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:
- We will generate an array of random elements.
- The algorithm will be called on the array, and for visualization purposes, the yield statement will be used instead of the return statement.
- We will generate the current state of the array after comparison and exchange. Therefore, the algorithm will return a generator object.
- Matplotlib This animation will be used to compare and exchange visual arrays.
- 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.
- 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.
- The repeated animation function will set the height of the rectangle equal to the value of the element.
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
# 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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