Shape Operations

function reshape(a: ArrayLike, newShape: number[]): NDArray

import * as np from 'numpy-ts';

const a = np.arange(12);

// Reshape to 3x4 matrix
const b = np.reshape(a, [3, 4]);
console.log(b.shape);  // [3, 4]

// Use -1 to auto-infer one dimension
const c = np.reshape(a, [2, -1]);
console.log(c.shape);  // [2, 6]

// Method chaining
const d = np.arange(24).reshape([2, 3, 4]);
console.log(d.shape);  // [2, 3, 4]

function flatten(a: ArrayLike): NDArray

import * as np from 'numpy-ts';

const a = np.array([[1, 2, 3], [4, 5, 6]]);

const flat = np.flatten(a);
console.log(flat.shape);    // [6]
console.log(flat.toArray()); // [1, 2, 3, 4, 5, 6]

// Modifying the flattened array does not affect the original
flat.set([0], 99);
console.log(a.item(0, 0));  // 1 (unchanged)

function ravel(a: ArrayLike): NDArray

import * as np from 'numpy-ts';

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

// ravel returns a view when possible
const r = np.ravel(a);
console.log(r.shape);     // [4]
console.log(r.toArray());  // [1, 2, 3, 4]

// For C-contiguous arrays, ravel shares memory with the original
console.log(r.base !== null);  // true (it is a view)

function squeeze(a: ArrayLike, axis?: number): NDArray

import * as np from 'numpy-ts';

const a = np.array([[[1], [2], [3]]]);
console.log(a.shape);  // [1, 3, 1]

// Remove all size-1 dimensions
const b = np.squeeze(a);
console.log(b.shape);  // [3]

// Remove only axis 0
const c = np.squeeze(a, 0);
console.log(c.shape);  // [3, 1]

function expand_dims(a: ArrayLike, axis: number): NDArray

import * as np from 'numpy-ts';

const a = np.array([1, 2, 3]);
console.log(a.shape);  // [3]

// Add a leading dimension (row vector -> 2D)
const b = np.expand_dims(a, 0);
console.log(b.shape);  // [1, 3]

// Add a trailing dimension (column vector -> 2D)
const c = np.expand_dims(a, 1);
console.log(c.shape);  // [3, 1]

// Negative axis
const d = np.expand_dims(a, -1);
console.log(d.shape);  // [3, 1]

function resize(a: ArrayLike, new_shape: number[]): NDArray

import * as np from 'numpy-ts';

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

// Grow: elements repeat to fill the new shape
const b = np.resize(a, [2, 4]);
console.log(b.toArray());
// [[1, 2, 3, 1],
//  [2, 3, 1, 2]]

// Shrink: elements are truncated
const c = np.resize(a, [2]);
console.log(c.toArray());  // [1, 2]