gemseo / wrappers

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disc_from_exe module

Make a discipline from an executable.

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=Parser.TEMPLATE, write_input_file_method=None, parse_out_separator='=', use_shell=True)[source]

Bases: MDODiscipline

Generic wrapper for executables.

This MDODiscipline uses template files describing the input and output variables.

The templates can be generated with a graphical user interface (GUI). by executing the module template_grammar_editor.

An input template file is a JSON file formatted as

{
   "a": GEMSEO_INPUT{a::1.0},
   "b": GEMSEO_INPUT{b::2.0},
   "c": GEMSEO_INPUT{c::3.0}
}

where "a" is the name of an input, and 1.0 is its default value. Similarly, an output template file is a JSON file formatted as

{
   "a": GEMSEO_OUTPUT{a::1.0},
   "b": GEMSEO_OUTPUT{b::2.0},
   "c": GEMSEO_OUTPUT{c::3.0}
}

where "a" is the name of an output, and 1.0 is its default value.

The current limitations are

  • Only one input and one output template 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.

  • For security reasons, the executable is executed via the Python subprocess library with no shell. If a shell is needed, you may override this in a derived class.

Initialize self. See help(type(self)) for accurate signature.

Parameters:
  • input_template (str | Path) – The path to the input template file. The input locations in the file are marked by GEMSEO_INPUT{input_name::1.0}, where input_name is the name of the input variable, and 1.0 is its default value.

  • output_template (str | Path) – The path to the output template file. The output locations in the file are marked by GEMSEO_OUTPUT{output_name::1.0}, where output_name is the name of the output variable, and 1.0 is its default value.

  • output_folder_basepath (str | Path) – The base path of the execution directories.

  • executable_command (str) – The command to run the executable. E.g. python my_script.py -i input.txt -o output.txt

  • input_filename (str | Path) – The name of the input file to be generated in the output folder. E.g. "input.txt".

  • output_filename (str | Path) – The name of the output file to be generated in the output folder. E.g. "output.txt".

  • folders_iter (FoldersIter) –

    The type of unique identifiers for the output folders. If NUMBERED, the generated output folders will be f"output_folder_basepath{i+1}", where i is the maximum value of the already existing f"output_folder_basepath{i}" folders. Otherwise, a unique number based on the UUID function is generated. This last option shall be used if multiple MDO processes are run in the same work folder.

    By default it is set to “NUMBERED”.

  • name (str | None) – The name of the discipline. If None, use the class name.

  • parse_outfile_method (Parser | OutputParser) –

    The optional method that can be provided by the user to parse the output template file. If the KEY_VALUE is used as output parser, the user may specify the separator key.

    By default it is set to “TEMPLATE”.

  • write_input_file_method (InputWriter | None) – The method to write the input data file. If None, use write_input_file().

  • parse_out_separator (str) –

    The separator used for the KEY_VALUE output parser.

    By default it is set to “=”.

  • use_shell (bool) –

    This argument is ignored and will be removed, the shell is not used.

    By default it is set to True.

Raises:

TypeError – If the provided parse_outfile_method is not callable. If the provided write_input_file_method is not callable.

class ApproximationMode(value)

Bases: StrEnum

The approximation derivation modes.

COMPLEX_STEP = 'complex_step'

The complex step method used to approximate the Jacobians by perturbing each variable with a small complex number.

FINITE_DIFFERENCES = 'finite_differences'

The finite differences method used to approximate the Jacobians by perturbing each variable with a small real number.

class CacheType(value)

Bases: StrEnum

The name of the cache class.

HDF5 = 'HDF5Cache'
MEMORY_FULL = 'MemoryFullCache'
NONE = ''

No cache is used.

SIMPLE = 'SimpleCache'
class ExecutionStatus(value)

Bases: StrEnum

The execution statuses of a discipline.

DONE = 'DONE'
FAILED = 'FAILED'
LINEARIZE = 'LINEARIZE'
PENDING = 'PENDING'
RUNNING = 'RUNNING'
VIRTUAL = 'VIRTUAL'
class GrammarType(value)

Bases: StrEnum

The name of the grammar class.

JSON = 'JSONGrammar'
PYDANTIC = 'PydanticGrammar'
SIMPLE = 'SimpleGrammar'
class InitJacobianType(value)

Bases: StrEnum

The way to initialize Jacobian matrices.

DENSE = 'dense'

The Jacobian is initialized as a NumPy ndarray filled in with zeros.

EMPTY = 'empty'

The Jacobian is initialized as an empty NumPy ndarray.

SPARSE = 'sparse'

The Jacobian is initialized as a SciPy CSR array with zero elements.

class LinearizationMode(value)

Bases: StrEnum

An enumeration.

ADJOINT = 'adjoint'
AUTO = 'auto'
COMPLEX_STEP = 'complex_step'
DIRECT = 'direct'
FINITE_DIFFERENCES = 'finite_differences'
REVERSE = 'reverse'
class ReExecutionPolicy(value)

Bases: StrEnum

The re-execution policy of a discipline.

DONE = 'RE_EXEC_DONE'
NEVER = 'RE_EXEC_NEVER'
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 the inputs against which to differentiate the outputs.

If the discipline grammar type is MDODiscipline.GrammarType.JSON and an input is either a non-numeric array or not an array, it will be ignored. If an input is declared as an array but the type of its items is not defined, it is assumed as a numeric array.

If the discipline grammar type is MDODiscipline.GrammarType.SIMPLE and an input is not an array, it will be ignored. Keep in mind that in this case the array subtype is not checked.

Parameters:

inputs (Iterable[str] | None) – The input variables against which to differentiate the outputs. If None, all the inputs of the discipline are used.

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 the outputs to be differentiated.

If the discipline grammar type is MDODiscipline.GrammarType.JSON and an output is either a non-numeric array or not an array, it will be ignored. If an output is declared as an array but the type of its items is not defined, it is assumed as a numeric array.

If the discipline grammar type is MDODiscipline.GrammarType.SIMPLE and an output is not an array, it will be ignored. Keep in mind that in this case the array subtype is not checked.

Parameters:

outputs (Iterable[str] | None) – 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.

Return type:

None

add_namespace_to_input(name, namespace)

Add a namespace prefix to an existing input grammar element.

The updated input grammar element name will be namespace + namespaces_separator + name.

Parameters:
  • name (str) – The element name to rename.

  • namespace (str) – The name of the namespace.

Return type:

None

add_namespace_to_output(name, namespace)

Add a namespace prefix to an existing output grammar element.

The updated output grammar element name will be namespace + namespaces_separator + name.

Parameters:
  • name (str) – The element name to rename.

  • namespace (str) – The name of the namespace.

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 the discipline.

Search in the directory comp_dir for either an input grammar file named name + "_input.json" or an output grammar file named name + "_output.json".

Parameters:
  • is_input (bool) –

    Whether to search for an input or output grammar file.

    By default it is set to True.

  • name (str | None) – The name to be searched in the file names. If None, use the name of the discipline class.

  • comp_dir (str | Path | None) – The directory in which to search the grammar file. If None, use the GRAMMAR_DIRECTORY if any, or the directory of the discipline class module.

Returns:

The grammar file path.

Return type:

Path

check_input_data(input_data, raise_exception=True)

Check the input data validity.

Parameters:
  • input_data (Mapping[str, Any]) – The input data needed to execute the discipline according to the discipline input grammar.

  • raise_exception (bool) –

    Whether to raise on error.

    By default it is set to True.

Return type:

None

check_jacobian(input_data=None, derr_approx=ApproximationMode.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, fig_size_x=10, fig_size_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 (Mapping[str, ndarray] | None) – The input data needed to execute the discipline according to the discipline input grammar. If None, use the MDODiscipline.default_inputs.

  • derr_approx (ApproximationMode) –

    The approximation method, either “complex_step” or “finite_differences”.

    By default it is set to “finite_differences”.

  • threshold (float) –

    The acceptance threshold for the Jacobian error.

    By default it is set to 1e-08.

  • linearization_mode (str) –

    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 (Iterable[str] | None) – The names of the inputs wrt which to differentiate the outputs.

  • outputs (Iterable[str] | None) – The names of the outputs to be differentiated.

  • step (float) –

    The differentiation step.

    By default it is set to 1e-07.

  • parallel (bool) –

    Whether to differentiate the discipline in parallel.

    By default it is set to False.

  • n_processes (int) –

    The maximum simultaneous number of threads, if use_threading is True, or processes otherwise, used to parallelize the execution.

    By default it is set to 2.

  • 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.

  • wait_time_between_fork (float) –

    The time waited between two forks of the process / thread.

    By default it is set to 0.

  • auto_set_step (bool) –

    Whether to compute the optimal step for a forward first order finite differences gradient approximation.

    By default it is set to False.

  • plot_result (bool) –

    Whether to plot the result of the validation (computed vs approximated Jacobians).

    By default it is set to False.

  • file_path (str | Path) –

    The path to the output file if plot_result is True.

    By default it is set to “jacobian_errors.pdf”.

  • show (bool) –

    Whether to open the figure.

    By default it is set to False.

  • fig_size_x (float) –

    The x-size of the figure in inches.

    By default it is set to 10.

  • fig_size_y (float) –

    The y-size of the figure in inches.

    By default it is set to 10.

  • reference_jacobian_path (str | Path | None) – The path of the reference Jacobian file.

  • save_reference_jacobian (bool) –

    Whether to save the reference Jacobian.

    By default it is set to False.

  • indices (Iterable[int] | None) – 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.

Return type:

bool

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

execute(input_data=None)

Execute the discipline.

This method executes the discipline:

Parameters:

input_data (Mapping[str, Any] | None) – The input data needed to execute the discipline according to the discipline input grammar. If None, use the MDODiscipline.default_inputs.

Returns:

The discipline local data after execution.

Raises:

RuntimeError – When residual_variables are declared but self.run_solves_residuals is False. This is not supported yet.

Return type:

dict[str, Any]

static from_pickle(file_path)

Deserialize a discipline from a file.

Parameters:

file_path (str | Path) – The path to the file containing the discipline.

Returns:

The discipline instance.

Return type:

MDODiscipline

get_all_inputs()

Return the local input data as a list.

The order is given by MDODiscipline.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 MDODiscipline.get_output_data_names().

Returns:

The local output data.

Return type:

list[Any]

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 (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:

Any | Iterator[Any]

get_disciplines_in_dataflow_chain()

Return the disciplines that must be shown as blocks in the XDSM.

By default, only the discipline itself is shown. This function can be differently implemented for any type of inherited discipline.

Returns:

The disciplines shown in the XDSM chain.

Return type:

list[gemseo.core.discipline.MDODiscipline]

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:

AtomicExecSequence

get_input_data(with_namespaces=True)

Return the local input data as a dictionary.

Parameters:

with_namespaces (bool) –

Whether to keep the namespace prefix of the input names, if any.

By default it is set to True.

Returns:

The local input data.

Return type:

dict[str, Any]

get_input_data_names(with_namespaces=True)

Return the names of the input variables.

Parameters:

with_namespaces (bool) –

Whether to keep the namespace prefix of the input names, if any.

By default it is set to True.

Returns:

The names of the input variables.

Return type:

list[str]

get_input_output_data_names(with_namespaces=True)

Return the names of the input and output variables.

Args:
with_namespaces: Whether to keep the namespace prefix of the

output names, if any.

Returns:

The name of the input and output variables.

Parameters:

with_namespaces (bool) –

By default it is set to True.

Return type:

list[str]

get_inputs_asarray()

Return the local output data as a large NumPy array.

The order is the one of MDODiscipline.get_all_outputs().

Returns:

The local output data.

Return type:

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 a discipline input name.

Return type:

Generator[Any]

get_output_data(with_namespaces=True)

Return the local output data as a dictionary.

Parameters:

with_namespaces (bool) –

Whether to keep the namespace prefix of the output names, if any.

By default it is set to True.

Returns:

The local output data.

Return type:

dict[str, Any]

get_output_data_names(with_namespaces=True)

Return the names of the output variables.

Parameters:

with_namespaces (bool) –

Whether to keep the namespace prefix of the output names, if any.

By default it is set to True.

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 MDODiscipline.get_all_inputs().

Returns:

The local input data.

Return type:

ndarray

get_outputs_by_name(data_names)

Return the local data associated with output variables.

Parameters:

data_names (Iterator[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(recursive=False)

Determine the sub-disciplines.

This method lists the sub-disciplines’ disciplines. It will list up to one level of disciplines contained inside another one unless the recursive argument is set to True.

Parameters:

recursive (bool) –

If True, the method will look inside any discipline that has other disciplines inside until it reaches a discipline without sub-disciplines, in this case the return value will not include any discipline that has sub-disciplines. If False, the method will list up to one level of disciplines contained inside another one, in this case the return value may include disciplines that contain sub-disciplines.

By default it is set to False.

Returns:

The sub-disciplines.

Return type:

list[gemseo.core.discipline.MDODiscipline]

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

linearize(input_data=None, compute_all_jacobians=False, execute=True)

Compute the Jacobians of some outputs with respect to some inputs.

Parameters:
  • input_data (Mapping[str, Any] | None) – The input data for which to compute the Jacobian. If None, use the MDODiscipline.default_inputs.

  • compute_all_jacobians (bool) –

    Whether to compute the Jacobians of all the output with respect to all the inputs. Otherwise, set the input variables against which to differentiate the output ones with add_differentiated_inputs() and set these output variables to differentiate with add_differentiated_outputs().

    By default it is set to False.

  • execute (bool) –

    Whether to start by executing the discipline with the input data for which to compute the Jacobian; this allows to ensure that the discipline was executed with the right input data; it can be almost free if the corresponding output data have been stored in the cache.

    By default it is set to True.

Returns:

The Jacobian of the discipline shaped as {output_name: {input_name: jacobian_array}} where jacobian_array[i, j] is the partial derivative of output_name[i] with respect to input_name[j].

Raises:

ValueError – When either the inputs for which to differentiate the outputs or the outputs to differentiate are missing.

Return type:

dict[str, dict[str, NDArray[float]]]

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 MDODiscipline.ExecutionStatus.PENDING.

Raises:

ValueError – When the discipline cannot be run because of its status.

Return type:

None

set_cache_policy(cache_type=CacheType.SIMPLE, cache_tolerance=0.0, cache_hdf_file=None, cache_hdf_node_path=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 and HDF5Cache. Caching data can be either in-memory, e.g. SimpleCache and MemoryFullCache, or on the disk, e.g. HDF5Cache.

The attribute CacheFactory.caches provides the available caches types.

Parameters:
  • cache_type (CacheType) –

    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 (str | Path | None) – The path to the HDF file to store the data; this argument is mandatory when the MDODiscipline.CacheType.HDF5 policy is used.

  • cache_hdf_node_path (str | None) – The name of the HDF file node to store the discipline data, possibly passed as a path root_name/.../group_name/.../node_name. If None, MDODiscipline.name is used.

  • 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=ApproximationMode.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 MDODiscipline.linearize().

Parameters:
  • jac_approx_type (ApproximationMode) –

    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 simultaneous number of threads, if jac_approx_use_threading is True, or processes otherwise, used to parallelize the execution.

    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, compute_all_jacobians=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 (round-off 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 Differentiation”

Parameters:
  • inputs (Iterable[str] | None) – The inputs wrt which the outputs are linearized. If None, use the MDODiscipline._differentiated_inputs.

  • outputs (Iterable[str] | None) – The outputs to be linearized. If None, use the MDODiscipline._differentiated_outputs.

  • compute_all_jacobians (bool) –

    Whether to compute the Jacobians of all the output with respect to all the inputs. Otherwise, set the input variables against which to differentiate the output ones with add_differentiated_inputs() and set these output variables to differentiate with add_differentiated_outputs().

    By default it is set to False.

  • print_errors (bool) –

    Whether to display the estimated errors.

    By default it is set to False.

  • numerical_error (float) –

    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.

Return type:

ndarray

store_local_data(**kwargs)

Store discipline data in local data.

Parameters:

**kwargs (Any) – The data to be stored in MDODiscipline.local_data.

Return type:

None

to_pickle(file_path)

Serialize the discipline and store it in a file.

Parameters:

file_path (str | Path) – The path to the file to store the discipline.

Return type:

None

GRAMMAR_DIRECTORY: ClassVar[str | None] = None

The directory in which to search for the grammar files if not the class one.

N_CPUS = 2
activate_cache: bool = True

Whether to cache the discipline evaluations by default.

activate_counters: ClassVar[bool] = True

Whether to activate the counters (execution time, calls and linearizations).

activate_input_data_check: ClassVar[bool] = True

Whether to check the input data respect the input grammar.

activate_output_data_check: ClassVar[bool] = True

Whether to check the output data respect the output grammar.

cache: AbstractCache | None

The cache containing one or several executions of the discipline according to the cache policy.

property cache_tol: 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).

Raises:

ValueError – When the discipline does not have a cache.

data_processor: DataProcessor

A data processor to be used before the execution of the discipline.

property default_inputs: Defaults

The default inputs.

property default_outputs: Defaults

The default outputs used when virtual_execution is True.

property disciplines: list[gemseo.core.discipline.MDODiscipline]

The sub-disciplines, if any.

exec_for_lin: bool

Whether the last execution was due to a linearization.

property exec_time: float | None

The cumulated execution time of the discipline.

This property is multiprocessing safe.

Raises:

RuntimeError – When the discipline counters are disabled.

executable_command: str

The executable command.

property grammar_type: GrammarType

The type of grammar to be used for inputs and outputs declaration.

input_filename: str

The name of the input file.

input_grammar: BaseGrammar

The input grammar.

input_template: str

The path to the input template file.

jac: dict[str, dict[str, ndarray]]

The Jacobians of the outputs wrt inputs.

The structure is {output: {input: matrix}}.

property linearization_mode: LinearizationMode

The linearization mode among MDODiscipline.LinearizationMode.

Raises:

ValueError – When the linearization mode is unknown.

property local_data: DisciplineData

The current input and output data.

property n_calls: int | None

The number of times the discipline was executed.

This property is multiprocessing safe.

Raises:

RuntimeError – When the discipline counters are disabled.

property n_calls_linearize: int | None

The number of times the discipline was linearized.

This property is multiprocessing safe.

Raises:

RuntimeError – When the discipline counters are disabled.

name: str

The name of the discipline.

output_filename: str

The name of the output file.

output_grammar: BaseGrammar

The output grammar.

output_template: str

The path to the output template file.

parse_outfile: OutputParser

The function used to parse the output template file.

re_exec_policy: ReExecutionPolicy

The policy to re-execute the same discipline.

residual_variables: Mapping[str, str]

The output variables mapping to their inputs, to be considered as residuals; they shall be equal to zero.

run_solves_residuals: bool

Whether the run method shall solve the residuals.

property status: ExecutionStatus

The status of the discipline.

The status aims at monitoring the process and give the user a simplified view on the state (the process state = execution or linearize or done) of the disciplines. The core part of the execution is _run, the core part of linearize is _compute_jacobian or approximate jacobian computation.

time_stamps = None
virtual_execution: ClassVar[bool] = False

Whether to skip the _run() method during execution and return the default_outputs, whatever the inputs.

write_input_file: InputWriter

The function used to write the input template file.

class gemseo.wrappers.disc_from_exe.Parser(value)[source]

Bases: StrEnum

Built-in parser types.

KEY_VALUE = 'KEY_VALUE'

The output file is expected to have a key-value structure for each line.

TEMPLATE = 'TEMPLATE'

The output is expected as a JSON file with the following format:

{

“a”: GEMSEO_OUTPUT{a::1.0}, “b”: GEMSEO_OUTPUT{b::2.0}, “c”: GEMSEO_OUTPUT{c::3.0}

}

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.

Raises:

ValueError – If the amount of separators in the lines are not consistent with the keys and values. If the float values cannot be parsed.

Return type:

dict[str, float]

gemseo.wrappers.disc_from_exe.parse_outfile(output_positions, out_lines)[source]

Parse the output template file from the expected text positions.

Parameters:
  • output_positions (Mapping[str, tuple[int]]) – The output position for each output variable, specified as {"name": (start, end, line_number)}, where "name" is the name of the output variable, start is the index of the starting point in the file, end is the index of the end character in the file, and line_number is the index of the line in the file. An index is a line index, i.e. a character number on the line.

  • out_lines (Sequence[str]) – The lines of the file.

Returns:

The output data.

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.

This function parses the input (or output) template. It returns the tuple (names_to_values, names_to_positions), where:

  • names_to_values is the {name:value} dict:

    • name is the data name

    • value is the parsed input or output value in the template

  • names_to_positions describes the template format {name:(start,end,line_number)}:

    • 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

Parameters:
  • template_lines (Sequence[str]) – The lines of the template file.

  • grammar_is_input (bool) – Whether the template file describes input variables.

Returns:

A data structure containing the parsed input or output template.

Return type:

tuple[dict[str, str], dict[str, tuple[int, int, 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 template from the input data.

Parameters:
  • input_file_path (str | Path) – The absolute path to the file to be written.

  • data (Mapping[str, ndarray]) – The local data of the discipline.

  • input_positions (Mapping[str, tuple[int, int, int]]) – The positions of the input variables, formatted as {"name": (start, end, line_number)}, where "name" is the name of the input variable, start is the index of the starting point in the file, end is the index of the end character in the file, and line_number is the index of the line in the file. An index is a line index, i.e. a character number on the line.

  • input_lines (MutableSequence[str]) – The lines of the file.

  • float_format (str) –

    The format of the input data in the file.

    By default it is set to “{:1.18g}”.

Return type:

None

gemseo.wrappers.disc_from_exe.InputWriter(*args, **kwargs)

Input writer type.

alias of Callable[[Union[str, Path], Mapping[str, ndarray], Mapping[str, Tuple[int, int, int]], MutableSequence[str]], None]

gemseo.wrappers.disc_from_exe.OutputParser(*args, **kwargs)

Output parser type.

alias of Callable[[Mapping[str, Tuple[int]], Sequence[str]], Mapping[str, Union[ndarray, float]]]