def test_idempotence1(caplog):
"""Confirm that a work graph can be run repeatedly, even after completed.
Use gmx.run and avoid extra references held by user code.
"""
md = gmx.workflow.from_tpr(tpr_filename, threads_per_rank=1)
gmx.run(md)
gmx.run(md)
gmx.run(md)
md = gmx.workflow.from_tpr(tpr_filename, threads_per_rank=1)
gmx.run(md)
md = gmx.workflow.from_tpr(tpr_filename, threads_per_rank=1)
gmx.run(md)
def test_modifiedInput(caplog):
"""Load a work specification with a single TPR file and updated params."""
md = gmx.workflow.from_tpr(tpr_filename,
threads_per_rank=1,
end_time='0.02')
context = gmx.get_context(md)
with context as session:
session.run()
md = gmx.workflow.from_tpr(tpr_filename,
threads_per_rank=1,
end_time='0.03')
context.work = md
with context as session:
session.run()
md = gmx.workflow.from_tpr(tpr_filename,
threads_per_rank=1,
end_time='0.04')
gmx.run(md)
converge = gmx.subgraph(variables={
'conformation': initial_input,
})
with converge:
modified_input = gmx.modify_input(input=initial_input,
structure=converge.conformation)
myplugin.converge_restraint(label='converging_potential',
params=train_loop.training_potential.output)
brer_tools.converge_analyzer(
converge.converging_potential.output.distances,
label='is_converged',
)
md = gmx.mdrun(input=modified_input,
potential=converge.converging_potential)
conv_loop = gmx.while_loop(operation=converge,
condition=gmx.logical_not(converge.is_converged))
production_input = gmx.modify_input(input=initial_input,
structure=converge.conformation)
prod_potential = myplugin.production_restraint(
params=converge.converging_potential.output)
prod_md = gmx.mdrun(input=production_input, potential=prod_potential)
gmx.run()
print('Final alpha value was {}'.format(
train_loop.training_potential.output.alpha.result()))
# also can extract conformation filenames, etc.
def run(self):
gmx.run(work=self.workflow)
# Compare the distribution from the current iteration to the experimental
# data and look for a threshold of low J-S divergence
# We perform the calculation using all of the ensemble data.
js_1 = calculate_js(
input={
'params': restraint1_params,
'simulation_distances': gmx.gather(potential1.output.pair_distance)
})
js_2 = calculate_js(
input={
'params': restraint2_params,
'simulation_distances': gmx.gather(potential2.output.pair_distance)
})
gmx.logical_and(js_1.is_converged, js_2.is_converged, label='is_converged')
converge.pair_distance1 = potential1.output.pair_distance
converge.pair_distance2 = potential2.output.pair_distance
work = gmx.while_loop(operation=converge,
condition=gmx.logical_not(converge.is_converged))
# Command-line arguments for mdrun can be added to gmx run as below.
# Settings for a 20 core HPC node. Use 18 threads for domain decomposition for pair potentials
# and the remaining 2 threads for PME electrostatics.
gmx.run(work,
tmpi=20,
grid=gmx.NDArray([3, 3, 2]),
ntomp_pme=1,
npme=2,
ntomp=1)
def test_simpleSimulation(caplog):
"""Load a work specification with a single TPR file and run."""
# use case 1: simple high-level
md = gmx.workflow.from_tpr(tpr_filename, threads_per_rank=1)
gmx.run(md)