NumPy Migration Guide

import numpy as np

import * as np from 'numpy-ts';

// Or, for tree-shakeable imports (no method chaining):
import { array, add, reshape } from 'numpy-ts/core';

a = np.array([1, 2, 3])
b = np.array([[1, 2], [3, 4]], dtype=np.float32)
z = np.zeros((3, 3))
r = np.arange(0, 10, 2)
l = np.linspace(0, 1, 5)
I = np.eye(3)

const a = np.array([1, 2, 3]);
const b = np.array([[1, 2], [3, 4]], 'float32');
const z = np.zeros([3, 3]);
const r = np.arange(0, 10, 2);
const l = np.linspace(0, 1, 5);
const I = np.eye(3);

c = a + b
d = a * b
e = a ** 2
f = np.sqrt(a)
g = np.dot(a, b)

const c = np.add(a, b);       // or a.add(b)
const d = np.multiply(a, b);  // or a.multiply(b)
const e = np.power(a, 2);     // or a.power(2)
const f = np.sqrt(a);         // or a.sqrt()
const g = np.dot(a, b);

a = np.arange(12).reshape(3, 4)

a[0, 2]           # Single element
a[0:2, :]         # First 2 rows
a[:, 1:3]         # Columns 1-2
a[::-1, :]        # Reverse rows
a[-1, :]          # Last row
a[1]              # Second row (implicit full slice on remaining dims)

const a = np.arange(12).reshape([3, 4]);

a.item(0, 2);            // Single element
a.slice('0:2', ':');     // First 2 rows
a.slice(':', '1:3');     // Columns 1-2
a.slice('::-1', ':');    // Reverse rows
a.slice('-1', ':');      // Last row
a.slice('1', ':');       // Second row

b = a.reshape(3, 4)        # Separate args
c = a.reshape((3, 4))      # Tuple arg
d = a.T                    # Transpose
e = a.flatten()
f = np.squeeze(a)

const b = a.reshape([3, 4]);    // Array arg (only option)
const c = a.reshape([3, 4]);    // Same
const d = a.T;                  // Transpose (property, not method)
const e = a.flatten();
const f = np.squeeze(a);

np.sum(a)
np.sum(a, axis=0)
np.mean(a, axis=1)
np.std(a)
np.var(a)
np.min(a)
np.max(a)
np.argmax(a, axis=0)

np.sum(a);
np.sum(a, 0);        // axis is positional, not keyword
np.mean(a, 1);
np.std(a);
np.variance(a);      // 'var' is reserved in JS
np.min(a);
np.max(a);
np.argmax(a, 0);

np.linalg.inv(A)
np.linalg.det(A)
np.linalg.eig(A)
np.linalg.svd(A)
np.linalg.norm(v)
np.linalg.solve(A, b)
np.linalg.qr(A)
np.linalg.cholesky(A)
np.linalg.lstsq(A, b)
np.linalg.matrix_rank(A)

np.linalg.inv(A);
np.linalg.det(A);
np.linalg.eig(A);
np.linalg.svd(A);
np.linalg.norm(v);
np.linalg.solve(A, b);
np.linalg.qr(A);
np.linalg.cholesky(A);
np.linalg.lstsq(A, b);
np.linalg.matrix_rank(A);

np.random.seed(42)
np.random.random((3, 3))
np.random.normal(0, 1, (1000,))
np.random.randint(0, 10, size=(5,))
np.random.choice(arr, size=5, replace=False)
np.random.shuffle(arr)

np.random.seed(42);
np.random.random([3, 3]);
np.random.normal(0, 1, [1000]);
np.random.randint(0, 10, [5]);
np.random.choice(arr, 5, false);
np.random.shuffle(arr);

spectrum = np.fft.fft(signal)
freqs = np.fft.fftfreq(n, d=1/256)
shifted = np.fft.fftshift(spectrum)

const spectrum = np.fft.fft(signal);
const freqs = np.fft.fftfreq(n, 1/256);
const shifted = np.fft.fftshift(spectrum);

arr = np.load('data.npy')
np.save('output.npy', arr)
data = np.load('data.npz')
np.savez('output.npz', x=arr1, y=arr2)
table = np.loadtxt('data.csv', delimiter=',')
np.savetxt('out.csv', arr, delimiter=',')

import * as np from 'numpy-ts';

const arr = await np.loadNpy('data.npy');
await np.saveNpy('output.npy', arr);
const data = await np.loadNpzFile('data.npz');
await np.savez('output.npz', { x: arr1, y: arr2 });
const table = await np.loadtxt('data.csv', { delimiter: ',' });
await np.savetxt('out.csv', arr, { delimiter: ',' });