Measurement Utilities for QCoDeS¶

Qanary simplifies measurement workflows and replaces QCoDeS’s own measurement layer. You declare the parameters to sweep and the quantities to record; Qanary walks the sweep, stores the result as an xarray.Dataset, and publishes it for live visualization and analysis to Qimchi while it runs.

Why Qanary?¶

Measurement code should describe the experiment, not the bookkeeping. Qanary takes over dataset creation, incremental persistence, live publication and metadata capture, so a sweep becomes a handful of declarative lines rather than a nested loop with save logic threaded through it.

Highlights¶

Declarative sweeps: nest Sweep objects; the stepper walks them and fills a pre-allocated dataset.
Buffered acquisition: hand a block of points to the instrument and read it back in one go, instead of a round trip per point. Much faster, where supported.
Live visualization: Qanary publishes each measurement to Qimchi as it runs through qimchi-connect. Other measurement libraries and custom acquisition scripts can use its live_measurement context manager to publish their own live data.
xarray datasets: completed measurements are stored as netCDF files and load directly as xarray.Dataset objects.
Lab-tested: built for and used by SQUAD Lab at Forschungszentrum Jülich, Germany.

Get Started¶

Qanary is a Python package managed with uv. See Installation to set up a measurement project, then Tutorials for a first sweep.