disc_from_exe module¶
Make a discipline from an executable¶
Classes:
|
Generic wrapper for executables. |
|
An enumeration. |
|
An enumeration. |
Functions:
|
Parse the output file from the expected text positions. |
|
Parse the output file from the expected text positions. |
|
Parse the input or output template. |
|
Write the input file from the input data. |
- class gemseo.wrappers.disc_from_exe.DiscFromExe(input_template, output_template, output_folder_basepath, executable_command, input_filename, output_filename, folders_iter=FoldersIter.NUMBERED, name=None, parse_outfile_method=Parsers.TEMPLATE_PARSER, write_input_file_method=None, parse_out_separator='=', use_shell=True)[source]¶
Bases:
gemseo.core.discipline.MDODiscipline
Generic wrapper for executables.
The DiscFromExe is a generic wrapper for executables. It generates a MDODiscipline from an executable and in inputs/output files wrappers. The input and output files are described by templates. The templates can be generated by executing the module
template_grammar_editor
to open a GUI.It requires the creation of templates for input and output file, for instance, from the following input JSON file:
{ "a": 1.01515112125, "b": 2.00151511213, "c": 3.00151511213 }
A template that declares the inputs must be generated under this format, where “a” is the name of the input, and “1.0” is the default input. GEMSEO_INPUT declares an input, GEMSEO_OUTPUT declares an output, similarly.
{ "a": GEMSEO_INPUT{a::1.0}, "b": GEMSEO_INPUT{b::2.0}, "c": GEMSEO_INPUT{c::3.0} }
Current limitations :
Only one input and one output file, otherwise,
inherit from this class and modify the parsers. Only limited input writing and output parser strategies are implemented. To change that, you can pass custom parsing and writing methods to the constructor.
The only limitation in the current file format is that
it must be a plain text file and not a binary file. In this case, the way of interfacing it is to provide a specific parser to the DiscFromExe, with the write_input_file_method and parse_outfile_method arguments of the constructor.
- input_grammar¶
The input grammar.
- Type
- output_grammar¶
The output grammar.
- Type
- grammar_type¶
The type of grammar to be used for inputs and outputs declaration.
- Type
str
- comp_dir¶
The path to the directory of the discipline module file if any.
- Type
str
- data_processor¶
A data processor to be used before the execution of the discipline.
- Type
- re_exec_policy¶
The policy to re-execute the same discipline.
- Type
str
- residual_variables¶
The output variables to be considered as residuals; they shall be equal to zero.
- Type
List[str]
- jac¶
The Jacobians of the outputs wrt inputs of the form
{output: {input: matrix}}
.- Type
Dict[str, Dict[str, ndarray]]
- exec_for_lin¶
Whether the last execution was due to a linearization.
- Type
bool
- name¶
The name of the discipline.
- Type
str
- cache¶
The cache containing one or several executions of the discipline according to the cache policy.
- Type
- local_data¶
The last input and output data.
- Type
Dict[str, Any]
- input_template¶
The path to the input template file.
- Type
str
- ouput_template¶
The path to the output template file.
- Type
str
- input_filename¶
The name of the input file.
- Type
str
- output_filename¶
The name of the ouput file.
- Type
str
- executable_command¶
The executable command.
- Type
str
- parse_outfile¶
The function used to parse the output file.
- Type
Callable[Mapping[str, Tuple[int]], Sequence[str]]
- write_input_file¶
(Callable[str, Mapping[str, ndarray], Mapping[str, Tuple[int]], Sequence[int], str): The function used to write the input file.
- folder_iter¶
The method to be used to name new execution directories.
- Type
str
- output_folder_basepath¶
The base path of the execution directories.
- Type
str
Initialize self. See help(type(self)) for accurate signature.
- Parameters
input_template (str) – The path to the input file template. The input locations in the file are marked by GEMSEO_INPUT{input_name::1.0}, where “input_name” is the input name, and 1.0 is here the default input.
output_template (str) – The path to the output file template. The input locations in the file are marked by GEMSEO_OUTPUT{output_name::1.0}, where “output_name” is the input name.
executable_command (str) – The command to run the executable. Will be called through a system call. Example: “python myscript.py -i input.txt -o output.txt
input_filename (str) – The name of the input file. This will determine the name of the input file generated in the output folder. Example “input.txt”.
output_filename (str) – The name of the output file. This will determine the name of the output file generated in the output folder. Example “output.txt”.
(Union[str (folders_iter) – The type of unique identifiers for the output folders. If NUMBERED the generated output folders will be “output_folder_basepath”+str(i+1), where i is the maximum value of the already existing “output_folder_basepath”+str(i) folders. Otherwise, a unique number based on the UUID function is generated. This last option shall be used if multiple MDO processes are runned in the same work directory.
FoldersIter] – The type of unique identifiers for the output folders. If NUMBERED the generated output folders will be “output_folder_basepath”+str(i+1), where i is the maximum value of the already existing “output_folder_basepath”+str(i) folders. Otherwise, a unique number based on the UUID function is generated. This last option shall be used if multiple MDO processes are runned in the same work directory.
name (Optional[str]) –
the name of the discipline. If None, use the class name.
By default it is set to None.
parse_outfile_method (Union[str, Parsers]) –
The optional method that can be provided by the user to parse the output file. To see the signature of the method, see the parse_outfile method of this file. If the KEY_VALUE_PARSER is used as output parser, specify the separator key (default : “=”).
By default it is set to TEMPLATE_PARSER.
write_input_file_method (Optional[str]) –
The method to write the input file. If None, use this modules’ write_input_file. To see the signature of the method, see the write_input_file method of this file.
By default it is set to None.
parse_out_separator (str) –
The separator used for the output parser.
By default it is set to =.
use_shell (bool) –
If True, run the command using the default shell. Otherwise, run directly the command.
By default it is set to True.
output_folder_basepath (str) –
folders_iter (Union[str, FoldersIter]) –
By default it is set to NUMBERED.
- Raises
TypeError – If the provided write_input_file_method is not callable.
- Return type
None
Attributes:
The cache input tolerance.
The default inputs.
The cumulated execution time of the discipline.
Getter/Setter for folders_iter.
The grammar type.
The linearization mode among
LINEARIZE_MODE_LIST
.The number of times the discipline was executed.
The number of times the discipline was linearized.
The status of the discipline.
Methods:
Activate the time stamps.
add_differentiated_inputs
([inputs])Add inputs against which to differentiate the outputs.
add_differentiated_outputs
([outputs])Add outputs to be differentiated.
add_status_observer
(obs)Add an observer for the status.
auto_get_grammar_file
([is_input, name, comp_dir])Use a naming convention to associate a grammar file to a discipline.
check_input_data
(input_data[, raise_exception])Check the input data validity.
check_jacobian
([input_data, derr_approx, ...])Check if the analytical Jacobian is correct with respect to a reference one.
check_output_data
([raise_exception])Check the output data validity.
Deactivate the time stamps.
deserialize
(in_file)Deserialize a discipline from a file.
execute
([input_data])Execute the discipline.
Generate an unique identifier for the execution directory.
Return the local input data as a list.
Return the local output data as a list.
Define the names of the attributes to be serialized.
get_data_list_from_dict
(keys, data_dict)Filter the dict from a list of keys or a single key.
Return the expected data exchange sequence.
Return the expected execution sequence.
Return the local input data as a dictionary.
Return the names of the input variables.
Return the names of the input and output variables.
Return the local output data as a large NumPy array.
get_inputs_by_name
(data_names)Return the local data associated with input variables.
get_local_data_by_name
(data_names)Return the local data of the discipline associated with variables names.
Return the local output data as a dictionary.
Return the names of the output variables.
Return the local input data as a large NumPy array.
get_outputs_by_name
(data_names)Return the local data associated with output variables.
Return the sub-disciplines if any.
is_all_inputs_existing
(data_names)Test if several variables are discipline inputs.
is_all_outputs_existing
(data_names)Test if several variables are discipline outputs.
is_input_existing
(data_name)Test if a variable is a discipline input.
is_output_existing
(data_name)Test if a variable is a discipline output.
Whether the discipline is a scenario.
linearize
([input_data, force_all, force_no_exec])Execute the linearized version of the code.
Notify all status observers that the status has changed.
Remove an observer for the status.
Set all the statuses to
PENDING
.serialize
(out_file)Serialize the discipline and store it in a file.
set_cache_policy
([cache_type, ...])Set the type of cache to use and the tolerance level.
set_disciplines_statuses
(status)Set the sub-disciplines statuses.
set_jacobian_approximation
([...])Set the Jacobian approximation method.
set_optimal_fd_step
([outputs, inputs, ...])Compute the optimal finite-difference step.
store_local_data
(**kwargs)Store discipline data in local data.
- APPROX_MODES = ['finite_differences', 'complex_step']¶
- AVAILABLE_MODES = ('auto', 'direct', 'adjoint', 'reverse', 'finite_differences', 'complex_step')¶
- COMPLEX_STEP = 'complex_step'¶
- FINITE_DIFFERENCES = 'finite_differences'¶
- HDF5_CACHE = 'HDF5Cache'¶
- JSON_GRAMMAR_TYPE = 'JSONGrammar'¶
- MEMORY_FULL_CACHE = 'MemoryFullCache'¶
- N_CPUS = 2¶
- RE_EXECUTE_DONE_POLICY = 'RE_EXEC_DONE'¶
- RE_EXECUTE_NEVER_POLICY = 'RE_EXEC_NEVER'¶
- SIMPLE_CACHE = 'SimpleCache'¶
- SIMPLE_GRAMMAR_TYPE = 'SimpleGrammar'¶
- STATUS_DONE = 'DONE'¶
- STATUS_FAILED = 'FAILED'¶
- STATUS_PENDING = 'PENDING'¶
- STATUS_RUNNING = 'RUNNING'¶
- STATUS_VIRTUAL = 'VIRTUAL'¶
- classmethod activate_time_stamps()¶
Activate the time stamps.
For storing start and end times of execution and linearizations.
- Return type
None
- add_differentiated_inputs(inputs=None)¶
Add inputs against which to differentiate the outputs.
This method updates
_differentiated_inputs
withinputs
.- Parameters
inputs (Optional[Iterable[str]]) –
The input variables against which to differentiate the outputs. If None, all the inputs of the discipline are used.
By default it is set to None.
- Raises
ValueError – When the inputs wrt which differentiate the discipline are not inputs of the latter.
- Return type
None
- add_differentiated_outputs(outputs=None)¶
Add outputs to be differentiated.
This method updates
_differentiated_outputs
withoutputs
.- Parameters
outputs (Optional[Iterable[str]]) –
The output variables to be differentiated. If None, all the outputs of the discipline are used.
By default it is set to None.
- Raises
ValueError – When the outputs to differentiate are not discipline outputs.
- Return type
None
- add_status_observer(obs)¶
Add an observer for the status.
Add an observer for the status to be notified when self changes of status.
- Parameters
obs (Any) – The observer to add.
- Return type
None
- auto_get_grammar_file(is_input=True, name=None, comp_dir=None)¶
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”``.- Parameters
is_input (bool) –
If True, autodetect the input grammar file; otherwise, autodetect the output grammar file.
By default it is set to True.
name (Optional[str]) –
The name to be searched in the file names. If None, use the
name
name of the discipline.By default it is set to None.
comp_dir (Optional[Union[str, pathlib.Path]]) –
The directory in which to search the grammar file. If None, use
comp_dir
.By default it is set to None.
- Returns
The grammar file path.
- Return type
pathlib.Path
- property cache_tol¶
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 callingself.execute(input_data)
.
- check_input_data(input_data, raise_exception=True)¶
Check the input data validity.
- Parameters
input_data (Dict[str, Any]) – The input data needed to execute the discipline according to the discipline input grammar.
raise_exception (bool) –
By default it is set to True.
- Return type
None
- check_jacobian(input_data=None, derr_approx='finite_differences', step=1e-07, threshold=1e-08, linearization_mode='auto', inputs=None, outputs=None, parallel=False, n_processes=2, use_threading=False, wait_time_between_fork=0, auto_set_step=False, plot_result=False, file_path='jacobian_errors.pdf', show=False, figsize_x=10, figsize_y=10, reference_jacobian_path=None, save_reference_jacobian=False, indices=None)¶
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.
- Parameters
input_data –
The input data needed to execute the discipline according to the discipline input grammar. If None, use the
default_inputs
.By default it is set to None.
derr_approx –
The approximation method, either “complex_step” or “finite_differences”.
By default it is set to finite_differences.
threshold –
The acceptance threshold for the Jacobian error.
By default it is set to 1e-08.
linearization_mode –
the mode of linearization: direct, adjoint or automated switch depending on dimensions of inputs and outputs (Default value = ‘auto’)
By default it is set to auto.
inputs –
The names of the inputs wrt which to differentiate the outputs.
By default it is set to None.
outputs –
The names of the outputs to be differentiated.
By default it is set to None.
step –
The differentiation step.
By default it is set to 1e-07.
parallel –
Whether to differentiate the discipline in parallel.
By default it is set to False.
n_processes –
The maximum number of processors on which to run.
By default it is set to 2.
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.
By default it is set to False.
wait_time_between_fork –
The time waited between two forks of the process / thread.
By default it is set to 0.
auto_set_step –
Whether to compute the optimal step for a forward first order finite differences gradient approximation.
By default it is set to False.
plot_result –
Whether to plot the result of the validation (computed vs approximated Jacobians).
By default it is set to False.
file_path –
The path to the output file if
plot_result
isTrue
.By default it is set to jacobian_errors.pdf.
show –
Whether to open the figure.
By default it is set to False.
figsize_x –
The x-size of the figure in inches.
By default it is set to 10.
figsize_y –
The y-size of the figure in inches.
By default it is set to 10.
reference_jacobian_path –
The path of the reference Jacobian file.
By default it is set to None.
save_reference_jacobian –
Whether to save the reference Jacobian.
By default it is set to False.
indices –
The indices of the inputs and outputs for the different sub-Jacobian matrices, formatted as
{variable_name: variable_components}
wherevariable_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 theinputs
andoutputs
.By default it is set to None.
- Returns
Whether the analytical Jacobian is correct with respect to the reference one.
- check_output_data(raise_exception=True)¶
Check the output data validity.
- Parameters
raise_exception (bool) –
Whether to raise an exception when the data is invalid.
By default it is set to True.
- Return type
None
- classmethod deactivate_time_stamps()¶
Deactivate the time stamps.
For storing start and end times of execution and linearizations.
- Return type
None
- property default_inputs¶
The default inputs.
- Raises
TypeError – When the default inputs are not passed as a dictionary.
- static deserialize(in_file)¶
Deserialize a discipline from a file.
- Parameters
in_file (Union[str, pathlib.Path]) – The path to the file containing the discipline.
- Returns
The discipline instance.
- Return type
- property exec_time¶
The cumulated execution time of the discipline.
Note
This property is multiprocessing safe.
- execute(input_data=None)¶
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_default_inputs
.Checks whether the last execution of the discipline was called with identical inputs, ie. cached in
cache
; if so, directly returnsself.cache.get_output_cache(inputs)
.Caches the inputs.
Checks the input data against
input_grammar
.If
data_processor
is not None, runs the preprocessor.Updates the status to
RUNNING
.Calls the
_run()
method, that shall be defined.If
data_processor
is not None, runs the postprocessor.Checks the output data.
Caches the outputs.
Updates the status to
DONE
orFAILED
.Updates summed execution time.
- Parameters
input_data (Optional[Dict[str, Any]]) –
The input data needed to execute the discipline according to the discipline input grammar. If None, use the
default_inputs
.By default it is set to None.
- Returns
The discipline local data after execution.
- Return type
Dict[str, Any]
- property folders_iter¶
Getter/Setter for folders_iter.
The setter will check that the value provided for folder_iter is valid. This check is done by checking its presence in FOLDERS_ITER.
- Raises
ValueError – If the value provided to the setter is not present in the accepted list of folders_iters list.
- generate_uid()[source]¶
Generate an unique identifier for the execution directory.
Generate a unique identifier for the current execution. If the folders_iter strategy is NUMBERED, the successive iterations are named by an integer 1, 2, 3 etc. This is multiprocess safe. Otherwise, a unique number based on the UUID function is generated. This last option shall be used if multiple MDO processes are runned in the same workdir.
- Returns
An unique string identifier (either a number or a UUID).
- Return type
str
- get_all_inputs()¶
Return the local input data as a list.
The order is given by
get_input_data_names()
.- Returns
The local input data.
- Return type
List[Any]
- get_all_outputs()¶
Return the local output data as a list.
The order is given by
get_output_data_names()
.- Returns
The local output data.
- Return type
List[Any]
- get_attributes_to_serialize()¶
Define the names of the attributes to be serialized.
Shall be overloaded by disciplines
- Returns
The names of the attributes to be serialized.
- static get_data_list_from_dict(keys, data_dict)¶
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.
- Parameters
keys (Union[str, Iterable]) – One or several names.
data_dict (Dict[str, Any]) – The mapping from which to get the data.
- Returns
Either a data or a generator of data.
- Return type
Union[Any, Generator[Any]]
- get_expected_dataflow()¶
Return the expected data exchange sequence.
This method is used for the XDSM representation.
The default expected data exchange sequence is an empty list.
See also
MDOFormulation.get_expected_dataflow
- Returns
The data exchange arcs.
- Return type
List[Tuple[gemseo.core.discipline.MDODiscipline, gemseo.core.discipline.MDODiscipline, List[str]]]
- get_expected_workflow()¶
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.
See also
MDOFormulation.get_expected_workflow
- Returns
The expected execution sequence.
- Return type
- get_input_data()¶
Return the local input data as a dictionary.
- Returns
The local input data.
- Return type
Dict[str, Any]
- get_input_data_names()¶
Return the names of the input variables.
- Returns
The names of the input variables.
- Return type
List[str]
- get_input_output_data_names()¶
Return the names of the input and output variables.
- Returns
The name of the input and output variables.
- Return type
List[str]
- get_inputs_asarray()¶
Return the local output data as a large NumPy array.
The order is the one of
get_all_outputs()
.- Returns
The local output data.
- Return type
numpy.ndarray
- get_inputs_by_name(data_names)¶
Return the local data associated with input variables.
- Parameters
data_names (Iterable[str]) – The names of the input variables.
- Returns
The local data for the given input variables.
- Raises
ValueError – When a variable is not an input of the discipline.
- Return type
List[Any]
- get_local_data_by_name(data_names)¶
Return the local data of the discipline associated with variables names.
- Parameters
data_names (Iterable[str]) – 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.
- Return type
Generator[Any]
- get_output_data()¶
Return the local output data as a dictionary.
- Returns
The local output data.
- Return type
Dict[str, Any]
- get_output_data_names()¶
Return the names of the output variables.
- Returns
The names of the output variables.
- Return type
List[str]
- get_outputs_asarray()¶
Return the local input data as a large NumPy array.
The order is the one of
get_all_inputs()
.- Returns
The local input data.
- Return type
numpy.ndarray
- get_outputs_by_name(data_names)¶
Return the local data associated with output variables.
- Parameters
data_names (Iterable[str]) – 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.
- Return type
List[Any]
- get_sub_disciplines()¶
Return the sub-disciplines if any.
- Returns
The sub-disciplines.
- Return type
- property grammar_type¶
The grammar type.
- is_all_inputs_existing(data_names)¶
Test if several variables are discipline inputs.
- Parameters
data_names (Iterable[str]) – The names of the variables.
- Returns
Whether all the variables are discipline inputs.
- Return type
bool
- is_all_outputs_existing(data_names)¶
Test if several variables are discipline outputs.
- Parameters
data_names (Iterable[str]) – The names of the variables.
- Returns
Whether all the variables are discipline outputs.
- Return type
bool
- is_input_existing(data_name)¶
Test if a variable is a discipline input.
- Parameters
data_name (str) – The name of the variable.
- Returns
Whether the variable is a discipline input.
- Return type
bool
- is_output_existing(data_name)¶
Test if a variable is a discipline output.
- Parameters
data_name (str) – The name of the variable.
- Returns
Whether the variable is a discipline output.
- Return type
bool
- static is_scenario()¶
Whether the discipline is a scenario.
- Return type
bool
- property linearization_mode¶
The linearization mode among
LINEARIZE_MODE_LIST
.- Raises
ValueError – When the linearization mode is unknown.
- linearize(input_data=None, force_all=False, force_no_exec=False)¶
Execute the linearized version of the code.
- Parameters
input_data (Optional[Dict[str, Any]]) –
The input data needed to linearize the discipline according to the discipline input grammar. If None, use the
default_inputs
.By default it is set to None.
force_all (bool) –
If False,
_differentiated_inputs
anddifferentiated_output
are used to filter the differentiated variables. otherwise, all outputs are differentiated wrt all inputs.By default it is set to False.
force_no_exec (bool) –
If True, the discipline is not re executed, cache is loaded anyway.
By default it is set to False.
- Returns
The Jacobian of the discipline.
- Return type
Dict[str, Dict[str, numpy.ndarray]]
- property n_calls¶
The number of times the discipline was executed.
Note
This property is multiprocessing safe.
- property n_calls_linearize¶
The number of times the discipline was linearized.
Note
This property is multiprocessing safe.
- notify_status_observers()¶
Notify all status observers that the status has changed.
- Return type
None
- remove_status_observer(obs)¶
Remove an observer for the status.
- Parameters
obs (Any) – The observer to remove.
- Return type
None
- reset_statuses_for_run()¶
Set all the statuses to
PENDING
.- Raises
ValueError – When the discipline cannot be run because of its status.
- Return type
None
- serialize(out_file)¶
Serialize the discipline and store it in a file.
- Parameters
out_file (Union[str, pathlib.Path]) – The path to the file to store the discipline.
- Return type
None
- set_cache_policy(cache_type='SimpleCache', cache_tolerance=0.0, cache_hdf_file=None, cache_hdf_node_name=None, is_memory_shared=True)¶
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
SimpleCache
recording the last execution or a cache storing all executions, e.g.MemoryFullCache
andHDF5Cache
. Caching data can be either in-memory, e.g.SimpleCache
andMemoryFullCache
, or on the disk, e.g.HDF5Cache
.The attribute
CacheFactory.caches
provides the available caches types.- Parameters
cache_type (str) –
The type of cache.
By default it is set to SimpleCache.
cache_tolerance (float) –
The maximum relative norm of the difference between two input arrays to consider that two input arrays are equal.
By default it is set to 0.0.
cache_hdf_file (Optional[Union[str, pathlib.Path]]) –
The path to the HDF file to store the data; this argument is mandatory when the
HDF5Cache
policy is used.By default it is set to None.
cache_hdf_node_name (Optional[str]) –
The name of the HDF file node to store the discipline data. If None,
name
is used.By default it is set to None.
is_memory_shared (bool) –
Whether to store the data with a shared memory dictionary, which makes the cache compatible with multiprocessing.
By default it is set to True.
- Return type
None
- set_disciplines_statuses(status)¶
Set the sub-disciplines statuses.
To be implemented in subclasses.
- Parameters
status (str) – The status.
- Return type
None
- set_jacobian_approximation(jac_approx_type='finite_differences', jax_approx_step=1e-07, jac_approx_n_processes=1, jac_approx_use_threading=False, jac_approx_wait_time=0)¶
Set the Jacobian approximation method.
Sets the linearization mode to approx_method, sets the parameters of the approximation for further use when calling
linearize()
.- Parameters
jac_approx_type (str) –
The approximation method, either “complex_step” or “finite_differences”.
By default it is set to finite_differences.
jax_approx_step (float) –
The differentiation step.
By default it is set to 1e-07.
jac_approx_n_processes (int) –
The maximum number of processors on which to run.
By default it is set to 1.
jac_approx_use_threading (bool) –
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.
By default it is set to False.
jac_approx_wait_time (float) –
The time waited between two forks of the process / thread.
By default it is set to 0.
- Return type
None
- set_optimal_fd_step(outputs=None, inputs=None, force_all=False, print_errors=False, numerical_error=2.220446049250313e-16)¶
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.
See also
https://en.wikipedia.org/wiki/Numerical_differentiation and “Numerical Algorithms and Digital Representation”, Knut Morken , Chapter 11, “Numerical Differenciation”
- Parameters
inputs –
The inputs wrt which the outputs are linearized. If None, use the
_differentiated_inputs
.By default it is set to None.
outputs –
The outputs to be linearized. If None, use the
_differentiated_outputs
.By default it is set to None.
force_all –
Whether to consider all the inputs and outputs of the discipline;
By default it is set to False.
print_errors –
Whether to display the estimated errors.
By default it is set to False.
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.
By default it is set to 2.220446049250313e-16.
- Returns
The estimated errors of truncation and cancellation error.
- Raises
ValueError – When the Jacobian approximation method has not been set.
- property status¶
The status of the discipline.
- store_local_data(**kwargs)¶
Store discipline data in local data.
- Parameters
kwargs – The data to be stored in
local_data
.**kwargs (Any) –
- Return type
None
- time_stamps = None¶
- class gemseo.wrappers.disc_from_exe.FoldersIter(value)[source]¶
Bases:
gemseo.utils.base_enum.BaseEnum
An enumeration.
Attributes:
- NUMBERED = 0¶
- UUID = 1¶
- class gemseo.wrappers.disc_from_exe.Parsers(value)[source]¶
Bases:
gemseo.utils.base_enum.BaseEnum
An enumeration.
Attributes:
- CUSTOM_CALLABLE = 2¶
- KEY_VALUE_PARSER = 0¶
- TEMPLATE_PARSER = 1¶
- gemseo.wrappers.disc_from_exe.parse_key_value_file(_, out_lines, separator='=')[source]¶
Parse the output file from the expected text positions.
- Parameters
out_lines (Sequence[str]) – The lines of the output file template.
separator (str) –
The separating characters of the key=value format.
By default it is set to =.
- Returns
The output data in .MDODiscipline friendly data structure (e.g. Dict[str, ndarray]).
- Return type
Dict[str, numpy.ndarray]
- gemseo.wrappers.disc_from_exe.parse_outfile(output_positions, out_lines)[source]¶
Parse the output file from the expected text positions.
- Parameters
output_positions (Mapping[str, Tuple[int]]) – The output position for each data name. The information from the template format {data_name:(start,end,dictionary)}, where name is the name of the output data, start is the index of the starting point in the input file template. This index is a line index (character number on the line) end is the index of the end character in the template line_number is the index of the line in the file
out_lines (Sequence[str]) – The lines of the output file template.
- Returns
The output data in .MDODiscipline friendly data structure (e.g. Dict[str, ndarray]).
- Return type
Dict[str, numpy.ndarray]
- gemseo.wrappers.disc_from_exe.parse_template(template_lines, grammar_is_input)[source]¶
Parse the input or output template.
- Parameters
template_lines (Sequence[str]) – The lines of the template file.
grammar_is_input (bool) – True for an input template, False otherwise.
- Return type
Tuple[Dict[str, numpy.ndarray], Dict[str, Tuple[int]]]
This function parses the input (or output) template. It returns the tuple (data_dict, pos_dict), where:
- data_dict is the {name:value} dict:
name is the data name
value is the parsed input or output value in the template
- pos_dict describes the template format {data_name:(start,end,line_number)}:
data_name is the name of the input data
start is the index of the starting point in the input file template.
This index is a line index (character number on the line)
end is the index of the end character in the template
line_number is the index of the line in the file
- Returns
A data structure containing the parsed inpout or output template.
- Parameters
template_lines (Sequence[str]) –
grammar_is_input (bool) –
- Return type
Tuple[Dict[str, numpy.ndarray], Dict[str, Tuple[int]]]
- gemseo.wrappers.disc_from_exe.write_input_file(input_file_path, data, input_positions, input_lines, float_format='{:1.18g}')[source]¶
Write the input file from the input data.
- Parameters
input_file_path (str) – The absolute path to the file to be written.
data (Mapping[str, numpy.ndarray]) – The local data of the discipline.
input_positions (Mapping[str, Tuple[int]]) – The information from the template format {data_name:(start,end,line_number)}, where name is the name of the input data, start is the index of the starting point in the input file template. This index is a line index (character number on the line). end is the index of the end character in the template, line_number is the index of the line in the file.
input_lines (Sequence[int]) – The lines of the input file template.
float_format (str) –
The formating of the input data in the file (Default value = “{:1.18g}”).
By default it is set to {:1.18g}.
- Return type
None