dcccpy 0.1.3

Python wrapper for the DCCCcore PET biomarker command-line tool

dcccpy

dcccpy is a Python wrapper for the DCCCcore command-line tool from DCCCSlicer. It keeps the native C++ core as the execution engine and provides a small Python API for running common PET biomarker workflows.

Install

The default package is slim:

pip install dcccpy

The first call downloads the matching DCCCcore release package into the local user cache if no native runtime is otherwise available. The slim package also installs nibabel for image loading and nibabel-style image inputs.

The downloaded GitHub release is the full DCCCcore runtime. The optional PyPI runtime wheels are smaller and omit the fast_and_acc registration model/config and ADAD decoupler ONNX ensemble.

Users who prefer pip install to include the native runtime and avoid first-run download can use the platform-selecting runtime extra:

pip install "dcccpy[runtime]"

Platform-specific runtime extras are also available:

pip install "dcccpy[linux-runtime]"
pip install "dcccpy[windows-runtime]"

The platform-specific extras are guarded by environment markers, so they only install a runtime wheel on the matching platform.

Python API

import dcccpy

result = dcccpy.centiloid("amyloid_pet.nii", skip_normalization=True)
print(result.metrics)
print(result.output)

The output argument is optional for single-image Python calls. When omitted, dcccpy creates a temporary output NIfTI path and returns it in the result. Relative input and output paths are resolved from the current Python working directory before DCCCcore is invoked.

result = dcccpy.centiloid("amyloid_pet.nii")
print(result.output)           # temporary output path
print(result.metrics.get("fbp"))

The wrapper accepts nibabel-style image objects with to_filename():

import nibabel as nib
import dcccpy

image = nib.load("amyloid_pet.nii.gz")
result = dcccpy.centiloid(image, skip_normalization=True)
output_image = result.load_output()

Common helpers mirror DCCCcore subcommands:

dcccpy.centiloid("amyloid.nii", suvr=True)
dcccpy.centaurz("tau.nii", report_detailed_regions=True)
dcccpy.fillstates("fdg.nii", tracer="fdg")
dcccpy.normalize("pet.nii", iterative=True)
dcccpy.run(["centiloid", "--input", "a.nii", "--output", "b.nii"])

Each helper returns DCCCResult with:

• returncode
• stdout / stderr
• output
• metrics, parsed from numeric lines in stdout
• load_output(), which loads the output with nibabel

Command Line

dcccpy also forwards raw arguments to DCCCcore:

dcccpy --help
dcccpy centiloid --input amyloid_pet.nii --output result.nii

Runtime lookup

At runtime, dcccpy looks for DCCCcore in this order:

1. DCCCPY_DCCCCORE environment variable.
2. A vendored binary inside the installed dcccpy wheel.
3. A binary from dcccpy-linux-runtime or dcccpy-windows-runtime, installed by dcccpy[runtime] or the platform-specific runtime extras.
4. The local dcccpy cache populated by automatic download.
5. DCCCcore on PATH.
6. Automatic download from GitHub releases, unless disabled.

Useful environment variables:

• DCCCPY_DCCCCORE: exact path to a DCCCcore executable.
• DCCCPY_AUTO_DOWNLOAD=0: disable first-run automatic download.
• DCCCPY_CACHE_DIR: override the runtime cache directory.
• DCCCPY_RELEASE_REPO: override the GitHub release repository.
• DCCCPY_DCCCCORE_URL: override the release asset URL.

Runtime Packaging

Release wheels should vendor the matching DCCCcore runtime tree before build:

python scripts/vendor_dccccore.py --version 4.2.3 --release-platform ubuntu-latest-x64
python -m build --wheel

The source tree intentionally does not commit the vendored runtime because it contains large ONNX model and NIfTI runtime assets.

The preferred distribution layout is:

• dcccpy: slim Python package with nibabel; downloads runtime on first use.
• dcccpy-linux-runtime: optional Linux runtime wheel.
• dcccpy-windows-runtime: optional Windows runtime wheel.
• dcccpy[runtime]: installs the matching runtime package on supported platforms.

Packaging note

The runtime wheels use a PyPI-size profile for version 4.2.3. They omit the fast_and_acc registration model/config and the ADAD decoupler ONNX ensemble, while keeping the default spatial normalization model and assets needed by common Centiloid/CenTauR/CenTauRz workflows.

The plain dcccpy package remains slim and downloads the full matching DCCCcore release package from GitHub on first use when no installed runtime is available.