def setUp (self):
sbml = SBML ()
sbml.load_file ('input/simple_enzymatic.xml')
self.__model = sbml_to_odes (sbml)
self.__experiments = ExperimentSet ('input/simple_enzymatic.data')
self.__theta_priors = define_sbml_params_priors (sbml,
'input/simple_enzymatic.priors')
def run_task (model_file, priors_file, experiment_file, \
iterations_phase1, sigma_update_n, iterations_phase2, \
iterations_phase3, nof_process, signetms_path, seed=42):
# pylint: disable=reimported
import time
start_time = time.time()
# importing local modules...
sys.path.insert(0, signetms_path)
from model.SBML import SBML
from experiment.ExperimentSet import ExperimentSet
from model.SBMLtoODES import sbml_to_odes
from marginal_likelihood.MarginalLikelihood \
import MarginalLikelihood
from model.PriorsReader import define_sbml_params_priors
import seed_manager
# Now the actual code...
seed_manager.set_seed(seed)
sbml = SBML()
sbml.load_file(model_file)
odes = sbml_to_odes(sbml)
experiments = ExperimentSet(experiment_file)
theta_priors = define_sbml_params_priors(sbml, priors_file)
ml = MarginalLikelihood (iterations_phase1,
sigma_update_n,
iterations_phase2,
iterations_phase3, 20, 2, \
verbose=False, n_process=nof_process)
log_l = ml.estimate_marginal_likelihood(experiments, odes, theta_priors)
elapsed_time = time.time() - start_time
return log_l, elapsed_time
def test_sbml_to_odes (self):
""" Tests if you can create an ODES object from an SBML model.
"""
model = SBML ()
model.load_file ("input/model2.xml")
odes = sbml_to_odes (model)
t = [0, .1, .2]
y = odes.evaluate_on ([0])
self.assertListEqual (y["EGF"], [0])
self.assertListEqual (y["ERK"], [10000])
t = [0, .1, .2]
y = odes.evaluate_on (t)
def perform_marginal_likelihood (sbml_file, priors_file, \
experiment_file, burnin1_iterations, sigma_update_n, \
burnin2_iterations, sampling_iterations, verbose=False, \
n_process=0, sample_output_file=None, seed=0):
print ("Performing marginal likelihood calculations of model: " + \
sbml_file)
sbml = SBML()
sbml.load_file(sbml_file)
odes = sbml_to_odes(sbml)
experiments = ExperimentSet(experiment_file)
theta_priors = define_sbml_params_priors(sbml, priors_file)
seed_manager.set_seed(seed)
ml = MarginalLikelihood (burnin1_iterations,
sigma_update_n,
burnin2_iterations,
sampling_iterations, 20, 2, \
verbose=verbose, n_process=n_process)
log_l = ml.estimate_marginal_likelihood(experiments, odes, theta_priors)
ml.print_sample(output_file=sample_output_file)
print("log_l = " + str(log_l))
return log_l
# Prints all chemical reactions defined in an SBML file
import sys
import os
current_path = os.path.abspath(__file__)
sys.path.insert(0, '/'.join(current_path.split('/')[:-2]))
import argparse
from model.SBML import SBML
from model.SBMLtoODES import sbml_to_odes
parser = argparse.ArgumentParser()
parser.add_argument("model", help="SBML file with model definition.")
args = parser.parse_args()
sbml_file = args.model
sbml = SBML()
sbml.load_file(sbml_file)
odes = sbml_to_odes(sbml)
odes.print_equations()
from experiment.ExperimentSet import ExperimentSet
if len (sys.argv) != 4:
print ("Usage: " + sys.argv[0] + " trace_file model_file " + \
"experiment_file")
exit()
trace_file = sys.argv[1]
model_file = sys.argv[2]
experiment_file = sys.argv[3]
# define model
sbml = SBML ()
sbml.load_file (model_file)
ode = sbml_to_odes (sbml)
ode.print_equations ()
# get param names
param_names = []
for param in sbml.get_all_param ():
param_names.append (param)
# experiment info
exp_set = ExperimentSet (experiment_file)
experiment_times = exp_set[-1].times
experiment_measure = exp_set[-1].measure_expression
experiment_observations = []
for exp in exp_set:
obs = exp.values
experiment_observations.append (obs)
