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Python 中使用 Matplotlib 的插入排序可视化

原文:https://www.geeksforgeeks.org/insert-sort-visualization-using-matplotlib-in-python/

先决条件: 插入排序使用 Matplotlib 制作动画

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，以绘制条形图来表示数组的元素，

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. 数组将存储在 matplotlib bar 容器对象(“rects”)中，其中每个 bar 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. 重复调用动画函数会将矩形的高度设置为等于元素的值。

蟒蛇 3

# import all the modules
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import matplotlib as mp
import numpy as np
import random

# set the style of the graph
plt.style.use('fivethirtyeight')

# input the size of the array (list here)
# and shuffle the elements to create
# a random list
n = int(input("enter array size\n"))
a = [i for i in range(1, n+1)]
random.shuffle(a)

# insertion sort

def insertionsort(a):
    for j in range(1, len(a)):
        key = a[j]
        i = j-1

        while(i >= 0 and a[i] > key):
            a[i+1] = a[i]
            i -= 1

            # yield the current position
            # of elements in a
            yield a
        a[i+1] = key
        yield a

# generator object returned by the function
generator = insertionsort(a)

# to set the 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()

# the bar container
rects = ax.bar(range(len(a)), a, align="edge",
               color=color_map(data_normalizer(range(n))))

# setting the view limit of x and y axes
ax.set_xlim(0, len(a))
ax.set_ylim(0, int(1.1*len(a)))

# 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)
iteration = [0]

# function to be called repeatedly to animate

def animate(A, rects, iteration):

    # setting the size of each bar equal
    # to the value of the elements
    for rect, val in zip(rects, A):
        rect.set_height(val)

    iteration[0] += 1
    text.set_text("iterations : {}".format(iteration[0]))

anim = FuncAnimation(fig, func=animate,
                     fargs=(rects, iteration), frames=generator, interval=50,
                     repeat=False)

plt.show()

输出:

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