def test_fr15(spc_water_box, caplog):
"""FR15: Simulation input modification.
* *gmx.modify_input produces new (tpr) simulation input in data flow operation*
(requires interaction with library development)
"""
try:
from mpi4py import MPI
current_rank = MPI.COMM_WORLD.Get_rank()
ensemble_size = MPI.COMM_WORLD.Get_size()
except ImportError:
current_rank = 0
ensemble_size = 1
with caplog.at_level(logging.DEBUG):
caplog.handler.setFormatter(formatter)
with caplog.at_level(logging.WARNING, 'gmxapi'), \
caplog.at_level(logging.DEBUG, 'gmxapi.mdrun'), \
caplog.at_level(logging.DEBUG, 'gmxapi.modify_input'), \
caplog.at_level(logging.DEBUG, 'gmxapi.read_tpr'), \
caplog.at_level(logging.DEBUG, 'gmxapi.simulation'):
initial_input = gmx.read_tpr(
[spc_water_box for _ in range(ensemble_size)])
parameters = list([{'ld-seed': i} for i in range(ensemble_size)])
param_sweep = gmx.modify_input(input=initial_input,
parameters=parameters)
md = gmx.mdrun(param_sweep)
# TODO: (#3179) Handle ensembles of size 1 more elegantly.
if md.output.ensemble_width > 1:
result_list = md.output.parameters['ld-seed'].result()
else:
result_list = [md.output.parameters['ld-seed'].result()]
for expected, actual in zip(parameters, result_list):
assert expected['ld-seed'] == actual
def test_run_from_modify_input_op(spc_water_box, caplog):
with caplog.at_level(logging.DEBUG):
simulation_input = gmx.read_tpr(spc_water_box)
modified_input = gmx.modify_input(input=simulation_input,
parameters={'nsteps': 4})
md = gmx.mdrun(input=modified_input)
md.run()
def test_fr15():
"""FR15: Simulation input modification.
* *gmx.modify_input produces new (tpr) simulation input in data flow operation*
(requires interaction with library development)
* gmx.make_input dispatches appropriate preprocessing for file or in-memory simulation input.
"""
initial_input = gmx.read_tpr([tpr_filename for _ in range(10)])
tau_t = list([i / 10. for i in range(10)])
param_sweep = gmx.modify_input(input=initial_input,
parameters={'tau_t': tau_t})
md = gmx.mdrun(param_sweep)
for tau_expected, tau_actual in zip(tau_t,
md.output.params['tau_t'].extract()):
assert tau_expected == tau_actual
def test_fr15():
"""FR15: Simulation input modification.
* *gmx.modify_input produces new (tpr) simulation input in data flow operation*
(requires interaction with library development)
* gmx.make_input dispatches appropriate preprocessing for file or in-memory simulation input.
"""
initial_input = gmx.read_tpr([tpr_filename for _ in range(10)])
tau_t = list([i/10. for i in range(10)])
param_sweep = gmx.modify_input(input=initial_input,
parameters={
'tau_t': tau_t
}
)
md = gmx.mdrun(param_sweep)
for tau_expected, tau_actual in zip(tau_t, md.output.params['tau_t'].extract()):
assert tau_expected == tau_actual
def test_fr4():
"""FR4: Dimensionality and typing of named data causes generation of correct work topologies."""
N = 10
simulation_input = gmx.read_tpr(initial_tpr)
# Array inputs imply array outputs.
input_array = gmx.modify_input(
simulation_input, params={'tau-t': [t / 10.0 for t in range(N)]})
md = gmx.mdrun(input_array) # An array of simulations
rmsf = gmx.commandline_operation(
'gmx',
'rmsf',
input={
'-f': md.output.trajectory,
'-s': initial_tpr
},
output={'-o': gmx.FileName(suffix='.xvg')})
def test_fr4():
"""FR4: Dimensionality and typing of named data causes generation of correct work topologies."""
N = 10
simulation_input = gmx.read_tpr(initial_tpr)
# Array inputs imply array outputs.
input_array = gmx.modify_input(
simulation_input, params={'tau-t': [t / 10.0 for t in range(N)]})
md = gmx.mdrun(input_array) # An array of simulations
rmsf = gmx.commandline_operation(
'gmx',
'rmsf',
input={
'-f': md.output.trajectory,
'-s': initial_tpr
},
output={'-o': gmx.FileName(suffix='.xvg')})
def test_fr10():
"""FR10: 10: fused operations for use in looping constructs
* gmx.subgraph fuses operations
* gmx.while creates an operation wrapping a dynamic number of iterations of a subgraph
"""
train = gmx.subgraph(variables={'conformation': initial_input})
with train:
myplugin.training_restraint(label='training_potential',
params=my_dict_params)
modified_input = gmx.modify_input(input=initial_input,
structure=train.conformation)
md = gmx.mdrun(input=modified_input,
potential=train.training_potential)
# Alternate syntax to facilitate adding multiple potentials:
# md.interface.potential.add(train.training_potential)
brer_tools.training_analyzer(
label='is_converged', params=train.training_potential.output.alpha)
train.conformation = md.output.conformation
train_loop = gmx.while_loop(operation=train,
condition=gmx.logical_not(train.is_converged))
def test_fr10():
"""FR10: 10: fused operations for use in looping constructs
* gmx.subgraph fuses operations
* gmx.while creates an operation wrapping a dynamic number of iterations of a subgraph
"""
train = gmx.subgraph(variables={'conformation': initial_input})
with train:
myplugin.training_restraint(
label='training_potential',
params=my_dict_params)
modified_input = gmx.modify_input(
input=initial_input, structure=train.conformation)
md = gmx.mdrun(input=modified_input, potential=train.training_potential)
# Alternate syntax to facilitate adding multiple potentials:
# md.interface.potential.add(train.training_potential)
brer_tools.training_analyzer(
label='is_converged',
params=train.training_potential.output.alpha)
train.conformation = md.output.conformation
train_loop = gmx.while_loop(
operation=train,
condition=gmx.logical_not(train.is_converged))