Traces

A trace is one parsed x/y (and often complex) series from a file on a dataset: S21, S11, a CSV column pair, a mixed-mode result you published.

Dataset vs trace

The dataset is the measurement event. S21 is a child of that event. Questions like “which S21s in lot 15?” mean: filter datasets, then read each dataset’s S21 trace.

There is no collection-level datasets.traces["S21"] in the SDK yet. Pull S21 per dataset:

for ds in cs.projects["cable-qual"].datasets.filter(cs.params.lot == "15"):
    s21 = ds.traces["S21"]
    x, y = s21.x, s21.y

Bracket names are case-sensitive and exact (S21, not s21, unless the file happened to parse with a lower-case name).

Notebooks add traces

When you build a review overlay, add the traces you mean (commonly S21). Adding a dataset as “primary” can dump every S-parameter onto the plot. See Notebooks.

Computed traces

Analysis (filter, mixed-mode, correlation average) happens in Python. You publish the outputs as a new dataset; the new traces show up like any other measurement. They are not sidecar files on the source. See Compute and publish results.

scikit-rf

network = s21.to_trace().to_network()  # needs cloudsprite[rf]

Use a Network when you need the full matrix (mixed-mode, cascade). Publish only the traces the rest of the team will plot.