body_hash
stringlengths 64
64
| body
stringlengths 23
109k
| docstring
stringlengths 1
57k
| path
stringlengths 4
198
| name
stringlengths 1
115
| repository_name
stringlengths 7
111
| repository_stars
float64 0
191k
| lang
stringclasses 1
value | body_without_docstring
stringlengths 14
108k
| unified
stringlengths 45
133k
|
|---|---|---|---|---|---|---|---|---|---|
8cd5220b750084ed02c23b99d76c506a8c494fb7d97430b4ed562467b6ad4730
|
@storage_service_port.setter
def storage_service_port(self, storage_service_port):
'Sets the storage_service_port of this VCloudRestCloud.\n\n\n :param storage_service_port: The storage_service_port of this VCloudRestCloud. # noqa: E501\n :type: int\n '
self._storage_service_port = storage_service_port
|
Sets the storage_service_port of this VCloudRestCloud.
:param storage_service_port: The storage_service_port of this VCloudRestCloud. # noqa: E501
:type: int
|
cons3rt/models/v_cloud_rest_cloud.py
|
storage_service_port
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@storage_service_port.setter
def storage_service_port(self, storage_service_port):
'Sets the storage_service_port of this VCloudRestCloud.\n\n\n :param storage_service_port: The storage_service_port of this VCloudRestCloud. # noqa: E501\n :type: int\n '
self._storage_service_port = storage_service_port
|
@storage_service_port.setter
def storage_service_port(self, storage_service_port):
'Sets the storage_service_port of this VCloudRestCloud.\n\n\n :param storage_service_port: The storage_service_port of this VCloudRestCloud. # noqa: E501\n :type: int\n '
self._storage_service_port = storage_service_port<|docstring|>Sets the storage_service_port of this VCloudRestCloud.
:param storage_service_port: The storage_service_port of this VCloudRestCloud. # noqa: E501
:type: int<|endoftext|>
|
54ed451f6fdaea3a56baa9a71e784fe5f7f36ead57e0ad73b99f30d3da9e489b
|
@property
def storage_service_protocol(self):
'Gets the storage_service_protocol of this VCloudRestCloud. # noqa: E501\n\n\n :return: The storage_service_protocol of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._storage_service_protocol
|
Gets the storage_service_protocol of this VCloudRestCloud. # noqa: E501
:return: The storage_service_protocol of this VCloudRestCloud. # noqa: E501
:rtype: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
storage_service_protocol
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@property
def storage_service_protocol(self):
'Gets the storage_service_protocol of this VCloudRestCloud. # noqa: E501\n\n\n :return: The storage_service_protocol of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._storage_service_protocol
|
@property
def storage_service_protocol(self):
'Gets the storage_service_protocol of this VCloudRestCloud. # noqa: E501\n\n\n :return: The storage_service_protocol of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._storage_service_protocol<|docstring|>Gets the storage_service_protocol of this VCloudRestCloud. # noqa: E501
:return: The storage_service_protocol of this VCloudRestCloud. # noqa: E501
:rtype: str<|endoftext|>
|
19d520d08feabe96133f6cc7909c280f7e3ca23fd0f927fd7102c4311997bf5d
|
@storage_service_protocol.setter
def storage_service_protocol(self, storage_service_protocol):
'Sets the storage_service_protocol of this VCloudRestCloud.\n\n\n :param storage_service_protocol: The storage_service_protocol of this VCloudRestCloud. # noqa: E501\n :type: str\n '
self._storage_service_protocol = storage_service_protocol
|
Sets the storage_service_protocol of this VCloudRestCloud.
:param storage_service_protocol: The storage_service_protocol of this VCloudRestCloud. # noqa: E501
:type: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
storage_service_protocol
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@storage_service_protocol.setter
def storage_service_protocol(self, storage_service_protocol):
'Sets the storage_service_protocol of this VCloudRestCloud.\n\n\n :param storage_service_protocol: The storage_service_protocol of this VCloudRestCloud. # noqa: E501\n :type: str\n '
self._storage_service_protocol = storage_service_protocol
|
@storage_service_protocol.setter
def storage_service_protocol(self, storage_service_protocol):
'Sets the storage_service_protocol of this VCloudRestCloud.\n\n\n :param storage_service_protocol: The storage_service_protocol of this VCloudRestCloud. # noqa: E501\n :type: str\n '
self._storage_service_protocol = storage_service_protocol<|docstring|>Sets the storage_service_protocol of this VCloudRestCloud.
:param storage_service_protocol: The storage_service_protocol of this VCloudRestCloud. # noqa: E501
:type: str<|endoftext|>
|
ed836ea8798d22a3f69bad9eb6ca1757ecf45264eb546cd151687a357173345c
|
@property
def storage_service_username(self):
'Gets the storage_service_username of this VCloudRestCloud. # noqa: E501\n\n\n :return: The storage_service_username of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._storage_service_username
|
Gets the storage_service_username of this VCloudRestCloud. # noqa: E501
:return: The storage_service_username of this VCloudRestCloud. # noqa: E501
:rtype: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
storage_service_username
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@property
def storage_service_username(self):
'Gets the storage_service_username of this VCloudRestCloud. # noqa: E501\n\n\n :return: The storage_service_username of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._storage_service_username
|
@property
def storage_service_username(self):
'Gets the storage_service_username of this VCloudRestCloud. # noqa: E501\n\n\n :return: The storage_service_username of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._storage_service_username<|docstring|>Gets the storage_service_username of this VCloudRestCloud. # noqa: E501
:return: The storage_service_username of this VCloudRestCloud. # noqa: E501
:rtype: str<|endoftext|>
|
ba164f63dff3262ced9798bb3026e3649a170d2ed18115f5708bb0be8c3808e8
|
@storage_service_username.setter
def storage_service_username(self, storage_service_username):
'Sets the storage_service_username of this VCloudRestCloud.\n\n\n :param storage_service_username: The storage_service_username of this VCloudRestCloud. # noqa: E501\n :type: str\n '
self._storage_service_username = storage_service_username
|
Sets the storage_service_username of this VCloudRestCloud.
:param storage_service_username: The storage_service_username of this VCloudRestCloud. # noqa: E501
:type: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
storage_service_username
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@storage_service_username.setter
def storage_service_username(self, storage_service_username):
'Sets the storage_service_username of this VCloudRestCloud.\n\n\n :param storage_service_username: The storage_service_username of this VCloudRestCloud. # noqa: E501\n :type: str\n '
self._storage_service_username = storage_service_username
|
@storage_service_username.setter
def storage_service_username(self, storage_service_username):
'Sets the storage_service_username of this VCloudRestCloud.\n\n\n :param storage_service_username: The storage_service_username of this VCloudRestCloud. # noqa: E501\n :type: str\n '
self._storage_service_username = storage_service_username<|docstring|>Sets the storage_service_username of this VCloudRestCloud.
:param storage_service_username: The storage_service_username of this VCloudRestCloud. # noqa: E501
:type: str<|endoftext|>
|
512788ca1643ead00980389290fdcb5bad3fcce131125121ec8cf68e4a542313
|
@property
def username(self):
'Gets the username of this VCloudRestCloud. # noqa: E501\n\n\n :return: The username of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._username
|
Gets the username of this VCloudRestCloud. # noqa: E501
:return: The username of this VCloudRestCloud. # noqa: E501
:rtype: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
username
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@property
def username(self):
'Gets the username of this VCloudRestCloud. # noqa: E501\n\n\n :return: The username of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._username
|
@property
def username(self):
'Gets the username of this VCloudRestCloud. # noqa: E501\n\n\n :return: The username of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._username<|docstring|>Gets the username of this VCloudRestCloud. # noqa: E501
:return: The username of this VCloudRestCloud. # noqa: E501
:rtype: str<|endoftext|>
|
121f37305a7a886713f0d177769940f1dbe51b7799380b09fb710357424f632a
|
@username.setter
def username(self, username):
'Sets the username of this VCloudRestCloud.\n\n\n :param username: The username of this VCloudRestCloud. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (username is None)):
raise ValueError('Invalid value for `username`, must not be `None`')
self._username = username
|
Sets the username of this VCloudRestCloud.
:param username: The username of this VCloudRestCloud. # noqa: E501
:type: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
username
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@username.setter
def username(self, username):
'Sets the username of this VCloudRestCloud.\n\n\n :param username: The username of this VCloudRestCloud. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (username is None)):
raise ValueError('Invalid value for `username`, must not be `None`')
self._username = username
|
@username.setter
def username(self, username):
'Sets the username of this VCloudRestCloud.\n\n\n :param username: The username of this VCloudRestCloud. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (username is None)):
raise ValueError('Invalid value for `username`, must not be `None`')
self._username = username<|docstring|>Sets the username of this VCloudRestCloud.
:param username: The username of this VCloudRestCloud. # noqa: E501
:type: str<|endoftext|>
|
4475fa8d40c383e06258c9e2f9c46ebdb40fd277495ced28dcdfe085cffacc48
|
@property
def vsphere_api_version(self):
'Gets the vsphere_api_version of this VCloudRestCloud. # noqa: E501\n\n\n :return: The vsphere_api_version of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._vsphere_api_version
|
Gets the vsphere_api_version of this VCloudRestCloud. # noqa: E501
:return: The vsphere_api_version of this VCloudRestCloud. # noqa: E501
:rtype: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
vsphere_api_version
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@property
def vsphere_api_version(self):
'Gets the vsphere_api_version of this VCloudRestCloud. # noqa: E501\n\n\n :return: The vsphere_api_version of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._vsphere_api_version
|
@property
def vsphere_api_version(self):
'Gets the vsphere_api_version of this VCloudRestCloud. # noqa: E501\n\n\n :return: The vsphere_api_version of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._vsphere_api_version<|docstring|>Gets the vsphere_api_version of this VCloudRestCloud. # noqa: E501
:return: The vsphere_api_version of this VCloudRestCloud. # noqa: E501
:rtype: str<|endoftext|>
|
24a89ada2df1ff4c788ac10bc381b4e600d318cd935e504cd4592e7c7c5d1a48
|
@vsphere_api_version.setter
def vsphere_api_version(self, vsphere_api_version):
'Sets the vsphere_api_version of this VCloudRestCloud.\n\n\n :param vsphere_api_version: The vsphere_api_version of this VCloudRestCloud. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (vsphere_api_version is not None) and (len(vsphere_api_version) > 6)):
raise ValueError('Invalid value for `vsphere_api_version`, length must be less than or equal to `6`')
if (self.local_vars_configuration.client_side_validation and (vsphere_api_version is not None) and (len(vsphere_api_version) < 1)):
raise ValueError('Invalid value for `vsphere_api_version`, length must be greater than or equal to `1`')
self._vsphere_api_version = vsphere_api_version
|
Sets the vsphere_api_version of this VCloudRestCloud.
:param vsphere_api_version: The vsphere_api_version of this VCloudRestCloud. # noqa: E501
:type: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
vsphere_api_version
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@vsphere_api_version.setter
def vsphere_api_version(self, vsphere_api_version):
'Sets the vsphere_api_version of this VCloudRestCloud.\n\n\n :param vsphere_api_version: The vsphere_api_version of this VCloudRestCloud. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (vsphere_api_version is not None) and (len(vsphere_api_version) > 6)):
raise ValueError('Invalid value for `vsphere_api_version`, length must be less than or equal to `6`')
if (self.local_vars_configuration.client_side_validation and (vsphere_api_version is not None) and (len(vsphere_api_version) < 1)):
raise ValueError('Invalid value for `vsphere_api_version`, length must be greater than or equal to `1`')
self._vsphere_api_version = vsphere_api_version
|
@vsphere_api_version.setter
def vsphere_api_version(self, vsphere_api_version):
'Sets the vsphere_api_version of this VCloudRestCloud.\n\n\n :param vsphere_api_version: The vsphere_api_version of this VCloudRestCloud. # noqa: E501\n :type: str\n '
if (self.local_vars_configuration.client_side_validation and (vsphere_api_version is not None) and (len(vsphere_api_version) > 6)):
raise ValueError('Invalid value for `vsphere_api_version`, length must be less than or equal to `6`')
if (self.local_vars_configuration.client_side_validation and (vsphere_api_version is not None) and (len(vsphere_api_version) < 1)):
raise ValueError('Invalid value for `vsphere_api_version`, length must be greater than or equal to `1`')
self._vsphere_api_version = vsphere_api_version<|docstring|>Sets the vsphere_api_version of this VCloudRestCloud.
:param vsphere_api_version: The vsphere_api_version of this VCloudRestCloud. # noqa: E501
:type: str<|endoftext|>
|
5374a99ebae71e16277de35f589145cb317cbded3fa6616ff9cfbc6b1c138d4a
|
@property
def vsphere_host(self):
'Gets the vsphere_host of this VCloudRestCloud. # noqa: E501\n\n\n :return: The vsphere_host of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._vsphere_host
|
Gets the vsphere_host of this VCloudRestCloud. # noqa: E501
:return: The vsphere_host of this VCloudRestCloud. # noqa: E501
:rtype: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
vsphere_host
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@property
def vsphere_host(self):
'Gets the vsphere_host of this VCloudRestCloud. # noqa: E501\n\n\n :return: The vsphere_host of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._vsphere_host
|
@property
def vsphere_host(self):
'Gets the vsphere_host of this VCloudRestCloud. # noqa: E501\n\n\n :return: The vsphere_host of this VCloudRestCloud. # noqa: E501\n :rtype: str\n '
return self._vsphere_host<|docstring|>Gets the vsphere_host of this VCloudRestCloud. # noqa: E501
:return: The vsphere_host of this VCloudRestCloud. # noqa: E501
:rtype: str<|endoftext|>
|
c717f55584408d4b3221d9468e32d65e1bc258a7b537b92bbc07f6a88284c8d1
|
@vsphere_host.setter
def vsphere_host(self, vsphere_host):
'Sets the vsphere_host of this VCloudRestCloud.\n\n\n :param vsphere_host: The vsphere_host of this VCloudRestCloud. # noqa: E501\n :type: str\n '
self._vsphere_host = vsphere_host
|
Sets the vsphere_host of this VCloudRestCloud.
:param vsphere_host: The vsphere_host of this VCloudRestCloud. # noqa: E501
:type: str
|
cons3rt/models/v_cloud_rest_cloud.py
|
vsphere_host
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@vsphere_host.setter
def vsphere_host(self, vsphere_host):
'Sets the vsphere_host of this VCloudRestCloud.\n\n\n :param vsphere_host: The vsphere_host of this VCloudRestCloud. # noqa: E501\n :type: str\n '
self._vsphere_host = vsphere_host
|
@vsphere_host.setter
def vsphere_host(self, vsphere_host):
'Sets the vsphere_host of this VCloudRestCloud.\n\n\n :param vsphere_host: The vsphere_host of this VCloudRestCloud. # noqa: E501\n :type: str\n '
self._vsphere_host = vsphere_host<|docstring|>Sets the vsphere_host of this VCloudRestCloud.
:param vsphere_host: The vsphere_host of this VCloudRestCloud. # noqa: E501
:type: str<|endoftext|>
|
88e74ba67ad2e85c349a810dc265074e76124002e35d6764fbcd2d2513b6c1eb
|
@property
def vsphere_port(self):
'Gets the vsphere_port of this VCloudRestCloud. # noqa: E501\n\n\n :return: The vsphere_port of this VCloudRestCloud. # noqa: E501\n :rtype: int\n '
return self._vsphere_port
|
Gets the vsphere_port of this VCloudRestCloud. # noqa: E501
:return: The vsphere_port of this VCloudRestCloud. # noqa: E501
:rtype: int
|
cons3rt/models/v_cloud_rest_cloud.py
|
vsphere_port
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@property
def vsphere_port(self):
'Gets the vsphere_port of this VCloudRestCloud. # noqa: E501\n\n\n :return: The vsphere_port of this VCloudRestCloud. # noqa: E501\n :rtype: int\n '
return self._vsphere_port
|
@property
def vsphere_port(self):
'Gets the vsphere_port of this VCloudRestCloud. # noqa: E501\n\n\n :return: The vsphere_port of this VCloudRestCloud. # noqa: E501\n :rtype: int\n '
return self._vsphere_port<|docstring|>Gets the vsphere_port of this VCloudRestCloud. # noqa: E501
:return: The vsphere_port of this VCloudRestCloud. # noqa: E501
:rtype: int<|endoftext|>
|
279bf8720a0051233cd8302969a89790c8e6dc80f142cbbe3df01ea0e9e380be
|
@vsphere_port.setter
def vsphere_port(self, vsphere_port):
'Sets the vsphere_port of this VCloudRestCloud.\n\n\n :param vsphere_port: The vsphere_port of this VCloudRestCloud. # noqa: E501\n :type: int\n '
self._vsphere_port = vsphere_port
|
Sets the vsphere_port of this VCloudRestCloud.
:param vsphere_port: The vsphere_port of this VCloudRestCloud. # noqa: E501
:type: int
|
cons3rt/models/v_cloud_rest_cloud.py
|
vsphere_port
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
@vsphere_port.setter
def vsphere_port(self, vsphere_port):
'Sets the vsphere_port of this VCloudRestCloud.\n\n\n :param vsphere_port: The vsphere_port of this VCloudRestCloud. # noqa: E501\n :type: int\n '
self._vsphere_port = vsphere_port
|
@vsphere_port.setter
def vsphere_port(self, vsphere_port):
'Sets the vsphere_port of this VCloudRestCloud.\n\n\n :param vsphere_port: The vsphere_port of this VCloudRestCloud. # noqa: E501\n :type: int\n '
self._vsphere_port = vsphere_port<|docstring|>Sets the vsphere_port of this VCloudRestCloud.
:param vsphere_port: The vsphere_port of this VCloudRestCloud. # noqa: E501
:type: int<|endoftext|>
|
5a4e41bb6a0def746593298cb605df98f1366e957c4ca89b12010ea7db707963
|
def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result
|
Returns the model properties as a dict
|
cons3rt/models/v_cloud_rest_cloud.py
|
to_dict
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result
|
def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
return result<|docstring|>Returns the model properties as a dict<|endoftext|>
|
cbb19eaa2fc8a113d9e32f924ef280a7e97563f8915f94f65dab438997af2e99
|
def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict())
|
Returns the string representation of the model
|
cons3rt/models/v_cloud_rest_cloud.py
|
to_str
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
def to_str(self):
return pprint.pformat(self.to_dict())
|
def to_str(self):
return pprint.pformat(self.to_dict())<|docstring|>Returns the string representation of the model<|endoftext|>
|
772243a2c2b3261a9b954d07aaf295e3c1242a579a495e2d6a5679c677861703
|
def __repr__(self):
'For `print` and `pprint`'
return self.to_str()
|
For `print` and `pprint`
|
cons3rt/models/v_cloud_rest_cloud.py
|
__repr__
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
def __repr__(self):
return self.to_str()
|
def __repr__(self):
return self.to_str()<|docstring|>For `print` and `pprint`<|endoftext|>
|
75c9485b0d88c948840ea411e5f3404ed1b67d735bd968bd5cb549c6382ee480
|
def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, VCloudRestCloud)):
return False
return (self.to_dict() == other.to_dict())
|
Returns true if both objects are equal
|
cons3rt/models/v_cloud_rest_cloud.py
|
__eq__
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
def __eq__(self, other):
if (not isinstance(other, VCloudRestCloud)):
return False
return (self.to_dict() == other.to_dict())
|
def __eq__(self, other):
if (not isinstance(other, VCloudRestCloud)):
return False
return (self.to_dict() == other.to_dict())<|docstring|>Returns true if both objects are equal<|endoftext|>
|
315657c2974adf5a6512059304866be1f46fb6bef28122f7f6b9d91d4edb464d
|
def __ne__(self, other):
'Returns true if both objects are not equal'
if (not isinstance(other, VCloudRestCloud)):
return True
return (self.to_dict() != other.to_dict())
|
Returns true if both objects are not equal
|
cons3rt/models/v_cloud_rest_cloud.py
|
__ne__
|
cons3rt/cons3rt-python-sdk
| 0 |
python
|
def __ne__(self, other):
if (not isinstance(other, VCloudRestCloud)):
return True
return (self.to_dict() != other.to_dict())
|
def __ne__(self, other):
if (not isinstance(other, VCloudRestCloud)):
return True
return (self.to_dict() != other.to_dict())<|docstring|>Returns true if both objects are not equal<|endoftext|>
|
0f21fba14581c5a1234a4c1755980ff400da8b234df474dd1a48091aa5b8e4bd
|
@event('manager.daemon.started')
@event('manager.config_updated')
def setup_scheduler(manager):
'Starts, stops or restarts the scheduler when config changes.'
if (not manager.is_daemon):
return
scheduler = Scheduler(manager)
if scheduler.is_alive():
scheduler.stop()
if manager.config.get('schedules', True):
scheduler.start()
|
Starts, stops or restarts the scheduler when config changes.
|
flexget/plugins/daemon/scheduler.py
|
setup_scheduler
|
fcharlier/Flexget
| 0 |
python
|
@event('manager.daemon.started')
@event('manager.config_updated')
def setup_scheduler(manager):
if (not manager.is_daemon):
return
scheduler = Scheduler(manager)
if scheduler.is_alive():
scheduler.stop()
if manager.config.get('schedules', True):
scheduler.start()
|
@event('manager.daemon.started')
@event('manager.config_updated')
def setup_scheduler(manager):
if (not manager.is_daemon):
return
scheduler = Scheduler(manager)
if scheduler.is_alive():
scheduler.stop()
if manager.config.get('schedules', True):
scheduler.start()<|docstring|>Starts, stops or restarts the scheduler when config changes.<|endoftext|>
|
4cee429c4d167d0bf6e4538b6b76825ed334b801dfcde71be2f8e3968b4f4625
|
def load_schedules(self):
'Clears current schedules and loads them from the config.'
with self.triggers_lock:
self.triggers = []
if ('schedules' not in self.manager.config):
log.info('No schedules defined in config. Defaulting to run all tasks on a 1 hour interval.')
for item in self.manager.config.get('schedules', [{'tasks': ['*'], 'interval': {'hours': 1}}]):
tasks = item['tasks']
if (not isinstance(tasks, list)):
tasks = [tasks]
self.triggers.append(Trigger(item['interval'], tasks, options={'cron': True}))
|
Clears current schedules and loads them from the config.
|
flexget/plugins/daemon/scheduler.py
|
load_schedules
|
fcharlier/Flexget
| 0 |
python
|
def load_schedules(self):
with self.triggers_lock:
self.triggers = []
if ('schedules' not in self.manager.config):
log.info('No schedules defined in config. Defaulting to run all tasks on a 1 hour interval.')
for item in self.manager.config.get('schedules', [{'tasks': ['*'], 'interval': {'hours': 1}}]):
tasks = item['tasks']
if (not isinstance(tasks, list)):
tasks = [tasks]
self.triggers.append(Trigger(item['interval'], tasks, options={'cron': True}))
|
def load_schedules(self):
with self.triggers_lock:
self.triggers = []
if ('schedules' not in self.manager.config):
log.info('No schedules defined in config. Defaulting to run all tasks on a 1 hour interval.')
for item in self.manager.config.get('schedules', [{'tasks': ['*'], 'interval': {'hours': 1}}]):
tasks = item['tasks']
if (not isinstance(tasks, list)):
tasks = [tasks]
self.triggers.append(Trigger(item['interval'], tasks, options={'cron': True}))<|docstring|>Clears current schedules and loads them from the config.<|endoftext|>
|
7fa49bb445ff3f6c722707b1f2ea918f23dc19e10c737e5b75d631de59bc9dff
|
def __init__(self, interval, tasks, options=None):
'\n :param dict interval: An interval dictionary from the config.\n :param list tasks: List of task names specified to run. Wildcards are allowed.\n :param dict options: Dictionary of options that should be applied to this run.\n '
self.tasks = tasks
self.options = options
self.unit = None
self.amount = None
self.on_day = None
self.at_time = None
self.last_run = None
self.run_at = None
self.interval = interval
self._get_db_last_run()
self.schedule_next_run()
|
:param dict interval: An interval dictionary from the config.
:param list tasks: List of task names specified to run. Wildcards are allowed.
:param dict options: Dictionary of options that should be applied to this run.
|
flexget/plugins/daemon/scheduler.py
|
__init__
|
fcharlier/Flexget
| 0 |
python
|
def __init__(self, interval, tasks, options=None):
'\n :param dict interval: An interval dictionary from the config.\n :param list tasks: List of task names specified to run. Wildcards are allowed.\n :param dict options: Dictionary of options that should be applied to this run.\n '
self.tasks = tasks
self.options = options
self.unit = None
self.amount = None
self.on_day = None
self.at_time = None
self.last_run = None
self.run_at = None
self.interval = interval
self._get_db_last_run()
self.schedule_next_run()
|
def __init__(self, interval, tasks, options=None):
'\n :param dict interval: An interval dictionary from the config.\n :param list tasks: List of task names specified to run. Wildcards are allowed.\n :param dict options: Dictionary of options that should be applied to this run.\n '
self.tasks = tasks
self.options = options
self.unit = None
self.amount = None
self.on_day = None
self.at_time = None
self.last_run = None
self.run_at = None
self.interval = interval
self._get_db_last_run()
self.schedule_next_run()<|docstring|>:param dict interval: An interval dictionary from the config.
:param list tasks: List of task names specified to run. Wildcards are allowed.
:param dict options: Dictionary of options that should be applied to this run.<|endoftext|>
|
522c1dbffbeaf8318db5a928f82e2ab38b8576549c220fb735a107639f53e904
|
def trigger(self):
'Call when trigger is activated. Records current run time and schedules next run.'
self.last_run = datetime.now()
self._set_db_last_run()
self.schedule_next_run()
|
Call when trigger is activated. Records current run time and schedules next run.
|
flexget/plugins/daemon/scheduler.py
|
trigger
|
fcharlier/Flexget
| 0 |
python
|
def trigger(self):
self.last_run = datetime.now()
self._set_db_last_run()
self.schedule_next_run()
|
def trigger(self):
self.last_run = datetime.now()
self._set_db_last_run()
self.schedule_next_run()<|docstring|>Call when trigger is activated. Records current run time and schedules next run.<|endoftext|>
|
902f6f33e058b1eabb8d03e4e85c760eb878fe9f39abcc5beb181fefecb4e638
|
def __hash__(self):
'A unique id which describes this trigger.'
return hash((tuple(sorted(self.interval.iteritems())) + tuple(sorted(self.tasks))))
|
A unique id which describes this trigger.
|
flexget/plugins/daemon/scheduler.py
|
__hash__
|
fcharlier/Flexget
| 0 |
python
|
def __hash__(self):
return hash((tuple(sorted(self.interval.iteritems())) + tuple(sorted(self.tasks))))
|
def __hash__(self):
return hash((tuple(sorted(self.interval.iteritems())) + tuple(sorted(self.tasks))))<|docstring|>A unique id which describes this trigger.<|endoftext|>
|
48196dd13abf47b136970e2fe1616407526b7e94d16482bc6b72e4d036051811
|
def alphanum_key(s):
' Turn a string into a list of string and number chunks.\n "z23a" -> ["z", 23, "a"]\n '
return [tryint(c) for c in re.split('([0-9]+)', s)]
|
Turn a string into a list of string and number chunks.
"z23a" -> ["z", 23, "a"]
|
scripts/benchmark_kcl/merge_hemis.py
|
alphanum_key
|
amiralansary/BrainSurfaceTK
| 0 |
python
|
def alphanum_key(s):
' Turn a string into a list of string and number chunks.\n "z23a" -> ["z", 23, "a"]\n '
return [tryint(c) for c in re.split('([0-9]+)', s)]
|
def alphanum_key(s):
' Turn a string into a list of string and number chunks.\n "z23a" -> ["z", 23, "a"]\n '
return [tryint(c) for c in re.split('([0-9]+)', s)]<|docstring|>Turn a string into a list of string and number chunks.
"z23a" -> ["z", 23, "a"]<|endoftext|>
|
4abfa0763f4c698d3a23904e5b2affffd1dcdb4eef513292e21aba5463df9756
|
def make_dirs(path):
'make a new directory if path does not exist'
if os.path.exists(path):
return
os.makedirs(path)
|
make a new directory if path does not exist
|
scripts/benchmark_kcl/merge_hemis.py
|
make_dirs
|
amiralansary/BrainSurfaceTK
| 0 |
python
|
def make_dirs(path):
if os.path.exists(path):
return
os.makedirs(path)
|
def make_dirs(path):
if os.path.exists(path):
return
os.makedirs(path)<|docstring|>make a new directory if path does not exist<|endoftext|>
|
a3dc7b5997abec29f84a1585fdb63d65c742d94ac8754710c1ea438da818043f
|
def decimate_surface(target_file, reduce_by, save_dir, surf, tags='', ext='.vtk'):
'\n :param ext:\n :param tag:\n :param surf:\n :param reduce_by:\n :param save_path:\n :param target_file:\n :type surfaces: object\n '
filename = target_file.split('/')[(- 1)][:(- 4)]
print((((20 * '-') + filename) + (20 * '-')))
for (i, tag) in enumerate(tags):
save_reduce_path = os.path.join(save_dir, ('reducedto_' + tag))
output_dir = os.path.join(save_reduce_path, surf, ext[1:])
make_dirs(output_dir)
output_file = os.path.join(output_dir, (((filename + '_') + tag) + ext))
cmd = (((((('mirtk decimate-surface ' + target_file) + ' ') + output_file) + ' -reduceby ') + str(reduce_by[i])) + ' -preservetopology 1 -splitangle 75')
print((((10 * '-') + ' decimate-surface ') + (10 * '-')))
print(cmd)
os.system(cmd)
print('--')
|
:param ext:
:param tag:
:param surf:
:param reduce_by:
:param save_path:
:param target_file:
:type surfaces: object
|
scripts/benchmark_kcl/merge_hemis.py
|
decimate_surface
|
amiralansary/BrainSurfaceTK
| 0 |
python
|
def decimate_surface(target_file, reduce_by, save_dir, surf, tags=, ext='.vtk'):
'\n :param ext:\n :param tag:\n :param surf:\n :param reduce_by:\n :param save_path:\n :param target_file:\n :type surfaces: object\n '
filename = target_file.split('/')[(- 1)][:(- 4)]
print((((20 * '-') + filename) + (20 * '-')))
for (i, tag) in enumerate(tags):
save_reduce_path = os.path.join(save_dir, ('reducedto_' + tag))
output_dir = os.path.join(save_reduce_path, surf, ext[1:])
make_dirs(output_dir)
output_file = os.path.join(output_dir, (((filename + '_') + tag) + ext))
cmd = (((((('mirtk decimate-surface ' + target_file) + ' ') + output_file) + ' -reduceby ') + str(reduce_by[i])) + ' -preservetopology 1 -splitangle 75')
print((((10 * '-') + ' decimate-surface ') + (10 * '-')))
print(cmd)
os.system(cmd)
print('--')
|
def decimate_surface(target_file, reduce_by, save_dir, surf, tags=, ext='.vtk'):
'\n :param ext:\n :param tag:\n :param surf:\n :param reduce_by:\n :param save_path:\n :param target_file:\n :type surfaces: object\n '
filename = target_file.split('/')[(- 1)][:(- 4)]
print((((20 * '-') + filename) + (20 * '-')))
for (i, tag) in enumerate(tags):
save_reduce_path = os.path.join(save_dir, ('reducedto_' + tag))
output_dir = os.path.join(save_reduce_path, surf, ext[1:])
make_dirs(output_dir)
output_file = os.path.join(output_dir, (((filename + '_') + tag) + ext))
cmd = (((((('mirtk decimate-surface ' + target_file) + ' ') + output_file) + ' -reduceby ') + str(reduce_by[i])) + ' -preservetopology 1 -splitangle 75')
print((((10 * '-') + ' decimate-surface ') + (10 * '-')))
print(cmd)
os.system(cmd)
print('--')<|docstring|>:param ext:
:param tag:
:param surf:
:param reduce_by:
:param save_path:
:param target_file:
:type surfaces: object<|endoftext|>
|
0562cebec081580930e2240b6f687f718025d02fc919626b6dba4a84c2a55ee0
|
def make_image_key(video_id, timestamp):
'Returns a unique identifier for a video id & timestamp.'
return ('%s,%04d' % (video_id, int(timestamp)))
|
Returns a unique identifier for a video id & timestamp.
|
evaluation/get_ava_performance_custom.py
|
make_image_key
|
oulutan/ActorConditionedAttentionMaps
| 23 |
python
|
def make_image_key(video_id, timestamp):
return ('%s,%04d' % (video_id, int(timestamp)))
|
def make_image_key(video_id, timestamp):
return ('%s,%04d' % (video_id, int(timestamp)))<|docstring|>Returns a unique identifier for a video id & timestamp.<|endoftext|>
|
937cac49154fd19773f83952e2fd70a6d4b649ec921e649da7b1d0aae33fb163
|
def read_csv(csv_file, class_whitelist=None):
'Loads boxes and class labels from a CSV file in the AVA format.\n\n CSV file format described at https://research.google.com/ava/download.html.\n\n Args:\n csv_file: A file object.\n class_whitelist: If provided, boxes corresponding to (integer) class labels\n not in this set are skipped.\n\n Returns:\n boxes: A dictionary mapping each unique image key (string) to a list of\n boxes, given as coordinates [y1, x1, y2, x2].\n labels: A dictionary mapping each unique image key (string) to a list of\n integer class lables, matching the corresponding box in `boxes`.\n scores: A dictionary mapping each unique image key (string) to a list of\n score values lables, matching the corresponding label in `labels`. If\n scores are not provided in the csv, then they will default to 1.0.\n '
start = time.time()
boxes = defaultdict(list)
labels = defaultdict(list)
scores = defaultdict(list)
reader = csv.reader(csv_file)
for row in reader:
assert (len(row) in [7, 8]), ('Wrong number of columns: ' + row)
image_key = make_image_key(row[0], row[1])
(x1, y1, x2, y2) = [float(n) for n in row[2:6]]
action_id = int(row[6])
if (class_whitelist and (action_id not in class_whitelist)):
continue
score = 1.0
if (len(row) == 8):
score = float(row[7])
boxes[image_key].append([y1, x1, y2, x2])
labels[image_key].append(action_id)
scores[image_key].append(score)
print_time(('read file ' + csv_file.name), start)
return (boxes, labels, scores)
|
Loads boxes and class labels from a CSV file in the AVA format.
CSV file format described at https://research.google.com/ava/download.html.
Args:
csv_file: A file object.
class_whitelist: If provided, boxes corresponding to (integer) class labels
not in this set are skipped.
Returns:
boxes: A dictionary mapping each unique image key (string) to a list of
boxes, given as coordinates [y1, x1, y2, x2].
labels: A dictionary mapping each unique image key (string) to a list of
integer class lables, matching the corresponding box in `boxes`.
scores: A dictionary mapping each unique image key (string) to a list of
score values lables, matching the corresponding label in `labels`. If
scores are not provided in the csv, then they will default to 1.0.
|
evaluation/get_ava_performance_custom.py
|
read_csv
|
oulutan/ActorConditionedAttentionMaps
| 23 |
python
|
def read_csv(csv_file, class_whitelist=None):
'Loads boxes and class labels from a CSV file in the AVA format.\n\n CSV file format described at https://research.google.com/ava/download.html.\n\n Args:\n csv_file: A file object.\n class_whitelist: If provided, boxes corresponding to (integer) class labels\n not in this set are skipped.\n\n Returns:\n boxes: A dictionary mapping each unique image key (string) to a list of\n boxes, given as coordinates [y1, x1, y2, x2].\n labels: A dictionary mapping each unique image key (string) to a list of\n integer class lables, matching the corresponding box in `boxes`.\n scores: A dictionary mapping each unique image key (string) to a list of\n score values lables, matching the corresponding label in `labels`. If\n scores are not provided in the csv, then they will default to 1.0.\n '
start = time.time()
boxes = defaultdict(list)
labels = defaultdict(list)
scores = defaultdict(list)
reader = csv.reader(csv_file)
for row in reader:
assert (len(row) in [7, 8]), ('Wrong number of columns: ' + row)
image_key = make_image_key(row[0], row[1])
(x1, y1, x2, y2) = [float(n) for n in row[2:6]]
action_id = int(row[6])
if (class_whitelist and (action_id not in class_whitelist)):
continue
score = 1.0
if (len(row) == 8):
score = float(row[7])
boxes[image_key].append([y1, x1, y2, x2])
labels[image_key].append(action_id)
scores[image_key].append(score)
print_time(('read file ' + csv_file.name), start)
return (boxes, labels, scores)
|
def read_csv(csv_file, class_whitelist=None):
'Loads boxes and class labels from a CSV file in the AVA format.\n\n CSV file format described at https://research.google.com/ava/download.html.\n\n Args:\n csv_file: A file object.\n class_whitelist: If provided, boxes corresponding to (integer) class labels\n not in this set are skipped.\n\n Returns:\n boxes: A dictionary mapping each unique image key (string) to a list of\n boxes, given as coordinates [y1, x1, y2, x2].\n labels: A dictionary mapping each unique image key (string) to a list of\n integer class lables, matching the corresponding box in `boxes`.\n scores: A dictionary mapping each unique image key (string) to a list of\n score values lables, matching the corresponding label in `labels`. If\n scores are not provided in the csv, then they will default to 1.0.\n '
start = time.time()
boxes = defaultdict(list)
labels = defaultdict(list)
scores = defaultdict(list)
reader = csv.reader(csv_file)
for row in reader:
assert (len(row) in [7, 8]), ('Wrong number of columns: ' + row)
image_key = make_image_key(row[0], row[1])
(x1, y1, x2, y2) = [float(n) for n in row[2:6]]
action_id = int(row[6])
if (class_whitelist and (action_id not in class_whitelist)):
continue
score = 1.0
if (len(row) == 8):
score = float(row[7])
boxes[image_key].append([y1, x1, y2, x2])
labels[image_key].append(action_id)
scores[image_key].append(score)
print_time(('read file ' + csv_file.name), start)
return (boxes, labels, scores)<|docstring|>Loads boxes and class labels from a CSV file in the AVA format.
CSV file format described at https://research.google.com/ava/download.html.
Args:
csv_file: A file object.
class_whitelist: If provided, boxes corresponding to (integer) class labels
not in this set are skipped.
Returns:
boxes: A dictionary mapping each unique image key (string) to a list of
boxes, given as coordinates [y1, x1, y2, x2].
labels: A dictionary mapping each unique image key (string) to a list of
integer class lables, matching the corresponding box in `boxes`.
scores: A dictionary mapping each unique image key (string) to a list of
score values lables, matching the corresponding label in `labels`. If
scores are not provided in the csv, then they will default to 1.0.<|endoftext|>
|
2a4a2597a691ed16e7290fa3b47e8444646dcd9aac8b39fbf54810c662967923
|
def read_exclusions(exclusions_file):
'Reads a CSV file of excluded timestamps.\n\n Args:\n exclusions_file: A file object containing a csv of video-id,timestamp.\n\n Returns:\n A set of strings containing excluded image keys, e.g. "aaaaaaaaaaa,0904",\n or an empty set if exclusions file is None.\n '
excluded = set()
if exclusions_file:
reader = csv.reader(exclusions_file)
for row in reader:
assert (len(row) == 2), ('Expected only 2 columns, got: ' + row)
excluded.add(make_image_key(row[0], row[1]))
return excluded
|
Reads a CSV file of excluded timestamps.
Args:
exclusions_file: A file object containing a csv of video-id,timestamp.
Returns:
A set of strings containing excluded image keys, e.g. "aaaaaaaaaaa,0904",
or an empty set if exclusions file is None.
|
evaluation/get_ava_performance_custom.py
|
read_exclusions
|
oulutan/ActorConditionedAttentionMaps
| 23 |
python
|
def read_exclusions(exclusions_file):
'Reads a CSV file of excluded timestamps.\n\n Args:\n exclusions_file: A file object containing a csv of video-id,timestamp.\n\n Returns:\n A set of strings containing excluded image keys, e.g. "aaaaaaaaaaa,0904",\n or an empty set if exclusions file is None.\n '
excluded = set()
if exclusions_file:
reader = csv.reader(exclusions_file)
for row in reader:
assert (len(row) == 2), ('Expected only 2 columns, got: ' + row)
excluded.add(make_image_key(row[0], row[1]))
return excluded
|
def read_exclusions(exclusions_file):
'Reads a CSV file of excluded timestamps.\n\n Args:\n exclusions_file: A file object containing a csv of video-id,timestamp.\n\n Returns:\n A set of strings containing excluded image keys, e.g. "aaaaaaaaaaa,0904",\n or an empty set if exclusions file is None.\n '
excluded = set()
if exclusions_file:
reader = csv.reader(exclusions_file)
for row in reader:
assert (len(row) == 2), ('Expected only 2 columns, got: ' + row)
excluded.add(make_image_key(row[0], row[1]))
return excluded<|docstring|>Reads a CSV file of excluded timestamps.
Args:
exclusions_file: A file object containing a csv of video-id,timestamp.
Returns:
A set of strings containing excluded image keys, e.g. "aaaaaaaaaaa,0904",
or an empty set if exclusions file is None.<|endoftext|>
|
ca8ea7540e95784ab4a37a5acee59b39444f633385ee8a82bd35663bfcddee72
|
def read_labelmap(labelmap_file):
'Reads a labelmap without the dependency on protocol buffers.\n\n Args:\n labelmap_file: A file object containing a label map protocol buffer.\n\n Returns:\n labelmap: The label map in the form used by the object_detection_evaluation\n module - a list of {"id": integer, "name": classname } dicts.\n class_ids: A set containing all of the valid class id integers.\n '
labelmap = []
class_ids = set()
name = ''
class_id = ''
for line in labelmap_file:
if line.startswith(' name:'):
name = line.split('"')[1]
elif (line.startswith(' id:') or line.startswith(' label_id:')):
class_id = int(line.strip().split(' ')[(- 1)])
labelmap.append({'id': class_id, 'name': name})
class_ids.add(class_id)
return (labelmap, class_ids)
|
Reads a labelmap without the dependency on protocol buffers.
Args:
labelmap_file: A file object containing a label map protocol buffer.
Returns:
labelmap: The label map in the form used by the object_detection_evaluation
module - a list of {"id": integer, "name": classname } dicts.
class_ids: A set containing all of the valid class id integers.
|
evaluation/get_ava_performance_custom.py
|
read_labelmap
|
oulutan/ActorConditionedAttentionMaps
| 23 |
python
|
def read_labelmap(labelmap_file):
'Reads a labelmap without the dependency on protocol buffers.\n\n Args:\n labelmap_file: A file object containing a label map protocol buffer.\n\n Returns:\n labelmap: The label map in the form used by the object_detection_evaluation\n module - a list of {"id": integer, "name": classname } dicts.\n class_ids: A set containing all of the valid class id integers.\n '
labelmap = []
class_ids = set()
name =
class_id =
for line in labelmap_file:
if line.startswith(' name:'):
name = line.split('"')[1]
elif (line.startswith(' id:') or line.startswith(' label_id:')):
class_id = int(line.strip().split(' ')[(- 1)])
labelmap.append({'id': class_id, 'name': name})
class_ids.add(class_id)
return (labelmap, class_ids)
|
def read_labelmap(labelmap_file):
'Reads a labelmap without the dependency on protocol buffers.\n\n Args:\n labelmap_file: A file object containing a label map protocol buffer.\n\n Returns:\n labelmap: The label map in the form used by the object_detection_evaluation\n module - a list of {"id": integer, "name": classname } dicts.\n class_ids: A set containing all of the valid class id integers.\n '
labelmap = []
class_ids = set()
name =
class_id =
for line in labelmap_file:
if line.startswith(' name:'):
name = line.split('"')[1]
elif (line.startswith(' id:') or line.startswith(' label_id:')):
class_id = int(line.strip().split(' ')[(- 1)])
labelmap.append({'id': class_id, 'name': name})
class_ids.add(class_id)
return (labelmap, class_ids)<|docstring|>Reads a labelmap without the dependency on protocol buffers.
Args:
labelmap_file: A file object containing a label map protocol buffer.
Returns:
labelmap: The label map in the form used by the object_detection_evaluation
module - a list of {"id": integer, "name": classname } dicts.
class_ids: A set containing all of the valid class id integers.<|endoftext|>
|
1705f6755381a1ae538aa1c7bf86a5c8120a3de672eda916d57e18d30072033f
|
def run_evaluation(labelmap, groundtruth, detections, exclusions):
'Runs evaluations given input files.\n\n Args:\n labelmap: file object containing map of labels to consider, in pbtxt format\n groundtruth: file object\n detections: file object\n exclusions: file object or None.\n '
(categories, class_whitelist) = read_labelmap(labelmap)
logging.info('CATEGORIES (%d):\n%s', len(categories), pprint.pformat(categories, indent=2))
excluded_keys = read_exclusions(exclusions)
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(categories)
(boxes, labels, _) = read_csv(groundtruth, class_whitelist)
start = time.time()
for image_key in boxes:
if (image_key in excluded_keys):
logging.info('Found excluded timestamp in ground truth: %s. It will be ignored.', image_key)
continue
pascal_evaluator.add_single_ground_truth_image_info(image_key, {standard_fields.InputDataFields.groundtruth_boxes: np.array(boxes[image_key], dtype=float), standard_fields.InputDataFields.groundtruth_classes: np.array(labels[image_key], dtype=int), standard_fields.InputDataFields.groundtruth_difficult: np.zeros(len(boxes[image_key]), dtype=bool)})
print_time('convert groundtruth', start)
(boxes, labels, scores) = read_csv(detections, class_whitelist)
start = time.time()
for image_key in boxes:
if (image_key in excluded_keys):
logging.info('Found excluded timestamp in detections: %s. It will be ignored.', image_key)
continue
pascal_evaluator.add_single_detected_image_info(image_key, {standard_fields.DetectionResultFields.detection_boxes: np.array(boxes[image_key], dtype=float), standard_fields.DetectionResultFields.detection_classes: np.array(labels[image_key], dtype=int), standard_fields.DetectionResultFields.detection_scores: np.array(scores[image_key], dtype=float)})
print_time('convert detections', start)
start = time.time()
metrics = pascal_evaluator.evaluate()
print_time('run_evaluator', start)
return metrics
|
Runs evaluations given input files.
Args:
labelmap: file object containing map of labels to consider, in pbtxt format
groundtruth: file object
detections: file object
exclusions: file object or None.
|
evaluation/get_ava_performance_custom.py
|
run_evaluation
|
oulutan/ActorConditionedAttentionMaps
| 23 |
python
|
def run_evaluation(labelmap, groundtruth, detections, exclusions):
'Runs evaluations given input files.\n\n Args:\n labelmap: file object containing map of labels to consider, in pbtxt format\n groundtruth: file object\n detections: file object\n exclusions: file object or None.\n '
(categories, class_whitelist) = read_labelmap(labelmap)
logging.info('CATEGORIES (%d):\n%s', len(categories), pprint.pformat(categories, indent=2))
excluded_keys = read_exclusions(exclusions)
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(categories)
(boxes, labels, _) = read_csv(groundtruth, class_whitelist)
start = time.time()
for image_key in boxes:
if (image_key in excluded_keys):
logging.info('Found excluded timestamp in ground truth: %s. It will be ignored.', image_key)
continue
pascal_evaluator.add_single_ground_truth_image_info(image_key, {standard_fields.InputDataFields.groundtruth_boxes: np.array(boxes[image_key], dtype=float), standard_fields.InputDataFields.groundtruth_classes: np.array(labels[image_key], dtype=int), standard_fields.InputDataFields.groundtruth_difficult: np.zeros(len(boxes[image_key]), dtype=bool)})
print_time('convert groundtruth', start)
(boxes, labels, scores) = read_csv(detections, class_whitelist)
start = time.time()
for image_key in boxes:
if (image_key in excluded_keys):
logging.info('Found excluded timestamp in detections: %s. It will be ignored.', image_key)
continue
pascal_evaluator.add_single_detected_image_info(image_key, {standard_fields.DetectionResultFields.detection_boxes: np.array(boxes[image_key], dtype=float), standard_fields.DetectionResultFields.detection_classes: np.array(labels[image_key], dtype=int), standard_fields.DetectionResultFields.detection_scores: np.array(scores[image_key], dtype=float)})
print_time('convert detections', start)
start = time.time()
metrics = pascal_evaluator.evaluate()
print_time('run_evaluator', start)
return metrics
|
def run_evaluation(labelmap, groundtruth, detections, exclusions):
'Runs evaluations given input files.\n\n Args:\n labelmap: file object containing map of labels to consider, in pbtxt format\n groundtruth: file object\n detections: file object\n exclusions: file object or None.\n '
(categories, class_whitelist) = read_labelmap(labelmap)
logging.info('CATEGORIES (%d):\n%s', len(categories), pprint.pformat(categories, indent=2))
excluded_keys = read_exclusions(exclusions)
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(categories)
(boxes, labels, _) = read_csv(groundtruth, class_whitelist)
start = time.time()
for image_key in boxes:
if (image_key in excluded_keys):
logging.info('Found excluded timestamp in ground truth: %s. It will be ignored.', image_key)
continue
pascal_evaluator.add_single_ground_truth_image_info(image_key, {standard_fields.InputDataFields.groundtruth_boxes: np.array(boxes[image_key], dtype=float), standard_fields.InputDataFields.groundtruth_classes: np.array(labels[image_key], dtype=int), standard_fields.InputDataFields.groundtruth_difficult: np.zeros(len(boxes[image_key]), dtype=bool)})
print_time('convert groundtruth', start)
(boxes, labels, scores) = read_csv(detections, class_whitelist)
start = time.time()
for image_key in boxes:
if (image_key in excluded_keys):
logging.info('Found excluded timestamp in detections: %s. It will be ignored.', image_key)
continue
pascal_evaluator.add_single_detected_image_info(image_key, {standard_fields.DetectionResultFields.detection_boxes: np.array(boxes[image_key], dtype=float), standard_fields.DetectionResultFields.detection_classes: np.array(labels[image_key], dtype=int), standard_fields.DetectionResultFields.detection_scores: np.array(scores[image_key], dtype=float)})
print_time('convert detections', start)
start = time.time()
metrics = pascal_evaluator.evaluate()
print_time('run_evaluator', start)
return metrics<|docstring|>Runs evaluations given input files.
Args:
labelmap: file object containing map of labels to consider, in pbtxt format
groundtruth: file object
detections: file object
exclusions: file object or None.<|endoftext|>
|
16fc7af2f8adecfa44787cac6fb65da2f310559563ca8724514189cc8487fc18
|
def parse_arguments():
'Parses command-line flags.\n\n Returns:\n args: a named tuple containing three file objects args.labelmap,\n args.groundtruth, and args.detections.\n '
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--labelmap', help='Filename of label map', type=argparse.FileType('r'), default='ava/ava_action_list_v2.1_for_activitynet_2018.pbtxt.txt')
parser.add_argument('-g', '--groundtruth', help='CSV file containing ground truth.', type=argparse.FileType('r'), required=True)
parser.add_argument('-d', '--detections', help='CSV file containing inferred action detections.', type=argparse.FileType('r'), required=True)
parser.add_argument('-e', '--exclusions', help='Optional CSV file containing videoid,timestamp pairs to exclude from evaluation.', type=argparse.FileType('r'), required=False)
return parser.parse_args()
|
Parses command-line flags.
Returns:
args: a named tuple containing three file objects args.labelmap,
args.groundtruth, and args.detections.
|
evaluation/get_ava_performance_custom.py
|
parse_arguments
|
oulutan/ActorConditionedAttentionMaps
| 23 |
python
|
def parse_arguments():
'Parses command-line flags.\n\n Returns:\n args: a named tuple containing three file objects args.labelmap,\n args.groundtruth, and args.detections.\n '
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--labelmap', help='Filename of label map', type=argparse.FileType('r'), default='ava/ava_action_list_v2.1_for_activitynet_2018.pbtxt.txt')
parser.add_argument('-g', '--groundtruth', help='CSV file containing ground truth.', type=argparse.FileType('r'), required=True)
parser.add_argument('-d', '--detections', help='CSV file containing inferred action detections.', type=argparse.FileType('r'), required=True)
parser.add_argument('-e', '--exclusions', help='Optional CSV file containing videoid,timestamp pairs to exclude from evaluation.', type=argparse.FileType('r'), required=False)
return parser.parse_args()
|
def parse_arguments():
'Parses command-line flags.\n\n Returns:\n args: a named tuple containing three file objects args.labelmap,\n args.groundtruth, and args.detections.\n '
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--labelmap', help='Filename of label map', type=argparse.FileType('r'), default='ava/ava_action_list_v2.1_for_activitynet_2018.pbtxt.txt')
parser.add_argument('-g', '--groundtruth', help='CSV file containing ground truth.', type=argparse.FileType('r'), required=True)
parser.add_argument('-d', '--detections', help='CSV file containing inferred action detections.', type=argparse.FileType('r'), required=True)
parser.add_argument('-e', '--exclusions', help='Optional CSV file containing videoid,timestamp pairs to exclude from evaluation.', type=argparse.FileType('r'), required=False)
return parser.parse_args()<|docstring|>Parses command-line flags.
Returns:
args: a named tuple containing three file objects args.labelmap,
args.groundtruth, and args.detections.<|endoftext|>
|
b0d8987cd35a81ca2b21b485171306ac846ad799761fa416850dbd2655a48b71
|
def make_instance(self, include_optional):
'Test VehicleResource\n include_option is a boolean, when False only required\n params are included, when True both required and\n optional params are included '
if include_optional:
return VehicleResource(type='0', relationships=openapi_client.models.vehicle_resource_relationships.VehicleResource_relationships(trip=openapi_client.models.prediction_resource_relationships_trip.PredictionResource_relationships_trip(links=openapi_client.models.prediction_resource_relationships_trip_links.PredictionResource_relationships_trip_links(self='0', related='0'), data=openapi_client.models.prediction_resource_relationships_trip_data.PredictionResource_relationships_trip_data(type='0', id='0')), stop=openapi_client.models.prediction_resource_relationships_stop.PredictionResource_relationships_stop(), route=openapi_client.models.prediction_resource_relationships_route.PredictionResource_relationships_route()), links=None, id='0', attributes=openapi_client.models.vehicle_resource_attributes.VehicleResource_attributes(updated_at='2017-08-14T16:04:44-04:00', speed=16.0, longitude=42.32941818237305, latitude=(- 71.27239990234375), label='1817', direction_id=56, current_stop_sequence=8, current_status='IN_TRANSIT_TO', bearing=174))
else:
return VehicleResource()
|
Test VehicleResource
include_option is a boolean, when False only required
params are included, when True both required and
optional params are included
|
test/test_vehicle_resource.py
|
make_instance
|
hypostulate/mbta-api-client
| 0 |
python
|
def make_instance(self, include_optional):
'Test VehicleResource\n include_option is a boolean, when False only required\n params are included, when True both required and\n optional params are included '
if include_optional:
return VehicleResource(type='0', relationships=openapi_client.models.vehicle_resource_relationships.VehicleResource_relationships(trip=openapi_client.models.prediction_resource_relationships_trip.PredictionResource_relationships_trip(links=openapi_client.models.prediction_resource_relationships_trip_links.PredictionResource_relationships_trip_links(self='0', related='0'), data=openapi_client.models.prediction_resource_relationships_trip_data.PredictionResource_relationships_trip_data(type='0', id='0')), stop=openapi_client.models.prediction_resource_relationships_stop.PredictionResource_relationships_stop(), route=openapi_client.models.prediction_resource_relationships_route.PredictionResource_relationships_route()), links=None, id='0', attributes=openapi_client.models.vehicle_resource_attributes.VehicleResource_attributes(updated_at='2017-08-14T16:04:44-04:00', speed=16.0, longitude=42.32941818237305, latitude=(- 71.27239990234375), label='1817', direction_id=56, current_stop_sequence=8, current_status='IN_TRANSIT_TO', bearing=174))
else:
return VehicleResource()
|
def make_instance(self, include_optional):
'Test VehicleResource\n include_option is a boolean, when False only required\n params are included, when True both required and\n optional params are included '
if include_optional:
return VehicleResource(type='0', relationships=openapi_client.models.vehicle_resource_relationships.VehicleResource_relationships(trip=openapi_client.models.prediction_resource_relationships_trip.PredictionResource_relationships_trip(links=openapi_client.models.prediction_resource_relationships_trip_links.PredictionResource_relationships_trip_links(self='0', related='0'), data=openapi_client.models.prediction_resource_relationships_trip_data.PredictionResource_relationships_trip_data(type='0', id='0')), stop=openapi_client.models.prediction_resource_relationships_stop.PredictionResource_relationships_stop(), route=openapi_client.models.prediction_resource_relationships_route.PredictionResource_relationships_route()), links=None, id='0', attributes=openapi_client.models.vehicle_resource_attributes.VehicleResource_attributes(updated_at='2017-08-14T16:04:44-04:00', speed=16.0, longitude=42.32941818237305, latitude=(- 71.27239990234375), label='1817', direction_id=56, current_stop_sequence=8, current_status='IN_TRANSIT_TO', bearing=174))
else:
return VehicleResource()<|docstring|>Test VehicleResource
include_option is a boolean, when False only required
params are included, when True both required and
optional params are included<|endoftext|>
|
702295050ddbf1c7391aaadee35d10e1c8faee3efec6f35aebf7ffbf11f27ee5
|
def testVehicleResource(self):
'Test VehicleResource'
inst_req_only = self.make_instance(include_optional=False)
inst_req_and_optional = self.make_instance(include_optional=True)
|
Test VehicleResource
|
test/test_vehicle_resource.py
|
testVehicleResource
|
hypostulate/mbta-api-client
| 0 |
python
|
def testVehicleResource(self):
inst_req_only = self.make_instance(include_optional=False)
inst_req_and_optional = self.make_instance(include_optional=True)
|
def testVehicleResource(self):
inst_req_only = self.make_instance(include_optional=False)
inst_req_and_optional = self.make_instance(include_optional=True)<|docstring|>Test VehicleResource<|endoftext|>
|
946bdc3077a04568d1206f7bdb8e303992e74bc3e7d39cf760f887927ef5fe4a
|
def kf_model(input_val, y_val, fn, fn_kwargs={}, k_folds=5, random_state=15, verbose=False):
'\n K-fold task, mean and std of results are calculated over K folds\n \n Params: \n input_val: (np.array) 2-D array holding instances (features) of validation set.\n y_val: (np.array) 1-D array holding y-values for validation set.\n fn: (class) a method used for calibration\n l2: (float) L2 regulariation value.\n k_folds: (int) how many crossvalidation folds are used.\n comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)\n \n returns: \n mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier\n '
kf = KFold(n_splits=k_folds, shuffle=True, random_state=random_state)
kf_results = []
models = []
for (train_index, test_index) in kf.split(input_val):
(X_train_c, X_val_c) = (input_val[train_index], input_val[test_index])
(y_train_c, y_val_c) = (y_val[train_index], y_val[test_index])
t1 = time.time()
model = fn(**fn_kwargs)
model.fit(X_train_c, y_train_c)
print('Model trained:', (time.time() - t1))
probs_holdout = model.predict_proba(X_val_c)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(probs_holdout, y_val_c, verbose=False)
kf_results.append([error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
models.append(model)
return (models, np.mean(kf_results, axis=0))
|
K-fold task, mean and std of results are calculated over K folds
Params:
input_val: (np.array) 2-D array holding instances (features) of validation set.
y_val: (np.array) 1-D array holding y-values for validation set.
fn: (class) a method used for calibration
l2: (float) L2 regulariation value.
k_folds: (int) how many crossvalidation folds are used.
comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)
returns:
mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier
|
Confidence_Calibration/calibration/calibration_functions.py
|
kf_model
|
heatherwan/Automatic-Validation-of-Simulation-Results
| 0 |
python
|
def kf_model(input_val, y_val, fn, fn_kwargs={}, k_folds=5, random_state=15, verbose=False):
'\n K-fold task, mean and std of results are calculated over K folds\n \n Params: \n input_val: (np.array) 2-D array holding instances (features) of validation set.\n y_val: (np.array) 1-D array holding y-values for validation set.\n fn: (class) a method used for calibration\n l2: (float) L2 regulariation value.\n k_folds: (int) how many crossvalidation folds are used.\n comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)\n \n returns: \n mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier\n '
kf = KFold(n_splits=k_folds, shuffle=True, random_state=random_state)
kf_results = []
models = []
for (train_index, test_index) in kf.split(input_val):
(X_train_c, X_val_c) = (input_val[train_index], input_val[test_index])
(y_train_c, y_val_c) = (y_val[train_index], y_val[test_index])
t1 = time.time()
model = fn(**fn_kwargs)
model.fit(X_train_c, y_train_c)
print('Model trained:', (time.time() - t1))
probs_holdout = model.predict_proba(X_val_c)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(probs_holdout, y_val_c, verbose=False)
kf_results.append([error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
models.append(model)
return (models, np.mean(kf_results, axis=0))
|
def kf_model(input_val, y_val, fn, fn_kwargs={}, k_folds=5, random_state=15, verbose=False):
'\n K-fold task, mean and std of results are calculated over K folds\n \n Params: \n input_val: (np.array) 2-D array holding instances (features) of validation set.\n y_val: (np.array) 1-D array holding y-values for validation set.\n fn: (class) a method used for calibration\n l2: (float) L2 regulariation value.\n k_folds: (int) how many crossvalidation folds are used.\n comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)\n \n returns: \n mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier\n '
kf = KFold(n_splits=k_folds, shuffle=True, random_state=random_state)
kf_results = []
models = []
for (train_index, test_index) in kf.split(input_val):
(X_train_c, X_val_c) = (input_val[train_index], input_val[test_index])
(y_train_c, y_val_c) = (y_val[train_index], y_val[test_index])
t1 = time.time()
model = fn(**fn_kwargs)
model.fit(X_train_c, y_train_c)
print('Model trained:', (time.time() - t1))
probs_holdout = model.predict_proba(X_val_c)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(probs_holdout, y_val_c, verbose=False)
kf_results.append([error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
models.append(model)
return (models, np.mean(kf_results, axis=0))<|docstring|>K-fold task, mean and std of results are calculated over K folds
Params:
input_val: (np.array) 2-D array holding instances (features) of validation set.
y_val: (np.array) 1-D array holding y-values for validation set.
fn: (class) a method used for calibration
l2: (float) L2 regulariation value.
k_folds: (int) how many crossvalidation folds are used.
comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)
returns:
mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier<|endoftext|>
|
2572e78addd375929753c5e2966e751571e89b2daeebcaa39fdd37dbbf36a93a
|
def one_model(input_val, y_val, fn, fn_kwargs={}, k_folds=1, random_state=15, verbose=False):
'\n 1-fold task, mean and std of results are calculated over 1 folds\n \n Params: \n input_val: (np.array) 2-D array holding instances (features) of validation set.\n y_val: (np.array) 1-D array holding y-values for validation set.\n fn: (class) a method used for calibration\n l2: (float) L2 regulariation value.\n k_folds: (int) how many crossvalidation folds are used.\n comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)\n \n returns: \n mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier\n '
kf_results = []
models = []
t1 = time.time()
model = fn(**fn_kwargs)
model.fit(input_val, y_val)
print('Model trained:', (time.time() - t1))
probs_holdout = model.predict_proba(input_val)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(probs_holdout, y_val, verbose=False)
kf_results.append([error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
models.append(model)
return (models, (np.mean(kf_results, axis=0), np.std(np.array(kf_results)[(:, (- 2):)], axis=0)))
|
1-fold task, mean and std of results are calculated over 1 folds
Params:
input_val: (np.array) 2-D array holding instances (features) of validation set.
y_val: (np.array) 1-D array holding y-values for validation set.
fn: (class) a method used for calibration
l2: (float) L2 regulariation value.
k_folds: (int) how many crossvalidation folds are used.
comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)
returns:
mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier
|
Confidence_Calibration/calibration/calibration_functions.py
|
one_model
|
heatherwan/Automatic-Validation-of-Simulation-Results
| 0 |
python
|
def one_model(input_val, y_val, fn, fn_kwargs={}, k_folds=1, random_state=15, verbose=False):
'\n 1-fold task, mean and std of results are calculated over 1 folds\n \n Params: \n input_val: (np.array) 2-D array holding instances (features) of validation set.\n y_val: (np.array) 1-D array holding y-values for validation set.\n fn: (class) a method used for calibration\n l2: (float) L2 regulariation value.\n k_folds: (int) how many crossvalidation folds are used.\n comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)\n \n returns: \n mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier\n '
kf_results = []
models = []
t1 = time.time()
model = fn(**fn_kwargs)
model.fit(input_val, y_val)
print('Model trained:', (time.time() - t1))
probs_holdout = model.predict_proba(input_val)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(probs_holdout, y_val, verbose=False)
kf_results.append([error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
models.append(model)
return (models, (np.mean(kf_results, axis=0), np.std(np.array(kf_results)[(:, (- 2):)], axis=0)))
|
def one_model(input_val, y_val, fn, fn_kwargs={}, k_folds=1, random_state=15, verbose=False):
'\n 1-fold task, mean and std of results are calculated over 1 folds\n \n Params: \n input_val: (np.array) 2-D array holding instances (features) of validation set.\n y_val: (np.array) 1-D array holding y-values for validation set.\n fn: (class) a method used for calibration\n l2: (float) L2 regulariation value.\n k_folds: (int) how many crossvalidation folds are used.\n comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)\n \n returns: \n mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier\n '
kf_results = []
models = []
t1 = time.time()
model = fn(**fn_kwargs)
model.fit(input_val, y_val)
print('Model trained:', (time.time() - t1))
probs_holdout = model.predict_proba(input_val)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(probs_holdout, y_val, verbose=False)
kf_results.append([error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
models.append(model)
return (models, (np.mean(kf_results, axis=0), np.std(np.array(kf_results)[(:, (- 2):)], axis=0)))<|docstring|>1-fold task, mean and std of results are calculated over 1 folds
Params:
input_val: (np.array) 2-D array holding instances (features) of validation set.
y_val: (np.array) 1-D array holding y-values for validation set.
fn: (class) a method used for calibration
l2: (float) L2 regulariation value.
k_folds: (int) how many crossvalidation folds are used.
comp_l2: (bool) use reversed L2 matrix for regulariation (default = False)
returns:
mean_error, mean_ece, mean_mce, mean_loss, mean_brier, std_loss, std_brier<|endoftext|>
|
ce197edcaa4ec167a7d9b18ad3cab89db9f1393a0a65c267483918b30bf3651d
|
def tune_dir_nn_heather(name, method, files, lambdas, mus, k_folds=5, random_state=15, verbose=True, double_learning=False, model_dir='models_dump', loss_fn='sparse_categorical_crossentropy', comp_l2=True, use_logits=False, use_scipy=False):
'\n\n Params:\n fn (class): class of the calibration method used. It must contain methods "fit" and "predict",\n where first fits the models and second outputs calibrated probabilities.\n path (string): path to the folder with logits files\n files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))\n comp_l2 (bool): use reversed L2 matrix for regulariation (default = False)\n\n Returns:\n df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.\n\n '
df_columns = ['Name', 'L2', 'mu', 'Error', 'ECE', 'ECE2', 'ECE_CW', 'ECE_CW2', 'ECE_FULL', 'ECE_FULL2', 'MCE', 'MCE2', 'Loss', 'Brier']
results = []
results2 = []
if (not os.path.exists(model_dir)):
os.makedirs(model_dir)
t1 = time.time()
val_df = pd.read_csv(files[0], sep='\t', index_col=False)
test_df = pd.read_csv(files[1], sep='\t', index_col=False)
logits_val = val_df.iloc[(:, 3:)].to_numpy()
y_val = val_df.iloc[(:, 1:2)].to_numpy().ravel()
logits_test = test_df.iloc[(:, 3:)].to_numpy()
y_test = test_df.iloc[(:, 1:2)].to_numpy().ravel()
if use_logits:
input_val = logits_val
input_test = logits_test
else:
input_val = softmax(logits_val)
input_test = softmax(logits_test)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(softmax(logits_val), y_val, verbose=False)
print(('Uncal Val: Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f; brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
results.append([(name + 'val_uncal'), error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(softmax(logits_test), y_test, verbose=False)
print(('Uncal Test: Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f; brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
results.append([(name + 'test_uncal'), error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
for l2 in lambdas:
for mu in mus:
if (mu is None):
mu = l2
if use_scipy:
temp_res = kf_model(input_val, y_val, LogisticCalibration, {'C': np.true_divide(1, l2)}, k_folds=k_folds, random_state=random_state, verbose=verbose)
elif (k_folds > 1):
temp_res = kf_model(input_val, y_val, Dirichlet_NN, {'l2': l2, 'mu': mu, 'patience': 15, 'loss': loss_fn, 'double_fit': double_learning, 'comp': comp_l2, 'use_logits': use_logits}, k_folds=k_folds, random_state=random_state, verbose=verbose)
else:
temp_res = one_model(input_val, y_val, Dirichlet_NN, {'l2': l2, 'mu': mu, 'patience': 15, 'loss': loss_fn, 'double_fit': double_learning, 'comp': comp_l2, 'use_logits': use_logits}, k_folds=k_folds, random_state=random_state, verbose=verbose)
(models, (avg_error, avg_ece, avg_ece2, avg_ece_cw, avg_ece_cw2, avg_ece_full, avg_ece_full2, avg_mce, avg_mce2, avg_loss, avg_brier)) = temp_res
results.append([(name + 'val_cal'), l2, mu, avg_error, avg_ece, avg_ece2, avg_ece_cw, avg_ece_cw2, avg_ece_full, avg_ece_full2, avg_mce, avg_mce2, avg_loss, avg_brier])
fname = f'model_{method}_{name}_l2={l2}_mu={mu}.p'
model_weights = []
for mod in models:
if (not use_scipy):
model_weights.append(mod.model.get_weights())
else:
model_weights.append([mod.coef_, mod.intercept_])
with open(join(model_dir, fname), 'wb') as f:
pickle.dump((model_weights, temp_res[1], (name, l2, mu)), f)
print(f'L2 = {l2}, Mu= {mu}, Validation Error {avg_error}; ece {avg_ece}; ece2 {avg_ece2}; ece_cw {avg_ece_cw}; ece_cw2 {avg_ece_cw2}; ece_full {avg_ece_full}; ece_full2 {avg_ece_full2}; mce {avg_mce}; mce2 {avg_mce2}; loss {avg_loss}; brier {avg_brier}')
with open(f'result/{name}_{method}_val_{l2}_{mu}.txt', 'wb') as f:
np.savetxt(f, input_val)
np.savetxt(f, get_cal_prob(models, input_val))
with open(f'result/{name}_{method}_test_{l2}_{mu}.txt', 'wb') as f2:
np.savetxt(f2, input_test)
np.savetxt(f2, get_cal_prob(models, input_test))
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = get_test_scores(models, input_test, y_test)
print(f'L2 = {l2}, Mu= {mu}, Test Error {error}; ece {ece}; ece2 {ece2}; ece_cw {ece_cw}; ece_cw2 {ece_cw2}; ece_full {ece_full}; ece_full2 {ece_full2}; mce {mce}; mce2 {mce2}; loss {loss}; brier {brier}')
results.append([(name + '_cal_test'), l2, mu, error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
print('Ensembled results:')
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = get_test_scores2(models, input_test, y_test)
print(f'L2 = {l2}, Mu= {mu}, Test Error {error}; ece {ece}; ece2 {ece2}; ece_cw {ece_cw}; ece_cw2 {ece_cw2}; ece_full {ece_full}; ece_full2 {ece_full2}; mce {mce}; mce2 {mce2}; loss {loss}; brier {brier}')
results2.append([name, l2, mu, error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
K.clear_session()
for mod in models:
del mod
del models
del temp_res
K.clear_session()
gc.collect()
t2 = time.time()
print('Time taken:', (t2 - t1), '\n')
df = pd.DataFrame(results, columns=df_columns)
df2 = pd.DataFrame(results2, columns=df_columns)
return (df, df2)
|
Params:
fn (class): class of the calibration method used. It must contain methods "fit" and "predict",
where first fits the models and second outputs calibrated probabilities.
path (string): path to the folder with logits files
files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))
comp_l2 (bool): use reversed L2 matrix for regulariation (default = False)
Returns:
df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.
|
Confidence_Calibration/calibration/calibration_functions.py
|
tune_dir_nn_heather
|
heatherwan/Automatic-Validation-of-Simulation-Results
| 0 |
python
|
def tune_dir_nn_heather(name, method, files, lambdas, mus, k_folds=5, random_state=15, verbose=True, double_learning=False, model_dir='models_dump', loss_fn='sparse_categorical_crossentropy', comp_l2=True, use_logits=False, use_scipy=False):
'\n\n Params:\n fn (class): class of the calibration method used. It must contain methods "fit" and "predict",\n where first fits the models and second outputs calibrated probabilities.\n path (string): path to the folder with logits files\n files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))\n comp_l2 (bool): use reversed L2 matrix for regulariation (default = False)\n\n Returns:\n df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.\n\n '
df_columns = ['Name', 'L2', 'mu', 'Error', 'ECE', 'ECE2', 'ECE_CW', 'ECE_CW2', 'ECE_FULL', 'ECE_FULL2', 'MCE', 'MCE2', 'Loss', 'Brier']
results = []
results2 = []
if (not os.path.exists(model_dir)):
os.makedirs(model_dir)
t1 = time.time()
val_df = pd.read_csv(files[0], sep='\t', index_col=False)
test_df = pd.read_csv(files[1], sep='\t', index_col=False)
logits_val = val_df.iloc[(:, 3:)].to_numpy()
y_val = val_df.iloc[(:, 1:2)].to_numpy().ravel()
logits_test = test_df.iloc[(:, 3:)].to_numpy()
y_test = test_df.iloc[(:, 1:2)].to_numpy().ravel()
if use_logits:
input_val = logits_val
input_test = logits_test
else:
input_val = softmax(logits_val)
input_test = softmax(logits_test)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(softmax(logits_val), y_val, verbose=False)
print(('Uncal Val: Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f; brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
results.append([(name + 'val_uncal'), error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(softmax(logits_test), y_test, verbose=False)
print(('Uncal Test: Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f; brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
results.append([(name + 'test_uncal'), error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
for l2 in lambdas:
for mu in mus:
if (mu is None):
mu = l2
if use_scipy:
temp_res = kf_model(input_val, y_val, LogisticCalibration, {'C': np.true_divide(1, l2)}, k_folds=k_folds, random_state=random_state, verbose=verbose)
elif (k_folds > 1):
temp_res = kf_model(input_val, y_val, Dirichlet_NN, {'l2': l2, 'mu': mu, 'patience': 15, 'loss': loss_fn, 'double_fit': double_learning, 'comp': comp_l2, 'use_logits': use_logits}, k_folds=k_folds, random_state=random_state, verbose=verbose)
else:
temp_res = one_model(input_val, y_val, Dirichlet_NN, {'l2': l2, 'mu': mu, 'patience': 15, 'loss': loss_fn, 'double_fit': double_learning, 'comp': comp_l2, 'use_logits': use_logits}, k_folds=k_folds, random_state=random_state, verbose=verbose)
(models, (avg_error, avg_ece, avg_ece2, avg_ece_cw, avg_ece_cw2, avg_ece_full, avg_ece_full2, avg_mce, avg_mce2, avg_loss, avg_brier)) = temp_res
results.append([(name + 'val_cal'), l2, mu, avg_error, avg_ece, avg_ece2, avg_ece_cw, avg_ece_cw2, avg_ece_full, avg_ece_full2, avg_mce, avg_mce2, avg_loss, avg_brier])
fname = f'model_{method}_{name}_l2={l2}_mu={mu}.p'
model_weights = []
for mod in models:
if (not use_scipy):
model_weights.append(mod.model.get_weights())
else:
model_weights.append([mod.coef_, mod.intercept_])
with open(join(model_dir, fname), 'wb') as f:
pickle.dump((model_weights, temp_res[1], (name, l2, mu)), f)
print(f'L2 = {l2}, Mu= {mu}, Validation Error {avg_error}; ece {avg_ece}; ece2 {avg_ece2}; ece_cw {avg_ece_cw}; ece_cw2 {avg_ece_cw2}; ece_full {avg_ece_full}; ece_full2 {avg_ece_full2}; mce {avg_mce}; mce2 {avg_mce2}; loss {avg_loss}; brier {avg_brier}')
with open(f'result/{name}_{method}_val_{l2}_{mu}.txt', 'wb') as f:
np.savetxt(f, input_val)
np.savetxt(f, get_cal_prob(models, input_val))
with open(f'result/{name}_{method}_test_{l2}_{mu}.txt', 'wb') as f2:
np.savetxt(f2, input_test)
np.savetxt(f2, get_cal_prob(models, input_test))
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = get_test_scores(models, input_test, y_test)
print(f'L2 = {l2}, Mu= {mu}, Test Error {error}; ece {ece}; ece2 {ece2}; ece_cw {ece_cw}; ece_cw2 {ece_cw2}; ece_full {ece_full}; ece_full2 {ece_full2}; mce {mce}; mce2 {mce2}; loss {loss}; brier {brier}')
results.append([(name + '_cal_test'), l2, mu, error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
print('Ensembled results:')
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = get_test_scores2(models, input_test, y_test)
print(f'L2 = {l2}, Mu= {mu}, Test Error {error}; ece {ece}; ece2 {ece2}; ece_cw {ece_cw}; ece_cw2 {ece_cw2}; ece_full {ece_full}; ece_full2 {ece_full2}; mce {mce}; mce2 {mce2}; loss {loss}; brier {brier}')
results2.append([name, l2, mu, error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
K.clear_session()
for mod in models:
del mod
del models
del temp_res
K.clear_session()
gc.collect()
t2 = time.time()
print('Time taken:', (t2 - t1), '\n')
df = pd.DataFrame(results, columns=df_columns)
df2 = pd.DataFrame(results2, columns=df_columns)
return (df, df2)
|
def tune_dir_nn_heather(name, method, files, lambdas, mus, k_folds=5, random_state=15, verbose=True, double_learning=False, model_dir='models_dump', loss_fn='sparse_categorical_crossentropy', comp_l2=True, use_logits=False, use_scipy=False):
'\n\n Params:\n fn (class): class of the calibration method used. It must contain methods "fit" and "predict",\n where first fits the models and second outputs calibrated probabilities.\n path (string): path to the folder with logits files\n files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))\n comp_l2 (bool): use reversed L2 matrix for regulariation (default = False)\n\n Returns:\n df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.\n\n '
df_columns = ['Name', 'L2', 'mu', 'Error', 'ECE', 'ECE2', 'ECE_CW', 'ECE_CW2', 'ECE_FULL', 'ECE_FULL2', 'MCE', 'MCE2', 'Loss', 'Brier']
results = []
results2 = []
if (not os.path.exists(model_dir)):
os.makedirs(model_dir)
t1 = time.time()
val_df = pd.read_csv(files[0], sep='\t', index_col=False)
test_df = pd.read_csv(files[1], sep='\t', index_col=False)
logits_val = val_df.iloc[(:, 3:)].to_numpy()
y_val = val_df.iloc[(:, 1:2)].to_numpy().ravel()
logits_test = test_df.iloc[(:, 3:)].to_numpy()
y_test = test_df.iloc[(:, 1:2)].to_numpy().ravel()
if use_logits:
input_val = logits_val
input_test = logits_test
else:
input_val = softmax(logits_val)
input_test = softmax(logits_test)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(softmax(logits_val), y_val, verbose=False)
print(('Uncal Val: Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f; brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
results.append([(name + 'val_uncal'), error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate_rip(softmax(logits_test), y_test, verbose=False)
print(('Uncal Test: Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f; brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
results.append([(name + 'test_uncal'), error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
for l2 in lambdas:
for mu in mus:
if (mu is None):
mu = l2
if use_scipy:
temp_res = kf_model(input_val, y_val, LogisticCalibration, {'C': np.true_divide(1, l2)}, k_folds=k_folds, random_state=random_state, verbose=verbose)
elif (k_folds > 1):
temp_res = kf_model(input_val, y_val, Dirichlet_NN, {'l2': l2, 'mu': mu, 'patience': 15, 'loss': loss_fn, 'double_fit': double_learning, 'comp': comp_l2, 'use_logits': use_logits}, k_folds=k_folds, random_state=random_state, verbose=verbose)
else:
temp_res = one_model(input_val, y_val, Dirichlet_NN, {'l2': l2, 'mu': mu, 'patience': 15, 'loss': loss_fn, 'double_fit': double_learning, 'comp': comp_l2, 'use_logits': use_logits}, k_folds=k_folds, random_state=random_state, verbose=verbose)
(models, (avg_error, avg_ece, avg_ece2, avg_ece_cw, avg_ece_cw2, avg_ece_full, avg_ece_full2, avg_mce, avg_mce2, avg_loss, avg_brier)) = temp_res
results.append([(name + 'val_cal'), l2, mu, avg_error, avg_ece, avg_ece2, avg_ece_cw, avg_ece_cw2, avg_ece_full, avg_ece_full2, avg_mce, avg_mce2, avg_loss, avg_brier])
fname = f'model_{method}_{name}_l2={l2}_mu={mu}.p'
model_weights = []
for mod in models:
if (not use_scipy):
model_weights.append(mod.model.get_weights())
else:
model_weights.append([mod.coef_, mod.intercept_])
with open(join(model_dir, fname), 'wb') as f:
pickle.dump((model_weights, temp_res[1], (name, l2, mu)), f)
print(f'L2 = {l2}, Mu= {mu}, Validation Error {avg_error}; ece {avg_ece}; ece2 {avg_ece2}; ece_cw {avg_ece_cw}; ece_cw2 {avg_ece_cw2}; ece_full {avg_ece_full}; ece_full2 {avg_ece_full2}; mce {avg_mce}; mce2 {avg_mce2}; loss {avg_loss}; brier {avg_brier}')
with open(f'result/{name}_{method}_val_{l2}_{mu}.txt', 'wb') as f:
np.savetxt(f, input_val)
np.savetxt(f, get_cal_prob(models, input_val))
with open(f'result/{name}_{method}_test_{l2}_{mu}.txt', 'wb') as f2:
np.savetxt(f2, input_test)
np.savetxt(f2, get_cal_prob(models, input_test))
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = get_test_scores(models, input_test, y_test)
print(f'L2 = {l2}, Mu= {mu}, Test Error {error}; ece {ece}; ece2 {ece2}; ece_cw {ece_cw}; ece_cw2 {ece_cw2}; ece_full {ece_full}; ece_full2 {ece_full2}; mce {mce}; mce2 {mce2}; loss {loss}; brier {brier}')
results.append([(name + '_cal_test'), l2, mu, error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
print('Ensembled results:')
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = get_test_scores2(models, input_test, y_test)
print(f'L2 = {l2}, Mu= {mu}, Test Error {error}; ece {ece}; ece2 {ece2}; ece_cw {ece_cw}; ece_cw2 {ece_cw2}; ece_full {ece_full}; ece_full2 {ece_full2}; mce {mce}; mce2 {mce2}; loss {loss}; brier {brier}')
results2.append([name, l2, mu, error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier])
K.clear_session()
for mod in models:
del mod
del models
del temp_res
K.clear_session()
gc.collect()
t2 = time.time()
print('Time taken:', (t2 - t1), '\n')
df = pd.DataFrame(results, columns=df_columns)
df2 = pd.DataFrame(results2, columns=df_columns)
return (df, df2)<|docstring|>Params:
fn (class): class of the calibration method used. It must contain methods "fit" and "predict",
where first fits the models and second outputs calibrated probabilities.
path (string): path to the folder with logits files
files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))
comp_l2 (bool): use reversed L2 matrix for regulariation (default = False)
Returns:
df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.<|endoftext|>
|
9b84a0d3a9c15c24d153c6c1127bca00f8a4a16ed2b12a1fb5c5a3ef8329f219
|
def cal_TS_results(name, method, files, m_kwargs={}, approach='all'):
'\n Calibrate models scores, using output from logits files and given function (fn).\n There are implemented to different approaches "all" and "1-vs-K" for calibration,\n the approach of calibration should match with function used for calibration.\n\n Params:\n fn (class): class of the calibration method used. It must contain methods "fit" and "predict",\n where first fits the models and second outputs calibrated probabilities.\n path (string): path to the folder with logits files\n files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))\n m_kwargs (dictionary): keyword arguments for the calibration class initialization\n approach (string): "all" for multiclass calibration and "1-vs-K" for 1-vs-K approach.\n input (string): "probabilities" or "logits", specific to calibration method\n\n Returns:\n df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.\n\n '
df = pd.DataFrame(columns=['Name', 'Error', 'ECE', 'ECE2', 'ECE_CW', 'ECE_CW2', 'ECE_FULL', 'ECE_FULL2', 'MCE', 'MCE2', 'Loss', 'Brier'])
t1 = time.time()
val_df = pd.read_csv(files[0], sep='\t', index_col=False)
test_df = pd.read_csv(files[1], sep='\t', index_col=False)
logits_val = val_df.iloc[(:, 3:)].to_numpy()
y_val = val_df.iloc[(:, 1:2)].to_numpy()
logits_test = test_df.iloc[(:, 3:)].to_numpy()
y_test = test_df.iloc[(:, 1:2)].to_numpy()
input_val = logits_val
input_test = logits_test
if (approach == 'all'):
y_val_flat = y_val.flatten()
model = TemperatureScaling(**m_kwargs)
opt = model.fit(input_val, y_val_flat)
print(f'the optimal temperature is {opt.x[0]}')
file1 = open(f'model_weights/model_TS_{name}.txt', 'w')
file1.write(str(opt.x[0]))
probs_val = model.predict(input_val)
probs_test = model.predict(input_test)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate(softmax(logits_val), y_val, verbose=False)
(error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1) = evaluate(softmax(logits_test), y_test, verbose=False)
(error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2) = evaluate(probs_test, y_test, verbose=False)
(error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3) = evaluate(probs_val, y_val, verbose=False)
print(('Uncal Valid Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
print(('Uncal Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1)))
print(('Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2)))
print(('Validation Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3)))
else:
K = input_test.shape[1]
probs_val = np.zeros_like(input_val)
probs_test = np.zeros_like(input_test)
for k in range(K):
y_cal = np.array((y_val == k), dtype='int')[(:, 0)]
model = TemperatureScaling(**m_kwargs)
model.fit(input_val[(:, k)], y_cal)
probs_val[(:, k)] = model.predict(input_val[(:, k)])
probs_test[(:, k)] = model.predict(input_test[(:, k)])
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate(softmax(logits_val), y_val, verbose=False)
(error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1) = evaluate(softmax(logits_test), y_test, verbose=False)
(error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2) = evaluate(probs_test, y_test, verbose=False)
(error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3) = evaluate(probs_val, y_val, verbose=False)
print(('Uncal Valid Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
print(('Uncal Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1)))
print(('Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2)))
print(('Validation Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3)))
with open(f'result/{name}_{method}_val.txt', 'wb') as f:
np.savetxt(f, softmax(logits_val))
np.savetxt(f, probs_val)
with open(f'result/{name}_{method}_test.txt', 'wb') as f2:
np.savetxt(f2, softmax(logits_test))
np.savetxt(f2, probs_test)
df.loc[0] = [(name + '_val_uncalib'), error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier]
df.loc[1] = [(name + '_test_uncalib'), error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1]
df.loc[2] = [(name + '_test_calib'), error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2]
df.loc[3] = [(name + '_val_calib'), error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3]
t2 = time.time()
print('Time taken:', (t2 - t1), '\n')
return df
|
Calibrate models scores, using output from logits files and given function (fn).
There are implemented to different approaches "all" and "1-vs-K" for calibration,
the approach of calibration should match with function used for calibration.
Params:
fn (class): class of the calibration method used. It must contain methods "fit" and "predict",
where first fits the models and second outputs calibrated probabilities.
path (string): path to the folder with logits files
files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))
m_kwargs (dictionary): keyword arguments for the calibration class initialization
approach (string): "all" for multiclass calibration and "1-vs-K" for 1-vs-K approach.
input (string): "probabilities" or "logits", specific to calibration method
Returns:
df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.
|
Confidence_Calibration/calibration/calibration_functions.py
|
cal_TS_results
|
heatherwan/Automatic-Validation-of-Simulation-Results
| 0 |
python
|
def cal_TS_results(name, method, files, m_kwargs={}, approach='all'):
'\n Calibrate models scores, using output from logits files and given function (fn).\n There are implemented to different approaches "all" and "1-vs-K" for calibration,\n the approach of calibration should match with function used for calibration.\n\n Params:\n fn (class): class of the calibration method used. It must contain methods "fit" and "predict",\n where first fits the models and second outputs calibrated probabilities.\n path (string): path to the folder with logits files\n files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))\n m_kwargs (dictionary): keyword arguments for the calibration class initialization\n approach (string): "all" for multiclass calibration and "1-vs-K" for 1-vs-K approach.\n input (string): "probabilities" or "logits", specific to calibration method\n\n Returns:\n df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.\n\n '
df = pd.DataFrame(columns=['Name', 'Error', 'ECE', 'ECE2', 'ECE_CW', 'ECE_CW2', 'ECE_FULL', 'ECE_FULL2', 'MCE', 'MCE2', 'Loss', 'Brier'])
t1 = time.time()
val_df = pd.read_csv(files[0], sep='\t', index_col=False)
test_df = pd.read_csv(files[1], sep='\t', index_col=False)
logits_val = val_df.iloc[(:, 3:)].to_numpy()
y_val = val_df.iloc[(:, 1:2)].to_numpy()
logits_test = test_df.iloc[(:, 3:)].to_numpy()
y_test = test_df.iloc[(:, 1:2)].to_numpy()
input_val = logits_val
input_test = logits_test
if (approach == 'all'):
y_val_flat = y_val.flatten()
model = TemperatureScaling(**m_kwargs)
opt = model.fit(input_val, y_val_flat)
print(f'the optimal temperature is {opt.x[0]}')
file1 = open(f'model_weights/model_TS_{name}.txt', 'w')
file1.write(str(opt.x[0]))
probs_val = model.predict(input_val)
probs_test = model.predict(input_test)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate(softmax(logits_val), y_val, verbose=False)
(error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1) = evaluate(softmax(logits_test), y_test, verbose=False)
(error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2) = evaluate(probs_test, y_test, verbose=False)
(error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3) = evaluate(probs_val, y_val, verbose=False)
print(('Uncal Valid Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
print(('Uncal Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1)))
print(('Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2)))
print(('Validation Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3)))
else:
K = input_test.shape[1]
probs_val = np.zeros_like(input_val)
probs_test = np.zeros_like(input_test)
for k in range(K):
y_cal = np.array((y_val == k), dtype='int')[(:, 0)]
model = TemperatureScaling(**m_kwargs)
model.fit(input_val[(:, k)], y_cal)
probs_val[(:, k)] = model.predict(input_val[(:, k)])
probs_test[(:, k)] = model.predict(input_test[(:, k)])
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate(softmax(logits_val), y_val, verbose=False)
(error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1) = evaluate(softmax(logits_test), y_test, verbose=False)
(error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2) = evaluate(probs_test, y_test, verbose=False)
(error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3) = evaluate(probs_val, y_val, verbose=False)
print(('Uncal Valid Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
print(('Uncal Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1)))
print(('Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2)))
print(('Validation Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3)))
with open(f'result/{name}_{method}_val.txt', 'wb') as f:
np.savetxt(f, softmax(logits_val))
np.savetxt(f, probs_val)
with open(f'result/{name}_{method}_test.txt', 'wb') as f2:
np.savetxt(f2, softmax(logits_test))
np.savetxt(f2, probs_test)
df.loc[0] = [(name + '_val_uncalib'), error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier]
df.loc[1] = [(name + '_test_uncalib'), error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1]
df.loc[2] = [(name + '_test_calib'), error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2]
df.loc[3] = [(name + '_val_calib'), error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3]
t2 = time.time()
print('Time taken:', (t2 - t1), '\n')
return df
|
def cal_TS_results(name, method, files, m_kwargs={}, approach='all'):
'\n Calibrate models scores, using output from logits files and given function (fn).\n There are implemented to different approaches "all" and "1-vs-K" for calibration,\n the approach of calibration should match with function used for calibration.\n\n Params:\n fn (class): class of the calibration method used. It must contain methods "fit" and "predict",\n where first fits the models and second outputs calibrated probabilities.\n path (string): path to the folder with logits files\n files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))\n m_kwargs (dictionary): keyword arguments for the calibration class initialization\n approach (string): "all" for multiclass calibration and "1-vs-K" for 1-vs-K approach.\n input (string): "probabilities" or "logits", specific to calibration method\n\n Returns:\n df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.\n\n '
df = pd.DataFrame(columns=['Name', 'Error', 'ECE', 'ECE2', 'ECE_CW', 'ECE_CW2', 'ECE_FULL', 'ECE_FULL2', 'MCE', 'MCE2', 'Loss', 'Brier'])
t1 = time.time()
val_df = pd.read_csv(files[0], sep='\t', index_col=False)
test_df = pd.read_csv(files[1], sep='\t', index_col=False)
logits_val = val_df.iloc[(:, 3:)].to_numpy()
y_val = val_df.iloc[(:, 1:2)].to_numpy()
logits_test = test_df.iloc[(:, 3:)].to_numpy()
y_test = test_df.iloc[(:, 1:2)].to_numpy()
input_val = logits_val
input_test = logits_test
if (approach == 'all'):
y_val_flat = y_val.flatten()
model = TemperatureScaling(**m_kwargs)
opt = model.fit(input_val, y_val_flat)
print(f'the optimal temperature is {opt.x[0]}')
file1 = open(f'model_weights/model_TS_{name}.txt', 'w')
file1.write(str(opt.x[0]))
probs_val = model.predict(input_val)
probs_test = model.predict(input_test)
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate(softmax(logits_val), y_val, verbose=False)
(error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1) = evaluate(softmax(logits_test), y_test, verbose=False)
(error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2) = evaluate(probs_test, y_test, verbose=False)
(error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3) = evaluate(probs_val, y_val, verbose=False)
print(('Uncal Valid Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
print(('Uncal Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1)))
print(('Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2)))
print(('Validation Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3)))
else:
K = input_test.shape[1]
probs_val = np.zeros_like(input_val)
probs_test = np.zeros_like(input_test)
for k in range(K):
y_cal = np.array((y_val == k), dtype='int')[(:, 0)]
model = TemperatureScaling(**m_kwargs)
model.fit(input_val[(:, k)], y_cal)
probs_val[(:, k)] = model.predict(input_val[(:, k)])
probs_test[(:, k)] = model.predict(input_test[(:, k)])
(error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier) = evaluate(softmax(logits_val), y_val, verbose=False)
(error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1) = evaluate(softmax(logits_test), y_test, verbose=False)
(error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2) = evaluate(probs_test, y_test, verbose=False)
(error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3) = evaluate(probs_val, y_val, verbose=False)
print(('Uncal Valid Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier)))
print(('Uncal Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1)))
print(('Test Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2)))
print(('Validation Error %f; ece %f; ece2 %f; ece_cw %f; ece_cw2 %f; ece_full %f; ece_full2 %f; mce %f; mce2 %f; loss %f, brier %f' % (error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3)))
with open(f'result/{name}_{method}_val.txt', 'wb') as f:
np.savetxt(f, softmax(logits_val))
np.savetxt(f, probs_val)
with open(f'result/{name}_{method}_test.txt', 'wb') as f2:
np.savetxt(f2, softmax(logits_test))
np.savetxt(f2, probs_test)
df.loc[0] = [(name + '_val_uncalib'), error, ece, ece2, ece_cw, ece_cw2, ece_full, ece_full2, mce, mce2, loss, brier]
df.loc[1] = [(name + '_test_uncalib'), error1, ece1, ece1_2, ece_cw1_1, ece_cw1_2, ece_full1_1, ece_full1_2, mce1_1, mce1_2, loss1, brier1]
df.loc[2] = [(name + '_test_calib'), error2, ece2_1, ece2_2, ece_cw2_1, ece_cw2_2, ece_full2_1, ece_full2_2, mce2_1, mce2_2, loss2, brier2]
df.loc[3] = [(name + '_val_calib'), error3, ece3_1, ece3_2, ece_cw3_1, ece_cw3_2, ece_full3_1, ece_full3_2, mce3_1, mce3_2, loss3, brier3]
t2 = time.time()
print('Time taken:', (t2 - t1), '\n')
return df<|docstring|>Calibrate models scores, using output from logits files and given function (fn).
There are implemented to different approaches "all" and "1-vs-K" for calibration,
the approach of calibration should match with function used for calibration.
Params:
fn (class): class of the calibration method used. It must contain methods "fit" and "predict",
where first fits the models and second outputs calibrated probabilities.
path (string): path to the folder with logits files
files (list of strings): pickled logits files ((logits_val, y_val), (logits_test, y_test))
m_kwargs (dictionary): keyword arguments for the calibration class initialization
approach (string): "all" for multiclass calibration and "1-vs-K" for 1-vs-K approach.
input (string): "probabilities" or "logits", specific to calibration method
Returns:
df (pandas.DataFrame): dataframe with calibrated and uncalibrated results for all the input files.<|endoftext|>
|
5dc17b5423528daf5de8853dd4f4aa055ebeaebafbbe854c67956b5cdddbb284
|
def _ninja_impl(ctx):
"The implementation of the `ninja` rule\n\n Args:\n ctx (ctx): The rule's context object\n\n Returns:\n list: A list of providers. See `cc_external_rule_impl`\n "
ninja_data = get_ninja_data(ctx)
tools_deps = (ctx.attr.tools_deps + ninja_data.deps)
attrs = create_attrs(ctx.attr, configure_name='Ninja', create_configure_script=_create_ninja_script, tools_deps=tools_deps, ninja_path=ninja_data.path)
return cc_external_rule_impl(ctx, attrs)
|
The implementation of the `ninja` rule
Args:
ctx (ctx): The rule's context object
Returns:
list: A list of providers. See `cc_external_rule_impl`
|
foreign_cc/ninja.bzl
|
_ninja_impl
|
rubensf/rules_foreign_cc
| 521 |
python
|
def _ninja_impl(ctx):
"The implementation of the `ninja` rule\n\n Args:\n ctx (ctx): The rule's context object\n\n Returns:\n list: A list of providers. See `cc_external_rule_impl`\n "
ninja_data = get_ninja_data(ctx)
tools_deps = (ctx.attr.tools_deps + ninja_data.deps)
attrs = create_attrs(ctx.attr, configure_name='Ninja', create_configure_script=_create_ninja_script, tools_deps=tools_deps, ninja_path=ninja_data.path)
return cc_external_rule_impl(ctx, attrs)
|
def _ninja_impl(ctx):
"The implementation of the `ninja` rule\n\n Args:\n ctx (ctx): The rule's context object\n\n Returns:\n list: A list of providers. See `cc_external_rule_impl`\n "
ninja_data = get_ninja_data(ctx)
tools_deps = (ctx.attr.tools_deps + ninja_data.deps)
attrs = create_attrs(ctx.attr, configure_name='Ninja', create_configure_script=_create_ninja_script, tools_deps=tools_deps, ninja_path=ninja_data.path)
return cc_external_rule_impl(ctx, attrs)<|docstring|>The implementation of the `ninja` rule
Args:
ctx (ctx): The rule's context object
Returns:
list: A list of providers. See `cc_external_rule_impl`<|endoftext|>
|
44aa8ff68f600488f643ee3b0f1ea26cc1bf5e8885cfbe0800521a03db5888d4
|
def _create_ninja_script(configureParameters):
'Creates the bash commands for invoking commands to build ninja projects\n\n Args:\n configureParameters (struct): See `ConfigureParameters`\n\n Returns:\n str: A string representing a section of a bash script\n '
ctx = configureParameters.ctx
attrs = configureParameters.attrs
script = []
root = detect_root(ctx.attr.lib_source)
script.append('##symlink_contents_to_dir## $$EXT_BUILD_ROOT$$/{} $$BUILD_TMPDIR$$'.format(root))
data = (ctx.attr.data + ctx.attr.build_data)
args = ' '.join([ctx.expand_location(arg, data) for arg in ctx.attr.args])
directory = '$$EXT_BUILD_ROOT$$/{}'.format(root)
if ctx.attr.directory:
directory = ctx.expand_location(ctx.attr.directory, data)
prefix = ('{} '.format(expand_locations(ctx, attrs.tool_prefix, data)) if attrs.tool_prefix else '')
for target in (ctx.attr.targets or ['']):
script.append('{prefix}{ninja} -C {dir} {args} {target}'.format(prefix=prefix, ninja=attrs.ninja_path, dir=directory, args=args, target=target))
return script
|
Creates the bash commands for invoking commands to build ninja projects
Args:
configureParameters (struct): See `ConfigureParameters`
Returns:
str: A string representing a section of a bash script
|
foreign_cc/ninja.bzl
|
_create_ninja_script
|
rubensf/rules_foreign_cc
| 521 |
python
|
def _create_ninja_script(configureParameters):
'Creates the bash commands for invoking commands to build ninja projects\n\n Args:\n configureParameters (struct): See `ConfigureParameters`\n\n Returns:\n str: A string representing a section of a bash script\n '
ctx = configureParameters.ctx
attrs = configureParameters.attrs
script = []
root = detect_root(ctx.attr.lib_source)
script.append('##symlink_contents_to_dir## $$EXT_BUILD_ROOT$$/{} $$BUILD_TMPDIR$$'.format(root))
data = (ctx.attr.data + ctx.attr.build_data)
args = ' '.join([ctx.expand_location(arg, data) for arg in ctx.attr.args])
directory = '$$EXT_BUILD_ROOT$$/{}'.format(root)
if ctx.attr.directory:
directory = ctx.expand_location(ctx.attr.directory, data)
prefix = ('{} '.format(expand_locations(ctx, attrs.tool_prefix, data)) if attrs.tool_prefix else )
for target in (ctx.attr.targets or []):
script.append('{prefix}{ninja} -C {dir} {args} {target}'.format(prefix=prefix, ninja=attrs.ninja_path, dir=directory, args=args, target=target))
return script
|
def _create_ninja_script(configureParameters):
'Creates the bash commands for invoking commands to build ninja projects\n\n Args:\n configureParameters (struct): See `ConfigureParameters`\n\n Returns:\n str: A string representing a section of a bash script\n '
ctx = configureParameters.ctx
attrs = configureParameters.attrs
script = []
root = detect_root(ctx.attr.lib_source)
script.append('##symlink_contents_to_dir## $$EXT_BUILD_ROOT$$/{} $$BUILD_TMPDIR$$'.format(root))
data = (ctx.attr.data + ctx.attr.build_data)
args = ' '.join([ctx.expand_location(arg, data) for arg in ctx.attr.args])
directory = '$$EXT_BUILD_ROOT$$/{}'.format(root)
if ctx.attr.directory:
directory = ctx.expand_location(ctx.attr.directory, data)
prefix = ('{} '.format(expand_locations(ctx, attrs.tool_prefix, data)) if attrs.tool_prefix else )
for target in (ctx.attr.targets or []):
script.append('{prefix}{ninja} -C {dir} {args} {target}'.format(prefix=prefix, ninja=attrs.ninja_path, dir=directory, args=args, target=target))
return script<|docstring|>Creates the bash commands for invoking commands to build ninja projects
Args:
configureParameters (struct): See `ConfigureParameters`
Returns:
str: A string representing a section of a bash script<|endoftext|>
|
4dc21acc576b5e04e3fcc9e96e1c94b2f4372622985d8e647c0a345b20301b71
|
def _attrs():
'Modifies the common set of attributes used by rules_foreign_cc and sets Ninja specific attrs\n\n Returns:\n dict: Attributes of the `ninja` rule\n '
attrs = dict(CC_EXTERNAL_RULE_ATTRIBUTES)
attrs.update({'args': attr.string_list(doc='A list of arguments to pass to the call to `ninja`'), 'directory': attr.string(doc=('A directory to pass as the `-C` argument. The rule will always use the root ' + 'directory of the `lib_sources` attribute if this attribute is not set'))})
return attrs
|
Modifies the common set of attributes used by rules_foreign_cc and sets Ninja specific attrs
Returns:
dict: Attributes of the `ninja` rule
|
foreign_cc/ninja.bzl
|
_attrs
|
rubensf/rules_foreign_cc
| 521 |
python
|
def _attrs():
'Modifies the common set of attributes used by rules_foreign_cc and sets Ninja specific attrs\n\n Returns:\n dict: Attributes of the `ninja` rule\n '
attrs = dict(CC_EXTERNAL_RULE_ATTRIBUTES)
attrs.update({'args': attr.string_list(doc='A list of arguments to pass to the call to `ninja`'), 'directory': attr.string(doc=('A directory to pass as the `-C` argument. The rule will always use the root ' + 'directory of the `lib_sources` attribute if this attribute is not set'))})
return attrs
|
def _attrs():
'Modifies the common set of attributes used by rules_foreign_cc and sets Ninja specific attrs\n\n Returns:\n dict: Attributes of the `ninja` rule\n '
attrs = dict(CC_EXTERNAL_RULE_ATTRIBUTES)
attrs.update({'args': attr.string_list(doc='A list of arguments to pass to the call to `ninja`'), 'directory': attr.string(doc=('A directory to pass as the `-C` argument. The rule will always use the root ' + 'directory of the `lib_sources` attribute if this attribute is not set'))})
return attrs<|docstring|>Modifies the common set of attributes used by rules_foreign_cc and sets Ninja specific attrs
Returns:
dict: Attributes of the `ninja` rule<|endoftext|>
|
b2aa3ce0151c9b0c4072c2d7a5c2161c03536fa765444d55d40bb0f72dd6ce21
|
@classmethod
def init_from_url(cls, url, rows=None):
'\n Initializes from url\n\n :param url: screener url\n :type url: string\n :param rows: total number of rows to get\n :type rows: int\n '
split_query = urlparse_qs(urlparse(url).query)
tickers = (split_query['t'][0].split(',') if ('t' in split_query) else None)
filters = (split_query['f'][0].split(',') if ('f' in split_query) else None)
custom = (split_query['c'][0].split(',') if ('c' in split_query) else None)
order = (split_query['o'][0] if ('o' in split_query) else '')
signal = (split_query['s'][0] if ('s' in split_query) else '')
table = 'Overview'
if ('v' in split_query):
table_numbers_types = {v: k for (k, v) in TABLE_TYPES.items()}
table_number_string = split_query['v'][0][0:3]
try:
table = table_numbers_types[table_number_string]
except KeyError:
raise InvalidTableType(split_query['v'][0])
return cls(tickers, filters, rows, order, signal, table, custom)
|
Initializes from url
:param url: screener url
:type url: string
:param rows: total number of rows to get
:type rows: int
|
finviz/screener.py
|
init_from_url
|
diveyez/finviz
| 746 |
python
|
@classmethod
def init_from_url(cls, url, rows=None):
'\n Initializes from url\n\n :param url: screener url\n :type url: string\n :param rows: total number of rows to get\n :type rows: int\n '
split_query = urlparse_qs(urlparse(url).query)
tickers = (split_query['t'][0].split(',') if ('t' in split_query) else None)
filters = (split_query['f'][0].split(',') if ('f' in split_query) else None)
custom = (split_query['c'][0].split(',') if ('c' in split_query) else None)
order = (split_query['o'][0] if ('o' in split_query) else )
signal = (split_query['s'][0] if ('s' in split_query) else )
table = 'Overview'
if ('v' in split_query):
table_numbers_types = {v: k for (k, v) in TABLE_TYPES.items()}
table_number_string = split_query['v'][0][0:3]
try:
table = table_numbers_types[table_number_string]
except KeyError:
raise InvalidTableType(split_query['v'][0])
return cls(tickers, filters, rows, order, signal, table, custom)
|
@classmethod
def init_from_url(cls, url, rows=None):
'\n Initializes from url\n\n :param url: screener url\n :type url: string\n :param rows: total number of rows to get\n :type rows: int\n '
split_query = urlparse_qs(urlparse(url).query)
tickers = (split_query['t'][0].split(',') if ('t' in split_query) else None)
filters = (split_query['f'][0].split(',') if ('f' in split_query) else None)
custom = (split_query['c'][0].split(',') if ('c' in split_query) else None)
order = (split_query['o'][0] if ('o' in split_query) else )
signal = (split_query['s'][0] if ('s' in split_query) else )
table = 'Overview'
if ('v' in split_query):
table_numbers_types = {v: k for (k, v) in TABLE_TYPES.items()}
table_number_string = split_query['v'][0][0:3]
try:
table = table_numbers_types[table_number_string]
except KeyError:
raise InvalidTableType(split_query['v'][0])
return cls(tickers, filters, rows, order, signal, table, custom)<|docstring|>Initializes from url
:param url: screener url
:type url: string
:param rows: total number of rows to get
:type rows: int<|endoftext|>
|
5b84d45833f61eb22a63315a611d4598f63adfe82d7e72bf387792ce25562ff8
|
def __init__(self, tickers=None, filters=None, rows=None, order='', signal='', table=None, custom=None, user_agent=generate_user_agent(), request_method='sequential'):
"\n Initializes all variables to its values\n\n :param tickers: collection of ticker strings eg.: ['AAPL', 'AMD', 'WMT']\n :type tickers: list\n :param filters: collection of filters strings eg.: ['exch_nasd', 'idx_sp500', 'fa_div_none']\n :type filters: list\n :param rows: total number of rows to get\n :type rows: int\n :param order: table order eg.: '-price' (to sort table by descending price)\n :type order: str\n :param signal: show by signal eg.: 'n_majornews' (for stocks with major news)\n :type signal: str\n :param table: table type eg.: 'Performance'\n :type table: str\n :param custom: collection of custom columns eg.: ['1', '21', '23', '45']\n :type custom: list\n :var self.data: list of dictionaries containing row data\n :type self.data: list\n "
if (tickers is None):
self._tickers = []
else:
self._tickers = tickers
if (filters is None):
self._filters = []
else:
self._filters = filters
if (table is None):
self._table = '111'
else:
self._table = self.__check_table(table)
if (custom is None):
self._custom = []
else:
self._table = '152'
self._custom = custom
if ('0' not in self._custom):
self._custom = (['0'] + self._custom)
self._rows = rows
self._order = order
self._signal = signal
self._user_agent = user_agent
self._request_method = request_method
self.analysis = []
self.data = self.__search_screener()
|
Initializes all variables to its values
:param tickers: collection of ticker strings eg.: ['AAPL', 'AMD', 'WMT']
:type tickers: list
:param filters: collection of filters strings eg.: ['exch_nasd', 'idx_sp500', 'fa_div_none']
:type filters: list
:param rows: total number of rows to get
:type rows: int
:param order: table order eg.: '-price' (to sort table by descending price)
:type order: str
:param signal: show by signal eg.: 'n_majornews' (for stocks with major news)
:type signal: str
:param table: table type eg.: 'Performance'
:type table: str
:param custom: collection of custom columns eg.: ['1', '21', '23', '45']
:type custom: list
:var self.data: list of dictionaries containing row data
:type self.data: list
|
finviz/screener.py
|
__init__
|
diveyez/finviz
| 746 |
python
|
def __init__(self, tickers=None, filters=None, rows=None, order=, signal=, table=None, custom=None, user_agent=generate_user_agent(), request_method='sequential'):
"\n Initializes all variables to its values\n\n :param tickers: collection of ticker strings eg.: ['AAPL', 'AMD', 'WMT']\n :type tickers: list\n :param filters: collection of filters strings eg.: ['exch_nasd', 'idx_sp500', 'fa_div_none']\n :type filters: list\n :param rows: total number of rows to get\n :type rows: int\n :param order: table order eg.: '-price' (to sort table by descending price)\n :type order: str\n :param signal: show by signal eg.: 'n_majornews' (for stocks with major news)\n :type signal: str\n :param table: table type eg.: 'Performance'\n :type table: str\n :param custom: collection of custom columns eg.: ['1', '21', '23', '45']\n :type custom: list\n :var self.data: list of dictionaries containing row data\n :type self.data: list\n "
if (tickers is None):
self._tickers = []
else:
self._tickers = tickers
if (filters is None):
self._filters = []
else:
self._filters = filters
if (table is None):
self._table = '111'
else:
self._table = self.__check_table(table)
if (custom is None):
self._custom = []
else:
self._table = '152'
self._custom = custom
if ('0' not in self._custom):
self._custom = (['0'] + self._custom)
self._rows = rows
self._order = order
self._signal = signal
self._user_agent = user_agent
self._request_method = request_method
self.analysis = []
self.data = self.__search_screener()
|
def __init__(self, tickers=None, filters=None, rows=None, order=, signal=, table=None, custom=None, user_agent=generate_user_agent(), request_method='sequential'):
"\n Initializes all variables to its values\n\n :param tickers: collection of ticker strings eg.: ['AAPL', 'AMD', 'WMT']\n :type tickers: list\n :param filters: collection of filters strings eg.: ['exch_nasd', 'idx_sp500', 'fa_div_none']\n :type filters: list\n :param rows: total number of rows to get\n :type rows: int\n :param order: table order eg.: '-price' (to sort table by descending price)\n :type order: str\n :param signal: show by signal eg.: 'n_majornews' (for stocks with major news)\n :type signal: str\n :param table: table type eg.: 'Performance'\n :type table: str\n :param custom: collection of custom columns eg.: ['1', '21', '23', '45']\n :type custom: list\n :var self.data: list of dictionaries containing row data\n :type self.data: list\n "
if (tickers is None):
self._tickers = []
else:
self._tickers = tickers
if (filters is None):
self._filters = []
else:
self._filters = filters
if (table is None):
self._table = '111'
else:
self._table = self.__check_table(table)
if (custom is None):
self._custom = []
else:
self._table = '152'
self._custom = custom
if ('0' not in self._custom):
self._custom = (['0'] + self._custom)
self._rows = rows
self._order = order
self._signal = signal
self._user_agent = user_agent
self._request_method = request_method
self.analysis = []
self.data = self.__search_screener()<|docstring|>Initializes all variables to its values
:param tickers: collection of ticker strings eg.: ['AAPL', 'AMD', 'WMT']
:type tickers: list
:param filters: collection of filters strings eg.: ['exch_nasd', 'idx_sp500', 'fa_div_none']
:type filters: list
:param rows: total number of rows to get
:type rows: int
:param order: table order eg.: '-price' (to sort table by descending price)
:type order: str
:param signal: show by signal eg.: 'n_majornews' (for stocks with major news)
:type signal: str
:param table: table type eg.: 'Performance'
:type table: str
:param custom: collection of custom columns eg.: ['1', '21', '23', '45']
:type custom: list
:var self.data: list of dictionaries containing row data
:type self.data: list<|endoftext|>
|
f7adc5310cb9ab69449a2a48e77916c4bfb7b15d896e15fe1006673ce493e76d
|
def __call__(self, tickers=None, filters=None, rows=None, order='', signal='', table=None, custom=None):
"\n Adds more filters to the screener. Example usage:\n\n stock_list = Screener(filters=['cap_large']) # All the stocks with large market cap\n # After analyzing you decide you want to see which of the stocks have high dividend yield\n # and show their performance:\n stock_list(filters=['fa_div_high'], table='Performance')\n # Shows performance of stocks with large market cap and high dividend yield\n "
if tickers:
[self._tickers.append(item) for item in tickers]
if filters:
[self._filters.append(item) for item in filters]
if table:
self._table = self.__check_table(table)
if order:
self._order = order
if signal:
self._signal = signal
if rows:
self._rows = rows
if custom:
self._custom = custom
self.analysis = []
self.data = self.__search_screener()
|
Adds more filters to the screener. Example usage:
stock_list = Screener(filters=['cap_large']) # All the stocks with large market cap
# After analyzing you decide you want to see which of the stocks have high dividend yield
# and show their performance:
stock_list(filters=['fa_div_high'], table='Performance')
# Shows performance of stocks with large market cap and high dividend yield
|
finviz/screener.py
|
__call__
|
diveyez/finviz
| 746 |
python
|
def __call__(self, tickers=None, filters=None, rows=None, order=, signal=, table=None, custom=None):
"\n Adds more filters to the screener. Example usage:\n\n stock_list = Screener(filters=['cap_large']) # All the stocks with large market cap\n # After analyzing you decide you want to see which of the stocks have high dividend yield\n # and show their performance:\n stock_list(filters=['fa_div_high'], table='Performance')\n # Shows performance of stocks with large market cap and high dividend yield\n "
if tickers:
[self._tickers.append(item) for item in tickers]
if filters:
[self._filters.append(item) for item in filters]
if table:
self._table = self.__check_table(table)
if order:
self._order = order
if signal:
self._signal = signal
if rows:
self._rows = rows
if custom:
self._custom = custom
self.analysis = []
self.data = self.__search_screener()
|
def __call__(self, tickers=None, filters=None, rows=None, order=, signal=, table=None, custom=None):
"\n Adds more filters to the screener. Example usage:\n\n stock_list = Screener(filters=['cap_large']) # All the stocks with large market cap\n # After analyzing you decide you want to see which of the stocks have high dividend yield\n # and show their performance:\n stock_list(filters=['fa_div_high'], table='Performance')\n # Shows performance of stocks with large market cap and high dividend yield\n "
if tickers:
[self._tickers.append(item) for item in tickers]
if filters:
[self._filters.append(item) for item in filters]
if table:
self._table = self.__check_table(table)
if order:
self._order = order
if signal:
self._signal = signal
if rows:
self._rows = rows
if custom:
self._custom = custom
self.analysis = []
self.data = self.__search_screener()<|docstring|>Adds more filters to the screener. Example usage:
stock_list = Screener(filters=['cap_large']) # All the stocks with large market cap
# After analyzing you decide you want to see which of the stocks have high dividend yield
# and show their performance:
stock_list(filters=['fa_div_high'], table='Performance')
# Shows performance of stocks with large market cap and high dividend yield<|endoftext|>
|
e169c361e6d9a32f951b3b24847dca3922b4f3fee32628e789683b7d3d1afbb3
|
def __str__(self):
' Returns a readable representation of a table. '
table_list = [self.headers]
for row in self.data:
table_list.append([(row[col] or '') for col in self.headers])
return create_table_string(table_list)
|
Returns a readable representation of a table.
|
finviz/screener.py
|
__str__
|
diveyez/finviz
| 746 |
python
|
def __str__(self):
' '
table_list = [self.headers]
for row in self.data:
table_list.append([(row[col] or ) for col in self.headers])
return create_table_string(table_list)
|
def __str__(self):
' '
table_list = [self.headers]
for row in self.data:
table_list.append([(row[col] or ) for col in self.headers])
return create_table_string(table_list)<|docstring|>Returns a readable representation of a table.<|endoftext|>
|
1b549b81355aedd7ab64eadec7d18459989c83ada61b46519d3603f562ead92b
|
def __repr__(self):
" Returns a string representation of the parameter's values. "
values = f'''tickers: {tuple(self._tickers)}
filters: {tuple(self._filters)}
rows: {self._rows}
order: {self._order}
signal: {self._signal}
table: {self._table}
table: {self._custom}'''
return values
|
Returns a string representation of the parameter's values.
|
finviz/screener.py
|
__repr__
|
diveyez/finviz
| 746 |
python
|
def __repr__(self):
" "
values = f'tickers: {tuple(self._tickers)}
filters: {tuple(self._filters)}
rows: {self._rows}
order: {self._order}
signal: {self._signal}
table: {self._table}
table: {self._custom}'
return values
|
def __repr__(self):
" "
values = f'tickers: {tuple(self._tickers)}
filters: {tuple(self._filters)}
rows: {self._rows}
order: {self._order}
signal: {self._signal}
table: {self._table}
table: {self._custom}'
return values<|docstring|>Returns a string representation of the parameter's values.<|endoftext|>
|
b356b3ca8cfcdf73836ce6156286d69abe4d9818694c773aa304de53c7a97b2a
|
def __len__(self):
' Returns an int with the number of total rows. '
return int(self._rows)
|
Returns an int with the number of total rows.
|
finviz/screener.py
|
__len__
|
diveyez/finviz
| 746 |
python
|
def __len__(self):
' '
return int(self._rows)
|
def __len__(self):
' '
return int(self._rows)<|docstring|>Returns an int with the number of total rows.<|endoftext|>
|
bbfd85e067b999be0260eaa894f6d521dcef75cf9a277ce4e6066605d6b53389
|
def __getitem__(self, position):
' Returns a dictionary containing specific row data. '
return self.data[position]
|
Returns a dictionary containing specific row data.
|
finviz/screener.py
|
__getitem__
|
diveyez/finviz
| 746 |
python
|
def __getitem__(self, position):
' '
return self.data[position]
|
def __getitem__(self, position):
' '
return self.data[position]<|docstring|>Returns a dictionary containing specific row data.<|endoftext|>
|
e432bdfc6cde9e495292e6e550d0f58220a30cf43075fbca8ea5af2baa0e1635
|
@staticmethod
def __check_table(input_table):
' Checks if the user input for table type is correct. Otherwise, raises an InvalidTableType error. '
try:
table = TABLE_TYPES[input_table]
return table
except KeyError:
raise InvalidTableType(input_table)
|
Checks if the user input for table type is correct. Otherwise, raises an InvalidTableType error.
|
finviz/screener.py
|
__check_table
|
diveyez/finviz
| 746 |
python
|
@staticmethod
def __check_table(input_table):
' '
try:
table = TABLE_TYPES[input_table]
return table
except KeyError:
raise InvalidTableType(input_table)
|
@staticmethod
def __check_table(input_table):
' '
try:
table = TABLE_TYPES[input_table]
return table
except KeyError:
raise InvalidTableType(input_table)<|docstring|>Checks if the user input for table type is correct. Otherwise, raises an InvalidTableType error.<|endoftext|>
|
adaa95e6c6b790ac79e919010948dbf9efd66fdcd34f87f77f565bc3cf993113
|
@staticmethod
def load_filter_dict(reload=True):
"\n Get dict of available filters. File containing json specification of filters will be built if it doesn't exist\n or if reload is False\n "
json_directory = pathlib.Path(__file__).parent
json_file = pathlib.Path.joinpath(json_directory, 'filters.json')
if (reload and json_file.is_file()):
with open(json_file, 'r') as fp:
return json.load(fp)
hdr = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11'}
url = 'https://finviz.com/screener.ashx?ft=4'
req = urllib.request.Request(url, headers=hdr)
with urllib.request.urlopen(req) as response:
html = response.read().decode('utf-8')
bs = BeautifulSoup(html, 'html.parser')
filters_table = None
for td in bs.find_all('td'):
if (td.get_text().strip() == 'Exchange'):
filters_table = td.find_parent('table')
if (filters_table is None):
raise Exception('Could not locate filter parameters')
for div in filters_table.find_all('div'):
div.decompose()
filter_dict = {}
td_list = filters_table.find_all('td')
for i in range(0, (len(td_list) - 2), 2):
current_dict = {}
if (td_list[i].get_text().strip() == ''):
continue
filter_text = td_list[i].get_text().strip()
selections = td_list[(i + 1)].find('select')
filter_name = selections.get('data-filter').strip()
options = selections.find_all('option', {'value': True})
for opt in options:
value = opt.get('value').strip()
text = opt.get_text()
if ((value is None) or ('Elite' in text)):
continue
current_dict[text] = f'{filter_name}_{value}'
filter_dict[filter_text] = current_dict
try:
with open(json_file, 'w') as fp:
json.dump(filter_dict, fp)
except Exception as e:
print(e)
print('Unable to write to file{}'.format(json_file))
return filter_dict
|
Get dict of available filters. File containing json specification of filters will be built if it doesn't exist
or if reload is False
|
finviz/screener.py
|
load_filter_dict
|
diveyez/finviz
| 746 |
python
|
@staticmethod
def load_filter_dict(reload=True):
"\n Get dict of available filters. File containing json specification of filters will be built if it doesn't exist\n or if reload is False\n "
json_directory = pathlib.Path(__file__).parent
json_file = pathlib.Path.joinpath(json_directory, 'filters.json')
if (reload and json_file.is_file()):
with open(json_file, 'r') as fp:
return json.load(fp)
hdr = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11'}
url = 'https://finviz.com/screener.ashx?ft=4'
req = urllib.request.Request(url, headers=hdr)
with urllib.request.urlopen(req) as response:
html = response.read().decode('utf-8')
bs = BeautifulSoup(html, 'html.parser')
filters_table = None
for td in bs.find_all('td'):
if (td.get_text().strip() == 'Exchange'):
filters_table = td.find_parent('table')
if (filters_table is None):
raise Exception('Could not locate filter parameters')
for div in filters_table.find_all('div'):
div.decompose()
filter_dict = {}
td_list = filters_table.find_all('td')
for i in range(0, (len(td_list) - 2), 2):
current_dict = {}
if (td_list[i].get_text().strip() == ):
continue
filter_text = td_list[i].get_text().strip()
selections = td_list[(i + 1)].find('select')
filter_name = selections.get('data-filter').strip()
options = selections.find_all('option', {'value': True})
for opt in options:
value = opt.get('value').strip()
text = opt.get_text()
if ((value is None) or ('Elite' in text)):
continue
current_dict[text] = f'{filter_name}_{value}'
filter_dict[filter_text] = current_dict
try:
with open(json_file, 'w') as fp:
json.dump(filter_dict, fp)
except Exception as e:
print(e)
print('Unable to write to file{}'.format(json_file))
return filter_dict
|
@staticmethod
def load_filter_dict(reload=True):
"\n Get dict of available filters. File containing json specification of filters will be built if it doesn't exist\n or if reload is False\n "
json_directory = pathlib.Path(__file__).parent
json_file = pathlib.Path.joinpath(json_directory, 'filters.json')
if (reload and json_file.is_file()):
with open(json_file, 'r') as fp:
return json.load(fp)
hdr = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11'}
url = 'https://finviz.com/screener.ashx?ft=4'
req = urllib.request.Request(url, headers=hdr)
with urllib.request.urlopen(req) as response:
html = response.read().decode('utf-8')
bs = BeautifulSoup(html, 'html.parser')
filters_table = None
for td in bs.find_all('td'):
if (td.get_text().strip() == 'Exchange'):
filters_table = td.find_parent('table')
if (filters_table is None):
raise Exception('Could not locate filter parameters')
for div in filters_table.find_all('div'):
div.decompose()
filter_dict = {}
td_list = filters_table.find_all('td')
for i in range(0, (len(td_list) - 2), 2):
current_dict = {}
if (td_list[i].get_text().strip() == ):
continue
filter_text = td_list[i].get_text().strip()
selections = td_list[(i + 1)].find('select')
filter_name = selections.get('data-filter').strip()
options = selections.find_all('option', {'value': True})
for opt in options:
value = opt.get('value').strip()
text = opt.get_text()
if ((value is None) or ('Elite' in text)):
continue
current_dict[text] = f'{filter_name}_{value}'
filter_dict[filter_text] = current_dict
try:
with open(json_file, 'w') as fp:
json.dump(filter_dict, fp)
except Exception as e:
print(e)
print('Unable to write to file{}'.format(json_file))
return filter_dict<|docstring|>Get dict of available filters. File containing json specification of filters will be built if it doesn't exist
or if reload is False<|endoftext|>
|
f25178b70943c84b71728446eeb200aab6c8fb1721914f17704e3ad12715071a
|
def to_sqlite(self, filename):
'Exports the generated table into a SQLite database.\n\n :param filename: SQLite database file path\n :type filename: str\n '
export_to_db(self.headers, self.data, filename)
|
Exports the generated table into a SQLite database.
:param filename: SQLite database file path
:type filename: str
|
finviz/screener.py
|
to_sqlite
|
diveyez/finviz
| 746 |
python
|
def to_sqlite(self, filename):
'Exports the generated table into a SQLite database.\n\n :param filename: SQLite database file path\n :type filename: str\n '
export_to_db(self.headers, self.data, filename)
|
def to_sqlite(self, filename):
'Exports the generated table into a SQLite database.\n\n :param filename: SQLite database file path\n :type filename: str\n '
export_to_db(self.headers, self.data, filename)<|docstring|>Exports the generated table into a SQLite database.
:param filename: SQLite database file path
:type filename: str<|endoftext|>
|
ad7971e4b84fe25a994d43bc9e880192bf61bf0ba3bc57f2d9dc3e94830a4b35
|
def to_csv(self, filename: str):
'Exports the generated table into a CSV file.\n Returns a CSV string if filename is None.\n\n :param filename: CSV file path\n :type filename: str\n '
if (filename and filename.endswith('.csv')):
filename = filename[:(- 4)]
if (len(self.analysis) > 0):
export_to_csv(['ticker', 'date', 'category', 'analyst', 'rating', 'price_from', 'price_to'], self.analysis, f'{filename}-analysts.csv')
return export_to_csv(self.headers, self.data, f'{filename}.csv')
|
Exports the generated table into a CSV file.
Returns a CSV string if filename is None.
:param filename: CSV file path
:type filename: str
|
finviz/screener.py
|
to_csv
|
diveyez/finviz
| 746 |
python
|
def to_csv(self, filename: str):
'Exports the generated table into a CSV file.\n Returns a CSV string if filename is None.\n\n :param filename: CSV file path\n :type filename: str\n '
if (filename and filename.endswith('.csv')):
filename = filename[:(- 4)]
if (len(self.analysis) > 0):
export_to_csv(['ticker', 'date', 'category', 'analyst', 'rating', 'price_from', 'price_to'], self.analysis, f'{filename}-analysts.csv')
return export_to_csv(self.headers, self.data, f'{filename}.csv')
|
def to_csv(self, filename: str):
'Exports the generated table into a CSV file.\n Returns a CSV string if filename is None.\n\n :param filename: CSV file path\n :type filename: str\n '
if (filename and filename.endswith('.csv')):
filename = filename[:(- 4)]
if (len(self.analysis) > 0):
export_to_csv(['ticker', 'date', 'category', 'analyst', 'rating', 'price_from', 'price_to'], self.analysis, f'{filename}-analysts.csv')
return export_to_csv(self.headers, self.data, f'{filename}.csv')<|docstring|>Exports the generated table into a CSV file.
Returns a CSV string if filename is None.
:param filename: CSV file path
:type filename: str<|endoftext|>
|
d3053a941625a38d82ea97c66419422d491bfa8394d644402bfcb3837d99f421
|
def get_charts(self, period='d', size='l', chart_type='c', ta='1'):
"\n Downloads the charts of all tickers shown by the table.\n\n :param period: table period eg. : 'd', 'w' or 'm' for daily, weekly and monthly periods\n :type period: str\n :param size: table size eg.: 'l' for large or 's' for small - choose large for better quality but higher size\n :type size: str\n :param chart_type: chart type: 'c' for candles or 'l' for lines\n :type chart_type: str\n :param ta: technical analysis eg.: '1' to show ta '0' to hide ta\n :type ta: str\n "
encoded_payload = urlencode({'ty': chart_type, 'ta': ta, 'p': period, 's': size})
sequential_data_scrape(scrape.download_chart_image, [f"https://finviz.com/chart.ashx?{encoded_payload}&t={row.get('Ticker')}" for row in self.data], self._user_agent)
|
Downloads the charts of all tickers shown by the table.
:param period: table period eg. : 'd', 'w' or 'm' for daily, weekly and monthly periods
:type period: str
:param size: table size eg.: 'l' for large or 's' for small - choose large for better quality but higher size
:type size: str
:param chart_type: chart type: 'c' for candles or 'l' for lines
:type chart_type: str
:param ta: technical analysis eg.: '1' to show ta '0' to hide ta
:type ta: str
|
finviz/screener.py
|
get_charts
|
diveyez/finviz
| 746 |
python
|
def get_charts(self, period='d', size='l', chart_type='c', ta='1'):
"\n Downloads the charts of all tickers shown by the table.\n\n :param period: table period eg. : 'd', 'w' or 'm' for daily, weekly and monthly periods\n :type period: str\n :param size: table size eg.: 'l' for large or 's' for small - choose large for better quality but higher size\n :type size: str\n :param chart_type: chart type: 'c' for candles or 'l' for lines\n :type chart_type: str\n :param ta: technical analysis eg.: '1' to show ta '0' to hide ta\n :type ta: str\n "
encoded_payload = urlencode({'ty': chart_type, 'ta': ta, 'p': period, 's': size})
sequential_data_scrape(scrape.download_chart_image, [f"https://finviz.com/chart.ashx?{encoded_payload}&t={row.get('Ticker')}" for row in self.data], self._user_agent)
|
def get_charts(self, period='d', size='l', chart_type='c', ta='1'):
"\n Downloads the charts of all tickers shown by the table.\n\n :param period: table period eg. : 'd', 'w' or 'm' for daily, weekly and monthly periods\n :type period: str\n :param size: table size eg.: 'l' for large or 's' for small - choose large for better quality but higher size\n :type size: str\n :param chart_type: chart type: 'c' for candles or 'l' for lines\n :type chart_type: str\n :param ta: technical analysis eg.: '1' to show ta '0' to hide ta\n :type ta: str\n "
encoded_payload = urlencode({'ty': chart_type, 'ta': ta, 'p': period, 's': size})
sequential_data_scrape(scrape.download_chart_image, [f"https://finviz.com/chart.ashx?{encoded_payload}&t={row.get('Ticker')}" for row in self.data], self._user_agent)<|docstring|>Downloads the charts of all tickers shown by the table.
:param period: table period eg. : 'd', 'w' or 'm' for daily, weekly and monthly periods
:type period: str
:param size: table size eg.: 'l' for large or 's' for small - choose large for better quality but higher size
:type size: str
:param chart_type: chart type: 'c' for candles or 'l' for lines
:type chart_type: str
:param ta: technical analysis eg.: '1' to show ta '0' to hide ta
:type ta: str<|endoftext|>
|
e9a28ad663f6d4c271a23388e15b16060b88b1e0dcfc8e32fe5693a41ff9df7b
|
def get_ticker_details(self):
'\n Downloads the details of all tickers shown by the table.\n '
ticker_data = sequential_data_scrape(scrape.download_ticker_details, [f"https://finviz.com/quote.ashx?&t={row.get('Ticker')}" for row in self.data], self._user_agent)
for entry in ticker_data:
for (key, value) in entry.items():
for ticker_generic in self.data:
if (ticker_generic.get('Ticker') == key):
if ('Sales' not in self.headers):
self.headers.extend(list(value[0].keys()))
ticker_generic.update(value[0])
self.analysis.extend(value[1])
return self.data
|
Downloads the details of all tickers shown by the table.
|
finviz/screener.py
|
get_ticker_details
|
diveyez/finviz
| 746 |
python
|
def get_ticker_details(self):
'\n \n '
ticker_data = sequential_data_scrape(scrape.download_ticker_details, [f"https://finviz.com/quote.ashx?&t={row.get('Ticker')}" for row in self.data], self._user_agent)
for entry in ticker_data:
for (key, value) in entry.items():
for ticker_generic in self.data:
if (ticker_generic.get('Ticker') == key):
if ('Sales' not in self.headers):
self.headers.extend(list(value[0].keys()))
ticker_generic.update(value[0])
self.analysis.extend(value[1])
return self.data
|
def get_ticker_details(self):
'\n \n '
ticker_data = sequential_data_scrape(scrape.download_ticker_details, [f"https://finviz.com/quote.ashx?&t={row.get('Ticker')}" for row in self.data], self._user_agent)
for entry in ticker_data:
for (key, value) in entry.items():
for ticker_generic in self.data:
if (ticker_generic.get('Ticker') == key):
if ('Sales' not in self.headers):
self.headers.extend(list(value[0].keys()))
ticker_generic.update(value[0])
self.analysis.extend(value[1])
return self.data<|docstring|>Downloads the details of all tickers shown by the table.<|endoftext|>
|
3d6eedcec70d919fdf1ae9f06105191dd656fab0a466d8cea342936e0d5944fe
|
def __check_rows(self):
'\n Checks if the user input for row number is correct.\n Otherwise, modifies the number or raises NoResults error.\n '
self._total_rows = scrape.get_total_rows(self._page_content)
if (self._total_rows == 0):
raise NoResults(self._url.split('?')[1])
elif ((self._rows is None) or (self._rows > self._total_rows)):
return self._total_rows
else:
return self._rows
|
Checks if the user input for row number is correct.
Otherwise, modifies the number or raises NoResults error.
|
finviz/screener.py
|
__check_rows
|
diveyez/finviz
| 746 |
python
|
def __check_rows(self):
'\n Checks if the user input for row number is correct.\n Otherwise, modifies the number or raises NoResults error.\n '
self._total_rows = scrape.get_total_rows(self._page_content)
if (self._total_rows == 0):
raise NoResults(self._url.split('?')[1])
elif ((self._rows is None) or (self._rows > self._total_rows)):
return self._total_rows
else:
return self._rows
|
def __check_rows(self):
'\n Checks if the user input for row number is correct.\n Otherwise, modifies the number or raises NoResults error.\n '
self._total_rows = scrape.get_total_rows(self._page_content)
if (self._total_rows == 0):
raise NoResults(self._url.split('?')[1])
elif ((self._rows is None) or (self._rows > self._total_rows)):
return self._total_rows
else:
return self._rows<|docstring|>Checks if the user input for row number is correct.
Otherwise, modifies the number or raises NoResults error.<|endoftext|>
|
3334462c93ed830d7d5022c3ca33d8737e626c1db84a88ddc4d81b7d8ce9262e
|
def __get_table_headers(self):
' Private function used to return table headers. '
return self._page_content.cssselect('tr[valign="middle"]')[0].xpath('td//text()')
|
Private function used to return table headers.
|
finviz/screener.py
|
__get_table_headers
|
diveyez/finviz
| 746 |
python
|
def __get_table_headers(self):
' '
return self._page_content.cssselect('tr[valign="middle"]')[0].xpath('td//text()')
|
def __get_table_headers(self):
' '
return self._page_content.cssselect('tr[valign="middle"]')[0].xpath('td//text()')<|docstring|>Private function used to return table headers.<|endoftext|>
|
94f477bd517d00c37125db69743e0d85900dc6a570773dce9cd89c552c832f96
|
def __search_screener(self):
' Private function used to return data from the FinViz screener. '
(self._page_content, self._url) = http_request_get('https://finviz.com/screener.ashx', payload={'v': self._table, 't': ','.join(self._tickers), 'f': ','.join(self._filters), 'o': self._order, 's': self._signal, 'c': ','.join(self._custom)}, user_agent=self._user_agent)
self._rows = self.__check_rows()
self.headers = self.__get_table_headers()
if (self._request_method == 'async'):
async_connector = Connector(scrape.get_table, scrape.get_page_urls(self._page_content, self._rows, self._url), self._user_agent, self.headers, self._rows, css_select=True)
pages_data = async_connector.run_connector()
else:
pages_data = sequential_data_scrape(scrape.get_table, scrape.get_page_urls(self._page_content, self._rows, self._url), self._user_agent, self.headers, self._rows)
data = []
for page in pages_data:
for row in page:
data.append(row)
return data
|
Private function used to return data from the FinViz screener.
|
finviz/screener.py
|
__search_screener
|
diveyez/finviz
| 746 |
python
|
def __search_screener(self):
' '
(self._page_content, self._url) = http_request_get('https://finviz.com/screener.ashx', payload={'v': self._table, 't': ','.join(self._tickers), 'f': ','.join(self._filters), 'o': self._order, 's': self._signal, 'c': ','.join(self._custom)}, user_agent=self._user_agent)
self._rows = self.__check_rows()
self.headers = self.__get_table_headers()
if (self._request_method == 'async'):
async_connector = Connector(scrape.get_table, scrape.get_page_urls(self._page_content, self._rows, self._url), self._user_agent, self.headers, self._rows, css_select=True)
pages_data = async_connector.run_connector()
else:
pages_data = sequential_data_scrape(scrape.get_table, scrape.get_page_urls(self._page_content, self._rows, self._url), self._user_agent, self.headers, self._rows)
data = []
for page in pages_data:
for row in page:
data.append(row)
return data
|
def __search_screener(self):
' '
(self._page_content, self._url) = http_request_get('https://finviz.com/screener.ashx', payload={'v': self._table, 't': ','.join(self._tickers), 'f': ','.join(self._filters), 'o': self._order, 's': self._signal, 'c': ','.join(self._custom)}, user_agent=self._user_agent)
self._rows = self.__check_rows()
self.headers = self.__get_table_headers()
if (self._request_method == 'async'):
async_connector = Connector(scrape.get_table, scrape.get_page_urls(self._page_content, self._rows, self._url), self._user_agent, self.headers, self._rows, css_select=True)
pages_data = async_connector.run_connector()
else:
pages_data = sequential_data_scrape(scrape.get_table, scrape.get_page_urls(self._page_content, self._rows, self._url), self._user_agent, self.headers, self._rows)
data = []
for page in pages_data:
for row in page:
data.append(row)
return data<|docstring|>Private function used to return data from the FinViz screener.<|endoftext|>
|
6ad6bd97d725ca8feab0ff43a5918dc3e13eb25915de1164d0353764d43101ee
|
def is_adb_available():
'Checks if adb is available as a command line tool.\n\n Returns:\n True if adb binary is available in console, False otherwise.\n '
(ret, out, err) = utils.run_command('which adb', shell=True)
clean_out = out.decode('utf-8').strip()
if clean_out:
return True
return False
|
Checks if adb is available as a command line tool.
Returns:
True if adb binary is available in console, False otherwise.
|
mobly/controllers/android_device_lib/adb.py
|
is_adb_available
|
xianyuanjia/mobly
| 532 |
python
|
def is_adb_available():
'Checks if adb is available as a command line tool.\n\n Returns:\n True if adb binary is available in console, False otherwise.\n '
(ret, out, err) = utils.run_command('which adb', shell=True)
clean_out = out.decode('utf-8').strip()
if clean_out:
return True
return False
|
def is_adb_available():
'Checks if adb is available as a command line tool.\n\n Returns:\n True if adb binary is available in console, False otherwise.\n '
(ret, out, err) = utils.run_command('which adb', shell=True)
clean_out = out.decode('utf-8').strip()
if clean_out:
return True
return False<|docstring|>Checks if adb is available as a command line tool.
Returns:
True if adb binary is available in console, False otherwise.<|endoftext|>
|
92524a3bb0eefa2364b0b2edd650189e120229bf7d6e889820691db1ab1dd1fe
|
def list_occupied_adb_ports():
'Lists all the host ports occupied by adb forward.\n\n This is useful because adb will silently override the binding if an attempt\n to bind to a port already used by adb was made, instead of throwing binding\n error. So one should always check what ports adb is using before trying to\n bind to a port with adb.\n\n Returns:\n A list of integers representing occupied host ports.\n '
out = AdbProxy().forward('--list')
clean_lines = str(out, 'utf-8').strip().split('\n')
used_ports = []
for line in clean_lines:
tokens = line.split(' tcp:')
if (len(tokens) != 3):
continue
used_ports.append(int(tokens[1]))
return used_ports
|
Lists all the host ports occupied by adb forward.
This is useful because adb will silently override the binding if an attempt
to bind to a port already used by adb was made, instead of throwing binding
error. So one should always check what ports adb is using before trying to
bind to a port with adb.
Returns:
A list of integers representing occupied host ports.
|
mobly/controllers/android_device_lib/adb.py
|
list_occupied_adb_ports
|
xianyuanjia/mobly
| 532 |
python
|
def list_occupied_adb_ports():
'Lists all the host ports occupied by adb forward.\n\n This is useful because adb will silently override the binding if an attempt\n to bind to a port already used by adb was made, instead of throwing binding\n error. So one should always check what ports adb is using before trying to\n bind to a port with adb.\n\n Returns:\n A list of integers representing occupied host ports.\n '
out = AdbProxy().forward('--list')
clean_lines = str(out, 'utf-8').strip().split('\n')
used_ports = []
for line in clean_lines:
tokens = line.split(' tcp:')
if (len(tokens) != 3):
continue
used_ports.append(int(tokens[1]))
return used_ports
|
def list_occupied_adb_ports():
'Lists all the host ports occupied by adb forward.\n\n This is useful because adb will silently override the binding if an attempt\n to bind to a port already used by adb was made, instead of throwing binding\n error. So one should always check what ports adb is using before trying to\n bind to a port with adb.\n\n Returns:\n A list of integers representing occupied host ports.\n '
out = AdbProxy().forward('--list')
clean_lines = str(out, 'utf-8').strip().split('\n')
used_ports = []
for line in clean_lines:
tokens = line.split(' tcp:')
if (len(tokens) != 3):
continue
used_ports.append(int(tokens[1]))
return used_ports<|docstring|>Lists all the host ports occupied by adb forward.
This is useful because adb will silently override the binding if an attempt
to bind to a port already used by adb was made, instead of throwing binding
error. So one should always check what ports adb is using before trying to
bind to a port with adb.
Returns:
A list of integers representing occupied host ports.<|endoftext|>
|
869e40735517e335b8ea68b5225a06dd9f69ffd7cffe93a2ff09e6b28c115f06
|
def _exec_cmd(self, args, shell, timeout, stderr):
'Executes adb commands.\n\n Args:\n args: string or list of strings, program arguments.\n See subprocess.Popen() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Popen() docs.\n timeout: float, the number of seconds to wait before timing out.\n If not specified, no timeout takes effect.\n stderr: a Byte stream, like io.BytesIO, stderr of the command will\n be written to this object if provided.\n\n Returns:\n The output of the adb command run if exit code is 0.\n\n Raises:\n ValueError: timeout value is invalid.\n AdbError: The adb command exit code is not 0.\n AdbTimeoutError: The adb command timed out.\n '
if (timeout and (timeout <= 0)):
raise ValueError(('Timeout is not a positive value: %s' % timeout))
try:
(ret, out, err) = utils.run_command(args, shell=shell, timeout=timeout)
except psutil.TimeoutExpired:
raise AdbTimeoutError(cmd=args, timeout=timeout, serial=self.serial)
if stderr:
stderr.write(err)
logging.debug('cmd: %s, stdout: %s, stderr: %s, ret: %s', utils.cli_cmd_to_string(args), out, err, ret)
if (ret == 0):
return out
else:
raise AdbError(cmd=args, stdout=out, stderr=err, ret_code=ret, serial=self.serial)
|
Executes adb commands.
Args:
args: string or list of strings, program arguments.
See subprocess.Popen() documentation.
shell: bool, True to run this command through the system shell,
False to invoke it directly. See subprocess.Popen() docs.
timeout: float, the number of seconds to wait before timing out.
If not specified, no timeout takes effect.
stderr: a Byte stream, like io.BytesIO, stderr of the command will
be written to this object if provided.
Returns:
The output of the adb command run if exit code is 0.
Raises:
ValueError: timeout value is invalid.
AdbError: The adb command exit code is not 0.
AdbTimeoutError: The adb command timed out.
|
mobly/controllers/android_device_lib/adb.py
|
_exec_cmd
|
xianyuanjia/mobly
| 532 |
python
|
def _exec_cmd(self, args, shell, timeout, stderr):
'Executes adb commands.\n\n Args:\n args: string or list of strings, program arguments.\n See subprocess.Popen() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Popen() docs.\n timeout: float, the number of seconds to wait before timing out.\n If not specified, no timeout takes effect.\n stderr: a Byte stream, like io.BytesIO, stderr of the command will\n be written to this object if provided.\n\n Returns:\n The output of the adb command run if exit code is 0.\n\n Raises:\n ValueError: timeout value is invalid.\n AdbError: The adb command exit code is not 0.\n AdbTimeoutError: The adb command timed out.\n '
if (timeout and (timeout <= 0)):
raise ValueError(('Timeout is not a positive value: %s' % timeout))
try:
(ret, out, err) = utils.run_command(args, shell=shell, timeout=timeout)
except psutil.TimeoutExpired:
raise AdbTimeoutError(cmd=args, timeout=timeout, serial=self.serial)
if stderr:
stderr.write(err)
logging.debug('cmd: %s, stdout: %s, stderr: %s, ret: %s', utils.cli_cmd_to_string(args), out, err, ret)
if (ret == 0):
return out
else:
raise AdbError(cmd=args, stdout=out, stderr=err, ret_code=ret, serial=self.serial)
|
def _exec_cmd(self, args, shell, timeout, stderr):
'Executes adb commands.\n\n Args:\n args: string or list of strings, program arguments.\n See subprocess.Popen() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Popen() docs.\n timeout: float, the number of seconds to wait before timing out.\n If not specified, no timeout takes effect.\n stderr: a Byte stream, like io.BytesIO, stderr of the command will\n be written to this object if provided.\n\n Returns:\n The output of the adb command run if exit code is 0.\n\n Raises:\n ValueError: timeout value is invalid.\n AdbError: The adb command exit code is not 0.\n AdbTimeoutError: The adb command timed out.\n '
if (timeout and (timeout <= 0)):
raise ValueError(('Timeout is not a positive value: %s' % timeout))
try:
(ret, out, err) = utils.run_command(args, shell=shell, timeout=timeout)
except psutil.TimeoutExpired:
raise AdbTimeoutError(cmd=args, timeout=timeout, serial=self.serial)
if stderr:
stderr.write(err)
logging.debug('cmd: %s, stdout: %s, stderr: %s, ret: %s', utils.cli_cmd_to_string(args), out, err, ret)
if (ret == 0):
return out
else:
raise AdbError(cmd=args, stdout=out, stderr=err, ret_code=ret, serial=self.serial)<|docstring|>Executes adb commands.
Args:
args: string or list of strings, program arguments.
See subprocess.Popen() documentation.
shell: bool, True to run this command through the system shell,
False to invoke it directly. See subprocess.Popen() docs.
timeout: float, the number of seconds to wait before timing out.
If not specified, no timeout takes effect.
stderr: a Byte stream, like io.BytesIO, stderr of the command will
be written to this object if provided.
Returns:
The output of the adb command run if exit code is 0.
Raises:
ValueError: timeout value is invalid.
AdbError: The adb command exit code is not 0.
AdbTimeoutError: The adb command timed out.<|endoftext|>
|
04af845a7df574abf67dd2e47e5987d3becfd1ac513de951b9fff3b8d1d4959a
|
def _execute_and_process_stdout(self, args, shell, handler):
'Executes adb commands and processes the stdout with a handler.\n\n Args:\n args: string or list of strings, program arguments.\n See subprocess.Popen() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Popen() docs.\n handler: func, a function to handle adb stdout line by line.\n\n Returns:\n The stderr of the adb command run if exit code is 0.\n\n Raises:\n AdbError: The adb command exit code is not 0.\n '
proc = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell, bufsize=1)
out = '[elided, processed via handler]'
try:
while True:
line = proc.stdout.readline()
if line:
handler(line)
else:
break
finally:
(unexpected_out, err) = proc.communicate()
if unexpected_out:
out = ('[unexpected stdout] %s' % unexpected_out)
for line in unexpected_out.splitlines():
handler(line)
ret = proc.returncode
logging.debug('cmd: %s, stdout: %s, stderr: %s, ret: %s', utils.cli_cmd_to_string(args), out, err, ret)
if (ret == 0):
return err
else:
raise AdbError(cmd=args, stdout=out, stderr=err, ret_code=ret)
|
Executes adb commands and processes the stdout with a handler.
Args:
args: string or list of strings, program arguments.
See subprocess.Popen() documentation.
shell: bool, True to run this command through the system shell,
False to invoke it directly. See subprocess.Popen() docs.
handler: func, a function to handle adb stdout line by line.
Returns:
The stderr of the adb command run if exit code is 0.
Raises:
AdbError: The adb command exit code is not 0.
|
mobly/controllers/android_device_lib/adb.py
|
_execute_and_process_stdout
|
xianyuanjia/mobly
| 532 |
python
|
def _execute_and_process_stdout(self, args, shell, handler):
'Executes adb commands and processes the stdout with a handler.\n\n Args:\n args: string or list of strings, program arguments.\n See subprocess.Popen() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Popen() docs.\n handler: func, a function to handle adb stdout line by line.\n\n Returns:\n The stderr of the adb command run if exit code is 0.\n\n Raises:\n AdbError: The adb command exit code is not 0.\n '
proc = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell, bufsize=1)
out = '[elided, processed via handler]'
try:
while True:
line = proc.stdout.readline()
if line:
handler(line)
else:
break
finally:
(unexpected_out, err) = proc.communicate()
if unexpected_out:
out = ('[unexpected stdout] %s' % unexpected_out)
for line in unexpected_out.splitlines():
handler(line)
ret = proc.returncode
logging.debug('cmd: %s, stdout: %s, stderr: %s, ret: %s', utils.cli_cmd_to_string(args), out, err, ret)
if (ret == 0):
return err
else:
raise AdbError(cmd=args, stdout=out, stderr=err, ret_code=ret)
|
def _execute_and_process_stdout(self, args, shell, handler):
'Executes adb commands and processes the stdout with a handler.\n\n Args:\n args: string or list of strings, program arguments.\n See subprocess.Popen() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Popen() docs.\n handler: func, a function to handle adb stdout line by line.\n\n Returns:\n The stderr of the adb command run if exit code is 0.\n\n Raises:\n AdbError: The adb command exit code is not 0.\n '
proc = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell, bufsize=1)
out = '[elided, processed via handler]'
try:
while True:
line = proc.stdout.readline()
if line:
handler(line)
else:
break
finally:
(unexpected_out, err) = proc.communicate()
if unexpected_out:
out = ('[unexpected stdout] %s' % unexpected_out)
for line in unexpected_out.splitlines():
handler(line)
ret = proc.returncode
logging.debug('cmd: %s, stdout: %s, stderr: %s, ret: %s', utils.cli_cmd_to_string(args), out, err, ret)
if (ret == 0):
return err
else:
raise AdbError(cmd=args, stdout=out, stderr=err, ret_code=ret)<|docstring|>Executes adb commands and processes the stdout with a handler.
Args:
args: string or list of strings, program arguments.
See subprocess.Popen() documentation.
shell: bool, True to run this command through the system shell,
False to invoke it directly. See subprocess.Popen() docs.
handler: func, a function to handle adb stdout line by line.
Returns:
The stderr of the adb command run if exit code is 0.
Raises:
AdbError: The adb command exit code is not 0.<|endoftext|>
|
88c4cbd2a97fdcb60a7265d190848dc7d612e71d26567c7e9fea22418db0c58d
|
def _construct_adb_cmd(self, raw_name, args, shell):
'Constructs an adb command with arguments for a subprocess call.\n\n Args:\n raw_name: string, the raw unsanitized name of the adb command to\n format.\n args: string or list of strings, arguments to the adb command.\n See subprocess.Proc() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Proc() docs.\n\n Returns:\n The adb command in a format appropriate for subprocess. If shell is\n True, then this is a string; otherwise, this is a list of\n strings.\n '
args = (args or '')
name = raw_name.replace('_', '-')
if shell:
args = utils.cli_cmd_to_string(args)
if self.serial:
adb_cmd = ('"%s" -s "%s" %s %s' % (ADB, self.serial, name, args))
else:
adb_cmd = ('"%s" %s %s' % (ADB, name, args))
else:
adb_cmd = [ADB]
if self.serial:
adb_cmd.extend(['-s', self.serial])
adb_cmd.append(name)
if args:
if isinstance(args, str):
adb_cmd.append(args)
else:
adb_cmd.extend(args)
return adb_cmd
|
Constructs an adb command with arguments for a subprocess call.
Args:
raw_name: string, the raw unsanitized name of the adb command to
format.
args: string or list of strings, arguments to the adb command.
See subprocess.Proc() documentation.
shell: bool, True to run this command through the system shell,
False to invoke it directly. See subprocess.Proc() docs.
Returns:
The adb command in a format appropriate for subprocess. If shell is
True, then this is a string; otherwise, this is a list of
strings.
|
mobly/controllers/android_device_lib/adb.py
|
_construct_adb_cmd
|
xianyuanjia/mobly
| 532 |
python
|
def _construct_adb_cmd(self, raw_name, args, shell):
'Constructs an adb command with arguments for a subprocess call.\n\n Args:\n raw_name: string, the raw unsanitized name of the adb command to\n format.\n args: string or list of strings, arguments to the adb command.\n See subprocess.Proc() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Proc() docs.\n\n Returns:\n The adb command in a format appropriate for subprocess. If shell is\n True, then this is a string; otherwise, this is a list of\n strings.\n '
args = (args or )
name = raw_name.replace('_', '-')
if shell:
args = utils.cli_cmd_to_string(args)
if self.serial:
adb_cmd = ('"%s" -s "%s" %s %s' % (ADB, self.serial, name, args))
else:
adb_cmd = ('"%s" %s %s' % (ADB, name, args))
else:
adb_cmd = [ADB]
if self.serial:
adb_cmd.extend(['-s', self.serial])
adb_cmd.append(name)
if args:
if isinstance(args, str):
adb_cmd.append(args)
else:
adb_cmd.extend(args)
return adb_cmd
|
def _construct_adb_cmd(self, raw_name, args, shell):
'Constructs an adb command with arguments for a subprocess call.\n\n Args:\n raw_name: string, the raw unsanitized name of the adb command to\n format.\n args: string or list of strings, arguments to the adb command.\n See subprocess.Proc() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Proc() docs.\n\n Returns:\n The adb command in a format appropriate for subprocess. If shell is\n True, then this is a string; otherwise, this is a list of\n strings.\n '
args = (args or )
name = raw_name.replace('_', '-')
if shell:
args = utils.cli_cmd_to_string(args)
if self.serial:
adb_cmd = ('"%s" -s "%s" %s %s' % (ADB, self.serial, name, args))
else:
adb_cmd = ('"%s" %s %s' % (ADB, name, args))
else:
adb_cmd = [ADB]
if self.serial:
adb_cmd.extend(['-s', self.serial])
adb_cmd.append(name)
if args:
if isinstance(args, str):
adb_cmd.append(args)
else:
adb_cmd.extend(args)
return adb_cmd<|docstring|>Constructs an adb command with arguments for a subprocess call.
Args:
raw_name: string, the raw unsanitized name of the adb command to
format.
args: string or list of strings, arguments to the adb command.
See subprocess.Proc() documentation.
shell: bool, True to run this command through the system shell,
False to invoke it directly. See subprocess.Proc() docs.
Returns:
The adb command in a format appropriate for subprocess. If shell is
True, then this is a string; otherwise, this is a list of
strings.<|endoftext|>
|
40b23fe4755fd43bbb518c9ca98d30446050ffb368e5e99e903183cdfb7e927d
|
def _parse_getprop_output(self, output):
'Parses the raw output of `adb shell getprop` into a dictionary.\n\n Args:\n output: byte str, the raw output of the `adb shell getprop` call.\n\n Returns:\n dict, name-value pairs of the properties.\n '
output = output.decode('utf-8', errors='ignore').replace('\r\n', '\n')
results = {}
for line in output.split(']\n'):
if (not line):
continue
try:
(name, value) = line.split(': ', 1)
except ValueError:
logging.debug('Failed to parse adb getprop line %s', line)
continue
name = name.strip()[1:(- 1)]
if (value and (value[0] == '[')):
value = value[1:]
results[name] = value
return results
|
Parses the raw output of `adb shell getprop` into a dictionary.
Args:
output: byte str, the raw output of the `adb shell getprop` call.
Returns:
dict, name-value pairs of the properties.
|
mobly/controllers/android_device_lib/adb.py
|
_parse_getprop_output
|
xianyuanjia/mobly
| 532 |
python
|
def _parse_getprop_output(self, output):
'Parses the raw output of `adb shell getprop` into a dictionary.\n\n Args:\n output: byte str, the raw output of the `adb shell getprop` call.\n\n Returns:\n dict, name-value pairs of the properties.\n '
output = output.decode('utf-8', errors='ignore').replace('\r\n', '\n')
results = {}
for line in output.split(']\n'):
if (not line):
continue
try:
(name, value) = line.split(': ', 1)
except ValueError:
logging.debug('Failed to parse adb getprop line %s', line)
continue
name = name.strip()[1:(- 1)]
if (value and (value[0] == '[')):
value = value[1:]
results[name] = value
return results
|
def _parse_getprop_output(self, output):
'Parses the raw output of `adb shell getprop` into a dictionary.\n\n Args:\n output: byte str, the raw output of the `adb shell getprop` call.\n\n Returns:\n dict, name-value pairs of the properties.\n '
output = output.decode('utf-8', errors='ignore').replace('\r\n', '\n')
results = {}
for line in output.split(']\n'):
if (not line):
continue
try:
(name, value) = line.split(': ', 1)
except ValueError:
logging.debug('Failed to parse adb getprop line %s', line)
continue
name = name.strip()[1:(- 1)]
if (value and (value[0] == '[')):
value = value[1:]
results[name] = value
return results<|docstring|>Parses the raw output of `adb shell getprop` into a dictionary.
Args:
output: byte str, the raw output of the `adb shell getprop` call.
Returns:
dict, name-value pairs of the properties.<|endoftext|>
|
f992d6397b4c3798bad3ad8a13fe2d0000c25414ffbded3ac9a76d156a179d75
|
@property
def current_user_id(self):
'The integer ID of the current Android user.\n\n Some adb commands require specifying a user ID to work properly. Use\n this to get the current user ID.\n\n Note a "user" is not the same as an "account" in Android. See AOSP\'s\n documentation for details.\n https://source.android.com/devices/tech/admin/multi-user\n '
sdk_int = int(self.getprop('ro.build.version.sdk'))
if (sdk_int >= 24):
return int(self.shell(['am', 'get-current-user']))
if (sdk_int >= 21):
user_info_str = self.shell(['dumpsys', 'user']).decode('utf-8')
return int(re.findall('\\{(\\d+):', user_info_str)[0])
return 0
|
The integer ID of the current Android user.
Some adb commands require specifying a user ID to work properly. Use
this to get the current user ID.
Note a "user" is not the same as an "account" in Android. See AOSP's
documentation for details.
https://source.android.com/devices/tech/admin/multi-user
|
mobly/controllers/android_device_lib/adb.py
|
current_user_id
|
xianyuanjia/mobly
| 532 |
python
|
@property
def current_user_id(self):
'The integer ID of the current Android user.\n\n Some adb commands require specifying a user ID to work properly. Use\n this to get the current user ID.\n\n Note a "user" is not the same as an "account" in Android. See AOSP\'s\n documentation for details.\n https://source.android.com/devices/tech/admin/multi-user\n '
sdk_int = int(self.getprop('ro.build.version.sdk'))
if (sdk_int >= 24):
return int(self.shell(['am', 'get-current-user']))
if (sdk_int >= 21):
user_info_str = self.shell(['dumpsys', 'user']).decode('utf-8')
return int(re.findall('\\{(\\d+):', user_info_str)[0])
return 0
|
@property
def current_user_id(self):
'The integer ID of the current Android user.\n\n Some adb commands require specifying a user ID to work properly. Use\n this to get the current user ID.\n\n Note a "user" is not the same as an "account" in Android. See AOSP\'s\n documentation for details.\n https://source.android.com/devices/tech/admin/multi-user\n '
sdk_int = int(self.getprop('ro.build.version.sdk'))
if (sdk_int >= 24):
return int(self.shell(['am', 'get-current-user']))
if (sdk_int >= 21):
user_info_str = self.shell(['dumpsys', 'user']).decode('utf-8')
return int(re.findall('\\{(\\d+):', user_info_str)[0])
return 0<|docstring|>The integer ID of the current Android user.
Some adb commands require specifying a user ID to work properly. Use
this to get the current user ID.
Note a "user" is not the same as an "account" in Android. See AOSP's
documentation for details.
https://source.android.com/devices/tech/admin/multi-user<|endoftext|>
|
6f8d135056cc4afc42f79b74892a85fe47f8d3b14f0287f532fd4614d789de4e
|
def connect(self, address):
'Executes the `adb connect` command with proper status checking.\n\n Args:\n address: string, the address of the Android instance to connect to.\n\n Returns:\n The stdout content.\n\n Raises:\n AdbError: if the connection failed.\n '
stdout = self._exec_adb_cmd('connect', address, shell=False, timeout=None, stderr=None)
if (PATTERN_ADB_CONNECT_SUCCESS.match(stdout.decode('utf-8')) is None):
raise AdbError(cmd=f'connect {address}', stdout=stdout, stderr='', ret_code=0)
return stdout
|
Executes the `adb connect` command with proper status checking.
Args:
address: string, the address of the Android instance to connect to.
Returns:
The stdout content.
Raises:
AdbError: if the connection failed.
|
mobly/controllers/android_device_lib/adb.py
|
connect
|
xianyuanjia/mobly
| 532 |
python
|
def connect(self, address):
'Executes the `adb connect` command with proper status checking.\n\n Args:\n address: string, the address of the Android instance to connect to.\n\n Returns:\n The stdout content.\n\n Raises:\n AdbError: if the connection failed.\n '
stdout = self._exec_adb_cmd('connect', address, shell=False, timeout=None, stderr=None)
if (PATTERN_ADB_CONNECT_SUCCESS.match(stdout.decode('utf-8')) is None):
raise AdbError(cmd=f'connect {address}', stdout=stdout, stderr=, ret_code=0)
return stdout
|
def connect(self, address):
'Executes the `adb connect` command with proper status checking.\n\n Args:\n address: string, the address of the Android instance to connect to.\n\n Returns:\n The stdout content.\n\n Raises:\n AdbError: if the connection failed.\n '
stdout = self._exec_adb_cmd('connect', address, shell=False, timeout=None, stderr=None)
if (PATTERN_ADB_CONNECT_SUCCESS.match(stdout.decode('utf-8')) is None):
raise AdbError(cmd=f'connect {address}', stdout=stdout, stderr=, ret_code=0)
return stdout<|docstring|>Executes the `adb connect` command with proper status checking.
Args:
address: string, the address of the Android instance to connect to.
Returns:
The stdout content.
Raises:
AdbError: if the connection failed.<|endoftext|>
|
d49c85bcd98964f716a1791abb3bf6dd3c06ea765c4ff9f4e0ee56e6e9551ec9
|
def getprop(self, prop_name):
"Get a property of the device.\n\n This is a convenience wrapper for `adb shell getprop xxx`.\n\n Args:\n prop_name: A string that is the name of the property to get.\n\n Returns:\n A string that is the value of the property, or None if the property\n doesn't exist.\n "
return self.shell(['getprop', prop_name], timeout=DEFAULT_GETPROP_TIMEOUT_SEC).decode('utf-8').strip()
|
Get a property of the device.
This is a convenience wrapper for `adb shell getprop xxx`.
Args:
prop_name: A string that is the name of the property to get.
Returns:
A string that is the value of the property, or None if the property
doesn't exist.
|
mobly/controllers/android_device_lib/adb.py
|
getprop
|
xianyuanjia/mobly
| 532 |
python
|
def getprop(self, prop_name):
"Get a property of the device.\n\n This is a convenience wrapper for `adb shell getprop xxx`.\n\n Args:\n prop_name: A string that is the name of the property to get.\n\n Returns:\n A string that is the value of the property, or None if the property\n doesn't exist.\n "
return self.shell(['getprop', prop_name], timeout=DEFAULT_GETPROP_TIMEOUT_SEC).decode('utf-8').strip()
|
def getprop(self, prop_name):
"Get a property of the device.\n\n This is a convenience wrapper for `adb shell getprop xxx`.\n\n Args:\n prop_name: A string that is the name of the property to get.\n\n Returns:\n A string that is the value of the property, or None if the property\n doesn't exist.\n "
return self.shell(['getprop', prop_name], timeout=DEFAULT_GETPROP_TIMEOUT_SEC).decode('utf-8').strip()<|docstring|>Get a property of the device.
This is a convenience wrapper for `adb shell getprop xxx`.
Args:
prop_name: A string that is the name of the property to get.
Returns:
A string that is the value of the property, or None if the property
doesn't exist.<|endoftext|>
|
85d2dc7cff2f9a1a63b756b3a35453bc101ea0f948f19795425c8af9be496b24
|
def getprops(self, prop_names):
'Get multiple properties of the device.\n\n This is a convenience wrapper for `adb shell getprop`. Use this to\n reduce the number of adb calls when getting multiple properties.\n\n Args:\n prop_names: list of strings, the names of the properties to get.\n\n Returns:\n A dict containing name-value pairs of the properties requested, if\n they exist.\n '
attempts = DEFAULT_GETPROPS_ATTEMPTS
results = {}
for attempt in range(attempts):
raw_output = self.shell(['getprop'], timeout=DEFAULT_GETPROP_TIMEOUT_SEC)
properties = self._parse_getprop_output(raw_output)
if properties:
for name in prop_names:
if (name in properties):
results[name] = properties[name]
break
if (attempt < (attempts - 1)):
time.sleep(DEFAULT_GETPROPS_RETRY_SLEEP_SEC)
return results
|
Get multiple properties of the device.
This is a convenience wrapper for `adb shell getprop`. Use this to
reduce the number of adb calls when getting multiple properties.
Args:
prop_names: list of strings, the names of the properties to get.
Returns:
A dict containing name-value pairs of the properties requested, if
they exist.
|
mobly/controllers/android_device_lib/adb.py
|
getprops
|
xianyuanjia/mobly
| 532 |
python
|
def getprops(self, prop_names):
'Get multiple properties of the device.\n\n This is a convenience wrapper for `adb shell getprop`. Use this to\n reduce the number of adb calls when getting multiple properties.\n\n Args:\n prop_names: list of strings, the names of the properties to get.\n\n Returns:\n A dict containing name-value pairs of the properties requested, if\n they exist.\n '
attempts = DEFAULT_GETPROPS_ATTEMPTS
results = {}
for attempt in range(attempts):
raw_output = self.shell(['getprop'], timeout=DEFAULT_GETPROP_TIMEOUT_SEC)
properties = self._parse_getprop_output(raw_output)
if properties:
for name in prop_names:
if (name in properties):
results[name] = properties[name]
break
if (attempt < (attempts - 1)):
time.sleep(DEFAULT_GETPROPS_RETRY_SLEEP_SEC)
return results
|
def getprops(self, prop_names):
'Get multiple properties of the device.\n\n This is a convenience wrapper for `adb shell getprop`. Use this to\n reduce the number of adb calls when getting multiple properties.\n\n Args:\n prop_names: list of strings, the names of the properties to get.\n\n Returns:\n A dict containing name-value pairs of the properties requested, if\n they exist.\n '
attempts = DEFAULT_GETPROPS_ATTEMPTS
results = {}
for attempt in range(attempts):
raw_output = self.shell(['getprop'], timeout=DEFAULT_GETPROP_TIMEOUT_SEC)
properties = self._parse_getprop_output(raw_output)
if properties:
for name in prop_names:
if (name in properties):
results[name] = properties[name]
break
if (attempt < (attempts - 1)):
time.sleep(DEFAULT_GETPROPS_RETRY_SLEEP_SEC)
return results<|docstring|>Get multiple properties of the device.
This is a convenience wrapper for `adb shell getprop`. Use this to
reduce the number of adb calls when getting multiple properties.
Args:
prop_names: list of strings, the names of the properties to get.
Returns:
A dict containing name-value pairs of the properties requested, if
they exist.<|endoftext|>
|
328a41850106a39d5b869addd9e39499aaf9a3d596bdff7908637fdf50bd8a82
|
def has_shell_command(self, command):
'Checks to see if a given check command exists on the device.\n\n Args:\n command: A string that is the name of the command to check.\n\n Returns:\n A boolean that is True if the command exists and False otherwise.\n '
try:
output = self.shell(['command', '-v', command]).decode('utf-8').strip()
return (command in output)
except AdbError:
return False
|
Checks to see if a given check command exists on the device.
Args:
command: A string that is the name of the command to check.
Returns:
A boolean that is True if the command exists and False otherwise.
|
mobly/controllers/android_device_lib/adb.py
|
has_shell_command
|
xianyuanjia/mobly
| 532 |
python
|
def has_shell_command(self, command):
'Checks to see if a given check command exists on the device.\n\n Args:\n command: A string that is the name of the command to check.\n\n Returns:\n A boolean that is True if the command exists and False otherwise.\n '
try:
output = self.shell(['command', '-v', command]).decode('utf-8').strip()
return (command in output)
except AdbError:
return False
|
def has_shell_command(self, command):
'Checks to see if a given check command exists on the device.\n\n Args:\n command: A string that is the name of the command to check.\n\n Returns:\n A boolean that is True if the command exists and False otherwise.\n '
try:
output = self.shell(['command', '-v', command]).decode('utf-8').strip()
return (command in output)
except AdbError:
return False<|docstring|>Checks to see if a given check command exists on the device.
Args:
command: A string that is the name of the command to check.
Returns:
A boolean that is True if the command exists and False otherwise.<|endoftext|>
|
e7a0a1ea65ed5b1536b82306cda4c4f87b77de07d07758ccf6dd8f7e15c9ea62
|
def instrument(self, package, options=None, runner=None, handler=None):
"Runs an instrumentation command on the device.\n\n This is a convenience wrapper to avoid parameter formatting.\n\n Example:\n\n .. code-block:: python\n\n device.instrument(\n 'com.my.package.test',\n options = {\n 'class': 'com.my.package.test.TestSuite',\n },\n )\n\n Args:\n package: string, the package of the instrumentation tests.\n options: dict, the instrumentation options including the test\n class.\n runner: string, the test runner name, which defaults to\n DEFAULT_INSTRUMENTATION_RUNNER.\n handler: optional func, when specified the function is used to parse\n the instrumentation stdout line by line as the output is\n generated; otherwise, the stdout is simply returned once the\n instrumentation is finished.\n\n Returns:\n The stdout of instrumentation command or the stderr if the handler\n is set.\n "
if (runner is None):
runner = DEFAULT_INSTRUMENTATION_RUNNER
if (options is None):
options = {}
options_list = []
for (option_key, option_value) in options.items():
options_list.append(('-e %s %s' % (option_key, option_value)))
options_string = ' '.join(options_list)
instrumentation_command = ('am instrument -r -w %s %s/%s' % (options_string, package, runner))
logging.info('AndroidDevice|%s: Executing adb shell %s', self.serial, instrumentation_command)
if (handler is None):
return self._exec_adb_cmd('shell', instrumentation_command, shell=False, timeout=None, stderr=None)
else:
return self._execute_adb_and_process_stdout('shell', instrumentation_command, shell=False, handler=handler)
|
Runs an instrumentation command on the device.
This is a convenience wrapper to avoid parameter formatting.
Example:
.. code-block:: python
device.instrument(
'com.my.package.test',
options = {
'class': 'com.my.package.test.TestSuite',
},
)
Args:
package: string, the package of the instrumentation tests.
options: dict, the instrumentation options including the test
class.
runner: string, the test runner name, which defaults to
DEFAULT_INSTRUMENTATION_RUNNER.
handler: optional func, when specified the function is used to parse
the instrumentation stdout line by line as the output is
generated; otherwise, the stdout is simply returned once the
instrumentation is finished.
Returns:
The stdout of instrumentation command or the stderr if the handler
is set.
|
mobly/controllers/android_device_lib/adb.py
|
instrument
|
xianyuanjia/mobly
| 532 |
python
|
def instrument(self, package, options=None, runner=None, handler=None):
"Runs an instrumentation command on the device.\n\n This is a convenience wrapper to avoid parameter formatting.\n\n Example:\n\n .. code-block:: python\n\n device.instrument(\n 'com.my.package.test',\n options = {\n 'class': 'com.my.package.test.TestSuite',\n },\n )\n\n Args:\n package: string, the package of the instrumentation tests.\n options: dict, the instrumentation options including the test\n class.\n runner: string, the test runner name, which defaults to\n DEFAULT_INSTRUMENTATION_RUNNER.\n handler: optional func, when specified the function is used to parse\n the instrumentation stdout line by line as the output is\n generated; otherwise, the stdout is simply returned once the\n instrumentation is finished.\n\n Returns:\n The stdout of instrumentation command or the stderr if the handler\n is set.\n "
if (runner is None):
runner = DEFAULT_INSTRUMENTATION_RUNNER
if (options is None):
options = {}
options_list = []
for (option_key, option_value) in options.items():
options_list.append(('-e %s %s' % (option_key, option_value)))
options_string = ' '.join(options_list)
instrumentation_command = ('am instrument -r -w %s %s/%s' % (options_string, package, runner))
logging.info('AndroidDevice|%s: Executing adb shell %s', self.serial, instrumentation_command)
if (handler is None):
return self._exec_adb_cmd('shell', instrumentation_command, shell=False, timeout=None, stderr=None)
else:
return self._execute_adb_and_process_stdout('shell', instrumentation_command, shell=False, handler=handler)
|
def instrument(self, package, options=None, runner=None, handler=None):
"Runs an instrumentation command on the device.\n\n This is a convenience wrapper to avoid parameter formatting.\n\n Example:\n\n .. code-block:: python\n\n device.instrument(\n 'com.my.package.test',\n options = {\n 'class': 'com.my.package.test.TestSuite',\n },\n )\n\n Args:\n package: string, the package of the instrumentation tests.\n options: dict, the instrumentation options including the test\n class.\n runner: string, the test runner name, which defaults to\n DEFAULT_INSTRUMENTATION_RUNNER.\n handler: optional func, when specified the function is used to parse\n the instrumentation stdout line by line as the output is\n generated; otherwise, the stdout is simply returned once the\n instrumentation is finished.\n\n Returns:\n The stdout of instrumentation command or the stderr if the handler\n is set.\n "
if (runner is None):
runner = DEFAULT_INSTRUMENTATION_RUNNER
if (options is None):
options = {}
options_list = []
for (option_key, option_value) in options.items():
options_list.append(('-e %s %s' % (option_key, option_value)))
options_string = ' '.join(options_list)
instrumentation_command = ('am instrument -r -w %s %s/%s' % (options_string, package, runner))
logging.info('AndroidDevice|%s: Executing adb shell %s', self.serial, instrumentation_command)
if (handler is None):
return self._exec_adb_cmd('shell', instrumentation_command, shell=False, timeout=None, stderr=None)
else:
return self._execute_adb_and_process_stdout('shell', instrumentation_command, shell=False, handler=handler)<|docstring|>Runs an instrumentation command on the device.
This is a convenience wrapper to avoid parameter formatting.
Example:
.. code-block:: python
device.instrument(
'com.my.package.test',
options = {
'class': 'com.my.package.test.TestSuite',
},
)
Args:
package: string, the package of the instrumentation tests.
options: dict, the instrumentation options including the test
class.
runner: string, the test runner name, which defaults to
DEFAULT_INSTRUMENTATION_RUNNER.
handler: optional func, when specified the function is used to parse
the instrumentation stdout line by line as the output is
generated; otherwise, the stdout is simply returned once the
instrumentation is finished.
Returns:
The stdout of instrumentation command or the stderr if the handler
is set.<|endoftext|>
|
2559a563eaa65586dc8e6e6447e275a7be4265c6c4f1e5171713553857feb96e
|
def root(self):
'Enables ADB root mode on the device.\n\n This method will retry to execute the command `adb root` when an\n AdbError occurs, since sometimes the error `adb: unable to connect\n for root: closed` is raised when executing `adb root` immediately after\n the device is booted to OS.\n\n Returns:\n A string that is the stdout of root command.\n\n Raises:\n AdbError: If the command exit code is not 0.\n '
for attempt in range(ADB_ROOT_RETRY_ATTMEPTS):
try:
return self._exec_adb_cmd('root', args=None, shell=False, timeout=None, stderr=None)
except AdbError as e:
if ((attempt + 1) < ADB_ROOT_RETRY_ATTMEPTS):
logging.debug(('Retry the command "%s" since Error "%s" occurred.' % (utils.cli_cmd_to_string(e.cmd), e.stderr.decode('utf-8').strip())))
time.sleep(ADB_ROOT_RETRY_ATTEMPT_INTERVAL_SEC)
else:
raise e
|
Enables ADB root mode on the device.
This method will retry to execute the command `adb root` when an
AdbError occurs, since sometimes the error `adb: unable to connect
for root: closed` is raised when executing `adb root` immediately after
the device is booted to OS.
Returns:
A string that is the stdout of root command.
Raises:
AdbError: If the command exit code is not 0.
|
mobly/controllers/android_device_lib/adb.py
|
root
|
xianyuanjia/mobly
| 532 |
python
|
def root(self):
'Enables ADB root mode on the device.\n\n This method will retry to execute the command `adb root` when an\n AdbError occurs, since sometimes the error `adb: unable to connect\n for root: closed` is raised when executing `adb root` immediately after\n the device is booted to OS.\n\n Returns:\n A string that is the stdout of root command.\n\n Raises:\n AdbError: If the command exit code is not 0.\n '
for attempt in range(ADB_ROOT_RETRY_ATTMEPTS):
try:
return self._exec_adb_cmd('root', args=None, shell=False, timeout=None, stderr=None)
except AdbError as e:
if ((attempt + 1) < ADB_ROOT_RETRY_ATTMEPTS):
logging.debug(('Retry the command "%s" since Error "%s" occurred.' % (utils.cli_cmd_to_string(e.cmd), e.stderr.decode('utf-8').strip())))
time.sleep(ADB_ROOT_RETRY_ATTEMPT_INTERVAL_SEC)
else:
raise e
|
def root(self):
'Enables ADB root mode on the device.\n\n This method will retry to execute the command `adb root` when an\n AdbError occurs, since sometimes the error `adb: unable to connect\n for root: closed` is raised when executing `adb root` immediately after\n the device is booted to OS.\n\n Returns:\n A string that is the stdout of root command.\n\n Raises:\n AdbError: If the command exit code is not 0.\n '
for attempt in range(ADB_ROOT_RETRY_ATTMEPTS):
try:
return self._exec_adb_cmd('root', args=None, shell=False, timeout=None, stderr=None)
except AdbError as e:
if ((attempt + 1) < ADB_ROOT_RETRY_ATTMEPTS):
logging.debug(('Retry the command "%s" since Error "%s" occurred.' % (utils.cli_cmd_to_string(e.cmd), e.stderr.decode('utf-8').strip())))
time.sleep(ADB_ROOT_RETRY_ATTEMPT_INTERVAL_SEC)
else:
raise e<|docstring|>Enables ADB root mode on the device.
This method will retry to execute the command `adb root` when an
AdbError occurs, since sometimes the error `adb: unable to connect
for root: closed` is raised when executing `adb root` immediately after
the device is booted to OS.
Returns:
A string that is the stdout of root command.
Raises:
AdbError: If the command exit code is not 0.<|endoftext|>
|
4ba2e42e0674d00a95db5da6bb1cffd957ce8b2e0cc88cc3c828e7a9a0d82b14
|
def adb_call(args=None, shell=False, timeout=None, stderr=None):
'Wrapper for an ADB command.\n\n Args:\n args: string or list of strings, arguments to the adb command.\n See subprocess.Proc() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Proc() docs.\n timeout: float, the number of seconds to wait before timing out.\n If not specified, no timeout takes effect.\n stderr: a Byte stream, like io.BytesIO, stderr of the command\n will be written to this object if provided.\n\n Returns:\n The output of the adb command run if exit code is 0.\n '
return self._exec_adb_cmd(name, args, shell=shell, timeout=timeout, stderr=stderr)
|
Wrapper for an ADB command.
Args:
args: string or list of strings, arguments to the adb command.
See subprocess.Proc() documentation.
shell: bool, True to run this command through the system shell,
False to invoke it directly. See subprocess.Proc() docs.
timeout: float, the number of seconds to wait before timing out.
If not specified, no timeout takes effect.
stderr: a Byte stream, like io.BytesIO, stderr of the command
will be written to this object if provided.
Returns:
The output of the adb command run if exit code is 0.
|
mobly/controllers/android_device_lib/adb.py
|
adb_call
|
xianyuanjia/mobly
| 532 |
python
|
def adb_call(args=None, shell=False, timeout=None, stderr=None):
'Wrapper for an ADB command.\n\n Args:\n args: string or list of strings, arguments to the adb command.\n See subprocess.Proc() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Proc() docs.\n timeout: float, the number of seconds to wait before timing out.\n If not specified, no timeout takes effect.\n stderr: a Byte stream, like io.BytesIO, stderr of the command\n will be written to this object if provided.\n\n Returns:\n The output of the adb command run if exit code is 0.\n '
return self._exec_adb_cmd(name, args, shell=shell, timeout=timeout, stderr=stderr)
|
def adb_call(args=None, shell=False, timeout=None, stderr=None):
'Wrapper for an ADB command.\n\n Args:\n args: string or list of strings, arguments to the adb command.\n See subprocess.Proc() documentation.\n shell: bool, True to run this command through the system shell,\n False to invoke it directly. See subprocess.Proc() docs.\n timeout: float, the number of seconds to wait before timing out.\n If not specified, no timeout takes effect.\n stderr: a Byte stream, like io.BytesIO, stderr of the command\n will be written to this object if provided.\n\n Returns:\n The output of the adb command run if exit code is 0.\n '
return self._exec_adb_cmd(name, args, shell=shell, timeout=timeout, stderr=stderr)<|docstring|>Wrapper for an ADB command.
Args:
args: string or list of strings, arguments to the adb command.
See subprocess.Proc() documentation.
shell: bool, True to run this command through the system shell,
False to invoke it directly. See subprocess.Proc() docs.
timeout: float, the number of seconds to wait before timing out.
If not specified, no timeout takes effect.
stderr: a Byte stream, like io.BytesIO, stderr of the command
will be written to this object if provided.
Returns:
The output of the adb command run if exit code is 0.<|endoftext|>
|
d75d9ce9350d7bad0462aadbad08d509fd88f6157e38ef000b8fb17bffddf8cb
|
def chapter_current() -> int:
'Return current chapter number'
|
Return current chapter number
|
code/chapters.py
|
chapter_current
|
hanuele/knausj_talon
| 298 |
python
|
def chapter_current() -> int:
|
def chapter_current() -> int:
<|docstring|>Return current chapter number<|endoftext|>
|
0ec4a225429196ca70f25ef5c14d425f6a3d6e53538bb260d80df8dd863dcdad
|
def chapter_next():
'Go to next chapter'
actions.user.chapter_jump((actions.user.chapter_current() + 1))
|
Go to next chapter
|
code/chapters.py
|
chapter_next
|
hanuele/knausj_talon
| 298 |
python
|
def chapter_next():
actions.user.chapter_jump((actions.user.chapter_current() + 1))
|
def chapter_next():
actions.user.chapter_jump((actions.user.chapter_current() + 1))<|docstring|>Go to next chapter<|endoftext|>
|
7c32194a1747165edcfda3614dfd940a7f3eaa9ef2af389f17c37c335a4db1ba
|
def chapter_previous():
'Go to previous chapter'
actions.user.chapter_jump((actions.user.chapter_current() - 1))
|
Go to previous chapter
|
code/chapters.py
|
chapter_previous
|
hanuele/knausj_talon
| 298 |
python
|
def chapter_previous():
actions.user.chapter_jump((actions.user.chapter_current() - 1))
|
def chapter_previous():
actions.user.chapter_jump((actions.user.chapter_current() - 1))<|docstring|>Go to previous chapter<|endoftext|>
|
1259410c3505f95c238c8805a87e4844e5f2842fcd5f4e925554bdc8e5c4ff0a
|
def chapter_jump(number: int):
'Go to chapter number'
|
Go to chapter number
|
code/chapters.py
|
chapter_jump
|
hanuele/knausj_talon
| 298 |
python
|
def chapter_jump(number: int):
|
def chapter_jump(number: int):
<|docstring|>Go to chapter number<|endoftext|>
|
184438187d213184696d03f7b4543071fd6c9e10c294ef78b1f42ca4b05082a6
|
def chapter_final():
'Go to final chapter'
|
Go to final chapter
|
code/chapters.py
|
chapter_final
|
hanuele/knausj_talon
| 298 |
python
|
def chapter_final():
|
def chapter_final():
<|docstring|>Go to final chapter<|endoftext|>
|
78d6c0f34249eb3c54697a1170548afababcd7e85cf6489622797749e72a10b6
|
async def async_setup_platform(hass: HomeAssistant, _: ConfigType, add_entities: AddEntitiesCallback, discovery_info: (DiscoveryInfoType | None)=None) -> None:
'Add lights from the main Qwikswitch component.'
if (discovery_info is None):
return
qsusb = hass.data[QWIKSWITCH]
devs = [QSLight(qsid, qsusb) for qsid in discovery_info[QWIKSWITCH]]
add_entities(devs)
|
Add lights from the main Qwikswitch component.
|
homeassistant/components/qwikswitch/light.py
|
async_setup_platform
|
a-p-z/core
| 30,023 |
python
|
async def async_setup_platform(hass: HomeAssistant, _: ConfigType, add_entities: AddEntitiesCallback, discovery_info: (DiscoveryInfoType | None)=None) -> None:
if (discovery_info is None):
return
qsusb = hass.data[QWIKSWITCH]
devs = [QSLight(qsid, qsusb) for qsid in discovery_info[QWIKSWITCH]]
add_entities(devs)
|
async def async_setup_platform(hass: HomeAssistant, _: ConfigType, add_entities: AddEntitiesCallback, discovery_info: (DiscoveryInfoType | None)=None) -> None:
if (discovery_info is None):
return
qsusb = hass.data[QWIKSWITCH]
devs = [QSLight(qsid, qsusb) for qsid in discovery_info[QWIKSWITCH]]
add_entities(devs)<|docstring|>Add lights from the main Qwikswitch component.<|endoftext|>
|
fae9ec509428c3c0ef0f4e92f4068ce79d8c1ab287dbe4013d56b36084829877
|
@property
def brightness(self):
'Return the brightness of this light (0-255).'
return (self.device.value if self.device.is_dimmer else None)
|
Return the brightness of this light (0-255).
|
homeassistant/components/qwikswitch/light.py
|
brightness
|
a-p-z/core
| 30,023 |
python
|
@property
def brightness(self):
return (self.device.value if self.device.is_dimmer else None)
|
@property
def brightness(self):
return (self.device.value if self.device.is_dimmer else None)<|docstring|>Return the brightness of this light (0-255).<|endoftext|>
|
11f9dd03330054ce354ce5ffdcd2e555216b0de41fb0cbb894a4354c998389e7
|
@property
def color_mode(self) -> ColorMode:
'Return the color mode of the light.'
return (ColorMode.BRIGHTNESS if self.device.is_dimmer else ColorMode.ONOFF)
|
Return the color mode of the light.
|
homeassistant/components/qwikswitch/light.py
|
color_mode
|
a-p-z/core
| 30,023 |
python
|
@property
def color_mode(self) -> ColorMode:
return (ColorMode.BRIGHTNESS if self.device.is_dimmer else ColorMode.ONOFF)
|
@property
def color_mode(self) -> ColorMode:
return (ColorMode.BRIGHTNESS if self.device.is_dimmer else ColorMode.ONOFF)<|docstring|>Return the color mode of the light.<|endoftext|>
|
e1334a939068e3e3dda5ddf15c7f852364b3335c4fef788a84aaf85326f6ff85
|
@property
def supported_color_modes(self) -> set[ColorMode]:
'Flag supported color modes.'
return {self.color_mode}
|
Flag supported color modes.
|
homeassistant/components/qwikswitch/light.py
|
supported_color_modes
|
a-p-z/core
| 30,023 |
python
|
@property
def supported_color_modes(self) -> set[ColorMode]:
return {self.color_mode}
|
@property
def supported_color_modes(self) -> set[ColorMode]:
return {self.color_mode}<|docstring|>Flag supported color modes.<|endoftext|>
|
d49db9a90b848a70bb6e46939e350e3003452f08c817915b3f8ff0e9c2aa8388
|
@distributed_trace
def list_by_product(self, resource_group_name: str, service_name: str, product_id: str, filter: Optional[str]=None, top: Optional[int]=None, skip: Optional[int]=None, **kwargs: Any) -> Iterable['_models.GroupCollection']:
'Lists the collection of developer groups associated with the specified product.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param filter: | Field | Usage | Supported operators | Supported\n functions |</br>|-------------|-------------|-------------|-------------|</br>| name |\n filter | ge, le, eq, ne, gt, lt | |</br>| displayName | filter | eq, ne | |</br>|\n description | filter | eq, ne | |</br>.\n :type filter: str\n :param top: Number of records to return.\n :type top: int\n :param skip: Number of records to skip.\n :type skip: int\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either GroupCollection or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~api_management_client.models.GroupCollection]\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_product_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, subscription_id=self._config.subscription_id, filter=filter, top=top, skip=skip, template_url=self.list_by_product.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_list_by_product_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, subscription_id=self._config.subscription_id, filter=filter, top=top, skip=skip, 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('GroupCollection', 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 the collection of developer groups associated with the specified product.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param service_name: The name of the API Management service.
:type service_name: str
:param product_id: Product identifier. Must be unique in the current API Management service
instance.
:type product_id: str
:param filter: | Field | Usage | Supported operators | Supported
functions |</br>|-------------|-------------|-------------|-------------|</br>| name |
filter | ge, le, eq, ne, gt, lt | |</br>| displayName | filter | eq, ne | |</br>|
description | filter | eq, ne | |</br>.
:type filter: str
:param top: Number of records to return.
:type top: int
:param skip: Number of records to skip.
:type skip: int
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either GroupCollection or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~api_management_client.models.GroupCollection]
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/apimanagement/azure-mgmt-apimanagement/azure/mgmt/apimanagement/operations/_product_group_operations.py
|
list_by_product
|
AFengKK/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def list_by_product(self, resource_group_name: str, service_name: str, product_id: str, filter: Optional[str]=None, top: Optional[int]=None, skip: Optional[int]=None, **kwargs: Any) -> Iterable['_models.GroupCollection']:
'Lists the collection of developer groups associated with the specified product.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param filter: | Field | Usage | Supported operators | Supported\n functions |</br>|-------------|-------------|-------------|-------------|</br>| name |\n filter | ge, le, eq, ne, gt, lt | |</br>| displayName | filter | eq, ne | |</br>|\n description | filter | eq, ne | |</br>.\n :type filter: str\n :param top: Number of records to return.\n :type top: int\n :param skip: Number of records to skip.\n :type skip: int\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either GroupCollection or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~api_management_client.models.GroupCollection]\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_product_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, subscription_id=self._config.subscription_id, filter=filter, top=top, skip=skip, template_url=self.list_by_product.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_list_by_product_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, subscription_id=self._config.subscription_id, filter=filter, top=top, skip=skip, 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('GroupCollection', 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_product(self, resource_group_name: str, service_name: str, product_id: str, filter: Optional[str]=None, top: Optional[int]=None, skip: Optional[int]=None, **kwargs: Any) -> Iterable['_models.GroupCollection']:
'Lists the collection of developer groups associated with the specified product.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param filter: | Field | Usage | Supported operators | Supported\n functions |</br>|-------------|-------------|-------------|-------------|</br>| name |\n filter | ge, le, eq, ne, gt, lt | |</br>| displayName | filter | eq, ne | |</br>|\n description | filter | eq, ne | |</br>.\n :type filter: str\n :param top: Number of records to return.\n :type top: int\n :param skip: Number of records to skip.\n :type skip: int\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: An iterator like instance of either GroupCollection or the result of cls(response)\n :rtype: ~azure.core.paging.ItemPaged[~api_management_client.models.GroupCollection]\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_product_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, subscription_id=self._config.subscription_id, filter=filter, top=top, skip=skip, template_url=self.list_by_product.metadata['url'])
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
request = build_list_by_product_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, subscription_id=self._config.subscription_id, filter=filter, top=top, skip=skip, 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('GroupCollection', 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 the collection of developer groups associated with the specified product.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param service_name: The name of the API Management service.
:type service_name: str
:param product_id: Product identifier. Must be unique in the current API Management service
instance.
:type product_id: str
:param filter: | Field | Usage | Supported operators | Supported
functions |</br>|-------------|-------------|-------------|-------------|</br>| name |
filter | ge, le, eq, ne, gt, lt | |</br>| displayName | filter | eq, ne | |</br>|
description | filter | eq, ne | |</br>.
:type filter: str
:param top: Number of records to return.
:type top: int
:param skip: Number of records to skip.
:type skip: int
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either GroupCollection or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~api_management_client.models.GroupCollection]
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
6e96bf2157dcf1fc8003af87482c3828baa8878a6f98def6ff6cfdffebe0fca6
|
@distributed_trace
def check_entity_exists(self, resource_group_name: str, service_name: str, product_id: str, group_id: str, **kwargs: Any) -> bool:
'Checks that Group entity specified by identifier is associated with the Product entity.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param group_id: Group identifier. Must be unique in the current API Management service\n instance.\n :type group_id: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: bool, or the result of cls(response)\n :rtype: bool\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_check_entity_exists_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, group_id=group_id, subscription_id=self._config.subscription_id, template_url=self.check_entity_exists.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 [204]):
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)
if cls:
return cls(pipeline_response, None, {})
return (200 <= response.status_code <= 299)
|
Checks that Group entity specified by identifier is associated with the Product entity.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param service_name: The name of the API Management service.
:type service_name: str
:param product_id: Product identifier. Must be unique in the current API Management service
instance.
:type product_id: str
:param group_id: Group identifier. Must be unique in the current API Management service
instance.
:type group_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: bool, or the result of cls(response)
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/apimanagement/azure-mgmt-apimanagement/azure/mgmt/apimanagement/operations/_product_group_operations.py
|
check_entity_exists
|
AFengKK/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def check_entity_exists(self, resource_group_name: str, service_name: str, product_id: str, group_id: str, **kwargs: Any) -> bool:
'Checks that Group entity specified by identifier is associated with the Product entity.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param group_id: Group identifier. Must be unique in the current API Management service\n instance.\n :type group_id: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: bool, or the result of cls(response)\n :rtype: bool\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_check_entity_exists_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, group_id=group_id, subscription_id=self._config.subscription_id, template_url=self.check_entity_exists.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 [204]):
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)
if cls:
return cls(pipeline_response, None, {})
return (200 <= response.status_code <= 299)
|
@distributed_trace
def check_entity_exists(self, resource_group_name: str, service_name: str, product_id: str, group_id: str, **kwargs: Any) -> bool:
'Checks that Group entity specified by identifier is associated with the Product entity.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param group_id: Group identifier. Must be unique in the current API Management service\n instance.\n :type group_id: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: bool, or the result of cls(response)\n :rtype: bool\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_check_entity_exists_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, group_id=group_id, subscription_id=self._config.subscription_id, template_url=self.check_entity_exists.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 [204]):
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)
if cls:
return cls(pipeline_response, None, {})
return (200 <= response.status_code <= 299)<|docstring|>Checks that Group entity specified by identifier is associated with the Product entity.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param service_name: The name of the API Management service.
:type service_name: str
:param product_id: Product identifier. Must be unique in the current API Management service
instance.
:type product_id: str
:param group_id: Group identifier. Must be unique in the current API Management service
instance.
:type group_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: bool, or the result of cls(response)
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
28f6b262d02bc173c6a6ef4636fb2f796332d4866c296f8dc453bc1e6644dd23
|
@distributed_trace
def create_or_update(self, resource_group_name: str, service_name: str, product_id: str, group_id: str, **kwargs: Any) -> '_models.GroupContract':
'Adds the association between the specified developer group with the specified product.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param group_id: Group identifier. Must be unique in the current API Management service\n instance.\n :type group_id: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: GroupContract, or the result of cls(response)\n :rtype: ~api_management_client.models.GroupContract\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_create_or_update_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, group_id=group_id, subscription_id=self._config.subscription_id, template_url=self.create_or_update.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, 201]):
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)
if (response.status_code == 200):
deserialized = self._deserialize('GroupContract', pipeline_response)
if (response.status_code == 201):
deserialized = self._deserialize('GroupContract', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
|
Adds the association between the specified developer group with the specified product.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param service_name: The name of the API Management service.
:type service_name: str
:param product_id: Product identifier. Must be unique in the current API Management service
instance.
:type product_id: str
:param group_id: Group identifier. Must be unique in the current API Management service
instance.
:type group_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: GroupContract, or the result of cls(response)
:rtype: ~api_management_client.models.GroupContract
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/apimanagement/azure-mgmt-apimanagement/azure/mgmt/apimanagement/operations/_product_group_operations.py
|
create_or_update
|
AFengKK/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def create_or_update(self, resource_group_name: str, service_name: str, product_id: str, group_id: str, **kwargs: Any) -> '_models.GroupContract':
'Adds the association between the specified developer group with the specified product.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param group_id: Group identifier. Must be unique in the current API Management service\n instance.\n :type group_id: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: GroupContract, or the result of cls(response)\n :rtype: ~api_management_client.models.GroupContract\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_create_or_update_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, group_id=group_id, subscription_id=self._config.subscription_id, template_url=self.create_or_update.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, 201]):
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)
if (response.status_code == 200):
deserialized = self._deserialize('GroupContract', pipeline_response)
if (response.status_code == 201):
deserialized = self._deserialize('GroupContract', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
|
@distributed_trace
def create_or_update(self, resource_group_name: str, service_name: str, product_id: str, group_id: str, **kwargs: Any) -> '_models.GroupContract':
'Adds the association between the specified developer group with the specified product.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param group_id: Group identifier. Must be unique in the current API Management service\n instance.\n :type group_id: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: GroupContract, or the result of cls(response)\n :rtype: ~api_management_client.models.GroupContract\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_create_or_update_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, group_id=group_id, subscription_id=self._config.subscription_id, template_url=self.create_or_update.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, 201]):
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)
if (response.status_code == 200):
deserialized = self._deserialize('GroupContract', pipeline_response)
if (response.status_code == 201):
deserialized = self._deserialize('GroupContract', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized<|docstring|>Adds the association between the specified developer group with the specified product.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param service_name: The name of the API Management service.
:type service_name: str
:param product_id: Product identifier. Must be unique in the current API Management service
instance.
:type product_id: str
:param group_id: Group identifier. Must be unique in the current API Management service
instance.
:type group_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: GroupContract, or the result of cls(response)
:rtype: ~api_management_client.models.GroupContract
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
d133dd6a3806fc637fd062fb4bd50118f4de2f14ee2ef486697d8f60041c7e0b
|
@distributed_trace
def delete(self, resource_group_name: str, service_name: str, product_id: str, group_id: str, **kwargs: Any) -> None:
'Deletes the association between the specified group and product.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param group_id: Group identifier. Must be unique in the current API Management service\n instance.\n :type group_id: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\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_delete_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, group_id=group_id, subscription_id=self._config.subscription_id, template_url=self.delete.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, 204]):
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)
if cls:
return cls(pipeline_response, None, {})
|
Deletes the association between the specified group and product.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param service_name: The name of the API Management service.
:type service_name: str
:param product_id: Product identifier. Must be unique in the current API Management service
instance.
:type product_id: str
:param group_id: Group identifier. Must be unique in the current API Management service
instance.
:type group_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
|
sdk/apimanagement/azure-mgmt-apimanagement/azure/mgmt/apimanagement/operations/_product_group_operations.py
|
delete
|
AFengKK/azure-sdk-for-python
| 1 |
python
|
@distributed_trace
def delete(self, resource_group_name: str, service_name: str, product_id: str, group_id: str, **kwargs: Any) -> None:
'Deletes the association between the specified group and product.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param group_id: Group identifier. Must be unique in the current API Management service\n instance.\n :type group_id: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\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_delete_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, group_id=group_id, subscription_id=self._config.subscription_id, template_url=self.delete.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, 204]):
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)
if cls:
return cls(pipeline_response, None, {})
|
@distributed_trace
def delete(self, resource_group_name: str, service_name: str, product_id: str, group_id: str, **kwargs: Any) -> None:
'Deletes the association between the specified group and product.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param service_name: The name of the API Management service.\n :type service_name: str\n :param product_id: Product identifier. Must be unique in the current API Management service\n instance.\n :type product_id: str\n :param group_id: Group identifier. Must be unique in the current API Management service\n instance.\n :type group_id: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: None, or the result of cls(response)\n :rtype: None\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_delete_request(resource_group_name=resource_group_name, service_name=service_name, product_id=product_id, group_id=group_id, subscription_id=self._config.subscription_id, template_url=self.delete.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, 204]):
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)
if cls:
return cls(pipeline_response, None, {})<|docstring|>Deletes the association between the specified group and product.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param service_name: The name of the API Management service.
:type service_name: str
:param product_id: Product identifier. Must be unique in the current API Management service
instance.
:type product_id: str
:param group_id: Group identifier. Must be unique in the current API Management service
instance.
:type group_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError<|endoftext|>
|
c314707ebb716bc7664f32ae7caa44630970cb3178b1bf8d485b59fec5b22039
|
def average_distributed_scalar(scalar, args):
' Average a scalar over the nodes if we are in distributed training. We use this for distributed evaluation. '
if (args.local_rank == (- 1)):
return scalar
scalar_t = (torch.tensor(scalar, dtype=torch.float, device=args.device) / torch.distributed.get_world_size())
torch.distributed.all_reduce(scalar_t, op=torch.distributed.ReduceOp.SUM)
return scalar_t.item()
|
Average a scalar over the nodes if we are in distributed training. We use this for distributed evaluation.
|
utils/auxiliary.py
|
average_distributed_scalar
|
lxchtan/Dialogue-Generation
| 2 |
python
|
def average_distributed_scalar(scalar, args):
' '
if (args.local_rank == (- 1)):
return scalar
scalar_t = (torch.tensor(scalar, dtype=torch.float, device=args.device) / torch.distributed.get_world_size())
torch.distributed.all_reduce(scalar_t, op=torch.distributed.ReduceOp.SUM)
return scalar_t.item()
|
def average_distributed_scalar(scalar, args):
' '
if (args.local_rank == (- 1)):
return scalar
scalar_t = (torch.tensor(scalar, dtype=torch.float, device=args.device) / torch.distributed.get_world_size())
torch.distributed.all_reduce(scalar_t, op=torch.distributed.ReduceOp.SUM)
return scalar_t.item()<|docstring|>Average a scalar over the nodes if we are in distributed training. We use this for distributed evaluation.<|endoftext|>
|
bc25e14b9f700c7e5723c16de3410b4f7a5ca5dd3dacac2c56affd81d8700483
|
def top_filtering(logits, top_k=0, top_p=0.0, threshold=(- float('Inf')), filter_value=(- float('Inf'))):
' Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering\n Args:\n logits: logits distribution shape (..., vocabulary size)\n top_k: <=0: no filtering, >0: keep only top k tokens with highest probability.\n top_p: <=0.0: no filtering, >0.0: keep only a subset S of candidates, where S is the smallest subset\n whose total probability mass is greater than or equal to the threshold top_p.\n In practice, we select the highest probability tokens whose cumulative probability mass exceeds\n the threshold top_p.\n threshold: a minimal threshold to keep logits\n '
top_k = min(top_k, logits.size((- 1)))
if (top_k > 0):
indices_to_remove = (logits < torch.topk(logits, top_k)[0][(..., (- 1), None)])
logits[indices_to_remove] = filter_value
if (top_p > 0.0):
(sorted_logits, sorted_indices) = torch.sort(logits, descending=True)
cumulative_probabilities = torch.cumsum(F.softmax(sorted_logits, dim=(- 1)), dim=(- 1))
sorted_indices_to_remove = (cumulative_probabilities > top_p)
sorted_indices_to_remove[(..., 1:)] = sorted_indices_to_remove[(..., :(- 1))].clone()
sorted_indices_to_remove[(..., 0)] = 0
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[indices_to_remove] = filter_value
indices_to_remove = (logits < threshold)
logits[indices_to_remove] = filter_value
return logits
|
Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering
Args:
logits: logits distribution shape (..., vocabulary size)
top_k: <=0: no filtering, >0: keep only top k tokens with highest probability.
top_p: <=0.0: no filtering, >0.0: keep only a subset S of candidates, where S is the smallest subset
whose total probability mass is greater than or equal to the threshold top_p.
In practice, we select the highest probability tokens whose cumulative probability mass exceeds
the threshold top_p.
threshold: a minimal threshold to keep logits
|
utils/auxiliary.py
|
top_filtering
|
lxchtan/Dialogue-Generation
| 2 |
python
|
def top_filtering(logits, top_k=0, top_p=0.0, threshold=(- float('Inf')), filter_value=(- float('Inf'))):
' Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering\n Args:\n logits: logits distribution shape (..., vocabulary size)\n top_k: <=0: no filtering, >0: keep only top k tokens with highest probability.\n top_p: <=0.0: no filtering, >0.0: keep only a subset S of candidates, where S is the smallest subset\n whose total probability mass is greater than or equal to the threshold top_p.\n In practice, we select the highest probability tokens whose cumulative probability mass exceeds\n the threshold top_p.\n threshold: a minimal threshold to keep logits\n '
top_k = min(top_k, logits.size((- 1)))
if (top_k > 0):
indices_to_remove = (logits < torch.topk(logits, top_k)[0][(..., (- 1), None)])
logits[indices_to_remove] = filter_value
if (top_p > 0.0):
(sorted_logits, sorted_indices) = torch.sort(logits, descending=True)
cumulative_probabilities = torch.cumsum(F.softmax(sorted_logits, dim=(- 1)), dim=(- 1))
sorted_indices_to_remove = (cumulative_probabilities > top_p)
sorted_indices_to_remove[(..., 1:)] = sorted_indices_to_remove[(..., :(- 1))].clone()
sorted_indices_to_remove[(..., 0)] = 0
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[indices_to_remove] = filter_value
indices_to_remove = (logits < threshold)
logits[indices_to_remove] = filter_value
return logits
|
def top_filtering(logits, top_k=0, top_p=0.0, threshold=(- float('Inf')), filter_value=(- float('Inf'))):
' Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering\n Args:\n logits: logits distribution shape (..., vocabulary size)\n top_k: <=0: no filtering, >0: keep only top k tokens with highest probability.\n top_p: <=0.0: no filtering, >0.0: keep only a subset S of candidates, where S is the smallest subset\n whose total probability mass is greater than or equal to the threshold top_p.\n In practice, we select the highest probability tokens whose cumulative probability mass exceeds\n the threshold top_p.\n threshold: a minimal threshold to keep logits\n '
top_k = min(top_k, logits.size((- 1)))
if (top_k > 0):
indices_to_remove = (logits < torch.topk(logits, top_k)[0][(..., (- 1), None)])
logits[indices_to_remove] = filter_value
if (top_p > 0.0):
(sorted_logits, sorted_indices) = torch.sort(logits, descending=True)
cumulative_probabilities = torch.cumsum(F.softmax(sorted_logits, dim=(- 1)), dim=(- 1))
sorted_indices_to_remove = (cumulative_probabilities > top_p)
sorted_indices_to_remove[(..., 1:)] = sorted_indices_to_remove[(..., :(- 1))].clone()
sorted_indices_to_remove[(..., 0)] = 0
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[indices_to_remove] = filter_value
indices_to_remove = (logits < threshold)
logits[indices_to_remove] = filter_value
return logits<|docstring|>Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering
Args:
logits: logits distribution shape (..., vocabulary size)
top_k: <=0: no filtering, >0: keep only top k tokens with highest probability.
top_p: <=0.0: no filtering, >0.0: keep only a subset S of candidates, where S is the smallest subset
whose total probability mass is greater than or equal to the threshold top_p.
In practice, we select the highest probability tokens whose cumulative probability mass exceeds
the threshold top_p.
threshold: a minimal threshold to keep logits<|endoftext|>
|
704a0ea42d94364e22110ab522330e0e9e73528dc501f9d7a6ef6f9153dde8f8
|
def grading_context_for_course(course):
'\n Same as grading_context, but takes in a course key.\n '
course_structure = get_course_in_cache(course.id)
return grading_context(course, course_structure)
|
Same as grading_context, but takes in a course key.
|
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/grades/context.py
|
grading_context_for_course
|
osoco/better-ways-of-thinking-about-software
| 3 |
python
|
def grading_context_for_course(course):
'\n \n '
course_structure = get_course_in_cache(course.id)
return grading_context(course, course_structure)
|
def grading_context_for_course(course):
'\n \n '
course_structure = get_course_in_cache(course.id)
return grading_context(course, course_structure)<|docstring|>Same as grading_context, but takes in a course key.<|endoftext|>
|
c0c6bb3c33e7e1638611fd266b15921dbe658c5767f6ae673eda1eb39289eba7
|
def graded_subsections_for_course(course_structure):
'\n Given a course block structure, yields the subsections of the course that are graded\n and visible to non-staff users.\n Args:\n course_structure: A course structure object.\n '
for chapter_key in course_structure.get_children(course_structure.root_block_usage_key):
for subsection_key in course_structure.get_children(chapter_key):
subsection = course_structure[subsection_key]
if ((not _visible_to_staff_only(subsection)) and subsection.graded):
(yield subsection)
|
Given a course block structure, yields the subsections of the course that are graded
and visible to non-staff users.
Args:
course_structure: A course structure object.
|
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/grades/context.py
|
graded_subsections_for_course
|
osoco/better-ways-of-thinking-about-software
| 3 |
python
|
def graded_subsections_for_course(course_structure):
'\n Given a course block structure, yields the subsections of the course that are graded\n and visible to non-staff users.\n Args:\n course_structure: A course structure object.\n '
for chapter_key in course_structure.get_children(course_structure.root_block_usage_key):
for subsection_key in course_structure.get_children(chapter_key):
subsection = course_structure[subsection_key]
if ((not _visible_to_staff_only(subsection)) and subsection.graded):
(yield subsection)
|
def graded_subsections_for_course(course_structure):
'\n Given a course block structure, yields the subsections of the course that are graded\n and visible to non-staff users.\n Args:\n course_structure: A course structure object.\n '
for chapter_key in course_structure.get_children(course_structure.root_block_usage_key):
for subsection_key in course_structure.get_children(chapter_key):
subsection = course_structure[subsection_key]
if ((not _visible_to_staff_only(subsection)) and subsection.graded):
(yield subsection)<|docstring|>Given a course block structure, yields the subsections of the course that are graded
and visible to non-staff users.
Args:
course_structure: A course structure object.<|endoftext|>
|
b16f5e29cc2e918fa9c7955c34fcdb282b26a4407bc58b84e9804e66c4002f88
|
def grading_context(course, course_structure):
'\n This returns a dictionary with keys necessary for quickly grading\n a student.\n\n The grading context has two keys:\n all_graded_subsections_by_type - This contains all subsections that are\n graded, keyed by subsection format (assignment type).\n\n The values are arrays of dictionaries containing\n "subsection_block" : The subsection block\n "scored_descendants" : An array of usage keys for blocks\n that could possibly be in the subsection, for any student\n\n all_graded_blocks - This contains a list of all blocks that can\n affect grading a student. This is used to efficiently fetch\n all the xmodule state for a FieldDataCache without walking\n the descriptor tree again.\n\n '
count_all_graded_blocks = 0
all_graded_subsections_by_type = OrderedDict()
for subsection in graded_subsections_for_course(course_structure):
scored_descendants_of_subsection = []
for descendant_key in course_structure.post_order_traversal(filter_func=possibly_scored, start_node=subsection.location):
scored_descendants_of_subsection.append(course_structure[descendant_key])
subsection_info = {'subsection_block': subsection, 'scored_descendants': [child for child in scored_descendants_of_subsection if getattr(child, 'has_score', None)]}
subsection_format = getattr(subsection, 'format', '')
if (subsection_format not in all_graded_subsections_by_type):
all_graded_subsections_by_type[subsection_format] = []
all_graded_subsections_by_type[subsection_format].append(subsection_info)
count_all_graded_blocks += len(scored_descendants_of_subsection)
return {'all_graded_subsections_by_type': all_graded_subsections_by_type, 'count_all_graded_blocks': count_all_graded_blocks, 'subsection_type_graders': CourseGrade.get_subsection_type_graders(course)}
|
This returns a dictionary with keys necessary for quickly grading
a student.
The grading context has two keys:
all_graded_subsections_by_type - This contains all subsections that are
graded, keyed by subsection format (assignment type).
The values are arrays of dictionaries containing
"subsection_block" : The subsection block
"scored_descendants" : An array of usage keys for blocks
that could possibly be in the subsection, for any student
all_graded_blocks - This contains a list of all blocks that can
affect grading a student. This is used to efficiently fetch
all the xmodule state for a FieldDataCache without walking
the descriptor tree again.
|
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/grades/context.py
|
grading_context
|
osoco/better-ways-of-thinking-about-software
| 3 |
python
|
def grading_context(course, course_structure):
'\n This returns a dictionary with keys necessary for quickly grading\n a student.\n\n The grading context has two keys:\n all_graded_subsections_by_type - This contains all subsections that are\n graded, keyed by subsection format (assignment type).\n\n The values are arrays of dictionaries containing\n "subsection_block" : The subsection block\n "scored_descendants" : An array of usage keys for blocks\n that could possibly be in the subsection, for any student\n\n all_graded_blocks - This contains a list of all blocks that can\n affect grading a student. This is used to efficiently fetch\n all the xmodule state for a FieldDataCache without walking\n the descriptor tree again.\n\n '
count_all_graded_blocks = 0
all_graded_subsections_by_type = OrderedDict()
for subsection in graded_subsections_for_course(course_structure):
scored_descendants_of_subsection = []
for descendant_key in course_structure.post_order_traversal(filter_func=possibly_scored, start_node=subsection.location):
scored_descendants_of_subsection.append(course_structure[descendant_key])
subsection_info = {'subsection_block': subsection, 'scored_descendants': [child for child in scored_descendants_of_subsection if getattr(child, 'has_score', None)]}
subsection_format = getattr(subsection, 'format', )
if (subsection_format not in all_graded_subsections_by_type):
all_graded_subsections_by_type[subsection_format] = []
all_graded_subsections_by_type[subsection_format].append(subsection_info)
count_all_graded_blocks += len(scored_descendants_of_subsection)
return {'all_graded_subsections_by_type': all_graded_subsections_by_type, 'count_all_graded_blocks': count_all_graded_blocks, 'subsection_type_graders': CourseGrade.get_subsection_type_graders(course)}
|
def grading_context(course, course_structure):
'\n This returns a dictionary with keys necessary for quickly grading\n a student.\n\n The grading context has two keys:\n all_graded_subsections_by_type - This contains all subsections that are\n graded, keyed by subsection format (assignment type).\n\n The values are arrays of dictionaries containing\n "subsection_block" : The subsection block\n "scored_descendants" : An array of usage keys for blocks\n that could possibly be in the subsection, for any student\n\n all_graded_blocks - This contains a list of all blocks that can\n affect grading a student. This is used to efficiently fetch\n all the xmodule state for a FieldDataCache without walking\n the descriptor tree again.\n\n '
count_all_graded_blocks = 0
all_graded_subsections_by_type = OrderedDict()
for subsection in graded_subsections_for_course(course_structure):
scored_descendants_of_subsection = []
for descendant_key in course_structure.post_order_traversal(filter_func=possibly_scored, start_node=subsection.location):
scored_descendants_of_subsection.append(course_structure[descendant_key])
subsection_info = {'subsection_block': subsection, 'scored_descendants': [child for child in scored_descendants_of_subsection if getattr(child, 'has_score', None)]}
subsection_format = getattr(subsection, 'format', )
if (subsection_format not in all_graded_subsections_by_type):
all_graded_subsections_by_type[subsection_format] = []
all_graded_subsections_by_type[subsection_format].append(subsection_info)
count_all_graded_blocks += len(scored_descendants_of_subsection)
return {'all_graded_subsections_by_type': all_graded_subsections_by_type, 'count_all_graded_blocks': count_all_graded_blocks, 'subsection_type_graders': CourseGrade.get_subsection_type_graders(course)}<|docstring|>This returns a dictionary with keys necessary for quickly grading
a student.
The grading context has two keys:
all_graded_subsections_by_type - This contains all subsections that are
graded, keyed by subsection format (assignment type).
The values are arrays of dictionaries containing
"subsection_block" : The subsection block
"scored_descendants" : An array of usage keys for blocks
that could possibly be in the subsection, for any student
all_graded_blocks - This contains a list of all blocks that can
affect grading a student. This is used to efficiently fetch
all the xmodule state for a FieldDataCache without walking
the descriptor tree again.<|endoftext|>
|
ced47cb260584a565ede41cecee2915c0999d6755d57c66128fcc8b8c409eb83
|
def _visible_to_staff_only(subsection):
'\n Returns True if the given subsection is visible to staff only else False\n '
try:
return subsection.transformer_data['visibility'].fields['merged_visible_to_staff_only']
except KeyError:
return False
|
Returns True if the given subsection is visible to staff only else False
|
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/grades/context.py
|
_visible_to_staff_only
|
osoco/better-ways-of-thinking-about-software
| 3 |
python
|
def _visible_to_staff_only(subsection):
'\n \n '
try:
return subsection.transformer_data['visibility'].fields['merged_visible_to_staff_only']
except KeyError:
return False
|
def _visible_to_staff_only(subsection):
'\n \n '
try:
return subsection.transformer_data['visibility'].fields['merged_visible_to_staff_only']
except KeyError:
return False<|docstring|>Returns True if the given subsection is visible to staff only else False<|endoftext|>
|
06dbfec78c18f7ff0d20ac8f0d78145168de497fce14d87600999a76dc786f96
|
def display_errors_summary(build_errors: Dict[(str, List[DocBuildError])]) -> None:
'Displays summary of errors'
print(('#' * 20), 'Docs build errors summary', ('#' * 20))
for (package_name, errors) in build_errors.items():
if package_name:
print(('=' * 20), package_name, ('=' * 20))
else:
print(('=' * 20), 'General', ('=' * 20))
for (warning_no, error) in enumerate(sorted(errors), 1):
print(('-' * 20), f'Error {warning_no:3}', ('-' * 20))
print(error.message)
print()
if (error.file_path and (error.file_path != '<unknown>') and error.line_no):
print(f'File path: {os.path.relpath(error.file_path, start=DOCS_DIR)} ({error.line_no})')
print()
print(prepare_code_snippet(error.file_path, error.line_no))
elif error.file_path:
print(f'File path: {error.file_path}')
print(('#' * 50))
|
Displays summary of errors
|
docs/exts/docs_build/errors.py
|
display_errors_summary
|
smowden/airflow
| 79 |
python
|
def display_errors_summary(build_errors: Dict[(str, List[DocBuildError])]) -> None:
print(('#' * 20), 'Docs build errors summary', ('#' * 20))
for (package_name, errors) in build_errors.items():
if package_name:
print(('=' * 20), package_name, ('=' * 20))
else:
print(('=' * 20), 'General', ('=' * 20))
for (warning_no, error) in enumerate(sorted(errors), 1):
print(('-' * 20), f'Error {warning_no:3}', ('-' * 20))
print(error.message)
print()
if (error.file_path and (error.file_path != '<unknown>') and error.line_no):
print(f'File path: {os.path.relpath(error.file_path, start=DOCS_DIR)} ({error.line_no})')
print()
print(prepare_code_snippet(error.file_path, error.line_no))
elif error.file_path:
print(f'File path: {error.file_path}')
print(('#' * 50))
|
def display_errors_summary(build_errors: Dict[(str, List[DocBuildError])]) -> None:
print(('#' * 20), 'Docs build errors summary', ('#' * 20))
for (package_name, errors) in build_errors.items():
if package_name:
print(('=' * 20), package_name, ('=' * 20))
else:
print(('=' * 20), 'General', ('=' * 20))
for (warning_no, error) in enumerate(sorted(errors), 1):
print(('-' * 20), f'Error {warning_no:3}', ('-' * 20))
print(error.message)
print()
if (error.file_path and (error.file_path != '<unknown>') and error.line_no):
print(f'File path: {os.path.relpath(error.file_path, start=DOCS_DIR)} ({error.line_no})')
print()
print(prepare_code_snippet(error.file_path, error.line_no))
elif error.file_path:
print(f'File path: {error.file_path}')
print(('#' * 50))<|docstring|>Displays summary of errors<|endoftext|>
|
e47c88721b7175b04f954c6c20708675a7a2fb6ab9132b9e46851376cb768190
|
def parse_sphinx_warnings(warning_text: str, docs_dir: str) -> List[DocBuildError]:
'\n Parses warnings from Sphinx.\n\n :param warning_text: warning to parse\n :return: list of DocBuildErrors.\n '
sphinx_build_errors = []
for sphinx_warning in warning_text.split('\n'):
if (not sphinx_warning):
continue
warning_parts = sphinx_warning.split(':', 2)
if (len(warning_parts) == 3):
try:
sphinx_build_errors.append(DocBuildError(file_path=os.path.join(docs_dir, warning_parts[0]), line_no=int(warning_parts[1]), message=warning_parts[2]))
except Exception:
sphinx_build_errors.append(DocBuildError(file_path=None, line_no=None, message=sphinx_warning))
else:
sphinx_build_errors.append(DocBuildError(file_path=None, line_no=None, message=sphinx_warning))
return sphinx_build_errors
|
Parses warnings from Sphinx.
:param warning_text: warning to parse
:return: list of DocBuildErrors.
|
docs/exts/docs_build/errors.py
|
parse_sphinx_warnings
|
smowden/airflow
| 79 |
python
|
def parse_sphinx_warnings(warning_text: str, docs_dir: str) -> List[DocBuildError]:
'\n Parses warnings from Sphinx.\n\n :param warning_text: warning to parse\n :return: list of DocBuildErrors.\n '
sphinx_build_errors = []
for sphinx_warning in warning_text.split('\n'):
if (not sphinx_warning):
continue
warning_parts = sphinx_warning.split(':', 2)
if (len(warning_parts) == 3):
try:
sphinx_build_errors.append(DocBuildError(file_path=os.path.join(docs_dir, warning_parts[0]), line_no=int(warning_parts[1]), message=warning_parts[2]))
except Exception:
sphinx_build_errors.append(DocBuildError(file_path=None, line_no=None, message=sphinx_warning))
else:
sphinx_build_errors.append(DocBuildError(file_path=None, line_no=None, message=sphinx_warning))
return sphinx_build_errors
|
def parse_sphinx_warnings(warning_text: str, docs_dir: str) -> List[DocBuildError]:
'\n Parses warnings from Sphinx.\n\n :param warning_text: warning to parse\n :return: list of DocBuildErrors.\n '
sphinx_build_errors = []
for sphinx_warning in warning_text.split('\n'):
if (not sphinx_warning):
continue
warning_parts = sphinx_warning.split(':', 2)
if (len(warning_parts) == 3):
try:
sphinx_build_errors.append(DocBuildError(file_path=os.path.join(docs_dir, warning_parts[0]), line_no=int(warning_parts[1]), message=warning_parts[2]))
except Exception:
sphinx_build_errors.append(DocBuildError(file_path=None, line_no=None, message=sphinx_warning))
else:
sphinx_build_errors.append(DocBuildError(file_path=None, line_no=None, message=sphinx_warning))
return sphinx_build_errors<|docstring|>Parses warnings from Sphinx.
:param warning_text: warning to parse
:return: list of DocBuildErrors.<|endoftext|>
|
852f5bdc74cf84a2a8f082761553ad036994f0a10d0221d8bdac8491532ea3e2
|
@pytest.yield_fixture(autouse=True, scope='session')
def prevent_dialog_box():
'Do not open dreaded dialog box on segfault on Windows'
import ctypes
SEM_NOGPFAULTERRORBOX = 2
old_err_mode = ctypes.windll.kernel32.GetErrorMode()
new_err_mode = (old_err_mode | SEM_NOGPFAULTERRORBOX)
ctypes.windll.kernel32.SetErrorMode(new_err_mode)
(yield)
ctypes.windll.kernel32.SetErrorMode(old_err_mode)
|
Do not open dreaded dialog box on segfault on Windows
|
pypy/module/cpyext/test/conftest.py
|
prevent_dialog_box
|
prg-titech/pypy
| 333 |
python
|
@pytest.yield_fixture(autouse=True, scope='session')
def prevent_dialog_box():
import ctypes
SEM_NOGPFAULTERRORBOX = 2
old_err_mode = ctypes.windll.kernel32.GetErrorMode()
new_err_mode = (old_err_mode | SEM_NOGPFAULTERRORBOX)
ctypes.windll.kernel32.SetErrorMode(new_err_mode)
(yield)
ctypes.windll.kernel32.SetErrorMode(old_err_mode)
|
@pytest.yield_fixture(autouse=True, scope='session')
def prevent_dialog_box():
import ctypes
SEM_NOGPFAULTERRORBOX = 2
old_err_mode = ctypes.windll.kernel32.GetErrorMode()
new_err_mode = (old_err_mode | SEM_NOGPFAULTERRORBOX)
ctypes.windll.kernel32.SetErrorMode(new_err_mode)
(yield)
ctypes.windll.kernel32.SetErrorMode(old_err_mode)<|docstring|>Do not open dreaded dialog box on segfault on Windows<|endoftext|>
|
7276e10f3b60b9e910326e1b6bacb006de4b03c085483dcc0895e1cba712083a
|
def do_um_synchro_task(u_id: str, um_socks) -> UmTask:
'\n 开始执行友盟同步任务\n :param u_id:\n :param um_socks:\n :return:\n '
task: UmTask = UmTask(u_id=u_id, um_socks=um_socks)
(key_master, key_slaves) = _get_um_key_config(u_id=u_id)
if (not key_master):
return None
task.down_events(um_keys=([key_master] + list(key_slaves)))
for um_key in key_slaves:
task.synchro_um_data(um_key=um_key, um_key_master=key_master)
return task
|
开始执行友盟同步任务
:param u_id:
:param um_socks:
:return:
|
api/um/um_tasks.py
|
do_um_synchro_task
|
Samge0/UmengEventManage
| 0 |
python
|
def do_um_synchro_task(u_id: str, um_socks) -> UmTask:
'\n 开始执行友盟同步任务\n :param u_id:\n :param um_socks:\n :return:\n '
task: UmTask = UmTask(u_id=u_id, um_socks=um_socks)
(key_master, key_slaves) = _get_um_key_config(u_id=u_id)
if (not key_master):
return None
task.down_events(um_keys=([key_master] + list(key_slaves)))
for um_key in key_slaves:
task.synchro_um_data(um_key=um_key, um_key_master=key_master)
return task
|
def do_um_synchro_task(u_id: str, um_socks) -> UmTask:
'\n 开始执行友盟同步任务\n :param u_id:\n :param um_socks:\n :return:\n '
task: UmTask = UmTask(u_id=u_id, um_socks=um_socks)
(key_master, key_slaves) = _get_um_key_config(u_id=u_id)
if (not key_master):
return None
task.down_events(um_keys=([key_master] + list(key_slaves)))
for um_key in key_slaves:
task.synchro_um_data(um_key=um_key, um_key_master=key_master)
return task<|docstring|>开始执行友盟同步任务
:param u_id:
:param um_socks:
:return:<|endoftext|>
|
b6243aad2a6de3221087249a1222accbdb45a014b66c1c4d1fb564aa29eda72e
|
def do_add_or_update_task(u_id: str, um_socks) -> UmTask:
'\n 执行添加/更新友盟自定义事件的任务\n :param u_id:\n :param um_socks:\n :return:\n '
task: UmTask = UmTask(u_id=u_id, um_socks=um_socks)
(key_master, key_slaves) = _get_um_key_config(u_id=u_id)
if (not key_master):
return None
task.down_events(um_keys=([key_master] + list(key_slaves)))
task.add_or_update_event_by_file(um_key=key_master)
task.update_local_db_events(um_key=key_master)
return task
|
执行添加/更新友盟自定义事件的任务
:param u_id:
:param um_socks:
:return:
|
api/um/um_tasks.py
|
do_add_or_update_task
|
Samge0/UmengEventManage
| 0 |
python
|
def do_add_or_update_task(u_id: str, um_socks) -> UmTask:
'\n 执行添加/更新友盟自定义事件的任务\n :param u_id:\n :param um_socks:\n :return:\n '
task: UmTask = UmTask(u_id=u_id, um_socks=um_socks)
(key_master, key_slaves) = _get_um_key_config(u_id=u_id)
if (not key_master):
return None
task.down_events(um_keys=([key_master] + list(key_slaves)))
task.add_or_update_event_by_file(um_key=key_master)
task.update_local_db_events(um_key=key_master)
return task
|
def do_add_or_update_task(u_id: str, um_socks) -> UmTask:
'\n 执行添加/更新友盟自定义事件的任务\n :param u_id:\n :param um_socks:\n :return:\n '
task: UmTask = UmTask(u_id=u_id, um_socks=um_socks)
(key_master, key_slaves) = _get_um_key_config(u_id=u_id)
if (not key_master):
return None
task.down_events(um_keys=([key_master] + list(key_slaves)))
task.add_or_update_event_by_file(um_key=key_master)
task.update_local_db_events(um_key=key_master)
return task<|docstring|>执行添加/更新友盟自定义事件的任务
:param u_id:
:param um_socks:
:return:<|endoftext|>
|
a82a343b64909696ccdace76ab086bb8265aea3630b900ed6eee5ee880385a2e
|
def load_analysis_event_file(u_id: str, um_key: str, refresh: bool):
'\n 获取友盟所有自定义事件列表（有效的&暂停的）\n :param u_id:\n :param um_key:\n :param refresh: 是否需要从网络中重新获取数据\n :return:\n '
task: UmTask = UmTask(u_id=u_id, um_socks=None)
need_refresh: bool = (refresh or (task.is_exists_pause(um_key=um_key) is False) or (task.is_exists_normal_analysis(um_key=um_key) is False))
if need_refresh:
task.update_local_db_events(um_key=um_key)
|
获取友盟所有自定义事件列表（有效的&暂停的）
:param u_id:
:param um_key:
:param refresh: 是否需要从网络中重新获取数据
:return:
|
api/um/um_tasks.py
|
load_analysis_event_file
|
Samge0/UmengEventManage
| 0 |
python
|
def load_analysis_event_file(u_id: str, um_key: str, refresh: bool):
'\n 获取友盟所有自定义事件列表（有效的&暂停的）\n :param u_id:\n :param um_key:\n :param refresh: 是否需要从网络中重新获取数据\n :return:\n '
task: UmTask = UmTask(u_id=u_id, um_socks=None)
need_refresh: bool = (refresh or (task.is_exists_pause(um_key=um_key) is False) or (task.is_exists_normal_analysis(um_key=um_key) is False))
if need_refresh:
task.update_local_db_events(um_key=um_key)
|
def load_analysis_event_file(u_id: str, um_key: str, refresh: bool):
'\n 获取友盟所有自定义事件列表（有效的&暂停的）\n :param u_id:\n :param um_key:\n :param refresh: 是否需要从网络中重新获取数据\n :return:\n '
task: UmTask = UmTask(u_id=u_id, um_socks=None)
need_refresh: bool = (refresh or (task.is_exists_pause(um_key=um_key) is False) or (task.is_exists_normal_analysis(um_key=um_key) is False))
if need_refresh:
task.update_local_db_events(um_key=um_key)<|docstring|>获取友盟所有自定义事件列表（有效的&暂停的）
:param u_id:
:param um_key:
:param refresh: 是否需要从网络中重新获取数据
:return:<|endoftext|>
|
6d4a584ab9c38cd003f077ce839b5bd01e6d9f51b58cb6bb76a60f450e95c4a5
|
def _get_um_key_config(u_id: str) -> (str, list):
'\n 获取友盟key配置信息\n :param u_id:\n :return:\n '
values = (UserConfig.objects.filter(u_id=u_id).values() or [])
if (len(values) == 0):
return ('', [])
uc_key_master: str = values[0].get('uc_key_master')
uc_key_slaves: str = values[0].get('uc_key_slaves')
if uc_key_slaves:
return (uc_key_master, uc_key_slaves.split('|'))
else:
return (uc_key_master, [])
|
获取友盟key配置信息
:param u_id:
:return:
|
api/um/um_tasks.py
|
_get_um_key_config
|
Samge0/UmengEventManage
| 0 |
python
|
def _get_um_key_config(u_id: str) -> (str, list):
'\n 获取友盟key配置信息\n :param u_id:\n :return:\n '
values = (UserConfig.objects.filter(u_id=u_id).values() or [])
if (len(values) == 0):
return (, [])
uc_key_master: str = values[0].get('uc_key_master')
uc_key_slaves: str = values[0].get('uc_key_slaves')
if uc_key_slaves:
return (uc_key_master, uc_key_slaves.split('|'))
else:
return (uc_key_master, [])
|
def _get_um_key_config(u_id: str) -> (str, list):
'\n 获取友盟key配置信息\n :param u_id:\n :return:\n '
values = (UserConfig.objects.filter(u_id=u_id).values() or [])
if (len(values) == 0):
return (, [])
uc_key_master: str = values[0].get('uc_key_master')
uc_key_slaves: str = values[0].get('uc_key_slaves')
if uc_key_slaves:
return (uc_key_master, uc_key_slaves.split('|'))
else:
return (uc_key_master, [])<|docstring|>获取友盟key配置信息
:param u_id:
:return:<|endoftext|>
|
cecda7bf3bd23cd24e2901e4ac31ec6c5e94892e830b9822f9c3ed7d55b1ddb9
|
def __init__(self, models, tgt_dict, beam_size=1, max_len_a=0, max_len_b=200, min_len=1, normalize_scores=True, len_penalty=1.0, unk_penalty=0.0, desired_length=(- 1), retain_dropout=False, temperature=1.0, match_source_len=False, no_repeat_ngram_size=0, search_strategy=None, eos=None):
'Generates translations of a given source sentence.\n\n Args:\n models (List[~fairseq.models.FairseqModel]): ensemble of models,\n currently support fairseq.models.TransformerModel for scripting\n beam_size (int, optional): beam width (default: 1)\n max_len_a/b (int, optional): generate sequences of maximum length\n ax + b, where x is the source length\n min_len (int, optional): the minimum length of the generated output\n (not including end-of-sentence)\n normalize_scores (bool, optional): normalize scores by the length\n of the output (default: True)\n len_penalty (float, optional): length penalty, where <1.0 favors\n shorter, >1.0 favors longer sentences (default: 1.0)\n unk_penalty (float, optional): unknown word penalty, where <0\n produces more unks, >0 produces fewer (default: 0.0)\n retain_dropout (bool, optional): use dropout when generating\n (default: False)\n temperature (float, optional): temperature, where values\n >1.0 produce more uniform samples and values <1.0 produce\n sharper samples (default: 1.0)\n match_source_len (bool, optional): outputs should match the source\n length (default: False)\n '
super().__init__()
if isinstance(models, EnsembleModel):
self.model = models
else:
self.model = EnsembleModel(models)
self.pad = tgt_dict.pad()
self.unk = tgt_dict.unk()
self.eos = (tgt_dict.eos() if (eos is None) else eos)
self.vocab_size = len(tgt_dict)
self.beam_size = beam_size
self.beam_size = min(beam_size, (self.vocab_size - 1))
self.max_len_a = max_len_a
self.max_len_b = max_len_b
self.min_len = min_len
self.normalize_scores = normalize_scores
self.len_penalty = len_penalty
self.unk_penalty = unk_penalty
self.desired_length = desired_length
self.retain_dropout = retain_dropout
self.temperature = temperature
self.match_source_len = match_source_len
self.no_repeat_ngram_size = no_repeat_ngram_size
assert (temperature > 0), '--temperature must be greater than 0'
self.search = (search.BeamSearch(tgt_dict) if (search_strategy is None) else search_strategy)
if (not self.retain_dropout):
self.model.eval()
|
Generates translations of a given source sentence.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models,
currently support fairseq.models.TransformerModel for scripting
beam_size (int, optional): beam width (default: 1)
max_len_a/b (int, optional): generate sequences of maximum length
ax + b, where x is the source length
min_len (int, optional): the minimum length of the generated output
(not including end-of-sentence)
normalize_scores (bool, optional): normalize scores by the length
of the output (default: True)
len_penalty (float, optional): length penalty, where <1.0 favors
shorter, >1.0 favors longer sentences (default: 1.0)
unk_penalty (float, optional): unknown word penalty, where <0
produces more unks, >0 produces fewer (default: 0.0)
retain_dropout (bool, optional): use dropout when generating
(default: False)
temperature (float, optional): temperature, where values
>1.0 produce more uniform samples and values <1.0 produce
sharper samples (default: 1.0)
match_source_len (bool, optional): outputs should match the source
length (default: False)
|
fairseq/sequence_generator.py
|
__init__
|
takase/alone_seq2seq
| 25 |
python
|
def __init__(self, models, tgt_dict, beam_size=1, max_len_a=0, max_len_b=200, min_len=1, normalize_scores=True, len_penalty=1.0, unk_penalty=0.0, desired_length=(- 1), retain_dropout=False, temperature=1.0, match_source_len=False, no_repeat_ngram_size=0, search_strategy=None, eos=None):
'Generates translations of a given source sentence.\n\n Args:\n models (List[~fairseq.models.FairseqModel]): ensemble of models,\n currently support fairseq.models.TransformerModel for scripting\n beam_size (int, optional): beam width (default: 1)\n max_len_a/b (int, optional): generate sequences of maximum length\n ax + b, where x is the source length\n min_len (int, optional): the minimum length of the generated output\n (not including end-of-sentence)\n normalize_scores (bool, optional): normalize scores by the length\n of the output (default: True)\n len_penalty (float, optional): length penalty, where <1.0 favors\n shorter, >1.0 favors longer sentences (default: 1.0)\n unk_penalty (float, optional): unknown word penalty, where <0\n produces more unks, >0 produces fewer (default: 0.0)\n retain_dropout (bool, optional): use dropout when generating\n (default: False)\n temperature (float, optional): temperature, where values\n >1.0 produce more uniform samples and values <1.0 produce\n sharper samples (default: 1.0)\n match_source_len (bool, optional): outputs should match the source\n length (default: False)\n '
super().__init__()
if isinstance(models, EnsembleModel):
self.model = models
else:
self.model = EnsembleModel(models)
self.pad = tgt_dict.pad()
self.unk = tgt_dict.unk()
self.eos = (tgt_dict.eos() if (eos is None) else eos)
self.vocab_size = len(tgt_dict)
self.beam_size = beam_size
self.beam_size = min(beam_size, (self.vocab_size - 1))
self.max_len_a = max_len_a
self.max_len_b = max_len_b
self.min_len = min_len
self.normalize_scores = normalize_scores
self.len_penalty = len_penalty
self.unk_penalty = unk_penalty
self.desired_length = desired_length
self.retain_dropout = retain_dropout
self.temperature = temperature
self.match_source_len = match_source_len
self.no_repeat_ngram_size = no_repeat_ngram_size
assert (temperature > 0), '--temperature must be greater than 0'
self.search = (search.BeamSearch(tgt_dict) if (search_strategy is None) else search_strategy)
if (not self.retain_dropout):
self.model.eval()
|
def __init__(self, models, tgt_dict, beam_size=1, max_len_a=0, max_len_b=200, min_len=1, normalize_scores=True, len_penalty=1.0, unk_penalty=0.0, desired_length=(- 1), retain_dropout=False, temperature=1.0, match_source_len=False, no_repeat_ngram_size=0, search_strategy=None, eos=None):
'Generates translations of a given source sentence.\n\n Args:\n models (List[~fairseq.models.FairseqModel]): ensemble of models,\n currently support fairseq.models.TransformerModel for scripting\n beam_size (int, optional): beam width (default: 1)\n max_len_a/b (int, optional): generate sequences of maximum length\n ax + b, where x is the source length\n min_len (int, optional): the minimum length of the generated output\n (not including end-of-sentence)\n normalize_scores (bool, optional): normalize scores by the length\n of the output (default: True)\n len_penalty (float, optional): length penalty, where <1.0 favors\n shorter, >1.0 favors longer sentences (default: 1.0)\n unk_penalty (float, optional): unknown word penalty, where <0\n produces more unks, >0 produces fewer (default: 0.0)\n retain_dropout (bool, optional): use dropout when generating\n (default: False)\n temperature (float, optional): temperature, where values\n >1.0 produce more uniform samples and values <1.0 produce\n sharper samples (default: 1.0)\n match_source_len (bool, optional): outputs should match the source\n length (default: False)\n '
super().__init__()
if isinstance(models, EnsembleModel):
self.model = models
else:
self.model = EnsembleModel(models)
self.pad = tgt_dict.pad()
self.unk = tgt_dict.unk()
self.eos = (tgt_dict.eos() if (eos is None) else eos)
self.vocab_size = len(tgt_dict)
self.beam_size = beam_size
self.beam_size = min(beam_size, (self.vocab_size - 1))
self.max_len_a = max_len_a
self.max_len_b = max_len_b
self.min_len = min_len
self.normalize_scores = normalize_scores
self.len_penalty = len_penalty
self.unk_penalty = unk_penalty
self.desired_length = desired_length
self.retain_dropout = retain_dropout
self.temperature = temperature
self.match_source_len = match_source_len
self.no_repeat_ngram_size = no_repeat_ngram_size
assert (temperature > 0), '--temperature must be greater than 0'
self.search = (search.BeamSearch(tgt_dict) if (search_strategy is None) else search_strategy)
if (not self.retain_dropout):
self.model.eval()<|docstring|>Generates translations of a given source sentence.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models,
currently support fairseq.models.TransformerModel for scripting
beam_size (int, optional): beam width (default: 1)
max_len_a/b (int, optional): generate sequences of maximum length
ax + b, where x is the source length
min_len (int, optional): the minimum length of the generated output
(not including end-of-sentence)
normalize_scores (bool, optional): normalize scores by the length
of the output (default: True)
len_penalty (float, optional): length penalty, where <1.0 favors
shorter, >1.0 favors longer sentences (default: 1.0)
unk_penalty (float, optional): unknown word penalty, where <0
produces more unks, >0 produces fewer (default: 0.0)
retain_dropout (bool, optional): use dropout when generating
(default: False)
temperature (float, optional): temperature, where values
>1.0 produce more uniform samples and values <1.0 produce
sharper samples (default: 1.0)
match_source_len (bool, optional): outputs should match the source
length (default: False)<|endoftext|>
|
74c150b01ba597ebb4fcad69386626606f8ce7f4ae20bcacc8759b8f0d6cd521
|
@torch.no_grad()
def forward(self, sample: Dict[(str, Dict[(str, Tensor)])], prefix_tokens: Optional[Tensor]=None, bos_token: Optional[int]=None):
'Generate a batch of translations.\n\n Args:\n sample (dict): batch\n prefix_tokens (torch.LongTensor, optional): force decoder to begin\n with these tokens\n bos_token (int, optional): beginning of sentence token\n (default: self.eos)\n '
self.model.reset_incremental_state()
return self._generate(sample, prefix_tokens, bos_token)
|
Generate a batch of translations.
Args:
sample (dict): batch
prefix_tokens (torch.LongTensor, optional): force decoder to begin
with these tokens
bos_token (int, optional): beginning of sentence token
(default: self.eos)
|
fairseq/sequence_generator.py
|
forward
|
takase/alone_seq2seq
| 25 |
python
|
@torch.no_grad()
def forward(self, sample: Dict[(str, Dict[(str, Tensor)])], prefix_tokens: Optional[Tensor]=None, bos_token: Optional[int]=None):
'Generate a batch of translations.\n\n Args:\n sample (dict): batch\n prefix_tokens (torch.LongTensor, optional): force decoder to begin\n with these tokens\n bos_token (int, optional): beginning of sentence token\n (default: self.eos)\n '
self.model.reset_incremental_state()
return self._generate(sample, prefix_tokens, bos_token)
|
@torch.no_grad()
def forward(self, sample: Dict[(str, Dict[(str, Tensor)])], prefix_tokens: Optional[Tensor]=None, bos_token: Optional[int]=None):
'Generate a batch of translations.\n\n Args:\n sample (dict): batch\n prefix_tokens (torch.LongTensor, optional): force decoder to begin\n with these tokens\n bos_token (int, optional): beginning of sentence token\n (default: self.eos)\n '
self.model.reset_incremental_state()
return self._generate(sample, prefix_tokens, bos_token)<|docstring|>Generate a batch of translations.
Args:
sample (dict): batch
prefix_tokens (torch.LongTensor, optional): force decoder to begin
with these tokens
bos_token (int, optional): beginning of sentence token
(default: self.eos)<|endoftext|>
|
d7b4b7081ddd571a763f203d593131bdfdaeefd5c6af014d003361ff0bb5dede
|
def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None):
'Iterate over a batched dataset and yield individual translations.\n Args:\n cuda (bool, optional): use GPU for generation\n timer (StopwatchMeter, optional): time generations\n '
for sample in data_itr:
s = (utils.move_to_cuda(sample) if cuda else sample)
if ('net_input' not in s):
continue
input = s['net_input']
encoder_input = {k: v for (k, v) in input.items() if (k != 'prev_output_tokens')}
if (timer is not None):
timer.start()
with torch.no_grad():
hypos = self.generate(encoder_input)
if (timer is not None):
timer.stop(sum((len(h[0]['tokens']) for h in hypos)))
for (i, id) in enumerate(s['id'].data):
src = utils.strip_pad(input['src_tokens'].data[(i, :)], self.pad)
ref = (utils.strip_pad(s['target'].data[(i, :)], self.pad) if (s['target'] is not None) else None)
(yield (id, src, ref, hypos[i]))
|
Iterate over a batched dataset and yield individual translations.
Args:
cuda (bool, optional): use GPU for generation
timer (StopwatchMeter, optional): time generations
|
fairseq/sequence_generator.py
|
generate_batched_itr
|
takase/alone_seq2seq
| 25 |
python
|
def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None):
'Iterate over a batched dataset and yield individual translations.\n Args:\n cuda (bool, optional): use GPU for generation\n timer (StopwatchMeter, optional): time generations\n '
for sample in data_itr:
s = (utils.move_to_cuda(sample) if cuda else sample)
if ('net_input' not in s):
continue
input = s['net_input']
encoder_input = {k: v for (k, v) in input.items() if (k != 'prev_output_tokens')}
if (timer is not None):
timer.start()
with torch.no_grad():
hypos = self.generate(encoder_input)
if (timer is not None):
timer.stop(sum((len(h[0]['tokens']) for h in hypos)))
for (i, id) in enumerate(s['id'].data):
src = utils.strip_pad(input['src_tokens'].data[(i, :)], self.pad)
ref = (utils.strip_pad(s['target'].data[(i, :)], self.pad) if (s['target'] is not None) else None)
(yield (id, src, ref, hypos[i]))
|
def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None):
'Iterate over a batched dataset and yield individual translations.\n Args:\n cuda (bool, optional): use GPU for generation\n timer (StopwatchMeter, optional): time generations\n '
for sample in data_itr:
s = (utils.move_to_cuda(sample) if cuda else sample)
if ('net_input' not in s):
continue
input = s['net_input']
encoder_input = {k: v for (k, v) in input.items() if (k != 'prev_output_tokens')}
if (timer is not None):
timer.start()
with torch.no_grad():
hypos = self.generate(encoder_input)
if (timer is not None):
timer.stop(sum((len(h[0]['tokens']) for h in hypos)))
for (i, id) in enumerate(s['id'].data):
src = utils.strip_pad(input['src_tokens'].data[(i, :)], self.pad)
ref = (utils.strip_pad(s['target'].data[(i, :)], self.pad) if (s['target'] is not None) else None)
(yield (id, src, ref, hypos[i]))<|docstring|>Iterate over a batched dataset and yield individual translations.
Args:
cuda (bool, optional): use GPU for generation
timer (StopwatchMeter, optional): time generations<|endoftext|>
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.