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mesoscope / cellpack

Python algorithm to pack 3D models
https://mesoscope.github.io/cellpack/
MIT License
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cellPack

Continuous Integration Documentation Code Coverage

algorithm to pack molecular recipes

Prerequisite

  1. Install Conda: https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html

Setup

  1. create a virtual env: conda create -n autopack python=3.9
  2. conda activate autopack
  3. pip install -e .[dev]

Run pack code

  1. example pack v1 recipe : pack -r examples/recipes/v1/NM_Analysis_FigureB1.0.json -c examples/packing-configs/run.json
  2. example pack v2 recipe : pack -r examples/recipes/v2/one_sphere.json -c examples/packing-configs/run.json
  3. example pack from remote : pack -r github:recipes/NM_Analysis_FigureB1.0.json -c examples/packing-configs/run.json

Run conversion code

Stable Release: pip install cellpack
Development Head: pip install git+https://github.com/mesoscope/cellpack.git

Documentation

For full package documentation please visit mesoscope.github.io/cellpack.

Development

See CONTRIBUTING.md for information related to developing the code.

Contributing cheat sheet

  1. pip install -e .[dev]

    This will install your package in editable mode with all the required development dependencies (i.e. tox).

  2. make build

    This will run tox which will run all your tests in both Python 3.7 and Python 3.8 as well as linting your code.

  3. make clean

    This will clean up various Python and build generated files so that you can ensure that you are working in a clean environment.

  4. make docs

    This will generate and launch a web browser to view the most up-to-date documentation for your Python package.

Suggested Git Branch Strategy

  1. main is for the most up-to-date development, very rarely should you directly commit to this branch. GitHub Actions will run on every push and on a CRON to this branch but still recommended to commit to your development branches and make pull requests to main. If you push a tagged commit with bumpversion, this will also release to PyPI.
  2. Your day-to-day work should exist on branches separate from main. Even if it is just yourself working on the repository, make a PR from your working branch to main so that you can ensure your commits don't break the development head. GitHub Actions will run on every push to any branch or any pull request from any branch to any other branch.
  3. It is recommended to use "Squash and Merge" commits when committing PR's. It makes each set of changes to main atomic and as a side effect naturally encourages small well defined PR's.

Introduction to Remote Databases

AWS S3

  1. Pre-requisites

    • Obtain an AWS account for AICS. Please contact the IT team or the code owner.
    • Generate an aws_access_key_id and aws_secret_access_key in your AWS account.

  2. Step-by-step Guide

    • Download and install the AWS CLI
    • Configure AWS CLI by running aws configure, then enter your credentials as prompted.
    • Ensure that Boto3, the AWS SDK for Python is installed and included in the requirements section of setup.py.

Firebase Firestore

  1. Step-by-step Guide
    • For dev database:
      • Create a Firebase project in test mode with your google account, select firebase_admin as the SDK. Firebase Firestore tutorial
      • Generate a new private key by navigating to "Project settings">"Service account" in the project's dashboard.
    • For staging database:
      • Reach out to the code owner for the necessary credentials.
      • Set up an .env file as instructed.

MIT license