index
int64 0
731k
| package
stringlengths 2
98
⌀ | name
stringlengths 1
76
| docstring
stringlengths 0
281k
⌀ | code
stringlengths 4
8.19k
| signature
stringlengths 2
42.8k
⌀ | embed_func_code
listlengths 768
768
|
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52,513 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
confusion
| null |
def confusion():
rule_config = _get_rule_config("Confusion")
return rule_config
|
()
|
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] |
52,514 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
create_xgboost_report
| null |
def create_xgboost_report():
rule_config = _get_rule_config("CreateXgboostReport")
return rule_config
|
()
|
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52,515 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
dead_relu
| null |
def dead_relu():
rule_config = _get_rule_config("DeadRelu")
return rule_config
|
()
|
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52,517 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
exploding_tensor
| null |
def exploding_tensor():
rule_config = _get_rule_config("ExplodingTensor")
return rule_config
|
()
|
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] |
52,518 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
feature_importance_overweight
| null |
def feature_importance_overweight():
rule_config = _get_rule_config("FeatureImportanceOverweight")
return rule_config
|
()
|
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] |
52,519 |
smdebug_rulesconfig.debugger_rules._collections
|
get_collection
| null |
def get_collection(collection_name):
return _get_collection_config(collection_name)
|
(collection_name)
|
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] |
52,520 |
smdebug_rulesconfig.actions.actions
|
is_valid_action_object
|
Helper function to be used by the sagemaker SDK to determine whether the provided object is a valid action object
or not (must be of type `Action` or `ActionList`.
:param actions: actions object specified by the user when calling `Rule.sagemaker` in the sagemaker SDK.
:return: Boolean for whether the provided actions object is valid or not.
|
def is_valid_action_object(actions: Union[Action, ActionList]):
"""
Helper function to be used by the sagemaker SDK to determine whether the provided object is a valid action object
or not (must be of type `Action` or `ActionList`.
:param actions: actions object specified by the user when calling `Rule.sagemaker` in the sagemaker SDK.
:return: Boolean for whether the provided actions object is valid or not.
"""
return isinstance(actions, Action) or isinstance(actions, ActionList)
|
(actions: Union[smdebug_rulesconfig.actions.actions.Action, smdebug_rulesconfig.actions.actions.ActionList])
|
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] |
52,521 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
loss_not_decreasing
| null |
def loss_not_decreasing():
rule_config = _get_rule_config("LossNotDecreasing")
return rule_config
|
()
|
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] |
52,522 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
nlp_sequence_ratio
| null |
def nlp_sequence_ratio():
rule_config = _get_rule_config("NLPSequenceRatio")
return rule_config
|
()
|
[
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] |
52,523 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
overfit
| null |
def overfit():
rule_config = _get_rule_config("Overfit")
return rule_config
|
()
|
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52,524 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
overtraining
| null |
def overtraining():
rule_config = _get_rule_config("Overtraining")
return rule_config
|
()
|
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] |
52,525 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
poor_weight_initialization
| null |
def poor_weight_initialization():
rule_config = _get_rule_config("PoorWeightInitialization")
return rule_config
|
()
|
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] |
52,527 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
saturated_activation
| null |
def saturated_activation():
rule_config = _get_rule_config("SaturatedActivation")
return rule_config
|
()
|
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] |
52,528 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
similar_across_runs
| null |
def similar_across_runs():
rule_config = _get_rule_config("SimilarAcrossRuns")
return rule_config
|
()
|
[
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] |
52,529 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
stalled_training_rule
| null |
def stalled_training_rule():
rule_config = _get_rule_config("StalledTrainingRule")
return rule_config
|
()
|
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52,530 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
tensor_variance
| null |
def tensor_variance():
rule_config = _get_rule_config("TensorVariance")
return rule_config
|
()
|
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52,531 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
tree_depth
| null |
def tree_depth():
rule_config = _get_rule_config("TreeDepth")
return rule_config
|
()
|
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] |
52,532 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
unchanged_tensor
| null |
def unchanged_tensor():
rule_config = _get_rule_config("UnchangedTensor")
return rule_config
|
()
|
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] |
52,533 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
vanishing_gradient
| null |
def vanishing_gradient():
rule_config = _get_rule_config("VanishingGradient")
return rule_config
|
()
|
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] |
52,534 |
smdebug_rulesconfig.debugger_rules.builtin_rules
|
weight_update_ratio
| null |
def weight_update_ratio():
rule_config = _get_rule_config("WeightUpdateRatio")
return rule_config
|
()
|
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] |
52,535 |
contextlib
|
AbstractContextManager
|
An abstract base class for context managers.
|
class AbstractContextManager(abc.ABC):
"""An abstract base class for context managers."""
__class_getitem__ = classmethod(GenericAlias)
def __enter__(self):
"""Return `self` upon entering the runtime context."""
return self
@abc.abstractmethod
def __exit__(self, exc_type, exc_value, traceback):
"""Raise any exception triggered within the runtime context."""
return None
@classmethod
def __subclasshook__(cls, C):
if cls is AbstractContextManager:
return _collections_abc._check_methods(C, "__enter__", "__exit__")
return NotImplemented
|
()
|
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52,536 |
contextlib
|
__enter__
|
Return `self` upon entering the runtime context.
|
def __enter__(self):
"""Return `self` upon entering the runtime context."""
return self
|
(self)
|
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] |
52,537 |
contextlib
|
__exit__
|
Raise any exception triggered within the runtime context.
|
@abc.abstractmethod
def __exit__(self, exc_type, exc_value, traceback):
"""Raise any exception triggered within the runtime context."""
return None
|
(self, exc_type, exc_value, traceback)
|
[
0.012388192117214203,
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0.015052788890898228,
0.04368191957473755,
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] |
52,538 |
asyncache
|
IdentityFunction
|
Type for a function returning the same type as the one it received.
|
class IdentityFunction(Protocol): # pylint: disable=too-few-public-methods
"""
Type for a function returning the same type as the one it received.
"""
def __call__(self, __x: _T) -> _T:
...
|
(*args, **kwargs)
|
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] |
52,539 |
asyncache
|
__call__
| null |
def __call__(self, __x: _T) -> _T:
...
|
(self, _IdentityFunction__x: ~_T) -> ~_T
|
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] |
52,540 |
typing
|
_no_init_or_replace_init
| null |
def _no_init_or_replace_init(self, *args, **kwargs):
cls = type(self)
if cls._is_protocol:
raise TypeError('Protocols cannot be instantiated')
# Already using a custom `__init__`. No need to calculate correct
# `__init__` to call. This can lead to RecursionError. See bpo-45121.
if cls.__init__ is not _no_init_or_replace_init:
return
# Initially, `__init__` of a protocol subclass is set to `_no_init_or_replace_init`.
# The first instantiation of the subclass will call `_no_init_or_replace_init` which
# searches for a proper new `__init__` in the MRO. The new `__init__`
# replaces the subclass' old `__init__` (ie `_no_init_or_replace_init`). Subsequent
# instantiation of the protocol subclass will thus use the new
# `__init__` and no longer call `_no_init_or_replace_init`.
for base in cls.__mro__:
init = base.__dict__.get('__init__', _no_init_or_replace_init)
if init is not _no_init_or_replace_init:
cls.__init__ = init
break
else:
# should not happen
cls.__init__ = object.__init__
cls.__init__(self, *args, **kwargs)
|
(self, *args, **kwargs)
|
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] |
52,542 |
asyncache
|
NullContext
|
A class for noop context managers.
|
class NullContext:
"""A class for noop context managers."""
def __enter__(self):
"""Return ``self`` upon entering the runtime context."""
return self
def __exit__(self, exc_type, exc_value, traceback):
"""Raise any exception triggered within the runtime context."""
return None
async def __aenter__(self):
"""Return ``self`` upon entering the runtime context."""
return self
async def __aexit__(self, exc_type, exc_value, traceback):
"""Raise any exception triggered within the runtime context."""
return None
|
()
|
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] |
52,543 |
asyncache
|
__aenter__
|
Return ``self`` upon entering the runtime context.
|
def __exit__(self, exc_type, exc_value, traceback):
"""Raise any exception triggered within the runtime context."""
return None
|
(self)
|
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] |
52,544 |
asyncache
|
__aexit__
|
Raise any exception triggered within the runtime context.
|
def __exit__(self, exc_type, exc_value, traceback):
"""Raise any exception triggered within the runtime context."""
return None
|
(self, exc_type, exc_value, traceback)
|
[
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] |
52,545 |
asyncache
|
__enter__
|
Return ``self`` upon entering the runtime context.
|
def __enter__(self):
"""Return ``self`` upon entering the runtime context."""
return self
|
(self)
|
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] |
52,557 |
asyncache
|
cached
|
Decorator to wrap a function or a coroutine with a memoizing callable
that saves results in a cache.
When ``lock`` is provided for a standard function, it's expected to
implement ``__enter__`` and ``__exit__`` that will be used to lock
the cache when gets updated. If it wraps a coroutine, ``lock``
must implement ``__aenter__`` and ``__aexit__``.
|
def cached(
cache: Optional[MutableMapping[_KT, Any]],
# ignoring the mypy error to be consistent with the type used
# in https://github.com/python/typeshed/tree/master/stubs/cachetools
key: Callable[..., _KT] = keys.hashkey, # type:ignore
lock: Optional["AbstractContextManager[Any]"] = None,
) -> IdentityFunction:
"""
Decorator to wrap a function or a coroutine with a memoizing callable
that saves results in a cache.
When ``lock`` is provided for a standard function, it's expected to
implement ``__enter__`` and ``__exit__`` that will be used to lock
the cache when gets updated. If it wraps a coroutine, ``lock``
must implement ``__aenter__`` and ``__aexit__``.
"""
lock = lock or NullContext()
def decorator(func):
if asyncio.iscoroutinefunction(func):
async def wrapper(*args, **kwargs):
k = key(*args, **kwargs)
try:
async with lock:
return cache[k]
except KeyError:
pass # key not found
val = await func(*args, **kwargs)
try:
async with lock:
cache[k] = val
except ValueError:
pass # val too large
return val
else:
def wrapper(*args, **kwargs):
k = key(*args, **kwargs)
try:
with lock:
return cache[k]
except KeyError:
pass # key not found
val = func(*args, **kwargs)
try:
with lock:
cache[k] = val
except ValueError:
pass # val too large
return val
return functools.wraps(func)(wrapper)
return decorator
|
(cache: Optional[MutableMapping[~_KT, Any]], key: Callable[..., ~_KT] = <function hashkey at 0x7f76318b20e0>, lock: Optional[contextlib.AbstractContextManager[Any]] = None) -> asyncache.IdentityFunction
|
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] |
52,558 |
asyncache
|
cachedmethod
|
Decorator to wrap a class or instance method with a memoizing
callable that saves results in a cache. This works similarly to
`cached`, but the arguments `cache` and `lock` are callables that
return the cache object and the lock respectively.
|
def cachedmethod(
cache: Callable[[Any], Optional[MutableMapping[_KT, Any]]],
# ignoring the mypy error to be consistent with the type used
# in https://github.com/python/typeshed/tree/master/stubs/cachetools
key: Callable[..., _KT] = keys.hashkey, # type:ignore
lock: Optional[Callable[[Any], "AbstractContextManager[Any]"]] = None,
) -> IdentityFunction:
"""Decorator to wrap a class or instance method with a memoizing
callable that saves results in a cache. This works similarly to
`cached`, but the arguments `cache` and `lock` are callables that
return the cache object and the lock respectively.
"""
lock = lock or (lambda _: NullContext())
def decorator(method):
if asyncio.iscoroutinefunction(method):
async def wrapper(self, *args, **kwargs):
method_cache = cache(self)
if method_cache is None:
return await method(self, *args, **kwargs)
k = key(self, *args, **kwargs)
try:
async with lock(self):
return method_cache[k]
except KeyError:
pass # key not found
val = await method(self, *args, **kwargs)
try:
async with lock(self):
method_cache[k] = val
except ValueError:
pass # val too large
return val
else:
def wrapper(self, *args, **kwargs):
method_cache = cache(self)
if method_cache is None:
return method(self, *args, **kwargs)
k = key(*args, **kwargs)
try:
with lock(self):
return method_cache[k]
except KeyError:
pass # key not found
val = method(self, *args, **kwargs)
try:
with lock(self):
method_cache[k] = val
except ValueError:
pass # val too large
return val
return functools.wraps(method)(wrapper)
return decorator
|
(cache: Callable[[Any], Optional[MutableMapping[~_KT, Any]]], key: Callable[..., ~_KT] = <function hashkey at 0x7f76318b20e0>, lock: Optional[Callable[[Any], contextlib.AbstractContextManager[Any]]] = None) -> asyncache.IdentityFunction
|
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] |
52,561 |
boxdiff.models.core
|
BoundingBox
|
Identified 2D bounding box: label + position, width, and height.
|
class BoundingBox:
"""
Identified 2D bounding box: label + position, width, and height.
"""
id: ID
label: str
x: float
y: float
width: float
height: float
def __sub__(self, other: 'BoundingBox') -> BoundingBoxDelta:
"""
Compute the delta between two bounding boxes.
"""
return BoundingBoxDelta(
self.id,
other.label,
self.label,
self.x - other.x,
self.y - other.y,
self.width - other.width,
self.height - other.height,
)
def __add__(self, delta: BoundingBoxDelta) -> 'BoundingBox':
return BoundingBox(
delta.id,
delta.label_new,
self.x + delta.x_delta,
self.y + delta.y_delta,
self.width + delta.width_delta,
self.height + delta.height_delta,
)
def __iadd__(self, delta: BoundingBoxDelta) -> 'BoundingBox':
self.label = delta.label_new
self.x += delta.x_delta
self.y += delta.y_delta
self.width += delta.width_delta
self.height += delta.height_delta
return self
@property
def points(self) -> Tuple[float]:
"""
Return the 4 points of the bounding box.
"""
return self.x, self.y, self.x + self.width, self.y + self.height
@property
def area(self) -> float:
"""
Return the area of the bounding box.
"""
return self.width * self.height
def __and__(self, other: 'BoundingBox') -> Optional['BoundingBox']:
"""
Compute the intersection of two bounding boxes, or None if they don't overlap.
"""
x1, y1, x2, y2 = self.points
x3, y3, x4, y4 = other.points
x = max(x1, x3)
y = max(y1, y3)
w = min(x2, x4) - x
h = min(y2, y4) - y
if w <= 0 or h <= 0:
return None
return BoundingBox(self.id, self.label, x, y, w, h)
def iou(self, other: 'BoundingBox') -> float:
"""
Compute the intersection over union of two bounding boxes.
"""
intersection = self & other
if intersection is None:
return 0
return intersection.area / (self.area + other.area - intersection.area)
|
(id: ~ID, label: str, x: float, y: float, width: float, height: float) -> None
|
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] |
52,562 |
boxdiff.models.core
|
__add__
| null |
def __add__(self, delta: BoundingBoxDelta) -> 'BoundingBox':
return BoundingBox(
delta.id,
delta.label_new,
self.x + delta.x_delta,
self.y + delta.y_delta,
self.width + delta.width_delta,
self.height + delta.height_delta,
)
|
(self, delta: boxdiff.models.deltas.BoundingBoxDelta) -> boxdiff.models.core.BoundingBox
|
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] |
52,563 |
boxdiff.models.core
|
__and__
|
Compute the intersection of two bounding boxes, or None if they don't overlap.
|
def __and__(self, other: 'BoundingBox') -> Optional['BoundingBox']:
"""
Compute the intersection of two bounding boxes, or None if they don't overlap.
"""
x1, y1, x2, y2 = self.points
x3, y3, x4, y4 = other.points
x = max(x1, x3)
y = max(y1, y3)
w = min(x2, x4) - x
h = min(y2, y4) - y
if w <= 0 or h <= 0:
return None
return BoundingBox(self.id, self.label, x, y, w, h)
|
(self, other: boxdiff.models.core.BoundingBox) -> Optional[boxdiff.models.core.BoundingBox]
|
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] |
52,564 |
boxdiff.models.core
|
__eq__
| null |
from typing import List, Optional, Tuple, TypeVar
from uuid import UUID
from pydantic.dataclasses import dataclass
from dataclasses_json import dataclass_json
from boxdiff.models.deltas import BoundingBoxDelta, ImageDelta, ImageSetDelta
ID = TypeVar('ID', int, UUID, str) # Parses in order: int, UUID, then str
@dataclass_json
@dataclass
class BoundingBox:
"""
Identified 2D bounding box: label + position, width, and height.
"""
id: ID
label: str
x: float
y: float
width: float
height: float
def __sub__(self, other: 'BoundingBox') -> BoundingBoxDelta:
"""
Compute the delta between two bounding boxes.
"""
return BoundingBoxDelta(
self.id,
other.label,
self.label,
self.x - other.x,
self.y - other.y,
self.width - other.width,
self.height - other.height,
)
def __add__(self, delta: BoundingBoxDelta) -> 'BoundingBox':
return BoundingBox(
delta.id,
delta.label_new,
self.x + delta.x_delta,
self.y + delta.y_delta,
self.width + delta.width_delta,
self.height + delta.height_delta,
)
def __iadd__(self, delta: BoundingBoxDelta) -> 'BoundingBox':
self.label = delta.label_new
self.x += delta.x_delta
self.y += delta.y_delta
self.width += delta.width_delta
self.height += delta.height_delta
return self
@property
def points(self) -> Tuple[float]:
"""
Return the 4 points of the bounding box.
"""
return self.x, self.y, self.x + self.width, self.y + self.height
@property
def area(self) -> float:
"""
Return the area of the bounding box.
"""
return self.width * self.height
def __and__(self, other: 'BoundingBox') -> Optional['BoundingBox']:
"""
Compute the intersection of two bounding boxes, or None if they don't overlap.
"""
x1, y1, x2, y2 = self.points
x3, y3, x4, y4 = other.points
x = max(x1, x3)
y = max(y1, y3)
w = min(x2, x4) - x
h = min(y2, y4) - y
if w <= 0 or h <= 0:
return None
return BoundingBox(self.id, self.label, x, y, w, h)
def iou(self, other: 'BoundingBox') -> float:
"""
Compute the intersection over union of two bounding boxes.
"""
intersection = self & other
if intersection is None:
return 0
return intersection.area / (self.area + other.area - intersection.area)
|
(self, other)
|
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] |
52,565 |
boxdiff.models.core
|
__iadd__
| null |
def __iadd__(self, delta: BoundingBoxDelta) -> 'BoundingBox':
self.label = delta.label_new
self.x += delta.x_delta
self.y += delta.y_delta
self.width += delta.width_delta
self.height += delta.height_delta
return self
|
(self, delta: boxdiff.models.deltas.BoundingBoxDelta) -> boxdiff.models.core.BoundingBox
|
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] |
52,566 |
pydantic._internal._dataclasses
|
__init__
| null |
def complete_dataclass(
cls: type[Any],
config_wrapper: _config.ConfigWrapper,
*,
raise_errors: bool = True,
types_namespace: dict[str, Any] | None,
) -> bool:
"""Finish building a pydantic dataclass.
This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`.
This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`.
Args:
cls: The class.
config_wrapper: The config wrapper instance.
raise_errors: Whether to raise errors, defaults to `True`.
types_namespace: The types namespace.
Returns:
`True` if building a pydantic dataclass is successfully completed, `False` otherwise.
Raises:
PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations.
"""
if hasattr(cls, '__post_init_post_parse__'):
warnings.warn(
'Support for `__post_init_post_parse__` has been dropped, the method will not be called', DeprecationWarning
)
if types_namespace is None:
types_namespace = _typing_extra.get_cls_types_namespace(cls)
set_dataclass_fields(cls, types_namespace, config_wrapper=config_wrapper)
typevars_map = get_standard_typevars_map(cls)
gen_schema = GenerateSchema(
config_wrapper,
types_namespace,
typevars_map,
)
# This needs to be called before we change the __init__
sig = generate_pydantic_signature(
init=cls.__init__,
fields=cls.__pydantic_fields__, # type: ignore
config_wrapper=config_wrapper,
is_dataclass=True,
)
# dataclass.__init__ must be defined here so its `__qualname__` can be changed since functions can't be copied.
def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) -> None:
__tracebackhide__ = True
s = __dataclass_self__
s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
__init__.__qualname__ = f'{cls.__qualname__}.__init__'
cls.__init__ = __init__ # type: ignore
cls.__pydantic_config__ = config_wrapper.config_dict # type: ignore
cls.__signature__ = sig # type: ignore
get_core_schema = getattr(cls, '__get_pydantic_core_schema__', None)
try:
if get_core_schema:
schema = get_core_schema(
cls,
CallbackGetCoreSchemaHandler(
partial(gen_schema.generate_schema, from_dunder_get_core_schema=False),
gen_schema,
ref_mode='unpack',
),
)
else:
schema = gen_schema.generate_schema(cls, from_dunder_get_core_schema=False)
except PydanticUndefinedAnnotation as e:
if raise_errors:
raise
set_dataclass_mocks(cls, cls.__name__, f'`{e.name}`')
return False
core_config = config_wrapper.core_config(cls)
try:
schema = gen_schema.clean_schema(schema)
except gen_schema.CollectedInvalid:
set_dataclass_mocks(cls, cls.__name__, 'all referenced types')
return False
# We are about to set all the remaining required properties expected for this cast;
# __pydantic_decorators__ and __pydantic_fields__ should already be set
cls = typing.cast('type[PydanticDataclass]', cls)
# debug(schema)
cls.__pydantic_core_schema__ = schema
cls.__pydantic_validator__ = validator = create_schema_validator(
schema, cls, cls.__module__, cls.__qualname__, 'dataclass', core_config, config_wrapper.plugin_settings
)
cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config)
if config_wrapper.validate_assignment:
@wraps(cls.__setattr__)
def validated_setattr(instance: Any, field: str, value: str, /) -> None:
validator.validate_assignment(instance, field, value)
cls.__setattr__ = validated_setattr.__get__(None, cls) # type: ignore
return True
|
(__dataclass_self__: 'PydanticDataclass', *args: 'Any', **kwargs: 'Any') -> 'None'
|
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] |
52,568 |
boxdiff.models.core
|
__sub__
|
Compute the delta between two bounding boxes.
|
def __sub__(self, other: 'BoundingBox') -> BoundingBoxDelta:
"""
Compute the delta between two bounding boxes.
"""
return BoundingBoxDelta(
self.id,
other.label,
self.label,
self.x - other.x,
self.y - other.y,
self.width - other.width,
self.height - other.height,
)
|
(self, other: boxdiff.models.core.BoundingBox) -> boxdiff.models.deltas.BoundingBoxDelta
|
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] |
52,569 |
boxdiff.models.core
|
iou
|
Compute the intersection over union of two bounding boxes.
|
def iou(self, other: 'BoundingBox') -> float:
"""
Compute the intersection over union of two bounding boxes.
"""
intersection = self & other
if intersection is None:
return 0
return intersection.area / (self.area + other.area - intersection.area)
|
(self, other: boxdiff.models.core.BoundingBox) -> float
|
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] |
52,570 |
dataclasses_json.api
|
to_dict
| null |
def to_dict(self, encode_json=False) -> Dict[str, Json]:
return _asdict(self, encode_json=encode_json)
|
(self, encode_json=False) -> Dict[str, Union[dict, list, str, int, float, bool, NoneType]]
|
[
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] |
52,571 |
dataclasses_json.api
|
to_json
| null |
def to_json(self,
*,
skipkeys: bool = False,
ensure_ascii: bool = True,
check_circular: bool = True,
allow_nan: bool = True,
indent: Optional[Union[int, str]] = None,
separators: Optional[Tuple[str, str]] = None,
default: Optional[Callable] = None,
sort_keys: bool = False,
**kw) -> str:
return json.dumps(self.to_dict(encode_json=False),
cls=_ExtendedEncoder,
skipkeys=skipkeys,
ensure_ascii=ensure_ascii,
check_circular=check_circular,
allow_nan=allow_nan,
indent=indent,
separators=separators,
default=default,
sort_keys=sort_keys,
**kw)
|
(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, NoneType] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) -> str
|
[
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] |
52,572 |
boxdiff.models.deltas
|
BoundingBoxDelta
|
BoundingBoxDelta(id: 'ID', label_old: str, label_new: str, x_delta: float, y_delta: float, width_delta: float, height_delta: float)
|
class BoundingBoxDelta:
id: 'ID'
label_old: str
label_new: str
x_delta: float
y_delta: float
width_delta: float
height_delta: float
@property
def flags(self) -> BoundingBoxDifference:
f = BoundingBoxDifference(0)
if self.label_old != self.label_new:
f |= BoundingBoxDifference.RELABELED
if self.x_delta != 0 or self.y_delta != 0:
f |= BoundingBoxDifference.MOVED
if self.width_delta != 0 or self.height_delta != 0:
f |= BoundingBoxDifference.RESIZED
return f
|
(id: 'ID', label_old: str, label_new: str, x_delta: float, y_delta: float, width_delta: float, height_delta: float) -> None
|
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] |
52,573 |
boxdiff.models.deltas
|
__eq__
| null |
from typing import List, TYPE_CHECKING
from dataclasses import dataclass
if TYPE_CHECKING:
from boxdiff.models.core import ID, BoundingBox
from boxdiff.models.flags import (
BoundingBoxDifference,
ImageDifference,
ImageSetDifference,
)
@dataclass
class BoundingBoxDelta:
id: 'ID'
label_old: str
label_new: str
x_delta: float
y_delta: float
width_delta: float
height_delta: float
@property
def flags(self) -> BoundingBoxDifference:
f = BoundingBoxDifference(0)
if self.label_old != self.label_new:
f |= BoundingBoxDifference.RELABELED
if self.x_delta != 0 or self.y_delta != 0:
f |= BoundingBoxDifference.MOVED
if self.width_delta != 0 or self.height_delta != 0:
f |= BoundingBoxDifference.RESIZED
return f
|
(self, other)
|
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] |
52,576 |
boxdiff.models.flags
|
BoundingBoxDifference
|
Flag enum for the different types of box-level differences.
|
class BoundingBoxDifference(Flag):
"""
Flag enum for the different types of box-level differences.
"""
MOVED = auto()
RESIZED = auto()
RELABELED = auto()
|
(value, names=None, *, module=None, qualname=None, type=None, start=1)
|
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] |
52,577 |
boxdiff.models.core
|
Image
|
Identified list of bounding boxes.
|
class Image:
"""
Identified list of bounding boxes.
"""
id: ID
bounding_boxes: List[BoundingBox]
def __sub__(self, other: 'Image') -> 'ImageDelta':
"""
Compute the delta between two images.
"""
# Get the unique set of IDs for the boxes in each image
box_ids_self = {box.id for box in self.bounding_boxes}
box_ids_other = {box.id for box in other.bounding_boxes}
# Find the boxes that are in one image but not the other
boxes_added = [
box for box in other.bounding_boxes if box.id not in box_ids_self
]
boxes_removed = [
box for box in self.bounding_boxes if box.id not in box_ids_other
]
# Find the boxes that are in both images
box_ids_common = box_ids_self & box_ids_other
boxes_common_self = sorted(
[box for box in self.bounding_boxes if box.id in box_ids_common],
key=lambda box: box.id,
)
boxes_common_other = sorted(
[box for box in other.bounding_boxes if box.id in box_ids_common],
key=lambda box: box.id,
)
assert len(boxes_common_self) == len(
boxes_common_other
), 'Common box count mismatch'
# Compute the deltas between the common boxes
box_deltas = []
for box_self, box_other in zip(boxes_common_self, boxes_common_other):
box_deltas.append(box_self - box_other)
return ImageDelta(self.id, boxes_added, boxes_removed, box_deltas)
|
(id: ~ID, bounding_boxes: List[boxdiff.models.core.BoundingBox]) -> None
|
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] |
52,581 |
boxdiff.models.core
|
__sub__
|
Compute the delta between two images.
|
def __sub__(self, other: 'Image') -> 'ImageDelta':
"""
Compute the delta between two images.
"""
# Get the unique set of IDs for the boxes in each image
box_ids_self = {box.id for box in self.bounding_boxes}
box_ids_other = {box.id for box in other.bounding_boxes}
# Find the boxes that are in one image but not the other
boxes_added = [
box for box in other.bounding_boxes if box.id not in box_ids_self
]
boxes_removed = [
box for box in self.bounding_boxes if box.id not in box_ids_other
]
# Find the boxes that are in both images
box_ids_common = box_ids_self & box_ids_other
boxes_common_self = sorted(
[box for box in self.bounding_boxes if box.id in box_ids_common],
key=lambda box: box.id,
)
boxes_common_other = sorted(
[box for box in other.bounding_boxes if box.id in box_ids_common],
key=lambda box: box.id,
)
assert len(boxes_common_self) == len(
boxes_common_other
), 'Common box count mismatch'
# Compute the deltas between the common boxes
box_deltas = []
for box_self, box_other in zip(boxes_common_self, boxes_common_other):
box_deltas.append(box_self - box_other)
return ImageDelta(self.id, boxes_added, boxes_removed, box_deltas)
|
(self, other: boxdiff.models.core.Image) -> boxdiff.models.deltas.ImageDelta
|
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] |
52,584 |
boxdiff.models.deltas
|
ImageDelta
|
ImageDelta(id: 'ID', boxes_added: List[ForwardRef('BoundingBox')], boxes_removed: List[ForwardRef('BoundingBox')], box_deltas: List[boxdiff.models.deltas.BoundingBoxDelta])
|
class ImageDelta:
id: 'ID'
boxes_added: List['BoundingBox']
boxes_removed: List['BoundingBox']
box_deltas: List[BoundingBoxDelta]
@property
def flags(self) -> ImageDifference:
f = ImageDifference(0)
if self.boxes_added:
f |= ImageDifference.BOXES_ADDED
if self.boxes_removed:
f |= ImageDifference.BOXES_REMOVED
if any(delta.flags for delta in self.box_deltas):
f |= ImageDifference.BOXES_MODIFIED
return f
|
(id: 'ID', boxes_added: List[ForwardRef('BoundingBox')], boxes_removed: List[ForwardRef('BoundingBox')], box_deltas: List[boxdiff.models.deltas.BoundingBoxDelta]) -> None
|
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] |
52,588 |
boxdiff.models.flags
|
ImageDifference
|
Flag enum for the different types of image-level differences.
|
class ImageDifference(Flag):
"""
Flag enum for the different types of image-level differences.
"""
BOXES_ADDED = auto()
BOXES_REMOVED = auto()
BOXES_MODIFIED = auto()
|
(value, names=None, *, module=None, qualname=None, type=None, start=1)
|
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] |
52,589 |
boxdiff.models.core
|
ImageSet
|
Identified list of images.
|
class ImageSet:
"""
Identified list of images.
"""
id: ID
images: List[Image]
def __sub__(self, other: 'ImageSet') -> ImageSetDelta:
"""
Compute the delta between two image sets.
"""
# Get the unique set of IDs for the images in each set
image_ids_self = {image.id for image in self.images}
image_ids_other = {image.id for image in other.images}
# Find the images that are in one set but not the other
images_added = [
image for image in other.images if image.id not in image_ids_self
]
images_removed = [
image for image in self.images if image.id not in image_ids_other
]
# Find the images that are in both sets
image_ids_common = image_ids_self & image_ids_other
images_common_self = sorted(
[image for image in self.images if image.id in image_ids_common],
key=lambda im: im.id,
)
images_common_other = sorted(
[image for image in other.images if image.id in image_ids_common],
key=lambda im: im.id,
)
assert len(images_common_self) == len(
images_common_other
), 'Common image count mismatch'
# Compute the deltas between the common images
image_deltas = []
for image_self, image_other in zip(images_common_self, images_common_other):
image_deltas.append(image_self - image_other)
return ImageSetDelta(self.id, images_added, images_removed, image_deltas)
|
(id: ~ID, images: List[boxdiff.models.core.Image]) -> None
|
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] |
52,593 |
boxdiff.models.core
|
__sub__
|
Compute the delta between two image sets.
|
def __sub__(self, other: 'ImageSet') -> ImageSetDelta:
"""
Compute the delta between two image sets.
"""
# Get the unique set of IDs for the images in each set
image_ids_self = {image.id for image in self.images}
image_ids_other = {image.id for image in other.images}
# Find the images that are in one set but not the other
images_added = [
image for image in other.images if image.id not in image_ids_self
]
images_removed = [
image for image in self.images if image.id not in image_ids_other
]
# Find the images that are in both sets
image_ids_common = image_ids_self & image_ids_other
images_common_self = sorted(
[image for image in self.images if image.id in image_ids_common],
key=lambda im: im.id,
)
images_common_other = sorted(
[image for image in other.images if image.id in image_ids_common],
key=lambda im: im.id,
)
assert len(images_common_self) == len(
images_common_other
), 'Common image count mismatch'
# Compute the deltas between the common images
image_deltas = []
for image_self, image_other in zip(images_common_self, images_common_other):
image_deltas.append(image_self - image_other)
return ImageSetDelta(self.id, images_added, images_removed, image_deltas)
|
(self, other: boxdiff.models.core.ImageSet) -> boxdiff.models.deltas.ImageSetDelta
|
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] |
52,596 |
boxdiff.models.deltas
|
ImageSetDelta
|
ImageSetDelta(id: 'ID', images_added: List[boxdiff.models.deltas.ImageDelta], images_removed: List[boxdiff.models.deltas.ImageDelta], image_deltas: List[boxdiff.models.deltas.ImageDelta])
|
class ImageSetDelta:
id: 'ID'
images_added: List[ImageDelta]
images_removed: List[ImageDelta]
image_deltas: List[ImageDelta]
@property
def flags(self) -> ImageSetDifference:
f = ImageSetDifference(0)
if self.images_added:
f |= ImageSetDifference.IMAGES_ADDED
if self.images_removed:
f |= ImageSetDifference.IMAGES_REMOVED
if any(delta.flags for delta in self.image_deltas):
f |= ImageSetDifference.IMAGES_MODIFIED
return f
|
(id: 'ID', images_added: List[boxdiff.models.deltas.ImageDelta], images_removed: List[boxdiff.models.deltas.ImageDelta], image_deltas: List[boxdiff.models.deltas.ImageDelta]) -> None
|
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] |
52,600 |
boxdiff.models.flags
|
ImageSetDifference
|
Flag enum for the different types of image-set-level differences.
|
class ImageSetDifference(Flag):
"""
Flag enum for the different types of image-set-level differences.
"""
IMAGES_ADDED = auto()
IMAGES_REMOVED = auto()
IMAGES_MODIFIED = auto()
|
(value, names=None, *, module=None, qualname=None, type=None, start=1)
|
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] |
52,602 |
causalimpact.analysis
|
CausalImpact
|
CausalImpact() performs causal inference through counterfactual
predictions using a Bayesian structural time-series model.
Parameters:
----------
data : pandas dataframe
the response variable must be in the first column, and any covariates
in subsequent columns.
pre_period : list
A list specifying the first and the last time point of the
pre-intervention period in the response column. This period can be
thought of as a training period, used to determine the relationship
between the response variable and the covariates.
post_period : list
A vector specifying the first and the last day of the post-intervention
period we wish to study. This is the period after the intervention has
begun whose effect we are interested in. The relationship between
response variable and covariates, as determined during the pre-period,
will be used to predict how the response variable should have evolved
during the post-period had no intervention taken place.
model_args : dict
Optional arguments that can be used to adjust the default construction
of the state-space model used for inference.
For full control over the model, you can construct your own model using
the statsmodels package and feed the model into CausalImpact().
ucm_model : statsmodels.tsa.statespace.structural.UnobservedComponents
Instead of passing in data and having CausalImpact construct a
model, it is possible to construct a model yourself using the
statsmodel package. In this case, omit data, pre_period, and
post_period. Instead only pass in ucm_model, y_post, alpha (optional).
The model must have been fitted on data where the response variable was
set to np.nan during the post-treatment period. The actual observed data
during this period must then be passed to the function in y_post.
post_period_response : list | pd.Series | np.Array
Actual observed data during the post-intervention period. This is required
if and only if a fitted ucm_model is passed instead of data.
alpha : float
Desired tail-area probability for posterior intervals. Defaults to 0.05,
which will produce central 95% intervals.
Returns
-------
CausalImpact Object
|
class CausalImpact:
"""CausalImpact() performs causal inference through counterfactual
predictions using a Bayesian structural time-series model.
Parameters:
----------
data : pandas dataframe
the response variable must be in the first column, and any covariates
in subsequent columns.
pre_period : list
A list specifying the first and the last time point of the
pre-intervention period in the response column. This period can be
thought of as a training period, used to determine the relationship
between the response variable and the covariates.
post_period : list
A vector specifying the first and the last day of the post-intervention
period we wish to study. This is the period after the intervention has
begun whose effect we are interested in. The relationship between
response variable and covariates, as determined during the pre-period,
will be used to predict how the response variable should have evolved
during the post-period had no intervention taken place.
model_args : dict
Optional arguments that can be used to adjust the default construction
of the state-space model used for inference.
For full control over the model, you can construct your own model using
the statsmodels package and feed the model into CausalImpact().
ucm_model : statsmodels.tsa.statespace.structural.UnobservedComponents
Instead of passing in data and having CausalImpact construct a
model, it is possible to construct a model yourself using the
statsmodel package. In this case, omit data, pre_period, and
post_period. Instead only pass in ucm_model, y_post, alpha (optional).
The model must have been fitted on data where the response variable was
set to np.nan during the post-treatment period. The actual observed data
during this period must then be passed to the function in y_post.
post_period_response : list | pd.Series | np.Array
Actual observed data during the post-intervention period. This is required
if and only if a fitted ucm_model is passed instead of data.
alpha : float
Desired tail-area probability for posterior intervals. Defaults to 0.05,
which will produce central 95% intervals.
Returns
-------
CausalImpact Object
"""
def __init__(
self,
data=None,
pre_period=None,
post_period=None,
model_args=None,
ucm_model=None,
post_period_response=None,
alpha=0.05,
estimation="MLE",
):
self.series = None
self.model = {}
if isinstance(data, pd.DataFrame):
self.data = data.copy()
else:
self.data = data
self.params = {
"data": data,
"pre_period": pre_period,
"post_period": post_period,
"model_args": model_args,
"ucm_model": ucm_model,
"post_period_response": post_period_response,
"alpha": alpha,
"estimation": estimation,
}
self.inferences = None
self.results = None
def run(self):
kwargs = self._format_input(
self.params["data"],
self.params["pre_period"],
self.params["post_period"],
self.params["model_args"],
self.params["ucm_model"],
self.params["post_period_response"],
self.params["alpha"],
)
# Depending on input, dispatch to the appropriate Run* method()
if self.data is not None:
self._run_with_data(
kwargs["data"],
kwargs["pre_period"],
kwargs["post_period"],
kwargs["model_args"],
kwargs["alpha"],
self.params["estimation"],
)
else:
self._run_with_ucm(
kwargs["ucm_model"],
kwargs["post_period_response"],
kwargs["alpha"],
kwargs["model_args"],
self.params["estimation"],
)
@staticmethod
def _format_input_data(data):
"""Check and format the data argument provided to CausalImpact().
Args:
data: Pandas DataFrame
Returns:
correctly formatted Pandas DataFrame
"""
# If <data> is a Pandas DataFrame and the first column is 'date',
# try to convert
if (
isinstance(data, pd.DataFrame)
and isinstance(data.columns[0], str)
and data.columns[0].lower() in ["date", "time"]
):
data = data.set_index(data.columns[0])
# Try to convert to Pandas DataFrame
try:
data = pd.DataFrame(data)
except ValueError:
raise ValueError("could not convert input data to Pandas " + "DataFrame")
# Must have at least 3 time points
if len(data.index) < 3:
raise ValueError("data must have at least 3 time points")
# Must not have NA in covariates (if any)
if len(data.columns) >= 2 and pd.isnull(data.iloc[:, 1:]).any(axis=None):
raise ValueError("covariates must not contain null values")
return data
@staticmethod
def _check_periods_are_valid(pre_period, post_period):
if not isinstance(pre_period, list) or not isinstance(post_period, list):
raise ValueError("pre_period and post_period must both be lists")
if len(pre_period) != 2 or len(post_period) != 2:
raise ValueError("pre_period and post_period must both be of " + "length 2")
if pd.isnull(pre_period).any(axis=None) or pd.isnull(post_period).any(
axis=None
):
raise ValueError(
"pre_period and post period must not contain " + "null values"
)
@staticmethod
def _align_periods_dtypes(pre_period, post_period, data):
"""align the dtypes of the pre_period and post_period to the data index.
Args:
pre_period: two-element list
post_period: two-element list
data: already-checked Pandas DataFrame, for reference only
"""
pre_dtype = np.array(pre_period).dtype
post_dtype = np.array(post_period).dtype
# if index is datetime then convert pre and post to datetimes
if isinstance(data.index, pd.core.indexes.datetimes.DatetimeIndex):
pre_period = [pd.to_datetime(date) for date in pre_period]
post_period = [pd.to_datetime(date) for date in post_period]
pd.core.dtypes.common.is_datetime_or_timedelta_dtype(pre_period)
# if index is not datetime then error if datetime pre and post is passed
elif pd.core.dtypes.common.is_datetime_or_timedelta_dtype(
pd.Series(pre_period)
) or pd.core.dtypes.common.is_datetime_or_timedelta_dtype(
pd.Series(post_period)
):
raise ValueError(
"pre_period ("
+ pre_dtype.name
+ ") and post_period ("
+ post_dtype.name
+ ") should have the same class as the "
+ "time points in the data ("
+ data.index.dtype.name
+ ")"
)
# if index is int
elif pd.api.types.is_int64_dtype(data.index):
pre_period = [int(elem) for elem in pre_period]
post_period = [int(elem) for elem in post_period]
# if index is int
elif pd.api.types.is_float_dtype(data.index):
pre_period = [float(elem) for elem in pre_period]
post_period = [float(elem) for elem in post_period]
# if index is string
elif pd.api.types.is_string_dtype(data.index):
if pd.api.types.is_numeric_dtype(
np.array(pre_period)
) or pd.api.types.is_nu
|
(data=None, pre_period=None, post_period=None, model_args=None, ucm_model=None, post_period_response=None, alpha=0.05, estimation='MLE')
|
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] |
52,603 |
causalimpact.analysis
|
__init__
| null |
def __init__(
self,
data=None,
pre_period=None,
post_period=None,
model_args=None,
ucm_model=None,
post_period_response=None,
alpha=0.05,
estimation="MLE",
):
self.series = None
self.model = {}
if isinstance(data, pd.DataFrame):
self.data = data.copy()
else:
self.data = data
self.params = {
"data": data,
"pre_period": pre_period,
"post_period": post_period,
"model_args": model_args,
"ucm_model": ucm_model,
"post_period_response": post_period_response,
"alpha": alpha,
"estimation": estimation,
}
self.inferences = None
self.results = None
|
(self, data=None, pre_period=None, post_period=None, model_args=None, ucm_model=None, post_period_response=None, alpha=0.05, estimation='MLE')
|
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] |
52,604 |
causalimpact.analysis
|
_align_periods_dtypes
|
align the dtypes of the pre_period and post_period to the data index.
Args:
pre_period: two-element list
post_period: two-element list
data: already-checked Pandas DataFrame, for reference only
|
@staticmethod
def _align_periods_dtypes(pre_period, post_period, data):
"""align the dtypes of the pre_period and post_period to the data index.
Args:
pre_period: two-element list
post_period: two-element list
data: already-checked Pandas DataFrame, for reference only
"""
pre_dtype = np.array(pre_period).dtype
post_dtype = np.array(post_period).dtype
# if index is datetime then convert pre and post to datetimes
if isinstance(data.index, pd.core.indexes.datetimes.DatetimeIndex):
pre_period = [pd.to_datetime(date) for date in pre_period]
post_period = [pd.to_datetime(date) for date in post_period]
pd.core.dtypes.common.is_datetime_or_timedelta_dtype(pre_period)
# if index is not datetime then error if datetime pre and post is passed
elif pd.core.dtypes.common.is_datetime_or_timedelta_dtype(
pd.Series(pre_period)
) or pd.core.dtypes.common.is_datetime_or_timedelta_dtype(
pd.Series(post_period)
):
raise ValueError(
"pre_period ("
+ pre_dtype.name
+ ") and post_period ("
+ post_dtype.name
+ ") should have the same class as the "
+ "time points in the data ("
+ data.index.dtype.name
+ ")"
)
# if index is int
elif pd.api.types.is_int64_dtype(data.index):
pre_period = [int(elem) for elem in pre_period]
post_period = [int(elem) for elem in post_period]
# if index is int
elif pd.api.types.is_float_dtype(data.index):
pre_period = [float(elem) for elem in pre_period]
post_period = [float(elem) for elem in post_period]
# if index is string
elif pd.api.types.is_string_dtype(data.index):
if pd.api.types.is_numeric_dtype(
np.array(pre_period)
) or pd.api.types.is_numeric_dtype(np.array(post_period)):
raise ValueError(
"pre_period ("
+ pre_dtype.name
+ ") and post_period ("
+ post_dtype.name
+ ") should have the same class as the "
+ "time points in the data ("
+ data.index.dtype.name
+ ")"
)
else:
pre_period = [str(idx) for idx in pre_period]
post_period = [str(idx) for idx in post_period]
else:
raise ValueError(
"pre_period ("
+ pre_dtype.name
+ ") and post_period ("
+ post_dtype.name
+ ") should have the same class as the "
+ "time points in the data ("
+ data.index.dtype.name
+ ")"
)
return [pre_period, post_period]
|
(pre_period, post_period, data)
|
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] |
52,605 |
causalimpact.analysis
|
_check_periods_are_valid
| null |
@staticmethod
def _check_periods_are_valid(pre_period, post_period):
if not isinstance(pre_period, list) or not isinstance(post_period, list):
raise ValueError("pre_period and post_period must both be lists")
if len(pre_period) != 2 or len(post_period) != 2:
raise ValueError("pre_period and post_period must both be of " + "length 2")
if pd.isnull(pre_period).any(axis=None) or pd.isnull(post_period).any(
axis=None
):
raise ValueError(
"pre_period and post period must not contain " + "null values"
)
|
(pre_period, post_period)
|
[
0.007482768502086401,
0.01664959266781807,
-0.01703154481947422,
0.040486812591552734,
-0.05496622994542122,
0.005603395402431488,
0.0009863452287390828,
-0.02647615224123001,
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] |
52,606 |
causalimpact.analysis
|
_check_valid_alpha
| null |
@staticmethod
def _check_valid_alpha(alpha):
if alpha is None:
raise ValueError("alpha must not be None")
if not np.isreal(alpha):
raise ValueError("alpha must be a real number")
if np.isnan(alpha):
raise ValueError("alpha must not be NA")
if alpha <= 0 or alpha >= 1:
raise ValueError("alpha must be between 0 and 1")
|
(alpha)
|
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] |
52,607 |
causalimpact.analysis
|
_check_valid_args_combo
| null |
@staticmethod
def _check_valid_args_combo(args):
data_model_args = [True, True, True, False, False]
ucm_model_args = [False, False, False, True, True]
if np.any(pd.isnull(args) != data_model_args) and np.any(
pd.isnull(args) != ucm_model_args
):
raise SyntaxError(
"Must either provide ``data``, ``pre_period``"
+ " ,``post_period``, ``model_args``"
" or ``ucm_model" + "and ``post_period_response``"
)
|
(args)
|
[
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] |
52,608 |
causalimpact.analysis
|
_format_input
|
Check and format all input arguments supplied to CausalImpact().
See the documentation of CausalImpact() for details
Args:
data: Pandas DataFrame or data frame
pre_period: beginning and end of pre-period
post_period: beginning and end of post-period
model_args: dict of additional arguments for the model
ucm_model: UnobservedComponents model (instead of data)
post_period_response: observed response in the post-period
alpha: tail-area for posterior intervals
estimation: method of estimation for model fitting
Returns:
list of checked (and possibly reformatted) input arguments
|
def _format_input(
self,
data,
pre_period,
post_period,
model_args,
ucm_model,
post_period_response,
alpha,
):
"""Check and format all input arguments supplied to CausalImpact().
See the documentation of CausalImpact() for details
Args:
data: Pandas DataFrame or data frame
pre_period: beginning and end of pre-period
post_period: beginning and end of post-period
model_args: dict of additional arguments for the model
ucm_model: UnobservedComponents model (instead of data)
post_period_response: observed response in the post-period
alpha: tail-area for posterior intervals
estimation: method of estimation for model fitting
Returns:
list of checked (and possibly reformatted) input arguments
"""
from statsmodels.tsa.statespace.structural import UnobservedComponents
# Check that a consistent set of variables has been provided
args = [data, pre_period, post_period, ucm_model, post_period_response]
self._check_valid_args_combo(args)
# Check <data> and convert to Pandas DataFrame, with rows
# representing time points
if data is not None:
data = self._format_input_data(data)
# Check <pre_period> and <post_period>
if data is not None:
checked = self._format_input_prepost(pre_period, post_period, data)
pre_period = checked["pre_period"]
post_period = checked["post_period"]
self.params["pre_period"] = pre_period
self.params["post_period"] = post_period
# Parse <model_args>, fill gaps using <_defaults>
_defaults = {
"ndraws": 1000,
"nburn": 100,
"niter": 1000,
"standardize_data": True,
"prior_level_sd": 0.01,
"nseasons": 1,
"season_duration": 1,
"dynamic_regression": False,
}
if model_args is None:
model_args = _defaults
else:
missing = [key for key in _defaults if key not in model_args]
for arg in missing:
model_args[arg] = _defaults[arg]
# Check <standardize_data>
if not isinstance(model_args["standardize_data"], bool):
raise ValueError("model_args.standardize_data must be a" + " boolean value")
# Check <ucm_model>
if ucm_model is not None and not isinstance(ucm_model, UnobservedComponents):
raise ValueError(
"ucm_model must be an object of class "
"statsmodels.tsa.statespace.structural.UnobservedComponents "
"instead received " + str(type(ucm_model))[8:-2]
)
# Check <post_period_response>
if ucm_model is not None:
if not is_list_like(post_period_response):
raise ValueError("post_period_response must be list-like")
if np.array(post_period_response).dtype.num == 17:
raise ValueError(
"post_period_response should not be" + " datetime values"
)
if not np.all(np.isreal(post_period_response)):
raise ValueError(
"post_period_response must contain all" + " real values"
)
# Check <alpha>
self._check_valid_alpha(alpha)
# Return updated arguments
kwargs = {
"data": data,
"pre_period": pre_period,
"post_period": post_period,
"model_args": model_args,
"ucm_model": ucm_model,
"post_period_response": post_period_response,
"alpha": alpha,
}
return kwargs
|
(self, data, pre_period, post_period, model_args, ucm_model, post_period_response, alpha)
|
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0.01734498329460621,
-0.009985909797251225,
0.043564144521951675
] |
52,609 |
causalimpact.analysis
|
_format_input_data
|
Check and format the data argument provided to CausalImpact().
Args:
data: Pandas DataFrame
Returns:
correctly formatted Pandas DataFrame
|
@staticmethod
def _format_input_data(data):
"""Check and format the data argument provided to CausalImpact().
Args:
data: Pandas DataFrame
Returns:
correctly formatted Pandas DataFrame
"""
# If <data> is a Pandas DataFrame and the first column is 'date',
# try to convert
if (
isinstance(data, pd.DataFrame)
and isinstance(data.columns[0], str)
and data.columns[0].lower() in ["date", "time"]
):
data = data.set_index(data.columns[0])
# Try to convert to Pandas DataFrame
try:
data = pd.DataFrame(data)
except ValueError:
raise ValueError("could not convert input data to Pandas " + "DataFrame")
# Must have at least 3 time points
if len(data.index) < 3:
raise ValueError("data must have at least 3 time points")
# Must not have NA in covariates (if any)
if len(data.columns) >= 2 and pd.isnull(data.iloc[:, 1:]).any(axis=None):
raise ValueError("covariates must not contain null values")
return data
|
(data)
|
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] |
52,610 |
causalimpact.analysis
|
_format_input_prepost
|
Check and format the pre_period and post_period input arguments.
Args:
pre_period: two-element list
post_period: two-element list
data: already-checked Pandas DataFrame, for reference only
|
def _format_input_prepost(self, pre_period, post_period, data):
"""Check and format the pre_period and post_period input arguments.
Args:
pre_period: two-element list
post_period: two-element list
data: already-checked Pandas DataFrame, for reference only
"""
self._check_periods_are_valid(pre_period, post_period)
pre_period, post_period = self._align_periods_dtypes(
pre_period, post_period, data
)
if pre_period[1] > post_period[0]:
raise ValueError(
"post period must start at least 1 observation"
+ " after the end of the pre_period"
)
if isinstance(data.index, pd.RangeIndex):
loc3 = post_period[0]
loc4 = post_period[1]
else:
loc3 = data.index.get_loc(post_period[0])
loc4 = data.index.get_loc(post_period[1])
if loc4 < loc3:
raise ValueError(
"post_period[1] must not be earlier than " + "post_period[0]"
)
if pre_period[0] < data.index.min():
pre_period[0] = data.index.min()
if post_period[1] > data.index.max():
post_period[1] = data.index.max()
return {"pre_period": pre_period, "post_period": post_period}
|
(self, pre_period, post_period, data)
|
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] |
52,611 |
causalimpact.analysis
|
_print_report
| null |
@staticmethod
def _print_report(
mean_pred_fmt,
mean_resp_fmt,
mean_lower_fmt,
mean_upper_fmt,
abs_effect_fmt,
abs_effect_upper_fmt,
abs_effect_lower_fmt,
rel_effect_fmt,
rel_effect_upper_fmt,
rel_effect_lower_fmt,
cum_resp_fmt,
cum_pred_fmt,
cum_lower_fmt,
cum_upper_fmt,
confidence,
cum_rel_effect_lower,
cum_rel_effect_upper,
cum_rel_effect,
width,
p_value,
alpha,
):
sig = not (cum_rel_effect_lower < 0 < cum_rel_effect_upper)
pos = cum_rel_effect > 0
# Summarize averages
stmt = textwrap.dedent(
"""During the post-intervention period, the response
variable had an average value of
approx. {mean_resp}.
""".format(
mean_resp=mean_resp_fmt
)
)
if sig:
stmt += " By contrast, in "
else:
stmt += " In "
stmt += textwrap.dedent(
"""
the absence of an intervention, we would have
expected an average response of {mean_pred}. The
{confidence} interval of this counterfactual
prediction is [{mean_lower}, {mean_upper}].
Subtracting this prediction from the observed
response yields an estimate of the causal effect
the intervention had on the response variable.
This effect is {abs_effect} with a
{confidence} interval of [{abs_lower},
{abs_upper}]. For a discussion of the
significance of this effect,
see below.
""".format(
mean_pred=mean_pred_fmt,
confidence=confidence,
mean_lower=mean_lower_fmt,
mean_upper=mean_upper_fmt,
abs_effect=abs_effect_fmt,
abs_upper=abs_effect_upper_fmt,
abs_lower=abs_effect_lower_fmt,
)
)
# Summarize sums
stmt2 = textwrap.dedent(
"""
Summing up the individual data points during the
post-intervention period (which can only sometimes be
meaningfully interpreted), the response variable had an
overall value of {cum_resp}.
""".format(
cum_resp=cum_resp_fmt
)
)
if sig:
stmt2 += " By contrast, had "
else:
stmt2 += " Had "
stmt2 += textwrap.dedent(
"""
the intervention not taken place, we would have expected
a sum of {cum_pred}. The {confidence} interval of this
prediction is [{cum_pred_lower}, {cum_pred_upper}]
""".format(
cum_pred=cum_pred_fmt,
confidence=confidence,
cum_pred_lower=cum_lower_fmt,
cum_pred_upper=cum_upper_fmt,
)
)
# Summarize relative numbers (in which case row [1] = row [2])
stmt3 = textwrap.dedent(
"""
The above results are given in terms
of absolute numbers. In relative terms, the
response variable showed
"""
)
if pos:
stmt3 += " an increase of "
else:
stmt3 += " a decrease of "
stmt3 += textwrap.dedent(
"""
{rel_effect}. The {confidence} interval of this
percentage is [{rel_effect_lower},
{rel_effect_upper}]
""".format(
confidence=confidence,
rel_effect=rel_effect_fmt,
rel_effect_lower=rel_effect_lower_fmt,
rel_effect_upper=rel_effect_upper_fmt,
)
)
# Comment on significance
if sig and pos:
stmt4 = textwrap.dedent(
"""
This means that the positive effect observed
during the intervention period is statistically
significant and unlikely to be due to random
fluctuations. It should be noted, however, that
the question of whether this increase also bears
substantive significance can only be answered by
comparing the absolute effect {abs_effect} to
the original goal of the underlying
intervention.
""".format(
abs_effect=abs_effect_fmt
)
)
elif sig and not pos:
stmt4 = textwrap.dedent(
"""
This means that the negative effect observed
during the intervention period is statistically
significant. If the experimenter had expected a
positive effect, it is recommended to double-check
whether anomalies in the control variables may have
caused an overly optimistic expectation of what
should have happened in the response variable in the
absence of the intervention.
"""
)
elif not sig and pos:
stmt4 = textwrap.dedent(
"""
This means that, although the intervention
appears to have caused a positive effect, this
effect is not statistically significant when
considering the post-intervention period as a whole.
Individual days or shorter stretches within the
intervention period may of course still have had a
significant effect, as indicated whenever the lower
limit of the impact time series (lower plot) was
above zero.
"""
)
elif not sig and not pos:
stmt4 = textwrap.dedent(
"""
This means that, although it may look as though
the intervention has exerted a negative effect on
the response variable when considering the
intervention period as a whole, this effect is not
statistically significant, and so cannot be
meaningfully interpreted.
"""
)
if not sig:
stmt4 += textwrap.dedent(
"""
The apparent effect could be the result of random
fluctuations that are unrelated to the intervention.
This is often the case when the intervention period
is very long and includes much of the time when the
effect has already worn off. It can also be the case
when the intervention period is too short to
distinguish the signal from the noise. Finally,
failing to find a significant effect can happen when
there are not enough control variables or when these
variables do not correlate well with the response
variable during the learning period."""
)
if p_value < alpha:
stmt5 = textwrap.dedent(
"""The probability of obtaining this effect by
chance is very small (Bayesian one-sided tail-area
probability {p}). This means the
causal effect can be considered statistically
significant.""".format(
p=np.round(p_value, 3)
)
)
else:
stmt5 = """The probability of obtaining this effect by
chance is p = ", round(p, 3), "). This means the effect may
be spurious and would generally not be considered
statistically significant.""".format()
print(textwrap.fill(stmt, width=width))
print("\n")
print(textwrap.fill(stmt2, width=width))
print("\n")
print(textwrap.fill(stmt3, width=width))
print("\n")
print(textwrap.fill(stmt4, width=width))
print("\n")
print(textwrap.fill(stmt5, width=width))
|
(mean_pred_fmt, mean_resp_fmt, mean_lower_fmt, mean_upper_fmt, abs_effect_fmt, abs_effect_upper_fmt, abs_effect_lower_fmt, rel_effect_fmt, rel_effect_upper_fmt, rel_effect_lower_fmt, cum_resp_fmt, cum_pred_fmt, cum_lower_fmt, cum_upper_fmt, confidence, cum_rel_effect_lower, cum_rel_effect_upper, cum_rel_effect, width, p_value, alpha)
|
[
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0.08389701694250107,
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0.026158232241868973,
0.02188790589570999,
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0.06157214939594269
] |
52,612 |
causalimpact.analysis
|
_run_with_data
| null |
def _run_with_data(
self, data, pre_period, post_period, model_args, alpha, estimation
):
# Zoom in on data in modeling range
if data.shape[1] == 1: # no exogenous values provided
raise ValueError("data contains no exogenous variables")
data_modeling = data.copy()
df_pre = data_modeling.loc[pre_period[0] : pre_period[1], :]
df_post = data_modeling.loc[post_period[0] : post_period[1], :]
# Standardize all variables
orig_std_params = (0, 1)
if model_args["standardize_data"]:
sd_results = standardize_all_variables(
data_modeling, pre_period, post_period
)
df_pre = sd_results["data_pre"]
df_post = sd_results["data_post"]
orig_std_params = sd_results["orig_std_params"]
# Construct model and perform inference
model = construct_model(df_pre, model_args)
self.model = model
model_results = model_fit(model, estimation, model_args)
inferences = compile_inferences(
model_results,
data,
df_pre,
df_post,
None,
alpha,
orig_std_params,
estimation,
)
# "append" to 'CausalImpact' object
self.inferences = inferences["series"]
self.results = model_results
|
(self, data, pre_period, post_period, model_args, alpha, estimation)
|
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] |
52,613 |
causalimpact.analysis
|
_run_with_ucm
|
Runs an impact analysis on top of a ucm model.
Args:
ucm_model: Model as returned by UnobservedComponents(),
in which the data during the post-period was set to NA
post_period_response: observed data during the post-intervention
period
alpha: tail-probabilities of posterior intervals
|
def _run_with_ucm(
self, ucm_model, post_period_response, alpha, model_args, estimation
):
"""Runs an impact analysis on top of a ucm model.
Args:
ucm_model: Model as returned by UnobservedComponents(),
in which the data during the post-period was set to NA
post_period_response: observed data during the post-intervention
period
alpha: tail-probabilities of posterior intervals"""
df_pre = ucm_model.data.orig_endog[: -len(post_period_response)]
df_pre = pd.DataFrame(df_pre)
post_period_response = pd.DataFrame(post_period_response)
data = pd.DataFrame(
np.concatenate([df_pre.values, post_period_response.values])
)
orig_std_params = (0, 1)
model_results = model_fit(ucm_model, estimation, model_args)
# Compile posterior inferences
inferences = compile_inferences(
model_results,
data,
df_pre,
None,
post_period_response,
alpha,
orig_std_params,
estimation,
)
obs_inter = model_results.model_nobs - len(post_period_response)
self.params["pre_period"] = [0, obs_inter - 1]
self.params["post_period"] = [obs_inter, -1]
self.data = pd.concat([df_pre, post_period_response])
self.inferences = inferences["series"]
self.results = model_results
|
(self, ucm_model, post_period_response, alpha, model_args, estimation)
|
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] |
52,614 |
causalimpact.analysis
|
plot
| null |
def plot(
self,
panels=None,
figsize=(15, 12),
fname=None,
):
if panels is None:
panels = ["original", "pointwise", "cumulative"]
plt = get_matplotlib()
fig = plt.figure(figsize=figsize)
data_inter = self.params["pre_period"][1]
if isinstance(data_inter, pd.DatetimeIndex):
data_inter = pd.Timestamp(data_inter)
inferences = self.inferences.iloc[1:, :]
# Observation and regression components
if "original" in panels:
ax1 = plt.subplot(3, 1, 1)
plt.plot(inferences.point_pred, "r--", linewidth=2, label="model")
plt.plot(inferences.response, "k", linewidth=2, label="endog")
plt.axvline(data_inter, c="k", linestyle="--")
plt.fill_between(
inferences.index,
inferences.point_pred_lower,
inferences.point_pred_upper,
facecolor="gray",
interpolate=True,
alpha=0.25,
)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.legend(loc="upper left")
plt.title("Observation vs prediction")
if "pointwise" in panels:
# Pointwise difference
if "ax1" in locals():
ax2 = plt.subplot(312, sharex=ax1)
else:
ax2 = plt.subplot(312)
lift = inferences.point_effect
plt.plot(lift, "r--", linewidth=2)
plt.plot(self.data.index, np.zeros(self.data.shape[0]), "g-", linewidth=2)
plt.axvline(data_inter, c="k", linestyle="--")
lift_lower = inferences.point_effect_lower
lift_upper = inferences.point_effect_upper
plt.fill_between(
inferences.index,
lift_lower,
lift_upper,
facecolor="gray",
interpolate=True,
alpha=0.25,
)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.title("Difference")
# Cumulative impact
if "cumulative" in panels:
if "ax1" in locals():
plt.subplot(313, sharex=ax1)
elif "ax2" in locals():
plt.subplot(313, sharex=ax2)
else:
plt.subplot(313)
plt.plot(
inferences.index,
inferences.cum_effect,
"r--",
linewidth=2,
)
plt.plot(self.data.index, np.zeros(self.data.shape[0]), "g-", linewidth=2)
plt.axvline(data_inter, c="k", linestyle="--")
plt.fill_between(
inferences.index,
inferences.cum_effect_lower,
inferences.cum_effect_upper,
facecolor="gray",
interpolate=True,
alpha=0.25,
)
plt.axis([inferences.index[0], inferences.index[-1], None, None])
plt.title("Cumulative Impact")
plt.xlabel("$T$")
if fname is None:
plt.show()
else:
fig.savefig(fname, bbox_inches="tight")
plt.close(fig)
|
(self, panels=None, figsize=(15, 12), fname=None)
|
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] |
52,615 |
causalimpact.analysis
|
run
| null |
def run(self):
kwargs = self._format_input(
self.params["data"],
self.params["pre_period"],
self.params["post_period"],
self.params["model_args"],
self.params["ucm_model"],
self.params["post_period_response"],
self.params["alpha"],
)
# Depending on input, dispatch to the appropriate Run* method()
if self.data is not None:
self._run_with_data(
kwargs["data"],
kwargs["pre_period"],
kwargs["post_period"],
kwargs["model_args"],
kwargs["alpha"],
self.params["estimation"],
)
else:
self._run_with_ucm(
kwargs["ucm_model"],
kwargs["post_period_response"],
kwargs["alpha"],
kwargs["model_args"],
self.params["estimation"],
)
|
(self)
|
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] |
52,616 |
causalimpact.analysis
|
summary
|
reports a summary of the results
Parameters
----------
output: str
can be summary or report. summary outputs a table.
report outputs a natural language description of the
findings
width : int
line width of the output. Only relevant if output == report
path : str
path to output summary to csv. Only relevant if output == summary
|
def summary(self, output="summary", width=120, path=None):
"""reports a summary of the results
Parameters
----------
output: str
can be summary or report. summary outputs a table.
report outputs a natural language description of the
findings
width : int
line width of the output. Only relevant if output == report
path : str
path to output summary to csv. Only relevant if output == summary
"""
alpha = self.params["alpha"]
confidence = "{}%".format(int((1 - alpha) * 100))
post_period = self.params["post_period"]
post_inf = self.inferences.loc[post_period[0] : post_period[1], :]
post_point_resp = post_inf.loc[:, "response"]
post_point_pred = post_inf.loc[:, "point_pred"]
post_point_upper = post_inf.loc[:, "point_pred_upper"]
post_point_lower = post_inf.loc[:, "point_pred_lower"]
mean_resp = post_point_resp.mean()
mean_resp_fmt = int(mean_resp)
cum_resp = post_point_resp.sum()
cum_resp_fmt = int(cum_resp)
mean_pred = post_point_pred.mean()
mean_pred_fmt = int(post_point_pred.mean())
cum_pred = post_point_pred.sum()
cum_pred_fmt = int(cum_pred)
mean_lower = post_point_lower.mean()
mean_lower_fmt = int(mean_lower)
mean_upper = post_point_upper.mean()
mean_upper_fmt = int(mean_upper)
mean_ci_fmt = [mean_lower_fmt, mean_upper_fmt]
cum_lower = post_point_lower.sum()
cum_lower_fmt = int(cum_lower)
cum_upper = post_point_upper.sum()
cum_upper_fmt = int(cum_upper)
cum_ci_fmt = [cum_lower_fmt, cum_upper_fmt]
abs_effect = (post_point_resp - post_point_pred).mean()
abs_effect_fmt = int(abs_effect)
cum_abs_effect = (post_point_resp - post_point_pred).sum()
cum_abs_effect_fmt = int(cum_abs_effect)
abs_effect_lower = (post_point_resp - post_point_lower).mean()
abs_effect_lower_fmt = int(abs_effect_lower)
abs_effect_upper = (post_point_resp - post_point_upper).mean()
abs_effect_upper_fmt = int(abs_effect_upper)
abs_effect_ci_fmt = [abs_effect_lower_fmt, abs_effect_upper_fmt]
cum_abs_lower = (post_point_resp - post_point_lower).sum()
cum_abs_lower_fmt = int(cum_abs_lower)
cum_abs_upper = (post_point_resp - post_point_upper).sum()
cum_abs_upper_fmt = int(cum_abs_upper)
cum_abs_effect_ci_fmt = [cum_abs_lower_fmt, cum_abs_upper_fmt]
rel_effect = abs_effect / mean_pred * 100
rel_effect_fmt = "{:.1f}%".format(rel_effect)
cum_rel_effect = cum_abs_effect / cum_pred * 100
cum_rel_effect_fmt = "{:.1f}%".format(cum_rel_effect)
rel_effect_lower = abs_effect_lower / mean_pred * 100
rel_effect_lower_fmt = "{:.1f}%".format(rel_effect_lower)
rel_effect_upper = abs_effect_upper / mean_pred * 100
rel_effect_upper_fmt = "{:.1f}%".format(rel_effect_upper)
rel_effect_ci_fmt = [rel_effect_lower_fmt, rel_effect_upper_fmt]
cum_rel_effect_lower = cum_abs_lower / cum_pred * 100
cum_rel_effect_lower_fmt = "{:.1f}%".format(cum_rel_effect_lower)
cum_rel_effect_upper = cum_abs_upper / cum_pred * 100
cum_rel_effect_upper_fmt = "{:.1f}%".format(cum_rel_effect_upper)
cum_rel_effect_ci_fmt = [cum_rel_effect_lower_fmt, cum_rel_effect_upper_fmt]
# assuming approximately normal distribution
# calculate standard deviation from the 95% conf interval
std_pred = (
mean_upper - mean_pred
) / 1.96 # from mean_upper = mean_pred + 1.96 * std
# calculate z score
z_score = (0 - mean_pred) / std_pred
# pvalue from zscore
p_value = st.norm.cdf(z_score)
prob_causal = 1 - p_value
p_value_perc = p_value * 100
prob_causal_perc = prob_causal * 100
if output == "summary":
# Posterior inference {CausalImpact}
summary = [
[mean_resp_fmt, cum_resp_fmt],
[mean_pred_fmt, cum_pred_fmt],
[mean_ci_fmt, cum_ci_fmt],
[" ", " "],
[abs_effect_fmt, cum_abs_effect_fmt],
[abs_effect_ci_fmt, cum_abs_effect_ci_fmt],
[" ", " "],
[rel_effect_fmt, cum_rel_effect_fmt],
[rel_effect_ci_fmt, cum_rel_effect_ci_fmt],
[" ", " "],
["{:.1f}%".format(p_value_perc), " "],
["{:.1f}%".format(prob_causal_perc), " "],
]
summary = pd.DataFrame(
summary,
columns=["Average", "Cumulative"],
index=[
"Actual",
"Predicted",
"95% CI",
" ",
"Absolute Effect",
"95% CI",
" ",
"Relative Effect",
"95% CI",
" ",
"P-value",
"Prob. of Causal Effect",
],
)
df_print(summary, path)
elif output == "report":
self._print_report(
mean_pred_fmt,
mean_resp_fmt,
mean_lower_fmt,
mean_upper_fmt,
abs_effect_fmt,
abs_effect_upper_fmt,
abs_effect_lower_fmt,
rel_effect_fmt,
rel_effect_upper_fmt,
rel_effect_lower_fmt,
cum_resp_fmt,
cum_pred_fmt,
cum_lower_fmt,
cum_upper_fmt,
confidence,
cum_rel_effect_lower,
cum_rel_effect_upper,
cum_rel_effect,
width,
p_value,
alpha,
)
else:
raise ValueError(
"Output argument must be either 'summary' " + "or 'report'"
)
|
(self, output='summary', width=120, path=None)
|
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] |
52,622 |
streamx.stream
|
AsyncStream
| null |
class AsyncStream(Generic[T]):
def __init__(self) -> None:
self._consuming_tasks: list[asyncio.Task] = []
self._closed: bool = False
self._event = SharedEvent[T]()
self._listeners = set[AsyncStreamListener[T]]()
@property
def listeners(self) -> set[AsyncStreamListener[T]]:
return self._listeners
@property
def closed(self) -> bool:
return self._closed
async def push(self, item: T) -> None:
if self._closed:
raise StreamClosedError("Can't push item into a closed stream.")
current_task = asyncio.current_task()
if current_task in self._consuming_tasks:
raise StreamShortCircuitError(
"Can't push an item while the task is listening to this stream."
)
await self._event.share(asyncio.sleep(0, item))
async def close(self) -> None:
if self._closed:
return
try:
await self.push(StopAsyncIteration) # type: ignore
except StreamShortCircuitError:
raise StreamShortCircuitError(
"Can't close a stream from a task that is listening to it."
) from None
self._closed = True
for listener in self._listeners:
listener.close()
@contextmanager
def listen(self) -> Iterator[AsyncStreamListener[T]]:
if self._closed:
raise StreamClosedError("Can't listen to a closed stream.")
current_task = asyncio.current_task()
if current_task in self._consuming_tasks:
raise StreamShortCircuitError("Task is already listening to this stream.")
listener = None
try:
with self._event.listen() as event_listener:
listener = AsyncStreamListener(event_listener)
self._listeners.add(listener)
if listener.current_task:
self._consuming_tasks.append(listener.current_task)
yield listener
finally:
if listener:
listener.close()
self._listeners.remove(listener)
if listener.current_task:
self._consuming_tasks.remove(listener.current_task)
|
() -> None
|
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52,623 |
streamx.stream
|
__init__
| null |
def __init__(self) -> None:
self._consuming_tasks: list[asyncio.Task] = []
self._closed: bool = False
self._event = SharedEvent[T]()
self._listeners = set[AsyncStreamListener[T]]()
|
(self) -> NoneType
|
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] |
52,627 |
streamx.stream
|
AsyncStreamIterator
| null |
class AsyncStreamIterator(AsyncIterator[T], Generic[T], AsyncIterable[T]):
def __init__(self, event_listener: SharedEventListener[T]) -> None:
self._event_listener = event_listener
async def __anext__(self) -> T:
item = await self._event_listener.wait()
if item is StopAsyncIteration:
raise StopAsyncIteration
return item
|
(event_listener: streamx.event.SharedEventListener[~T]) -> None
|
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streamx.stream
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__anext__
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def __init__(self, event_listener: SharedEventListener[T]) -> None:
self._event_listener = event_listener
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(self) -> ~T
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] |
52,631 |
streamx.stream
|
AsyncStreamListener
| null |
class AsyncStreamListener(Generic[T]):
def __init__(self, event_listener: SharedEventListener[T]) -> None:
self._event_listener = event_listener
self._current_task = asyncio.current_task()
self._closed = False
@property
def current_task(self) -> asyncio.Task | None:
return self._current_task
@property
def closed(self) -> bool:
return self._closed
def close(self) -> None:
self._closed = True
def __aiter__(self) -> AsyncStreamIterator[T]:
if self._closed:
raise StreamClosedError("Can't iterate over a closed stream.")
return AsyncStreamIterator(self._event_listener)
|
(event_listener: streamx.event.SharedEventListener[~T]) -> None
|
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52,632 |
streamx.stream
|
__aiter__
| null |
def __aiter__(self) -> AsyncStreamIterator[T]:
if self._closed:
raise StreamClosedError("Can't iterate over a closed stream.")
return AsyncStreamIterator(self._event_listener)
|
(self) -> streamx.stream.AsyncStreamIterator[~T]
|
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52,633 |
streamx.stream
|
__init__
| null |
def __init__(self, event_listener: SharedEventListener[T]) -> None:
self._event_listener = event_listener
self._current_task = asyncio.current_task()
self._closed = False
|
(self, event_listener: streamx.event.SharedEventListener[~T]) -> NoneType
|
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52,634 |
streamx.stream
|
close
| null |
def close(self) -> None:
self._closed = True
|
(self) -> NoneType
|
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] |
52,635 |
streamx.event
|
SharedEvent
| null |
class SharedEvent(Generic[T]):
def __init__(self, loop: asyncio.AbstractEventLoop | None = None):
self.loop = loop or asyncio.get_event_loop()
self._listeners: set[SharedEventListener[T]] = set()
@contextmanager
def listen(self) -> Iterator[SharedEventListener[T]]:
listener = None
try:
listener = SharedEventListener(self.loop)
self._listeners.add(listener)
yield listener
finally:
if listener:
self._listeners.remove(listener)
async def share(self, coro: ...) -> T:
value: T = await coro
await asyncio.gather(*[listener.ready.wait() for listener in self._listeners])
for listener in self._listeners:
listener.push(value)
return value
|
(loop: asyncio.events.AbstractEventLoop | None = None)
|
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] |
52,636 |
streamx.event
|
__init__
| null |
def __init__(self, loop: asyncio.AbstractEventLoop | None = None):
self.loop = loop or asyncio.get_event_loop()
self._listeners: set[SharedEventListener[T]] = set()
|
(self, loop: Optional[asyncio.events.AbstractEventLoop] = None)
|
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] |
52,638 |
streamx.event
|
share
| null |
@contextmanager
def listen(self) -> Iterator[SharedEventListener[T]]:
listener = None
try:
listener = SharedEventListener(self.loop)
self._listeners.add(listener)
yield listener
finally:
if listener:
self._listeners.remove(listener)
|
(self, coro: Ellipsis) -> ~T
|
[
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] |
52,639 |
streamx.event
|
SharedEventListener
| null |
class SharedEventListener(Generic[T]):
def __init__(self, loop: asyncio.AbstractEventLoop | None = None) -> None:
self._loop = loop or asyncio.get_event_loop()
self._waiter: asyncio.Future[T] = self._loop.create_future()
self._ready = asyncio.Event()
@property
def ready(self) -> asyncio.Event:
return self._ready
def push(self, value: T) -> None:
self._waiter.set_result(value)
async def wait(self) -> T:
try:
self._ready.set()
return await self._waiter
finally:
self._ready.clear()
self._waiter = self._loop.create_future()
|
(loop: asyncio.events.AbstractEventLoop | None = None) -> None
|
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] |
52,640 |
streamx.event
|
__init__
| null |
def __init__(self, loop: asyncio.AbstractEventLoop | None = None) -> None:
self._loop = loop or asyncio.get_event_loop()
self._waiter: asyncio.Future[T] = self._loop.create_future()
self._ready = asyncio.Event()
|
(self, loop: Optional[asyncio.events.AbstractEventLoop] = None) -> NoneType
|
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52,641 |
streamx.event
|
push
| null |
def push(self, value: T) -> None:
self._waiter.set_result(value)
|
(self, value: ~T) -> NoneType
|
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] |
52,643 |
streamx.errors
|
StreamClosedError
|
Raised when operations are performed on a closed stream
|
class StreamClosedError(StreamError):
"""
Raised when operations are performed on a closed stream
"""
| null |
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0.005573011469095945
] |
52,644 |
streamx.errors
|
StreamError
|
Base class for all streamx exceptions
|
class StreamError(Exception):
"""Base class for all streamx exceptions"""
| null |
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] |
52,645 |
streamx.errors
|
StreamShortCircuitError
|
Raised when a stream is being consumed by the same task
and a new item is pushed
|
class StreamShortCircuitError(StreamError):
"""
Raised when a stream is being consumed by the same task
and a new item is pushed
"""
| null |
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] |
52,649 |
bmitzkus_pulumi_onepassword.get_item
|
AwaitableGetItemResult
| null |
class AwaitableGetItemResult(GetItemResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return GetItemResult(
category=self.category,
database=self.database,
hostname=self.hostname,
id=self.id,
note_value=self.note_value,
password=self.password,
port=self.port,
sections=self.sections,
tags=self.tags,
title=self.title,
type=self.type,
url=self.url,
username=self.username,
uuid=self.uuid,
vault=self.vault)
|
(category=None, database=None, hostname=None, id=None, note_value=None, password=None, port=None, sections=None, tags=None, title=None, type=None, url=None, username=None, uuid=None, vault=None)
|
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] |
52,650 |
bmitzkus_pulumi_onepassword.get_item
|
__await__
| null |
def __await__(self):
if False:
yield self
return GetItemResult(
category=self.category,
database=self.database,
hostname=self.hostname,
id=self.id,
note_value=self.note_value,
password=self.password,
port=self.port,
sections=self.sections,
tags=self.tags,
title=self.title,
type=self.type,
url=self.url,
username=self.username,
uuid=self.uuid,
vault=self.vault)
|
(self)
|
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0.022858958691358566,
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0.01840568147599697,
0.04600522294640541,
0.013027630746364594,
0.05092537775635719,
0.045251037925481796,
-0.025247206911444664,
0.041516032069921494,
0.0467953197658062,
0.03630857169628143,
0.030400797724723816,
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0.06403381377458572,
-0.10055786371231079,
0.022320255637168884,
0.027617499232292175,
0.0373859778046608
] |
52,652 |
bmitzkus_pulumi_onepassword.get_item
|
__init__
| null |
def __init__(__self__, category=None, database=None, hostname=None, id=None, note_value=None, password=None, port=None, sections=None, tags=None, title=None, type=None, url=None, username=None, uuid=None, vault=None):
if category and not isinstance(category, str):
raise TypeError("Expected argument 'category' to be a str")
pulumi.set(__self__, "category", category)
if database and not isinstance(database, str):
raise TypeError("Expected argument 'database' to be a str")
pulumi.set(__self__, "database", database)
if hostname and not isinstance(hostname, str):
raise TypeError("Expected argument 'hostname' to be a str")
pulumi.set(__self__, "hostname", hostname)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if note_value and not isinstance(note_value, str):
raise TypeError("Expected argument 'note_value' to be a str")
pulumi.set(__self__, "note_value", note_value)
if password and not isinstance(password, str):
raise TypeError("Expected argument 'password' to be a str")
pulumi.set(__self__, "password", password)
if port and not isinstance(port, str):
raise TypeError("Expected argument 'port' to be a str")
pulumi.set(__self__, "port", port)
if sections and not isinstance(sections, list):
raise TypeError("Expected argument 'sections' to be a list")
pulumi.set(__self__, "sections", sections)
if tags and not isinstance(tags, list):
raise TypeError("Expected argument 'tags' to be a list")
pulumi.set(__self__, "tags", tags)
if title and not isinstance(title, str):
raise TypeError("Expected argument 'title' to be a str")
pulumi.set(__self__, "title", title)
if type and not isinstance(type, str):
raise TypeError("Expected argument 'type' to be a str")
pulumi.set(__self__, "type", type)
if url and not isinstance(url, str):
raise TypeError("Expected argument 'url' to be a str")
pulumi.set(__self__, "url", url)
if username and not isinstance(username, str):
raise TypeError("Expected argument 'username' to be a str")
pulumi.set(__self__, "username", username)
if uuid and not isinstance(uuid, str):
raise TypeError("Expected argument 'uuid' to be a str")
pulumi.set(__self__, "uuid", uuid)
if vault and not isinstance(vault, str):
raise TypeError("Expected argument 'vault' to be a str")
pulumi.set(__self__, "vault", vault)
|
(__self__, category=None, database=None, hostname=None, id=None, note_value=None, password=None, port=None, sections=None, tags=None, title=None, type=None, url=None, username=None, uuid=None, vault=None)
|
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] |
52,653 |
bmitzkus_pulumi_onepassword.get_vault
|
AwaitableGetVaultResult
| null |
class AwaitableGetVaultResult(GetVaultResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return GetVaultResult(
description=self.description,
id=self.id,
name=self.name,
uuid=self.uuid)
|
(description=None, id=None, name=None, uuid=None)
|
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] |
52,654 |
bmitzkus_pulumi_onepassword.get_vault
|
__await__
| null |
def __await__(self):
if False:
yield self
return GetVaultResult(
description=self.description,
id=self.id,
name=self.name,
uuid=self.uuid)
|
(self)
|
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] |
52,656 |
bmitzkus_pulumi_onepassword.get_vault
|
__init__
| null |
def __init__(__self__, description=None, id=None, name=None, uuid=None):
if description and not isinstance(description, str):
raise TypeError("Expected argument 'description' to be a str")
pulumi.set(__self__, "description", description)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if name and not isinstance(name, str):
raise TypeError("Expected argument 'name' to be a str")
pulumi.set(__self__, "name", name)
if uuid and not isinstance(uuid, str):
raise TypeError("Expected argument 'uuid' to be a str")
pulumi.set(__self__, "uuid", uuid)
|
(__self__, description=None, id=None, name=None, uuid=None)
|
[
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0.045718688517808914,
0.002184953074902296,
0.018587740138173103
] |
52,657 |
bmitzkus_pulumi_onepassword.get_item
|
GetItemResult
|
A collection of values returned by getItem.
|
class GetItemResult:
"""
A collection of values returned by getItem.
"""
def __init__(__self__, category=None, database=None, hostname=None, id=None, note_value=None, password=None, port=None, sections=None, tags=None, title=None, type=None, url=None, username=None, uuid=None, vault=None):
if category and not isinstance(category, str):
raise TypeError("Expected argument 'category' to be a str")
pulumi.set(__self__, "category", category)
if database and not isinstance(database, str):
raise TypeError("Expected argument 'database' to be a str")
pulumi.set(__self__, "database", database)
if hostname and not isinstance(hostname, str):
raise TypeError("Expected argument 'hostname' to be a str")
pulumi.set(__self__, "hostname", hostname)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if note_value and not isinstance(note_value, str):
raise TypeError("Expected argument 'note_value' to be a str")
pulumi.set(__self__, "note_value", note_value)
if password and not isinstance(password, str):
raise TypeError("Expected argument 'password' to be a str")
pulumi.set(__self__, "password", password)
if port and not isinstance(port, str):
raise TypeError("Expected argument 'port' to be a str")
pulumi.set(__self__, "port", port)
if sections and not isinstance(sections, list):
raise TypeError("Expected argument 'sections' to be a list")
pulumi.set(__self__, "sections", sections)
if tags and not isinstance(tags, list):
raise TypeError("Expected argument 'tags' to be a list")
pulumi.set(__self__, "tags", tags)
if title and not isinstance(title, str):
raise TypeError("Expected argument 'title' to be a str")
pulumi.set(__self__, "title", title)
if type and not isinstance(type, str):
raise TypeError("Expected argument 'type' to be a str")
pulumi.set(__self__, "type", type)
if url and not isinstance(url, str):
raise TypeError("Expected argument 'url' to be a str")
pulumi.set(__self__, "url", url)
if username and not isinstance(username, str):
raise TypeError("Expected argument 'username' to be a str")
pulumi.set(__self__, "username", username)
if uuid and not isinstance(uuid, str):
raise TypeError("Expected argument 'uuid' to be a str")
pulumi.set(__self__, "uuid", uuid)
if vault and not isinstance(vault, str):
raise TypeError("Expected argument 'vault' to be a str")
pulumi.set(__self__, "vault", vault)
@property
@pulumi.getter
def category(self) -> str:
"""
The category of the item. One of ["login" "password" "database"]
"""
return pulumi.get(self, "category")
@property
@pulumi.getter
def database(self) -> str:
"""
(Only applies to the database category) The name of the database.
"""
return pulumi.get(self, "database")
@property
@pulumi.getter
def hostname(self) -> str:
"""
(Only applies to the database category) The address where the database can be found
"""
return pulumi.get(self, "hostname")
@property
@pulumi.getter
def id(self) -> str:
return pulumi.get(self, "id")
@property
@pulumi.getter(name="noteValue")
def note_value(self) -> str:
"""
Secure Note value.
"""
return pulumi.get(self, "note_value")
@property
@pulumi.getter
def password(self) -> str:
"""
Password for this item.
"""
return pulumi.get(self, "password")
@property
@pulumi.getter
def port(self) -> str:
"""
(Only applies to the database category) The port the database is listening on.
"""
return pulumi.get(self, "port")
@property
@pulumi.getter
def sections(self) -> Sequence['outputs.GetItemSectionResult']:
"""
A list of custom sections in an item
"""
return pulumi.get(self, "sections")
@property
@pulumi.getter
def tags(self) -> Sequence[str]:
"""
An array of strings of the tags assigned to the item.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter
def title(self) -> str:
"""
The title of the item to retrieve. This field will be populated with the title of the item if the item it looked up by its UUID.
"""
return pulumi.get(self, "title")
@property
@pulumi.getter
def type(self) -> str:
"""
(Only applies to the database category) The type of database. One of ["db2" "filemaker" "msaccess" "mssql" "mysql" "oracle" "postgresql" "sqlite" "other"]
"""
return pulumi.get(self, "type")
@property
@pulumi.getter
def url(self) -> str:
"""
The primary URL for the item.
"""
return pulumi.get(self, "url")
@property
@pulumi.getter
def username(self) -> str:
"""
Username for this item.
"""
return pulumi.get(self, "username")
@property
@pulumi.getter
def uuid(self) -> str:
"""
The UUID of the item to retrieve. This field will be populated with the UUID of the item if the item it looked up by its title.
"""
return pulumi.get(self, "uuid")
@property
@pulumi.getter
def vault(self) -> str:
"""
The UUID of the vault the item is in.
"""
return pulumi.get(self, "vault")
|
(category=None, database=None, hostname=None, id=None, note_value=None, password=None, port=None, sections=None, tags=None, title=None, type=None, url=None, username=None, uuid=None, vault=None)
|
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] |
52,660 |
bmitzkus_pulumi_onepassword.get_vault
|
GetVaultResult
|
A collection of values returned by getVault.
|
class GetVaultResult:
"""
A collection of values returned by getVault.
"""
def __init__(__self__, description=None, id=None, name=None, uuid=None):
if description and not isinstance(description, str):
raise TypeError("Expected argument 'description' to be a str")
pulumi.set(__self__, "description", description)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if name and not isinstance(name, str):
raise TypeError("Expected argument 'name' to be a str")
pulumi.set(__self__, "name", name)
if uuid and not isinstance(uuid, str):
raise TypeError("Expected argument 'uuid' to be a str")
pulumi.set(__self__, "uuid", uuid)
@property
@pulumi.getter
def description(self) -> str:
"""
The description of the vault.
"""
return pulumi.get(self, "description")
@property
@pulumi.getter
def id(self) -> str:
return pulumi.get(self, "id")
@property
@pulumi.getter
def name(self) -> str:
"""
The name of the vault to retrieve. This field will be populated with the name of the vault if the vault it looked up by its UUID.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def uuid(self) -> str:
"""
The UUID of the vault to retrieve. This field will be populated with the UUID of the vault if the vault it looked up by its name.
"""
return pulumi.get(self, "uuid")
|
(description=None, id=None, name=None, uuid=None)
|
[
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0.028172042220830917,
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] |
52,663 |
bmitzkus_pulumi_onepassword.item
|
Item
| null |
class Item(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
category: Optional[pulumi.Input[str]] = None,
database: Optional[pulumi.Input[str]] = None,
hostname: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
password_recipe: Optional[pulumi.Input[pulumi.InputType['ItemPasswordRecipeArgs']]] = None,
port: Optional[pulumi.Input[str]] = None,
sections: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ItemSectionArgs']]]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
title: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None,
username: Optional[pulumi.Input[str]] = None,
vault: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
A 1Password item.
## Example Usage
```python
import pulumi
import bmitzkus_pulumi_onepassword as onepassword
demo_password = onepassword.Item("demoPassword",
vault=var["demo_vault"],
title="Demo Password Recipe",
category="password",
password_recipe=onepassword.ItemPasswordRecipeArgs(
length=40,
symbols=False,
))
demo_login = onepassword.Item("demoLogin",
vault=var["demo_vault"],
title="Demo Terraform Login",
category="login",
username="[email protected]")
demo_db = onepassword.Item("demoDb",
vault=var["demo_vault"],
category="database",
type="mysql",
title="Demo TF Database",
username="root",
database="Example MySQL Instance",
hostname="localhost",
port="3306")
```
## Import
import an existing 1Password item
```sh
$ pulumi import onepassword:index/item:Item myitem vaults/<vault uuid>/items/<item uuid>
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] category: The category of the item. One of ["login" "password" "database"]
:param pulumi.Input[str] database: (Only applies to the database category) The name of the database.
:param pulumi.Input[str] hostname: (Only applies to the database category) The address where the database can be found
:param pulumi.Input[str] password: Password for this item.
:param pulumi.Input[pulumi.InputType['ItemPasswordRecipeArgs']] password_recipe: Password for this item.
:param pulumi.Input[str] port: (Only applies to the database category) The port the database is listening on.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ItemSectionArgs']]]] sections: A list of custom sections in an item
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of strings of the tags assigned to the item.
:param pulumi.Input[str] title: The title of the item.
:param pulumi.Input[str] type: The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
:param pulumi.Input[str] url: The primary URL for the item.
:param pulumi.Input[str] username: Username for this item.
:param pulumi.Input[str] vault: The UUID of the vault the item is in.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: ItemArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
A 1Password item.
## Example Usage
```python
import pulumi
import bmitzkus_pulumi_onepassword as onepassword
demo_password = onepassword.Item("demoPassword",
vault=var["demo_vault"],
title="Demo Password Recipe",
category="password",
password_recipe=onepassword.ItemPasswordRecipeArgs(
length=40,
symbols=False,
))
demo_login = onepassword.Item("demoLogin",
vault=var["demo_vault"],
title="Demo Terraform Login",
category="login",
username="[email protected]")
demo_db = onepassword.Item("demoDb",
vault=var["demo_vault"],
category="database",
type="mysql",
title="Demo TF Database",
username="root",
database="Example MySQL Instance",
hostname="localhost",
port="3306")
```
## Import
import an existing 1Password item
```sh
$ pulumi import onepassword:index/item:Item myitem vaults/<vault uuid>/items/<item uuid>
```
:param str resource_name: The name of the resource.
:param ItemArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ItemArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
category: Optional[pulumi.Input[str]] = None,
database: Optional[pulumi.Input[str]] = None,
hostname: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
password_recipe: Optional[pulumi.Input[pulumi.InputType['ItemPasswordRecipeArgs']]] = None,
port: Optional[pulumi.Input[str]] = None,
sections: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ItemSectionArgs']]]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
title: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None,
username: Optional[pulumi.Input[str]] = None,
vault: Optional[pulumi.Input[str]] = None,
__props__=None):
opts = pulumi.ResourceOptions.merge(_utilities.get_resource_opts_defaults(), opts)
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ItemArgs.__new__(ItemArgs)
__props__.__dict__["category"] = category
__props__.__dict__["database"] = database
__props__.__dict__["hostname"] = hostname
__props__.__dict__["password"] = None if password is None else pulumi.Output.secret(password)
__props__.__dict__["password_recipe"] = password_recipe
__props__.__dict__["port"] = port
__props__.__dict__["sections"] = sections
__props__.__dict__["tags"] = tags
__props__.__dict__["title"] = title
__props__.__dict__["type"] = type
__props__.__dict__["url"] = url
__props__.__dict__["username"] = username
if vault is None and not opts.urn:
raise TypeError("Missing required property 'vault'")
__props__.__dict__["v
|
(resource_name: str, *args, **kwargs)
|
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] |
52,664 |
bmitzkus_pulumi_onepassword.item
|
__init__
| null |
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ItemArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
|
(__self__, resource_name: str, *args, **kwargs)
|
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] |
52,666 |
bmitzkus_pulumi_onepassword.item
|
_internal_init
| null |
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
category: Optional[pulumi.Input[str]] = None,
database: Optional[pulumi.Input[str]] = None,
hostname: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
password_recipe: Optional[pulumi.Input[pulumi.InputType['ItemPasswordRecipeArgs']]] = None,
port: Optional[pulumi.Input[str]] = None,
sections: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ItemSectionArgs']]]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
title: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None,
username: Optional[pulumi.Input[str]] = None,
vault: Optional[pulumi.Input[str]] = None,
__props__=None):
opts = pulumi.ResourceOptions.merge(_utilities.get_resource_opts_defaults(), opts)
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ItemArgs.__new__(ItemArgs)
__props__.__dict__["category"] = category
__props__.__dict__["database"] = database
__props__.__dict__["hostname"] = hostname
__props__.__dict__["password"] = None if password is None else pulumi.Output.secret(password)
__props__.__dict__["password_recipe"] = password_recipe
__props__.__dict__["port"] = port
__props__.__dict__["sections"] = sections
__props__.__dict__["tags"] = tags
__props__.__dict__["title"] = title
__props__.__dict__["type"] = type
__props__.__dict__["url"] = url
__props__.__dict__["username"] = username
if vault is None and not opts.urn:
raise TypeError("Missing required property 'vault'")
__props__.__dict__["vault"] = vault
__props__.__dict__["uuid"] = None
secret_opts = pulumi.ResourceOptions(additional_secret_outputs=["password"])
opts = pulumi.ResourceOptions.merge(opts, secret_opts)
super(Item, __self__).__init__(
'onepassword:index/item:Item',
resource_name,
__props__,
opts)
|
(__self__, resource_name: str, opts: Optional[pulumi.resource.ResourceOptions] = None, category: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, database: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, hostname: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password_recipe: Union[ForwardRef('ItemPasswordRecipeArgs'), Mapping[str, Any], Awaitable[Union[ForwardRef('ItemPasswordRecipeArgs'), Mapping[str, Any]]], ForwardRef('Output[T]'), NoneType] = None, port: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, sections: Union[Sequence[Union[ForwardRef('ItemSectionArgs'), Mapping[str, Any], Awaitable[Union[ForwardRef('ItemSectionArgs'), Mapping[str, Any]]], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[ForwardRef('ItemSectionArgs'), Mapping[str, Any], Awaitable[Union[ForwardRef('ItemSectionArgs'), Mapping[str, Any]]], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, tags: Union[Sequence[Union[str, Awaitable[str], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[str, Awaitable[str], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, title: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, type: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, url: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, username: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, vault: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, __props__=None)
|
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] |
52,667 |
bmitzkus_pulumi_onepassword.item
|
get
|
Get an existing Item resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] category: The category of the item. One of ["login" "password" "database"]
:param pulumi.Input[str] database: (Only applies to the database category) The name of the database.
:param pulumi.Input[str] hostname: (Only applies to the database category) The address where the database can be found
:param pulumi.Input[str] password: Password for this item.
:param pulumi.Input[pulumi.InputType['ItemPasswordRecipeArgs']] password_recipe: Password for this item.
:param pulumi.Input[str] port: (Only applies to the database category) The port the database is listening on.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ItemSectionArgs']]]] sections: A list of custom sections in an item
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of strings of the tags assigned to the item.
:param pulumi.Input[str] title: The title of the item.
:param pulumi.Input[str] type: The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
:param pulumi.Input[str] url: The primary URL for the item.
:param pulumi.Input[str] username: Username for this item.
:param pulumi.Input[str] uuid: The UUID of the item. Item identifiers are unique within a specific vault.
:param pulumi.Input[str] vault: The UUID of the vault the item is in.
|
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
category: Optional[pulumi.Input[str]] = None,
database: Optional[pulumi.Input[str]] = None,
hostname: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
password_recipe: Optional[pulumi.Input[pulumi.InputType['ItemPasswordRecipeArgs']]] = None,
port: Optional[pulumi.Input[str]] = None,
sections: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ItemSectionArgs']]]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
title: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None,
username: Optional[pulumi.Input[str]] = None,
uuid: Optional[pulumi.Input[str]] = None,
vault: Optional[pulumi.Input[str]] = None) -> 'Item':
"""
Get an existing Item resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] category: The category of the item. One of ["login" "password" "database"]
:param pulumi.Input[str] database: (Only applies to the database category) The name of the database.
:param pulumi.Input[str] hostname: (Only applies to the database category) The address where the database can be found
:param pulumi.Input[str] password: Password for this item.
:param pulumi.Input[pulumi.InputType['ItemPasswordRecipeArgs']] password_recipe: Password for this item.
:param pulumi.Input[str] port: (Only applies to the database category) The port the database is listening on.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ItemSectionArgs']]]] sections: A list of custom sections in an item
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of strings of the tags assigned to the item.
:param pulumi.Input[str] title: The title of the item.
:param pulumi.Input[str] type: The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
:param pulumi.Input[str] url: The primary URL for the item.
:param pulumi.Input[str] username: Username for this item.
:param pulumi.Input[str] uuid: The UUID of the item. Item identifiers are unique within a specific vault.
:param pulumi.Input[str] vault: The UUID of the vault the item is in.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _ItemState.__new__(_ItemState)
__props__.__dict__["category"] = category
__props__.__dict__["database"] = database
__props__.__dict__["hostname"] = hostname
__props__.__dict__["password"] = password
__props__.__dict__["password_recipe"] = password_recipe
__props__.__dict__["port"] = port
__props__.__dict__["sections"] = sections
__props__.__dict__["tags"] = tags
__props__.__dict__["title"] = title
__props__.__dict__["type"] = type
__props__.__dict__["url"] = url
__props__.__dict__["username"] = username
__props__.__dict__["uuid"] = uuid
__props__.__dict__["vault"] = vault
return Item(resource_name, opts=opts, __props__=__props__)
|
(resource_name: str, id: Union[str, Awaitable[str], ForwardRef('Output[T]')], opts: Optional[pulumi.resource.ResourceOptions] = None, category: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, database: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, hostname: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password_recipe: Union[ForwardRef('ItemPasswordRecipeArgs'), Mapping[str, Any], Awaitable[Union[ForwardRef('ItemPasswordRecipeArgs'), Mapping[str, Any]]], ForwardRef('Output[T]'), NoneType] = None, port: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, sections: Union[Sequence[Union[ForwardRef('ItemSectionArgs'), Mapping[str, Any], Awaitable[Union[ForwardRef('ItemSectionArgs'), Mapping[str, Any]]], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[ForwardRef('ItemSectionArgs'), Mapping[str, Any], Awaitable[Union[ForwardRef('ItemSectionArgs'), Mapping[str, Any]]], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, tags: Union[Sequence[Union[str, Awaitable[str], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[str, Awaitable[str], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, title: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, type: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, url: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, username: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, uuid: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, vault: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None) -> 'Item'
|
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] |
52,671 |
bmitzkus_pulumi_onepassword.item
|
ItemArgs
| null |
class ItemArgs:
def __init__(__self__, *,
vault: pulumi.Input[str],
category: Optional[pulumi.Input[str]] = None,
database: Optional[pulumi.Input[str]] = None,
hostname: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
password_recipe: Optional[pulumi.Input['ItemPasswordRecipeArgs']] = None,
port: Optional[pulumi.Input[str]] = None,
sections: Optional[pulumi.Input[Sequence[pulumi.Input['ItemSectionArgs']]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
title: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None,
username: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a Item resource.
:param pulumi.Input[str] vault: The UUID of the vault the item is in.
:param pulumi.Input[str] category: The category of the item. One of ["login" "password" "database"]
:param pulumi.Input[str] database: (Only applies to the database category) The name of the database.
:param pulumi.Input[str] hostname: (Only applies to the database category) The address where the database can be found
:param pulumi.Input[str] password: Password for this item.
:param pulumi.Input['ItemPasswordRecipeArgs'] password_recipe: Password for this item.
:param pulumi.Input[str] port: (Only applies to the database category) The port the database is listening on.
:param pulumi.Input[Sequence[pulumi.Input['ItemSectionArgs']]] sections: A list of custom sections in an item
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of strings of the tags assigned to the item.
:param pulumi.Input[str] title: The title of the item.
:param pulumi.Input[str] type: The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
:param pulumi.Input[str] url: The primary URL for the item.
:param pulumi.Input[str] username: Username for this item.
"""
pulumi.set(__self__, "vault", vault)
if category is not None:
pulumi.set(__self__, "category", category)
if database is not None:
pulumi.set(__self__, "database", database)
if hostname is not None:
pulumi.set(__self__, "hostname", hostname)
if password is not None:
pulumi.set(__self__, "password", password)
if password_recipe is not None:
pulumi.set(__self__, "password_recipe", password_recipe)
if port is not None:
pulumi.set(__self__, "port", port)
if sections is not None:
pulumi.set(__self__, "sections", sections)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if title is not None:
pulumi.set(__self__, "title", title)
if type is not None:
pulumi.set(__self__, "type", type)
if url is not None:
pulumi.set(__self__, "url", url)
if username is not None:
pulumi.set(__self__, "username", username)
@property
@pulumi.getter
def vault(self) -> pulumi.Input[str]:
"""
The UUID of the vault the item is in.
"""
return pulumi.get(self, "vault")
@vault.setter
def vault(self, value: pulumi.Input[str]):
pulumi.set(self, "vault", value)
@property
@pulumi.getter
def category(self) -> Optional[pulumi.Input[str]]:
"""
The category of the item. One of ["login" "password" "database"]
"""
return pulumi.get(self, "category")
@category.setter
def category(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "category", value)
@property
@pulumi.getter
def database(self) -> Optional[pulumi.Input[str]]:
"""
(Only applies to the database category) The name of the database.
"""
return pulumi.get(self, "database")
@database.setter
def database(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "database", value)
@property
@pulumi.getter
def hostname(self) -> Optional[pulumi.Input[str]]:
"""
(Only applies to the database category) The address where the database can be found
"""
return pulumi.get(self, "hostname")
@hostname.setter
def hostname(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "hostname", value)
@property
@pulumi.getter
def password(self) -> Optional[pulumi.Input[str]]:
"""
Password for this item.
"""
return pulumi.get(self, "password")
@password.setter
def password(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "password", value)
@property
@pulumi.getter(name="passwordRecipe")
def password_recipe(self) -> Optional[pulumi.Input['ItemPasswordRecipeArgs']]:
"""
Password for this item.
"""
return pulumi.get(self, "password_recipe")
@password_recipe.setter
def password_recipe(self, value: Optional[pulumi.Input['ItemPasswordRecipeArgs']]):
pulumi.set(self, "password_recipe", value)
@property
@pulumi.getter
def port(self) -> Optional[pulumi.Input[str]]:
"""
(Only applies to the database category) The port the database is listening on.
"""
return pulumi.get(self, "port")
@port.setter
def port(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "port", value)
@property
@pulumi.getter
def sections(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ItemSectionArgs']]]]:
"""
A list of custom sections in an item
"""
return pulumi.get(self, "sections")
@sections.setter
def sections(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ItemSectionArgs']]]]):
pulumi.set(self, "sections", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
An array of strings of the tags assigned to the item.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
@property
@pulumi.getter
def title(self) -> Optional[pulumi.Input[str]]:
"""
The title of the item.
"""
return pulumi.get(self, "title")
@title.setter
def title(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "title", value)
@property
@pulumi.getter
def type(self) -> Optional[pulumi.Input[str]]:
"""
The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "type", value)
@property
@pulumi.getter
def url(self) -> Optional[pulumi.Input[str]]:
"""
The primary URL for the item.
"""
return pulumi.get(self, "url")
@url.setter
def url(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "url", value)
@property
@pulumi.getter
def username(self) -> Optional[pulumi.Input[str]]:
"""
Username for this item.
"""
return pulumi.get(self, "username")
@username.setter
def username(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "username", value)
|
(*, vault: Union[str, Awaitable[str], ForwardRef('Output[T]')], category: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, database: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, hostname: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password_recipe: Union[ForwardRef('ItemPasswordRecipeArgs'), Awaitable[ForwardRef('ItemPasswordRecipeArgs')], ForwardRef('Output[T]'), NoneType] = None, port: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, sections: Union[Sequence[Union[ForwardRef('ItemSectionArgs'), Awaitable[ForwardRef('ItemSectionArgs')], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[ForwardRef('ItemSectionArgs'), Awaitable[ForwardRef('ItemSectionArgs')], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, tags: Union[Sequence[Union[str, Awaitable[str], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[str, Awaitable[str], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, title: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, type: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, url: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, username: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None)
|
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] |
52,673 |
bmitzkus_pulumi_onepassword.item
|
__init__
|
The set of arguments for constructing a Item resource.
:param pulumi.Input[str] vault: The UUID of the vault the item is in.
:param pulumi.Input[str] category: The category of the item. One of ["login" "password" "database"]
:param pulumi.Input[str] database: (Only applies to the database category) The name of the database.
:param pulumi.Input[str] hostname: (Only applies to the database category) The address where the database can be found
:param pulumi.Input[str] password: Password for this item.
:param pulumi.Input['ItemPasswordRecipeArgs'] password_recipe: Password for this item.
:param pulumi.Input[str] port: (Only applies to the database category) The port the database is listening on.
:param pulumi.Input[Sequence[pulumi.Input['ItemSectionArgs']]] sections: A list of custom sections in an item
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of strings of the tags assigned to the item.
:param pulumi.Input[str] title: The title of the item.
:param pulumi.Input[str] type: The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
:param pulumi.Input[str] url: The primary URL for the item.
:param pulumi.Input[str] username: Username for this item.
|
def __init__(__self__, *,
vault: pulumi.Input[str],
category: Optional[pulumi.Input[str]] = None,
database: Optional[pulumi.Input[str]] = None,
hostname: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
password_recipe: Optional[pulumi.Input['ItemPasswordRecipeArgs']] = None,
port: Optional[pulumi.Input[str]] = None,
sections: Optional[pulumi.Input[Sequence[pulumi.Input['ItemSectionArgs']]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
title: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
url: Optional[pulumi.Input[str]] = None,
username: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a Item resource.
:param pulumi.Input[str] vault: The UUID of the vault the item is in.
:param pulumi.Input[str] category: The category of the item. One of ["login" "password" "database"]
:param pulumi.Input[str] database: (Only applies to the database category) The name of the database.
:param pulumi.Input[str] hostname: (Only applies to the database category) The address where the database can be found
:param pulumi.Input[str] password: Password for this item.
:param pulumi.Input['ItemPasswordRecipeArgs'] password_recipe: Password for this item.
:param pulumi.Input[str] port: (Only applies to the database category) The port the database is listening on.
:param pulumi.Input[Sequence[pulumi.Input['ItemSectionArgs']]] sections: A list of custom sections in an item
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: An array of strings of the tags assigned to the item.
:param pulumi.Input[str] title: The title of the item.
:param pulumi.Input[str] type: The type of value stored in the field. One of ["STRING" "EMAIL" "CONCEALED" "URL" "OTP" "DATE" "MONTH_YEAR" "MENU"]
:param pulumi.Input[str] url: The primary URL for the item.
:param pulumi.Input[str] username: Username for this item.
"""
pulumi.set(__self__, "vault", vault)
if category is not None:
pulumi.set(__self__, "category", category)
if database is not None:
pulumi.set(__self__, "database", database)
if hostname is not None:
pulumi.set(__self__, "hostname", hostname)
if password is not None:
pulumi.set(__self__, "password", password)
if password_recipe is not None:
pulumi.set(__self__, "password_recipe", password_recipe)
if port is not None:
pulumi.set(__self__, "port", port)
if sections is not None:
pulumi.set(__self__, "sections", sections)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if title is not None:
pulumi.set(__self__, "title", title)
if type is not None:
pulumi.set(__self__, "type", type)
if url is not None:
pulumi.set(__self__, "url", url)
if username is not None:
pulumi.set(__self__, "username", username)
|
(__self__, *, vault: Union[str, Awaitable[str], ForwardRef('Output[T]')], category: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, database: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, hostname: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, password_recipe: Union[ForwardRef('ItemPasswordRecipeArgs'), Awaitable[ForwardRef('ItemPasswordRecipeArgs')], ForwardRef('Output[T]'), NoneType] = None, port: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, sections: Union[Sequence[Union[ForwardRef('ItemSectionArgs'), Awaitable[ForwardRef('ItemSectionArgs')], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[ForwardRef('ItemSectionArgs'), Awaitable[ForwardRef('ItemSectionArgs')], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, tags: Union[Sequence[Union[str, Awaitable[str], ForwardRef('Output[T]')]], Awaitable[Sequence[Union[str, Awaitable[str], ForwardRef('Output[T]')]]], ForwardRef('Output[T]'), NoneType] = None, title: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, type: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, url: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None, username: Union[str, Awaitable[str], ForwardRef('Output[T]'), NoneType] = None)
|
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] |
52,674 |
bmitzkus_pulumi_onepassword._inputs
|
ItemPasswordRecipeArgs
| null |
class ItemPasswordRecipeArgs:
def __init__(__self__, *,
digits: Optional[pulumi.Input[bool]] = None,
length: Optional[pulumi.Input[int]] = None,
letters: Optional[pulumi.Input[bool]] = None,
symbols: Optional[pulumi.Input[bool]] = None):
"""
:param pulumi.Input[bool] digits: Use digits [0-9] when generating the password.
:param pulumi.Input[int] length: The length of the password to be generated.
:param pulumi.Input[bool] letters: Use letters [a-zA-Z] when generating the password.
:param pulumi.Input[bool] symbols: Use symbols [[email protected]_*] when generating the password.
"""
if digits is not None:
pulumi.set(__self__, "digits", digits)
if length is not None:
pulumi.set(__self__, "length", length)
if letters is not None:
pulumi.set(__self__, "letters", letters)
if symbols is not None:
pulumi.set(__self__, "symbols", symbols)
@property
@pulumi.getter
def digits(self) -> Optional[pulumi.Input[bool]]:
"""
Use digits [0-9] when generating the password.
"""
return pulumi.get(self, "digits")
@digits.setter
def digits(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "digits", value)
@property
@pulumi.getter
def length(self) -> Optional[pulumi.Input[int]]:
"""
The length of the password to be generated.
"""
return pulumi.get(self, "length")
@length.setter
def length(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "length", value)
@property
@pulumi.getter
def letters(self) -> Optional[pulumi.Input[bool]]:
"""
Use letters [a-zA-Z] when generating the password.
"""
return pulumi.get(self, "letters")
@letters.setter
def letters(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "letters", value)
@property
@pulumi.getter
def symbols(self) -> Optional[pulumi.Input[bool]]:
"""
Use symbols [[email protected]_*] when generating the password.
"""
return pulumi.get(self, "symbols")
@symbols.setter
def symbols(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "symbols", value)
|
(*, digits: Union[bool, Awaitable[bool], ForwardRef('Output[T]'), NoneType] = None, length: Union[int, Awaitable[int], ForwardRef('Output[T]'), NoneType] = None, letters: Union[bool, Awaitable[bool], ForwardRef('Output[T]'), NoneType] = None, symbols: Union[bool, Awaitable[bool], ForwardRef('Output[T]'), NoneType] = None)
|
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