gemseo / disciplines

auto_py module¶

A discipline interfacing a Python function.

class gemseo.disciplines.auto_py.AutoDiscDataProcessor(out_names)[source]¶

Bases: DataProcessor

A data processor forcing input data to float and output data to arrays.

Convert all GEMSEO scalar input data to floats, and convert all discipline output data to NumPy arrays.

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

Parameters:: out_names (Sequence[str]) – The names of the outputs.

post_process_data(data)[source]¶

Post-process the output data.

Execute a post-processing of the output data after the _run() method of the discipline is called, and before they are checked by check_output_data().

Parameters:: data (dict[str, Union[float, numpy.ndarray]]) – The data to be processed.
Returns:: The processed data with NumPy arrays as values.
Return type:: dict[str, numpy.ndarray]

pre_process_data(data)[source]¶

Pre-process the input data.

Execute a pre-processing of input data after they are checked by check_input_data(), and before the _run() method of the discipline is called.

Parameters:: data (dict[str, Union[float, numpy.ndarray]]) – The data to be processed.
Returns:: The processed data where one-length NumPy arrays have been replaced with floats.
Return type:: dict[str, Union[float, numpy.ndarray]]

docstring_processor(parent_doc: Optional[str], child_func: Callable) → None: Callable[[Optional[str], Callable], str] = <docstring_inheritance.processors.google.GoogleDocstringProcessor object>¶

Parameters:

parent_doc (Optional[str]) –
child_func (Callable) –

Return type:

None

one_output: bool¶: Whether there is a single output.

out_names: Sequence[str]¶: The names of the outputs.

class gemseo.disciplines.auto_py.AutoPyDiscipline(py_func, py_jac=None, name=None, use_arrays=False, grammar_type='JSONGrammar')[source]¶

Bases: MDODiscipline

Wrap a Python function into a discipline.

A simplified and straightforward way of integrating a discipline from a Python function.

The Python function can take and return only numbers and NumPy arrays.

The Python function may or may not include default values for input arguments, however, if the resulting AutoPyDiscipline is going to be placed inside an MDF, a BiLevel formulation or an MDA with strong couplings, then the Python function must assign default values for its input arguments.

Example

>>> from gemseo.disciplines.auto_py import AutoPyDiscipline
>>> from numpy import array
>>> def my_function(x=0., y=0.):
>>>     z1 = x + 2*y
>>>     z2 = x + 2*y + 1
>>>     return z1, z2
>>>
>>> discipline = AutoPyDiscipline(py_func=my_function)
>>> discipline.execute()
{'x': array([0.]), 'y': array([0.]), 'z1': array([0.]), 'z2': array([1.])}
>>> discipline.execute({'x': array([1.]), 'y':array([-3.2])})
{'x': array([1.]), 'y': array([-3.2]), 'z1': array([-5.4]), 'z2': array([-4.4])}

Parameters:

py_func (Callable[[DataType, ..., DataType], DataType]) – The Python function to compute the outputs from the inputs.
py_jac (Callable[[DataType, ..., DataType], ndarray] | None) – The Python function to compute the Jacobian from the inputs; its output value must be a 2D NumPy array with rows correspond to the outputs and columns to the inputs.
name (str | None) – The name of the discipline. If None, use the name of the Python function.
use_arrays (bool) –
Whether the function is expected to take arrays as inputs and give outputs as arrays.

By default it is set to False.
grammar_type (str) –
The type of grammar to define the input and output variables, e.g. MDODiscipline.JSON_GRAMMAR_TYPE or MDODiscipline.SIMPLE_GRAMMAR_TYPE.

By default it is set to “JSONGrammar”.

Raises:

TypeError – When py_func is not callable.

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.JSON_GRAMMAR_TYPE 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.SIMPLE_GRAMMAR_TYPE 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.JSON_GRAMMAR_TYPE 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.SIMPLE_GRAMMAR_TYPE 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.

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.

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:

str

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) –
Whether to raise on error.

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, 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 (dict[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 (str) –
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.

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

static deserialize(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

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 MDODiscipline.default_inputs.
Checks whether the last execution of the discipline was called with identical inputs, i.e. cached in MDODiscipline.cache; if so, directly returns self.cache.get_output_cache(inputs).
Caches the inputs.
Checks the input data against MDODiscipline.input_grammar.
If MDODiscipline.data_processor is not None, runs the preprocessor.
Updates the status to MDODiscipline.STATUS_RUNNING.
Calls the MDODiscipline._run() method, that shall be defined.
If MDODiscipline.data_processor is not None, runs the postprocessor.
Checks the output data.
Caches the outputs.
Updates the status to MDODiscipline.STATUS_DONE or MDODiscipline.STATUS_FAILED.
Updates summed execution time.

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]

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]

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.
Return type:: list[str]

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: