# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# 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 - initial API and implementation and/or initial
# documentation
# :author: Francois Gallard
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""Abstraction of processes."""
from __future__ import division, unicode_literals
import inspect
import logging
import os
import sys
from collections import defaultdict
from copy import deepcopy
from multiprocessing import Manager, Value, cpu_count
from multiprocessing.sharedctypes import Synchronized
from timeit import default_timer as timer
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Iterable,
List,
Optional,
Tuple,
Union,
)
import six
from custom_inherit import DocInheritMeta
from numpy import concatenate, empty, ndarray, zeros
if TYPE_CHECKING:
from gemseo.core.execution_sequence import SerialExecSequence
from gemseo.caches.cache_factory import CacheFactory
from gemseo.core.grammar import AbstractGrammar, InvalidDataException
from gemseo.core.grammars.factory import GrammarFactory
from gemseo.core.jacobian_assembly import JacobianAssembly
from gemseo.utils.derivatives_approx import EPSILON, DisciplineJacApprox
from gemseo.utils.py23_compat import Path
from gemseo.utils.string_tools import MultiLineString, pretty_repr
# TODO: remove try except when py2 is no longer supported
try:
import cPickle as pickle # noqa: N813
except ImportError:
import pickle
LOGGER = logging.getLogger(__name__)
[docs]def default_dict_factory(): # type: (...) -> Dict
"""Instantiate a defaultdict(None) object."""
return defaultdict(None)
[docs]@six.add_metaclass(
DocInheritMeta(
abstract_base_class=True,
style="google_with_merge",
include_special_methods=True,
)
)
class MDODiscipline(object):
"""A software integrated in the workflow.
The inputs and outputs are defined in a grammar, which can be
either a SimpleGrammar or a JSONGrammar, or your own which
derives from the Grammar abstract class.
To be used, use a subclass and implement the _run method
which defined the execution of the software.
Typically, in the _run method, get the inputs from the
input grammar, call your software, and write the outputs
to the output grammar.
The JSONGrammar files are automatically detected when in the same
folder as your subclass module and named "CLASSNAME_input.json"
use ``auto_detect_grammar_files=True`` to activate this option.
Attributes:
input_grammar (AbstractGrammar): The input grammar.
output_grammar (AbstractGrammar): The output grammar.
grammar_type (str): The type of grammar
to be used for inputs and outputs declaration.
comp_dir (str): The path to the directory of the discipline module file if any.
data_processor (DataProcessor): A tool to pre- and post-process discipline data.
re_exec_policy (str): The policy to re-execute the same discipline.
residual_variables (List[str]): The output variables
to be considered as residuals; they shall be equal to zero.
jac (Dict[str, Dict[str, ndarray]]): The Jacobians of the outputs wrt inputs
of the form ``{output: {input: matrix}}``.
exec_for_lin (bool): Whether the last execution was due to a linearization.
name (str): The name of the discipline.
cache (AbstractCache): The cache
containing one or several executions of the discipline
according to the cache policy.
local_data (Dict[str, Any]): The last input and output data.
"""
STATUS_VIRTUAL = "VIRTUAL"
STATUS_PENDING = "PENDING"
STATUS_DONE = "DONE"
STATUS_RUNNING = "RUNNING"
STATUS_FAILED = "FAILED"
__DEPRECATED_GRAMMAR_TYPES = {"JSON": "JSONGrammar", "Simple": "SimpleGrammar"}
JSON_GRAMMAR_TYPE = "JSONGrammar"
SIMPLE_GRAMMAR_TYPE = "SimpleGrammar"
COMPLEX_STEP = "complex_step"
FINITE_DIFFERENCES = "finite_differences"
SIMPLE_CACHE = "SimpleCache"
HDF5_CACHE = "HDF5Cache"
MEMORY_FULL_CACHE = "MemoryFullCache"
APPROX_MODES = [FINITE_DIFFERENCES, COMPLEX_STEP]
AVAILABLE_MODES = (
JacobianAssembly.AUTO_MODE,
JacobianAssembly.DIRECT_MODE,
JacobianAssembly.ADJOINT_MODE,
JacobianAssembly.REVERSE_MODE,
FINITE_DIFFERENCES,
COMPLEX_STEP,
)
RE_EXECUTE_DONE_POLICY = "RE_EXEC_DONE"
RE_EXECUTE_NEVER_POLICY = "RE_EXEC_NEVER"
N_CPUS = cpu_count()
_ATTR_TO_SERIALIZE = (
"residual_variables",
"output_grammar",
"name",
"local_data",
"jac",
"input_grammar",
"_status",
"cache",
"n_calls",
"n_calls_linearize",
"_differentiated_inputs",
"_differentiated_outputs",
"data_processor",
"_is_linearized",
"_linearization_mode",
"_default_inputs",
"re_exec_policy",
"exec_time",
"_cache_type",
"_cache_file_path",
"_cache_tolerance",
"_cache_hdf_node_name",
"_linearize_on_last_state",
"_cache_was_loaded",
"_grammar_type",
"comp_dir",
"exec_for_lin",
"_in_data_hash_dict",
"_jac_approx",
)
__time_stamps_mp_manager = None
time_stamps = None
def __init__(
self,
name=None, # type: Optional[str]
input_grammar_file=None, # type: Optional[Union[str, Path]]
output_grammar_file=None, # type: Optional[Union[str, Path]]
auto_detect_grammar_files=False, # type: bool
grammar_type=JSON_GRAMMAR_TYPE, # type: str
cache_type=SIMPLE_CACHE, # type: str
cache_file_path=None, # type: Optional[Union[str, Path]]
): # type: (...) -> None
"""
Args:
name: The name of the discipline.
If None, use the class name.
input_grammar_file: The input grammar file path.
If None and ``auto_detect_grammar_files=True``,
use the file naming convention ``name + "_input.json"``
and look for it in the directory containing the discipline source file
If None and ``auto_detect_grammar_files=False``,
do not initialize the input grammar from a schema file.
output_grammar_file: The output grammar file path.
If None and ``auto_detect_grammar_files=True``,
use the file naming convention ``name + "_output.json"``
and look for it in the directory containing the discipline source file
If None and ``auto_detect_grammar_files=False``,
do not initialize the output grammar from a schema file.
auto_detect_grammar_files: Whether to use the naming convention
when ``input_grammar_file`` and ``output_grammar_file`` are None.
If so,
search for these file names in the directory
containing the discipline source file.
grammar_type: The type of grammar to use for inputs and outputs declaration,
e.g. :attr:`.JSON_GRAMMAR_TYPE` or :attr:`.SIMPLE_GRAMMAR_TYPE`.
cache_type: The type of policy to cache the discipline evaluations,
e.g. :attr:`.SIMPLE_CACHE` or :attr:`.HDF5_CACHE`.
cache_file_path: The HDF file path
when ``grammar_type`` is :attr:`.HDF5_CACHE`.
"""
self.input_grammar = None # : input grammar
self.output_grammar = None # : output grammar
self._grammar_type = grammar_type
self.comp_dir = None
# : data converters between execute and _run
self.data_processor = None
self._default_inputs = {} # : default data to be set if not passed
# Allow to re execute the same discipline twice, only if did not fail
# and not running
self.re_exec_policy = self.RE_EXECUTE_DONE_POLICY
# : list of outputs that shall be null, to be considered as residuals
self.residual_variables = []
self._differentiated_inputs = [] # : outputs to differentiate
# : inputs to be used for differentiation
self._differentiated_outputs = []
self._n_calls = None # : number of calls to execute()
self._exec_time = None # : cumulated execution time
# : number of calls to linearize()
self._n_calls_linearize = None
self._in_data_hash_dict = {}
self.jac = None # : Jacobians of outputs wrt inputs dictionary
# : True if linearize() has already been called
self._is_linearized = False
self._jac_approx = None # Jacobian approximation object
self._linearize_on_last_state = False # If true, the linearization
# is performed on the state computed by the disciplines
# (MDAs for instance) otherwise, the inputs that are also outputs
# are reset at the previous inputs value be fore calling
# _compute_jacobian
# the last execution was due to a linearization
self.exec_for_lin = False
if name is None:
self.name = self.__class__.__name__
else:
self.name = name
self.cache = None
self._cache_type = cache_type
self._cache_file_path = cache_file_path
self._cache_tolerance = 0.0
self._cache_hdf_node_name = None
# By default, dont use approximate cache
# It is up to the user to choose to optimize CPU time with this or not
self.set_cache_policy(cache_type=cache_type, cache_hdf_file=cache_file_path)
# linearize mode :auto, adjoint, direct
self._linearization_mode = JacobianAssembly.AUTO_MODE
self_module = sys.modules.get(self.__class__.__module__)
has_file = hasattr(self_module, "__file__")
if has_file:
self.comp_dir = str(Path(inspect.getfile(self.__class__)).parent.absolute())
if input_grammar_file is None and auto_detect_grammar_files:
input_grammar_file = self.auto_get_grammar_file(True)
if output_grammar_file is None and auto_detect_grammar_files:
output_grammar_file = self.auto_get_grammar_file(False)
self._instantiate_grammars(
input_grammar_file, output_grammar_file, self._grammar_type
)
self.local_data = {} # : the inputs and outputs data
# : The current status of execution
self._status = self.STATUS_PENDING
self._cache_was_loaded = False
self._init_shared_attrs()
self._status_observers = []
def __str__(self): # type: (...) -> str
return self.name
def __repr__(self): # type: (...) -> str
msg = MultiLineString()
msg.add(self.name)
msg.indent()
inputs = sorted(self.get_input_data_names())
outputs = sorted(self.get_output_data_names())
msg.add("Inputs: {}", pretty_repr(inputs))
msg.add("Outputs: {}", pretty_repr(outputs))
return str(msg)
def _init_shared_attrs(self): # type: (...) -> None
"""Initialize the shared attributes in multiprocessing."""
self._n_calls = Value("i", 0)
self._exec_time = Value("d", 0.0)
self._n_calls_linearize = Value("i", 0)
def __init_cache_attr(self): # type: (...) -> None
"""Initialize the cache attributes."""
if self._cache_type == self.HDF5_CACHE:
self.cache = None
self.set_cache_policy(
self.HDF5_CACHE,
self._cache_tolerance,
self._cache_file_path,
self._cache_hdf_node_name,
)
@property
def n_calls(self): # type: (...) -> int
"""The number of times the discipline was executed.
.. note::
This property is multiprocessing safe.
"""
return self._n_calls.value
@n_calls.setter
def n_calls(
self,
value, # type: int
): # type: (...) -> None
self._n_calls.value = value
@property
def exec_time(self): # type: (...) -> float
"""The cumulated execution time of the discipline.
.. note::
This property is multiprocessing safe.
"""
return self._exec_time.value
@exec_time.setter
def exec_time(
self,
value, # type: float
): # type: (...) -> None
self._exec_time.value = value
@property
def n_calls_linearize(self): # type: (...) -> int
"""The number of times the discipline was linearized.
.. note::
This property is multiprocessing safe.
"""
return self._n_calls_linearize.value
@n_calls_linearize.setter
def n_calls_linearize(
self,
value, # type: int
): # type: (...) -> None
self._n_calls_linearize.value = value
@property
def grammar_type(self): # type: (...) -> AbstractGrammar
"""The grammar type."""
return self._grammar_type
[docs] def auto_get_grammar_file(
self,
is_input=True, # type: bool
name=None, # type: Optional[str]
comp_dir=None, # type: Optional[Union[str, Path]]
): # type: (...) -> Path
"""Use a naming convention to associate a grammar file to a discipline.
This method searches in a directory for
either an input grammar file named ``name + "_input.json"``
or an output grammar file named``name + "_output.json"``.
Args:
is_input: If True,
autodetect the input grammar file;
otherwise,
autodetect the output grammar file.
name: The name to be searched in the file names.
If None,
use the :attr:`.name` name of the discipline.
comp_dir: The directory in which to search the grammar file.
If None, use :attr:`.comp_dir`.
Returns:
The grammar file path.
"""
if comp_dir is None:
comp_dir = self.comp_dir
if name is None:
name = self.name
if is_input:
suffix = "input"
else:
suffix = "output"
return Path(comp_dir) / "{}_{}.json".format(name, suffix)
[docs] def add_differentiated_outputs(
self,
outputs=None, # type: Optional[Iterable[str]]
): # type: (...) -> None
"""Add outputs to be differentiated.
This method updates :attr:`._differentiated_outputs` with ``outputs``.
Args:
outputs: The output variables to be differentiated.
If None, all the outputs of the discipline are used.
Raises:
ValueError: When the outputs to differentiate are not discipline outputs.
"""
if (outputs is not None) and (not self.is_all_outputs_existing(outputs)):
raise ValueError(
"Cannot differentiate {} "
"that are not among the discipline outputs {}.".format(
self.name, self.get_output_data_names()
)
)
out_diff = self._differentiated_outputs
if outputs is None:
outputs = self.get_output_data_names()
self._differentiated_outputs = list(set(out_diff) | set(outputs))
[docs] def set_cache_policy(
self,
cache_type=SIMPLE_CACHE, # type: str
cache_tolerance=0.0, # type: float
cache_hdf_file=None, # type: Optional[Union[str, Path]]
cache_hdf_node_name=None, # type: Optional[str]
is_memory_shared=True, # type: bool
): # type: (...) -> None
"""Set the type of cache to use and the tolerance level.
This method defines when the output data have to be cached
according to the distance between the corresponding input data
and the input data already cached for which output data are also cached.
The cache can be either a :class:`SimpleCache` recording the last execution
or a cache storing all executions,
e.g. :class:`.MemoryFullCache` and :class:`.HDF5Cache`.
Caching data can be either in-memory,
e.g. :class:`.SimpleCache` and :class:`.MemoryFullCache`,
or on the disk,
e.g. :class:`.HDF5Cache`.
The attribute :attr:`.CacheFactory.caches` provides the available caches types.
Args:
cache_type: The type of cache.
cache_tolerance: The maximum relative norm
of the difference between two input arrays
to consider that two input arrays are equal.
cache_hdf_file: The path to the HDF file to store the data;
this argument is mandatory when the :attr:`.HDF5Cache` policy is used.
cache_hdf_node_name: The name of the HDF file node
to store the discipline data.
If None, :attr:`.name` is used.
is_memory_shared: Whether to store the data with a shared memory dictionary,
which makes the cache compatible with multiprocessing.
.. warning:
If set to False,
and multiple disciplines point
to the same cache or the process is multiprocessed,
there may be duplicate computations
because the cache will not be shared among the processes.
"""
create_cache = CacheFactory().create
if cache_type == self.HDF5_CACHE:
not_same_file = cache_hdf_file != self._cache_file_path
not_same_node = cache_hdf_node_name != self._cache_hdf_node_name
cache_none = self.cache is None
already_hdf = self._cache_type == self.HDF5_CACHE
not_already_hdf = self._cache_type != self.HDF5_CACHE
if cache_none or (
(already_hdf and (not_same_file or not_same_node)) or not_already_hdf
):
node_path = cache_hdf_node_name or self.name
self.cache = create_cache(
self.HDF5_CACHE,
hdf_file_path=cache_hdf_file,
hdf_node_path=node_path,
tolerance=cache_tolerance,
name=self.name,
)
self._cache_hdf_node_name = cache_hdf_node_name
self._cache_file_path = cache_hdf_file
elif cache_type != self._cache_type or self.cache is None:
if cache_type == self.MEMORY_FULL_CACHE:
self.cache = create_cache(
cache_type,
tolerance=cache_tolerance,
name=self.name,
is_memory_shared=is_memory_shared,
)
else:
self.cache = create_cache(
cache_type, tolerance=cache_tolerance, name=self.name
)
else:
LOGGER.warning(
"Cache policy is already set to %s. To clear the"
" discipline cache, use its clear() method.",
cache_type,
)
self._cache_type = cache_type
self._cache_tolerance = cache_tolerance
[docs] def get_sub_disciplines(
self,
): # type: (...) -> List[MDODiscipline] # pylint: disable=R0201
"""Return the sub-disciplines if any.
Returns:
The sub-disciplines.
"""
return []
[docs] def get_expected_workflow(self): # type: (...) -> SerialExecSequence
"""Return the expected execution sequence.
This method is used for the XDSM representation.
The default expected execution sequence
is the execution of the discipline itself.
.. seealso::
MDOFormulation.get_expected_workflow
Returns:
The expected execution sequence.
"""
# avoid circular dependency
from gemseo.core.execution_sequence import ExecutionSequenceFactory
return ExecutionSequenceFactory.serial(self)
[docs] def get_expected_dataflow(
self,
): # type: (...) -> List[Tuple[MDODiscipline,MDODiscipline,List[str]]]
"""Return the expected data exchange sequence.
This method is used for the XDSM representation.
The default expected data exchange sequence is an empty list.
.. seealso::
MDOFormulation.get_expected_dataflow
Returns:
The data exchange arcs.
"""
return []
def _instantiate_grammars(
self,
input_grammar_file, # type: Optional[Union[str, Path]]
output_grammar_file, # type: Optional[Union[str, Path]]
grammar_type=JSON_GRAMMAR_TYPE, # type: str
): # type: (...) -> None
"""Create the input and output grammars.
Args:
input_grammar_file: The input grammar file path.
If None, do not initialize the input grammar from a schema file.
output_grammar_file: The output grammar file path.
If None, do not initialize the output grammar from a schema file.
grammar_type: The type of grammar,
e.g. :attr:`.JSONGrammar` or :attr:`.SimpleGrammar`.
"""
factory = GrammarFactory()
grammar_type = self.__DEPRECATED_GRAMMAR_TYPES.get(grammar_type, grammar_type)
# TODO: deprecate this at some point.
self.input_grammar = factory.create(
grammar_type,
name="{}_input".format(self.name),
schema_file=input_grammar_file,
)
self.output_grammar = factory.create(
grammar_type,
name="{}_output".format(self.name),
schema_file=output_grammar_file,
)
def _run(self): # type: (...) -> None
"""Define the execution of the process, given that data has been checked.
To be overloaded by subclasses.
"""
raise NotImplementedError()
def _filter_inputs(
self,
input_data=None, # type: Optional[Dict[str, Any]]
): # type: (...) -> Dict[str, Any]
"""Filter data with the discipline inputs and use the default values if missing.
Args:
input_data: The data to be filtered.
Returns:
The values of the input variables based on the provided data.
Raises:
TypeError: When the input data are not passed as a dictionary.
"""
if input_data is None:
return deepcopy(self.default_inputs)
if not isinstance(input_data, dict):
raise TypeError(
"Input data must be of dict type, "
"got {} instead.".format(type(input_data))
)
# Take default inputs if not in input_data
filt_inputs = self._default_inputs.copy() # Shallow copy
filt_inputs.update(input_data)
# Remove inputs that should not be there
in_names = self.get_input_data_names()
filt_inputs = {key: val for key, val in filt_inputs.items() if key in in_names}
return filt_inputs
def _filter_local_data(self): # type: (...) -> None
"""Filter the local data after execution.
This method removes data that are neither inputs nor outputs.
"""
all_data_names = self.get_input_output_data_names()
self.local_data = {
key: val for key, val in self.local_data.items() if key in all_data_names
}
def _check_status_before_run(self): # type: (...) -> None
"""Check the status of the discipline before calling :meth:`._run`.
Check the status of the discipline depending on :attr:`.re_execute_policy`.
If ``re_exec_policy == RE_EXECUTE_NEVER_POLICY``,
the status shall be either :attr:`.PENDING` or :attr:`.VIRTUAL`.
If ``self.re_exec_policy == RE_EXECUTE_NEVER_POLICY``,
- if status is :attr:`.DONE`,
:meth:`.reset_statuses_for_run` is called prior :meth:`._run`,
- otherwise status must be :attr:`.VIRTUAL` or :attr:`.PENDING`.
Raises:
ValueError:
* When the re-execution policy is unknown.
* When the discipline status and the re-execution policy
are no consistent.
"""
status_ok = True
if self.status == self.STATUS_RUNNING:
status_ok = False
if self.re_exec_policy == self.RE_EXECUTE_NEVER_POLICY:
if self.status not in [self.STATUS_PENDING, self.STATUS_VIRTUAL]:
status_ok = False
elif self.re_exec_policy == self.RE_EXECUTE_DONE_POLICY:
if self.status == self.STATUS_DONE:
self.reset_statuses_for_run()
status_ok = True
elif self.status not in [self.STATUS_PENDING, self.STATUS_VIRTUAL]:
status_ok = False
else:
raise ValueError("Unknown re_exec_policy: {}.".format(self.re_exec_policy))
if not status_ok:
raise ValueError(
"Trying to run a discipline {} with status: {} "
"while re_exec_policy is {}.".format(
type(self), self.status, self.re_exec_policy
)
)
def __get_input_data_for_cache(
self,
input_data, # type: Dict[str, Any]
in_names, # type: Iterable[str]
): # type: (...) -> Dict[str, Any]
"""Prepare the input data for caching.
Args:
input_data: The values of the inputs.
in_names: The names of the inputs.
Returns:
The input data to be cached.
"""
in_and_out = set(in_names) & set(self.get_output_data_names())
cached_inputs = dict(input_data.items())
for key in in_and_out:
val = input_data.get(key)
if val is not None:
# If also an output, keeps a copy of the original input value
cached_inputs[key] = deepcopy(val)
return cached_inputs
[docs] def execute(
self,
input_data=None, # type:Optional[Dict[str, Any]]
): # type: (...) -> Dict[str, Any]
"""Execute the discipline.
This method executes the discipline:
* Adds the default inputs to the ``input_data``
if some inputs are not defined in input_data
but exist in :attr:`._default_inputs`.
* Checks whether the last execution of the discipline was called
with identical inputs, ie. cached in :attr:`.cache`;
if so, directly returns ``self.cache.get_output_cache(inputs)``.
* Caches the inputs.
* Checks the input data against :attr:`.input_grammar`.
* If :attr:`.data_processor` is not None, runs the preprocessor.
* Updates the status to :attr:`.RUNNING`.
* Calls the :meth:`._run` method, that shall be defined.
* If :attr:`.data_processor` is not None, runs the postprocessor.
* Checks the output data.
* Caches the outputs.
* Updates the status to :attr:`.DONE` or :attr:`.FAILED`.
* Updates summed execution time.
Args:
input_data: The input data needed to execute the discipline
according to the discipline input grammar.
If None, use the :attr:`.default_inputs`.
Returns:
The discipline local data after execution.
"""
# Load the default_inputs if the user did not provide all required data
input_data = self._filter_inputs(input_data)
# Check if the cache already the contains outputs associated to these
# inputs
in_names = self.get_input_data_names()
out_cached, out_jac = self.cache.get_outputs(input_data, in_names)
if out_cached is not None:
self.__update_local_data_from_cache(input_data, out_cached, out_jac)
return self.local_data
# Cache was not loaded, see self.linearize
self._cache_was_loaded = False
# Save the state of the inputs
cached_inputs = self.__get_input_data_for_cache(input_data, in_names)
self._check_status_before_run()
self.check_input_data(input_data)
self.local_data = {}
self.local_data.update(input_data)
processor = self.data_processor
# If the data processor is set, pre-process the data before _run
# See gemseo.core.data_processor module
if processor is not None:
self.local_data = processor.pre_process_data(input_data)
self.status = self.STATUS_RUNNING
self._is_linearized = False
self.__increment_n_calls()
t_0 = timer()
try:
# Effectively run the discipline, the _run method has to be
# Defined by the subclasses
self._run()
except Exception:
self.status = self.STATUS_FAILED
# Update the status but
# raise the same exception
raise
self.__increment_exec_time(t_0)
self.status = self.STATUS_DONE
# If the data processor is set, post process the data after _run
# See gemseo.core.data_processor module
if processor is not None:
self.local_data = processor.post_process_data(self.local_data)
# Filter data that is neither outputs nor inputs
self._filter_local_data()
self.check_output_data()
# Caches output data in case the discipline is called twice in a row
# with the same inputs
out_names = self.get_output_data_names()
self.cache.cache_outputs(cached_inputs, in_names, self.local_data, out_names)
# Some disciplines are always linearized during execution, cache the
# jac in this case
if self._is_linearized:
self.cache.cache_jacobian(cached_inputs, in_names, self.jac)
return self.local_data
def __update_local_data_from_cache(
self,
input_data, # type: Dict[str, Any]
out_cached, # type: Dict[str, Any]
out_jac, # type: Dict[str, ndarray]
): # type: (...) -> None
"""Update the local data from the cache.
Args:
input_data: The input data.
out_cached: The output data retrieved from the cache.
out_jac: The Jacobian data retrieved from the cache.
"""
self.local_data = {}
self.local_data.update(input_data)
self.local_data.update(out_cached)
if out_jac is not None:
self.jac = out_jac
self._is_linearized = True
else: # Erase jacobian which is unknown
self.jac = None
self._is_linearized = False
self.check_output_data()
self._cache_was_loaded = True
def __increment_n_calls(self): # type: (...) -> None
"""Increment by 1 the number of executions.."""
with self._n_calls.get_lock():
self._n_calls.value += 1
def __increment_n_calls_lin(self): # type: (...) -> None
"""Increment by 1 the number of linearizations."""
with self._n_calls_linearize.get_lock():
self._n_calls_linearize.value += 1
def __increment_exec_time(
self,
t_0, # type: float
linearize=False, # type: bool
): # type: (...) -> None
"""Increment the execution time of the discipline.
The execution can be either an evaluation or a linearization.
Args:
t_0: The time of the execution start.
linearize: Whether it is a linearization.
"""
curr_t = timer()
with self._exec_time.get_lock():
self._exec_time.value += curr_t - t_0
time_stamps = MDODiscipline.time_stamps
if time_stamps is not None:
disc_stamps = time_stamps.get(self.name)
if disc_stamps is None:
if os.name == "nt":
disc_stamps = []
else:
disc_stamps = MDODiscipline.__time_stamps_mp_manager.list()
stamp = (t_0, curr_t, linearize)
disc_stamps.append(stamp)
time_stamps[self.name] = disc_stamps
def _retrieve_diff_inouts(
self,
force_all=False, # type: bool
): # type: (...) -> Tuple[List[str], List[str]]
"""Get the inputs and outputs used in the differentiation of the discipline.
Args:
force_all: If True,
consider all the inputs and outputs of the discipline;
otherwise,
consider :attr:`_differentiated_inputs`
and :attr:`_differentiated_outputs`.
"""
if force_all:
inputs = self.get_input_data_names()
outputs = self.get_output_data_names()
else:
inputs = self._differentiated_inputs
outputs = self._differentiated_outputs
return inputs, outputs
_retreive_diff_inouts = _retrieve_diff_inouts
# TODO: deprecate it at some point
[docs] @classmethod
def activate_time_stamps(cls): # type: (...) -> None
"""Activate the time stamps.
For storing start and end times of execution and linearizations.
"""
if os.name == "nt": # No multiprocessing under windows
MDODiscipline.time_stamps = {}
else:
manager = Manager()
MDODiscipline.__time_stamps_mp_manager = manager
MDODiscipline.time_stamps = manager.dict()
[docs] @classmethod
def deactivate_time_stamps(cls): # type: (...) -> None
"""Deactivate the time stamps.
For storing start and end times of execution and linearizations.
"""
MDODiscipline.time_stamps = None
MDODiscipline.__time_stamps_mp_manager = None
[docs] def linearize(
self,
input_data=None, # type: Optional[Dict[str, Any]]
force_all=False, # type: bool
force_no_exec=False, # type: bool
): # type: (...) -> Dict[str, Dict[str, ndarray]]
"""Execute the linearized version of the code.
Args:
input_data: The input data needed to linearize the discipline
according to the discipline input grammar.
If None, use the :attr:`.default_inputs`.
force_all: If False,
:attr:`._differentiated_inputs` and :attr:`.differentiated_output`
are used to filter the differentiated variables.
otherwise, all outputs are differentiated wrt all inputs.
force_no_exec: If True,
the discipline is not re executed, cache is loaded anyway.
Returns:
The Jacobian of the discipline.
"""
# TODO: remove the execution when no option exec_before_lin
# is set to True
inputs, outputs = self._retrieve_diff_inouts(force_all)
if not outputs:
self.jac = {}
return self.jac
# Save inputs dict for caching
input_data = self._filter_inputs(input_data)
# if force_no_exec, we do not re execute the discipline
# otherwise, we ensure that the discipline was executed
# with the right input_data.
# This may trigger caching (see self.execute()
if not force_no_exec:
self.reset_statuses_for_run()
self.exec_for_lin = True
self.execute(input_data)
self.exec_for_lin = False
# The local_data shall be reset to their original values in case
# an input is also an output, if we don't want to keep the computed
# state (as in MDAs)
if not self._linearize_on_last_state:
self.local_data.update(input_data)
# If the caching was triggered, check if the jacobian
# was loaded,
# Or the discipline._run method also linearizes the discipline
if self._cache_was_loaded or self._is_linearized:
if self.jac:
# for cases when linearization is called
# twice with different i/o
# while cache_was_loaded=True, the check_jacobian_shape raises
# a KeyError.
try:
self._check_jacobian_shape(inputs, outputs)
return self.jac
except KeyError:
# in this case, another computation of jacobian is
# triggered.
pass
t_0 = timer()
approximate_jac = self.linearization_mode in self.APPROX_MODES
if approximate_jac: # Time already counted in execute()
self.jac = self._jac_approx.compute_approx_jac(outputs, inputs)
else:
self._compute_jacobian(inputs, outputs)
self.__increment_exec_time(t_0, linearize=True)
self.__increment_n_calls_lin()
self._check_jacobian_shape(inputs, outputs)
# Cache the Jacobian matrix
self.cache.cache_jacobian(input_data, self.get_input_data_names(), self.jac)
return self.jac
[docs] def set_jacobian_approximation(
self,
jac_approx_type=FINITE_DIFFERENCES, # type: str
jax_approx_step=1e-7, # type: float
jac_approx_n_processes=1, # type: int
jac_approx_use_threading=False, # type: bool
jac_approx_wait_time=0, # type: float
): # type: (...) -> None
"""Set the Jacobian approximation method.
Sets the linearization mode to approx_method,
sets the parameters of the approximation for further use
when calling :meth:`.linearize`.
Args:
jac_approx_type: The approximation method,
either "complex_step" or "finite_differences".
jax_approx_step: The differentiation step.
jac_approx_n_processes: The maximum number of processors on which to run.
jac_approx_use_threading: Whether to use threads instead of processes
to parallelize the execution;
multiprocessing will copy (serialize) all the disciplines,
while threading will share all the memory
This is important to note
if you want to execute the same discipline multiple times,
you shall use multiprocessing.
jac_approx_wait_time: The time waited between two forks
of the process / thread.
"""
appx = DisciplineJacApprox(
self,
approx_method=jac_approx_type,
step=jax_approx_step,
parallel=jac_approx_n_processes > 1,
n_processes=jac_approx_n_processes,
use_threading=jac_approx_use_threading,
wait_time_between_fork=jac_approx_wait_time,
)
self._jac_approx = appx
self.linearization_mode = jac_approx_type
[docs] def set_optimal_fd_step(
self,
outputs=None, # type: Optional[Iterable[str]]
inputs=None, # type: Optional[Iterable[str]]
force_all=False, # type: bool
print_errors=False, # type: bool
numerical_error=EPSILON, # type: float
):
"""Compute the optimal finite-difference step.
Compute the optimal step
for a forward first order finite differences gradient approximation.
Requires a first evaluation of the perturbed functions values.
The optimal step is reached when the truncation error
(cut in the Taylor development),
and the numerical cancellation errors
(roundoff when doing f(x+step)-f(x))
are approximately equal.
.. warning::
This calls the discipline execution twice per input variables.
.. seealso::
https://en.wikipedia.org/wiki/Numerical_differentiation
and
"Numerical Algorithms and Digital Representation", Knut Morken ,
Chapter 11, "Numerical Differenciation"
Args:
inputs: The inputs wrt which the outputs are linearized.
If None, use the :attr:`_differentiated_inputs`.
outputs: The outputs to be linearized.
If None, use the :attr:`_differentiated_outputs`.
force_all: Whether to consider all the inputs and outputs of the discipline;
print_errors: Whether to display the estimated errors.
numerical_error: The numerical error associated to the calculation of f.
By default, this is the machine epsilon (appx 1e-16),
but can be higher
when the calculation of f requires a numerical resolution.
Returns:
The estimated errors of truncation and cancellation error.
Raises:
ValueError: When the Jacobian approximation method has not been set.
"""
if self._jac_approx is None:
raise ValueError(
"set_jacobian_approximation must be called "
"before setting an optimal step."
)
inpts, outps = self._retrieve_diff_inouts(force_all=force_all)
if outputs is None or force_all:
outputs = outps
if inputs is None or force_all:
inputs = inpts
errors, steps = self._jac_approx.auto_set_step(
outputs, inputs, print_errors, numerical_error=numerical_error
)
return errors, steps
@staticmethod
def __get_len(container): # type: (...) -> int
"""Measure the length of a container."""
if container is None:
return -1
try:
return len(container)
except TypeError:
return 1
def _check_jacobian_shape(
self,
inputs, # type: Iterable[str]
outputs, # type: Iterable[str]
): # type: (...) -> None
"""Check that the Jacobian is a dictionary of dictionaries of 2D NumPy arrays.
Args:
inputs: The inputs wrt the outputs are linearized.
outputs: The outputs to be linearized.
Raises:
ValueError:
* When the discipline was not linearized.
* When the Jacobian is not of the right shape.
KeyError:
* When outputs are missing in the Jacobian of the discipline.
* When inputs are missing for an output in the Jacobian of the discipline.
"""
if self.jac is None:
raise ValueError("The discipline {} was not linearized.".format(self.name))
out_set = set(outputs)
in_set = set(inputs)
out_jac_set = set(self.jac.keys())
if not out_set.issubset(out_jac_set):
msg = "Missing outputs in Jacobian of discipline {}: {}."
missing_outputs = out_set.difference(out_jac_set)
raise KeyError(msg.format(self.name, missing_outputs))
for j_o in outputs:
j_out = self.jac[j_o]
out_dv_set = set(j_out.keys())
output_vals = self.local_data.get(j_o)
n_out_j = self.__get_len(output_vals)
if not in_set.issubset(out_dv_set):
msg = "Missing inputs {} in Jacobian of discipline {}, for output: {}."
missing_inputs = in_set.difference(out_dv_set)
raise KeyError(msg.format(missing_inputs, self.name, j_o))
for j_i in inputs:
input_vals = self.local_data.get(j_i)
n_in_j = self.__get_len(input_vals)
j_mat = j_out[j_i]
expected_shape = (n_out_j, n_in_j)
if -1 in expected_shape:
# At least one of the dimensions is unknown
# Dont check shape
continue
if j_mat.shape != expected_shape:
msg = (
"Jacobian matrix of discipline {} d{}/d{}"
"is not of the right shape.\n "
"Expected: ({},{}), got: {}"
)
data = [self.name, j_o, j_i, n_out_j, n_in_j, j_mat.shape]
raise ValueError(msg.format(*data))
# Discard imaginary part of Jacobian
for jac_loc in self.jac.values():
for desv, jac_out in jac_loc.items():
jac_loc[desv] = jac_out.real
@property
def cache_tol(self): # type: (...) -> float
"""The cache input tolerance.
This is the tolerance for equality of the inputs in the cache.
If norm(stored_input_data-input_data) <= cache_tol * norm(stored_input_data),
the cached data for ``stored_input_data`` is returned
when calling ``self.execute(input_data)``.
"""
return self.cache.tolerance
@cache_tol.setter
def cache_tol(
self,
cache_tol, # type: float
): # type: (...) -> None
self._set_cache_tol(cache_tol)
def _set_cache_tol(
self,
cache_tol, # type: float
): # type: (...) -> None
"""Set to the cache input tolerance.
To be overloaded by subclasses.
Args:
cache_tol: The cache tolerance.
"""
self.cache.tolerance = cache_tol or 0.0
@property
def default_inputs(self): # type: (...) -> Dict[str, Any]
"""The default inputs.
Raises:
TypeError: When the default inputs are not passed as a dictionary.
"""
return self._default_inputs
@default_inputs.setter
def default_inputs(
self, default_inputs # type: Dict[str,Any]
): # type: (...) -> None
if not isinstance(default_inputs, dict):
raise TypeError(
"MDODiscipline default inputs must be of dict type, "
"got {} instead.".format(type(default_inputs))
)
self._default_inputs = default_inputs
def _compute_jacobian(
self,
inputs=None, # type:Optional[Iterable[str]]
outputs=None, # type:Optional[Iterable[str]]
): # type: (...)-> None
"""Compute the Jacobian matrix.
To be overloaded by subclasses, actual computation of the Jacobian matrix.
Args:
inputs: The inputs wrt the outputs are linearized.
If None,
the linearization should be performed wrt all inputs.
outputs: The outputs to be linearized.
If None,
the linearization should be performed on all outputs.
"""
def _init_jacobian(
self,
inputs=None, # type:Optional[Iterable[str]]
outputs=None, # type:Optional[Iterable[str]]
with_zeros=False, # type: bool
fill_missing_keys=False, # type: bool
): # type: (...) -> None
"""Initialize the Jacobian dictionary of the form ``{input: {output: matrix}}``.
Args:
inputs: The inputs wrt the outputs are linearized.
If None,
the linearization should be performed wrt all inputs.
outputs: The outputs to be linearized.
If None,
the linearization should be performed on all outputs.
fill_missing_keys: if True, just fill the missing items
with_zeros: If True,
the matrices are set to zero
otherwise,
they are empty matrices.
fill_missing_keys: If True,
just fill the missing items with zeros/empty
but do not override the existing data.
"""
if inputs is None:
inputs_names = self._differentiated_inputs
else:
inputs_names = inputs
inputs_vals = []
for diff_name in inputs_names:
inputs_vals.append(self.get_inputs_by_name(diff_name))
if outputs is None:
outputs_names = self._differentiated_outputs
else:
outputs_names = outputs
outputs_vals = []
for diff_name in outputs_names:
outputs_vals.append(self.get_outputs_by_name(diff_name))
if with_zeros:
np_method = zeros
else:
np_method = empty
if not fill_missing_keys:
# When a key is not in the default dict, ie a function is not in
# the Jacobian; return an empty defaultdict(None)
jac = defaultdict(default_dict_factory)
for out_n, out_v in zip(outputs_names, outputs_vals):
jac_loc = defaultdict(None)
jac[out_n] = jac_loc
# When a key is not in the default dict,
# ie a variable is not in the
# Jacobian; return a defaultdict(None)
for in_n, in_v in zip(inputs_names, inputs_vals):
jac_loc[in_n] = np_method((len(out_v), len(in_v)))
self.jac = jac
else:
jac = self.jac
# Only fill the missing sub jacobians
for out_n, out_v in zip(outputs_names, outputs_vals):
jac_loc = jac.get(out_n)
if jac_loc is None:
jac_loc = defaultdict(None)
jac[out_n] = jac_loc
for in_n, in_v in zip(inputs_names, inputs_vals):
sub_jac = jac_loc.get(in_n)
if sub_jac is None:
jac_loc[in_n] = np_method((len(out_v), len(in_v)))
@property
def linearization_mode(self): # type: (...) -> str
"""The linearization mode among :attr:`.LINEARIZE_MODE_LIST`.
Raises:
ValueError: When the linearization mode is unknown.
"""
return self._linearization_mode
@linearization_mode.setter
def linearization_mode(
self,
linearization_mode, # type: str
): # type: (...) -> None
if linearization_mode not in self.AVAILABLE_MODES:
msg = "Linearization mode '{}' is unknown; it must be one of {}."
raise ValueError(msg.format(linearization_mode, self.AVAILABLE_MODES))
self._linearization_mode = linearization_mode
if linearization_mode in self.APPROX_MODES and self._jac_approx is None:
self.set_jacobian_approximation(linearization_mode)
[docs] def check_jacobian(
self,
input_data=None, # type: Optional[Dict[str, ndarray]]
derr_approx=FINITE_DIFFERENCES, # type: str
step=1e-7, # type: float
threshold=1e-8, # type: float
linearization_mode="auto", # type: str
inputs=None, # type: Optional[Iterable[str]]
outputs=None, # type: Optional[Iterable[str]]
parallel=False, # type: bool
n_processes=N_CPUS, # type: int
use_threading=False, # type: bool
wait_time_between_fork=0, # type: float
auto_set_step=False, # type: bool
plot_result=False, # type: bool
file_path="jacobian_errors.pdf", # type: Union[str, Path]
show=False, # type: bool
figsize_x=10, # type: float
figsize_y=10, # type: float
reference_jacobian_path=None, # type: Optional[str, Path]
save_reference_jacobian=False, # type: bool
indices=None, # type: Optional[Iterable[int]]
):
"""Check if the analytical Jacobian is correct with respect to a reference one.
If `reference_jacobian_path` is not `None`
and `save_reference_jacobian` is `True`,
compute the reference Jacobian with the approximation method
and save it in `reference_jacobian_path`.
If `reference_jacobian_path` is not `None`
and `save_reference_jacobian` is `False`,
do not compute the reference Jacobian
but read it from `reference_jacobian_path`.
If `reference_jacobian_path` is `None`,
compute the reference Jacobian without saving it.
Args:
input_data: The input data needed to execute the discipline
according to the discipline input grammar.
If None, use the :attr:`.default_inputs`.
derr_approx: The approximation method,
either "complex_step" or "finite_differences".
threshold: The acceptance threshold for the Jacobian error.
linearization_mode: the mode of linearization: direct, adjoint
or automated switch depending on dimensions
of inputs and outputs (Default value = 'auto')
inputs: The names of the inputs wrt which to differentiate the outputs.
outputs: The names of the outputs to be differentiated.
step: The differentiation step.
parallel: Whether to differentiate the discipline in parallel.
n_processes: The maximum number of processors on which to run.
use_threading: Whether to use threads instead of processes
to parallelize the execution;
multiprocessing will copy (serialize) all the disciplines,
while threading will share all the memory
This is important to note
if you want to execute the same discipline multiple times,
you shall use multiprocessing.
wait_time_between_fork: The time waited between two forks
of the process / thread.
auto_set_step: Whether to compute the optimal step
for a forward first order finite differences gradient approximation.
plot_result: Whether to plot the result of the validation
(computed vs approximated Jacobians).
file_path: The path to the output file if ``plot_result`` is ``True``.
show: Whether to open the figure.
figsize_x: The x-size of the figure in inches.
figsize_y: The y-size of the figure in inches.
reference_jacobian_path: The path of the reference Jacobian file.
save_reference_jacobian: Whether to save the reference Jacobian.
indices: The indices of the inputs and outputs
for the different sub-Jacobian matrices,
formatted as ``{variable_name: variable_components}``
where ``variable_components`` can be either
an integer, e.g. `2`
a sequence of integers, e.g. `[0, 3]`,
a slice, e.g. `slice(0,3)`,
the ellipsis symbol (`...`)
or `None`, which is the same as ellipsis.
If a variable name is missing, consider all its components.
If None,
consider all the components of all the ``inputs`` and ``outputs``.
Returns:
Whether the analytical Jacobian is correct
with respect to the reference one.
"""
# Do not use self._jac_approx because we may want to check complex
# step approximation with the finite differences for instance
approx = DisciplineJacApprox(
self,
derr_approx,
step,
parallel,
n_processes,
use_threading,
wait_time_between_fork,
)
if inputs is None:
inputs = self.get_input_data_names()
if outputs is None:
outputs = self.get_output_data_names()
if auto_set_step:
approx.auto_set_step(outputs, inputs, print_errors=True)
# Differentiate analytically
self.add_differentiated_inputs(inputs)
self.add_differentiated_outputs(outputs)
self.linearization_mode = linearization_mode
self.reset_statuses_for_run()
# Linearize performs execute() if needed
self.linearize(input_data)
o_k = approx.check_jacobian(
self.jac,
outputs,
inputs,
self,
threshold,
plot_result=plot_result,
file_path=file_path,
show=show,
figsize_x=figsize_x,
figsize_y=figsize_y,
reference_jacobian_path=reference_jacobian_path,
save_reference_jacobian=save_reference_jacobian,
indices=indices,
)
return o_k
@property
def status(self): # type: (...) -> str
"""The status of the discipline."""
return self._status
def _check_status(
self,
status, # type: str
): # type: (...) -> None
"""Check the status according to possible statuses.
Raises:
ValueError: When the status is unknown.
"""
if status not in [
self.STATUS_PENDING,
self.STATUS_VIRTUAL,
self.STATUS_DONE,
self.STATUS_RUNNING,
self.STATUS_FAILED,
]:
raise ValueError("Unknown status: {}.".format(status))
[docs] def set_disciplines_statuses(
self,
status, # type: str
): # type: (...) -> None
"""Set the sub-disciplines statuses.
To be implemented in subclasses.
Args:
status: The status.
"""
[docs] def is_output_existing(
self,
data_name, # type: str
): # type: (...) -> bool
"""Test if a variable is a discipline output.
Args:
data_name: The name of the variable.
Returns:
Whether the variable is a discipline output.
"""
return self.output_grammar.is_data_name_existing(data_name)
[docs] def is_all_outputs_existing(
self,
data_names, # type: Iterable[str]
): # type: (...) -> bool
"""Test if several variables are discipline outputs.
Args:
data_names: The names of the variables.
Returns:
Whether all the variables are discipline outputs.
"""
return self.output_grammar.is_all_data_names_existing(data_names)
def _is_status_ok_for_run_again(
self,
status, # type: str
): # type: (...) -> bool
"""Check if the discipline can be run again.
Args:
status: The status of the discipline.
Returns:
Whether the discipline can be run again.
"""
return status not in [self.STATUS_RUNNING]
[docs] def reset_statuses_for_run(self): # type: (...) -> None
"""Set all the statuses to :attr:`.PENDING`.
Raises:
ValueError: When the discipline cannot be run because of its status.
"""
if not self._is_status_ok_for_run_again(self.status):
raise ValueError(
"Cannot run discipline {} with status {}.".format(
self.name, self.status
)
)
self.status = self.STATUS_PENDING
@status.setter
def status(
self,
status, # type: str
): # type: (...) -> None
self._check_status(status)
self._status = status
self.notify_status_observers()
[docs] def add_status_observer(
self,
obs, # type: Any
): # type: (...) -> None
"""Add an observer for the status.
Add an observer for the status
to be notified when self changes of status.
Args:
obs: The observer to add.
"""
if obs not in self._status_observers:
self._status_observers.append(obs)
[docs] def remove_status_observer(
self,
obs, # type: Any
): # type: (...) -> None
"""Remove an observer for the status.
Args:
obs: The observer to remove.
"""
if obs in self._status_observers:
self._status_observers.remove(obs)
[docs] def notify_status_observers(self): # type: (...) -> None
"""Notify all status observers that the status has changed."""
for obs in self._status_observers[:]:
obs.update_status(self)
[docs] def store_local_data(
self, **kwargs # type:Any
): # type: (...) -> None
"""Store discipline data in local data.
Args:
kwargs: The data to be stored in :attr:`.local_data`.
"""
self.local_data.update(kwargs)
[docs] def check_output_data(
self,
raise_exception=True, # type: bool
): # type: (...) -> None
"""Check the output data validity.
Args:
raise_exception: Whether to raise an exception when the data is invalid.
"""
try:
self.output_grammar.load_data(self.local_data, raise_exception)
except InvalidDataException as err:
err.args = (
err.args[0].replace("Invalid data", "Invalid output data")
+ " in discipline {}".format(self.name),
)
raise
[docs] def get_outputs_asarray(self): # type: (...) -> ndarray
"""Return the local input data as a large NumPy array.
The order is the one of :meth:`.get_all_inputs`.
Returns:
The local input data.
"""
return concatenate(list(self.get_all_outputs()))
[docs] def get_outputs_by_name(
self,
data_names, # type: Iterable[str]
): # type: (...) -> List[Any]
"""Return the local data associated with output variables.
Args:
data_names: The names of the output variables.
Returns:
The local data for the given output variables.
Raises:
ValueError: When a variable is not an output of the discipline.
"""
try:
return self.get_data_list_from_dict(data_names, self.local_data)
except KeyError as err:
raise ValueError(
"Discipline {} has no output named {}.".format(self.name, err)
)
[docs] def get_output_data_names(self): # type: (...) -> List[str]
"""Return the names of the output variables.
Returns:
The names of the output variables.
"""
return self.output_grammar.get_data_names()
[docs] def get_all_outputs(self): # type: (...) -> List[Any]
"""Return the local output data as a list.
The order is given by :meth:`.get_output_data_names`.
Returns:
The local output data.
"""
return self.get_outputs_by_name(self.get_output_data_names())
[docs] def get_output_data(self): # type: (...) -> Dict[str, Any]
"""Return the local output data as a dictionary.
Returns:
The local output data.
"""
return dict(
(k, v) for k, v in self.local_data.items() if self.is_output_existing(k)
)
[docs] def serialize(
self,
out_file, # type: Union[str, Path]
): # type: (...) -> None
"""Serialize the discipline and store it in a file.
Args:
out_file: The path to the file to store the discipline.
"""
out_file = Path(out_file)
with out_file.open("wb") as outfobj:
pickler = pickle.Pickler(outfobj, protocol=2)
pickler.dump(self)
[docs] @staticmethod
def deserialize(
in_file, # type: Union[str, Path]
): # type: (...) -> MDODiscipline
"""Deserialize a discipline from a file.
Args:
in_file: The path to the file containing the discipline.
Returns:
The discipline instance.
"""
in_file = Path(in_file)
with in_file.open("rb") as in_fobj:
pickler = pickle.Unpickler(in_fobj)
return pickler.load()
[docs] def get_attributes_to_serialize(self): # pylint: disable=R0201
"""Define the names of the attributes to be serialized.
Shall be overloaded by disciplines
Returns:
The names of the attributes to be serialized.
"""
# pylint warning ==> method could be a function but when overriden,
# it is a function==> self is required
return list(self._ATTR_TO_SERIALIZE)
def __getstate__(self): # type: (...) -> Dict[str, Any]
"""Used by pickle to define what to serialize.
Returns:
The attributes to be serialized.
Raises:
AttributeError: When an attribute to be serialized is undefined.
TypeError: When an attribute has an undefined type.
"""
out_d = {}
for keep_name in self.get_attributes_to_serialize():
if keep_name not in self.__dict__:
if "_" + keep_name not in self.__dict__ and not hasattr(
self, keep_name
):
msg = (
"Discipline {} defined attribute '{}' "
"as required for serialization, "
"but it appears to "
"be undefined.".format(self.name, keep_name)
)
raise AttributeError(msg)
prop = self.__dict__.get("_" + keep_name)
# May appear for properties that overload public attrs of super class
if hasattr(self, keep_name) and not isinstance(prop, Synchronized):
continue
if not isinstance(prop, Synchronized):
raise TypeError(
"Cant handle attribute {} serialization "
"of undefined type.".format(keep_name)
)
# Dont serialize shared memory object,
# this is meaningless, save the value instead
out_d[keep_name] = prop.value
else:
out_d[keep_name] = self.__dict__[keep_name]
if self._cache_type == self.HDF5_CACHE:
out_d.pop("cache")
return out_d
def __setstate__(
self,
state_dict, # type: Dict[str, Any]
): # type: (...) -> None
"""Used by pickle to define what to deserialize.
Args:
state_dict: Update ``self.__dict__`` from ``state_dict``
to deserialize the discipline.
"""
self._init_shared_attrs()
self._status_observers = []
out_d = self.__dict__
shared_attrs = list(out_d.keys())
for key, val in state_dict.items():
if "_" + key not in shared_attrs:
out_d[key] = val
else:
out_d["_" + key].value = val
self.__init_cache_attr()
[docs] def get_local_data_by_name(
self,
data_names, # type: Iterable[str]
): # type: (...) -> Generator[Any]
"""Return the local data of the discipline associated with variables names.
Args:
data_names: The names of the variables.
Returns:
The local data associated with the variables names.
Raises:
ValueError: When a name is not not a discipline input name.
"""
try:
return self.get_data_list_from_dict(data_names, self.local_data)
except KeyError as err:
raise ValueError(
"Discipline {} has no local_data named {}.".format(self.name, err)
)
[docs] @staticmethod
def is_scenario(): # type: (...) -> bool
"""Whether the discipline is a scenario."""
return False
[docs] @staticmethod
def get_data_list_from_dict(
keys, # type: Union[str, Iterable]
data_dict, # type: Dict[str, Any]
): # type: (...) -> Union[Any, Generator[Any]]
"""Filter the dict from a list of keys or a single key.
If keys is a string, then the method return the value associated to the
key.
If keys is a list of strings, then the method returns a generator of
value corresponding to the keys which can be iterated.
Args:
keys: One or several names.
data_dict: The mapping from which to get the data.
Returns:
Either a data or a generator of data.
"""
if isinstance(keys, six.string_types):
return data_dict[keys]
return (data_dict[name] for name in keys)
