neptoon
neptoon is a Python package for processing Cosmic-Ray Neutron Sensor (CRNS) data to produce field-scale soil moisture estimates.
Key Features
- Modular Correction Pipeline: Apply multiple correction methods for pressure, incoming intensity, humidity, and biomass
- Quality Assessment: Built-in data quality checks integrated with SaQC
- Sensor Calibration: Tools for N0 calibration using soil sampling data
- External Data Integration: Automatic integration with NMDB.eu for incoming neutron corrections
- Multiple Interfaces: Use via Python API, configuration files, or GUI
- Published Science: Implementations based on peer-reviewed methodologies
- Reproducibility: Built-in reporting, reproducible workflows, and comprehensive documentation
Installation
pip install neptoon
Isolated Environment with uv (recommended):
uv init --python 3.11
uv add neptoon
Isolated Environment with conda:
conda create -n neptoon python=3.11 ipykernel
conda activate neptoon
pip install neptoon
For more detailed instructions, see the installation documentation.
Quick Start
from neptoon.config import ConfigurationManager
from neptoon.workflow import ProcessWithConfig
# Load configurations
config = ConfigurationManager()
config.load_configuration(file_path="path/to/sensor_config.yaml")
config.load_configuration(file_path="path/to/processing_config.yaml")
# Process data
yaml_processor = ProcessWithConfig(configuration_object=config)
yaml_processor.run_full_process()
Ready-to-use configurations, sample data, scripts, and notebooks are indexed in examples/README.md.
Documentation
Comprehensive documentation is available at:
- www.neptoon.org - Main documentation
- User Guide - Detailed workflow description
- Examples - Practical examples and tutorials
Project Status
Neptoon is currently in active development. Version 1.0, focusing on stability and robustness, is expected soon. Future plans include:
- Roving CRNS processing capabilities
- Server/Docker versions for automated processing
Support and Contribution
- Contact: Email us at contact@neptoon.org
- Issues: Report bugs or request features through GitLab issues
- Contributing: See the contribution guidelines for details on how to contribute
Authors and Acknowledgments
Lead Developers:
- Daniel Power — ORCID · Email
- Martin Schrön — ORCID · Homepage · Email
- Louis Trinkle — Email
- Markus Köhli — ORCID · Homepage · Email
Acknowledgments:
- Fredo Erxleben — ORCID
- Steffen Zacharias — ORCID
- Rafael Rosolem — ORCID · Homepage
- Miguel Rico-Ramirez — ORCID
- Till Francke — ORCID
- Daniel Rasche — ORCID
License
Neptoon is licensed under the MIT License. See the LICENSE file for details.
Citation
Cite the version you used. The DOI below always names the latest release; the badge above uses the concept DOI, which covers all versions. DOIs for older versions are listed on the Zenodo record.
Power, Daniel, Köhli, Markus, Schrön, Markus, Trinkle, Louis, Erxleben, Fredo, Rosolem, Rafael, & Zacharias, Steffen (2026). "Neptoon". Zenodo. doi:10.5281/zenodo.22088972
BibTex
@software{Neptoon,
author = {Power, Daniel and Köhli, Markus and Schrön, Markus and Trinkle, Louis and Erxleben, Fredo and Rosolem, Rafael and Zacharias, Steffen},
title = {Neptoon},
year = 2026,
publisher = {Zenodo},
version = {v0.77.3},
doi = {10.5281/zenodo.22088972},
url = {https://doi.org/10.5281/zenodo.22088972},
}
More Detailed Documentation
Scientific literature
The scientific methods implemented or discussed by Neptoon are linked to their primary and supporting references in the scientific literature documentation.
Conventions
These documents define the coding standards and architectural patterns used throughout the project. They serve as the single source of truth for both human contributors and AI assistants.
- Logging - Logger setup and log level conventions.
- Testing - Test organization, fixtures, markers, and CI pipeline.
- ColumnInfo - Column naming system and enum usage.
- Configuration - Pydantic config models and YAML loading.
- Hub Pattern - CRNSDataHub, DataFrames, and quality flags.
- Corrections - Factory/Builder pattern for the correction pipeline.
- Git Workflow - Branching, CI pipeline, and release process.
AI-Assisted Development
The repository includes durable guidance for coding agents and contributors:
AGENTS.md— universal entry point and toolchain rulesagent/code-overview.md— detailed find/edit/test mapagent/architecture.md— stable architecture summaryagent/conventions/— recurring implementation conventionsagent/decisions/— architecture and policy recordsagent/playbooks/convention-audit.md— project-specific guidance audit
Temporary plans, session memory, current priorities, contributor profiles, and agent
personas are local concerns and are intentionally not tracked. See
agent/README.md for the repository/local boundary.
3 maintainers
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