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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
QSBinarySensor.__init__ | (self, sensor) | Initialize the sensor. | Initialize the sensor. | def __init__(self, sensor):
"""Initialize the sensor."""
super().__init__(sensor["id"], sensor["name"])
self.channel = sensor["channel"]
sensor_type = sensor["type"]
self._decode, _ = SENSORS[sensor_type]
self._invert = not sensor.get("invert", False)
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QSBinarySensor.update_packet | (self, packet) | Receive update packet from QSUSB. | Receive update packet from QSUSB. | def update_packet(self, packet):
"""Receive update packet from QSUSB."""
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QSBinarySensor.is_on | (self) | Check if device is on (non-zero). | Check if device is on (non-zero). | def is_on(self):
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QSBinarySensor.unique_id | (self) | Return a unique identifier for this sensor. | Return a unique identifier for this sensor. | def unique_id(self):
"""Return a unique identifier for this sensor."""
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QSBinarySensor.device_class | (self) | Return the class of this sensor. | Return the class of this sensor. | def device_class(self):
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distance2bbox | (points, distance, max_shape=None) | Decode distance prediction to bounding box.
Args:
points (Tensor): Shape (n, 2), [x, y].
distance (Tensor): Distance from the given point to 4
boundaries (left, top, right, bottom).
max_shape (tuple): Shape of the image.
Returns:
Tensor: Decoded bboxes.
| Decode distance prediction to bounding box. | def distance2bbox(points, distance, max_shape=None):
"""Decode distance prediction to bounding box.
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points (Tensor): Shape (n, 2), [x, y].
distance (Tensor): Distance from the given point to 4
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distance2kps | (points, distance, max_shape=None) | Decode distance prediction to bounding box.
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points (Tensor): Shape (n, 2), [x, y].
distance (Tensor): Distance from the given point to 4
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max_shape (tuple): Shape of the image.
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| Decode distance prediction to bounding box. | def distance2kps(points, distance, max_shape=None):
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distance (Tensor): Distance from the given point to 4
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async_setup_platform | (hass, config, async_add_entities, discovery_info=None) | Old way of setting up the Daikin HVAC platform.
Can only be called when a user accidentally mentions the platform in their
config. But even in that case it would have been ignored.
| Old way of setting up the Daikin HVAC platform. | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Old way of setting up the Daikin HVAC platform.
Can only be called when a user accidentally mentions the platform in their
config. But even in that case it would have been ignored.
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async_setup_entry | (hass, entry, async_add_entities) | Set up Daikin climate based on config_entry. | Set up Daikin climate based on config_entry. | async def async_setup_entry(hass, entry, async_add_entities):
"""Set up Daikin climate based on config_entry."""
daikin_api = hass.data[DAIKIN_DOMAIN].get(entry.entry_id)
async_add_entities([DaikinClimate(daikin_api)], update_before_add=True) | [
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DaikinClimate.__init__ | (self, api) | Initialize the climate device. | Initialize the climate device. | def __init__(self, api):
"""Initialize the climate device."""
self._api = api
self._list = {
ATTR_HVAC_MODE: list(HA_STATE_TO_DAIKIN),
ATTR_FAN_MODE: self._api.device.fan_rate,
ATTR_SWING_MODE: self._api.device.swing_modes,
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DaikinClimate._set | (self, settings) | Set device settings using API. | Set device settings using API. | async def _set(self, settings):
"""Set device settings using API."""
values = {}
for attr in [ATTR_TEMPERATURE, ATTR_FAN_MODE, ATTR_SWING_MODE, ATTR_HVAC_MODE]:
value = settings.get(attr)
if value is None:
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DaikinClimate.supported_features | (self) | Return the list of supported features. | Return the list of supported features. | def supported_features(self):
"""Return the list of supported features."""
return self._supported_features | [
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DaikinClimate.name | (self) | Return the name of the thermostat, if any. | Return the name of the thermostat, if any. | def name(self):
"""Return the name of the thermostat, if any."""
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DaikinClimate.unique_id | (self) | Return a unique ID. | Return a unique ID. | def unique_id(self):
"""Return a unique ID."""
return self._api.device.mac | [
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DaikinClimate.temperature_unit | (self) | Return the unit of measurement which this thermostat uses. | Return the unit of measurement which this thermostat uses. | def temperature_unit(self):
"""Return the unit of measurement which this thermostat uses."""
return TEMP_CELSIUS | [
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DaikinClimate.current_temperature | (self) | Return the current temperature. | Return the current temperature. | def current_temperature(self):
"""Return the current temperature."""
return self._api.device.inside_temperature | [
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DaikinClimate.target_temperature | (self) | Return the temperature we try to reach. | Return the temperature we try to reach. | def target_temperature(self):
"""Return the temperature we try to reach."""
return self._api.device.target_temperature | [
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DaikinClimate.target_temperature_step | (self) | Return the supported step of target temperature. | Return the supported step of target temperature. | def target_temperature_step(self):
"""Return the supported step of target temperature."""
return 1 | [
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DaikinClimate.async_set_temperature | (self, **kwargs) | Set new target temperature. | Set new target temperature. | async def async_set_temperature(self, **kwargs):
"""Set new target temperature."""
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DaikinClimate.hvac_mode | (self) | Return current operation ie. heat, cool, idle. | Return current operation ie. heat, cool, idle. | def hvac_mode(self):
"""Return current operation ie. heat, cool, idle."""
daikin_mode = self._api.device.represent(HA_ATTR_TO_DAIKIN[ATTR_HVAC_MODE])[1]
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DaikinClimate.hvac_modes | (self) | Return the list of available operation modes. | Return the list of available operation modes. | def hvac_modes(self):
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DaikinClimate.async_set_hvac_mode | (self, hvac_mode) | Set HVAC mode. | Set HVAC mode. | async def async_set_hvac_mode(self, hvac_mode):
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DaikinClimate.fan_mode | (self) | Return the fan setting. | Return the fan setting. | def fan_mode(self):
"""Return the fan setting."""
return self._api.device.represent(HA_ATTR_TO_DAIKIN[ATTR_FAN_MODE])[1].title() | [
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DaikinClimate.async_set_fan_mode | (self, fan_mode) | Set fan mode. | Set fan mode. | async def async_set_fan_mode(self, fan_mode):
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DaikinClimate.fan_modes | (self) | List of available fan modes. | List of available fan modes. | def fan_modes(self):
"""List of available fan modes."""
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DaikinClimate.swing_mode | (self) | Return the fan setting. | Return the fan setting. | def swing_mode(self):
"""Return the fan setting."""
return self._api.device.represent(HA_ATTR_TO_DAIKIN[ATTR_SWING_MODE])[1].title() | [
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DaikinClimate.async_set_swing_mode | (self, swing_mode) | Set new target temperature. | Set new target temperature. | async def async_set_swing_mode(self, swing_mode):
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DaikinClimate.swing_modes | (self) | List of available swing modes. | List of available swing modes. | def swing_modes(self):
"""List of available swing modes."""
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DaikinClimate.preset_mode | (self) | Return the preset_mode. | Return the preset_mode. | def preset_mode(self):
"""Return the preset_mode."""
if (
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DaikinClimate.async_set_preset_mode | (self, preset_mode) | Set preset mode. | Set preset mode. | async def async_set_preset_mode(self, preset_mode):
"""Set preset mode."""
if preset_mode == PRESET_AWAY:
await self._api.device.set_holiday(ATTR_STATE_ON)
elif preset_mode == PRESET_BOOST:
await self._api.device.set_advanced_mode(
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DaikinClimate.preset_modes | (self) | List of available preset modes. | List of available preset modes. | def preset_modes(self):
"""List of available preset modes."""
ret = [PRESET_NONE]
if self._api.device.support_away_mode:
ret.append(PRESET_AWAY)
if self._api.device.support_advanced_modes:
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DaikinClimate.async_update | (self) | Retrieve latest state. | Retrieve latest state. | async def async_update(self):
"""Retrieve latest state."""
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DaikinClimate.async_turn_on | (self) | Turn device on. | Turn device on. | async def async_turn_on(self):
"""Turn device on."""
await self._api.device.set({}) | [
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DaikinClimate.async_turn_off | (self) | Turn device off. | Turn device off. | async def async_turn_off(self):
"""Turn device off."""
await self._api.device.set(
{HA_ATTR_TO_DAIKIN[ATTR_HVAC_MODE]: HA_STATE_TO_DAIKIN[HVAC_MODE_OFF]}
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DaikinClimate.device_info | (self) | Return a device description for device registry. | Return a device description for device registry. | def device_info(self):
"""Return a device description for device registry."""
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async_setup_entry | (hass, config_entry, async_add_entities) | Set up the Tesla binary_sensors by config_entry. | Set up the Tesla binary_sensors by config_entry. | async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the Tesla binary_sensors by config_entry."""
entities = [
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device,
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TeslaLock.async_lock | (self, **kwargs) | Send the lock command. | Send the lock command. | async def async_lock(self, **kwargs):
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TeslaLock.async_unlock | (self, **kwargs) | Send the unlock command. | Send the unlock command. | async def async_unlock(self, **kwargs):
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TeslaLock.is_locked | (self) | Get whether the lock is in locked state. | Get whether the lock is in locked state. | def is_locked(self):
"""Get whether the lock is in locked state."""
if self.tesla_device.is_locked() is None:
return None
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setup_ozw | (hass, entry=None, fixture=None) | Set up OZW and load a dump. | Set up OZW and load a dump. | async def setup_ozw(hass, entry=None, fixture=None):
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hass.config.components.add("mqtt")
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MQTTMessage.__init__ | (self, topic, payload) | Set up message. | Set up message. | def __init__(self, topic, payload):
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MQTTMessage.decode | (self) | Decode message payload from a string to a json dict. | Decode message payload from a string to a json dict. | def decode(self):
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MQTTMessage.encode | (self) | Encode message payload into a string. | Encode message payload into a string. | def encode(self):
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DotAttention.get_pre_compute | (self, s) |
:param s: [src_sequence, batch_size, src_dim]
:return: [src_sequence, batch_size. hidden_dim]
|
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DotAttention.get_att | (self, s, prob) |
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SamsungTVConfigFlow.__init__ | (self) | Initialize flow. | Initialize flow. | def __init__(self):
"""Initialize flow."""
self._host = None
self._ip = None
self._manufacturer = None
self._model = None
self._name = None
self._title = None
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SamsungTVConfigFlow._try_connect | (self) | Try to connect and check auth. | Try to connect and check auth. | def _try_connect(self):
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for method in SUPPORTED_METHODS:
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SamsungTVConfigFlow.async_step_import | (self, user_input=None) | Handle configuration by yaml file. | Handle configuration by yaml file. | async def async_step_import(self, user_input=None):
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SamsungTVConfigFlow.async_step_user | (self, user_input=None) | Handle a flow initialized by the user. | Handle a flow initialized by the user. | async def async_step_user(self, user_input=None):
"""Handle a flow initialized by the user."""
if user_input is not None:
ip_address = await self.hass.async_add_executor_job(
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SamsungTVConfigFlow.async_step_ssdp | (self, discovery_info) | Handle a flow initialized by discovery. | Handle a flow initialized by discovery. | async def async_step_ssdp(self, discovery_info):
"""Handle a flow initialized by discovery."""
host = urlparse(discovery_info[ATTR_SSDP_LOCATION]).hostname
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SamsungTVConfigFlow.async_step_confirm | (self, user_input=None) | Handle user-confirmation of discovered node. | Handle user-confirmation of discovered node. | async def async_step_confirm(self, user_input=None):
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SamsungTVConfigFlow.async_step_reauth | (self, user_input=None) | Handle configuration by re-auth. | Handle configuration by re-auth. | async def async_step_reauth(self, user_input=None):
"""Handle configuration by re-auth."""
self._host = user_input[CONF_HOST]
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list_jobs | () | List the jobs in the cluster.
Returns:
None.
| List the jobs in the cluster. | def list_jobs():
"""List the jobs in the cluster.
Returns:
None.
"""
name_to_job_details = redis_controller.get_name_to_job_details()
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get_job | (job_name: str) | Get the job with job_name.
Returns:
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| Get the job with job_name. | def get_job(job_name: str):
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create_job | (**kwargs) | Create a job.
Returns:
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| Create a job. | def create_job(**kwargs):
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delete_job | (job_name: str) | Delete a job.
Returns:
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| Delete a job. | def delete_job(job_name: str):
"""Delete a job.
Returns:
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redis_controller.remove_pending_job_ticket(job_name=job_name)
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stop_job | (job_name: str) | Stop a job.
Returns:
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| Stop a job. | def stop_job(job_name: str):
"""Stop a job.
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redis_controller.remove_pending_job_ticket(job_name=job_name)
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clean_jobs | () | Clean all jobs in the cluster.
Returns:
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| Clean all jobs in the cluster. | def clean_jobs():
"""Clean all jobs in the cluster.
Returns:
None.
"""
# Delete all job related resources.
redis_controller.delete_pending_jobs_queue()
redis_controller.delete_killed_jobs_queue()
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122,
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same_channel_sets | (channel_sets: list) | Validate that all the channel sets are consistent, return false if not | Validate that all the channel sets are consistent, return false if not | def same_channel_sets(channel_sets: list):
"""Validate that all the channel sets are consistent, return false if not"""
for chs in channel_sets[1:]:
if chs.shape[0] != channel_sets[0].shape[0] or chs.shape[1] != channel_sets[0].shape[1]:
return False
# if not np.all(channel_sets[0] =... | [
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InstanceTransform.__init__ | (self, only_trial_data=True) |
Trial transforms are, for the most part, simply operations that are performed on the loaded tensors when they are
fetched via the :meth:`__call__` method. Ideally this is implemented with pytorch operations for ease of execution
graph integration.
|
Trial transforms are, for the most part, simply operations that are performed on the loaded tensors when they are
fetched via the :meth:`__call__` method. Ideally this is implemented with pytorch operations for ease of execution
graph integration.
| def __init__(self, only_trial_data=True):
"""
Trial transforms are, for the most part, simply operations that are performed on the loaded tensors when they are
fetched via the :meth:`__call__` method. Ideally this is implemented with pytorch operations for ease of execution
graph integra... | [
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InstanceTransform.__call__ | (self, *x) |
Modifies a batch of tensors.
Parameters
----------
x : torch.Tensor, tuple
The trial tensor, not including a batch-dimension. If initialized with `only_trial_data=False`, then this
is a tuple of all ids, labels, etc. being propagated.
Returns
----... |
Modifies a batch of tensors.
Parameters
----------
x : torch.Tensor, tuple
The trial tensor, not including a batch-dimension. If initialized with `only_trial_data=False`, then this
is a tuple of all ids, labels, etc. being propagated.
Returns
----... | def __call__(self, *x):
"""
Modifies a batch of tensors.
Parameters
----------
x : torch.Tensor, tuple
The trial tensor, not including a batch-dimension. If initialized with `only_trial_data=False`, then this
is a tuple of all ids, labels, etc. being propa... | [
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51,
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InstanceTransform.new_channels | (self, old_channels) |
This is an optional method that indicates the transformation modifies the representation and/or presence of
channels.
Parameters
----------
old_channels : ndarray
An array whose last two dimensions are channel names and channel types.
Returns
... |
This is an optional method that indicates the transformation modifies the representation and/or presence of
channels. | def new_channels(self, old_channels):
"""
This is an optional method that indicates the transformation modifies the representation and/or presence of
channels.
Parameters
----------
old_channels : ndarray
An array whose last two dimensions are chan... | [
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InstanceTransform.new_sfreq | (self, old_sfreq) |
This is an optional method that indicates the transformation modifies the sampling frequency of the underlying
time-series.
Parameters
----------
old_sfreq : float
Returns
-------
new_sfreq : float
|
This is an optional method that indicates the transformation modifies the sampling frequency of the underlying
time-series. | def new_sfreq(self, old_sfreq):
"""
This is an optional method that indicates the transformation modifies the sampling frequency of the underlying
time-series.
Parameters
----------
old_sfreq : float
Returns
-------
new_sfreq : float
"""
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InstanceTransform.new_sequence_length | (self, old_sequence_length) |
This is an optional method that indicates the transformation modifies the length of the acquired extracts,
specified in number of samples.
Parameters
----------
old_sequence_length : int
Returns
-------
new_sequence_length : int
|
This is an optional method that indicates the transformation modifies the length of the acquired extracts,
specified in number of samples. | def new_sequence_length(self, old_sequence_length):
"""
This is an optional method that indicates the transformation modifies the length of the acquired extracts,
specified in number of samples.
Parameters
----------
old_sequence_length : int
Returns
---... | [
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TemporalPadding.__init__ | (self, start_padding, end_padding, mode='constant', constant_value=0) |
Pad the number of samples.
Parameters
----------
start_padding : int
The number of padded samples to add to the beginning of a trial
end_padding : int
The number of padded samples to add to the end of a trial
mode : str
... |
Pad the number of samples. | def __init__(self, start_padding, end_padding, mode='constant', constant_value=0):
"""
Pad the number of samples.
Parameters
----------
start_padding : int
The number of padded samples to add to the beginning of a trial
end_padding : int
... | [
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TemporalInterpolation.__init__ | (self, desired_sequence_length, mode='nearest', new_sfreq=None) |
This is in essence a DN3 wrapper for the pytorch function
`interpolate() <https://pytorch.org/docs/stable/nn.functional.html>`_
Currently only supports single dimensional samples (i.e. channels have not been projected into more dimensions)
Warnings
--------
Using this ... |
This is in essence a DN3 wrapper for the pytorch function
`interpolate() <https://pytorch.org/docs/stable/nn.functional.html>`_ | def __init__(self, desired_sequence_length, mode='nearest', new_sfreq=None):
"""
This is in essence a DN3 wrapper for the pytorch function
`interpolate() <https://pytorch.org/docs/stable/nn.functional.html>`_
Currently only supports single dimensional samples (i.e. channels have not bee... | [
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192,
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TemporalCrop.__init__ | (self, cropped_length, start_offset=None) |
Crop to a new length of `cropped_length` from the specified `start_offset`, or randomly select an offset.
Parameters
----------
cropped_length : int
The cropped sequence length (in samples).
start_offset : int, None, List[int]
If ... |
Crop to a new length of `cropped_length` from the specified `start_offset`, or randomly select an offset. | def __init__(self, cropped_length, start_offset=None):
"""
Crop to a new length of `cropped_length` from the specified `start_offset`, or randomly select an offset.
Parameters
----------
cropped_length : int
The cropped sequence length (in samples).
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MappingDeep1010.__init__ | (self, dataset, add_scale_ind=True, return_mask=False) |
Creates a Deep10-10 mapping for the provided dataset.
Parameters
----------
dataset : Dataset
add_scale_ind : bool
If `True` (default), the scale ind is filled with the relative scale of the trial with respect
to the data min and... |
Creates a Deep10-10 mapping for the provided dataset. | def __init__(self, dataset, add_scale_ind=True, return_mask=False):
"""
Creates a Deep10-10 mapping for the provided dataset.
Parameters
----------
dataset : Dataset
add_scale_ind : bool
If `True` (default), the scale ind is filled with the relat... | [
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To1020.__init__ | (self, only_trial_data=True, include_scale_ch=True, include_ref_chs=False) |
Transforms incoming Deep1010 data into exclusively the more limited 1020 channel set.
|
Transforms incoming Deep1010 data into exclusively the more limited 1020 channel set.
| def __init__(self, only_trial_data=True, include_scale_ch=True, include_ref_chs=False):
"""
Transforms incoming Deep1010 data into exclusively the more limited 1020 channel set.
"""
super(To1020, self).__init__(only_trial_data=only_trial_data)
self._inds_20_div = [DEEP_1010_CHS_L... | [
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UniformTransformSelection.__init__ | (self, transform_list, weights=None, suppress_warnings=False) |
Uniformly selects a transform from the `transform_list` with probabilities according to `p`.
Parameters
----------
transform_list: List[InstanceTransform]
List of transforms to select from.
weights: None, List[float]
This is either `None`, in ... |
Uniformly selects a transform from the `transform_list` with probabilities according to `p`. | def __init__(self, transform_list, weights=None, suppress_warnings=False):
"""
Uniformly selects a transform from the `transform_list` with probabilities according to `p`.
Parameters
----------
transform_list: List[InstanceTransform]
List of transforms to... | [
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async_setup | (hass, config) | Initialize the FreeDNS component. | Initialize the FreeDNS component. | async def async_setup(hass, config):
"""Initialize the FreeDNS component."""
conf = config[DOMAIN]
url = conf.get(CONF_URL)
auth_token = conf.get(CONF_ACCESS_TOKEN)
update_interval = conf[CONF_SCAN_INTERVAL]
session = hass.helpers.aiohttp_client.async_get_clientsession()
result = await _up... | [
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_update_freedns | (hass, session, url, auth_token) | Update FreeDNS. | Update FreeDNS. | async def _update_freedns(hass, session, url, auth_token):
"""Update FreeDNS."""
params = None
if url is None:
url = UPDATE_URL
if auth_token is not None:
params = {}
params[auth_token] = ""
try:
with async_timeout.timeout(TIMEOUT):
resp = await session... | [
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alexa_client | (loop, hass, hass_client) | Initialize a Home Assistant server for testing this module. | Initialize a Home Assistant server for testing this module. | def alexa_client(loop, hass, hass_client):
"""Initialize a Home Assistant server for testing this module."""
@callback
def mock_service(call):
calls.append(call)
hass.services.async_register("test", "alexa", mock_service)
assert loop.run_until_complete(
async_setup_component(
... | [
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test_flash_briefing_invalid_id | (alexa_client) | Test an invalid Flash Briefing ID. | Test an invalid Flash Briefing ID. | async def test_flash_briefing_invalid_id(alexa_client):
"""Test an invalid Flash Briefing ID."""
req = await _flash_briefing_req(alexa_client, 10000)
assert req.status == HTTP_NOT_FOUND
text = await req.text()
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test_flash_briefing_no_password | (alexa_client) | Test for no Flash Briefing password. | Test for no Flash Briefing password. | async def test_flash_briefing_no_password(alexa_client):
"""Test for no Flash Briefing password."""
req = await _flash_briefing_req(alexa_client, "weather", password=None)
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test_flash_briefing_invalid_password | (alexa_client) | Test an invalid Flash Briefing password. | Test an invalid Flash Briefing password. | async def test_flash_briefing_invalid_password(alexa_client):
"""Test an invalid Flash Briefing password."""
req = await _flash_briefing_req(alexa_client, "weather", password="wrongpass")
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test_flash_briefing_request_for_password | (alexa_client) | Test for "password" Flash Briefing. | Test for "password" Flash Briefing. | async def test_flash_briefing_request_for_password(alexa_client):
"""Test for "password" Flash Briefing."""
req = await _flash_briefing_req(alexa_client, "password")
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test_flash_briefing_date_from_str | (alexa_client) | Test the response has a valid date parsed from string. | Test the response has a valid date parsed from string. | async def test_flash_briefing_date_from_str(alexa_client):
"""Test the response has a valid date parsed from string."""
req = await _flash_briefing_req(alexa_client, "weather")
assert req.status == 200
data = await req.json()
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datetime.datetime.strptime(
data[0]... | [
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test_flash_briefing_valid | (alexa_client) | Test the response is valid. | Test the response is valid. | async def test_flash_briefing_valid(alexa_client):
"""Test the response is valid."""
data = [
{
"titleText": "NPR",
"redirectionURL": "https://npr.org",
"streamUrl": NPR_NEWS_MP3_URL,
"mainText": "",
"uid": "uuid",
"updateDate": "20... | [
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setup_platform | (hass, config, add_entities, disc_info=None) | Set up the WebDav Calendar platform. | Set up the WebDav Calendar platform. | def setup_platform(hass, config, add_entities, disc_info=None):
"""Set up the WebDav Calendar platform."""
url = config[CONF_URL]
username = config.get(CONF_USERNAME)
password = config.get(CONF_PASSWORD)
days = config[CONF_DAYS]
client = caldav.DAVClient(
url, None, username, password, ... | [
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WebDavCalendarEventDevice.__init__ | (self, name, calendar, entity_id, days, all_day=False, search=None) | Create the WebDav Calendar Event Device. | Create the WebDav Calendar Event Device. | def __init__(self, name, calendar, entity_id, days, all_day=False, search=None):
"""Create the WebDav Calendar Event Device."""
self.data = WebDavCalendarData(calendar, days, all_day, search)
self.entity_id = entity_id
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self._name = name
self._offset_rea... | [
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36
] | python | en | ['en', 'pt', 'en'] | True |
WebDavCalendarEventDevice.device_state_attributes | (self) | Return the device state attributes. | Return the device state attributes. | def device_state_attributes(self):
"""Return the device state attributes."""
return {"offset_reached": self._offset_reached} | [
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WebDavCalendarEventDevice.event | (self) | Return the next upcoming event. | Return the next upcoming event. | def event(self):
"""Return the next upcoming event."""
return self._event | [
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WebDavCalendarEventDevice.name | (self) | Return the name of the entity. | Return the name of the entity. | def name(self):
"""Return the name of the entity."""
return self._name | [
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WebDavCalendarEventDevice.async_get_events | (self, hass, start_date, end_date) | Get all events in a specific time frame. | Get all events in a specific time frame. | async def async_get_events(self, hass, start_date, end_date):
"""Get all events in a specific time frame."""
return await self.data.async_get_events(hass, start_date, end_date) | [
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WebDavCalendarEventDevice.update | (self) | Update event data. | Update event data. | def update(self):
"""Update event data."""
self.data.update()
event = copy.deepcopy(self.data.event)
if event is None:
self._event = event
return
event = calculate_offset(event, OFFSET)
self._offset_reached = is_offset_reached(event)
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WebDavCalendarData.__init__ | (self, calendar, days, include_all_day, search) | Set up how we are going to search the WebDav calendar. | Set up how we are going to search the WebDav calendar. | def __init__(self, calendar, days, include_all_day, search):
"""Set up how we are going to search the WebDav calendar."""
self.calendar = calendar
self.days = days
self.include_all_day = include_all_day
self.search = search
self.event = None | [
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WebDavCalendarData.async_get_events | (self, hass, start_date, end_date) | Get all events in a specific time frame. | Get all events in a specific time frame. | async def async_get_events(self, hass, start_date, end_date):
"""Get all events in a specific time frame."""
# Get event list from the current calendar
vevent_list = await hass.async_add_executor_job(
self.calendar.date_search, start_date, end_date
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event_list = []
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WebDavCalendarData.update | (self) | Get the latest data. | Get the latest data. | def update(self):
"""Get the latest data."""
start_of_today = dt.start_of_local_day()
start_of_tomorrow = dt.start_of_local_day() + timedelta(days=self.days)
# We have to retrieve the results for the whole day as the server
# won't return events that have already started
... | [
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WebDavCalendarData.is_matching | (vevent, search) | Return if the event matches the filter criteria. | Return if the event matches the filter criteria. | def is_matching(vevent, search):
"""Return if the event matches the filter criteria."""
if search is None:
return True
pattern = re.compile(search)
return (
hasattr(vevent, "summary")
and pattern.match(vevent.summary.value)
or hasattr(veve... | [
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WebDavCalendarData.is_all_day | (vevent) | Return if the event last the whole day. | Return if the event last the whole day. | def is_all_day(vevent):
"""Return if the event last the whole day."""
return not isinstance(vevent.dtstart.value, datetime) | [
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WebDavCalendarData.is_over | (vevent) | Return if the event is over. | Return if the event is over. | def is_over(vevent):
"""Return if the event is over."""
return dt.now() >= WebDavCalendarData.to_datetime(
WebDavCalendarData.get_end_date(vevent)
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] | python | en | ['en', 'en', 'en'] | True |
WebDavCalendarData.get_hass_date | (obj) | Return if the event matches. | Return if the event matches. | def get_hass_date(obj):
"""Return if the event matches."""
if isinstance(obj, datetime):
return {"dateTime": obj.isoformat()}
return {"date": obj.isoformat()} | [
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] | [
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] | python | en | ['en', 'en', 'en'] | True |
WebDavCalendarData.to_datetime | (obj) | Return a datetime. | Return a datetime. | def to_datetime(obj):
"""Return a datetime."""
if isinstance(obj, datetime):
if obj.tzinfo is None:
# floating value, not bound to any time zone in particular
# represent same time regardless of which time zone is currently being observed
retur... | [
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] | [
310,
71
] | python | en | ['en', 'ro', 'en'] | True |
WebDavCalendarData.get_attr_value | (obj, attribute) | Return the value of the attribute if defined. | Return the value of the attribute if defined. | def get_attr_value(obj, attribute):
"""Return the value of the attribute if defined."""
if hasattr(obj, attribute):
return getattr(obj, attribute).value
return None | [
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] | python | en | ['en', 'en', 'en'] | True |
WebDavCalendarData.get_end_date | (obj) | Return the end datetime as determined by dtend or duration. | Return the end datetime as determined by dtend or duration. | def get_end_date(obj):
"""Return the end datetime as determined by dtend or duration."""
if hasattr(obj, "dtend"):
enddate = obj.dtend.value
elif hasattr(obj, "duration"):
enddate = obj.dtstart.value + obj.duration.value
else:
enddate = obj.dtstart.v... | [
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] | [
331,
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] | python | en | ['en', 'en', 'en'] | True |
CascadeEvent.add_immediate_event | (self, event, is_head: bool = False) | Add an immediate event, that will be processed right after the current event.
Immediate events are only supported to be inserted into the head or tail of the immediate event list.
By default, the events will be appended to the end.
NOTE:
The tick of the event to insert must be the ... | Add an immediate event, that will be processed right after the current event. | def add_immediate_event(self, event, is_head: bool = False) -> bool:
"""Add an immediate event, that will be processed right after the current event.
Immediate events are only supported to be inserted into the head or tail of the immediate event list.
By default, the events will be appended to ... | [
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] | python | en | ['en', 'en', 'en'] | True |
async_setup_platform | (hass, config, async_add_entities, discovery_info=None) | Configure the platform and add the sensors. | Configure the platform and add the sensors. | async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Configure the platform and add the sensors."""
websession = aiohttp_client.async_get_clientsession(hass)
client = SeventeenTrackClient(websession)
try:
login_result = await client.profile.login(
... | [
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] | [
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] | python | en | ['en', 'en', 'en'] | True |
SeventeenTrackSummarySensor.__init__ | (self, data, status, initial_state) | Initialize. | Initialize. | def __init__(self, data, status, initial_state):
"""Initialize."""
self._attrs = {ATTR_ATTRIBUTION: DEFAULT_ATTRIBUTION}
self._data = data
self._state = initial_state
self._status = status | [
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] | [
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] | python | en | ['en', 'en', 'it'] | False |
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