使用 Matplotlib 可视化合并排序
原文:https://www . geesforgeks . org/visualization-of-merge-sort-using-matplotlib/
先决条件:Matplotlib 简介合并排序
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:
- 我们将生成一个包含随机元素的数组。
- 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。
- 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。
- Matplotlib 动画将用于可视化数组的比较和交换。
- 数组将存储在 matplotlib bar 容器对象(“bar_rects”)中，其中每个 bar 的大小将等于数组中元素的相应值。
- 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。
- 重复调用动画函数会将矩形的高度设置为等于元素的值。
下面是上述方法的实现。
Python 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
# function to recursively divide the arra
def mergesort(A, start, end):
if end <= start:
return
mid = start + ((end - start + 1) // 2) - 1
# yield from statements have been used to yield
# the array from the functions
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=20
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, 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()
输出:
[https://media.geeksforgeeks.org/wp-content/uploads/20200723011743/merge2d.mp4](https://media.geeksforgeeks.org/wp-content/uploads/20200723011743/merge2d.mp4)版权属于：月萌API www.moonapi.com，转载请注明出处
