Source code for gemseo.utils.xdsmizer

# Copyright 2021 IRT Saint Exupéry,
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# Lesser General Public License for more details.
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
# Contributors:
#    INITIAL AUTHORS - API and implementation and/or documentation
#       :author: Remi Lafage
"""Creation of a XDSM diagram from a scenario.

The :class:`.XDSMizer` generates a JSON file.

The latter is used by
the `XDSMjs javascript library <>`_
to produce an interactive web XDSM
and by the pyxdsm python library
to produce TIKZ and PDF versions of the XDSM.

For more information, see:
A. B. Lambe and J. R. R. A. Martins, “Extensions to the Design Structure Matrix for
the Description of Multidisciplinary Design, Analysis, and Optimization Processes”,
Structural and Multidisciplinary Optimization, vol. 46, no. 2, p. 273-284, 2012.
from __future__ import annotations

import logging
import webbrowser
from json import dumps
from multiprocessing import RLock
from os.path import basename
from os.path import splitext
from pathlib import Path
from tempfile import mkdtemp
from typing import Any
from typing import Dict
from typing import List
from typing import Mapping
from typing import Union

from gemseo.core.discipline import MDODiscipline
from gemseo.core.doe_scenario import DOEScenario
from gemseo.core.execution_sequence import AtomicExecSequence
from gemseo.core.execution_sequence import CompositeExecSequence
from gemseo.core.execution_sequence import LoopExecSequence
from gemseo.core.execution_sequence import ParallelExecSequence
from gemseo.core.execution_sequence import SerialExecSequence
from gemseo.core.mdo_scenario import MDOScenario
from gemseo.core.monitoring import Monitoring
from gemseo.core.scenario import Scenario
from gemseo.disciplines.scenario_adapter import MDOScenarioAdapter
from gemseo.mda.mda import MDA
from gemseo.utils.locks import synchronized
from gemseo.utils.show_utils import generate_xdsm_html
from gemseo.utils.xdsm_to_pdf import xdsm_data_to_pdf

LOGGER = logging.getLogger(__name__)


EdgeType = Dict[str, Union[MDODiscipline, List[str]]]
NodeType = Dict[str, str]
IdsType = Any

XdsmType = Dict[str, Any]

[docs]class XDSMizer: """Build the XDSM diagram of a scenario as a JSON structure.""" def __init__( self, scenario: Scenario, hashref: str = "root", level: int = 0, expected_workflow: CompositeExecSequence | None = None, ) -> None: """ Args: scenario: The scenario to be represented as an XDSM diagram. hashref: The keyword used in the JSON structure to reference the dictionary data structure whose keys are "nodes", "edges", "workflow" and "optpb". level: The depth of the scenario. Root scenario is level 0. expected_workflow: The expected workflow, describing the sequence of execution of the different disciplines (:class:`.MDODiscipline`, :class:`.Scenario`, :class:`.MDA`, etc.) """ self.scenario = scenario self.level = level self.hashref = hashref self.lock = RLock() self._monitor = None self.outdir = "." self.outfilename = "xdsm.json" self.to_hashref = {} self.to_id = {} # dictionary to map AtomicExecSequence to XDSM id self.initialize(expected_workflow) self.print_statuses = False # Prints the statuses in the console self.latex_output = False
[docs] def initialize( self, workflow: CompositeExecSequence | None = None, ) -> None: """Initialize the XDSM from a workflow. The initialization also creates sub-XDSM diagram accordingly. Args: workflow: The composite execution sequence. If None, use the scenario's one. """ self.sub_xdsmizers = [] # Find disciplines from workflow structure if workflow: self.workflow = workflow else: self.workflow = self.scenario.get_expected_workflow() self.atoms = XDSMizer._get_single_level_atoms(self.workflow) self.to_hashref = {} level = self.level + 1 num = 1 for atom in self.atoms: if atom.discipline.is_scenario(): if atom.discipline == self.scenario: self.to_hashref[atom] = "root" self.root_atom = atom else: # sub-scenario name = self.to_hashref[atom] = f"{name}_scn-{level}-{num}" sub_workflow = XDSMizer._find_sub_workflow(self.workflow, atom) self.sub_xdsmizers.append( XDSMizer( atom.discipline, self.to_hashref[atom], level, sub_workflow ) ) num += 1
[docs] def monitor( self, outdir: str | None = ".", outfilename: str = "xdsm.json", print_statuses: bool = False, latex_output: bool = False, ) -> None: """Monitor the discipline execution by generating XDSM json file on discipline status update. Args: outdir: The name of the directory to store the different files. If None, the current working directory is used. outfilename: The name of the JSON file. print_statuses: If True, print the statuses in the console at each update. latex_output: If True, save the XDSM to tikz, tex and pdf files. """ self._monitor = Monitoring(self.scenario) self._monitor.add_observer(self) # have to reinitialize with monitored workflow self.initialize(self._monitor.workflow) self.outdir = outdir self.outfilename = outfilename self.print_statuses = print_statuses self.latex_output = latex_output
[docs] def update( self, atom: AtomicExecSequence, ) -> None: # pylint: disable=unused-argument """Generate a new XDSM regarding the atom status update. Args: atom: The discipline which status is monitored. """ output_directory_path=self.outdir, outfilename=self.outfilename, latex_output=self.latex_output, ) if self.print_statuses:
[docs] def run( self, output_directory_path: str | None = None, latex_output: bool = False, outfilename: str = "xdsm.html", html_output: bool = True, json_output: bool = False, open_browser: bool = False, ) -> XdsmType: """Generate a XDSM diagram from the process. By default, a self contained HTML file is generated, that can be viewed in a browser. Args: output_directory_path: The name of the directory to store the JSON file. If None, the current working directory is used. If open_browser is True and outdir is None, the file is stored in a temporary directory. outfilename: The name of the JSON file. latex_output: If True, save the XDSM to tikz, tex and pdf files. open_browser: If True, open the web browser and display the XDSM. html_output: If True, save the XDSM in a self-contained HTML file json_output: If True, save the JSON file. Returns: The XDSM structure expressed as a dictionary whose keys are "nodes", "edges", "workflow" and "optpb". """ xdsm = self.xdsmize() xdsm_json = dumps(xdsm, indent=2, ensure_ascii=False) base = basename(outfilename) outfile_basename = splitext(base)[0] no_html_loc = False if output_directory_path is None: output_directory_path = Path.cwd() no_html_loc = True else: output_directory_path = Path(output_directory_path) if json_output: json_path = output_directory_path / f"{outfile_basename}.json" with"w") as file_stream: file_stream.write(xdsm_json) if latex_output: xdsm_data_to_pdf(xdsm, output_directory_path, outfile_basename) if html_output or open_browser: if no_html_loc: output_directory_path = Path(mkdtemp(suffix="", prefix="tmp", dir=None)) out_file_path = (output_directory_path / outfile_basename).with_suffix( ".html" )"Generating HTML XDSM file in : %s", out_file_path) generate_xdsm_html(xdsm, out_file_path) if open_browser: url = f"file://{out_file_path}", new=2) # open in new tab return out_file_path return xdsm
[docs] def get_all_sub_xdsmizers(self) -> list[XDSMizer]: """Retrieve all the sub-xdsmizers corresponding to the sub-scenarios. Returns: The sub-xdsmizers. """ result = [] for sub in self.sub_xdsmizers: result.append(sub) result.extend(sub.get_all_sub_xdsmizers()) return result
[docs] @synchronized def xdsmize( self, algoname: str = "Optimizer", ) -> dict[str, Any]: """Build the data structure to be used to generate the JSON file. Args: algoname: The name under which a scenario appears in an XDSM. Returns: The XDSM structure expressed as a dictionary whose keys are "nodes", "edges", "workflow" and "optpb". """ nodes = self._create_nodes(algoname) edges = self._create_edges() workflow = self._create_workflow() optpb = str(self.scenario.formulation.opt_problem) if self.level == 0: res = { self.hashref: { "nodes": nodes, "edges": edges, "workflow": workflow, "optpb": optpb, } } for sub_xdsmizer in self.get_all_sub_xdsmizers(): if"ing"): name = f"{[:-3]}er" elif"Scenario"): if isinstance(sub_xdsmizer.scenario, DOEScenario): name = "Trade-Off" elif isinstance(sub_xdsmizer.scenario, MDOScenario): name = "Optimizer" else: name = else: name = res[sub_xdsmizer.hashref] = sub_xdsmizer.xdsmize(name) return res return {"nodes": nodes, "edges": edges, "workflow": workflow, "optpb": optpb}
def _create_nodes( self, algoname: str, ) -> list[NodeType]: # pylint: disable=too-many-branches """Create the nodes of the XDSM from the scenarios and the disciplines. Args: algoname: The name under which a scenario appears in an XDSM. """ nodes = [] self.to_id = {} statuses = self.workflow.get_statuses() # Optimization self.to_id[self.root_atom] = OPT_ID opt_node = {"id": OPT_ID, "name": algoname, "type": "optimization"} if statuses[self.root_atom.uuid]: opt_node["status"] = statuses[self.root_atom.uuid] nodes.append(opt_node) # Disciplines for atom_id, atom in enumerate( self.atoms ): # pylint: disable=too-many-nested-blocks # if a node already created from an atom with same discipline # at one level just reference the same node for ref_atom in self.to_id: if atom.discipline == ref_atom.discipline: self.to_id[atom] = self.to_id[ref_atom] if ( atom.status and atom.parent.status is MDODiscipline.STATUS_RUNNING ): node = None for a_node in nodes: if a_node["id"] == self.to_id[atom]: node = a_node break if not node: # TODO: add specific exception? raise "Node " + self.to_id[ atom ] + " not found in " + nodes # pragma: no cover node["status"] = atom.status break if atom in self.to_id: continue self.to_id[atom] = "Dis" + str(atom_id) node = {"id": self.to_id[atom], "name":} # node type if isinstance(atom.discipline, MDA): node["type"] = "mda" elif atom.discipline.is_scenario(): node["type"] = "mdo" node["subxdsm"] = self.to_hashref[atom] node["name"] = self.to_hashref[atom] else: node["type"] = "analysis" if statuses[atom.uuid]: node["status"] = statuses[atom.uuid] nodes.append(node) return nodes def _create_edges(self) -> list[EdgeType]: """Create the edges of the XDSM from the dataflow of the scenario.""" edges = [] # convenient method to factorize code for creating and appending edges def add_edge( from_edge: MDODiscipline, to_edge: MDODiscipline, varnames: list[str], ) -> None: """Add an edge from a discipline to another with variables names as label. Args: from_edge: The starting discipline. to_edge: The end discipline. varnames: The names of the variables going from the starting discipline to the end one. """ edge = {"from": from_edge, "to": to_edge, "name": ", ".join(varnames)} edges.append(edge) # For User to/from optimization opt_pb = self.scenario.formulation.opt_problem # fct names such as -y4 functions_names = opt_pb.get_all_functions_names() # output variables used by the fonction (eg y4) fct_varnames = [f.outvars for f in opt_pb.get_all_functions()] function_varnames = [] for fvars in fct_varnames: function_varnames.extend(fvars) to_user = functions_names to_opt = self.scenario.get_optim_variables_names() user_pattern = "L({})" if == "Sampling" else "{}^(0)" opt_pattern = "{}^(1:N)" if == "Sampling" else "{}^*" add_edge(USER_ID, OPT_ID, [user_pattern.format(x) for x in to_opt]) add_edge(OPT_ID, USER_ID, [opt_pattern.format(x) for x in to_user]) # Disciplines to/from optimization for atom in self.atoms: if atom is not self.root_atom: varnames = set(atom.discipline.get_input_data_names()) & set( self.scenario.get_optim_variables_names() ) if varnames: add_edge(OPT_ID, self.to_id[atom], varnames) varnames = set(atom.discipline.get_output_data_names()) & set( function_varnames ) # print set(disc.get_output_data_names()), set(functions_names) if varnames: add_edge(self.to_id[atom], OPT_ID, varnames) # Disciplines to User/Optimization (from User is already handled at # optimizer level) disc_to_opt = function_varnames for atom in self.atoms: if atom is not self.root_atom: # special case MDA : skipped if isinstance(atom.discipline, MDA): continue out_to_user = [ o for o in atom.discipline.get_output_data_names() if o not in disc_to_opt ] out_to_opt = [ o for o in atom.discipline.get_output_data_names() if o in disc_to_opt ] if out_to_user: add_edge(self.to_id[atom], USER_ID, [x + "^*" for x in out_to_user]) if out_to_opt: add_edge(self.to_id[atom], OPT_ID, out_to_opt) # Disciplines to/from disciplines for coupling in self.scenario.get_expected_dataflow(): (disc1, disc2, varnames) = coupling add_edge( self.to_id[self._find_atom(disc1)], self.to_id[self._find_atom(disc2)], varnames, ) return edges @staticmethod def _get_single_level_atoms( workflow: CompositeExecSequence, ) -> list[AtomicExecSequence]: """Retrieve the list of atoms of the given workflow. This method does not look into the loop execution sequences coming from the scenario. Thus, it retrieves the atoms for a one level XDSM diagram. Args: The composite execution sequence. Returns: The atomic execution sequences. """ atoms = [] for sequence in workflow.sequences: if isinstance(sequence, LoopExecSequence): atoms.append(sequence.atom_controller) if not sequence.atom_controller.discipline.is_scenario(): atoms += XDSMizer._get_single_level_atoms( sequence.iteration_sequence ) elif isinstance(sequence, AtomicExecSequence): atoms.append(sequence) else: atoms += XDSMizer._get_single_level_atoms(sequence) return atoms def _find_atom( self, discipline: MDODiscipline, ) -> AtomicExecSequence: """Find the atomic sequence corresponding to a given discipline. Args: discipline: A discipline. Returns: The atomic sequence corresponding to the given discipline. Raises: ValueError: If the atomic sequence is not found. """ atom = None if isinstance(discipline, MDOScenarioAdapter): atom = self._find_atom(discipline.scenario) else: for atom_i in self.atoms: if discipline == atom_i.discipline: atom = atom_i if atom is None: disciplines = [a.discipline for a in self.atoms] raise ValueError(f"Discipline {discipline} not found in {disciplines}") return atom @staticmethod def _find_sub_workflow( workflow: CompositeExecSequence, atom_controller: AtomicExecSequence, ) -> LoopExecSequence | None: """Find the sub-workflow from a workflow and controller atom in it. Args: workflow: The workflow from which to find a sub-workflow. atom_controller: The atomic execution sequence that controls the loop execution sequence to find. Returns: The sub-workflow. None if the list of execution sequences of the original workflow is empty. """ sub_workflow = None for sequence in workflow.sequences: if isinstance(sequence, LoopExecSequence): if sequence.atom_controller.uuid == atom_controller.uuid: sub_workflow = sequence return sub_workflow sub_workflow = sub_workflow or XDSMizer._find_sub_workflow( sequence.iteration_sequence, atom_controller ) elif not isinstance(sequence, AtomicExecSequence): sub_workflow = sub_workflow or XDSMizer._find_sub_workflow( sequence, atom_controller ) return sub_workflow def _create_workflow(self) -> list[str, IdsType]: """Manage the creation of the XDSM workflow creation from a formulation one.""" workflow = [USER_ID, expand(self.workflow, self.to_id)] return workflow
[docs]def expand( wks: CompositeExecSequence, to_id: Mapping[str, str], ) -> IdsType: """Expand the workflow structure as an ids structure using to_id mapping. The expansion preserve the structure while replacing the object by its id in all case except when a tuple is encountered as cdr then the expansion transforms loop[A, (B,C)] in [idA, {'parallel': [idB, idC]}]. Args: wks: The workflow structure. to_id: The mapping dict from object to id. Returns: The ids structure valid to be used as XDSM json chains. """ if isinstance(wks, SerialExecSequence): res = [] for sequence in wks.sequences: res += expand(sequence, to_id) ids = res elif isinstance(wks, ParallelExecSequence): res = [] for sequence in wks.sequences: res += expand(sequence, to_id) ids = [{"parallel": res}] elif isinstance(wks, LoopExecSequence): if ( wks.atom_controller.discipline.is_scenario() and to_id[wks.atom_controller] != OPT_ID ): # sub-scnario consider only the controller ids = [to_id[wks.atom_controller]] else: ids = [to_id[wks.atom_controller], expand(wks.iteration_sequence, to_id)] elif isinstance(wks, AtomicExecSequence): ids = [to_id[wks]] else: raise Exception(f"Bad execution sequence: found {wks}") return ids