{
  "language": "python",
  "code": "# Example of AI-generated data analysis code\nimport pandas as pd\nimport numpy as np\n\n# AI-generated function for sales analysis\ndef analyze_sales_trends(filepath):\n    try:\n        data = pd.read_csv(filepath)\n        \n        # Validate data structure (safety check)\n        if 'date' not in data.columns or 'sales' not in data.columns:\n            raise ValueError('Missing required columns')\n        \n        # Perform trend analysis\n        data['date'] = pd.to_datetime(data['date'])\n        monthly_trends = data.groupby(data['date'].dt.to_period('M'))['sales'].agg(['sum', 'mean', 'count'])\n        \n        print(f'Analyzed {len(data)} sales records')\n        print(f'Found trends across {len(monthly_trends)} months')\n        \n        return monthly_trends\n    except Exception as e:\n        print(f'Analysis failed safely: {e}')\n        return None\n\n# Execute AI-generated analysis safely\nresult = analyze_sales_trends('/sandbox/data/sales.csv')\nif result is not None:\n    result.to_csv('/sandbox/output/ai_analysis.csv')",
  "timeout": 300,
  "installPackages": "pandas numpy matplotlib"
}

# Efficient data processing pattern
import pandas as pd
import numpy as np

# Read large files in chunks
chunk_size = 10000
processed_chunks = []

for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size):
    # Vectorized operations are faster
    chunk['processed_value'] = chunk['value'] * np.sqrt(chunk['multiplier'])
    
    # Efficient filtering
    valid_chunk = chunk[chunk['processed_value'] > 0]
    processed_chunks.append(valid_chunk)

# Combine results efficiently
final_result = pd.concat(processed_chunks, ignore_index=True)
final_result.to_csv('processed_output.csv', index=False)

print(f"Processed {len(final_result)} records")

# Python resource monitoring
import psutil
import time

def monitor_execution():
    start_time = time.time()
    start_memory = psutil.virtual_memory().used
    
    # Your code execution here
    result = perform_computation()
    
    # Monitor resources
    end_time = time.time()
    end_memory = psutil.virtual_memory().used
    
    execution_stats = {
        'execution_time': end_time - start_time,
        'memory_used': end_memory - start_memory,
        'cpu_percent': psutil.cpu_percent(interval=1)
    }
    
    print(f"Execution stats: {execution_stats}")
    return result, execution_stats

// JavaScript performance monitoring
const { performance } = require('perf_hooks');

function monitorExecution() {
    const startTime = performance.now();
    const startMemory = process.memoryUsage();
    
    // Your code execution here
    const result = performComputation();
    
    // Monitor resources
    const endTime = performance.now();
    const endMemory = process.memoryUsage();
    
    const executionStats = {
        executionTime: (endTime - startTime) / 1000, // Convert to seconds
        memoryUsed: endMemory.heapUsed - startMemory.heapUsed,
        heapTotal: endMemory.heapTotal
    };
    
    console.log('Execution stats:', executionStats);
    return { result, executionStats };
}

# Complete workflow: Upload → Process → Download
import pandas as pd

# 1. File uploaded via file system tools
input_file = 'uploaded_data.csv'

# 2. Process with code execution
try:
    # Load and validate data
    data = pd.read_csv(input_file)
    
    # Perform analysis
    summary = data.groupby('category').agg({
        'value': ['sum', 'mean', 'count'],
        'date': 'max'
    }).round(2)
    
    # Generate insights
    insights = {
        'total_records': len(data),
        'categories': len(summary),
        'summary_stats': summary.to_dict()
    }
    
    # Save results for download
    summary.to_csv('analysis_summary.csv')
    with open('insights.json', 'w') as f:
        json.dump(insights, f, indent=2)
        
    print(f"Analysis complete: {insights['total_records']} records processed")
    
except Exception as e:
    print(f"Error processing data: {str(e)}")
    # Create error report
    with open('error_report.txt', 'w') as f:
        f.write(f"Processing failed: {str(e)}")

// Workflow: Web Data → Process → Generate Report
const fs = require('fs').promises;

async function processWebData() {
    try {
        // 1. Data collected via web tools
        const webData = await fs.readFile('web_search_results.json', 'utf8');
        const searchResults = JSON.parse(webData);
        
        // 2. Process and analyze data
        const analysis = searchResults.map(result => ({
            url: result.url,
            title: result.title,
            wordCount: result.content.split(' ').length,
            sentiment: analyzeSentiment(result.content),
            keywords: extractKeywords(result.content),
            processedAt: new Date().toISOString()
        }));
        
        // 3. Generate summary statistics
        const summary = {
            totalResults: analysis.length,
            averageWordCount: analysis.reduce((sum, item) => sum + item.wordCount, 0) / analysis.length,
            sentimentDistribution: calculateSentimentDistribution(analysis),
            topKeywords: getTopKeywords(analysis)
        };
        
        // 4. Save results for document generation
        await fs.writeFile('processed_analysis.json', JSON.stringify(analysis, null, 2));
        await fs.writeFile('summary_report.json', JSON.stringify(summary, null, 2));
        
        console.log(`Processed ${analysis.length} web search results`);
        return summary;
        
    } catch (error) {
        console.error('Web data processing failed:', error.message);
        throw error;
    }
}

function analyzeSentiment(text) {
    // Simple sentiment analysis
    const positiveWords = ['good', 'great', 'excellent', 'amazing', 'wonderful'];
    const negativeWords = ['bad', 'terrible', 'awful', 'horrible', 'disappointing'];
    
    const words = text.toLowerCase().split(' ');
    const positiveCount = words.filter(word => positiveWords.includes(word)).length;
    const negativeCount = words.filter(word => negativeWords.includes(word)).length;
    
    if (positiveCount > negativeCount) return 'positive';
    if (negativeCount > positiveCount) return 'negative';
    return 'neutral';
}

// Execute processing
processWebData();

# Python debugging information
import sys
import os

def get_debug_info():
    debug_info = {
        'python_version': sys.version,
        'working_directory': os.getcwd(),
        'environment_variables': dict(os.environ),
        'available_modules': [module for module in sys.modules.keys()],
        'file_list': os.listdir('.') if os.path.exists('.') else []
    }
    return debug_info

# Print debug info
print(json.dumps(get_debug_info(), indent=2, default=str))

// JavaScript debugging information
const fs = require('fs');
const os = require('os');

function getDebugInfo() {
    const debugInfo = {
        nodeVersion: process.version,
        platform: process.platform,
        workingDirectory: process.cwd(),
        environmentVariables: process.env,
        memoryUsage: process.memoryUsage(),
        fileList: fs.existsSync('.') ? fs.readdirSync('.') : []
    };
    return debugInfo;
}

// Print debug info
console.log(JSON.stringify(getDebugInfo(), null, 2));