Detector Characterization Framework

A modular Python framework for processing and analysis of imaging detector data (CCD/CMOS).
Supports direct task usage in scripts and orchestration in config‑driven DAG workflows.

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Key Features

• Modular tasks — preprocessing, calibration, analysis, and image generation.
• Dual usage — call tasks directly in scripts or chain them in a pipeline.
• Reusable image ops — shared combine/stack core functionalities in core.
• Config‑driven orchestration — Prefect‑ready flows kept in pipeline.
• Optional lazy execution — support for generator‑style tasks.

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Project Layout

eregion/
├── core/                    # reusable core algorithms
│   └── image_operations.py   # image combine/stack ops
├── data/                     # sample/raw data for testing
│   └── deimos_raw/
│       ├── *.fits
├── datamodels/
│   ├── detector_config.py    # For loading and parsing detector configuration files
│   ├── image.py              # DetImage (and child) data classes to hold image data and metadata
│   └── image_utils.py        # utilities for manipulating image data from numpy arrays to xarray DataArrays
├── pipeline/                 # Prefect flows / runners (orchestration layer)
├── playground/
│   ├── basic_ccd.yaml        # example config(s)
│   ├── deimos.yaml           # DEIMOS example config
│   └── test.ipynb            # notebook for quick testing and exmaple usage
├── tasks/                    # modular processing/analysis tasks
│   ├── analysis.py           # analysis tasks (e.g., ptc, linearity)
│   ├── calibration.py        # calibration tasks (e.g., masterbias, masterflat)
│   ├── imagegen.py           # For generating DetImage instances from detector config and input image data
│   ├── preprocessing.py      # preprocessing tasks (e.g., overscan trim, bias subtract)
│   └── task.py               # base Task and LazyTask abstract classes
├── tests/                    # unit tests
└── README.md

Usage

Scripted: import task classes from tasks/* and call run(...) or call(...).

Orchestrated: define Prefect flows in pipeline/ that load a YAML from playground/ and execute steps using a generic runner.

Status

Early development. More tasks and flows to be added.

Installation

Clone the repository

  git clone git@github.com:CaltechOpticalObservatories/eregion.git
  cd eregion

Create and activate a virtual environment

  python3 -m venv venv
  source venv/bin/activate

Install the package

• Standard installation:

  pip install .

• Development installation:

  pip install -e .

Install additional dependencies for testing and development

  pip install -e .[dev]