diff --git a/pixi.lock b/pixi.lock
index eb033db0..0c633805 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,13 +1,13 @@
 version: 7
 platforms:
-- name: p1
+- name: linux-64-glibc-2-35
   subdir: linux-64
   virtual-packages:
   - __glibc=2.35
   - __unix=0=0
   - __linux=4.18
   - __archspec=0=x86_64
-- name: p2
+- name: osx-arm64-macos-14-0
   subdir: osx-arm64
   virtual-packages:
   - __osx=14.0
@@ -25,7 +25,7 @@ environments:
     indexes:
     - https://pypi.org/simple
     packages:
-      p1:
+      linux-64-glibc-2-35:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
@@ -298,7 +298,7 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/fe/3b/8ec5074bcfc450fe84273713b4b0a0dd47c0249358f5d82eb8104ffe2520/multidict-6.7.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/fe/d7/a71b12b625c44cfcb95d41397752edccc0a731903081609cffd6720f671d/asteval-1.0.9-py3-none-any.whl
-      p2:
+      osx-arm64-macos-14-0:
       - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_3.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.14.2-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda
@@ -832,7 +832,7 @@ environments:
     indexes:
     - https://pypi.org/simple
     packages:
-      p1:
+      linux-64-glibc-2-35:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py311h49ec1c0_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
@@ -1103,7 +1103,7 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/fb/e2/79c688af8b210d232694e31e59da9f6ec747bae31c3f5946e4e9b98860d5/click-8.4.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/fe/d7/a71b12b625c44cfcb95d41397752edccc0a731903081609cffd6720f671d/asteval-1.0.9-py3-none-any.whl
-      p2:
+      osx-arm64-macos-14-0:
       - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.14.2-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda
@@ -1628,7 +1628,7 @@ environments:
     indexes:
     - https://pypi.org/simple
     packages:
-      p1:
+      linux-64-glibc-2-35:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
@@ -1901,7 +1901,7 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/fe/3b/8ec5074bcfc450fe84273713b4b0a0dd47c0249358f5d82eb8104ffe2520/multidict-6.7.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/fe/d7/a71b12b625c44cfcb95d41397752edccc0a731903081609cffd6720f671d/asteval-1.0.9-py3-none-any.whl
-      p2:
+      osx-arm64-macos-14-0:
       - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_3.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.14.2-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda
@@ -2435,7 +2435,7 @@ environments:
     indexes:
     - https://pypi.org/simple
     packages:
-      p1:
+      linux-64-glibc-2-35:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
@@ -2598,7 +2598,7 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/fe/3b/8ec5074bcfc450fe84273713b4b0a0dd47c0249358f5d82eb8104ffe2520/multidict-6.7.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/fe/d7/a71b12b625c44cfcb95d41397752edccc0a731903081609cffd6720f671d/asteval-1.0.9-py3-none-any.whl
-      p2:
+      osx-arm64-macos-14-0:
       - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_3.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.14.2-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda
diff --git a/pixi.toml b/pixi.toml
index a3237349..4873d04f 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -3,14 +3,13 @@
 ###########
 
 [workspace]
-
 # Supported platforms for the lock file (pixi.lock)
 platforms = [
   'win-64',
   # Set minimum supported version for glibc to be 2.35 to ensure packages
   # like `crysfml` that only have wheels for glibc 2.35+
   # (manylinux_2_35_x86_64) are used.
-  #libc = { family = 'glibc', version = '2.35' }
+  # libc = { family = 'glibc', version = '2.35' }
   { platform = 'linux-64', glibc = '2.35' },
   # Set minimum supported version for macOS to be 14.0 to ensure packages
   # like `scipp` that only have wheels for macOS 14.0+ (macosx_14_0_arm64)
@@ -44,8 +43,8 @@ python = '3.14.*'
 # editable installations.
 
 [feature.dev.dependencies]
-nodejs = '*'      # Required for Prettier (non-Python formatting)
-pixi-kernel = '*' # Pixi Jupyter kernel
+nodejs = '*'  # Required for Prettier (non-Python formatting)
+pixi-kernel = '*'  # Pixi Jupyter kernel
 
 [feature.dev.pypi-dependencies]
 pip = '*'
@@ -55,7 +54,7 @@ easyscience = { path = '.', editable = true, extras = ['dev'] }
 # of editable installations.
 
 [feature.user.dependencies]
-pixi-kernel = '*' # Pixi Jupyter kernel
+pixi-kernel = '*'  # Pixi Jupyter kernel
 
 [feature.user.pypi-dependencies]
 pip = '*'
@@ -66,7 +65,6 @@ easyscience = '*'
 ##############
 
 [environments]
-
 # The `default` feature is always included in all environments.
 # Additional features can be specified per environment.
 
@@ -87,7 +85,6 @@ user = { features = ['py-max', 'user'] }
 #######
 
 [tasks]
-
 ##################
 # 🧪 Testing Tasks
 ##################
@@ -172,9 +169,9 @@ notebook-tweak = 'python tools/tweak_notebooks.py docs/docs/tutorials/'
 notebook-exec = 'python -m pytest --nbmake docs/docs/tutorials/ --nbmake-timeout=1200 --overwrite --color=yes -n auto -v'
 
 notebook-prepare = { depends-on = [
-  #'notebook-convert',
+  # 'notebook-convert',
   'notebook-strip',
-  #'notebook-tweak',
+  # 'notebook-tweak',
 ] }
 
 ########################
@@ -266,7 +263,7 @@ clean-pycache = "find . -type d -name '__pycache__' -prune -exec rm -rf '{}' +"
 post-install = { depends-on = [
   'npm-config',
   'prettier-install',
-  #'pre-commit-setup',
+  # 'pre-commit-setup',
 ] }
 
 ##########################
diff --git a/prettierrc.toml b/prettierrc.toml
index b98c86eb..d8a956f5 100644
--- a/prettierrc.toml
+++ b/prettierrc.toml
@@ -1,22 +1,24 @@
 plugins = [
-  "prettier-plugin-toml", # use the TOML plugin
+  'prettier-plugin-toml',  # use the TOML plugin
 ]
 
-endOfLine = 'lf'     # change line endings to LF
-proseWrap = 'always' # change wrapping in Markdown files
-semi = false         # remove semicolons
-singleQuote = true   # use single quotes instead of double quotes
-tabWidth = 2         # change tab width to 2 spaces
-useTabs = false      # use spaces instead of tabs
+endOfLine = 'lf'  # change line endings to LF
+proseWrap = 'always'  # change wrapping in Markdown files
+semi = false  # remove semicolons
+singleQuote = true  # use single quotes instead of double quotes
+tabWidth = 2  # change tab width to 2 spaces
+useTabs = false  # use spaces instead of tabs
 
-printWidth = 79 # wrap lines at 79 characters
+printWidth = 79  # wrap lines at 79 characters
 
 [[overrides]]
-files = ["*.md"]
+files = ['*.md']
+
 [overrides.options]
-printWidth = 72 # wrap Markdown files at 72 characters
+printWidth = 72  # wrap Markdown files at 72 characters
 
 [[overrides]]
-files = ["*.yml", "*.yaml"]
+files = ['*.yml', '*.yaml']
+
 [overrides.options]
-printWidth = 88 # wrap YAML files at 88 characters
+printWidth = 88  # wrap YAML files at 88 characters
diff --git a/pyproject.toml b/pyproject.toml
index 89d28050..aa57296e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@
 
 [project]
 name = 'easyscience'
-dynamic = ['version'] # Use versioningit to manage the version
+dynamic = ['version']  # Use versioningit to manage the version
 description = 'Core building blocks for EasyScience'
 authors = [{ name = 'EasyScience contributors' }]
 readme = 'README.md'
@@ -24,54 +24,54 @@ classifiers = [
 ]
 requires-python = '>=3.11'
 dependencies = [
-  'asteval',      # Safely evaluate Python expressions from strings
-  'lmfit',        # Non-linear least squares fitting
-  'bumps>=1.0.4', # Bayesian uncertainty estimation
-  'dfo-ls',       # Derivative-free optimization
-  'numpy',        # Numerical computing
-  'scipp',        # Handling and analysis of scientific data
+  'asteval',  # Safely evaluate Python expressions from strings
+  'lmfit',  # Non-linear least squares fitting
+  'bumps>=1.0.4',  # Bayesian uncertainty estimation
+  'dfo-ls',  # Derivative-free optimization
+  'numpy',  # Numerical computing
+  'scipp',  # Handling and analysis of scientific data
 ]
 
 [project.optional-dependencies]
 dev = [
-  'pooch',                           # Data downloader
-  'matplotlib',                      # Plotting library
-  'jupyterlab',                      # Jupyter notebooks
-  'ipykernel',                       # Jupyter kernel (required for running notebooks)
-  'ipywidgets',                      # Widgets (needed for interactive matplotlib backends)
-  'ipympl',                          # Matplotlib Jupyter widget backend (%matplotlib widget)
-  'IPython',                         # Interactive Python shell
-  'GitPython',                       # Interact with Git repositories
-  'build',                           # Building the package
-  'pre-commit',                      # Pre-commit hooks
-  'jinja2',                          # Templating
-  'nbmake',                          # Building notebooks
-  'nbstripout',                      # Strip output from notebooks
-  'nbqa',                            # Linting and formatting notebooks
-  'pytest',                          # Testing
-  'pytest-cov',                      # Test coverage
-  'pytest-xdist',                    # Enable parallel testing
-  'ruff',                            # Linting and formatting code
-  'radon',                           # Code complexity and maintainability
-  'validate-pyproject[all]',         # Validate pyproject.toml
-  'versioningit',                    # Automatic versioning from git tags
-  'jupytext',                        # Jupyter notebook text format support
-  'jupyterquiz',                     # Quizzes in Jupyter notebooks
-  'pydoclint',                       # Docstring linter
-  'format-docstring',                # Docstring formatter
-  'docstripy',                       # Convert docstrings to other formats
-  'interrogate',                     # Docstring coverage checker
-  'copier',                          # Template management
-  'mike',                            # MkDocs: Versioned documentation support
-  'mkdocs',                          # Static site generator
-  'mkdocs-material',                 # Documentation framework on top of MkDocs
-  'mkdocs-autorefs',                 # MkDocs: Auto-references support
-  'mkdocs-jupyter',                  # MkDocs: Jupyter notebook support
-  'mkdocs-plugin-inline-svg',        # MkDocs: Inline SVG support
-  'mkdocs-markdownextradata-plugin', # MkDocs: Markdown extra data support, such as global variables
-  'mkdocstrings-python',             # MkDocs: Python docstring support
-  'pyyaml',                          # YAML parser
-  'spdx-headers',                    # SPDX license header validation
+  'pooch',  # Data downloader
+  'matplotlib',  # Plotting library
+  'jupyterlab',  # Jupyter notebooks
+  'ipykernel',  # Jupyter kernel (required for running notebooks)
+  'ipywidgets',  # Widgets (needed for interactive matplotlib backends)
+  'ipympl',  # Matplotlib Jupyter widget backend (%matplotlib widget)
+  'IPython',  # Interactive Python shell
+  'GitPython',  # Interact with Git repositories
+  'build',  # Building the package
+  'pre-commit',  # Pre-commit hooks
+  'jinja2',  # Templating
+  'nbmake',  # Building notebooks
+  'nbstripout',  # Strip output from notebooks
+  'nbqa',  # Linting and formatting notebooks
+  'pytest',  # Testing
+  'pytest-cov',  # Test coverage
+  'pytest-xdist',  # Enable parallel testing
+  'ruff',  # Linting and formatting code
+  'radon',  # Code complexity and maintainability
+  'validate-pyproject[all]',  # Validate pyproject.toml
+  'versioningit',  # Automatic versioning from git tags
+  'jupytext',  # Jupyter notebook text format support
+  'jupyterquiz',  # Quizzes in Jupyter notebooks
+  'pydoclint',  # Docstring linter
+  'format-docstring',  # Docstring formatter
+  'docstripy',  # Convert docstrings to other formats
+  'interrogate',  # Docstring coverage checker
+  'copier',  # Template management
+  'mike',  # MkDocs: Versioned documentation support
+  'mkdocs',  # Static site generator
+  'mkdocs-material',  # Documentation framework on top of MkDocs
+  'mkdocs-autorefs',  # MkDocs: Auto-references support
+  'mkdocs-jupyter',  # MkDocs: Jupyter notebook support
+  'mkdocs-plugin-inline-svg',  # MkDocs: Inline SVG support
+  'mkdocs-markdownextradata-plugin',  # MkDocs: Markdown extra data support, such as global variables
+  'mkdocstrings-python',  # MkDocs: Python docstring support
+  'pyyaml',  # YAML parser
+  'spdx-headers',  # SPDX license header validation
 ]
 
 [project.urls]
@@ -102,7 +102,7 @@ packages = ['src/easyscience']
 allow-direct-references = true
 
 [tool.hatch.version]
-source = 'versioningit' # Use versioningit to manage the version
+source = 'versioningit'  # Use versioningit to manage the version
 
 ################################
 # Configuration for versioningit
@@ -118,9 +118,9 @@ source = 'versioningit' # Use versioningit to manage the version
 # pixi.lock update without any changes to the source code.
 
 [tool.versioningit.format]
-distance = '{base_version}+dev{distance}'            # example: 1.2.3.post4+dev3
-dirty = '{base_version}+dirty{distance}'             # example: 0.5.8+dirty3
-distance-dirty = '{base_version}+devdirty{distance}' # example: 0.5.8+devdirty3
+distance = '{base_version}+dev{distance}'  # example: 1.2.3.post4+dev3
+dirty = '{base_version}+dirty{distance}'  # example: 0.5.8+dirty3
+distance-dirty = '{base_version}+devdirty{distance}'  # example: 0.5.8+devdirty3
 
 # Configure how versioningit detects versions from Git
 # - 'match' ensures it only considers tags starting with 'v'
@@ -138,7 +138,7 @@ default-tag = 'v999.0.0'
 # https://interrogate.readthedocs.io/en/latest/
 
 [tool.interrogate]
-fail-under = 45                      # Minimum docstring coverage percentage to pass
+fail-under = 45  # Minimum docstring coverage percentage to pass
 verbose = 1
 exclude = ['src/easyscience/legacy']
 
@@ -150,13 +150,13 @@ exclude = ['src/easyscience/legacy']
 # https://coverage.readthedocs.io/en/latest/
 
 [tool.coverage.run]
-branch = true    # Measure branch coverage as well
-source = ['src'] # Limit coverage to the source code directory
+branch = true  # Measure branch coverage as well
+source = ['src']  # Limit coverage to the source code directory
 
 [tool.coverage.report]
 show_missing = true  # Show missing lines
-skip_covered = false # Skip files with 100% coverage in the report
-fail_under = 80      # Minimum coverage percentage to pass
+skip_covered = false  # Skip files with 100% coverage in the report
+fail_under = 80  # Minimum coverage percentage to pass
 
 ##########################
 # Configuration for pytest
@@ -180,31 +180,31 @@ testpaths = ['tests']
 [tool.ruff]
 exclude = ['tests', 'legacy', 'src/easyscience/legacy']
 indent-width = 4
-line-length = 99 # See also `max-line-length` in [tool.ruff.lint.pycodestyle]
-preview = true # Enable new rules that are not yet stable, like DOC
+line-length = 99  # See also `max-line-length` in [tool.ruff.lint.pycodestyle]
+preview = true  # Enable new rules that are not yet stable, like DOC
 builtins = [
   'display',
-] # Clutch-fix/patch to https://github.com/nbQA-dev/nbQA/issues/882
+]  # Clutch-fix/patch to https://github.com/nbQA-dev/nbQA/issues/882
 # Formatting options for Ruff
 
 [tool.ruff.format]
-docstring-code-format = true    # Whether to format code snippets in docstrings
-docstring-code-line-length = 72 # Line length for code snippets in docstrings
-indent-style = 'space'          # PEP 8 recommends using spaces over tabs
-quote-style = 'single'          # But double quotes in docstrings (PEP 8, PEP 257)
+docstring-code-format = true  # Whether to format code snippets in docstrings
+docstring-code-line-length = 72  # Line length for code snippets in docstrings
+indent-style = 'space'  # PEP 8 recommends using spaces over tabs
+quote-style = 'single'  # But double quotes in docstrings (PEP 8, PEP 257)
 
 # Linting rules to use with Ruff
 
 [tool.ruff.lint]
 select = [
-  #  'E',   # General PEP 8 style errors
-  'E9', # Runtime errors (e.g., syntax errors, undefined names)
-  #  'F',   # Pyflakes-specific checks (e.g., unused variables, imports)
-  'F63', # Issues related to invalid escape sequences in strings
+  # 'E',   # General PEP 8 style errors
+  'E9',  # Runtime errors (e.g., syntax errors, undefined names)
+  # 'F',   # Pyflakes-specific checks (e.g., unused variables, imports)
+  'F63',  # Issues related to invalid escape sequences in strings
   'F7',  # Type-related errors (e.g., mismatched argument types, unsupported operations)
-  'F82', # Import-related errors (e.g., unresolved imports, reimported modules)
-  'I',   # Import sorting issues (e.g., unsorted imports)
-  #  'S',   # Security-related issues (e.g., use of insecure functions or libraries)
+  'F82',  # Import-related errors (e.g., unresolved imports, reimported modules)
+  'I',  # Import sorting issues (e.g., unsorted imports)
+  # 'S',   # Security-related issues (e.g., use of insecure functions or libraries)
   #  'W',   # General PEP 8 warnings (e.g., lines too long, trailing whitespace)
 ]
 
@@ -212,31 +212,31 @@ select = [
 
 # Ignore specific rules globally
 ignore = [
-  'COM812', # https://docs.astral.sh/ruff/rules/missing-trailing-comma/
+  'COM812',  # https://docs.astral.sh/ruff/rules/missing-trailing-comma/
   # Replaced by 'D' plus pydocstyle and pydoclint
-  'DOC', # https://docs.astral.sh/ruff/rules/#pydoclint-doc
+  'DOC',  # https://docs.astral.sh/ruff/rules/#pydoclint-doc
   # Disable, as [tool.format_docstring] split one-line docstrings into the canonical multi-line layout
-  'D200', # https://docs.astral.sh/ruff/rules/unnecessary-multiline-docstring/
+  'D200',  # https://docs.astral.sh/ruff/rules/unnecessary-multiline-docstring/
 ]
 
 # Ignore specific rules in certain files or directories
 [tool.ruff.lint.per-file-ignores]
 '*/__init__.py' = [
-  'F401', # re-exports are intentional in __init__.py
+  'F401',  # re-exports are intentional in __init__.py
 ]
 'tests/**' = [
-  'ANN',    # https://docs.astral.sh/ruff/rules/#flake8-annotations-ann
-  'D',      # https://docs.astral.sh/ruff/rules/#pydocstyle-d
-  'DOC',    # https://docs.astral.sh/ruff/rules/#pydoclint-doc
-  'INP001', # https://docs.astral.sh/ruff/rules/implicit-namespace-package/
-  'S101',   # https://docs.astral.sh/ruff/rules/assert/
+  'ANN',  # https://docs.astral.sh/ruff/rules/#flake8-annotations-ann
+  'D',  # https://docs.astral.sh/ruff/rules/#pydocstyle-d
+  'DOC',  # https://docs.astral.sh/ruff/rules/#pydoclint-doc
+  'INP001',  # https://docs.astral.sh/ruff/rules/implicit-namespace-package/
+  'S101',  # https://docs.astral.sh/ruff/rules/assert/
 ]
 'docs/**' = [
-  'INP001', # https://docs.astral.sh/ruff/rules/implicit-namespace-package/
-  'T201',   # https://docs.astral.sh/ruff/rules/print/
+  'INP001',  # https://docs.astral.sh/ruff/rules/implicit-namespace-package/
+  'T201',  # https://docs.astral.sh/ruff/rules/print/
 ]
 'docs/docs/tutorials/**' = [
-  'E402', # https://docs.astral.sh/ruff/rules/module-import-not-at-top-of-file/
+  'E402',  # https://docs.astral.sh/ruff/rules/module-import-not-at-top-of-file/
 ]
 
 # Specific options for certain rules
@@ -258,7 +258,7 @@ max-complexity = 10
 # https://peps.python.org/pep-0008/#maximum-line-length
 # Use 99 characters as the project-wide maximum for regular code lines.
 # Use 72 characters for docstrings.
-max-line-length = 99 # See also `line-length` in [tool.ruff]
+max-line-length = 99  # See also `line-length` in [tool.ruff]
 max-doc-length = 72
 
 [tool.ruff.lint.pydocstyle]
diff --git a/src/easyscience/fitting/minimizers/minimizer_bumps.py b/src/easyscience/fitting/minimizers/minimizer_bumps.py
index 315d856c..500edce6 100644
--- a/src/easyscience/fitting/minimizers/minimizer_bumps.py
+++ b/src/easyscience/fitting/minimizers/minimizer_bumps.py
@@ -417,42 +417,45 @@ def mcmc_sample(
         weights : np.ndarray
             Flattened weight array.
         samples : int, default=10000
-            Number of raw samples to draw across all chains, before thinning.
-            A guaranteed minimum, not an exact count: DREAM advances in
-            blocks of 10 generations (one generation = one draw per chain)
-            and stops at the first block boundary at or past ``samples``.
+            Number of raw samples to draw across all chains, before
+            thinning. A guaranteed minimum, not an exact count: DREAM
+            advances in blocks of 10 generations (one generation = one
+            draw per chain) and stops at the first block boundary at or
+            past ``samples``.
         burn : int, default=2000
             Burn-in generations to discard. BUMPS counts ``burn`` in
-            generations while ``samples`` counts raw draws, so ``burn=500``
-            discards ``500 * n_chains`` raw samples.
+            generations while ``samples`` counts raw draws, so
+            ``burn=500`` discards ``500 * n_chains`` raw samples.
         thin : int, default=10
-            Thinning interval — only every ``thin``-th generation is stored.
+            Thinning interval — only every ``thin``-th generation is
+            stored.
         population : int | None, default=None
-            BUMPS DREAM population count per parameter (number of parallel
-            chains): BUMPS creates ``ceil(population * n_parameters)`` chains.
+            BUMPS DREAM population count per parameter (number of
+            parallel chains): BUMPS creates ``ceil(population *
+            n_parameters)`` chains.
         resume_state : MCMCDraw | None, default=None
             A BUMPS ``MCMCDraw`` state object from a previous
-            ``mcmc_sample()`` call (e.g. ``PosteriorResults.sampler_state``).
-            When provided, DREAM **continues** the saved chain instead of
-            starting cold.  The population, parameter count, and parameter
-            names must match the current model — a ``ValueError`` is raised
-            otherwise.
-
-            ``samples`` must be the **total** number of raw samples, not an
-            increment: to extend an existing chain of ``N`` raw samples by
-            ``M``, pass ``samples=N + M`` (DREAM keeps only the last
-            ``samples`` draws in its buffer). The `Sampler.extend` helper
-            computes this for you.
-
-            ``burn`` is forced to 0 on resume: a previously-converged chain is
-            never re-burned.
-
-            The ``population`` and ``initializer`` parameters
-            have **no effect** when ``resume_state`` is provided — they
-            are determined by the saved state.
-
-            Resuming against *different* data is undefined behaviour (the
-            chain's likelihood changes underneath it).
+            ``mcmc_sample()`` call (e.g.
+            ``PosteriorResults.sampler_state``). When provided, DREAM
+            **continues** the saved chain instead of starting cold.  The
+            population, parameter count, and parameter names must match
+            the current model — a ``ValueError`` is raised otherwise.
+
+            ``samples`` must be the **total** number of raw samples, not
+            an increment: to extend an existing chain of ``N`` raw
+            samples by ``M``, pass ``samples=N + M`` (DREAM keeps only
+            the last ``samples`` draws in its buffer). The
+            ``Sampler.extend`` helper computes this for you.
+
+            ``burn`` is forced to 0 on resume: a previously-converged
+            chain is never re-burned.
+
+            The ``population`` and ``initializer`` parameters have **no
+            effect** when ``resume_state`` is provided — they are
+            determined by the saved state.
+
+            Resuming against *different* data is undefined behaviour
+            (the chain's likelihood changes underneath it).
         sampler_kwargs : dict | None, default=None
             Additional keyword arguments forwarded to
             ``bumps.fitters.fit``.
@@ -475,9 +478,9 @@ def mcmc_sample(
         ------
         ValueError
             If the input shapes or weights are invalid, if
-            ``progress_callback`` is not callable, or if ``resume_state``
-            is incompatible with the current model (parameter count,
-            names/order, or population mismatch).
+            ``progress_callback`` is not callable, or if
+            ``resume_state`` is incompatible with the current model
+            (parameter count, names/order, or population mismatch).
         FitError
             If DREAM sampling was aborted by the user (via
             ``abort_test``).
@@ -608,28 +611,32 @@ def _validate_resume_state(
         population: int | None,
         burn: int,
     ) -> tuple[int, int]:
-        """Check that ``resume_state`` is compatible with ``problem`` and
+        """
+        Check that ``resume_state`` is compatible with ``problem`` and
         resolve the population and burn values to use when resuming.
 
         Parameters
         ----------
         problem : FitProblem
-            The freshly built BUMPS ``FitProblem`` for the current model.
+            The freshly built BUMPS ``FitProblem`` for the current
+            model.
         resume_state : MCMCDraw
             The saved chain state to resume from.
         population : int | None
             The caller-supplied population scale factor, or ``None``.
         burn : int
-            The caller-supplied burn-in, ignored (with a warning) on resume.
+            The caller-supplied burn-in, ignored (with a warning) on
+            resume.
 
         Returns
         -------
         tuple[int, int]
             ``(population, burn)`` to pass to DREAM. The population is
             returned as a **negative** number, which BUMPS'
-            ``initpop.generate`` reads as an absolute chain count, exactly
-            reproducing the saved state's population. ``burn`` is always 0:
-            a previously converged chain is never re-burned.
+            ``initpop.generate`` reads as an absolute chain count,
+            exactly reproducing the saved state's population. ``burn``
+            is always 0: a previously converged chain is never
+            re-burned.
 
         Raises
         ------
diff --git a/src/easyscience/fitting/sampler.py b/src/easyscience/fitting/sampler.py
index d3915115..3917a5bc 100644
--- a/src/easyscience/fitting/sampler.py
+++ b/src/easyscience/fitting/sampler.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
-"""Bayesian MCMC sampling — the ``Sampler`` class and persistence helpers."""
+"""
+Bayesian MCMC sampling — the ``Sampler`` class and persistence helpers.
+"""
 
 from __future__ import annotations
 
@@ -38,7 +40,9 @@ def _data_fingerprint(
     y_list: list,
     w_list: list,
 ) -> str | None:
-    """Return a SHA-256 hex digest of concatenated (x|y|weights), or None."""
+    """
+    Return a SHA-256 hex digest of concatenated (x|y|weights), or None.
+    """
     try:
         h = hashlib.sha256()
         for arr in list(x_list) + list(y_list) + list(w_list):
@@ -53,7 +57,8 @@ def _validate_dataset_arrays(
     data: np.ndarray | list | tuple,
     allow_none_entries: bool = False,
 ) -> None:
-    """Check that ``data`` (an array or list of arrays) holds numeric,
+    """
+    Check that ``data`` (an array or list of arrays) holds numeric,
     at-least-1-D, non-empty arrays.
 
     Structural checks (array vs list, matching dataset counts) are in
@@ -101,12 +106,13 @@ def _validate_dataset_arrays(
 
 
 def _copy_data(data):
-    """Copy an array (or list of arrays) and mark the copies read-only.
+    """
+    Copy an array (or list of arrays) and mark the copies read-only.
 
-    The ``Sampler`` binds its data at construction; copying decouples the
-    bound data from the caller's arrays, and the read-only flag stops
-    in-place mutation of the copies, so the chain and the ``save()``
-    fingerprint always describe the data actually sampled.
+    The ``Sampler`` binds its data at construction; copying decouples
+    the bound data from the caller's arrays, and the read-only flag
+    stops in-place mutation of the copies, so the chain and the
+    ``save()`` fingerprint always describe the data actually sampled.
     """
     if data is None:
         return None
@@ -118,10 +124,13 @@ def _copy_data(data):
 
 
 def _validate_chain_path(path: str | os.PathLike, skip: int = 0) -> str:
-    """Validate the persistence arguments shared by the save/load functions.
+    """
+    Validate the persistence arguments shared by the save/load
+    functions.
 
-    Returns ``path`` coerced to ``str``. Raises ``TypeError`` if ``path`` is
-    not path-like and ``ValueError`` if ``skip`` is not a non-negative int.
+    Returns ``path`` coerced to ``str``. Raises ``TypeError`` if
+    ``path`` is not path-like and ``ValueError`` if ``skip`` is not a
+    non-negative int.
     """
     if not isinstance(path, (str, os.PathLike)):
         raise TypeError(f'path must be a str or os.PathLike, got {type(path).__name__}.')
@@ -131,14 +140,15 @@ def _validate_chain_path(path: str | os.PathLike, skip: int = 0) -> str:
 
 
 def _load_bumps_state(path: str, skip: int = 0) -> MCMCDraw:
-    """Read a BUMPS chain from disk, working around a BUMPS bug.
+    """
+    Read a BUMPS chain from disk, working around a BUMPS bug.
 
     ``bumps.dream.state.load_state`` reads the saved buffers with
-    ``numpy.loadtxt``, which collapses a single-row file to a 1-D array. A
-    short chain stores a single CR-weight update row, so ``load_state``'s
-    subsequent ``stats[:, 0]`` indexing raises ``IndexError: too many indices
-    for array``. We coerce each buffer read back to 2-D before ``load_state``
-    consumes it.
+    ``numpy.loadtxt``, which collapses a single-row file to a 1-D array.
+    A short chain stores a single CR-weight update row, so
+    ``load_state``'s subsequent ``stats[:, 0]`` indexing raises
+    ``IndexError: too many indices for array``. We coerce each buffer
+    read back to 2-D before ``load_state`` consumes it.
 
     Parameters
     ----------
@@ -170,28 +180,30 @@ def _loadtxt_2d(file, report: int = 0) -> np.ndarray:
 
 
 def load_chain(path: str | os.PathLike, skip: int = 0) -> tuple[MCMCDraw, list[str] | None, dict]:
-    """Reload a DREAM chain state saved by ``Sampler.save``.
+    """
+    Reload a DREAM chain state saved by ``Sampler.save``.
 
-    This is the standalone reader: unlike ``Sampler.load_state`` it needs no
-    fitter, model or data, so a saved chain can be inspected or post-processed
-    on a machine that does not have the model. Parameter names are restored
-    from the sidecar when available (schema versions 1 and 2), falling back to
-    the state's labels with the minimizer prefix stripped.
+    This is the standalone reader: unlike ``Sampler.load_state`` it
+    needs no fitter, model or data, so a saved chain can be inspected or
+    post-processed on a machine that does not have the model. Parameter
+    names are restored from the sidecar when available (schema versions
+    1 and 2), falling back to the state's labels with the minimizer
+    prefix stripped.
 
     Parameters
     ----------
     path : str | os.PathLike
         File path prefix used when saving.
     skip : int, default=0
-        Discard the first ``skip`` saved generations on load, forwarded to
-        ``bumps.dream.state.load_state``.
+        Discard the first ``skip`` saved generations on load, forwarded
+        to ``bumps.dream.state.load_state``.
 
     Returns
     -------
     tuple[MCMCDraw, list[str] | None, dict]
-        The reloaded BUMPS chain state, the parameter names (or ``None`` if
-        neither sidecar nor labels yielded them), and the raw sidecar dict
-        (empty if absent/unreadable).
+        The reloaded BUMPS chain state, the parameter names (or ``None``
+        if neither sidecar nor labels yielded them), and the raw sidecar
+        dict (empty if absent/unreadable).
 
     Raises
     ------
@@ -229,15 +241,18 @@ def load_chain(path: str | os.PathLike, skip: int = 0) -> tuple[MCMCDraw, list[s
 
 @dataclass
 class SamplingResults:
-    """Structured result of an MCMC sampling run (analogous to ``FitResults``).
+    """
+    Structured result of an MCMC sampling run (analogous to
+    ``FitResults``).
 
     Attributes
     ----------
     draws : np.ndarray
-        Posterior samples, shape ``(n_samples, n_params)``. For results from
-        ``Sampler.sample()``/``Sampler.extend()`` this is a *trimmed,
-        outlier-filtered* view of the chain, not the raw buffer, so it holds
-        fewer rows than ``samples / thin`` — see the notes on ``Sampler``.
+        Posterior samples, shape ``(n_samples, n_params)``. For results
+        from ``Sampler.sample()``/``Sampler.extend()`` this is a
+        *trimmed, outlier-filtered* view of the chain, not the raw
+        buffer, so it holds fewer rows than ``samples / thin`` — see the
+        notes on ``Sampler``.
     param_names : list[str]
         Parameter names (one per column of ``draws``).
     logp : np.ndarray
@@ -252,8 +267,10 @@ class SamplingResults:
     state: MCMCDraw
 
     def to_legacy_dict(self) -> dict:
-        """Return the legacy dict shape produced by the deprecated
-        ``mcmc_sample()`` APIs."""
+        """
+        Return the legacy dict shape produced by the deprecated
+        ``mcmc_sample()`` APIs.
+        """
         return {
             'draws': self.draws,
             'param_names': self.param_names,
@@ -263,58 +280,64 @@ def to_legacy_dict(self) -> dict:
 
 
 class Sampler:
-    """Bayesian MCMC sampler for one dataset, backed by a Fitter's BUMPS minimizer.
-
-    One ``Sampler`` instance represents one chain over one ``(x, y, weights)``
-    dataset. The data is bound at construction; ``sample()`` and ``extend()``
-    take no data arguments, so a chain can never be extended against different
-    data (undefined behaviour in BUMPS). The bound data is a defensive,
-    read-only copy of the caller's arrays, exposed via the ``x``, ``y`` and
-    ``weights`` properties — mutating the originals after construction has no
-    effect on the sampler, and there are deliberately no setters: to sample
-    different data, create a new ``Sampler``.
-
-    Construct directly with a configured ``Fitter`` (or ``MultiFitter``) whose
-    minimizer has been switched to ``AvailableMinimizers.Bumps``. **Running a
-    fit first is not required** — the ``Fitter`` supplies the model and the
-    minimizer, not a fit result, and sampling from the initial parameter values
-    works fine.
-
-    It is often worth fitting first anyway. DREAM seeds its whole starting
-    population inside a tiny ball around the parameters' *current* values
-    (BUMPS' default ``init='eps'``), so sampling from fitted values starts the
-    chain in the right region and shortens the burn-in needed to reach the
-    typical set. From a poor initial guess, expect to burn for longer.
-
-    The sampler is BUMPS/DREAM-specific for now: the BUMPS check in ``_run()``
-    is the seam where another backend would plug in.
+    """
+    Bayesian MCMC sampler for one dataset, backed by a Fitter's BUMPS
+    minimizer.
+
+    One ``Sampler`` instance represents one chain over one ``(x, y,
+    weights)`` dataset. The data is bound at construction; ``sample()``
+    and ``extend()`` take no data arguments, so a chain can never be
+    extended against different data (undefined behaviour in BUMPS). The
+    bound data is a defensive, read-only copy of the caller's arrays,
+    exposed via the ``x``, ``y`` and ``weights`` properties — mutating
+    the originals after construction has no effect on the sampler, and
+    there are deliberately no setters: to sample different data, create
+    a new ``Sampler``.
+
+    Construct directly with a configured ``Fitter`` (or ``MultiFitter``)
+    whose minimizer has been switched to ``AvailableMinimizers.Bumps``.
+    **Running a fit first is not required** — the ``Fitter`` supplies
+    the model and the minimizer, not a fit result, and sampling from the
+    initial parameter values works fine.
+
+    It is often worth fitting first anyway. DREAM seeds its whole
+    starting population inside a tiny ball around the parameters'
+    *current* values (BUMPS' default ``init='eps'``), so sampling from
+    fitted values starts the chain in the right region and shortens the
+    burn-in needed to reach the typical set. From a poor initial guess,
+    expect to burn for longer.
+
+    The sampler is BUMPS/DREAM-specific for now: the BUMPS check in
+    ``_run()`` is the seam where another backend would plug in.
 
     Parameters
     ----------
-    fitter : Fitter
-        A configured ``Fitter`` (or ``MultiFitter``) whose minimizer has been
-        switched to ``AvailableMinimizers.Bumps``.
+    fitter : 'Fitter'
+        A configured ``Fitter`` (or ``MultiFitter``) whose minimizer has
+        been switched to ``AvailableMinimizers.Bumps``.
     x : np.ndarray | list[np.ndarray]
-        Independent variable array (or list of arrays for ``MultiFitter``).
+        Independent variable array (or list of arrays for
+        ``MultiFitter``).
     y : np.ndarray | list[np.ndarray]
-        Dependent variable array (or list of arrays for ``MultiFitter``).
+        Dependent variable array (or list of arrays for
+        ``MultiFitter``).
     weights : np.ndarray | list[np.ndarray | None] | None, default=None
         Weight array (or list of arrays for ``MultiFitter``).
     vectorized : bool, default=False
         When ``True``, each x array may be multi-dimensional (e.g. an
         ``(N, M, 2)`` grid for a 2D model) and is left as-is.
     sampler_kwargs : dict | None, default=None
-        Per-instance default keyword arguments forwarded to the BUMPS DREAM
-        sampler on every run, merged with (and overridden by) per-call
-        ``sampler_kwargs``.
+        Per-instance default keyword arguments forwarded to the BUMPS
+        DREAM sampler on every run, merged with (and overridden by)
+        per-call ``sampler_kwargs``.
 
     Raises
     ------
     TypeError
-        If ``fitter`` is not Fitter-shaped (no ``minimizer``/``fit_function``),
-        if any dataset in ``x``/``y``/``weights`` is not a numeric array
-        (e.g. a string), or ``vectorized``/``sampler_kwargs`` have the wrong
-        type.
+        If ``fitter`` is not Fitter-shaped (no
+        ``minimizer``/``fit_function``), if any dataset in
+        ``x``/``y``/``weights`` is not a numeric array (e.g. a string),
+        or ``vectorized``/``sampler_kwargs`` have the wrong type.
     ValueError
         If ``x``, ``y`` and ``weights`` do not hold matching structures
         (all arrays, or lists of the same length), or any dataset is a
@@ -322,42 +345,44 @@ class Sampler:
 
     Notes
     -----
-    **The retained draws are a trimmed view, not the whole chain.** BUMPS'
-    DREAM sampler defaults to ``trim=True``: once sampling finishes it runs a
-    convergence-based burn-point detector over the chain and returns only the
-    portion after that point. ``state.draw()`` additionally drops chains
-    flagged as outliers. So ``results.draws`` is usually *smaller* than the
-    chain, and its length is not deterministic — the detector re-runs from
-    scratch on every ``sample()`` and ``extend()`` call and may place the burn
-    point differently each time. Read the count off the array; do not predict
-    it.
-
-    Nor is the chain itself exactly ``samples / thin`` rows: ``samples`` is a
-    guaranteed minimum, not an exact count. DREAM advances in blocks of 10
-    generations (one generation = one draw per chain) and only checks its
-    stopping condition between blocks, so the raw chain length is ``samples``
-    rounded up to a multiple of ``10 * n_chains``.
-
-    The trimming is only a *view* — nothing is dropped from the buffer. To
-    read the full untrimmed chain::
+    **The retained draws are a trimmed view, not the whole chain.**
+    BUMPS' DREAM sampler defaults to ``trim=True``: once sampling
+    finishes it runs a convergence-based burn-point detector over the
+    chain and returns only the portion after that point.
+    ``state.draw()`` additionally drops chains flagged as outliers. So
+    ``results.draws`` is usually *smaller* than the chain, and its
+    length is not deterministic — the detector re-runs from scratch on
+    every ``sample()`` and ``extend()`` call and may place the burn
+    point differently each time. Read the count off the array; do not
+    predict it.
+
+    Nor is the chain itself exactly ``samples / thin`` rows: ``samples``
+    is a guaranteed minimum, not an exact count. DREAM advances in
+    blocks of 10 generations (one generation = one draw per chain) and
+    only checks its stopping condition between blocks, so the raw chain
+    length is ``samples`` rounded up to a multiple of ``10 * n_chains``.
+
+    The trimming is only a *view* — nothing is dropped from the buffer.
+    To read the full untrimmed chain::
 
         results = sampler.sample(samples=10000, burn=500, thin=2)
         full = results.state.draw(portion=1.0, outliers=True)
         full.points  # (n_chain_rows, n_params)
         full.logp
 
-    To switch the automatic trimming off entirely, pass BUMPS' own ``trim``
-    option straight through ``sampler_kwargs``; ``results.draws`` then holds
-    the whole chain::
+    To switch the automatic trimming off entirely, pass BUMPS' own
+    ``trim`` option straight through ``sampler_kwargs``;
+    ``results.draws`` then holds the whole chain::
 
         sampler = Sampler(
             fitter, x, y, weights=w, sampler_kwargs={'trim': False}
         )
 
-    Note also that trimming does not survive a ``save()``/``load_state()``
-    round-trip: BUMPS does not persist the trim point, so a reloaded chain
-    comes back untrimmed and ``load_state()`` reports *more* draws than the
-    ``sample()`` call that created it. The chain itself is identical.
+    Note also that trimming does not survive a
+    ``save()``/``load_state()`` round-trip: BUMPS does not persist the
+    trim point, so a reloaded chain comes back untrimmed and
+    ``load_state()`` reports *more* draws than the ``sample()`` call
+    that created it. The chain itself is identical.
     """
 
     def __init__(
@@ -435,7 +460,9 @@ def weights(self) -> np.ndarray | list[np.ndarray | None] | None:
 
     @property
     def state(self) -> MCMCDraw | None:
-        """Raw BUMPS MCMCDraw state (None before first sample/load_state)."""
+        """
+        Raw BUMPS MCMCDraw state (None before first sample/load_state).
+        """
         return self._state
 
     @property
@@ -459,7 +486,9 @@ def logp(self) -> np.ndarray | None:
         return self._results.logp if self._results is not None else None
 
     def _fingerprint(self) -> str | None:
-        """SHA-256 fingerprint of the bound (x, y, weights) data, or None."""
+        """
+        SHA-256 fingerprint of the bound (x, y, weights) data, or None.
+        """
         x_list = list(self._x) if isinstance(self._x, (list, tuple)) else [self._x]
         y_list = list(self._y) if isinstance(self._y, (list, tuple)) else [self._y]
         if self._weights is None:
@@ -481,7 +510,8 @@ def _run(
         progress_callback: Callable[[dict], bool | None] | None,
         abort_test: Callable[[], bool] | None,
     ) -> SamplingResults:
-        """Shared sampling engine for ``sample()`` and ``extend()``.
+        """
+        Shared sampling engine for ``sample()`` and ``extend()``.
 
         Argument validation for ``samples``/``burn``/``thin`` lives in
         ``Bumps.mcmc_sample`` (single source of truth).
@@ -545,37 +575,43 @@ def sample(
         progress_callback: Callable[[dict], bool | None] | None = None,
         abort_test: Callable[[], bool] | None = None,
     ) -> SamplingResults:
-        """Run fresh Bayesian MCMC sampling on the bound data.
+        """
+        Run fresh Bayesian MCMC sampling on the bound data.
 
-        Calling ``sample()`` on a sampler that already holds a chain starts a
-        **fresh** chain — the previous state and results are replaced. Use
-        ``extend()`` to continue an existing chain.
+        Calling ``sample()`` on a sampler that already holds a chain
+        starts a **fresh** chain — the previous state and results are
+        replaced. Use ``extend()`` to continue an existing chain.
 
         Parameters
         ----------
         samples : int, default=10000
-            Number of raw samples to draw across all chains, before thinning.
-            This is a guaranteed minimum, not an exact count: DREAM advances
-            in blocks of 10 generations (one generation = one draw per chain)
-            and stops at the first block boundary at or past ``samples``.
+            Number of raw samples to draw across all chains, before
+            thinning. This is a guaranteed minimum, not an exact count:
+            DREAM advances in blocks of 10 generations (one generation =
+            one draw per chain) and stops at the first block boundary at
+            or past ``samples``.
         burn : int, default=2000
-            Burn-in generations to discard before collecting samples. Note
-            BUMPS counts ``burn`` in generations while ``samples`` counts raw
-            draws, so ``burn=500`` discards ``500 * n_chains`` raw samples.
+            Burn-in generations to discard before collecting samples.
+            Note BUMPS counts ``burn`` in generations while ``samples``
+            counts raw draws, so ``burn=500`` discards ``500 *
+            n_chains`` raw samples.
         thin : int, default=10
-            Thinning interval — only every ``thin``-th generation is kept,
-            which reduces autocorrelation between consecutive draws.
+            Thinning interval — only every ``thin``-th generation is
+            kept, which reduces autocorrelation between consecutive
+            draws.
         population : int | None, default=None
-            DREAM population **scale factor** (not an absolute chain count):
-            BUMPS creates ``ceil(population * n_parameters)`` parallel chains.
+            DREAM population **scale factor** (not an absolute chain
+            count): BUMPS creates ``ceil(population * n_parameters)``
+            parallel chains.
         sampler_kwargs : dict | None, default=None
             Additional keyword arguments forwarded to the BUMPS DREAM
             sampler (merged over the instance defaults).
         progress_callback : Callable[[dict], bool | None] | None, default=None
-            Optional callback invoked at each DREAM generation. The payload
-            dict includes ``iteration`` and ``sampling: True``.
+            Optional callback invoked at each DREAM generation. The
+            payload dict includes ``iteration`` and ``sampling: True``.
         abort_test : Callable[[], bool] | None, default=None
-            Optional callable that returns ``True`` to abort sampling early.
+            Optional callable that returns ``True`` to abort sampling
+            early.
 
         Returns
         -------
@@ -584,14 +620,16 @@ def sample(
 
         Notes
         -----
-        ``results.draws`` is a trimmed, outlier-filtered view of the chain,
-        so it is usually smaller than the chain and its length is not
-        predictable from ``samples``/``thin``. See the ``Sampler`` class notes
-        for how to read the full chain or switch trimming off.
+        ``results.draws`` is a trimmed, outlier-filtered view of the
+        chain, so it is usually smaller than the chain and its length is
+        not predictable from ``samples``/``thin``. See the ``Sampler``
+        class notes for how to read the full chain or switch trimming
+        off.
 
         Exceptions propagate from the sampling engine: ``ValueError`` if
-        ``samples``, ``burn``, or ``thin`` are invalid, and ``RuntimeError``
-        if the active minimizer is not a BUMPS instance.
+        ``samples``, ``burn``, or ``thin`` are invalid, and
+        ``RuntimeError`` if the active minimizer is not a BUMPS
+        instance.
         """
         if self._state is not None:
             global_object.log.getLogger('fitting').warning(
@@ -618,38 +656,43 @@ def extend(
         progress_callback: Callable[[dict], bool | None] | None = None,
         abort_test: Callable[[], bool] | None = None,
     ) -> SamplingResults:
-        """Continue the existing chain with additional samples.
+        """
+        Continue the existing chain with additional samples.
 
         DREAM stores draws in a fixed-size ring buffer sized to its
-        ``samples`` parameter; this method does the ring-buffer arithmetic
-        for you (``samples = stored_generations * population +
-        additional_samples``) so no existing draws are dropped from the
-        buffer, regardless of the thinning interval. Runs with ``burn=0`` —
-        re-burning a converged chain is usually a mistake, and BUMPS forces
-        it to 0 on resume in any case. The DREAM population is recovered from
-        the saved state and cannot be changed on extend.
+        ``samples`` parameter; this method does the ring-buffer
+        arithmetic for you (``samples = stored_generations * population
+        + additional_samples``) so no existing draws are dropped from
+        the buffer, regardless of the thinning interval. Runs with
+        ``burn=0`` — re-burning a converged chain is usually a mistake,
+        and BUMPS forces it to 0 on resume in any case. The DREAM
+        population is recovered from the saved state and cannot be
+        changed on extend.
 
         Parameters
         ----------
         additional_samples : int, default=5000
-            Number of additional DREAM samples to draw, in the same units
-            as ``samples`` in ``sample()``. This grows the ring buffer by
-            exactly ``additional_samples``; how many of the new draws become
-            *visible* in ``results.draws`` depends on the thinning interval
-            and on BUMPS' automatic trimming (see Notes).
+            Number of additional DREAM samples to draw, in the same
+            units as ``samples`` in ``sample()``. This grows the ring
+            buffer by exactly ``additional_samples``; how many of the
+            new draws become *visible* in ``results.draws`` depends on
+            the thinning interval and on BUMPS' automatic trimming (see
+            Notes).
         thin : int, default=10
             Thinning interval for the retained draws.
         total_samples : int | None, default=None
-            Advanced: total retained samples requested from the ring buffer,
-            **overriding** the ``additional_samples`` arithmetic. With
-            ``total_samples=N``, only the last N draws are retained.
+            Advanced: total retained samples requested from the ring
+            buffer, **overriding** the ``additional_samples``
+            arithmetic. With ``total_samples=N``, only the last N draws
+            are retained.
         sampler_kwargs : dict | None, default=None
             Additional keyword arguments forwarded to the BUMPS DREAM
             sampler (merged over the instance defaults).
         progress_callback : Callable[[dict], bool | None] | None, default=None
             Optional callback invoked at each DREAM generation.
         abort_test : Callable[[], bool] | None, default=None
-            Optional callable that returns ``True`` to abort sampling early.
+            Optional callable that returns ``True`` to abort sampling
+            early.
 
         Returns
         -------
@@ -665,16 +708,17 @@ def extend(
         Notes
         -----
         ``results.draws`` will **not** grow by exactly
-        ``additional_samples / thin``. Two things get in the way, neither of
-        them a re-applied burn-in: BUMPS re-runs its burn-point detector over
-        the whole extended chain and re-trims the returned view (so the
-        visible count can even shrink), and DREAM advances in blocks of 10
-        generations, so the raw growth is ``additional_samples`` rounded up
-        to a multiple of ``10 * n_chains`` (i.e. at least
-        ``additional_samples / thin`` retained rows). To see the chain
-        itself, compare ``results.state.draw(portion=1.0, outliers=True)``
-        before and after, or run with ``sampler_kwargs={'trim': False}``. See
-        the ``Sampler`` class notes.
+        ``additional_samples / thin``. Two things get in the way,
+        neither of them a re-applied burn-in: BUMPS re-runs its
+        burn-point detector over the whole extended chain and re-trims
+        the returned view (so the visible count can even shrink), and
+        DREAM advances in blocks of 10 generations, so the raw growth is
+        ``additional_samples`` rounded up to a multiple of ``10 *
+        n_chains`` (i.e. at least ``additional_samples / thin`` retained
+        rows). To see the chain itself, compare
+        ``results.state.draw(portion=1.0, outliers=True)`` before and
+        after, or run with ``sampler_kwargs={'trim': False}``. See the
+        ``Sampler`` class notes.
         """
         if self._state is None:
             raise RuntimeError('No chain to extend. Call sample() or load_state() first.')
@@ -698,14 +742,15 @@ def extend(
         )
 
     def save(self, path: str | os.PathLike) -> None:
-        """Persist the chain state and metadata to disk.
+        """
+        Persist the chain state and metadata to disk.
 
         Writes the BUMPS native files (``<path>-chain.mc.gz``,
         ``<path>-point.mc.gz`` and ``<path>-stats.mc.gz``) plus a
         ``<path>.params.json`` sidecar with the parameter names, the
-        easyscience version, and a fingerprint of the bound data (verified
-        with a warning on ``load_state()``). Use ``load_chain`` to read the
-        files back without a fitter.
+        easyscience version, and a fingerprint of the bound data
+        (verified with a warning on ``load_state()``). Use
+        ``load_chain`` to read the files back without a fitter.
 
         Parameters
         ----------
@@ -746,13 +791,14 @@ def save(self, path: str | os.PathLike) -> None:
             json.dump(sidecar, f, indent=2)
 
     def load_state(self, path: str | os.PathLike, skip: int = 0) -> SamplingResults:
-        """Load a previously saved chain into this sampler.
+        """
+        Load a previously saved chain into this sampler.
 
-        The sampler must be constructed with the same fitter and data used to
-        create the chain — ``extend()`` then continues the saved chain. If the
-        sidecar carries a data fingerprint and it does not match this
-        sampler's bound data, a warning is logged (extending a chain against
-        different data is undefined behaviour).
+        The sampler must be constructed with the same fitter and data
+        used to create the chain — ``extend()`` then continues the saved
+        chain. If the sidecar carries a data fingerprint and it does not
+        match this sampler's bound data, a warning is logged (extending
+        a chain against different data is undefined behaviour).
 
         Populates ``state`` and ``results`` (draws, log-posterior and
         parameter names) from the saved chain, so summaries and
@@ -763,8 +809,8 @@ def load_state(self, path: str | os.PathLike, skip: int = 0) -> SamplingResults:
         path : str | os.PathLike
             File path prefix used in ``save()``.
         skip : int, default=0
-            Discard the first ``skip`` saved generations on load. Useful for
-            trimming additional burn-in without re-sampling.
+            Discard the first ``skip`` saved generations on load. Useful
+            for trimming additional burn-in without re-sampling.
 
         Returns
         -------
@@ -780,11 +826,11 @@ def load_state(self, path: str | os.PathLike, skip: int = 0) -> SamplingResults:
 
         Notes
         -----
-        BUMPS does not persist its automatic trim point, so a reloaded chain
-        comes back **untrimmed**: this method reports more draws than the
-        ``sample()`` call that wrote the file, even though the chain is
-        identical. Use ``skip`` to discard leading generations explicitly.
-        See the ``Sampler`` class notes.
+        BUMPS does not persist its automatic trim point, so a reloaded
+        chain comes back **untrimmed**: this method reports more draws
+        than the ``sample()`` call that wrote the file, even though the
+        chain is identical. Use ``skip`` to discard leading generations
+        explicitly. See the ``Sampler`` class notes.
         """  # noqa: DOC502 -- raised in _validate_chain_path via load_chain
         state, param_names, sidecar = load_chain(path, skip=skip)
 
diff --git a/tests/unit/base_classes/test_easy_list.py b/tests/unit/base_classes/test_easy_list.py
index 978d5571..7978292e 100644
--- a/tests/unit/base_classes/test_easy_list.py
+++ b/tests/unit/base_classes/test_easy_list.py
@@ -32,7 +32,9 @@ def __init__(self, unique_name=None, display_name=None):
 class MockModel(ModelBase):
     """A ModelBase subclass with a Parameter and a DescriptorNumber for testing get_all_variables."""
 
-    def __init__(self, unique_name=None, display_name=None, temperature=25, volume=1.0, items=None):
+    def __init__(
+        self, unique_name=None, display_name=None, temperature=25, volume=1.0, items=None
+    ):
         super().__init__(unique_name=unique_name, display_name=display_name)
         self._temperature = Parameter(name='temperature', value=temperature)
         self._volume = DescriptorNumber(name='volume', value=volume)
@@ -643,9 +645,12 @@ def test_from_dict_missing_data_key(self):
         el_dict = {
             '@module': 'easyscience',
             '@class': 'EasyList',
-            'protected_types': [{'@module': 'easyscience.base_classes', '@class': 'ModelBase'}]
+            'protected_types': [{'@module': 'easyscience.base_classes', '@class': 'ModelBase'}],
         }
-        with pytest.raises(ValueError, match='The provided dictionary does not represent an EasyList. Missing the "data" key.'):
+        with pytest.raises(
+            ValueError,
+            match='The provided dictionary does not represent an EasyList. Missing the "data" key.',
+        ):
             EasyList.from_dict(el_dict)
 
     # --- get_all_variables ---
diff --git a/tests/unit/base_classes/test_new_base.py b/tests/unit/base_classes/test_new_base.py
index 7d74cac2..f3b7cbe6 100644
--- a/tests/unit/base_classes/test_new_base.py
+++ b/tests/unit/base_classes/test_new_base.py
@@ -168,7 +168,6 @@ def test_to_dict_nested_new_base_reverse(self):
         assert 'unique_name' in obj_dict['nested_new_base']
         assert 'display_name' in obj_dict['nested_new_base']
 
-
     def test_from_dict(self):
         # When
         obj_dict = {