def test_massaction_from_parameters():
mak = MassAction(k_forward=ParameterEntry("k", .1))
assert mak.k_forward == .1
mak = MassAction(k_forward=ParameterEntry("k", .1),
k_reverse=ParameterEntry("k", .01))
assert mak.k_reverse == .01
def test_massaction_forward_rate():
with pytest.raises(ValueError, match=r"Propensity parameters must be Parameters or floats with positive values.*"):
MassAction(k_forward=0)
with pytest.raises(ValueError, match=r"Propensity parameters must be Parameters or floats with positive values.*"):
MassAction(k_forward=-1)
def test_massaction_reserve_rate():
with pytest.raises(TypeError, match=r"missing 1 required positional argument: 'k_forward'"):
MassAction(k_reverse=0.1)
with pytest.raises(ValueError, match=r"Propensity parameters must be Parameters or floats with positive values.*"):
MassAction(k_forward=1, k_reverse=0)
with pytest.raises(ValueError, match=r"Propensity parameters must be Parameters or floats with positive values.*"):
MassAction(k_forward=1, k_reverse=-1)
def test_reaction_equality():
"""test for the_equality operator"""
sp1 = Species(name='test_species_a')
sp2 = Species(name='test_species_b')
rxn1 = Reaction(inputs=[sp1, sp2], outputs=[sp2, sp2], propensity_type=MassAction(k_forward=1))
rxn2 = Reaction(inputs=[sp2, sp1], outputs=[sp2, sp2], propensity_type=MassAction(k_forward=1))
rxn3 = Reaction(inputs=[sp2, sp1], outputs=[sp2, sp2], propensity_type=MassAction(k_forward=10))
rxn4 = Reaction(inputs=[sp2, sp1], outputs=[sp2], propensity_type=MassAction(k_forward=1))
assert rxn1 == rxn2
assert rxn1 != rxn3
assert rxn1 != rxn4
def test_species_merging():
sp1 = Species(name='test_species_a')
chem_complexes = [WeightedSpecies(species=sp1, stoichiometry=2),
WeightedSpecies(species=sp1, stoichiometry=1)]
rxn = Reaction(inputs=chem_complexes, outputs=[], propensity_type=MassAction(k_forward=1))
# same species with different stoichiometry gets merged into one species
assert len(rxn.inputs) == 1
sp1 = Species(name='test_species_a')
chem_complexes = [WeightedSpecies(species=sp1, stoichiometry=2),
WeightedSpecies(species=sp1, stoichiometry=1)]
rxn = Reaction(inputs=[], outputs=chem_complexes, propensity_type=MassAction(k_forward=1))
# same species with different stoichiometry gets merged into one species
assert len(rxn.outputs) == 1
def test_reaction_list_flattening():
sp1 = Species(name='test_species_a')
sp2 = Species(name='test_species_b')
k_f = 1
mak = MassAction(k_forward=k_f)
rxn1 = Reaction(inputs=[sp1, [sp1, sp2]], outputs=[[sp2, sp2], sp1], propensity_type=mak)
rxn2 = Reaction(inputs=[sp1, sp1, sp2], outputs=[sp1, sp2, sp2], propensity_type=mak)
assert rxn1 == rxn2
def test_propensity_dict_massaction():
k1 = ParameterEntry(parameter_value='1', parameter_name='k1')
k2 = ParameterEntry(parameter_value='2', parameter_name='k2')
#Should store the ParameterEntry in this case
P1 = MassAction(k_forward=k1, k_reverse=k2)
assert P1.propensity_dict["parameters"]["k_forward"] == k1
assert P1.propensity_dict["parameters"]["k_reverse"] == k2
#assert getters work (should return values instead of ParameterEntries)
assert P1.k_forward == k1.value
assert P1.k_reverse == k2.value
#Should store a numerical value in this case
P2 = MassAction(k_forward=k1.value, k_reverse=k2.value)
assert P2.propensity_dict["parameters"]["k_forward"] == k1.value
assert P2.propensity_dict["parameters"]["k_reverse"] == k2.value
#assert getters work
assert P2.k_forward == k1.value
assert P2.k_reverse == k2.value
def test_reaction_initialization(self):
# warns if both input and output species are empty
mak = MassAction(k_forward=0.1)
with self.assertWarns(Warning):
Reaction(inputs=[], outputs=[], propensity_type=mak)
# test for invalid propensity type
with self.assertRaises(ValueError):
Reaction(inputs=[], outputs=[], propensity_type=Species)
# input must be a valid species object
with self.assertRaises(TypeError):
Reaction(inputs=['a'], outputs=[], propensity_type=mak)
# output must be a valid species object
with self.assertRaises(TypeError):
Reaction(inputs=[], outputs=['b'], propensity_type=mak)
rxn = Reaction.from_massaction(inputs=[], outputs=[], k_forward=0.1, k_reverse=1)
# test whether the reaction is registered as reversible
self.assertTrue(rxn.is_reversible)
# test whether the reaction is registered as massaction
self.assertTrue(isinstance(rxn.propensity_type, MassAction))
# test WeightedSpecies inputs
sp1 = Species(name='test_species_a')
sp2 = Species(name='test_species_b')
chem_com_sp1 = WeightedSpecies(species=sp1, stoichiometry=2)
chem_com_sp2 = WeightedSpecies(species=sp2, stoichiometry=1)
Reaction(inputs=[chem_com_sp1], outputs=[chem_com_sp2], propensity_type=MassAction(k_forward=1))
# test different input and output lists
Reaction(inputs=[chem_com_sp1], outputs=[sp2], propensity_type=MassAction(k_forward=1))
# mixing WeightedSpecies and Species is not allowed
with self.assertRaises(TypeError):
Reaction(inputs=[chem_com_sp1, sp2], outputs=[sp1], propensity_type=MassAction(k_forward=1))
def test_massaction_is_reverable():
mak = MassAction(k_forward=1, k_reverse=0.1)
assert mak.is_reversible
