Bidirectional integration enabling Python's data science ecosystem within .NET's FrameworX

Platform→ Python and .NET Integration | Tutorial | Reference

Language Selection Guide

Quick Selection Criteria

• C# - Maximum performance, real-time control, type safety
• Python - Data science, machine learning, rapid prototyping
• Both - C# for control logic, Python for analytics

Integration Patterns

1. Embedded Python Scripts

Python code runs within FrameworX's Script Tasks and Classes, with full access to tags and namespaces:

# Python accessing FrameworX tags
@Tag.Temperature = @Tag.SetPoint + 5
result = ProcessData(@Tag.ProductionRate)

2. Shell Integration

Execute external Python files while maintaining tag access:

result = TK.ExecutePythonShell("analytics.py", [@Tag.Input1, @Tag.Input2])
@Tag.Result = result

3. Cross-Language Calls

C# calling Python:

// C# code calling Python class
var result = @Script.Class.PythonAnalytics.ProcessML(data);

Python calling C#:

# Python code calling C# method
result = @Script.Class.CSharpLogic.Calculate(param1, param2)

Direct Access Architecture

Python code accesses FrameworX objects directly through the same '@' notation used in C#:

• @Tag - Real-time tag values
• @Dataset - Database queries
• @Alarm - Alarm states
• @Historian - Time-series data

No marshaling, conversion, or intermediate layers, Python operates on the same objects as .NET code.

Development Environment

Solution-Specific Python

Each solution specifies its Python interpreter, enabling:

• Different Python versions per project
• Isolated package environments
• Consistent deployment across development and production

In-Designer Features

• Code Editor - Python syntax highlighting and IntelliSense
• Testing - Run Python scripts directly in Designer
• Debugging - Output window for print statements and errors
• Tag Access - Auto-completion for all namespace objects

Industrial Use Cases

Machine Learning Integration

# Use scikit-learn for anomaly detection
from sklearn.ensemble import IsolationForest
model = IsolationForest()
data = GetHistorianData(@Tag.Equipment)
anomalies = model.fit_predict(data)

Data Analysis

# Pandas for production analytics
import pandas as pd
df = pd.DataFrame(@Dataset.Query("SELECT * FROM Production"))
daily_avg = df.groupby('Day')['Output'].mean()
@Tag.DailyAverage = daily_avg.iloc[-1]

Custom Protocols

# Implement proprietary protocol
import struct
packet = struct.pack('>HH', @Tag.DeviceID, @Tag.Command)
response = SendCustomProtocol(packet)
@Tag.Response = struct.unpack('>H', response)[0]

System Requirements

• Python Version: 3.7 or later (3.11 recommended)
• Python.NET: Required for integration (pip install pythonnet)
• Installation: System-wide (all users) for service deployment
• Path: Python.exe must be in system PATH

Performance Considerations

Key Advantages

• No Bridge Required - Direct integration via Python.NET
• Shared Memory Space - Zero-copy data access between languages
• Production Ready - Runs as Windows service, handles 24/7 operation
• Version Flexibility - Multiple Python versions on same server
• Library Access - Full Python Package Index (PyPI) availability

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