jacscanomaly.AnomalyResult

class jacscanomaly.AnomalyResult(time, flux, ferr, fit, residual, model_flux, chi2_dof, seasons, clusters_all, grid_metrics_all, best)[source]

Output of scanomaly.finder.Finder.run().

This object is designed to be convenient for plotting and downstream analysis. Arrays are stored on CPU as NumPy arrays.

Parameters:

time (np.ndarray)
flux (np.ndarray)
ferr (np.ndarray)
fit (SingleLensFitResult)
residual (np.ndarray)
model_flux (np.ndarray)
chi2_dof (float)
seasons (List[SeasonSummary])
clusters_all (np.ndarray)
grid_metrics_all (np.ndarray)
best (Optional[BestCandidate])

time, flux, ferr

Input light curve arrays (1D).

Type:: np.ndarray

fit

PSPL fitting result (contains params, fs, fb, chi2, model_flux, residual, etc.).

Type:: SingleLensFitResult

residual

Flux residuals on CPU: flux - model_flux.

Type:: np.ndarray

model_flux

PSPL model flux on CPU.

Type:: np.ndarray

chi2_dof

Reduced chi-square of the PSPL fit.

Type:: float

seasons

Per-season summaries including clusters.

Type:: list[SeasonSummary]

clusters_all

Flattened clusters across all seasons, shape (N, 3) with rows [t0, teff, dchi2].

Type:: np.ndarray

grid_metrics_all

Flattened per-grid diagnostics, shape (M, 9), columns: [t0, teff, dchi2, n_window, n_contrib, n_eff, peak_frac, rho1, longest_run].

Type:: np.ndarray

best

Best candidate over all clusters, or None if no candidate exists.

Type:: BestCandidate | None

__init__(time, flux, ferr, fit, residual, model_flux, chi2_dof, seasons, clusters_all, grid_metrics_all, best)

Parameters:

time (np.ndarray)
flux (np.ndarray)
ferr (np.ndarray)
fit (SingleLensFitResult)
residual (np.ndarray)
model_flux (np.ndarray)
chi2_dof (float)
seasons (List[SeasonSummary])
clusters_all (np.ndarray)
grid_metrics_all (np.ndarray)
best (Optional[BestCandidate])

Return type:

None

Methods

`__init__`(time, flux, ferr, fit, residual, ...)
`print_summary`()	Print a CLI-friendly summary.
`summary_dict`()	Return a compact summary dictionary suitable for logging/serialization.
`summary_table`()	Return a notebook-friendly single-row table.
`summary_text`()	Return a CLI-friendly multi-line summary.

Attributes

`time`
`flux`
`ferr`
`fit`
`residual`
`model_flux`
`chi2_dof`
`seasons`
`clusters_all`
`grid_metrics_all`
`best`