orbit-gui

An assessment-forward GUI for the analysis of functional neural optical imaging data: validation metrics are computed and surfaced at every pipeline stage (motion correction, source identification, demixing, ...), not bolted on at the end.

Layout

  • src/orbit/ -- pure-Python algorithm and validation-metric core, no Qt. Every stage and every metric is a standalone function (e.g. orbit.projections.mean_projection), independently unit-tested, and called by the GUI rather than implemented inside it.
  • src/orbitapp/ -- the PySide6 GUI shell. One tab per pipeline stage; tabs only call orbit functions and display results. Mirrors the architecture of the sibling pyGraFT project (AppState + per-stage tabs), and reuses the MovieSliderWidget bundled with the roiapp distribution for movie playback.

Install

pip install orbit-gui[gui]

or, for a development checkout:

pip install -e ".[test,gui]"

orbit's native (C++) accelerator for the slower per-pixel/per-trace operations (local correlation and mode projections, OASIS deconvolution, the per-pixel Ljung-Box test) builds automatically whenever a C++ compiler is available (via setup.py's ext_modules -- see PyPI's published wheels, which ship it prebuilt) -- falls back to pure numpy/Python if no compiler is found, so this never blocks pip install. To rebuild it in place after editing the .cpp source, without reinstalling:

src/orbit/_native/build_native.sh

Real-SEUDO's own per-cell FISTA solve has a separate optional accelerator (also falls back to pure Python if skipped) -- needs FFTW3 in addition to a C++14 compiler and pybind11 (Debian/Ubuntu: apt-get install libfftw3-dev). It builds automatically alongside the accelerator above wherever FFTW3 is found (including in PyPI's published wheels, on the platforms where FFTW3 could be provisioned in CI). To rebuild it in place after editing the .cpp source, without reinstalling:

src/orbit/seudo/_native/build_native.sh

Run

python3 -m orbitapp

Develop

pytest

Status

  • Load: a movie (TIFF/folder-of-TIFFs/NPY/H5/MAT), previewed via an embedded, scrubbable movie player.
  • Data Projections: mean/median/mode/variance/Fano-factor/local-correlation projections, computed lazily and cached.
  • Motion Correction: rigid, piecewise-rigid, or PatchWarp-style piecewise-affine registration, with quality metrics (mMD/mCM/ECC, singular-value-spectrum tightening, spatial PC maps) shown alongside the before/after images. Runs in the background; results are explicit candidates previewed in the tab until committed to the active dataset, which is then recorded in the header's pipeline breadcrumb.

Later phases add source identification and demixing/contamination assessment -- see project memory for the full roadmap.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for adamshch from gravatar.com adamshch

Credits

Author: Adam Charles

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

test gui

Tags

calcium-imaging neuroscience microscopy source-extraction roi image-processing

Classifiers

Report project as malware

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