documentation

Author: valentinwust

.github/workflows/ci.yaml

@@ -28,4 +28,4 @@ jobs:
       - name: Smoke test
         run: |
           pip install dist/*.whl --no-deps
-          python -c "from cytovanni.version import __version__; print(__version__)"
+          python -c "import importlib.metadata; print(importlib.metadata.version('cytovanni'))"

.readthedocs.yaml

@@ -0,0 +1,13 @@
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+
+sphinx:
+  configuration: docs/conf.py
+
+python:
+  install:
+    - requirements: docs/requirements.txt

container/test.def

@@ -0,0 +1,12 @@
+Bootstrap: docker
+From: pytorch/pytorch:2.8.0-cuda12.9-cudnn9-devel
+
+%post
+    pip install "cytovanni[geom]"
+
+%runscript
+    python -c "
+import cytovanni; print(cytovanni.__version__)
+import geomloss; print('geomloss ok')
+import pykeops; print('pykeops ok')
+"

docs/conf.py

@@ -0,0 +1,16 @@
+import re
+
+project = "Cytovanni"
+author = "Valentin Wüst, Simon Anders"
+
+with open("../src/cytovanni/version.py") as f:
+    release = re.search(r'__version__ = ["\']([^"\']+)["\']', f.read()).group(1)
+
+extensions = ["myst_nb"]
+
+nb_execution_mode = "off"
+
+html_theme = "pydata_sphinx_theme"
+html_theme_options = {
+    "github_url": "https://github.com/anders-biostat/Cytovanni",
+}

docs/index.md

@@ -9,3 +9,14 @@ Computational Standardization of Flow Cytometry Data
 The first tutorial shows how to use [rainbow beads to standardize the flow cytometer](tutorials/integration_fixed-spectra/integration_fixed-spectra-rainbow.ipynb), and [fit scaling factors for each marker using reference samples](tutorials/integration_fixed-spectra/integration_fixed-spectra-ref.ipynb). While we strongly encourage the use of rainbow beads, the scaling factors can also be used independently from the cytometer standardization, and will be the dominant source of batch effects in many setups.
 
 The second tutorial also uses the [rainbow bead standardization](tutorials/integration_variable-spectra/integration_variable-spectra-rainbow.ipynb), which is applied to the samples, as well as to single stain spectra. We then [model the dye variability](tutorials/integration_variable-spectra/integration_variable-spectra-dye.ipynb), which allows us to [fit a full integration from overlapping samples](tutorials/integration_variable-spectra/integration_variable-spectra-model.ipynb) including both marker scaling factors, as well as to extract dye spectra for every batch from the overlapping samples.
+
+```{toctree}
+:hidden:
+:maxdepth: 2
+
+tutorials/integration_fixed-spectra/integration_fixed-spectra-rainbow
+tutorials/integration_fixed-spectra/integration_fixed-spectra-ref
+tutorials/integration_variable-spectra/integration_variable-spectra-rainbow
+tutorials/integration_variable-spectra/integration_variable-spectra-dye
+tutorials/integration_variable-spectra/integration_variable-spectra-model
+```

docs/requirements.txt

@@ -0,0 +1,3 @@
+sphinx
+myst-nb
+pydata-sphinx-theme