def cluster(self, window): """ Cluster the dict array, assuming it has the relevant Coinc colums, time1, time2, stat, and timeslide_id """ from pycbc.events import cluster_coincs interval = self.attrs["timeslide_interval"] cid = cluster_coincs(self.stat, self.time1, self.time2, self.timeslide_id, interval, window) return self.select(cid)
def cluster(self, window): """ Cluster the dict array, assuming it has the relevant Coinc colums, time1, time2, stat, and timeslide_id """ # If no events, do nothing if len(self.time1) == 0 or len(self.time2) == 0: return self from pycbc.events import cluster_coincs interval = self.attrs['timeslide_interval'] cid = cluster_coincs(self.stat, self.time1, self.time2, self.timeslide_id, interval, window) return self.select(cid)
def cluster(self, window): """ Cluster the dict array, assuming it has the relevant Coinc colums, time1, time2, stat, and timeslide_id """ # If no events, do nothing pivot_ifo = self.attrs['pivot'] fixed_ifo = self.attrs['fixed'] if len(self.data['%s/time' % pivot_ifo]) == 0 or len( self.data['%s/time' % fixed_ifo]) == 0: return self from pycbc.events import cluster_coincs interval = self.attrs['timeslide_interval'] cid = cluster_coincs(self.stat, self.data['%s/time' % pivot_ifo], self.data['%s/time' % fixed_ifo], self.timeslide_id, interval, window) return self.select(cid)
def cluster(self, window): """ Cluster the dict array, assuming it has the relevant Coinc colums, time1, time2, stat, and timeslide_id """ # If no events, do nothing pivot_ifo = self.attrs['pivot'] fixed_ifo = self.attrs['fixed'] if len(self.data['%s/time' % pivot_ifo]) == 0 or len(self.data['%s/time' % fixed_ifo]) == 0: return self from pycbc.events import cluster_coincs interval = self.attrs['timeslide_interval'] cid = cluster_coincs(self.stat, self.data['%s/time' % pivot_ifo], self.data['%s/time' % fixed_ifo], self.timeslide_id, interval, window) return self.select(cid)