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pyngb - NETZSCH STA File Parser

Welcome to pyngb, a comprehensive Python library for parsing and analyzing NETZSCH TA (Thermal Analysis) NGB files.

What is pyngb?

pyngb is a high-performance, production-ready library that enables scientists and engineers to:

  • Parse NGB Files: Extract data and metadata from NETZSCH binary files
  • Analyze Thermal Data: Work with temperature programs, mass loss, and DSC signals
  • Process in Batches: Handle multiple files efficiently with parallel processing
  • Validate Data Quality: Built-in tools for data validation and quality checking
  • Export Results: Convert to modern formats like Parquet, CSV, and JSON

Key Features

🚀 High Performance

  • Optimized binary parsing with NumPy and PyArrow
  • Memory-efficient processing of large files
  • Parallel batch processing capabilities
  • Typical parsing speed: 0.1-1 seconds per file

📊 Rich Data Extraction

  • Complete metadata extraction (instrument settings, sample info, conditions)
  • Full temperature program details
  • All measurement channels (temperature, mass, DSC, gas flows)
  • Calibration constants and system parameters

🛠️ Production Ready

  • Comprehensive error handling and validation
  • Thread-safe operations
  • Command-line interface for automation
  • 300+ tests including integration and stress tests

🔧 Flexible APIs

  • Simple functions for common use cases
  • Advanced parser configuration options
  • Dataset management tools
  • Data validation and quality checking

Quick Example

from pyngb import read_ngb
import polars as pl
import json

# Load NGB file
table = read_ngb("experiment.ngb-ss3")

# Convert to DataFrame for analysis
df = pl.from_arrow(table)
print(f"Loaded {df.height} data points with {df.width} measurements")

# Access metadata
metadata = json.loads(table.schema.metadata[b'file_metadata'])
print(f"Sample: {metadata.get('sample_name', 'Unknown')}")
print(f"Instrument: {metadata.get('instrument', 'Unknown')}")

# Basic analysis
if "sample_temperature" in df.columns:
    temp_range = df["sample_temperature"].min(), df["sample_temperature"].max()
    print(f"Temperature range: {temp_range[0]:.1f} to {temp_range[1]:.1f} °C")

if "mass" in df.columns:
    mass_loss = (df["mass"].max() - df["mass"].min()) / df["mass"].max() * 100
    print(f"Mass loss: {mass_loss:.2f}%")

Use Cases

Research and Development

  • Data Exploration: Quick analysis of thermal behavior
  • Method Development: Validation of temperature programs
  • Quality Control: Automated data validation
  • Comparative Studies: Batch analysis of multiple samples

Production Environments

  • Automated Processing: CLI integration with production systems
  • Data Pipelines: Integration with laboratory information systems
  • Quality Assurance: Automated validation of measurement data
  • Reporting: Export to standard formats for analysis tools

Educational Applications

  • Teaching: Demonstrate thermal analysis concepts
  • Research Training: Provide tools for student projects
  • Method Comparison: Compare different analytical approaches

Architecture Overview

pyngb uses a modular architecture designed for performance, extensibility, and reliability:

```mermaid graph TB A[User Code] --> B[API Layer] B --> C[Core Parser] C --> D[Binary Parser] C --> E[Metadata Extractor] C --> F[Stream Processor] D --> G[Data Type Handlers] B --> H[Batch Processor] B --> I[Validation Tools]

subgraph "Output Formats"
    J[PyArrow Tables]
    K[Parquet Files]
    L[CSV Files]
    M[JSON Metadata]
end

B --> J
H --> K
H --> L
H --> M

```

Core Components

  • API Layer: High-level functions for common use cases
  • Core Parser: Orchestrates parsing operations
  • Binary Parser: Low-level binary format handling
  • Metadata Extractor: Specialized metadata extraction
  • Stream Processor: Data stream parsing and conversion
  • Batch Processor: Parallel processing of multiple files
  • Validation Tools: Data quality checking and validation

Supported Data

File Formats

  • Primary: .ngb-ss3 and .ngb-bs3 files from NETZSCH STA instruments
  • Output: Parquet, CSV, JSON metadata

Measurement Types

  • Temperature: Sample and furnace temperatures
  • Mass: Sample mass (TGA data)
  • Heat Flow: DSC signals and heat capacity
  • Gas Flows: Purge and protective gas flows
  • Environmental: Pressure, acceleration (if available)

Metadata Categories

  • Instrument: Model, version, calibration data
  • Sample: Name, mass, material, crucible information
  • Experimental: Operator, date, lab, project details
  • Method: Temperature program, gas settings, acquisition parameters
  • System: PID settings, MFC configurations

Getting Started

Ready to start using pyngb? Here are your next steps:

  1. Installation: Install pyngb and its dependencies
  2. Quick Start: Learn the basics with hands-on examples
  3. API Reference: Detailed documentation of all functions
  4. Advanced Usage: Custom configurations and extensions

Performance Characteristics

Metric Typical Value
Parse Speed 0.1-1 seconds per file
Memory Usage ~2x file size
Batch Processing 100 files in ~30 seconds (4 cores)
File Size Support KB to GB range
Concurrent Operations Thread-safe

System Requirements

  • Python: 3.9 or higher
  • Operating System: Windows, macOS, Linux
  • Memory: 4GB+ recommended for large files
  • Dependencies: polars, pyarrow, numpy

Support and Community

License and Disclaimer

pyngb is released under the MIT License. This is an independent project not affiliated with NETZSCH-Gerätebau GmbH.

⚠️ Disclaimer: This package and its author are not affiliated with, endorsed by, or approved by NETZSCH-Gerätebau GmbH. NETZSCH is a trademark of NETZSCH-Gerätebau GmbH.

Ready to parse your first NGB file? Head to the Quick Start Guide!