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#!/usr/bin/python3

# -*- coding: utf-8 -*-

# Author: xurongzhong#126.com 技术支持qq群：630011153

# CreateDate: 2018-04-12

import numpy as np

# Chapter 2 Beginning with NumPy fundamentals

#

# Demonstrates array stacking.

#

# Run from the commandline with

#

# python stacking.py

print("In: a = arange(9).reshape(3,3)")

a = np.arange(9).reshape(3,3)

print("In: a")

print(a)

#Out:

#array([[0, 1, 2],

# [3, 4, 5],

# [6, 7, 8]])

print("In: b = 2 * a")

b = 2 * a

print("In: b")

print(b)

#Out:

#array([[ 0, 2, 4],

# [ 6, 8, 10],

# [12, 14, 16]])

# 水平组合

print("In: hstack((a, b))")

print(np.hstack((a, b)))

#Out:

#array([[ 0, 1, 2, 0, 2, 4],

# [ 3, 4, 5, 6, 8, 10],

# [ 6, 7, 8, 12, 14, 16]])

print("In: concatenate((a, b), axis=1)")

print(np.concatenate((a, b), axis=1))

#Out:

#array([[ 0, 1, 2, 0, 2, 4],

# [ 3, 4, 5, 6, 8, 10],

# [ 6, 7, 8, 12, 14, 16]])

# 垂直组合

print("In: vstack((a, b))")

print(np.vstack((a, b)))

#Out:

#array([[ 0, 1, 2],

# [ 3, 4, 5],

# [ 6, 7, 8],

# [ 0, 2, 4],

# [ 6, 8, 10],

# [12, 14, 16]])

#

print("In: concatenate((a, b), axis=0)")

print(np.concatenate((a, b), axis=0))

#Out:

#array([[ 0, 1, 2],

# [ 3, 4, 5],

# [ 6, 7, 8],

# [ 0, 2, 4],

# [ 6, 8, 10],

# [12, 14, 16]])

# 深度组合

print("In: dstack((a, b))")

print(np.dstack((a, b)))

#Out:

#array([[[ 0, 0],

# [ 1, 2],

# [ 2, 4]],

#

# [[ 3, 6],

# [ 4, 8],

# [ 5, 10]],

#

# [[ 6, 12],

# [ 7, 14],

# [ 8, 16]]])

# 列组合

print("In: oned = arange(2)")

oned = np.arange(2)

print("In: oned")

print(oned)

#Out: array([0, 1])

print("In: twice_oned = 2 * oned")

twice_oned = 2 * oned

print("In: twice_oned")

print(twice_oned)

#Out: array([0, 2])

print("In: column_stack((oned, twice_oned))")

print(np.column_stack((oned, twice_oned)))

#Out:

#array([[0, 0],

# [1, 2]])

print("In: column_stack((a, b))")

print(np.column_stack((a, b)))

#Out:

#array([[ 0, 1, 2, 0, 2, 4],

# [ 3, 4, 5, 6, 8, 10],

# [ 6, 7, 8, 12, 14, 16]])

print("In: column_stack((a, b)) == hstack((a, b))")

print(np.column_stack((a, b)) == np.hstack((a, b)))

#Out:

#array([[ True, True, True, True, True, True],

# [ True, True, True, True, True, True],

# [ True, True, True, True, True, True]], dtype=bool)

# 行组合

# 对于两个一维数组，将直接层叠起来组合成一个二维数组。

print("In: row_stack((oned, twice_oned))")

print(np.row_stack((oned, twice_oned)))

#Out:

#array([[0, 1],

# [0, 2]])

# 对于二维数组， row_stack 与 vstack 的效果是相同的：

print("In: row_stack((a, b))")

print(np.row_stack((a, b)))

#Out:

#array([[ 0, 1, 2],

# [ 3, 4, 5],

# [ 6, 7, 8],

# [ 0, 2, 4],

# [ 6, 8, 10],

# [12, 14, 16]])

print("In: row_stack((a,b)) == vstack((a, b))")

print(np.row_stack((a,b)) == np.vstack((a, b)))

#Out:

#array([[ True, True, True],

# [ True, True, True],

# [ True, True, True],

# [ True, True, True],

# [ True, True, True],

# [ True, True, True]], dtype=bool)