pyhfm 0.1.0

Python package for reading and analyzing Heat Flow Meter (HFM) data files

PyHFM - Heat Flow Meter Data Analysis

PyHFM is a Python package for reading and analyzing Heat Flow Meter (HFM) data files. It provides a clean, modern API for parsing HFM measurement data and extracting thermal conductivity and volumetric heat capacity measurements.

Features

• Simple API: Clean read_hfm() function for easy data loading
• Multiple Formats: Support for thermal conductivity and volumetric heat capacity measurements
• Rich Metadata: Comprehensive metadata extraction and validation
• Modern Data Stack: Built on PyArrow for efficient data handling
• Type Safety: Full type annotations for better development experience
• Comprehensive Testing: Extensive test suite for reliability
• CLI Support: Command-line interface for quick data conversion

Installation

pip install pyhfm

For development installation:

git clone https://github.com/GraysonBellamy/pyhfm.git
cd pyhfm
pip install -e ".[dev,test]"

Quick Start

Basic Usage

import pyhfm

# Read HFM file and get PyArrow table
table = pyhfm.read_hfm("sample.tst")

# Convert to polars DataFrame for analysis
import polars as pl
df = pl.from_arrow(table)
print(df.head())

Access Metadata

# Get both metadata and data
metadata, table = pyhfm.read_hfm("sample.tst", return_metadata=True)

print(f"Sample: {metadata['sample_id']}")
print(f"Type: {metadata['type']}")
print(f"Setpoints: {metadata['number_of_setpoints']}")

Custom Configuration

# Override default settings
config = {"default_encoding": "utf-8"}
table = pyhfm.read_hfm("sample.tst", config=config)

Data Structure

PyHFM returns data in PyArrow tables with the following schemas:

Thermal Conductivity Measurements

Column	Type	Description
setpoint	int32	Setpoint number
upper_temperature	float64	Upper plate temperature
lower_temperature	float64	Lower plate temperature
upper_thermal_conductivity	float64	Upper thermal conductivity result
lower_thermal_conductivity	float64	Lower thermal conductivity result

Volumetric Heat Capacity Measurements

Column	Type	Description
setpoint	int32	Setpoint number
average_temperature	float64	Average temperature
volumetric_heat_capacity	float64	Volumetric heat capacity result

Command Line Interface

PyHFM includes a CLI for quick data conversion:

# Convert to CSV
pyhfm sample.tst --output sample.csv --format csv

# Convert to Parquet with metadata
pyhfm sample.tst --output sample.parquet --format parquet --metadata

# Print to stdout as JSON
pyhfm sample.tst --format json

Advanced Usage

Custom Parser

from pyhfm import HFMParser

# Create parser with custom configuration
parser = HFMParser(config={"default_encoding": "utf-16le"})
table = parser.parse_file("sample.tst")

Data Extraction

from pyhfm import DataExtractor

# Extract data from metadata
extractor = DataExtractor()
table = extractor.extract_data(metadata)

Error Handling

import pyhfm

try:
    table = pyhfm.read_hfm("sample.tst")
except pyhfm.HFMFileError as e:
    print(f"File error: {e}")
except pyhfm.HFMParsingError as e:
    print(f"Parsing error: {e}")
except pyhfm.HFMValidationError as e:
    print(f"Validation error: {e}")

Supported File Formats

• HFM Test Files (.tst): Standard HFM output format
• Encodings: UTF-16LE (default), UTF-8, and auto-detection
• Measurements: Thermal conductivity and volumetric heat capacity

Architecture

PyHFM follows a modular architecture inspired by the pynetzsch package:

pyhfm/
   api/           # High-level user API
   core/          # Core parsing logic
   extractors/    # Data extraction components
   constants.py   # Configuration and data types
   exceptions.py  # Custom exception hierarchy
   __init__.py    # Public API exports

Development

Setup Development Environment

git clone https://github.com/GraysonBellamy/pyhfm.git
cd pyhfm
pip install -e ".[dev,test]"
pre-commit install

Run Tests

pytest

Code Quality

ruff check .          # Linting
ruff format .         # Formatting
mypy src/pyhfm        # Type checking

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for your changes
5. Ensure all tests pass (pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

v0.1.0 (2024-01-01)

• Initial release
• Support for thermal conductivity and volumetric heat capacity measurements
• PyArrow-based data handling
• Comprehensive metadata extraction
• CLI interface
• Full type annotations

Related Projects

• pynetzsch - Python package for NETZSCH thermal analysis data

Support

If you encounter any issues or have questions, please:

1. Check the documentation
2. Search existing issues
3. Create a new issue if needed