PeriodicDetectMetric¶
- class rubin_sim.maf.metrics.PeriodicDetectMetric(mjd_col='observationStartMJD', periods=2.0, amplitudes=0.1, m5_col='fiveSigmaDepth', metric_name='PeriodicDetectMetric', filter_col='filter', star_mags=20, sig_level=0.05, sed_template='F', **kwargs)¶
Bases:
BaseMetric
Determine if we would be able to classify an object as periodic/non-uniform, using an F-test The idea here is that if a periodic source is aliased, it will be indistinguishable from a constant source, so we can find a best-fit constant, and if the reduced chi-squared is ~1, we know we are aliased.
- Parameters:
- periodfloat (2) or array
The period of the star (days). Can be a single value, or an array. If an array, amplitude and starMag should be arrays of equal length.
- amplitudefloar (0.1)
The amplitude of the stellar variablility (mags).
- starMagfloat (20.)
The mean magnitude of the star in r (mags).
- sig_levelfloat (0.05)
The value to use to compare to the p-value when deciding if we can reject the null hypothesis.
- sed_templatestr (‘F’)
The stellar SED template to use to generate realistic colors (default is an F star, so RR Lyrae-like)
- Returns:
- 1 if we would detect star is variable, 0 if it is well-fit by a constant value. If using arrays to test multiple
- period-amplitude-mag combinations, will be the sum of the number of detected stars.
Methods Summary
run
(data_slice[, slice_point])Calculate metric values.
Methods Documentation
- run(data_slice, slice_point=None)¶
Calculate metric values.
- Parameters:
- data_slice
numpy.recarray
Values passed to metric by the slicer, which the metric will use to calculate metric values at each slice_point.
- slice_point
dict
or None Dictionary of slice_point metadata passed to each metric. E.g. the ra/dec of the healpix pixel or opsim fieldId.
- data_slice
- Returns: