NetCdfFileUtil

Value Members

1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. object Create

7. object OutDim

8. def anomalies(in: NetcdfFile, out: File, varName: String, timeName: String = "time", windowYears: Int = 30, useMedian: Boolean = false): Processor[Unit] with Prepared

   Calculates anomalies of a time series.

   Calculates anomalies of a time series. It assumes that the time resolution in the input is months! The output will have a matrix of the same size as the input, where each cell is the difference between the input cell and the normal value for that cell at that time.

   in

   the input file to process

   out

   the output file to create

   varName

   the variable to process

   timeName

   the time dimension in the variable

   windowYears

   the number of years to average across

9. final def asInstanceOf[T0]: T0

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10. def clone(): AnyRef

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    protected[java.lang]

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11. def concat(in1: NetcdfFile, in2: NetcdfFile, out: File, varName: String, dimName: String = "time"): Processor[Unit] with Prepared

    Creates a new NetCDF file that contains one variable resulting from the concatenation of that variable present in two input files.

    Creates a new NetCDF file that contains one variable resulting from the concatenation of that variable present in two input files.

    in1

    the first input file (data will appear first)

    in2

    the second input file (data will appear second)

    out

    the output file to write to

    varName

    the name of the variable to take from the inputs and concatenate

    dimName

    the dimension along which to the variable is split across the two inputs

12. def concatAndWait(in1: NetcdfFile, in2: NetcdfFile, out: File, varName: String, dimName: String = "time"): Unit

13. final def eq(arg0: AnyRef): Boolean

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14. def equals(arg0: Any): Boolean

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15. def finalize(): Unit

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    protected[java.lang]

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16. final def getClass(): Class[_]

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17. def hashCode(): Int

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18. final def isInstanceOf[T0]: Boolean

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19. final def ne(arg0: AnyRef): Boolean

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20. final def notify(): Unit

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21. final def notifyAll(): Unit

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

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23. def toString(): String

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24. def transform(in: NetcdfFile, out: File, varName: String, inDims: IndexedSeq[String], outDimsSpec: IndexedSeq[OutDim])(fun: (IndexedSeq[Int], Array) ⇒ Array): Processor[Unit] with Prepared

    Transforms an input NetCDF file into an output NetCDF file, by copying a given variable and applying an optional transform to the data.

    Transforms an input NetCDF file into an output NetCDF file, by copying a given variable and applying an optional transform to the data.

    Note: This is not optimized for speed, yet.

    in

    the input file to transform

    out

    the file to which the output will be written

    varName

    the variable to copy/transform

    inDims

    the dimensions of the variable to transform. these will be removed from the target variable

    outDimsSpec

    the dimensions of the output variable. each spec can either indicate a verbatim copy (Keep) or the result of the transformation (Create)

    fun

    a function that will transform the variable's data matrix. It is passed the origin in the kept dimensions (origin of the output shape minus the created dimensions) and an object of dimension inDims.size and is required to output an object of dimension outDims.filterNot(_.isCopy).size. The dimensions are sorted to correspond with inDims. The function is called repeatedly, iterating over all other input dimensions except those in inDims.

25. def transformSelection(in: NetcdfFile, out: File, sel: VariableSection, inDims: IndexedSeq[String], outDimsSpec: IndexedSeq[OutDim])(fun: (IndexedSeq[Int], Array) ⇒ Array): Processor[Unit] with Prepared

26. final def wait(): Unit

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

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

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