def get_hits(self, hashes):
        # Allocate to largest possible number of hits
        nhashes = np.shape(hashes)[0]
        hits = np.zeros((nhashes*self.depth, 4), np.int32)
        nhits = 0
        maxtimemask = (1 << self.maxtimebits) - 1
        # Fill in
        for ix in xrange(nhashes):
            time_ = hashes[ix][0]
            hash_ = hashes[ix][1]
            nids = min(self.depth, self.counts[hash_])
            tabvals = self.table[hash_, :nids]
            hitrows = nhits + np.arange(nids)
            hits[hitrows, 0] = tabvals >> self.maxtimebits
            hits[hitrows, 1] = (tabvals & maxtimemask) - time_
            hits[hitrows, 2] = hash_
            hits[hitrows, 3] = time_
            nhits += nids
        # Discard the excess rows
        hits.resize( (nhits, 4) )
        return hits
Initializer for the class attributes.
dataset/ETHPy150Open dpwe/audfprint/hash_table.py/HashTable.get_hits--crossover with--dataset/ETHPy150Open CollabQ/CollabQ/.google_appengine/google/appengine/tools/appcfg.py/AppCfgApp.Action.__init__