This repository contains Python tools and Jupyter notebooks for working with microscopy imaging data, focusing on smart event-driven acquisition (EDA) workflows.
It provides utilities for reading, reshaping, and visualising image datasets, with an emphasis on converting raw datasets into cropped TIFFs for downstream analysis.
analysis_visualisation_tools/ # Helper functions and classes
notebooks/ # Jupyter notebooks
results/ # Output figures, plots, movies
tests/ # Unit tests
pyproject.toml # Poetry environment definition
poetry.lock # Poetry lock file
This notebook reads all smart EDA measurements and related metadata from a given folder, and generates cropped multi-channel TIFF stacks, ready for screening and downstream analysis. Main features:
- Reads ome.zarr EDA datasets.
- Extracts all events detected during acquisition
- Saves the events cropped in time and space as TIFF files.
This notebook loads and processes batches of cropped TIFFs produced in the previous step.
Main features:
- Display all the events in a friendly way.
- Allow to select interesting events, and categorise them.
- Copy selected events in different folders, divided by event category.
- All events metadata are easily accessible in a dedicated JSON file.
This project uses Poetry for dependency management.
Clone the repository and install dependencies:
git clone <https://github.com/GiorgioTortarolo/analysis-visualisation-tools.git>
cd <analysis_visualisation_tools>
poetry install