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stringlengths 4
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stringlengths 1
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stringlengths 7
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value | body_without_docstring
stringlengths 14
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|---|---|---|---|---|---|---|---|---|---|
6bf8a4ca977d2680170ad1d459b657d5413641165bf4f674a487a8dcea1b0a9a
|
def _parent(self, i: int) -> (int | None):
'Returns parent index of given index if exists, else None.'
return (((i - 1) // 2) if (0 < i < self.size) else None)
|
Returns parent index of given index if exists, else None.
|
cs/structures/heap/binary_heap.py
|
_parent
|
TylerYep/workshop
| 1 |
python
|
def _parent(self, i: int) -> (int | None):
return (((i - 1) // 2) if (0 < i < self.size) else None)
|
def _parent(self, i: int) -> (int | None):
return (((i - 1) // 2) if (0 < i < self.size) else None)<|docstring|>Returns parent index of given index if exists, else None.<|endoftext|>
|
0f4fce31d8c22419bdfef0d2485e95b5f86003e10b1de8d93a13906d0c043818
|
def _left(self, i: int) -> (int | None):
'Returns left-child-index of given index if exists, else None.'
left = int(((2 * i) + 1))
return (left if (0 < left < self.size) else None)
|
Returns left-child-index of given index if exists, else None.
|
cs/structures/heap/binary_heap.py
|
_left
|
TylerYep/workshop
| 1 |
python
|
def _left(self, i: int) -> (int | None):
left = int(((2 * i) + 1))
return (left if (0 < left < self.size) else None)
|
def _left(self, i: int) -> (int | None):
left = int(((2 * i) + 1))
return (left if (0 < left < self.size) else None)<|docstring|>Returns left-child-index of given index if exists, else None.<|endoftext|>
|
8195fcfe927aac4cb45d0f10c872b752c8343b77cfb507b8e3a53a2130282712
|
def _right(self, i: int) -> (int | None):
'Returns right-child-index of given index if exists, else None.'
right = int(((2 * i) + 2))
return (right if (0 < right < self.size) else None)
|
Returns right-child-index of given index if exists, else None.
|
cs/structures/heap/binary_heap.py
|
_right
|
TylerYep/workshop
| 1 |
python
|
def _right(self, i: int) -> (int | None):
right = int(((2 * i) + 2))
return (right if (0 < right < self.size) else None)
|
def _right(self, i: int) -> (int | None):
right = int(((2 * i) + 2))
return (right if (0 < right < self.size) else None)<|docstring|>Returns right-child-index of given index if exists, else None.<|endoftext|>
|
558cbefa86f5c45a8e60f4accd6fbc9a666e94b9ff2534b0873fd0f619e80f42
|
def _swap(self, i: int, j: int) -> None:
'Performs changes required for swapping two elements in the heap.'
(self.elem_to_index[self._heap[i]], self.elem_to_index[self._heap[j]]) = (self.elem_to_index[self._heap[j]], self.elem_to_index[self._heap[i]])
(self._heap[i], self._heap[j]) = (self._heap[j], self._heap[i])
|
Performs changes required for swapping two elements in the heap.
|
cs/structures/heap/binary_heap.py
|
_swap
|
TylerYep/workshop
| 1 |
python
|
def _swap(self, i: int, j: int) -> None:
(self.elem_to_index[self._heap[i]], self.elem_to_index[self._heap[j]]) = (self.elem_to_index[self._heap[j]], self.elem_to_index[self._heap[i]])
(self._heap[i], self._heap[j]) = (self._heap[j], self._heap[i])
|
def _swap(self, i: int, j: int) -> None:
(self.elem_to_index[self._heap[i]], self.elem_to_index[self._heap[j]]) = (self.elem_to_index[self._heap[j]], self.elem_to_index[self._heap[i]])
(self._heap[i], self._heap[j]) = (self._heap[j], self._heap[i])<|docstring|>Performs changes required for swapping two elements in the heap.<|endoftext|>
|
ff29bda101c4a7a8cf9fccd050f14d6c5098c7ef682ce5877c7701e60cde5663
|
def _cmp(self, i: int, j: int) -> bool:
'Compares the two items using default comparison.'
return (self.key(self._heap[i]) < self.key(self._heap[j]))
|
Compares the two items using default comparison.
|
cs/structures/heap/binary_heap.py
|
_cmp
|
TylerYep/workshop
| 1 |
python
|
def _cmp(self, i: int, j: int) -> bool:
return (self.key(self._heap[i]) < self.key(self._heap[j]))
|
def _cmp(self, i: int, j: int) -> bool:
return (self.key(self._heap[i]) < self.key(self._heap[j]))<|docstring|>Compares the two items using default comparison.<|endoftext|>
|
2e04cdb3f74664a00988f3ce105ea6d8387a1459e0ad6b16318e82049db3e491
|
def _get_valid_parent(self, i: int) -> int:
'\n Returns index of valid parent as per desired ordering among given index and\n both its children.\n '
(left, right) = (self._left(i), self._right(i))
valid_parent = i
if ((left is not None) and (not self._cmp(left, valid_parent))):
valid_parent = left
if ((right is not None) and (not self._cmp(right, valid_parent))):
valid_parent = right
return valid_parent
|
Returns index of valid parent as per desired ordering among given index and
both its children.
|
cs/structures/heap/binary_heap.py
|
_get_valid_parent
|
TylerYep/workshop
| 1 |
python
|
def _get_valid_parent(self, i: int) -> int:
'\n Returns index of valid parent as per desired ordering among given index and\n both its children.\n '
(left, right) = (self._left(i), self._right(i))
valid_parent = i
if ((left is not None) and (not self._cmp(left, valid_parent))):
valid_parent = left
if ((right is not None) and (not self._cmp(right, valid_parent))):
valid_parent = right
return valid_parent
|
def _get_valid_parent(self, i: int) -> int:
'\n Returns index of valid parent as per desired ordering among given index and\n both its children.\n '
(left, right) = (self._left(i), self._right(i))
valid_parent = i
if ((left is not None) and (not self._cmp(left, valid_parent))):
valid_parent = left
if ((right is not None) and (not self._cmp(right, valid_parent))):
valid_parent = right
return valid_parent<|docstring|>Returns index of valid parent as per desired ordering among given index and
both its children.<|endoftext|>
|
7c253e58e6acf7cc57dd0a1c89cf263ead3d60f514c0cc266334db77bd2f5b29
|
def _heapify_up(self, index: int) -> None:
'Fixes the heap in upward direction of given index.'
parent = self._parent(index)
while ((parent is not None) and (not self._cmp(index, parent))):
self._swap(index, parent)
(index, parent) = (parent, self._parent(parent))
|
Fixes the heap in upward direction of given index.
|
cs/structures/heap/binary_heap.py
|
_heapify_up
|
TylerYep/workshop
| 1 |
python
|
def _heapify_up(self, index: int) -> None:
parent = self._parent(index)
while ((parent is not None) and (not self._cmp(index, parent))):
self._swap(index, parent)
(index, parent) = (parent, self._parent(parent))
|
def _heapify_up(self, index: int) -> None:
parent = self._parent(index)
while ((parent is not None) and (not self._cmp(index, parent))):
self._swap(index, parent)
(index, parent) = (parent, self._parent(parent))<|docstring|>Fixes the heap in upward direction of given index.<|endoftext|>
|
150ba0adaa201a72e422beda02a0e10b2df43c3e75c672cbde2119f3c23b72e3
|
def _heapify_down(self, index: int) -> None:
'Fixes the heap in downward direction of given index.'
valid_parent = self._get_valid_parent(index)
while (valid_parent != index):
self._swap(index, valid_parent)
(index, valid_parent) = (valid_parent, self._get_valid_parent(valid_parent))
|
Fixes the heap in downward direction of given index.
|
cs/structures/heap/binary_heap.py
|
_heapify_down
|
TylerYep/workshop
| 1 |
python
|
def _heapify_down(self, index: int) -> None:
valid_parent = self._get_valid_parent(index)
while (valid_parent != index):
self._swap(index, valid_parent)
(index, valid_parent) = (valid_parent, self._get_valid_parent(valid_parent))
|
def _heapify_down(self, index: int) -> None:
valid_parent = self._get_valid_parent(index)
while (valid_parent != index):
self._swap(index, valid_parent)
(index, valid_parent) = (valid_parent, self._get_valid_parent(valid_parent))<|docstring|>Fixes the heap in downward direction of given index.<|endoftext|>
|
3674b30dbe0415224720ac2c7186b761e7d0a629e374fcff0774703a52a8d978
|
def __init__(self, **kwargs):
'\n Initializes a new UserDefinedFunctionValidationSummary object with values from keyword arguments.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param total_message_count:\n The value to assign to the total_message_count property of this UserDefinedFunctionValidationSummary.\n :type total_message_count: int\n\n :param error_message_count:\n The value to assign to the error_message_count property of this UserDefinedFunctionValidationSummary.\n :type error_message_count: int\n\n :param warn_message_count:\n The value to assign to the warn_message_count property of this UserDefinedFunctionValidationSummary.\n :type warn_message_count: int\n\n :param info_message_count:\n The value to assign to the info_message_count property of this UserDefinedFunctionValidationSummary.\n :type info_message_count: int\n\n :param validation_messages:\n The value to assign to the validation_messages property of this UserDefinedFunctionValidationSummary.\n :type validation_messages: dict(str, list[ValidationMessage])\n\n :param key:\n The value to assign to the key property of this UserDefinedFunctionValidationSummary.\n :type key: str\n\n :param model_type:\n The value to assign to the model_type property of this UserDefinedFunctionValidationSummary.\n :type model_type: str\n\n :param model_version:\n The value to assign to the model_version property of this UserDefinedFunctionValidationSummary.\n :type model_version: str\n\n :param parent_ref:\n The value to assign to the parent_ref property of this UserDefinedFunctionValidationSummary.\n :type parent_ref: oci.data_integration.models.ParentReference\n\n :param name:\n The value to assign to the name property of this UserDefinedFunctionValidationSummary.\n :type name: str\n\n :param description:\n The value to assign to the description property of this UserDefinedFunctionValidationSummary.\n :type description: str\n\n :param object_version:\n The value to assign to the object_version property of this UserDefinedFunctionValidationSummary.\n :type object_version: int\n\n :param object_status:\n The value to assign to the object_status property of this UserDefinedFunctionValidationSummary.\n :type object_status: int\n\n :param identifier:\n The value to assign to the identifier property of this UserDefinedFunctionValidationSummary.\n :type identifier: str\n\n :param metadata:\n The value to assign to the metadata property of this UserDefinedFunctionValidationSummary.\n :type metadata: oci.data_integration.models.ObjectMetadata\n\n '
self.swagger_types = {'total_message_count': 'int', 'error_message_count': 'int', 'warn_message_count': 'int', 'info_message_count': 'int', 'validation_messages': 'dict(str, list[ValidationMessage])', 'key': 'str', 'model_type': 'str', 'model_version': 'str', 'parent_ref': 'ParentReference', 'name': 'str', 'description': 'str', 'object_version': 'int', 'object_status': 'int', 'identifier': 'str', 'metadata': 'ObjectMetadata'}
self.attribute_map = {'total_message_count': 'totalMessageCount', 'error_message_count': 'errorMessageCount', 'warn_message_count': 'warnMessageCount', 'info_message_count': 'infoMessageCount', 'validation_messages': 'validationMessages', 'key': 'key', 'model_type': 'modelType', 'model_version': 'modelVersion', 'parent_ref': 'parentRef', 'name': 'name', 'description': 'description', 'object_version': 'objectVersion', 'object_status': 'objectStatus', 'identifier': 'identifier', 'metadata': 'metadata'}
self._total_message_count = None
self._error_message_count = None
self._warn_message_count = None
self._info_message_count = None
self._validation_messages = None
self._key = None
self._model_type = None
self._model_version = None
self._parent_ref = None
self._name = None
self._description = None
self._object_version = None
self._object_status = None
self._identifier = None
self._metadata = None
|
Initializes a new UserDefinedFunctionValidationSummary object with values from keyword arguments.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param total_message_count:
The value to assign to the total_message_count property of this UserDefinedFunctionValidationSummary.
:type total_message_count: int
:param error_message_count:
The value to assign to the error_message_count property of this UserDefinedFunctionValidationSummary.
:type error_message_count: int
:param warn_message_count:
The value to assign to the warn_message_count property of this UserDefinedFunctionValidationSummary.
:type warn_message_count: int
:param info_message_count:
The value to assign to the info_message_count property of this UserDefinedFunctionValidationSummary.
:type info_message_count: int
:param validation_messages:
The value to assign to the validation_messages property of this UserDefinedFunctionValidationSummary.
:type validation_messages: dict(str, list[ValidationMessage])
:param key:
The value to assign to the key property of this UserDefinedFunctionValidationSummary.
:type key: str
:param model_type:
The value to assign to the model_type property of this UserDefinedFunctionValidationSummary.
:type model_type: str
:param model_version:
The value to assign to the model_version property of this UserDefinedFunctionValidationSummary.
:type model_version: str
:param parent_ref:
The value to assign to the parent_ref property of this UserDefinedFunctionValidationSummary.
:type parent_ref: oci.data_integration.models.ParentReference
:param name:
The value to assign to the name property of this UserDefinedFunctionValidationSummary.
:type name: str
:param description:
The value to assign to the description property of this UserDefinedFunctionValidationSummary.
:type description: str
:param object_version:
The value to assign to the object_version property of this UserDefinedFunctionValidationSummary.
:type object_version: int
:param object_status:
The value to assign to the object_status property of this UserDefinedFunctionValidationSummary.
:type object_status: int
:param identifier:
The value to assign to the identifier property of this UserDefinedFunctionValidationSummary.
:type identifier: str
:param metadata:
The value to assign to the metadata property of this UserDefinedFunctionValidationSummary.
:type metadata: oci.data_integration.models.ObjectMetadata
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
__init__
|
ezequielramos/oci-python-sdk
| 249 |
python
|
def __init__(self, **kwargs):
'\n Initializes a new UserDefinedFunctionValidationSummary object with values from keyword arguments.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param total_message_count:\n The value to assign to the total_message_count property of this UserDefinedFunctionValidationSummary.\n :type total_message_count: int\n\n :param error_message_count:\n The value to assign to the error_message_count property of this UserDefinedFunctionValidationSummary.\n :type error_message_count: int\n\n :param warn_message_count:\n The value to assign to the warn_message_count property of this UserDefinedFunctionValidationSummary.\n :type warn_message_count: int\n\n :param info_message_count:\n The value to assign to the info_message_count property of this UserDefinedFunctionValidationSummary.\n :type info_message_count: int\n\n :param validation_messages:\n The value to assign to the validation_messages property of this UserDefinedFunctionValidationSummary.\n :type validation_messages: dict(str, list[ValidationMessage])\n\n :param key:\n The value to assign to the key property of this UserDefinedFunctionValidationSummary.\n :type key: str\n\n :param model_type:\n The value to assign to the model_type property of this UserDefinedFunctionValidationSummary.\n :type model_type: str\n\n :param model_version:\n The value to assign to the model_version property of this UserDefinedFunctionValidationSummary.\n :type model_version: str\n\n :param parent_ref:\n The value to assign to the parent_ref property of this UserDefinedFunctionValidationSummary.\n :type parent_ref: oci.data_integration.models.ParentReference\n\n :param name:\n The value to assign to the name property of this UserDefinedFunctionValidationSummary.\n :type name: str\n\n :param description:\n The value to assign to the description property of this UserDefinedFunctionValidationSummary.\n :type description: str\n\n :param object_version:\n The value to assign to the object_version property of this UserDefinedFunctionValidationSummary.\n :type object_version: int\n\n :param object_status:\n The value to assign to the object_status property of this UserDefinedFunctionValidationSummary.\n :type object_status: int\n\n :param identifier:\n The value to assign to the identifier property of this UserDefinedFunctionValidationSummary.\n :type identifier: str\n\n :param metadata:\n The value to assign to the metadata property of this UserDefinedFunctionValidationSummary.\n :type metadata: oci.data_integration.models.ObjectMetadata\n\n '
self.swagger_types = {'total_message_count': 'int', 'error_message_count': 'int', 'warn_message_count': 'int', 'info_message_count': 'int', 'validation_messages': 'dict(str, list[ValidationMessage])', 'key': 'str', 'model_type': 'str', 'model_version': 'str', 'parent_ref': 'ParentReference', 'name': 'str', 'description': 'str', 'object_version': 'int', 'object_status': 'int', 'identifier': 'str', 'metadata': 'ObjectMetadata'}
self.attribute_map = {'total_message_count': 'totalMessageCount', 'error_message_count': 'errorMessageCount', 'warn_message_count': 'warnMessageCount', 'info_message_count': 'infoMessageCount', 'validation_messages': 'validationMessages', 'key': 'key', 'model_type': 'modelType', 'model_version': 'modelVersion', 'parent_ref': 'parentRef', 'name': 'name', 'description': 'description', 'object_version': 'objectVersion', 'object_status': 'objectStatus', 'identifier': 'identifier', 'metadata': 'metadata'}
self._total_message_count = None
self._error_message_count = None
self._warn_message_count = None
self._info_message_count = None
self._validation_messages = None
self._key = None
self._model_type = None
self._model_version = None
self._parent_ref = None
self._name = None
self._description = None
self._object_version = None
self._object_status = None
self._identifier = None
self._metadata = None
|
def __init__(self, **kwargs):
'\n Initializes a new UserDefinedFunctionValidationSummary object with values from keyword arguments.\n The following keyword arguments are supported (corresponding to the getters/setters of this class):\n\n :param total_message_count:\n The value to assign to the total_message_count property of this UserDefinedFunctionValidationSummary.\n :type total_message_count: int\n\n :param error_message_count:\n The value to assign to the error_message_count property of this UserDefinedFunctionValidationSummary.\n :type error_message_count: int\n\n :param warn_message_count:\n The value to assign to the warn_message_count property of this UserDefinedFunctionValidationSummary.\n :type warn_message_count: int\n\n :param info_message_count:\n The value to assign to the info_message_count property of this UserDefinedFunctionValidationSummary.\n :type info_message_count: int\n\n :param validation_messages:\n The value to assign to the validation_messages property of this UserDefinedFunctionValidationSummary.\n :type validation_messages: dict(str, list[ValidationMessage])\n\n :param key:\n The value to assign to the key property of this UserDefinedFunctionValidationSummary.\n :type key: str\n\n :param model_type:\n The value to assign to the model_type property of this UserDefinedFunctionValidationSummary.\n :type model_type: str\n\n :param model_version:\n The value to assign to the model_version property of this UserDefinedFunctionValidationSummary.\n :type model_version: str\n\n :param parent_ref:\n The value to assign to the parent_ref property of this UserDefinedFunctionValidationSummary.\n :type parent_ref: oci.data_integration.models.ParentReference\n\n :param name:\n The value to assign to the name property of this UserDefinedFunctionValidationSummary.\n :type name: str\n\n :param description:\n The value to assign to the description property of this UserDefinedFunctionValidationSummary.\n :type description: str\n\n :param object_version:\n The value to assign to the object_version property of this UserDefinedFunctionValidationSummary.\n :type object_version: int\n\n :param object_status:\n The value to assign to the object_status property of this UserDefinedFunctionValidationSummary.\n :type object_status: int\n\n :param identifier:\n The value to assign to the identifier property of this UserDefinedFunctionValidationSummary.\n :type identifier: str\n\n :param metadata:\n The value to assign to the metadata property of this UserDefinedFunctionValidationSummary.\n :type metadata: oci.data_integration.models.ObjectMetadata\n\n '
self.swagger_types = {'total_message_count': 'int', 'error_message_count': 'int', 'warn_message_count': 'int', 'info_message_count': 'int', 'validation_messages': 'dict(str, list[ValidationMessage])', 'key': 'str', 'model_type': 'str', 'model_version': 'str', 'parent_ref': 'ParentReference', 'name': 'str', 'description': 'str', 'object_version': 'int', 'object_status': 'int', 'identifier': 'str', 'metadata': 'ObjectMetadata'}
self.attribute_map = {'total_message_count': 'totalMessageCount', 'error_message_count': 'errorMessageCount', 'warn_message_count': 'warnMessageCount', 'info_message_count': 'infoMessageCount', 'validation_messages': 'validationMessages', 'key': 'key', 'model_type': 'modelType', 'model_version': 'modelVersion', 'parent_ref': 'parentRef', 'name': 'name', 'description': 'description', 'object_version': 'objectVersion', 'object_status': 'objectStatus', 'identifier': 'identifier', 'metadata': 'metadata'}
self._total_message_count = None
self._error_message_count = None
self._warn_message_count = None
self._info_message_count = None
self._validation_messages = None
self._key = None
self._model_type = None
self._model_version = None
self._parent_ref = None
self._name = None
self._description = None
self._object_version = None
self._object_status = None
self._identifier = None
self._metadata = None<|docstring|>Initializes a new UserDefinedFunctionValidationSummary object with values from keyword arguments.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param total_message_count:
The value to assign to the total_message_count property of this UserDefinedFunctionValidationSummary.
:type total_message_count: int
:param error_message_count:
The value to assign to the error_message_count property of this UserDefinedFunctionValidationSummary.
:type error_message_count: int
:param warn_message_count:
The value to assign to the warn_message_count property of this UserDefinedFunctionValidationSummary.
:type warn_message_count: int
:param info_message_count:
The value to assign to the info_message_count property of this UserDefinedFunctionValidationSummary.
:type info_message_count: int
:param validation_messages:
The value to assign to the validation_messages property of this UserDefinedFunctionValidationSummary.
:type validation_messages: dict(str, list[ValidationMessage])
:param key:
The value to assign to the key property of this UserDefinedFunctionValidationSummary.
:type key: str
:param model_type:
The value to assign to the model_type property of this UserDefinedFunctionValidationSummary.
:type model_type: str
:param model_version:
The value to assign to the model_version property of this UserDefinedFunctionValidationSummary.
:type model_version: str
:param parent_ref:
The value to assign to the parent_ref property of this UserDefinedFunctionValidationSummary.
:type parent_ref: oci.data_integration.models.ParentReference
:param name:
The value to assign to the name property of this UserDefinedFunctionValidationSummary.
:type name: str
:param description:
The value to assign to the description property of this UserDefinedFunctionValidationSummary.
:type description: str
:param object_version:
The value to assign to the object_version property of this UserDefinedFunctionValidationSummary.
:type object_version: int
:param object_status:
The value to assign to the object_status property of this UserDefinedFunctionValidationSummary.
:type object_status: int
:param identifier:
The value to assign to the identifier property of this UserDefinedFunctionValidationSummary.
:type identifier: str
:param metadata:
The value to assign to the metadata property of this UserDefinedFunctionValidationSummary.
:type metadata: oci.data_integration.models.ObjectMetadata<|endoftext|>
|
fdec9f2cd6f6fb0c68f33da772cf38e4e5bb5350f24e69a2c4413e31eb890638
|
@property
def total_message_count(self):
'\n Gets the total_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation messages.\n\n\n :return: The total_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._total_message_count
|
Gets the total_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation messages.
:return: The total_message_count of this UserDefinedFunctionValidationSummary.
:rtype: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
total_message_count
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def total_message_count(self):
'\n Gets the total_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation messages.\n\n\n :return: The total_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._total_message_count
|
@property
def total_message_count(self):
'\n Gets the total_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation messages.\n\n\n :return: The total_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._total_message_count<|docstring|>Gets the total_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation messages.
:return: The total_message_count of this UserDefinedFunctionValidationSummary.
:rtype: int<|endoftext|>
|
576a98f785c79a06d52072872f82d7a85f3958f5287b3c3b1979c9bdeea545ea
|
@total_message_count.setter
def total_message_count(self, total_message_count):
'\n Sets the total_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation messages.\n\n\n :param total_message_count: The total_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._total_message_count = total_message_count
|
Sets the total_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation messages.
:param total_message_count: The total_message_count of this UserDefinedFunctionValidationSummary.
:type: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
total_message_count
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@total_message_count.setter
def total_message_count(self, total_message_count):
'\n Sets the total_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation messages.\n\n\n :param total_message_count: The total_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._total_message_count = total_message_count
|
@total_message_count.setter
def total_message_count(self, total_message_count):
'\n Sets the total_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation messages.\n\n\n :param total_message_count: The total_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._total_message_count = total_message_count<|docstring|>Sets the total_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation messages.
:param total_message_count: The total_message_count of this UserDefinedFunctionValidationSummary.
:type: int<|endoftext|>
|
0e06c555cbd23e16049c59f84430c2998b8729bd936dccae1333ddc583eed20d
|
@property
def error_message_count(self):
'\n Gets the error_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation error messages.\n\n\n :return: The error_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._error_message_count
|
Gets the error_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation error messages.
:return: The error_message_count of this UserDefinedFunctionValidationSummary.
:rtype: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
error_message_count
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def error_message_count(self):
'\n Gets the error_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation error messages.\n\n\n :return: The error_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._error_message_count
|
@property
def error_message_count(self):
'\n Gets the error_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation error messages.\n\n\n :return: The error_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._error_message_count<|docstring|>Gets the error_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation error messages.
:return: The error_message_count of this UserDefinedFunctionValidationSummary.
:rtype: int<|endoftext|>
|
ca8e46f99589df1f8eb3287eb94af8c5cb1ef1a30bf607978223186fa5ff539f
|
@error_message_count.setter
def error_message_count(self, error_message_count):
'\n Sets the error_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation error messages.\n\n\n :param error_message_count: The error_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._error_message_count = error_message_count
|
Sets the error_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation error messages.
:param error_message_count: The error_message_count of this UserDefinedFunctionValidationSummary.
:type: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
error_message_count
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@error_message_count.setter
def error_message_count(self, error_message_count):
'\n Sets the error_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation error messages.\n\n\n :param error_message_count: The error_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._error_message_count = error_message_count
|
@error_message_count.setter
def error_message_count(self, error_message_count):
'\n Sets the error_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation error messages.\n\n\n :param error_message_count: The error_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._error_message_count = error_message_count<|docstring|>Sets the error_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation error messages.
:param error_message_count: The error_message_count of this UserDefinedFunctionValidationSummary.
:type: int<|endoftext|>
|
2660fa89febc120e47e41e23a3337a597909293935b82805e6759412cfe0157c
|
@property
def warn_message_count(self):
'\n Gets the warn_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation warning messages.\n\n\n :return: The warn_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._warn_message_count
|
Gets the warn_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation warning messages.
:return: The warn_message_count of this UserDefinedFunctionValidationSummary.
:rtype: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
warn_message_count
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def warn_message_count(self):
'\n Gets the warn_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation warning messages.\n\n\n :return: The warn_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._warn_message_count
|
@property
def warn_message_count(self):
'\n Gets the warn_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation warning messages.\n\n\n :return: The warn_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._warn_message_count<|docstring|>Gets the warn_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation warning messages.
:return: The warn_message_count of this UserDefinedFunctionValidationSummary.
:rtype: int<|endoftext|>
|
4f334857f3422ff79db537fe2224f758872d89a9f3a04174ab2c68289f1d45dd
|
@warn_message_count.setter
def warn_message_count(self, warn_message_count):
'\n Sets the warn_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation warning messages.\n\n\n :param warn_message_count: The warn_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._warn_message_count = warn_message_count
|
Sets the warn_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation warning messages.
:param warn_message_count: The warn_message_count of this UserDefinedFunctionValidationSummary.
:type: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
warn_message_count
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@warn_message_count.setter
def warn_message_count(self, warn_message_count):
'\n Sets the warn_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation warning messages.\n\n\n :param warn_message_count: The warn_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._warn_message_count = warn_message_count
|
@warn_message_count.setter
def warn_message_count(self, warn_message_count):
'\n Sets the warn_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation warning messages.\n\n\n :param warn_message_count: The warn_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._warn_message_count = warn_message_count<|docstring|>Sets the warn_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation warning messages.
:param warn_message_count: The warn_message_count of this UserDefinedFunctionValidationSummary.
:type: int<|endoftext|>
|
fa47ce80751324a19fe14ec4986f61824b634b3fbb062d607fe94a90ed7cbc50
|
@property
def info_message_count(self):
'\n Gets the info_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation information messages.\n\n\n :return: The info_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._info_message_count
|
Gets the info_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation information messages.
:return: The info_message_count of this UserDefinedFunctionValidationSummary.
:rtype: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
info_message_count
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def info_message_count(self):
'\n Gets the info_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation information messages.\n\n\n :return: The info_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._info_message_count
|
@property
def info_message_count(self):
'\n Gets the info_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation information messages.\n\n\n :return: The info_message_count of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._info_message_count<|docstring|>Gets the info_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation information messages.
:return: The info_message_count of this UserDefinedFunctionValidationSummary.
:rtype: int<|endoftext|>
|
b2d45e5839c2a9b3ca8988cc1f042578b2abdaf2d39916f7e3757ef824271463
|
@info_message_count.setter
def info_message_count(self, info_message_count):
'\n Sets the info_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation information messages.\n\n\n :param info_message_count: The info_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._info_message_count = info_message_count
|
Sets the info_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation information messages.
:param info_message_count: The info_message_count of this UserDefinedFunctionValidationSummary.
:type: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
info_message_count
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@info_message_count.setter
def info_message_count(self, info_message_count):
'\n Sets the info_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation information messages.\n\n\n :param info_message_count: The info_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._info_message_count = info_message_count
|
@info_message_count.setter
def info_message_count(self, info_message_count):
'\n Sets the info_message_count of this UserDefinedFunctionValidationSummary.\n The total number of validation information messages.\n\n\n :param info_message_count: The info_message_count of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._info_message_count = info_message_count<|docstring|>Sets the info_message_count of this UserDefinedFunctionValidationSummary.
The total number of validation information messages.
:param info_message_count: The info_message_count of this UserDefinedFunctionValidationSummary.
:type: int<|endoftext|>
|
0d8c15dfb86c08513bb6e3975aa3dfa95747080f4f0e187a398392556571506b
|
@property
def validation_messages(self):
'\n Gets the validation_messages of this UserDefinedFunctionValidationSummary.\n The detailed information of the UserDefinedFunction object validation.\n\n\n :return: The validation_messages of this UserDefinedFunctionValidationSummary.\n :rtype: dict(str, list[ValidationMessage])\n '
return self._validation_messages
|
Gets the validation_messages of this UserDefinedFunctionValidationSummary.
The detailed information of the UserDefinedFunction object validation.
:return: The validation_messages of this UserDefinedFunctionValidationSummary.
:rtype: dict(str, list[ValidationMessage])
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
validation_messages
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def validation_messages(self):
'\n Gets the validation_messages of this UserDefinedFunctionValidationSummary.\n The detailed information of the UserDefinedFunction object validation.\n\n\n :return: The validation_messages of this UserDefinedFunctionValidationSummary.\n :rtype: dict(str, list[ValidationMessage])\n '
return self._validation_messages
|
@property
def validation_messages(self):
'\n Gets the validation_messages of this UserDefinedFunctionValidationSummary.\n The detailed information of the UserDefinedFunction object validation.\n\n\n :return: The validation_messages of this UserDefinedFunctionValidationSummary.\n :rtype: dict(str, list[ValidationMessage])\n '
return self._validation_messages<|docstring|>Gets the validation_messages of this UserDefinedFunctionValidationSummary.
The detailed information of the UserDefinedFunction object validation.
:return: The validation_messages of this UserDefinedFunctionValidationSummary.
:rtype: dict(str, list[ValidationMessage])<|endoftext|>
|
3542307f2e71c5c7837b25464e12715897ac91aaf4207b2043f5f5f55c5a6c63
|
@validation_messages.setter
def validation_messages(self, validation_messages):
'\n Sets the validation_messages of this UserDefinedFunctionValidationSummary.\n The detailed information of the UserDefinedFunction object validation.\n\n\n :param validation_messages: The validation_messages of this UserDefinedFunctionValidationSummary.\n :type: dict(str, list[ValidationMessage])\n '
self._validation_messages = validation_messages
|
Sets the validation_messages of this UserDefinedFunctionValidationSummary.
The detailed information of the UserDefinedFunction object validation.
:param validation_messages: The validation_messages of this UserDefinedFunctionValidationSummary.
:type: dict(str, list[ValidationMessage])
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
validation_messages
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@validation_messages.setter
def validation_messages(self, validation_messages):
'\n Sets the validation_messages of this UserDefinedFunctionValidationSummary.\n The detailed information of the UserDefinedFunction object validation.\n\n\n :param validation_messages: The validation_messages of this UserDefinedFunctionValidationSummary.\n :type: dict(str, list[ValidationMessage])\n '
self._validation_messages = validation_messages
|
@validation_messages.setter
def validation_messages(self, validation_messages):
'\n Sets the validation_messages of this UserDefinedFunctionValidationSummary.\n The detailed information of the UserDefinedFunction object validation.\n\n\n :param validation_messages: The validation_messages of this UserDefinedFunctionValidationSummary.\n :type: dict(str, list[ValidationMessage])\n '
self._validation_messages = validation_messages<|docstring|>Sets the validation_messages of this UserDefinedFunctionValidationSummary.
The detailed information of the UserDefinedFunction object validation.
:param validation_messages: The validation_messages of this UserDefinedFunctionValidationSummary.
:type: dict(str, list[ValidationMessage])<|endoftext|>
|
171aba8baf9e311c6d3a4e54ab9f678bfd9950969f5a0bd8899e5f43cb002c22
|
@property
def key(self):
'\n Gets the key of this UserDefinedFunctionValidationSummary.\n Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.\n\n\n :return: The key of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._key
|
Gets the key of this UserDefinedFunctionValidationSummary.
Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.
:return: The key of this UserDefinedFunctionValidationSummary.
:rtype: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
key
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def key(self):
'\n Gets the key of this UserDefinedFunctionValidationSummary.\n Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.\n\n\n :return: The key of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._key
|
@property
def key(self):
'\n Gets the key of this UserDefinedFunctionValidationSummary.\n Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.\n\n\n :return: The key of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._key<|docstring|>Gets the key of this UserDefinedFunctionValidationSummary.
Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.
:return: The key of this UserDefinedFunctionValidationSummary.
:rtype: str<|endoftext|>
|
02d7c69d5960d0ed8d2ec54c2f05fca5b8304be9bc3ddde34dfb7a83ce49ced9
|
@key.setter
def key(self, key):
'\n Sets the key of this UserDefinedFunctionValidationSummary.\n Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.\n\n\n :param key: The key of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._key = key
|
Sets the key of this UserDefinedFunctionValidationSummary.
Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.
:param key: The key of this UserDefinedFunctionValidationSummary.
:type: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
key
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@key.setter
def key(self, key):
'\n Sets the key of this UserDefinedFunctionValidationSummary.\n Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.\n\n\n :param key: The key of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._key = key
|
@key.setter
def key(self, key):
'\n Sets the key of this UserDefinedFunctionValidationSummary.\n Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.\n\n\n :param key: The key of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._key = key<|docstring|>Sets the key of this UserDefinedFunctionValidationSummary.
Objects will use a 36 character key as unique ID. It is system generated and cannot be modified.
:param key: The key of this UserDefinedFunctionValidationSummary.
:type: str<|endoftext|>
|
2d8d706e3788b5e611d892e4a1e17ec20e0ba618cd4a10a6fac27835dce53430
|
@property
def model_type(self):
'\n Gets the model_type of this UserDefinedFunctionValidationSummary.\n The type of the object.\n\n\n :return: The model_type of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._model_type
|
Gets the model_type of this UserDefinedFunctionValidationSummary.
The type of the object.
:return: The model_type of this UserDefinedFunctionValidationSummary.
:rtype: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
model_type
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def model_type(self):
'\n Gets the model_type of this UserDefinedFunctionValidationSummary.\n The type of the object.\n\n\n :return: The model_type of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._model_type
|
@property
def model_type(self):
'\n Gets the model_type of this UserDefinedFunctionValidationSummary.\n The type of the object.\n\n\n :return: The model_type of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._model_type<|docstring|>Gets the model_type of this UserDefinedFunctionValidationSummary.
The type of the object.
:return: The model_type of this UserDefinedFunctionValidationSummary.
:rtype: str<|endoftext|>
|
713c1828bf2c8ac1e845e3a112645df707612ad3fd1f24b6910bf8f671c116b7
|
@model_type.setter
def model_type(self, model_type):
'\n Sets the model_type of this UserDefinedFunctionValidationSummary.\n The type of the object.\n\n\n :param model_type: The model_type of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._model_type = model_type
|
Sets the model_type of this UserDefinedFunctionValidationSummary.
The type of the object.
:param model_type: The model_type of this UserDefinedFunctionValidationSummary.
:type: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
model_type
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@model_type.setter
def model_type(self, model_type):
'\n Sets the model_type of this UserDefinedFunctionValidationSummary.\n The type of the object.\n\n\n :param model_type: The model_type of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._model_type = model_type
|
@model_type.setter
def model_type(self, model_type):
'\n Sets the model_type of this UserDefinedFunctionValidationSummary.\n The type of the object.\n\n\n :param model_type: The model_type of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._model_type = model_type<|docstring|>Sets the model_type of this UserDefinedFunctionValidationSummary.
The type of the object.
:param model_type: The model_type of this UserDefinedFunctionValidationSummary.
:type: str<|endoftext|>
|
3ed7b985d71d4c92135de689d42b38b7cf17f3b92bab23896bff947a4169fe75
|
@property
def model_version(self):
'\n Gets the model_version of this UserDefinedFunctionValidationSummary.\n The model version of the object.\n\n\n :return: The model_version of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._model_version
|
Gets the model_version of this UserDefinedFunctionValidationSummary.
The model version of the object.
:return: The model_version of this UserDefinedFunctionValidationSummary.
:rtype: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
model_version
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def model_version(self):
'\n Gets the model_version of this UserDefinedFunctionValidationSummary.\n The model version of the object.\n\n\n :return: The model_version of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._model_version
|
@property
def model_version(self):
'\n Gets the model_version of this UserDefinedFunctionValidationSummary.\n The model version of the object.\n\n\n :return: The model_version of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._model_version<|docstring|>Gets the model_version of this UserDefinedFunctionValidationSummary.
The model version of the object.
:return: The model_version of this UserDefinedFunctionValidationSummary.
:rtype: str<|endoftext|>
|
02a873e338010e6870c953f357c6009f6275ff3df9b99cf87638a5fb25b59236
|
@model_version.setter
def model_version(self, model_version):
'\n Sets the model_version of this UserDefinedFunctionValidationSummary.\n The model version of the object.\n\n\n :param model_version: The model_version of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._model_version = model_version
|
Sets the model_version of this UserDefinedFunctionValidationSummary.
The model version of the object.
:param model_version: The model_version of this UserDefinedFunctionValidationSummary.
:type: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
model_version
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@model_version.setter
def model_version(self, model_version):
'\n Sets the model_version of this UserDefinedFunctionValidationSummary.\n The model version of the object.\n\n\n :param model_version: The model_version of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._model_version = model_version
|
@model_version.setter
def model_version(self, model_version):
'\n Sets the model_version of this UserDefinedFunctionValidationSummary.\n The model version of the object.\n\n\n :param model_version: The model_version of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._model_version = model_version<|docstring|>Sets the model_version of this UserDefinedFunctionValidationSummary.
The model version of the object.
:param model_version: The model_version of this UserDefinedFunctionValidationSummary.
:type: str<|endoftext|>
|
1cfaa8c7377918e144e8722e8e378a1bbc4ed482afb6ff893fb7a5e0a6ee0b45
|
@property
def parent_ref(self):
'\n Gets the parent_ref of this UserDefinedFunctionValidationSummary.\n\n :return: The parent_ref of this UserDefinedFunctionValidationSummary.\n :rtype: oci.data_integration.models.ParentReference\n '
return self._parent_ref
|
Gets the parent_ref of this UserDefinedFunctionValidationSummary.
:return: The parent_ref of this UserDefinedFunctionValidationSummary.
:rtype: oci.data_integration.models.ParentReference
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
parent_ref
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def parent_ref(self):
'\n Gets the parent_ref of this UserDefinedFunctionValidationSummary.\n\n :return: The parent_ref of this UserDefinedFunctionValidationSummary.\n :rtype: oci.data_integration.models.ParentReference\n '
return self._parent_ref
|
@property
def parent_ref(self):
'\n Gets the parent_ref of this UserDefinedFunctionValidationSummary.\n\n :return: The parent_ref of this UserDefinedFunctionValidationSummary.\n :rtype: oci.data_integration.models.ParentReference\n '
return self._parent_ref<|docstring|>Gets the parent_ref of this UserDefinedFunctionValidationSummary.
:return: The parent_ref of this UserDefinedFunctionValidationSummary.
:rtype: oci.data_integration.models.ParentReference<|endoftext|>
|
e0d0d7c16795b0ba531297f6517f26028e823817c1fab8b9dc1b4bd9fa56117e
|
@parent_ref.setter
def parent_ref(self, parent_ref):
'\n Sets the parent_ref of this UserDefinedFunctionValidationSummary.\n\n :param parent_ref: The parent_ref of this UserDefinedFunctionValidationSummary.\n :type: oci.data_integration.models.ParentReference\n '
self._parent_ref = parent_ref
|
Sets the parent_ref of this UserDefinedFunctionValidationSummary.
:param parent_ref: The parent_ref of this UserDefinedFunctionValidationSummary.
:type: oci.data_integration.models.ParentReference
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
parent_ref
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@parent_ref.setter
def parent_ref(self, parent_ref):
'\n Sets the parent_ref of this UserDefinedFunctionValidationSummary.\n\n :param parent_ref: The parent_ref of this UserDefinedFunctionValidationSummary.\n :type: oci.data_integration.models.ParentReference\n '
self._parent_ref = parent_ref
|
@parent_ref.setter
def parent_ref(self, parent_ref):
'\n Sets the parent_ref of this UserDefinedFunctionValidationSummary.\n\n :param parent_ref: The parent_ref of this UserDefinedFunctionValidationSummary.\n :type: oci.data_integration.models.ParentReference\n '
self._parent_ref = parent_ref<|docstring|>Sets the parent_ref of this UserDefinedFunctionValidationSummary.
:param parent_ref: The parent_ref of this UserDefinedFunctionValidationSummary.
:type: oci.data_integration.models.ParentReference<|endoftext|>
|
8fcd625d2fb4a3e881e2a2b8010c27e432fb3c92302e354fdcdafe2810db68b3
|
@property
def name(self):
'\n Gets the name of this UserDefinedFunctionValidationSummary.\n Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.\n\n\n :return: The name of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._name
|
Gets the name of this UserDefinedFunctionValidationSummary.
Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.
:return: The name of this UserDefinedFunctionValidationSummary.
:rtype: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
name
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def name(self):
'\n Gets the name of this UserDefinedFunctionValidationSummary.\n Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.\n\n\n :return: The name of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._name
|
@property
def name(self):
'\n Gets the name of this UserDefinedFunctionValidationSummary.\n Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.\n\n\n :return: The name of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._name<|docstring|>Gets the name of this UserDefinedFunctionValidationSummary.
Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.
:return: The name of this UserDefinedFunctionValidationSummary.
:rtype: str<|endoftext|>
|
dc968158c12c7135f8a545e525b200f0c60e7edff3e71c103cb3fe1e4a29f0d4
|
@name.setter
def name(self, name):
'\n Sets the name of this UserDefinedFunctionValidationSummary.\n Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.\n\n\n :param name: The name of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._name = name
|
Sets the name of this UserDefinedFunctionValidationSummary.
Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.
:param name: The name of this UserDefinedFunctionValidationSummary.
:type: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
name
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@name.setter
def name(self, name):
'\n Sets the name of this UserDefinedFunctionValidationSummary.\n Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.\n\n\n :param name: The name of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._name = name
|
@name.setter
def name(self, name):
'\n Sets the name of this UserDefinedFunctionValidationSummary.\n Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.\n\n\n :param name: The name of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._name = name<|docstring|>Sets the name of this UserDefinedFunctionValidationSummary.
Free form text without any restriction on permitted characters. Name can have letters, numbers, and special characters. The value is editable and is restricted to 1000 characters.
:param name: The name of this UserDefinedFunctionValidationSummary.
:type: str<|endoftext|>
|
c8dace48a770ef9775a5380e2aac6260b864851f656608987555f97ea6989424
|
@property
def description(self):
'\n Gets the description of this UserDefinedFunctionValidationSummary.\n Detailed description for the object.\n\n\n :return: The description of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._description
|
Gets the description of this UserDefinedFunctionValidationSummary.
Detailed description for the object.
:return: The description of this UserDefinedFunctionValidationSummary.
:rtype: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
description
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def description(self):
'\n Gets the description of this UserDefinedFunctionValidationSummary.\n Detailed description for the object.\n\n\n :return: The description of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._description
|
@property
def description(self):
'\n Gets the description of this UserDefinedFunctionValidationSummary.\n Detailed description for the object.\n\n\n :return: The description of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._description<|docstring|>Gets the description of this UserDefinedFunctionValidationSummary.
Detailed description for the object.
:return: The description of this UserDefinedFunctionValidationSummary.
:rtype: str<|endoftext|>
|
754518b6e135d27c563fd9b40ff86c2db13f4c16e0900173b781176607d6e581
|
@description.setter
def description(self, description):
'\n Sets the description of this UserDefinedFunctionValidationSummary.\n Detailed description for the object.\n\n\n :param description: The description of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._description = description
|
Sets the description of this UserDefinedFunctionValidationSummary.
Detailed description for the object.
:param description: The description of this UserDefinedFunctionValidationSummary.
:type: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
description
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@description.setter
def description(self, description):
'\n Sets the description of this UserDefinedFunctionValidationSummary.\n Detailed description for the object.\n\n\n :param description: The description of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._description = description
|
@description.setter
def description(self, description):
'\n Sets the description of this UserDefinedFunctionValidationSummary.\n Detailed description for the object.\n\n\n :param description: The description of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._description = description<|docstring|>Sets the description of this UserDefinedFunctionValidationSummary.
Detailed description for the object.
:param description: The description of this UserDefinedFunctionValidationSummary.
:type: str<|endoftext|>
|
25d23f88e54b1501e0e1b119b25fac39e4d75575927d5e231753e59b7e794a71
|
@property
def object_version(self):
'\n Gets the object_version of this UserDefinedFunctionValidationSummary.\n The version of the object that is used to track changes in the object instance.\n\n\n :return: The object_version of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._object_version
|
Gets the object_version of this UserDefinedFunctionValidationSummary.
The version of the object that is used to track changes in the object instance.
:return: The object_version of this UserDefinedFunctionValidationSummary.
:rtype: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
object_version
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def object_version(self):
'\n Gets the object_version of this UserDefinedFunctionValidationSummary.\n The version of the object that is used to track changes in the object instance.\n\n\n :return: The object_version of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._object_version
|
@property
def object_version(self):
'\n Gets the object_version of this UserDefinedFunctionValidationSummary.\n The version of the object that is used to track changes in the object instance.\n\n\n :return: The object_version of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._object_version<|docstring|>Gets the object_version of this UserDefinedFunctionValidationSummary.
The version of the object that is used to track changes in the object instance.
:return: The object_version of this UserDefinedFunctionValidationSummary.
:rtype: int<|endoftext|>
|
63321b5bb16ba9671649d9f8a33e5ce955490ed9f2c75ef1e976c629c18a1416
|
@object_version.setter
def object_version(self, object_version):
'\n Sets the object_version of this UserDefinedFunctionValidationSummary.\n The version of the object that is used to track changes in the object instance.\n\n\n :param object_version: The object_version of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._object_version = object_version
|
Sets the object_version of this UserDefinedFunctionValidationSummary.
The version of the object that is used to track changes in the object instance.
:param object_version: The object_version of this UserDefinedFunctionValidationSummary.
:type: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
object_version
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@object_version.setter
def object_version(self, object_version):
'\n Sets the object_version of this UserDefinedFunctionValidationSummary.\n The version of the object that is used to track changes in the object instance.\n\n\n :param object_version: The object_version of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._object_version = object_version
|
@object_version.setter
def object_version(self, object_version):
'\n Sets the object_version of this UserDefinedFunctionValidationSummary.\n The version of the object that is used to track changes in the object instance.\n\n\n :param object_version: The object_version of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._object_version = object_version<|docstring|>Sets the object_version of this UserDefinedFunctionValidationSummary.
The version of the object that is used to track changes in the object instance.
:param object_version: The object_version of this UserDefinedFunctionValidationSummary.
:type: int<|endoftext|>
|
fc4aa37536262bd41970976c53294c10fa19fa1aad8434664496c5b6e916f5b4
|
@property
def object_status(self):
'\n Gets the object_status of this UserDefinedFunctionValidationSummary.\n The status of an object that can be set to value 1 for shallow references across objects, other values reserved.\n\n\n :return: The object_status of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._object_status
|
Gets the object_status of this UserDefinedFunctionValidationSummary.
The status of an object that can be set to value 1 for shallow references across objects, other values reserved.
:return: The object_status of this UserDefinedFunctionValidationSummary.
:rtype: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
object_status
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def object_status(self):
'\n Gets the object_status of this UserDefinedFunctionValidationSummary.\n The status of an object that can be set to value 1 for shallow references across objects, other values reserved.\n\n\n :return: The object_status of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._object_status
|
@property
def object_status(self):
'\n Gets the object_status of this UserDefinedFunctionValidationSummary.\n The status of an object that can be set to value 1 for shallow references across objects, other values reserved.\n\n\n :return: The object_status of this UserDefinedFunctionValidationSummary.\n :rtype: int\n '
return self._object_status<|docstring|>Gets the object_status of this UserDefinedFunctionValidationSummary.
The status of an object that can be set to value 1 for shallow references across objects, other values reserved.
:return: The object_status of this UserDefinedFunctionValidationSummary.
:rtype: int<|endoftext|>
|
4cd725345f2184c25fc4f762aef7c5efbaa85d30057158a8fc6bd39e5d34fd2b
|
@object_status.setter
def object_status(self, object_status):
'\n Sets the object_status of this UserDefinedFunctionValidationSummary.\n The status of an object that can be set to value 1 for shallow references across objects, other values reserved.\n\n\n :param object_status: The object_status of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._object_status = object_status
|
Sets the object_status of this UserDefinedFunctionValidationSummary.
The status of an object that can be set to value 1 for shallow references across objects, other values reserved.
:param object_status: The object_status of this UserDefinedFunctionValidationSummary.
:type: int
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
object_status
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@object_status.setter
def object_status(self, object_status):
'\n Sets the object_status of this UserDefinedFunctionValidationSummary.\n The status of an object that can be set to value 1 for shallow references across objects, other values reserved.\n\n\n :param object_status: The object_status of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._object_status = object_status
|
@object_status.setter
def object_status(self, object_status):
'\n Sets the object_status of this UserDefinedFunctionValidationSummary.\n The status of an object that can be set to value 1 for shallow references across objects, other values reserved.\n\n\n :param object_status: The object_status of this UserDefinedFunctionValidationSummary.\n :type: int\n '
self._object_status = object_status<|docstring|>Sets the object_status of this UserDefinedFunctionValidationSummary.
The status of an object that can be set to value 1 for shallow references across objects, other values reserved.
:param object_status: The object_status of this UserDefinedFunctionValidationSummary.
:type: int<|endoftext|>
|
be6b0e838b8e5b35eef63514901e2ee9d858ea0a93a5faa07d2fc074e0ad1dda
|
@property
def identifier(self):
'\n Gets the identifier of this UserDefinedFunctionValidationSummary.\n Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.\n\n\n :return: The identifier of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._identifier
|
Gets the identifier of this UserDefinedFunctionValidationSummary.
Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.
:return: The identifier of this UserDefinedFunctionValidationSummary.
:rtype: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
identifier
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def identifier(self):
'\n Gets the identifier of this UserDefinedFunctionValidationSummary.\n Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.\n\n\n :return: The identifier of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._identifier
|
@property
def identifier(self):
'\n Gets the identifier of this UserDefinedFunctionValidationSummary.\n Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.\n\n\n :return: The identifier of this UserDefinedFunctionValidationSummary.\n :rtype: str\n '
return self._identifier<|docstring|>Gets the identifier of this UserDefinedFunctionValidationSummary.
Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.
:return: The identifier of this UserDefinedFunctionValidationSummary.
:rtype: str<|endoftext|>
|
3d5cfecd38ea9e47d9b13829f8519c65636fc540673f64d84594ada7264c7528
|
@identifier.setter
def identifier(self, identifier):
'\n Sets the identifier of this UserDefinedFunctionValidationSummary.\n Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.\n\n\n :param identifier: The identifier of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._identifier = identifier
|
Sets the identifier of this UserDefinedFunctionValidationSummary.
Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.
:param identifier: The identifier of this UserDefinedFunctionValidationSummary.
:type: str
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
identifier
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@identifier.setter
def identifier(self, identifier):
'\n Sets the identifier of this UserDefinedFunctionValidationSummary.\n Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.\n\n\n :param identifier: The identifier of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._identifier = identifier
|
@identifier.setter
def identifier(self, identifier):
'\n Sets the identifier of this UserDefinedFunctionValidationSummary.\n Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.\n\n\n :param identifier: The identifier of this UserDefinedFunctionValidationSummary.\n :type: str\n '
self._identifier = identifier<|docstring|>Sets the identifier of this UserDefinedFunctionValidationSummary.
Value can only contain upper case letters, underscore, and numbers. It should begin with upper case letter or underscore. The value can be modified.
:param identifier: The identifier of this UserDefinedFunctionValidationSummary.
:type: str<|endoftext|>
|
e8f65a3b81cb79a439adf53aab6f94b46a5e39d3eeb4361b1c80f99b73089c99
|
@property
def metadata(self):
'\n Gets the metadata of this UserDefinedFunctionValidationSummary.\n\n :return: The metadata of this UserDefinedFunctionValidationSummary.\n :rtype: oci.data_integration.models.ObjectMetadata\n '
return self._metadata
|
Gets the metadata of this UserDefinedFunctionValidationSummary.
:return: The metadata of this UserDefinedFunctionValidationSummary.
:rtype: oci.data_integration.models.ObjectMetadata
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
metadata
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@property
def metadata(self):
'\n Gets the metadata of this UserDefinedFunctionValidationSummary.\n\n :return: The metadata of this UserDefinedFunctionValidationSummary.\n :rtype: oci.data_integration.models.ObjectMetadata\n '
return self._metadata
|
@property
def metadata(self):
'\n Gets the metadata of this UserDefinedFunctionValidationSummary.\n\n :return: The metadata of this UserDefinedFunctionValidationSummary.\n :rtype: oci.data_integration.models.ObjectMetadata\n '
return self._metadata<|docstring|>Gets the metadata of this UserDefinedFunctionValidationSummary.
:return: The metadata of this UserDefinedFunctionValidationSummary.
:rtype: oci.data_integration.models.ObjectMetadata<|endoftext|>
|
11b5de2f6113c45f3686dfad93a712f6b26fe0dc7a41e211330190da9eb30012
|
@metadata.setter
def metadata(self, metadata):
'\n Sets the metadata of this UserDefinedFunctionValidationSummary.\n\n :param metadata: The metadata of this UserDefinedFunctionValidationSummary.\n :type: oci.data_integration.models.ObjectMetadata\n '
self._metadata = metadata
|
Sets the metadata of this UserDefinedFunctionValidationSummary.
:param metadata: The metadata of this UserDefinedFunctionValidationSummary.
:type: oci.data_integration.models.ObjectMetadata
|
src/oci/data_integration/models/user_defined_function_validation_summary.py
|
metadata
|
ezequielramos/oci-python-sdk
| 249 |
python
|
@metadata.setter
def metadata(self, metadata):
'\n Sets the metadata of this UserDefinedFunctionValidationSummary.\n\n :param metadata: The metadata of this UserDefinedFunctionValidationSummary.\n :type: oci.data_integration.models.ObjectMetadata\n '
self._metadata = metadata
|
@metadata.setter
def metadata(self, metadata):
'\n Sets the metadata of this UserDefinedFunctionValidationSummary.\n\n :param metadata: The metadata of this UserDefinedFunctionValidationSummary.\n :type: oci.data_integration.models.ObjectMetadata\n '
self._metadata = metadata<|docstring|>Sets the metadata of this UserDefinedFunctionValidationSummary.
:param metadata: The metadata of this UserDefinedFunctionValidationSummary.
:type: oci.data_integration.models.ObjectMetadata<|endoftext|>
|
a11015e2c9ea8328fe67cfb6e2fa9b2c583e93a32ce66bb78d9b2e85d31ff620
|
@distributed_trace
def list(self, **kwargs: Any) -> Iterable['_models.FrontDoorListResult']:
'Lists all of the Front Doors within an Azure subscription.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either FrontDoorListResult or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
def prepare_request(next_link=None):
if (not next_link):
request = build_list_request(subscription_id=self._config.subscription_id, template_url=self.list.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_list_request(subscription_id=self._config.subscription_id, template_url=next_link)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = 'GET'
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('FrontDoorListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), iter(list_of_elem))
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)
|
Lists all of the Front Doors within an Azure subscription.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either FrontDoorListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/network/azure-mgmt-frontdoor/azure/mgmt/frontdoor/operations/_front_doors_operations.py
|
list
|
NateLehman/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def list(self, **kwargs: Any) -> Iterable['_models.FrontDoorListResult']:
'Lists all of the Front Doors within an Azure subscription.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either FrontDoorListResult or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
def prepare_request(next_link=None):
if (not next_link):
request = build_list_request(subscription_id=self._config.subscription_id, template_url=self.list.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_list_request(subscription_id=self._config.subscription_id, template_url=next_link)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = 'GET'
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('FrontDoorListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), iter(list_of_elem))
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)
|
@distributed_trace
def list(self, **kwargs: Any) -> Iterable['_models.FrontDoorListResult']:
'Lists all of the Front Doors within an Azure subscription.\n\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either FrontDoorListResult or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
def prepare_request(next_link=None):
if (not next_link):
request = build_list_request(subscription_id=self._config.subscription_id, template_url=self.list.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_list_request(subscription_id=self._config.subscription_id, template_url=next_link)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = 'GET'
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('FrontDoorListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), iter(list_of_elem))
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)<|docstring|>Lists all of the Front Doors within an Azure subscription.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either FrontDoorListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
337e6fb39484a1e59a6e04bdab472f93669a3ad3ed8a903b7f0845bfcaedff70
|
@distributed_trace
def list_by_resource_group(self, resource_group_name: str, **kwargs: Any) -> Iterable['_models.FrontDoorListResult']:
'Lists all of the Front Doors within a resource group under a subscription.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either FrontDoorListResult or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
def prepare_request(next_link=None):
if (not next_link):
request = build_list_by_resource_group_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, template_url=self.list_by_resource_group.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_list_by_resource_group_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, template_url=next_link)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = 'GET'
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('FrontDoorListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), iter(list_of_elem))
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)
|
Lists all of the Front Doors within a resource group under a subscription.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either FrontDoorListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/network/azure-mgmt-frontdoor/azure/mgmt/frontdoor/operations/_front_doors_operations.py
|
list_by_resource_group
|
NateLehman/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def list_by_resource_group(self, resource_group_name: str, **kwargs: Any) -> Iterable['_models.FrontDoorListResult']:
'Lists all of the Front Doors within a resource group under a subscription.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either FrontDoorListResult or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
def prepare_request(next_link=None):
if (not next_link):
request = build_list_by_resource_group_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, template_url=self.list_by_resource_group.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_list_by_resource_group_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, template_url=next_link)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = 'GET'
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('FrontDoorListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), iter(list_of_elem))
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)
|
@distributed_trace
def list_by_resource_group(self, resource_group_name: str, **kwargs: Any) -> Iterable['_models.FrontDoorListResult']:
'Lists all of the Front Doors within a resource group under a subscription.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either FrontDoorListResult or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
def prepare_request(next_link=None):
if (not next_link):
request = build_list_by_resource_group_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, template_url=self.list_by_resource_group.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_list_by_resource_group_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, template_url=next_link)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = 'GET'
return request
def extract_data(pipeline_response):
deserialized = self._deserialize('FrontDoorListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return ((deserialized.next_link or None), iter(list_of_elem))
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)<|docstring|>Lists all of the Front Doors within a resource group under a subscription.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either FrontDoorListResult or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.frontdoor.models.FrontDoorListResult]
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
ea8b7b95ba5d233a0cc2330a00ee3ab1c1ab34857e08d4d11ac887c5edebdac4
|
@distributed_trace
def get(self, resource_group_name: str, front_door_name: str, **kwargs: Any) -> '_models.FrontDoor':
'Gets a Front Door with the specified Front Door name under the specified subscription and\n resource group.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: FrontDoor, or the result of cls(response)\n :rtype: ~azure.mgmt.frontdoor.models.FrontDoor\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, front_door_name=front_door_name, template_url=self.get.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize('FrontDoor', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
|
Gets a Front Door with the specified Front Door name under the specified subscription and
resource group.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:param front_door_name: Name of the Front Door which is globally unique.
:type front_door_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FrontDoor, or the result of cls(response)
:rtype: ~azure.mgmt.frontdoor.models.FrontDoor
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/network/azure-mgmt-frontdoor/azure/mgmt/frontdoor/operations/_front_doors_operations.py
|
get
|
NateLehman/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def get(self, resource_group_name: str, front_door_name: str, **kwargs: Any) -> '_models.FrontDoor':
'Gets a Front Door with the specified Front Door name under the specified subscription and\n resource group.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: FrontDoor, or the result of cls(response)\n :rtype: ~azure.mgmt.frontdoor.models.FrontDoor\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, front_door_name=front_door_name, template_url=self.get.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize('FrontDoor', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
|
@distributed_trace
def get(self, resource_group_name: str, front_door_name: str, **kwargs: Any) -> '_models.FrontDoor':
'Gets a Front Door with the specified Front Door name under the specified subscription and\n resource group.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: FrontDoor, or the result of cls(response)\n :rtype: ~azure.mgmt.frontdoor.models.FrontDoor\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, front_door_name=front_door_name, template_url=self.get.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize('FrontDoor', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized<|docstring|>Gets a Front Door with the specified Front Door name under the specified subscription and
resource group.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:param front_door_name: Name of the Front Door which is globally unique.
:type front_door_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FrontDoor, or the result of cls(response)
:rtype: ~azure.mgmt.frontdoor.models.FrontDoor
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
e13e91e23ce8429029eedb6bac107a077b3faa28e1584ead91fc4395f22331ae
|
@distributed_trace
def begin_create_or_update(self, resource_group_name: str, front_door_name: str, front_door_parameters: '_models.FrontDoor', **kwargs: Any) -> LROPoller['_models.FrontDoor']:
'Creates a new Front Door with a Front Door name under the specified subscription and resource\n group.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :param front_door_parameters: Front Door properties needed to create a new Front Door.\n :type front_door_parameters: ~azure.mgmt.frontdoor.models.FrontDoor\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this\n operation to not poll, or pass in your own initialized polling object for a personal polling\n strategy.\n :paramtype polling: bool or ~azure.core.polling.PollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of LROPoller that returns either FrontDoor or the result of cls(response)\n :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.frontdoor.models.FrontDoor]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
content_type = kwargs.pop('content_type', 'application/json')
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = self._create_or_update_initial(resource_group_name=resource_group_name, front_door_name=front_door_name, front_door_parameters=front_door_parameters, content_type=content_type, cls=(lambda x, y, z: x), **kwargs)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
response = pipeline_response.http_response
deserialized = self._deserialize('FrontDoor', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if (polling is True):
polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs)
elif (polling is False):
polling_method = NoPolling()
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
|
Creates a new Front Door with a Front Door name under the specified subscription and resource
group.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:param front_door_name: Name of the Front Door which is globally unique.
:type front_door_name: str
:param front_door_parameters: Front Door properties needed to create a new Front Door.
:type front_door_parameters: ~azure.mgmt.frontdoor.models.FrontDoor
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
operation to not poll, or pass in your own initialized polling object for a personal polling
strategy.
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of LROPoller that returns either FrontDoor or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[~azure.mgmt.frontdoor.models.FrontDoor]
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/network/azure-mgmt-frontdoor/azure/mgmt/frontdoor/operations/_front_doors_operations.py
|
begin_create_or_update
|
NateLehman/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def begin_create_or_update(self, resource_group_name: str, front_door_name: str, front_door_parameters: '_models.FrontDoor', **kwargs: Any) -> LROPoller['_models.FrontDoor']:
'Creates a new Front Door with a Front Door name under the specified subscription and resource\n group.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :param front_door_parameters: Front Door properties needed to create a new Front Door.\n :type front_door_parameters: ~azure.mgmt.frontdoor.models.FrontDoor\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this\n operation to not poll, or pass in your own initialized polling object for a personal polling\n strategy.\n :paramtype polling: bool or ~azure.core.polling.PollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of LROPoller that returns either FrontDoor or the result of cls(response)\n :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.frontdoor.models.FrontDoor]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
content_type = kwargs.pop('content_type', 'application/json')
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = self._create_or_update_initial(resource_group_name=resource_group_name, front_door_name=front_door_name, front_door_parameters=front_door_parameters, content_type=content_type, cls=(lambda x, y, z: x), **kwargs)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
response = pipeline_response.http_response
deserialized = self._deserialize('FrontDoor', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if (polling is True):
polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs)
elif (polling is False):
polling_method = NoPolling()
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
|
@distributed_trace
def begin_create_or_update(self, resource_group_name: str, front_door_name: str, front_door_parameters: '_models.FrontDoor', **kwargs: Any) -> LROPoller['_models.FrontDoor']:
'Creates a new Front Door with a Front Door name under the specified subscription and resource\n group.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :param front_door_parameters: Front Door properties needed to create a new Front Door.\n :type front_door_parameters: ~azure.mgmt.frontdoor.models.FrontDoor\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this\n operation to not poll, or pass in your own initialized polling object for a personal polling\n strategy.\n :paramtype polling: bool or ~azure.core.polling.PollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of LROPoller that returns either FrontDoor or the result of cls(response)\n :rtype: ~azure.core.polling.LROPoller[~azure.mgmt.frontdoor.models.FrontDoor]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
content_type = kwargs.pop('content_type', 'application/json')
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = self._create_or_update_initial(resource_group_name=resource_group_name, front_door_name=front_door_name, front_door_parameters=front_door_parameters, content_type=content_type, cls=(lambda x, y, z: x), **kwargs)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
response = pipeline_response.http_response
deserialized = self._deserialize('FrontDoor', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if (polling is True):
polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs)
elif (polling is False):
polling_method = NoPolling()
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)<|docstring|>Creates a new Front Door with a Front Door name under the specified subscription and resource
group.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:param front_door_name: Name of the Front Door which is globally unique.
:type front_door_name: str
:param front_door_parameters: Front Door properties needed to create a new Front Door.
:type front_door_parameters: ~azure.mgmt.frontdoor.models.FrontDoor
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
operation to not poll, or pass in your own initialized polling object for a personal polling
strategy.
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of LROPoller that returns either FrontDoor or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[~azure.mgmt.frontdoor.models.FrontDoor]
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
352ba858e143ca1a47cebaccca2851eb1d35727d4485006252c7bf84c8fcd327
|
@distributed_trace
def begin_delete(self, resource_group_name: str, front_door_name: str, **kwargs: Any) -> LROPoller[None]:
'Deletes an existing Front Door with the specified parameters.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this\n operation to not poll, or pass in your own initialized polling object for a personal polling\n strategy.\n :paramtype polling: bool or ~azure.core.polling.PollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of LROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.LROPoller[None]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = self._delete_initial(resource_group_name=resource_group_name, front_door_name=front_door_name, cls=(lambda x, y, z: x), **kwargs)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if (polling is True):
polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs)
elif (polling is False):
polling_method = NoPolling()
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
|
Deletes an existing Front Door with the specified parameters.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:param front_door_name: Name of the Front Door which is globally unique.
:type front_door_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
operation to not poll, or pass in your own initialized polling object for a personal polling
strategy.
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of LROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[None]
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/network/azure-mgmt-frontdoor/azure/mgmt/frontdoor/operations/_front_doors_operations.py
|
begin_delete
|
NateLehman/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def begin_delete(self, resource_group_name: str, front_door_name: str, **kwargs: Any) -> LROPoller[None]:
'Deletes an existing Front Door with the specified parameters.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this\n operation to not poll, or pass in your own initialized polling object for a personal polling\n strategy.\n :paramtype polling: bool or ~azure.core.polling.PollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of LROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.LROPoller[None]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = self._delete_initial(resource_group_name=resource_group_name, front_door_name=front_door_name, cls=(lambda x, y, z: x), **kwargs)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if (polling is True):
polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs)
elif (polling is False):
polling_method = NoPolling()
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
|
@distributed_trace
def begin_delete(self, resource_group_name: str, front_door_name: str, **kwargs: Any) -> LROPoller[None]:
'Deletes an existing Front Door with the specified parameters.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :keyword str continuation_token: A continuation token to restart a poller from a saved state.\n :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this\n operation to not poll, or pass in your own initialized polling object for a personal polling\n strategy.\n :paramtype polling: bool or ~azure.core.polling.PollingMethod\n :keyword int polling_interval: Default waiting time between two polls for LRO operations if no\n Retry-After header is present.\n :return: An instance of LROPoller that returns either None or the result of cls(response)\n :rtype: ~azure.core.polling.LROPoller[None]\n :raises: ~azure.core.exceptions.HttpResponseError\n '
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop('polling_interval', self._config.polling_interval)
cont_token = kwargs.pop('continuation_token', None)
if (cont_token is None):
raw_result = self._delete_initial(resource_group_name=resource_group_name, front_door_name=front_door_name, cls=(lambda x, y, z: x), **kwargs)
kwargs.pop('error_map', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
if (polling is True):
polling_method = ARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs)
elif (polling is False):
polling_method = NoPolling()
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output)
else:
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)<|docstring|>Deletes an existing Front Door with the specified parameters.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:param front_door_name: Name of the Front Door which is globally unique.
:type front_door_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
operation to not poll, or pass in your own initialized polling object for a personal polling
strategy.
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of LROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[None]
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
ba06a7e9798a32824ce3b1af6d3a06e0824e01d3523ebcca9695faf64aa7ba1d
|
@distributed_trace
def validate_custom_domain(self, resource_group_name: str, front_door_name: str, custom_domain_properties: '_models.ValidateCustomDomainInput', **kwargs: Any) -> '_models.ValidateCustomDomainOutput':
'Validates the custom domain mapping to ensure it maps to the correct Front Door endpoint in\n DNS.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :param custom_domain_properties: Custom domain to be validated.\n :type custom_domain_properties: ~azure.mgmt.frontdoor.models.ValidateCustomDomainInput\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: ValidateCustomDomainOutput, or the result of cls(response)\n :rtype: ~azure.mgmt.frontdoor.models.ValidateCustomDomainOutput\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', 'application/json')
_json = self._serialize.body(custom_domain_properties, 'ValidateCustomDomainInput')
request = build_validate_custom_domain_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, front_door_name=front_door_name, content_type=content_type, json=_json, template_url=self.validate_custom_domain.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize('ValidateCustomDomainOutput', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
|
Validates the custom domain mapping to ensure it maps to the correct Front Door endpoint in
DNS.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:param front_door_name: Name of the Front Door which is globally unique.
:type front_door_name: str
:param custom_domain_properties: Custom domain to be validated.
:type custom_domain_properties: ~azure.mgmt.frontdoor.models.ValidateCustomDomainInput
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ValidateCustomDomainOutput, or the result of cls(response)
:rtype: ~azure.mgmt.frontdoor.models.ValidateCustomDomainOutput
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/network/azure-mgmt-frontdoor/azure/mgmt/frontdoor/operations/_front_doors_operations.py
|
validate_custom_domain
|
NateLehman/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def validate_custom_domain(self, resource_group_name: str, front_door_name: str, custom_domain_properties: '_models.ValidateCustomDomainInput', **kwargs: Any) -> '_models.ValidateCustomDomainOutput':
'Validates the custom domain mapping to ensure it maps to the correct Front Door endpoint in\n DNS.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :param custom_domain_properties: Custom domain to be validated.\n :type custom_domain_properties: ~azure.mgmt.frontdoor.models.ValidateCustomDomainInput\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: ValidateCustomDomainOutput, or the result of cls(response)\n :rtype: ~azure.mgmt.frontdoor.models.ValidateCustomDomainOutput\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', 'application/json')
_json = self._serialize.body(custom_domain_properties, 'ValidateCustomDomainInput')
request = build_validate_custom_domain_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, front_door_name=front_door_name, content_type=content_type, json=_json, template_url=self.validate_custom_domain.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize('ValidateCustomDomainOutput', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
|
@distributed_trace
def validate_custom_domain(self, resource_group_name: str, front_door_name: str, custom_domain_properties: '_models.ValidateCustomDomainInput', **kwargs: Any) -> '_models.ValidateCustomDomainOutput':
'Validates the custom domain mapping to ensure it maps to the correct Front Door endpoint in\n DNS.\n\n :param resource_group_name: Name of the Resource group within the Azure subscription.\n :type resource_group_name: str\n :param front_door_name: Name of the Front Door which is globally unique.\n :type front_door_name: str\n :param custom_domain_properties: Custom domain to be validated.\n :type custom_domain_properties: ~azure.mgmt.frontdoor.models.ValidateCustomDomainInput\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: ValidateCustomDomainOutput, or the result of cls(response)\n :rtype: ~azure.mgmt.frontdoor.models.ValidateCustomDomainOutput\n :raises: ~azure.core.exceptions.HttpResponseError\n '
cls = kwargs.pop('cls', None)
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', 'application/json')
_json = self._serialize.body(custom_domain_properties, 'ValidateCustomDomainInput')
request = build_validate_custom_domain_request(subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, front_door_name=front_door_name, content_type=content_type, json=_json, template_url=self.validate_custom_domain.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if (response.status_code not in [200]):
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize('ValidateCustomDomainOutput', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized<|docstring|>Validates the custom domain mapping to ensure it maps to the correct Front Door endpoint in
DNS.
:param resource_group_name: Name of the Resource group within the Azure subscription.
:type resource_group_name: str
:param front_door_name: Name of the Front Door which is globally unique.
:type front_door_name: str
:param custom_domain_properties: Custom domain to be validated.
:type custom_domain_properties: ~azure.mgmt.frontdoor.models.ValidateCustomDomainInput
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ValidateCustomDomainOutput, or the result of cls(response)
:rtype: ~azure.mgmt.frontdoor.models.ValidateCustomDomainOutput
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
dc92024fdb3ab3b39b49f23078948c4e1c598ca9b360a1de9380dd03013bcacd
|
def run_ridge(data, subjects_data, cv=10, alphas=None, train_sizes=None, n_jobs=None):
'Run ridge resgression.\n\n Parameters\n ----------\n data : pandas.DataFrame\n Features to be used for predictions.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
subjects = data.index.values
y = subjects_data.loc[subjects]['age']
reg = make_pipeline(StandardScaler(), RidgeCV(alphas))
cv = check_cv(cv)
index = data.index
mae = list()
r2 = list()
df_pred = pd.DataFrame(columns=['fold', 'y_pred'], index=subjects, dtype=float)
for (ii, (train, test)) in enumerate(cv.split(data.values)):
train_mask = (~ data.loc[index[train]].isna().values[(:, 0)])
test_mask = (~ data.loc[index[test]].isna().values[(:, 0)])
X_train = data.loc[index[train]].values[train_mask]
X_test = data.loc[index[test]].values[test_mask]
y_train = y.loc[index[train]].values[train_mask]
y_test = y.loc[index[test]].values[test_mask]
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)
mae.append((- mean_absolute_error(y_true=y_test, y_pred=y_pred)))
r2.append(r2_score(y_true=y_test, y_pred=y_pred))
df_pred.loc[(index[test[test_mask]], 'y_pred')] = y_pred
df_pred.loc[(index[test], 'fold')] = ii
mask = (~ data.isna().values[(:, 0)])
(train_sizes, train_scores, test_scores) = learning_curve(reg, data[mask].values, y[mask].values, cv=cv, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
return (df_pred, np.array(mae), np.array(r2), train_sizes, train_scores, test_scores)
|
Run ridge resgression.
Parameters
----------
data : pandas.DataFrame
Features to be used for predictions.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
|
camcan/utils/evaluation.py
|
run_ridge
|
dengemann/engemann-2020-multimodal-brain-age
| 6 |
python
|
def run_ridge(data, subjects_data, cv=10, alphas=None, train_sizes=None, n_jobs=None):
'Run ridge resgression.\n\n Parameters\n ----------\n data : pandas.DataFrame\n Features to be used for predictions.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
subjects = data.index.values
y = subjects_data.loc[subjects]['age']
reg = make_pipeline(StandardScaler(), RidgeCV(alphas))
cv = check_cv(cv)
index = data.index
mae = list()
r2 = list()
df_pred = pd.DataFrame(columns=['fold', 'y_pred'], index=subjects, dtype=float)
for (ii, (train, test)) in enumerate(cv.split(data.values)):
train_mask = (~ data.loc[index[train]].isna().values[(:, 0)])
test_mask = (~ data.loc[index[test]].isna().values[(:, 0)])
X_train = data.loc[index[train]].values[train_mask]
X_test = data.loc[index[test]].values[test_mask]
y_train = y.loc[index[train]].values[train_mask]
y_test = y.loc[index[test]].values[test_mask]
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)
mae.append((- mean_absolute_error(y_true=y_test, y_pred=y_pred)))
r2.append(r2_score(y_true=y_test, y_pred=y_pred))
df_pred.loc[(index[test[test_mask]], 'y_pred')] = y_pred
df_pred.loc[(index[test], 'fold')] = ii
mask = (~ data.isna().values[(:, 0)])
(train_sizes, train_scores, test_scores) = learning_curve(reg, data[mask].values, y[mask].values, cv=cv, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
return (df_pred, np.array(mae), np.array(r2), train_sizes, train_scores, test_scores)
|
def run_ridge(data, subjects_data, cv=10, alphas=None, train_sizes=None, n_jobs=None):
'Run ridge resgression.\n\n Parameters\n ----------\n data : pandas.DataFrame\n Features to be used for predictions.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
subjects = data.index.values
y = subjects_data.loc[subjects]['age']
reg = make_pipeline(StandardScaler(), RidgeCV(alphas))
cv = check_cv(cv)
index = data.index
mae = list()
r2 = list()
df_pred = pd.DataFrame(columns=['fold', 'y_pred'], index=subjects, dtype=float)
for (ii, (train, test)) in enumerate(cv.split(data.values)):
train_mask = (~ data.loc[index[train]].isna().values[(:, 0)])
test_mask = (~ data.loc[index[test]].isna().values[(:, 0)])
X_train = data.loc[index[train]].values[train_mask]
X_test = data.loc[index[test]].values[test_mask]
y_train = y.loc[index[train]].values[train_mask]
y_test = y.loc[index[test]].values[test_mask]
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)
mae.append((- mean_absolute_error(y_true=y_test, y_pred=y_pred)))
r2.append(r2_score(y_true=y_test, y_pred=y_pred))
df_pred.loc[(index[test[test_mask]], 'y_pred')] = y_pred
df_pred.loc[(index[test], 'fold')] = ii
mask = (~ data.isna().values[(:, 0)])
(train_sizes, train_scores, test_scores) = learning_curve(reg, data[mask].values, y[mask].values, cv=cv, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
return (df_pred, np.array(mae), np.array(r2), train_sizes, train_scores, test_scores)<|docstring|>Run ridge resgression.
Parameters
----------
data : pandas.DataFrame
Features to be used for predictions.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.<|endoftext|>
|
7d6081ffda9dd50acc82013cd574ee11885aa0356eb4b99b86edd3b01267b9c0
|
def run_meg_spoc(data, subjects_data, cv=10, alphas=None, train_sizes=None, fbands=None, n_jobs=None):
'Run ridge resgression on MEG data with SPoC.\n\n Parameters\n ----------\n data : pandas.DataFrame\n Features to be used for predictions.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested using RidgeCV. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n fbands : [(float, float)]\n List of frequency bands to be checked with SPoC.\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
data_path = sample.data_path()
raw_fname = path.join(data_path, 'MEG/sample/sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(raw_fname)
info = raw.info
picks = mne.pick_types(info, meg='mag')
subjects = [d['subject'] for d in data if ('subject' in d)]
covs = np.array(tuple((d['covs'][(:, picks)][(:, :, picks)] for d in data if ('subject' in d))))
y = subjects_data.loc[subjects].age.values
X = np.arange(len(y))
spoc = SPoC(covs=covs, fbands=fbands, spoc=True, n_components=len(picks), alpha=0.01)
reg = make_pipeline(spoc, StandardScaler(), RidgeCV(alphas))
cv_ss = ShuffleSplit(n_splits=cv, random_state=42)
mae = cross_val_score(reg, X, y, scoring='neg_mean_absolute_error', cv=cv_ss, n_jobs=n_jobs)
r2 = cross_val_score(reg, X, y, scoring='r2', cv=cv_ss, n_jobs=n_jobs)
y_pred = cross_val_predict(reg, X, y, cv=cv, n_jobs=n_jobs)
(train_sizes, train_scores, test_scores) = learning_curve(reg, X, y, cv=cv_ss, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
df_pred = pd.DataFrame(y_pred, index=subjects, dtype=float)
return (df_pred, mae, r2, train_sizes, train_scores, test_scores)
|
Run ridge resgression on MEG data with SPoC.
Parameters
----------
data : pandas.DataFrame
Features to be used for predictions.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested using RidgeCV. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
fbands : [(float, float)]
List of frequency bands to be checked with SPoC.
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
|
camcan/utils/evaluation.py
|
run_meg_spoc
|
dengemann/engemann-2020-multimodal-brain-age
| 6 |
python
|
def run_meg_spoc(data, subjects_data, cv=10, alphas=None, train_sizes=None, fbands=None, n_jobs=None):
'Run ridge resgression on MEG data with SPoC.\n\n Parameters\n ----------\n data : pandas.DataFrame\n Features to be used for predictions.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested using RidgeCV. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n fbands : [(float, float)]\n List of frequency bands to be checked with SPoC.\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
data_path = sample.data_path()
raw_fname = path.join(data_path, 'MEG/sample/sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(raw_fname)
info = raw.info
picks = mne.pick_types(info, meg='mag')
subjects = [d['subject'] for d in data if ('subject' in d)]
covs = np.array(tuple((d['covs'][(:, picks)][(:, :, picks)] for d in data if ('subject' in d))))
y = subjects_data.loc[subjects].age.values
X = np.arange(len(y))
spoc = SPoC(covs=covs, fbands=fbands, spoc=True, n_components=len(picks), alpha=0.01)
reg = make_pipeline(spoc, StandardScaler(), RidgeCV(alphas))
cv_ss = ShuffleSplit(n_splits=cv, random_state=42)
mae = cross_val_score(reg, X, y, scoring='neg_mean_absolute_error', cv=cv_ss, n_jobs=n_jobs)
r2 = cross_val_score(reg, X, y, scoring='r2', cv=cv_ss, n_jobs=n_jobs)
y_pred = cross_val_predict(reg, X, y, cv=cv, n_jobs=n_jobs)
(train_sizes, train_scores, test_scores) = learning_curve(reg, X, y, cv=cv_ss, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
df_pred = pd.DataFrame(y_pred, index=subjects, dtype=float)
return (df_pred, mae, r2, train_sizes, train_scores, test_scores)
|
def run_meg_spoc(data, subjects_data, cv=10, alphas=None, train_sizes=None, fbands=None, n_jobs=None):
'Run ridge resgression on MEG data with SPoC.\n\n Parameters\n ----------\n data : pandas.DataFrame\n Features to be used for predictions.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested using RidgeCV. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n fbands : [(float, float)]\n List of frequency bands to be checked with SPoC.\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
data_path = sample.data_path()
raw_fname = path.join(data_path, 'MEG/sample/sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(raw_fname)
info = raw.info
picks = mne.pick_types(info, meg='mag')
subjects = [d['subject'] for d in data if ('subject' in d)]
covs = np.array(tuple((d['covs'][(:, picks)][(:, :, picks)] for d in data if ('subject' in d))))
y = subjects_data.loc[subjects].age.values
X = np.arange(len(y))
spoc = SPoC(covs=covs, fbands=fbands, spoc=True, n_components=len(picks), alpha=0.01)
reg = make_pipeline(spoc, StandardScaler(), RidgeCV(alphas))
cv_ss = ShuffleSplit(n_splits=cv, random_state=42)
mae = cross_val_score(reg, X, y, scoring='neg_mean_absolute_error', cv=cv_ss, n_jobs=n_jobs)
r2 = cross_val_score(reg, X, y, scoring='r2', cv=cv_ss, n_jobs=n_jobs)
y_pred = cross_val_predict(reg, X, y, cv=cv, n_jobs=n_jobs)
(train_sizes, train_scores, test_scores) = learning_curve(reg, X, y, cv=cv_ss, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
df_pred = pd.DataFrame(y_pred, index=subjects, dtype=float)
return (df_pred, mae, r2, train_sizes, train_scores, test_scores)<|docstring|>Run ridge resgression on MEG data with SPoC.
Parameters
----------
data : pandas.DataFrame
Features to be used for predictions.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested using RidgeCV. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
fbands : [(float, float)]
List of frequency bands to be checked with SPoC.
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.<|endoftext|>
|
9c26592accc4ada56031d2e2ee96d969cf8b3b3afe53de07ec9879b69a69cc88
|
def run_stacking_spoc(named_data, subjects_data, cv=10, alphas=None, train_sizes=None, fbands=None, n_jobs=None):
'Run stacking.\n\n Parameters\n ----------\n named_data : list(tuple(str, pandas.DataFrame))\n List of tuples (name, data) with name and corresponding features\n to be used for predictions by linear models.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n fbands : [(float, float)]\n List of frequency bands to be checked with SPoC.\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
rnd_state = 42
names = []
combined_data = []
meg_data = None
for (name, data) in named_data:
names.append(name)
if (name == 'meg'):
meg_data = data
meg_subjects = tuple((d['subject'] for d in data if ('subject' in d)))
pseudo_data = np.arange(len(meg_subjects))
combined_data.append(pd.DataFrame(pseudo_data, index=meg_subjects))
else:
combined_data.append(data)
data = pd.concat(combined_data, axis=1, join='inner')
feature_col_lens = tuple((d.shape[1] for d in combined_data))
estimators = []
subjects = data.index.values
for (i_data, _) in enumerate(named_data):
feature_transformers = []
ft_begin = 0
ft_end = 0
for (i_ct, (name, col_len)) in enumerate(zip(names, feature_col_lens)):
trans_name = (('pass_' if (i_data == i_ct) else 'drop_') + name)
transformer = ('passthrough' if (i_data == i_ct) else 'drop')
ft_end = (ft_end + col_len)
trans_slice = slice(ft_begin, ft_end)
ft_begin = (ft_begin + col_len)
feature_transformers.append((trans_name, transformer, trans_slice))
est_name = ('reg_' + named_data[i_data][0])
if (est_name == 'reg_meg'):
if (fbands is None):
raise ValueError('fbands should be given for MEG classifier.')
data_path = sample.data_path()
raw_fname = path.join(data_path, 'MEG/sample', 'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(raw_fname)
info = raw.info
picks = mne.pick_types(info, meg='mag')
covs = np.array(tuple((d['covs'][(:, picks)][(:, :, picks)] for d in meg_data if ('subject' in d))))
spoc = SPoC(covs=covs, fbands=fbands, spoc=True, n_components=len(picks), alpha=0.01)
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), spoc, StandardScaler(), RidgeCV(alphas))
else:
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), StandardScaler(), RidgeCV(alphas))
estimators.append((est_name, est_pipeline))
final_estimator = RandomForestRegressor(n_estimators=100, random_state=rnd_state, oob_score=True)
reg = StackingRegressor(estimators=estimators, final_estimator=final_estimator, cv=cv, random_state=rnd_state, n_jobs=n_jobs)
y = subjects_data.loc[subjects].age.values
X = data.values
cv = check_cv(cv)
mae = cross_val_score(reg, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=n_jobs)
r2 = cross_val_score(reg, X, y, scoring='r2', cv=cv, n_jobs=n_jobs)
y_pred = cross_val_predict(reg, X, y, cv=cv, n_jobs=n_jobs)
(train_sizes, train_scores, test_scores) = learning_curve(reg, X, y, cv=cv, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
fold = _get_fold_indices(cv, X, y)
df_pred = pd.DataFrame(dict(y=y_pred, fold=fold), index=subjects, dtype=float)
return (df_pred, mae, r2, train_sizes, train_scores, test_scores)
|
Run stacking.
Parameters
----------
named_data : list(tuple(str, pandas.DataFrame))
List of tuples (name, data) with name and corresponding features
to be used for predictions by linear models.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
fbands : [(float, float)]
List of frequency bands to be checked with SPoC.
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
|
camcan/utils/evaluation.py
|
run_stacking_spoc
|
dengemann/engemann-2020-multimodal-brain-age
| 6 |
python
|
def run_stacking_spoc(named_data, subjects_data, cv=10, alphas=None, train_sizes=None, fbands=None, n_jobs=None):
'Run stacking.\n\n Parameters\n ----------\n named_data : list(tuple(str, pandas.DataFrame))\n List of tuples (name, data) with name and corresponding features\n to be used for predictions by linear models.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n fbands : [(float, float)]\n List of frequency bands to be checked with SPoC.\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
rnd_state = 42
names = []
combined_data = []
meg_data = None
for (name, data) in named_data:
names.append(name)
if (name == 'meg'):
meg_data = data
meg_subjects = tuple((d['subject'] for d in data if ('subject' in d)))
pseudo_data = np.arange(len(meg_subjects))
combined_data.append(pd.DataFrame(pseudo_data, index=meg_subjects))
else:
combined_data.append(data)
data = pd.concat(combined_data, axis=1, join='inner')
feature_col_lens = tuple((d.shape[1] for d in combined_data))
estimators = []
subjects = data.index.values
for (i_data, _) in enumerate(named_data):
feature_transformers = []
ft_begin = 0
ft_end = 0
for (i_ct, (name, col_len)) in enumerate(zip(names, feature_col_lens)):
trans_name = (('pass_' if (i_data == i_ct) else 'drop_') + name)
transformer = ('passthrough' if (i_data == i_ct) else 'drop')
ft_end = (ft_end + col_len)
trans_slice = slice(ft_begin, ft_end)
ft_begin = (ft_begin + col_len)
feature_transformers.append((trans_name, transformer, trans_slice))
est_name = ('reg_' + named_data[i_data][0])
if (est_name == 'reg_meg'):
if (fbands is None):
raise ValueError('fbands should be given for MEG classifier.')
data_path = sample.data_path()
raw_fname = path.join(data_path, 'MEG/sample', 'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(raw_fname)
info = raw.info
picks = mne.pick_types(info, meg='mag')
covs = np.array(tuple((d['covs'][(:, picks)][(:, :, picks)] for d in meg_data if ('subject' in d))))
spoc = SPoC(covs=covs, fbands=fbands, spoc=True, n_components=len(picks), alpha=0.01)
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), spoc, StandardScaler(), RidgeCV(alphas))
else:
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), StandardScaler(), RidgeCV(alphas))
estimators.append((est_name, est_pipeline))
final_estimator = RandomForestRegressor(n_estimators=100, random_state=rnd_state, oob_score=True)
reg = StackingRegressor(estimators=estimators, final_estimator=final_estimator, cv=cv, random_state=rnd_state, n_jobs=n_jobs)
y = subjects_data.loc[subjects].age.values
X = data.values
cv = check_cv(cv)
mae = cross_val_score(reg, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=n_jobs)
r2 = cross_val_score(reg, X, y, scoring='r2', cv=cv, n_jobs=n_jobs)
y_pred = cross_val_predict(reg, X, y, cv=cv, n_jobs=n_jobs)
(train_sizes, train_scores, test_scores) = learning_curve(reg, X, y, cv=cv, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
fold = _get_fold_indices(cv, X, y)
df_pred = pd.DataFrame(dict(y=y_pred, fold=fold), index=subjects, dtype=float)
return (df_pred, mae, r2, train_sizes, train_scores, test_scores)
|
def run_stacking_spoc(named_data, subjects_data, cv=10, alphas=None, train_sizes=None, fbands=None, n_jobs=None):
'Run stacking.\n\n Parameters\n ----------\n named_data : list(tuple(str, pandas.DataFrame))\n List of tuples (name, data) with name and corresponding features\n to be used for predictions by linear models.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n fbands : [(float, float)]\n List of frequency bands to be checked with SPoC.\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
rnd_state = 42
names = []
combined_data = []
meg_data = None
for (name, data) in named_data:
names.append(name)
if (name == 'meg'):
meg_data = data
meg_subjects = tuple((d['subject'] for d in data if ('subject' in d)))
pseudo_data = np.arange(len(meg_subjects))
combined_data.append(pd.DataFrame(pseudo_data, index=meg_subjects))
else:
combined_data.append(data)
data = pd.concat(combined_data, axis=1, join='inner')
feature_col_lens = tuple((d.shape[1] for d in combined_data))
estimators = []
subjects = data.index.values
for (i_data, _) in enumerate(named_data):
feature_transformers = []
ft_begin = 0
ft_end = 0
for (i_ct, (name, col_len)) in enumerate(zip(names, feature_col_lens)):
trans_name = (('pass_' if (i_data == i_ct) else 'drop_') + name)
transformer = ('passthrough' if (i_data == i_ct) else 'drop')
ft_end = (ft_end + col_len)
trans_slice = slice(ft_begin, ft_end)
ft_begin = (ft_begin + col_len)
feature_transformers.append((trans_name, transformer, trans_slice))
est_name = ('reg_' + named_data[i_data][0])
if (est_name == 'reg_meg'):
if (fbands is None):
raise ValueError('fbands should be given for MEG classifier.')
data_path = sample.data_path()
raw_fname = path.join(data_path, 'MEG/sample', 'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(raw_fname)
info = raw.info
picks = mne.pick_types(info, meg='mag')
covs = np.array(tuple((d['covs'][(:, picks)][(:, :, picks)] for d in meg_data if ('subject' in d))))
spoc = SPoC(covs=covs, fbands=fbands, spoc=True, n_components=len(picks), alpha=0.01)
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), spoc, StandardScaler(), RidgeCV(alphas))
else:
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), StandardScaler(), RidgeCV(alphas))
estimators.append((est_name, est_pipeline))
final_estimator = RandomForestRegressor(n_estimators=100, random_state=rnd_state, oob_score=True)
reg = StackingRegressor(estimators=estimators, final_estimator=final_estimator, cv=cv, random_state=rnd_state, n_jobs=n_jobs)
y = subjects_data.loc[subjects].age.values
X = data.values
cv = check_cv(cv)
mae = cross_val_score(reg, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=n_jobs)
r2 = cross_val_score(reg, X, y, scoring='r2', cv=cv, n_jobs=n_jobs)
y_pred = cross_val_predict(reg, X, y, cv=cv, n_jobs=n_jobs)
(train_sizes, train_scores, test_scores) = learning_curve(reg, X, y, cv=cv, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
fold = _get_fold_indices(cv, X, y)
df_pred = pd.DataFrame(dict(y=y_pred, fold=fold), index=subjects, dtype=float)
return (df_pred, mae, r2, train_sizes, train_scores, test_scores)<|docstring|>Run stacking.
Parameters
----------
named_data : list(tuple(str, pandas.DataFrame))
List of tuples (name, data) with name and corresponding features
to be used for predictions by linear models.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
fbands : [(float, float)]
List of frequency bands to be checked with SPoC.
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.<|endoftext|>
|
583b8919539f3c315cd6a64036cffe6ab6eb57d7e5189a5e4afaf9d070a67dc3
|
def run_stacking(named_data, subjects_data, cv=10, alphas=None, train_sizes=None, n_jobs=None):
'Run stacking.\n\n Parameters\n ----------\n named_data : list(tuple(str, pandas.DataFrame))\n List of tuples (name, data) with name and corresponding features\n to be used for predictions by linear models.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
rnd_state = 42
names = []
combined_data = []
for (name, data) in named_data:
names.append(name)
combined_data.append(data)
data = pd.concat(combined_data, axis=1, join='inner')
feature_col_lens = tuple((d.shape[1] for d in combined_data))
estimators = []
subjects = data.index.values
for (i_data, _) in enumerate(named_data):
feature_transformers = []
ft_begin = 0
ft_end = 0
for (i_ct, (name, col_len)) in enumerate(zip(names, feature_col_lens)):
trans_name = (('pass_' if (i_data == i_ct) else 'drop_') + name)
transformer = ('passthrough' if (i_data == i_ct) else 'drop')
ft_end = (ft_end + col_len)
trans_slice = slice(ft_begin, ft_end)
ft_begin = (ft_begin + col_len)
feature_transformers.append((trans_name, transformer, trans_slice))
est_name = ('reg_' + named_data[i_data][0])
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), StandardScaler(), RidgeCV(alphas))
estimators.append((est_name, est_pipeline))
final_estimator = RandomForestRegressor(n_estimators=100, random_state=rnd_state, oob_score=True)
reg = StackingRegressor(estimators=estimators, final_estimator=final_estimator, cv=cv, random_state=rnd_state, n_jobs=n_jobs)
y = subjects_data.loc[subjects].age.values
X = data.values
cv = check_cv(cv)
mae = cross_val_score(reg, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=n_jobs)
r2 = cross_val_score(reg, X, y, scoring='r2', cv=cv, n_jobs=n_jobs)
y_pred = cross_val_predict(reg, X, y, cv=cv, n_jobs=n_jobs)
(train_sizes, train_scores, test_scores) = learning_curve(reg, X, y, cv=cv, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
fold = _get_fold_indices(cv, X, y)
df_pred = pd.DataFrame(dict(y=y_pred, fold=fold), index=subjects, dtype=float)
return (df_pred, mae, r2, train_sizes, train_scores, test_scores)
|
Run stacking.
Parameters
----------
named_data : list(tuple(str, pandas.DataFrame))
List of tuples (name, data) with name and corresponding features
to be used for predictions by linear models.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
|
camcan/utils/evaluation.py
|
run_stacking
|
dengemann/engemann-2020-multimodal-brain-age
| 6 |
python
|
def run_stacking(named_data, subjects_data, cv=10, alphas=None, train_sizes=None, n_jobs=None):
'Run stacking.\n\n Parameters\n ----------\n named_data : list(tuple(str, pandas.DataFrame))\n List of tuples (name, data) with name and corresponding features\n to be used for predictions by linear models.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
rnd_state = 42
names = []
combined_data = []
for (name, data) in named_data:
names.append(name)
combined_data.append(data)
data = pd.concat(combined_data, axis=1, join='inner')
feature_col_lens = tuple((d.shape[1] for d in combined_data))
estimators = []
subjects = data.index.values
for (i_data, _) in enumerate(named_data):
feature_transformers = []
ft_begin = 0
ft_end = 0
for (i_ct, (name, col_len)) in enumerate(zip(names, feature_col_lens)):
trans_name = (('pass_' if (i_data == i_ct) else 'drop_') + name)
transformer = ('passthrough' if (i_data == i_ct) else 'drop')
ft_end = (ft_end + col_len)
trans_slice = slice(ft_begin, ft_end)
ft_begin = (ft_begin + col_len)
feature_transformers.append((trans_name, transformer, trans_slice))
est_name = ('reg_' + named_data[i_data][0])
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), StandardScaler(), RidgeCV(alphas))
estimators.append((est_name, est_pipeline))
final_estimator = RandomForestRegressor(n_estimators=100, random_state=rnd_state, oob_score=True)
reg = StackingRegressor(estimators=estimators, final_estimator=final_estimator, cv=cv, random_state=rnd_state, n_jobs=n_jobs)
y = subjects_data.loc[subjects].age.values
X = data.values
cv = check_cv(cv)
mae = cross_val_score(reg, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=n_jobs)
r2 = cross_val_score(reg, X, y, scoring='r2', cv=cv, n_jobs=n_jobs)
y_pred = cross_val_predict(reg, X, y, cv=cv, n_jobs=n_jobs)
(train_sizes, train_scores, test_scores) = learning_curve(reg, X, y, cv=cv, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
fold = _get_fold_indices(cv, X, y)
df_pred = pd.DataFrame(dict(y=y_pred, fold=fold), index=subjects, dtype=float)
return (df_pred, mae, r2, train_sizes, train_scores, test_scores)
|
def run_stacking(named_data, subjects_data, cv=10, alphas=None, train_sizes=None, n_jobs=None):
'Run stacking.\n\n Parameters\n ----------\n named_data : list(tuple(str, pandas.DataFrame))\n List of tuples (name, data) with name and corresponding features\n to be used for predictions by linear models.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
rnd_state = 42
names = []
combined_data = []
for (name, data) in named_data:
names.append(name)
combined_data.append(data)
data = pd.concat(combined_data, axis=1, join='inner')
feature_col_lens = tuple((d.shape[1] for d in combined_data))
estimators = []
subjects = data.index.values
for (i_data, _) in enumerate(named_data):
feature_transformers = []
ft_begin = 0
ft_end = 0
for (i_ct, (name, col_len)) in enumerate(zip(names, feature_col_lens)):
trans_name = (('pass_' if (i_data == i_ct) else 'drop_') + name)
transformer = ('passthrough' if (i_data == i_ct) else 'drop')
ft_end = (ft_end + col_len)
trans_slice = slice(ft_begin, ft_end)
ft_begin = (ft_begin + col_len)
feature_transformers.append((trans_name, transformer, trans_slice))
est_name = ('reg_' + named_data[i_data][0])
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), StandardScaler(), RidgeCV(alphas))
estimators.append((est_name, est_pipeline))
final_estimator = RandomForestRegressor(n_estimators=100, random_state=rnd_state, oob_score=True)
reg = StackingRegressor(estimators=estimators, final_estimator=final_estimator, cv=cv, random_state=rnd_state, n_jobs=n_jobs)
y = subjects_data.loc[subjects].age.values
X = data.values
cv = check_cv(cv)
mae = cross_val_score(reg, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=n_jobs)
r2 = cross_val_score(reg, X, y, scoring='r2', cv=cv, n_jobs=n_jobs)
y_pred = cross_val_predict(reg, X, y, cv=cv, n_jobs=n_jobs)
(train_sizes, train_scores, test_scores) = learning_curve(reg, X, y, cv=cv, train_sizes=train_sizes, scoring='neg_mean_absolute_error', n_jobs=n_jobs)
fold = _get_fold_indices(cv, X, y)
df_pred = pd.DataFrame(dict(y=y_pred, fold=fold), index=subjects, dtype=float)
return (df_pred, mae, r2, train_sizes, train_scores, test_scores)<|docstring|>Run stacking.
Parameters
----------
named_data : list(tuple(str, pandas.DataFrame))
List of tuples (name, data) with name and corresponding features
to be used for predictions by linear models.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.<|endoftext|>
|
af4f16d0d3d1e4fc2f3aa216def6841937b95be0510b78586ef8464636765e9d
|
def train_stacked_regressor(named_data, subjects_data, cv=10, alphas=None, train_sizes=None, n_jobs=None):
'Return stacked classifier trained on provided data.\n\n For MEG data features estimated in the source space should be provided.\n\n Parameters\n ----------\n named_data : list(tuple(str, pandas.DataFrame))\n List of tuples (name, data) with name and corresponding features\n to be used for predictions by linear models.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
rnd_state = 42
names = []
combined_data = []
for (name, data) in named_data:
names.append(name)
combined_data.append(data)
data = pd.concat(combined_data, axis=1, join='inner')
feature_col_lens = tuple((d.shape[1] for d in combined_data))
estimators = []
subjects = data.index.values
for (i_data, _) in enumerate(named_data):
feature_transformers = []
ft_begin = 0
ft_end = 0
for (i_ct, (name, col_len)) in enumerate(zip(names, feature_col_lens)):
trans_name = (('pass_' if (i_data == i_ct) else 'drop_') + name)
transformer = ('passthrough' if (i_data == i_ct) else 'drop')
ft_end = (ft_end + col_len)
trans_slice = slice(ft_begin, ft_end)
ft_begin = (ft_begin + col_len)
feature_transformers.append((trans_name, transformer, trans_slice))
est_name = ('reg_' + named_data[i_data][0])
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), StandardScaler(), RidgeCV(alphas))
estimators.append((est_name, est_pipeline))
final_estimator = RandomForestRegressor(n_estimators=100, random_state=rnd_state, oob_score=True)
reg = StackingRegressor(estimators=estimators, final_estimator=final_estimator, cv=cv, random_state=rnd_state, n_jobs=n_jobs)
y = subjects_data.loc[subjects].age.values
X = data.values
reg.fit(X, y)
return (reg, X, y)
|
Return stacked classifier trained on provided data.
For MEG data features estimated in the source space should be provided.
Parameters
----------
named_data : list(tuple(str, pandas.DataFrame))
List of tuples (name, data) with name and corresponding features
to be used for predictions by linear models.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
|
camcan/utils/evaluation.py
|
train_stacked_regressor
|
dengemann/engemann-2020-multimodal-brain-age
| 6 |
python
|
def train_stacked_regressor(named_data, subjects_data, cv=10, alphas=None, train_sizes=None, n_jobs=None):
'Return stacked classifier trained on provided data.\n\n For MEG data features estimated in the source space should be provided.\n\n Parameters\n ----------\n named_data : list(tuple(str, pandas.DataFrame))\n List of tuples (name, data) with name and corresponding features\n to be used for predictions by linear models.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
rnd_state = 42
names = []
combined_data = []
for (name, data) in named_data:
names.append(name)
combined_data.append(data)
data = pd.concat(combined_data, axis=1, join='inner')
feature_col_lens = tuple((d.shape[1] for d in combined_data))
estimators = []
subjects = data.index.values
for (i_data, _) in enumerate(named_data):
feature_transformers = []
ft_begin = 0
ft_end = 0
for (i_ct, (name, col_len)) in enumerate(zip(names, feature_col_lens)):
trans_name = (('pass_' if (i_data == i_ct) else 'drop_') + name)
transformer = ('passthrough' if (i_data == i_ct) else 'drop')
ft_end = (ft_end + col_len)
trans_slice = slice(ft_begin, ft_end)
ft_begin = (ft_begin + col_len)
feature_transformers.append((trans_name, transformer, trans_slice))
est_name = ('reg_' + named_data[i_data][0])
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), StandardScaler(), RidgeCV(alphas))
estimators.append((est_name, est_pipeline))
final_estimator = RandomForestRegressor(n_estimators=100, random_state=rnd_state, oob_score=True)
reg = StackingRegressor(estimators=estimators, final_estimator=final_estimator, cv=cv, random_state=rnd_state, n_jobs=n_jobs)
y = subjects_data.loc[subjects].age.values
X = data.values
reg.fit(X, y)
return (reg, X, y)
|
def train_stacked_regressor(named_data, subjects_data, cv=10, alphas=None, train_sizes=None, n_jobs=None):
'Return stacked classifier trained on provided data.\n\n For MEG data features estimated in the source space should be provided.\n\n Parameters\n ----------\n named_data : list(tuple(str, pandas.DataFrame))\n List of tuples (name, data) with name and corresponding features\n to be used for predictions by linear models.\n\n subjects_data : pandas.DataFrame\n Information about subjects from CamCAN dataset.\n\n cv : int, cross-validation generator or an iterable, optional\n Determines the cross-validation splitting strategy.\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices.\n\n For integer/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used.\n\n alphas : numpy.ndarray\n Values for parameter alpha to be tested. Default is\n np.logspace(start=-3, stop=1, num=50, base=10.0).\n\n train_sizes : array-like, shape (n_ticks,), dtype float or int\n Relative or absolute numbers of training examples that will be used to\n generate the learning curve. If the dtype is float, it is regarded as a\n fraction of the maximum size of the training set (that is determined\n by the selected validation method), i.e. it has to be within (0, 1].\n Otherwise it is interpreted as absolute sizes of the training sets.\n Note that for classification the number of samples usually have to\n be big enough to contain at least one sample from each class.\n (default: np.linspace(0.1, 1.0, 5))\n\n n_jobs : int or None, optional (default=None)\n The number of CPUs to use to do the computation.\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n for more details.\n\n '
if (alphas is None):
alphas = np.logspace((- 3), 5, 100)
if (train_sizes is None):
train_sizes = np.linspace(0.1, 1.0, 5)
rnd_state = 42
names = []
combined_data = []
for (name, data) in named_data:
names.append(name)
combined_data.append(data)
data = pd.concat(combined_data, axis=1, join='inner')
feature_col_lens = tuple((d.shape[1] for d in combined_data))
estimators = []
subjects = data.index.values
for (i_data, _) in enumerate(named_data):
feature_transformers = []
ft_begin = 0
ft_end = 0
for (i_ct, (name, col_len)) in enumerate(zip(names, feature_col_lens)):
trans_name = (('pass_' if (i_data == i_ct) else 'drop_') + name)
transformer = ('passthrough' if (i_data == i_ct) else 'drop')
ft_end = (ft_end + col_len)
trans_slice = slice(ft_begin, ft_end)
ft_begin = (ft_begin + col_len)
feature_transformers.append((trans_name, transformer, trans_slice))
est_name = ('reg_' + named_data[i_data][0])
est_pipeline = make_pipeline(ColumnTransformer(feature_transformers), StandardScaler(), RidgeCV(alphas))
estimators.append((est_name, est_pipeline))
final_estimator = RandomForestRegressor(n_estimators=100, random_state=rnd_state, oob_score=True)
reg = StackingRegressor(estimators=estimators, final_estimator=final_estimator, cv=cv, random_state=rnd_state, n_jobs=n_jobs)
y = subjects_data.loc[subjects].age.values
X = data.values
reg.fit(X, y)
return (reg, X, y)<|docstring|>Return stacked classifier trained on provided data.
For MEG data features estimated in the source space should be provided.
Parameters
----------
named_data : list(tuple(str, pandas.DataFrame))
List of tuples (name, data) with name and corresponding features
to be used for predictions by linear models.
subjects_data : pandas.DataFrame
Information about subjects from CamCAN dataset.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
alphas : numpy.ndarray
Values for parameter alpha to be tested. Default is
np.logspace(start=-3, stop=1, num=50, base=10.0).
train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
generate the learning curve. If the dtype is float, it is regarded as a
fraction of the maximum size of the training set (that is determined
by the selected validation method), i.e. it has to be within (0, 1].
Otherwise it is interpreted as absolute sizes of the training sets.
Note that for classification the number of samples usually have to
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.<|endoftext|>
|
0a5ccce848965e5ed31e7009290441260c7398dc146bb250c81a740d49ea053a
|
def find_by_text(browser, tag, text):
'\n Encontrar o elemeto com o texto\n Argumentos:\n - browser = Instancia do browser [firefox, chrome, ...]\n - text = conteúdo que deve estar na tag\n - tag = tag onde o texto será procurado\n\n '
elementos = browser.find_elements_by_tag_name(tag)
for elemento in elementos:
if (elemento.text == text):
return elemento
|
Encontrar o elemeto com o texto
Argumentos:
- browser = Instancia do browser [firefox, chrome, ...]
- text = conteúdo que deve estar na tag
- tag = tag onde o texto será procurado
|
Aula04/04_selenium.py
|
find_by_text
|
vfamim/Selenium_Curso
| 0 |
python
|
def find_by_text(browser, tag, text):
'\n Encontrar o elemeto com o texto\n Argumentos:\n - browser = Instancia do browser [firefox, chrome, ...]\n - text = conteúdo que deve estar na tag\n - tag = tag onde o texto será procurado\n\n '
elementos = browser.find_elements_by_tag_name(tag)
for elemento in elementos:
if (elemento.text == text):
return elemento
|
def find_by_text(browser, tag, text):
'\n Encontrar o elemeto com o texto\n Argumentos:\n - browser = Instancia do browser [firefox, chrome, ...]\n - text = conteúdo que deve estar na tag\n - tag = tag onde o texto será procurado\n\n '
elementos = browser.find_elements_by_tag_name(tag)
for elemento in elementos:
if (elemento.text == text):
return elemento<|docstring|>Encontrar o elemeto com o texto
Argumentos:
- browser = Instancia do browser [firefox, chrome, ...]
- text = conteúdo que deve estar na tag
- tag = tag onde o texto será procurado<|endoftext|>
|
6e59bd311e09658c539846f78e60332aa65308db0d135a7dffd99bdd04dd09f8
|
def find_by_href(browser, link):
"Encontrar o elemento 'a' com o link 'link'\n Argumentos:\n - browser = Instância do browser [firefox, chrome, ...]\n - link = link que será procurado em todos as tags 'a'\n "
elementos = browser.find_elements_by_tag_name('a')
for elemento in elementos:
if (link in elemento.get_attribute('href')):
return elemento
|
Encontrar o elemento 'a' com o link 'link'
Argumentos:
- browser = Instância do browser [firefox, chrome, ...]
- link = link que será procurado em todos as tags 'a'
|
Aula04/04_selenium.py
|
find_by_href
|
vfamim/Selenium_Curso
| 0 |
python
|
def find_by_href(browser, link):
"Encontrar o elemento 'a' com o link 'link'\n Argumentos:\n - browser = Instância do browser [firefox, chrome, ...]\n - link = link que será procurado em todos as tags 'a'\n "
elementos = browser.find_elements_by_tag_name('a')
for elemento in elementos:
if (link in elemento.get_attribute('href')):
return elemento
|
def find_by_href(browser, link):
"Encontrar o elemento 'a' com o link 'link'\n Argumentos:\n - browser = Instância do browser [firefox, chrome, ...]\n - link = link que será procurado em todos as tags 'a'\n "
elementos = browser.find_elements_by_tag_name('a')
for elemento in elementos:
if (link in elemento.get_attribute('href')):
return elemento<|docstring|>Encontrar o elemento 'a' com o link 'link'
Argumentos:
- browser = Instância do browser [firefox, chrome, ...]
- link = link que será procurado em todos as tags 'a'<|endoftext|>
|
3371e934fb92a3619c0bffc231bffb76a642fda12dbcebf85ba5e38c1f44e6f4
|
def _comment(self, msg: str) -> str:
' Returns an XML comment (which contains <msg>) as a string.\n '
return '<!-- {} -->'.format(msg)
|
Returns an XML comment (which contains <msg>) as a string.
|
test/units/test_file_writers.py
|
_comment
|
andrewjunyoung/symboard
| 5 |
python
|
def _comment(self, msg: str) -> str:
' \n '
return '<!-- {} -->'.format(msg)
|
def _comment(self, msg: str) -> str:
' \n '
return '<!-- {} -->'.format(msg)<|docstring|>Returns an XML comment (which contains <msg>) as a string.<|endoftext|>
|
3b0a54a33595d398c97498034aa508aa0930532e105bcf64d83a42c09b601346
|
def _test_comment_with_strftime(self, function, comment):
' This class tests that <function> returns a comment with the string\n <comment>. It mocks out the time.strftime method, which allows it to\n test comments which include times in them.\n\n For symboard, it can be used to test both the «_created» and «_updated»\n functions.\n '
time = Mock()
time.strftime = MagicMock(return_value=self.expected_time)
self.assertEqual(self._comment('Created by Symboard version {} at {}'.format(VERSION, self.expected_time)), self.file_writer._created(time))
|
This class tests that <function> returns a comment with the string
<comment>. It mocks out the time.strftime method, which allows it to
test comments which include times in them.
For symboard, it can be used to test both the «_created» and «_updated»
functions.
|
test/units/test_file_writers.py
|
_test_comment_with_strftime
|
andrewjunyoung/symboard
| 5 |
python
|
def _test_comment_with_strftime(self, function, comment):
' This class tests that <function> returns a comment with the string\n <comment>. It mocks out the time.strftime method, which allows it to\n test comments which include times in them.\n\n For symboard, it can be used to test both the «_created» and «_updated»\n functions.\n '
time = Mock()
time.strftime = MagicMock(return_value=self.expected_time)
self.assertEqual(self._comment('Created by Symboard version {} at {}'.format(VERSION, self.expected_time)), self.file_writer._created(time))
|
def _test_comment_with_strftime(self, function, comment):
' This class tests that <function> returns a comment with the string\n <comment>. It mocks out the time.strftime method, which allows it to\n test comments which include times in them.\n\n For symboard, it can be used to test both the «_created» and «_updated»\n functions.\n '
time = Mock()
time.strftime = MagicMock(return_value=self.expected_time)
self.assertEqual(self._comment('Created by Symboard version {} at {}'.format(VERSION, self.expected_time)), self.file_writer._created(time))<|docstring|>This class tests that <function> returns a comment with the string
<comment>. It mocks out the time.strftime method, which allows it to
test comments which include times in them.
For symboard, it can be used to test both the «_created» and «_updated»
functions.<|endoftext|>
|
09b7a03c010d0a4e663ec1815daf31088cbaa33f235bfbe3c1451e118740f76a
|
def _assert_properties_of_XML_tag_returned_from(self, function, expected_tag, expected_attributes, keylayout=None, args=None):
" Assert that a function, when called on <keylayout> with an Element\n node and other arguments (provided by <args>) creates a sub-elem with\n the expected tag and attributes.\n\n <function>, given an Element node, should add a sub-elem to that\n node, and return the sub-elem.\n\n child_node = function(keylayout, Element('root'), *args)\n\n Args:\n function (Callable): The function under test.\n keylayout (Keylayout): The Keylayout, or mock object, being used to\n test <function>.\n expected_tag (str): The name that the sub-elem created by\n <function> is expected to have.\n expected_attributes (Dict[str, str]): A map of the expected\n attributes which the sub-elem created by <function> is expected\n to have.\n\n Raises:\n AssertionError: If the child is incorrectly created, or if either\n the expected tag or attributes are different to their actual values\n created by <function>.\n "
(root, child) = self._execute_function_on_elem(function, keylayout, args)
self.assertEqual([child], list(root))
self.assertEqual(expected_tag, child.tag)
self.assertEqual(expected_attributes, child.attrib)
|
Assert that a function, when called on <keylayout> with an Element
node and other arguments (provided by <args>) creates a sub-elem with
the expected tag and attributes.
<function>, given an Element node, should add a sub-elem to that
node, and return the sub-elem.
child_node = function(keylayout, Element('root'), *args)
Args:
function (Callable): The function under test.
keylayout (Keylayout): The Keylayout, or mock object, being used to
test <function>.
expected_tag (str): The name that the sub-elem created by
<function> is expected to have.
expected_attributes (Dict[str, str]): A map of the expected
attributes which the sub-elem created by <function> is expected
to have.
Raises:
AssertionError: If the child is incorrectly created, or if either
the expected tag or attributes are different to their actual values
created by <function>.
|
test/units/test_file_writers.py
|
_assert_properties_of_XML_tag_returned_from
|
andrewjunyoung/symboard
| 5 |
python
|
def _assert_properties_of_XML_tag_returned_from(self, function, expected_tag, expected_attributes, keylayout=None, args=None):
" Assert that a function, when called on <keylayout> with an Element\n node and other arguments (provided by <args>) creates a sub-elem with\n the expected tag and attributes.\n\n <function>, given an Element node, should add a sub-elem to that\n node, and return the sub-elem.\n\n child_node = function(keylayout, Element('root'), *args)\n\n Args:\n function (Callable): The function under test.\n keylayout (Keylayout): The Keylayout, or mock object, being used to\n test <function>.\n expected_tag (str): The name that the sub-elem created by\n <function> is expected to have.\n expected_attributes (Dict[str, str]): A map of the expected\n attributes which the sub-elem created by <function> is expected\n to have.\n\n Raises:\n AssertionError: If the child is incorrectly created, or if either\n the expected tag or attributes are different to their actual values\n created by <function>.\n "
(root, child) = self._execute_function_on_elem(function, keylayout, args)
self.assertEqual([child], list(root))
self.assertEqual(expected_tag, child.tag)
self.assertEqual(expected_attributes, child.attrib)
|
def _assert_properties_of_XML_tag_returned_from(self, function, expected_tag, expected_attributes, keylayout=None, args=None):
" Assert that a function, when called on <keylayout> with an Element\n node and other arguments (provided by <args>) creates a sub-elem with\n the expected tag and attributes.\n\n <function>, given an Element node, should add a sub-elem to that\n node, and return the sub-elem.\n\n child_node = function(keylayout, Element('root'), *args)\n\n Args:\n function (Callable): The function under test.\n keylayout (Keylayout): The Keylayout, or mock object, being used to\n test <function>.\n expected_tag (str): The name that the sub-elem created by\n <function> is expected to have.\n expected_attributes (Dict[str, str]): A map of the expected\n attributes which the sub-elem created by <function> is expected\n to have.\n\n Raises:\n AssertionError: If the child is incorrectly created, or if either\n the expected tag or attributes are different to their actual values\n created by <function>.\n "
(root, child) = self._execute_function_on_elem(function, keylayout, args)
self.assertEqual([child], list(root))
self.assertEqual(expected_tag, child.tag)
self.assertEqual(expected_attributes, child.attrib)<|docstring|>Assert that a function, when called on <keylayout> with an Element
node and other arguments (provided by <args>) creates a sub-elem with
the expected tag and attributes.
<function>, given an Element node, should add a sub-elem to that
node, and return the sub-elem.
child_node = function(keylayout, Element('root'), *args)
Args:
function (Callable): The function under test.
keylayout (Keylayout): The Keylayout, or mock object, being used to
test <function>.
expected_tag (str): The name that the sub-elem created by
<function> is expected to have.
expected_attributes (Dict[str, str]): A map of the expected
attributes which the sub-elem created by <function> is expected
to have.
Raises:
AssertionError: If the child is incorrectly created, or if either
the expected tag or attributes are different to their actual values
created by <function>.<|endoftext|>
|
f67d3c5fdb71920cbc660cc032b5bd618505e1d0f348d48337702d44fd5897ef
|
def test_layouts_creates_a_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_layouts» meets the\n required specification.\n '
mock_keylayout = MagicMock(layouts=[])
self._assert_properties_of_XML_tag_returned_from(self.file_writer._layouts, keylayout=mock_keylayout, expected_tag='layouts', expected_attributes={})
|
Asserts that the sub_elem created by «_layouts» meets the
required specification.
|
test/units/test_file_writers.py
|
test_layouts_creates_a_well_formed_sub_elem
|
andrewjunyoung/symboard
| 5 |
python
|
def test_layouts_creates_a_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_layouts» meets the\n required specification.\n '
mock_keylayout = MagicMock(layouts=[])
self._assert_properties_of_XML_tag_returned_from(self.file_writer._layouts, keylayout=mock_keylayout, expected_tag='layouts', expected_attributes={})
|
def test_layouts_creates_a_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_layouts» meets the\n required specification.\n '
mock_keylayout = MagicMock(layouts=[])
self._assert_properties_of_XML_tag_returned_from(self.file_writer._layouts, keylayout=mock_keylayout, expected_tag='layouts', expected_attributes={})<|docstring|>Asserts that the sub_elem created by «_layouts» meets the
required specification.<|endoftext|>
|
873de6d7436f47c57cc17cabd2b1b38b10b38704ce7638258a82e7e8ebf944bb
|
def test_modifier_map_creates_a_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_modifier_map» meets the\n required specification.\n '
EXPECTED_DEFAULT_INDEX = 3
modifiers = 'Modifiers'
mock_keylayout = MagicMock(layouts=[{'modifiers': modifiers}], default_index=str(EXPECTED_DEFAULT_INDEX))
self._assert_properties_of_XML_tag_returned_from(self.file_writer._modifier_map, keylayout=mock_keylayout, expected_tag='modifierMap', expected_attributes={'id': modifiers, 'defaultIndex': str(EXPECTED_DEFAULT_INDEX)})
|
Asserts that the sub_elem created by «_modifier_map» meets the
required specification.
|
test/units/test_file_writers.py
|
test_modifier_map_creates_a_well_formed_sub_elem
|
andrewjunyoung/symboard
| 5 |
python
|
def test_modifier_map_creates_a_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_modifier_map» meets the\n required specification.\n '
EXPECTED_DEFAULT_INDEX = 3
modifiers = 'Modifiers'
mock_keylayout = MagicMock(layouts=[{'modifiers': modifiers}], default_index=str(EXPECTED_DEFAULT_INDEX))
self._assert_properties_of_XML_tag_returned_from(self.file_writer._modifier_map, keylayout=mock_keylayout, expected_tag='modifierMap', expected_attributes={'id': modifiers, 'defaultIndex': str(EXPECTED_DEFAULT_INDEX)})
|
def test_modifier_map_creates_a_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_modifier_map» meets the\n required specification.\n '
EXPECTED_DEFAULT_INDEX = 3
modifiers = 'Modifiers'
mock_keylayout = MagicMock(layouts=[{'modifiers': modifiers}], default_index=str(EXPECTED_DEFAULT_INDEX))
self._assert_properties_of_XML_tag_returned_from(self.file_writer._modifier_map, keylayout=mock_keylayout, expected_tag='modifierMap', expected_attributes={'id': modifiers, 'defaultIndex': str(EXPECTED_DEFAULT_INDEX)})<|docstring|>Asserts that the sub_elem created by «_modifier_map» meets the
required specification.<|endoftext|>
|
ebd235f6e3f566b04405cf1b894f53e144d380e3053303979db713dfed7c8698
|
def test_key_map_set_creates_a_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_key_map_set» meets the\n required specification.\n '
mock_keylayout = MagicMock(key_map={})
self._assert_properties_of_XML_tag_returned_from(self.file_writer._key_map_set, keylayout=mock_keylayout, expected_tag='keyMapSet', expected_attributes={'id': 'ANSI'})
|
Asserts that the sub_elem created by «_key_map_set» meets the
required specification.
|
test/units/test_file_writers.py
|
test_key_map_set_creates_a_well_formed_sub_elem
|
andrewjunyoung/symboard
| 5 |
python
|
def test_key_map_set_creates_a_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_key_map_set» meets the\n required specification.\n '
mock_keylayout = MagicMock(key_map={})
self._assert_properties_of_XML_tag_returned_from(self.file_writer._key_map_set, keylayout=mock_keylayout, expected_tag='keyMapSet', expected_attributes={'id': 'ANSI'})
|
def test_key_map_set_creates_a_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_key_map_set» meets the\n required specification.\n '
mock_keylayout = MagicMock(key_map={})
self._assert_properties_of_XML_tag_returned_from(self.file_writer._key_map_set, keylayout=mock_keylayout, expected_tag='keyMapSet', expected_attributes={'id': 'ANSI'})<|docstring|>Asserts that the sub_elem created by «_key_map_set» meets the
required specification.<|endoftext|>
|
a1ef4057c363f0e719f64a019025d55a0674acc509fdd6ed1cb1eaaf08e5ec38
|
def test_action_creates_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_action» meets the\n required specification.\n '
self._assert_properties_of_XML_tag_returned_from(self.file_writer._action, keylayout=self.states_mocker, args=[self.action], expected_tag='action', expected_attributes={'id': self.action.id_})
|
Asserts that the sub_elem created by «_action» meets the
required specification.
|
test/units/test_file_writers.py
|
test_action_creates_well_formed_sub_elem
|
andrewjunyoung/symboard
| 5 |
python
|
def test_action_creates_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_action» meets the\n required specification.\n '
self._assert_properties_of_XML_tag_returned_from(self.file_writer._action, keylayout=self.states_mocker, args=[self.action], expected_tag='action', expected_attributes={'id': self.action.id_})
|
def test_action_creates_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_action» meets the\n required specification.\n '
self._assert_properties_of_XML_tag_returned_from(self.file_writer._action, keylayout=self.states_mocker, args=[self.action], expected_tag='action', expected_attributes={'id': self.action.id_})<|docstring|>Asserts that the sub_elem created by «_action» meets the
required specification.<|endoftext|>
|
88031ca833029e8a6f1f277905a9842d02637c99bd963cf28a9805cf31c814ac
|
def test_terminators_creates_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_terminators» meets the\n required specification.\n '
mock_keylayout = MagicMock(states=[])
self._assert_properties_of_XML_tag_returned_from(self.file_writer._terminators, keylayout=mock_keylayout, expected_tag='terminators', expected_attributes={})
|
Asserts that the sub_elem created by «_terminators» meets the
required specification.
|
test/units/test_file_writers.py
|
test_terminators_creates_well_formed_sub_elem
|
andrewjunyoung/symboard
| 5 |
python
|
def test_terminators_creates_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_terminators» meets the\n required specification.\n '
mock_keylayout = MagicMock(states=[])
self._assert_properties_of_XML_tag_returned_from(self.file_writer._terminators, keylayout=mock_keylayout, expected_tag='terminators', expected_attributes={})
|
def test_terminators_creates_well_formed_sub_elem(self):
' Asserts that the sub_elem created by «_terminators» meets the\n required specification.\n '
mock_keylayout = MagicMock(states=[])
self._assert_properties_of_XML_tag_returned_from(self.file_writer._terminators, keylayout=mock_keylayout, expected_tag='terminators', expected_attributes={})<|docstring|>Asserts that the sub_elem created by «_terminators» meets the
required specification.<|endoftext|>
|
f0650151f4c7ffcc91c6da37cd836b9dff66e2a3c2c543430bba25dace49f525
|
def rename_permissions(apps, schema_editor):
'\n This fixes the typo in delete_associated_propositiondocument and renames delete_propositiondocument to\n delete_all_propositiondocument so it is consistent with corresponding permission of evidencedocument.\n It also removed _investmentproject_ from proposition permissions to make it consistent.\n '
permission_model = apps.get_model('auth', 'Permission')
old_new_codename = (('deleted_associated_propositiondocument', 'delete_associated_propositiondocument'), ('delete_propositiondocument', 'delete_all_propositiondocument'), ('view_associated_investmentproject_proposition', 'view_associated_proposition'), ('change_associated_investmentproject_proposition', 'change_associated_proposition'), ('add_associated_investmentproject_proposition', 'add_associated_proposition'))
for (old_codename, new_codename) in old_new_codename:
permission_model.objects.filter(codename=old_codename).update(codename=new_codename)
|
This fixes the typo in delete_associated_propositiondocument and renames delete_propositiondocument to
delete_all_propositiondocument so it is consistent with corresponding permission of evidencedocument.
It also removed _investmentproject_ from proposition permissions to make it consistent.
|
datahub/investment/project/proposition/migrations/0006_rename_permissions.py
|
rename_permissions
|
Staberinde/data-hub-api
| 6 |
python
|
def rename_permissions(apps, schema_editor):
'\n This fixes the typo in delete_associated_propositiondocument and renames delete_propositiondocument to\n delete_all_propositiondocument so it is consistent with corresponding permission of evidencedocument.\n It also removed _investmentproject_ from proposition permissions to make it consistent.\n '
permission_model = apps.get_model('auth', 'Permission')
old_new_codename = (('deleted_associated_propositiondocument', 'delete_associated_propositiondocument'), ('delete_propositiondocument', 'delete_all_propositiondocument'), ('view_associated_investmentproject_proposition', 'view_associated_proposition'), ('change_associated_investmentproject_proposition', 'change_associated_proposition'), ('add_associated_investmentproject_proposition', 'add_associated_proposition'))
for (old_codename, new_codename) in old_new_codename:
permission_model.objects.filter(codename=old_codename).update(codename=new_codename)
|
def rename_permissions(apps, schema_editor):
'\n This fixes the typo in delete_associated_propositiondocument and renames delete_propositiondocument to\n delete_all_propositiondocument so it is consistent with corresponding permission of evidencedocument.\n It also removed _investmentproject_ from proposition permissions to make it consistent.\n '
permission_model = apps.get_model('auth', 'Permission')
old_new_codename = (('deleted_associated_propositiondocument', 'delete_associated_propositiondocument'), ('delete_propositiondocument', 'delete_all_propositiondocument'), ('view_associated_investmentproject_proposition', 'view_associated_proposition'), ('change_associated_investmentproject_proposition', 'change_associated_proposition'), ('add_associated_investmentproject_proposition', 'add_associated_proposition'))
for (old_codename, new_codename) in old_new_codename:
permission_model.objects.filter(codename=old_codename).update(codename=new_codename)<|docstring|>This fixes the typo in delete_associated_propositiondocument and renames delete_propositiondocument to
delete_all_propositiondocument so it is consistent with corresponding permission of evidencedocument.
It also removed _investmentproject_ from proposition permissions to make it consistent.<|endoftext|>
|
6065f05dc71b6e8a6814cc124a63545ff7fe974f8a632aaea748e37ed1526295
|
def user_by_login(rstate: Rstate, login: str) -> (User | None):
'Return a user identified by login.'
return _get_unique_x_by_y(rstate, users, 'login', login)
|
Return a user identified by login.
|
src/ghaudit/schema.py
|
user_by_login
|
DistantThunder/ghaudit
| 1 |
python
|
def user_by_login(rstate: Rstate, login: str) -> (User | None):
return _get_unique_x_by_y(rstate, users, 'login', login)
|
def user_by_login(rstate: Rstate, login: str) -> (User | None):
return _get_unique_x_by_y(rstate, users, 'login', login)<|docstring|>Return a user identified by login.<|endoftext|>
|
e7c5e771f7cfdc6f23c5e709fdd437a9a596e13eb6ac77fd3c2d011b5401a1f9
|
def user_by_id(rstate: Rstate, user_id: UserID) -> User:
"Return a user identified by ID.\n\n :raises: RuntimeError if the identifier `id' is not found.\n "
user = _user_by_id_noexcept(rstate, user_id)
if (not user):
raise RuntimeError('User not found from ID: "{}"'.format(user_id))
return user
|
Return a user identified by ID.
:raises: RuntimeError if the identifier `id' is not found.
|
src/ghaudit/schema.py
|
user_by_id
|
DistantThunder/ghaudit
| 1 |
python
|
def user_by_id(rstate: Rstate, user_id: UserID) -> User:
"Return a user identified by ID.\n\n :raises: RuntimeError if the identifier `id' is not found.\n "
user = _user_by_id_noexcept(rstate, user_id)
if (not user):
raise RuntimeError('User not found from ID: "{}"'.format(user_id))
return user
|
def user_by_id(rstate: Rstate, user_id: UserID) -> User:
"Return a user identified by ID.\n\n :raises: RuntimeError if the identifier `id' is not found.\n "
user = _user_by_id_noexcept(rstate, user_id)
if (not user):
raise RuntimeError('User not found from ID: "{}"'.format(user_id))
return user<|docstring|>Return a user identified by ID.
:raises: RuntimeError if the identifier `id' is not found.<|endoftext|>
|
93d56d38de8e70a4ac006d0d7e71ead16e2fb7b451ae533c697dcc020a760bad
|
def users(rstate: Rstate) -> Collection[User]:
'Return the list of all known users from the remote state.'
return rstate['data']['users'].values()
|
Return the list of all known users from the remote state.
|
src/ghaudit/schema.py
|
users
|
DistantThunder/ghaudit
| 1 |
python
|
def users(rstate: Rstate) -> Collection[User]:
return rstate['data']['users'].values()
|
def users(rstate: Rstate) -> Collection[User]:
return rstate['data']['users'].values()<|docstring|>Return the list of all known users from the remote state.<|endoftext|>
|
b2b9884a1c157cc49beddf53978e14bf993dae2e3f1e18d764d828070c00d9ba
|
def org_repositories(rstate: Rstate) -> List[Repo]:
'Return the list of repositories in the organisation.'
return _get_org_repos(rstate)
|
Return the list of repositories in the organisation.
|
src/ghaudit/schema.py
|
org_repositories
|
DistantThunder/ghaudit
| 1 |
python
|
def org_repositories(rstate: Rstate) -> List[Repo]:
return _get_org_repos(rstate)
|
def org_repositories(rstate: Rstate) -> List[Repo]:
return _get_org_repos(rstate)<|docstring|>Return the list of repositories in the organisation.<|endoftext|>
|
df3f08b6c0926150b27d1ccae683ba7b09d2690ba8802e88984541915a2e6991
|
def org_teams(rstate: Rstate) -> List[Team]:
'Return the list of teams in the organisation.'
return _get_org_teams(rstate)
|
Return the list of teams in the organisation.
|
src/ghaudit/schema.py
|
org_teams
|
DistantThunder/ghaudit
| 1 |
python
|
def org_teams(rstate: Rstate) -> List[Team]:
return _get_org_teams(rstate)
|
def org_teams(rstate: Rstate) -> List[Team]:
return _get_org_teams(rstate)<|docstring|>Return the list of teams in the organisation.<|endoftext|>
|
11b6e7f4f668313dda492f3adf1ee52f20ef8c2633c7b7bebe8c01261432e6f3
|
def org_members(rstate: Rstate) -> List[UserWithOrgRole]:
'Return the list of members of the organisation.'
return [cast(UserWithOrgRole, user_by_id(rstate, x)) for x in _get_org_members(rstate)]
|
Return the list of members of the organisation.
|
src/ghaudit/schema.py
|
org_members
|
DistantThunder/ghaudit
| 1 |
python
|
def org_members(rstate: Rstate) -> List[UserWithOrgRole]:
return [cast(UserWithOrgRole, user_by_id(rstate, x)) for x in _get_org_members(rstate)]
|
def org_members(rstate: Rstate) -> List[UserWithOrgRole]:
return [cast(UserWithOrgRole, user_by_id(rstate, x)) for x in _get_org_members(rstate)]<|docstring|>Return the list of members of the organisation.<|endoftext|>
|
cb9dbaecaeaf2f328fad835c5f46322e2340c7e3da8e062ad2fd67758eb8a0b0
|
def org_team_by_id(rstate: Rstate, team_id: TeamID) -> Team:
'Return a team from the organisation identified by ID.'
return _get_unique_x_by_y(rstate, _get_org_teams, 'id', team_id)
|
Return a team from the organisation identified by ID.
|
src/ghaudit/schema.py
|
org_team_by_id
|
DistantThunder/ghaudit
| 1 |
python
|
def org_team_by_id(rstate: Rstate, team_id: TeamID) -> Team:
return _get_unique_x_by_y(rstate, _get_org_teams, 'id', team_id)
|
def org_team_by_id(rstate: Rstate, team_id: TeamID) -> Team:
return _get_unique_x_by_y(rstate, _get_org_teams, 'id', team_id)<|docstring|>Return a team from the organisation identified by ID.<|endoftext|>
|
def5a8982ec807d7d2288382b1ba8de4b40eae7ff6e0add5c911cace85297a4e
|
def org_team_by_name(rstate: Rstate, name: str) -> Team:
'Return a team from the organisation identified by name.'
return _get_unique_x_by_y(rstate, _get_org_teams, 'name', name)
|
Return a team from the organisation identified by name.
|
src/ghaudit/schema.py
|
org_team_by_name
|
DistantThunder/ghaudit
| 1 |
python
|
def org_team_by_name(rstate: Rstate, name: str) -> Team:
return _get_unique_x_by_y(rstate, _get_org_teams, 'name', name)
|
def org_team_by_name(rstate: Rstate, name: str) -> Team:
return _get_unique_x_by_y(rstate, _get_org_teams, 'name', name)<|docstring|>Return a team from the organisation identified by name.<|endoftext|>
|
9edea6327f4b054256019591740852f30a0934bd036f334b9f190095cf41a8fb
|
def org_repo_by_id(rstate: Rstate, repo_id: RepoID) -> Repo:
'Return a repository from the organisation identified by ID.'
return _get_unique_x_by_y(rstate, _get_org_repos, 'id', repo_id)
|
Return a repository from the organisation identified by ID.
|
src/ghaudit/schema.py
|
org_repo_by_id
|
DistantThunder/ghaudit
| 1 |
python
|
def org_repo_by_id(rstate: Rstate, repo_id: RepoID) -> Repo:
return _get_unique_x_by_y(rstate, _get_org_repos, 'id', repo_id)
|
def org_repo_by_id(rstate: Rstate, repo_id: RepoID) -> Repo:
return _get_unique_x_by_y(rstate, _get_org_repos, 'id', repo_id)<|docstring|>Return a repository from the organisation identified by ID.<|endoftext|>
|
15d3ef6897281130cd6c54538156199f106d8eb37151ec383abf2c9323692cb7
|
def org_repo_by_name(rstate: Rstate, name: str) -> Repo:
'Return a repository from the organisation identified by name.'
return _get_unique_x_by_y(rstate, _get_org_repos, 'name', name)
|
Return a repository from the organisation identified by name.
|
src/ghaudit/schema.py
|
org_repo_by_name
|
DistantThunder/ghaudit
| 1 |
python
|
def org_repo_by_name(rstate: Rstate, name: str) -> Repo:
return _get_unique_x_by_y(rstate, _get_org_repos, 'name', name)
|
def org_repo_by_name(rstate: Rstate, name: str) -> Repo:
return _get_unique_x_by_y(rstate, _get_org_repos, 'name', name)<|docstring|>Return a repository from the organisation identified by name.<|endoftext|>
|
269bad07e5c6bbe248c32f105a433ac32a10b8f9c260828c36a9524f9a9b01e7
|
def repo_archived(repo: Repo) -> bool:
'Return whether the given repository is archived.'
return repo['node']['isArchived']
|
Return whether the given repository is archived.
|
src/ghaudit/schema.py
|
repo_archived
|
DistantThunder/ghaudit
| 1 |
python
|
def repo_archived(repo: Repo) -> bool:
return repo['node']['isArchived']
|
def repo_archived(repo: Repo) -> bool:
return repo['node']['isArchived']<|docstring|>Return whether the given repository is archived.<|endoftext|>
|
a222a6cdec7e1e3df0c85a4635e2a1041e8c7063b02a2f48498e4a496876d81b
|
def repo_forked(repo: Repo) -> bool:
'Return whether the given repository is a fork.'
return repo['node']['isFork']
|
Return whether the given repository is a fork.
|
src/ghaudit/schema.py
|
repo_forked
|
DistantThunder/ghaudit
| 1 |
python
|
def repo_forked(repo: Repo) -> bool:
return repo['node']['isFork']
|
def repo_forked(repo: Repo) -> bool:
return repo['node']['isFork']<|docstring|>Return whether the given repository is a fork.<|endoftext|>
|
fba2e1bd669dccfcadb5db3fcbf2c98abea8cc0e7329edbe4351e8a187d27958
|
def repo_private(repo: Repo) -> bool:
'Return whether the given repository has the visibility to private.'
return repo['node']['isPrivate']
|
Return whether the given repository has the visibility to private.
|
src/ghaudit/schema.py
|
repo_private
|
DistantThunder/ghaudit
| 1 |
python
|
def repo_private(repo: Repo) -> bool:
return repo['node']['isPrivate']
|
def repo_private(repo: Repo) -> bool:
return repo['node']['isPrivate']<|docstring|>Return whether the given repository has the visibility to private.<|endoftext|>
|
267eb460479835e0499ef05fc8bc638c2f07266506c77e8f72c8b284ec0ca611
|
def repo_name(repo: Repo) -> str:
'Return the name of a given repository.'
return repo['node']['name']
|
Return the name of a given repository.
|
src/ghaudit/schema.py
|
repo_name
|
DistantThunder/ghaudit
| 1 |
python
|
def repo_name(repo: Repo) -> str:
return repo['node']['name']
|
def repo_name(repo: Repo) -> str:
return repo['node']['name']<|docstring|>Return the name of a given repository.<|endoftext|>
|
50636417f04697988ca43b429bdbfb1d4e7638f08f0f25456f6057aa11f20fbd
|
def repo_description(repo: Repo) -> str:
'Return the description of a given repository.'
return repo['node']['description']
|
Return the description of a given repository.
|
src/ghaudit/schema.py
|
repo_description
|
DistantThunder/ghaudit
| 1 |
python
|
def repo_description(repo: Repo) -> str:
return repo['node']['description']
|
def repo_description(repo: Repo) -> str:
return repo['node']['description']<|docstring|>Return the description of a given repository.<|endoftext|>
|
d430bdcaf4324f6b8875ddae8c4989b8c3be91f49257fba7ef585a085fded52a
|
def repo_collaborators(rstate: Rstate, repo: Repo) -> List[RepoCollaborator]:
'Return the list of collaborators to the given repository.\n\n The collaborators may be organisation members or external collaborators\n with access to the repository.\n '
def mkobj(rstate: Rstate, edge: RepoCollaboratorEdge) -> RepoCollaborator:
return {'role': edge['permission'], 'node': user_by_id(rstate, edge['node']['id'])['node']}
if (('collaborators' in repo['node']) and repo['node']['collaborators']):
collaborators = repo['node']['collaborators']['edges']
return [mkobj(rstate, x) for x in collaborators if (x is not None)]
return []
|
Return the list of collaborators to the given repository.
The collaborators may be organisation members or external collaborators
with access to the repository.
|
src/ghaudit/schema.py
|
repo_collaborators
|
DistantThunder/ghaudit
| 1 |
python
|
def repo_collaborators(rstate: Rstate, repo: Repo) -> List[RepoCollaborator]:
'Return the list of collaborators to the given repository.\n\n The collaborators may be organisation members or external collaborators\n with access to the repository.\n '
def mkobj(rstate: Rstate, edge: RepoCollaboratorEdge) -> RepoCollaborator:
return {'role': edge['permission'], 'node': user_by_id(rstate, edge['node']['id'])['node']}
if (('collaborators' in repo['node']) and repo['node']['collaborators']):
collaborators = repo['node']['collaborators']['edges']
return [mkobj(rstate, x) for x in collaborators if (x is not None)]
return []
|
def repo_collaborators(rstate: Rstate, repo: Repo) -> List[RepoCollaborator]:
'Return the list of collaborators to the given repository.\n\n The collaborators may be organisation members or external collaborators\n with access to the repository.\n '
def mkobj(rstate: Rstate, edge: RepoCollaboratorEdge) -> RepoCollaborator:
return {'role': edge['permission'], 'node': user_by_id(rstate, edge['node']['id'])['node']}
if (('collaborators' in repo['node']) and repo['node']['collaborators']):
collaborators = repo['node']['collaborators']['edges']
return [mkobj(rstate, x) for x in collaborators if (x is not None)]
return []<|docstring|>Return the list of collaborators to the given repository.
The collaborators may be organisation members or external collaborators
with access to the repository.<|endoftext|>
|
9a5e4ab1ed03390fb64d8cfd66fcaf9fd64fe9d5965ed94a9669a8cbc4effcca
|
def repo_branch_protection_rules(repo: Repo) -> List[BranchProtectionRuleNode]:
'Return the list of branch protection rules from a given repository.'
return repo['node']['branchProtectionRules']['nodes']
|
Return the list of branch protection rules from a given repository.
|
src/ghaudit/schema.py
|
repo_branch_protection_rules
|
DistantThunder/ghaudit
| 1 |
python
|
def repo_branch_protection_rules(repo: Repo) -> List[BranchProtectionRuleNode]:
return repo['node']['branchProtectionRules']['nodes']
|
def repo_branch_protection_rules(repo: Repo) -> List[BranchProtectionRuleNode]:
return repo['node']['branchProtectionRules']['nodes']<|docstring|>Return the list of branch protection rules from a given repository.<|endoftext|>
|
4012fe08e165577b330a860238acc27cdc2e3ab4f2ce66aad1f919f828e7d77c
|
def repo_branch_protection_rule(repo: Repo, pattern: str) -> Optional[BranchProtectionRuleNode]:
'Return a branch protection rule given a branch name pattern.'
rules = repo_branch_protection_rules(repo)
elems = [x for x in rules if (branch_protection_pattern(x) == pattern)]
assert (len(elems) <= 1)
if elems:
return elems[0]
return None
|
Return a branch protection rule given a branch name pattern.
|
src/ghaudit/schema.py
|
repo_branch_protection_rule
|
DistantThunder/ghaudit
| 1 |
python
|
def repo_branch_protection_rule(repo: Repo, pattern: str) -> Optional[BranchProtectionRuleNode]:
rules = repo_branch_protection_rules(repo)
elems = [x for x in rules if (branch_protection_pattern(x) == pattern)]
assert (len(elems) <= 1)
if elems:
return elems[0]
return None
|
def repo_branch_protection_rule(repo: Repo, pattern: str) -> Optional[BranchProtectionRuleNode]:
rules = repo_branch_protection_rules(repo)
elems = [x for x in rules if (branch_protection_pattern(x) == pattern)]
assert (len(elems) <= 1)
if elems:
return elems[0]
return None<|docstring|>Return a branch protection rule given a branch name pattern.<|endoftext|>
|
46122bcc0cfdf77f926833a2ac374c8f92fd23f24a50b4bf239ad17c85057558
|
def team_name(team: Team) -> str:
'Return the name of a given team.'
return team['node']['name']
|
Return the name of a given team.
|
src/ghaudit/schema.py
|
team_name
|
DistantThunder/ghaudit
| 1 |
python
|
def team_name(team: Team) -> str:
return team['node']['name']
|
def team_name(team: Team) -> str:
return team['node']['name']<|docstring|>Return the name of a given team.<|endoftext|>
|
3f88ec11eed3839accf122041c88ae260a4be44c27c6c8f28d50be9978238ece
|
def team_description(team: Team) -> str:
'Return the description of a given team.'
return team['node']['description']
|
Return the description of a given team.
|
src/ghaudit/schema.py
|
team_description
|
DistantThunder/ghaudit
| 1 |
python
|
def team_description(team: Team) -> str:
return team['node']['description']
|
def team_description(team: Team) -> str:
return team['node']['description']<|docstring|>Return the description of a given team.<|endoftext|>
|
5414667584dac73dab1a29d88c4aca1bac7a86ecb9741020dcdb9fcea31cbba9
|
def team_repos(rstate: Rstate, team: Team) -> List[RepoWithPerms]:
'Return the list of repositories a team has effective access to.'
def mkobj(rstate: Rstate, edge: TeamRepoEdge) -> RepoWithPerms:
return {'permission': edge['permission'], 'node': org_repo_by_id(rstate, edge['node']['id'])['node']}
if (('repositories' in team['node']) and team['node']['repositories']):
repositories = team['node']['repositories']['edges']
return [mkobj(rstate, x) for x in repositories if (x is not None)]
return []
|
Return the list of repositories a team has effective access to.
|
src/ghaudit/schema.py
|
team_repos
|
DistantThunder/ghaudit
| 1 |
python
|
def team_repos(rstate: Rstate, team: Team) -> List[RepoWithPerms]:
def mkobj(rstate: Rstate, edge: TeamRepoEdge) -> RepoWithPerms:
return {'permission': edge['permission'], 'node': org_repo_by_id(rstate, edge['node']['id'])['node']}
if (('repositories' in team['node']) and team['node']['repositories']):
repositories = team['node']['repositories']['edges']
return [mkobj(rstate, x) for x in repositories if (x is not None)]
return []
|
def team_repos(rstate: Rstate, team: Team) -> List[RepoWithPerms]:
def mkobj(rstate: Rstate, edge: TeamRepoEdge) -> RepoWithPerms:
return {'permission': edge['permission'], 'node': org_repo_by_id(rstate, edge['node']['id'])['node']}
if (('repositories' in team['node']) and team['node']['repositories']):
repositories = team['node']['repositories']['edges']
return [mkobj(rstate, x) for x in repositories if (x is not None)]
return []<|docstring|>Return the list of repositories a team has effective access to.<|endoftext|>
|
0d67906cbb5600711eef93ffd671e0b17e8345f6d9b5de915a73a2e9c290f6b0
|
def team_members(rstate: Rstate, team: Team) -> List[UserWithRole]:
'Return the list of members of a given team.'
def mkobj(rstate: Rstate, edge: TeamMemberEdge) -> UserWithRole:
return {'role': edge['role'], 'node': user_by_id(rstate, edge['node']['id'])['node']}
if (('members' in team['node']) and team['node']['members']):
members = team['node']['members']['edges']
return [mkobj(rstate, x) for x in members if (x is not None)]
return []
|
Return the list of members of a given team.
|
src/ghaudit/schema.py
|
team_members
|
DistantThunder/ghaudit
| 1 |
python
|
def team_members(rstate: Rstate, team: Team) -> List[UserWithRole]:
def mkobj(rstate: Rstate, edge: TeamMemberEdge) -> UserWithRole:
return {'role': edge['role'], 'node': user_by_id(rstate, edge['node']['id'])['node']}
if (('members' in team['node']) and team['node']['members']):
members = team['node']['members']['edges']
return [mkobj(rstate, x) for x in members if (x is not None)]
return []
|
def team_members(rstate: Rstate, team: Team) -> List[UserWithRole]:
def mkobj(rstate: Rstate, edge: TeamMemberEdge) -> UserWithRole:
return {'role': edge['role'], 'node': user_by_id(rstate, edge['node']['id'])['node']}
if (('members' in team['node']) and team['node']['members']):
members = team['node']['members']['edges']
return [mkobj(rstate, x) for x in members if (x is not None)]
return []<|docstring|>Return the list of members of a given team.<|endoftext|>
|
1b070c58d5690257082f16411619a2a966d7a3088e9f9a3c03450b272c14685f
|
def team_parent(rstate: Rstate, team: Team) -> Optional[Team]:
'Return the parent of a given team if it exists.'
parent_team = team['node']['parentTeam']
if parent_team:
return org_team_by_id(rstate, parent_team)
return None
|
Return the parent of a given team if it exists.
|
src/ghaudit/schema.py
|
team_parent
|
DistantThunder/ghaudit
| 1 |
python
|
def team_parent(rstate: Rstate, team: Team) -> Optional[Team]:
parent_team = team['node']['parentTeam']
if parent_team:
return org_team_by_id(rstate, parent_team)
return None
|
def team_parent(rstate: Rstate, team: Team) -> Optional[Team]:
parent_team = team['node']['parentTeam']
if parent_team:
return org_team_by_id(rstate, parent_team)
return None<|docstring|>Return the parent of a given team if it exists.<|endoftext|>
|
56072288ff50da94f6feb451b2a441cd3c8de4f4c9899fff0c44c86f4762821a
|
def team_children(rstate: Rstate, team: Team) -> List[Team]:
'Return the list of direct children teams of a given team.'
def mkobj(rstate: Rstate, edge: ChildTeam) -> Team:
return {'node': org_team_by_id(rstate, edge['node']['id'])['node']}
if (('childTeams' in team['node']) and team['node']['childTeams']):
children = team['node']['childTeams']['edges']
return [mkobj(rstate, x) for x in children if (x is not None)]
return []
|
Return the list of direct children teams of a given team.
|
src/ghaudit/schema.py
|
team_children
|
DistantThunder/ghaudit
| 1 |
python
|
def team_children(rstate: Rstate, team: Team) -> List[Team]:
def mkobj(rstate: Rstate, edge: ChildTeam) -> Team:
return {'node': org_team_by_id(rstate, edge['node']['id'])['node']}
if (('childTeams' in team['node']) and team['node']['childTeams']):
children = team['node']['childTeams']['edges']
return [mkobj(rstate, x) for x in children if (x is not None)]
return []
|
def team_children(rstate: Rstate, team: Team) -> List[Team]:
def mkobj(rstate: Rstate, edge: ChildTeam) -> Team:
return {'node': org_team_by_id(rstate, edge['node']['id'])['node']}
if (('childTeams' in team['node']) and team['node']['childTeams']):
children = team['node']['childTeams']['edges']
return [mkobj(rstate, x) for x in children if (x is not None)]
return []<|docstring|>Return the list of direct children teams of a given team.<|endoftext|>
|
13072a2686e5fe678bd808370720ce1179729bdb0fb5571fdda33a9e33ccdb54
|
def user_name(user: User) -> Optional[str]:
'Return the name of a given user.'
return user['node']['name']
|
Return the name of a given user.
|
src/ghaudit/schema.py
|
user_name
|
DistantThunder/ghaudit
| 1 |
python
|
def user_name(user: User) -> Optional[str]:
return user['node']['name']
|
def user_name(user: User) -> Optional[str]:
return user['node']['name']<|docstring|>Return the name of a given user.<|endoftext|>
|
a9f344d61d2b2a8946520c6d0f75bb06a350a7819b88d7fa5716a926648c7419
|
def user_login(user: User) -> str:
'Return the login of a given user.'
return user['node']['login']
|
Return the login of a given user.
|
src/ghaudit/schema.py
|
user_login
|
DistantThunder/ghaudit
| 1 |
python
|
def user_login(user: User) -> str:
return user['node']['login']
|
def user_login(user: User) -> str:
return user['node']['login']<|docstring|>Return the login of a given user.<|endoftext|>
|
1fdfaa300d7e1046f7583203daa36fedb09cfe2b0bf711c25ce8ff98886b42df
|
def user_email(user: User) -> str:
'Return the email of a given user, as set in their account.'
return user['node']['email']
|
Return the email of a given user, as set in their account.
|
src/ghaudit/schema.py
|
user_email
|
DistantThunder/ghaudit
| 1 |
python
|
def user_email(user: User) -> str:
return user['node']['email']
|
def user_email(user: User) -> str:
return user['node']['email']<|docstring|>Return the email of a given user, as set in their account.<|endoftext|>
|
aaec58022297c4b79fd51eaf5325eb5b571ead1172d472c13412a03788cb82c5
|
def user_company(user: User) -> str:
'Return the company of a given user, as set in their account.'
return user['node']['company']
|
Return the company of a given user, as set in their account.
|
src/ghaudit/schema.py
|
user_company
|
DistantThunder/ghaudit
| 1 |
python
|
def user_company(user: User) -> str:
return user['node']['company']
|
def user_company(user: User) -> str:
return user['node']['company']<|docstring|>Return the company of a given user, as set in their account.<|endoftext|>
|
aa52b5e216a72ded2e72344477acb53c5accad8bb648eb2b52a109f1f5171d3a
|
def user_is_owner(user: UserWithOrgRole) -> bool:
'Return whether a given user has the owner role in the organisation.'
return (('role' in user) and (user['role'] == 'ADMIN'))
|
Return whether a given user has the owner role in the organisation.
|
src/ghaudit/schema.py
|
user_is_owner
|
DistantThunder/ghaudit
| 1 |
python
|
def user_is_owner(user: UserWithOrgRole) -> bool:
return (('role' in user) and (user['role'] == 'ADMIN'))
|
def user_is_owner(user: UserWithOrgRole) -> bool:
return (('role' in user) and (user['role'] == 'ADMIN'))<|docstring|>Return whether a given user has the owner role in the organisation.<|endoftext|>
|
fb52dc41d5d7103f29374d1dfb00099682495e26ba79f596b106e62d91b6a928
|
def branch_protection_id(rule: BranchProtectionRuleNode) -> Hashable:
'Return the ID of a given branch protection rule.'
return rule['id']
|
Return the ID of a given branch protection rule.
|
src/ghaudit/schema.py
|
branch_protection_id
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_id(rule: BranchProtectionRuleNode) -> Hashable:
return rule['id']
|
def branch_protection_id(rule: BranchProtectionRuleNode) -> Hashable:
return rule['id']<|docstring|>Return the ID of a given branch protection rule.<|endoftext|>
|
468501b02a21b980d9ec3d35e8107e40480d4e8c025519ee547ec43afe7e67b2
|
def branch_protection_pattern(rule: BranchProtectionRuleNode) -> str:
'Return the branch pattern name of a given branch protection rule.'
return rule['pattern']
|
Return the branch pattern name of a given branch protection rule.
|
src/ghaudit/schema.py
|
branch_protection_pattern
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_pattern(rule: BranchProtectionRuleNode) -> str:
return rule['pattern']
|
def branch_protection_pattern(rule: BranchProtectionRuleNode) -> str:
return rule['pattern']<|docstring|>Return the branch pattern name of a given branch protection rule.<|endoftext|>
|
1344070642ed964c57e009b0e4f2ecd089e7bc469724acdbd1054e359b5c9086
|
def branch_protection_admin_enforced(rule: BranchProtectionRuleNode) -> bool:
'Return whether an admin can overwrite branches.\n\n Return whether an admin can overwrite branches with a name mathing to the\n pattern of the given branch protection.\n '
return rule['isAdminEnforced']
|
Return whether an admin can overwrite branches.
Return whether an admin can overwrite branches with a name mathing to the
pattern of the given branch protection.
|
src/ghaudit/schema.py
|
branch_protection_admin_enforced
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_admin_enforced(rule: BranchProtectionRuleNode) -> bool:
'Return whether an admin can overwrite branches.\n\n Return whether an admin can overwrite branches with a name mathing to the\n pattern of the given branch protection.\n '
return rule['isAdminEnforced']
|
def branch_protection_admin_enforced(rule: BranchProtectionRuleNode) -> bool:
'Return whether an admin can overwrite branches.\n\n Return whether an admin can overwrite branches with a name mathing to the\n pattern of the given branch protection.\n '
return rule['isAdminEnforced']<|docstring|>Return whether an admin can overwrite branches.
Return whether an admin can overwrite branches with a name mathing to the
pattern of the given branch protection.<|endoftext|>
|
6d0387225039b999df6c9a01fb773fe244585339946e926b30084ae23a0be787
|
def branch_protection_approvals(rule: BranchProtectionRuleNode) -> int:
'Are approving reviews required to update matching branches.'
if rule['requiresApprovingReviews']:
return rule['requiredApprovingReviewCount']
return 0
|
Are approving reviews required to update matching branches.
|
src/ghaudit/schema.py
|
branch_protection_approvals
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_approvals(rule: BranchProtectionRuleNode) -> int:
if rule['requiresApprovingReviews']:
return rule['requiredApprovingReviewCount']
return 0
|
def branch_protection_approvals(rule: BranchProtectionRuleNode) -> int:
if rule['requiresApprovingReviews']:
return rule['requiredApprovingReviewCount']
return 0<|docstring|>Are approving reviews required to update matching branches.<|endoftext|>
|
fdcb93f832bf945bc9083674585ddf435b1bae74c48be36dd835aa07209bd2c1
|
def branch_protection_owner_approval(rule: BranchProtectionRuleNode) -> bool:
'Are approving reviews required to update matching branches.'
return rule['requiresCodeOwnerReviews']
|
Are approving reviews required to update matching branches.
|
src/ghaudit/schema.py
|
branch_protection_owner_approval
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_owner_approval(rule: BranchProtectionRuleNode) -> bool:
return rule['requiresCodeOwnerReviews']
|
def branch_protection_owner_approval(rule: BranchProtectionRuleNode) -> bool:
return rule['requiresCodeOwnerReviews']<|docstring|>Are approving reviews required to update matching branches.<|endoftext|>
|
29c53bd4057af9f8915af49d2a514aced04f75ab1c515d826acf06db765141bc
|
def branch_protection_commit_signatures(rule: BranchProtectionRuleNode) -> bool:
'Are commits required to be signed to update matching branches.'
return rule['requiresCommitSignatures']
|
Are commits required to be signed to update matching branches.
|
src/ghaudit/schema.py
|
branch_protection_commit_signatures
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_commit_signatures(rule: BranchProtectionRuleNode) -> bool:
return rule['requiresCommitSignatures']
|
def branch_protection_commit_signatures(rule: BranchProtectionRuleNode) -> bool:
return rule['requiresCommitSignatures']<|docstring|>Are commits required to be signed to update matching branches.<|endoftext|>
|
b165a00572f71fdfe62fc3e952e93a0b21dae96096f25f9b8be130079d0dea55
|
def branch_protection_linear_history(rule: BranchProtectionRuleNode) -> bool:
'Are merge commits prohibited from being pushed to matching branches.'
return rule['requiresLinearHistory']
|
Are merge commits prohibited from being pushed to matching branches.
|
src/ghaudit/schema.py
|
branch_protection_linear_history
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_linear_history(rule: BranchProtectionRuleNode) -> bool:
return rule['requiresLinearHistory']
|
def branch_protection_linear_history(rule: BranchProtectionRuleNode) -> bool:
return rule['requiresLinearHistory']<|docstring|>Are merge commits prohibited from being pushed to matching branches.<|endoftext|>
|
a6ca7871de44398e503f36ada5250b0b04848786cb7ccf77f0e72428663f2707
|
def branch_protection_restrict_pushes(rule: BranchProtectionRuleNode) -> bool:
'Is pushing to matching branches restricted.'
return rule['restrictsPushes']
|
Is pushing to matching branches restricted.
|
src/ghaudit/schema.py
|
branch_protection_restrict_pushes
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_restrict_pushes(rule: BranchProtectionRuleNode) -> bool:
return rule['restrictsPushes']
|
def branch_protection_restrict_pushes(rule: BranchProtectionRuleNode) -> bool:
return rule['restrictsPushes']<|docstring|>Is pushing to matching branches restricted.<|endoftext|>
|
18a5af7dda4e32092cf67ea272473c258647268c95da198a5d106fc17565c133
|
def branch_protection_restrict_deletion(rule: BranchProtectionRuleNode) -> bool:
'Can matching branches be deleted.'
return (not rule['allowsDeletions'])
|
Can matching branches be deleted.
|
src/ghaudit/schema.py
|
branch_protection_restrict_deletion
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_restrict_deletion(rule: BranchProtectionRuleNode) -> bool:
return (not rule['allowsDeletions'])
|
def branch_protection_restrict_deletion(rule: BranchProtectionRuleNode) -> bool:
return (not rule['allowsDeletions'])<|docstring|>Can matching branches be deleted.<|endoftext|>
|
3de1ea710bbfdd301be7b33be442957573a92a3a75b45959acf142fc3a80b61a
|
def branch_protection_creator(rule: BranchProtectionRuleNode) -> str:
'Return the actor who created the given branch protection rule.'
return rule['creator']['login']
|
Return the actor who created the given branch protection rule.
|
src/ghaudit/schema.py
|
branch_protection_creator
|
DistantThunder/ghaudit
| 1 |
python
|
def branch_protection_creator(rule: BranchProtectionRuleNode) -> str:
return rule['creator']['login']
|
def branch_protection_creator(rule: BranchProtectionRuleNode) -> str:
return rule['creator']['login']<|docstring|>Return the actor who created the given branch protection rule.<|endoftext|>
|
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