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 Maths (The Eclipse January API Documentation)
org.eclipse.january.dataset

Class Maths

• java.lang.Object
• org.eclipse.january.dataset.internal.GeneratedMaths
• org.eclipse.january.dataset.Maths

• public class Maths
extends org.eclipse.january.dataset.internal.GeneratedMaths
Mathematics class
• Constructor Summary

Constructors
Constructor and Description
Maths()
• Method Summary

Methods
Modifier and Type Method and Description
static Dataset abs(Object a)
abs - absolute value of each element
static Dataset abs(Object a, Dataset o)
abs - absolute value of each element
static Dataset 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 angle(Object a)
Create a dataset of the arguments from a complex dataset
static Dataset angle(Object a, boolean inDegrees)
Create a dataset of the arguments from a complex dataset
static Dataset angle(Object a, boolean inDegrees, Dataset o)
Create a dataset of the arguments from a complex dataset
static Dataset angle(Object a, Dataset o)
Create a dataset of the arguments from a complex dataset
static Dataset arctan2(Object a, Object b)
static Dataset arctan2(Object a, Object b, Dataset o)
static Dataset centralDifference(Dataset a, int axis)
Discrete difference of dataset along axis using finite central difference
static Dataset conjugate(Object a)
static Dataset conjugate(Object a, Dataset o)
static Dataset 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 difference(Dataset a, int n, int axis)
Discrete difference of dataset along axis using finite difference
static Dataset floorDivide(Object a, Object b)
static Dataset floorDivide(Object a, Object b, Dataset o)
static Dataset floorRemainder(Object a, Object b)
static Dataset floorRemainder(Object a, Object b, Dataset o)
static void getBilinear(double[] values, CompoundDataset d, double x0, double x1)
static double getBilinear(IDataset d, double x0, double x1)
static double getBilinear(IDataset d, IDataset m, double x0, double x1)
static void getLinear(double[] values, CompoundDataset d, double x0)
static double getLinear(IDataset d, double x0)
Deprecated.
static List<Dataset> gradient(Dataset y, Dataset... x)
Calculate gradient (or partial derivatives) by central difference
static Dataset hypot(Object a, Object b)
static Dataset hypot(Object a, Object b, Dataset o)
static double interpolate(Dataset d, Dataset m, double... x)
Linearly interpolate a value at a point in a n-D dataset with a mask.
static double interpolate(Dataset d, Dataset m, double x0)
Linearly interpolate a value at a point in a 1D dataset with a mask.
static double 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 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 interpolate(Dataset d, double... x)
Linearly interpolate a value at a point in a n-D dataset.
static double interpolate(Dataset d, double x0)
Linearly interpolate a value at a point in a 1D dataset.
static double interpolate(Dataset d, double x0, double x1)
Linearly interpolate a value at a point in a 2D dataset.
static void interpolate(double[] values, CompoundDataset d, double... x)
Linearly interpolate an array of values at a point in a compound n-D dataset.
static void interpolate(double[] values, CompoundDataset d, double x0)
Linearly interpolate an array of values at a point in a compound 1D dataset.
static void interpolate(double[] values, CompoundDataset d, double x0, double x1)
Linearly interpolate an array of values at a point in a compound 2D dataset.
static Dataset multiply(Collection<IDataset> sets, boolean requireClone)
Multiplies all sets passed in together The first IDataset must cast to Dataset
static Dataset phaseAsComplexNumber(Object a, boolean keepZeros)
Create a phase only dataset.
static Dataset phaseAsComplexNumber(Object a, Dataset o, boolean keepZeros)
Create a phase only dataset.
static Dataset reciprocal(Object a)
Find reciprocal from dataset
static Dataset reciprocal(Object a, Dataset o)
Find reciprocal from dataset
static Object unwrap(Dataset o, Object... a)
Unwrap result from mathematical methods if necessary
static Object unwrap(Dataset o, Object a)
Unwrap result from mathematical methods if necessary
static Object unwrap(Dataset o, Object a, Object b)
Unwrap result from mathematical methods if necessary
• Methods inherited from class org.eclipse.january.dataset.internal.GeneratedMaths

add, add, addBinaryOperatorName, addFunctionName, addFunctionName, arccos, arccos, arccosh, arccosh, arcsin, arcsin, arcsinh, arcsinh, arctan, arctan, arctanh, arctanh, bitwiseAnd, bitwiseAnd, bitwiseInvert, bitwiseInvert, bitwiseOr, bitwiseOr, bitwiseXor, bitwiseXor, cbrt, cbrt, ceil, ceil, clip, clip, cos, cos, cosh, cosh, divide, divide, divideTowardsFloor, divideTowardsFloor, dividez, dividez, exp, exp, expm1, expm1, floor, floor, leftShift, leftShift, log, log, log10, log10, log1p, log1p, log2, log2, maximum, maximum, minimum, minimum, multiply, multiply, negative, negative, power, power, remainder, remainder, rightShift, rightShift, rint, rint, signum, signum, sin, sin, sinh, sinh, sqrt, sqrt, square, square, subtract, subtract, tan, tan, tanh, tanh, toDegrees, toDegrees, toLong, toRadians, toRadians, truncate, truncate, unsignedRightShift, unsignedRightShift
• Constructor Detail

• Method Detail

• unwrap

public static Object unwrap(Dataset o,
Object a)
Unwrap result from mathematical methods if necessary
Parameters:
o -
a -
Returns:
a dataset if a is a dataset or an object of the same class as o
• unwrap

public static Object unwrap(Dataset o,
Object a,
Object b)
Unwrap result from mathematical methods if necessary
Parameters:
o -
a -
Returns:
a dataset if either a and b are datasets or an object of the same class as o
• unwrap

public static Object unwrap(Dataset o,
Object... a)
Unwrap result from mathematical methods if necessary
Parameters:
o -
a -
Returns:
a dataset if any inputs are datasets or an object of the same class as o
• floorDivide

public static Dataset floorDivide(Object a,
Object b,
Dataset o)
Parameters:
a -
b -
o - output can be null - in which case, a new dataset is created
Returns:
floor divide of a and b
• floorRemainder

public static Dataset floorRemainder(Object a,
Object b,
Dataset o)
Parameters:
a -
b -
o - output can be null - in which case, a new dataset is created
Returns:
floor remainder of a and b
• reciprocal

public static Dataset reciprocal(Object a)
Find reciprocal from dataset
Parameters:
a -
Returns:
reciprocal dataset
• reciprocal

public static Dataset reciprocal(Object a,
Dataset o)
Find reciprocal from dataset
Parameters:
a -
o - output can be null - in which case, a new dataset is created
Returns:
reciprocal dataset
• abs

public static Dataset abs(Object a)
abs - absolute value of each element
Parameters:
a -
Returns:
dataset
• abs

public static Dataset abs(Object a,
Dataset o)
abs - absolute value of each element
Parameters:
a -
o - output can be null - in which case, a new dataset is created
Returns:
dataset
• conjugate

public static Dataset conjugate(Object a,
Dataset o)
Parameters:
a -
o - output can be null - in which case, a new dataset is created
Returns:
a^*, complex conjugate of a
• hypot

public static Dataset hypot(Object a,
Object b)
Parameters:
a - side of right-angled triangle
b - side of right-angled triangle
Returns:
hypotenuse of right-angled triangle: sqrt(a^2 + a^2)
• hypot

public static Dataset hypot(Object a,
Object b,
Dataset o)
Parameters:
a - side of right-angled triangle
b - side of right-angled triangle
o - output can be null - in which case, a new dataset is created
Returns:
hypotenuse of right-angled triangle: sqrt(a^2 + a^2)
• arctan2

public static Dataset arctan2(Object a,
Object b)
Parameters:
a - opposite side of right-angled triangle
b - adjacent side of right-angled triangle
Returns:
angle of triangle: atan(b/a)
• arctan2

public static Dataset arctan2(Object a,
Object b,
Dataset o)
Parameters:
a - opposite side of right-angled triangle
b - adjacent side of right-angled triangle
o - output can be null - in which case, a new dataset is created
Returns:
angle of triangle: atan(b/a)
• angle

public static Dataset angle(Object a)
Create a dataset of the arguments from a complex dataset
Parameters:
a -
Returns:
dataset of angles in radians
• angle

public static Dataset angle(Object a,
boolean inDegrees)
Create a dataset of the arguments from a complex dataset
Parameters:
a -
inDegrees - if true then return angles in degrees else in radians
Returns:
dataset of angles
• angle

public static Dataset angle(Object a,
Dataset o)
Create a dataset of the arguments from a complex dataset
Parameters:
a -
o - output can be null - in which case, a new dataset is created
Returns:
dataset of angles in radians
• angle

public static Dataset angle(Object a,
boolean inDegrees,
Dataset o)
Create a dataset of the arguments from a complex dataset
Parameters:
a -
inDegrees - if true then return angles in degrees else in radians
o - output can be null - in which case, a new dataset is created
Returns:
dataset of angles
• phaseAsComplexNumber

public static Dataset phaseAsComplexNumber(Object a,
boolean keepZeros)
Create a phase only dataset. NB it will contain NaNs if there are any items with zero amplitude
Parameters:
a - dataset
keepZeros - if true then zero items are returned as zero rather than NaNs
Returns:
complex dataset where items have unit amplitude
• phaseAsComplexNumber

public static Dataset phaseAsComplexNumber(Object a,
Dataset o,
boolean keepZeros)
Create a phase only dataset. NB it will contain NaNs if there are any items with zero amplitude
Parameters:
a - dataset
o - output can be null - in which case, a new dataset is created
keepZeros - if true then zero items are returned as zero rather than NaNs
Returns:
complex dataset where items have unit amplitude
• add

public static Dataset 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. Otherwise a new data set is returned, the sum of those passed in.
Parameters:
sets -
requireClone -
Returns:
sum of collection
• multiply

public static Dataset multiply(Collection<IDataset> sets,
boolean requireClone)
Multiplies all sets passed in together The first IDataset must cast to Dataset
Parameters:
sets -
requireClone -
Returns:
product of collection
• interpolate

public static Dataset interpolate(Dataset x,
Dataset d,
IDataset x0,
Number left,
Number right)
Linearly interpolate values at points in a 1D dataset corresponding to given coordinates.
Parameters:
x - input 1-D coordinate dataset (must be ordered)
d - input 1-D dataset
x0 - coordinate values
left - value to use when x0 lies left of domain. If null, then interpolate to zero by using leftmost interval
right - value to use when x0 lies right of domain. If null, then interpolate to zero by using rightmost interval
Returns:
interpolated values
• interpolate

public static double interpolate(Dataset d,
double x0)
Linearly interpolate a value at a point in a 1D dataset. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero.
Parameters:
d - input dataset
x0 - coordinate
Returns:
interpolated value
• interpolate

public static double interpolate(Dataset d,
Dataset m,
double x0)
Linearly interpolate a value at a point in a 1D dataset with a mask. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero.
Parameters:
d - input dataset
m - mask dataset
x0 - coordinate
Returns:
interpolated value
• interpolate

public static void interpolate(double[] values,
CompoundDataset d,
double x0)
Linearly interpolate an array of values at a point in a compound 1D dataset. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero.
Parameters:
values - interpolated array
d - input dataset
x0 - coordinate
• interpolate

public static double interpolate(Dataset d,
double x0,
double x1)
Linearly interpolate a value at a point in a 2D dataset. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero.
Parameters:
d - input dataset
x0 - coordinate
x1 - coordinate
Returns:
bilinear interpolation
• interpolate

public static double interpolate(Dataset d,
Dataset m,
double x0,
double x1)
Linearly interpolate a value at a point in a 2D dataset with a mask. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero.
Parameters:
d - input dataset
m - mask dataset
x0 - coordinate
x1 - coordinate
Returns:
bilinear interpolation
• interpolate

public static void interpolate(double[] values,
CompoundDataset d,
double x0,
double x1)
Linearly interpolate an array of values at a point in a compound 2D dataset. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero.
Parameters:
values - bilinear interpolated array
d -
x0 -
x1 -
• interpolate

public static double interpolate(Dataset d,
double... x)
Linearly interpolate a value at a point in a n-D dataset. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero. The number of coordinates must match the rank of the dataset.
Parameters:
d - input dataset
x - coordinates
Returns:
interpolated value
• interpolate

public static double interpolate(Dataset d,
Dataset m,
double... x)
Linearly interpolate a value at a point in a n-D dataset with a mask. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero. The number of coordinates must match the rank of the dataset.
Parameters:
d - input dataset
m - mask dataset (can be null)
x - coordinates
Returns:
interpolated value
• interpolate

public static void interpolate(double[] values,
CompoundDataset d,
double... x)
Linearly interpolate an array of values at a point in a compound n-D dataset. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero.
Parameters:
values - linearly interpolated array
d -
x -
• getLinear

@Deprecated
public static double getLinear(IDataset d,
double x0)
Linearly interpolate a value at a point in a 1D dataset. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero.
Parameters:
d - input dataset
x0 - coordinate
Returns:
interpolated value
• getLinear

@Deprecated
public static void getLinear(double[] values,
CompoundDataset d,
double x0)
Linearly interpolate a value at a point in a compound 1D dataset. The dataset is considered to have zero support outside its bounds. Thus points just outside are interpolated from the boundary value to zero.
Parameters:
values - interpolated array
d - input dataset
x0 - coordinate
• difference

public static Dataset difference(Dataset a,
int n,
int axis)
Discrete difference of dataset along axis using finite difference
Parameters:
a -
n - order of difference
axis -
Returns:
difference
• derivative

public static Dataset 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
Parameters:
x - The x values of the function to take the derivative of.
y - The y values of the function to take the derivative of.
n - The spread the derivative is calculated from, i.e. the smoothing, the higher the value, the more smoothing occurs.
Returns:
A dataset which contains all the derivative point for point.
• centralDifference

public static Dataset centralDifference(Dataset a,
int axis)
Discrete difference of dataset along axis using finite central difference
Parameters:
a -
axis -
Returns:
difference
• gradient

public static List<Datasetgradient(Dataset y,
Dataset... x)
Calculate gradient (or partial derivatives) by central difference
Parameters:
y -
x - one or more datasets for dependent variables
Returns:
a list of datasets (one for each dimension in y)

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