Minor fixes

.github/workflows/ci.yml

@@ -17,10 +17,10 @@ jobs:
     name: Lint & type-check
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v8.2.0
         with:
           enable-cache: true
 
@@ -41,10 +41,10 @@ jobs:
     name: Build docs
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v8.2.0
         with:
           enable-cache: true
 
@@ -62,10 +62,10 @@ jobs:
       matrix:
         python-version: ["3.13"]
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v8.2.0
         with:
           enable-cache: true
 
@@ -86,7 +86,7 @@ jobs:
 
       - name: Upload coverage
         if: matrix.python-version == '3.12'
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
         with:
           name: coverage-xml
           path: coverage.xml

.github/workflows/docs.yml

@@ -1,30 +1,35 @@
-name: Deploy docs
+name: Docs
 
 on:
   push:
-    branches: [master]
+    branches: [main]
   workflow_dispatch:
 
+# Deploy via the GitHub Pages "GitHub Actions" source (set it in repo
+# Settings -> Pages). Zensical has no `gh-deploy`; we build and upload the
+# site as a Pages artifact instead.
 permissions:
-  contents: write
-
-concurrency:
-  group: docs-${{ github.ref }}
-  cancel-in-progress: true
+  contents: read
+  pages: write
+  id-token: write
 
 jobs:
   deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v5
+      - uses: actions/configure-pages@v6
+      - uses: actions/checkout@v7
+      - uses: astral-sh/setup-uv@v8.2.0
         with:
+          python-version: "3.13"
           enable-cache: true
-
-      - name: Sync (docs group)
-        run: uv sync --group docs
-
-      - name: Deploy to GitHub Pages
-        run: uv run mkdocs gh-deploy --force
+      - run: uv sync --dev
+      - run: uv run zensical build
+      - uses: actions/upload-pages-artifact@v6
+        with:
+          path: site
+      - uses: actions/deploy-pages@v6
+        id: deployment

.github/workflows/release.yml

@@ -19,10 +19,10 @@ jobs:
     name: Build distributions
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v8.2.0
 
       - name: Build sdist + wheel
         run: uv build
@@ -31,7 +31,7 @@ jobs:
         run: uvx twine check dist/*
 
       - name: Upload artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
         with:
           name: dist
           path: dist/*
@@ -44,7 +44,7 @@ jobs:
     permissions:
       id-token: write  # OIDC for Trusted Publishing
     steps:
-      - uses: actions/download-artifact@v4
+      - uses: actions/download-artifact@v8
         with:
           name: dist
           path: dist

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.15.15
+    rev: v0.15.16
     hooks:
       # Run the linter.
       - id: ruff

CITATION.cff

@@ -4,7 +4,8 @@ message: >-
   fork and also cite the original TopOMetry work listed under references.
 type: software
 title: topometry-nosc
-version: 0.1.0
+version: 0.3.0
+date-released: 2026-06-10
 authors:
   - family-names: Madsen
     given-names: Jakob
@@ -34,6 +35,6 @@ references:
     journal: eLife
     year: 2024
     volume: 13
-    article-number: RP100361
+    number: RP100361
     doi: 10.7554/eLife.100361.2
     url: "https://doi.org/10.7554/eLife.100361.2"

README.md

@@ -1,3 +1,4 @@
+[![PyPI](https://img.shields.io/pypi/v/topometry-nosc)](https://pypi.org/project/topometry-nosc/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/HauserGroup/topometryNoSC/blob/master/notebooks/example.ipynb)
 [![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
@@ -161,9 +162,14 @@ Example `TopOGraph` fit:
 
 ## Changelog
 
-**0.2.0** — Core-only release
-- Removed single-cell / scanpy / AnnData wrappers (now a standalone geometry toolkit)
-- Core API unchanged: `TopOGraph`, spectral scaffolds, graph operators, layouts, metrics, plotting
+**0.3.0** — First PyPI release
+- Published to PyPI as [`topometry-nosc`](https://pypi.org/project/topometry-nosc/)
+- Simplified internals delegate to scikit-learn/SciPy/umap-learn where possible;
+  slimmer runtime dependencies (ANN backends and extra layouts are opt-in extras)
+- Standalone building blocks (`kNN`, `Kernel`, `cknn_graph`, `EigenDecomposition`,
+  `LE`, `Projector`) usable and tested outside the `TopOGraph` orchestrator
+
+See [CHANGELOG.md](CHANGELOG.md) for the full history.
 
 #### Citation
 

docs/changelog.md

@@ -1,13 +1 @@
-# Changelog
-
-## Unreleased
-
-- Documentation migrated to MkDocs Material.
-- Added API reference through mkdocstrings.
-- Added provenance and citation documentation.
-
-## v2.0.0 — Core-only release
-
-- Removed single-cell / scanpy / AnnData wrappers (now a standalone geometry toolkit).
-- Core API unchanged: `TopOGraph`, spectral scaffolds, graph operators, layouts,
-  metrics, plotting.
+--8<-- "CHANGELOG.md"

src/topo/layouts/map.py

@@ -141,11 +141,12 @@ def fuzzy_embedding(
     euclidean_output : bool, default=True
         Whether to use the faster code specialised for euclidean output metrics
     save_every : int or None, optional
-        If provided and >0, store the embedding every `save_every` epochs into
+        If provided and >0, store the final embedding into
         `aux_data["checkpoints"]` as a list of dicts:
             [{"epoch": e, "embedding": Y_e}, ...]
-        WARNING: storing many snapshots can be memory intensive. Consider
-        passing `save_callback` to stream snapshots to disk.
+        Per-epoch checkpointing is not implemented since layout optimization
+        is delegated to `umap-learn`; at most the initial (epoch 0) and final
+        snapshots are captured.
     save_limit : int or None, optional
         Maximum number of snapshots to keep in-memory in `aux_data`.
         If exceeded, the earliest snapshots are discarded (FIFO).

src/topo/layouts/projector.py

@@ -23,7 +23,7 @@
 from topo.base.ann import kNN
 from topo.layouts.isomap import Isomap
 from topo.layouts.map import fuzzy_embedding
-from topo.spectral.eigen import EigenDecomposition
+from topo.spectral import LE
 from topo.tpgraph.kernels import Kernel
 from topo.utils._utils import get_landmark_indices
 
@@ -73,10 +73,11 @@ class Projector(BaseEstimator, TransformerMixin):
 
     projection_method : str, default='Isomap'
         Which projection method to use. `UMAP` delegates to `umap-learn`; `MAP`
-        is TopoMetry's local checkpoint-aware graph-layout optimizer. Current options are:
+        is TopoMetry's graph-layout optimizer (optional initial/final snapshot
+        capture). Current options are:
             * 'Isomap' - one of the first manifold learning methods
             * ['t-SNE'](https://github.com/DmitryUlyanov/Multicore-TSNE) - a classic manifold learning method
-            * 'MAP' - local checkpoint-aware graph-layout optimization
+            * 'MAP' - graph-layout optimization with initial/final snapshot capture
             * ['UMAP'](https://umap-learn.readthedocs.io/en/latest/index.html) - upstream `umap-learn` estimator
             * ['PaCMAP'](http://jmlr.org/papers/v22/20-1061.html) (Pairwise-controlled Manifold Approximation and Projection) - for balanced visualizations
             * ['TriMAP'](https://github.com/eamid/trimap) - dimensionality reduction using triplets
@@ -125,11 +126,11 @@ def __init__(
         keep_estimator=False,
         random_state=None,
         verbose=False,
-        # ---- NEW: checkpointing passthrough to MAP ----
-        save_every=None,  # int or None: store Y every `save_every` epochs
+        # ---- checkpointing passthrough to MAP ----
+        save_every=None,  # int or None: store final MAP embedding in aux checkpoints
         save_limit=None,  # cap snapshots kept in memory
         save_callback=None,  # callable(epoch:int, Y:np.ndarray) -> None
-        include_init_snapshot=True,  # store epoch=0 (post-init) snapshot
+        include_init_snapshot=True,  # store epoch=0 snapshot before SGD
     ):
         self.n_components = n_components
         self.metric = metric
@@ -317,17 +318,22 @@ def fit(self, X: np.ndarray | csr_matrix | Kernel, **kwargs: Any) -> "Projector"
             if self.init == "spectral":
                 try:
                     self.init_Y_ = np.asarray(
-                        EigenDecomposition(
-                            n_components=self.n_components
-                        ).fit_transform(K)
+                        LE(
+                            K,
+                            n_eigs=int(self.n_components),
+                            laplacian_type="normalized",
+                            drop_first=True,
+                            return_evals=False,
+                        ),
+                        dtype=np.float32,
                     )
                 except Exception:
                     warnings.warn(
                         "Spectral layout initialization failed, falling back to random initialization..."
                     )
                     self.init_Y_ = self.random_state.randn(
                         _n_rows(K, "projection graph"), self.n_components
-                    )
+                    ).astype(np.float32)
             else:
                 self.init_Y_ = self.random_state.randn(
                     _n_rows(K, "projection graph"), self.n_components

src/topo/spectral/eigen.py

@@ -164,7 +164,7 @@ def eigendecompose(
             G_csr,
             k=k,
             which="LM" if largest else "SM",
-            tol=float(eigen_tol),  # type: ignore
+            tol=_eigsh_tol(eigen_tol),
         )
     except ArpackError as exc:
         raise RuntimeError(