def run(self):
r"""
Overrides the default run() method.
Performs the complete analysis on the model specified during initialisation.
:return: an ODE problem which can be further used in inference and simulation.
:rtype: :class:`~means.core.problems.ODEProblem`
"""
max_order = self.__max_order
stoichiometry_matrix = self.model.stoichiometry_matrix
propensities = self.model.propensities
species = self.model.species
# compute n_counter and k_counter; the "n" and "k" vectors in equations, respectively.
n_counter, k_counter = generate_n_and_k_counters(max_order, species)
# dmu_over_dt has row per species and one col per element of n_counter (eq. 6)
dmu_over_dt = generate_dmu_over_dt(species, propensities, n_counter, stoichiometry_matrix)
# Calculate expressions to use in central moments equations (eq. 9)
central_moments_exprs = eq_central_moments(n_counter, k_counter, dmu_over_dt, species, propensities, stoichiometry_matrix, max_order)
# Expresses central moments in terms of raw moments (and central moments) (eq. 8)
central_from_raw_exprs = raw_to_central(n_counter, species, k_counter)
# Substitute raw moment, in central_moments, with expressions depending only on central moments
central_moments_exprs = self._substitute_raw_with_central(central_moments_exprs, central_from_raw_exprs, n_counter, k_counter)
# Get final right hand side expressions for each moment in a vector
mfk = self._generate_mass_fluctuation_kinetics(central_moments_exprs, dmu_over_dt, n_counter)
# Applies moment expansion closure, that is replaces last order central moments by parametric expressions
mfk = self.closure.close(mfk, central_from_raw_exprs, n_counter, k_counter)
# These are the left hand sign symbols referring to the mfk
prob_lhs = self._generate_problem_left_hand_side(n_counter, k_counter)
# Finally, we build the problem
out_problem = ODEProblem("MEA", prob_lhs, mfk, sp.Matrix(self.model.parameters))
return out_problem
def _compute_moments(self, all_trajectories, max_moment_order):
mean_trajectories = [sum(trajs)/float(len(trajs)) for trajs in zip(*all_trajectories)]
if max_moment_order == 1:
return mean_trajectories
n_counter, _ = generate_n_and_k_counters(max_moment_order - 1, self.__problem.species)
out_trajects = mean_trajectories[:]
for n in n_counter:
if n.order == 0:
continue
out_trajects.append(self._compute_one_moment(all_trajectories, mean_trajectories, n))
return out_trajects
def run(self):
r"""
Overrides the default run() method.
Performs the complete analysis on the model specified during initialisation.
:return: an ODE problem which can be further used in inference and simulation.
:rtype: :class:`~means.core.problems.ODEProblem`
"""
max_order = self.__max_order
stoichiometry_matrix = self.model.stoichiometry_matrix
propensities = self.model.propensities
species = self.model.species
# compute n_counter and k_counter; the "n" and "k" vectors in equations, respectively.
n_counter, k_counter = generate_n_and_k_counters(max_order, species)
# dmu_over_dt has row per species and one col per element of n_counter (eq. 6)
dmu_over_dt = generate_dmu_over_dt(species, propensities, n_counter,
stoichiometry_matrix)
# Calculate expressions to use in central moments equations (eq. 9)
central_moments_exprs = eq_central_moments(n_counter, k_counter,
dmu_over_dt, species,
propensities,
stoichiometry_matrix,
max_order)
# Expresses central moments in terms of raw moments (and central moments) (eq. 8)
central_from_raw_exprs = raw_to_central(n_counter, species, k_counter)
# Substitute raw moment, in central_moments, with expressions depending only on central moments
central_moments_exprs = self._substitute_raw_with_central(
central_moments_exprs, central_from_raw_exprs, n_counter,
k_counter)
# Get final right hand side expressions for each moment in a vector
mfk = self._generate_mass_fluctuation_kinetics(central_moments_exprs,
dmu_over_dt, n_counter)
# Applies moment expansion closure, that is replaces last order central moments by parametric expressions
mfk = self.closure.close(mfk, central_from_raw_exprs, n_counter,
k_counter)
# These are the left hand sign symbols referring to the mfk
prob_lhs = self._generate_problem_left_hand_side(n_counter, k_counter)
# Finally, we build the problem
out_problem = ODEProblem("MEA", prob_lhs, mfk,
sp.Matrix(self.model.parameters))
return out_problem
