Source code for gmxapi.operation

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"""Define gmxapi-compliant Operations

Provide decorators and base classes to generate and validate gmxapi Operations.

Nodes in a work graph are created as instances of Operations. An Operation factory
accepts well-defined inputs as key word arguments. The object returned by such
a factory is a handle to the node in the work graph. It's ``output`` attribute
is a collection of the Operation's results.

function_wrapper(...) produces a wrapper that converts a function to an Operation
factory. The Operation is defined when the wrapper is called. The Operation is
instantiated when the factory is called. The function is executed when the Operation
instance is run.

The framework ensures that an Operation instance is executed no more than once.

__all__ = ['computed_result',

import abc
import collections
import functools
import inspect
import typing
import weakref
from contextlib import contextmanager

import gmxapi as gmx
from gmxapi import datamodel
from gmxapi import exceptions
from gmxapi import logger as root_logger
from import OperationImplementation, MutableResource, Node
from gmxapi.typing import _Context, ResultTypeVar, SourceTypeVar, valid_result_types, valid_source_types
from gmxapi.typing import Future as _Future

# Initialize module-level logger
logger = root_logger.getChild('operation')'Importing {}'.format(__name__))

class ResultDescription:
    """Describe what will be returned when `result()` is called."""

    def __init__(self, dtype=None, width=1):
        assert isinstance(dtype, type)
        assert issubclass(dtype, valid_result_types)
        assert isinstance(width, int)
        self._dtype = dtype
        self._width = width

    def dtype(self) -> type:
        """node output type"""
        return self._dtype

    def width(self) -> int:
        """ensemble width"""
        return self._width

    def __repr__(self):
        return '{}(dtype={}, width={})'.format(self.__class__.__name__, self.dtype, self.width)

class OutputData(object):
    """Encapsulate the description and storage of a data output."""

    def __init__(self, name: str, description: ResultDescription):
        assert name != ''
        self._name = name
        assert isinstance(description, ResultDescription)
        self._description = description
        self._done = [False] * self._description.width
        self._data = [None] * self._description.width

    def name(self):
        """The name of the published output."""
        return self._name

    # TODO: Change to regular member function and add ensemble member arg.
    def done(self):
        """Ensemble completion status for this output."""
        return all(self._done)

    def data(self, member: int = None):
        """Access the raw data for localized output for the ensemble or the specified member."""
        if not self.done:
            raise exceptions.ApiError('Attempt to read before data has been published.')
        if self._data is None or None in self._data:
            raise exceptions.ApiError('Data marked "done" but contains null value.')
        if member is not None:
            return self._data[member]
            return self._data

    def set(self, value, member: int):
        """Set local data and mark as completed.

        Used internally to maintain the data store.
        if self._description.dtype == datamodel.NDArray:
            self._data[member] = datamodel.ndarray(value)
            self._data[member] = self._description.dtype(value)
        self._done[member] = True

    def reset(self):
        """Reinitialize the data store.

            This is a workaround until the execution model is more developed.

            Remove this method when all operation handles provide factories to
            reinstantiate on new Contexts and/or with new inputs.

        self._done = [False] * self._description.width
        self._data = [None] * self._description.width

class EnsembleDataSource(
    """A single source of data with ensemble data flow annotations.

    Note that data sources may be Futures.

    def __init__(self, source=None, width=1, dtype=None):
        self.source = source
        self.width = width
        self.dtype = dtype

    def node(self, member: int):
        return self.source[member]

    def reset(self):
        protocols = ('reset', '_reset')
        for protocol in protocols:
            if hasattr(self.source, protocol):
                getattr(self.source, protocol)()

class DataSourceCollection(collections.OrderedDict):
    """Store and describe input data handles for an operation.

    When created from InputCollectionDescription.bind(), the DataSourceCollection
    has had default values inserted.

    Note: DataSourceCollection is the input resource collection type for NodeBuilder.
    To use with the NodeBuilder interface, first create the collection, then
    iteratively add its resources.

    TODO: We should probably normalize the collection aspect of input. Is this a
          single input? Are all inputs added as collections? Should this be split
          to a standard iterable? Does a resource factory always produce a mapping?

    def __init__(self, **kwargs):
        """Initialize from key/value pairs of named data sources.

        Data sources may be any of the basic gmxapi data types, gmxapi Futures
        of those types, or gmxapi ensemble data bundles of the above.
        super(DataSourceCollection, self).__init__()
        for name, value in kwargs.items():
            self[name] = value

    def __setitem__(self, key: str, value: SourceTypeVar) -> None:
        if not isinstance(key, str):
            raise exceptions.TypeError('Data must be named with str type.')
        # TODO: Encapsulate handling of proferred data sources to Context details.
        # Preprocessed input should be self-describing gmxapi data types. Structured
        # input must be recursively (depth-first) converted to gmxapi data types.
        # TODO: Handle gmxapi Futures stored as dictionary elements!
        if not isinstance(value, valid_source_types):
            if isinstance(value,
                # Iterables here are treated as arrays, but we do not have a robust typing system.
                # Warning: In the initial implementation, the iterable may contain Future objects.
                # TODO: (#2993) Revisit as we sort out data shape and Future protocol.
                    value = datamodel.ndarray(value)
                except (exceptions.ValueError, exceptions.TypeError) as e:
                    raise exceptions.TypeError('Iterable could not be converted to NDArray: {}'.format(value)) from e
            elif hasattr(value, 'result'):
                # A Future object.
                raise exceptions.ApiError('Cannot process data source {}'.format(value))
        super().__setitem__(key, value)

    def reset(self):
        """Reset all sources in the collection."""
        for source in self.values():
            if hasattr(source, 'reset'):
            if hasattr(source, '_reset'):

    def __hash__(self):
        """Provide some sort of unique identifier.

        We need a more deterministic fingerprinting scheme with well-specified
        uniqueness semantics, but right now we just need something reasonably
        hashed_keys_and_values = tuple(hash(entity) for item in self.items() for entity in item)
        return hash(hashed_keys_and_values)

def computed_result(function):
    """Decorate a function to get a helper that produces an object with Result behavior.

    When called, the new function produces an ImmediateResult object.

    The new function has the same signature as the original function, but can accept
    gmxapi data proxies, assuming the provided proxy objects represent types
    compatible with the original signature.

    Calls to `result()` return the value that `function` would return when executed
    in the local context with the inputs fully resolved.

    The API does not specify when input data dependencies will be resolved
    or when the wrapped function will be executed. That is, ``@computed_result``
    functions may force immediate resolution of data dependencies and/or may
    be called more than once to satisfy dependent operation inputs.

    # Attempt to inspect function signature. Introspection can fail, so we catch
    # the various exceptions. We re-raise as ApiError, indicating a bug, because
    # we should do more to handle this intelligently and provide better user
    # feedback.
    # TODO: Figure out what to do with exceptions where this introspection
    #  and rebinding won't work.
    # ref:
        sig = inspect.signature(function)
    except TypeError as T:
        raise exceptions.ApiError('Can not inspect type of provided function argument.') from T
    except ValueError as V:
        raise exceptions.ApiError('Can not inspect provided function signature.') from V

    wrapped_function = function_wrapper()(function)

    def new_function(*args, **kwargs):
        # The signature of the new function will accept abstractions
        # of whatever types it originally accepted. This wrapper must
        # * Create a mapping to the original call signature from `input`
        # * Add handling for typed abstractions in wrapper function.
        # * Process arguments to the wrapper function into `input`

        # 1. Inspect the return annotation to determine valid gmxapi type(s)
        # 2. Generate a Result object advertising the correct type, bound to the
        #    Input and implementing function.
        # 3. Transform the result() data to the correct type.

        # TODO: (FR3+) create a serializable data structure for inputs discovered
        #  from function introspection.

        for name, param in sig.parameters.items():
            assert not param.kind == param.POSITIONAL_ONLY
        bound_arguments = sig.bind(*args, **kwargs)
        handle = wrapped_function(**bound_arguments.arguments)
        output = handle.output
        # TODO: Find a type hinting / generic way to assert output attributes.

    return new_function

class OutputCollectionDescription(collections.OrderedDict):
    def __init__(self, **kwargs):
        """Create the output description for an operation node from a dictionary of names and types."""
        outputs = []
        for name, flavor in kwargs.items():
            if not isinstance(name, str):
                raise exceptions.TypeError('Output descriptions are keyed by Python strings.')
            # Multidimensional outputs are explicitly NDArray
            if issubclass(flavor, (list, tuple)):
                flavor = datamodel.NDArray
            assert issubclass(flavor, valid_result_types)
            outputs.append((name, flavor))

class InputCollectionDescription(collections.OrderedDict):
    """Describe acceptable inputs for an Operation.

    Generally, an InputCollectionDescription is an aspect of the public API by
    which an Operation expresses its possible inputs. This class includes details
    of the Python package.

    Keyword Arguments:
        parameters : A sequence of named parameter descriptions.

    Parameter descriptions are objects containing an `annotation` attribute
    declaring the data type of the parameter and, optionally, a `default`
    attribute declaring a default value for the parameter.

    Instances can be used as an ordered map of parameter names to gmxapi data types.

    Analogous to inspect.Signature, but generalized for gmxapi Operations.
    Additional notable differences: typing is normalized at initialization, and
    the bind() method does not return an object that can be directly used as
    function input. The object returned from bind() is used to construct a data
    graph Edge for subsequent execution.

    def __init__(self, parameters: typing.Iterable[typing.Tuple[str, inspect.Parameter]]):
        """Create the input description for an operation node from a dictionary of names and types."""
        inputs = []
        for name, param in parameters:
            if not isinstance(name, str):
                raise exceptions.TypeError('Input descriptions are keyed by Python strings.')
            # Multidimensional inputs are explicitly NDArray
            dtype = param.annotation
            if issubclass(dtype, \
                    and not issubclass(dtype, (str, bytes,
                # TODO: we can relax this with some more input conditioning.
                if dtype != datamodel.NDArray:
                    raise exceptions.UsageError(
                        'Cannot accept input type {}. Sequence type inputs must use NDArray.'.format(param))
            assert issubclass(dtype, valid_result_types)
            if hasattr(param, 'kind'):
                disallowed = any([param.kind == param.POSITIONAL_ONLY,
                                  param.kind == param.VAR_POSITIONAL,
                                  param.kind == param.VAR_KEYWORD])
                if disallowed:
                    raise exceptions.ProtocolError(
                        'Cannot wrap function. Operations must have well-defined parameter names.')
                kind = param.kind
                kind = inspect.Parameter.POSITIONAL_OR_KEYWORD
            if hasattr(param, 'default'):
                default = param.default
                default = inspect.Parameter.empty
            inputs.append(inspect.Parameter(name, kind, default=default, annotation=dtype))
        super().__init__([(, input.annotation) for input in inputs])
        self.signature = inspect.Signature(inputs)

    def from_function(function):
        """Inspect a function to be wrapped.

        Used internally by gmxapi.operation.function_wrapper()

                exceptions.ProtocolError if function signature cannot be determined to be valid.

                InputCollectionDescription for the function input signature.
        # First, inspect the function.
        assert callable(function)
        signature = inspect.signature(function)
        # The function must have clear and static input schema
        # Make sure that all parameters have clear names, whether or not they are used in a call.
        for name, param in signature.parameters.items():
            disallowed = any([param.kind == param.POSITIONAL_ONLY,
                              param.kind == param.VAR_POSITIONAL,
                              param.kind == param.VAR_KEYWORD])
            if disallowed:
                raise exceptions.ProtocolError(
                    'Cannot wrap function. Operations must have well-defined parameter names.')
            if == 'input':
                raise exceptions.ProtocolError('Function signature includes the (reserved) "input" keyword argument.')
        description = collections.OrderedDict()
        for param in signature.parameters.values():
            if == 'output':
                # Wrapped functions may accept the output parameter to publish results, but
                # that is not part of the Operation input signature.
            if param.annotation == param.empty:
                if param.default == param.empty or param.default is None:
                    raise exceptions.ProtocolError('Could not infer parameter type for {}'.format(
                dtype = type(param.default)
                if isinstance(dtype, \
                        and not isinstance(dtype, (str, bytes,
                    dtype = datamodel.NDArray
                dtype = param.annotation
            description[] = param.replace(annotation=dtype)
        return InputCollectionDescription(description.items())

    def bind(self, *args, **kwargs) -> DataSourceCollection:
        """Create a compatible DataSourceCollection from provided arguments.

        Pre-process input and function signature to get named input arguments.

        This is a helper function to allow calling code to characterize the
        arguments in a Python function call with hints from the factory that is
        initializing an operation. Its most useful functionality is to  allows a
        factory to accept positional arguments where named inputs are usually
        required. It also allows data sources to participate in multiple
        DataSourceCollections with minimal constraints.

        Note that the returned object has had data populated from any defaults
        described in the InputCollectionDescription.

        See wrapped_function_runner() and describe_function_input().
        # For convenience, accept *args, but convert to **kwargs to pass to Operation.
        # Factory accepts an unadvertised `input` keyword argument that is used as a default kwargs dict.
        # If present, kwargs['input'] is treated as an input "pack" providing _default_ values.
        input_kwargs = collections.OrderedDict()
        if 'input' in kwargs:
            provided_input = kwargs.pop('input')
            if provided_input is not None:
        # `function` may accept an `output` keyword argument that should not be supplied to the factory.
        for key, value in kwargs.items():
            if key == 'output':
                raise exceptions.UsageError('Invalid keyword argument: output (reserved).')
            input_kwargs[key] = value
            bound_arguments = self.signature.bind_partial(*args, **input_kwargs)
        except TypeError as e:
            raise exceptions.UsageError('Could not bind operation parameters to function signature.') from e
        assert 'output' not in bound_arguments.arguments
        assert 'input' not in bound_arguments.arguments
        input_kwargs = collections.OrderedDict([pair for pair in bound_arguments.arguments.items()])
        if 'output' in input_kwargs:
        return DataSourceCollection(**input_kwargs)

class ProxyDataDescriptor(object):
    """Base class for data descriptors used in DataProxyBase subclasses.

    Subclasses should either not define __init__ or should call the base class
    __init__ explicitly: super().__init__(self, name, dtype)

    def __init__(self, name: str, dtype: ResultTypeVar = None):
        self._name = name
        # TODO: We should not allow dtype==None, but we currently have a weak data
        #  model that does not allow good support of structured Futures.
        if dtype is not None:
            assert isinstance(dtype, type)
            assert issubclass(dtype, valid_result_types)
        self._dtype = dtype

class DataProxyMeta(abc.ABCMeta):
    # Key word arguments consumed by __prepare__
    _prepare_keywords = ('descriptors',)

    def __prepare__(mcs, name, bases, descriptors: = None):
        """Allow dynamic sub-classing.

        DataProxy class definitions are collections of data descriptors. This
        class method allows subclasses to give the descriptor names and type(s)
        in the class declaration as arguments instead of as class attribute

            class MyProxy(DataProxyBase, descriptors={name: MyDescriptor() for name in datanames}): pass

            If we are only using this metaclass for the __prepare__ hook by the
            time we require Python >= 3.6, we could reimplement __prepare__ as
            DataProxyBase.__init_subclass__ and remove this metaclass.
        if descriptors is None:
            return {}
        elif isinstance(descriptors, tuple):
            namespace = collections.OrderedDict([(d._name, d) for d in descriptors])
            return namespace
            assert isinstance(descriptors,
            return descriptors

    def __new__(cls, name, bases: typing.Iterable, namespace, **kwargs):
        for key in kwargs:
            if key not in DataProxyMeta._prepare_keywords:
                raise exceptions.ApiError('Unexpected class creation keyword: {}'.format(key))
        # See note about DataProxyBase._reserved.
        if '_reserved' not in namespace and not any(hasattr(base, '_reserved') for base in bases):
            raise exceptions.ApiError(
                'We currently expect DataProxy classes to provide a list of reserved attribute names.')
        for key in namespace:
            # Here we can check conformance with naming and typing rules.
            assert isinstance(key, str)
            if key.startswith('__'):
                # Skip non-public attributes.
            descriptor = namespace[key]
            # The purpose of the current data proxies is to serve as a limited namespace
            # containing only descriptors of a certain type. In the future, these proxies
            # may be flattened into a facet of a richer OperationHandle class
            # (this metaclass may become a decorator on an implementation class),
            # but for now we check that the class is being used according to the
            # documented framework. A nearer term update could be to restrict the
            # type of the data descriptor:
            # TODO: Use a member type of the derived cls (or a mix-in base) to specify a particular
            #  ProxyDataDescriptor subclass.
            # Also, see note about DataProxyBase._reserved
            if not isinstance(descriptor, ProxyDataDescriptor):
                if key not in namespace['_reserved'] and not any(key in getattr(base, '_reserved') for base in
                                                                 bases if hasattr(base, '_reserved')):
                    raise exceptions.ApiError('Unexpected data proxy attribute {}: {}'.format(key, repr(descriptor)))
                assert isinstance(descriptor, ProxyDataDescriptor)
                if not isinstance(descriptor._name, str) or descriptor._name == '':
                    descriptor._name = key
                    if descriptor._name != key:
                        raise exceptions.ApiError(
                            'Descriptor internal name {} does not match attribute name {}'.format(
                                descriptor._name, key))
        return super().__new__(cls, name, bases, namespace)

    # TODO: This keyword argument stripping is not necessary in more recent Python versions.
    # When Python minimum required version is increased, check if we can remove this.
    def __init__(cls, name, bases, namespace, **kwargs):
        for key in kwargs:
            if key not in DataProxyMeta._prepare_keywords:
                raise exceptions.ApiError('Unexpected class initialization keyword: {}'.format(key))
        super().__init__(name, bases, namespace)

    # TODO: See if we can use __dir__ in the metaclass to help hint class attributes for better tab completion.
    #  Ref:
    # def __dir__(self) -> Iterable[str]:
    #     return super().__dir__()

class DataProxyBase(, metaclass=DataProxyMeta):
    """Limited interface to managed resources.

    Inherit from DataProxy to specialize an interface to an ``instance``.
    In the derived class, either do not define ``__init__`` or be sure to
    initialize the super class (DataProxy) with an instance of the object
    to be proxied.

    A class deriving from DataProxyBase allows its instances to provide a namespace
    for proxies to named data by defining attributes that are data descriptors
    (subclasses of ProxyDataDescriptor).
    The ProxyDataDescriptors are accessed as attributes of the
    data proxy instance or by iterating on items(). Attributes that are not
    ProxyDataDescriptors are possible, but will not be returned by items() which
    is a necessary part of gmxapi execution protocol.

    Acts as an owning handle to the resources provide by ``instance``,
    preventing the reference count of ``instance`` from going to zero for the
    lifetime of the proxy object.

    When sub-classing DataProxyBase, data descriptors can be passed as a mapping
    to the ``descriptors`` key word argument in the class declaration. This
    allows data proxy subclasses to be easily defined dynamically.

        mydescriptors = {'foo': Publisher('foo', int), 'data': Publisher('data', float)}
        class MyDataProxy(DataProxyBase, descriptors=mydescriptors): pass
        assert hasattr(MyDataProxy, 'foo')

    # This class attribute (which subclasses are free to replace to augment) is an
    # indication of a problem with the current data model. If we are allowing
    # reserved words that would otherwise be valid data names, there is not a
    # compelling reason for separate data proxy classes: we throw away the assertion
    # that we are preparing a clean namespace and we could have accomplished the
    # class responsibilities in the Operation handle with just descriptor classes.
    # If we want the clean namespace, we should figure out how to keep this interface
    # from growing and/or have some "hidden" internal interface.
    _reserved = ('ensemble_width', 'items', '_reserved')

    # This class can be expanded to be the attachment point for a metaclass for
    # data proxies such as PublishingDataProxy or OutputDataProxy, which may be
    # defined very dynamically and concisely as a set of Descriptors and a type()
    # call.
    # If development in this direction does not materialize, then this base
    # class is not very useful and should be removed.
    def __init__(self, instance: 'SourceResource', client_id: int = None):
        """Get partial ownership of a resource provider.

            instance : resource-owning object
            client_id : identifier for client holding the resource handle (e.g. ensemble member id)

        If client_id is not provided, the proxy scope is for all clients.
        # TODO: Decide whether _resource_instance is public or not.
        # Note: currently commonly needed for subclass implementations.
        self._resource_instance = instance
        # Developer note subclasses should handle self._client_identifier == None
        self._client_identifier = client_id
        # Collection is fixed by the time of instance creation, so cache it.
        self.__keys = tuple([key for key, _ in self.items()])
        self.__length = len(self.__keys)

    def ensemble_width(self) -> int:
        return self._resource_instance.width()

    def items(cls):
        """Generator for tuples of attribute name and descriptor instance.

        This almost certainly doesn't do quite what we want...
        for name, value in cls.__dict__.items():
            if isinstance(value, ProxyDataDescriptor):
                yield name, value

    def __getitem__(self, k):
        if hasattr(self, k):
            return getattr(self, k)

    def __len__(self):
        return self.__length

    def __iter__(self):
        for key in self.__keys:
            yield key

class Publisher(ProxyDataDescriptor):
    """Data descriptor for write access to a specific named data resource.

    For a wrapped function receiving an ``output`` argument, provides the
    accessors for an attribute on the object passed as ``output``. Maps
    read and write access by the wrapped function to appropriate details of
    the resource manager.

    Used internally to implement settable attributes on PublishingDataProxy.
    Allows PublishingDataProxy to be dynamically defined in the scope of the
    operation.function_wrapper closure. Each named output is represented by
    an instance of Publisher in the PublishingDataProxy class definition for
    the operation.


    Relies on implementation details of ResourceManager.

    def __get__(self, instance: DataProxyBase, owner):
        if instance is None:
            # The current access has come through the class attribute of owner class
            return self
        resource_manager = instance._resource_instance
        client_id = instance._client_identifier
        # TODO: Fix API scope.
        # Either this class is a detail of the same implementation as ResourceManager,
        # or we need to enforce that instance._resource_instance provides _data (or equivalent)
        assert isinstance(resource_manager, ResourceManager)
        if client_id is None:
            return getattr(resource_manager._data, self._name)
            return getattr(resource_manager._data, self._name)[client_id]

    def __set__(self, instance: DataProxyBase, value):
        resource_manager = instance._resource_instance
        # TODO: Fix API scope.
        # Either this class is a detail of the same implementation as ResourceManager,
        # or we need to enforce that instance._resource_instance provides _data (or equivalent)
        assert isinstance(resource_manager, ResourceManager)
        client_id = instance._client_identifier
        resource_manager.set_result(name=self._name, value=value, member=client_id)

    def __repr__(self):
        return '{}(name={}, dtype={})'.format(self.__class__.__name__,

def define_publishing_data_proxy(output_description) -> typing.Type[DataProxyBase]:
    """Returns a class definition for a PublishingDataProxy for the provided output description."""
    # This dynamic type creation hides collaborations with things like make_datastore.
    # We should encapsulate these relationships in Context details, explicit collaborations
    # between specific operations and Contexts, and in groups of Operation definition helpers.

    descriptors = collections.OrderedDict([(name, Publisher(name)) for name in output_description])

    class PublishingDataProxy(DataProxyBase, descriptors=descriptors):
        """Handler for write access to the `output` of an operation.

        Acts as a sort of PublisherCollection.

    return PublishingDataProxy

# get symbols we can use to annotate input and output types more specifically.
_OutputDataProxyType = typing.TypeVar('_OutputDataProxyType', bound=DataProxyBase)
_PublishingDataProxyType = typing.TypeVar('_PublishingDataProxyType', bound=DataProxyBase)
# Currently, the ClientID type is an integer, but this may change.
ClientID = typing.NewType('ClientID', int)

class _Resources(typing.Generic[_PublishingDataProxyType]):

# TODO: Why generic in publishingdataproxytype?
class SourceResource(typing.Generic[_OutputDataProxyType, _PublishingDataProxyType]):
    """Resource Manager for a data provider.

    Supports Future instances in a particular context.

    # Note: ResourceManager members not yet included:
    # future(), _data, set_result.

    # This might not belong here. Maybe separate out for a OperationHandleManager?
    def data(self) -> _OutputDataProxyType:
        """Get the output data proxy."""
        # Warning: this should probably be renamed, but "output_data_proxy" is already
        # a member in at least one derived class.

    def is_done(self, name: str) -> bool:
        return False

    def get(self, name: str) -> 'OutputData':

    def update_output(self):
        """Bring the _data member up to date and local."""

    def reset(self):
        """Recursively reinitialize resources.

        Set the resource manager to its initialized state.
        All outputs are marked not "done".
        All inputs supporting the interface have ``_reset()`` called on them.

    def width(self) -> int:
        """Ensemble width of the managed resources."""

    def future(self, name: str, description: ResultDescription) -> 'Future':
        """Get a Future handle for managed data.

        Resource managers owned by subclasses of gmx.operation.Context provide
        this method to get references to output data.

        In addition to the interface described by, returned objects
        provide the interface described by gmx.operation.Future.

class StaticSourceManager(SourceResource[_OutputDataProxyType, _PublishingDataProxyType]):
    """Provide the resource manager interface for local static data.

    Allow data transformations on the proxied resource.

    Keyword Args:
        proxied_data: A gmxapi supported data object.
        width: Size of (one-dimensional) shaped data produced by function.
        function: Transformation to perform on the managed data.

    The callable passed as ``function`` must accept a single argument. The
    argument will be an iterable when proxied_data represents an ensemble,
    or an object of the same type as proxied_data otherwise.

    def __init__(self, *, name: str, proxied_data, width: int, function: typing.Callable):
        assert not isinstance(proxied_data, Future)
        if hasattr(proxied_data, 'width'):
            # Ensemble data source
            assert hasattr(proxied_data, 'source')
            self._result = function(proxied_data.source)
            self._result = function(proxied_data)
        if width > 1:
            if isinstance(self._result, (str, bytes)):
                # In this case, do not implicitly broadcast
                raise exceptions.ValueError('"function" produced data incompatible with "width".')
                if not isinstance(self._result,
                    raise exceptions.DataShapeError(
                        'Expected iterable of size {} but "function" result is not iterable.')
            data = list(self._result)
            size = len(data)
            if len(data) != width:
                raise exceptions.DataShapeError(
                    'Expected iterable of size {} but "function" produced a {} of size {}'.format(width, type(data),
            dtype = type(data[0])
            if width != 1:
                raise exceptions.ValueError('width must be an integer 1 or greater.')
            dtype = type(self._result)
            if issubclass(dtype, (list, tuple)):
                dtype = datamodel.NDArray
                data = [datamodel.ndarray(self._result)]
            elif isinstance(self._result,
                if not isinstance(self._result, (str, bytes, dict)):
                    raise exceptions.ValueError(
                        'Expecting width 1 but "function" produced iterable type {}.'.format(type(self._result)))
                    dtype = str
                    data = [str(self._result)]
                data = [self._result]
        description = ResultDescription(dtype=dtype, width=width)
        self._data = OutputData(name=name, description=description)
        for member in range(width):
            self._data.set(data[member], member=member)

        output_collection_description = OutputCollectionDescription(**{name: dtype})
        self.output_data_proxy = define_output_data_proxy(output_description=output_collection_description)

    def is_done(self, name: str) -> bool:
        return True

    def get(self, name: str) -> 'OutputData':
        assert == name
        return self._data

    def data(self) -> _OutputDataProxyType:
        return self.output_data_proxy(self)

    def width(self) -> int:
        # TODO: It looks like the OutputData ResultDescription probably belongs
        #  in the public interface.
        return self._data._description.width

    def update_output(self):

    def reset(self):

    def future(self, name: str, description: ResultDescription) -> 'Future':
        return Future(self, name, description=description)

class ProxyResourceManager(SourceResource[_OutputDataProxyType, _PublishingDataProxyType]):
    """Act as a resource manager for data managed by another resource manager.

    Allow data transformations on the proxied resource.

    Keyword Args:
        proxied_future: An object implementing the Future interface.
        width: Size of (one-dimensional) shaped data produced by function.
        function: Transformation to perform on the result of proxied_future.

    The callable passed as ``function`` must accept a single argument, which will
    be an iterable when proxied_future represents an ensemble, or an object of
    type proxied_future.description.dtype otherwise.

    def __init__(self, proxied_future: 'Future', width: int, function: typing.Callable):
        self._done = False
        self._proxied_future = proxied_future
        self._width = width =
        self._result = None
        assert callable(function)
        self.function = function

    def width(self) -> int:
        return self._width

    def reset(self):
        self._done = False
        self._result = None

    def is_done(self, name: str) -> bool:
        return self._done

    def get(self, name: str):
        if name !=
            raise exceptions.ValueError('Request for unknown data.')
        if not self.is_done(name):
            raise exceptions.ProtocolError('Data not ready.')
        result = self.function(self._result)
        if self._width != 1:
            # TODO Fix this typing nightmare:
            #  ResultDescription should be fully knowable and defined when the resource manager is initialized.
            data = OutputData(, description=ResultDescription(dtype=type(result[0]), width=self._width))
            for member, value in enumerate(result):
                data.set(value, member)
            data = OutputData(, description=ResultDescription(dtype=type(result), width=self._width))
            data.set(result, 0)
        return data

    def update_output(self):
        self._result = self._proxied_future.result()
        self._done = True

    def data(self) -> _OutputDataProxyType:
        raise exceptions.ApiError('ProxyResourceManager cannot yet manage a full OutputDataProxy.')

    def future(self, name: str, description: ResultDescription):
        return Future(self, name, description=description)

class AbstractOperation(typing.Generic[_OutputDataProxyType]):
    """Client interface to an operation instance (graph node).

    Note that this is a generic abstract class. Subclasses should provide a
    class subscript to help static type checkers.

    def run(self):
        """Assert execution of an operation.

        After calling run(), the operation results are guaranteed to be available
        in the local context.

    def output(self) -> _OutputDataProxyType:
        """Get a proxy collection to the output of the operation.

        Developer note: The 'output' property exists to isolate the namespace of
        output data from other operation handle attributes and we should consider
        whether it is actually necessary or helpful. To facilitate its possible
        future removal, do not enrich its interface beyond that of a collection
        of OutputDescriptor attributes.

class OperationRegistryKey(collections.namedtuple('OperationRegistryKey', 'namespace name'),
    """Helper class to define the key type for OperationRegistry."""

    def __hash__(self):
        return hash((self.namespace,

    def __eq__(self, other):
        """Test equivalence rather than identity.

        Note: Use `is` to test identity.
        return other.namespace == self.namespace and ==

    def __str__(self):
        return '.'.join([self.namespace,])

    def __repr__(self):
        return '{}(namespace={}, name={})'.format(self.__qualname__, self.namespace,

def _make_registry_key(*args) -> OperationRegistryKey:
    """Normalize input to an OperationRegistryKey.

    Used to implement OperationRegistry.__getitem__(), which catches and converts
    the various exceptions this helper function can produce.
    if len(args) > 1:
        return OperationRegistryKey(*args)
        if len(args) != 1:
            raise exceptions.UsageError('Empty index value passed to OperationRegistry instance[].')
        item = args[0]
    if isinstance(item, OperationRegistryKey):
        return item
    if isinstance(item, str):
        namespace, name = item.rsplit(sep='.', maxsplit=1)
        return OperationRegistryKey(namespace=namespace, name=name)
    # Item could be a class object or an instance object...
    if hasattr(item, 'namespace') and hasattr(item, 'name'):
        if callable(item.namespace):
            namespace = item.namespace()
            namespace = item.namespace
        if callable(
            name =
            name =
        return OperationRegistryKey(namespace=namespace, name=name)
    raise exceptions.ValueError('Not a usable OperationRegistryKey: {}'.format(item))

class OperationRegistry(collections.UserDict):
    """Helper class to map identifiers to Operation implementation instances.

    This is an implementation detail of gmxapi.operation and should not be used from
    outside of the package until a stable interface can be specified.

    def __getitem__(self, item: OperationRegistryKey):

    def __getitem__(self, item: str):

    def __getitem__(self, *args):
        """Fetch the requested operation registrant.

        The getter is overloaded to support look-ups in multiple forms.

        The key can be given in the following forms.
        * As a period-delimited string of the form "namespace.operation".
        * As an OperationRegistryKey object.
        * As a sequence of positional arguments accepted by OperationRegistryKey.
        * As an object conforming to the OperationRegistryKey interface.
            item = _make_registry_key(*args)
        except exceptions.Error as e:
            raise exceptions.TypeError('Could not interpret key as a OperationRegistryKey.') from e

# Module level data store for locating operation implementations at run time.
# TODO: This may make sense as instance data of a root Context instance, but we
#  don't have a stable interface for communicating between Contexts yet.
# Alternatively, it could be held as class data in a RegisteredOperation class,
# but note that the "register" member function would behave less like the abc.ABCMeta
# support for "virtual subclassing" and more like the generic function machinery
# of e.g. functools.singledispatch.
_operation_registry = OperationRegistry()

def _register_operation(cls: typing.Type[OperationImplementation]):
    assert isinstance(cls, type)
    assert issubclass(cls, OperationImplementation)
    operation = _make_registry_key(cls)
    if operation in _operation_registry:
        full_name = str(operation)
        raise exceptions.ProtocolError('Attempting to redefine operation {}.'.format(full_name))
    _operation_registry[operation] = cls

# TODO: replace with a generic function that we dispatch on so the type checker can infer a return type.
def _get_operation_director(operation, context:

    :param operation:
    :param context:
    registrant = _operation_registry[operation]
    director = registrant.director(context=context)
    return director

class InputDescription(abc.ABC):
    """Node input description for gmxapi.operation module.

    Provides the support needed to understand operation inputs in gmxapi.operation
    module Contexts.

    .. todo:: Implementation base class with heritable behavior and/or helpers to
              compose this functionality from more normative description of
              operation inputs. This will probably become a facet of the ResourceDirector
              when specialized for gmxapi.operation.Context.

    def signature(self) -> InputCollectionDescription:
        """Mapping of named inputs and input type.

        Used to determine valid inputs before an Operation node is created.

            Related to the operation resource factory for this context.

        ..  todo::
            Better unification of this protocol, InputCollectionDescription, and
            resource factory.
            Note, also, that the *bind* method of the returned InputCollectionDescription
            serves as the resource factory for input to the node builder.

    def make_uid(self, input: 'DataEdge') -> str:
        """The unique identity of an operation node tags the output with respect to the input.

        Combines information on the Operation details and the input to uniquely
        identify the Operation node.

            input : A (collection of) data source(s) that can provide Fingerprints.

        Used internally by the Context to manage ownership of data sources, to
        locate resources for nodes in work graphs, and to manage serialization,
        deserialization, and checkpointing of the work graph.

        The UID is a detail of the generic Operation that _should_ be independent
        of the Context details to allow the framework to manage when and where
        an operation is executed.

        TODO: We probably don't want to allow Operations to single-handedly determine their
         own uniqueness, but they probably should participate in the determination with the Context.

        TODO: Context implementations should be allowed to optimize handling of
         equivalent operations in different sessions or work graphs, but we do not
         yet guarantee that UIDs are globally unique!

        To be refined...

class ConcreteInputDescription(InputDescription):
    """Simple composed InputDescription."""

    def __init__(self,
                 input_signature: InputCollectionDescription,
                 uid_helper: typing.Callable[['DataEdge'], str]
        self._input_signature_description = input_signature
        self._uid_helper = uid_helper

    def signature(self) -> InputCollectionDescription:
        return self._input_signature_description

    def make_uid(self, input: 'DataEdge') -> str:
        return self._uid_helper(input)

class OperationMeta(abc.ABCMeta):
    """Metaclass to manage the definition of Operation implementation classes.

    Note that this metaclass can be superseded by `__init_subclass__()` when
    the minimum Python version is increased to Python 3.6+.

    def __new__(meta, name, bases, class_dict):
        cls = super().__new__(meta, name, bases, class_dict)
        # Register subclasses, but not the base class.
        if issubclass(cls, OperationImplementation) and cls is not OperationImplementation:
            # TODO: Remove OperationDetailsBase layer and this extra check.
            # Note: we do not yet register the Operations built dynamically because we
            # don't have a clear definition of unique implementations yet. For instance,
            # a new OperationDetails class is defined for each call to gmx.join_arrays
            # TODO: Properly register and reuse Operations defined dynamically
            #  through function_wrapper (currently encompassed by OperationDetailsBase subclasses)
            if name != 'OperationDetailsBase':
                if OperationDetailsBase not in bases:
        return cls

class OperationDetailsBase(OperationImplementation, InputDescription,
    """Abstract base class for Operation details in this module's Python Context.

    Provides necessary interface for use with gmxapi.operation.ResourceManager.
    Separates the details of an Operation from those of the ResourceManager in
    a given Context.

    OperationDetails classes are almost stateless, serving mainly to compose implementation
    details. Instances (operation objects) provide the Context-dependent interfaces
    for a specific node in a work graph.

    OperationDetails subclasses are created dynamically by function_wrapper and

    Developer note: when subclassing, note that the ResourceManager is responsible
    for managing Operation state. Do not add instance data members related to
    computation or output state.

    TODO: determine what is acceptable instance data and/or initialization information.
    Note that currently the subclass in function_wrapper has _no_ initialization input,
    but does not yet handle input-dependent output specification or node fingerprinting.
    It seems likely that instance initialization will require some characterization of
    supplied input, but nothing else. Even that much is not necessary if the instance
    is completely stateless, but that would require additional parameters to the member
    functions. However, an instance should be tied to a specific ResourceManager and
    Context, so weak references to these would be reasonable.

    def output_description(self) -> OutputCollectionDescription:
        """Mapping of available outputs and types for an existing Operation node."""

    def publishing_data_proxy(self, *,
                              instance: SourceResource[typing.Any, _PublishingDataProxyType],
                              client_id) -> _PublishingDataProxyType:
        """Factory for Operation output publishing resources.

        Used internally when the operation is run with resources provided by instance."""

    def output_data_proxy(self,
                          instance: SourceResource[_OutputDataProxyType, typing.Any]
                          ) -> _OutputDataProxyType:
        """Get an object that can provide Futures for output data managed by instance."""

    def __call__(self, resources: _Resources):
        """Execute the operation with provided resources.

        Resources are prepared in an execution context with aid of resource_director()

        After the first call, output data has been published and is trivially
        available through the output_data_proxy()

    def resource_director(cls, *, input, output: _PublishingDataProxyType) -> _Resources[_PublishingDataProxyType]:
        """a Director factory that helps build the Session Resources for the function.

        The Session launcher provides the director with all of the resources previously
        requested/negotiated/registered by the Operation. The director uses details of
        the operation to build the resources object required by the operation runner.

        For the Python Context, the protocol is for the Context to call the
        resource_director instance method, passing input and output containers.
        (See, for example, gmxapi.operation.PyFunctionRunnerResources)

    # TODO: Don't run the director. Just return the correct callable.
    def operation_director(cls, *args, context: 'Context', label=None, **kwargs) -> AbstractOperation:
        """Dispatching Director for adding a work node.

        A Director for input of a particular sort knows how to reconcile
        input with the requirements of the Operation and Context node builder.
        The Director (using a less flexible / more standard interface)
        builds the operation node using a node builder provided by the Context.

        This is essentially the creation method, instead of __init__, but the
        object is created and owned by the framework, and the caller receives
        an OperationHandle instead of a reference to an instance of cls.

        # TODO: We need a way to compose this functionality for arbitrary Contexts.
        # That likely requires traits on the Contexts, and registration of Context
        # implementations. It seems likely that an Operation will register Director
        # implementations on import, and dispatching will be moved to the Context
        # implementations, which can either find an appropriate OperationDirector
        # or raise a compatibility error. To avoid requirements on import order of
        # Operations and Context implementations, we can change this to a non-abstract
        # dispatching method, requiring registration in the global gmxapi.context
        # module, or get rid of this method and use something like pkg_resources
        # "entry point" groups for independent registration of Directors and Contexts,
        # each annotated with relevant traits. E.g.:
        if not isinstance(context, Context):
            raise exceptions.UsageError('Context instance needed for dispatch.')
        # TODO: use Context characteristics rather than isinstance checks.
        if isinstance(context, ModuleContext):
            construct = OperationDirector(*args, operation_details=cls, context=context, label=label, **kwargs)
            return construct()
        elif isinstance(context, SubgraphContext):
            construct = OperationDirector(*args, operation_details=cls, context=context, label=label, **kwargs)
            return construct()
            raise exceptions.ApiError('Cannot dispatch operation_director for context {}'.format(context))

# TODO: Implement observer pattern for edge->node data flow.
# Step 0: implement subject interface subscribe()
# Step 1: implement subject interface get_state()
# Step 2: implement observer interface update()
# Step 3: implement subject interface notify()
# Step 4: implement observer hook to support substantial change in source that
#         invalidates downstream fingerprinting, such as a new subgraph iteration.
# class Observer(object):
#     """Abstract base class for data observers."""
#     def rebind(self, edge: DataEdge):
#         """Recreate the Operation at the consuming end of the DataEdge."""

class Future(_Future):
    """gmxapi data handle.

    Future is currently more than a Future right now. (should be corrected / clarified.)
    Future is also a facade to other features of the data provider.

    Future objects are the most atomic interface between Contexts. User scripts
    may hold Futures from which they extract data with result(). Operation output
    used as input for another Operation can be decomposed such that the Operation
    factory has only Future objects in its input.

    TODO: ``subscribe`` method allows consumers to bind as Observers.

    TODO: extract the abstract class for input inspection?
    Currently abstraction is handled through SourceResource subclassing.

        description (ResultDescription): Describes the result to be obtained from this Future.


    def __init__(self, resource_manager: SourceResource, name: str, description: ResultDescription): = name
        if not isinstance(description, ResultDescription):
            raise exceptions.ValueError('Need description of requested data.')
        self.description = description  # type: ResultDescription
        self.resource_manager = resource_manager

        # Deprecated. We should not "reset" futures, but reconstitute them, but we
        # need to move the data model to a subscription-based system so that we can
        # make Futures properly immutable and issue new ones across subgraph iterations.
        self._number_of_resets = 0

    def __eq__(self, other):
        # This function is defined because we have defined __hash__().
        # Before customizing __eq__(), recall that Python objects that compare
        # equal should hash to the same value.
        # Please keep the two functions semantically correct.
        return object.__eq__(self, other)

    def __hash__(self):
        # We cannot properly determine equivalency beyond the scope of a ResourceManager instance
        # without more developed data flow fingerprinting.
        return hash((id(self.resource_manager),, self.description, self._number_of_resets))

    def __str__(self):
        return '<Future: name={}, description={}>'.format(, self.description)

    def result(self) -> ResultTypeVar:
        """Fetch data to the caller's Context.

        Returns an object of the concrete type specified according to
        the operation that produces this Result.

        Ensemble data are returned as a list. Scalar results or results from single
        member ensembles are returned as scalars.
        # Return ownership of concrete data
        handle = self.resource_manager.get(

        # For intuitive use in non-ensemble cases, we represent data as bare scalars
        # when possible. It is easier for users to cast scalars to lists of length 1
        # than to introspect their own code to determine if a list of length 1 is
        # part of an ensemble or not. The data model will become clearer as we
        # develop more robust handling of multidimensional data and data flow topologies.
        # In the future,
        # we may distinguish between data of shape () and shape (1,), but we will need
        # to be careful with semantics. We are already starting to adopt a rule-of-thumb
        # that data objects assume the minimum dimensionality necessary unless told
        # otherwise, and we could make that a hard rule if it doesn't make other things
        # too difficult.
        if self.description.width == 1:
        # TODO: Richer output behavior.
        # return self._Result(self)

    def _reset(self):
        """Mark the Future "not done" to allow reexecution.

        Invalidates cached results, resets "done" markers in data sources, and
        triggers _reset recursively.

        Note: this is a hack that is inconsistent with the plan of unique mappings
        of inputs to outputs, but allows a quick prototype for looping operations.
        self._number_of_resets += 1

    def dtype(self):
        return self.description.dtype

    def __getitem__(self, item):
        """Get a more limited view on the Future."""
        description = ResultDescription(dtype=self.dtype, width=self.description.width)
        # TODO: Use explicit typing when we have more thorough typing.
        description._dtype = None
        if self.description.width == 1:
            proxy = ProxyResourceManager(self,
                                         function=lambda value, key=item: value[key])
            proxy = ProxyResourceManager(self,
                                         function=lambda value, key=item:
                                         [subscriptable[key] for subscriptable in value])
        return proxy.future(, description=description)

class OutputDataDescriptor(ProxyDataDescriptor):
    """Read-only data descriptor for proxied access to output data.

    Knows how to get a Future from the resource manager.

    # TODO: Reconcile the internal implementation details with the visibility and
    #  usages of this class.

    def __get__(self, proxy: DataProxyBase, owner):
        if proxy is None:
            # Access through class attribute of owner class
            return self
        result_description = ResultDescription(dtype=self._dtype, width=proxy.ensemble_width)

        return proxy._resource_instance.future(name=self._name, description=result_description)

class MutableResourceDescriptor(ProxyDataDescriptor):
    """Accessor for rich binding interfaces.

    Allows operations to access resources beyond the scope of the current
    resource manager. Used by operations whose interactions are more complicated
    than standard typed data flow at the scope of the current Context.

    Instead of a Future interface, the returned object is a MutableResource with
    which a subscriber can collaborate with lower-level protocols.

    def __get__(self, proxy: DataProxyBase, owner) -> typing.Union[MutableResource, 'MutableResourceDescriptor']:
        if proxy is None:
            # Access through class attribute of owner class. We don't have a
            # specified use case for that, so allow inspection of the data
            # descriptor instance, itself.
            return self
        # TODO: implement.
        # The protocol for MD extension plugins requires that the simulation operation
        # subscribe to the plugin. Then the Context allows the plugin to access the
        # MdRunner interface as the simulation is launched.
        # The protocol for modify_input and for mdrun to consume the TPR payload
        # of read_tpr or modify_input should allow us to use the gmxapi 0.0.7
        # WorkSpec to configure and launch a simulation, which we can do by feeding
        # forward and building a fused operation at the mdrun node. The information
        # fed forward can just be references to the inputs and parameters of the
        # earlier operations, with annotations so that we know the intended behavior.

def define_output_data_proxy(output_description: OutputCollectionDescription) -> typing.Type[DataProxyBase]:
    descriptors = {name: OutputDataDescriptor(name, description) for name, description in output_description.items()}

    class OutputDataProxy(DataProxyBase, descriptors=descriptors):
        """Handler for read access to the `output` member of an operation handle.

        Acts as a sort of ResultCollection.

        A ResourceManager creates an OutputDataProxy instance at initialization to
        provide the ``output`` property of an operation handle.

    # Note: the OutputDataProxy has an inherent ensemble shape in the context
    # in which it is used, but that is an instance characteristic, not part of this type definition.
    # TODO: (FR5) The current tool does not support topology changing operations.
    return OutputDataProxy

# Encapsulate the description of the input data flow.
PyFuncInput = collections.namedtuple('Input', ('args', 'kwargs', 'dependencies'))

class SinkTerminal(object):
    """Operation input end of a data edge.

    In addition to the information in an InputCollectionDescription, includes
    topological information for the Operation node (ensemble width).

    Collaborations: Required for creation of a DataEdge. Created with knowledge
    of a DataSourceCollection instance and a InputCollectionDescription.

    # TODO: This clearly maps to a Builder pattern.
    # I think we want to get the sink terminal builder from a factory parameterized by InputCollectionDescription,
    # add data source collections, and then build the sink terminal for the data edge.
    def __init__(self, input_collection_description: InputCollectionDescription):
        """Define an appropriate data sink for a new operation node.

        Resolve data sources and input description to determine connectability,
        topology, and any necessary implicit data transformations.

        :param input_collection_description: Available inputs for Operation
        :return: Fully formed description of the Sink terminal for a data edge to be created.

        Collaborations: Execution Context implementation.
        self.ensemble_width = 1
        self.inputs = input_collection_description

    def __str__(self):
        return '<SinkTerminal: ensemble_width={}>'.format(self.ensemble_width)

    def update_width(self, width: int):
        if not isinstance(width, int):
                width = int(width)
            except TypeError:
                raise exceptions.TypeError('Need an integer width > 0.')
        if width < 1:
            raise exceptions.ValueError('Nonsensical ensemble width: {}'.format(int(width)))
        if self.ensemble_width != 1:
            if width != self.ensemble_width:
                raise exceptions.ValueError(
                    'Cannot change ensemble width {} to width {}.'.format(self.ensemble_width, width))
        self.ensemble_width = width

    def update(self, data_source_collection: DataSourceCollection):
        """Update the SinkTerminal with the proposed data provider."""
        for name, sink_dtype in self.inputs.items():
            if name not in data_source_collection:
                # If/when we accept data from multiple sources, we'll need some additional sanity checking.
                if not hasattr(self.inputs.signature.parameters[name], 'default'):
                    raise exceptions.UsageError('No data or default for {}'.format(name))
                # With a single data source, we need data to be in the source or have a default
                assert name in data_source_collection
                assert issubclass(sink_dtype, valid_result_types)
                source = data_source_collection[name]
                logger.debug('Updating Sink for source {}: {}.'.format(name, source))
                if isinstance(source, sink_dtype):
                    logger.debug('Source matches sink. No update necessary.')
                    if isinstance(source, and not isinstance(source, (
                            str, bytes,
                        assert isinstance(source, datamodel.NDArray)
                        if sink_dtype != datamodel.NDArray:
                            # Source is NDArray, but sink is not. Implicitly scatter.
                    if hasattr(source, 'description'):
                        source_description = typing.cast(ResultDescription, source.description)
                        source_dtype = source_description.dtype
                        assert isinstance(sink_dtype, type)
                        # TODO: Handle typing of Future slices when we have a better data model.
                        if source_dtype is not None:
                            assert isinstance(source_dtype, type)
                            if not issubclass(source_dtype, sink_dtype):
                                raise exceptions.TypeError('Expected {} but got {}.'.format(sink_dtype, source_dtype))
                        source_width = source.description.width

class DataEdge(object):
    """State and description of a data flow edge.

    A DataEdge connects a data source collection to a data sink. A sink is an
    input or collection of inputs of an operation (or fused operation). An operation's
    inputs may be fed from multiple data source collections, but an operation
    cannot be fully instantiated until all of its inputs are bound, so the DataEdge
    is instantiated at the same time the operation is instantiated because the
    required topology of a graph edge may be determined by the required topology
    of another graph edge.

    A data edge has a well-defined topology only when it is terminated by both
    a source and sink. Creation requires that a source collection is compared to
    a sink description.

    Calling code initiates edge creation by passing well-described data sources
    to an operation factory. The data sources may be annotated with explicit scatter
    or gather commands.

    The resource manager for the new operation determines the
    required shape of the sink to handle all of the offered input.

    and transformations of the data sources are then determined and the edge is

    At that point, the fingerprint of the input data at each operation
    becomes available to the resource manager for the operation. The fingerprint
    has sufficient information for the resource manager of the operation to
    request and receive data through the execution context.

    Instantiating operations and data edges implicitly involves collaboration with
    a Context instance. The state of a given Context or the availability of a
    default Context through a module function may affect the ability to instantiate
    an operation or edge. In other words, behavior may be different for connections
    being made in the scripting environment versus the running Session, and implementation
    details can determine whether or not new operations or data flow can occur in
    different code environments.

    class ConstantResolver(object):
        def __init__(self, value):
            self.value = value

        def __call__(self, member=None):
            return self.value

    def __init__(self, source_collection: DataSourceCollection, sink_terminal: SinkTerminal):
        # Adapters are callables that transform a source and node ID to local data.
        # Every key in the sink has an adapter.
        self.adapters = {}
        self.source_collection = source_collection
        self.sink_terminal = sink_terminal
        for name in sink_terminal.inputs:
            if name not in source_collection:
                if hasattr(sink_terminal.inputs[name], 'default'):
                    self.adapters[name] = self.ConstantResolver(sink_terminal.inputs[name])
                    # TODO: Initialize with multiple DataSourceCollections?
                    raise exceptions.ValueError('No source or default for required input "{}".'.format(name))
                source = source_collection[name]
                sink = sink_terminal.inputs[name]
                if isinstance(source, (str, bool, int, float, dict)):
                    if issubclass(sink, (str, bool, int, float, dict)):
                        self.adapters[name] = self.ConstantResolver(source)
                        assert issubclass(sink, datamodel.NDArray)
                        self.adapters[name] = self.ConstantResolver(datamodel.ndarray([source]))
                elif isinstance(source, datamodel.NDArray):
                    if issubclass(sink, datamodel.NDArray):
                        # TODO: shape checking
                        # Implicit broadcast may not be what is intended
                        self.adapters[name] = self.ConstantResolver(source)
                        if source.shape[0] != sink_terminal.ensemble_width:
                            raise exceptions.ValueError(
                                'Implicit broadcast could not match array source to ensemble sink')
                            self.adapters[name] = lambda member, source=source: source[member]
                elif hasattr(source, 'result'):
                    # Handle data futures...
                    # If the Future is part of an ensemble, result() will return a list.
                    # Otherwise, it will return a single object.
                    ensemble_width = source.description.width
                    # TODO: subscribe to futures so results can be pushed.
                    if ensemble_width == 1:
                        self.adapters[name] = lambda member, source=source: source.result()
                        self.adapters[name] = lambda member, source=source: source.result()[member]
                    assert isinstance(source, EnsembleDataSource)
                    self.adapters[name] = lambda member, source=source: source.node(member)

    def __str__(self):
        return '<DataEdge: source_collection={}, sink_terminal={}>'.format(self.source_collection, self.sink_terminal)

    def reset(self):

    def resolve(self, key: str, member: int):
        return self.adapters[key](member=member)

    def sink(self, node: int) -> dict:
        """Consume data for the specified sink terminal node.

        Run-time utility delivers data from the bound data source(s) for the
        specified terminal that was configured when the edge was created.

        Terminal node is identified by a member index number.

            A Python dictionary of the provided inputs as local data (not Future).
        results = {}
        sink_ports = self.sink_terminal.inputs
        for key in sink_ports:
            results[key] = self.resolve(key, node)
        return results

class ResourceManager(SourceResource[_OutputDataProxyType, _PublishingDataProxyType]):
    """Provides data publication and subscription services.

        Owns the data published by the operation implementation or served to consumers.
        Mediates read and write access to the managed data streams.

        This ResourceManager implementation is defined in conjunction with a
        run-time definition of an Operation that wraps a Python callable (function).
        ResourceManager is instantiated with a reference to the callable.

        When the Operation is run, the resource manager prepares resources for the wrapped
        function. Inputs provided to the Operation factory are provided to the
        function as keyword arguments. The wrapped function publishes its output
        through the (additional) ``output`` key word argument. This argument is
        a short-lived resource, prepared by the ResourceManager, with writable
        attributes named in the call to function_wrapper().

        After the Operation has run and the outputs published, the data managed
        by the ResourceManager is marked "done."


        The data() method produces a read-only collection of outputs named for
        the Operation when the Operation's ``output`` attribute is accessed.

        publishing_resources() can be called once during the ResourceManager lifetime
        to provide the ``output`` object for the wrapped function. (Used by update_output().)

        update_output() brings the managed output data up-to-date with the input
        when the Operation results are needed. If the Operation has not run, an
        execution session is prepared with input and output arguments for the
        wrapped Python callable. Output is publishable only during this session.

    TODO: This functionality should evolve to be a facet of Context implementations.
     There should be no more than one ResourceManager instance per work graph
     node in a Context. This will soon be at odds with letting the ResourceManager
     be owned by an operation instance handle.
    TODO: The publisher and data objects can be more strongly defined through
     interaction between the Context and clients.

    Design notes:

    The normative pattern for updating data is to execute a node in the work
    graph, passing Resources for an execution Session to an operation runner.
    The resources and runner are dependent on the implementation details of
    the operation and the execution context, so logical execution may look
    like the following.

        resource_builder = ResourcesBuilder()
        runner_builder = RunnerBuilder()
        input_resource_director = input_resource_factory.director(input)
        output_resource_director = publishing_resource_factory.director(output)
        input_resource_director(resource_builder, runner_builder)
        output_resource_director(resource_builder, runner_builder)
        resources =
        runner =

    Only the final line is intended to be literal. The preceding code, if it
    exists in entirety, may be spread across several code comments.

    TODO: Data should be pushed, not pulled.
    Early implementations executed operation code and extracted results directly.
    While we need to be able to "wait for" results and alert the data provider that
    we are ready for input, we want to defer execution management and data flow to
    the framework.

    def __publishing_context(self, ensemble_member=0) -> typing.Iterator[_PublishingDataProxyType]:
        """Get a context manager for resolving the data dependencies of this node.

        The returned object is a Python context manager (used to open a `with` block)
        to define the scope in which the operation's output can be published.
        'output' type resources can be published exactly once, and only while the
        publishing context is active. (See operation.function_wrapper())

        Used internally to implement ResourceManager.publishing_resources()

        Responsibilities of the context manager are to:
            * (TODO) Make sure dependencies are resolved.
            * Make sure outputs are marked 'done' when leaving the context.


        # TODO:
        # if self._data.done():
        #     raise exceptions.ProtocolError('Resources have already been published.')

        # I don't think we want the OperationDetails to need to know about ensemble data,
        # (though the should probably be allowed to), so we may need a separate interface
        # for the resource manager with built-in scope-limiting to a single ensemble member.
        # Right now, one Operation handle owns one ResourceManager (which takes care of
        # the ensemble details), which owns one OperationDetails (which has no ensemble knowledge).
        # It is the responsibility of the calling code to make sure the PublishingDataProxy
        # gets used correctly.

        # ref:
            if not self._done[ensemble_member]:
                resource = self.__publishing_data_proxy(instance=weakref.proxy(self),
                yield resource
        except Exception as e:
            message = 'Uncaught {} while providing output-publishing resources for {}.'
            message.format(repr(e), self.operation_id)
            raise exceptions.ApiError(message) from e
            logger.debug('Published output for {} member {}'.format(self.operation_id, ensemble_member))
            self._done[ensemble_member] = True

    def __init__(self, *,
                 source: DataEdge,
                 output_description: OutputCollectionDescription,
                 output_data_proxy: typing.Type[_OutputDataProxyType],
                 publishing_data_proxy: typing.Type[_PublishingDataProxyType],
                 output_context: 'Context'):
        """Initialize a resource manager for the inputs and outputs of an operation.
        # Note: This implementation assumes there is one ResourceManager instance per data source,
        # so we only stash the inputs and dependency information for a single set of resources.
        # TODO: validate input_fingerprint as its interface becomes clear.
        self._input_edge = source
        self.ensemble_width = self._input_edge.sink_terminal.ensemble_width

        # Node UID.
        self.operation_id = operation_id

        if isinstance(output_context, Context):
            self._output_context = output_context
            message = 'Provide an instance of gmxapi.operation.Context for output_context'
            raise exceptions.UsageError(message)
        assert self._output_context is not None

        self._output_data_proxy = output_data_proxy
        assert self._output_data_proxy is not None
        assert callable(self._output_data_proxy)

        self._output_description = output_description
        assert self._output_description is not None

        self.__publishing_data_proxy = publishing_data_proxy
        assert self.__publishing_data_proxy is not None
        assert callable(self.__publishing_data_proxy)

        self._runner_director = runner_director
        assert self._runner_director is not None
        self._resource_factory = resource_factory
        assert self._resource_factory is not None

        self._data = _make_datastore(output_description=self._output_description,

        # We store a rereference to the publishing context manager implementation
        # in a data structure that can only produce one per Python interpreter
        # (using list.pop()).
        # TODO: reimplement as a data descriptor
        #  so that PublishingDataProxy does not need a bound circular reference.
        self.__publishing_resources = [self.__publishing_context]

        self._done = [False] * self.ensemble_width
        self.__operation_entrance_counter = 0

    def width(self) -> int:
        return self.ensemble_width

    def reset(self):
        self.__operation_entrance_counter = 0
        self._done = [False] * self.ensemble_width
        self.__publishing_resources = [self.__publishing_context]
        for data in self._data.values():
        assert self.__operation_entrance_counter == 0

    def done(self, member=None):
        if member is None:
            return all(self._done)
            return self._done[member]

    def set_result(self, name, value, member: int):
        if not isinstance(value, (str, bytes)):
                for item in value:
                    # In this specification, it is antithetical to publish Futures.
                    if hasattr(item, 'result'):
                        raise exceptions.ApiError('Operation produced Future instead of real output.')
            except TypeError:
                # Ignore when `item` is not iterable.
        self._data[name].set(value=value, member=member)

    def is_done(self, name):
        return self._data[name].done

    def get(self, name: str):

        Raises exceptions.ProtocolError if requested data is not local yet.
        Raises exceptions.ValueError if data is requested for an unknown name.
        if name not in self._data:
            raise exceptions.ValueError('Request for unknown data.')
        if not self.is_done(name):
            raise exceptions.ProtocolError('Data not ready.')
        assert isinstance(self._data[name], OutputData)
        return self._data[name]

    # TODO: Normalize. This is the no-argument, no-return callable member of an
    #  operation handle that dispatches to another Context (the operation implementation).
    # TODO: Allow update of a single ensemble member. As written, this always updates all ensemble members. That can
    #  be the default behavior, but we don't want to require non-local updates in all cases.
    def update_output(self):
        """Bring the output of the bound operation up to date.

        Execute the bound operation once if and only if it has not
        yet been run in the lifetime of this resource manager.

        Used internally to implement Futures for the local operation
        associated with this resource manager.

        TODO: We need a different implementation for an operation whose output
         is served by multiple resource managers. E.g. an operation whose output
         is available across the ensemble, but which should only be executed on
         a single ensemble member.
        # This code is not intended to be reentrant. We make a modest attempt to
        # catch unexpected reentrance, but this is not (yet) intended to be a thread-safe
        # resource manager implementation.
        # TODO: Handle checking just the ensemble members this resource manager is responsible for.
        # TODO: Replace with a managed observer pattern. Update once when input is available in the Context.
        if not self.done():
            # Note: This check could also be encapsulated in a run_once decorator that
            # could even set a data descriptor to change behavior.
            self.__operation_entrance_counter += 1
            if self.__operation_entrance_counter > 1:
                raise exceptions.ProtocolError('Bug detected: resource manager tried to execute operation twice.')
            if not self.done():
                # Note! This is a detail of the ResourceManager in a SerialContext
                # TODO: rewrite with the pattern that this block is directing and then resolving an operation in the
                #  operation's library/implementation context.
                publishing_resources = self.publishing_resources()
                for i in range(self.ensemble_width):
                    # TODO: rewrite the following expression as a call to a resource factory.
                    # TODO: Consider whether the resource_factory behavior should be normalized
                    #  to always use `with` blocks to indicate the lifetime of a resource handle.
                    #  That implies that an operation handle can expire, but the operation handle
                    #  could be "yield"ed
                    #  from within the `with` block to keep the resource scope alive until the resulting
                    #  generator is exhausted. Not sure what that looks like or what the use case would be.
                    with self.local_input(i) as input:
                        # Note: Resources are marked "done" by the publishing system
                        # before the following context manager finishes exiting.
                        with publishing_resources(ensemble_member=i) as output:
                            # self._runner(*input.args, output=output, **input.kwargs)
                            # Here we can make _runner a thing that accepts session resources, and
                            # is created by specializable builders. Separate out the expression of
                            # inputs.
                            # resource_builder = OperationDetails.ResourcesBuilder(context)
                            # runner_builder = OperationDetails.RunnerBuilder(context)
                            # input_resource_director = self._input_resource_factory.director(input)
                            # output_resource_director = self._publishing_resource_factory.director(output)
                            # input_resource_director(resource_builder, runner_builder)
                            # output_resource_director(resource_builder, runner_builder)
                            # resources =
                            # runner =
                            # runner(resources)
                            # This resource factory signature might need to be inverted or broken up
                            # into a builder for consistency. I.e.
                            # option 1: Make the input and output resources with separate factories and add_resource on
                            # the runner builder.
                            # option 2: Pass resource_builder to input_director and then output_director.
                            resources = self._resource_factory(input=input, output=output)
                            runner = self._runner_director(resources)
            if not self.done():
                raise exceptions.ApiError('update_output implementation failed to update all outputs.')

    def future(self, name: str, description: ResultDescription):
        """Retrieve a Future for a named output.

        Provide a description of the expected result to check for compatibility or
        implicit topological conversion.

        TODO: (FR5+) Normalize this part of the interface between operation definitions and
         resource managers.
        if not isinstance(name, str) or name not in self._data:
            raise exceptions.ValueError('"name" argument must name an output.')
        assert description is not None
        requested_dtype = description.dtype
        available_dtype = self._data[name]._description.dtype
        if requested_dtype != available_dtype:
            # TODO: framework to check for implicit conversions
            message = 'Requested Future of type {} is not compatible with available type {}.'
            message = message.format(requested_dtype, available_dtype)
            raise exceptions.ApiError(message)
        return Future(self, name, description)

    def data(self) -> _OutputDataProxyType:
        """Get an adapter to the output resources to access results."""
        return self._output_data_proxy(self)

    def local_input(self, member: int = None):
        """In an API session, get a handle to fully resolved locally available input data.

        Execution dependencies are resolved on creation of the context manager. Input data
        becomes available in the ``as`` object when entering the context manager, which
        becomes invalid after exiting the context manager. Resources allocated to hold the
        input data may be released when exiting the context manager.

        It is left as an implementation detail whether the context manager is reusable and
        under what circumstances one may be obtained.
        # Localize data
        kwargs = self._input_edge.sink(node=member)
        assert 'input' not in kwargs

        # Check that we have real data
        for key, value in kwargs.items():
            assert not hasattr(value, 'result')
            assert not hasattr(value, 'run')
            value_list = []
            if isinstance(value, list):
                value_list = value
            if isinstance(value, datamodel.NDArray):
                value_list = value._values
            if isinstance(value,
                value_list = value.values()
            assert not isinstance(value_list, Future)
            assert not hasattr(value_list, 'result')
            assert not hasattr(value_list, 'run')
            for item in value_list:
                assert not hasattr(item, 'result')

        input_pack = InputPack(kwargs=kwargs)

        # Prepare input data structure
        # Note: we use 'yield' instead of 'return' for the protocol expected by
        # the @contextmanager decorator
        yield input_pack

    def publishing_resources(self):
        """Get a context manager for resolving the data dependencies of this node.

        Use the returned object as a Python context manager.
        'output' type resources can be published exactly once, and only while the
        publishing context is active.

        Write access to publishing resources can be granted exactly once during the
        resource manager lifetime and conveys exclusive access.
        return self.__publishing_resources.pop()

class PyFunctionRunnerResources(collections.UserDict):
    """Runtime resources for Python functions.

    Produced by a ResourceDirector for a particular Operation.

    def output(self):
        if 'output' in self:
            return self['output']
            return None

    def input(self):
        return {key: value for key, value in self.items() if key != 'output'}

class PyFunctionRunner(abc.ABC):
    def __init__(self, *, function: typing.Callable, output_description: OutputCollectionDescription):
        assert callable(function)
        self.function = function
        self.output_description = output_description

    def __call__(self, resources: PyFunctionRunnerResources):
        self.function(output=resources.output(), **resources.input())

class CapturedOutputRunner(PyFunctionRunner):
    """Function runner that captures return value as"""

    def __call__(self, resources: PyFunctionRunnerResources):
        resources['output'].data = self.function(**resources.input())

class OutputParameterRunner(PyFunctionRunner):
    """Function runner that uses output parameter to let function publish output."""

    def __call__(self, resources: PyFunctionRunnerResources):

def wrapped_function_runner(function, output_description: OutputCollectionDescription = None) -> PyFunctionRunner:
    """Get an adapter for a function to be wrapped.

    If the function does not accept a publishing data proxy as an `output`
    key word argument, the returned object has a `capture_output` attribute that
    must be re-assigned by the calling code before calling the runner. `capture_output`
    must be assigned to be a callable that will receive the output of the wrapped

        Callable with a signature `__call__(*args, **kwargs)` and no return value

        OperationDetails.resource_director assigns the `capture_output` member of the returned object.
    assert callable(function)
    signature = inspect.signature(function)

    # Implementation note: this function dispatches an implementation with the
    # logic below. A better factoring would be a "chain of responsibility" in
    # which the concrete Runners would be tried in sequence and determine internally
    # whether to create a runner, raise an error, or defer.

    # Determine output details for proper dispatching.
    # First check for signature with output parameter.
    # TODO FR4: standardize typing
    if 'output' in signature.parameters:
        if not isinstance(output_description, OutputCollectionDescription):
            if not isinstance(output_description,
                raise exceptions.UsageError(
                    'Function passes output through call argument, but output is not described.')
            return OutputParameterRunner(
            return OutputParameterRunner(function=function,
    # Next try output_description parameter or function return annotation.
        if isinstance(output_description, OutputCollectionDescription):
            return_type = output_description['data'].gmxapi_datatype
        elif output_description is not None:
            # output_description should be None for inferred output or
            # a singular mapping of the key 'data' to a gmxapi type.
            if not isinstance(output_description, \
                    or set(output_description.keys()) != {'data'}:
                raise exceptions.ApiError(
                    'invalid output description for wrapped function: {}'.format(output_description))
            if signature.return_annotation != signature.empty:
                if signature.return_annotation != output_description['data']:
                    raise exceptions.ApiError(
                        'Wrapped function with return-value-capture provided with non-matching output description.')
            return_type = output_description['data']
            # Use return type inferred from function signature.
            return_type = signature.return_annotation
        if return_type == signature.empty or return_type is None:
            raise exceptions.ApiError('No return annotation or output_description for {}'.format(function))
        return CapturedOutputRunner(function=function,

# TODO: Refactor in terms of reference to a node in a Context.
#  ResourceManager is an implementation detail of how the Context
#  manages a node.
class OperationHandle(AbstractOperation[_OutputDataProxyType]):
    """Generic Operation handle for dynamically defined operations.

    Define a gmxapi Operation for the functionality being wrapped by the enclosing code.

    An Operation type definition encapsulates description of allowed inputs
    of an Operation. An Operation instance represents a node in a work graph
    with uniquely fingerprinted inputs and well-defined output. The implementation
    of the operation is a collaboration with the resource managers resolving
    data flow for output Futures, which may depend on the execution context.

    def __init__(self, resource_manager: SourceResource[_OutputDataProxyType, typing.Any]):
        """Initialization defines the unique input requirements of a work graph node.

        Initialization parameters map to the parameters of the wrapped function with
        addition(s) to support gmxapi data flow and deferred execution.

        If provided, an ``input`` keyword argument is interpreted as a parameter pack
        of base input. Inputs also present as standalone keyword arguments override
        values in ``input``.

        Inputs that are handles to gmxapi operations or outputs induce data flow
        dependencies that the framework promises to satisfy before the Operation
        executes and produces output.
        # TODO: When the resource manager can be kept alive by an enclosing or
        #  module-level Context, convert to a weakref.
        self.__resource_manager = resource_manager
        # The unique identifier for the operation node allows the Context implementation
        # to manage the state of the handle. Reproducibility of node_uid is TBD, but
        # it must be unique in a Context where it references a different operation node.
        self.node_uid = None

    def output(self) -> _OutputDataProxyType:
        # TODO: We can configure `output` as a data descriptor
        #  instead of a property so that we can get more information
        #  from the class attribute before creating an instance of OperationDetails.OutputDataProxy.
        # The C++ equivalence would probably be a templated free function for examining traits.

    def run(self):
        """Make a single attempt to resolve data flow conditions.

        This is a public method, but should not need to be called by users. Instead,
        just use the `output` data proxy for result handles, or force data flow to be
        resolved with the `result` methods on the result handles.

        `run()` may be useful to try to trigger computation (such as for remotely
        dispatched work) without retrieving results locally right away.

        `run()` is also useful internally as a facade to the Context implementation details
        that allow `result()` calls to ask for their data dependencies to be resolved.
        Typically, `run()` will cause results to be published to subscribing operations as
        they are calculated, so the `run()` hook allows execution dependency to be slightly
        decoupled from data dependency, as well as to allow some optimizations or to allow
        data flow to be resolved opportunistically. `result()` should not call `run()`
        directly, but should cause the resource manager / Context implementation to process
        the data flow graph.

        In one conception, `run()` can have a return value that supports control flow
        by itself being either runnable or not. The idea would be to support
        fault tolerance, implementations that require multiple iterations / triggers
        to complete, or looping operations.
        # Note: `run()` is a synonym for `resolve` or `update` or whatever we choose
        #  to generically describe the request to bring a node up-to-date: i.e. the
        #  non-returning callable on the object produced by a director.

class OperationPlaceholder(AbstractOperation):
    """Placeholder for Operation handle during subgraph definition."""

    def __init__(self, subgraph_resource_manager):

    def run(self):
        raise exceptions.UsageError('This placeholder operation handle is not in an executable context.')

    def output(self):
        """Allow subgraph components to be connected without instantiating actual operations."""
        if not isinstance(current_context(), SubgraphContext):
            raise exceptions.UsageError('Invalid access to subgraph internals.')

_HandleType = typing.TypeVar('_HandleType',

class NodeBuilder(
    """Add an operation node to be managed by a Context.

    The NodeBuilder interface implies minimal internal logic, and instead
    specifies the set of information that must or may be provided to construct
    a node.

    def __init__(self,
                 context: 'Context',
                 label: typing.Optional[str] = None):
        """Initialize the base NodeBuilder for gmxapi.operation module Nodes.

            Convert the *operation* argument to be the registrant in the Operation registry.
            Requires confirmation of conforming behavior for dynamically defined operations.

        self.context = context
        self.label = label
            key = _make_registry_key(operation)
        except Exception as e:
            error = 'Could not create an operation registry key from {}'.format(operation)
            raise exceptions.ValueError(error) from e
            # TODO: sensibly handle dynamically defined operations.
            if key not in _operation_registry and not issubclass(operation, OperationDetailsBase):
                error = '{} must be initialized with a registered operation. Got {}.'
                raise exceptions.ValueError(error.format(__class__.__qualname__, operation))
        self.sources = DataSourceCollection()
        self._input_description = None
        self._resource_factory = None
        self._runner_director = None
        self._handle = None
        self._output_factory = None

        self._resource_manager = ResourceManager

    def set_input_description(self, input_description: InputDescription):
        self._input_description = input_description

    def set_resource_factory(self, factory):
        self._resource_factory = factory

    def set_runner_director(self, factory):
        self._runner_director = factory

    def set_handle(self, factory):
        self._handle = factory

    def set_output_factory(self, factory: 'OutputFactory'):
        self._output_factory = factory

    def set_resource_manager(self, implementation: typing.Type[ResourceManager]):
        """Allow overriding the default ResourceManager implementation.

        This is a workaround until we figure out what parts of the ResourceManager
        could be composed, registered separately with the Context, or be customized
        through other dispatching. One likely part of the solution is for clients
        of the NodeBuilder to assert requirements of the Context.
        assert issubclass(implementation, ResourceManager)
        self._resource_manager = implementation

    # TODO: Let the Context use the handle factory to produce a dynamically typed handle,
    #  and figure out the right way to annotate the return value.
    def build(self):
        """Create node and return a handle of the appropriate type."""

        # Check for the ability to instantiate operations.
        missing_details = list()
        for builder_resource in ['input_description',
            detail = '_' + builder_resource
            if getattr(self, detail, None) is None:
        if len(missing_details) > 0:
            raise exceptions.UsageError(
                'Missing details needed for operation node: {}'.format(
                    ', '.join(missing_details)

        assert hasattr(self._input_description, 'signature')
        input_sink = SinkTerminal(self._input_description.signature())
        logger.debug('SinkTerminal configured: {}'.format(SinkTerminal))
        edge = DataEdge(self.sources, input_sink)
        logger.debug('Created data edge {} with Sink {}'.format(edge, edge.sink_terminal))
        # TODO: Fingerprinting: Each operation instance has unique output based on the unique input.
        #            input_data_fingerprint = edge.fingerprint()

        # Set up output proxy.
        assert hasattr(self._input_description, 'make_uid')
        uid = self._input_description.make_uid(edge)
        # TODO: ResourceManager should fetch the relevant factories from the Context
        #  instead of getting an OperationDetails instance.
        output_data_proxy = self._output_factory.output_proxy()
        output_description = self._output_factory.output_description()
        publishing_data_proxy = self._output_factory.publishing_data_proxy()
        manager = self._resource_manager(output_context=self.context,
        self.context.work_graph[uid] = manager
        # TODO: Replace with a call to Node.handle()
        handle = self._handle(self.context.work_graph[uid])
        handle.node_uid = uid
        return handle

    def add_input(self, name, source):
        # TODO: We can move some input checking here as the data model matures.
        self.sources[name] = source

class InputPack(object):
    """Input container for data sources provided to resource factories.

    When gmxapi.operation Contexts provide run time inputs to operations,
    instances of this class are provided to the operation's registered
    Resource factory.

        kwargs (dict): collection of named data sources.

    def __init__(self, kwargs: typing.Mapping[str, SourceTypeVar]):
        self.kwargs = kwargs

class Context(
    """API Context.

    All gmxapi data and operations are owned by a Context instance. The Context
    manages the details of how work is run and how data is managed.

    Context implementations are not required to inherit from gmxapi.context.Context,
    but this class definition serves to specify the current Context API.

    If subclassing is used to implement new Contexts, be sure to initialize the
    base class when providing a new __init__

    def node(self, node_id) -> Node:
        if node_id in self.labels:
            return self.labels[node_id]
        elif node_id in self.work_graph:
            return self.work_graph[node_id]
            raise exceptions.ValueError('Could not find a node identified by {}'.format(node_id))

    def __init__(self):
        self.operations = dict()
        self.labels = dict()
        self.work_graph = collections.OrderedDict()

    def node_builder(self, *, operation,
                     label: typing.Optional[str] = None) -> NodeBuilder:
        """Get a builder for a new work graph node.

        Nodes are elements of computational work, with resources and execution
        managed by the Context. The Context handles parallelism resources, data
        placement, work scheduling, and data flow / execution dependencies.

        This method is used by Operation director code and helper functions to
        add work to the graph.

            operation: a registered gmxapi operation
            label: optional user-provided identifier to provide human-readable node locators.

    # TODO: *node()* accessor.
    # @abc.abstractmethod
    # def node(self, node_identifier) -> AbstractOperation:
    #     ...

class ModuleNodeBuilder(NodeBuilder):
    """Builder for work nodes in gmxapi.operation.ModuleContext."""

class ModuleContext(Context):
    """Context implementation for the gmxapi.operation module.

    __version__ = 0

    def node_builder(self, operation, label=None) -> NodeBuilder:
        """Get a builder for a new work node to add an operation in this context."""
        if label is not None:
            if label in self.labels:
                raise exceptions.ValueError('Label {} is already in use.'.format(label))
                # The builder should update the labeled node when it is done.
                self.labels[label] = None

        return ModuleNodeBuilder(context=weakref.proxy(self), operation=operation, label=label)

# Context stack.
__current_context = [ModuleContext()]

def current_context() -> Context:
    """Get a reference to the currently active Context.

    The imported gmxapi.context module maintains some state for the convenience
    of the scripting environment. Internally, all gmxapi activity occurs under
    the management of an API Context, explicitly or implicitly. Some actions or
    code constructs will generate separate contexts or sub-contexts. This utility
    command retrieves a reference to the currently active Context.
    return __current_context[-1]

def push_context(context) -> Context:
    """Enter a sub-context by pushing a context to the global context stack.
    return current_context()

def pop_context() -> Context:
    """Exit the current Context by popping it from the stack."""
    return __current_context.pop()

class OutputFactory(object):
    """Encapsulate the details of Operation output implementation in the gmxapi.operation Context.

    Currently, OutputFactory objects are containers that compose functionality
    with which to implement the required internal interface.

    def __init__(self, *,
                 output_proxy: typing.Callable[[SourceResource], _OutputDataProxyType],
                 output_description: OutputCollectionDescription,
                 publishing_data_proxy: typing.Callable[[SourceResource, ClientID], _PublishingDataProxyType]):
        """Package the output details for an operation.

            output_proxy: factory to produce the *output* facet of an operation instance (node)
            output_description: fully formed output description
            publishing_data_proxy: factory to produce the run time output publishing resources

        if not callable(output_proxy):
            raise exceptions.ValueError('output_proxy argument must be a callable.')
        if not callable(publishing_data_proxy):
            raise exceptions.ValueError('publishing_data_proxy argument must be a callable.')
        if not isinstance(output_description, OutputCollectionDescription):
            raise exceptions.ValueError('output_description must be an instance of '
        self._output_proxy = output_proxy
        self._output_description = output_description
        self._publishing_data_proxy = publishing_data_proxy

    def output_proxy(self) -> typing.Callable[[_OutputDataProxyType, SourceResource], _OutputDataProxyType]:
        return self._output_proxy

    def output_description(self) -> OutputCollectionDescription:
        return self._output_description

    def publishing_data_proxy(self) -> typing.Callable[[SourceResource, ClientID],
        return self._publishing_data_proxy

# TODO: Refactor in terms of[_Op, gmx.operation.Context]
# Normalizing this OperationDirector may require other updates to the function_wrapper facilities.
class OperationDirector(object):
    """Direct the construction of an operation node in the gmxapi.operation module Context.

    Collaboration: used by OperationDetails.operation_director, which
    will likely dispatch to different implementations depending on
    requirements of work or context.

    def __init__(self,
                 operation_details: typing.Type[OperationDetailsBase],
                 context: Context,
        self.operation_details = operation_details
        self.context = weakref.proxy(context)
        self.args = args
        self.kwargs = kwargs
        self.label = label

    def __call__(self) -> AbstractOperation:
        builder = self.context.node_builder(operation=self.operation_details, label=self.label)


        operation_details = self.operation_details()
        node_input_factory = operation_details.signature().bind
        data_source_collection = node_input_factory(*self.args, **self.kwargs)
        for name, source in data_source_collection.items():
            builder.add_input(name, source)

        def runner_director(resources):
            def runner():
            return runner

        # Note that the call-backs held by OutputFactory cannot be annotated with
        # key word arguments under PEP 484, producing a weak warning in some cases.
        # We can consider more in the future how to balance call-back simplicity,
        # type annotation, and key word explicitness in helper functions like these.
        output_factory = OutputFactory(output_description=operation_details.output_description(),

        handle =
        return handle

def _make_datastore(output_description: OutputCollectionDescription, ensemble_width: int):
    """Create the data store for an operation with the described output.

    Create a container to hold the resources for an operation node.
    Used internally by the resource manager when setting up the node.
    Evolution of the C++ framework for creating the Operation SessionResources
    object will inform the future of this and the resource_director method, but
    this data store is how the Context manages output data sources for resources
    that it manages.

    datastore = collections.OrderedDict()
    for name, dtype in output_description.items():
        assert isinstance(dtype, type)
        result_description = ResultDescription(dtype=dtype, width=ensemble_width)
        datastore[name] = OutputData(name=name, description=result_description)
    return datastore

# TODO: For outputs, distinguish between "results" and "events".
#  Both are published to the resource manager in the same way, but the relationship
#  with subscribers is potentially different.
[docs]def function_wrapper(output: dict = None): # Suppress warnings in the example code. # noinspection PyUnresolvedReferences """Generate a decorator for wrapped functions with signature manipulation. New function accepts the same arguments, with additional arguments required by the API. The new function returns an object with an `output` attribute containing the named outputs. Example: >>> @function_wrapper(output={'spam': str, 'foo': str}) ... def myfunc(parameter: str = None, output=None): ... output.spam = parameter ... = parameter + ' ' + parameter ... >>> operation1 = myfunc(parameter='spam spam') >>> assert operation1.output.spam.result() == 'spam spam' >>> assert == 'spam spam spam spam' If 'output' is provided to the wrapper, a data structure will be passed to the wrapped functions with the named attributes so that the function can easily publish multiple named results. Otherwise, the `output` of the generated operation will just capture the return value of the wrapped function. .. todo:: gmxapi typing stub file(s). The way this wrapper uses parameter annotations is not completely compatible with static type checking (PEP 484). If we decide to keep the convenience functionality by which operation details are inferred from parameter annotations, we should provide a separate stub file (.pyi) to support static type checking of the API. """ if output is not None and not isinstance(output, raise exceptions.TypeError('If provided, `output` argument must be a mapping of data names to types.') # TODO: (FR5+) gmxapi operations need to allow a context-dependent way to generate an implementation with input. # This function wrapper reproduces the wrapped function's kwargs, but does not allow chaining a # dynamic `input` kwarg and does not dispatch according to a `context` kwarg. We should allow # a default implementation and registration of alternate implementations. We don't have to do that # with functools.singledispatch, but we could, if we add yet another layer to generate a wrapper # that takes the context as the first argument. (`singledispatch` inspects the first argument rather # that a named argument) # Implementation note: The closure of the current function is used to # dynamically define several classes that support the operation to be # created by the returned decorator. def decorator(function) -> typing.Callable: # Explicitly capture `function` and `output` references. provided_output_map = output # Note: Allow operations to be defined entirely in template headers to facilitate # compile-time optimization of fused operations. Consider what distinction, if any, # exists between a fused operation and a more basic operation. Probably it amounts # to aspects related to interaction with the Context that get combined in a fused # operation, such as the resource director, builder, etc. class OperationDetails(OperationDetailsBase): # Warning: function.__qualname__ is not rigorous since function may be in a local scope. # TODO: Improve base identifier. # Suggest registering directly in the Context instead of in this local class definition. __basename = '.'.join((str(function.__module__), function.__qualname__)) __last_uid = 0 _input_signature_description = InputCollectionDescription.from_function(function) # TODO: Separate the class and instance logic for the runner. # Logically, the runner is a detail of a context-specific implementation class, # though the output is not generally fully knowable until an instance is initialized # for a certain input fingerprint. # Note: We are almost at a point where this class can be subsumed into two # possible return types for wrapped_function_runner, acting as an operation helper. _runner = wrapped_function_runner(function, provided_output_map) _output_description = _runner.output_description _output_data_proxy_type = define_output_data_proxy(_output_description) _publishing_data_proxy_type = define_publishing_data_proxy(_output_description) _SourceResource = SourceResource[_output_data_proxy_type, _publishing_data_proxy_type] @classmethod def name(cls) -> str: return cls.__basename.split('.')[-1] @classmethod def namespace(cls) -> str: return cls.__basename.rstrip('.' + @classmethod def director(cls, context: _Context): return cls.operation_director @classmethod def signature(cls) -> InputCollectionDescription: """Mapping of named inputs and input type. Used to determine valid inputs before an Operation node is created. Overrides OperationDetailsBase.signature() to provide an implementation for the bound operation. """ return cls._input_signature_description def output_description(self) -> OutputCollectionDescription: """Mapping of available outputs and types for an existing Operation node. Overrides OperationDetailsBase.output_description() to provide an implementation for the bound operation. """ return self._output_description def publishing_data_proxy(self, *, instance: _SourceResource, client_id: int ) -> _publishing_data_proxy_type: """Factory for Operation output publishing resources. Used internally when the operation is run with resources provided by instance. Overrides OperationDetailsBase.publishing_data_proxy() to provide an implementation for the bound operation. """ assert isinstance(instance, ResourceManager) return self._publishing_data_proxy_type(instance=instance, client_id=client_id) def output_data_proxy(self, instance: _SourceResource) -> _output_data_proxy_type: assert isinstance(instance, ResourceManager) return self._output_data_proxy_type(instance=instance) def __call__(self, resources: PyFunctionRunnerResources): """Execute the operation with provided resources. Resources are prepared in an execution context with aid of resource_director() After the first call, output data has been published and is trivially available through the output_data_proxy() Overrides OperationDetailsBase.__call__(). """ self._runner(resources) @classmethod def make_uid(cls, input): """The unique identity of an operation node tags the output with respect to the input. Combines information on the Operation details and the input to uniquely identify the Operation node. Arguments: input : A (collection of) data source(s) that can provide Fingerprints. Used internally by the Context to manage ownership of data sources, to locate resources for nodes in work graphs, and to manage serialization, deserialization, and checkpointing of the work graph. The UID is a detail of the generic Operation that _should_ be independent of the Context details to allow the framework to manage when and where an operation is executed. Design notes on further refinement: TODO: Operations should not single-handedly determine their own uniqueness (but they should participate in the determination with the Context). Context implementations should be allowed to optimize handling of equivalent operations in different sessions or work graphs, but we do not yet TODO: guarantee that UIDs are globally unique! The UID should uniquely indicate an operation node based on that node's input. We need input fingerprinting to identify equivalent nodes in a work graph or distinguish nodes across work graphs. """ uid = str(cls.__basename) + str(cls.__last_uid) cls.__last_uid += 1 return uid @classmethod def resource_director(cls, *, input=None, output: _publishing_data_proxy_type = None) -> PyFunctionRunnerResources: """a Director factory that helps build the Session Resources for the function. The Session launcher provides the director with all of the resources previously requested/negotiated/registered by the Operation. The director uses details of the operation to build the resources object required by the operation runner. For the Python Context, the protocol is for the Context to call the resource_director instance method, passing input and output containers. """ resources = PyFunctionRunnerResources() resources.update(input.kwargs) resources.update({'output': output}) # TODO: Remove this hack when we can better handle Futures of Containers and Future slicing. for name in resources: if isinstance(resources[name], (list, tuple)): resources[name] = datamodel.ndarray(resources[name]) # Check data compatibility for name, value in resources.items(): if name != 'output': expected = cls.signature()[name] got = type(value) if got != expected: raise exceptions.TypeError('Expected {} but got {}.'.format(expected, got)) return resources # TODO: (FR4) Update annotations with gmxapi data types. E.g. return -> Future. @functools.wraps(function) def helper(*args, context=None, **kwargs): # Description of the Operation input (and output) occurs in the # decorator closure. By the time this factory is (dynamically) defined, # the OperationDetails and ResourceManager are well defined, but not # yet instantiated. # Inspection of the offered input occurs when this factory is called, # and OperationDetails, ResourceManager, and Operation are instantiated. # This operation factory is specialized for the default package Context. if context is None: context = current_context() else: raise exceptions.ApiError('Non-default context handling not implemented.') # This calls a dispatching function that may not be able to reconcile the input # and Context capabilities. This is the place to handle various exceptions for # whatever reasons this reconciliation cannot occur. handle = OperationDetails.operation_director(*args, context=context, label=None, **kwargs) # TODO: NOW: The input fingerprint describes the provided input # as (a) ensemble input, (b) static, (c) future. By the time the # operation is instantiated, the topology of the node is known. # When compared to the InputCollectionDescription, the data compatibility # can be determined. return handle # to do: The factory itself needs to be able to register a factory with # the context that will be responsible for the Operation handle. # The factories need to be able to serve as dispatchers for themselves, # since an operation in one context may need to be reconstituted in a # different context. # The dispatching factory produces a director for a Context, # which will register a factory with the operation in that context. # The factory function has a DirectorFactory. Director instances talk to a NodeBuilder for a Context to # get handles to new operation nodes managed by the context. Part of that process includes registering # a DirectorFactory with the Context. return helper return decorator
class GraphVariableDescriptor(object): def __init__(self, name: str = None, dtype=None, default=None): = name self.dtype = dtype self.default = default self.state = None @property def internal_name(self): try: return '_' + except TypeError: return None def __get__(self, instance, owner): if instance is None: # Access is through the class attribute of the owning class. # Allows the descriptor itself to be inspected or reconfigured after # class definition. # TODO: There is probably some usage checking that can be performed here. return self try: value = getattr(instance, self.internal_name) except AttributeError: value = self.default # Lazily initialize the instance value from the class default. if value is not None: try: setattr(instance, self.internal_name, value) except Exception as e: message = 'Could not assign default value to {} attribute of {}'.format( self.internal_name, instance) raise exceptions.ApiError(message) from e return value # Implementation note: If we have a version of the descriptor class with no `__set__` method, # it is a non-data descriptor that can be overridden by a data descriptor on an instance. # This could be one way to handle the polymorphism associated with state changes. def __set__(self, instance, value): if instance._editing: # Update the internal connections defining the subgraph. setattr(instance, self.internal_name, value) else: raise AttributeError('{} not assignable on {}'.format(, instance)) class GraphMeta(type): """Meta-class for gmxapi data flow graphs and subgraphs. Used to implement ``subgraph`` as GraphMeta.__new__(...). Also allows subgraphs to be defined as Python class definitions by inheriting from Subgraph or by using the ``metaclass=GraphMeta`` hint in the class statement arguments. The Python class creation protocol allows Subgraphs to be defined in as follows. See the Subgraph class documentation for customization of instances through the Python context manager protocol. """ _prepare_keywords = ('variables',) # TODO: Python 3.7.2 introduces typing.OrderedDict # In practice, we are using collections.OrderedDict, but we should use the generic # ABC from the typing module to avoid being overly restrictive with type hints. try: from typing import OrderedDict except ImportError: from collections import OrderedDict @classmethod def __prepare__(mcs, name, bases, variables: OrderedDict = None, **kwargs): """Prepare the class namespace. Keyword Args: variables: mapping of persistent graph variables to type / default value (optional) """ # Python runs this before executing the class body of Subgraph or its # subclasses. This is our chance to handle key word arguments given in the # class declaration. if kwargs is not None: for keyword in kwargs: raise exceptions.UsageError('Unexpected key word argument: {}'.format(keyword)) namespace = collections.OrderedDict() if variables is not None: if isinstance(variables, for name, value in variables.items(): if isinstance(value, type): dtype = value if hasattr(value, 'default'): default = value.default else: default = None else: default = value if hasattr(default, 'dtype'): dtype = default.dtype else: dtype = type(default) namespace[name] = GraphVariableDescriptor(name, default=default, dtype=dtype) # Note: we are not currently using the hook used by `inspect` # to annotate with the class that defined the attribute. # namespace[name].__objclass__ = mcs assert not hasattr(namespace[name], '__objclass__') else: raise exceptions.ValueError('"variables" must be a mapping of graph variables to types or defaults.') return namespace def __new__(cls, name, bases, namespace, **kwargs): for key in kwargs: if key not in GraphMeta._prepare_keywords: raise exceptions.ApiError('Unexpected class creation keyword: {}'.format(key)) return type.__new__(cls, name, bases, namespace) # TODO: This is keyword argument stripping is not necessary in more recent Python versions. # When Python minimum required version is increased, check if we can remove this. def __init__(cls, name, bases, namespace, **kwargs): for key in kwargs: if key not in GraphMeta._prepare_keywords: raise exceptions.ApiError('Unexpected class initialization keyword: {}'.format(key)) super().__init__(name, bases, namespace) class SubgraphNodeBuilder(NodeBuilder): def __init__(self, context: 'SubgraphContext', operation, label: typing.Optional[str] = None): super().__init__(context, operation, label) def add_input(self, name: str, source): """Add an input resource for the Node under construction. Extends NodeBuilder.add_input() """ # Inspect inputs. # * Are they from outside the subgraph? # * Subgraph variables? # * Subgraph internal nodes? # Inputs from outside the subgraph are (provisionally) subgraph inputs. # Inputs that are subgraph variables or subgraph internal nodes mark operations that will need to be re-run. # For first implementation, let all operations be recreated, but we need to # manage the right input proxies. # For zeroeth implementation, try just tracking the entities that need a reset() method called. assert isinstance(self.context, SubgraphContext) if hasattr(source, 'reset'): self.context.add_resetter(source.reset) elif hasattr(source, '_reset'): self.context.add_resetter(source._reset) super().add_input(name, source) def build(self) -> OperationPlaceholder: """Get a reference to the internal node in the subgraph definition. In the SubgraphContext, these handles cannot represent uniquely identifiable results. They are placeholders for relative positions in graphs that are not defined until the subgraph is being executed. Such references should be tracked and invalidated when exiting the subgraph context. Within the subgraph context, they are used to establish the recipe for updating the subgraph's outputs or persistent data during execution. """ # Placeholder handles in the subgraph definition don't have real resource managers. # Check for the ability to instantiate operations. handle = super().build() # handle = OperationPlaceholder() return typing.cast(OperationPlaceholder, handle) class SubgraphContext(Context): """Provide a Python context manager in which to set up a graph of operations. Allows operations to be configured without adding nodes to the global execution context. """ def __init__(self): super().__init__() self.resetters = set() def node_builder(self, operation, label=None) -> NodeBuilder: if label is not None: if label in self.labels: raise exceptions.ValueError('Label {} is already in use.'.format(label)) else: # The builder should update the labeled node when it is done. self.labels[label] = None return SubgraphNodeBuilder(context=weakref.proxy(self), operation=operation, label=label) def add_resetter(self, function): assert callable(function) self.resetters.add(function) class Subgraph(object, metaclass=GraphMeta): """ When subclassing from Subgraph, aspects of the subgraph's data ports can be specified with keyword arguments in the class statement. Example:: >>> class MySubgraph(Subgraph, variables={'int_with_default': 1, 'boolData': bool}): pass ... The key word *variables* is used in the class declaration to map the types of subgraph Variables with (optional) default values. Execution model: Subgraph execution must follow a well-defined protocol in order to sensibly resolve data Futures at predictable points. Note that subgraphs act as operations that can be automatically redefined at run time (in limited cases), such as to automatically form iteration chains to implement "while" loops. We refer to one copy / iteration / generation of a subgraph as an "element" below. When a subgraph element begins execution, each of its variables with an "updated" state from the previous iteration has the "updated" state moved to the new element's "initial" state, and the "updated" state is voided. appends an element of the subgraph to the chain. Subsequent calls to bring the new outputs up to date (and then call next()). Thus, for a subgraph with an output called ``is_converged``, calling ``while (not subgraph.is_converged):`` has the desired effect, but is likely suboptimal for execution. Instead use the gmxapi while_loop. If the subgraph is not currently executing, it can be in one of two states: "editing" or "ready". In the "ready" state, class and instance Variables cannot be assigned, but can be read through the data descriptors. In this state, the descriptors only have a single state. If "editing," variables on the class object can be assigned to update the data flow defined for the subgraph. While "editing", reading or writing instance Variables is undefined behavior. When "editing" begins, each variable is readable as a proxy to the "initial" state in an element. Assignments while "editing" put variables in temporary states accessible only during editing. When "editing" finishes, the "updated" output data source of each variable for the element is set, if appropriate. # TODO: Use a get_context to allow operation factories or accessors to mark # references for update/annotation when exiting the 'with' block. """ class SubgraphBuilder(object): """Helper for defining new Subgraphs. Manages a Python context in which to define a new Subgraph type. Can be used in a Python ``with`` construct exactly once to provide the Subgraph class body. When the ``with`` block is exited (or ``build()`` is called explicitly), a new type instance becomes available. Subsequent calls to SubgraphBuilder.__call__(self, ...) are dispatched to the Subgraph constructor. Outside of the ``with`` block, read access to data members is proxied to descriptors on the built Subgraph. Instances of SubgraphBuilder are returned by the ``subgraph()`` utility function. """ def __init__(self, variables): self.__dict__.update({'variables': variables, '_staging': collections.OrderedDict(), '_editing': False, '_subgraph_context': None, '_subgraph_instance': None, '_fused_operation': None, '_factory': None}) # Return a placeholder that we can update during iteration. # Long term, this is probably implemented with data descriptors # that will be moved to a new Subgraph type object. for name in self.variables: if not isinstance(self.variables[name], Future): self.variables[name] = gmx.make_constant(self.variables[name]) # class MySubgraph(Subgraph, variables=variables): # pass # # self._subgraph_instance = MySubgraph() def __getattr__(self, item): if self._editing: if item in self.variables: if item in self._staging: logger.debug('Read access to intermediate value of subgraph variable {}'.format(item)) return self._staging[item] else: logger.debug('Read access to subgraph variable {}'.format(item)) return self.variables[item] else: raise AttributeError('Invalid attribute: {}'.format(item)) else: # TODO: this is not quite the described interface... return lambda obj: obj.values[item] def __setattr__(self, key, value): """Part of the builder interface.""" if key in self.__dict__: self.__dict__[key] = value else: if self._editing: self.add_update(key, value) else: raise exceptions.UsageError('Subgraph is not in an editable state.') def add_update(self, key, value): """Add a variable update to the internal subgraph.""" if key not in self.variables: raise AttributeError('No such attribute: {}'.format(key)) if not self._editing: raise exceptions.UsageError('Subgraph is not in an editable state.') # Else, stage the potential final value for the iteration. logger.debug('Staging subgraph update {} = {}'.format(key, value)) # Return a placeholder that we can update during iteration. # Long term, this is probably implemented with data descriptors # that will be moved to a new Subgraph type object. if not isinstance(value, Future): value = gmx.make_constant(value) self._staging[key] = value self._staging.move_to_end(key) def __enter__(self): """Enter a Context managed by the subgraph to capture operation additions. Allows the internal data flow of the subgraph to be defined in the same manner as the default work graph while the Python context manager is active. The subgraph Context is activated when entering a ``with`` block and finalized at the end of the block. """ # While editing the subgraph in the SubgraphContext, we need __get__ and __set__ # data descriptor access on the Subgraph subclass type, but outside of the # context manager, the descriptor should be non-writable and more opaque, # while the instance should be readable, but not writable. self.__dict__['_editing'] = True # TODO: this probably needs to be configured with variables... self.__dict__['_subgraph_context'] = SubgraphContext() push_context(self._subgraph_context) return self def build(self): """Build the subgraph by defining some new operations. Examine the subgraph variables. Variables with handles to data sources in the SubgraphContext represent work that needs to be executed on a subgraph execution or iteration. Variables with handles to data sources outside of the subgraph represent work that needs to be executed once on only the first iteration to initialize the subgraph. Construct a factory for the fused operation that performs the work to update the variables on a single iteration. Construct a factory for the fused operation that performs the work to update the variables on subsequent iterations and which will be fed the outputs of a previous iteration. Both of the generated operations have the same output signature. Construct and wrap the generator function to recursively append work to the graph and update until condition is satisfied. TODO: Explore how to drop work from the graph once there are no more references to its output, including check-point machinery. """ logger.debug('Finalizing subgraph definition.') inputs = collections.OrderedDict() for key, value in self.variables.items(): # TODO: What if we don't want to provide default values? inputs[key] = value updates = self._staging class Subgraph(object): def __init__(self, input_futures, update_sources): self.values = collections.OrderedDict([(key, value.result()) for key, value in input_futures.items()]) logger.debug('subgraph initialized with {}'.format( ', '.join(['{}: {}'.format(key, value) for key, value in self.values.items()]))) self.futures = collections.OrderedDict([(key, value) for key, value in input_futures.items()]) self.update_sources = collections.OrderedDict([(key, value) for key, value in update_sources.items()]) logger.debug('Subgraph updates staged:') for update, source in self.update_sources.items(): logger.debug(' {} = {}'.format(update, source)) def run(self): for name in self.update_sources: result = self.update_sources[name].result() logger.debug('Update: {} = {}'.format(name, result)) self.values[name] = result # Replace the data sources in the futures. for name in self.update_sources: self.futures[name].resource_manager = gmx.make_constant(self.values[name]).resource_manager for name in self.update_sources: self.update_sources[name]._reset() subgraph = Subgraph(inputs, updates) return lambda subgraph=subgraph: subgraph def __exit__(self, exc_type, exc_val, exc_tb): """End the Subgraph editing session and finalize the Subgraph build. After exiting, this instance forwards __call__() to a factory for an operation that carries out the work in the subgraph with inputs bound in the current context as defined by ``variables``. """ self._factory = context = pop_context() assert context is self._subgraph_context self.__dict__['_editing'] = False # Returning False causes exceptions in the `with` block to be reraised. # Remember to switch this to return True if we want to transform or suppress # such an exception (we probably do). if exc_type is not None: logger.error('Got exception {} while editing subgraph {}.'.format(exc_val, self)) logger.debug('Subgraph exception traceback: \n{}'.format(exc_tb)) return False def __call__(self): # TODO: After build() has been called, this should dispatch to a factory # that returns an OperationHandle. return self._factory()
[docs]def while_loop(*, operation, condition, max_iteration=10): """Generate and run a chain of operations such that condition evaluates True. Returns and operation instance that acts like a single node in the current work graph, but which is a proxy to the operation at the end of a dynamically generated chain of operations. At run time, condition is evaluated for the last element in the current chain. If condition evaluates False, the chain is extended and the next element is executed. When condition evaluates True, the object returned by ``while_loop`` becomes a proxy for the last element in the chain. Equivalent to calling operation.while(condition), where available. Arguments: operation: a callable that produces an instance of an operation when called with no arguments. condition: a callable that accepts an object (returned by ``operation``) that returns a boolean. max_iteration: execute the loop no more than this many times (default 10) Warning: *max_iteration* is provided in part to minimize the cost of bugs in early versions of this software. The default value may be changed or removed on short notice. Warning: The protocol by which ``while_loop`` interacts with ``operation`` and ``condition`` is very unstable right now. Please refer to this documentation when installing new versions of the package. Protocol: Warning: This protocol will be changed before the 0.1 API is finalized. When called, ``while_loop`` calls ``operation`` without arguments and captures the return value captured as ``_operation``. The object produced by ``operation()`` must have a ``reset``, a ``run`` method, and an ``output`` attribute. This is inspected to determine the output data proxy for the operation produced by the call to ``while_loop``. When that operation is called, it does the equivalent of while(condition(self._operation)): self._operation.reset() Then, the output data proxy of ``self`` is updated with the results from self._operation.output. """ # In the first implementation, Subgraph is NOT and OperationHandle. # if not isinstance(obj, AbstractOperationHandle): # raise exceptions.UsageError( # '"operation" key word argument must be a callable that produces an Operation handle.') # outputs = {} # for name, descriptor in obj.output.items(): # outputs[name] = descriptor._dtype # 1. Get the initial inputs. # 2. Initialize the subgraph with the initial inputs. # 3. Run the subgraph. # 4. Get the outputs. # 5. Initialize the subgraph with the outputs. # 6. Go to 3 if condition is not met. obj = operation() assert hasattr(obj, 'values') outputs = collections.OrderedDict([(key, type(value)) for key, value in obj.values.items()]) @function_wrapper(output=outputs) def run_loop(output: OutputCollectionDescription): iteration = 0 obj = operation() logger.debug('Created object {}'.format(obj)) logger.debug(', '.join(['{}: {}'.format(key, obj.values[key]) for key in obj.values])) logger.debug('Condition: {}'.format(condition(obj))) while (condition(obj)): logger.debug('Running iteration {}'.format(iteration)) logger.debug( ', '.join(['{}: {}'.format(key, obj.values[key]) for key in obj.values])) logger.debug('Condition: {}'.format(condition(obj))) iteration += 1 if iteration > max_iteration: break for name in outputs: setattr(output, name, obj.values[name]) return obj return run_loop
[docs]def subgraph(variables=None): """Allow operations to be configured in a sub-context. The object returned functions as a Python context manager. When entering the context manager (the beginning of the ``with`` block), the object has an attribute for each of the named ``variables``. Reading from these variables gets a proxy for the initial value or its update from a previous loop iteration. At the end of the ``with`` block, any values or data flows assigned to these attributes become the output for an iteration. After leaving the ``with`` block, the variables are no longer assignable, but can be called as bound methods to get the current value of a variable. When the object is run, operations bound to the variables are ``reset`` and run to update the variables. """ # Implementation note: # A Subgraph (type) has a subgraph context associated with it. The subgraph's # ability to capture operation additions is implemented in terms of the # subgraph context. logger.debug('Declare a new subgraph with variables {}'.format(variables)) return SubgraphBuilder(variables)
[docs]@computed_result def join_arrays(*, front: datamodel.NDArray = (), back: datamodel.NDArray = ()) -> datamodel.NDArray: """Operation that consumes two sequences and produces a concatenated single sequence. Note that the exact signature of the operation is not determined until this helper is called. Helper functions may dispatch to factories for different operations based on the inputs. In this case, the dtype and shape of the inputs determines dtype and shape of the output. An operation instance must have strongly typed output, but the input must be strongly typed on an object definition so that a Context can make runtime decisions about dispatching work and data before instantiating. # TODO: elaborate and clarify. # TODO: check type and shape. # TODO: figure out a better annotation. """ # TODO: (FR4) Returned list should be an NDArray. if isinstance(front, (str, bytes)) or isinstance(back, (str, bytes)): raise exceptions.ValueError('Input must be a pair of lists.') assert isinstance(front, datamodel.NDArray) assert isinstance(back, datamodel.NDArray) new_list = list(front._values) new_list.extend(back._values) return datamodel.NDArray(new_list)
# TODO: Constrain Scalar = typing.TypeVar('Scalar')
[docs]def concatenate_lists(sublists: list = ()) -> _Future[gmx.datamodel.NDArray]: """Combine data sources into a single list. A trivial data flow restructuring operation. """ if isinstance(sublists, (str, bytes)): raise exceptions.ValueError('Input must be a list of lists.') if len(sublists) == 0: return datamodel.ndarray([]) else: # TODO: Fix the data model so that this can type-check properly. return join_arrays(front=sublists[0], back=typing.cast(datamodel.NDArray, concatenate_lists(sublists[1:])))
[docs]def make_constant(value: Scalar) -> _Future: """Provide a predetermined value at run time. This is a trivial operation that provides a (typed) value, primarily for internally use to manage gmxapi data flow. Accepts a value of any type. The object returned has a definite type and provides same interface as other gmxapi outputs. Additional constraints or guarantees on data type may appear in future versions. """ dtype = type(value) source = StaticSourceManager(name='data', proxied_data=value, width=1, function=lambda x: x) description = ResultDescription(dtype=dtype, width=1) future = Future(source, 'data', description=description) return future
[docs]def logical_not(value: bool) -> _Future: """Boolean negation. If the argument is a gmxapi compatible Data or Future object, a new View or Future is created that proxies the boolean opposite of the input. If the argument is a callable, logical_not returns a wrapper function that returns a Future for the logical opposite of the callable's result. """ # TODO: Small data transformations like this don't need to be formal Operations. # This could be essentially a data annotation that affects the resolver in a # DataEdge. As an API detail, coding for different Contexts and optimizations # within those Context implementations could be simplified. operation = function_wrapper(output={'data': bool})(lambda data=bool(): not bool(data)) return operation(data=value)