002_Numoy索引和切片

定义可视化函数

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import math
from matplotlib import cm 

def visualize_2D(array, vmax, vmin):
    
    fig_width = math.ceil(array.shape[1] * 0.5)
    fig_length = math.ceil(array.shape[0] * 0.5)
    
    fig, ax = plt.subplots(figsize = (fig_width, fig_length))
    
    sns.heatmap(array,
               vmax = vmax,
               vmin = vmin,
               annot = True,
               fmt = '.0f',
               square = True,
               cmap = 'RdYlBu_r',
               linewidth = .5,
               cbar = False,
               xticklabels = False,
               yticklabels = False,
               ax = ax)

def visual_1D(array):
    fix, ax = plt.subplots()
    
    colors = cm.RdYlBu_r(np.linspace(0, 1, len(array)))
    
    for idx,num in enumerate(array):
        circle_idx = plt.Circle((idx, 0 ), 
                                0.5,
                               facecolor = colors[idx],
                               edgecolor = 'w')
        ax.add_patch(circle_idx)
        ax.text(idx, 0, s = str(array[idx]),
                horizontalalignment = 'center',
                verticalalignment = 'center'
               )
    ax.set_xlim(-0.6, 0.6 + len(array))
    ax.set_ylim(-0.6, 0.6)
    ax.axis('off')
    
    ax.set_aspect('equal', adjustable = 'box')

1.一维数组

索引 行向量、列向量、切片、整数索引、切片、布尔索引切片

a = np.arange(-5, 6)
visual_1D(a)

a.shape

(11,)

a[0],a[-11],a[10],a[-1]

(-5, -5, 5, 5)

行向量、列向量

升维 a[:,np.newaxis] 让数组当前增加一个维度
降维 np.squeeze() 用来给压缩数组shape为1的维度

visualize_2D(a[:,np.newaxis],5,-5)
a[:,np.newaxis]

array([[-5],
       [-4],
       [-3],
       [-2],
       [-1],
       [ 0],
       [ 1],
       [ 2],
       [ 3],
       [ 4],
       [ 5]])

visualize_2D(a[np.newaxis,:],5,-5)
a[np.newaxis,:]

array([[-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5]])

a[np.newaxis,:].ndim

2

a[:,np.newaxis,np.newaxis].shape,np.squeeze(a[:,np.newaxis,np.newaxis]).ndim

((11, 1, 1), 1)

切片

visual_1D(a[0:3])

visual_1D(a[::2]) #奇数
visual_1D(a[1::2]) #偶数
visual_1D(a[::-1]) #倒序输出

整数索引

visual_1D(a[np.r_[0,1,2,-1]])

visual_1D(a[[0,1,2,-1]])

布尔索引、切片

visual_1D(a[a>0]) 
visual_1D(a[a<0])

2.二维数组

A_2D = np.array([[-7,-6,-5,-4,-3],
                 [-2,-1, 0, 1, 2],
                 [ 3, 4, 5, 6, 7]])
visualize_2D(A_2D, 7 , -8)

取出行

visualize_2D(A_2D[:1,:],7,-1)
visualize_2D(A_2D[2:3,:],7,-1)

取出列

visualize_2D(A_2D[:,:1],7,-7)  
visualize_2D(A_2D[:,2:],7,-7)

同时对行列进行操作

visualize_2D(A_2D[1:2,1:3],7,-7)