Value Members

1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

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6.  final def eq(arg0: AnyRef): Boolean

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7.  def equals(arg0: Any): Boolean

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8.  def finalize(): Unit

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9.  def fromNTSeq(compatible: Index, read: NTSeq)(implicit spark: SparkSession): Index

   Construct a new counting index from the given sequence.

   Construct a new counting index from the given sequence. K-mers will not be normalized. This method is not intended for large amounts of data, as everything has to go through the Spark driver.

   compatible

   A compatible index to copy parameters from

   read

   Sequence to index

10.  def fromNTSeqs(compatible: Index, reads: Seq[NTSeq])(implicit spark: SparkSession): Index

    Construct a new counting index from the given sequences.

    Construct a new counting index from the given sequences. K-mers will not be normalized. This method is not intended for large amounts of data, as everything has to go through the Spark driver.

    compatible

    A compatible index to copy parameters from

    reads

    Sequences to index

11.  def fromNTSeqs(compatible: Index, reads: Dataset[NTSeq])(implicit spark: SparkSession): Index

    Construct a new counting index from the given sequences.

    Construct a new counting index from the given sequences. K-mers will not be normalized.

    compatible

    A compatible index to copy parameters from

    reads

    Sequences to index

12.  final def getClass(): Class[_]

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13.  def getIndexSplitter(location: String, k: Int)(implicit spark: SparkSession): AnyMinSplitter
14.  def hashCode(): Int

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15.  final def isInstanceOf[T0]: Boolean

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16.  final def ne(arg0: AnyRef): Boolean

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17.  final def notify(): Unit

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18.  final def notifyAll(): Unit

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19.  val random: SplittableRandom
20.  def randomTableName: String
21.  def reSplitBuckets(input: Dataset[ReducibleBucket], reducer: Reducer, spl: Broadcast[AnyMinSplitter])(implicit spark: SparkSession): Dataset[ReducibleBucket]

    Split buckets into supermer/tag pairs according to a new minimizer ordering, constructing new buckets from the result.

    Split buckets into supermer/tag pairs according to a new minimizer ordering, constructing new buckets from the result. The resulting buckets will have shorter supermers but will respect the new ordering.

    input

    buckets to split

    reducer

    reducer to use for compacting the result

    spl

    a splitter that reflects the new ordering

22.  def read(location: String, knownParams: Option[IndexParams] = None)(implicit spark: SparkSession): Index
23.  final def synchronized[T0](arg0: ⇒ T0): T0

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24.  def toString(): String

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25.  final def wait(): Unit

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26.  final def wait(arg0: Long, arg1: Int): Unit

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27.  final def wait(arg0: Long): Unit

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28.  def write(data: DataFrame, location: String, numBuckets: Int): Unit