class rubin_sim.maf.metrics.SFUncertMetric(mag=22, times_col='observationStartMJD', m5_col='fiveSigmaDepth', all_gaps=True, units='mag', bins=array([1.00000000e+00, 1.72777104e+00, 2.98519278e+00, 5.15772963e+00, 8.91137591e+00, 1.53968172e+01, 2.66021750e+01, 4.59624676e+01, 7.94126206e+01, 1.37206826e+02, 2.37061981e+02, 4.09588826e+02, 7.07675714e+02, 1.22270161e+03, 2.11254843e+03, 3.65000000e+03]), weight=None, metric_name='Structure Function Uncert', snr_cut=5, filter_col='filter', dust=True, **kwargs)

Bases: BaseMetric

Structure Function (SF) Uncertainty Metric. Developed on top of LogTGaps

Adapted from Weixiang Yu & Gordon Richards at: https://github.com/RichardsGroup/LSST_SF_Metric/blob/main/notebooks/00_SFErrorMetric.ipynb

mag: `float` (22)

The magnitude of the fiducial object. Default 22.

times_col: `str` (‘observationStartMJD’)

Time column name. Defaults to “observationStartMJD”.

all_gaps: `bool` (True)

Whether to use all gaps (between any two pairs of observations). If False, only use consecutive paris. Defaults to True.

units: `str` (‘mag’)

Unit of this metric. Defaults to “mag”.

bins: `object`

An array of bin edges. Defaults to “np.logspace(0, np.log10(3650), 16)” for a total of 15 (final) bins.

weight: `object

The weight assigned to each delta_t bin for deriving the final metric. Defaults to flat weighting with sum of 1. Should have length 1 less than bins.

snr_cutfloat (5)

Ignore observations below an SNR limit, default 5.

dustbool (True)

Apply dust extinction to the fiducial object magnitude. Default True.

Methods Summary

run(data_slice[, slice_point])

Code executed at each healpix pixel to compute the metric

Methods Documentation

run(data_slice, slice_point=None)

Code executed at each healpix pixel to compute the metric