Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
convert_broadcast_greater | (node, **kwargs) | Map MXNet's broadcast_greater operator attributes to onnx's Greater operator
and return the created node.
| Map MXNet's broadcast_greater operator attributes to onnx's Greater operator
and return the created node.
| def convert_broadcast_greater(node, **kwargs):
"""Map MXNet's broadcast_greater operator attributes to onnx's Greater operator
and return the created node.
"""
return create_basic_op_node('Greater', node, kwargs) | [
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convert_broadcast_equal | (node, **kwargs) | Map MXNet's broadcast_equal operator attributes to onnx's Equal operator
and return the created node.
| Map MXNet's broadcast_equal operator attributes to onnx's Equal operator
and return the created node.
| def convert_broadcast_equal(node, **kwargs):
"""Map MXNet's broadcast_equal operator attributes to onnx's Equal operator
and return the created node.
"""
return create_basic_op_node('Equal', node, kwargs) | [
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convert_broadcast_logical_and | (node, **kwargs) | Map MXNet's broadcast logical and operator attributes to onnx's Add operator
and return the created node.
| Map MXNet's broadcast logical and operator attributes to onnx's Add operator
and return the created node.
| def convert_broadcast_logical_and(node, **kwargs):
"""Map MXNet's broadcast logical and operator attributes to onnx's Add operator
and return the created node.
"""
return create_basic_op_node('And', node, kwargs) | [
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convert_broadcast_logical_or | (node, **kwargs) | Map MXNet's broadcast logical or operator attributes to onnx's Or operator
and return the created node.
| Map MXNet's broadcast logical or operator attributes to onnx's Or operator
and return the created node.
| def convert_broadcast_logical_or(node, **kwargs):
"""Map MXNet's broadcast logical or operator attributes to onnx's Or operator
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return create_basic_op_node('Or', node, kwargs) | [
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convert_broadcast_logical_xor | (node, **kwargs) | Map MXNet's broadcast logical xor operator attributes to onnx's Xor operator
and return the created node.
| Map MXNet's broadcast logical xor operator attributes to onnx's Xor operator
and return the created node.
| def convert_broadcast_logical_xor(node, **kwargs):
"""Map MXNet's broadcast logical xor operator attributes to onnx's Xor operator
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"""
return create_basic_op_node('Xor', node, kwargs) | [
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convert_logical_not | (node, **kwargs) | Map MXNet's logical not operator attributes to onnx's Not operator
and return the created node.
| Map MXNet's logical not operator attributes to onnx's Not operator
and return the created node.
| def convert_logical_not(node, **kwargs):
"""Map MXNet's logical not operator attributes to onnx's Not operator
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return create_basic_op_node('Not', node, kwargs) | [
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convert_size | (node, **kwargs) | Map MXNet's size_array operator attributes to onnx's Size operator
and return the created node.
| Map MXNet's size_array operator attributes to onnx's Size operator
and return the created node.
| def convert_size(node, **kwargs):
"""Map MXNet's size_array operator attributes to onnx's Size operator
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"""
return create_basic_op_node('Size', node, kwargs) | [
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convert_logsoftmax | (node, **kwargs) | Map MXNet's log_softmax operator attributes to onnx's LogSoftMax operator
and return the created node.
| Map MXNet's log_softmax operator attributes to onnx's LogSoftMax operator
and return the created node.
| def convert_logsoftmax(node, **kwargs):
"""Map MXNet's log_softmax operator attributes to onnx's LogSoftMax operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
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axis = int(attrs.get("axis", -1))
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convert_norm | (node, **kwargs) | Map MXNet's norm operator attributes to onnx's ReduceL1 and ReduceL2 operators
and return the created node.
| Map MXNet's norm operator attributes to onnx's ReduceL1 and ReduceL2 operators
and return the created node.
| def convert_norm(node, **kwargs):
"""Map MXNet's norm operator attributes to onnx's ReduceL1 and ReduceL2 operators
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
mx_axis = attrs.get("axis", None)
axes = convert_string_to_list(str(mx_axis)) if mx_axis else ... | [
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convert_multinomial | (node, **kwargs) | Map MXNet's multinomial operator attributes to onnx's
Multinomial operator and return the created node.
| Map MXNet's multinomial operator attributes to onnx's
Multinomial operator and return the created node.
| def convert_multinomial(node, **kwargs):
"""Map MXNet's multinomial operator attributes to onnx's
Multinomial operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get("dtype", 'int32'))]
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convert_random_uniform | (node, **kwargs) | Map MXNet's random_uniform operator attributes to onnx's RandomUniform
operator and return the created node.
| Map MXNet's random_uniform operator attributes to onnx's RandomUniform
operator and return the created node.
| def convert_random_uniform(node, **kwargs):
"""Map MXNet's random_uniform operator attributes to onnx's RandomUniform
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
low = float(attrs.get("low", 0))
high = float(attrs.get... | [
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convert_random_normal | (node, **kwargs) | Map MXNet's random_normal operator attributes to onnx's RandomNormal
operator and return the created node.
| Map MXNet's random_normal operator attributes to onnx's RandomNormal
operator and return the created node.
| def convert_random_normal(node, **kwargs):
"""Map MXNet's random_normal operator attributes to onnx's RandomNormal
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
mean = float(attrs.get("loc", 0))
scale = float(attrs.get(... | [
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convert_roipooling | (node, **kwargs) | Map MXNet's ROIPooling operator attributes to onnx's MaxRoiPool
operator and return the created node.
| Map MXNet's ROIPooling operator attributes to onnx's MaxRoiPool
operator and return the created node.
| def convert_roipooling(node, **kwargs):
"""Map MXNet's ROIPooling operator attributes to onnx's MaxRoiPool
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
pooled_shape = convert_string_to_list(attrs.get('pooled_size'))
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convert_tile | (node, **kwargs) | Map MXNet's Tile operator attributes to onnx's Tile
operator and return the created node.
| Map MXNet's Tile operator attributes to onnx's Tile
operator and return the created node.
| def convert_tile(node, **kwargs):
"""Map MXNet's Tile operator attributes to onnx's Tile
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
reps_list = convert_string_to_list(attrs["reps"])
initializer = kwargs["initializer"]
reps_shape_np = np.ar... | [
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convert_broadcast_to | (node, **kwargs) | Map MXNet's broadcast_to operator attributes to onnx's Expand
operator and return the created node.
| Map MXNet's broadcast_to operator attributes to onnx's Expand
operator and return the created node.
| def convert_broadcast_to(node, **kwargs):
"""Map MXNet's broadcast_to operator attributes to onnx's Expand
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
shape_list = convert_string_to_list(attrs["shape"])
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convert_topk | (node, **kwargs) | Map MXNet's topk operator attributes to onnx's TopK operator
and return the created node.
| Map MXNet's topk operator attributes to onnx's TopK operator
and return the created node.
| def convert_topk(node, **kwargs):
"""Map MXNet's topk operator attributes to onnx's TopK operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get('axis', '-1'))
k = int(attrs.get('k', '1'))
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convert_take | (node, **kwargs) | Map MXNet's Take operator attributes to onnx's Gather operator.
| Map MXNet's Take operator attributes to onnx's Gather operator.
| def convert_take(node, **kwargs):
"""Map MXNet's Take operator attributes to onnx's Gather operator.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get('axis', 0))
node = onnx.helper.make_node(
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choice_blocks | (layers_configs, stage_ops) |
Create list of layer candidates for NNI one-shot NAS.
Parameters
----------
layers_configs : list
the layer config: [input_channel, output_channel, stride, height]
stage_ops : dict
the pairs of op name and layer operator
Returns
-------
output: list
list of lay... |
Create list of layer candidates for NNI one-shot NAS. | def choice_blocks(layers_configs, stage_ops):
"""
Create list of layer candidates for NNI one-shot NAS.
Parameters
----------
layers_configs : list
the layer config: [input_channel, output_channel, stride, height]
stage_ops : dict
the pairs of op name and layer operator
Ret... | [
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SingleOperation.__init__ | (self, layers_configs, stage_ops, sampled_op="") |
Parameters
----------
layers_configs : list
the layer config: [input_channel, output_channel, stride, height]
stage_ops : dict
the pairs of op name and layer operator
sampled_op : str
the searched layer name
|
Parameters
----------
layers_configs : list
the layer config: [input_channel, output_channel, stride, height]
stage_ops : dict
the pairs of op name and layer operator
sampled_op : str
the searched layer name
| def __init__(self, layers_configs, stage_ops, sampled_op=""):
"""
Parameters
----------
layers_configs : list
the layer config: [input_channel, output_channel, stride, height]
stage_ops : dict
the pairs of op name and layer operator
sampled_op : st... | [
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async_setup_platform | (hass, config, async_add_entities, discovery_info=None) | Set up the Fido sensor. | Set up the Fido sensor. | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Set up the Fido sensor."""
username = config.get(CONF_USERNAME)
password = config.get(CONF_PASSWORD)
httpsession = hass.helpers.aiohttp_client.async_get_clientsession()
fido_data = FidoData(username, password,... | [
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FidoSensor.__init__ | (self, fido_data, sensor_type, name, number) | Initialize the sensor. | Initialize the sensor. | def __init__(self, fido_data, sensor_type, name, number):
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self.client_name = name
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FidoSensor.name | (self) | Return the name of the sensor. | Return the name of the sensor. | def name(self):
"""Return the name of the sensor."""
return f"{self.client_name} {self._number} {self._name}" | [
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FidoSensor.state | (self) | Return the state of the sensor. | Return the state of the sensor. | def state(self):
"""Return the state of the sensor."""
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FidoSensor.unit_of_measurement | (self) | Return the unit of measurement of this entity, if any. | Return the unit of measurement of this entity, if any. | def unit_of_measurement(self):
"""Return the unit of measurement of this entity, if any."""
return self._unit_of_measurement | [
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FidoSensor.icon | (self) | Icon to use in the frontend, if any. | Icon to use in the frontend, if any. | def icon(self):
"""Icon to use in the frontend, if any."""
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FidoSensor.device_state_attributes | (self) | Return the state attributes of the sensor. | Return the state attributes of the sensor. | def device_state_attributes(self):
"""Return the state attributes of the sensor."""
return {"number": self._number} | [
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FidoSensor.async_update | (self) | Get the latest data from Fido and update the state. | Get the latest data from Fido and update the state. | async def async_update(self):
"""Get the latest data from Fido and update the state."""
await self.fido_data.async_update()
if self.type == "balance":
if self.fido_data.data.get(self.type) is not None:
self._state = round(self.fido_data.data[self.type], 2)
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FidoData.__init__ | (self, username, password, httpsession) | Initialize the data object. | Initialize the data object. | def __init__(self, username, password, httpsession):
"""Initialize the data object."""
self.client = FidoClient(username, password, REQUESTS_TIMEOUT, httpsession)
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FidoData.async_update | (self) | Get the latest data from Fido. | Get the latest data from Fido. | async def async_update(self):
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Model.fork | (self) |
Create a new model which has same topology, names, and IDs to current one.
Can only be invoked on a frozen model.
The new model will be in `Mutating` state.
This API is used in mutator base class.
|
Create a new model which has same topology, names, and IDs to current one. | def fork(self) -> 'Model':
"""
Create a new model which has same topology, names, and IDs to current one.
Can only be invoked on a frozen model.
The new model will be in `Mutating` state.
This API is used in mutator base class.
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Model.get_nodes | (self) |
Traverse through all the nodes.
|
Traverse through all the nodes.
| def get_nodes(self) -> Iterable['Node']:
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Traverse through all the nodes.
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Model.get_nodes_by_label | (self, label: str) |
Traverse all the nodes to find the matched node(s) with the given label.
There could be multiple nodes with the same label. Name space name can uniquely
identify a graph or node.
NOTE: the implementation does not support the class abstration
|
Traverse all the nodes to find the matched node(s) with the given label.
There could be multiple nodes with the same label. Name space name can uniquely
identify a graph or node. | def get_nodes_by_label(self, label: str) -> List['Node']:
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Traverse all the nodes to find the matched node(s) with the given label.
There could be multiple nodes with the same label. Name space name can uniquely
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Model.get_nodes_by_type | (self, type_name: str) |
Traverse all the nodes to find the matched node(s) with the given type.
|
Traverse all the nodes to find the matched node(s) with the given type.
| def get_nodes_by_type(self, type_name: str) -> List['Node']:
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Traverse all the nodes to find the matched node(s) with the given type.
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Model.get_node_by_name | (self, node_name: str) |
Traverse all the nodes to find the matched node with the given name.
|
Traverse all the nodes to find the matched node with the given name.
| def get_node_by_name(self, node_name: str) -> 'Node':
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Traverse all the nodes to find the matched node with the given name.
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Graph.get_node_by_name | (self, name: str) |
Returns the node which has specified name; or returns `None` if no node has this name.
|
Returns the node which has specified name; or returns `None` if no node has this name.
| def get_node_by_name(self, name: str) -> Optional['Node']:
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Returns the node which has specified name; or returns `None` if no node has this name.
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Graph.get_nodes_by_type | (self, operation_type: str) |
Returns nodes whose operation is specified typed.
|
Returns nodes whose operation is specified typed.
| def get_nodes_by_type(self, operation_type: str) -> List['Node']:
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Graph.get_node_by_id | (self, node_id: int) |
Returns the node which has specified name; or returns `None` if no node has this name.
|
Returns the node which has specified name; or returns `None` if no node has this name.
| def get_node_by_id(self, node_id: int) -> Optional['Node']:
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Graph.fork | (self) |
Fork the model and returns corresponding graph in new model.
This shortcut might be helpful because many algorithms only cares about "stem" subgraph instead of whole model.
|
Fork the model and returns corresponding graph in new model.
This shortcut might be helpful because many algorithms only cares about "stem" subgraph instead of whole model.
| def fork(self) -> 'Graph':
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Fork the model and returns corresponding graph in new model.
This shortcut might be helpful because many algorithms only cares about "stem" subgraph instead of whole model.
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Node.specialize_cell | (self) |
Only available if the operation is a cell.
Duplicate the cell template and let this node reference to newly created copy.
|
Only available if the operation is a cell.
Duplicate the cell template and let this node reference to newly created copy.
| def specialize_cell(self) -> Graph:
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Only available if the operation is a cell.
Duplicate the cell template and let this node reference to newly created copy.
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_retrieve_online_state | (data, detail) | Get the latest state of the sensor. | Get the latest state of the sensor. | def _retrieve_online_state(data, detail):
"""Get the latest state of the sensor."""
# The doorbell will go into standby mode when there is no motion
# for a short while. It will wake by itself when needed so we need
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_activity_time_based_state | (data, device_id, activity_types) | Get the latest state of the sensor. | Get the latest state of the sensor. | def _activity_time_based_state(data, device_id, activity_types):
"""Get the latest state of the sensor."""
latest = data.activity_stream.get_latest_device_activity(device_id, activity_types)
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async_setup_entry | (hass, config_entry, async_add_entities) | Set up the August binary sensors. | Set up the August binary sensors. | async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the August binary sensors."""
data = hass.data[DOMAIN][config_entry.entry_id][DATA_AUGUST]
devices = []
for door in data.locks:
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AugustDoorBinarySensor.__init__ | (self, data, sensor_type, device) | Initialize the sensor. | Initialize the sensor. | def __init__(self, data, sensor_type, device):
"""Initialize the sensor."""
super().__init__(data, device)
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AugustDoorBinarySensor.available | (self) | Return the availability of this sensor. | Return the availability of this sensor. | def available(self):
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AugustDoorBinarySensor.is_on | (self) | Return true if the binary sensor is on. | Return true if the binary sensor is on. | def is_on(self):
"""Return true if the binary sensor is on."""
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AugustDoorBinarySensor.device_class | (self) | Return the class of this device. | Return the class of this device. | def device_class(self):
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AugustDoorBinarySensor.name | (self) | Return the name of the binary sensor. | Return the name of the binary sensor. | def name(self):
"""Return the name of the binary sensor."""
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AugustDoorBinarySensor._update_from_data | (self) | Get the latest state of the sensor and update activity. | Get the latest state of the sensor and update activity. | def _update_from_data(self):
"""Get the latest state of the sensor and update activity."""
door_activity = self._data.activity_stream.get_latest_device_activity(
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AugustDoorBinarySensor.unique_id | (self) | Get the unique of the door open binary sensor. | Get the unique of the door open binary sensor. | def unique_id(self) -> str:
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AugustDoorbellBinarySensor.__init__ | (self, data, sensor_type, device) | Initialize the sensor. | Initialize the sensor. | def __init__(self, data, sensor_type, device):
"""Initialize the sensor."""
super().__init__(data, device)
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AugustDoorbellBinarySensor.available | (self) | Return the availability of this sensor. | Return the availability of this sensor. | def available(self):
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AugustDoorbellBinarySensor.is_on | (self) | Return true if the binary sensor is on. | Return true if the binary sensor is on. | def is_on(self):
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AugustDoorbellBinarySensor.device_class | (self) | Return the class of this device, from component DEVICE_CLASSES. | Return the class of this device, from component DEVICE_CLASSES. | def device_class(self):
"""Return the class of this device, from component DEVICE_CLASSES."""
return SENSOR_TYPES_DOORBELL[self._sensor_type][SENSOR_DEVICE_CLASS] | [
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AugustDoorbellBinarySensor.name | (self) | Return the name of the binary sensor. | Return the name of the binary sensor. | def name(self):
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AugustDoorbellBinarySensor._state_provider | (self) | Return the state provider for the binary sensor. | Return the state provider for the binary sensor. | def _state_provider(self):
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192,
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] | [
194,
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] | python | en | ['en', 'no', 'en'] | True |
AugustDoorbellBinarySensor._is_time_based | (self) | Return true of false if the sensor is time based. | Return true of false if the sensor is time based. | def _is_time_based(self):
"""Return true of false if the sensor is time based."""
return SENSOR_TYPES_DOORBELL[self._sensor_type][SENSOR_STATE_IS_TIME_BASED] | [
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AugustDoorbellBinarySensor._update_from_data | (self) | Get the latest state of the sensor. | Get the latest state of the sensor. | def _update_from_data(self):
"""Get the latest state of the sensor."""
self._cancel_any_pending_updates()
self._state = self._state_provider(self._data, self._detail)
if self._is_time_based:
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AugustDoorbellBinarySensor._schedule_update_to_recheck_turn_off_sensor | (self) | Schedule an update to recheck the sensor to see if it is ready to turn off. | Schedule an update to recheck the sensor to see if it is ready to turn off. | def _schedule_update_to_recheck_turn_off_sensor(self):
"""Schedule an update to recheck the sensor to see if it is ready to turn off."""
# If the sensor is already off there is nothing to do
if not self._state:
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# self.hass is only available after setup is completed
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AugustDoorbellBinarySensor._cancel_any_pending_updates | (self) | Cancel any updates to recheck a sensor to see if it is ready to turn off. | Cancel any updates to recheck a sensor to see if it is ready to turn off. | def _cancel_any_pending_updates(self):
"""Cancel any updates to recheck a sensor to see if it is ready to turn off."""
if self._check_for_off_update_listener:
_LOGGER.debug("%s: canceled pending update", self.entity_id)
self._check_for_off_update_listener()
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AugustDoorbellBinarySensor.async_added_to_hass | (self) | Call the mixin to subscribe and setup an async_track_point_in_utc_time to turn off the sensor if needed. | Call the mixin to subscribe and setup an async_track_point_in_utc_time to turn off the sensor if needed. | async def async_added_to_hass(self):
"""Call the mixin to subscribe and setup an async_track_point_in_utc_time to turn off the sensor if needed."""
self._schedule_update_to_recheck_turn_off_sensor()
await super().async_added_to_hass() | [
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AugustDoorbellBinarySensor.unique_id | (self) | Get the unique id of the doorbell sensor. | Get the unique id of the doorbell sensor. | def unique_id(self) -> str:
"""Get the unique id of the doorbell sensor."""
return (
f"{self._device_id}_"
f"{SENSOR_TYPES_DOORBELL[self._sensor_type][SENSOR_NAME].lower()}"
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_assert_tensors_equal | (a, b, atol=1e-12, prefix="") | If tensors not close, or a and b arent both tensors, raise a nice Assertion error. | If tensors not close, or a and b arent both tensors, raise a nice Assertion error. | def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
"""If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
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mock_webhook_id | () | Mock webhook_id. | Mock webhook_id. | def mock_webhook_id():
"""Mock webhook_id."""
with patch(
"homeassistant.components.webhook.async_generate_id", return_value=WEBHOOK_ID
):
yield | [
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29,
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mock_secret | () | Mock secret. | Mock secret. | def mock_secret():
"""Mock secret."""
with patch("secrets.token_hex", return_value=SECRET):
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36,
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mock_not_supports_encryption | () | Mock non successful nacl import. | Mock non successful nacl import. | def mock_not_supports_encryption():
"""Mock non successful nacl import."""
with patch(
"homeassistant.components.owntracks.config_flow.supports_encryption",
return_value=False,
):
yield | [
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46,
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] | python | de | ['de', 'it', 'en'] | False |
init_config_flow | (hass) | Init a configuration flow. | Init a configuration flow. | async def init_config_flow(hass):
"""Init a configuration flow."""
await async_process_ha_core_config(
hass,
{"external_url": BASE_URL},
)
flow = config_flow.OwnTracksFlow()
flow.hass = hass
return flow | [
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test_user | (hass, webhook_id, secret) | Test user step. | Test user step. | async def test_user(hass, webhook_id, secret):
"""Test user step."""
flow = await init_config_flow(hass)
result = await flow.async_step_user()
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "user"
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74,
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test_import | (hass, webhook_id, secret) | Test import step. | Test import step. | async def test_import(hass, webhook_id, secret):
"""Test import step."""
flow = await init_config_flow(hass)
result = await flow.async_step_import({})
assert result["type"] == data_entry_flow.RESULT_TYPE_CREATE_ENTRY
assert result["title"] == "OwnTracks"
assert result["data"][CONF_WEBHOOK_ID] =... | [
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] | python | de | ['de', 'sd', 'en'] | False |
test_import_setup | (hass) | Test that we automatically create a config flow. | Test that we automatically create a config flow. | async def test_import_setup(hass):
"""Test that we automatically create a config flow."""
await async_process_ha_core_config(
hass,
{"external_url": "http://example.com"},
)
assert not hass.config_entries.async_entries(DOMAIN)
assert await async_setup_component(hass, DOMAIN, {"owntr... | [
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test_abort_if_already_setup | (hass) | Test that we can't add more than one instance. | Test that we can't add more than one instance. | async def test_abort_if_already_setup(hass):
"""Test that we can't add more than one instance."""
flow = await init_config_flow(hass)
MockConfigEntry(domain=DOMAIN, data={}).add_to_hass(hass)
assert hass.config_entries.async_entries(DOMAIN)
# Should fail, already setup (import)
result = await ... | [
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test_user_not_supports_encryption | (hass, not_supports_encryption) | Test user step. | Test user step. | async def test_user_not_supports_encryption(hass, not_supports_encryption):
"""Test user step."""
flow = await init_config_flow(hass)
result = await flow.async_step_user({})
assert result["type"] == data_entry_flow.RESULT_TYPE_CREATE_ENTRY
assert (
result["description_placeholders"]["secret... | [
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130,
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test_unload | (hass) | Test unloading a config flow. | Test unloading a config flow. | async def test_unload(hass):
"""Test unloading a config flow."""
await async_process_ha_core_config(
hass,
{"external_url": "http://example.com"},
)
with patch(
"homeassistant.config_entries.ConfigEntries.async_forward_entry_setup"
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result = await h... | [
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test_with_cloud_sub | (hass) | Test creating a config flow while subscribed. | Test creating a config flow while subscribed. | async def test_with_cloud_sub(hass):
"""Test creating a config flow while subscribed."""
hass.config.components.add("cloud")
with patch(
"homeassistant.components.cloud.async_active_subscription", return_value=True
), patch(
"homeassistant.components.cloud.async_create_cloudhook",
... | [
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184,
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] | python | en | ['en', 'en', 'en'] | True |
setup | (hass, config) | Set up the smarty environment. | Set up the smarty environment. | def setup(hass, config):
"""Set up the smarty environment."""
conf = config[DOMAIN]
host = conf[CONF_HOST]
name = conf[CONF_NAME]
_LOGGER.debug("Name: %s, host: %s", name, host)
smarty = Smarty(host=host)
hass.data[DOMAIN] = {"api": smarty, "name": name}
# Initial update
smarty... | [
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convert_tf_weight_name_to_pt_weight_name | (tf_name, start_prefix_to_remove="") |
Convert a TF 2.0 model variable name in a pytorch model weight name.
Conventions for TF2.0 scopes -> PyTorch attribute names conversions:
- '$1___$2' is replaced by $2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch)
- '_._' is replaced by a new level separation (can be used to... |
Convert a TF 2.0 model variable name in a pytorch model weight name. | def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=""):
"""
Convert a TF 2.0 model variable name in a pytorch model weight name.
Conventions for TF2.0 scopes -> PyTorch attribute names conversions:
- '$1___$2' is replaced by $2 (can be used to duplicate or remove layers i... | [
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load_pytorch_checkpoint_in_tf2_model | (tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False) | Load pytorch checkpoints in a TF 2.0 model | Load pytorch checkpoints in a TF 2.0 model | def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False):
"""Load pytorch checkpoints in a TF 2.0 model"""
try:
import tensorflow as tf # noqa: F401
import torch # noqa: F401
except ImportError:
logger.error(
... | [
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load_pytorch_model_in_tf2_model | (tf_model, pt_model, tf_inputs=None, allow_missing_keys=False) | Load pytorch checkpoints in a TF 2.0 model | Load pytorch checkpoints in a TF 2.0 model | def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_missing_keys=False):
"""Load pytorch checkpoints in a TF 2.0 model"""
pt_state_dict = pt_model.state_dict()
return load_pytorch_weights_in_tf2_model(
tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_... | [
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load_pytorch_weights_in_tf2_model | (tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False) | Load pytorch state_dict in a TF 2.0 model. | Load pytorch state_dict in a TF 2.0 model. | def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False):
"""Load pytorch state_dict in a TF 2.0 model."""
try:
import tensorflow as tf # noqa: F401
import torch # noqa: F401
from tensorflow.python.keras import backend as K
except Imp... | [
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load_tf2_checkpoint_in_pytorch_model | (pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False) |
Load TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to easily do transfer learning (see
https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357).
|
Load TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to easily do transfer learning (see
https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357).
| def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False):
"""
Load TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to easily do transfer learning (see
https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorfl... | [
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293,
101
] | python | en | ['en', 'error', 'th'] | False |
load_tf2_model_in_pytorch_model | (pt_model, tf_model, allow_missing_keys=False) | Load TF 2.0 model in a pytorch model | Load TF 2.0 model in a pytorch model | def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False):
"""Load TF 2.0 model in a pytorch model"""
weights = tf_model.weights
return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys) | [
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296,
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300,
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load_tf2_weights_in_pytorch_model | (pt_model, tf_weights, allow_missing_keys=False) | Load TF2.0 symbolic weights in a PyTorch model | Load TF2.0 symbolic weights in a PyTorch model | def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False):
"""Load TF2.0 symbolic weights in a PyTorch model"""
try:
import tensorflow as tf # noqa: F401
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a TensorFlow model ... | [
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"logge... | [
303,
0
] | [
408,
19
] | python | en | ['en', 'en', 'en'] | True |
lazyprop | (fun) |
Decorator which only evaluates the specified function when accessed.
|
Decorator which only evaluates the specified function when accessed.
| def lazyprop(fun):
"""
Decorator which only evaluates the specified function when accessed.
"""
name = '_lazy_' + fun.__name__
@property
def _lazy(self):
val = getattr(self, name, None)
if val is None:
val = fun(self)
setattr(self, name, val)
retu... | [
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"... | [
35,
0
] | [
49,
16
] | python | en | ['en', 'error', 'th'] | False |
taskd_cmd | (cmd, *args, **kwargs) |
Invoke taskd with the specified command with the privileges of the 'taskd'
user and 'taskd' group.
If 'capture_stdout' is passed as a keyword argument with the value True,
the return value are the contents the command printed to stdout.
|
Invoke taskd with the specified command with the privileges of the 'taskd'
user and 'taskd' group. | def taskd_cmd(cmd, *args, **kwargs):
"""
Invoke taskd with the specified command with the privileges of the 'taskd'
user and 'taskd' group.
If 'capture_stdout' is passed as a keyword argument with the value True,
the return value are the contents the command printed to stdout.
"""
capture_s... | [
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... | [
63,
0
] | [
77,
5
] | python | en | ['en', 'error', 'th'] | False |
certtool_cmd | (*args, **kwargs) |
Invoke certtool from GNUTLS and return the output of the command.
The provided arguments are added to the certtool command and keyword
arguments are added to subprocess.check_output().
Note that this will suppress all output of certtool and it will only be
printed whenever there is an unsuccessfu... |
Invoke certtool from GNUTLS and return the output of the command. | def certtool_cmd(*args, **kwargs):
"""
Invoke certtool from GNUTLS and return the output of the command.
The provided arguments are added to the certtool command and keyword
arguments are added to subprocess.check_output().
Note that this will suppress all output of certtool and it will only be
... | [
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... | [
80,
0
] | [
95,
5
] | python | en | ['en', 'error', 'th'] | False |
mark_imperative | (*path) |
Mark the specified path as being imperatively managed by creating an empty
file called ".imperative", so that it doesn't interfere with the
declarative configuration.
|
Mark the specified path as being imperatively managed by creating an empty
file called ".imperative", so that it doesn't interfere with the
declarative configuration.
| def mark_imperative(*path):
"""
Mark the specified path as being imperatively managed by creating an empty
file called ".imperative", so that it doesn't interfere with the
declarative configuration.
"""
open(os.path.join(mkpath(*path), ".imperative"), 'a').close() | [
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] | [
107,
0
] | [
113,
65
] | python | en | ['en', 'error', 'th'] | False |
is_imperative | (*path) |
Check whether the given path is marked as imperative, see mark_imperative()
for more information.
|
Check whether the given path is marked as imperative, see mark_imperative()
for more information.
| def is_imperative(*path):
"""
Check whether the given path is marked as imperative, see mark_imperative()
for more information.
"""
full_path = []
for component in path:
full_path.append(component)
if os.path.exists(os.path.join(mkpath(*full_path), ".imperative")):
re... | [
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"... | [
116,
0
] | [
126,
16
] | python | en | ['en', 'error', 'th'] | False |
create_template | (contents) |
Generate a temporary file with the specified contents as a list of strings
and yield its path as the context.
|
Generate a temporary file with the specified contents as a list of strings
and yield its path as the context.
| def create_template(contents):
"""
Generate a temporary file with the specified contents as a list of strings
and yield its path as the context.
"""
template = NamedTemporaryFile(mode="w", prefix="certtool-template")
template.writelines(map(lambda l: l + "\n", contents))
template.flush()
... | [
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"\"\\n\"",
... | [
139,
0
] | [
148,
20
] | python | en | ['en', 'error', 'th'] | False |
cli | (ctx) |
Manage Taskserver users and certificates
|
Manage Taskserver users and certificates
| def cli(ctx):
"""
Manage Taskserver users and certificates
"""
if not IS_AUTO_CONFIG:
return
for path in (CA_KEY, CA_CERT, CRL_FILE):
if not os.path.exists(path):
msg = "CA setup not done or incomplete, missing file {}."
ctx.fail(msg.format(path)) | [
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"=",
"\"... | [
446,
0
] | [
455,
38
] | python | en | ['en', 'error', 'th'] | False |
org_cli | () |
Manage organisations
|
Manage organisations
| def org_cli():
"""
Manage organisations
"""
pass | [
"def",
"org_cli",
"(",
")",
":",
"pass"
] | [
459,
0
] | [
463,
8
] | python | en | ['en', 'error', 'th'] | False |
user_cli | () |
Manage users
|
Manage users
| def user_cli():
"""
Manage users
"""
pass | [
"def",
"user_cli",
"(",
")",
":",
"pass"
] | [
467,
0
] | [
471,
8
] | python | en | ['en', 'error', 'th'] | False |
group_cli | () |
Manage groups
|
Manage groups
| def group_cli():
"""
Manage groups
"""
pass | [
"def",
"group_cli",
"(",
")",
":",
"pass"
] | [
475,
0
] | [
479,
8
] | python | en | ['en', 'error', 'th'] | False |
list_users | (organisation) |
List all users belonging to the specified organisation.
|
List all users belonging to the specified organisation.
| def list_users(organisation):
"""
List all users belonging to the specified organisation.
"""
label("The following users exists for {}:".format(organisation.name))
for user in organisation.users.values():
sys.stdout.write(user.name + "\n") | [
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"."... | [
484,
0
] | [
490,
42
] | python | en | ['en', 'error', 'th'] | False |
list_groups | (organisation) |
List all users belonging to the specified organisation.
|
List all users belonging to the specified organisation.
| def list_groups(organisation):
"""
List all users belonging to the specified organisation.
"""
label("The following users exists for {}:".format(organisation.name))
for group in organisation.groups.values():
sys.stdout.write(group.name + "\n") | [
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... | [
495,
0
] | [
501,
43
] | python | en | ['en', 'error', 'th'] | False |
list_orgs | () |
List available organisations
|
List available organisations
| def list_orgs():
"""
List available organisations
"""
label("The following organisations exist:")
for org in Manager().orgs:
sys.stdout.write(org.name + "\n") | [
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"\"\\n\"",
")"
] | [
505,
0
] | [
511,
41
] | python | en | ['en', 'error', 'th'] | False |
get_uuid | (organisation, user) |
Get the UUID of the specified user belonging to the specified organisation.
|
Get the UUID of the specified user belonging to the specified organisation.
| def get_uuid(organisation, user):
"""
Get the UUID of the specified user belonging to the specified organisation.
"""
userobj = organisation.get_user(user)
if userobj is None:
msg = "User {} doesn't exist in organisation {}."
sys.exit(msg.format(userobj.name, organisation.name))
... | [
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"exit",
"(",
"msg",
... | [
517,
0
] | [
527,
37
] | python | en | ['en', 'error', 'th'] | False |
export_user | (organisation, user) |
Export user of the specified organisation as a series of shell commands
that can be used on the client side to easily import the certificates.
Note that the private key will be exported as well, so use this with care!
|
Export user of the specified organisation as a series of shell commands
that can be used on the client side to easily import the certificates. | def export_user(organisation, user):
"""
Export user of the specified organisation as a series of shell commands
that can be used on the client side to easily import the certificates.
Note that the private key will be exported as well, so use this with care!
"""
userobj = organisation.get_user(... | [
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"exit",
"(",
"msg"... | [
533,
0
] | [
545,
38
] | python | en | ['en', 'error', 'th'] | False |
add_org | (name) |
Create an organisation with the specified name.
|
Create an organisation with the specified name.
| def add_org(name):
"""
Create an organisation with the specified name.
"""
if os.path.exists(mkpath(name)):
msg = "Organisation with name {} already exists."
sys.exit(msg.format(name))
taskd_cmd("add", "org", name)
mark_imperative(name) | [
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"(",
"name",
")",
")... | [
550,
0
] | [
559,
25
] | python | en | ['en', 'error', 'th'] | False |
del_org | (name) |
Delete the organisation with the specified name.
All of the users and groups will be deleted as well and client certificates
will be revoked.
|
Delete the organisation with the specified name. | def del_org(name):
"""
Delete the organisation with the specified name.
All of the users and groups will be deleted as well and client certificates
will be revoked.
"""
Manager().del_org(name)
msg = ("Organisation {} deleted. Be sure to restart the Taskserver"
" using 'systemctl ... | [
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"\" using 'systemctl restart taskserver.service' in order for\"",
"\" the certificate revocation to... | [
564,
0
] | [
575,
42
] | python | en | ['en', 'error', 'th'] | False |
add_user | (organisation, user) |
Create a user for the given organisation along with a client certificate
and print the key of the new user.
The client certificate along with it's public key can be shown via the
'user export' subcommand.
|
Create a user for the given organisation along with a client certificate
and print the key of the new user. | def add_user(organisation, user):
"""
Create a user for the given organisation along with a client certificate
and print the key of the new user.
The client certificate along with it's public key can be shown via the
'user export' subcommand.
"""
userobj = organisation.add_user(user)
if... | [
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"msg",
... | [
581,
0
] | [
594,
64
] | python | en | ['en', 'error', 'th'] | False |
del_user | (organisation, user) |
Delete a user from the given organisation.
This will also revoke the client certificate of the given user.
|
Delete a user from the given organisation. | def del_user(organisation, user):
"""
Delete a user from the given organisation.
This will also revoke the client certificate of the given user.
"""
organisation.del_user(user)
msg = ("User {} deleted. Be sure to restart the Taskserver using"
" 'systemctl restart taskserver.service' ... | [
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"\" certificate rev... | [
600,
0
] | [
610,
42
] | python | en | ['en', 'error', 'th'] | False |
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