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https://github.com/metabarcoding/obitools4.git
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b49aba9c09
Ajout d'une fonctionnalité pour le filtrage unique qui prend en compte à la fois la séquence et les catégories. - Modification de la fonction ISequenceChunk pour accepter un classifieur unique optionnel - Implémentation du traitement unique sur disque en utilisant un classifieur composite - Mise à jour du classifieur utilisé pour le tri sur disque - Correction de la gestion des clés de unicité en utilisant le code et la valeur du classifieur - Mise à jour du numéro de commit
109 lines
2.4 KiB
Go
109 lines
2.4 KiB
Go
package obichunk
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import (
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"sync"
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log "github.com/sirupsen/logrus"
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"git.metabarcoding.org/obitools/obitools4/obitools4/pkg/obiiter"
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"git.metabarcoding.org/obitools/obitools4/obitools4/pkg/obiseq"
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)
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// Runs dereplication algorithm on a obiiter.IBioSequenceBatch
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// iterator.
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func IUniqueSequence(iterator obiiter.IBioSequence,
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options ...WithOption) (obiiter.IBioSequence, error) {
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var err error
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opts := MakeOptions(options)
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nworkers := opts.ParallelWorkers()
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iUnique := obiiter.MakeIBioSequence()
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iterator = iterator.Speed("Splitting data set")
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log.Infoln("Starting data splitting")
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cat := opts.Categories()
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na := opts.NAValue()
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// Classifier for bucketing: Hash only to control number of chunks
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bucketClassifier := obiseq.HashClassifier(opts.BatchCount())
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// Classifier for uniqueness: Sequence + categories
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var uniqueClassifier *obiseq.BioSequenceClassifier
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if len(cat) > 0 {
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cls := make([]*obiseq.BioSequenceClassifier, len(cat)+1)
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cls[0] = obiseq.SequenceClassifier()
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for i, c := range cat {
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cls[i+1] = obiseq.AnnotationClassifier(c, na)
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}
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uniqueClassifier = obiseq.CompositeClassifier(cls...)
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} else {
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uniqueClassifier = obiseq.SequenceClassifier()
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}
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if opts.SortOnDisk() {
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nworkers = 1
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iterator, err = ISequenceChunkOnDisk(iterator, bucketClassifier, true, na, opts.StatsOn(), uniqueClassifier)
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if err != nil {
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return obiiter.NilIBioSequence, err
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}
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} else {
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iterator, err = ISequenceChunkOnMemory(iterator, bucketClassifier)
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if err != nil {
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return obiiter.NilIBioSequence, err
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}
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}
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log.Infoln("End of the data splitting")
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iUnique.Add(nworkers)
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go func() {
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iUnique.Wait()
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iUnique.Close()
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}()
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omutex := sync.Mutex{}
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order := 0
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nextOrder := func() int {
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omutex.Lock()
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neworder := order
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order++
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omutex.Unlock()
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return neworder
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}
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ff := func(input obiiter.IBioSequence,
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classifier *obiseq.BioSequenceClassifier) {
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input, err = ISequenceSubChunk(input,
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classifier,
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1)
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for input.Next() {
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batch := input.Get()
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if !(opts.NoSingleton() && len(batch.Slice()) == 1 && batch.Slice()[0].Count() == 1) {
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iUnique.Push(batch.Reorder(nextOrder()))
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}
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}
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iUnique.Done()
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}
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for i := 0; i < nworkers-1; i++ {
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go ff(iterator.Split(), uniqueClassifier.Clone())
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}
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go ff(iterator, uniqueClassifier)
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iMerged := iUnique.IMergeSequenceBatch(opts.NAValue(),
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opts.StatsOn(),
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)
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return iMerged, nil
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}
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