在Python中使用 Matplotlib 进行快速排序的 3D 可视化

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。算法的 3D 可视化不太常见，为此我们将使用 Matplotlib 绘制条形图并对其进行动画处理以表示数组的元素。

让我们看看快速排序的 3D 可视化：

方法：

1. We will generate an array with random elements.
2. The algorithm will be called on that array and yield statement will be used instead of return statement for visualization purposes.
3. We will yield the current states of the array after comparing and swapping. Hence, the algorithm will return a generator object.
4. Matplotlib animation will be used to visualize the comparing and swapping of the array.
5. We will then plot the graph, which will return an object of Poly3dCollection using which further animation will be done.

下面是实现：

Python3

# importing all required  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 = plt.figure()
     
    ax = fig.add_subplot(projection='3d')
     
    # z values and positions of the bars
    z = np.zeros(n)
    dx = np.ones(n)
    dy = np.ones(n)
     
    dz = [i for i in range(len(a))]
    # Poly3dCollection returned
    # into variable rects
    rects = ax.bar3d(range(len(a)), a, z, dx,
                     dy, dz,
                     color = color_map(data_normalizer(range(n))))
     
    # setting and x and y limits
    # equal to the length of the array
    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'})
    # text to plot on the chart
    text = ax.text2D(0.1,0.95, "", horizontalalignment = 'center',
                     verticalalignment = 'center',
                     transform=ax.transAxes,
                     color = "#E4365D")
    iteration = [0]
 
    # animation function to be
    # repeatedly called
    def animate(A, rects, iteration):
       
        # to clear the bars from
        # the Poly3DCollection object
        ax.collections.clear()
        ax.bar3d(range(len(a)), A, z, dx,
                 dy, dz,
                 color = color_map(data_normalizer(range(n))))
         
        iteration[0] += 1
        text.set_text("iterations : {}".format(iteration[0]))
             
    # animate function is called here
    # and the generator object is passed
    anim = FuncAnimation(fig, func=animate,
        fargs = (rects, iteration),
       frames = generator, interval=50,
        repeat=False)
     
    # show the plot
    plt.show()
 
# function call
showGraph()

输出：

对于数组大小 20