使用 Matplotlib 可视化快速排序

通过分析和比较为比较和交换元素而发生的操作数量，可视化算法可以更容易地理解它们。为此，我们将使用 matplotlib 绘制条形图来表示数组的元素，

方法 ：

1. 我们将生成一个包含随机元素的数组。
2. 该算法将在该数组上调用，并且出于可视化目的，将使用 yield 语句而不是 return 语句。
3. 在比较和交换之后，我们将产生数组的当前状态。因此该算法将返回一个生成器对象。
4. Matplotlib 动画将用于可视化数组的比较和交换。
5. 该数组将存储在 matplotlib 条形容器对象 ('bar_rects') 中，其中每个条形的大小将等于数组中元素的相应值。
6. matplotlib 动画的内置 FuncAnimation 方法会将容器和生成器对象传递给用于创建动画的函数。动画的每一帧对应于生成器的一次迭代。
7. 重复调用的动画函数会将矩形的高度设置为元素的值。

python3

# import all the modules
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from mpl_toolkits.mplot3d import axes3d
import matplotlib as mp
import numpy as np
import random
 
# quicksort function
def quicksort(a, l, r):
    if l >= r:
        return
    x = a[l]
    j = l
    for i in range(l + 1, r + 1):
        if a[i] <= x:
            j += 1
            a[j], a[i] = a[i], a[j]
        yield a
    a[l], a[j]= a[j], a[l]
    yield a
 
    # yield from statement used to yield
    # the array after dividing
    yield from quicksort(a, l, j-1)
    yield from quicksort(a, j + 1, r)
 
# function to plot bars
def showGraph():
 
    # for random unique values
    n = int(input("enter array size\n"))
    a = [i for i in range(1, n + 1)]
    random.shuffle(a)
    datasetName ='Random'
 
    # generator object returned by the function
    generator = quicksort(a, 0, n-1)
    algoName = 'Quick Sort'
 
    # style of the chart
    plt.style.use('fivethirtyeight')
 
    # set colors of the bars
    data_normalizer = mp.colors.Normalize()
    color_map = mp.colors.LinearSegmentedColormap(
        "my_map",
        {
            "red": [(0, 1.0, 1.0),
                    (1.0, .5, .5)],
            "green": [(0, 0.5, 0.5),
                      (1.0, 0, 0)],
            "blue": [(0, 0.50, 0.5),
                     (1.0, 0, 0)]
        }
    )
 
    fig, ax = plt.subplots()
 
    # bar container
    bar_rects = ax.bar(range(len(a)), a, align ="edge",
                       color = color_map(data_normalizer(range(n))))
 
    # setting the limits of x and y axes
    ax.set_xlim(0, len(a))
    ax.set_ylim(0, int(1.1 * len(a)))
    ax.set_title("ALGORITHM : "+ algoName + "\n" + "DATA SET : " +
             datasetName, fontdict = {'fontsize': 13, 'fontweight':
                                      'medium', 'color' : '#E4365D'})
 
    # the text to be shown on the upper left indicating the number of iterations
    # transform indicates the position with relevance to the axes coordinates.
    text = ax.text(0.01, 0.95, "", transform = ax.transAxes, color = "#E4365D")
    iteration = [0]
 
    def animate(A, rects, iteration):
        for rect, val in zip(rects, A):
 
            # setting the size of each bar equal to the value of the elements
            rect.set_height(val)
        iteration[0] += 1
        text.set_text("iterations : {}".format(iteration[0]))
 
    # call animate function repeatedly
    anim = FuncAnimation(fig, func = animate,
        fargs = (bar_rects, iteration), frames = generator, interval = 50,
        repeat = False)
    plt.show()
 
showGraph()

输出 ：

对于数组大小 20