MoCompletenessMetric

class rubin_sim.maf.metrics.MoCompletenessMetric(threshold=1, nbins=20, min_hrange=1.0, cumulative=None, hindex=0.33, **kwargs)

Bases: BaseMoMetric

Calculate the fraction of the population that meets threshold value or higher. This is equivalent to calculating the completeness (relative to the entire population) given the output of a Discovery_N_Chances metric, or the fraction of the population that meets a given cutoff value for Color determination metrics.

Any moving object metric that outputs a float value can thus have the ‘fraction of the population’ with greater than X value calculated here, as a summary statistic.

Parameters:

thresholdint, optional

Count the fraction of the population that exceeds this value. Default = 1.

nbinsint, optional

If the H values for the metric are not a cloned distribution, then split up H into this many bins. Default 20.

min_hrangefloat, optional

If the H values for the metric are not a cloned distribution, then split up H into at least this range (otherwise just use the min/max of the H values). Default 1.0

cumulativebool, optional

If False, simply report the differential fractional value (or differential completeness). If True, integrate over the H distribution (using IntegrateOverH) to report a cumulative fraction. Default None which becomes True; if metric_name is set and starts with ‘Differential’ this will then set to False.

hindexfloat, optional

Use hindex as the power law to integrate over H, if cumulative is True. Default 0.3.

Methods Summary

run(metric_values, h_vals)

Calculate the metric value.

Methods Documentation

run(metric_values, h_vals)

Calculate the metric value.

Parameters:

sso_obs: np.ndarray

The input data to the metric (same as the parent metric).

orb: np.ndarray

The information about the orbit for which the metric is being calculated.

hvalfloat

The H value for which the metric is being calculated.

Returns:

float or np.ndarray or dict