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IdentityPython/pysaml2 | 6badb32d212257bd83ffcc816f9b625f68281b47 | src/saml2/authn_context/sslcert.py | python | activation_limit_usages_type__from_string | (xml_string) | return saml2.create_class_from_xml_string(ActivationLimitUsagesType_,
xml_string) | [] | def activation_limit_usages_type__from_string(xml_string):
return saml2.create_class_from_xml_string(ActivationLimitUsagesType_,
xml_string) | [
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OpenAgricultureFoundation/openag-device-software | a51d2de399c0a6781ae51d0dcfaae1583d75f346 | device/iot/pubsub.py | python | PubSub.load_mqtt_config | (self) | Loads mqtt config. | Loads mqtt config. | [
"Loads",
"mqtt",
"config",
"."
] | def load_mqtt_config(self) -> None:
"""Loads mqtt config."""
self.logger.debug("Loading mqtt config")
# Load settings from environment variables
try:
self.project_id = os.environ["GCLOUD_PROJECT"]
self.cloud_region = os.environ["GCLOUD_REGION"]
self.registry_id = os.environ["GCLOUD_DEV_REG"]
self.device_id = registration.device_id()
self.private_key_filepath = os.environ["IOT_PRIVATE_KEY"]
self.ca_certs = os.environ["CA_CERTS"]
except KeyError as e:
message = "Unable to load pubsub config, key {} is required".format(e)
self.logger.critical(message)
raise
# Initialize client id
self.client_id = "projects/{}/locations/{}/registries/{}/devices/{}".format(
self.project_id, self.cloud_region, self.registry_id, self.device_id
)
# Initialize config topic
self.config_topic = "/devices/{}/config".format(self.device_id)
# Initialize commands topic
self.command_topic = "/devices/{}/commands/#".format(self.device_id)
# Initialize event topic
test_telemetry_topic = os.environ.get("IOT_TEST_TOPIC")
if test_telemetry_topic is not None:
self.telemetry_topic = "/devices/{}/{}".format(self.device_id, test_telemetry_topic)
self.logger.debug("Publishing to test topic: {}".format(self.telemetry_topic))
else:
self.telemetry_topic = "/devices/{}/events".format(self.device_id) | [
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ring04h/wyportmap | c4201e2313504e780a7f25238eba2a2d3223e739 | sqlalchemy/ext/associationproxy.py | python | _AssociationList.__iter__ | (self) | Iterate over proxied values.
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"""Iterate over proxied values.
For the actual domain objects, iterate over .col instead or
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"""
for member in self.col:
yield self._get(member)
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bitcraze/crazyflie-clients-python | 65d433a945b097333e5681a937354045dd4b66f4 | src/cfclient/utils/config_manager.py | python | ConfigManager.get_list_of_configs | (self) | return self._list_of_configs | Reload the configurations from file | Reload the configurations from file | [
"Reload",
"the",
"configurations",
"from",
"file"
] | def get_list_of_configs(self):
"""Reload the configurations from file"""
try:
configs = [os.path.basename(f) for f in
glob.glob(self.configs_dir + "/[A-Za-z]*.json")]
self._input_config = []
self._input_settings = []
self._list_of_configs = []
for conf in configs:
logger.debug("Parsing [%s]", conf)
json_data = open(self.configs_dir + "/%s" % conf)
data = json.load(json_data)
new_input_device = {}
new_input_settings = {"updateperiod": 10,
"springythrottle": True,
"rp_dead_band": 0.05}
for s in data["inputconfig"]["inputdevice"]:
if s == "axis":
for a in data["inputconfig"]["inputdevice"]["axis"]:
axis = {}
axis["scale"] = a["scale"]
axis["offset"] = a[
"offset"] if "offset" in a else 0.0
axis["type"] = a["type"]
axis["key"] = a["key"]
axis["name"] = a["name"]
self._translate_for_backwards_compatibility(axis)
try:
ids = a["ids"]
except Exception:
ids = [a["id"]]
for id in ids:
locaxis = copy.deepcopy(axis)
if "ids" in a:
if id == a["ids"][0]:
locaxis["scale"] = locaxis[
"scale"] * -1
locaxis["id"] = id
# 'type'-'id' defines unique index for axis
index = "%s-%d" % (a["type"], id)
new_input_device[index] = locaxis
else:
new_input_settings[s] = data[
"inputconfig"]["inputdevice"][s]
self._input_config.append(new_input_device)
self._input_settings.append(new_input_settings)
json_data.close()
self._list_of_configs.append(conf[:-5])
except Exception as e:
logger.warning("Exception while parsing inputconfig file: %s ", e)
return self._list_of_configs | [
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emesene/emesene | 4548a4098310e21b16437bb36223a7f632a4f7bc | emesene/e3/papylib/papyon/papyon/media/stream.py | python | MediaStream.local_candidates_prepared | (self) | Called by the stream handler once all the local candidates are found | Called by the stream handler once all the local candidates are found | [
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"""Called by the stream handler once all the local candidates are found"""
if self._local_candidates_prepared:
return
logger.debug("Media stream %s is prepared" % self.name)
self._local_candidates_prepared = True
if self.prepared:
self.emit("prepared") | [
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ifwe/digsby | f5fe00244744aa131e07f09348d10563f3d8fa99 | digsby/lib/pyxmpp/jabber/muc.py | python | MucRoomState.change_nick | (self,new_nick) | Send a nick change request to the room.
:Parameters:
- `new_nick`: the new nickname requested.
:Types:
- `new_nick`: `unicode` | Send a nick change request to the room. | [
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] | def change_nick(self,new_nick):
"""
Send a nick change request to the room.
:Parameters:
- `new_nick`: the new nickname requested.
:Types:
- `new_nick`: `unicode`
"""
new_room_jid=JID(self.room_jid.node,self.room_jid.domain,new_nick)
p=Presence(to_jid=new_room_jid)
self.manager.stream.send(p) | [
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rndusr/stig | 334f03e2e3eda7c1856dd5489f0265a47b9861b6 | stig/tui/views/base.py | python | ListWidgetBase.mark | (self, toggle=False, all=False) | Mark the currently focused item or all items | Mark the currently focused item or all items | [
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"""Mark the currently focused item or all items"""
self._set_mark(True, toggle=toggle, all=all) | [
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/data_safe/data_safe_client.py | python | DataSafeClient.list_users | (self, user_assessment_id, **kwargs) | Gets a list of users of the specified user assessment. The result contains the database user details for each user, such
as user type, account status, last login time, user creation time, authentication type, user profile, and the date and time
of the latest password change. It also contains the user category derived from these user details as well as privileges
granted to each user.
:param str user_assessment_id: (required)
The OCID of the user assessment.
:param int limit: (optional)
For list pagination. The maximum number of items to return per page in a paginated \"List\" call. For details about how pagination works, see `List Pagination`__.
__ https://docs.cloud.oracle.com/en-us/iaas/Content/API/Concepts/usingapi.htm#nine
:param bool compartment_id_in_subtree: (optional)
Default is false.
When set to true, the hierarchy of compartments is traversed and all compartments and subcompartments in the tenancy are returned. Depends on the 'accessLevel' setting.
:param str access_level: (optional)
Valid values are RESTRICTED and ACCESSIBLE. Default is RESTRICTED.
Setting this to ACCESSIBLE returns only those compartments for which the
user has INSPECT permissions directly or indirectly (permissions can be on a
resource in a subcompartment). When set to RESTRICTED permissions are checked and no partial results are displayed.
Allowed values are: "RESTRICTED", "ACCESSIBLE"
:param str user_category: (optional)
A filter to return only items that match the specified user category.
:param str user_key: (optional)
A filter to return only items that match the specified user key.
:param str account_status: (optional)
A filter to return only items that match the specified account status.
:param str authentication_type: (optional)
A filter to return only items that match the specified authentication type.
:param str user_name: (optional)
A filter to return only items that match the specified user name.
:param str target_id: (optional)
A filter to return only items that match the specified target.
:param datetime time_last_login_greater_than_or_equal_to: (optional)
A filter to return users whose last login time in the database is greater than or equal to the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_last_login_less_than: (optional)
A filter to return users whose last login time in the database is less than the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_user_created_greater_than_or_equal_to: (optional)
A filter to return users whose creation time in the database is greater than or equal to the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_user_created_less_than: (optional)
A filter to return users whose creation time in the database is less than the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_password_last_changed_greater_than_or_equal_to: (optional)
A filter to return users whose last password change in the database is greater than or equal to the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_password_last_changed_less_than: (optional)
A filter to return users whose last password change in the database is less than the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param str page: (optional)
For list pagination. The page token representing the page at which to start retrieving results. It is usually retrieved from a previous \"List\" call. For details about how pagination works, see `List Pagination`__.
__ https://docs.cloud.oracle.com/en-us/iaas/Content/API/Concepts/usingapi.htm#nine
:param str sort_order: (optional)
The sort order to use, either ascending (ASC) or descending (DESC).
Allowed values are: "ASC", "DESC"
:param str sort_by: (optional)
The field to sort by. You can specify only one sort order (sortOrder). The default order for userName is ascending.
Allowed values are: "userName", "userCategory", "accountStatus", "timeLastLogin", "targetId", "timeUserCreated", "authenticationType", "timePasswordChanged"
:param str opc_request_id: (optional)
Unique identifier for the request.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type list of :class:`~oci.data_safe.models.UserSummary`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/datasafe/list_users.py.html>`__ to see an example of how to use list_users API. | Gets a list of users of the specified user assessment. The result contains the database user details for each user, such
as user type, account status, last login time, user creation time, authentication type, user profile, and the date and time
of the latest password change. It also contains the user category derived from these user details as well as privileges
granted to each user. | [
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"for",
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"user",
"such",
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"user",
"type",
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"login",
"time",
"... | def list_users(self, user_assessment_id, **kwargs):
"""
Gets a list of users of the specified user assessment. The result contains the database user details for each user, such
as user type, account status, last login time, user creation time, authentication type, user profile, and the date and time
of the latest password change. It also contains the user category derived from these user details as well as privileges
granted to each user.
:param str user_assessment_id: (required)
The OCID of the user assessment.
:param int limit: (optional)
For list pagination. The maximum number of items to return per page in a paginated \"List\" call. For details about how pagination works, see `List Pagination`__.
__ https://docs.cloud.oracle.com/en-us/iaas/Content/API/Concepts/usingapi.htm#nine
:param bool compartment_id_in_subtree: (optional)
Default is false.
When set to true, the hierarchy of compartments is traversed and all compartments and subcompartments in the tenancy are returned. Depends on the 'accessLevel' setting.
:param str access_level: (optional)
Valid values are RESTRICTED and ACCESSIBLE. Default is RESTRICTED.
Setting this to ACCESSIBLE returns only those compartments for which the
user has INSPECT permissions directly or indirectly (permissions can be on a
resource in a subcompartment). When set to RESTRICTED permissions are checked and no partial results are displayed.
Allowed values are: "RESTRICTED", "ACCESSIBLE"
:param str user_category: (optional)
A filter to return only items that match the specified user category.
:param str user_key: (optional)
A filter to return only items that match the specified user key.
:param str account_status: (optional)
A filter to return only items that match the specified account status.
:param str authentication_type: (optional)
A filter to return only items that match the specified authentication type.
:param str user_name: (optional)
A filter to return only items that match the specified user name.
:param str target_id: (optional)
A filter to return only items that match the specified target.
:param datetime time_last_login_greater_than_or_equal_to: (optional)
A filter to return users whose last login time in the database is greater than or equal to the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_last_login_less_than: (optional)
A filter to return users whose last login time in the database is less than the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_user_created_greater_than_or_equal_to: (optional)
A filter to return users whose creation time in the database is greater than or equal to the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_user_created_less_than: (optional)
A filter to return users whose creation time in the database is less than the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_password_last_changed_greater_than_or_equal_to: (optional)
A filter to return users whose last password change in the database is greater than or equal to the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param datetime time_password_last_changed_less_than: (optional)
A filter to return users whose last password change in the database is less than the date and time specified, in the format defined by `RFC3339`__.
**Example:** 2016-12-19T16:39:57.600Z
__ https://tools.ietf.org/html/rfc3339
:param str page: (optional)
For list pagination. The page token representing the page at which to start retrieving results. It is usually retrieved from a previous \"List\" call. For details about how pagination works, see `List Pagination`__.
__ https://docs.cloud.oracle.com/en-us/iaas/Content/API/Concepts/usingapi.htm#nine
:param str sort_order: (optional)
The sort order to use, either ascending (ASC) or descending (DESC).
Allowed values are: "ASC", "DESC"
:param str sort_by: (optional)
The field to sort by. You can specify only one sort order (sortOrder). The default order for userName is ascending.
Allowed values are: "userName", "userCategory", "accountStatus", "timeLastLogin", "targetId", "timeUserCreated", "authenticationType", "timePasswordChanged"
:param str opc_request_id: (optional)
Unique identifier for the request.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type list of :class:`~oci.data_safe.models.UserSummary`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/datasafe/list_users.py.html>`__ to see an example of how to use list_users API.
"""
resource_path = "/userAssessments/{userAssessmentId}/users"
method = "GET"
# Don't accept unknown kwargs
expected_kwargs = [
"retry_strategy",
"limit",
"compartment_id_in_subtree",
"access_level",
"user_category",
"user_key",
"account_status",
"authentication_type",
"user_name",
"target_id",
"time_last_login_greater_than_or_equal_to",
"time_last_login_less_than",
"time_user_created_greater_than_or_equal_to",
"time_user_created_less_than",
"time_password_last_changed_greater_than_or_equal_to",
"time_password_last_changed_less_than",
"page",
"sort_order",
"sort_by",
"opc_request_id"
]
extra_kwargs = [_key for _key in six.iterkeys(kwargs) if _key not in expected_kwargs]
if extra_kwargs:
raise ValueError(
"list_users got unknown kwargs: {!r}".format(extra_kwargs))
path_params = {
"userAssessmentId": user_assessment_id
}
path_params = {k: v for (k, v) in six.iteritems(path_params) if v is not missing}
for (k, v) in six.iteritems(path_params):
if v is None or (isinstance(v, six.string_types) and len(v.strip()) == 0):
raise ValueError('Parameter {} cannot be None, whitespace or empty string'.format(k))
if 'access_level' in kwargs:
access_level_allowed_values = ["RESTRICTED", "ACCESSIBLE"]
if kwargs['access_level'] not in access_level_allowed_values:
raise ValueError(
"Invalid value for `access_level`, must be one of {0}".format(access_level_allowed_values)
)
if 'sort_order' in kwargs:
sort_order_allowed_values = ["ASC", "DESC"]
if kwargs['sort_order'] not in sort_order_allowed_values:
raise ValueError(
"Invalid value for `sort_order`, must be one of {0}".format(sort_order_allowed_values)
)
if 'sort_by' in kwargs:
sort_by_allowed_values = ["userName", "userCategory", "accountStatus", "timeLastLogin", "targetId", "timeUserCreated", "authenticationType", "timePasswordChanged"]
if kwargs['sort_by'] not in sort_by_allowed_values:
raise ValueError(
"Invalid value for `sort_by`, must be one of {0}".format(sort_by_allowed_values)
)
query_params = {
"limit": kwargs.get("limit", missing),
"compartmentIdInSubtree": kwargs.get("compartment_id_in_subtree", missing),
"accessLevel": kwargs.get("access_level", missing),
"userCategory": kwargs.get("user_category", missing),
"userKey": kwargs.get("user_key", missing),
"accountStatus": kwargs.get("account_status", missing),
"authenticationType": kwargs.get("authentication_type", missing),
"userName": kwargs.get("user_name", missing),
"targetId": kwargs.get("target_id", missing),
"timeLastLoginGreaterThanOrEqualTo": kwargs.get("time_last_login_greater_than_or_equal_to", missing),
"timeLastLoginLessThan": kwargs.get("time_last_login_less_than", missing),
"timeUserCreatedGreaterThanOrEqualTo": kwargs.get("time_user_created_greater_than_or_equal_to", missing),
"timeUserCreatedLessThan": kwargs.get("time_user_created_less_than", missing),
"timePasswordLastChangedGreaterThanOrEqualTo": kwargs.get("time_password_last_changed_greater_than_or_equal_to", missing),
"timePasswordLastChangedLessThan": kwargs.get("time_password_last_changed_less_than", missing),
"page": kwargs.get("page", missing),
"sortOrder": kwargs.get("sort_order", missing),
"sortBy": kwargs.get("sort_by", missing)
}
query_params = {k: v for (k, v) in six.iteritems(query_params) if v is not missing and v is not None}
header_params = {
"accept": "application/json",
"content-type": "application/json",
"opc-request-id": kwargs.get("opc_request_id", missing)
}
header_params = {k: v for (k, v) in six.iteritems(header_params) if v is not missing and v is not None}
retry_strategy = self.base_client.get_preferred_retry_strategy(
operation_retry_strategy=kwargs.get('retry_strategy'),
client_retry_strategy=self.retry_strategy
)
if retry_strategy:
if not isinstance(retry_strategy, retry.NoneRetryStrategy):
self.base_client.add_opc_client_retries_header(header_params)
retry_strategy.add_circuit_breaker_callback(self.circuit_breaker_callback)
return retry_strategy.make_retrying_call(
self.base_client.call_api,
resource_path=resource_path,
method=method,
path_params=path_params,
query_params=query_params,
header_params=header_params,
response_type="list[UserSummary]")
else:
return self.base_client.call_api(
resource_path=resource_path,
method=method,
path_params=path_params,
query_params=query_params,
header_params=header_params,
response_type="list[UserSummary]") | [
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isnowfy/pydown | 71ecc891868cd2a34b7e5fe662c99474f2d0fd7f | main.py | python | slides_split | (slides) | [] | def slides_split(slides):
data = ''
css = ''
for item in slides.split('\n'):
if item.startswith('!SLIDE'):
yield css, data
css = ' '.join(item.split(' ')[1:])
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/site-packages/setuptools/command/build_py.py | python | build_py.build_package_data | (self) | Copy data files into build directory | Copy data files into build directory | [
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] | def build_package_data(self):
"""Copy data files into build directory"""
for package, src_dir, build_dir, filenames in self.data_files:
for filename in filenames:
target = os.path.join(build_dir, filename)
self.mkpath(os.path.dirname(target))
srcfile = os.path.join(src_dir, filename)
outf, copied = self.copy_file(srcfile, target)
srcfile = os.path.abspath(srcfile)
if (copied and
srcfile in self.distribution.convert_2to3_doctests):
self.__doctests_2to3.append(outf) | [
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/progress.py | python | Bar.__init__ | (
self,
label="",
width=32,
hide=None,
empty_char=BAR_EMPTY_CHAR,
filled_char=BAR_FILLED_CHAR,
expected_size=None,
every=1,
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self,
label="",
width=32,
hide=None,
empty_char=BAR_EMPTY_CHAR,
filled_char=BAR_FILLED_CHAR,
expected_size=None,
every=1,
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self.label = label
self.width = width
self.hide = hide
# Only show bar in terminals by default (better for piping, logging etc.)
if hide is None:
try:
self.hide = not STREAM.isatty()
except AttributeError: # output does not support isatty()
self.hide = True
self.empty_char = empty_char
self.filled_char = filled_char
self.expected_size = expected_size
self.every = every
self.start = time.time()
self.ittimes = []
self.eta = 0
self.etadelta = time.time()
self.etadisp = self.format_time(self.eta)
self.last_progress = 0
if self.expected_size:
self.show(0) | [
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deepinsight/insightface | c0b25f998a649f662c7136eb389abcacd7900e9d | python-package/insightface/utils/filesystem.py | python | try_import_cv2 | () | return try_import('cv2', msg) | Try import cv2 at runtime.
Returns
-------
cv2 module if found. Raise ImportError otherwise | Try import cv2 at runtime. | [
"Try",
"import",
"cv2",
"at",
"runtime",
"."
] | def try_import_cv2():
"""Try import cv2 at runtime.
Returns
-------
cv2 module if found. Raise ImportError otherwise
"""
msg = "cv2 is required, you can install by package manager, e.g. 'apt-get', \
or `pip install opencv-python --user` (note that this is unofficial PYPI package)."
return try_import('cv2', msg) | [
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iterative/dvc | 13238e97168007cb5ba21966368457776274b9ca | dvc/machine/backend/terraform.py | python | TerraformBackend.rename | (self, name: str, new: str, **config) | rename a dvc machine instance to a new name | rename a dvc machine instance to a new name | [
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] | def rename(self, name: str, new: str, **config):
"""rename a dvc machine instance to a new name"""
import shutil
mtype = "iterative_machine"
new_dir = os.path.join(self.tmp_dir, new)
old_dir = os.path.join(self.tmp_dir, name)
if os.path.exists(new_dir):
raise DvcException(f"rename failed: path {new_dir} already exists")
if not os.path.exists(old_dir):
return
with self.make_tpi(name) as tpi:
tpi.state_mv(f"{mtype}.{name}", f"{mtype}.{new}", **config)
shutil.move(old_dir, new_dir) | [
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apeterswu/RL4NMT | 3c66a2d8142abc5ce73db63e05d3cc9bf4663b65 | tensor2tensor/layers/common_attention.py | python | combine_last_two_dimensions | (x) | return ret | Reshape x so that the last two dimension become one.
Args:
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Returns:
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"""Reshape x so that the last two dimension become one.
Args:
x: a Tensor with shape [..., a, b]
Returns:
a Tensor with shape [..., ab]
"""
old_shape = x.get_shape().dims
a, b = old_shape[-2:]
new_shape = old_shape[:-2] + [a * b if a and b else None]
ret = tf.reshape(x, tf.concat([tf.shape(x)[:-2], [-1]], 0))
ret.set_shape(new_shape)
return ret | [
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pabigot/pyxb | 14737c23a125fd12c954823ad64fc4497816fae3 | pyxb/namespace/__init__.py | python | Namespace.initialNamespaceContext | (self) | return self.__initialNamespaceContext | Obtain the namespace context to be used when creating components in this namespace.
Usually applies only to built-in namespaces, but is also used in the
autotests when creating a namespace without a xs:schema element. .
Note that we must create the instance dynamically, since the
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Usually applies only to built-in namespaces, but is also used in the
autotests when creating a namespace without a xs:schema element. .
Note that we must create the instance dynamically, since the
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resolved until this module has been completely loaded."""
if self.__initialNamespaceContext is None:
isn = { }
if self.__contextInScopeNamespaces is not None:
for (k, v) in six.iteritems(self.__contextInScopeNamespaces):
isn[k] = self.__identifyNamespace(v)
kw = { 'target_namespace' : self
, 'default_namespace' : self.__identifyNamespace(self.__contextDefaultNamespace)
, 'in_scope_namespaces' : isn }
self.__initialNamespaceContext = resolution.NamespaceContext(None, **kw)
return self.__initialNamespaceContext | [
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openbmc/openbmc | 5f4109adae05f4d6925bfe960007d52f98c61086 | poky/bitbake/lib/toaster/orm/models.py | python | Target.clone_sdk_artifacts_from | (self, target) | Clone TargetSDKFile objects from target and associate them with this
target. | Clone TargetSDKFile objects from target and associate them with this
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"""
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target.
"""
sdk_files = target.targetsdkfile_set.all()
for sdk_file in sdk_files:
sdk_file.pk = None
sdk_file.target = self
sdk_file.save() | [
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/FeedMitreAttackv2/Integrations/FeedMitreAttackv2/FeedMitreAttackv2.py | python | create_relationship | (item_json, id_to_name) | return None | Create a single relation with the given arguments. | Create a single relation with the given arguments. | [
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] | def create_relationship(item_json, id_to_name):
"""
Create a single relation with the given arguments.
"""
a_type = item_json.get('source_ref').split('--')[0]
a_type = MITRE_TYPE_TO_DEMISTO_TYPE.get(a_type)
b_type = item_json.get('target_ref').split('--')[0]
b_type = MITRE_TYPE_TO_DEMISTO_TYPE.get(b_type)
mapping_fields = {
'description': item_json.get('description'),
'lastseenbysource': item_json.get('modified'),
'firstseenbysource': item_json.get('created')
}
if item_json.get('relationship_type') not in RELATIONSHIP_TYPES:
demisto.debug(f"Invalid relation type: {item_json.get('relationship_type')}")
return
entity_a = id_to_name.get(item_json.get('source_ref'))
entity_b = id_to_name.get(item_json.get('target_ref'))
if entity_b and entity_a:
return EntityRelationship(name=item_json.get('relationship_type'),
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entity_a_type=a_type,
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BMW-InnovationLab/BMW-TensorFlow-Training-GUI | 4f10d1f00f9ac312ca833e5b28fd0f8952cfee17 | training_api/research/object_detection/utils/np_box_mask_list_ops.py | python | concatenate | (box_mask_lists, fields=None) | return box_list_to_box_mask_list(
np_box_list_ops.concatenate(boxlists=box_mask_lists, fields=fields)) | Concatenate list of box_mask_lists.
This op concatenates a list of input box_mask_lists into a larger
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handles concatenation of box_mask_list fields as long as the field tensor
shapes are equal except for the first dimension.
Args:
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fields: optional list of fields to also concatenate. By default, all
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Returns:
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] | def concatenate(box_mask_lists, fields=None):
"""Concatenate list of box_mask_lists.
This op concatenates a list of input box_mask_lists into a larger
box_mask_list. It also
handles concatenation of box_mask_list fields as long as the field tensor
shapes are equal except for the first dimension.
Args:
box_mask_lists: list of np_box_mask_list.BoxMaskList objects
fields: optional list of fields to also concatenate. By default, all
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concatenation.
Returns:
a box_mask_list with number of boxes equal to
sum([box_mask_list.num_boxes() for box_mask_list in box_mask_list])
Raises:
ValueError: if box_mask_lists is invalid (i.e., is not a list, is empty, or
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"""
if fields is not None:
if 'masks' not in fields:
fields.append('masks')
return box_list_to_box_mask_list(
np_box_list_ops.concatenate(boxlists=box_mask_lists, fields=fields)) | [
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mozilla/crontabber | d3a116cf2b70fa0d1030f671ceaf5cf11e825aa8 | crontabber/app.py | python | CronTabberBase.time_to_run | (self, class_, time_) | return False | return true if it's time to run the job.
This is true if there is no previous information about its last run
or if the last time it ran and set its next_run to a date that is now
past. | return true if it's time to run the job.
This is true if there is no previous information about its last run
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"""return true if it's time to run the job.
This is true if there is no previous information about its last run
or if the last time it ran and set its next_run to a date that is now
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"""
app_name = class_.app_name
try:
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except KeyError:
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# only run if this hour and minute is < now
now = utc_now()
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return True
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return True
return False
else:
# no past information, run now
return True
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# It has never run before.
# If it has an active ongoing status it means two
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# If that's the case, let it through because it will
# commence and break due to RowLevelLockError in the
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return bool(info['ongoing'])
if next_run < utc_now():
return True
return False | [
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... | https://github.com/mozilla/crontabber/blob/d3a116cf2b70fa0d1030f671ceaf5cf11e825aa8/crontabber/app.py#L1195-L1232 | |
spectacles/CodeComplice | 8ca8ee4236f72b58caa4209d2fbd5fa56bd31d62 | libs/codeintel2/lang_perl.py | python | PerlLangIntel.async_eval_at_trg | (self, buf, trg, ctlr) | [] | def async_eval_at_trg(self, buf, trg, ctlr):
if _xpcom_:
trg = UnwrapObject(trg)
ctlr = UnwrapObject(ctlr)
assert trg.lang == "Perl"
ctlr.start(buf, trg)
if trg.id == ("Perl", TRG_FORM_CPLN, "available-imports"):
evalr = PerlImportsEvaluator(ctlr, buf, trg)
buf.mgr.request_eval(evalr)
return
# Remaining triggers all use this parsed CITDL expr.
# Extract the leading CITDL expression (and possible filter,
# i.e. '$', '@', ...).
try:
citdl_expr, filter \
= self.citdl_expr_and_prefix_filter_from_trg(buf, trg)
except CodeIntelError as ex:
ctlr.error(str(ex))
ctlr.done("error")
return
# Perl's trg_from_pos doesn't distinguish btwn "package-subs"
# and "object-subs" trigger type -- calling them both "*-subs".
# Let's do so here.
if trg.type == "*-subs":
assert citdl_expr
if isident(citdl_expr[0]):
trg.type = "package-subs"
else:
trg.type = "object-subs"
if trg.id == ("Perl", TRG_FORM_CPLN, "package-members"):
# [prefix]SomePackage::<|>
# Note: This trigger has the "prefix" extra attr which could
# be used instead of the leading CITDL expr parse.
line = buf.accessor.line_from_pos(trg.pos)
evalr = PerlPackageMembersTreeEvaluator(ctlr, buf, trg, citdl_expr,
line, filter)
buf.mgr.request_eval(evalr)
elif trg.id == ("Perl", TRG_FORM_CPLN, "package-subs"):
# SomePackage-><|>
assert not filter, "shouldn't be Perl filter prefix for " \
"'complete-package-subs': %r" % filter
line = buf.accessor.line_from_pos(trg.pos)
evalr = PerlPackageSubsTreeEvaluator(
ctlr, buf, trg, citdl_expr, line)
buf.mgr.request_eval(evalr)
# TODO: Might want to handle TRG_FORM_DEFN differently.
else:
if citdl_expr is None:
ctlr.info("no CITDL expression found for %s" % trg)
ctlr.done("no trigger")
return
line = buf.accessor.line_from_pos(trg.pos)
if trg.id[1] == TRG_FORM_DEFN and citdl_expr[0] == '$':
current_pos = trg.pos
lim = buf.accessor.length()
while buf.accessor.style_at_pos(current_pos) == ScintillaConstants.SCE_PL_SCALAR and current_pos < lim:
current_pos += 1
c = buf.accessor.char_at_pos(current_pos)
if c == '[':
citdl_expr = '@' + citdl_expr[1:]
elif c == '{':
citdl_expr = '%' + citdl_expr[1:]
evalr = PerlTreeEvaluator(ctlr, buf, trg, citdl_expr,
line, filter)
buf.mgr.request_eval(evalr) | [
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zhaoolee/StarsAndClown | b2d4039cad2f9232b691e5976f787b49a0a2c113 | node_modules/npmi/node_modules/npm/node_modules/node-gyp/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings.GetLdflags | (self, config, gyp_to_build_path, expand_special,
manifest_base_name, output_name, is_executable, build_dir) | return ldflags, intermediate_manifest, manifest_files | Returns the flags that need to be added to link commands, and the
manifest files. | Returns the flags that need to be added to link commands, and the
manifest files. | [
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] | def GetLdflags(self, config, gyp_to_build_path, expand_special,
manifest_base_name, output_name, is_executable, build_dir):
"""Returns the flags that need to be added to link commands, and the
manifest files."""
config = self._TargetConfig(config)
ldflags = []
ld = self._GetWrapper(self, self.msvs_settings[config],
'VCLinkerTool', append=ldflags)
self._GetDefFileAsLdflags(ldflags, gyp_to_build_path)
ld('GenerateDebugInformation', map={'true': '/DEBUG'})
ld('TargetMachine', map={'1': 'X86', '17': 'X64', '3': 'ARM'},
prefix='/MACHINE:')
ldflags.extend(self._GetAdditionalLibraryDirectories(
'VCLinkerTool', config, gyp_to_build_path))
ld('DelayLoadDLLs', prefix='/DELAYLOAD:')
ld('TreatLinkerWarningAsErrors', prefix='/WX',
map={'true': '', 'false': ':NO'})
out = self.GetOutputName(config, expand_special)
if out:
ldflags.append('/OUT:' + out)
pdb = self.GetPDBName(config, expand_special, output_name + '.pdb')
if pdb:
ldflags.append('/PDB:' + pdb)
pgd = self.GetPGDName(config, expand_special)
if pgd:
ldflags.append('/PGD:' + pgd)
map_file = self.GetMapFileName(config, expand_special)
ld('GenerateMapFile', map={'true': '/MAP:' + map_file if map_file
else '/MAP'})
ld('MapExports', map={'true': '/MAPINFO:EXPORTS'})
ld('AdditionalOptions', prefix='')
minimum_required_version = self._Setting(
('VCLinkerTool', 'MinimumRequiredVersion'), config, default='')
if minimum_required_version:
minimum_required_version = ',' + minimum_required_version
ld('SubSystem',
map={'1': 'CONSOLE%s' % minimum_required_version,
'2': 'WINDOWS%s' % minimum_required_version},
prefix='/SUBSYSTEM:')
stack_reserve_size = self._Setting(
('VCLinkerTool', 'StackReserveSize'), config, default='')
if stack_reserve_size:
stack_commit_size = self._Setting(
('VCLinkerTool', 'StackCommitSize'), config, default='')
if stack_commit_size:
stack_commit_size = ',' + stack_commit_size
ldflags.append('/STACK:%s%s' % (stack_reserve_size, stack_commit_size))
ld('TerminalServerAware', map={'1': ':NO', '2': ''}, prefix='/TSAWARE')
ld('LinkIncremental', map={'1': ':NO', '2': ''}, prefix='/INCREMENTAL')
ld('BaseAddress', prefix='/BASE:')
ld('FixedBaseAddress', map={'1': ':NO', '2': ''}, prefix='/FIXED')
ld('RandomizedBaseAddress',
map={'1': ':NO', '2': ''}, prefix='/DYNAMICBASE')
ld('DataExecutionPrevention',
map={'1': ':NO', '2': ''}, prefix='/NXCOMPAT')
ld('OptimizeReferences', map={'1': 'NOREF', '2': 'REF'}, prefix='/OPT:')
ld('ForceSymbolReferences', prefix='/INCLUDE:')
ld('EnableCOMDATFolding', map={'1': 'NOICF', '2': 'ICF'}, prefix='/OPT:')
ld('LinkTimeCodeGeneration',
map={'1': '', '2': ':PGINSTRUMENT', '3': ':PGOPTIMIZE',
'4': ':PGUPDATE'},
prefix='/LTCG')
ld('IgnoreDefaultLibraryNames', prefix='/NODEFAULTLIB:')
ld('ResourceOnlyDLL', map={'true': '/NOENTRY'})
ld('EntryPointSymbol', prefix='/ENTRY:')
ld('Profile', map={'true': '/PROFILE'})
ld('LargeAddressAware',
map={'1': ':NO', '2': ''}, prefix='/LARGEADDRESSAWARE')
# TODO(scottmg): This should sort of be somewhere else (not really a flag).
ld('AdditionalDependencies', prefix='')
if self.GetArch(config) == 'x86':
safeseh_default = 'true'
else:
safeseh_default = None
ld('ImageHasSafeExceptionHandlers',
map={'false': ':NO', 'true': ''}, prefix='/SAFESEH',
default=safeseh_default)
# If the base address is not specifically controlled, DYNAMICBASE should
# be on by default.
base_flags = filter(lambda x: 'DYNAMICBASE' in x or x == '/FIXED',
ldflags)
if not base_flags:
ldflags.append('/DYNAMICBASE')
# If the NXCOMPAT flag has not been specified, default to on. Despite the
# documentation that says this only defaults to on when the subsystem is
# Vista or greater (which applies to the linker), the IDE defaults it on
# unless it's explicitly off.
if not filter(lambda x: 'NXCOMPAT' in x, ldflags):
ldflags.append('/NXCOMPAT')
have_def_file = filter(lambda x: x.startswith('/DEF:'), ldflags)
manifest_flags, intermediate_manifest, manifest_files = \
self._GetLdManifestFlags(config, manifest_base_name, gyp_to_build_path,
is_executable and not have_def_file, build_dir)
ldflags.extend(manifest_flags)
return ldflags, intermediate_manifest, manifest_files | [
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idaapi.py | python | area_t.__eq__ | (self, *args) | return _idaapi.area_t___eq__(self, *args) | __eq__(self, r) -> bool | __eq__(self, r) -> bool | [
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"""
__eq__(self, r) -> bool
"""
return _idaapi.area_t___eq__(self, *args) | [
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lad1337/XDM | 0c1b7009fe00f06f102a6f67c793478f515e7efe | site-packages/logilab/astng/as_string.py | python | AsStringVisitor.visit_yield | (self, node) | return 'yield' + yi_val | yield an ast.Yield node as string | yield an ast.Yield node as string | [
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] | def visit_yield(self, node):
"""yield an ast.Yield node as string"""
yi_val = node.value and (" " + node.value.accept(self)) or ""
return 'yield' + yi_val | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-47/fabmetheus_utilities/geometry/geometry_utilities/boolean_solid.py | python | getLoopsIntersectionByPair | (importRadius, loopsFirst, loopsLast) | return triangle_mesh.getDescendingAreaOrientedLoops(allPoints, corners, importRadius) | Get intersection loops for a pair of loop lists. | Get intersection loops for a pair of loop lists. | [
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] | def getLoopsIntersectionByPair(importRadius, loopsFirst, loopsLast):
'Get intersection loops for a pair of loop lists.'
halfImportRadius = 0.5 * importRadius # so that there are no misses on shallow angles
radiusSide = 0.01 * importRadius
corners = []
corners += getInsetPointsByInsetLoops(loopsFirst, True, loopsLast, radiusSide)
corners += getInsetPointsByInsetLoops(loopsLast, True, loopsFirst, radiusSide)
allPoints = corners[:]
allPoints += getInsetPointsByInsetLoops(getInBetweenLoopsFromLoops(loopsFirst, halfImportRadius), True, loopsLast, radiusSide)
allPoints += getInsetPointsByInsetLoops(getInBetweenLoopsFromLoops(loopsLast, halfImportRadius), True, loopsFirst, radiusSide)
return triangle_mesh.getDescendingAreaOrientedLoops(allPoints, corners, importRadius) | [
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viewfinderco/viewfinder | 453845b5d64ab5b3b826c08b02546d1ca0a07c14 | backend/db/health_report.py | python | HealthCriteria.GetCriteriaList | (cls) | return cls._criteria_list | Gets the list of criteria for server health checks. This is implemented as a class
method to ensure that the static counters variable is completely loaded before accessing
it, rather than depending on python module loading order. | Gets the list of criteria for server health checks. This is implemented as a class
method to ensure that the static counters variable is completely loaded before accessing
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"""Gets the list of criteria for server health checks. This is implemented as a class
method to ensure that the static counters variable is completely loaded before accessing
it, rather than depending on python module loading order.
"""
if hasattr(cls, '_criteria_list'):
return cls._criteria_list
cls._criteria_list = [
HealthCriteria('Errors',
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HealthCriteria('MissingMetrics',
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]
return cls._criteria_list | [
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christiansandberg/canopen | a142fb583f236e0ea3c561af32f72ab1c423b95b | canopen/objectdictionary/__init__.py | python | Array.add_member | (self, variable: "Variable") | Adds a :class:`~canopen.objectdictionary.Variable` to the record. | Adds a :class:`~canopen.objectdictionary.Variable` to the record. | [
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] | def add_member(self, variable: "Variable") -> None:
"""Adds a :class:`~canopen.objectdictionary.Variable` to the record."""
variable.parent = self
self.subindices[variable.subindex] = variable
self.names[variable.name] = variable | [
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google/apitools | 31cad2d904f356872d2965687e84b2d87ee2cdd3 | apitools/base/py/gzip.py | python | GzipFile.__init__ | (self, filename=None, mode=None,
compresslevel=9, fileobj=None, mtime=None) | Constructor for the GzipFile class.
At least one of fileobj and filename must be given a
non-trivial value.
The new class instance is based on fileobj, which can be a regular
file, an io.BytesIO object, or any other object which simulates a file.
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and in this case the original filename is not included in the header.
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depending on whether the file will be read or written. The default
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'wb', and 'a' and 'ab'.
The compresslevel argument is an integer from 0 to 9 controlling the
level of compression; 1 is fastest and produces the least compression,
and 9 is slowest and produces the most compression. 0 is no compression
at all. The default is 9.
The mtime argument is an optional numeric timestamp to be written
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are required to contain a timestamp. If omitted or None, the
current time is used. This module ignores the timestamp when
decompressing; however, some programs, such as gunzip, make use
of it. The format of the timestamp is the same as that of the
return value of time.time() and of the st_mtime member of the
object returned by os.stat(). | Constructor for the GzipFile class. | [
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] | def __init__(self, filename=None, mode=None,
compresslevel=9, fileobj=None, mtime=None):
"""Constructor for the GzipFile class.
At least one of fileobj and filename must be given a
non-trivial value.
The new class instance is based on fileobj, which can be a regular
file, an io.BytesIO object, or any other object which simulates a file.
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and in this case the original filename is not included in the header.
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and 9 is slowest and produces the most compression. 0 is no compression
at all. The default is 9.
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are required to contain a timestamp. If omitted or None, the
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decompressing; however, some programs, such as gunzip, make use
of it. The format of the timestamp is the same as that of the
return value of time.time() and of the st_mtime member of the
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"""
if mode and ('t' in mode or 'U' in mode):
raise ValueError("Invalid mode: {!r}".format(mode))
if mode and 'b' not in mode:
mode += 'b'
if fileobj is None:
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
if filename is None:
filename = getattr(fileobj, 'name', '')
if not isinstance(filename, six.string_types):
filename = ''
if mode is None:
mode = getattr(fileobj, 'mode', 'rb')
if mode.startswith('r'):
self.mode = READ
# Set flag indicating start of a new member
self._new_member = True
# Buffer data read from gzip file. extrastart is offset in
# stream where buffer starts. extrasize is number of
# bytes remaining in buffer from current stream position.
self.extrabuf = b""
self.extrasize = 0
self.extrastart = 0
self.name = filename
# Starts small, scales exponentially
self.min_readsize = 100
fileobj = _PaddedFile(fileobj)
elif mode.startswith(('w', 'a')):
self.mode = WRITE
self._init_write(filename)
self.compress = zlib.compressobj(compresslevel,
zlib.DEFLATED,
-zlib.MAX_WBITS,
zlib.DEF_MEM_LEVEL,
0)
else:
raise ValueError("Invalid mode: {!r}".format(mode))
self.fileobj = fileobj
self.offset = 0
self.mtime = mtime
if self.mode == WRITE:
self._write_gzip_header() | [
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aiidateam/aiida-core | c743a335480f8bb3a5e4ebd2463a31f9f3b9f9b2 | aiida/orm/utils/loaders.py | python | CodeEntityLoader._get_query_builder_label_identifier | (cls, identifier, classes, operator='==', project='*') | return builder | Return the query builder instance that attempts to map the identifier onto an entity of the orm class,
defined for this loader class, interpreting the identifier as a LABEL like identifier
:param identifier: the LABEL identifier
:param classes: a tuple of orm classes to which the identifier should be mapped
:param operator: the operator to use in the query
:param project: the property or properties to project for entities matching the query
:returns: the query builder instance that should retrieve the entity corresponding to the identifier
:raises ValueError: if the identifier is invalid
:raises aiida.common.NotExistent: if the orm base class does not support a LABEL like identifier | Return the query builder instance that attempts to map the identifier onto an entity of the orm class,
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"onto",
"an",
"entity",
"of",
"the",
"orm",
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"defined",
"for",
"this",
"loader",
"class",
"interpreting",
"the",
"identifier",
"as",
"a",
"LABEL",
... | def _get_query_builder_label_identifier(cls, identifier, classes, operator='==', project='*'):
"""
Return the query builder instance that attempts to map the identifier onto an entity of the orm class,
defined for this loader class, interpreting the identifier as a LABEL like identifier
:param identifier: the LABEL identifier
:param classes: a tuple of orm classes to which the identifier should be mapped
:param operator: the operator to use in the query
:param project: the property or properties to project for entities matching the query
:returns: the query builder instance that should retrieve the entity corresponding to the identifier
:raises ValueError: if the identifier is invalid
:raises aiida.common.NotExistent: if the orm base class does not support a LABEL like identifier
"""
from aiida.common.escaping import escape_for_sql_like
from aiida.orm import Computer
try:
identifier, _, machinename = identifier.partition('@')
except AttributeError:
raise ValueError('the identifier needs to be a string')
if operator == 'like':
identifier = f'{escape_for_sql_like(identifier)}%'
builder = QueryBuilder()
builder.append(cls=classes, tag='code', project=project, filters={'label': {operator: identifier}})
if machinename:
builder.append(Computer, filters={'label': {'==': machinename}}, with_node='code')
return builder | [
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CyberDiscovery/cyberdisc-bot | e7d3a0789148aec1ef3e4153955a8c6f4d2cf219 | cdbot/cogs/fun.py | python | Fun.angryj | (self, ctx: Context, *, text: str) | Creates an image of Angry Agent J with the specified text. | Creates an image of Angry Agent J with the specified text. | [
"Creates",
"an",
"image",
"of",
"Angry",
"Agent",
"J",
"with",
"the",
"specified",
"text",
"."
] | async def angryj(self, ctx: Context, *, text: str):
"""
Creates an image of Angry Agent J with the specified text.
"""
await self.create_text_image(ctx, "AngryJ", text) | [
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antonylesuisse/qweb | 2d6964a3e5cae90414c4f873eb770591f569dfe0 | ajaxterm/ajaxterm.py | python | Terminal.esc_0x08 | (self,s) | [] | def esc_0x08(self,s):
self.cx=max(0,self.cx-1) | [
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] | https://github.com/antonylesuisse/qweb/blob/2d6964a3e5cae90414c4f873eb770591f569dfe0/ajaxterm/ajaxterm.py#L143-L144 | ||||
AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.3/django/contrib/gis/geos/geometry.py | python | GEOSGeometry.union | (self, other) | return self._topology(capi.geos_union(self.ptr, other.ptr)) | Returns a Geometry representing all the points in this Geometry and other. | Returns a Geometry representing all the points in this Geometry and other. | [
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"Geometry",
"representing",
"all",
"the",
"points",
"in",
"this",
"Geometry",
"and",
"other",
"."
] | def union(self, other):
"Returns a Geometry representing all the points in this Geometry and other."
return self._topology(capi.geos_union(self.ptr, other.ptr)) | [
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tribe29/checkmk | 6260f2512e159e311f426e16b84b19d0b8e9ad0c | cmk/base/agent_based/checking/_cluster_modes.py | python | _unfit_for_clustering | (**_kw) | A cluster_check_function that displays a generic warning | A cluster_check_function that displays a generic warning | [
"A",
"cluster_check_function",
"that",
"displays",
"a",
"generic",
"warning"
] | def _unfit_for_clustering(**_kw) -> CheckResult:
"""A cluster_check_function that displays a generic warning"""
yield Result(
state=State.UNKNOWN,
summary=(
"This service does not implement a native cluster mode. Please change your "
"configuration using the rule 'Aggregation options for clustered services', "
"and select one of the other available aggregation modes."
),
) | [
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/dts/transfer_device_client.py | python | TransferDeviceClient.create_transfer_device | (self, id, create_transfer_device_details, **kwargs) | Create a new Transfer Device
:param str id: (required)
ID of the Transfer Job
:param oci.dts.models.CreateTransferDeviceDetails create_transfer_device_details: (required)
Creates a New Transfer Device
:param str opc_retry_token: (optional)
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.dts.models.NewTransferDevice`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/dts/create_transfer_device.py.html>`__ to see an example of how to use create_transfer_device API. | Create a new Transfer Device | [
"Create",
"a",
"new",
"Transfer",
"Device"
] | def create_transfer_device(self, id, create_transfer_device_details, **kwargs):
"""
Create a new Transfer Device
:param str id: (required)
ID of the Transfer Job
:param oci.dts.models.CreateTransferDeviceDetails create_transfer_device_details: (required)
Creates a New Transfer Device
:param str opc_retry_token: (optional)
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.dts.models.NewTransferDevice`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/dts/create_transfer_device.py.html>`__ to see an example of how to use create_transfer_device API.
"""
resource_path = "/transferJobs/{id}/transferDevices"
method = "POST"
# Don't accept unknown kwargs
expected_kwargs = [
"retry_strategy",
"opc_retry_token"
]
extra_kwargs = [_key for _key in six.iterkeys(kwargs) if _key not in expected_kwargs]
if extra_kwargs:
raise ValueError(
"create_transfer_device got unknown kwargs: {!r}".format(extra_kwargs))
path_params = {
"id": id
}
path_params = {k: v for (k, v) in six.iteritems(path_params) if v is not missing}
for (k, v) in six.iteritems(path_params):
if v is None or (isinstance(v, six.string_types) and len(v.strip()) == 0):
raise ValueError('Parameter {} cannot be None, whitespace or empty string'.format(k))
header_params = {
"accept": "application/json",
"content-type": "application/json",
"opc-retry-token": kwargs.get("opc_retry_token", missing)
}
header_params = {k: v for (k, v) in six.iteritems(header_params) if v is not missing and v is not None}
retry_strategy = self.base_client.get_preferred_retry_strategy(
operation_retry_strategy=kwargs.get('retry_strategy'),
client_retry_strategy=self.retry_strategy
)
if retry_strategy:
if not isinstance(retry_strategy, retry.NoneRetryStrategy):
self.base_client.add_opc_retry_token_if_needed(header_params)
self.base_client.add_opc_client_retries_header(header_params)
retry_strategy.add_circuit_breaker_callback(self.circuit_breaker_callback)
return retry_strategy.make_retrying_call(
self.base_client.call_api,
resource_path=resource_path,
method=method,
path_params=path_params,
header_params=header_params,
body=create_transfer_device_details,
response_type="NewTransferDevice")
else:
return self.base_client.call_api(
resource_path=resource_path,
method=method,
path_params=path_params,
header_params=header_params,
body=create_transfer_device_details,
response_type="NewTransferDevice") | [
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/difflib.py | python | SequenceMatcher.get_grouped_opcodes | (self, n=3) | Isolate change clusters by eliminating ranges with no changes.
Return a generator of groups with up to n lines of context.
Each group is in the same format as returned by get_opcodes().
>>> from pprint import pprint
>>> a = list(map(str, range(1,40)))
>>> b = a[:]
>>> b[8:8] = ['i'] # Make an insertion
>>> b[20] += 'x' # Make a replacement
>>> b[23:28] = [] # Make a deletion
>>> b[30] += 'y' # Make another replacement
>>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))
[[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],
[('equal', 16, 19, 17, 20),
('replace', 19, 20, 20, 21),
('equal', 20, 22, 21, 23),
('delete', 22, 27, 23, 23),
('equal', 27, 30, 23, 26)],
[('equal', 31, 34, 27, 30),
('replace', 34, 35, 30, 31),
('equal', 35, 38, 31, 34)]] | Isolate change clusters by eliminating ranges with no changes. | [
"Isolate",
"change",
"clusters",
"by",
"eliminating",
"ranges",
"with",
"no",
"changes",
"."
] | def get_grouped_opcodes(self, n=3):
""" Isolate change clusters by eliminating ranges with no changes.
Return a generator of groups with up to n lines of context.
Each group is in the same format as returned by get_opcodes().
>>> from pprint import pprint
>>> a = list(map(str, range(1,40)))
>>> b = a[:]
>>> b[8:8] = ['i'] # Make an insertion
>>> b[20] += 'x' # Make a replacement
>>> b[23:28] = [] # Make a deletion
>>> b[30] += 'y' # Make another replacement
>>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))
[[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],
[('equal', 16, 19, 17, 20),
('replace', 19, 20, 20, 21),
('equal', 20, 22, 21, 23),
('delete', 22, 27, 23, 23),
('equal', 27, 30, 23, 26)],
[('equal', 31, 34, 27, 30),
('replace', 34, 35, 30, 31),
('equal', 35, 38, 31, 34)]]
"""
codes = self.get_opcodes()
if not codes:
codes = [("equal", 0, 1, 0, 1)]
# Fixup leading and trailing groups if they show no changes.
if codes[0][0] == 'equal':
tag, i1, i2, j1, j2 = codes[0]
codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2
if codes[-1][0] == 'equal':
tag, i1, i2, j1, j2 = codes[-1]
codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n)
nn = n + n
group = []
for tag, i1, i2, j1, j2 in codes:
# End the current group and start a new one whenever
# there is a large range with no changes.
if tag == 'equal' and i2-i1 > nn:
group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n)))
yield group
group = []
i1, j1 = max(i1, i2-n), max(j1, j2-n)
group.append((tag, i1, i2, j1 ,j2))
if group and not (len(group)==1 and group[0][0] == 'equal'):
yield group | [
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hhyo/Archery | c9b057d37e47894ca8531e5cd10afdb064cd0122 | sql/engines/__init__.py | python | EngineBase.get_execute_percentage | (self) | 获取执行进度 | 获取执行进度 | [
"获取执行进度"
] | def get_execute_percentage(self):
"""获取执行进度""" | [
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] | https://github.com/hhyo/Archery/blob/c9b057d37e47894ca8531e5cd10afdb064cd0122/sql/engines/__init__.py#L132-L133 | ||
Qirky/FoxDot | 76318f9630bede48ff3994146ed644affa27bfa4 | FoxDot/lib/Workspace/Simple/SimpleEditor.py | python | Editor.alt_return | (self, event) | return | Evaluates current line of code | Evaluates current line of code | [
"Evaluates",
"current",
"line",
"of",
"code"
] | def alt_return(self, event):
""" Evaluates current line of code """
row = self.text.get_current_row()
code = self.text.get_block_text(row, row)
if code:
execute(code)
self.flash(row, row)
return | [
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open-mmlab/mmdetection | ff9bc39913cb3ff5dde79d3933add7dc2561bab7 | mmdet/core/export/onnx_helper.py | python | get_k_for_topk | (k, size) | return ret_k | Get k of TopK for onnx exporting.
The K of TopK in TensorRT should not be a Tensor, while in ONNX Runtime
it could be a Tensor.Due to dynamic shape feature, we have to decide
whether to do TopK and what K it should be while exporting to ONNX.
If returned K is less than zero, it means we do not have to do
TopK operation.
Args:
k (int or Tensor): The set k value for nms from config file.
size (Tensor or torch.Size): The number of elements of \
TopK's input tensor
Returns:
tuple: (int or Tensor): The final K for TopK. | Get k of TopK for onnx exporting. | [
"Get",
"k",
"of",
"TopK",
"for",
"onnx",
"exporting",
"."
] | def get_k_for_topk(k, size):
"""Get k of TopK for onnx exporting.
The K of TopK in TensorRT should not be a Tensor, while in ONNX Runtime
it could be a Tensor.Due to dynamic shape feature, we have to decide
whether to do TopK and what K it should be while exporting to ONNX.
If returned K is less than zero, it means we do not have to do
TopK operation.
Args:
k (int or Tensor): The set k value for nms from config file.
size (Tensor or torch.Size): The number of elements of \
TopK's input tensor
Returns:
tuple: (int or Tensor): The final K for TopK.
"""
ret_k = -1
if k <= 0 or size <= 0:
return ret_k
if torch.onnx.is_in_onnx_export():
is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT'
if is_trt_backend:
# TensorRT does not support dynamic K with TopK op
if 0 < k < size:
ret_k = k
else:
# Always keep topk op for dynamic input in onnx for ONNX Runtime
ret_k = torch.where(k < size, k, size)
elif k < size:
ret_k = k
else:
# ret_k is -1
pass
return ret_k | [
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CouchPotato/CouchPotatoServer | 7260c12f72447ddb6f062367c6dfbda03ecd4e9c | libs/suds/mx/appender.py | python | ContentAppender.append | (self, parent, content) | Select an appender and append the content to parent.
@param parent: A parent node.
@type parent: L{Element}
@param content: The content to append.
@type content: L{Content} | Select an appender and append the content to parent. | [
"Select",
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"and",
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] | def append(self, parent, content):
"""
Select an appender and append the content to parent.
@param parent: A parent node.
@type parent: L{Element}
@param content: The content to append.
@type content: L{Content}
"""
appender = self.default
for a in self.appenders:
if a[0] == content.value:
appender = a[1]
break
appender.append(parent, content) | [
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tenable/poc | 361b6b191eda0bdc4f7338a58c2e2fe79bb8afca | Ubiquiti/UniFi_Protect/cve_2020_8213_unifi_protect_username_discovery.py | python | test_username | (username, password, conn, headers, valid_usernames, valid_passwords) | return None | Test passed username and password and store valid usernames and valid username / password
combinations in the passed lists. | Test passed username and password and store valid usernames and valid username / password
combinations in the passed lists. | [
"Test",
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"usernames",
"and",
"valid",
"username",
"/",
"password",
"combinations",
"in",
"the",
"passed",
"lists",
"."
] | def test_username(username, password, conn, headers, valid_usernames, valid_passwords):
"""
Test passed username and password and store valid usernames and valid username / password
combinations in the passed lists.
"""
debug("Trying username: {}".format(username))
data = {}
data["username"] = username
data["password"] = password
body = json.dumps(data)
try:
conn.request("POST", "/api/auth", body=body, headers=headers)
except OSError:
exit("OSError sending to target port. Use --no-ssl or -n if target port is not an SSL port.")
start = time.time()
res = None
while time.time() - start < TIMEOUT:
try:
res = conn.getresponse()
except http.client.ResponseNotReady:
continue
except http.client.RemoteDisconnected:
exit("Remote disconnected when getting response from target port. Do not use --no-ssl or -n if target port is an SSL port.")
res.read()
break
if not res:
debug("Response to authentication request for username '{}' timed out after {} seconds".format(username, TIMEOUT))
return None
if res.status == 400:
return False
if res.status == 401:
print("[!] Found potentially valid username: {}".format(username))
valid_usernames.append(username)
return True
if res.status == 200:
print("[!] Found valid username / password: {} / {}".format(username, password))
valid_usernames.append(username)
valid_passwords.append({"username":username, "password":password})
return True
debug("Unexpected response: {} {}".format(res.status, res.reason))
return None | [
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shuup/shuup | 25f78cfe370109b9885b903e503faac295c7b7f2 | shuup/admin/modules/orders/views/shipment.py | python | OrderCreateShipmentView.create_shipment | (self, order, product_quantities, shipment) | return order.create_shipment(product_quantities=product_quantities, shipment=shipment) | This function exists so subclasses can implement custom logic without
overriding the entire form_valid method.
:param order: Order to create the shipment for.
:type order: shuup.core.models.Order
:param product_quantities: a dict mapping Product instances to quantities to ship.
:type product_quantities: dict[shuup.shop.models.Product, decimal.Decimal]
:param shipment: unsaved Shipment for ShipmentProduct's.
:type shipment: shuup.core.models.Shipment
:return: Saved, complete Shipment object.
:rtype: shuup.core.models.Shipment | This function exists so subclasses can implement custom logic without
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"""
This function exists so subclasses can implement custom logic without
overriding the entire form_valid method.
:param order: Order to create the shipment for.
:type order: shuup.core.models.Order
:param product_quantities: a dict mapping Product instances to quantities to ship.
:type product_quantities: dict[shuup.shop.models.Product, decimal.Decimal]
:param shipment: unsaved Shipment for ShipmentProduct's.
:type shipment: shuup.core.models.Shipment
:return: Saved, complete Shipment object.
:rtype: shuup.core.models.Shipment
"""
return order.create_shipment(product_quantities=product_quantities, shipment=shipment) | [
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compas-dev/compas | 0b33f8786481f710115fb1ae5fe79abc2a9a5175 | src/compas/datastructures/mesh/operations/weld.py | python | mesh_unweld_edges | (mesh, edges) | Unwelds a mesh along edges.
Parameters
----------
mesh : Mesh
A mesh.
edges: list
List of edges as tuples of vertex keys. | Unwelds a mesh along edges. | [
"Unwelds",
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"edges",
"."
] | def mesh_unweld_edges(mesh, edges):
"""Unwelds a mesh along edges.
Parameters
----------
mesh : Mesh
A mesh.
edges: list
List of edges as tuples of vertex keys.
"""
# set of vertices in edges to unweld
vertices = set([i for edge in edges for i in edge])
# to store changes to do all at once
vertex_changes = {}
for vkey in vertices:
# maps between old mesh face index and new network vertex index
old_to_new = {nbr: i for i, nbr in enumerate(mesh.vertex_faces(vkey))}
new_to_old = {i: nbr for i, nbr in enumerate(mesh.vertex_faces(vkey))}
# get adjacency network of faces around the vertex excluding adjacency
# through the edges to unweld
network_edges = []
for nbr in mesh.vertex_neighbors(vkey):
if not mesh.is_edge_on_boundary(vkey, nbr) and (vkey, nbr) not in edges and (nbr, vkey) not in edges:
network_edges.append((old_to_new[mesh.halfedge[vkey][nbr]], old_to_new[mesh.halfedge[nbr][vkey]]))
adjacency = adjacency_from_edges(network_edges)
for key, values in adjacency.items():
adjacency[key] = {value: None for value in values}
# include non connected vertices
edge_vertices = list(set([i for edge in network_edges for i in edge]))
for i in range(len(mesh.vertex_faces(vkey))):
if i not in edge_vertices:
adjacency[i] = {}
# collect the disconnected parts around the vertex due to unwelding
vertex_changes[vkey] = [[new_to_old[key] for key in part] for part in connected_components(adjacency)]
for vkey, changes in vertex_changes.items():
# for each disconnected part replace the vertex by a new vertex in the
# faces of the part
for change in changes:
mesh_substitute_vertex_in_faces(mesh, vkey, mesh.add_vertex(attr_dict=mesh.vertex[vkey]), change)
# delete old vertices
mesh.delete_vertex(vkey) | [
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tensorly/tensorly | 87b435b3f3343447b49d47ebb5461118f6c8a9ab | tensorly/tenalg/einsum_tenalg/generalised_inner_product.py | python | inner | (tensor1, tensor2, n_modes=None) | return T.einsum(equation, tensor1, tensor2) | Generalised inner products between tensors
Takes the inner product between the last (respectively first)
`n_modes` of `tensor1` (respectively `tensor2`)
Parameters
----------
tensor1, tensor2 : tensor
n_modes : int, default is None
* if None, the traditional inner product is returned (i.e. a float)
* otherwise, the product between the `n_modes` last modes of `tensor1`
and the `n_modes` first modes of `tensor2` is returned.
The resulting tensor's order is `ndim(tensor1) - n_modes`.
Returns
-------
inner_product : float if n_modes is None, tensor otherwise | Generalised inner products between tensors | [
"Generalised",
"inner",
"products",
"between",
"tensors"
] | def inner(tensor1, tensor2, n_modes=None):
"""Generalised inner products between tensors
Takes the inner product between the last (respectively first)
`n_modes` of `tensor1` (respectively `tensor2`)
Parameters
----------
tensor1, tensor2 : tensor
n_modes : int, default is None
* if None, the traditional inner product is returned (i.e. a float)
* otherwise, the product between the `n_modes` last modes of `tensor1`
and the `n_modes` first modes of `tensor2` is returned.
The resulting tensor's order is `ndim(tensor1) - n_modes`.
Returns
-------
inner_product : float if n_modes is None, tensor otherwise
"""
# Traditional inner product
if n_modes is None:
if tensor1.shape != tensor2.shape:
raise ValueError('Taking a generalised product between two tensors without specifying common modes'
' is equivalent to taking inner product.'
'This requires tensor1.shape == tensor2.shape.'
'However, got tensor1.shape={} and tensor2.shape={}'.format(tensor1.shape, tensor2.shape))
return T.sum(tensor1*tensor2)
shape_t1 = list(T.shape(tensor1))
shape_t2 = list(T.shape(tensor2))
offset = len(shape_t1) - n_modes
if shape_t1[offset:] != shape_t2[:n_modes]:
raise ValueError('Incorrect shapes for inner product along {} common modes.'
'tensor_1.shape={}, tensor_2.shape={}'.format(n_modes, shape_t1, shape_t2))
# Inner product along `n_modes` common modes
start = ord('a')
modes_t1 = ''.join(chr(i+start) for i in range(T.ndim(tensor1)))
modes_t2 = ''.join(chr(i + start + offset) for i in range(T.ndim(tensor2)))
output_modes = modes_t1[:offset] + modes_t2[n_modes:]
equation = f'{modes_t1},{modes_t2}->{output_modes}'
return T.einsum(equation, tensor1, tensor2) | [
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ebroecker/canmatrix | 219a19adf4639b0b4fd5328f039563c6d4060887 | src/canmatrix/formats/arxml.py | python | decode_can_helper | (ea, float_factory, ignore_cluster_info) | return found_matrixes | [] | def decode_can_helper(ea, float_factory, ignore_cluster_info):
found_matrixes = {}
if ignore_cluster_info is True:
ccs = [lxml.etree.Element("ignoreClusterInfo")] # type: typing.Sequence[_Element]
else:
ccs = ea.findall('CAN-CLUSTER')
headers_are_littleendian = containters_are_little_endian(ea)
nodes = {} # type: typing.Dict[_Element, canmatrix.Ecu]
for cc in ccs: # type: _Element
db = canmatrix.CanMatrix()
# Defines not jet imported...
db.add_ecu_defines("NWM-Stationsadresse", 'HEX 0 63')
db.add_ecu_defines("NWM-Knoten", 'ENUM "nein","ja"')
db.add_signal_defines("LongName", 'STRING')
db.add_frame_defines("GenMsgDelayTime", 'INT 0 65535')
db.add_frame_defines("GenMsgNrOfRepetitions", 'INT 0 65535')
db.add_frame_defines("GenMsgStartValue", 'STRING')
db.add_frame_defines("GenMsgStartDelayTime", 'INT 0 65535')
db.add_frame_defines(
"GenMsgSendType",
'ENUM "cyclicX","spontanX","cyclicIfActiveX","spontanWithDelay","cyclicAndSpontanX","cyclicAndSpontanWithDelay","spontanWithRepitition","cyclicIfActiveAndSpontanWD","cyclicIfActiveFast","cyclicWithRepeatOnDemand","none"')
if ignore_cluster_info is True:
can_frame_trig = ea.findall('CAN-FRAME-TRIGGERING')
bus_name = ""
else:
speed = ea.get_child(cc, "SPEED")
baudrate_elem = ea.find("BAUDRATE", cc)
fd_baudrate_elem = ea.find("CAN-FD-BAUDRATE", cc)
logger.debug("Busname: " + ea.get_element_name(cc))
bus_name = ea.get_element_name(cc)
if speed is not None:
db.baudrate = int(speed.text)
elif baudrate_elem is not None:
db.baudrate = int(baudrate_elem.text)
logger.debug("Baudrate: " + str(db.baudrate))
if fd_baudrate_elem is not None:
db.fd_baudrate = int(fd_baudrate_elem.text)
can_frame_trig = ea.selector(cc, "/CAN-PHYSICAL-CHANNEL//CAN-FRAME-TRIGGERING")
multiplex_translation = {} # type: typing.Dict[str, str]
for frameTrig in can_frame_trig: # type: _Element
frame = get_frame(frameTrig, ea, multiplex_translation, float_factory, headers_are_littleendian)
if frame is not None:
comm_direction = ea.selector(frameTrig, ">>FRAME-PORT-REF/COMMUNICATION-DIRECTION")
if len(comm_direction) > 0:
ecu_elem = ea.get_ecu_instance(element=comm_direction[0])
if ecu_elem is not None:
if ecu_elem in nodes:
ecu = nodes[ecu_elem]
else:
ecu = process_ecu(ecu_elem, ea)
nodes[ecu_elem] = ecu
if comm_direction[0].text == "OUT":
frame.add_transmitter(ecu.name)
else:
frame.add_receiver(ecu.name)
db.add_ecu(ecu)
db.add_frame(frame)
for frame in db.frames:
if frame.is_pdu_container:
continue
sig_value_hash = dict()
for sig in frame.signals:
sig.receivers = list(set(frame.receivers).intersection(sig.receivers))
try:
sig_value_hash[sig.name] = sig.phys2raw()
except AttributeError:
sig_value_hash[sig.name] = 0
frame_data = frame.encode(sig_value_hash)
frame.add_attribute("GenMsgStartValue", "".join(["%02x" % x for x in frame_data]))
# frame.update_receiver()
found_matrixes[bus_name] = db
return found_matrixes | [
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CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idaapi.py | python | is_debugger_memory | (*args) | return _idaapi.is_debugger_memory(*args) | is_debugger_memory(ea) -> bool | is_debugger_memory(ea) -> bool | [
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"""
is_debugger_memory(ea) -> bool
"""
return _idaapi.is_debugger_memory(*args) | [
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evait-security/weeman | 4aff9c0332a8cfba4124f033cff0a85d0dcb849d | lib/bs4/element.py | python | PageElement._is_xml | (self) | return self.parent._is_xml | Is this element part of an XML tree or an HTML tree?
This is used when mapping a formatter name ("minimal") to an
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"""Is this element part of an XML tree or an HTML tree?
This is used when mapping a formatter name ("minimal") to an
appropriate function (one that performs entity-substitution on
the contents of <script> and <style> tags, or not). It can be
inefficient, but it should be called very rarely.
"""
if self.known_xml is not None:
# Most of the time we will have determined this when the
# document is parsed.
return self.known_xml
# Otherwise, it's likely that this element was created by
# direct invocation of the constructor from within the user's
# Python code.
if self.parent is None:
# This is the top-level object. It should have .known_xml set
# from tree creation. If not, take a guess--BS is usually
# used on HTML markup.
return getattr(self, 'is_xml', False)
return self.parent._is_xml | [
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robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/physics/robot_physics_randomizer.py | python | RobotPhysicsRandomizer.__init__ | (self, body, links=None, joints=None, **kwargs) | Initialize the robot physics randomizer.
Args:
body (Body): multi-body object.
links (int, list of int, LinkPhysicsRandomizer, list of LinkPhysicsRandomizer, None): link id(s) or link
physics randomizer(s).
joints (int, list of int, JointPhysicsRandomizer, list JointPhysicsRandomizer, None): joint id(s) or joint
physics randomizer.
**kwargs (dict): range of possible physical properties. If given one value, that property won't be
randomized. Each range is a tuple of two values `[lower_bound, upper_bound]`. | Initialize the robot physics randomizer. | [
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] | def __init__(self, body, links=None, joints=None, **kwargs):
"""
Initialize the robot physics randomizer.
Args:
body (Body): multi-body object.
links (int, list of int, LinkPhysicsRandomizer, list of LinkPhysicsRandomizer, None): link id(s) or link
physics randomizer(s).
joints (int, list of int, JointPhysicsRandomizer, list JointPhysicsRandomizer, None): joint id(s) or joint
physics randomizer.
**kwargs (dict): range of possible physical properties. If given one value, that property won't be
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"""
super(RobotPhysicsRandomizer, self).__init__(body)
self.links = links
self.joints = joints | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/flaskbb/utils/helpers.py | python | ReverseProxyPathFix.__call__ | (self, environ, start_response) | return self.app(environ, start_response) | [] | def __call__(self, environ, start_response):
script_name = environ.get('HTTP_X_SCRIPT_NAME', '')
if script_name:
environ['SCRIPT_NAME'] = script_name
path_info = environ.get('PATH_INFO', '')
if path_info and path_info.startswith(script_name):
environ['PATH_INFO'] = path_info[len(script_name):]
server = environ.get('HTTP_X_FORWARDED_SERVER_CUSTOM',
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if server:
environ['HTTP_HOST'] = server
scheme = environ.get('HTTP_X_SCHEME', '')
if scheme:
environ['wsgi.url_scheme'] = scheme
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environ['wsgi.url_scheme'] = 'https'
return self.app(environ, start_response) | [
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pyvista/pyvista | 012dbb95a9aae406c3cd4cd94fc8c477f871e426 | pyvista/core/errors.py | python | DeprecationError.__init__ | (self, message='This feature has been depreciated') | Empty init. | Empty init. | [
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ShannonAI/glyce | 2ecefe68dcbe45d19c3fd043e02d04d337afdc15 | glyce/utils/file_utils.py | python | s3_etag | (url: str) | return s3_object.e_tag | Check ETag on S3 object. | Check ETag on S3 object. | [
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] | def s3_etag(url: str) -> Optional[str]:
"""Check ETag on S3 object."""
s3_resource = boto3.resource("s3")
bucket_name, s3_path = split_s3_path(url)
s3_object = s3_resource.Object(bucket_name, s3_path)
return s3_object.e_tag | [
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travisgoodspeed/goodfet | 1750cc1e8588af5470385e52fa098ca7364c2863 | client/GoodFETSPI.py | python | GoodFETSPIFlash.SPIjedecmanstr | (self) | return man | Grab the JEDEC manufacturer string. Call after SPIjedec(). | Grab the JEDEC manufacturer string. Call after SPIjedec(). | [
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"""Grab the JEDEC manufacturer string. Call after SPIjedec()."""
man=self.JEDECmanufacturers.get(self.JEDECmanufacturer)
if man==0:
man="UNKNOWN";
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daoluan/decode-Django | d46a858b45b56de48b0355f50dd9e45402d04cfd | Django-1.5.1/django/db/backends/__init__.py | python | set_dirty | (self) | Sets a dirty flag for the current thread and code streak. This can be used
to decide in a managed block of code to decide whether there are open
changes waiting for commit. | Sets a dirty flag for the current thread and code streak. This can be used
to decide in a managed block of code to decide whether there are open
changes waiting for commit. | [
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"""
Sets a dirty flag for the current thread and code streak. This can be used
to decide in a managed block of code to decide whether there are open
changes waiting for commit.
"""
if self._dirty is not None:
self._dirty = True
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Komodo/KomodoEdit | 61edab75dce2bdb03943b387b0608ea36f548e8e | src/codeintel/lib/codeintel2/rubycile.py | python | scan_multilang | (tokens, module_elem) | return csl_tokens, css_tokens, tokenizer.has_ruby_code() | Build the Ruby module CIX element tree.
"tokens" is a generator of UDL tokens for this UDL-based
multi-lang document.
"module_elem" is the <module> element of a CIX element tree on
which the Ruby module should be built.
This should return a tuple of:
* the list of the CSL and CSS tokens in the token stream,
* whether or not the document contains any Ruby tokens (style UDL_SSL...) | Build the Ruby module CIX element tree. | [
"Build",
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"module",
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"tree",
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] | def scan_multilang(tokens, module_elem):
"""Build the Ruby module CIX element tree.
"tokens" is a generator of UDL tokens for this UDL-based
multi-lang document.
"module_elem" is the <module> element of a CIX element tree on
which the Ruby module should be built.
This should return a tuple of:
* the list of the CSL and CSS tokens in the token stream,
* whether or not the document contains any Ruby tokens (style UDL_SSL...)
"""
tokenizer = ruby_lexer.RubyMultiLangLexer(tokens)
parser = ruby_parser.Parser(tokenizer, "RHTML")
parse_tree = parser.parse()
parser_cix.produce_elementTree_contents_cix(parse_tree, module_elem)
csl_tokens = tokenizer.get_csl_tokens()
css_tokens = tokenizer.get_css_tokens()
return csl_tokens, css_tokens, tokenizer.has_ruby_code() | [
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postgres/pgadmin4 | 374c5e952fa594d749fadf1f88076c1cba8c5f64 | web/pgadmin/browser/server_groups/servers/tablespaces/__init__.py | python | TablespaceView.variable_options | (self, gid, sid) | return make_json_response(
data=rset['rows'],
status=200
) | Args:
gid:
sid:
Returns:
This function will return list of variables available for
table spaces. | Args:
gid:
sid: | [
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"""
Args:
gid:
sid:
Returns:
This function will return list of variables available for
table spaces.
"""
ver = self.manager.version
if ver >= 90600:
SQL = render_template(
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)
else:
SQL = render_template(
"/".join([self.template_path, 'variables.sql'])
)
status, rset = self.conn.execute_dict(SQL)
if not status:
return internal_server_error(errormsg=rset)
return make_json_response(
data=rset['rows'],
status=200
) | [
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pm4py/pm4py-core | 7807b09a088b02199cd0149d724d0e28793971bf | pm4py/streaming/algo/discovery/dfg/algorithm.py | python | apply | (variant=DEFAULT_VARIANT, parameters=None) | return exec_utils.get_variant(variant).apply(parameters=parameters) | Discovers a DFG from an event stream
Parameters
--------------
variant
Variant of the algorithm (default: Variants.FREQUENCY)
Returns
--------------
stream_dfg_obj
Streaming DFG discovery object | Discovers a DFG from an event stream | [
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"""
Discovers a DFG from an event stream
Parameters
--------------
variant
Variant of the algorithm (default: Variants.FREQUENCY)
Returns
--------------
stream_dfg_obj
Streaming DFG discovery object
"""
if parameters is None:
parameters = {}
return exec_utils.get_variant(variant).apply(parameters=parameters) | [
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ryanmcgrath/twython | 0c405604285364457f3c309969f11ba68163bd05 | twython/endpoints.py | python | EndpointsMixin.create_block | (self, **params) | return self.post('blocks/create', params=params) | Blocks the specified user from following the authenticating user.
Docs:
https://developer.twitter.com/en/docs/accounts-and-users/mute-block-report-users/api-reference/post-blocks-create | Blocks the specified user from following the authenticating user. | [
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] | def create_block(self, **params):
"""Blocks the specified user from following the authenticating user.
Docs:
https://developer.twitter.com/en/docs/accounts-and-users/mute-block-report-users/api-reference/post-blocks-create
"""
return self.post('blocks/create', params=params) | [
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kbandla/dpkt | 7a91ae53bb20563607f32e6781ef40d2efe6520d | dpkt/dns.py | python | DNS.pack_q | (self, buf, q) | return buf + pack_name(q.name, len(buf), self.label_ptrs) + struct.pack('>HH', q.type, q.cls) | Append packed DNS question and return buf. | Append packed DNS question and return buf. | [
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"""Append packed DNS question and return buf."""
return buf + pack_name(q.name, len(buf), self.label_ptrs) + struct.pack('>HH', q.type, q.cls) | [
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gnuradio/pybombs | 17044241bf835b93571026b112f179f2db7448a4 | pybombs/config_file.py | python | PBConfigFile.save | (self, newdata=None) | Write the contents of the data cache to the file. | Write the contents of the data cache to the file. | [
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" Write the contents of the data cache to the file. "
if newdata is not None:
assert isinstance(newdata, dict)
self.data = newdata
fpath = os.path.split(self._filename)[0]
if len(fpath):
sysutils.mkdirp_writable(fpath)
with open(self._filename, 'w') as fn:
self.yaml.dump(self.data, fn) | [
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t4ngo/dragonfly | 3c885cbf1a63b373fd725d4bbfcb716e162dc92c | dragonfly/grammar/grammar_base.py | python | Grammar.disable | (self) | Disable this grammar so that it is not active to
receive recognitions. | Disable this grammar so that it is not active to
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"""
Disable this grammar so that it is not active to
receive recognitions.
"""
self._enabled = False | [
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CuriousAI/mean-teacher | 546348ff863c998c26be4339021425df973b4a36 | tensorflow/mean_teacher/model.py | python | tower | (inputs,
is_training,
dropout_probability,
input_noise,
normalize_input,
flip_horizontally,
translate,
num_logits,
is_initialization=False,
name=None) | [] | def tower(inputs,
is_training,
dropout_probability,
input_noise,
normalize_input,
flip_horizontally,
translate,
num_logits,
is_initialization=False,
name=None):
with tf.name_scope(name, "tower"):
default_conv_args = dict(
padding='SAME',
kernel_size=[3, 3],
activation_fn=nn.lrelu,
init=is_initialization
)
training_mode_funcs = [
nn.random_translate, nn.flip_randomly, nn.gaussian_noise, slim.dropout,
wn.fully_connected, wn.conv2d
]
training_args = dict(
is_training=is_training
)
with \
slim.arg_scope([wn.conv2d], **default_conv_args), \
slim.arg_scope(training_mode_funcs, **training_args):
net = inputs
assert_shape(net, [None, 32, 32, 3])
net = tf.cond(normalize_input,
lambda: slim.layer_norm(net,
scale=False,
center=False,
scope='normalize_inputs'),
lambda: net)
assert_shape(net, [None, 32, 32, 3])
net = nn.flip_randomly(net,
horizontally=flip_horizontally,
vertically=False,
name='random_flip')
net = tf.cond(translate,
lambda: nn.random_translate(net, scale=2, name='random_translate'),
lambda: net)
net = nn.gaussian_noise(net, scale=input_noise, name='gaussian_noise')
net = wn.conv2d(net, 128, scope="conv_1_1")
net = wn.conv2d(net, 128, scope="conv_1_2")
net = wn.conv2d(net, 128, scope="conv_1_3")
net = slim.max_pool2d(net, [2, 2], scope='max_pool_1')
net = slim.dropout(net, 1 - dropout_probability, scope='dropout_probability_1')
assert_shape(net, [None, 16, 16, 128])
net = wn.conv2d(net, 256, scope="conv_2_1")
net = wn.conv2d(net, 256, scope="conv_2_2")
net = wn.conv2d(net, 256, scope="conv_2_3")
net = slim.max_pool2d(net, [2, 2], scope='max_pool_2')
net = slim.dropout(net, 1 - dropout_probability, scope='dropout_probability_2')
assert_shape(net, [None, 8, 8, 256])
net = wn.conv2d(net, 512, padding='VALID', scope="conv_3_1")
assert_shape(net, [None, 6, 6, 512])
net = wn.conv2d(net, 256, kernel_size=[1, 1], scope="conv_3_2")
net = wn.conv2d(net, 128, kernel_size=[1, 1], scope="conv_3_3")
net = slim.avg_pool2d(net, [6, 6], scope='avg_pool')
assert_shape(net, [None, 1, 1, 128])
net = slim.flatten(net)
assert_shape(net, [None, 128])
primary_logits = wn.fully_connected(net, 10, init=is_initialization)
secondary_logits = wn.fully_connected(net, 10, init=is_initialization)
with tf.control_dependencies([tf.assert_greater_equal(num_logits, 1),
tf.assert_less_equal(num_logits, 2)]):
secondary_logits = tf.case([
(tf.equal(num_logits, 1), lambda: primary_logits),
(tf.equal(num_logits, 2), lambda: secondary_logits),
], exclusive=True, default=lambda: primary_logits)
assert_shape(primary_logits, [None, 10])
assert_shape(secondary_logits, [None, 10])
return primary_logits, secondary_logits | [
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/matplotlib/pyplot.py | python | bone | () | Set the colormap to "bone".
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information. | Set the colormap to "bone". | [
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] | def bone():
"""
Set the colormap to "bone".
This changes the default colormap as well as the colormap of the current
image if there is one. See ``help(colormaps)`` for more information.
"""
set_cmap("bone") | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/prompt_toolkit/document.py | python | Document.selection_range_at_line | (self, row) | If the selection spans a portion of the given line, return a (from, to) tuple.
The returned upper boundary is not included in the selection, so
`(0, 0)` is an empty selection. `(0, 1)`, is a one character selection.
Returns None if the selection doesn't cover this line at all. | If the selection spans a portion of the given line, return a (from, to) tuple. | [
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] | def selection_range_at_line(self, row):
"""
If the selection spans a portion of the given line, return a (from, to) tuple.
The returned upper boundary is not included in the selection, so
`(0, 0)` is an empty selection. `(0, 1)`, is a one character selection.
Returns None if the selection doesn't cover this line at all.
"""
if self.selection:
line = self.lines[row]
row_start = self.translate_row_col_to_index(row, 0)
row_end = self.translate_row_col_to_index(row, len(line))
from_, to = sorted([self.cursor_position, self.selection.original_cursor_position])
# Take the intersection of the current line and the selection.
intersection_start = max(row_start, from_)
intersection_end = min(row_end, to)
if intersection_start <= intersection_end:
if self.selection.type == SelectionType.LINES:
intersection_start = row_start
intersection_end = row_end
elif self.selection.type == SelectionType.BLOCK:
_, col1 = self.translate_index_to_position(from_)
_, col2 = self.translate_index_to_position(to)
col1, col2 = sorted([col1, col2])
if col1 > len(line):
return # Block selection doesn't cross this line.
intersection_start = self.translate_row_col_to_index(row, col1)
intersection_end = self.translate_row_col_to_index(row, col2)
_, from_column = self.translate_index_to_position(intersection_start)
_, to_column = self.translate_index_to_position(intersection_end)
# In Vi mode, the upper boundary is always included. For Emacs
# mode, that's not the case.
if vi_mode():
to_column += 1
return from_column, to_column | [
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holoviz/geoviews | b7a5ce742ebc5d7576e4dbd0249aafc0f1d0fb88 | geoviews/data/iris.py | python | CubeInterface.length | (cls, dataset) | return np.product([len(d.points) for d in dataset.data.coords(dim_coords=True)], dtype=np.intp) | Returns the total number of samples in the dataset. | Returns the total number of samples in the dataset. | [
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"""
Returns the total number of samples in the dataset.
"""
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mrlesmithjr/Ansible | d44f0dc0d942bdf3bf7334b307e6048f0ee16e36 | roles/ansible-vsphere-management/scripts/pdns/lib/python2.7/site-packages/pkg_resources/_vendor/appdirs.py | python | site_data_dir | (appname=None, appauthor=None, version=None, multipath=False) | return path | Return full path to the user-shared data dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
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"version" is an optional version path element to append to the
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of your app to be able to run independently. If used, this
would typically be "<major>.<minor>".
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"multipath" is an optional parameter only applicable to *nix
which indicates that the entire list of data dirs should be
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Mac OS X: /Library/Application Support/<AppName>
Unix: /usr/local/share/<AppName> or /usr/share/<AppName>
Win XP: C:\Documents and Settings\All Users\Application Data\<AppAuthor>\<AppName>
Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
Win 7: C:\ProgramData\<AppAuthor>\<AppName> # Hidden, but writeable on Win 7.
For Unix, this is using the $XDG_DATA_DIRS[0] default.
WARNING: Do not use this on Windows. See the Vista-Fail note above for why. | Return full path to the user-shared data dir for this application. | [
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] | def site_data_dir(appname=None, appauthor=None, version=None, multipath=False):
"""Return full path to the user-shared data dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
pass False to disable it.
"version" is an optional version path element to append to the
path. You might want to use this if you want multiple versions
of your app to be able to run independently. If used, this
would typically be "<major>.<minor>".
Only applied when appname is present.
"multipath" is an optional parameter only applicable to *nix
which indicates that the entire list of data dirs should be
returned. By default, the first item from XDG_DATA_DIRS is
returned, or '/usr/local/share/<AppName>',
if XDG_DATA_DIRS is not set
Typical user data directories are:
Mac OS X: /Library/Application Support/<AppName>
Unix: /usr/local/share/<AppName> or /usr/share/<AppName>
Win XP: C:\Documents and Settings\All Users\Application Data\<AppAuthor>\<AppName>
Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
Win 7: C:\ProgramData\<AppAuthor>\<AppName> # Hidden, but writeable on Win 7.
For Unix, this is using the $XDG_DATA_DIRS[0] default.
WARNING: Do not use this on Windows. See the Vista-Fail note above for why.
"""
if system == "win32":
if appauthor is None:
appauthor = appname
path = os.path.normpath(_get_win_folder("CSIDL_COMMON_APPDATA"))
if appname:
if appauthor is not False:
path = os.path.join(path, appauthor, appname)
else:
path = os.path.join(path, appname)
elif system == 'darwin':
path = os.path.expanduser('/Library/Application Support')
if appname:
path = os.path.join(path, appname)
else:
# XDG default for $XDG_DATA_DIRS
# only first, if multipath is False
path = os.getenv('XDG_DATA_DIRS',
os.pathsep.join(['/usr/local/share', '/usr/share']))
pathlist = [os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep)]
if appname:
if version:
appname = os.path.join(appname, version)
pathlist = [os.sep.join([x, appname]) for x in pathlist]
if multipath:
path = os.pathsep.join(pathlist)
else:
path = pathlist[0]
return path
if appname and version:
path = os.path.join(path, version)
return path | [
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kivy/kivy | fbf561f73ddba9941b1b7e771f86264c6e6eef36 | kivy/vector.py | python | Vector.normalize | (self) | return self / self.length() | Returns a new vector that has the same direction as vec,
but has a length of one.
>>> v = Vector(88, 33).normalize()
>>> v
[0.93632917756904444, 0.3511234415883917]
>>> v.length()
1.0 | Returns a new vector that has the same direction as vec,
but has a length of one. | [
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] | def normalize(self):
'''Returns a new vector that has the same direction as vec,
but has a length of one.
>>> v = Vector(88, 33).normalize()
>>> v
[0.93632917756904444, 0.3511234415883917]
>>> v.length()
1.0
'''
if self[0] == 0. and self[1] == 0.:
return Vector(0., 0.)
return self / self.length() | [
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pyeventsourcing/eventsourcing | f5a36f434ab2631890092b6c7714b8fb8c94dc7c | eventsourcing/persistence.py | python | EventStore.get | (
self,
originator_id: UUID,
gt: Optional[int] = None,
lte: Optional[int] = None,
desc: bool = False,
limit: Optional[int] = None,
) | return cast(
Iterator[TDomainEvent],
map(
self.mapper.to_domain_event,
self.recorder.select_events(
originator_id=originator_id,
gt=gt,
lte=lte,
desc=desc,
limit=limit,
),
),
) | Retrieves domain events from aggregate sequence. | Retrieves domain events from aggregate sequence. | [
"Retrieves",
"domain",
"events",
"from",
"aggregate",
"sequence",
"."
] | def get(
self,
originator_id: UUID,
gt: Optional[int] = None,
lte: Optional[int] = None,
desc: bool = False,
limit: Optional[int] = None,
) -> Iterator[TDomainEvent]:
"""
Retrieves domain events from aggregate sequence.
"""
return cast(
Iterator[TDomainEvent],
map(
self.mapper.to_domain_event,
self.recorder.select_events(
originator_id=originator_id,
gt=gt,
lte=lte,
desc=desc,
limit=limit,
),
),
) | [
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knownsec/Pocsuite | 877d1b1604629b8dcd6e53b167c3c98249e5e94f | pocsuite/thirdparty/requests/packages/urllib3/packages/ordered_dict.py | python | OrderedDict.viewvalues | (self) | return ValuesView(self) | od.viewvalues() -> an object providing a view on od's values | od.viewvalues() -> an object providing a view on od's values | [
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] | def viewvalues(self):
"od.viewvalues() -> an object providing a view on od's values"
return ValuesView(self) | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-35/fabmetheus_utilities/settings.py | python | HelpPage.getOpenFromAfterWWW | ( self, afterWWW ) | return self.openPage | Get the open help page function from the afterWWW of the address after the www. | Get the open help page function from the afterWWW of the address after the www. | [
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"from",
"the",
"afterWWW",
"of",
"the",
"address",
"after",
"the",
"www",
"."
] | def getOpenFromAfterWWW( self, afterWWW ):
"Get the open help page function from the afterWWW of the address after the www."
self.hypertextAddress = 'http://www.' + afterWWW
return self.openPage | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/matplotlib-3.0.3-py3.7-macosx-10.9-x86_64.egg/matplotlib/backends/backend_agg.py | python | RendererAgg.draw_mathtext | (self, gc, x, y, s, prop, angle) | Draw the math text using matplotlib.mathtext | Draw the math text using matplotlib.mathtext | [
"Draw",
"the",
"math",
"text",
"using",
"matplotlib",
".",
"mathtext"
] | def draw_mathtext(self, gc, x, y, s, prop, angle):
"""
Draw the math text using matplotlib.mathtext
"""
ox, oy, width, height, descent, font_image, used_characters = \
self.mathtext_parser.parse(s, self.dpi, prop)
xd = descent * sin(radians(angle))
yd = descent * cos(radians(angle))
x = np.round(x + ox + xd)
y = np.round(y - oy + yd)
self._renderer.draw_text_image(font_image, x, y + 1, angle, gc) | [
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playframework/play1 | 0ecac3bc2421ae2dbec27a368bf671eda1c9cba5 | python/Lib/ntpath.py | python | relpath | (path, start=curdir) | return join(*rel_list) | Return a relative version of a path | Return a relative version of a path | [
"Return",
"a",
"relative",
"version",
"of",
"a",
"path"
] | def relpath(path, start=curdir):
"""Return a relative version of a path"""
if not path:
raise ValueError("no path specified")
start_is_unc, start_prefix, start_list = _abspath_split(start)
path_is_unc, path_prefix, path_list = _abspath_split(path)
if path_is_unc ^ start_is_unc:
raise ValueError("Cannot mix UNC and non-UNC paths (%s and %s)"
% (path, start))
if path_prefix.lower() != start_prefix.lower():
if path_is_unc:
raise ValueError("path is on UNC root %s, start on UNC root %s"
% (path_prefix, start_prefix))
else:
raise ValueError("path is on drive %s, start on drive %s"
% (path_prefix, start_prefix))
# Work out how much of the filepath is shared by start and path.
i = 0
for e1, e2 in zip(start_list, path_list):
if e1.lower() != e2.lower():
break
i += 1
rel_list = [pardir] * (len(start_list)-i) + path_list[i:]
if not rel_list:
return curdir
return join(*rel_list) | [
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CharlesShang/FastMaskRCNN | bdae07702acccd85803e658f5e49690981efcdb2 | libs/nets/resnet_v1.py | python | resnet_v1 | (inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope=None) | Generator for v1 ResNet models.
This function generates a family of ResNet v1 models. See the resnet_v1_*()
methods for specific model instantiations, obtained by selecting different
block instantiations that produce ResNets of various depths.
Training for image classification on Imagenet is usually done with [224, 224]
inputs, resulting in [7, 7] feature maps at the output of the last ResNet
block for the ResNets defined in [1] that have nominal stride equal to 32.
However, for dense prediction tasks we advise that one uses inputs with
spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In
this case the feature maps at the ResNet output will have spatial shape
[(height - 1) / output_stride + 1, (width - 1) / output_stride + 1]
and corners exactly aligned with the input image corners, which greatly
facilitates alignment of the features to the image. Using as input [225, 225]
images results in [8, 8] feature maps at the output of the last ResNet block.
For dense prediction tasks, the ResNet needs to run in fully-convolutional
(FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all
have nominal stride equal to 32 and a good choice in FCN mode is to use
output_stride=16 in order to increase the density of the computed features at
small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915.
Args:
inputs: A tensor of size [batch, height_in, width_in, channels].
blocks: A list of length equal to the number of ResNet blocks. Each element
is a resnet_utils.Block object describing the units in the block.
num_classes: Number of predicted classes for classification tasks. If None
we return the features before the logit layer.
is_training: whether is training or not.
global_pool: If True, we perform global average pooling before computing the
logits. Set to True for image classification, False for dense prediction.
output_stride: If None, then the output will be computed at the nominal
network stride. If output_stride is not None, it specifies the requested
ratio of input to output spatial resolution.
include_root_block: If True, include the initial convolution followed by
max-pooling, if False excludes it.
spatial_squeeze: if True, logits is of shape [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
If global_pool is False, then height_out and width_out are reduced by a
factor of output_stride compared to the respective height_in and width_in,
else both height_out and width_out equal one. If num_classes is None, then
net is the output of the last ResNet block, potentially after global
average pooling. If num_classes is not None, net contains the pre-softmax
activations.
end_points: A dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: If the target output_stride is not valid. | Generator for v1 ResNet models. | [
"Generator",
"for",
"v1",
"ResNet",
"models",
"."
] | def resnet_v1(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope=None):
"""Generator for v1 ResNet models.
This function generates a family of ResNet v1 models. See the resnet_v1_*()
methods for specific model instantiations, obtained by selecting different
block instantiations that produce ResNets of various depths.
Training for image classification on Imagenet is usually done with [224, 224]
inputs, resulting in [7, 7] feature maps at the output of the last ResNet
block for the ResNets defined in [1] that have nominal stride equal to 32.
However, for dense prediction tasks we advise that one uses inputs with
spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In
this case the feature maps at the ResNet output will have spatial shape
[(height - 1) / output_stride + 1, (width - 1) / output_stride + 1]
and corners exactly aligned with the input image corners, which greatly
facilitates alignment of the features to the image. Using as input [225, 225]
images results in [8, 8] feature maps at the output of the last ResNet block.
For dense prediction tasks, the ResNet needs to run in fully-convolutional
(FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all
have nominal stride equal to 32 and a good choice in FCN mode is to use
output_stride=16 in order to increase the density of the computed features at
small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915.
Args:
inputs: A tensor of size [batch, height_in, width_in, channels].
blocks: A list of length equal to the number of ResNet blocks. Each element
is a resnet_utils.Block object describing the units in the block.
num_classes: Number of predicted classes for classification tasks. If None
we return the features before the logit layer.
is_training: whether is training or not.
global_pool: If True, we perform global average pooling before computing the
logits. Set to True for image classification, False for dense prediction.
output_stride: If None, then the output will be computed at the nominal
network stride. If output_stride is not None, it specifies the requested
ratio of input to output spatial resolution.
include_root_block: If True, include the initial convolution followed by
max-pooling, if False excludes it.
spatial_squeeze: if True, logits is of shape [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
If global_pool is False, then height_out and width_out are reduced by a
factor of output_stride compared to the respective height_in and width_in,
else both height_out and width_out equal one. If num_classes is None, then
net is the output of the last ResNet block, potentially after global
average pooling. If num_classes is not None, net contains the pre-softmax
activations.
end_points: A dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: If the target output_stride is not valid.
"""
with tf.variable_scope(scope, 'resnet_v1', [inputs], reuse=reuse) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope([slim.conv2d, bottleneck,
resnet_utils.stack_blocks_dense],
outputs_collections=end_points_collection):
with slim.arg_scope([slim.batch_norm], is_training=is_training):
net = inputs
if include_root_block:
if output_stride is not None:
if output_stride % 4 != 0:
raise ValueError('The output_stride needs to be a multiple of 4.')
output_stride /= 4
net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)
if global_pool:
# Global average pooling.
net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)
if num_classes is not None:
net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, scope='logits')
if spatial_squeeze:
logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
# Convert end_points_collection into a dictionary of end_points.
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
if num_classes is not None:
end_points['predictions'] = slim.softmax(logits, scope='predictions')
return logits, end_points | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/feature_extraction_utils.py | python | FeatureExtractionMixin.to_json_string | (self) | return json.dumps(dictionary, indent=2, sort_keys=True) + "\n" | Serializes this instance to a JSON string.
Returns:
`str`: String containing all the attributes that make up this feature_extractor instance in JSON format. | Serializes this instance to a JSON string. | [
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"""
Serializes this instance to a JSON string.
Returns:
`str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
"""
dictionary = self.to_dict()
for key, value in dictionary.items():
if isinstance(value, np.ndarray):
dictionary[key] = value.tolist()
# make sure private name "_processor_class" is correctly
# saved as "processor_class"
if dictionary.get("_processor_class", None) is not None:
dictionary["processor_class"] = dictionary.pop("_processor_class")
return json.dumps(dictionary, indent=2, sort_keys=True) + "\n" | [
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UKPLab/sentence-transformers | 2158fff3aa96651b10fe367c41fdd5008a33c5c6 | sentence_transformers/readers/STSDataReader.py | python | STSDataReader.get_examples | (self, filename, max_examples=0) | return examples | filename specified which data split to use (train.csv, dev.csv, test.csv). | filename specified which data split to use (train.csv, dev.csv, test.csv). | [
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] | def get_examples(self, filename, max_examples=0):
"""
filename specified which data split to use (train.csv, dev.csv, test.csv).
"""
filepath = os.path.join(self.dataset_folder, filename)
with gzip.open(filepath, 'rt', encoding='utf8') if filename.endswith('.gz') else open(filepath, encoding="utf-8") as fIn:
data = csv.reader(fIn, delimiter=self.delimiter, quoting=self.quoting)
examples = []
for id, row in enumerate(data):
score = float(row[self.score_col_idx])
if self.normalize_scores: # Normalize to a 0...1 value
score = (score - self.min_score) / (self.max_score - self.min_score)
s1 = row[self.s1_col_idx]
s2 = row[self.s2_col_idx]
examples.append(InputExample(guid=filename+str(id), texts=[s1, s2], label=score))
if max_examples > 0 and len(examples) >= max_examples:
break
return examples | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.5/django/contrib/gis/gdal/layer.py | python | Layer.get_fields | (self, field_name) | return [feat.get(field_name) for feat in self] | Returns a list containing the given field name for every Feature
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"""
if not field_name in self.fields:
raise OGRException('invalid field name: %s' % field_name)
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Pymol-Scripts/Pymol-script-repo | bcd7bb7812dc6db1595953dfa4471fa15fb68c77 | modules/ADT/MolKit/pdb2pqr/src/psize.py | python | Psize.parseInput | (self, filename) | Parse input structure file in PDB or PQR format | Parse input structure file in PDB or PQR format | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/nbformat/sign.py | python | signature_removed | (nb) | Context manager for operating on a notebook with its signature removed
Used for excluding the previous signature when computing a notebook's signature. | Context manager for operating on a notebook with its signature removed
Used for excluding the previous signature when computing a notebook's signature. | [
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"""Context manager for operating on a notebook with its signature removed
Used for excluding the previous signature when computing a notebook's signature.
"""
save_signature = nb['metadata'].pop('signature', None)
try:
yield
finally:
if save_signature is not None:
nb['metadata']['signature'] = save_signature | [
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open-mmlab/mmdetection3d | c7272063e818bcf33aebc498a017a95c8d065143 | mmdet3d/core/bbox/transforms.py | python | bbox3d2result | (bboxes, scores, labels, attrs=None) | return result_dict | Convert detection results to a list of numpy arrays.
Args:
bboxes (torch.Tensor): Bounding boxes with shape of (n, 5).
labels (torch.Tensor): Labels with shape of (n, ).
scores (torch.Tensor): Scores with shape of (n, ).
attrs (torch.Tensor, optional): Attributes with shape of (n, ). \
Defaults to None.
Returns:
dict[str, torch.Tensor]: Bounding box results in cpu mode.
- boxes_3d (torch.Tensor): 3D boxes.
- scores (torch.Tensor): Prediction scores.
- labels_3d (torch.Tensor): Box labels.
- attrs_3d (torch.Tensor, optional): Box attributes. | Convert detection results to a list of numpy arrays. | [
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] | def bbox3d2result(bboxes, scores, labels, attrs=None):
"""Convert detection results to a list of numpy arrays.
Args:
bboxes (torch.Tensor): Bounding boxes with shape of (n, 5).
labels (torch.Tensor): Labels with shape of (n, ).
scores (torch.Tensor): Scores with shape of (n, ).
attrs (torch.Tensor, optional): Attributes with shape of (n, ). \
Defaults to None.
Returns:
dict[str, torch.Tensor]: Bounding box results in cpu mode.
- boxes_3d (torch.Tensor): 3D boxes.
- scores (torch.Tensor): Prediction scores.
- labels_3d (torch.Tensor): Box labels.
- attrs_3d (torch.Tensor, optional): Box attributes.
"""
result_dict = dict(
boxes_3d=bboxes.to('cpu'),
scores_3d=scores.cpu(),
labels_3d=labels.cpu())
if attrs is not None:
result_dict['attrs_3d'] = attrs.cpu()
return result_dict | [
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suurjaak/Skyperious | 6a4f264dbac8d326c2fa8aeb5483dbca987860bf | skyperious/gui.py | python | ChatContentSTC.IsMessageShown | (self, message_id) | return (message_id in self._message_positions) | Returns whether the specified message is currently shown. | Returns whether the specified message is currently shown. | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/email/_header_value_parser.py | python | get_local_part | (value) | return local_part, value | local-part = dot-atom / quoted-string / obs-local-part | local-part = dot-atom / quoted-string / obs-local-part | [
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""" local-part = dot-atom / quoted-string / obs-local-part
"""
local_part = LocalPart()
leader = None
if value[0] in CFWS_LEADER:
leader, value = get_cfws(value)
if not value:
raise errors.HeaderParseError(
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try:
token, value = get_dot_atom(value)
except errors.HeaderParseError:
try:
token, value = get_word(value)
except errors.HeaderParseError:
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local_part.defects.append(errors.ObsoleteHeaderDefect(
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local_part[0] = obs_local_part
try:
local_part.value.encode('ascii')
except UnicodeEncodeError:
local_part.defects.append(errors.NonASCIILocalPartDefect(
"local-part contains non-ASCII characters)"))
return local_part, value | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/Lib/distutils/archive_util.py | python | _get_gid | (name) | return None | Returns a gid, given a group name. | Returns a gid, given a group name. | [
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] | def _get_gid(name):
"""Returns a gid, given a group name."""
if getgrnam is None or name is None:
return None
try:
result = getgrnam(name)
except KeyError:
result = None
if result is not None:
return result[2]
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ring04h/wyportmap | c4201e2313504e780a7f25238eba2a2d3223e739 | sqlalchemy/sql/elements.py | python | ColumnElement.label | (self, name) | return Label(name, self, self.type) | Produce a column label, i.e. ``<columnname> AS <name>``.
This is a shortcut to the :func:`~.expression.label` function.
if 'name' is None, an anonymous label name will be generated. | Produce a column label, i.e. ``<columnname> AS <name>``. | [
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"""Produce a column label, i.e. ``<columnname> AS <name>``.
This is a shortcut to the :func:`~.expression.label` function.
if 'name' is None, an anonymous label name will be generated.
"""
return Label(name, self, self.type) | [
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JacquesLucke/animation_nodes | b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1 | animation_nodes/nodes/mesh/offset_polygons.py | python | OffsetPolygonsNode.drawAdvanced | (self, layout) | [] | def drawAdvanced(self, layout):
layout.prop(self, "pivotSource", text = "Local Pivots")
if self.pivotSource == "CUSTOM_MATRICES" and "LOCAL_AXIS" not in self.rotationMode:
layout.label(text = "Try to change the rotation mode.", icon = "INFO")
self.drawAdvanced_MatrixTransformationProperties(layout) | [
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enthought/comtypes | 8f3abc93c213fccdf9cae54aa88bfc1dfc17b8e9 | comtypes/client/_code_cache.py | python | _is_writeable | (path) | return os.access(path[0], os.W_OK) | Check if the first part, if any, on path is a directory in
which we can create files. | Check if the first part, if any, on path is a directory in
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"""Check if the first part, if any, on path is a directory in
which we can create files."""
if not path:
return False
# TODO: should we add os.X_OK flag as well? It seems unnecessary on Windows.
return os.access(path[0], os.W_OK) | [
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abhik/pebl | 5e7d694eb1e4f90e0f1410000b958ba62698a268 | src/pebl/result.py | python | LearnerResult.tofile | (self, filename=None) | Save the result to a python pickle file.
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"""
filename = filename or config.get('result.filename')
with open(filename, 'w') as fp:
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msr-fiddle/pipedream | 7db6a1c3e64996d5b319faec6ca38cb31bfea1c4 | profiler/translation/seq2seq/inference/beam_search.py | python | SequenceGenerator.__init__ | (self, model, beam_size=5, max_seq_len=100, cuda=False,
len_norm_factor=0.6, len_norm_const=5,
cov_penalty_factor=0.1) | Constructor for the SequenceGenerator.
Beam search decoding supports coverage penalty and length
normalization. For details, refer to Section 7 of the GNMT paper
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:param model: model which implements generate method
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:param cuda: whether to use cuda
:param len_norm_factor: length normalization factor
:param len_norm_const: length normalization constant
:param cov_penalty_factor: coverage penalty factor | Constructor for the SequenceGenerator. | [
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] | def __init__(self, model, beam_size=5, max_seq_len=100, cuda=False,
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cov_penalty_factor=0.1):
"""
Constructor for the SequenceGenerator.
Beam search decoding supports coverage penalty and length
normalization. For details, refer to Section 7 of the GNMT paper
(https://arxiv.org/pdf/1609.08144.pdf).
:param model: model which implements generate method
:param beam_size: decoder beam size
:param max_seq_len: maximum decoder sequence length
:param cuda: whether to use cuda
:param len_norm_factor: length normalization factor
:param len_norm_const: length normalization constant
:param cov_penalty_factor: coverage penalty factor
"""
self.model = model
self.cuda = cuda
self.beam_size = beam_size
self.max_seq_len = max_seq_len
self.len_norm_factor = len_norm_factor
self.len_norm_const = len_norm_const
self.cov_penalty_factor = cov_penalty_factor
self.batch_first = self.model.batch_first
gnmt_print(key=mlperf_log.EVAL_HP_BEAM_SIZE,
value=self.beam_size)
gnmt_print(key=mlperf_log.EVAL_HP_MAX_SEQ_LEN,
value=self.max_seq_len)
gnmt_print(key=mlperf_log.EVAL_HP_LEN_NORM_CONST,
value=self.len_norm_const)
gnmt_print(key=mlperf_log.EVAL_HP_LEN_NORM_FACTOR,
value=self.len_norm_factor)
gnmt_print(key=mlperf_log.EVAL_HP_COV_PENALTY_FACTOR,
value=self.cov_penalty_factor) | [
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clinton-hall/nzbToMedia | 27669389216902d1085660167e7bda0bd8527ecf | libs/common/beetsplug/bpd/gstplayer.py | python | GstPlayer._get_state | (self) | return self.player.get_state(Gst.CLOCK_TIME_NONE)[1] | Returns the current state flag of the playbin. | Returns the current state flag of the playbin. | [
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# gst's get_state function returns a 3-tuple; we just want the
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return self.player.get_state(Gst.CLOCK_TIME_NONE)[1] | [
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/concurrent/futures/_base.py | python | Future.result | (self, timeout=None) | Return the result of the call that the future represents.
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Args:
timeout: The number of seconds to wait for the result if the future
isn't done. If None, then there is no limit on the wait time.
Returns:
The result of the call that the future represents.
Raises:
CancelledError: If the future was cancelled.
TimeoutError: If the future didn't finish executing before the given
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Exception: If the call raised then that exception will be raised.
"""
with self._condition:
if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
raise CancelledError()
elif self._state == FINISHED:
return self.__get_result()
self._condition.wait(timeout)
if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
raise CancelledError()
elif self._state == FINISHED:
return self.__get_result()
else:
raise TimeoutError() | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/whoosh/matching/wrappers.py | python | InverseMatcher.is_active | (self) | return self._id < self.limit | [] | def is_active(self):
return self._id < self.limit | [
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qutip/qutip | 52d01da181a21b810c3407812c670f35fdc647e8 | qutip/visualization.py | python | matrix_histogram_complex | (M, xlabels=None, ylabels=None,
title=None, limits=None, phase_limits=None,
colorbar=True, fig=None, ax=None,
threshold=None) | return fig, ax | Draw a histogram for the amplitudes of matrix M, using the argument
of each element for coloring the bars, with the given x and y labels
and title.
Parameters
----------
M : Matrix of Qobj
The matrix to visualize
xlabels : list of strings
list of x labels
ylabels : list of strings
list of y labels
title : string
title of the plot (optional)
limits : list/array with two float numbers
The z-axis limits [min, max] (optional)
phase_limits : list/array with two float numbers
The phase-axis (colorbar) limits [min, max] (optional)
ax : a matplotlib axes instance
The axes context in which the plot will be drawn.
threshold: float (None)
Threshold for when bars of smaller height should be transparent. If
not set, all bars are colored according to the color map.
Returns
-------
fig, ax : tuple
A tuple of the matplotlib figure and axes instances used to produce
the figure.
Raises
------
ValueError
Input argument is not valid. | Draw a histogram for the amplitudes of matrix M, using the argument
of each element for coloring the bars, with the given x and y labels
and title. | [
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"title",
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] | def matrix_histogram_complex(M, xlabels=None, ylabels=None,
title=None, limits=None, phase_limits=None,
colorbar=True, fig=None, ax=None,
threshold=None):
"""
Draw a histogram for the amplitudes of matrix M, using the argument
of each element for coloring the bars, with the given x and y labels
and title.
Parameters
----------
M : Matrix of Qobj
The matrix to visualize
xlabels : list of strings
list of x labels
ylabels : list of strings
list of y labels
title : string
title of the plot (optional)
limits : list/array with two float numbers
The z-axis limits [min, max] (optional)
phase_limits : list/array with two float numbers
The phase-axis (colorbar) limits [min, max] (optional)
ax : a matplotlib axes instance
The axes context in which the plot will be drawn.
threshold: float (None)
Threshold for when bars of smaller height should be transparent. If
not set, all bars are colored according to the color map.
Returns
-------
fig, ax : tuple
A tuple of the matplotlib figure and axes instances used to produce
the figure.
Raises
------
ValueError
Input argument is not valid.
"""
if isinstance(M, Qobj):
# extract matrix data from Qobj
M = M.full()
n = np.size(M)
xpos, ypos = np.meshgrid(range(M.shape[0]), range(M.shape[1]))
xpos = xpos.T.flatten() - 0.5
ypos = ypos.T.flatten() - 0.5
zpos = np.zeros(n)
dx = dy = 0.8 * np.ones(n)
Mvec = M.flatten()
dz = abs(Mvec)
# make small numbers real, to avoid random colors
idx, = np.where(abs(Mvec) < 0.001)
Mvec[idx] = abs(Mvec[idx])
if phase_limits: # check that limits is a list type
phase_min = phase_limits[0]
phase_max = phase_limits[1]
else:
phase_min = -pi
phase_max = pi
norm = mpl.colors.Normalize(phase_min, phase_max)
cmap = complex_phase_cmap()
colors = cmap(norm(angle(Mvec)))
if threshold is not None:
colors[:, 3] = 1 * (dz > threshold)
if ax is None:
fig = plt.figure()
ax = _axes3D(fig, azim=-35, elev=35)
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=colors)
if title and fig:
ax.set_title(title)
# x axis
xtics = -0.5 + np.arange(M.shape[0])
ax.axes.w_xaxis.set_major_locator(plt.FixedLocator(xtics))
if xlabels:
nxlabels = len(xlabels)
if nxlabels != len(xtics):
raise ValueError(f"got {nxlabels} xlabels but needed {len(xtics)}")
ax.set_xticklabels(xlabels)
ax.tick_params(axis='x', labelsize=12)
# y axis
ytics = -0.5 + np.arange(M.shape[1])
ax.axes.w_yaxis.set_major_locator(plt.FixedLocator(ytics))
if ylabels:
nylabels = len(ylabels)
if nylabels != len(ytics):
raise ValueError(f"got {nylabels} ylabels but needed {len(ytics)}")
ax.set_yticklabels(ylabels)
ax.tick_params(axis='y', labelsize=12)
# z axis
if limits and isinstance(limits, list):
ax.set_zlim3d(limits)
else:
ax.set_zlim3d([0, 1]) # use min/max
# ax.set_zlabel('abs')
# color axis
if colorbar:
cax, kw = mpl.colorbar.make_axes(ax, shrink=.75, pad=.0)
cb = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm)
cb.set_ticks([-pi, -pi / 2, 0, pi / 2, pi])
cb.set_ticklabels(
(r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'))
cb.set_label('arg')
return fig, ax | [
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snipsco/ntm-lasagne | 65c950b01f52afb87cf3dccc963d8bbc5b1dbf69 | utils/generators.py | python | DyckWordsTask.get_random_non_dyck | (self, n) | return w | Return a random balanced non-Dyck word of semilength `n`
The algorithm is based on the bijection between words in the
language `L = S(u^{n-1} d^{n+1})` and the balanced words of length
2n that are not Dyck words. This transformation is given by the
reflection of the letters after the first letter that violates
the Dyck property (i.e. the first right parenthesis that does
not have a matching left counterpart). The reflexion transformation
is defined by transforming any left parenthesis in a right one
and vice-versa.
To summarize, here is the pseudo-code for this algorithm:
- Pick a random word `w` in the language `L = S(u^{n-1} d^{n+1})`
- Find the first letter violating the Dyck property
- Apply the reflection transformation to the following letters | Return a random balanced non-Dyck word of semilength `n` | [
"Return",
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"random",
"balanced",
"non",
"-",
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"of",
"semilength",
"n"
] | def get_random_non_dyck(self, n):
"""
Return a random balanced non-Dyck word of semilength `n`
The algorithm is based on the bijection between words in the
language `L = S(u^{n-1} d^{n+1})` and the balanced words of length
2n that are not Dyck words. This transformation is given by the
reflection of the letters after the first letter that violates
the Dyck property (i.e. the first right parenthesis that does
not have a matching left counterpart). The reflexion transformation
is defined by transforming any left parenthesis in a right one
and vice-versa.
To summarize, here is the pseudo-code for this algorithm:
- Pick a random word `w` in the language `L = S(u^{n-1} d^{n+1})`
- Find the first letter violating the Dyck property
- Apply the reflection transformation to the following letters
"""
w = [0] * (n - 1) + [1] * (n + 1)
np.random.shuffle(w)
stack, reflection = (0, False)
for i in range(2 * n):
if reflection:
w[i] = 1 * (not w[i])
else:
if w[i]: stack -= 1
else: stack += 1
reflection = (stack < 0)
return w | [
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tensorflow/graphics | 86997957324bfbdd85848daae989b4c02588faa0 | tensorflow_graphics/util/asserts.py | python | assert_at_least_k_non_zero_entries | (
tensor: type_alias.TensorLike,
k: int = 1,
name: str = 'assert_at_least_k_non_zero_entries') | Checks if `tensor` has at least k non-zero entries in the last dimension.
Given a tensor with `M` dimensions in its last axis, this function checks
whether at least `k` out of `M` dimensions are non-zero.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
tensor: A tensor of shape `[A1, ..., An, M]`.
k: An integer, corresponding to the minimum number of non-zero entries.
name: A name for this op. Defaults to 'assert_at_least_k_non_zero_entries'.
Raises:
InvalidArgumentError: If `tensor` has less than `k` non-zero entries in its
last axis.
Returns:
The input tensor, with dependence on the assertion operator in the graph. | Checks if `tensor` has at least k non-zero entries in the last dimension. | [
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] | def assert_at_least_k_non_zero_entries(
tensor: type_alias.TensorLike,
k: int = 1,
name: str = 'assert_at_least_k_non_zero_entries') -> tf.Tensor:
"""Checks if `tensor` has at least k non-zero entries in the last dimension.
Given a tensor with `M` dimensions in its last axis, this function checks
whether at least `k` out of `M` dimensions are non-zero.
Note:
In the following, A1 to An are optional batch dimensions.
Args:
tensor: A tensor of shape `[A1, ..., An, M]`.
k: An integer, corresponding to the minimum number of non-zero entries.
name: A name for this op. Defaults to 'assert_at_least_k_non_zero_entries'.
Raises:
InvalidArgumentError: If `tensor` has less than `k` non-zero entries in its
last axis.
Returns:
The input tensor, with dependence on the assertion operator in the graph.
"""
if not FLAGS[tfg_flags.TFG_ADD_ASSERTS_TO_GRAPH].value:
return tensor
with tf.name_scope(name):
tensor = tf.convert_to_tensor(value=tensor)
indicator = tf.cast(tf.math.greater(tensor, 0.0), dtype=tensor.dtype)
indicator_sum = tf.reduce_sum(input_tensor=indicator, axis=-1)
assert_op = tf.debugging.assert_greater_equal(
indicator_sum, tf.cast(k, dtype=tensor.dtype))
with tf.control_dependencies([assert_op]):
return tf.identity(tensor) | [
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beurtschipper/crackcoin | 73ae99b8f6957f1df3f4549074beaf616e4588d7 | crackcoin/networks.py | python | crackcoinNetwork.handleServerInput | (self, data, ip) | Handle crackcoin packets | Handle crackcoin packets | [
"Handle",
"crackcoin",
"packets"
] | def handleServerInput(self, data, ip):
''' Handle crackcoin packets '''
# reply to broadcast packets
if hasPacketPrefix(data, packet_sync_request):
# todo: do proper sync, not send everything
transactions = crackcoin.db.doQuery("SELECT hash from transactions WHERE hash != 'd34db33f'", result='all') # everything except genesis transaction
for transaction in transactions:
transactionHash = transaction[0]
# send transaction
transactionJSON = crackcoin.transactions.getJSONForTransaction(transactionHash)
crackcoin.network.sendToNode(ip, packet_payment_new + zlib.compress(transactionJSON) )
# send current confirmation
difficulty,addition = crackcoin.db.doQuery("select difficulty,addition from confirmations where transactionHash = ?", (transactionHash,), result='one')
data = "%s,%s,%s" % (transactionHash,str(difficulty),addition)
crackcoin.network.sendToNode(ip, packet_payment_confirmation + data)
if hasPacketPrefix(data, packet_payment_new):
crackcoin.transactions.addTransactionJSON( zlib.decompress(data[len(packet_payment_new):]) )
if hasPacketPrefix(data, packet_payment_confirmation):
crackcoin.transactions.addConfirmationCSV(data[len(packet_payment_confirmation):]) | [
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... | https://github.com/beurtschipper/crackcoin/blob/73ae99b8f6957f1df3f4549074beaf616e4588d7/crackcoin/networks.py#L51-L76 | ||
lazylibrarian/LazyLibrarian | ae3c14e9db9328ce81765e094ab2a14ed7155624 | cherrypy/lib/sessions.py | python | Session.save | (self) | Save session data. | Save session data. | [
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"."
] | def save(self):
"""Save session data."""
try:
# If session data has never been loaded then it's never been
# accessed: no need to save it
if self.loaded:
t = datetime.timedelta(seconds=self.timeout * 60)
expiration_time = self.now() + t
if self.debug:
cherrypy.log('Saving session %r with expiry %s' %
(self.id, expiration_time),
'TOOLS.SESSIONS')
self._save(expiration_time)
else:
if self.debug:
cherrypy.log(
'Skipping save of session %r (no session loaded).' %
self.id, 'TOOLS.SESSIONS')
finally:
if self.locked:
# Always release the lock if the user didn't release it
self.release_lock()
if self.debug:
cherrypy.log('Lock released after save.', 'TOOLS.SESSIONS') | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/netatmo/sensor.py | python | NetatmoSensor.async_update_callback | (self) | Update the entity's state. | Update the entity's state. | [
"Update",
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"entity",
"s",
"state",
"."
] | def async_update_callback(self) -> None:
"""Update the entity's state."""
data = self._data.get_last_data(station_id=self._station_id, exclude=3600).get(
self._id
)
if data is None:
if self.state:
_LOGGER.debug(
"No data found for %s - %s (%s)",
self.name,
self._device_name,
self._id,
)
self._attr_native_value = None
return
try:
state = data[self.entity_description.netatmo_name]
if self.entity_description.key in {"temperature", "pressure", "sum_rain_1"}:
self._attr_native_value = round(state, 1)
elif self.entity_description.key in {"windangle_value", "gustangle_value"}:
self._attr_native_value = fix_angle(state)
elif self.entity_description.key in {"windangle", "gustangle"}:
self._attr_native_value = process_angle(fix_angle(state))
elif self.entity_description.key == "rf_status":
self._attr_native_value = process_rf(state)
elif self.entity_description.key == "wifi_status":
self._attr_native_value = process_wifi(state)
elif self.entity_description.key == "health_idx":
self._attr_native_value = process_health(state)
else:
self._attr_native_value = state
except KeyError:
if self.state:
_LOGGER.debug(
"No %s data found for %s",
self.entity_description.key,
self._device_name,
)
self._attr_native_value = None
return
self.async_write_ha_state() | [
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hzlzh/AlfredWorkflow.com | 7055f14f6922c80ea5943839eb0caff11ae57255 | Sources/Workflows/SearchKippt/kippt/requests/cookies.py | python | RequestsCookieJar.copy | (self) | return new_cj | Return a copy of this RequestsCookieJar. | Return a copy of this RequestsCookieJar. | [
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] | def copy(self):
"""Return a copy of this RequestsCookieJar."""
new_cj = RequestsCookieJar()
new_cj.update(self)
return new_cj | [
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google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/nql/nql/util.py | python | ModelBuilder.build_estimator | (self, model_dir=None, params=None) | return tf.estimator.Estimator(
model_fn=self.build_model_fn(), model_dir=model_dir, params=params) | Produce an Estimator for this Model.
Args:
model_dir: passed in to Estimator - location of tmp files used by
Estimator to checkpoint models
params: passed in to estimator - dict of model_fn parameters
Returns:
a tf.estimator.Estimator | Produce an Estimator for this Model. | [
"Produce",
"an",
"Estimator",
"for",
"this",
"Model",
"."
] | def build_estimator(self, model_dir=None, params=None):
"""Produce an Estimator for this Model.
Args:
model_dir: passed in to Estimator - location of tmp files used by
Estimator to checkpoint models
params: passed in to estimator - dict of model_fn parameters
Returns:
a tf.estimator.Estimator
"""
return tf.estimator.Estimator(
model_fn=self.build_model_fn(), model_dir=model_dir, params=params) | [
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napari/napari | dbf4158e801fa7a429de8ef1cdee73bf6d64c61e | napari/utils/colormaps/vendored/cm.py | python | revcmap | (data) | return data_r | Can only handle specification *data* in dictionary format. | Can only handle specification *data* in dictionary format. | [
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] | def revcmap(data):
"""Can only handle specification *data* in dictionary format."""
data_r = {}
for key, val in data.items():
if callable(val):
valnew = _reverser(val)
# This doesn't work: lambda x: val(1-x)
# The same "val" (the first one) is used
# each time, so the colors are identical
# and the result is shades of gray.
else:
# Flip x and exchange the y values facing x = 0 and x = 1.
valnew = [(1.0 - x, y1, y0) for x, y0, y1 in reversed(val)]
data_r[key] = valnew
return data_r | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/manifolds/local_frame.py | python | LocalCoFrame.at | (self, point) | return self._basis.at(point).dual_basis() | r"""
Return the value of ``self`` at a given point on the base space, this
value being a basis of the dual vector bundle at this point.
INPUT:
- ``point`` -- :class:`~sage.manifolds.point.ManifoldPoint`;
point `p` in the domain `U` of the coframe (denoted `f` hereafter)
OUTPUT:
- :class:`~sage.tensor.modules.free_module_basis.FreeModuleCoBasis`
representing the basis `f(p)` of the vector space `E^*_p`,
dual to the vector bundle fiber `E_p`
EXAMPLES:
Cobasis of a vector bundle fiber::
sage: M = Manifold(2, 'M', structure='top', start_index=1)
sage: X.<x,y> = M.chart()
sage: E = M.vector_bundle(2, 'E')
sage: e = E.local_frame('e')
sage: e_dual = e.coframe(); e_dual
Local coframe (E|_M, (e^1,e^2))
sage: p = M.point((-1,2), name='p')
sage: e_dual_p = e_dual.at(p) ; e_dual_p
Dual basis (e^1,e^2) on the Fiber of E at Point p on the
2-dimensional topological manifold M
sage: type(e_dual_p)
<class 'sage.tensor.modules.free_module_basis.FreeModuleCoBasis'>
sage: e_dual_p[1]
Linear form e^1 on the Fiber of E at Point p on the 2-dimensional
topological manifold M
sage: e_dual_p[2]
Linear form e^2 on the Fiber of E at Point p on the 2-dimensional
topological manifold M
sage: e_dual_p is e.at(p).dual_basis()
True | r"""
Return the value of ``self`` at a given point on the base space, this
value being a basis of the dual vector bundle at this point. | [
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] | def at(self, point):
r"""
Return the value of ``self`` at a given point on the base space, this
value being a basis of the dual vector bundle at this point.
INPUT:
- ``point`` -- :class:`~sage.manifolds.point.ManifoldPoint`;
point `p` in the domain `U` of the coframe (denoted `f` hereafter)
OUTPUT:
- :class:`~sage.tensor.modules.free_module_basis.FreeModuleCoBasis`
representing the basis `f(p)` of the vector space `E^*_p`,
dual to the vector bundle fiber `E_p`
EXAMPLES:
Cobasis of a vector bundle fiber::
sage: M = Manifold(2, 'M', structure='top', start_index=1)
sage: X.<x,y> = M.chart()
sage: E = M.vector_bundle(2, 'E')
sage: e = E.local_frame('e')
sage: e_dual = e.coframe(); e_dual
Local coframe (E|_M, (e^1,e^2))
sage: p = M.point((-1,2), name='p')
sage: e_dual_p = e_dual.at(p) ; e_dual_p
Dual basis (e^1,e^2) on the Fiber of E at Point p on the
2-dimensional topological manifold M
sage: type(e_dual_p)
<class 'sage.tensor.modules.free_module_basis.FreeModuleCoBasis'>
sage: e_dual_p[1]
Linear form e^1 on the Fiber of E at Point p on the 2-dimensional
topological manifold M
sage: e_dual_p[2]
Linear form e^2 on the Fiber of E at Point p on the 2-dimensional
topological manifold M
sage: e_dual_p is e.at(p).dual_basis()
True
"""
return self._basis.at(point).dual_basis() | [
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sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | modules/s3cfg.py | python | S3Config.get_br_case_activity_updates | (self) | return self.br.get("case_activity_updates", False) | Use case activity update journal (inline-component) | Use case activity update journal (inline-component) | [
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] | def get_br_case_activity_updates(self):
"""
Use case activity update journal (inline-component)
"""
return self.br.get("case_activity_updates", False) | [
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] | https://github.com/sahana/eden/blob/1696fa50e90ce967df69f66b571af45356cc18da/modules/s3cfg.py#L3164-L3168 | |
pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/patched/notpip/_vendor/html5lib/html5parser.py | python | impliedTagToken | (name, type="EndTag", attributes=None,
selfClosing=False) | return {"type": tokenTypes[type], "name": name, "data": attributes,
"selfClosing": selfClosing} | [] | def impliedTagToken(name, type="EndTag", attributes=None,
selfClosing=False):
if attributes is None:
attributes = {}
return {"type": tokenTypes[type], "name": name, "data": attributes,
"selfClosing": selfClosing} | [
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Nuitka/Nuitka | 39262276993757fa4e299f497654065600453fc9 | nuitka/build/inline_copy/tqdm/tqdm/keras.py | python | TqdmCallback.display | (self) | Displays in the current cell in Notebooks. | Displays in the current cell in Notebooks. | [
"Displays",
"in",
"the",
"current",
"cell",
"in",
"Notebooks",
"."
] | def display(self):
"""Displays in the current cell in Notebooks."""
container = getattr(self.epoch_bar, 'container', None)
if container is None:
return
from .notebook import display
display(container)
batch_bar = getattr(self, 'batch_bar', None)
if batch_bar is not None:
display(batch_bar.container) | [
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