This repository contains code and configuration for processing and analysing images of plankton samples. It's experimental, serving as much as a proposed template for new projects than as a project in itself.
It's a companion project to an R-shiny based image annotation app that is not yet released, written by researchers and data scientists at the UK Centre for Ecology and Hydrology in the early stages of a collaboration that was placed on hold.
Create a fresh virtual environment in the repository root using Python >=3.12 and (e.g.) venv:
python -m venv venv
Next, install the package using pip:
python -m pip install .
Most likely you are interested in developing and/or experimenting, so you will probably want to install the package in 'editable' mode (-e), along with dev tools and jupyter notebook functionality
python -m pip install -e .[all]
Use anaconda or miniconda to create a python environment using the included environment.yml
conda env create -f environment.yml
conda activate cyto_ml
Next install this package without dependencies:
python -m pip install --no-deps -e .
.env contains environment variable names for S3 connection details for the JASMIN object store. Fill these in with your own credentials. If you're not sure what the ENDPOINT should be, please reach out to one of the project contributors listed below.
pytest or py.test
scripts/intake_metadata.py is a proof of concept that creates a configuration file for an intake catalogue - a utility to make reading analytical datasets into analysis workflows more reproducible and less effortful.
Experiment testing workflows by using this plankton model from SciVision to extract features from images for use in similarity search, clustering, etc.
The notebooks/ directory contains Markdown (.md) representations of the notebooks.
To create Jupyter notebooks (.ipynb), run the following command with the conda environment activated:
jupytext --sync notebooks/*If you modify the contents of a notebook, run the command after closing the notebook to re-sync the .ipynb and .md representations before committing.
For more information see the Jupytext docs.
Streamlit app based off the text embeddings for EIDC catalogue metadata one
streamlit run cyto_ml/visualisation/visualisation_app.py
The demo should automatically open in your browser when you run streamlit. If it does not, connect using: http://localhost:8501.
Jo Walsh Alba Gomez Segura Ezra Kitson
Please see CONTRIBUTING.md