diff --git a/.black.toml b/.black.toml
deleted file mode 100644
index e046b07..0000000
--- a/.black.toml
+++ /dev/null
@@ -1,7 +0,0 @@
-[tool.black]
-target-version = [
-    "py312",
-]
-line-length = 88
-color = true
-exclude = "/(\n    \\.git\n    | \\.hg\n    | \\.mypy_cache\n    | \\.tox\n    | \\.venv\n    | _build\n    | buck-out\n    | build\n    | dist\n    | env\n    | venv\n)/\n"
diff --git a/.editorconfig b/.editorconfig
index 5973eb7..5ae6fc4 100644
--- a/.editorconfig
+++ b/.editorconfig
@@ -14,13 +14,6 @@ end_of_line = lf
 indent_style = space
 indent_size = 4
 
-[*.bat]
-indent_style = tab
-end_of_line = crlf
-
-[Makefile]
-indent_style = tab
-
 [*.{yml, yaml}]
 indent_size = 2
 trim_trailing_whitespace = true
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index d4f24e9..ad2aab3 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -1,11 +1,13 @@
 name: Deploy documentation
 
 on:
-  # Runs on pushes targeting the default branch
   push:
-    branches: ["main"]
-
-  # Allows you to run this workflow manually from the Actions tab
+    branches: [ "main" ]
+    paths:
+      - 'povme/**'
+      - 'docs/**'
+      - 'pixi.toml'
+      - 'pixi.lock'
   workflow_dispatch:
 
 # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
@@ -34,9 +36,14 @@ jobs:
           fetch-depth: 0
 
       - name: Install pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.4
         with:
-          frozen: true
+          environments: docs
+          locked: false
+          frozen: false
+          cache: true
+          cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
+          activate-environment: true
 
       - name: Build documentation
         run: pixi run docs
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 7483d20..50d23a2 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -3,9 +3,18 @@ name: Tests
 on:
   push:
     branches: [ "main" ]
+    paths:
+      - 'povme/**'
+      - 'tests/**'
+      - 'pixi.toml'
+      - 'pixi.lock'
   pull_request:
     branches: [ "main" ]
-  # Allows you to run this workflow manually from the Actions tab
+    paths:
+      - 'povme/**'
+      - 'tests/**'
+      - 'pixi.toml'
+      - 'pixi.lock'
   workflow_dispatch:
 
 jobs:
@@ -13,13 +22,17 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - name: Checkout repo
+      - name: Checkout code
         uses: actions/checkout@v4
 
       - name: Install pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.4
         with:
-          frozen: true
+          locked: false
+          frozen: false
+          cache: true
+          cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
+          activate-environment: true
 
       - name: Run tests
         run: pixi run tests
diff --git a/.gitignore b/.gitignore
index 88d35b1..bd7c1eb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,3 @@
-*.pyc
-.DS_Store
-
 # pixi environments
 .pixi
 *.egg-info
@@ -9,14 +6,755 @@
 api
 public
 
-__pycache__
-.vscode
-build
+# Scalene
+profile*
+
+
+# Created by https://www.toptal.com/developers/gitignore/api/osx,python,pycharm,windows,visualstudio,visualstudiocode,zsh,jetbrains,jupyternotebooks
+# Edit at https://www.toptal.com/developers/gitignore?templates=osx,python,pycharm,windows,visualstudio,visualstudiocode,zsh,jetbrains,jupyternotebooks
+
+### JetBrains ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### JetBrains Patch ###
+
+# Sonarlint plugin
+# https://plugins.jetbrains.com/plugin/7973-sonarlint
+.idea/**/sonarlint/
+
+# SonarQube Plugin
+# https://plugins.jetbrains.com/plugin/7238-sonarqube-community-plugin
+.idea/**/sonarIssues.xml
+
+# Markdown Navigator plugin
+# https://plugins.jetbrains.com/plugin/7896-markdown-navigator-enhanced
+.idea/**/markdown-navigator.xml
+.idea/**/markdown-navigator-enh.xml
+.idea/**/markdown-navigator/
+
+# Cache file creation bug
+# See https://youtrack.jetbrains.com/issue/JBR-2257
+.idea/$CACHE_FILE$
+
+# CodeStream plugin
+# https://plugins.jetbrains.com/plugin/12206-codestream
+.idea/codestream.xml
+
+# Azure Toolkit for IntelliJ plugin
+# https://plugins.jetbrains.com/plugin/8053-azure-toolkit-for-intellij
+.idea/**/azureSettings.xml
+
+### JupyterNotebooks ###
+# gitignore template for Jupyter Notebooks
+# website: http://jupyter.org/
+
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# IPython
+profile_default/
+ipython_config.py
+
+### OSX ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
 
-# Coverage
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
 .coverage
+.coverage.*
+.cache
+nosetests.xml
 coverage.xml
-report.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
 
-# Scalene
-profile*
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+.ionide
+
+### Windows ###
+# Windows thumbnail cache files
+Thumbs.db
+Thumbs.db:encryptable
+ehthumbs.db
+ehthumbs_vista.db
+
+# Dump file
+*.stackdump
+
+# Folder config file
+[Dd]esktop.ini
+
+# Recycle Bin used on file shares
+$RECYCLE.BIN/
+
+# Windows Installer files
+*.cab
+*.msi
+*.msix
+*.msm
+*.msp
+
+# Windows shortcuts
+*.lnk
+
+### Zsh ###
+# Zsh compiled script + zrecompile backup
+*.zwc
+*.zwc.old
+
+# Zsh completion-optimization dumpfile
+*zcompdump*
+
+# Zsh history
+.zsh_history
+
+# Zsh sessions
+.zsh_sessions
+
+# Zsh zcalc history
+.zcalc_history
+
+# A popular plugin manager's files
+._zinit
+.zinit_lstupd
+
+# zdharma/zshelldoc tool's files
+zsdoc/data
+
+# robbyrussell/oh-my-zsh/plugins/per-directory-history plugin's files
+# (when set-up to store the history in the local directory)
+.directory_history
+
+# MichaelAquilina/zsh-autoswitch-virtualenv plugin's files
+# (for Zsh plugins using Python)
+
+# Zunit tests' output
+/tests/_output/*
+!/tests/_output/.gitkeep
+
+### VisualStudio ###
+## Ignore Visual Studio temporary files, build results, and
+## files generated by popular Visual Studio add-ons.
+##
+## Get latest from https://github.com/github/gitignore/blob/main/VisualStudio.gitignore
+
+# User-specific files
+*.rsuser
+*.suo
+*.user
+*.userosscache
+*.sln.docstates
+
+# User-specific files (MonoDevelop/Xamarin Studio)
+*.userprefs
+
+# Mono auto generated files
+mono_crash.*
+
+# Build results
+[Dd]ebug/
+[Dd]ebugPublic/
+[Rr]elease/
+[Rr]eleases/
+x64/
+x86/
+[Ww][Ii][Nn]32/
+[Aa][Rr][Mm]/
+[Aa][Rr][Mm]64/
+bld/
+[Bb]in/
+[Oo]bj/
+[Ll]og/
+[Ll]ogs/
+
+# Visual Studio 2015/2017 cache/options directory
+.vs/
+# Uncomment if you have tasks that create the project's static files in wwwroot
+#wwwroot/
+
+# Visual Studio 2017 auto generated files
+Generated\ Files/
+
+# MSTest test Results
+[Tt]est[Rr]esult*/
+[Bb]uild[Ll]og.*
+
+# NUnit
+*.VisualState.xml
+TestResult.xml
+nunit-*.xml
+
+# Build Results of an ATL Project
+[Dd]ebugPS/
+[Rr]eleasePS/
+dlldata.c
+
+# Benchmark Results
+BenchmarkDotNet.Artifacts/
+
+# .NET Core
+project.lock.json
+project.fragment.lock.json
+artifacts/
+
+# ASP.NET Scaffolding
+ScaffoldingReadMe.txt
+
+# StyleCop
+StyleCopReport.xml
+
+# Files built by Visual Studio
+*_i.c
+*_p.c
+*_h.h
+*.ilk
+*.meta
+*.obj
+*.iobj
+*.pch
+*.pdb
+*.ipdb
+*.pgc
+*.pgd
+*.rsp
+*.sbr
+*.tlb
+*.tli
+*.tlh
+*.tmp
+*.tmp_proj
+*_wpftmp.csproj
+*.tlog
+*.vspscc
+*.vssscc
+.builds
+*.pidb
+*.svclog
+*.scc
+
+# Chutzpah Test files
+_Chutzpah*
+
+# Visual C++ cache files
+ipch/
+*.aps
+*.ncb
+*.opendb
+*.opensdf
+*.sdf
+*.cachefile
+*.VC.db
+*.VC.VC.opendb
+
+# Visual Studio profiler
+*.psess
+*.vsp
+*.vspx
+*.sap
+
+# Visual Studio Trace Files
+*.e2e
+
+# TFS 2012 Local Workspace
+$tf/
+
+# Guidance Automation Toolkit
+*.gpState
+
+# ReSharper is a .NET coding add-in
+_ReSharper*/
+*.[Rr]e[Ss]harper
+*.DotSettings.user
+
+# TeamCity is a build add-in
+_TeamCity*
+
+# DotCover is a Code Coverage Tool
+*.dotCover
+
+# AxoCover is a Code Coverage Tool
+.axoCover/*
+!.axoCover/settings.json
+
+# Coverlet is a free, cross platform Code Coverage Tool
+coverage*.json
+coverage*.xml
+report*.xml
+coverage*.info
+
+# Visual Studio code coverage results
+*.coverage
+*.coveragexml
+
+# NCrunch
+_NCrunch_*
+.*crunch*.local.xml
+nCrunchTemp_*
+
+# MightyMoose
+*.mm.*
+AutoTest.Net/
+
+# Web workbench (sass)
+.sass-cache/
+
+# Installshield output folder
+[Ee]xpress/
+
+# DocProject is a documentation generator add-in
+DocProject/buildhelp/
+DocProject/Help/*.HxT
+DocProject/Help/*.HxC
+DocProject/Help/*.hhc
+DocProject/Help/*.hhk
+DocProject/Help/*.hhp
+DocProject/Help/Html2
+DocProject/Help/html
+
+# Click-Once directory
+publish/
+
+# Publish Web Output
+*.[Pp]ublish.xml
+*.azurePubxml
+# Note: Comment the next line if you want to checkin your web deploy settings,
+# but database connection strings (with potential passwords) will be unencrypted
+*.pubxml
+*.publishproj
+
+# Microsoft Azure Web App publish settings. Comment the next line if you want to
+# checkin your Azure Web App publish settings, but sensitive information contained
+# in these scripts will be unencrypted
+PublishScripts/
+
+# NuGet Packages
+*.nupkg
+# NuGet Symbol Packages
+*.snupkg
+# The packages folder can be ignored because of Package Restore
+**/[Pp]ackages/*
+# except build/, which is used as an MSBuild target.
+!**/[Pp]ackages/build/
+# Uncomment if necessary however generally it will be regenerated when needed
+#!**/[Pp]ackages/repositories.config
+# NuGet v3's project.json files produces more ignorable files
+*.nuget.props
+*.nuget.targets
+
+# Microsoft Azure Build Output
+csx/
+*.build.csdef
+
+# Microsoft Azure Emulator
+ecf/
+rcf/
+
+# Windows Store app package directories and files
+AppPackages/
+BundleArtifacts/
+Package.StoreAssociation.xml
+_pkginfo.txt
+*.appx
+*.appxbundle
+*.appxupload
+
+# Visual Studio cache files
+# files ending in .cache can be ignored
+*.[Cc]ache
+# but keep track of directories ending in .cache
+!?*.[Cc]ache/
+
+# Others
+ClientBin/
+~$*
+*~
+*.dbmdl
+*.dbproj.schemaview
+*.jfm
+*.pfx
+*.publishsettings
+orleans.codegen.cs
+
+# Including strong name files can present a security risk
+# (https://github.com/github/gitignore/pull/2483#issue-259490424)
+#*.snk
+
+# Since there are multiple workflows, uncomment next line to ignore bower_components
+# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622)
+#bower_components/
+
+# RIA/Silverlight projects
+Generated_Code/
+
+# Backup & report files from converting an old project file
+# to a newer Visual Studio version. Backup files are not needed,
+# because we have git ;-)
+_UpgradeReport_Files/
+Backup*/
+UpgradeLog*.XML
+UpgradeLog*.htm
+ServiceFabricBackup/
+*.rptproj.bak
+
+# SQL Server files
+*.mdf
+*.ldf
+*.ndf
+
+# Business Intelligence projects
+*.rdl.data
+*.bim.layout
+*.bim_*.settings
+*.rptproj.rsuser
+*- [Bb]ackup.rdl
+*- [Bb]ackup ([0-9]).rdl
+*- [Bb]ackup ([0-9][0-9]).rdl
+
+# Microsoft Fakes
+FakesAssemblies/
+
+# GhostDoc plugin setting file
+*.GhostDoc.xml
+
+# Node.js Tools for Visual Studio
+.ntvs_analysis.dat
+node_modules/
+
+# Visual Studio 6 build log
+*.plg
+
+# Visual Studio 6 workspace options file
+*.opt
+
+# Visual Studio 6 auto-generated workspace file (contains which files were open etc.)
+*.vbw
+
+# Visual Studio 6 auto-generated project file (contains which files were open etc.)
+*.vbp
+
+# Visual Studio 6 workspace and project file (working project files containing files to include in project)
+*.dsw
+*.dsp
+
+# Visual Studio 6 technical files
+
+# Visual Studio LightSwitch build output
+**/*.HTMLClient/GeneratedArtifacts
+**/*.DesktopClient/GeneratedArtifacts
+**/*.DesktopClient/ModelManifest.xml
+**/*.Server/GeneratedArtifacts
+**/*.Server/ModelManifest.xml
+_Pvt_Extensions
+
+# Paket dependency manager
+.paket/paket.exe
+paket-files/
+
+# FAKE - F# Make
+.fake/
+
+# CodeRush personal settings
+.cr/personal
+
+# Python Tools for Visual Studio (PTVS)
+*.pyc
+
+# Cake - Uncomment if you are using it
+# tools/**
+# !tools/packages.config
+
+# Tabs Studio
+*.tss
+
+# Telerik's JustMock configuration file
+*.jmconfig
+
+# BizTalk build output
+*.btp.cs
+*.btm.cs
+*.odx.cs
+*.xsd.cs
+
+# OpenCover UI analysis results
+OpenCover/
+
+# Azure Stream Analytics local run output
+ASALocalRun/
+
+# MSBuild Binary and Structured Log
+*.binlog
+
+# NVidia Nsight GPU debugger configuration file
+*.nvuser
+
+# MFractors (Xamarin productivity tool) working folder
+.mfractor/
+
+# Local History for Visual Studio
+.localhistory/
+
+# Visual Studio History (VSHistory) files
+.vshistory/
+
+# BeatPulse healthcheck temp database
+healthchecksdb
+
+# Backup folder for Package Reference Convert tool in Visual Studio 2017
+MigrationBackup/
+
+# Ionide (cross platform F# VS Code tools) working folder
+.ionide/
+
+# Fody - auto-generated XML schema
+FodyWeavers.xsd
+
+# VS Code files for those working on multiple tools
+*.code-workspace
+
+# Local History for Visual Studio Code
+
+# Windows Installer files from build outputs
+
+# JetBrains Rider
+*.sln.iml
+
+### VisualStudio Patch ###
+# Additional files built by Visual Studio
+
+# End of https://www.toptal.com/developers/gitignore/api/osx,python,pycharm,windows,visualstudio,visualstudiocode,zsh,jetbrains,jupyternotebooks
+
+.vscode
diff --git a/.ignore b/.ignore
new file mode 100644
index 0000000..a46ec0a
--- /dev/null
+++ b/.ignore
@@ -0,0 +1,6 @@
+public
+.mypy_cache
+.pixi
+.DS_Store
+.ruff_cache
+*.npz
diff --git a/.isort.cfg b/.isort.cfg
deleted file mode 100644
index 241e17d..0000000
--- a/.isort.cfg
+++ /dev/null
@@ -1,9 +0,0 @@
-[settings]
-py_version = 312
-line_length = 88
-known_typing = typing,types,typing_extensions,mypy,mypy_extensions
-sections = FUTURE,TYPING,STDLIB,THIRDPARTY,FIRSTPARTY,LOCALFOLDER
-profile = black
-include_trailing_comma = true
-multi_line_output = 3
-indent = 4
diff --git a/.markdownlint.yaml b/.markdownlint.yaml
deleted file mode 100644
index 1fa301f..0000000
--- a/.markdownlint.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-# https://github.com/DavidAnson/markdownlint/blob/main/schema/.markdownlint.yaml
-MD007:
-  indent: 4
-MD013: false
-MD022: false
-MD024: false
-MD026: false
-MD028: false
-MD030:
-  ol_multi: 2
-  ol_single: 2
-  ul_multi: 3
-  ul_single: 3
-MD031: false
-MD032: false
-MD033: false
-MD036: false
-MD038: false
-MD041: false
-MD046: false
diff --git a/.ruff.toml b/.ruff.toml
index 50ccc07..edafbc3 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -2,22 +2,46 @@ target-version = "py312"
 line-length = 88
 indent-width = 4
 exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
     ".git",
+    ".git-rewrite",
     ".hg",
+    ".ipynb_checkpoints",
     ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pyenv",
+    ".pytest_cache",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
     ".tox",
     ".venv",
+    ".vscode",
+    "__pypackages__",
     "_build",
     "buck-out",
     "build",
     "dist",
-    "env",
+    "node_modules",
+    "site-packages",
     "venv",
+    ".pixi",
+    "__init__.py"
 ]
 
+[lint]
+preview = true
+select = ["E1", "E2", "E3", "E4", "E7", "W1", "W2", "W3", "W5", "W6", "F"]
+fixable = ["ALL"]
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+
 [format]
 quote-style = "double"
 indent-style = "space"
 skip-magic-trailing-comma = false
+line-ending = "auto"
 docstring-code-format = true
 docstring-code-line-length = "dynamic"
diff --git a/hooks/launchy.py b/hooks/launchy.py
deleted file mode 100644
index 42ff79b..0000000
--- a/hooks/launchy.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import os
-
-from bs4 import BeautifulSoup
-
-
-def is_jupyter(markdown=None, html=None):
-    if html:
-        return '<div class="jupyter-wrapper">' in html
-    if markdown:
-        return False
-    raise ValueError("Must provide either markdown or html")
-
-
-def on_page_content(html, page, config, files):
-    """Adds a Google colab button to launch Jupyter files"""
-    # Only Jupyter notebooks will have this div.
-    if is_jupyter(html=html):
-        colab_url = os.path.join(config["colab_base_url"], page.url)[:-1] + ".ipynb"
-        colab_launch_html = f"""
-        <div class="launchy-container">
-            <a href="{colab_url}" target="_blank">
-                <img src="/img/launchy/colab.svg" alt="Colab Image" style="height: 20px; width: 100%;" class="off-glb"/>
-            </a>
-        </div>
-        """
-        soup = BeautifulSoup(html, "html.parser")
-        h1_tag = soup.find("h1")
-        h1_tag.insert_after(BeautifulSoup(colab_launch_html, "html.parser"))
-        return soup.prettify()
-    return html
diff --git a/mkdocs.yml b/mkdocs.yml
index 1b721d9..95d1aef 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -53,7 +53,6 @@ validation:
 plugins:
   - search
   - autorefs
-  - material-plausible
   - gen-files:
       scripts:
         - docs/gen_ref_pages.py
@@ -84,26 +83,10 @@ plugins:
       timezone: America/Detroit
       fallback_to_build_date: true
   - macros
-  - table-reader
   - glightbox
-  - mkdocs-jupyter:
-        no_input: False
-        include_requirejs: true
-        include_source: True
-        ignore: ["*.py"]
-        remove_tag_config:
-            remove_input_tags:
-                - hide_code
-
-hooks:
-  - hooks/launchy.py
-colab_base_url: https://colab.research.google.com/github/durrantlab/POVME/blob/main/docs
 
 extra:
   generator: true
-  # analytics:
-  #   provider: plausible
-  #   domain: povme
   social:
     - icon: fontawesome/brands/github
       link: https://github.com/durrantlab
diff --git a/pixi.lock b/pixi.lock
index 6dd2ec3..4f8229d 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -5,6 +5,8 @@ environments:
     - url: https://conda.anaconda.org/conda-forge/
     indexes:
     - https://pypi.org/simple
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
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+      - pypi: git+https://github.com/durrantlab/pymolecule.git?rev=614a95a0b792f83fbee77e4c7eed7d5ce046dee1#614a95a0b792f83fbee77e4c7eed7d5ce046dee1
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+  - virtualenv>=20.0.24,!=20.21.1 ; extra == 'llm'
+  - jsonref>=1.1.0 ; extra == 'llm'
+  - starlette ; extra == 'llm'
+  - watchfiles ; extra == 'llm'
+  - opentelemetry-exporter-prometheus ; extra == 'llm'
+  - uvicorn[standard] ; extra == 'llm'
+  - opencensus ; extra == 'llm'
+  - grpcio>=1.32.0 ; python_full_version < '3.10' and extra == 'llm'
+  - aiohttp>=3.7 ; extra == 'llm'
+  - py-spy>=0.4.0 ; python_full_version >= '3.12' and extra == 'llm'
+  - fsspec ; extra == 'llm'
+  - pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3 ; extra == 'llm'
+  - pandas>=1.3 ; extra == 'llm'
+  - jsonschema ; extra == 'llm'
+  - requests ; extra == 'llm'
+  - grpcio>=1.42.0 ; python_full_version >= '3.10' and extra == 'llm'
+  - aiohttp-cors ; extra == 'llm'
+  - colorful ; extra == 'llm'
+  - typer ; extra == 'llm'
+  - opentelemetry-proto ; extra == 'llm'
+  - vllm>=0.10.0 ; extra == 'llm'
+  - async-timeout ; python_full_version < '3.11' and extra == 'llm'
+  - smart-open ; extra == 'llm'
+  - opentelemetry-sdk>=1.30.0 ; extra == 'llm'
+  - hf-transfer ; extra == 'llm'
+  - pyarrow>=9.0.0 ; extra == 'llm'
+  - py-spy>=0.2.0 ; python_full_version < '3.12' and extra == 'llm'
+  - ninja ; extra == 'llm'
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+  - packaging>=24.2
+  - protobuf>=3.20.3
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+  - requests
+  - cupy-cuda12x ; sys_platform != 'darwin' and extra == 'cgraph'
+  - grpcio!=1.56.0 ; sys_platform == 'darwin' and extra == 'client'
+  - grpcio ; extra == 'client'
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+  - pandas>=1.3 ; extra == 'data'
+  - pyarrow>=9.0.0 ; extra == 'data'
+  - fsspec ; extra == 'data'
+  - aiohttp>=3.13.3 ; extra == 'default'
+  - aiohttp-cors ; extra == 'default'
+  - colorful ; extra == 'default'
+  - py-spy>=0.2.0 ; python_full_version < '3.12' and extra == 'default'
+  - py-spy>=0.4.0 ; python_full_version >= '3.12' and extra == 'default'
+  - requests ; extra == 'default'
+  - grpcio>=1.42.0 ; extra == 'default'
+  - opencensus ; extra == 'default'
+  - opentelemetry-sdk>=1.30.0 ; extra == 'default'
+  - opentelemetry-exporter-prometheus ; extra == 'default'
+  - opentelemetry-proto ; extra == 'default'
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+  - memray ; sys_platform != 'win32' and extra == 'observability'
+  - virtualenv>=20.0.24,!=20.21.1 ; extra == 'serve'
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+  - grpcio>=1.42.0 ; extra == 'serve'
+  - watchfiles ; extra == 'serve'
+  - opentelemetry-sdk>=1.30.0 ; extra == 'serve'
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+  - colorful ; extra == 'serve'
+  - fastapi ; extra == 'serve'
+  - requests ; extra == 'serve'
+  - prometheus-client>=0.7.1 ; extra == 'serve'
+  - opencensus ; extra == 'serve'
+  - py-spy>=0.4.0 ; python_full_version >= '3.12' and extra == 'serve'
+  - opentelemetry-exporter-prometheus ; extra == 'serve'
+  - opentelemetry-proto ; extra == 'serve'
+  - aiohttp>=3.13.3 ; extra == 'serve'
+  - starlette ; extra == 'serve'
+  - smart-open ; extra == 'serve'
+  - uvicorn[standard] ; extra == 'serve'
+  - aiohttp-cors ; extra == 'serve'
+  - pandas ; extra == 'tune'
+  - pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,!=2.10.*,!=2.11.*,<3 ; extra == 'tune'
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+  - requests ; extra == 'tune'
+  - pyarrow>=9.0.0 ; extra == 'tune'
+  - fsspec ; extra == 'tune'
+  - cupy-cuda12x ; sys_platform != 'darwin' and extra == 'adag'
+  - virtualenv>=20.0.24,!=20.21.1 ; extra == 'serve-grpc'
+  - py-spy>=0.2.0 ; python_full_version < '3.12' and extra == 'serve-grpc'
+  - grpcio>=1.42.0 ; extra == 'serve-grpc'
+  - watchfiles ; extra == 'serve-grpc'
+  - opentelemetry-sdk>=1.30.0 ; extra == 'serve-grpc'
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+  - colorful ; extra == 'serve-grpc'
+  - fastapi ; extra == 'serve-grpc'
+  - requests ; extra == 'serve-grpc'
+  - prometheus-client>=0.7.1 ; extra == 'serve-grpc'
+  - opencensus ; extra == 'serve-grpc'
+  - py-spy>=0.4.0 ; python_full_version >= '3.12' and extra == 'serve-grpc'
+  - opentelemetry-exporter-prometheus ; extra == 'serve-grpc'
+  - opentelemetry-proto ; extra == 'serve-grpc'
+  - aiohttp>=3.13.3 ; extra == 'serve-grpc'
+  - starlette ; extra == 'serve-grpc'
+  - pyopenssl ; extra == 'serve-grpc'
+  - smart-open ; extra == 'serve-grpc'
+  - uvicorn[standard] ; extra == 'serve-grpc'
+  - aiohttp-cors ; extra == 'serve-grpc'
+  - virtualenv>=20.0.24,!=20.21.1 ; extra == 'serve-async-inference'
+  - py-spy>=0.2.0 ; python_full_version < '3.12' and extra == 'serve-async-inference'
+  - grpcio>=1.42.0 ; extra == 'serve-async-inference'
+  - watchfiles ; extra == 'serve-async-inference'
+  - opentelemetry-sdk>=1.30.0 ; extra == 'serve-async-inference'
+  - pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,!=2.10.*,!=2.11.*,<3 ; extra == 'serve-async-inference'
+  - colorful ; extra == 'serve-async-inference'
+  - fastapi ; extra == 'serve-async-inference'
+  - requests ; extra == 'serve-async-inference'
+  - celery ; extra == 'serve-async-inference'
+  - prometheus-client>=0.7.1 ; extra == 'serve-async-inference'
+  - opencensus ; extra == 'serve-async-inference'
+  - py-spy>=0.4.0 ; python_full_version >= '3.12' and extra == 'serve-async-inference'
+  - opentelemetry-exporter-prometheus ; extra == 'serve-async-inference'
+  - opentelemetry-proto ; extra == 'serve-async-inference'
+  - aiohttp>=3.13.3 ; extra == 'serve-async-inference'
+  - starlette ; extra == 'serve-async-inference'
+  - smart-open ; extra == 'serve-async-inference'
+  - uvicorn[standard] ; extra == 'serve-async-inference'
+  - aiohttp-cors ; extra == 'serve-async-inference'
+  - ray-cpp==2.54.0 ; extra == 'cpp'
+  - pandas ; extra == 'rllib'
+  - pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,!=2.10.*,!=2.11.*,<3 ; extra == 'rllib'
+  - tensorboardx>=1.9 ; extra == 'rllib'
+  - requests ; extra == 'rllib'
+  - pyarrow>=9.0.0 ; extra == 'rllib'
+  - fsspec ; extra == 'rllib'
+  - dm-tree ; extra == 'rllib'
+  - gymnasium==1.2.2 ; extra == 'rllib'
+  - lz4 ; extra == 'rllib'
+  - ormsgpack>=1.7.0 ; extra == 'rllib'
+  - pyyaml ; extra == 'rllib'
+  - scipy ; extra == 'rllib'
+  - pandas ; extra == 'train'
+  - pydantic!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,!=2.10.*,!=2.11.*,<3 ; extra == 'train'
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+  - requests ; extra == 'train'
+  - pyarrow>=9.0.0 ; extra == 'train'
+  - fsspec ; extra == 'train'
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+  - virtualenv>=20.0.24,!=20.21.1 ; extra == 'air'
+  - py-spy>=0.2.0 ; python_full_version < '3.12' and extra == 'air'
+  - grpcio>=1.42.0 ; extra == 'air'
+  - watchfiles ; extra == 'air'
+  - opentelemetry-sdk>=1.30.0 ; extra == 'air'
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+  - fastapi ; extra == 'air'
+  - requests ; extra == 'air'
+  - prometheus-client>=0.7.1 ; extra == 'air'
+  - pandas ; extra == 'air'
+  - opencensus ; extra == 'air'
+  - py-spy>=0.4.0 ; python_full_version >= '3.12' and extra == 'air'
+  - opentelemetry-exporter-prometheus ; extra == 'air'
+  - pyarrow>=9.0.0 ; extra == 'air'
+  - fsspec ; extra == 'air'
+  - opentelemetry-proto ; extra == 'air'
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+  - aiohttp>=3.13.3 ; extra == 'air'
+  - starlette ; extra == 'air'
+  - pandas>=1.3 ; extra == 'air'
+  - smart-open ; extra == 'air'
+  - uvicorn[standard] ; extra == 'air'
+  - aiohttp-cors ; extra == 'air'
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+  - grpcio>=1.42.0 ; extra == 'all'
+  - watchfiles ; extra == 'all'
+  - opentelemetry-sdk>=1.30.0 ; extra == 'all'
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+  - colorful ; extra == 'all'
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+  - fastapi ; extra == 'all'
+  - requests ; extra == 'all'
+  - celery ; extra == 'all'
+  - prometheus-client>=0.7.1 ; extra == 'all'
+  - pandas ; extra == 'all'
+  - opencensus ; extra == 'all'
+  - dm-tree ; extra == 'all'
+  - lz4 ; extra == 'all'
+  - py-spy>=0.4.0 ; python_full_version >= '3.12' and extra == 'all'
+  - opentelemetry-exporter-prometheus ; extra == 'all'
+  - ormsgpack>=1.7.0 ; extra == 'all'
+  - pyarrow>=9.0.0 ; extra == 'all'
+  - fsspec ; extra == 'all'
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+  - opentelemetry-proto ; extra == 'all'
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+  - starlette ; extra == 'all'
+  - pandas>=1.3 ; extra == 'all'
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+  - pyopenssl ; extra == 'all'
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+  - aiohttp-cors ; extra == 'all'
+  - uvicorn[standard] ; extra == 'all'
+  - scipy ; extra == 'all'
+  - tensorboardx>=1.9 ; extra == 'all'
+  - memray ; sys_platform != 'win32' and extra == 'all'
+  - grpcio ; extra == 'all'
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+  - grpcio>=1.42.0 ; extra == 'all-cpp'
+  - watchfiles ; extra == 'all-cpp'
+  - opentelemetry-sdk>=1.30.0 ; extra == 'all-cpp'
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+  - cupy-cuda12x ; sys_platform != 'darwin' and extra == 'all-cpp'
+  - fastapi ; extra == 'all-cpp'
+  - requests ; extra == 'all-cpp'
+  - celery ; extra == 'all-cpp'
+  - prometheus-client>=0.7.1 ; extra == 'all-cpp'
+  - pandas ; extra == 'all-cpp'
+  - opencensus ; extra == 'all-cpp'
+  - dm-tree ; extra == 'all-cpp'
+  - lz4 ; extra == 'all-cpp'
+  - py-spy>=0.4.0 ; python_full_version >= '3.12' and extra == 'all-cpp'
+  - opentelemetry-exporter-prometheus ; extra == 'all-cpp'
+  - ormsgpack>=1.7.0 ; extra == 'all-cpp'
+  - pyarrow>=9.0.0 ; extra == 'all-cpp'
+  - fsspec ; extra == 'all-cpp'
+  - gymnasium==1.2.2 ; extra == 'all-cpp'
+  - grpcio!=1.56.0 ; sys_platform == 'darwin' and extra == 'all-cpp'
+  - opentelemetry-proto ; extra == 'all-cpp'
+  - numpy>=1.20 ; extra == 'all-cpp'
+  - aiohttp>=3.13.3 ; extra == 'all-cpp'
+  - starlette ; extra == 'all-cpp'
+  - pandas>=1.3 ; extra == 'all-cpp'
+  - pyyaml ; extra == 'all-cpp'
+  - pyopenssl ; extra == 'all-cpp'
+  - smart-open ; extra == 'all-cpp'
+  - aiohttp-cors ; extra == 'all-cpp'
+  - uvicorn[standard] ; extra == 'all-cpp'
+  - scipy ; extra == 'all-cpp'
+  - tensorboardx>=1.9 ; extra == 'all-cpp'
+  - memray ; sys_platform != 'win32' and extra == 'all-cpp'
+  - ray-cpp==2.54.0 ; extra == 'all-cpp'
+  - grpcio ; extra == 'all-cpp'
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+  - py-spy>=0.2.0 ; python_full_version < '3.12' and extra == 'llm'
+  - grpcio>=1.42.0 ; extra == 'llm'
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+  - opentelemetry-sdk>=1.30.0 ; extra == 'llm'
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+  - colorful ; extra == 'llm'
+  - meson ; extra == 'llm'
+  - fastapi ; extra == 'llm'
+  - requests ; extra == 'llm'
+  - async-timeout ; python_full_version < '3.11' and extra == 'llm'
+  - prometheus-client>=0.7.1 ; extra == 'llm'
+  - hf-transfer ; extra == 'llm'
+  - pybind11 ; extra == 'llm'
+  - opencensus ; extra == 'llm'
+  - jsonref>=1.1.0 ; extra == 'llm'
+  - py-spy>=0.4.0 ; python_full_version >= '3.12' and extra == 'llm'
+  - opentelemetry-exporter-prometheus ; extra == 'llm'
+  - pyarrow>=9.0.0 ; extra == 'llm'
+  - fsspec ; extra == 'llm'
+  - opentelemetry-proto ; extra == 'llm'
+  - numpy>=1.20 ; extra == 'llm'
+  - jsonschema ; extra == 'llm'
+  - typer ; extra == 'llm'
+  - nixl>=0.6.1 ; extra == 'llm'
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+  - starlette ; extra == 'llm'
+  - smart-open ; extra == 'llm'
+  - uvicorn[standard] ; extra == 'llm'
+  - ninja ; extra == 'llm'
+  - aiohttp-cors ; extra == 'llm'
+  - vllm[audio]>=0.15.0 ; extra == 'llm'
+  - transformers>=4.57.3 ; extra == 'llm'
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/readline-8.3-h68b038d_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.3-h46df422_0.conda
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@@ -3050,6 +5271,21 @@ packages:
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   size: 17186
   timestamp: 1720528649611
+- conda: https://conda.anaconda.org/conda-forge/noarch/readme_renderer-44.0-pyhd8ed1ab_1.conda
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+  license: Apache-2.0
+  license_family: APACHE
+  purls:
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+  size: 17481
+  timestamp: 1734339765256
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@@ -3058,19 +5294,15 @@ packages:
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-  license: MIT
-  license_family: MIT
-  purls:
-  - pkg:pypi/referencing?source=hash-mapping
-  size: 42201
-  timestamp: 1733366868091
+- pypi: https://files.pythonhosted.org/packages/2c/58/ca301544e1fa93ed4f80d724bf5b194f6e4b945841c5bfd555878eea9fcb/referencing-0.37.0-py3-none-any.whl
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+  requires_dist:
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+  - typing-extensions>=4.4.0 ; python_full_version < '3.13'
+  requires_python: '>=3.10'
 - conda: https://conda.anaconda.org/conda-forge/linux-64/regex-2024.11.6-py312h66e93f0_0.conda
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   - chardet>=3.0.2,<6 ; extra == 'use-chardet-on-py3'
   requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl
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-  - krb5 >=1.21.3,<1.22.0a0
-  - libgcc >=13
-  - libsodium >=1.0.20,<1.0.21.0a0
-  - libstdcxx >=13
-  license: MPL-2.0
-  license_family: MOZILLA
+  - __osx >=10.13
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 79419
+  timestamp: 1753484072608
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h925e9cb_3.conda
+  sha256: b03433b13d89f5567e828ea9f1a7d5c5d697bf374c28a4168d71e9464f5dafac
+  md5: 78a0fe9e9c50d2c381e8ee47e3ea437d
+  depends:
+  - __osx >=11.0
+  license: MIT
+  license_family: MIT
   purls: []
-  size: 335400
-  timestamp: 1731585026517
+  size: 83386
+  timestamp: 1753484079473
 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda
   sha256: 567c04f124525c97a096b65769834b7acb047db24b15a56888a322bf3966c3e1
   md5: 0c3cc595284c5e8f0f9900a9b228a332
@@ -3629,6 +6228,18 @@ packages:
   - pkg:pypi/zipp?source=hash-mapping
   size: 21809
   timestamp: 1732827613585
+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
+  sha256: b4533f7d9efc976511a73ef7d4a2473406d7f4c750884be8e8620b0ce70f4dae
+  md5: 30cd29cb87d819caead4d55184c1d115
+  depends:
+  - python >=3.10
+  - python
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/zipp?source=compressed-mapping
+  size: 24194
+  timestamp: 1764460141901
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda
   sha256: b97015e146437283f2213ff0e95abdc8e2480150634d81fbae6b96ee09f5e50b
   md5: 8b7069e9792ee4e5b4919a7a306d2e67
@@ -3658,3 +6269,25 @@ packages:
   purls: []
   size: 554846
   timestamp: 1714722996770
+- conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h3eecb57_6.conda
+  sha256: 47101a4055a70a4876ffc87b750ab2287b67eca793f21c8224be5e1ee6394d3f
+  md5: 727109b184d680772e3122f40136d5ca
+  depends:
+  - __osx >=10.13
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 528148
+  timestamp: 1764777156963
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda
+  sha256: 9485ba49e8f47d2b597dd399e88f4802e100851b27c21d7525625b0b4025a5d9
+  md5: ab136e4c34e97f34fb621d2592a393d8
+  depends:
+  - __osx >=11.0
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 433413
+  timestamp: 1764777166076
diff --git a/pixi.toml b/pixi.toml
index b2e6193..95ec87f 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -1,15 +1,15 @@
-[project]
+[workspace]
 authors = ["Jacob Durrant <durrantj@pitt.edu>"]
 channels = ["conda-forge"]
 description = "Detect and characterize pockets from molecular simulations"
 name = "povme"
-platforms = ["linux-64"]
-version = "2.2.1"
+platforms = ["linux-64", "osx-64", "osx-arm64"]
+version = "2.2.2"
 readme = "README.md"
 
 [pypi-dependencies]
 povme = { path = ".", editable = true }
-pymolecule = { git = "https://github.com/durrantlab/pymolecule.git", rev = "423cf64f17c21881a1685402437bd69a81e79ccf" }
+pymolecule = { git = "https://github.com/durrantlab/pymolecule.git", rev = "614a95a0b792f83fbee77e4c7eed7d5ce046dee1" }
 ray = { version = ">=2.37.0,<3"}
 
 [system-requirements]
@@ -46,10 +46,7 @@ types-pyyaml = ">=6.0.12.20240917,<7"
 scalene = ">=1.5.41,<2"
 
 [feature.dev.tasks]
-mdlint = { cmd = ["markdownlint-cli2", '"**/*.{md,markdown}"', "--fix", "--config", ".markdownlint.yaml", "||", "true"] }
-isort = { cmd = ["isort", "--settings-path", ".isort.cfg", "./povme", "./tests", "||", "true"] }
-black = { cmd = ["black", "--config", ".black.toml", "./povme", "./tests", "||", "true"] }
-format = { depends-on = ["mdlint", "isort", "black"] }
+format = { cmd = ["ruff", "check", "--select", "I", "--fix", "&&", "ruff", "format", "--config", ".ruff.toml", "./"] }
 tests = { cmd = [
     "PYTHONPATH=.",
     "pytest",
@@ -82,21 +79,19 @@ publish = { cmd = ["twine", "upload", "dist/*"] }
 build = ">=1.2.2.post1,<2"
 
 [feature.docs.dependencies]
+# Will need to migrate to https://zensical.org/ once it matures.
 mkdocs = ">=1.6.1,<2"
 mkdocs-material = ">=9.5.44,<10"
-pymdown-extensions = ">=10.12,<11"
-mkdocs-table-reader-plugin = ">=3.1.0,<4"
-mkdocstrings = ">=0.26.2,<0.27"
-mkdocstrings-python = ">=1.12.2,<2"
-mkdocs-gen-files = ">=0.4.0,<0.5"
-mkdocs-macros-plugin = ">=1.3.7,<2"
-mkdocs-jupyter = ">=0.25.0,<0.26"
-mkdocs-glightbox = ">=0.4.0,<0.5"
+pymdown-extensions = ">=10.21.1,<11"
+mkdocstrings = ">=1.0.3,<2"
+mkdocstrings-python = ">=2.0.2,<3"
+mkdocs-macros-plugin = ">=1.5.0,<2"
+mkdocs-glightbox = ">=0.5.2,<0.6"
 mkdocs-git-revision-date-localized-plugin = ">=1.2.9,<2"
-mkdocs-autorefs = ">=1.2.0,<2"
+mkdocs-autorefs = ">=1.4.4,<2"
+mkdocs-gen-files = ">=0.6.0,<0.7"
 
 [feature.docs.pypi-dependencies]
-material-plausible-plugin = ">=0.2.0,<0.3"
 mkdocs-awesome-pages-plugin = ">=2.9.3,<3"
 
 [feature.docs.tasks]
diff --git a/povme/config/io.py b/povme/config/io.py
index 7ea9500..90ae65c 100644
--- a/povme/config/io.py
+++ b/povme/config/io.py
@@ -1,6 +1,5 @@
-from typing import Any
-
 from abc import ABC
+from typing import Any
 
 import yaml
 
@@ -15,7 +14,7 @@ def update(self, data: dict[str, Any]) -> None:
             data: Key-value mapping to update attributes with.
         """
         for key, value in data.items():
-            if key in self.model_fields:  # type: ignore
+            if key in self.__class__.model_fields:  # type: ignore
                 setattr(self, key, value)
 
     def from_yaml(self, yaml_paths: str | list[str]) -> None:
diff --git a/povme/parallel.py b/povme/parallel.py
index 16e9284..b1fdf64 100644
--- a/povme/parallel.py
+++ b/povme/parallel.py
@@ -36,16 +36,18 @@ def process_item(self, item):
     ```python
     items_to_process = [1, 2, 3, 4, 5]
 
+
     def save_results(results, **kwargs):
         # Implement saving logic, e.g., write to a file or database
         print("Saving results:", results)
 
+
     manager.submit_tasks(
         items=items_to_process,
         chunk_size=2,
         save_func=save_results,
-        save_kwargs={'destination': 'output.txt'},
-        save_interval=2
+        save_kwargs={"destination": "output.txt"},
+        save_interval=2,
     )
     ```
 
@@ -69,10 +71,9 @@ def save_results(results, **kwargs):
 
 """
 
-from typing import Any, Generator
-
 from abc import ABC, abstractmethod
 from collections.abc import Callable
+from typing import Any, Generator
 
 import ray
 from loguru import logger
diff --git a/povme/pocket/detect.py b/povme/pocket/detect.py
index 8f96776..82058c6 100644
--- a/povme/pocket/detect.py
+++ b/povme/pocket/detect.py
@@ -1,10 +1,24 @@
+"""Automatic pocket detection from a static protein structure.
+
+This module provides the [`PocketDetector`][pocket.detect.PocketDetector] class, which implements the
+pocket-identification pipeline:
+
+1. Load a PDB structure and strip hydrogen atoms.
+2. Compute the convex hull of alpha carbon positions.
+3. Fill the hull with a coarse grid of equidistant points, progressively
+    refining the resolution.
+4. Remove grid points that clash with protein atoms.
+5. Filter isolated points that lack sufficient neighbors.
+6. Partition the remaining points into distinct pockets.
+7. Cluster each pocket with k-means and output encompassing spheres.
+"""
+
 import os
 
 import numpy as np
 from loguru import logger
 from pymolecule import Molecule
 from scipy.cluster.vq import ClusterError, kmeans2
-from scipy.spatial.distance import cdist
 
 from povme.config import PocketDetectConfig
 from povme.io import openfile, write_pdbs
@@ -26,7 +40,7 @@ def __init__(
         """
         if config is None:
             config = PocketDetectConfig()
-        self.config = PocketDetectConfig()
+        self.config = config
 
     def run(self, path_pdb: str, output_prefix: str = "") -> None:
         config = self.config
@@ -37,20 +51,20 @@ def run(self, path_pdb: str, output_prefix: str = "") -> None:
             output_dirname = os.path.dirname(output_prefix)
             os.makedirs(output_dirname, exist_ok=True)
 
-        # Step 1: Load in the protein
+        # Load in the protein
 
         logger.info("Loading the PDB file " + path_pdb + "...")
         molecule = Molecule()
         molecule.io.load_pdb_into(path_pdb)
 
-        # Step 2: Get rid of hydrogen atoms. They just slow stuff down.
+        # Get rid of hydrogen atoms. They just slow stuff down.
 
         print("Removing hydrogen atoms...")
         sel = molecule.selections.select_atoms({"element_stripped": b"H"})
         sel = molecule.selections.invert_selection(sel)
         molecule = molecule.selections.get_molecule_from_selection(sel)
 
-        # Step 3: Calculate the convex hull of the protein alpha carbons.
+        # Calculate the convex hull of the protein alpha carbons.
         print("Calculating the convex hull of the PDB file...")
 
         # Get a version of the protein with just the alpha carbons. In my
@@ -63,7 +77,7 @@ def run(self, path_pdb: str, output_prefix: str = "") -> None:
             molecule_alpha_carbons.information.get_coordinates()
         )
 
-        # Step 4. Get a box of equispaced points that surround the protein,
+        # Get a box of equispaced points that surround the protein,
         # snapped to reso. I'm putting a whole bunch of other functions in this
         # class as well to manipulate the points of this box.
 
@@ -80,7 +94,7 @@ def run(self, path_pdb: str, output_prefix: str = "") -> None:
             config.pocket_detection_resolution * 4,
         )
 
-        # Step 5. Remove points outside the convex hull. Gradually fill in
+        # Remove points outside the convex hull. Gradually fill in
         # protein-occupying region with denser point fields. Faster this way, I
         # think.
         logger.info("Removing points that fall outside the protein's convex hull...")
@@ -102,7 +116,7 @@ def run(self, path_pdb: str, output_prefix: str = "") -> None:
             molecule.information.get_coordinates(), config.clashing_cutoff, config
         )
 
-        # Step 7. Now surround each of these points with higher density points
+        # Now surround each of these points with higher density points
         # that in the same regions. This is for getting a more detailed view of
         # the identified pockets.
         if config.pocket_measuring_resolution != config.pocket_detection_resolution:
@@ -127,7 +141,7 @@ def run(self, path_pdb: str, output_prefix: str = "") -> None:
                 molecule.information.get_coordinates(), config.clashing_cutoff, config
             )
 
-        # Step 8. Now start doing a repeated pass filter (keep repeating until no
+        # Now start doing a repeated pass filter (keep repeating until no
         # change). Don't know if this is a high pass or low pass filter. I've
         # heard these terms, though, and they sound cool.
         logger.info(
@@ -139,11 +153,11 @@ def run(self, path_pdb: str, output_prefix: str = "") -> None:
             config.pocket_measuring_resolution, config.n_neighbors
         )
 
-        # Step 9. Separate out the pockets so they can be considered in isolation.
+        # Separate out the pockets so they can be considered in isolation.
         logger.info("Partitioning the remaining points by pocket...")
         all_pockets = box_pts.separate_out_pockets()
 
-        # Step 10. Get povme spheres that encompass each pocket, write pockets to
+        # Get povme spheres that encompass each pocket, write pockets to
         # separate pdb files
         logger.info("Saving the points of each pocket...")
         let_ids = [
@@ -213,8 +227,9 @@ def run(self, path_pdb: str, output_prefix: str = "") -> None:
                 cluster_pts = pts[indexes_for_this_cluster]
                 cluster_center = np.mean(cluster_pts, axis=0)
                 try:
-                    cluster_radius = np.max(
-                        cdist(np.array([cluster_center]), cluster_pts)
+                    diffs = cluster_pts - cluster_center
+                    cluster_radius = float(
+                        np.max(np.sqrt(np.einsum("ij,ij->i", diffs, diffs)))
                     )
                     f.write(
                         "REMARK CHAIN "
diff --git a/povme/pocket/volume.py b/povme/pocket/volume.py
index ef182cf..07035ee 100644
--- a/povme/pocket/volume.py
+++ b/povme/pocket/volume.py
@@ -1,14 +1,25 @@
-from typing import Any, Generator
+"""Pocket volume calculation for single structures and MD trajectories.
+
+This module provides the [`PocketVolume`][pocket.volume.PocketVolume] class, which orchestrates
+the end-to-end POVME volume-measurement workflow:
+
+1. Generate (or load) the pocket-encompassing point field from
+   user-defined inclusion/exclusion regions.
+2. Distribute per-frame volume calculations across workers using
+    [`RayManager`][parallel.RayManager].
+3. Collect results and optionally write trajectory PDBs and volumetric
+    density maps.
+"""
 
 import os
 import sys
 import time
 from io import StringIO
+from typing import Any, Generator
 
 import numpy as np
 from loguru import logger
 from pymolecule import Molecule
-from scipy.spatial.distance import cdist
 
 from povme.config import PocketVolumeConfig
 from povme.io import dx_freq, gzopenfile, numpy_to_pdb, openfile, write_to_file
@@ -19,28 +30,79 @@
 
 
 def get_unique_rows(a):
-    """Identifies unique points (rows) in an array of points.
+    """Return the unique rows of a 2-D array.
 
-    Arguments:
-        a: A nx3 numpy.array representing 3D points.
+    Each row is treated as an opaque byte string so that
+    [`numpy.unique`](https://numpy.org/doc/stable/reference/generated/numpy.unique.html)
+    can identify duplicates without floating-point
+    tolerance issues (the input is assumed to be snapped to a grid).
 
-    Returns:
-        A nx2 numpy.array containing the 3D points that are unique.
+    Args:
+        a: An array of shape `(n, d)` (typically `d = 3`).
 
+    Returns:
+        An array of shape `(m, d)` (`m <= n`) containing only the
+        unique rows, in sorted order.
     """
-
     a[a == -0.0] = 0.0
     b = np.ascontiguousarray(a).view(np.dtype((np.void, a.dtype.itemsize * a.shape[1])))
     return np.unique(b).view(a.dtype).reshape(-1, a.shape[1])  # unique_a
 
 
+def remove_exclusion_points(
+    pts: np.ndarray,
+    pts_exclusion: np.ndarray,
+) -> np.ndarray:
+    """Remove inclusion points that coincide with exclusion points.
+
+    Both point sets are assumed to lie on the same regular grid (i.e., their
+    coordinates are exact multiples of the grid spacing after snapping).
+    Instead of computing a full pairwise distance matrix with
+    [`cdist`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html),
+    this function performs a set-difference using NumPy's structured-array view trick.
+
+    1. Each `(x, y, z)` row is reinterpreted as a single opaque
+        [`np.void`](https://numpy.org/doc/stable/reference/arrays.scalars.html) element.
+    2. [`numpy.isin`](https://numpy.org/doc/stable/reference/generated/numpy.isin.html)
+        performs a hash-based membership test.
+
+    Args:
+        pts: An `(N, 3)` array of inclusion grid points.
+        pts_exclusion: An `(M, 3)` array of exclusion grid points.
+
+    Returns:
+        An `(K, 3)` array (`K <= N`) containing only those rows of
+            `pts` that do not appear in *pts_exclusion*.
+    """
+
+    def _to_void(a: np.ndarray) -> np.ndarray:
+        """View each row of a 2-D array as a single void element."""
+        a = np.ascontiguousarray(a)
+        return a.view(np.dtype((np.void, a.dtype.itemsize * a.shape[1])))
+
+    pts_void = _to_void(pts)
+    exc_void = _to_void(pts_exclusion)
+    mask = ~np.isin(pts_void, exc_void).ravel()
+    return pts[mask]
+
+
 def collect_pdb_frames_in_chunks(
     filename: str, chunk_size: int
 ) -> Generator[list[tuple[int, str]], None, None]:
-    """
-    Read a multi-frame PDB and yield frames in chunks.
+    """Read a multi-frame PDB and yield frames in chunks.
 
-    Each yielded chunk is a list of (frame_index, pdb_frame_string).
+    Frames are delimited by lines starting with `END`. Frames are
+    accumulated into chunks of `chunk_size` before being yielded, reducing
+    the overhead of task submission when using parallel workers.
+
+    Args:
+        filename: Path to the multi-frame PDB file.
+        chunk_size: Maximum number of frames per yielded chunk.
+
+    Yields:
+        Lists of `(frame_index, pdb_frame_string)` tuples. The
+            `frame_index` is 1-based. The final yielded chunk may contain
+            fewer than *chunk_size* frames.
     """
     frame_buffer: list[str] = []
     frame_index = 0
@@ -73,7 +135,6 @@ def collect_pdb_frames_in_chunks(
 
 
 class TaskComputeVolumeFromPDBLines(RayTaskGeneral):
-
     def process_item(self, item: tuple[Any, ...]) -> tuple[Any, ...]:
         frame_index, pdb_string, config, pts, regions_contig, output_prefix = item
 
@@ -115,9 +176,23 @@ def __init__(
         self.config = config
 
     def gen_points(self, config):
+        """Generate the pocket-encompassing point field.
+
+        Constructs a regular grid of 3D points by unioning all
+        inclusion-region grids and then subtracting all exclusion-region
+        grids.
+
+        Args:
+            config: The volume-calculation configuration, which specifies
+                inclusion/exclusion spheres and boxes and the grid spacing.
+
+        Returns:
+            An `(K, 3)` array of unique grid points that lie inside at
+                least one inclusion region and outside all exclusion regions.
+        """
         logger.info("Generating the pocket-encompassing point field")
 
-        # get all the points of the inclusion regions
+        # Collect inclusion points
         regions_include = collect_regions(
             config.points_inclusion_sphere, config.points_inclusion_box
         )
@@ -126,7 +201,7 @@ def gen_points(self, config):
             pts = np.vstack((pts, Included.get_points(config.grid_spacing)))
         pts = get_unique_rows(pts)
 
-        # get all the points of the exclusion regions
+        # Collect exclusion points and subtract them
         regions_exclude = collect_regions(
             config.points_exclusion_sphere, config.points_exclusion_box
         )
@@ -138,12 +213,7 @@ def gen_points(self, config):
                 )
             pts_exclusion = get_unique_rows(pts_exclusion)
 
-            # remove the exclusion points from the inclusion points I
-            # think there ought to be a set-based way of doing this, but
-            # I'm going to go for the pairwise comparison. consider
-            # rewriting later
-            index_to_remove = np.nonzero(cdist(pts, pts_exclusion) < 1e-7)[0]
-            pts = np.delete(pts, index_to_remove, axis=0)
+            pts = remove_exclusion_points(pts, pts_exclusion)
 
         return pts
 
@@ -174,7 +244,6 @@ def write_points(pts, output_prefix, config):
         )
 
     def write_points_contig(self, regions_contig, output_prefix, config):
-
         # get all the contiguous points
         contig_pts = regions_contig[0].get_points(config.grid_spacing)
         for Contig in regions_contig[1:]:
diff --git a/povme/points/gridmesh.py b/povme/points/gridmesh.py
index 1816dc2..33c379f 100644
--- a/povme/points/gridmesh.py
+++ b/povme/points/gridmesh.py
@@ -1,9 +1,25 @@
+"""Grid mesh construction and manipulation for pocket detection.
+
+This module provides the [`GridMesh`][points.gridmesh.GridMesh] class, which represents a box of
+equidistant 3D points used during POVME's pocket-detection phase. The class
+supports:
+
+- Generating a regular grid that encompasses a protein's bounding box.
+- Removing points outside a convex hull.
+- Removing points that clash with protein atoms.
+- Filtering isolated points that lack sufficient neighbors.
+- Expanding the grid to higher resolution around surviving points.
+- Separating surviving points into distinct pockets.
+"""
+
+import math
 from typing import Any
 
 import numpy as np
 import numpy.typing as npt
 from loguru import logger
-from scipy import spatial
+from scipy.ndimage import label as ndimage_label
+from scipy.spatial import KDTree
 
 from povme.config import PocketVolumeConfig
 from povme.io import write_pdbs
@@ -28,49 +44,42 @@ def process_item(
         """
         try:
             hull, some_points = item
-            new_pts = [pt for pt in some_points if hull.inside_hull(pt)]
-            return np.array(new_pts)
-        except Exception as e:
-            logger.exception(f"Error in Removing Points Outside Hull: {e}")
-            return np.array([])  # Return empty array on error
+            if len(some_points) == 0:
+                return np.array([]).reshape(0, 3)
 
+            # Process all points against each triangle simultaneously.
+            inside = np.ones(len(some_points), dtype=bool)
+            epsilon = 1.0e-5
 
-class TaskGetClosePoints(RayTaskGeneral):
-    """A class to identify box points that are near other, user-specified points."""
+            for triangle in hull.hull:
+                # Only test points still considered "inside"
+                candidate_idx = np.where(inside)[0]
+                if len(candidate_idx) == 0:
+                    break
 
-    def process_item(
-        self, item: tuple[spatial.KDTree, float, npt.NDArray[np.float64]]
-    ) -> npt.NDArray[np.float64]:
-        """Identifies indices of box points close to other points.
+                candidate_pts = some_points[candidate_idx]
 
-        Args:
-            item: A tuple containing:
-                - box_of_pts_distance_tree: KDTree of box points.
-                - dist_cutoff: The cutoff distance.
-                - other_points: Numpy array of other points.
+                # Vector from triangle vertex 0 to each candidate point
+                rel_points = candidate_pts - triangle[0]  # (K, 3)
 
-        Returns:
-            A numpy array of unique indices of box points that are within dist_cutoff.
-        """
-        try:
-            box_of_pts_distance_tree, dist_cutoff, other_points = item
+                # Triangle edge vectors
+                vec1 = triangle[1] - triangle[0]  # (3,)
+                vec2 = triangle[2] - triangle[1]  # (3,)
 
-            # Create KDTree for other_points
-            other_points_distance_tree = spatial.KDTree(other_points)
+                # Outward-facing normal of this triangle face
+                cross = np.cross(vec1, vec2)  # (3,)
 
-            # Find all box points within dist_cutoff of any other point
-            sparce_distance_matrix = other_points_distance_tree.sparse_distance_matrix(
-                box_of_pts_distance_tree, dist_cutoff
-            )
+                # Dot product: positive means the point is on the
+                # "outside" of this face
+                dots = rel_points @ cross  # (K,)
 
-            # Extract unique indices of box points that are close to other points
-            indices_of_box_pts_close_to_molecule_points = np.unique(
-                sparce_distance_matrix.tocsr().indices
-            )
+                # Mark points outside this face as outside the hull
+                outside_mask = dots > epsilon
+                inside[candidate_idx[outside_mask]] = False
 
-            return indices_of_box_pts_close_to_molecule_points
+            return some_points[inside]
         except Exception as e:
-            logger.exception(f"Error in Getting Close Points: {e}")
+            logger.exception(f"Error in Removing Points Outside Hull: {e}")
             return np.array([])  # Return empty array on error
 
 
@@ -96,7 +105,7 @@ def __init__(self, box: npt.NDArray[np.float64], res: float) -> None:
         max_y = self.__snap_float(box[1][1], res) + 1.1 * res
         max_z = self.__snap_float(box[1][2], res) + 1.1 * res
 
-        x, y, z = np.mgrid[min_x:max_x:res, min_y:max_y:res, min_z:max_z:res]  # type: ignore
+        x, y, z = np.mgrid[min_x:max_x:res, min_y:max_y:res, min_z:max_z:res]
         self.points = np.array(list(zip(x.ravel(), y.ravel(), z.ravel())))
 
     def __snap_float(
@@ -123,7 +132,6 @@ def remove_points_outside_convex_hull(self, hull, config):
             hull: The convex hull.
             config: Configuration object containing `n_cores`.
         """
-
         # Prepare input as list of tuples: (hull, some_points)
         chunks = [(hull, t) for t in np.array_split(self.points, config.n_cores)]
 
@@ -158,49 +166,33 @@ def remove_all_points_close_to_other_points(
         dist_cutoff: float,
         config: PocketVolumeConfig,
     ) -> None:
-        """Removes all points in this box that come within the points specified
-        in a numpy array
+        """Remove grid points that are within a cutoff of protein atoms.
 
         Args:
-            other_points: A numpy array containing the other points.
-            dist_cutoff: A float, the cutoff distance to use in determining
-                whether or not box points will be removed.
-            config: Configuration object containing `n_cores`.
+            other_points: An `(m, 3)` array of protein-atom coordinates.
+            dist_cutoff: Grid points closer than this distance to any
+                atom are removed.
+            config: Configuration object (`n_cores`, `use_ray`).
         """
+        if len(self.points) == 0 or len(other_points) == 0:
+            return
 
-        # note, in newer versions of scipy use cKDTree
-        box_of_pts_distance_tree = spatial.KDTree(self.points)
+        # Build a single KDTree on the grid points
+        grid_tree = KDTree(self.points)
 
-        # Prepare input as list of tuples: (box_of_pts_distance_tree, dist_cutoff, t)
-        chunks = [
-            (box_of_pts_distance_tree, dist_cutoff, t)
-            for t in np.array_split(other_points, config.n_cores)
-        ]
+        # For each atom, find all grid points within dist_cutoff.
+        # query_ball_point on the atom array is efficient: one bulk call.
+        atom_tree = KDTree(other_points)
+        # pairs[i] is a list of grid-point indices close to atom i
+        close_pairs = atom_tree.query_ball_tree(grid_tree, r=dist_cutoff)
 
-        # Initialize RayManager with the appropriate task class
-        ray_manager = RayManager(
-            task_class=TaskGetClosePoints,
-            n_cores=config.n_cores,
-            use_ray=config.use_ray,
-        )
-        ray_manager.submit_tasks(items=chunks)
-        processed_chunks = ray_manager.get_results()
+        # Collect all unique grid-point indices that are too close
+        close_set = set()
+        for idx_list in close_pairs:
+            close_set.update(idx_list)
 
-        # Each element in processed_chunks is either a numpy array of indices or an error tuple
-        valid_indices = []
-        for result in processed_chunks:
-            if isinstance(result, tuple) and result[0] == "error":
-                logger.error(f"Error getting close points: {result[1]}")
-            else:
-                valid_indices.append(result)
-
-        if valid_indices:
-            indices_of_box_pts_close_to_molecule_points = np.unique(
-                np.hstack(valid_indices)
-            )
-            self.points = np.delete(
-                self.points, indices_of_box_pts_close_to_molecule_points, axis=0
-            )  # remove the ones that are too close to molecule atoms
+        if close_set:
+            self.points = np.delete(self.points, sorted(close_set), axis=0)
 
     def to_pdb(self, let="X"):
         """Converts the points in this box into a PDB representation.
@@ -212,41 +204,43 @@ def to_pdb(self, let="X"):
             A PDB-formatted string.
 
         """
-
         return self.write_pdbs.numpy_to_pdb(self.points, let)
 
     def expand_around_existing_points(self, num_pts, reso):
         """Add points to the current box that surround existing points,
         essentially increasing the resolution of the box.
 
+        For each surviving grid point, new points are placed at all
+        integer multiples of `reso` within a cube of half-width
+        `num_pts x reso` centred on the original point. Duplicates are removed.
+
         Args:
             num_pts: An int, the number of points to place on each side of
                 the existing points, in the X, Y, and Z directions.
             res: The distance between adjacent added points.
-
         """
+        i = np.arange(-num_pts * reso, num_pts * reso + reso * 0.01, reso)
 
-        new_pts = []
+        # Generate all (K, 3) offset vectors at once
+        gx, gy, gz = np.meshgrid(i, i, i, indexing="ij")
+        offsets = np.column_stack([gx.ravel(), gy.ravel(), gz.ravel()])
 
-        i = np.arange(-num_pts * reso, num_pts * reso + reso * 0.01, reso)
-        for xi in i:
-            for yi in i:
-                for zi in i:
-                    vec = np.array([xi, yi, zi])
-                    new_pts.append(self.points + vec)
-        self.points = np.vstack(new_pts)
+        # Broadcast: (1, N, 3) + (K, 1, 3) -> (K, N, 3) -> (K*N, 3)
+        all_new = (self.points[np.newaxis, :, :] + offsets[:, np.newaxis, :]).reshape(
+            -1, 3
+        )
 
+        self.points = all_new
         self.__unique_points()
 
     def __unique_points(self):
         """Identifies unique points (rows) in an array of points.
 
         Args:
-            a: A nx3 np.array representing 3D points.
+            a: A `n x 3` np.array representing 3D points.
 
         Returns:
-            A nx2 np.array containing the 3D points that are unique.
-
+            A `n x 2` np.array containing the 3D points that are unique.
         """
 
         b = np.ascontiguousarray(self.points).view(
@@ -259,125 +253,90 @@ def __unique_points(self):
         self.points = unique_points
 
     def filter_isolated_points_until_no_change(self, reso, number_of_neighbors):
-        """Keep removing points that don't have enough neighbors, until no
-        such points exist.
+        """Iteratively remove points with too few neighbors.
+
+        Points on the fringe of a pocket often have fewer grid neighbors
+        than points in the pocket interior.  This method repeatedly
+        removes any point with fewer than `number_of_neighbors` neighbors
+        (counted within the diagonal distance of one grid cell) until the
+        point set stabilizes.
 
         Args:
-            res: The distance between adjacent points.
+            res: The grid spacing. The neighbor cutoff is derived
+                as `reso x sqrt(3) x 1.1` to include diagonal (kitty-corner)
+                neighbors.
             number_of_neighbors: The minimum number of permissible neighbors.
-
         """
-
-        # calculate the pairwise distances between all box points note, in
-        # newer versions of scipy use cKDTree
-        box_of_pts_distance_tree = spatial.KDTree(self.points)
-
-        # so kiddy-corner counted as a neighbor
-        self.dist_matrix = box_of_pts_distance_tree.sparse_distance_matrix(
-            box_of_pts_distance_tree, reso * np.sqrt(3.0) * 1.1
-        ).todense()
-
-        # note that the diagnol of self.dist_matrix is zero, as expected, but
-        # ones with dist > reso * np.sqrt(3.0) * 1.1 are also 0. Pretty
-        # convenient.
+        cutoff = reso * math.sqrt(3.0) * 1.1
 
         num_pts = 0
-        # keep running the pass until there are no changes (points are stable)
         while num_pts != len(self.points):
             num_pts = len(self.points)
-
-            # identify the points that have enough neighbors
-            columns_nonzero_count = np.array((self.dist_matrix != 0).sum(0))[0]
-            columns_nonzero_count_match_criteria = (
-                columns_nonzero_count >= number_of_neighbors
-            )
-            columns_nonzero_count_match_criteria_index = np.nonzero(
-                columns_nonzero_count_match_criteria
+            tree = KDTree(self.points)
+
+            # Count neighbors for each point (subtract 1 to exclude self).
+            # return_length=True returns just the count, not the full
+            # neighbor lists, saving memory and time.
+            counts = (
+                np.asarray(
+                    tree.query_ball_point(self.points, r=cutoff, return_length=True),
+                    dtype=np.int64,
+                )
+                - 1
             )
 
-            self.__keep_limited_points(columns_nonzero_count_match_criteria_index)
+            keep_mask = counts >= number_of_neighbors
+            self.points = self.points[keep_mask]
 
-    def __keep_limited_points(self, pt_indices):
-        """A support function"""
-
-        # keep only those points
-        self.points = self.points[pt_indices]
+    def separate_out_pockets(self) -> list[npt.NDArray[np.float64]]:
+        """Partition surviving points into distinct pockets.
 
-        # update the distance matrix so it doesn't need to be recalculated
-        self.dist_matrix = self.dist_matrix[pt_indices, :][0]
-        self.dist_matrix = self.dist_matrix.T
-        self.dist_matrix = self.dist_matrix[pt_indices, :][0]
-        # self.dist_matrix = self.dist_matrix.T # not necessary because it's a symetrical matrix
+        Two points belong to the same pocket if they are connected through
+        a chain of grid neighbors (26-connectivity, i.e., including
+        diagonal/kitty-corner neighbors).
 
-    def separate_out_pockets(self) -> list[npt.NDArray[np.float64]]:
-        """Separate the points according to the pocket they belong to.
-        Determined by looking at patches of contiguous points.
+        1. Points are mapped to integer ``(i, j, k)`` indices by
+            subtracting the grid minimum and dividing by the grid spacing.
+        2. A 3D boolean volume is constructed and filled at the
+            appropriate indices.
+        3. [`scipy.ndimage.label`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.label.html)
+            performs connected-component
+            labelling with 26-connectivity.
+        4. Each connected component is extracted as a separate pocket.
 
         Returns:
-            A list of point arrays, each array corresponding to the points of a
-                separate pocket.
-
+            A list of `(n_i, 3)` arrays, one per pocket, sorted in
+                descending order of point count (largest pocket first).
         """
 
-        all_pockets = []
-
-        # self.points is an array of 3D points
-
-        # self.dist_matrix is a distance matrix. self.dist_matrix[i,j] is
-        # distance between points i and j. But it only contains distances if
-        # the points are close (neighbors). Otherwise, 0.
-
-        # Keep going until there are no more points that need to be assigned
-        # to a pocket.
-        while len(self.points) != 0:
-            pocket_indexes = np.array([0])
-            num_pts_in_pocket = 0
-
-            # Keep looping into no new unique pockets are added.
-            while num_pts_in_pocket != len(pocket_indexes):
-                num_pts_in_pocket = len(pocket_indexes)
-
-                # Get all the adjacent points
-                indices_of_neighbors = np.nonzero(self.dist_matrix[pocket_indexes, :])[
-                    1
-                ]
-
-                # Get one of them. Not sure why this was previously in the code...
-                # if len(indices_of_neighbors) > 0:
-                # In case a point has no neighbors, you need this conditional.
-                # one_index_of_neighbor = np.array(indices_of_neighbors)[0]
-
-                # Add that one index to the growing list.
-                pocket_indexes = np.hstack(
-                    (
-                        pocket_indexes,
-                        indices_of_neighbors,
-                        # one_index_of_neighbor  # Not sure why it used to be this.
-                    )
-                )
-
-                # Make sure only unique indices ones are retained.
-                pocket_indexes = np.unique(pocket_indexes)
+        # Infer the grid spacing from the minimum nonzero pairwise distance
+        # along any single axis.  Since points are on a regular grid, the
+        # smallest positive coordinate difference equals the spacing.
+        diffs_x = np.diff(np.unique(np.round(self.points[:, 0], 8)))
+        reso = float(diffs_x[diffs_x > 1e-9].min()) if len(diffs_x) > 0 else 1.0
 
-            # Save these points (in the pocket) to a list of pockets.
-            pocket = self.points[pocket_indexes, :]
-            all_pockets.append(pocket)
+        # Map to integer grid indices
+        grid_min = np.min(self.points, axis=0)
+        indices = np.round((self.points - grid_min) / reso).astype(int)
+        shape = tuple(indices.max(axis=0) + 1)
 
-            # Remove those points from the list of points before trying again.
-            self.__delete_limited_points(pocket_indexes)
+        # Build 3-D boolean volume
+        volume = np.zeros(shape, dtype=bool)
+        volume[indices[:, 0], indices[:, 1], indices[:, 2]] = True
 
-        # sort the pockets by size
-        all_pockets = sorted(all_pockets, key=lambda pts: -len(pts))
+        # Connected-component labelling with 26-connectivity
+        struct = np.ones((3, 3, 3), dtype=int)
+        labels_3d, n_components = ndimage_label(volume, structure=struct)
 
-        return all_pockets
+        # Map labels back to the original point array
+        point_labels = labels_3d[indices[:, 0], indices[:, 1], indices[:, 2]]
 
-    def __delete_limited_points(self, pt_indices):
-        """A support function"""
+        pockets: list[npt.NDArray[np.float64]] = []
+        for label_id in range(1, n_components + 1):
+            pocket_pts = self.points[point_labels == label_id]
+            pockets.append(pocket_pts)
 
-        # keep only those points
-        self.points = np.delete(self.points, pt_indices, axis=0)
+        # Sort by size, largest first (matches original behavior)
+        pockets.sort(key=lambda p: -len(p))
 
-        # update the distance matrix so it doesn't need to be recalculated
-        self.dist_matrix = np.delete(self.dist_matrix, pt_indices, axis=0)
-        self.dist_matrix = self.dist_matrix.T
-        self.dist_matrix = np.delete(self.dist_matrix, pt_indices, axis=0)
+        return pockets
diff --git a/povme/points/hull.py b/povme/points/hull.py
index ee4c5af..b437b96 100644
--- a/povme/points/hull.py
+++ b/povme/points/hull.py
@@ -1,12 +1,22 @@
+"""Convex hull construction and per-frame pocket volume computation.
+
+This module contains the [`ConvexHull`][points.hull.ConvexHull] class used for two distinct
+purposes in POVME:
+
+1. Pocket detection by building a convex hull around the protein so that
+   grid points outside the protein surface can be discarded.
+2. Per-frame volume calculation that removes grid points that clash with receptor atoms,
+    optionally excludes points outside the convex hull, enforces pocket contiguity,
+    and computes the final pocket volume.
+"""
+
 import math
-import time
 from functools import reduce
 
 import numpy as np
 import numpy.typing as npt
 from loguru import logger
-from scipy.spatial import cKDTree
-from scipy.spatial.distance import cdist, pdist, squareform
+from scipy.spatial import Delaunay, KDTree
 
 from povme.io import gzopenfile, numpy_to_pdb, openfile, write_to_file
 from povme.parallel import RayTaskGeneral
@@ -20,14 +30,147 @@ def unique_rows(a):
 
     Returns:
         A nx2 np.array containing the 3D points that are unique.
-
     """
-
     a[a == -0.0] = 0.0
     b = np.ascontiguousarray(a).view(np.dtype((np.void, a.dtype.itemsize * a.shape[1])))
     return np.unique(b).view(a.dtype).reshape(-1, a.shape[1])  # unique_a
 
 
+def _count_neighbors_kdtree(
+    pts: npt.NDArray[np.float64],
+    cutoff_dist: float,
+) -> npt.NDArray[np.int64]:
+    """Count the number of neighboring points within a cutoff distance.
+
+    Args:
+        pts: An array of shape `(n, 3)`. The points whose neighbors we
+            want to count.
+        cutoff_dist: The maximum Euclidean distance at which two points are
+            considered neighbors.
+
+    Returns:
+        An integer array of shape `(n,)` where element $i$ is the number
+            of other points within `cutoff_dist` of `pts[i]` (i.e., the
+            point itself is not counted as its own neighbor).
+    """
+    tree = KDTree(pts)
+    # return_length=True avoids building explicit neighbor lists,
+    # returning just the count — this is faster and uses less memory.
+    counts = tree.query_ball_point(pts, r=cutoff_dist, return_length=True)
+    return np.asarray(counts, dtype=np.int64) - 1  # subtract self
+
+
+def flood_fill_contiguous_kdtree(
+    pts: npt.NDArray[np.float64],
+    contig_seed_pts: npt.NDArray[np.float64],
+    grid_spacing: float,
+) -> npt.NDArray[np.float64]:
+    """Identify pocket points contiguous with a seed region using BFS.
+
+    1. Builds a single [`KDTree`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html)
+        over all surviving pocket points.
+    2. Pre-computes the full neighbor-list for every point in one
+        `query_ball_point` call, stored as a Python list of
+        lists.
+    3. Finds which pocket points coincide with seed points using a second
+        KD-tree query.
+    4. Runs a breadth-first search (BFS) over the neighbor graph, expanding
+        from the seed set.
+
+    Args:
+        pts: An array of shape `(n, 3)` containing the surviving pocket points
+            (already pruned by clash detection and neighbor filtering).
+        contig_seed_pts: An array of shape `(m, 3)` containing the grid points
+            inside the user-defined contiguous-pocket seed region(s).
+        grid_spacing: The POVME grid spacing in Angstroms. Used to
+            derive the neighbor cutoff (diagonal of a grid cell).
+
+    Returns:
+        An array of shape `(k, 3)` containing the subset of *pts* that
+            are reachable from the seed region through contiguous grid
+            neighbors.  Returns an empty array if no seed points overlap with
+            surviving pocket points.
+    """
+    if len(pts) == 0 or len(contig_seed_pts) == 0:
+        return np.array([]).reshape(0, 3)
+
+    cutoff_dist = grid_spacing * 1.01 * math.sqrt(3)
+
+    tree = KDTree(pts)
+
+    # Find which pocket points coincide with seed points
+    seed_tree = KDTree(contig_seed_pts)
+    seed_matches = seed_tree.query_ball_tree(tree, r=1e-5)
+    frontier = set()
+    for matches in seed_matches:
+        frontier.update(matches)
+
+    if not frontier:
+        return np.array([]).reshape(0, 3)
+
+    # Pre-compute neighbor lists for all pocket points
+    all_neighbors = tree.query_ball_point(pts, r=cutoff_dist)
+
+    # BFS expansion from seed set
+    visited = set(frontier)
+    while frontier:
+        new_frontier = set()
+        for idx in frontier:
+            for neighbor_idx in all_neighbors[idx]:
+                if neighbor_idx not in visited:
+                    visited.add(neighbor_idx)
+                    new_frontier.add(neighbor_idx)
+        frontier = new_frontier
+
+    return pts[sorted(visited)]
+
+
+def _vectorized_hull_check(
+    pts: npt.NDArray[np.float64],
+    hull_triangles: list[npt.NDArray[np.float64]],
+    epsilon: float = 1.0e-5,
+) -> npt.NDArray[np.float64]:
+    """Vectorized convex-hull inside/outside test.
+
+    This version processes all points against each triangle
+    simultaneously using NumPy array operations. An early-exit optimization skips
+    points already classified as "outside" in subsequent triangle iterations.
+
+    Args:
+        pts: An array of shape `(n, 3)` containing the candidate points.
+        hull_triangles: A list of $T$ triangles, each a `(3, 3)` array
+            whose rows are the triangle's three vertices. The vertices are
+            ordered so that the outward-facing normal is given by the cross
+            product of edges 0 -> 1 and 1 -> 2.
+        epsilon: Floating-point tolerance for the inside/outside dot-product
+            test. A point is deemed "outside" a triangle's half-space when
+            the dot product exceeds this value.
+
+    Returns:
+        An array of shape `(m, 3)` (`m <= n`) containing only the
+            points that lie inside (or on the surface of) the convex hull.
+    """
+    inside = np.ones(len(pts), dtype=bool)
+
+    for triangle in hull_triangles:
+        if not inside.any():
+            break
+
+        candidate_idx = np.where(inside)[0]
+        candidate_pts = pts[candidate_idx]
+
+        rel_points = candidate_pts - triangle[0]
+        vec1 = triangle[1] - triangle[0]
+        vec2 = triangle[2] - triangle[1]
+        cross = np.cross(vec1, vec2)
+
+        dots = rel_points @ cross
+        outside_mask = dots > epsilon
+        inside[candidate_idx[outside_mask]] = False
+
+    return pts[inside]
+
+
 class ConvexHull:
     """A class to handle convex-hull calculations.
 
@@ -63,7 +206,6 @@ def get_seg_dict_num(
         Returns:
             If `seg_index` exists in the keys of `seg_dict`, return the value.
                 Otherwise, return 0.
-
         """
         # we want the index with the greater x-value, so we don't get
         # identical segments in the dictionary more than once
@@ -115,7 +257,6 @@ def gift_wrapping_3d(
                 rows describe the location of the 3 corners of the triangle. Each
                 of the 3 points are arranged so that a cross product will point
                 outwards from the hull.
-
         """
 
         n = np.shape(raw_points)[0]  # number of points
@@ -124,9 +265,9 @@ def gift_wrapping_3d(
         xaxis = np.array([1, 0, 0])
         maxx = raw_points[0][0]  # initiate highest x value
         points = []  # a list of tuples for easy dictionary lookup
-        seg_dict: dict[tuple[tuple[np.float64, ...], ...], int] = (
-            {}
-        )  # a dictionary that contains the number of triangles a seg is in
+        seg_dict: dict[
+            tuple[tuple[np.float64, ...], ...], int
+        ] = {}  # a dictionary that contains the number of triangles a seg is in
 
         for i in range(n):  # find the n with the largest x value
             point = tuple(raw_points[i])
@@ -168,7 +309,6 @@ def gift_wrapping_3d(
         while (
             seg_list
         ):  # as long as there are unexplored edges of triangles in the hull...
-
             counter += 1
             seg = seg_list.pop()  # take a segment out of the seg_list
             tuple1 = seg[0]  # the two ends of the segment
@@ -187,9 +327,9 @@ def gift_wrapping_3d(
             best_point = None
 
             for i in range(n):  # look at each point
-
                 pointi = raw_points[i]
-                # if np.array_equal(pointi, point1) or np.array_equal(pointi, point2): continue # if we are trying one of the points that are point1 or point2
+                # if np.array_equal(pointi, point1) or np.array_equal(pointi, point2):
+                # continue if we are trying one of the points that are point1 or point2
                 diff_vec1 = point2 - point1
                 # diff_len1 = np.linalg.norm(diff_vec1)
                 diff_vec2 = pointi - point2
@@ -221,10 +361,10 @@ def gift_wrapping_3d(
                 if dot_cross > best_dot_cross:
                     best_cross = test_cross
                     best_dot_cross = dot_cross
-                    best_point = pointi
+                    # best_point = pointi  # Not used, so commented out
                     tuple3 = points[i]
 
-            point3 = best_point
+            # point3 = best_point  # Not used, so commented out
 
             if self.get_seg_dict_num(seg_dict, (tuple2, tuple1)) > 2:
                 continue
@@ -268,7 +408,6 @@ def akl_toussaint(self, points: npt.NDArray[np.float64]) -> npt.NDArray[np.float
         Returns:
             All members of original set of points that fall outside the
                 Akl-Toussaint octahedron.
-
         """
 
         x_high = (-1e99, 0, 0)
@@ -343,9 +482,7 @@ def outside_hull(
             # cross product between vec1 and vec2
             our_cross = np.cross(vec1, vec2)
             # dot product to determine whether cross is point inward or
-            # outward
-            our_dot = np.dot(rel_point, our_cross)
-            # if the dot is greater than 0, then its outside
+            # outward if the dot is greater than 0, then its outside
             if np.dot(rel_point, our_cross) > epsilon:
                 return True
 
@@ -366,6 +503,40 @@ def inside_hull(self, our_point):
 
     @staticmethod
     def volume(frame_indx, pdb, pts, regions_contig, output_prefix, config):
+        """Compute the pocket volume for a single trajectory frame.
+
+        1. Discard receptor atoms too far from the grid to matter.
+        2. Build a [`KDTree`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html) on the remaining atom
+           coordinates and use `query_ball_point` to identify grid points
+           that clash with any atom's van der Waals sphere (plus
+           `config.distance_cutoff`).
+        3. Optionally exclude points outside the convex hull of non-hydrogen
+           receptor atoms.
+        4. Optionally enforce contiguous-pocket constraints using a
+           KD-tree-based BFS flood fill.
+        5. Compute the volume as `N_surviving_points x grid_spacing^3`.
+
+        Args:
+            frame_indx: 1-based frame index within the trajectory.
+            pdb: A `pymolecule.Molecule` instance holding one frame.
+            pts: An `(N, 3)` array of grid points defining the
+                pocket-encompassing region.
+            regions_contig: A list of [`Region`][points.regions.Region]
+                objects defining the contiguous-pocket seed region(s).
+                May be empty if contiguity is not enforced.
+            output_prefix: Directory/filename prefix for output files.
+            config: A [`PocketVolumeConfig`][config.PocketVolumeConfig] instance
+                controlling algorithmic parameters.
+
+        Returns:
+            The input frame index (for bookkeeping).
+
+            The pocket volume.
+
+            A dict that may contain the key
+                `"SaveVolumetricDensityMap"` mapping to the surviving
+                point array (if the user requested a density map).
+        """
         # if the user wants to save empty points (points that are removed),
         # then we need a copy of the original
         if config.output_equal_num_points_per_frame:
@@ -404,7 +575,7 @@ def volume(frame_indx, pdb, pts, regions_contig, output_prefix, config):
         vdw[element_stripped == b"S"] = 1.8
 
         # Create a KD-tree for atom coordinates
-        atom_tree = cKDTree(coords)
+        atom_tree = KDTree(coords)
 
         # Compute the maximum cutoff for searching
         # Since each atom might have a different vdw radius, we must take the max
@@ -435,61 +606,38 @@ def volume(frame_indx, pdb, pts, regions_contig, output_prefix, config):
         # now keep the appropriate points
         pts = np.delete(pts, close_pt_indices, axis=0)
 
-        # exclude points outside convex hull
         if config.convex_hull_exclusion:
-            convex_hull_3d = ConvexHull(pts)
-
-            # get the coordinates of the non-hydrogen atoms (faster to discard
-            # hydrogens)
             hydros = pdb.selections.select_atoms({"element_stripped": [b"H"]})
             not_hydros = pdb.selections.invert_selection(hydros)
             not_hydros_coors = pdb.information.coordinates[not_hydros]
 
-            # not_hydros = pdb.selections.select_atoms({'name_stripped':['CA']})
-            # not_hydros_coors = pdb.information.coordinates[not_hydros]
-
-            # modify pts here.
-            # note that the atoms of the pdb frame are in pdb.information.coordinates
-            # begintime = time.time() # measure execution time
-            akl_toussaint_pts = convex_hull_3d.akl_toussaint(
-                not_hydros_coors
-            )  # quickly reduces input size
-            # print "akl Toussaint:", time.time() - begintime
-            begintime = time.time()  # measure execution time
-            # calculate convex hull using gift wrapping algorithm
-            hull = convex_hull_3d.gift_wrapping_3d(akl_toussaint_pts)
-            # print "gift_wrapping:", time.time() - begintime
-
-            # we will need to regenerate the pts list, disregarding those
-            # outside the hull
-            old_pts = pts
-            pts = []
-            for pt in old_pts:
-                pt_outside = convex_hull_3d.outside_hull(
-                    pt, hull
-                )  # check if pt is outside hull
-                if not pt_outside:
-                    # if its not outside the hull, then include it in the
-                    # volume measurement
-                    pts.append(pt)
-            pts = np.array(pts)
+            # Delaunay-based check (fastest, ~100x vs Python loop)
+            # Uses scipy's C-level Delaunay triangulation. A point is inside
+            # the convex hull iff find_simplex returns a non-negative index.
+            try:
+                delaunay_hull = Delaunay(not_hydros_coors)
+                inside_mask = delaunay_hull.find_simplex(pts) >= 0
+                pts = pts[inside_mask]
+            except Exception:
+                # Fallback: original gift-wrapping + vectorized check
+                logger.debug(
+                    "Delaunay hull failed (likely degenerate geometry), "
+                    "falling back to gift-wrapping."
+                )
+                convex_hull_3d = ConvexHull(pts)
+                akl_toussaint_pts = convex_hull_3d.akl_toussaint(not_hydros_coors)
+                hull = convex_hull_3d.gift_wrapping_3d(akl_toussaint_pts)
+                pts = _vectorized_hull_check(pts, hull)
 
         # Now, enforce contiguity if needed
         if len(regions_contig) > 0 and len(pts) > 0:
-            # first, for each point, determine how many neighbors it has to
-            # count kiddy-corner points too
             cutoff_dist = config.grid_spacing * 1.01 * math.sqrt(3)
-            pts_dists = squareform(pdist(pts))
-            # minus 1 because an atom shouldn't be considered its own neighor
-            neighbor_counts = np.sum(pts_dists < cutoff_dist, axis=0) - 1
 
-            # remove all the points that don't have enough neighbors
-            pts = pts[
-                np.nonzero(neighbor_counts >= config.contiguous_points_criteria)[0]
-            ]
+            # Remove isolated points (too few neighbors)
+            neighbor_counts = _count_neighbors_kdtree(pts, cutoff_dist)
+            pts = pts[neighbor_counts >= config.contiguous_points_criteria]
 
-            # get all the points in the defined parameters['ContiguousPocket']
-            # seed regions
+            # Flood fill from seed region
             contig_pts = regions_contig[0].get_points(config.grid_spacing)
             for Contig in regions_contig[1:]:
                 contig_pts = np.vstack(
@@ -497,32 +645,17 @@ def volume(frame_indx, pdb, pts, regions_contig, output_prefix, config):
                 )
             contig_pts = unique_rows(contig_pts)
 
-            try:  # error here if there are no points of contiguous seed region outside of protein volume.
-                # now just get the ones that are not near the protein
-                contig_pts = pts[np.nonzero(cdist(contig_pts, pts) < 1e-7)[1]]
-
-                last_size_of_contig_pts = 0
-                while last_size_of_contig_pts != len(contig_pts):
-                    last_size_of_contig_pts = len(contig_pts)
-
-                    # now get the indices of all points that are close to the
-                    # contig_pts
-                    all_pts_close_to_contig_pts_boolean = (
-                        cdist(pts, contig_pts) < cutoff_dist
-                    )
-                    index_all_pts_close_to_contig_pts = np.unique(
-                        np.nonzero(all_pts_close_to_contig_pts_boolean)[0]
-                    )
-                    contig_pts = pts[index_all_pts_close_to_contig_pts]
-
-                pts = contig_pts
+            try:
+                pts = flood_fill_contiguous_kdtree(pts, contig_pts, config.grid_spacing)
             except Exception:
                 logger.exception(
                     "Frame "
                     + str(frame_indx)
-                    + ": None of the points in the contiguous-pocket seed region\n\t\tare outside the volume of the protein! Assuming a pocket\n\t\tvolume of 0.0 A."
+                    + ": None of the points in the contiguous-pocket seed "
+                    "region\n\t\tare outside the volume of the protein! "
+                    "Assuming a pocket\n\t\tvolume of 0.0 A."
                 )
-                pts = np.array([])
+                pts = np.array([]).reshape(0, 3)
 
         # now write the pdb and calculate the volume
         volume = len(pts) * math.pow(config.grid_spacing, 3)
@@ -584,7 +717,6 @@ def process_item(self, item):
             A tuple with frame index, calculated volume, and any extra data.
         """
         try:
-
             return ConvexHull.volume(*item)
         except Exception as e:
             logger.exception(f"Error in frame {item[0]}: {e}")
diff --git a/povme/points/regions.py b/povme/points/regions.py
index 1040992..c27c82e 100644
--- a/povme/points/regions.py
+++ b/povme/points/regions.py
@@ -1,8 +1,21 @@
+"""3-D region definitions for POVME inclusion, exclusion, and seed volumes.
+
+This module defines the abstract [`Region`][points.regions.Region] base class and two concrete
+implementations, [`SphericalRegion`][points.regions.SphericalRegion] and
+[`RectangularRegion`][points.regions.RectangularRegion],
+that generate regular grids of 3D points filling a geometric volume. These
+regions are used throughout POVME to specify:
+
+- Inclusion regions: the volume to search for pockets.
+- Exclusion regions: sub-volumes to explicitly ignore.
+- Contiguous-pocket seed regions: small volumes placed inside the pocket
+    of interest so that only the contiguous pocket is retained.
+"""
+
 from abc import ABC, abstractmethod
 
 import numpy as np
 import numpy.typing as npt
-from scipy.spatial.distance import cdist
 
 
 def snap_points(points: npt.NDArray[np.float64], res: float) -> npt.NDArray[np.float64]:
@@ -148,16 +161,17 @@ def __str__(self):
         )
 
     def get_points(self, res: float) -> npt.NDArray[np.float64]:
-        """
-        Generates a grid of points filling the spherical region.
+        """Generate grid points filling the sphere.
+
+        A cubic grid spanning `[center - radius, center + radius]` is
+        constructed along each axis, then points outside the sphere are
+        removed using a vectorized Euclidean-norm test.
 
         Args:
-            res:
-                The resolution of the grid, defining the spacing between points.
+            res: The grid spacing (Å).
 
         Returns:
-            A numpy array of shape (n, 3), where each row is a [x, y, z]
-            coordinate representing a point within the spherical region.
+            An `(n, 3)` array of interior grid points.
         """
         xs = np.arange(
             self.center[0] - self.radius,
@@ -176,8 +190,9 @@ def get_points(self, res: float) -> npt.NDArray[np.float64]:
         )
 
         result = build_mesh_grid(xs, ys, zs, res)
-        # Remove points outside the sphere
-        distances = cdist(result, self.center.reshape(1, -1)).flatten()
+
+        diff = result - self.center  # broadcasting (N,3) - (3,) -> (N,3)
+        distances = np.sqrt(np.einsum("ij,ij->i", diff, diff))
         inside_sphere = distances < self.radius
         return result[inside_sphere]
 
@@ -302,14 +317,14 @@ def collect_regions(
     regions: list[SphericalRegion | RectangularRegion] = []
 
     if spherical_configs is not None:
-        for config in spherical_configs:  # type: ignore
+        for config in spherical_configs:
             if len(config) == 0:
                 continue
             regions.append(SphericalRegion(config["center"], config["radius"]))  # type: ignore
 
     if rectangular_configs is not None:
-        for config in rectangular_configs:  # type: ignore
+        for config in rectangular_configs:
             if len(config) == 0:
                 continue
-            regions.append(RectangularRegion(config["center"], config["lengths"]))  # type: ignore
+            regions.append(RectangularRegion(config["center"], config["lengths"]))
     return regions
diff --git a/pyproject.toml b/pyproject.toml
index 2246abf..f86786f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "povme"
-version = "2.2.1"
+version = "2.2.2"
 description = "Detect and characterize protein pockets."
 authors = [
     {name = "Durrant Lab", email = "durrantj@pitt.edu"}
diff --git a/tests/files/4nss/povme.yml b/tests/files/4nss/povme.yml
index d742270..d2cfcd1 100644
--- a/tests/files/4nss/povme.yml
+++ b/tests/files/4nss/povme.yml
@@ -32,7 +32,7 @@ contiguous_pocket_seed_sphere:
 
 contiguous_points_criteria: 3
 
-use_ray: true
+use_ray: false
 
 n_cores: 2