python 传统文化

根据提供的引用内容，我们可以看到Python与太极文化的结合为我们提供了一个思考和创造的平台，既能够提升编程技能，又能够深入了解和传播太极文化的价值。那么，Python如何与传统文化相结合呢？以下是一些例子：

1. 使用Python绘制太极图案

import turtle

# 设置画布大小
turtle.setup(600, 600)

# 设置画笔颜色和大小
turtle.pensize(5)
turtle.pencolor('black')

# 绘制黑色半圆
turtle.fillcolor('black')
turtle.begin_fill()
turtle.circle(200, 180)
turtle.end_fill()

# 绘制白色半圆
turtle.fillcolor('white')
turtle.begin_fill()
turtle.circle(200, -180)
turtle.end_fill()

# 绘制两个小圆
turtle.pencolor('black')
turtle.fillcolor('black')
turtle.penup()
turtle.goto(-60, 80)
turtle.pendown()
turtle.begin_fill()
turtle.circle(30)
turtle.end_fill()
turtle.penup()
turtle.goto(60, -80)
turtle.pendown()
turtle.begin_fill()
turtle.circle(30)
turtle.end_fill()

# 隐藏画笔
turtle.hideturtle()

# 显示绘制结果
turtle.done()

2. 使用Python实现太极八卦

import turtle

# 设置画布大小
turtle.setup(600, 600)

# 设置画笔颜色和大小
turtle.pensize(5)
turtle.pencolor('black')

# 绘制黑色半圆
turtle.fillcolor('black')
turtle.begin_fill()
turtle.circle(200, 180)
turtle.end_fill()

# 绘制白色半圆
turtle.fillcolor('white')
turtle.begin_fill()
turtle.circle(200, -180)
turtle.end_fill()

# 绘制两个小圆
turtle.pencolor('black')
turtle.fillcolor('black')
turtle.penup()
turtle.goto(-60, 80)
turtle.pendown()
turtle.begin_fill()
turtle.circle(30)
turtle.end_fill()
turtle.penup()
turtle.goto(60, -80)
turtle.pendown()
turtle.begin_fill()
turtle.circle(30)
turtle.end_fill()

# 绘制八卦图案
turtle.pencolor('black')
turtle.penup()
turtle.goto(0, 0)
turtle.pendown()
turtle.circle(200, -45)
turtle.circle(100, -135)
turtle.circle(-100, -135)
turtle.circle(-200, -45)

# 隐藏画笔
turtle.hideturtle()

# 显示绘制结果
turtle.done()

3. 使用Python实现太极拳动作识别

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# 读取数据集
data = pd.read_csv('taichi.csv')

# 将标签转换为数字
data['label'] = data['label'].map({'A': 0, 'B': 1})

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=42)

# 训练决策树模型
clf = DecisionTreeClassifier(random_state=42)
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)