def get_attribute_data(self, attribute_name, limit, offset,
fromdate=None, todate=None, operation=None):
# clearing previous metadata
db.metadata.clear()
attrs = attribute_name.split(',')
attributes = Attributes.get_by_name_in(attrs)
data = []
for attribute in attributes:
model = ModelClass(attribute.table_name.lower())
count = db.session.query(model).count()
values = []
if fromdate is not None and todate is not None:
if operation is None:
values = db.session.query(model) \
.filter(model.api_timestamp >= fromdate) \
.filter(model.api_timestamp <= todate) \
.limit(limit).offset(abs(count - offset)) \
.all()
else:
values = db.session.query(model) \
.filter(model.api_timestamp >= fromdate) \
.filter(model.api_timestamp <= todate) \
.all()
else:
if operation is None:
### refactored the query to fetch the latest values by default
values = db.session.query(model).order_by(desc(model.api_timestamp)).limit(limit).all() # \
# values = db.session.query(model).limit(limit) \
# .offset(abs(count - offset)).all()
else:
values = db.session.query(model).all()
_common = {
'Attribute_Table': attribute.table_name,
'Attribute_Name': attribute.name,
'Attribute_Description': attribute.description,
'Attribute_Unit_Value': attribute.unit_value,
'Total_Records': count
}
temp = []
if operation is None:
for i in range(len(values)-1, -1, -1):
temp.append({
'Sensor_id': values[i].s_id,
'Value': values[i].value,
'Timestamp': str(values[i].api_timestamp)
})
_common['Attribute_Values'] = temp
else:
_values = [v.value for v in values]
_int_values = list(map(float, _values))
_operation_result = 0
if operation == 'sum':
_operation_result = sum(_int_values)
elif operation == 'mean':
_operation_result = sum(_int_values) / len(_int_values)
elif operation == 'median':
_operation_result = statistics.median(_int_values)
_common['Result_' + operation] = _operation_result
data.append(_common)
return data
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
def get(self):
args = self.parser.parse_args()
attribute_data, attributes, sensorid, n_predictions, predictions, grouped, harmonising_method, per_sensor, freq = None, [], None, 100, None, None, None, None, '1H'
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 '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 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
return {
"error": "error occured while processing request"
}, 400