def spark_work(model_with_parameters):
import tellurium as te
if(antimony == "antimony"):
model_roadrunner = te.loada(model_with_parameters[0])
else:
model_roadrunner = te.loadSBMLModel(model_with_parameters[0])
parameter_scan_initilisation = te.ParameterScan(model_roadrunner,**model_with_parameters[1])
simulator = getattr(parameter_scan_initilisation, function_name)
return(simulator())
def sbml_dom_path():
import os
test_path = os.path.dirname(__file__)
model_path = os.path.join(os.path.dirname(test_path), 'examples', 'sbml_example', 'double_enzymatic_sbml.xml')
r = te.loadSBMLModel(model_path)
model = SbmlModel(r)
sim = SbmlSimulation()
r.selections = ['time', '__s0', '__s1', '__s2', '__s3', '__s4', '__s5', 'r0', 'r1', 'r2', 'r3']
r.simulate(0, 100, 100)
sim.add_simulation(r)
dom = ds.SbmlDomPath(model, sim)
return dom
def test_roadrunner(self):
# load test model as SBML
sbml = te.getTestModel('feedback.xml')
rr = te.loadSBMLModel(sbml)
# simulate
s = rr.simulate(0, 100.0, 200)
rr.plot(s)
self.assertIsNotNone(rr)
self.assertIsNotNone(s)
self.assertEqual(s.shape[0], 200)
self.assertEqual(s["time"][0], 0)
self.assertAlmostEqual(s["time"][-1], 100.0)
def __init__(self,
sbml,
settings_kwargs={},
species_kwargs={},
conditions_kwargs={}):
self.model = te.loadSBMLModel(sbml)
self._config = ChainMap()
self.settings_kwargs = settings_kwargs
self.species_kwargs = species_kwargs
self.conditions_kwargs = conditions_kwargs
self.config = self._create_config()
super().__init__(self.config)
def test_roadrunner(self):
# load test model as SBML
sbml = te.getTestModel('feedback.xml')
rr = te.loadSBMLModel(sbml)
# simulate
s = rr.simulate(0, 100.0, 200)
rr.plot(s)
self.assertIsNotNone(rr)
self.assertIsNotNone(s)
self.assertEqual(s.shape[0], 200)
self.assertEqual(s["time"][0], 0)
self.assertAlmostEqual(s["time"][-1], 100.0)
def test_roadrunner(self):
# load test model as SBML
from tellurium.tests.testdata import FEEDBACK_SBML
# sbml = te.getTestModel('feedback.xml')
rr = te.loadSBMLModel(FEEDBACK_SBML)
# simulate
s = rr.simulate(0, 100.0, 200)
rr.plot(s)
self.assertIsNotNone(rr)
self.assertIsNotNone(s)
self.assertEqual(s.shape[0], 200)
self.assertEqual(s["time"][0], 0)
self.assertAlmostEqual(s["time"][-1], 100.0)
def stochastic_work(model_object):
import tellurium as te
if model_type == "antimony":
model_roadrunner = te.loada(model_object.model)
else:
model_roadrunner = te.loadSBMLModel(model_object.model)
model_roadrunner.integrator = model_object.integrator
model_roadrunner.setSeed(random.randint(1000, 9999))
model_roadrunner.variable_step_size = model_object.variable_step_size
model_roadrunner.reset()
simulated_data = model_roadrunner.simulate(model_object.from_time,
model_object.to_time,
model_object.step_points)
return ([simulated_data.colnames, np.array(simulated_data)])
def test_roadrunner(self):
# load test model as SBML
from tellurium.tests.testdata import FEEDBACK_SBML
# sbml = te.getTestModel('feedback.xml')
rr = te.loadSBMLModel(FEEDBACK_SBML)
# simulate
s = rr.simulate(0, 100.0, 200)
rr.plot(s)
self.assertIsNotNone(rr)
self.assertIsNotNone(s)
self.assertEqual(s.shape[0], 200)
self.assertEqual(s["time"][0], 0)
self.assertAlmostEqual(s["time"][-1], 100.0)
def getSBMLFromAntimony(self, input_string):
"""
This method runs in a different process from the caller
and so can import tellurium.
"""
import tellurium as te
# Convert input to string.
inputs = self.__class__._convert(input_string)
# Try different ways to load the model
try:
rr = te.loada(inputs)
except:
rr = te.loadSBMLModel(inputs)
sbml = rr.getSBML()
sys.stdout.writelines(sbml)
def stochastic_work(model_object):
import tellurium as te
if model_type == "antimony":
model_roadrunner = te.loada(model_object.model)
else:
model_roadrunner = te.loadSBMLModel(model_object.model)
model_roadrunner.integrator = model_object.integrator
# seed the randint method with the current time
random.seed()
# it is now safe to use random.randint
model_roadrunner.setSeed(random.randint(1000, 99999))
model_roadrunner.integrator.variable_step_size = model_object.variable_step_size
model_roadrunner.reset()
simulated_data = model_roadrunner.simulate(model_object.from_time, model_object.to_time, model_object.step_points)
return([simulated_data.colnames,np.array(simulated_data)])
def __init__(self, sbml, config):
self.sbml = sbml
self.config = config
self.model = te.loadSBMLModel(sbml)
self.nspecies = len(self.config['species'])
self.n_iterations = self.config['settings']['n_iterations']
self.start = self.config['settings']['integration_options'][
'start_time']
self.end = self.config['settings']['integration_options']['end_time']
self.intervals = self.config['settings']['integration_options'][
'intervals']
self.ics = np.zeros(self.n_iterations * self.nspecies).reshape(
self.n_iterations, self.nspecies)
width = self.nspecies + 1 + 1 # +1 for time +1 for iteration id
self.results = np.zeros(width * self.intervals * self.n_iterations) \
.reshape(self.intervals * self.n_iterations, width)
self.current_iter = 0
self.sampler = MonteCarloSampler(self.sbml, self.config)
"""
return ['ki', 'kr', 'k_T1R', 'k_T2R',
'kdeg_T1R', 'kdeg_T2R', 'kdeg_LRC', 'kdeg_TGF_beta', 'klid', 'ka_LRC', 'kdiss_LRC', 'kimp_Smad2',
'kexp_Smad2', 'kimp_Smad4', 'kexp_Smad4', 'kpho_Smad2', 'kon_Smads', 'koff_Smads', 'kimp_Smads',
'kdepho_Smad2', 'kon_ns', 'KD_ns']
# assign, and now freeParameters is callable bound to ExtendedRoadrunner types
ExtendedRoadRunner.freeParameters = freeParameters
#####################################################################################3
# model and data generation
#
# globally scoped model
r = te.loadSBMLModel(r"D:\SRES\csres\test\zi2011.xml")
print(r.getGlobalParameterIds())
def get_data(**params) -> Tuple[np.ndarray, np.ndarray, List[str]]:
r.reset()
for k, v in params.items():
setattr(r, k, v)
try:
# Simulate "experimental" data
m = r.simulate(0, 20, 21)
except Exception:
return None
x_data = m["time"] # only time
import tellurium as te
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sedx/_te_ClockSedML'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel(os.path.join(workingDir, 'Clock_BIOMD21.xml'))
# Model <model2>
model2 = te.loadSBMLModel(os.path.join(workingDir, 'Clock_BIOMD21.xml'))
# /sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='V_dT']/@value 4.8
model2['V_dT'] = 4.8
# /sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='V_mT']/@value 0.28
model2['V_mT'] = 0.28
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.timeCourseSelections = ['[Mt]', 'time']
import tellurium as te
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel(os.path.join(workingDir, '../models/lorenz.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.timeCourseSelections = ['[Z]', '[X]', '[Y]', 'time']
task1[0] = model1.simulate(start=0.0, end=200.0, steps=1000)
# --------------------------------------------------------
# DataGenerators
# --------------------------------------------------------
# DataGenerator <time1>
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r"/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/_te_case_03"
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <mod1>
mod1 = te.loadSBMLModel(os.path.join(workingDir, "case_03.xml"))
# Model <mod2>
mod2 = te.loadSBMLModel(os.path.join(workingDir, "case_03.xml"))
__var__S1 = mod2["init([S1])"]
mod2["init([S2])"] = __var__S1 + 4
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
mod1.setIntegrator("cvode")
mod1.timeCourseSelections = ["[S1]", "[S2]", "time"]
task1[0] = mod1.simulate(start=0.0, end=10.0, steps=100)
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r'/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/_te_case_01'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model0>
model0 = te.loadSBMLModel(os.path.join(workingDir, 'case_01.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task0>
# Task: <task0>
task0 = [None]
model0.setIntegrator('cvode')
model0.timeCourseSelections = ['[S1]', 'time']
task0[0] = model0.simulate(start=0.0, end=10.0, steps=100)
# --------------------------------------------------------
# DataGenerators
# --------------------------------------------------------
# DataGenerator <plot_0_0_0>
from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path Config.LOADSBMLOPTIONS_RECOMPILE = True workingDir = r'/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <Application0> Application0 = te.loadSBMLModel(os.path.join(workingDir, '../models/asedmlComplex.xml')) # Model <Application0_0> Application0_0 = te.loadSBMLModel(os.path.join(workingDir, '../models/asedmlComplex.xml')) # /sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='s0'] 25.0 Application0_0['init([s0])'] = 25.0 # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task_0_0> # not part of any DataGenerator: task_0_0 # Task <repeatedTask_0_0> repeatedTask_0_0 = [] __range__range_0_0_s1_init_uM = np.linspace(start=5.0, stop=15.0, num=4)
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = '/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/_te_oneStep'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel(os.path.join(workingDir, 'oneStep.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task0>
# not part of any DataGenerator: task0
# Task <task1>
task1 = []
__range__x = np.linspace(start=0.0, stop=10.0, num=101)
for __k__x, __value__x in enumerate(__range__x):
if __k__x == 0:
model1.reset()
# Task: <task0>
elif i[3] == 0:
m.addReaction(["S" + str(i[0]), "S" + str(i[1])],
["S" + str(i[2])], "k" + str(count) + "*S" +
str(i[0]) + "*S" + str(i[1]))
elif i[1] == 0:
m.addReaction(["S" + str(i[0])], ["S" + str(i[1]),
"S" + str(i[2])], "k" + str(count) + "*S" +
str(i[0]))
else:
m.addReaction(["S" + str(i[0]), "S" + str(i[1])], ["S" + str(i[2]),
"S" + str(i[3])], "k" + str(count) + "*S" +
str(i[0]) + "*S" + str(i[1]))
te.saveToFile('ProteinModel.xml', str(m))
r = te.loadSBMLModel('ProteinModel.xml')
try:
if forbidZeros:
r.simulate()
for i in r.getSteadyStateValues():
if i < 1e-5:
raise ValueError
r.simulate(end=1000)
steady = r.steadyState()
if steady < 10e-6:
print "Success!"
print ("Number of tries = " + str(zcount))
zcount = 0
for i in data:
zcount += 1
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r'/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <Application0>
Application0 = te.loadSBMLModel(
os.path.join(workingDir, '../models/asedmlComplex.xml'))
# Model <Application0_0>
Application0_0 = te.loadSBMLModel(
os.path.join(workingDir, '../models/asedmlComplex.xml'))
# /sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='s0'] 25.0
Application0_0['init([s0])'] = 25.0
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task_0_0>
# not part of any DataGenerator: task_0_0
# Task <repeatedTask_0_0>
repeatedTask_0_0 = []
from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path Config.LOADSBMLOPTIONS_RECOMPILE = True workingDir = r'/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/results/_te_repeatedStochastic' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <model1> model1 = te.loadSBMLModel(os.path.join(workingDir, 'repeatedStochastic.xml')) # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task1> # not part of any DataGenerator: task1 # Task <task2> # not part of any DataGenerator: task2 # Task <repeat1> repeat1 = [] __range__x = np.linspace(start=0.0, stop=10.0, num=11) for __k__x, __value__x in enumerate(__range__x):
import tellurium as te from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <model1> model1 = te.loadSBMLModel(os.path.join(workingDir, '../models/oscli.xml')) # Model <model2> model2 = te.loadSBMLModel(os.path.join(workingDir, '../models/oscli.xml')) __var__J3_k2 = model2['J3_k2'] model2['J3_k2'] = __var__J3_k2 / 2 __var__S2 = model2['[S2]'] model2['init([S2])'] = __var__S2 / 2 __var__S1 = model2['[S1]'] model2['init([S1])'] = __var__S1 / 2 # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task1> # Task: <task1> task1 = [None]
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r'/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <Application0>
Application0 = te.loadSBMLModel(os.path.join(workingDir, '../models/app2sim.xml'))
# Model <Application0_0>
Application0_0 = te.loadSBMLModel(os.path.join(workingDir, '../models/app2sim.xml'))
# /sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='s1'] 10.0
Application0_0['init([s1])'] = 10.0
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task_0_0>
# Task: <task_0_0>
task_0_0 = [None]
Application0.setIntegrator('cvode')
Application0.timeCourseSelections = ['[s0]', '[s1]', 'time']
task_0_0[0] = Application0.simulate(start=0.0, end=20.0, steps=1000)
M_df = pd.DataFrame([[round(x, 4) for x in line] for line in M],
columns=species_names)
with open(filepath, "w") as outfile:
outfile.write(M_df.to_csv(sep="\t"))
def write_eigenvalues_to_file(model, filepath, species_names):
M_df = pd.DataFrame([model.getFullEigenValues()], columns=species_names)
with open(filepath, "w") as outfile:
outfile.write(M_df.to_csv(sep="\t"))
# We start with the smallest subsystem: Amino Sugar and Nucleotide Sugar Metabolism
asansm_libsbml_doc = libsbml.readSBML(asansm_sbml)
asansm_libsbml = asansm_libsbml_doc.getModel()
asansm_model = te.loadSBMLModel(asansm_sbml)
S_asansm = asansm_model.getFullStoichiometryMatrix()
S_asansm.colnames = get_reaction_names(asansm_libsbml.getListOfReactions(),
S_asansm.colnames)
S_asansm.rownames = get_metabolite_names(asansm_libsbml.getListOfSpecies(),
S_asansm.rownames)
asansm_right_nullspace = Matrix(S_asansm).nullspace()
asansm_left_nullspace = Matrix(S_asansm.T).nullspace()
asansm_rank = Matrix(S_asansm).rank()
# Next subsystem: Pyrimidine Metabolism
# This one is a little special because Matrix().nullspace computes an empty right nullspace here, but there is one!
# So we do it the pedestrian way...
pm_libsbml_doc = libsbml.readSBML(pm_sbml)
pm_libsbml = pm_libsbml_doc.getModel()
pm_model = te.loadSBMLModel(pm_sbml)
def test_loadSBMLModel_file(self):
r = te.loadSBMLModel(self.sbml_file)
self.assertIsNotNone(r)
def test_loadSBMLModel_file(self):
r = te.loadSBMLModel(self.sbml_file)
self.assertIsNotNone(r)
import tellurium as te from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path workingDir = '/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/_te_case_04' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <mod1> mod1 = te.loadSBMLModel(os.path.join(workingDir, 'case_04.xml')) # Model <mod2> mod2 = te.loadSBMLModel(os.path.join(workingDir, 'case_04.xml')) __var__S1 = mod2['S1'] mod2['S2'] = __var__S1 + 4 # Model <mod3> mod3 = te.loadSBMLModel(os.path.join(workingDir, 'case_04.xml')) __var__S1 = mod3['S1'] mod3['S2'] = __var__S1 + 4 # /sbml:sbml/sbml:model/listOfSpecies/species[@id='S1']/@initialConcentration 20 mod3['init([S1])'] = 20 # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task1>
import tellurium as te from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <model1> model1 = te.loadSBMLModel(os.path.join(workingDir, '../models/oscli.xml')) # Model <model2> model2 = te.loadSBMLModel(os.path.join(workingDir, '../models/oscli.xml')) __var__J3_k2 = model2['J3_k2'] model2['J3_k2'] = __var__J3_k2 / 2 __var__S2 = model2['[S2]'] model2['init([S2])'] = __var__S2 / 2 __var__S1 = model2['[S1]'] model2['init([S1])'] = __var__S1 / 2 # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task1> # Task: <task1> task1 = [None]
from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path Config.LOADSBMLOPTIONS_RECOMPILE = True workingDir = r'/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/results/_te_case_10' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <mod1> mod1 = te.loadSBMLModel(os.path.join(workingDir, 'case_10.xml')) # Model <mod2> mod2 = te.loadSBMLModel(os.path.join(workingDir, 'case_10.xml')) # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task1> # not part of any DataGenerator: task1 # Task <task2> # not part of any DataGenerator: task2 # Task <repeat1> repeat1 = []
import tellurium as te
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel(os.path.join(workingDir, '../models/BorisEJB.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.timeCourseSelections = [
'[MKKK]', '[MKKK_P]', '[MAPK]', '[MAPK_PP]', '[MKK_P]', '[MAPK_P]', 'time',
'[MKK]'
]
task1[0] = model1.simulate(start=0.0, end=4000.0, steps=1000)
# --------------------------------------------------------
from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path Config.LOADSBMLOPTIONS_RECOMPILE = True workingDir = r'/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/results/_te_repeatedStochastic' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <model1> model1 = te.loadSBMLModel(os.path.join(workingDir, 'repeatedStochastic.xml')) # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task1> # not part of any DataGenerator: task1 # Task <task2> # not part of any DataGenerator: task2 # Task <repeat1> repeat1 = [] __range__x = np.linspace(start=0.0, stop=10.0, num=11) for __k__x, __value__x in enumerate(__range__x):
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r'/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <repressor_activator_oscillations>
repressor_activator_oscillations = te.loadSBMLModel(
os.path.join(workingDir,
'../models/BioModel1_repressor_activator_oscillations.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task_0_0>
# not part of any DataGenerator: task_0_0
# Task <repeatedTask_0_0>
repeatedTask_0_0 = []
__range__range_0_0_common_delta_A = np.linspace(start=0.2, stop=1.0, num=4)
for __k__range_0_0_common_delta_A, __value__range_0_0_common_delta_A in enumerate(
__range__range_0_0_common_delta_A):
repressor_activator_oscillations.reset()
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
import tellurium.temiriam as temiriam
__model1_sbml = temiriam.getSBMLFromBiomodelsURN(
'urn:miriam:biomodels.db:BIOMD0000000021')
model1 = te.loadSBMLModel(__model1_sbml)
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.timeCourseSelections = [
'[P0]', '[T1]', '[T0]', '[CC]', '[P2]', '[T2]', '[P1]', '[Cn]', '[Mp]',
'time', '[Mt]'
]
task1[0] = model1.simulate(start=0.0, end=100.0, steps=1000)
# --------------------------------------------------------
import tellurium as te
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sedx/_te_BIOMD0000000012'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel('http://www.ebi.ac.uk/biomodels/models-main/publ/BIOMD0000000012/BIOMD0000000012.xml')
# Model <model2>
model2 = te.loadSBMLModel('http://www.ebi.ac.uk/biomodels/models-main/publ/BIOMD0000000012/BIOMD0000000012.xml')
# /sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id="ps_a"]/@value 0.013
model2['ps_a'] = 0.013
# /sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id="ps_0"]/@value 1.3e-5
model2['ps_0'] = 1.3e-5
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.timeCourseSelections = ['[Z]', '[X]', '[Y]', 'time']
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sedx/_te_BIOMD0000000139'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
import tellurium.temiriam as temiriam
__model1_sbml = temiriam.getSBMLFromBiomodelsURN('urn:miriam:biomodels.db:BIOMD0000000139')
model1 = te.loadSBMLModel(__model1_sbml)
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.timeCourseSelections = ['Total_NFkBn', 'time']
task1[0] = model1.simulate(start=0.0, end=2500.0, steps=1000)
# --------------------------------------------------------
# DataGenerators
# --------------------------------------------------------
# DataGenerator <time>
s.join([k2, sp2]) + "+" + s.join([k3, sp1, sp2]) + ")")
elif i[6] == 8:
m.addReaction([], [sp3], "(1+" + s.join([k1, sp1, sp2]) +
")/(1+" + s.join([k1, sp1]) + "+" + s.join([k2, sp2]) + "+"
+ s.join([k3, sp1, sp2]) + ")")
elif i[6] == 9:
m.addReaction([], [sp3], "(" + s.join([k1, sp1]) + ")/(1+" +
s.join([k1, sp1]) + "+" + s.join([k2, sp2]) + "+" +
s.join([k3, sp1, sp2]) + ")")
decayData = [] # Holds protein decay information
re.proteindecay()
te.saveToFile('GeneticModel.xml', str(m))
r = te.loadSBMLModel('GeneticModel.xml')
try:
if forbidZeros:
r.simulate()
for i in r.getSteadyStateValues():
if i < 1e-5:
raise ValueError
steady = r.steadyState() # Determine if final concentrations steady out
if steady < 10e-6: # If they do, then it's a successful model
print "Success!"
print ("Number of tries = " + str(zcount))
zcount = 0
for i in data:
zcount += 1
print ("Number of reactions = " + str(zcount))
if printReactions:
if i[1] == 0 and i[3] == 0:
m.addReaction([sp1], [sp2], "k" + str(count) + "*" + sp1)
elif i[3] == 0:
m.addReaction([sp1, sp2], [sp3], "k" + str(count) + "*" + sp1
+ "*" + sp2)
elif i[1] == 0:
m.addReaction([sp1], [sp2, sp3], "k" + str(count) + "*" + sp1)
else:
m.addReaction([sp1, sp2], [sp3, sp4], "k" + str(count) + "*" + sp1
+ "*" + sp2)
te.saveToFile('Model.xml', str(m))
r = te.loadSBMLModel('Model.xml')
try:
if forbidZeros:
r.simulate()
for i in r.getSteadyStateValues():
if i < 1e-5:
raise ValueError
if limitBoundarySpecies:
if speciesType.count(1) > maxBoundarySpecies:
raise ValueError
steady = r.steadyState() #Determine if concentrations converge
if steady < 10e-6: # If they do, it's a successful model
print "Success!"
print ("Number of tries = " + str(zcount))
zcount = 0
for i in data:
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r'/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <Application0>
Application0 = te.loadSBMLModel(
os.path.join(workingDir, '../models/app2sim.xml'))
# Model <Application0_0>
Application0_0 = te.loadSBMLModel(
os.path.join(workingDir, '../models/app2sim.xml'))
# /sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='s1'] 10.0
Application0_0['init([s1])'] = 10.0
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task_0_0>
# Task: <task_0_0>
task_0_0 = [None]
Application0.setIntegrator('cvode')
Application0.timeCourseSelections = ['[s0]', '[s1]', 'time']
task_0_0[0] = Application0.simulate(start=0.0, end=20.0, steps=1000)
from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path Config.LOADSBMLOPTIONS_RECOMPILE = True workingDir = r'/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/results/_te_case_02' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <model0> model0 = te.loadSBMLModel(os.path.join(workingDir, 'case_02.xml')) # Model <model1> model1 = te.loadSBMLModel(os.path.join(workingDir, 'case_02.xml')) # /sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='S1']/@initialConcentration 5 model1['init([S1])'] = 5 # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task0> # not part of any DataGenerator: task0 # Task <task1> task1 = [] __range__uniform_linear_for_k1 = np.linspace(start=0.0, stop=5.0, num=6)
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r"/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml"
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <repressor_activator_oscillations>
repressor_activator_oscillations = te.loadSBMLModel(
os.path.join(workingDir, "../models/BioModel1_repressor_activator_oscillations.xml")
)
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task_0_0>
# not part of any DataGenerator: task_0_0
# Task <repeatedTask_0_0>
repeatedTask_0_0 = []
__range__range_0_0_common_delta_A = np.linspace(start=0.2, stop=1.0, num=4)
for __k__range_0_0_common_delta_A, __value__range_0_0_common_delta_A in enumerate(__range__range_0_0_common_delta_A):
repressor_activator_oscillations.reset()
# Task: <task_0_0>
def write_left_nullspace_to_file(M, filepath, species_names): # pass rownames of corresponding S as species_names
M_df = pd.DataFrame([[round(x, 4) for x in line] for line in M], columns=species_names)
with open(filepath, "w") as outfile:
outfile.write(M_df.to_csv(sep="\t"))
def write_eigenvalues_to_file(model, filepath, species_names):
M_df = pd.DataFrame([model.getFullEigenValues()], columns=species_names)
with open(filepath, "w") as outfile:
outfile.write(M_df.to_csv(sep="\t"))
# We start with the smallest subsystem: Amino Sugar and Nucleotide Sugar Metabolism
asansm_libsbml_doc = libsbml.readSBML(asansm_sbml)
asansm_libsbml = asansm_libsbml_doc.getModel()
asansm_model = te.loadSBMLModel(asansm_sbml)
S_asansm = asansm_model.getFullStoichiometryMatrix()
S_asansm.colnames = get_reaction_names(asansm_libsbml.getListOfReactions(), S_asansm.colnames)
S_asansm.rownames = get_metabolite_names(asansm_libsbml.getListOfSpecies(), S_asansm.rownames)
asansm_right_nullspace = Matrix(S_asansm).nullspace()
asansm_left_nullspace = Matrix(S_asansm.T).nullspace()
asansm_rank = Matrix(S_asansm).rank()
# Next subsystem: Pyrimidine Metabolism
# This one is a little special because Matrix().nullspace computes an empty right nullspace here, but there is one!
# So we do it the pedestrian way...
pm_libsbml_doc = libsbml.readSBML(pm_sbml)
pm_libsbml = pm_libsbml_doc.getModel()
pm_model = te.loadSBMLModel(pm_sbml)
S_pm = pm_model.getFullStoichiometryMatrix()
S_pm.colnames = get_reaction_names(pm_libsbml.getListOfReactions(), S_pm.colnames)
class Parameters:
# Input data ==========================================================
INPUT = None
READ_SETTINGS = None
# Test models =========================================================
# 'FFL', 'Linear', 'Nested', 'Branched'
modelType = 'Linear_m'
# General settings ====================================================
# Number of generations
n_gen = 100
# Size of output ensemble
ens_size = 100
# Number of models passed on the next generation without mutation
pass_size = int(ens_size/10)
# Number of models to mutate
mut_size = int(ens_size/2)
# Maximum iteration allowed for random generation
maxIter_gen = 20
# Maximum iteration allowed for mutation
maxIter_mut = 20
# Set conserved moiety
conservedMoiety = False
# Optimizer settings ==================================================
# Maximum iteration allowed for optimizer
optiMaxIter = 100
optiTol = 1.
optiPolish = False
# Weight for control coefficients when calculating the distance
w1 = 16
# Weight for steady-state and flux when calculating the distance
w2 = 1.0
FLUX = False
# Random settings =====================================================
# random seed
r_seed = 123123
# Flag for adding Gaussian noise to steady-state and control coefficiant values
NOISE = False
# Standard deviation of Gaussian noise
ABS_NOISE_STD = 0.01
# Standard deviation of Gaussian noise
REL_NOISE_STD = 0.1
# Plotting settings ===================================================
# Flag for plots
PLOT = True
# Flag for saving plots
SAVE_PLOT = True
# Data settings =======================================================
# Flag for collecting models
EXPORT_ALL_MODELS = True
# Flag for saving collected models
EXPORT_OUTPUT = True
# Flag for saving current settings
EXPORT_SETTINGS = False
# Path to save the output
EXPORT_PATH = './USE/output_Linear_m_update'
# Flag to run algorithm
RUN = True
#%%
if conservedMoiety:
roadrunner.Config.setValue(roadrunner.Config.LOADSBMLOPTIONS_CONSERVED_MOIETIES, True)
roadrunner.Config.setValue(roadrunner.Config.STEADYSTATE_APPROX, True)
roadrunner.Config.setValue(roadrunner.Config.STEADYSTATE_APPROX_MAX_STEPS, 100)
roadrunner.Config.setValue(roadrunner.Config.STEADYSTATE_APPROX_TIME, 1000000)
# roadrunner.Config.setValue(roadrunner.Config.STEADYSTATE_APPROX_TOL, 1e-3)
# Using one of the test models
realModel = ioutils.testModels(modelType)
if os.path.exists(realModel):
realRR = te.loadSBMLModel(realModel)
else:
realRR = te.loada(realModel)
realNumBoundary = realRR.getNumBoundarySpecies()
realNumFloating = realRR.getNumFloatingSpecies()
realFloatingIds = np.sort(realRR.getFloatingSpeciesIds())
realFloatingIdsInd = list(map(int, [s.strip('S') for s in realRR.getFloatingSpeciesIds()]))
realFloatingIdsInd.sort()
realBoundaryIds = np.sort(realRR.getBoundarySpeciesIds())
realBoundaryIdsInd = list(map(int,[s.strip('S') for s in realRR.getBoundarySpeciesIds()]))
realBoundaryIdsInd.sort()
realBoundaryVal = realRR.getBoundarySpeciesConcentrations()
realGlobalParameterIds = realRR.getGlobalParameterIds()
realRR.steadyState()
realSteadyState = realRR.getFloatingSpeciesConcentrations()
realSteadyStateRatio = np.divide(realSteadyState, np.min(realSteadyState))
realFlux = realRR.getReactionRates()
realRR.reset()
realRR.steadyState()
realFluxCC = realRR.getScaledFluxControlCoefficientMatrix()
realConcCC = realRR.getScaledConcentrationControlCoefficientMatrix()
realFluxCC[np.abs(realFluxCC) < 1e-12] = 0
realConcCC[np.abs(realConcCC) < 1e-12] = 0
# Ordering
realFluxCCrow = realFluxCC.rownames
realFluxCCcol = realFluxCC.colnames
realFluxCC = realFluxCC[np.argsort(realFluxCCrow)]
realFluxCC = realFluxCC[:,np.argsort(realFluxCCcol)]
realConcCCrow = realConcCC.rownames
realConcCCcol = realConcCC.colnames
realConcCC = realConcCC[np.argsort(realConcCCrow)]
realConcCC = realConcCC[:,np.argsort(realConcCCcol)]
realFlux = realFlux[np.argsort(realRR.getReactionIds())]
ns = realNumBoundary + realNumFloating # Number of species
nr = realRR.getNumReactions() # Number of reactions
realReactionList = ng.generateReactionListFromAntimony(realModel)
knownReactionList = ng.generateKnownReactionListFromAntimony(realModel)
n_range = range(1, n_gen)
ens_range = range(ens_size)
mut_range = range(mut_size)
r_range = range(nr)
def test_loadSBMLModel_str(self):
r = te.loadSBMLModel(self.sbml_str)
self.assertIsNotNone(r)
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r'/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <Application0>
Application0 = te.loadSBMLModel(
os.path.join(workingDir, '../models/asedml3repeat.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task_0_0>
# not part of any DataGenerator: task_0_0
# Task <repeatedTask_0_0>
repeatedTask_0_0 = []
__range__range_0_0_s1_init_uM = np.linspace(start=10.0, stop=30.0, num=4)
for __k__range_0_0_s1_init_uM, __value__range_0_0_s1_init_uM in enumerate(
__range__range_0_0_s1_init_uM):
Application0.reset()
# Task: <task_0_0>
from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path Config.LOADSBMLOPTIONS_RECOMPILE = True workingDir = r'/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/results/_te_case_12' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <mod1> mod1 = te.loadSBMLModel(os.path.join(workingDir, 'case_12.xml')) # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task1> # not part of any DataGenerator: task1 # Task <task2> # not part of any DataGenerator: task2 # Task <repeat1> repeat1 = [] __range__uniform_linear_for_S1 = np.linspace(start=0.0, stop=10.0, num=5) for __k__uniform_linear_for_S1, __value__uniform_linear_for_S1 in enumerate(
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r'/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/results/_te_lorenz'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel(os.path.join(workingDir, 'lorenz.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.timeCourseSelections = ['x', 'z']
task1[0] = model1.simulate(start=0.0, end=15.0, steps=2000)
# --------------------------------------------------------
# DataGenerators
# --------------------------------------------------------
# DataGenerator <plot_0_0_0>
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
Config.LOADSBMLOPTIONS_RECOMPILE = True
workingDir = r'/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <Application0>
Application0 = te.loadSBMLModel(os.path.join(workingDir, '../models/asedml3repeat.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task_0_0>
# not part of any DataGenerator: task_0_0
# Task <repeatedTask_0_0>
repeatedTask_0_0 = []
__range__range_0_0_s1_init_uM = np.linspace(start=10.0, stop=30.0, num=4)
for __k__range_0_0_s1_init_uM, __value__range_0_0_s1_init_uM in enumerate(__range__range_0_0_s1_init_uM):
Application0.reset()
# Task: <task_0_0>
task_0_0 = [None]
from __future__ import print_function, division
import libsbml
import tellurium as te
import lxml.etree as ET
from io import StringIO
import tempfile
r = te.loada('''
J0: S1 -> S2; k1*S1;
S1=10.0; S2=0.0; k1=1.0;
''')
# create SBML file
f = tempfile.NamedTemporaryFile('w', suffix=".xml")
r.exportToSBML(f.name)
# create C14N canonical XML
et = ET.parse(f.name)
output = StringIO.StringIO()
et.write_c14n(output)
c14n_xml = output.getvalue()
# TODO: in addition sort all elements of the listOfs
# read with libsbml
doc = libsbml.readSBMLFromString(c14n_xml)
# read with roadrunner
te.loadSBMLModel(c14n_xml)
import tellurium as te
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sedx/_te_BorisEJB-test'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel(os.path.join(workingDir, 'model1.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.timeCourseSelections = [
'[MKKK]', '[MKKK_P]', '[MAPK]', '[MAPK_PP]', '[MKK_P]', '[MAPK_P]', 'time',
'[MKK]'
]
task1[0] = model1.simulate(start=0.0, end=4000.0, steps=1000)
# --------------------------------------------------------
import tellurium as te
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel(os.path.join(workingDir, '../models/BorisEJB.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.conservedMoietyAnalysis = True
model1.steadyStateSelections = ['[MKKK]', '[MKKK_P]', '[MAPK]', '[MAPK_PP]', '[MKK_P]', '[MAPK_P]', 'time', '[MKK]']
model1.simulate()
task1[0] = model1.steadyState()
model1.conservedMoietyAnalysis = False
# --------------------------------------------------------
def evolveBifurcation(model,
bifurType='oscillator',
parameterList=None,
paramRanges=None,
maxGenerations=100,
numMembers=50,
mutationConst=0.5,
recombinationConst=0.5,
thresholdType='fitness',
threshold=5.0,
localMin=False,
displayOpt=False):
"""
Author: Veronica Porubsky
evolveBifurcation(model, bifurType = 'oscillator', parameterList = None, paramRanges = None, maxGenerations = 100, numMembers = 50, mutationConst = 0.5, recombinationConst = 0.5, thresholdType = 'fitness', threshold = 5.0, localMin = False, displayOpt = False)
======================
The module will evolve global parameters, floating species initial
concentrations, and boundary species of the loaded antimony or sbml model
to optimize for eigenvalues characteristic of Hopf or turning point
bifurcations.
Inputs:
model : executable model string, SBML (.xml) or Antimony (.ant) model
string file evolveBifurcation takes models in the form of Antimony or
SBML strings which have either an existing instance or which are saved
independently as files with the extension .ant or .xml and entered as
type str
bifurType : str
The bifurcation type that the user wishes to optimize for - either for
Hopf bifurcations to evolve parameters which produce an oscillatory
output or for a turning point bifurcation which will attempt to evolve
parameters that may generate bistability. Should select either:
- 'oscillator'
- 'turningPoint'
parameterList : list
parameterList specifies the parameters to be optimized. If the model
contains parameters named k1, k3_deg, and k5, which the user wishes to
optimize, these should be entered as follows:
parameterList = ['k1', 'k3_deg', 'k5']. The default value is
parameterList = None, in which the algorithm automatically optimizes
all global parameters and all floating and boundary species initial
conditions.
paramRanges : list of tuples
paramRanges imposes a minimum and maximum on the range of values
that the parameters being optimized can be assigned. Should be entered
as follows: paramRanges = [(lower bound, upper bound)]. The lower and
upper bound should both of type float. If the user wishes to assign
unique parameter ranges for each parameter being optimized, they must
ensure that the length of the paramRanges list is the same as the length
of the parameterList. For example, if the user wishes to independently
specify three parameter ranges for parameterList = ['k1', 'k3_deg', 'k5'],
they must enter paramRanges = [(0.1, 10.0), (0.5, 1.0), (5.0, 10.0)].
maxGenerations : int
maxGenerations sets the determines the maximum number of generations
the differential evolution algorithm will complete.
numMembers : int
Specifies the number of members included in the population for
differential evolution. Increasing the number of members may slow
convergence but improve the quality of the solution.
mutationConst : float
The mutationConst parameter specifies the scaling factor during
recombinationConst : float
The recombinationConst is used to determine whether or not
a parameter value in the mutant trial vector will undergo mutation or
whether it will accept the value of the original member.
thresholdType : str
The thresholdType determines whether the specified threshold should be
checked with respect to the best fitness value of the differential
evolution algorithm or to the smallest eigenvalue real component
magnitude. Should select either:
- 'fitness'
- 'eigenvalue'
threshold : float
The threshold is used to fully define stopping criteria for
the differential evolution algorithm.
localMin : bool
Setting this argument to True will allow the optimization algorithm to
perform a final local minimization using the
Broyden-Fletcher-Goldfarb-Shanno algorithm, maintaining the bounded
parameter ranges used during global minimization with differential
evolution.
displayOpt : bool
Use diplayOpt = True to display the progress of optimization. Will
print to the console the optimized objective function evaluation at the
current generation of differential evolution or iteration of BFGS-B.
Returns:
bifurcationModel : executable model/ RoadRunner object
The resulting model with all differential evolution-optimized
parameters to produce the specified bifurcation type. This can be
accessed from the first element of the evolveBifurcation return, and
can be used directly for simulation in tellurium.
fitness : float
Returns the minimized objective function evaluation, which is the
fitness value for the returned model.
:Example:
>>> import tellurium
>>> from bifurcationEvolution import evolveBifurcation
>>> r, funEval = evolveBifurcation('oscillatoryModel.xml', 'oscillator') # assign model with evolved parameters to roadrunner object 'r'
>>> r.simulate() # simulate the updated model which is now available as a roadrunner object
"""
try:
if type(model) == str:
if model.endswith('.xml'):
try:
bifurcationModel = te.loadSBMLModel(model)
bmSBMLPP = bifurcationModel.getParamPromotedSBML(
bifurcationModel)
bifurcationModel = te.loadSBMLModel(bmSBMLPP)
except:
print(
'Could not promote local parameters to global parameters. Continuing optimization without parameter promotion.'
)
bifurcationModel = te.loadSBMLModel(model)
elif model.endswith('.ant'):
try:
bifurcationModel = te.loada(model)
bmSBML = bifurcationModel.getCurrentSBML()
bmSBMLPP = bifurcationModel.getParamPromotedSBML(bmSBML)
bifurcationModel = te.loadSBMLModel(bmSBMLPP)
except:
print(
'Could not promote local parameters to global parameters. Continuing optimization without parameter promotion.'
)
bifurcationModel = te.loada(model)
elif isinstance(model, RoadRunner):
try:
bifurcationModel = model
bmSBML = bifurcationModel.getCurrentSBML()
bmSBMLPP = bifurcationModel.getParamPromotedSBML(bmSBML)
bifurcationModel = te.loadSBMLModel(bmSBMLPP)
except:
print(
'Could not promote local parameters to global parameters. Continuing optimization without parameter promotion.'
)
bifurcationModel = model
else:
raise RuntimeError(
'Input not supported: pass an exisiting roadrunner.RoadRunner instance, Antimony (.ant) file, or an SBML (.xml) file.'
)
except Exception as e:
print(e)
if not bifurType in ['oscillator', 'turningPoint']:
raise RuntimeError(
"bifurType must be either 'oscillator' or 'turningPoint'.")
bifurcationModel.reset()
bifurcationModel.conservedMoietyAnalysis = True
parameterList, paramValues = generateParameterList(bifurcationModel,
parameterList)
if paramRanges:
if len(paramRanges) == 1:
paramRanges = paramRanges * len(parameterList)
else:
paramRanges = generateParameterRanges(paramValues)
toBifurcationObjFunc = (bifurcationModel, bifurType, parameterList)
try:
bifurcationOptParams, fitness = differentialEvolution(
bifurcationObjFunc,
parameterRanges=paramRanges,
maxGenerations=maxGenerations,
displayDEProgress=displayOpt,
populationSize=numMembers,
crossoverProbability=recombinationConst,
mutationConstant=mutationConst,
thresholdType=thresholdType,
threshold=threshold,
bfgsMin=localMin,
differentialEvArguments=toBifurcationObjFunc)
except Exception as e:
print(e)
bifurcationModel = setModelParams(bifurcationModel, parameterList,
bifurcationOptParams)
return bifurcationModel, fitness
import tellurium as te
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = '/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sed-ml'
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel(os.path.join(workingDir, '../models/curien.xml'))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator('cvode')
model1.timeCourseSelections = ['[Phser]', 'time']
task1[0] = model1.simulate(start=0.0, end=500.0, steps=500)
# --------------------------------------------------------
# DataGenerators
# --------------------------------------------------------
# DataGenerator <time>
import tellurium as te
from tellurium.sedml.mathml import *
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import libsedml
import pandas
import os.path
workingDir = "/home/mkoenig/git/tellurium/tellurium/tests/testdata/sedml/sedx/_te_BM12"
# --------------------------------------------------------
# Models
# --------------------------------------------------------
# Model <model1>
model1 = te.loadSBMLModel(os.path.join(workingDir, "model1.xml"))
# --------------------------------------------------------
# Tasks
# --------------------------------------------------------
# Task <task1>
# Task: <task1>
task1 = [None]
model1.setIntegrator("cvode")
model1.timeCourseSelections = ["[Z]", "[X]", "[Y]", "time"]
task1[0] = model1.simulate(start=0.0, end=100.0, steps=1000)
# --------------------------------------------------------
# DataGenerators
# --------------------------------------------------------
# DataGenerator <time>
def test_loadSBMLModel_str(self):
r = te.loadSBMLModel(self.sbml_str)
self.assertIsNotNone(r)
from tellurium.sedml.mathml import * import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d import libsedml import pandas import os.path Config.LOADSBMLOPTIONS_RECOMPILE = True workingDir = r'/home/mkoenig/git/tellurium/examples/tellurium-files/phrasedml/results/_te_parameterScan2D' # -------------------------------------------------------- # Models # -------------------------------------------------------- # Model <model_3> model_3 = te.loadSBMLModel(os.path.join(workingDir, 'parameterScan2D.xml')) # -------------------------------------------------------- # Tasks # -------------------------------------------------------- # Task <task_1> # not part of any DataGenerator: task_1 # Task <repeatedtask_1> # not part of any DataGenerator: repeatedtask_1 # Task <repeatedtask_2> repeatedtask_2 = [] __range__uniform_linear_for_J4_KK5 = np.linspace(start=1.0, stop=40.0, num=11) for __k__uniform_linear_for_J4_KK5, __value__uniform_linear_for_J4_KK5 in enumerate(__range__uniform_linear_for_J4_KK5):
# coding: utf-8
# Back to the main [Index](../index.ipynb)
# ### Model loading from BioModels
# Models can be easily retrieved from BioModels via their identifier.
# In[1]:
#!!! DO NOT CHANGE !!! THIS FILE WAS CREATED AUTOMATICALLY FROM NOTEBOOKS !!! CHANGES WILL BE OVERWRITTEN !!! CHANGE CORRESPONDING NOTEBOOK FILE !!!
from __future__ import print_function
import tellurium as te
# Load model from biomodels.
r = te.loadSBMLModel("http://www.ebi.ac.uk/biomodels-main/download?mid=BIOMD0000000010")
result = r.simulate(0, 3000, 5000)
r.plot(result);
# In[2]:
