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schedula.utils.asy.executors
__init__
null
def __init__(self, *args, **state): super(ProcessExecutor, self).__init__() import threading self.lock = threading.Lock() for k, v in state.items(): setattr(self, k, v)
(self, *args, **state)
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24,646
schedula.utils.asy.executors
__reduce__
null
def __reduce__(self): return self.__class__, (), { '_init': self._init, '_submit': self._submit, '_shutdown': self._shutdown, '_init_args': self._init_args, '_init_kwargs': self._init_kwargs }
(self)
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24,647
schedula.utils.asy.executors
_set_future
null
def _set_future(self, fut, res): self.tasks.pop(fut) if 'err' in res: _safe_set_exception(fut, res['err']) else: _safe_set_result(fut, res['res']) return fut
(self, fut, res)
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24,648
schedula.utils.asy.executors
_submit
null
def _submit(self, func, args, kwargs): # noinspection PyUnresolvedReferences from multiprocess import get_context ctx = get_context() fut, (c0, c1) = Future(), ctx.Pipe(duplex=False) self.tasks[fut] = task = ctx.Process( target=self._target, args=(c1.send, func, args, kwargs) ) task.start() return self._set_future(fut, c0.recv())
(self, func, args, kwargs)
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24,649
schedula.utils.asy.executors
_target
null
@staticmethod def _target(send, func, args, kwargs): try: obj = {'res': func(*args, **kwargs)} except BaseException as ex: obj = {'err': ex} if send: send(obj) else: return obj
(send, func, args, kwargs)
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24,650
schedula.utils.asy.executors
init
null
def init(self): if self._init: with self.lock: self._init()
(self)
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24,651
schedula.utils.asy.executors
shutdown
null
def shutdown(self, wait=True): tasks = super(ProcessExecutor, self).shutdown(wait) if self._shutdown: with self.lock: self._shutdown() return tasks
(self, wait=True)
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24,652
schedula.utils.asy.executors
submit
null
def submit(self, func, *args, **kwargs): self.init() return self._submit(func, args, kwargs)
(self, func, *args, **kwargs)
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24,653
schedula.utils.asy.executors
ProcessPoolExecutor
Process Pool Executor
class ProcessPoolExecutor(ProcessExecutor): """Process Pool Executor""" def _init(self): if getattr(self, 'pool', None) is None: # noinspection PyUnresolvedReferences from multiprocess import get_context ctx = get_context() self.pool = ctx.Pool(*self._init_args, **self._init_kwargs) def _submit(self, func, args, kwargs): fut = Future() callback = functools.partial(_safe_set_result, fut) error_callback = functools.partial(_safe_set_exception, fut) self.tasks[fut] = self.pool.apply_async( func, args, kwargs, callback, error_callback ) fut.add_done_callback(self.tasks.pop) return fut def _shutdown(self): if getattr(self, 'pool', None): self.pool.terminate() self.pool.join()
(*args, **state)
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24,656
schedula.utils.asy.executors
_init
null
def _init(self): if getattr(self, 'pool', None) is None: # noinspection PyUnresolvedReferences from multiprocess import get_context ctx = get_context() self.pool = ctx.Pool(*self._init_args, **self._init_kwargs)
(self)
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24,658
schedula.utils.asy.executors
_shutdown
null
def _shutdown(self): if getattr(self, 'pool', None): self.pool.terminate() self.pool.join()
(self)
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24,659
schedula.utils.asy.executors
_submit
null
def _submit(self, func, args, kwargs): fut = Future() callback = functools.partial(_safe_set_result, fut) error_callback = functools.partial(_safe_set_exception, fut) self.tasks[fut] = self.pool.apply_async( func, args, kwargs, callback, error_callback ) fut.add_done_callback(self.tasks.pop) return fut
(self, func, args, kwargs)
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24,664
schedula.utils.exc
SkipNode
null
class SkipNode(BaseException): def __init__(self, *args, ex=None, **kwargs): # noinspection PyArgumentList super(SkipNode, self).__init__(*args, **kwargs) self.ex = ex
(*args, ex=None, **kwargs)
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24,665
schedula.utils.exc
__init__
null
def __init__(self, *args, ex=None, **kwargs): # noinspection PyArgumentList super(SkipNode, self).__init__(*args, **kwargs) self.ex = ex
(self, *args, ex=None, **kwargs)
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24,666
schedula.utils.dsp
SubDispatch
It dispatches a given :class:`~schedula.dispatcher.Dispatcher` like a function. This function takes a sequence of dictionaries as input that will be combined before the dispatching. :return: A function that executes the dispatch of the given :class:`~schedula.dispatcher.Dispatcher`. :rtype: callable .. seealso:: :func:`~schedula.dispatcher.Dispatcher.dispatch`, :func:`combine_dicts` Example: .. dispatcher:: dsp :opt: graph_attr={'ratio': '1'}, depth=-1 :code: >>> from schedula import Dispatcher >>> sub_dsp = Dispatcher(name='Sub-dispatcher') ... >>> def fun(a): ... return a + 1, a - 1 ... >>> sub_dsp.add_function('fun', fun, ['a'], ['b', 'c']) 'fun' >>> dispatch = SubDispatch(sub_dsp, ['a', 'b', 'c'], output_type='dict') >>> dsp = Dispatcher(name='Dispatcher') >>> dsp.add_function('Sub-dispatch', dispatch, ['d'], ['e']) 'Sub-dispatch' The Dispatcher output is: .. dispatcher:: o :opt: graph_attr={'ratio': '1'}, depth=-1 :code: >>> o = dsp.dispatch(inputs={'d': {'a': 3}}) while, the Sub-dispatch is: .. dispatcher:: sol :opt: graph_attr={'ratio': '1'}, depth=-1 :code: >>> sol = o.workflow.nodes['Sub-dispatch']['solution'] >>> sol Solution([('a', 3), ('b', 4), ('c', 2)]) >>> sol == o['e'] True
class SubDispatch(Base): """ It dispatches a given :class:`~schedula.dispatcher.Dispatcher` like a function. This function takes a sequence of dictionaries as input that will be combined before the dispatching. :return: A function that executes the dispatch of the given :class:`~schedula.dispatcher.Dispatcher`. :rtype: callable .. seealso:: :func:`~schedula.dispatcher.Dispatcher.dispatch`, :func:`combine_dicts` Example: .. dispatcher:: dsp :opt: graph_attr={'ratio': '1'}, depth=-1 :code: >>> from schedula import Dispatcher >>> sub_dsp = Dispatcher(name='Sub-dispatcher') ... >>> def fun(a): ... return a + 1, a - 1 ... >>> sub_dsp.add_function('fun', fun, ['a'], ['b', 'c']) 'fun' >>> dispatch = SubDispatch(sub_dsp, ['a', 'b', 'c'], output_type='dict') >>> dsp = Dispatcher(name='Dispatcher') >>> dsp.add_function('Sub-dispatch', dispatch, ['d'], ['e']) 'Sub-dispatch' The Dispatcher output is: .. dispatcher:: o :opt: graph_attr={'ratio': '1'}, depth=-1 :code: >>> o = dsp.dispatch(inputs={'d': {'a': 3}}) while, the Sub-dispatch is: .. dispatcher:: sol :opt: graph_attr={'ratio': '1'}, depth=-1 :code: >>> sol = o.workflow.nodes['Sub-dispatch']['solution'] >>> sol Solution([('a', 3), ('b', 4), ('c', 2)]) >>> sol == o['e'] True """ def __new__(cls, dsp=None, *args, **kwargs): from .blue import Blueprint if isinstance(dsp, Blueprint): return Blueprint(dsp, *args, **kwargs)._set_cls(cls) return super(SubDispatch, cls).__new__(cls) def __getstate__(self): state = self.__dict__.copy() state['solution'] = state['solution'].__class__(state['dsp']) del state['__name__'] return state def __setstate__(self, d): self.__dict__ = d self.__name__ = self.name def __init__(self, dsp, outputs=None, inputs_dist=None, wildcard=False, no_call=False, shrink=False, rm_unused_nds=False, output_type='all', function_id=None, output_type_kw=None): """ Initializes the Sub-dispatch. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher | schedula.utils.blue.BlueDispatcher :param outputs: Ending data nodes. :type outputs: list[str], iterable :param inputs_dist: Initial distances of input data nodes. :type inputs_dist: dict[str, int | float], optional :param wildcard: If True, when the data node is used as input and target in the ArciDispatch algorithm, the input value will be used as input for the connected functions, but not as output. :type wildcard: bool, optional :param no_call: If True data node estimation function is not used. :type no_call: bool, optional :param shrink: If True the dispatcher is shrink before the dispatch. :type shrink: bool, optional :param rm_unused_nds: If True unused function and sub-dispatcher nodes are removed from workflow. :type rm_unused_nds: bool, optional :param output_type: Type of function output: + 'all': a dictionary with all dispatch outputs. + 'list': a list with all outputs listed in `outputs`. + 'dict': a dictionary with any outputs listed in `outputs`. :type output_type: str, optional :param output_type_kw: Extra kwargs to pass to the `selector` function. :type output_type_kw: dict, optional :param function_id: Function name. :type function_id: str, optional """ self.dsp = dsp self.outputs = outputs self.wildcard = wildcard self.no_call = no_call self.shrink = shrink self.output_type = output_type self.output_type_kw = output_type_kw or {} self.inputs_dist = inputs_dist self.rm_unused_nds = rm_unused_nds self.name = self.__name__ = function_id or dsp.name self.__doc__ = dsp.__doc__ self.solution = dsp.solution.__class__(dsp) def blue(self, memo=None, depth=-1): """ Constructs a Blueprint out of the current object. :param memo: A dictionary to cache Blueprints. :type memo: dict[T,schedula.utils.blue.Blueprint] :param depth: Depth of sub-dispatch blue. If negative all levels are bluprinted. :type depth: int, optional :return: A Blueprint of the current object. :rtype: schedula.utils.blue.Blueprint """ if depth == 0: return self depth -= 1 memo = {} if memo is None else memo if self not in memo: import inspect from .blue import Blueprint, _parent_blue keys = tuple(inspect.signature(self.__init__).parameters) memo[self] = Blueprint(**{ k: _parent_blue(v, memo, depth) for k, v in self.__dict__.items() if k in keys })._set_cls(self.__class__) return memo[self] def __call__(self, *input_dicts, copy_input_dicts=False, _stopper=None, _executor=False, _sol_name=(), _verbose=False): # Combine input dictionaries. i = combine_dicts(*input_dicts, copy=copy_input_dicts) # Dispatch the function calls. self.solution = self.dsp.dispatch( i, self.outputs, self.inputs_dist, self.wildcard, self.no_call, self.shrink, self.rm_unused_nds, stopper=_stopper, executor=_executor, sol_name=_sol_name, verbose=_verbose ) return self._return(self.solution) def _return(self, solution): outs = self.outputs solution.result() solution.parent = self # Set output. if self.output_type != 'all': try: # Save outputs. return selector( outs, solution, output_type=self.output_type, **self.output_type_kw ) except KeyError: # Outputs not reached. missed = {k for k in outs if k not in solution} # Raise error msg = '\n Unreachable output-targets: {}\n Available ' \ 'outputs: {}'.format(missed, list(solution.keys())) raise DispatcherError(msg, sol=solution) return solution # Return outputs. def copy(self): return _copy.deepcopy(self)
(dsp=None, *args, **kwargs)
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24,667
schedula.utils.dsp
__call__
null
def __call__(self, *input_dicts, copy_input_dicts=False, _stopper=None, _executor=False, _sol_name=(), _verbose=False): # Combine input dictionaries. i = combine_dicts(*input_dicts, copy=copy_input_dicts) # Dispatch the function calls. self.solution = self.dsp.dispatch( i, self.outputs, self.inputs_dist, self.wildcard, self.no_call, self.shrink, self.rm_unused_nds, stopper=_stopper, executor=_executor, sol_name=_sol_name, verbose=_verbose ) return self._return(self.solution)
(self, *input_dicts, copy_input_dicts=False, _stopper=None, _executor=False, _sol_name=(), _verbose=False)
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24,670
schedula.utils.dsp
__init__
Initializes the Sub-dispatch. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher | schedula.utils.blue.BlueDispatcher :param outputs: Ending data nodes. :type outputs: list[str], iterable :param inputs_dist: Initial distances of input data nodes. :type inputs_dist: dict[str, int | float], optional :param wildcard: If True, when the data node is used as input and target in the ArciDispatch algorithm, the input value will be used as input for the connected functions, but not as output. :type wildcard: bool, optional :param no_call: If True data node estimation function is not used. :type no_call: bool, optional :param shrink: If True the dispatcher is shrink before the dispatch. :type shrink: bool, optional :param rm_unused_nds: If True unused function and sub-dispatcher nodes are removed from workflow. :type rm_unused_nds: bool, optional :param output_type: Type of function output: + 'all': a dictionary with all dispatch outputs. + 'list': a list with all outputs listed in `outputs`. + 'dict': a dictionary with any outputs listed in `outputs`. :type output_type: str, optional :param output_type_kw: Extra kwargs to pass to the `selector` function. :type output_type_kw: dict, optional :param function_id: Function name. :type function_id: str, optional
def __init__(self, dsp, outputs=None, inputs_dist=None, wildcard=False, no_call=False, shrink=False, rm_unused_nds=False, output_type='all', function_id=None, output_type_kw=None): """ Initializes the Sub-dispatch. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher | schedula.utils.blue.BlueDispatcher :param outputs: Ending data nodes. :type outputs: list[str], iterable :param inputs_dist: Initial distances of input data nodes. :type inputs_dist: dict[str, int | float], optional :param wildcard: If True, when the data node is used as input and target in the ArciDispatch algorithm, the input value will be used as input for the connected functions, but not as output. :type wildcard: bool, optional :param no_call: If True data node estimation function is not used. :type no_call: bool, optional :param shrink: If True the dispatcher is shrink before the dispatch. :type shrink: bool, optional :param rm_unused_nds: If True unused function and sub-dispatcher nodes are removed from workflow. :type rm_unused_nds: bool, optional :param output_type: Type of function output: + 'all': a dictionary with all dispatch outputs. + 'list': a list with all outputs listed in `outputs`. + 'dict': a dictionary with any outputs listed in `outputs`. :type output_type: str, optional :param output_type_kw: Extra kwargs to pass to the `selector` function. :type output_type_kw: dict, optional :param function_id: Function name. :type function_id: str, optional """ self.dsp = dsp self.outputs = outputs self.wildcard = wildcard self.no_call = no_call self.shrink = shrink self.output_type = output_type self.output_type_kw = output_type_kw or {} self.inputs_dist = inputs_dist self.rm_unused_nds = rm_unused_nds self.name = self.__name__ = function_id or dsp.name self.__doc__ = dsp.__doc__ self.solution = dsp.solution.__class__(dsp)
(self, dsp, outputs=None, inputs_dist=None, wildcard=False, no_call=False, shrink=False, rm_unused_nds=False, output_type='all', function_id=None, output_type_kw=None)
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24,680
schedula.utils.dsp
SubDispatchFunction
It converts a :class:`~schedula.dispatcher.Dispatcher` into a function. This function takes a sequence of arguments or a key values as input of the dispatch. :return: A function that executes the dispatch of the given `dsp`. :rtype: callable .. seealso:: :func:`~schedula.dispatcher.Dispatcher.dispatch`, :func:`~schedula.dispatcher.Dispatcher.shrink_dsp` **Example**: A dispatcher with two functions `max` and `min` and an unresolved cycle (i.e., `a` --> `max` --> `c` --> `min` --> `a`): .. dispatcher:: dsp :opt: graph_attr={'ratio': '1'} >>> from schedula import Dispatcher >>> dsp = Dispatcher(name='Dispatcher') >>> dsp.add_function('max', max, inputs=['a', 'b'], outputs=['c']) 'max' >>> from math import log >>> def my_log(x): ... return log(x - 1) >>> dsp.add_function('log(x - 1)', my_log, inputs=['c'], ... outputs=['a'], input_domain=lambda c: c > 1) 'log(x - 1)' Extract a static function node, i.e. the inputs `a` and `b` and the output `a` are fixed:: >>> fun = SubDispatchFunction(dsp, 'myF', ['a', 'b'], ['a']) >>> fun.__name__ 'myF' >>> fun(b=1, a=2) 0.0 .. dispatcher:: fun :opt: workflow=True, graph_attr={'ratio': '1'} >>> fun.dsp.name = 'Created function internal' The created function raises a ValueError if un-valid inputs are provided: .. dispatcher:: fun :opt: workflow=True, graph_attr={'ratio': '1'} :code: >>> fun(1, 0) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... DispatcherError: Unreachable output-targets: ... Available outputs: ...
class SubDispatchFunction(SubDispatch): """ It converts a :class:`~schedula.dispatcher.Dispatcher` into a function. This function takes a sequence of arguments or a key values as input of the dispatch. :return: A function that executes the dispatch of the given `dsp`. :rtype: callable .. seealso:: :func:`~schedula.dispatcher.Dispatcher.dispatch`, :func:`~schedula.dispatcher.Dispatcher.shrink_dsp` **Example**: A dispatcher with two functions `max` and `min` and an unresolved cycle (i.e., `a` --> `max` --> `c` --> `min` --> `a`): .. dispatcher:: dsp :opt: graph_attr={'ratio': '1'} >>> from schedula import Dispatcher >>> dsp = Dispatcher(name='Dispatcher') >>> dsp.add_function('max', max, inputs=['a', 'b'], outputs=['c']) 'max' >>> from math import log >>> def my_log(x): ... return log(x - 1) >>> dsp.add_function('log(x - 1)', my_log, inputs=['c'], ... outputs=['a'], input_domain=lambda c: c > 1) 'log(x - 1)' Extract a static function node, i.e. the inputs `a` and `b` and the output `a` are fixed:: >>> fun = SubDispatchFunction(dsp, 'myF', ['a', 'b'], ['a']) >>> fun.__name__ 'myF' >>> fun(b=1, a=2) 0.0 .. dispatcher:: fun :opt: workflow=True, graph_attr={'ratio': '1'} >>> fun.dsp.name = 'Created function internal' The created function raises a ValueError if un-valid inputs are provided: .. dispatcher:: fun :opt: workflow=True, graph_attr={'ratio': '1'} :code: >>> fun(1, 0) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... DispatcherError: Unreachable output-targets: ... Available outputs: ... """ var_keyword = 'kw' def __init__(self, dsp, function_id=None, inputs=None, outputs=None, inputs_dist=None, shrink=True, wildcard=True, output_type=None, output_type_kw=None, first_arg_as_kw=False): """ Initializes the Sub-dispatch Function. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher | schedula.utils.blue.BlueDispatcher :param function_id: Function name. :type function_id: str, optional :param inputs: Input data nodes. :type inputs: list[str], iterable, optional :param outputs: Ending data nodes. :type outputs: list[str], iterable, optional :param inputs_dist: Initial distances of input data nodes. :type inputs_dist: dict[str, int | float], optional :param shrink: If True the dispatcher is shrink before the dispatch. :type shrink: bool, optional :param wildcard: If True, when the data node is used as input and target in the ArciDispatch algorithm, the input value will be used as input for the connected functions, but not as output. :type wildcard: bool, optional :param output_type: Type of function output: + 'all': a dictionary with all dispatch outputs. + 'list': a list with all outputs listed in `outputs`. + 'dict': a dictionary with any outputs listed in `outputs`. :type output_type: str, optional :param output_type_kw: Extra kwargs to pass to the `selector` function. :type output_type_kw: dict, optional :param first_arg_as_kw: Uses the first argument of the __call__ method as `kwargs`. :type output_type_kw: bool """ if shrink: dsp = dsp.shrink_dsp( inputs, outputs, inputs_dist=inputs_dist, wildcard=wildcard ) if outputs: # Outputs not reached. missed = {k for k in outputs if k not in dsp.nodes} if missed: # If outputs are missing raise error. available = list(dsp.data_nodes.keys()) # Available data nodes. # Raise error msg = '\n Unreachable output-targets: {}\n Available ' \ 'outputs: {}'.format(missed, available) raise ValueError(msg) # Set internal proprieties self.inputs = inputs # Set dsp name equal to function id. self.function_id = dsp.name = function_id or dsp.name or 'fun' no_call = False self._sol = dsp.solution.__class__( dsp, dict.fromkeys(inputs or (), None), outputs, wildcard, None, inputs_dist, no_call, False ) # Initialize as sub dispatch. super(SubDispatchFunction, self).__init__( dsp, outputs, inputs_dist, wildcard, no_call, True, True, 'list', output_type_kw=output_type_kw ) # Define the function to return outputs sorted. if output_type is not None: self.output_type = output_type elif outputs is None: self.output_type = 'all' elif len(outputs) == 1: self.output_type = 'values' self.first_arg_as_kw = first_arg_as_kw @property def __signature__(self): import inspect dfl, p = self.dsp.default_values, [] for name in self.inputs or (): par = inspect.Parameter('par', inspect._POSITIONAL_OR_KEYWORD) par._name = name if name in dfl: par._default = dfl[name]['value'] p.append(par) if self.var_keyword: p.append(inspect.Parameter(self.var_keyword, inspect._VAR_KEYWORD)) return inspect.Signature(p, __validate_parameters__=False) def _parse_inputs(self, *args, **kw): if self.first_arg_as_kw: for k in sorted(args[0]): if k in kw: msg = 'multiple values for argument %r' raise TypeError(msg % k) from None kw.update(args[0]) args = args[1:] defaults, inputs = self.dsp.default_values, {} for i, k in enumerate(self.inputs or ()): try: inputs[k] = args[i] if k in kw: msg = 'multiple values for argument %r' raise TypeError(msg % k) from None except IndexError: if k in kw: inputs[k] = kw.pop(k) elif k in defaults: inputs[k] = defaults[k]['value'] else: msg = 'missing a required argument: %r' raise TypeError(msg % k) from None if len(inputs) < len(args): raise TypeError('too many positional arguments') from None if self.var_keyword: inputs.update(kw) elif not all(k in inputs for k in kw): k = next(k for k in sorted(kw) if k not in inputs) msg = 'got an unexpected keyword argument %r' raise TypeError(msg % k) from None return inputs def __call__(self, *args, _stopper=None, _executor=False, _sol_name=(), _verbose=False, **kw): # Namespace shortcuts. self.solution = sol = self._sol._copy_structure() sol.verbose = _verbose self.solution.full_name, dfl = _sol_name, self.dsp.default_values # Parse inputs. inp = self._parse_inputs(*args, **kw) i = tuple(k for k in inp if k not in self.dsp.data_nodes) if i: msg = "%s() got an unexpected keyword argument '%s'" raise TypeError(msg % (self.function_id, min(i))) inputs_dist = combine_dicts( sol.inputs_dist, dict.fromkeys(inp, 0), self.inputs_dist or {} ) inp.update({k: v['value'] for k, v in dfl.items() if k not in inp}) # Initialize. sol._init_workflow(inp, inputs_dist=inputs_dist, clean=False) # Dispatch outputs. sol._run(stopper=_stopper, executor=_executor) # Return
null
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24,681
schedula.utils.dsp
__call__
null
def __call__(self, *args, _stopper=None, _executor=False, _sol_name=(), _verbose=False, **kw): # Namespace shortcuts. self.solution = sol = self._sol._copy_structure() sol.verbose = _verbose self.solution.full_name, dfl = _sol_name, self.dsp.default_values # Parse inputs. inp = self._parse_inputs(*args, **kw) i = tuple(k for k in inp if k not in self.dsp.data_nodes) if i: msg = "%s() got an unexpected keyword argument '%s'" raise TypeError(msg % (self.function_id, min(i))) inputs_dist = combine_dicts( sol.inputs_dist, dict.fromkeys(inp, 0), self.inputs_dist or {} ) inp.update({k: v['value'] for k, v in dfl.items() if k not in inp}) # Initialize. sol._init_workflow(inp, inputs_dist=inputs_dist, clean=False) # Dispatch outputs. sol._run(stopper=_stopper, executor=_executor) # Return outputs sorted. return self._return(sol)
(self, *args, _stopper=None, _executor=False, _sol_name=(), _verbose=False, **kw)
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24,684
schedula.utils.dsp
__init__
Initializes the Sub-dispatch Function. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher | schedula.utils.blue.BlueDispatcher :param function_id: Function name. :type function_id: str, optional :param inputs: Input data nodes. :type inputs: list[str], iterable, optional :param outputs: Ending data nodes. :type outputs: list[str], iterable, optional :param inputs_dist: Initial distances of input data nodes. :type inputs_dist: dict[str, int | float], optional :param shrink: If True the dispatcher is shrink before the dispatch. :type shrink: bool, optional :param wildcard: If True, when the data node is used as input and target in the ArciDispatch algorithm, the input value will be used as input for the connected functions, but not as output. :type wildcard: bool, optional :param output_type: Type of function output: + 'all': a dictionary with all dispatch outputs. + 'list': a list with all outputs listed in `outputs`. + 'dict': a dictionary with any outputs listed in `outputs`. :type output_type: str, optional :param output_type_kw: Extra kwargs to pass to the `selector` function. :type output_type_kw: dict, optional :param first_arg_as_kw: Uses the first argument of the __call__ method as `kwargs`. :type output_type_kw: bool
def __init__(self, dsp, function_id=None, inputs=None, outputs=None, inputs_dist=None, shrink=True, wildcard=True, output_type=None, output_type_kw=None, first_arg_as_kw=False): """ Initializes the Sub-dispatch Function. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher | schedula.utils.blue.BlueDispatcher :param function_id: Function name. :type function_id: str, optional :param inputs: Input data nodes. :type inputs: list[str], iterable, optional :param outputs: Ending data nodes. :type outputs: list[str], iterable, optional :param inputs_dist: Initial distances of input data nodes. :type inputs_dist: dict[str, int | float], optional :param shrink: If True the dispatcher is shrink before the dispatch. :type shrink: bool, optional :param wildcard: If True, when the data node is used as input and target in the ArciDispatch algorithm, the input value will be used as input for the connected functions, but not as output. :type wildcard: bool, optional :param output_type: Type of function output: + 'all': a dictionary with all dispatch outputs. + 'list': a list with all outputs listed in `outputs`. + 'dict': a dictionary with any outputs listed in `outputs`. :type output_type: str, optional :param output_type_kw: Extra kwargs to pass to the `selector` function. :type output_type_kw: dict, optional :param first_arg_as_kw: Uses the first argument of the __call__ method as `kwargs`. :type output_type_kw: bool """ if shrink: dsp = dsp.shrink_dsp( inputs, outputs, inputs_dist=inputs_dist, wildcard=wildcard ) if outputs: # Outputs not reached. missed = {k for k in outputs if k not in dsp.nodes} if missed: # If outputs are missing raise error. available = list(dsp.data_nodes.keys()) # Available data nodes. # Raise error msg = '\n Unreachable output-targets: {}\n Available ' \ 'outputs: {}'.format(missed, available) raise ValueError(msg) # Set internal proprieties self.inputs = inputs # Set dsp name equal to function id. self.function_id = dsp.name = function_id or dsp.name or 'fun' no_call = False self._sol = dsp.solution.__class__( dsp, dict.fromkeys(inputs or (), None), outputs, wildcard, None, inputs_dist, no_call, False ) # Initialize as sub dispatch. super(SubDispatchFunction, self).__init__( dsp, outputs, inputs_dist, wildcard, no_call, True, True, 'list', output_type_kw=output_type_kw ) # Define the function to return outputs sorted. if output_type is not None: self.output_type = output_type elif outputs is None: self.output_type = 'all' elif len(outputs) == 1: self.output_type = 'values' self.first_arg_as_kw = first_arg_as_kw
(self, dsp, function_id=None, inputs=None, outputs=None, inputs_dist=None, shrink=True, wildcard=True, output_type=None, output_type_kw=None, first_arg_as_kw=False)
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24,695
schedula.utils.dsp
SubDispatchPipe
It converts a :class:`~schedula.dispatcher.Dispatcher` into a function. This function takes a sequence of arguments as input of the dispatch. :return: A function that executes the pipe of the given `dsp`. :rtype: callable .. seealso:: :func:`~schedula.dispatcher.Dispatcher.dispatch`, :func:`~schedula.dispatcher.Dispatcher.shrink_dsp` **Example**: A dispatcher with two functions `max` and `min` and an unresolved cycle (i.e., `a` --> `max` --> `c` --> `min` --> `a`): .. dispatcher:: dsp :opt: graph_attr={'ratio': '1'} >>> from schedula import Dispatcher >>> dsp = Dispatcher(name='Dispatcher') >>> dsp.add_function('max', max, inputs=['a', 'b'], outputs=['c']) 'max' >>> def func(x): ... return x - 1 >>> dsp.add_function('x - 1', func, inputs=['c'], outputs=['a']) 'x - 1' Extract a static function node, i.e. the inputs `a` and `b` and the output `a` are fixed:: >>> fun = SubDispatchPipe(dsp, 'myF', ['a', 'b'], ['a']) >>> fun.__name__ 'myF' >>> fun(2, 1) 1 .. dispatcher:: fun :opt: workflow=True, graph_attr={'ratio': '1'} >>> fun.dsp.name = 'Created function internal' The created function raises a ValueError if un-valid inputs are provided: .. dispatcher:: fun :opt: workflow=True, graph_attr={'ratio': '1'} :code: >>> fun(1, 0) 0
class SubDispatchPipe(SubDispatchFunction): """ It converts a :class:`~schedula.dispatcher.Dispatcher` into a function. This function takes a sequence of arguments as input of the dispatch. :return: A function that executes the pipe of the given `dsp`. :rtype: callable .. seealso:: :func:`~schedula.dispatcher.Dispatcher.dispatch`, :func:`~schedula.dispatcher.Dispatcher.shrink_dsp` **Example**: A dispatcher with two functions `max` and `min` and an unresolved cycle (i.e., `a` --> `max` --> `c` --> `min` --> `a`): .. dispatcher:: dsp :opt: graph_attr={'ratio': '1'} >>> from schedula import Dispatcher >>> dsp = Dispatcher(name='Dispatcher') >>> dsp.add_function('max', max, inputs=['a', 'b'], outputs=['c']) 'max' >>> def func(x): ... return x - 1 >>> dsp.add_function('x - 1', func, inputs=['c'], outputs=['a']) 'x - 1' Extract a static function node, i.e. the inputs `a` and `b` and the output `a` are fixed:: >>> fun = SubDispatchPipe(dsp, 'myF', ['a', 'b'], ['a']) >>> fun.__name__ 'myF' >>> fun(2, 1) 1 .. dispatcher:: fun :opt: workflow=True, graph_attr={'ratio': '1'} >>> fun.dsp.name = 'Created function internal' The created function raises a ValueError if un-valid inputs are provided: .. dispatcher:: fun :opt: workflow=True, graph_attr={'ratio': '1'} :code: >>> fun(1, 0) 0 """ var_keyword = None def __init__(self, dsp, function_id=None, inputs=None, outputs=None, inputs_dist=None, no_domain=True, wildcard=True, shrink=True, output_type=None, output_type_kw=None, first_arg_as_kw=False): """ Initializes the Sub-dispatch Function. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher | schedula.utils.blue.BlueDispatcher :param function_id: Function name. :type function_id: str :param inputs: Input data nodes. :type inputs: list[str], iterable :param outputs: Ending data nodes. :type outputs: list[str], iterable, optional :param inputs_dist: Initial distances of input data nodes. :type inputs_dist: dict[str, int | float], optional :param no_domain: Skip the domain check. :type no_domain: bool, optional :param shrink: If True the dispatcher is shrink before the dispatch. :type shrink: bool, optional :param wildcard: If True, when the data node is used as input and target in the ArciDispatch algorithm, the input value will be used as input for the connected functions, but not as output. :type wildcard: bool, optional :param output_type: Type of function output: + 'all': a dictionary with all dispatch outputs. + 'list': a list with all outputs listed in `outputs`. + 'dict': a dictionary with any outputs listed in `outputs`. :type output_type: str, optional :param output_type_kw: Extra kwargs to pass to the `selector` function. :type output_type_kw: dict, optional :param first_arg_as_kw: Converts first argument of the __call__ method as `kwargs`. :type output_type_kw: bool """ self.solution = sol = dsp.solution.__class__( dsp, inputs, outputs, wildcard, inputs_dist, True, True, no_domain=no_domain ) sol._run() if shrink: from .alg import _union_workflow, _convert_bfs bfs = _union_workflow(sol) o, bfs = outputs or sol, _convert_bfs(bfs) dsp = dsp._get_dsp_from_bfs(o, bfs_graphs=bfs) super(SubDispatchPipe, self).__init__( dsp, function_id, inputs, outputs=outputs, inputs_dist=inputs_dist, shrink=False, wildcard=wildcard, output_type=output_type, output_type_kw=output_type_kw, first_arg_as_kw=first_arg_as_kw ) self._reset_sol() self.pipe = self._set_pipe() def _reset_sol(self): self._sol.no_call = True self._sol._init_workflow() self._sol._run() self._sol.no_call = False def _set_pipe(self): def _make_tks(task): v, s = task[-1] if v is START: nxt_nds = s.dsp.dmap[v] else: nxt_nds = s.workflow[v] nxt_dsp = [n for n in nxt_nds if s.nodes[n]['type'] == 'dispatcher'] nxt_dsp = [(n, s._edge_length(s.dmap[v][n], s.nodes[n])) for n in nxt_dsp] return (task[0], task[1], (v, s)), nxt_nds, nxt_dsp return [_make_tks(v['task']) for v in self._sol.pipe.values()] def _init_new_solution(self, full_name, verbose): key_map, sub_sol = {}, {} for k, s in self._sol.sub_sol.items(): ns = s._copy_structure(dist=1) ns.verbose = verbose ns.fringe = None ns.sub_sol = sub_sol ns.full_name = full_name + s.full_name key_map[s] = ns sub_sol[ns.index] = ns return key_map[self._sol], lambda x: key_map[x] def _init_workflows(self, inputs): self.solution.inputs.update(inputs) for s in self.solution.sub_sol.values(): s._init_workflow(clean=False) def _callback_pipe_failure(self): pass def _pipe_append(self): return self.solution._pipe.append def __call__(self, *args, _stopper=None, _executor=False, _sol_name=(), _verbose=False, **kw): self.solution, key_map = self._init_new_solution(_sol_name, _verbose) pipe_append = self._pipe_append() self._init_workflows(self._parse_inputs(*args, **kw)) for x, nxt_nds, nxt_dsp in self.pipe: v, s = x[-1] s = key_map(s) pipe_append(x[:2] + ((v, s),)) if not s._set_node_output( v, False, next_nds=nxt_nds, stopper=_stopper, executor=_executor): self._callback_pipe_failure() break for n, vw_d in nxt_dsp: s._set_sub_dsp_node_input(v, n, [], False, vw_d) s._see_remote_link_node(v) # Return outputs sorted. return self._return(self.solution)
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24,702
schedula.utils.dsp
_callback_pipe_failure
null
def _callback_pipe_failure(self): pass
(self)
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24,703
schedula.utils.dsp
_init_new_solution
null
def _init_new_solution(self, full_name, verbose): key_map, sub_sol = {}, {} for k, s in self._sol.sub_sol.items(): ns = s._copy_structure(dist=1) ns.verbose = verbose ns.fringe = None ns.sub_sol = sub_sol ns.full_name = full_name + s.full_name key_map[s] = ns sub_sol[ns.index] = ns return key_map[self._sol], lambda x: key_map[x]
(self, full_name, verbose)
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24,704
schedula.utils.dsp
_init_workflows
null
def _init_workflows(self, inputs): self.solution.inputs.update(inputs) for s in self.solution.sub_sol.values(): s._init_workflow(clean=False)
(self, inputs)
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24,706
schedula.utils.dsp
_pipe_append
null
def _pipe_append(self): return self.solution._pipe.append
(self)
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24,716
schedula.utils.asy.executors
ThreadExecutor
Multi Thread Executor
class ThreadExecutor(Executor): """Multi Thread Executor""" def submit(self, func, *args, **kwargs): import threading fut, send = Future(), lambda res: self._set_future(fut, res) task = threading.Thread( target=self._target, args=(send, func, args, kwargs) ) self.tasks[fut], task.daemon = task, True task.start() return fut
()
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24,717
schedula.utils.asy.executors
__init__
null
def __init__(self): self.tasks = {} finalize(self, self.shutdown, False)
(self)
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24,718
schedula.utils.asy.executors
__reduce__
null
def __reduce__(self): return self.__class__, ()
(self)
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24,721
schedula.utils.asy.executors
shutdown
null
def shutdown(self, wait=True): tasks = dict(self.tasks) if wait: from concurrent.futures import wait as _wait_fut # noinspection PyCallingNonCallable _wait_fut(tasks) for fut, task in tasks.items(): _safe_set_exception(fut, ExecutorShutdown) for _ in range(100): try: hasattr(task, 'terminate') and task.terminate() break except AttributeError: time.sleep(0.01) pass else: raise ValueError('Task could not terminate!') return tasks
(self, wait=True)
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24,722
schedula.utils.asy.executors
submit
null
def submit(self, func, *args, **kwargs): import threading fut, send = Future(), lambda res: self._set_future(fut, res) task = threading.Thread( target=self._target, args=(send, func, args, kwargs) ) self.tasks[fut], task.daemon = task, True task.start() return fut
(self, func, *args, **kwargs)
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24,723
schedula.utils.gen
Token
It constructs a unique constant that behaves like a string. Example:: >>> s = Token('string') >>> s string >>> s == 'string' False >>> s == Token('string') False >>> {s: 1, Token('string'): 1} {string: 1, string: 1} >>> s.capitalize() 'String'
class Token(_Token, str): """ It constructs a unique constant that behaves like a string. Example:: >>> s = Token('string') >>> s string >>> s == 'string' False >>> s == Token('string') False >>> {s: 1, Token('string'): 1} {string: 1, string: 1} >>> s.capitalize() 'String' """
(*args)
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24,726
schedula.utils.gen
__eq__
null
def __eq__(self, other): return self is other
(self, other)
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24,728
schedula.utils.gen
__init__
null
def __init__(self, *args): import inspect f = inspect.currentframe() self.module_name = f and f.f_back and f.f_back.f_globals.get('__name__') del f
(self, *args)
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24,729
schedula.utils.gen
__ne__
null
def __ne__(self, other): return self is not other
(self, other)
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24,730
schedula.utils.gen
__reduce__
null
def __reduce__(self): if self.module_name: import importlib # noinspection PyTypeChecker mdl = importlib.import_module(self.module_name) for name, obj in mdl.__dict__.items(): if obj is self: return getattr, (mdl, name) return super(_Token, self).__reduce__()
(self)
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24,731
schedula.utils.gen
__repr__
null
def __repr__(self): return self
(self)
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24,732
schedula.utils.exc
WebResponse
null
class WebResponse(BaseException): def __init__(self, response): self.response = response
(response)
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24,733
schedula.utils.exc
__init__
null
def __init__(self, response): self.response = response
(self, response)
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24,734
schedula.utils.dsp
add_args
null
class add_args: """ Adds arguments to a function (left side). :param func: Function to wrap. :type func: callable :param n: Number of unused arguments to add to the left side. :type n: int :return: Wrapped function. :rtype: callable Example:: >>> import inspect >>> def original_func(a, b, *args, c=0): ... '''Doc''' ... return a + b + c >>> func = add_args(original_func, n=2) >>> func.__name__, func.__doc__ ('original_func', 'Doc') >>> func(1, 2, 3, 4, c=5) 12 >>> str(inspect.signature(func)) '(none, none, a, b, *args, c=0)' """ __name__ = __doc__ = None _args = ('func', 'n', 'callback') def __init__(self, func, n=1, callback=None): self.n = n self.callback = callback self.func = func for i in range(2): # noinspection PyBroadException try: self.__name__ = func.__name__ self.__doc__ = func.__doc__ break except AttributeError: func = parent_func(func) @property def __signature__(self): return _get_signature(self.func, self.n) def __call__(self, *args, **kwargs): res = self.func(*args[self.n:], **kwargs) if self.callback: self.callback(res, *args, **kwargs) return res
null
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24,735
schedula.utils.dsp
__call__
null
def __call__(self, *args, **kwargs): res = self.func(*args[self.n:], **kwargs) if self.callback: self.callback(res, *args, **kwargs) return res
(self, *args, **kwargs)
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0.00994347408413887, 0.05868162959814072, 0.07570215314626694 ]
24,736
schedula.utils.dsp
__init__
null
def __init__(self, func, n=1, callback=None): self.n = n self.callback = callback self.func = func for i in range(2): # noinspection PyBroadException try: self.__name__ = func.__name__ self.__doc__ = func.__doc__ break except AttributeError: func = parent_func(func)
(self, func, n=1, callback=None)
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24,737
schedula.utils.dsp
add_function
Decorator to add a function to a dispatcher. :param dsp: A dispatcher. :type dsp: schedula.Dispatcher | schedula.blue.BlueDispatcher :param inputs_kwargs: Do you want to include kwargs as inputs? :type inputs_kwargs: bool :param inputs_defaults: Do you want to set default values? :type inputs_defaults: bool :param kw: See :func:~`schedula.dispatcher.Dispatcher.add_function`. :return: Decorator. :rtype: callable **------------------------------------------------------------------------** **Example**: .. dispatcher:: sol :opt: graph_attr={'ratio': '1'} :code: >>> import schedula as sh >>> dsp = sh.Dispatcher(name='Dispatcher') >>> @sh.add_function(dsp, outputs=['e']) ... @sh.add_function(dsp, False, True, outputs=['i'], inputs='ecah') ... @sh.add_function(dsp, True, outputs=['l']) ... def f(a, b, c, d=1): ... return (a + b) - c + d >>> @sh.add_function(dsp, True, outputs=['d']) ... def g(e, i, *args, d=0): ... return e + i + d >>> sol = dsp({'a': 1, 'b': 2, 'c': 3}); sol Solution([('a', 1), ('b', 2), ('c', 3), ('h', 1), ('e', 1), ('i', 4), ('d', 5), ('l', 5)])
def add_function(dsp, inputs_kwargs=False, inputs_defaults=False, **kw): """ Decorator to add a function to a dispatcher. :param dsp: A dispatcher. :type dsp: schedula.Dispatcher | schedula.blue.BlueDispatcher :param inputs_kwargs: Do you want to include kwargs as inputs? :type inputs_kwargs: bool :param inputs_defaults: Do you want to set default values? :type inputs_defaults: bool :param kw: See :func:~`schedula.dispatcher.Dispatcher.add_function`. :return: Decorator. :rtype: callable **------------------------------------------------------------------------** **Example**: .. dispatcher:: sol :opt: graph_attr={'ratio': '1'} :code: >>> import schedula as sh >>> dsp = sh.Dispatcher(name='Dispatcher') >>> @sh.add_function(dsp, outputs=['e']) ... @sh.add_function(dsp, False, True, outputs=['i'], inputs='ecah') ... @sh.add_function(dsp, True, outputs=['l']) ... def f(a, b, c, d=1): ... return (a + b) - c + d >>> @sh.add_function(dsp, True, outputs=['d']) ... def g(e, i, *args, d=0): ... return e + i + d >>> sol = dsp({'a': 1, 'b': 2, 'c': 3}); sol Solution([('a', 1), ('b', 2), ('c', 3), ('h', 1), ('e', 1), ('i', 4), ('d', 5), ('l', 5)]) """ def decorator(f): dsp.add_func( f, inputs_kwargs=inputs_kwargs, inputs_defaults=inputs_defaults, **kw ) return f return decorator
(dsp, inputs_kwargs=False, inputs_defaults=False, **kw)
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24,738
schedula.utils.dsp
are_in_nested_dicts
Nested keys are inside of nested-dictionaries. :param nested_dict: Nested dictionary. :type nested_dict: dict :param keys: Nested keys. :type keys: object :return: True if nested keys are inside of nested-dictionaries, otherwise False. :rtype: bool
def are_in_nested_dicts(nested_dict, *keys): """ Nested keys are inside of nested-dictionaries. :param nested_dict: Nested dictionary. :type nested_dict: dict :param keys: Nested keys. :type keys: object :return: True if nested keys are inside of nested-dictionaries, otherwise False. :rtype: bool """ if keys: # noinspection PyBroadException try: return are_in_nested_dicts(nested_dict[keys[0]], *keys[1:]) except Exception: # Key error or not a dict. return False return True
(nested_dict, *keys)
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24,739
schedula.utils.asy
await_result
Return the result of a `Future` object. :param obj: Value object. :type obj: concurrent.futures.Future | object :param timeout: The number of seconds to wait for the result if the future isn't done. If None, then there is no limit on the wait time. :type timeout: int :return: Result. :rtype: object Example:: >>> from concurrent.futures import Future >>> fut = Future() >>> fut.set_result(3) >>> await_result(fut), await_result(4) (3, 4)
def await_result(obj, timeout=None): """ Return the result of a `Future` object. :param obj: Value object. :type obj: concurrent.futures.Future | object :param timeout: The number of seconds to wait for the result if the future isn't done. If None, then there is no limit on the wait time. :type timeout: int :return: Result. :rtype: object Example:: >>> from concurrent.futures import Future >>> fut = Future() >>> fut.set_result(3) >>> await_result(fut), await_result(4) (3, 4) """ return obj.result(timeout) if isinstance(obj, Future) else obj
(obj, timeout=None)
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24,740
schedula.utils.dsp
bypass
Returns the same arguments. :param inputs: Inputs values. :type inputs: T :param copy: If True, it returns a deepcopy of input values. :type copy: bool, optional :return: Same input values. :rtype: (T, ...), T Example:: >>> bypass('a', 'b', 'c') ('a', 'b', 'c') >>> bypass('a') 'a'
def bypass(*inputs, copy=False): """ Returns the same arguments. :param inputs: Inputs values. :type inputs: T :param copy: If True, it returns a deepcopy of input values. :type copy: bool, optional :return: Same input values. :rtype: (T, ...), T Example:: >>> bypass('a', 'b', 'c') ('a', 'b', 'c') >>> bypass('a') 'a' """ if len(inputs) == 1: inputs = inputs[0] # Same inputs. return _copy.deepcopy(inputs) if copy else inputs # Return inputs.
(*inputs, copy=False)
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24,741
schedula.utils.dsp
combine_dicts
Combines multiple dicts in one. :param dicts: A sequence of dicts. :type dicts: dict :param copy: If True, it returns a deepcopy of input values. :type copy: bool, optional :param base: Base dict where combine multiple dicts in one. :type base: dict, optional :return: A unique dict. :rtype: dict Example:: >>> sorted(combine_dicts({'a': 3, 'c': 3}, {'a': 1, 'b': 2}).items()) [('a', 1), ('b', 2), ('c', 3)]
def combine_dicts(*dicts, copy=False, base=None): """ Combines multiple dicts in one. :param dicts: A sequence of dicts. :type dicts: dict :param copy: If True, it returns a deepcopy of input values. :type copy: bool, optional :param base: Base dict where combine multiple dicts in one. :type base: dict, optional :return: A unique dict. :rtype: dict Example:: >>> sorted(combine_dicts({'a': 3, 'c': 3}, {'a': 1, 'b': 2}).items()) [('a', 1), ('b', 2), ('c', 3)] """ if len(dicts) == 1 and base is None: # Only one input dict. cd = dicts[0].copy() else: cd = {} if base is None else base # Initialize empty dict. for d in dicts: # Combine dicts. if d: # noinspection PyTypeChecker cd.update(d) # Return combined dict. return {k: _copy.deepcopy(v) for k, v in cd.items()} if copy else cd
(*dicts, copy=False, base=None)
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24,742
schedula.utils.dsp
combine_nested_dicts
Merge nested-dictionaries. :param nested_dicts: Nested dictionaries. :type nested_dicts: dict :param depth: Maximum keys depth. :type depth: int, optional :param base: Base dict where combine multiple dicts in one. :type base: dict, optional :return: Combined nested-dictionary. :rtype: dict
def combine_nested_dicts(*nested_dicts, depth=-1, base=None): """ Merge nested-dictionaries. :param nested_dicts: Nested dictionaries. :type nested_dicts: dict :param depth: Maximum keys depth. :type depth: int, optional :param base: Base dict where combine multiple dicts in one. :type base: dict, optional :return: Combined nested-dictionary. :rtype: dict """ if base is None: base = {} for nested_dict in nested_dicts: for k, v in stack_nested_keys(nested_dict, depth=depth): while k: # noinspection PyBroadException try: get_nested_dicts(base, *k[:-1])[k[-1]] = v break except Exception: # A branch of the nested_dict is longer than the base. k = k[:-1] v = get_nested_dicts(nested_dict, *k) return base
(*nested_dicts, depth=-1, base=None)
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24,743
schedula.utils.gen
counter
Return a object whose .__call__() method returns consecutive values. :param start: Start value. :type start: int, float, optional :param step: Step value. :type step: int, float, optional
def counter(start=0, step=1): """ Return a object whose .__call__() method returns consecutive values. :param start: Start value. :type start: int, float, optional :param step: Step value. :type step: int, float, optional """ return itertools.count(start, step).__next__
(start=0, step=1)
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24,744
schedula.utils.dsp
get_nested_dicts
Get/Initialize the value of nested-dictionaries. :param nested_dict: Nested dictionary. :type nested_dict: dict :param keys: Nested keys. :type keys: object :param default: Function used to initialize a new value. :type default: callable, optional :param init_nesting: Function used to initialize a new intermediate nesting dict. :type init_nesting: callable, optional :return: Value of nested-dictionary. :rtype: generator
def get_nested_dicts(nested_dict, *keys, default=None, init_nesting=dict): """ Get/Initialize the value of nested-dictionaries. :param nested_dict: Nested dictionary. :type nested_dict: dict :param keys: Nested keys. :type keys: object :param default: Function used to initialize a new value. :type default: callable, optional :param init_nesting: Function used to initialize a new intermediate nesting dict. :type init_nesting: callable, optional :return: Value of nested-dictionary. :rtype: generator """ if keys: default = default or init_nesting if keys[0] in nested_dict: nd = nested_dict[keys[0]] else: d = default() if len(keys) == 1 else init_nesting() nd = nested_dict[keys[0]] = d return get_nested_dicts(nd, *keys[1:], default=default, init_nesting=init_nesting) return nested_dict
(nested_dict, *keys, default=None, init_nesting=<class 'dict'>)
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24,745
schedula.utils.dsp
inf
Class to model infinite numbers for workflow distance.
class inf: """Class to model infinite numbers for workflow distance.""" _inf: float = 0 _num: float = 0 def __iter__(self): yield self._inf yield self._num @staticmethod def format(val): if not isinstance(val, tuple): val = 0, val return inf(*val) def __repr__(self): if self._inf == 0: return str(self._num) return 'inf(inf={}, num={})'.format(*self) def __add__(self, other): if isinstance(other, self.__class__): return inf(self._inf + other._inf, self._num + other._num) return inf(self._inf, self._num + other) def __sub__(self, other): other = isinstance(other, self.__class__) and other or (0, other) return inf(*(x - y for x, y in zip(self, other))) def __rsub__(self, other): other = isinstance(other, self.__class__) and other or (0, other) return inf(*(x - y for x, y in zip(other, self))) def __mul__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x * y for x, y in zip(self, other))) def __truediv__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x / y for x, y in zip(self, other))) def __rtruediv__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x / y for x, y in zip(other, self))) def __pow__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x ** y for x, y in zip(self, other))) def __rpow__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x ** y for x, y in zip(other, self))) def __mod__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x % y for x, y in zip(self, other))) def __rmod__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x % y for x, y in zip(other, self))) def __floordiv__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x // y for x, y in zip(self, other))) def __rfloordiv__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x // y for x, y in zip(other, self))) def __neg__(self): return inf(*(-x for x in self)) def __pos__(self): return inf(*(+x for x in self)) def __abs__(self): return inf(*(map(abs, self))) def __trunc__(self): return inf(*(map(math.trunc, self))) def __floor__(self): return inf(*(map(math.floor, self))) def __ceil__(self): return inf(*(map(math.ceil, self))) def __round__(self, n=None): return inf(*(round(x, n) for x in self)) __radd__ = __add__ __rmul__ = __mul__ def __ge__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) >= other def __gt__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) > other def __eq__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) == other def __le__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) <= other def __lt__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) < other def __ne__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) != other
(_inf: float = 0, _num: float = 0) -> None
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24,746
schedula.utils.dsp
__abs__
null
def __abs__(self): return inf(*(map(abs, self)))
(self)
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24,747
schedula.utils.dsp
__add__
null
def __add__(self, other): if isinstance(other, self.__class__): return inf(self._inf + other._inf, self._num + other._num) return inf(self._inf, self._num + other)
(self, other)
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24,748
schedula.utils.dsp
__ceil__
null
def __ceil__(self): return inf(*(map(math.ceil, self)))
(self)
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24,749
schedula.utils.dsp
__delattr__
null
#!/usr/bin/env python # -*- coding: UTF-8 -*- # # Copyright 2015-2024, Vincenzo Arcidiacono; # Licensed under the EUPL (the 'Licence'); # You may not use this work except in compliance with the Licence. # You may obtain a copy of the Licence at: http://ec.europa.eu/idabc/eupl """ It provides tools to create models with the :class:`~schedula.dispatcher.Dispatcher`. """ import math import inspect import functools import itertools import collections import copy as _copy from .cst import START from .gen import Token from .base import Base from .exc import DispatcherError from dataclasses import dataclass __author__ = 'Vincenzo Arcidiacono <[email protected]>' def stlp(s): """ Converts a string in a tuple. """ if isinstance(s, str): return s, return s
(self, name)
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24,750
schedula.utils.dsp
__eq__
null
def __eq__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) == other
(self, other)
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24,751
schedula.utils.dsp
__floor__
null
def __floor__(self): return inf(*(map(math.floor, self)))
(self)
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0.049033910036087036, 0.01652449555695057, 0.03473516181111336 ]
24,752
schedula.utils.dsp
__floordiv__
null
def __floordiv__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x // y for x, y in zip(self, other)))
(self, other)
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24,753
schedula.utils.dsp
__ge__
null
def __ge__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) >= other
(self, other)
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24,754
schedula.utils.dsp
__gt__
null
def __gt__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) > other
(self, other)
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24,756
schedula.utils.dsp
__iter__
null
def __iter__(self): yield self._inf yield self._num
(self)
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0.06662910431623459, 0.06724636256694794, 0.031308479607105255 ]
24,757
schedula.utils.dsp
__le__
null
def __le__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) <= other
(self, other)
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24,758
schedula.utils.dsp
__lt__
null
def __lt__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) < other
(self, other)
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24,759
schedula.utils.dsp
__mod__
null
def __mod__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x % y for x, y in zip(self, other)))
(self, other)
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24,760
schedula.utils.dsp
__mul__
null
def __mul__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x * y for x, y in zip(self, other)))
(self, other)
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24,761
schedula.utils.dsp
__ne__
null
def __ne__(self, other): other = isinstance(other, self.__class__) and tuple(other) or (0, other) return tuple(self) != other
(self, other)
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24,762
schedula.utils.dsp
__neg__
null
def __neg__(self): return inf(*(-x for x in self))
(self)
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24,763
schedula.utils.dsp
__pos__
null
def __pos__(self): return inf(*(+x for x in self))
(self)
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24,764
schedula.utils.dsp
__pow__
null
def __pow__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x ** y for x, y in zip(self, other)))
(self, other)
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24,766
schedula.utils.dsp
__repr__
null
def __repr__(self): if self._inf == 0: return str(self._num) return 'inf(inf={}, num={})'.format(*self)
(self)
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24,767
schedula.utils.dsp
__rfloordiv__
null
def __rfloordiv__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x // y for x, y in zip(other, self)))
(self, other)
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24,768
schedula.utils.dsp
__rmod__
null
def __rmod__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x % y for x, y in zip(other, self)))
(self, other)
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24,770
schedula.utils.dsp
__round__
null
def __round__(self, n=None): return inf(*(round(x, n) for x in self))
(self, n=None)
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24,771
schedula.utils.dsp
__rpow__
null
def __rpow__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x ** y for x, y in zip(other, self)))
(self, other)
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24,772
schedula.utils.dsp
__rsub__
null
def __rsub__(self, other): other = isinstance(other, self.__class__) and other or (0, other) return inf(*(x - y for x, y in zip(other, self)))
(self, other)
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24,773
schedula.utils.dsp
__rtruediv__
null
def __rtruediv__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x / y for x, y in zip(other, self)))
(self, other)
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24,775
schedula.utils.dsp
__sub__
null
def __sub__(self, other): other = isinstance(other, self.__class__) and other or (0, other) return inf(*(x - y for x, y in zip(self, other)))
(self, other)
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24,776
schedula.utils.dsp
__truediv__
null
def __truediv__(self, other): other = isinstance(other, self.__class__) and other or (other, other) return inf(*(x / y for x, y in zip(self, other)))
(self, other)
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24,777
schedula.utils.dsp
__trunc__
null
def __trunc__(self): return inf(*(map(math.trunc, self)))
(self)
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24,778
schedula.utils.dsp
format
null
@staticmethod def format(val): if not isinstance(val, tuple): val = 0, val return inf(*val)
(val)
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24,779
schedula.utils.dsp
kk_dict
Merges and defines dictionaries with values identical to keys. :param kk: A sequence of keys and/or dictionaries. :type kk: object | dict, optional :param adict: A dictionary. :type adict: dict, optional :return: Merged dictionary. :rtype: dict Example:: >>> sorted(kk_dict('a', 'b', 'c').items()) [('a', 'a'), ('b', 'b'), ('c', 'c')] >>> sorted(kk_dict('a', 'b', **{'a-c': 'c'}).items()) [('a', 'a'), ('a-c', 'c'), ('b', 'b')] >>> sorted(kk_dict('a', {'b': 'c'}, 'c').items()) [('a', 'a'), ('b', 'c'), ('c', 'c')] >>> sorted(kk_dict('a', 'b', **{'b': 'c'}).items()) Traceback (most recent call last): ... ValueError: keyword argument repeated (b) >>> sorted(kk_dict({'a': 0, 'b': 1}, **{'b': 2, 'a': 3}).items()) Traceback (most recent call last): ... ValueError: keyword argument repeated (a, b)
def kk_dict(*kk, **adict): """ Merges and defines dictionaries with values identical to keys. :param kk: A sequence of keys and/or dictionaries. :type kk: object | dict, optional :param adict: A dictionary. :type adict: dict, optional :return: Merged dictionary. :rtype: dict Example:: >>> sorted(kk_dict('a', 'b', 'c').items()) [('a', 'a'), ('b', 'b'), ('c', 'c')] >>> sorted(kk_dict('a', 'b', **{'a-c': 'c'}).items()) [('a', 'a'), ('a-c', 'c'), ('b', 'b')] >>> sorted(kk_dict('a', {'b': 'c'}, 'c').items()) [('a', 'a'), ('b', 'c'), ('c', 'c')] >>> sorted(kk_dict('a', 'b', **{'b': 'c'}).items()) Traceback (most recent call last): ... ValueError: keyword argument repeated (b) >>> sorted(kk_dict({'a': 0, 'b': 1}, **{'b': 2, 'a': 3}).items()) Traceback (most recent call last): ... ValueError: keyword argument repeated (a, b) """ for k in kk: if isinstance(k, dict): if any(i in adict for i in k): k = ', '.join(sorted(set(k).intersection(adict))) raise ValueError('keyword argument repeated ({})'.format(k)) adict.update(k) elif k in adict: raise ValueError('keyword argument repeated ({})'.format(k)) else: adict[k] = k return adict
(*kk, **adict)
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24,780
schedula.utils.io
load_default_values
Load Dispatcher default values in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be uncompressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_data('a', default_value=1) 'a' >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_default_values(dsp, file_name) >>> dsp = Dispatcher(dmap=dsp.dmap) >>> load_default_values(dsp, file_name) >>> dsp.dispatch(inputs={'b': 3})['c'] 3
def load_default_values(dsp, path): """ Load Dispatcher default values in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be uncompressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_data('a', default_value=1) 'a' >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_default_values(dsp, file_name) >>> dsp = Dispatcher(dmap=dsp.dmap) >>> load_default_values(dsp, file_name) >>> dsp.dispatch(inputs={'b': 3})['c'] 3 """ import dill # noinspection PyArgumentList with open(path, 'rb') as f: dsp.__init__(dmap=dsp.dmap, default_values=dill.load(f))
(dsp, path)
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24,781
schedula.utils.io
load_dispatcher
Load Dispatcher object in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param path: File or filename to write. File names ending in .gz or .bz2 will be uncompressed. :type path: str, file :return: A dispatcher that identifies the model adopted. :rtype: schedula.Dispatcher .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_data('a', default_value=1) 'a' >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_dispatcher(dsp, file_name) >>> dsp = load_dispatcher(file_name) >>> dsp.dispatch(inputs={'b': 3})['c'] 3
def load_dispatcher(path): """ Load Dispatcher object in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param path: File or filename to write. File names ending in .gz or .bz2 will be uncompressed. :type path: str, file :return: A dispatcher that identifies the model adopted. :rtype: schedula.Dispatcher .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_data('a', default_value=1) 'a' >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_dispatcher(dsp, file_name) >>> dsp = load_dispatcher(file_name) >>> dsp.dispatch(inputs={'b': 3})['c'] 3 """ import dill # noinspection PyArgumentList with open(path, 'rb') as f: return dill.load(f)
(path)
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24,782
schedula.utils.io
load_map
Load Dispatcher map in Python pickle format. :param dsp: A dispatcher that identifies the model to be upgraded. :type dsp: schedula.schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be uncompressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_map(dsp, file_name) >>> dsp = Dispatcher() >>> load_map(dsp, file_name) >>> dsp.dispatch(inputs={'a': 1, 'b': 3})['c'] 3
def load_map(dsp, path): """ Load Dispatcher map in Python pickle format. :param dsp: A dispatcher that identifies the model to be upgraded. :type dsp: schedula.schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be uncompressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_map(dsp, file_name) >>> dsp = Dispatcher() >>> load_map(dsp, file_name) >>> dsp.dispatch(inputs={'a': 1, 'b': 3})['c'] 3 """ import dill with open(path, 'rb') as f: dsp.__init__(dmap=dill.load(f), default_values=dsp.default_values)
(dsp, path)
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24,783
schedula.utils.dsp
map_dict
Returns a dict with new key values. :param key_map: A dictionary that maps the dict keys ({old key: new key}. :type key_map: dict :param dicts: A sequence of dicts. :type dicts: dict :param copy: If True, it returns a deepcopy of input values. :type copy: bool, optional :param base: Base dict where combine multiple dicts in one. :type base: dict, optional :return: A unique dict with new key values. :rtype: dict Example:: >>> d = map_dict({'a': 'c', 'b': 'd'}, {'a': 1, 'b': 1}, {'b': 2}) >>> sorted(d.items()) [('c', 1), ('d', 2)]
def map_dict(key_map, *dicts, copy=False, base=None): """ Returns a dict with new key values. :param key_map: A dictionary that maps the dict keys ({old key: new key}. :type key_map: dict :param dicts: A sequence of dicts. :type dicts: dict :param copy: If True, it returns a deepcopy of input values. :type copy: bool, optional :param base: Base dict where combine multiple dicts in one. :type base: dict, optional :return: A unique dict with new key values. :rtype: dict Example:: >>> d = map_dict({'a': 'c', 'b': 'd'}, {'a': 1, 'b': 1}, {'b': 2}) >>> sorted(d.items()) [('c', 1), ('d', 2)] """ it = combine_dicts(*dicts).items() # Combine dicts. get = key_map.get # Namespace shortcut. # Return mapped dict. return combine_dicts({get(k, k): v for k, v in it}, copy=copy, base=base)
(key_map, *dicts, copy=False, base=None)
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24,784
schedula.utils.dsp
map_list
Returns a new dict. :param key_map: A list that maps the dict keys ({old key: new key} :type key_map: list[str | dict | list] :param inputs: A sequence of data. :type inputs: iterable | dict | int | float | list | tuple :param copy: If True, it returns a deepcopy of input values. :type copy: bool, optional :param base: Base dict where combine multiple dicts in one. :type base: dict, optional :return: A unique dict with new values. :rtype: dict Example:: >>> key_map = [ ... 'a', ... {'a': 'c'}, ... [ ... 'a', ... {'a': 'd'} ... ] ... ] >>> inputs = ( ... 2, ... {'a': 3, 'b': 2}, ... [ ... 1, ... {'a': 4} ... ] ... ) >>> d = map_list(key_map, *inputs) >>> sorted(d.items()) [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
def map_list(key_map, *inputs, copy=False, base=None): """ Returns a new dict. :param key_map: A list that maps the dict keys ({old key: new key} :type key_map: list[str | dict | list] :param inputs: A sequence of data. :type inputs: iterable | dict | int | float | list | tuple :param copy: If True, it returns a deepcopy of input values. :type copy: bool, optional :param base: Base dict where combine multiple dicts in one. :type base: dict, optional :return: A unique dict with new values. :rtype: dict Example:: >>> key_map = [ ... 'a', ... {'a': 'c'}, ... [ ... 'a', ... {'a': 'd'} ... ] ... ] >>> inputs = ( ... 2, ... {'a': 3, 'b': 2}, ... [ ... 1, ... {'a': 4} ... ] ... ) >>> d = map_list(key_map, *inputs) >>> sorted(d.items()) [('a', 1), ('b', 2), ('c', 3), ('d', 4)] """ d = {} if base is None else base # Initialize empty dict. for m, v in zip(key_map, inputs): if isinstance(m, dict): map_dict(m, v, base=d) # Apply a map dict. elif isinstance(m, list): map_list(m, *v, base=d) # Apply a map list. else: d[m] = v # Apply map. return combine_dicts(copy=copy, base=d) # Return dict.
(key_map, *inputs, copy=False, base=None)
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24,785
schedula.utils.dsp
parent_func
Return the parent function of a wrapped function (wrapped with :class:`functools.partial` and :class:`add_args`). :param func: Wrapped function. :type func: callable :param input_id: Index of the first input of the wrapped function. :type input_id: int :return: Parent function. :rtype: callable
def parent_func(func, input_id=None): """ Return the parent function of a wrapped function (wrapped with :class:`functools.partial` and :class:`add_args`). :param func: Wrapped function. :type func: callable :param input_id: Index of the first input of the wrapped function. :type input_id: int :return: Parent function. :rtype: callable """ if isinstance(func, add_args): if input_id is not None: input_id -= func.n return parent_func(func.func, input_id=input_id) elif isinstance(func, partial): if input_id is not None: # noinspection PyTypeChecker input_id += len(func.args) return parent_func(func.func, input_id=input_id) if input_id is None: return func else: return func, input_id
(func, input_id=None)
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24,787
schedula.utils.asy
register_executor
Register a new executor type. :param name: Executor name. :type name: str :param init: Function to initialize the executor. :type init: callable :param executors: Executor factory. :type executors: ExecutorFactory
def register_executor(name, init, executors=None): """ Register a new executor type. :param name: Executor name. :type name: str :param init: Function to initialize the executor. :type init: callable :param executors: Executor factory. :type executors: ExecutorFactory """ if executors is None: executors = EXECUTORS executors[name] = init
(name, init, executors=None)
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24,788
schedula.utils.dsp
replicate_value
Replicates `n` times the input value. :param n: Number of replications. :type n: int :param value: Value to be replicated. :type value: T :param copy: If True the list contains deep-copies of the value. :type copy: bool :return: A list with the value replicated `n` times. :rtype: list Example:: >>> import schedula as sh >>> fun = sh.partial(replicate_value, n=5) >>> fun({'a': 3}) ({'a': 3}, {'a': 3}, {'a': 3}, {'a': 3}, {'a': 3})
def replicate_value(value, n=2, copy=True): """ Replicates `n` times the input value. :param n: Number of replications. :type n: int :param value: Value to be replicated. :type value: T :param copy: If True the list contains deep-copies of the value. :type copy: bool :return: A list with the value replicated `n` times. :rtype: list Example:: >>> import schedula as sh >>> fun = sh.partial(replicate_value, n=5) >>> fun({'a': 3}) ({'a': 3}, {'a': 3}, {'a': 3}, {'a': 3}, {'a': 3}) """ return bypass(*[value] * n, copy=copy) # Return replicated values.
(value, n=2, copy=True)
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24,789
schedula.utils.dsp
run_model
It is an utility function to execute dynamically generated function/models and - if Dispatcher based - add their workflows to the parent solution. :return: A function that executes the dispatch of the given `dsp`. :rtype: callable **Example**: Follows a simple example on how to use the :func:`~schedula.utils.dsp.run_model`: .. dispatcher:: dsp :opt: graph_attr={'ratio': '1'} >>> from schedula import Dispatcher >>> dsp = Dispatcher(name='Dispatcher') >>> dsp.add_function( ... function_id='execute_dsp', function=run_model, ... inputs=['dsp_model', 'inputs'], outputs=['outputs'] ... ) 'execute_dsp' >>> dsp_model = Dispatcher(name='Model') >>> dsp_model.add_function('max', max, inputs=['a', 'b'], outputs=['c']) 'max' >>> sol = dsp({'dsp_model': dsp_model, 'inputs': {'b': 1, 'a': 2}}) >>> sol['outputs'] Solution([('a', 2), ('b', 1), ('c', 2)]) >>> sol.workflow.nodes['execute_dsp']['solution'] Solution([('a', 2), ('b', 1), ('c', 2)]) Moreover, it can be used also with all :func:`~schedula.utils.dsp.SubDispatcher` like objects:: >>> sub_dsp = SubDispatch(dsp_model, outputs=['c'], output_type='list') >>> sol = dsp({'dsp_model': sub_dsp, 'inputs': {'b': 1, 'a': 2}}) >>> sol['outputs'] [2] >>> sol.workflow.nodes['execute_dsp']['solution'] Solution([('a', 2), ('b', 1), ('c', 2)])
class run_model: """ It is an utility function to execute dynamically generated function/models and - if Dispatcher based - add their workflows to the parent solution. :return: A function that executes the dispatch of the given `dsp`. :rtype: callable **Example**: Follows a simple example on how to use the :func:`~schedula.utils.dsp.run_model`: .. dispatcher:: dsp :opt: graph_attr={'ratio': '1'} >>> from schedula import Dispatcher >>> dsp = Dispatcher(name='Dispatcher') >>> dsp.add_function( ... function_id='execute_dsp', function=run_model, ... inputs=['dsp_model', 'inputs'], outputs=['outputs'] ... ) 'execute_dsp' >>> dsp_model = Dispatcher(name='Model') >>> dsp_model.add_function('max', max, inputs=['a', 'b'], outputs=['c']) 'max' >>> sol = dsp({'dsp_model': dsp_model, 'inputs': {'b': 1, 'a': 2}}) >>> sol['outputs'] Solution([('a', 2), ('b', 1), ('c', 2)]) >>> sol.workflow.nodes['execute_dsp']['solution'] Solution([('a', 2), ('b', 1), ('c', 2)]) Moreover, it can be used also with all :func:`~schedula.utils.dsp.SubDispatcher` like objects:: >>> sub_dsp = SubDispatch(dsp_model, outputs=['c'], output_type='list') >>> sol = dsp({'dsp_model': sub_dsp, 'inputs': {'b': 1, 'a': 2}}) >>> sol['outputs'] [2] >>> sol.workflow.nodes['execute_dsp']['solution'] Solution([('a', 2), ('b', 1), ('c', 2)]) """ def __init__(self, func, *args, _init=None, **kwargs): from .blue import Blueprint if isinstance(func, Blueprint): func = func.register(memo={}) self.func = func if _init: args, kwargs = _init(*args, **kwargs) self.args = args self.kwargs = kwargs def __call__(self, **kwargs): return self.func(*self.args, **self.kwargs, **kwargs)
(func, *args, _init=None, **kwargs)
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24,790
schedula.utils.dsp
__call__
null
def __call__(self, **kwargs): return self.func(*self.args, **self.kwargs, **kwargs)
(self, **kwargs)
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24,791
schedula.utils.dsp
__init__
null
def __init__(self, func, *args, _init=None, **kwargs): from .blue import Blueprint if isinstance(func, Blueprint): func = func.register(memo={}) self.func = func if _init: args, kwargs = _init(*args, **kwargs) self.args = args self.kwargs = kwargs
(self, func, *args, _init=None, **kwargs)
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24,792
schedula.utils.io
save_default_values
Write Dispatcher default values in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be compressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_data('a', default_value=1) 'a' >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_default_values(dsp, file_name)
def save_default_values(dsp, path): """ Write Dispatcher default values in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be compressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_data('a', default_value=1) 'a' >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_default_values(dsp, file_name) """ import dill with open(path, 'wb') as f: dill.dump(dsp.default_values, f)
(dsp, path)
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24,793
schedula.utils.io
save_dispatcher
Write Dispatcher object in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be compressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_data('a', default_value=1) 'a' >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_dispatcher(dsp, file_name)
def save_dispatcher(dsp, path): """ Write Dispatcher object in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be compressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_data('a', default_value=1) 'a' >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_dispatcher(dsp, file_name) """ import dill with open(path, 'wb') as f: dill.dump(dsp, f)
(dsp, path)
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24,794
schedula.utils.io
save_map
Write Dispatcher graph object in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be compressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_map(dsp, file_name)
def save_map(dsp, path): """ Write Dispatcher graph object in Python pickle format. Pickles are a serialized byte stream of a Python object. This format will preserve Python objects used as nodes or edges. :param dsp: A dispatcher that identifies the model adopted. :type dsp: schedula.Dispatcher :param path: File or filename to write. File names ending in .gz or .bz2 will be compressed. :type path: str, file .. testsetup:: >>> from tempfile import mkstemp >>> file_name = mkstemp()[1] Example:: >>> from schedula import Dispatcher >>> dsp = Dispatcher() >>> dsp.add_function(function=max, inputs=['a', 'b'], outputs=['c']) 'max' >>> save_map(dsp, file_name) """ import dill with open(path, 'wb') as f: dill.dump(dsp.dmap, f)
(dsp, path)
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24,795
schedula.utils.dsp
selector
Selects the chosen dictionary keys from the given dictionary. :param keys: Keys to select. :type keys: list, tuple, set :param dictionary: A dictionary. :type dictionary: dict :param copy: If True the output contains deep-copies of the values. :type copy: bool :param output_type: Type of function output: + 'list': a list with all values listed in `keys`. + 'dict': a dictionary with any outputs listed in `keys`. + 'values': if output length == 1 return a single value otherwise a tuple with all values listed in `keys`. :type output_type: str, optional :param allow_miss: If True it does not raise when some key is missing in the dictionary. :type allow_miss: bool :return: A dictionary with chosen dictionary keys if present in the sequence of dictionaries. These are combined with :func:`combine_dicts`. :rtype: dict Example:: >>> import schedula as sh >>> fun = sh.partial(selector, ['a', 'b']) >>> sorted(fun({'a': 1, 'b': 2, 'c': 3}).items()) [('a', 1), ('b', 2)]
def selector(keys, dictionary, copy=False, output_type='dict', allow_miss=False): """ Selects the chosen dictionary keys from the given dictionary. :param keys: Keys to select. :type keys: list, tuple, set :param dictionary: A dictionary. :type dictionary: dict :param copy: If True the output contains deep-copies of the values. :type copy: bool :param output_type: Type of function output: + 'list': a list with all values listed in `keys`. + 'dict': a dictionary with any outputs listed in `keys`. + 'values': if output length == 1 return a single value otherwise a tuple with all values listed in `keys`. :type output_type: str, optional :param allow_miss: If True it does not raise when some key is missing in the dictionary. :type allow_miss: bool :return: A dictionary with chosen dictionary keys if present in the sequence of dictionaries. These are combined with :func:`combine_dicts`. :rtype: dict Example:: >>> import schedula as sh >>> fun = sh.partial(selector, ['a', 'b']) >>> sorted(fun({'a': 1, 'b': 2, 'c': 3}).items()) [('a', 1), ('b', 2)] """ if not allow_miss: # noinspection PyUnusedLocal def check(key): return True else: def check(key): return key in dictionary if output_type == 'list': # Select as list. res = [dictionary[k] for k in keys if check(k)] return _copy.deepcopy(res) if copy else res elif output_type == 'values': return bypass(*[dictionary[k] for k in keys if check(k)], copy=copy) # Select as dict. return bypass({k: dictionary[k] for k in keys if check(k)}, copy=copy)
(keys, dictionary, copy=False, output_type='dict', allow_miss=False)
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24,796
schedula.utils.asy
shutdown_executor
Clean-up the resources associated with the Executor. :param name: Executor name. :type name: str :param sol_id: Solution id. :type sol_id: int :param wait: If True then shutdown will not return until all running futures have finished executing and the resources used by the executor have been reclaimed. :type wait: bool :param executors: Executor factory. :type executors: ExecutorFactory :return: Shutdown pool executor. :rtype: dict[concurrent.futures.Future,Thread|Process]
def shutdown_executor(name=EMPTY, sol_id=EMPTY, wait=True, executors=None): """ Clean-up the resources associated with the Executor. :param name: Executor name. :type name: str :param sol_id: Solution id. :type sol_id: int :param wait: If True then shutdown will not return until all running futures have finished executing and the resources used by the executor have been reclaimed. :type wait: bool :param executors: Executor factory. :type executors: ExecutorFactory :return: Shutdown pool executor. :rtype: dict[concurrent.futures.Future,Thread|Process] """ if executors is None: executors = EXECUTORS return executors.shutdown_executor(name, sol_id, wait)
(name=empty, sol_id=empty, wait=True, executors=None)
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24,797
schedula.utils.asy
shutdown_executors
Clean-up the resources of all initialized executors. :param wait: If True then shutdown will not return until all running futures have finished executing and the resources used by the executors have been reclaimed. :type wait: bool :param executors: Executor factory. :type executors: ExecutorFactory :return: Shutdown pool executor. :rtype: dict[str,dict]
def shutdown_executors(wait=True, executors=None): """ Clean-up the resources of all initialized executors. :param wait: If True then shutdown will not return until all running futures have finished executing and the resources used by the executors have been reclaimed. :type wait: bool :param executors: Executor factory. :type executors: ExecutorFactory :return: Shutdown pool executor. :rtype: dict[str,dict] """ return shutdown_executor(wait=wait, executors=executors)
(wait=True, executors=None)
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24,798
schedula.utils.dsp
stack_nested_keys
Stacks the keys of nested-dictionaries into tuples and yields a list of k-v pairs. :param nested_dict: Nested dictionary. :type nested_dict: dict :param key: Initial keys. :type key: tuple, optional :param depth: Maximum keys depth. :type depth: int, optional :return: List of k-v pairs. :rtype: generator
def stack_nested_keys(nested_dict, key=(), depth=-1): """ Stacks the keys of nested-dictionaries into tuples and yields a list of k-v pairs. :param nested_dict: Nested dictionary. :type nested_dict: dict :param key: Initial keys. :type key: tuple, optional :param depth: Maximum keys depth. :type depth: int, optional :return: List of k-v pairs. :rtype: generator """ if depth != 0 and hasattr(nested_dict, 'items'): for k, v in nested_dict.items(): yield from stack_nested_keys(v, key=key + (k,), depth=depth - 1) else: yield key, nested_dict
(nested_dict, key=(), depth=-1)
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24,799
schedula.utils.dsp
stlp
Converts a string in a tuple.
def stlp(s): """ Converts a string in a tuple. """ if isinstance(s, str): return s, return s
(s)
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24,800
schedula.utils.dsp
summation
Sums inputs values. :param inputs: Inputs values. :type inputs: int, float :return: Sum of the input values. :rtype: int, float Example:: >>> summation(1, 3.0, 4, 2) 10.0
def summation(*inputs): """ Sums inputs values. :param inputs: Inputs values. :type inputs: int, float :return: Sum of the input values. :rtype: int, float Example:: >>> summation(1, 3.0, 4, 2) 10.0 """ # Return the sum of the input values. return functools.reduce(lambda x, y: x + y, inputs)
(*inputs)
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