def do_UA_simulation(compound, T_range, properties, n_points, biasing_factor,
optimum_matching, prior):
print('Starting UA')
UA_simulation = RJMC_Simulation(compound, T_range, properties, n_points,
1 * 10**6, 0.0, biasing_factor,
optimum_matching)
UA_simulation.prepare_data()
compound_2CLJ = LennardJones_2C(UA_simulation.M_w)
UA_simulation.gen_Tmatrix(prior, compound_2CLJ)
UA_simulation.set_initial_state(prior, compound_2CLJ, initial_model='UA')
UA_simulation.RJMC_Outerloop(prior, compound_2CLJ)
UA_simulation.Report()
UA_logp_trace = UA_simulation.logp_trace
print('UA complete')
return UA_logp_trace
def basic(simulation_params):
print(simulation_params['priors'])
prior = RJMC_Prior(simulation_params['priors'])
prior.epsilon_prior()
prior.sigma_prior()
prior.L_prior()
prior.Q_prior()
rjmc_simulator = RJMC_Simulation(
simulation_params['compound'], simulation_params['trange'],
simulation_params['properties'],
simulation_params['number_data_points'], simulation_params['steps'],
simulation_params['swap_freq'], simulation_params['biasing_factor'],
simulation_params['optimum_matching'])
rjmc_simulator.prepare_data()
print('Simulation Attributes:', rjmc_simulator.get_attributes())
compound_2CLJ = LennardJones_2C(rjmc_simulator.M_w)
rjmc_simulator.optimum_bounds = simulation_params['opt_bounds']
rjmc_simulator.gen_Tmatrix(prior, compound_2CLJ)
# print(rjmc_simulator.opt_params_AUA)
rjmc_simulator.set_initial_state(prior, compound_2CLJ)
rjmc_simulator.RJMC_Outerloop(prior, compound_2CLJ)
trace, logp_trace, percent_dev_trace, BAR_trace = rjmc_simulator.Report(
USE_BAR=simulation_params['USE_BAR'])
rjmc_simulator.write_output(simulation_params['priors'],
tag=simulation_params['label'],
save_traj=simulation_params['save_traj'])
path = 'output/' + simulation_params['compound'] + '/' + simulation_params['properties'] + '/' + simulation_params['compound'] + \
'_' + simulation_params['properties'] + '_' + str(simulation_params['steps']) + '_' + simulation_params['label'] + '_' + str(date.today()) +'/runfile.yaml'
with open(path, 'w') as outfile:
yaml.dump(simulation_params, outfile, default_flow_style=False)
def main():
parser = argparse.ArgumentParser(description='Process input parameters for RJMC 2CLJQ')
parser.add_argument('--compound', '-c',
type=str,
help='Compound to simulate parameters for',
required=True)
parser.add_argument('--trange', '-t',
type=list,
help='Temperature range for data to include',
required=False)
parser.add_argument('--steps', '-s',
type=int,
help='Number of MCMC steps',
required=True)
parser.add_argument('--properties', '-p',
type=str,
help='Properties to include in computation',
required=True)
parser.add_argument('--priors', '-r',
type=dict,
help='Values and types of prior distribution',
required=False)
parser.add_argument('--optimum_matching', '-o',
type=list,
help='Whether to use optimum matching in transitions',
required=False)
parser.add_argument('--number_data_points', '-d',
type=int,
help='Number of data points to include',
required=False)
parser.add_argument('--swap_freq', '-f',
type=float,
help='Frequency of model jump proposal',
required=False)
parser.add_argument('--biasing_factor', '-b',
type=str,
help='Biasing factors for each model',
required=False)
parser.add_argument('--label', '-l',
type=str,
help='label for files',
required=False)
parser.add_argument('--save_traj', '-j',
type=bool,
help='Whether to save trajectory files',
required=False)
T_range = [0.55, 0.95]
n_points = 10
swap_freq = 0.1
biasing_factor = [0, 0, 0]
optimum_matching = ['True', 'True']
tag = ''
save_traj = False
prior_values = {
'epsilon': ['exponential', [0,400]],
'sigma': ['exponential', [0,5]],
'L': ['exponential', [0,3]],
'Q': ['exponential', [0,1]]}
args = parser.parse_args()
print(args.compound)
print(args.biasing_factor)
compound = args.compound
# T_range=args.trange
steps = args.steps
properties = args.properties
if args.trange is not None:
T_range = args.trange
if args.number_data_points is not None:
n_points = args.number_data_points
if args.swap_freq is not None:
swap_freq = args.swap_freq
if args.biasing_factor is not None:
biasing_factor_raw = list(args.biasing_factor.split(','))
biasing_factor = [float(i) for i in biasing_factor_raw]
if args.optimum_matching is not None:
optimum_matching = args.optimum_matching
if args.priors is not None:
prior_values = args.priors
if args.label is not None:
tag = args.label
if args.save_traj is not None:
save_traj = args.save_traj
prior = RJMC_Prior(prior_values)
prior.epsilon_prior()
prior.sigma_prior()
prior.L_prior()
prior.Q_prior()
mcmc_prior_simulation = RJMC_Simulation(compound,
T_range,
properties,
n_points,
5 * 10**5,
0.0,
biasing_factor,
optimum_matching)
mcmc_prior_simulation.prepare_data()
compound_2CLJ = LennardJones_2C(mcmc_prior_simulation.M_w)
mcmc_prior_simulation.gen_Tmatrix(prior, compound_2CLJ)
mcmc_prior_simulation.set_initial_state(prior,
compound_2CLJ,
initial_model='AUA+Q')
mcmc_prior_simulation.RJMC_Outerloop(prior, compound_2CLJ)
mcmc_prior_simulation.Report()
prior_values['Q'] = mcmc_prior_simulation.refit_prior('exponential')
print(prior_values['Q'])
#plt.hist(mcmc_prior_simulation.trace_model_1[:,4],bins=50,density=True)
#plt.plot(np.linspace(0,1,num=500),gengamma.pdf(np.linspace(0,1,num=500),*prior_values['Q'][1]))
#plt.plot(np.linspace(0,1,num=500),expon.pdf(np.linspace(0,1,num=500),0,400))
#plt.show()
print('Refitting Prior for Q')
prior = RJMC_Prior(prior_values)
prior.epsilon_prior()
prior.sigma_prior()
prior.L_prior()
prior.Q_prior()
print(prior.Q_prior_values)
'''
plt.hist(mcmc_prior_simulation.trace_model_1[:,4],bins=50,density=True)
plt.plot(np.linspace(0,1,num=500),expon.pdf(np.linspace(0,1,num=500),*prior.Q_prior_values))
plt.show()
'''
aua_path = 'output/C2H2/rhol+Psat/C2H2_rhol+Psat_2000000_aua_only_2019-10-25/trace/trace.npy'
auaq_path = 'output/C2H2/rhol+Psat/C2H2_rhol+Psat_2000000_auaq_only_2019-10-25/trace/trace.npy'
aua_max,auaq_max = create_map(aua_path,auaq_path)
rjmc_simulator = RJMC_Simulation(compound,
T_range,
properties,
n_points,
steps,
swap_freq,
biasing_factor,
optimum_matching)
rjmc_simulator.prepare_data()
print('Simulation Attributes:', rjmc_simulator.get_attributes())
compound_2CLJ = LennardJones_2C(rjmc_simulator.M_w)
rjmc_simulator.gen_Tmatrix(prior, compound_2CLJ)
rjmc_simulator.set_initial_state(prior, compound_2CLJ)
custom_map = list([list(aua_max),list(auaq_max),list(rjmc_simulator.opt_params_UA)])
rjmc_simulator.opt_params_AUA,rjmc_simulator.opt_params_AUA_Q,rjmc_simulator.opt_params_UA = rjmc_simulator.load_custom_map(custom_map)
rjmc_simulator.RJMC_Outerloop(prior, compound_2CLJ)
trace, logp_trace, percent_dev_trace,BAR_trace = rjmc_simulator.Report(USE_BAR=True)
rjmc_simulator.write_output(prior_values, tag=tag, save_traj=save_traj)
print('Finished!')
def main():
T_range = [0.55, 0.95]
n_points = 10
swap_freq = 0.1
biasing_factor = [0, 0, 0]
optimum_matching = ['True', 'True']
tag = ''
save_traj = False
prior_values = {
'epsilon': ['exponential', [0, 400]],
'sigma': ['exponential', [0, 5]],
'L': ['exponential', [0, 3]],
'Q': ['exponential', [0, 1]]
}
args = parse_args()
print(args.compound)
compound = args.compound
steps = args.steps
properties = args.properties
if args.trange is not None:
T_range = args.trange
if args.number_data_points is not None:
n_points = args.number_data_points
if args.swap_freq is not None:
swap_freq = args.swap_freq
if args.biasing_factor is not None:
biasing_factor = args.biasing_factor
if args.optimum_matching is not None:
optimum_matching = args.optimum_matching
if args.priors is not None:
prior_values = args.priors
if args.label is not None:
tag = args.label
if args.save_traj is not None:
save_traj = args.save_traj
prior = RJMC_Prior(prior_values)
prior.epsilon_prior()
prior.sigma_prior()
prior.L_prior()
prior.Q_prior()
'''
mcmc_prior_simulation = RJMC_Simulation(compound,
T_range,
properties,
n_points,
1 * 10**6,
0.0,
biasing_factor,
optimum_matching)
mcmc_prior_simulation.prepare_data()
compound_2CLJ = LennardJones_2C(mcmc_prior_simulation.M_w)
mcmc_prior_simulation.gen_Tmatrix(prior, compound_2CLJ)
mcmc_prior_simulation.set_initial_state(prior,
compound_2CLJ,
initial_model='AUA+Q')
mcmc_prior_simulation.RJMC_Outerloop(prior, compound_2CLJ)
mcmc_prior_simulation.Report()
prior_values['Q'] = mcmc_prior_simulation.refit_prior('gamma')
print('Refitting Prior for Q')
prior = RJMC_Prior(prior_values)
prior.epsilon_prior()
prior.sigma_prior()
prior.L_prior()
prior.Q_prior()
'''
UA_logp_trace = do_UA_simulation(compound, T_range, properties, n_points,
biasing_factor, optimum_matching, prior)
AUA_Q_logp_trace = do_AUA_Q_simulation(compound, T_range, properties,
n_points, biasing_factor,
optimum_matching, prior)
AUA_logp_trace = do_AUA_simulation(compound, T_range, properties, n_points,
biasing_factor, optimum_matching, prior)
UA_biasing_factor, AUA_Q_biasing_factor = compute_biasing_factors(
UA_logp_trace, AUA_logp_trace, AUA_Q_logp_trace)
biasing_factor = np.asarray([0, AUA_Q_biasing_factor, UA_biasing_factor])
print('Biasing factor', biasing_factor)
rjmc_simulator = RJMC_Simulation(compound, T_range, properties, n_points,
steps, swap_freq, biasing_factor,
optimum_matching)
rjmc_simulator.prepare_data()
print('Simulation Attributes:', rjmc_simulator.get_attributes())
compound_2CLJ = LennardJones_2C(rjmc_simulator.M_w)
rjmc_simulator.gen_Tmatrix(prior, compound_2CLJ)
print(rjmc_simulator.opt_params_AUA)
rjmc_simulator.set_initial_state(prior, compound_2CLJ)
rjmc_simulator.RJMC_Outerloop(prior, compound_2CLJ)
trace, logp_trace, percent_dev_trace, BAR_trace = rjmc_simulator.Report(
USE_BAR=True)
rjmc_simulator.write_output(prior_values, tag=tag, save_traj=save_traj)
prob = rjmc_simulator.prob
unbias_prob = unbias_simulation(biasing_factor, prob)
print('Unbias Prob: ', unbias_prob)
unbias_BF = [
unbias_prob[0] / unbias_prob[1], unbias_prob[0] / unbias_prob[2]
]
print('Unbiased Bayes factors: ', unbias_BF)
BF = rjmc_simulator.BF_BAR
BAR_LB = [BF[0][1][0], BF[1][1][0]]
BAR_UB = [BF[0][1][1], BF[1][1][1]]
BAR_prob_LB = undo_bar(BAR_LB)
BAR_prob_UB = undo_bar(BAR_UB)
BF_prob = undo_bar([BF[0][0], BF[1][0]])
print('Prob from BAR: ', BF_prob)
print('LB prob from BAR: ', BAR_prob_LB)
print('UB prob from BAR: ', BAR_prob_UB)
unbias_prob_BAR = unbias_simulation(biasing_factor, BF_prob)
unbias_prob_BAR_LB = unbias_simulation(biasing_factor, BAR_prob_LB)
unbias_prob_BAR_UB = unbias_simulation(biasing_factor, BAR_prob_UB)
print('Unbias prob BAR: ', unbias_prob_BAR)
print('Unbias prob LB BAR: ', unbias_prob_BAR_LB)
print('Unbias prob UB BAR: ', unbias_prob_BAR_UB)
unbias_BF_BAR = [
unbias_prob_BAR[0] / unbias_prob_BAR[1],
unbias_prob_BAR[0] / unbias_prob_BAR[2]
]
unbias_BF_BAR_LB = [
unbias_prob_BAR_LB[0] / unbias_prob_BAR_LB[1],
unbias_prob_BAR_LB[0] / unbias_prob_BAR_LB[2]
]
unbias_BF_BAR_UB = [
unbias_prob_BAR_UB[0] / unbias_prob_BAR_UB[1],
unbias_prob_BAR_UB[0] / unbias_prob_BAR_UB[2]
]
print('Unbias BF from BAR: ', unbias_BF_BAR)
print('Unbias BF LB from BAR: ', unbias_BF_BAR_LB)
print('Unbias BF UB from BAR: ', unbias_BF_BAR_UB)
print('Finished!')
def refit_prior(simulation_params):
prior = RJMC_Prior(simulation_params['priors'])
prior.epsilon_prior()
prior.sigma_prior()
prior.L_prior()
prior.Q_prior()
print('Approximating AUA+Q posterior distribution')
mcmc_prior_simulation = RJMC_Simulation(
simulation_params['compound'], simulation_params['trange'],
simulation_params['properties'],
simulation_params['number_data_points'],
simulation_params['single_simulation_length'], 0.0, [0, 0, 0],
simulation_params['optimum_matching'])
mcmc_prior_simulation.prepare_data()
compound_2CLJ = LennardJones_2C(mcmc_prior_simulation.M_w)
mcmc_prior_simulation.optimum_bounds = simulation_params['opt_bounds']
mcmc_prior_simulation.gen_Tmatrix(prior, compound_2CLJ)
mcmc_prior_simulation.set_initial_state(prior,
compound_2CLJ,
initial_model='AUA+Q')
mcmc_prior_simulation.RJMC_Outerloop(prior, compound_2CLJ)
mcmc_prior_simulation.Report()
simulation_params['priors']['Q'] = mcmc_prior_simulation.refit_prior(
simulation_params['refit_prior_to'])
print('Refitting Q prior')
print(simulation_params['priors']['Q'])
prior = RJMC_Prior(simulation_params['priors'])
prior.epsilon_prior()
prior.sigma_prior()
prior.L_prior()
prior.Q_prior()
if simulation_params['use_MAP'] is True:
aua_max, auaq_max = create_map(simulation_params['MAP_simulations'][0],
simulation_params['MAP_simulations'][1])
rjmc_simulator = RJMC_Simulation(
simulation_params['compound'], simulation_params['trange'],
simulation_params['properties'],
simulation_params['number_data_points'], simulation_params['steps'],
simulation_params['swap_freq'], simulation_params['biasing_factor'],
simulation_params['optimum_matching'])
rjmc_simulator.prepare_data()
print('Simulation Attributes:', rjmc_simulator.get_attributes())
compound_2CLJ = LennardJones_2C(rjmc_simulator.M_w)
rjmc_simulator.optimum_bounds = simulation_params['opt_bounds']
rjmc_simulator.gen_Tmatrix(prior, compound_2CLJ)
rjmc_simulator.set_initial_state(prior, compound_2CLJ)
if simulation_params['use_MAP'] is True:
custom_map = list([
list(aua_max),
list(auaq_max),
list(rjmc_simulator.opt_params_UA)
])
rjmc_simulator.opt_params_AUA, rjmc_simulator.opt_params_AUA_Q, rjmc_simulator.opt_params_UA = rjmc_simulator.load_custom_map(
custom_map)
rjmc_simulator.RJMC_Outerloop(prior, compound_2CLJ)
trace, logp_trace, percent_dev_trace, BAR_trace = rjmc_simulator.Report(
USE_BAR=simulation_params['USE_BAR'])
rjmc_simulator.write_output(simulation_params['priors'],
tag=simulation_params['label'],
save_traj=simulation_params['save_traj'])
path = 'output/' + simulation_params['compound'] + '/' + simulation_params['properties'] + '/' + simulation_params['compound'] + \
'_' + simulation_params['properties'] + '_' + str(simulation_params['steps']) + '_' + simulation_params['label'] + '_' + str(date.today()) +'/runfile.yaml'
with open(path, 'w') as outfile:
yaml.dump(simulation_params, outfile, default_flow_style=False)
def biasing_factor(simulation_params):
if simulation_params['refit_prior'] is True:
prior = RJMC_Prior(simulation_params['priors'])
prior.epsilon_prior()
prior.sigma_prior()
prior.L_prior()
prior.Q_prior()
print('Approximating AUA+Q posterior distribution')
mcmc_prior_simulation = RJMC_Simulation(
simulation_params['compound'], simulation_params['trange'],
simulation_params['properties'],
simulation_params['number_data_points'],
simulation_params['single_simulation_length'], 0.0, [0, 0, 0],
simulation_params['optimum_matching'])
mcmc_prior_simulation.prepare_data()
compound_2CLJ = LennardJones_2C(mcmc_prior_simulation.M_w)
mcmc_prior_simulation.gen_Tmatrix(prior, compound_2CLJ)
mcmc_prior_simulation.set_initial_state(prior,
compound_2CLJ,
initial_model='AUA+Q')
mcmc_prior_simulation.RJMC_Outerloop(prior, compound_2CLJ)
mcmc_prior_simulation.Report()
simulation_params['priors']['Q'] = mcmc_prior_simulation.refit_prior(
simulation_params['refit_prior_to'])
print('Refitting Q prior')
print(simulation_params['priors']['Q'])
print('Starting AUA')
AUA_simulation = RJMC_Simulation(
simulation_params['compound'], simulation_params['trange'],
simulation_params['properties'],
simulation_params['number_data_points'],
simulation_params['biasing_factor_simulation_length'], 0.0, [0, 0, 0],
simulation_params['optimum_matching'])
AUA_simulation.prepare_data()
compound_2CLJ = LennardJones_2C(AUA_simulation.M_w)
AUA_simulation.gen_Tmatrix(prior, compound_2CLJ)
AUA_simulation.set_initial_state(prior, compound_2CLJ, initial_model='AUA')
AUA_simulation.RJMC_Outerloop(prior, compound_2CLJ)
AUA_simulation.Report()
AUA_logp_trace = AUA_simulation.logp_trace
print('AUA Complete!')
print('Starting AUA+Q')
AUAQ_simulation = RJMC_Simulation(
simulation_params['compound'], simulation_params['trange'],
simulation_params['properties'],
simulation_params['number_data_points'],
simulation_params['biasing_factor_simulation_length'], 0.0, [0, 0, 0],
simulation_params['optimum_matching'])
AUAQ_simulation.prepare_data()
compound_2CLJ = LennardJones_2C(AUAQ_simulation.M_w)
AUAQ_simulation.gen_Tmatrix(prior, compound_2CLJ)
AUAQ_simulation.set_initial_state(prior,
compound_2CLJ,
initial_model='AUA+Q')
AUAQ_simulation.RJMC_Outerloop(prior, compound_2CLJ)
AUAQ_simulation.Report()
AUAQ_logp_trace = AUAQ_simulation.logp_trace
print('AUA Complete!')
print('Starting UA')
UA_simulation = RJMC_Simulation(
simulation_params['compound'], simulation_params['trange'],
simulation_params['properties'],
simulation_params['number_data_points'],
simulation_params['biasing_factor_simulation_length'], 0.0, [0, 0, 0],
simulation_params['optimum_matching'])
UA_simulation.prepare_data()
compound_2CLJ = LennardJones_2C(UA_simulation.M_w)
UA_simulation.gen_Tmatrix(prior, compound_2CLJ)
UA_simulation.set_initial_state(prior, compound_2CLJ, initial_model='UA')
UA_simulation.RJMC_Outerloop(prior, compound_2CLJ)
UA_simulation.Report()
UA_logp_trace = UA_simulation.logp_trace
print('UA Complete!')
UA_biasing_factor, AUAQ_biasing_factor = compute_biasing_factors(
UA_logp_trace, AUA_logp_trace, AUAQ_logp_trace)
biasing_factor = np.asarray([0, AUAQ_biasing_factor, UA_biasing_factor])
print('Biasing factor', biasing_factor)
rjmc_simulator = RJMC_Simulation(
simulation_params['compound'], simulation_params['trange'],
simulation_params['properties'],
simulation_params['number_data_points'], simulation_params['steps'],
simulation_params['swap_freq'], biasing_factor,
simulation_params['optimum_matching'])
rjmc_simulator.prepare_data()
print('Simulation Attributes:', rjmc_simulator.get_attributes())
compound_2CLJ = LennardJones_2C(rjmc_simulator.M_w)
rjmc_simulator.optimum_bounds = simulation_params['opt_bounds']
rjmc_simulator.gen_Tmatrix(prior, compound_2CLJ)
# print(rjmc_simulator.opt_params_AUA)
rjmc_simulator.set_initial_state(prior, compound_2CLJ)
rjmc_simulator.RJMC_Outerloop(prior, compound_2CLJ)
trace, logp_trace, percent_dev_trace, BAR_trace = rjmc_simulator.Report(
USE_BAR=simulation_params['USE_BAR'])
rjmc_simulator.write_output(simulation_params['priors'],
tag=simulation_params['label'],
save_traj=simulation_params['save_traj'])
path = 'output/' + simulation_params['compound'] + '/' + simulation_params['properties'] + '/' + simulation_params['compound'] + \
'_' + simulation_params['properties'] + '_' + str(simulation_params['steps']) + '_' + simulation_params['label'] + '_' + str(date.today()) +'/runfile.yaml'
with open(path, 'w') as outfile:
yaml.dump(simulation_params, outfile, default_flow_style=False)
def main():
parser = argparse.ArgumentParser(description='Process input parameters for RJMC 2CLJQ')
parser.add_argument('--compound', '-c',
type=str,
help='Compound to simulate parameters for',
required=True)
parser.add_argument('--trange', '-t',
type=list,
help='Temperature range for data to include',
required=False)
parser.add_argument('--steps', '-s',
type=int,
help='Number of MCMC steps',
required=True)
parser.add_argument('--properties', '-p',
type=str,
help='Properties to include in computation',
required=True)
parser.add_argument('--priors', '-r',
type=dict,
help='Values and types of prior distribution',
required=False)
parser.add_argument('--optimum_matching', '-o',
type=list,
help='Whether to use optimum matching in transitions',
required=False)
parser.add_argument('--number_data_points', '-d',
type=int,
help='Number of data points to include',
required=False)
parser.add_argument('--swap_freq', '-f',
type=float,
help='Frequency of model jump proposal',
required=False)
parser.add_argument('--biasing_factor', '-b',
type=str,
help='Biasing factors for each model',
required=False)
parser.add_argument('--label', '-l',
type=str,
help='label for files',
required=False)
parser.add_argument('--save_traj', '-j',
type=bool,
help='Whether to save trajectory files',
required=False)
T_range = [0.55, 0.95]
n_points = 10
swap_freq = 0.1
biasing_factor = [0, 0, 0]
optimum_matching = ['True', 'True']
tag = ''
save_traj = False
prior_values = {
'epsilon': ['exponential', [400]],
'sigma': ['exponential', [5]],
'L': ['exponential', [3]],
'Q': ['exponential', [1]]}
args = parser.parse_args()
print(args.compound)
print(args.biasing_factor)
compound = args.compound
#T_range=args.trange
steps = args.steps
properties = args.properties
if args.trange is not None:
T_range = args.trange
if args.number_data_points is not None:
n_points = args.number_data_points
if args.swap_freq is not None:
swap_freq = args.swap_freq
if args.biasing_factor is not None:
biasing_factor_raw = list(args.biasing_factor.split(','))
biasing_factor = [float(i) for i in biasing_factor_raw]
if args.optimum_matching is not None:
optimum_matching = args.optimum_matching
if args.priors is not None:
prior_values = args.priors
if args.label is not None:
tag = args.label
if args.save_traj is not None:
save_traj = args.save_traj
prior = RJMC_Prior(prior_values)
prior.epsilon_prior()
prior.sigma_prior()
prior.L_prior()
prior.Q_prior()
rjmc_simulation = RJMC_Simulation(compound, T_range, properties, n_points, steps,
0.0, biasing_factor, optimum_matching)
rjmc_simulation.prepare_data()
compound_2CLJ = LennardJones_2C(rjmc_simulation.M_w)
rjmc_simulation.gen_Tmatrix(prior, compound_2CLJ)
rjmc_simulation.set_initial_state(prior,
compound_2CLJ,
initial_model='AUA+Q')
rjmc_simulation.RJMC_Outerloop(prior, compound_2CLJ)
rjmc_simulation.Report()
rjmc_simulation.write_output(prior_values, tag='auaq_only', save_traj=save_traj)
rjmc_simulator = RJMC_Simulation(compound,
T_range,
properties,
n_points,
steps,
0.0,
biasing_factor,
optimum_matching)
rjmc_simulator.prepare_data()
print('Simulation Attributes:', rjmc_simulator.get_attributes())
compound_2CLJ = LennardJones_2C(rjmc_simulator.M_w)
rjmc_simulator.gen_Tmatrix(prior, compound_2CLJ)
print(rjmc_simulator.opt_params_AUA)
rjmc_simulator.set_initial_state(prior, compound_2CLJ,initial_model = 'AUA')
rjmc_simulator.RJMC_Outerloop(prior, compound_2CLJ)
rjmc_simulator.Report()
rjmc_simulator.write_output(prior_values, tag='aua_only', save_traj=save_traj)
print('Finished!')