## Uses of Interface
org.eclipse.january.dataset.Dataset

- Packages that use Dataset
  Package | Description
  --- | ---
  org.eclipse.january.asserts |
  org.eclipse.january.dataset |
  org.eclipse.january.metadata |

- - ### Uses of Dataset in org.eclipse.january.asserts

    Methods in org.eclipse.january.asserts with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `static void` | TestUtils.`assertDatasetEquals(Dataset expected, Dataset actual)` Assert equality of datasets where each item is true if they are equal or for floating point datasets, if `abs(a - b) ≤ 1e-12 + 1e-12*abs(b)`
    `static void` | TestUtils.`assertDatasetEquals(Dataset expected, Dataset actual, boolean testDTypeAndItemSize)` Assert equality of datasets where each element is true if they are equal or for floating point datasets, if `abs(a - b) ≤ 1e-12 + 1e-12*abs(b)`
    `static void` | TestUtils.`assertDatasetEquals(Dataset expected, Dataset actual, boolean testDTypeAndItemSize, double relTolerance, double absTolerance)` Assert equality of datasets where each element is true if they are equal or for floating point datasets, if `abs(a - b) ≤ absTolerance + relTolerance*abs(b)`.
    `static void` | TestUtils.`assertDatasetEquals(Dataset expected, Dataset actual, double relTolerance, double absTolerance)` Assert equality of datasets where each element is true if they are equal or for floating point datasets, if `abs(a - b) ≤ absTolerance + relTolerance*abs(b)`.

  - ### Uses of Dataset in org.eclipse.january.dataset

    Subinterfaces of Dataset in org.eclipse.january.dataset
    Modifier and Type | Interface and Description
    --- | ---
    `interface` | `CompoundDataset`
    `interface` | `DateDataset` Interface for a dataset containing `Date`s.

    Classes in org.eclipse.january.dataset that implement Dataset
    Modifier and Type | Class and Description
    --- | ---
    `class` | `AbstractCompoundDataset` Generic container class for data that is compound in nature Each subclass has an array of compound types, items of this array are composed of primitive types Data items can be Complex, Vector, etc
    `class` | `AbstractDataset` Generic container class for data
    `class` | `BooleanDataset` Extend boolean base dataset for boolean values
    `class` | `BooleanDatasetBase` Extend dataset for boolean values // PRIM_TYPE
    `class` | `ByteDataset` Extend dataset for byte values // PRIM_TYPE
    `class` | `ComplexDoubleDataset` Extend compound dataset to hold complex double values // PRIM_TYPE
    `class` | `ComplexFloatDataset` Extend compound dataset to hold complex float values // PRIM_TYPE
    `class` | `CompoundByteDataset` Extend compound dataset for byte values // PRIM_TYPE
    `class` | `CompoundDoubleDataset` Extend compound dataset for double values // PRIM_TYPE
    `class` | `CompoundFloatDataset` Extend compound dataset for float values // PRIM_TYPE
    `class` | `CompoundIntegerDataset` Extend compound dataset for int values // PRIM_TYPE
    `class` | `CompoundLongDataset` Extend compound dataset for long values // PRIM_TYPE
    `class` | `CompoundShortDataset` Extend compound dataset for short values // PRIM_TYPE
    `class` | `DateDatasetImpl`
    `class` | `DoubleDataset` Extend dataset for double values // PRIM_TYPE
    `class` | `FloatDataset` Extend dataset for float values // PRIM_TYPE
    `class` | `IntegerDataset` Extend dataset for int values // PRIM_TYPE
    `class` | `LongDataset` Extend dataset for long values // PRIM_TYPE
    `class` | `ObjectDataset` Extend dataset for objects
    `class` | `ObjectDatasetBase` Extend dataset for Object values // PRIM_TYPE
    `class` | `RGBByteDataset` Class to hold colour datasets as red, green, blue tuples of byte integers.
    `class` | `RGBDataset` Class to hold colour datasets as red, green, blue tuples of short integers
    `class` | `ShortDataset` Extend dataset for short values // PRIM_TYPE
    `class` | `StringDataset` Extend dataset for objects
    `class` | `StringDatasetBase` Extend dataset for String values // PRIM_TYPE

    Fields in org.eclipse.january.dataset declared as Dataset
    Modifier and Type | Field and Description
    --- | ---
    `protected Dataset` | BooleanIterator.`aDataset`
    `protected Dataset` | BroadcastIteratorBase.`aDataset`
    `protected Dataset` | BroadcastIteratorBase.`bDataset`
    `protected Dataset` | BooleanIterator.`cDataset`
    `protected Dataset` | BooleanIterator.`oDataset` Output dataset
    `protected Dataset` | BroadcastIterator.`oDataset` Output dataset

    Methods in org.eclipse.january.dataset with type parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `<T extends Dataset> T` | AbstractDataset.`cast(Class<T> clazz)`
    `<T extends Dataset> T` | Dataset.`cast(Class<T> clazz)` Cast a dataset
    `static <T extends Dataset> T` | DatasetUtils.`cast(Class<T> clazz, IDataset d)` Cast a dataset
    `<T extends Dataset> T` | AbstractDataset.`cast(int isize, Class<T> clazz, boolean repeat)`
    `<T extends Dataset> T` | Dataset.`cast(int isize, Class<T> clazz, boolean repeat)` Cast a dataset
    `static <T extends Dataset> T` | DatasetUtils.`cast(int isize, Class<T> clazz, IDataset d, boolean repeat)` Cast a dataset
    `<T extends Dataset> T` | AbstractDataset.`copy(Class<T> clazz)`
    `<T extends Dataset> T` | Dataset.`copy(Class<T> clazz)` Copy and cast a dataset
    `static <T extends Dataset> T` | DatasetUtils.`copy(Class<T> clazz, IDataset d)` Cast a dataset
    `<T extends Dataset> T` | RGBByteDataset.`createBlueDataset(Class<T> clazz)` Extract blue colour channel
    `<T extends Dataset> T` | RGBDataset.`createBlueDataset(Class<T> clazz)` Extract blue colour channel
    `static <T extends Dataset> T` | DatasetFactory.`createFromList(Class<T> clazz, List<?> objectList)` Create dataset of given class from list
    `static <T extends Dataset> T` | DatasetFactory.`createFromObject(Class<T> clazz, Object obj)` Create a dataset from object
    `static <T extends Dataset> T` | DatasetFactory.`createFromObject(Class<T> clazz, Object obj, int... shape)` Create a dataset from object
    `static <T extends Dataset> T` | DatasetFactory.`createFromObject(int itemSize, Class<T> clazz, Object obj, int... shape)` Create a compound dataset from object
    `<T extends Dataset> T` | RGBByteDataset.`createGreenDataset(Class<T> clazz)` Extract green colour channel
    `<T extends Dataset> T` | RGBDataset.`createGreenDataset(Class<T> clazz)` Extract green colour channel
    `<T extends Dataset> T` | RGBByteDataset.`createGreyDataset(Class<T> clazz)` Convert colour dataset to a grey-scale one using the NTSC formula, aka ITU-R BT.601, for RGB to luma mapping
    `<T extends Dataset> T` | RGBDataset.`createGreyDataset(Class<T> clazz)` Convert colour dataset to a grey-scale one using the NTSC formula, aka ITU-R BT.601, for RGB to luma mapping
    `<T extends Dataset> T` | RGBByteDataset.`createGreyDataset(Class<T> clazz, double red, double green, double blue)` Convert colour dataset to a grey-scale one using given RGB to luma mapping
    `<T extends Dataset> T` | RGBDataset.`createGreyDataset(Class<T> clazz, double red, double green, double blue)` Convert colour dataset to a grey-scale one using given RGB to luma mapping
    `static <T extends Dataset> T` | DatasetFactory.`createLinearSpace(Class<T> clazz, double start, double stop, int length)` Create a 1D dataset of linearly spaced values in closed interval
    `static <T extends Dataset> T` | DatasetFactory.`createLogSpace(Class<T> clazz, double start, double stop, int length, double base)` Create a 1D dataset of logarithmically spaced values in closed interval.
    `static <T extends Dataset> T` | DatasetFactory.`createRange(Class<T> clazz, double stop)` Create dataset with items ranging from 0 up to given stop in steps of 1
    `static <T extends Dataset> T` | DatasetFactory.`createRange(Class<T> clazz, double start, double stop, double step)` Create dataset with items ranging from given start up to given stop in given steps
    `<T extends Dataset> T` | RGBByteDataset.`createRedDataset(Class<T> clazz)` Extract red colour channel
    `<T extends Dataset> T` | RGBDataset.`createRedDataset(Class<T> clazz)` Extract red colour channel
    `static <T extends Dataset> T` | DatasetUtils.`diag(T a, int offset)` Create a (off-)diagonal matrix from items in dataset
    `static <T extends Dataset> T` | DatasetUtils.`eye(Class<T> clazz, int rows, int cols, int offset)`
    `static <T extends Dataset> T` | DatasetUtils.`flipLeftRight(T a)` Flip items in left/right direction, column-wise, or along second axis
    `static <T extends Dataset> T` | DatasetUtils.`flipUpDown(T a)` Flip items in up/down direction, row-wise, or along first axis
    `static <T extends Dataset> T` | DatasetFactory.`ones(Class<T> clazz, int... shape)`
    `static <T extends Dataset> T` | DatasetFactory.`ones(Dataset dataset, Class<T> clazz)`
    `static <T extends Dataset> T` | DatasetFactory.`ones(int itemSize, Class<T> clazz, int... shape)`
    `static <T extends Dataset> T` | DatasetFactory.`ones(T dataset)`
    `static <T extends Dataset> T` | DatasetUtils.`put(T a, Dataset indices, Object values)` Changes specific items of dataset by replacing them with other array
    `static <T extends Dataset> T` | DatasetUtils.`put(T a, int[] indices, Object values)` Changes specific items of dataset by replacing them with other array
    `static <T extends Dataset> T` | DatasetUtils.`repeat(T a, int[] repeats, int axis)` Constructs a dataset which has its elements along an axis replicated from the original dataset by the number of times given in the repeats array.
    `static <T extends Dataset> T` | DatasetUtils.`resize(T a, int... shape)` Resize a dataset
    `static <T extends Dataset> T` | DatasetUtils.`roll(T a, int shift, Integer axis)` Roll items over given axis by given amount
    `static <T extends Dataset> T` | DatasetUtils.`rollAxis(T a, int axis, int start)` Roll the specified axis backwards until it lies in given position
    `static <T extends Dataset> T` | DatasetUtils.`rotate90(T a)` Rotate items in first two dimension by 90 degrees anti-clockwise
    `static <T extends Dataset> T` | DatasetUtils.`rotate90(T a, int k)` Rotate items in first two dimension by 90 degrees anti-clockwise
    `static <T extends Dataset> T` | DatasetUtils.`sort(T a)`
    `static <T extends Dataset> T` | DatasetUtils.`sort(T a, Integer axis)`
    `static <T extends Dataset> T` | DatasetUtils.`take(T a, Dataset indices, Integer axis)` Take items from dataset along an axis
    `static <T extends Dataset> T` | DatasetUtils.`take(T a, int[] indices, Integer axis)` Take items from dataset along an axis
    `static <T extends Dataset> T` | Stats.`typedProduct(Class<T> clazz, Dataset a, int[] axes, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | Stats.`typedProduct(Class<T> clazz, Dataset a, int axis, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | Stats.`typedSum(Class<T> clazz, Dataset a, int[] axes, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | Stats.`typedSum(Class<T> clazz, Dataset a, int axis, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | DatasetFactory.`zeros(Class<T> clazz, int... shape)`
    `static <T extends Dataset> T` | DatasetFactory.`zeros(Dataset dataset, Class<T> clazz)`
    `static <T extends Dataset> T` | DatasetFactory.`zeros(int itemSize, Class<T> clazz, int... shape)`
    `static <T extends Dataset> T` | DatasetFactory.`zeros(T dataset)`
    `static <T extends Dataset> T` | DatasetFactory.`zeros(T dataset, int... shape)`

    Methods in org.eclipse.january.dataset that return Dataset
    Modifier and Type | Method and Description
    --- | ---
    `static Dataset` | Maths.`abs(Object a)` abs - absolute value of each element
    `static Dataset` | Maths.`abs(Object a, Dataset o)` abs - absolute value of each element
    `static Dataset` | Maths.`add(Collection<IDataset> sets, boolean requireClone)` Adds all sets passed in together The first IDataset must cast to Dataset For memory efficiency sake if add(...) is called with a set of size one, no clone is done, the original object is returned directly.
    `static Dataset` | Maths.`add(Object a, Object b)` add operator
    `static Dataset` | Maths.`add(Object a, Object b, Dataset o)` add operator
    `Dataset` | Dataset.`all(int axis)`
    `static Dataset` | Maths.`angle(Object a)` Create a dataset of the arguments from a complex dataset
    `static Dataset` | Maths.`angle(Object a, boolean inDegrees)` Create a dataset of the arguments from a complex dataset
    `static Dataset` | Maths.`angle(Object a, boolean inDegrees, Dataset o)` Create a dataset of the arguments from a complex dataset
    `static Dataset` | Maths.`angle(Object a, Dataset o)` Create a dataset of the arguments from a complex dataset
    `Dataset` | Dataset.`any(int axis)`
    `static Dataset` | DatasetUtils.`append(IDataset a, IDataset b, int axis)` Append copy of dataset with another dataset along n-th axis
    `static Dataset` | Maths.`arccos(Object a)` arccos - evaluate the inverse cosine function on each element of the dataset
    `static Dataset` | Maths.`arccos(Object a, Dataset o)` arccos - evaluate the inverse cosine function on each element of the dataset
    `static Dataset` | Maths.`arccosh(Object a)` arccosh - evaluate the inverse hyperbolic cosine function on each element of the dataset
    `static Dataset` | Maths.`arccosh(Object a, Dataset o)` arccosh - evaluate the inverse hyperbolic cosine function on each element of the dataset
    `static Dataset` | Maths.`arcsin(Object a)` arcsin - evaluate the inverse sine function on each element of the dataset
    `static Dataset` | Maths.`arcsin(Object a, Dataset o)` arcsin - evaluate the inverse sine function on each element of the dataset
    `static Dataset` | Maths.`arcsinh(Object a)` arcsinh - evaluate the inverse hyperbolic sine function on each element of the dataset
    `static Dataset` | Maths.`arcsinh(Object a, Dataset o)` arcsinh - evaluate the inverse hyperbolic sine function on each element of the dataset
    `static Dataset` | Maths.`arctan(Object a)` arctan - evaluate the inverse tangent function on each element of the dataset
    `static Dataset` | Maths.`arctan(Object a, Dataset o)` arctan - evaluate the inverse tangent function on each element of the dataset
    `static Dataset` | Maths.`arctan2(Object a, Object b)`
    `static Dataset` | Maths.`arctan2(Object a, Object b, Dataset o)`
    `static Dataset` | Maths.`arctanh(Object a)` arctanh - evaluate the inverse hyperbolic tangent function on each element of the dataset
    `static Dataset` | Maths.`arctanh(Object a, Dataset o)` arctanh - evaluate the inverse hyperbolic tangent function on each element of the dataset
    `Dataset` | Dataset.`argMax(int axis, boolean... ignoreInvalids)` Find indices of maximum values along given axis
    `Dataset` | Dataset.`argMin(int axis, boolean... ignoreInvalids)` Find indices of minimum values along given axis
    `Dataset` | CompoundDataset.`asNonCompoundDataset(boolean shareData)` Get a non-compound dataset version
    `static Dataset` | Maths.`bitwiseAnd(Object a, Object b)` bitwiseAnd operator
    `static Dataset` | Maths.`bitwiseAnd(Object a, Object b, Dataset o)` bitwiseAnd operator
    `static Dataset` | Maths.`bitwiseInvert(Object a)` bitwiseInvert - `~a`, bitwise invert (or NOT) each element
    `static Dataset` | Maths.`bitwiseInvert(Object a, Dataset o)` bitwiseInvert - `~a`, bitwise invert (or NOT) each element
    `static Dataset` | Maths.`bitwiseOr(Object a, Object b)` bitwiseOr operator
    `static Dataset` | Maths.`bitwiseOr(Object a, Object b, Dataset o)` bitwiseOr operator
    `static Dataset` | Maths.`bitwiseXor(Object a, Object b)` bitwiseXor operator
    `static Dataset` | Maths.`bitwiseXor(Object a, Object b, Dataset o)` bitwiseXor operator
    `static Dataset` | LinearAlgebra.`calcCholeskyDecomposition(Dataset a)` Calculate Cholesky decomposition `A = L L^T`
    `static Dataset` | LinearAlgebra.`calcConjugateGradient(Dataset a, Dataset v)` Calculation `A x = v` by conjugate gradient method with the stopping criterion being that the estimated residual `r = v - A x` satisfies `||r|| < ||v||` with maximum of 100 iterations
    `static Dataset` | LinearAlgebra.`calcConjugateGradient(Dataset a, Dataset v, int maxIterations, double delta)` Calculation `A x = v` by conjugate gradient method with the stopping criterion being that the estimated residual `r = v - A x` satisfies `||r|| < delta ||v||`
    `static Dataset[]` | LinearAlgebra.`calcEigenDecomposition(Dataset a)` Calculate eigen-decomposition `A = V D V^T`
    `static Dataset` | LinearAlgebra.`calcEigenvalues(Dataset a)`
    `static Dataset` | LinearAlgebra.`calcInverse(Dataset a)` Calculate inverse of square dataset
    `static Dataset[]` | LinearAlgebra.`calcLUDecomposition(Dataset a)` Calculate LU decomposition `A = P^-1 L U`
    `static Dataset` | LinearAlgebra.`calcPseudoInverse(Dataset a)` Calculate (Moore-Penrose) pseudo-inverse
    `static Dataset[]` | LinearAlgebra.`calcQRDecomposition(Dataset a)` Calculate QR decomposition `A = Q R`
    `static Dataset[]` | LinearAlgebra.`calcSingularValueDecomposition(Dataset a)` Calculate singular value decomposition `A = U S V^T`
    `Dataset` | AbstractDataset.`cast(boolean repeat, int dtype, int isize)`
    `Dataset` | Dataset.`cast(boolean repeat, int dtype, int isize)` Deprecated. Please use class-based method `cast(int, Class, boolean)`
    `static Dataset` | DatasetUtils.`cast(IDataset d, boolean repeat, int dtype, int isize)` Deprecated. Please use the class-based methods in DatasetUtils, such as `DatasetUtils.cast(Class, IDataset)`
    `static Dataset` | DatasetUtils.`cast(IDataset d, int dtype)` Deprecated. Please use the class-based methods in DatasetUtils, such as `DatasetUtils.cast(Class, IDataset)`
    `Dataset` | AbstractDataset.`cast(int dtype)`
    `Dataset` | Dataset.`cast(int dtype)` Deprecated. Please use class-based method `cast(Class)`
    `static Dataset` | Maths.`cbrt(Object a)` cbrt - evaluate the cube root function on each element of the dataset
    `static Dataset` | Maths.`cbrt(Object a, Dataset o)` cbrt - evaluate the cube root function on each element of the dataset
    `static Dataset` | Maths.`ceil(Object a)` ceil - evaluate the ceiling function on each element of the dataset
    `static Dataset` | Maths.`ceil(Object a, Dataset o)` ceil - evaluate the ceiling function on each element of the dataset
    `static Dataset` | Maths.`centralDifference(Dataset a, int axis)` Discrete difference of dataset along axis using finite central difference
    `static Dataset` | DatasetUtils.`choose(IntegerDataset index, Object[] choices, boolean throwAIOOBE, boolean clip)` Choose content from choices where condition is true, otherwise use default.
    `static Dataset` | Maths.`clip(Object a, Dataset o, Object pa, Object pb)` clip - clip elements to limits
    `static Dataset` | Maths.`clip(Object a, Object pa, Object pb)` clip - clip elements to limits
    `Dataset` | Dataset.`clone()`
    `static Dataset` | DatasetUtils.`coerce(Dataset a, Object obj)` Create a copy that has been coerced to an appropriate dataset type depending on the input object's class
    `static Dataset` | DatasetUtils.`concatenate(IDataset[] as, int axis)` Concatenate the set of datasets along given axis
    `static Dataset` | Maths.`conjugate(Object a)`
    `static Dataset` | Maths.`conjugate(Object a, Dataset o)`
    `static Dataset[]` | BroadcastUtils.`convertAndBroadcast(Object... objects)` Converts and broadcast all objects as datasets of same shape
    `static Dataset` | DatasetUtils.`convertToDataset(IDataset data)` Convert (if necessary) a dataset obeying the interface to our implementation
    `static Dataset` | DatasetUtils.`copy(IDataset d, int dtype)` Deprecated. Please use the class-based methods in DatasetUtils, such as `DatasetUtils.copy(Class, IDataset)`
    `Dataset` | AbstractDataset.`copy(int dtype)`
    `Dataset` | Dataset.`copy(int dtype)` Deprecated. Please use class-based method `copy(Class)`
    `static Dataset` | Maths.`cos(Object a)` cos - evaluate the cosine function on each element of the dataset
    `static Dataset` | Maths.`cos(Object a, Dataset o)` cos - evaluate the cosine function on each element of the dataset
    `static Dataset` | Maths.`cosh(Object a)` cosh - evaluate the hyperbolic cosine function on each element of the dataset
    `static Dataset` | Maths.`cosh(Object a, Dataset o)` cosh - evaluate the hyperbolic cosine function on each element of the dataset
    `Dataset` | AbstractDataset.`count(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`count(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | AbstractDataset.`count(int axis, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`count(int axis, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`covariance(Dataset a)` See `Stats.covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof)` with b = null, rowvar = true, bias = false and ddof = null.
    `static Dataset` | Stats.`covariance(Dataset a, boolean rowvar, boolean bias, Integer ddof)` See `Stats.covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof)` with b = null.
    `static Dataset` | Stats.`covariance(Dataset a, Dataset b)` See `Stats.covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof)` with b = null, rowvar = true, bias = false and ddof = null
    `static Dataset` | Stats.`covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof)` Calculate the covariance matrix (array) of a concatenated with b.
    `Dataset` | RGBByteDataset.`createBlueDataset(int dtype)` Deprecated. Use `RGBByteDataset.createBlueDataset(java.lang.Class<T>)`
    `Dataset` | RGBDataset.`createBlueDataset(int dtype)` Deprecated. Use `RGBDataset.createBlueDataset(java.lang.Class<T>)`
    `static Dataset` | DatasetUtils.`createDatasetFromCompoundDataset(CompoundDataset a, boolean shareData)` Create a dataset from a compound dataset by using elements of an item as last axis
    `static Dataset` | DatasetFactory.`createFromList(int dtype, List<?> objectList)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.createFromList(Class, List)`
    `static Dataset` | DatasetFactory.`createFromList(List<?> objectList)` Create dataset of appropriate type from list
    `static Dataset` | DatasetFactory.`createFromObject(boolean isUnsigned, Object obj)` Create a dataset from object (automatically detect dataset type)
    `static Dataset` | DatasetFactory.`createFromObject(int itemSize, int dtype, Object obj, int... shape)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.createFromObject(int, Class, Object, int...)`
    `static Dataset` | DatasetFactory.`createFromObject(int dtype, Object obj)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.createFromObject(Class, Object, int...)`
    `static Dataset` | DatasetFactory.`createFromObject(int dtype, Object obj, int... shape)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.createFromObject(Class, Object, int...)`
    `static Dataset` | DatasetFactory.`createFromObject(Object obj)` Create a dataset from object (automatically detect dataset type)
    `static Dataset` | DatasetFactory.`createFromObject(Object obj, int... shape)` Create a dataset from object (automatically detect dataset type)
    `Dataset` | RGBByteDataset.`createGreenDataset(int dtype)` Deprecated. Use `RGBByteDataset.createGreenDataset(java.lang.Class<T>)`
    `Dataset` | RGBDataset.`createGreenDataset(int dtype)` Deprecated. Use `RGBDataset.createGreenDataset(java.lang.Class<T>)`
    `Dataset` | RGBByteDataset.`createGreyDataset(double red, double green, double blue, int dtype)` Deprecated. Use `RGBByteDataset.createGreyDataset(Class, double, double, double)`
    `Dataset` | RGBDataset.`createGreyDataset(double red, double green, double blue, int dtype)` Deprecated. Use `RGBDataset.createGreyDataset(Class, double, double, double)`
    `Dataset` | RGBByteDataset.`createGreyDataset(int dtype)` Deprecated. Use `RGBByteDataset.createGreyDataset(Class)`
    `Dataset` | RGBDataset.`createGreyDataset(int dtype)` Deprecated. Use `RGBDataset.createGreyDataset(Class)`
    `static Dataset` | DatasetFactory.`createLinearSpace(double start, double stop, int length, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.createLinearSpace(Class, double, double, int)`
    `static Dataset` | DatasetFactory.`createLogSpace(double start, double stop, int length, double base, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.createLogSpace(Class, double, double, int, double)`
    `static Dataset` | DatasetFactory.`createRange(double start, double stop, double step, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.createRange(Class, double, double, double)`
    `static Dataset` | DatasetFactory.`createRange(double stop, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.createRange(Class, double)`
    `Dataset` | RGBByteDataset.`createRedDataset(int dtype)` Deprecated. Use `RGBByteDataset.createRedDataset(java.lang.Class<T>)`
    `Dataset` | RGBDataset.`createRedDataset(int dtype)` Deprecated. Use `RGBDataset.createRedDataset(java.lang.Class<T>)`
    `static Dataset` | LinearAlgebra.`crossProduct(Dataset a, Dataset b)` Calculate the cross product of two datasets.
    `static Dataset` | LinearAlgebra.`crossProduct(Dataset a, Dataset b, int axisA, int axisB, int axisC)` Calculate the cross product of two datasets.
    `static Dataset` | Stats.`cumulativeProduct(Dataset a, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`cumulativeProduct(Dataset a, int axis, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`cumulativeSum(Dataset a, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`cumulativeSum(Dataset a, int axis, boolean... ignoreInvalids)`
    `static Dataset` | Maths.`derivative(Dataset x, Dataset y, int n)` Calculates the derivative of a line described by two datasets (x,y) given a spread of n either side of the point
    `static Dataset` | Maths.`difference(Dataset a, int n, int axis)` Discrete difference of dataset along axis using finite difference
    `static Dataset` | Maths.`divide(Object a, Object b)` divide operator
    `static Dataset` | Maths.`divide(Object a, Object b, Dataset o)` divide operator
    `static Dataset` | Maths.`divideTowardsFloor(Object a, Object b)` divideTowardsFloor operator
    `static Dataset` | Maths.`divideTowardsFloor(Object a, Object b, Dataset o)` divideTowardsFloor operator
    `static Dataset` | Maths.`dividez(Object a, Object b)` dividez operator
    `static Dataset` | Maths.`dividez(Object a, Object b, Dataset o)` dividez operator
    `static Dataset` | LinearAlgebra.`dotProduct(Dataset a, Dataset b)` Calculate the dot product of two datasets.
    `static Dataset` | Maths.`exp(Object a)` exp - evaluate the exponential function on each element of the dataset
    `static Dataset` | Maths.`exp(Object a, Dataset o)` exp - evaluate the exponential function on each element of the dataset
    `static Dataset` | Maths.`expm1(Object a)` expm1 - evaluate the exponential function - 1 on each element of the dataset
    `static Dataset` | Maths.`expm1(Object a, Dataset o)` expm1 - evaluate the exponential function - 1 on each element of the dataset
    `static Dataset` | DatasetUtils.`extract(IDataset data, IDataset condition)` Extract values where condition is non-zero.
    `static Dataset` | DatasetUtils.`eye(int rows, int cols, int offset, int dtype)` Deprecated. Please use the class-based methods in DatasetUtils, such as `DatasetUtils.eye(Class, int, int, int)`
    `Dataset` | Dataset.`fill(Object obj)` Fill dataset with given object
    `Dataset` | AbstractDataset.`flatten()`
    `Dataset` | Dataset.`flatten()` Flatten shape
    `static Dataset` | Maths.`floor(Object a)` floor - evaluate the floor function on each element of the dataset
    `static Dataset` | Maths.`floor(Object a, Dataset o)` floor - evaluate the floor function on each element of the dataset
    `static Dataset` | Maths.`floorDivide(Object a, Object b)`
    `static Dataset` | Maths.`floorDivide(Object a, Object b, Dataset o)`
    `static Dataset` | Maths.`floorRemainder(Object a, Object b)`
    `static Dataset` | Maths.`floorRemainder(Object a, Object b, Dataset o)`
    `Dataset` | AbstractDataset.`getBroadcastView(int... broadcastShape)`
    `Dataset` | Dataset.`getBroadcastView(int... shape)`
    `Dataset` | AbstractDataset.`getBy1DIndex(IntegerDataset index)`
    `Dataset` | Dataset.`getBy1DIndex(IntegerDataset index)` This is modelled after the NumPy get item with an index dataset
    `Dataset` | AbstractDataset.`getByBoolean(Dataset selection)`
    `Dataset` | Dataset.`getByBoolean(Dataset selection)` This is modelled after the NumPy get item with a condition specified by a boolean dataset
    `Dataset` | AbstractDataset.`getByIndexes(Object... indexes)`
    `Dataset` | Dataset.`getByIndexes(Object... indexes)` This is modelled after the NumPy get item with an array of indexing objects
    `Dataset` | BooleanIterator.`getChoice()`
    `Dataset` | RunningAverage.`getCurrentAverage()`
    `Dataset` | CompoundDataset.`getElements(int element)` Get chosen elements from each item as a dataset
    `Dataset` | CompoundDataset.`getElementsView(int element)` Get chosen elements from each item as a view on dataset
    `Dataset` | AbstractDataset.`getErrorBuffer()`
    `Dataset` | Dataset.`getErrorBuffer()` Get the (un-broadcasted) dataset that backs the (squared) error data
    `Dataset` | AbstractDataset.`getErrors()`
    `Dataset` | Dataset.`getErrors()` Get the error array from the dataset of same shape.
    `protected Dataset` | AbstractDataset.`getInternalSquaredError()`
    `Dataset` | BooleanIterator.`getOutput()`
    `Dataset` | BroadcastIterator.`getOutput()`
    `Dataset` | SingleInputBroadcastIterator.`getOutput()`
    `Dataset` | AbstractDataset.`getRealPart()`
    `Dataset` | Dataset.`getRealPart()`
    `Dataset` | AbstractDataset.`getRealView()`
    `Dataset` | Dataset.`getRealView()`
    `Dataset` | AbstractDataset.`getSlice(IMonitor monitor, int[] start, int[] stop, int[] step)`
    `Dataset` | AggregateDataset.`getSlice(IMonitor monitor, int[] start, int[] stop, int[] step)`
    `Dataset` | Dataset.`getSlice(IMonitor mon, int[] start, int[] stop, int[] step)`
    `Dataset` | LazyDataset.`getSlice(IMonitor monitor, int[] start, int[] stop, int[] step)`
    `Dataset` | AbstractDataset.`getSlice(IMonitor monitor, Slice... slice)`
    `Dataset` | AggregateDataset.`getSlice(IMonitor monitor, Slice... slice)`
    `Dataset` | Dataset.`getSlice(IMonitor mon, Slice... slice)`
    `Dataset` | LazyDataset.`getSlice(IMonitor monitor, Slice... slice)`
    `Dataset` | AbstractDataset.`getSlice(IMonitor monitor, SliceND slice)`
    `Dataset` | AggregateDataset.`getSlice(IMonitor monitor, SliceND slice)`
    `Dataset` | Dataset.`getSlice(IMonitor mon, SliceND slice)`
    `Dataset` | LazyDataset.`getSlice(IMonitor monitor, SliceND slice)`
    `Dataset` | AbstractDataset.`getSlice(int[] start, int[] stop, int[] step)`
    `Dataset` | AggregateDataset.`getSlice(int[] start, int[] stop, int[] step)`
    `Dataset` | Dataset.`getSlice(int[] start, int[] stop, int[] step)`
    `Dataset` | LazyDataset.`getSlice(int[] start, int[] stop, int[] step)`
    `Dataset` | AbstractDataset.`getSlice(Slice... slice)`
    `Dataset` | AggregateDataset.`getSlice(Slice... slice)`
    `Dataset` | Dataset.`getSlice(Slice... slice)`
    `Dataset` | LazyDataset.`getSlice(Slice... slice)`
    `Dataset` | AbstractDataset.`getSlice(SliceND slice)` Get a slice of the dataset.
    `Dataset` | AggregateDataset.`getSlice(SliceND slice)`
    `Dataset` | Dataset.`getSlice(SliceND slice)`
    `Dataset` | LazyDataset.`getSlice(SliceND slice)`
    `Dataset` | AbstractDataset.`getSliceView(int[] start, int[] stop, int[] step)`
    `Dataset` | Dataset.`getSliceView(int[] start, int[] stop, int[] step)`
    `Dataset` | AbstractDataset.`getSliceView(Slice... slice)`
    `Dataset` | Dataset.`getSliceView(Slice... slice)`
    `Dataset` | AbstractDataset.`getSliceView(SliceND slice)` Get a slice of the dataset.
    `Dataset` | Dataset.`getSliceView(SliceND slice)`
    `Dataset` | AbstractDataset.`getTransposedView(int... axes)`
    `Dataset` | Dataset.`getTransposedView(int... axes)`
    `Dataset` | AbstractCompoundDataset.`getUniqueItems()`
    `Dataset` | Dataset.`getUniqueItems()` Get unique items
    `Dataset` | Dataset.`getView(boolean deepCopyMetadata)`
    `static Dataset` | Maths.`hypot(Object a, Object b)`
    `static Dataset` | Maths.`hypot(Object a, Object b, Dataset o)`
    `Dataset` | Dataset.`iadd(Object o)` In-place addition
    `Dataset` | Dataset.`idivide(Object o)` In-place division
    `Dataset` | Dataset.`ifloor()` In-place floor
    `Dataset` | AbstractDataset.`ifloorDivide(Object o)`
    `Dataset` | Dataset.`ifloorDivide(Object o)` In-place floor division
    `Dataset` | Dataset.`imultiply(Object o)` In-place multiplication
    `protected Dataset` | LazyDataset.`internalGetSlice(IMonitor monitor, SliceND slice)`
    `static Dataset` | Maths.`interpolate(Dataset x, Dataset d, IDataset x0, Number left, Number right)` Linearly interpolate values at points in a 1D dataset corresponding to given coordinates.
    `Dataset` | Dataset.`ipower(Object o)` In-place raise to power of argument
    `static Dataset` | Stats.`iqr(Dataset a, int axis)` Interquartile range: Q3 - Q1
    `Dataset` | Dataset.`iremainder(Object o)` In-place remainder of division
    `Dataset` | Dataset.`isubtract(Object o)` In-place subtraction
    `static Dataset` | LinearAlgebra.`kroneckerProduct(Dataset a, Dataset b)` Create the Kronecker product as defined by `kron[k0,...,kN] = a[i0,...,iN] * b[j0,...,jN]` where `kn = sn * in + jn` for `n = 0...N` and `s` is shape of `b`
    `static Dataset` | Stats.`kurtosis(Dataset a, int axis, boolean... ignoreInvalids)`
    `static Dataset` | Maths.`leftShift(Object a, Object b)` leftShift operator
    `static Dataset` | Maths.`leftShift(Object a, Object b, Dataset o)` leftShift operator
    `static Dataset` | DatasetUtils.`lnnorm(Dataset a)` Function that returns a normalised dataset which is bounded between 0 and 1 and has been distributed on a natural log scale
    `static Dataset` | Maths.`log(Object a)` log - evaluate the logarithm function on each element of the dataset
    `static Dataset` | Maths.`log(Object a, Dataset o)` log - evaluate the logarithm function on each element of the dataset
    `static Dataset` | Maths.`log10(Object a)` log10 - evaluate the logarithm function on each element of the dataset
    `static Dataset` | Maths.`log10(Object a, Dataset o)` log10 - evaluate the logarithm function on each element of the dataset
    `static Dataset` | Maths.`log1p(Object a)` log1p - evaluate the logarithm function of 1 plus on each element of the dataset
    `static Dataset` | Maths.`log1p(Object a, Dataset o)` log1p - evaluate the logarithm function of 1 plus on each element of the dataset
    `static Dataset` | Maths.`log2(Object a)` log2 - evaluate the logarithm function on each element of the dataset
    `static Dataset` | Maths.`log2(Object a, Dataset o)` log2 - evaluate the logarithm function on each element of the dataset
    `static Dataset` | DatasetUtils.`lognorm(Dataset a)` Function that returns a normalised dataset which is bounded between 0 and 1 and has been distributed on a log10 scale
    `static Dataset` | Maths.`lowerClip(Object a, Dataset o, Object pa)` lowerClip - clip elements to lower limit
    `static Dataset` | Maths.`lowerClip(Object a, Object pa)` lowerClip - clip elements to lower limit
    `static Dataset` | DatasetUtils.`makeUnsigned(IDataset a)` Make a dataset unsigned by promoting it to a wider dataset type and unwrapping the signs of its contents
    `static Dataset` | DatasetUtils.`makeUnsigned(IDataset a, boolean check)` Make a dataset unsigned by promoting it to a wider dataset type and unwrapping the signs of its contents
    `static Dataset` | LazyMaths.`max(ILazyDataset data, int... axes)`
    `Dataset` | AbstractDataset.`max(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`max(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | AbstractDataset.`max(int axis, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`max(int axis, boolean... ignoreInvalids)`
    `static Dataset` | Maths.`maximum(Object a, Object b)` maximum operator
    `static Dataset` | Maths.`maximum(Object a, Object b, Dataset o)` maximum operator
    `static Dataset` | LazyMaths.`mean(ILazyDataset data, int... ignoreAxes)`
    `Dataset` | AbstractDataset.`mean(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`mean(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | AbstractDataset.`mean(int axis, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`mean(int axis, boolean... ignoreInvalids)`
    `static Dataset` | LazyMaths.`mean(int start, int stop, ILazyDataset data, int... ignoreAxes)`
    `static Dataset` | CollectionStats.`mean(List<IDataset> sets)` Used to get a mean image from a set of images for instance.
    `static Dataset` | Stats.`median(Dataset a, int axis)`
    `static Dataset` | CollectionStats.`median(List<IDataset> sets)` Used to get a median image from a set of images for instance.
    `static Dataset` | LazyMaths.`min(ILazyDataset data, int... axes)`
    `Dataset` | AbstractDataset.`min(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`min(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | AbstractDataset.`min(int axis, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`min(int axis, boolean... ignoreInvalids)`
    `static Dataset` | Maths.`minimum(Object a, Object b)` minimum operator
    `static Dataset` | Maths.`minimum(Object a, Object b, Dataset o)` minimum operator
    `static Dataset` | Maths.`multiply(Collection<IDataset> sets, boolean requireClone)` Multiplies all sets passed in together The first IDataset must cast to Dataset
    `static Dataset` | Maths.`multiply(Object a, Object b)` multiply operator
    `static Dataset` | Maths.`multiply(Object a, Object b, Dataset o)` multiply operator
    `static Dataset` | Maths.`negative(Object a)` negative - negative value of each element
    `static Dataset` | Maths.`negative(Object a, Dataset o)` negative - negative value of each element
    `static Dataset` | DatasetUtils.`norm(Dataset a)` Function that returns a normalised dataset which is bounded between 0 and 1
    `static Dataset` | DatasetFactory.`ones(Dataset dataset, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.ones(Dataset, Class)`
    `static Dataset` | DatasetFactory.`ones(int[] shape, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.ones(Class, int...)`
    `static Dataset` | DatasetFactory.`ones(int itemSize, int[] shape, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.ones(Class, int...)`
    `static Dataset` | Operations.`operate(BinaryOperation op, Object a, Object b, Dataset o)` Operate on a dataset
    `static Dataset` | Operations.`operate(UnaryOperation op, Object a, Dataset o)` Operate on a dataset
    `static Dataset` | LinearAlgebra.`outerProduct(Dataset a, Dataset b)` Calculate the outer product of two datasets
    `Dataset` | AbstractDataset.`peakToPeak(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`peakToPeak(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | AbstractDataset.`peakToPeak(int axis, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`peakToPeak(int axis, boolean... ignoreInvalids)`
    `static Dataset` | Maths.`phaseAsComplexNumber(Object a, boolean keepZeros)` Create a phase only dataset.
    `static Dataset` | Maths.`phaseAsComplexNumber(Object a, Dataset o, boolean keepZeros)` Create a phase only dataset.
    `static Dataset` | LinearAlgebra.`power(Dataset a, int n)` Raise dataset to given power by matrix multiplication
    `static Dataset` | Maths.`power(Object a, Object b)` power operator
    `static Dataset` | Maths.`power(Object a, Object b, Dataset o)` power operator
    `static Dataset` | Stats.`product(Dataset a, int[] axes, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`product(Dataset a, int axis, boolean... ignoreInvalids)`
    `static Dataset` | LazyMaths.`product(ILazyDataset data, int axis)`
    `Dataset` | AbstractDataset.`product(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`product(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | AbstractDataset.`product(int axis, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`product(int axis, boolean... ignoreInvalids)`
    `static Dataset[]` | Stats.`quantile(Dataset a, int axis, double... values)` Calculate quantiles of dataset which is defined as the inverse of the cumulative distribution function (CDF)
    `static Dataset` | Maths.`reciprocal(Object a)` Find reciprocal from dataset
    `static Dataset` | Maths.`reciprocal(Object a, Dataset o)` Find reciprocal from dataset
    `static Dataset` | InterpolatorUtils.`regrid(Dataset data, Dataset x, Dataset y, Dataset gridX, Dataset gridY)`
    `static Dataset` | InterpolatorUtils.`regridOld(Dataset data, Dataset x, Dataset y, Dataset gridX, Dataset gridY)`
    `static Dataset` | Maths.`remainder(Object a, Object b)` remainder operator
    `static Dataset` | Maths.`remainder(Object a, Object b, Dataset o)` remainder operator
    `static Dataset` | InterpolatorUtils.`remap1D(Dataset dataset, Dataset axis, Dataset outputAxis)`
    `static Dataset` | InterpolatorUtils.`remapAxis(Dataset dataset, int axisIndex, Dataset originalAxisForCorrection, Dataset outputAxis)`
    `static Dataset` | InterpolatorUtils.`remapOneAxis(Dataset dataset, int axisIndex, Dataset corrections, Dataset originalAxisForCorrection, Dataset outputAxis)`
    `Dataset` | AbstractDataset.`reshape(int... shape)`
    `Dataset` | Dataset.`reshape(int... shape)` Returns new dataset with new shape but old data if possible, otherwise a copy is made
    `static Dataset` | Maths.`rightShift(Object a, Object b)` rightShift operator
    `static Dataset` | Maths.`rightShift(Object a, Object b, Dataset o)` rightShift operator
    `static Dataset` | Maths.`rint(Object a)` rint - round each element of the dataset
    `static Dataset` | Maths.`rint(Object a, Dataset o)` rint - round each element of the dataset
    `Dataset` | AbstractDataset.`rootMeanSquare(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`rootMeanSquare(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | AbstractDataset.`rootMeanSquare(int axis, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`rootMeanSquare(int axis, boolean... ignoreInvalids)`
    `static Dataset` | DatasetUtils.`select(BooleanDataset[] conditions, Object[] choices, Object def)` Select content from choices where condition is true, otherwise use default.
    `static Dataset` | DatasetUtils.`select(BooleanDataset condition, Object x, Object y)` Select content according where condition is true.
    `static Dataset` | InterpolatorUtils.`selectDatasetRegion(Dataset dataset, int x, int y, int xSize, int ySize)`
    `Dataset` | Dataset.`setBy1DIndex(Object obj, Dataset index)` This is modelled after the NumPy set item with an index dataset
    `Dataset` | Dataset.`setByBoolean(Object obj, Dataset selection)` This is modelled after the NumPy set item with a condition specified by a boolean dataset
    `Dataset` | Dataset.`setByIndexes(Object obj, Object... indexes)` This is modelled after the NumPy set item with an array of indexing objects
    `Dataset` | Dataset.`setSlice(Object obj, IndexIterator iterator)`
    `Dataset` | AbstractDataset.`setSlice(Object obj, int[] start, int[] stop, int[] step)`
    `Dataset` | Dataset.`setSlice(Object obj, int[] start, int[] stop, int[] step)` This is modelled after the NumPy array slice
    `Dataset` | AbstractDataset.`setSlice(Object obj, Slice... slice)`
    `Dataset` | Dataset.`setSlice(Object obj, Slice... slice)` This is modelled after the NumPy array slice
    `Dataset` | AbstractDataset.`setSlice(Object obj, SliceND slice)`
    `Dataset` | Dataset.`setSlice(Object obj, SliceND slice)` This is modelled after the NumPy array slice
    `static Dataset` | Maths.`signum(Object a)` signum - sign of each element
    `static Dataset` | Maths.`signum(Object a, Dataset o)` signum - sign of each element
    `static Dataset` | Maths.`sin(Object a)` sin - evaluate the sine function on each element of the dataset
    `static Dataset` | Maths.`sin(Object a, Dataset o)` sin - evaluate the sine function on each element of the dataset
    `static Dataset` | Maths.`sinh(Object a)` sinh - evaluate the hyperbolic sine function on each element of the dataset
    `static Dataset` | Maths.`sinh(Object a, Dataset o)` sinh - evaluate the hyperbolic sine function on each element of the dataset
    `static Dataset` | Stats.`skewness(Dataset a, int axis, boolean... ignoreInvalids)`
    `static Dataset` | DatasetUtils.`sliceAndConvertLazyDataset(ILazyDataset lazy)` Slice (or fully load), if necessary, a lazy dataset, otherwise take a slice view and convert to our dataset implementation.
    `static Dataset` | LinearAlgebra.`solve(Dataset a, Dataset v)` Solve linear matrix equation `A x = v`
    `static Dataset` | LinearAlgebra.`solveSVD(Dataset a, Dataset v)` Solve least squares matrix equation `A x = v` by SVD
    `Dataset` | Dataset.`sort(Integer axis)` In-place sort of dataset
    `static Dataset` | Maths.`sqrt(Object a)` sqrt - evaluate the square root function on each element of the dataset
    `static Dataset` | Maths.`sqrt(Object a, Dataset o)` sqrt - evaluate the square root function on each element of the dataset
    `static Dataset` | Maths.`square(Object a)` square - square each element
    `static Dataset` | Maths.`square(Object a, Dataset o)` square - square each element
    `Dataset` | AbstractDataset.`squeeze()`
    `Dataset` | Dataset.`squeeze()`
    `Dataset` | AbstractDataset.`squeeze(boolean onlyFromEnds)`
    `Dataset` | Dataset.`squeeze(boolean onlyFromEnds)`
    `Dataset` | AbstractDataset.`squeezeEnds()`
    `Dataset` | Dataset.`squeezeEnds()`
    `Dataset` | AbstractDataset.`stdDeviation(int axis)`
    `Dataset` | Dataset.`stdDeviation(int axis)` Standard deviation is square root of the variance
    `Dataset` | AbstractDataset.`stdDeviation(int[] axes)`
    `Dataset` | Dataset.`stdDeviation(int[] axes)` Standard deviation is square root of the variance
    `Dataset` | AbstractDataset.`stdDeviation(int[] axes, boolean isWholePopulation, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`stdDeviation(int[] axes, boolean isWholePopulation, boolean... ignoreInvalids)` Standard deviation is square root of the variance
    `Dataset` | AbstractDataset.`stdDeviation(int axis, boolean isWholePopulation, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`stdDeviation(int axis, boolean isWholePopulation, boolean... ignoreInvalids)` Standard deviation is square root of the variance
    `static Dataset` | Maths.`subtract(Object a, Object b)` subtract operator
    `static Dataset` | Maths.`subtract(Object a, Object b, Dataset o)` subtract operator
    `static Dataset` | LazyMaths.`sum(ILazyDataset data, boolean ignore, int... axes)`
    `static Dataset` | LazyMaths.`sum(ILazyDataset data, int... ignoreAxes)`
    `static Dataset` | LazyMaths.`sum(ILazyDataset data, int axis)`
    `Dataset` | AbstractDataset.`sum(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`sum(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | AbstractDataset.`sum(int axis, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`sum(int axis, boolean... ignoreInvalids)`
    `static Dataset` | DatasetUtils.`swapAxes(IDataset a, int axis1, int axis2)` Swap two axes in dataset
    `Dataset` | AbstractDataset.`swapAxes(int axis1, int axis2)`
    `Dataset` | Dataset.`swapAxes(int axis1, int axis2)` Swap two axes in dataset
    `Dataset` | AbstractDataset.`synchronizedCopy()`
    `Dataset` | Dataset.`synchronizedCopy()` This is a synchronized version of the clone method
    `static Dataset` | Maths.`tan(Object a)` tan - evaluate the tangent function on each element of the dataset
    `static Dataset` | Maths.`tan(Object a, Dataset o)` tan - evaluate the tangent function on each element of the dataset
    `static Dataset` | Maths.`tanh(Object a)` tanh - evaluate the tangent hyperbolic function on each element of the dataset
    `static Dataset` | Maths.`tanh(Object a, Dataset o)` tanh - evaluate the tangent hyperbolic function on each element of the dataset
    `static Dataset` | LinearAlgebra.`tensorDotProduct(Dataset a, Dataset b, int[] axisa, int[] axisb)` Calculate the tensor dot product over given axes.
    `static Dataset` | LinearAlgebra.`tensorDotProduct(Dataset a, Dataset b, int axisa, int axisb)` Calculate the tensor dot product over given axes.
    `static Dataset` | DatasetUtils.`tile(IDataset a, int... reps)` Construct a dataset that contains the original dataset repeated the number of times in each axis given by corresponding entries in the reps array
    `static Dataset` | Maths.`toDegrees(Object a)` toDegrees - convert to degrees
    `static Dataset` | Maths.`toDegrees(Object a, Dataset o)` toDegrees - convert to degrees
    `static Dataset` | Maths.`toRadians(Object a)` toRadians - convert to radians
    `static Dataset` | Maths.`toRadians(Object a, Dataset o)` toRadians - convert to radians
    `static Dataset` | LinearAlgebra.`trace(Dataset a)` Calculate trace of dataset - sum of values over 1st axis and 2nd axis
    `static Dataset` | LinearAlgebra.`trace(Dataset a, int offset, int axis1, int axis2)` Calculate trace of dataset - sum of values over axis1 and axis2 where axis2 is offset
    `static Dataset` | DatasetUtils.`transpose(IDataset a, int... axes)` Permute copy of dataset's axes so that given order is old order:
    `Dataset` | AbstractDataset.`transpose(int... axes)`
    `Dataset` | Dataset.`transpose(int... axes)` See `getTransposedView(int...)`
    `static Dataset` | Maths.`truncate(Object a)` truncate - truncate each element to integers of the dataset
    `static Dataset` | Maths.`truncate(Object a, Dataset o)` truncate - truncate each element to integers of the dataset
    `static Dataset` | Stats.`typedProduct(Dataset a, int dtype, int[] axes, boolean... ignoreInvalids)` Deprecated. Please use the class-based method `Stats.typedProduct(Class, Dataset, int[], boolean...)`
    `static Dataset` | Stats.`typedProduct(Dataset a, int dtype, int axis, boolean... ignoreInvalids)` Deprecated. Please use the class-based method `Stats.typedProduct(Class, Dataset, int, boolean...)`
    `static Dataset` | Stats.`typedSum(Dataset a, int dtype, int axis, boolean... ignoreInvalids)` Deprecated. Please use the class-based method `Stats.typedSum(Class, Dataset, int, boolean...)`
    `static Dataset` | Maths.`unsignedRightShift(Object a, Object b)` unsignedRightShift operator
    `static Dataset` | Maths.`unsignedRightShift(Object a, Object b, Dataset o)` unsignedRightShift operator
    `static Dataset` | Maths.`upperClip(Object a, Dataset o, Object pa)` upperClip - clip elements to upper limit
    `static Dataset` | Maths.`upperClip(Object a, Object pa)` upperClip - clip elements to upper limit
    `Dataset` | AbstractDataset.`variance(int axis)`
    `Dataset` | Dataset.`variance(int axis)`
    `Dataset` | AbstractDataset.`variance(int[] axes)`
    `Dataset` | Dataset.`variance(int[] axes)`
    `Dataset` | AbstractDataset.`variance(int[] axes, boolean isWholePopulation, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`variance(int[] axes, boolean isWholePopulation, boolean... ignoreInvalids)`
    `Dataset` | AbstractDataset.`variance(int axis, boolean isWholePopulation, boolean... ignoreInvalids)`
    `Dataset` | Dataset.`variance(int axis, boolean isWholePopulation, boolean... ignoreInvalids)`
    `static Dataset` | DatasetFactory.`zeros(Dataset dataset, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.zeros(Dataset, Class)`
    `static Dataset` | DatasetFactory.`zeros(int[] shape, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.zeros(Class, int...)`
    `static Dataset` | DatasetFactory.`zeros(int itemSize, int[] shape, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.zeros(int, Class, int...)`

    Methods in org.eclipse.january.dataset that return types with arguments of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `static Class<? extends Dataset>` | InterfaceUtils.`findSubInterface(Class<? extends Dataset> clazz)` Find sub-interface of Dataset
    `static Class<? extends Dataset>` | InterfaceUtils.`getBestFloatInterface(Class<? extends Dataset> clazz)` Find floating point dataset interface that best fits given types.
    `static Class<? extends Dataset>` | InterfaceUtils.`getBestInterface(Class<? extends Dataset> a, Class<? extends Dataset> b)` Find dataset interface that best fits given classes.
    `static Class<? extends Dataset>` | InterfaceUtils.`getElementalInterface(Class<? extends Dataset> clazz)`
    `Class<? extends Dataset>` | LazyDataset.`getInterface()`
    `static Class<? extends Dataset>` | DTypeUtils.`getInterface(int dtype)`
    `static Class<? extends Dataset>` | InterfaceUtils.`getInterface(Object obj)` Get dataset interface from an object.
    `static Class<? extends Dataset>` | InterfaceUtils.`getInterfaceFromClass(int elementsPerItem, Class<?> elementClass)`
    `static Class<? extends Dataset>` | DTypeUtils.`getLargestDataset(Class<? extends Dataset> clazz)` The largest dataset class suitable for a summation of around a few thousand items without changing from the "kind" of dataset
    `static Class<? extends Dataset>` | InterfaceUtils.`getLargestInterface(Dataset a)` The largest dataset type suitable for a summation of around a few thousand items without changing from the "kind" of dataset
    `static List<Dataset>` | Maths.`gradient(Dataset y, Dataset... x)` Calculate gradient (or partial derivatives) by central difference
    `static List<Dataset>` | DatasetUtils.`meshGrid(Dataset... axes)` Construct a list of datasets where each represents a coordinate varying over the hypergrid formed by the input list of axes
    `static List<Dataset>` | DatasetUtils.`split(Dataset a, int[] indices, int axis)` Split a dataset into parts along given axis
    `static List<Dataset>` | DatasetUtils.`split(Dataset a, int sections, int axis, boolean checkEqual)` Split a dataset into equal sections along given axis

    Methods in org.eclipse.january.dataset with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `static Dataset` | Maths.`abs(Object a, Dataset o)` abs - absolute value of each element
    `static Dataset` | Maths.`add(Object a, Object b, Dataset o)` add operator
    `protected static void` | Maths.`addBinaryOperatorName(Dataset a, Dataset b, Dataset dataset, String oname)`
    `protected static void` | Maths.`addFunctionName(Dataset a, Dataset b, Dataset dataset, String fname)`
    `protected static void` | Maths.`addFunctionName(Dataset dataset, String fname)`
    `static Dataset` | Maths.`angle(Object a, boolean inDegrees, Dataset o)` Create a dataset of the arguments from a complex dataset
    `static Dataset` | Maths.`angle(Object a, Dataset o)` Create a dataset of the arguments from a complex dataset
    `static Dataset` | Maths.`arccos(Object a, Dataset o)` arccos - evaluate the inverse cosine function on each element of the dataset
    `static Dataset` | Maths.`arccosh(Object a, Dataset o)` arccosh - evaluate the inverse hyperbolic cosine function on each element of the dataset
    `static Dataset` | Maths.`arcsin(Object a, Dataset o)` arcsin - evaluate the inverse sine function on each element of the dataset
    `static Dataset` | Maths.`arcsinh(Object a, Dataset o)` arcsinh - evaluate the inverse hyperbolic sine function on each element of the dataset
    `static Dataset` | Maths.`arctan(Object a, Dataset o)` arctan - evaluate the inverse tangent function on each element of the dataset
    `static Dataset` | Maths.`arctan2(Object a, Object b, Dataset o)`
    `static Dataset` | Maths.`arctanh(Object a, Dataset o)` arctanh - evaluate the inverse hyperbolic tangent function on each element of the dataset
    `static Object` | Stats.`averageDeviation(Dataset a)`
    `static Dataset` | Maths.`bitwiseAnd(Object a, Object b, Dataset o)` bitwiseAnd operator
    `static Dataset` | Maths.`bitwiseInvert(Object a, Dataset o)` bitwiseInvert - `~a`, bitwise invert (or NOT) each element
    `static Dataset` | Maths.`bitwiseOr(Object a, Object b, Dataset o)` bitwiseOr operator
    `static Dataset` | Maths.`bitwiseXor(Object a, Object b, Dataset o)` bitwiseXor operator
    `static Dataset` | LinearAlgebra.`calcCholeskyDecomposition(Dataset a)` Calculate Cholesky decomposition `A = L L^T`
    `static double` | LinearAlgebra.`calcConditionNumber(Dataset a)` Calculate condition number of matrix by singular value decomposition method
    `static Dataset` | LinearAlgebra.`calcConjugateGradient(Dataset a, Dataset v)` Calculation `A x = v` by conjugate gradient method with the stopping criterion being that the estimated residual `r = v - A x` satisfies `||r|| < ||v||` with maximum of 100 iterations
    `static Dataset` | LinearAlgebra.`calcConjugateGradient(Dataset a, Dataset v, int maxIterations, double delta)` Calculation `A x = v` by conjugate gradient method with the stopping criterion being that the estimated residual `r = v - A x` satisfies `||r|| < delta ||v||`
    `static double` | LinearAlgebra.`calcDeterminant(Dataset a)`
    `static Dataset[]` | LinearAlgebra.`calcEigenDecomposition(Dataset a)` Calculate eigen-decomposition `A = V D V^T`
    `static Dataset` | LinearAlgebra.`calcEigenvalues(Dataset a)`
    `static Dataset` | LinearAlgebra.`calcInverse(Dataset a)` Calculate inverse of square dataset
    `static Dataset[]` | LinearAlgebra.`calcLUDecomposition(Dataset a)` Calculate LU decomposition `A = P^-1 L U`
    `static int` | LinearAlgebra.`calcMatrixRank(Dataset a)` Calculate matrix rank by singular value decomposition method
    `static List<IntegerDataset>` | DatasetUtils.`calcPositionsFromIndexes(Dataset indices, int[] shape)` Calculate positions in given shape from a dataset of 1-D indexes
    `static Dataset` | LinearAlgebra.`calcPseudoInverse(Dataset a)` Calculate (Moore-Penrose) pseudo-inverse
    `static Dataset[]` | LinearAlgebra.`calcQRDecomposition(Dataset a)` Calculate QR decomposition `A = Q R`
    `static Dataset[]` | LinearAlgebra.`calcSingularValueDecomposition(Dataset a)` Calculate singular value decomposition `A = U S V^T`
    `static double[]` | LinearAlgebra.`calcSingularValues(Dataset a)`
    `static CompoundDataset` | DatasetUtils.`cast(Class<? extends Dataset> clazz, Dataset... a)` Cast array of datasets to a compound dataset
    `static CompoundDataset` | DatasetUtils.`cast(Dataset[] a, int dtype)` Deprecated. Please use the class-based methods in DatasetUtils, such as `DatasetUtils.cast(Class, Dataset...)`
    `static Dataset` | Maths.`cbrt(Object a, Dataset o)` cbrt - evaluate the cube root function on each element of the dataset
    `static Dataset` | Maths.`ceil(Object a, Dataset o)` ceil - evaluate the ceiling function on each element of the dataset
    `static Dataset` | Maths.`centralDifference(Dataset a, int axis)` Discrete difference of dataset along axis using finite central difference
    `static double[]` | DatasetUtils.`centroid(Dataset a, Dataset... bases)` Get the centroid value of a dataset, this function works out the centroid in every direction
    `static double[]` | DatasetUtils.`centroid(Dataset a, Dataset... bases)` Get the centroid value of a dataset, this function works out the centroid in every direction
    `static Dataset` | Maths.`clip(Object a, Dataset o, Object pa, Object pb)` clip - clip elements to limits
    `static Dataset` | DatasetUtils.`coerce(Dataset a, Object obj)` Create a copy that has been coerced to an appropriate dataset type depending on the input object's class
    `static <T extends CompoundDataset> T` | DatasetUtils.`compoundCast(Class<T> clazz, Dataset... a)` Cast array of datasets to a compound dataset
    `static Dataset` | Maths.`conjugate(Object a, Dataset o)`
    `void` | CompoundByteDataset.`copyElements(Dataset destination, int element)`
    `void` | CompoundDataset.`copyElements(Dataset destination, int element)` Copy chosen elements from each item to another dataset
    `void` | CompoundDoubleDataset.`copyElements(Dataset destination, int element)`
    `void` | CompoundFloatDataset.`copyElements(Dataset destination, int element)`
    `void` | CompoundIntegerDataset.`copyElements(Dataset destination, int element)`
    `void` | CompoundLongDataset.`copyElements(Dataset destination, int element)`
    `void` | CompoundShortDataset.`copyElements(Dataset destination, int element)`
    `void` | BooleanDatasetBase.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | ByteDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | CompoundByteDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | CompoundDoubleDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | CompoundFloatDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | CompoundIntegerDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | CompoundLongDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | CompoundShortDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | Dataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)` Copy content from axes in given position to array
    `void` | DoubleDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | FloatDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | IntegerDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | LongDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | ObjectDatasetBase.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | ShortDataset.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `void` | StringDatasetBase.`copyItemsFromAxes(int[] pos, boolean[] axes, Dataset dest)`
    `protected static void` | AbstractDataset.`copyToView(Dataset orig, AbstractDataset view, boolean clone, boolean cloneMetadata)` Copy fields from original to view
    `static Dataset` | Maths.`cos(Object a, Dataset o)` cos - evaluate the cosine function on each element of the dataset
    `static Dataset` | Maths.`cosh(Object a, Dataset o)` cosh - evaluate the hyperbolic cosine function on each element of the dataset
    `static Dataset` | Stats.`covariance(Dataset a)` See `Stats.covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof)` with b = null, rowvar = true, bias = false and ddof = null.
    `static Dataset` | Stats.`covariance(Dataset a, boolean rowvar, boolean bias, Integer ddof)` See `Stats.covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof)` with b = null.
    `static Dataset` | Stats.`covariance(Dataset a, Dataset b)` See `Stats.covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof)` with b = null, rowvar = true, bias = false and ddof = null
    `static Dataset` | Stats.`covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof)` Calculate the covariance matrix (array) of a concatenated with b.
    `static int[]` | BroadcastUtils.`createBroadcastStrides(Dataset a, int[] broadcastShape)` Create a stride array from a dataset to a broadcast shape
    `static <T extends CompoundDataset> T` | DatasetUtils.`createCompoundDataset(Class<T> clazz, Dataset... datasets)` Create a compound dataset from copying given datasets
    `static CompoundDataset` | DatasetUtils.`createCompoundDataset(Dataset... datasets)` Create a compound dataset from given datasets
    `static CompoundDataset` | DatasetUtils.`createCompoundDataset(Dataset dataset, int itemSize)` Create a compound dataset from given dataset, sharing data
    `static CompoundDataset` | DatasetUtils.`createCompoundDataset(int dtype, Dataset... datasets)` Deprecated. Please use the class-based methods in DatasetUtils, such as `DatasetUtils.createCompoundDataset(Class, Dataset...)`
    `static CompoundDataset` | DatasetUtils.`createCompoundDatasetFromLastAxis(Dataset a, boolean shareData)` Create a compound dataset by using last axis as elements of an item
    `static CompoundByteDataset` | CompoundByteDataset.`createCompoundDatasetWithLastDimension(Dataset a, boolean shareData)` Create a compound dataset using last dimension of given dataset
    `static CompoundDoubleDataset` | CompoundDoubleDataset.`createCompoundDatasetWithLastDimension(Dataset a, boolean shareData)` Create a compound dataset using last dimension of given dataset
    `static CompoundFloatDataset` | CompoundFloatDataset.`createCompoundDatasetWithLastDimension(Dataset a, boolean shareData)` Create a compound dataset using last dimension of given dataset
    `static CompoundIntegerDataset` | CompoundIntegerDataset.`createCompoundDatasetWithLastDimension(Dataset a, boolean shareData)` Create a compound dataset using last dimension of given dataset
    `static CompoundLongDataset` | CompoundLongDataset.`createCompoundDatasetWithLastDimension(Dataset a, boolean shareData)` Create a compound dataset using last dimension of given dataset
    `static CompoundShortDataset` | CompoundShortDataset.`createCompoundDatasetWithLastDimension(Dataset a, boolean shareData)` Create a compound dataset using last dimension of given dataset
    `static RGBByteDataset` | RGBByteDataset.`createFromHSL(Dataset hue, Dataset saturation, Dataset lightness)` Create a RGB dataset from hue, saturation and lightness dataset
    `static RGBDataset` | RGBDataset.`createFromHSL(Dataset hue, Dataset saturation, Dataset lightness)` Create a RGB dataset from hue, saturation and lightness dataset
    `static RGBByteDataset` | RGBByteDataset.`createFromHSV(Dataset hue, Dataset saturation, Dataset value)` Create a RGB dataset from hue, saturation and value dataset
    `static RGBDataset` | RGBDataset.`createFromHSV(Dataset hue, Dataset saturation, Dataset value)` Create a RGB dataset from hue, saturation and value dataset
    `static BooleanIterator` | BooleanIterator.`createIterator(boolean v, Dataset a, Dataset c, Dataset o)` Create a boolean iterator that stops at every position in the choice dataset where its value matches the given boolean
    `static BooleanIterator` | BooleanIterator.`createIterator(boolean v, Dataset a, Dataset c, Dataset o, boolean createIfNull)` Create a boolean iterator that stops at every position in the choice dataset where its value matches the given boolean
    `static BooleanIterator` | BooleanIterator.`createIterator(Dataset a, Dataset c)` Create a boolean iterator that stops at every position in the choice dataset with a true value
    `static BroadcastIterator` | BroadcastIterator.`createIterator(Dataset a, Dataset b)`
    `static BroadcastSelfIterator` | BroadcastSelfIterator.`createIterator(Dataset a, Dataset b)`
    `static BooleanIterator` | BooleanIterator.`createIterator(Dataset a, Dataset c, Dataset o)` Create a boolean iterator that stops at every position in the choice dataset with a true value
    `static BroadcastIterator` | BroadcastIterator.`createIterator(Dataset a, Dataset b, Dataset o)`
    `static BroadcastIterator` | BroadcastIterator.`createIterator(Dataset a, Dataset b, Dataset o, boolean createIfNull)`
    `static Object` | DatasetUtils.`createJavaArray(Dataset a)` Create Java array (of arrays) from dataset
    `static LazyDataset` | LazyDataset.`createLazyDataset(Dataset dataset)` Create a lazy dataset based on in-memory data (handy for testing)
    `static LazyWriteableDataset` | LazyWriteableDataset.`createLazyDataset(Dataset dataset)` Create a lazy writeable dataset based on in-memory data (handy for testing)
    `static LazyWriteableDataset` | LazyWriteableDataset.`createLazyDataset(Dataset dataset, int[] maxShape)` Create a lazy writeable dataset based on in-memory data (handy for testing)
    `static int[]` | AbstractDataset.`createStrides(Dataset a, int[] offset)` Create a stride array from dataset
    `static int[]` | AbstractDataset.`createStrides(SliceND slice, Dataset a, int[] stride, int[] offset)` Create a stride array from slice information and a dataset
    `static List<Double>` | DatasetUtils.`crossings(Dataset xAxis, Dataset yAxis, double yValue)` Find x values of all the crossing points of the dataset with the given y value
    `static List<Double>` | DatasetUtils.`crossings(Dataset xAxis, Dataset yAxis, double yValue, double xRangeProportion)` Function that uses the crossings function but prunes the result, so that multiple crossings within a certain proportion of the overall range of the x values
    `static List<Double>` | DatasetUtils.`crossings(Dataset d, double value)` Find linearly-interpolated crossing points where the given dataset crosses the given value
    `static Dataset` | LinearAlgebra.`crossProduct(Dataset a, Dataset b)` Calculate the cross product of two datasets.
    `static Dataset` | LinearAlgebra.`crossProduct(Dataset a, Dataset b, int axisA, int axisB, int axisC)` Calculate the cross product of two datasets.
    `static Dataset` | Stats.`cumulativeProduct(Dataset a, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`cumulativeProduct(Dataset a, int axis, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`cumulativeSum(Dataset a, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`cumulativeSum(Dataset a, int axis, boolean... ignoreInvalids)`
    `static Dataset` | Maths.`derivative(Dataset x, Dataset y, int n)` Calculates the derivative of a line described by two datasets (x,y) given a spread of n either side of the point
    `static Dataset` | Maths.`difference(Dataset a, int n, int axis)` Discrete difference of dataset along axis using finite difference
    `static Dataset` | Maths.`divide(Object a, Object b, Dataset o)` divide operator
    `static Dataset` | Maths.`divideTowardsFloor(Object a, Object b, Dataset o)` divideTowardsFloor operator
    `static Dataset` | Maths.`dividez(Object a, Object b, Dataset o)` dividez operator
    `static Dataset` | LinearAlgebra.`dotProduct(Dataset a, Dataset b)` Calculate the dot product of two datasets.
    `static Dataset` | Maths.`exp(Object a, Dataset o)` exp - evaluate the exponential function on each element of the dataset
    `static Dataset` | Maths.`expm1(Object a, Dataset o)` expm1 - evaluate the exponential function - 1 on each element of the dataset
    `void` | BooleanDatasetBase.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | ByteDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | CompoundByteDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | CompoundDoubleDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | CompoundFloatDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | CompoundIntegerDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | CompoundLongDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | CompoundShortDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | Dataset.`fillDataset(Dataset other, IndexIterator iter)` Populate another dataset with part of current dataset
    `void` | DoubleDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | FloatDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | IntegerDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | LongDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | ObjectDatasetBase.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | ShortDataset.`fillDataset(Dataset result, IndexIterator iter)`
    `void` | StringDatasetBase.`fillDataset(Dataset result, IndexIterator iter)`
    `static IntegerDataset` | DatasetUtils.`findFirstOccurrences(Dataset a, Dataset values)` Find first occurrences in one dataset of values given in another sorted dataset
    `static int` | DatasetUtils.`findIndexEqualTo(Dataset a, double n)` Find absolute index of first value in dataset that is equal to given number
    `static IntegerDataset` | DatasetUtils.`findIndexesForValues(Dataset a, Dataset values)` Find indexes in sorted dataset of values for each value in other dataset
    `static int` | DatasetUtils.`findIndexGreaterThan(Dataset a, double n)` Find absolute index of first value in dataset that is greater than given number
    `static int` | DatasetUtils.`findIndexGreaterThanOrEqualTo(Dataset a, double n)` Find absolute index of first value in dataset that is greater than or equal to given number
    `static int` | DatasetUtils.`findIndexLessThan(Dataset a, double n)` Find absolute index of first value in dataset that is less than given number
    `static int` | DatasetUtils.`findIndexLessThanOrEqualTo(Dataset a, double n)` Find absolute index of first value in dataset that is less than or equal to given number
    `static Dataset` | Maths.`floor(Object a, Dataset o)` floor - evaluate the floor function on each element of the dataset
    `static Dataset` | Maths.`floorDivide(Object a, Object b, Dataset o)`
    `static Dataset` | Maths.`floorRemainder(Object a, Object b, Dataset o)`
    `BooleanIterator` | AbstractDataset.`getBooleanIterator(Dataset choice)`
    `BooleanIterator` | Dataset.`getBooleanIterator(Dataset choice)` Get an iterator that visits every item in this dataset where the corresponding item in choice dataset is true
    `BooleanIterator` | AbstractDataset.`getBooleanIterator(Dataset choice, boolean value)`
    `BooleanIterator` | Dataset.`getBooleanIterator(Dataset choice, boolean value)` Get an iterator that visits every item in this dataset where the corresponding item in choice dataset is given by value
    `CompoundDataset` | AbstractCompoundDataset.`getByBoolean(Dataset selection)`
    `Dataset` | AbstractDataset.`getByBoolean(Dataset selection)`
    `CompoundDataset` | CompoundDataset.`getByBoolean(Dataset selection)`
    `Dataset` | Dataset.`getByBoolean(Dataset selection)` This is modelled after the NumPy get item with a condition specified by a boolean dataset
    `static String` | DTypeUtils.`getDatasetName(Dataset a)`
    `static String` | DTypeUtils.`getDTypeName(Dataset a)`
    `static Class<? extends Dataset>` | InterfaceUtils.`getLargestInterface(Dataset a)` The largest dataset type suitable for a summation of around a few thousand items without changing from the "kind" of dataset
    `static List<Dataset>` | Maths.`gradient(Dataset y, Dataset... x)` Calculate gradient (or partial derivatives) by central difference
    `static List<Dataset>` | Maths.`gradient(Dataset y, Dataset... x)` Calculate gradient (or partial derivatives) by central difference
    `static double` | Outliers.`highMed(Dataset data)` Returns the himed
    `static Dataset` | Maths.`hypot(Object a, Object b, Dataset o)`
    `static IntegerDataset` | DatasetUtils.`indexSort(Dataset a, Integer axis)` Indirectly sort along given axis
    `static double` | Maths.`interpolate(Dataset d, Dataset m, double... x)` Linearly interpolate a value at a point in an n-D dataset with a mask.
    `static double` | Maths.`interpolate(Dataset d, Dataset m, double x0)` Linearly interpolate a value at a point in a 1D dataset with a mask.
    `static double` | Maths.`interpolate(Dataset d, Dataset m, double x0, double x1)` Linearly interpolate a value at a point in a 2D dataset with a mask.
    `static Dataset` | Maths.`interpolate(Dataset x, Dataset d, IDataset x0, Number left, Number right)` Linearly interpolate values at points in a 1D dataset corresponding to given coordinates.
    `static double` | Maths.`interpolate(Dataset d, double... x)` Linearly interpolate a value at a point in an n-D dataset.
    `static double` | Maths.`interpolate(Dataset d, double x0)` Linearly interpolate a value at a point in a 1D dataset.
    `static double` | Maths.`interpolate(Dataset d, double x0, double x1)` Linearly interpolate a value at a point in a 2D dataset.
    `static Object` | Stats.`iqr(Dataset a)` Interquartile range: Q3 - Q1
    `static Dataset` | Stats.`iqr(Dataset a, int axis)` Interquartile range: Q3 - Q1
    `static Dataset` | LinearAlgebra.`kroneckerProduct(Dataset a, Dataset b)` Create the Kronecker product as defined by `kron[k0,...,kN] = a[i0,...,iN] * b[j0,...,jN]` where `kn = sn * in + jn` for `n = 0...N` and `s` is shape of `b`
    `static Object` | Stats.`kurtosis(Dataset a, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`kurtosis(Dataset a, int axis, boolean... ignoreInvalids)`
    `static Dataset` | Maths.`leftShift(Object a, Object b, Dataset o)` leftShift operator
    `static Dataset` | DatasetUtils.`lnnorm(Dataset a)` Function that returns a normalised dataset which is bounded between 0 and 1 and has been distributed on a natural log scale
    `static Dataset` | Maths.`log(Object a, Dataset o)` log - evaluate the logarithm function on each element of the dataset
    `static Dataset` | Maths.`log10(Object a, Dataset o)` log10 - evaluate the logarithm function on each element of the dataset
    `static Dataset` | Maths.`log1p(Object a, Dataset o)` log1p - evaluate the logarithm function of 1 plus on each element of the dataset
    `static Dataset` | Maths.`log2(Object a, Dataset o)` log2 - evaluate the logarithm function on each element of the dataset
    `static Dataset` | DatasetUtils.`lognorm(Dataset a)` Function that returns a normalised dataset which is bounded between 0 and 1 and has been distributed on a log10 scale
    `static Dataset` | Maths.`lowerClip(Object a, Dataset o, Object pa)` lowerClip - clip elements to lower limit
    `static double` | Outliers.`lowMed(Dataset data)` Returns the lomed
    `static void` | DatasetUtils.`makeFinite(Dataset a)` Make floating point datasets contain only finite values.
    `static Dataset` | Maths.`maximum(Object a, Object b, Dataset o)` maximum operator
    `static Object` | Stats.`median(Dataset a)`
    `static Dataset` | Stats.`median(Dataset a, int axis)`
    `static double[]` | Outliers.`medianAbsoluteDeviation(Dataset data)` Returns the Median Absolute Deviation (MAD) and the median.
    `static List<Dataset>` | DatasetUtils.`meshGrid(Dataset... axes)` Construct a list of datasets where each represents a coordinate varying over the hypergrid formed by the input list of axes
    `static Dataset` | Maths.`minimum(Object a, Object b, Dataset o)` minimum operator
    `static Dataset` | Maths.`multiply(Object a, Object b, Dataset o)` multiply operator
    `static Dataset` | Maths.`negative(Object a, Dataset o)` negative - negative value of each element
    `static List<IntegerDataset>` | Comparisons.`nonZero(Dataset a)` Create a list of indices of positions where items are non-zero
    `static Dataset` | DatasetUtils.`norm(Dataset a)` Function that returns a normalised dataset which is bounded between 0 and 1
    `static double` | LinearAlgebra.`norm(Dataset a)`
    `static double` | LinearAlgebra.`norm(Dataset a, double p)`
    `static double` | LinearAlgebra.`norm(Dataset a, LinearAlgebra.NormOrder order)`
    `static <T extends Dataset> T` | DatasetFactory.`ones(Dataset dataset, Class<T> clazz)`
    `static Dataset` | DatasetFactory.`ones(Dataset dataset, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.ones(Dataset, Class)`
    `static Dataset` | Operations.`operate(BinaryOperation op, Object a, Object b, Dataset o)` Operate on a dataset
    `static Dataset` | Operations.`operate(UnaryOperation op, Object a, Dataset o)` Operate on a dataset
    `static Dataset` | LinearAlgebra.`outerProduct(Dataset a, Dataset b)` Calculate the outer product of two datasets
    `static double[]` | Stats.`outlierValues(Dataset a, Dataset mask, boolean value, double lo, double hi, int length)` Calculate approximate outlier values.
    `static double[]` | Stats.`outlierValues(Dataset a, double lo, double hi, int length)` Calculate approximate outlier values.
    `static Dataset` | Maths.`phaseAsComplexNumber(Object a, Dataset o, boolean keepZeros)` Create a phase only dataset.
    `static Dataset` | LinearAlgebra.`power(Dataset a, int n)` Raise dataset to given power by matrix multiplication
    `static Dataset` | Maths.`power(Object a, Object b, Dataset o)` power operator
    `static Object` | Stats.`product(Dataset a, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`product(Dataset a, int[] axes, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`product(Dataset a, int axis, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | DatasetUtils.`put(T a, Dataset indices, Object values)` Changes specific items of dataset by replacing them with other array
    `static double[]` | Stats.`quantile(Dataset a, double... values)` Calculate quantiles of dataset which is defined as the inverse of the cumulative distribution function (CDF)
    `static double` | Stats.`quantile(Dataset a, double q)` Calculate quantile of dataset which is defined as the inverse of the cumulative distribution function (CDF)
    `static Dataset[]` | Stats.`quantile(Dataset a, int axis, double... values)` Calculate quantiles of dataset which is defined as the inverse of the cumulative distribution function (CDF)
    `static Dataset` | Maths.`reciprocal(Object a, Dataset o)` Find reciprocal from dataset
    `static Dataset` | InterpolatorUtils.`regrid(Dataset data, Dataset x, Dataset y, Dataset gridX, Dataset gridY)`
    `static Dataset` | InterpolatorUtils.`regridOld(Dataset data, Dataset x, Dataset y, Dataset gridX, Dataset gridY)`
    `static Dataset` | Maths.`remainder(Object a, Object b, Dataset o)` remainder operator
    `static Dataset` | InterpolatorUtils.`remap1D(Dataset dataset, Dataset axis, Dataset outputAxis)`
    `static Dataset` | InterpolatorUtils.`remapAxis(Dataset dataset, int axisIndex, Dataset originalAxisForCorrection, Dataset outputAxis)`
    `static Dataset` | InterpolatorUtils.`remapOneAxis(Dataset dataset, int axisIndex, Dataset corrections, Dataset originalAxisForCorrection, Dataset outputAxis)`
    `static void` | DatasetUtils.`removeNansAndInfinities(Dataset a, Number value)` Removes NaNs and infinities from floating point datasets.
    `static double` | Stats.`residual(Dataset a, Dataset b)` The residual is the sum of squared differences
    `double` | BooleanDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | BooleanDatasetBase.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | ByteDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | ComplexDoubleDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | ComplexFloatDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | CompoundByteDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | CompoundDoubleDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | CompoundFloatDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | CompoundIntegerDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | CompoundLongDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | CompoundShortDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | Dataset.`residual(Object o, Dataset weight, boolean ignoreNaNs)` Calculate residual of dataset with object and weight.
    `double` | DoubleDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | FloatDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | IntegerDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | LongDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | ObjectDatasetBase.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | ShortDataset.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `double` | StringDatasetBase.`residual(Object b, Dataset w, boolean ignoreNaNs)`
    `static Dataset` | Maths.`rightShift(Object a, Object b, Dataset o)` rightShift operator
    `static Dataset` | Maths.`rint(Object a, Dataset o)` rint - round each element of the dataset
    `static Dataset` | InterpolatorUtils.`selectDatasetRegion(Dataset dataset, int x, int y, int xSize, int ySize)`
    `BooleanDatasetBase` | BooleanDatasetBase.`setBy1DIndex(Object obj, Dataset index)`
    `ByteDataset` | ByteDataset.`setBy1DIndex(Object obj, Dataset index)`
    `CompoundByteDataset` | CompoundByteDataset.`setBy1DIndex(Object o, Dataset index)`
    `CompoundDataset` | CompoundDataset.`setBy1DIndex(Object obj, Dataset index)`
    `CompoundDoubleDataset` | CompoundDoubleDataset.`setBy1DIndex(Object o, Dataset index)`
    `CompoundFloatDataset` | CompoundFloatDataset.`setBy1DIndex(Object o, Dataset index)`
    `CompoundIntegerDataset` | CompoundIntegerDataset.`setBy1DIndex(Object o, Dataset index)`
    `CompoundLongDataset` | CompoundLongDataset.`setBy1DIndex(Object o, Dataset index)`
    `CompoundShortDataset` | CompoundShortDataset.`setBy1DIndex(Object o, Dataset index)`
    `Dataset` | Dataset.`setBy1DIndex(Object obj, Dataset index)` This is modelled after the NumPy set item with an index dataset
    `DoubleDataset` | DoubleDataset.`setBy1DIndex(Object obj, Dataset index)`
    `FloatDataset` | FloatDataset.`setBy1DIndex(Object obj, Dataset index)`
    `IntegerDataset` | IntegerDataset.`setBy1DIndex(Object obj, Dataset index)`
    `LongDataset` | LongDataset.`setBy1DIndex(Object obj, Dataset index)`
    `ObjectDatasetBase` | ObjectDatasetBase.`setBy1DIndex(Object obj, Dataset index)`
    `ShortDataset` | ShortDataset.`setBy1DIndex(Object obj, Dataset index)`
    `StringDatasetBase` | StringDatasetBase.`setBy1DIndex(Object obj, Dataset index)`
    `BooleanDatasetBase` | BooleanDatasetBase.`setByBoolean(Object obj, Dataset selection)`
    `ByteDataset` | ByteDataset.`setByBoolean(Object obj, Dataset selection)`
    `CompoundByteDataset` | CompoundByteDataset.`setByBoolean(Object o, Dataset selection)`
    `CompoundDataset` | CompoundDataset.`setByBoolean(Object obj, Dataset selection)`
    `CompoundDoubleDataset` | CompoundDoubleDataset.`setByBoolean(Object o, Dataset selection)`
    `CompoundFloatDataset` | CompoundFloatDataset.`setByBoolean(Object o, Dataset selection)`
    `CompoundIntegerDataset` | CompoundIntegerDataset.`setByBoolean(Object o, Dataset selection)`
    `CompoundLongDataset` | CompoundLongDataset.`setByBoolean(Object o, Dataset selection)`
    `CompoundShortDataset` | CompoundShortDataset.`setByBoolean(Object o, Dataset selection)`
    `Dataset` | Dataset.`setByBoolean(Object obj, Dataset selection)` This is modelled after the NumPy set item with a condition specified by a boolean dataset
    `DoubleDataset` | DoubleDataset.`setByBoolean(Object obj, Dataset selection)`
    `FloatDataset` | FloatDataset.`setByBoolean(Object obj, Dataset selection)`
    `IntegerDataset` | IntegerDataset.`setByBoolean(Object obj, Dataset selection)`
    `LongDataset` | LongDataset.`setByBoolean(Object obj, Dataset selection)`
    `ObjectDatasetBase` | ObjectDatasetBase.`setByBoolean(Object obj, Dataset selection)`
    `ShortDataset` | ShortDataset.`setByBoolean(Object obj, Dataset selection)`
    `StringDatasetBase` | StringDatasetBase.`setByBoolean(Object obj, Dataset selection)`
    `void` | CompoundByteDataset.`setElements(Dataset source, int element)`
    `void` | CompoundDataset.`setElements(Dataset source, int element)` Set values of chosen elements from each item according to source dataset
    `void` | CompoundDoubleDataset.`setElements(Dataset source, int element)`
    `void` | CompoundFloatDataset.`setElements(Dataset source, int element)`
    `void` | CompoundIntegerDataset.`setElements(Dataset source, int element)`
    `void` | CompoundLongDataset.`setElements(Dataset source, int element)`
    `void` | CompoundShortDataset.`setElements(Dataset source, int element)`
    `static Dataset` | Maths.`signum(Object a, Dataset o)` signum - sign of each element
    `static Dataset` | Maths.`sin(Object a, Dataset o)` sin - evaluate the sine function on each element of the dataset
    `static Dataset` | Maths.`sinh(Object a, Dataset o)` sinh - evaluate the hyperbolic sine function on each element of the dataset
    `static Object` | Stats.`skewness(Dataset a, boolean... ignoreInvalids)`
    `static Dataset` | Stats.`skewness(Dataset a, int axis, boolean... ignoreInvalids)`
    `static double` | Outliers.`snFast(Dataset data)` Returns the Sn estimator of Croux and Rousseeuw.
    `static double` | Outliers.`snNaive(Dataset data)` Returns the Sn estimator of Croux and Rousseeuw.
    `static Dataset` | LinearAlgebra.`solve(Dataset a, Dataset v)` Solve linear matrix equation `A x = v`
    `static Dataset` | LinearAlgebra.`solveSVD(Dataset a, Dataset v)` Solve least squares matrix equation `A x = v` by SVD
    `static void` | DatasetUtils.`sort(Dataset a, Dataset... b)` Sort in place given dataset and reorder ancillary datasets too
    `static void` | DatasetUtils.`sort(Dataset a, Dataset... b)` Sort in place given dataset and reorder ancillary datasets too
    `static List<Dataset>` | DatasetUtils.`split(Dataset a, int[] indices, int axis)` Split a dataset into parts along given axis
    `static List<Dataset>` | DatasetUtils.`split(Dataset a, int sections, int axis, boolean checkEqual)` Split a dataset into equal sections along given axis
    `static Dataset` | Maths.`sqrt(Object a, Dataset o)` sqrt - evaluate the square root function on each element of the dataset
    `static Dataset` | Maths.`square(Object a, Dataset o)` square - square each element
    `static Dataset` | Maths.`subtract(Object a, Object b, Dataset o)` subtract operator
    `static Object` | Stats.`sum(Dataset a, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | DatasetUtils.`take(T a, Dataset indices, Integer axis)` Take items from dataset along an axis
    `static Dataset` | Maths.`tan(Object a, Dataset o)` tan - evaluate the tangent function on each element of the dataset
    `static Dataset` | Maths.`tanh(Object a, Dataset o)` tanh - evaluate the tangent hyperbolic function on each element of the dataset
    `static Dataset` | LinearAlgebra.`tensorDotProduct(Dataset a, Dataset b, int[] axisa, int[] axisb)` Calculate the tensor dot product over given axes.
    `static Dataset` | LinearAlgebra.`tensorDotProduct(Dataset a, Dataset b, int axisa, int axisb)` Calculate the tensor dot product over given axes.
    `static Dataset` | Maths.`toDegrees(Object a, Dataset o)` toDegrees - convert to degrees
    `static Dataset` | Maths.`toRadians(Object a, Dataset o)` toRadians - convert to radians
    `static Dataset` | LinearAlgebra.`trace(Dataset a)` Calculate trace of dataset - sum of values over 1st axis and 2nd axis
    `static Dataset` | LinearAlgebra.`trace(Dataset a, int offset, int axis1, int axis2)` Calculate trace of dataset - sum of values over axis1 and axis2 where axis2 is offset
    `static Dataset` | Maths.`truncate(Object a, Dataset o)` truncate - truncate each element to integers of the dataset
    `static Object` | Stats.`typedProduct(Class<? extends Dataset> clazz, Dataset a, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | Stats.`typedProduct(Class<T> clazz, Dataset a, int[] axes, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | Stats.`typedProduct(Class<T> clazz, Dataset a, int axis, boolean... ignoreInvalids)`
    `static Object` | Stats.`typedProduct(Dataset a, int dtype, boolean... ignoreInvalids)` Deprecated. Please use the class-based method `Stats.typedProduct(Class, Dataset, boolean...)`
    `static Dataset` | Stats.`typedProduct(Dataset a, int dtype, int[] axes, boolean... ignoreInvalids)` Deprecated. Please use the class-based method `Stats.typedProduct(Class, Dataset, int[], boolean...)`
    `static Dataset` | Stats.`typedProduct(Dataset a, int dtype, int axis, boolean... ignoreInvalids)` Deprecated. Please use the class-based method `Stats.typedProduct(Class, Dataset, int, boolean...)`
    `static Object` | Stats.`typedSum(Class<? extends Dataset> clazz, Dataset a, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | Stats.`typedSum(Class<T> clazz, Dataset a, int[] axes, boolean... ignoreInvalids)`
    `static <T extends Dataset> T` | Stats.`typedSum(Class<T> clazz, Dataset a, int axis, boolean... ignoreInvalids)`
    `static Object` | Stats.`typedSum(Dataset a, int dtype, boolean... ignoreInvalids)` Deprecated. Please use the class-based method `Stats.typedSum(Class, Dataset, boolean...)`
    `static Dataset` | Stats.`typedSum(Dataset a, int dtype, int axis, boolean... ignoreInvalids)` Deprecated. Please use the class-based method `Stats.typedSum(Class, Dataset, int, boolean...)`
    `static Dataset` | Maths.`unsignedRightShift(Object a, Object b, Dataset o)` unsignedRightShift operator
    `static Object` | Maths.`unwrap(Dataset o, Object... a)` Unwrap result from mathematical methods if necessary
    `static Object` | Maths.`unwrap(Dataset o, Object a)` Unwrap result from mathematical methods if necessary
    `static Object` | Maths.`unwrap(Dataset o, Object a, Object b)` Unwrap result from mathematical methods if necessary
    `static void` | DatasetUtils.`unwrapUnsigned(Dataset a, int bitWidth)` Unwrap dataset elements so that all elements are unsigned
    `static Dataset` | Maths.`upperClip(Object a, Dataset o, Object pa)` upperClip - clip elements to upper limit
    `static double` | Stats.`weightedResidual(Dataset a, Dataset b, Dataset w)` The residual is the sum of squared differences
    `static <T extends Dataset> T` | DatasetFactory.`zeros(Dataset dataset, Class<T> clazz)`
    `static Dataset` | DatasetFactory.`zeros(Dataset dataset, int dtype)` Deprecated. Please use the class-based methods in DatasetFactory, such as `DatasetFactory.zeros(Dataset, Class)`

    Method parameters in org.eclipse.january.dataset with type arguments of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `static IntegerDataset` | DatasetUtils.`calcIndexesFromPositions(List<? extends Dataset> positions, int[] shape, int... mode)` Calculate indexes in given shape from datasets of position
    `static CompoundDataset` | DatasetUtils.`cast(Class<? extends Dataset> clazz, Dataset... a)` Cast array of datasets to a compound dataset
    `static <T extends CompoundDataset> T` | DatasetFactory.`createComplexDataset(Class<? extends Dataset> clazz, Object real, Object imag)` Create complex dataset of given class from real and imaginary parts
    `static Class<? extends Dataset>` | InterfaceUtils.`findSubInterface(Class<? extends Dataset> clazz)` Find sub-interface of Dataset
    `static Object` | InterfaceUtils.`fromDoublesToBiggestPrimitives(Class<? extends Dataset> clazz, double[] x)` Convert double array to primitive array
    `static Number` | InterfaceUtils.`fromDoubleToBiggestNumber(Class<? extends Dataset> clazz, double x)` Convert double to number
    `static Number` | InterfaceUtils.`fromDoubleToNumber(Class<? extends Dataset> clazz, double x)` Convert double to number
    `static Class<? extends Dataset>` | InterfaceUtils.`getBestFloatInterface(Class<? extends Dataset> clazz)` Find floating point dataset interface that best fits given types.
    `static Class<? extends Dataset>` | InterfaceUtils.`getBestInterface(Class<? extends Dataset> a, Class<? extends Dataset> b)` Find dataset interface that best fits given classes.
    `static Class<? extends Dataset>` | InterfaceUtils.`getBestInterface(Class<? extends Dataset> a, Class<? extends Dataset> b)` Find dataset interface that best fits given classes.
    `static Class<? extends CompoundDataset>` | InterfaceUtils.`getCompoundInterface(Class<? extends Dataset> clazz)`
    `static String` | DTypeUtils.`getDatasetName(Class<? extends Dataset> clazz, int itemSize)`
    `static int` | DTypeUtils.`getDType(Class<? extends Dataset> clazz)`
    `static Class<? extends Dataset>` | InterfaceUtils.`getElementalInterface(Class<? extends Dataset> clazz)`
    `static Class<?>` | InterfaceUtils.`getElementClass(Class<? extends Dataset> clazz)`
    `static int` | InterfaceUtils.`getElementsPerItem(Class<? extends Dataset> clazz)`
    `static int` | InterfaceUtils.`getItemBytes(int isize, Class<? extends Dataset> clazz)`
    `static Class<? extends Dataset>` | DTypeUtils.`getLargestDataset(Class<? extends Dataset> clazz)` The largest dataset class suitable for a summation of around a few thousand items without changing from the "kind" of dataset
    `static boolean` | InterfaceUtils.`hasInterface(Class<? extends Dataset> clazz, Class<? extends Dataset>... dInterfaces)`
    `static boolean` | InterfaceUtils.`hasInterface(Class<? extends Dataset> clazz, Class<? extends Dataset> dInterface)`
    `static boolean` | InterfaceUtils.`hasInterface(Class<? extends Dataset> clazz, Class<? extends Dataset> dInterface)`
    `static boolean` | InterfaceUtils.`isComplex(Class<? extends Dataset> clazz)`
    `static boolean` | InterfaceUtils.`isCompound(Class<? extends Dataset> clazz)`
    `static boolean` | InterfaceUtils.`isElemental(Class<? extends Dataset> clazz)`
    `static boolean` | InterfaceUtils.`isFloating(Class<? extends Dataset> clazz)`
    `static boolean` | InterfaceUtils.`isInstance(Object object, Class<? extends Dataset> dInterface)`
    `static boolean` | InterfaceUtils.`isInteger(Class<? extends Dataset> clazz)`
    `static boolean` | InterfaceUtils.`isNumerical(Class<? extends Dataset> clazz)`
    `static ILazyDataset` | Random.`lazyRand(String name, Class<? extends Dataset> clazz, int... shape)`
    `void` | LazyDataset.`setInterface(Class<? extends Dataset> clazz)` Set interface
    `static Number` | InterfaceUtils.`toBiggestNumber(Class<? extends Dataset> clazz, Number x)`
    `static Object` | Stats.`typedProduct(Class<? extends Dataset> clazz, Dataset a, boolean... ignoreInvalids)`
    `static Object` | Stats.`typedSum(Class<? extends Dataset> clazz, Dataset a, boolean... ignoreInvalids)`

    Constructors in org.eclipse.january.dataset with parameters of type Dataset
    Constructor and Description
    `BooleanBroadcastIterator(boolean v, Dataset a, Dataset c, Dataset o, boolean createIfNull)` Construct a boolean iterator that stops at every position in the choice dataset where its value matches the given boolean
    `BooleanContiguousIterator(boolean v, Dataset a, Dataset c, Dataset o, boolean createIfNull)` Construct a boolean iterator that stops at the position in choice dataset where its value matches the given boolean
    `BooleanIterator(boolean v, Dataset a, Dataset c, Dataset o)` Construct a boolean iterator that stops at the given boolean value in choice dataset
    `BooleanNullIterator(Dataset a, Dataset o)`
    `BooleanNullIterator(Dataset a, Dataset o, boolean createIfNull)`
    `BooleanNullIterator(Dataset a, Dataset o, boolean createIfNull, boolean allowInteger, boolean allowComplex)`
    `BroadcastIterator(Dataset a, Dataset b, Dataset o)`
    `BroadcastIteratorBase(Dataset a, Dataset b)`
    `BroadcastPairIterator(Dataset a, Dataset b, Dataset o, boolean createIfNull)`
    `BroadcastSelfIterator(Dataset a, Dataset b)`
    `BroadcastSingleIterator(Dataset a, Dataset b)`
    `ContiguousPairIterator(Dataset a, Dataset b, Dataset o, boolean createIfNull)`
    `ContiguousSingleIterator(Dataset a, Dataset b)`
    `ContiguousSingleIteratorElemental(Dataset a, Dataset b)`
    `IntegerIterator(Dataset index, int length)` Constructor for an iterator over the items of an integer dataset
    `IntegerIterator(Dataset index, int length, int isize)` Constructor for an iterator over the items of an integer dataset
    `RGBByteDataset(Dataset grey)` Create a dataset using given grey data
    `RGBByteDataset(Dataset red, Dataset green, Dataset blue)` Create a dataset using given colour data (colour components are given separately)
    `RGBDataset(Dataset grey)` Create a dataset using given grey data
    `RGBDataset(Dataset red, Dataset green, Dataset blue)` Create a dataset using given colour data (colour components are given separately)
    `SingleInputBroadcastIterator(Dataset a, Dataset o)`
    `SingleInputBroadcastIterator(Dataset a, Dataset o, boolean createIfNull)`
    `SingleInputBroadcastIterator(Dataset a, Dataset o, boolean createIfNull, boolean allowInteger, boolean allowComplex)`

    Constructor parameters in org.eclipse.january.dataset with type arguments of type Dataset
    Constructor and Description
    `LazyDataset(ILazyLoader loader, String name, Class<? extends Dataset> clazz, int... shape)` Create a lazy dataset
    `LazyDataset(ILazyLoader loader, String name, int elements, Class<? extends Dataset> clazz, int... shape)` Create a lazy dataset
    `LazyDynamicDataset(ILazyLoader loader, String name, int elements, Class<? extends Dataset> clazz, int[] shape, int[] maxShape)` Create a dynamic lazy dataset
    `LazyDynamicDataset(String name, int elements, Class<? extends Dataset> clazz, int[] shape, int[] maxShape, ILazyLoader loader)` Deprecated. Use `LazyDynamicDataset.LazyDynamicDataset(ILazyLoader, String, int, Class, int[], int[])`
    `LazyWriteableDataset(ILazySaver saver, String name, Class<? extends Dataset> clazz, int[] shape, int[] maxShape, int[] chunks)` Create a lazy dataset
    `LazyWriteableDataset(ILazySaver saver, String name, int elements, Class<? extends Dataset> clazz, int[] shape, int[] maxShape, int[] chunks)` Create a lazy dataset

  - ### Uses of Dataset in org.eclipse.january.metadata

    Methods in org.eclipse.january.metadata that return Dataset
    Modifier and Type | Method and Description
    --- | ---
    `Dataset` | StatisticsMetadata.`getArgMaximum(int axis, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getArgMinimum(int axis, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getCount(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getCount(int axis, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getMaximum(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getMaximum(int axis, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getMean(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getMean(int axis, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getMinimum(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getMinimum(int axis, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getSum(int[] axes, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getSum(int axis, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getVariance(int[] axes, boolean isWholePopulation, boolean... ignoreInvalids)`
    `Dataset` | StatisticsMetadata.`getVariance(int axis, boolean isWholePopulation, boolean... ignoreInvalids)`

    Methods in org.eclipse.january.metadata with parameters of type Dataset
    Modifier and Type | Method and Description
    --- | ---
    `void` | StatisticsMetadata.`initialize(Dataset dataset)`
    `boolean` | StatisticsMetadata.`isDirty(Dataset dataset)`

Copyright © 2014–2022 Eclipse Foundation. All rights reserved.