def test_entropy_barrier_timescale_elber(toy_elber_model):
"""
Test entropy barrier system for if it recreates reasonable
kinetics timescales.
"""
num_steps = 100000
fwd_rev_interval = 100
long_timescale_residence_time_in_ps = 1263.06
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")
analysis = analyze.analyze(toy_elber_model, num_error_samples=100,
skip_checks=False)
#assert np.isclose(analysis.MFPTs[('anchor_0', 'bulk')],
# long_timescale_residence_time_in_ps,
# rtol=0.5)
assert analysis.MFPTs is not None
assert analysis.k_off is not None
analysis.print_results()
image_directory = common_analyze.make_image_directory(toy_elber_model, None)
analysis.save_plots(image_directory)
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_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_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
def test_calc_RMSD_conv_amount_elber(toy_elber_model):
for anchor in toy_elber_model.anchors:
runner_openmm.cleanse_anchor_outputs(toy_elber_model, anchor)
anchor1 = toy_elber_model.anchors[1]
anchor1_output_filename = os.path.join(toy_elber_model.anchor_rootdir,
anchor1.directory,
anchor1.production_directory,
"forward.restart1.out")
copyfile(test_output_filename_elber, anchor1_output_filename)
image_directory = common_analyze.make_image_directory(
toy_elber_model, None)
data_sample_list = converge.converge(toy_elber_model,
0,
image_directory=image_directory,
verbose=True)
convergence_results = common_converge.calc_RMSD_conv_amount(
toy_elber_model, data_sample_list)
for alpha, anchor in enumerate(toy_elber_model.anchors):
if anchor.bulkstate:
continue
if anchor.index == 1:
assert convergence_results[alpha] < 1.0
else:
assert convergence_results[alpha] == 1e99
return
def test_calc_transition_steps_elber(toy_elber_model):
for anchor in toy_elber_model.anchors:
runner_openmm.cleanse_anchor_outputs(toy_elber_model, anchor)
anchor1 = toy_elber_model.anchors[1]
anchor1_output_filename = os.path.join(toy_elber_model.anchor_rootdir,
anchor1.directory,
anchor1.production_directory,
"forward.restart1.out")
copyfile(test_output_filename_elber, anchor1_output_filename)
image_directory = common_analyze.make_image_directory(
toy_elber_model, None)
data_sample_list = converge.converge(toy_elber_model,
0,
image_directory=image_directory,
verbose=True)
transition_minima, transition_prob_results, transition_time_results \
= common_converge.calc_transition_steps(
toy_elber_model, data_sample_list[-1])
for alpha, anchor in enumerate(toy_elber_model.anchors):
if anchor.bulkstate:
continue
if anchor.index == 1:
assert transition_minima[alpha] == 3
assert transition_prob_results[alpha][(1, 2)] == 5
assert transition_prob_results[alpha][(1, 0)] == 3
assert np.isclose(transition_time_results[alpha][1], (1.324 / 5))
else:
assert transition_minima[alpha] == 0
for key in transition_prob_results[alpha]:
assert transition_prob_results[alpha][key] == 0
for key in transition_time_results[alpha]:
assert transition_time_results[alpha][key] == 0
return
argparser.add_argument(
"-T", "--maximum_time", dest="maximum_time", default=None, type=float,
help="A user may wish to stop the analysis of simulation time for "\
"each anchor at a particular time. Enter the time (in ps) at which to "\
"end the analysis at a given anchor if the simulation time exceeds it.")
args = argparser.parse_args() # parse the args into a dictionary
args = vars(args)
model_file = args["model_file"]
force_warning = args["force_warning"]
num_error_samples = args["num_error_samples"]
stride_error_samples = args["stride_error_samples"]
skip_error_samples = args["skip_error_samples"]
image_directory = args["image_directory"]
skip_checks = args["skip_checks"]
min_time = args["minimum_time"]
max_time = args["maximum_time"]
model = base.load_model(model_file)
image_directory = common_analyze.make_image_directory(
model, image_directory)
if not skip_checks:
check.check_post_simulation_all(model, long_check=True)
analysis = analyze(model, force_warning=force_warning,
num_error_samples=num_error_samples,
stride_error_samples=stride_error_samples,
skip_error_samples=skip_error_samples,
skip_checks=skip_checks, min_time=min_time, max_time=max_time)
analysis.print_results()
print("All plots being saved to:", image_directory)
analysis.save_plots(image_directory)
