rail.evaluation.metrics.pointestimates module

class rail.evaluation.metrics.pointestimates.PointBias(pzvec, szvec)[source]

Bases: PointStatsEz

calculates the bias of the ez and ez_magcut samples.

In keeping with the Science Book, this is just the median of the ez values

evaluate()[source]: Returns: bias: median of the full ez sample

class rail.evaluation.metrics.pointestimates.PointOutlierRate(pzvec, szvec)[source]

Bases: PointStatsEz

Calculates the catastrophic outlier rate, defined in the Science Book as the number of galaxies with ez larger than max(0.06,3sigma). This keeps the fraction reasonable when sigma is very small.

evaluate()[source]: Returns: frac: fraction of catastrophic outliers for full sample

class rail.evaluation.metrics.pointestimates.PointSigmaIQR(pzvec, szvec)[source]

Bases: PointStatsEz

Calculate sigmaIQR

evaluate()[source]: Calculate the width of the e_z distribution using the Interquartile range Parameters: imagcut: float: i-band magnitude cut for the sample Returns: sigma_IQR float: width of ez distribution for full sample sigma_IQR_magcut float: width of ez distribution for magcut sample

class rail.evaluation.metrics.pointestimates.PointSigmaMAD(pzvec, szvec)[source]

Bases: PointStatsEz

Function to calculate median absolute deviation and sigma based on MAD (just scaled up by 1.4826) for the full and magnitude trimmed samples of ez values

evaluate()[source]: Returns: sigma_mad: sigma_MAD for full sample

class rail.evaluation.metrics.pointestimates.PointStatsEz(pzvec, szvec)[source]

Bases: MetricEvaluator

Copied from PZDC1paper repo. Adapted to remove the cut based on magnitude.

evaluate()[source]: Return the ez values