nbnode 1.1.0.post1.dev1

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/:

.. code-block:: bash

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