Quick Start

This page shows the basic flow of Matft: create an array, compute, slice and read values back. If you know Numpy, the table below is almost everything you need.

Numpy	Matft
import numpy as np	import Matft
np.array([[1, 2], [3, 4]])	MfArray([[1, 2], [3, 4]])
np.arange(0, 6).reshape(2, 3)	Matft.arange(start: 0, to: 6, by: 1, shape: [2, 3])
a.dtype = np.float32	a.astype(.Float)
a[:, 1]	a[Matft.all, 1] or a[0~<, 1]
a[1:3] / a[::-1]	a[1~<3] / a[Matft.reverse]
a @ b	a *& b
np.sin(a)	Matft.math.sin(a)
a.sum(axis=0)	a.sum(axis: 0)
a.tolist()	a.toArray()

Create

import Matft

let a = MfArray([[1, 2, 3],
                 [4, 5, 6]])
let b = Matft.arange(start: 0, to: 6, by: 1, shape: [2, 3])
print(a)
/*
mfarray = 
[[	1,		2,		3],
[	4,		5,		6]], type=Int, shape=[2, 3]
*/

Compute

Operators work element-wise, and arrays with different shapes are broadcast.

print(a + b)
/*
mfarray = 
[[	1,		3,		5],
[	7,		9,		11]], type=Int, shape=[2, 3]
*/
print(a * MfArray([10, 100, 1000]))
/*
mfarray = 
[[	10,		200,		3000],
[	40,		500,		6000]], type=Int, shape=[2, 3]
*/
print(a.sum(axis: 0))
/*
mfarray = 
[	5,		7,		9], type=Int, shape=[3]
*/

Slice

Use ~< instead of Python's :. The result is a view of the original array.

print(a[Matft.all, 1~<3]) // a[:, 1:3]
/*
mfarray = 
[[	2,		3],
[	5,		6]], type=Int, shape=[2, 2]
*/

Read values back

print(a.toArray() as! [[Int]])
// [[1, 2, 3], [4, 5, 6]]
print(a.item(index: 4, type: Int.self))
// 5

Next, read the Guide or look up a function in the NumPy Mapping.