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Python 中使用 Matplotlib 快速排序的三维可视化

原文:https://www.geeksforgeeks.org/3d-visualization-of-quick-sort-use-matplotlib-in-python/

通过分析和比较为比较和交换元素而进行的操作数量，可视化算法使理解它们变得更加容易。算法的三维可视化不太常见，为此，我们将使用 Matplotlib 绘制条形图并制作动画来表示数组的元素。

让我们看看快速排序的三维可视化:

进场:

  1. We will generate an array with random elements.
  2. The algorithm will be called on the array, and for visualization purposes, the yield statement will be used instead of the return statement.
  3. We will get the current state of the array after comparison and exchange. Therefore, the algorithm will return a generator object.
  4. Matplotlib animation will be used to compare and exchange visual arrays.
  5. Then we will draw a graph, which will return a Poly3dCollection object, which will be used for further animation.

下面是实现:

蟒蛇 3

# 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 posistions 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:

对于阵列大小 20

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