def run_short_ci(model_input, cuda_device_index, long_check=True):
start_dir = os.getcwd()
model, xml_path = prepare.prepare(model_input, force_overwrite=False)
model_dir = os.path.dirname(xml_path)
model.anchor_rootdir = os.path.abspath(model_dir)
check.check_pre_simulation_all(model)
run.run(model,
"any",
min_b_surface_simulation_length=1000,
num_rev_launches=10,
cuda_device_index=cuda_device_index,
save_state_file=True)
data_sample_list = converge.converge(model, k_on_state=0)
rmsd_convergence_results = common_converge.calc_RMSD_conv_amount(
model, data_sample_list)
transition_minima, transition_details, transition_times \
= common_converge.calc_transition_steps(
model, data_sample_list[-1])
converge.print_convergence_results(
model,
rmsd_convergence_results,
cutoff=0.1,
transition_results=transition_details,
transition_time_results=transition_times,
minimum_anchor_transitions=10,
bd_transition_counts=data_sample_list[-1].bd_transition_counts)
check.check_post_simulation_all(model, long_check=long_check)
analysis = analyze.analyze(model)
analysis.print_results()
os.chdir(start_dir)
return
def test_converge_default_bd_only_mmvt(host_guest_mmvt_model):
model = host_guest_mmvt_model
bd_directory = os.path.join(host_guest_mmvt_model.anchor_rootdir,
"b_surface")
runner_browndye2.run_bd_top(model.browndye_settings.browndye_bin_dir,
bd_directory,
force_overwrite=True)
runner_browndye2.run_nam_simulation(
model.browndye_settings.browndye_bin_dir, bd_directory,
model.k_on_info.bd_output_glob)
cutoff = 0.1
minimum_anchor_transitions = 100
image_directory = common_analyze.make_image_directory(
host_guest_mmvt_model, None)
k_on_state = 0
data_sample_list = converge.converge(host_guest_mmvt_model,
k_on_state,
image_directory=image_directory,
verbose=True)
rmsd_convergence_results = common_converge.calc_RMSD_conv_amount(
host_guest_mmvt_model, data_sample_list)
transition_minima, transition_prob_results, transition_time_results \
= common_converge.calc_transition_steps(
host_guest_mmvt_model, data_sample_list[-1])
bd_transition_counts = data_sample_list[-1].bd_transition_counts
converge.print_convergence_results(host_guest_mmvt_model,
rmsd_convergence_results, cutoff,
transition_prob_results,
transition_time_results,
minimum_anchor_transitions,
bd_transition_counts)
return
def test_converge_incomplete_md_only_mmvt(toy_mmvt_model):
num_steps = 100000
toy_mmvt_model.openmm_settings.cuda_platform_settings = None
toy_mmvt_model.openmm_settings.reference_platform = True
toy_mmvt_model.openmm_settings.langevin_integrator.friction_coefficient \
= 100.0
toy_mmvt_model.calculation_settings.num_production_steps = num_steps
toy_mmvt_model.calculation_settings.energy_reporter_interval = num_steps
run.run(toy_mmvt_model, "any", force_overwrite=True)
runner_openmm.cleanse_anchor_outputs(toy_mmvt_model,
toy_mmvt_model.anchors[3])
cutoff = 0.1
minimum_anchor_transitions = 100
image_directory = common_analyze.make_image_directory(toy_mmvt_model, None)
k_on_state = None
data_sample_list = converge.converge(toy_mmvt_model,
k_on_state,
image_directory=image_directory,
verbose=True)
rmsd_convergence_results = common_converge.calc_RMSD_conv_amount(
toy_mmvt_model, data_sample_list)
transition_minima, transition_prob_results, transition_time_results \
= common_converge.calc_transition_steps(
toy_mmvt_model, data_sample_list[-1])
bd_transition_counts = data_sample_list[-1].bd_transition_counts
converge.print_convergence_results(toy_mmvt_model,
rmsd_convergence_results, cutoff,
transition_prob_results,
transition_time_results,
minimum_anchor_transitions,
bd_transition_counts)
return
def test_converge_default_md_only_elber(toy_elber_model):
num_steps = 100000
fwd_rev_interval = 100
toy_elber_model.openmm_settings.cuda_platform_settings = None
toy_elber_model.openmm_settings.reference_platform = True
toy_elber_model.openmm_settings.langevin_integrator.friction_coefficient \
= 100.0
toy_elber_model.calculation_settings.num_umbrella_stage_steps = num_steps
toy_elber_model.calculation_settings.fwd_rev_interval = fwd_rev_interval
run.run(toy_elber_model, "any")
cutoff = 0.1
minimum_anchor_transitions = 100
image_directory = common_analyze.make_image_directory(
toy_elber_model, None)
k_on_state = None
data_sample_list = converge.converge(toy_elber_model,
k_on_state,
image_directory=image_directory,
verbose=True)
rmsd_convergence_results = common_converge.calc_RMSD_conv_amount(
toy_elber_model, data_sample_list)
transition_minima, transition_prob_results, transition_time_results \
= common_converge.calc_transition_steps(
toy_elber_model, data_sample_list[-1])
bd_transition_counts = data_sample_list[-1].bd_transition_counts
converge.print_convergence_results(toy_elber_model,
rmsd_convergence_results, cutoff,
transition_prob_results,
transition_time_results,
minimum_anchor_transitions,
bd_transition_counts)
return