def _create_datasource(self,
headers=json.loads(HEADERS_K.replace("'", '"'))):
super()._create_datasource(headers)
self.df = self.create_dataframe(object_separator=None)
self.df['latitude'] = 0.
self.df['longitude'] = 0.
for i in range(0, len(self.df)):
lid = Sensor.get_by_name_in(
[self.df.parkingSpotSensorCode.iloc[i]])[0].l_id
loc = location.Location.get_by_id_in([lid])[0]
self.df.set_value(i, 'latitude', loc.lat)
self.df.set_value(i, 'longitude', loc.lon)
self.df['api_timestamp_tag'] = pd.to_datetime(self.df['datetime'])
self.df['api_timestamp_tag'] = self.df['api_timestamp_tag'].astype(int)
loc = Location('latitude', 'longitude')
self.create_datasource(dataframe=self.df,
sensor_tag='parkingSpotSensorCode',
attribute_tag=['state'],
unit_value=[],
bespoke_unit_tag=[],
description=['No Description'],
bespoke_sub_theme=[],
location_tag=loc,
sensor_prefix='',
api_timestamp_tag='api_timestamp_tag')
def get(self):
args = self.parser.parse_args()
sensor, sensor_name, sensor_attribute = None, None, None
if 'sensor' in args and args['sensor'] is not None:
sensor = args['sensor']
if sensor != '':
if sensor == 'all':
sensors = Sensor.get_all()
return [a.json() for a in sensors], 200
else:
return (Sensor.get_by_id(sensor)).json(), 200
if 'sensorname' in args and args['sensorname'] is not None:
sensor_name = args['sensorname']
if sensor_name != '':
_sensors = sensor_name.split(',')
_by_name = Sensor.get_by_name_in(_sensors)
return [a.json() for a in _by_name], 200
if 'sensorattribute' in args and args['sensorattribute'] is not None:
sensor_attribute = args['sensorattribute']
if sensor_attribute != '':
_sen_attrs_ids = sensor_attribute.split(',')
_sen_attrs = SensorAttribute.get_by_id_in(_sen_attrs_ids)
attrs_ids = [_id.a_id for _id in _sen_attrs]
_attributes = Attributes.get_by_id_in(attrs_ids)
return [a.json() for a in _attributes], 200
return {
"error": "error occured while processing request"
}, 400
def _create_datasource(self, headers: Union[str, None] = None) -> None:
"""
Create DataSource
:param headers: Request Headers
"""
try:
super()._create_datasource(headers)
self.df = self.create_dataframe(ignore_object_tags=['fieldAliases', 'fields'])
names = self.df['lotcode'].tolist()
name_set = set()
location_sensor = {}
sensor_location = {}
latitude = []
longitude = []
for s in names:
name_set.add('smart_parking_2_' + str(s))
sensors = Sensor.get_by_name_in(name_set)
loc_ids = []
for s in sensors:
loc_ids.append(s.l_id)
location_sensor[s.l_id] = s
locations = location.Location.get_by_id_in(loc_ids)
for loc in locations:
if loc.id in location_sensor:
_sensor = location_sensor[loc.id]
sensor_location[_sensor.name] = loc
for s in names:
_s = 'smart_parking_2_' + str(s)
if _s in sensor_location:
latitude.append(sensor_location[_s].lat)
longitude.append(sensor_location[_s].lon)
self.df['latitude'] = latitude
self.df['longitude'] = longitude
self.df.rename(index=str, columns={'free': 'free_2',
'isoffline': 'isoffline_2',
'occupied': 'occupied_2'},
inplace=True)
loc = Location('latitude', 'longitude')
self.create_datasource(dataframe=self.df, sensor_tag='lotcode',
attribute_tag=['free_2', 'isoffline_2', 'occupied_2'],
unit_value=[], bespoke_unit_tag=[], description=[], bespoke_sub_theme=[],
location_tag=loc,
sensor_prefix='smart_parking_2_', api_timestamp_tag='run_time_stamp',
is_dependent=True)
self.importer_status.status = Status.success(__class__.__name__)
except Exception as e:
self.importer_status.status = Status.failure(__class__.__name__, e.__str__(), traceback.format_exc())
def _create_datasource(self, headers: Union[str, None] = None) -> None:
"""
Create DataSource
:param headers: Request Headers
"""
try:
if not headers:
headers = json.loads(self.HEADERS.replace("'", '"'))
super()._create_datasource(headers)
self.df = self.create_dataframe(object_separator=None)
names = self.df['dev_eui'].tolist()
name_set = set()
location_sensor = {}
for s in names:
name_set.add(str(s))
sensors = Sensor.get_by_name_in(name_set)
loc_ids = []
for s in sensors:
loc_ids.append(s.l_id)
location_sensor[s.l_id] = s
locations = location.Location.get_by_id_in(loc_ids)
d = dict(zip([n.name for n in sensors], locations))
self.df['latitude'] = self.df['dev_eui'].apply(
lambda x: d.get(x).lat)
self.df['longitude'] = self.df['dev_eui'].apply(
lambda x: d.get(x).lon)
self.df['api_timestamp_tag'] = pd.to_datetime(self.df['data'])
self.df['api_timestamp_tag'] = self.df['api_timestamp_tag'].astype(
int)
loc = Location('latitude', 'longitude')
self.create_datasource(
dataframe=self.df,
sensor_tag='dev_eui',
attribute_tag=['pressione', 'temperatura', 'umidita'],
unit_value=[],
bespoke_unit_tag=[],
description=['No Description'],
bespoke_sub_theme=[],
location_tag=loc,
sensor_prefix='',
api_timestamp_tag='api_timestamp_tag')
self.importer_status.status = Status.success(__class__.__name__)
except Exception as e:
self.importer_status.status = Status.failure(
__class__.__name__, e.__str__(), traceback.format_exc())
def _create_datasource(self, headers=None):
super()._create_datasource(headers)
self.df = self.create_dataframe(ignore_object_tags=['fieldAliases', 'fields'])
names = self.df['lotcode'].tolist()
name_set = set()
location_sensor = {}
sensor_location = {}
latitude = []
longitude = []
for s in names:
name_set.add('smart_parking_2_' + str(s))
sensors = Sensor.get_by_name_in(name_set)
loc_ids = []
for s in sensors:
loc_ids.append(s.l_id)
location_sensor[s.l_id] = s
locations = location.Location.get_by_id_in(loc_ids)
for loc in locations:
if loc.id in location_sensor:
_sensor = location_sensor[loc.id]
sensor_location[_sensor.name] = loc
for s in names:
_s = 'smart_parking_2_' + str(s)
if _s in sensor_location:
latitude.append(sensor_location[_s].lat)
longitude.append(sensor_location[_s].lon)
self.df['latitude'] = latitude
self.df['longitude'] = longitude
### Renaming the columns so that they are not confused with GreenwichOCC
self.df.rename(index=str, columns={'free': 'free_2',
'isoffline': 'isoffline_2',
'occupied' : 'occupied_2'},
inplace=True)
loc = Location('latitude', 'longitude')
self.create_datasource(dataframe= self.df, sensor_tag='lotcode', attribute_tag=['free_2', 'isoffline_2', 'occupied_2'],
unit_value=[], bespoke_unit_tag=[], description=[], bespoke_sub_theme=[], location_tag=loc,
sensor_prefix='smart_parking_2_', api_timestamp_tag='run_time_stamp', is_dependent=True)
def _create_datasource(self,
headers=json.loads(HEADERS_SM.replace("'", '"'))):
super()._create_datasource(headers)
self.df = self.create_dataframe(object_separator=None)
names = self.df['dev_eui'].tolist()
name_set = set()
location_sensor = {}
sensor_location = {}
sensor_name_location = {}
latitude = []
longitude = []
for s in names:
name_set.add(str(s))
sensors = Sensor.get_by_name_in(name_set)
loc_ids = []
for s in sensors:
loc_ids.append(s.l_id)
location_sensor[s.l_id] = s
locations = location.Location.get_by_id_in(loc_ids)
d = dict(zip([n.name for n in sensors], locations))
self.df['latitude'] = self.df['dev_eui'].apply(lambda x: d.get(x).lat)
self.df['longitude'] = self.df['dev_eui'].apply(lambda x: d.get(x).lon)
self.df['api_timestamp_tag'] = pd.to_datetime(self.df['data'])
self.df['api_timestamp_tag'] = self.df['api_timestamp_tag'].astype(int)
loc = Location('latitude', 'longitude')
self.create_datasource(
dataframe=self.df,
sensor_tag='dev_eui',
attribute_tag=['pressione', 'temperatura', 'umidita'],
unit_value=[],
bespoke_unit_tag=[],
description=['No Description'],
bespoke_sub_theme=[],
location_tag=loc,
sensor_prefix='',
api_timestamp_tag='api_timestamp_tag')
def _create_datasource(self, headers: Union[str, None] = None) -> None:
"""
Create DataSource
:param headers: Request Headers
"""
try:
if not headers:
headers = json.loads(self.HEADERS.replace("'", '"'))
super()._create_datasource(headers)
self.df = self.create_dataframe(object_separator=None)
self.df['latitude'] = 0.
self.df['longitude'] = 0.
for i in range(0, len(self.df)):
lid = Sensor.get_by_name_in(
[self.df.parkingSpotSensorCode.iloc[i]])[0].l_id
loc = location.Location.get_by_id_in([lid])[0]
self.df.set_value(i, 'latitude', loc.lat)
self.df.set_value(i, 'longitude', loc.lon)
self.df['api_timestamp_tag'] = pd.to_datetime(self.df['datetime'])
self.df['api_timestamp_tag'] = self.df['api_timestamp_tag'].astype(
int)
loc = Location('latitude', 'longitude')
self.create_datasource(dataframe=self.df,
sensor_tag='parkingSpotSensorCode',
attribute_tag=['state'],
unit_value=[],
bespoke_unit_tag=[],
description=['No Description'],
bespoke_sub_theme=[],
location_tag=loc,
sensor_prefix='',
api_timestamp_tag='api_timestamp_tag')
self.importer_status.status = Status.success(__class__.__name__)
except Exception as e:
self.importer_status.status = Status.failure(
__class__.__name__, e.__str__(), traceback.format_exc())
def get(self):
args = self.parser.parse_args()
theme, subtheme, attribute_data, sensor, sensor_name, sensor_attribute, attributes, sensorid, n_predictions, predictions, grouped, harmonising_method, per_sensor, freq = None, None, None, None, None, None, [], None, 100, None, None, None, None, '1H'
if 'theme' in args:
theme = args['theme']
if 'subtheme' in args:
subtheme = args['subtheme']
if 'attributedata' in args:
attribute_data = args['attributedata']
if 'attribute' in args and args['attribute'] is not None:
_attributes = args['attribute']
if _attributes != '':
attributes = _attributes.split(',')
if 'sensor' in args and args['sensor'] is not None:
sensor = args['sensor']
if sensor != '':
if sensor == 'all':
sensors = Sensor.get_all()
return [a.json() for a in sensors], 200
else:
return (Sensor.get_by_id(sensor)).json(), 200
if 'sensorname' in args and args['sensorname'] is not None:
sensor_name = args['sensorname']
if sensor_name != '':
_sensors = sensor_name.split(',')
_by_name = Sensor.get_by_name_in(_sensors)
return [a.json() for a in _by_name], 200
if 'sensorattribute' in args and args['sensorattribute'] is not None:
sensor_attribute = args['sensorattribute']
if sensor_attribute != '':
_sen_attrs_ids = sensor_attribute.split(',')
_sen_attrs = SensorAttribute.get_by_id_in(_sen_attrs_ids)
attrs_ids = [_id.a_id for _id in _sen_attrs]
_attributes = Attributes.get_by_id_in(attrs_ids)
return [a.json() for a in _attributes], 200
if 'grouped' in args:
grouped = args['grouped']
if 'harmonising_method' in args:
harmonising_method = args['harmonising_method']
if 'per_sensor' in args:
per_sensor = args['per_sensor']
if 'freq' in args:
freq = args['freq']
if 'predictions' in args:
predictions = args['predictions']
if predictions >=100:
predictions = 100
if 'n_predictions' in args:
n_predictions = args['n_predictions']
if 'sensorid' in args:
sensorid = args['sensorid']
if theme is None and subtheme is None \
and len(attributes) == 0 and attribute_data is None \
and sensor is None and sensor_name is None and sensor_attribute is None:
themes = Theme.get_all()
return [a.json() for a in themes], 200
if attribute_data is not None:
global LIMIT, OFFSET
data = None
operation = None
if 'limit' in args and args['limit'] is not None:
LIMIT = args['limit']
if 'offset' in args and args['offset'] is not None:
OFFSET = args['offset']
if 'operation' in args and args['operation'] is not None:
operation = args['operation']
if ('fromdate' in args and args['fromdate'] is not None
and 'todate' in args and args['todate'] is not None):
data = self.get_attribute_data(attribute_data, LIMIT, OFFSET,
args['fromdate'], args['todate'], operation)
if predictions:
data.append(self.get_predictions(attribute_table = data[0]["Attribute_Table"],
sensor_id = sensorid,
n_pred = n_predictions))
else:
if grouped:
if harmonising_method:
data = self.get_attribute_data(attribute_data, LIMIT, OFFSET, operation=operation)
data = request_harmonised_data(data, harmonising_method=harmonising_method)
else:
data = self.get_attribute_data(attribute_data, LIMIT, OFFSET, operation=operation)
data = request_grouped_data(data, per_sensor=per_sensor, freq=freq)
else:
data = self.get_attribute_data(attribute_data, LIMIT, OFFSET, operation=operation)
if predictions:
#### Ceck for data
if data[0]["Total_Records"] != 0:
#### Check for non numeric data
if is_number(data[0]["Attribute_Values"][0]["Value"]):
data.append(self.get_predictions(attribute_table = data[0]["Attribute_Table"],
sensor_id = sensorid,
n_pred = n_predictions))
else:
print("Cannot predict non-numeric data")
pass
else:
pass
return data, 200
if attributes:
_attrs = []
attr = Attributes.get_by_name_in(attributes)
for a in attr:
_attrs.append(a.json())
return _attrs, 200
if subtheme is not None and subtheme != '':
attributes = Attributes.get_by_sub_theme_id(subtheme)
return [a.json() for a in attributes], 200
if theme is not None and theme != '':
subthemes = SubTheme.get_by_theme_id(theme)
return [a.json() for a in subthemes], 200
return {
"error": "error occured while processing request"
}, 400