Realization of numpy Array Combination and Matrix Splicing

concatenate, append, stack classes (including hsatck, vstack, dstack, row_stack, column_stack), r_, c_ and other classes and functions are provided in Numpy for array splicing operations.

The characteristics and differences of various functions are as follows:

concatenate	提供了axis参数，用于指定拼接方向
append	默认先ravel再拼接成1维数组，也可指定axis
stack	提供了axis参数，用于生成新的维度
hstack	水平拼接，沿着行的方向，对列进行拼接
vstack	垂直拼接，沿着列的方向，对行进行拼接
dstack	沿着第3个轴（深度方向）进行拼接
column_stack	水平拼接，沿着行的方向，对列进行拼接
row_stack	垂直拼接，沿着列的方向，对行进行拼接
r_	垂直拼接，沿着列的方向，对行进行拼接
c_	水平拼接，沿着行的方向，对列进行拼接

Direct merger

Combine two 1-dimensional arrays into one 2-dimensional array:

import torch
import numpy as np
import matplotlib.pyplot as plt
a = np.arange(0,15,0.1)
b = 1.088 * a + 0.638 + np.random.rand() * 10

print(a.shape,b.shape)
points = np.array([a,b])
print(points.shape)


(150,) (150,)
(2, 150)

append splicing

append(arr, values, axis=None)

arr	待合并的数组的复制（特别主页是复制，所以要多耗费很多内存）
values	用来合并到上述数组复制的值。如果指定了下面的参数axis的话，则这些值必须和arr的shape1致(shape[axis]之外都相等)，否则的话，则没有要求。
axis	要合并的轴.

>>> import numpy as np
>>> ar1 = np.array([[1,2,3], [4,5,6]])
>>> ar2 = np.array([[7,8,9], [11,12,13]])

>>> np.append(ar1, ar2) #  First ravel Flattening and splicing, so the return value is 1 A 1 Dimensional array 
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13])

>>> np.append(ar1, ar2, axis=0)  #  Along the first 1 Axis splicing, here is the direction of the row  
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])

>>> np.append(ar1, ar2, axis=1)  #  Along the first 2 Axis splicing, here is the direction of the column  
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])

concatenate splicing

concatenate(a_tuple, axis=0, out=None)

a_tuple:	对需要合并的数组用元组的形式给出
axis	待合并的轴，默认为0

 >>> import numpy as np
>>> ar1 = np.array([[1,2,3], [4,5,6]])
>>> ar2 = np.array([[7,8,9], [11,12,13]])
>>> ar1
array([[1, 2, 3],
  [4, 5, 6]])
>>> ar2
array([[ 7, 8, 9],
  [11, 12, 13]])

>>> np.concatenate((ar1, ar2)) #  The number here 1 Shaft (axis 0) Is the line direction 
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])

>>> np.concatenate((ar1, ar2),axis=1) #  Here along the first 2 Three axes, that is, the column direction, are spliced 
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])

>>> ar3 = np.array([[14,15,16]]) # shape For ( 1 , 3 ) 2 Dimensional array 
>>> np.concatenate((ar1, ar3)) # 1 Go on like concatenate Operational array Adj. shape Need 1 To, of course, if array In splicing axis Directional size No 1 Sample, can also be completed 
>>> np.concatenate((ar1, ar3)) # ar3 Although in axis0 The length of the direction is not 1 To, but axis1 In the direction 1 To, so along axis0 Can be spliced 
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [14, 15, 16]])
>>> np.concatenate((ar1, ar3), axis=1) # ar3 And ar1 In axis0 The length of the direction is not 1 To, so report an error 

hstack

>>> np.hstack((ar1,ar2)) #  Horizontal splicing, splicing columns along the direction of rows 
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])

vstack

>>> np.vstack((ar1,ar2)) #  Vertical splicing, splicing rows in the direction of columns 
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])

vstack

>>> np.dstack((ar1,ar2)) #  For 2 Dimension array, along the first 3 Shaft (depth direction) for splicing ,  The effect is equivalent to stack(axis=-1)
array([[[ 1, 7],
  [ 2, 8],
  [ 3, 9]],
  [[ 4, 11],
  [ 5, 12],
  [ 6, 13]]])

column_stack and row_stack

>>> np.column_stack((ar1,ar2)) #  Horizontal splicing, splicing columns along the direction of rows 
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])

>>> np.row_stack((ar1,ar2)) #  Vertical splicing, splicing rows in the direction of columns 
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])

np.r_ and np.c_

Commonly used to quickly generate ndarray data

>>> np.r_[ar1,ar2]  #  Vertical splicing, splicing rows in the direction of columns 
array([[ 1, 2, 3],
  [ 4, 5, 6],
  [ 7, 8, 9],
  [11, 12, 13]])
 
>>> np.c_[ar1,ar2] #  Horizontal splicing, splicing columns along the direction of rows 
array([[ 1, 2, 3, 7, 8, 9],
  [ 4, 5, 6, 11, 12, 13]])