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fixes for publication
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‎.github/workflows/publish.yml‎

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name: Publish to PyPI
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# Publishes teal-bio whenever a GitHub Release is published.
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# Uses PyPI Trusted Publishing (OIDC): there is no API token to create or leak.
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# One-time setup on PyPI: Publishing -> Add a new pending publisher
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# owner: IronSheep16 repo: Teal workflow: publish.yml environment: pypi
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on:
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release:
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types: [published]
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Build sdist and wheel
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run: |
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python -m pip install --upgrade build
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python -m build
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- name: Check the metadata renders on PyPI
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run: |
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python -m pip install --upgrade twine
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python -m twine check dist/*
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- uses: actions/upload-artifact@v4
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with:
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name: dist
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path: dist/
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publish:
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needs: build
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runs-on: ubuntu-latest
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environment: pypi
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permissions:
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id-token: write # required for trusted publishing
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steps:
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- uses: actions/download-artifact@v4
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with:
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name: dist
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path: dist/
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- uses: pypa/gh-action-pypi-publish@release/v1

‎.github/workflows/tests.yml‎

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name: Tests
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on:
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push:
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branches: [main]
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pull_request:
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.9", "3.11", "3.12"]
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install teal and its test deps
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run: |
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python -m pip install --upgrade pip
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python -m pip install -e .
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python -m pip install pytest
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- name: Run the test suite
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run: pytest tests/ -v
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- name: Check the examples still run end to end
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run: |
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python examples/minimal_pipeline.py
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python examples/multi_file_pipeline.py

‎CITATION.cff‎

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cff-version: 1.2.0
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title: "Teal: Timelapse Express Analysis Library"
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message: "If you use Teal in your research, please cite it as below."
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type: software
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authors:
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- given-names: Benjamin
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family-names: Lapointe
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email: benjamin.lapointe@umontreal.ca
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affiliation: Université de Montréal
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repository-code: "https://github.com/IronSheep16/Teal"
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abstract: >-
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Teal analyses cell lineage data from timelapse microscopy. It builds a single
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spatiotemporal graph in which every cell is a node carrying its attributes, its
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timepoint and the mesh labels it came from, connected by lineage edges (parent to
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child) and spatial edges (neighbouring cells). Samples exported as several
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overlapping meshes are merged into one graph, including cells imaged in more than
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one mesh and cells whose parent lies in another mesh. Built to round-trip with
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MorphoGraphX, though any connected cell mesh works.
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keywords:
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- cell lineage
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- lineage tracing
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- timelapse microscopy
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- morphogenesis
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- MorphoGraphX
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- bioimage analysis
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license: GPL-2.0
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version: 0.1.0
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# once a release is archived on Zenodo, add the DOI it mints:
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# doi: 10.5281/zenodo.XXXXXXX

‎README.md‎

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A Python library for analyzing cell lineage data from timelapse microscopy, with a focus on plant tissue development.
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Meant to be used in combination with MorphoGraphX (Pierre Barbier de Reuille et al., 2015), but having an otherwise connected cell mesh is sufficient for usage.
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![TEAL Overview](teal/Teal.png)
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![TEAL Overview](https://raw.githubusercontent.com/IronSheep16/Teal/main/teal/Teal.png)
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---
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## Installation
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```bash
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# Clone the repository
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git clone https://github.com/IronSheep16/Teal.git
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cd Teal
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pip install teal-bio
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```
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# Install dependencies
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pip install -r requirements.txt
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The distribution is called `teal-bio` (the name `teal` was already taken on PyPI), but you
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still import it as `teal`:
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```python
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import teal
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```
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# Install in development mode
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To work on the library itself:
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```bash
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git clone https://github.com/IronSheep16/Teal.git
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cd Teal
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pip install -e .
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```
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### Requirements
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- Python 3.8+
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- numpy
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- pandas
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- matplotlib
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- networkX
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Python 3.9+. `numpy`, `pandas`, `networkx` and `matplotlib` are installed automatically.
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---
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## Quick Start
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### 1. Export the cell graph from MorphoGraphX
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Teal reads the cell connectivity of a segmented mesh as a `.ply`. In MGX, run this once per
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timepoint on the segmented mesh you want to analyse:
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```python
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Process.Mesh__Export__Cell_Graph_Ply_File_Save(filename, 'No', 'Active Mesh')
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```
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Name the files so the timepoint is readable from the filename (`02_T0_cellGraph.ply`,
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`02_T1_cellGraph.ply`, …) — that is what `files_by_t` keys them by. Attributes (growth, size,
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zones, …) and parent labels are the CSVs MGX already exports.
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### 2. Build and analyse in Teal
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```python
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import teal as tl
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from teal import files_by_t
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tmlps.lineages('GROWTH')
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```
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Results go back to MGX as heatmap CSVs:
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```python
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tmlps.heat('GROWTH_heat', 'GROWTH') # one CSV per timepoint, per mesh
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```
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---
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## Core Concepts

‎pyproject.toml‎

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build-backend = "setuptools.build_meta"
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[project]
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name = "Teal"
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# 'teal' is already taken on PyPI, so the distribution ships as teal-bio.
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# the import name is unaffected: users still write `import teal`.
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name = "teal-bio"
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version = "0.1.0"
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description = "Conversion of cell (nodes) timelapse to a spruce graph, including some analysis tools."
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authors = [{ name = "Benjamin Lapointe" }]
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description = "Cell lineage analysis for timelapse microscopy: one spatiotemporal graph of lineage and spatial neighbours, built from MorphoGraphX meshes."
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authors = [{ name = "Benjamin Lapointe", email = "benjamin.lapointe@umontreal.ca" }]
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readme = "README.md"
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requires-python = ">=3.9"
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dependencies = ["numpy", "pandas", "networkx"]
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license = { file = "LICENSE" }
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keywords = [
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"cell-lineage",
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"lineage-tracing",
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"timelapse",
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"microscopy",
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"morphographx",
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"morphogenesis",
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"bioimage-analysis",
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]
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classifiers = [
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"Programming Language :: Python :: 3",
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"Intended Audience :: Science/Research",
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"Topic :: Scientific/Engineering :: Bio-Informatics",
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"Topic :: Scientific/Engineering :: Image Processing",
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"Operating System :: OS Independent",
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]
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dependencies = [
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"numpy",
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"pandas",
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"networkx",
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"matplotlib", # graph_viz only; imported lazily so it never slows down `import teal`
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]
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[project.urls]
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Homepage = "https://github.com/IronSheep16/Teal"
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Repository = "https://github.com/IronSheep16/Teal"
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[tool.setuptools]
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packages = ["teal"]

‎teal/core.py‎

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if d.get("edge_type") == edge_type
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]
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graph = graph.edge_subgraph(edges_to_keep).copy()
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A = nx.nx_agraph.to_agraph(graph)
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for n in graph.nodes():
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node_attr = graph.nodes[n].get(attr_name, n) if attr_name else n
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A.get_node(n).attr.update(
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label=str(node_attr),
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rank=str(graph.nodes[n]["layer"])
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)
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A.graph_attr.update(
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rankdir="LR",
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ranksep="2.0",
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nodesep="0.2"
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import matplotlib.pyplot as plt # only needed to draw, so keep it out of the import
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# one column per timepoint: 'layer' already ranks every cell, so it lays itself out
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pos = nx.multipartite_layout(graph, subset_key="layer")
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labels = {
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n: str(graph.nodes[n].get(attr_name, n) if attr_name else n)
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for n in graph.nodes()
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}
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fig, ax = plt.subplots(figsize=(12, 8))
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nx.draw_networkx(
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graph, pos, ax=ax, labels=labels,
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node_size=300, node_color="lightsteelblue",
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edge_color="grey", arrowsize=8, font_size=6,
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)
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A.layout(prog="dot")
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A.draw(path=path or "graph.png", format="png", prog="dot", args="-Gdpi=200")
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ax.set_axis_off()
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fig.tight_layout()
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fig.savefig(path or "graph.png", dpi=200)
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plt.close(fig)
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return graph
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# checking up on common graph errors

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