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train | Dataset.get_maintainer | Get the dataset's maintainer.
Returns:
User: Dataset's maintainer | src/hdx/data/dataset.py | def get_maintainer(self):
# type: () -> hdx.data.user.User
"""Get the dataset's maintainer.
Returns:
User: Dataset's maintainer
"""
return hdx.data.user.User.read_from_hdx(self.data['maintainer'], configuration=self.configuration) | def get_maintainer(self):
# type: () -> hdx.data.user.User
"""Get the dataset's maintainer.
Returns:
User: Dataset's maintainer
"""
return hdx.data.user.User.read_from_hdx(self.data['maintainer'], configuration=self.configuration) | [
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train | Dataset.set_maintainer | Set the dataset's maintainer.
Args:
maintainer (Union[User,Dict,str]): Either a user id or User metadata from a User object or dictionary.
Returns:
None | src/hdx/data/dataset.py | def set_maintainer(self, maintainer):
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"""Set the dataset's maintainer.
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maintainer (Union[User,Dict,str]): Either a user id or User metadata from a User object or dictionary.
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... | def set_maintainer(self, maintainer):
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maintainer (Union[User,Dict,str]): Either a user id or User metadata from a User object or dictionary.
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train | Dataset.get_organization | Get the dataset's organization.
Returns:
Organization: Dataset's organization | src/hdx/data/dataset.py | def get_organization(self):
# type: () -> hdx.data.organization.Organization
"""Get the dataset's organization.
Returns:
Organization: Dataset's organization
"""
return hdx.data.organization.Organization.read_from_hdx(self.data['owner_org'], configuration=self.conf... | def get_organization(self):
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"""Get the dataset's organization.
Returns:
Organization: Dataset's organization
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train | Dataset.set_organization | Set the dataset's organization.
Args:
organization (Union[Organization,Dict,str]): Either an Organization id or Organization metadata from an Organization object or dictionary.
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# type: (Union[hdx.data.organization.Organization,Dict,str]) -> None
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train | Dataset.get_showcases | Get any showcases the dataset is in
Returns:
List[Showcase]: list of showcases | src/hdx/data/dataset.py | def get_showcases(self):
# type: () -> List[hdx.data.showcase.Showcase]
"""Get any showcases the dataset is in
Returns:
List[Showcase]: list of showcases
"""
assoc_result, showcases_dicts = self._read_from_hdx('showcase', self.data['id'], fieldname='package_id',
... | def get_showcases(self):
# type: () -> List[hdx.data.showcase.Showcase]
"""Get any showcases the dataset is in
Returns:
List[Showcase]: list of showcases
"""
assoc_result, showcases_dicts = self._read_from_hdx('showcase', self.data['id'], fieldname='package_id',
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train | Dataset._get_dataset_showcase_dict | Get dataset showcase dict
Args:
showcase (Union[Showcase,Dict,str]): Either a showcase id or Showcase metadata from a Showcase object or dictionary
Returns:
dict: dataset showcase dict | src/hdx/data/dataset.py | def _get_dataset_showcase_dict(self, showcase):
# type: (Union[hdx.data.showcase.Showcase, Dict,str]) -> Dict
"""Get dataset showcase dict
Args:
showcase (Union[Showcase,Dict,str]): Either a showcase id or Showcase metadata from a Showcase object or dictionary
Returns:
... | def _get_dataset_showcase_dict(self, showcase):
# type: (Union[hdx.data.showcase.Showcase, Dict,str]) -> Dict
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Args:
showcase (Union[Showcase,Dict,str]): Either a showcase id or Showcase metadata from a Showcase object or dictionary
Returns:
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train | Dataset.add_showcase | Add dataset to showcase
Args:
showcase (Union[Showcase,Dict,str]): Either a showcase id or showcase metadata from a Showcase object or dictionary
showcases_to_check (List[Showcase]): list of showcases against which to check existence of showcase. Defaults to showcases containing dataset... | src/hdx/data/dataset.py | def add_showcase(self, showcase, showcases_to_check=None):
# type: (Union[hdx.data.showcase.Showcase,Dict,str], List[hdx.data.showcase.Showcase]) -> bool
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showcase (Union[Showcase,Dict,str]): Either a showcase id or showcase metadata from a Showcase o... | def add_showcase(self, showcase, showcases_to_check=None):
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train | Dataset.add_showcases | Add dataset to multiple showcases
Args:
showcases (List[Union[Showcase,Dict,str]]): A list of either showcase ids or showcase metadata from Showcase objects or dictionaries
showcases_to_check (List[Showcase]): list of showcases against which to check existence of showcase. Defaults to s... | src/hdx/data/dataset.py | def add_showcases(self, showcases, showcases_to_check=None):
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train | Dataset.remove_showcase | Remove dataset from showcase
Args:
showcase (Union[Showcase,Dict,str]): Either a showcase id string or showcase metadata from a Showcase object or dictionary
Returns:
None | src/hdx/data/dataset.py | def remove_showcase(self, showcase):
# type: (Union[hdx.data.showcase.Showcase,Dict,str]) -> None
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Args:
showcase (Union[Showcase,Dict,str]): Either a showcase id string or showcase metadata from a Showcase object or dictionary
Returns:
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# type: (Union[hdx.data.showcase.Showcase,Dict,str]) -> None
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Args:
showcase (Union[Showcase,Dict,str]): Either a showcase id string or showcase metadata from a Showcase object or dictionary
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train | Dataset.set_requestable | Set the dataset to be of type requestable or not
Args:
requestable (bool): Set whether dataset is requestable. Defaults to True.
Returns:
None | src/hdx/data/dataset.py | def set_requestable(self, requestable=True):
# type: (bool) -> None
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Args:
requestable (bool): Set whether dataset is requestable. Defaults to True.
Returns:
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train | Dataset.get_filetypes | Return list of filetypes in your data
Returns:
List[str]: List of filetypes | src/hdx/data/dataset.py | def get_filetypes(self):
# type: () -> List[str]
"""Return list of filetypes in your data
Returns:
List[str]: List of filetypes
"""
if not self.is_requestable():
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List[str]: List of filetypes
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train | Dataset.clean_dataset_tags | Clean dataset tags according to tags cleanup spreadsheet and return if any changes occurred
Returns:
Tuple[bool, bool]: Returns (True if tags changed or False if not, True if error or False if not) | src/hdx/data/dataset.py | def clean_dataset_tags(self):
# type: () -> Tuple[bool, bool]
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Returns:
Tuple[bool, bool]: Returns (True if tags changed or False if not, True if error or False if not)
"""
... | def clean_dataset_tags(self):
# type: () -> Tuple[bool, bool]
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Returns:
Tuple[bool, bool]: Returns (True if tags changed or False if not, True if error or False if not)
"""
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train | Dataset.set_quickchart_resource | Set the resource that will be used for displaying QuickCharts in dataset preview
Args:
resource (Union[hdx.data.resource.Resource,Dict,str,int]): Either resource id or name, resource metadata from a Resource object or a dictionary or position
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Args:
resource (Union[hdx.data.resource.Resource,Dict,str,int]): Either resource id or na... | def set_quickchart_resource(self, resource):
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train | Dataset.create_default_views | Create default resource views for all resources in dataset
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create_datastore_views (bool): Whether to try to create resource views that point to the datastore
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# type: (bool) -> None
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Args:
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train | Configuration._get_credentials | Return HDX site username and password
Returns:
Optional[Tuple[str, str]]: HDX site username and password or None | src/hdx/hdx_configuration.py | def _get_credentials(self):
# type: () -> Optional[Tuple[str, str]]
"""
Return HDX site username and password
Returns:
Optional[Tuple[str, str]]: HDX site username and password or None
"""
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Return HDX site username and password
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train | Configuration.call_remoteckan | Calls the remote CKAN
Args:
*args: Arguments to pass to remote CKAN call_action method
**kwargs: Keyword arguments to pass to remote CKAN call_action method
Returns:
Dict: The response from the remote CKAN call_action method | src/hdx/hdx_configuration.py | def call_remoteckan(self, *args, **kwargs):
# type: (Any, Any) -> Dict
"""
Calls the remote CKAN
Args:
*args: Arguments to pass to remote CKAN call_action method
**kwargs: Keyword arguments to pass to remote CKAN call_action method
Returns:
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# type: (Any, Any) -> Dict
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Calls the remote CKAN
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*args: Arguments to pass to remote CKAN call_action method
**kwargs: Keyword arguments to pass to remote CKAN call_action method
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train | Configuration._environment_variables | Overwrite keyword arguments with environment variables
Args:
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hdx_site (str): HDX site to use eg. prod, test. Defaults to test.
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Overwrite keyword arguments with environment variables
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train | Configuration.create_remoteckan | Create remote CKAN instance from configuration
Args:
site_url (str): Site url.
user_agent (Optional[str]): User agent string. HDXPythonLibrary/X.X.X- is prefixed.
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train | Configuration.setup_remoteckan | Set up remote CKAN from provided CKAN or by creating from configuration
Args:
remoteckan (Optional[ckanapi.RemoteCKAN]): CKAN instance. Defaults to setting one up from configuration.
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Set up remote CKAN from provided CKAN or by creating from configuration
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train | Configuration.setup | Set up the HDX configuration
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train | Configuration._create | Create HDX configuration
Args:
configuration (Optional[Configuration]): Configuration instance. Defaults to setting one up from passed arguments.
remoteckan (Optional[ckanapi.RemoteCKAN]): CKAN instance. Defaults to setting one up from configuration.
**kwargs: See below
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Create HDX configuration
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Create HDX configuration
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train | kwargs_to_variable_assignment | Convert a dictionary into a string with assignments
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train | is_jsonable | Check if an object is jsonable.
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An object is jsonable if it is json serialisable and by loading its json representation the same object is recovered.
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Check if an expresion can be literal_eval.
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train | find_duplicates | Return the duplicates in a list.
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"""
Return the duplicates in a list.
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----------
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-------
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train | sort_dict | Sort a dictionary by key or value.
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Sort a dictionary by key or value.
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Sort a dictionary by key or value.
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train | group_dict_by_value | Group a dictionary by values.
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Output dictionary. The keys are the values of the initial dictionary
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Group a dictionary by values.
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----------
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Returns
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train | variable_status | Find the possible parameters and "global" variables from a python code.
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Find the possible parameters and "global" variables from a python code.
This is achieved by parsing the abstract syntax tree.
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... | def variable_status(code: str,
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"""
Find the possible parameters and "global" variables from a python code.
This is achieved by parsing the abstract syntax tree.
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train | increment_name | Increment the name where the incremental part is given by parameters.
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Increment the name where the incremental part is given by parameters.
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Increment the name where the incremental part is given by parameters.
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train | ResourceView.read_from_hdx | Reads the resource view given by identifier from HDX and returns ResourceView object
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identifier (str): Identifier of resource view
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train | ResourceView.get_all_for_resource | Read all resource views for a resource given by identifier from HDX and returns list of ResourceView objects
Args:
identifier (str): Identifier of resource
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identifier (str): Identifier of resource
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identifier (str): Identifier of resource
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train | ResourceView._update_resource_view | Check if resource view exists in HDX and if so, update resource view
Returns:
bool: True if updated and False if not | src/hdx/data/resource_view.py | def _update_resource_view(self, log=False):
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bool: True if updated and False if not
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train | ResourceView.create_in_hdx | Check if resource view exists in HDX and if so, update it, otherwise create resource view
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# type: () -> None
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train | ResourceView.copy | Copies all fields except id, resource_id and package_id from another resource view.
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resource_view (Union[ResourceView,Dict,str]): Either a resource view id or resource view metadata either from a ResourceView object or a dictionary
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train | Tags.tagscleanupdicts | Get tags cleanup dictionaries
Args:
configuration (Optional[Configuration]): HDX configuration. Defaults to global configuration.
url (Optional[str]): Url of tags cleanup spreadsheet. Defaults to None (internal configuration parameter).
keycolumn (int): Column number of tag ... | src/hdx/hdx_tagscleanup.py | def tagscleanupdicts(configuration=None, url=None, keycolumn=5, failchained=True):
# type: (Optional[Configuration], Optional[str], int, bool) -> Tuple[Dict,List]
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Get tags cleanup dictionaries
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configuration (Optional[Configuration]): HDX configuration. Defaults to ... | def tagscleanupdicts(configuration=None, url=None, keycolumn=5, failchained=True):
# type: (Optional[Configuration], Optional[str], int, bool) -> Tuple[Dict,List]
"""
Get tags cleanup dictionaries
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configuration (Optional[Configuration]): HDX configuration. Defaults to ... | [
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train | User.read_from_hdx | Reads the user given by identifier from HDX and returns User object
Args:
identifier (str): Identifier of user
configuration (Optional[Configuration]): HDX configuration. Defaults to global configuration.
Returns:
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# type: (str, Optional[Configuration]) -> Optional['User']
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train | User.update_in_hdx | Check if user exists in HDX and if so, update user
Returns:
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# type: () -> None
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Returns:
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train | User.create_in_hdx | Check if user exists in HDX and if so, update it, otherwise create user
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train | User.email | Emails a user.
Args:
subject (str): Email subject
text_body (str): Plain text email body
html_body (str): HTML email body
sender (Optional[str]): Email sender. Defaults to SMTP username.
**kwargs: See below
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# type: (str, str, Optional[str], Optional[str], Any) -> None
"""Emails a user.
Args:
subject (str): Email subject
text_body (str): Plain text email body
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# type: (str, str, Optional[str], Optional[str], Any) -> None
"""Emails a user.
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subject (str): Email subject
text_body (str): Plain text email body
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train | User.get_all_users | Get all users in HDX
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configuration (Optional[Configuration]): HDX configuration. Defaults to global configuration.
**kwargs: See below
q (str): Restrict to names containing a string. Defaults to all users.
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# type: (Optional[Configuration], Any) -> List['User']
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Args:
configuration (Optional[Configuration]): HDX configuration. Defaults to global configuration.
**kwargs: See below
q (str): Re... | def get_all_users(configuration=None, **kwargs):
# type: (Optional[Configuration], Any) -> List['User']
"""Get all users in HDX
Args:
configuration (Optional[Configuration]): HDX configuration. Defaults to global configuration.
**kwargs: See below
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train | User.email_users | Email a list of users
Args:
users (List[User]): List of users
subject (str): Email subject
text_body (str): Plain text email body
html_body (str): HTML email body
sender (Optional[str]): Email sender. Defaults to SMTP username.
configurati... | src/hdx/data/user.py | def email_users(users, subject, text_body, html_body=None, sender=None, configuration=None, **kwargs):
# type: (List['User'], str, str, Optional[str], Optional[str], Optional[Configuration], Any) -> None
"""Email a list of users
Args:
users (List[User]): List of users
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# type: (List['User'], str, str, Optional[str], Optional[str], Optional[Configuration], Any) -> None
"""Email a list of users
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users (List[User]): List of users
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train | User.get_organizations | Get organizations in HDX that this user is a member of.
Args:
permission (str): Permission to check for. Defaults to 'read'.
Returns:
List[Organization]: List of organizations in HDX that this user is a member of | src/hdx/data/user.py | def get_organizations(self, permission='read'):
# type: (str) -> List['Organization']
"""Get organizations in HDX that this user is a member of.
Args:
permission (str): Permission to check for. Defaults to 'read'.
Returns:
List[Organization]: List of organizatio... | def get_organizations(self, permission='read'):
# type: (str) -> List['Organization']
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permission (str): Permission to check for. Defaults to 'read'.
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train | _remove_old_snapshots | Remove old snapshots
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train | watch | Start watching a new volume
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:type interval: str
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:type connection: boto.ec2.connection.EC2Connection
:param connection: EC2 connection object
:type volume_id: str
:param volume_id: VolumeID to add to the watchlist
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train | get_volume_id | Get Volume ID from the given volume. Input can be volume id
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:param connection: EC2 connection object
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"""
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:param connection: EC2 connection object
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:param volume: Volume ID or Volume Name
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train | watch_from_file | Start watching a new volume
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:param file_name: path to config file
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:type connection: boto.ec2.connection.EC2Connection
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:param file_name: path to config file
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train | unwatch_from_file | Start watching a new volume
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:type connection: boto.ec2.connection.EC2Connection
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:type file_name: str
:param file_name: path to config file
:returns: None
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:param file_name: path to config file
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train | list_snapshots | List all snapshots for the volume
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:param connection: EC2 connection object
:type volume: str
:param volume: Volume ID or Volume Name
:returns: None | automated_ebs_snapshots/volume_manager.py | def list_snapshots(connection, volume):
""" List all snapshots for the volume
:type connection: boto.ec2.connection.EC2Connection
:param connection: EC2 connection object
:type volume: str
:param volume: Volume ID or Volume Name
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:param connection: EC2 connection object
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:param volume: Volume ID or Volume Name
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train | Morfologik.stem | Get stems for the words using a given parser
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stemmer.stem(['ja tańczę a ona śpi], parser)
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Example:
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[
... | def stem(self, words, parser, **kwargs):
"""
Get stems for the words using a given parser
Example:
from .parsing import ListParser
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stemmer = Morfologik()
stemmer.stem(['ja tańczę a ona śpi], parser)
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train | Morfologik._run_morfologik | Runs morfologik java jar and assumes that input and output is
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train | Showcase.read_from_hdx | Reads the showcase given by identifier from HDX and returns Showcase object
Args:
identifier (str): Identifier of showcase
configuration (Optional[Configuration]): HDX configuration. Defaults to global configuration.
Returns:
Optional[Showcase]: Showcase object if s... | src/hdx/data/showcase.py | def read_from_hdx(identifier, configuration=None):
# type: (str, Optional[Configuration]) -> Optional['Showcase']
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Args:
identifier (str): Identifier of showcase
configuration (Optional[Configu... | def read_from_hdx(identifier, configuration=None):
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train | Showcase.get_datasets | Get any datasets in the showcase
Returns:
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# type: () -> List[hdx.data.dataset.Dataset]
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Returns:
List[Dataset]: List of datasets
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assoc_result, datasets_dicts = self._read_from_hdx('showcase', self.data['id'], fieldname='showcase_id',
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List[Dataset]: List of datasets
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showcase (Union[Showcase,Dict,str]): Either a showcase id or Showcase metadata from a Showcase object or dictionary
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Args:
showcase (Union[Showcase,Dict,str]): Either a showcase id or Showcase metadata from a Showcase object or dictionary
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Args:
showcase (Union[Showcase,Dict,str]): Either a showcase id or Showcase metadata from a Showcase object or dictionary
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train | Showcase.add_dataset | Add a dataset
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dataset (Union[Dataset,Dict,str]): Either a dataset id or dataset metadata either from a Dataset object or a dictionary
datasets_to_check (List[Dataset]): List of datasets against which to check existence of dataset. Defaults to datasets in showcase.
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Args:
dataset (Union[Dataset,Dict,str]): Either a dataset id or dataset metadata either from a Dataset object or a dict... | def add_dataset(self, dataset, datasets_to_check=None):
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train | Showcase.add_datasets | Add multiple datasets
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datasets (List[Union[Dataset,Dict,str]]): A list of either dataset ids or dataset metadata from Dataset objects or dictionaries
datasets_to_check (List[Dataset]): List of datasets against which to check existence of dataset. Defaults to datasets in showcase.
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Find the possible parameters from a jupyter notebook (python3 only).
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For safety reasons, in order to avoid any code injection,
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train | ListWrapper.join | Join values and convert to string
Example:
>>> from ww import l
>>> lst = l('012')
>>> lst.join(',')
u'0,1,2'
>>> lst.join(',', template="{}#")
u'0#,1#,2#'
>>> string = lst.join(',',\
formatte... | src/ww/wrappers/lists.py | def join(self, joiner, formatter=lambda s, t: t.format(s),
template="{}"):
"""Join values and convert to string
Example:
>>> from ww import l
>>> lst = l('012')
>>> lst.join(',')
u'0,1,2'
>>> lst.join(',', template="{}#")
... | def join(self, joiner, formatter=lambda s, t: t.format(s),
template="{}"):
"""Join values and convert to string
Example:
>>> from ww import l
>>> lst = l('012')
>>> lst.join(',')
u'0,1,2'
>>> lst.join(',', template="{}#")
... | [
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train | ListWrapper.append | Append values at the end of the list
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>>> from ww import l
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train | ListWrapper.extend | Add all values of all iterables at the end of the list
Args:
iterables: iterable which content to add at the end
Example:
>>> from ww import l
>>> lst = l([])
>>> lst.extend([1, 2])
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>>> lst
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Args:
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>>> from ww import l
>>> lst = l([])
>>> lst.extend([1, 2])
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"""Add all values of all iterables at the end of the list
Args:
iterables: iterable which content to add at the end
Example:
>>> from ww import l
>>> lst = l([])
>>> lst.extend([1, 2])
[1, 2]
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train | normalize_cell_value | Process value for writing into a cell.
Args:
value: any type of variable
Returns:
json serialized value if value is list or dict, else value | housecanary/excel/utilities.py | def normalize_cell_value(value):
"""Process value for writing into a cell.
Args:
value: any type of variable
Returns:
json serialized value if value is list or dict, else value
"""
if isinstance(value, dict) or isinstance(value, list):
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Args:
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json serialized value if value is list or dict, else value
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if isinstance(value, dict) or isinstance(value, list):
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train | get_addresses_from_input_file | Read addresses from input file into list of tuples.
This only supports address and zipcode headers | housecanary/excel/utilities.py | def get_addresses_from_input_file(input_file_name):
"""Read addresses from input file into list of tuples.
This only supports address and zipcode headers
"""
mode = 'r'
if sys.version_info[0] < 3:
mode = 'rb'
with io.open(input_file_name, mode) as input_file:
reader = csv.read... | def get_addresses_from_input_file(input_file_name):
"""Read addresses from input file into list of tuples.
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mode = 'r'
if sys.version_info[0] < 3:
mode = 'rb'
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train | get_identifiers_from_input_file | Read identifiers from input file into list of dicts with the header row values
as keys, and the rest of the rows as values. | housecanary/excel/utilities.py | def get_identifiers_from_input_file(input_file_name):
"""Read identifiers from input file into list of dicts with the header row values
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"""
valid_identifiers = ['address', 'zipcode', 'unit', 'city', 'state', 'slug', 'block_id', 'msa',
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train | get_real_time_locate | function takes the ipAddress of a specific host and issues a RESTFUL call to get the device and interface that the
target host is currently connected to. Note: Although intended to return a single location, Multiple locations may
be returned for a single host due to a partially discovered network or misconfigur... | build/lib/pyhpeimc/plat/termaccess.py | def get_real_time_locate(ipAddress, auth, url):
"""
function takes the ipAddress of a specific host and issues a RESTFUL call to get the device and interface that the
target host is currently connected to. Note: Although intended to return a single location, Multiple locations may
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train | get_ip_mac_arp_list | function takes devid of specific device and issues a RESTFUL call to get the IP/MAC/ARP list from the target device.
:param devId: int or str value of the target device.
:param auth: requests auth object #usually auth.creds from auth pyhpeimc.auth.class
:param url: base url of IMC RS interface #usually a... | build/lib/pyhpeimc/plat/termaccess.py | def get_ip_mac_arp_list(devId, auth,url):
"""
function takes devid of specific device and issues a RESTFUL call to get the IP/MAC/ARP list from the target device.
:param devId: int or str value of the target device.
:param auth: requests auth object #usually auth.creds from auth pyhpeimc.auth.class
... | def get_ip_mac_arp_list(devId, auth,url):
"""
function takes devid of specific device and issues a RESTFUL call to get the IP/MAC/ARP list from the target device.
:param devId: int or str value of the target device.
:param auth: requests auth object #usually auth.creds from auth pyhpeimc.auth.class
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train | get_ip_scope_detail | function requires no inputs and returns all IP address scopes currently configured on the HPE IMC server. If the
optional scopeId parameter is included, this will automatically return only the desired scope id.
:param scopeId: integer of the desired scope id ( optional )
:param auth: requests auth object #... | build/lib/pyhpeimc/plat/termaccess.py | def get_ip_scope_detail(scopeId, auth, url ):
"""
function requires no inputs and returns all IP address scopes currently configured on the HPE IMC server. If the
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function requires no inputs and returns all IP address scopes currently configured on the HPE IMC server. If the
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train | delete_ip_scope | Function to delete an entire IP segment from the IMC IP Address management under terminal access
:param network_address
:param auth
:param url
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.termaccess import *
>>> auth = IMCAuth("http://", "10.101.0.203", "8080", "admin", "admin")
... | build/lib/pyhpeimc/plat/termaccess.py | def delete_ip_scope(network_address, auth, url):
'''Function to delete an entire IP segment from the IMC IP Address management under terminal access
:param network_address
:param auth
:param url
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.termaccess import *
>>> auth = IMCAuth(... | def delete_ip_scope(network_address, auth, url):
'''Function to delete an entire IP segment from the IMC IP Address management under terminal access
:param network_address
:param auth
:param url
>>> from pyhpeimc.auth import *
>>> from pyhpeimc.plat.termaccess import *
>>> auth = IMCAuth(... | [
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train | add_scope_ip | Function to add new host IP address allocation to existing scope ID
:param ipaddress:
:param name: name of the owner of this host
:param description: Description of the host
:param auth: requests auth object #usually auth.creds from auth pyhpeimc.auth.class
:param url: base url of IMC RS interf... | build/lib/pyhpeimc/plat/termaccess.py | def add_scope_ip(ipaddress, name, description, scopeid, auth, url):
"""
Function to add new host IP address allocation to existing scope ID
:param ipaddress:
:param name: name of the owner of this host
:param description: Description of the host
:param auth: requests auth object #usually aut... | def add_scope_ip(ipaddress, name, description, scopeid, auth, url):
"""
Function to add new host IP address allocation to existing scope ID
:param ipaddress:
:param name: name of the owner of this host
:param description: Description of the host
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train | remove_scope_ip | Function to add remove IP address allocation
:param hostid: Host id of the host to be deleted
:param auth: requests auth object #usually auth.creds from auth pyhpeimc.auth.class
:param url: base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
:return: String of HTTP response c... | build/lib/pyhpeimc/plat/termaccess.py | def remove_scope_ip(hostid, auth, url):
"""
Function to add remove IP address allocation
:param hostid: Host id of the host to be deleted
:param auth: requests auth object #usually auth.creds from auth pyhpeimc.auth.class
:param url: base url of IMC RS interface #usually auth.url from pyhpeimc.au... | def remove_scope_ip(hostid, auth, url):
"""
Function to add remove IP address allocation
:param hostid: Host id of the host to be deleted
:param auth: requests auth object #usually auth.creds from auth pyhpeimc.auth.class
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train | get_ip_scope_hosts | Function requires input of scope ID and returns list of allocated IP address for the specified scope
:param auth: requests auth object #usually auth.creds from auth pyhpeimc.auth.class
:param url: base url of IMC RS interface #usually auth.url from pyhpeimc.auth.authclass
:param scopeId: Interger of teh ... | build/lib/pyhpeimc/plat/termaccess.py | def get_ip_scope_hosts( scopeId, auth, url):
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:param ipaddress:
:param name: name of the owner of this host
:param description: Description of the host
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''' Function to abstract existing add_scope_ip_function. Allows for use of network address rather than forcing human
to learn the scope_id
:param ipaddress:
:param name: name of the owner of this host
:param descrip... | def add_host_to_segment(ipaddress, name, description, network_address, auth, url):
''' Function to abstract existing add_scope_ip_function. Allows for use of network address rather than forcing human
to learn the scope_id
:param ipaddress:
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train | delete_host_from_segment | Function to abstract | build/lib/pyhpeimc/plat/termaccess.py | def delete_host_from_segment(ipaddress, networkaddress, auth, url):
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'''
host_id = get_host_id(ipaddress, networkaddress, auth, url)
remove_scope_ip(host_id, auth.creds, auth.url) | def delete_host_from_segment(ipaddress, networkaddress, auth, url):
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train | generate_signature | Generates the API request signature from the given parameters. | src/ai/backend/client/auth.py | def generate_signature(method, version, endpoint,
date, rel_url, content_type, content,
access_key, secret_key, hash_type):
'''
Generates the API request signature from the given parameters.
'''
hash_type = hash_type
hostname = endpoint._val.netloc # FI... | def generate_signature(method, version, endpoint,
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Generates the API request signature from the given parameters.
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hash_type = hash_type
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train | Agent.list_with_limit | Fetches the list of agents with the given status with limit and offset for
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:param limit: number of agents to get
:param offset: offset index of agents to get
:param status: An upper-cased string constant representing agent
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'''
Fetches the list of agents with the given status with limit an... | async def list_with_limit(cls,
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train | Request.set_content | Sets the content of the request. | src/ai/backend/client/request.py | def set_content(self, value: RequestContent, *,
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Sets the content of the request.
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Sets the content of the request.
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train | Request.set_json | A shortcut for set_content() with JSON objects. | src/ai/backend/client/request.py | def set_json(self, value: object):
'''
A shortcut for set_content() with JSON objects.
'''
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train | Request.attach_files | Attach a list of files represented as AttachedFile. | src/ai/backend/client/request.py | def attach_files(self, files: Sequence[AttachedFile]):
'''
Attach a list of files represented as AttachedFile.
'''
assert not self._content, 'content must be empty to attach files.'
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self._attached_files = files | def attach_files(self, files: Sequence[AttachedFile]):
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train | Request._sign | Calculates the signature of the given request and adds the
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It should be called at the very end of request preparation and before
sending the request to the server. | src/ai/backend/client/request.py | def _sign(self, rel_url, access_key=None, secret_key=None, hash_type=None):
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Calculates the signature of the given request and adds the
Authorization HTTP header.
It should be called at the very end of request preparation and before
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Calculates the signature of the given request and adds the
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train | Request.fetch | Sends the request to the server and reads the response.
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.. code-block:: python3
from ai.backend.client.request import Request
from ai.backend.client.sess... | src/ai/backend/client/request.py | def fetch(self, **kwargs) -> 'FetchContextManager':
'''
Sends the request to the server and reads the response.
You may use this method either with plain synchronous Session or
AsyncSession. Both the followings patterns are valid:
.. code-block:: python3
from ai.bac... | def fetch(self, **kwargs) -> 'FetchContextManager':
'''
Sends the request to the server and reads the response.
You may use this method either with plain synchronous Session or
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.. code-block:: python3
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train | Request.connect_websocket | Creates a WebSocket connection.
.. warning::
This method only works with
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'''
Creates a WebSocket connection.
.. warning::
This method only works with
:class:`~ai.backend.client.session.AsyncSession`.
'''
assert isinstance(self.session, AsyncSession), \
... | def connect_websocket(self, **kwargs) -> 'WebSocketContextManager':
'''
Creates a WebSocket connection.
.. warning::
This method only works with
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'''
assert isinstance(self.session, AsyncSession), \
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train | main | Main function.
:return:
None. | src/aoiklivereload/demo/tornado_demo.py | def main():
"""
Main function.
:return:
None.
"""
try:
# Get the `src` directory's absolute path
src_path = os.path.dirname(
# `aoiklivereload` directory's absolute path
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# `demo` directory's absolute path
... | def main():
"""
Main function.
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# `aoiklivereload` directory's absolute path
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train | add_perf_task | function takes the a python dict containing all necessary fields for a performance tasks,
transforms the dict into JSON and issues a RESTFUL call to create the performance task. device.
:param task: dictionary containing all required fields for performance tasks
:param auth: requests auth object #usually ... | pyhpeimc/plat/perf.py | def add_perf_task(task, auth, url):
"""
function takes the a python dict containing all necessary fields for a performance tasks,
transforms the dict into JSON and issues a RESTFUL call to create the performance task. device.
:param task: dictionary containing all required fields for performance tasks
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function takes the a python dict containing all necessary fields for a performance tasks,
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train | get_perf_task | function takes the a str object containing the name of an existing performance tasks and
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function takes the a str object containing the name of an existing performance tasks and
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:param task_name: str containing the name of the performance task
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train | delete_perf_task | Function takes a str of the target task_name to be deleted and retrieves task_id using
the get_perf_task function. Once the task_id has been successfully retrieved it is
populated into the task_id variable and an DELETE call is made against the HPE IMC REST
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:param ta... | pyhpeimc/plat/perf.py | def delete_perf_task(task_name, auth, url):
"""
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populated into the task_id variable and an DELETE call is made against the HPE IMC RES... | def delete_perf_task(task_name, auth, url):
"""
Function takes a str of the target task_name to be deleted and retrieves task_id using
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