts = data.index
# Define objective function
# obj_resid = cs.sum_square(cs.vertcat(
# [(self._get_interp(ti, self._states_indicies) - xi)/ xs.max(0)
# for ti, xi in zip(ts, xs)]))
alpha = cs.SX.sym("alpha")
obj_lasso = alpha * cs.sumRows(cs.fabs(self._P))
self._obj = obj_resid + obj_lasso
# Create the solver object
self._nlp = cs.SXFunction("nlp", cs.nlpIn(x=self._V, p=alpha), cs.nlpOut(f=self._obj, g=self._g))
def test_sum(self):
self.message("sum")
D=DMatrix([[1,2,3],[4,5,6],[7,8,9]])
self.checkarray(c.sumRows(D),array([[12,15,18]]),'sum()')
self.checkarray(c.sumCols(D),array([[6,15,24]]).T,'sum()')
def test_sum(self):
self.message("sum")
D = DMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
self.checkarray(c.sumRows(D), array([[12, 15, 18]]), 'sum()')
self.checkarray(c.sumCols(D), array([[6, 15, 24]]).T, 'sum()')
