#!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Experiment
==========
Tools to build Yank experiments from a YAML configuration file.
This is not something that should be normally invoked by the user, and instead
created by going through the Command Line Interface with the ``yank script`` command.
"""
# =============================================================================
# GLOBAL IMPORTS
# =============================================================================
import collections
import copy
import gc
import logging
import os
import numpy as np
import cerberus
import cerberus.errors
import openmmtools as mmtools
import openmoltools as moltools
import yaml
from simtk import unit, openmm
from simtk.openmm.app import PDBFile, AmberPrmtopFile
from . import utils, pipeline, mpi, restraints, schema, multistate
from .yank import AlchemicalPhase, Topography
logger = logging.getLogger(__name__)
# =============================================================================
# CONSTANTS
# =============================================================================
HIGHEST_VERSION = '1.3' # highest version of YAML syntax
# =============================================================================
# UTILITY FUNCTIONS
# =============================================================================
[docs]def get_openmm_nonbonded_methods_strings():
"""
Get the list of valid OpenMM Nonbonded methods YANK can process
Returns
-------
valid_methods : list of str
"""
return ['NoCutoff', 'CutoffPeriodic', 'CutoffNonPeriodic', 'Ewald', 'PME']
[docs]def get_openmm_implicit_nb_method_strings():
"""
Get the subset of nonbonded method strings which work for implicit solvent
Returns
-------
valid_methods : list of str
"""
return get_openmm_nonbonded_methods_strings()[:1]
[docs]def get_openmm_explicit_nb_method_strings():
"""
Get the subset of nonbonded method strings which work for explicit solvent
Returns
-------
valid_methods : list of str
"""
return get_openmm_nonbonded_methods_strings()[1:]
[docs]def to_openmm_app(input_string):
"""
Converter function to be used with :func:`yank.utils.validate_parameters`.
Parameters
----------
input_string : str
Method name of openmm.app to fetch
Returns
-------
method : Method of openmm.app
Returns openmm.app.{input_string}
"""
return getattr(openmm.app, input_string)
def _is_phase_completed(status, number_of_iterations):
"""Check if the stored simulation is completed.
When the simulation is resumed, the number of iterations to run
in the YAML script could be updated, so we can't rely entirely on
the is_completed field in the ReplicaExchange.Status object.
Parameters
----------
status : namedtuple
The status object returned by ``yank.AlchemicalPhase``.
number_of_iterations : int
The total number of iterations that the simulation must perform.
"""
# TODO allow users to change online analysis options on resuming.
if status.target_error is None and status.iteration < number_of_iterations:
is_completed = False
else:
is_completed = status.is_completed
return is_completed
# ==============================================================================
# UTILITY CLASSES
# ==============================================================================
[docs]class YamlParseError(Exception):
"""Represent errors occurring during parsing of Yank YAML file."""
def __init__(self, message):
super(YamlParseError, self).__init__(message)
logger.error(message)
[docs]class YankLoader(yaml.Loader):
"""PyYAML Loader that recognized !Combinatorial nodes and load OrderedDicts."""
def __init__(self, *args, **kwargs):
super(YankLoader, self).__init__(*args, **kwargs)
self.add_constructor(u'!Combinatorial', self.combinatorial_constructor)
self.add_constructor(u'!Ordered', self.ordered_constructor)
@staticmethod
[docs] def combinatorial_constructor(loader, node):
"""Constructor for YAML !Combinatorial entries."""
return utils.CombinatorialLeaf(loader.construct_sequence(node))
@staticmethod
[docs] def ordered_constructor(loader, node):
"""Constructor for YAML !Ordered tag."""
loader.flatten_mapping(node)
return collections.OrderedDict(loader.construct_pairs(node))
[docs]class YankDumper(yaml.Dumper):
"""PyYAML Dumper that always return sequences in flow style and maps in block style."""
def __init__(self, *args, **kwargs):
super(YankDumper, self).__init__(*args, **kwargs)
self.add_representer(utils.CombinatorialLeaf, self.combinatorial_representer)
self.add_representer(collections.OrderedDict, self.ordered_representer)
def represent_sequence(self, tag, sequence, flow_style=None):
return yaml.Dumper.represent_sequence(self, tag, sequence, flow_style=True)
def represent_mapping(self, tag, mapping, flow_style=None):
return yaml.Dumper.represent_mapping(self, tag, mapping, flow_style=False)
@staticmethod
[docs] def combinatorial_representer(dumper, data):
"""YAML representer CombinatorialLeaf nodes."""
return dumper.represent_sequence(u'!Combinatorial', data)
@staticmethod
[docs] def ordered_representer(dumper, data):
"""YAML representer OrderedDict nodes."""
return dumper.represent_mapping(u'!Ordered', data)
# ==============================================================================
# BUILDER CLASS
# ==============================================================================
[docs]class AlchemicalPhaseFactory(object):
"""
YANK simulation phase entirely contained as one object.
Creates a full phase to simulate, expects Replica Exchange simulation for now.
Parameters
----------
sampler : yank.multistate.MultiStateSampler
Sampler which will carry out the simulation
thermodynamic_state : openmmtools.states.ThermodynamicState
Reference thermodynamic state without any alchemical modifications
sampler_states : openmmtools.states.SamplerState
Sampler state to initialize from, including the positions of the atoms
topography : yank.yank.Topography
Topography defining the ligand atoms and other atoms
protocol : dict of lists
Alchemical protocol to create states from.
Format should be ``{parameter_name : parameter_values}``
where ``parameter_name`` is the name of the specific alchemical
parameter (e.g. ``lambda_sterics``), and ``parameter_values`` is a list of values for that parameter where each
entry is one state.
Each of the ``parameter_values`` lists for every ``parameter_name`` should be the same length.
storage : yank.multistate.MultiStateReporter or str
Reporter object to use, or file path to create the reporter at
Will be a :class:`yank.multistate.MultiStateReporter` internally if str is given
restraint : yank.restraint.ReceptorLigandRestraint or None, Optional, Default: None
Optional restraint to apply to the system
alchemical_regions : openmmtools.alchemy.AlchemicalRegion or None, Optional, Default: None
Alchemical regions which define which atoms to modify.
alchemical_factory : openmmtools.alchemy.AbsoluteAlchemicalFactory, Optional, Default: None
Alchemical factory with which to create the alchemical system with if you don't want to use all the previously
defined options.
This is passed on to :class:`yank.yank.AlchemicalPhase`
metadata : dict
Additional metdata to pass on to :class:`yank.yank.AlchemicalPhase`
options : dict
Additional options to setup the rest of the process.
See the DEFAULT_OPTIONS for this class in the source code or look at the *options* header for the YAML options.
"""
DEFAULT_OPTIONS = {
'anisotropic_dispersion_cutoff': 'auto',
'minimize': True,
'minimize_tolerance': 1.0 * unit.kilojoules_per_mole/unit.nanometers,
'minimize_max_iterations': 1000,
'randomize_ligand': False,
'randomize_ligand_sigma_multiplier': 2.0,
'randomize_ligand_close_cutoff': 1.5 * unit.angstrom,
'number_of_equilibration_iterations': 0,
'equilibration_timestep': 1.0 * unit.femtosecond,
'checkpoint_interval': 50,
'store_solute_trajectory': True
}
def __init__(self, sampler, thermodynamic_state, sampler_states, topography,
protocol, storage, restraint=None, alchemical_regions=None,
alchemical_factory=None, metadata=None, **options):
self.sampler = sampler
self.thermodynamic_state = thermodynamic_state
self.sampler_states = sampler_states
self.topography = topography
self.protocol = protocol
self.storage = storage
self.restraint = restraint
self.alchemical_regions = alchemical_regions
self.alchemical_factory = alchemical_factory
self.metadata = metadata
self.options = self.DEFAULT_OPTIONS.copy()
self.options.update(options)
[docs] def create_alchemical_phase(self):
"""
Create the alchemical phase based on all the options
This only creates it, but does nothing else to prepare for simulations. The ``initialize_alchemical_phase``
will actually minimize, randomize ligand, and/or equilibrate if requested.
Returns
-------
alchemical_phase : yank.yank.AlchemicalPhase
See Also
--------
initialize_alchemical_phase
"""
alchemical_phase = AlchemicalPhase(self.sampler)
create_kwargs = self.__dict__.copy()
create_kwargs.pop('options')
create_kwargs.pop('sampler')
# Create a reporter if this is only a path.
if isinstance(self.storage, str):
checkpoint_interval = self.options['checkpoint_interval']
# Get the solute atoms
if self.options['store_solute_trajectory']:
# "Solute" is basically just not water. Includes all non-water atoms and ions
# Topography ensures the union of solute_atoms and ions_atoms is a null set
solute_atoms = self.topography.solute_atoms + self.topography.ions_atoms
if checkpoint_interval == 1:
logger.warning("WARNING! You have specified both a solute-only trajectory AND a checkpoint "
"interval of 1! You are about write the trajectory of the solute twice!\n"
"This can be okay if you are running explicit solvent and want faster retrieval "
"of the solute atoms, but in implicit solvent, this is redundant.")
else:
solute_atoms = ()
# We don't allow checkpoint file overwriting in YAML file
reporter = multistate.MultiStateReporter(self.storage, checkpoint_interval=checkpoint_interval,
analysis_particle_indices=solute_atoms)
create_kwargs['storage'] = reporter
self.storage = reporter
dispersion_cutoff = self.options['anisotropic_dispersion_cutoff'] # This will be None or an option
alchemical_phase.create(anisotropic_dispersion_cutoff=dispersion_cutoff,
**create_kwargs)
return alchemical_phase
[docs] def initialize_alchemical_phase(self):
"""
Create and set all the initial options for the alchemical phase
This minimizes, randomizes_ligand, and equilibrates the alchemical_phase on top of creating it, if the various
options are set
Returns
-------
alchemical_phase : yank.yank.AlchemicalPhase
"""
alchemical_phase = self.create_alchemical_phase()
# Minimize if requested.
if self.options['minimize']:
tolerance = self.options['minimize_tolerance']
max_iterations = self.options['minimize_max_iterations']
alchemical_phase.minimize(tolerance=tolerance, max_iterations=max_iterations)
# Randomize ligand if requested.
if self.options['randomize_ligand']:
sigma_multiplier = self.options['randomize_ligand_sigma_multiplier']
close_cutoff = self.options['randomize_ligand_close_cutoff']
alchemical_phase.randomize_ligand(sigma_multiplier=sigma_multiplier,
close_cutoff=close_cutoff)
# Equilibrate if requested.
if self.options['number_of_equilibration_iterations'] > 0:
n_iterations = self.options['number_of_equilibration_iterations']
# Get main propagation move. If this is a sequence, find first IntegratorMove.
mcmc_move = self.sampler.mcmc_moves[0]
try:
integrator_moves = [move for move in mcmc_move.move_list
if isinstance(move, mmtools.mcmc.BaseIntegratorMove)]
mcmc_move = copy.deepcopy(integrator_moves[0])
except AttributeError:
mcmc_move = copy.deepcopy(mcmc_move)
logger.debug('Using {} for equilibration.'.format(mcmc_move))
# Fix move parameters for equilibration.
move_parameters = dict(
n_steps=500,
n_restart_attempts=6,
timestep=self.options['equilibration_timestep'],
collision_rate=90.0/unit.picosecond,
measure_shadow_work=False,
measure_heat=False
)
for parameter_name, parameter_value in move_parameters.items():
if hasattr(mcmc_move, parameter_name):
setattr(mcmc_move, parameter_name, parameter_value)
# Run equilibration.
alchemical_phase.equilibrate(n_iterations, mcmc_moves=mcmc_move)
return alchemical_phase
[docs]class Experiment(object):
"""
An experiment built by :class:`ExperimentBuilder`.
This is a completely defined experiment with all parameters and settings ready to go.
It is highly recommended to **NOT** use this class directly, and instead rely on the :class:`ExperimentBuilder`
class to parse all options, configure all phases, properly set up the experiments, and even run them.
These experiments are frequently created with the :func:`ExperimentBuilder.build_experiments` method.
Parameters
----------
phases : list of yank.yank.AlchemicalPhases
Phases to run for the experiment
number_of_iterations : int or infinity
Total number of iterations each phase will be run for. Both
``float('inf')`` and ``numpy.inf`` are accepted for infinity.
switch_phase_interval : int
Number of iterations each phase will be run before the cycling to the next phase
Attributes
----------
iteration
See Also
--------
ExperimentBuilder
"""
def __init__(self, phases, number_of_iterations, switch_phase_interval):
self.phases = phases
self.number_of_iterations = number_of_iterations
self.switch_phase_interval = switch_phase_interval
self._phases_last_iterations = [None, None]
self._are_phases_completed = [False, False]
@property
def iteration(self):
"""pair of int, Current total number of iterations which have been run for each phase."""
if None in self._phases_last_iterations:
return 0, 0
return self._phases_last_iterations
@property
def is_completed(self):
return all(self._are_phases_completed)
[docs] def run(self, n_iterations=None):
"""
Run the experiment.
Runs until either the maximum number of iterations have been reached or the sampler
for that phase reports its own completion (e.g. online analysis)
Parameters
----------
n_iterations : int or None, Optional, Default: None
Optional parameter to run for a finite number of iterations instead of up to the maximum number of
iterations.
"""
# Handle default argument.
if n_iterations is None:
n_iterations = self.number_of_iterations
# Handle case in which we don't alternate between phases.
if self.switch_phase_interval <= 0:
switch_phase_interval = self.number_of_iterations
else:
switch_phase_interval = self.switch_phase_interval
# Count down the iterations to run.
iterations_left = [None, None]
while iterations_left != [0, 0]:
# Alternate phases every switch_phase_interval iterations.
for phase_id, phase in enumerate(self.phases):
# Phases may get out of sync if the user delete the storage
# file of only one phase and restart. Here we check that the
# phase still has iterations to run before creating it.
if self._are_phases_completed[phase_id]:
iterations_left[phase_id] = 0
continue
# If this is a new simulation, initialize alchemical phase.
if isinstance(phase, AlchemicalPhaseFactory):
alchemical_phase = phase.initialize_alchemical_phase()
self.phases[phase_id] = phase.storage # Should automatically be a Reporter class
else: # Resume previously created simulation.
# Check the status before loading the full alchemical phase object.
status = AlchemicalPhase.read_status(phase)
if _is_phase_completed(status, self.number_of_iterations):
self._are_phases_completed[phase_id] = True
iterations_left[phase_id] = 0
continue
alchemical_phase = AlchemicalPhase.from_storage(phase)
# TODO allow users to change online analysis options on resuming.
# Update total number of iterations. This may write the new number
# of iterations in the storage file so we do it only if necessary.
if alchemical_phase.number_of_iterations != self.number_of_iterations:
alchemical_phase.number_of_iterations = self.number_of_iterations
# Determine number of iterations to run in this function call.
if iterations_left[phase_id] is None:
total_iterations_left = self.number_of_iterations - alchemical_phase.iteration
iterations_left[phase_id] = min(n_iterations, total_iterations_left)
# Run simulation for iterations_left or until we have to switch phase.
iterations_to_run = min(iterations_left[phase_id], switch_phase_interval)
try:
alchemical_phase.run(n_iterations=iterations_to_run)
except multistate.SimulationNaNError:
# Simulation has NaN'd, this experiment is done, flag phases as done and send error up stack
self._are_phases_completed = [True] * len(self._are_phases_completed)
raise
# Check if the phase has converged.
self._are_phases_completed[phase_id] = alchemical_phase.is_completed
# Update phase iteration info. iterations_to_run may be infinity
# if number_of_iterations is.
if iterations_to_run == float('inf') and self._are_phases_completed[phase_id]:
iterations_left[phase_id] = 0
else:
iterations_left[phase_id] -= iterations_to_run
self._phases_last_iterations[phase_id] = alchemical_phase.iteration
# Delete alchemical phase and prepare switching. We force garbage
# collection to make sure that everything finalizes correctly.
del alchemical_phase
gc.collect()
[docs]class ExperimentBuilder(object):
"""Parse YAML configuration file and build the experiment.
The relative paths indicated in the script are assumed to be relative to
the script directory. However, if ExperimentBuilder is initiated with a string
rather than a file path, the paths will be relative to the user's working
directory.
The class firstly perform a dry run to check if this is going to overwrite
some files and raises an exception if it finds already existing output folders
unless the options resume_setup or resume_simulation are True.
Parameters
----------
script : str or dict
A path to the YAML script or the YAML content. If not specified, you
can load it later by using :func:`parse` (default is None).
job_id : None or int
If you want to split the experiments among different executions,
you can set this to an integer 1 <= job_id <= n_jobs, and this
:class:`ExperimentBuilder` will run only 1/n_jobs of the experiments.
n_jobs : None or int
If ``job_id`` is specified, this is the total number of jobs that
you are running in parallel from your script.
See Also
--------
Experiment
Examples
--------
>>> import textwrap
>>> import openmmtools as mmtools
>>> import yank.utils
>>> setup_dir = yank.utils.get_data_filename(os.path.join('..', 'examples',
... 'p-xylene-implicit', 'input'))
>>> pxylene_path = os.path.join(setup_dir, 'p-xylene.mol2')
>>> lysozyme_path = os.path.join(setup_dir, '181L-pdbfixer.pdb')
>>> with mmtools.utils.temporary_directory() as tmp_dir:
... yaml_content = '''
... ---
... options:
... default_number_of_iterations: 1
... output_dir: {}
... molecules:
... T4lysozyme:
... filepath: {}
... p-xylene:
... filepath: {}
... antechamber:
... charge_method: bcc
... solvents:
... vacuum:
... nonbonded_method: NoCutoff
... systems:
... my_system:
... receptor: T4lysozyme
... ligand: p-xylene
... solvent: vacuum
... leap:
... parameters: [leaprc.gaff, leaprc.ff14SB]
... protocols:
... absolute-binding:
... complex:
... alchemical_path:
... lambda_electrostatics: [1.0, 0.9, 0.8, 0.6, 0.4, 0.2, 0.0]
... lambda_sterics: [1.0, 0.9, 0.8, 0.6, 0.4, 0.2, 0.0]
... solvent:
... alchemical_path:
... lambda_electrostatics: [1.0, 0.8, 0.6, 0.3, 0.0]
... lambda_sterics: [1.0, 0.8, 0.6, 0.3, 0.0]
... experiments:
... system: my_system
... protocol: absolute-binding
... '''.format(tmp_dir, lysozyme_path, pxylene_path)
>>> yaml_builder = ExperimentBuilder(textwrap.dedent(yaml_content))
>>> yaml_builder.run_experiments()
"""
# --------------------------------------------------------------------------
# Public API
# --------------------------------------------------------------------------
# These are options that can be specified only in the main "options" section.
GENERAL_DEFAULT_OPTIONS = {
'verbose': False,
'resume_setup': True,
'resume_simulation': True,
'output_dir': 'output',
'setup_dir': 'setup',
'experiments_dir': 'experiments',
'platform': 'fastest',
'precision': 'auto',
'max_n_contexts': 3,
'switch_experiment_interval': 0,
'processes_per_experiment': 'auto'
}
# These options can be overwritten also in the "experiment"
# section and they can be thus combinatorially expanded.
EXPERIMENT_DEFAULT_OPTIONS = {
'switch_phase_interval': 0,
'temperature': 298 * unit.kelvin,
'pressure': 1 * unit.atmosphere,
'constraints': openmm.app.HBonds,
'hydrogen_mass': 1 * unit.amu,
'default_nsteps_per_iteration': 500,
'default_timestep': 2.0 * unit.femtosecond,
'default_number_of_iterations': 5000
}
def __init__(self, script=None, job_id=None, n_jobs=None):
"""
Constructor.
"""
# Check consistency job_id and n_jobs.
if job_id is not None:
if n_jobs is None:
raise ValueError('n_jobs must be specified together with job_id')
if not 1 <= job_id <= n_jobs:
raise ValueError('job_id must be between 1 and n_jobs ({})'.format(n_jobs))
self._job_id = job_id
self._n_jobs = n_jobs
self._options = self.GENERAL_DEFAULT_OPTIONS.copy()
self._options.update(self.EXPERIMENT_DEFAULT_OPTIONS.copy())
self._version = None
self._script_dir = os.getcwd() # basic dir for relative paths
self._db = None # Database containing molecules created in parse()
self._raw_yaml = {} # Unconverted input YAML script, helpful for
self._expanded_raw_yaml = {} # Raw YAML with selective keys chosen and blank dictionaries for missing keys
self._protocols = {} # Alchemical protocols description
self._experiments = {} # Experiments description
# Parse YAML script
if script is not None:
self.parse(script)
[docs] def update_yaml(self, script):
"""
Update the current yaml content and reparse it
Parameters
----------
script : str or dict
String which accepts multiple forms of YAML content that is one of the following:
File path to the YAML file
String containing all the YAML data
Dict of yaml content you wish to replace
See Also
--------
utils.update_nested_dict
"""
current_content = self._raw_yaml
try:
with open(script, 'r') as f:
new_content = yaml.load(f, Loader=YankLoader)
except IOError: # string
new_content = yaml.load(script, Loader=YankLoader)
except TypeError: # dict
new_content = script.copy()
combined_content = utils.update_nested_dict(current_content, new_content)
self.parse(combined_content)
[docs] def parse(self, script):
"""Parse the given YAML configuration file.
Validate the syntax and load the script into memory. This does not build
the actual experiment.
Parameters
----------
script : str or dict
A path to the YAML script or the YAML content.
Raises
------
YamlParseError
If the input YAML script is syntactically incorrect.
"""
# TODO check version of yank-yaml language
# TODO what if there are multiple streams in the YAML file?
# Load YAML script and decide working directory for relative paths
try:
with open(script, 'r') as f:
yaml_content = yaml.load(f, Loader=YankLoader)
self._script_dir = os.path.dirname(script)
except IOError: # string
yaml_content = yaml.load(script, Loader=YankLoader)
except TypeError: # dict
yaml_content = script.copy()
self._raw_yaml = yaml_content.copy()
# Check that YAML loading was successful
if yaml_content is None:
raise YamlParseError('The YAML file is empty!')
if not isinstance(yaml_content, dict):
raise YamlParseError('Cannot load YAML from source: {}'.format(script))
# Check version (currently there's only one)
try:
self._version = yaml_content['version']
except KeyError:
self._version = HIGHEST_VERSION
else:
if self._version != HIGHEST_VERSION:
raise ValueError('Unsupported syntax version {}'.format(self._version))
# Expand combinatorial molecules and systems
yaml_content = self._expand_molecules(yaml_content)
yaml_content = self._expand_systems(yaml_content)
# Save raw YAML content that will be needed when generating the YAML files
self._expanded_raw_yaml = copy.deepcopy({key: yaml_content.get(key, {})
for key in ['options', 'molecules', 'solvents',
'systems', 'protocols']})
# Validate options and overwrite defaults
self._options.update(self._validate_options(yaml_content.get('options', {}),
validate_general_options=True))
# Setup general logging
utils.config_root_logger(self._options['verbose'], log_file_path=None)
# Configure ContextCache, platform and precision. A Yank simulation
# currently needs 3 contexts: 1 for the alchemical states and 2 for
# the states with expanded cutoff.
platform = self._configure_platform(self._options['platform'],
self._options['precision'])
try:
mmtools.cache.global_context_cache.platform = platform
except RuntimeError:
# The cache has been already used. Empty it before switching platform.
mmtools.cache.global_context_cache.empty()
mmtools.cache.global_context_cache.platform = platform
mmtools.cache.global_context_cache.capacity = self._options['max_n_contexts']
# Initialize and configure database with molecules, solvents and systems
setup_dir = os.path.join(self._options['output_dir'], self._options['setup_dir'])
self._db = pipeline.SetupDatabase(setup_dir=setup_dir)
self._db.molecules = self._validate_molecules(yaml_content.get('molecules', {}))
self._db.solvents = self._validate_solvents(yaml_content.get('solvents', {}))
self._db.systems = self._validate_systems(yaml_content.get('systems', {}))
# Validate protocols
self._mcmc_moves = self._validate_mcmc_moves(yaml_content)
self._samplers = self._validate_samplers(yaml_content)
self._protocols = self._validate_protocols(yaml_content.get('protocols', {}))
# Validate experiments
self._parse_experiments(yaml_content)
[docs] def run_experiments(self):
"""
Set up and run all the Yank experiments.
See Also
--------
Experiment
"""
# Throw exception if there are no experiments
if len(self._experiments) == 0:
raise YamlParseError('No experiments specified!')
# Setup and run all experiments with paths relative to the script directory.
with moltools.utils.temporary_cd(self._script_dir):
self._check_resume()
self._setup_experiments()
self._generate_experiments_protocols()
# Find all the experiments to distribute among mpicomms.
all_experiments = list(self._expand_experiments())
# Cycle between experiments every switch_experiment_interval iterations
# until all of them are done.
while len(all_experiments) > 0:
# Allocate the MPI processes to the experiments that still have to be completed.
group_size = self._get_experiment_mpi_group_size(all_experiments)
if group_size is None:
completed = [False] * len(all_experiments)
for exp_index, exp in enumerate(all_experiments):
completed[exp_index] = self._run_experiment(exp)
else:
completed = mpi.distribute(self._run_experiment,
distributed_args=all_experiments,
group_size=group_size,
send_results_to='all')
# Remove any completed experiments, releasing possible parallel resources
# to be reused. Evaluate in reverse order to avoid shuffling indices.
for exp_index in range(len(all_experiments)-1, -1, -1):
if completed[exp_index]:
all_experiments.pop(exp_index)
[docs] def build_experiments(self):
"""
Generator to configure, build, and yield an experiment
Yields
------
Experiment
"""
# Throw exception if there are no experiments
if len(self._experiments) == 0:
raise YamlParseError('No experiments specified!')
# Setup and iterate over all experiments with paths relative to the script directory
with moltools.utils.temporary_cd(self._script_dir):
self._check_resume()
self._setup_experiments()
self._generate_experiments_protocols()
for experiment_path, combination in self._expand_experiments():
yield self._build_experiment(experiment_path, combination)
[docs] def setup_experiments(self):
"""
Set up all systems required for the Yank experiments without running them.
"""
# All paths must be relative to the script directory
with moltools.utils.temporary_cd(self._script_dir):
self._check_resume(check_experiments=False)
self._setup_experiments()
[docs] def status(self):
"""Iterate over the status of all experiments in dictionary form.
The status of each experiment is set to "completed" if both phases
in the experiments have been completed, "pending" if they are both
pending, and "ongoing" otherwise.
Yields
------
experiment_status : namedtuple
The status of the experiment. It contains the following fields:
name : str
The name of the experiment.
status : str
One between "completed", "ongoing", or "pending".
number_of_iterations : int
The total number of iteration set for this experiment.
job_id : int or None
If njobs is specified, this includes the job id associated
to this experiment.
phases : dict
phases[phase_name] is a namedtuple describing the status
of phase ``phase_name``. The namedtuple has two fields:
``iteration`` and ``status``.
"""
# TODO use Python 3.6 namedtuple syntax when we drop Python 3.5 support.
PhaseStatus = collections.namedtuple('PhaseStatus', [
'status',
'iteration'
])
ExperimentStatus = collections.namedtuple('ExperimentStatus', [
'name',
'status',
'phases',
'number_of_iterations',
'job_id'
])
for experiment_idx, (exp_path, exp_description) in enumerate(self._expand_experiments()):
# Determine the final number of iterations for this experiment.
number_of_iterations = self._get_experiment_number_of_iterations(exp_description)
# Determine the phases status.
phases = collections.OrderedDict()
for phase_nc_path in self._get_nc_file_paths(exp_path, exp_description):
# Determine the status of the phase.
try:
phase_status = AlchemicalPhase.read_status(phase_nc_path)
except FileNotFoundError:
iteration = None
phase_status = 'pending'
else:
iteration = phase_status.iteration
if _is_phase_completed(phase_status, number_of_iterations):
phase_status = 'completed'
else:
phase_status = 'ongoing'
phase_name = os.path.splitext(os.path.basename(phase_nc_path))[0]
phases[phase_name] = PhaseStatus(status=phase_status, iteration=iteration)
# Determine the status of the whole experiment.
phase_statuses = [phase.status for phase in phases.values()]
if phase_statuses[0] == phase_statuses[1]:
# This covers the completed and pending status.
exp_status = phase_statuses[0]
else:
exp_status = 'ongoing'
# Determine jobid if requested.
if self._n_jobs is not None:
job_id = experiment_idx % self._n_jobs + 1
else:
job_id = None
yield ExperimentStatus(name=exp_path, status=exp_status,
phases=phases, job_id=job_id,
number_of_iterations=number_of_iterations)
# --------------------------------------------------------------------------
# Properties
# --------------------------------------------------------------------------
@property
def verbose(self):
"""bool: the log verbosity."""
return self._options['verbose']
@verbose.setter
def verbose(self, new_verbose):
self._options['verbose'] = new_verbose
utils.config_root_logger(self._options['verbose'], log_file_path=None)
@property
def output_dir(self):
"""The path to the main output directory."""
return self._options['output_dir']
@output_dir.setter
def output_dir(self, new_output_dir):
self._options['output_dir'] = new_output_dir
self._db.setup_dir = os.path.join(new_output_dir, self.setup_dir)
@property
def setup_dir(self):
"""The path to the setup files directory relative to the output folder.."""
return self._options['setup_dir']
@setup_dir.setter
def setup_dir(self, new_setup_dir):
self._options['setup_dir'] = new_setup_dir
self._db.setup_dir = os.path.join(self.output_dir, new_setup_dir)
# --------------------------------------------------------------------------
# Options handling
# --------------------------------------------------------------------------
def _determine_experiment_options(self, experiment):
"""Determine all the options required to build the experiment.
Merge the options specified in the experiment section with the ones
in the options section, and divide them into several dictionaries to
feed to different main classes necessary to create an AlchemicalPhase.
Parameters
----------
experiment : dict
The dictionary encoding the experiment.
Returns
-------
experiment_options : dict
The ExperimentBuilder experiment options. This does not contain
the general ExperimentBuilder options that are accessible through
self._options.
phase_options : dict
The options to pass to the AlchemicalPhaseFactory constructor.
alchemical_region_options : dict
The options to pass to AlchemicalRegion.
alchemical_factory_options : dict
The options to pass to AlchemicalFactory.
"""
# First discard general options.
options = {name: value for name, value in self._options.items()
if name not in self.GENERAL_DEFAULT_OPTIONS}
# Then update with specific experiment options.
options.update(self._validate_options(experiment.get('options', {}),
validate_general_options=False))
def _filter_options(reference_options):
return {name: value for name, value in options.items()
if name in reference_options}
experiment_options = _filter_options(self.EXPERIMENT_DEFAULT_OPTIONS)
phase_options = _filter_options(AlchemicalPhaseFactory.DEFAULT_OPTIONS)
alchemical_region_options = _filter_options(mmtools.alchemy._ALCHEMICAL_REGION_ARGS)
alchemical_factory_options = _filter_options(utils.get_keyword_args(
mmtools.alchemy.AbsoluteAlchemicalFactory.__init__))
return (experiment_options, phase_options,
alchemical_region_options, alchemical_factory_options)
# --------------------------------------------------------------------------
# Combinatorial expansion
# --------------------------------------------------------------------------
def _expand_molecules(self, yaml_content):
"""Expand combinatorial molecules.
Generate new YAML content with no combinatorial molecules. The new content
is identical to the old one but combinatorial molecules are substituted by
the description of all the non-combinatorial molecules that they generate.
Moreover, systems that use combinatorial molecules are updated with the new
molecules ids.
Parameters
----------
yaml_content : dict
The YAML content as returned by yaml.load().
Returns
-------
expanded_content : dict
The new YAML content with combinatorial molecules expanded.
"""
expanded_content = copy.deepcopy(yaml_content)
if 'molecules' not in expanded_content:
return expanded_content
# First substitute all 'select: all' with the correct combination of indices
for comb_mol_name, comb_molecule in expanded_content['molecules'].items():
if 'select' in comb_molecule and comb_molecule['select'] == 'all':
# Get the number of models in the file
try:
extension = os.path.splitext(comb_molecule['filepath'])[1][1:] # remove dot
except KeyError:
# Trap an error caused by missing a filepath from the combinatorial expansion
# This will ultimately fail cerberus validation, but will be easier to debug than
# random Python error
continue
with moltools.utils.temporary_cd(self._script_dir):
if extension == 'pdb':
n_models = PDBFile(comb_molecule['filepath']).getNumFrames()
elif extension == 'csv' or extension == 'smiles':
n_models = len(pipeline.read_csv_lines(comb_molecule['filepath'], lines='all'))
elif extension == 'sdf' or extension == 'mol2':
if not utils.is_openeye_installed(oetools=('oechem',)):
err_msg = 'Molecule {}: Cannot "select" from {} file without OpenEye toolkit'
raise RuntimeError(err_msg.format(comb_mol_name, extension))
n_models = len(utils.load_oe_molecules(comb_molecule['filepath']))
else:
raise YamlParseError('Molecule {}: Cannot "select" from {} file'.format(
comb_mol_name, extension))
# Substitute select: all with list of all models indices to trigger combinations
comb_molecule['select'] = utils.CombinatorialLeaf(range(n_models))
# Expand molecules and update molecule ids in systems
expanded_content = utils.CombinatorialTree(expanded_content)
update_nodes_paths = [('systems', '*', 'receptor'), ('systems', '*', 'ligand'),
('systems', '*', 'solute')]
expanded_content = expanded_content.expand_id_nodes('molecules', update_nodes_paths)
return expanded_content
def _expand_systems(self, yaml_content):
"""Expand combinatorial systems.
Generate new YAML content with no combinatorial systems. The new content
is identical to the old one but combinatorial systems are substituted by
the description of all the non-combinatorial systems that they generate.
Moreover, the experiments that use combinatorial systems are updated with
the new system ids.
Molecules must be already expanded when calling this function.
Parameters
----------
yaml_content : dict
The YAML content as returned by _expand_molecules().
Returns
-------
expanded_content : dict
The new YAML content with combinatorial systems expanded.
"""
expanded_content = copy.deepcopy(yaml_content)
if 'systems' not in expanded_content:
return expanded_content
# Check if we have a sequence of experiments or a single one
try:
if isinstance(expanded_content['experiments'], list): # sequence of experiments
experiment_names = expanded_content['experiments']
else:
experiment_names = ['experiments']
except KeyError:
experiment_names = []
# Expand molecules and update molecule ids in experiments
expanded_content = utils.CombinatorialTree(expanded_content)
update_nodes_paths = [(e, 'system') for e in experiment_names]
expanded_content = expanded_content.expand_id_nodes('systems', update_nodes_paths)
return expanded_content
def _expand_experiments(self):
"""Generates all possible combinations of experiment.
Each generated experiment is uniquely named. If job_id and n_jobs are
set, this returns only the experiments assigned to this particular job.
Yields
------
experiment_path : str
A unique path where to save the experiment output files relative to
the main output directory specified by the user in the options.
combination : dict
The dictionary describing a single experiment.
"""
# We need to distribute experiments among jobs, but different
# experiments sections may have a different number of combinations,
# so we need to count them.
experiment_id = 0
output_dir = ''
for exp_name, experiment in self._experiments.items():
if len(self._experiments) > 1:
output_dir = exp_name
# Loop over all combinations
for name, combination in experiment.named_combinations(separator='_', max_name_length=50):
# Both self._job_id and self._job_id-1 work (self._job_id is 1-based),
# but we use the latter just because it makes it easier to identify in
# advance which job ids are associated to which experiments.
if self._job_id is None or experiment_id % self._n_jobs == self._job_id-1:
yield os.path.join(output_dir, name), combination
experiment_id += 1
# --------------------------------------------------------------------------
# Parsing and syntax validation
# --------------------------------------------------------------------------
# Shared schema for leap parameters. Molecules, solvents and systems use it.
# Simple strings in "parameters" are converted to list of strings.
_LEAP_PARAMETERS_DEFAULT_SCHEMA = yaml.load("""
leap:
required: no
type: dict
default_setter: no_parameters
schema:
parameters:
type: list
coerce: single_str_to_list
schema:
type: string
""")
@classmethod
def _validate_options(cls, options, validate_general_options):
"""Validate molecules syntax.
Parameters
----------
options : dict
A dictionary with the options to validate.
validate_general_options : bool
If False only the options that can be specified in the
experiment section are validated.
Returns
-------
validated_options : dict
The validated options.
Raises
------
YamlParseError
If the syntax for any option is not valid.
"""
template_options = cls.EXPERIMENT_DEFAULT_OPTIONS.copy()
template_options.update(AlchemicalPhaseFactory.DEFAULT_OPTIONS)
template_options.update(mmtools.alchemy._ALCHEMICAL_REGION_ARGS)
template_options.update(utils.get_keyword_args(
mmtools.alchemy.AbsoluteAlchemicalFactory.__init__))
if validate_general_options is True:
template_options.update(cls.GENERAL_DEFAULT_OPTIONS.copy())
# Remove options that are not supported.
template_options.pop('alchemical_atoms') # AlchemicalRegion
template_options.pop('alchemical_bonds')
template_options.pop('alchemical_angles')
template_options.pop('alchemical_torsions')
template_options.pop('switch_width') # AbsoluteAlchemicalFactory
def check_anisotropic_cutoff(cutoff):
if cutoff == 'auto':
return cutoff
else:
return utils.quantity_from_string(cutoff, unit.angstroms)
def check_processes_per_experiment(processes_per_experiment):
if processes_per_experiment == 'auto' or processes_per_experiment is None:
return processes_per_experiment
return int(processes_per_experiment)
special_conversions = {'constraints': to_openmm_app,
'default_number_of_iterations': schema.to_integer_or_infinity_coercer,
'anisotropic_dispersion_cutoff': check_anisotropic_cutoff,
'processes_per_experiment': check_processes_per_experiment}
# Validate parameters.
try:
validated_options = utils.validate_parameters(options, template_options, check_unknown=True,
process_units_str=True, float_to_int=True,
special_conversions=special_conversions)
except (TypeError, ValueError) as e:
raise YamlParseError(str(e))
# Overwrite defaults.
defaults_to_overwrite = {
# With the analytical dispersion correction for alchemical atoms,
# the computation of the energy matrix becomes super slow.
'disable_alchemical_dispersion_correction': True,
}
for option, default_value in defaults_to_overwrite.items():
if option not in validated_options:
validated_options[option] = default_value
return validated_options
@classmethod
def _validate_molecules(cls, molecules_description):
"""Validate molecules syntax.
Parameters
----------
molecules_description : dict
A dictionary representing molecules.
Returns
-------
validated_molecules : dict
The validated molecules description.
Raises
------
YamlParseError
If the syntax for any molecule is not valid.
"""
regions_schema_yaml = '''
regions:
type: dict
required: no
keyschema:
type: string
valueschema:
anyof:
- type: list
validator: positive_int_list
- type: string
- type: integer
min: 0
'''
regions_schema = yaml.load(regions_schema_yaml)
# Define the LEAP schema
leap_schema = cls._LEAP_PARAMETERS_DEFAULT_SCHEMA
# Setup the common schema across ALL molecules
common_molecules_schema = {**leap_schema, **regions_schema}
# Setup the small molecules schemas
small_molecule_schema_yaml = """
smiles:
type: string
excludes: [name, filepath]
required: yes
name:
type: string
excludes: [smiles, filepath]
required: yes
filepath:
type: string
excludes: [smiles, name]
required: yes
validator: [is_small_molecule, file_exists]
openeye:
required: no
type: dict
schema:
quacpac:
required: yes
type: string
allowed: [am1-bcc]
antechamber:
required: no
type: dict
schema:
charge_method:
required: yes
type: string
nullable: yes
net_charge:
required: no
type: integer
nullable: yes
select:
required: no
dependencies: filepath
validator: int_or_all_string
"""
# Build small molecule Epik by hand as dict since we are fetching from another source
epik_schema = schema.generate_signature_schema(moltools.schrodinger.run_epik,
update_keys={'select': {'required': False, 'type': 'integer'}},
exclude_keys=['extract_range'])
epik_schema = {'epik': {
'required': False,
'type': 'dict',
'schema': epik_schema
}
}
small_molecule_schema = {**yaml.load(small_molecule_schema_yaml), **epik_schema, **common_molecules_schema}
# Peptide schema has some keys excluded from small_molecule checks
peptide_schema_yaml = """
filepath:
required: yes
type: string
validator: [is_peptide, file_exists]
select:
required: no
dependencies: filepath
validator: int_or_all_string
strip_protons:
required: no
type: boolean
dependencies: filepath
pdbfixer:
required: no
dependencies: filepath
modeller:
required: no
dependencies: filepath
"""
peptide_schema = {**yaml.load(peptide_schema_yaml), **common_molecules_schema}
validated_molecules = molecules_description.copy()
# Schema validation
for molecule_id, molecule_descr in molecules_description.items():
small_molecule_validator = schema.YANKCerberusValidator(small_molecule_schema)
peptide_validator = schema.YANKCerberusValidator(peptide_schema)
# Test for small molecule
if small_molecule_validator.validate(molecule_descr):
validated_molecules[molecule_id] = small_molecule_validator.document
# Test for peptide
elif peptide_validator.validate(molecule_descr):
validated_molecules[molecule_id] = peptide_validator.document
else:
# Both failed, lets figure out why
# Check the is peptide w/ only excluded errors
if (cerberus.errors.EXCLUDES_FIELD in peptide_validator._errors and
peptide_validator.document_error_tree['filepath'] is None):
error = ("Molecule {} appears to be a peptide, but uses items exclusive to small molecules:\n"
"Please change either the options to peptide-only entries, or your molecule to a "
"small molecule.\n"
"====== Peptide Schema =====\n"
"{}\n"
"===========================\n")
error = error.format(molecule_id, yaml.dump(peptide_validator.errors))
# We don't know exactly what went wrong, run both error blocks
else:
error = ("Molecule {} failed to validate against one of the following schemes\n"
"Please check the following schemes for errors:\n"
"===========================\n"
"== Small Molecule Schema ==\n"
"{}\n"
"===========================\n\n" # Blank line
"===========================\n"
"====== Peptide Schema =====\n"
"{}\n"
"===========================\n")
error = error.format(molecule_id, yaml.dump(small_molecule_validator.errors),
yaml.dump(peptide_validator.errors))
raise YamlParseError(error)
# Check OpenEye charges - antechamber consistency
if 'openeye' in validated_molecules[molecule_id]:
if 'antechamber' not in validated_molecules[molecule_id]:
raise YamlParseError('Cannot specify openeye charges without antechamber')
if validated_molecules[molecule_id]['antechamber']['charge_method'] is not None:
raise YamlParseError('Antechamber charge_method must be "null" to read '
'OpenEye charges')
# Convert epik "select" to "extract_range" which is accepted by run_epik()
try:
extract_range = validated_molecules[molecule_id]['epik'].pop('select')
validated_molecules[molecule_id]['epik']['extract_range'] = extract_range
except (AttributeError, KeyError):
pass
return validated_molecules
@classmethod
def _validate_solvents(cls, solvents_description):
"""Validate molecules syntax.
Parameters
----------
solvents_description : dict
A dictionary representing solvents.
Returns
-------
validated_solvents : dict
The validated solvents description.
Raises
------
YamlParseError
If the syntax for any solvent is not valid.
"""
openmm_nonbonded_strings = get_openmm_nonbonded_methods_strings()
mapped_openmm_nonbonded_methods = {nb_method: to_openmm_app(nb_method) for
nb_method in openmm_nonbonded_strings}
explicit_strings = get_openmm_explicit_nb_method_strings()
mapped_explicit_methods = [mapped_openmm_nonbonded_methods[method] for method in explicit_strings]
all_valid_explicit = explicit_strings + mapped_explicit_methods
implicit_strings = get_openmm_implicit_nb_method_strings()
mapped_implicit_methods = [mapped_openmm_nonbonded_methods[method] for method in implicit_strings]
all_valid_implicit = implicit_strings + mapped_implicit_methods
def is_supported_solvent_model(field, solvent_model, error):
"""Check that solvent model name is supported."""
if solvent_model not in pipeline._OPENMM_LEAP_SOLVENT_MODELS_MAP:
error(field, "{} not in the known solvent models map!".format(solvent_model))
def ionic_strength_if_explicit_else_none(document, default="0.0*molar"):
"""Set the ionic strength IFF solvent model is explicit"""
if document['nonbonded_method'] in all_valid_explicit:
return default
else:
return None
def solvent_model_if_explicit_else_none(document, default='tip4pew'):
"""Set the solvent_model IFF solvent model is explicit"""
if document['nonbonded_method'] in all_valid_explicit:
return default
else:
return None
def to_openmm_app_unless_none(input_string):
"""
Extension method of the :func:`to_openmm_app` method which returns None if None is given
Primarily used by the schema validators
Parameters
----------
input_string : str or None
Method name of openmm.app to fetch
Returns
-------
method : Method of openmm.app or None
Returns openmm.app.{input_string}
"""
return to_openmm_app(input_string) if input_string is not None else None
def to_unit_unless_none_coercer(compatible_units):
"""
Extension to the :func:`utils.to_unit_coercer` method which also allows a None object to be set
See call to :func:`utils.to_unit_coercer` for call
"""
unit_validator = schema.to_unit_coercer(compatible_units)
def _to_unit_unless_none(input_quantity):
if input_quantity is None:
return None
else:
return unit_validator(input_quantity)
return _to_unit_unless_none
# Define solvents Schema
# Create the basic solvent schema, ignoring things which have a dependency
# Some keys we manually tweak
base_solvent_schema = schema.generate_signature_schema(AmberPrmtopFile.createSystem,
exclude_keys=['nonbonded_method'])
implicit_solvent_default_schema = {'implicit_solvent': base_solvent_schema.pop('implicit_solvent')}
rigid_water_default_schema = {'rigid_water': base_solvent_schema.pop('rigid_water')}
nonbonded_cutoff_default_schema = {'nonbonded_cutoff': base_solvent_schema.pop('nonbonded_cutoff')}
# Cerberus Schema Processing hierarchy:
# Input Value -> {default value} -> default setter -> coerce -> allowed/validate
# Handle the use cases for special keys
# nonbonded_method
base_solvent_schema['nonbonded_method'] = {
'allowed': [value for _, value in mapped_openmm_nonbonded_methods.items()], # Only use valid openmm methods
'coerce': to_openmm_app, # Cast the string first to valid method
'required': True, # This must be set
'default': openmm_nonbonded_strings[0], # Choose a default mapping
}
# Explicit solvent keys, populate required and dependencies in batch
explicit_only_keys = {
'clearance': {
'type': 'quantity',
'coerce': schema.to_unit_coercer(unit.angstrom),
},
'solvent_model': {
'type': 'string',
'nullable': True,
'validator': is_supported_solvent_model,
'default_setter': solvent_model_if_explicit_else_none
},
'positive_ion': {
'type': 'string'
},
'negative_ion': {
'type': 'string'
},
'ionic_strength': {
'type': 'quantity',
'coerce': to_unit_unless_none_coercer(unit.molar),
'default_setter': ionic_strength_if_explicit_else_none,
'nullable': True
},
**nonbonded_cutoff_default_schema
}
# Batch the explicit dependencies
for key in explicit_only_keys.keys():
explicit_only_keys[key]['dependencies'] = {'nonbonded_method': [mapped_openmm_nonbonded_methods[value] for
value in
get_openmm_explicit_nb_method_strings()]}
explicit_only_keys[key]['required'] = False
# Implicit solvent keys
# Input Value -> {default value} -> default setter -> coerce -> allowed/validate
implicit_only_keys = {**implicit_solvent_default_schema}
implicit_only_keys['implicit_solvent']['coerce'] = to_openmm_app_unless_none
implicit_only_keys['implicit_solvent']['dependencies'] = {'nonbonded_method': all_valid_implicit}
# Batch the implicit dependencies
for key in implicit_only_keys.keys():
implicit_only_keys[key]['dependencies'] = {'nonbonded_method': [mapped_openmm_nonbonded_methods[value] for
value in
get_openmm_implicit_nb_method_strings()]}
implicit_only_keys[key]['required'] = False
# Vacuum solvent is implicitly defined when the `NoCutoff` scheme is selected and `implicit_solvent` is None
# Finally, stitch the schema together
solvent_schema = {**base_solvent_schema, **explicit_only_keys, **implicit_only_keys,
**rigid_water_default_schema, **cls._LEAP_PARAMETERS_DEFAULT_SCHEMA}
solvent_validator = schema.YANKCerberusValidator(solvent_schema)
validated_solvents = solvents_description.copy()
# Schema validation
for solvent_id, solvent_descr in solvents_description.items():
if solvent_validator.validate(solvent_descr):
validated_solvents[solvent_id] = solvent_validator.document
else:
error = "Solvent '{}' did not validate! Check the schema error below for details\n{}"
raise YamlParseError(error.format(solvent_id, yaml.dump(solvent_validator.errors)))
return validated_solvents
@staticmethod
def _validate_protocols(protocols_description):
"""Validate protocols.
Parameters
----------
protocols_description : dict
A dictionary representing protocols.
Returns
-------
validated_protocols : dict
The validated protocols description.
Raises
------
YamlParseError
If the syntax for any protocol is not valid.
"""
def sort_protocol(protocol):
"""Reorder phases in dictionary to have complex/solvent1 first."""
sortables = [('complex', 'solvent'), ('solvent1', 'solvent2')]
for sortable in sortables:
# Phases names must be unambiguous, they can't contain both names
phase1 = [(k, v) for k, v in protocol.items()
if (sortable[0] in k and sortable[1] not in k)]
phase2 = [(k, v) for k, v in protocol.items()
if (sortable[1] in k and sortable[0] not in k)]
# Phases names must be unique
if len(phase1) == 1 and len(phase2) == 1:
return collections.OrderedDict([phase1[0], phase2[0]])
# Could not find any sortable
raise YamlParseError('Non-ordered phases must contain either "complex" and "solvent" '
'OR "solvent1" and "solvent2", the phase names must also be non-ambiguous so each '
'keyword can only appear in a single phase, not multiple.')
def validate_string_auto(field, value, error):
if isinstance(value, str) and value != 'auto':
error(field, "Only the exact string 'auto' is accepted as a string argument, not {}.".format(value))
def cast_quantity_strings(value):
"""Take an object and try to cast quantity strings to quantity, otherwise return object"""
if isinstance(value, str):
value = utils.quantity_from_string(value)
return value
def validate_required_entries_dict(field, value, error):
"""Ensure the required entries are in the dict, string is checked by a separate validator"""
if isinstance(value, dict) or isinstance(value, collections.OrderedDict):
if 'lambda_sterics' not in value.keys() or 'lambda_electrostatics' not in value.keys():
error(field, "Missing required keys lambda_sterics and/or lambda_electrostatics")
def validate_lambda_min_max(field, value, error):
"""Ensure keys which are lambda values are in fact between 0 and 1"""
base_error = "Entries with a 'lambda_' must be a float between 0 and 1, inclusive. Values {} are not."
collected_bad_values = []
if "lambda_" in field:
for single_value in value:
if not (isinstance(single_value, float) and 0 <= single_value <= 1.0):
collected_bad_values.append(single_value)
if len(collected_bad_values):
error(field, base_error.format(collected_bad_values))
# Define protocol Schema
# Note: Cannot cleanly do yaml.dump(v.errors) for nested `schema`/`*of` logic from Cerberus until its 1.2
protocol_value_schema = {
'alchemical_path': { # The only literal key
'required': True, # Key is required
'type': ['string', 'dict'], # Must be a string or dictionary
# Use this to check the string value until Cerberus 1.2 for `oneof`
# Check string with validate_string_auto, pass other values to next validator
# Check the dict values with validate_required_entries_doct, ignore other values
'validator': [validate_string_auto, validate_required_entries_dict],
'keyschema': { # Validate the keys of this sub-dictionary against this schema
'type': 'string'
},
'valueschema': { # Validate the values of this sub-dictionary against this schema
'type': 'list', # They must be a list (dont accept single values)
# Check if it has the `lambda_` string that its a float within [0,1]
'validator': validate_lambda_min_max,
'schema': {
'type': ['float', 'quantity'], # Ensure the output type is float or quantity
# Cast strings to quantity. Everything else had to validate down to this point
'coerce': cast_quantity_strings,
}
}
}
}
# Validate the top level keys (cannot be done with Cerberus)
def validate_protocol_keys_and_values(protocol_id, protocol):
# Ensure the protocol is 2 keys
if len(protocol) != 2:
raise YamlParseError('Protocol {} must only have two phases, found {}'.format(protocol_id,
len(protocol)))
# Ensure the protocol keys are in fact strings
keys_not_strings = []
key_string_error = 'Protocol {} has keys which are not strings: '.format(protocol_id)
for key in protocol.keys():
if not isinstance(key, str):
keys_not_strings.append(key)
if len(keys_not_strings) > 0:
# The join(list_comprehension) forces invalid keys to a string so the join command works
raise YamlParseError(key_string_error + ', '.join(['{}'.format(key) for key in keys_not_strings]))
# Check for ordered dict or the sorted keys
if not isinstance(protocol, collections.OrderedDict):
protocol = sort_protocol(protocol)
# Now user cerberus to validate the alchemical path part
errored_phases = []
for phase_key, phase_entry in protocol.items():
phase_validator = schema.YANKCerberusValidator(protocol_value_schema)
# test the phase
if phase_validator.validate(phase_entry):
protocol[phase_key] = phase_validator.document
else:
# collect the errors
errored_phases.append([phase_key, yaml.dump(phase_validator.errors)])
if len(errored_phases) > 0:
# Throw error
error = "Protocol {} failed because one or more of the phases did not validate, see the errors below " \
"for more information.\n".format(protocol_id)
for phase_id, phase_error in errored_phases:
error += "Phase: {}\n----\n{}\n====\n".format(phase_id, phase_error)
raise YamlParseError(error)
# Finally return if everything is fine
return protocol
validated_protocols = protocols_description.copy()
# Schema validation
for protocol_id, protocol_descr in protocols_description.items():
# Error is raised in the function
validated_protocols[protocol_id] = validate_protocol_keys_and_values(protocol_id, protocol_descr)
return validated_protocols
def _validate_systems(self, systems_description):
"""Validate systems.
Receptors, ligands, and solvents must be already loaded. If they are not
found an exception is raised.
Parameters
----------
yaml_content : dict
The dictionary representing the YAML script loaded by yaml.load()
Returns
-------
validated_systems : dict
The validated systems description.
Raises
------
YamlParseError
If the syntax for any experiment is not valid.
"""
def generate_region_clash_validator(system_descr, mol_class1, mol_class2=None):
"""
Check that the regions have non clashing names by looking at regions in each molecule for a given
system, this is generated at run time per-system
"""
base_error = ("Cannot resolve molecular regions! "
"Found regions(s) clashing for a {}/{} pair!".format(mol_class1, mol_class2))
error_collection = []
mol_description1 = self._db.molecules.get(system_descr.get(mol_class1, ''), {})
mol_description2 = self._db.molecules.get(
system_descr.get(mol_class2, ''), {}) if mol_class2 is not None else {}
# Fetch the regions or an empty dict
regions_1_names = mol_description1.get('regions', {}).keys()
regions_2_names = mol_description2.get('regions', {}).keys()
for region_1_name in regions_1_names:
if region_1_name in regions_2_names:
error_collection.append("\n- Region {}".format(region_1_name))
def _region_clash_validator(field, value, error):
if len(error_collection) > 0:
error(field, base_error + ''.join(error_collection))
return _region_clash_validator
def generate_is_pipeline_solvent_with_receptor_validator(system_descr, cross_id):
def _is_pipeline_solvent_with_receptor(field, solvent_id, error):
if cross_id in system_descr:
solvent = self._db.solvents.get(solvent_id, {})
if (solvent.get('nonbonded_method') != openmm.app.NoCutoff and
'clearance' not in solvent):
error(field, 'Explicit solvent {} does not specify clearance.'.format(solvent_id))
return _is_pipeline_solvent_with_receptor
# Define systems Schema
systems_schema_yaml = """
# DSL Schema
ligand_dsl:
required: no
type: string
dependencies: [phase1_path, phase2_path]
solvent_dsl:
required: no
type: string
dependencies: [phase1_path, phase2_path]
# Phase paths
phase1_path:
required: no
type: list
schema:
type: string
validator: file_exists
dependencies: phase2_path
validator: supported_system_files
phase2_path:
required: no
type: list
schema:
type: string
validator: file_exists
validator: supported_system_files
gromacs_include_dir:
required: no
type: string
dependencies: [phase1_path, phase2_path]
validator: directory_exists
# Solvents
solvent:
required: no
type: string
excludes: [solvent1, solvent2]
allowed: SOLVENT_IDS_POPULATED_AT_RUNTIME
validator: PIPELINE_SOLVENT_DETERMINED_AT_RUNTIME_WITH_RECEPTOR
oneof:
- dependencies: [phase1_path, phase2_path]
- dependencies: [receptor, ligand]
solvent1:
required: no
type: string
excludes: solvent
allowed: SOLVENT_IDS_POPULATED_AT_RUNTIME
validator: PIPELINE_SOLVENT_DETERMINED_AT_RUNTIME_WITH_SOLUTE
oneof:
- dependencies: [phase1_path, phase2_path, solvent2]
- dependencies: [solute, solvent2]
solvent2:
required: no
type: string
excludes: solvent
allowed: SOLVENT_IDS_POPULATED_AT_RUNTIME
validator: PIPELINE_SOLVENT_DETERMINED_AT_RUNTIME_WITH_SOLUTE
oneof:
- dependencies: [phase1_path, phase2_path, solvent1]
- dependencies: [solute, solvent1]
# Automatic pipeline
receptor:
required: no
type: string
dependencies: [ligand, solvent]
allowed: MOLECULE_IDS_POPULATED_AT_RUNTIME
excludes: [solute, phase1_path, phase2_path]
validator: REGION_CLASH_DETERMINED_AT_RUNTIME_WITH_LIGAND
ligand:
required: no
type: string
dependencies: [receptor, solvent]
allowed: MOLECULE_IDS_POPULATED_AT_RUNTIME
excludes: [solute, phase1_path, phase2_path]
pack:
# Technically requires receptor, but with default interjects itself even if receptor is not present
required: no
type: boolean
default: no
solute:
required: no
type: string
allowed: MOLECULE_IDS_POPULATED_AT_RUNTIME
dependencies: [solvent1, solvent2]
validator: REGION_CLASH_DETERMINED_AT_RUNTIME
excludes: [receptor, ligand]
"""
# Load the YAML into a schema into dict format
system_schema = yaml.load(systems_schema_yaml)
# Add the LEAP schema
leap_schema = self._LEAP_PARAMETERS_DEFAULT_SCHEMA
# Handle dependencies
# This does nothing in the case of phase1_path/phase2_path, but I wanted to leave this here in case
# we decide to actually make these required. The problem is that because this has a `default` key, it inserts
# itself into the scheme, but then fails if its a phase1_path and phase2_path set. So I silenced the line for
# now if we decided to engineer this in later.
# leap_schema['leap']['oneof'] = [{'dependencies': ['receptor', 'ligand']}, {'dependencies': 'solute'}]
system_schema = {**system_schema, **leap_schema}
# Handle the populations
# Molecules
for molecule_id_key in ['receptor', 'ligand', 'solute']:
system_schema[molecule_id_key]['allowed'] = [str(key) for key in self._db.molecules.keys()]
# Solvents
for solvent_id_key in ['solvent', 'solvent1', 'solvent2']:
system_schema[solvent_id_key]['allowed'] = [str(key) for key in self._db.solvents.keys()]
def generate_runtime_schema_for_system(system_descr):
new_schema = system_schema.copy()
# Handle the validators
# Spin up the region clash calculators
new_schema['receptor']['validator'] = generate_region_clash_validator(system_descr, 'ligand', 'receptor')
new_schema['solute']['validator'] = generate_region_clash_validator(system_descr, 'solute')
# "solvent"
new_schema['solvent']['validator'] = generate_is_pipeline_solvent_with_receptor_validator(system_descr,
'receptor')
# "solvent1" and "solvent2
new_schema['solvent1']['validator'] = generate_is_pipeline_solvent_with_receptor_validator(system_descr,
'solute')
new_schema['solvent2']['validator'] = generate_is_pipeline_solvent_with_receptor_validator(system_descr,
'solute')
return new_schema
validated_systems = systems_description.copy()
# Schema validation
for system_id, system_descr in systems_description.items():
runtime_system_schema = generate_runtime_schema_for_system(system_descr)
system_validator = schema.YANKCerberusValidator(runtime_system_schema)
if system_validator.validate(system_descr):
validated_systems[system_id] = system_validator.document
else:
error = "System '{}' did not validate! Check the schema error below for details\n{}"
raise YamlParseError(error.format(system_id, yaml.dump(system_validator.errors)))
return validated_systems
@classmethod
def _validate_mcmc_moves(cls, yaml_content):
"""Validate mcmc_moves section."""
mcmc_move_descriptions = yaml_content.get('mcmc_moves', None)
if mcmc_move_descriptions is None:
return {}
mcmc_move_schema = """
mcmc_moves:
keyschema:
type: string
valueschema:
type: dict
validator: is_mcmc_move_constructor
keyschema:
type: string
"""
mcmc_move_schema = yaml.load(mcmc_move_schema)
mcmc_move_validator = schema.YANKCerberusValidator(mcmc_move_schema)
if mcmc_move_validator.validate({'mcmc_moves': mcmc_move_descriptions}):
validated_mcmc_moves = mcmc_move_validator.document
else:
error = "MCMC moves validation failed with:\n{}"
raise YamlParseError(error.format(yaml.dump(mcmc_move_validator.errors)))
return validated_mcmc_moves['mcmc_moves']
def _validate_samplers(self, yaml_content):
"""Validate samplers section."""
sampler_descriptions = yaml_content.get('samplers', None)
if sampler_descriptions is None:
return {}
sampler_schema = """
samplers:
keyschema:
type: string
valueschema:
type: dict
validator: is_sampler_constructor
allow_unknown: yes
schema:
mcmc_moves:
type: string
allowed: {MCMC_MOVE_IDS}
""".format(MCMC_MOVE_IDS=list(self._mcmc_moves.keys()))
sampler_schema = yaml.load(sampler_schema)
# Special case for "checkpoint" in online analysis
# Handle 1-case where its set by user to something other than checkpoint and exclude it, then process if not it
options_description = yaml_content.get('options', {})
for sampler_description in sampler_descriptions:
if not ("online_analysis_interval" in sampler_descriptions[sampler_description] and
sampler_descriptions[sampler_description]["online_analysis_interval"] != "checkpoint"):
sampler_descriptions[sampler_description]["online_analysis_interval"] = \
options_description.get('checkpoint_interval',
AlchemicalPhaseFactory.DEFAULT_OPTIONS['checkpoint_interval'])
sampler_validator = schema.YANKCerberusValidator(sampler_schema)
if sampler_validator.validate({'samplers': sampler_descriptions}):
validated_samplers = sampler_validator.document
else:
error = "Samplers validation failed with:\n{}"
raise YamlParseError(error.format(yaml.dump(sampler_validator.errors)))
return validated_samplers['samplers']
def _parse_experiments(self, yaml_content):
"""Validate experiments.
Perform dry run and validate system, protocol and options of every combination.
Systems and protocols must be already loaded. If they are not found, an exception
is raised. Experiments options are validated as well.
Parameters
----------
yaml_content : dict
The dictionary representing the YAML script loaded by yaml.load()
Raises
------
YamlParseError
If the syntax for any experiment is not valid.
"""
def coerce_and_validate_options_here_against_existing(options):
coerced_and_validated = {}
errors = ""
for option, value in options.items():
try:
validated = ExperimentBuilder._validate_options({option:value}, validate_general_options=False)
coerced_and_validated = {**coerced_and_validated, **validated}
except YamlParseError as yaml_err:
# Collect all errors.
coerced_and_validated[option] = value
errors += "\n{}".format(yaml_err)
if errors != "":
raise YamlParseError(errors)
return coerced_and_validated
def ensure_restraint_type_is_key(field, restraints_dict, error):
if 'type' not in restraints_dict:
error(field, "'type' must be a sub-key in the `restraints` dict")
rest_type = restraints_dict.get('type')
if rest_type is not None and not isinstance(rest_type, str):
error(field, "Restraint type must be a string or None")
# Check if there is a sequence of experiments or a single one.
# We need to have a deterministic order of experiments so that
# if we run multiple experiments in parallel, we won't have
# multiple processes running the same one.
try:
if isinstance(yaml_content['experiments'], list):
combinatorial_trees = [(exp_name, utils.CombinatorialTree(yaml_content[exp_name]))
for exp_name in yaml_content['experiments']]
else:
combinatorial_trees = [('experiments', utils.CombinatorialTree(yaml_content['experiments']))]
self._experiments = collections.OrderedDict(combinatorial_trees)
except KeyError:
self._experiments = collections.OrderedDict()
return
# Experiments Schema
experiment_schema_yaml = """
system:
required: yes
type: string
allowed: SYSTEM_IDS_POPULATED_AT_RUNTIME
protocol:
required: yes
type: string
allowed: PROTOCOL_IDS_POPULATED_AT_RUNTIME
sampler:
required: no
type: string
allowed: SAMPLER_IDS_POPULATED_AT_RUNTIME
options:
required: no
type: dict
coerce: coerce_and_validate_options_here_against_existing
restraint:
required: no
type: dict
validator: is_restraint_constructor
keyschema:
type: string
"""
experiment_schema = yaml.load(experiment_schema_yaml)
# Populate valid types
experiment_schema['system']['allowed'] = [str(key) for key in self._db.systems.keys()]
experiment_schema['protocol']['allowed'] = [str(key) for key in self._protocols.keys()]
experiment_schema['sampler']['allowed'] = [str(key) for key in self._samplers.keys()]
# Options validator
experiment_schema['options']['coerce'] = coerce_and_validate_options_here_against_existing
experiment_validator = schema.YANKCerberusValidator(experiment_schema)
# Schema validation
for experiment_path, experiment_descr in self._expand_experiments():
if not experiment_validator.validate(experiment_descr):
error = "Experiment '{}' did not validate! Check the schema error below for details\n{}"
raise YamlParseError(error.format(experiment_path, yaml.dump(experiment_validator.errors)))
# --------------------------------------------------------------------------
# File paths utilities
# --------------------------------------------------------------------------
def _get_experiment_dir(self, experiment_path):
"""Return the path to the directory where the experiment output files
should be stored.
Parameters
----------
experiment_path : str
The relative path w.r.t. the main experiments directory (determined
through the options) of the experiment-specific subfolder.
"""
return os.path.join(self._options['output_dir'], self._options['experiments_dir'],
experiment_path)
[docs] def get_experiment_directories(self):
"""
Helper function to return the experiment directory for each experiment in the yaml file
Returns
-------
directories : tuple of str
Tuple of strings providing the relative paths to each experiment from this location
"""
directories = []
for exp_name, _ in self._expand_experiments():
# Normalize the path here to be consistent
directories.append(os.path.normpath(self._get_experiment_dir(exp_name)))
return tuple(directories)
def _get_nc_file_paths(self, experiment_path, experiment):
"""Return the paths to the two output .nc files of the experiment.
Parameters
----------
experiment_path : str
The relative path w.r.t. the main experiments directory of the
experiment-specific subfolder.
experiment : dict
The dictionary describing the single experiment.
Returns
-------
list of str
A list with the path of the .nc files for the two phases.
"""
protocol = self._protocols[experiment['protocol']]
experiment_dir = self._get_experiment_dir(experiment_path)
# The order of the phases needs to be well defined for this to make sense.
assert isinstance(protocol, collections.OrderedDict)
return [os.path.join(experiment_dir, name + '.nc') for name in protocol.keys()]
def _get_experiment_file_name(self, experiment_path):
"""Return the extension-less path to use for files referring to the experiment.
Parameters
----------
experiment_path : str
The relative path w.r.t. the main experiments directory of the
experiment-specific subfolder.
"""
experiment_dir = self._get_experiment_dir(experiment_path)
if experiment_path == '':
log_file_name = 'experiments'
else:
# Normalize path to drop eventual final slash character.
log_file_name = os.path.basename(os.path.normpath(experiment_path))
return os.path.join(experiment_dir, log_file_name)
def _get_generated_yaml_script_path(self, experiment_path):
"""Return the path for the generated single-experiment YAML script."""
return self._get_experiment_file_name(experiment_path) + '.yaml'
def _get_experiment_log_path(self, experiment_path):
"""Return the path for the experiment log file."""
return self._get_experiment_file_name(experiment_path) + '.log'
# --------------------------------------------------------------------------
# Resuming
# --------------------------------------------------------------------------
def _check_resume_experiment(self, experiment_path, experiment):
"""Check if Yank output files already exist.
Parameters
----------
experiment_path : str
The relative path w.r.t. the main experiments directory of the
experiment-specific subfolder.
experiment : dict
The dictionary describing the single experiment.
Returns
-------
bool
True if NetCDF output files already exist, or if the protocol
needs to be found automatically, and the generated YAML file
exists, False otherwise.
"""
# If protocol has automatic alchemical paths to generate,
# check if generated YAML script exist.
protocol = self._protocols[experiment['protocol']]
automatic_phases = self._find_automatic_protocol_phases(protocol)
yaml_generated_script_path = self._get_generated_yaml_script_path(experiment_path)
if len(automatic_phases) > 0 and os.path.exists(yaml_generated_script_path):
return True
# Look for existing .nc files in the folder
phase_paths = self._get_nc_file_paths(experiment_path, experiment)
for phase_path in phase_paths:
if os.path.isfile(phase_path) and os.path.getsize(phase_path) > 0:
return True
return False
@mpi.on_single_node(0, sync_nodes=True)
def _check_resume(self, check_setup=True, check_experiments=True):
"""Perform dry run to check if we are going to overwrite files.
If we find folders that ExperimentBuilder should create we raise an exception
unless resume_setup or resume_simulation are found, in which case we
assume we need to use the existing files. We never overwrite files, the
user is responsible to delete them or move them.
It's important to check all possible combinations at the beginning to
avoid interrupting the user simulation after few experiments.
Parameters
----------
check_setup : bool
Check if we are going to overwrite setup files (default is True).
check_experiments : bool
Check if we are going to overwrite experiment files (default is True).
Raises
------
YamlParseError
If files to write already exist and we resuming options are not set.
"""
err_msg = ''
for experiment_path, combination in self._expand_experiments():
if check_experiments:
resume_sim = self._options['resume_simulation']
if not resume_sim and self._check_resume_experiment(experiment_path,
combination):
experiment_dir = self._get_experiment_dir(experiment_path)
err_msg = 'experiment files in directory {}'.format(experiment_dir)
solving_option = 'resume_simulation'
if check_setup and err_msg == '':
resume_setup = self._options['resume_setup']
system_id = combination['system']
# Check system and molecule setup dirs
is_sys_setup, is_sys_processed = self._db.is_system_setup(system_id)
if is_sys_processed and not resume_setup:
system_dir = os.path.dirname(
self._db.get_system_files_paths(system_id)[0].position_path)
err_msg = 'system setup directory {}'.format(system_dir)
elif not is_sys_setup: # then this must go through the pipeline
try: # binding free energy system
receptor_id = self._db.systems[system_id]['receptor']
ligand_id = self._db.systems[system_id]['ligand']
molecule_ids = [receptor_id, ligand_id]
except KeyError: # partition/solvation free energy system
molecule_ids = [self._db.systems[system_id]['solute']]
for molecule_id in molecule_ids:
is_processed = self._db.is_molecule_setup(molecule_id)[1]
if is_processed and not resume_setup:
err_msg = 'molecule {} file'.format(molecule_id)
break
if err_msg != '':
solving_option = 'resume_setup'
# Check for errors
if err_msg != '':
err_msg += (' already exists; cowardly refusing to proceed. Move/delete '
'directory or set {} options').format(solving_option)
raise YamlParseError(err_msg)
# --------------------------------------------------------------------------
# OpenMM Platform configuration
# --------------------------------------------------------------------------
@staticmethod
def _opencl_device_support_precision(precision_model):
"""
Check if this device supports the given precision model for OpenCL platform.
Some OpenCL devices do not support double precision. This offers a test
function.
Returns
-------
is_supported : bool
True if this device supports double precision for OpenCL, False
otherwise.
"""
opencl_platform = openmm.Platform.getPlatformByName('OpenCL')
# Platforms are singleton so we need to store
# the old precision model before modifying it
old_precision = opencl_platform.getPropertyDefaultValue('OpenCLPrecision')
# Test support by creating a toy context
opencl_platform.setPropertyDefaultValue('Precision', precision_model)
system = openmm.System()
system.addParticle(1.0 * unit.amu) # system needs at least 1 particle
integrator = openmm.VerletIntegrator(1.0 * unit.femtoseconds)
try:
context = openmm.Context(system, integrator, opencl_platform)
is_supported = True
except Exception:
is_supported = False
else:
del context
del integrator
# Restore old precision
opencl_platform.setPropertyDefaultValue('Precision', old_precision)
return is_supported
@classmethod
def _configure_platform(cls, platform_name, platform_precision):
"""
Configure the platform to be used for simulation for the given precision.
Parameters
----------
platform_name : str
The name of the platform to be used for execution. If 'fastest',
the fastest available platform is used.
platform_precision : str or None
The precision to be used. If 'auto' the default value is used,
which is always mixed precision except for Reference that only
supports double precision, and OpenCL when the device supports
only single precision. If None, the precision mode won't be
set, so OpenMM default value will be used which is always
'single' for CUDA and OpenCL.
Returns
-------
platform : simtk.openmm.Platform
The configured platform.
Raises
------
RuntimeError
If the given precision model selected is not compatible with the
platform.
"""
# Determine the platform to configure
if platform_name == 'fastest':
platform = mmtools.utils.get_fastest_platform()
platform_name = platform.getName()
else:
platform = openmm.Platform.getPlatformByName(platform_name)
# Set CUDA DeterministicForces (necessary for MBAR).
if platform_name == 'CUDA':
platform.setPropertyDefaultValue('DeterministicForces', 'true')
# Use only a single CPU thread if we are using the CPU platform.
# TODO: Since there is an environment variable that can control this,
# TODO: we may want to avoid doing this.
mpicomm = mpi.get_mpicomm()
if platform_name == 'CPU' and mpicomm is not None:
logger.debug("Setting 'CpuThreads' to 1 because MPI is active.")
platform.setPropertyDefaultValue('CpuThreads', '1')
# If user doesn't specify precision, determine default value
if platform_precision == 'auto':
if platform_name == 'CUDA':
platform_precision = 'mixed'
elif platform_name == 'OpenCL':
if cls._opencl_device_support_precision('mixed'):
platform_precision = 'mixed'
else:
logger.info("This device does not support double precision for OpenCL. "
"Setting OpenCL precision to 'single'")
platform_precision = 'single'
elif platform_name == 'Reference' or platform_name == 'CPU':
platform_precision = None # leave OpenMM default precision
# Set platform precision
if platform_precision is not None:
logger.info("Setting {} platform to use precision model "
"'{}'.".format(platform_name, platform_precision))
if platform_name == 'CUDA':
platform.setPropertyDefaultValue('Precision', platform_precision)
elif platform_name == 'OpenCL':
# Some OpenCL devices do not support double precision so we need to test it
if cls._opencl_device_support_precision(platform_precision):
platform.setPropertyDefaultValue('Precision', platform_precision)
else:
raise RuntimeError('This device does not support double precision for OpenCL.')
elif platform_name == 'Reference':
if platform_precision != 'double':
raise RuntimeError("Reference platform does not support precision model '{}';"
"only 'double' is supported.".format(platform_precision))
elif platform_name == 'CPU':
if platform_precision != 'mixed':
raise RuntimeError("CPU platform does not support precision model '{}';"
"only 'mixed' is supported.".format(platform_precision))
else: # This is an unkown platform
raise RuntimeError("Found unknown platform '{}'.".format(platform_name))
return platform
# --------------------------------------------------------------------------
# Experiment setup
# --------------------------------------------------------------------------
def _setup_experiments(self):
"""Set up all experiments without running them.
IMPORTANT: This does not check if we are about to overwrite files, nor it
cd into the script directory! Use setup_experiments() for that.
"""
# Create setup directory if it doesn't exist.
os.makedirs(self._db.setup_dir, exist_ok=True)
# Configure log file for setup.
setup_log_file_path = os.path.join(self._db.setup_dir, 'setup.log')
utils.config_root_logger(self._options['verbose'], setup_log_file_path)
# Setup all systems.
self._db.setup_all_systems()
# --------------------------------------------------------------------------
# Automatic alchemical path generation
# --------------------------------------------------------------------------
@staticmethod
def _find_automatic_protocol_phases(protocol):
"""Return the list of phase names in the protocol whose alchemical
path must be generated automatically."""
assert isinstance(protocol, collections.OrderedDict)
phases_to_generate = []
for phase_name in protocol:
if protocol[phase_name]['alchemical_path'] == 'auto':
phases_to_generate.append(phase_name)
return phases_to_generate
def _generate_experiments_protocols(self):
"""Go through all experiments and generate auto alchemical paths."""
# Find all experiments that have at least one phase whose
# alchemical path needs to be generated automatically.
experiments_to_generate = []
for experiment_path, experiment in self._expand_experiments():
# First check if we have already generated the path for it.
script_filepath = self._get_generated_yaml_script_path(experiment_path)
if os.path.isfile(script_filepath):
continue
# Determine output directory and create it if it doesn't exist.
os.makedirs(os.path.dirname(script_filepath), exist_ok=True)
# Check if any of the phases needs to have its path generated.
protocol = self._protocols[experiment['protocol']]
phases_to_generate = self._find_automatic_protocol_phases(protocol)
if len(phases_to_generate) > 0:
experiments_to_generate.append((experiment_path, experiment))
else:
# Export YAML file for reproducibility.
self._generate_yaml(experiment, script_filepath)
# Parallelize generation of all protocols among nodes.
mpi.distribute(self._generate_experiment_protocol,
distributed_args=experiments_to_generate,
send_results_to=None, group_size=1, sync_nodes=True)
def _generate_experiment_protocol(self, experiment, constrain_receptor=True,
n_equilibration_iterations=None, **kwargs):
"""Generate auto alchemical paths for the given experiment.
Creates a YAML script in the experiment folder with the found protocol.
Parameters
----------
experiment : tuple (str, dict)
A tuple with the experiment path and the experiment description.
constrain_receptor : bool, optional
If True, the receptor in a receptor-ligand system will have its
CA atoms constrained during optimization (default is True).
n_equilibration_iterations : None or int
The number of equilibration iterations to perform before running
the path search. If None, the function will determine the number
of iterations to run based on the system dimension.
Other Parameters
----------------
kwargs : dict
Other parameters to pass to pipeline.find_alchemical_protocol().
"""
class DummyReporter(object):
"""A dummy reporter since we don't need to store MultiState stuff on disk."""
def nothing(self, *args, **kwargs):
"""This serves both as an attribute and a callable."""
pass
def __getattr__(self, _):
return self.nothing
# Unpack experiment argument that has been distributed among nodes.
experiment_path, experiment = experiment
# Maybe only a subset of the phases need to be generated.
protocol = self._protocols[experiment['protocol']]
phases_to_generate = self._find_automatic_protocol_phases(protocol)
# Build experiment. Use a dummy protocol for building since it hasn't been generated yet.
exp = self._build_experiment(experiment_path, experiment, use_dummy_protocol=True)
# Generate protocols.
optimal_protocols = collections.OrderedDict.fromkeys(phases_to_generate)
for phase_idx, phase_name in enumerate(phases_to_generate):
logger.debug('Generating alchemical path for {}.{}'.format(experiment_path, phase_name))
phase = exp.phases[phase_idx]
state_parameters = []
is_vacuum = len(phase.topography.receptor_atoms) == 0 and len(phase.topography.solvent_atoms) == 0
# We may need to slowly turn on a Boresch restraint.
if isinstance(phase.restraint, restraints.BoreschLike):
state_parameters.append(('lambda_restraints', [0.0, 1.0]))
# We support only lambda sterics and electrostatics for now.
if is_vacuum and not phase.alchemical_regions.annihilate_electrostatics:
state_parameters.append(('lambda_electrostatics', [1.0, 1.0]))
else:
state_parameters.append(('lambda_electrostatics', [1.0, 0.0]))
if is_vacuum and not phase.alchemical_regions.annihilate_sterics:
state_parameters.append(('lambda_sterics', [1.0, 1.0]))
else:
state_parameters.append(('lambda_sterics', [1.0, 0.0]))
# Turn the RMSD restraints off slowly at the end
if isinstance(phase.restraint, restraints.RMSD):
state_parameters.append(('lambda_restraints', [1.0, 0.0]))
# We only need to create a single state.
phase.protocol = {par[0]: [par[1][0]] for par in state_parameters}
# Remove unsampled state that we don't need for the optimization.
phase.options['anisotropic_dispersion_correction'] = False
# If default argument is used, determine number of equilibration iterations.
# TODO automatic equilibration?
if n_equilibration_iterations is None:
if is_vacuum: # Vacuum or small molecule in implicit solvent.
n_equilibration_iterations = 0
elif len(phase.topography.receptor_atoms) == 0: # Explicit solvent phase.
n_equilibration_iterations = 250
elif len(phase.topography.solvent_atoms) == 0: # Implicit complex phase.
n_equilibration_iterations = 500
else: # Explicit complex phase
n_equilibration_iterations = 1000
# Set number of equilibration iterations.
phase.options['number_of_equilibration_iterations'] = n_equilibration_iterations
# Use a reporter that doesn't write anything to save time.
phase.storage = DummyReporter()
# Create the thermodynamic state exactly as AlchemicalPhase would make it.
alchemical_phase = phase.initialize_alchemical_phase()
# Get sampler and thermodynamic state and delete alchemical phase.
thermodynamic_state = alchemical_phase._sampler._thermodynamic_states[0]
sampler_state = alchemical_phase._sampler._sampler_states[0]
mcmc_move = alchemical_phase._sampler.mcmc_moves[0]
del alchemical_phase
gc.collect()
# Restrain the receptor heavy atoms to avoid drastic
# conformational changes (possibly after equilibration).
if len(phase.topography.receptor_atoms) != 0 and constrain_receptor:
receptor_atoms_set = set(phase.topography.receptor_atoms)
# Check first if there are alpha carbons. If not, restrain all carbons.
restrained_atoms = [atom.index for atom in phase.topography.topology.atoms
if atom.name is 'CA' and atom.index in receptor_atoms_set]
if len(restrained_atoms) == 0:
# Select all carbon atoms of the receptor.
restrained_atoms = [atom.index for atom in phase.topography.topology.atoms
if atom.element.symbol is 'C' and atom.index in receptor_atoms_set]
mmtools.forcefactories.restrain_atoms(thermodynamic_state, sampler_state,
restrained_atoms, sigma=3.0*unit.angstroms)
# Find protocol.
alchemical_path = pipeline.trailblaze_alchemical_protocol(thermodynamic_state, sampler_state,
mcmc_move, state_parameters,
**kwargs)
optimal_protocols[phase_name] = alchemical_path
# Generate yaml script with updated protocol.
script_path = self._get_generated_yaml_script_path(experiment_path)
protocol = copy.deepcopy(self._protocols[experiment['protocol']])
for phase_name, alchemical_path in optimal_protocols.items():
protocol[phase_name]['alchemical_path'] = alchemical_path
self._generate_yaml(experiment, script_path, overwrite_protocol=protocol)
@mpi.on_single_node(rank=0, sync_nodes=True)
def _generate_yaml(self, experiment, file_path, overwrite_protocol=None):
"""Generate the minimum YAML file needed to reproduce the experiment.
Parameters
----------
experiment : dict
The dictionary describing a single experiment.
file_path : str
The path to the file to save.
overwrite_protocol : None or dict
If not None, this protocol description will be used instead
of the one in the original YAML file.
"""
yaml_dir = os.path.dirname(file_path)
sys_descr = self._db.systems[experiment['system']] # system description
# Molecules section data
try:
try: # binding free energy
molecule_ids = [sys_descr['receptor'], sys_descr['ligand']]
except KeyError: # partition/solvation free energy
molecule_ids = [sys_descr['solute']]
mol_section = {mol_id: self._expanded_raw_yaml['molecules'][mol_id]
for mol_id in molecule_ids}
# Copy to avoid modifying _expanded_raw_yaml when updating paths
mol_section = copy.deepcopy(mol_section)
except KeyError: # user provided directly system files
mol_section = {}
# Solvents section data
try: # binding free energy
solvent_ids = [sys_descr['solvent']]
except KeyError: # partition/solvation free energy
try:
solvent_ids = [sys_descr['solvent1'], sys_descr['solvent2']]
except KeyError: # from xml/pdb system files
assert 'phase1_path' in sys_descr
solvent_ids = []
sol_section = {sol_id: self._expanded_raw_yaml['solvents'][sol_id]
for sol_id in solvent_ids}
# Systems section data
system_id = experiment['system']
sys_section = {system_id: copy.deepcopy(self._expanded_raw_yaml['systems'][system_id])}
# Protocols section data
protocol_id = experiment['protocol']
if overwrite_protocol is None:
prot_section = {protocol_id: self._expanded_raw_yaml['protocols'][protocol_id]}
else:
prot_section = {protocol_id: overwrite_protocol}
# We pop the options section in experiment and merge it to the general one
exp_section = experiment.copy()
opt_section = self._expanded_raw_yaml['options'].copy()
opt_section.update(exp_section.pop('options', {}))
# Convert relative paths to new script directory
for molecule in mol_section.values():
if 'filepath' in molecule and not os.path.isabs(molecule['filepath']):
molecule['filepath'] = os.path.relpath(molecule['filepath'], yaml_dir)
try: # systems for which user has specified directly system files
for phase in ['phase2_path', 'phase1_path']:
for path in sys_section[system_id][phase]:
sys_section[system_id][path] = os.path.relpath(path, yaml_dir)
except KeyError: # system went through pipeline
pass
try: # output directory
output_dir = opt_section['output_dir']
except KeyError:
output_dir = self.GENERAL_DEFAULT_OPTIONS['output_dir']
if not os.path.isabs(output_dir):
opt_section['output_dir'] = os.path.relpath(output_dir, yaml_dir)
# If we are converting a combinatorial experiment into a
# single one we must set the correct experiment directory
experiment_dir = os.path.relpath(yaml_dir, output_dir)
if experiment_dir != self.GENERAL_DEFAULT_OPTIONS['experiments_dir']:
opt_section['experiments_dir'] = experiment_dir
# Create YAML with the sections in order
dump_options = {'Dumper': YankDumper, 'line_break': '\n', 'indent': 4}
yaml_content = yaml.dump({'version': self._version}, explicit_start=True, **dump_options)
yaml_content += yaml.dump({'options': opt_section}, **dump_options)
if mol_section:
yaml_content += yaml.dump({'molecules': mol_section}, **dump_options)
if sol_section:
yaml_content += yaml.dump({'solvents': sol_section}, **dump_options)
yaml_content += yaml.dump({'systems': sys_section}, **dump_options)
yaml_content += yaml.dump({'protocols': prot_section}, **dump_options)
yaml_content += yaml.dump({'experiments': exp_section}, **dump_options)
# Export YAML into a file
with open(file_path, 'w') as f:
f.write(yaml_content)
def _get_experiment_protocol(self, experiment_path, experiment_description,
use_dummy_protocol=False):
"""Obtain the protocol for this experiment.
This masks whether the protocol is hardcoded in the input YAML
script or it has been generated automatically.
Parameters
----------
experiment_path : str
The directory where to store the output files relative to the main
output directory as specified by the user in the YAML script.
experiment_description : dict
A dictionary describing a single experiment.
use_dummy_protocol : bool, optional
If True, automatically-generated protocols that have not been found
are substituted by a dummy protocol.
Returns
-------
protocol : OrderedDict
A dictionary thermodynamic_variable -> list of values.
"""
protocol_id = experiment_description['protocol']
protocol = copy.deepcopy(self._protocols[protocol_id])
# Check if there are automatically-generated protocols.
generated_alchemical_paths = self._find_automatic_protocol_phases(protocol)
if len(generated_alchemical_paths) > 0:
yaml_script_file_path = self._get_generated_yaml_script_path(experiment_path)
# Use a dummy protocol if the file doesn't exist.
try:
with open(yaml_script_file_path, 'r') as f:
yaml_script = yaml.load(f, Loader=YankLoader)
except FileNotFoundError:
if not use_dummy_protocol:
raise
for phase_name in generated_alchemical_paths:
protocol[phase_name]['alchemical_path'] = {}
else:
protocol = yaml_script['protocols'][protocol_id]
return protocol
# --------------------------------------------------------------------------
# Experiment building
# --------------------------------------------------------------------------
@staticmethod
def _save_analysis_script(results_dir, phase_names):
"""Store the analysis information about phase signs for analyze."""
analysis = [[phase_names[0], 1], [phase_names[1], -1]]
analysis_script_path = os.path.join(results_dir, 'analysis.yaml')
with open(analysis_script_path, 'w') as f:
yaml.dump(analysis, f)
def _get_experiment_mpi_group_size(self, experiments):
"""Return the MPI group size to pass when executing the experiments.
For SAMS simulations, only 1 process per experiment is currently supported.
For repex, if processes_per_experiment >= n_available_mpi_processes, the
experiments are run sequentially using all the MPI processes. Otherwise,
the heuristic tries to allocate the MPI processes among the experiments
roughly according to their computational costs using the number of states
of the first phase (either complex or solvent1).
Parameters
----------
experiments : list of pairs
Each pair contains (experiment_path, experiment_description) of an
experiment that needs to be run (i.e. that hasn't been completed yet).
Returns
-------
groups_size : list of integers
The MPI processes groups to pass to mpi.distribute(). group_size[i]
is the number of MPI processes assigned to experiments[i].
"""
mpicomm = mpi.get_mpicomm()
n_experiments = len(experiments)
processes_per_experiment = self._options['processes_per_experiment']
# Check if we need to run the experiments sequentially.
if mpicomm is None:
return None
n_mpi_processes = mpicomm.size
# If we are using SAMS samplers, use only 1 process for all experiments.
sampler_names = {self._create_experiment_sampler(exp[1], []).__class__.__name__ for exp in experiments}
if 'SAMSSampler' in sampler_names:
if processes_per_experiment != 'auto':
logger.warning('The option "processes_per_experiment" will be overwritten as SAMS '
'simulations are currently only compatible with processes_per_experiment=1')
if n_mpi_processes > n_experiments:
logger.warning('One MPI process will be assigned to each experiment but there are '
'more MPI processes than experiments. Some process will be unused.')
return 1
# Check if the user has specified an hardcoded
# number of processes per experiments.
if processes_per_experiment != 'auto':
# If more processes are requested than MPI processes, run
# experiments in sequence using all the MPI processes.
if processes_per_experiment is not None and processes_per_experiment >= n_mpi_processes:
return None
return processes_per_experiment
# If there are less MPI processes than experiments, completely split the MPI comm.
if n_mpi_processes <= n_experiments:
return 1
# Distribute the MPI processes using an heuristic that assigns more MPI
# processes to experiments that have a higher number of states in the
# first thermodynamic leg.
# ---------------------------------------------------------------------
# Split the mpicomm among the experiments.
group_size = int(n_mpi_processes / n_experiments)
# Estimate the computational cost of each experiment taken as the
# number of thermodynamic states of the complex phase.
experiment_costs = np.zeros(n_experiments)
for experiment_idx, (experiment_path, experiment_description) in enumerate(experiments):
protocol = self._get_experiment_protocol(experiment_path, experiment_description)
first_phase_name = next(iter(protocol)) # protocol is an OrderedDict
n_states = len(protocol[first_phase_name]['alchemical_path']['lambda_electrostatics'])
experiment_costs[experiment_idx] = n_states
# Find the index of the most expensive jobs.
n_expensive_experiments = n_mpi_processes % n_experiments
expensive_experiment_indices = list(reversed(np.argsort(experiment_costs)))
expensive_experiment_indices = expensive_experiment_indices[:n_expensive_experiments]
# The most expensive jobs are allocated an extra MPI process.
group_size = [group_size for _ in range(n_experiments)]
for expensive_experiment_idx in expensive_experiment_indices:
group_size[expensive_experiment_idx] += 1
return group_size
def _create_experiment_restraint(self, experiment_description):
"""Create a restraint object for the experiment."""
# Determine restraint description (None if not specified).
restraint_description = experiment_description.get('restraint', None)
if restraint_description is not None:
return schema.call_restraint_constructor(restraint_description)
return None
def _create_default_mcmc_move(self, experiment_description, mc_atoms):
"""Instantiate the default MCMCMove."""
experiment_options = self._determine_experiment_options(experiment_description)[0]
integrator_move = mmtools.mcmc.LangevinSplittingDynamicsMove(
timestep=experiment_options['default_timestep'],
collision_rate=1.0 / unit.picosecond,
n_steps=experiment_options['default_nsteps_per_iteration'],
reassign_velocities=True,
n_restart_attempts=6,
measure_shadow_work=False,
measure_heat=False
)
# Apply MC rotation displacement to ligand if there are MC atoms.
if len(mc_atoms) > 0:
move_list = [
mmtools.mcmc.MCDisplacementMove(atom_subset=mc_atoms),
mmtools.mcmc.MCRotationMove(atom_subset=mc_atoms),
integrator_move
]
else:
return integrator_move
return mmtools.mcmc.SequenceMove(move_list=move_list)
def _get_experiment_sampler_constructor(self, experiment_description):
"""Return the experiment sampler constructor description or the default if None is specified."""
# Check if we need to use the default sampler.
sampler_id = experiment_description.get('sampler', None)
if sampler_id is None:
constructor_description = {'type': 'ReplicaExchangeSampler'}
else:
constructor_description = copy.deepcopy(self._samplers[sampler_id])
# Overwrite default number of iterations if not specified.
experiment_options, phase_options, _, _ = self._determine_experiment_options(experiment_description)
if 'number_of_iterations' not in constructor_description:
default_number_of_iterations = experiment_options['default_number_of_iterations']
constructor_description['number_of_iterations'] = default_number_of_iterations
# Overwrite the online analysis interval if not specified
if not ("online_analysis_interval" in constructor_description and
constructor_description["online_analysis_interval"] != "checkpoint"):
constructor_description["online_analysis_interval"] = \
phase_options.get('checkpoint_interval',
AlchemicalPhaseFactory.DEFAULT_OPTIONS['checkpoint_interval'])
return constructor_description
def _get_experiment_number_of_iterations(self, experiment_description):
"""Return the number of iterations for the experiment.
Resolve the priority between default_number_of_iterations and the
options specified in the sampler used for the experiment.
"""
constructor_description = self._get_experiment_sampler_constructor(experiment_description)
return constructor_description['number_of_iterations']
def _create_experiment_sampler(self, experiment_description, default_mc_atoms):
"""Create the sampler object associated to the given experiment."""
# Obtain the sampler's constructor description.
constructor_description = self._get_experiment_sampler_constructor(experiment_description)
# Create the MCMCMove for the sampler.
mcmc_move_id = constructor_description.get('mcmc_moves', None)
if mcmc_move_id is None:
mcmc_move = self._create_default_mcmc_move(experiment_description, default_mc_atoms)
else:
mcmc_move = schema.call_mcmc_move_constructor(self._mcmc_moves[mcmc_move_id],
atom_subset=default_mc_atoms)
constructor_description['mcmc_moves'] = mcmc_move
# Create the sampler.
return schema.call_sampler_constructor(constructor_description)
def _build_experiment(self, experiment_path, experiment, use_dummy_protocol=False):
"""Prepare a single experiment.
Parameters
----------
experiment_path : str
The directory where to store the output files relative to the main
output directory as specified by the user in the YAML script.
experiment : dict
A dictionary describing a single experiment
use_dummy_protocol : bool, optional
If True, automatically-generated protocols that have not been found
are substituted by a dummy protocol.
Returns
-------
yaml_experiment : Experiment
A Experiment object.
"""
system_id = experiment['system']
# Get and validate experiment sub-options and divide them by class.
exp_opts = self._determine_experiment_options(experiment)
(exp_opts, phase_opts, alchemical_region_opts, alchemical_factory_opts) = exp_opts
# Configure logger file for this experiment.
experiment_log_file_path = self._get_experiment_log_path(experiment_path)
utils.config_root_logger(self._options['verbose'], experiment_log_file_path)
# Initialize alchemical factory.
alchemical_factory = mmtools.alchemy.AbsoluteAlchemicalFactory(**alchemical_factory_opts)
# Get ligand resname for alchemical atom selection. If we can't
# find it, this is a solvation free energy calculation.
ligand_dsl = None
try:
# First try for systems that went through pipeline.
ligand_molecule_id = self._db.systems[system_id]['ligand']
except KeyError:
# Try with system from system files.
try:
ligand_dsl = self._db.systems[system_id]['ligand_dsl']
except KeyError:
# This is a solvation free energy.
pass
else:
# Make sure that molecule filepath points to the mol2 file
self._db.is_molecule_setup(ligand_molecule_id)
ligand_descr = self._db.molecules[ligand_molecule_id]
ligand_resname = utils.Mol2File(ligand_descr['filepath']).resname
ligand_dsl = 'resname ' + ligand_resname
if ligand_dsl is None:
logger.debug('Cannot find ligand specification. '
'Alchemically modifying the whole solute.')
else:
logger.debug('DSL string for the ligand: "{}"'.format(ligand_dsl))
# Determine solvent DSL.
try:
solvent_dsl = self._db.systems[system_id]['solvent_dsl']
except KeyError:
solvent_dsl = 'auto' # Topography uses common solvent resnames.
logger.debug('DSL string for the solvent: "{}"'.format(solvent_dsl))
# Determine complex and solvent phase solvents while also getting regions
system_description = self._db.systems[system_id]
try: # binding free energy calculations
solvent_ids = [system_description['solvent'],
system_description['solvent']]
ligand_regions = self._db.molecules.get(system_description.get('ligand'), {}).get('regions', {})
receptor_regions = self._db.molecules.get(system_description.get('receptor'), {}).get('regions', {})
# Name clashes have been resolved in the yaml validation
regions = {'ligand_atoms': ligand_regions, 'receptor_atoms': receptor_regions}
except KeyError: # partition/solvation free energy calculations
try:
solvent_ids = [system_description['solvent1'],
system_description['solvent2']]
regions = {'solute_atoms':
self._db.molecules.get(system_description.get('solute'), {}).get('regions', {})}
except KeyError: # from xml/pdb system files
assert 'phase1_path' in system_description
solvent_ids = [None, None]
regions = {}
# Obtain the protocol for this experiment.
protocol = self._get_experiment_protocol(experiment_path, experiment, use_dummy_protocol)
# Get system files.
system_files_paths = self._db.get_system(system_id)
gromacs_include_dir = self._db.systems[system_id].get('gromacs_include_dir', None)
# Prepare Yank arguments
phases = [None, None]
# protocol is an OrderedDict so phases are in the correct
# order (e.g. [complex, solvent] or [solvent1, solvent2]).
assert isinstance(protocol, collections.OrderedDict)
phase_names = list(protocol.keys())
phase_paths = self._get_nc_file_paths(experiment_path, experiment)
for phase_idx, (phase_name, phase_path) in enumerate(zip(phase_names, phase_paths)):
# Check if we need to resume a phase. If the phase has been
# already created, Experiment will resume from the storage.
if os.path.isfile(phase_path):
phases[phase_idx] = phase_path
continue
# Create system, topology and sampler state from system files.
solvent_id = solvent_ids[phase_idx]
positions_file_path = system_files_paths[phase_idx].position_path
parameters_file_path = system_files_paths[phase_idx].parameters_path
if solvent_id is None:
system_options = None
else:
system_options = {**self._db.solvents[solvent_id], **exp_opts}
logger.info("Reading phase {}".format(phase_name))
system, topology, sampler_state = pipeline.read_system_files(
positions_file_path, parameters_file_path, system_options,
gromacs_include_dir=gromacs_include_dir)
# Identify system components. There is a ligand only in the complex phase.
if phase_idx == 0:
ligand_atoms = ligand_dsl
else:
ligand_atoms = None
topography = Topography(topology, ligand_atoms=ligand_atoms,
solvent_atoms=solvent_dsl)
# Add regions
for sub_region, specific_regions in regions.items():
for region_name, region_description in specific_regions.items():
topography.add_region(region_name, region_description, subset=sub_region)
# Create reference thermodynamic state.
if system.usesPeriodicBoundaryConditions():
pressure = exp_opts['pressure']
else:
pressure = None
thermodynamic_state = mmtools.states.ThermodynamicState(system, exp_opts['temperature'],
pressure=pressure)
# Start from AlchemicalPhase default alchemical region
# and modified it according to the user options.
phase_protocol = protocol[phase_name]['alchemical_path']
alchemical_region = AlchemicalPhase._build_default_alchemical_region(system, topography,
phase_protocol)
alchemical_region = alchemical_region._replace(**alchemical_region_opts)
# Apply restraint only if this is the first phase. AlchemicalPhase
# will take care of raising an error if the phase type does not support it.
if phase_idx == 0:
restraint = self._create_experiment_restraint(experiment)
else:
restraint = None
# Create MCMC moves and sampler. Apply MC rotation displacement to ligand.
# We don't try displacing and rotating the ligand with a Boresch restraint
# since the attempts would likely always fail.
if len(topography.ligand_atoms) > 0 and not isinstance(restraint, restraints.BoreschLike):
mc_atoms = topography.ligand_atoms
else:
mc_atoms = []
sampler = self._create_experiment_sampler(experiment, mc_atoms)
# Create phases.
phases[phase_idx] = AlchemicalPhaseFactory(sampler, thermodynamic_state, sampler_state,
topography, phase_protocol, storage=phase_path,
restraint=restraint, alchemical_regions=alchemical_region,
alchemical_factory=alchemical_factory, **phase_opts)
# Dump analysis script
results_dir = self._get_experiment_dir(experiment_path)
mpi.run_single_node(0, self._save_analysis_script, results_dir, phase_names)
# Return new Experiment object.
number_of_iterations = self._get_experiment_number_of_iterations(experiment)
return Experiment(phases, number_of_iterations, exp_opts['switch_phase_interval'])
# --------------------------------------------------------------------------
# Experiment run
# --------------------------------------------------------------------------
def _run_experiment(self, experiment):
"""Run a single experiment.
This runs the experiment only for ``switch_experiment_interval``
iterations (if specified).
Parameters
----------
experiment : tuple (str, dict)
A tuple with the experiment path and the experiment description.
Returns
-------
is_completed
True if the experiment has completed the number of iterations
requested or if it has reached the target statistical error.
"""
# Unpack experiment argument that has been distributed among nodes.
experiment_path, experiment = experiment
# Handle case where we don't have to switch between experiments.
if self._options['switch_experiment_interval'] <= 0:
# Run Experiment for number_of_iterations.
switch_experiment_interval = None
else:
switch_experiment_interval = self._options['switch_experiment_interval']
built_experiment = self._build_experiment(experiment_path, experiment)
# Trap a NaN'd simulation by capturing only the error we can handle, let all others raise normally
try:
built_experiment.run(n_iterations=switch_experiment_interval)
except multistate.SimulationNaNError:
# Print out to critical logger.
nan_warning_string = ('\n\n' # Initial blank line for spacing.
'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n'
'! CRITICAL: Experiment NaN !\n'
'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n'
'The following experiment threw a NaN! It should NOT be considered!\n'
'Experiment: {}\n'
'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n'
'!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n'
).format(self._get_experiment_dir(experiment_path))
logger.critical(nan_warning_string)
# Flag the experiment as completed to avoid continuing in the next cycle.
return True
return built_experiment.is_completed
if __name__ == "__main__":
import doctest
doctest.testmod()