JobCreationContext

Qualified name: api.simulation.studies.job_setup.JobCreationContext

class api.simulation.studies.job_setup.JobCreationContext(study_parameters_table_setup, job_name)

Bases: ABC

This class handles the creation of jobs in studies.

Creation of this class should happen through the API Gateway, see the example: The variable ‘job_setup’ will hold a reference to an object of this class

Example

>>> from more.api import ApiGateway
>>> api = ApiGateway(proj=proj)
>>> simulation_setup = api.create_simulation_setup()
>>> study_setup = simulation_setup.create_study_setup()
>>> print(study_setup.get_available_job_type_names()) # Check which job type names are available
[...]
>>> job_setup = study_setup.create_job_setup(job_name='Static Job') # Adds a static job and returns a job creation pipeline object
>>> print("Available general parameters: {}".format(job_setup.get_general_job_settings_description())) # To check which general settings are available
Available general parameters: "job_type": Job type parameter ...
>>> print("Available job parameters: {}".format(job_setup.get_job_type_specific_settings_description())) # To check which general settings are available
Available job parameters: "single_load_case_choice": Single list choice parameter ...

This class works by first preparing the job and then applying the changes to the study using the create() method as in the example given:

Example

Assuming that the ‘job_setup’ variable holds the reference from the example above:

>>> lcc_setup = simulation_setup.create_load_case_container_setup().set_name('example_load_case_container')

>>> study_setup.add_tags(tags=['some_tag'])
<more...>
>>> job_setup = job_setup.set_general_job_settings_parameter(parameter_name='tags', value=['some_tag']) \
...          .set_general_job_settings_parameter(parameter_name='label', value='example_job') \
...          .set_job_specific_parameter(parameter_name='single_load_case_choice', value='example_load_case_container') \
...          .create()

Methods

create	Adds a job to the Study with the settings that were added before.
get_general_job_settings_description	Returns a description of which settings are available for this study in the 'Study settings' tab
get_job_type_specific_settings_description	Returns a description of which settings are available for the current job type
get_tunable_parameters_description_for_object	Returns a description of which tunable parameters are available for the given object
set_general_job_settings_parameter	Sets a tunable parameter available in the 'Study settings' tab to a specific value for this job
set_job_specific_parameter	Sets a tunable job specific parameter to a specific value for this job
set_parameter	Adds a tunable parameter to this job
set_running_job_settings_parameter	Deprecated version of set_general_job_settings_parameter()

abstract create() → JobCreationContext

Adds a job to the Study with the settings that were added before.

Example

>>> from more.api import ApiGateway
>>> api = ApiGateway(proj=proj)
>>> simulation_setup = api.create_simulation_setup()
>>> study_setup = simulation_setup.create_study_setup()
>>> job_setup = study_setup.create_job_setup(job_name='Static Job')
>>> job_setup.set_general_job_settings_parameter(parameter_name='label', value='first_job') \
...          .create()
<more...>

Returns:

self

Return type:

JobCreationContext

abstract get_general_job_settings_description() → str

Returns a description of which settings are available for this study in the ‘Study settings’ tab

Example

>>> from more.api import ApiGateway
>>> api = ApiGateway(proj=proj)
>>> simulation_setup = api.create_simulation_setup()
>>> study_setup = simulation_setup.create_study_setup()
>>> job_setup = study_setup.create_job_setup(job_name='FRF Job')
>>> print("Available general parameters: {}".format(job_setup.get_general_job_settings_description())) # To check which general settings are available
Available general parameters: "job_type": Job type parameter ...

Returns:

A description of the parameters available under the ‘Study settings’ tab including the names they have to be referenced by to use the set_general_job_settings_parameter() method.

Return type:

str

abstract get_job_type_specific_settings_description() → str

Returns a description of which settings are available for the current job type

Example

>>> from more.api import ApiGateway
>>> api = ApiGateway(proj=proj)
>>> simulation_setup = api.create_simulation_setup()
>>> study_setup = simulation_setup.create_study_setup()
>>> job_setup = study_setup.create_job_setup(job_name='FRF Job')
>>> print("Available job parameters: {}".format(job_setup.get_job_type_specific_settings_description())) # To check which general settings are available
Available job parameters: ...

Returns:

A description of the parameters available for this job type including the names they have to be referenced by to use the set_job_specific_parameter() method.

Return type:

str

abstract get_tunable_parameters_description_for_object(obj) → str

Returns a description of which tunable parameters are available for the given object

Example

>>> from more.api import ApiGateway
>>> api = ApiGateway(proj=proj)
>>> simulation_setup = api.create_simulation_setup()
>>> study_setup = simulation_setup.create_study_setup()
>>> job_setup = study_setup.create_job_setup(job_name='Static Job') # Adds a static job and returns a job creation pipeline object

>>> particular_property = proj.comp.find_link_properties(linkpropname='property_name').properties[0] # Assumes that a link property with this names exists and has at least one particular property
>>> print(job_setup.get_tunable_parameters_description_for_object(obj=particular_property)) # Shows the parameters available to the supplied object
"ku": Float parameter - ...

Returns:

A description of the tunable parameters available the given object including the names they have to be referenced by to use the set_parameter() method.

Return type:

str

abstract set_general_job_settings_parameter(parameter_name: str, value: Any) → JobCreationContext

Sets a tunable parameter available in the ‘Study settings’ tab to a specific value for this job

Example

>>> from more.api import ApiGateway
>>> api = ApiGateway(proj=proj)
>>> simulation_setup = api.create_simulation_setup()
>>> study_setup = simulation_setup.create_study_setup()
>>> job_setup = study_setup.create_job_setup(job_name='FRF Job')
>>> print("Available general parameters: {}".format(job_setup.get_general_job_settings_description())) # To check which general settings are available
Available general parameters: "job_type": Job type parameter ...
>>> job_setup = job_setup.set_general_job_settings_parameter(parameter_name='save_result', value=False) \
...          .create()

Parameters:

• parameter_name (str) – The name of tunable parameter

• value (Any) – The value to set

Returns:

self

Return type:

JobCreationContext

Raises:

• NotCompatibleError – Raised if a wrong value type is given for the general job settings

• NameNotFoundError – Raised if the general job settings do not have a parameter with the given name

abstract set_job_specific_parameter(parameter_name: str, value: Any) → JobCreationContext

Sets a tunable job specific parameter to a specific value for this job

Example

>>> from more.api import ApiGateway
>>> api = ApiGateway(proj=proj)
>>> simulation_setup = api.create_simulation_setup()
>>> study_setup = simulation_setup.create_study_setup()
>>> job_setup = study_setup.create_job_setup(job_name='FRF Job')
>>> print("Available job parameters: {}".format(job_setup.get_job_type_specific_settings_description())) # To check which general settings are available
Available job parameters: ...
>>> job_setup.set_job_specific_parameter(parameter_name='n_points', value=100) \
...          .create()
<more...>

Parameters:

• parameter_name (str) – The name of the job specific parameter to set

• value (Any) – The value to set for the job specific parameter

Returns:

self

Return type:

JobCreationContext

Raises:

• NotCompatibleError – Raised if a wrong value type is given for the job type specific settings parameter

• NameNotFoundError – Raised if the job type specific settings do not have a parameter with the given name

abstract set_parameter(obj: Any, parameter_name: str, value: Any) → JobCreationContext

Adds a tunable parameter to this job

Example

>>> from more.api import ApiGateway
>>> api = ApiGateway(proj=proj)
>>> simulation_setup = api.create_simulation_setup()
>>> study_setup = simulation_setup.create_study_setup()
>>> job_setup = study_setup.create_job_setup(job_name='Static Job') # Adds a static job and returns a job creation pipeline object

>>> particular_property = proj.comp.find_link_properties(linkpropname='property_name').properties[0] # Assumes that a link property with this names exists and has at least one particular property
>>> print(job_setup.get_tunable_parameters_description_for_object(obj=particular_property)) # Shows the parameters available to the supplied object
"ku": Float parameter - ...
>>> job_setup.set_parameter(obj=particular_property, parameter_name='ku', value=1e6) \
...          .create()
<more...>

>>> # Setup objects are interpreted as if they were their underlying objects
>>> load_setup = simulation_setup \
... .create_load_case_container_setup() \
... .create_load_case_setup(lc_type='Mechanical load case') \
... .create_load_setup(load_type='Link load')

>>> job_setup.set_parameter(obj=load_setup, parameter_name='u', value=1e6)
<more...>

Returns:

self

Return type:

JobCreationContext

Raises:

• NotCompatibleError – Raised if a wrong value is given for the parameter

• NameNotFoundError – Raised if the given object has no parameter with the given name

abstract set_running_job_settings_parameter(parameter_name: str, value: Any) → JobCreationContext

Deprecated version of set_general_job_settings_parameter()

Deprecated

set_running_job_settings_parameter will be removed in a future MORe release, it is replaced by set_parameter()