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haddock3 / data /src /haddock /libs /libworkflow.py
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introvoyz042
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"""HADDOCK3 workflow logic."""
import importlib
import sys
from pathlib import Path
from time import time
from haddock import log
from haddock.clis.cli_analyse import main as cli_analyse
from haddock.clis.cli_traceback import main as cli_traceback
from haddock.core.exceptions import HaddockError, HaddockTermination, StepError
from haddock.core.typing import Any, ModuleParams, Optional
from haddock.gear.clean_steps import clean_output
from haddock.gear.config import get_module_name
from haddock.gear.zerofill import zero_fill
from haddock.libs.libtimer import convert_seconds_to_min_sec, log_time
from haddock.libs.libutil import recursive_dict_update
from haddock.modules import (
modules_category,
non_mandatory_general_parameters_defaults,
)
class WorkflowManager:
"""Read and execute workflows."""
def __init__(
self,
workflow_params: ModuleParams,
start: Optional[int] = 0,
**other_params: Any,
) -> None:
self.start = 0 if start is None else start
self.recipe = Workflow(workflow_params, start=0, **other_params)
# terminate is used to synchronize the `clean` option with the
# `exit` module. If the `exit` module is removed in the future,
# you can also remove and clean the `terminate` part here.
self._terminated = None
def run(self) -> None:
"""High level workflow composer."""
for i, step in enumerate(self.recipe.steps[self.start :], start=self.start):
try:
step.execute()
except HaddockTermination:
self._terminated = i # type: ignore
break
# Forward any params generated at runtime by some module, for example
# auto-generated ligand topology/parameter files to every subsequent
# step that exposes the same key in its config,
# Do this ONLY when the user has not already set a value for that key.
output_params = getattr(step.module, "_output_params", {})
if output_params:
for future_step in self.recipe.steps[i + 1 :]:
for key, value in output_params.items():
if future_step.config.get(key) in (None, ""):
future_step.config[key] = value
def clean(self, terminated: Optional[int] = None) -> None:
"""
Clean steps.
Parameters
----------
terminated : int, None
At which index of the workflow to stop the cleaning. If ``None``,
uses the internal class configuration.
"""
terminated = self._terminated if terminated is None else terminated
for step in self.recipe.steps[:terminated]:
step.clean()
def postprocess(self, self_contained: bool = False) -> None:
"""Postprocess the workflow."""
# is the workflow going to be cleaned?
is_cleaned = self.recipe.steps[0].config["clean"]
# Is the workflow supposed to run offline
offline = self.recipe.steps[0].config["offline"]
# running mode
mode = self.recipe.steps[0].config["mode"]
# ncores
ncores = self.recipe.steps[0].config["ncores"]
capri_steps: list[int] = []
for step in self.recipe.steps:
if step.module_name == "caprieval":
capri_steps.append(step.order) # type: ignore
# call cli_analyse (no need for capri_dicts, it's all precalculated)
cli_analyse(
"./",
capri_steps,
top_clusters=10,
format=None,
scale=None,
inter=False,
is_cleaned=is_cleaned,
offline=offline,
mode=mode,
ncores=ncores,
self_contained=self_contained,
)
# call cli_traceback. If it fails, it's not a big deal
try:
cli_traceback("./", offline=offline)
except Exception as e:
log.warning(f"Error running traceback: {e}")
class Workflow:
"""Represent a set of stages to be executed by HADDOCK."""
def __init__(
self,
modules_parameters: ModuleParams,
start: Optional[int] = 0,
**other_params: Any,
) -> None:
if start is None:
start = 0
# filter out those parameters not belonging to the modules
general_modules = {
k: v
for k, v in other_params.items()
if k in non_mandatory_general_parameters_defaults
}
# Create the list of steps contained in this workflow
self.steps: list[Step] = []
_items = enumerate(modules_parameters.items(), start=start)
for num_stage, (stage_name, params) in _items:
stage_name = get_module_name(stage_name)
log.info(f"Reading instructions step {num_stage}_{stage_name}")
# updates the module's specific parameter with global parameters
# that are applicable to the modules. But keep priority to the local
# level
params_up = recursive_dict_update(general_modules, params)
try:
_ = Step(
stage_name,
order=num_stage,
**params_up,
)
self.steps.append(_)
except StepError as re:
log.error(f"Error found while parsing course {stage_name}")
raise HaddockError from re
class Step:
"""Represents a Step of the Workflow."""
def __init__(
self,
module_name: str,
order: Optional[int] = None,
**config_params: Any,
) -> None:
self.config = config_params
self.module_name = module_name
self.order = order
self.working_path = Path(zero_fill.fill(self.module_name, self.order)) # type: ignore
self.module = None
def execute(self) -> None:
"""Execute simulation step."""
self.working_path.resolve().mkdir(parents=False, exist_ok=False)
# Import the module given by the mode or default
module_name = ".".join(
["haddock", "modules", modules_category[self.module_name], self.module_name]
)
module_lib = importlib.import_module(module_name)
self.module = module_lib.HaddockModule(order=self.order, path=self.working_path)
# Run module
start = time()
try:
self.module.update_params(**self.config) # type: ignore
self.module.save_config(Path(self.working_path, "params.cfg")) # type: ignore
self.module.run() # type: ignore
except KeyboardInterrupt:
log.info("You have halted subprocess execution by hitting Ctrl+c")
log.info("Exiting...")
sys.exit(1)
end = time()
elapsed = convert_seconds_to_min_sec(end - start)
self.module.log(f"took {elapsed}") # type: ignore
def clean(self) -> None:
"""Clean step output."""
if self.module is None and self.config["clean"]:
with log_time("cleaning output files took"):
clean_output(self.working_path, self.config["ncores"])
elif self.module is not None and self.module.params["clean"]:
self.module.clean_output()