讲解Python3中NumPy数组寻找特定元素下标的两种方法


						引子

Matlab中有一个函数叫做find,可以很方便地寻找数组内特定元素的下标，即：Find indices and values of nonzero elements。 

这个函数非常有用。比如，我们想计算图1中点Q（x0, y0）抛物线的最短距离。一个可以实施的方法是：计算出抛物线上所有点到Q点的距离，找到最小值，用find函数找到最小值对应的下标，即M点横坐标和纵坐标对应的元素的下标，M点到Q点的距离就是最短距离。 



 



首先给出Matlab使用find函数实现的代码：





a = linspace(-5,5,1000);

b = a .^2;

x0 = 4;

y0 = 4;

dis = sqrt((a - x0).^2 + (b - y0).^2);

mm = find (dis == min(dis));

a0 = a(mm);

b0 = b(mm);

disMin = sqrt((a0 - x0).^2 + (b0 - y0).^2);

plot(a, b);

hold on;

scatter(x0, y0, 'k*');

scatter(a0, b0, 'k*');

xx = [a0, x0];

yy = [b0, y0];

plot(xx, yy);





NumPy中的where函数

Syntax: np.where(conditions, [x,y]) 



具体实现代码如下：





import numpy as np

import math

import matplotlib.pyplot as plt



a = np.linspace(-5, 5, 10000)

b = a * a

x0 = 4

y0 =4

num = np.linspace(0, len(a) - 1, len(a))

dis = np.linspace(0, 0, len(a))

for k in num:

  k = int(k)

  dis[k] = dis[k] + math.sqrt((a[k] -x0) **2 + (b[k] - y0) **2)

disMin = min(dis)

disMinIndex = np.where(dis == disMin)

disMin0 = math.sqrt((a[disMinIndex] - x0) **2 + (b[disMinIndex] - y0) **2)

print('The mininum distance:',disMin)

print('The mininum distance:',disMin0)

print(type(dis))

a0 = a[disMinIndex]

b0 = b[disMinIndex]

fig = plt.figure(figsize = (6,6), dpi = 200)

ax1 = plt.subplot(1,1,1)

line11 = ax1.scatter(a,b,s = 1)

line12 = ax1.scatter(x0, y0, s = 100, marker = '*', color = 'darkorange')

line13 = ax1.scatter(a0, b0, s = 100, marker = '*', color = 'darkorange')

line14 = ax1.plot([x0,a0],[y0,b0], color = 'darkorange')

line15 = ax1.text(4.2,4,'Q(x0,y0)')

line16 = ax1.text(0.6,5, 'M(a0,b0)')

line18 = plt.xlim(-5,5)

line17 = plt.ylim(0,25)

plt.savefig('C:/Users/BRIAR/Desktop/index.png')

plt.show()



The mininum distance: 1.943317035 

The mininum distance: 1.9433170350024023 

class ‘numpy.ndarray' 





List中的index函数

Syntax: List.index(aimElement) 

注意：此处需将NumPy数组转换成List格式的数据。 

具体实现代码如下：





import numpy as np

import math

import matplotlib.pyplot as plt



a = np.linspace(-5, 5, 10000)

b = a * a

x0 = 4

y0 =4

num = np.linspace(0, len(a) - 1, len(a))

dis = np.linspace(0, 0, len(a))

for k in num:

  k = int(k)

  dis[k] = dis[k] + math.sqrt((a[k] -x0) **2 + (b[k] - y0) **2)

disMin = min(dis)

disList = dis.tolist()

disMinIndex = disList.index(disMin)

disMin0 = math.sqrt((a[disMinIndex] - x0) **2 + (b[disMinIndex] - y0) **2)

print('The mininum distance:',disMin)

print('The mininum distance:',disMin0)

print(type(disList))

a0 = a[disMinIndex]

b0 = b[disMinIndex]

fig = plt.figure(figsize = (6,6), dpi = 200)

ax1 = plt.subplot(1,1,1)

line11 = ax1.scatter(a,b,s = 1)

line12 = ax1.scatter(x0, y0, s = 100, marker = '*', color = 'darkorange')

line13 = ax1.scatter(a0, b0, s = 100, marker = '*', color = 'darkorange')

line14 = ax1.plot([x0,a0],[y0,b0], color = 'darkorange')

line15 = ax1.text(4.2,4,'Q(x0,y0)')

line16 = ax1.text(0.6,5, 'M(a0,b0)')

line18 = plt.xlim(-5,5)

line17 = plt.ylim(0,25)

plt.savefig('C:/Users/BRIAR/Desktop/index.png')

plt.show()



The mininum distance: 1.943317035 

The mininum distance: 1.9433170350024023 

class ‘list' 





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