async-httpd-data-collector 2.0.1

Gateway facilitating asyncronous communication between sensory data-emitting devices, InfluxDB and the user.

async-httpd-data-collector

Interface handling the communication between sensory data-emitting devices, InfluxDB and the user.

The most important object that a user would use is DatabseInterface within ahttpdc.reads.interface module. This class facilitates the communication between the fetcher and the querying apis of InfluxDB.

In order to control fetching, there are two methods:

• interface.daemon.enableg();
• interface.daemon.disable().

Those methods control the thread within which fetching process is contained.

You can query data from the database using methods with query_ prefix. For now there are three:

• interface.query_latest(), which queries the lastest measurement;
• interface.query_historical(), which queries data from a given time range or relative time (eg. -3h);
• interface.query(), which can takes user's given query as an argument.

Some examples will be presented below:

1.1 Connecting to the database

import json
from ahttpdc.reads.interface import DatabaseInterface

# load the secrets
with open('../../../secrets/secrets.json', 'r') as f:
    secrets = json.load(f)

# define sensors
sensors = {
    'bmp180': ['altitude', 'pressure', 'seaLevelPressure'],
    'mq135': ['aceton', 'alcohol', 'co', 'co2', 'nh4', 'toulen'],
    'ds18b20': ['temperature'],
    'dht22': ['humidity'],
}

# define the interface to the database
interface = DatabaseInterface(
    secrets['host'],
    secrets['port'],
    secrets['token'],
    secrets['organization'],
    secrets['bucket'],
    sensors,
    secrets['dev_ip'],
    80,
    secrets['handle'],
)

1.2 Extracting the dataframe from the database

import pandas as pd
import asyncio
from datetime import datetime, timedelta
from pathlib import Path

# if there is readings.csv file, load it
# if not - create it
readings_path = Path('../data/readings.csv')
if readings_path.is_file():
    sensor = pd.read_csv(readings_path)
else:
    sensor = await interface.query_historical('-30d')
    sensor.to_csv(readings_path)

sensor

	time	aceton	alcohol	altitude	co	co2	humidity	nh4	pressure	seaLevelPressure	temperature	toulen
0	2024-05-16 17:43:59.196399+00:00	0.41	1.17	149.92	3.38	402.54	37.4	3.93	999.35	1017.31	24.40	0.48
1	2024-05-16 17:44:01.768738+00:00	0.47	1.32	149.76	3.94	402.84	30.5	4.33	997.61	1015.56	24.03	0.55
2	2024-05-16 17:44:03.255309+00:00	0.96	2.62	149.54	9.16	405.25	49.1	7.35	999.14	1017.08	23.16	1.15
3	2024-05-16 17:44:04.618203+00:00	0.30	0.86	149.38	2.32	401.94	32.9	3.10	999.09	1017.02	23.05	0.35
4	2024-05-16 17:44:05.954714+00:00	1.31	3.50	149.37	13.13	406.82	48.8	9.21	998.04	1015.93	23.92	1.57
...	...	...	...	...	...	...	...	...	...	...	...	...
284122	2024-05-21 14:42:57.894312+00:00	1.35	3.62	150.08	13.68	407.03	47.6	9.46	998.85	1016.81	24.35	1.63
284123	2024-05-21 14:42:59.277937+00:00	1.08	2.92	149.87	10.48	405.79	49.3	8.00	998.58	1016.53	23.41	1.29
284124	2024-05-21 14:43:00.594968+00:00	0.38	1.09	149.97	3.09	402.38	33.8	3.71	999.59	1017.54	24.88	0.44
284125	2024-05-21 14:43:01.918239+00:00	1.41	3.77	150.13	14.38	407.29	44.4	9.76	998.51	1016.48	23.54	1.70
284126	2024-05-21 14:43:03.248095+00:00	1.24	3.32	150.50	12.29	406.50	48.8	8.84	998.85	1016.85	22.44	1.49

284127 rows × 12 columns