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使用 Matplotlib 可视化快速排序

原文:https://www.geeksforgeeks.org/visualization-of-quick-sort-use-matplotlib/

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。为此，我们将使用 matplotlib 绘制条形图来表示数组的元素，

进场:

  1. We will generate an array of 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 generate the current state of the array after comparison and exchange. Therefore, the algorithm will return a generator object.
  4. Matplotlib This animation will be used to compare and exchange visual arrays.
  5. The array will be stored in the matplotlib bar container object ('bar_rects'), where the size of each bar will be equal to the corresponding value of the elements in the array.
  6. Matplotlib animation's built-in FuncAnimation method passes the container and generator objects to the function used to create the animation. Each frame of the animation corresponds to an iteration of the generator.
  7. The repeated animation function will set the height of the rectangle equal to the value of the element.

蟒 3

# 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()
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