Skip to main content

Cell Maps ImmunoFluorescent Image Embedder

The Cell Maps Image Embedder is part of the Cell Mapping Toolkit

a b Documentation Status Zenodo DOI badge

Generate embeddings from ImmunoFluorescent image data from Human Protein Atlas

Dependencies

Compatibility

  • Python 3.8+

Installation

git clone https://github.com/idekerlab/cellmaps_image_embedding
cd cellmaps_image_embedding
make dist
pip install dist/cellmaps_image_embedding*whl

Run make command with no arguments to see other build/deploy options including creation of Docker image

make

Output:

clean                remove all build, test, coverage and Python artifacts
clean-build          remove build artifacts
clean-pyc            remove Python file artifacts
clean-test           remove test and coverage artifacts
lint                 check style with flake8
test                 run tests quickly with the default Python
test-all             run tests on every Python version with tox
coverage             check code coverage quickly with the default Python
docs                 generate Sphinx HTML documentation, including API docs
servedocs            compile the docs watching for changes
testrelease          package and upload a TEST release
release              package and upload a release
dist                 builds source and wheel package
install              install the package to the active Python's site-packages
dockerbuild          build docker image and store in local repository
dockerpush           push image to dockerhub

Before running tests, please install: pip install -r requirements_dev.txt.

For developers

To deploy development versions of this package

Below are steps to make changes to this code base, deploy, and then run against those changes.

  1. Make changes

    Modify code in this repo as desired

  2. Build and deploy

# From base directory of this repo cellmaps_image_embedding
pip uninstall cellmaps_image_embedding -y ; make clean dist; pip install dist/cellmaps_image_embedding*whl

Needed files

The output directory for the image downloads is required (see Cell Maps Image Downloader). Optionally, a path to the image embedding model can be provided.

Usage

For information invoke cellmaps_image_embeddingcmd.py -h

Example usage

cellmaps_image_embeddingcmd.py ./cellmaps_image_embedding_outdir --inputdir ./cellmaps_imagedownloader_outdir

Via Docker

Example usage

Coming soon...

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.3.0 (2024-12-02)

  • Added README generation.

  • Refactor code.

0.2.1 (2024-09-06)

  • Bug fix in --inputdir argument.

0.2.0 (2024-08-29)

  • Added --provenance flag to pass a path to json file with provenance information. This removes the necessity of input directory to be an RO-Crate.

  • Bug fixes
    • Resolved an issue in embedding generation process where images associated with multiple genes were not correctly handled (ambiguous antibodies).

0.1.0 (2024-02-01)

  • First release on PyPI.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cellmaps_image_embedding-0.3.0.tar.gz (35.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

cellmaps_image_embedding-0.3.0-py2.py3-none-any.whl (22.7 kB view details)

Uploaded Python 2Python 3

File details

Details for the file cellmaps_image_embedding-0.3.0.tar.gz.

File metadata

File hashes

Hashes for cellmaps_image_embedding-0.3.0.tar.gz
Algorithm Hash digest
SHA256 e6e8ea79ae054252683e37f4f49461020cc930345b195c2fb805bbd36be828e9
MD5 b4d1ce302394a32e4cce4f43e517d2a5
BLAKE2b-256 2bba3e117263fa6658c36d1b62efffb90f8d3c0eb4ed234e4cd215525d01630f

See more details on using hashes here.

File details

Details for the file cellmaps_image_embedding-0.3.0-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for cellmaps_image_embedding-0.3.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 ecc598c0ea4cb7afe9806bfd262b70ba01edb748ad5fa63c44a6262c13c1ba8e
MD5 e42c196c54894bef290fa061ebe283ff
BLAKE2b-256 88d7dc78b23feb184d7f02aca2c1e775b4c3460f6c5373b536813cda9362239a

See more details on using hashes here.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page