# 示例：一个简单的脚本来可视化提交趋势
import matplotlib.pyplot as plt

# 每年提交量的样本数据（2025年 vs 2026年）
years = ['2025', '2026']
commits = [1500000000, 1775000000]  # 样本数据

plt.bar(years, commits, color=['blue', 'green'])
plt.xlabel('年份')
plt.ylabel('提交数量')
plt.title('同比提交增长')
plt.show()

# 示例：带有开发者代理的GitHub Actions工作流程
name: 自动化开发工作流程

on: [push]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
    - name: 检出代码
      uses: actions/checkout@v2

    - name: 运行开发者代理
      run: |
        # 初始化开发者代理
        dev-agent init

        # 为缺失的函数生成代码片段
        dev-agent generate --file=app.py --missing-functions

        # 运行自动化代码审查
        dev-agent review

        # 提交并推送更改
        git add app.py
        git commit -m "自动化代码生成和审查"
        git push

# 示例：使用开源库进行医学影像分析的Python脚本
import medical_image_processing as mip

# 加载医学影像
image = mip.load("patient_xray.png")

# 执行影像分析
results = mip.analyze(image)

# 生成报告
mip.generate_report(results, "patient_report.pdf")

# Example: A simple script to visualize commit trends
import matplotlib.pyplot as plt

# Sample data for commits per year (2025 vs 2026)
years = ['2025', '2026']
commits = [1500000000, 1775000000]  # Sample numbers

plt.bar(years, commits, color=['blue', 'green'])
plt.xlabel('Year')
plt.ylabel('Number of Commits')
plt.title('Year-over-Year Commit Growth')
plt.show()

# Example: GitHub Actions workflow with a developer agent
name: Automated Development Workflow

on: [push]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Run developer agent
      run: |
        # Initialize the developer agent
        dev-agent init

        # Generate code snippets for missing functions
        dev-agent generate --file=app.py --missing-functions

        # Run automated code reviews
        dev-agent review

        # Commit and push changes
        git add app.py
        git commit -m "Automated code generation and review"
        git push

# Example: A Python script using an open-source library for medical imaging analysis
import medical_image_processing as mip

# Load a medical image
image = mip.load("patient_xray.png")

# Perform image analysis
results = mip.analyze(image)

# Generate a report
mip.generate_report(results, "patient_report.pdf")