from __future__ import annotations

from numpy import array

from gemseo import configure_logger
from gemseo import create_discipline
from gemseo import generate_coupling_graph
from gemseo import generate_n2_plot
from gemseo import get_available_disciplines
from gemseo import get_discipline_inputs_schema
from gemseo import get_discipline_options_defaults
from gemseo import get_discipline_options_schema
from gemseo import get_discipline_outputs_schema
from gemseo.core.discipline import MDODiscipline
from gemseo.disciplines.utils import get_all_inputs
from gemseo.disciplines.utils import get_all_outputs

<RootLogger root (INFO)>

In this example, we will discover the different functions of the API related to disciplines, which are the GEMSEO’ objects dedicated to the representation of an input-output process. All classes implementing disciplines inherit from MDODiscipline which is an abstract class.

Get available disciplines

The get_available_disciplines() function can list the available disciplines:

['Aerodynamics', 'AnalyticDiscipline', 'AutoPyDiscipline', 'BaseDiscipline', 'CalibrationScenario', 'Calibrator', 'Concatenater', 'ConstraintAggregation', 'DOEScenario', 'DensityFilter', 'DiscFromExe', 'FilteringDiscipline', 'FininiteElementAnalysis', 'JEPJavaDiscipline', 'JavaDiscipline', 'JobSchedulerDisciplineWrapper', 'LSF', 'LinearCombination', 'LinearDiscipline', 'MDOAdditiveChain', 'MDOChain', 'MDODiscipline', 'MDOObjectiveScenarioAdapter', 'MDOParallelChain', 'MDOScenario', 'MDOScenarioAdapter', 'MDOWarmStartedChain', 'MainDiscipline', 'MaterialModelInterpolation', 'MatlabDiscipline', 'Mission', 'PropaneComb1', 'PropaneComb2', 'PropaneComb3', 'PropaneReaction', 'RemappingDiscipline', 'RosenMF', 'SLURM', 'ScalableDiscipline', 'Scenario', 'ScilabDiscipline', 'Sellar1', 'Sellar2', 'SellarSystem', 'SobieskiAerodynamics', 'SobieskiAerodynamicsSG', 'SobieskiChain', 'SobieskiDiscipline', 'SobieskiDisciplineWithSimpleGrammar', 'SobieskiMDAGaussSeidel', 'SobieskiMDAJacobi', 'SobieskiMission', 'SobieskiMissionSG', 'SobieskiPropulsion', 'SobieskiPropulsionSG', 'SobieskiStructure', 'SobieskiStructureSG', 'Splitter', 'Structure', 'SurrogateDiscipline', 'TaylorDiscipline', 'VolumeFraction', 'XLSDiscipline']

Create a discipline

The create_discipline() function can create a MDODiscipline or a list of MDODiscipline by using its class name. Specific **options can be provided in argument. E.g.

disciplines = create_discipline([
type(disciplines), type(disciplines[0]), isinstance(disciplines[0], MDODiscipline)
(<class 'list'>, <class 'gemseo.problems.sobieski.disciplines.SobieskiPropulsion'>, True)

This function can also be used to create a particular MDODiscipline from scratch, such as AnalyticDiscipline or AutoPyDiscipline. E.g.

addition = create_discipline("AnalyticDiscipline", expressions={"y": "x1+x2"})
addition.execute({"x1": array([1.0]), "x2": array([2.0])})
{'x1': array([1.]), 'x2': array([2.]), 'y': array([3.])}

Get all inputs/outputs

The get_all_inputs() function can list all the inputs of a list of disciplines, including the sub-disciplines if the argument recursive (default: False) is True, merging the input data from the discipline grammars. E.g.

['c_0', 'c_1', 'c_2', 'c_3', 'c_4', 'x_1', 'x_2', 'x_3', 'x_shared', 'y_12', 'y_14', 'y_21', 'y_23', 'y_24', 'y_31', 'y_32', 'y_34']

The get_all_outputs() function can list all the inputs of a list of disciplines, including the sub-disciplines if the argument recursive (default: False) is True, merging the input data from the discipline grammars. E.g.

['g_1', 'g_2', 'g_3', 'y_1', 'y_11', 'y_12', 'y_14', 'y_2', 'y_21', 'y_23', 'y_24', 'y_3', 'y_31', 'y_32', 'y_34', 'y_4']

Get discipline schemas for inputs, outputs and options

{'$schema': 'http://json-schema.org/draft-04/schema', 'type': 'object', 'properties': {'y_23': {'type': 'array', 'items': {'type': 'number'}}, 'x_3': {'type': 'array', 'items': {'type': 'number'}}, 'x_shared': {'type': 'array', 'items': {'type': 'number'}}, 'c_3': {'description': 'engine weight reference', 'type': 'array', 'items': {'type': 'number'}}}, 'required': ['c_3', 'x_3', 'x_shared', 'y_23']}
{'$schema': 'http://json-schema.org/draft-04/schema', 'type': 'object', 'properties': {'y_32': {'type': 'array', 'items': {'type': 'number'}}, 'y_31': {'type': 'array', 'items': {'type': 'number'}}, 'g_3': {'type': 'array', 'items': {'type': 'number'}}, 'y_3': {'type': 'array', 'items': {'type': 'number'}}, 'y_34': {'type': 'array', 'items': {'type': 'number'}}}, 'required': ['g_3', 'y_3', 'y_31', 'y_32', 'y_34']}
{'$schema': 'http://json-schema.org/draft-04/schema', 'type': 'object', 'properties': {'linearization_mode': {'description': 'Linearization mode', 'enum': ['auto', 'direct', 'reverse', 'adjoint'], 'type': 'string'}, 'jac_approx_type': {'description': 'Jacobian approximation type', 'enum': ['finite_differences', 'complex_step'], 'type': 'string'}, 'jax_approx_step': {'minimum': 0, 'exclusiveMinimum': True, 'description': 'Step for finite differences or complex step for Jacobian approximation', 'type': 'number'}, 'jac_approx_use_threading': {'description': 'if True, use Threads instead of processes\n to parallelize the execution. \nMultiprocessing will serialize all the disciplines, \nwhile multithreading will share all the memory.\n This is important to note if you want to execute the same\n  discipline multiple times, you shall use multiprocessing', 'type': 'boolean'}, 'jac_approx_wait_time': {'description': 'Time waited between two forks of the process or thread when using parallel jacobian approximations (parallel=True)', 'minimum': 0, 'type': 'number'}, 'jac_approx_n_processes': {'minimum': 1, 'description': 'maximum number of processors or threads on \nwhich the jacobian approximation is performed\n by default, 1 means no parallel calculations', 'type': 'integer'}, 'cache_type': {'description': 'Type of cache to be used.  \nBy default, simple cache stores the last execution inputs and outputs  \nin memory only to avoid computation of the outputs if the inputs are identical.\n To store more executions, use HDF5 caches, which stores data on the disk.\n There is a hashing mechanism which avoids reading on the disk for every calculation.', 'type': 'string'}, 'cache_tolerance': {'minimum': 0, 'description': 'Numerical tolerance on the relative norm of input vectors \n to consider that two sets of inputs are equal, and that the outputs may therefore be returned from the cache without calculations.', 'type': 'number'}, 'cache_hdf_file': {'format': 'uri', 'description': 'Path to the HDF5 file to store the cache data.', 'type': 'string'}, 'cache_hdf_node_path': {'description': 'Name of the HDF dataset to store the discipline\n data. If ``None``, the discipline name is used.', 'type': 'string'}, 'dtype': {'description': 'The data type for the NumPy arrays, either "float64" or "complex128".', 'type': 'string'}, 'enable_delay': {'description': 'If ``True``, wait one second before computation. If a positive number, wait the corresponding number of seconds. If ``False``, compute directly.', 'type': 'boolean'}}}
{'dtype': <DataType.FLOAT: 'float64'>, 'enable_delay': False}

Plot coupling structure

The generate_coupling_graph() function plots the coupling graph of a set of MDODiscipline:

generate_coupling_graph(disciplines, file_path="full_coupling_graph.pdf")
    disciplines, file_path="condensed_coupling_graph.pdf", full=False
%3 MDA of SobieskiPropulsion, SobieskiAerodynamics, SobieskiStructure MDA of SobieskiPropulsion, SobieskiAerodynamics, SobieskiStructure SobieskiMission SobieskiMission MDA of SobieskiPropulsion, SobieskiAerodynamics, SobieskiStructure->SobieskiMission y_14,y_24,y_34 SobieskiMission->_0 y_4

The generate_n2_plot() function plots the N2 diagram of a set of MDODiscipline:

generate_n2_plot(disciplines, save=False, show=True)
plot discipline

Total running time of the script: (0 minutes 0.741 seconds)

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