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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.  final def getClass(): Class[_]

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10.  def hashCode(): Int

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11.  final def isInstanceOf[T0]: Boolean

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

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13.  final def notify(): Unit

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14.  final def notifyAll(): Unit

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15.  final def synchronized[T0](arg0: ⇒ T0): T0

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16.  def toDf(spatialPairRDD: JavaPairRDD[Geometry, Geometry], schema: StructType, sparkSession: SparkSession): DataFrame

    Convert a JavaPairRDD to DataFrame with a specified schema.

    Convert a JavaPairRDD to DataFrame with a specified schema.

    The schema is expected to follow the format: [left geometry, (left user data columns), right geometry, (right user data columns)]

    Note that the schema _must_ provide columns for left and right user data, even if they do not exist in the original data.

    Also note that some complex types, such as MapType, and schemas as strings are not supported.

    schema

    Desired output schema

    sparkSession

    Spark session

    returns

    Spatial pair RDD as a DataFrame with the desired schema

17.  def toDf(spatialPairRDD: JavaPairRDD[Geometry, Geometry], leftFieldnames: Seq[String], rightFieldNames: Seq[String], sparkSession: SparkSession): DataFrame
18.  def toDf(spatialPairRDD: JavaPairRDD[Geometry, Geometry], sparkSession: SparkSession): DataFrame
19.  def toDf[T <: Geometry](spatialRDD: SpatialRDD[T], schema: StructType, sparkSession: SparkSession): DataFrame

    Convert a spatial RDD to DataFrame with a given schema.

    Convert a spatial RDD to DataFrame with a given schema.

    T

    Geometry

    spatialRDD

    Spatial RDD

    schema

    Desired schema

    sparkSession

    Spark Session

    returns

    DataFrame with the specified schema

20.  def toDf[T <: Geometry](spatialRDD: SpatialRDD[T], fieldNames: Seq[String], sparkSession: SparkSession): DataFrame
21.  def toDf[T <: Geometry](spatialRDD: SpatialRDD[T], sparkSession: SparkSession): DataFrame
22.  def toSpatialRdd(dataFrame: DataFrame, geometryColId: Int): SpatialRDD[Geometry]

    Convert a Spatial DF to a Spatial RDD.

    Convert a Spatial DF to a Spatial RDD. The geometry column can be at any place in the DF

23.  def toSpatialRdd(dataFrame: DataFrame, geometryColId: Int, fieldNames: Seq[String]): SpatialRDD[Geometry]

    Convert a Spatial DF to a Spatial RDD with a list of user-supplied col names (except geom col).

    Convert a Spatial DF to a Spatial RDD with a list of user-supplied col names (except geom col). The geometry column can be at any place in the DF.

24.  def toSpatialRdd(dataFrame: DataFrame, geometryFieldName: String, fieldNames: Seq[String]): SpatialRDD[Geometry]

    Convert a Spatial DF to a Spatial RDD with a list of user-supplied col names (except geom col).

    Convert a Spatial DF to a Spatial RDD with a list of user-supplied col names (except geom col). The geometry column can be at any place in the DF.

25.  def toSpatialRdd(dataFrame: DataFrame, geometryFieldName: String): SpatialRDD[Geometry]

    Convert a Spatial DF to a Spatial RDD.

    Convert a Spatial DF to a Spatial RDD. The geometry column can be at any place in the DF

26.  def toString(): String

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27.  final def wait(): Unit

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

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

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