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使用 Matplotlib 进行合并排序的三维可视化

原文:https://www.geesforgeks.org/3d-可视化-合并-排序-使用-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。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。

进场:

  1. 我们将生成一个包含随机元素的数组。
  2. 将在该数组上调用该算法，并将使用 yield 语句而不是 return 语句来实现可视化。
  3. 在比较和交换之后，我们将得出阵列的当前状态。因此，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. 然后我们将绘制图形，这将返回一个 Poly3dCollection 的对象，使用该对象可以进行进一步的动画制作。
# 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

# merge sort function to divide the array
def mergesort(A, start, end):
    if end <= start:
        return

    mid = start + ((end - start + 1) // 2) - 1

    # yield from statement is used to 
    # yield the array from the merge function
    yield from mergesort(A, start, mid)
    yield from mergesort(A, mid + 1, end)
    yield from merge(A, start, mid, end)

# function to merge the array
def merge(A, start, mid, end):
    merged = []
    leftIdx = start
    rightIdx = mid + 1

    while leftIdx <= mid and rightIdx <= end:
        if A[leftIdx] < A[rightIdx]:
            merged.append(A[leftIdx])
            leftIdx += 1
        else:
            merged.append(A[rightIdx])
            rightIdx += 1

    while leftIdx <= mid:
        merged.append(A[leftIdx])
        leftIdx += 1

    while rightIdx <= end:
        merged.append(A[rightIdx])
        rightIdx += 1

    for i in range(len(merged)):
        A[start + i] = merged[i]
        yield A

# 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 = mergesort(a, 0, len(a)-1)
    algoName = 'Merge 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)
    plt.show()

showGraph()

输出:

对于阵列大小 20

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