def static_sensor_data(sensor_id, start_time, end_time):
maxVal = db.query_dataframe(f"""SELECT max FROM sensor_data where sensor_id = '{sensor_id}' AND time BETWEEN '{start_time}' AND '{end_time}' ORDER BY time ASC""")
#sampling_rate = 359.754
sampling_rate = 1
start_time = pd.Timestamp(start_time)
end_time = pd.Timestamp(end_time)
diff = end_time - start_time
num = diff.total_seconds()
time_np = np.linspace(0,int(num),int(num*sampling_rate) +1)
time = pd.DataFrame(data=time_np)
return time, maxVal
def fetch_sensor_db_data(sensor_id, start,end):
assert end > start
db.init()
static_data = db.query_dataframe("""
SELECT time,min,max,average,variance
FROM monitoring.sensor_data
WHERE sensor_id = %s
AND time >= %s
AND time <= %s
ORDER BY time ASC
""", (sensor_id, start,end))
return static_data
def fetch_sensor_db_data_gapfill(sensor_id, start, end):
assert end > start
db.init()
data = db.query_dataframe("""
SELECT time_bucket_gapfill('1 second', time) as time,
COALESCE(avg(min), 'NaN') as min,
COALESCE(avg(max), 'NaN') as max,
COALESCE(avg(average), 'NaN') as average,
COALESCE(avg(variance), 'NaN') as variance
FROM monitoring.sensor_data
WHERE sensor_id = %s
AND time > %s
AND time < %s
GROUP BY 1
ORDER BY 1 ASC""", (sensor_id, start, end))
return data