def __init__(self, parameter_values=None):
# First call the gillespy2.Model initializer.
super().__init__(self)
# Define parameters for the rates of creation and dissociation.
k_c = gillespy2.Parameter(name='k_c', expression=0.005)
k_d = gillespy2.Parameter(name='k_d', expression=0.08)
self.add_parameter([k_c, k_d])
# Define variables for the molecular species representing M and D.
m = gillespy2.Species(name='monomer', initial_value=30)
d = gillespy2.Species(name='dimer', initial_value=0)
self.add_species([m, d])
# Define the reactions representing the process. In GillesPy2,
# the list of reactants and products for a Reaction object are each a
# Python dictionary in which the dictionary keys are Species objects
# and the values are stoichiometries of the species in the reaction.
r_creation = gillespy2.Reaction(name="r_creation",
rate=k_c,
reactants={m: 2},
products={d: 1})
r_dissociation = gillespy2.Reaction(name="r_dissociation",
rate=k_d,
reactants={d: 1},
products={m: 2})
self.add_reaction([r_creation, r_dissociation])
# Set the timespan for the simulation.
self.timespan(numpy.linspace(0, 100, 101))
def __init__(self, parameter_values=None):
# Initialize the model.
gillespy2.Model.__init__(self, name="toggle_switch")
# Parameters
alpha1 = gillespy2.Parameter(name='alpha1', expression=1)
alpha2 = gillespy2.Parameter(name='alpha2', expression=1)
beta = gillespy2.Parameter(name='beta', expression="2.0")
gamma = gillespy2.Parameter(name='gamma', expression="2.0")
mu = gillespy2.Parameter(name='mu', expression=1.0)
self.add_parameter([alpha1, alpha2, beta, gamma, mu])
# Species
U = gillespy2.Species(name='U', initial_value=10)
V = gillespy2.Species(name='V', initial_value=10)
self.add_species([U, V])
# Reactions
cu = gillespy2.Reaction(name="r1",
reactants={},
products={U: 1},
propensity_function="alpha1/(1+pow(V,beta))")
cv = gillespy2.Reaction(name="r2",
reactants={},
products={V: 1},
propensity_function="alpha2/(1+pow(U,gamma))")
du = gillespy2.Reaction(name="r3",
reactants={U: 1},
products={},
rate=mu)
dv = gillespy2.Reaction(name="r4",
reactants={V: 1},
products={},
rate=mu)
self.add_reaction([cu, cv, du, dv])
self.timespan(np.linspace(0, 50, 101))
def __init__(self, t, s0, i0, r0, beta, gamma):
# First call the gillespy2.Model initializer.
gillespy2.Model.__init__(self, name='StochasticSIR')
# Define parameters for the rates of creation and dissociation.
k_i = gillespy2.Parameter(name='k_i', expression=beta / N)
k_r = gillespy2.Parameter(name='k_r', expression=gamma)
self.add_parameter([k_i, k_r])
# Define variables for the molecular species representing M and D.
s = gillespy2.Species(name='S', initial_value=s0)
i = gillespy2.Species(name='I', initial_value=i0)
r = gillespy2.Species(name='R', initial_value=r0)
self.add_species([s, i, r])
# The list of reactants and products for a Reaction object are each a
# Python dictionary in which the dictionary keys are Species objects
# and the values are stoichiometries of the species in the reaction.
r_i = gillespy2.Reaction(name="infection",
rate=k_i,
reactants={
s: 1,
i: 1
},
products={i: 2})
r_r = gillespy2.Reaction(name="recovery",
rate=k_r,
reactants={i: 1},
products={r: 1})
self.add_reaction([r_i, r_r])
# Set the timespan for the simulation.
self.timespan(t)
def __init__(self, parameter_values=None, volume=1.0):
# Initialize the model.
gillespy2.Model.__init__(self, name="simple1", volume=volume)
# Parameters
k1 = gillespy2.Parameter(name='k1', expression=0.03)
self.add_parameter(k1)
# Species
r1 = gillespy2.Species(name='r1', initial_value=100)
self.add_species(r1)
r2 = gillespy2.Species(name='r2', initial_value=100)
self.add_species(r2)
# Reactions
rxn1 = gillespy2.Reaction(name='r1d',
reactants={r1: 2},
products={},
rate=k1)
self.add_reaction(rxn1)
rxn2 = gillespy2.Reaction(name='r2d',
reactants={r2: 2},
products={},
propensity_function='k1/2 * r2*(r2-1)/vol')
self.add_reaction(rxn2)
def __init__(self, parameter_values=None, init_v=1):
#initialize Model
gillespy2.Model.__init__(self, name="Simple_Hybrid_Model")
#Species
A = gillespy2.Species(name='A', initial_value=init_v)
V = gillespy2.Species(name='V', initial_value=init_v)
self.add_species([A, V])
#parameters
rate1 = gillespy2.Parameter(name='rate1', expression=20.0)
rate2 = gillespy2.Parameter(name='rate2', expression=10.0)
# rate_rule1 = gillespy2.RateRule(V, "cos(t)")
self.add_parameter([rate1, rate2])
# self.add_rate_rule(rate_rule1)
#reactions
r1 = gillespy2.Reaction(name="r1",
reactants={},
products={A: 1},
propensity_function="rate1 * V")
r2 = gillespy2.Reaction(name="r2",
reactants={A: 1},
products={},
rate=rate2)
self.add_reaction([r1, r2])
self.timespan(numpy.linspace(0, 100, 101))
def test_compile_w_spaces(self):
import gillespy2
self.solvers = [
gillespy2.ODECSolver, gillespy2.SSACSolver,
gillespy2.TauLeapingCSolver, gillespy2.TauHybridCSolver
]
# create a model
model = gillespy2.Model(name="Michaelis_Menten")
# parameters
rate1 = gillespy2.Parameter(name='rate1', expression=0.0017)
rate2 = gillespy2.Parameter(name='rate2', expression=0.5)
rate3 = gillespy2.Parameter(name='rate3', expression=0.1)
model.add_parameter([rate1, rate2, rate3])
# Species
A = gillespy2.Species(name='A', initial_value=301)
B = gillespy2.Species(name='B', initial_value=120)
C = gillespy2.Species(name='C', initial_value=0)
D = gillespy2.Species(name='D', initial_value=0)
model.add_species([A, B, C, D])
# reactions
r1 = gillespy2.Reaction(name="r1",
reactants={
A: 1,
B: 1
},
products={C: 1},
rate=rate1)
r2 = gillespy2.Reaction(name="r2",
reactants={C: 1},
products={
A: 1,
B: 1
},
rate=rate2)
r3 = gillespy2.Reaction(name="r3",
reactants={C: 1},
products={
B: 1,
D: 1
},
rate=rate3)
model.add_reaction([r1, r2, r3])
model.timespan(gillespy2.TimeSpan.linspace(t=100, num_points=101))
# run the model
for solver in self.solvers:
with self.subTest(solver=solver.name):
if platform.system() == "Windows":
with self.assertRaises(gillespy2.SimulationError):
model.run(solver=solver)
else:
result = model.run(solver=solver)
self.assertTrue(gillespy2.__file__ == os.path.join(
self.prefix_base_dir, 'A SPACE/gillespy2/__init__.py'))
def create_boundary_condition_test_model(parameter_values=None):
model = gillespy2.Model(name="BoundaryConditionTestModel")
s1 = gillespy2.Species(name="S1", boundary_condition=True, initial_value=0.001, mode='continuous')
s2 = gillespy2.Species(name="S2", boundary_condition=False, initial_value=0.002, mode='continuous')
s3 = gillespy2.Species(name="S3", boundary_condition=False, initial_value=0.001, mode='continuous')
s4 = gillespy2.Species(name="S4", boundary_condition=True, initial_value=0.002, mode='continuous')
model.add_species([s1, s2, s3, s4])
k1 = gillespy2.Parameter(name="k1", expression="0.75")
k2 = gillespy2.Parameter(name="k2", expression="0.25")
model.add_parameter([k1, k2])
reaction1 = gillespy2.Reaction(name="reaction1", propensity_function="vol*k1*S1*S2",
reactants={s1: 1, s2: 1},
products={s3: 1})
reaction2 = gillespy2.Reaction(name="reaction2", propensity_function="vol*k2*S3",
reactants={s3: 1},
products={s1: 1, s2: 1})
reaction3 = gillespy2.Reaction(name="reaction3", propensity_function="vol*k2*S3",
reactants={s1: 1, s2: 2, s3: 1},
products={s4: 1})
model.add_reaction([reaction1, reaction2, reaction3])
model.timespan(numpy.linspace(0, 20, 51))
return model
def __init__(self, parameter_values=None):
# Initialize the model.
gillespy2.Model.__init__(self, name="simple1")
# Parameters
k1 = gillespy2.Parameter(name='k1', expression=parameter_values[0])
k2 = gillespy2.Parameter(name='k2', expression=parameter_values[1])
k3 = gillespy2.Parameter(name='k3', expression=parameter_values[2])
self.add_parameter([k1, k2, k3])
# Species
S1 = gillespy2.Species(name='S1', initial_value=100)
S2 = gillespy2.Species(name='S2', initial_value=0)
self.add_species([S1, S2])
# Reactions
rxn1 = gillespy2.Reaction(name='S1 production',
reactants={},
products={S1: 1},
rate=k1)
rxn2 = gillespy2.Reaction(name='dimer formation',
reactants={S1: 2},
products={S2: 1},
rate=k2)
rxn3 = gillespy2.Reaction(name='dimer degradation',
reactants={S2: 1},
products={},
rate=k3)
self.add_reaction([rxn1, rxn2, rxn3])
self.timespan(np.linspace(0, 100, 101))
def test_add_rate_rule_dict(self):
species2 = gillespy2.Species('test_species2',initial_value=2, mode ='continuous')
species3 = gillespy2.Species('test_species3',initial_value=3, mode='continuous')
rule2 = gillespy2.RateRule(species2, 'cos(t)')
rule3 = gillespy2.RateRule(species3, 'sin(t)')
rate_rule_dict = {'rule2' :rule2, 'rule3':rule3}
self.model.add_species([species2,species3])
with self.assertRaises(ParameterError):
self.model.add_rate_rule(rate_rule_dict)
def __init__(self, parameter_values=None):
gillespy2.Model.__init__(self, name='StochTest1')
A = gillespy2.Species(name='A', initial_value=10)
B = gillespy2.Species(name='B', initial_value=0)
self.add_species([A, B])
k = gillespy2.Parameter(name='k', expression=10)
self.add_parameter([k])
r = gillespy2.Reaction(name='r', reactants={A: 2}, products={B:1}, rate=k)
self.add_reaction([r])
self.timespan(np.linspace(0, 100, 101))
def create_stoch_test_1(parameter_values=None):
model = gillespy2.Model(name='StochTest1')
A = gillespy2.Species(name='A', initial_value=10)
B = gillespy2.Species(name='B', initial_value=0)
model.add_species([A, B])
k = gillespy2.Parameter(name='k', expression=10)
model.add_parameter([k])
r = gillespy2.Reaction(name='r',
reactants={A: 2},
products={B: 1},
rate=k)
model.add_reaction([r])
model.timespan(np.linspace(0, 100, 101))
return model
def create_truncated_state_model(parameter_values=None):
model = gillespy2.Model(name="TruncatedStateModel")
S1 = gillespy2.Species(name="S1", initial_value=0, mode="discrete")
rate = gillespy2.Species(name="rate",
initial_value=0.9999,
mode="continuous")
model.add_species([S1, rate])
model.add_rate_rule(
gillespy2.RateRule(variable="rate",
formula="-1/((t+0.9999)**2)"))
model.add_reaction(
# Because S1 is a "discrete" species, our reaction will be marked "stochastic."
gillespy2.Reaction(products={S1: 1},
propensity_function="10.0*rate"))
return model
def create_stoch_test_1(parameter_values=None):
model = gillespy2.Model(name='StochTest1')
A = gillespy2.Species(name='A', initial_value=10)
B = gillespy2.Species(name='B', initial_value=0)
model.add_species([A, B])
k = gillespy2.Parameter(name='k', expression=10)
model.add_parameter([k])
r = gillespy2.Reaction(name='r',
reactants={A: 1},
products={B: 1},
propensity_function="k*A/vol"
) # testing if 'vol' is a pre-set variable
model.add_reaction([r])
model.timespan(np.linspace(0, 100, 101))
return model
def create_protein_decay(parameter_values=None):
# Instantiate Model
model = gillespy2.Model(name="Protein Decay")
# Define Variables (GillesPy2.Species)
protein = gillespy2.Species(name='protein', initial_value=50)
# Add Variables to Model
model.add_species(protein)
# Define Parameters
decayrate = gillespy2.Parameter(name='decayrate', expression=0.05)
# Add Parameters to Model
model.add_parameter(decayrate)
# Define Reactions
reaction = gillespy2.Reaction(
name="decay", rate='decayrate', reactants={'protein': 1}, products={}
)
# Add Reactions to Model
model.add_reaction(reaction)
# Define Timespan
tspan = gillespy2.TimeSpan.linspace(t=100, num_points=101)
# Set Model Timespan
model.timespan(tspan)
return model
def __get_events(sbml_model, gillespy_model):
for i in range(sbml_model.getNumEvents()):
event = sbml_model.getEvent(i)
gillespy_assignments = []
trigger = event.getTrigger()
delay = event.getDelay()
if delay is not None:
delay = libsbml.formulaToL3String(delay.getMath())
expression=libsbml.formulaToL3String(trigger.getMath())
expression = expression.replace('&&', ' and ').replace('||', ' or ')
initial_value = trigger.getInitialValue()
persistent = trigger.getPersistent()
use_values_from_trigger_time = event.getUseValuesFromTriggerTime()
gillespy_trigger = gillespy2.EventTrigger(expression=expression,
initial_value=initial_value, persistent=persistent)
assignments = event.getListOfEventAssignments()
for a in assignments:
# Convert Non-Constant Parameter to Species
if a.getVariable() in gillespy_model.listOfParameters:
gillespy_species = gillespy2.Species(name=a.getVariable(),
initial_value=gillespy_model.listOfParameters[a.getVariable()].expression,
mode='continuous', allow_negative_populations=True)
gillespy_model.delete_parameter(a.getVariable())
gillespy_model.add_species([gillespy_species])
gillespy_assignment = gillespy2.EventAssignment(a.getVariable(),
__get_math(a.getMath()))
gillespy_assignments.append(gillespy_assignment)
gillespy_event = gillespy2.Event(
name=event.getId(), trigger=gillespy_trigger,
assignments=gillespy_assignments, delay=delay,
use_values_from_trigger_time=use_values_from_trigger_time)
gillespy_model.add_event(gillespy_event)
def cbsa2gillespy(cbsa_model):
import gillespy2 as glp
gilles = glp.Model(name="Model")
k = [glp.Parameter(name='k'+str(i), expression=cbsa_model.exp_k[i]) for i in range(1,cbsa_model.exp_n_reactions)]
gilles.add_parameter(k)
mols = [glp.Species(name='M'+str(i), initial_value=int(cbsa_model.exp_x0[i])) for i in range(1,cbsa_model.exp_n_molecules)]
gilles.add_species(mols)
reactions = []
for i in range(1,cbsa_model.exp_n_reactions):
reactants = list(np.where(cbsa_model.expS[:,i] < 0)[0])
reactants_sto = list(cbsa_model.expS[:,i][reactants]*-1)
modifiers = list(np.where(cbsa_model.expR[:,i] > 0)[0])
modifiers_sto = list(cbsa_model.expR[:,i][modifiers])
products = list(np.where(cbsa_model.expS[:,i] > 0)[0])
products_sto = list(cbsa_model.expS[:,i][products])
reactants += modifiers
reactants_sto += modifiers_sto
products += modifiers
products_sto += modifiers_sto
reactions.append(glp.Reaction(name="R"+str(i),
rate=k[i-1],
reactants={mols[reactants[j]-1]:reactants_sto[j] for j in range(len(reactants))},
products={mols[products[j]-1]:products_sto[j] for j in range(len(products))}
)
)
gilles.add_reaction(reactions)
return gilles
def __init__(self, parameter_values=None):
# First call the gillespy2.Model initializer.
super().__init__(self)
# Define a parameter that represents the rate of decay.
decayrate = gillespy2.Parameter(name='decayrate', expression=0.05)
self.add_parameter([decayrate])
# Define a molecular species representing the protein; "initial_value"
# sets the number of molecules initial present of simulation.
protein = gillespy2.Species(name='protein', initial_value=50)
self.add_species([protein])
# Define the reaction representing the decay process. In GillesPy2,
# the list of reactants and products for a Reaction object are each a
# Python dictionary in which the dictionary keys are Species objects
# and the values are stoichiometries of the species in the reaction.
# (In this example, we have only one reactant, and no products.) The
# "rate" for the Reaction object is a Parameter representing the
# propensity of this reaction firing.)
reaction = gillespy2.Reaction(name="decay",
rate=decayrate,
reactants={protein: 1},
products={})
self.add_reaction([reaction])
# Set the timespan for the simulation.
self.timespan(numpy.linspace(0, 100, 101))
def create_event_test_model(parameter_values=None):
model = gillespy2.Model(name='Event Test Model')
model.add_species([gillespy2.Species(name='S', initial_value=0)])
model.add_parameter(
gillespy2.Parameter(name='event_tracker', expression=99))
model.add_parameter(
gillespy2.Parameter(name='event_tracker2', expression=0))
model.add_reaction(
gillespy2.Reaction(
name='r1',
products={'S': 1},
rate=model.listOfParameters['event_tracker2']))
eventTrig1 = gillespy2.EventTrigger(expression='t>=2')
event1 = gillespy2.Event(name='event1', trigger=eventTrig1)
event1.add_assignment(
gillespy2.EventAssignment(variable='event_tracker',
expression='t'))
eventTrig2 = gillespy2.EventTrigger(
expression='t >= event_tracker + 2')
event2 = gillespy2.Event(name='event2', trigger=eventTrig2)
event2.add_assignment(
gillespy2.EventAssignment(variable='event_tracker2',
expression='t'))
model.add_event([event1, event2])
return model
def test_add_rate_rule_dict(self):
model = create_decay()
species2 = gillespy2.Species('test_species2',
initial_value=2,
mode='continuous')
species3 = gillespy2.Species('test_species3',
initial_value=3,
mode='continuous')
rule2 = gillespy2.RateRule('rule2', species2, 'cos(t)')
rule3 = gillespy2.RateRule(name='rule3',
variable=species3,
formula='sin(t)')
rate_rule_dict = {'rule2': rule2, 'rule3': rule3}
model.add_species([species2, species3])
with self.assertRaises(ModelError):
model.add_rate_rule(rate_rule_dict)
def test_null_timeout(self):
model = create_decay()
species = gillespy2.Species('test_species', initial_value=1)
model.add_species([species])
solver = TauHybridSolver(model=model)
results = solver.run()
self.assertTrue(len(results) > 0)
def test_model_add__multiple_components__in_order(self):
import gillespy2
s1 = gillespy2.Species(name="s1", initial_value=29)
k1 = gillespy2.Parameter(name="k1", expression=29)
r1 = gillespy2.Reaction(name="r1", reactants={"s1": 1}, rate="k1")
rr1 = gillespy2.RateRule(name="rr1", variable="k1", formula="29")
ar1 = gillespy2.AssignmentRule(name="ar1", variable="s1", formula="29")
ea = gillespy2.EventAssignment(name="ea",
variable="k1",
expression="29")
et = gillespy2.EventTrigger(expression="t > 29")
e1 = gillespy2.Event(name="e1", trigger=et, assignments=[ea])
divide = gillespy2.FunctionDefinition(name="divide",
function="x / y",
args=["x", "y"])
tspan = gillespy2.TimeSpan(range(100))
model = gillespy2.Model(name="Test Model")
model.add([s1, k1, r1, rr1, ar1, e1, divide, tspan])
self.assertIn("ar1", model.listOfAssignmentRules)
self.assertIn("e1", model.listOfEvents)
self.assertIn("divide", model.listOfFunctionDefinitions)
self.assertIn("k1", model.listOfParameters)
self.assertIn("rr1", model.listOfRateRules)
self.assertIn("r1", model.listOfReactions)
self.assertIn("s1", model.listOfSpecies)
self.assertEqual(tspan, model.tspan)
def test_add_rate_rule(self):
model = Example()
species = gillespy2.Species('test_species', initial_value=1)
rule = gillespy2.RateRule(name='rr1',formula='test_species+1',variable='test_species')
model.add_species([species])
model.add_rate_rule([rule])
results = model.run()
self.assertEqual(results[0].solver_name, 'TauHybridSolver')
def test_add_rate_rule(self):
species = gillespy2.Species('test_species',
initial_value=1,
mode='continuous')
rule = gillespy2.RateRule(species, 'cos(t)')
self.model.add_species([species])
self.model.add_rate_rule([rule])
self.model.run(solver=BasicTauHybridSolver)
def test_add_bad_expression_rate_rule_dict(self):
model = create_decay()
species2 = gillespy2.Species('test_species2',
initial_value=2,
mode='continuous')
rule = gillespy2.RateRule(variable=species2, formula='')
with self.assertRaises(ModelError):
model.add_rate_rule(rule)
def test_math_name_overlap(self):
gamma = gillespy2.Species('gamma',initial_value=2, mode='continuous')
self.model.add_species([gamma])
k2 = gillespy2.Parameter(name='k2', expression=1)
self.model.add_parameter([k2])
gamma_react = gillespy2.Reaction(name='gamma_react', reactants={'gamma':1}, products={}, rate=k2)
self.model.add_reaction([gamma_react])
self.model.run(solver=BasicTauHybridSolver)
def create_rate_rule_test_model(parameter_values=None):
model = gillespy2.Model(name="RateRuleTestModel")
s1 = gillespy2.Species(name="S1", initial_value=0.015, mode='continuous')
s2 = gillespy2.Species(name="S2", initial_value=0.0, mode='continuous')
s3 = gillespy2.Species(name="S3", initial_value=1.0, mode='continuous')
model.add_species([s1, s2, s3])
k1 = gillespy2.Parameter(name="k1", expression="1.0")
model.add_parameter([k1])
rule1 = gillespy2.RateRule(name="rule1", variable="S1", formula="-k1*S1")
rule2 = gillespy2.RateRule(name="rule2", variable="S2", formula="k1*S1")
rule3 = gillespy2.RateRule(name="rule3", variable="S3", formula="0.015-(S1+S2)")
model.add_rate_rule([rule1, rule2, rule3])
model.timespan(numpy.linspace(0, 20, 51))
return model
def convert_species(self):
'''
Method to convert species from non-gillespy2 Random DNA Chemistry to the gillespy2 Random DNA Chemistry
'''
gillespy2_species = []
if self.previous_gillespy2_result == None:
for u, ucon in self.randomDNAChem.concentration_lookup[
'conU'].items():
gillespy2_u = gillespy2.Species(name=u, initial_value=ucon[0])
gillespy2_species.append(gillespy2_u)
for l, lcon in self.randomDNAChem.concentration_lookup[
'conL'].items():
gillespy2_l = gillespy2.Species(name=l, initial_value=lcon[0])
gillespy2_species.append(gillespy2_l)
for f, fcon in self.randomDNAChem.concentration_lookup[
'conF'].items():
gillespy2_f = gillespy2.Species(name=f, initial_value=fcon[0])
gillespy2_species.append(gillespy2_f)
for p, pcon in self.randomDNAChem.concentration_lookup[
'conP'].items():
gillespy2_p = gillespy2.Species(name=p, initial_value=pcon[0])
gillespy2_species.append(gillespy2_p)
else:
for u, ucon in self.randomDNAChem.concentration_lookup[
'conU'].items():
gillespy2_u = gillespy2.Species(
name=u,
initial_value=int(self.previous_gillespy2_result[u][-1]))
gillespy2_species.append(gillespy2_u)
for l, lcon in self.randomDNAChem.concentration_lookup[
'conL'].items():
gillespy2_l = gillespy2.Species(
name=l,
initial_value=int(self.previous_gillespy2_result[l][-1]))
gillespy2_species.append(gillespy2_l)
for f, fcon in self.randomDNAChem.concentration_lookup[
'conF'].items():
gillespy2_f = gillespy2.Species(
name=f,
initial_value=int(self.previous_gillespy2_result[f][-1]))
gillespy2_species.append(gillespy2_f)
for p, pcon in self.randomDNAChem.concentration_lookup[
'conP'].items():
gillespy2_p = gillespy2.Species(
name=p,
initial_value=int(self.previous_gillespy2_result[p][-1]))
gillespy2_species.append(gillespy2_p)
self.add_species(gillespy2_species)
def __init__(self, endtime, timestep):
"""
Initialize the model.
Parameters
----------
endtime : endtime of simulations
timestep : time-step of simulations
"""
super().__init__(
endtime=endtime,
timestep=timestep,
model_name="SIR",
)
beta = gillespy.Parameter(name='beta', expression=self.params['beta'])
gamma = gillespy.Parameter(name='gamma',
expression=self.params['gamma'])
self.add_parameter([beta, gamma])
# Species
S = gillespy.Species(name='S', initial_value=100)
I = gillespy.Species(name='I', initial_value=100)
R = gillespy.Species(name='R', initial_value=100)
self.add_species([S, I, R])
# Reactions
infection = gillespy.Reaction(
name='infection',
reactants={
S: 1,
I: 1
},
products={I: 2},
propensity_function='beta*S*I/(S+I+R)',
)
recover = gillespy.Reaction(
name='recover',
reactants={I: 1},
products={R: 1},
rate=gamma,
)
self.add_reaction([infection, recover])
nb_of_steps = int(math.ceil((endtime / timestep))) + 1
self.timespan(np.linspace(0, endtime, nb_of_steps))
def test_ensure_hybrid_continuous_species(self):
model = create_decay()
species1 = gillespy2.Species('test_species1',
initial_value=1,
mode='continuous')
model.add_species(species1)
results = model.run()
valid_solvers = ('TauHybridSolver', 'TauHybridCSolver')
self.assertIn(results[0].solver_name, valid_solvers)
def test_ensure_continuous_dynamic_timeout_warning(self):
model = create_decay()
species1 = gillespy2.Species('test_species1',
initial_value=1,
mode='dynamic')
model.add_species(species1)
with self.assertLogs('GillesPy2', level='WARN'):
solver = TauHybridSolver(model=model)
results = model.run(solver=solver, timeout=1)
