deteqt 0.1.1

Python client for DeteQT metrology and quantum-chip diagnostics on InfluxDB v2.

deteqt

Python client for DeteQT metrology and quantum-chip diagnostics on InfluxDB v2.

Documentation

• Quickstart
• Usage
• API reference

Quick setup

If uv is not installed yet on your machine, install it first:

Linux/macOS

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Installing deteqt package

# Create a virtual environment:
uv venv deteqt-env --python 313

# Activate virtual environment:
source deteqt-env/bin/activate  # Linux/macOS

# Or if you are on Windows:
# .\deteqt-env\Scripts\activate

# Install local editable package with all tool groups:
uv pip install -e . --group all

# Verify installation and version:
uv run --active python -c "import deteqt; print(deteqt.__version__)"

Writing and querying data

To write and query data, first edit settings.toml at the project root with values provided by your administrator:

url = "your-url-here"
org = "your-org-here"
bucket = "your-bucket-here"
token = "your-token-here"

Write one point to InfluxDB

from deteqt import write_point

write_point(qoi="frequency", value=1.01, uncertainty=0.01)

Write a batch of points

from deteqt import write_batch
my_data = write_batch(
    [
        {"qoi": "frequency", "value": 1.01},
        {"qoi": "phase", "value": 0.12, "uncertainty": 0.01},
    ],
)

Query rows

from deteqt import build_query, query_rows

query = build_query(
    bucket="your-bucket-here",
    measurement="frequency",
    start="-1h",
)

rows = query_rows(
    measurement="frequency",
    start="-1h",
)

Ingest data from a folder recursively, process it, and write to InfluxDB

from deteqt import ingest_data, process_data, write_batch
raw_data = ingest_data(
    folder="quantify-data",
    file_name="quantities_of_interest.json",  # or "quantities.csv"
)

processed_data = process_data(
    raw_data,
    keep_keys=("qoi", "value", "uncertainty", "run_id", "cycle_id"),
)

summary = write_batch(processed_data)