def _batch_run(dir='unnamed',
batch_id='template',
space=None,
save_data_in_hdf5=False,
single_method=single_run,
process_method=null_processing,
post_process_method=None,
final_process_method=None,
multiprocessing=True,
resumable=True,
overwrite=False,
sim_config=None,
params=None,
config=None,
post_kwargs={},
run_kwargs={}):
saved_args = locals()
# print(locals())
traj_name = f'{batch_id}_traj'
parent_dir_path = f'{BatchRunFolder}/{dir}'
dir_path = os.path.join(parent_dir_path, batch_id)
plot_path = os.path.join(dir_path, f'{batch_id}.pdf')
data_path = os.path.join(dir_path, f'{batch_id}.csv')
filename = f'{dir_path}/{batch_id}.hdf5'
build_new = True
if os.path.exists(parent_dir_path) and os.path.exists(
dir_path) and overwrite == False:
build_new = False
try:
print('Trying to resume existing trajectory')
env = Environment(continuable=True)
env.resume(trajectory_name=traj_name, resume_folder=dir_path)
print('Resumed existing trajectory')
build_new = False
except:
try:
print('Trying to load existing trajectory')
traj = load_trajectory(filename=filename,
name=traj_name,
load_all=0)
env = Environment(trajectory=traj)
traj.f_load(index=None, load_parameters=2, load_results=0)
traj.f_expand(space)
print('Loaded existing trajectory')
build_new = False
except:
print(
'Neither of resuming or expanding of existing trajectory worked'
)
if build_new:
if multiprocessing:
multiproc = True
resumable = False
wrap_mode = pypetconstants.WRAP_MODE_QUEUE
else:
multiproc = False
resumable = True
wrap_mode = pypetconstants.WRAP_MODE_LOCK
# try:
print('Trying to create novel environment')
env = Environment(
trajectory=traj_name,
filename=filename,
file_title=batch_id,
comment=f'{batch_id} batch run!',
large_overview_tables=True,
overwrite_file=True,
resumable=False,
resume_folder=dir_path,
multiproc=multiproc,
ncores=4,
use_pool=
True, # Our runs are inexpensive we can get rid of overhead by using a pool
freeze_input=
True, # We can avoid some overhead by freezing the input to the pool
wrap_mode=wrap_mode,
graceful_exit=True)
traj = env.traj
print('Created novel environment')
fly_params, env_params, sim_params = sim_config[
'fly_params'], sim_config['env_params'], sim_config['sim_params']
if all(v is not None for v in [sim_params, env_params, fly_params]):
traj = load_default_configuration(traj,
sim_params=sim_params,
env_params=env_params,
fly_params=fly_params)
elif params is not None:
for p in params:
traj.f_apar(p, 0.0)
if config is not None:
for k, v in config.items():
traj.f_aconf(k, v)
traj.f_aconf('parent_dir_path',
parent_dir_path,
comment='The parent directory')
traj.f_aconf('dir_path',
dir_path,
comment='The directory path for saving data')
traj.f_aconf('plot_path',
plot_path,
comment='The file path for saving plot')
traj.f_aconf('data_path',
data_path,
comment='The file path for saving data')
traj.f_aconf('dataset_path',
f'{dir_path}/{batch_id}',
comment='The directory path for saving datasets')
traj.f_explore(space)
# except:
# raise ValueError(f'Failed to perform batch run {batch_id}')
if post_process_method is not None:
env.add_postprocessing(post_process_method, **post_kwargs)
env.run(single_method,
process_method,
save_data_in_hdf5=save_data_in_hdf5,
save_to=dir_path,
common_folder=batch_id,
**run_kwargs)
env.disable_logging()
print('Batch run complete')
if final_process_method is not None:
return final_process_method(env.traj)
def _batch_run(dir='unnamed',
batch_id='template',
space=None,
save_data_in_hdf5=False,
single_method=single_run,
process_method=null_processing,
post_process_method=None,
final_process_method=None,
multiprocessing=True,
resumable=True,
overwrite=False,
sim_config=None,
params=None,
optimization=None,
post_kwargs={},
run_kwargs={}):
saved_args = locals()
traj_name = f'{batch_id}_traj'
parent_dir_path = f'{paths.BatchRunFolder}/{dir}'
dir_path = os.path.join(parent_dir_path, batch_id)
filename = f'{dir_path}/{batch_id}.hdf5'
build_new = True
if os.path.exists(parent_dir_path) and os.path.exists(
dir_path) and overwrite == False:
build_new = False
try:
# print('Trying to resume existing trajectory')
env = Environment(continuable=True)
env.resume(trajectory_name=traj_name, resume_folder=dir_path)
print('Resumed existing trajectory')
build_new = False
except:
try:
# print('Trying to load existing trajectory')
traj = load_trajectory(filename=filename,
name=traj_name,
load_all=0)
env = Environment(trajectory=traj, multiproc=True, ncores=4)
traj = config_traj(traj, optimization)
traj.f_load(index=None, load_parameters=2, load_results=0)
traj.f_expand(space)
print('Loaded existing trajectory')
build_new = False
except:
print(
'Neither of resuming or expanding of existing trajectory worked'
)
if build_new:
if multiprocessing:
multiproc = True
resumable = False
wrap_mode = pypetconstants.WRAP_MODE_QUEUE
else:
multiproc = False
resumable = True
wrap_mode = pypetconstants.WRAP_MODE_LOCK
# print('Trying to create novel environment')
env = Environment(
trajectory=traj_name,
filename=filename,
file_title=batch_id,
comment=f'{batch_id} batch run!',
large_overview_tables=True,
overwrite_file=True,
resumable=False,
resume_folder=dir_path,
multiproc=multiproc,
ncores=4,
use_pool=
True, # Our runs are inexpensive we can get rid of overhead by using a pool
freeze_input=
True, # We can avoid some overhead by freezing the input to the pool
wrap_mode=wrap_mode,
graceful_exit=True)
print('Created novel environment')
traj = prepare_traj(env.traj, sim_config, params, batch_id,
parent_dir_path, dir_path)
traj = config_traj(traj, optimization)
traj.f_explore(space)
if post_process_method is not None:
env.add_postprocessing(post_process_method, **post_kwargs)
env.run(single_method,
process_method,
save_data_in_hdf5=save_data_in_hdf5,
save_to=dir_path,
**run_kwargs)
env.disable_logging()
print('Batch run complete')
if final_process_method is not None:
results = final_process_method(env.traj)
# print(results)
return results
