code stringlengths 3 6.57k |
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logging.getLogger(__name__) |
setup_platform(hass, config, add_entities, discovery_info=None) |
FibaroBinarySensor(device) |
FibaroBinarySensor(FibaroDevice, BinarySensorDevice) |
__init__(self, fibaro_device) |
super() |
__init__(fibaro_device) |
ENTITY_ID_FORMAT.format(self.ha_id) |
devconf.get(CONF_ICON, self._icon) |
icon(self) |
device_class(self) |
is_on(self) |
update(self) |
find_packages() |
events (typically incidents or alerts) |
logging.getLogger(__name__) |
timedelta(minutes=5) |
vol.Required(CONF_URL) |
vol.Optional(CONF_LATITUDE) |
vol.Optional(CONF_LONGITUDE) |
vol.Optional(CONF_RADIUS, default=DEFAULT_RADIUS_IN_KM) |
vol.Coerce(float) |
vol.Optional(CONF_NAME, default=DEFAULT_NAME) |
vol.Optional(CONF_CATEGORIES, default=[]) |
vol.All(cv.ensure_list, [cv.string]) |
setup_platform(hass, config, add_entities, discovery_info=None) |
config.get(CONF_LATITUDE, hass.config.latitude) |
config.get(CONF_LONGITUDE, hass.config.longitude) |
config.get(CONF_URL) |
config.get(CONF_RADIUS) |
config.get(CONF_NAME) |
config.get(CONF_CATEGORIES) |
config.get(CONF_UNIT_OF_MEASUREMENT) |
GeoRssServiceSensor((latitude, longitude) |
devices.append(device) |
GeoRssServiceSensor((latitude, longitude) |
devices.append(device) |
add_entities(devices, True) |
GeoRssServiceSensor(Entity) |
name(self) |
state(self) |
unit_of_measurement(self) |
icon(self) |
device_state_attributes(self) |
update(self) |
self._feed.update() |
len(feed_entries) |
mcode(input) |
input.charCodeAt(i++) |
input.charCodeAt(i++) |
input.charCodeAt(i++) |
if (isNaN(chr2) |
if (isNaN(chr3) |
keyStr.charAt(enc1) |
keyStr.charAt(enc2) |
keyStr.charAt(enc3) |
keyStr.charAt(enc4) |
while (i < input.length) |
stock_rank_forecast_cninfo(date: str = "20210910") |
join([date[:4], date[4:6], date[6:]]) |
str(int(time.time() |
py_mini_racer.MiniRacer() |
js_code.eval(js_str) |
js_code.call("mcode", random_time_str) |
requests.post(url, params=params, headers=headers) |
r.json() |
pd.DataFrame(data_json["records"]) |
pd.to_numeric(temp_df["目标价格-上限"], errors="coerce") |
pd.to_numeric(temp_df["目标价格-下限"], errors="coerce") |
stock_rank_forecast_cninfo(date="20210907") |
print(stock_rank_forecast_cninfo_df) |
Copyright (c) |
_try_to_transform(conv_op, scale_op, block) |
shape (1,) |
isinstance(scale, np.ndarray) |
scale.tolist() |
len(conv_weight.shape) |
np.product(scale.shape) |
len(scale.shape) |
len(conv_weight.shape) |
len(scale.shape) |
len(conv_weight.shape) |
np.zeros(Cout) |
conv_bias.astype(conv_weight_type) |
np.array(conv_bias * scale) |
astype(conv_weight_type) |
np.array(conv_weight * scale) |
astype(conv_weight_type) |
np.reshape(scale, (Cout) |
np.array(conv_bias * scale) |
astype(conv_weight_type) |
np.transpose(conv_weight, [1, 0, 2] if is_conv_1d else [1, 0, 2, 3]) |
np.reshape(conv_weight, [Cout, Cin // groups] + list(conv_weight.shape[2:]) |
range(Cout) |
new_conv_weight.append(_conv_weight) |
np.array(new_conv_weight) |
astype(conv_weight_type) |
np.reshape(new_conv_weight, [Cout // groups, Cin] + list(new_conv_weight.shape[2:]) |
np.transpose(new_conv_weight, [1, 0, 2] if is_conv_1d else [1, 0, 2, 3]) |
conv_op.inputs.items() |
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