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41,669 | pyglove.core.patching.pattern_based | patch_on_value | Recursively patch values on matched values.
Example::
d = pg.Dict(a={'x': 1}, b=1)
print(pg.patching.patch_on_value(d, 1, value=3))
# {a={x=3}, b=3}
Args:
src: symbolic value to patch.
old_value: Old value to match.
value: New value for field that satisfy `condition`.
value_fn: Callable object that produces new value based on old value.
If not None, `value` must be None.
skip_notification: If True, `on_change` event will not be triggered for this
operation. If None, the behavior is decided by `pg.notify_on_rebind`.
Please see `symbolic.Symbolic.rebind` for details.
Returns:
`src` after being patched.
| def patch_on_value(
src: symbolic.Symbolic,
old_value: Any,
value: Any = None,
value_fn: Optional[Callable[[Any], Any]] = None,
skip_notification: Optional[bool] = None) -> Any:
"""Recursively patch values on matched values.
Example::
d = pg.Dict(a={'x': 1}, b=1)
print(pg.patching.patch_on_value(d, 1, value=3))
# {a={x=3}, b=3}
Args:
src: symbolic value to patch.
old_value: Old value to match.
value: New value for field that satisfy `condition`.
value_fn: Callable object that produces new value based on old value.
If not None, `value` must be None.
skip_notification: If True, `on_change` event will not be triggered for this
operation. If None, the behavior is decided by `pg.notify_on_rebind`.
Please see `symbolic.Symbolic.rebind` for details.
Returns:
`src` after being patched.
"""
return _conditional_patch(
src, lambda k, v, p: v == old_value,
value, value_fn, skip_notification)
| (src: pyglove.core.symbolic.base.Symbolic, old_value: Any, value: Optional[Any] = None, value_fn: Optional[Callable[[Any], Any]] = None, skip_notification: Optional[bool] = None) -> Any | [
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|
41,670 | pyglove.core.patching.rule_based | patcher | Decorate a function into a Patcher and register it.
A patcher function is defined as:
`<patcher_fun> := <fun_name>(<target>, [parameters])`
The signature takes at least one argument as the patching target,
with additional arguments as patching parameters to control the details of
this patch.
Example::
@pg.patching.patcher([
('x': pg.typing.Int())
])
def increment(v, x=1):
return pg.symbolic.get_rebind_dict(
lambda k, v, p: v + x if isinstance(v, int) else v)
# This patcher can be called via:
# pg.patching.apply(v, [increment(x=2)])
# or pg.patching.apply(v, ['increment?x=2'])
Args:
args: A list of (arg_name, arg_value_spec) to schematize patcher arguments.
name: String to be used as patcher name in URI. If None, function name will
be used as patcher name.
Returns:
A decorator that converts a function into a Patcher subclass.
| def patcher(
args: Optional[List[Tuple[str, pg_typing.ValueSpec]]] = None,
name: Optional[str] = None) -> Any:
"""Decorate a function into a Patcher and register it.
A patcher function is defined as:
`<patcher_fun> := <fun_name>(<target>, [parameters])`
The signature takes at least one argument as the patching target,
with additional arguments as patching parameters to control the details of
this patch.
Example::
@pg.patching.patcher([
('x': pg.typing.Int())
])
def increment(v, x=1):
return pg.symbolic.get_rebind_dict(
lambda k, v, p: v + x if isinstance(v, int) else v)
# This patcher can be called via:
# pg.patching.apply(v, [increment(x=2)])
# or pg.patching.apply(v, ['increment?x=2'])
Args:
args: A list of (arg_name, arg_value_spec) to schematize patcher arguments.
name: String to be used as patcher name in URI. If None, function name will
be used as patcher name.
Returns:
A decorator that converts a function into a Patcher subclass.
"""
functor_decorator = symbolic.functor(args, base_class=Patcher)
def _decorator(fn):
"""Returns decorated Patcher class."""
cls = functor_decorator(fn)
_PATCHER_REGISTRY.register(name or fn.__name__,
typing.cast(Type[Patcher], cls))
arg_specs = cls.__signature__.args
if len(arg_specs) < 1:
raise TypeError(
'Patcher function should have at least 1 argument '
f'as patching target. (Patcher={cls.__type_name__!r})'
)
if not _is_patcher_target_spec(arg_specs[0].value_spec):
raise TypeError(
f'{arg_specs[0].value_spec!r} cannot be used for constraining '
f'Patcher target. (Patcher={cls.__type_name__!r}, '
f'Argument={arg_specs[0].name!r})\n'
'Acceptable value spec types are: '
'Any, Callable, Dict, Functor, List, Object.'
)
for arg_spec in arg_specs[1:]:
if not _is_patcher_parameter_spec(arg_spec.value_spec):
raise TypeError(
f'{arg_spec.value_spec!r} cannot be used for constraining '
f'Patcher argument. (Patcher={cls.__type_name__!r}, '
f'Argument={arg_spec.name!r})\n'
'Consider to treat it as string and parse yourself.'
)
return cls
return _decorator
| (args: Optional[List[Tuple[str, pyglove.core.typing.class_schema.ValueSpec]]] = None, name: Optional[str] = None) -> Any | [
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|
41,672 | pyglove.core.hyper.categorical | permutate | Permuatation of candidates.
Example::
@pg.members([
('x', pg.typing.Int())
])
class A(pg.Object):
pass
# Permutates the candidates.
v = pg.permutate([1, 2, 3])
# A complex type as candidate.
v1 = pg.permutate(['a', {'x': 1}, A(1)])
# A hierarchical categorical choice:
v2 = pg.permutate([
'foo',
'bar',
A(pg.oneof([1, 2, 3]))
])
.. note::
Under symbolic mode (by default), `pg.manyof` returns a ``pg.hyper.ManyOf``
object. Under dynamic evaluate mode, which is called under the context of
:meth:`pyglove.hyper.DynamicEvaluationContext.collect` or
:meth:`pyglove.hyper.DynamicEvaluationContext.apply`, it evaluates to
a concrete candidate value.
To use conditional search space in dynamic evaluate mode, the candidate
should be wrapped with a `lambda` function, which is not necessary under
symbolic mode. For example::
pg.permutate([
lambda: pg.oneof([0, 1], name='sub_a'),
lambda: pg.floatv(0.0, 1.0, name='sub_b'),
lambda: pg.manyof(2, ['a', 'b', 'c'], name='sub_c')
], name='root')
See also:
* :class:`pyglove.hyper.ManyOf`
* :func:`pyglove.oneof`
* :func:`pyglove.manyof`
* :func:`pyglove.floatv`
* :func:`pyglove.evolve`
Args:
candidates: Candidates to select from. Items of candidate can be any type,
therefore it can have nested hyper primitives, which forms a hierarchical
search space.
name: A name that can be used to identify a decision point in the search
space. This is needed when the code to instantiate the same hyper
primitive may be called multiple times under a
`pg.DynamicEvaluationContext.collect` context or a
`pg.DynamicEvaluationContext.apply` context.
hints: An optional value which acts as a hint for the controller.
Returns:
In symbolic mode, this function returns a `Choices`.
In dynamic evaluate mode, this function returns a permutation from
`candidates`.
If evaluated under an `pg.DynamicEvaluationContext.apply` scope,
this function will return a permutation of candidates based on controller
decisions.
If evaluated under a `pg.DynamicEvaluationContext.collect`
scope, it will return the first valid permutation.
For example::
# Evaluates to [0, 1, 2, 3, 4].
permutate(range(5), name='numbers')
| def permutate(candidates: Iterable[Any],
name: Optional[str] = None,
hints: Optional[Any] = None) -> Any:
"""Permuatation of candidates.
Example::
@pg.members([
('x', pg.typing.Int())
])
class A(pg.Object):
pass
# Permutates the candidates.
v = pg.permutate([1, 2, 3])
# A complex type as candidate.
v1 = pg.permutate(['a', {'x': 1}, A(1)])
# A hierarchical categorical choice:
v2 = pg.permutate([
'foo',
'bar',
A(pg.oneof([1, 2, 3]))
])
.. note::
Under symbolic mode (by default), `pg.manyof` returns a ``pg.hyper.ManyOf``
object. Under dynamic evaluate mode, which is called under the context of
:meth:`pyglove.hyper.DynamicEvaluationContext.collect` or
:meth:`pyglove.hyper.DynamicEvaluationContext.apply`, it evaluates to
a concrete candidate value.
To use conditional search space in dynamic evaluate mode, the candidate
should be wrapped with a `lambda` function, which is not necessary under
symbolic mode. For example::
pg.permutate([
lambda: pg.oneof([0, 1], name='sub_a'),
lambda: pg.floatv(0.0, 1.0, name='sub_b'),
lambda: pg.manyof(2, ['a', 'b', 'c'], name='sub_c')
], name='root')
See also:
* :class:`pyglove.hyper.ManyOf`
* :func:`pyglove.oneof`
* :func:`pyglove.manyof`
* :func:`pyglove.floatv`
* :func:`pyglove.evolve`
Args:
candidates: Candidates to select from. Items of candidate can be any type,
therefore it can have nested hyper primitives, which forms a hierarchical
search space.
name: A name that can be used to identify a decision point in the search
space. This is needed when the code to instantiate the same hyper
primitive may be called multiple times under a
`pg.DynamicEvaluationContext.collect` context or a
`pg.DynamicEvaluationContext.apply` context.
hints: An optional value which acts as a hint for the controller.
Returns:
In symbolic mode, this function returns a `Choices`.
In dynamic evaluate mode, this function returns a permutation from
`candidates`.
If evaluated under an `pg.DynamicEvaluationContext.apply` scope,
this function will return a permutation of candidates based on controller
decisions.
If evaluated under a `pg.DynamicEvaluationContext.collect`
scope, it will return the first valid permutation.
For example::
# Evaluates to [0, 1, 2, 3, 4].
permutate(range(5), name='numbers')
"""
candidates = list(candidates)
return manyof(
len(candidates), candidates,
choices_distinct=True, choices_sorted=False, name=name, hints=hints)
| (candidates: Iterable[Any], name: Optional[str] = None, hints: Optional[Any] = None) -> Any | [
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|
41,673 | pyglove.core.tuning.backend | poll_result | Gets tuning result by name. | def poll_result(
name: str,
backend: Optional[str] = None,
**kwargs) -> Result:
"""Gets tuning result by name."""
return get_backend_cls(backend).poll_result(name, **kwargs)
| (name: str, backend: Optional[str] = None, **kwargs) -> pyglove.core.tuning.protocols.Result | [
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|
41,674 | pyglove.core.object_utils.formatting | printv | Prints formatted value. | def printv(v: Any, **kwargs):
"""Prints formatted value."""
fs = kwargs.pop('file', sys.stdout)
print(format(v, **kwargs), file=fs)
| (v: Any, **kwargs) | [
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|
41,675 | pyglove.core.symbolic.base | query | Queries a (maybe) symbolic value.
Example::
@pg.members([
('x', pg.typing.Int()),
('y', pg.typing.Int())
])
class A(pg.Object):
pass
value = {
'a1': A(x=0, y=1),
'a2': [A(x=1, y=1), A(x=1, y=2)],
'a3': {
'p': A(x=2, y=1),
'q': A(x=2, y=2)
}
}
# Query by path regex.
# Shall print:
# {'a3.p': A(x=2, y=1)}
print(pg.query(value, r'.*p'))
# Query by value.
# Shall print:
# {
# 'a2[1].y': 2,
# 'a3.p.x': 2,
# 'a3.q.x': 2,
# 'a3.q.y': 2,
# }
print(pg.query(value, where=lambda v: v==2))
# Query by path, value and parent.
# Shall print:
# {
# 'a2[1].y': 2,
# }
print(pg.query(
value, r'.*y',
where=lambda v, p: v > 1 and isinstance(p, A) and p.x == 1))
Args:
x: A nested structure that may contains symbolic value.
path_regex: Optional regex expression to constrain path.
where: Optional callable to constrain value and parent when path matches
with `path_regex` or `path_regex` is not provided. The signature is:
`(value) -> should_select` or `(value, parent) -> should_select`
enter_selected: If True, if a node is selected, enter the node and query
its sub-nodes.
custom_selector: Optional callable object as custom selector. When
`custom_selector` is provided, `path_regex` and `where` must be None.
The signature of `custom_selector` is:
`(key_path, value) -> should_select`
or `(key_path, value, parent) -> should_select`
Returns:
A dict of key path to value as results for selected values.
| def query(
x: Any,
path_regex: Optional[str] = None,
where: Optional[Union[Callable[[Any], bool],
Callable[[Any, Any], bool]]] = None,
enter_selected: bool = False,
custom_selector: Optional[Union[
Callable[[object_utils.KeyPath, Any], bool],
Callable[[object_utils.KeyPath, Any, Any], bool]]] = None
) -> Dict[str, Any]:
"""Queries a (maybe) symbolic value.
Example::
@pg.members([
('x', pg.typing.Int()),
('y', pg.typing.Int())
])
class A(pg.Object):
pass
value = {
'a1': A(x=0, y=1),
'a2': [A(x=1, y=1), A(x=1, y=2)],
'a3': {
'p': A(x=2, y=1),
'q': A(x=2, y=2)
}
}
# Query by path regex.
# Shall print:
# {'a3.p': A(x=2, y=1)}
print(pg.query(value, r'.*p'))
# Query by value.
# Shall print:
# {
# 'a2[1].y': 2,
# 'a3.p.x': 2,
# 'a3.q.x': 2,
# 'a3.q.y': 2,
# }
print(pg.query(value, where=lambda v: v==2))
# Query by path, value and parent.
# Shall print:
# {
# 'a2[1].y': 2,
# }
print(pg.query(
value, r'.*y',
where=lambda v, p: v > 1 and isinstance(p, A) and p.x == 1))
Args:
x: A nested structure that may contains symbolic value.
path_regex: Optional regex expression to constrain path.
where: Optional callable to constrain value and parent when path matches
with `path_regex` or `path_regex` is not provided. The signature is:
`(value) -> should_select` or `(value, parent) -> should_select`
enter_selected: If True, if a node is selected, enter the node and query
its sub-nodes.
custom_selector: Optional callable object as custom selector. When
`custom_selector` is provided, `path_regex` and `where` must be None.
The signature of `custom_selector` is:
`(key_path, value) -> should_select`
or `(key_path, value, parent) -> should_select`
Returns:
A dict of key path to value as results for selected values.
"""
regex = re.compile(path_regex) if path_regex else None
if custom_selector is not None:
if path_regex is not None or where is not None:
raise ValueError('\'path_regex\' and \'where\' must be None when '
'\'custom_selector\' is provided.')
signature = pg_typing.get_signature(custom_selector)
if len(signature.args) == 2:
select_fn = lambda k, v, p: custom_selector(k, v) # pytype: disable=wrong-arg-count
elif len(signature.args) == 3:
select_fn = custom_selector
else:
raise TypeError(
f'Custom selector \'{signature.id}\' should accept 2 or 3 arguments. '
f'(key_path, value, [parent]). Encountered: {signature.args}')
else:
if where is not None:
signature = pg_typing.get_signature(where)
if len(signature.args) == 1:
where_fn = lambda v, p: where(v) # pytype: disable=wrong-arg-count
elif len(signature.args) == 2:
where_fn = where
else:
raise TypeError(
f'Where function \'{signature.id}\' should accept 1 or 2 '
f'arguments: (value, [parent]). Encountered: {signature.args}.')
else:
where_fn = lambda v, p: True
def select_fn(k, v, p):
if regex is not None and not regex.match(str(k)):
return False
return where_fn(v, p) # pytype: disable=wrong-arg-count
results = {}
def _preorder_visitor(path: object_utils.KeyPath, v: Any,
parent: Any) -> TraverseAction:
if select_fn(path, v, parent): # pytype: disable=wrong-arg-count
results[str(path)] = v
return TraverseAction.ENTER if enter_selected else TraverseAction.CONTINUE
return TraverseAction.ENTER
traverse(x, preorder_visitor_fn=_preorder_visitor)
return results
| (x: Any, path_regex: Optional[str] = None, where: Union[Callable[[Any], bool], Callable[[Any, Any], bool], NoneType] = None, enter_selected: bool = False, custom_selector: Union[Callable[[pyglove.core.object_utils.value_location.KeyPath, Any], bool], Callable[[pyglove.core.object_utils.value_location.KeyPath, Any, Any], bool], NoneType] = None) -> Dict[str, Any] | [
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|
41,676 | pyglove.core.geno.random | random_dna | Generates a random DNA from a DNASpec.
Example::
spec = pg.geno.space([
pg.geno.oneof([
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant()
]),
pg.geno.floatv(0.1, 0.2)
])
print(pg.random_dna(spec))
# DNA([2, 0.1123])
Args:
dna_spec: a DNASpec object.
random_generator: a Python random generator.
attach_spec: If True, attach the DNASpec to generated DNA.
previous_dna: An optional DNA representing previous DNA. This field might
be useful for generating stateful random DNAs.
Returns:
A DNA object.
| def random_dna(
dna_spec: DNASpec,
random_generator: Union[None, types.ModuleType, random.Random] = None,
attach_spec: bool = True,
previous_dna: Optional[DNA] = None
) -> DNA:
"""Generates a random DNA from a DNASpec.
Example::
spec = pg.geno.space([
pg.geno.oneof([
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant()
]),
pg.geno.floatv(0.1, 0.2)
])
print(pg.random_dna(spec))
# DNA([2, 0.1123])
Args:
dna_spec: a DNASpec object.
random_generator: a Python random generator.
attach_spec: If True, attach the DNASpec to generated DNA.
previous_dna: An optional DNA representing previous DNA. This field might
be useful for generating stateful random DNAs.
Returns:
A DNA object.
"""
return dna_spec.random_dna(
random_generator or random, attach_spec, previous_dna)
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|
41,677 | pyglove.core.hyper.iter | random_sample | Returns an iterator of random sampled examples.
Example::
hyper_dict = pg.Dict(x=pg.oneof(range(3)), y=pg.floatv(0.0, 1.0))
# Generate one random example from the hyper_dict.
d = next(pg.random_sample(hyper_dict))
# Generate 5 random examples with random seed.
ds = list(pg.random_sample(hyper_dict, 5, seed=1))
# Generate 3 random examples of `x` with `y` intact.
ds = list(pg.random_sample(hyper_dict, 3,
where=lambda x: isinstance(x, pg.hyper.OneOf)))
Args:
value: A (maybe) hyper value.
num_examples: An optional integer as number of examples to propose. If None,
propose will return an iterator that iterates forever.
where: Function to filter hyper primitives. If None, all hyper primitives in
`value` will be included in the encoding/decoding process. Otherwise only
the hyper primitives on which 'where' returns True will be included.
`where` can be useful to partition a search space into separate
optimization processes. Please see 'Template' docstr for details.
seed: An optional integer as random seed.
Returns:
Iterator of random examples.
| def random_sample(
value: Any,
num_examples: Optional[int] = None,
where: Optional[Callable[[base.HyperPrimitive], bool]] = None,
seed: Optional[int] = None):
"""Returns an iterator of random sampled examples.
Example::
hyper_dict = pg.Dict(x=pg.oneof(range(3)), y=pg.floatv(0.0, 1.0))
# Generate one random example from the hyper_dict.
d = next(pg.random_sample(hyper_dict))
# Generate 5 random examples with random seed.
ds = list(pg.random_sample(hyper_dict, 5, seed=1))
# Generate 3 random examples of `x` with `y` intact.
ds = list(pg.random_sample(hyper_dict, 3,
where=lambda x: isinstance(x, pg.hyper.OneOf)))
Args:
value: A (maybe) hyper value.
num_examples: An optional integer as number of examples to propose. If None,
propose will return an iterator that iterates forever.
where: Function to filter hyper primitives. If None, all hyper primitives in
`value` will be included in the encoding/decoding process. Otherwise only
the hyper primitives on which 'where' returns True will be included.
`where` can be useful to partition a search space into separate
optimization processes. Please see 'Template' docstr for details.
seed: An optional integer as random seed.
Returns:
Iterator of random examples.
"""
return iterate(
value, num_examples, geno.Random(seed), where=where)
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|
41,678 | pyglove.core.typing.type_conversion | register_converter | Register converter from source type to destination type.
Examples::
# Add converter from int to float.
pg.typing.register_converter(int, float, float)
assert pg.typing.Float().apply(1) is 1.0
# Add converter from a dict to class A.
def from_dict(d):
return A(**d)
assert isinstance(pg.typing.Object(A).apply({'x': 1, 'y': 2}), A)
Args:
src_type: Source value type.
dest_type: Target value type.
convert_fn: Function that performs the conversion, in signature
(src_type) -> dest_type.
| def register_converter(
src_type: Union[Type[Any], Tuple[Type[Any], ...]],
dest_type: Union[Type[Any], Tuple[Type[Any], ...]],
convert_fn: Callable[[Any], Any]) -> None:
"""Register converter from source type to destination type.
Examples::
# Add converter from int to float.
pg.typing.register_converter(int, float, float)
assert pg.typing.Float().apply(1) is 1.0
# Add converter from a dict to class A.
def from_dict(d):
return A(**d)
assert isinstance(pg.typing.Object(A).apply({'x': 1, 'y': 2}), A)
Args:
src_type: Source value type.
dest_type: Target value type.
convert_fn: Function that performs the conversion, in signature
(src_type) -> dest_type.
"""
_TYPE_CONVERTER_REGISTRY.register(src_type, dest_type, convert_fn)
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|
41,679 | pyglove.core.object_utils.json_conversion | registered_types | Returns an iterator of registered (serialization key, class) tuples. | def registered_types() -> Iterable[Tuple[str, Type[JSONConvertible]]]:
"""Returns an iterator of registered (serialization key, class) tuples."""
return JSONConvertible.registered_types()
| () -> Iterable[Tuple[str, Type[pyglove.core.object_utils.json_conversion.JSONConvertible]]] | [
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|
41,680 | pyglove.core.object_utils.common_traits | repr_format | Context manager for setting the default format kwargs for __repr__. | def repr_format(**kwargs) -> ContextManager[Dict[str, Any]]:
"""Context manager for setting the default format kwargs for __repr__."""
return thread_local.thread_local_arg_scope(_TLS_REPR_FORMAT_KWARGS, **kwargs)
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|
41,681 | pyglove.core.tuning.sample | sample | Yields an example and its feedback sampled from a hyper value.
Example 1: sample a search space defined by a symbolic hyper value::
for example, feedback in pg.sample(
pg.Dict(x=pg.floatv(-1, 1))),
pg.geno.Random(),
num_examples=10,
name='my_search'):
# We can access trial ID (staring from 1) and DNA from the feedback.
print(feedback.id, feedback.dna)
# We can report the reward computed on the example using
# `feedback.add_measurement`, which can be called
# multiple times to report the rewards incrementally.
# Once a trial is done, we call `feedback.done` to mark evaluation on
# current example as completed, or use `feedback.skip` to move to the
# next sample without passing any feedback to the algorithm.
# Without `feedback.done` or `feedback.skip`, the same trial will be
# iterated over and over again for failover handling purpose.
# Besides the reward and the step, metrics and checkpoint can be added
# to each measurement. Additional meta-data and related links (URLs) can
# be passed to `feedback.done` which can be retrieved via
# `pg.poll_result` later.
if example.x >= 0:
# If we only want to add one measurement for each example, a
# shortcut expression for the next two lines can be written as
# follows:
# `feedback(reward=math.sqrt(example.x), step=1)`
feedback.add_measurement(reward=math.sqrt(example.x), step=1)
feedback.done()
else:
feedback.skip()
# IMPORTANT: to stop the loop among all workers, we can call
# `feedback.end_loop`. As a result, each worker will quit their loop
# after current iteration, while using `break` in the for-loop is
# only effective for the local process rather than remotely.
if session.id > 1000:
feedback.end_loop()
# At any time, we can poll the search result via `pg.poll_result`, please
# see `pg.tuning.Result` for more details.
result = pg.poll_result('my_search')
print(result.best_trial)
Example 2: sample a search space defined by `pg.hyper.trace`::
def fun():
return pg.oneof([
lambda: pg.oneof([1, 2, 3]),
lambda: pg.float(0.1, 1.0),
3]) + sum(pg.manyof(2, [1, 2, 3]))
for example, feedback in pg.sample(
pg.hyper.trace(fun),
pg.geno.Random(),
num_examples=10,
name='my_search'):
# When space is a `pg.hyper.DynamicEvaluationContext` object,
# the `example` yielded at each iteration is a context manager under which
# the hyper primitives (e.g. pg.oneof) will be materialized into concrete
# values according to the controller decision.
with example():
reward = fun()
feedback(reward)
Example 3: sample DNAs from an abstract search space represented by
`pg.DNASpec`::
for dna, feedback in pg.sample(
pg.List([pg.oneof(range(3))] * 5).dna_spec(),
pg.geno.Random(),
num_examples=10,
name='my_search'):
reward = evaluate_dna(dna)
feedback(reward)
**Using `pg.sample` in distributed environment**
`pg.sample` is designed with distributed sampling in mind, in which multiple
processes can work on the trials of the same sampling loop. While the default
'in-memory' backend works only within a single process without failover
handling, other backends may support distributed computing environment with
persistent state. Nevertheless, the `pg.sample` API is the same
among different backends, users can switch the backend easily by passing a
different value to the `backend` argument, or set the default value globally
via `pg.tuning.set_default_backend`.
**Identifying a sampling**
To identify a distributed loop, a unique `name` is introduced, which will
also be used to poll the latest sampling result via `pg.poll_result`.
**Failover handling**
In a distributed setup, worker processes may incidentally die and restart.
Unless a trial is explicitly marked as done (via `feedback(reward)` or
`feedback.done()`) or skipped (via feedback.skip()), a worker will try to
resume its work on the trial from where it left off.
**Workroup**
In a distributed setup, worker processes may or may not work on the same
trials. Worker group is introduced to serve this purpose, which is identified
by an integer or a string named `group`. If `group` is not specified or
having different values among the workers, every worker will work on
different trials of the loop. On the contrary, workers having the same
`group` will co-work on the same trials. Group is useful when evaluation on
one example can be parallelized - for example - an example in the outer loop
of a nested search. However, feedback on the same example should be fed back
to the search algorithm only once. Therefore, workers in the same group need
to communicate with each other to avoid duplicated evaluation and feedbacks.
To facilitate such communication, per-trial metadata is supported, and can be
accessed via `feedback.set_metadata/get_metadata` methods. The consistency in
reading and writing the metadata is defined by the backend used. For the
'in-memory' backend, all the trials and their metadata is stored in memory,
thus will be lost if the process get restarted. On the contrary, the backends
built on distributed computing environment may store both the trials and
metadata in a persistent storage with varing read/write QPS and read/write
consistency guarentees.
**Switch between backends**
The `backend` argument of `pg.sample` lets users choose a backend used in
current sampling loop. Users can use different backends in the same process
to achieve a best performance trade-off, e.g., using `in-memory` backend when
the communication cost overweighs the redudant evaluation cost upon worker
failure.
Helper function :func:`pyglove.tuning.set_default_backend` is introduced to
set the default tuning backend for the entire process.
Args:
space: One of (a hyper value, a `pg.hyper.DynamicEvaluationContext`,
a `pg.DNASpec`) to sample from.
A hyper value is an object with to-be-determined values specified by
`pg.oneof`, `pg.manyof`, `pg.floatv` and etc, representing a search space.
A `pg.hyper.DynamicEvaluationContext` object represents a search space
that is traced via dynamic evaluation.
A `pg.DNASpec` represents an abstract search space that emits DNAs.
algorithm: The search algorithm that samples the search space. For example:
`pg.geno.Random()`, `pg.evolution.regularized_evolution(...)`, and
etc.
num_examples: An optional integer as the max number of examples to
sample. If None, sample will return an iterator of infinite examples.
early_stopping_policy: An optional early stopping policy for user to tell
if incremental evaluation (which reports multiple measurements) on each
example can be early short circuited.
After each call to `feedback.add_measurement`, users can use method
`feedback.should_stop_early` to check whether current example worth
further evaluation or not.
where: Function to filter the hyper values. If None, all decision points
from the `space` will be included for the algorithm to make
decisions. Otherwise only the decision points on which 'where' returns
True will be included. The rest decision points will be passed through
in the example, intact, which is a sub-space of the search space
represented by the `space`. `where` is usually used in nested search
flows. Please see 'hyper.Template' docstr for details.
name: A string as a unique identifier for current sampling. Two separate
calls to `pg.sample` with the same `name` (also the same algorithm) will
share the same sampling queue, whose examples are proposed by the same
search algorithm.
group: An string or integer as the group ID of current process in
distributed sampling, which will be used to group different workers into
co-worker groups. Workers with the same group id will work on the same
trial. On the contrary, workers in different groups will always be working
with different trials. If not specified, each worker in current sampling
will be in different groups. `group` is usually used in the outer
loops of nested search, in order to allow workers to work on the same
higher-order item.
backend: An optional string to specify the backend name for sampling.
if None, the default backend set by `pg.tuning.set_default_backend`
will be used.
metrics_to_optimize: A sequence of string as the names of the metrics
to be optimized by the algorithm, which is ['reward'] by default.
When specified, it should have only 1 item for single-objective algorithm
and can have multiple items for algorithms that support multi-objective
optimization.
**kwargs: Arguments passed to the `BackendFactory` subclass registered with
the requested backend.
Yields:
An iterator of tuples (example, feedback) as examples sampled from the
search space defined by the `space` through `algorithm`.
Raises:
ValueError: `space` is a fixed value, or requested `backend` is not
available.
| def sample(space: Union[HyperValue,
hyper.DynamicEvaluationContext,
geno.DNASpec],
algorithm: geno.DNAGenerator,
num_examples: Optional[int] = None,
early_stopping_policy: Optional[EarlyStoppingPolicy] = None,
where: Optional[Callable[[hyper.HyperPrimitive], bool]] = None,
name: Optional[str] = None,
group: Union[None, int, str] = None,
backend: Optional[str] = None,
metrics_to_optimize: Optional[Sequence[str]] = None,
**kwargs):
"""Yields an example and its feedback sampled from a hyper value.
Example 1: sample a search space defined by a symbolic hyper value::
for example, feedback in pg.sample(
pg.Dict(x=pg.floatv(-1, 1))),
pg.geno.Random(),
num_examples=10,
name='my_search'):
# We can access trial ID (staring from 1) and DNA from the feedback.
print(feedback.id, feedback.dna)
# We can report the reward computed on the example using
# `feedback.add_measurement`, which can be called
# multiple times to report the rewards incrementally.
# Once a trial is done, we call `feedback.done` to mark evaluation on
# current example as completed, or use `feedback.skip` to move to the
# next sample without passing any feedback to the algorithm.
# Without `feedback.done` or `feedback.skip`, the same trial will be
# iterated over and over again for failover handling purpose.
# Besides the reward and the step, metrics and checkpoint can be added
# to each measurement. Additional meta-data and related links (URLs) can
# be passed to `feedback.done` which can be retrieved via
# `pg.poll_result` later.
if example.x >= 0:
# If we only want to add one measurement for each example, a
# shortcut expression for the next two lines can be written as
# follows:
# `feedback(reward=math.sqrt(example.x), step=1)`
feedback.add_measurement(reward=math.sqrt(example.x), step=1)
feedback.done()
else:
feedback.skip()
# IMPORTANT: to stop the loop among all workers, we can call
# `feedback.end_loop`. As a result, each worker will quit their loop
# after current iteration, while using `break` in the for-loop is
# only effective for the local process rather than remotely.
if session.id > 1000:
feedback.end_loop()
# At any time, we can poll the search result via `pg.poll_result`, please
# see `pg.tuning.Result` for more details.
result = pg.poll_result('my_search')
print(result.best_trial)
Example 2: sample a search space defined by `pg.hyper.trace`::
def fun():
return pg.oneof([
lambda: pg.oneof([1, 2, 3]),
lambda: pg.float(0.1, 1.0),
3]) + sum(pg.manyof(2, [1, 2, 3]))
for example, feedback in pg.sample(
pg.hyper.trace(fun),
pg.geno.Random(),
num_examples=10,
name='my_search'):
# When space is a `pg.hyper.DynamicEvaluationContext` object,
# the `example` yielded at each iteration is a context manager under which
# the hyper primitives (e.g. pg.oneof) will be materialized into concrete
# values according to the controller decision.
with example():
reward = fun()
feedback(reward)
Example 3: sample DNAs from an abstract search space represented by
`pg.DNASpec`::
for dna, feedback in pg.sample(
pg.List([pg.oneof(range(3))] * 5).dna_spec(),
pg.geno.Random(),
num_examples=10,
name='my_search'):
reward = evaluate_dna(dna)
feedback(reward)
**Using `pg.sample` in distributed environment**
`pg.sample` is designed with distributed sampling in mind, in which multiple
processes can work on the trials of the same sampling loop. While the default
'in-memory' backend works only within a single process without failover
handling, other backends may support distributed computing environment with
persistent state. Nevertheless, the `pg.sample` API is the same
among different backends, users can switch the backend easily by passing a
different value to the `backend` argument, or set the default value globally
via `pg.tuning.set_default_backend`.
**Identifying a sampling**
To identify a distributed loop, a unique `name` is introduced, which will
also be used to poll the latest sampling result via `pg.poll_result`.
**Failover handling**
In a distributed setup, worker processes may incidentally die and restart.
Unless a trial is explicitly marked as done (via `feedback(reward)` or
`feedback.done()`) or skipped (via feedback.skip()), a worker will try to
resume its work on the trial from where it left off.
**Workroup**
In a distributed setup, worker processes may or may not work on the same
trials. Worker group is introduced to serve this purpose, which is identified
by an integer or a string named `group`. If `group` is not specified or
having different values among the workers, every worker will work on
different trials of the loop. On the contrary, workers having the same
`group` will co-work on the same trials. Group is useful when evaluation on
one example can be parallelized - for example - an example in the outer loop
of a nested search. However, feedback on the same example should be fed back
to the search algorithm only once. Therefore, workers in the same group need
to communicate with each other to avoid duplicated evaluation and feedbacks.
To facilitate such communication, per-trial metadata is supported, and can be
accessed via `feedback.set_metadata/get_metadata` methods. The consistency in
reading and writing the metadata is defined by the backend used. For the
'in-memory' backend, all the trials and their metadata is stored in memory,
thus will be lost if the process get restarted. On the contrary, the backends
built on distributed computing environment may store both the trials and
metadata in a persistent storage with varing read/write QPS and read/write
consistency guarentees.
**Switch between backends**
The `backend` argument of `pg.sample` lets users choose a backend used in
current sampling loop. Users can use different backends in the same process
to achieve a best performance trade-off, e.g., using `in-memory` backend when
the communication cost overweighs the redudant evaluation cost upon worker
failure.
Helper function :func:`pyglove.tuning.set_default_backend` is introduced to
set the default tuning backend for the entire process.
Args:
space: One of (a hyper value, a `pg.hyper.DynamicEvaluationContext`,
a `pg.DNASpec`) to sample from.
A hyper value is an object with to-be-determined values specified by
`pg.oneof`, `pg.manyof`, `pg.floatv` and etc, representing a search space.
A `pg.hyper.DynamicEvaluationContext` object represents a search space
that is traced via dynamic evaluation.
A `pg.DNASpec` represents an abstract search space that emits DNAs.
algorithm: The search algorithm that samples the search space. For example:
`pg.geno.Random()`, `pg.evolution.regularized_evolution(...)`, and
etc.
num_examples: An optional integer as the max number of examples to
sample. If None, sample will return an iterator of infinite examples.
early_stopping_policy: An optional early stopping policy for user to tell
if incremental evaluation (which reports multiple measurements) on each
example can be early short circuited.
After each call to `feedback.add_measurement`, users can use method
`feedback.should_stop_early` to check whether current example worth
further evaluation or not.
where: Function to filter the hyper values. If None, all decision points
from the `space` will be included for the algorithm to make
decisions. Otherwise only the decision points on which 'where' returns
True will be included. The rest decision points will be passed through
in the example, intact, which is a sub-s | (space: Union[Any, pyglove.core.hyper.dynamic_evaluation.DynamicEvaluationContext, pyglove.core.geno.base.DNASpec], algorithm: pyglove.core.geno.dna_generator.DNAGenerator, num_examples: Optional[int] = None, early_stopping_policy: Optional[pyglove.core.tuning.early_stopping.EarlyStoppingPolicy] = None, where: Optional[Callable[[pyglove.core.hyper.base.HyperPrimitive], bool]] = None, name: Optional[str] = None, group: Union[NoneType, int, str] = None, backend: Optional[str] = None, metrics_to_optimize: Optional[Sequence[str]] = None, **kwargs) | [
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|
41,682 | pyglove.core.symbolic.base | save | Save a symbolic value using the global save handler.
Example::
@pg.members([
('x', pg.typing.Any())
])
class A(pg.Object):
pass
a1 = A(1)
file = 'my_file.json'
a1.save(file)
a2 = pg.load(file)
assert pg.eq(a1, a2)
Args:
value: value to save.
path: A path string for saving `value`.
*args: Positional arguments that will be passed through to the global
save handler.
**kwargs: Keyword arguments that will be passed through to the global
save handler.
Returns:
Return value from the global save handler.
Raises:
RuntimeError: if global save handler is not set.
| def save(value: Any, path: str, *args, **kwargs) -> Any:
"""Save a symbolic value using the global save handler.
Example::
@pg.members([
('x', pg.typing.Any())
])
class A(pg.Object):
pass
a1 = A(1)
file = 'my_file.json'
a1.save(file)
a2 = pg.load(file)
assert pg.eq(a1, a2)
Args:
value: value to save.
path: A path string for saving `value`.
*args: Positional arguments that will be passed through to the global
save handler.
**kwargs: Keyword arguments that will be passed through to the global
save handler.
Returns:
Return value from the global save handler.
Raises:
RuntimeError: if global save handler is not set.
"""
save_handler = flags.get_save_handler() or default_save_handler
return save_handler(value, path, *args, **kwargs)
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|
41,685 | pyglove.core.symbolic.flags | set_load_handler | Sets global load handler.
Args:
load_handler: A callable object that takes arbitrary arguments and returns
a value. `symbolic.load` method will pass through all arguments to this
handler and return its return value.
Returns:
Previous global load handler.
| def set_load_handler(
load_handler: Optional[Callable[..., Any]]) -> Optional[Callable[..., Any]]:
"""Sets global load handler.
Args:
load_handler: A callable object that takes arbitrary arguments and returns
a value. `symbolic.load` method will pass through all arguments to this
handler and return its return value.
Returns:
Previous global load handler.
"""
if load_handler and not callable(load_handler):
raise ValueError('`load_handler` must be callable.')
global _LOAD_HANDLER
old_handler = _LOAD_HANDLER
_LOAD_HANDLER = load_handler
return old_handler
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|
41,686 | pyglove.core.symbolic.flags | set_origin_stacktrace_limit | Set stack trace limit for origin tracking. | def set_origin_stacktrace_limit(limit: int) -> None:
"""Set stack trace limit for origin tracking."""
global _ORIGIN_STACKTRACE_LIMIT
_ORIGIN_STACKTRACE_LIMIT = limit
| (limit: int) -> NoneType | [
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|
41,687 | pyglove.core.symbolic.flags | set_save_handler | Sets global save handler.
Args:
save_handler: A callable object that takes at least one argument as value to
save. `symbolic.save` method will pass through all arguments to this
handler and return its return value.
Returns:
Previous global save handler.
| def set_save_handler(
save_handler: Optional[Callable[..., Any]]) -> Optional[Callable[..., Any]]:
"""Sets global save handler.
Args:
save_handler: A callable object that takes at least one argument as value to
save. `symbolic.save` method will pass through all arguments to this
handler and return its return value.
Returns:
Previous global save handler.
"""
if save_handler and not callable(save_handler):
raise ValueError('`save_handler` must be callable.')
global _SAVE_HANDLER
old_handler = _SAVE_HANDLER
_SAVE_HANDLER = save_handler
return old_handler
| (save_handler: Optional[Callable[..., Any]]) -> Optional[Callable[..., Any]] | [
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|
41,688 | pyglove.core.object_utils.common_traits | str_format | Context manager for setting the default format kwargs for __str__. | def str_format(**kwargs) -> ContextManager[Dict[str, Any]]:
"""Context manager for setting the default format kwargs for __str__."""
return thread_local.thread_local_arg_scope(_TLS_STR_FORMAT_KWARGS, **kwargs)
| (**kwargs) -> ContextManager[Dict[str, Any]] | [
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|
41,691 | pyglove.core.symbolic.symbolize | symbolize | Make a symbolic class/function out of a regular Python class/function.
``pg.symbolize`` is introduced for the purpose of making existing
classes/functions symbolically programmable. For use cases that build
symbolic classes from scratch (native PyGlove classes), extending `pg.Object`
with `@pg.members` that declares the symbolic properties is the recommended
way, which automatically generates the ``__init__`` method and allow
symbolic attributes to be accessed via `self.<member>`.
``pg.symbolize`` can be invoked as a class/function decorator, or as a
function. When it is used as a decorator, the decorated class or function
will be converted to a symbolic type (via :func:`pyglove.wrap` and
:func:`pyglove.functor_class`). This is preferred when user can modify the
files of existing classes/functions. For example::
@pg.symbolize
def foo(a, b):
return a + b
f = foo(1, 2)
f.rebind(a=2)
f() # Returns 4
@pg.symbolize([
# (Optional) add symbolic constraint for __init__ argument 'a'.
('a', pg.typing.Int(min_value=0), 'Description for `a`.')
])
class Foo:
def __init__(self, a, b):
self._a = a
self._b = b
def result(self):
return self._a + self._b
f = Foo(1, 2)
f.rebind(a=2, b=3)
f.result() # Returns 5
When it used as a function, the input class or function will not be modified.
Instead, a new symbolic type will be created and returned. This is helpful
when users want to create new symbolic types from existing classes/functions
without modifying their original source code. For example::
def foo(a, b):
return a + b
# Create a new symbolic type with constraint on 'a'.
symbolic_foo = pg.symbolize(foo, [
('a', pg.typing.Int(min_value=0))
], returns=pg.typing.Int())
foo(1, 2) # Returns 3 (foo is kept intact).
f = symbolic_foo(1, 2)
f.rebind(a=2)
f() # Returns 4.
class Foo:
def __init__(self, a, b):
self._a = a
self._b = b
def result(self):
return self._a + self._b
SymbolicFoo = pg.symbolize(Foo)
f = SymbolicFoo(2, 2)
f.rebind(a=3)
f.result() # Returns 5.
Args:
*args: The positional arguments for `symbolize` are:
* `class_or_fn`: applicable when `symbolize` is called in function mode.
* `constraints`: an optional list of tuples that allows users to specify
the constraints for arguments from the `__init__` method (for class)
or the arguments from the function signature (for function).
Each tuple should be in format:
`(<arg_name>, <value_spec>, [description], [arg_metadata])`
Where `arg_name` is an argument name that is acceptable to the
`__init__` method of the class, or the function signature;
'value_spec' is a `pg.ValueSpec` object that validates the value of
the argument.
`description` and `arg_metadata` are optional, for documentation and
meta-programming purposes.
**kwargs: Keyword arguments will be passsed through to :func:`pyglove.wrap`
(for symbolizing classes) and :func:`pyglove.functor_class` (for
symbolizing functions).
Returns:
A Symbolic subclass for the decorated/input type.
Raises:
TypeError: input type cannot be symbolized, or it's not a type.
| def symbolize(*args, **kwargs):
"""Make a symbolic class/function out of a regular Python class/function.
``pg.symbolize`` is introduced for the purpose of making existing
classes/functions symbolically programmable. For use cases that build
symbolic classes from scratch (native PyGlove classes), extending `pg.Object`
with `@pg.members` that declares the symbolic properties is the recommended
way, which automatically generates the ``__init__`` method and allow
symbolic attributes to be accessed via `self.<member>`.
``pg.symbolize`` can be invoked as a class/function decorator, or as a
function. When it is used as a decorator, the decorated class or function
will be converted to a symbolic type (via :func:`pyglove.wrap` and
:func:`pyglove.functor_class`). This is preferred when user can modify the
files of existing classes/functions. For example::
@pg.symbolize
def foo(a, b):
return a + b
f = foo(1, 2)
f.rebind(a=2)
f() # Returns 4
@pg.symbolize([
# (Optional) add symbolic constraint for __init__ argument 'a'.
('a', pg.typing.Int(min_value=0), 'Description for `a`.')
])
class Foo:
def __init__(self, a, b):
self._a = a
self._b = b
def result(self):
return self._a + self._b
f = Foo(1, 2)
f.rebind(a=2, b=3)
f.result() # Returns 5
When it used as a function, the input class or function will not be modified.
Instead, a new symbolic type will be created and returned. This is helpful
when users want to create new symbolic types from existing classes/functions
without modifying their original source code. For example::
def foo(a, b):
return a + b
# Create a new symbolic type with constraint on 'a'.
symbolic_foo = pg.symbolize(foo, [
('a', pg.typing.Int(min_value=0))
], returns=pg.typing.Int())
foo(1, 2) # Returns 3 (foo is kept intact).
f = symbolic_foo(1, 2)
f.rebind(a=2)
f() # Returns 4.
class Foo:
def __init__(self, a, b):
self._a = a
self._b = b
def result(self):
return self._a + self._b
SymbolicFoo = pg.symbolize(Foo)
f = SymbolicFoo(2, 2)
f.rebind(a=3)
f.result() # Returns 5.
Args:
*args: The positional arguments for `symbolize` are:
* `class_or_fn`: applicable when `symbolize` is called in function mode.
* `constraints`: an optional list of tuples that allows users to specify
the constraints for arguments from the `__init__` method (for class)
or the arguments from the function signature (for function).
Each tuple should be in format:
`(<arg_name>, <value_spec>, [description], [arg_metadata])`
Where `arg_name` is an argument name that is acceptable to the
`__init__` method of the class, or the function signature;
'value_spec' is a `pg.ValueSpec` object that validates the value of
the argument.
`description` and `arg_metadata` are optional, for documentation and
meta-programming purposes.
**kwargs: Keyword arguments will be passsed through to :func:`pyglove.wrap`
(for symbolizing classes) and :func:`pyglove.functor_class` (for
symbolizing functions).
Returns:
A Symbolic subclass for the decorated/input type.
Raises:
TypeError: input type cannot be symbolized, or it's not a type.
"""
cls_or_fn = None
if args:
if inspect.isclass(args[0]) or inspect.isfunction(args[0]): # pytype: disable=not-supported-yet
cls_or_fn = args[0]
if cls_or_fn is dict or cls_or_fn is list:
if len(args) != 1 or kwargs:
raise ValueError(
f'Constraints are not supported in symbolic {cls_or_fn!r}. '
f'Encountered: constraints={args[1]!r}.')
return pg_dict.Dict if cls_or_fn is dict else pg_list.List
args = args[1:]
if len(args) > 1:
raise ValueError(
f'Only `constraint` is supported as positional arguments. '
f'Encountered {args!r}.')
elif not isinstance(args[0], list):
raise TypeError(f'{args[0]!r} cannot be symbolized.')
def _symbolize(cls_or_fn):
if inspect.isclass(cls_or_fn):
if (issubclass(cls_or_fn, base.Symbolic)
and not issubclass(cls_or_fn, class_wrapper.ClassWrapper)):
raise ValueError(
f'Cannot symbolize {cls_or_fn!r}: {cls_or_fn.__name__} is already '
f'a dataclass-like symbolic class derived from `pg.Object`. '
f'Consider to use `pg.members` to add new symbolic attributes.')
return class_wrapper.wrap(cls_or_fn, *args, **kwargs)
assert inspect.isfunction(cls_or_fn), (
f'Unexpected: {cls_or_fn!r} should be a class or function.')
return functor_class(
cls_or_fn, add_to_registry=True, *args, **kwargs)
if cls_or_fn is not None:
# When `cls_or_fn` is provided, `symbolize` is called under function mode
# such as `SymbolicFoo = pg.symbolize(Foo)` or being used as a decorator
# with no arguments, e.g:
# ```
# @symbolize
# class Foo:
# pass
# ```
# In both case, we return the symbolic type of `cls_or_fn`.
return _symbolize(cls_or_fn)
else:
# Otherwise a factory method is returned to create the symbolic type from
# a late-bound `cls_or_fn` input, which is the case when `symbolize` is used
# as a decorator with provided arguments.
return _symbolize
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|
41,692 | pyglove.core.hyper.object_template | template | Creates an object template from the input.
Example::
d = pg.Dict(x=pg.oneof(['a', 'b', 'c'], y=pg.manyof(2, range(4))))
t = pg.template(d)
assert t.dna_spec() == pg.geno.space([
pg.geno.oneof([
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
], location='x'),
pg.geno.manyof([
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
], location='y')
])
assert t.encode(pg.Dict(x='a', y=0)) == pg.DNA([0, 0])
assert t.decode(pg.DNA([0, 0])) == pg.Dict(x='a', y=0)
t = pg.template(d, where=lambda x: isinstance(x, pg.hyper.ManyOf))
assert t.dna_spec() == pg.geno.space([
pg.geno.manyof([
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
], location='y')
])
assert t.encode(pg.Dict(x=pg.oneof(['a', 'b', 'c']), y=0)) == pg.DNA(0)
assert t.decode(pg.DNA(0)) == pg.Dict(x=pg.oneof(['a', 'b', 'c']), y=0)
Args:
value: A value based on which the template is created.
where: Function to filter hyper values. If None, all hyper primitives from
`value` will be included in the encoding/decoding process. Otherwise
only the hyper values on which 'where' returns True will be included.
`where` can be useful to partition a search space into separate
optimization processes. Please see 'ObjectTemplate' docstr for details.
Returns:
A template object.
| def template(
value: Any,
where: Optional[Callable[[base.HyperPrimitive], bool]] = None
) -> ObjectTemplate:
"""Creates an object template from the input.
Example::
d = pg.Dict(x=pg.oneof(['a', 'b', 'c'], y=pg.manyof(2, range(4))))
t = pg.template(d)
assert t.dna_spec() == pg.geno.space([
pg.geno.oneof([
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
], location='x'),
pg.geno.manyof([
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
], location='y')
])
assert t.encode(pg.Dict(x='a', y=0)) == pg.DNA([0, 0])
assert t.decode(pg.DNA([0, 0])) == pg.Dict(x='a', y=0)
t = pg.template(d, where=lambda x: isinstance(x, pg.hyper.ManyOf))
assert t.dna_spec() == pg.geno.space([
pg.geno.manyof([
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
pg.geno.constant(),
], location='y')
])
assert t.encode(pg.Dict(x=pg.oneof(['a', 'b', 'c']), y=0)) == pg.DNA(0)
assert t.decode(pg.DNA(0)) == pg.Dict(x=pg.oneof(['a', 'b', 'c']), y=0)
Args:
value: A value based on which the template is created.
where: Function to filter hyper values. If None, all hyper primitives from
`value` will be included in the encoding/decoding process. Otherwise
only the hyper values on which 'where' returns True will be included.
`where` can be useful to partition a search space into separate
optimization processes. Please see 'ObjectTemplate' docstr for details.
Returns:
A template object.
"""
return ObjectTemplate(value, compute_derived=True, where=where)
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|
41,693 | pyglove.core.symbolic.base | to_json | Serializes a (maybe) symbolic value into a plain Python object.
Example::
@pg.members([
('x', pg.typing.Any())
])
class A(pg.Object):
pass
a1 = A(1)
json = a1.to_json()
a2 = pg.from_json(json)
assert pg.eq(a1, a2)
Args:
value: value to serialize. Applicable value types are:
* Builtin python types: None, bool, int, float, string;
* JSONConvertible types;
* List types;
* Tuple types;
* Dict types.
**kwargs: Keyword arguments to pass to value.to_json if value is
JSONConvertible.
Returns:
JSON value.
| def to_json(value: Any, **kwargs) -> Any:
"""Serializes a (maybe) symbolic value into a plain Python object.
Example::
@pg.members([
('x', pg.typing.Any())
])
class A(pg.Object):
pass
a1 = A(1)
json = a1.to_json()
a2 = pg.from_json(json)
assert pg.eq(a1, a2)
Args:
value: value to serialize. Applicable value types are:
* Builtin python types: None, bool, int, float, string;
* JSONConvertible types;
* List types;
* Tuple types;
* Dict types.
**kwargs: Keyword arguments to pass to value.to_json if value is
JSONConvertible.
Returns:
JSON value.
"""
# NOTE(daiyip): special handling `sym_jsonify` since symbolized
# classes may have conflicting `to_json` method in their existing classes.
if isinstance(value, Symbolic):
return value.sym_jsonify(**kwargs)
return object_utils.to_json(value, **kwargs)
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|
41,694 | pyglove.core.symbolic.base | to_json_str | Serializes a (maybe) symbolic value into a JSON string.
Example::
@pg.members([
('x', pg.typing.Any())
])
class A(pg.Object):
pass
a1 = A(1)
json_str = a1.to_json_str()
a2 = pg.from_json_str(json_str)
assert pg.eq(a1, a2)
Args:
value: Value to serialize.
json_indent: The size of indentation for JSON format.
**kwargs: Additional keyword arguments that are passed to ``pg.to_json``.
Returns:
A JSON string.
| def to_json_str(value: Any,
*,
json_indent=None,
**kwargs) -> str:
"""Serializes a (maybe) symbolic value into a JSON string.
Example::
@pg.members([
('x', pg.typing.Any())
])
class A(pg.Object):
pass
a1 = A(1)
json_str = a1.to_json_str()
a2 = pg.from_json_str(json_str)
assert pg.eq(a1, a2)
Args:
value: Value to serialize.
json_indent: The size of indentation for JSON format.
**kwargs: Additional keyword arguments that are passed to ``pg.to_json``.
Returns:
A JSON string.
"""
return json.dumps(to_json(value, **kwargs), indent=json_indent)
| (value: Any, *, json_indent=None, **kwargs) -> str | [
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|
41,695 | pyglove.core.symbolic.flags | track_origin | Returns a context manager to enable or disable origin tracking.
`track_origin` is thread-safe and can be nested. For example::
a = pg.Dict(x=1)
with pg.track_origin(False):
with pg.track_origin(True):
# b's origin will be tracked, which can be accessed by `b.sym_origin`.
b = a.clone()
# c's origin will not be tracked, `c.sym_origin` returns None.
c = a.clone()
Args:
enabled: If True, the origin of symbolic values will be tracked during
object cloning and retuning from functors under current scope.
Returns:
A context manager for enable or disable origin tracking.
| def track_origin(enabled: bool = True) -> ContextManager[None]:
"""Returns a context manager to enable or disable origin tracking.
`track_origin` is thread-safe and can be nested. For example::
a = pg.Dict(x=1)
with pg.track_origin(False):
with pg.track_origin(True):
# b's origin will be tracked, which can be accessed by `b.sym_origin`.
b = a.clone()
# c's origin will not be tracked, `c.sym_origin` returns None.
c = a.clone()
Args:
enabled: If True, the origin of symbolic values will be tracked during
object cloning and retuning from functors under current scope.
Returns:
A context manager for enable or disable origin tracking.
"""
return thread_local.thread_local_value_scope(
_TLS_ENABLE_ORIGIN_TRACKING, enabled, False
)
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|
41,696 | pyglove.core.symbolic.base | traverse | Traverse a (maybe) symbolic value using visitor functions.
Example::
@pg.members([
('x', pg.typing.Int())
])
class A(pg.Object):
pass
v = [{'a': A(1)}, A(2)]
integers = []
def track_integers(k, v, p):
if isinstance(v, int):
integers.append((k, v))
return pg.TraverseAction.ENTER
pg.traverse(v, track_integers)
assert integers == [('[0].a.x', 1), ('[1].x', 2)]
Args:
x: Maybe symbolic value.
preorder_visitor_fn: preorder visitor function. Function signature is
`(path, value, parent) -> should_continue`.
postorder_visitor_fn: postorder visitor function. Function signature is
`(path, value, parent) -> should_continue`.
root_path: KeyPath of root value.
parent: Optional parent of the root node.
Returns:
True if both `preorder_visitor_fn` and `postorder_visitor_fn` return
either `TraverseAction.ENTER` or `TraverseAction.CONTINUE` for all nodes.
Otherwise False.
| def traverse(x: Any,
preorder_visitor_fn: Optional[
Callable[[object_utils.KeyPath, Any, Any],
Optional[TraverseAction]]] = None,
postorder_visitor_fn: Optional[
Callable[[object_utils.KeyPath, Any, Any],
Optional[TraverseAction]]] = None,
root_path: Optional[object_utils.KeyPath] = None,
parent: Optional[Any] = None) -> bool:
"""Traverse a (maybe) symbolic value using visitor functions.
Example::
@pg.members([
('x', pg.typing.Int())
])
class A(pg.Object):
pass
v = [{'a': A(1)}, A(2)]
integers = []
def track_integers(k, v, p):
if isinstance(v, int):
integers.append((k, v))
return pg.TraverseAction.ENTER
pg.traverse(v, track_integers)
assert integers == [('[0].a.x', 1), ('[1].x', 2)]
Args:
x: Maybe symbolic value.
preorder_visitor_fn: preorder visitor function. Function signature is
`(path, value, parent) -> should_continue`.
postorder_visitor_fn: postorder visitor function. Function signature is
`(path, value, parent) -> should_continue`.
root_path: KeyPath of root value.
parent: Optional parent of the root node.
Returns:
True if both `preorder_visitor_fn` and `postorder_visitor_fn` return
either `TraverseAction.ENTER` or `TraverseAction.CONTINUE` for all nodes.
Otherwise False.
"""
root_path = root_path or object_utils.KeyPath()
def no_op_visitor(path, value, parent):
del path, value, parent
return TraverseAction.ENTER
if preorder_visitor_fn is None:
preorder_visitor_fn = no_op_visitor
if postorder_visitor_fn is None:
postorder_visitor_fn = no_op_visitor
preorder_action = preorder_visitor_fn(root_path, x, parent)
if preorder_action is None or preorder_action == TraverseAction.ENTER:
if isinstance(x, dict):
for k, v in x.items():
if not traverse(v, preorder_visitor_fn, postorder_visitor_fn,
object_utils.KeyPath(k, root_path), x):
preorder_action = TraverseAction.STOP
break
elif isinstance(x, list):
for i, v in enumerate(x):
if not traverse(v, preorder_visitor_fn, postorder_visitor_fn,
object_utils.KeyPath(i, root_path), x):
preorder_action = TraverseAction.STOP
break
elif isinstance(x, Symbolic.ObjectType): # pytype: disable=wrong-arg-types
for k, v in x.sym_items():
if not traverse(v, preorder_visitor_fn, postorder_visitor_fn,
object_utils.KeyPath(k, root_path), x):
preorder_action = TraverseAction.STOP
break
postorder_action = postorder_visitor_fn(root_path, x, parent)
if (preorder_action == TraverseAction.STOP or
postorder_action == TraverseAction.STOP):
return False
return True
| (x: Any, preorder_visitor_fn: Optional[Callable[[pyglove.core.object_utils.value_location.KeyPath, Any, Any], Optional[pyglove.core.symbolic.base.TraverseAction]]] = None, postorder_visitor_fn: Optional[Callable[[pyglove.core.object_utils.value_location.KeyPath, Any, Any], Optional[pyglove.core.symbolic.base.TraverseAction]]] = None, root_path: Optional[pyglove.core.object_utils.value_location.KeyPath] = None, parent: Optional[Any] = None) -> bool | [
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|
41,699 | pyglove.core.symbolic.object | use_init_args | Decorator for updating the `__init__` signature of a `pg.Object` subclass.
Examples::
@pg.use_init_args(['x', 'y', '*z'])
class Foo(pg.Object):
y: int
x: str
z: list[int]
f = Foo('abc', 1, 2, 3)
assert f.x == 'abc'
assert f.y == 1
assert f.z == [2, 3]
Args:
init_arg_list: A sequence of attribute names that will be used as the
positional arguments of `__init__`. The last element could be the name of
a list-type attribute, indicating it's used as `*args`. Keyword-only
arguments are not needed to be present in this list, which will be figured
out automatically based on class' schema.
Returns:
a decorator function that updates the `__init__` signature.
| def use_init_args(init_arg_list: Sequence[str]) -> pg_typing.Decorator:
"""Decorator for updating the `__init__` signature of a `pg.Object` subclass.
Examples::
@pg.use_init_args(['x', 'y', '*z'])
class Foo(pg.Object):
y: int
x: str
z: list[int]
f = Foo('abc', 1, 2, 3)
assert f.x == 'abc'
assert f.y == 1
assert f.z == [2, 3]
Args:
init_arg_list: A sequence of attribute names that will be used as the
positional arguments of `__init__`. The last element could be the name of
a list-type attribute, indicating it's used as `*args`. Keyword-only
arguments are not needed to be present in this list, which will be figured
out automatically based on class' schema.
Returns:
a decorator function that updates the `__init__` signature.
"""
def _decorator(cls):
schema_utils.update_schema(
cls, [], extend=True, init_arg_list=init_arg_list
)
return cls
return typing.cast(pg_typing.Decorator, _decorator)
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|
41,700 | pyglove.core.symbolic.class_wrapper | wrap | Makes a symbolic class wrapper from a regular Python class.
``pg.wrap`` is called by :func:`pyglove.symbolize` for symbolizing existing
Python classes. For example::
class A:
def __init__(self, x):
self.x = x
# The following two lines are equivalent.
A1 = pg.symbolize(A)
A2 = pg.wrap(A)
Besides passing the source class, ``pg.wrap`` allows the user to pass symbolic
field definitions for the init arguments. For example::
A3 = pg.wrap(A, [
('x', pg.typing.Int())
])
Moreover, multiple flags are provided to determine whether or not to use the
symbolic operations as the default behaviors. For example::
A4 = pg.wrap(
A,
[],
# Instead clearing out all internal states (default),
# do not reset internal state.
reset_state_fn=lambda self: None,
# Use symbolic representation for __repr__ and __str__.
repr=True,
# use symbolic equality for __eq__, __ne__ and __hash__.
eq=True,
# Customize the class name obtained (the default behaivor
# is to use the source class name).
class_name='A4'
# Customize the module name for created class (the default
# behavior is to use the source module name).
module_name='my_module')
Args:
cls: Class to wrap.
init_args: An optional list of field definitions for the arguments of
__init__. It can be a sparse value specifications for argument in the
__init__ method of `cls`.
reset_state_fn: An optional callable object to reset the internal state of
the user class when rebind happens.
repr: Options for generating `__repr__` and `__str__`. If True (default),
use symbolic representation if the user class does not define its own.
Otherwise use the user class' definition. If False, always use
non-symbolic representations, which falls back to `object.__repr__` and
`object.__str__` if the user class does not define them.
eq: Options for generating `__eq__`, `__ne__` and `__hash__`. If True and
the `user_cls` defines `__eq__`, `__ne__` and `__hash__`, use the
definitions from the `user_cls`. If True and the `user_cls` does not
define `__eq__`, `__ne__` and `__hash__`, use symbolic eq/hash. If False
(default), use `user_cls`'s definition if present, or the definitions from
the `object` class.
class_name: An optional string used as class name for the wrapper class. If
None, the wrapper class will use the class name of the wrapped class.
module_name: An optional string used as module name for the wrapper class.
If None, the wrapper class will use the module name of the wrapped class.
auto_doc: If True, the descriptions for init argument fields will be
extracted from docstring if present.
auto_typing: If True, PyGlove typing (runtime-typing) will be enabled based
on type annotations inspected from the `__init__` method.
serialization_key: An optional string to be used as the serialization key
for the class during `sym_jsonify`. If None, `cls.__type_name__` will be
used. This is introduced for scenarios when we want to relocate a class,
before the downstream can recognize the new location, we need the class to
serialize it using previous key.
additional_keys: An optional list of strings as additional keys to
deserialize an object of the registered class. This can be useful when we
need to relocate or rename the registered class while being able to load
existing serialized JSON values.
override: Additional class attributes to override.
Returns:
A subclass of `cls` and `ClassWrapper`.
Raises:
TypeError: input `cls` is not a class.
| def wrap(
cls,
init_args: Optional[List[Union[
Tuple[Union[Text, pg_typing.KeySpec], pg_typing.ValueSpec, Text],
Tuple[Union[Text, pg_typing.KeySpec], pg_typing.ValueSpec, Text, Any]
]]] = None,
*,
reset_state_fn: Optional[Callable[[Any], None]] = None,
repr: bool = True, # pylint: disable=redefined-builtin
eq: bool = False,
class_name: Optional[str] = None,
module_name: Optional[str] = None,
auto_doc: bool = False,
auto_typing: bool = False,
serialization_key: Optional[str] = None,
additional_keys: Optional[List[str]] = None,
override: Optional[Dict[str, Any]] = None
) -> Type['ClassWrapper']:
"""Makes a symbolic class wrapper from a regular Python class.
``pg.wrap`` is called by :func:`pyglove.symbolize` for symbolizing existing
Python classes. For example::
class A:
def __init__(self, x):
self.x = x
# The following two lines are equivalent.
A1 = pg.symbolize(A)
A2 = pg.wrap(A)
Besides passing the source class, ``pg.wrap`` allows the user to pass symbolic
field definitions for the init arguments. For example::
A3 = pg.wrap(A, [
('x', pg.typing.Int())
])
Moreover, multiple flags are provided to determine whether or not to use the
symbolic operations as the default behaviors. For example::
A4 = pg.wrap(
A,
[],
# Instead clearing out all internal states (default),
# do not reset internal state.
reset_state_fn=lambda self: None,
# Use symbolic representation for __repr__ and __str__.
repr=True,
# use symbolic equality for __eq__, __ne__ and __hash__.
eq=True,
# Customize the class name obtained (the default behaivor
# is to use the source class name).
class_name='A4'
# Customize the module name for created class (the default
# behavior is to use the source module name).
module_name='my_module')
Args:
cls: Class to wrap.
init_args: An optional list of field definitions for the arguments of
__init__. It can be a sparse value specifications for argument in the
__init__ method of `cls`.
reset_state_fn: An optional callable object to reset the internal state of
the user class when rebind happens.
repr: Options for generating `__repr__` and `__str__`. If True (default),
use symbolic representation if the user class does not define its own.
Otherwise use the user class' definition. If False, always use
non-symbolic representations, which falls back to `object.__repr__` and
`object.__str__` if the user class does not define them.
eq: Options for generating `__eq__`, `__ne__` and `__hash__`. If True and
the `user_cls` defines `__eq__`, `__ne__` and `__hash__`, use the
definitions from the `user_cls`. If True and the `user_cls` does not
define `__eq__`, `__ne__` and `__hash__`, use symbolic eq/hash. If False
(default), use `user_cls`'s definition if present, or the definitions from
the `object` class.
class_name: An optional string used as class name for the wrapper class. If
None, the wrapper class will use the class name of the wrapped class.
module_name: An optional string used as module name for the wrapper class.
If None, the wrapper class will use the module name of the wrapped class.
auto_doc: If True, the descriptions for init argument fields will be
extracted from docstring if present.
auto_typing: If True, PyGlove typing (runtime-typing) will be enabled based
on type annotations inspected from the `__init__` method.
serialization_key: An optional string to be used as the serialization key
for the class during `sym_jsonify`. If None, `cls.__type_name__` will be
used. This is introduced for scenarios when we want to relocate a class,
before the downstream can recognize the new location, we need the class to
serialize it using previous key.
additional_keys: An optional list of strings as additional keys to
deserialize an object of the registered class. This can be useful when we
need to relocate or rename the registered class while being able to load
existing serialized JSON values.
override: Additional class attributes to override.
Returns:
A subclass of `cls` and `ClassWrapper`.
Raises:
TypeError: input `cls` is not a class.
"""
if not inspect.isclass(cls):
raise TypeError(f'Class wrapper can only be created from classes. '
f'Encountered: {cls!r}.')
if not issubclass(cls, ClassWrapper):
cls = _subclassed_wrapper(
cls,
use_symbolic_repr=repr,
use_symbolic_comp=eq,
reset_state_fn=reset_state_fn,
class_name=class_name,
module_name=module_name,
use_auto_doc=auto_doc,
use_auto_typing=auto_typing)
if issubclass(cls, ClassWrapper):
# Update init argument specifications according to user specified specs.
# Replace schema instead of extending it.
description, init_arg_list, arg_fields = _extract_init_signature(
cls, init_args, auto_doc=auto_doc, auto_typing=auto_typing)
schema_utils.update_schema(
cls,
arg_fields,
init_arg_list=init_arg_list,
extend=False,
description=description,
serialization_key=serialization_key,
additional_keys=additional_keys)
if override:
for k, v in override.items():
setattr(cls, k, v)
return cls
| (cls, init_args: Optional[List[Union[Tuple[Union[str, pyglove.core.typing.class_schema.KeySpec], pyglove.core.typing.class_schema.ValueSpec, str], Tuple[Union[str, pyglove.core.typing.class_schema.KeySpec], pyglove.core.typing.class_schema.ValueSpec, str, Any]]]] = None, *, reset_state_fn: Optional[Callable[[Any], NoneType]] = None, repr: bool = True, eq: bool = False, class_name: Optional[str] = None, module_name: Optional[str] = None, auto_doc: bool = False, auto_typing: bool = False, serialization_key: Optional[str] = None, additional_keys: Optional[List[str]] = None, override: Optional[Dict[str, Any]] = None) -> Type[pyglove.core.symbolic.class_wrapper.ClassWrapper] | [
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|
41,701 | pyglove.core.symbolic.class_wrapper | wrap_module | Wrap classes from a module.
For example, users can wrap all subclasses of `xxx.Base` under module `xxx`::
import xxx
pg.wrap_module(
xxx, where=lambda c: isinstance(c, xxx.Base))
Args:
module: A container that contains classes to wrap.
names: An optional list of class names. If not provided, all classes under
`module` will be considered candidates.
where: An optional filter function in signature (user_class) -> bool.
Only the classes under `module` with True return value will be wrapped.
export_to: An optional module to export the wrapper classes.
**kwargs: Keyword arguments passed to `wrap`
Returns:
Wrapper classes.
| def wrap_module(
module,
names: Optional[Sequence[Text]] = None,
where: Optional[Callable[[Type['ClassWrapper']], bool]] = None,
export_to: Optional[types.ModuleType] = None,
**kwargs):
"""Wrap classes from a module.
For example, users can wrap all subclasses of `xxx.Base` under module `xxx`::
import xxx
pg.wrap_module(
xxx, where=lambda c: isinstance(c, xxx.Base))
Args:
module: A container that contains classes to wrap.
names: An optional list of class names. If not provided, all classes under
`module` will be considered candidates.
where: An optional filter function in signature (user_class) -> bool.
Only the classes under `module` with True return value will be wrapped.
export_to: An optional module to export the wrapper classes.
**kwargs: Keyword arguments passed to `wrap`
Returns:
Wrapper classes.
"""
wrapper_classes = []
module_name = export_to.__name__ if export_to else None
origin_cls_to_wrap_cls = {}
for symbol_name in (names or dir(module)):
s = getattr(module, symbol_name)
if inspect.isclass(s) and (not where or where(s)):
# NOTE(daiyip): It's possible that a name under a module is an alias for
# another class. In such case, we do not create duplicated wrappers but
# shares the same wrapper classes with different names.
if s in origin_cls_to_wrap_cls:
wrapper_class = origin_cls_to_wrap_cls[s]
else:
wrapper_class = wrap(s, module_name=module_name, **kwargs)
origin_cls_to_wrap_cls[s] = wrapper_class
wrapper_classes.append(wrapper_class)
if export_to:
setattr(export_to, symbol_name, wrapper_class)
return wrapper_classes
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|
41,703 | builtins | type | type(object) -> the object's type
type(name, bases, dict, **kwds) -> a new type | from builtins import type
| null | [
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]
|
41,707 | comm.base_comm | BaseComm | Class for communicating between a Frontend and a Kernel
Must be subclassed with a publish_msg method implementation which
sends comm messages through the iopub channel.
| class BaseComm:
"""Class for communicating between a Frontend and a Kernel
Must be subclassed with a publish_msg method implementation which
sends comm messages through the iopub channel.
"""
def __init__(
self,
target_name: str = "comm",
data: MaybeDict = None,
metadata: MaybeDict = None,
buffers: BuffersType = None,
comm_id: str | None = None,
primary: bool = True,
target_module: str | None = None,
topic: bytes | None = None,
_open_data: MaybeDict = None,
_close_data: MaybeDict = None,
**kwargs: t.Any,
) -> None:
super().__init__(**kwargs)
self.comm_id = comm_id if comm_id else uuid.uuid4().hex
self.primary = primary
self.target_name = target_name
self.target_module = target_module
self.topic = topic if topic else ("comm-%s" % self.comm_id).encode("ascii")
self._open_data = _open_data if _open_data else {}
self._close_data = _close_data if _close_data else {}
self._msg_callback: CommCallback | None = None
self._close_callback: CommCallback | None = None
self._closed = True
if self.primary:
# I am primary, open my peer.
self.open(data=data, metadata=metadata, buffers=buffers)
else:
self._closed = False
def publish_msg(
self,
msg_type: str, # noqa: ARG002
data: MaybeDict = None, # noqa: ARG002
metadata: MaybeDict = None, # noqa: ARG002
buffers: BuffersType = None, # noqa: ARG002
**keys: t.Any, # noqa: ARG002
) -> None:
msg = "publish_msg Comm method is not implemented"
raise NotImplementedError(msg)
def __del__(self) -> None:
"""trigger close on gc"""
with contextlib.suppress(Exception):
# any number of things can have gone horribly wrong
# when called during interpreter teardown
self.close(deleting=True)
# publishing messages
def open(
self, data: MaybeDict = None, metadata: MaybeDict = None, buffers: BuffersType = None
) -> None:
"""Open the frontend-side version of this comm"""
if data is None:
data = self._open_data
comm_manager = comm.get_comm_manager()
if comm_manager is None:
msg = "Comms cannot be opened without a comm_manager." # type:ignore[unreachable]
raise RuntimeError(msg)
comm_manager.register_comm(self)
try:
self.publish_msg(
"comm_open",
data=data,
metadata=metadata,
buffers=buffers,
target_name=self.target_name,
target_module=self.target_module,
)
self._closed = False
except Exception:
comm_manager.unregister_comm(self)
raise
def close(
self,
data: MaybeDict = None,
metadata: MaybeDict = None,
buffers: BuffersType = None,
deleting: bool = False,
) -> None:
"""Close the frontend-side version of this comm"""
if self._closed:
# only close once
return
self._closed = True
if data is None:
data = self._close_data
self.publish_msg(
"comm_close",
data=data,
metadata=metadata,
buffers=buffers,
)
if not deleting:
# If deleting, the comm can't be registered
comm.get_comm_manager().unregister_comm(self)
def send(
self, data: MaybeDict = None, metadata: MaybeDict = None, buffers: BuffersType = None
) -> None:
"""Send a message to the frontend-side version of this comm"""
self.publish_msg(
"comm_msg",
data=data,
metadata=metadata,
buffers=buffers,
)
# registering callbacks
def on_close(self, callback: CommCallback | None) -> None:
"""Register a callback for comm_close
Will be called with the `data` of the close message.
Call `on_close(None)` to disable an existing callback.
"""
self._close_callback = callback
def on_msg(self, callback: CommCallback | None) -> None:
"""Register a callback for comm_msg
Will be called with the `data` of any comm_msg messages.
Call `on_msg(None)` to disable an existing callback.
"""
self._msg_callback = callback
# handling of incoming messages
def handle_close(self, msg: MessageType) -> None:
"""Handle a comm_close message"""
logger.debug("handle_close[%s](%s)", self.comm_id, msg)
if self._close_callback:
self._close_callback(msg)
def handle_msg(self, msg: MessageType) -> None:
"""Handle a comm_msg message"""
logger.debug("handle_msg[%s](%s)", self.comm_id, msg)
if self._msg_callback:
from IPython import get_ipython
shell = get_ipython()
if shell:
shell.events.trigger("pre_execute")
self._msg_callback(msg)
if shell:
shell.events.trigger("post_execute")
| (target_name: 'str' = 'comm', data: 'MaybeDict' = None, metadata: 'MaybeDict' = None, buffers: 'BuffersType' = None, comm_id: 'str | None' = None, primary: 'bool' = True, target_module: 'str | None' = None, topic: 'bytes | None' = None, _open_data: 'MaybeDict' = None, _close_data: 'MaybeDict' = None, **kwargs: 't.Any') -> 'None' | [
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|
41,708 | comm.base_comm | __del__ | trigger close on gc | def __del__(self) -> None:
"""trigger close on gc"""
with contextlib.suppress(Exception):
# any number of things can have gone horribly wrong
# when called during interpreter teardown
self.close(deleting=True)
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|
41,709 | comm.base_comm | __init__ | null | def __init__(
self,
target_name: str = "comm",
data: MaybeDict = None,
metadata: MaybeDict = None,
buffers: BuffersType = None,
comm_id: str | None = None,
primary: bool = True,
target_module: str | None = None,
topic: bytes | None = None,
_open_data: MaybeDict = None,
_close_data: MaybeDict = None,
**kwargs: t.Any,
) -> None:
super().__init__(**kwargs)
self.comm_id = comm_id if comm_id else uuid.uuid4().hex
self.primary = primary
self.target_name = target_name
self.target_module = target_module
self.topic = topic if topic else ("comm-%s" % self.comm_id).encode("ascii")
self._open_data = _open_data if _open_data else {}
self._close_data = _close_data if _close_data else {}
self._msg_callback: CommCallback | None = None
self._close_callback: CommCallback | None = None
self._closed = True
if self.primary:
# I am primary, open my peer.
self.open(data=data, metadata=metadata, buffers=buffers)
else:
self._closed = False
| (self, target_name: str = 'comm', data: Optional[Dict[str, Any]] = None, metadata: Optional[Dict[str, Any]] = None, buffers: Optional[List[bytes]] = None, comm_id: Optional[str] = None, primary: bool = True, target_module: Optional[str] = None, topic: Optional[bytes] = None, _open_data: Optional[Dict[str, Any]] = None, _close_data: Optional[Dict[str, Any]] = None, **kwargs: Any) -> NoneType | [
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|
41,710 | comm.base_comm | close | Close the frontend-side version of this comm | def close(
self,
data: MaybeDict = None,
metadata: MaybeDict = None,
buffers: BuffersType = None,
deleting: bool = False,
) -> None:
"""Close the frontend-side version of this comm"""
if self._closed:
# only close once
return
self._closed = True
if data is None:
data = self._close_data
self.publish_msg(
"comm_close",
data=data,
metadata=metadata,
buffers=buffers,
)
if not deleting:
# If deleting, the comm can't be registered
comm.get_comm_manager().unregister_comm(self)
| (self, data: Optional[Dict[str, Any]] = None, metadata: Optional[Dict[str, Any]] = None, buffers: Optional[List[bytes]] = None, deleting: bool = False) -> NoneType | [
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|
41,711 | comm.base_comm | handle_close | Handle a comm_close message | def handle_close(self, msg: MessageType) -> None:
"""Handle a comm_close message"""
logger.debug("handle_close[%s](%s)", self.comm_id, msg)
if self._close_callback:
self._close_callback(msg)
| (self, msg: Dict[str, Any]) -> NoneType | [
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|
41,712 | comm.base_comm | handle_msg | Handle a comm_msg message | def handle_msg(self, msg: MessageType) -> None:
"""Handle a comm_msg message"""
logger.debug("handle_msg[%s](%s)", self.comm_id, msg)
if self._msg_callback:
from IPython import get_ipython
shell = get_ipython()
if shell:
shell.events.trigger("pre_execute")
self._msg_callback(msg)
if shell:
shell.events.trigger("post_execute")
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|
41,713 | comm.base_comm | on_close | Register a callback for comm_close
Will be called with the `data` of the close message.
Call `on_close(None)` to disable an existing callback.
| def on_close(self, callback: CommCallback | None) -> None:
"""Register a callback for comm_close
Will be called with the `data` of the close message.
Call `on_close(None)` to disable an existing callback.
"""
self._close_callback = callback
| (self, callback: Optional[Callable[[Dict[str, Any]], NoneType]]) -> NoneType | [
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|
41,714 | comm.base_comm | on_msg | Register a callback for comm_msg
Will be called with the `data` of any comm_msg messages.
Call `on_msg(None)` to disable an existing callback.
| def on_msg(self, callback: CommCallback | None) -> None:
"""Register a callback for comm_msg
Will be called with the `data` of any comm_msg messages.
Call `on_msg(None)` to disable an existing callback.
"""
self._msg_callback = callback
| (self, callback: Optional[Callable[[Dict[str, Any]], NoneType]]) -> NoneType | [
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|
41,715 | comm.base_comm | open | Open the frontend-side version of this comm | def open(
self, data: MaybeDict = None, metadata: MaybeDict = None, buffers: BuffersType = None
) -> None:
"""Open the frontend-side version of this comm"""
if data is None:
data = self._open_data
comm_manager = comm.get_comm_manager()
if comm_manager is None:
msg = "Comms cannot be opened without a comm_manager." # type:ignore[unreachable]
raise RuntimeError(msg)
comm_manager.register_comm(self)
try:
self.publish_msg(
"comm_open",
data=data,
metadata=metadata,
buffers=buffers,
target_name=self.target_name,
target_module=self.target_module,
)
self._closed = False
except Exception:
comm_manager.unregister_comm(self)
raise
| (self, data: Optional[Dict[str, Any]] = None, metadata: Optional[Dict[str, Any]] = None, buffers: Optional[List[bytes]] = None) -> NoneType | [
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|
41,716 | comm.base_comm | publish_msg | null | def publish_msg(
self,
msg_type: str, # noqa: ARG002
data: MaybeDict = None, # noqa: ARG002
metadata: MaybeDict = None, # noqa: ARG002
buffers: BuffersType = None, # noqa: ARG002
**keys: t.Any, # noqa: ARG002
) -> None:
msg = "publish_msg Comm method is not implemented"
raise NotImplementedError(msg)
| (self, msg_type: str, data: Optional[Dict[str, Any]] = None, metadata: Optional[Dict[str, Any]] = None, buffers: Optional[List[bytes]] = None, **keys: Any) -> NoneType | [
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|
41,717 | comm.base_comm | send | Send a message to the frontend-side version of this comm | def send(
self, data: MaybeDict = None, metadata: MaybeDict = None, buffers: BuffersType = None
) -> None:
"""Send a message to the frontend-side version of this comm"""
self.publish_msg(
"comm_msg",
data=data,
metadata=metadata,
buffers=buffers,
)
| (self, data: Optional[Dict[str, Any]] = None, metadata: Optional[Dict[str, Any]] = None, buffers: Optional[List[bytes]] = None) -> NoneType | [
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|
41,718 | comm.base_comm | CommManager | Default CommManager singleton implementation for Comms in the Kernel | class CommManager:
"""Default CommManager singleton implementation for Comms in the Kernel"""
# Public APIs
def __init__(self) -> None:
self.comms: dict[str, BaseComm] = {}
self.targets: dict[str, CommTargetCallback] = {}
def register_target(self, target_name: str, f: CommTargetCallback | str) -> None:
"""Register a callable f for a given target name
f will be called with two arguments when a comm_open message is received with `target`:
- the Comm instance
- the `comm_open` message itself.
f can be a Python callable or an import string for one.
"""
if isinstance(f, str):
f = import_item(f)
self.targets[target_name] = t.cast(CommTargetCallback, f)
def unregister_target(self, target_name: str, f: CommTargetCallback) -> CommTargetCallback: # noqa: ARG002
"""Unregister a callable registered with register_target"""
return self.targets.pop(target_name)
def register_comm(self, comm: BaseComm) -> str:
"""Register a new comm"""
comm_id = comm.comm_id
self.comms[comm_id] = comm
return comm_id
def unregister_comm(self, comm: BaseComm) -> None:
"""Unregister a comm, and close its counterpart"""
# unlike get_comm, this should raise a KeyError
comm = self.comms.pop(comm.comm_id)
def get_comm(self, comm_id: str) -> BaseComm | None:
"""Get a comm with a particular id
Returns the comm if found, otherwise None.
This will not raise an error,
it will log messages if the comm cannot be found.
"""
try:
return self.comms[comm_id]
except KeyError:
logger.warning("No such comm: %s", comm_id)
if logger.isEnabledFor(logging.DEBUG):
# don't create the list of keys if debug messages aren't enabled
logger.debug("Current comms: %s", list(self.comms.keys()))
return None
# Message handlers
def comm_open(self, stream: ZMQStream, ident: str, msg: MessageType) -> None: # noqa: ARG002
"""Handler for comm_open messages"""
from comm import create_comm
content = msg["content"]
comm_id = content["comm_id"]
target_name = content["target_name"]
f = self.targets.get(target_name, None)
comm = create_comm(
comm_id=comm_id,
primary=False,
target_name=target_name,
)
self.register_comm(comm)
if f is None:
logger.error("No such comm target registered: %s", target_name)
else:
try:
f(comm, msg)
return
except Exception:
logger.error("Exception opening comm with target: %s", target_name, exc_info=True)
# Failure.
try:
comm.close()
except Exception:
logger.error(
"""Could not close comm during `comm_open` failure
clean-up. The comm may not have been opened yet.""",
exc_info=True,
)
def comm_msg(self, stream: ZMQStream, ident: str, msg: MessageType) -> None: # noqa: ARG002
"""Handler for comm_msg messages"""
content = msg["content"]
comm_id = content["comm_id"]
comm = self.get_comm(comm_id)
if comm is None:
return
try:
comm.handle_msg(msg)
except Exception:
logger.error("Exception in comm_msg for %s", comm_id, exc_info=True)
def comm_close(self, stream: ZMQStream, ident: str, msg: MessageType) -> None: # noqa: ARG002
"""Handler for comm_close messages"""
content = msg["content"]
comm_id = content["comm_id"]
comm = self.get_comm(comm_id)
if comm is None:
return
self.comms[comm_id]._closed = True
del self.comms[comm_id]
try:
comm.handle_close(msg)
except Exception:
logger.error("Exception in comm_close for %s", comm_id, exc_info=True)
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|
41,719 | comm.base_comm | __init__ | null | def __init__(self) -> None:
self.comms: dict[str, BaseComm] = {}
self.targets: dict[str, CommTargetCallback] = {}
| (self) -> NoneType | [
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|
41,720 | comm.base_comm | comm_close | Handler for comm_close messages | def comm_close(self, stream: ZMQStream, ident: str, msg: MessageType) -> None: # noqa: ARG002
"""Handler for comm_close messages"""
content = msg["content"]
comm_id = content["comm_id"]
comm = self.get_comm(comm_id)
if comm is None:
return
self.comms[comm_id]._closed = True
del self.comms[comm_id]
try:
comm.handle_close(msg)
except Exception:
logger.error("Exception in comm_close for %s", comm_id, exc_info=True)
| (self, stream: 'ZMQStream', ident: 'str', msg: 'MessageType') -> 'None' | [
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|
41,721 | comm.base_comm | comm_msg | Handler for comm_msg messages | def comm_msg(self, stream: ZMQStream, ident: str, msg: MessageType) -> None: # noqa: ARG002
"""Handler for comm_msg messages"""
content = msg["content"]
comm_id = content["comm_id"]
comm = self.get_comm(comm_id)
if comm is None:
return
try:
comm.handle_msg(msg)
except Exception:
logger.error("Exception in comm_msg for %s", comm_id, exc_info=True)
| (self, stream: 'ZMQStream', ident: 'str', msg: 'MessageType') -> 'None' | [
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|
41,722 | comm.base_comm | comm_open | Handler for comm_open messages | def comm_open(self, stream: ZMQStream, ident: str, msg: MessageType) -> None: # noqa: ARG002
"""Handler for comm_open messages"""
from comm import create_comm
content = msg["content"]
comm_id = content["comm_id"]
target_name = content["target_name"]
f = self.targets.get(target_name, None)
comm = create_comm(
comm_id=comm_id,
primary=False,
target_name=target_name,
)
self.register_comm(comm)
if f is None:
logger.error("No such comm target registered: %s", target_name)
else:
try:
f(comm, msg)
return
except Exception:
logger.error("Exception opening comm with target: %s", target_name, exc_info=True)
# Failure.
try:
comm.close()
except Exception:
logger.error(
"""Could not close comm during `comm_open` failure
clean-up. The comm may not have been opened yet.""",
exc_info=True,
)
| (self, stream: 'ZMQStream', ident: 'str', msg: 'MessageType') -> 'None' | [
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|
41,723 | comm.base_comm | get_comm | Get a comm with a particular id
Returns the comm if found, otherwise None.
This will not raise an error,
it will log messages if the comm cannot be found.
| def get_comm(self, comm_id: str) -> BaseComm | None:
"""Get a comm with a particular id
Returns the comm if found, otherwise None.
This will not raise an error,
it will log messages if the comm cannot be found.
"""
try:
return self.comms[comm_id]
except KeyError:
logger.warning("No such comm: %s", comm_id)
if logger.isEnabledFor(logging.DEBUG):
# don't create the list of keys if debug messages aren't enabled
logger.debug("Current comms: %s", list(self.comms.keys()))
return None
| (self, comm_id: str) -> comm.base_comm.BaseComm | None | [
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|
41,724 | comm.base_comm | register_comm | Register a new comm | def register_comm(self, comm: BaseComm) -> str:
"""Register a new comm"""
comm_id = comm.comm_id
self.comms[comm_id] = comm
return comm_id
| (self, comm: comm.base_comm.BaseComm) -> str | [
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|
41,725 | comm.base_comm | register_target | Register a callable f for a given target name
f will be called with two arguments when a comm_open message is received with `target`:
- the Comm instance
- the `comm_open` message itself.
f can be a Python callable or an import string for one.
| def register_target(self, target_name: str, f: CommTargetCallback | str) -> None:
"""Register a callable f for a given target name
f will be called with two arguments when a comm_open message is received with `target`:
- the Comm instance
- the `comm_open` message itself.
f can be a Python callable or an import string for one.
"""
if isinstance(f, str):
f = import_item(f)
self.targets[target_name] = t.cast(CommTargetCallback, f)
| (self, target_name: str, f: Union[Callable[[comm.base_comm.BaseComm, Dict[str, Any]], NoneType], str]) -> NoneType | [
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|
41,726 | comm.base_comm | unregister_comm | Unregister a comm, and close its counterpart | def unregister_comm(self, comm: BaseComm) -> None:
"""Unregister a comm, and close its counterpart"""
# unlike get_comm, this should raise a KeyError
comm = self.comms.pop(comm.comm_id)
| (self, comm: comm.base_comm.BaseComm) -> NoneType | [
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|
41,727 | comm.base_comm | unregister_target | Unregister a callable registered with register_target | def unregister_target(self, target_name: str, f: CommTargetCallback) -> CommTargetCallback: # noqa: ARG002
"""Unregister a callable registered with register_target"""
return self.targets.pop(target_name)
| (self, target_name: str, f: Callable[[comm.base_comm.BaseComm, Dict[str, Any]], NoneType]) -> Callable[[comm.base_comm.BaseComm, Dict[str, Any]], NoneType] | [
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|
41,728 | comm | DummyComm | null | class DummyComm(BaseComm):
def publish_msg(
self,
msg_type: str,
data: MaybeDict = None,
metadata: MaybeDict = None,
buffers: BuffersType = None,
**keys: Any,
) -> None:
pass
| (target_name: 'str' = 'comm', data: 'MaybeDict' = None, metadata: 'MaybeDict' = None, buffers: 'BuffersType' = None, comm_id: 'str | None' = None, primary: 'bool' = True, target_module: 'str | None' = None, topic: 'bytes | None' = None, _open_data: 'MaybeDict' = None, _close_data: 'MaybeDict' = None, **kwargs: 't.Any') -> 'None' | [
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|
41,737 | comm | publish_msg | null | def publish_msg(
self,
msg_type: str,
data: MaybeDict = None,
metadata: MaybeDict = None,
buffers: BuffersType = None,
**keys: Any,
) -> None:
pass
| (self, msg_type: str, data: Optional[Dict[str, Any]] = None, metadata: Optional[Dict[str, Any]] = None, buffers: Optional[List[bytes]] = None, **keys: Any) -> NoneType | [
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|
41,739 | comm | _create_comm | Create a Comm.
This method is intended to be replaced, so that it returns your Comm instance.
| def _create_comm(*args: Any, **kwargs: Any) -> BaseComm:
"""Create a Comm.
This method is intended to be replaced, so that it returns your Comm instance.
"""
return DummyComm(*args, **kwargs)
| (*args: Any, **kwargs: Any) -> comm.base_comm.BaseComm | [
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|
41,740 | comm | _get_comm_manager | Get the current Comm manager, creates one if there is none.
This method is intended to be replaced if needed (if you want to manage multiple CommManagers).
| def _get_comm_manager() -> CommManager:
"""Get the current Comm manager, creates one if there is none.
This method is intended to be replaced if needed (if you want to manage multiple CommManagers).
"""
global _comm_manager # noqa: PLW0603
if _comm_manager is None:
_comm_manager = CommManager()
return _comm_manager
| () -> comm.base_comm.CommManager | [
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|
41,745 | tokenize_rt | Offset | Offset(line, utf8_byte_offset) | class Offset(NamedTuple):
line: int | None = None
utf8_byte_offset: int | None = None
| (line: int | None = None, utf8_byte_offset: int | None = None) | [
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|
41,747 | namedtuple_Offset | __new__ | Create new instance of Offset(line, utf8_byte_offset) | from builtins import function
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|
41,750 | collections | _replace | Return a new Offset object replacing specified fields with new values | def namedtuple(typename, field_names, *, rename=False, defaults=None, module=None):
"""Returns a new subclass of tuple with named fields.
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Point.__doc__ # docstring for the new class
'Point(x, y)'
>>> p = Point(11, y=22) # instantiate with positional args or keywords
>>> p[0] + p[1] # indexable like a plain tuple
33
>>> x, y = p # unpack like a regular tuple
>>> x, y
(11, 22)
>>> p.x + p.y # fields also accessible by name
33
>>> d = p._asdict() # convert to a dictionary
>>> d['x']
11
>>> Point(**d) # convert from a dictionary
Point(x=11, y=22)
>>> p._replace(x=100) # _replace() is like str.replace() but targets named fields
Point(x=100, y=22)
"""
# Validate the field names. At the user's option, either generate an error
# message or automatically replace the field name with a valid name.
if isinstance(field_names, str):
field_names = field_names.replace(',', ' ').split()
field_names = list(map(str, field_names))
typename = _sys.intern(str(typename))
if rename:
seen = set()
for index, name in enumerate(field_names):
if (not name.isidentifier()
or _iskeyword(name)
or name.startswith('_')
or name in seen):
field_names[index] = f'_{index}'
seen.add(name)
for name in [typename] + field_names:
if type(name) is not str:
raise TypeError('Type names and field names must be strings')
if not name.isidentifier():
raise ValueError('Type names and field names must be valid '
f'identifiers: {name!r}')
if _iskeyword(name):
raise ValueError('Type names and field names cannot be a '
f'keyword: {name!r}')
seen = set()
for name in field_names:
if name.startswith('_') and not rename:
raise ValueError('Field names cannot start with an underscore: '
f'{name!r}')
if name in seen:
raise ValueError(f'Encountered duplicate field name: {name!r}')
seen.add(name)
field_defaults = {}
if defaults is not None:
defaults = tuple(defaults)
if len(defaults) > len(field_names):
raise TypeError('Got more default values than field names')
field_defaults = dict(reversed(list(zip(reversed(field_names),
reversed(defaults)))))
# Variables used in the methods and docstrings
field_names = tuple(map(_sys.intern, field_names))
num_fields = len(field_names)
arg_list = ', '.join(field_names)
if num_fields == 1:
arg_list += ','
repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')'
tuple_new = tuple.__new__
_dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip
# Create all the named tuple methods to be added to the class namespace
namespace = {
'_tuple_new': tuple_new,
'__builtins__': {},
'__name__': f'namedtuple_{typename}',
}
code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))'
__new__ = eval(code, namespace)
__new__.__name__ = '__new__'
__new__.__doc__ = f'Create new instance of {typename}({arg_list})'
if defaults is not None:
__new__.__defaults__ = defaults
@classmethod
def _make(cls, iterable):
result = tuple_new(cls, iterable)
if _len(result) != num_fields:
raise TypeError(f'Expected {num_fields} arguments, got {len(result)}')
return result
_make.__func__.__doc__ = (f'Make a new {typename} object from a sequence '
'or iterable')
def _replace(self, /, **kwds):
result = self._make(_map(kwds.pop, field_names, self))
if kwds:
raise ValueError(f'Got unexpected field names: {list(kwds)!r}')
return result
_replace.__doc__ = (f'Return a new {typename} object replacing specified '
'fields with new values')
def __repr__(self):
'Return a nicely formatted representation string'
return self.__class__.__name__ + repr_fmt % self
def _asdict(self):
'Return a new dict which maps field names to their values.'
return _dict(_zip(self._fields, self))
def __getnewargs__(self):
'Return self as a plain tuple. Used by copy and pickle.'
return _tuple(self)
# Modify function metadata to help with introspection and debugging
for method in (
__new__,
_make.__func__,
_replace,
__repr__,
_asdict,
__getnewargs__,
):
method.__qualname__ = f'{typename}.{method.__name__}'
# Build-up the class namespace dictionary
# and use type() to build the result class
class_namespace = {
'__doc__': f'{typename}({arg_list})',
'__slots__': (),
'_fields': field_names,
'_field_defaults': field_defaults,
'__new__': __new__,
'_make': _make,
'_replace': _replace,
'__repr__': __repr__,
'_asdict': _asdict,
'__getnewargs__': __getnewargs__,
'__match_args__': field_names,
}
for index, name in enumerate(field_names):
doc = _sys.intern(f'Alias for field number {index}')
class_namespace[name] = _tuplegetter(index, doc)
result = type(typename, (tuple,), class_namespace)
# For pickling to work, the __module__ variable needs to be set to the frame
# where the named tuple is created. Bypass this step in environments where
# sys._getframe is not defined (Jython for example) or sys._getframe is not
# defined for arguments greater than 0 (IronPython), or where the user has
# specified a particular module.
if module is None:
try:
module = _sys._getframe(1).f_globals.get('__name__', '__main__')
except (AttributeError, ValueError):
pass
if module is not None:
result.__module__ = module
return result
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|
41,751 | tokenize_rt | Token | Token(name, src, line, utf8_byte_offset) | class Token(NamedTuple):
name: str
src: str
line: int | None = None
utf8_byte_offset: int | None = None
@property
def offset(self) -> Offset:
return Offset(self.line, self.utf8_byte_offset)
def matches(self, *, name: str, src: str) -> bool:
return self.name == name and self.src == src
| (name: str, src: str, line: int | None = None, utf8_byte_offset: int | None = None) | [
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|
41,753 | namedtuple_Token | __new__ | Create new instance of Token(name, src, line, utf8_byte_offset) | from builtins import function
| (_cls, name: ForwardRef('str'), src: ForwardRef('str'), line: ForwardRef('int | None') = None, utf8_byte_offset: ForwardRef('int | None') = None) | [
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|
41,756 | collections | _replace | Return a new Token object replacing specified fields with new values | def namedtuple(typename, field_names, *, rename=False, defaults=None, module=None):
"""Returns a new subclass of tuple with named fields.
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Point.__doc__ # docstring for the new class
'Point(x, y)'
>>> p = Point(11, y=22) # instantiate with positional args or keywords
>>> p[0] + p[1] # indexable like a plain tuple
33
>>> x, y = p # unpack like a regular tuple
>>> x, y
(11, 22)
>>> p.x + p.y # fields also accessible by name
33
>>> d = p._asdict() # convert to a dictionary
>>> d['x']
11
>>> Point(**d) # convert from a dictionary
Point(x=11, y=22)
>>> p._replace(x=100) # _replace() is like str.replace() but targets named fields
Point(x=100, y=22)
"""
# Validate the field names. At the user's option, either generate an error
# message or automatically replace the field name with a valid name.
if isinstance(field_names, str):
field_names = field_names.replace(',', ' ').split()
field_names = list(map(str, field_names))
typename = _sys.intern(str(typename))
if rename:
seen = set()
for index, name in enumerate(field_names):
if (not name.isidentifier()
or _iskeyword(name)
or name.startswith('_')
or name in seen):
field_names[index] = f'_{index}'
seen.add(name)
for name in [typename] + field_names:
if type(name) is not str:
raise TypeError('Type names and field names must be strings')
if not name.isidentifier():
raise ValueError('Type names and field names must be valid '
f'identifiers: {name!r}')
if _iskeyword(name):
raise ValueError('Type names and field names cannot be a '
f'keyword: {name!r}')
seen = set()
for name in field_names:
if name.startswith('_') and not rename:
raise ValueError('Field names cannot start with an underscore: '
f'{name!r}')
if name in seen:
raise ValueError(f'Encountered duplicate field name: {name!r}')
seen.add(name)
field_defaults = {}
if defaults is not None:
defaults = tuple(defaults)
if len(defaults) > len(field_names):
raise TypeError('Got more default values than field names')
field_defaults = dict(reversed(list(zip(reversed(field_names),
reversed(defaults)))))
# Variables used in the methods and docstrings
field_names = tuple(map(_sys.intern, field_names))
num_fields = len(field_names)
arg_list = ', '.join(field_names)
if num_fields == 1:
arg_list += ','
repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')'
tuple_new = tuple.__new__
_dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip
# Create all the named tuple methods to be added to the class namespace
namespace = {
'_tuple_new': tuple_new,
'__builtins__': {},
'__name__': f'namedtuple_{typename}',
}
code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))'
__new__ = eval(code, namespace)
__new__.__name__ = '__new__'
__new__.__doc__ = f'Create new instance of {typename}({arg_list})'
if defaults is not None:
__new__.__defaults__ = defaults
@classmethod
def _make(cls, iterable):
result = tuple_new(cls, iterable)
if _len(result) != num_fields:
raise TypeError(f'Expected {num_fields} arguments, got {len(result)}')
return result
_make.__func__.__doc__ = (f'Make a new {typename} object from a sequence '
'or iterable')
def _replace(self, /, **kwds):
result = self._make(_map(kwds.pop, field_names, self))
if kwds:
raise ValueError(f'Got unexpected field names: {list(kwds)!r}')
return result
_replace.__doc__ = (f'Return a new {typename} object replacing specified '
'fields with new values')
def __repr__(self):
'Return a nicely formatted representation string'
return self.__class__.__name__ + repr_fmt % self
def _asdict(self):
'Return a new dict which maps field names to their values.'
return _dict(_zip(self._fields, self))
def __getnewargs__(self):
'Return self as a plain tuple. Used by copy and pickle.'
return _tuple(self)
# Modify function metadata to help with introspection and debugging
for method in (
__new__,
_make.__func__,
_replace,
__repr__,
_asdict,
__getnewargs__,
):
method.__qualname__ = f'{typename}.{method.__name__}'
# Build-up the class namespace dictionary
# and use type() to build the result class
class_namespace = {
'__doc__': f'{typename}({arg_list})',
'__slots__': (),
'_fields': field_names,
'_field_defaults': field_defaults,
'__new__': __new__,
'_make': _make,
'_replace': _replace,
'__repr__': __repr__,
'_asdict': _asdict,
'__getnewargs__': __getnewargs__,
'__match_args__': field_names,
}
for index, name in enumerate(field_names):
doc = _sys.intern(f'Alias for field number {index}')
class_namespace[name] = _tuplegetter(index, doc)
result = type(typename, (tuple,), class_namespace)
# For pickling to work, the __module__ variable needs to be set to the frame
# where the named tuple is created. Bypass this step in environments where
# sys._getframe is not defined (Jython for example) or sys._getframe is not
# defined for arguments greater than 0 (IronPython), or where the user has
# specified a particular module.
if module is None:
try:
module = _sys._getframe(1).f_globals.get('__name__', '__main__')
except (AttributeError, ValueError):
pass
if module is not None:
result.__module__ = module
return result
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|
41,757 | tokenize_rt | matches | null | def matches(self, *, name: str, src: str) -> bool:
return self.name == name and self.src == src
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|
41,758 | tokenize_rt | _re_partition | null | def _re_partition(regex: Pattern[str], s: str) -> tuple[str, str, str]:
match = regex.search(s)
if match:
return s[:match.start()], s[slice(*match.span())], s[match.end():]
else:
return (s, '', '')
| (regex: Pattern[str], s: str) -> tuple[str, str, str] | [
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|
41,762 | tokenize_rt | main | null | def main(argv: Sequence[str] | None = None) -> int:
parser = argparse.ArgumentParser()
parser.add_argument('filename')
args = parser.parse_args(argv)
with open(args.filename) as f:
tokens = src_to_tokens(f.read())
for token in tokens:
line, col = str(token.line), str(token.utf8_byte_offset)
print(f'{line}:{col} {token.name} {token.src!r}')
return 0
| (argv: Optional[Sequence[str]] = None) -> int | [
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|
41,763 | tokenize_rt | parse_string_literal | parse a string literal's source into (prefix, string) | def parse_string_literal(src: str) -> tuple[str, str]:
"""parse a string literal's source into (prefix, string)"""
match = _string_re.match(src)
assert match is not None
return match.group(1), match.group(2)
| (src: str) -> tuple[str, str] | [
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|
41,765 | tokenize_rt | reversed_enumerate | null | def reversed_enumerate(
tokens: Sequence[Token],
) -> Generator[tuple[int, Token], None, None]:
for i in reversed(range(len(tokens))):
yield i, tokens[i]
| (tokens: Sequence[tokenize_rt.Token]) -> Generator[tuple[int, tokenize_rt.Token], NoneType, NoneType] | [
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|
41,766 | tokenize_rt | rfind_string_parts | find the indicies of the string parts of a (joined) string literal
- `i` should start at the end of the string literal
- returns `()` (an empty tuple) for things which are not string literals
| def rfind_string_parts(tokens: Sequence[Token], i: int) -> tuple[int, ...]:
"""find the indicies of the string parts of a (joined) string literal
- `i` should start at the end of the string literal
- returns `()` (an empty tuple) for things which are not string literals
"""
ret = []
depth = 0
for i in range(i, -1, -1):
token = tokens[i]
if token.name == 'STRING':
ret.append(i)
elif token.name in NON_CODING_TOKENS:
pass
elif token.src == ')':
depth += 1
elif depth and token.src == '(':
depth -= 1
# if we closed the paren(s) make sure it was a parenthesized string
# and not actually a call
if depth == 0:
for j in range(i - 1, -1, -1):
tok = tokens[j]
if tok.name in NON_CODING_TOKENS:
pass
# this was actually a call and not a parenthesized string
elif (
tok.src in {']', ')'} or (
tok.name == 'NAME' and
tok.src not in keyword.kwlist
)
):
return ()
else:
break
break
elif depth: # it looked like a string but wasn't
return ()
else:
break
return tuple(reversed(ret))
| (tokens: Sequence[tokenize_rt.Token], i: int) -> tuple[int, ...] | [
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-0.010573334991931915,
0.008818951435387135,
0.019641568884253502,
0.009317516349256039,
-0.03715718165040016,
0.05903758853673935,
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|
41,767 | tokenize_rt | src_to_tokens | null | def src_to_tokens(src: str) -> list[Token]:
tokenize_target = io.StringIO(src)
lines = ('',) + tuple(tokenize_target)
tokenize_target.seek(0)
tokens = []
last_line = 1
last_col = 0
end_offset = 0
gen = tokenize.generate_tokens(tokenize_target.readline)
for tok_type, tok_text, (sline, scol), (eline, ecol), line in gen:
if sline > last_line:
newtok = lines[last_line][last_col:]
for lineno in range(last_line + 1, sline):
newtok += lines[lineno]
if scol > 0:
newtok += lines[sline][:scol]
# a multiline unimportant whitespace may contain escaped newlines
while _escaped_nl_re.search(newtok):
ws, nl, newtok = _re_partition(_escaped_nl_re, newtok)
if ws:
tokens.append(
Token(UNIMPORTANT_WS, ws, last_line, end_offset),
)
end_offset += len(ws.encode())
tokens.append(Token(ESCAPED_NL, nl, last_line, end_offset))
end_offset = 0
last_line += 1
if newtok:
tokens.append(Token(UNIMPORTANT_WS, newtok, sline, 0))
end_offset = len(newtok.encode())
else:
end_offset = 0
elif scol > last_col:
newtok = line[last_col:scol]
tokens.append(Token(UNIMPORTANT_WS, newtok, sline, end_offset))
end_offset += len(newtok.encode())
tok_name = tokenize.tok_name[tok_type]
tokens.append(Token(tok_name, tok_text, sline, end_offset))
last_line, last_col = eline, ecol
if sline != eline:
end_offset = len(lines[last_line][:last_col].encode())
else:
end_offset += len(tok_text.encode())
return tokens
| (src: str) -> list[tokenize_rt.Token] | [
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|
41,770 | tokenize_rt | tokens_to_src | null | def tokens_to_src(tokens: Iterable[Token]) -> str:
return ''.join(tok.src for tok in tokens)
| (tokens: Iterable[tokenize_rt.Token]) -> str | [
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|
41,771 | wield.bunch.bunch | Bunch |
Cookbook method for creating bunches
Often we want to just collect a bunch of stuff together, naming each
item of the bunch; a dictionary's OK for that, but a small do-nothing
class is even handier, and prettier to use. Whenever you want to
group a few variables:
>>> point = Bunch(datum=2, squared=4, coord=12)
>>> point.datum
taken from matplotlib's cbook.py
By: Alex Martelli
From: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52308
| class Bunch(object):
"""
Cookbook method for creating bunches
Often we want to just collect a bunch of stuff together, naming each
item of the bunch; a dictionary's OK for that, but a small do-nothing
class is even handier, and prettier to use. Whenever you want to
group a few variables:
>>> point = Bunch(datum=2, squared=4, coord=12)
>>> point.datum
taken from matplotlib's cbook.py
By: Alex Martelli
From: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52308
"""
def __init__(self, inner_dict=None, *args, **kwds):
if inner_dict is None or args or kwds:
if args:
_mydict = dict(inner_dict, *args, **kwds)
else:
_mydict = dict(**kwds)
else:
_mydict = inner_dict
self.__dict__["_mydict"] = _mydict
return
@classmethod
def as_bunch(cls, data):
if isinstance(data, cls):
return data
return cls(data)
def __repr__(self):
keys = list(self._mydict.keys())
return "{0}({1})".format(
self.__class__.__name__,
", \n ".join(
["".join((str(k), "=", repr(self._mydict[k]))) for k in keys]
),
)
def _repr_pretty_(self, p, cycle):
if cycle:
p.text("Bunch(<recurse>)")
else:
with p.group(4, "Bunch(", ")"):
first = True
for k, v in sorted(self._mydict.items()):
if not first:
p.text(",")
p.breakable()
else:
p.breakable()
first = False
if isinstance(k, str):
p.text(k)
else:
p.pretty(k)
p.text(" = ")
p.pretty(v)
if not first:
p.text(",")
p.breakable()
return
def __dir__(self):
items = [k for k in self._mydict.keys() if isinstance(k, str)]
# items += dir(super(Bunch, self))
return items
def __getitem__(self, key):
if isinstance(key, (slice, np.ndarray, MutableSequence)):
rebuild = dict()
for vkey, val in self._mydict.items():
if isinstance(val, np.ndarray):
val = val[key]
rebuild[vkey] = val
if not rebuild:
raise RuntimeError("Not holding arrays to index by {0}".format(key))
return Bunch(rebuild)
else:
return self._mydict[key]
def domain_sort(self, key):
argsort = np.argsort(self[key])
return self[argsort]
def __getattr__(self, key):
try:
item = self._mydict[key]
except KeyError as E:
raise AttributeError(E)
if type(item) is dict:
return self.__class__(item)
return item
def __setattr__(self, key, item):
self._mydict[key] = item
return
def __delattr__(self, key):
del self._mydict[key]
def __delitem__(self, key):
del self._mydict[key]
def __deepcopy__(self, memo):
return self.__class__(copy.deepcopy(self._mydict, memo))
def copy(self):
return self.__class__(self._mydict.copy())
__contains__ = gen_func("__contains__")
__eq__ = gen_func("__eq__")
__format__ = gen_func("__format__")
__ge__ = gen_func("__ge__")
__gt__ = gen_func("__gt__")
__iter__ = gen_func("__iter__")
__le__ = gen_func("__le__")
__len__ = gen_func("__len__")
__lt__ = gen_func("__lt__")
__ne__ = gen_func("__ne__")
__setitem__ = gen_func("__setitem__")
__sizeof__ = gen_func("__sizeof__")
__str__ = gen_func("__str__")
clear = gen_func("clear")
fromkeys = gen_func("fromkeys")
get = gen_func("get")
items = gen_func("items")
keys = gen_func("keys")
pop = gen_func("pop")
popitem = gen_func("popitem")
setdefault = gen_func("setdefault")
update = gen_func("update")
values = gen_func("values")
| (inner_dict=None, *args, **kwds) | [
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|
41,772 | wield.bunch.bunch | __contains__ | True if the dictionary has the specified key, else False. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,773 | wield.bunch.bunch | __deepcopy__ | null | def __deepcopy__(self, memo):
return self.__class__(copy.deepcopy(self._mydict, memo))
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|
41,774 | wield.bunch.bunch | __delattr__ | null | def __delattr__(self, key):
del self._mydict[key]
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|
41,775 | wield.bunch.bunch | __delitem__ | null | def __delitem__(self, key):
del self._mydict[key]
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|
41,776 | wield.bunch.bunch | __dir__ | null | def __dir__(self):
items = [k for k in self._mydict.keys() if isinstance(k, str)]
# items += dir(super(Bunch, self))
return items
| (self) | [
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|
41,777 | wield.bunch.bunch | __eq__ | Return self==value. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,778 | wield.bunch.bunch | __format__ | Default object formatter. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,779 | wield.bunch.bunch | __ge__ | Return self>=value. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,780 | wield.bunch.bunch | __getattr__ | null | def __getattr__(self, key):
try:
item = self._mydict[key]
except KeyError as E:
raise AttributeError(E)
if type(item) is dict:
return self.__class__(item)
return item
| (self, key) | [
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|
41,781 | wield.bunch.bunch | __getitem__ | null | def __getitem__(self, key):
if isinstance(key, (slice, np.ndarray, MutableSequence)):
rebuild = dict()
for vkey, val in self._mydict.items():
if isinstance(val, np.ndarray):
val = val[key]
rebuild[vkey] = val
if not rebuild:
raise RuntimeError("Not holding arrays to index by {0}".format(key))
return Bunch(rebuild)
else:
return self._mydict[key]
| (self, key) | [
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|
41,782 | wield.bunch.bunch | __gt__ | Return self>value. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,783 | wield.bunch.bunch | __init__ | null | def __init__(self, inner_dict=None, *args, **kwds):
if inner_dict is None or args or kwds:
if args:
_mydict = dict(inner_dict, *args, **kwds)
else:
_mydict = dict(**kwds)
else:
_mydict = inner_dict
self.__dict__["_mydict"] = _mydict
return
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|
41,784 | wield.bunch.bunch | __iter__ | Implement iter(self). | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
| (self, *args, **kwargs) | [
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|
41,785 | wield.bunch.bunch | __le__ | Return self<=value. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,786 | wield.bunch.bunch | __len__ | Return len(self). | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,787 | wield.bunch.bunch | __lt__ | Return self<value. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,788 | wield.bunch.bunch | __ne__ | Return self!=value. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,789 | wield.bunch.bunch | __repr__ | null | def __repr__(self):
keys = list(self._mydict.keys())
return "{0}({1})".format(
self.__class__.__name__,
", \n ".join(
["".join((str(k), "=", repr(self._mydict[k]))) for k in keys]
),
)
| (self) | [
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|
41,790 | wield.bunch.bunch | __setattr__ | null | def __setattr__(self, key, item):
self._mydict[key] = item
return
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|
41,791 | wield.bunch.bunch | __setitem__ | Set self[key] to value. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,792 | wield.bunch.bunch | __sizeof__ | D.__sizeof__() -> size of D in memory, in bytes | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,793 | wield.bunch.bunch | __str__ | Return str(self). | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
| (self, *args, **kwargs) | [
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|
41,794 | wield.bunch.bunch | _repr_pretty_ | null | def _repr_pretty_(self, p, cycle):
if cycle:
p.text("Bunch(<recurse>)")
else:
with p.group(4, "Bunch(", ")"):
first = True
for k, v in sorted(self._mydict.items()):
if not first:
p.text(",")
p.breakable()
else:
p.breakable()
first = False
if isinstance(k, str):
p.text(k)
else:
p.pretty(k)
p.text(" = ")
p.pretty(v)
if not first:
p.text(",")
p.breakable()
return
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|
41,795 | wield.bunch.bunch | clear | D.clear() -> None. Remove all items from D. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,796 | wield.bunch.bunch | copy | null | def copy(self):
return self.__class__(self._mydict.copy())
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|
41,797 | wield.bunch.bunch | domain_sort | null | def domain_sort(self, key):
argsort = np.argsort(self[key])
return self[argsort]
| (self, key) | [
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|
41,798 | wield.bunch.bunch | fromkeys | Create a new dictionary with keys from iterable and values set to value. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
| (self, *args, **kwargs) | [
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|
41,799 | wield.bunch.bunch | get | Return the value for key if key is in the dictionary, else default. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,800 | wield.bunch.bunch | items | D.items() -> a set-like object providing a view on D's items | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,801 | wield.bunch.bunch | keys | D.keys() -> a set-like object providing a view on D's keys | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,802 | wield.bunch.bunch | pop | D.pop(k[,d]) -> v, remove specified key and return the corresponding value.
If the key is not found, return the default if given; otherwise,
raise a KeyError. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
| (self, *args, **kwargs) | [
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|
41,803 | wield.bunch.bunch | popitem | Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
41,804 | wield.bunch.bunch | setdefault | Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default. | def gen_func(mname):
def func(self, *args, **kwargs):
return getattr(self._mydict, mname)(*args, **kwargs)
orig_func = getattr(dict, mname)
if orig_func is None:
return
func.__name__ = orig_func.__name__
func.__doc__ = orig_func.__doc__
return func
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|
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