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MQTTRoomSensor.update
(self)
Update the state for absent devices.
Update the state for absent devices.
def update(self): """Update the state for absent devices.""" if ( self._updated and self._consider_home and dt.utcnow() - self._updated > self._consider_home ): self._state = STATE_NOT_HOME
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[ 141, 4 ]
[ 148, 40 ]
python
en
['en', 'en', 'en']
True
setup_platform
(hass, config, add_entities, discovery_info=None)
Set up the Alpha Vantage sensor.
Set up the Alpha Vantage sensor.
def setup_platform(hass, config, add_entities, discovery_info=None): """Set up the Alpha Vantage sensor.""" api_key = config[CONF_API_KEY] symbols = config.get(CONF_SYMBOLS, []) conversions = config.get(CONF_FOREIGN_EXCHANGE, []) if not symbols and not conversions: msg = "No symbols or currencies configured." hass.components.persistent_notification.create(msg, "Sensor alpha_vantage") _LOGGER.warning(msg) return timeseries = TimeSeries(key=api_key) dev = [] for symbol in symbols: try: _LOGGER.debug("Configuring timeseries for symbols: %s", symbol[CONF_SYMBOL]) timeseries.get_intraday(symbol[CONF_SYMBOL]) except ValueError: _LOGGER.error("API Key is not valid or symbol '%s' not known", symbol) dev.append(AlphaVantageSensor(timeseries, symbol)) forex = ForeignExchange(key=api_key) for conversion in conversions: from_cur = conversion.get(CONF_FROM) to_cur = conversion.get(CONF_TO) try: _LOGGER.debug("Configuring forex %s - %s", from_cur, to_cur) forex.get_currency_exchange_rate(from_currency=from_cur, to_currency=to_cur) except ValueError as error: _LOGGER.error( "API Key is not valid or currencies '%s'/'%s' not known", from_cur, to_cur, ) _LOGGER.debug(str(error)) dev.append(AlphaVantageForeignExchange(forex, conversion)) add_entities(dev, True) _LOGGER.debug("Setup completed")
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[ 64, 0 ]
[ 104, 36 ]
python
en
['en', 'ca', 'en']
True
AlphaVantageSensor.__init__
(self, timeseries, symbol)
Initialize the sensor.
Initialize the sensor.
def __init__(self, timeseries, symbol): """Initialize the sensor.""" self._symbol = symbol[CONF_SYMBOL] self._name = symbol.get(CONF_NAME, self._symbol) self._timeseries = timeseries self.values = None self._unit_of_measurement = symbol.get(CONF_CURRENCY, self._symbol) self._icon = ICONS.get(symbol.get(CONF_CURRENCY, "USD"))
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[ 110, 4 ]
[ 117, 64 ]
python
en
['en', 'en', 'en']
True
AlphaVantageSensor.name
(self)
Return the name of the sensor.
Return the name of the sensor.
def name(self): """Return the name of the sensor.""" return self._name
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[ 120, 4 ]
[ 122, 25 ]
python
en
['en', 'mi', 'en']
True
AlphaVantageSensor.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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[ 125, 4 ]
[ 127, 40 ]
python
en
['en', 'en', 'en']
True
AlphaVantageSensor.state
(self)
Return the state of the sensor.
Return the state of the sensor.
def state(self): """Return the state of the sensor.""" return self.values["1. open"]
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[ 130, 4 ]
[ 132, 37 ]
python
en
['en', 'en', 'en']
True
AlphaVantageSensor.device_state_attributes
(self)
Return the state attributes.
Return the state attributes.
def device_state_attributes(self): """Return the state attributes.""" if self.values is not None: return { ATTR_ATTRIBUTION: ATTRIBUTION, ATTR_CLOSE: self.values["4. close"], ATTR_HIGH: self.values["2. high"], ATTR_LOW: self.values["3. low"], }
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[ 135, 4 ]
[ 143, 13 ]
python
en
['en', 'en', 'en']
True
AlphaVantageSensor.icon
(self)
Return the icon to use in the frontend, if any.
Return the icon to use in the frontend, if any.
def icon(self): """Return the icon to use in the frontend, if any.""" return self._icon
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[ 146, 4 ]
[ 148, 25 ]
python
en
['en', 'en', 'en']
True
AlphaVantageSensor.update
(self)
Get the latest data and updates the states.
Get the latest data and updates the states.
def update(self): """Get the latest data and updates the states.""" _LOGGER.debug("Requesting new data for symbol %s", self._symbol) all_values, _ = self._timeseries.get_intraday(self._symbol) self.values = next(iter(all_values.values())) _LOGGER.debug("Received new values for symbol %s", self._symbol)
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[ 150, 4 ]
[ 155, 72 ]
python
en
['en', 'en', 'en']
True
AlphaVantageForeignExchange.__init__
(self, foreign_exchange, config)
Initialize the sensor.
Initialize the sensor.
def __init__(self, foreign_exchange, config): """Initialize the sensor.""" self._foreign_exchange = foreign_exchange self._from_currency = config[CONF_FROM] self._to_currency = config[CONF_TO] if CONF_NAME in config: self._name = config.get(CONF_NAME) else: self._name = f"{self._to_currency}/{self._from_currency}" self._unit_of_measurement = self._to_currency self._icon = ICONS.get(self._from_currency, "USD") self.values = None
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[ 161, 4 ]
[ 172, 26 ]
python
en
['en', 'en', 'en']
True
AlphaVantageForeignExchange.name
(self)
Return the name of the sensor.
Return the name of the sensor.
def name(self): """Return the name of the sensor.""" return self._name
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[ 175, 4 ]
[ 177, 25 ]
python
en
['en', 'mi', 'en']
True
AlphaVantageForeignExchange.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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[ 180, 4 ]
[ 182, 40 ]
python
en
['en', 'en', 'en']
True
AlphaVantageForeignExchange.state
(self)
Return the state of the sensor.
Return the state of the sensor.
def state(self): """Return the state of the sensor.""" return round(float(self.values["5. Exchange Rate"]), 4)
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[ 185, 4 ]
[ 187, 63 ]
python
en
['en', 'en', 'en']
True
AlphaVantageForeignExchange.icon
(self)
Return the icon to use in the frontend, if any.
Return the icon to use in the frontend, if any.
def icon(self): """Return the icon to use in the frontend, if any.""" return self._icon
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[ 190, 4 ]
[ 192, 25 ]
python
en
['en', 'en', 'en']
True
AlphaVantageForeignExchange.device_state_attributes
(self)
Return the state attributes.
Return the state attributes.
def device_state_attributes(self): """Return the state attributes.""" if self.values is not None: return { ATTR_ATTRIBUTION: ATTRIBUTION, CONF_FROM: self._from_currency, CONF_TO: self._to_currency, }
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[ 195, 4 ]
[ 202, 13 ]
python
en
['en', 'en', 'en']
True
AlphaVantageForeignExchange.update
(self)
Get the latest data and updates the states.
Get the latest data and updates the states.
def update(self): """Get the latest data and updates the states.""" _LOGGER.debug( "Requesting new data for forex %s - %s", self._from_currency, self._to_currency, ) self.values, _ = self._foreign_exchange.get_currency_exchange_rate( from_currency=self._from_currency, to_currency=self._to_currency ) _LOGGER.debug( "Received new data for forex %s - %s", self._from_currency, self._to_currency, )
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[ 204, 4 ]
[ 218, 9 ]
python
en
['en', 'en', 'en']
True
create_lat_lons_for_NZTMgrid
(extent_w=1.2e6, extent_e=1.4e6, extent_n=5.13e6, extent_s=4.82e6, resolution=250)
create grids of latitude and longitude corresponding to grid centres of data in nztm grid
create grids of latitude and longitude corresponding to grid centres of data in nztm grid
def create_lat_lons_for_NZTMgrid(extent_w=1.2e6, extent_e=1.4e6, extent_n=5.13e6, extent_s=4.82e6, resolution=250): """create grids of latitude and longitude corresponding to grid centres of data in nztm grid """ # create coordinates x_centres = np.arange(extent_w + resolution / 2, extent_e, resolution) y_centres = np.arange(extent_s + resolution / 2, extent_n, resolution) y_array, x_array = np.meshgrid(y_centres, x_centres, indexing='ij') lat_array, lon_array = nztm_to_wgs84(y_array, x_array) return lat_array, lon_array
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[ 111, 0 ]
[ 119, 31 ]
python
en
['en', 'en', 'en']
True
write_nztm_grids_to_netcdf
(fname, list_of_data_arrays, var_names, datetime_list, northings, eastings, lat_array, lon_array, elevation, no_time=False)
Write a netCDF file containing fractional snow covered area data :param fname: string, full pathname of file to be created :param list_of_data_arrays: list, list containing data arrays to be saved [[time, northings, eastings],[time, northings, eastings]] :param var_names: list of strings corresponding to names of data arrays :param datetime_list: list of datetime objects corresponding to data :param northings: vector containing northings associated with data grid :param eastings: vector containing eastings associated with data grid :param lat_array: array containing longitudes of data grid :param lon_array: array containing latitudes of data grid :param elevation: array containing elevation of data grid :return:
Write a netCDF file containing fractional snow covered area data :param fname: string, full pathname of file to be created :param list_of_data_arrays: list, list containing data arrays to be saved [[time, northings, eastings],[time, northings, eastings]] :param var_names: list of strings corresponding to names of data arrays :param datetime_list: list of datetime objects corresponding to data :param northings: vector containing northings associated with data grid :param eastings: vector containing eastings associated with data grid :param lat_array: array containing longitudes of data grid :param lon_array: array containing latitudes of data grid :param elevation: array containing elevation of data grid
def write_nztm_grids_to_netcdf(fname, list_of_data_arrays, var_names, datetime_list, northings, eastings, lat_array, lon_array, elevation, no_time=False): """ Write a netCDF file containing fractional snow covered area data :param fname: string, full pathname of file to be created :param list_of_data_arrays: list, list containing data arrays to be saved [[time, northings, eastings],[time, northings, eastings]] :param var_names: list of strings corresponding to names of data arrays :param datetime_list: list of datetime objects corresponding to data :param northings: vector containing northings associated with data grid :param eastings: vector containing eastings associated with data grid :param lat_array: array containing longitudes of data grid :param lon_array: array containing latitudes of data grid :param elevation: array containing elevation of data grid :return: """ ds = nc.Dataset(fname, 'w') # add common attributes ds.institution = "Bodeker Scientific" ds.title = '' ds.source = '' ds.history = '' ds.references = '' ds.author = '' ds.email = '' ds.created = strftime("%Y-%m-%d %H:%M:%S", gmtime()) if no_time == False: ds.featureType = "timeSeries" else: ds.comment = 'timestamp {}'.format(datetime_list.strftime('%Y%m%d%H%M')) ds.Conventions = "CF-1.6" if no_time == False: ds.createDimension('time', ) t = ds.createVariable('time', 'f8', ('time',)) t.long_name = "time" t.units = 'days since 1900-01-01 00:00:00' t[:] = nc.date2num(datetime_list, units=t.units) ds.createDimension('northing', len(northings)) ds.createDimension('easting', len(eastings)) ds.createDimension('latitude', len(northings)) ds.createDimension('longitude', len(eastings)) # add northing and easting dimensions as well as lat/lon variables t = ds.createVariable('northing', 'f8', ('northing',)) t.axis = 'Y' t.long_name = "northing in NZTM" t.units = 'metres' t[:] = northings t = ds.createVariable('easting', 'f8', ('easting',)) t.axis = 'X' t.long_name = "easting in NZTM" t.units = 'metres' t[:] = eastings t = ds.createVariable('lat', 'f8', ('northing', 'easting',)) t.long_name = "latitude" t.standard_name = "latitude" t.units = "degrees north" t[:] = lat_array t = ds.createVariable('lon', 'f8', ('northing', 'easting',)) t.long_name = "longitude" t.standard_name = "longitude" t.units = "degrees east" t[:] = lon_array elevation_var = ds.createVariable('elevation', 'f8', ('northing', 'easting',), fill_value=-9999.) elevation_var.long_name = "elevation (meters)" elevation_var.standard_name = "surface_altitude" elevation_var.units = "meters" elevation_var[:] = elevation if 'precipitation_amount' in var_names: precip_var = create_ncvar_precipitation(ds, no_time=no_time) precip_var[:] = list_of_data_arrays[var_names.index('precipitation_amount')] if 'air_temperature' in var_names: temp_var = create_ncvar_temperaure(ds, no_time=no_time) temp_var[:] = list_of_data_arrays[var_names.index('air_temperature')] if 'surface_downwelling_shortwave_flux' in var_names: temp_var = create_ncvar_shortwave(ds, no_time=no_time) temp_var[:] = list_of_data_arrays[var_names.index('surface_downwelling_shortwave_flux')] if 'fsca' in var_names: fsca_var = create_ncvar_fsca(ds) fsca_var[:] = list_of_data_arrays[var_names.index('fsca')] if 'swe' in var_names: swe_var = create_ncvar_swe(ds) swe_var[:] = list_of_data_arrays[var_names.index('swe')] ds.close()
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[ 219, 14 ]
python
en
['en', 'error', 'th']
False
setup_nztm_grid_netcdf
(fname, list_of_data_arrays, var_names, datetime_list, northings, eastings, lat_array, lon_array, elevation, no_time=False)
Write a netCDF file containing fractional snow covered area data :param fname: string, full pathname of file to be created :param list_of_data_arrays: list, list containing data arrays to be saved [[time, northings, eastings],[time, northings, eastings]] :param var_names: list of strings corresponding to names of data arrays :param datetime_list: list of datetime objects corresponding to data :param northings: vector containing northings associated with data grid :param eastings: vector containing eastings associated with data grid :param lat_array: array containing longitudes of data grid :param lon_array: array containing latitudes of data grid :param elevation: array containing elevation of data grid :return:
Write a netCDF file containing fractional snow covered area data :param fname: string, full pathname of file to be created :param list_of_data_arrays: list, list containing data arrays to be saved [[time, northings, eastings],[time, northings, eastings]] :param var_names: list of strings corresponding to names of data arrays :param datetime_list: list of datetime objects corresponding to data :param northings: vector containing northings associated with data grid :param eastings: vector containing eastings associated with data grid :param lat_array: array containing longitudes of data grid :param lon_array: array containing latitudes of data grid :param elevation: array containing elevation of data grid
def setup_nztm_grid_netcdf(fname, list_of_data_arrays, var_names, datetime_list, northings, eastings, lat_array, lon_array, elevation, no_time=False): """ Write a netCDF file containing fractional snow covered area data :param fname: string, full pathname of file to be created :param list_of_data_arrays: list, list containing data arrays to be saved [[time, northings, eastings],[time, northings, eastings]] :param var_names: list of strings corresponding to names of data arrays :param datetime_list: list of datetime objects corresponding to data :param northings: vector containing northings associated with data grid :param eastings: vector containing eastings associated with data grid :param lat_array: array containing longitudes of data grid :param lon_array: array containing latitudes of data grid :param elevation: array containing elevation of data grid :return: """ ds = nc.Dataset(fname, 'w') # add common attributes ds.institution = "Bodeker Scientific" ds.title = '' ds.source = '' ds.history = '' ds.references = '' ds.author = '' ds.email = '' ds.created = strftime("%Y-%m-%d %H:%M:%S", gmtime()) if no_time == False: ds.featureType = "timeSeries" else: ds.comment = 'timestamp {}'.format(datetime_list.strftime('%Y%m%d%H%M')) ds.Conventions = "CF-1.6" if no_time == False: ds.createDimension('time', ) t = ds.createVariable('time', 'f8', ('time',)) t.long_name = "time" t.units = 'days since 1900-01-01 00:00:00' t[:] = nc.date2num(datetime_list, units=t.units) ds.createDimension('northing', len(northings)) ds.createDimension('easting', len(eastings)) ds.createDimension('latitude', len(northings)) ds.createDimension('longitude', len(eastings)) # add northing and easting dimensions as well as lat/lon variables t = ds.createVariable('northing', 'f8', ('northing',)) t.axis = 'Y' t.long_name = "northing in NZTM" t.units = 'metres' t[:] = northings t = ds.createVariable('easting', 'f8', ('easting',)) t.axis = 'X' t.long_name = "easting in NZTM" t.units = 'metres' t[:] = eastings t = ds.createVariable('lat', 'f8', ('northing', 'easting',)) t.long_name = "latitude" t.standard_name = "latitude" t.units = "degrees north" t[:] = lat_array t = ds.createVariable('lon', 'f8', ('northing', 'easting',)) t.long_name = "longitude" t.standard_name = "longitude" t.units = "degrees east" t[:] = lon_array elevation_var = ds.createVariable('elevation', 'f8', ('northing', 'easting',), fill_value=-9999.) elevation_var.long_name = "elevation (meters)" elevation_var.standard_name = "surface_altitude" elevation_var.units = "meters" elevation_var[:] = elevation if 'precipitation_amount' in var_names: precip_var = create_ncvar_precipitation(ds, no_time=no_time) if 'air_temperature' in var_names: temp_var = create_ncvar_temperaure(ds, no_time=no_time) if 'surface_downwelling_shortwave_flux' in var_names: temp_var = create_ncvar_shortwave(ds, no_time=no_time) if 'fsca' in var_names: fsca_var = create_ncvar_fsca(ds) if 'swe' in var_names: swe_var = create_ncvar_swe(ds) if 'acc' in var_names: acc_var = create_ncvar_acc(ds) if 'melt' in var_names: melt_var = create_ncvar_melt(ds) return ds
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[ 222, 0 ]
[ 320, 13 ]
python
en
['en', 'error', 'th']
False
setup_platform
(hass, config, add_entities, discovery_info=None)
Set up the Blinkt Light platform.
Set up the Blinkt Light platform.
def setup_platform(hass, config, add_entities, discovery_info=None): """Set up the Blinkt Light platform.""" # pylint: disable=no-member blinkt = importlib.import_module("blinkt") # ensure that the lights are off when exiting blinkt.set_clear_on_exit() name = config[CONF_NAME] add_entities( [BlinktLight(blinkt, name, index) for index in range(blinkt.NUM_PIXELS)] )
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python
en
['en', 'da', 'en']
True
BlinktLight.__init__
(self, blinkt, name, index)
Initialize a Blinkt Light. Default brightness and white color.
Initialize a Blinkt Light.
def __init__(self, blinkt, name, index): """Initialize a Blinkt Light. Default brightness and white color. """ self._blinkt = blinkt self._name = f"{name}_{index}" self._index = index self._is_on = False self._brightness = 255 self._hs_color = [0, 0]
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python
en
['en', 'lb', 'it']
False
BlinktLight.name
(self)
Return the display name of this light.
Return the display name of this light.
def name(self): """Return the display name of this light.""" return self._name
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python
en
['en', 'en', 'en']
True
BlinktLight.brightness
(self)
Read back the brightness of the light. Returns integer in the range of 1-255.
Read back the brightness of the light.
def brightness(self): """Read back the brightness of the light. Returns integer in the range of 1-255. """ return self._brightness
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python
en
['en', 'en', 'en']
True
BlinktLight.hs_color
(self)
Read back the color of the light.
Read back the color of the light.
def hs_color(self): """Read back the color of the light.""" return self._hs_color
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python
en
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True
BlinktLight.supported_features
(self)
Flag supported features.
Flag supported features.
def supported_features(self): """Flag supported features.""" return SUPPORT_BLINKT
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python
en
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BlinktLight.is_on
(self)
Return true if light is on.
Return true if light is on.
def is_on(self): """Return true if light is on.""" return self._is_on
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python
en
['en', 'et', 'en']
True
BlinktLight.should_poll
(self)
Return if we should poll this device.
Return if we should poll this device.
def should_poll(self): """Return if we should poll this device.""" return False
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python
en
['en', 'en', 'en']
True
BlinktLight.assumed_state
(self)
Return True if unable to access real state of the entity.
Return True if unable to access real state of the entity.
def assumed_state(self) -> bool: """Return True if unable to access real state of the entity.""" return True
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python
en
['en', 'en', 'en']
True
BlinktLight.turn_on
(self, **kwargs)
Instruct the light to turn on and set correct brightness & color.
Instruct the light to turn on and set correct brightness & color.
def turn_on(self, **kwargs): """Instruct the light to turn on and set correct brightness & color.""" if ATTR_HS_COLOR in kwargs: self._hs_color = kwargs[ATTR_HS_COLOR] if ATTR_BRIGHTNESS in kwargs: self._brightness = kwargs[ATTR_BRIGHTNESS] percent_bright = self._brightness / 255 rgb_color = color_util.color_hs_to_RGB(*self._hs_color) self._blinkt.set_pixel( self._index, rgb_color[0], rgb_color[1], rgb_color[2], percent_bright ) self._blinkt.show() self._is_on = True self.schedule_update_ha_state()
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[ 94, 4 ]
[ 110, 39 ]
python
en
['en', 'en', 'en']
True
BlinktLight.turn_off
(self, **kwargs)
Instruct the light to turn off.
Instruct the light to turn off.
def turn_off(self, **kwargs): """Instruct the light to turn off.""" self._blinkt.set_pixel(self._index, 0, 0, 0, 0) self._blinkt.show() self._is_on = False self.schedule_update_ha_state()
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[ 112, 4 ]
[ 117, 39 ]
python
en
['en', 'en', 'en']
True
test_min_sensor
(hass)
Test the min sensor.
Test the min sensor.
async def test_min_sensor(hass): """Test the min sensor.""" config = { "sensor": { "platform": "min_max", "name": "test_min", "type": "min", "entity_ids": ["sensor.test_1", "sensor.test_2", "sensor.test_3"], } } assert await async_setup_component(hass, "sensor", config) await hass.async_block_till_done() entity_ids = config["sensor"]["entity_ids"] for entity_id, value in dict(zip(entity_ids, VALUES)).items(): hass.states.async_set(entity_id, value) await hass.async_block_till_done() state = hass.states.get("sensor.test_min") assert str(float(MIN_VALUE)) == state.state assert entity_ids[2] == state.attributes.get("min_entity_id") assert MAX_VALUE == state.attributes.get("max_value") assert entity_ids[1] == state.attributes.get("max_entity_id") assert MEAN == state.attributes.get("mean") assert MEDIAN == state.attributes.get("median")
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[ 29, 0 ]
[ 56, 51 ]
python
en
['en', 'sq', 'en']
True
test_max_sensor
(hass)
Test the max sensor.
Test the max sensor.
async def test_max_sensor(hass): """Test the max sensor.""" config = { "sensor": { "platform": "min_max", "name": "test_max", "type": "max", "entity_ids": ["sensor.test_1", "sensor.test_2", "sensor.test_3"], } } assert await async_setup_component(hass, "sensor", config) await hass.async_block_till_done() entity_ids = config["sensor"]["entity_ids"] for entity_id, value in dict(zip(entity_ids, VALUES)).items(): hass.states.async_set(entity_id, value) await hass.async_block_till_done() state = hass.states.get("sensor.test_max") assert str(float(MAX_VALUE)) == state.state assert entity_ids[2] == state.attributes.get("min_entity_id") assert MIN_VALUE == state.attributes.get("min_value") assert entity_ids[1] == state.attributes.get("max_entity_id") assert MEAN == state.attributes.get("mean") assert MEDIAN == state.attributes.get("median")
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[ 59, 0 ]
[ 86, 51 ]
python
en
['en', 'ca', 'en']
True
test_mean_sensor
(hass)
Test the mean sensor.
Test the mean sensor.
async def test_mean_sensor(hass): """Test the mean sensor.""" config = { "sensor": { "platform": "min_max", "name": "test_mean", "type": "mean", "entity_ids": ["sensor.test_1", "sensor.test_2", "sensor.test_3"], } } assert await async_setup_component(hass, "sensor", config) await hass.async_block_till_done() entity_ids = config["sensor"]["entity_ids"] for entity_id, value in dict(zip(entity_ids, VALUES)).items(): hass.states.async_set(entity_id, value) await hass.async_block_till_done() state = hass.states.get("sensor.test_mean") assert str(float(MEAN)) == state.state assert MIN_VALUE == state.attributes.get("min_value") assert entity_ids[2] == state.attributes.get("min_entity_id") assert MAX_VALUE == state.attributes.get("max_value") assert entity_ids[1] == state.attributes.get("max_entity_id") assert MEDIAN == state.attributes.get("median")
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[ 89, 0 ]
[ 116, 51 ]
python
en
['en', 'da', 'en']
True
test_mean_1_digit_sensor
(hass)
Test the mean with 1-digit precision sensor.
Test the mean with 1-digit precision sensor.
async def test_mean_1_digit_sensor(hass): """Test the mean with 1-digit precision sensor.""" config = { "sensor": { "platform": "min_max", "name": "test_mean", "type": "mean", "round_digits": 1, "entity_ids": ["sensor.test_1", "sensor.test_2", "sensor.test_3"], } } assert await async_setup_component(hass, "sensor", config) await hass.async_block_till_done() entity_ids = config["sensor"]["entity_ids"] for entity_id, value in dict(zip(entity_ids, VALUES)).items(): hass.states.async_set(entity_id, value) await hass.async_block_till_done() state = hass.states.get("sensor.test_mean") assert str(float(MEAN_1_DIGIT)) == state.state assert MIN_VALUE == state.attributes.get("min_value") assert entity_ids[2] == state.attributes.get("min_entity_id") assert MAX_VALUE == state.attributes.get("max_value") assert entity_ids[1] == state.attributes.get("max_entity_id") assert MEDIAN == state.attributes.get("median")
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[ 119, 0 ]
[ 147, 51 ]
python
en
['en', 'en', 'en']
True
test_mean_4_digit_sensor
(hass)
Test the mean with 1-digit precision sensor.
Test the mean with 1-digit precision sensor.
async def test_mean_4_digit_sensor(hass): """Test the mean with 1-digit precision sensor.""" config = { "sensor": { "platform": "min_max", "name": "test_mean", "type": "mean", "round_digits": 4, "entity_ids": ["sensor.test_1", "sensor.test_2", "sensor.test_3"], } } assert await async_setup_component(hass, "sensor", config) await hass.async_block_till_done() entity_ids = config["sensor"]["entity_ids"] for entity_id, value in dict(zip(entity_ids, VALUES)).items(): hass.states.async_set(entity_id, value) await hass.async_block_till_done() state = hass.states.get("sensor.test_mean") assert str(float(MEAN_4_DIGITS)) == state.state assert MIN_VALUE == state.attributes.get("min_value") assert entity_ids[2] == state.attributes.get("min_entity_id") assert MAX_VALUE == state.attributes.get("max_value") assert entity_ids[1] == state.attributes.get("max_entity_id") assert MEDIAN == state.attributes.get("median")
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[ 150, 0 ]
[ 178, 51 ]
python
en
['en', 'en', 'en']
True
test_median_sensor
(hass)
Test the median sensor.
Test the median sensor.
async def test_median_sensor(hass): """Test the median sensor.""" config = { "sensor": { "platform": "min_max", "name": "test_median", "type": "median", "entity_ids": ["sensor.test_1", "sensor.test_2", "sensor.test_3"], } } assert await async_setup_component(hass, "sensor", config) await hass.async_block_till_done() entity_ids = config["sensor"]["entity_ids"] for entity_id, value in dict(zip(entity_ids, VALUES)).items(): hass.states.async_set(entity_id, value) await hass.async_block_till_done() state = hass.states.get("sensor.test_median") assert str(float(MEDIAN)) == state.state assert MIN_VALUE == state.attributes.get("min_value") assert entity_ids[2] == state.attributes.get("min_entity_id") assert MAX_VALUE == state.attributes.get("max_value") assert entity_ids[1] == state.attributes.get("max_entity_id") assert MEAN == state.attributes.get("mean")
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[ 181, 0 ]
[ 208, 47 ]
python
en
['en', 'da', 'en']
True
test_not_enough_sensor_value
(hass)
Test that there is nothing done if not enough values available.
Test that there is nothing done if not enough values available.
async def test_not_enough_sensor_value(hass): """Test that there is nothing done if not enough values available.""" config = { "sensor": { "platform": "min_max", "name": "test_max", "type": "max", "entity_ids": ["sensor.test_1", "sensor.test_2", "sensor.test_3"], } } assert await async_setup_component(hass, "sensor", config) await hass.async_block_till_done() entity_ids = config["sensor"]["entity_ids"] hass.states.async_set(entity_ids[0], STATE_UNKNOWN) await hass.async_block_till_done() state = hass.states.get("sensor.test_max") assert STATE_UNKNOWN == state.state assert state.attributes.get("min_entity_id") is None assert state.attributes.get("min_value") is None assert state.attributes.get("max_entity_id") is None assert state.attributes.get("max_value") is None assert state.attributes.get("median") is None hass.states.async_set(entity_ids[1], VALUES[1]) await hass.async_block_till_done() state = hass.states.get("sensor.test_max") assert STATE_UNKNOWN != state.state assert entity_ids[1] == state.attributes.get("min_entity_id") assert VALUES[1] == state.attributes.get("min_value") assert entity_ids[1] == state.attributes.get("max_entity_id") assert VALUES[1] == state.attributes.get("max_value") hass.states.async_set(entity_ids[2], STATE_UNKNOWN) await hass.async_block_till_done() state = hass.states.get("sensor.test_max") assert STATE_UNKNOWN != state.state assert entity_ids[1] == state.attributes.get("min_entity_id") assert VALUES[1] == state.attributes.get("min_value") assert entity_ids[1] == state.attributes.get("max_entity_id") assert VALUES[1] == state.attributes.get("max_value") hass.states.async_set(entity_ids[1], STATE_UNAVAILABLE) await hass.async_block_till_done() state = hass.states.get("sensor.test_max") assert STATE_UNKNOWN == state.state assert state.attributes.get("min_entity_id") is None assert state.attributes.get("min_value") is None assert state.attributes.get("max_entity_id") is None assert state.attributes.get("max_value") is None
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[ 211, 0 ]
[ 266, 52 ]
python
en
['en', 'en', 'en']
True
test_different_unit_of_measurement
(hass)
Test for different unit of measurement.
Test for different unit of measurement.
async def test_different_unit_of_measurement(hass): """Test for different unit of measurement.""" config = { "sensor": { "platform": "min_max", "name": "test", "type": "mean", "entity_ids": ["sensor.test_1", "sensor.test_2", "sensor.test_3"], } } assert await async_setup_component(hass, "sensor", config) await hass.async_block_till_done() entity_ids = config["sensor"]["entity_ids"] hass.states.async_set( entity_ids[0], VALUES[0], {ATTR_UNIT_OF_MEASUREMENT: TEMP_CELSIUS} ) await hass.async_block_till_done() state = hass.states.get("sensor.test") assert str(float(VALUES[0])) == state.state assert state.attributes.get("unit_of_measurement") == TEMP_CELSIUS hass.states.async_set( entity_ids[1], VALUES[1], {ATTR_UNIT_OF_MEASUREMENT: TEMP_FAHRENHEIT} ) await hass.async_block_till_done() state = hass.states.get("sensor.test") assert STATE_UNKNOWN == state.state assert state.attributes.get("unit_of_measurement") == "ERR" hass.states.async_set( entity_ids[2], VALUES[2], {ATTR_UNIT_OF_MEASUREMENT: PERCENTAGE} ) await hass.async_block_till_done() state = hass.states.get("sensor.test") assert STATE_UNKNOWN == state.state assert state.attributes.get("unit_of_measurement") == "ERR"
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[ 269, 0 ]
[ 313, 63 ]
python
en
['en', 'en', 'en']
True
test_last_sensor
(hass)
Test the last sensor.
Test the last sensor.
async def test_last_sensor(hass): """Test the last sensor.""" config = { "sensor": { "platform": "min_max", "name": "test_last", "type": "last", "entity_ids": ["sensor.test_1", "sensor.test_2", "sensor.test_3"], } } assert await async_setup_component(hass, "sensor", config) await hass.async_block_till_done() entity_ids = config["sensor"]["entity_ids"] for entity_id, value in dict(zip(entity_ids, VALUES)).items(): hass.states.async_set(entity_id, value) await hass.async_block_till_done() state = hass.states.get("sensor.test_last") assert str(float(value)) == state.state assert entity_id == state.attributes.get("last_entity_id") assert MIN_VALUE == state.attributes.get("min_value") assert MAX_VALUE == state.attributes.get("max_value") assert MEAN == state.attributes.get("mean") assert MEDIAN == state.attributes.get("median")
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[ 316, 0 ]
[ 342, 51 ]
python
en
['en', 'et', 'en']
True
test_reload
(hass)
Verify we can reload filter sensors.
Verify we can reload filter sensors.
async def test_reload(hass): """Verify we can reload filter sensors.""" hass.states.async_set("sensor.test_1", 12345) hass.states.async_set("sensor.test_2", 45678) await async_setup_component( hass, "sensor", { "sensor": { "platform": "min_max", "name": "test", "type": "mean", "entity_ids": ["sensor.test_1", "sensor.test_2"], } }, ) await hass.async_block_till_done() assert len(hass.states.async_all()) == 3 assert hass.states.get("sensor.test") yaml_path = path.join( _get_fixtures_base_path(), "fixtures", "min_max/configuration.yaml", ) with patch.object(hass_config, "YAML_CONFIG_FILE", yaml_path): await hass.services.async_call( DOMAIN, SERVICE_RELOAD, {}, blocking=True, ) await hass.async_block_till_done() assert len(hass.states.async_all()) == 3 assert hass.states.get("sensor.test") is None assert hass.states.get("sensor.second_test")
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[ 345, 0 ]
[ 385, 48 ]
python
en
['en', 'da', 'en']
True
test_water_heater
( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker )
Test the creation of Atag water heater.
Test the creation of Atag water heater.
async def test_water_heater( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker ) -> None: """Test the creation of Atag water heater.""" with patch("pyatag.entities.DHW.status"): entry = await init_integration(hass, aioclient_mock) registry = await hass.helpers.entity_registry.async_get_registry() assert registry.async_is_registered(WATER_HEATER_ID) entry = registry.async_get(WATER_HEATER_ID) assert entry.unique_id == f"{UID}-{WATER_HEATER}"
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[ 14, 0 ]
[ 24, 57 ]
python
en
['en', 'en', 'en']
True
test_setting_target_temperature
( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker )
Test setting the water heater device.
Test setting the water heater device.
async def test_setting_target_temperature( hass: HomeAssistant, aioclient_mock: AiohttpClientMocker ) -> None: """Test setting the water heater device.""" await init_integration(hass, aioclient_mock) with patch("pyatag.entities.DHW.set_temp") as mock_set_temp: await hass.services.async_call( WATER_HEATER, SERVICE_SET_TEMPERATURE, {ATTR_ENTITY_ID: WATER_HEATER_ID, ATTR_TEMPERATURE: 50}, blocking=True, ) await hass.async_block_till_done() mock_set_temp.assert_called_once_with(50)
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[ 27, 0 ]
[ 40, 49 ]
python
en
['en', 'en', 'en']
True
browse_media
(zone_id, roon_server, media_content_type=None, media_content_id=None)
Implement the websocket media browsing helper.
Implement the websocket media browsing helper.
def browse_media(zone_id, roon_server, media_content_type=None, media_content_id=None): """Implement the websocket media browsing helper.""" try: _LOGGER.debug("browse_media: %s: %s", media_content_type, media_content_id) if media_content_type in [None, "library"]: return library_payload(roon_server, zone_id, media_content_id) except UnknownMediaType as err: raise BrowseError( f"Media not found: {media_content_type} / {media_content_id}" ) from err
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[ 40, 0 ]
[ 50, 18 ]
python
en
['en', 'af', 'en']
True
item_payload
(roon_server, item, list_image_id)
Create response payload for a single media item.
Create response payload for a single media item.
def item_payload(roon_server, item, list_image_id): """Create response payload for a single media item.""" title = item["title"] subtitle = item.get("subtitle") if subtitle is None: display_title = title else: display_title = f"{title} ({subtitle})" image_id = item.get("image_key") or list_image_id image = None if image_id: image = roon_server.roonapi.get_image(image_id) media_content_id = item["item_key"] media_content_type = "library" hint = item.get("hint") if hint == "list": media_class = MEDIA_CLASS_DIRECTORY can_expand = True elif hint == "action_list": media_class = MEDIA_CLASS_PLAYLIST can_expand = False elif hint == "action": media_content_type = "track" media_class = MEDIA_CLASS_TRACK can_expand = False else: # Roon API says to treat unknown as a list media_class = MEDIA_CLASS_DIRECTORY can_expand = True _LOGGER.warning("Unknown hint %s - %s", title, hint) payload = { "title": display_title, "media_class": media_class, "media_content_id": media_content_id, "media_content_type": media_content_type, "can_play": True, "can_expand": can_expand, "thumbnail": image, } return BrowseMedia(**payload)
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[ 53, 0 ]
[ 99, 33 ]
python
en
['en', 'en', 'en']
True
library_payload
(roon_server, zone_id, media_content_id)
Create response payload for the library.
Create response payload for the library.
def library_payload(roon_server, zone_id, media_content_id): """Create response payload for the library.""" opts = { "hierarchy": "browse", "zone_or_output_id": zone_id, "count": ITEM_LIMIT, } # Roon starts browsing for a zone where it left off - so start from the top unless otherwise specified if media_content_id is None or media_content_id == "Explore": opts["pop_all"] = True content_id = "Explore" else: opts["item_key"] = media_content_id content_id = media_content_id result_header = roon_server.roonapi.browse_browse(opts) _LOGGER.debug("Result header %s", result_header) header = result_header["list"] title = header.get("title") subtitle = header.get("subtitle") if subtitle is None: list_title = title else: list_title = f"{title} ({subtitle})" total_count = header["count"] library_image_id = header.get("image_key") library_info = BrowseMedia( title=list_title, media_content_id=content_id, media_content_type="library", media_class=MEDIA_CLASS_DIRECTORY, can_play=False, can_expand=True, children=[], ) result_detail = roon_server.roonapi.browse_load(opts) _LOGGER.debug("Result detail %s", result_detail) items = result_detail["items"] count = len(items) if count < total_count: _LOGGER.debug( "Exceeded limit of %d, loaded %d/%d", ITEM_LIMIT, count, total_count ) for item in items: if item.get("title") in EXCLUDE_ITEMS: continue entry = item_payload(roon_server, item, library_image_id) library_info.children.append(entry) return library_info
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[ 102, 0 ]
[ 162, 23 ]
python
en
['en', 'en', 'en']
True
BaseModel.init_weights
(self, module)
Initialize the weights.
Initialize the weights.
def init_weights(self, module): """ Initialize the weights. """ if isinstance(module, (nn.Linear, nn.Embedding)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, BertLayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_()
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[ 13, 4 ]
[ 24, 36 ]
python
en
['en', 'en', 'en']
True
async_setup_platform
(hass, config, async_add_entities, discovery_info=None)
Set up the mysensors platform for switches.
Set up the mysensors platform for switches.
async def async_setup_platform(hass, config, async_add_entities, discovery_info=None): """Set up the mysensors platform for switches.""" device_class_map = { "S_DOOR": MySensorsSwitch, "S_MOTION": MySensorsSwitch, "S_SMOKE": MySensorsSwitch, "S_LIGHT": MySensorsSwitch, "S_LOCK": MySensorsSwitch, "S_IR": MySensorsIRSwitch, "S_BINARY": MySensorsSwitch, "S_SPRINKLER": MySensorsSwitch, "S_WATER_LEAK": MySensorsSwitch, "S_SOUND": MySensorsSwitch, "S_VIBRATION": MySensorsSwitch, "S_MOISTURE": MySensorsSwitch, "S_WATER_QUALITY": MySensorsSwitch, } mysensors.setup_mysensors_platform( hass, DOMAIN, discovery_info, device_class_map, async_add_entities=async_add_entities, ) async def async_send_ir_code_service(service): """Set IR code as device state attribute.""" entity_ids = service.data.get(ATTR_ENTITY_ID) ir_code = service.data.get(ATTR_IR_CODE) devices = mysensors.get_mysensors_devices(hass, DOMAIN) if entity_ids: _devices = [ device for device in devices.values() if isinstance(device, MySensorsIRSwitch) and device.entity_id in entity_ids ] else: _devices = [ device for device in devices.values() if isinstance(device, MySensorsIRSwitch) ] kwargs = {ATTR_IR_CODE: ir_code} for device in _devices: await device.async_turn_on(**kwargs) hass.services.async_register( MYSENSORS_DOMAIN, SERVICE_SEND_IR_CODE, async_send_ir_code_service, schema=SEND_IR_CODE_SERVICE_SCHEMA, )
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[ 17, 0 ]
[ 71, 5 ]
python
en
['en', 'en', 'en']
True
MySensorsSwitch.assumed_state
(self)
Return True if unable to access real state of entity.
Return True if unable to access real state of entity.
def assumed_state(self): """Return True if unable to access real state of entity.""" return self.gateway.optimistic
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[ 78, 4 ]
[ 80, 38 ]
python
en
['en', 'en', 'en']
True
MySensorsSwitch.current_power_w
(self)
Return the current power usage in W.
Return the current power usage in W.
def current_power_w(self): """Return the current power usage in W.""" set_req = self.gateway.const.SetReq return self._values.get(set_req.V_WATT)
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[ 83, 4 ]
[ 86, 47 ]
python
en
['en', 'en', 'en']
True
MySensorsSwitch.is_on
(self)
Return True if switch is on.
Return True if switch is on.
def is_on(self): """Return True if switch is on.""" return self._values.get(self.value_type) == STATE_ON
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[ 89, 4 ]
[ 91, 60 ]
python
en
['en', 'fy', 'en']
True
MySensorsSwitch.async_turn_on
(self, **kwargs)
Turn the switch on.
Turn the switch on.
async def async_turn_on(self, **kwargs): """Turn the switch on.""" self.gateway.set_child_value( self.node_id, self.child_id, self.value_type, 1, ack=1 ) if self.gateway.optimistic: # Optimistically assume that switch has changed state self._values[self.value_type] = STATE_ON self.async_write_ha_state()
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[ 93, 4 ]
[ 101, 39 ]
python
en
['en', 'en', 'en']
True
MySensorsSwitch.async_turn_off
(self, **kwargs)
Turn the switch off.
Turn the switch off.
async def async_turn_off(self, **kwargs): """Turn the switch off.""" self.gateway.set_child_value( self.node_id, self.child_id, self.value_type, 0, ack=1 ) if self.gateway.optimistic: # Optimistically assume that switch has changed state self._values[self.value_type] = STATE_OFF self.async_write_ha_state()
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[ 103, 4 ]
[ 111, 39 ]
python
en
['en', 'en', 'en']
True
MySensorsIRSwitch.__init__
(self, *args)
Set up instance attributes.
Set up instance attributes.
def __init__(self, *args): """Set up instance attributes.""" super().__init__(*args) self._ir_code = None
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[ 117, 4 ]
[ 120, 28 ]
python
en
['en', 'en', 'en']
True
MySensorsIRSwitch.is_on
(self)
Return True if switch is on.
Return True if switch is on.
def is_on(self): """Return True if switch is on.""" set_req = self.gateway.const.SetReq return self._values.get(set_req.V_LIGHT) == STATE_ON
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[ 123, 4 ]
[ 126, 60 ]
python
en
['en', 'fy', 'en']
True
MySensorsIRSwitch.async_turn_on
(self, **kwargs)
Turn the IR switch on.
Turn the IR switch on.
async def async_turn_on(self, **kwargs): """Turn the IR switch on.""" set_req = self.gateway.const.SetReq if ATTR_IR_CODE in kwargs: self._ir_code = kwargs[ATTR_IR_CODE] self.gateway.set_child_value( self.node_id, self.child_id, self.value_type, self._ir_code ) self.gateway.set_child_value( self.node_id, self.child_id, set_req.V_LIGHT, 1, ack=1 ) if self.gateway.optimistic: # Optimistically assume that switch has changed state self._values[self.value_type] = self._ir_code self._values[set_req.V_LIGHT] = STATE_ON self.async_write_ha_state() # Turn off switch after switch was turned on await self.async_turn_off()
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[ 128, 4 ]
[ 145, 39 ]
python
en
['en', 'zh', 'en']
True
MySensorsIRSwitch.async_turn_off
(self, **kwargs)
Turn the IR switch off.
Turn the IR switch off.
async def async_turn_off(self, **kwargs): """Turn the IR switch off.""" set_req = self.gateway.const.SetReq self.gateway.set_child_value( self.node_id, self.child_id, set_req.V_LIGHT, 0, ack=1 ) if self.gateway.optimistic: # Optimistically assume that switch has changed state self._values[set_req.V_LIGHT] = STATE_OFF self.async_write_ha_state()
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[ 147, 4 ]
[ 156, 39 ]
python
en
['en', 'zh', 'en']
True
MySensorsIRSwitch.async_update
(self)
Update the controller with the latest value from a sensor.
Update the controller with the latest value from a sensor.
async def async_update(self): """Update the controller with the latest value from a sensor.""" await super().async_update() self._ir_code = self._values.get(self.value_type)
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[ 158, 4 ]
[ 161, 57 ]
python
en
['en', 'en', 'en']
True
async_get_actions
(hass: HomeAssistant, device_id: str)
List device actions for Alarm control panel devices.
List device actions for Alarm control panel devices.
async def async_get_actions(hass: HomeAssistant, device_id: str) -> List[dict]: """List device actions for Alarm control panel devices.""" registry = await entity_registry.async_get_registry(hass) actions = [] # Get all the integrations entities for this device for entry in entity_registry.async_entries_for_device(registry, device_id): if entry.domain != DOMAIN: continue state = hass.states.get(entry.entity_id) # We need a state or else we can't populate the HVAC and preset modes. if state is None: continue supported_features = state.attributes[ATTR_SUPPORTED_FEATURES] # Add actions for each entity that belongs to this integration if supported_features & SUPPORT_ALARM_ARM_AWAY: actions.append( { CONF_DEVICE_ID: device_id, CONF_DOMAIN: DOMAIN, CONF_ENTITY_ID: entry.entity_id, CONF_TYPE: "arm_away", } ) if supported_features & SUPPORT_ALARM_ARM_HOME: actions.append( { CONF_DEVICE_ID: device_id, CONF_DOMAIN: DOMAIN, CONF_ENTITY_ID: entry.entity_id, CONF_TYPE: "arm_home", } ) if supported_features & SUPPORT_ALARM_ARM_NIGHT: actions.append( { CONF_DEVICE_ID: device_id, CONF_DOMAIN: DOMAIN, CONF_ENTITY_ID: entry.entity_id, CONF_TYPE: "arm_night", } ) actions.append( { CONF_DEVICE_ID: device_id, CONF_DOMAIN: DOMAIN, CONF_ENTITY_ID: entry.entity_id, CONF_TYPE: "disarm", } ) if supported_features & SUPPORT_ALARM_TRIGGER: actions.append( { CONF_DEVICE_ID: device_id, CONF_DOMAIN: DOMAIN, CONF_ENTITY_ID: entry.entity_id, CONF_TYPE: "trigger", } ) return actions
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[ 43, 0 ]
[ 107, 18 ]
python
en
['fr', 'en', 'en']
True
async_call_action_from_config
( hass: HomeAssistant, config: dict, variables: dict, context: Optional[Context] )
Execute a device action.
Execute a device action.
async def async_call_action_from_config( hass: HomeAssistant, config: dict, variables: dict, context: Optional[Context] ) -> None: """Execute a device action.""" config = ACTION_SCHEMA(config) service_data = {ATTR_ENTITY_ID: config[CONF_ENTITY_ID]} if CONF_CODE in config: service_data[ATTR_CODE] = config[CONF_CODE] if config[CONF_TYPE] == "arm_away": service = SERVICE_ALARM_ARM_AWAY elif config[CONF_TYPE] == "arm_home": service = SERVICE_ALARM_ARM_HOME elif config[CONF_TYPE] == "arm_night": service = SERVICE_ALARM_ARM_NIGHT elif config[CONF_TYPE] == "disarm": service = SERVICE_ALARM_DISARM elif config[CONF_TYPE] == "trigger": service = SERVICE_ALARM_TRIGGER await hass.services.async_call( DOMAIN, service, service_data, blocking=True, context=context )
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[ 110, 0 ]
[ 133, 5 ]
python
en
['ro', 'en', 'en']
True
async_get_action_capabilities
(hass, config)
List action capabilities.
List action capabilities.
async def async_get_action_capabilities(hass, config): """List action capabilities.""" state = hass.states.get(config[CONF_ENTITY_ID]) code_required = state.attributes.get(ATTR_CODE_ARM_REQUIRED) if state else False if config[CONF_TYPE] == "trigger" or ( config[CONF_TYPE] != "disarm" and not code_required ): return {} return {"extra_fields": vol.Schema({vol.Optional(CONF_CODE): str})}
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[ 136, 0 ]
[ 146, 71 ]
python
en
['ro', 'ga', 'en']
False
convert
(value: float, unit_1: str, unit_2: str)
Convert one unit of measurement to another.
Convert one unit of measurement to another.
def convert(value: float, unit_1: str, unit_2: str) -> float: """Convert one unit of measurement to another.""" if unit_1 not in VALID_UNITS: raise ValueError(UNIT_NOT_RECOGNIZED_TEMPLATE.format(unit_1, LENGTH)) if unit_2 not in VALID_UNITS: raise ValueError(UNIT_NOT_RECOGNIZED_TEMPLATE.format(unit_2, LENGTH)) if not isinstance(value, Number): raise TypeError(f"{value} is not of numeric type") if unit_1 == unit_2 or unit_1 not in VALID_UNITS: return value meters: float = TO_METERS[unit_1](value) return METERS_TO[unit_2](meters)
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[ 51, 0 ]
[ 66, 36 ]
python
en
['en', 'en', 'en']
True
glue_convert_examples_to_features
( examples: Union[List[InputExample], "tf.data.Dataset"], tokenizer: PreTrainedTokenizer, max_length: Optional[int] = None, task=None, label_list=None, output_mode=None, )
Loads a data file into a list of ``InputFeatures`` Args: examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples. tokenizer: Instance of a tokenizer that will tokenize the examples max_length: Maximum example length. Defaults to the tokenizer's max_len task: GLUE task label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method output_mode: String indicating the output mode. Either ``regression`` or ``classification`` Returns: If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset`` containing the task-specific features. If the input is a list of ``InputExamples``, will return a list of task-specific ``InputFeatures`` which can be fed to the model.
Loads a data file into a list of ``InputFeatures``
def glue_convert_examples_to_features( examples: Union[List[InputExample], "tf.data.Dataset"], tokenizer: PreTrainedTokenizer, max_length: Optional[int] = None, task=None, label_list=None, output_mode=None, ): """ Loads a data file into a list of ``InputFeatures`` Args: examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples. tokenizer: Instance of a tokenizer that will tokenize the examples max_length: Maximum example length. Defaults to the tokenizer's max_len task: GLUE task label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method output_mode: String indicating the output mode. Either ``regression`` or ``classification`` Returns: If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset`` containing the task-specific features. If the input is a list of ``InputExamples``, will return a list of task-specific ``InputFeatures`` which can be fed to the model. """ warnings.warn(DEPRECATION_WARNING.format("function"), FutureWarning) if is_tf_available() and isinstance(examples, tf.data.Dataset): if task is None: raise ValueError("When calling glue_convert_examples_to_features from TF, the task parameter is required.") return _tf_glue_convert_examples_to_features(examples, tokenizer, max_length=max_length, task=task) return _glue_convert_examples_to_features( examples, tokenizer, max_length=max_length, task=task, label_list=label_list, output_mode=output_mode )
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[ 41, 0 ]
[ 73, 5 ]
python
en
['en', 'error', 'th']
False
MrpcProcessor.get_example_from_tensor_dict
(self, tensor_dict)
See base class.
See base class.
def get_example_from_tensor_dict(self, tensor_dict): """See base class.""" return InputExample( tensor_dict["idx"].numpy(), tensor_dict["sentence1"].numpy().decode("utf-8"), tensor_dict["sentence2"].numpy().decode("utf-8"), str(tensor_dict["label"].numpy()), )
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[ 176, 4 ]
[ 183, 9 ]
python
en
['en', 'en', 'en']
True
MrpcProcessor.get_train_examples
(self, data_dir)
See base class.
See base class.
def get_train_examples(self, data_dir): """See base class.""" logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv"))) return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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[ 185, 4 ]
[ 188, 98 ]
python
en
['en', 'en', 'en']
True
MrpcProcessor.get_dev_examples
(self, data_dir)
See base class.
See base class.
def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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[ 190, 4 ]
[ 192, 94 ]
python
en
['en', 'en', 'en']
True
MrpcProcessor.get_test_examples
(self, data_dir)
See base class.
See base class.
def get_test_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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[ 194, 4 ]
[ 196, 96 ]
python
en
['en', 'en', 'en']
True
MrpcProcessor.get_labels
(self)
See base class.
See base class.
def get_labels(self): """See base class.""" return ["0", "1"]
[ "def", "get_labels", "(", "self", ")", ":", "return", "[", "\"0\"", ",", "\"1\"", "]" ]
[ 198, 4 ]
[ 200, 25 ]
python
en
['en', 'en', 'en']
True
MrpcProcessor._create_examples
(self, lines, set_type)
Creates examples for the training, dev and test sets.
Creates examples for the training, dev and test sets.
def _create_examples(self, lines, set_type): """Creates examples for the training, dev and test sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, i) text_a = line[3] text_b = line[4] label = None if set_type == "test" else line[0] examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples
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[ 202, 4 ]
[ 213, 23 ]
python
en
['en', 'en', 'en']
True
MnliProcessor.get_example_from_tensor_dict
(self, tensor_dict)
See base class.
See base class.
def get_example_from_tensor_dict(self, tensor_dict): """See base class.""" return InputExample( tensor_dict["idx"].numpy(), tensor_dict["premise"].numpy().decode("utf-8"), tensor_dict["hypothesis"].numpy().decode("utf-8"), str(tensor_dict["label"].numpy()), )
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[ 223, 4 ]
[ 230, 9 ]
python
en
['en', 'en', 'en']
True
MnliProcessor.get_train_examples
(self, data_dir)
See base class.
See base class.
def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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[ 232, 4 ]
[ 234, 98 ]
python
en
['en', 'en', 'en']
True
MnliProcessor.get_dev_examples
(self, data_dir)
See base class.
See base class.
def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched")
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[ 236, 4 ]
[ 238, 110 ]
python
en
['en', 'en', 'en']
True
MnliProcessor.get_test_examples
(self, data_dir)
See base class.
See base class.
def get_test_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test_matched")
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[ 240, 4 ]
[ 242, 112 ]
python
en
['en', 'en', 'en']
True
MnliProcessor.get_labels
(self)
See base class.
See base class.
def get_labels(self): """See base class.""" return ["contradiction", "entailment", "neutral"]
[ "def", "get_labels", "(", "self", ")", ":", "return", "[", "\"contradiction\"", ",", "\"entailment\"", ",", "\"neutral\"", "]" ]
[ 244, 4 ]
[ 246, 57 ]
python
en
['en', 'en', 'en']
True
MnliProcessor._create_examples
(self, lines, set_type)
Creates examples for the training, dev and test sets.
Creates examples for the training, dev and test sets.
def _create_examples(self, lines, set_type): """Creates examples for the training, dev and test sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[8] text_b = line[9] label = None if set_type.startswith("test") else line[-1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples
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[ 248, 4 ]
[ 259, 23 ]
python
en
['en', 'en', 'en']
True
MnliMismatchedProcessor.get_dev_examples
(self, data_dir)
See base class.
See base class.
def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")), "dev_mismatched")
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[ 269, 4 ]
[ 271, 116 ]
python
en
['en', 'en', 'en']
True
MnliMismatchedProcessor.get_test_examples
(self, data_dir)
See base class.
See base class.
def get_test_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_mismatched.tsv")), "test_mismatched")
[ "def", "get_test_examples", "(", "self", ",", "data_dir", ")", ":", "return", "self", ".", "_create_examples", "(", "self", ".", "_read_tsv", "(", "os", ".", "path", ".", "join", "(", "data_dir", ",", "\"test_mismatched.tsv\"", ")", ")", ",", "\"test_mismatched\"", ")" ]
[ 273, 4 ]
[ 275, 118 ]
python
en
['en', 'en', 'en']
True
ColaProcessor.get_example_from_tensor_dict
(self, tensor_dict)
See base class.
See base class.
def get_example_from_tensor_dict(self, tensor_dict): """See base class.""" return InputExample( tensor_dict["idx"].numpy(), tensor_dict["sentence"].numpy().decode("utf-8"), None, str(tensor_dict["label"].numpy()), )
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[ 285, 4 ]
[ 292, 9 ]
python
en
['en', 'en', 'en']
True
ColaProcessor.get_train_examples
(self, data_dir)
See base class.
See base class.
def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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[ 294, 4 ]
[ 296, 98 ]
python
en
['en', 'en', 'en']
True
ColaProcessor.get_dev_examples
(self, data_dir)
See base class.
See base class.
def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
[ "def", "get_dev_examples", "(", "self", ",", "data_dir", ")", ":", "return", "self", ".", "_create_examples", "(", "self", ".", "_read_tsv", "(", "os", ".", "path", ".", "join", "(", "data_dir", ",", "\"dev.tsv\"", ")", ")", ",", "\"dev\"", ")" ]
[ 298, 4 ]
[ 300, 94 ]
python
en
['en', 'en', 'en']
True
ColaProcessor.get_test_examples
(self, data_dir)
See base class.
See base class.
def get_test_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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[ 302, 4 ]
[ 304, 96 ]
python
en
['en', 'en', 'en']
True
ColaProcessor.get_labels
(self)
See base class.
See base class.
def get_labels(self): """See base class.""" return ["0", "1"]
[ "def", "get_labels", "(", "self", ")", ":", "return", "[", "\"0\"", ",", "\"1\"", "]" ]
[ 306, 4 ]
[ 308, 25 ]
python
en
['en', 'en', 'en']
True
ColaProcessor._create_examples
(self, lines, set_type)
Creates examples for the training, dev and test sets.
Creates examples for the training, dev and test sets.
def _create_examples(self, lines, set_type): """Creates examples for the training, dev and test sets.""" test_mode = set_type == "test" if test_mode: lines = lines[1:] text_index = 1 if test_mode else 3 examples = [] for (i, line) in enumerate(lines): guid = "%s-%s" % (set_type, i) text_a = line[text_index] label = None if test_mode else line[1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples
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[ 310, 4 ]
[ 322, 23 ]
python
en
['en', 'en', 'en']
True
Sst2Processor.get_example_from_tensor_dict
(self, tensor_dict)
See base class.
See base class.
def get_example_from_tensor_dict(self, tensor_dict): """See base class.""" return InputExample( tensor_dict["idx"].numpy(), tensor_dict["sentence"].numpy().decode("utf-8"), None, str(tensor_dict["label"].numpy()), )
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[ 332, 4 ]
[ 339, 9 ]
python
en
['en', 'en', 'en']
True
Sst2Processor.get_train_examples
(self, data_dir)
See base class.
See base class.
def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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[ 341, 4 ]
[ 343, 98 ]
python
en
['en', 'en', 'en']
True
Sst2Processor.get_dev_examples
(self, data_dir)
See base class.
See base class.
def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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[ 345, 4 ]
[ 347, 94 ]
python
en
['en', 'en', 'en']
True
Sst2Processor.get_test_examples
(self, data_dir)
See base class.
See base class.
def get_test_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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[ 349, 4 ]
[ 351, 96 ]
python
en
['en', 'en', 'en']
True
Sst2Processor.get_labels
(self)
See base class.
See base class.
def get_labels(self): """See base class.""" return ["0", "1"]
[ "def", "get_labels", "(", "self", ")", ":", "return", "[", "\"0\"", ",", "\"1\"", "]" ]
[ 353, 4 ]
[ 355, 25 ]
python
en
['en', 'en', 'en']
True
Sst2Processor._create_examples
(self, lines, set_type)
Creates examples for the training, dev and test sets.
Creates examples for the training, dev and test sets.
def _create_examples(self, lines, set_type): """Creates examples for the training, dev and test sets.""" examples = [] text_index = 1 if set_type == "test" else 0 for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, i) text_a = line[text_index] label = None if set_type == "test" else line[1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples
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[ 357, 4 ]
[ 368, 23 ]
python
en
['en', 'en', 'en']
True
StsbProcessor.get_example_from_tensor_dict
(self, tensor_dict)
See base class.
See base class.
def get_example_from_tensor_dict(self, tensor_dict): """See base class.""" return InputExample( tensor_dict["idx"].numpy(), tensor_dict["sentence1"].numpy().decode("utf-8"), tensor_dict["sentence2"].numpy().decode("utf-8"), str(tensor_dict["label"].numpy()), )
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[ 378, 4 ]
[ 385, 9 ]
python
en
['en', 'en', 'en']
True
StsbProcessor.get_train_examples
(self, data_dir)
See base class.
See base class.
def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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[ 387, 4 ]
[ 389, 98 ]
python
en
['en', 'en', 'en']
True
StsbProcessor.get_dev_examples
(self, data_dir)
See base class.
See base class.
def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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[ 391, 4 ]
[ 393, 94 ]
python
en
['en', 'en', 'en']
True
StsbProcessor.get_test_examples
(self, data_dir)
See base class.
See base class.
def get_test_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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[ 395, 4 ]
[ 397, 96 ]
python
en
['en', 'en', 'en']
True
StsbProcessor.get_labels
(self)
See base class.
See base class.
def get_labels(self): """See base class.""" return [None]
[ "def", "get_labels", "(", "self", ")", ":", "return", "[", "None", "]" ]
[ 399, 4 ]
[ 401, 21 ]
python
en
['en', 'en', 'en']
True
StsbProcessor._create_examples
(self, lines, set_type)
Creates examples for the training, dev and test sets.
Creates examples for the training, dev and test sets.
def _create_examples(self, lines, set_type): """Creates examples for the training, dev and test sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[7] text_b = line[8] label = None if set_type == "test" else line[-1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples
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[ 403, 4 ]
[ 414, 23 ]
python
en
['en', 'en', 'en']
True
QqpProcessor.get_example_from_tensor_dict
(self, tensor_dict)
See base class.
See base class.
def get_example_from_tensor_dict(self, tensor_dict): """See base class.""" return InputExample( tensor_dict["idx"].numpy(), tensor_dict["question1"].numpy().decode("utf-8"), tensor_dict["question2"].numpy().decode("utf-8"), str(tensor_dict["label"].numpy()), )
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[ 424, 4 ]
[ 431, 9 ]
python
en
['en', 'en', 'en']
True
QqpProcessor.get_train_examples
(self, data_dir)
See base class.
See base class.
def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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[ 433, 4 ]
[ 435, 98 ]
python
en
['en', 'en', 'en']
True
QqpProcessor.get_dev_examples
(self, data_dir)
See base class.
See base class.
def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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[ 437, 4 ]
[ 439, 94 ]
python
en
['en', 'en', 'en']
True
QqpProcessor.get_test_examples
(self, data_dir)
See base class.
See base class.
def get_test_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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[ 441, 4 ]
[ 443, 96 ]
python
en
['en', 'en', 'en']
True
QqpProcessor.get_labels
(self)
See base class.
See base class.
def get_labels(self): """See base class.""" return ["0", "1"]
[ "def", "get_labels", "(", "self", ")", ":", "return", "[", "\"0\"", ",", "\"1\"", "]" ]
[ 445, 4 ]
[ 447, 25 ]
python
en
['en', 'en', 'en']
True
QqpProcessor._create_examples
(self, lines, set_type)
Creates examples for the training, dev and test sets.
Creates examples for the training, dev and test sets.
def _create_examples(self, lines, set_type): """Creates examples for the training, dev and test sets.""" test_mode = set_type == "test" q1_index = 1 if test_mode else 3 q2_index = 2 if test_mode else 4 examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) try: text_a = line[q1_index] text_b = line[q2_index] label = None if test_mode else line[5] except IndexError: continue examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples
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[ 449, 4 ]
[ 466, 23 ]
python
en
['en', 'en', 'en']
True