def _set_measurement_deviations(self):
self._measurement_deviations = ci.vertcat([ \
ci.vec(self._discretization.measurements) - \
self._measurement_data_vectorized + \
ci.vec(self._discretization.optimization_variables["V"])
])
def _set_measurement_deviations(self):
self._measurement_deviations = ci.vertcat([ \
ci.vec(self._measurements_controls_applied) - \
self._measurement_data_vectorized + \
ci.vec(self._discretization.optimization_variables["V"])
])
def _set_measurement_data(self):
# The DOE problem does not depend on actual measurement values,
# the measurement deviations are only needed to set up the objective;
# therefore, dummy-values for the measurements can be used
# (see issue #7 for further information)
measurement_data = np.zeros((self._discretization.system.nphi, \
self._discretization.number_of_intervals + 1))
self._measurement_data_vectorized = ci.vec(measurement_data)
def _set_measurement_data(self):
# The DOE problem does not depend on actual measurement values,
# the measurement deviations are only needed to set up the objective;
# therefore, dummy-values for the measurements can be used
# (see issue #7 for further information)
measurement_data = np.zeros((self._discretization.system.nphi, \
self._discretization.number_of_intervals + 1))
self._measurement_data_vectorized = ci.vec(measurement_data)
def _set_measurement_data(self, ydata):
measurement_data = inputchecks.check_measurement_data(ydata, \
self._discretization.system.nphi, \
self._discretization.number_of_intervals + 1)
self._measurement_data_vectorized = ci.vec(measurement_data)
def _set_measurement_data(self, ydata):
measurement_data = inputchecks.check_measurement_data(ydata, \
self._discretization.system.nphi, \
self._discretization.number_of_intervals + 1)
self._measurement_data_vectorized = ci.vec(measurement_data)