def testIDReprBond(self):
rxn = BO4
# noinspection PyTypeChecker
event1 = Event(rxn, [1, 2], DEF_RXN_RATES[rxn][(G, S)][MON_OLI], (4, 8))
good_str = "Forming bo4 bond between indices [1, 2] (adjacency_matrix update (4, 8))"
self.assertTrue(str(event1) == good_str)
self.assertTrue(repr(event1) == good_str)
def testB1BondGenMol(self):
ini_mono_type_list = [S, S, S, G, S]
sg_ratio = 1.0
max_monos = 12
random_num = 55
initial_monomers = [
Monomer(mono_type, i)
for i, mono_type in enumerate(ini_mono_type_list)
]
initial_events = create_initial_events(initial_monomers, DEF_RXN_RATES)
initial_events.append(Event(GROW, [], rate=1e4))
initial_state = create_initial_state(initial_events, initial_monomers)
result = run_kmc(DEF_RXN_RATES,
initial_state,
initial_events,
n_max=max_monos,
t_max=2,
random_seed=random_num,
sg_ratio=sg_ratio)
nodes = result[MONO_LIST]
adj = result[ADJ_MATRIX]
# generate_mol(adj, nodes)
with capture_stderr(generate_mol, adj, nodes) as output:
self.assertFalse(output)
mol = MolFromMolBlock(generate_mol(adj, nodes))
mols = GetMolFrags(mol)
analysis = analyze_adj_matrix(adj)
frag_sizes = analysis[CHAIN_LEN]
# Make sure there are the same number of separate fragments calculated by RDKIT
# as we get from just separating the alternate B1
self.assertEqual(np.sum(list(frag_sizes.values())), len(mols))
def create_initial_events(initial_monomers, rxn_rates):
"""
# Create event_dict that will oxidize every monomer
:param initial_monomers: a list of Monomer objects
:param rxn_rates: dict of dict of dicts of reaction rates in 1/s
:return: a list of oxidation Event objects to initialize the state by allowing oxidation of every monomer
"""
return [Event(OX, [mon.identity], rxn_rates[OX][mon.type][MONOMER]) for mon in initial_monomers]
def create_sample_kmc_result_c_lignin(num_monos=2, max_monos=12, seed=10):
initial_monomers = [Monomer(C, i) for i in range(num_monos)]
# noinspection PyTypeChecker
initial_events = create_initial_events(initial_monomers, DEF_RXN_RATES)
initial_state = create_initial_state(initial_events, initial_monomers)
initial_events.append(Event(GROW, [], rate=1e4))
result = run_kmc(DEF_RXN_RATES, initial_state, sorted(initial_events), n_max=max_monos, t_max=2, random_seed=seed)
return result
def testMissingRequiredSGRatio(self):
# set up variable to allow running run_kmc without specifying sg_ratio
initial_sg_ratio = 0.75
num_initial_monos = 3
monomer_draw = np.around(np.random.rand(num_initial_monos), MAX_NUM_DECIMAL)
# these are tested separately
initial_monomers = create_initial_monomers(initial_sg_ratio, monomer_draw)
initial_events = create_initial_events(initial_monomers, DEF_RXN_RATES)
initial_state = create_initial_state(initial_events, initial_monomers)
events = {initial_events[i] for i in range(num_initial_monos)}
events.add(Event(GROW, [], rate=1e4))
try:
run_kmc(DEF_RXN_RATES, initial_state, sorted(events), n_max=20, t_max=1, random_seed=10)
self.assertFalse("Should not arrive here; An error should have be raised")
except InvalidDataError as e:
self.assertTrue("A numeric sg_ratio" in e.args[0])
def generate_lignin(num_monomers: int = 1) -> Chem.Mol:
"""Generates lignin molecule.
parameters
----------
num_monomers : int
Number of monomers in lignin molecule.
"""
# Set the percentage of S
sg_ratio = 0
pct_s = sg_ratio / (1 + sg_ratio)
# Set the initial and maximum number of monomers to be modeled.
ini_num_monos = 1
max_num_monos = num_monomers
# Maximum time to simulate, in seconds
t_max = 1 # seconds
mono_add_rate = 1e4 # monomers/second
# Use a random number and the given sg_ratio to determine the monolignol types to be initially modeled
monomer_draw = np.random.rand(ini_num_monos)
initial_monomers = create_initial_monomers(pct_s, monomer_draw)
# Initially allow only oxidation events. After they are used to determine the initial state, add
# GROW to the events, which allows additional monomers to be added to the reaction at the
# specified rate and with the specified ratio
initial_events = create_initial_events(initial_monomers, rxn_rates)
initial_state = create_initial_state(initial_events, initial_monomers)
initial_events.append(Event(GROW, [], rate=mono_add_rate))
# simulate lignin creation
result = run_kmc(rxn_rates,
initial_state,
initial_events,
n_max=max_num_monos,
t_max=t_max,
sg_ratio=sg_ratio)
# using RDKit
nodes = result[MONO_LIST]
adj = result[ADJ_MATRIX]
block = generate_mol(adj, nodes)
mol = MolFromMolBlock(block)
mol = Chem.AddHs(mol)
return mol
def testIniRates(self):
# Note: this test did not increase coverage. Added to help debug notebook.
# run_multi = False
# if run_multi:
# fun = par.delayed(run_kmc)
# num_jobs = num_repeats
# else:
num_repeats = 4
# fun = None
# num_jobs = None
sg_ratio = 1.1
# minimize random calls
monomer_type_list = [S, G]
initial_monomers = [Monomer(mono_type, i) for i, mono_type in enumerate(monomer_type_list)]
max_monos = 12
initial_events = create_initial_events(initial_monomers, DEF_RXN_RATES)
# FYI: np.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)[source]
num_rates = 3
add_rates = np.logspace(4, 12, num_rates)
add_rates_result_list = []
# will add to random seed in the iterations to insure using a different seed for each repeat
random_seed = 2
for add_rate in add_rates:
initial_state = create_initial_state(initial_events, initial_monomers)
initial_events.append(Event(GROW, [], rate=add_rate))
# if run_multi:
# results = par.Parallel(n_jobs=num_jobs)([fun(DEF_RXN_RATES, initial_state, initial_events,
# n_max=max_monos, t_max=1, sg_ratio=sg_ratio,
# random_seed=(random_seed + i))
# for i in range(num_repeats)])
# else:
results = [run_kmc(DEF_RXN_RATES, initial_state, initial_events, n_max=max_monos, t_max=1,
sg_ratio=sg_ratio, random_seed=(random_seed + i)) for i in range(num_repeats)]
add_rates_result_list.append(results)
av_bo4_bonds, std_bo4_bonds = get_avg_num_bonds(BO4, num_rates, add_rates_result_list, num_repeats)
good_av_bo4 = [0.3680555555555555, 0.2863636363636364, 0.03125]
good_std_bo4 = [0.08187379251771941, 0.013636363636363641, 0.05412658773652741]
self.assertTrue(np.allclose(av_bo4_bonds, good_av_bo4))
self.assertTrue(np.allclose(std_bo4_bonds, good_std_bo4))
def initiate_state(add_rate, cfg, rep, sg_ratio):
pct_s = sg_ratio / (1 + sg_ratio)
ini_num_monos = cfg[INI_MONOS]
if cfg[RANDOM_SEED]:
# we don't want the same random seed for every iteration
np.random.seed(cfg[RANDOM_SEED] + int(add_rate / 100 + sg_ratio * 10) + rep)
monomer_draw = np.around(np.random.rand(ini_num_monos), MAX_NUM_DECIMAL)
else:
monomer_draw = np.random.rand(ini_num_monos)
initial_monomers = create_initial_monomers(pct_s, monomer_draw)
# initial event must be oxidation to create reactive species; all monomers may be oxidized
initial_events = create_initial_events(initial_monomers, cfg[RXN_RATES])
# initial_monomers and initial_events are grouped into the initial state
initial_state = create_initial_state(initial_events, initial_monomers)
if cfg[MAX_MONOS] > cfg[INI_MONOS]:
initial_events.append(Event(GROW, [], rate=add_rate))
elif cfg[MAX_MONOS] < cfg[INI_MONOS]:
warning(f"The specified maximum number of monomers ({cfg[MAX_MONOS]}) is less than the "
f"specified initial number of monomers ({cfg[INI_MONOS]}). \n The program will "
f"proceed with the with the initial number of monomers with no addition of monomers.")
return initial_events, initial_state
def create_sample_kmc_result(max_time=1., num_initial_monos=3, max_monos=10, sg_ratio=0.75, seed=10):
# The set lists are to minimize randomness in testing (adding while debugging source of randomness in some tests;
# leaving because it doesn't hurt a thing; also leaving option to make a monomer_draw of arbitrary length
# using a seed, but rounding those numbers because the machine precision differences in floats was the bug
np.random.seed(seed)
if num_initial_monos == 3:
monomer_draw = MONO_DRAW_3
elif num_initial_monos == 20:
monomer_draw = [0.77132064, 0.02075195, 0.63364823, 0.74880388, 0.49850701, 0.22479665, 0.19806286,
0.76053071, 0.16911084, 0.08833981, 0.68535982, 0.95339335, 0.00394827, 0.51219226,
0.81262096, 0.61252607, 0.72175532, 0.29187607, 0.91777412, 0.71457578]
else:
monomer_draw = np.around(np.random.rand(num_initial_monos), MAX_NUM_DECIMAL)
# these are tested separately elsewhere
initial_monomers = create_initial_monomers(sg_ratio, monomer_draw)
initial_events = create_initial_events(initial_monomers, DEF_RXN_RATES)
initial_state = OrderedDict(create_initial_state(initial_events, initial_monomers))
initial_events.append(Event(GROW, [], rate=1e4))
result = run_kmc(DEF_RXN_RATES, initial_state, initial_events, n_max=max_monos, t_max=max_time,
random_seed=10, sg_ratio=sg_ratio)
return result
max_num_monos = 10
# Maximum time to simulate, in seconds
t_max = 1 # seconds
mono_add_rate = 1e4 # monomers/second
# Use a random number and the given sg_ratio to determine the monolignol types to be initially modeled
monomer_draw = np.random.rand(ini_num_monos)
initial_monomers = create_initial_monomers(pct_s, monomer_draw)
# Initially allow only oxidation events. After they are used to determine the initial state, add
# GROW to the events, which allows additional monomers to be added to the reaction at the
# specified rate and with the specified ratio
initial_events = create_initial_events(initial_monomers, rxn_rates)
initial_state = create_initial_state(initial_events, initial_monomers)
initial_events.append(Event(GROW, [], rate=mono_add_rate))
result = run_kmc(rxn_rates,
initial_state,
initial_events,
n_max=max_num_monos,
t_max=t_max,
sg_ratio=sg_ratio)
# Convert the sparse matrix to a full array before printing
print("The adjacency matrix for the simulated lignin is:")
print(result[ADJ_MATRIX].toarray())
# From the list of monomers and the adjacency matrix, we can use LigninKMC to write out a tcl script for psfgen to
# turn into a .psf file.
# fname and sgnames are things that we'd want to change; file name always the same as the segname
def testIDRepr(self):
rxn = OX
# noinspection PyTypeChecker
event1 = Event(rxn, [2], DEF_RXN_RATES[rxn][G][MONOMER])
self.assertTrue(str(event1) == "Performing oxidation on index 2")
