scentience 2.1.0

Python client for Scentience olfaction instruments over BLE Bluetooth

scentience

Python client for Scentience olfaction instruments over BLE Bluetooth.

Installation

BLE only:

pip install scentience

BLE + COLIP embedding models:

pip install "scentience[models]"

Requirements

• Python 3.8+
• Bluetooth 4.0+ adapter
• A Scentience developer API key (obtain from the Scentience portal)
• For embedding models: torch, torchvision, transformers, huggingface-hub, Pillow (installed via [models] extra)

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Connecting a single device

Auto-discover

Scans for the first Scentience device in range and connects to it.

import scentience as scn

device = scn.ScentienceDevice(api_key="YOUR_API_KEY")
device.connect_ble(char_uuid="YOUR_CHAR_UUID")

Target a specific device

Pass the device UID (serial number or BLE name) to connect to a particular instrument.

device.connect_ble(char_uuid="YOUR_CHAR_UUID", device_uid="A00022")

Take a single reading

data = device.sample_ble()   # returns dict
print(data)

Stream continuously

def on_sample(data: dict) -> None:
    print(data["UID"], data.get("CO2"), data.get("ENV_temperatureC"))

device.stream_ble(callback=on_sample)

# ... your application runs here ...

device.stop_stream()
device.disconnect()

Context manager

disconnect() is called automatically on exit.

with scn.ScentienceDevice(api_key="YOUR_API_KEY") as device:
    device.connect_ble(char_uuid="YOUR_CHAR_UUID", device_uid="A00022")
    data = device.sample_ble()
    print(data)

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Connecting multiple devices

Pass a list of device UIDs to connect_ble. All devices are scanned for in a single BLE scan and connected concurrently. The API key and characteristic UUID are the same for every device.

Snapshot from all devices

import scentience as scn

device = scn.ScentienceDevice(api_key="YOUR_API_KEY")
device.connect_ble(
    char_uuid="YOUR_CHAR_UUID",
    device_uids=["A00022", "A00010"],   # as many UIDs as needed
)

readings = device.sample_ble()   # returns List[dict], one entry per device
for r in readings:
    print(r)

Stream from all devices simultaneously

The same callback receives packets from every device. Use the UID field to identify the source.

def on_sample(data: dict) -> None:
    print(f"[{data['UID']}]  CO2={data.get('CO2')}  temp={data.get('ENV_temperatureC')}°C")

device.stream_ble(callback=on_sample)

# ... your application runs here ...

device.stop_stream()
device.disconnect()

Context manager (multiple devices)

with scn.ScentienceDevice(api_key="YOUR_API_KEY") as device:
    device.connect_ble(
        char_uuid="YOUR_CHAR_UUID",
        device_uids=["A00022", "A00010"],
    )
    readings = device.sample_ble()

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Discovering devices

Before connecting, use scan_devices() to see exactly what BLE devices are visible and confirm the names your instruments advertise.

nearby = scn.ScentienceDevice.scan_devices(timeout=10.0)
for d in nearby:
    print(d["name"], d["address"], d["rssi"])

Pass any value from the name column as device_uid / device_uids.

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Logging and export

All readings from sample_ble() and stream_ble() are automatically buffered in memory across all connected devices.

print(device.log)             # list of dicts

device.export_json("readings.json")
device.export_csv("readings.csv")

device.clear_log()

CSV column order: UID and TIMESTAMP first, then all remaining fields alphabetically. Missing fields are written as empty values.

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Response payload

Each reading dict may contain:

Key	Description
UID	Device serial number
TIMESTAMP	Reading timestamp
ENV_temperatureC	Ambient temperature (°C)
ENV_humidity	Relative humidity (%)
ENV_pressureHpa	Barometric pressure (hPa)
BATT_health	Battery health
BATT_v	Battery voltage
BATT_charge	Battery charge (%)
BATT_time	Estimated battery time remaining
STATUS_opuA	Operational status
CO2, NH3, NO, NO2, CO, C2H5OH	Chemical compounds (non-zero only)
H2, CH4, C3H8, C4H10, H2S, HCHO, SO2, VOC	Chemical compounds (non-zero only)

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COLIP embedding models

ColipModel integrates the Olfaction-Vision-Language Embeddings to produce joint embeddings across olfaction, vision, and language modalities.

Four variants are available:

Variant	Embed dim	Architecture	Best for
colip-small-base	512	base GNN	Fast inference / edge devices
colip-small-gat	512	GAT	Higher accuracy on edge devices
colip-large-base	2048	base GNN	Accuracy-critical tasks
colip-large-gat	2048	GAT	Highest accuracy, slower inference

Weights are downloaded from HuggingFace Hub and cached locally on first use.

Example — small base model

import scentience as scn
from PIL import Image

model = scn.ColipModel.from_pretrained("colip-small-base")
# ColipModel(variant='colip-small-base', embed_dim=512, device='cpu')

image   = Image.open("scene.jpg")
olf_vec = [0.0] * 138          # 138-dimensional olfactory descriptor

embedding = model.embed(image, olf_vec)       # torch.Tensor, shape (512,)
arr       = model.embed_numpy(image, olf_vec) # numpy array

Example — large GAT model

model = scn.ColipModel.from_pretrained("colip-large-gat")
embedding = model.embed(image, olf_vec)       # torch.Tensor, shape (2048,)

Example — BLE streaming + real-time embedding

import scentience as scn
from PIL import Image

model  = scn.ColipModel.from_pretrained("colip-small-base")
device = scn.ScentienceDevice(api_key="YOUR_API_KEY")
device.connect_ble(char_uuid="YOUR_CHAR_UUID", device_uid="A00022")

scene = Image.open("scene.jpg")

def on_sample(data: dict) -> None:
    olf_vec   = [data.get(k, 0.0) for k in sorted(data) if k not in ("UID", "TIMESTAMP")]
    embedding = model.embed(scene, olf_vec)
    print(f"embedding shape: {embedding.shape}, norm: {embedding.norm():.4f}")

device.stream_ble(callback=on_sample)

Targeting a specific compute backend

model = scn.ColipModel.from_pretrained("colip-large-base", device="cuda")  # GPU
model = scn.ColipModel(variant="colip-small-gat", device="mps")             # Apple Silicon

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API reference

See the full BLE API documentation.

License

Apache 2.0