def init_project(p_name,
sys_name=None,
m_freq=None,
p_freq=None,
platform=None,
dbhost=None,
w_threads=None):
#def init_project(p_name, **freq):
from adaptivemd import Project
#if p_name in Project.list():
# print(project.name, "Deleting existing version of this test project")
# Project.delete(p_name)
if dbhost is not None:
Project.set_dbhost(dbhost)
project = Project(p_name)
if project.name in Project.list():
print(
project.name,
"Project {0} exists, reading it from database".format(
project.name))
else:
from adaptivemd import File, OpenMMEngine
from adaptivemd.analysis.pyemma import PyEMMAAnalysis
#####################################
# NEW initialize sequence
configuration_file = 'configuration.cfg'
project.initialize(configuration_file)
#
# OLD initialize sequence
#from adaptivemd import LocalResource
#resource = LocalResource('/lustre/atlas/scratch/jrossyra/bip149/admd/')
#project.initialize(resource)
#####################################
f_name = '{0}.pdb'.format(sys_name)
# only works if filestructure is preserved as described in 'jro_ntl9.ipynb'
# and something akin to job script in 'admd_workers.pbs' is used
f_base = 'file:///$ADAPTIVEMD/examples/files/{0}/'.format(sys_name)
f_structure = File(f_base + f_name).load()
f_system_2 = File(f_base + 'system-2.xml').load()
f_integrator_2 = File(f_base + 'integrator-2.xml').load()
f_system_5 = File(f_base + 'system-5.xml').load()
f_integrator_5 = File(f_base + 'integrator-5.xml').load()
sim_args = '-r -p {0}'.format(platform)
if platform == 'CPU':
print(
project.name,
"Using CPU simulation platform with {0} threads per worker".
format(w_threads))
sim_args += ' --cpu-cpu-threads {0}'.format(w_threads)
engine_2 = OpenMMEngine(f_system_2, f_integrator_2, f_structure,
sim_args).named('openmm-2')
engine_5 = OpenMMEngine(f_system_5, f_integrator_5, f_structure,
sim_args).named('openmm-5')
m_freq_2 = m_freq
p_freq_2 = p_freq
m_freq_5 = m_freq * 2 / 5
p_freq_5 = p_freq * 2 / 5
engine_2.add_output_type('master', 'allatoms.dcd', stride=m_freq_2)
engine_2.add_output_type('protein',
'protein.dcd',
stride=p_freq_2,
selection='protein')
engine_5.add_output_type('master', 'allatoms.dcd', stride=m_freq_5)
engine_5.add_output_type('protein',
'protein.dcd',
stride=p_freq_5,
selection='protein')
ca_features = {'add_distances_ca': None}
#features = {'add_inverse_distances': {'select_Backbone': None}}
ca_modeller_2 = PyEMMAAnalysis(engine_2, 'protein',
ca_features).named('pyemma-ca-2')
ca_modeller_5 = PyEMMAAnalysis(engine_5, 'protein',
ca_features).named('pyemma-ca-5')
pos = [
'(rescode K and mass > 13) ' + 'or (rescode R and mass > 13) ' +
'or (rescode H and mass > 13)'
]
neg = ['(rescode D and mass > 13) ' + 'or (rescode E and mass > 13)']
ionic_features = {
'add_distances': {
'select': pos
},
'kwargs': {
'indices2': {
'select': neg
}
}
}
all_features = [ca_features, ionic_features]
#ok#ionic_modeller = {'add_distances': {'select':
#ok# ['rescode K or rescode R or rescode H']},
#ok# 'kwargs': {'indices2': {'select':
#ok# 'rescode D or rescode E']}}}
#contact_features = [ {'add_inverse_distances':
# {'select_Backbone': None}},
# {'add_residue_mindist': None,
# 'kwargs': {'threshold': 0.6}}
# ]
all_modeller_2 = PyEMMAAnalysis(engine_2, 'protein',
all_features).named('pyemma-ionic-2')
all_modeller_5 = PyEMMAAnalysis(engine_5, 'protein',
all_features).named('pyemma-ionic-5')
project.generators.add(ca_modeller_2)
project.generators.add(all_modeller_2)
project.generators.add(ca_modeller_5)
project.generators.add(all_modeller_5)
project.generators.add(engine_2)
project.generators.add(engine_5)
[print(g) for g in project.generators]
return project
resource = LocalResource(15, 2)
resource.add_path(amp.path_conda_local_jhp)
elif resource_id == 'local.sheep':
resource = LocalResource(15, 2)
resource.add_path(amp.path_conda_local_sheep)
elif resource_id == 'fub.allegro':
resource = AllegroCluster(15, 4, 'big')
resource.add_path(amp.path_conda_allegro_jhp)
else:
resource = LocalResource(1, 2)
# --------------------------------------------------------------------------
# CREATE THE ENGINE
# the instance to create trajectories
# --------------------------------------------------------------------------
pdb_file = File('file://input.pdb')
engine = OpenMMEngine(pdb_file=pdb_file,
system_file=File('file://system.xml'),
integrator_file=File('file://integrator.xml'))
engine.args = '-r --report-interval 1 -p fastest --store-interval 1'
# --------------------------------------------------------------------------
# CREATE THE CLUSTER
# the instance that runs the simulations on the resource
# --------------------------------------------------------------------------
cluster = MDCluster(system='alanine', resource=resource, report=report)
# add the path to CONDA if now already in the default
project = Project(project_name)
if len(sys.argv) == 3:
if resource_id == 'local.jhp':
project.initialize(LocalJHP)
elif resource_id == 'local.sheep':
project.initialize(LocalSheep)
elif resource_id == 'fub.allegro':
project.initialize(AllegroCluster)
# --------------------------------------------------------------------------
# CREATE THE ENGINE
# the instance to create trajectories
# --------------------------------------------------------------------------
pdb_file = File('file://files/input.pdb')
engine = OpenMMEngine(
pdb_file=pdb_file,
system_file=File('file://files/system.xml'),
integrator_file=File('file://files/integrator.xml'),
args='-r --report-interval 1 -p CPU --store-interval 1')
# --------------------------------------------------------------------------
# CREATE THE MODELLER
# the instance to create msm models
# --------------------------------------------------------------------------
modeller = PyEMMAAnalysis(pdb_file=pdb_file,
source_folder=File('../staging_area/ntl9/trajs'))
# --------------------------------------------------------------------------
if __name__ == '__main__':
project = Project('testcase')
# --------------------------------------------------------------------------
# CREATE THE RESOURCE
# the instance to know about the place where we run simulations
# --------------------------------------------------------------------------
project.initialize(LocalResource('$HOME/miniconda2/bin'))
# --------------------------------------------------------------------------
# CREATE THE ENGINE
# the instance to create trajectories
# --------------------------------------------------------------------------
pdb_file = File('file://../files/alanine/alanine.pdb').named('initial_pdb')
engine = OpenMMEngine(
pdb_file=pdb_file,
system_file=File('file://../files/alanine/system.xml'),
integrator_file=File('file://../files/alanine/integrator.xml'),
args='-r --report-interval 1 -p CPU --store-interval 1').named(
'openmm')
# --------------------------------------------------------------------------
# CREATE AN ANALYZER
# the instance that knows how to compute a msm from the trajectories
# --------------------------------------------------------------------------
modeller = PyEMMAAnalysis(pdb_file=pdb_file).named('pyemma')
def init_project(p_name,
sys_name=None,
m_freq=None,
p_freq=None,
platform=None,
reinitialize=False): #, dblocation=None):
#def init_project(p_name, **freq):
from adaptivemd import Project
#if p_name in Project.list():
# print("Deleting existing version of this test project")
# Project.delete(p_name)
dburl = os.environ.get("ADMD_DBURL", 0)
if dburl:
logger.info("Set ADMD_DBURL to: " + dburl)
Project.set_dburl(dburl)
# if dblocation is not None:
# Project.set_dblocation(dblocation)
if reinitialize:
logger.info(
"Project {0} exists, deleting it from database to reinialize".
format(p_name))
Project.delete(p_name)
if p_name in Project.list():
logger.info(
"Project {0} exists, reading it from database".format(p_name))
project = Project(p_name)
elif not all([sys_name, m_freq, p_freq, platform]):
raise ValueError(
"Must define all parameters [{0}] to initialize new project\nHave: {1}"
.format("sys_name,m_freq,p_freq,platform",
[sys_name, m_freq, p_freq, platform].__repr__()))
else:
project = Project(p_name)
from adaptivemd import File, OpenMMEngine
from adaptivemd.analysis.pyemma import PyEMMAAnalysis
# Initialize w/ config file: 1 of multiple options
# TODO add config filename argument
configuration_file = 'configuration.cfg'
project.initialize(configuration_file)
f_name = '{0}.pdb'.format(sys_name)
# FIXME add system specifications to configuration file
f_base = 'file:///$ADMD_FILES/{0}/'.format(sys_name)
f_structure = File(f_base + f_name).load()
f_system_2 = File(f_base + 'system-2.xml').load()
f_integrator_2 = File(f_base + 'integrator-2.xml').load()
f_system_5 = File(f_base + 'system-5.xml').load()
f_integrator_5 = File(f_base + 'integrator-5.xml').load()
sim_args = '-r -p {0}'.format(platform)
engine_2 = OpenMMEngine(f_system_2, f_integrator_2, f_structure,
sim_args).named('openmm-2')
engine_5 = OpenMMEngine(f_system_5, f_integrator_5, f_structure,
sim_args).named('openmm-5')
# FIXME this is dumb and hard for user to deal with
# TODO engine selection by name
m_freq_2 = m_freq
p_freq_2 = p_freq
m_freq_5 = m_freq * 2 / 5
p_freq_5 = p_freq * 2 / 5
engine_2.add_output_type('master', 'allatoms.dcd', stride=m_freq_2)
engine_2.add_output_type('protein',
'protein.dcd',
stride=p_freq_2,
selection='protein')
engine_5.add_output_type('master', 'allatoms.dcd', stride=m_freq_5)
engine_5.add_output_type('protein',
'protein.dcd',
stride=p_freq_5,
selection='protein')
ca_features = {'add_distances_ca': None}
#features = {'add_inverse_distances': {'select_Backbone': None}}
ca_modeller_2 = PyEMMAAnalysis(engine_2, 'protein',
ca_features).named('pyemma-ca-2')
ca_modeller_5 = PyEMMAAnalysis(engine_5, 'protein',
ca_features).named('pyemma-ca-5')
pos = [
'(rescode K and mass > 13) ' + 'or (rescode R and mass > 13) ' +
'or (rescode H and mass > 13)'
]
neg = ['(rescode D and mass > 13) ' + 'or (rescode E and mass > 13)']
ionic_features = {
'add_distances': {
'select': pos
},
'kwargs': {
'indices2': {
'select': neg
}
}
}
all_features = [ca_features, ionic_features]
inv_ca_features = {'add_inverse_distances': {'select_Ca': None}}
#ok#ionic_modeller = {'add_distances': {'select':
#ok# ['rescode K or rescode R or rescode H']},
#ok# 'kwargs': {'indices2': {'select':
#ok# 'rescode D or rescode E']}}}
#contact_features = [ {'add_inverse_distances':
# {'select_Backbone': None}},
# {'add_residue_mindist': None,
# 'kwargs': {'threshold': 0.6}}
# ]
all_modeller_2 = PyEMMAAnalysis(engine_2, 'protein',
all_features).named('pyemma-ionic-2')
all_modeller_5 = PyEMMAAnalysis(engine_5, 'protein',
all_features).named('pyemma-ionic-5')
inv_modeller_2 = PyEMMAAnalysis(
engine_2, 'protein', inv_ca_features).named('pyemma-invca-2')
inv_modeller_5 = PyEMMAAnalysis(
engine_5, 'protein', inv_ca_features).named('pyemma-invca-5')
project.generators.add(ca_modeller_2)
project.generators.add(all_modeller_2)
project.generators.add(inv_modeller_2)
project.generators.add(ca_modeller_5)
project.generators.add(all_modeller_5)
project.generators.add(inv_modeller_5)
project.generators.add(engine_2)
project.generators.add(engine_5)
#[print(g) for g in project.generators]
return project
def test(self):
# ----------------------------------------------------------------------
# CREATE THE ENGINE
# the instance to create trajectories
# ----------------------------------------------------------------------
pdb_file = File('file://{0}alanine.pdb'.format(
self.f_base)).named('initial_pdb').load()
engine = OpenMMEngine(
pdb_file=pdb_file,
system_file=File('file://{0}system.xml'.format(
self.f_base)).load(),
integrator_file=File('file://{0}integrator.xml'.format(
self.f_base)).load(),
args='-r --report-interval 1 -p CPU --store-interval 1 -v').named(
'openmm')
# ----------------------------------------------------------------------
# CREATE AN ANALYZER
# the instance that knows how to compute a msm from the trajectories
# ----------------------------------------------------------------------
modeller = PyEMMAAnalysis(engine=engine).named('pyemma')
self.project.generators.add(engine)
self.project.generators.add(modeller)
def strategy(loops=1, trajs_per_loop=1, length=1):
initial_traj = self.project.new_trajectory(frame=pdb_file,
length=length)
task = engine.run(initial_traj)
self.project.queue(task)
yield task.is_done
for loop in range(loops):
# submit some trajectory tasks
trajectories = self.project.new_ml_trajectory(
engine=engine, length=length, number=trajs_per_loop)
tasks = tuple(map(engine.run, trajectories))
self.project.queue(tasks)
print("queued %s tasks" % len(tasks))
# continue if ALL of the tasks are done (can be failed)
yield [task.is_done for task in tasks]
# submit a model job
task = modeller.execute(list(self.project.trajectories))
self.project.queue(task)
print("queued modeller task")
# when it is done do next loop
yield task.is_done
# TODO worker/MD running in subprocess thread horribly slow
# - can it be made to run a bit faster?
n_loops = 1
trajs_per_loop = 1
self.project.add_event(
strategy(loops=n_loops, trajs_per_loop=trajs_per_loop))
self.project.run()
self.project.wait_until(self.project.on_ntraj(n_loops *
trajs_per_loop))
self.assertEqual(len(list(self.project.trajectories)),
n_loops * trajs_per_loop)
self.project.close()
def test(self):
# ----------------------------------------------------------------------
# CREATE THE ENGINE
# the instance to create trajectories
# ----------------------------------------------------------------------
pdb_file = File('file://{0}alanine.pdb'.format(
self.f_base)).named('initial_pdb').load()
engine = OpenMMEngine(
pdb_file=pdb_file,
system_file=File('file://{0}system.xml'.format(
self.f_base)).load(),
integrator_file=File('file://{0}integrator.xml'.format(
self.f_base)).load(),
args='-r --report-interval 1 -p CPU --store-interval 1').named(
'openmm')
# ----------------------------------------------------------------------
# CREATE AN ANALYZER
# the instance that knows how to compute a msm from the trajectories
# ----------------------------------------------------------------------
modeller = PyEMMAAnalysis(engine=engine).named('pyemma')
self.project.generators.add(engine)
self.project.generators.add(modeller)
# ----------------------------------------------------------------------
# CREATE THE CLUSTER
# the instance that runs the simulations on the resource
# ----------------------------------------------------------------------
traj_len = 1
trajectory = self.project.new_trajectory(engine['pdb_file'], traj_len,
engine)
task = engine.run(trajectory)
# self.project.queue(task)
pdb = md.load('{0}alanine.pdb'.format(self.f_base))
# this part fakes a running worker without starting the worker process
worker = WorkerScheduler(self.project.configuration, verbose=True)
worker.enter(self.project)
worker.submit(task)
self.assertEqual(len(self.project.trajectories), 0)
while not task.is_done():
worker.advance()
try:
assert (len(self.project.trajectories) == 1)
except AssertionError:
print("stderr from worker task: \n%s" % task.stderr)
print("stdout from worker task: \n%s" % task.stdout)
raise
print("stdout of worker:\n%s" % task.stdout)
# FIXME: the worker space is cleared, so the trajectory paths are not valid anymore.
# traj_path = os.path.join(
# worker.path,
# 'workers',
# 'worker.' + hex(task.__uuid__),
# worker.replace_prefix(self.project.trajectories.one.url)
# )
# this is a workaround, but assumes that sandbox:// lives on the same fs.
traj_path = os.path.join(self.shared_path,
self.project.trajectories.one.dirname[1:],
'output.dcd')
assert (os.path.exists(traj_path)), traj_path
# go back to the place where we ran the test
traj = md.load(traj_path, top=pdb)
assert (len(traj) == traj_len + 1), len(traj)
# well, we have a 100 step trajectory which matches the size of the initial PDB
# that is a good sign
# extend the trajectory by 10
task2 = task.extend(10)
worker.submit(task2)
while not task2.is_done():
worker.advance()
# should still be one, since we have the same trajectory
assert (len(self.project.trajectories) == 1)
traj = md.load(traj_path, top=pdb)
self.assertEqual(len(traj), traj_len + 10 + 1)
# after extension it is traj_len + 10 frames. Excellent
self.project.close()