Exponential & Logarithmic

All functions in this section are element-wise unary operations unless noted otherwise.

exp

Compute the exponential e**x for each element.

function exp(x: ArrayLike): NDArray

Name	Type	Default	Description
`x`	`ArrayLike`	-	Input array (exponents).

Returns: NDArray — Element-wise e**x.

import * as np from 'numpy-ts';

const a = np.exp(np.array([0, 1, 2]));
// array([1, 2.71828..., 7.38906...])

const b = np.exp(np.array([np.log(2)]));
// array([2])

import { exp, array } from 'numpy-ts/core';

const a = exp(array([0, 1, 2]));

exp2

Compute 2**x for each element.

function exp2(x: ArrayLike): NDArray

Name	Type	Default	Description
`x`	`ArrayLike`	-	Input array (exponents).

Returns: NDArray — Element-wise 2**x.

import * as np from 'numpy-ts';

const a = np.exp2(np.array([0, 1, 2, 3, 8]));
// array([1, 2, 4, 8, 256])

import { exp2, array } from 'numpy-ts/core';

const a = exp2(array([0, 1, 2, 3, 8]));

expm1

Compute exp(x) - 1 for each element. More accurate than exp(x) - 1 for small values of x.

function expm1(x: ArrayLike): NDArray

Name	Type	Default	Description
`x`	`ArrayLike`	-	Input array.

Returns: NDArray — Element-wise e**x - 1.

import * as np from 'numpy-ts';

// More accurate than np.exp(x) - 1 for small x
const a = np.expm1(np.array([1e-10, 0, 1]));
// array([1e-10, 0, 1.71828...])

import { expm1, array } from 'numpy-ts/core';

const a = expm1(array([1e-10, 0, 1]));

log

Natural logarithm, element-wise. The natural log is the inverse of the exponential function: if y = exp(x), then x = log(y).

function log(x: ArrayLike): NDArray

Name	Type	Default	Description
`x`	`ArrayLike`	-	Input array. Values must be positive.

Returns: NDArray — Element-wise natural logarithm (base e).

import * as np from 'numpy-ts';

const a = np.log(np.array([1, Math.E, Math.E ** 2]));
// array([0, 1, 2])

import { log, array } from 'numpy-ts/core';

const a = log(array([1, Math.E, Math.E ** 2]));

log2

Base-2 logarithm, element-wise.

function log2(x: ArrayLike): NDArray

Name	Type	Default	Description
`x`	`ArrayLike`	-	Input array. Values must be positive.

Returns: NDArray — Element-wise base-2 logarithm.

import * as np from 'numpy-ts';

const a = np.log2(np.array([1, 2, 4, 8, 256]));
// array([0, 1, 2, 3, 8])

import { log2, array } from 'numpy-ts/core';

const a = log2(array([1, 2, 4, 8, 256]));

log10

Base-10 logarithm, element-wise.

function log10(x: ArrayLike): NDArray

Name	Type	Default	Description
`x`	`ArrayLike`	-	Input array. Values must be positive.

Returns: NDArray — Element-wise base-10 logarithm.

import * as np from 'numpy-ts';

const a = np.log10(np.array([1, 10, 100, 1000]));
// array([0, 1, 2, 3])

import { log10, array } from 'numpy-ts/core';

const a = log10(array([1, 10, 100, 1000]));

log1p

Natural logarithm of 1 + x, element-wise. More accurate than log(1 + x) for small values of x.

function log1p(x: ArrayLike): NDArray

Name	Type	Default	Description
`x`	`ArrayLike`	-	Input array. Values must be greater than `-1`.

Returns: NDArray — Element-wise ln(1 + x).

import * as np from 'numpy-ts';

// More accurate than np.log(1 + x) for small x
const a = np.log1p(np.array([1e-10, 0, 1]));
// array([1e-10, 0, 0.69315...])

import { log1p, array } from 'numpy-ts/core';

const a = log1p(array([1e-10, 0, 1]));

logaddexp

Logarithm of the sum of exponentiations: log(exp(x1) + exp(x2)). Useful for computing log-probabilities. This is a binary operation.

function logaddexp(x1: ArrayLike, x2: ArrayLike | number): NDArray

Name	Type	Default	Description
`x1`	`ArrayLike`	-	First input array.
`x2`	`ArrayLike \| number`	-	Second input array or scalar.

Returns: NDArray — Element-wise log(exp(x1) + exp(x2)).

import * as np from 'numpy-ts';

// Adding log-probabilities
const logp1 = np.array([-1, -2, -3]);
const logp2 = np.array([-1, -3, -5]);
const result = np.logaddexp(logp1, logp2);

import { logaddexp, array } from 'numpy-ts/core';

const logp1 = array([-1, -2, -3]);
const logp2 = array([-1, -3, -5]);
const result = logaddexp(logp1, logp2);

logaddexp2

Logarithm base 2 of the sum of exponentiations: log2(2**x1 + 2**x2). This is a binary operation.

function logaddexp2(x1: ArrayLike, x2: ArrayLike | number): NDArray

Name	Type	Default	Description
`x1`	`ArrayLike`	-	First input array.
`x2`	`ArrayLike \| number`	-	Second input array or scalar.

Returns: NDArray — Element-wise log2(2**x1 + 2**x2).

import * as np from 'numpy-ts';

const a = np.logaddexp2(np.array([1, 2, 3]), np.array([1, 2, 3]));
// array([2, 3, 4])  -- log2(2^1 + 2^1) = log2(4) = 2, etc.

import { logaddexp2, array } from 'numpy-ts/core';

const a = logaddexp2(array([1, 2, 3]), array([1, 2, 3]));

Overview

NDArray

Array Creation

Math

Array Manipulation

Reductions & Statistics

Linear Algebra

Logic & Comparison

Sorting & Searching

Set Operations

Bitwise

Indexing

Gradient & Differences

Polynomials

Random (np.random)

FFT (np.fft)

I/O

Utilities

Exponential & Logarithmic

exp

exp2

expm1

log

log2

log10

log1p

logaddexp

logaddexp2

​exp

​exp2

​expm1

​log

​log2

​log10

​log1p

​logaddexp

​logaddexp2

exp

exp2

expm1

log

log2

log10

log1p

logaddexp

logaddexp2