jacscanomaly.BestCandidate

class jacscanomaly.BestCandidate(t0, teff, dchi2, med_others, std_others, score, quality)[source]

Best anomaly candidate selected from all extracted clusters.

Parameters:

t0 (float)
teff (float)
dchi2 (float)
med_others (float)
std_others (float)
score (float)
quality (CandidateQuality)

t0

Candidate center time.

Type:: float

teff

Candidate effective timescale.

Type:: float

dchi2

Improvement in chi-square: chi2_null - chi2_anom (larger is better).

Type:: float

med_others

Median dchi2 among the bulk of the other candidates (excluding the best, with the upper tail trimmed when configured).

Type:: float

std_others

Standard deviation of dchi2 among the bulk of the other candidates (excluding the best, with the upper tail trimmed when configured).

Type:: float

score

Standardized score of the best candidate. Computed as (dchi2_best - med_others) / std_others. (may be NaN/inf depending on the number of candidates / std_others).

Type:: float

quality

Per-point support and temporal diagnostics for this candidate.

Type:: CandidateQuality

__init__(t0, teff, dchi2, med_others, std_others, score, quality)

Parameters:

t0 (float)
teff (float)
dchi2 (float)
med_others (float)
std_others (float)
score (float)
quality (CandidateQuality)

Return type:

None

Methods

__init__(t0, teff, dchi2, med_others, ...)

Attributes

`t0`
`teff`
`dchi2`
`med_others`
`std_others`
`score`
`quality`