Skip to main content
Project generated with PyScaffold

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 git@git.uni-regensburg.de: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:

https://zenodo.org/badge/DOI/10.5281/zenodo.7890571.svg
pip install requests
python tests/specific_analyses/e02_download_intraassay_zenodo.py

Metadata

Release files for nbnode 1.1.0.post2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for nbnode 1.1.0.post2
File Size Uploaded
nbnode-1.1.0.post2.tar.gz 356.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for nbnode 1.1.0.post2
File Interpreter ABI Platform
nbnode-1.1.0.post2-py3-none-any.whl Python 3 none any Details

Total release size: 408.1 kB

Release files / nbnode-1.1.0.post2.tar.gz

Download URL nbnode-1.1.0.post2.tar.gz
Size 356.6 kB
Tags Source
SHA-256 checksum
How to use checksums
051aaffd7669d7ae6e1df8862e256683c0fe5246ade241848faf268e301bc938
BLAKE2b-256 checksum
How to use checksums
f60242bff77ba979aeecbc4590cfbc2290e836bdf292809d2674f91860ae77d6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.0.0 CPython/3.10.12

Release files / nbnode-1.1.0.post2-py3-none-any.whl

Download URL nbnode-1.1.0.post2-py3-none-any.whl
Size 51.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
22180b1b97b21fb189af33e82666901ce5e08478e82c4d55b7bef97829a539c4
BLAKE2b-256 checksum
How to use checksums
3c6fa7e08b50ee5b477df610827e0db8513a90cf1750885379ca0354038ef7d1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.0.0 CPython/3.10.12
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