ngimager 1.1.0

Event-cone imaging (neutron & gamma ray) with fastmode and list-mode pipelines.

ng-imager

ng-imager is a modular Python toolkit for imaging neutrons and gamma rays in the NOVO detector system.

It turns detector-level hit data into images via a clean, restartable pipeline:

hits → events → cones → images

and is designed to replace a monolithic legacy script with:

• Explicit separation of physics, geometry, imaging, configuration, and I/O
• Flexible energy strategies (ELUT, ToF, fixed–energy, direct Edep)
• Unified neutron and gamma imaging, including proton vs carbon recoil handling
• Structured hit / event / cone filters with detailed counters
• Self-describing HDF5 output suitable for long-term analysis and archiving

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Repository vs Python Package Name

• Repository name: ng-imager
• Python package (import name): ngimager
• PyPI package: ngimager

After installing from source, you import the package as:

import ngimager

and use the CLI entry points:

• ng-run – main imaging pipeline
• ng-viz – visualization / re-rendering of summed images
• ng-inspect – inspect a single cone and its pixel footprint (optional tool)

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Install

From PyPI (recommended)

pip install ngimager

Check that the package and CLI are available:

python -c "import ngimager; print(ngimager.__file__)"
ng-run --help
ng-viz --help

From Source (development install)

git clone https://github.com/Lindt8/ng-imager.git
cd ng-imager

python -m venv .venv
source .venv/bin/activate      # on Windows: .venv\Scripts\activate

pip install -e ".[dev]"

This installs ngimager in editable mode along with development dependencies (tests, docs, etc.). For a minimal runtime install, you can also use:

pip install -e .

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First Run: Example PHITS Config

The repository includes a simple PHITS usrdef example:

• Config: examples/configs/phits_usrdef_simple.toml
• Input: examples/imaging_datasets/PHITS_simple_ng_source/usrdef.out

Run the unified pipeline with:

ng-run examples/configs/phits_usrdef_simple.toml

or equivalently:

python -m ngimager.pipelines.core examples/configs/phits_usrdef_simple.toml

This will:

1. Read PHITS usrdef events via the adapter.

2. Construct hits and apply hit-level filters.

3. Shape and type events (neutron 2-hit, gamma 3-hit).

4. Apply event- and cone-level filters (ToF windows, light thresholds, Δθ, incident energy).

5. Build neutron and gamma cones (with proton vs carbon recoil handling for neutrons).

6. Reconstruct a simple back-projection (SBP) image on the configured plane.

7. Write an HDF5 file containing:

   • /images/summed/n, /images/summed/g, and /images/summed/all (where available)
   • /cones/... with per-cone geometry, species, recoil code, and incident energy
   • /lm/... with per-event and per-hit physics and optional list-mode cone pixel indices
   • /meta/counters/... summarizing all filters and pipeline stages
   • A snapshot of the TOML config used for the run

You can then use ng-viz to re-render images from that HDF5 file:

ng-viz summed results/example.h5

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Documentation

Full documentation is hosted at:

• https://Lindt8.github.io/ng-imager/

Key sections:

• Quickstart – run the example config and inspect the results.
• Configuration – complete TOML reference (docs/config.md).
• HDF5 Layout – how to interpret the output file (docs/hdf5.md).
• Architecture – design overview and long-term roadmap (docs/architecture.md).
• API Reference – auto-generated documentation from Python docstrings (docs/api/index.md).

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Status

• Neutron imaging: functional and validated against the legacy NOVO imaging code.
• Gamma imaging: functional and validated on legacy-formatted model data.
• Proton vs carbon recoil hypotheses: implemented, selected via priors, and stored in HDF5.
• Hit / event / cone filters: implemented with counters and HDF5 export.
• Fast and list modes: wired through configuration and I/O.

The codebase is under active development, but the core architecture and HDF5 layout are intended to be stable enough for experimental usage and future extension.

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Contributing

Contributions are very welcome. Useful areas include:

• New I/O adapters (additional simulation formats, experimental data).
• Alternative imaging algorithms / uncertainty models.
• Improved tests, examples, and documentation.

If you’d like to help:

1. File an issue describing your idea or bug.
2. Open a pull request from a feature branch.
3. Use the existing example config and tests to validate your changes.

Repository:

https://github.com/Lindt8/ng-imager

The aim is to keep ng-imager physically transparent, modular, and pleasant to work with for both simulation and experimental campaigns.