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API reference / Analyses

tsdive.SpcAnalysis dataclass 

SpcAnalysis(
    baseline: Window,
    monitor: Window,
    limits: ControlLimits,
    sigma: float,
    n_monitored: int,
    hits: list[RuleHit],
)

An individuals chart: limits from the baseline, rule hits in the window.

frame holds one row per rule hit: timestamp, rule, detail.

Methods:

Name Description
render

The report tsdive spc prints for this window, without colour.
to_dict

The document tsdive spc --json prints, ready for json.dumps.

Attributes:

Name Type Description
baseline Window
monitor Window
limits ControlLimits
sigma float
n_monitored int
hits list[RuleHit]
identity TagIdentity
frame DataFrame

baseline instance-attribute 

baseline: Window

monitor instance-attribute 

monitor: Window

limits instance-attribute 

limits: ControlLimits

sigma instance-attribute 

sigma: float

n_monitored instance-attribute 

n_monitored: int

hits instance-attribute 

hits: list[RuleHit]

identity property 

identity: TagIdentity

frame property 

frame: DataFrame

render 

render() -> str

The report tsdive spc prints for this window, without colour.

Examples:

>>> import tsdive
>>> c = tsdive.spc("data/demo/fic101_demo.parquet",
...                "2024-03-30T20:00:00Z/2024-03-31T01:00:00Z",
...                "2024-03-31T01:00:00Z/2024-03-31T06:00:00Z")
>>> print(c.render().splitlines()[0])
demo:FIC101.PV  37 rule hits in 300 samples

to_dict 

to_dict() -> dict[str, object]

The document tsdive spc --json prints, ready for json.dumps.

The command adds result_kind and tsdive_version in front.