diff --git a/.github/workflows/build-windows-wheels.yml b/.github/workflows/build-windows-wheels.yml
new file mode 100644
index 0000000..abab1fb
--- /dev/null
+++ b/.github/workflows/build-windows-wheels.yml
@@ -0,0 +1,80 @@
+name: Windows Wheels
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  build-windows:
+    runs-on: windows-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install Poetry
+      run: pip install --upgrade poetry
+
+    - name: Build wheel
+      run: poetry build -f wheel
+
+    - name: Verify wheel contains binary
+      shell: pwsh
+      run: |
+        Add-Type -AssemblyName System.IO.Compression.FileSystem
+        $wheel = Get-ChildItem dist/*.whl | Select-Object -First 1
+        Write-Host "Inspecting $($wheel.Name)"
+        $zip = [System.IO.Compression.ZipFile]::OpenRead($wheel.FullName)
+        $entries = $zip.Entries | Where-Object { $_.Name -match '\.(pyd|dll)$' }
+        if ($entries.Count -eq 0) {
+          Write-Error "No .pyd or .dll found in wheel"
+          $zip.Dispose()
+          exit 1
+        }
+        $entries | ForEach-Object { Write-Host "Found: $($_.FullName)" }
+        $zip.Dispose()
+
+    - name: Smoke test — install and import
+      shell: pwsh
+      run: |
+        python -m venv test-env
+        test-env\Scripts\Activate.ps1
+        pip install (Get-ChildItem dist\*.whl).FullName
+        python -c "import ellphi; print('version:', ellphi.__version__)"
+        python -c "from ellphi.solver import has_cpp_backend; assert has_cpp_backend(), 'C++ backend not available'"
+        python -c "from ellphi.solver import build_info; print(build_info())"
+
+    - name: Smoke test — run solver
+      shell: pwsh
+      run: |
+        test-env\Scripts\Activate.ps1
+        python -c @"
+        import numpy as np
+        from ellphi import tangency, coef_from_cov
+        c1 = coef_from_cov(np.array([[0.0, 0.0]]), np.eye(2).reshape(1, 2, 2))
+        c2 = coef_from_cov(np.array([[3.0, 0.0]]), np.eye(2).reshape(1, 2, 2))
+        result = tangency(c1, c2)
+        print('tangency t =', result.t)
+        assert result.t > 0, 'Expected positive tangency time'
+        print('Solver smoke test passed.')
+        "@
+
+    - name: Upload wheel artifact
+      uses: actions/upload-artifact@v4
+      with:
+        name: wheel-windows-py${{ matrix.python-version }}
+        path: dist/*.whl
+        retention-days: 30
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 0000000..f12f545
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,37 @@
+name: docs
+
+on:
+  push:
+    branches:
+      - main
+      - "ellphi-*"
+  pull_request:
+  workflow_dispatch:
+
+permissions:
+  contents: write
+
+jobs:
+  docs:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Install poetry
+        run: pipx install poetry
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+          cache: "poetry"
+
+      - name: Install dependencies
+        run: poetry install --with docs
+
+      - name: Build docs
+        run: poetry run mkdocs build
+
+      - name: Deploy to gh-pages
+        if: github.event_name == 'push' && github.ref == 'refs/heads/main'
+        run: poetry run mkdocs gh-deploy --force
diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
index f4ea5a7..0a88614 100644
--- a/.github/workflows/python-app.yml
+++ b/.github/workflows/python-app.yml
@@ -17,23 +17,29 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
+    env:
+      ELLPHI_USE_EIGEN: "1"
+      ELLPHI_EIGEN_INCLUDE: "/usr/include/eigen3"
 
     steps:
     - uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
         cache: "pip"
 
+    - name: Install Eigen headers
+      run: sudo apt-get update && sudo apt-get install -y libeigen3-dev
+
     # -- Poetry ------------------------------------------------------
     - name: Install Poetry
       run: pip install --upgrade poetry
 
     - name: Cache Poetry env
-      uses: actions/cache@v3
+      uses: actions/cache@v4
       with:
         path: ~/.cache/pypoetry
         key: ${{ runner.os }}-poetry-${{ hashFiles('pyproject.toml') }}-v2
@@ -60,20 +66,26 @@ jobs:
   # 3.11 限定で mypy
   typecheck:
     runs-on: ubuntu-latest
+    env:
+      ELLPHI_USE_EIGEN: "1"
+      ELLPHI_EIGEN_INCLUDE: "/usr/include/eigen3"
     steps:
     - uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: "3.11"
         cache: "pip"
 
+    - name: Install Eigen headers
+      run: sudo apt-get update && sudo apt-get install -y libeigen3-dev
+
     - name: Install Poetry
       run: pip install --upgrade poetry
 
     - name: Cache Poetry env
-      uses: actions/cache@v3
+      uses: actions/cache@v4
       with:
         path: ~/.cache/pypoetry
         key: ${{ runner.os }}-poetry-${{ hashFiles('pyproject.toml') }}-v2
@@ -89,15 +101,21 @@ jobs:
 
   package-test:
     runs-on: ubuntu-latest
+    env:
+      ELLPHI_USE_EIGEN: "1"
+      ELLPHI_EIGEN_INCLUDE: "/usr/include/eigen3"
     steps:
     - uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: "3.11"
         cache: "pip"
 
+    - name: Install Eigen headers
+      run: sudo apt-get update && sudo apt-get install -y libeigen3-dev
+
     - name: Install Poetry
       run: pip install --upgrade poetry
 
@@ -139,13 +157,13 @@ jobs:
         cd test_run
         pytest ../tests
 
-  build-docs:
+  demo-install:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: "3.11"
         cache: "pip"
@@ -154,14 +172,10 @@ jobs:
       run: pip install --upgrade poetry
 
     - name: Cache Poetry env
-      uses: actions/cache@v3
+      uses: actions/cache@v4
       with:
         path: ~/.cache/pypoetry
         key: ${{ runner.os }}-poetry-${{ hashFiles('pyproject.toml') }}-v2
 
     - name: Install project (demo extras)
-      run: poetry install --with demo # Install core and demo dependencies
-
-    # Add steps for building documentation here, e.g., mkdocs build or sphinx build
-    # - name: Build documentation
-    #   run: poetry run mkdocs build
+      run: poetry install --with demo # Ensure the demo dependency set resolves
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 0000000..ad8acff
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,163 @@
+# Release workflow — publishes to PyPI or TestPyPI based on tag name.
+#
+# Routing:
+#   v*.dev*  → TestPyPI (publish-testpypi job)
+#   v*.*.*   → PyPI     (publish-pypi job)
+#
+# Prerequisites (one-time setup):
+#
+# 1. GitHub Environments (Settings → Environments):
+#    - "pypi"     — Deployment tags: v[0-9]*.[0-9]*.[0-9]*
+#                    and v[0-9]*.[0-9]*.[0-9]*.post[0-9]*
+#                    Optional: add Required reviewers for release approval.
+#    - "testpypi" — Deployment tags: v* (allow all)
+#
+# 2. PyPI Trusted Publishing (pypi.org → ellphi → Publishing):
+#    Owner: t-uda, Repository: ellphi,
+#    Workflow: release.yml, Environment: pypi
+#
+# 3. TestPyPI Trusted Publishing (test.pypi.org → ellphi → Publishing):
+#    Owner: t-uda, Repository: ellphi,
+#    Workflow: release.yml, Environment: testpypi
+#
+# Tag examples:
+#   v0.1.2       → PyPI
+#   v0.1.2.post1 → PyPI
+#   v0.1.2.dev1  → TestPyPI
+#   v0.1.2a1     → blocked by "pypi" environment tag rule → manual decision
+#   v0.1.2rc1    → blocked by "pypi" environment tag rule → manual decision
+
+name: Release
+
+on:
+  push:
+    tags: ["v*"]
+
+permissions:
+  contents: read
+
+jobs:
+  build-sdist:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+
+    - name: Install Poetry
+      run: pip install --upgrade poetry
+
+    - name: Build sdist
+      run: poetry build -f sdist
+
+    - name: Upload sdist
+      uses: actions/upload-artifact@v4
+      with:
+        name: dist-sdist
+        path: dist/*.tar.gz
+
+  build-linux:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+    env:
+      ELLPHI_USE_EIGEN: "1"
+      ELLPHI_EIGEN_INCLUDE: "/usr/include/eigen3"
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install Eigen headers
+      run: sudo apt-get update && sudo apt-get install -y libeigen3-dev
+
+    - name: Install Poetry
+      run: pip install --upgrade poetry
+
+    - name: Build wheel
+      run: poetry build -f wheel
+
+    - name: Upload wheel
+      uses: actions/upload-artifact@v4
+      with:
+        name: dist-linux-py${{ matrix.python-version }}
+        path: dist/*.whl
+
+  build-windows:
+    runs-on: windows-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install Poetry
+      run: pip install --upgrade poetry
+
+    - name: Build wheel
+      run: poetry build -f wheel
+
+    - name: Upload wheel
+      uses: actions/upload-artifact@v4
+      with:
+        name: dist-windows-py${{ matrix.python-version }}
+        path: dist/*.whl
+
+  publish-testpypi:
+    if: contains(github.ref_name, 'dev')
+    needs: [build-sdist, build-linux, build-windows]
+    runs-on: ubuntu-latest
+    environment: testpypi
+    permissions:
+      id-token: write
+    steps:
+    - name: Download all artifacts
+      uses: actions/download-artifact@v4
+      with:
+        path: dist
+        merge-multiple: true
+
+    - name: List distribution files
+      run: ls -la dist/
+
+    - name: Publish to TestPyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        repository-url: https://test.pypi.org/legacy/
+        packages-dir: dist/
+
+  publish-pypi:
+    if: ${{ !contains(github.ref_name, 'dev') }}
+    needs: [build-sdist, build-linux, build-windows]
+    runs-on: ubuntu-latest
+    environment: pypi
+    permissions:
+      id-token: write
+    steps:
+    - name: Download all artifacts
+      uses: actions/download-artifact@v4
+      with:
+        path: dist
+        merge-multiple: true
+
+    - name: List distribution files
+      run: ls -la dist/
+
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        packages-dir: dist/
diff --git a/.gitignore b/.gitignore
index c4dba2f..76425e8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,9 @@
 # Gemini
 .gemini/
 GEMINI.md
+# Claude
+.claude/
+CLAUDE.md
 # Serena AI Helper
 .serena/
 
diff --git a/AGENTS.md b/AGENTS.md
index a662edf..8fcfc0f 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -18,6 +18,13 @@ poetry install
 
 This command installs all Python dependencies and also compiles the C++ backend for the tangency solver. If the C++ code (`src/ellphi/_tangency_cpp_impl.cpp`) is modified, the backend will be automatically rebuilt the next time you run `poetry install`.
 
+Optional Eigen build (C++ linear algebra):
+- Install Eigen headers (Ubuntu: `apt-get install libeigen3-dev`, macOS: `brew install eigen`).
+- Rebuild with `ELLPHI_USE_EIGEN=1` and `ELLPHI_EIGEN_INCLUDE` set to the Eigen include path.
+  - Linux default: `/usr/include/eigen3`
+  - macOS default: `/opt/homebrew/include/eigen3` (Apple Silicon) or `/usr/local/include/eigen3` (Intel)
+- Verify with `python -m ellphi --build-info` and check `cpp_linalg_kind`.
+
 ### A Note on Managing Dependencies
 
 If you need to add, remove, or update dependencies in `pyproject.toml`, you must also update the `poetry.lock` file to reflect these changes. After modifying `pyproject.toml`, run the following command:
@@ -98,3 +105,15 @@ Before requesting review or handing work back to the user:
 *   **Coding Style:** All Python code should adhere to the [PEP 8 style guide](https://peps.python.org/pep-0008/).
 *   **Docstrings:** Docstrings must be written in **English**. They can include simple mathematical expressions where appropriate.
 *   **Comments:** Code comments should be short and simple. Do not add verbose trivial comments.
+
+## Documentation-only Tasks
+
+Docs-only changes (files under `docs/`, `mkdocs.yml`, `docs/javascripts/`)
+do **not** require the full Python CI checklist (black/flake8/mypy/pytest).
+Instead, run:
+
+```bash
+poetry run mkdocs build
+```
+
+and confirm it exits without errors before committing.
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..c729ff4
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,37 @@
+# Claude Code Guidelines
+
+This file is loaded automatically by Claude Code at session start.
+For general project conventions and CI requirements, read `AGENTS.md` first.
+
+## Subagent Permissions
+
+When orchestrating work through Claude Code subagents, the following
+permissions must be present in `.claude/settings.local.json`:
+
+```json
+{
+  "permissions": {
+    "allow": [
+      "Edit",
+      "Write",
+      "Bash(git:*)",
+      "Bash(cp:*)",
+      "Bash(python3:*)"
+    ]
+  }
+}
+```
+
+Notes:
+- Permission changes take effect after a session restart.
+- Binary file processing (e.g. image transparency) should be handled by
+  the main agent, not delegated to subagents.
+- Simple one-off shell commands that fall outside the patterns above
+  can be run by the main agent directly.
+
+## Orchestration Style
+
+- Delegate file editing and git commits to background subagents.
+- Use the main agent for discussion, planning, and binary/image processing.
+- Commit granularity: one logical change per commit; a subagent handling
+  multiple related files in one pass may bundle them into a single commit.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..6430c42
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,180 @@
+# Contributing
+
+This document describes a simple, low-overhead workflow for developing and
+releasing ellphi. It keeps `main` as a released version and uses a dedicated
+release branch for ongoing work.
+
+## Before You Start
+
+Always check the project-wide guidelines and CI expectations:
+
+- `AGENTS.md`
+- `.github/workflows/python-app.yml`
+- `.github/workflows/docs.yml` (for docs / release-note changes)
+
+They define the required local checks and the order CI will run them.
+
+## Branching Model
+
+- `main` always points to the latest released version.
+- Each release is developed on its own branch (e.g., `ellphi-0.1.2`).
+- Feature PRs are merged into the release branch, not into `main`.
+
+## Versioning
+
+We only bump versions at two moments:
+
+1. Start of a new release cycle: set a dev version (e.g., `0.1.2.dev0`).
+2. Release: set the final version (e.g., `0.1.2`).
+
+Use the version sync script so that metadata and runtime version stay aligned:
+
+```bash
+poetry run python scripts/update_version.py 0.1.2.dev0
+```
+
+## Release Notes
+
+Create an "Unreleased" section at the start of the cycle and update it as
+features land:
+
+```text
+## Unreleased
+```
+
+Right before the release, replace it with a dated heading such as:
+
+```text
+## 0.1.2 - YYYY-MM-DD
+```
+
+## Standard Workflow (Simple)
+
+1. Create a release branch from `main`.
+2. Bump to a dev version once (e.g., `0.1.2.dev0`) and commit it.
+3. Merge feature PRs into the release branch.
+4. When ready for the final release, create a short-lived version-bump branch
+   from the release branch.
+5. On that version-bump branch:
+   - finalize `RELEASE_NOTES.md`
+   - run `poetry run python scripts/update_version.py 0.1.2`
+   - run all required local checks
+   - run `poetry run mkdocs build`
+6. Open a PR from the version-bump branch back into the release branch and
+   merge it after CI passes.
+7. Open a PR from the release branch into `main` and merge it.
+8. Create the final release tag (e.g., `v0.1.2`) from the commit now on
+   `main`, then push the tag.
+
+Do not tag a side branch or an unmerged release candidate commit. Tag the exact
+commit that now represents the released state on `main`.
+
+## Publishing
+
+Releases are published automatically by the CI release workflow
+(`.github/workflows/release.yml`).
+
+| Tag | Destination |
+|-----|-------------|
+| `v0.1.2.dev1` | TestPyPI |
+| `v0.1.2` | PyPI |
+| `v0.1.2.post1` | PyPI |
+| `v0.1.2a1`, `v0.1.2rc1` | Blocked by environment rule — manual decision |
+
+Additional `v0.1.2.devN` tags are optional. Create another dev release only if
+you want a fresh TestPyPI snapshot for validation before the final release.
+
+```bash
+# Example: publish a dev build to TestPyPI
+git tag v0.1.2.dev1
+git push origin v0.1.2.dev1
+
+# Example: after the release branch has been merged into main,
+# tag the main commit that now contains version 0.1.2
+git tag v0.1.2
+git push origin v0.1.2
+```
+
+The workflow uses PyPI Trusted Publishing (no API tokens needed).
+See the comments at the top of `release.yml` for one-time setup steps.
+
+## Required Local Checks
+
+Mirror CI locally. Run the exact commands listed in `AGENTS.md` and
+`.github/workflows/python-app.yml`, in the same order when relevant. For docs
+and release-note changes, also mirror `.github/workflows/docs.yml` by running
+`poetry run mkdocs build`. Do not skip steps. If something cannot run, stop
+and document the blocker.
+
+## Optional Eigen Build (C++ linear algebra)
+
+If you want to test the Eigen build locally:
+
+1. Install Eigen headers:
+   - Ubuntu: `apt-get install libeigen3-dev`
+   - macOS (Homebrew): `brew install eigen`
+2. Rebuild the extension with:
+
+```bash
+ELLPHI_USE_EIGEN=1 ELLPHI_EIGEN_INCLUDE=/usr/include/eigen3 poetry install
+```
+
+On macOS, use `/opt/homebrew/include/eigen3` (Apple Silicon) or
+`/usr/local/include/eigen3` (Intel).
+
+Verify via:
+
+```bash
+python -m ellphi --build-info
+```
+
+## Docs CI and Deployment
+
+- `.github/workflows/docs.yml` runs `mkdocs build` on pull requests, on pushes
+  to `ellphi-*`, and via `workflow_dispatch`.
+- It deploys to `gh-pages` only on pushes to `main`.
+- `.github/workflows/python-app.yml` contains a `demo-install` job that checks
+  the demo dependency set; it is not the docs deployment workflow.
+
+## Documentation
+
+### Building and previewing
+
+```bash
+# One-off build
+poetry run mkdocs build
+
+# Live-reload server (http://127.0.0.1:8000)
+poetry run mkdocs serve
+```
+
+Run `poetry run mkdocs build` before committing any docs change and confirm
+the build completes without errors.
+
+### English style
+
+All documentation is written in **British English**
+(`optimisation`, `normalise`, `characterisation`, `centred`, etc.).
+
+### Mathematical notation
+
+| Element | Convention | Example |
+|---|---|---|
+| Inline math | `\(...\)` | `\(t \ge 0\)` |
+| Display math | `\[...\]` or `$$...$$` (block only) | — |
+| Vectors | `\bm{·}` (bold italic via `\boldsymbol`) | `\(\bm{x}_c\)` |
+| Matrices | Plain capital letter | `\(A\)`, `\(\Sigma\)` |
+| Scalars | Plain italic | `\(t\)`, `\(\mu\)` |
+
+`\bm` is defined as a MathJax macro in `docs/javascripts/mathjax.js`.
+Inline math must not span multiple source lines.
+
+### Assets
+
+Logo assets live in `docs/assets/`:
+
+- `logo.png` — full logo (icon + wordmark), transparent background
+- `logo-icon.png` — icon only, used for navbar and favicon
+
+The root `ellphi-logo.png` is kept in sync with `docs/assets/logo.png`
+(used by the README).
diff --git a/README.md b/README.md
index 1a66522..dddcdf7 100644
--- a/README.md
+++ b/README.md
@@ -28,6 +28,25 @@ To install with demo dependencies for notebooks and examples:
 pip install ellphi[demo]
 ```
 
+## Optional Eigen build (from source)
+
+If you build from source (for example, with `poetry install`), you can enable
+the C++ linear algebra implementation based on Eigen.
+
+Install Eigen headers:
+
+- Ubuntu: `apt-get install libeigen3-dev`
+- macOS (Homebrew): `brew install eigen`
+
+Then build with:
+
+```bash
+ELLPHI_USE_EIGEN=1 ELLPHI_EIGEN_INCLUDE=/usr/include/eigen3 poetry install
+```
+
+On macOS, use `/opt/homebrew/include/eigen3` (Apple Silicon) or
+`/usr/local/include/eigen3` (Intel).
+
 ## Supported Python Versions
 
 Python 3.10 or later.
@@ -98,10 +117,20 @@ From the shell:
 python -m ellphi --version
 ```
 
+Build info (including the C++ linear algebra choice):
+
+```bash
+python -m ellphi --build-info
+```
+
 ## Contributing
 
 Interested in contributing? We welcome pull requests!
 
+Please read `CONTRIBUTING.md` for the branching/versioning flow and
+`AGENTS.md` (plus `.github/workflows/python-app.yml`) for the required local
+checks before opening a PR.
+
 To get started with development, clone the repository and set up your environment:
 
 ```bash
diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md
index e489196..6edfbe1 100644
--- a/RELEASE_NOTES.md
+++ b/RELEASE_NOTES.md
@@ -1,3 +1,49 @@
+## 0.1.2 - 2026-03-25
+
+### Added
+
+- Added the new `ellphi.grad` API for differentiable workflows:
+  `tangency_grad(...)` for single-pair gradients,
+  `pdist_tangency_grad(...)` for condensed pairwise distances plus a VJP
+  pullback, and `coef_from_cov_grad(...)` for differentiable conversion from
+  centres/covariances to packed conic coefficients.
+- Added C++ backend build metadata helpers:
+  `ellphi.build_info()`, `ellphi.cpp_linalg_kind()`, and
+  `ellphi --build-info` / `python -m ellphi --build-info`.
+- Added `EllipseCloud.from_cov(...)` for constructing clouds directly from
+  precomputed centres/covariances, and `EllipseCloud.distance_matrix()` as a
+  square-matrix convenience wrapper around `pdist_tangency`.
+
+### Changed
+
+- Source builds can now opt into an Eigen-based C++ linear-algebra backend via
+  `ELLPHI_USE_EIGEN=1` (with `ELLPHI_EIGEN_INCLUDE` when needed). Build
+  metadata now records the linear-algebra implementation and embedded backend
+  version.
+
+### Fixed
+
+- `ellphi.visualization.ellipse_patch(...)` now preserves explicit
+  `facecolor` / `fc` arguments while keeping hollow ellipses as the default.
+- `coef_from_cov_grad(...)` now validates batch and dimensionality mismatches
+  consistently and returns `NaN` outputs for singular covariances in line with
+  `coef_from_cov(...)`.
+- The C++ backend build cache is now invalidated when switching between the
+  internal and Eigen linear-algebra modes, preventing stale extension reuse.
+
+### Documentation
+
+- Added a MkDocs documentation site with Getting Started, TDA workflow,
+  differentiable tangency guide, API reference pages, and a notebook index
+  that now includes a topology-optimisation example.
+
+### Packaging
+
+- Added Windows wheel build support (`win_amd64`, Python 3.10-3.12).
+- Added release automation for TestPyPI/PyPI publishing via Trusted
+  Publishing.
+- Added Python 3.12 to the CI test matrix.
+
 ## 0.1.1 - 2025-12-14
 
 ### Added
diff --git a/contrib/agent-skill/SKILL.md b/contrib/agent-skill/SKILL.md
new file mode 100644
index 0000000..2a599c8
--- /dev/null
+++ b/contrib/agent-skill/SKILL.md
@@ -0,0 +1,105 @@
+---
+name: ellphi-api
+description: >
+  EllPHi library usage guide for anisotropic persistent homology.
+  Use when: (1) writing code that imports ellphi, (2) working with
+  anisotropic persistent homology or ellipsoid tangency distances,
+  (3) debugging ellphi solver issues or understanding coefficient format.
+---
+
+# EllPHi API Skill
+
+## Version check
+
+```bash
+python -c "import ellphi; print(ellphi.__version__)"
+```
+
+Require >= 0.1.2 for the `grad` module. If older: `pip install -U ellphi`.
+
+## Core concepts
+
+### Ellipsoid coefficient format
+
+Each ellipsoid is a quadratic `Q(x) = x^T A x + 2 b^T x + c`, packed into a flat vector of length `m = (d+1)(d+2)/2` (m=6 for d=2, m=10 for d=3). Packing order: upper-triangular A, then b (length d), then c (scalar).
+
+**Critical: b = -A x_bar** (negative sign). To recover center: `x_bar = -A^{-1} b`. Use `unpack_conic` to extract (A, b, c).
+
+### Tangency distance
+
+The inflation factor `t` at which two ellipsoids become externally tangent. The solver finds pencil parameter `mu` in [0,1] minimizing the discriminant, then `t = sqrt(alpha)`.
+
+## Quick recipe: point cloud to PH
+
+```python
+import ellphi
+
+cloud = ellphi.ellipse_cloud(X, k=5)   # X: (N, d) point cloud
+dists = ellphi.pdist_tangency(cloud)    # condensed distance matrix (like scipy pdist)
+
+# Feed to ripser, GUDHI, etc.
+```
+
+## Quick recipe: gradient optimization
+
+```python
+from ellphi.grad import pdist_tangency_grad
+
+dists, vjp = pdist_tangency_grad(cloud.coef)  # cloud.coef: (N, m)
+grad_coefs = vjp(upstream_grad)               # upstream: (N*(N-1)//2,) -> (N, m)
+```
+
+### End-to-end: centers + covariances → loss
+
+```python
+from ellphi.grad import coef_from_cov_grad, pdist_tangency_grad
+
+# Forward + VJP for coef_from_cov
+coefs, vjp_coef = coef_from_cov_grad(centers, covs)
+
+# Forward + VJP for tangency distances
+dists, vjp_dist = pdist_tangency_grad(coefs)
+
+# Backward: chain the two VJPs
+loss = dists.sum()
+grad_coefs = vjp_dist(np.ones_like(dists))    # (N, m)
+grad_centers, grad_covs = vjp_coef(grad_coefs) # (N,d), (N,d,d)
+```
+
+## Solver methods
+
+Pass `method=` to `tangency()`:
+
+| Method | Notes |
+|--------|-------|
+| `"brentq+newton"` | **(default)** Brentq bracketing + Newton polish |
+| `"brentq"` | Pure Brentq. Robust, slower |
+| `"bisect"` | Most robust, slowest |
+| `"newton"` | Requires `x0`. Fast but can diverge |
+
+## Backends
+
+`backend=` in `tangency()` / `pdist_tangency()`: `"auto"` (default, C++ if available), `"python"`, `"cpp"`.
+
+## Common pitfalls
+
+1. **b sign**: `b = -A x_bar`. Forgetting the negative sign is the #1 bug.
+2. **Condensed distances**: `pdist_tangency` returns 1-D like `scipy.spatial.distance.pdist`. Use `squareform` for a full matrix.
+3. **Degenerate inputs**: `tangency_grad` raises `ZeroDivisionError` for identical/concentric ellipsoids.
+4. **Singular covariance**: `coef_from_cov` returns NaN for singular matrices.
+
+## Envelope theorem (grad module)
+
+See [references/grad_module.md](references/grad_module.md) for how the envelope theorem enables efficient exact gradients.
+
+## API details
+
+For full function signatures and parameters:
+
+```bash
+python -c "import ellphi; help(ellphi.tangency)"
+python -c "from ellphi.grad import tangency_grad; help(tangency_grad)"
+```
+
+- Current exports: `src/ellphi/__init__.py`
+- Full reference: the project's MkDocs site (`mkdocs serve` or published docs)
diff --git a/contrib/agent-skill/references/grad_module.md b/contrib/agent-skill/references/grad_module.md
new file mode 100644
index 0000000..1c17f0c
--- /dev/null
+++ b/contrib/agent-skill/references/grad_module.md
@@ -0,0 +1,51 @@
+# EllPHi Grad Module — Conceptual Background
+
+## Envelope theorem approach
+
+The tangency distance `t` depends on coefficient vectors `p` and `q` through:
+
+1. The pencil parameter `mu*` (optimal blend), itself a function of (p, q).
+2. The tangent point `x*(mu*)`, the pencil ellipsoid center at `mu*`.
+
+By the envelope theorem, at the optimum `mu*`, the derivative of the
+tangent point `x*(mu*)` w.r.t. `p` drops out of the gradient formula.
+The `d_mu/dp` terms remain (computed via implicit differentiation):
+
+```
+dt/dp_i = (1/(2t)) * [(1 - mu*) * base_i + scalar * d_mu/dp_i]
+dt/dq_i = (1/(2t)) * [mu* * base_i + scalar * d_mu/dq_i]
+```
+
+where `base` is the monomial basis at the tangent point, and
+`scalar = base . (q - p)`.
+
+The `d_mu/dp` terms come from implicit differentiation of the optimality
+condition `F(mu, p, q) = 0` (handled internally by `solve_mu_gradients`
+in `ellphi.differentiable_solver`).
+
+## coef_from_cov_grad: differentiable geometry
+
+`coef_from_cov_grad(X, cov, *, scale)` is the differentiable version of
+`coef_from_cov`. It returns `(coefs, vjp)` where the VJP maps
+`grad_coefs (N, m)` back to `(grad_centers (N, d), grad_cov (N, d, d))`.
+
+Internally it uses the matrix inverse derivative identity:
+`d(Σ⁻¹) = -Σ⁻¹ dΣ Σ⁻¹` to propagate gradients through `A = Σ⁻¹/s²`.
+
+Combined with `pdist_tangency_grad`, this enables end-to-end gradient
+computation from geometric parameters (centers, covariances) through to
+a topological loss, without finite differences.
+
+## Key design points
+
+- `tangency_grad` / `pdist_tangency_grad` are the public API (in `ellphi.grad`).
+- They handle the full chain: mu gradients -> t gradients, including the
+  `1/(2t)` factor and monomial basis evaluation.
+- `pdist_tangency_grad` returns a VJP (vector-Jacobian product) pullback that
+  accumulates pair contributions into per-ellipsoid gradients.
+- `coef_from_cov_grad` completes the chain by differentiating through
+  the (centers, covs) → coefs mapping. Works for arbitrary dimension d.
+- Degenerate configurations (identical/concentric ellipsoids) raise
+  `ZeroDivisionError` because the implicit function theorem breaks down.
+
+For function signatures: `python -c "from ellphi.grad import tangency_grad; help(tangency_grad)"`
diff --git a/docs/algebraic_sigmoid_newton.md b/docs/algebraic_sigmoid_newton.md
index e02ea1d..b02c087 100644
--- a/docs/algebraic_sigmoid_newton.md
+++ b/docs/algebraic_sigmoid_newton.md
@@ -2,15 +2,13 @@
 
 This document outlines the `algebraic-sigmoid+newton` strategy, a root-finding algorithm designed for functions on the interval $(0, 1)$. This method is particularly useful for objective functions, such as the difference of Mahalanobis distances, where the scale can vary dramatically at the boundaries, causing standard Newton methods to become unstable.
 
-To ensure that the optimization variable remains within the $(0, 1)$ domain, this strategy employs a variable transformation known as the **Algebraic Sigmoid function**. This reparameterization allows Newton's method to be applied in an unbounded space, enhancing stability.
+To ensure that the optimisation variable remains within the $(0, 1)$ domain, this strategy employs a variable transformation known as the **Algebraic Sigmoid function**. This reparameterisation allows Newton's method to be applied in an unbounded space, enhancing stability.
 
 ## 1. Mathematical Formulation
 
 ### 1.1. Variable Transformation
 
-The core of the strategy is a mapping from the unbounded real line $u 
- \in \mathbb{R}$ to the interval $x 
- \in (0, 1)$. This is achieved using the algebraic sigmoid function:
+The core of the strategy is a mapping from the unbounded real line $u \in \mathbb{R}$ to the interval $x \in (0, 1)$. This is achieved using the algebraic sigmoid function:
 
 $$
  x(u) = \frac{1}{2} \left( 1 + \frac{u}{\sqrt{1 + u^2}} \right) 
@@ -20,8 +18,7 @@ This function smoothly maps $(-\infty, \infty)$ to $(0, 1)$ and has a fixed poin
 
 ### 1.2. Newton's Method in the Transformed Space
 
-Let the original objective function be $f(x)$, defined on $x 
- \in (0, 1)$. We define a new function $F(u)$ in the transformed space:
+Let the original objective function be $f(x)$, defined on $x \in (0, 1)$. We define a new function $F(u)$ in the transformed space:
 
 $$ 
  F(u) = f(x(u)) 
diff --git a/docs/assets/logo-icon.png b/docs/assets/logo-icon.png
new file mode 100644
index 0000000..c474a3f
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diff --git a/docs/assets/logo.png b/docs/assets/logo.png
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diff --git a/docs/eigen_backend_plan.md b/docs/eigen_backend_plan.md
new file mode 100644
index 0000000..9590164
--- /dev/null
+++ b/docs/eigen_backend_plan.md
@@ -0,0 +1,81 @@
+# Eigen backend plan
+
+## Decision
+We will keep Eigen as the only optional accelerated backend. For moderate dimensions
+(~100x100), Eigen provides enough speed without extra build and packaging
+complexity. As a result, we do not maintain a special manylinux cibuildwheel
+pipeline.
+
+## Dependencies (C++)
+- Optional: Eigen headers. Required only when building with `ELLPHI_USE_EIGEN=1`.
+- Default builds do not require Eigen and use the internal C++ solver.
+
+## Build knobs in this repo
+The build script supports:
+- `ELLPHI_USE_EIGEN=1` to enable Eigen code paths at compile time.
+- `ELLPHI_EIGEN_INCLUDE` to set Eigen include paths (space-separated).
+
+Notes:
+- Eigen is header-only, so no runtime shared libraries are bundled.
+
+## Build without Eigen
+If Eigen headers are not available, build without enabling it:
+1) Do not set `ELLPHI_USE_EIGEN`.
+2) Run `poetry install` to build the C++ extension with the internal solver.
+3) If `ELLPHI_USE_EIGEN=1` is set without headers, the build fails with a
+   clear error asking for `ELLPHI_EIGEN_INCLUDE`.
+
+## Local developer workflow
+1) Install Eigen headers (examples):
+   - Ubuntu: `apt-get install libeigen3-dev`
+   - macOS (Homebrew): `brew install eigen`
+2) If Eigen is not in a standard include location, set:
+   - `ELLPHI_EIGEN_INCLUDE="/path/to/eigen3"`
+3) Rebuild the extension:
+   - `poetry install`
+   - If needed, delete `src/ellphi/_tangency_cpp_impl*.so` before reinstalling.
+4) Quick sanity check:
+   ```bash
+   python - <<'PY'
+   import ellphi; print(ellphi.has_cpp_backend())
+   PY
+   ```
+
+## CI/release approach
+- Use the normal wheel build process; no manylinux customization is required
+  for Eigen because it is header-only.
+- If Eigen-enabled wheels are desired, set `ELLPHI_USE_EIGEN=1` and
+  `ELLPHI_EIGEN_INCLUDE` in the build environment.
+- No `auditwheel` bundling is needed.
+
+## Benchmark summary (Eigen vs internal)
+Notes:
+- Host: Ubuntu aarch64 (local dev machine)
+- Eigen: 3.4.0 (`libeigen3-dev`)
+- Build: deleted `_tangency_cpp_impl.so` then rebuilt for each mode via `poetry install`
+- Run: `OMP_NUM_THREADS=1 poetry run python scripts/benchmark_dim_scale.py`
+- joblib emitted a permissions warning and ran in serial mode
+
+CPP results (avg_time_ms):
+```
+dim,Eigen,Internal
+2,0.0245,0.0235
+3,0.0243,0.0232
+5,0.0262,0.0243
+8,0.0305,0.0277
+10,0.0338,0.0301
+15,0.0457,0.0426
+20,0.0560,0.0583
+30,0.0928,0.1663
+40,0.1570,0.2003
+50,0.2404,0.3486
+60,0.3323,0.5255
+80,0.5687,1.0683
+100,0.9809,2.3383
+```
+
+## Validation checklist
+- Confirm `ellphi.has_cpp_backend()` returns `True` for Eigen builds.
+- Confirm `ellphi.cpp_linalg_kind()` returns `eigen` when Eigen is enabled.
+- Use `ellphi.build_info()` to inspect solver backend choices and C++ build details.
+- Run a small tangency call or the benchmark script to confirm performance.
diff --git a/docs/guide/differentiable.md b/docs/guide/differentiable.md
new file mode 100644
index 0000000..105a2f3
--- /dev/null
+++ b/docs/guide/differentiable.md
@@ -0,0 +1,87 @@
+# Differentiable Tangency
+
+The `ellphi.grad` module exposes gradient-enabled versions of the tangency solver.
+Gradients are computed analytically via the **envelope theorem**: because the tangency
+point $\bm{x}_c$ is optimal at the solution, its implicit derivative with respect to the
+parameters drops out, and only the explicit dependence of the pencil on the coefficient
+vectors $\bm{p}$ and $\bm{q}$ contributes.
+
+## Single-pair gradient
+
+`tangency_grad` returns a [`TangencyGrad`][ellphi.grad.TangencyGrad] dataclass with the
+tangency distance $t$ and the gradients $\partial t/\partial \bm{p}$ and
+$\partial t/\partial \bm{q}$:
+
+```python
+import numpy as np
+from ellphi import ellipse_cloud
+from ellphi.grad import tangency_grad
+
+rng = np.random.default_rng(0)
+X = rng.standard_normal((10, 2))
+cloud = ellipse_cloud(X, k=5)
+
+p, q = cloud[0], cloud[1]
+g = tangency_grad(p, q)
+
+print(f"t = {g.t:.4f}")
+print(f"∂t/∂p shape: {g.dt_dp.shape}")  # (6,) for 2D
+```
+
+## Batch pairwise VJP
+
+`pdist_tangency_grad` computes all pairwise distances and returns a **VJP (pullback)**
+function.  This is the building block for optimising a loss that depends on the full
+distance matrix:
+
+```python
+import numpy as np
+from ellphi import ellipse_cloud
+from ellphi.grad import pdist_tangency_grad
+
+rng = np.random.default_rng(1)
+X = rng.standard_normal((20, 2))
+cloud = ellipse_cloud(X, k=5)
+
+dists, vjp = pdist_tangency_grad(cloud.coef)
+
+# Suppose the loss is the sum of all tangency distances.
+# Its gradient w.r.t. each distance is 1.
+grad_coefs = vjp(np.ones_like(dists))   # shape (20, 6)
+```
+
+## Gradient-based optimisation
+
+The VJP integrates naturally into any gradient descent loop:
+
+```python
+import numpy as np
+from ellphi import ellipse_cloud
+from ellphi.grad import pdist_tangency_grad
+
+rng = np.random.default_rng(2)
+X = rng.standard_normal((15, 2))
+cloud = ellipse_cloud(X, k=5)
+coefs = cloud.coef.copy()
+
+lr = 1e-3
+for step in range(50):
+    dists, vjp = pdist_tangency_grad(coefs)
+    loss = dists.sum()
+    grad = vjp(np.ones_like(dists))    # ∂loss/∂coefs
+    coefs -= lr * grad
+    if step % 10 == 0:
+        print(f"step {step:3d}  loss={loss:.4f}")
+```
+
+## Persistent-homology backprop
+
+When combined with modern TDA libraries, `pdist_tangency_grad` enables differentiating
+through a persistence diagram — see `notebooks/topology_optimization.ipynb` for a
+worked example.
+
+## Degenerate inputs
+
+`tangency_grad` may raise `ZeroDivisionError` for degenerate configurations (identical
+or concentric ellipsoids) where $\partial F/\partial \mu$ vanishes at the solution.
+Duplicate ellipsoids should be removed from the inputs beforehand.
diff --git a/docs/guide/notebooks.md b/docs/guide/notebooks.md
new file mode 100644
index 0000000..14f6628
--- /dev/null
+++ b/docs/guide/notebooks.md
@@ -0,0 +1,38 @@
+# Notebook Examples
+
+The notebooks below demonstrate EllPHi's key workflows end-to-end.
+They are hosted alongside the source code on GitHub and can be viewed
+directly in the browser or cloned and run locally.
+
+!!! note "Future plans"
+    The notebook collection is planned to move to a dedicated repository
+    with interactive [Marimo](https://marimo.io) versions that run
+    entirely in the browser via WebAssembly — no local installation
+    required.  Links on this page will be updated when that migration
+    is complete.
+
+## Getting started
+
+| Notebook | Description |
+|---|---|
+| [Quick Start](https://github.com/t-uda/ellphi/blob/main/notebooks/quickstart.ipynb) | Install EllPHi, build an ellipse cloud, and compute pairwise tangency distances in a few lines. |
+
+## Persistent homology workflows
+
+| Notebook | Description |
+|---|---|
+| [Anisotropy-aware PH — 6 rings](https://github.com/t-uda/ellphi/blob/main/notebooks/eph-6rings-PH.ipynb) | Full pipeline from a six-ring point cloud to a persistence diagram using elliptic tangency distances. |
+| [PH figures — standard vs elliptic](https://github.com/t-uda/ellphi/blob/main/notebooks/eph-6rings-PH-figures.ipynb) | Side-by-side comparison of standard (Euclidean) and anisotropic (elliptic) persistent homology on the same data. |
+| [n-dimensional demo (3D)](https://github.com/t-uda/ellphi/blob/main/notebooks/ndim-demo-3d.ipynb) | Extends the quick-start to a 3-D point cloud, showing that the solver and distance pipeline work in any dimension. |
+
+## Differentiable solver
+
+| Notebook | Description |
+|---|---|
+| [Topology Optimisation with Gradients](https://github.com/t-uda/ellphi/blob/main/notebooks/topology_optimization.ipynb) | Gradient-based optimisation of ellipsoid configurations using `ellphi.grad` and the VJP interface. |
+
+## Benchmarks
+
+| Notebook | Description |
+|---|---|
+| [Performance Benchmark](https://github.com/t-uda/ellphi/blob/main/notebooks/performance_benchmark.ipynb) | Compares per-call timings of `pdist_tangency` across the pure-Python and compiled C++ backends. |
diff --git a/docs/guide/quickstart.md b/docs/guide/quickstart.md
new file mode 100644
index 0000000..04e0a59
--- /dev/null
+++ b/docs/guide/quickstart.md
@@ -0,0 +1,62 @@
+# Getting Started
+
+## Installation
+
+```bash
+pip install ellphi
+```
+
+The C++ backend (recommended for performance) is compiled during installation when a suitable compiler is available. Check whether it loaded:
+
+```python
+import ellphi
+print(ellphi.build_info())
+```
+
+## Create an ellipse cloud
+
+`ellipse_cloud` fits a local-covariance ellipse around the *k*-nearest neighbours of each point:
+
+```python
+import numpy as np
+from ellphi import ellipse_cloud
+
+rng = np.random.default_rng(42)
+X = rng.standard_normal((80, 2))   # 80 points in ℝ²
+
+cloud = ellipse_cloud(X, k=5)
+print(cloud)  # EllipseCloud(coef=array<(N, 6)>, ...)
+```
+
+Each ellipse is stored as a packed conic coefficient vector of length
+$m = (d+1)(d+2)/2$ (6 for 2D, 10 for 3D).
+
+## Compute pairwise tangency distances
+
+```python
+dists = cloud.pdist_tangency()   # condensed array, same layout as scipy.pdist
+print(dists.shape)               # (N*(N-1)//2,)
+```
+
+Convert to a square matrix when needed:
+
+```python
+D = cloud.distance_matrix()
+```
+
+## Visualise
+
+```python
+import matplotlib.pyplot as plt
+
+ax = cloud.plot(alpha=0.4, edgecolor="steelblue")
+ax.scatter(X[:, 0], X[:, 1], s=10, c="k")
+ax.set_aspect("equal")
+plt.show()
+```
+
+## Next steps
+
+- [TDA Workflow](tda_workflow.md) — build a persistence diagram from the tangency distance matrix.
+- [Differentiable Tangency](differentiable.md) — gradient-based optimisation with `ellphi.grad`.
+- [API Reference](../reference/index.md) — full module documentation.
diff --git a/docs/guide/tda_workflow.md b/docs/guide/tda_workflow.md
new file mode 100644
index 0000000..904d4cb
--- /dev/null
+++ b/docs/guide/tda_workflow.md
@@ -0,0 +1,108 @@
+# TDA Workflow
+
+This page shows how to go from a raw point cloud to a persistence diagram using elliptic tangency distances.
+
+## Overview
+
+1. Sample a point cloud $X \subset \mathbb{R}^d$.
+2. Fit an ellipse cloud with `ellipse_cloud`.
+3. Compute the condensed pairwise tangency distance matrix with `pdist_tangency`.
+4. Feed the matrix to a persistent-homology library (e.g. [HomCloud](https://homcloud.dev/) or [Ripser](https://github.com/scikit-tda/ripser.py)).
+
+## Step-by-step example
+
+```python
+import numpy as np
+from ellphi import ellipse_cloud
+
+# 1. Point cloud — six interlocking rings
+rng = np.random.default_rng(0)
+angles = np.linspace(0, 2 * np.pi, 30, endpoint=False)
+rings = [
+    np.column_stack([np.cos(angles) + dx, np.sin(angles) + dy])
+    for dx, dy in [(0, 0), (1.5, 0), (3, 0), (0.75, 1.3), (2.25, 1.3), (1.5, 2.6)]
+]
+X = np.vstack(rings) + rng.normal(scale=0.05, size=(180, 2))
+
+# 2. Ellipse cloud
+cloud = ellipse_cloud(X, k=8)
+
+# 3. Pairwise tangency distances
+dists = cloud.pdist_tangency()
+```
+
+### Persistence diagram with HomCloud
+
+```python
+import homcloud.interface as hc
+
+pd = hc.PDList.from_rips_filtration(dists, maxdim=1, metric="precomputed")
+pd.histogram(1).plot()          # H₁ barcode
+```
+
+### Persistence diagram with Ripser
+
+```python
+from ripser import ripser
+from persim import plot_diagrams
+
+D = cloud.distance_matrix()
+result = ripser(D, metric="precomputed", maxdim=1)
+plot_diagrams(result["dgms"])
+```
+
+## Notes on distance semantics
+
+Let $p$ and $q$ be generic (nondegenerate) input quadratic polynomials. More precisely,
+$p$ and $q$ are squared Mahalanobis distances whose level sets are ellipsoids.
+The solver finds $\mu \in (0, 1)$ such that the center $\bm{x}_c$ of the pencil of conics
+$(1-\mu)p + \mu q$ satisfies $p(\bm{x}_c) = q(\bm{x}_c)$. The tangency distance is
+
+$$t(E_p, E_q) = \sqrt{p(\bm{x}_c)} = \sqrt{q(\bm{x}_c)}.$$
+
+Because $\bm{x}_c$ is the center of the pencil element, the gradient of the pencil element
+vanishes at $x = \bm{x}_c$, which gives
+
+$$
+(1 - \mu)\,\nabla p(\bm{x}_c) + \mu\,\nabla q(\bm{x}_c) = 0.
+$$
+
+For $t > 0$, the normals to the level sets $\{p = t^2\}$ and $\{q = t^2\}$ are
+anti-parallel at $\bm{x}_c$: these level sets are the reference ellipsoids dilated by a factor of $t$ about their
+respective centres, and they are externally tangent at $\bm{x}_c$.
+
+Equivalently, $t$ is the smallest scale at which the inflated sublevel sets meet:
+
+$$
+t = \inf\bigl\{s \ge 0 : E_p(s) \cap E_q(s) \neq \varnothing\bigr\},
+\qquad E_p(s) = \{\bm{x} : p(\bm{x}) \le s^2\},
+$$
+
+or $t^2 = \min_x \max\{p(x),\, q(x)\}$. This characterisation yields a coarse
+trichotomy for the reference ellipsoids $E_p = \{p \le 1\}$, $E_q = \{q \le 1\}$:
+
+- $0 \le t < 1$: the reference ellipsoids have non-empty intersection (overlap,
+  containment, or internal tangency).
+- $t = 1$: $E_p$ and $E_q$ are externally tangent.
+- $t > 1$: $E_p$ and $E_q$ are disjoint; both must be scaled by $t$ to touch.
+
+Degenerate cases (identical or concentric ellipsoids) yield $t = 0$; the zero level
+sets reduce to the respective ellipsoid centres rather than forming two tangent ellipsoids.
+
+Note that $t$ is non-negative and unbounded above.
+
+## Choosing $k$
+
+By default, `ellipse_cloud` computes an ellipsoid cloud by $k$-nearest neighbour
+local covariance construction.
+
+Larger $k$ produces fatter, more overlapping ellipsoids and tends to smooth out local
+noise.  A good starting range is $k \in [5, 15]$ for 2D data; for higher-dimensional
+data increase $k$ proportionally.
+
+## Rescaling
+
+The covariance construction estimates local metrics at each data point, but this process
+may distort the scale compared to the global Euclidean metric derived from the input.
+Use `cloud.rescale(method="median")` to normalise the scale of the ellipses before
+computing tangency distances if you want to compare with the Euclidean setting.
diff --git a/docs/hybrid_tuning.md b/docs/hybrid_tuning.md
index 9c300e6..c5ce6ce 100644
--- a/docs/hybrid_tuning.md
+++ b/docs/hybrid_tuning.md
@@ -35,7 +35,7 @@ those quadratic values and the reported scaling factor, normalised by
 
 * `brentq+newton` with 28 Brent iterations followed by 3 Newton steps
   delivered zero failures across 120 cases while keeping mean absolute
-  μ error below 5×10⁻⁹.
+  \(\mu\) error below \(5 \times 10^{-9}\).
 * The tuned hybrid (`28×3`) halves the mean runtime compared to
   pure bisection (1.09 ms vs. 2.32 ms) while retaining the robustness of
 the bracketing stage.
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..0aab1fd
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,38 @@
+![EllPHi](assets/logo.png){ width=220 style="display:block;margin:0 auto 1rem;" }
+
+# EllPHi
+
+**EllPHi** is a fast ellipse (and ellipsoid) tangency solver for Python, designed for applications in Topological Data Analysis (TDA) and Computational Geometry.
+
+## Features
+
+- **Ellipse cloud construction** — fit local covariance ellipses to a point cloud with `ellipse_cloud`.
+- **Fast tangency distances** — compute the pairwise tangency distance matrix with `pdist_tangency`, dispatching automatically to a compiled C++ backend (Eigen) when available.
+- **Differentiable solver** — `ellphi.grad` provides per-pair gradients (`tangency_grad`) and a batch pairwise VJP (`pdist_tangency_grad`) for gradient-based optimisation and persistent-homology backprop.
+- **n-dimensional support** — geometry and solver work in any dimension ≥ 2.
+
+## Quick install
+
+```bash
+pip install ellphi
+```
+
+## Five-line example
+
+```python
+import numpy as np
+from ellphi import ellipse_cloud, pdist_tangency
+
+rng = np.random.default_rng(0)
+X = rng.standard_normal((60, 2))         # 60 points in 2D
+cloud = ellipse_cloud(X, k=5)            # fit local-covariance ellipses
+dists = cloud.pdist_tangency()           # condensed distance matrix (1770,)
+print(dists.min(), dists.max())
+```
+
+## Documentation
+
+- [Getting Started](guide/quickstart.md) — install and first steps
+- [TDA Workflow](guide/tda_workflow.md) — full pipeline from point cloud to persistence diagram
+- [Differentiable Tangency](guide/differentiable.md) — gradient-based optimisation with `ellphi.grad`
+- [API Reference](reference/index.md) — full module documentation
diff --git a/docs/javascripts/mathjax.js b/docs/javascripts/mathjax.js
new file mode 100644
index 0000000..93a539c
--- /dev/null
+++ b/docs/javascripts/mathjax.js
@@ -0,0 +1,20 @@
+window.MathJax = {
+  tex: {
+    packages: {'[+]': ['boldsymbol']},
+    inlineMath: [["\\(", "\\)"]],
+    displayMath: [["\\[", "\\]"]],
+    processEscapes: true,
+    processEnvironments: true,
+    macros: {
+      bm: ["\\boldsymbol{#1}", 1]
+    }
+  },
+  options: {
+    ignoreHtmlClass: ".*|",
+    processHtmlClass: "arithmatex"
+  }
+};
+
+document$.subscribe(() => { 
+  MathJax.typesetPromise()
+})
diff --git a/docs/reference/ellcloud.md b/docs/reference/ellcloud.md
new file mode 100644
index 0000000..f06ade4
--- /dev/null
+++ b/docs/reference/ellcloud.md
@@ -0,0 +1,19 @@
+# ellphi.ellcloud
+
+The `EllipseCloud` class is the central data structure of EllPHi.  It holds an array of ellipsoid coefficient vectors together with the original means and covariances, and provides convenience methods for computing tangency distances and visualisation.
+
+The `ellipse_cloud` factory function (an alias for `EllipseCloud.from_point_cloud`) is the recommended entry point:
+
+```python
+from ellphi import ellipse_cloud
+cloud = ellipse_cloud(X, k=5)
+```
+
+When centres and covariance matrices are already available, prefer the direct
+constructor:
+
+```python
+cloud = EllipseCloud.from_cov(centers, covs)
+```
+
+::: ellphi.ellcloud
diff --git a/docs/reference/geometry.md b/docs/reference/geometry.md
new file mode 100644
index 0000000..cbbde90
--- /dev/null
+++ b/docs/reference/geometry.md
@@ -0,0 +1,13 @@
+# ellphi.geometry
+
+Utilities for encoding and decoding ellipsoid geometry as packed conic coefficient vectors.
+
+A *d*-dimensional ellipsoid centred at $\bm{x}_0$ with covariance $\Sigma$ is represented by the quadratic form
+
+$$
+E = \{ \bm{x} : (\bm{x} - \bm{x}_0)^\top \Sigma^{-1} (\bm{x} - \bm{x}_0) \le 1 \}
+$$
+
+The coefficient vector packs the upper-triangular entries of $A = \Sigma^{-1}$, followed by the linear term $\bm{b} = -A\bm{x}_0$ and the scalar $c = \bm{x}_0^\top A \bm{x}_0$, into a flat array of length $m = (d+1)(d+2)/2$.  The internal evaluator applies a factor of 2 to $\bm{b}$, so the effective evaluation is $(\bm{x} - \bm{x}_0)^\top A (\bm{x} - \bm{x}_0)$, which equals 1 on the ellipsoid boundary and 0 at its center.
+
+::: ellphi.geometry
diff --git a/docs/reference/grad.md b/docs/reference/grad.md
new file mode 100644
index 0000000..ad8f5c2
--- /dev/null
+++ b/docs/reference/grad.md
@@ -0,0 +1,21 @@
+# ellphi.grad
+
+Differentiable tangency distances for gradient-based optimisation and persistent-homology backprop.
+
+Gradients are derived via the **envelope theorem**: the derivative through the optimal tangent point $\bm{x}^*$ vanishes, so only the explicit dependence of the pencil on the input coefficients survives.  This makes the gradient cheap — no second solver pass is required.
+
+```python
+from ellphi.grad import tangency_grad, pdist_tangency_grad
+
+# Single pair
+g = tangency_grad(p, q)
+# g.t, g.dt_dp, g.dt_dq
+
+# Batch pairwise with VJP
+dists, vjp = pdist_tangency_grad(coefs)
+grad_coefs = vjp(upstream_grad)   # ∂loss/∂coefs
+```
+
+See the [Differentiable Tangency guide](../guide/differentiable.md) for full examples.
+
+::: ellphi.grad
diff --git a/docs/reference/index.md b/docs/reference/index.md
new file mode 100644
index 0000000..fcfe5be
--- /dev/null
+++ b/docs/reference/index.md
@@ -0,0 +1,24 @@
+# API Reference
+
+EllPHi's public API is organised into the following modules:
+
+| Module | Description |
+|--------|-------------|
+| [`ellphi.geometry`](geometry.md) | Conic coefficient encoding/decoding and covariance-to-ellipse conversion. |
+| [`ellphi.ellcloud`](ellcloud.md) | `EllipseCloud` container and the `ellipse_cloud` factory function. |
+| [`ellphi.solver`](solver.md) | Tangency solver — `tangency` (single pair) and `pdist_tangency` (pairwise). |
+| [`ellphi.grad`](grad.md) | Differentiable solver — gradients and VJP for gradient-based optimisation. |
+| [`ellphi.visualization`](visualization.md) | Matplotlib helpers for visualising ellipse clouds. |
+
+The top-level `ellphi` namespace re-exports the most commonly used symbols:
+
+```python
+from ellphi import (
+    ellipse_cloud,       # factory: point cloud → EllipseCloud
+    EllipseCloud,
+    tangency,            # single-pair tangency distance
+    pdist_tangency,      # pairwise condensed distance matrix
+    TangencyResult,      # NamedTuple: t, point, mu
+    build_info,          # solver backend details
+)
+```
diff --git a/docs/reference/solver.md b/docs/reference/solver.md
new file mode 100644
index 0000000..80c5bf2
--- /dev/null
+++ b/docs/reference/solver.md
@@ -0,0 +1,21 @@
+# ellphi.solver
+
+The solver module dispatches tangency computations to the best available backend:
+
+- **C++ / Eigen** — compiled extension, used automatically when available (`backend="auto"`).
+- **Pure Python** — SciPy-based fallback, always available.
+
+The default method `"brentq+newton"` uses a short bracketing phase followed by Newton refinement, which is significantly faster than pure Brent's method for high-dimensional inputs.
+
+```python
+from ellphi import tangency, pdist_tangency, build_info
+
+print(build_info())         # shows cpp_backend_available, cpp_linalg_kind, etc.
+
+result = tangency(p, q)
+print(result.t, result.point, result.mu)
+
+dists = pdist_tangency(cloud)   # condensed array
+```
+
+::: ellphi.solver
diff --git a/docs/reference/visualization.md b/docs/reference/visualization.md
new file mode 100644
index 0000000..d435350
--- /dev/null
+++ b/docs/reference/visualization.md
@@ -0,0 +1,16 @@
+# ellphi.visualization
+
+Matplotlib helpers for rendering ellipse clouds.  For most use cases, `EllipseCloud.plot()` is the recommended entry point — it calls `ellipse_patch` internally.
+
+```python
+import matplotlib.pyplot as plt
+from ellphi.visualization import ellipse_patch
+
+fig, ax = plt.subplots()
+patch = ellipse_patch(center, r_major, r_minor, theta, edgecolor="teal", alpha=0.5)
+ax.add_patch(patch)
+ax.set_aspect("equal")
+plt.show()
+```
+
+::: ellphi.visualization
diff --git a/ellphi-logo.png b/ellphi-logo.png
index 673218b..b591304 100644
Binary files a/ellphi-logo.png and b/ellphi-logo.png differ
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..5503958
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,83 @@
+site_name: EllPHi
+site_url: https://t-uda.github.io/ellphi/
+repo_url: https://github.com/t-uda/ellphi
+
+theme:
+  name: material
+  logo: assets/logo-icon.png
+  favicon: assets/logo-icon.png
+  features:
+    - navigation.tabs
+    - navigation.instant
+    - content.code.copy
+  palette:
+    # Palette toggle for automatic mode
+    - media: "(prefers-color-scheme)"
+      toggle:
+        icon: material/brightness-auto
+        name: Switch to light mode
+    # Palette toggle for light mode
+    - media: "(prefers-color-scheme: light)"
+      scheme: default
+      primary: teal
+      accent: purple
+      toggle:
+        icon: material/brightness-7
+        name: Switch to dark mode
+    # Palette toggle for dark mode
+    - media: "(prefers-color-scheme: dark)"
+      scheme: slate
+      primary: teal
+      accent: lime
+      toggle:
+        icon: material/brightness-4
+        name: Switch to system preference
+
+plugins:
+  - search
+  - mkdocstrings:
+      handlers:
+        python:
+          paths: [src]
+          options:
+            docstring_style: google  # or numpy, depending on your codebase style
+            show_source: true
+            show_root_heading: true
+
+markdown_extensions:
+  - attr_list
+  - admonition
+  - pymdownx.details
+  - pymdownx.superfences
+  - pymdownx.highlight:
+      anchor_linenums: true
+      line_spans: __span
+      pygments_lang_class: true
+  - pymdownx.inlinehilite
+  - pymdownx.snippets
+  - pymdownx.arithmatex:
+      generic: true
+
+extra_javascript:
+  - javascripts/mathjax.js
+  - https://polyfill.io/v3/polyfill.min.js?features=es6
+  - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml-full.js
+
+nav:
+  - Home: index.md
+  - Getting Started: guide/quickstart.md
+  - User Guide:
+    - TDA Workflow: guide/tda_workflow.md
+    - Differentiable Tangency: guide/differentiable.md
+    - Notebook Examples: guide/notebooks.md
+  - API Reference:
+    - Overview: reference/index.md
+    - ellphi.geometry: reference/geometry.md
+    - ellphi.ellcloud: reference/ellcloud.md
+    - ellphi.solver: reference/solver.md
+    - ellphi.grad: reference/grad.md
+    - ellphi.visualization: reference/visualization.md
+  - Design Notes:
+    - Algebraic Sigmoid + Newton: algebraic_sigmoid_newton.md
+    - Hybrid Solver Tuning: hybrid_tuning.md
+    - Eigen Backend Plan: eigen_backend_plan.md
diff --git a/notebooks/topology_optimization.ipynb b/notebooks/topology_optimization.ipynb
new file mode 100644
index 0000000..d6a7db5
--- /dev/null
+++ b/notebooks/topology_optimization.ipynb
@@ -0,0 +1,658 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9b76127b",
+   "metadata": {},
+   "source": [
+    "# Topology Optimisation with EllPHi Gradients\n",
+    "\n",
+    "This notebook demonstrates how to use `ellphi.grad` for gradient-based\n",
+    "optimisation of ellipsoid configurations. No dependencies beyond **ellphi**\n",
+    "and **scipy** are required.\n",
+    "\n",
+    "**Sections**\n",
+    "1. Gradient-aware tangency (single pair)\n",
+    "2. Batch gradients and the VJP pattern\n",
+    "3. [Placeholder] Persistence diagram optimisation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "36a9cd5b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-20T11:10:45.703843Z",
+     "iopub.status.busy": "2026-03-20T11:10:45.703712Z",
+     "iopub.status.idle": "2026-03-20T11:10:46.165826Z",
+     "shell.execute_reply": "2026-03-20T11:10:46.165469Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ellphi version: 0.1.2.dev0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import ellphi\n",
+    "from ellphi import (\n",
+    "    tangency_grad,\n",
+    "    pdist_tangency_grad,\n",
+    "    pdist_tangency,\n",
+    "    coef_from_axes,\n",
+    "    coef_from_cov,\n",
+    "    tangency,\n",
+    ")\n",
+    "\n",
+    "rng = np.random.default_rng(42)\n",
+    "print('ellphi version:', ellphi.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "866f83a9",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 1. Gradient-aware tangency (single pair)\n",
+    "\n",
+    "`tangency_grad(p, q)` returns a `TangencyGrad` dataclass with three fields:\n",
+    "\n",
+    "| Field | Shape | Meaning |\n",
+    "|-------|-------|---------|\n",
+    "| `t` | scalar | Tangency distance (same as `tangency(p, q).t`) |\n",
+    "| `dt_dp` | `(m,)` | Gradient of `t` w.r.t. first-ellipsoid coefficients |\n",
+    "| `dt_dq` | `(m,)` | Gradient of `t` w.r.t. second-ellipsoid coefficients |\n",
+    "\n",
+    "`coef_from_axes(X, r0, r1, theta)` builds a single ellipse coefficient vector\n",
+    "from a center `X`, semi-axes `r0`/`r1`, and orientation angle `theta`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8a14d47a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-20T11:10:46.168191Z",
+     "iopub.status.busy": "2026-03-20T11:10:46.167996Z",
+     "iopub.status.idle": "2026-03-20T11:10:46.172330Z",
+     "shell.execute_reply": "2026-03-20T11:10:46.172027Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t          = 1.042287\n",
+      "dt_dp      = [3.13295714 2.49616215 0.49720002 2.2102748  0.88051065 0.38983255]\n",
+      "dt_dq      = [0.72235141 0.57552854 0.11463711 0.50961282 0.20301526 0.08988188]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build two 2-D ellipses from axes/angle representation\n",
+    "p = coef_from_axes(np.array([0.0, 0.0]), 3.0, 1.0, 0.3)\n",
+    "q = coef_from_axes(np.array([4.0, 1.0]), 2.0, 0.5, -0.5)\n",
+    "\n",
+    "g = tangency_grad(p, q)\n",
+    "print(f't          = {g.t:.6f}')\n",
+    "print(f'dt_dp      = {g.dt_dp}')\n",
+    "print(f'dt_dq      = {g.dt_dq}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3149595e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-20T11:10:46.173730Z",
+     "iopub.status.busy": "2026-03-20T11:10:46.173629Z",
+     "iopub.status.idle": "2026-03-20T11:10:46.176233Z",
+     "shell.execute_reply": "2026-03-20T11:10:46.175982Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "analytic dt_dp[0] = 3.13295714\n",
+      "finite-diff       = 3.13295714\n",
+      "relative error    = 2.69e-11\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Quick finite-difference check for dt_dp[0]\n",
+    "h = 1e-6\n",
+    "p_plus = p.copy()\n",
+    "p_plus[0] += h\n",
+    "p_minus = p.copy()\n",
+    "p_minus[0] -= h\n",
+    "fd = (tangency(p_plus, q).t - tangency(p_minus, q).t) / (2 * h)\n",
+    "print(f'analytic dt_dp[0] = {g.dt_dp[0]:.8f}')\n",
+    "print(f'finite-diff       = {fd:.8f}')\n",
+    "print(f'relative error    = {abs(g.dt_dp[0] - fd) / abs(fd):.2e}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "922864c6",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 2. Batch gradients and the VJP pattern\n",
+    "\n",
+    "`pdist_tangency_grad(coefs)` returns:\n",
+    "- `dists`: condensed pairwise distance array (same as `pdist_tangency`)\n",
+    "- `vjp`: a pullback `grad_dists → grad_coefs` that accumulates upstream\n",
+    "  gradients into per-ellipsoid coefficient gradients\n",
+    "\n",
+    "The VJP can be chained with any scalar loss `L(dists)` via the chain rule:\n",
+    "`∂L/∂coefs = vjp(∂L/∂dists)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6f1fa22b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-20T11:10:46.177401Z",
+     "iopub.status.busy": "2026-03-20T11:10:46.177321Z",
+     "iopub.status.idle": "2026-03-20T11:10:46.183603Z",
+     "shell.execute_reply": "2026-03-20T11:10:46.183360Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max |dists - ref|: 0.0\n",
+      "distances: [2.29796195 8.07432226 3.18499214 5.91116232 7.6311246  1.21815661\n",
+      " 6.87865889 6.05896354 5.01377611 6.56044731]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build a small cloud of N=5 random 2-D ellipses\n",
+    "N = 5\n",
+    "means = rng.uniform(-20, 20, (N, 2))\n",
+    "covs = np.stack([\n",
+    "    (lambda F: F @ F.T + 4 * np.eye(2))(rng.standard_normal((2, 2)))\n",
+    "    for _ in range(N)\n",
+    "])\n",
+    "coefs = coef_from_cov(means, covs)\n",
+    "\n",
+    "dists, vjp = pdist_tangency_grad(coefs)\n",
+    "\n",
+    "# Verify distances match pdist_tangency\n",
+    "ref = pdist_tangency(coefs)\n",
+    "print('max |dists - ref|:', np.max(np.abs(dists - ref)))\n",
+    "print('distances:', dists)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1bdb2c81",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-20T11:10:46.184782Z",
+     "iopub.status.busy": "2026-03-20T11:10:46.184695Z",
+     "iopub.status.idle": "2026-03-20T11:10:46.186730Z",
+     "shell.execute_reply": "2026-03-20T11:10:46.186495Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "grad_coefs shape: (5, 6)\n",
+      "grad_coefs[0]: [29.27797444 16.4591706   5.01931062  4.31358709  1.63785166  0.26589981]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example: gradient of sum(dists) w.r.t. all coefficients\n",
+    "grad_coefs = vjp(np.ones_like(dists))\n",
+    "print('grad_coefs shape:', grad_coefs.shape)  # (N, m)\n",
+    "print('grad_coefs[0]:', grad_coefs[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d2c0deb",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 3. Persistence Diagram Optimisation via VJP (homcloud)\n",
+    "\n",
+    "This section drives gradient descent on ellipsoid centres using a\n",
+    "**persistence-diagram loss** on H1 features computed with\n",
+    "[homcloud](https://homcloud.dev).\n",
+    "\n",
+    "### Setup\n",
+    "\n",
+    "* N = 8 two-dimensional ellipsoids with **fixed** isotropic shape\n",
+    "  (covariance `1.5 I`) whose centres are optimised.\n",
+    "* The N × N tangency-distance matrix `D` is fed to\n",
+    "  `hc.PDList.from_rips_filtration` to build a **flag (clique)\n",
+    "  filtration** on the complete weighted graph.  Because `t(P, Q)` is a\n",
+    "  dissimilarity (not a metric — the triangle inequality fails in\n",
+    "  general), the resulting persistence diagram is interpreted as the PH\n",
+    "  of a weighted graph, not as Vietoris–Rips PH of a metric space.\n",
+    "\n",
+    "### Loss and gradient chain\n",
+    "\n",
+    "A single H1 birth–death pair `(b, d)` is targeted.  The loss is\n",
+    "\n",
+    "    L = (b − b*)² + (d − d*)²\n",
+    "\n",
+    "where `(b*, d*)` is a feasible target with `b* < d*`.\n",
+    "\n",
+    "The gradient flows through the following chain:\n",
+    "\n",
+    "    ∂L/∂dists   (sparse: only the birth edge and the death edge are non-zero)\n",
+    "         ↓  vjp\n",
+    "    ∂L/∂coefs   (shape N × m)\n",
+    "         ↓  Jacobian of coef_from_cov w.r.t. centres\n",
+    "    ∂L/∂centres (shape N × 2)\n",
+    "\n",
+    "For a Rips H1 pair in generic position, `b` is the weight of a unique\n",
+    "birth edge and `d` is the weight of a unique death-controlling edge, so\n",
+    "`∂b/∂dists[k]` and `∂d/∂dists[k]` are 0 or 1.  Both edges are\n",
+    "identified by scanning `D` (float32) for unique matches; a non-unique\n",
+    "match indicates a degenerate (non-smooth) step and the loop stops.\n",
+    "\n",
+    "**Float32 approximation note.** HomCloud / ripser uses float32 distances\n",
+    "internally.  Critical edges are selected from the float32 matrix, while\n",
+    "the VJP pullback operates on the original float64 tangency distances.\n",
+    "This is a reasonable numerical approximation; strictly speaking it is\n",
+    "the gradient of the ideal real-weight filtration value, not of the\n",
+    "exact float32-rounded pipeline.\n",
+    "\n",
+    "**Smoothness note.** The map from centres to the selected pair is only\n",
+    "*locally* smooth.  As long as the critical edges and the selected H1\n",
+    "pair do not change, the update is the **ordinary gradient** of that\n",
+    "local smooth branch.  When ties or pairing switches occur the objective\n",
+    "is non-smooth; the same step acts as a heuristic continuation and offers\n",
+    "no convergence guarantee.\n",
+    "\n",
+    "### Centre-only Jacobian\n",
+    "\n",
+    "With fixed inverse covariance `Q = cov⁻¹` and centre `X`, the packed\n",
+    "ellphi coefficients satisfy `ℓ = −Q X` and `c = Xᵀ Q X`.  The\n",
+    "Jacobian `J = d(coef)/d(X)` is therefore\n",
+    "\n",
+    "    J = [ 0,  0  ]   ← quad block (fixed)\n",
+    "        [ 0,  0  ]\n",
+    "        [ 0,  0  ]\n",
+    "        [−Q₁₁, −Q₁₂]  ← linear block\n",
+    "        [−Q₁₂, −Q₂₂]\n",
+    "        [2(QX)₁, 2(QX)₂]  ← constant\n",
+    "\n",
+    "and `grad_X = Jᵀ @ grad_coef`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9akahxlx11v",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-20T11:10:46.188153Z",
+     "iopub.status.busy": "2026-03-20T11:10:46.188063Z",
+     "iopub.status.idle": "2026-03-20T11:10:46.778690Z",
+     "shell.execute_reply": "2026-03-20T11:10:46.778360Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial H1 pair: birth = 1.1027, death = 2.1507, persistence = 1.0480\n"
+     ]
+    }
+   ],
+   "source": [
+    "import homcloud.interface as hc\n",
+    "import tempfile\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# --- Initial configuration ---\n",
+    "# N ellipsoids with fixed isotropic covariance arranged roughly on a circle.\n",
+    "# Using a separate RNG so this section is reproducible independently.\n",
+    "rng_opt = np.random.default_rng(7)\n",
+    "N_OPT = 8\n",
+    "angles_opt = np.linspace(0, 2 * np.pi, N_OPT, endpoint=False)\n",
+    "centers_init = np.column_stack(\n",
+    "    [3.0 * np.cos(angles_opt), 3.0 * np.sin(angles_opt)]\n",
+    ") + rng_opt.normal(0, 0.2, (N_OPT, 2))\n",
+    "\n",
+    "cov_shape = 1.5 * np.eye(2)\n",
+    "covs_opt = np.stack([cov_shape] * N_OPT)\n",
+    "inv_covs_opt = np.linalg.inv(covs_opt)  # Q = cov^{-1}, fixed throughout\n",
+    "\n",
+    "\n",
+    "def build_D_and_pd(centers, covs, N):\n",
+    "    \"\"\"Compute pairwise tangency distances and the H1 persistence diagram.\"\"\"\n",
+    "    coefs = coef_from_cov(centers, covs)\n",
+    "    dists, vjp = pdist_tangency_grad(coefs)\n",
+    "    # Build float32 distance matrix (homcloud / ripser uses float32 internally)\n",
+    "    D = np.zeros((N, N), dtype=np.float32)\n",
+    "    k = 0\n",
+    "    for i in range(N):\n",
+    "        for j in range(i + 1, N):\n",
+    "            D[i, j] = D[j, i] = dists[k]\n",
+    "            k += 1\n",
+    "    with tempfile.NamedTemporaryFile(suffix=\".pdgm\", delete=False) as f:\n",
+    "        tmp = f.name\n",
+    "    pdlist = hc.PDList.from_rips_filtration(D, maxdim=1, save_to=tmp)\n",
+    "    pd1 = pdlist.dth_diagram(1)\n",
+    "    os.unlink(tmp)\n",
+    "    return dists, vjp, D, pd1\n",
+    "\n",
+    "\n",
+    "# Verify initial configuration has exactly one H1 pair\n",
+    "_, _, D_init, pd1_init = build_D_and_pd(centers_init, covs_opt, N_OPT)\n",
+    "pairs_init = pd1_init.pairs()\n",
+    "assert len(pairs_init) == 1, (\n",
+    "    f\"Expected exactly 1 H1 pair for the demo, got {len(pairs_init)}. \"\n",
+    "    \"Try a different initial configuration.\"\n",
+    ")\n",
+    "p0 = pairs_init[0]\n",
+    "b0, d0 = float(p0.birth), float(p0.death)\n",
+    "print(f\"Initial H1 pair: birth = {b0:.4f}, death = {d0:.4f}, \"\n",
+    "      f\"persistence = {d0 - b0:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "l91i3dahq3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-20T11:10:46.780304Z",
+     "iopub.status.busy": "2026-03-20T11:10:46.780124Z",
+     "iopub.status.idle": "2026-03-20T11:10:46.783523Z",
+     "shell.execute_reply": "2026-03-20T11:10:46.783252Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def condensed_idx(i, j, N):\n",
+    "    \"\"\"SciPy-style condensed index for the unordered edge {i, j}.\"\"\"\n",
+    "    if i > j:\n",
+    "        i, j = j, i\n",
+    "    return N * i - i * (i + 1) // 2 + j - i - 1\n",
+    "\n",
+    "\n",
+    "def find_unique_edge(D_f32, val_f32, N):\n",
+    "    \"\"\"Return the unique edge (i, j) with D_f32[i, j] == val_f32.\n",
+    "\n",
+    "    The genericity assumption required for the gradient to be well-defined\n",
+    "    is that the birth (or death) value is attained by exactly one edge in the\n",
+    "    float32 distance matrix seen by homcloud / ripser.  If more than one edge\n",
+    "    matches, the configuration is degenerate and the caller should stop.\n",
+    "\n",
+    "    Note: homcloud 4.8.0 does not expose birth/death simplex vertex indices\n",
+    "    for Rips filtrations (RipsResolver.resolve_cells is not implemented).\n",
+    "    This scan is the correct fallback under the generic-position assumption.\n",
+    "    \"\"\"\n",
+    "    matches = [\n",
+    "        (i, j)\n",
+    "        for i in range(N)\n",
+    "        for j in range(i + 1, N)\n",
+    "        if D_f32[i, j] == val_f32\n",
+    "    ]\n",
+    "    return matches[0] if len(matches) == 1 else None\n",
+    "\n",
+    "\n",
+    "def center_gradient(grad_coefs, centers, inv_covs):\n",
+    "    \"\"\"Backpropagate grad_coefs to centre coordinates.\n",
+    "\n",
+    "    With fixed Q = inv_cov, the packed ellphi conic coefficients are:\n",
+    "      quad block  (indices 0..n_quad-1): constant, no centre dependence\n",
+    "      linear block (indices n_quad..n_quad+n-1): ℓ = −Q X\n",
+    "      constant    (index -1):              c  = Xᵀ Q X\n",
+    "\n",
+    "    The Jacobian J = d(coef)/d(X) has:\n",
+    "      rows for quad:    0\n",
+    "      rows for linear: −Q   (n × n matrix)\n",
+    "      row  for const:  2(QX)ᵀ\n",
+    "\n",
+    "    Hence grad_X = Jᵀ @ g = −Q @ g_linear + 2*(Q @ X) * g_const.\n",
+    "\n",
+    "    For 2D ellipses (n=2), the packed layout is\n",
+    "      [Q11, Q12, Q22, l1, l2, c], so n_quad = 3.\n",
+    "    \"\"\"\n",
+    "    N = len(centers)\n",
+    "    n = centers.shape[1]          # spatial dimension\n",
+    "    n_quad = n * (n + 1) // 2    # number of upper-triangular quad entries\n",
+    "    grad_centers = np.zeros_like(centers)\n",
+    "    for i in range(N):\n",
+    "        Q = inv_covs[i]\n",
+    "        X = centers[i]\n",
+    "        g = grad_coefs[i]\n",
+    "        g_linear = g[n_quad: n_quad + n]\n",
+    "        g_const = g[-1]\n",
+    "        grad_centers[i] = -Q @ g_linear + 2.0 * (Q @ X) * g_const\n",
+    "    return grad_centers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "07owbfuamkkh",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-20T11:10:46.784833Z",
+     "iopub.status.busy": "2026-03-20T11:10:46.784745Z",
+     "iopub.status.idle": "2026-03-20T11:10:47.166662Z",
+     "shell.execute_reply": "2026-03-20T11:10:47.166298Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target: b* = 1.3027, d* = 1.9507  (delta = 0.2000)\n",
+      "Step   1: b = 1.1027, d = 2.1507, L = 0.080000\n",
+      "Step  10: b = 1.1553, d = 2.1065, L = 0.045975\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step  20: b = 1.1977, d = 2.0748, L = 0.026426\n",
+      "Step  30: b = 1.2279, d = 2.0497, L = 0.015384\n",
+      "Step  40: b = 1.2494, d = 2.0296, L = 0.009065\n",
+      "\n",
+      "Final H1 pair: birth = 1.2494, death = 2.0296\n",
+      "Target:        b*    = 1.3027,  d*    = 1.9507\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- Optimisation target ---\n",
+    "# Shrink persistence: push birth up and death down by delta,\n",
+    "# ensuring b* < d* (feasibility).\n",
+    "delta = min(0.2, 0.25 * (d0 - b0))\n",
+    "b_star = b0 + delta\n",
+    "d_star = d0 - delta\n",
+    "print(f\"Target: b* = {b_star:.4f}, d* = {d_star:.4f}  (delta = {delta:.4f})\")\n",
+    "\n",
+    "# --- Gradient-descent loop ---\n",
+    "# On each locally smooth branch (fixed critical edges and H1 pairing)\n",
+    "# this is ordinary gradient descent.  When pairing switches or ties\n",
+    "# occur it acts as a heuristic continuation step.\n",
+    "lr = 0.05\n",
+    "n_steps = 40\n",
+    "\n",
+    "centers_cur = centers_init.copy()\n",
+    "history = [(b0, d0)]\n",
+    "losses = []\n",
+    "\n",
+    "for step in range(n_steps):\n",
+    "    dists, vjp, D, pd1 = build_D_and_pd(centers_cur, covs_opt, N_OPT)\n",
+    "    pairs = pd1.pairs()\n",
+    "\n",
+    "    # Stop if the number of H1 pairs has changed\n",
+    "    if len(pairs) != 1:\n",
+    "        print(f\"Step {step}: H1 count = {len(pairs)}, stopping.\")\n",
+    "        break\n",
+    "\n",
+    "    # Exactly one H1 pair at this point; take it directly\n",
+    "    p = pairs[0]\n",
+    "    b, d = float(p.birth), float(p.death)\n",
+    "    history.append((b, d))\n",
+    "    loss = (b - b_star) ** 2 + (d - d_star) ** 2\n",
+    "    losses.append(loss)\n",
+    "\n",
+    "    if (step + 1) % 10 == 0 or step == 0:\n",
+    "        print(f\"Step {step + 1:3d}: b = {b:.4f}, d = {d:.4f}, L = {loss:.6f}\")\n",
+    "\n",
+    "    # Identify the birth and death edges from the float32 distance matrix.\n",
+    "    # Under the generic-position assumption (no two edges share the same\n",
+    "    # float32 weight), each filtration value corresponds to a unique edge.\n",
+    "    # The gradient is then the exact derivative of that locally smooth branch;\n",
+    "    # the float64→float32 rounding in the filtration step is not back-propagated.\n",
+    "    birth_edge = find_unique_edge(D, np.float32(b), N_OPT)\n",
+    "    death_edge = find_unique_edge(D, np.float32(d), N_OPT)\n",
+    "\n",
+    "    if birth_edge is None or death_edge is None:\n",
+    "        # Non-unique edge: degenerate (non-smooth) configuration.\n",
+    "        print(f\"Step {step}: non-unique birth or death edge, stopping.\")\n",
+    "        break\n",
+    "\n",
+    "    # Assemble the sparse upstream gradient in distance space.\n",
+    "    # ∂L/∂dists is non-zero only at the two critical edges.\n",
+    "    grad_dists = np.zeros_like(dists)\n",
+    "    grad_dists[condensed_idx(*birth_edge, N_OPT)] += 2.0 * (b - b_star)\n",
+    "    grad_dists[condensed_idx(*death_edge, N_OPT)] += 2.0 * (d - d_star)\n",
+    "\n",
+    "    # Pull back through the VJP to coefficient space, then to centre space.\n",
+    "    grad_coefs = vjp(grad_dists)\n",
+    "    grad_c = center_gradient(grad_coefs, centers_cur, inv_covs_opt)\n",
+    "\n",
+    "    centers_cur = centers_cur - lr * grad_c\n",
+    "\n",
+    "print(f\"\\nFinal H1 pair: birth = {history[-1][0]:.4f}, death = {history[-1][1]:.4f}\")\n",
+    "print(f\"Target:        b*    = {b_star:.4f},  d*    = {d_star:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "s9qfzlroai",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-20T11:10:47.168608Z",
+     "iopub.status.busy": "2026-03-20T11:10:47.168452Z",
+     "iopub.status.idle": "2026-03-20T11:10:47.453332Z",
+     "shell.execute_reply": "2026-03-20T11:10:47.453034Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "history = np.array(history)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+    "\n",
+    "# --- Panel 1: loss curve ---\n",
+    "ax = axes[0]\n",
+    "ax.plot(losses, color=\"steelblue\")\n",
+    "ax.set_xlabel(\"step\")\n",
+    "ax.set_ylabel(\"L\")\n",
+    "ax.set_title(\"Loss  L = (b − b*)² + (d − d*)²\")\n",
+    "ax.set_yscale(\"log\")\n",
+    "\n",
+    "# --- Panel 2: H1 pair trajectory in PD space ---\n",
+    "ax = axes[1]\n",
+    "diag_lo = min(history[:, 0].min(), b_star, d_star) - 0.1\n",
+    "diag_hi = max(history[:, 1].max(), b_star, d_star) + 0.1\n",
+    "ax.plot([diag_lo, diag_hi], [diag_lo, diag_hi], \"gray\", lw=0.8, ls=\"--\")\n",
+    "\n",
+    "sc = ax.scatter(\n",
+    "    history[:, 0], history[:, 1],\n",
+    "    c=np.arange(len(history)), cmap=\"viridis\",\n",
+    "    s=20, zorder=3,\n",
+    ")\n",
+    "ax.plot(history[0, 0], history[0, 1], \"go\", ms=8, label=\"start\", zorder=4)\n",
+    "ax.plot(history[-1, 0], history[-1, 1], \"r*\", ms=12, label=\"end\", zorder=4)\n",
+    "ax.plot(b_star, d_star, \"kx\", ms=10, mew=2, label=\"target\", zorder=5)\n",
+    "plt.colorbar(sc, ax=ax, label=\"step\")\n",
+    "ax.set_xlabel(\"birth\")\n",
+    "ax.set_ylabel(\"death\")\n",
+    "ax.set_title(\"H1 pair trajectory in PD space\")\n",
+    "ax.legend(fontsize=8)\n",
+    "\n",
+    "# --- Panel 3: initial vs final ellipsoid centres ---\n",
+    "ax = axes[2]\n",
+    "r_ell = np.sqrt(cov_shape[0, 0])   # radius of isotropic ellipsoid\n",
+    "theta_ell = np.linspace(0, 2 * np.pi, 60)\n",
+    "cos_t, sin_t = np.cos(theta_ell), np.sin(theta_ell)\n",
+    "for i in range(N_OPT):\n",
+    "    ci = centers_init[i]\n",
+    "    cf = centers_cur[i]\n",
+    "    ax.plot(ci[0] + r_ell * cos_t, ci[1] + r_ell * sin_t, \"g--\", alpha=0.4)\n",
+    "    ax.plot(cf[0] + r_ell * cos_t, cf[1] + r_ell * sin_t, \"b-\", alpha=0.7)\n",
+    "    ax.annotate(\"\", xy=cf, xytext=ci,\n",
+    "                arrowprops=dict(arrowstyle=\"->\", color=\"orange\", lw=1.2))\n",
+    "ax.set_aspect(\"equal\")\n",
+    "ax.set_title(\"Centres: initial (green dashed) → final (blue)\\norange arrows show displacement\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Poetry ellphi 2025",
+   "language": "python",
+   "name": "poetry-ellphi2025"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/poetry.lock b/poetry.lock
index 6fb3e56..2fb69e3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,26 +2,23 @@
 
 [[package]]
 name = "anyio"
-version = "4.9.0"
-description = "High level compatibility layer for multiple asynchronous event loop implementations"
+version = "4.12.0"
+description = "High-level concurrency and networking framework on top of asyncio or Trio"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
-    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
+    {file = "anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb"},
+    {file = "anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0"},
 ]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
-sniffio = ">=1.1"
 typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""]
-trio = ["trio (>=0.26.1)"]
+trio = ["trio (>=0.31.0) ; python_version < \"3.10\"", "trio (>=0.32.0) ; python_version >= \"3.10\""]
 
 [[package]]
 name = "appnope"
@@ -53,77 +50,81 @@ argon2-cffi-bindings = "*"
 
 [[package]]
 name = "argon2-cffi-bindings"
-version = "21.2.0"
+version = "25.1.0"
 description = "Low-level CFFI bindings for Argon2"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3"},
-    {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367"},
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 ]
 
 [package.dependencies]
-cffi = ">=1.0.1"
-
-[package.extras]
-dev = ["cogapp", "pre-commit", "pytest", "wheel"]
-tests = ["pytest"]
+cffi = [
+    {version = ">=1.0.1", markers = "python_version < \"3.14\""},
+    {version = ">=2.0.0b1", markers = "python_version >= \"3.14\""},
+]
 
 [[package]]
 name = "arrow"
-version = "1.3.0"
+version = "1.4.0"
 description = "Better dates & times for Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo"]
 files = [
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+    {file = "arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7"},
 ]
 
 [package.dependencies]
 python-dateutil = ">=2.7.0"
-types-python-dateutil = ">=2.8.10"
+tzdata = {version = "*", markers = "python_version >= \"3.9\""}
 
 [package.extras]
 doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
-test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"]
+test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2025.2)", "simplejson (==3.*)"]
 
 [[package]]
 name = "asttokens"
-version = "3.0.0"
+version = "3.0.1"
 description = "Annotate AST trees with source code positions"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo", "dev"]
 files = [
-    {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
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+    {file = "asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7"},
 ]
 
 [package.extras]
-astroid = ["astroid (>=2,<4)"]
-test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
+astroid = ["astroid (>=2,<5)"]
+test = ["astroid (>=2,<5)", "pytest (<9.0)", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "async-lru"
@@ -142,31 +143,23 @@ typing_extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "attrs"
-version = "25.3.0"
+version = "25.4.0"
 description = "Classes Without Boilerplate"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
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+    {file = "attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373"},
+    {file = "attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11"},
 ]
 
-[package.extras]
-benchmark = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
-cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
-dev = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
-docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"]
-tests = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
-tests-mypy = ["mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\""]
-
 [[package]]
 name = "babel"
 version = "2.17.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "demo"]
+groups = ["main", "demo", "docs"]
 files = [
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@@ -175,20 +168,40 @@ files = [
 [package.extras]
 dev = ["backports.zoneinfo ; python_version < \"3.9\"", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata ; sys_platform == \"win32\""]
 
+[[package]]
+name = "backrefs"
+version = "6.1"
+description = "A wrapper around re and regex that adds additional back references."
+optional = false
+python-versions = ">=3.9"
+groups = ["docs"]
+files = [
+    {file = "backrefs-6.1-py310-none-any.whl", hash = "sha256:2a2ccb96302337ce61ee4717ceacfbf26ba4efb1d55af86564b8bbaeda39cac1"},
+    {file = "backrefs-6.1-py311-none-any.whl", hash = "sha256:e82bba3875ee4430f4de4b6db19429a27275d95a5f3773c57e9e18abc23fd2b7"},
+    {file = "backrefs-6.1-py312-none-any.whl", hash = "sha256:c64698c8d2269343d88947c0735cb4b78745bd3ba590e10313fbf3f78c34da5a"},
+    {file = "backrefs-6.1-py313-none-any.whl", hash = "sha256:4c9d3dc1e2e558965202c012304f33d4e0e477e1c103663fd2c3cc9bb18b0d05"},
+    {file = "backrefs-6.1-py314-none-any.whl", hash = "sha256:13eafbc9ccd5222e9c1f0bec563e6d2a6d21514962f11e7fc79872fd56cbc853"},
+    {file = "backrefs-6.1-py39-none-any.whl", hash = "sha256:a9e99b8a4867852cad177a6430e31b0f6e495d65f8c6c134b68c14c3c95bf4b0"},
+    {file = "backrefs-6.1.tar.gz", hash = "sha256:3bba1749aafe1db9b915f00e0dd166cba613b6f788ffd63060ac3485dc9be231"},
+]
+
+[package.extras]
+extras = ["regex"]
+
 [[package]]
 name = "beautifulsoup4"
-version = "4.13.4"
+version = "4.14.3"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.7.0"
 groups = ["main", "demo"]
 files = [
-    {file = "beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b"},
-    {file = "beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195"},
+    {file = "beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb"},
+    {file = "beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86"},
 ]
 
 [package.dependencies]
-soupsieve = ">1.2"
+soupsieve = ">=1.6.1"
 typing-extensions = ">=4.0.0"
 
 [package.extras]
@@ -200,34 +213,39 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
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 [package.dependencies]
@@ -237,6 +255,7 @@ mypy-extensions = ">=0.4.3"
 packaging = ">=22.0"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
+pytokens = ">=0.3.0"
 tokenize-rt = {version = ">=3.2.0", optional = true, markers = "extra == \"jupyter\""}
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
@@ -249,14 +268,14 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "bleach"
-version = "6.2.0"
+version = "6.3.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["main", "demo"]
 files = [
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 [package.dependencies]
@@ -280,208 +299,246 @@ files = [
 
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-version = "2025.7.14"
+version = "2025.11.12"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.7"
-groups = ["main", "demo"]
+groups = ["main", "demo", "docs"]
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 ]
 
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 name = "cffi"
-version = "1.17.1"
+version = "2.0.0"
 description = "Foreign Function Interface for Python calling C code."
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-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 [package.dependencies]
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 ]
 
 [[package]]
 name = "click"
-version = "8.2.1"
+version = "8.3.1"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.10"
-groups = ["dev"]
+groups = ["dev", "docs"]
 files = [
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+    {file = "click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6"},
+    {file = "click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a"},
 ]
 
 [package.dependencies]
@@ -493,28 +550,25 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
-groups = ["main", "demo", "dev"]
-markers = "sys_platform == \"win32\" or platform_system == \"Windows\""
+groups = ["main", "demo", "dev", "docs"]
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
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 ]
+markers = {main = "sys_platform == \"win32\" or platform_system == \"Windows\"", demo = "sys_platform == \"win32\" or platform_system == \"Windows\"", dev = "sys_platform == \"win32\" or platform_system == \"Windows\""}
 
 [[package]]
 name = "comm"
-version = "0.2.2"
+version = "0.2.3"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo"]
 files = [
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-    {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"},
+    {file = "comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417"},
+    {file = "comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971"},
 ]
 
-[package.dependencies]
-traitlets = ">=4"
-
 [package.extras]
 test = ["pytest"]
 
@@ -525,6 +579,7 @@ description = "Python library for calculating contours of 2D quadrilateral grids
 optional = false
 python-versions = ">=3.10"
 groups = ["main", "demo"]
+markers = "python_version == \"3.10\""
 files = [
     {file = "contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934"},
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@@ -595,106 +650,199 @@ mypy = ["bokeh", "contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.15.0)", "
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
 
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.11"
+groups = ["main", "demo"]
+markers = "python_version >= \"3.11\""
+files = [
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 [package.dependencies]
@@ -721,110 +869,91 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
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 [[package]]
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 description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
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@@ -890,14 +1019,14 @@ test = ["pytest (>=6)"]
 
 [[package]]
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 description = "Get the currently executing AST node of a frame, and other information"
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 [[package]]
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+version = "2.21.2"
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 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
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 symfont = ["sympy"]
 type1 = ["xattr ; sys_platform == \"darwin\""]
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 [[package]]
@@ -1007,11 +1144,61 @@ description = "Validate fully-qualified domain names compliant to RFC 1035 and t
 optional = false
 python-versions = "*"
 groups = ["main", "demo"]
+markers = "python_version >= \"3.14\""
 files = [
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 ]
 
+[[package]]
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+version = "1.5.1"
+description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
+optional = false
+python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
+groups = ["main", "demo"]
+markers = "python_version < \"3.14\""
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+optional = false
+python-versions = "*"
+groups = ["docs"]
+files = [
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+
+[package.extras]
+dev = ["flake8", "markdown", "twine", "wheel"]
+
+[[package]]
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+version = "1.15.0"
+description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
+optional = false
+python-versions = ">=3.10"
+groups = ["docs"]
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+
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+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -1121,14 +1308,14 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "idna"
-version = "3.10"
+version = "3.11"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.6"
-groups = ["main", "demo"]
+python-versions = ">=3.8"
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@@ -1136,14 +1323,14 @@ all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2
 
 [[package]]
 name = "imageio"
-version = "2.37.0"
-description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
+version = "2.37.2"
+description = "Read and write images and video across all major formats. Supports scientific and volumetric data."
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
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-    {file = "imageio-2.37.0.tar.gz", hash = "sha256:71b57b3669666272c818497aebba2b4c5f20d5b37c81720e5e1a56d59c492996"},
+    {file = "imageio-2.37.2-py3-none-any.whl", hash = "sha256:ad9adfb20335d718c03de457358ed69f141021a333c40a53e57273d8a5bd0b9b"},
+    {file = "imageio-2.37.2.tar.gz", hash = "sha256:0212ef2727ac9caa5ca4b2c75ae89454312f440a756fcfc8ef1993e718f50f8a"},
 ]
 
 [package.dependencies]
@@ -1151,14 +1338,14 @@ numpy = "*"
 pillow = ">=8.3.2"
 
 [package.extras]
-all-plugins = ["astropy", "av", "imageio-ffmpeg", "numpy (>2)", "pillow-heif", "psutil", "rawpy", "tifffile"]
-all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
-build = ["wheel"]
+all-plugins = ["astropy", "av", "fsspec[http]", "imageio-ffmpeg", "numpy (>2)", "pillow-heif", "psutil", "rawpy", "tifffile"]
+all-plugins-pypy = ["fsspec[http]", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
 dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
 docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
 ffmpeg = ["imageio-ffmpeg", "psutil"]
 fits = ["astropy"]
-full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpy (>2)", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "rawpy", "sphinx (<6)", "tifffile", "wheel"]
+freeimage = ["fsspec[http]"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github,http]", "imageio-ffmpeg", "numpy (>2)", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "rawpy", "sphinx (<6)", "tifffile"]
 gdal = ["gdal"]
 itk = ["itk"]
 linting = ["black", "flake8"]
@@ -1170,26 +1357,26 @@ tifffile = ["tifffile"]
 
 [[package]]
 name = "iniconfig"
-version = "2.1.0"
+version = "2.3.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["dev"]
 files = [
-    {file = "iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760"},
-    {file = "iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7"},
+    {file = "iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12"},
+    {file = "iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730"},
 ]
 
 [[package]]
 name = "ipykernel"
-version = "6.30.0"
+version = "7.1.0"
 description = "IPython Kernel for Jupyter"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["main", "demo"]
 files = [
-    {file = "ipykernel-6.30.0-py3-none-any.whl", hash = "sha256:fd2936e55c4a1c2ee8b1e5fa6a372b8eecc0ab1338750dee76f48fa5cca1301e"},
-    {file = "ipykernel-6.30.0.tar.gz", hash = "sha256:b7b808ddb2d261aae2df3a26ff3ff810046e6de3dfbc6f7de8c98ea0a6cb632c"},
+    {file = "ipykernel-7.1.0-py3-none-any.whl", hash = "sha256:763b5ec6c5b7776f6a8d7ce09b267693b4e5ce75cb50ae696aaefb3c85e1ea4c"},
+    {file = "ipykernel-7.1.0.tar.gz", hash = "sha256:58a3fc88533d5930c3546dc7eac66c6d288acde4f801e2001e65edc5dc9cf0db"},
 ]
 
 [package.dependencies]
@@ -1209,7 +1396,7 @@ traitlets = ">=5.4.0"
 
 [package.extras]
 cov = ["coverage[toml]", "matplotlib", "pytest-cov", "trio"]
-docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx (<8.2.0)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
 test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0,<9)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
@@ -1221,6 +1408,7 @@ description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 groups = ["main", "demo", "dev"]
+markers = "python_version == \"3.10\""
 files = [
     {file = "ipython-8.37.0-py3-none-any.whl", hash = "sha256:ed87326596b878932dbcb171e3e698845434d8c61b8d8cd474bf663041a9dcf2"},
     {file = "ipython-8.37.0.tar.gz", hash = "sha256:ca815841e1a41a1e6b73a0b08f3038af9b2252564d01fc405356d34033012216"},
@@ -1253,6 +1441,56 @@ qtconsole = ["qtconsole"]
 test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
+[[package]]
+name = "ipython"
+version = "9.8.0"
+description = "IPython: Productive Interactive Computing"
+optional = false
+python-versions = ">=3.11"
+groups = ["main", "demo", "dev"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "ipython-9.8.0-py3-none-any.whl", hash = "sha256:ebe6d1d58d7d988fbf23ff8ff6d8e1622cfdb194daf4b7b73b792c4ec3b85385"},
+    {file = "ipython-9.8.0.tar.gz", hash = "sha256:8e4ce129a627eb9dd221c41b1d2cdaed4ef7c9da8c17c63f6f578fe231141f83"},
+]
+
+[package.dependencies]
+colorama = {version = ">=0.4.4", markers = "sys_platform == \"win32\""}
+decorator = ">=4.3.2"
+ipython-pygments-lexers = ">=1.0.0"
+jedi = ">=0.18.1"
+matplotlib-inline = ">=0.1.5"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
+prompt_toolkit = ">=3.0.41,<3.1.0"
+pygments = ">=2.11.0"
+stack_data = ">=0.6.0"
+traitlets = ">=5.13.0"
+typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
+
+[package.extras]
+all = ["ipython[doc,matplotlib,test,test-extra]"]
+black = ["black"]
+doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[matplotlib,test]", "setuptools (>=70.0)", "sphinx (>=8.0)", "sphinx-rtd-theme (>=0.1.8)", "sphinx_toml (==0.0.4)", "typing_extensions"]
+matplotlib = ["matplotlib (>3.9)"]
+test = ["packaging (>=20.1.0)", "pytest (>=7.0.0)", "pytest-asyncio (>=1.0.0)", "setuptools (>=61.2)", "testpath (>=0.2)"]
+test-extra = ["curio", "ipykernel (>6.30)", "ipython[matplotlib]", "ipython[test]", "jupyter_ai", "nbclient", "nbformat", "numpy (>=1.27)", "pandas (>2.1)", "trio (>=0.1.0)"]
+
+[[package]]
+name = "ipython-pygments-lexers"
+version = "1.1.1"
+description = "Defines a variety of Pygments lexers for highlighting IPython code."
+optional = false
+python-versions = ">=3.8"
+groups = ["main", "demo", "dev"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c"},
+    {file = "ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81"},
+]
+
+[package.dependencies]
+pygments = "*"
+
 [[package]]
 name = "isoduration"
 version = "20.11.0"
@@ -1294,7 +1532,7 @@ version = "3.1.6"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
-groups = ["main", "demo"]
+groups = ["main", "demo", "docs"]
 files = [
     {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"},
     {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"},
@@ -1308,26 +1546,26 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.5.1"
+version = "1.5.3"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "joblib-1.5.1-py3-none-any.whl", hash = "sha256:4719a31f054c7d766948dcd83e9613686b27114f190f717cec7eaa2084f8a74a"},
-    {file = "joblib-1.5.1.tar.gz", hash = "sha256:f4f86e351f39fe3d0d32a9f2c3d8af1ee4cec285aafcb27003dda5205576b444"},
+    {file = "joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713"},
+    {file = "joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3"},
 ]
 
 [[package]]
 name = "json5"
-version = "0.12.0"
+version = "0.12.1"
 description = "A Python implementation of the JSON5 data format."
 optional = false
 python-versions = ">=3.8.0"
 groups = ["main", "demo"]
 files = [
-    {file = "json5-0.12.0-py3-none-any.whl", hash = "sha256:6d37aa6c08b0609f16e1ec5ff94697e2cbbfbad5ac112afa05794da9ab7810db"},
-    {file = "json5-0.12.0.tar.gz", hash = "sha256:0b4b6ff56801a1c7dc817b0241bca4ce474a0e6a163bfef3fc594d3fd263ff3a"},
+    {file = "json5-0.12.1-py3-none-any.whl", hash = "sha256:d9c9b3bc34a5f54d43c35e11ef7cb87d8bdd098c6ace87117a7b7e83e705c1d5"},
+    {file = "json5-0.12.1.tar.gz", hash = "sha256:b2743e77b3242f8d03c143dd975a6ec7c52e2f2afe76ed934e53503dd4ad4990"},
 ]
 
 [package.extras]
@@ -1347,14 +1585,14 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.25.0"
+version = "4.25.1"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "jsonschema-4.25.0-py3-none-any.whl", hash = "sha256:24c2e8da302de79c8b9382fee3e76b355e44d2a4364bb207159ce10b517bd716"},
-    {file = "jsonschema-4.25.0.tar.gz", hash = "sha256:e63acf5c11762c0e6672ffb61482bdf57f0876684d8d249c0fe2d730d48bc55f"},
+    {file = "jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63"},
+    {file = "jsonschema-4.25.1.tar.gz", hash = "sha256:e4a9655ce0da0c0b67a085847e00a3a51449e1157f4f75e9fb5aa545e122eb85"},
 ]
 
 [package.dependencies]
@@ -1378,14 +1616,14 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2025.4.1"
+version = "2025.9.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "jsonschema_specifications-2025.4.1-py3-none-any.whl", hash = "sha256:4653bffbd6584f7de83a67e0d620ef16900b390ddc7939d56684d6c81e33f1af"},
-    {file = "jsonschema_specifications-2025.4.1.tar.gz", hash = "sha256:630159c9f4dbea161a6a2205c3011cc4f18ff381b189fff48bb39b9bf26ae608"},
+    {file = "jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe"},
+    {file = "jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d"},
 ]
 
 [package.dependencies]
@@ -1393,42 +1631,41 @@ referencing = ">=0.31.0"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.3"
+version = "8.7.0"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main", "demo"]
 files = [
-    {file = "jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f"},
-    {file = "jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419"},
+    {file = "jupyter_client-8.7.0-py3-none-any.whl", hash = "sha256:3671a94fd25e62f5f2f554f5e95389c2294d89822378a5f2dd24353e1494a9e0"},
+    {file = "jupyter_client-8.7.0.tar.gz", hash = "sha256:3357212d9cbe01209e59190f67a3a7e1f387a4f4e88d1e0433ad84d7b262531d"},
 ]
 
 [package.dependencies]
-jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+jupyter-core = ">=5.1"
 python-dateutil = ">=2.8.2"
-pyzmq = ">=23.0"
-tornado = ">=6.2"
+pyzmq = ">=25.0"
+tornado = ">=6.4.1"
 traitlets = ">=5.3"
 
 [package.extras]
 docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+test = ["anyio", "coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.6.2)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.8.1"
+version = "5.9.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 groups = ["main", "demo"]
 files = [
-    {file = "jupyter_core-5.8.1-py3-none-any.whl", hash = "sha256:c28d268fc90fb53f1338ded2eb410704c5449a358406e8a948b75706e24863d0"},
-    {file = "jupyter_core-5.8.1.tar.gz", hash = "sha256:0a5f9706f70e64786b75acba995988915ebd4601c8a52e534a40b51c95f59941"},
+    {file = "jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407"},
+    {file = "jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508"},
 ]
 
 [package.dependencies]
 platformdirs = ">=2.5"
-pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
 traitlets = ">=5.3"
 
 [package.extras]
@@ -1464,14 +1701,14 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.6"
+version = "2.3.0"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo"]
 files = [
-    {file = "jupyter_lsp-2.2.6-py3-none-any.whl", hash = "sha256:283783752bf0b459ee7fa88effa72104d87dd343b82d5c06cf113ef755b15b6d"},
-    {file = "jupyter_lsp-2.2.6.tar.gz", hash = "sha256:0566bd9bb04fd9e6774a937ed01522b555ba78be37bebef787c8ab22de4c0361"},
+    {file = "jupyter_lsp-2.3.0-py3-none-any.whl", hash = "sha256:e914a3cb2addf48b1c7710914771aaf1819d46b2e5a79b0f917b5478ec93f34f"},
+    {file = "jupyter_lsp-2.3.0.tar.gz", hash = "sha256:458aa59339dc868fb784d73364f17dbce8836e906cd75fd471a325cba02e0245"},
 ]
 
 [package.dependencies]
@@ -1479,14 +1716,14 @@ jupyter_server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.16.0"
+version = "2.17.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "jupyter_server-2.16.0-py3-none-any.whl", hash = "sha256:3d8db5be3bc64403b1c65b400a1d7f4647a5ce743f3b20dbdefe8ddb7b55af9e"},
-    {file = "jupyter_server-2.16.0.tar.gz", hash = "sha256:65d4b44fdf2dcbbdfe0aa1ace4a842d4aaf746a2b7b168134d5aaed35621b7f6"},
+    {file = "jupyter_server-2.17.0-py3-none-any.whl", hash = "sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f"},
+    {file = "jupyter_server-2.17.0.tar.gz", hash = "sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5"},
 ]
 
 [package.dependencies]
@@ -1499,7 +1736,7 @@ jupyter-events = ">=0.11.0"
 jupyter-server-terminals = ">=0.4.4"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
-overrides = ">=5.0"
+overrides = {version = ">=5.0", markers = "python_version < \"3.12\""}
 packaging = ">=22.0"
 prometheus-client = ">=0.9"
 pywinpty = {version = ">=2.0.1", markers = "os_name == \"nt\""}
@@ -1536,25 +1773,25 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.4.5"
+version = "4.5.1"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "jupyterlab-4.4.5-py3-none-any.whl", hash = "sha256:e76244cceb2d1fb4a99341f3edc866f2a13a9e14c50368d730d75d8017be0863"},
-    {file = "jupyterlab-4.4.5.tar.gz", hash = "sha256:0bd6c18e6a3c3d91388af6540afa3d0bb0b2e76287a7b88ddf20ab41b336e595"},
+    {file = "jupyterlab-4.5.1-py3-none-any.whl", hash = "sha256:31b059de96de0754ff1f2ce6279774b6aab8c34d7082e9752db58207c99bd514"},
+    {file = "jupyterlab-4.5.1.tar.gz", hash = "sha256:09da1ddfbd9eec18b5101dbb8515612aa1e47443321fb99503725a88e93d20d9"},
 ]
 
 [package.dependencies]
 async-lru = ">=1.0.0"
-httpx = ">=0.25.0"
-ipykernel = ">=6.5.0"
+httpx = ">=0.25.0,<1"
+ipykernel = ">=6.5.0,<6.30.0 || >6.30.0"
 jinja2 = ">=3.0.3"
 jupyter-core = "*"
 jupyter-lsp = ">=2.0.0"
 jupyter-server = ">=2.4.0,<3"
-jupyterlab-server = ">=2.27.1,<3"
+jupyterlab-server = ">=2.28.0,<3"
 notebook-shim = ">=0.2"
 packaging = "*"
 setuptools = ">=41.1.0"
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 traitlets = "*"
 
 [package.extras]
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+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.11.12)"]
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 [[package]]
 name = "lark"
-version = "1.2.2"
+version = "1.3.1"
 description = "a modern parsing library"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo"]
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 ]
 
 [package.extras]
@@ -1715,119 +1973,271 @@ interegular = ["interegular (>=0.3.1,<0.4.0)"]
 nearley = ["js2py"]
 regex = ["regex"]
 
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 ]
 
 [package.dependencies]
@@ -1838,7 +2248,7 @@ kiwisolver = ">=1.3.1"
 numpy = ">=1.23"
 packaging = ">=20.0"
 pillow = ">=8"
-pyparsing = ">=2.3.1"
+pyparsing = ">=3"
 python-dateutil = ">=2.7"
 
 [package.extras]
@@ -1846,19 +2256,22 @@ dev = ["meson-python (>=0.13.1,<0.17.0)", "pybind11 (>=2.13.2,!=2.13.3)", "setup
 
 [[package]]
 name = "matplotlib-inline"
-version = "0.1.7"
+version = "0.2.1"
 description = "Inline Matplotlib backend for Jupyter"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main", "demo", "dev"]
 files = [
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 ]
 
 [package.dependencies]
 traitlets = "*"
 
+[package.extras]
+test = ["flake8", "nbdime", "nbval", "notebook", "pytest"]
+
 [[package]]
 name = "mccabe"
 version = "0.7.0"
@@ -1871,133 +2284,307 @@ files = [
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 ]
 
+[[package]]
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+version = "1.3.4"
+description = "A deep merge function for 🐍."
+optional = false
+python-versions = ">=3.6"
+groups = ["docs"]
+files = [
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+]
+
 [[package]]
 name = "mistune"
-version = "3.1.3"
+version = "3.2.0"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo"]
 files = [
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 ]
 
 [package.dependencies]
 typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
 
+[[package]]
+name = "mkdocs"
+version = "1.6.1"
+description = "Project documentation with Markdown."
+optional = false
+python-versions = ">=3.8"
+groups = ["docs"]
+files = [
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+pathspec = ">=0.11.1"
+pyyaml = ">=5.1"
+pyyaml-env-tag = ">=0.1"
+watchdog = ">=2.0"
+
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+
+[[package]]
+name = "mkdocs-autorefs"
+version = "1.3.1"
+description = "Automatically link across pages in MkDocs."
+optional = false
+python-versions = ">=3.9"
+groups = ["docs"]
+files = [
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+markupsafe = ">=2.0.1"
+mkdocs = ">=1.1"
+
+[[package]]
+name = "mkdocs-get-deps"
+version = "0.2.0"
+description = "MkDocs extension that lists all dependencies according to a mkdocs.yml file"
+optional = false
+python-versions = ">=3.8"
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 [package.dependencies]
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 mypy_extensions = ">=1.0.0"
 pathspec = ">=0.9.0"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
@@ -2024,20 +2611,20 @@ files = [
 
 [[package]]
 name = "narwhals"
-version = "1.48.0"
+version = "2.14.0"
 description = "Extremely lightweight compatibility layer between dataframe libraries"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
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 [package.extras]
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 dask = ["dask[dataframe] (>=2024.8)"]
-duckdb = ["duckdb (>=1.0)"]
+duckdb = ["duckdb (>=1.1)"]
 ibis = ["ibis-framework (>=6.0.0)", "packaging", "pyarrow-hotfix", "rich"]
 modin = ["modin"]
 pandas = ["pandas (>=1.1.3)"]
@@ -2045,30 +2632,30 @@ polars = ["polars (>=0.20.4)"]
 pyarrow = ["pyarrow (>=13.0.0)"]
 pyspark = ["pyspark (>=3.5.0)"]
 pyspark-connect = ["pyspark[connect] (>=3.5.0)"]
-sqlframe = ["sqlframe (>=3.22.0)"]
+sqlframe = ["sqlframe (>=3.22.0,!=3.39.3)"]
 
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+version = "0.10.4"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 optional = false
-python-versions = ">=3.9.0"
+python-versions = ">=3.10.0"
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 traitlets = ">=5.4"
 
 [package.extras]
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 [[package]]
 name = "nbconvert"
@@ -2166,6 +2753,7 @@ description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.10"
 groups = ["main", "demo"]
+markers = "python_version == \"3.10\""
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 ]
 
+[[package]]
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+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.11"
+groups = ["main", "demo", "dev"]
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 [package.dependencies]
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+numpy-typing-compat = {version = ">=20250818.1.25,<20251207", optional = true, markers = "extra == \"numpy\""}
 typing-extensions = {version = ">=4.10", markers = "python_full_version < \"3.13.0\""}
 
 [package.extras]
-numpy = ["numpy (>=1.25)"]
+numpy = ["numpy (>=1.25)", "numpy-typing-compat (>=20250818.1.25,<20251207)"]
 
 [[package]]
 name = "overrides"
@@ -2250,6 +2939,7 @@ description = "A decorator to automatically detect mismatch when overriding a me
 optional = false
 python-versions = ">=3.6"
 groups = ["main", "demo"]
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 optional = false
 python-versions = ">=3.8"
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 python-versions = ">=3.8"
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+    {file = "plotly-6.5.0-py3-none-any.whl", hash = "sha256:5ac851e100367735250206788a2b1325412aa4a4917a4fe3e6f0bc5aa6f3d90a"},
+    {file = "plotly-6.5.0.tar.gz", hash = "sha256:d5d38224883fd38c1409bef7d6a8dc32b74348d39313f3c52ca998b8e447f5c8"},
 ]
 
 [package.dependencies]
@@ -2598,7 +3301,7 @@ dev-build = ["build", "jupyter", "plotly[dev-core]"]
 dev-core = ["pytest", "requests", "ruff (==0.11.12)"]
 dev-optional = ["anywidget", "colorcet", "fiona (<=1.9.6) ; python_version <= \"3.8\"", "geopandas", "inflect", "numpy", "orjson", "pandas", "pdfrw", "pillow", "plotly-geo", "plotly[dev-build]", "plotly[kaleido]", "polars[timezone]", "pyarrow", "pyshp", "pytz", "scikit-image", "scipy", "shapely", "statsmodels", "vaex ; python_version <= \"3.9\"", "xarray"]
 express = ["numpy"]
-kaleido = ["kaleido (>=1.0.0)"]
+kaleido = ["kaleido (>=1.1.0)"]
 
 [[package]]
 name = "pluggy"
@@ -2640,14 +3343,14 @@ xxhash = ["xxhash (>=1.4.3)"]
 
 [[package]]
 name = "prometheus-client"
-version = "0.22.1"
+version = "0.23.1"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "prometheus_client-0.22.1-py3-none-any.whl", hash = "sha256:cca895342e308174341b2cbf99a56bef291fbc0ef7b9e5412a0f26d653ba7094"},
-    {file = "prometheus_client-0.22.1.tar.gz", hash = "sha256:190f1331e783cf21eb60bca559354e0a4d4378facecf78f5428c39b675d20d28"},
+    {file = "prometheus_client-0.23.1-py3-none-any.whl", hash = "sha256:dd1913e6e76b59cfe44e7a4b83e01afc9873c1bdfd2ed8739f1e76aeca115f99"},
+    {file = "prometheus_client-0.23.1.tar.gz", hash = "sha256:6ae8f9081eaaaf153a2e959d2e6c4f4fb57b12ef76c8c7980202f1e57b48b2ce"},
 ]
 
 [package.extras]
@@ -2655,14 +3358,14 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.51"
+version = "3.0.52"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo", "dev"]
 files = [
-    {file = "prompt_toolkit-3.0.51-py3-none-any.whl", hash = "sha256:52742911fde84e2d423e2f9a4cf1de7d7ac4e51958f648d9540e0fb8db077b07"},
-    {file = "prompt_toolkit-3.0.51.tar.gz", hash = "sha256:931a162e3b27fc90c86f1b48bb1fb2c528c2761475e57c9c06de13311c7b54ed"},
+    {file = "prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955"},
+    {file = "prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855"},
 ]
 
 [package.dependencies]
@@ -2670,27 +3373,38 @@ wcwidth = "*"
 
 [[package]]
 name = "psutil"
-version = "7.0.0"
-description = "Cross-platform lib for process and system monitoring in Python.  NOTE: the syntax of this script MUST be kept compatible with Python 2.7."
+version = "7.2.1"
+description = "Cross-platform lib for process and system monitoring."
 optional = false
 python-versions = ">=3.6"
 groups = ["main", "demo"]
 files = [
-    {file = "psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25"},
-    {file = "psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da"},
-    {file = "psutil-7.0.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fcee592b4c6f146991ca55919ea3d1f8926497a713ed7faaf8225e174581e91"},
-    {file = "psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b1388a4f6875d7e2aff5c4ca1cc16c545ed41dd8bb596cefea80111db353a34"},
-    {file = "psutil-7.0.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5f098451abc2828f7dc6b58d44b532b22f2088f4999a937557b603ce72b1993"},
-    {file = "psutil-7.0.0-cp36-cp36m-win32.whl", hash = "sha256:84df4eb63e16849689f76b1ffcb36db7b8de703d1bc1fe41773db487621b6c17"},
-    {file = "psutil-7.0.0-cp36-cp36m-win_amd64.whl", hash = "sha256:1e744154a6580bc968a0195fd25e80432d3afec619daf145b9e5ba16cc1d688e"},
-    {file = "psutil-7.0.0-cp37-abi3-win32.whl", hash = "sha256:ba3fcef7523064a6c9da440fc4d6bd07da93ac726b5733c29027d7dc95b39d99"},
-    {file = "psutil-7.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:4cf3d4eb1aa9b348dec30105c55cd9b7d4629285735a102beb4441e38db90553"},
-    {file = "psutil-7.0.0.tar.gz", hash = "sha256:7be9c3eba38beccb6495ea33afd982a44074b78f28c434a1f51cc07fd315c456"},
+    {file = "psutil-7.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:ba9f33bb525b14c3ea563b2fd521a84d2fa214ec59e3e6a2858f78d0844dd60d"},
+    {file = "psutil-7.2.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:81442dac7abfc2f4f4385ea9e12ddf5a796721c0f6133260687fec5c3780fa49"},
+    {file = "psutil-7.2.1-cp313-cp313t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ea46c0d060491051d39f0d2cff4f98d5c72b288289f57a21556cc7d504db37fc"},
+    {file = "psutil-7.2.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:35630d5af80d5d0d49cfc4d64c1c13838baf6717a13effb35869a5919b854cdf"},
+    {file = "psutil-7.2.1-cp313-cp313t-win_amd64.whl", hash = "sha256:923f8653416604e356073e6e0bccbe7c09990acef442def2f5640dd0faa9689f"},
+    {file = "psutil-7.2.1-cp313-cp313t-win_arm64.whl", hash = "sha256:cfbe6b40ca48019a51827f20d830887b3107a74a79b01ceb8cc8de4ccb17b672"},
+    {file = "psutil-7.2.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:494c513ccc53225ae23eec7fe6e1482f1b8a44674241b54561f755a898650679"},
+    {file = "psutil-7.2.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3fce5f92c22b00cdefd1645aa58ab4877a01679e901555067b1bd77039aa589f"},
+    {file = "psutil-7.2.1-cp314-cp314t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:93f3f7b0bb07711b49626e7940d6fe52aa9940ad86e8f7e74842e73189712129"},
+    {file = "psutil-7.2.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d34d2ca888208eea2b5c68186841336a7f5e0b990edec929be909353a202768a"},
+    {file = "psutil-7.2.1-cp314-cp314t-win_amd64.whl", hash = "sha256:2ceae842a78d1603753561132d5ad1b2f8a7979cb0c283f5b52fb4e6e14b1a79"},
+    {file = "psutil-7.2.1-cp314-cp314t-win_arm64.whl", hash = "sha256:08a2f175e48a898c8eb8eace45ce01777f4785bc744c90aa2cc7f2fa5462a266"},
+    {file = "psutil-7.2.1-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:b2e953fcfaedcfbc952b44744f22d16575d3aa78eb4f51ae74165b4e96e55f42"},
+    {file = "psutil-7.2.1-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:05cc68dbb8c174828624062e73078e7e35406f4ca2d0866c272c2410d8ef06d1"},
+    {file = "psutil-7.2.1-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5e38404ca2bb30ed7267a46c02f06ff842e92da3bb8c5bfdadbd35a5722314d8"},
+    {file = "psutil-7.2.1-cp36-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ab2b98c9fc19f13f59628d94df5cc4cc4844bc572467d113a8b517d634e362c6"},
+    {file = "psutil-7.2.1-cp36-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:f78baafb38436d5a128f837fab2d92c276dfb48af01a240b861ae02b2413ada8"},
+    {file = "psutil-7.2.1-cp36-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:99a4cd17a5fdd1f3d014396502daa70b5ec21bf4ffe38393e152f8e449757d67"},
+    {file = "psutil-7.2.1-cp37-abi3-win_amd64.whl", hash = "sha256:b1b0671619343aa71c20ff9767eced0483e4fc9e1f489d50923738caf6a03c17"},
+    {file = "psutil-7.2.1-cp37-abi3-win_arm64.whl", hash = "sha256:0d67c1822c355aa6f7314d92018fb4268a76668a536f133599b91edd48759442"},
+    {file = "psutil-7.2.1.tar.gz", hash = "sha256:f7583aec590485b43ca601dd9cea0dcd65bd7bb21d30ef4ddbf4ea6b5ed1bdd3"},
 ]
 
 [package.extras]
-dev = ["abi3audit", "black (==24.10.0)", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest", "pytest-cov", "pytest-xdist", "requests", "rstcheck", "ruff", "setuptools", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "vulture", "wheel"]
-test = ["pytest", "pytest-xdist", "setuptools"]
+dev = ["abi3audit", "black", "check-manifest", "coverage", "packaging", "psleak", "pylint", "pyperf", "pypinfo", "pytest", "pytest-cov", "pytest-instafail", "pytest-xdist", "requests", "rstcheck", "ruff", "setuptools", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "validate-pyproject[all]", "virtualenv", "vulture", "wheel"]
+test = ["psleak", "pytest", "pytest-instafail", "pytest-xdist", "setuptools"]
 
 [[package]]
 name = "ptyprocess"
@@ -2707,19 +3421,26 @@ markers = {main = "sys_platform != \"win32\" and sys_platform != \"emscripten\"
 
 [[package]]
 name = "pulp"
-version = "3.2.1"
+version = "3.3.0"
 description = "PuLP is an LP modeler written in python. PuLP can generate MPS or LP files and call GLPK, COIN CLP/CBC, CPLEX, and GUROBI to solve linear problems."
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "pulp-3.2.1-py3-none-any.whl", hash = "sha256:c6cf7fe84cef15795bc7c27e2f3c6784db5cf6ebf68e94d5a659b02415f982c5"},
-    {file = "pulp-3.2.1.tar.gz", hash = "sha256:fc6c02c47c06342c586b175924add753cad7638ff6149b3b43e87ac6709ac469"},
+    {file = "pulp-3.3.0-py3-none-any.whl", hash = "sha256:dd6ad2d63f196d1254eddf9dcff5cd224912c1f046120cb7c143c5b0eda63fae"},
+    {file = "pulp-3.3.0.tar.gz", hash = "sha256:7eb99b9ce7beeb8bbb7ea9d1c919f02f003ab7867e0d1e322f2f2c26dd31c8ba"},
 ]
 
 [package.extras]
+copt = ["coptpy"]
+cplex = ["cplex ; sys_platform != \"darwin\" or python_full_version < \"3.12.0\""]
+gurobi = ["gurobipy"]
+highs = ["highspy"]
+mosek = ["mosek"]
 open-py = ["cylp", "highspy", "pyscipopt"]
-public-py = ["coptpy", "gurobipy", "xpress"]
+public-py = ["coptpy", "cplex ; sys_platform != \"darwin\" or python_full_version < \"3.12.0\"", "gurobipy", "xpress"]
+scip = ["pyscipopt"]
+xpress = ["xpress"]
 
 [[package]]
 name = "pure-eval"
@@ -2750,14 +3471,15 @@ files = [
 
 [[package]]
 name = "pycparser"
-version = "2.22"
+version = "2.23"
 description = "C parser in Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo"]
+markers = "implementation_name != \"PyPy\""
 files = [
-    {file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"},
-    {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
+    {file = "pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934"},
+    {file = "pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2"},
 ]
 
 [[package]]
@@ -2778,7 +3500,7 @@ version = "2.19.2"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "demo", "dev"]
+groups = ["main", "demo", "dev", "docs"]
 files = [
     {file = "pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b"},
     {file = "pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887"},
@@ -2787,16 +3509,35 @@ files = [
 [package.extras]
 windows-terminal = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "pymdown-extensions"
+version = "10.20"
+description = "Extension pack for Python Markdown."
+optional = false
+python-versions = ">=3.9"
+groups = ["docs"]
+files = [
+    {file = "pymdown_extensions-10.20-py3-none-any.whl", hash = "sha256:ea9e62add865da80a271d00bfa1c0fa085b20d133fb3fc97afdc88e682f60b2f"},
+    {file = "pymdown_extensions-10.20.tar.gz", hash = "sha256:5c73566ab0cf38c6ba084cb7c5ea64a119ae0500cce754ccb682761dfea13a52"},
+]
+
+[package.dependencies]
+markdown = ">=3.6"
+pyyaml = "*"
+
+[package.extras]
+extra = ["pygments (>=2.19.1)"]
+
 [[package]]
 name = "pyparsing"
-version = "3.2.3"
-description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+version = "3.3.1"
+description = "pyparsing - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
-    {file = "pyparsing-3.2.3-py3-none-any.whl", hash = "sha256:a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf"},
-    {file = "pyparsing-3.2.3.tar.gz", hash = "sha256:b9c13f1ab8b3b542f72e28f634bad4de758ab3ce4546e4301970ad6fa77c38be"},
+    {file = "pyparsing-3.3.1-py3-none-any.whl", hash = "sha256:023b5e7e5520ad96642e2c6db4cb683d3970bd640cdf7115049a6e9c3682df82"},
+    {file = "pyparsing-3.3.1.tar.gz", hash = "sha256:47fad0f17ac1e2cad3de3b458570fbc9b03560aa029ed5e16ee5554da9a2251c"},
 ]
 
 [package.extras]
@@ -2804,14 +3545,14 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "8.4.1"
+version = "8.4.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "pytest-8.4.1-py3-none-any.whl", hash = "sha256:539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7"},
-    {file = "pytest-8.4.1.tar.gz", hash = "sha256:7c67fd69174877359ed9371ec3af8a3d2b04741818c51e5e99cc1742251fa93c"},
+    {file = "pytest-8.4.2-py3-none-any.whl", hash = "sha256:872f880de3fc3a5bdc88a11b39c9710c3497a547cfa9320bc3c5e62fbf272e79"},
+    {file = "pytest-8.4.2.tar.gz", hash = "sha256:86c0d0b93306b961d58d62a4db4879f27fe25513d4b969df351abdddb3c30e01"},
 ]
 
 [package.dependencies]
@@ -2852,7 +3593,7 @@ version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
-groups = ["main", "demo"]
+groups = ["main", "demo", "docs"]
 files = [
     {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
     {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
@@ -2863,19 +3604,34 @@ six = ">=1.5"
 
 [[package]]
 name = "python-json-logger"
-version = "3.3.0"
+version = "4.0.0"
 description = "JSON Log Formatter for the Python Logging Package"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo"]
 files = [
-    {file = "python_json_logger-3.3.0-py3-none-any.whl", hash = "sha256:dd980fae8cffb24c13caf6e158d3d61c0d6d22342f932cb6e9deedab3d35eec7"},
-    {file = "python_json_logger-3.3.0.tar.gz", hash = "sha256:12b7e74b17775e7d565129296105bbe3910842d9d0eb083fc83a6a617aa8df84"},
+    {file = "python_json_logger-4.0.0-py3-none-any.whl", hash = "sha256:af09c9daf6a813aa4cc7180395f50f2a9e5fa056034c9953aec92e381c5ba1e2"},
+    {file = "python_json_logger-4.0.0.tar.gz", hash = "sha256:f58e68eb46e1faed27e0f574a55a0455eecd7b8a5b88b85a784519ba3cff047f"},
 ]
 
 [package.extras]
 dev = ["backports.zoneinfo ; python_version < \"3.9\"", "black", "build", "freezegun", "mdx_truly_sane_lists", "mike", "mkdocs", "mkdocs-awesome-pages-plugin", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-material (>=8.5)", "mkdocstrings[python]", "msgspec ; implementation_name != \"pypy\"", "mypy", "orjson ; implementation_name != \"pypy\"", "pylint", "pytest", "tzdata", "validate-pyproject[all]"]
 
+[[package]]
+name = "pytokens"
+version = "0.3.0"
+description = "A Fast, spec compliant Python 3.14+ tokenizer that runs on older Pythons."
+optional = false
+python-versions = ">=3.8"
+groups = ["dev"]
+files = [
+    {file = "pytokens-0.3.0-py3-none-any.whl", hash = "sha256:95b2b5eaf832e469d141a378872480ede3f251a5a5041b8ec6e581d3ac71bbf3"},
+    {file = "pytokens-0.3.0.tar.gz", hash = "sha256:2f932b14ed08de5fcf0b391ace2642f858f1394c0857202959000b68ed7a458a"},
+]
+
+[package.extras]
+dev = ["black", "build", "mypy", "pytest", "pytest-cov", "setuptools", "tox", "twine", "wheel"]
+
 [[package]]
 name = "pytz"
 version = "2025.2"
@@ -2890,14 +3646,14 @@ files = [
 
 [[package]]
 name = "pyvista"
-version = "0.45.3"
+version = "0.46.4"
 description = "Easier Pythonic interface to VTK"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "demo"]
 files = [
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 pillow = "*"
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-typing-extensions = "*"
-vtk = "<9.4.0 || >9.4.0,<9.4.1 || >9.4.1,<9.5.0"
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+vtk = "<9.4.0 || >9.4.0,<9.4.1 || >9.4.1,<9.6.0"
 
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 all = ["pyvista[colormaps,io,jupyter]"]
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+colormaps = ["cmcrameri", "cmocean", "colorcet"]
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 ]
 
 [package.dependencies]
@@ -3270,198 +4061,175 @@ scikit-learn = "*"
 scipy = "*"
 
 [package.extras]
-docs = ["sktda_docs_config", "sphinx (<8.2.0)"]
-examples = ["jupyter", "pillow", "tadasets"]
+docs = ["jupyter", "pillow", "sktda_docs_config", "sphinx (<8.2.0)"]
+ruff = ["ruff (<0.12.0)"]
 testing = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "rpds-py"
-version = "0.26.0"
+version = "0.30.0"
 description = "Python bindings to Rust's persistent data structures (rpds)"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["main", "demo"]
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 maintenance = ["conda-lock (==3.0.1)"]
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+[[package]]
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 [package.extras]
-scipy = ["scipy (>=1.16.0,<1.17)"]
+scipy = ["scipy (>=1.17.0rc2,<1.18)"]
 
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 name = "scooby"
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+version = "0.11.0"
 description = "A Great Dane turned Python environment detective"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo"]
 files = [
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@@ -3601,14 +4513,14 @@ stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
 [[package]]
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-version = "1.8.3"
+version = "2.0.0"
 description = "Send file to trash natively under Mac OS X, Windows and Linux"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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 groups = ["main", "demo"]
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 description = "Python 2 and 3 compatibility utilities"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
-groups = ["main", "demo"]
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 files = [
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 [[package]]
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 description = "A modern CSS selector implementation for Beautiful Soup."
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+python-versions = ">=3.9"
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 [[package]]
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 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "demo", "dev"]
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 test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
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-
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 python-versions = ">=3.9"
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 ]
 
 [[package]]
 name = "tzdata"
-version = "2025.2"
+version = "2025.3"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 groups = ["main", "demo"]
 files = [
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 ]
 
 [[package]]
@@ -3914,53 +4812,59 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
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+version = "2.6.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "demo"]
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 [package.extras]
-brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
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 h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
-zstd = ["zstandard (>=0.18.0)"]
+zstd = ["backports-zstd (>=1.0.0) ; python_version < \"3.14\""]
 
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@@ -3970,28 +4874,71 @@ matplotlib = ">=2.0.0"
 numpy = ["numpy (>=1.9)"]
 web = ["wslink (>=1.0.4)"]
 
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 optional = false
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 [[package]]
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 description = "WebSocket client for Python with low level API options"
 optional = false
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 [package.extras]
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 optional = ["python-socks", "wsaccel"]
-test = ["websockets"]
+test = ["pytest", "websockets"]
 
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+    {file = "wrapt-2.0.1.tar.gz", hash = "sha256:9c9c635e78497cacb81e84f8b11b23e0aacac7a136e73b8e5b2109a1d9fc468f"},
 ]
 
+[package.extras]
+dev = ["pytest", "setuptools"]
+
 [extras]
 demo = ["homcloud", "ipykernel", "jupyterlab", "nbformat", "pandas", "plotly", "seaborn", "tqdm"]
 
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10"
-content-hash = "fcba08f8a6a29bfb3fd876e3a72862170aee8ab67838ba2a3d87a91a997d024c"
+content-hash = "f48e31a467c31ba9bc25de096029f851d7cb1a3a51e100ca6d75842c9c8a6950"
diff --git a/pyproject.toml b/pyproject.toml
index 2770e85..353e61c 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ellphi"
-version = "0.1.1.post1"
+version = "0.1.2"
 description = "Fast ellipse tangency solver"
 authors = [
   { name = "Tomoki Uda", email = "tomoki.uda@gmail.com" }
@@ -95,6 +95,15 @@ homcloud = "^4.8.0"
 tqdm = "^4.67.1"
 seaborn = "^0.13"
 
+[tool.poetry.group.docs]
+optional = true
+
+[tool.poetry.group.docs.dependencies]
+mkdocs = "^1.6.0"
+mkdocs-material = "^9.5.0"
+mkdocstrings = {extras = ["python"], version = "0.25.0"}
+mkdocs-autorefs = "1.3.1"
+
 [tool.mypy]
 mypy_path = "src"
 
diff --git a/scripts/benchmark_dim_scale.py b/scripts/benchmark_dim_scale.py
new file mode 100644
index 0000000..4c1b4fa
--- /dev/null
+++ b/scripts/benchmark_dim_scale.py
@@ -0,0 +1,66 @@
+import time
+import numpy as np
+import ellphi
+import sys
+
+
+def generate_random_ellipse(dim):
+    # Random center
+    center = np.random.rand(dim) * 10.0
+
+    # Random positive definite matrix
+    X = np.random.rand(dim, dim)
+    cov = X @ X.T + np.eye(dim) * 0.1
+
+    # Convert to coefficients
+    # coef_from_cov returns (n, m) array
+    # We pass a list of centers and list of covs (length 1)
+    coefs = ellphi.coef_from_cov([center], [cov])
+    return coefs[0]
+
+
+def benchmark():
+    dims = [2, 3, 5, 8, 10, 15, 20, 30, 40, 50, 60, 80, 100]
+    n_trials = 20  # Number of different ellipse pairs to test per dimension
+    n_repeats = 20  # Number of repeats for timing per pair
+
+    print("dim,backend,avg_time_ms")
+
+    for dim in dims:
+        # Pre-generate ellipse pairs to avoid measuring generation time
+        pairs = []
+        for _ in range(n_trials):
+            p = generate_random_ellipse(dim)
+            q = generate_random_ellipse(dim)
+            pairs.append((p, q))
+
+        # Benchmark Python backend
+        py_times = []
+        for p, q in pairs:
+            start = time.perf_counter()
+            for _ in range(n_repeats):
+                ellphi.tangency(p, q, backend="python")
+            end = time.perf_counter()
+            py_times.append((end - start) / n_repeats)
+        avg_py = np.mean(py_times) * 1000.0
+        print(f"{dim},python,{avg_py:.4f}")
+
+        # Benchmark C++ backend
+        if ellphi.has_cpp_backend():
+            cpp_times = []
+            for p, q in pairs:
+                start = time.perf_counter()
+                for _ in range(n_repeats):
+                    ellphi.tangency(p, q, backend="cpp")
+                end = time.perf_counter()
+                cpp_times.append((end - start) / n_repeats)
+            avg_cpp = np.mean(cpp_times) * 1000.0
+            print(f"{dim},cpp,{avg_cpp:.4f}")
+        else:
+            print(f"{dim},cpp,NaN")
+
+        sys.stdout.flush()
+
+
+if __name__ == "__main__":
+    benchmark()
diff --git a/scripts/build_tangency_cpp.py b/scripts/build_tangency_cpp.py
index dbb403c..8f2f0f0 100644
--- a/scripts/build_tangency_cpp.py
+++ b/scripts/build_tangency_cpp.py
@@ -4,7 +4,9 @@
 from __future__ import annotations
 
 import ctypes
+import os
 import re
+import shlex
 import sys
 import sysconfig
 from pathlib import Path
@@ -19,6 +21,23 @@
 _LIB_NAME = "_tangency_cpp_impl"
 
 
+def _use_eigen() -> bool:
+    value = os.getenv("ELLPHI_USE_EIGEN", "")
+    return value.lower() in {"1", "true", "yes", "on"}
+
+
+def _eigen_include_dirs() -> list[str]:
+    raw = os.getenv("ELLPHI_EIGEN_INCLUDE", "")
+    if raw:
+        return shlex.split(raw)
+    candidates = [
+        "/usr/include/eigen3",
+        "/usr/local/include/eigen3",
+        "/opt/homebrew/include/eigen3",
+    ]
+    return [path for path in candidates if Path(path).exists()]
+
+
 def _library_suffix() -> str:
     suffix = sysconfig.get_config_var("SHLIB_SUFFIX")
     if suffix:
@@ -88,6 +107,24 @@ def _existing_library_version(path: Path) -> str | None:
     return value.decode("utf-8", errors="replace")
 
 
+def _existing_library_linalg_kind(path: Path) -> str | None:
+    try:
+        lib = ctypes.CDLL(str(path))
+    except OSError:
+        return None
+
+    try:
+        func = lib.tangency_linalg_kind
+    except AttributeError:
+        return None
+
+    func.restype = ctypes.c_char_p
+    value = func()
+    if not value:
+        return None
+    return value.decode("utf-8", errors="replace")
+
+
 def _ensure_parent(path: Path) -> None:
     path.parent.mkdir(parents=True, exist_ok=True)
 
@@ -95,11 +132,24 @@ def _ensure_parent(path: Path) -> None:
 class _TangencyExtension(Extension):
     def __init__(self) -> None:
         version = _project_version()
+        define_macros = [("TANGENCY_VERSION", f'"{version}"')]
+        extra_link_args: list[str] = []
+        include_dirs: list[str] = []
+        if _use_eigen():
+            define_macros.append(("ELLPHI_USE_EIGEN", "1"))
+            include_dirs = _eigen_include_dirs()
+            if not include_dirs:
+                raise RuntimeError(
+                    "ELLPHI_USE_EIGEN is set but no Eigen include path was found. "
+                    "Set ELLPHI_EIGEN_INCLUDE to the Eigen headers."
+                )
         super().__init__(
             name=_LIB_NAME,
             sources=[str(_source_path())],
             language="c++",
-            define_macros=[("TANGENCY_VERSION", f'"{version}"')],
+            define_macros=define_macros,
+            extra_link_args=extra_link_args,
+            include_dirs=include_dirs,
         )
 
 
@@ -121,6 +171,10 @@ def build_extension(self, ext: Extension) -> None:  # type: ignore[override]
         ctype = compiler.compiler_type
         compile_opts = list(self.c_opts.get(ctype, []))
         link_opts = list(self.l_opts.get(ctype, []))
+        if getattr(ext, "extra_compile_args", None):
+            compile_opts.extend(ext.extra_compile_args)
+        if getattr(ext, "extra_link_args", None):
+            link_opts.extend(ext.extra_link_args)
 
         objects = compiler.compile(
             ext.sources,  # type: ignore[arg-type]
@@ -160,10 +214,13 @@ def build() -> Path:
         raise FileNotFoundError(f"C++ source not found: {source}")
 
     existing_version = _existing_library_version(output)
+    expected_kind = "eigen" if _use_eigen() else "internal"
+    existing_kind = _existing_library_linalg_kind(output)
     if (
         output.exists()
         and output.stat().st_mtime >= source.stat().st_mtime
         and existing_version == project_version
+        and existing_kind == expected_kind
     ):
         return output
 
diff --git a/scripts/hybrid_tuning.py b/scripts/hybrid_tuning.py
index 4425c4c..e772f18 100644
--- a/scripts/hybrid_tuning.py
+++ b/scripts/hybrid_tuning.py
@@ -2,7 +2,7 @@
 """Benchmark tangency backends/methods to tune the hybrid solver.
 
 The script generates a mix of well-conditioned and highly eccentric
-ellipsoids, calls :func:`ellphi.solver.tangency` with different
+ellipsoids, calls `ellphi.solver.tangency` with different
 backends/methods, and reports aggregated + per-dimension timing/accuracy
 statistics (markdown table by default).  The output guides the selection
 of iteration budgets that generalise beyond 2D while remaining faster
diff --git a/src/ellphi/__init__.py b/src/ellphi/__init__.py
index 53e7166..9a58db7 100644
--- a/src/ellphi/__init__.py
+++ b/src/ellphi/__init__.py
@@ -38,9 +38,14 @@
     pdist_tangency,
     TangencyResult,
     has_cpp_backend,
+    cpp_linalg_kind,
+    BuildInfo,
+    build_info,
 )
 
 
+from .grad import TangencyGrad, tangency_grad, pdist_tangency_grad, coef_from_cov_grad
+
 FloatArray = NDArray[np.float64]
 
 
@@ -62,19 +67,27 @@
     "pdist_tangency",
     "TangencyResult",
     "has_cpp_backend",
+    "cpp_linalg_kind",
+    "BuildInfo",
+    "build_info",
+    # grad
+    "TangencyGrad",
+    "tangency_grad",
+    "pdist_tangency_grad",
+    "coef_from_cov_grad",
     "__version__",
     "version_info",
 ]
 
 
 def version_info() -> str:
-    """Return the current :mod:`ellphi` version string."""
+    """Return the current `ellphi` version string."""
 
     return __version__
 
 
 def _main() -> None:
-    """A minimal CLI for printing the current version."""
+    """A minimal CLI for printing the current version and build info."""
 
     import argparse
 
@@ -84,10 +97,17 @@ def _main() -> None:
         action="store_true",
         help="Print the installed ellphi version and exit",
     )
+    parser.add_argument(
+        "--build-info",
+        action="store_true",
+        help="Print the ellphi build info and exit",
+    )
 
     args = parser.parse_args()
 
     if args.version:
         print(version_info())
-    else:
+    if args.build_info:
+        print(build_info())
+    if not args.version and not args.build_info:
         parser.print_help()
diff --git a/src/ellphi/__init__.pyi b/src/ellphi/__init__.pyi
index 5411710..d98f65c 100644
--- a/src/ellphi/__init__.pyi
+++ b/src/ellphi/__init__.pyi
@@ -15,13 +15,22 @@ from .geometry import (
     unit_vector as unit_vector,
 )
 from .solver import (
+    BuildInfo as BuildInfo,
     TangencyResult as TangencyResult,
+    cpp_linalg_kind as cpp_linalg_kind,
+    build_info as build_info,
     has_cpp_backend as has_cpp_backend,
     pdist_tangency as pdist_tangency,
     pencil as pencil,
     quad_eval as quad_eval,
     tangency as tangency,
 )
+from .grad import (
+    TangencyGrad as TangencyGrad,
+    tangency_grad as tangency_grad,
+    pdist_tangency_grad as pdist_tangency_grad,
+    coef_from_cov_grad as coef_from_cov_grad,
+)
 from ._version import __version__ as __version__
 
 def version_info() -> str: ...
@@ -41,6 +50,13 @@ __all__ = [
     "pdist_tangency",
     "TangencyResult",
     "has_cpp_backend",
+    "cpp_linalg_kind",
+    "BuildInfo",
+    "build_info",
+    "TangencyGrad",
+    "tangency_grad",
+    "pdist_tangency_grad",
+    "coef_from_cov_grad",
     "FloatArray",
     "__version__",
     "version_info",
diff --git a/src/ellphi/__main__.py b/src/ellphi/__main__.py
index 26ac131..d2d3393 100644
--- a/src/ellphi/__main__.py
+++ b/src/ellphi/__main__.py
@@ -1,7 +1,7 @@
-"""Command-line entry point for :mod:`ellphi`.
+"""Command-line entry point for `ellphi`.
 
 Allows executing ``python -m ellphi`` to access the minimal CLI that exposes
-version information.
+version/build information.
 """
 
 from . import _main
diff --git a/src/ellphi/_solver_python.py b/src/ellphi/_solver_python.py
index befed31..6ffab85 100644
--- a/src/ellphi/_solver_python.py
+++ b/src/ellphi/_solver_python.py
@@ -11,6 +11,7 @@
     Callable,
     Iterator,
     Literal,
+    NamedTuple,
     Tuple,
     cast,
 )
@@ -110,7 +111,7 @@ def _gaussian_elimination(matrix: numpy.ndarray, rhs: numpy.ndarray) -> numpy.nd
     x = numpy.zeros(dim, dtype=float)
     for i in range(dim - 1, -1, -1):
         diag = A[i, i]
-        if diag == 0.0:
+        if diag == 0.0:  # pragma: no cover - guarded by pivot checks
             raise numpy.linalg.LinAlgError("Matrix is singular")
         residual = b[i] - A[i, i + 1 :] @ x[i + 1 :]
         x[i] = residual / diag
@@ -152,7 +153,20 @@ def pencil(p: numpy.ndarray, q: numpy.ndarray, mu: float) -> numpy.ndarray:
     return (1.0 - mu) * p + mu * q
 
 
-TangencyResult = namedtuple("TangencyResult", ["t", "point", "mu"])
+class TangencyResult(NamedTuple):
+    """Result of a tangency point calculation.
+
+    Attributes:
+        t: The tangency time (distance).
+        point: The tangent point as a NumPy array.
+        mu: The pencil parameter μ at which the two ellipses are tangent.
+    """
+
+    t: float
+    point: numpy.ndarray
+    mu: float
+
+
 NewtonResult = namedtuple("NewtonResult", ["root", "converged"])
 
 
@@ -349,7 +363,7 @@ def wrapper(mu: float) -> float:
         return wrapper
 
     def solve_single_stage(method_name: SingleStageMethodName, **kwargs: Any) -> float:
-        if method_name == "newton":
+        if method_name == "newton":  # pragma: no cover - currently unused
             kwargs.setdefault("fprime", curry_df)
         result = root_scalar(curry_f, method=method_name, **kwargs)
         return float(result.root)
@@ -479,10 +493,7 @@ def tangency(
         failsafe: If `True`, a failsafe fallback is enabled.
 
     Returns:
-        A `TangencyResult` named tuple with the following fields:
-        - `t`: The tangency time.
-        - `point`: The tangent point.
-        - `mu`: The pencil parameter `μ` at which the two ellipses are tangent.
+        A `TangencyResult` containing the tangency time, point, and pencil parameter.
 
     Raises:
         RuntimeError: If the solver fails to converge to a valid solution
diff --git a/src/ellphi/_solver_python.pyi b/src/ellphi/_solver_python.pyi
index b190537..b859733 100644
--- a/src/ellphi/_solver_python.pyi
+++ b/src/ellphi/_solver_python.pyi
@@ -15,6 +15,7 @@ __all__ = [
 MethodName = str  # Simplified for stub
 
 def _gaussian_elimination(matrix: FloatArray, rhs: FloatArray) -> FloatArray: ...
+def _u_from_x(x: float | FloatArray) -> float | FloatArray: ...
 def _center(coef: FloatArray) -> FloatArray: ...
 def quad_eval(coef: FloatArray, center: Tuple[float, ...] | FloatArray) -> float: ...
 def pencil(p: FloatArray, q: FloatArray, mu: float) -> FloatArray: ...
diff --git a/src/ellphi/_tangency_cpp.py b/src/ellphi/_tangency_cpp.py
index df02238..740c3ed 100644
--- a/src/ellphi/_tangency_cpp.py
+++ b/src/ellphi/_tangency_cpp.py
@@ -99,6 +99,41 @@ def is_available() -> bool:
     return _LIB is not None
 
 
+def linalg_kind() -> str | None:
+    """Return the C++ linear algebra build kind.
+
+    Returns:
+        The build kind ("eigen" or "internal") if the C++ backend is available,
+        otherwise `None`.
+    """
+    if _LIB is None:
+        return None
+    try:
+        func = _LIB.tangency_linalg_kind
+    except AttributeError as exc:
+        raise RuntimeError(
+            "C++ backend is missing linear algebra metadata. "
+            "Please rebuild the extension."
+        ) from exc
+    func.restype = ctypes.c_char_p
+    value = func()
+    if value is None:
+        raise RuntimeError("C++ backend returned an empty linear algebra kind")
+    return value.decode("utf-8", errors="replace")
+
+
+def backend_version() -> str | None:
+    """Return the C++ backend build version string.
+
+    Returns:
+        The embedded build version if the C++ backend is available, otherwise
+        `None`.
+    """
+    if _LIB is None:
+        return None
+    return _library_version(_LIB)
+
+
 def _ensure_available() -> ctypes.CDLL:
     if _LIB is None:
         message = (
diff --git a/src/ellphi/_tangency_cpp.pyi b/src/ellphi/_tangency_cpp.pyi
index ccc1cde..aca13b9 100644
--- a/src/ellphi/_tangency_cpp.pyi
+++ b/src/ellphi/_tangency_cpp.pyi
@@ -3,6 +3,8 @@ from ellphi import FloatArray
 from ._solver_python import TangencyResult
 
 def is_available() -> bool: ...
+def linalg_kind() -> str | None: ...
+def backend_version() -> str | None: ...
 def tangency(
     pcoef: FloatArray,
     qcoef: FloatArray,
diff --git a/src/ellphi/_tangency_cpp_impl.cpp b/src/ellphi/_tangency_cpp_impl.cpp
index 739d18e..afbfc30 100644
--- a/src/ellphi/_tangency_cpp_impl.cpp
+++ b/src/ellphi/_tangency_cpp_impl.cpp
@@ -10,11 +10,36 @@
 #include <utility>
 #include <vector>
 
+#if defined(ELLPHI_USE_EIGEN)
+#include <Eigen/Cholesky>
+#include <Eigen/Core>
+#endif
+
 #ifndef TANGENCY_VERSION
 #define TANGENCY_VERSION "0+unknown"
 #endif
 
-extern "C" const char* tangency_backend_version() { return TANGENCY_VERSION; }
+#ifndef TANGENCY_LINALG_KIND
+#if defined(ELLPHI_USE_EIGEN)
+#define TANGENCY_LINALG_KIND "eigen"
+#else
+#define TANGENCY_LINALG_KIND "internal"
+#endif
+#endif
+
+#if defined(_WIN32) || defined(__CYGWIN__)
+#define ELLPHI_EXPORT __declspec(dllexport)
+#else
+#define ELLPHI_EXPORT
+#endif
+
+ELLPHI_EXPORT extern "C" const char* tangency_backend_version() {
+    return TANGENCY_VERSION;
+}
+
+ELLPHI_EXPORT extern "C" const char* tangency_linalg_kind() {
+    return TANGENCY_LINALG_KIND;
+}
 
 namespace {
 
@@ -74,52 +99,89 @@ struct DecodedConic {
     double constant;
 };
 
+// Forward declarations for optimization functions
+void decode_conic_into(const std::vector<double>& coef, DecodedConic& out);
+void pencil_into(const std::vector<double>& p, const std::vector<double>& q, double mu, std::vector<double>& out);
+void cholesky_factor_into(const std::vector<double>& matrix, int dim, std::vector<double>& out);
+void solve_with_cholesky_into(const std::vector<double>& chol, const std::vector<double>& rhs, int dim, std::vector<double>& out);
+void cholesky_factor_into_internal(const std::vector<double>& matrix, int dim, std::vector<double>& out);
+void solve_with_cholesky_into_internal(const std::vector<double>& chol, const std::vector<double>& rhs, int dim, std::vector<double>& out);
+#if defined(ELLPHI_USE_EIGEN)
+void cholesky_factor_into_eigen(const std::vector<double>& matrix, int dim, std::vector<double>& out);
+void solve_with_cholesky_into_eigen(const std::vector<double>& chol, const std::vector<double>& rhs, int dim, std::vector<double>& out);
+#endif
+void gaussian_elimination_into(std::vector<double> matrix, std::vector<double> rhs, int dim, std::vector<double>& out);
+void matvec_into(const std::vector<double>& matrix, const std::vector<double>& vec, int dim, std::vector<double>& out);
+void center_with_fallback_into(const DecodedConic& conic, std::vector<double>& out_center, std::vector<double>& work_rhs, std::vector<double>& work_chol);
+
 DecodedConic decode_conic(const std::vector<double>& coef) {
     DecodedConic decoded{};
-    decoded.dim = infer_dim_from_coef_length(coef.size());
-    const int dim = decoded.dim;
-    const std::size_t quad_entries = static_cast<std::size_t>(dim * (dim + 1) / 2);
-    decoded.quad.assign(static_cast<std::size_t>(dim * dim), 0.0);
-    decoded.linear.assign(dim, 0.0);
+    decode_conic_into(coef, decoded);
+    return decoded;
+}
+
+void decode_conic_into(const std::vector<double>& coef, DecodedConic& out) {
+    out.dim = infer_dim_from_coef_length(coef.size());
+    const int dim = out.dim;
+    const std::size_t quad_size = static_cast<std::size_t>(dim * dim);
+    if (out.quad.size() != quad_size) {
+        out.quad.resize(quad_size);
+    }
+    if (out.linear.size() != static_cast<std::size_t>(dim)) {
+        out.linear.resize(dim);
+    }
+
+    std::fill(out.quad.begin(), out.quad.end(), 0.0);
 
     std::size_t idx = 0;
     for (int i = 0; i < dim; ++i) {
         for (int j = i; j < dim; ++j) {
             double value = coef[idx++];
-            decoded.quad[static_cast<std::size_t>(i * dim + j)] = value;
-            decoded.quad[static_cast<std::size_t>(j * dim + i)] = value;
+            out.quad[static_cast<std::size_t>(i * dim + j)] = value;
+            out.quad[static_cast<std::size_t>(j * dim + i)] = value;
         }
     }
 
     for (int i = 0; i < dim; ++i) {
-        decoded.linear[static_cast<std::size_t>(i)] = coef[idx++];
+        out.linear[static_cast<std::size_t>(i)] = coef[idx++];
     }
     if (idx >= coef.size()) {
         raise("Coefficient vector too short to contain constant term");
     }
-    decoded.constant = coef[idx];
+    out.constant = coef[idx];
 
-    const std::size_t expected_length = quad_entries + static_cast<std::size_t>(dim) + 1U;
+    const std::size_t expected_length = idx + 1;
     if (coef.size() != expected_length) {
         raise("Coefficient length mismatch during decoding");
     }
-    return decoded;
 }
-
 std::vector<double> pencil(
     const std::vector<double>& p,
     const std::vector<double>& q,
     double mu
+) {
+    std::vector<double> result(p.size());
+    pencil_into(p, q, mu, result);
+    return result;
+}
+
+void pencil_into(
+    const std::vector<double>& p,
+    const std::vector<double>& q,
+    double mu,
+    std::vector<double>& out
 ) {
     if (p.size() != q.size()) {
         raise("Coefficient vectors must have the same length");
     }
-    std::vector<double> result(p.size(), 0.0);
+    if (out.size() != p.size()) {
+        out.resize(p.size());
+    }
     const double alpha = 1.0 - mu;
-    for (std::size_t i = 0; i < result.size(); ++i) {
-        result[i] = alpha * p[i] + mu * q[i];
+    const std::size_t n = p.size();
+    for (std::size_t i = 0; i < n; ++i) {
+        out[i] = alpha * p[i] + mu * q[i];
     }
-    return result;
 }
 
 struct SolverContext {
@@ -129,6 +191,18 @@ struct SolverContext {
     DecodedConic q_dec;
     std::vector<double> diff_coef;
     DecodedConic diff_dec;
+    
+    // Working buffers to avoid reallocation
+    std::vector<double> work_coef;
+    DecodedConic work_conic;
+    std::vector<double> work_chol;
+    std::vector<double> work_rhs;
+    std::vector<double> work_center;
+    std::vector<double> work_residual;
+    std::vector<double> work_solved;
+    double cache_mu = std::numeric_limits<double>::quiet_NaN();
+    bool cache_valid = false;
+    bool cache_chol_ok = false;
 };
 
 SolverContext build_solver_context(
@@ -144,82 +218,184 @@ SolverContext build_solver_context(
         decode_conic(pcoef),
         decode_conic(qcoef),
         {},
-        {}
+        {},
+        {}, {}, {}, {}, {}, {}, {}
     };
     ctx.diff_coef.resize(pcoef.size());
     for (std::size_t i = 0; i < pcoef.size(); ++i) {
         ctx.diff_coef[i] = pcoef[i] - qcoef[i];
     }
     ctx.diff_dec = decode_conic(ctx.diff_coef);
+    
+    // Initialize working buffers
+    int dim = ctx.p_dec.dim;
+    ctx.work_coef.resize(pcoef.size());
+    // work_conic will be resized in decode_conic_into
+    ctx.work_chol.resize(static_cast<std::size_t>(dim * dim));
+    ctx.work_rhs.resize(dim);
+    ctx.work_center.resize(dim);
+    ctx.work_residual.resize(dim);
+    ctx.work_solved.resize(dim);
+    
     return ctx;
 }
 
-std::vector<double> cholesky_factor(const std::vector<double>& matrix, int dim) {
-    std::vector<double> chol(static_cast<std::size_t>(dim * dim), 0.0);
+
+
+void cholesky_factor_into_internal(const std::vector<double>& matrix, int dim, std::vector<double>& out) {
+    const std::size_t size = static_cast<std::size_t>(dim * dim);
+    if (out.size() != size) {
+        out.resize(size);
+    }
+    std::fill(out.begin(), out.end(), 0.0);
+    
+    // Pointer access for optimization
+    const double* mat_ptr = matrix.data();
+    double* out_ptr = out.data();
+
     for (int i = 0; i < dim; ++i) {
+        double* row_i = out_ptr + i * dim;
+        const double* mat_row_i = mat_ptr + i * dim;
+
         for (int j = 0; j <= i; ++j) {
+            double* row_j = out_ptr + j * dim;
             double sum = 0.0;
+            
+            // Vectorizable loop
             for (int k = 0; k < j; ++k) {
-                sum += chol[static_cast<std::size_t>(i * dim + k)] *
-                    chol[static_cast<std::size_t>(j * dim + k)];
+                sum += row_i[k] * row_j[k];
             }
+
             if (i == j) {
-                double value = matrix[static_cast<std::size_t>(i * dim + i)] - sum;
+                double value = mat_row_i[i] - sum;
                 if (value <= 0.0) {
                     raise("Degenerate conic (determinant zero)");
                 }
-                chol[static_cast<std::size_t>(i * dim + j)] = std::sqrt(value);
+                row_i[j] = std::sqrt(value);
             } else {
-                double diag = chol[static_cast<std::size_t>(j * dim + j)];
+                double diag = row_j[j];
                 if (diag == 0.0) {
                     raise("Degenerate conic (determinant zero)");
                 }
-                chol[static_cast<std::size_t>(i * dim + j)] =
-                    (matrix[static_cast<std::size_t>(i * dim + j)] - sum) / diag;
+                row_i[j] = (mat_row_i[j] - sum) / diag;
             }
         }
     }
-    return chol;
 }
 
-std::vector<double> solve_with_cholesky(
+
+
+void solve_with_cholesky_into_internal(
     const std::vector<double>& chol,
     const std::vector<double>& rhs,
-    int dim
+    int dim,
+    std::vector<double>& out
 ) {
-    std::vector<double> y(dim, 0.0);
+    if (out.size() != static_cast<std::size_t>(dim)) {
+        out.resize(dim);
+    }
+    // Forward substitution (Ly = b)
+    // Reuse out as y
     for (int i = 0; i < dim; ++i) {
         double sum = 0.0;
         for (int k = 0; k < i; ++k) {
-            sum += chol[static_cast<std::size_t>(i * dim + k)] * y[static_cast<std::size_t>(k)];
+            sum += chol[static_cast<std::size_t>(i * dim + k)] * out[static_cast<std::size_t>(k)];
         }
         double diag = chol[static_cast<std::size_t>(i * dim + i)];
         if (diag == 0.0) {
             raise("Degenerate conic (determinant zero)");
         }
-        y[static_cast<std::size_t>(i)] = (rhs[static_cast<std::size_t>(i)] - sum) / diag;
+        out[static_cast<std::size_t>(i)] = (rhs[static_cast<std::size_t>(i)] - sum) / diag;
     }
 
-    std::vector<double> x(dim, 0.0);
+    // Backward substitution (L^T x = y)
+    // Reuse out as x (in-place)
     for (int i = dim - 1; i >= 0; --i) {
         double sum = 0.0;
         for (int k = i + 1; k < dim; ++k) {
-            sum += chol[static_cast<std::size_t>(k * dim + i)] * x[static_cast<std::size_t>(k)];
+            sum += chol[static_cast<std::size_t>(k * dim + i)] * out[static_cast<std::size_t>(k)];
         }
         double diag = chol[static_cast<std::size_t>(i * dim + i)];
         if (diag == 0.0) {
             raise("Degenerate conic (determinant zero)");
         }
-        x[static_cast<std::size_t>(i)] = (y[static_cast<std::size_t>(i)] - sum) / diag;
+        out[static_cast<std::size_t>(i)] = (out[static_cast<std::size_t>(i)] - sum) / diag;
+    }
+}
+
+#if defined(ELLPHI_USE_EIGEN)
+void cholesky_factor_into_eigen(const std::vector<double>& matrix, int dim, std::vector<double>& out) {
+    const std::size_t size = static_cast<std::size_t>(dim * dim);
+    if (out.size() != size) {
+        out.resize(size);
+    }
+    std::fill(out.begin(), out.end(), 0.0);
+
+    using RowMajorMat = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+    Eigen::Map<const RowMajorMat> mat(matrix.data(), dim, dim);
+    Eigen::LLT<RowMajorMat> llt(mat);
+    if (llt.info() != Eigen::Success) {
+        raise("Degenerate conic (determinant zero)");
+    }
+    const auto L = llt.matrixL();
+    for (int i = 0; i < dim; ++i) {
+        for (int j = 0; j <= i; ++j) {
+            out[static_cast<std::size_t>(i * dim + j)] = L(i, j);
+        }
+    }
+}
+
+void solve_with_cholesky_into_eigen(
+    const std::vector<double>& chol,
+    const std::vector<double>& rhs,
+    int dim,
+    std::vector<double>& out
+) {
+    if (out.size() != static_cast<std::size_t>(dim)) {
+        out.resize(dim);
     }
-    return x;
+    using RowMajorMat = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+    using Vec = Eigen::Matrix<double, Eigen::Dynamic, 1>;
+    Eigen::Map<const RowMajorMat> L(chol.data(), dim, dim);
+    Eigen::Map<const Vec> b(rhs.data(), dim);
+    Eigen::Map<Vec> x(out.data(), dim);
+    x = L.template triangularView<Eigen::Lower>().solve(b);
+    x = L.transpose().template triangularView<Eigen::Upper>().solve(x);
+}
+#endif
+
+void cholesky_factor_into(const std::vector<double>& matrix, int dim, std::vector<double>& out) {
+#if defined(ELLPHI_USE_EIGEN)
+    cholesky_factor_into_eigen(matrix, dim, out);
+#else
+    cholesky_factor_into_internal(matrix, dim, out);
+#endif
+}
+
+void solve_with_cholesky_into(
+    const std::vector<double>& chol,
+    const std::vector<double>& rhs,
+    int dim,
+    std::vector<double>& out
+) {
+#if defined(ELLPHI_USE_EIGEN)
+    solve_with_cholesky_into_eigen(chol, rhs, dim, out);
+#else
+    solve_with_cholesky_into_internal(chol, rhs, dim, out);
+#endif
 }
 
-std::vector<double> gaussian_elimination(
-    std::vector<double> matrix,
-    std::vector<double> rhs,
-    int dim
+
+
+void gaussian_elimination_into(
+    std::vector<double> matrix, // Pass by value to copy
+    std::vector<double> rhs,    // Pass by value to copy
+    int dim,
+    std::vector<double>& out
 ) {
+    if (out.size() != static_cast<std::size_t>(dim)) {
+        out.resize(dim);
+    }
     for (int k = 0; k < dim; ++k) {
         int pivot = k;
         double pivot_value = std::abs(matrix[static_cast<std::size_t>(k * dim + k)]);
@@ -253,35 +429,37 @@ std::vector<double> gaussian_elimination(
         }
     }
 
-    std::vector<double> x(dim, 0.0);
     for (int i = dim - 1; i >= 0; --i) {
         double sum = rhs[static_cast<std::size_t>(i)];
         for (int j = i + 1; j < dim; ++j) {
-            sum -= matrix[static_cast<std::size_t>(i * dim + j)] * x[static_cast<std::size_t>(j)];
+            sum -= matrix[static_cast<std::size_t>(i * dim + j)] * out[static_cast<std::size_t>(j)];
         }
         double diag = matrix[static_cast<std::size_t>(i * dim + i)];
         if (diag == 0.0) {
             raise("Degenerate conic (determinant zero)");
         }
-        x[static_cast<std::size_t>(i)] = sum / diag;
+        out[static_cast<std::size_t>(i)] = sum / diag;
     }
-    return x;
 }
 
-std::vector<double> matvec(
+
+
+void matvec_into(
     const std::vector<double>& matrix,
     const std::vector<double>& vec,
-    int dim
+    int dim,
+    std::vector<double>& out
 ) {
-    std::vector<double> result(dim, 0.0);
+    if (out.size() != static_cast<std::size_t>(dim)) {
+        out.resize(dim);
+    }
     for (int i = 0; i < dim; ++i) {
         double sum = 0.0;
         for (int j = 0; j < dim; ++j) {
             sum += matrix[static_cast<std::size_t>(i * dim + j)] * vec[static_cast<std::size_t>(j)];
         }
-        result[static_cast<std::size_t>(i)] = sum;
+        out[static_cast<std::size_t>(i)] = sum;
     }
-    return result;
 }
 
 double quad_eval(const DecodedConic& conic, const std::vector<double>& point) {
@@ -313,56 +491,108 @@ struct PencilGeometry {
 };
 
 std::vector<double> center_with_fallback(const DecodedConic& conic) {
-    std::vector<double> rhs = conic.linear;
-    for (double& value : rhs) {
-        value = -value;
+    std::vector<double> center(conic.dim);
+    std::vector<double> work_rhs(conic.dim);
+    std::vector<double> work_chol(static_cast<std::size_t>(conic.dim * conic.dim));
+    center_with_fallback_into(conic, center, work_rhs, work_chol);
+    return center;
+}
+
+void center_with_fallback_into(
+    const DecodedConic& conic,
+    std::vector<double>& out_center,
+    std::vector<double>& work_rhs,
+    std::vector<double>& work_chol
+) {
+    const int dim = conic.dim;
+    if (work_rhs.size() != static_cast<std::size_t>(dim)) {
+        work_rhs.resize(dim);
     }
+    // Copy linear to work_rhs and negate
+    for(int i=0; i<dim; ++i) {
+        work_rhs[i] = -conic.linear[i];
+    }
+    
     try {
-        std::vector<double> chol = cholesky_factor(conic.quad, conic.dim);
-        return solve_with_cholesky(chol, rhs, conic.dim);
+        cholesky_factor_into(conic.quad, dim, work_chol);
+        solve_with_cholesky_into(work_chol, work_rhs, dim, out_center);
     } catch (const std::runtime_error& ex) {
         if (!is_degenerate_error(ex)) {
             throw;
         }
-        return gaussian_elimination(conic.quad, rhs, conic.dim);
+        // Fallback: gaussian_elimination copies inputs, so we can pass quad and work_rhs
+        gaussian_elimination_into(conic.quad, work_rhs, dim, out_center);
     }
 }
 
-PencilGeometry build_pencil_geometry(double mu, const SolverContext& ctx) {
-    PencilGeometry geom;
-    geom.conic = decode_conic(pencil(ctx.pcoef, ctx.qcoef, mu));
-    geom.chol = cholesky_factor(geom.conic.quad, geom.conic.dim);
-    std::vector<double> rhs = geom.conic.linear;
-    for (double& value : rhs) {
-        value = -value;
+void compute_geometry_for_mu(double mu, SolverContext& ctx) {
+    if (ctx.cache_valid && mu == ctx.cache_mu) {
+        return;
     }
-    geom.center = solve_with_cholesky(geom.chol, rhs, geom.conic.dim);
-    return geom;
+    ctx.cache_valid = false;
+    ctx.cache_chol_ok = false;
+
+    pencil_into(ctx.pcoef, ctx.qcoef, mu, ctx.work_coef);
+    decode_conic_into(ctx.work_coef, ctx.work_conic);
+
+    const int dim = ctx.work_conic.dim;
+    if (ctx.work_rhs.size() != static_cast<std::size_t>(dim)) {
+        ctx.work_rhs.resize(dim);
+    }
+    for (int i = 0; i < dim; ++i) {
+        ctx.work_rhs[i] = -ctx.work_conic.linear[i];
+    }
+
+    try {
+        cholesky_factor_into(ctx.work_conic.quad, dim, ctx.work_chol);
+        solve_with_cholesky_into(ctx.work_chol, ctx.work_rhs, dim, ctx.work_center);
+        ctx.cache_chol_ok = true;
+    } catch (const std::runtime_error& ex) {
+        if (!is_degenerate_error(ex)) {
+            throw;
+        }
+        gaussian_elimination_into(ctx.work_conic.quad, ctx.work_rhs, dim, ctx.work_center);
+    }
+
+    ctx.cache_mu = mu;
+    ctx.cache_valid = true;
 }
 
-double target(double mu, const SolverContext& ctx) {
-    DecodedConic conic = decode_conic(pencil(ctx.pcoef, ctx.qcoef, mu));
-    std::vector<double> center = center_with_fallback(conic);
-    double p_value = quad_eval(ctx.p_dec, center);
-    double q_value = quad_eval(ctx.q_dec, center);
+double target(double mu, SolverContext& ctx) {
+    compute_geometry_for_mu(mu, ctx);
+
+    double p_value = quad_eval(ctx.p_dec, ctx.work_center);
+    double q_value = quad_eval(ctx.q_dec, ctx.work_center);
     return p_value - q_value;
 }
 
-double target_prime(double mu, const SolverContext& ctx) {
+double target_prime(double mu, SolverContext& ctx) {
     try {
-        PencilGeometry geom = build_pencil_geometry(mu, ctx);
-        const int dim = geom.conic.dim;
+        compute_geometry_for_mu(mu, ctx);
+        if (!ctx.cache_chol_ok) {
+            return std::numeric_limits<double>::quiet_NaN();
+        }
+
+        const int dim = ctx.work_conic.dim;
+        
+        // Now compute derivative
         if (ctx.diff_dec.dim != dim) {
             raise("Dimension mismatch while computing derivative");
         }
-        std::vector<double> mat_center = matvec(ctx.diff_dec.quad, geom.center, dim);
-        std::vector<double> residual(dim, 0.0);
+        
+        // matvec_into(ctx.diff_dec.quad, geom.center, dim, work_residual)
+        matvec_into(ctx.diff_dec.quad, ctx.work_center, dim, ctx.work_residual);
+        
+        // residual = -(mat_center + diff_linear)
         for (int i = 0; i < dim; ++i) {
-            residual[static_cast<std::size_t>(i)] =
-                -(mat_center[static_cast<std::size_t>(i)] + ctx.diff_dec.linear[static_cast<std::size_t>(i)]);
+            ctx.work_residual[i] = -(ctx.work_residual[i] + ctx.diff_dec.linear[i]);
         }
-        std::vector<double> solved = solve_with_cholesky(geom.chol, residual, dim);
-        double dot = std::inner_product(residual.begin(), residual.end(), solved.begin(), 0.0);
+        
+        // solved = solve_with_cholesky(chol, residual)
+        // use work_solved
+        solve_with_cholesky_into(ctx.work_chol, ctx.work_residual, dim, ctx.work_solved);
+        
+        double dot = std::inner_product(ctx.work_residual.begin(), ctx.work_residual.end(), ctx.work_solved.begin(), 0.0);
         return 2.0 * dot;
     } catch (const std::runtime_error& ex) {
         if (is_degenerate_error(ex)) {
@@ -881,12 +1111,6 @@ void copy_error(char* buffer, std::size_t size, const std::string& message) {
 
 }  // namespace
 
-#if defined(_WIN32) || defined(__CYGWIN__)
-#define ELLPHI_EXPORT __declspec(dllexport)
-#else
-#define ELLPHI_EXPORT
-#endif
-
 ELLPHI_EXPORT extern "C" int tangency_solve(
     const double* pcoef,
     const double* qcoef,
diff --git a/src/ellphi/_version.py b/src/ellphi/_version.py
index 716c94e..e793082 100644
--- a/src/ellphi/_version.py
+++ b/src/ellphi/_version.py
@@ -1,3 +1,3 @@
 """Canonical package version."""
 
-__version__ = "0.1.1.post1"
+__version__ = "0.1.2"
diff --git a/src/ellphi/ellcloud.py b/src/ellphi/ellcloud.py
index e532bc4..01df549 100644
--- a/src/ellphi/ellcloud.py
+++ b/src/ellphi/ellcloud.py
@@ -179,6 +179,39 @@ def pdist_tangency(
             backend=backend,
         )
 
+    def distance_matrix(
+        self,
+        *,
+        parallel: bool = True,
+        n_jobs: int | None = -1,
+        backend: str = "auto",
+    ) -> numpy.ndarray:
+        """Computes the square pairwise tangency distance matrix.
+
+        This is a convenience wrapper around `pdist_tangency` that returns the
+        full square matrix using the same layout as
+        `scipy.spatial.distance.squareform`.
+
+        Args:
+            parallel: If `True`, the computation is performed in parallel.
+            n_jobs: The number of jobs to run in parallel. See
+                `ellphi.solver.pdist_tangency` for more details.
+            backend: The backend to use for the computation. Can be one of
+                "auto", "python", or "cpp".
+
+        Returns:
+            A square matrix of tangency distances with shape `(n, n)`.
+        """
+        if self.n == 0:
+            return numpy.empty((0, 0), dtype=float)
+        return squareform(
+            self.pdist_tangency(
+                parallel=parallel,
+                n_jobs=n_jobs,
+                backend=backend,
+            )
+        )
+
     @classmethod
     def from_point_cloud(
         cls: type[EllipseCloud],
@@ -217,6 +250,47 @@ def from_point_cloud(
             ellcloud.rescale(method=rescaling)
         return ellcloud
 
+    @classmethod
+    def from_cov(
+        cls: type[EllipseCloud],
+        X: numpy.ndarray,
+        cov: numpy.ndarray,
+        *,
+        rescaling: str = "none",
+    ) -> EllipseCloud:
+        """Creates an `EllipseCloud` from centers and covariance matrices.
+
+        This class method is the recommended entry point when ellipsoid
+        covariances are already known and no k-nearest-neighbour structure is
+        involved.
+
+        Args:
+            X: An array of centers with shape `(n, d)` or a single center with
+                shape `(d,)`.
+            cov: An array of covariance matrices with shape `(n, d, d)` or a
+                single covariance matrix with shape `(d, d)`.
+            rescaling: The method to use for rescaling the ellipses. Can be
+                one of "none", "median", or "average".
+
+        Returns:
+            An `EllipseCloud` object created from the provided centers and
+            covariances.
+        """
+        centers = numpy.array(X, dtype=float, copy=True)
+        covariances = numpy.array(cov, dtype=float, copy=True)
+
+        if centers.ndim == 1:
+            centers = centers[numpy.newaxis, :]
+        if covariances.ndim == 2:
+            covariances = covariances[numpy.newaxis, :, :]
+
+        coefs = coef_from_cov(centers, covariances)
+        nbd = numpy.empty((centers.shape[0], 0), dtype=int)
+        ellcloud = cls(coefs, centers, covariances, k=0, nbd=nbd)
+        if rescaling != "none":
+            ellcloud.rescale(method=rescaling)
+        return ellcloud
+
     @classmethod
     def from_local_cov(
         cls: type[EllipseCloud], X: numpy.ndarray, *, k: int = 5
diff --git a/src/ellphi/ellcloud.pyi b/src/ellphi/ellcloud.pyi
index 9003e22..108c98d 100644
--- a/src/ellphi/ellcloud.pyi
+++ b/src/ellphi/ellcloud.pyi
@@ -32,6 +32,9 @@ class EllipseCloud:
     def pdist_tangency(
         self, *, parallel: bool = True, n_jobs: int | None = -1, backend: str = "auto"
     ) -> FloatArray: ...
+    def distance_matrix(
+        self, *, parallel: bool = True, n_jobs: int | None = -1, backend: str = "auto"
+    ) -> FloatArray: ...
     @classmethod
     def from_point_cloud(
         cls,
@@ -42,6 +45,14 @@ class EllipseCloud:
         **kwgs: Any,
     ) -> EllipseCloud: ...
     @classmethod
+    def from_cov(
+        cls,
+        X: FloatArray,
+        cov: FloatArray,
+        *,
+        rescaling: str = "none",
+    ) -> EllipseCloud: ...
+    @classmethod
     def from_local_cov(cls, X: FloatArray, *, k: int = 5) -> EllipseCloud: ...
     def rescale(self, *, method: str = "median") -> float: ...
 
diff --git a/src/ellphi/geometry.py b/src/ellphi/geometry.py
index ed666fc..89f4dbd 100644
--- a/src/ellphi/geometry.py
+++ b/src/ellphi/geometry.py
@@ -1,4 +1,4 @@
-"""Geometry helpers shared across the :mod:`ellphi` package.
+"""Geometry helpers shared across the `ellphi` package.
 
 This module historically provided 2D-only utilities for working with
 ellipses represented as quadratic forms.  For the n-dimensional extension
@@ -7,14 +7,14 @@
 
 Key API (all return ``numpy.float64`` arrays):
 
-* :func:`unit_vector`
-* :func:`axes_from_cov`
-* :func:`coef_from_axes`
-* :func:`coef_from_cov`
-* :func:`pack_conic`
-* :func:`unpack_conic`
-* :func:`unpack_single_conic`
-* :func:`infer_dim_from_coef_length`
+* [`unit_vector`][ellphi.geometry.unit_vector]
+* [`axes_from_cov`][ellphi.geometry.axes_from_cov]
+* [`coef_from_axes`][ellphi.geometry.coef_from_axes]
+* [`coef_from_cov`][ellphi.geometry.coef_from_cov]
+* [`pack_conic`][ellphi.geometry.pack_conic]
+* [`unpack_conic`][ellphi.geometry.unpack_conic]
+* [`unpack_single_conic`][ellphi.geometry.unpack_single_conic]
+* [`infer_dim_from_coef_length`][ellphi.geometry.infer_dim_from_coef_length]
 """
 
 from __future__ import annotations
@@ -63,8 +63,16 @@ def axes_from_cov(cov: numpy.ndarray, /, *, scale: float = 1.0):
 
     Returns:
         A tuple containing the major axis, minor axis, and angle of the major
-        axis in radians. The major axis is always greater than or equal to the
-        minor axis.
+            axis in radians. The major axis is always greater than or equal to the
+            minor axis.
+
+    Examples:
+        >>> import numpy as np
+        >>> from ellphi.geometry import axes_from_cov
+        >>> cov = np.array([[4.0, 0.0], [0.0, 1.0]])
+        >>> r_major, r_minor, theta = axes_from_cov(cov)
+        >>> float(r_major), float(r_minor)
+        (2.0, 1.0)
     """
     if len(cov.shape) <= 2:
         cov = cov[None, :, :]
@@ -125,7 +133,7 @@ def infer_dim_from_coef_length(length: int) -> int:
             "quadratic form"
         )
     n = (sqrt_disc - 3) // 2
-    if n < 2 or ((n + 1) * (n + 2)) // 2 != length:
+    if n < 2 or ((n + 1) * (n + 2)) // 2 != length:  # pragma: no cover
         raise ValueError(
             f"Coefficient length {length} does not correspond to a valid " "dimension"
         )
@@ -158,7 +166,7 @@ def pack_conic(
 
     Returns:
         A NumPy array of shape `(..., m)` where `m = (n + 1)(n + 2) / 2`,
-        representing the packed conic coefficients.
+            representing the packed conic coefficients.
     """
     matrices = numpy.asarray(matrices, dtype=float)
     linear = numpy.asarray(linear, dtype=float)
@@ -192,7 +200,8 @@ def unpack_conic(
 ) -> tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]:
     """Unpacks a flattened coefficient vector into a conic's components.
 
-    This function is the inverse of :func:`pack_conic`. It takes a flattened
+    This function is the inverse of
+    [`pack_conic`][ellphi.geometry.pack_conic]. It takes a flattened
     coefficient vector and returns the quadratic, linear, and constant terms
     of the conic's equation.
 
@@ -201,7 +210,7 @@ def unpack_conic(
 
     Returns:
         A tuple containing the quadratic matrix `A`, the linear coefficients
-        `b`, and the constant term `c`.
+            `b`, and the constant term `c`.
     """
     coef = numpy.asarray(coef, dtype=float)
     squeeze = False
@@ -234,7 +243,8 @@ def unpack_single_conic(
 ) -> tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]:
     """Unpacks a single flattened coefficient vector into a conic's components.
 
-    This function is a convenience wrapper around :func:`unpack_conic` for a
+    This function is a convenience wrapper around
+    [`unpack_conic`][ellphi.geometry.unpack_conic] for a
     single conic. It accepts a coefficient array of shape `(m,)` or `(1, m)`
     and returns the conic's components.
 
@@ -243,7 +253,7 @@ def unpack_single_conic(
 
     Returns:
         A tuple containing the quadratic matrix `A`, the linear coefficients
-        `b`, and the constant term `c`.
+            `b`, and the constant term `c`.
     """
     matrices, linear, constant = unpack_conic(coef)
     if matrices.ndim == 3:
@@ -294,7 +304,16 @@ def coef_from_cov(
 
     Returns:
         A NumPy array of packed conic coefficients with shape `(n, m)`, where
-        `m = (d + 1)(d + 2) / 2`.
+            `m = (d + 1)(d + 2) / 2`.
+
+    Examples:
+        >>> import numpy as np
+        >>> from ellphi.geometry import coef_from_cov
+        >>> center = np.array([1.0, 0.0])
+        >>> cov = np.array([[2.0, 0.0], [0.0, 1.0]])
+        >>> coef = coef_from_cov(center, cov)
+        >>> coef.shape
+        (1, 6)
     """
     centers = numpy.asarray(X, dtype=float)
     cov = numpy.asarray(cov, dtype=float)
diff --git a/src/ellphi/grad.py b/src/ellphi/grad.py
new file mode 100644
index 0000000..92cc0e5
--- /dev/null
+++ b/src/ellphi/grad.py
@@ -0,0 +1,276 @@
+"""Differentiable tangency distances and geometry helpers.
+
+Gradient-based optimisation support for ellphi.
+
+This module exposes ``tangency_grad`` (single-pair gradient),
+``pdist_tangency_grad`` (batch pairwise, with VJP pullback), and
+``coef_from_cov_grad`` (differentiable coefficient computation).
+"""
+
+from __future__ import annotations
+
+import dataclasses
+from itertools import combinations
+from typing import Callable
+
+import numpy as np
+
+from .differentiable_solver import solve_mu_gradients
+from .solver import tangency
+
+__all__ = ["TangencyGrad", "tangency_grad", "pdist_tangency_grad", "coef_from_cov_grad"]
+
+
+@dataclasses.dataclass(slots=True, frozen=True)
+class TangencyGrad:
+    """Tangency distance and its gradients with respect to both ellipsoids.
+
+    Attributes:
+        t:      Tangency distance (same as ``TangencyResult.t``).
+        dt_dp:  Gradient of ``t`` w.r.t. ``p`` (shape ``(m,)``).
+        dt_dq:  Gradient of ``t`` w.r.t. ``q`` (shape ``(m,)``).
+    """
+
+    t: float
+    dt_dp: np.ndarray
+    dt_dq: np.ndarray
+
+
+def tangency_grad(p: np.ndarray, q: np.ndarray, **solver_kwargs) -> TangencyGrad:
+    """Return the tangency distance and its gradients w.r.t. ``p`` and ``q``.
+
+    Uses the envelope theorem: the chain through the optimal center vanishes,
+    so only the explicit dependence of the pencil coefficients on ``p``/``q``
+    contributes.
+
+    Args:
+        p: Coefficient vector of the first ellipsoid.
+        q: Coefficient vector of the second ellipsoid.
+        **solver_kwargs: Forwarded to ``tangency()`` (e.g. ``method``,
+            ``backend``).
+
+    Returns:
+        A [`TangencyGrad`][ellphi.grad.TangencyGrad] with ``t``,
+        ``dt_dp``, and ``dt_dq``.
+
+    Raises:
+        ZeroDivisionError: When the pencil derivative ``∂F/∂μ`` vanishes at
+            the solution — which occurs for degenerate configurations such as
+            identical or concentric nested ellipsoids.  These cases make both
+            the implicit-function step in `solve_mu_gradients` and the
+            ``1/(2t)`` factor in the gradient formula ill-defined.
+            Note: ``tangency()`` itself returns a small non-zero ``t`` for
+            such inputs (never exactly ``0.0``), so this error surfaces from
+            ``solve_mu_gradients`` rather than from the ``1/(2t)`` term.
+    """
+    p = np.asarray(p, dtype=float).reshape(-1)
+    q = np.asarray(q, dtype=float).reshape(-1)
+
+    res = tangency(p, q, **solver_kwargs)
+    mu, center, t = res.mu, res.point, res.t
+
+    _, d_mu_dp, d_mu_dq = solve_mu_gradients(p, q, mu=mu)
+
+    # Monomial basis evaluated at center: [x_i*x_j (i<=j), 2*x_k, 1]
+    tri_i, tri_j = np.triu_indices(center.shape[0])
+    quad_entries = np.where(
+        tri_i == tri_j,
+        center[tri_i] ** 2,
+        2.0 * center[tri_i] * center[tri_j],
+    )
+    base = np.concatenate([quad_entries, 2.0 * center, [1.0]])
+
+    scalar = float(np.dot(base, q - p))
+    inv2t = 0.5 / t
+
+    return TangencyGrad(
+        t=t,
+        dt_dp=inv2t * ((1.0 - mu) * base + scalar * d_mu_dp),
+        dt_dq=inv2t * (mu * base + scalar * d_mu_dq),
+    )
+
+
+def pdist_tangency_grad(
+    coefs: np.ndarray,
+) -> tuple[np.ndarray, Callable[[np.ndarray], np.ndarray]]:
+    """Pairwise tangency distances and a VJP (pullback) for all pairs.
+
+    Computes the same condensed distance array as
+    [`pdist_tangency`][ellphi.pdist_tangency]
+    and additionally returns a VJP function that maps upstream gradient vectors
+    back to per-ellipsoid coefficient gradients.
+
+    Args:
+        coefs: Array of shape ``(N, m)`` containing ``N`` ellipsoid coefficient
+            vectors.
+
+    Returns:
+        A tuple ``(dists, vjp)`` where:
+
+        - ``dists`` is a 1-D array of shape ``(N*(N-1)//2,)`` with pairwise
+            tangency distances in the same order as ``scipy.spatial.distance.pdist``.
+        - ``vjp`` is a callable ``(grad_dists,) -> grad_coefs`` that accumulates
+            upstream gradients into an array of shape ``(N, m)``.
+
+    Examples:
+        >>> import numpy as np
+        >>> from ellphi import ellipse_cloud
+        >>> from ellphi.grad import pdist_tangency_grad
+        >>> rng = np.random.default_rng(0)
+        >>> cloud = ellipse_cloud(rng.standard_normal((6, 2)), k=3)
+        >>> dists, vjp = pdist_tangency_grad(cloud.coef)
+        >>> dists.shape        # N*(N-1)//2 = 15
+        (15,)
+        >>> vjp(np.ones(15)).shape   # (N, m) = (6, 6)
+        (6, 6)
+    """
+    coefs = np.asarray(coefs, dtype=float)
+    if coefs.ndim == 3 and coefs.shape[1] == 1:
+        coefs = coefs[:, 0, :]
+    if coefs.ndim != 2:
+        raise ValueError("Expected coefficient array with shape (N, m)")
+    N = len(coefs)
+    pairs = list(combinations(range(N), 2))
+    dists = np.empty(len(pairs))
+    store: list[tuple[int, int, np.ndarray, np.ndarray]] = []
+
+    for k, (i, j) in enumerate(pairs):
+        g = tangency_grad(coefs[i], coefs[j])
+        dists[k] = g.t
+        store.append((i, j, g.dt_dp, g.dt_dq))
+
+    def vjp(grad_dists: np.ndarray) -> np.ndarray:
+        g_coefs = np.zeros_like(coefs)
+        for k, (i, j, dt_dp, dt_dq) in enumerate(store):
+            g_coefs[i] += grad_dists[k] * dt_dp
+            g_coefs[j] += grad_dists[k] * dt_dq
+        return g_coefs
+
+    return dists, vjp
+
+
+def coef_from_cov_grad(
+    X: np.ndarray,
+    cov: np.ndarray,
+    /,
+    *,
+    scale: float = 1.0,
+) -> tuple[np.ndarray, Callable[[np.ndarray], tuple[np.ndarray, np.ndarray]]]:
+    """Converts centers and covariances to packed conic coefficients, with VJP.
+
+    This is the differentiable version of ``coef_from_cov``. It returns the same
+    coefficient array plus a VJP (vector-Jacobian product) pullback function.
+
+    Args:
+        X: Centers array, shape ``(n, d)`` or ``(d,)``.
+        cov: Covariance matrices, shape ``(n, d, d)`` or ``(d, d)``.
+        scale: Optional scaling factor for covariance matrices.
+
+    Returns:
+        A tuple ``(coefs, vjp)`` where:
+
+        - ``coefs``: shape ``(n, m)``, same as ``coef_from_cov`` output
+        - ``vjp``: callable ``(grad_coefs,) -> (grad_X, grad_cov)`` where
+          ``grad_X`` has shape ``(n, d)`` and ``grad_cov`` has shape ``(n, d, d)``
+    """
+    from .geometry import pack_conic
+
+    centers = np.asarray(X, dtype=float)
+    cov_arr = np.asarray(cov, dtype=float)
+
+    if centers.ndim == 1:
+        centers = centers[np.newaxis, :]
+    if cov_arr.ndim == 2:
+        cov_arr = cov_arr[np.newaxis, :, :]
+
+    if centers.shape[0] != cov_arr.shape[0]:
+        raise ValueError("Mismatch between number of centres and covariance matrices")
+    if cov_arr.shape[-1] != cov_arr.shape[-2]:
+        raise ValueError("Covariance matrices must be square")
+
+    n, d = centers.shape
+
+    if cov_arr.shape[-1] != d:
+        raise ValueError("Centre dimensionality and covariance size must agree")
+    s2 = scale**2
+
+    # Forward pass – handle singular covariances the same way coef_from_cov
+    # does: return NaN coefficients so callers can detect degeneracy.
+    try:
+        inv_cov = np.linalg.inv(cov_arr)  # (n, d, d)
+    except np.linalg.LinAlgError:
+        m = (d + 1) * (d + 2) // 2
+        coefs = np.full((n, m), np.nan, dtype=float)
+
+        def vjp_nan(
+            grad_coefs: np.ndarray,
+        ) -> tuple[np.ndarray, np.ndarray]:
+            return (
+                np.full_like(centers, np.nan),
+                np.full_like(cov_arr, np.nan),
+            )
+
+        return coefs, vjp_nan
+
+    A = inv_cov / s2  # (n, d, d)
+    b = -np.einsum("nij,nj->ni", A, centers)  # (n, d)
+    c = np.einsum("ni,nij,nj->n", centers, A, centers)  # (n,)
+
+    coefs = pack_conic(A, b, c)
+
+    # Precompute indices for unpacking
+    tri_i, tri_j = np.triu_indices(d)
+    n_quad = tri_i.size
+
+    def vjp(
+        grad_coefs: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        gc = np.asarray(grad_coefs, dtype=float)
+
+        # Unpack grad_coefs into grad_A_packed, grad_b, grad_c
+        grad_A_packed = gc[:, :n_quad]  # (n, n_quad)
+        grad_b = gc[:, n_quad : n_quad + d]  # (n, d)
+        grad_c = gc[:, n_quad + d]  # (n,)
+
+        # Reconstruct grad_A from upper-tri packed entries.
+        # pack_conic extracts A[tri_i, tri_j] (upper triangle only), so
+        # the gradient from packing only touches those entries.
+        grad_A = np.zeros((n, d, d), dtype=float)
+        grad_A[:, tri_i, tri_j] = grad_A_packed
+
+        # Accumulate contributions from b = -A @ center into grad_A.
+        # b_k = -sum_j A[k,j] * center[j], so dL/dA[k,j] += -grad_b[k]*center[j]
+        # This contribution is for the FULL matrix (not just upper triangle).
+        grad_A += -np.einsum("ni,nj->nij", grad_b, centers)
+
+        # Accumulate contributions from c = center^T A center into grad_A.
+        # c = sum_{i,j} center[i] A[i,j] center[j], so
+        # dL/dA[i,j] += grad_c * center[i] * center[j]
+        grad_A += grad_c[:, None, None] * np.einsum("ni,nj->nij", centers, centers)
+
+        # A is parameterized by symmetric covariances, so project the
+        # unconstrained matrix gradient back onto the symmetric subspace before
+        # exposing grad_cov or differentiating through inv(cov).
+        grad_A = 0.5 * (grad_A + np.swapaxes(grad_A, -2, -1))
+
+        # --- grad w.r.t. centers ---
+        # From b = -A @ center: grad_center += -A^T @ grad_b
+        grad_centers = -np.einsum("nji,nj->ni", A, grad_b)
+        # From c = center^T A center: grad_center += 2 * A @ center * grad_c
+        #   = -2 * b * grad_c
+        grad_centers += -2.0 * b * grad_c[:, None]
+
+        # --- grad w.r.t. cov (via A = inv(cov)/s^2) ---
+        # A = inv(cov) / s^2
+        # dA = -inv(cov) @ dCov @ inv(cov) / s^2
+        # <grad_A, dA> = tr(grad_A^T (-inv(cov) dCov inv(cov) / s^2))
+        #              = -1/s^2 * tr(inv(cov)^T grad_A^T inv(cov)^T dCov)
+        # Since inv(cov) and grad_A are symmetric:
+        #   grad_cov = -inv(cov) @ grad_A @ inv(cov) / s^2
+        #            = -s^2 A @ grad_A @ s^2 A / s^2
+        #            = -s^2 * A @ grad_A @ A
+        grad_cov = -s2 * np.einsum("nij,njk,nkl->nil", A, grad_A, A)
+
+        return grad_centers, grad_cov
+
+    return coefs, vjp
diff --git a/src/ellphi/solver.py b/src/ellphi/solver.py
index ec7bd3c..9c58c8c 100644
--- a/src/ellphi/solver.py
+++ b/src/ellphi/solver.py
@@ -2,12 +2,14 @@
 
 """Tangency solver dispatching between Python and C++ backends."""
 
+from dataclasses import dataclass
 from typing import Iterable, Tuple, cast, get_args
 
 import numpy
 
 from . import _solver_python as _py
 from . import _tangency_cpp as _cpp
+from ._version import __version__
 from .geometry import infer_dim_from_coef_length
 
 __all__ = [
@@ -20,6 +22,9 @@
     "tangency_python",
     "pdist_tangency_python",
     "has_cpp_backend",
+    "cpp_linalg_kind",
+    "BuildInfo",
+    "build_info",
     "MethodName",
 ]
 
@@ -40,7 +45,26 @@
 
 
 BackendLiteral = tuple[str, ...]
-_BACKEND_NAMES: BackendLiteral = ("auto", "python", "cpp")
+_BACKEND_CHOICES: BackendLiteral = ("auto", "python", "cpp")
+
+
+@dataclass(frozen=True)
+class BuildInfo:
+    """Summary of solver backends and C++ build details.
+
+    Notes:
+        - ``backend_choices`` are the accepted values for the ``backend`` argument
+          in solver functions ("auto", "python", "cpp").
+        - ``cpp_linalg_kind`` reports the linear algebra implementation used by the
+          C++ implementation ("eigen" or "internal").
+    """
+
+    version: str
+    backend_choices: tuple[str, ...]
+    backend_default: str
+    cpp_backend_available: bool
+    cpp_linalg_kind: str | None
+    cpp_backend_version: str | None
 
 
 def has_cpp_backend() -> bool:
@@ -52,6 +76,28 @@ def has_cpp_backend() -> bool:
     return _cpp.is_available()
 
 
+def cpp_linalg_kind() -> str | None:
+    """Return the C++ linear algebra build kind.
+
+    Returns:
+        The linear algebra kind ("eigen" or "internal") if the C++ backend is
+        available, otherwise `None`.
+    """
+    return _cpp.linalg_kind()
+
+
+def build_info() -> BuildInfo:
+    """Return build information for solver selection and C++ backend details."""
+    return BuildInfo(
+        version=__version__,
+        backend_choices=_BACKEND_CHOICES,
+        backend_default="auto",
+        cpp_backend_available=has_cpp_backend(),
+        cpp_linalg_kind=cpp_linalg_kind(),
+        cpp_backend_version=_cpp.backend_version(),
+    )
+
+
 def _extract_coef_array(ellcloud: Iterable[numpy.ndarray]) -> numpy.ndarray:
     coef = getattr(ellcloud, "coef", ellcloud)
     array = numpy.asarray(coef, dtype=float)
@@ -64,9 +110,10 @@ def _extract_coef_array(ellcloud: Iterable[numpy.ndarray]) -> numpy.ndarray:
 
 
 def _should_use_cpp(backend: str) -> bool:
-    if backend not in _BACKEND_NAMES:
+    if backend not in _BACKEND_CHOICES:
         raise ValueError(
-            f"Unknown backend '{backend}'. Expected one of {', '.join(_BACKEND_NAMES)}"
+            "Unknown backend "
+            f"'{backend}'. Expected one of {', '.join(_BACKEND_CHOICES)}"
         )
     if backend == "cpp":
         if not has_cpp_backend():
@@ -129,10 +176,7 @@ def tangency(
             falls back to the high-precision Brent's method.
 
     Returns:
-        A `TangencyResult` named tuple with the following fields:
-        - `t`: The tangency time.
-        - `point`: The tangent point.
-        - `mu`: The pencil parameter `μ` at which the two ellipses are tangent.
+        A `TangencyResult` containing the tangency time, point, and pencil parameter.
     """
     method_literal = _normalize_method(method)
     pcoef_arr = numpy.asarray(pcoef, dtype=float).reshape(-1)
@@ -164,9 +208,10 @@ def tangency(
             default_newton if hybrid_newton_maxiter is None else hybrid_newton_maxiter
         )
 
-    if backend not in _BACKEND_NAMES:
+    if backend not in _BACKEND_CHOICES:
         raise ValueError(
-            f"Unknown backend '{backend}'. Expected one of {', '.join(_BACKEND_NAMES)}"
+            "Unknown backend "
+            f"'{backend}'. Expected one of {', '.join(_BACKEND_CHOICES)}"
         )
     use_cpp = backend in {"cpp", "auto"} and _should_use_cpp(backend)
 
@@ -214,9 +259,10 @@ def pdist_tangency(
     Returns:
         A condensed distance matrix of tangency distances.
     """
-    if backend not in _BACKEND_NAMES:
+    if backend not in _BACKEND_CHOICES:
         raise ValueError(
-            f"Unknown backend '{backend}'. Expected one of {', '.join(_BACKEND_NAMES)}"
+            "Unknown backend "
+            f"'{backend}'. Expected one of {', '.join(_BACKEND_CHOICES)}"
         )
 
     if backend in {"cpp", "auto"}:
diff --git a/src/ellphi/solver.pyi b/src/ellphi/solver.pyi
index d66d155..9c0101b 100644
--- a/src/ellphi/solver.pyi
+++ b/src/ellphi/solver.pyi
@@ -1,3 +1,4 @@
+from dataclasses import dataclass
 from typing import Iterable, Tuple
 from ellphi import FloatArray
 from ._solver_python import TangencyResult, MethodName
@@ -12,6 +13,9 @@ __all__ = [
     "tangency_python",
     "pdist_tangency_python",
     "has_cpp_backend",
+    "cpp_linalg_kind",
+    "BuildInfo",
+    "build_info",
     "MethodName",
 ]
 
@@ -33,6 +37,17 @@ def pdist_tangency_python(
 BackendLiteral = Tuple[str, ...]
 
 def has_cpp_backend() -> bool: ...
+def cpp_linalg_kind() -> str | None: ...
+@dataclass(frozen=True)
+class BuildInfo:
+    version: str
+    backend_choices: Tuple[str, ...]
+    backend_default: str
+    cpp_backend_available: bool
+    cpp_linalg_kind: str | None
+    cpp_backend_version: str | None
+
+def build_info() -> BuildInfo: ...
 def tangency(
     pcoef: FloatArray,
     qcoef: FloatArray,
diff --git a/src/ellphi/visualization.py b/src/ellphi/visualization.py
index 8946f30..5f4f83c 100644
--- a/src/ellphi/visualization.py
+++ b/src/ellphi/visualization.py
@@ -29,15 +29,23 @@ def ellipse_patch(X, r_major=1, r_minor=1, theta=0, *, cov=None, scale=1, **kwgs
 
     Returns:
         A `matplotlib.patches.Ellipse` object.
+
+    Examples:
+        >>> import matplotlib.pyplot as plt
+        >>> from ellphi.visualization import ellipse_patch
+        >>> patch = ellipse_patch([0, 0], r_major=2.0, r_minor=1.0, theta=0.0)
+        >>> type(patch).__name__
+        'Ellipse'
     """
     if cov is not None:
         r_major, r_minor, theta = axes_from_cov(cov)
+    if "facecolor" not in kwgs and "fc" not in kwgs:
+        kwgs["facecolor"] = "none"
     ellipse = plt.matplotlib.patches.Ellipse(
         X,
         width=2 * r_major * scale,
         height=2 * r_minor * scale,
         angle=numpy.degrees(theta),
-        facecolor="none",
         **kwgs,
     )
     return ellipse
diff --git a/tests/test_cli.py b/tests/test_cli.py
index ea749a3..0697b8e 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -1,5 +1,6 @@
 """Tests for the ellphi CLI."""
 
+import runpy
 import unittest.mock
 from contextlib import redirect_stdout
 import io
@@ -17,6 +18,26 @@ def test_cli_version():
     assert output == version_info()
 
 
+def test_cli_build_info():
+    """Test the --build-info argument."""
+    with unittest.mock.patch("sys.argv", ["ellphi", "--build-info"]):
+        with io.StringIO() as buf, redirect_stdout(buf):
+            _main()
+            output = buf.getvalue().strip().splitlines()
+    assert len(output) == 1
+    assert output[0].startswith("BuildInfo(")
+
+
+def test_cli_version_and_build_info():
+    """Test --version and --build-info together."""
+    with unittest.mock.patch("sys.argv", ["ellphi", "--version", "--build-info"]):
+        with io.StringIO() as buf, redirect_stdout(buf):
+            _main()
+            output = buf.getvalue().strip().splitlines()
+    assert output[0] == version_info()
+    assert output[1].startswith("BuildInfo(")
+
+
 def test_cli_help():
     """Test the --help argument."""
     with unittest.mock.patch("sys.argv", ["ellphi", "--help"]):
@@ -34,3 +55,12 @@ def test_cli_no_args():
             _main()
             output = buf.getvalue()
     assert "usage: ellphi" in output
+
+
+def test_cli_module_entrypoint():
+    """Test running the module entry point."""
+    with unittest.mock.patch("sys.argv", ["ellphi", "--version"]):
+        with io.StringIO() as buf, redirect_stdout(buf):
+            runpy.run_module("ellphi", run_name="__main__")
+            output = buf.getvalue().strip()
+    assert output == version_info()
diff --git a/tests/test_coef_from_cov_grad.py b/tests/test_coef_from_cov_grad.py
new file mode 100644
index 0000000..367f029
--- /dev/null
+++ b/tests/test_coef_from_cov_grad.py
@@ -0,0 +1,228 @@
+"""Tests for ellphi.grad.coef_from_cov_grad."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from ellphi import coef_from_cov_grad
+from ellphi.geometry import coef_from_cov
+
+from .factories import random_covariance
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _numerical_jacobian_X(centers, covs, scale, h=1e-6):
+    """Central FD Jacobian of coef_from_cov w.r.t. centers."""
+    n, d = centers.shape
+    coefs0 = coef_from_cov(centers, covs, scale=scale)
+    m = coefs0.shape[-1]
+    jac = np.zeros((n, m, n, d))
+    for i in range(n):
+        for j in range(d):
+            X_plus = centers.copy()
+            X_plus[i, j] += h
+            X_minus = centers.copy()
+            X_minus[i, j] -= h
+            c_plus = coef_from_cov(X_plus, covs, scale=scale)
+            c_minus = coef_from_cov(X_minus, covs, scale=scale)
+            jac[:, :, i, j] = (c_plus - c_minus) / (2 * h)
+    return jac
+
+
+def _numerical_jacobian_cov(centers, covs, scale, h=1e-6):
+    """Central FD Jacobian of coef_from_cov w.r.t. cov entries.
+
+    Perturbs each entry of the symmetric covariance matrix while maintaining
+    symmetry. For the symmetric parameterization, we perturb both (j,k) and
+    (k,j) together and split the off-diagonal derivative evenly across the
+    returned matrix entries.
+    """
+    n, d = centers.shape
+    coefs0 = coef_from_cov(centers, covs, scale=scale)
+    m = coefs0.shape[-1]
+    jac = np.zeros((n, m, n, d, d))
+    for i in range(n):
+        for j in range(d):
+            for k in range(j, d):
+                cov_plus = covs.copy()
+                cov_plus[i, j, k] += h
+                if j != k:
+                    cov_plus[i, k, j] += h
+                cov_minus = covs.copy()
+                cov_minus[i, j, k] -= h
+                if j != k:
+                    cov_minus[i, k, j] -= h
+                c_plus = coef_from_cov(centers, cov_plus, scale=scale)
+                c_minus = coef_from_cov(centers, cov_minus, scale=scale)
+                deriv = (c_plus - c_minus) / (2 * h)
+                if j == k:
+                    jac[:, :, i, j, k] = deriv
+                else:
+                    jac[:, :, i, j, k] = 0.5 * deriv
+                    jac[:, :, i, k, j] = 0.5 * deriv
+    return jac
+
+
+def _check_vjp_against_fd(centers, covs, scale, rng):
+    """Check VJP matches numerical Jacobian for given inputs."""
+    n, d = centers.shape
+
+    coefs, vjp = coef_from_cov_grad(centers, covs, scale=scale)
+    m = coefs.shape[-1]
+
+    # Check forward pass matches coef_from_cov
+    ref = coef_from_cov(centers, covs, scale=scale)
+    np.testing.assert_allclose(coefs, ref, atol=1e-12)
+
+    # Random upstream gradient
+    g_coefs = rng.standard_normal((n, m))
+    grad_X, grad_cov = vjp(g_coefs)
+
+    # --- Check grad_X via FD ---
+    jac_X = _numerical_jacobian_X(centers, covs, scale)
+    # Expected grad_X[i, j] = sum over (a, b) of g_coefs[a, b] * jac_X[a, b, i, j]
+    expected_grad_X = np.einsum("ab,abij->ij", g_coefs, jac_X)
+    np.testing.assert_allclose(grad_X, expected_grad_X, rtol=1e-5, atol=1e-8)
+
+    # --- Check grad_cov via FD ---
+    np.testing.assert_allclose(grad_cov, np.swapaxes(grad_cov, -2, -1), atol=1e-12)
+    jac_cov = _numerical_jacobian_cov(centers, covs, scale)
+    expected_grad_cov = np.einsum("ab,abijk->ijk", g_coefs, jac_cov)
+    np.testing.assert_allclose(grad_cov, expected_grad_cov, rtol=1e-5, atol=1e-8)
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+
+def test_forward_matches_coef_from_cov_d2(rng):
+    """Forward pass matches coef_from_cov for d=2."""
+    centers = rng.uniform(-5, 5, size=(3, 2))
+    covs = np.stack([random_covariance(rng, dim=2) for _ in range(3)])
+    coefs, _ = coef_from_cov_grad(centers, covs)
+    ref = coef_from_cov(centers, covs)
+    np.testing.assert_allclose(coefs, ref, atol=1e-12)
+
+
+def test_vjp_d2_identity_cov(rng):
+    """VJP correct for d=2 with identity covariance."""
+    centers = rng.uniform(-5, 5, size=(2, 2))
+    covs = np.stack([np.eye(2)] * 2)
+    _check_vjp_against_fd(centers, covs, scale=1.0, rng=rng)
+
+
+def test_vjp_d2_random_cov(rng):
+    """VJP correct for d=2 with random covariances."""
+    centers = rng.uniform(-5, 5, size=(3, 2))
+    covs = np.stack([random_covariance(rng, dim=2) for _ in range(3)])
+    _check_vjp_against_fd(centers, covs, scale=1.0, rng=rng)
+
+
+def test_vjp_d3(rng):
+    """VJP correct for d=3."""
+    centers = rng.uniform(-5, 5, size=(2, 3))
+    covs = np.stack([random_covariance(rng, dim=3) for _ in range(2)])
+    _check_vjp_against_fd(centers, covs, scale=1.0, rng=rng)
+
+
+def test_vjp_rotated_cov(rng):
+    """VJP correct for non-identity covariance with rotation (d=2)."""
+    angle = rng.uniform(0, np.pi)
+    cos, sin = np.cos(angle), np.sin(angle)
+    rot = np.array([[cos, -sin], [sin, cos]])
+    diag = np.diag([3.0, 0.5])
+    cov = rot @ diag @ rot.T
+
+    centers = rng.uniform(-5, 5, size=(2, 2))
+    covs = np.stack([cov, cov])
+    _check_vjp_against_fd(centers, covs, scale=1.0, rng=rng)
+
+
+def test_vjp_with_scale(rng):
+    """VJP correct for non-unit scale values."""
+    centers = rng.uniform(-5, 5, size=(2, 2))
+    covs = np.stack([random_covariance(rng, dim=2) for _ in range(2)])
+    for scale in [0.5, 2.0, 3.7]:
+        _check_vjp_against_fd(centers, covs, scale=scale, rng=rng)
+
+
+def test_vjp_single_ellipsoid(rng):
+    """VJP works for a single ellipsoid (d,) input."""
+    center = rng.uniform(-5, 5, size=(2,))
+    cov = random_covariance(rng, dim=2)
+
+    coefs, vjp = coef_from_cov_grad(center, cov)
+    ref = coef_from_cov(center, cov)
+    np.testing.assert_allclose(coefs, ref, atol=1e-12)
+
+    m = coefs.shape[-1]
+    g = rng.standard_normal((1, m))
+    grad_X, grad_cov = vjp(g)
+    assert grad_X.shape == (1, 2)
+    assert grad_cov.shape == (1, 2, 2)
+
+
+def test_vjp_d4(rng):
+    """VJP correct for d=4 (higher dimension)."""
+    centers = rng.uniform(-3, 3, size=(2, 4))
+    covs = np.stack([random_covariance(rng, dim=4) for _ in range(2)])
+    _check_vjp_against_fd(centers, covs, scale=1.0, rng=rng)
+
+
+def test_grad_shapes(rng):
+    """Output shapes are correct."""
+    n, d = 4, 3
+    centers = rng.uniform(-5, 5, size=(n, d))
+    covs = np.stack([random_covariance(rng, dim=d) for _ in range(n)])
+
+    coefs, vjp = coef_from_cov_grad(centers, covs)
+    m = (d + 1) * (d + 2) // 2
+    assert coefs.shape == (n, m)
+
+    g = rng.standard_normal((n, m))
+    grad_X, grad_cov = vjp(g)
+    assert grad_X.shape == (n, d)
+    assert grad_cov.shape == (n, d, d)
+
+
+# ---------------------------------------------------------------------------
+# Input validation
+# ---------------------------------------------------------------------------
+
+
+def test_batch_size_mismatch():
+    """Mismatched centre/covariance batch sizes raise ValueError."""
+    with pytest.raises(ValueError, match="Mismatch"):
+        coef_from_cov_grad(np.zeros((3, 2)), np.eye(2)[np.newaxis])
+
+
+def test_non_square_cov():
+    """Non-square covariance raises ValueError."""
+    with pytest.raises(ValueError, match="square"):
+        coef_from_cov_grad(np.zeros((2, 2)), np.zeros((2, 3, 2)))
+
+
+def test_dimension_mismatch():
+    """Centre/covariance dimension mismatch raises ValueError."""
+    with pytest.raises(ValueError, match="dimensionality"):
+        coef_from_cov_grad(np.zeros((2, 2)), np.eye(3)[np.newaxis].repeat(2, axis=0))
+
+
+def test_singular_cov_returns_nan():
+    """Singular covariance returns NaN coefficients, matching coef_from_cov."""
+    centers = np.zeros((1, 2))
+    cov_sing = np.zeros((1, 2, 2))
+
+    coefs, vjp = coef_from_cov_grad(centers, cov_sing)
+    ref = coef_from_cov(centers, cov_sing)
+    np.testing.assert_array_equal(np.isnan(coefs), np.isnan(ref))
+
+    grad_X, grad_cov = vjp(np.ones_like(coefs))
+    assert np.all(np.isnan(grad_X))
+    assert np.all(np.isnan(grad_cov))
diff --git a/tests/test_cpp_backend.py b/tests/test_cpp_backend.py
index 9a95b96..5e3f03a 100644
--- a/tests/test_cpp_backend.py
+++ b/tests/test_cpp_backend.py
@@ -1,8 +1,12 @@
 from __future__ import annotations
 
+import types
+
 import numpy
 import pytest
 
+import ellphi._tangency_cpp as _cpp
+from ellphi import build_info, cpp_linalg_kind
 from ellphi.geometry import coef_from_cov
 from ellphi.solver import (
     has_cpp_backend,
@@ -24,6 +28,55 @@ def example_coefficients():
     return numpy.stack([p, q, r], axis=0)
 
 
+def test_build_info():
+    info = build_info()
+    assert info.version
+    assert info.backend_default == "auto"
+    assert info.backend_choices == ("auto", "python", "cpp")
+    assert info.cpp_backend_available == has_cpp_backend()
+    assert info.cpp_linalg_kind == cpp_linalg_kind()
+    if has_cpp_backend():
+        assert info.cpp_linalg_kind in {"eigen", "internal"}
+        assert info.cpp_backend_version is not None
+    else:
+        assert info.cpp_linalg_kind is None
+        assert info.cpp_backend_version is None
+
+
+def test_cpp_linalg_kind_missing_backend(monkeypatch):
+    monkeypatch.setattr(_cpp, "_LIB", None)
+    assert _cpp.is_available() is False
+    assert _cpp.linalg_kind() is None
+    assert _cpp.backend_version() is None
+
+
+def test_cpp_linalg_kind_missing_symbol(monkeypatch):
+    monkeypatch.setattr(_cpp, "_LIB", types.SimpleNamespace())
+    with pytest.raises(RuntimeError, match="linear algebra metadata"):
+        _cpp.linalg_kind()
+
+
+def test_cpp_linalg_kind_empty_value(monkeypatch):
+    def _linalg_kind():
+        return None
+
+    monkeypatch.setattr(
+        _cpp, "_LIB", types.SimpleNamespace(tangency_linalg_kind=_linalg_kind)
+    )
+    with pytest.raises(RuntimeError, match="empty linear algebra kind"):
+        _cpp.linalg_kind()
+
+
+def test_cpp_linalg_kind_dummy_value(monkeypatch):
+    def _linalg_kind():
+        return b"eigen"
+
+    monkeypatch.setattr(
+        _cpp, "_LIB", types.SimpleNamespace(tangency_linalg_kind=_linalg_kind)
+    )
+    assert _cpp.linalg_kind() == "eigen"
+
+
 @pytest.mark.skipif(not has_cpp_backend(), reason="C++ backend not available")
 def test_tangency_cpp_matches_python(example_coefficients):
     p, q, _ = example_coefficients
diff --git a/tests/test_cpp_backend_edge_cases.py b/tests/test_cpp_backend_edge_cases.py
new file mode 100644
index 0000000..5739740
--- /dev/null
+++ b/tests/test_cpp_backend_edge_cases.py
@@ -0,0 +1,160 @@
+import types
+from importlib.metadata import PackageNotFoundError
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+import ellphi._tangency_cpp as _cpp
+
+
+def test_library_suffix_prefers_sysconfig(monkeypatch):
+    monkeypatch.setattr(_cpp.sysconfig, "get_config_var", lambda _: ".pyd")
+    assert _cpp._library_suffix() == ".pyd"
+
+
+@pytest.mark.parametrize(
+    "platform, expected",
+    [
+        ("win32", ".dll"),
+        ("darwin", ".dylib"),
+        ("linux", ".so"),
+    ],
+)
+def test_library_suffix_platform_fallback(monkeypatch, platform, expected):
+    monkeypatch.setattr(_cpp.sysconfig, "get_config_var", lambda _: None)
+    monkeypatch.setattr(_cpp.sys, "platform", platform)
+    assert _cpp._library_suffix() == expected
+
+
+def test_expected_backend_version_uses_package_metadata(monkeypatch):
+    monkeypatch.setattr(_cpp, "__version__", "0+unknown")
+    monkeypatch.setattr(_cpp, "package_version", lambda _: "9.9.9")
+    assert _cpp._expected_backend_version() == "9.9.9"
+
+
+def test_expected_backend_version_falls_back_on_missing_package(monkeypatch):
+    monkeypatch.setattr(_cpp, "__version__", "0+unknown")
+
+    def raise_not_found(_: str) -> str:
+        raise PackageNotFoundError
+
+    monkeypatch.setattr(_cpp, "package_version", raise_not_found)
+    assert _cpp._expected_backend_version() == "0+unknown"
+
+
+def test_library_version_requires_metadata():
+    with pytest.raises(RuntimeError, match="version metadata"):
+        _cpp._library_version(types.SimpleNamespace())
+
+
+def test_library_version_rejects_empty_value():
+    def version_func():
+        return None
+
+    lib = types.SimpleNamespace(tangency_backend_version=version_func)
+    with pytest.raises(RuntimeError, match="empty version string"):
+        _cpp._library_version(lib)
+
+
+def test_library_version_decodes_bytes():
+    def version_func():
+        return b"1.2.3"
+
+    lib = types.SimpleNamespace(tangency_backend_version=version_func)
+    assert _cpp._library_version(lib) == "1.2.3"
+
+
+def test_validate_library_version_reports_mismatch(monkeypatch):
+    monkeypatch.setattr(_cpp, "_expected_backend_version", lambda: "expected")
+    monkeypatch.setattr(_cpp, "_library_version", lambda _: "actual")
+    with pytest.raises(RuntimeError, match="version mismatch"):
+        _cpp._validate_library_version(object())
+
+
+def test_load_library_raises_on_missing_file(monkeypatch, tmp_path):
+    missing = tmp_path / "missing.so"
+    monkeypatch.setattr(_cpp, "_library_path", lambda: Path(missing))
+    with pytest.raises(FileNotFoundError, match="library missing"):
+        _cpp._load_library()
+
+
+def test_ensure_available_uses_error_message(monkeypatch):
+    monkeypatch.setattr(_cpp, "_LIB", None)
+    monkeypatch.setattr(_cpp, "_LIB_ERROR", "boom")
+    with pytest.raises(RuntimeError, match="boom"):
+        _cpp._ensure_available()
+
+
+def test_ensure_available_defaults_message(monkeypatch):
+    monkeypatch.setattr(_cpp, "_LIB", None)
+    monkeypatch.setattr(_cpp, "_LIB_ERROR", None)
+    with pytest.raises(RuntimeError, match="C\\+\\+ backend not available"):
+        _cpp._ensure_available()
+
+
+@pytest.mark.parametrize(
+    "message, exc_type",
+    [
+        ("x0 must be provided for Newton method", ValueError),
+        ("Degenerate conic", ZeroDivisionError),
+        ("other", RuntimeError),
+        ("", RuntimeError),
+    ],
+)
+def test_raise_backend_error(message, exc_type):
+    with pytest.raises(exc_type):
+        _cpp._raise_backend_error(message)
+
+
+def test_tangency_rejects_non_1d_inputs(monkeypatch):
+    def tangency_solve(*_args, **_kwargs):
+        return 0
+
+    dummy_lib = types.SimpleNamespace(tangency_solve=tangency_solve)
+    monkeypatch.setattr(_cpp, "_ensure_available", lambda: dummy_lib)
+    pcoef = np.zeros((1, 6))
+    qcoef = np.zeros((6,))
+    with pytest.raises(ValueError, match="one-dimensional"):
+        _cpp.tangency(
+            pcoef,
+            qcoef,
+            method="brentq",
+            bracket=(0.0, 1.0),
+            x0=None,
+            hybrid_bracket_maxiter=1,
+            hybrid_newton_maxiter=1,
+            failsafe=True,
+        )
+
+
+def test_tangency_rejects_shape_mismatch(monkeypatch):
+    def tangency_solve(*_args, **_kwargs):
+        return 0
+
+    dummy_lib = types.SimpleNamespace(tangency_solve=tangency_solve)
+    monkeypatch.setattr(_cpp, "_ensure_available", lambda: dummy_lib)
+    pcoef = np.zeros((6,))
+    qcoef = np.zeros((7,))
+    with pytest.raises(ValueError, match="same length"):
+        _cpp.tangency(
+            pcoef,
+            qcoef,
+            method="brentq",
+            bracket=(0.0, 1.0),
+            x0=None,
+            hybrid_bracket_maxiter=1,
+            hybrid_newton_maxiter=1,
+            failsafe=True,
+        )
+
+
+def test_pdist_tangency_rejects_invalid_shape(monkeypatch):
+    def pdist_tangency(*_args, **_kwargs):
+        return 0
+
+    dummy_lib = types.SimpleNamespace(pdist_tangency=pdist_tangency)
+    monkeypatch.setattr(_cpp, "_ensure_available", lambda: dummy_lib)
+    coef = np.zeros((6,))
+    with pytest.raises(ValueError, match="shape \\(m, n\\)"):
+        _cpp.pdist_tangency(coef)
diff --git a/tests/test_differentiable_solver.py b/tests/test_differentiable_solver.py
index e49775e..0b9ba39 100644
--- a/tests/test_differentiable_solver.py
+++ b/tests/test_differentiable_solver.py
@@ -1,6 +1,8 @@
 import numpy as np
 import pytest
+from types import SimpleNamespace
 
+import ellphi.differentiable_solver as diff_solver
 from ellphi.differentiable_solver import (
     solve_mu_gradients,
     solve_mu_numerical_diff,
@@ -96,3 +98,18 @@ def test_analytic_gradients_high_dimension(rng):
 
     mu_direct = solve_mu(p, q)
     assert mu == pytest.approx(mu_direct)
+
+
+def test_solve_mu_gradients_rejects_zero_derivative(monkeypatch, rng):
+    p, q = random_coef_pair(rng)
+
+    def fake_pencil(*_args, **_kwargs):
+        return SimpleNamespace(center=np.zeros(2))
+
+    monkeypatch.setattr(diff_solver, "build_tangent_pencil", fake_pencil)
+    monkeypatch.setattr(diff_solver, "target_prime_from_pencil", lambda *_args: 0.0)
+
+    with pytest.raises(
+        ZeroDivisionError, match="Derivative with respect to mu is numerically zero"
+    ):
+        diff_solver.solve_mu_gradients(p, q, mu=0.5)
diff --git a/tests/test_ellcloud.py b/tests/test_ellcloud.py
index 0e362ce..0a3f5a8 100644
--- a/tests/test_ellcloud.py
+++ b/tests/test_ellcloud.py
@@ -1,8 +1,13 @@
 import numpy as np
 import pytest
+import matplotlib.pyplot as plt
+from scipy.spatial.distance import squareform
 
 from ellphi.ellcloud import EllipseCloud
-from ellphi.geometry import coef_from_cov
+from ellphi.geometry import coef_from_axes, coef_from_cov
+from ellphi.solver import pdist_tangency
+
+from .factories import random_cloud
 
 
 def test_local_cov_uses_actual_neighbourhood_size():
@@ -96,6 +101,179 @@ def test_post_init_value_errors():
         EllipseCloud(coef, mean, cov, 2, np.zeros((3, 2)))
 
 
+def test_post_init_validates_mean_shape():
+    coef = np.stack(
+        [
+            coef_from_axes([0.0, 0.0], 1.0, 1.0, 0.0),
+            coef_from_axes([1.0, 0.5], 1.5, 1.2, 0.3),
+        ],
+        axis=0,
+    )
+    mean = np.zeros((3, 2))
+    cov = np.stack([np.eye(2), np.eye(2)], axis=0)
+    nbd = np.zeros((2, 0), dtype=int)
+    with pytest.raises(ValueError, match="Mean array has shape"):
+        EllipseCloud(coef, mean, cov, k=0, nbd=nbd)
+
+
+def test_post_init_validates_cov_shape():
+    coef = np.stack(
+        [
+            coef_from_axes([0.0, 0.0], 1.0, 1.0, 0.0),
+            coef_from_axes([1.0, 0.5], 1.5, 1.2, 0.3),
+        ],
+        axis=0,
+    )
+    mean = np.zeros((2, 2))
+    cov = np.zeros((2, 3, 3))
+    nbd = np.zeros((2, 0), dtype=int)
+    with pytest.raises(ValueError, match="Covariance array has shape"):
+        EllipseCloud(coef, mean, cov, k=0, nbd=nbd)
+
+
+def test_post_init_validates_neighbourhood_shape():
+    coef = np.stack(
+        [
+            coef_from_axes([0.0, 0.0], 1.0, 1.0, 0.0),
+            coef_from_axes([1.0, 0.5], 1.5, 1.2, 0.3),
+        ],
+        axis=0,
+    )
+    mean = np.zeros((2, 2))
+    cov = np.stack([np.eye(2), np.eye(2)], axis=0)
+    nbd = np.zeros((3, 1), dtype=int)
+    with pytest.raises(ValueError, match="Neighbourhood index array"):
+        EllipseCloud(coef, mean, cov, k=0, nbd=nbd)
+
+
+def test_iter_returns_coefficients():
+    coef = np.stack(
+        [
+            coef_from_axes([0.0, 0.0], 1.0, 1.0, 0.0),
+            coef_from_axes([1.0, 0.5], 1.5, 1.2, 0.3),
+        ],
+        axis=0,
+    )
+    mean = np.zeros((2, 2))
+    cov = np.stack([np.eye(2), np.eye(2)], axis=0)
+    nbd = np.zeros((2, 0), dtype=int)
+    cloud = EllipseCloud(coef, mean, cov, k=0, nbd=nbd)
+    first = next(iter(cloud))
+    np.testing.assert_allclose(first, coef[0])
+
+
+def test_plot_creates_axes_and_patches():
+    coef = np.stack(
+        [
+            coef_from_axes([0.0, 0.0], 1.0, 1.0, 0.0),
+            coef_from_axes([1.0, 0.5], 1.5, 1.2, 0.3),
+        ],
+        axis=0,
+    )
+    mean = np.array([[0.0, 0.0], [1.0, 0.5]])
+    cov = np.stack([np.eye(2), np.eye(2)], axis=0)
+    nbd = np.zeros((2, 0), dtype=int)
+    cloud = EllipseCloud(coef, mean, cov, k=0, nbd=nbd)
+    ax = cloud.plot()
+    assert len(ax.patches) == 2
+    plt.close(ax.figure)
+
+
+def test_from_cov_accepts_single_2d_sample():
+    center = np.array([1.0, 2.0])
+    cov = np.array([[2.0, 0.1], [0.1, 1.5]])
+
+    cloud = EllipseCloud.from_cov(center, cov)
+
+    assert cloud.k == 0
+    assert cloud.n == 1
+    assert cloud.nbd.shape == (1, 0)
+    np.testing.assert_allclose(cloud.mean, center[np.newaxis, :])
+    np.testing.assert_allclose(cloud.cov, cov[np.newaxis, :, :])
+    np.testing.assert_allclose(cloud.coef, coef_from_cov(center, cov))
+
+
+def test_from_cov_matches_manual_constructor_3d(rng):
+    expected = random_cloud(rng, n_ellipses=4, dim=3)
+
+    cloud = EllipseCloud.from_cov(expected.mean, expected.cov)
+
+    assert cloud.k == 0
+    assert cloud.nbd.shape == (expected.n, 0)
+    np.testing.assert_allclose(cloud.mean, expected.mean)
+    np.testing.assert_allclose(cloud.cov, expected.cov)
+    np.testing.assert_allclose(cloud.coef, expected.coef)
+
+
+@pytest.mark.parametrize("method", ["median", "average"])
+def test_from_cov_rescaling_matches_manual_rescale(rng, method):
+    original = random_cloud(rng, n_ellipses=5)
+    manual = EllipseCloud(
+        coef=original.coef.copy(),
+        mean=original.mean.copy(),
+        cov=original.cov.copy(),
+        k=0,
+        nbd=np.empty((original.n, 0), dtype=int),
+    )
+    manual.rescale(method=method)
+
+    cloud = EllipseCloud.from_cov(
+        original.mean.copy(),
+        original.cov.copy(),
+        rescaling=method,
+    )
+
+    np.testing.assert_allclose(cloud.mean, manual.mean)
+    np.testing.assert_allclose(cloud.cov, manual.cov)
+    np.testing.assert_allclose(cloud.coef, manual.coef)
+
+
+def test_pdist_tangency_wrapper_matches_solver(rng):
+    cloud = random_cloud(rng, n_ellipses=4)
+    wrapper = cloud.pdist_tangency(backend="python", parallel=False)
+    direct = pdist_tangency(cloud, backend="python", parallel=False)
+    np.testing.assert_allclose(wrapper, direct)
+
+
+def test_distance_matrix_matches_squareform(rng):
+    cloud = random_cloud(rng, n_ellipses=4)
+
+    matrix = cloud.distance_matrix(backend="python", parallel=False)
+    expected = squareform(cloud.pdist_tangency(backend="python", parallel=False))
+
+    assert matrix.shape == (cloud.n, cloud.n)
+    np.testing.assert_allclose(matrix, expected)
+
+
+def test_distance_matrix_empty_cloud_returns_empty_square():
+    cloud = EllipseCloud(
+        coef=np.empty((0, 6), dtype=float),
+        mean=np.empty((0, 2), dtype=float),
+        cov=np.empty((0, 2, 2), dtype=float),
+        k=0,
+        nbd=np.empty((0, 0), dtype=int),
+    )
+
+    matrix = cloud.distance_matrix()
+
+    assert matrix.shape == (0, 0)
+
+
+def test_from_cov_rescaling_does_not_mutate_inputs(rng):
+    original = random_cloud(rng, n_ellipses=5)
+    means = original.mean.copy()
+    covs = original.cov.copy()
+    means_before = means.copy()
+    covs_before = covs.copy()
+
+    cloud = EllipseCloud.from_cov(means, covs, rescaling="median")
+
+    np.testing.assert_allclose(means, means_before)
+    np.testing.assert_allclose(covs, covs_before)
+    assert not np.shares_memory(cloud.mean, means)
+    assert not np.shares_memory(cloud.cov, covs)
+
+
 def test_str_method():
     X = np.array([[0.0, 0.0], [1.0, 1.0], [2.0, 0.0]])
     ellcloud = EllipseCloud.from_local_cov(X, k=3)
diff --git a/tests/test_ellcloud_rescale.py b/tests/test_ellcloud_rescale.py
index 3cad35b..2c48c85 100644
--- a/tests/test_ellcloud_rescale.py
+++ b/tests/test_ellcloud_rescale.py
@@ -1,4 +1,4 @@
-"""Regression tests for :mod:`ellphi.ellcloud`."""
+"""Regression tests for `ellphi.ellcloud`."""
 
 from __future__ import annotations
 
diff --git a/tests/test_geometry_edge_cases.py b/tests/test_geometry_edge_cases.py
new file mode 100644
index 0000000..fcffb2a
--- /dev/null
+++ b/tests/test_geometry_edge_cases.py
@@ -0,0 +1,63 @@
+import numpy as np
+import pytest
+
+from ellphi.geometry import (
+    coef_from_cov,
+    infer_dim_from_coef_length,
+    pack_conic,
+    unpack_conic,
+    unpack_single_conic,
+)
+
+
+def test_infer_dim_from_coef_length_rejects_invalid_discriminant():
+    with pytest.raises(ValueError, match="symmetric quadratic form"):
+        infer_dim_from_coef_length(7)
+
+
+def test_pack_conic_requires_square_matrix():
+    matrix = np.zeros((2, 3))
+    linear = np.zeros(3)
+    with pytest.raises(ValueError, match="Quadratic matrices must have shape"):
+        pack_conic(matrix, linear, 0.0)
+
+
+def test_pack_conic_requires_linear_match():
+    matrix = np.eye(2)
+    linear = np.zeros(3)
+    with pytest.raises(ValueError, match="Linear term incompatible"):
+        pack_conic(matrix, linear, 0.0)
+
+
+def test_unpack_conic_rejects_invalid_rank():
+    coef = np.zeros((2, 2, 2))
+    with pytest.raises(ValueError, match="one- or two-dimensional"):
+        unpack_conic(coef)
+
+
+def test_unpack_single_conic_rejects_multiple_entries():
+    centers = np.array([[0.0, 0.0], [1.0, 1.0]])
+    covs = np.stack([np.eye(2), 2.0 * np.eye(2)])
+    coef = coef_from_cov(centers, covs)
+    with pytest.raises(ValueError, match="Expected coefficients for a single conic"):
+        unpack_single_conic(coef)
+
+
+@pytest.mark.parametrize(
+    "centers, covs, message",
+    [
+        (np.zeros((2, 2)), np.zeros((1, 2, 2)), "number of centres"),
+        (np.zeros((1, 2)), np.zeros((1, 2, 3)), "Covariance matrices must be square"),
+        (np.zeros((1, 3)), np.zeros((1, 2, 2)), "dimensionality"),
+    ],
+)
+def test_coef_from_cov_validates_shapes(centers, covs, message):
+    with pytest.raises(ValueError, match=message):
+        coef_from_cov(centers, covs)
+
+
+def test_coef_from_cov_singular_returns_nan():
+    center = np.array([0.0, 0.0])
+    cov = np.array([[1.0, 0.0], [0.0, 0.0]])
+    coef = coef_from_cov(center, cov)
+    assert np.isnan(coef).all()
diff --git a/tests/test_grad.py b/tests/test_grad.py
new file mode 100644
index 0000000..981ebae
--- /dev/null
+++ b/tests/test_grad.py
@@ -0,0 +1,175 @@
+"""Tests for ellphi.grad (tangency_grad and pdist_tangency_grad)."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from ellphi import tangency_grad, pdist_tangency_grad, pdist_tangency, TangencyGrad
+from ellphi.solver import tangency
+
+from .factories import random_coef_pair, random_covariance
+from ellphi.geometry import coef_from_cov
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _finite_diff_t_dp(p, q, h=1e-6):
+    """Central finite difference of tangency().t w.r.t. p."""
+    grad = np.zeros_like(p)
+    for i in range(len(p)):
+        p_plus = p.copy()
+        p_plus[i] += h
+        p_minus = p.copy()
+        p_minus[i] -= h
+        grad[i] = (tangency(p_plus, q).t - tangency(p_minus, q).t) / (2 * h)
+    return grad
+
+
+def _finite_diff_t_dq(p, q, h=1e-6):
+    """Central finite difference of tangency().t w.r.t. q."""
+    grad = np.zeros_like(q)
+    for i in range(len(q)):
+        q_plus = q.copy()
+        q_plus[i] += h
+        q_minus = q.copy()
+        q_minus[i] -= h
+        grad[i] = (tangency(p, q_plus).t - tangency(p, q_minus).t) / (2 * h)
+    return grad
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+
+def test_tangency_grad_dt_dp(rng):
+    """tangency_grad().dt_dp matches central finite differences."""
+    p, q = random_coef_pair(rng)
+    g = tangency_grad(p, q)
+    fd = _finite_diff_t_dp(p, q)
+    np.testing.assert_allclose(g.dt_dp, fd, rtol=1e-5, atol=1e-8)
+
+
+def test_tangency_grad_dt_dq(rng):
+    """tangency_grad().dt_dq matches central finite differences."""
+    p, q = random_coef_pair(rng)
+    g = tangency_grad(p, q)
+    fd = _finite_diff_t_dq(p, q)
+    np.testing.assert_allclose(g.dt_dq, fd, rtol=1e-5, atol=1e-8)
+
+
+def test_tangency_grad_returns_correct_t(rng):
+    """tangency_grad().t equals tangency().t."""
+    p, q = random_coef_pair(rng)
+    g = tangency_grad(p, q)
+    ref = tangency(p, q).t
+    assert g.t == pytest.approx(ref)
+
+
+def test_tangency_grad_is_frozen_dataclass(rng):
+    """TangencyGrad is immutable."""
+    p, q = random_coef_pair(rng)
+    g = tangency_grad(p, q)
+    assert isinstance(g, TangencyGrad)
+    with pytest.raises((AttributeError, TypeError)):
+        g.t = 999.0  # type: ignore[misc]
+
+
+def test_pdist_tangency_grad_values_match(rng):
+    """pdist_tangency_grad distances equal pdist_tangency to high precision."""
+    n = 5
+    means = rng.uniform(-20.0, 20.0, size=(n, 2))
+    covs = np.stack([random_covariance(rng) for _ in range(n)])
+    coefs = coef_from_cov(means, covs)
+
+    dists, _ = pdist_tangency_grad(coefs)
+    ref = pdist_tangency(coefs)
+
+    np.testing.assert_allclose(dists, ref, atol=1e-12)
+
+
+def test_pdist_tangency_grad_vjp(rng):
+    """VJP directional-derivative check: g·dists_pert ≈ g·vjp(g)."""
+    n = 4
+    means = rng.uniform(-20.0, 20.0, size=(n, 2))
+    covs = np.stack([random_covariance(rng) for _ in range(n)])
+    coefs = coef_from_cov(means, covs)
+
+    dists, vjp = pdist_tangency_grad(coefs)
+    n_pairs = len(dists)
+
+    # Random upstream gradient vector
+    g_upstream = rng.standard_normal(n_pairs)
+    g_coefs = vjp(g_upstream)
+
+    # Directional derivative via finite difference
+    h = 1e-5
+    direction = rng.standard_normal(coefs.shape)
+    direction /= np.linalg.norm(direction)
+    dists_plus, _ = pdist_tangency_grad(coefs + h * direction)
+    dists_minus, _ = pdist_tangency_grad(coefs - h * direction)
+    fd_directional = np.dot(g_upstream, (dists_plus - dists_minus) / (2 * h))
+
+    # VJP directional: <g_coefs, direction>
+    vjp_directional = float(np.dot(g_coefs.ravel(), direction.ravel()))
+
+    assert fd_directional == pytest.approx(vjp_directional, rel=1e-4)
+
+
+def test_tangency_grad_accepts_list_input(rng):
+    """tangency_grad accepts list inputs, matching tangency() behaviour."""
+    p, q = random_coef_pair(rng)
+    g_arr = tangency_grad(p, q)
+    g_list = tangency_grad(list(p), list(q))
+    assert g_list.t == pytest.approx(g_arr.t)
+    np.testing.assert_array_equal(g_list.dt_dp, g_arr.dt_dp)
+    np.testing.assert_array_equal(g_list.dt_dq, g_arr.dt_dq)
+
+
+def test_tangency_grad_accepts_row_vector_input(rng):
+    """tangency_grad accepts (1, m) row-vector inputs."""
+    p, q = random_coef_pair(rng)
+    g_arr = tangency_grad(p, q)
+    g_row = tangency_grad(p[None, :], q[None, :])
+    assert g_row.t == pytest.approx(g_arr.t)
+    np.testing.assert_array_equal(g_row.dt_dp, g_arr.dt_dp)
+    np.testing.assert_array_equal(g_row.dt_dq, g_arr.dt_dq)
+
+
+def test_pdist_tangency_grad_rejects_bad_shape(rng):
+    """pdist_tangency_grad raises ValueError for unsupported array layouts."""
+    n = 4
+    means = rng.uniform(-20.0, 20.0, size=(n, 2))
+    covs = np.stack([random_covariance(rng) for _ in range(n)])
+    coefs = coef_from_cov(means, covs)
+
+    with pytest.raises(ValueError, match="Expected coefficient array"):
+        pdist_tangency_grad(coefs[:, :, None])  # (N, m, 1) — wrong layout
+
+
+def test_pdist_tangency_grad_accepts_n1m_input(rng):
+    """pdist_tangency_grad accepts (N, 1, m) arrays, matching pdist_tangency()."""
+    n = 4
+    means = rng.uniform(-20.0, 20.0, size=(n, 2))
+    covs = np.stack([random_covariance(rng) for _ in range(n)])
+    coefs = coef_from_cov(means, covs)
+
+    dists_2d, _ = pdist_tangency_grad(coefs)
+    dists_3d, _ = pdist_tangency_grad(coefs[:, None, :])  # (N, 1, m)
+    np.testing.assert_array_equal(dists_2d, dists_3d)
+
+
+def test_tangency_grad_3d(rng):
+    """tangency_grad works for 3-D ellipsoids."""
+    p, q = random_coef_pair(rng, dim=3)
+    g = tangency_grad(p, q)
+    assert g.dt_dp.shape == p.shape
+    assert g.dt_dq.shape == q.shape
+    fd_dp = _finite_diff_t_dp(p, q)
+    fd_dq = _finite_diff_t_dq(p, q)
+    np.testing.assert_allclose(g.dt_dp, fd_dp, rtol=1e-5, atol=1e-8)
+    np.testing.assert_allclose(g.dt_dq, fd_dq, rtol=1e-5, atol=1e-8)
diff --git a/tests/test_init.py b/tests/test_init.py
index 8a9d57c..d09fd1c 100644
--- a/tests/test_init.py
+++ b/tests/test_init.py
@@ -1,4 +1,4 @@
-"""Tests for the :mod:`ellphi` package initialisation."""
+"""Tests for the `ellphi` package initialisation."""
 
 import importlib
 import sys
diff --git a/tests/test_solver_dispatch_edge_cases.py b/tests/test_solver_dispatch_edge_cases.py
new file mode 100644
index 0000000..820f085
--- /dev/null
+++ b/tests/test_solver_dispatch_edge_cases.py
@@ -0,0 +1,51 @@
+import numpy as np
+import pytest
+
+import ellphi.solver as solver_mod
+from ellphi.geometry import coef_from_axes
+
+from .factories import random_cloud
+
+
+def test_extract_coef_array_requires_2d():
+    with pytest.raises(ValueError, match="shape"):
+        solver_mod._extract_coef_array(np.zeros(6))
+
+
+def test_should_use_cpp_rejects_unknown_backend():
+    with pytest.raises(ValueError, match="Unknown backend"):
+        solver_mod._should_use_cpp("unknown")
+
+
+def test_should_use_cpp_python_returns_false():
+    assert solver_mod._should_use_cpp("python") is False
+
+
+def test_should_use_cpp_requires_available_backend(monkeypatch):
+    monkeypatch.setattr(solver_mod._cpp, "is_available", lambda: False)
+    with pytest.raises(RuntimeError, match="C\\+\\+ backend requested"):
+        solver_mod._should_use_cpp("cpp")
+
+
+def test_normalize_method_rejects_unknown():
+    with pytest.raises(ValueError, match="Unknown method"):
+        solver_mod._normalize_method("not-a-method")
+
+
+def test_tangency_rejects_mismatched_coefficients():
+    p = np.zeros(6)
+    q = np.zeros(7)
+    with pytest.raises(ValueError, match="same length"):
+        solver_mod.tangency(p, q, backend="python")
+
+
+def test_tangency_rejects_unknown_backend():
+    p = coef_from_axes([0.0, 0.0], 1.0, 1.0, 0.0)
+    with pytest.raises(ValueError, match="Unknown backend"):
+        solver_mod.tangency(p, p, backend="unknown")
+
+
+def test_pdist_tangency_rejects_unknown_backend(rng):
+    cloud = random_cloud(rng, n_ellipses=2)
+    with pytest.raises(ValueError, match="Unknown backend"):
+        solver_mod.pdist_tangency(cloud, backend="unknown")
diff --git a/tests/test_solver_python_edge_cases.py b/tests/test_solver_python_edge_cases.py
new file mode 100644
index 0000000..0fb9fca
--- /dev/null
+++ b/tests/test_solver_python_edge_cases.py
@@ -0,0 +1,253 @@
+import numpy as np
+import pytest
+from types import SimpleNamespace
+
+import ellphi._solver_python as solver_py
+from ellphi.geometry import coef_from_axes
+
+from .factories import random_cloud, random_coef_pair
+
+
+def test_gaussian_elimination_requires_square_matrix():
+    matrix = np.zeros((2, 3))
+    rhs = np.zeros(2)
+    with pytest.raises(np.linalg.LinAlgError, match="square"):
+        solver_py._gaussian_elimination(matrix, rhs)
+
+
+def test_gaussian_elimination_detects_singular_pivot():
+    matrix = np.array([[0.0, 1.0], [0.0, 2.0]])
+    rhs = np.array([1.0, 2.0])
+    with pytest.raises(np.linalg.LinAlgError, match="singular"):
+        solver_py._gaussian_elimination(matrix, rhs)
+
+
+def test_gaussian_elimination_detects_zero_diagonal():
+    matrix = np.array([[1.0, 1.0], [2.0, 2.0]])
+    rhs = np.array([1.0, 2.0])
+    with pytest.raises(np.linalg.LinAlgError, match="singular"):
+        solver_py._gaussian_elimination(matrix, rhs)
+
+
+def test_quad_eval_dimension_mismatch():
+    coef = coef_from_axes([0.0, 0.0], 1.0, 1.0, 0.0)
+    with pytest.raises(ValueError, match="dimensionality"):
+        solver_py.quad_eval(coef, [0.0, 0.0, 0.0])
+
+
+def test_algsig_newton_nan_x0_fallback_converges():
+    def f(x: float) -> float:
+        return x - 0.5
+
+    def df(x: float) -> float:
+        return 1.0
+
+    result = solver_py._algsig_newton_py(f, df, np.nan, maxiter=3, xtol=1e-12, rtol=0.0)
+    assert result.converged
+    assert result.root == pytest.approx(0.5)
+
+
+def test_algsig_newton_nonfinite_target_returns_failure():
+    def f(x: float) -> float:
+        return np.nan
+
+    def df(x: float) -> float:
+        return 1.0
+
+    result = solver_py._algsig_newton_py(f, df, 0.5, maxiter=2, xtol=1e-12, rtol=0.0)
+    assert not result.converged
+
+
+def test_algsig_newton_zero_derivative_returns_failure():
+    def f(x: float) -> float:
+        return 1.0
+
+    def df(x: float) -> float:
+        return 0.0
+
+    result = solver_py._algsig_newton_py(f, df, 0.5, maxiter=2, xtol=1e-12, rtol=0.0)
+    assert not result.converged
+
+
+def test_algsig_newton_nonfinite_candidate_backtracks_and_fails():
+    def f(x: float) -> float:
+        return 1e308
+
+    def df(x: float) -> float:
+        return 1e-308
+
+    result = solver_py._algsig_newton_py(f, df, 0.5, maxiter=1, xtol=1e-12, rtol=0.0)
+    assert not result.converged
+
+
+def test_algsig_newton_stops_after_max_iterations():
+    def f(x: float) -> float:
+        return x - 0.25
+
+    def df(x: float) -> float:
+        return 1.0
+
+    result = solver_py._algsig_newton_py(f, df, 0.9, maxiter=1, xtol=0.0, rtol=0.0)
+    assert not result.converged
+
+
+def test_target_prime_returns_nan_when_pencil_build_fails(monkeypatch):
+    def raise_error(*args, **kwargs):
+        raise np.linalg.LinAlgError("boom")
+
+    monkeypatch.setattr(solver_py, "build_tangent_pencil", raise_error)
+    p = coef_from_axes([0.0, 0.0], 1.0, 1.0, 0.0)
+    q = coef_from_axes([1.0, 0.0], 1.0, 1.0, 0.0)
+    value = solver_py._target_prime(0.5, p, q)
+    assert np.isnan(value)
+
+
+def test_initial_mu_for_newton_defaults_on_all_nan_scores():
+    def df(mu: float) -> float:
+        return np.nan
+
+    mu = solver_py._initial_mu_for_newton(df)
+    assert mu == 0.5
+
+
+def test_initial_mu_for_newton_handles_exceptions():
+    def df(mu: float) -> float:
+        raise RuntimeError("boom")
+
+    mu = solver_py._initial_mu_for_newton(df)
+    assert mu == 0.5
+
+
+def test_initial_mu_for_newton_prefers_larger_gradient():
+    def df(mu: float) -> float:
+        return 1.0 if mu < 0.5 else 2.0
+
+    mu = solver_py._initial_mu_for_newton(df)
+    assert mu == pytest.approx(1.0 - 1e-5)
+
+
+def test_solve_mu_brentq_newton_failsafe_uses_brentq(rng, monkeypatch):
+    p, q = random_coef_pair(rng)
+    expected = solver_py.solve_mu(p, q, method="brentq")
+
+    def raise_newton(*args, **kwargs):
+        raise RuntimeError("boom")
+
+    monkeypatch.setattr(solver_py, "scipy_newton", raise_newton)
+    result = solver_py.solve_mu(p, q, method="brentq+newton", failsafe=True)
+    assert result == pytest.approx(expected)
+
+
+def test_solve_mu_brentq_newton_rejects_invalid_maxiter(rng):
+    p, q = random_coef_pair(rng)
+    with pytest.raises(ValueError, match="hybrid_bracket_maxiter"):
+        solver_py.solve_mu(
+            p, q, method="brentq+newton", hybrid_bracket_maxiter=0, x0=0.5
+        )
+    with pytest.raises(ValueError, match="hybrid_newton_maxiter"):
+        solver_py.solve_mu(
+            p, q, method="brentq+newton", hybrid_newton_maxiter=0, x0=0.5
+        )
+
+
+def test_solve_mu_brentq_newton_without_failsafe_returns_mu0(rng, monkeypatch):
+    p, q = random_coef_pair(rng)
+    expected = solver_py.solve_mu(p, q, method="brentq")
+
+    def fake_newton(*args, **kwargs):
+        return 0.123, SimpleNamespace(converged=False)
+
+    monkeypatch.setattr(solver_py, "scipy_newton", fake_newton)
+    result = solver_py.solve_mu(p, q, method="brentq+newton", failsafe=False)
+    assert result == pytest.approx(expected)
+
+
+def test_solve_mu_algsig_newton_failsafe_returns_brentq(rng, monkeypatch):
+    p, q = random_coef_pair(rng)
+    expected = solver_py.solve_mu(p, q, method="brentq")
+
+    monkeypatch.setattr(
+        solver_py,
+        "_algsig_newton_py",
+        lambda *args, **kwargs: solver_py.NewtonResult(0.5, False),
+    )
+
+    result = solver_py.solve_mu(p, q, method="algsig+newton", failsafe=True, x0=0.5)
+    assert result == pytest.approx(expected)
+
+
+def test_solve_mu_algsig_newton_without_failsafe_raises(rng, monkeypatch):
+    p, q = random_coef_pair(rng)
+    monkeypatch.setattr(
+        solver_py,
+        "_algsig_newton_py",
+        lambda *args, **kwargs: solver_py.NewtonResult(0.5, False),
+    )
+
+    with pytest.raises(RuntimeError, match="algsig\\+newton failed to converge"):
+        solver_py.solve_mu(p, q, method="algsig+newton", failsafe=False, x0=0.5)
+
+
+def test_solve_mu_newton_nonconverged_failsafe_returns_brentq(rng, monkeypatch):
+    p, q = random_coef_pair(rng)
+    expected = solver_py.solve_mu(p, q, method="brentq")
+
+    def fake_newton(*args, **kwargs):
+        return 0.123, SimpleNamespace(converged=False)
+
+    monkeypatch.setattr(solver_py, "scipy_newton", fake_newton)
+    result = solver_py.solve_mu(p, q, method="newton", x0=0.5, failsafe=True)
+    assert result == pytest.approx(expected)
+
+
+def test_solve_mu_newton_nonconverged_without_failsafe_raises(rng, monkeypatch):
+    p, q = random_coef_pair(rng)
+
+    def fake_newton(*args, **kwargs):
+        return 0.123, SimpleNamespace(converged=False)
+
+    monkeypatch.setattr(solver_py, "scipy_newton", fake_newton)
+    with pytest.raises(RuntimeError, match="Newton method failed to converge"):
+        solver_py.solve_mu(p, q, method="newton", x0=0.5, failsafe=False)
+
+
+def test_solve_mu_rejects_unknown_method(rng):
+    p, q = random_coef_pair(rng)
+    with pytest.raises(ValueError, match="Unknown method"):
+        solver_py.solve_mu(p, q, method="not-a-method")
+
+
+def test_solve_mu_rejects_nonfinite_target(monkeypatch, rng):
+    p, q = random_coef_pair(rng)
+    monkeypatch.setattr(solver_py, "_target", lambda mu, p, q: np.nan)
+    with pytest.raises(RuntimeError, match="Non-finite target value"):
+        solver_py.solve_mu(p, q, method="newton", x0=0.5, failsafe=False)
+
+
+def test_tangency_rejects_mu_outside_bracket(monkeypatch):
+    p = coef_from_axes([0.0, 0.0], 1.0, 1.0, 0.0)
+    q = coef_from_axes([1.0, 0.0], 1.0, 1.0, 0.0)
+    monkeypatch.setattr(solver_py, "solve_mu", lambda *args, **kwargs: 2.0)
+    with pytest.raises(RuntimeError, match="within the bracket"):
+        solver_py.tangency(p, q, bracket=(0.0, 1.0))
+
+
+def test_pdist_tangency_parallel_returns_empty_for_single_ellipse(rng):
+    cloud = random_cloud(rng, n_ellipses=1)
+    result = solver_py._pdist_tangency_parallel(cloud)
+    assert result.shape == (0,)
+    assert result.dtype == float
+
+
+def test_pdist_tangency_serial_branch_matches_serial(rng):
+    cloud = random_cloud(rng, n_ellipses=3)
+    result = solver_py.pdist_tangency(cloud, parallel=False)
+    expected = solver_py._pdist_tangency_serial(cloud)
+    np.testing.assert_allclose(result, expected)
+
+
+def test_pdist_tangency_parallel_branch_matches_parallel(rng):
+    cloud = random_cloud(rng, n_ellipses=3)
+    result = solver_py.pdist_tangency(cloud, parallel=True, n_jobs=1)
+    expected = solver_py._pdist_tangency_parallel(cloud, n_jobs=1)
+    np.testing.assert_allclose(result, expected)
diff --git a/tests/test_visualization.py b/tests/test_visualization.py
index b9a1641..09ef1f5 100644
--- a/tests/test_visualization.py
+++ b/tests/test_visualization.py
@@ -1,6 +1,7 @@
 """Tests for the ellphi visualization module."""
 
 import numpy as np
+from matplotlib.colors import to_rgba
 from matplotlib.patches import Ellipse
 from ellphi.visualization import ellipse_patch
 
@@ -38,3 +39,18 @@ def test_ellipse_patch_direct_params():
     assert np.isclose(patch.width, 2 * r_major)
     assert np.isclose(patch.height, 2 * r_minor)
     assert np.isclose(patch.angle, np.degrees(theta))
+    assert np.allclose(patch.get_facecolor(), to_rgba("none"))
+
+
+def test_ellipse_patch_respects_explicit_facecolor():
+    """Explicit facecolor/fill arguments should not be overridden."""
+    patch = ellipse_patch(
+        [0.0, 0.0],
+        r_major=1.5,
+        r_minor=0.5,
+        theta=0.0,
+        facecolor="steelblue",
+        alpha=0.4,
+    )
+
+    assert np.allclose(patch.get_facecolor(), to_rgba("steelblue", alpha=0.4))