API reference#

Generated from the source. If you are new to acia, start with the tutorials instead — this page is for looking things up.

acia itself exports only the pint registry used throughout the library: ureg (the application registry), Q_ (Quantity) and U_ (Unit). Everything else is imported from the submodules below.

Core data model#

Detections, overlays and the image-sequence abstraction that every reader implements.

`acia.base`	All basic functionality for acia
`acia.utils`	Global utilities
`acia.colors`	Named channel colors for ImageSequenceSource.to_rgb(colors=...) composites.
`acia.timing`	Helpers for physical calibration (time and pixel size) in pint units.

Reading image sequences#

`acia.segm.open`	Format-agnostic lazy opener for multi-position acquisitions.
`acia.segm.local`	Local segmentation functionality dealing with files from HDD.
`acia.segm.folder_source`	A folder of per-timepoint TIFFs exposed as one lazy time series.
`acia.segm.nd2_source`	Lazy, memory-bounded image source for Nikon ND2 files.
`acia.segm.czi_source`	Lazy, memory-bounded image source for Zeiss CZI files.
`acia.segm.tiff_metadata`	Best-effort calibration reader for OME-TIFF / ImageJ-hyperstack metadata.
`acia.segm.tiff_export`	Optional lazy TIFF export of a (cropped) image sequence.
`acia.segm.formats`	Functions for different segmentation formats
`acia.segm.utils`	Utils for segmentation data handling
`acia.segm.omero.storer`	Classes for OMERO storage interaction
`acia.segm.omero.utils`	Utils for OMERO segmenation data
`acia.segm.omero.shapeUtils`	Utilities for working with OMERO shape objects
`acia.config`	Filesystem credentials store for image sources.

Segmentation#

`acia.segm.processor.canny`	Canny edge detection based segmentation processor
`acia.segm.processor.cellpose`	Segmenter using Cellpose (below v4)
`acia.segm.processor.cellpose_sam`	Segmenter using CellposeSAM: https://doi.org/10.1101/2025.04.28.651001
`acia.segm.processor.omnipose`	Omnipose segmentation implementation
`acia.segm.processor.flowpose_rt`	flowpose-rt segmentation implementation
`acia.segm.processor.cpn`	Module for Contour Proposal Networks
`acia.segm.processor.yolo`	Segmenter using YOLO: ultralytics/ultralytics
`acia.segm.processor.offline`	Offline image processors to perform segmentation
`acia.segm.processor.online`	Processors to apply remote models for segmentation
`acia.segm.processor.predict`	Helper file to generate contours from masks
`acia.segm.filter`	Filters for segmentating overlay objects

Tracking#

`acia.tracking`	Tracking module contains all tools to work with tracking formats
`acia.tracking.formats`	Module to convert tracking formats
`acia.tracking.utils`	Utilities for tracking
`acia.tracking.output`	Tracking dataset exporters
`acia.tracking.processor.trackastra`	Trackastra based tracking
`acia.tracking.processor.ultrack`	Ultrack based tracking
`acia.tracking.processor.laptrack`	LAP tracking processor: https://doi.org/10.1093/bioinformatics/btac799
`acia.tracking.processor.pyuat`	PyUAT based tracking
`acia.tracking.processor.utils`	Utility functions for the tracking processors

Analysis#

`acia.analysis`	Functionality for single-cell analysis
`acia.analysis.properties`	Before/after property-distribution histograms for filtered cell populations.
`acia.analysis.growth_rate`	Unit-aware exponential growth-rate estimation from extractor tables.
`acia.analysis.doubling_time`	Per-cell doubling time from lineage topology, and its temporal evolution.
`acia.analysis.units`	Unit representations for property-extractor tables.
`acia.analysis.stage`	Shared run context for a chain of analysis notebooks ("stages").

Visualization#

`acia.viz`	Module for general visualization functionality
`acia.viz.compose`	Compose image sequences into a single sequence -- side by side, stacked, gridded.
`acia.viz.utils`	Utilities for the visualization
`acia.segm.output`	Functionality for creating outputs (export) the segmentation information

Preprocessing and curation#

Drift correction, ROI selection and the provenance helpers.

`acia.registration`	Pluggable frame-to-frame registration methods for drift correction.
`acia.registration_persistence`	Registration manifest — persist drift-correction transforms and reconstruct lazy corrected sources.
`acia.selection`	Selection manifest — persist curation and reconstruct lazy crops.
`acia.crop_capture`	Persist manual crops as reloadable specs plus training-data captures.
`acia.attribute`	Attribute usage

Notebook integration#

The Jupyter display mixin and the interactive curation widgets.

acia.notebook

Jupyter notebook visualization mixin for image sequence sources.