toolsandogh
A Python toolbox for iSCAT microscopy data analysis, developed by the Sandoghdar Division of the Max Planck Institute for the Science of Light (MPL).
Overview
toolsandogh provides a small, reusable library for loading, processing, analyzing, and storing microscopy videos. It is designed around a canonical 5D data model---T (time), C (channel), Z (slice), Y (row), X (column)---implemented as lazy xarray arrays backed by Dask.
Features
- Lazy, out-of-core computation: Videos are kept as Dask arrays so you can work with datasets larger than memory.
- Labeled, metadata-rich arrays: Results are
xarray.DataArrayobjects withTCZYXdimensions and OME metadata. - Multiple microscopy formats: Read and write RAW/BIN, TIFF, OME-TIFF, OME-Zarr, ND2, and other formats supported by bioio.
- Reusable analysis primitives: Rolling statistics, radial variance transform, synthetic data generation, and more.
Installation
This project uses the uv package manager. Clone the repository and run:
uv sync
To install in editable mode for development:
uv pip install -e ".[dev]"
Quick start
import toolsandogh as sand
# Load a video (any bioio-supported format or raw BIN/RAW file).
video = sand.load_video("movie.nd2")
# Apply a rolling average along the time axis.
smoothed = sand.rolling_average(video, window_size=5, dim="T")
# Save the result back to disk.
sand.store_video(smoothed, "smoothed.ome.tiff")
Use help(sand.load_video) or help(sand.store_video) for details on the available arguments.
Repository layout
src/toolsandogh/--- the main Python package.src/toolsandogh/tests/---pytesttest suite.DESIGN.md--- design conventions and coding standards.
Contributing
Please see DESIGN.md for coding conventions, documentation standards, and architecture decisions. In brief:
- All public functions must be type-hinted and documented in numpydoc style.
- Code is linted with
ruffand type-checked withpyright. - Prefer free functions; keep data in
xarray.DataArray/Datasetform. - Tests go in
src/toolsandogh/tests/.
License
GPL-3.0-or-later.
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