aiida.engine.processes.workchains package

Module for the WorkChain process and related utilities.

aiida.engine.processes.workchains.ToContext

alias of builtins.dict

aiida.engine.processes.workchains.assign_(target)

Convenience function that will construct an Awaitable for a given class instance with the context action set to ASSIGN. When the awaitable target is completed it will be assigned to the context for a key that is to be defined later

Parameters

target – an instance of a Process or Awaitable

Returns

the awaitable

Return type

Awaitable

aiida.engine.processes.workchains.append_(target)

Convenience function that will construct an Awaitable for a given class instance with the context action set to APPEND. When the awaitable target is completed it will be appended to a list in the context for a key that is to be defined later

Parameters

target – an instance of a Process or Awaitable

Returns

the awaitable

Return type

Awaitable

class aiida.engine.processes.workchains.WorkChain(inputs=None, logger=None, runner=None, enable_persistence=True)

Bases: aiida.engine.processes.process.Process

The WorkChain class is the principle component to implement workflows in AiiDA.

_CONTEXT = 'CONTEXT'

_Process__called = True

_STEPPER_STATE = 'stepper_state'

__abstractmethods__ = frozenset({})

__init__(inputs=None, logger=None, runner=None, enable_persistence=True)

Construct a WorkChain instance.

Construct the instance only if it is a sub class of WorkChain, otherwise raise InvalidOperation.

Parameters

• inputs (dict) – work chain inputs

• logger (logging.Logger) – aiida logger

• runner – work chain runner

• enable_persistence (bool) – whether to persist this work chain

Type

aiida.engine.runners.Runner

__module__ = 'aiida.engine.processes.workchains.workchain'

_abc_impl = <_abc_data object>

_auto_persist = {'_CREATION_TIME', '_awaitables', '_enable_persistence', '_future', '_parent_pid', '_paused', '_pid', '_pre_paused_status', '_status'}

_do_step()

Execute the next step in the outline and return the result.

If the stepper returns a non-finished status and the return value is of type ToContext, the contents of the ToContext container will be turned into awaitables if necessary. If any awaitables were created, the process will enter in the Wait state, otherwise it will go to Continue. When the stepper returns that it is done, the stepper result will be converted to None and returned, unless it is an integer or instance of ExitCode.

_node_class

alias of aiida.orm.nodes.process.workflow.workchain.WorkChainNode

_spec = <aiida.engine.processes.workchains.workchain.WorkChainSpec object>

_spec_class

alias of WorkChainSpec

_store_nodes(data)

Recurse through a data structure and store any unstored nodes that are found along the way

Parameters

data – a data structure potentially containing unstored nodes

_update_process_status()

Set the process status with a message accounting the current sub processes that we are waiting for.

action_awaitables()

Handle the awaitables that are currently registered with the work chain

Depending on the class type of the awaitable’s target a different callback function will be bound with the awaitable and the runner will be asked to call it when the target is completed

property ctx

Get context.

Return type

aiida.common.extendeddicts.AttributeDict

insert_awaitable(awaitable)

Insert an awaitable that should be terminated before before continuing to the next step.

Parameters

awaitable (aiida.engine.processes.workchains.awaitable.Awaitable) – the thing to await

load_instance_state(saved_state, load_context)

Load instance state.

Parameters

• saved_state – saved instance state

• load_context (plumpy.persistence.LoadSaveContext) –

on_exiting()

Ensure that any unstored nodes in the context are stored, before the state is exited

After the state is exited the next state will be entered and if persistence is enabled, a checkpoint will be saved. If the context contains unstored nodes, the serialization necessary for checkpointing will fail.

on_process_finished(awaitable, pk)

Callback function called by the runner when the process instance identified by pk is completed.

The awaitable will be effectuated on the context of the work chain and removed from the internal list. If all awaitables have been dealt with, the work chain process is resumed.

Parameters

• awaitable – an Awaitable instance

• pk (int) – the pk of the awaitable’s target

on_run()

on_wait(awaitables)

remove_awaitable(awaitable)

Remove an awaitable.

Precondition: must be an awaitable that was previously inserted.

Parameters

awaitable – the awaitable to remove

run()

save_instance_state(out_state, save_context)

Save instance stace.

Parameters

• out_state – state to save in

• save_context (plumpy.persistence.LoadSaveContext) –

to_context(**kwargs)

Add a dictionary of awaitables to the context.

This is a convenience method that provides syntactic sugar, for a user to add multiple intersteps that will assign a certain value to the corresponding key in the context of the work chain.

aiida.engine.processes.workchains.if_(condition)

A conditional that can be used in a workchain outline.

Use as:

if_(cls.conditional)(
  cls.step1,
  cls.step2
)

Each step can, of course, also be any valid workchain step e.g. conditional.

Parameters

condition – The workchain method that will return True or False

aiida.engine.processes.workchains.while_(condition)

A while loop that can be used in a workchain outline.

Use as:

while_(cls.conditional)(
  cls.step1,
  cls.step2
)

Each step can, of course, also be any valid workchain step e.g. conditional.

Parameters

condition – The workchain method that will return True or False

Submodules

Enums and function for the awaitables of Processes.

class aiida.engine.processes.workchains.awaitable.Awaitable(**kwargs)

Bases: plumpy.utils.AttributesDict

An attribute dictionary that represents an action that a Process could be waiting for to finish.

__module__ = 'aiida.engine.processes.workchains.awaitable'

class aiida.engine.processes.workchains.awaitable.AwaitableTarget

Bases: enum.Enum

Enum that describes the class of the target a given awaitable.

PROCESS = 'process'

__module__ = 'aiida.engine.processes.workchains.awaitable'

class aiida.engine.processes.workchains.awaitable.AwaitableAction

Bases: enum.Enum

Enum that describes the action to be taken for a given awaitable.

APPEND = 'append'

ASSIGN = 'assign'

__module__ = 'aiida.engine.processes.workchains.awaitable'

aiida.engine.processes.workchains.awaitable.construct_awaitable(target)

Construct an instance of the Awaitable class that will contain the information related to the action to be taken with respect to the context once the awaitable object is completed.

The awaitable is a simple dictionary with the following keys

• pk: the pk of the node that is being waited on

• action: the context action to be performed upon completion

• outputs: a boolean that toggles whether the node itself

Currently the only awaitable classes are ProcessNode and Workflow The only awaitable actions are the Assign and Append operators

Convenience functions to add awaitables to the Context of a WorkChain.

aiida.engine.processes.workchains.context.ToContext

alias of builtins.dict

aiida.engine.processes.workchains.context.assign_(target)

Convenience function that will construct an Awaitable for a given class instance with the context action set to ASSIGN. When the awaitable target is completed it will be assigned to the context for a key that is to be defined later

Parameters

target – an instance of a Process or Awaitable

Returns

the awaitable

Return type

Awaitable

aiida.engine.processes.workchains.context.append_(target)

Convenience function that will construct an Awaitable for a given class instance with the context action set to APPEND. When the awaitable target is completed it will be appended to a list in the context for a key that is to be defined later

Parameters

target – an instance of a Process or Awaitable

Returns

the awaitable

Return type

Awaitable

Components for the WorkChain concept of the workflow engine.

class aiida.engine.processes.workchains.workchain.WorkChain(inputs=None, logger=None, runner=None, enable_persistence=True)

Bases: aiida.engine.processes.process.Process

The WorkChain class is the principle component to implement workflows in AiiDA.

_CONTEXT = 'CONTEXT'

_Process__called = True

_STEPPER_STATE = 'stepper_state'

__abstractmethods__ = frozenset({})

__init__(inputs=None, logger=None, runner=None, enable_persistence=True)

Construct a WorkChain instance.

Construct the instance only if it is a sub class of WorkChain, otherwise raise InvalidOperation.

Parameters

• inputs (dict) – work chain inputs

• logger (logging.Logger) – aiida logger

• runner – work chain runner

• enable_persistence (bool) – whether to persist this work chain

Type

aiida.engine.runners.Runner

__module__ = 'aiida.engine.processes.workchains.workchain'

_abc_impl = <_abc_data object>

_auto_persist = {'_CREATION_TIME', '_awaitables', '_enable_persistence', '_future', '_parent_pid', '_paused', '_pid', '_pre_paused_status', '_status'}

_do_step()

Execute the next step in the outline and return the result.

If the stepper returns a non-finished status and the return value is of type ToContext, the contents of the ToContext container will be turned into awaitables if necessary. If any awaitables were created, the process will enter in the Wait state, otherwise it will go to Continue. When the stepper returns that it is done, the stepper result will be converted to None and returned, unless it is an integer or instance of ExitCode.

_node_class

alias of aiida.orm.nodes.process.workflow.workchain.WorkChainNode

_spec = <aiida.engine.processes.workchains.workchain.WorkChainSpec object>

_spec_class

alias of WorkChainSpec

_store_nodes(data)

Recurse through a data structure and store any unstored nodes that are found along the way

Parameters

data – a data structure potentially containing unstored nodes

_update_process_status()

Set the process status with a message accounting the current sub processes that we are waiting for.

action_awaitables()

Handle the awaitables that are currently registered with the work chain

Depending on the class type of the awaitable’s target a different callback function will be bound with the awaitable and the runner will be asked to call it when the target is completed

property ctx

Get context.

Return type

aiida.common.extendeddicts.AttributeDict

insert_awaitable(awaitable)

Insert an awaitable that should be terminated before before continuing to the next step.

Parameters

awaitable (aiida.engine.processes.workchains.awaitable.Awaitable) – the thing to await

load_instance_state(saved_state, load_context)

Load instance state.

Parameters

• saved_state – saved instance state

• load_context (plumpy.persistence.LoadSaveContext) –

on_exiting()

Ensure that any unstored nodes in the context are stored, before the state is exited

After the state is exited the next state will be entered and if persistence is enabled, a checkpoint will be saved. If the context contains unstored nodes, the serialization necessary for checkpointing will fail.

on_process_finished(awaitable, pk)

Callback function called by the runner when the process instance identified by pk is completed.

The awaitable will be effectuated on the context of the work chain and removed from the internal list. If all awaitables have been dealt with, the work chain process is resumed.

Parameters

• awaitable – an Awaitable instance

• pk (int) – the pk of the awaitable’s target

on_run()

on_wait(awaitables)

remove_awaitable(awaitable)

Remove an awaitable.

Precondition: must be an awaitable that was previously inserted.

Parameters

awaitable – the awaitable to remove

run()

save_instance_state(out_state, save_context)

Save instance stace.

Parameters

• out_state – state to save in

• save_context (plumpy.persistence.LoadSaveContext) –

to_context(**kwargs)

Add a dictionary of awaitables to the context.

This is a convenience method that provides syntactic sugar, for a user to add multiple intersteps that will assign a certain value to the corresponding key in the context of the work chain.

aiida.engine.processes.workchains.workchain.if_(condition)

A conditional that can be used in a workchain outline.

Use as:

if_(cls.conditional)(
  cls.step1,
  cls.step2
)

Each step can, of course, also be any valid workchain step e.g. conditional.

Parameters

condition – The workchain method that will return True or False

aiida.engine.processes.workchains.workchain.while_(condition)

A while loop that can be used in a workchain outline.

Use as:

while_(cls.conditional)(
  cls.step1,
  cls.step2
)

Each step can, of course, also be any valid workchain step e.g. conditional.

Parameters

condition – The workchain method that will return True or False