AggregateFunctions

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. 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. def rf_agg_approx_histogram(col: Column, numBuckets: Int): TypedColumn[Any, CellHistogram]

    

    Compute the approximate aggregate floating point histogram using a streaming algorithm, with the given number of buckets.

16. def rf_agg_approx_histogram(tile: Column): TypedColumn[Any, CellHistogram]

    

    Compute the approximate aggregate floating point histogram using a streaming algorithm, with the default of 80 buckets.

17. def rf_agg_approx_quantiles(tile: Column, probabilities: Seq[Double], relativeError: Double = 0.00001): TypedColumn[Any, Seq[Double]]

    

    Calculates the approximate quantiles of a tile column of a DataFrame.

    Calculates the approximate quantiles of a tile column of a DataFrame.

    tile

    tile column to extract cells from.

    probabilities

    a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the minimum, 0.5 is the median, 1 is the maximum.

    relativeError

    The relative target precision to achieve (greater than or equal to 0).

    returns

    the approximate quantiles at the given probabilities of each column

18. def rf_agg_data_cells(tile: Column): TypedColumn[Any, Long]

    

    Computes the number of non-NoData cells in a column.

19. def rf_agg_extent(extent: Column): TypedColumn[Any, Extent]

    

    Compute the aggregate extent over a column.

    Compute the aggregate extent over a column. Assumes CRS homogeneity.

20. def rf_agg_local_data_cells(tile: Column): TypedColumn[Any, Tile]

    

    Compute the cellwise/local count of non-NoData cells for all Tiles in a column.

21. def rf_agg_local_max(tile: Column): TypedColumn[Any, Tile]

    

    Compute the cell-wise/local max operation between Tiles in a column.

22. def rf_agg_local_mean(tile: Column): TypedColumn[Any, Tile]

    

    Compute the cellwise/local mean operation between Tiles in a column.

23. def rf_agg_local_min(tile: Column): TypedColumn[Any, Tile]

    

    Compute the cellwise/local min operation between Tiles in a column.

24. def rf_agg_local_no_data_cells(tile: Column): TypedColumn[Any, Tile]

    

    Compute the cellwise/local count of NoData cells for all Tiles in a column.

25. def rf_agg_local_stats(tile: Column): TypedColumn[Any, LocalCellStatistics]

    

    Compute cell-local aggregate descriptive statistics for a column of Tiles.

26. def rf_agg_mean(tile: Column): TypedColumn[Any, Double]

    

    Computes the column aggregate mean.

27. def rf_agg_no_data_cells(tile: Column): TypedColumn[Any, Long]

    

    Computes the number of NoData cells in a column.

28. def rf_agg_overview_raster(tile: Column, tileExtent: Column, tileCRS: Column, cols: Int, rows: Int, areaOfInterest: Extent, sampler: ResampleMethod): TypedColumn[Any, Tile]

    

    Construct an overview raster of size colsxrows where data in tile intersects the areaOfInterest in web-mercator.

    Construct an overview raster of size colsxrows where data in tile intersects the areaOfInterest in web-mercator. Allows specification of one of these sampling methods:

    • geotrellis.raster.resample.NearestNeighbor
    • geotrellis.raster.resample.Bilinear
    • geotrellis.raster.resample.CubicConvolution
    • geotrellis.raster.resample.CubicSpline
    • geotrellis.raster.resample.Lanczos

29. def rf_agg_overview_raster(tile: Column, tileExtent: Column, tileCRS: Column, cols: Int, rows: Int, areaOfInterest: Extent): TypedColumn[Any, Tile]

    

    Construct an overview raster of size colsxrows where data in tile intersects the areaOfInterest in web-mercator.

    Construct an overview raster of size colsxrows where data in tile intersects the areaOfInterest in web-mercator. Uses nearest bi-linear sampling method.

30. def rf_agg_overview_raster(proj_raster: Column, cols: Int, rows: Int, areaOfInterest: Extent): TypedColumn[Any, Tile]

    

    Construct an overview raster of size colsxrows where data in proj_raster intersects the areaOfInterest in web-mercator.

    Construct an overview raster of size colsxrows where data in proj_raster intersects the areaOfInterest in web-mercator. Uses bi-linear sampling method.

31. def rf_agg_reprojected_extent(extent: Column, srcCRS: Column, destCRS: CRS): TypedColumn[Any, Extent]

    

    Compute the aggregate extent over a column after reprojecting from the rows source CRS into the given destination CRS .

32. def rf_agg_stats(tile: Column): TypedColumn[Any, CellStatistics]

    

    Compute the full column aggregate floating point statistics.

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

    

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34. def toString(): String

    

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35. final def wait(): Unit

    

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

    

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37. final def wait(arg0: Long): Unit

    

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