nbnode 1.1.0.post1.dev2

A python package for non-binary trees and simulation of flow cytometry data.

NBNode

A python package for non-binary trees and simulation of flow cytometry data.

NBNode enables non-binary decision trees with multiple decisions at each node. Additionally it enables a dirichlet distribution based simulation of flow cytometry data.

Installation

conda create -y -n conda_nbnode python=3.8
conda activate conda_nbnode
git clone https://git.uni-regensburg.de/ccc_verse/nbnode
cd nbnode
pip install --upgrade pip
pip install .

Tutorials

See the documentation and the (within) presented jupyter notebooks https://ggrlab.github.io/nbnode/

Package setup

This project has been set up using PyScaffold 4.4. For details and usage information on PyScaffold see https://pyscaffold.org/.

pip install pyscaffold
putup nbnode
cd nbnode
# Create nbnode within gitlab, without README
git branch -m master main
git remote add origin [e-mail]:ccc_verse/nbnode.git
git push -u origin --all
conda create -y -n conda_nbnode python=3.8
conda activate conda_nbnode
# Select conda_nbnode as default python interpreter in VsCode
#   select a single python file, then on the bottom right, the current python interpreter name
#   pops up. Click on it and select the "conda_nbnode" interpreter.
# Make sure that the correct pip is used:
#   Something like: /home/gugl/.conda_envs/conda_nbnode/bin/pip
which pip
pip install tox
tox --help

# Have a clean git, then add gitlab-ci with pyscaffold
putup --update . --gitlab
pip install pre-commit
pre-commit autoupdate

# Migration to github:

    putup --update . --github

# pypi:
# login, then create a new token https://pypi.org/manage/account/token/
# re-comment (after I commented them out)
# .github/workflows/ci.yml:
#       publish: [...]

Tests

For some tests you need data files, which are not included in the repository. Especially all tests in tests/specific_analyses need data. You can obtain the data by downloading the data from zenodo:

pip install requests
python tests/specific_analyses/e02_download_intraassay_zenodo.py