sorcha.modules.PPLinkingFilter
Functions
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A function which mimics the effects of the SSP linking process by looking |
Module Contents
- PPLinkingFilter(observations, detection_efficiency, min_observations, min_tracklets, tracklet_interval, minimum_separation, maximum_time, night_start_utc, survey_name='rubin_sim', drop_unlinked=True)[source]
A function which mimics the effects of the SSP linking process by looking for valid tracklets within valid tracks and only outputting observations which would be thus successfully "linked" by SSP.
Parameters:
- observationspandas data frame
dataframe of observations of each input object
- detection_efficiencyfloat
the fractional percentage of successfully linked detections.
- min_observationsint
the minimum number of observations in a night required to form a tracklet.
- min_trackletsint
the minimum number of tracklets required to form a valid track.
- tracklet_intervalint
the time window (in days) in which the minimum number of tracklets must occur to form a valid track.
- minimum_separationfloat
the minimum separation inside a tracklet for it to be recognised as motion between images (in arcseconds).
- maximum_timefloat
Maximum time separation (in days) between subsequent observations in a tracklet.
- rngnumpy Generator object
numpy random number generator object.
- survey_namestr, default= "rubin_sim"
a string with the survey name. used for time-zone purposes. Currently only accepts "rubin_sim", "RUBIN_SIM", "lsst", "LSST".
- drop_unlinkedboolean, default=True
rejects all observations that are considered to not be linked.
Returns:
- observations_outpandas dataframe
a pandas dataframe containing observations of linked objects only.