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Documentation Index

Fetch the complete documentation index at: https://numpyts.dev/llms.txt

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frombuffer

Interpret a buffer as a 1-D array. 
function frombuffer(
  buffer: ArrayBuffer | TypedArray,
  dtype?: DType,
  count?: number,
  offset?: number
): NDArray
NameTypeDefaultDescription
bufferArrayBuffer | TypedArray-An object that exposes the buffer interface.
dtypeDType'float64'Data type of the returned array.
countnumber-1Number of items to read. -1 means all data in the buffer.
offsetnumber0Start reading the buffer from this offset (in bytes for ArrayBuffer, in elements for TypedArray).
Returns: NDArray — 1-D array created from the buffer data. 
import * as np from 'numpy-ts';

const buf = new Float64Array([1.0, 2.0, 3.0, 4.0]).buffer;
const a = np.frombuffer(buf);
// array([1, 2, 3, 4])

const b = np.frombuffer(buf, 'float64', 2);
// array([1, 2])

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fromfile

Create an array from data in a file. This function requires Node.js file system access. 
function fromfile(
  _file: string,
  _dtype?: DType,
  _count?: number,
  _sep?: string
): NDArray
NameTypeDefaultDescription
_filestring-File path to read from.
_dtypeDType'float64'Data type of the returned array.
_countnumber-1Number of items to read. -1 reads the entire file.
_sepstring''Separator between items in a text file. Empty string means binary read.
Returns: NDArray — 1-D array created from file data.
fromfile requires Node.js. It will throw an error in browser environments. Use numpy-ts/node for file I/O support.
import * as np from 'numpy-ts/node';

const a = await np.loadNpy('data.npy');
// Use loadNpy for .npy files instead of fromfile

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fromfunction

Construct an array by executing a function over each coordinate. The function is called with N arguments (one per dimension) for each element, where each argument is the index along that dimension. 
function fromfunction(
  func: (...args: number[]) => number,
  shape: number[],
  dtype?: DType
): NDArray
NameTypeDefaultDescription
func(...args: number[]) => number-Function called with coordinate indices. Should return a single number.
shapenumber[]-Shape of the output array.
dtypeDType'float64'Data type of the output array.
Returns: NDArray — Array of the given shape, with each element set to func(i, j, ...). 
import * as np from 'numpy-ts';

// Sum of indices
const a = np.fromfunction((i, j) => i + j, [3, 3]);
// array([[0, 1, 2],
//        [1, 2, 3],
//        [2, 3, 4]])

// Boolean-like: 1 where i == j
const b = np.fromfunction((i, j) => i === j ? 1 : 0, [3, 3]);
// array([[1, 0, 0],
//        [0, 1, 0],
//        [0, 0, 1]])

​
fromiter

Create a 1-D array from an iterable. 
function fromiter(
  iter: Iterable<number>,
  dtype?: DType,
  count?: number
): NDArray
NameTypeDefaultDescription
iterIterable<number>-Any iterable yielding numbers (arrays, generators, sets, etc.).
dtypeDType'float64'Data type of the output array.
countnumber-1Maximum number of items to read. -1 reads all items.
Returns: NDArray — 1-D array from the iterable values. 
import * as np from 'numpy-ts';

function* fibonacci() {
  let a = 0, b = 1;
  while (true) { yield a; [a, b] = [b, a + b]; }
}

const a = np.fromiter(fibonacci(), 'float64', 8);
// array([0, 1, 1, 2, 3, 5, 8, 13])

const b = np.fromiter(new Set([10, 20, 30]));
// array([10, 20, 30])

​
fromstring

Create a 1-D array from a string of numbers. 
function fromstring(
  string: string,
  dtype?: DType,
  count?: number,
  sep?: string
): NDArray
NameTypeDefaultDescription
stringstring-A string containing the data.
dtypeDType'float64'Data type of the output array.
countnumber-1Number of items to read. -1 reads all items.
sepstringwhitespaceSeparator between values. Defaults to whitespace splitting.
Returns: NDArray — 1-D array parsed from the string. 
import * as np from 'numpy-ts';

const a = np.fromstring('1 2 3 4 5');
// array([1, 2, 3, 4, 5])

const b = np.fromstring('1.5,2.5,3.5', 'float64', -1, ',');
// array([1.5, 2.5, 3.5])

const c = np.fromstring('10 20 30 40 50', 'int32', 3);
// array([10, 20, 30])

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meshgrid

Return coordinate matrices from coordinate vectors. Given N 1-D arrays, meshgrid returns N N-D arrays for vectorized evaluations of N-D scalar fields. 
function meshgrid(...args: (ArrayLike | { indexing?: 'xy' | 'ij'; })[]): NDArray[]
NameTypeDefaultDescription
...args(ArrayLike | { indexing?: 'xy' | 'ij' })[]-1-D arrays representing coordinates of a grid, optionally followed by options.
Returns: NDArray[] — Array of N-D coordinate arrays, one per input. 
import * as np from 'numpy-ts';

const x = np.array([1, 2, 3]);
const y = np.array([4, 5]);

const [X, Y] = np.meshgrid(x, y);
// X = array([[1, 2, 3],
//            [1, 2, 3]])
// Y = array([[4, 4, 4],
//            [5, 5, 5]])

// Evaluate x^2 + y^2 on the grid
const Z = np.add(np.square(X), np.square(Y));