Parameters and tags¶

Two labeling systems, different jobs.

Parameters are test conditions on a dataset: lot=15, temp=25, instrument=182, sn=6. They are how you ask “which measurements were taken at 25 °C?”

Tags are team-wide stickers: needs-review, golden, rma. Create a tag once on the team, then apply it to many datasets.

Find by parameter¶

project = cs.projects["cable-qual"]
lot15 = list(project.datasets.filter(cs.params.lot == "15"))

Values are stored as strings. == 15 (integer) will not match "15".

Chain in Python when you need two keys:

matches = [
    ds
    for ds in project.datasets.filter(cs.params.lot == "15")
    if ds.params.get("instrument") == "182"
]

Change parameters¶

Add or update a key on one dataset from the dataset page, or in bulk from a script. Bulk edits must preview. Show the dataset names and the count, then apply:

Add instrument=182 to 7 datasets: sweep-1 … sweep-7. Proceed?

Skipped rows (already set, missing permission) belong in the result, not only in a log file.

Tags¶

Tags belong to the team, not the project. needs-review in RF Lab is one tag object; do not create a second because you are in another project on the same team.

Typical flow:

Filter the datasets you mean (parameters first).
Get-or-create the tag by name.
Apply it to that set.
Use the tag later to build a notebook (Lot 15 review) or to ask “what is still unreviewed?”

Notebooks vs tags¶

A notebook is a view. A tag is an attribute of the dataset that survives after you close the plot. If the question is “which of these still need a serial number?”, filter datasets missing sn — do not rely on notebook titles.