diff --git a/docs/docs/tutorials/components.ipynb b/docs/docs/tutorials/components.ipynb
index 1b4af94d6..0fa5a8884 100644
--- a/docs/docs/tutorials/components.ipynb
+++ b/docs/docs/tutorials/components.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Components\n",
     "\n",
-    "Components are the basic ingredients for all models. Currently, the available components are Gaussian, Lorentzian, Voigt (the convolution of a Gaussian with a Lorentzian), delta function, damped harmonic oscillator, stretched exponential and polynomial. This notebooks shows how to use the components. \n",
+    "Components are the basic ingredients for all models. Currently, the available components are Gaussian, Lorentzian, Voigt (the convolution of a Gaussian with a Lorentzian), delta function, damped harmonic oscillator and polynomial. This notebooks shows how to use the components. \n",
     "\n",
     "Note in particular that a Gaussian, Lorentzian, Voigt or delta function where the center has not been given will be centered at 0."
    ]
@@ -203,36 +203,11 @@
     "plt.legend()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e09b85d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stretched_lorz = edyn.StretchedExponential(area=1.0, relaxation_time=10.0, beta=1.0)\n",
-    "lorentzian = edyn.Lorentzian(center=0.0, width=0.6582 / 10, area=1.0)\n",
-    "stretched = edyn.StretchedExponential(area=1.0, relaxation_time=10.0, beta=0.5)\n",
-    "\n",
-    "x = np.linspace(-1, 1, 1001)\n",
-    "y = stretched_lorz.evaluate(x)\n",
-    "y2 = lorentzian.evaluate(x)\n",
-    "y3 = stretched.evaluate(x)\n",
-    "plt.figure()\n",
-    "plt.plot(x, y, label='Stretched exponential (β=1)')\n",
-    "plt.plot(x, y2, label='Lorentzian', linestyle='dotted')\n",
-    "plt.plot(x, y3, label='Stretched exponential (β=0.5)')\n",
-    "plt.xlabel('Energy (meV)')\n",
-    "plt.ylabel('Intensity (arb. units)')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "DEFAULT",
+   "display_name": "default",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/docs/user-guide/concept.md b/docs/docs/user-guide/concept.md
index 2aad691a0..17014b924 100644
--- a/docs/docs/user-guide/concept.md
+++ b/docs/docs/user-guide/concept.md
@@ -19,8 +19,8 @@ a list of `ComponentCollection`s (one for each Q) describing the model
 of the measured data. A `ComponentCollection` is essentially a list of
 `ModelComponents`. A `ModelComponent` can be any of `Gaussian`,
 `Lorentzian`, `Voigt` (the convolution of a `Gaussian` and
-`Lorentzian`), `DeltaFunction`, `DampedHarmonicOscillator`,
-`StretchedExponential` and `Polynomium`.
+`Lorentzian`), `DeltaFunction`, `DampedHarmonicOscillator` and
+`Polynomium`.
 
 Each `ModelComponent` has a number of `Parameter`s. The `Gaussian`, for
 example, has `area`, `center` and `width`. Each of these `Parameter`s
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-  - anywidget>=0.9.0 ; extra == 'test'
-  requires_python: '>=3.11'
 - pypi: https://files.pythonhosted.org/packages/7f/b9/87fea2769fe1c47c1b5b01d8310772c9d1a85d485de7cf386ef7a3332b02/numpy-2.5.2-cp312-cp312-win_amd64.whl
   name: numpy
   version: 2.5.2
@@ -10499,6 +10469,36 @@ packages:
   - pyparsing>=3
   - python-dateutil>=2.7
   requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/ce/27/bdaaf32952f052c15c048fab82d971d30f92b63d54b61486f04a241fa994/plopp-26.7.0-py3-none-any.whl
+  name: plopp
+  version: 26.7.0
+  sha256: 2082beccfc0df47750600ecdd68f5379bfee004a929bfcb0420e2b180b565fb2
+  requires_dist:
+  - lazy-loader>=0.4
+  - matplotlib>=3.8
+  - scipp>=25.11.0
+  - anywidget>=0.9.0 ; extra == 'all'
+  - ipympl>0.8.4 ; extra == 'all'
+  - pythreejs>=2.4.1 ; extra == 'all'
+  - mpltoolbox>=24.6.0 ; extra == 'all'
+  - ipywidgets>=8.1.5 ; extra == 'all'
+  - graphviz>=0.20.3 ; extra == 'all'
+  - graphviz>=0.20.3 ; extra == 'test'
+  - h5py>=3.12 ; extra == 'test'
+  - ipympl>=0.8.4 ; extra == 'test'
+  - ipywidgets>=8.1.5 ; extra == 'test'
+  - ipykernel>=6.26,<7 ; extra == 'test'
+  - mpltoolbox>=24.6.0 ; extra == 'test'
+  - pandas>=2.2.2 ; extra == 'test'
+  - plotly>=5.15.0 ; extra == 'test'
+  - pooch>=1.5 ; extra == 'test'
+  - pyarrow>=13.0.0 ; extra == 'test'
+  - pytest>=8.0 ; extra == 'test'
+  - pythreejs>=2.4.1 ; extra == 'test'
+  - scipy>=1.10.0 ; extra == 'test'
+  - xarray>=2024.5.0 ; extra == 'test'
+  - anywidget>=0.9.0 ; extra == 'test'
+  requires_python: '>=3.11'
 - pypi: https://files.pythonhosted.org/packages/cf/52/6daa2ee9d95e5c98b8128f8df91eb692eb423ab274b8cf08db52152fad26/yarl-1.24.5-cp314-cp314-win_amd64.whl
   name: yarl
   version: 1.24.5
diff --git a/pixi.toml b/pixi.toml
index 406b2019b..007abbce4 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -3,13 +3,14 @@
 ###########
 
 [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)
@@ -44,10 +45,10 @@ python = '3.14.*'
 # editable installations.
 
 [feature.dev.dependencies]
-nodejs = '*'  # Required for Prettier (non-Python formatting)
+nodejs = '*'      # Required for Prettier (non-Python formatting)
 jupyterlab = '*'  # Jupyter notebooks
-ipython = '*'  # Interactive Python shell
-pixi-kernel = '*'  # Pixi Jupyter kernel
+ipython = '*'     # Interactive Python shell
+pixi-kernel = '*' # Pixi Jupyter kernel
 
 [feature.dev.pypi-dependencies]
 pip = '*'
@@ -58,8 +59,8 @@ easydynamics = { path = '.', editable = true, extras = ['dev'] }
 
 [feature.user.dependencies]
 jupyterlab = '*'  # Jupyter notebooks
-ipython = '*'  # Interactive Python shell
-pixi-kernel = '*'  # Pixi Jupyter kernel
+ipython = '*'     # Interactive Python shell
+pixi-kernel = '*' # Pixi Jupyter kernel
 
 [feature.user.pypi-dependencies]
 pip = '*'
@@ -70,6 +71,7 @@ easydynamics = '*'
 ##############
 
 [environments]
+
 # The `default` feature is always included in all environments.
 # Additional features can be specified per environment.
 
@@ -90,6 +92,7 @@ user = { features = ['py-max', 'user'] }
 #######
 
 [tasks]
+
 ##################
 # 🧪 Testing Tasks
 ##################
@@ -187,9 +190,9 @@ notebook-exec = { cmd = 'python -m pytest --nbmake docs/docs/tutorials/ --nbmake
 ] }
 
 notebook-prepare = { depends-on = [
-  # 'notebook-convert',
+  #'notebook-convert',
   'notebook-strip',
-  # 'notebook-tweak',
+  #'notebook-tweak',
 ] }
 
 ########################
@@ -284,7 +287,7 @@ 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 d8a956f5e..b98c86eb7 100644
--- a/prettierrc.toml
+++ b/prettierrc.toml
@@ -1,24 +1,22 @@
 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 7f1f68d8b..c8ff0397c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@
 
 [project]
 name = 'easydynamics'
-dynamic = ['version']  # Use versioningit to manage the version
+dynamic = ['version'] # Use versioningit to manage the version
 description = 'QENS data analysis'
 authors = [{ name = 'EasyScience contributors' }]
 readme = 'README.md'
@@ -23,58 +23,58 @@ classifiers = [
 ]
 requires-python = '>=3.12'
 dependencies = [
-  'easyscience>=2.5.1',  # The base library of the EasyScience framework. 2.5.1 adds fitting.Sampler
-  'numpy',  # Numerical arrays (used directly throughout the library)
-  'scipy',  # Numerical routines (convolution, interpolation, special functions)
-  'scipp',  # Labelled multi-dimensional arrays; backs Experiment data handling
-  'h5py',  # HDF5 backend for scipp's HDF5 I/O (Experiment.load_hdf5)
-  'matplotlib',  # Plotting (posterior trace, corner, and predictive plots)
-  'pooch',  # Data downloader
-  'darkdetect',  # Detecting dark mode (system-level)
-  'plopp>=26.9.0',  # Plotting library
-  'jupyterlab',  # Jupyter notebooks
-  'pixi-kernel',  # Pixi Jupyter kernel
-  '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
-  'sympy',  # Symbolic mathematics (used for expression components)
+  'easyscience>=2.5.1', # The base library of the EasyScience framework. 2.5.1 adds fitting.Sampler
+  'numpy',              # Numerical arrays (used directly throughout the library)
+  'scipy',              # Numerical routines (convolution, interpolation, special functions)
+  'scipp',              # Labelled multi-dimensional arrays; backs Experiment data handling
+  'h5py',               # HDF5 backend for scipp's HDF5 I/O (Experiment.load_hdf5)
+  'matplotlib',         # Plotting (posterior trace, corner, and predictive plots)
+  'pooch',              # Data downloader
+  'darkdetect',         # Detecting dark mode (system-level)
+  'plopp',              # Plotting library
+  'jupyterlab',         # Jupyter notebooks
+  'pixi-kernel',        # Pixi Jupyter kernel
+  '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
+  'sympy',              # Symbolic mathematics (used for expression components)
 ]
 
 [project.optional-dependencies]
 dev = [
-  '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
-  'docstring-parser-fork!=0.0.15',  # Parser used by pydoclint. 0.0.15 splits NumPy-style defaults -> DOC105 failures
-  '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
+  '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
+  'docstring-parser-fork!=0.0.15',   # Parser used by pydoclint. 0.0.15 splits NumPy-style defaults -> DOC105 failures
+  '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]
@@ -106,7 +106,7 @@ packages = ['src/easydynamics']
 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
@@ -122,9 +122,9 @@ 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'
@@ -142,9 +142,9 @@ default-tag = 'v999.0.0'
 # https://interrogate.readthedocs.io/en/latest/
 
 [tool.interrogate]
-fail-under = 0  # Minimum docstring coverage percentage to pass
+fail-under = 0 # Minimum docstring coverage percentage to pass
 verbose = 1
-# exclude = ['src/**/__init__.py']
+#exclude = ['src/**/__init__.py']
 
 #######################################
 # Configuration for coverage/pytest-cov
@@ -154,13 +154,13 @@ verbose = 1
 # 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 = 0  # Minimum coverage percentage to pass
+skip_covered = false # Skip files with 100% coverage in the report
+fail_under = 0       # Minimum coverage percentage to pass
 
 ##########################
 # Configuration for pytest
@@ -188,74 +188,74 @@ testpaths = ['tests']
 exclude = ['tmp']
 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
+preview = true    # Enable new rules that are not yet stable, like DOC
 
 # Formatting options for Ruff
 
 [tool.ruff.format]
-docstring-code-format = true  # Whether to format code snippets in docstrings
-docstring-code-line-length = 99  # 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 = 99 # 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 = [
   # Various rules
-  # 'C90',  # https://docs.astral.sh/ruff/rules/#mccabe-c90
-  # 'D',   # https://docs.astral.sh/ruff/rules/#pydocstyle-d
-  'F',  # https://docs.astral.sh/ruff/rules/#pyflakes-f
-  # 'FLY',  # https://docs.astral.sh/ruff/rules/#flynt-fly
-  # 'FURB', # https://docs.astral.sh/ruff/rules/#refurb-furb
-  'I',  # https://docs.astral.sh/ruff/rules/#isort-i
-  # 'N',    # https://docs.astral.sh/ruff/rules/#pep8-naming-n
-  'NPY',  # https://docs.astral.sh/ruff/rules/#numpy-specific-rules-npy
-  # 'PGH',  # https://docs.astral.sh/ruff/rules/#pygrep-hooks-pgh
-  'PERF',  # https://docs.astral.sh/ruff/rules/#perflint-perf
+  #'C90',  # https://docs.astral.sh/ruff/rules/#mccabe-c90
+  #'D',   # https://docs.astral.sh/ruff/rules/#pydocstyle-d
+  'F', # https://docs.astral.sh/ruff/rules/#pyflakes-f
+  #'FLY',  # https://docs.astral.sh/ruff/rules/#flynt-fly
+  #'FURB', # https://docs.astral.sh/ruff/rules/#refurb-furb
+  'I', # https://docs.astral.sh/ruff/rules/#isort-i
+  #'N',    # https://docs.astral.sh/ruff/rules/#pep8-naming-n
+  'NPY', # https://docs.astral.sh/ruff/rules/#numpy-specific-rules-npy
+  #'PGH',  # https://docs.astral.sh/ruff/rules/#pygrep-hooks-pgh
+  'PERF', # https://docs.astral.sh/ruff/rules/#perflint-perf
   'RUF',  # https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf
-  # 'TRY',  # https://docs.astral.sh/ruff/rules/#tryceratops-try # overly restrictive
-  'UP',  # https://docs.astral.sh/ruff/rules/#pyupgrade-up
+  #'TRY',  # https://docs.astral.sh/ruff/rules/#tryceratops-try # overly restrictive
+  'UP', # https://docs.astral.sh/ruff/rules/#pyupgrade-up
   # pycodestyle (E, W) rules
-  'E',  # https://docs.astral.sh/ruff/rules/#error-e
-  'W',  # https://docs.astral.sh/ruff/rules/#warning-w
+  'E', # https://docs.astral.sh/ruff/rules/#error-e
+  'W', # https://docs.astral.sh/ruff/rules/#warning-w
   # Pylint (PL) rules
-  # 'PLC', # https://docs.astral.sh/ruff/rules/#convention-plc
-  # 'PLE', # https://docs.astral.sh/ruff/rules/#error-ple
-  # 'PLR', # https://docs.astral.sh/ruff/rules/#refactor-plr
-  # 'PLW', # https://docs.astral.sh/ruff/rules/#warning-plw # Good to enable
+  #'PLC', # https://docs.astral.sh/ruff/rules/#convention-plc
+  #'PLE', # https://docs.astral.sh/ruff/rules/#error-ple
+  #'PLR', # https://docs.astral.sh/ruff/rules/#refactor-plr
+  #'PLW', # https://docs.astral.sh/ruff/rules/#warning-plw # Good to enable
   # flake8 rules
-  'A',  # https://docs.astral.sh/ruff/rules/#flake8-builtins-a
-  'ANN',  # https://docs.astral.sh/ruff/rules/#flake8-annotations-ann
-  'ARG',  # https://docs.astral.sh/ruff/rules/#flake8-unused-arguments-arg
-  'ASYNC',  # https://docs.astral.sh/ruff/rules/#flake8-async-async
-  'B',  # https://docs.astral.sh/ruff/rules/#flake8-bugbear-b
-  # 'BLE',   # https://docs.astral.sh/ruff/rules/#flake8-blind-except-ble # enable when base classes have been merged in
+  'A',     # https://docs.astral.sh/ruff/rules/#flake8-builtins-a
+  'ANN',   # https://docs.astral.sh/ruff/rules/#flake8-annotations-ann
+  'ARG',   # https://docs.astral.sh/ruff/rules/#flake8-unused-arguments-arg
+  'ASYNC', # https://docs.astral.sh/ruff/rules/#flake8-async-async
+  'B',     # https://docs.astral.sh/ruff/rules/#flake8-bugbear-b
+  #'BLE',   # https://docs.astral.sh/ruff/rules/#flake8-blind-except-ble # enable when base classes have been merged in
   'C4',  # https://docs.astral.sh/ruff/rules/#flake8-comprehensions-c4
-  'COM',  # https://docs.astral.sh/ruff/rules/#flake8-commas-com
-  'DTZ',  # https://docs.astral.sh/ruff/rules/#flake8-datetimez-dtz
-  # 'EM',    # https://docs.astral.sh/ruff/rules/#flake8-errmsg-em # Unsure if I want it - requires all error messages to be rewritten
-  'FA',  # https://docs.astral.sh/ruff/rules/#flake8-future-annotations-fa
-  # 'FBT',   # https://docs.astral.sh/ruff/rules/#flake8-boolean-trap-fbt # should eventually be enabled, but may break serialization
-  'FIX',  # https://docs.astral.sh/ruff/rules/#flake8-fixme-fix
-  'G',  # https://docs.astral.sh/ruff/rules/#flake8-logging-format-g
-  'ICN',  # https://docs.astral.sh/ruff/rules/#flake8-import-conventions-icn
-  'INP',  # https://docs.astral.sh/ruff/rules/#flake8-no-pep420-inp
-  'ISC',  # https://docs.astral.sh/ruff/rules/#flake8-implicit-str-concat-isc
-  'LOG',  # https://docs.astral.sh/ruff/rules/#flake8-logging-log
-  'PIE',  # https://docs.astral.sh/ruff/rules/#flake8-pie-pie
-  # 'PT',    # https://docs.astral.sh/ruff/rules/#flake8-pytest-style-pt # Should eventually be enabled, but quite a few errors to fix
+  'COM', # https://docs.astral.sh/ruff/rules/#flake8-commas-com
+  'DTZ', # https://docs.astral.sh/ruff/rules/#flake8-datetimez-dtz
+  #'EM',    # https://docs.astral.sh/ruff/rules/#flake8-errmsg-em # Unsure if I want it - requires all error messages to be rewritten
+  'FA', # https://docs.astral.sh/ruff/rules/#flake8-future-annotations-fa
+  #'FBT',   # https://docs.astral.sh/ruff/rules/#flake8-boolean-trap-fbt # should eventually be enabled, but may break serialization
+  'FIX', # https://docs.astral.sh/ruff/rules/#flake8-fixme-fix
+  'G',   # https://docs.astral.sh/ruff/rules/#flake8-logging-format-g
+  'ICN', # https://docs.astral.sh/ruff/rules/#flake8-import-conventions-icn
+  'INP', # https://docs.astral.sh/ruff/rules/#flake8-no-pep420-inp
+  'ISC', # https://docs.astral.sh/ruff/rules/#flake8-implicit-str-concat-isc
+  'LOG', # https://docs.astral.sh/ruff/rules/#flake8-logging-log
+  'PIE', # https://docs.astral.sh/ruff/rules/#flake8-pie-pie
+  #'PT',    # https://docs.astral.sh/ruff/rules/#flake8-pytest-style-pt # Should eventually be enabled, but quite a few errors to fix
   'PTH',  # https://docs.astral.sh/ruff/rules/#flake8-use-pathlib-pth
   'PYI',  # https://docs.astral.sh/ruff/rules/#flake8-pyi-pyi
   'RET',  # https://docs.astral.sh/ruff/rules/#flake8-return-ret
   'RSE',  # https://docs.astral.sh/ruff/rules/#flake8-raise-rse
-  'S',  # https://docs.astral.sh/ruff/rules/#flake8-bandit-s
+  'S',    # https://docs.astral.sh/ruff/rules/#flake8-bandit-s
   'SIM',  # https://docs.astral.sh/ruff/rules/#flake8-simplify-sim
   'SLF',  # https://docs.astral.sh/ruff/rules/#flake8-self-slf
-  'SLOT',  # https://docs.astral.sh/ruff/rules/#flake8-slots-slot
+  'SLOT', # https://docs.astral.sh/ruff/rules/#flake8-slots-slot
   'T20',  # https://docs.astral.sh/ruff/rules/#flake8-print-t20
-  'TC',  # https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc
-  'TD',  # https://docs.astral.sh/ruff/rules/#flake8-todos-td
+  'TC',   # https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc
+  'TD',   # https://docs.astral.sh/ruff/rules/#flake8-todos-td
   'TID',  # https://docs.astral.sh/ruff/rules/#flake8-tidy-imports-tid
 ]
 
@@ -263,28 +263,28 @@ 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/
   # The following is replaced by 'D'/[tool.ruff.lint.pydocstyle] and [tool.pydoclint]  '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/
-  'SLF',  # https://docs.astral.sh/ruff/rules/#flake8-self-slf # may want to eventually enable it, but accessing private methods is quite useful in 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/
+  'SLF',    # https://docs.astral.sh/ruff/rules/#flake8-self-slf # may want to eventually enable it, but accessing private methods is quite useful in tests
 ]
 '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/
 ]
 
 # Specific options for certain rules
@@ -306,7 +306,7 @@ 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 99 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 = 99
 
 [tool.ruff.lint.pydocstyle]
@@ -333,7 +333,7 @@ max-positional-args = 6
 # the parameter declarations in the code (in function's signature).
 
 [tool.pydoclint]
-# exclude = '\.' # Temporarily disable pydoclint until we are ready
+#exclude = '\.' # Temporarily disable pydoclint until we are ready
 style = 'numpy'
 check-style-mismatch = true
 check-arg-defaults = true
@@ -347,7 +347,7 @@ allow-init-docstring = true
 # https://github.com/jsh9/format-docstring
 
 [tool.format_docstring]
-# exclude = '\.' # Temporarily disable format-docstring until we are ready
+#exclude = '\.' # Temporarily disable format-docstring until we are ready
 docstring_style = 'numpy'
 line_length = 99
 fix_rst_backticks = true
diff --git a/src/easydynamics/__init__.py b/src/easydynamics/__init__.py
index a0f9d570d..de7391225 100644
--- a/src/easydynamics/__init__.py
+++ b/src/easydynamics/__init__.py
@@ -38,7 +38,6 @@
 from easydynamics.sample_model import Polynomial
 from easydynamics.sample_model import ResolutionModel
 from easydynamics.sample_model import SampleModel
-from easydynamics.sample_model import StretchedExponential
 from easydynamics.sample_model import Voigt
 from easydynamics.settings import ConvolutionSettings
 from easydynamics.settings import DetailedBalanceSettings
@@ -82,7 +81,6 @@
     'PosteriorSummary',
     'ResolutionModel',
     'SampleModel',
-    'StretchedExponential',
     'Voigt',
     'detailed_balance_factor',
     'hbar',
diff --git a/src/easydynamics/analysis/analysis.py b/src/easydynamics/analysis/analysis.py
index 48910fb7f..f06a818bd 100644
--- a/src/easydynamics/analysis/analysis.py
+++ b/src/easydynamics/analysis/analysis.py
@@ -11,7 +11,7 @@
 from easyscience.fitting.minimizers.utils import FitResults
 from easyscience.fitting.multi_fitter import MultiFitter
 from easyscience.variable import Parameter
-from plopp.backends.matplotlib.figure import WidgetFigure
+from plopp.backends.matplotlib.figure import InteractiveFigure
 from scipp import UnitError
 
 from easydynamics.analysis.analysis1d import Analysis1d
@@ -391,7 +391,7 @@ def plot_data_and_model(
         plot_residuals: bool = False,
         energy: sc.Variable | None = None,
         **kwargs: dict[str, Any],
-    ) -> WidgetFigure:
+    ) -> InteractiveFigure:
         """
         Plot the experimental data and the model prediction.
 
@@ -426,8 +426,8 @@ def plot_data_and_model(
 
         Returns
         -------
-        WidgetFigure
-            A Plopp WidgetFigure containing the plot of the data and model.
+        InteractiveFigure
+            A Plopp InteractiveFigure containing the plot of the data and model.
         """
         verify_Q_index(Q_index=Q_index, Q=self.Q, allow_none=True)
         if Q_index is not None:
@@ -647,7 +647,7 @@ def plot_parameters(
         self,
         names: str | list[str] | None = None,
         **kwargs: dict[str, Any],
-    ) -> WidgetFigure:
+    ) -> InteractiveFigure:
         """
         Plot fitted parameters as a function of Q.
 
@@ -668,8 +668,8 @@ def plot_parameters(
 
         Returns
         -------
-        WidgetFigure
-            A Plopp WidgetFigure containing the plot of the parameters.
+        InteractiveFigure
+            A Plopp InteractiveFigure containing the plot of the parameters.
         """
 
         ds = self.parameters_to_dataset()
diff --git a/src/easydynamics/analysis/analysis1d.py b/src/easydynamics/analysis/analysis1d.py
index 0e0acdab2..6ac0b7eb3 100644
--- a/src/easydynamics/analysis/analysis1d.py
+++ b/src/easydynamics/analysis/analysis1d.py
@@ -11,7 +11,7 @@
 from easyscience.fitting.minimizers.utils import FitResults
 from easyscience.variable import DescriptorNumber
 from easyscience.variable import Parameter
-from plopp.backends.matplotlib.figure import WidgetFigure
+from plopp.backends.matplotlib.figure import InteractiveFigure
 
 from easydynamics.analysis.analysis_base import AnalysisBase
 from easydynamics.analysis.posterior_labels import ParameterLabels
@@ -452,7 +452,7 @@ def plot_data_and_model(
         plot_residuals: bool = False,
         energy: sc.Variable | None = None,
         **kwargs: dict[str, Any],
-    ) -> WidgetFigure:
+    ) -> InteractiveFigure:
         """
         Plot the experimental data and the model prediction for the chosen Q index. Optionally also
         plot the individual components of the model.
@@ -476,7 +476,7 @@ def plot_data_and_model(
 
         Returns
         -------
-        WidgetFigure
+        InteractiveFigure
             A plot of the data and model.
         """
         data_and_model = self.data_and_model_to_datagroup(
diff --git a/src/easydynamics/analysis/parameter_analysis.py b/src/easydynamics/analysis/parameter_analysis.py
index 6413a963f..1e99f006a 100644
--- a/src/easydynamics/analysis/parameter_analysis.py
+++ b/src/easydynamics/analysis/parameter_analysis.py
@@ -11,7 +11,7 @@
 from easyscience.fitting.multi_fitter import MultiFitter
 from easyscience.variable import Parameter
 from matplotlib import rcParams
-from plopp.backends.matplotlib.figure import WidgetFigure
+from plopp.backends.matplotlib.figure import InteractiveFigure
 
 from easydynamics.analysis.analysis import Analysis
 from easydynamics.analysis.fit_binding import FitBinding
@@ -412,7 +412,9 @@ def _chain_parameters(self) -> list[Parameter]:
                 parameters.setdefault(parameter.unique_name, parameter)
         return list(parameters.values())
 
-    def plot(self, names: str | list[str] | None = None, **kwargs: dict[str, Any]) -> WidgetFigure:
+    def plot(
+        self, names: str | list[str] | None = None, **kwargs: dict[str, Any]
+    ) -> InteractiveFigure:
         """
         Plot the parameters and fit results.
 
@@ -425,7 +427,7 @@ def plot(self, names: str | list[str] | None = None, **kwargs: dict[str, Any]) -
 
         Returns
         -------
-        WidgetFigure
+        InteractiveFigure
             An interactive figure containing the plots of the parameters and fit results.
 
         Raises
diff --git a/src/easydynamics/analysis/posterior_sampling.py b/src/easydynamics/analysis/posterior_sampling.py
index e1187a9c0..7d2615938 100644
--- a/src/easydynamics/analysis/posterior_sampling.py
+++ b/src/easydynamics/analysis/posterior_sampling.py
@@ -42,7 +42,7 @@
     from easyscience.variable import Parameter
     from ipywidgets import VBox
     from matplotlib.figure import Figure
-    from plopp.backends.matplotlib.figure import WidgetFigure
+    from plopp.backends.matplotlib.figure import InteractiveFigure
 
     from easydynamics.analysis.posterior import BoundsSuggestions
 
@@ -1584,7 +1584,7 @@ def plot_posterior_predictive(
         credible_interval: float = 68.0,
         Q_index: int | None = None,
         **kwargs: dict[str, Any],
-    ) -> Figure | WidgetFigure:
+    ) -> Figure | InteractiveFigure:
         """
         Plot the data against the credible band implied by the posterior.
 
@@ -1610,7 +1610,7 @@ def plot_posterior_predictive(
 
         Returns
         -------
-        Figure | WidgetFigure
+        Figure | InteractiveFigure
             The matplotlib Figure for one Q, or the plopp figure with a Q slider.
 
         Raises
@@ -1748,7 +1748,7 @@ def _predictive_with_q_slider(
         n_draws: int,
         credible_interval: float,
         **kwargs: dict[str, Any],
-    ) -> WidgetFigure:
+    ) -> InteractiveFigure:
         """
         Build the posterior-predictive figure with a Q slider from the per-Q chains.
 
@@ -1770,7 +1770,7 @@ def _predictive_with_q_slider(
 
         Returns
         -------
-        WidgetFigure
+        InteractiveFigure
             The plopp figure with its Q slider.
 
         Raises
diff --git a/src/easydynamics/experiment/experiment.py b/src/easydynamics/experiment/experiment.py
index 3beeeb737..058df9abe 100644
--- a/src/easydynamics/experiment/experiment.py
+++ b/src/easydynamics/experiment/experiment.py
@@ -7,7 +7,7 @@
 import numpy as np
 import plopp as pp
 import scipp as sc
-from plopp.backends.matplotlib.figure import WidgetFigure
+from plopp.backends.matplotlib.figure import InteractiveFigure
 from scipp.io import load_hdf5 as sc_load_hdf5
 from scipp.io import save_hdf5 as sc_save_hdf5
 
@@ -440,7 +440,7 @@ def plot_data(
         slicer: bool = False,
         transpose_axes: bool = False,
         **kwargs: dict,
-    ) -> WidgetFigure:
+    ) -> InteractiveFigure:
         """
         Plot the dataset using plopp: https://scipp.github.io/plopp/.
 
@@ -456,7 +456,7 @@ def plot_data(
 
         Returns
         -------
-        WidgetFigure
+        InteractiveFigure
             A plot of the data and model.
 
         Raises
diff --git a/src/easydynamics/sample_model/__init__.py b/src/easydynamics/sample_model/__init__.py
index 97a788aab..07f434a18 100644
--- a/src/easydynamics/sample_model/__init__.py
+++ b/src/easydynamics/sample_model/__init__.py
@@ -12,7 +12,6 @@
 from easydynamics.sample_model.components.gaussian import Gaussian
 from easydynamics.sample_model.components.lorentzian import Lorentzian
 from easydynamics.sample_model.components.polynomial import Polynomial
-from easydynamics.sample_model.components.stretched_exponential import StretchedExponential
 from easydynamics.sample_model.components.voigt import Voigt
 from easydynamics.sample_model.diffusion_model.brownian_translational_diffusion import (
     BrownianTranslationalDiffusion,
@@ -41,6 +40,5 @@
     'Polynomial',
     'ResolutionModel',
     'SampleModel',
-    'StretchedExponential',
     'Voigt',
 ]
diff --git a/src/easydynamics/sample_model/components/__init__.py b/src/easydynamics/sample_model/components/__init__.py
index 62206663d..940f41a7e 100644
--- a/src/easydynamics/sample_model/components/__init__.py
+++ b/src/easydynamics/sample_model/components/__init__.py
@@ -10,7 +10,6 @@
 from easydynamics.sample_model.components.gaussian import Gaussian
 from easydynamics.sample_model.components.lorentzian import Lorentzian
 from easydynamics.sample_model.components.polynomial import Polynomial
-from easydynamics.sample_model.components.stretched_exponential import StretchedExponential
 from easydynamics.sample_model.components.voigt import Voigt
 
 __all__ = [
@@ -21,6 +20,5 @@
     'Gaussian',
     'Lorentzian',
     'Polynomial',
-    'StretchedExponential',
     'Voigt',
 ]
diff --git a/src/easydynamics/sample_model/components/stretched_exponential.py b/src/easydynamics/sample_model/components/stretched_exponential.py
deleted file mode 100644
index 9e33fd5c0..000000000
--- a/src/easydynamics/sample_model/components/stretched_exponential.py
+++ /dev/null
@@ -1,784 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-
-r"""
-The stretched exponential (Kohlrausch-Williams-Watts) relaxation, Fourier transformed to energy.
-
-The model is defined in time, as $I(t) = A e^{-(|t| / \tau)^\beta}$. To evaluate the Fourier
-transform, we need to evaluate the following integral:
-
-$$ G_\beta(w) = \int_0^\infty e^{-u^\beta} \cos(w u) \, du $$
-
-The problem is that $\cos(w u)$ oscillates forever at constant amplitude, and $e^{-u^\beta}$ decays
-extremely slowly once $\beta$ is small: at $\beta = 0.3$ it is still significant at $u \sim 10^5$.
-Evaluating $G_\beta$ therefore means summing an enormous number of nearly cancelling oscillations.
-Furthermore, large $w$ makes the oscillation fast, so the steps must be tiny, while small $\beta$
-stretches the tail over more decades, so the range must be huge.
-
-An FFT of the sampled relaxation cannot span that tail at small $\beta$ and, because $e^{-(t /
-\tau)^\beta}$ has a cusp at $t = 0$, converges only as $(\Delta t)^{1 + \beta}$.
-``scipy.stats.levy_stable`` is mathematically the same function, but it is 70-100x slower and not
-accurate for $\alpha$ close to 1.
-
-Instead, the integral is evaluated in the complex plane described in :func:`_kww_shape`.
-
-The four helpers below are module-level functions rather than methods. :func:`_quadrature_nodes`
-and :func:`_reduced_hwhm` must be, because ``@lru_cache`` on a method would key on ``self``: every
-instance would rebuild its own grid and the cache would keep every component ever created alive.
-:func:`_kww_shape` is a pure function of ``(w, beta)`` that touches no component state: area,
-center and units are applied afterwards in ``_evaluate_values``, so it does not belong in the
-class, and :func:`_hwhm_dependency_expression` only assembles a string from module constants.
-
-:func:`_reduced_hwhm` is not on any evaluation path.  It solves the half width exactly, and the
-closed form that ``width`` actually resolves through is fitted to it; it stays here as the
-reference that defines `_HWHM_POLY_COEFFS` and as what the tests check that fit against.
-"""
-
-from __future__ import annotations
-
-import contextlib
-from functools import lru_cache
-from typing import TYPE_CHECKING
-
-import numpy as np
-from easyscience.variable import DescriptorNumber
-from easyscience.variable import Parameter
-from scipp import UnitError
-from scipy.optimize import brentq
-
-from easydynamics.sample_model.components.mixins import CreateParametersMixin
-from easydynamics.sample_model.components.model_component import ModelComponent
-from easydynamics.utils.utils import Numeric
-from easydynamics.utils.utils import convert_value_unit
-from easydynamics.utils.utils import hbar
-
-if TYPE_CHECKING:
-    import scipp as sc
-
-MINIMUM_RELAXATION_TIME = 1e-10  # ps. Avoids a division by zero in hbar / tau
-MINIMUM_BETA = 0.05  # Below this the quadrature grid can no longer resolve the time tail
-MAXIMUM_BETA = 2.0  # Above this the transform is no longer positive, so unphysical
-
-# Tuning of the exp-sinh quadrature used by _kww_shape. The step and the half-range were chosen
-# together so the transform reproduces its analytic special cases (beta = 1 and beta = 2) to
-# ~1e-11 relative over w in [0, 1e6]; see test_stretched_exponential.py.
-_QUAD_STEP = 0.03
-_QUAD_HALF_RANGE = 4.3
-# exp(-u**beta) has fallen below exp(-_QUAD_DECADES) at the far end of the grid.
-_QUAD_DECADES = 50.0
-# The quadrature holds one (energies x nodes) temporary, so long energy axes are evaluated in
-# blocks to keep that intermediate at a few tens of MB instead of scaling with the axis.
-_MAX_BLOCK_ELEMENTS = 1 << 21
-# The reduced half width spans 1.67 at beta = 2 down to ~7e-27 at beta = 0.05, so it is
-# bracketed in log10(w).  The lower end stays clear of the subnormal range, where the
-# 1 / (w sin theta) rescaling inside _kww_shape would overflow.
-_HWHM_LOG_BRACKET = (-300.0, 3.0)
-
-# The width is exposed as a dependent Parameter, and easyscience resolves a dependency from a
-# string expression, so the root solved by _reduced_hwhm has to be written in closed form.  It
-# very nearly is one: beta * ln(HWHM) - ln(beta) is a small, smooth function of beta alone, so
-# HWHM = exp((ln(beta) + p(beta)) / beta) with p the polynomial below, in ascending powers.  The
-# coefficients are a degree 8 least-squares fit over 400 log-spaced beta in [MINIMUM_BETA,
-# MAXIMUM_BETA], and reproduce _reduced_hwhm to 1.4e-5 relative for beta >= 0.1.  Below that the
-# fit degrades to ~1e-2, but so does the root it is fitted to, and both are tens of orders of
-# magnitude under any energy grid step by then.  Regenerate, or check these against the root they
-# are fitted to, with tools/fit_kww_hwhm_coefficients.py.
-_HWHM_POLY_COEFFS = (
-    -5.5129123891501144e-05,
-    -0.24257355020449564,
-    0.2875849387004671,
-    -0.04773226324334423,
-    -0.04385316873071495,
-    0.11064865473723488,
-    -0.09366816058690182,
-    0.03431170767881221,
-    -0.004659825239152873,
-)
-
-
-@lru_cache(maxsize=8)
-def _quadrature_nodes(half_steps: int) -> tuple[np.ndarray, np.ndarray]:
-    r"""
-    Build the exp-sinh quadrature nodes and weights for ``2 * half_steps + 1`` points.
-
-    The exp-sinh (double-exponential) rule maps the trapezoidal rule on the whole real line onto
-    the half line via $s = e^{(\pi / 2) \sinh t}$, which makes the node density follow the decades
-    of $s$ rather than its absolute size.  The grids are small and reused across evaluations, so
-    they are cached rather than rebuilt on every call.
-
-    Parameters
-    ----------
-    half_steps : int
-        Number of steps of size ``_QUAD_STEP`` on each side of $t = 0$.
-
-    Returns
-    -------
-    tuple[np.ndarray, np.ndarray]
-        The node positions $s$ on the half line and their quadrature weights.
-    """
-    t = np.arange(-half_steps, half_steps + 1) * _QUAD_STEP
-    nodes = np.exp(0.5 * np.pi * np.sinh(t))
-    weights = _QUAD_STEP * nodes * (0.5 * np.pi) * np.cosh(t)
-    return nodes, weights
-
-
-def _kww_shape(w: np.ndarray, beta: float) -> np.ndarray:
-    r"""
-    Evaluate the reduced Fourier cosine transform of a stretched exponential.
-
-    $$ G_\beta(w) = \int_0^\infty e^{-u^\beta} \cos(w u) \, du $$
-
-    The physical spectrum is $\frac{A}{\pi \Gamma} G_\beta(x / \Gamma)$ with $\Gamma = \hbar /
-    \tau$, so $w$ is the reduced energy $x / \Gamma$ and everything else is a scale factor.
-
-    The integrand oscillates without decaying, so it is integrated along the rotated ray $u = s
-    e^{i \theta}$ instead of the real axis.  Writing $\cos(w u) = \mathrm{Re}\, e^{i w u}$ makes
-    the integrand analytic in a wedge around the positive real axis: $e^{-u^\beta}$ keeps decaying
-    as long as $\arg(u) < \pi / (2 \beta)$, and the arc at infinity vanishes, so by Cauchy's
-    theorem the ray may be swung up to that angle without changing the value.
-
-    On the tilted ray $e^{i w u}$ becomes $e^{i w s \cos\theta} e^{-w s \sin\theta}$: the
-    oscillation now decays with increasing $w$.  The faster the oscillation, the faster it is
-    damped, so the number of oscillations before the integrand dies is bounded *independently of*
-    $w$.
-
-    The angle $\theta = \pi / (4 \beta)$ is half of the $\pi / (2 \beta)$ limit, a deliberate
-    safety margin; it is capped at $0.45 \pi$ because for $\beta < 0.5$ the formula would exceed
-    $\pi / 2$ and the ray would cross the imaginary axis, where $e^{-u^\beta}$ grows.
-
-    Writing $e^{-u^\beta + i w u}$ out in real form on that ray, with the extra $\theta$ in the
-    phase coming from $du = e^{i \theta} ds$, turns the integral into
-
-    $$ G_\beta(w) = \int_0^\infty e^{-P(s)} \cos(Q(s) + \theta) \, ds $$
-
-    with $P = \cos(\beta \theta) s^\beta + w \sin(\theta) s$ (the decay) and $Q = w \cos(\theta) s
-    - \sin(\beta \theta) s^\beta$ (what is left of the oscillation), which an exp-sinh rule on a
-    grid rescaled to the decay length of $P$ integrates to near machine precision for every $w$.
-
-    The rescaling is the other half of the robustness.  $P$ has two terms, and whichever dies first
-    sets the decay length: $\cos(\beta \theta)^{-1 / \beta}$ for the $s^\beta$ term, $1 / (w
-    \sin\theta)$ for the $w s$ term.  Taking the smaller of the two and stretching the grid onto it
-    means the integrand always falls off around $s = 1$, so a single node layout serves every $w$
-    and every $\beta$.
-
-    Accuracy is therefore governed by $w$ and $\beta$ alone. For $\beta \le 1$ the result tracks
-    the exact power-law tail out to at least $w = 10^8$.  For $\beta > 1$ the profile decays fast
-    enough to reach the floor of double precision, and values below roughly $10^{-11}$ of the peak
-    are noise.
-
-    Parameters
-    ----------
-    w : np.ndarray
-        Reduced energies.  Only $|w|$ matters: the transform is even in $w$.
-    beta : float
-        Stretching exponent, in ``[MINIMUM_BETA, MAXIMUM_BETA]``.
-
-    Returns
-    -------
-    np.ndarray
-        The transform, clipped at zero.  It is non-negative for every $\beta \le 2$; the clip only
-        removes rounding noise where the true value has underflowed.
-    """
-    abs_w = np.abs(w)
-
-    theta = min(0.45 * np.pi, np.pi / (4.0 * beta))
-    cos_rotated = np.cos(beta * theta)
-    sin_rotated = np.sin(beta * theta)
-    sin_theta = np.sin(theta)
-    cos_theta = np.cos(theta)
-
-    # Reach far enough out in s that exp(-cos(beta theta) s**beta) has died: small beta stretches
-    # the tail over many more decades, so the grid has to follow it.  This inverts the node map
-    # s = exp(pi / 2 sinh(t)) at the s where cos(beta theta) s**beta equals _QUAD_DECADES, i.e.
-    # where the decaying factor has fallen to exp(-50).
-    half_range = np.arcsinh((2.0 / np.pi) * np.log(_QUAD_DECADES / cos_rotated) / beta)
-    half_steps = int(np.ceil(max(half_range, _QUAD_HALF_RANGE) / _QUAD_STEP))
-    nodes, weights = _quadrature_nodes(half_steps)
-    nodes_beta = nodes**beta
-
-    # Rescale the grid onto whichever of the two exponents in P decays first, so the integrand
-    # always falls off around s = 1 and the same node layout serves every w.  beta_scale is where
-    # the s**beta term dies, w_scale where the w s term does; w_scale is infinite at w = 0, where
-    # there is no oscillation to damp and beta_scale is the only length in the problem.
-    beta_scale = cos_rotated ** (-1.0 / beta)
-    w_scale = np.divide(1.0, abs_w * sin_theta, out=np.full(abs_w.shape, np.inf), where=abs_w > 0)
-    scale = np.minimum(beta_scale, w_scale)
-
-    out = np.empty(abs_w.shape)
-    block = max(1, _MAX_BLOCK_ELEMENTS // nodes.size)
-    for start in range(0, abs_w.size, block):
-        stop = start + block
-        block_w = abs_w[start:stop, np.newaxis]
-        block_scale = scale[start:stop, np.newaxis]
-
-        s = block_scale * nodes
-        s_beta = block_scale**beta * nodes_beta
-        decaying = cos_rotated * s_beta + (block_w * sin_theta) * s
-        oscillating = (block_w * cos_theta) * s - sin_rotated * s_beta
-
-        integral = (np.exp(-decaying) * np.cos(oscillating + theta) * weights).sum(axis=1)
-        out[start:stop] = integral * scale[start:stop]
-
-    return np.maximum(out, 0.0)
-
-
-@lru_cache(maxsize=128)
-def _reduced_hwhm(beta: float) -> float:
-    r"""
-    Solve for the half width at half maximum of $G_\beta$ in reduced energy $w = x / \Gamma$.
-
-    $\Gamma = \hbar / \tau$ is the HWHM only at $\beta = 1$.  Below that the profile sharpens
-    dramatically: the peak $G_\beta(0)$ grows while the area stays fixed, so the half width
-    collapses far faster than $\Gamma$ suggests -- 0.22 $\Gamma$ at $\beta = 0.5$, 2.7e-4 $\Gamma$
-    at $\beta = 0.2$, 8e-11 $\Gamma$ at $\beta = 0.1$.  There is no closed form, so the crossing of
-    $G_\beta(w) = \tfrac{1}{2} G_\beta(0)$ is bracketed numerically.
-
-    The search runs in $\log_{10} w$ because the root ranges over more than 25 decades across the
-    supported $\beta$; a linear bracket could not resolve the small-$\beta$ end.  Each solve costs
-    a few tens of single-point quadratures, under a millisecond, and the result is cached because
-    it depends on $\beta$ alone.
-
-    The root is well conditioned down to $\beta \approx 0.1$ and increasingly poorly below it: by
-    $\beta = 0.05$ the profile takes some 60 e-folds of $w$ to fall by half, so $G_\beta$ is nearly
-    flat in $\log w$ near the crossing and the result is only good to about a percent.  That is
-    immaterial for the one thing the half width is used for -- comparing against the convolution's
-    energy grid -- because at those $\beta$ it is already tens of orders of magnitude below any
-    grid step, and the comparison lands the same way either way.
-
-    Parameters
-    ----------
-    beta : float
-        Stretching exponent, in ``[MINIMUM_BETA, MAXIMUM_BETA]``.
-
-    Returns
-    -------
-    float
-        The HWHM in units of $\Gamma$.  Multiply by $\Gamma$ to get an energy.
-    """
-    half_peak = 0.5 * _kww_shape(np.array([0.0]), beta)[0]
-
-    def offset(log_w: float) -> float:
-        return _kww_shape(np.array([10.0**log_w]), beta)[0] - half_peak
-
-    return float(10.0 ** brentq(offset, *_HWHM_LOG_BRACKET, xtol=1e-12))
-
-
-def _hwhm_dependency_expression() -> str:
-    r"""
-    Write the half width of :func:`_reduced_hwhm` as an easyscience dependency expression.
-
-    The expression evaluates ``exp((log(beta) + p(beta)) / beta)`` from `_HWHM_POLY_COEFFS`, in
-    Horner form, and multiplies it by $\hbar / \tau$ to turn the reduced half width into an energy.
-
-    ``exp`` and ``log`` are not defined on DescriptorNumbers, so the reduced half width is built
-    from ``b.value`` and is a plain float; the surrounding ``* hbar / tau`` is what makes the whole
-    expression return a DescriptorNumber, as easyscience requires.  Referring to ``b.value`` rather
-    than ``b`` strips beta's uncertainty, which is why the width carries only the relaxation time's
-    contribution. When exp and log of DescriptorNumbers are supported, this can be rewritten to
-    propagate beta's uncertainty too.
-
-    Returns
-    -------
-    str
-        The dependency expression, over the mapped names ``b``, ``hbar`` and ``tau``.
-    """
-    horner = repr(_HWHM_POLY_COEFFS[-1])
-    for coefficient in _HWHM_POLY_COEFFS[-2::-1]:
-        horner = f'({horner}) * b.value + {coefficient!r}'
-    return f'exp((log(b.value) + ({horner})) / b.value) * hbar / tau'
-
-
-class StretchedExponential(CreateParametersMixin, ModelComponent):
-    r"""
-    Model of a stretched exponential (Kohlrausch-Williams-Watts) relaxation, Fourier transformed
-    from time to energy.
-
-    The model is defined by its intermediate scattering function
-
-    $$ I(t) = A \exp\left[-\left(\frac{|t|}{\tau}\right)^\beta\right] $$
-
-    where $\tau$ is the relaxation time and $\beta$ the stretching exponent.  Here we calculate the
-    Fourier transform:
-
-    $$ S(x) = \frac{1}{2\pi\hbar} \int I(t)\, e^{-i (x - x_0) t / \hbar} \, \mathrm{d}t =
-    \frac{A}{\pi \Gamma} \, G_\beta\!\left(\frac{x - x_0}{\Gamma}\right), \qquad \Gamma =
-    \frac{\hbar}{\tau} $$
-
-    with $G_\beta(w) = \int_0^\infty e^{-u^\beta} \cos(w u)\, \mathrm{d}u$.  $A$ is the area (the
-    profile integrates to $A$ over $x$), $x_0$ is the center, and $\Gamma$ is the energy scale set
-    by the relaxation time.  area has unit = x_unit * y_unit; center has unit = x_unit;
-    relaxation_time has unit ps; beta is dimensionless.
-
-    $\beta = 1$ recovers a Lorentzian of HWHM $\Gamma$ and $\beta = 2$ a Gaussian of standard
-    deviation $\sqrt{2}\,\Gamma$; in between there is no closed form and the transform is evaluated
-    numerically.  $\beta \le 1$ is the physically usual range.  $\beta$ is capped at 2 because
-    $\exp(-|t|^\beta)$ stops being positive definite beyond it, so the transform would go negative.
-
-    Note that the x-axis has to be an energy, since the relaxation time is turned into an energy
-    scale through $\hbar$.
-
-    Examples
-    --------
-    **Creating a stretched exponential**
-
-    By default the center is fixed at 0 like a Lorentzian::
-    ```python
-    import numpy as np
-    import easydynamics as edyn
-
-    kww = edyn.StretchedExponential(area=1.0, relaxation_time=5.0, beta=0.7)
-    x = np.linspace(-2, 2, 100)
-    values = kww.evaluate(x)
-    ```
-
-    **Modifying parameters after construction**
-
-    ```python
-    import easydynamics as edyn
-
-    kww = edyn.StretchedExponential(area=2.0, relaxation_time=10.0, beta=0.5, name='Polymer')
-    kww.relaxation_time = 20.0
-    kww.beta = 0.6
-    ```
-    """
-
-    def __init__(
-        self,
-        area: Numeric = 1.0,
-        center: Numeric | None = None,
-        relaxation_time: Numeric = 1.0,
-        beta: Numeric = 1.0,
-        x_unit: str | sc.Unit = 'meV',
-        y_unit: str | sc.Unit = 'dimensionless',
-        name: str = 'StretchedExponential',
-        display_name: str | None = None,
-        unique_name: str | None = None,
-    ) -> None:
-        r"""
-        Initialize the StretchedExponential component.
-
-        Parameters
-        ----------
-        area : Numeric, default=1.0
-            Integrated area under the transformed profile.  Unit is ``x_unit * y_unit``.
-        center : Numeric | None, default=None
-            Peak position in x_unit.  If None, defaults to 0 and the center parameter is fixed.
-        relaxation_time : Numeric, default=1.0
-            Relaxation time tau in ps.  Must be strictly positive.  It enters the spectrum as the
-            energy scale $\Gamma = \hbar / \tau$.
-        beta : Numeric, default=1.0
-            Stretching exponent.  Must lie in ``[0.05, 2.0]``; $\beta = 1$ is a Lorentzian and
-            $\beta = 2$ a Gaussian.
-        x_unit : str | sc.Unit, default='meV'
-            Unit of the x-axis.  Must be an energy, since $\hbar / \tau$ is converted into it.
-            center is stored in this unit. area_unit = x_unit * y_unit.
-        y_unit : str | sc.Unit, default='dimensionless'
-            Unit of the y-axis (output).
-        name : str, default='StretchedExponential'
-            Name of the component.
-        display_name : str | None, default=None
-            Display name shown when plotting.  Falls back to *name* if None.
-        unique_name : str | None, default=None
-            Globally unique identifier.  Auto-generated if None.
-        """
-        super().__init__(
-            x_unit=x_unit,
-            y_unit=y_unit,
-            name=name,
-            display_name=display_name,
-            unique_name=unique_name,
-        )
-
-        self._area = self._create_area_parameter(
-            area=area, name=name, x_unit=self.x_unit, y_unit=self.y_unit
-        )
-        self._center = self._create_center_parameter(
-            center=center, name=name, fix_if_none=True, x_unit=self.x_unit
-        )
-
-        self._validate_relaxation_time(relaxation_time)
-        self._relaxation_time = Parameter(
-            name=name + ' relaxation_time',
-            value=float(relaxation_time),
-            unit='ps',
-            min=MINIMUM_RELAXATION_TIME,
-        )
-
-        self._validate_beta(beta)
-        self._beta = Parameter(
-            name=name + ' beta',
-            value=float(beta),
-            unit='dimensionless',
-            min=MINIMUM_BETA,
-            max=MAXIMUM_BETA,
-        )
-
-        # hbar has to be in the dependency map, because carrying J*s through the expression is
-        # what lets the unit algebra turn the reduced half width into an energy.  It is a private
-        # copy rather than the shared ``utils.hbar`` deliberately: make_dependent_on attaches the
-        # width as an observer of every mapped variable, so mapping the module-level constant
-        # would append one observer per component to a list that is never emptied, keeping every
-        # StretchedExponential ever built alive and lengthening each of its notifications.
-        self._hbar = DescriptorNumber(name=name + ' hbar', value=hbar.value, unit=str(hbar.unit))
-        # Built here rather than on first access so that get_all_parameters reports the same set
-        # whether or not the width has been read yet.  A non-energy x_unit has no width to build
-        # and must still construct: the UnitError is deferred to the first read, as for evaluate.
-        self._width: Parameter | None = None
-        with contextlib.suppress(UnitError):
-            self._width = self._build_width()
-
-    ################################
-    # Validation
-    ################################
-
-    @staticmethod
-    def _validate_relaxation_time(value: Numeric) -> None:
-        """
-        Check that a relaxation time is a finite, strictly positive number.
-
-        Parameters
-        ----------
-        value : Numeric
-            The candidate relaxation time.
-
-        Raises
-        ------
-        TypeError
-            If *value* is not a numeric type.
-        ValueError
-            If *value* is not finite, or is smaller than ``MINIMUM_RELAXATION_TIME``.
-        """
-        if not isinstance(value, Numeric):
-            raise TypeError('relaxation_time must be a number.')
-        if not np.isfinite(value):
-            raise ValueError('relaxation_time must be a finite number.')
-        if float(value) < MINIMUM_RELAXATION_TIME:
-            raise ValueError('relaxation_time must be greater than zero.')
-
-    @staticmethod
-    def _validate_beta(value: Numeric) -> None:
-        """
-        Check that a stretching exponent is a finite number inside the supported range.
-
-        Parameters
-        ----------
-        value : Numeric
-            The candidate stretching exponent.
-
-        Raises
-        ------
-        TypeError
-            If *value* is not a numeric type.
-        ValueError
-            If *value* is not finite, or lies outside ``[MINIMUM_BETA, MAXIMUM_BETA]``.
-        """
-        if not isinstance(value, Numeric):
-            raise TypeError('beta must be a number.')
-        if not np.isfinite(value):
-            raise ValueError('beta must be a finite number.')
-        if not MINIMUM_BETA <= float(value) <= MAXIMUM_BETA:
-            raise ValueError(f'beta must be between {MINIMUM_BETA} and {MAXIMUM_BETA}.')
-
-    ################################
-    # Properties
-    ################################
-
-    @property
-    def area(self) -> Parameter:
-        """
-        Get the area parameter.
-
-        Returns
-        -------
-        Parameter
-            The area Parameter with unit ``x_unit * y_unit``.
-        """
-        return self._area
-
-    @area.setter
-    def area(self, value: Numeric) -> None:
-        """
-        Parameters
-        ----------
-        value : Numeric
-            New area value (in current area unit = x_unit * y_unit).
-
-        Notes
-        -----
-        A ``TypeError`` propagates from the shared value setter if *value* is not a numeric type,
-        and a ``ValueError`` propagates from it if *value* violates the area parameter's bounds
-        (e.g. a negative value when the area was created non-negative, giving it ``min=0``).
-        """
-        self._set_bounded_parameter_value(self._area, value, 'area')
-
-    @property
-    def center(self) -> Parameter:
-        """
-        Get the center parameter.
-
-        Returns
-        -------
-        Parameter
-            The center (x_0) Parameter with unit ``x_unit``.
-        """
-        return self._center
-
-    @center.setter
-    def center(self, value: Numeric | None) -> None:
-        """
-        Parameters
-        ----------
-        value : Numeric | None
-            New center value in x_unit.  If None, the center is set to 0 and the parameter is
-            fixed.
-
-        Raises
-        ------
-        TypeError
-            If *value* is not None and not a numeric type.
-        """
-        if value is None:
-            value = 0.0
-            self._center.fixed = True
-        if not isinstance(value, Numeric):
-            raise TypeError('center must be a number')
-        self._center.value = value
-
-    @property
-    def relaxation_time(self) -> Parameter:
-        """
-        Get the relaxation time parameter.
-
-        Returns
-        -------
-        Parameter
-            The relaxation time (tau) Parameter with unit ``ps``.
-        """
-        return self._relaxation_time
-
-    @relaxation_time.setter
-    def relaxation_time(self, value: Numeric) -> None:
-        """
-        Parameters
-        ----------
-        value : Numeric
-            New relaxation time in the parameter's current unit.  Must be strictly positive.
-
-        Notes
-        -----
-        A ``TypeError`` propagates from the validator if *value* is not a numeric type, and a
-        ``ValueError`` propagates from it if *value* is not finite or not positive, or from the
-        shared value setter if *value* violates the parameter's bounds.
-        """
-        self._validate_relaxation_time(value)
-        self._set_bounded_parameter_value(self._relaxation_time, value, 'relaxation_time')
-
-    @property
-    def beta(self) -> Parameter:
-        """
-        Get the stretching exponent parameter.
-
-        Returns
-        -------
-        Parameter
-            The stretching exponent (beta) Parameter, dimensionless.
-        """
-        return self._beta
-
-    @beta.setter
-    def beta(self, value: Numeric) -> None:
-        """
-        Parameters
-        ----------
-        value : Numeric
-            New stretching exponent.  Must lie in ``[MINIMUM_BETA, MAXIMUM_BETA]``.
-
-        Notes
-        -----
-        A ``TypeError`` propagates from the validator if *value* is not a numeric type, and a
-        ``ValueError`` propagates from it if *value* is not finite or lies outside the supported
-        range, or from the shared value setter if *value* violates the parameter's bounds.
-        """
-        self._validate_beta(value)
-        self._set_bounded_parameter_value(self._beta, value, 'beta')
-
-    def _build_width(self) -> Parameter:
-        r"""
-        Build the dependent width Parameter in the component's current x_unit.
-
-        Returns
-        -------
-        Parameter
-            A Parameter made dependent on :attr:`beta` and :attr:`relaxation_time`.
-
-        Raises
-        ------
-        UnitError
-            If x_unit is not an energy, so $\hbar / \tau$ cannot be expressed in it.
-        """
-        width = Parameter(name=self.name + ' width', value=1.0, unit=str(self.x_unit))
-        try:
-            # easyscience propagates inf bounds through arithmetic, producing inf/inf=nan as a
-            # transient intermediate; suppress the spurious numpy warnings as elsewhere.
-            with np.errstate(invalid='ignore', divide='ignore'):
-                width.make_dependent_on(
-                    dependency_expression=_hwhm_dependency_expression(),
-                    dependency_map={
-                        'b': self._beta,
-                        'hbar': self._hbar,
-                        'tau': self._relaxation_time,
-                    },
-                    desired_unit=str(self.x_unit),
-                )
-        except UnitError as e:
-            raise UnitError(
-                f'{self.__class__.__name__} needs an energy x_unit so that hbar / '
-                f'relaxation_time can be expressed in it, but got {self.x_unit}.'
-            ) from e
-        return width
-
-    @property
-    def width(self) -> Parameter:
-        r"""
-        Get the half width at half maximum of the peak.
-
-        This is a *dependent* Parameter, derived from :attr:`relaxation_time` and :attr:`beta`. It
-        is accurate to 1.4e-5 relative for $\beta \ge 0.1$. For better accuracy, use
-        :func:`_reduced_hwhm` directly and multiply by $\hbar / \tau$ in the desired unit. Note
-        that the uncertainty of the width may be underestimated, because the dependency expression
-        uses ``b.value`` rather than ``b`` to compute the width, so the uncertainty of beta is not
-        propagated into the width.
-
-        Returns
-        -------
-        Parameter
-            The HWHM expressed in the component's own x_unit.
-
-        Notes
-        -----
-        A ``UnitError`` propagates from :meth:`_build_width` if x_unit is not an energy, so $\hbar
-        / \tau$ cannot be expressed in it.
-        """
-        if self._width is None:
-            self._width = self._build_width()
-        return self._width
-
-    ################################
-    # Evaluation
-    ################################
-
-    def _energy_scale(self, eval_unit: str | None) -> float:
-        r"""
-        Get the energy scale $\Gamma = \hbar / \tau$ expressed in the evaluation unit.
-
-        Parameters
-        ----------
-        eval_unit : str | None
-            The unit x values are expressed in, or None for the component's own x_unit.
-
-        Returns
-        -------
-        float
-            Gamma in *eval_unit*.
-
-        Raises
-        ------
-        UnitError
-            If the target unit is not an energy, so $\hbar / \tau$ cannot be expressed in it.
-        """
-        target_unit = eval_unit if eval_unit is not None else self.x_unit
-        tau_unit = self._relaxation_time.unit
-        try:
-            hbar_value = convert_value_unit(hbar.value, hbar.unit, f'({target_unit})*({tau_unit})')
-        except UnitError as e:
-            raise UnitError(
-                f'{self.__class__.__name__} needs an energy x_unit so that hbar / '
-                f'relaxation_time can be expressed in it, but got {target_unit}.'
-            ) from e
-        return hbar_value / self._relaxation_time.value
-
-    def _evaluate_values(self, x_vals: np.ndarray, eval_unit: str | None) -> np.ndarray:
-        r"""
-        Evaluate the transformed stretched exponential at x_vals.
-
-        $$ S(x) = \frac{A}{\pi \Gamma} \, G_\beta\!\left(\frac{x - x_0}{\Gamma}\right), \qquad
-        \Gamma = \frac{\hbar}{\tau} $$
-
-        where *A* is ``area``, *x*₀ is ``center``, *tau* is ``relaxation_time``, *beta* is the
-        stretching exponent, and $G_\beta$ is the reduced transform computed by :func:`_kww_shape`.
-        Parameters in the model's own units are temporarily converted to eval_unit for the
-        computation.
-
-        Parameters
-        ----------
-        x_vals : np.ndarray
-            Raw x values expressed in eval_unit.
-        eval_unit : str | None
-            The unit of x_vals.
-
-        Returns
-        -------
-        np.ndarray
-            Evaluated values at x_vals.
-        """
-        center = self._resolve_param_value(self._center, eval_unit)
-        area = self._resolve_param_value(self._area, self._eval_area_unit(eval_unit))
-        energy_scale = self._energy_scale(eval_unit)
-
-        shape = _kww_shape((x_vals - center) / energy_scale, self._beta.value)
-        return area / (np.pi * energy_scale) * shape
-
-    ################################
-    # Unit conversion
-    ################################
-
-    def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None:
-        r"""
-        Convert the center and the area to new_x_unit.
-
-        The relaxation time carries a time unit and beta is dimensionless, so neither is affected;
-        the energy scale $\hbar / \tau$ is re-derived in the new unit on every evaluation.
-
-        Parameters
-        ----------
-        new_x_unit : str | sc.Unit
-            Target x-axis unit.  Must be dimensionally compatible with the current x_unit.
-        """
-        self._convert_x_unit_area_based(
-            new_x_unit=new_x_unit,
-            x_params=[self._center],
-            area_param=self._area,
-        )
-        # The dependency resolves into the unit it was built with, so rebuild it in the new one.
-        self._width = self._build_width()
-
-    def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None:
-        """
-        Convert the y-axis (output) unit by rescaling the area parameter.
-
-        The area is rescaled from ``x_unit * old_y_unit`` to ``x_unit * new_y_unit``.
-
-        Parameters
-        ----------
-        new_y_unit : str | sc.Unit
-            Target y-axis unit.
-        """
-        self._convert_y_unit_area_based(new_y_unit=new_y_unit, area_param=self._area)
-
-    def __repr__(self) -> str:
-        """
-        Return a string representation of the StretchedExponential.
-
-        Returns
-        -------
-        str
-            A string representation of the StretchedExponential.
-        """
-        return (
-            f'{self.__class__.__name__}(name = {self.name}, display_name = {self.display_name}, '
-            f'x_unit = {self.x_unit}, y_unit = {self.y_unit},\n '
-            f'    area = {self.area},\n '
-            f'    center = {self.center},\n '
-            f'    relaxation_time = {self.relaxation_time},\n '
-            f'    beta = {self.beta})'
-        )
diff --git a/src/easydynamics/utils/plotting.py b/src/easydynamics/utils/plotting.py
index 52360b518..cb8ee0a98 100644
--- a/src/easydynamics/utils/plotting.py
+++ b/src/easydynamics/utils/plotting.py
@@ -4,7 +4,7 @@
 
 import plopp as pp
 import scipp as sc
-from plopp.backends.matplotlib.figure import WidgetFigure
+from plopp.backends.matplotlib.figure import InteractiveFigure
 from plopp.plotting._slicer import SlicerPlot
 from plopp.plotting._slicer import _maybe_reduce_dim
 from plopp.widgets import slice_dims
@@ -17,7 +17,7 @@ def slicerplot_with_residuals(
     keep: list[str] | str | None = None,
     operation: str = 'sum',
     **kwargs: object,
-) -> WidgetFigure:
+) -> InteractiveFigure:
     """
     Create a SlicerPlot with an additional subplot for residuals.
 
@@ -56,7 +56,7 @@ def slicerplot_with_residuals(
 
     Returns
     -------
-    WidgetFigure
+    InteractiveFigure
         A figure containing the SlicerPlot and the residuals subplot.
 
     Raises
diff --git a/src/easydynamics/utils/posterior_plotting.py b/src/easydynamics/utils/posterior_plotting.py
index dc930de5a..d1e45b146 100644
--- a/src/easydynamics/utils/posterior_plotting.py
+++ b/src/easydynamics/utils/posterior_plotting.py
@@ -23,7 +23,7 @@
 if TYPE_CHECKING:
     from ipywidgets import VBox
     from matplotlib.figure import Figure
-    from plopp.backends.matplotlib.figure import WidgetFigure
+    from plopp.backends.matplotlib.figure import InteractiveFigure
 
 
 def plot_trace(
@@ -719,7 +719,7 @@ def predictive_with_slider(
     title: str | None = None,
     credible_interval: float = 68.0,
     **kwargs: dict[str, Any],
-) -> WidgetFigure:
+) -> InteractiveFigure:
     """
     Plot per-Q posterior-predictive bands behind a plopp Q slider.
 
@@ -763,7 +763,7 @@ def predictive_with_slider(
 
     Returns
     -------
-    WidgetFigure
+    InteractiveFigure
         The plopp figure with its Q slider.
 
     Raises
diff --git a/tests/integration/fitting/test_fitting_with_stretched_exponential.py b/tests/integration/fitting/test_fitting_with_stretched_exponential.py
deleted file mode 100644
index fbbac97dc..000000000
--- a/tests/integration/fitting/test_fitting_with_stretched_exponential.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-
-"""
-Round-trip fit of a StretchedExponential through Analysis1d.
-
-The transform of a stretched exponential has no closed form, so it is evaluated by quadrature.
-That makes it worth checking not just that the numbers are right at fixed parameters — the unit
-tests cover that — but that the profile stays smooth enough in the relaxation time and the
-stretching exponent for a least-squares fitter to walk back to the truth from a poor start.
-"""
-
-import numpy as np
-import pytest
-import scipp as sc
-
-from easydynamics.analysis.analysis1d import Analysis1d
-from easydynamics.experiment import Experiment
-from easydynamics.sample_model import InstrumentModel
-from easydynamics.sample_model import SampleModel
-from easydynamics.sample_model import StretchedExponential
-
-TRUE_AREA = 1.0
-TRUE_RELAXATION_TIME = 8.0
-TRUE_BETA = 0.6
-NOISE_FRACTION = 0.01
-
-TRUTHS = {
-    'StretchedExponential area': TRUE_AREA,
-    'StretchedExponential relaxation_time': TRUE_RELAXATION_TIME,
-    'StretchedExponential beta': TRUE_BETA,
-}
-
-
-def build_analysis() -> Analysis1d:
-    """Fit a badly-started StretchedExponential against noisy data drawn from the true one."""
-    energy_values = np.linspace(-1.5, 1.5, 301)
-    truth = StretchedExponential(
-        area=TRUE_AREA,
-        relaxation_time=TRUE_RELAXATION_TIME,
-        beta=TRUE_BETA,
-    )
-    profile = truth.evaluate(energy_values)
-    noise = NOISE_FRACTION * profile.max()
-    observed = profile + np.random.default_rng(0).normal(0.0, noise, size=profile.shape)
-
-    experiment = Experiment(
-        data=sc.DataArray(
-            data=sc.array(
-                dims=['Q', 'energy'],
-                values=observed[None, :],
-                variances=np.full_like(observed, noise**2)[None, :],
-            ),
-            coords={
-                'Q': sc.array(dims=['Q'], values=[1.0], unit='1/Angstrom'),
-                'energy': sc.array(dims=['energy'], values=energy_values, unit='meV'),
-            },
-        )
-    )
-
-    # Start far from the truth: half the relaxation time, an almost unstretched exponent and half
-    # again the area.  The centre is left at its default, fixed at zero.
-    analysis = Analysis1d(
-        display_name='StretchedExponentialIntegration',
-        experiment=experiment,
-        sample_model=SampleModel(
-            components=StretchedExponential(area=1.5, relaxation_time=4.0, beta=0.9)
-        ),
-        instrument_model=InstrumentModel(),
-        Q_index=0,
-    )
-    # The energy offset shifts the spectrum exactly as the component's centre does, so leaving
-    # both free would make the model unidentifiable.
-    analysis.instrument_model.fix_energy_offset(Q_index=0)
-    return analysis
-
-
-@pytest.fixture(scope='module')
-def fitted_analysis():
-    analysis = build_analysis()
-    analysis.fit()
-    return analysis
-
-
-class TestFittingWithStretchedExponential:
-    def test_fit_describes_the_data(self):
-        # WHEN
-        analysis = build_analysis()
-
-        # THEN
-        results = analysis.fit()
-
-        # EXPECT
-        assert results.success
-        assert results.reduced_chi2 < 1.5
-
-    def test_the_free_parameters_are_the_three_shape_parameters(self, fitted_analysis):
-        # THEN
-        names = {parameter.name for parameter in fitted_analysis.get_free_parameters()}
-
-        # EXPECT the centre and the energy offset stay fixed
-        assert names == set(TRUTHS)
-
-    @pytest.mark.parametrize('name', list(TRUTHS))
-    def test_fit_recovers_the_true_parameters(self, fitted_analysis, name):
-        # WHEN
-        parameter = next(p for p in fitted_analysis.get_free_parameters() if p.name == name)
-
-        # THEN EXPECT the truth within a few standard errors, on a usable uncertainty
-        assert np.isfinite(parameter.error)
-        assert parameter.error > 0.0
-        assert abs(parameter.value - TRUTHS[name]) < 4 * parameter.error
diff --git a/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py b/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py
index 4ce8db631..3d80f983b 100644
--- a/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py
+++ b/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py
@@ -9,7 +9,6 @@
 from easydynamics.convolution.energy_grid import EnergyGrid
 from easydynamics.convolution.numerical_convolution_base import NumericalConvolutionBase
 from easydynamics.sample_model import Gaussian
-from easydynamics.sample_model import StretchedExponential
 from easydynamics.sample_model import Voigt
 from easydynamics.sample_model.component_collection import ComponentCollection
 from easydynamics.settings.convolution_settings import ConvolutionSettings
@@ -606,30 +605,6 @@ def test_check_width_small_threshold(self, default_numerical_convolution_base):
                 model_name='ComponentCollection',
             )
 
-    def test_check_width_small_threshold_for_stretched_exponential(
-        self, default_numerical_convolution_base
-    ):
-        """
-        Regression: a stretched exponential at small beta is far narrower than its energy scale
-        hbar / tau, and used to report that scale as its width. The spike then fell between the
-        grid points with no warning at all.
-        """
-        # WHEN tau = 5 ps puts hbar / tau at 0.13 meV, well above the grid step, while the true
-        # half width at beta = 0.2 is 3.5e-5 meV, well below it
-        narrow_kww = StretchedExponential(
-            name='ComponentCollection', area=1.0, relaxation_time=5.0, beta=0.2
-        )
-
-        # THEN EXPECT
-        with pytest.warns(
-            UserWarning,
-            match='Increase upsample_factor to improve',
-        ):
-            default_numerical_convolution_base._check_width_thresholds(
-                model=narrow_kww,
-                model_name='ComponentCollection',
-            )
-
     def test_check_width_no_warnings(self, default_numerical_convolution_base):
         """
         Test that _check_width_thresholds does not warn when model
diff --git a/tests/unit/easydynamics/sample_model/components/test_stretched_exponential.py b/tests/unit/easydynamics/sample_model/components/test_stretched_exponential.py
deleted file mode 100644
index a5943c865..000000000
--- a/tests/unit/easydynamics/sample_model/components/test_stretched_exponential.py
+++ /dev/null
@@ -1,693 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-
-from copy import copy
-from itertools import pairwise
-
-import numpy as np
-import pytest
-import scipp as sc
-from easyscience.variable import Parameter
-from scipp import UnitError
-from scipy.integrate import simpson
-from scipy.special import gamma
-
-from easydynamics.sample_model import Gaussian
-from easydynamics.sample_model import Lorentzian
-from easydynamics.sample_model import StretchedExponential
-from easydynamics.sample_model.components.stretched_exponential import _kww_shape
-from easydynamics.sample_model.components.stretched_exponential import _reduced_hwhm
-
-# hbar in meV*ps (CODATA), so the tests derive the energy scale independently of the library.
-HBAR_MEV_PS = 0.6582119569509066
-
-
-def sinh_grid(reach: float, n_points: int = 20001) -> np.ndarray:
-    """A symmetric grid that is dense near zero and reaches *reach*, for the heavy KWW tails."""
-    t = np.linspace(-np.arcsinh(reach), np.arcsinh(reach), n_points)
-    return np.sinh(t)
-
-
-def naive_fft_transform(
-    area: float, tau: float, beta: float, n_time: int, t_max: float
-) -> tuple[np.ndarray, np.ndarray]:
-    """
-    Transform ``I(t) = area * exp(-(t / tau)**beta)`` to energy the obvious way, with an FFT.
-
-    This is the textbook route the implementation deliberately does not take: sample the
-    relaxation on a uniform time grid and let ``np.fft`` do the cosine transform.  It is an
-    independent check because it shares no code and no idea with the rotated-contour quadrature
-    in ``_kww_shape`` -- only the physics.
-
-    The profile is even in *t*, so the two-sided transform is twice the one-sided one and
-
-        S(x) = (1 / (pi hbar)) Re [ integral of I(t) exp(-i x t / hbar) dt over t >= 0 ],
-
-    which on the uniform grid is a trapezoid sum (hence the half-weighted t = 0 sample) evaluated
-    at the FFT frequencies ``x_n = 2 pi hbar n / (n_time dt)``.
-
-    Returns
-    -------
-    tuple[np.ndarray, np.ndarray]
-        The energy axis in meV and the spectrum on it.
-    """
-    dt = t_max / n_time
-    time = np.arange(n_time) * dt
-    intensity = area * np.exp(-((time / tau) ** beta))
-    spectrum = (np.real(np.fft.rfft(intensity)) - 0.5 * intensity[0]) * dt / (np.pi * HBAR_MEV_PS)
-    energy = 2.0 * np.pi * HBAR_MEV_PS * np.arange(spectrum.size) / (n_time * dt)
-    return energy, spectrum
-
-
-def fft_comparison(beta: float, n_time: int, tau: float = 5.0, area: float = 1.0) -> float:
-    """Largest relative gap between the component and the FFT, over the resolvable region."""
-    # Reach far enough in time that exp(-(t / tau)**beta) has fallen by exp(-40).
-    energy, reference = naive_fft_transform(area, tau, beta, n_time, tau * 40.0 ** (1.0 / beta))
-    inside = (energy > 0.0) & (energy < 2.0)
-    energy, reference = energy[inside], reference[inside]
-
-    stretched = StretchedExponential(area=area, relaxation_time=tau, beta=beta)
-    values = stretched.evaluate(energy)
-
-    # Below a thousandth of the peak the FFT reference is dominated by its own truncation error,
-    # so comparing there would measure the reference rather than the implementation.
-    resolvable = values > 1e-3 * stretched.evaluate(np.array([0.0]))[0]
-    return float(np.max(np.abs(values[resolvable] - reference[resolvable]) / values[resolvable]))
-
-
-#####################################
-# The Fourier transform: _kww_shape
-#####################################
-
-
-@pytest.mark.parametrize(
-    'beta, rel',
-    # The smallest supported beta spreads exp(-u**beta) over so many decades that the grid only
-    # just reaches its tail, which costs a few digits; everything above it is at machine precision.
-    [(0.05, 1e-8), (0.1, 1e-11), (0.3, 1e-12), (0.5, 1e-12), (1.0, 1e-12), (2.0, 1e-12)],
-)
-def test_kww_shape_at_zero(beta, rel):
-    # WHEN G(0) is the integral of exp(-u**beta), which is gamma(1 + 1/beta)
-
-    # THEN
-    value = _kww_shape(np.array([0.0]), beta)
-
-    # EXPECT
-    assert value[0] == pytest.approx(gamma(1.0 + 1.0 / beta), rel=rel)
-
-
-def test_kww_shape_matches_lorentzian_at_beta_one():
-    # WHEN beta = 1 the transform of exp(-u) is known in closed form
-    w = sinh_grid(1e6)
-
-    # THEN
-    value = _kww_shape(w, 1.0)
-
-    # EXPECT
-    np.testing.assert_allclose(value, 1.0 / (1.0 + w**2), rtol=1e-9)
-
-
-def test_kww_shape_matches_gaussian_at_beta_two():
-    # WHEN beta = 2 the transform of exp(-u**2) is again a Gaussian
-    w = np.linspace(-12.0, 12.0, 501)
-
-    # THEN
-    value = _kww_shape(w, 2.0)
-
-    # EXPECT
-    expected = 0.5 * np.sqrt(np.pi) * np.exp(-(w**2) / 4)
-    np.testing.assert_allclose(value, expected, atol=1e-14)
-
-
-@pytest.mark.parametrize('beta', [0.3, 0.6, 0.9])
-def test_kww_shape_matches_the_large_w_asymptote(beta):
-    # WHEN the leading term of the large-w expansion is
-    # gamma(beta + 1) sin(pi beta / 2) / w**(beta + 1).  The next term is smaller by w**-beta, so
-    # w has to be taken far out before the leading term alone is worth a few digits.
-    w = np.array([1e9, 1e11])
-
-    # THEN
-    value = _kww_shape(w, beta)
-
-    # EXPECT
-    expected = gamma(beta + 1) * np.sin(np.pi * beta / 2) / w ** (beta + 1)
-    np.testing.assert_allclose(value, expected, rtol=3e-3)
-
-
-@pytest.mark.parametrize('beta', [0.3, 0.5, 0.8, 1.0, 1.6, 2.0])
-def test_kww_shape_is_even_and_non_negative(beta):
-    # WHEN
-    w = sinh_grid(1e6, n_points=2001)
-
-    # THEN
-    value = _kww_shape(w, beta)
-
-    # EXPECT
-    np.testing.assert_allclose(value, _kww_shape(-w, beta), rtol=0, atol=0)
-    assert np.all(value >= 0.0)
-
-
-@pytest.mark.parametrize('beta', [0.3, 0.5, 0.8, 1.0, 1.6, 2.0])
-def test_kww_shape_integrates_to_pi(beta):
-    # WHEN the transform of a function that is 1 at t = 0 integrates to pi over w
-
-    # THEN
-    w = sinh_grid(1e8)
-    integral = simpson(_kww_shape(w, beta), x=w)
-
-    # EXPECT
-    assert integral == pytest.approx(np.pi, rel=5e-3)
-
-
-def test_kww_shape_is_blocked_without_changing_the_result():
-    # WHEN a long energy axis is evaluated in blocks to bound the memory of the quadrature
-    w = np.linspace(-40.0, 40.0, 997)
-    reference = _kww_shape(w, 0.6)
-
-    # THEN
-    blocked = np.concatenate([_kww_shape(chunk, 0.6) for chunk in np.array_split(w, 7)])
-
-    # EXPECT the block boundaries do not perturb any value
-    np.testing.assert_array_equal(blocked, reference)
-
-
-class TestStretchedExponential:
-    @pytest.fixture
-    def stretched_exponential(self):
-        return StretchedExponential(
-            name='StretchedName',
-            display_name='TestStretched',
-            area=2.0,
-            center=0.5,
-            relaxation_time=3.0,
-            beta=0.7,
-            x_unit='meV',
-        )
-
-    #############
-    # Creation
-    #############
-
-    def test_init_no_inputs(self):
-        # WHEN THEN
-        stretched = StretchedExponential()
-
-        # EXPECT
-        assert stretched.display_name == 'StretchedExponential'
-        assert stretched.area.value == pytest.approx(1.0)
-        assert stretched.center.value == pytest.approx(0.0)
-        assert stretched.relaxation_time.value == pytest.approx(1.0)
-        assert stretched.beta.value == pytest.approx(1.0)
-        assert stretched.x_unit == 'meV'
-        assert stretched.y_unit == 'dimensionless'
-        assert stretched.relaxation_time.unit == 'ps'
-        assert stretched.beta.unit == 'dimensionless'
-        assert stretched.center.fixed is True
-
-    def test_initialization(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN EXPECT
-        assert stretched_exponential.display_name == 'TestStretched'
-        assert stretched_exponential.area.value == pytest.approx(2.0)
-        assert stretched_exponential.center.value == pytest.approx(0.5)
-        assert stretched_exponential.relaxation_time.value == pytest.approx(3.0)
-        assert stretched_exponential.beta.value == pytest.approx(0.7)
-        assert stretched_exponential.center.fixed is False
-
-    @pytest.mark.parametrize(
-        'kwargs, expected_message',
-        [
-            ({'area': 'invalid'}, 'area must be a number'),
-            ({'center': 'invalid'}, 'center must be None or a number'),
-            ({'relaxation_time': 'invalid'}, 'relaxation_time must be a number'),
-            ({'beta': 'invalid'}, 'beta must be a number'),
-            ({'x_unit': 123}, 'unit must be None, a string'),
-            ({'y_unit': 123}, 'unit must be None, a string'),
-        ],
-    )
-    def test_input_type_validation_raises(self, kwargs, expected_message):
-        # WHEN THEN EXPECT
-        with pytest.raises(TypeError, match=expected_message):
-            StretchedExponential(**kwargs)
-
-    @pytest.mark.parametrize(
-        'kwargs, expected_message',
-        [
-            ({'relaxation_time': 0.0}, 'relaxation_time must be greater than zero'),
-            ({'relaxation_time': -1.0}, 'relaxation_time must be greater than zero'),
-            ({'relaxation_time': np.inf}, 'relaxation_time must be a finite number'),
-            ({'beta': 0.0}, 'beta must be between'),
-            ({'beta': 2.5}, 'beta must be between'),
-            ({'beta': np.nan}, 'beta must be a finite number'),
-        ],
-    )
-    def test_input_value_validation_raises(self, kwargs, expected_message):
-        # WHEN THEN EXPECT
-        with pytest.raises(ValueError, match=expected_message):
-            StretchedExponential(**kwargs)
-
-    def test_negative_area_warns(self):
-        # WHEN THEN EXPECT
-        with pytest.warns(UserWarning, match='may not be physically meaningful'):
-            StretchedExponential(area=-2.0)
-
-    def test_get_all_parameters(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN
-        params = stretched_exponential.get_all_parameters()
-
-        # EXPECT
-        assert all(isinstance(param, Parameter) for param in params)
-        assert {param.name for param in params} == {
-            'StretchedName area',
-            'StretchedName center',
-            'StretchedName relaxation_time',
-            'StretchedName beta',
-            'StretchedName width',
-        }
-
-    def test_copy(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN
-        stretched_copy = copy(stretched_exponential)
-
-        # EXPECT
-        assert stretched_copy is not stretched_exponential
-        assert stretched_copy.display_name == stretched_exponential.display_name
-        assert stretched_copy.area.value == stretched_exponential.area.value
-        assert stretched_copy.center.value == stretched_exponential.center.value
-        assert stretched_copy.relaxation_time.value == stretched_exponential.relaxation_time.value
-        assert stretched_copy.beta.value == stretched_exponential.beta.value
-        assert stretched_copy.x_unit == stretched_exponential.x_unit
-
-    def test_repr(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN
-        repr_str = repr(stretched_exponential)
-
-        # EXPECT
-        assert 'StretchedExponential' in repr_str
-        assert 'name = StretchedName' in repr_str
-        assert 'x_unit = meV' in repr_str
-        assert 'area =' in repr_str
-        assert 'center =' in repr_str
-        assert 'relaxation_time =' in repr_str
-        assert 'beta =' in repr_str
-
-    #############
-    # Parameters
-    #############
-
-    @pytest.mark.parametrize(
-        'prop, valid_value',
-        [('area', 3.0), ('center', 0.6), ('relaxation_time', 4.0), ('beta', 0.9)],
-    )
-    def test_property_setters(
-        self, stretched_exponential: StretchedExponential, prop, valid_value
-    ):
-        # WHEN: set a valid value
-        setattr(stretched_exponential, prop, valid_value)
-        # THEN EXPECT
-        assert getattr(stretched_exponential, prop).value == valid_value
-
-        # WHEN: set an invalid value — THEN EXPECT
-        with pytest.raises(TypeError, match=' must be a number'):
-            setattr(stretched_exponential, prop, 'invalid')
-
-    def test_relaxation_time_must_be_positive(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN EXPECT
-        with pytest.raises(ValueError, match='relaxation_time must be greater than zero'):
-            stretched_exponential.relaxation_time = -1.0
-        assert stretched_exponential.relaxation_time.value == pytest.approx(3.0)
-
-    @pytest.mark.parametrize('value', [0.01, 2.5])
-    def test_beta_outside_the_supported_range_raises(
-        self, stretched_exponential: StretchedExponential, value
-    ):
-        # WHEN THEN EXPECT
-        with pytest.raises(ValueError, match='beta must be between'):
-            stretched_exponential.beta = value
-        assert stretched_exponential.beta.value == pytest.approx(0.7)
-
-    def test_area_setter_out_of_bounds_raises(self, stretched_exponential: StretchedExponential):
-        # WHEN the fixture's area was created non-negative, so it carries min=0
-
-        # THEN EXPECT a negative assignment raises instead of being silently clamped to 0
-        with pytest.raises(ValueError, match='violates the parameter bounds'):
-            stretched_exponential.area = -1.0
-        assert stretched_exponential.area.value == pytest.approx(2.0)
-
-    def test_center_is_fixed_if_set_to_None(self, stretched_exponential: StretchedExponential):
-        # WHEN
-        assert stretched_exponential.center.fixed is False
-
-        # THEN
-        stretched_exponential.center = None
-
-        # EXPECT
-        assert stretched_exponential.center.value == pytest.approx(0.0)
-        assert stretched_exponential.center.fixed is True
-
-    def test_width_is_the_half_width_at_half_maximum(
-        self, stretched_exponential: StretchedExponential
-    ):
-        # WHEN width is not stored but solved for as the half maximum crossing of the profile
-        # THEN
-        width = stretched_exponential.width
-
-        # EXPECT the profile really has fallen to half its peak there, to the accuracy of the
-        # closed-form fit the dependency evaluates rather than to that of the root itself
-        center = stretched_exponential.center.value
-        peak = stretched_exponential.evaluate(np.array([center]))[0]
-        half = stretched_exponential.evaluate(np.array([center + width.value]))[0]
-        assert half == pytest.approx(0.5 * peak, rel=1e-4)
-        assert str(width.unit) == 'meV'
-
-    def test_width_is_below_the_energy_scale_for_stretched_profiles(self):
-        # WHEN beta < 1 the peak sharpens well beyond Gamma = hbar / tau, so reporting Gamma would
-        # hide a sub-grid spike from the convolution's width-versus-grid checks
-        gamma = HBAR_MEV_PS / 5.0
-
-        # THEN
-        widths = {
-            beta: StretchedExponential(relaxation_time=5.0, beta=beta).width.value
-            for beta in (1.0, 0.5, 0.2)
-        }
-
-        # EXPECT Gamma only at beta = 1, and orders of magnitude below it further down
-        assert widths[1.0] == pytest.approx(gamma, rel=2e-5)
-        assert widths[0.5] == pytest.approx(0.22355 * gamma, rel=1e-4)
-        assert widths[0.2] == pytest.approx(2.653e-4 * gamma, rel=1e-3)
-
-    def test_width_is_a_dependent_parameter(self, stretched_exponential: StretchedExponential):
-        # WHEN width is resolved from beta and relaxation_time by a dependency expression
-        # THEN
-        width = stretched_exponential.width
-
-        # EXPECT a Parameter, so the usual width attributes resolve, but a dependent one, so a fit
-        # can never pick it up as a free parameter
-        assert isinstance(width, Parameter)
-        assert width.independent is False
-        assert width not in stretched_exponential.get_fittable_parameters()
-
-    def test_width_matches_the_solved_half_width(self):
-        # WHEN the dependency evaluates a closed-form fit rather than the root itself
-        # THEN EXPECT it tracks _reduced_hwhm over the range the fit targets
-        for beta in (2.0, 1.0, 0.7, 0.5, 0.3, 0.2, 0.1):
-            stretched = StretchedExponential(relaxation_time=5.0, beta=beta)
-            expected = _reduced_hwhm(beta) * HBAR_MEV_PS / 5.0
-            assert stretched.width.value == pytest.approx(expected, rel=2e-5)
-
-    def test_width_is_recomputed_after_a_raw_beta_write(
-        self, stretched_exponential: StretchedExponential
-    ):
-        # WHEN a minimizer writes the beta Parameter directly, bypassing the component setter
-        before = stretched_exponential.width.value
-
-        # THEN
-        stretched_exponential.beta.value = 0.2
-
-        # EXPECT the dependency re-evaluates: beta is reached as b.value, but it is still a mapped
-        # variable, so a write to it retriggers the expression
-        assert stretched_exponential.width.value < before
-
-    def test_width_can_be_chained_onto(self, stretched_exponential: StretchedExponential):
-        # WHEN another component's width is made to follow this one
-        lorentzian = Lorentzian(name='Chained', area=1.0, width=0.1)
-        with np.errstate(invalid='ignore', divide='ignore'):
-            lorentzian.width.make_dependent_on(
-                dependency_expression='w * 2',
-                dependency_map={'w': stretched_exponential.width},
-                desired_unit='meV',
-            )
-
-        # THEN
-        stretched_exponential.beta.value = 0.2
-
-        # EXPECT the change reaches the chained parameter, not just this component
-        assert lorentzian.width.value == pytest.approx(2 * stretched_exponential.width.value)
-
-    def test_width_tracks_beta(self, stretched_exponential: StretchedExponential):
-        # WHEN
-        before = stretched_exponential.width.value
-
-        # THEN
-        stretched_exponential.beta = 0.3
-
-        # EXPECT a smaller beta gives a sharper peak, so a smaller half width
-        assert stretched_exponential.width.value < before
-
-    def test_width_tracks_the_relaxation_time(self, stretched_exponential: StretchedExponential):
-        # WHEN
-        before = stretched_exponential.width.value
-
-        # THEN
-        stretched_exponential.relaxation_time = 6.0
-
-        # EXPECT doubling the relaxation time halves the width
-        assert stretched_exponential.width.value == pytest.approx(before / 2.0)
-
-    def test_width_is_read_only(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN EXPECT the relaxation time is the fittable parameter, not the width
-        with pytest.raises(AttributeError):
-            stretched_exponential.width = 0.5
-
-    def test_width_follows_the_x_unit(self):
-        # WHEN the component measures energy in microeV
-        stretched = StretchedExponential(relaxation_time=5.0, beta=1.0, x_unit='ueV')
-
-        # THEN
-        width = stretched.width
-
-        # EXPECT the half width is expressed in that unit too
-        assert width.value == pytest.approx(1e3 * HBAR_MEV_PS / 5.0, rel=2e-5)
-        assert str(width.unit) == str(sc.Unit('ueV'))
-
-    def test_width_follows_an_x_unit_conversion(self):
-        # WHEN
-        stretched = StretchedExponential(relaxation_time=5.0, beta=1.0, x_unit='meV')
-        before = stretched.width.value
-
-        # THEN
-        stretched.convert_x_unit('ueV')
-
-        # EXPECT the half width is re-derived in the new unit rather than keeping the old one
-        assert stretched.width.value == pytest.approx(1e3 * before)
-        assert str(stretched.width.unit) == str(sc.Unit('ueV'))
-
-    def test_width_with_a_non_energy_x_unit_raises(self):
-        # WHEN THEN EXPECT hbar / relaxation_time cannot be expressed in metres
-        with pytest.raises(UnitError, match='needs an energy x_unit'):
-            _ = StretchedExponential(x_unit='m').width
-
-    def test_width_is_the_lorentzian_hwhm_at_beta_one(self):
-        # WHEN beta = 1 the transform is a Lorentzian of HWHM Gamma = hbar / tau.  Gamma is used
-        # directly rather than via width, so this pins the profile itself and stays independent of
-        # the closed-form fit the width dependency evaluates.
-        stretched = StretchedExponential(area=1.0, relaxation_time=4.0, beta=1.0)
-
-        # THEN
-        lorentzian = Lorentzian(area=1.0, width=HBAR_MEV_PS / 4.0)
-
-        # EXPECT
-        x = np.linspace(-2.0, 2.0, 101)
-        np.testing.assert_allclose(stretched.evaluate(x), lorentzian.evaluate(x), rtol=1e-12)
-
-    #############
-    # Evaluation
-    #############
-
-    def test_evaluate_reduces_to_a_lorentzian_at_beta_one(self):
-        # WHEN beta = 1, the transform is a Lorentzian of HWHM hbar / tau
-        stretched = StretchedExponential(area=2.5, relaxation_time=3.0, beta=1.0)
-        lorentzian = Lorentzian(area=2.5, width=HBAR_MEV_PS / 3.0)
-        x = np.linspace(-3.0, 3.0, 401)
-
-        # THEN
-        result = stretched.evaluate(x)
-
-        # EXPECT
-        np.testing.assert_allclose(result, lorentzian.evaluate(x), rtol=1e-10)
-
-    def test_evaluate_reduces_to_a_gaussian_at_beta_two(self):
-        # WHEN beta = 2, the transform is a Gaussian of standard deviation sqrt(2) hbar / tau
-        stretched = StretchedExponential(area=2.5, relaxation_time=3.0, beta=2.0)
-        gaussian = Gaussian(area=2.5, width=np.sqrt(2) * HBAR_MEV_PS / 3.0)
-        x = np.linspace(-3.0, 3.0, 401)
-
-        # THEN
-        result = stretched.evaluate(x)
-
-        # EXPECT
-        np.testing.assert_allclose(result, gaussian.evaluate(x), atol=1e-13)
-
-    def test_evaluate_peak_height(self, stretched_exponential: StretchedExponential):
-        # WHEN the peak sits at the center and equals area * gamma(1 + 1/beta) / (pi * Gamma)
-        energy_scale = HBAR_MEV_PS / 3.0
-
-        # THEN
-        result = stretched_exponential.evaluate(np.array([0.5]))
-
-        # EXPECT
-        expected = 2.0 * gamma(1.0 + 1.0 / 0.7) / (np.pi * energy_scale)
-        assert result[0] == pytest.approx(expected, rel=1e-10)
-
-    def test_evaluate_is_symmetric_about_the_center(
-        self, stretched_exponential: StretchedExponential
-    ):
-        # WHEN
-        offset = np.array([0.05, 0.3, 1.7])
-
-        # THEN
-        left = stretched_exponential.evaluate(0.5 - offset)
-        right = stretched_exponential.evaluate(0.5 + offset)
-
-        # EXPECT
-        np.testing.assert_allclose(left, right, rtol=1e-12)
-
-    @pytest.mark.parametrize('beta', [0.5, 0.7, 1.0, 1.5])
-    def test_area_matches_parameter(self, beta):
-        # WHEN the transform integrates to the area parameter over the whole axis
-        stretched = StretchedExponential(area=2.0, relaxation_time=5.0, beta=beta)
-        # The tails are heavy, so integrate on a grid that is dense near the peak and reaches far
-        x = sinh_grid(1e8) * (HBAR_MEV_PS / 5.0)
-
-        # THEN
-        numerical_area = simpson(stretched.evaluate(x), x=x)
-
-        # EXPECT
-        assert numerical_area == pytest.approx(2.0, rel=5e-3)
-
-    def test_relaxation_time_sets_the_width(self):
-        # WHEN a longer relaxation time means a narrower line
-        narrow = StretchedExponential(relaxation_time=20.0, beta=0.8)
-        wide = StretchedExponential(relaxation_time=2.0, beta=0.8)
-
-        # THEN
-        narrow_peak = narrow.evaluate(np.array([0.0]))[0]
-        wide_peak = wide.evaluate(np.array([0.0]))[0]
-
-        # EXPECT the peak scales as tau, since the area is conserved
-        assert narrow_peak == pytest.approx(10.0 * wide_peak, rel=1e-10)
-
-    def test_evaluate_scipp_output(self, stretched_exponential: StretchedExponential):
-        # WHEN
-        x = np.linspace(-5, 5, 50)
-
-        # THEN
-        result = stretched_exponential.evaluate(x, output='scipp')
-
-        # EXPECT
-        assert isinstance(result, sc.Variable)
-        assert result.unit == sc.Unit('dimensionless')
-        np.testing.assert_allclose(result.values, stretched_exponential.evaluate(x))
-
-    def test_evaluate_with_scipp_x_in_another_energy_unit(
-        self, stretched_exponential: StretchedExponential
-    ):
-        # WHEN x carries microeV rather than the component's meV
-        x = np.linspace(-2.0, 2.0, 51)
-
-        # THEN
-        result = stretched_exponential.evaluate(
-            sc.array(dims=['energy'], values=x * 1e3, unit='microeV')
-        )
-
-        # EXPECT the same profile, since the output carries y_unit either way
-        np.testing.assert_allclose(result, stretched_exponential.evaluate(x), rtol=1e-12)
-
-    def test_evaluate_with_a_non_energy_x_unit_raises(self):
-        # WHEN hbar / relaxation_time cannot be expressed in the x unit
-        stretched = StretchedExponential(x_unit='m')
-
-        # THEN EXPECT
-        with pytest.raises(UnitError, match='needs an energy x_unit'):
-            stretched.evaluate(np.array([0.0, 1.0]))
-
-    @pytest.mark.parametrize('beta', [0.6, 0.8, 1.0, 1.5, 2.0])
-    def test_evaluate_matches_a_naive_fft_transform(self, beta):
-        # WHEN the same physics is computed the obvious way, by FFT-ing the sampled relaxation
-
-        # THEN
-        largest_gap = fft_comparison(beta, n_time=2**20)
-
-        # EXPECT the two agree wherever the FFT itself is trustworthy
-        assert largest_gap < 1e-3
-
-    def test_the_naive_fft_converges_onto_evaluate(self):
-        # WHEN the FFT reference is limited by its own time step, not by the implementation.  Its
-        # error comes from the cusp of exp(-(t / tau)**beta) at t = 0, so the trapezoid sum
-        # converges as dt**(1 + beta) -- refining dt by four should shrink it by 4**1.6 ~ 9.
-        gaps = [fft_comparison(0.6, n_time=n) for n in (2**16, 2**18, 2**20)]
-
-        # THEN
-        ratios = [coarse / fine for coarse, fine in pairwise(gaps)]
-
-        # EXPECT the gap closes at the predicted rate, so the FFT is converging onto our values
-        assert all(ratio > 4.0 for ratio in ratios), (gaps, ratios)
-
-    ##################
-    # Unit conversion
-    ##################
-
-    def test_convert_x_unit(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN
-        stretched_exponential.convert_x_unit('microeV')
-
-        # EXPECT the time and the exponent are untouched: they carry no x unit
-        assert stretched_exponential.x_unit == 'microeV'
-        assert stretched_exponential.area.value == pytest.approx(2.0 * 1e3)
-        assert stretched_exponential.center.value == pytest.approx(0.5 * 1e3)
-        assert stretched_exponential.relaxation_time.value == pytest.approx(3.0)
-        assert stretched_exponential.relaxation_time.unit == 'ps'
-        assert stretched_exponential.beta.value == pytest.approx(0.7)
-
-    def test_convert_x_unit_keeps_the_profile(self, stretched_exponential: StretchedExponential):
-        # WHEN
-        x = np.linspace(-2.0, 2.0, 51)
-        before = stretched_exponential.evaluate(x)
-
-        # THEN
-        stretched_exponential.convert_x_unit('microeV')
-
-        # EXPECT the same curve, read off the rescaled axis
-        np.testing.assert_allclose(stretched_exponential.evaluate(x * 1e3), before, rtol=1e-12)
-
-    def test_convert_x_unit_invalid_type_raises(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN EXPECT
-        with pytest.raises(TypeError, match=r'x_unit must be a string or sc\.Unit'):
-            stretched_exponential.convert_x_unit(123)
-
-    def test_convert_x_unit_rollback_on_failure(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN
-        with pytest.raises(UnitError):
-            stretched_exponential.convert_x_unit('m')
-
-        # EXPECT: state rolled back
-        assert stretched_exponential.x_unit == 'meV'
-        assert stretched_exponential.area.value == pytest.approx(2.0)
-        assert stretched_exponential.center.value == pytest.approx(0.5)
-
-    def test_convert_y_unit(self):
-        # WHEN: x_unit='meV', y_unit='1/meV' → area_unit='dimensionless'
-        stretched = StretchedExponential(area=1.0, x_unit='meV', y_unit='1/meV')
-
-        # THEN: convert y_unit to '1/eV' (same dimension, different scale)
-        stretched.convert_y_unit('1/eV')
-
-        # EXPECT: y_unit updated and area value rescaled (1e3 factor)
-        assert stretched.y_unit == '1/eV'
-        assert stretched.area.value == pytest.approx(1e3)
-
-    def test_convert_y_unit_invalid_type_raises(self, stretched_exponential: StretchedExponential):
-        # WHEN THEN EXPECT
-        with pytest.raises(TypeError):
-            stretched_exponential.convert_y_unit(123)
-
-    def test_convert_y_unit_rollback_on_failure(self):
-        # WHEN
-        stretched = StretchedExponential(area=1.0, x_unit='meV')
-
-        # THEN
-        with pytest.raises(UnitError):
-            stretched.convert_y_unit('K')
-
-        # EXPECT: state rolled back
-        assert stretched.y_unit == 'dimensionless'
-        assert stretched.area.value == pytest.approx(1.0)
diff --git a/tools/fit_kww_hwhm_coefficients.py b/tools/fit_kww_hwhm_coefficients.py
deleted file mode 100644
index 3056d2ee9..000000000
--- a/tools/fit_kww_hwhm_coefficients.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
-# SPDX-License-Identifier: BSD-3-Clause
-"""
-Refit ``_HWHM_POLY_COEFFS`` in ``easydynamics.sample_model.components.stretched_exponential``.
-
-``StretchedExponential.width`` is a dependent easyscience ``Parameter``, and easyscience resolves
-a dependency from a string expression, so the half width has to be written in closed form.  The
-half width is not available in closed form: it is the ``w`` solving
-
-    G_beta(w) = 0.5 * G_beta(0),     G_beta(w) = integral of exp(-u**beta) cos(w u) du,
-
-which ``_reduced_hwhm`` brackets numerically.  It is very nearly closed form, though, because
-
-    eps(beta) := beta * ln(HWHM(beta)) - ln(beta)
-
-is small, smooth and O(0.06), so that
-
-    HWHM(beta) = exp((ln(beta) + eps(beta)) / beta)
-
-with ``eps`` a low-order polynomial.  This script fits that polynomial against ``_reduced_hwhm``
-and prints the coefficient block to paste back into the module.
-
-The fit degrades below beta = 0.1, but so does the root it is fitted to: by beta = 0.05 the
-profile takes some 60 e-folds of ``w`` to fall by half, so ``G_beta`` is nearly flat in ``log w``
-near the crossing.  Wuttke's reference implementation libkww (arXiv:0911.4796) likewise supports
-only ``0.1 <= beta <= 1.9``.  Both are far below any energy grid step by then, so the accuracy
-that matters is the one reported for ``beta >= 0.1``.
-
-Usage
------
-```
-pixi run python tools/fit_kww_hwhm_coefficients.py            # refit and print the block
-pixi run python tools/fit_kww_hwhm_coefficients.py --check    # verify the committed values
-pixi run python tools/fit_kww_hwhm_coefficients.py --degree 10 --samples 800
-```
-"""
-
-import argparse
-import sys
-
-import numpy as np
-from numpy.polynomial import polynomial as P
-
-from easydynamics.sample_model.components.stretched_exponential import _HWHM_POLY_COEFFS
-from easydynamics.sample_model.components.stretched_exponential import MAXIMUM_BETA
-from easydynamics.sample_model.components.stretched_exponential import MINIMUM_BETA
-from easydynamics.sample_model.components.stretched_exponential import _reduced_hwhm
-
-# The committed coefficients were produced with these settings; changing them changes the fit.
-DEFAULT_DEGREE = 8
-DEFAULT_SAMPLES = 400
-# Accuracy is quoted over the range the fit is meant to serve, and where libkww also stops.
-REPORTING_FLOOR = 0.1
-# Loose enough to absorb a scipy root-finder nudge, tight enough to catch a real regression.
-CHECK_TOLERANCE = 5e-5
-
-
-def sample_grid(samples: int) -> np.ndarray:
-    """
-    Build the beta grid the fit is evaluated on.
-
-    Log spacing matches how the half width actually varies: it spans some 26 decades across the
-    supported beta, almost all of that below beta = 0.5.
-
-    Parameters
-    ----------
-    samples : int
-        Number of grid points.
-
-    Returns
-    -------
-    np.ndarray
-        Log-spaced beta values over ``[MINIMUM_BETA, MAXIMUM_BETA]``.
-    """
-    return np.exp(np.linspace(np.log(MINIMUM_BETA), np.log(MAXIMUM_BETA), samples))
-
-
-def fit_coefficients(beta: np.ndarray, degree: int) -> np.ndarray:
-    """
-    Least-squares fit the eps polynomial against the solved half width.
-
-    Parameters
-    ----------
-    beta : np.ndarray
-        Beta values to fit over.
-    degree : int
-        Polynomial degree.
-
-    Returns
-    -------
-    np.ndarray
-        Coefficients in ascending powers of beta.
-    """
-    hwhm = np.array([_reduced_hwhm(b) for b in beta])
-    eps = beta * np.log(hwhm) - np.log(beta)
-    return P.polyfit(beta, eps, degree)
-
-
-def relative_error(beta: np.ndarray, coefficients: np.ndarray) -> np.ndarray:
-    """
-    Compare the closed form against the solved half width.
-
-    Parameters
-    ----------
-    beta : np.ndarray
-        Beta values to evaluate at.
-    coefficients : np.ndarray
-        Polynomial coefficients in ascending powers of beta.
-
-    Returns
-    -------
-    np.ndarray
-        Relative error of the closed form at each beta.
-    """
-    exact = np.array([_reduced_hwhm(b) for b in beta])
-    approx = np.exp((np.log(beta) + P.polyval(beta, coefficients)) / beta)
-    return np.abs(approx / exact - 1.0)
-
-
-def report(beta: np.ndarray, coefficients: np.ndarray) -> float:
-    """
-    Print the accuracy of a coefficient set and return the error over the reporting range.
-
-    Parameters
-    ----------
-    beta : np.ndarray
-        Beta values to evaluate at.
-    coefficients : np.ndarray
-        Polynomial coefficients in ascending powers of beta.
-
-    Returns
-    -------
-    float
-        Maximum relative error for ``beta >= REPORTING_FLOOR``.
-    """
-    error = relative_error(beta, coefficients)
-    above_floor = error[beta >= REPORTING_FLOOR].max()
-    print(f'  max relative error, all beta        : {error.max():.2e}')
-    print(f'  max relative error, beta >= {REPORTING_FLOOR}     : {above_floor:.2e}')
-    for probe in (2.0, 1.0, 0.5, 0.2, 0.1, MINIMUM_BETA):
-        single = relative_error(np.array([probe]), coefficients)[0]
-        print(f'    beta = {probe:<5} -> {single:.2e}')
-    return float(above_floor)
-
-
-def main() -> int:
-    """
-    Refit the coefficients, or check the committed ones.
-
-    Returns
-    -------
-    int
-        Process exit status.
-    """
-    parser = argparse.ArgumentParser(description=__doc__.splitlines()[1])
-    parser.add_argument(
-        '--check',
-        action='store_true',
-        help='verify the committed coefficients instead of refitting',
-    )
-    parser.add_argument('--degree', type=int, default=DEFAULT_DEGREE)
-    parser.add_argument('--samples', type=int, default=DEFAULT_SAMPLES)
-    args = parser.parse_args()
-
-    beta = sample_grid(args.samples)
-
-    if args.check:
-        print(f'Checking committed _HWHM_POLY_COEFFS ({len(_HWHM_POLY_COEFFS) - 1} degree):')
-        above_floor = report(beta, np.array(_HWHM_POLY_COEFFS))
-        if above_floor > CHECK_TOLERANCE:
-            print(f'\nFAIL: {above_floor:.2e} exceeds the {CHECK_TOLERANCE:.0e} tolerance.')
-            print('Rerun without --check and paste the new block into the module.')
-            return 1
-        print(f'\nOK: within the {CHECK_TOLERANCE:.0e} tolerance.')
-        return 0
-
-    coefficients = fit_coefficients(beta, args.degree)
-    print(
-        f'Fitted degree {args.degree} over {args.samples} log-spaced beta in '
-        f'[{MINIMUM_BETA}, {MAXIMUM_BETA}]:'
-    )
-    report(beta, coefficients)
-
-    print('\nPaste into stretched_exponential.py:\n')
-    print('_HWHM_POLY_COEFFS = (')
-    for coefficient in coefficients:
-        print(f'    {float(coefficient)!r},')
-    print(')')
-    return 0
-
-
-if __name__ == '__main__':
-    sys.exit(main())