def run_and_print_log(workflow, highlight=None):
"""Run workflow on multi-threaded worker cached with Sqlite3.
:param workflow: workflow to evaluate.
:param highlight: highlight these lines.
"""
from noodles.run.threading.sqlite3 import run_parallel
from noodles import serial
import io
import logging
log = io.StringIO()
log_handler = logging.StreamHandler(log)
formatter = logging.Formatter('%(asctime)s - %(message)s')
log_handler.setFormatter(formatter)
logger = logging.getLogger('noodles')
logger.setLevel(logging.INFO)
logger.handlers = [log_handler]
result = run_parallel(workflow,
n_threads=4,
registry=serial.base,
db_file='tutorial.db',
always_cache=True,
echo_log=False)
display_text(log.getvalue(), highlight or [], split_at=40)
return result
def call_default(wf, n_processes=1):
"""
Run locally using several threads.
Caching can be turned off by specifying cache=None
"""
return run_parallel(
wf, n_threads=n_processes, registry=registry,
db_file='cache.db', always_cache=True, echo_log=False)
def call_default(wf, n_processes, always_cache):
"""
Run locally using several threads.
Caching can be turned off by specifying cache=None
"""
return run_parallel(wf,
n_threads=n_processes,
registry=registry,
db_file='cache.db',
always_cache=always_cache,
echo_log=False)
def _multi_ligand_job(mol: Molecule, psf: PSFContainer, settings: QmSettings,
workdir: str, db_file: str,
**kwargs: Any) -> CP2KMM_Result:
"""Helper function for :func:`multi_ligand_job`."""
# Run MATCH on all unique ligands
lig_dict = _lig_from_psf(mol, psf)
ff: str = kwargs.pop('forcefield', 'top_all36_cgenff_new')
rtf_list, prm = _run_match(lig_dict.values(), forcefield=ff)
# Update the .psf file with all MATCH results
initial_charge = psf.charge.sum()
for id_range, rtf_file in zip(lig_dict.keys(), rtf_list):
overlay_rtf_file(psf, rtf_file, id_range)
# Update the charge
_constrain_charge(psf, settings, initial_charge)
# Fill in all missing core/ligand lennard-jones parameters with those from UFF
# TODO: Connect this with the newly improved Auto-FOX 0.8 parameter guessing schemes
prm_to_df(settings)
if settings.get('lennard_jones') is not None:
_fill_uff(psf, settings['lennard_jones'])
elif settings.get('lennard-jones') is not None:
_fill_uff(psf, settings['lennard-jones'])
# Write the new .prm and .psf files and update the CP2K settings
prm_name = join(workdir, 'mol.prm')
psf_name = join(workdir, 'mol.psf')
prm.write(prm_name)
psf.write(psf_name)
settings.prm = prm_name
settings.psf = psf_name
# Run the actual CP2K job
with SET_CONFIG_STDOUT:
job = cp2k_mm(mol=mol, settings=settings, **kwargs)
return run_parallel(job,
db_file=db_file,
n_threads=1,
always_cache=True,
registry=registry,
echo_log=False)
def call_default(wf: PromisedObject, n_processes: int,
always_cache: bool) -> Result:
"""Run locally using several threads.
Caching can be turned off by specifying ``cache=None``.
"""
# In case 'default_jobmanager' is not set (for some reason)
try:
workdir = plams.config.get('default_jobmanager').workdir
except AttributeError as ex:
raise plams.PlamsError(
"Failed to initialize the PLAMS jobmanager") from ex
db_file = join(workdir, 'cache.db')
return run_parallel(wf,
n_threads=n_processes,
registry=registry,
db_file=db_file,
always_cache=always_cache,
echo_log=False)
def run(wf):
result = run_parallel(
wf, n_threads=2, registry=registry, db_file=':memory:',
always_cache=False)
return result