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int64 0
731k
| package
stringlengths 2
98
⌀ | name
stringlengths 1
76
| docstring
stringlengths 0
281k
⌀ | code
stringlengths 4
8.19k
| signature
stringlengths 2
42.8k
⌀ | embed_func_code
listlengths 768
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24,645 | 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)
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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())
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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()
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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)
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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
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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
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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) | [
-0.023252779617905617,
0.0008441191748715937,
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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)
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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
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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)
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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
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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
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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
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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
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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
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24,732 | schedula.utils.exc | WebResponse | null | class WebResponse(BaseException):
def __init__(self, response):
self.response = response
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24,733 | schedula.utils.exc | __init__ | null | def __init__(self, response):
self.response = 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
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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
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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)
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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
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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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0.022967969998717308,
0.0008644045446999371,
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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
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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.
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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
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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
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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__
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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
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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)))
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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)))
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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
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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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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
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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
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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))
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24,763 | schedula.utils.dsp | __pos__ | null | def __pos__(self):
return inf(*(+x for x in 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)
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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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0.0386936254799366,
0.018072158098220825
] |
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)))
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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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0.003952901344746351,
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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)
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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)
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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.
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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
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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
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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.
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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)
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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
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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) | [
0.04253013804554939,
0.0014443485997617245,
-0.019739801064133644,
0.019349616020917892,
0.0032567125745117664,
-0.002793368184939027,
-0.08761420845985413,
-0.024829939007759094,
0.05267494171857834,
0.0038109521847218275,
0.018214533105492592,
-0.004544765688478947,
-0.044871244579553604,
0.04877309501171112,
0.03515210002660751,
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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)
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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)
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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)
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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
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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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