Storing data

Activity data collected during an experiment is usually represented by an array of records with the following data:

• time of measurement;

• measured activity value;

• time of day marker (night or day).

We recommend InfluxDB to store activity data.

Only InfluxDB v1.x is supported, don’t use InfluxDB v2.x.

Each searies of measurements should be represented by a separate InfluxDB table with the following fields:

• value – numeric values obtained by sensors;

• is_night – day/night marker (True for nighttime measurements and False for daytime measurements).

Other data (like sensor_id in case of multiple sensors) can also be included in the table as tags.

In InfluxDB, data can be stored as a field or tag. Tags are more efficient to construct WHERE-queries but they can hold only string data. So it’s better to have value and is_night stored as fields and other data (required for filtering purpuses) stored as tags.

For installing and configuring InfluxDB see the official documentation. Once your InfluxDB is initialized and configured, you are ready to write data to it.

Writing data to InfluxDB using Python

In the following example, random measurements are generated and written to database via InfluxDBClient which can be downloaded using pip:

$ pip install influxdb

from influxdb import InfluxDBClient
import json
from datetime import datetime, timedelta
import random

# Create a client to access the database.
client = InfluxDBClient('localhost', 8086, 'admin', 'pass', database='my_db')

# Generate random measurements.
measurements=[]
for i in range(5):
    measurements.append({
        'measurement': 'test_series',
        'tags': {
            'sensor_id': '1',
        },
        'time': datetime.fromisoformat('2021-01-01') + timedelta(hours=i),
        'fields': {
            'value': random.uniform(1, 10),
            'is_night': True,
        }
    })
measurements

[{'measurement': 'test_series',
  'tags': {'sensor_id': '1'},
  'time': datetime.datetime(2021, 1, 1, 0, 0),
  'fields': {'value': 2.636775703935856, 'is_night': True}},
 {'measurement': 'test_series',
  'tags': {'sensor_id': '1'},
  'time': datetime.datetime(2021, 1, 1, 1, 0),
  'fields': {'value': 9.634964674586048, 'is_night': True}},
 {'measurement': 'test_series',
  'tags': {'sensor_id': '1'},
  'time': datetime.datetime(2021, 1, 1, 2, 0),
  'fields': {'value': 5.488104584244404, 'is_night': True}},
 {'measurement': 'test_series',
  'tags': {'sensor_id': '1'},
  'time': datetime.datetime(2021, 1, 1, 3, 0),
  'fields': {'value': 2.3051122236289237, 'is_night': True}},
 {'measurement': 'test_series',
  'tags': {'sensor_id': '1'},
  'time': datetime.datetime(2021, 1, 1, 4, 0),
  'fields': {'value': 5.834270867285628, 'is_night': True}}]

# Insert generated data to the database.
client.write_points(measurements);

# Show the inserted data.
client.query('''SELECT (*) FROM "test_series"''')

ResultSet({'('test_series', None)': [{'time': '2021-01-01T00:00:00Z', 'is_night': True, 'sensor_id': '1', 'value': 2.636775703935856}, {'time': '2021-01-01T01:00:00Z', 'is_night': True, 'sensor_id': '1', 'value': 9.634964674586048}, {'time': '2021-01-01T02:00:00Z', 'is_night': True, 'sensor_id': '1', 'value': 5.488104584244404}, {'time': '2021-01-01T03:00:00Z', 'is_night': True, 'sensor_id': '1', 'value': 2.3051122236289237}, {'time': '2021-01-01T04:00:00Z', 'is_night': True, 'sensor_id': '1', 'value': 5.834270867285628}]})