code-analysis

Code Analysis Integration

Python code execution integration for data analysis, file processing, and automation tasks within Autohive workflows.

Features

• Execute arbitrary Python code in a sandboxed environment
• Process input files (CSV, Excel, JSON, images, PDFs, etc.)
• Automatically detect and return generated output files
• Access to popular data science and document processing libraries
• Base64-encoded file transfer for binary data

Authentication

No authentication required. This integration runs Python code locally.

Actions

execute_python_code

Execute Python code with optional input files and automatic output file collection.

Inputs:

• python_code (string, required): The Python code to execute
• files (array, optional): Input files to make available to the script
  • name (string): Filename (e.g., "data.csv")
  • content (string): Base64-encoded file content
  • contentType (string, optional): MIME type

Outputs:

• result (string): Standard output from the executed code
• error (string, optional): Error message/traceback if execution failed
• files (array): Generated output files
  • name (string): Output filename
  • content (string): Base64-encoded file content
  • contentType (string): MIME type

Available Libraries

The following third-party libraries are pre-installed:

• numpy - Numerical computing
• Pillow - Image processing
• PyPDF2 - PDF manipulation
• python-docx - Word document creation/editing
• reportlab - PDF generation
• openpyxl - Excel file reading/writing
• XlsxWriter - Excel file creation
• matplotlib - Data visualization and plotting
• python-pptx - PowerPoint presentation creation

Plus the entire Python standard library.

Usage Examples

Basic Calculation

# Input
python_code = """
result = sum([1, 2, 3, 4, 5])
print(f"Sum: {result}")
"""

Process CSV Data

# Input
python_code = """
import csv
import json

with open('data.csv', 'r') as f:
    reader = csv.DictReader(f)
    data = list(reader)

# Calculate totals
total = sum(float(row['amount']) for row in data)
print(json.dumps({'total': total, 'count': len(data)}))
"""

files = [
    {
        "name": "data.csv",
        "content": "<base64-encoded-csv>",
        "contentType": "text/csv"
    }
]

Generate Excel Report

# Input
python_code = """
import openpyxl
from openpyxl.styles import Font, Alignment

wb = openpyxl.Workbook()
ws = wb.active
ws.title = "Report"

# Add headers
ws['A1'] = 'Name'
ws['B1'] = 'Value'
ws['A1'].font = Font(bold=True)
ws['B1'].font = Font(bold=True)

# Add data
data = [('Item 1', 100), ('Item 2', 200), ('Item 3', 150)]
for i, (name, value) in enumerate(data, start=2):
    ws[f'A{i}'] = name
    ws[f'B{i}'] = value

wb.save('report.xlsx')
print("Report generated successfully")
"""

Create Chart with Matplotlib

# Input
python_code = """
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(10, 6))
plt.plot(x, y)
plt.title('Sine Wave')
plt.xlabel('X')
plt.ylabel('Y')
plt.grid(True)
plt.savefig('chart.png', dpi=150)
print("Chart saved")
"""

Convert Image Format

# Input
python_code = """
from PIL import Image

# Open input image
img = Image.open('input.png')

# Convert and save as JPEG
img = img.convert('RGB')
img.save('output.jpg', 'JPEG', quality=85)
print(f"Converted image: {img.size}")
"""

files = [
    {
        "name": "input.png",
        "content": "<base64-encoded-png>",
        "contentType": "image/png"
    }
]

Best Practices

1. Output results via print() - Any non-file results should be printed to stdout
2. Save files to current directory - Output files are auto-detected from the working directory
3. Use available libraries only - Don't import packages not in the pre-installed list
4. Handle errors gracefully - Use try/except for robust scripts
5. Close file handles - Use context managers (with statements) for file operations

Requirements

• autohive-integrations-sdk~=1.0.2
• numpy
• Pillow
• PyPDF2
• python-docx
• reportlab
• openpyxl
• XlsxWriter
• matplotlib
• python-pptx

Testing

To run the tests:

1. Navigate to the integration directory: cd code-analysis
2. Install dependencies: pip install -r requirements.txt -t dependencies
3. Run the tests: python tests/test_code_analysis.py

Version

1.0.0