dummysensors 0.2.0

Dummy sensor data generator for testing IoT/ML pipelines

dummysensors

A lightweight generator of dummy sensor data for IoT and ML testing. Provides a simple Python API and CLI, supports running multiple sensors in parallel and splitting output streams into files.

Installation

pip install -e .
# for development:
pip install -r requirements.txt

Quick Start (API)

from dummysensors import TemperatureSensor, VibrationSensor

# create sensors
temp = TemperatureSensor(min_val=18, max_val=24, noise=0.2)
vib  = VibrationSensor(base_hz=50.0, amp=1.0, noise=0.05)

print(temp.read())            # e.g. 21.3
print(vib.read(t_s=0.123))    # sinusoidal signal with noise

Quick Start (CLI)

Generate a single temperature stream to JSONL:

dummy-sensors generate --rate 5 --duration 2 --jsonl out/temp.jsonl

Run multiple sensors and devices, split by type into separate files:

dummy-sensors run \
  --rate 5 \
  --count 30 \
  --spec "device=engine-A: temp*1,vibration*2; device=room-101: temp*2" \
  --out "temp=out/temp.jsonl" \
  --out "vibration=out/vib.jsonl" \
  --out "*=stdout"

👉 Check out a full demo with live plotting and JSONL logging here:
dummysensors demo (ds-test)

Each record is a JSON line:

{
  "ts_ms": 1234,
  "device_id": "engine-A",
  "sensor_id": "temp-0",
  "type": "temp",
  "value": 21.04
}

--spec format

The --spec string describes devices and sensors:

device=<ID>: <type>*<count>[, <type>*<count> ...] ; device=<ID2>: ...

Examples:

• device=A: temp*3 — device A with three temperature sensors
• device=eng: temp*1,vibration*2; device=room: temp*2

As of v0.2, supported sensor types: temp, vibration. More sensors and YAML configs coming soon.

Python API

TemperatureSensor

• Parameters: min_val=15.0, max_val=30.0, noise=0.5, period_s=86400
• Methods:
  • read(t_s: float | None = None) -> float — one sample (random within range + Gaussian noise)

VibrationSensor

• Parameters: base_hz=50.0, amp=1.0, noise=0.1, spike_prob=0.0
• Methods:
  • read(t_s: float | None = None) -> float — sinusoidal signal at base_hz + noise

Sensor Registry

• dummysensors.registry.SENSOR_REGISTRY — maps string kind → class
• dummysensors.registry.make_sensor(kind: str, **params)

Orchestrator

• dummysensors.orchestrator.run_stream(spec_str, rate_hz, duration_s, total_count, writer_for_type)
  • Creates instances based on spec_str, emits samples at rate_hz.
  • writer_for_type is a dict type → callable(sample_dict). * = default writer.

Output Format

JSON Lines (one record per line):

{
  "ts_ms": 171234,
  "device_id": "engine-A",
  "sensor_id": "vibration-1",
  "type": "vibration",
  "value": -0.124
}

Planned: CSV, Kafka, Redis Stream, WebSocket.

Makefile

For convenience, run with make:

make venv        # create .venv and upgrade pip
make install     # pip install -e .
make test        # pytest
make demo        # generate demo data into demo_out/
make clean       # cleanup build and cache

After make demo, check the files:

head -n 3 demo_out/temp.jsonl
head -n 3 demo_out/vibration.jsonl

Development

• Project layout: src-layout
• Tests: pytest -q
• Lint/format: ruff check src tests and ruff format

Pull Requests welcome. Guidelines:

• Simple sensor classes
• No heavy runtime dependencies
• Test each public feature

Example Demo

• A complete example using dummysensors for live plotting and JSONL logging is available in the ds-test repository.

Roadmap

• v0.2
  • CSV writer
  • partition_by=device
  • YAML config (--config config.yaml)
• v0.3
  • AnomalyInjector (spike, dropout, drift)
  • New sensors: humidity, rpm, battery_voltage, gps (trajectory), accel 3-axis
• v0.4
  • Outputs: Kafka, Redis Stream
  • Live preview (WebSocket demo)

Versioning and Publishing

• Semantic versioning with tags vX.Y.Z.
• ​CI​: .github/workflows/ci.yml (lint + tests + build).
• ​Publish​: .github/workflows/publish.yml (Trusted Publishing to PyPI on release).

License

MIT © Mateusz Dalke