diff --git a/.gersemirc b/.gersemirc
index f2f71ebc..ebecb14c 100644
--- a/.gersemirc
+++ b/.gersemirc
@@ -1,4 +1,4 @@
-definitions: [./CMakeLists.txt, ./cmake, ./tests]
-line_length: 80
+definitions: [./CMakeLists.txt, ./tests]
+line_length: 100
 indent: 2
 warn_about_unknown_commands: false
diff --git a/.github/workflows/macos-linux-windows-pixi.yml b/.github/workflows/macos-linux-windows-pixi.yml
index f0006b5b..615cbc7e 100644
--- a/.github/workflows/macos-linux-windows-pixi.yml
+++ b/.github/workflows/macos-linux-windows-pixi.yml
@@ -1,4 +1,4 @@
-name: CI - MacOS/Linux/Windows via Pixi
+name: CI - Pixi (MacOS/Linux/Windows)
 
 on:
   push:
@@ -30,12 +30,12 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  nanoeigenpy-pixi:
-    name: Standard - ${{ matrix.os }} - Env ${{ matrix.environment }} ${{ matrix.build_type }}
+  pixi:
+    name: Pixi - ${{ matrix.os }} - Env ${{ matrix.environment }} ${{ matrix.build_type }}
     runs-on: ${{ matrix.os }}
     env:
-      CCACHE_BASEDIR: "${GITHUB_WORKSPACE}"
-      CCACHE_DIR: "${GITHUB_WORKSPACE}/.ccache"
+      CCACHE_BASEDIR: "${{ github.workspace }}"
+      CCACHE_DIR: "${{ github.workspace }}/.ccache"
       CCACHE_COMPRESS: true
       CCACHE_COMPRESSLEVEL: 6
       # Since pixi will install a compiler, the compiler mtime will be changed.
@@ -55,43 +55,44 @@ jobs:
 
     steps:
     - uses: actions/checkout@v6
-      with:
-        submodules: recursive
 
     - uses: actions/cache@v5
       with:
-        path: .ccache
-        key: ccache-macos-linux-windows-pixi-${{ matrix.os }}-${{ matrix.build_type }}-${{ matrix.environment }}-${{ github.sha }}
-        restore-keys: ccache-macos-linux-windows-pixi-${{ matrix.os }}-${{ matrix.build_type }}-${{ matrix.environment }}-
+        path: ${{ env.CCACHE_DIR }}
+        key: ccache-pixi-${{ matrix.os }}-${{ matrix.build_type }}-${{ github.sha }}
+        restore-keys: ccache-pixi-${{ matrix.os }}-${{ matrix.build_type }}-
 
     - uses: prefix-dev/setup-pixi@v0.9.5
       with:
-        cache: true
         environments: ${{ matrix.environment }}
 
     - name: Clear ccache statistics [MacOS/Linux/Windows]
       run: |
-        pixi run -e ${{ matrix.environment }} ccache -z
+        pixi run -e ${{ matrix.environment }} ccache -svz
 
-    - name: Build nanoeigenpy [MacOS/Linux/Windows]
+    - name: Configure nanoeigenpy [MacOS/Linux/Windows]
       env:
         NANOEIGENPY_BUILD_TYPE: ${{ matrix.build_type }}
+      run: |
+        pixi run -e ${{ matrix.environment }} configure
+
+    - name: Build nanoeigenpy [MacOS/Linux/Windows]
       run: |
         pixi run -e ${{ matrix.environment }} build
 
     - name: Test nanoeigenpy [MacOS/Linux/Windows]
       run: |
-        pixi run -e ${{ matrix.environment }} ctest --test-dir build --output-on-failure
+        pixi run -e ${{ matrix.environment }} test
 
     - name: Install nanoeigenpy [MacOS/Linux/Windows]
       run: |
-        pixi run -e ${{ matrix.environment }} cmake --build build --target install
+        pixi run -e ${{ matrix.environment }} install
 
     - name: Show ccache statistics [MacOS/Linux/Windows]
       run: |
         pixi run -e ${{ matrix.environment }} ccache -sv
 
-  # nanoeigenpy-pixi-build:
+  # pixi-build:
   #   name: Pixi build - ${{ matrix.os }}
   #   runs-on: ${{ matrix.os }}
 
@@ -102,14 +103,11 @@ jobs:
 
   #   steps:
   #   - uses: actions/checkout@v6
-  #     with:
-  #       submodules: recursive
 
   #   - uses: prefix-dev/setup-pixi@v0.9.5
   #     env:
   #       CMAKE_BUILD_PARALLEL_LEVEL: 2
   #     with:
-  #       cache: true
   #       environments: test-pixi-build
 
   #   - name: Test package [MacOS/Linux/Windows]
@@ -121,9 +119,10 @@ jobs:
     name: check-macos-linux-windows-pixi
 
     needs:
-    - nanoeigenpy-pixi
+    - pixi
+    # - pixi-build
 
-    runs-on: Ubuntu-latest
+    runs-on: ubuntu-latest
 
     steps:
     - name: Decide whether the needed jobs succeeded or failed
diff --git a/.github/workflows/nix.yml b/.github/workflows/nix.yml
index 1dc45e03..92a32e32 100644
--- a/.github/workflows/nix.yml
+++ b/.github/workflows/nix.yml
@@ -10,6 +10,9 @@ on:
 
 jobs:
   nix:
+    # ⚠️ temporarly disable the Nix job for https://github.com/Simple-Robotics/nanoeigenpy/pull/40 ⚠️
+    if: false
+
     runs-on: "${{ matrix.os }}-latest"
     strategy:
       matrix:
@@ -25,7 +28,10 @@ jobs:
       - run: nix build -L ".#nanoeigenpy${{ matrix.eigen }}"
 
   check:
-    if: always()
+    # ⚠️ temporarly disable the Nix job for https://github.com/Simple-Robotics/nanoeigenpy/pull/40 ⚠️
+    if: false
+    # if: always()
+
     name: check-macos-linux-nix
 
     needs:
diff --git a/.github/workflows/ros_ci.yml b/.github/workflows/ros_ci.yml
index b702039c..f09787dc 100644
--- a/.github/workflows/ros_ci.yml
+++ b/.github/workflows/ros_ci.yml
@@ -20,34 +20,50 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  CI:
+  ros:
+    name: ROS ${{ matrix.ROS_DISTRO }} - ${{ matrix.os }}
     strategy:
+      fail-fast: false
       matrix:
-        env:
-          - {ROS_DISTRO: humble}
-          - {ROS_DISTRO: jazzy}
-          - {ROS_DISTRO: kilted}
-          - {ROS_DISTRO: rolling}
+        include:
+          # ROS2 Humble Hawksbill (May 2022 - May 2027)
+          - ROS_DISTRO: humble
+            os: Ubuntu 22.04 (Jammy)
+
+          # ROS2 Jazzy Jalisco (May 2024 - May 2029)
+          - ROS_DISTRO: jazzy
+            os: Ubuntu 24.04 (Noble)
+
+          # ROS2 Kilted Kayu (May 2025 - December 2026)
+          - ROS_DISTRO: kilted
+            os: Ubuntu 24.04 (Noble)
+
+          # ROS2 Rolling Ridley
+          - ROS_DISTRO: rolling
+            os: Ubuntu 24.04 (Noble)
     env:
-      # PRERELEASE: true  # Fails due to issues in the underlying Docker image
-      BUILDER: colcon
+      VERBOSE_OUTPUT: true
+      VERBOSE_TESTS: true
+      ROS_DISTRO: ${{ matrix.ROS_DISTRO }}
+      CCACHE_DIR: ${{ github.workspace }}/.ccache
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v6
+      - uses: actions/cache@v4
         with:
-          submodules: recursive
-      # Run industrial_ci
-      - uses: 'ros-industrial/industrial_ci@c553397753252d630e88e7e91aa69c6c8a478ee0'
-        env: ${{ matrix.env }}
+          path: ${{ env.CCACHE_DIR }}
+          key: ccache-${{ matrix.ROS_DISTRO }}-${{github.run_id}}
+          restore-keys: ccache-${{ matrix.ROS_DISTRO }}-
+      - uses: 'ros-industrial/industrial_ci@ba2a3d0f830f8051b356711a8df2fedfc5d256cf'
 
   check:
     if: always()
     name: check-ros-ci
 
     needs:
-    - CI
+    - ros
 
-    runs-on: Ubuntu-latest
+    runs-on: ubuntu-latest
 
     steps:
     - name: Decide whether the needed jobs succeeded or failed
diff --git a/.gitignore b/.gitignore
index 36de0b59..86d7b592 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,16 @@
 build*/
+install*/
+.pytest_cache/
 .cache/
-__pycache__
-.pytest_cache
-
-# pixi environments
-.pixi
+.pixi/
+__pycache__/
+Xcode*
+*.pyc
+*~
 *.egg-info
+.ruff_cache/
+.DS_Store
+compile_commands.json
+cmake-profiling.json
+result
+*.conda
diff --git a/.gitmodules b/.gitmodules
deleted file mode 100644
index 955a435d..00000000
--- a/.gitmodules
+++ /dev/null
@@ -1,6 +0,0 @@
-[submodule "cmake"]
-	path = cmake
-	url = https://github.com/jrl-umi3218/jrl-cmakemodules.git
-[submodule "ext/nanobind"]
-	path = ext/nanobind
-	url = https://github.com/wjakob/nanobind
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 40722d15..73dda0c6 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,6 +16,9 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 - Python version update ([#25](https://github.com/Simple-Robotics/nanoeigenpy/pull/25)):
   - Project is now tested with Python 3.10 and 3.14
   - Python 3.10 is the minimal supported Python version
+- Switch to [JRL CMake modules v2](https://github.com/jrl-umi3218/jrl-cmakemodules/pull/798) ([#28](https://github.com/Simple-Robotics/nanoeigenpy/pull/28))
+- Remove submodules: archives can now be used
+- Migrate python tests to pytest
 
 ### Added
 - Add pixi-build support ([#25](https://github.com/Simple-Robotics/nanoeigenpy/pull/25))
diff --git a/CMakeLists.txt b/CMakeLists.txt
index af1a6e35..65fb58ce 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,247 +1,260 @@
-#
-# Copyright 2025 INRIA
-#
+# Copyright 2025-2026 Inria
 
 cmake_minimum_required(VERSION 3.22)
 
-set(PROJECT_NAME nanoeigenpy)
-set(PROJECT_URL https://github.com/Simple-Robotics/nanoeigenpy)
-set(
-  PROJECT_DESCRIPTION
-  "A support library for bindings between Eigen in C++ and Python, based on nanobind"
+project(
+  nanoeigenpy
+  VERSION 0.12.0
+  DESCRIPTION "A support library for bindings between Eigen in C++ and Python, based on nanobind"
+  HOMEPAGE_URL "https://github.com/Simple-Robotics/nanoeigenpy"
 )
-set(PROJECT_CUSTOM_HEADER_EXTENSION "hpp")
-set(PROJECT_USE_CMAKE_EXPORT True)
-
-# To enable jrl-cmakemodules compatibility with workspace we must define the two
-# following lines
-set(PROJECT_AUTO_RUN_FINALIZE FALSE)
-set(PROJECT_SOURCE_DIR ${CMAKE_CURRENT_LIST_DIR})
-
-# Check if the submodule cmake have been initialized
-set(JRL_CMAKE_MODULES "${CMAKE_CURRENT_LIST_DIR}/cmake")
-if(EXISTS "${JRL_CMAKE_MODULES}/base.cmake")
-  message(STATUS "JRL cmakemodules found in 'cmake/' git submodule")
-else()
-  find_package(jrl-cmakemodules QUIET CONFIG)
-  if(jrl-cmakemodules_FOUND)
-    get_property(
-      JRL_CMAKE_MODULES
-      TARGET jrl-cmakemodules::jrl-cmakemodules
-      PROPERTY INTERFACE_INCLUDE_DIRECTORIES
-    )
-    message(STATUS "JRL cmakemodules found on system at ${JRL_CMAKE_MODULES}")
-  elseif(${CMAKE_VERSION} VERSION_LESS "3.14.0")
-    message(
-      FATAL_ERROR
-      "\nCan't find jrl-cmakemodules. Please either:\n"
-      "  - use git submodule: 'git submodule update --init'\n"
-      "  - or install https://github.com/jrl-umi3218/jrl-cmakemodules\n"
-      "  - or upgrade your CMake version to >= 3.14 to allow automatic fetching\n"
-    )
-  else()
-    message(STATUS "JRL cmakemodules not found. Let's fetch it.")
-    include(FetchContent)
-    FetchContent_Declare(
-      "jrl-cmakemodules"
-      GIT_REPOSITORY "https://github.com/jrl-umi3218/jrl-cmakemodules.git"
-    )
-    FetchContent_MakeAvailable("jrl-cmakemodules")
-    FetchContent_GetProperties("jrl-cmakemodules" SOURCE_DIR JRL_CMAKE_MODULES)
-  endif()
-endif()
 
-option(INSTALL_DOCUMENTATION "Generate and install the documentation" OFF)
+include(cmake/get-jrl-cmakemodules.cmake)
 
-if(POLICY CMP0167)
-  cmake_policy(SET CMP0167 NEW)
-  set(CMAKE_POLICY_DEFAULT_CMP0167 NEW)
-endif()
-if(POLICY CMP0177)
-  cmake_policy(SET CMP0177 NEW)
-  set(CMAKE_POLICY_DEFAULT_CMP0177 NEW)
+jrl_configure_defaults()
+
+jrl_option(BUILD_TESTING "Build the tests" OFF)
+
+jrl_option(INSTALL_DOCUMENTATION "Generate and install the documentation" OFF)
+
+jrl_option(BUILD_WITH_CHOLMOD_SUPPORT
+      "Build with SparseSuite's Cholmod (LGPL). See CHOLMOD/Doc/License.txt for further details." OFF
+)
+
+jrl_option(BUILD_WITH_ACCELERATE_SUPPORT
+        "Build with the Accelerate framework (Apple only)" OFF
+)
+
+if(BUILD_WITH_ACCELERATE_SUPPORT AND NOT APPLE)
+  message(WARNING "Accelerate support is only available on APPLE systems")
 endif()
-include("${JRL_CMAKE_MODULES}/base.cmake")
-COMPUTE_PROJECT_ARGS(PROJECT_ARGS LANGUAGES CXX)
-include("${JRL_CMAKE_MODULES}/ide.cmake")
-include("${JRL_CMAKE_MODULES}/apple.cmake")
-project(${PROJECT_NAME} ${PROJECT_ARGS})
-
-string(REPLACE "-pedantic" "" CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
-string(REPLACE "-Wcast-qual" "" CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
-string(REPLACE "-Wconversion" "" CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
-
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/bin)
-set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/lib)
-set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/lib)
-
-if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
-  set(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build." FORCE)
-  set_property(
-    CACHE CMAKE_BUILD_TYPE
-    PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo"
-  )
+
+jrl_find_package(Eigen3 CONFIG REQUIRED)
+
+jrl_find_python(3.10 REQUIRED COMPONENTS Interpreter Development.Module)
+jrl_find_nanobind(2.5.0 CONFIG QUIET)
+
+if(nanobind_ROOT AND NOT nanobind_FOUND)
+  message(WARNING "nanobind found at ${nanobind_ROOT} but too old")
 endif()
 
-option(
-  BUILD_WITH_CHOLMOD_SUPPORT
-  "Build NanoEigenPy with the Cholmod support"
-  OFF
-)
+# On Ubuntu 24.04, the nanobind-dev package ships nanobind 1.9.2.
+# We require >=2.5.0 for nanobind_add_stub,
+# NB_SUPPRESS_WARNINGS, and visitor pattern (c++)
+if(NOT nanobind_FOUND)
+  set(nanobind_GIT_REPOSITORY "https://github.com/wjakob/nanobind.git")
+  set(nanobind_GIT_TAG "v2.10.2")
 
-if(APPLE)
-  option(
-    BUILD_WITH_ACCELERATE_SUPPORT
-    "Build EigenPy with the Accelerate support"
-    OFF
+  message(
+    STATUS
+    "nanobind: fallback to FetchContent from ${nanobind_GIT_REPOSITORY} (tag: ${nanobind_GIT_TAG})"
   )
-endif(APPLE)
 
-# Find dependencies
-ADD_PROJECT_DEPENDENCY(Eigen3 REQUIRED PKG_CONFIG_REQUIRES "eigen3 >= 3.3.1")
+  include(FetchContent)
+  FetchContent_Declare(
+    nanobind
+    GIT_REPOSITORY ${nanobind_GIT_REPOSITORY}
+    GIT_TAG ${nanobind_GIT_TAG}
+    GIT_SHALLOW True
+  )
+  FetchContent_MakeAvailable(nanobind)
+endif()
 
-find_package(Python REQUIRED COMPONENTS Interpreter Development)
-# On Windows Python_SITELIB contains \ that can create installation issues
-if(WIN32)
-  string(REPLACE "\\" "/" Python_SITELIB "${Python_SITELIB}")
+if(BUILD_WITH_CHOLMOD_SUPPORT)
+  jrl_find_package(CHOLMOD CONFIG REQUIRED)
 endif()
 
-# Detect the installed nanobind package and import it into CMake
-execute_process(
-  COMMAND "${Python_EXECUTABLE}" -m nanobind --cmake_dir
-  OUTPUT_STRIP_TRAILING_WHITESPACE
-  OUTPUT_VARIABLE nanobind_ROOT
-)
-find_package(nanobind 2.5.0 CONFIG)
-if(NOT nanobind_FOUND)
-  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ext/nanobind)
+if(BUILD_WITH_ACCELERATE_SUPPORT AND APPLE)
+  if(Eigen3_VERSION VERSION_LESS 5.0.0)
+    message(FATAL_ERROR "Accelerate requires Eigen >= 5.0.0 (provided ${Eigen3_VERSION})")
+  endif()
+
+  jrl_find_package(Accelerate REQUIRED)
 endif()
 
-# Setup main targets
-file(
-  GLOB_RECURSE ${PROJECT_NAME}_HEADERS
-  CONFIGURE_DEPENDS
-  include/nanoeigenpy/*.hpp
-)
+if(BUILD_TESTING)
+  jrl_find_package(Pytest REQUIRED)
+endif()
 
-add_library(nanoeigenpy_headers INTERFACE)
-target_include_directories(
-  nanoeigenpy_headers
-  INTERFACE
-    $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>
-    $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>
-    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+set(
+  nanoeigenpy_HEADERS
+  include/nanoeigenpy/decompositions/sparse/simplicial-cholesky.hpp
+  include/nanoeigenpy/decompositions/sparse/simplicial-ldlt.hpp
+  include/nanoeigenpy/decompositions/sparse/simplicial-llt.hpp
+  include/nanoeigenpy/decompositions/sparse/sparse-lu.hpp
+  include/nanoeigenpy/decompositions/sparse/sparse-qr.hpp
+  include/nanoeigenpy/decompositions/sparse/sparse-solver-base.hpp
+  include/nanoeigenpy/decompositions/bdcsvd.hpp
+  include/nanoeigenpy/decompositions/col-piv-householder-qr.hpp
+  include/nanoeigenpy/decompositions/complete-orthogonal-decomposition.hpp
+  include/nanoeigenpy/decompositions/complex-eigen-solver.hpp
+  include/nanoeigenpy/decompositions/complex-schur.hpp
+  include/nanoeigenpy/decompositions/eigen-solver.hpp
+  include/nanoeigenpy/decompositions/full-piv-householder-qr.hpp
+  include/nanoeigenpy/decompositions/full-piv-lu.hpp
+  include/nanoeigenpy/decompositions/generalized-eigen-solver.hpp
+  include/nanoeigenpy/decompositions/generalized-self-adjoint-eigen-solver.hpp
+  include/nanoeigenpy/decompositions/hessenberg-decomposition.hpp
+  include/nanoeigenpy/decompositions/householder-qr.hpp
+  include/nanoeigenpy/decompositions/jacobi-svd.hpp
+  include/nanoeigenpy/decompositions/ldlt.hpp
+  include/nanoeigenpy/decompositions/llt.hpp
+  include/nanoeigenpy/decompositions/partial-piv-lu.hpp
+  include/nanoeigenpy/decompositions/permutation-matrix.hpp
+  include/nanoeigenpy/decompositions/real-qz.hpp
+  include/nanoeigenpy/decompositions/real-schur.hpp
+  include/nanoeigenpy/decompositions/self-adjoint-eigen-solver.hpp
+  include/nanoeigenpy/decompositions/svd-base.hpp
+  include/nanoeigenpy/decompositions/tridiagonalization.hpp
+  include/nanoeigenpy/geometry/detail/rotation-base.hpp
+  include/nanoeigenpy/geometry/angle-axis.hpp
+  include/nanoeigenpy/geometry/hyperplane.hpp
+  include/nanoeigenpy/geometry/jacobi-rotation.hpp
+  include/nanoeigenpy/geometry/parametrized-line.hpp
+  include/nanoeigenpy/geometry/quaternion.hpp
+  include/nanoeigenpy/geometry/rotation-2d.hpp
+  include/nanoeigenpy/geometry/scaling.hpp
+  include/nanoeigenpy/geometry/translation.hpp
+  include/nanoeigenpy/solvers/basic-preconditioners.hpp
+  include/nanoeigenpy/solvers/bfgs-preconditioners.hpp
+  include/nanoeigenpy/solvers/bicgstab.hpp
+  include/nanoeigenpy/solvers/conjugate-gradient.hpp
+  include/nanoeigenpy/solvers/incomplete-cholesky.hpp
+  include/nanoeigenpy/solvers/incomplete-lut.hpp
+  include/nanoeigenpy/solvers/iterative-solver-base.hpp
+  include/nanoeigenpy/solvers/least-squares-conjugate-gradient.hpp
+  include/nanoeigenpy/solvers/minres.hpp
+  include/nanoeigenpy/utils/helpers.hpp
+  include/nanoeigenpy/utils/is-approx.hpp
+  include/nanoeigenpy/constants.hpp
+  include/nanoeigenpy/decompositions.hpp
+  include/nanoeigenpy/eigen-base.hpp
+  include/nanoeigenpy/fwd.hpp
+  include/nanoeigenpy/geometry.hpp
+  include/nanoeigenpy/id.hpp
+  include/nanoeigenpy/nanoeigenpy.hpp
+  include/nanoeigenpy/solvers.hpp
 )
-target_link_libraries(nanoeigenpy_headers INTERFACE Eigen3::Eigen)
-
-set(${PROJECT_NAME}_SOURCES src/module.cpp)
-nanobind_add_module(nanoeigenpy NB_STATIC NB_SUPPRESS_WARNINGS ${nanoeigenpy_SOURCES} ${nanoeigenpy_HEADERS})
-target_link_libraries(nanoeigenpy PRIVATE nanoeigenpy_headers)
 
-# Cholmod
 if(BUILD_WITH_CHOLMOD_SUPPORT)
-  set(
-    CMAKE_MODULE_PATH
-    ${JRL_CMAKE_MODULES}/find-external/CHOLMOD
-    ${CMAKE_MODULE_PATH}
-  )
-  ADD_PROJECT_DEPENDENCY(CHOLMOD REQUIRED FIND_EXTERNAL "CHOLMOD")
-  message(
-    STATUS
-    "Build with CHOLMOD support (LGPL). See CHOLMOD/Doc/License.txt for further details."
-  )
-  file(
-    GLOB ${PROJECT_NAME}_DECOMPOSITIONS_SPARSE_CHOLMOD_HEADERS
-    include/nanoeigenpy/decompositions/sparse/cholmod/*.hpp
-  )
   list(
     APPEND
-    ${PROJECT_NAME}_HEADERS
-    ${${PROJECT_NAME}_DECOMPOSITIONS_SPARSE_CHOLMOD_HEADERS}
-  )
-  target_link_libraries(nanoeigenpy PRIVATE CHOLMOD::CHOLMOD)
-else()
-  list(
-    FILTER ${PROJECT_NAME}_HEADERS
-    EXCLUDE
-    REGEX "include/nanoeigenpy/decompositions/sparse/cholmod/.*"
+    nanoeigenpy_HEADERS
+    include/nanoeigenpy/decompositions/sparse/cholmod/cholmod-base.hpp
+    include/nanoeigenpy/decompositions/sparse/cholmod/cholmod-decomposition.hpp
+    include/nanoeigenpy/decompositions/sparse/cholmod/cholmod-simplicial-ldlt.hpp
+    include/nanoeigenpy/decompositions/sparse/cholmod/cholmod-simplicial-llt.hpp
+    include/nanoeigenpy/decompositions/sparse/cholmod/cholmod-supernodal-llt.hpp
   )
-endif(BUILD_WITH_CHOLMOD_SUPPORT)
+endif()
 
-# Apple accelerate
 if(BUILD_WITH_ACCELERATE_SUPPORT)
-  if(NOT ${EIGEN_VERSION_NUMBER} VERSION_GREATER_EQUAL "5.0.0")
-    message(
-      FATAL_ERROR
-      "Your version of Eigen is too low. Should be at least 5.0.0. Current version is ${EIGEN_VERSION_NUMBER}."
-    )
-  else()
-    message(
-      STATUS
-      "Building with Accelerate support - Eigen version detected ${EIGEN_VERSION_NUMBER}"
-    )
-  endif()
-
-  set(
-    CMAKE_MODULE_PATH
-    ${JRL_CMAKE_MODULES}/find-external/Accelerate
-    ${CMAKE_MODULE_PATH}
-  )
-  find_package(Accelerate REQUIRED)
-  message(STATUS "Build with Accelerate support framework.")
-  target_link_libraries(nanoeigenpy PRIVATE Accelerate)
-  target_compile_definitions(
-    nanoeigenpy_headers
-    INTERFACE -DNANOEIGENPY_WITH_ACCELERATE_SUPPORT
-  )
-  file(
-    GLOB ${PROJECT_NAME}_DECOMPOSITIONS_SPARSE_ACCELERATE_HEADERS
-    include/nanoeigenpy/decompositions/sparse/accelerate/*.hpp
-  )
   list(
     APPEND
-    ${PROJECT_NAME}_HEADERS
-    ${${PROJECT_NAME}_DECOMPOSITIONS_SPARSE_ACCELERATE_HEADERS}
-  )
-else()
-  list(
-    FILTER ${PROJECT_NAME}_HEADERS
-    EXCLUDE
-    REGEX "include/nanoeigenpy/decompositions/sparse/accelerate/.*"
+    nanoeigenpy_HEADERS
+    include/nanoeigenpy/decompositions/sparse/accelerate/accelerate.hpp
   )
-endif(BUILD_WITH_ACCELERATE_SUPPORT)
-
-if(BUILD_TESTING)
-  add_subdirectory(tests)
 endif()
 
-nanobind_add_stub(
-  nanoeigenpy_stub
-  INSTALL_TIME
-  VERBOSE
-  MODULE nanoeigenpy
-  OUTPUT ${Python_SITELIB}/nanoeigenpy.pyi
-  PYTHON_PATH $<TARGET_FILE_DIR:nanoeigenpy>
+# ----------------------------------------------------------------------- #
+
+add_library(nanoeigenpy_headers INTERFACE)
+add_library(nanoeigenpy::nanoeigenpy_headers ALIAS nanoeigenpy_headers)
+target_compile_features(nanoeigenpy_headers INTERFACE cxx_std_17)
+
+jrl_target_generate_config_header(nanoeigenpy_headers INTERFACE
+  LIBRARY_NAME nanoeigenpy
+  VERSION ${PROJECT_VERSION}
 )
 
-# Install targets
-install(
-  TARGETS ${PROJECT_NAME}_headers
-  EXPORT ${TARGETS_EXPORT_NAME}
-  PUBLIC_HEADER DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
-  INCLUDES DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
-  LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
-  ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
-  RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
+jrl_target_generate_warning_header(nanoeigenpy_headers INTERFACE
+  LIBRARY_NAME nanoeigenpy
+)
+
+jrl_target_generate_deprecated_header(nanoeigenpy_headers INTERFACE
+  LIBRARY_NAME nanoeigenpy
 )
 
+target_include_directories(
+  nanoeigenpy_headers
+  INTERFACE
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+    $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>
+)
+
+target_link_libraries(
+  nanoeigenpy_headers
+  INTERFACE
+    Eigen3::Eigen
+    $<$<BOOL:${BUILD_WITH_CHOLMOD_SUPPORT}>:SuiteSparse::CHOLMOD>
+    $<$<BOOL:${BUILD_WITH_ACCELERATE_SUPPORT}>:Accelerate::Accelerate>
+)
+
+target_compile_definitions(
+  nanoeigenpy_headers
+  INTERFACE
+    $<$<BOOL:${BUILD_WITH_CHOLMOD_SUPPORT}>:NANOEIGENPY_HAS_CHOLMOD>
+    $<$<BOOL:${BUILD_WITH_ACCELERATE_SUPPORT}>:NANOEIGENPY_HAS_ACCELERATE>
+)
+
+set(nanoeigenpy_SOURCES src/module.cpp)
+
+nanobind_add_module(nanoeigenpy
+  NB_STATIC LTO NB_SUPPRESS_WARNINGS
+  ${nanoeigenpy_SOURCES}
+  ${nanoeigenpy_HEADERS}
+)
+jrl_target_set_output_directory(nanoeigenpy OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib/site-packages)
+jrl_target_enforce_msvc_conformance(nanoeigenpy PRIVATE)
+jrl_check_python_module_name(nanoeigenpy)
+
+target_link_libraries(nanoeigenpy PUBLIC nanoeigenpy_headers)
+
+# Stub generation requires typing-extensions
+# ROS Humble ships an incompatible typing-extensions with python3.10
+if(Python_VERSION VERSION_GREATER_EQUAL 3.11.0)
+  nanobind_add_stub(
+    nanoeigenpy_stub
+    VERBOSE
+    MODULE nanoeigenpy
+    OUTPUT ${CMAKE_BINARY_DIR}/lib/site-packages/nanoeigenpy.pyi
+    PYTHON_PATH $<TARGET_FILE_DIR:nanoeigenpy>
+    DEPENDS nanoeigenpy
+  )
+endif()
+
+jrl_python_compute_install_dir(python_install_dir)
+# NOTE: install the whole binary dir, we need the "/" at the end to copy the content.
+# In a next version, we might install in a nanoeigenpy directory, which contains all the files.
 install(
-  TARGETS ${PROJECT_NAME}
-  EXPORT ${TARGETS_EXPORT_NAME}
-  LIBRARY DESTINATION ${Python_SITELIB}
+  DIRECTORY ${CMAKE_BINARY_DIR}/lib/site-packages/
+  DESTINATION ${python_install_dir}
+  FILES_MATCHING
+  PATTERN "*.py"
+  PATTERN "*.pyc"
+  PATTERN "*.pyi"
+  PATTERN "*.typed"
+  PATTERN "*.so"
+  PATTERN "*.pyd"
 )
 
-ADD_HEADER_GROUP(${PROJECT_NAME}_HEADERS)
-ADD_SOURCE_GROUP(${PROJECT_NAME}_SOURCES)
+jrl_target_headers(nanoeigenpy_headers INTERFACE
+  HEADERS ${nanoeigenpy_HEADERS}
+  BASE_DIRS include
+)
+
+jrl_add_export_component(NAME nanoeigenpy_headers TARGETS nanoeigenpy_headers)
+jrl_export_package()
+
+if(BUILD_TESTING)
+  enable_testing()
+  add_subdirectory(tests)
+endif()
+
+jrl_generate_ros2_package_files(
+  INSTALL_CPP_PACKAGE_FILES [[NOT BUILD_STANDALONE_PYTHON_INTERFACE]]
+  INSTALL_PYTHON_PACKAGE_FILES [[BUILD_PYTHON_INTERFACE]]
+)
 
-SETUP_PROJECT_FINALIZE()
+jrl_print_dependencies_summary()
+jrl_print_options_summary()
diff --git a/cmake b/cmake
deleted file mode 160000
index e2df2bb1..00000000
--- a/cmake
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit e2df2bb1552848e7660d992cdc6102f70ff8c1dc
diff --git a/cmake/get-jrl-cmakemodules.cmake b/cmake/get-jrl-cmakemodules.cmake
new file mode 100644
index 00000000..c23f6b72
--- /dev/null
+++ b/cmake/get-jrl-cmakemodules.cmake
@@ -0,0 +1,49 @@
+# Get jrl-cmakemodules package
+
+# Option 1: pass -DJRL_CMAKEMODULES_SOURCE_DIR=... to cmake command line
+if(JRL_CMAKEMODULES_SOURCE_DIR)
+  message(
+    DEBUG
+    "JRL_CMAKEMODULES_SOURCE_DIR variable set, adding jrl-cmakemodules from source directory: ${JRL_CMAKEMODULES_SOURCE_DIR}"
+  )
+  add_subdirectory(${JRL_CMAKEMODULES_SOURCE_DIR} jrl-cmakemodules)
+  return()
+endif()
+
+# Option 2: use JRL_CMAKEMODULES_SOURCE_DIR environment variable (pixi might unset it, prefer option 1)
+if(ENV{JRL_CMAKEMODULES_SOURCE_DIR})
+  message(
+    DEBUG
+    "JRL_CMAKEMODULES_SOURCE_DIR environement variable set, adding jrl-cmakemodules from source directory: ${JRL_CMAKEMODULES_SOURCE_DIR}"
+  )
+  add_subdirectory(${JRL_CMAKEMODULES_SOURCE_DIR} jrl-cmakemodules)
+  return()
+endif()
+
+# Try to look for the installed package
+message(DEBUG "Looking for jrl-cmakemodules using find_package().")
+find_package(jrl-cmakemodules 1.2.0 CONFIG QUIET)
+
+# If we have the package, we are done.
+if(jrl-cmakemodules_FOUND)
+  message(DEBUG "Found jrl-cmakemodules package via find_package().")
+  return()
+else()
+  message(DEBUG "jrl-cmakemodules package not found using find_package().")
+endif()
+
+# Fallback to FetchContent if not found
+set(JRL_GIT_REPOSITORY "https://github.com/ahoarau/jrl-cmakemodules.git")
+set(JRL_GIT_TAG "jrl-next")
+
+message(
+  DEBUG
+  "Fetching jrl-cmakemodules using FetchContent:
+  GIT_REPOSITORY: ${JRL_GIT_REPOSITORY}
+  GIT_TAG       : ${JRL_GIT_TAG}
+"
+)
+
+include(FetchContent)
+FetchContent_Declare(jrl-cmakemodules GIT_REPOSITORY ${JRL_GIT_REPOSITORY} GIT_TAG ${JRL_GIT_TAG})
+FetchContent_MakeAvailable(jrl-cmakemodules)
diff --git a/ext/nanobind b/ext/nanobind
deleted file mode 160000
index 879bca48..00000000
--- a/ext/nanobind
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 879bca4869664bdc1446ee7f160ffe3c7028cd7a
diff --git a/include/nanoeigenpy/fwd.hpp b/include/nanoeigenpy/fwd.hpp
index 9546110a..3a08d4e1 100644
--- a/include/nanoeigenpy/fwd.hpp
+++ b/include/nanoeigenpy/fwd.hpp
@@ -7,43 +7,22 @@
 #include "nanoeigenpy/utils/helpers.hpp"
 #include <nanobind/nanobind.h>
 
+#ifdef NANOEIGENPY_HAS_CHOLMOD
+#include <cholmod.h>
+#include <Eigen/CholmodSupport>
+#endif
+
+#ifdef NANOEIGENPY_HAS_ACCELERATE
+#include <Accelerate/Accelerate.h>
+#include <Eigen/AccelerateSupport>
+#endif
+
 #include <Eigen/Core>
 #include <Eigen/Sparse>
 #include <Eigen/Geometry>
 #include <Eigen/IterativeLinearSolvers>
 #include <Eigen/Jacobi>
 
-#if defined(__clang__)
-#define NANOEIGENPY_CLANG_COMPILER
-#elif defined(__GNUC__)
-#define NANOEIGENPY_GCC_COMPILER
-#elif defined(_MSC_VER)
-#define NANOEIGENPY_MSVC_COMPILER
-#endif
-
-#if __has_include(<cholmod.h>)
-#define NANOEIGENPY_HAS_CHOLMOD
-#endif
-
-#if __has_include(<Accelerate.h>)
-#define NANOEIGENPY_HAS_ACCELERATE
-#endif
-
-#if (__cplusplus >= 202002L || (defined(_MSVC_LANG) && _MSVC_LANG >= 202002L))
-#define NANOEIGENPY_WITH_CXX20_SUPPORT
-#endif
-
-#define NANOEIGENPY_UNUSED_TYPE(Type) (void)(Type*)(NULL)
-
-#define NANOEIGENPY_MAKE_TYPEDEFS(Type, Options, TypeSuffix, Size, SizeSuffix) \
-  /** \ingroup matrixtypedefs */                                               \
-  typedef Eigen::Matrix<Type, Size, Size, Options>                             \
-      Matrix##SizeSuffix##TypeSuffix;                                          \
-  /** \ingroup matrixtypedefs */                                               \
-  typedef Eigen::Matrix<Type, Size, 1> Vector##SizeSuffix##TypeSuffix;         \
-  /** \ingroup matrixtypedefs */                                               \
-  typedef Eigen::Matrix<Type, 1, Size> RowVector##SizeSuffix##TypeSuffix;
-
 namespace nanoeigenpy {
 namespace nb = nanobind;
 }  // namespace nanoeigenpy
diff --git a/package.xml b/package.xml
index c06ddd37..a8498262 100644
--- a/package.xml
+++ b/package.xml
@@ -15,10 +15,12 @@
 
   <!-- The following tag is recommended by REP-136 -->
   <depend condition="$ROS_PYTHON_VERSION == 3">python3</depend>
+  <depend condition="$ROS_PYTHON_VERSION == 3">python3-pytest</depend>
   <depend condition="$ROS_PYTHON_VERSION == 3">python3-numpy</depend>
   <depend condition="$ROS_PYTHON_VERSION == 3">python3-scipy</depend>
   <depend>eigen</depend>
-  <depend>nanobind-dev</depend>
+  <!-- TODO: finish release of nanoeigenpy on ROS -->
+  <!-- <depend>nanobind-dev</depend> -->
 
   <buildtool_depend>cmake</buildtool_depend>
   <export>
diff --git a/pixi.lock b/pixi.lock
index 67d39009..e77e3837 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,10 +1,13 @@
-version: 6
+version: 7
+platforms:
+- name: linux-64
+- name: osx-64
+- name: osx-arm64
+- name: win-64
 environments:
   accelerate:
     channels:
     - url: https://conda.anaconda.org/conda-forge/
-    options:
-      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
@@ -14,87 +17,100 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.4.22-hbd8a1cb_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/ccache-4.13.4-hedf47ba_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ccache-4.13.6-hedf47ba_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cmake-4.3.2-hc85cc9f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_18.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/doxygen-1.13.2-h8e693c7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/eigen-5.0.1-hc65338a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hbdf3cc3_18.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h6f77f03_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-h235f0fe_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h50e9bb6_24.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/git-2.53.0-pl5321h6d3cee1_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_18.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/git-2.54.0-pl5321h6d3cee1_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h8a413ad_24.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-ha1258a1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-6_h4a7cf45_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-6_h0358290_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-7_h4a7cf45_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-7_h0358290_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.20.0-hcf29cc6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.5-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.0-hecca717_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_118.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_18.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.3.0-h5888daf_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-6_h47877c9_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-7_h47877c9_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.68.1-h877daf1_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.32-pthreads_h94d23a6_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.0-hf4e2dac_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.33-pthreads_h94d23a6_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_118.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42-h5347b49_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/nanobind-2.12.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ninja-1.13.2-h171cf75_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.3-py314h2b28147_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.2-h35e630c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.47-haa7fec5_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/perl-5.32.1-7_hd590300_perl5.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda
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-  - libcblas   3.11.0   6*_openblas
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-  license_family: BSD
-  size: 433413
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   md5: 053b84beec00b71ea8ff7a4f84b55207
@@ -8349,3 +9352,339 @@ packages:
   license_family: BSD
   size: 388453
   timestamp: 1764777142545
+- conda_source: nanoeigenpy[771252e4] @ .
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+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-22.1.5-h19cb2f5_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-devel-22.1.5-h7c275be_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libedit-3.1.20250104-pl5321ha958ccf_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libev-4.33-h10d778d_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.8.0-hcc62823_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libiconv-1.18-h57a12c2_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libllvm22-22.1.5-hab754da_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/liblzma-5.8.3-hbb4bfdb_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libnghttp2-1.68.1-h70048d4_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libsigtool-0.1.3-hc0f2934_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libssh2-1.11.1-hed3591d_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libuv-1.51.0-h58003a5_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libxml2-16-2.15.3-h0d7f165_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.15.3-h0712280_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.2-hbb4bfdb_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-tools-22-22.1.5-hc181bea_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-tools-22.1.5-h1637cdf_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/ncurses-6.6-hcc0dc9a_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/ninja-1.13.2-hfc0b2d5_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.6.2-hc881268_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/rhash-1.4.6-h6e16a3a_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/sigtool-codesign-0.1.3-hc0f2934_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/tapi-1600.0.11.8-h8d8e812_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h3eecb57_6.conda
+  host_packages:
+  - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.4.22-hbd8a1cb_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/nanobind-2.12.0-pyhd8ed1ab_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/_openmp_mutex-4.5-7_kmp_llvm.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h500dc9f_9.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/eigen-5.0.1-h4ff50a2_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/eigen-abi-5.0.1.80-h969a130_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/eigen-abi-devel-5.0.1.100-hf952dd3_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/icu-78.3-h25d91c4_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.11.0-7_he492b99_openblas.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.11.0-7_h9b27e0a_openblas.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-22.1.5-h19cb2f5_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.8.0-hcc62823_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libffi-3.5.2-hd1f9c09_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libgcc-15.2.0-h08519bb_19.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran-15.2.0-h7e5c614_19.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-15.2.0-hd16e46c_19.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.11.0-7_h859234e_openblas.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/liblzma-5.8.3-hbb4bfdb_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libmpdec-4.0.0-hf3981d6_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libopenblas-0.3.33-openmp_h9e49c7b_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.53.1-h8f8c405_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.2-hbb4bfdb_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-22.1.5-h0d3cbff_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/ncurses-6.6-hcc0dc9a_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.4.3-py314h7b24d9b_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.6.2-hc881268_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.14.4-h7c6738f_100_cp314.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/readline-8.3-h68b038d_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/tk-8.6.13-h7142dee_3.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h3eecb57_6.conda
+- conda_source: nanoeigenpy[c651a8ed] @ .
+  variants:
+    eigen: 5.*
+    target_platform: osx-arm64
+  depends:
+  - eigen >=3.4
+  - libcxx >=22
+  - eigen-abi >=5.0.1.100,<5.0.1.101.0a0
+  - python_abi 3.14.* *_cp314
+  - numpy >=1.23,<3
+  build_packages:
+  - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.4.22-hbd8a1cb_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/compiler-rt22_osx-arm64-22.1.5-h7e67a1e_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-arm64-22.1.5-hce30654_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-22.1.5-h707e725_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/sdkroot_env_osx-arm64-26.0-ha3f98da_7.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-hd037594_9.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.6-hc919400_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cctools_impl_osx-arm64-1030.6.3-llvm22_1_hb5e89dc_4.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cctools_osx-arm64-1030.6.3-llvm22_1_hbe26303_4.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clang-22-22.1.5-default_hd632d02_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clang-scan-deps-22.1.5-default_h8e162e0_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clang_impl_osx-arm64-22.1.5-default_h17d1ed9_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clang_osx-arm64-22.1.5-h1bea106_31.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clangxx_impl_osx-arm64-22.1.5-default_h17d1ed9_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clangxx_osx-arm64-22.1.5-h1bea106_31.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cmake-4.3.2-h8cb302d_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/compiler-rt-22.1.5-hce30654_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/compiler-rt22-22.1.5-hd34ed20_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/krb5-1.22.2-h385eeb1_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ld64_osx-arm64-956.6-llvm22_1_h692d5aa_4.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp22.1-22.1.5-default_h8e162e0_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang13-22.1.5-default_h6dd9417_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcompiler-rt-22.1.5-hd34ed20_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.20.0-hd5a2499_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-22.1.5-h55c6f16_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-22.1.5-h6dc3340_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libev-4.33-h93a5062_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.8.0-hf6b4638_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libiconv-1.18-h23cfdf5_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libllvm22-22.1.5-h89af1be_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblzma-5.8.3-h8088a28_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libnghttp2-1.68.1-h8f3e76b_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsigtool-0.1.3-h98dc951_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libssh2-1.11.1-h1590b86_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libuv-1.51.0-h6caf38d_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-16-2.15.3-h6967ea9_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.15.3-heed7d32_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.2-h8088a28_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-tools-22-22.1.5-hb545844_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-tools-22.1.5-hd34ed20_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.6-h1d4f5a5_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ninja-1.13.2-h49c215f_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.2-hd24854e_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rhash-1.4.6-h5505292_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/sigtool-codesign-0.1.3-h98dc951_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tapi-1600.0.11.8-h997e182_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda
+  host_packages:
+  - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.4.22-hbd8a1cb_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/nanobind-2.12.0-pyhd8ed1ab_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda
+  - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-hd037594_9.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/eigen-5.0.1-h44d0d2d_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/eigen-abi-5.0.1.100-h485a483_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/eigen-abi-devel-5.0.1.100-hac87611_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.11.0-7_h51639a9_openblas.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.11.0-7_hb0561ab_openblas.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-22.1.5-h55c6f16_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.8.0-hf6b4638_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_19.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_19.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_19.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.11.0-7_hd9741b5_openblas.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblzma-5.8.3-h8088a28_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libmpdec-4.0.0-h84a0fba_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.33-openmp_he657e61_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.53.1-h1b79a29_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.2-h8088a28_2.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-22.1.5-hc7d1edf_1.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.6-h1d4f5a5_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.3-py314h1569ea8_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.2-hd24854e_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.4-h4c637c5_100_cp314.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.3-h46df422_0.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda
+  - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda
diff --git a/pixi.toml b/pixi.toml
index 23adff29..ffe3f8dc 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -14,7 +14,7 @@ name = { workspace = true }
 version = { workspace = true }
 
 [package.build]
-backend = { name = "pixi-build-cmake", version = "0.3.*" }
+backend = { name = "pixi-build-cmake", version = "*" }
 
 [package.build.config]
 extra-args = [
@@ -47,6 +47,7 @@ python = ">=3.10"
 eigen = ">=3.4"
 numpy = ">=1.22"
 scipy = ">=1.10.0"
+pytest = ">=9.0.2,<10"
 
 # nanobind need to use OSX SDK >= 10.13.
 # But default version setup by rattler is 10.9
@@ -81,13 +82,42 @@ configure = { cmd = [
   "build",
   "-S",
   ".",
+  "-DJRL_CMAKEMODULES_SOURCE_DIR=$JRL_CMAKEMODULES_SOURCE_DIR",
   "-DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX",
+  "-DBUILD_TESTING=ON",
   "-DCMAKE_BUILD_TYPE=$NANOEIGENPY_BUILD_TYPE",
   "-DBUILD_WITH_CHOLMOD_SUPPORT=$NANOEIGENPY_CHOLMOD_SUPPORT",
   "-DBUILD_WITH_ACCELERATE_SUPPORT=$NANOEIGENPY_ACCELERATE_SUPPORT",
 ] }
-build = { cmd = "cmake --build build --target all", depends-on = ["configure"] }
+build = { cmd = "cmake --build build", depends-on = ["configure"] }
 clean = { cmd = "rm -rf build" }
+install = { cmd = "cmake --install build", depends-on = ["build"] }
+test = { cmd = "ctest --output-on-failure --test-dir build", depends-on = [
+  "build",
+] }
+
+test-import-python = { depends-on = [
+  "install",
+], cmd = [
+  "python",
+  "-c",
+  "import nanoeigenpy; print(nanoeigenpy.__version__)",
+] }
+_test-packaging-configure = { cmd = [
+  "cmake",
+  "-G",
+  "Ninja",
+  "-S",
+  "tests/packaging/cmake",
+  "-B",
+  "build/test-packaging",
+], depends-on = [
+  "install",
+] }
+_test-packaging-build = { cmd = "cmake --build build/test-packaging", depends-on = [
+  "_test-packaging-configure",
+] }
+test-packaging = { depends-on = ["_test-packaging-build"] }
 
 [feature.eigen3.dependencies]
 eigen = "3.4.*"
@@ -146,8 +176,8 @@ dependencies = { clangxx = "*", lld = "*" }
 
 # Absolute path is needed to avoid using system clang-cl
 [feature.clang-cl.activation.env]
-CC = "%CONDA_PREFIX%\\Library\\bin\\clang-cl"
-CXX = "%CONDA_PREFIX%\\Library\\bin\\clang-cl"
+CC = "%CONDA_PREFIX%/Library/bin/clang-cl"
+CXX = "%CONDA_PREFIX%/Library/bin/clang-cl"
 
 # Use clang on GNU/Linux
 [feature.clang]
@@ -155,13 +185,13 @@ platforms = ["linux-64"]
 activation = { scripts = ["development/scripts/pixi/activation_clang.sh"] }
 dependencies = { clangxx = "*", lld = "*" }
 
-# [feature.test-pixi-build]
-# dependencies = { nanoeigenpy = { path = "." }, cmake = ">=3.22", python = "*" }
+[feature.test-pixi-build]
+dependencies = { nanoeigenpy = { path = "." }, cmake = ">=3.22", python = "*", cxx-compiler = "*" }
 
-# [feature.test-pixi-build.tasks]
-# test-cmake = "cmake -S tests/packaging/pixi_build -B build_test_pixi_build"
-# test-python = "python -c 'import nanoeigenpy'"
-# test = { depends-on = ["test-cmake", "test-python"] }
+[feature.test-pixi-build.tasks]
+test-cmake = "cmake -S tests/packaging/cmake -B build/test_pixi_build"
+test-python = "python -c 'import nanoeigenpy; print(nanoeigenpy.__version__)'"
+test = { depends-on = ["test-cmake", "test-python"] }
 
 [environments]
 default = { features = ["test", "python-latest"], solve-group = "py-latest" }
@@ -214,4 +244,4 @@ eigen3-python-oldest = { features = [
   "python-oldest",
   "eigen3"
 ]}
-# test-pixi-build = { features = ["test-pixi-build"], no-default-feature = true }
+test-pixi-build = { features = ["test-pixi-build"], no-default-feature = true }
diff --git a/src/internal.h b/src/internal.h
deleted file mode 100644
index ea9cbe97..00000000
--- a/src/internal.h
+++ /dev/null
@@ -1,9 +0,0 @@
-#include <nanobind/nanobind.h>
-#include <Eigen/Core>
-
-namespace nb = nanobind;
-
-using Scalar = double;
-static constexpr int Options = Eigen::ColMajor;
-using Matrix = Eigen::Matrix<Scalar, -1, -1, Options>;
-using Vector = Eigen::Matrix<Scalar, -1, 1>;
diff --git a/src/module.cpp b/src/module.cpp
index f482f250..3ba5ac94 100644
--- a/src/module.cpp
+++ b/src/module.cpp
@@ -2,13 +2,19 @@
 
 #include <nanobind/stl/string.h>
 
+#include "nanoeigenpy/fwd.hpp"
 #include "nanoeigenpy/decompositions.hpp"
 #include "nanoeigenpy/geometry.hpp"
 #include "nanoeigenpy/solvers.hpp"
 #include "nanoeigenpy/constants.hpp"
 #include "nanoeigenpy/utils/is-approx.hpp"
 
-#include "./internal.h"
+namespace nb = nanobind;
+
+using Scalar = double;
+static constexpr int Options = Eigen::ColMajor;
+using Matrix = Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Options>;
+using Vector = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
 
 using namespace nanoeigenpy;
 
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 18cfe812..4ffd82ec 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -1,108 +1,59 @@
-# Copyright 2025 INRIA
-
-# Create a shared nanobind library for testing
-set(NANOBIND_TESTING_TARGET nanobind-testing)
-nanobind_build_library(${NANOBIND_TESTING_TARGET} SHARED)
-
-# On Win32, shared DLL libs are sent to RUNTIME_OUTPUT_DIRECTORY, *but*
-# we really need to send it to the lib dir so
-if(WIN32)
-  set_target_properties(
-    ${NANOBIND_TESTING_TARGET}
-    PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${PROJECT_BINARY_DIR}/lib
-  )
-endif()
-
-# Add a C++ extension module for tests
-function(add_tests_cpp_extension name)
-  set(filename ${name}.cpp)
-  add_library(${name} MODULE ${filename})
-  target_link_libraries(
-    ${name}
-    PRIVATE ${NANOBIND_TESTING_TARGET} nanoeigenpy_headers
-  )
-  # Use nanobind low-level interface to set properties
-  nanobind_set_visibility(${name})
-  nanobind_strip(${name})
-  nanobind_extension(${name})
-  nanobind_compile_options(${name})
-  nanobind_link_options(${name})
-
-  add_dependencies(build_tests ${name})
+nanobind_add_module(quaternion
+  NB_STATIC LTO NB_SUPPRESS_WARNINGS
+  quaternion.cpp
+)
+jrl_check_python_module_name(quaternion)
+target_link_libraries(quaternion PRIVATE Eigen3::Eigen)
+jrl_target_set_output_directory(quaternion OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/test-quaternion)
 
+function(nanoeigenpy_add_test name)
+  set(test_name "nanoeigenpy: ${name}")
   add_test(
-    NAME "${PROJECT_NAME}-import-${name}"
-    COMMAND ${Python_EXECUTABLE} -c "import ${name}"
-    WORKING_DIRECTORY $<TARGET_FILE_DIR:${name}>
+    NAME ${test_name}
+    COMMAND $<TARGET_FILE:Pytest::Pytest> ${CMAKE_CURRENT_SOURCE_DIR}/test_${name}.py
+  )
+  set_property(
+    TEST ${test_name}
+    PROPERTY
+      ENVIRONMENT_MODIFICATION
+        "PYTHONPATH=path_list_prepend:$<TARGET_FILE_DIR:nanoeigenpy>"
+        "PYTHONPATH=path_list_prepend:$<TARGET_FILE_DIR:quaternion>"
   )
 endfunction()
 
-# Add Python test module
-function(add_tests_py_module name)
-  set(filename tests/${name}.py)
-  set(test_target "${PROJECT_NAME}-${name}")
-  string(REPLACE "_" "-" test_target ${test_target})
-  set(PYTHON_EXECUTABLE ${Python_EXECUTABLE})
-  ADD_PYTHON_UNIT_TEST(${test_target} ${filename} "lib")
-  unset(PYTHON_EXECUTABLE)
-  set_tests_properties(${test_target} PROPERTIES DEPENDS nanoeigenpy)
-endfunction()
-
-add_dependencies(build_tests nanoeigenpy)
-
-add_test(
-  NAME "${PROJECT_NAME}-import-extension"
-  COMMAND ${Python_EXECUTABLE} -c "import ${PROJECT_NAME}"
-  WORKING_DIRECTORY $<TARGET_FILE_DIR:${PROJECT_NAME}>
-)
-
-add_tests_cpp_extension(quaternion)
-
-set(
-  TEST_NAMES
-  test_eigen_solver
-  test_complex_eigen_solver
-  test_generalized_eigen_solver
-  test_self_adjoint_eigen_solver
-  test_generalized_self_adjoint_eigen_solver
-  test_real_schur
-  test_complex_schur
-  test_hessenberg_decomposition
-  test_real_qz
-  test_tridiagonalization
-  test_bdcsvd
-  test_jacobi_svd
-  test_full_piv_lu
-  test_partial_piv_lu
-  test_ldlt
-  test_llt
-  test_qr
-  test_simplicial_llt
-  test_sparse_lu
-  test_sparse_qr
-  test_geometry
-  test_iterative_solvers
-  test_permutation_matrix
-  test_incomplete_lut
-  test_incomplete_cholesky
-)
+nanoeigenpy_add_test(bdcsvd)
+nanoeigenpy_add_test(complex_eigen_solver)
+nanoeigenpy_add_test(complex_schur)
+nanoeigenpy_add_test(eigen_solver)
+nanoeigenpy_add_test(full_piv_lu)
+nanoeigenpy_add_test(generalized_eigen_solver)
+nanoeigenpy_add_test(generalized_self_adjoint_eigen_solver)
+nanoeigenpy_add_test(geometry)
+nanoeigenpy_add_test(hessenberg_decomposition)
+nanoeigenpy_add_test(import_extension)
+nanoeigenpy_add_test(incomplete_cholesky)
+nanoeigenpy_add_test(incomplete_lut)
+nanoeigenpy_add_test(iterative_solvers)
+nanoeigenpy_add_test(jacobi_svd)
+nanoeigenpy_add_test(ldlt)
+nanoeigenpy_add_test(llt)
+nanoeigenpy_add_test(partial_piv_lu)
+nanoeigenpy_add_test(permutation_matrix)
+nanoeigenpy_add_test(qr)
+nanoeigenpy_add_test(real_qz)
+nanoeigenpy_add_test(real_schur)
+nanoeigenpy_add_test(self_adjoint_eigen_solver)
+nanoeigenpy_add_test(simplicial_llt)
+nanoeigenpy_add_test(sparse_lu)
+nanoeigenpy_add_test(sparse_qr)
+nanoeigenpy_add_test(tridiagonalization)
 
 if(BUILD_WITH_CHOLMOD_SUPPORT)
-  list(
-    APPEND
-    TEST_NAMES
-    test_cholmod_simplicial_ldlt
-    test_cholmod_simplicial_llt
-    test_cholmod_supernodal_llt
-  )
-endif(BUILD_WITH_CHOLMOD_SUPPORT)
-
-foreach(test_name ${TEST_NAMES})
-  message(STATUS "Adding Python test ${test_name}")
-  add_tests_py_module(${test_name})
-endforeach()
+  nanoeigenpy_add_test(cholmod_simplicial_ldlt)
+  nanoeigenpy_add_test(cholmod_simplicial_llt)
+  nanoeigenpy_add_test(cholmod_supernodal_llt)
+endif()
 
 if(BUILD_WITH_ACCELERATE_SUPPORT)
-  message(STATUS "Adding Python test test_accelerate")
-  add_tests_py_module(test_accelerate)
-endif(BUILD_WITH_ACCELERATE_SUPPORT)
+  nanoeigenpy_add_test(accelerate)
+endif()
diff --git a/tests/packaging/pixi_build/CMakeLists.txt b/tests/packaging/cmake/CMakeLists.txt
similarity index 61%
rename from tests/packaging/pixi_build/CMakeLists.txt
rename to tests/packaging/cmake/CMakeLists.txt
index 46e4847d..69713663 100644
--- a/tests/packaging/pixi_build/CMakeLists.txt
+++ b/tests/packaging/cmake/CMakeLists.txt
@@ -1,3 +1,4 @@
 cmake_minimum_required(VERSION 3.22)
 project(test_pixi_build)
 find_package(nanoeigenpy REQUIRED)
+message(STATUS "nanoeigenpy found: ${nanoeigenpy_VERSION}")
diff --git a/tests/test_accelerate.py b/tests/test_accelerate.py
index d21d46db..f02eebdb 100644
--- a/tests/test_accelerate.py
+++ b/tests/test_accelerate.py
@@ -1,11 +1,23 @@
 import nanoeigenpy
 import numpy as np
 from scipy.sparse import csc_matrix
+import pytest
 
 rng = np.random.default_rng()
 
 
-def test(SolverType: type):
+@pytest.mark.parametrize(
+    "SolverType",
+    [
+        nanoeigenpy.AccelerateLLT,
+        nanoeigenpy.AccelerateLDLT,
+        nanoeigenpy.AccelerateLDLTUnpivoted,
+        nanoeigenpy.AccelerateLDLTSBK,
+        nanoeigenpy.AccelerateLDLTTPP,
+        nanoeigenpy.AccelerateQR,
+    ],
+)
+def test_accelerate_solver(SolverType):
     dim = 100
     A = rng.random((dim, dim))
     A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
@@ -25,11 +37,3 @@ def test(SolverType: type):
 
     llt.analyzePattern(A)
     llt.factorize(A)
-
-
-test(nanoeigenpy.AccelerateLLT)
-test(nanoeigenpy.AccelerateLDLT)
-test(nanoeigenpy.AccelerateLDLTUnpivoted)
-test(nanoeigenpy.AccelerateLDLTSBK)
-test(nanoeigenpy.AccelerateLDLTTPP)
-test(nanoeigenpy.AccelerateQR)
diff --git a/tests/test_cholmod_simplicial_ldlt.py b/tests/test_cholmod_simplicial_ldlt.py
index cb1b0500..35324679 100644
--- a/tests/test_cholmod_simplicial_ldlt.py
+++ b/tests/test_cholmod_simplicial_ldlt.py
@@ -1,24 +1,25 @@
+import nanoeigenpy
 import numpy as np
 from scipy.sparse import csc_matrix
 
-import nanoeigenpy
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
+def test_cholmod_simplicial_ldlt():
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
-A = csc_matrix(A)
+    A = csc_matrix(A)
 
-llt = nanoeigenpy.CholmodSimplicialLDLT(A)
+    llt = nanoeigenpy.CholmodSimplicialLDLT(A)
 
-assert llt.info() == nanoeigenpy.ComputationInfo.Success
+    assert llt.info() == nanoeigenpy.ComputationInfo.Success
 
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = llt.solve(B)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = llt.solve(B)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
 
-llt.analyzePattern(A)
-llt.factorize(A)
+    llt.analyzePattern(A)
+    llt.factorize(A)
diff --git a/tests/test_cholmod_simplicial_llt.py b/tests/test_cholmod_simplicial_llt.py
index 763cfaab..2b8137c3 100644
--- a/tests/test_cholmod_simplicial_llt.py
+++ b/tests/test_cholmod_simplicial_llt.py
@@ -1,25 +1,26 @@
+import nanoeigenpy
 import numpy as np
 from scipy.sparse import csc_matrix
 
-import nanoeigenpy
 
-dim = 100
-rng = np.random.default_rng()
+def test_cholmod_simplicial_llt():
+    dim = 100
+    rng = np.random.default_rng()
 
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
-A = csc_matrix(A)
+    A = csc_matrix(A)
 
-llt = nanoeigenpy.CholmodSimplicialLLT(A)
+    llt = nanoeigenpy.CholmodSimplicialLLT(A)
 
-assert llt.info() == nanoeigenpy.ComputationInfo.Success
+    assert llt.info() == nanoeigenpy.ComputationInfo.Success
 
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = llt.solve(B)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = llt.solve(B)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
 
-llt.analyzePattern(A)
-llt.factorize(A)
+    llt.analyzePattern(A)
+    llt.factorize(A)
diff --git a/tests/test_cholmod_supernodal_llt.py b/tests/test_cholmod_supernodal_llt.py
index 15de556f..c938531a 100644
--- a/tests/test_cholmod_supernodal_llt.py
+++ b/tests/test_cholmod_supernodal_llt.py
@@ -1,25 +1,26 @@
+import nanoeigenpy
 import numpy as np
 from scipy.sparse import csc_matrix
 
-import nanoeigenpy
 
-dim = 100
-rng = np.random.default_rng()
+def test_cholmod_supernodal_llt():
+    dim = 100
+    rng = np.random.default_rng()
 
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
-A = csc_matrix(A)
+    A = csc_matrix(A)
 
-llt = nanoeigenpy.CholmodSupernodalLLT(A)
+    llt = nanoeigenpy.CholmodSupernodalLLT(A)
 
-assert llt.info() == nanoeigenpy.ComputationInfo.Success
+    assert llt.info() == nanoeigenpy.ComputationInfo.Success
 
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = llt.solve(B)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = llt.solve(B)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
 
-llt.analyzePattern(A)
-llt.factorize(A)
+    llt.analyzePattern(A)
+    llt.factorize(A)
diff --git a/tests/test_complex_eigen_solver.py b/tests/test_complex_eigen_solver.py
index df6527a4..3b3d85ae 100644
--- a/tests/test_complex_eigen_solver.py
+++ b/tests/test_complex_eigen_solver.py
@@ -1,32 +1,34 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
 
-es = nanoeigenpy.ComplexEigenSolver(A)
-assert es.info() == nanoeigenpy.ComputationInfo.Success
+def test_complex_eigen_solver():
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
 
-V = es.eigenvectors()
-D = es.eigenvalues()
-assert V.shape == (dim, dim)
-assert D.shape == (dim,)
+    es = nanoeigenpy.ComplexEigenSolver(A)
+    assert es.info() == nanoeigenpy.ComputationInfo.Success
 
-AV = A @ V
-VD = V @ np.diag(D)
-assert nanoeigenpy.is_approx(AV.real, VD.real)
-assert nanoeigenpy.is_approx(AV.imag, VD.imag)
+    V = es.eigenvectors()
+    D = es.eigenvalues()
+    assert V.shape == (dim, dim)
+    assert D.shape == (dim,)
 
-trace_A = np.trace(A)
-trace_D = np.sum(D)
-assert abs(trace_A - trace_D.real) < 1e-10
-assert abs(trace_D.imag) < 1e-10
+    AV = A @ V
+    VD = V @ np.diag(D)
+    assert nanoeigenpy.is_approx(AV.real, VD.real)
+    assert nanoeigenpy.is_approx(AV.imag, VD.imag)
 
-ces5 = nanoeigenpy.ComplexEigenSolver(A)
-ces6 = nanoeigenpy.ComplexEigenSolver(A)
-id5 = ces5.id()
-id6 = ces6.id()
-assert id5 != id6
-assert id5 == ces5.id()
-assert id6 == ces6.id()
+    trace_A = np.trace(A)
+    trace_D = np.sum(D)
+    assert abs(trace_A - trace_D.real) < 1e-10
+    assert abs(trace_D.imag) < 1e-10
+
+    ces5 = nanoeigenpy.ComplexEigenSolver(A)
+    ces6 = nanoeigenpy.ComplexEigenSolver(A)
+    id5 = ces5.id()
+    id6 = ces6.id()
+    assert id5 != id6
+    assert id5 == ces5.id()
+    assert id6 == ces6.id()
diff --git a/tests/test_complex_schur.py b/tests/test_complex_schur.py
index da1acb94..55a671b0 100644
--- a/tests/test_complex_schur.py
+++ b/tests/test_complex_schur.py
@@ -1,49 +1,51 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
 
-cs = nanoeigenpy.ComplexSchur(A)
-assert cs.info() == nanoeigenpy.ComputationInfo.Success
+def test_complex_schur():
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
 
-U = cs.matrixU()
-T = cs.matrixT()
+    cs = nanoeigenpy.ComplexSchur(A)
+    assert cs.info() == nanoeigenpy.ComputationInfo.Success
 
-A_complex = A.astype(complex)
-assert nanoeigenpy.is_approx(A_complex, U @ T @ U.conj().T)
-assert nanoeigenpy.is_approx(U @ U.conj().T, np.eye(dim))
+    U = cs.matrixU()
+    T = cs.matrixT()
 
-for row in range(1, dim):
-    for col in range(row):
-        assert abs(T[row, col]) < 1e-12
+    A_complex = A.astype(complex)
+    assert nanoeigenpy.is_approx(A_complex, U @ T @ U.conj().T)
+    assert nanoeigenpy.is_approx(U @ U.conj().T, np.eye(dim))
 
-A_triangular = np.triu(A)
-cs_triangular = nanoeigenpy.ComplexSchur(dim)
-cs_triangular.setMaxIterations(1)
-result_triangular = cs_triangular.compute(A_triangular)
-assert result_triangular.info() == nanoeigenpy.ComputationInfo.Success
+    for row in range(1, dim):
+        for col in range(row):
+            assert abs(T[row, col]) < 1e-12
 
-T_triangular = cs_triangular.matrixT()
-U_triangular = cs_triangular.matrixU()
+    A_triangular = np.triu(A)
+    cs_triangular = nanoeigenpy.ComplexSchur(dim)
+    cs_triangular.setMaxIterations(1)
+    result_triangular = cs_triangular.compute(A_triangular)
+    assert result_triangular.info() == nanoeigenpy.ComputationInfo.Success
 
-A_triangular_complex = A_triangular.astype(complex)
-assert nanoeigenpy.is_approx(T_triangular, A_triangular_complex)
-assert nanoeigenpy.is_approx(U_triangular, np.eye(dim, dtype=complex))
+    T_triangular = cs_triangular.matrixT()
+    U_triangular = cs_triangular.matrixU()
 
-hess = nanoeigenpy.HessenbergDecomposition(A)
-H = hess.matrixH()
-Q_hess = hess.matrixQ()
+    A_triangular_complex = A_triangular.astype(complex)
+    assert nanoeigenpy.is_approx(T_triangular, A_triangular_complex)
+    assert nanoeigenpy.is_approx(U_triangular, np.eye(dim, dtype=complex))
 
-cs_from_hess = nanoeigenpy.ComplexSchur(dim)
-result_from_hess = cs_from_hess.computeFromHessenberg(H, Q_hess, True)
-assert result_from_hess.info() == nanoeigenpy.ComputationInfo.Success
+    hess = nanoeigenpy.HessenbergDecomposition(A)
+    H = hess.matrixH()
+    Q_hess = hess.matrixQ()
 
-T_from_hess = cs_from_hess.matrixT()
-U_from_hess = cs_from_hess.matrixU()
+    cs_from_hess = nanoeigenpy.ComplexSchur(dim)
+    result_from_hess = cs_from_hess.computeFromHessenberg(H, Q_hess, True)
+    assert result_from_hess.info() == nanoeigenpy.ComputationInfo.Success
 
-A_complex = A.astype(complex)
-assert nanoeigenpy.is_approx(
-    A_complex, U_from_hess @ T_from_hess @ U_from_hess.conj().T
-)
+    T_from_hess = cs_from_hess.matrixT()
+    U_from_hess = cs_from_hess.matrixU()
+
+    A_complex = A.astype(complex)
+    assert nanoeigenpy.is_approx(
+        A_complex, U_from_hess @ T_from_hess @ U_from_hess.conj().T
+    )
diff --git a/tests/test_eigen_solver.py b/tests/test_eigen_solver.py
index 27a9594c..38f82339 100644
--- a/tests/test_eigen_solver.py
+++ b/tests/test_eigen_solver.py
@@ -1,40 +1,42 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
 
-A = rng.random((dim, dim))
+def test_eigen_solver():
+    dim = 100
+    rng = np.random.default_rng()
 
-es = nanoeigenpy.EigenSolver()
-es = nanoeigenpy.EigenSolver(dim)
-es = nanoeigenpy.EigenSolver(A)
-assert es.info() == nanoeigenpy.ComputationInfo.Success
+    A = rng.random((dim, dim))
 
-V = es.eigenvectors()
-D = es.eigenvalues()
+    es = nanoeigenpy.EigenSolver()
+    es = nanoeigenpy.EigenSolver(dim)
+    es = nanoeigenpy.EigenSolver(A)
+    assert es.info() == nanoeigenpy.ComputationInfo.Success
 
-assert nanoeigenpy.is_approx(A.dot(V).real, V.dot(np.diag(D)).real)
-assert nanoeigenpy.is_approx(A.dot(V).imag, V.dot(np.diag(D)).imag)
+    V = es.eigenvectors()
+    D = es.eigenvalues()
 
-es1 = nanoeigenpy.EigenSolver()
-es2 = nanoeigenpy.EigenSolver()
+    assert nanoeigenpy.is_approx(A.dot(V).real, V.dot(np.diag(D)).real)
+    assert nanoeigenpy.is_approx(A.dot(V).imag, V.dot(np.diag(D)).imag)
 
-id1 = es1.id()
-id2 = es2.id()
+    es1 = nanoeigenpy.EigenSolver()
+    es2 = nanoeigenpy.EigenSolver()
 
-assert id1 != id2
-assert id1 == es1.id()
-assert id2 == es2.id()
+    id1 = es1.id()
+    id2 = es2.id()
 
-dim_constructor = 3
+    assert id1 != id2
+    assert id1 == es1.id()
+    assert id2 == es2.id()
 
-es3 = nanoeigenpy.EigenSolver(dim_constructor)
-es4 = nanoeigenpy.EigenSolver(dim_constructor)
+    dim_constructor = 3
 
-id3 = es3.id()
-id4 = es4.id()
+    es3 = nanoeigenpy.EigenSolver(dim_constructor)
+    es4 = nanoeigenpy.EigenSolver(dim_constructor)
 
-assert id3 != id4
-assert id3 == es3.id()
-assert id4 == es4.id()
+    id3 = es3.id()
+    id4 = es4.id()
+
+    assert id3 != id4
+    assert id3 == es3.id()
+    assert id4 == es4.id()
diff --git a/tests/test_full_piv_lu.py b/tests/test_full_piv_lu.py
index 94e5dccd..25b1c0e9 100644
--- a/tests/test_full_piv_lu.py
+++ b/tests/test_full_piv_lu.py
@@ -1,109 +1,111 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
-fullpivlu = nanoeigenpy.FullPivLU(A)
-
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = fullpivlu.solve(B)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
-
-x = rng.random(dim)
-b = A.dot(x)
-x_est = fullpivlu.solve(b)
-assert nanoeigenpy.is_approx(x, x_est)
-assert nanoeigenpy.is_approx(A.dot(x_est), b)
-
-rows = fullpivlu.rows()
-cols = fullpivlu.cols()
-assert cols == dim
-assert rows == dim
-
-fullpivlu_compute = fullpivlu.compute(A)
-A_reconstructed = fullpivlu.reconstructedMatrix()
-assert nanoeigenpy.is_approx(A_reconstructed, A)
-
-nonzeropivots = fullpivlu.nonzeroPivots()
-maxpivot = fullpivlu.maxPivot()
-assert nonzeropivots == dim
-assert maxpivot > 0
-
-LU = fullpivlu.matrixLU()
-P_perm = fullpivlu.permutationP()
-Q_perm = fullpivlu.permutationQ()
-P = P_perm.toDenseMatrix()
-Q = Q_perm.toDenseMatrix()
-
-U = np.triu(LU)
-L = np.eye(dim) + np.tril(LU, -1)
-assert nanoeigenpy.is_approx(P @ A @ Q, L @ U)
-
-rank = fullpivlu.rank()
-dimkernel = fullpivlu.dimensionOfKernel()
-injective = fullpivlu.isInjective()
-surjective = fullpivlu.isSurjective()
-invertible = fullpivlu.isInvertible()
-assert rank == dim
-assert dimkernel == 0
-assert injective
-assert surjective
-assert invertible
-
-kernel = fullpivlu.kernel()
-image = fullpivlu.image(A)
-assert kernel.shape[1] == 1
-assert nanoeigenpy.is_approx(A @ kernel, np.zeros((dim, 1)))
-assert image.shape[1] == rank
-
-inverse = fullpivlu.inverse()
-assert nanoeigenpy.is_approx(A @ inverse, np.eye(dim))
-assert nanoeigenpy.is_approx(inverse @ A, np.eye(dim))
-
-rcond = fullpivlu.rcond()
-determinant = fullpivlu.determinant()
-det_numpy = np.linalg.det(A)
-assert rcond > 0
-assert abs(determinant - det_numpy) / abs(det_numpy) < 1e-10
-
-fullpivlu.setThreshold()
-default_threshold = fullpivlu.threshold()
-fullpivlu.setThreshold(1e-8)
-assert fullpivlu.threshold() == 1e-8
-
-P_inv = P_perm.inverse().toDenseMatrix()
-Q_inv = Q_perm.inverse().toDenseMatrix()
-assert nanoeigenpy.is_approx(P @ P_inv, np.eye(dim))
-assert nanoeigenpy.is_approx(Q @ Q_inv, np.eye(dim))
-assert nanoeigenpy.is_approx(P_inv @ P, np.eye(dim))
-assert nanoeigenpy.is_approx(Q_inv @ Q, np.eye(dim))
-
-rows_rect = 4
-cols_rect = 6
-A_rect = rng.random((rows_rect, cols_rect))
-fullpivlu_rect = nanoeigenpy.FullPivLU(A_rect)
-assert fullpivlu_rect.rows() == rows_rect
-assert fullpivlu_rect.cols() == cols_rect
-rank_rect = fullpivlu_rect.rank()
-assert rank_rect <= min(rows_rect, cols_rect)
-assert fullpivlu_rect.dimensionOfKernel() == cols_rect - rank_rect
-
-decomp1 = nanoeigenpy.FullPivLU()
-decomp2 = nanoeigenpy.FullPivLU()
-id1 = decomp1.id()
-id2 = decomp2.id()
-assert id1 != id2
-assert id1 == decomp1.id()
-assert id2 == decomp2.id()
-
-decomp3 = nanoeigenpy.FullPivLU(dim, dim)
-decomp4 = nanoeigenpy.FullPivLU(dim, dim)
-id3 = decomp3.id()
-id4 = decomp4.id()
-assert id3 != id4
-assert id3 == decomp3.id()
-assert id4 == decomp4.id()
+
+def test_full_piv_lu():
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
+    fullpivlu = nanoeigenpy.FullPivLU(A)
+
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = fullpivlu.solve(B)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
+
+    x = rng.random(dim)
+    b = A.dot(x)
+    x_est = fullpivlu.solve(b)
+    assert nanoeigenpy.is_approx(x, x_est)
+    assert nanoeigenpy.is_approx(A.dot(x_est), b)
+
+    rows = fullpivlu.rows()
+    cols = fullpivlu.cols()
+    assert cols == dim
+    assert rows == dim
+
+    fullpivlu_compute = fullpivlu.compute(A)  # noqa
+    A_reconstructed = fullpivlu.reconstructedMatrix()
+    assert nanoeigenpy.is_approx(A_reconstructed, A)
+
+    nonzeropivots = fullpivlu.nonzeroPivots()
+    maxpivot = fullpivlu.maxPivot()
+    assert nonzeropivots == dim
+    assert maxpivot > 0
+
+    LU = fullpivlu.matrixLU()
+    P_perm = fullpivlu.permutationP()
+    Q_perm = fullpivlu.permutationQ()
+    P = P_perm.toDenseMatrix()
+    Q = Q_perm.toDenseMatrix()
+
+    U = np.triu(LU)
+    L = np.eye(dim) + np.tril(LU, -1)
+    assert nanoeigenpy.is_approx(P @ A @ Q, L @ U)
+
+    rank = fullpivlu.rank()
+    dimkernel = fullpivlu.dimensionOfKernel()
+    injective = fullpivlu.isInjective()
+    surjective = fullpivlu.isSurjective()
+    invertible = fullpivlu.isInvertible()
+    assert rank == dim
+    assert dimkernel == 0
+    assert injective
+    assert surjective
+    assert invertible
+
+    kernel = fullpivlu.kernel()
+    image = fullpivlu.image(A)
+    assert kernel.shape[1] == 1
+    assert nanoeigenpy.is_approx(A @ kernel, np.zeros((dim, 1)))
+    assert image.shape[1] == rank
+
+    inverse = fullpivlu.inverse()
+    assert nanoeigenpy.is_approx(A @ inverse, np.eye(dim))
+    assert nanoeigenpy.is_approx(inverse @ A, np.eye(dim))
+
+    rcond = fullpivlu.rcond()
+    determinant = fullpivlu.determinant()
+    det_numpy = np.linalg.det(A)
+    assert rcond > 0
+    assert abs(determinant - det_numpy) / abs(det_numpy) < 1e-10
+
+    fullpivlu.setThreshold()
+    default_threshold = fullpivlu.threshold()  # noqa
+    fullpivlu.setThreshold(1e-8)
+    assert fullpivlu.threshold() == 1e-8
+
+    P_inv = P_perm.inverse().toDenseMatrix()
+    Q_inv = Q_perm.inverse().toDenseMatrix()
+    assert nanoeigenpy.is_approx(P @ P_inv, np.eye(dim))
+    assert nanoeigenpy.is_approx(Q @ Q_inv, np.eye(dim))
+    assert nanoeigenpy.is_approx(P_inv @ P, np.eye(dim))
+    assert nanoeigenpy.is_approx(Q_inv @ Q, np.eye(dim))
+
+    rows_rect = 4
+    cols_rect = 6
+    A_rect = rng.random((rows_rect, cols_rect))
+    fullpivlu_rect = nanoeigenpy.FullPivLU(A_rect)
+    assert fullpivlu_rect.rows() == rows_rect
+    assert fullpivlu_rect.cols() == cols_rect
+    rank_rect = fullpivlu_rect.rank()
+    assert rank_rect <= min(rows_rect, cols_rect)
+    assert fullpivlu_rect.dimensionOfKernel() == cols_rect - rank_rect
+
+    decomp1 = nanoeigenpy.FullPivLU()
+    decomp2 = nanoeigenpy.FullPivLU()
+    id1 = decomp1.id()
+    id2 = decomp2.id()
+    assert id1 != id2
+    assert id1 == decomp1.id()
+    assert id2 == decomp2.id()
+
+    decomp3 = nanoeigenpy.FullPivLU(dim, dim)
+    decomp4 = nanoeigenpy.FullPivLU(dim, dim)
+    id3 = decomp3.id()
+    id4 = decomp4.id()
+    assert id3 != id4
+    assert id3 == decomp3.id()
+    assert id4 == decomp4.id()
diff --git a/tests/test_generalized_eigen_solver.py b/tests/test_generalized_eigen_solver.py
index 4be542c0..2bf2c016 100644
--- a/tests/test_generalized_eigen_solver.py
+++ b/tests/test_generalized_eigen_solver.py
@@ -1,40 +1,42 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
-B = rng.random((dim, dim))
-B = (B + B.T) * 0.5 + np.diag(10.0 + rng.random(dim))
-
-ges_matrices = nanoeigenpy.GeneralizedEigenSolver(A, B)
-assert ges_matrices.info() == nanoeigenpy.ComputationInfo.Success
-
-alphas = ges_matrices.alphas()
-betas = ges_matrices.betas()
-eigenvectors = ges_matrices.eigenvectors()
-eigenvalues = ges_matrices.eigenvalues()
-
-for k in range(dim):
-    v = eigenvectors[:, k]
-    lambda_k = eigenvalues[k]
-
-    Av = A @ v
-    lambda_Bv = lambda_k * (B @ v)
-    assert nanoeigenpy.is_approx(Av.real, lambda_Bv.real, 1e-6)
-    assert nanoeigenpy.is_approx(Av.imag, lambda_Bv.imag, 1e-6)
-
-for k in range(dim):
-    v = eigenvectors[:, k]
-    alpha = alphas[k]
-    beta = betas[k]
-
-    alpha_Bv = alpha * (B @ v)
-    beta_Av = beta * (A @ v)
-    assert nanoeigenpy.is_approx(alpha_Bv.real, beta_Av.real, 1e-6)
-    assert nanoeigenpy.is_approx(alpha_Bv.imag, beta_Av.imag, 1e-6)
-
-for k in range(dim):
-    if abs(betas[k]) > 1e-12:
-        expected_eigenvalue = alphas[k] / betas[k]
-        assert abs(eigenvalues[k] - expected_eigenvalue) < 1e-12
+
+def test_generalized_eigen_solver():
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
+    B = rng.random((dim, dim))
+    B = (B + B.T) * 0.5 + np.diag(10.0 + rng.random(dim))
+
+    ges_matrices = nanoeigenpy.GeneralizedEigenSolver(A, B)
+    assert ges_matrices.info() == nanoeigenpy.ComputationInfo.Success
+
+    alphas = ges_matrices.alphas()
+    betas = ges_matrices.betas()
+    eigenvectors = ges_matrices.eigenvectors()
+    eigenvalues = ges_matrices.eigenvalues()
+
+    for k in range(dim):
+        v = eigenvectors[:, k]
+        lambda_k = eigenvalues[k]
+
+        Av = A @ v
+        lambda_Bv = lambda_k * (B @ v)
+        assert nanoeigenpy.is_approx(Av.real, lambda_Bv.real, 1e-6)
+        assert nanoeigenpy.is_approx(Av.imag, lambda_Bv.imag, 1e-6)
+
+    for k in range(dim):
+        v = eigenvectors[:, k]
+        alpha = alphas[k]
+        beta = betas[k]
+
+        alpha_Bv = alpha * (B @ v)
+        beta_Av = beta * (A @ v)
+        assert nanoeigenpy.is_approx(alpha_Bv.real, beta_Av.real, 1e-6)
+        assert nanoeigenpy.is_approx(alpha_Bv.imag, beta_Av.imag, 1e-6)
+
+    for k in range(dim):
+        if abs(betas[k]) > 1e-12:
+            expected_eigenvalue = alphas[k] / betas[k]
+            assert abs(eigenvalues[k] - expected_eigenvalue) < 1e-12
diff --git a/tests/test_geometry.py b/tests/test_geometry.py
index 1fe48abe..7097178f 100644
--- a/tests/test_geometry.py
+++ b/tests/test_geometry.py
@@ -1,7 +1,7 @@
-import numpy as np
-from numpy import cos
 import nanoeigenpy
 import quaternion
+import numpy as np
+from numpy import cos
 
 
 verbose = True
@@ -14,692 +14,699 @@ def isapprox(a, b, epsilon=1e-6):
         return abs(a - b) < epsilon
 
 
-# --- Quaternion ---------------------------------------------------------------
-verbose and print("[Quaternion] Coefficient initialisation")
-q = nanoeigenpy.Quaternion(1, 2, 3, 4)
-q.normalize()
-assert isapprox(np.linalg.norm(q.coeffs()), q.norm())
-assert isapprox(np.linalg.norm(q.coeffs()), 1)
-
-verbose and print("[Quaternion] Coefficient-vector initialisation")
-v = np.array([0.5, -0.5, 0.5, 0.5])
-for k in range(10000):
-    qv = nanoeigenpy.Quaternion(v)
-assert isapprox(qv.coeffs(), v)
-
-verbose and print("[Quaternion] AngleAxis initialisation")
-r = nanoeigenpy.AngleAxis(q)
-q2 = nanoeigenpy.Quaternion(r)
-assert q == q
-assert isapprox(q.coeffs(), q2.coeffs())
-assert q2.isApprox(q2)
-assert q2.isApprox(q2, 1e-2)
-
-Rq = q.matrix()
-Rr = r.matrix()
-assert isapprox(Rq.dot(Rq.T), np.eye(3))
-assert isapprox(Rr, Rq)
-
-verbose and print("[Quaternion] Rotation Matrix initialisation")
-qR = nanoeigenpy.Quaternion(Rr)
-assert q.isApprox(qR)
-assert isapprox(q.coeffs(), qR.coeffs())
-
-assert isapprox(qR[3], 1.0 / np.sqrt(30))
-try:
-    qR[5]
-    print("Error, this message should not appear.")
-except IndexError as e:
+def test_geometry():
+    # --- Quaternion ---------------------------------------------------------------
+    verbose and print("[Quaternion] Coefficient initialisation")
+    q = nanoeigenpy.Quaternion(1, 2, 3, 4)
+    q.normalize()
+    assert isapprox(np.linalg.norm(q.coeffs()), q.norm())
+    assert isapprox(np.linalg.norm(q.coeffs()), 1)
+
+    verbose and print("[Quaternion] Coefficient-vector initialisation")
+    v = np.array([0.5, -0.5, 0.5, 0.5])
+    for k in range(10000):
+        qv = nanoeigenpy.Quaternion(v)
+    assert isapprox(qv.coeffs(), v)
+
+    verbose and print("[Quaternion] AngleAxis initialisation")
+    r = nanoeigenpy.AngleAxis(q)
+    q2 = nanoeigenpy.Quaternion(r)
+    assert q == q
+    assert isapprox(q.coeffs(), q2.coeffs())
+    assert q2.isApprox(q2)
+    assert q2.isApprox(q2, 1e-2)
+
+    Rq = q.matrix()
+    Rr = r.matrix()
+    assert isapprox(Rq.dot(Rq.T), np.eye(3))
+    assert isapprox(Rr, Rq)
+
+    verbose and print("[Quaternion] Rotation Matrix initialisation")
+    qR = nanoeigenpy.Quaternion(Rr)
+    assert q.isApprox(qR)
+    assert isapprox(q.coeffs(), qR.coeffs())
+
+    assert isapprox(qR[3], 1.0 / np.sqrt(30))
+    try:
+        qR[5]
+        print("Error, this message should not appear.")
+    except IndexError as e:
+        if verbose:
+            print("As expected, caught exception: ", e)
+
+    x = quaternion.X(q)
+    assert x.a == q
+
+    # --- Angle Vector ------------------------------------------------
+    r = nanoeigenpy.AngleAxis(0.1, np.array([1, 0, 0], np.double))
     if verbose:
-        print("As expected, caught exception: ", e)
-
-x = quaternion.X(q)
-assert x.a == q
-
-# --- Angle Vector ------------------------------------------------
-r = nanoeigenpy.AngleAxis(0.1, np.array([1, 0, 0], np.double))
-if verbose:
-    print("Rx(.1) = \n\n", r.matrix(), "\n")
-assert isapprox(r.matrix()[2, 2], cos(r.angle))
-assert isapprox(r.axis, np.array([1.0, 0, 0]))
-assert isapprox(r.angle, 0.1)
-assert r.isApprox(r)
-assert r.isApprox(r, 1e-2)
-
-r.axis = np.array([0, 1, 0], np.double).T
-assert isapprox(r.matrix()[0, 0], cos(r.angle))
-
-ri = r.inverse()
-assert isapprox(ri.angle, -0.1)
-
-R = r.matrix()
-r2 = nanoeigenpy.AngleAxis(np.dot(R, R))
-assert isapprox(r2.angle, r.angle * 2)
-
-# --- Hyperplane ------------------------------------------------
-verbose and print("[Hyperplane] Normal and point construction")
-n = np.array([1.0, 0.0])
-p = np.array([2.0, 3.0])
-h = nanoeigenpy.Hyperplane(n, p)
-assert isapprox(h.normal(), n)
-assert isapprox(h.absDistance(p), 0.0)
-assert h.dim() == 2
-
-verbose and print("[Hyperplane] Normal and distance construction")
-d = -np.dot(n, p)
-h2 = nanoeigenpy.Hyperplane(n, d)
-assert isapprox(h.coeffs(), h2.coeffs())
-assert isapprox(h2.offset(), d)
-
-verbose and print("[Hyperplane] Through two points")
-p1 = np.array([0.0, 0.0])
-p2 = np.array([1.0, 1.0])
-h3 = nanoeigenpy.Hyperplane.Through(p1, p2)
-assert isapprox(h3.absDistance(p1), 0.0)
-assert isapprox(h3.absDistance(p2), 0.0)
-assert isapprox(np.linalg.norm(h3.normal()), 1.0)
-
-verbose and print("[Hyperplane] Through three points")
-p1_3d = np.array([1.0, 0.0, 0.0])
-p2_3d = np.array([0.0, 1.0, 0.0])
-p3_3d = np.array([0.0, 0.0, 1.0])
-h4 = nanoeigenpy.Hyperplane.Through(p1_3d, p2_3d, p3_3d)
-assert isapprox(h4.absDistance(p1_3d), 0.0)
-assert isapprox(h4.absDistance(p2_3d), 0.0)
-assert isapprox(h4.absDistance(p3_3d), 0.0)
-assert isapprox(np.linalg.norm(h4.normal()), 1.0)
-assert h4.dim() == 3
-
-verbose and print("[Hyperplane] Distance calculations")
-test_point = np.array([1.0, 0.0])
-signed_dist = h3.signedDistance(test_point)
-abs_dist = h3.absDistance(test_point)
-assert isapprox(abs_dist, abs(signed_dist))
-
-verbose and print("[Hyperplane] Projection")
-proj = h3.projection(test_point)
-assert isapprox(h3.absDistance(proj), 0.0)
-
-verbose and print("[Hyperplane] Normalization")
-h_copy = nanoeigenpy.Hyperplane(np.array([2.0, 0.0]), np.array([1.0, 0.0]))
-h_copy.normalize()
-assert isapprox(np.linalg.norm(h_copy.normal()), 1.0)
-
-verbose and print("[Hyperplane] Line intersection")
-h_line1 = nanoeigenpy.Hyperplane(np.array([1.0, 0.0]), 0.0)
-h_line2 = nanoeigenpy.Hyperplane(np.array([0.0, 1.0]), 0.0)
-intersection = h_line1.intersection(h_line2)
-assert isapprox(intersection, np.array([0.0, 0.0]))
-
-verbose and print("[Hyperplane] isApprox")
-h5 = nanoeigenpy.Hyperplane(h)
-assert h.isApprox(h5)
-assert h.isApprox(h5, 1e-12)
-
-# --- ParametrizedLine ------------------------------------------------
-verbose and print("[ParametrizedLine] Origin and direction construction")
-origin = np.array([1.0, 2.0])
-direction = np.array([1.0, 0.0])
-line = nanoeigenpy.ParametrizedLine(origin, direction)
-assert isapprox(line.origin(), origin)
-assert isapprox(line.direction(), direction)
-assert line.dim() == 2
-
-verbose and print("[ParametrizedLine] Default constructor")
-line_default = nanoeigenpy.ParametrizedLine()
-assert line_default.dim() == 0
-
-verbose and print("[ParametrizedLine] Dimension constructor")
-line_3d = nanoeigenpy.ParametrizedLine(3)
-assert line_3d.dim() == 3
-
-verbose and print("[ParametrizedLine] Copy constructor")
-line_copy = nanoeigenpy.ParametrizedLine(line)
-assert isapprox(line_copy.origin(), line.origin())
-assert isapprox(line_copy.direction(), line.direction())
-
-verbose and print("[ParametrizedLine] Construction from 2D hyperplane")
-h_2d = nanoeigenpy.Hyperplane(np.array([1.0, 0.0]), 0.0)
-line_from_h = nanoeigenpy.ParametrizedLine(h_2d)
-assert line_from_h.dim() == 2
-assert isapprox(line_from_h.origin(), np.array([0.0, 0.0]))
-assert isapprox(line_from_h.direction(), np.array([0.0, 1.0]))
-
-verbose and print("[ParametrizedLine] 3D hyperplane should fail")
-h_3d = nanoeigenpy.Hyperplane(np.array([1.0, 0.0, 0.0]), 0.0)
-try:
-    line_fail = nanoeigenpy.ParametrizedLine(h_3d)
-    print("Error, this message should not appear.")
-except ValueError as e:
-    if verbose:
-        print("As expected, caught exception:", e)
-
-verbose and print("[ParametrizedLine] Distance calculations")
-test_point = np.array([1.0, 0.0])
-line_x_axis = nanoeigenpy.ParametrizedLine(np.array([0.0, 0.0]), np.array([1.0, 0.0]))
-distance = line_x_axis.distance(test_point)
-squared_distance = line_x_axis.squaredDistance(test_point)
-assert isapprox(distance, 0.0)
-assert isapprox(squared_distance, 0.0)
-
-off_line_point = np.array([1.0, 1.0])
-distance_off = line_x_axis.distance(off_line_point)
-squared_distance_off = line_x_axis.squaredDistance(off_line_point)
-assert isapprox(distance_off, 1.0)
-assert isapprox(squared_distance_off, 1.0)
-assert isapprox(distance_off * distance_off, squared_distance_off)
-
-verbose and print("[ParametrizedLine] Projection")
-projection = line_x_axis.projection(off_line_point)
-assert isapprox(projection, np.array([1.0, 0.0]))
-assert isapprox(line_x_axis.distance(projection), 0.0)
-
-verbose and print("[ParametrizedLine] Intersection with hyperplane")
-line_diagonal = nanoeigenpy.ParametrizedLine(np.array([0.0, 0.0]), np.array([1.0, 1.0]))
-h_vertical = nanoeigenpy.Hyperplane(np.array([1.0, 0.0]), -1.0)
-
-intersection_param = line_diagonal.intersectionParameter(h_vertical)
-assert isapprox(intersection_param, 1.0)
-
-intersection_param_old = line_diagonal.intersection(h_vertical)
-assert isapprox(intersection_param_old, intersection_param)
-
-intersection_point = line_diagonal.intersectionPoint(h_vertical)
-expected_intersection = np.array([1.0, 1.0])
-assert isapprox(intersection_point, expected_intersection)
-assert isapprox(h_vertical.absDistance(intersection_point), 0.0)
-
-verbose and print("[ParametrizedLine] isApprox")
-line1 = nanoeigenpy.ParametrizedLine(np.array([0.0, 0.0]), np.array([1.0, 0.0]))
-line2 = nanoeigenpy.ParametrizedLine(np.array([0.0, 0.0]), np.array([1.0, 0.0]))
-line3 = nanoeigenpy.ParametrizedLine(np.array([0.0, 0.0]), np.array([0.0, 1.0]))
-assert line1.isApprox(line2)
-assert line1.isApprox(line2, 1e-12)
-assert not line1.isApprox(line3)
-
-verbose and print("[ParametrizedLine] Parallel lines")
-line_parallel1 = nanoeigenpy.ParametrizedLine(
-    np.array([0.0, 0.0]), np.array([1.0, 0.0])
-)
-line_parallel2 = nanoeigenpy.ParametrizedLine(
-    np.array([0.0, 1.0]), np.array([1.0, 0.0])
-)
-assert not line_parallel1.isApprox(line_parallel2)
-
-test_points = [np.array([i, 0.0]) for i in range(5)]
-distances = [line_parallel2.distance(p) for p in test_points]
-for d in distances:
-    assert isapprox(d, 1.0)
-
-verbose and print("[ParametrizedLine] Through two points")
-p0 = np.array([0.0, 0.0])
-p1 = np.array([1.0, 1.0])
-line_through = nanoeigenpy.ParametrizedLine.Through(p0, p1)
-direction = line_through.direction()
-expected_dir = (p1 - p0) / np.linalg.norm(p1 - p0)
-assert isapprox(line_through.origin(), p0)
-assert isapprox(np.linalg.norm(direction), 1.0)
-assert isapprox(direction, expected_dir)
-
-# --- Rotation2D ------------------------------------------------
-verbose and print("[Rotation2D] Default constructor")
-r_default = nanoeigenpy.Rotation2D()
-assert isapprox(r_default.angle, 0.0)
-
-verbose and print("[Rotation2D] Angle constructor")
-angle = np.pi / 4
-r_angle = nanoeigenpy.Rotation2D(angle)
-assert isapprox(r_angle.angle, angle)
-
-verbose and print("[Rotation2D] Copy constructor")
-r_copy = nanoeigenpy.Rotation2D(r_angle)
-assert isapprox(r_copy.angle, r_angle.angle)
-assert r_copy == r_angle
-
-verbose and print("[Rotation2D] Matrix constructor")
-theta = np.pi / 6
-rotation_matrix = np.array(
-    [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
-)
-r_matrix = nanoeigenpy.Rotation2D(rotation_matrix)
-assert isapprox(r_matrix.angle, theta)
-
-verbose and print("[Rotation2D] Angle property")
-r_prop = nanoeigenpy.Rotation2D()
-new_angle = np.pi / 3
-r_prop.angle = new_angle
-assert isapprox(r_prop.angle, new_angle)
-
-verbose and print("[Rotation2D] smallestPositiveAngle")
-r_negative = nanoeigenpy.Rotation2D(-np.pi / 4)
-positive_angle = r_negative.smallestPositiveAngle()
-assert positive_angle >= 0.0
-assert positive_angle < 2 * np.pi
-assert isapprox(positive_angle, 7 * np.pi / 4)
-
-verbose and print("[Rotation2D] smallestAngle")
-r_large = nanoeigenpy.Rotation2D(3 * np.pi)
-smallest_angle = r_large.smallestAngle()
-assert smallest_angle >= -np.pi
-assert smallest_angle <= np.pi
-assert isapprox(smallest_angle, np.pi)
-
-verbose and print("[Rotation2D] Identity")
-r_identity = nanoeigenpy.Rotation2D.Identity()
-assert isapprox(r_identity.angle, 0.0)
-
-verbose and print("[Rotation2D] fromRotationMatrix")
-r_from_matrix = nanoeigenpy.Rotation2D()
-theta2 = np.pi / 2
-matrix2 = np.array(
-    [[np.cos(theta2), -np.sin(theta2)], [np.sin(theta2), np.cos(theta2)]]
-)
-r_from_matrix.fromRotationMatrix(matrix2)
-assert isapprox(r_from_matrix.angle, theta2)
-
-verbose and print("[Rotation2D] Rotation composition")
-r1 = nanoeigenpy.Rotation2D(np.pi / 4)
-r2 = nanoeigenpy.Rotation2D(np.pi / 6)
-r_composed = r1 * r2
-expected_angle = np.pi / 4 + np.pi / 6
-assert isapprox(r_composed.angle, expected_angle)
-
-verbose and print("[Rotation2D] In-place multiplication")
-r_inplace = nanoeigenpy.Rotation2D(np.pi / 4)
-original_angle = r_inplace.angle
-r_inplace *= nanoeigenpy.Rotation2D(np.pi / 6)
-assert isapprox(r_inplace.angle, original_angle + np.pi / 6)
-
-verbose and print("[Rotation2D] Vector rotation")
-r_90 = nanoeigenpy.Rotation2D(np.pi / 2)
-vec = np.array([1.0, 0.0])
-rotated_vec = r_90 * vec
-expected_vec = np.array([0.0, 1.0])
-assert isapprox(rotated_vec, expected_vec)
-
-vec2 = np.array([1.0, 1.0])
-r_45 = nanoeigenpy.Rotation2D(np.pi / 4)
-rotated_vec2 = r_45 * vec2
-expected_vec2 = np.array([0.0, np.sqrt(2)])
-assert isapprox(rotated_vec2, expected_vec2)
-
-verbose and print("[Rotation2D] Equality operators")
-r_eq1 = nanoeigenpy.Rotation2D(np.pi / 3)
-r_eq2 = nanoeigenpy.Rotation2D(np.pi / 3)
-r_eq3 = nanoeigenpy.Rotation2D(np.pi / 4)
-
-assert r_eq1 == r_eq2
-assert not (r_eq1 == r_eq3)
-assert r_eq1 != r_eq3
-assert not (r_eq1 != r_eq2)
-
-verbose and print("[Rotation2D] Periodic angles")
-r_period1 = nanoeigenpy.Rotation2D(0.0)
-r_period2 = nanoeigenpy.Rotation2D(2 * np.pi)
-verbose and print("[Rotation2D] isApprox")
-r_approx1 = nanoeigenpy.Rotation2D(np.pi / 4)
-r_approx2 = nanoeigenpy.Rotation2D(np.pi / 4 + 1e-15)
-r_approx3 = nanoeigenpy.Rotation2D(np.pi / 3)
-
-assert r_approx1.isApprox(r_approx2)
-assert r_approx1.isApprox(r_approx2, 1e-12)
-assert not r_approx1.isApprox(r_approx3)
-
-verbose and print("[Rotation2D] slerp")
-r_start = nanoeigenpy.Rotation2D(0.0)
-r_end = nanoeigenpy.Rotation2D(np.pi / 2)
-r_middle = r_start.slerp(0.5, r_end)
-assert isapprox(r_middle.angle, np.pi / 4)
-
-r_slerp_0 = r_start.slerp(0.0, r_end)
-r_slerp_1 = r_start.slerp(1.0, r_end)
-assert isapprox(r_slerp_0.angle, r_start.angle)
-assert isapprox(r_slerp_1.angle, r_end.angle)
-
-verbose and print("[Rotation2D] Inverse rotation")
-try:
-    r_original = nanoeigenpy.Rotation2D(np.pi / 3)
-    r_inverse = r_original.inverse()
-    assert isapprox(r_inverse.angle, -np.pi / 3)
-
-    r_identity_test = r_original * r_inverse
-    assert isapprox(r_identity_test.angle, 0.0, 1e-12)
-except AttributeError:
-    if verbose:
-        print("inverse() method not exposed or not available")
-
-verbose and print("[Rotation2D] Matrix conversion")
-try:
-    r_matrix_test = nanoeigenpy.Rotation2D(np.pi / 6)
-    matrix = r_matrix_test.matrix()
-
-    assert matrix.shape == (2, 2)
-    assert isapprox(matrix @ matrix.T, np.eye(2))
-    assert isapprox(np.linalg.det(matrix), 1.0)
-
-    expected_matrix = np.array(
-        [
-            [np.cos(np.pi / 6), -np.sin(np.pi / 6)],
-            [np.sin(np.pi / 6), np.cos(np.pi / 6)],
-        ]
+        print("Rx(.1) = \n\n", r.matrix(), "\n")
+    assert isapprox(r.matrix()[2, 2], cos(r.angle))
+    assert isapprox(r.axis, np.array([1.0, 0, 0]))
+    assert isapprox(r.angle, 0.1)
+    assert r.isApprox(r)
+    assert r.isApprox(r, 1e-2)
+
+    r.axis = np.array([0, 1, 0], np.double).T
+    assert isapprox(r.matrix()[0, 0], cos(r.angle))
+
+    ri = r.inverse()
+    assert isapprox(ri.angle, -0.1)
+
+    R = r.matrix()
+    r2 = nanoeigenpy.AngleAxis(np.dot(R, R))
+    assert isapprox(r2.angle, r.angle * 2)
+
+    # --- Hyperplane ------------------------------------------------
+    verbose and print("[Hyperplane] Normal and point construction")
+    n = np.array([1.0, 0.0])
+    p = np.array([2.0, 3.0])
+    h = nanoeigenpy.Hyperplane(n, p)
+    assert isapprox(h.normal(), n)
+    assert isapprox(h.absDistance(p), 0.0)
+    assert h.dim() == 2
+
+    verbose and print("[Hyperplane] Normal and distance construction")
+    d = -np.dot(n, p)
+    h2 = nanoeigenpy.Hyperplane(n, d)
+    assert isapprox(h.coeffs(), h2.coeffs())
+    assert isapprox(h2.offset(), d)
+
+    verbose and print("[Hyperplane] Through two points")
+    p1 = np.array([0.0, 0.0])
+    p2 = np.array([1.0, 1.0])
+    h3 = nanoeigenpy.Hyperplane.Through(p1, p2)
+    assert isapprox(h3.absDistance(p1), 0.0)
+    assert isapprox(h3.absDistance(p2), 0.0)
+    assert isapprox(np.linalg.norm(h3.normal()), 1.0)
+
+    verbose and print("[Hyperplane] Through three points")
+    p1_3d = np.array([1.0, 0.0, 0.0])
+    p2_3d = np.array([0.0, 1.0, 0.0])
+    p3_3d = np.array([0.0, 0.0, 1.0])
+    h4 = nanoeigenpy.Hyperplane.Through(p1_3d, p2_3d, p3_3d)
+    assert isapprox(h4.absDistance(p1_3d), 0.0)
+    assert isapprox(h4.absDistance(p2_3d), 0.0)
+    assert isapprox(h4.absDistance(p3_3d), 0.0)
+    assert isapprox(np.linalg.norm(h4.normal()), 1.0)
+    assert h4.dim() == 3
+
+    verbose and print("[Hyperplane] Distance calculations")
+    test_point = np.array([1.0, 0.0])
+    signed_dist = h3.signedDistance(test_point)
+    abs_dist = h3.absDistance(test_point)
+    assert isapprox(abs_dist, abs(signed_dist))
+
+    verbose and print("[Hyperplane] Projection")
+    proj = h3.projection(test_point)
+    assert isapprox(h3.absDistance(proj), 0.0)
+
+    verbose and print("[Hyperplane] Normalization")
+    h_copy = nanoeigenpy.Hyperplane(np.array([2.0, 0.0]), np.array([1.0, 0.0]))
+    h_copy.normalize()
+    assert isapprox(np.linalg.norm(h_copy.normal()), 1.0)
+
+    verbose and print("[Hyperplane] Line intersection")
+    h_line1 = nanoeigenpy.Hyperplane(np.array([1.0, 0.0]), 0.0)
+    h_line2 = nanoeigenpy.Hyperplane(np.array([0.0, 1.0]), 0.0)
+    intersection = h_line1.intersection(h_line2)
+    assert isapprox(intersection, np.array([0.0, 0.0]))
+
+    verbose and print("[Hyperplane] isApprox")
+    h5 = nanoeigenpy.Hyperplane(h)
+    assert h.isApprox(h5)
+    assert h.isApprox(h5, 1e-12)
+
+    # --- ParametrizedLine ------------------------------------------------
+    verbose and print("[ParametrizedLine] Origin and direction construction")
+    origin = np.array([1.0, 2.0])
+    direction = np.array([1.0, 0.0])
+    line = nanoeigenpy.ParametrizedLine(origin, direction)
+    assert isapprox(line.origin(), origin)
+    assert isapprox(line.direction(), direction)
+    assert line.dim() == 2
+
+    verbose and print("[ParametrizedLine] Default constructor")
+    line_default = nanoeigenpy.ParametrizedLine()
+    assert line_default.dim() == 0
+
+    verbose and print("[ParametrizedLine] Dimension constructor")
+    line_3d = nanoeigenpy.ParametrizedLine(3)
+    assert line_3d.dim() == 3
+
+    verbose and print("[ParametrizedLine] Copy constructor")
+    line_copy = nanoeigenpy.ParametrizedLine(line)
+    assert isapprox(line_copy.origin(), line.origin())
+    assert isapprox(line_copy.direction(), line.direction())
+
+    verbose and print("[ParametrizedLine] Construction from 2D hyperplane")
+    h_2d = nanoeigenpy.Hyperplane(np.array([1.0, 0.0]), 0.0)
+    line_from_h = nanoeigenpy.ParametrizedLine(h_2d)
+    assert line_from_h.dim() == 2
+    assert isapprox(line_from_h.origin(), np.array([0.0, 0.0]))
+    assert isapprox(line_from_h.direction(), np.array([0.0, 1.0]))
+
+    verbose and print("[ParametrizedLine] 3D hyperplane should fail")
+    h_3d = nanoeigenpy.Hyperplane(np.array([1.0, 0.0, 0.0]), 0.0)
+    try:
+        line_fail = nanoeigenpy.ParametrizedLine(h_3d)  # noqa
+        print("Error, this message should not appear.")
+    except ValueError as e:
+        if verbose:
+            print("As expected, caught exception:", e)
+
+    verbose and print("[ParametrizedLine] Distance calculations")
+    test_point = np.array([1.0, 0.0])
+    line_x_axis = nanoeigenpy.ParametrizedLine(
+        np.array([0.0, 0.0]), np.array([1.0, 0.0])
     )
-    assert isapprox(matrix, expected_matrix)
-
-except AttributeError:
-    if verbose:
-        print("matrix() method not exposed or not available")
-
-verbose and print("[Rotation2D] Angle normalization")
-r_large_angle = nanoeigenpy.Rotation2D(3 * np.pi)
-vec_test = np.array([1.0, 0.0])
-rotated_large = r_large_angle * vec_test
-expected_large = np.array([-1.0, 0.0])
-assert isapprox(rotated_large, expected_large)
-
-# --- UniformScaling ------------------------------------------------
-verbose and print("[UniformScaling] Default constructor")
-s_default = nanoeigenpy.UniformScaling()
-
-verbose and print("[UniformScaling] Factor constructor")
-factor = 2.5
-s_factor = nanoeigenpy.UniformScaling(factor)
-assert isapprox(s_factor.factor(), factor)
-
-verbose and print("[UniformScaling] Copy constructor")
-s_copy = nanoeigenpy.UniformScaling(s_factor)
-assert isapprox(s_copy.factor(), s_factor.factor())
-
-verbose and print("[UniformScaling] Factor getter")
-s_test = nanoeigenpy.UniformScaling(3.0)
-assert isapprox(s_test.factor(), 3.0)
-
-verbose and print("[UniformScaling] Inverse scaling")
-s_original = nanoeigenpy.UniformScaling(4.0)
-s_inverse = s_original.inverse()
-assert isapprox(s_inverse.factor(), 1.0 / 4.0)
-
-s_identity_test = s_original * s_inverse
-assert isapprox(s_identity_test.factor(), 1.0)
-
-verbose and print("[UniformScaling] Concatenation of scalings")
-s1 = nanoeigenpy.UniformScaling(2.0)
-s2 = nanoeigenpy.UniformScaling(3.0)
-s_combined = s1 * s2
-assert isapprox(s_combined.factor(), 6.0)
-
-verbose and print("[UniformScaling] Multiplication with matrix")
-s_scale = nanoeigenpy.UniformScaling(2.0)
-matrix = np.array([[1.0, 2.0], [3.0, 4.0]])
-scaled_matrix = s_scale * matrix
-expected_matrix = matrix * 2.0
-assert isapprox(scaled_matrix, expected_matrix)
-
-identity = np.eye(3)
-s_identity_scale = nanoeigenpy.UniformScaling(5.0)
-scaled_identity = s_identity_scale * identity
-expected_identity = identity * 5.0
-assert isapprox(scaled_identity, expected_identity)
-
-verbose and print("[UniformScaling] Multiplication with AngleAxis")
-try:
-    angle_axis = nanoeigenpy.AngleAxis(np.pi / 4, np.array([0.0, 0.0, 1.0]))
-    s_with_rotation = nanoeigenpy.UniformScaling(2.0)
-    result_rotation = s_with_rotation * angle_axis
-
-    assert result_rotation.shape == (3, 3)
-    det = np.linalg.det(result_rotation)
-    assert isapprox(det, 2.0**3)
-
-except (AttributeError, NameError):
-    if verbose:
-        print("AngleAxis class not available or not exposed")
-
-verbose and print("[UniformScaling] Multiplication with Quaternion")
-try:
-    quat = nanoeigenpy.Quaternion(1, 0, 0, 0)
-    s_with_quat = nanoeigenpy.UniformScaling(3.0)
-    result_quat = s_with_quat * quat
-
-    assert result_quat.shape == (3, 3)
-    expected_scaled_identity = np.eye(3) * 3.0
-    assert isapprox(result_quat, expected_scaled_identity)
-
-except (AttributeError, NameError):
-    if verbose:
-        print("Quaternion class not available or not exposed")
-
-verbose and print("[UniformScaling] Multiplication with Rotation2D")
-try:
-    rotation_2d = nanoeigenpy.Rotation2D(np.pi / 4)
-    s_with_rot2d = nanoeigenpy.UniformScaling(2.0)
-    result_rot2d = s_with_rot2d * rotation_2d
-
-    assert result_rot2d.shape == (2, 2)
-    det_2d = np.linalg.det(result_rot2d)
-    assert isapprox(det_2d, 2.0**2)
-
-except (AttributeError, NameError):
-    if verbose:
-        print("Rotation2D class not available or not exposed")
-
-verbose and print("[UniformScaling] isApprox")
-s_approx1 = nanoeigenpy.UniformScaling(2.0)
-s_approx2 = nanoeigenpy.UniformScaling(2.0 + 1e-15)
-s_approx3 = nanoeigenpy.UniformScaling(3.0)
-
-assert s_approx1.isApprox(s_approx2)
-assert s_approx1.isApprox(s_approx2, 1e-12)
-assert not s_approx1.isApprox(s_approx3)
-
-verbose and print("[UniformScaling] Edge cases")
-s_zero = nanoeigenpy.UniformScaling(0.0)
-assert isapprox(s_zero.factor(), 0.0)
-try:
-    s_zero_inverse = s_zero.inverse()
-    if verbose:
-        print("Zero scaling inverse:", s_zero_inverse.factor())
-except Exception as e:
-    if verbose:
-        print("Zero scaling inverse threw exception (expected):", type(e).__name__)
-
-s_negative = nanoeigenpy.UniformScaling(-2.0)
-assert isapprox(s_negative.factor(), -2.0)
-s_negative_inverse = s_negative.inverse()
-assert isapprox(s_negative_inverse.factor(), -0.5)
-
-s_small = nanoeigenpy.UniformScaling(1e-10)
-s_small_inverse = s_small.inverse()
-assert isapprox(s_small_inverse.factor(), 1e10)
-
-verbose and print("[UniformScaling] Chain operations")
-s_chain1 = nanoeigenpy.UniformScaling(2.0)
-s_chain2 = nanoeigenpy.UniformScaling(3.0)
-s_chain3 = nanoeigenpy.UniformScaling(4.0)
-
-left_assoc = (s_chain1 * s_chain2) * s_chain3
-right_assoc = s_chain1 * (s_chain2 * s_chain3)
-assert isapprox(left_assoc.factor(), right_assoc.factor())
-assert isapprox(left_assoc.factor(), 24.0)
-
-verbose and print("[UniformScaling] Vector scaling")
-s_vector = nanoeigenpy.UniformScaling(2.0)
-vector = np.array([[1.0], [2.0], [3.0]])
-identity_3x3 = np.eye(3)
-scaled_identity = s_vector * identity_3x3
-scaled_vector = scaled_identity @ vector
-expected_vector = vector * 2.0
-assert isapprox(scaled_vector, expected_vector)
-
-# --- Translation ------------------------------------------------
-verbose and print("[Translation] Default constructor")
-t_default = nanoeigenpy.Translation()
-
-verbose and print("[Translation] 2D constructor with vector")
-t_2d = nanoeigenpy.Translation(np.array([1.0, 2.0]))
-assert isapprox(t_2d.x, 1.0)
-assert isapprox(t_2d.y, 2.0)
-
-verbose and print("[Translation] 3D constructor with vector")
-t_3d = nanoeigenpy.Translation(np.array([1.0, 2.0, 3.0]))
-assert isapprox(t_3d.x, 1.0)
-assert isapprox(t_3d.y, 2.0)
-assert isapprox(t_3d.z, 3.0)
-
-verbose and print("[Translation] Vector constructor")
-vector = np.array([1.5, 2.5, 3.5])
-t_vector = nanoeigenpy.Translation(vector)
-assert isapprox(t_vector.x, 1.5)
-assert isapprox(t_vector.y, 2.5)
-assert isapprox(t_vector.z, 3.5)
-
-verbose and print("[Translation] Copy constructor")
-t_copy = nanoeigenpy.Translation(t_3d)
-assert isapprox(t_copy.x, t_3d.x)
-assert isapprox(t_copy.y, t_3d.y)
-assert isapprox(t_copy.z, t_3d.z)
-
-verbose and print("[Translation] Property setters")
-t_test = nanoeigenpy.Translation(np.array([1.0, 2.0, 3.0]))
-t_test.x = 10.0
-t_test.y = 20.0
-t_test.z = 30.0
-assert isapprox(t_test.x, 10.0)
-assert isapprox(t_test.y, 20.0)
-assert isapprox(t_test.z, 30.0)
-
-verbose and print("[Translation] Vector and translation getters")
-vector_result = t_test.vector()
-translation_result = t_test.translation()
-assert isapprox(vector_result[0], 10.0)
-assert isapprox(translation_result[0], 10.0)
-
-verbose and print("[Translation] Inverse")
-t_original = nanoeigenpy.Translation(np.array([2.0, 3.0, 4.0]))
-t_inverse = t_original.inverse()
-assert isapprox(t_inverse.x, -2.0)
-assert isapprox(t_inverse.y, -3.0)
-assert isapprox(t_inverse.z, -4.0)
-
-verbose and print("[Translation] Concatenation")
-t1 = nanoeigenpy.Translation(np.array([1.0, 2.0, 3.0]))
-t2 = nanoeigenpy.Translation(np.array([4.0, 5.0, 6.0]))
-t_combined = t1 * t2
-assert isapprox(t_combined.x, 5.0)
-assert isapprox(t_combined.y, 7.0)
-assert isapprox(t_combined.z, 9.0)
-
-verbose and print("[Translation] isApprox")
-t_approx1 = nanoeigenpy.Translation(np.array([1.0, 2.0, 3.0]))
-t_approx2 = nanoeigenpy.Translation(np.array([1.0 + 1e-15, 2.0 + 1e-15, 3.0 + 1e-15]))
-t_approx3 = nanoeigenpy.Translation(np.array([1.1, 2.1, 3.1]))
-assert t_approx1.isApprox(t_approx2)
-assert t_approx1.isApprox(t_approx2, 1e-12)
-assert not t_approx1.isApprox(t_approx3)
-
-# --- JacobiRotation ---------------------------------------------------------------
-verbose and print("[JacobiRotation] Default constructor")
-j = nanoeigenpy.JacobiRotation()
-assert hasattr(j, "c")
-assert hasattr(j, "s")
-
-verbose and print("[JacobiRotation] Cosine-sine constructor")
-c_val = 0.8
-s_val = 0.6
-j = nanoeigenpy.JacobiRotation(c_val, s_val)
-assert isapprox(j.c, c_val)
-assert isapprox(j.s, s_val)
-
-verbose and print("[JacobiRotation] Property access")
-j.c = 0.8
-j.s = 0.6
-assert isapprox(j.c, 0.8)
-assert isapprox(j.s, 0.6)
-norm_squared = j.c**2 + j.s**2
-assert isapprox(norm_squared, 1.0, 1e-12)
-
-verbose and print("[JacobiRotation] Multiplication operator")
-j1 = nanoeigenpy.JacobiRotation(0.8, 0.6)
-j2 = nanoeigenpy.JacobiRotation(0.6, 0.8)
-j_mult = j1 * j2
-assert hasattr(j_mult, "c")
-assert hasattr(j_mult, "s")
-norm_mult = j_mult.c**2 + j_mult.s**2
-assert isapprox(norm_mult, 1.0, 1e-12)
-
-verbose and print("[JacobiRotation] Transpose")
-j = nanoeigenpy.JacobiRotation(0.8, 0.6)
-j_t = j.transpose()
-assert isapprox(j_t.c, j.c)
-assert isapprox(j_t.s, -j.s)
-
-verbose and print("[JacobiRotation] Adjoint")
-j = nanoeigenpy.JacobiRotation(0.8, 0.6)
-j_adj = j.adjoint()
-assert isapprox(j_adj.c, j.c)
-assert isapprox(j_adj.s, -j.s)
-
-verbose and print("[JacobiRotation] Identity property")
-j = nanoeigenpy.JacobiRotation(0.8, 0.6)
-j_t = j.transpose()
-identity = j * j_t
-assert isapprox(identity.c, 1.0, 1e-12)
-assert isapprox(identity.s, 0.0, 1e-12)
-
-verbose and print("[JacobiRotation] makeJacobi from scalars")
-j = nanoeigenpy.JacobiRotation()
-x, z = 4.0, 1.0
-y = 2.0
-result = j.makeJacobi(x, y, z)
-assert isinstance(result, bool)
-norm_after = j.c**2 + j.s**2
-assert isapprox(norm_after, 1.0, 1e-12)
-
-verbose and print("[JacobiRotation] makeJacobi from matrix")
-M = np.array([[4.0, 2.0, 1.0], [2.0, 3.0, 0.5], [1.0, 0.5, 1.0]])
-j = nanoeigenpy.JacobiRotation()
-result = j.makeJacobi(M, 0, 1)
-assert isinstance(result, bool)
-norm_matrix = j.c**2 + j.s**2
-assert isapprox(norm_matrix, 1.0, 1e-12)
-
-verbose and print("[JacobiRotation] makeGivens basic")
-j = nanoeigenpy.JacobiRotation()
-p_val = 3.0
-q_val = 4.0
-j.makeGivens(p_val, q_val)
-norm_givens = j.c**2 + j.s**2
-assert isapprox(norm_givens, 1.0, 1e-12)
-
-verbose and print("[JacobiRotation] makeGivens with r parameter")
-j = nanoeigenpy.JacobiRotation()
-p_val = 3.0
-q_val = 4.0
-r_container = np.array([0.0])
-j.makeGivens(p_val, q_val, r_container.ctypes.data)
-expected_r = np.sqrt(p_val**2 + q_val**2)
-
-verbose and print("[JacobiRotation] Edge cases")
-j_zero = nanoeigenpy.JacobiRotation(1.0, 0.0)
-assert isapprox(j_zero.c, 1.0)
-assert isapprox(j_zero.s, 0.0)
-
-j_90 = nanoeigenpy.JacobiRotation(0.0, 1.0)
-assert isapprox(j_90.c, 0.0)
-assert isapprox(j_90.s, 1.0)
-
-j = nanoeigenpy.JacobiRotation()
-j.makeGivens(5.0, 0.0)
-assert isapprox(abs(j.c), 1.0)
-assert isapprox(j.s, 0.0)
-
-j.makeGivens(0.0, 5.0)
-assert isapprox(j.c, 0.0)
-assert isapprox(abs(j.s), 1.0)
-
-verbose and print("[JacobiRotation] makeJacobi small off-diagonal")
-j = nanoeigenpy.JacobiRotation()
-result = j.makeJacobi(1.0, 1e-15, 2.0)
-assert isinstance(result, bool)
-if not result:
-    assert isapprox(j.c, 1.0)
+    distance = line_x_axis.distance(test_point)
+    squared_distance = line_x_axis.squaredDistance(test_point)
+    assert isapprox(distance, 0.0)
+    assert isapprox(squared_distance, 0.0)
+
+    off_line_point = np.array([1.0, 1.0])
+    distance_off = line_x_axis.distance(off_line_point)
+    squared_distance_off = line_x_axis.squaredDistance(off_line_point)
+    assert isapprox(distance_off, 1.0)
+    assert isapprox(squared_distance_off, 1.0)
+    assert isapprox(distance_off * distance_off, squared_distance_off)
+
+    verbose and print("[ParametrizedLine] Projection")
+    projection = line_x_axis.projection(off_line_point)
+    assert isapprox(projection, np.array([1.0, 0.0]))
+    assert isapprox(line_x_axis.distance(projection), 0.0)
+
+    verbose and print("[ParametrizedLine] Intersection with hyperplane")
+    line_diagonal = nanoeigenpy.ParametrizedLine(
+        np.array([0.0, 0.0]), np.array([1.0, 1.0])
+    )
+    h_vertical = nanoeigenpy.Hyperplane(np.array([1.0, 0.0]), -1.0)
+
+    intersection_param = line_diagonal.intersectionParameter(h_vertical)
+    assert isapprox(intersection_param, 1.0)
+
+    intersection_param_old = line_diagonal.intersection(h_vertical)
+    assert isapprox(intersection_param_old, intersection_param)
+
+    intersection_point = line_diagonal.intersectionPoint(h_vertical)
+    expected_intersection = np.array([1.0, 1.0])
+    assert isapprox(intersection_point, expected_intersection)
+    assert isapprox(h_vertical.absDistance(intersection_point), 0.0)
+
+    verbose and print("[ParametrizedLine] isApprox")
+    line1 = nanoeigenpy.ParametrizedLine(np.array([0.0, 0.0]), np.array([1.0, 0.0]))
+    line2 = nanoeigenpy.ParametrizedLine(np.array([0.0, 0.0]), np.array([1.0, 0.0]))
+    line3 = nanoeigenpy.ParametrizedLine(np.array([0.0, 0.0]), np.array([0.0, 1.0]))
+    assert line1.isApprox(line2)
+    assert line1.isApprox(line2, 1e-12)
+    assert not line1.isApprox(line3)
+
+    verbose and print("[ParametrizedLine] Parallel lines")
+    line_parallel1 = nanoeigenpy.ParametrizedLine(
+        np.array([0.0, 0.0]), np.array([1.0, 0.0])
+    )
+    line_parallel2 = nanoeigenpy.ParametrizedLine(
+        np.array([0.0, 1.0]), np.array([1.0, 0.0])
+    )
+    assert not line_parallel1.isApprox(line_parallel2)
+
+    test_points = [np.array([i, 0.0]) for i in range(5)]
+    distances = [line_parallel2.distance(p) for p in test_points]
+    for d in distances:
+        assert isapprox(d, 1.0)
+
+    verbose and print("[ParametrizedLine] Through two points")
+    p0 = np.array([0.0, 0.0])
+    p1 = np.array([1.0, 1.0])
+    line_through = nanoeigenpy.ParametrizedLine.Through(p0, p1)
+    direction = line_through.direction()
+    expected_dir = (p1 - p0) / np.linalg.norm(p1 - p0)
+    assert isapprox(line_through.origin(), p0)
+    assert isapprox(np.linalg.norm(direction), 1.0)
+    assert isapprox(direction, expected_dir)
+
+    # --- Rotation2D ------------------------------------------------
+    verbose and print("[Rotation2D] Default constructor")
+    r_default = nanoeigenpy.Rotation2D()
+    assert isapprox(r_default.angle, 0.0)
+
+    verbose and print("[Rotation2D] Angle constructor")
+    angle = np.pi / 4
+    r_angle = nanoeigenpy.Rotation2D(angle)
+    assert isapprox(r_angle.angle, angle)
+
+    verbose and print("[Rotation2D] Copy constructor")
+    r_copy = nanoeigenpy.Rotation2D(r_angle)
+    assert isapprox(r_copy.angle, r_angle.angle)
+    assert r_copy == r_angle
+
+    verbose and print("[Rotation2D] Matrix constructor")
+    theta = np.pi / 6
+    rotation_matrix = np.array(
+        [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
+    )
+    r_matrix = nanoeigenpy.Rotation2D(rotation_matrix)
+    assert isapprox(r_matrix.angle, theta)
+
+    verbose and print("[Rotation2D] Angle property")
+    r_prop = nanoeigenpy.Rotation2D()
+    new_angle = np.pi / 3
+    r_prop.angle = new_angle
+    assert isapprox(r_prop.angle, new_angle)
+
+    verbose and print("[Rotation2D] smallestPositiveAngle")
+    r_negative = nanoeigenpy.Rotation2D(-np.pi / 4)
+    positive_angle = r_negative.smallestPositiveAngle()
+    assert positive_angle >= 0.0
+    assert positive_angle < 2 * np.pi
+    assert isapprox(positive_angle, 7 * np.pi / 4)
+
+    verbose and print("[Rotation2D] smallestAngle")
+    r_large = nanoeigenpy.Rotation2D(3 * np.pi)
+    smallest_angle = r_large.smallestAngle()
+    assert smallest_angle >= -np.pi
+    assert smallest_angle <= np.pi
+    assert isapprox(smallest_angle, np.pi)
+
+    verbose and print("[Rotation2D] Identity")
+    r_identity = nanoeigenpy.Rotation2D.Identity()
+    assert isapprox(r_identity.angle, 0.0)
+
+    verbose and print("[Rotation2D] fromRotationMatrix")
+    r_from_matrix = nanoeigenpy.Rotation2D()
+    theta2 = np.pi / 2
+    matrix2 = np.array(
+        [[np.cos(theta2), -np.sin(theta2)], [np.sin(theta2), np.cos(theta2)]]
+    )
+    r_from_matrix.fromRotationMatrix(matrix2)
+    assert isapprox(r_from_matrix.angle, theta2)
+
+    verbose and print("[Rotation2D] Rotation composition")
+    r1 = nanoeigenpy.Rotation2D(np.pi / 4)
+    r2 = nanoeigenpy.Rotation2D(np.pi / 6)
+    r_composed = r1 * r2
+    expected_angle = np.pi / 4 + np.pi / 6
+    assert isapprox(r_composed.angle, expected_angle)
+
+    verbose and print("[Rotation2D] In-place multiplication")
+    r_inplace = nanoeigenpy.Rotation2D(np.pi / 4)
+    original_angle = r_inplace.angle
+    r_inplace *= nanoeigenpy.Rotation2D(np.pi / 6)
+    assert isapprox(r_inplace.angle, original_angle + np.pi / 6)
+
+    verbose and print("[Rotation2D] Vector rotation")
+    r_90 = nanoeigenpy.Rotation2D(np.pi / 2)
+    vec = np.array([1.0, 0.0])
+    rotated_vec = r_90 * vec
+    expected_vec = np.array([0.0, 1.0])
+    assert isapprox(rotated_vec, expected_vec)
+
+    vec2 = np.array([1.0, 1.0])
+    r_45 = nanoeigenpy.Rotation2D(np.pi / 4)
+    rotated_vec2 = r_45 * vec2
+    expected_vec2 = np.array([0.0, np.sqrt(2)])
+    assert isapprox(rotated_vec2, expected_vec2)
+
+    verbose and print("[Rotation2D] Equality operators")
+    r_eq1 = nanoeigenpy.Rotation2D(np.pi / 3)
+    r_eq2 = nanoeigenpy.Rotation2D(np.pi / 3)
+    r_eq3 = nanoeigenpy.Rotation2D(np.pi / 4)
+
+    assert r_eq1 == r_eq2
+    assert not (r_eq1 == r_eq3)
+    assert r_eq1 != r_eq3
+    assert not (r_eq1 != r_eq2)
+
+    verbose and print("[Rotation2D] Periodic angles")
+    r_period1 = nanoeigenpy.Rotation2D(0.0)  # noqa
+    r_period2 = nanoeigenpy.Rotation2D(2 * np.pi)  # noqa
+    verbose and print("[Rotation2D] isApprox")
+    r_approx1 = nanoeigenpy.Rotation2D(np.pi / 4)
+    r_approx2 = nanoeigenpy.Rotation2D(np.pi / 4 + 1e-15)
+    r_approx3 = nanoeigenpy.Rotation2D(np.pi / 3)
+
+    assert r_approx1.isApprox(r_approx2)
+    assert r_approx1.isApprox(r_approx2, 1e-12)
+    assert not r_approx1.isApprox(r_approx3)
+
+    verbose and print("[Rotation2D] slerp")
+    r_start = nanoeigenpy.Rotation2D(0.0)
+    r_end = nanoeigenpy.Rotation2D(np.pi / 2)
+    r_middle = r_start.slerp(0.5, r_end)
+    assert isapprox(r_middle.angle, np.pi / 4)
+
+    r_slerp_0 = r_start.slerp(0.0, r_end)
+    r_slerp_1 = r_start.slerp(1.0, r_end)
+    assert isapprox(r_slerp_0.angle, r_start.angle)
+    assert isapprox(r_slerp_1.angle, r_end.angle)
+
+    verbose and print("[Rotation2D] Inverse rotation")
+    try:
+        r_original = nanoeigenpy.Rotation2D(np.pi / 3)
+        r_inverse = r_original.inverse()
+        assert isapprox(r_inverse.angle, -np.pi / 3)
+
+        r_identity_test = r_original * r_inverse
+        assert isapprox(r_identity_test.angle, 0.0, 1e-12)
+    except AttributeError:
+        if verbose:
+            print("inverse() method not exposed or not available")
+
+    verbose and print("[Rotation2D] Matrix conversion")
+    try:
+        r_matrix_test = nanoeigenpy.Rotation2D(np.pi / 6)
+        matrix = r_matrix_test.matrix()
+
+        assert matrix.shape == (2, 2)
+        assert isapprox(matrix @ matrix.T, np.eye(2))
+        assert isapprox(np.linalg.det(matrix), 1.0)
+
+        expected_matrix = np.array(
+            [
+                [np.cos(np.pi / 6), -np.sin(np.pi / 6)],
+                [np.sin(np.pi / 6), np.cos(np.pi / 6)],
+            ]
+        )
+        assert isapprox(matrix, expected_matrix)
+
+    except AttributeError:
+        if verbose:
+            print("matrix() method not exposed or not available")
+
+    verbose and print("[Rotation2D] Angle normalization")
+    r_large_angle = nanoeigenpy.Rotation2D(3 * np.pi)
+    vec_test = np.array([1.0, 0.0])
+    rotated_large = r_large_angle * vec_test
+    expected_large = np.array([-1.0, 0.0])
+    assert isapprox(rotated_large, expected_large)
+
+    # --- UniformScaling ------------------------------------------------
+    verbose and print("[UniformScaling] Default constructor")
+    s_default = nanoeigenpy.UniformScaling()  # noqa
+
+    verbose and print("[UniformScaling] Factor constructor")
+    factor = 2.5
+    s_factor = nanoeigenpy.UniformScaling(factor)
+    assert isapprox(s_factor.factor(), factor)
+
+    verbose and print("[UniformScaling] Copy constructor")
+    s_copy = nanoeigenpy.UniformScaling(s_factor)
+    assert isapprox(s_copy.factor(), s_factor.factor())
+
+    verbose and print("[UniformScaling] Factor getter")
+    s_test = nanoeigenpy.UniformScaling(3.0)
+    assert isapprox(s_test.factor(), 3.0)
+
+    verbose and print("[UniformScaling] Inverse scaling")
+    s_original = nanoeigenpy.UniformScaling(4.0)
+    s_inverse = s_original.inverse()
+    assert isapprox(s_inverse.factor(), 1.0 / 4.0)
+
+    s_identity_test = s_original * s_inverse
+    assert isapprox(s_identity_test.factor(), 1.0)
+
+    verbose and print("[UniformScaling] Concatenation of scalings")
+    s1 = nanoeigenpy.UniformScaling(2.0)
+    s2 = nanoeigenpy.UniformScaling(3.0)
+    s_combined = s1 * s2
+    assert isapprox(s_combined.factor(), 6.0)
+
+    verbose and print("[UniformScaling] Multiplication with matrix")
+    s_scale = nanoeigenpy.UniformScaling(2.0)
+    matrix = np.array([[1.0, 2.0], [3.0, 4.0]])
+    scaled_matrix = s_scale * matrix
+    expected_matrix = matrix * 2.0
+    assert isapprox(scaled_matrix, expected_matrix)
+
+    identity = np.eye(3)
+    s_identity_scale = nanoeigenpy.UniformScaling(5.0)
+    scaled_identity = s_identity_scale * identity
+    expected_identity = identity * 5.0
+    assert isapprox(scaled_identity, expected_identity)
+
+    verbose and print("[UniformScaling] Multiplication with AngleAxis")
+    try:
+        angle_axis = nanoeigenpy.AngleAxis(np.pi / 4, np.array([0.0, 0.0, 1.0]))
+        s_with_rotation = nanoeigenpy.UniformScaling(2.0)
+        result_rotation = s_with_rotation * angle_axis
+
+        assert result_rotation.shape == (3, 3)
+        det = np.linalg.det(result_rotation)
+        assert isapprox(det, 2.0**3)
+
+    except (AttributeError, NameError):
+        if verbose:
+            print("AngleAxis class not available or not exposed")
+
+    verbose and print("[UniformScaling] Multiplication with Quaternion")
+    try:
+        quat = nanoeigenpy.Quaternion(1, 0, 0, 0)
+        s_with_quat = nanoeigenpy.UniformScaling(3.0)
+        result_quat = s_with_quat * quat
+
+        assert result_quat.shape == (3, 3)
+        expected_scaled_identity = np.eye(3) * 3.0
+        assert isapprox(result_quat, expected_scaled_identity)
+
+    except (AttributeError, NameError):
+        if verbose:
+            print("Quaternion class not available or not exposed")
+
+    verbose and print("[UniformScaling] Multiplication with Rotation2D")
+    try:
+        rotation_2d = nanoeigenpy.Rotation2D(np.pi / 4)
+        s_with_rot2d = nanoeigenpy.UniformScaling(2.0)
+        result_rot2d = s_with_rot2d * rotation_2d
+
+        assert result_rot2d.shape == (2, 2)
+        det_2d = np.linalg.det(result_rot2d)
+        assert isapprox(det_2d, 2.0**2)
+
+    except (AttributeError, NameError):
+        if verbose:
+            print("Rotation2D class not available or not exposed")
+
+    verbose and print("[UniformScaling] isApprox")
+    s_approx1 = nanoeigenpy.UniformScaling(2.0)
+    s_approx2 = nanoeigenpy.UniformScaling(2.0 + 1e-15)
+    s_approx3 = nanoeigenpy.UniformScaling(3.0)
+
+    assert s_approx1.isApprox(s_approx2)
+    assert s_approx1.isApprox(s_approx2, 1e-12)
+    assert not s_approx1.isApprox(s_approx3)
+
+    verbose and print("[UniformScaling] Edge cases")
+    s_zero = nanoeigenpy.UniformScaling(0.0)
+    assert isapprox(s_zero.factor(), 0.0)
+    try:
+        s_zero_inverse = s_zero.inverse()
+        if verbose:
+            print("Zero scaling inverse:", s_zero_inverse.factor())
+    except Exception as e:
+        if verbose:
+            print("Zero scaling inverse threw exception (expected):", type(e).__name__)
+
+    s_negative = nanoeigenpy.UniformScaling(-2.0)
+    assert isapprox(s_negative.factor(), -2.0)
+    s_negative_inverse = s_negative.inverse()
+    assert isapprox(s_negative_inverse.factor(), -0.5)
+
+    s_small = nanoeigenpy.UniformScaling(1e-10)
+    s_small_inverse = s_small.inverse()
+    assert isapprox(s_small_inverse.factor(), 1e10)
+
+    verbose and print("[UniformScaling] Chain operations")
+    s_chain1 = nanoeigenpy.UniformScaling(2.0)
+    s_chain2 = nanoeigenpy.UniformScaling(3.0)
+    s_chain3 = nanoeigenpy.UniformScaling(4.0)
+
+    left_assoc = (s_chain1 * s_chain2) * s_chain3
+    right_assoc = s_chain1 * (s_chain2 * s_chain3)
+    assert isapprox(left_assoc.factor(), right_assoc.factor())
+    assert isapprox(left_assoc.factor(), 24.0)
+
+    verbose and print("[UniformScaling] Vector scaling")
+    s_vector = nanoeigenpy.UniformScaling(2.0)
+    vector = np.array([[1.0], [2.0], [3.0]])
+    identity_3x3 = np.eye(3)
+    scaled_identity = s_vector * identity_3x3
+    scaled_vector = scaled_identity @ vector
+    expected_vector = vector * 2.0
+    assert isapprox(scaled_vector, expected_vector)
+
+    # --- Translation ------------------------------------------------
+    verbose and print("[Translation] Default constructor")
+    t_default = nanoeigenpy.Translation()  # noqa
+
+    verbose and print("[Translation] 2D constructor with vector")
+    t_2d = nanoeigenpy.Translation(np.array([1.0, 2.0]))
+    assert isapprox(t_2d.x, 1.0)
+    assert isapprox(t_2d.y, 2.0)
+
+    verbose and print("[Translation] 3D constructor with vector")
+    t_3d = nanoeigenpy.Translation(np.array([1.0, 2.0, 3.0]))
+    assert isapprox(t_3d.x, 1.0)
+    assert isapprox(t_3d.y, 2.0)
+    assert isapprox(t_3d.z, 3.0)
+
+    verbose and print("[Translation] Vector constructor")
+    vector = np.array([1.5, 2.5, 3.5])
+    t_vector = nanoeigenpy.Translation(vector)
+    assert isapprox(t_vector.x, 1.5)
+    assert isapprox(t_vector.y, 2.5)
+    assert isapprox(t_vector.z, 3.5)
+
+    verbose and print("[Translation] Copy constructor")
+    t_copy = nanoeigenpy.Translation(t_3d)
+    assert isapprox(t_copy.x, t_3d.x)
+    assert isapprox(t_copy.y, t_3d.y)
+    assert isapprox(t_copy.z, t_3d.z)
+
+    verbose and print("[Translation] Property setters")
+    t_test = nanoeigenpy.Translation(np.array([1.0, 2.0, 3.0]))
+    t_test.x = 10.0
+    t_test.y = 20.0
+    t_test.z = 30.0
+    assert isapprox(t_test.x, 10.0)
+    assert isapprox(t_test.y, 20.0)
+    assert isapprox(t_test.z, 30.0)
+
+    verbose and print("[Translation] Vector and translation getters")
+    vector_result = t_test.vector()
+    translation_result = t_test.translation()
+    assert isapprox(vector_result[0], 10.0)
+    assert isapprox(translation_result[0], 10.0)
+
+    verbose and print("[Translation] Inverse")
+    t_original = nanoeigenpy.Translation(np.array([2.0, 3.0, 4.0]))
+    t_inverse = t_original.inverse()
+    assert isapprox(t_inverse.x, -2.0)
+    assert isapprox(t_inverse.y, -3.0)
+    assert isapprox(t_inverse.z, -4.0)
+
+    verbose and print("[Translation] Concatenation")
+    t1 = nanoeigenpy.Translation(np.array([1.0, 2.0, 3.0]))
+    t2 = nanoeigenpy.Translation(np.array([4.0, 5.0, 6.0]))
+    t_combined = t1 * t2
+    assert isapprox(t_combined.x, 5.0)
+    assert isapprox(t_combined.y, 7.0)
+    assert isapprox(t_combined.z, 9.0)
+
+    verbose and print("[Translation] isApprox")
+    t_approx1 = nanoeigenpy.Translation(np.array([1.0, 2.0, 3.0]))
+    t_approx2 = nanoeigenpy.Translation(
+        np.array([1.0 + 1e-15, 2.0 + 1e-15, 3.0 + 1e-15])
+    )
+    t_approx3 = nanoeigenpy.Translation(np.array([1.1, 2.1, 3.1]))
+    assert t_approx1.isApprox(t_approx2)
+    assert t_approx1.isApprox(t_approx2, 1e-12)
+    assert not t_approx1.isApprox(t_approx3)
+
+    # --- JacobiRotation ---------------------------------------------------------------
+    verbose and print("[JacobiRotation] Default constructor")
+    j = nanoeigenpy.JacobiRotation()
+    assert hasattr(j, "c")
+    assert hasattr(j, "s")
+
+    verbose and print("[JacobiRotation] Cosine-sine constructor")
+    c_val = 0.8
+    s_val = 0.6
+    j = nanoeigenpy.JacobiRotation(c_val, s_val)
+    assert isapprox(j.c, c_val)
+    assert isapprox(j.s, s_val)
+
+    verbose and print("[JacobiRotation] Property access")
+    j.c = 0.8
+    j.s = 0.6
+    assert isapprox(j.c, 0.8)
+    assert isapprox(j.s, 0.6)
+    norm_squared = j.c**2 + j.s**2
+    assert isapprox(norm_squared, 1.0, 1e-12)
+
+    verbose and print("[JacobiRotation] Multiplication operator")
+    j1 = nanoeigenpy.JacobiRotation(0.8, 0.6)
+    j2 = nanoeigenpy.JacobiRotation(0.6, 0.8)
+    j_mult = j1 * j2
+    assert hasattr(j_mult, "c")
+    assert hasattr(j_mult, "s")
+    norm_mult = j_mult.c**2 + j_mult.s**2
+    assert isapprox(norm_mult, 1.0, 1e-12)
+
+    verbose and print("[JacobiRotation] Transpose")
+    j = nanoeigenpy.JacobiRotation(0.8, 0.6)
+    j_t = j.transpose()
+    assert isapprox(j_t.c, j.c)
+    assert isapprox(j_t.s, -j.s)
+
+    verbose and print("[JacobiRotation] Adjoint")
+    j = nanoeigenpy.JacobiRotation(0.8, 0.6)
+    j_adj = j.adjoint()
+    assert isapprox(j_adj.c, j.c)
+    assert isapprox(j_adj.s, -j.s)
+
+    verbose and print("[JacobiRotation] Identity property")
+    j = nanoeigenpy.JacobiRotation(0.8, 0.6)
+    j_t = j.transpose()
+    identity = j * j_t
+    assert isapprox(identity.c, 1.0, 1e-12)
+    assert isapprox(identity.s, 0.0, 1e-12)
+
+    verbose and print("[JacobiRotation] makeJacobi from scalars")
+    j = nanoeigenpy.JacobiRotation()
+    x, z = 4.0, 1.0
+    y = 2.0
+    result = j.makeJacobi(x, y, z)
+    assert isinstance(result, bool)
+    norm_after = j.c**2 + j.s**2
+    assert isapprox(norm_after, 1.0, 1e-12)
+
+    verbose and print("[JacobiRotation] makeJacobi from matrix")
+    M = np.array([[4.0, 2.0, 1.0], [2.0, 3.0, 0.5], [1.0, 0.5, 1.0]])
+    j = nanoeigenpy.JacobiRotation()
+    result = j.makeJacobi(M, 0, 1)
+    assert isinstance(result, bool)
+    norm_matrix = j.c**2 + j.s**2
+    assert isapprox(norm_matrix, 1.0, 1e-12)
+
+    verbose and print("[JacobiRotation] makeGivens basic")
+    j = nanoeigenpy.JacobiRotation()
+    p_val = 3.0
+    q_val = 4.0
+    j.makeGivens(p_val, q_val)
+    norm_givens = j.c**2 + j.s**2
+    assert isapprox(norm_givens, 1.0, 1e-12)
+
+    verbose and print("[JacobiRotation] makeGivens with r parameter")
+    j = nanoeigenpy.JacobiRotation()
+    p_val = 3.0
+    q_val = 4.0
+    r_container = np.array([0.0])
+    j.makeGivens(p_val, q_val, r_container.ctypes.data)
+    expected_r = np.sqrt(p_val**2 + q_val**2)  # noqa
+
+    verbose and print("[JacobiRotation] Edge cases")
+    j_zero = nanoeigenpy.JacobiRotation(1.0, 0.0)
+    assert isapprox(j_zero.c, 1.0)
+    assert isapprox(j_zero.s, 0.0)
+
+    j_90 = nanoeigenpy.JacobiRotation(0.0, 1.0)
+    assert isapprox(j_90.c, 0.0)
+    assert isapprox(j_90.s, 1.0)
+
+    j = nanoeigenpy.JacobiRotation()
+    j.makeGivens(5.0, 0.0)
+    assert isapprox(abs(j.c), 1.0)
     assert isapprox(j.s, 0.0)
+
+    j.makeGivens(0.0, 5.0)
+    assert isapprox(j.c, 0.0)
+    assert isapprox(abs(j.s), 1.0)
+
+    verbose and print("[JacobiRotation] makeJacobi small off-diagonal")
+    j = nanoeigenpy.JacobiRotation()
+    result = j.makeJacobi(1.0, 1e-15, 2.0)
+    assert isinstance(result, bool)
+    if not result:
+        assert isapprox(j.c, 1.0)
+        assert isapprox(j.s, 0.0)
diff --git a/tests/test_hessenberg_decomposition.py b/tests/test_hessenberg_decomposition.py
index 9a098027..a82d8d80 100644
--- a/tests/test_hessenberg_decomposition.py
+++ b/tests/test_hessenberg_decomposition.py
@@ -1,70 +1,72 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
-
-hess = nanoeigenpy.HessenbergDecomposition(A)
-
-Q = hess.matrixQ()
-H = hess.matrixH()
-
-if np.iscomplexobj(A):
-    A_reconstructed = Q @ H @ Q.conj().T
-else:
-    A_reconstructed = Q @ H @ Q.T
-assert nanoeigenpy.is_approx(A, A_reconstructed)
-
-for row in range(2, dim):
-    for col in range(row - 1):
-        assert abs(H[row, col]) < 1e-12
-
-if np.iscomplexobj(Q):
-    QQ_conj = Q @ Q.conj().T
-else:
-    QQ_conj = Q @ Q.T
-assert nanoeigenpy.is_approx(QQ_conj, np.eye(dim))
-
-A_test = rng.random((dim, dim))
-hess1 = nanoeigenpy.HessenbergDecomposition(dim)
-hess1.compute(A_test)
-hess2 = nanoeigenpy.HessenbergDecomposition(A_test)
-
-H1 = hess1.matrixH()
-H2 = hess2.matrixH()
-Q1 = hess1.matrixQ()
-Q2 = hess2.matrixQ()
-
-assert nanoeigenpy.is_approx(H1, H2)
-assert nanoeigenpy.is_approx(Q1, Q2)
-
-hCoeffs = hess.householderCoefficients()
-packed = hess.packedMatrix()
-
-assert hCoeffs.shape == (dim - 1,)
-assert packed.shape == (dim, dim)
-
-for i in range(dim):
-    for j in range(i - 1, dim):
-        if j >= 0:
-            assert abs(H[i, j] - packed[i, j]) < 1e-12
-
-hess_default = nanoeigenpy.HessenbergDecomposition(dim)
-hess_matrix = nanoeigenpy.HessenbergDecomposition(A)
-
-hess1_id = nanoeigenpy.HessenbergDecomposition(dim)
-hess2_id = nanoeigenpy.HessenbergDecomposition(dim)
-id1 = hess1_id.id()
-id2 = hess2_id.id()
-assert id1 != id2
-assert id1 == hess1_id.id()
-assert id2 == hess2_id.id()
-
-hess3_id = nanoeigenpy.HessenbergDecomposition(A)
-hess4_id = nanoeigenpy.HessenbergDecomposition(A)
-id3 = hess3_id.id()
-id4 = hess4_id.id()
-assert id3 != id4
-assert id3 == hess3_id.id()
-assert id4 == hess4_id.id()
+
+def test_hessenberg_decomposition():
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
+
+    hess = nanoeigenpy.HessenbergDecomposition(A)
+
+    Q = hess.matrixQ()
+    H = hess.matrixH()
+
+    if np.iscomplexobj(A):
+        A_reconstructed = Q @ H @ Q.conj().T
+    else:
+        A_reconstructed = Q @ H @ Q.T
+    assert nanoeigenpy.is_approx(A, A_reconstructed)
+
+    for row in range(2, dim):
+        for col in range(row - 1):
+            assert abs(H[row, col]) < 1e-12
+
+    if np.iscomplexobj(Q):
+        QQ_conj = Q @ Q.conj().T
+    else:
+        QQ_conj = Q @ Q.T
+    assert nanoeigenpy.is_approx(QQ_conj, np.eye(dim))
+
+    A_test = rng.random((dim, dim))
+    hess1 = nanoeigenpy.HessenbergDecomposition(dim)
+    hess1.compute(A_test)
+    hess2 = nanoeigenpy.HessenbergDecomposition(A_test)
+
+    H1 = hess1.matrixH()
+    H2 = hess2.matrixH()
+    Q1 = hess1.matrixQ()
+    Q2 = hess2.matrixQ()
+
+    assert nanoeigenpy.is_approx(H1, H2)
+    assert nanoeigenpy.is_approx(Q1, Q2)
+
+    hCoeffs = hess.householderCoefficients()
+    packed = hess.packedMatrix()
+
+    assert hCoeffs.shape == (dim - 1,)
+    assert packed.shape == (dim, dim)
+
+    for i in range(dim):
+        for j in range(i - 1, dim):
+            if j >= 0:
+                assert abs(H[i, j] - packed[i, j]) < 1e-12
+
+    hess_default = nanoeigenpy.HessenbergDecomposition(dim)  # noqa
+    hess_matrix = nanoeigenpy.HessenbergDecomposition(A)  # noqa
+
+    hess1_id = nanoeigenpy.HessenbergDecomposition(dim)
+    hess2_id = nanoeigenpy.HessenbergDecomposition(dim)
+    id1 = hess1_id.id()
+    id2 = hess2_id.id()
+    assert id1 != id2
+    assert id1 == hess1_id.id()
+    assert id2 == hess2_id.id()
+
+    hess3_id = nanoeigenpy.HessenbergDecomposition(A)
+    hess4_id = nanoeigenpy.HessenbergDecomposition(A)
+    id3 = hess3_id.id()
+    id4 = hess4_id.id()
+    assert id3 != id4
+    assert id3 == hess3_id.id()
+    assert id4 == hess4_id.id()
diff --git a/tests/test_import_extension.py b/tests/test_import_extension.py
new file mode 100644
index 00000000..56b01bb2
--- /dev/null
+++ b/tests/test_import_extension.py
@@ -0,0 +1,5 @@
+import nanoeigenpy
+
+
+def test_import_nanoeigenpy():
+    assert hasattr(nanoeigenpy, "EigenSolver")
diff --git a/tests/test_incomplete_cholesky.py b/tests/test_incomplete_cholesky.py
index 01f4f11f..3bd74b04 100644
--- a/tests/test_incomplete_cholesky.py
+++ b/tests/test_incomplete_cholesky.py
@@ -1,71 +1,72 @@
+import nanoeigenpy
 import numpy as np
 from scipy.sparse import csc_matrix
-import nanoeigenpy
 
 
-dim = 100
-rng = np.random.default_rng()
-
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5 + np.diag(5.0 + rng.random(dim))
-A = csc_matrix(A)
-
-ichol = nanoeigenpy.solvers.IncompleteCholesky(A)
-assert ichol.info() == nanoeigenpy.ComputationInfo.Success
-assert ichol.rows() == dim
-assert ichol.cols() == dim
-
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = ichol.solve(B)
-assert isinstance(X_est, np.ndarray)
-residual = np.linalg.norm(B - A.dot(X_est)) / np.linalg.norm(B)
-assert residual < 0.1
-
-x = rng.random(dim)
-b = A.dot(x)
-x_est = ichol.solve(b)
-assert isinstance(x_est, np.ndarray)
-residual = np.linalg.norm(b - A.dot(x_est)) / np.linalg.norm(b)
-assert residual < 0.1
-
-X_sparse = csc_matrix(rng.random((dim, 10)))
-B_sparse = A.dot(X_sparse).tocsc()
-if not B_sparse.has_sorted_indices:
-    B_sparse.sort_indices()
-X_est_sparse = ichol.solve(B_sparse)
-assert isinstance(X_est_sparse, csc_matrix)
-
-ichol.analyzePattern(A)
-ichol.factorize(A)
-ichol.compute(A)
-assert ichol.info() == nanoeigenpy.ComputationInfo.Success
-
-L = ichol.matrixL()
-S_diag = ichol.scalingS()
-perm = ichol.permutationP()
-P = perm.toDenseMatrix()
-
-assert isinstance(L, csc_matrix)
-assert isinstance(S_diag, np.ndarray)
-assert L.shape == (dim, dim)
-assert S_diag.shape == (dim,)
-
-L_dense = L.toarray()
-upper_part = np.triu(L_dense, k=1)
-assert np.allclose(upper_part, 0, atol=1e-12)
-
-assert np.all(S_diag > 0)
-
-S = csc_matrix((S_diag, (range(dim), range(dim))), shape=(dim, dim))
-
-PA = P @ A
-PAP = PA @ P.T
-SPAP = S @ PAP
-SPAPS = SPAP @ S
-
-LLT = L @ L.T
-
-diff = SPAPS - LLT
-relative_error = np.linalg.norm(diff.data) / np.linalg.norm(SPAPS.data)
-assert relative_error < 0.5
+def test_incomplete_cholesky():
+    dim = 100
+    rng = np.random.default_rng()
+
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(5.0 + rng.random(dim))
+    A = csc_matrix(A)
+
+    ichol = nanoeigenpy.solvers.IncompleteCholesky(A)
+    assert ichol.info() == nanoeigenpy.ComputationInfo.Success
+    assert ichol.rows() == dim
+    assert ichol.cols() == dim
+
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = ichol.solve(B)
+    assert isinstance(X_est, np.ndarray)
+    residual = np.linalg.norm(B - A.dot(X_est)) / np.linalg.norm(B)
+    assert residual < 0.1
+
+    x = rng.random(dim)
+    b = A.dot(x)
+    x_est = ichol.solve(b)
+    assert isinstance(x_est, np.ndarray)
+    residual = np.linalg.norm(b - A.dot(x_est)) / np.linalg.norm(b)
+    assert residual < 0.1
+
+    X_sparse = csc_matrix(rng.random((dim, 10)))
+    B_sparse = A.dot(X_sparse).tocsc()
+    if not B_sparse.has_sorted_indices:
+        B_sparse.sort_indices()
+    X_est_sparse = ichol.solve(B_sparse)
+    assert isinstance(X_est_sparse, csc_matrix)
+
+    ichol.analyzePattern(A)
+    ichol.factorize(A)
+    ichol.compute(A)
+    assert ichol.info() == nanoeigenpy.ComputationInfo.Success
+
+    L = ichol.matrixL()
+    S_diag = ichol.scalingS()
+    perm = ichol.permutationP()
+    P = perm.toDenseMatrix()
+
+    assert isinstance(L, csc_matrix)
+    assert isinstance(S_diag, np.ndarray)
+    assert L.shape == (dim, dim)
+    assert S_diag.shape == (dim,)
+
+    L_dense = L.toarray()
+    upper_part = np.triu(L_dense, k=1)
+    assert np.allclose(upper_part, 0, atol=1e-12)
+
+    assert np.all(S_diag > 0)
+
+    S = csc_matrix((S_diag, (range(dim), range(dim))), shape=(dim, dim))
+
+    PA = P @ A
+    PAP = PA @ P.T
+    SPAP = S @ PAP
+    SPAPS = SPAP @ S
+
+    LLT = L @ L.T
+
+    diff = SPAPS - LLT
+    relative_error = np.linalg.norm(diff.data) / np.linalg.norm(SPAPS.data)
+    assert relative_error < 0.5
diff --git a/tests/test_incomplete_lut.py b/tests/test_incomplete_lut.py
index 4405e937..16afeb22 100644
--- a/tests/test_incomplete_lut.py
+++ b/tests/test_incomplete_lut.py
@@ -1,49 +1,51 @@
+import nanoeigenpy
 import numpy as np
 from scipy.sparse import csc_matrix
-import nanoeigenpy
 
-dim = 100
-rng = np.random.default_rng()
-
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5 + np.diag(5.0 + rng.random(dim))
-A = csc_matrix(A)
-
-ilut = nanoeigenpy.solvers.IncompleteLUT(A)
-assert ilut.info() == nanoeigenpy.ComputationInfo.Success
-assert ilut.rows() == dim
-assert ilut.cols() == dim
-
-X = rng.random((dim, 100))
-B = A.dot(X)
-X_est = ilut.solve(B)
-assert isinstance(X_est, np.ndarray)
-residual = np.linalg.norm(B - A.dot(X_est)) / np.linalg.norm(B)
-assert residual < 0.1
-
-x = rng.random(dim)
-b = A.dot(x)
-x_est = ilut.solve(b)
-assert isinstance(x_est, np.ndarray)
-residual = np.linalg.norm(b - A.dot(x_est)) / np.linalg.norm(b)
-assert residual < 0.1
-
-X_sparse = csc_matrix(rng.random((dim, 10)))
-B_sparse = A.dot(X_sparse).tocsc()
-if not B_sparse.has_sorted_indices:
-    B_sparse.sort_indices()
-X_est_sparse = ilut.solve(B_sparse)
-assert isinstance(X_est_sparse, csc_matrix)
-
-ilut.analyzePattern(A)
-ilut.factorize(A)
-assert ilut.info() == nanoeigenpy.ComputationInfo.Success
-
-ilut_params = nanoeigenpy.solvers.IncompleteLUT(A, 1e-4, 15)
-assert ilut_params.info() == nanoeigenpy.ComputationInfo.Success
-
-ilut_set = nanoeigenpy.solvers.IncompleteLUT()
-ilut_set.setDroptol(1e-3)
-ilut_set.setFillfactor(20)
-ilut_set.compute(A)
-assert ilut_set.info() == nanoeigenpy.ComputationInfo.Success
+
+def test_incomplete_lut():
+    dim = 100
+    rng = np.random.default_rng()
+
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(5.0 + rng.random(dim))
+    A = csc_matrix(A)
+
+    ilut = nanoeigenpy.solvers.IncompleteLUT(A)
+    assert ilut.info() == nanoeigenpy.ComputationInfo.Success
+    assert ilut.rows() == dim
+    assert ilut.cols() == dim
+
+    X = rng.random((dim, 100))
+    B = A.dot(X)
+    X_est = ilut.solve(B)
+    assert isinstance(X_est, np.ndarray)
+    residual = np.linalg.norm(B - A.dot(X_est)) / np.linalg.norm(B)
+    assert residual < 0.1
+
+    x = rng.random(dim)
+    b = A.dot(x)
+    x_est = ilut.solve(b)
+    assert isinstance(x_est, np.ndarray)
+    residual = np.linalg.norm(b - A.dot(x_est)) / np.linalg.norm(b)
+    assert residual < 0.1
+
+    X_sparse = csc_matrix(rng.random((dim, 10)))
+    B_sparse = A.dot(X_sparse).tocsc()
+    if not B_sparse.has_sorted_indices:
+        B_sparse.sort_indices()
+    X_est_sparse = ilut.solve(B_sparse)
+    assert isinstance(X_est_sparse, csc_matrix)
+
+    ilut.analyzePattern(A)
+    ilut.factorize(A)
+    assert ilut.info() == nanoeigenpy.ComputationInfo.Success
+
+    ilut_params = nanoeigenpy.solvers.IncompleteLUT(A, 1e-4, 15)
+    assert ilut_params.info() == nanoeigenpy.ComputationInfo.Success
+
+    ilut_set = nanoeigenpy.solvers.IncompleteLUT()
+    ilut_set.setDroptol(1e-3)
+    ilut_set.setFillfactor(20)
+    ilut_set.compute(A)
+    assert ilut_set.info() == nanoeigenpy.ComputationInfo.Success
diff --git a/tests/test_jacobi_svd.py b/tests/test_jacobi_svd.py
index 8d0f0666..dd14c39e 100644
--- a/tests/test_jacobi_svd.py
+++ b/tests/test_jacobi_svd.py
@@ -111,9 +111,3 @@ def test_jacobi(cls, options):
         S_matrix = np.diag(S)
         A_reconstructed = U @ S_matrix @ V.T
         assert nanoeigenpy.is_approx(A, A_reconstructed)
-
-
-if __name__ == "__main__":
-    import sys
-
-    sys.exit(pytest.main(sys.argv))
diff --git a/tests/test_ldlt.py b/tests/test_ldlt.py
index d8448636..9a2484bf 100644
--- a/tests/test_ldlt.py
+++ b/tests/test_ldlt.py
@@ -1,96 +1,98 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-
-A_neg = -np.eye(dim)
-ldlt_neg = nanoeigenpy.LDLT(A_neg)
-assert ldlt_neg.isNegative()
-assert not ldlt_neg.isPositive()
-
-A_pos = np.eye(dim)
-ldlt_pos = nanoeigenpy.LDLT(A_pos)
-assert ldlt_pos.isPositive()
-assert not ldlt_pos.isNegative()
-
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
-
-ldlt = nanoeigenpy.LDLT(A)
-assert ldlt.info() == nanoeigenpy.ComputationInfo.Success
-
-L = ldlt.matrixL()
-D = ldlt.vectorD()
-P = ldlt.transpositionsP()
-assert nanoeigenpy.is_approx(
-    np.transpose(P).dot(L.dot(np.diag(D).dot(np.transpose(L).dot(P)))), A
-)
-
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = ldlt.solve(B)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
-
-x = rng.random(dim)
-b = A.dot(x)
-x_est = ldlt.solve(b)
-assert nanoeigenpy.is_approx(x, x_est)
-assert nanoeigenpy.is_approx(A.dot(x_est), b)
-
-A_reconstructed = ldlt.reconstructedMatrix()
-assert nanoeigenpy.is_approx(A_reconstructed, A)
-
-adjoint = ldlt.adjoint()
-assert adjoint is ldlt
-
-A_cond = np.eye(dim)
-ldlt_cond = nanoeigenpy.LDLT(A_cond)
-estimated_r_cond_num = ldlt_cond.rcond()
-assert abs(estimated_r_cond_num - 1) <= 1e-9
-
-ldlt_compute = ldlt.compute(A)
-
-LDLT = ldlt.matrixLDLT()
-LDLT_lower_without_diag = np.tril(LDLT, k=-1)
-L_lower_without_diag = np.tril(L, k=-1)
-assert nanoeigenpy.is_approx(LDLT_lower_without_diag, L_lower_without_diag)
-
-A_upper_without_diag = np.triu(A, k=1)
-LLT_upper_without_diag = np.triu(LDLT, k=1)
-assert nanoeigenpy.is_approx(A_upper_without_diag, LLT_upper_without_diag)
-
-LDLT_diag = np.diagonal(LDLT)
-assert nanoeigenpy.is_approx(LDLT_diag, D)
-
-sigma = 3
-w = np.ones(dim)
-ldlt.rankUpdate(w, sigma)
-L = ldlt.matrixL()
-D = ldlt.vectorD()
-P = ldlt.transpositionsP()
-A_updated = np.transpose(P).dot(L.dot(np.diag(D).dot(np.transpose(L).dot(P))))
-assert nanoeigenpy.is_approx(A_updated, A + sigma * w * np.transpose(w))
-
-ldlt1 = nanoeigenpy.LDLT()
-ldlt2 = nanoeigenpy.LDLT()
-
-id1 = ldlt1.id()
-id2 = ldlt2.id()
-
-assert id1 != id2
-assert id1 == ldlt1.id()
-assert id2 == ldlt2.id()
-
-dim_constructor = 3
-
-ldlt3 = nanoeigenpy.LDLT(dim_constructor)
-ldlt4 = nanoeigenpy.LDLT(dim_constructor)
-
-id3 = ldlt3.id()
-id4 = ldlt4.id()
-
-assert id3 != id4
-assert id3 == ldlt3.id()
-assert id4 == ldlt4.id()
+
+def test_ldlt():
+    dim = 100
+    rng = np.random.default_rng()
+
+    A_neg = -np.eye(dim)
+    ldlt_neg = nanoeigenpy.LDLT(A_neg)
+    assert ldlt_neg.isNegative()
+    assert not ldlt_neg.isPositive()
+
+    A_pos = np.eye(dim)
+    ldlt_pos = nanoeigenpy.LDLT(A_pos)
+    assert ldlt_pos.isPositive()
+    assert not ldlt_pos.isNegative()
+
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
+
+    ldlt = nanoeigenpy.LDLT(A)
+    assert ldlt.info() == nanoeigenpy.ComputationInfo.Success
+
+    L = ldlt.matrixL()
+    D = ldlt.vectorD()
+    P = ldlt.transpositionsP()
+    assert nanoeigenpy.is_approx(
+        np.transpose(P).dot(L.dot(np.diag(D).dot(np.transpose(L).dot(P)))), A
+    )
+
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = ldlt.solve(B)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
+
+    x = rng.random(dim)
+    b = A.dot(x)
+    x_est = ldlt.solve(b)
+    assert nanoeigenpy.is_approx(x, x_est)
+    assert nanoeigenpy.is_approx(A.dot(x_est), b)
+
+    A_reconstructed = ldlt.reconstructedMatrix()
+    assert nanoeigenpy.is_approx(A_reconstructed, A)
+
+    adjoint = ldlt.adjoint()
+    assert adjoint is ldlt
+
+    A_cond = np.eye(dim)
+    ldlt_cond = nanoeigenpy.LDLT(A_cond)
+    estimated_r_cond_num = ldlt_cond.rcond()
+    assert abs(estimated_r_cond_num - 1) <= 1e-9
+
+    ldlt_compute = ldlt.compute(A)  # noqa
+
+    LDLT = ldlt.matrixLDLT()
+    LDLT_lower_without_diag = np.tril(LDLT, k=-1)
+    L_lower_without_diag = np.tril(L, k=-1)
+    assert nanoeigenpy.is_approx(LDLT_lower_without_diag, L_lower_without_diag)
+
+    A_upper_without_diag = np.triu(A, k=1)
+    LLT_upper_without_diag = np.triu(LDLT, k=1)
+    assert nanoeigenpy.is_approx(A_upper_without_diag, LLT_upper_without_diag)
+
+    LDLT_diag = np.diagonal(LDLT)
+    assert nanoeigenpy.is_approx(LDLT_diag, D)
+
+    sigma = 3
+    w = np.ones(dim)
+    ldlt.rankUpdate(w, sigma)
+    L = ldlt.matrixL()
+    D = ldlt.vectorD()
+    P = ldlt.transpositionsP()
+    A_updated = np.transpose(P).dot(L.dot(np.diag(D).dot(np.transpose(L).dot(P))))
+    assert nanoeigenpy.is_approx(A_updated, A + sigma * w * np.transpose(w))
+
+    ldlt1 = nanoeigenpy.LDLT()
+    ldlt2 = nanoeigenpy.LDLT()
+
+    id1 = ldlt1.id()
+    id2 = ldlt2.id()
+
+    assert id1 != id2
+    assert id1 == ldlt1.id()
+    assert id2 == ldlt2.id()
+
+    dim_constructor = 3
+
+    ldlt3 = nanoeigenpy.LDLT(dim_constructor)
+    ldlt4 = nanoeigenpy.LDLT(dim_constructor)
+
+    id3 = ldlt3.id()
+    id4 = ldlt4.id()
+
+    assert id3 != id4
+    assert id3 == ldlt3.id()
+    assert id4 == ldlt4.id()
diff --git a/tests/test_llt.py b/tests/test_llt.py
index f2b953d5..dd0e6c96 100644
--- a/tests/test_llt.py
+++ b/tests/test_llt.py
@@ -1,80 +1,82 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
 
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
+def test_llt():
+    dim = 100
+    rng = np.random.default_rng()
 
-llt = nanoeigenpy.LLT(A)
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
 
-assert llt.info() == nanoeigenpy.ComputationInfo.Success
+    llt = nanoeigenpy.LLT(A)
 
-L = llt.matrixL()
-assert nanoeigenpy.is_approx(L.dot(np.transpose(L)), A)
+    assert llt.info() == nanoeigenpy.ComputationInfo.Success
 
-U = llt.matrixU()
-LU = L @ U
-assert nanoeigenpy.is_approx(LU, A)
+    L = llt.matrixL()
+    assert nanoeigenpy.is_approx(L.dot(np.transpose(L)), A)
 
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = llt.solve(B)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
+    U = llt.matrixU()
+    LU = L @ U
+    assert nanoeigenpy.is_approx(LU, A)
 
-x = rng.random(dim)
-b = A.dot(x)
-x_est = llt.solve(b)
-assert nanoeigenpy.is_approx(x, x_est)
-assert nanoeigenpy.is_approx(A.dot(x_est), b)
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = llt.solve(B)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
 
-LLT = llt.matrixLLT()
-LLT_lower = np.tril(LLT)
-assert nanoeigenpy.is_approx(LLT_lower, L)
+    x = rng.random(dim)
+    b = A.dot(x)
+    x_est = llt.solve(b)
+    assert nanoeigenpy.is_approx(x, x_est)
+    assert nanoeigenpy.is_approx(A.dot(x_est), b)
 
-A_upper = np.triu(A, k=1)
-LLT_upper = np.triu(LLT, k=1)
-assert nanoeigenpy.is_approx(A_upper, LLT_upper)
+    LLT = llt.matrixLLT()
+    LLT_lower = np.tril(LLT)
+    assert nanoeigenpy.is_approx(LLT_lower, L)
 
-A_reconstructed = llt.reconstructedMatrix()
-assert nanoeigenpy.is_approx(A_reconstructed, A)
+    A_upper = np.triu(A, k=1)
+    LLT_upper = np.triu(LLT, k=1)
+    assert nanoeigenpy.is_approx(A_upper, LLT_upper)
 
-adjoint = llt.adjoint()
-assert adjoint is llt
+    A_reconstructed = llt.reconstructedMatrix()
+    assert nanoeigenpy.is_approx(A_reconstructed, A)
 
-A_cond = np.eye(dim)
-llt_cond = nanoeigenpy.LLT(A_cond)
-estimated_r_cond_num = llt_cond.rcond()
-assert abs(estimated_r_cond_num - 1) <= 1e-9
+    adjoint = llt.adjoint()
+    assert adjoint is llt
 
-sigma = 3
-w = np.ones(dim)
-llt.rankUpdate(w, sigma)
-L = llt.matrixL()
-U = llt.matrixU()
-LU = L @ U
-assert nanoeigenpy.is_approx(LU, A + sigma * w * np.transpose(w))
+    A_cond = np.eye(dim)
+    llt_cond = nanoeigenpy.LLT(A_cond)
+    estimated_r_cond_num = llt_cond.rcond()
+    assert abs(estimated_r_cond_num - 1) <= 1e-9
 
-llt1 = nanoeigenpy.LLT()
-llt2 = nanoeigenpy.LLT()
+    sigma = 3
+    w = np.ones(dim)
+    llt.rankUpdate(w, sigma)
+    L = llt.matrixL()
+    U = llt.matrixU()
+    LU = L @ U
+    assert nanoeigenpy.is_approx(LU, A + sigma * w * np.transpose(w))
 
-id1 = llt1.id()
-id2 = llt2.id()
+    llt1 = nanoeigenpy.LLT()
+    llt2 = nanoeigenpy.LLT()
 
-assert id1 != id2
-assert id1 == llt1.id()
-assert id2 == llt2.id()
+    id1 = llt1.id()
+    id2 = llt2.id()
 
-dim_constructor = 3
+    assert id1 != id2
+    assert id1 == llt1.id()
+    assert id2 == llt2.id()
 
-llt3 = nanoeigenpy.LLT(dim_constructor)
-llt4 = nanoeigenpy.LLT(dim_constructor)
+    dim_constructor = 3
 
-id3 = llt3.id()
-id4 = llt4.id()
+    llt3 = nanoeigenpy.LLT(dim_constructor)
+    llt4 = nanoeigenpy.LLT(dim_constructor)
 
-assert id3 != id4
-assert id3 == llt3.id()
-assert id4 == llt4.id()
+    id3 = llt3.id()
+    id4 = llt4.id()
+
+    assert id3 != id4
+    assert id3 == llt3.id()
+    assert id4 == llt4.id()
diff --git a/tests/test_partial_piv_lu.py b/tests/test_partial_piv_lu.py
index 98554c60..1e6e5ee3 100644
--- a/tests/test_partial_piv_lu.py
+++ b/tests/test_partial_piv_lu.py
@@ -1,75 +1,77 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
-partialpivlu = nanoeigenpy.PartialPivLU(A)
 
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = partialpivlu.solve(B)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
+def test_partial_piv_lu():
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5 + np.diag(10.0 + rng.random(dim))
+    partialpivlu = nanoeigenpy.PartialPivLU(A)
 
-x = rng.random(dim)
-b = A.dot(x)
-x_est = partialpivlu.solve(b)
-assert nanoeigenpy.is_approx(x, x_est)
-assert nanoeigenpy.is_approx(A.dot(x_est), b)
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = partialpivlu.solve(B)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
 
-rows = partialpivlu.rows()
-cols = partialpivlu.cols()
-assert cols == dim
-assert rows == dim
+    x = rng.random(dim)
+    b = A.dot(x)
+    x_est = partialpivlu.solve(b)
+    assert nanoeigenpy.is_approx(x, x_est)
+    assert nanoeigenpy.is_approx(A.dot(x_est), b)
 
-partialpivlu_compute = partialpivlu.compute(A)
-A_reconstructed = partialpivlu.reconstructedMatrix()
-assert nanoeigenpy.is_approx(A_reconstructed, A)
+    rows = partialpivlu.rows()
+    cols = partialpivlu.cols()
+    assert cols == dim
+    assert rows == dim
 
-LU = partialpivlu.matrixLU()
-P_perm = partialpivlu.permutationP()
-P = P_perm.toDenseMatrix()
+    partialpivlu_compute = partialpivlu.compute(A)  # noqa
+    A_reconstructed = partialpivlu.reconstructedMatrix()
+    assert nanoeigenpy.is_approx(A_reconstructed, A)
 
-U = np.triu(LU)
-L = np.eye(dim) + np.tril(LU, -1)
-assert nanoeigenpy.is_approx(P @ A, L @ U)
+    LU = partialpivlu.matrixLU()
+    P_perm = partialpivlu.permutationP()
+    P = P_perm.toDenseMatrix()
 
-inverse = partialpivlu.inverse()
-assert nanoeigenpy.is_approx(A @ inverse, np.eye(dim))
-assert nanoeigenpy.is_approx(inverse @ A, np.eye(dim))
+    U = np.triu(LU)
+    L = np.eye(dim) + np.tril(LU, -1)
+    assert nanoeigenpy.is_approx(P @ A, L @ U)
 
-rcond = partialpivlu.rcond()
-determinant = partialpivlu.determinant()
-det_numpy = np.linalg.det(A)
-assert rcond > 0
-assert abs(determinant - det_numpy) / abs(det_numpy) < 1e-10
+    inverse = partialpivlu.inverse()
+    assert nanoeigenpy.is_approx(A @ inverse, np.eye(dim))
+    assert nanoeigenpy.is_approx(inverse @ A, np.eye(dim))
 
-P_inv = P_perm.inverse().toDenseMatrix()
-assert nanoeigenpy.is_approx(P @ P_inv, np.eye(dim))
-assert nanoeigenpy.is_approx(P_inv @ P, np.eye(dim))
+    rcond = partialpivlu.rcond()
+    determinant = partialpivlu.determinant()
+    det_numpy = np.linalg.det(A)
+    assert rcond > 0
+    assert abs(determinant - det_numpy) / abs(det_numpy) < 1e-10
 
-decomp1 = nanoeigenpy.PartialPivLU()
-decomp2 = nanoeigenpy.PartialPivLU()
-id1 = decomp1.id()
-id2 = decomp2.id()
-assert id1 != id2
-assert id1 == decomp1.id()
-assert id2 == decomp2.id()
+    P_inv = P_perm.inverse().toDenseMatrix()
+    assert nanoeigenpy.is_approx(P @ P_inv, np.eye(dim))
+    assert nanoeigenpy.is_approx(P_inv @ P, np.eye(dim))
 
-decomp3 = nanoeigenpy.PartialPivLU(dim)
-decomp4 = nanoeigenpy.PartialPivLU(dim)
-id3 = decomp3.id()
-id4 = decomp4.id()
-assert id3 != id4
-assert id3 == decomp3.id()
-assert id4 == decomp4.id()
+    decomp1 = nanoeigenpy.PartialPivLU()
+    decomp2 = nanoeigenpy.PartialPivLU()
+    id1 = decomp1.id()
+    id2 = decomp2.id()
+    assert id1 != id2
+    assert id1 == decomp1.id()
+    assert id2 == decomp2.id()
 
-decomp5 = nanoeigenpy.PartialPivLU(A)
-decomp6 = nanoeigenpy.PartialPivLU(A)
-id5 = decomp5.id()
-id6 = decomp6.id()
-assert id5 != id6
-assert id5 == decomp5.id()
-assert id6 == decomp6.id()
+    decomp3 = nanoeigenpy.PartialPivLU(dim)
+    decomp4 = nanoeigenpy.PartialPivLU(dim)
+    id3 = decomp3.id()
+    id4 = decomp4.id()
+    assert id3 != id4
+    assert id3 == decomp3.id()
+    assert id4 == decomp4.id()
+
+    decomp5 = nanoeigenpy.PartialPivLU(A)
+    decomp6 = nanoeigenpy.PartialPivLU(A)
+    id5 = decomp5.id()
+    id6 = decomp6.id()
+    assert id5 != id6
+    assert id5 == decomp5.id()
+    assert id6 == decomp6.id()
diff --git a/tests/test_permutation_matrix.py b/tests/test_permutation_matrix.py
index 29e76f8c..95c74f67 100644
--- a/tests/test_permutation_matrix.py
+++ b/tests/test_permutation_matrix.py
@@ -1,59 +1,61 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-indices = rng.permutation(dim)
 
-perm = nanoeigenpy.PermutationMatrix(dim)
-perm = nanoeigenpy.PermutationMatrix(indices)
+def test_permutation_matrix():
+    dim = 100
+    rng = np.random.default_rng()
+    indices = rng.permutation(dim)
 
-est_indices = perm.indices()
-assert est_indices.all() == indices.all()
+    perm = nanoeigenpy.PermutationMatrix(dim)
+    perm = nanoeigenpy.PermutationMatrix(indices)
 
-perm_left = perm.applyTranspositionOnTheLeft(0, 1)
-perm_left_right = perm_left.applyTranspositionOnTheRight(0, 1)
-assert perm_left_right.indices().all() == perm.indices().all()
+    est_indices = perm.indices()
+    assert est_indices.all() == indices.all()
 
-perm.setIdentity()
-assert perm.indices().all() == np.arange(dim).all()
-dim = dim + 1
-perm.setIdentity(dim)
-assert perm.indices().all() == np.arange(dim).all()
+    perm_left = perm.applyTranspositionOnTheLeft(0, 1)
+    perm_left_right = perm_left.applyTranspositionOnTheRight(0, 1)
+    assert perm_left_right.indices().all() == perm.indices().all()
 
-perm.setIdentity()
-dense = perm.toDenseMatrix()
-assert dense.all() == np.eye(dim).all()
+    perm.setIdentity()
+    assert perm.indices().all() == np.arange(dim).all()
+    dim = dim + 1
+    perm.setIdentity(dim)
+    assert perm.indices().all() == np.arange(dim).all()
 
-perm = nanoeigenpy.PermutationMatrix(np.array([1, 0, 2]))
-perm_t = perm.transpose()
-dense = perm.toDenseMatrix()
-dense_t = perm_t.toDenseMatrix()
-assert dense_t.all() == dense.T.all()
+    perm.setIdentity()
+    dense = perm.toDenseMatrix()
+    assert dense.all() == np.eye(dim).all()
 
-perm_inv = perm.inverse()
-result = perm * perm_inv
-identity = result.toDenseMatrix()
-assert identity.all() == np.eye(3).all()
+    perm = nanoeigenpy.PermutationMatrix(np.array([1, 0, 2]))
+    perm_t = perm.transpose()
+    dense = perm.toDenseMatrix()
+    dense_t = perm_t.toDenseMatrix()
+    assert dense_t.all() == dense.T.all()
 
-dim_constructor = 3
+    perm_inv = perm.inverse()
+    result = perm * perm_inv
+    identity = result.toDenseMatrix()
+    assert identity.all() == np.eye(3).all()
 
-perm1 = nanoeigenpy.PermutationMatrix(dim_constructor)
-perm2 = nanoeigenpy.PermutationMatrix(dim_constructor)
+    dim_constructor = 3
 
-id1 = perm1.id()
-id2 = perm2.id()
+    perm1 = nanoeigenpy.PermutationMatrix(dim_constructor)
+    perm2 = nanoeigenpy.PermutationMatrix(dim_constructor)
 
-assert id1 != id2
-assert id1 == perm1.id()
-assert id2 == perm2.id()
+    id1 = perm1.id()
+    id2 = perm2.id()
 
-es3 = nanoeigenpy.PermutationMatrix(indices)
-es4 = nanoeigenpy.PermutationMatrix(indices)
+    assert id1 != id2
+    assert id1 == perm1.id()
+    assert id2 == perm2.id()
 
-id3 = es3.id()
-id4 = es4.id()
+    es3 = nanoeigenpy.PermutationMatrix(indices)
+    es4 = nanoeigenpy.PermutationMatrix(indices)
 
-assert id3 != id4
-assert id3 == es3.id()
-assert id4 == es4.id()
+    id3 = es3.id()
+    id4 = es4.id()
+
+    assert id3 != id4
+    assert id3 == es3.id()
+    assert id4 == es4.id()
diff --git a/tests/test_qr.py b/tests/test_qr.py
index fa0f194b..5dacbbd4 100644
--- a/tests/test_qr.py
+++ b/tests/test_qr.py
@@ -1,100 +1,102 @@
 import nanoeigenpy
 import numpy as np
 
-rows = 20
-cols = 100
-rng = np.random.default_rng()
-
-A = rng.random((rows, cols))
-
-householder_qr = nanoeigenpy.HouseholderQR()
-householder_qr = nanoeigenpy.HouseholderQR(rows, cols)
-householder_qr = nanoeigenpy.HouseholderQR(A)
-
-householder_qr_eye = nanoeigenpy.HouseholderQR(np.eye(rows, rows))
-X = rng.random((rows, 20))
-assert householder_qr_eye.absDeterminant() == 1.0
-assert householder_qr_eye.logAbsDeterminant() == 0.0
-
-Y = householder_qr_eye.solve(X)
-assert (X == Y).all()
-
-x = rng.random(rows)
-y = householder_qr_eye.solve(x)
-assert (x == y).all()
-
-fullpiv_householder_qr = nanoeigenpy.FullPivHouseholderQR()
-fullpiv_householder_qr = nanoeigenpy.FullPivHouseholderQR(rows, cols)
-fullpiv_householder_qr = nanoeigenpy.FullPivHouseholderQR(A)
-
-fullpiv_householder_qr = nanoeigenpy.FullPivHouseholderQR(np.eye(rows, rows))
-assert fullpiv_householder_qr.isSurjective()
-assert fullpiv_householder_qr.isInjective()
-fullpiv_householder_qr.isInvertible()
-
-X = rng.random((rows, 20))
-assert fullpiv_householder_qr.absDeterminant() == 1.0
-assert fullpiv_householder_qr.logAbsDeterminant() == 0.0
-
-Y = fullpiv_householder_qr.solve(X)
-assert (X == Y).all()
-assert fullpiv_householder_qr.rank() == rows
-
-x = rng.random(rows)
-y = fullpiv_householder_qr.solve(x)
-assert (x == y).all()
-
-fullpiv_householder_qr.setThreshold()
-fullpiv_householder_qr.setThreshold(1e-8)
-assert fullpiv_householder_qr.threshold() == 1e-8
-assert nanoeigenpy.is_approx(np.eye(rows, rows), fullpiv_householder_qr.inverse())
-
-assert fullpiv_householder_qr.maxPivot() == 1.0
-assert fullpiv_householder_qr.nonzeroPivots() == rows
-assert fullpiv_householder_qr.dimensionOfKernel() == 0
-
-colpiv_householder_qr = nanoeigenpy.ColPivHouseholderQR(A)
-assert colpiv_householder_qr.info() == nanoeigenpy.ComputationInfo.Success
-
-colpiv_householder_qr = nanoeigenpy.ColPivHouseholderQR(np.eye(rows, rows))
-X = rng.random((rows, 20))
-assert colpiv_householder_qr.absDeterminant() == 1.0
-assert colpiv_householder_qr.logAbsDeterminant() == 0.0
-
-Y = colpiv_householder_qr.solve(X)
-assert (X == Y).all()
-assert colpiv_householder_qr.rank() == rows
-
-colpiv_householder_qr.setThreshold()
-colpiv_householder_qr.setThreshold(1e-8)
-assert colpiv_householder_qr.threshold() == 1e-8
-assert nanoeigenpy.is_approx(np.eye(rows, rows), colpiv_householder_qr.inverse())
-
-assert colpiv_householder_qr.maxPivot() == 1.0
-assert colpiv_householder_qr.nonzeroPivots() == rows
-assert colpiv_householder_qr.dimensionOfKernel() == 0
-
-cod = nanoeigenpy.CompleteOrthogonalDecomposition(A)
-assert cod.info() == nanoeigenpy.ComputationInfo.Success
-
-cod = nanoeigenpy.CompleteOrthogonalDecomposition(np.eye(rows, rows))
-X = rng.random((rows, 20))
-assert cod.absDeterminant() == 1.0
-assert cod.logAbsDeterminant() == 0.0
-
-Y = cod.solve(X)
-assert (X == Y).all()
-assert cod.rank() == rows
-
-x = rng.random(rows)
-y = cod.solve(x)
-assert (x == y).all()
-
-cod.setThreshold()
-cod.setThreshold(1e-8)
-assert cod.threshold() == 1e-8
-assert nanoeigenpy.is_approx(np.eye(rows, rows), cod.pseudoInverse())
-
-assert cod.maxPivot() == 1.0
-assert cod.nonzeroPivots() == rows
-assert cod.dimensionOfKernel() == 0
+
+def test_qr():
+    rows = 20
+    cols = 100
+    rng = np.random.default_rng()
+
+    A = rng.random((rows, cols))
+
+    householder_qr = nanoeigenpy.HouseholderQR()
+    householder_qr = nanoeigenpy.HouseholderQR(rows, cols)
+    householder_qr = nanoeigenpy.HouseholderQR(A)  # noqa
+
+    householder_qr_eye = nanoeigenpy.HouseholderQR(np.eye(rows, rows))
+    X = rng.random((rows, 20))
+    assert householder_qr_eye.absDeterminant() == 1.0
+    assert householder_qr_eye.logAbsDeterminant() == 0.0
+
+    Y = householder_qr_eye.solve(X)
+    assert (X == Y).all()
+
+    x = rng.random(rows)
+    y = householder_qr_eye.solve(x)
+    assert (x == y).all()
+
+    fullpiv_householder_qr = nanoeigenpy.FullPivHouseholderQR()
+    fullpiv_householder_qr = nanoeigenpy.FullPivHouseholderQR(rows, cols)
+    fullpiv_householder_qr = nanoeigenpy.FullPivHouseholderQR(A)
+
+    fullpiv_householder_qr = nanoeigenpy.FullPivHouseholderQR(np.eye(rows, rows))
+    assert fullpiv_householder_qr.isSurjective()
+    assert fullpiv_householder_qr.isInjective()
+    fullpiv_householder_qr.isInvertible()
+
+    X = rng.random((rows, 20))
+    assert fullpiv_householder_qr.absDeterminant() == 1.0
+    assert fullpiv_householder_qr.logAbsDeterminant() == 0.0
+
+    Y = fullpiv_householder_qr.solve(X)
+    assert (X == Y).all()
+    assert fullpiv_householder_qr.rank() == rows
+
+    x = rng.random(rows)
+    y = fullpiv_householder_qr.solve(x)
+    assert (x == y).all()
+
+    fullpiv_householder_qr.setThreshold()
+    fullpiv_householder_qr.setThreshold(1e-8)
+    assert fullpiv_householder_qr.threshold() == 1e-8
+    assert nanoeigenpy.is_approx(np.eye(rows, rows), fullpiv_householder_qr.inverse())
+
+    assert fullpiv_householder_qr.maxPivot() == 1.0
+    assert fullpiv_householder_qr.nonzeroPivots() == rows
+    assert fullpiv_householder_qr.dimensionOfKernel() == 0
+
+    colpiv_householder_qr = nanoeigenpy.ColPivHouseholderQR(A)
+    assert colpiv_householder_qr.info() == nanoeigenpy.ComputationInfo.Success
+
+    colpiv_householder_qr = nanoeigenpy.ColPivHouseholderQR(np.eye(rows, rows))
+    X = rng.random((rows, 20))
+    assert colpiv_householder_qr.absDeterminant() == 1.0
+    assert colpiv_householder_qr.logAbsDeterminant() == 0.0
+
+    Y = colpiv_householder_qr.solve(X)
+    assert (X == Y).all()
+    assert colpiv_householder_qr.rank() == rows
+
+    colpiv_householder_qr.setThreshold()
+    colpiv_householder_qr.setThreshold(1e-8)
+    assert colpiv_householder_qr.threshold() == 1e-8
+    assert nanoeigenpy.is_approx(np.eye(rows, rows), colpiv_householder_qr.inverse())
+
+    assert colpiv_householder_qr.maxPivot() == 1.0
+    assert colpiv_householder_qr.nonzeroPivots() == rows
+    assert colpiv_householder_qr.dimensionOfKernel() == 0
+
+    cod = nanoeigenpy.CompleteOrthogonalDecomposition(A)
+    assert cod.info() == nanoeigenpy.ComputationInfo.Success
+
+    cod = nanoeigenpy.CompleteOrthogonalDecomposition(np.eye(rows, rows))
+    X = rng.random((rows, 20))
+    assert cod.absDeterminant() == 1.0
+    assert cod.logAbsDeterminant() == 0.0
+
+    Y = cod.solve(X)
+    assert (X == Y).all()
+    assert cod.rank() == rows
+
+    x = rng.random(rows)
+    y = cod.solve(x)
+    assert (x == y).all()
+
+    cod.setThreshold()
+    cod.setThreshold(1e-8)
+    assert cod.threshold() == 1e-8
+    assert nanoeigenpy.is_approx(np.eye(rows, rows), cod.pseudoInverse())
+
+    assert cod.maxPivot() == 1.0
+    assert cod.nonzeroPivots() == rows
+    assert cod.dimensionOfKernel() == 0
diff --git a/tests/test_real_qz.py b/tests/test_real_qz.py
index 088708d9..e778f053 100644
--- a/tests/test_real_qz.py
+++ b/tests/test_real_qz.py
@@ -1,37 +1,39 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
-B = rng.random((dim, dim))
-
-realqz = nanoeigenpy.RealQZ(A, B)
-assert realqz.info() == nanoeigenpy.ComputationInfo.Success
-
-Q = realqz.matrixQ()
-S = realqz.matrixS()
-Z = realqz.matrixZ()
-T = realqz.matrixT()
-
-assert nanoeigenpy.is_approx(A, Q @ S @ Z)
-assert nanoeigenpy.is_approx(B, Q @ T @ Z)
-
-assert nanoeigenpy.is_approx(Q @ Q.T, np.eye(dim))
-assert nanoeigenpy.is_approx(Z @ Z.T, np.eye(dim))
-
-for i in range(dim):
-    for j in range(i):
-        assert abs(T[i, j]) < 1e-12
-
-for i in range(dim):
-    for j in range(i - 1):
-        assert abs(S[i, j]) < 1e-12
-
-realqz3_id = nanoeigenpy.RealQZ(A, B)
-realqz4_id = nanoeigenpy.RealQZ(A, B)
-id3 = realqz3_id.id()
-id4 = realqz4_id.id()
-assert id3 != id4
-assert id3 == realqz3_id.id()
-assert id4 == realqz4_id.id()
+
+def test_real_qz():
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
+    B = rng.random((dim, dim))
+
+    realqz = nanoeigenpy.RealQZ(A, B)
+    assert realqz.info() == nanoeigenpy.ComputationInfo.Success
+
+    Q = realqz.matrixQ()
+    S = realqz.matrixS()
+    Z = realqz.matrixZ()
+    T = realqz.matrixT()
+
+    assert nanoeigenpy.is_approx(A, Q @ S @ Z)
+    assert nanoeigenpy.is_approx(B, Q @ T @ Z)
+
+    assert nanoeigenpy.is_approx(Q @ Q.T, np.eye(dim))
+    assert nanoeigenpy.is_approx(Z @ Z.T, np.eye(dim))
+
+    for i in range(dim):
+        for j in range(i):
+            assert abs(T[i, j]) < 1e-12
+
+    for i in range(dim):
+        for j in range(i - 1):
+            assert abs(S[i, j]) < 1e-12
+
+    realqz3_id = nanoeigenpy.RealQZ(A, B)
+    realqz4_id = nanoeigenpy.RealQZ(A, B)
+    id3 = realqz3_id.id()
+    id4 = realqz4_id.id()
+    assert id3 != id4
+    assert id3 == realqz3_id.id()
+    assert id4 == realqz4_id.id()
diff --git a/tests/test_real_schur.py b/tests/test_real_schur.py
index 399675ae..9e9efe7b 100644
--- a/tests/test_real_schur.py
+++ b/tests/test_real_schur.py
@@ -2,47 +2,50 @@
 import numpy as np
 
 
-def verify_is_quasi_triangular(T):
-    size = T.shape[0]
+def test_real_schur():
+    def verify_is_quasi_triangular(T):
+        size = T.shape[0]
 
-    for row in range(2, size):
-        for col in range(row - 1):
-            assert abs(T[row, col]) < 1e-12
+        for row in range(2, size):
+            for col in range(row - 1):
+                assert abs(T[row, col]) < 1e-12
 
-    for row in range(1, size):
-        if abs(T[row, row - 1]) > 1e-12:
-            if row < size - 1:
-                assert abs(T[row + 1, row]) < 1e-12
+        for row in range(1, size):
+            if abs(T[row, row - 1]) > 1e-12:
+                if row < size - 1:
+                    assert abs(T[row + 1, row]) < 1e-12
 
-            tr = T[row - 1, row - 1] + T[row, row]
-            det = T[row - 1, row - 1] * T[row, row] - T[row - 1, row] * T[row, row - 1]
-            assert 4 * det > tr * tr
+                tr = T[row - 1, row - 1] + T[row, row]
+                det = (
+                    T[row - 1, row - 1] * T[row, row]
+                    - T[row - 1, row] * T[row, row - 1]
+                )
+                assert 4 * det > tr * tr
 
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
+    rs = nanoeigenpy.RealSchur(A)
+    assert rs.info() == nanoeigenpy.ComputationInfo.Success
 
-rs = nanoeigenpy.RealSchur(A)
-assert rs.info() == nanoeigenpy.ComputationInfo.Success
+    U = rs.matrixU()
+    T = rs.matrixT()
 
-U = rs.matrixU()
-T = rs.matrixT()
+    assert nanoeigenpy.is_approx(A, U @ T @ U.T)
+    assert nanoeigenpy.is_approx(U @ U.T, np.eye(dim))
 
-assert nanoeigenpy.is_approx(A, U @ T @ U.T)
-assert nanoeigenpy.is_approx(U @ U.T, np.eye(dim))
+    verify_is_quasi_triangular(T)
 
-verify_is_quasi_triangular(T)
+    hess = nanoeigenpy.HessenbergDecomposition(A)
+    H = hess.matrixH()
+    Q_hess = hess.matrixQ()
 
-hess = nanoeigenpy.HessenbergDecomposition(A)
-H = hess.matrixH()
-Q_hess = hess.matrixQ()
+    rs_from_hess = nanoeigenpy.RealSchur(dim)
+    result_from_hess = rs_from_hess.computeFromHessenberg(H, Q_hess, True)
+    assert result_from_hess.info() == nanoeigenpy.ComputationInfo.Success
 
-rs_from_hess = nanoeigenpy.RealSchur(dim)
-result_from_hess = rs_from_hess.computeFromHessenberg(H, Q_hess, True)
-assert result_from_hess.info() == nanoeigenpy.ComputationInfo.Success
+    T_from_hess = rs_from_hess.matrixT()
+    U_from_hess = rs_from_hess.matrixU()
 
-T_from_hess = rs_from_hess.matrixT()
-U_from_hess = rs_from_hess.matrixU()
-
-assert nanoeigenpy.is_approx(A, U_from_hess @ T_from_hess @ U_from_hess.T)
+    assert nanoeigenpy.is_approx(A, U_from_hess @ T_from_hess @ U_from_hess.T)
diff --git a/tests/test_self_adjoint_eigen_solver.py b/tests/test_self_adjoint_eigen_solver.py
index d53ed89f..ae03b73b 100644
--- a/tests/test_self_adjoint_eigen_solver.py
+++ b/tests/test_self_adjoint_eigen_solver.py
@@ -1,20 +1,22 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
 
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5
+def test_self_adjoint_eigen_solver():
+    dim = 100
+    rng = np.random.default_rng()
 
-es = nanoeigenpy.SelfAdjointEigenSolver(A)
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5
 
-assert es.info() == nanoeigenpy.ComputationInfo.Success
+    es = nanoeigenpy.SelfAdjointEigenSolver(A)
 
-V = es.eigenvectors()
-D = es.eigenvalues()
+    assert es.info() == nanoeigenpy.ComputationInfo.Success
 
-AdotV = A @ V
-VdotD = V @ np.diag(D)
+    V = es.eigenvectors()
+    D = es.eigenvalues()
 
-assert nanoeigenpy.is_approx(AdotV, VdotD, 1e-6)
+    AdotV = A @ V
+    VdotD = V @ np.diag(D)
+
+    assert nanoeigenpy.is_approx(AdotV, VdotD, 1e-6)
diff --git a/tests/test_simplicial_llt.py b/tests/test_simplicial_llt.py
index 12e8d460..d613fb78 100644
--- a/tests/test_simplicial_llt.py
+++ b/tests/test_simplicial_llt.py
@@ -2,45 +2,47 @@
 import numpy as np
 import scipy.sparse as spa
 
-dim = 100
-rng = np.random.default_rng()
-
-A_fac = spa.random(dim, dim, density=0.25, random_state=rng)
-A = A_fac.T @ A_fac
-A += spa.diags(10.0 * rng.standard_normal(dim) ** 2)
-A = A.tocsc(True)
-A.check_format()
-
-llt = nanoeigenpy.SimplicialLLT(A)
-
-assert llt.info() == nanoeigenpy.ComputationInfo.Success
-
-L = llt.matrixL()
-U = llt.matrixU()
-
-LU = L @ U
-perm = llt.permutationP().toDenseMatrix()
-perm_inv = llt.permutationP().inverse().toDenseMatrix()
-A_perm = perm @ A @ perm_inv
-assert nanoeigenpy.is_approx(LU.toarray(), A_perm)
-
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = llt.solve(B)
-assert isinstance(X_est, np.ndarray)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
-
-llt.analyzePattern(A)
-llt.factorize(A)
-
-X_sparse = spa.random(dim, 10, random_state=rng)
-B_sparse = A.dot(X_sparse)
-B_sparse: spa.csc_matrix = B_sparse.tocsc(True)
-if not B_sparse.has_sorted_indices:
-    B_sparse.sort_indices()
-
-X_est = llt.solve(B_sparse)
-assert isinstance(X_est, spa.csc_matrix)
-assert nanoeigenpy.is_approx(X_est.toarray(), X_sparse.toarray())
-assert nanoeigenpy.is_approx(A.dot(X_est.toarray()), B_sparse.toarray())
+
+def test_simplicial_llt():
+    dim = 100
+    rng = np.random.default_rng()
+
+    A_fac = spa.random(dim, dim, density=0.25, random_state=rng)
+    A = A_fac.T @ A_fac
+    A += spa.diags(10.0 * rng.standard_normal(dim) ** 2)
+    A = A.tocsc(True)
+    A.check_format()
+
+    llt = nanoeigenpy.SimplicialLLT(A)
+
+    assert llt.info() == nanoeigenpy.ComputationInfo.Success
+
+    L = llt.matrixL()
+    U = llt.matrixU()
+
+    LU = L @ U
+    perm = llt.permutationP().toDenseMatrix()
+    perm_inv = llt.permutationP().inverse().toDenseMatrix()
+    A_perm = perm @ A @ perm_inv
+    assert nanoeigenpy.is_approx(LU.toarray(), A_perm)
+
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = llt.solve(B)
+    assert isinstance(X_est, np.ndarray)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
+
+    llt.analyzePattern(A)
+    llt.factorize(A)
+
+    X_sparse = spa.random(dim, 10, random_state=rng)
+    B_sparse = A.dot(X_sparse)
+    B_sparse: spa.csc_matrix = B_sparse.tocsc(True)
+    if not B_sparse.has_sorted_indices:
+        B_sparse.sort_indices()
+
+    X_est = llt.solve(B_sparse)
+    assert isinstance(X_est, spa.csc_matrix)
+    assert nanoeigenpy.is_approx(X_est.toarray(), X_sparse.toarray())
+    assert nanoeigenpy.is_approx(A.dot(X_est.toarray()), B_sparse.toarray())
diff --git a/tests/test_sparse_lu.py b/tests/test_sparse_lu.py
index 859b1f7b..5c5fa724 100644
--- a/tests/test_sparse_lu.py
+++ b/tests/test_sparse_lu.py
@@ -2,62 +2,64 @@
 import numpy as np
 import scipy.sparse as spa
 
-dim = 100
-rng = np.random.default_rng()
-
-A_fac = spa.random(dim, dim, density=0.25, random_state=rng)
-A = A_fac.T @ A_fac
-A += spa.diags(10.0 * rng.standard_normal(dim) ** 2)
-A = A.tocsc(True)
-A.check_format()
-
-splu = nanoeigenpy.SparseLU(A)
-
-assert splu.info() == nanoeigenpy.ComputationInfo.Success
-
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = splu.solve(B)
-assert isinstance(X_est, np.ndarray)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
-
-splu.analyzePattern(A)
-splu.factorize(A)
-
-X_sparse = spa.random(dim, 10, random_state=rng)
-B_sparse = A.dot(X_sparse)
-B_sparse: spa.csc_matrix = B_sparse.tocsc(True)
-if not B_sparse.has_sorted_indices:
-    B_sparse.sort_indices()
-
-X_est = splu.solve(B_sparse)
-assert isinstance(X_est, spa.csc_matrix)
-assert nanoeigenpy.is_approx(X_est.toarray(), X_sparse.toarray())
-assert nanoeigenpy.is_approx(A.dot(X_est.toarray()), B_sparse.toarray())
-
-assert splu.nnzL() > 0
-assert splu.nnzU() > 0
-
-L = splu.matrixL()
-U = splu.matrixU()
-
-assert L.rows() == dim
-assert L.cols() == dim
-assert U.rows() == dim
-assert U.cols() == dim
-
-x_true = rng.random(dim)
-b_true = A.dot(x_true)
-P_rows_indices = splu.rowsPermutation().indices()
-P_cols_indices = splu.colsPermutation().indices()
-
-b_permuted = b_true[P_rows_indices]
-z = b_permuted.copy()
-L.solveInPlace(z)
-y = z.copy()
-U.solveInPlace(y)
-x_reconstructed = np.zeros(dim)
-x_reconstructed[P_cols_indices] = y
-
-assert nanoeigenpy.is_approx(x_reconstructed, x_true, 1e-6)
+
+def test_sparse_lu():
+    dim = 100
+    rng = np.random.default_rng()
+
+    A_fac = spa.random(dim, dim, density=0.25, random_state=rng)
+    A = A_fac.T @ A_fac
+    A += spa.diags(10.0 * rng.standard_normal(dim) ** 2)
+    A = A.tocsc(True)
+    A.check_format()
+
+    splu = nanoeigenpy.SparseLU(A)
+
+    assert splu.info() == nanoeigenpy.ComputationInfo.Success
+
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = splu.solve(B)
+    assert isinstance(X_est, np.ndarray)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
+
+    splu.analyzePattern(A)
+    splu.factorize(A)
+
+    X_sparse = spa.random(dim, 10, random_state=rng)
+    B_sparse = A.dot(X_sparse)
+    B_sparse: spa.csc_matrix = B_sparse.tocsc(True)
+    if not B_sparse.has_sorted_indices:
+        B_sparse.sort_indices()
+
+    X_est = splu.solve(B_sparse)
+    assert isinstance(X_est, spa.csc_matrix)
+    assert nanoeigenpy.is_approx(X_est.toarray(), X_sparse.toarray())
+    assert nanoeigenpy.is_approx(A.dot(X_est.toarray()), B_sparse.toarray())
+
+    assert splu.nnzL() > 0
+    assert splu.nnzU() > 0
+
+    L = splu.matrixL()
+    U = splu.matrixU()
+
+    assert L.rows() == dim
+    assert L.cols() == dim
+    assert U.rows() == dim
+    assert U.cols() == dim
+
+    x_true = rng.random(dim)
+    b_true = A.dot(x_true)
+    P_rows_indices = splu.rowsPermutation().indices()
+    P_cols_indices = splu.colsPermutation().indices()
+
+    b_permuted = b_true[P_rows_indices]
+    z = b_permuted.copy()
+    L.solveInPlace(z)
+    y = z.copy()
+    U.solveInPlace(y)
+    x_reconstructed = np.zeros(dim)
+    x_reconstructed[P_cols_indices] = y
+
+    assert nanoeigenpy.is_approx(x_reconstructed, x_true, 1e-6)
diff --git a/tests/test_sparse_qr.py b/tests/test_sparse_qr.py
index 7a825f8a..9544cf0b 100644
--- a/tests/test_sparse_qr.py
+++ b/tests/test_sparse_qr.py
@@ -2,72 +2,74 @@
 import numpy as np
 import scipy.sparse as spa
 
-dim = 100
-rng = np.random.default_rng()
-
-A_fac = spa.random(dim, dim, density=0.25, random_state=rng)
-A = A_fac.T @ A_fac
-A += spa.diags(10.0 * rng.standard_normal(dim) ** 2)
-A = A.tocsc(True)
-A.check_format()
-
-spqr = nanoeigenpy.SparseQR(A)
-
-assert spqr.info() == nanoeigenpy.ComputationInfo.Success
-
-X = rng.random((dim, 20))
-B = A.dot(X)
-X_est = spqr.solve(B)
-assert isinstance(X_est, np.ndarray)
-assert nanoeigenpy.is_approx(X, X_est)
-assert nanoeigenpy.is_approx(A.dot(X_est), B)
-
-spqr.analyzePattern(A)
-spqr.factorize(A)
-
-X_sparse = spa.random(dim, 10, random_state=rng)
-B_sparse = A.dot(X_sparse)
-B_sparse: spa.csc_matrix = B_sparse.tocsc(True)
-if not B_sparse.has_sorted_indices:
-    B_sparse.sort_indices()
-
-X_est = spqr.solve(B_sparse)
-assert isinstance(X_est, spa.csc_matrix)
-assert nanoeigenpy.is_approx(X_est.toarray(), X_sparse.toarray())
-assert nanoeigenpy.is_approx(A.dot(X_est.toarray()), B_sparse.toarray())
-
-Q = spqr.matrixQ()
-R = spqr.matrixR()
-P = spqr.colsPermutation()
-
-assert spqr.matrixQ().rows() == dim
-assert spqr.matrixQ().cols() == dim
-assert R.shape[0] == dim
-assert R.shape[1] == dim
-assert P.indices().size == dim
-
-test_vec = rng.random(dim)
-test_matrix = rng.random((dim, 20))
-
-Qv = Q @ test_vec
-QM = Q @ test_matrix
-Qt = Q.transpose()
-QtV = Qt @ test_vec
-QtM = Qt @ test_matrix
-
-assert Qv.shape == (dim,)
-assert QM.shape == (dim, 20)
-assert QtV.shape == (dim,)
-assert QtM.shape == (dim, 20)
-
-Qa_real_mat = Q.adjoint()
-QaV = Qa_real_mat @ test_vec
-assert nanoeigenpy.is_approx(QtV, QaV)
-
-A_dense = A.toarray()
-P_indices = np.array([P.indices()[i] for i in range(dim)])
-A_permuted = A_dense[:, P_indices]
-
-QtAP = Qt @ A_permuted
-R_dense = spqr.matrixR().toarray()
-assert nanoeigenpy.is_approx(QtAP, R_dense)
+
+def test_sparse_qr():
+    dim = 100
+    rng = np.random.default_rng()
+
+    A_fac = spa.random(dim, dim, density=0.25, random_state=rng)
+    A = A_fac.T @ A_fac
+    A += spa.diags(10.0 * rng.standard_normal(dim) ** 2)
+    A = A.tocsc(True)
+    A.check_format()
+
+    spqr = nanoeigenpy.SparseQR(A)
+
+    assert spqr.info() == nanoeigenpy.ComputationInfo.Success
+
+    X = rng.random((dim, 20))
+    B = A.dot(X)
+    X_est = spqr.solve(B)
+    assert isinstance(X_est, np.ndarray)
+    assert nanoeigenpy.is_approx(X, X_est)
+    assert nanoeigenpy.is_approx(A.dot(X_est), B)
+
+    spqr.analyzePattern(A)
+    spqr.factorize(A)
+
+    X_sparse = spa.random(dim, 10, random_state=rng)
+    B_sparse = A.dot(X_sparse)
+    B_sparse: spa.csc_matrix = B_sparse.tocsc(True)
+    if not B_sparse.has_sorted_indices:
+        B_sparse.sort_indices()
+
+    X_est = spqr.solve(B_sparse)
+    assert isinstance(X_est, spa.csc_matrix)
+    assert nanoeigenpy.is_approx(X_est.toarray(), X_sparse.toarray())
+    assert nanoeigenpy.is_approx(A.dot(X_est.toarray()), B_sparse.toarray())
+
+    Q = spqr.matrixQ()
+    R = spqr.matrixR()
+    P = spqr.colsPermutation()
+
+    assert spqr.matrixQ().rows() == dim
+    assert spqr.matrixQ().cols() == dim
+    assert R.shape[0] == dim
+    assert R.shape[1] == dim
+    assert P.indices().size == dim
+
+    test_vec = rng.random(dim)
+    test_matrix = rng.random((dim, 20))
+
+    Qv = Q @ test_vec
+    QM = Q @ test_matrix
+    Qt = Q.transpose()
+    QtV = Qt @ test_vec
+    QtM = Qt @ test_matrix
+
+    assert Qv.shape == (dim,)
+    assert QM.shape == (dim, 20)
+    assert QtV.shape == (dim,)
+    assert QtM.shape == (dim, 20)
+
+    Qa_real_mat = Q.adjoint()
+    QaV = Qa_real_mat @ test_vec
+    assert nanoeigenpy.is_approx(QtV, QaV)
+
+    A_dense = A.toarray()
+    P_indices = np.array([P.indices()[i] for i in range(dim)])
+    A_permuted = A_dense[:, P_indices]
+
+    QtAP = Qt @ A_permuted
+    R_dense = spqr.matrixR().toarray()
+    assert nanoeigenpy.is_approx(QtAP, R_dense)
diff --git a/tests/test_tridiagonalization.py b/tests/test_tridiagonalization.py
index 7b8a2f78..9a6d8b58 100644
--- a/tests/test_tridiagonalization.py
+++ b/tests/test_tridiagonalization.py
@@ -1,103 +1,104 @@
 import nanoeigenpy
 import numpy as np
 
-dim = 100
-rng = np.random.default_rng()
-A = rng.random((dim, dim))
-A = (A + A.T) * 0.5
-
-tri = nanoeigenpy.Tridiagonalization(A)
-
-Q = tri.matrixQ()
-T = tri.matrixT()
-
-assert nanoeigenpy.is_approx(A, Q @ T @ Q.T)
-assert nanoeigenpy.is_approx(Q @ Q.T, np.eye(dim))
-
-for i in range(dim):
-    for j in range(dim):
-        if abs(i - j) > 1:
-            assert abs(T[i, j]) < 1e-12
-
-assert nanoeigenpy.is_approx(T, T.T)
-
-diag = tri.diagonal()
-sub_diag = tri.subDiagonal()
-
-for i in range(dim):
-    assert abs(diag[i] - T[i, i]) < 1e-12
-
-for i in range(dim - 1):
-    assert abs(sub_diag[i] - T[i + 1, i]) < 1e-12
-
-A_test = rng.random((dim, dim))
-A_test = (A_test + A_test.T) * 0.5
-
-tri1 = nanoeigenpy.Tridiagonalization(dim)
-tri1.compute(A_test)
-tri2 = nanoeigenpy.Tridiagonalization(A_test)
-
-Q1 = tri1.matrixQ()
-T1 = tri1.matrixT()
-Q2 = tri2.matrixQ()
-T2 = tri2.matrixT()
-
-assert nanoeigenpy.is_approx(Q1, Q2)
-assert nanoeigenpy.is_approx(T1, T2)
-
-h_coeffs = tri.householderCoefficients()
-packed = tri.packedMatrix()
-
-assert h_coeffs.shape == (dim - 1,)
-assert packed.shape == (dim, dim)
-
-for i in range(dim):
-    for j in range(i + 1, dim):
-        assert abs(packed[i, j] - A[i, j]) < 1e-12
-
-for i in range(dim):
-    assert abs(packed[i, i] - T[i, i]) < 1e-12
-    if i < dim - 1:
-        assert abs(packed[i + 1, i] - T[i + 1, i]) < 1e-12
-
-A_diag = np.diag(rng.random(dim))
-tri_diag = nanoeigenpy.Tridiagonalization(A_diag)
-Q_diag = tri_diag.matrixQ()
-T_diag = tri_diag.matrixT()
-
-assert nanoeigenpy.is_approx(A_diag, Q_diag @ T_diag @ Q_diag.T)
-for i in range(dim):
-    for j in range(dim):
-        if i != j:
-            assert abs(T_diag[i, j]) < 1e-10
-
-A_tridiag = np.zeros((dim, dim))
-for i in range(dim):
-    A_tridiag[i, i] = rng.random()
-    if i < dim - 1:
-        val = rng.random()
-        A_tridiag[i, i + 1] = val
-        A_tridiag[i + 1, i] = val
-
-tri_tridiag = nanoeigenpy.Tridiagonalization(A_tridiag)
-Q_tridiag = tri_tridiag.matrixQ()
-T_tridiag = tri_tridiag.matrixT()
-
-assert nanoeigenpy.is_approx(A_tridiag, Q_tridiag @ T_tridiag @ Q_tridiag.T)
-
-
-tri1_id = nanoeigenpy.Tridiagonalization(dim)
-tri2_id = nanoeigenpy.Tridiagonalization(dim)
-id1 = tri1_id.id()
-id2 = tri2_id.id()
-assert id1 != id2
-assert id1 == tri1_id.id()
-assert id2 == tri2_id.id()
-
-tri3_id = nanoeigenpy.Tridiagonalization(A)
-tri4_id = nanoeigenpy.Tridiagonalization(A)
-id3 = tri3_id.id()
-id4 = tri4_id.id()
-assert id3 != id4
-assert id3 == tri3_id.id()
-assert id4 == tri4_id.id()
+
+def test_tridiagonalization():
+    dim = 100
+    rng = np.random.default_rng()
+    A = rng.random((dim, dim))
+    A = (A + A.T) * 0.5
+
+    tri = nanoeigenpy.Tridiagonalization(A)
+
+    Q = tri.matrixQ()
+    T = tri.matrixT()
+
+    assert nanoeigenpy.is_approx(A, Q @ T @ Q.T)
+    assert nanoeigenpy.is_approx(Q @ Q.T, np.eye(dim))
+
+    for i in range(dim):
+        for j in range(dim):
+            if abs(i - j) > 1:
+                assert abs(T[i, j]) < 1e-12
+
+    assert nanoeigenpy.is_approx(T, T.T)
+
+    diag = tri.diagonal()
+    sub_diag = tri.subDiagonal()
+
+    for i in range(dim):
+        assert abs(diag[i] - T[i, i]) < 1e-12
+
+    for i in range(dim - 1):
+        assert abs(sub_diag[i] - T[i + 1, i]) < 1e-12
+
+    A_test = rng.random((dim, dim))
+    A_test = (A_test + A_test.T) * 0.5
+
+    tri1 = nanoeigenpy.Tridiagonalization(dim)
+    tri1.compute(A_test)
+    tri2 = nanoeigenpy.Tridiagonalization(A_test)
+
+    Q1 = tri1.matrixQ()
+    T1 = tri1.matrixT()
+    Q2 = tri2.matrixQ()
+    T2 = tri2.matrixT()
+
+    assert nanoeigenpy.is_approx(Q1, Q2)
+    assert nanoeigenpy.is_approx(T1, T2)
+
+    h_coeffs = tri.householderCoefficients()
+    packed = tri.packedMatrix()
+
+    assert h_coeffs.shape == (dim - 1,)
+    assert packed.shape == (dim, dim)
+
+    for i in range(dim):
+        for j in range(i + 1, dim):
+            assert abs(packed[i, j] - A[i, j]) < 1e-12
+
+    for i in range(dim):
+        assert abs(packed[i, i] - T[i, i]) < 1e-12
+        if i < dim - 1:
+            assert abs(packed[i + 1, i] - T[i + 1, i]) < 1e-12
+
+    A_diag = np.diag(rng.random(dim))
+    tri_diag = nanoeigenpy.Tridiagonalization(A_diag)
+    Q_diag = tri_diag.matrixQ()
+    T_diag = tri_diag.matrixT()
+
+    assert nanoeigenpy.is_approx(A_diag, Q_diag @ T_diag @ Q_diag.T)
+    for i in range(dim):
+        for j in range(dim):
+            if i != j:
+                assert abs(T_diag[i, j]) < 1e-10
+
+    A_tridiag = np.zeros((dim, dim))
+    for i in range(dim):
+        A_tridiag[i, i] = rng.random()
+        if i < dim - 1:
+            val = rng.random()
+            A_tridiag[i, i + 1] = val
+            A_tridiag[i + 1, i] = val
+
+    tri_tridiag = nanoeigenpy.Tridiagonalization(A_tridiag)
+    Q_tridiag = tri_tridiag.matrixQ()
+    T_tridiag = tri_tridiag.matrixT()
+
+    assert nanoeigenpy.is_approx(A_tridiag, Q_tridiag @ T_tridiag @ Q_tridiag.T)
+
+    tri1_id = nanoeigenpy.Tridiagonalization(dim)
+    tri2_id = nanoeigenpy.Tridiagonalization(dim)
+    id1 = tri1_id.id()
+    id2 = tri2_id.id()
+    assert id1 != id2
+    assert id1 == tri1_id.id()
+    assert id2 == tri2_id.id()
+
+    tri3_id = nanoeigenpy.Tridiagonalization(A)
+    tri4_id = nanoeigenpy.Tridiagonalization(A)
+    id3 = tri3_id.id()
+    id4 = tri4_id.id()
+    assert id3 != id4
+    assert id3 == tri3_id.id()
+    assert id4 == tri4_id.id()