@aas395/numjs 中文文档教程
NumJs 是一个 npm/bower 包,用于使用 JavaScript 进行科学计算。 除其他外,它包含:
- a powerful N-dimensional array object
- linear algebra function
- fast Fourier transform
- tools for basic image processing
除了明显的科学用途外,NumJs 还可以用作通用数据的高效多维容器。
它适用于 node.js 和浏览器(有或没有 browserify)
NumJs 是根据 MIT license,几乎没有限制地重用。
请参阅此 jsfiddle,了解如何使用该库在浏览器中操作图像的具体示例。
Installation
on node.js
npm install numjs
var nj = require('numjs');
...
on the browser
bower install numjs
<script src="bower_packages/numjs/dist/numjs.min.js"></script>
<!-- or include it directly from a CDN -->
<script src="https://cdn.jsdelivr.net/gh/nicolaspanel/numjs@0.15.1/dist/numjs.min.js"></script>
Basics
Array Creation
> var a = nj.array([2,3,4]);
> a
array([ 2, 3, 4])
> var b = nj.array([[1,2,3], [4,5,6]]);
> b
array([[ 1, 2, 3],
[ 4, 5, 6]])
注意:默认数据容器是 Javascript Array
对象。 如果需要,您还可以使用类型化数组,例如 Uint8Array
:
> var a = nj.uint8([1,2,3]);
> a
array([ 1, 2, 3], dtype=uint8)
注意:可能的类型有 int8、uint8、int16、uint16、int32、uint32、float32、float64 和 array (默认值)
要创建具有给定形状的数组,您可以使用 zeros
、ones
或 random
函数:
> nj.zeros([2,3]);
array([[ 0, 0, 0],
[ 0, 0, 0]])
> nj.ones([2,3,4], 'int32') // dtype can also be specified
array([[[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]],
[[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]]], dtype=int32)
> nj.random([4,3])
array([[ 0.9182 , 0.85176, 0.22587],
[ 0.50088, 0.74376, 0.84024],
[ 0.74045, 0.23345, 0.20289],
[ 0.00612, 0.37732, 0.06932]])
要创建数字序列,NumJs 提供了一个名为arange
的函数:
> nj.arange(4);
array([ 0, 1, 2, 3])
> nj.arange( 10, 30, 5 )
array([ 10, 15, 20, 25])
> nj.arange(1, 5, 'uint8');
array([ 1, 2, 3, 4], dtype=uint8)
More info about the array
NumJs 的数组类称为NdArray
。 它也被称为 array
的别名。 NdArray
对象的更重要的属性是:
NdArray#ndim
: the number of axes (dimensions) of the array.NdArray#shape
: the dimensions of the array. This is a list of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be [n,m]. The length of the shape is therefore the number of dimensions, ndim.NdArray#size
: the total number of elements of the array. This is equal to the product of the elements of shape.NdArray#dtype
: a string describing the type of the elements in the array.int32
,int16
, andfloat64
are some examples. Default dtype isarray
.
NdArray
总是可以使用 NdArray#tolist( ) 方法。
示例:
> a = nj.arange(15).reshape(3, 5);
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14]])
> a.shape
[ 3, 5]
> a.ndim
2
> a.dtype
'array'
> a instanceof nj.NdArray
true
> a.tolist() instanceof Array
true
> a.get(1,1)
6
> a.set(0,0,1)
> a
array([[ 1, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14]])
Printing arrays
当您打印数组时,NumJs 以类似于嵌套列表的方式显示它,但具有以下布局:
- the last axis is printed from left to right,
- the second-to-last is printed from top to bottom,
- the rest are also printed from top to bottom, with each slice separated from the next by an empty line.
然后将一维数组打印为行,将二维数组打印为矩阵,将三维数组打印为列表矩阵。
> var a = nj.arange(6); // 1d array
> console.log(a);
array([ 0, 1, 2, 3, 4, 5])
>
> var b = nj.arange(12).reshape(4,3); // 2d array
> console.log(b);
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
>
> var c = nj.arange(24).reshape(2,3,4); // 3d array
> console.log(c);
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[ 12, 13, 14, 15],
[ 16, 17, 18, 19],
[ 20, 21, 22, 23]]])
如果数组太大而无法打印,NumJs 会自动跳过数组的中央部分,只打印边角:
> console.log(nj.arange(10000).reshape(100,100))
array([[ 0, 1, ..., 98, 99],
[ 100, 101, ..., 198, 199],
...
[ 9800, 9801, ..., 9898, 9899],
[ 9900, 9901, ..., 9998, 9999]])
要自定义此行为,您可以使用 nj.config 更改打印选项.printThreshold
(默认为7
):
> nj.config.printThreshold = 9;
> console.log(nj.arange(10000).reshape(100,100))
array([[ 0, 1, 2, 3, ..., 96, 97, 98, 99],
[ 100, 101, 102, 103, ..., 196, 197, 198, 199],
[ 200, 201, 202, 203, ..., 296, 297, 298, 299],
[ 300, 301, 302, 303, ..., 396, 397, 398, 399],
...
[ 9600, 9601, 9602, 9603, ..., 9696, 9697, 9698, 9699],
[ 9700, 9701, 9702, 9703, ..., 9796, 9797, 9798, 9799],
[ 9800, 9801, 9802, 9803, ..., 9896, 9897, 9898, 9899],
[ 9900, 9901, 9902, 9903, ..., 9996, 9997, 9998, 9999]])
Indexing
单元素索引使用get
和set
方法。 它是从 0 开始的,并且接受从数组末尾开始索引的负索引:
> var a = nj.array([0,1,2]);
> a.get(1)
1
>
> a.get(-1)
2
>
> var b = nj.arange(3*3).reshape(3,3);
> b
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8])
>
> b.get(1, 1);
4
>
> b.get(-1, -1);
8
> b.set(0,0,1);
> b
array([[ 1, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]])
Slicing and Striding
可以对数组进行切片和跨步以提取维数相同但大小与原始数组不同的数组。 切片和跨步的工作方式与它在 NumPy 中的工作方式完全相同:
> var a = nj.arange(5);
> a
array([ 0, 1, 2, 3, 4])
>
> a.slice(1) // skip the first item, same as a[1:]
array([ 1, 2, 3, 4])
>
> a.slice(-3) // takes the last 3 items, same as a[-3:]
array([ 2, 3, 4])
>
> a.slice([4]) // takes the first 4 items, same as a[:4]
array([ 0, 1, 2, 3])
>
> a.slice([-2]) // skip the last 2 items, same as a[:-2]
array([ 0, 1, 2])
>
> a.slice([1,4]) // same as a[1:4]
array([ 1, 2, 3])
>
> a.slice([1,4,-1]) // same as a[1:4:-1]
array([ 3, 2, 1])
>
> a.slice([null,null,-1]) // same as a[::-1]
array([ 4, 3, 2, 1, 0])
>
> var b = nj.arange(5*5).reshape(5,5);
> b
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14],
[ 15, 16, 17, 18, 19],
[ 20, 21, 22, 23, 24]])
>
> b.slice(1,2) // skip the first row and the 2 first columns, same as b[1:,2:]
array([[ 7, 8, 9],
[ 12, 13, 14],
[ 17, 18, 19],
[ 22, 23, 24]])
>
> b.slice(null, [null, null, -1]) // reverse rows, same as b[:, ::-1]
array([[ 4, 3, 2, 1, 0],
[ 9, 8, 7, 6, 5],
[ 14, 13, 12, 11, 10],
[ 19, 18, 17, 16, 15],
[ 24, 23, 22, 21, 20]])
请注意,切片不会复制内部数组数据,它会生成原始数据的新视图。
Basic operations
*
(multiply
)、+
(add
)、-
等算术运算符(subtract
), /
(divide
), **
(pow
), =
(assign
) 按元素应用。 创建一个新数组并用结果填充:
> zeros = nj.zeros([3,4]);
array([[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]])
>
> ones = nj.ones([3,4]);
array([[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]])
>
> ones.add(ones)
array([[ 2, 2, 2, 2],
[ 2, 2, 2, 2],
[ 2, 2, 2, 2]])
>
> ones.subtract(ones)
array([[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]])
>
> zeros.pow(zeros)
array([[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]])
>
要修改现有数组而不是创建一个新数组,您可以将 copy
参数设置为 false
:
> ones = nj.ones([3,4]);
array([[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]])
>
> ones.add(ones, false)
array([[ 2, 2, 2, 2],
[ 2, 2, 2, 2],
[ 2, 2, 2, 2]])
>
> ones
array([[ 2, 2, 2, 2],
[ 2, 2, 2, 2],
[ 2, 2, 2, 2]])
>
> zeros = nj.zeros([3,4])
> zeros.slice([1,-1],[1,-1]).assign(1, false);
> zeros
array([[ 0, 0, 0, 0],
[ 0, 1, 1, 0],
[ 0, 0, 0, 0]])
注意< /strong>:可用于加法
、减法
、乘法
、除法
、赋值
和 pow
方法。
可以使用 dot
函数执行矩阵乘积:
> a = nj.arange(12).reshape(3,4);
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> nj.dot(a.T, a)
array([[ 80, 92, 104, 116],
[ 92, 107, 122, 137],
[ 104, 122, 140, 158],
[ 116, 137, 158, 179]])
>
> nj.dot(a, a.T)
array([[ 14, 38, 62],
[ 38, 126, 214],
[ 62, 214, 366]])
许多一元运算,例如计算数组中所有元素的总和,都是作为 NdArray
类的方法实现的:
> a = nj.random([2,3])
array([[0.62755, 0.8278,0.21384],
[ 0.7029,0.27584,0.46472]])
> a.sum()
3.1126488673035055
>
> a.min()
0.2138431086204946
>
> a.max()
0.8278025290928781
>
> a.mean()
0.5187748112172509
>
> a.std()
0.22216977543691244
Universal Functions
NumJs 提供熟悉的数学函数,例如 sin
、cos
和 exp
。 元素操作,生成 NdArray
作为输出
> a = nj.array([-1, 0, 1])
array([-1, 0, 1])
>
> nj.negative(a)
array([ 1, 0,-1])
>
> nj.abs(a)
array([ 1, 0, 1])
>
> nj.exp(a)
array([ 0.36788, 1, 2.71828])
>
> nj.tanh(a)
array([-0.76159, 0, 0.76159])
>
> nj.softmax(a)
array([ 0.09003, 0.24473, 0.66524])
>
> nj.sigmoid(a)
array([ 0.26894, 0.5, 0.73106])
>
> nj.exp(a)
array([ 0.36788, 1, 2.71828])
>
> nj.log(nj.exp(a))
array([-1, 0, 1])
>
> nj.sqrt(nj.abs(a))
array([ 1, 0, 1])
>
> nj.sin(nj.arcsin(a))
array([-1, 0, 1])
>
> nj.cos(nj.arccos(a))
array([-1, 0, 1])
>
> nj.tan(nj.arctan(a))
array([-1, 0, 1])
Shape Manipulation
这些函数在数组上按
> a = nj.array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]);
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
> a.shape
[ 3, 4 ]
:
> a.flatten();
array([ 0, 1, 2, ..., 9, 10, 11])
>
> a.T // equivalent to a.transpose(1,0)
array([[ 0, 4, 8],
[ 1, 5, 9],
[ 2, 6, 10],
[ 3, 7, 11]])
>
> a.reshape(4,3)
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
>
由于 a
是矩阵,我们可能需要它的对角线:
> nj.diag(a)
array([ 0, 5, 10])
>
Identity matrix
单位数组是一个正方形数组,其中一个在主对角线上:
> nj.identity(3)
array([[ 1, 0, 0],
[ 0, 1, 0],
[ 0, 0, 1]])
Concatenate different arrays
可以使用 concatenate
函数将多个数组堆叠在一起:
> a = nj.arange(12).reshape(3,4)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> b = nj.arange(3)
array([ 0, 1, 2])
>
> nj.concatenate(a,b.reshape(3,1))
array([[ 0, 1, 2, 3, 0],
[ 4, 5, 6, 7, 1],
[ 8, 9, 10, 11, 2]])
注意:
- the arrays must have the same shape, except in the last dimension
- arrays are concatenated along the last axis
仍然可以使用换位沿其他维度连接:
> a = nj.arange(12).reshape(3,4)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> b = nj.arange(4)
array([ 0, 1, 2, 3])
>
> nj.concatenate(a.T,b.reshape(4,1)).T
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[ 0, 1, 2, 3]])
Stack multiple arrays
> a = nj.array([1, 2, 3])
> b = nj.array([2, 3, 4])
> np.stack([a, b])
array([[1, 2, 3],
[2, 3, 4]])
> np.stack([a, b], -1)
array([[1, 2],
[2, 3],
[3, 4]])
注意:
- the arrays must have the same shape
- take an optional axis argument which can be negative
Deep Copy
clone
方法可完整复制数组及其数据。
> a = nj.arange(12).reshape(3,4)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> b = a.clone()
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> a === b
false
>
> a.set(0,0,1)
> a
array([[ 1, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
> b
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
Fast Fourier Transform (FFT)
fft
和 ifft
函数可用于计算 N 维离散傅里叶变换及其逆变换。
示例:
> RI = nj.concatenate(nj.ones([10,1]), nj.zeros([10,1]))
array([[ 1, 0],
[ 1, 0],
[ 1, 0],
...
[ 1, 0],
[ 1, 0],
[ 1, 0]])
>
> fft = nj.fft(RI)
array([[ 10, 0],
[ 0, 0],
[ 0, 0],
...
[ 0, 0],
[ 0, 0],
[ 0, 0]])
>
> nj.ifft(fft)
array([[ 1, 0],
[ 1, 0],
[ 1, 0],
...
[ 1, 0],
[ 1, 0],
[ 1, 0]])
注意:fft
和ifft
都期望数组的最后一维包含 2 个值:实值和虚值
Convolution
卷积
函数计算两个多维数组的离散线性卷积。
注意:卷积积仅针对信号完全重叠的点给出。 信号边界外的值无效。 此行为也称为“有效”模式。
示例:
> x = nj.array([0,0,1,2,1,0,0])
array([ 0, 0, 1, 2, 1, 0, 0])
>
> nj.convolve(x, [-1,0,1])
array([-1,-2, 0, 2, 1])
>
> var a = nj.arange(25).reshape(5,5)
> a
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14],
[ 15, 16, 17, 18, 19],
[ 20, 21, 22, 23, 24]])
> nj.convolve(a, [[ 1, 2, 1], [ 0, 0, 0], [-1,-2,-1]])
array([[ 40, 40, 40],
[ 40, 40, 40],
[ 40, 40, 40]])
> nj.convolve(nj.convolve(a, [[1, 2, 1]]), [[1],[0],[-1]])
array([[ 40, 40, 40],
[ 40, 40, 40],
[ 40, 40, 40]])
注意:卷积
使用快速傅立叶变换 (FFT) 来加速大型数组的计算。
Other utils
rot90
> m = nj.array([[1,2],[3,4]], 'int')
> m
array([[1, 2],
[3, 4]])
> nj.rot90(m)
array([[2, 4],
[1, 3]])
> nj.rot90(m, 2)
array([[4, 3],
[2, 1]])
> m = nj.arange(8).reshape([2,2,2])
> nj.rot90(m, 1, [1,2])
array([[[1, 3],
[0, 2]],
[[5, 7],
[4, 6]]])
mod
(since v0.16.0)
> nj.mod(nj.arange(7), 5)
> m
array([0, 1, 2, 3, 4, 0, 1])
Images manipulation
NumJs 具有强大的图像处理功能。 这些函数位于 nj.images
模块中。
使用 NdArray
对象存储不同的色带/通道,例如灰度图像是 [H,W]
,RGB 图像是 [H, W,3]
而 RGBA 图像是 [H,W,4]
。
使用 nj.images.read
、nj.images.write
和 nj.images.resize
函数(分别)读取、写入或调整图像大小.
示例:
> nj.config.printThreshold = 28;
>
> var img = nj.images.data.digit; // WARN: this is a property, not a function. See also `nj.images.data.moon`, `nj.images.data.lenna` and `nj.images.data.node`
>
> img
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18, 126, 136, 175, 26, 166, 255, 247, 127, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253, 253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253, 253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253, 253, 198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253, 205, 11, 0, 43, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253, 90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253, 190, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190, 253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221, 253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253, 253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
> var resized = nj.images.resize(img, 14, 12)
>
> resized.shape
[ 14, 12 ]
>
> resized
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 6, 9, 66, 51, 106, 94, 0],
[ 0, 0, 13, 140, 189, 233, 253, 253, 143, 159, 75, 0],
[ 0, 0, 5, 178, 217, 241, 98, 172, 0, 0, 0, 0],
[ 0, 0, 0, 4, 74, 197, 1, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 3, 180, 114, 28, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 21, 182, 220, 51, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 4, 149, 236, 16, 0, 0],
[ 0, 0, 0, 0, 0, 47, 165, 236, 224, 1, 0, 0],
[ 0, 0, 0, 23, 152, 245, 240, 135, 20, 0, 0, 0],
[ 0, 57, 167, 245, 251, 148, 23, 0, 0, 0, 0, 0],
[ 0, 98, 127, 87, 37, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
另请参阅 this jsfiddle 以了解有关浏览器可能实现的功能的更多详细信息。
More ?
请参阅有关 numjs globals 和 NdArray 方法。
Credits
NumJs is a npm/bower package for scientific computing with JavaScript. It contains among other things:
- a powerful N-dimensional array object
- linear algebra function
- fast Fourier transform
- tools for basic image processing
Besides its obvious scientific uses, NumJs can also be used as an efficient multi-dimensional container of generic data.
It works both in node.js and in the browser (with or without browserify)
NumJs is licensed under the MIT license, enabling reuse with almost no restrictions.
See this jsfiddle for a concrete example of how to use the library to manipulate images in the browser.
Installation
on node.js
npm install numjs
var nj = require('numjs');
...
on the browser
bower install numjs
<script src="bower_packages/numjs/dist/numjs.min.js"></script>
<!-- or include it directly from a CDN -->
<script src="https://cdn.jsdelivr.net/gh/nicolaspanel/numjs@0.15.1/dist/numjs.min.js"></script>
Basics
Array Creation
> var a = nj.array([2,3,4]);
> a
array([ 2, 3, 4])
> var b = nj.array([[1,2,3], [4,5,6]]);
> b
array([[ 1, 2, 3],
[ 4, 5, 6]])
Note: Default data container is Javascript Array
object. If needed, you can also use typed array such as Uint8Array
:
> var a = nj.uint8([1,2,3]);
> a
array([ 1, 2, 3], dtype=uint8)
Note: possible types are int8, uint8, int16, uint16, int32, uint32, float32, float64 and array (the default)
To create arrays with a given shape, you can use zeros
, ones
or random
functions:
> nj.zeros([2,3]);
array([[ 0, 0, 0],
[ 0, 0, 0]])
> nj.ones([2,3,4], 'int32') // dtype can also be specified
array([[[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]],
[[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]]], dtype=int32)
> nj.random([4,3])
array([[ 0.9182 , 0.85176, 0.22587],
[ 0.50088, 0.74376, 0.84024],
[ 0.74045, 0.23345, 0.20289],
[ 0.00612, 0.37732, 0.06932]])
To create sequences of numbers, NumJs provides a function called arange
:
> nj.arange(4);
array([ 0, 1, 2, 3])
> nj.arange( 10, 30, 5 )
array([ 10, 15, 20, 25])
> nj.arange(1, 5, 'uint8');
array([ 1, 2, 3, 4], dtype=uint8)
More info about the array
NumJs’s array class is called NdArray
. It is also known by the alias array
. The more important properties of an NdArray
object are:
NdArray#ndim
: the number of axes (dimensions) of the array.NdArray#shape
: the dimensions of the array. This is a list of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be [n,m]. The length of the shape is therefore the number of dimensions, ndim.NdArray#size
: the total number of elements of the array. This is equal to the product of the elements of shape.NdArray#dtype
: a string describing the type of the elements in the array.int32
,int16
, andfloat64
are some examples. Default dtype isarray
.
An NdArray
can always be converted to a native JavaScript Array
using NdArray#tolist()
method.
Example:
> a = nj.arange(15).reshape(3, 5);
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14]])
> a.shape
[ 3, 5]
> a.ndim
2
> a.dtype
'array'
> a instanceof nj.NdArray
true
> a.tolist() instanceof Array
true
> a.get(1,1)
6
> a.set(0,0,1)
> a
array([[ 1, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14]])
Printing arrays
When you print an array, NumJs displays it in a similar way to nested lists, but with the following layout:
- the last axis is printed from left to right,
- the second-to-last is printed from top to bottom,
- the rest are also printed from top to bottom, with each slice separated from the next by an empty line.
One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices.
> var a = nj.arange(6); // 1d array
> console.log(a);
array([ 0, 1, 2, 3, 4, 5])
>
> var b = nj.arange(12).reshape(4,3); // 2d array
> console.log(b);
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
>
> var c = nj.arange(24).reshape(2,3,4); // 3d array
> console.log(c);
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[ 12, 13, 14, 15],
[ 16, 17, 18, 19],
[ 20, 21, 22, 23]]])
If an array is too large to be printed, NumJs automatically skips the central part of the array and only prints the corners:
> console.log(nj.arange(10000).reshape(100,100))
array([[ 0, 1, ..., 98, 99],
[ 100, 101, ..., 198, 199],
...
[ 9800, 9801, ..., 9898, 9899],
[ 9900, 9901, ..., 9998, 9999]])
To customize this behaviour, you can change the printing options using nj.config.printThreshold
(default is 7
):
> nj.config.printThreshold = 9;
> console.log(nj.arange(10000).reshape(100,100))
array([[ 0, 1, 2, 3, ..., 96, 97, 98, 99],
[ 100, 101, 102, 103, ..., 196, 197, 198, 199],
[ 200, 201, 202, 203, ..., 296, 297, 298, 299],
[ 300, 301, 302, 303, ..., 396, 397, 398, 399],
...
[ 9600, 9601, 9602, 9603, ..., 9696, 9697, 9698, 9699],
[ 9700, 9701, 9702, 9703, ..., 9796, 9797, 9798, 9799],
[ 9800, 9801, 9802, 9803, ..., 9896, 9897, 9898, 9899],
[ 9900, 9901, 9902, 9903, ..., 9996, 9997, 9998, 9999]])
Indexing
Single element indexing uses get
and set
methods. It is 0-based, and accepts negative indices for indexing from the end of the array:
> var a = nj.array([0,1,2]);
> a.get(1)
1
>
> a.get(-1)
2
>
> var b = nj.arange(3*3).reshape(3,3);
> b
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8])
>
> b.get(1, 1);
4
>
> b.get(-1, -1);
8
> b.set(0,0,1);
> b
array([[ 1, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]])
Slicing and Striding
It is possible to slice and stride arrays to extract arrays of the same number of dimensions, but of different sizes than the original. The slicing and striding works exactly the same way it does in NumPy:
> var a = nj.arange(5);
> a
array([ 0, 1, 2, 3, 4])
>
> a.slice(1) // skip the first item, same as a[1:]
array([ 1, 2, 3, 4])
>
> a.slice(-3) // takes the last 3 items, same as a[-3:]
array([ 2, 3, 4])
>
> a.slice([4]) // takes the first 4 items, same as a[:4]
array([ 0, 1, 2, 3])
>
> a.slice([-2]) // skip the last 2 items, same as a[:-2]
array([ 0, 1, 2])
>
> a.slice([1,4]) // same as a[1:4]
array([ 1, 2, 3])
>
> a.slice([1,4,-1]) // same as a[1:4:-1]
array([ 3, 2, 1])
>
> a.slice([null,null,-1]) // same as a[::-1]
array([ 4, 3, 2, 1, 0])
>
> var b = nj.arange(5*5).reshape(5,5);
> b
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14],
[ 15, 16, 17, 18, 19],
[ 20, 21, 22, 23, 24]])
>
> b.slice(1,2) // skip the first row and the 2 first columns, same as b[1:,2:]
array([[ 7, 8, 9],
[ 12, 13, 14],
[ 17, 18, 19],
[ 22, 23, 24]])
>
> b.slice(null, [null, null, -1]) // reverse rows, same as b[:, ::-1]
array([[ 4, 3, 2, 1, 0],
[ 9, 8, 7, 6, 5],
[ 14, 13, 12, 11, 10],
[ 19, 18, 17, 16, 15],
[ 24, 23, 22, 21, 20]])
Note that slices do not copy the internal array data, it produces a new views of the original data.
Basic operations
Arithmetic operators such as *
(multiply
), +
(add
), -
(subtract
), /
(divide
), **
(pow
), =
(assign
) apply elemen-twise. A new array is created and filled with the result:
> zeros = nj.zeros([3,4]);
array([[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]])
>
> ones = nj.ones([3,4]);
array([[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]])
>
> ones.add(ones)
array([[ 2, 2, 2, 2],
[ 2, 2, 2, 2],
[ 2, 2, 2, 2]])
>
> ones.subtract(ones)
array([[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]])
>
> zeros.pow(zeros)
array([[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]])
>
To modify an existing array rather than create a new one you can set the copy
parameter to false
:
> ones = nj.ones([3,4]);
array([[ 1, 1, 1, 1],
[ 1, 1, 1, 1],
[ 1, 1, 1, 1]])
>
> ones.add(ones, false)
array([[ 2, 2, 2, 2],
[ 2, 2, 2, 2],
[ 2, 2, 2, 2]])
>
> ones
array([[ 2, 2, 2, 2],
[ 2, 2, 2, 2],
[ 2, 2, 2, 2]])
>
> zeros = nj.zeros([3,4])
> zeros.slice([1,-1],[1,-1]).assign(1, false);
> zeros
array([[ 0, 0, 0, 0],
[ 0, 1, 1, 0],
[ 0, 0, 0, 0]])
Note: available for add
, subtract
, multiply
, divide
, assign
and pow
methods.
The matrix product can be performed using the dot
function:
> a = nj.arange(12).reshape(3,4);
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> nj.dot(a.T, a)
array([[ 80, 92, 104, 116],
[ 92, 107, 122, 137],
[ 104, 122, 140, 158],
[ 116, 137, 158, 179]])
>
> nj.dot(a, a.T)
array([[ 14, 38, 62],
[ 38, 126, 214],
[ 62, 214, 366]])
Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the NdArray
class:
> a = nj.random([2,3])
array([[0.62755, 0.8278,0.21384],
[ 0.7029,0.27584,0.46472]])
> a.sum()
3.1126488673035055
>
> a.min()
0.2138431086204946
>
> a.max()
0.8278025290928781
>
> a.mean()
0.5187748112172509
>
> a.std()
0.22216977543691244
Universal Functions
NumJs provides familiar mathematical functions such as sin
, cos
, and exp
. These functions operate element-wise on an array, producing an NdArray
as output:
> a = nj.array([-1, 0, 1])
array([-1, 0, 1])
>
> nj.negative(a)
array([ 1, 0,-1])
>
> nj.abs(a)
array([ 1, 0, 1])
>
> nj.exp(a)
array([ 0.36788, 1, 2.71828])
>
> nj.tanh(a)
array([-0.76159, 0, 0.76159])
>
> nj.softmax(a)
array([ 0.09003, 0.24473, 0.66524])
>
> nj.sigmoid(a)
array([ 0.26894, 0.5, 0.73106])
>
> nj.exp(a)
array([ 0.36788, 1, 2.71828])
>
> nj.log(nj.exp(a))
array([-1, 0, 1])
>
> nj.sqrt(nj.abs(a))
array([ 1, 0, 1])
>
> nj.sin(nj.arcsin(a))
array([-1, 0, 1])
>
> nj.cos(nj.arccos(a))
array([-1, 0, 1])
>
> nj.tan(nj.arctan(a))
array([-1, 0, 1])
Shape Manipulation
An array has a shape given by the number of elements along each axis:
> a = nj.array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]);
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
> a.shape
[ 3, 4 ]
The shape of an array can be changed with various commands:
> a.flatten();
array([ 0, 1, 2, ..., 9, 10, 11])
>
> a.T // equivalent to a.transpose(1,0)
array([[ 0, 4, 8],
[ 1, 5, 9],
[ 2, 6, 10],
[ 3, 7, 11]])
>
> a.reshape(4,3)
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
>
Since a
is matrix we may want its diagonal:
> nj.diag(a)
array([ 0, 5, 10])
>
Identity matrix
The identity array is a square array with ones on the main diagonal:
> nj.identity(3)
array([[ 1, 0, 0],
[ 0, 1, 0],
[ 0, 0, 1]])
Concatenate different arrays
Several arrays can be stacked together using concatenate
function:
> a = nj.arange(12).reshape(3,4)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> b = nj.arange(3)
array([ 0, 1, 2])
>
> nj.concatenate(a,b.reshape(3,1))
array([[ 0, 1, 2, 3, 0],
[ 4, 5, 6, 7, 1],
[ 8, 9, 10, 11, 2]])
Notes:
- the arrays must have the same shape, except in the last dimension
- arrays are concatenated along the last axis
It is still possible to concatenate along other dimensions using transpositions:
> a = nj.arange(12).reshape(3,4)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> b = nj.arange(4)
array([ 0, 1, 2, 3])
>
> nj.concatenate(a.T,b.reshape(4,1)).T
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[ 0, 1, 2, 3]])
Stack multiple arrays
> a = nj.array([1, 2, 3])
> b = nj.array([2, 3, 4])
> np.stack([a, b])
array([[1, 2, 3],
[2, 3, 4]])
> np.stack([a, b], -1)
array([[1, 2],
[2, 3],
[3, 4]])
Notes:
- the arrays must have the same shape
- take an optional axis argument which can be negative
Deep Copy
The clone
method makes a complete copy of the array and its data.
> a = nj.arange(12).reshape(3,4)
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> b = a.clone()
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>
> a === b
false
>
> a.set(0,0,1)
> a
array([[ 1, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
> b
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
Fast Fourier Transform (FFT)
fft
and ifft
functions can be used to compute the N-dimensional discrete Fourier Transform and its inverse.
Example:
> RI = nj.concatenate(nj.ones([10,1]), nj.zeros([10,1]))
array([[ 1, 0],
[ 1, 0],
[ 1, 0],
...
[ 1, 0],
[ 1, 0],
[ 1, 0]])
>
> fft = nj.fft(RI)
array([[ 10, 0],
[ 0, 0],
[ 0, 0],
...
[ 0, 0],
[ 0, 0],
[ 0, 0]])
>
> nj.ifft(fft)
array([[ 1, 0],
[ 1, 0],
[ 1, 0],
...
[ 1, 0],
[ 1, 0],
[ 1, 0]])
Note: both fft
and ifft
expect last dimension of the array to contain 2 values: the real and the imaginary value
Convolution
convolve
function compute the discrete, linear convolution of two multi-dimensional arrays.
Note: The convolution product is only given for points where the signals overlap completely. Values outside the signal boundary have no effect. This behaviour is also known as the 'valid' mode.
Example:
> x = nj.array([0,0,1,2,1,0,0])
array([ 0, 0, 1, 2, 1, 0, 0])
>
> nj.convolve(x, [-1,0,1])
array([-1,-2, 0, 2, 1])
>
> var a = nj.arange(25).reshape(5,5)
> a
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14],
[ 15, 16, 17, 18, 19],
[ 20, 21, 22, 23, 24]])
> nj.convolve(a, [[ 1, 2, 1], [ 0, 0, 0], [-1,-2,-1]])
array([[ 40, 40, 40],
[ 40, 40, 40],
[ 40, 40, 40]])
> nj.convolve(nj.convolve(a, [[1, 2, 1]]), [[1],[0],[-1]])
array([[ 40, 40, 40],
[ 40, 40, 40],
[ 40, 40, 40]])
Note: convolve
uses Fast Fourier Transform (FFT) to speed up computation on large arrays.
Other utils
rot90
> m = nj.array([[1,2],[3,4]], 'int')
> m
array([[1, 2],
[3, 4]])
> nj.rot90(m)
array([[2, 4],
[1, 3]])
> nj.rot90(m, 2)
array([[4, 3],
[2, 1]])
> m = nj.arange(8).reshape([2,2,2])
> nj.rot90(m, 1, [1,2])
array([[[1, 3],
[0, 2]],
[[5, 7],
[4, 6]]])
mod
(since v0.16.0)
> nj.mod(nj.arange(7), 5)
> m
array([0, 1, 2, 3, 4, 0, 1])
Images manipulation
NumJs’s comes with powerful functions for image processing. Theses function are located in nj.images
module.
The different color bands/channels are stored using the NdArray
object such that a grey-image is [H,W]
, an RGB-image is [H,W,3]
and an RGBA-image is [H,W,4]
.
Use nj.images.read
, nj.images.write
and nj.images.resize
functions to (respectively) read, write or resize images.
Example:
> nj.config.printThreshold = 28;
>
> var img = nj.images.data.digit; // WARN: this is a property, not a function. See also `nj.images.data.moon`, `nj.images.data.lenna` and `nj.images.data.node`
>
> img
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18, 126, 136, 175, 26, 166, 255, 247, 127, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253, 253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253, 253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253, 253, 198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253, 205, 11, 0, 43, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253, 90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253, 190, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190, 253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221, 253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253, 253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
> var resized = nj.images.resize(img, 14, 12)
>
> resized.shape
[ 14, 12 ]
>
> resized
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 6, 9, 66, 51, 106, 94, 0],
[ 0, 0, 13, 140, 189, 233, 253, 253, 143, 159, 75, 0],
[ 0, 0, 5, 178, 217, 241, 98, 172, 0, 0, 0, 0],
[ 0, 0, 0, 4, 74, 197, 1, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 3, 180, 114, 28, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 21, 182, 220, 51, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 4, 149, 236, 16, 0, 0],
[ 0, 0, 0, 0, 0, 47, 165, 236, 224, 1, 0, 0],
[ 0, 0, 0, 23, 152, 245, 240, 135, 20, 0, 0, 0],
[ 0, 57, 167, 245, 251, 148, 23, 0, 0, 0, 0, 0],
[ 0, 98, 127, 87, 37, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
See also this jsfiddle for more details on what is possible from the browser.
More ?
See documentation on numjs globals and NdArray methods.
Credits
NumJs is built on top of ndarray and uses many scijs packages