This is a helper library designed for reading microscopy data supported by Bioformats using Python. The package also includes a command-line interface for assessing differences between images.
- Version: "0.3.12"
You can get the library directly from PyPI
using pip:
pip install nima_io
Alternatively, you can use pipx to install it in an isolated environment:
pipx install nima_io
To enable auto completion for the cli command, follow these steps:
-
Generate the completion script by running the following command:
_IMGDIFF_COMPLETE=bash_source imgdiff > ~/.local/bin/imgdiff-complete.bash -
Source the generated completion script to enable auto completion:
source ~/.local/bin/imgdiff-complete.bash
You can check out the documentation on https://darosio.github.io/nima_io for up to date usage information and examples.
ii provides several command line interface tools for …
imgdiff --help
ii can be imported and used as a Python package. The following modules are available:
nima_io.read - TODO DESCRIBE
To use nima_io in your python:
from nima_io import read
Despite the comprehensive python-bioformats package, Bioformats reading in Python is not flawless. To assess correct reading and performance, I gathered a set of test input files from real working data and established various approaches for reading them:
- Utilizing the external "showinf" and parsing the generated XML metadata.
- Employing out-of-the-box python-bioformats.
- Leveraging bioformats through the Java API.
- Combining python-bioformats with Java for metadata (Download link: bio-formats 5.9.2).
At present, Solution No. 4 appears to be the most effective.
It's important to note that FEI files are not 100% OME compliant, and understanding OME metadata can be challenging. For instance, metadata.getXXX is sometimes equivalent to metadata.getRoot().getImage(i).getPixels().getPlane(index).
The use of parametrized tests enhances clarity and consistency. The approach of returning a wrapper to a Bioformats reader enables memory-mapped (a la memmap) operations.
Notebooks are included in the documentation tutorials to aid development and illustrate usage. Although there was an initial exploration of the TileStitch Java class, the decision was made to implement TileStitcher in Python.
Future improvements can be implemented in the code, particularly for the multichannel OME standard example, which currently lacks obj or resolutionX metadata. Additionally, support for various instrument, experiment, or plate metadata can be considered in future updates.
We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.
All code is licensed under the terms of the revised BSD license.
If you are interested in contributing to the project, please read our contributing and development environment guides, which outline the guidelines and conventions that we follow for contributing code, documentation, and other resources.
To begin development, follow these steps:
Create an .envrc file with the command:
echo "layout hatch" > .envrc
direnv allow
Update and initialize submodules:
git submodule update --init --recursive
Navigate to the tests/data/ directory:
cd tests/data/
git co master
Configure Git Annex for SSH caching:
git config annex.sshcaching true
Pull the necessary files using Git Annex:
git annex pull
These commands set up the development environment and fetch the required data for testing.
Modify tests/data.filenames.txt and tests/data.filenames.md5 as needed and run:
cd tests
./data.filenames.sh
This project was initialized using the Cookiecutter Python template.