class rubin_sim.maf.metrics.ProperMotionMetric(metric_name='properMotion', m5_col='fiveSigmaDepth', mjd_col='observationStartMJD', filter_col='filter', seeing_col='seeingFwhmGeom', rmag=20.0, sed_template='flat', badval=-666, atm_err=0.01, normalize=False, baseline=10.0, **kwargs)

Bases: BaseMetric

Calculate the uncertainty in the returned proper motion.

This metric assumes gaussian errors in the astrometry measurements.

metricNamestr, optional

Default ‘properMotion’.

m5_colstr, optional

The default column name for m5 information in the input data. Default fiveSigmaDepth.

mjd_colstr, optional

The column name for the exposure time. Default observationStartMJD.

filterColstr, optional

The column name for the filter information. Default filter.

seeing_colstr, optional

The column name for the seeing information. Since the astrometry errors are based on the physical size of the PSF, this should be the FWHM of the physical psf. Default seeingFwhmGeom.

rmagfloat, optional

The r magnitude of the fiducial star in r band. Other filters are sclaed using sedTemplate keyword. Default 20.0

SedTemplatestr, optional

The template to use. This can be ‘flat’ or ‘O’,’B’,’A’,’F’,’G’,’K’,’M’. Default flat.

atm_errfloat, optional

The expected centroiding error due to the atmosphere, in arcseconds. Default 0.01.

normalizebool, optional

Compare the astrometric uncertainty to the uncertainty that would result if half the observations were taken at the start and half at the end. A perfect survey will have a value close to 1, while a poorly scheduled survey will be close to 0. Default False.

baselinefloat, optional

The length of the survey used for the normalization, in years. Default 10.

badvalfloat, optional

The value to return when the metric value cannot be calculated. Default -666.

Methods Summary

run(data_slice[, slice_point])

Calculate metric values.

Methods Documentation

run(data_slice, slice_point=None)

Calculate metric values.

data_slicenumpy.ndarray, (N,)

Values passed to metric by the slicer, which the metric will use to calculate metric values at each slice_point.

slice_pointdict or None

Dictionary of slice_point metadata passed to each metric. E.g. the ra/dec of the healpix pixel or opsim fieldId.

metricValue: int float or object

The metric value at each slice_point.