diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml
index abcb91df..7c480941 100644
--- a/.github/ISSUE_TEMPLATE/bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -6,7 +6,7 @@ body:
   - type: textarea
     id: what-happened
     attributes:
-      label: What happened?
+      label: Description
       description: |
         Thanks for reporting a bug! Please describe what you were trying to get done.
         Tell us what happened, what went wrong.
@@ -16,7 +16,7 @@ body:
   - type: textarea
     id: what-did-you-expect-to-happen
     attributes:
-      label: What did you expect to happen?
+      label: Expected behavior
       description: |
         A clear and concise description of what you expected to happen.
     validations:
@@ -27,7 +27,8 @@ body:
     attributes:
       label: Minimal Complete Verifiable Example
       description: |
-        Minimal, self-contained copy-pastable example that demonstrates the issue.         This will be automatically formatted into code, so no need for markdown backticks.
+        Minimal, self-contained copy-pastable example that demonstrates the issue.
+        This will be automatically formatted into code, so no need for markdown backticks.
       render: Python
 
   - type: checkboxes
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
index 4e1b0118..d2c885ad 100644
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -2,4 +2,4 @@ blank_issues_enabled: false
 contact_links:
   - name: Ask a question or start a discussion
     url: https://github.com/kmnhan/erlabpy/discussions
-    about: Please ask and answer questions here.
+    about: Ask and answer questions on the discussions page!
diff --git a/.github/ISSUE_TEMPLATE/feature-request.yml b/.github/ISSUE_TEMPLATE/feature-request.yml
index 3fa28cfe..50d2a078 100644
--- a/.github/ISSUE_TEMPLATE/feature-request.yml
+++ b/.github/ISSUE_TEMPLATE/feature-request.yml
@@ -6,21 +6,22 @@ body:
   - type: textarea
     id: description
     attributes:
-      label: Is your feature request related to a problem? Please describe.
+      label: Description
       description: |
-        A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+        Is your feature request related to a problem?
+        Please provide a clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
     validations:
       required: true
   - type: textarea
     id: solution
     attributes:
-      label: Describe the solution you'd like
+      label: Possible solution
       description: |
         A clear and concise description of what you want to happen.
   - type: textarea
     id: alternatives
     attributes:
-      label: Describe alternatives you've considered
+      label: Alternatives
       description: |
         A clear and concise description of any alternative solutions or features you've considered.
     validations:
diff --git a/docs/environment.yml b/docs/environment.yml
index ee5796e7..7202b8d7 100644
--- a/docs/environment.yml
+++ b/docs/environment.yml
@@ -19,9 +19,8 @@ dependencies:
   - qtawesome>=1.3.1
   - qtpy>=2.4.1
   - scipy>=1.12.0
-  - superqt>=0.6.2
   - tqdm>=4.66.2
-  - uncertainties>=3.0.1
+  - uncertainties>=3.1.4
   - varname>=0.13.0
   - xarray>=2024.02.0
   - hvplot
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 0df5f11c..d734be72 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -14,9 +14,8 @@ pyqtgraph>=0.13.1
 qtawesome>=1.3.1
 qtpy>=2.4.1
 scipy>=1.12.0
-superqt>=0.6.2
 tqdm>=4.66.2
-uncertainties>=3.0.1
+uncertainties>=3.1.4
 varname>=0.13.0
 xarray>=2024.02.0
 sphinx
diff --git a/docs/source/conf.py b/docs/source/conf.py
index d041a5ee..777e6dbb 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -63,6 +63,8 @@
 # nitpicky = False
 # nitpick_ignore = [("py:class", "numpy.float64")]
 
+highlight_language = "python3"
+
 # -- Linkcode settings -------------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/extensions/linkcode.html
 
diff --git a/docs/source/contributing.rst b/docs/source/contributing.rst
index 60c0be3e..d41b985c 100644
--- a/docs/source/contributing.rst
+++ b/docs/source/contributing.rst
@@ -306,12 +306,9 @@ Code standards
 - Please try to add type annotations to your code. This will help with code completion
   and static analysis.
 
-- Although it would be great to enforce static type checking, our code base currently
-  does not pass the tests. It would require a large amount of work to get it to pass, so
-  we are not enforcing it at the moment, and it is unclear whether the extra effort is
-  worth it. See `this article
-  <https://typing.readthedocs.io/en/latest/source/typing_anti_pitch.html>`_ for some
-  reasons to avoid static type checking.
+- We are in the process of adding type annotations to the codebase, and most of it
+  should pass `mypy <https://mypy.readthedocs.io/en/stable/>`_ except for the io and
+  interactive modules.
 
 Documentation
 =============
diff --git a/docs/source/erlab.accessors.rst b/docs/source/erlab.accessors.rst
index 29f5eebe..22aaf2eb 100644
--- a/docs/source/erlab.accessors.rst
+++ b/docs/source/erlab.accessors.rst
@@ -1,14 +1,4 @@
-Extensions to xarray (:mod:`erlab.accessors`)
-=============================================
+Accessors (:mod:`erlab.accessors`)
+==================================
 
 .. automodule:: erlab.accessors
-
-
-   .. rubric:: Classes
-
-   .. autosummary::
-
-      PlotAccessor
-      ImageToolAccessor
-      SelectionAccessor
-      MomentumAccessor
diff --git a/docs/source/erlab.characterization.rst b/docs/source/erlab.characterization.rst
deleted file mode 100644
index b3fc0eb6..00000000
--- a/docs/source/erlab.characterization.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Characterization (:mod:`erlab.characterization`)
-================================================
-
-.. automodule:: erlab.characterization
diff --git a/docs/source/pyplots/norms.py b/docs/source/pyplots/norms.py
index ec73475d..f9c1ab36 100644
--- a/docs/source/pyplots/norms.py
+++ b/docs/source/pyplots/norms.py
@@ -59,11 +59,11 @@ def sample_plot(norms, labels, kw0, kw1, cmap):
         figsize=eplt.figwh(),
     )
 
-    for norm, label, k0, k1 in zip(norms, labels, kw0, kw1):
+    for norm, label, k0, k1 in zip(norms, labels, kw0, kw1, strict=True):
         axs[0].plot(x, norm(**k0, **k1)(x), label=label)
 
     bar_data = modulatedBarData(384, 256)
-    for i, (ax, norm, k1) in enumerate(zip(axs[1:], norms, kw1)):
+    for i, (ax, norm, k1) in enumerate(zip(axs[1:], norms, kw1, strict=True)):
         ax.plot(
             0.5,
             1,
diff --git a/docs/source/reference.rst b/docs/source/reference.rst
index 8decc1ab..eacba377 100644
--- a/docs/source/reference.rst
+++ b/docs/source/reference.rst
@@ -2,7 +2,7 @@
 API Reference
 *************
 
-ERLabPy is organized into multiple subpackages and submodules.
+ERLabPy is organized into multiple subpackages and submodules classified by their functionality. The following table lists the subpackages and submodules of ERLabPy.
 
 Subpackages
 ===========
@@ -10,11 +10,11 @@ Subpackages
 ========================   ========================
 Subpackage                 Description
 ========================   ========================
-`erlab.analysis`           Data analysis
-`erlab.io`                 Read & write ARPES data
-`erlab.plotting`           Plot
-`erlab.interactive`        Interactive plotting based on Qt and pyqtgraph
-`erlab.characterization`   Analyze sample characterization results such as XRD and transport measurements
+`erlab.analysis`           Routines for analyzing ARPES data.
+`erlab.io`                 Reading and writing data.
+`erlab.plotting`           Functions related to static plotting with matplotlib.
+`erlab.interactive`        Interactive tools and widgets based on Qt and pyqtgraph
+`erlab.accessors`          `xarray accessors <https://docs.xarray.dev/en/stable/internals/extending-xarray.html>`_. You will not need to import this module directly.
 ========================   ========================
 
 .. currentmodule:: erlab
@@ -26,7 +26,7 @@ Subpackage                 Description
    erlab.io
    erlab.plotting
    erlab.interactive
-   erlab.characterization
+   erlab.accessors
 
 Submodules
 ==========
@@ -35,9 +35,8 @@ Submodules
 Submodule           Description
 ==================  ==================
 `erlab.lattice`     Tools for working with real and reciprocal lattices.
-`erlab.constants`   Physical constants and unit conversion
-`erlab.accessors`   `xarray accessors <https://docs.xarray.dev/en/stable/internals/extending-xarray.html>`_
-`erlab.parallel`    Helpers for parallel processing
+`erlab.constants`   Physical constants and functions for unit conversion.
+`erlab.parallel`    Helpers for parallel processing.
 ==================  ==================
 
 .. toctree::
@@ -45,5 +44,4 @@ Submodule           Description
 
    erlab.lattice
    erlab.constants
-   erlab.accessors
    erlab.parallel
diff --git a/docs/source/user-guide/curve-fitting.ipynb b/docs/source/user-guide/curve-fitting.ipynb
index 09182765..dd9fac6c 100644
--- a/docs/source/user-guide/curve-fitting.ipynb
+++ b/docs/source/user-guide/curve-fitting.ipynb
@@ -17,25 +17,22 @@
                 "Curve fitting\n",
                 "=============\n",
                 "\n",
-                "ERLabPy provides two choices for curve fitting: `lmfit\n",
-                "<https://lmfit.github.io/lmfit-py/>`_ and `iminuit\n",
-                "<https://github.com/scikit-hep/iminuit>`_. \n",
+                "Curve fitting in ERLabPy largely relies on `lmfit <https://lmfit.github.io/lmfit-py/>`_.\n",
+                "Along with some convenient models for common fitting tasks, ERLabPy provides a powerful\n",
+                "accessor that streamlines curve fitting on multidimensional xarray objects.\n",
                 "\n",
-                "- `lmfit <https://lmfit.github.io/lmfit-py/>`_ provides a high-level interface to\n",
-                "  optimization and curve fitting problems for Python. It builds on and extends many of\n",
-                "  the optimization methods of :mod:`scipy.optimize`, and provides a common interface for\n",
-                "  all of its supported optimization methods.\n",
+                "ERLabPy also provides optional integration of lmfit models with `iminuit\n",
+                "<https://github.com/scikit-hep/iminuit>`_, which is a Python interface to the `Minuit\n",
+                "C++ library <https://root.cern.ch/doc/master/Minuit2Page.html>`_ developed at CERN.\n",
                 "\n",
-                "- `iminuit <https://github.com/scikit-hep/iminuit>`_ is a Python interface to the Minuit\n",
-                "  C++ library, highly compatible with Jupyter notebooks and the SciPy ecosystem.\n",
-                "  Although developed for high-energy physics, it is a simple and easy-to-use tool for\n",
-                "  solving optimization problems.\n",
+                "In this tutorial, we will start with the basics of curve fitting using lmfit, introduce\n",
+                "some models that are available in ERLabPy, and demonstrate curve fitting with the\n",
+                ":meth:`modelfit <erlab.accessors.fit.ModelFitDataArrayAccessor.__call__>` accessor to\n",
+                "fit multidimensional xarray objects. Finally, we will show how to use `iminuit\n",
+                "<https://github.com/scikit-hep/iminuit>`_ with lmfit models.\n",
                 "\n",
-                "In this tutorial, we will show how to use both libraries to fit a simple function to a\n",
-                "set of data points.\n",
-                "\n",
-                "Basic fitting with lmfit\n",
-                "------------------------"
+                "Basic fitting with ``lmfit``\n",
+                "----------------------------"
             ]
         },
         {
@@ -320,11 +317,16 @@
                 "<https://lmfit.github.io/lmfit-py/fitting.html>`_.\n",
                 "\n",
                 "Fitting with pre-defined models\n",
-                "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+                "-------------------------------\n",
                 "\n",
                 "Creating composite models with different prefixes every time can be cumbersome, so\n",
-                "ERLabPy provides some pre-defined models in :mod:`erlab.analysis.fit.models`. One\n",
-                "example is :class:`MultiPeakModel <erlab.analysis.fit.models.MultiPeakModel>`, which is\n",
+                "ERLabPy provides some pre-defined models in :mod:`erlab.analysis.fit.models`.\n",
+                "\n",
+                "\n",
+                "Fitting multiple peaks\n",
+                "~~~~~~~~~~~~~~~~~~~~~~\n",
+                "\n",
+                "One example is :class:`MultiPeakModel <erlab.analysis.fit.models.MultiPeakModel>`, which is\n",
                 "a composite model of multiple Gaussian or Lorentzian peaks on a linear background. By\n",
                 "supplying keyword arguments, you can specify the number of peaks, their shapes, whether\n",
                 "to multiply with a Fermi-Dirac distribution, and whether to convolve the result with\n",
@@ -388,7 +390,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "data = generate_data(bandshift=-0.2, count=5e+8).T\n",
+                "data = generate_data(bandshift=-0.2, count=5e8, seed=1).T\n",
                 "cut = data.qsel(ky=0.3)\n",
                 "cut.qplot(colorbar=True)"
             ]
@@ -468,8 +470,8 @@
                 }
             },
             "source": [
-                "Fitting xarray objects\n",
-                "----------------------\n",
+                "Fitting ``xarray`` objects\n",
+                "--------------------------\n",
                 "\n",
                 "ERLabPy provides accessors for xarray objects that allows you to fit data with lmfit\n",
                 "models: :meth:`xarray.DataArray.modelfit\n",
@@ -738,11 +740,25 @@
                 "        \"slope\": -0.1,\n",
                 "    },\n",
                 ")\n",
-                "\n",
+                "result_ds"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Let's overlay the fitted peak positions on the data."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
                 "result_ds.modelfit_data.qplot()\n",
-                "\n",
-                "center_fitted = result_ds.modelfit_coefficients.sel(param=\"center\")\n",
-                "plt.plot(center_fitted, center_fitted.beta, \".\")"
+                "result_center = result_ds.sel(param=\"center\")\n",
+                "plt.plot(result_center.modelfit_coefficients, result_center.beta, '.-')"
             ]
         },
         {
@@ -834,15 +850,15 @@
                 "          guess=True,\n",
                 "      )\n",
                 "\n",
-                ".. note ::\n",
-                "\n",
-                "    - Note that the initial run will take a long time due to the overhead of creating\n",
-                "      parallel workers. Subsequent calls will run faster, since joblib's default backend\n",
-                "      will try to reuse the workers.\n",
-                "    \n",
-                "    - The accessor has some intrinsic overhead due to post-processing. If you need the\n",
-                "      best performance, handle the parallelization yourself with joblib and\n",
-                "      :meth:`lmfit.Model.fit <lmfit.model.Model.fit>`.\n",
+                "  .. note ::\n",
+                "  \n",
+                "      - Note that the initial run will take a long time due to the overhead of creating\n",
+                "        parallel workers. Subsequent calls will run faster, since joblib's default backend\n",
+                "        will try to reuse the workers.\n",
+                "      \n",
+                "      - The accessor has some intrinsic overhead due to post-processing. If you need the\n",
+                "        best performance, handle the parallelization yourself with joblib and\n",
+                "        :meth:`lmfit.Model.fit <lmfit.model.Model.fit>`.\n",
                 "\n",
                 "Saving and loading fits\n",
                 "~~~~~~~~~~~~~~~~~~~~~~~\n",
@@ -948,8 +964,16 @@
                 "Also check out the interactive Fermi edge fitting tool,\n",
                 ":func:`erlab.interactive.goldtool`.\n",
                 "\n",
-                "Using iminuit\n",
-                "-------------\n",
+                "Using ``iminuit``\n",
+                "-----------------\n",
+                "\n",
+                ".. note::\n",
+                "\n",
+                "    This part requires `iminuit <https://github.com/scikit-hep/iminuit>`_.\n",
+                "\n",
+                "`iminuit <https://github.com/scikit-hep/iminuit>`_ is a powerful Python interface to the\n",
+                "`Minuit C++ library <https://root.cern.ch/doc/master/Minuit2Page.html>`_ developed at\n",
+                "CERN. To learn more, see the `iminuit documentation <http://scikit-hep.org/iminuit/>`_.\n",
                 "\n",
                 "ERLabPy provides a thin wrapper around :class:`iminuit.Minuit` that allows you to use\n",
                 "lmfit models with iminuit. The example below conducts the same fit as the previous one,\n",
@@ -1049,7 +1073,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.11.8"
+            "version": "3.11.9"
         }
     },
     "nbformat": 4,
diff --git a/docs/source/user-guide/indexing.ipynb b/docs/source/user-guide/indexing.ipynb
index 5ab8503e..5ae0fd74 100644
--- a/docs/source/user-guide/indexing.ipynb
+++ b/docs/source/user-guide/indexing.ipynb
@@ -47,7 +47,7 @@
             "source": [
                 "from erlab.io.exampledata import generate_data\n",
                 "\n",
-                "dat = generate_data()"
+                "dat = generate_data(seed=1)"
             ]
         },
         {
diff --git a/docs/source/user-guide/io.ipynb b/docs/source/user-guide/io.ipynb
index 6eb40d5d..3dae1c84 100644
--- a/docs/source/user-guide/io.ipynb
+++ b/docs/source/user-guide/io.ipynb
@@ -120,6 +120,19 @@
                 "Loading ARPES data\n",
                 "------------------\n",
                 "\n",
+                ".. warning::\n",
+                "\n",
+                "   ERLabPy is still in development and the API may change. Some major changes regarding\n",
+                "   data loading and handling are planned:\n",
+                "\n",
+                "   - The `xarray datatree structure <https://github.com/pydata/xarray/issues/8572>`_\n",
+                "     will enable much more intuitive and powerful data handling. Once the feature gets\n",
+                "     incorporated into xarray, ERLabPy will be updated to use it.\n",
+                "\n",
+                "   - A universal translation layer between true data header attributes and\n",
+                "     human-readable representations will be implemented. This will allow for more\n",
+                "     consistent and user-friendly data handling.\n",
+                "\n",
                 "ERLabPy's data loading framework consists of various plugins, or *loaders*, each\n",
                 "designed to load data from a different beamline or laboratory. Each loader is a class\n",
                 "that has a ``load`` method which takes a file path or sequence number and returns data.\n",
@@ -525,6 +538,7 @@
                 "        temp=temp,\n",
                 "        bandshift=bandshift,\n",
                 "        assign_attributes=False,\n",
+                "        seed=1,\n",
                 "    ).T\n",
                 "\n",
                 "    # Rename coordinates. The loader must rename them back to the original names.\n",
@@ -1070,7 +1084,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.11.8"
+            "version": "3.11.9"
         }
     },
     "nbformat": 4,
diff --git a/docs/source/user-guide/kconv.ipynb b/docs/source/user-guide/kconv.ipynb
index e5cda9d6..aebc5199 100644
--- a/docs/source/user-guide/kconv.ipynb
+++ b/docs/source/user-guide/kconv.ipynb
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "nbsphinx": "hidden"
    },
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -102,499 +102,11 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
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-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (eV: 500, beta: 60, alpha: 500)&gt;\n",
-       "2.358 3.405 2.24 1.645 0.6441 ... 0.0004334 8.253e-07 6.374e-09 6.121e-12\n",
-       "Coordinates:\n",
-       "  * alpha    (alpha) float64 -15.0 -14.94 -14.88 -14.82 ... 14.88 14.94 15.0\n",
-       "  * beta     (beta) float64 -15.0 -14.49 -13.98 -13.47 ... 13.98 14.49 15.0\n",
-       "  * eV       (eV) float64 -0.45 -0.4489 -0.4477 -0.4466 ... 0.1177 0.1189 0.12\n",
-       "    xi       float64 0.0\n",
-       "    delta    float64 0.0\n",
-       "    hv       float64 50.0\n",
-       "Attributes:\n",
-       "    configuration:        1\n",
-       "    temp_sample:          20.0\n",
-       "    sample_workfunction:  4.5</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>eV</span>: 500</li><li><span class='xr-has-index'>beta</span>: 60</li><li><span class='xr-has-index'>alpha</span>: 500</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1020b550-70ca-492f-ac9f-2f962592cede' class='xr-array-in' type='checkbox' ><label for='section-1020b550-70ca-492f-ac9f-2f962592cede' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.358 3.405 2.24 1.645 ... 0.0004334 8.253e-07 6.374e-09 6.121e-12</span></div><div class='xr-array-data'><pre>array([[[2.35844314e+00, 3.40539690e+00, 2.24044696e+00, ...,\n",
-       "         5.02574294e-01, 1.55053870e+00, 1.30280108e+00],\n",
-       "        [1.20444912e+00, 1.39253846e+00, 1.71992927e+00, ...,\n",
-       "         4.21230348e+00, 3.04978817e+00, 3.30209104e+00],\n",
-       "        [3.56771888e+00, 1.95369426e+00, 1.82055492e+00, ...,\n",
-       "         2.61856733e+00, 1.31759611e+00, 1.92138583e+00],\n",
-       "        ...,\n",
-       "        [1.41821726e+00, 2.10576288e+00, 1.32936210e+00, ...,\n",
-       "         2.71604710e+00, 2.50067901e+00, 2.75027138e+00],\n",
-       "        [1.41483168e+00, 1.97401752e+00, 8.94222283e-01, ...,\n",
-       "         1.64578091e+00, 1.44646315e+00, 1.72034631e+00],\n",
-       "        [8.88858486e-01, 5.67003353e-01, 1.27199662e+00, ...,\n",
-       "         2.41225205e-01, 5.93993651e-01, 2.37136064e+00]],\n",
-       "\n",
-       "       [[2.08461187e+00, 3.26674532e+00, 1.88255911e+00, ...,\n",
-       "         1.00640458e+00, 1.58633420e+00, 1.56119826e+00],\n",
-       "        [2.18766949e+00, 2.16155034e+00, 1.47755121e+00, ...,\n",
-       "         2.99517431e+00, 3.15513056e+00, 1.98056901e+00],\n",
-       "        [3.39034956e+00, 3.21152841e+00, 3.70286597e+00, ...,\n",
-       "         3.03350947e+00, 2.88574370e+00, 2.83433774e+00],\n",
-       "...\n",
-       "        [2.05162531e-06, 4.15822470e-07, 6.35454537e-09, ...,\n",
-       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
-       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
-       "         2.78294794e-02, 1.12012451e-01, 3.03825449e-02],\n",
-       "        [3.16729900e-28, 1.36692173e-21, 3.66796068e-16, ...,\n",
-       "         4.26418558e-04, 6.58576803e-06, 6.34880651e-09]],\n",
-       "\n",
-       "       [[2.82542493e-02, 1.11581089e-01, 2.78228874e-02, ...,\n",
-       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
-       "        [6.12046182e-12, 6.34880614e-09, 4.09498587e-07, ...,\n",
-       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
-       "        [4.15847393e-07, 4.31361972e-04, 2.78228873e-02, ...,\n",
-       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
-       "        ...,\n",
-       "        [1.22968026e-10, 2.49231028e-11, 3.80871644e-13, ...,\n",
-       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
-       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
-       "         1.37275271e-01, 5.50530035e-01, 1.39405612e-01],\n",
-       "        [1.89838030e-32, 8.19290282e-26, 2.19846131e-20, ...,\n",
-       "         8.25345980e-07, 6.37372924e-09, 6.12055470e-12]]])</pre></div></div></li><li class='xr-section-item'><input id='section-c8b62af8-618a-4912-b6cf-309f6acde597' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8b62af8-618a-4912-b6cf-309f6acde597' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alpha</span></div><div class='xr-var-dims'>(alpha)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-15.0 -14.94 -14.88 ... 14.94 15.0</div><input id='attrs-ff8015fe-254b-4817-ace9-40f2dd60b559' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff8015fe-254b-4817-ace9-40f2dd60b559' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67cb1c3f-d760-475f-97d6-8eaa18f353fc' class='xr-var-data-in' type='checkbox'><label for='data-67cb1c3f-d760-475f-97d6-8eaa18f353fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-15.     , -14.93988, -14.87976, ...,  14.87976,  14.93988,  15.     ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beta</span></div><div class='xr-var-dims'>(beta)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-15.0 -14.49 -13.98 ... 14.49 15.0</div><input id='attrs-ee1126f4-c040-4dce-b40d-0d63af7f3e5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee1126f4-c040-4dce-b40d-0d63af7f3e5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-baed2029-8669-463b-9817-89001173b7a1' class='xr-var-data-in' type='checkbox'><label for='data-baed2029-8669-463b-9817-89001173b7a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-15.      , -14.491525, -13.983051, -13.474576, -12.966102, -12.457627,\n",
-       "       -11.949153, -11.440678, -10.932203, -10.423729,  -9.915254,  -9.40678 ,\n",
-       "        -8.898305,  -8.389831,  -7.881356,  -7.372881,  -6.864407,  -6.355932,\n",
-       "        -5.847458,  -5.338983,  -4.830508,  -4.322034,  -3.813559,  -3.305085,\n",
-       "        -2.79661 ,  -2.288136,  -1.779661,  -1.271186,  -0.762712,  -0.254237,\n",
-       "         0.254237,   0.762712,   1.271186,   1.779661,   2.288136,   2.79661 ,\n",
-       "         3.305085,   3.813559,   4.322034,   4.830508,   5.338983,   5.847458,\n",
-       "         6.355932,   6.864407,   7.372881,   7.881356,   8.389831,   8.898305,\n",
-       "         9.40678 ,   9.915254,  10.423729,  10.932203,  11.440678,  11.949153,\n",
-       "        12.457627,  12.966102,  13.474576,  13.983051,  14.491525,  15.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eV</span></div><div class='xr-var-dims'>(eV)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.45 -0.4489 ... 0.1189 0.12</div><input id='attrs-acc16729-d3d6-4c02-8e93-d1c7e56b1a67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acc16729-d3d6-4c02-8e93-d1c7e56b1a67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-461908cc-4df3-49f2-935b-55b5dabae9ba' class='xr-var-data-in' type='checkbox'><label for='data-461908cc-4df3-49f2-935b-55b5dabae9ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.45    , -0.448858, -0.447715, ...,  0.117715,  0.118858,  0.12    ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xi</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-11f5182e-ade0-4f4d-9e6e-4b5b1f26da36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11f5182e-ade0-4f4d-9e6e-4b5b1f26da36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c672ca67-defd-4af8-a7c5-94c9b0642037' class='xr-var-data-in' type='checkbox'><label for='data-c672ca67-defd-4af8-a7c5-94c9b0642037' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-c65ee33e-a5df-4967-9cac-07bdc6fb6d5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c65ee33e-a5df-4967-9cac-07bdc6fb6d5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b19e583d-0af8-414f-aa7b-10c369d35e3f' class='xr-var-data-in' type='checkbox'><label for='data-b19e583d-0af8-414f-aa7b-10c369d35e3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hv</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>50.0</div><input id='attrs-6b10d910-52f1-473c-87ad-261b6ee960f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b10d910-52f1-473c-87ad-261b6ee960f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c11406c9-2547-44bd-b0ab-5334b69a72d6' class='xr-var-data-in' type='checkbox'><label for='data-c11406c9-2547-44bd-b0ab-5334b69a72d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(50.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a4797ff-3600-4181-bd2f-bfee30bd6493' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a4797ff-3600-4181-bd2f-bfee30bd6493' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>alpha</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-837291b6-39cf-46cf-9dc4-142773a1f8f5' class='xr-index-data-in' type='checkbox'/><label for='index-837291b6-39cf-46cf-9dc4-142773a1f8f5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -15.0, -14.939879759519037, -14.879759519038076,\n",
-       "       -14.819639278557114, -14.759519038076153,  -14.69939879759519,\n",
-       "       -14.639278557114228, -14.579158316633267, -14.519038076152304,\n",
-       "       -14.458917835671343,\n",
-       "       ...\n",
-       "        14.458917835671343,  14.519038076152302,  14.579158316633265,\n",
-       "        14.639278557114228,   14.69939879759519,   14.75951903807615,\n",
-       "        14.819639278557112,  14.879759519038075,  14.939879759519037,\n",
-       "                      15.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;alpha&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beta</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d8a1f92e-9ab8-456d-be9a-8ee492ccfb62' class='xr-index-data-in' type='checkbox'/><label for='index-d8a1f92e-9ab8-456d-be9a-8ee492ccfb62' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               -15.0,  -14.491525423728813,  -13.983050847457626,\n",
-       "        -13.474576271186441,  -12.966101694915254,  -12.457627118644067,\n",
-       "        -11.949152542372882,  -11.440677966101696,  -10.932203389830509,\n",
-       "        -10.423728813559322,   -9.915254237288135,    -9.40677966101695,\n",
-       "         -8.898305084745763,   -8.389830508474576,    -7.88135593220339,\n",
-       "         -7.372881355932204,   -6.864406779661017,  -6.3559322033898304,\n",
-       "         -5.847457627118644,  -5.3389830508474585,   -4.830508474576272,\n",
-       "         -4.322033898305085,  -3.8135593220338997,   -3.305084745762713,\n",
-       "         -2.796610169491526,   -2.288135593220339,  -1.7796610169491522,\n",
-       "        -1.2711864406779672,  -0.7627118644067803, -0.25423728813559343,\n",
-       "        0.25423728813559165,   0.7627118644067785,   1.2711864406779654,\n",
-       "         1.7796610169491522,    2.288135593220339,    2.796610169491526,\n",
-       "          3.305084745762713,    3.813559322033896,    4.322033898305083,\n",
-       "           4.83050847457627,    5.338983050847457,    5.847457627118644,\n",
-       "         6.3559322033898304,    6.864406779661017,    7.372881355932201,\n",
-       "         7.8813559322033875,    8.389830508474574,    8.898305084745761,\n",
-       "          9.406779661016948,    9.915254237288135,   10.423728813559322,\n",
-       "         10.932203389830509,   11.440677966101696,   11.949152542372879,\n",
-       "         12.457627118644066,   12.966101694915253,    13.47457627118644,\n",
-       "         13.983050847457626,   14.491525423728813,                 15.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;beta&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>eV</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2d229a5a-3188-4186-a4c8-cfe10c9ca244' class='xr-index-data-in' type='checkbox'/><label for='index-2d229a5a-3188-4186-a4c8-cfe10c9ca244' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               -0.45,  -0.4488577154308617, -0.44771543086172344,\n",
-       "       -0.44657314629258515,  -0.4454308617234469, -0.44428857715430864,\n",
-       "       -0.44314629258517035, -0.44200400801603207,  -0.4408617234468938,\n",
-       "        -0.4397194388777555,\n",
-       "       ...\n",
-       "        0.10971943887775554,  0.11086172344689377,  0.11200400801603211,\n",
-       "        0.11314629258517034,  0.11428857715430857,  0.11543086172344691,\n",
-       "        0.11657314629258514,  0.11771543086172348,  0.11885771543086171,\n",
-       "                       0.12],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;eV&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ad5279f-9d7a-49cb-85f8-a6d7472a03db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9ad5279f-9d7a-49cb-85f8-a6d7472a03db' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>configuration :</span></dt><dd>1</dd><dt><span>temp_sample :</span></dt><dd>20.0</dd><dt><span>sample_workfunction :</span></dt><dd>4.5</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (eV: 500, beta: 60, alpha: 500)>\n",
-       "2.358 3.405 2.24 1.645 0.6441 ... 0.0004334 8.253e-07 6.374e-09 6.121e-12\n",
-       "Coordinates:\n",
-       "  * alpha    (alpha) float64 -15.0 -14.94 -14.88 -14.82 ... 14.88 14.94 15.0\n",
-       "  * beta     (beta) float64 -15.0 -14.49 -13.98 -13.47 ... 13.98 14.49 15.0\n",
-       "  * eV       (eV) float64 -0.45 -0.4489 -0.4477 -0.4466 ... 0.1177 0.1189 0.12\n",
-       "    xi       float64 0.0\n",
-       "    delta    float64 0.0\n",
-       "    hv       float64 50.0\n",
-       "Attributes:\n",
-       "    configuration:        1\n",
-       "    temp_sample:          20.0\n",
-       "    sample_workfunction:  4.5"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from erlab.io.exampledata import generate_data_angles\n",
     "\n",
-    "dat = generate_data_angles(assign_attributes=True, seed=1).T\n",
+    "dat = generate_data_angles(shape=(200, 60, 300), assign_attributes=True, seed=1).T\n",
     "dat"
    ]
   },
@@ -607,672 +119,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x16bce1ed0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "application/pdf": 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-      "text/plain": [
-       "<Figure size 614.4x460.8 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cut = dat.sel(beta=10.0, method=\"nearest\")\n",
     "eplt.plot_array(cut)"
@@ -1302,493 +151,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Estimating bounds and resolution\n",
-      "Calculating destination coordinates\n",
-      "Converting ('eV', 'alpha', 'beta')  ->  ('eV', 'kx', 'ky')\n",
-      "Interpolated in 1.110 s\n"
-     ]
-    },
-    {
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-       "\n",
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-       ".xr-index-data,\n",
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-       "\n",
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-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (eV: 500, kx: 310, ky: 310)&gt;\n",
-       "nan nan nan nan nan nan nan nan nan ... 0.01834 0.02405 nan nan nan nan nan nan\n",
-       "Coordinates:\n",
-       "    xi       float64 0.0\n",
-       "    delta    float64 0.0\n",
-       "    hv       float64 50.0\n",
-       "  * eV       (eV) float64 -0.45 -0.4489 -0.4477 -0.4466 ... 0.1177 0.1189 0.12\n",
-       "  * kx       (kx) float64 -0.8956 -0.8898 -0.884 -0.8782 ... 0.884 0.8898 0.8956\n",
-       "  * ky       (ky) float64 -0.8956 -0.8898 -0.884 -0.8782 ... 0.884 0.8898 0.8956\n",
-       "Attributes:\n",
-       "    configuration:        1\n",
-       "    temp_sample:          20.0\n",
-       "    sample_workfunction:  4.5\n",
-       "    delta_offset:         0.0\n",
-       "    xi_offset:            0.0\n",
-       "    beta_offset:          0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>eV</span>: 500</li><li><span class='xr-has-index'>kx</span>: 310</li><li><span class='xr-has-index'>ky</span>: 310</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-36436c1f-81b6-47cc-9c2c-525588de9946' class='xr-array-in' type='checkbox' ><label for='section-36436c1f-81b6-47cc-9c2c-525588de9946' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... 0.02405 nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-974a5ebb-0818-493c-8ecb-acd5a4579a96' class='xr-section-summary-in' type='checkbox'  checked><label for='section-974a5ebb-0818-493c-8ecb-acd5a4579a96' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xi</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-a91f5cd8-2c4f-4dff-b6f9-ea7e28f3ad40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a91f5cd8-2c4f-4dff-b6f9-ea7e28f3ad40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32765cc1-e446-43d8-92f0-bfc0637a2291' class='xr-var-data-in' type='checkbox'><label for='data-32765cc1-e446-43d8-92f0-bfc0637a2291' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-0081edfa-35e7-468d-b7c3-126edd8efea1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0081edfa-35e7-468d-b7c3-126edd8efea1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbb2df06-3c81-4a0a-a015-db03691d038a' class='xr-var-data-in' type='checkbox'><label for='data-fbb2df06-3c81-4a0a-a015-db03691d038a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hv</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>50.0</div><input id='attrs-0336af32-8546-4cf4-a9ee-7ee32021a6cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0336af32-8546-4cf4-a9ee-7ee32021a6cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5341437a-ca21-4599-9eaa-0f54f816c01a' class='xr-var-data-in' type='checkbox'><label for='data-5341437a-ca21-4599-9eaa-0f54f816c01a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(50.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eV</span></div><div class='xr-var-dims'>(eV)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.45 -0.4489 ... 0.1189 0.12</div><input id='attrs-e4bd7800-55da-4935-b328-b1e9a006b38f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4bd7800-55da-4935-b328-b1e9a006b38f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ba573ea-ae97-4216-be79-62794013b6f4' class='xr-var-data-in' type='checkbox'><label for='data-5ba573ea-ae97-4216-be79-62794013b6f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.45    , -0.448858, -0.447715, ...,  0.117715,  0.118858,  0.12    ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>kx</span></div><div class='xr-var-dims'>(kx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.8956 -0.8898 ... 0.8898 0.8956</div><input id='attrs-2f5d0835-473a-4b04-a487-72f6ba52575b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f5d0835-473a-4b04-a487-72f6ba52575b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c885264-5cf9-4b18-b41a-d61eba18e0db' class='xr-var-data-in' type='checkbox'><label for='data-8c885264-5cf9-4b18-b41a-d61eba18e0db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.895597, -0.8898  , -0.884003, ...,  0.884003,  0.8898  ,  0.895597])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ky</span></div><div class='xr-var-dims'>(ky)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.8956 -0.8898 ... 0.8898 0.8956</div><input id='attrs-a821afa8-ee8e-466a-9e88-4e595e14f73e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a821afa8-ee8e-466a-9e88-4e595e14f73e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e4a3bad-c925-4445-b71e-13eb6773c5fa' class='xr-var-data-in' type='checkbox'><label for='data-0e4a3bad-c925-4445-b71e-13eb6773c5fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.895596, -0.8898  , -0.884003, ...,  0.884003,  0.8898  ,  0.895596])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ba63519-a786-465a-bf1b-b0b1de09bbdf' class='xr-section-summary-in' type='checkbox'  ><label for='section-3ba63519-a786-465a-bf1b-b0b1de09bbdf' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>eV</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad87b84a-db36-4c96-8259-03eb25ecf72f' class='xr-index-data-in' type='checkbox'/><label for='index-ad87b84a-db36-4c96-8259-03eb25ecf72f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               -0.45,  -0.4488577154308617, -0.44771543086172344,\n",
-       "       -0.44657314629258515,  -0.4454308617234469, -0.44428857715430864,\n",
-       "       -0.44314629258517035, -0.44200400801603207,  -0.4408617234468938,\n",
-       "        -0.4397194388777555,\n",
-       "       ...\n",
-       "        0.10971943887775554,  0.11086172344689377,  0.11200400801603211,\n",
-       "        0.11314629258517034,  0.11428857715430857,  0.11543086172344691,\n",
-       "        0.11657314629258514,  0.11771543086172348,  0.11885771543086171,\n",
-       "                       0.12],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;eV&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>kx</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c0fbf378-3e44-412a-be49-0159a3c6b7be' class='xr-index-data-in' type='checkbox'/><label for='index-c0fbf378-3e44-412a-be49-0159a3c6b7be' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-0.8955965305067839, -0.8897997892090054, -0.8840030479112269,\n",
-       "       -0.8782063066134482, -0.8724095653156697, -0.8666128240178912,\n",
-       "       -0.8608160827201127, -0.8550193414223342, -0.8492226001245556,\n",
-       "       -0.8434258588267771,\n",
-       "       ...\n",
-       "        0.8434258588267771,  0.8492226001245555,  0.8550193414223342,\n",
-       "        0.8608160827201126,  0.8666128240178912,  0.8724095653156698,\n",
-       "        0.8782063066134482,  0.8840030479112269,  0.8897997892090053,\n",
-       "        0.8955965305067839],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;kx&#x27;, length=310))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ky</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e607b999-bf0f-4b03-b0f0-d007e575f659' class='xr-index-data-in' type='checkbox'/><label for='index-e607b999-bf0f-4b03-b0f0-d007e575f659' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -0.895596407247807, -0.8897996667478212, -0.8840029262478354,\n",
-       "       -0.8782061857478496, -0.8724094452478638,  -0.866612704747878,\n",
-       "       -0.8608159642478921, -0.8550192237479063, -0.8492224832479205,\n",
-       "       -0.8434257427479347,\n",
-       "       ...\n",
-       "        0.8434257427479349,  0.8492224832479206,  0.8550192237479065,\n",
-       "        0.8608159642478922,  0.8666127047478781,  0.8724094452478638,\n",
-       "        0.8782061857478497,  0.8840029262478354,  0.8897996667478213,\n",
-       "         0.895596407247807],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;ky&#x27;, length=310))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4bb485b-a780-4e38-9681-46d48516e47a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4bb485b-a780-4e38-9681-46d48516e47a' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>configuration :</span></dt><dd>1</dd><dt><span>temp_sample :</span></dt><dd>20.0</dd><dt><span>sample_workfunction :</span></dt><dd>4.5</dd><dt><span>delta_offset :</span></dt><dd>0.0</dd><dt><span>xi_offset :</span></dt><dd>0.0</dd><dt><span>beta_offset :</span></dt><dd>0.0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (eV: 500, kx: 310, ky: 310)>\n",
-       "nan nan nan nan nan nan nan nan nan ... 0.01834 0.02405 nan nan nan nan nan nan\n",
-       "Coordinates:\n",
-       "    xi       float64 0.0\n",
-       "    delta    float64 0.0\n",
-       "    hv       float64 50.0\n",
-       "  * eV       (eV) float64 -0.45 -0.4489 -0.4477 -0.4466 ... 0.1177 0.1189 0.12\n",
-       "  * kx       (kx) float64 -0.8956 -0.8898 -0.884 -0.8782 ... 0.884 0.8898 0.8956\n",
-       "  * ky       (ky) float64 -0.8956 -0.8898 -0.884 -0.8782 ... 0.884 0.8898 0.8956\n",
-       "Attributes:\n",
-       "    configuration:        1\n",
-       "    temp_sample:          20.0\n",
-       "    sample_workfunction:  4.5\n",
-       "    delta_offset:         0.0\n",
-       "    xi_offset:            0.0\n",
-       "    beta_offset:          0.0"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dat_kconv = dat.kspace.convert()\n",
     "dat_kconv"
@@ -1810,867 +175,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x176b94c50>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "application/pdf": 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+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots(1, 2, layout=\"compressed\")\n",
     "eplt.plot_array(dat.sel(eV=-0.3, method=\"nearest\"), ax=axs[0], aspect=\"equal\")\n",
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   },
   {
    "cell_type": "code",
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+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-   ],
+   "outputs": [],
    "source": [
     "dat.kspace.offsets"
    ]
@@ -2739,516 +232,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-     "data": {
-      "text/html": [
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-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
+   "outputs": [],
    "source": [
     "dat.kspace.offsets.update(delta=60.0, beta=30.0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Estimating bounds and resolution\n",
-      "Calculating destination coordinates\n",
-      "Converting ('eV', 'alpha', 'beta')  ->  ('eV', 'kx', 'ky')\n",
-      "Interpolated in 0.494 s\n"
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-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (eV: 500, kx: 380, ky: 398)&gt;\n",
-       "nan nan nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan nan\n",
-       "Coordinates:\n",
-       "    xi       float64 0.0\n",
-       "    delta    float64 0.0\n",
-       "    hv       float64 50.0\n",
-       "  * eV       (eV) float64 -0.45 -0.4489 -0.4477 -0.4466 ... 0.1177 0.1189 0.12\n",
-       "  * kx       (kx) float64 -2.495 -2.489 -2.483 ... -0.3111 -0.3053 -0.2995\n",
-       "  * ky       (ky) float64 -0.3431 -0.3373 -0.3315 -0.3257 ... 1.946 1.952 1.957\n",
-       "Attributes:\n",
-       "    configuration:        1\n",
-       "    temp_sample:          20.0\n",
-       "    sample_workfunction:  4.5\n",
-       "    delta_offset:         60.0\n",
-       "    xi_offset:            0.0\n",
-       "    beta_offset:          30.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>eV</span>: 500</li><li><span class='xr-has-index'>kx</span>: 380</li><li><span class='xr-has-index'>ky</span>: 398</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ceef7a73-8afa-4bcd-9b1a-d1ae45a1e3db' class='xr-array-in' type='checkbox' ><label for='section-ceef7a73-8afa-4bcd-9b1a-d1ae45a1e3db' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-611d0d71-9e64-4c55-ab3c-8f73a7dadc00' class='xr-section-summary-in' type='checkbox'  checked><label for='section-611d0d71-9e64-4c55-ab3c-8f73a7dadc00' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xi</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-1a76c94c-885b-4951-8413-9f25fec540af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a76c94c-885b-4951-8413-9f25fec540af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10a82da7-64e7-453d-8aed-e2104af04365' class='xr-var-data-in' type='checkbox'><label for='data-10a82da7-64e7-453d-8aed-e2104af04365' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-810babdc-f7ab-4202-b54c-da045a7b7565' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-810babdc-f7ab-4202-b54c-da045a7b7565' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45ef4c17-88a2-4b19-946b-5b96f04ae088' class='xr-var-data-in' type='checkbox'><label for='data-45ef4c17-88a2-4b19-946b-5b96f04ae088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hv</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>50.0</div><input id='attrs-4bbb85ca-8d55-484d-b362-bbf59fb5b2ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bbb85ca-8d55-484d-b362-bbf59fb5b2ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e35a1b21-f6a7-45fd-b79a-75ebf67d6405' class='xr-var-data-in' type='checkbox'><label for='data-e35a1b21-f6a7-45fd-b79a-75ebf67d6405' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(50.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eV</span></div><div class='xr-var-dims'>(eV)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.45 -0.4489 ... 0.1189 0.12</div><input id='attrs-a79af0cd-2d10-4899-a42d-58131626f85c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a79af0cd-2d10-4899-a42d-58131626f85c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1ccec71-9005-431b-b2b0-7f032979938e' class='xr-var-data-in' type='checkbox'><label for='data-b1ccec71-9005-431b-b2b0-7f032979938e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.45    , -0.448858, -0.447715, ...,  0.117715,  0.118858,  0.12    ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>kx</span></div><div class='xr-var-dims'>(kx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.495 -2.489 ... -0.3053 -0.2995</div><input id='attrs-95d9c80e-6272-40ab-88fa-0df97e908d74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95d9c80e-6272-40ab-88fa-0df97e908d74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b78e8ff6-4950-4398-b5bc-2bbf684610fd' class='xr-var-data-in' type='checkbox'><label for='data-b78e8ff6-4950-4398-b5bc-2bbf684610fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2.494599, -2.488807, -2.483015, ..., -0.311078, -0.305286, -0.299494])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ky</span></div><div class='xr-var-dims'>(ky)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3431 -0.3373 ... 1.952 1.957</div><input id='attrs-e9dfba3b-78f0-4176-a2d0-9f5b074d3470' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9dfba3b-78f0-4176-a2d0-9f5b074d3470' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68484519-f9bb-4063-9009-f11815323836' class='xr-var-data-in' type='checkbox'><label for='data-68484519-f9bb-4063-9009-f11815323836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.343069, -0.337275, -0.331481, ...,  1.945741,  1.951536,  1.95733 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6152e859-427c-4351-8039-ccbe9e067ea0' class='xr-section-summary-in' type='checkbox'  ><label for='section-6152e859-427c-4351-8039-ccbe9e067ea0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>eV</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b5555216-1ed8-442b-beda-325acac9e10c' class='xr-index-data-in' type='checkbox'/><label for='index-b5555216-1ed8-442b-beda-325acac9e10c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               -0.45,  -0.4488577154308617, -0.44771543086172344,\n",
-       "       -0.44657314629258515,  -0.4454308617234469, -0.44428857715430864,\n",
-       "       -0.44314629258517035, -0.44200400801603207,  -0.4408617234468938,\n",
-       "        -0.4397194388777555,\n",
-       "       ...\n",
-       "        0.10971943887775554,  0.11086172344689377,  0.11200400801603211,\n",
-       "        0.11314629258517034,  0.11428857715430857,  0.11543086172344691,\n",
-       "        0.11657314629258514,  0.11771543086172348,  0.11885771543086171,\n",
-       "                       0.12],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;eV&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>kx</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-26867a3f-c00a-4c71-9778-b2afd743fe8f' class='xr-index-data-in' type='checkbox'/><label for='index-26867a3f-c00a-4c71-9778-b2afd743fe8f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -2.4945990927204136,  -2.4888072589636736,   -2.483015425206934,\n",
-       "         -2.477223591450194,  -2.4714317576934546,  -2.4656399239367146,\n",
-       "         -2.459848090179975,   -2.454056256423235,  -2.4482644226664956,\n",
-       "        -2.4424725889097556,\n",
-       "       ...\n",
-       "       -0.35162060272669704, -0.34582876896995707, -0.34003693521321754,\n",
-       "       -0.33424510145647757, -0.32845326769973804, -0.32266143394299807,\n",
-       "        -0.3168696001862581, -0.31107776642951857,  -0.3052859326727786,\n",
-       "        -0.2994940989160389],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;kx&#x27;, length=380))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ky</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4cfc4011-5dca-4d28-a18a-593adc692008' class='xr-index-data-in' type='checkbox'/><label for='index-4cfc4011-5dca-4d28-a18a-593adc692008' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-0.34306943758438574,   -0.337274979666854, -0.33148052174932224,\n",
-       "       -0.32568606383179055,  -0.3198916059142588, -0.31409714799672706,\n",
-       "        -0.3083026900791953, -0.30250823216166356, -0.29671377424413187,\n",
-       "        -0.2909193163266001,\n",
-       "       ...\n",
-       "          1.905180234417928,   1.9109746923354598,   1.9167691502529915,\n",
-       "         1.9225636081705233,    1.928358066088055,   1.9341525240055868,\n",
-       "         1.9399469819231185,   1.9457414398406503,    1.951535897758182,\n",
-       "         1.9573303556757138],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;ky&#x27;, length=398))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de6e63bd-5187-4522-b41c-c44212b4840e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de6e63bd-5187-4522-b41c-c44212b4840e' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>configuration :</span></dt><dd>1</dd><dt><span>temp_sample :</span></dt><dd>20.0</dd><dt><span>sample_workfunction :</span></dt><dd>4.5</dd><dt><span>delta_offset :</span></dt><dd>60.0</dd><dt><span>xi_offset :</span></dt><dd>0.0</dd><dt><span>beta_offset :</span></dt><dd>30.0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (eV: 500, kx: 380, ky: 398)>\n",
-       "nan nan nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan nan\n",
-       "Coordinates:\n",
-       "    xi       float64 0.0\n",
-       "    delta    float64 0.0\n",
-       "    hv       float64 50.0\n",
-       "  * eV       (eV) float64 -0.45 -0.4489 -0.4477 -0.4466 ... 0.1177 0.1189 0.12\n",
-       "  * kx       (kx) float64 -2.495 -2.489 -2.483 ... -0.3111 -0.3053 -0.2995\n",
-       "  * ky       (ky) float64 -0.3431 -0.3373 -0.3315 -0.3257 ... 1.946 1.952 1.957\n",
-       "Attributes:\n",
-       "    configuration:        1\n",
-       "    temp_sample:          20.0\n",
-       "    sample_workfunction:  4.5\n",
-       "    delta_offset:         60.0\n",
-       "    xi_offset:            0.0\n",
-       "    beta_offset:          30.0"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dat_kconv = dat.kspace.convert()\n",
     "dat_kconv"
@@ -3263,818 +258,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x176988810>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "application/pdf": 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-       "   </g>\n",
-       "  </g>\n",
-       " </g>\n",
-       "</svg>\n"
-      ],
-      "text/plain": [
-       "<Figure size 614.4x460.8 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots(1, 2, layout=\"compressed\")\n",
     "eplt.plot_array(dat.sel(eV=-0.3, method=\"nearest\"), ax=axs[0], aspect=\"equal\")\n",
@@ -4177,7 +363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/user-guide/plotting.ipynb b/docs/source/user-guide/plotting.ipynb
index 0b9801cf..b0ed0369 100644
--- a/docs/source/user-guide/plotting.ipynb
+++ b/docs/source/user-guide/plotting.ipynb
@@ -85,7 +85,7 @@
             "source": [
                 "from erlab.io.exampledata import generate_data\n",
                 "\n",
-                "dat = generate_data(bandshift=-0.2).T"
+                "dat = generate_data(bandshift=-0.2, seed=1).T"
             ]
         },
         {
@@ -494,14 +494,18 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "dat0, dat1 = generate_data(shape=(250, 250, 2), Erange=(-0.3, 0.3), temp=0.0).T\n",
+                "dat0, dat1 = generate_data(\n",
+                "    shape=(250, 250, 2), Erange=(-0.3, 0.3), temp=0.0, seed=1, count=1e6\n",
+                ").T\n",
                 "\n",
-                "eplt.plot_slices(\n",
+                "_, axs = eplt.plot_slices(\n",
                 "    [dat0, dat1],\n",
                 "    order=\"F\",\n",
                 "    subplot_kw={\"layout\": \"compressed\", \"sharey\": \"row\"},\n",
                 "    axis=\"scaled\",\n",
-                ")"
+                "    label=True,\n",
+                ")\n",
+                "# eplt.label_subplot_properties(axs, values=dict(Eb=[-0.3, 0.3]))"
             ]
         },
         {
@@ -517,11 +521,11 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "lightness = dat0 + dat1\n",
-                "color = (dat0 - dat1) / lightness\n",
+                "dat_sum = dat0 + dat1\n",
+                "dat_ndiff = (dat0 - dat1) / dat_sum\n",
                 "\n",
                 "eplt.plot_slices(\n",
-                "    [lightness, color],\n",
+                "    [dat_sum, dat_ndiff],\n",
                 "    order=\"F\",\n",
                 "    subplot_kw={\"layout\": \"compressed\", \"sharey\": \"row\"},\n",
                 "    cmap=[\"viridis\", \"bwr\"],\n",
@@ -534,7 +538,27 @@
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "The difference array is noisy for small values of the sum. We can plot using a 2D colomap to visualize the relevant features better."
+                "The difference array is noisy for small values of the sum. We can plot using a 2D\n",
+                "colomap, where `dat_ndiff` is mapped to the color along the colormap and `dat_sum` is\n",
+                "mapped to the lightness of the colormap."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "eplt.plot_array_2d(dat_sum, dat_ndiff)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "The color normalization for each axis can be set independently with `lnorm` and `cnorm`.\n",
+                "The appearance of the colorbar axes can be customized with the returned `Colorbar`\n",
+                "object."
             ]
         },
         {
@@ -544,13 +568,13 @@
             "outputs": [],
             "source": [
                 "_, cb = eplt.plot_array_2d(\n",
-                "    lightness,\n",
-                "    color,\n",
+                "    dat_sum,\n",
+                "    dat_ndiff,\n",
                 "    lnorm=eplt.InversePowerNorm(0.5),\n",
                 "    cnorm=eplt.CenteredInversePowerNorm(0.7, vcenter=0.0, halfrange=1.0),\n",
                 ")\n",
-                "cb.ax.set_xticks([])\n",
-                "eplt.fancy_labels()"
+                "cb.ax.set_xticks(cb.ax.get_xlim())\n",
+                "cb.ax.set_xticklabels([\"Min\", \"Max\"])"
             ]
         },
         {
@@ -637,7 +661,7 @@
             "source": [
                 "import hvplot.xarray\n",
                 "\n",
-                "cut.hvplot(x=\"kx\", y=\"eV\", cmap=\"Greys\")"
+                "cut.hvplot(x=\"kx\", y=\"eV\", cmap=\"Greys\", aspect=1.5)"
             ]
         },
         {
@@ -646,7 +670,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "dat.hvplot(x=\"kx\", y=\"ky\", cmap=\"Greys\", widget_location=\"bottom\")"
+                "dat.hvplot(x=\"kx\", y=\"ky\", cmap=\"Greys\", aspect=\"equal\", widget_location=\"bottom\")"
             ]
         },
         {
@@ -689,7 +713,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.11.8"
+            "version": "3.11.9"
         }
     },
     "nbformat": 4,
diff --git a/environment.yml b/environment.yml
index d3c38ab5..40ae75cd 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,7 +4,6 @@ channels:
 dependencies:
   - h5netcdf>=1.2.0
   - igor2>=0.5.6
-  - iminuit>=2.25.2
   - ipykernel
   - joblib>=1.3.2
   - lmfit>=1.2.0,!=1.3.0
@@ -19,9 +18,8 @@ dependencies:
   - qtawesome>=1.3.1
   - qtpy>=2.4.1
   - scipy>=1.12.0
-  - superqt>=0.6.2
   - tqdm>=4.66.2
-  - uncertainties>=3.0.1
+  - uncertainties>=3.1.4
   - varname>=0.13.0
   - xarray>=2024.02.0
   - pip:
diff --git a/pyproject.toml b/pyproject.toml
index 628ab6a6..5c93ebab 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,6 @@ dynamic = ["version"]
 dependencies = [
     "h5netcdf>=1.2.0",
     "igor2>=0.5.6",
-    "iminuit>=2.25.2",
     "joblib>=1.3.2",
     "lmfit>=1.2.0,!=1.3.0",
     "matplotlib>=3.8.0",
@@ -36,9 +35,8 @@ dependencies = [
     "qtawesome>=1.3.1",
     "qtpy>=2.4.1",
     "scipy>=1.12.0",
-    "superqt>=0.6.2",
     "tqdm>=4.66.2",
-    "uncertainties>=3.0.1",
+    "uncertainties>=3.1.4",
     "varname>=0.13.0",
     "xarray>=2024.02.0",
 ]
@@ -156,10 +154,30 @@ select = [
     "PERF",
     "RUF",
 ]
-ignore = ["B905", "ICN001", "TRY003", "RUF001", "RUF002", "RUF003", "RUF012"]
+ignore = [
+    "ICN001", # Import conventions
+    "TRY003", # Long exception messages
+]
 extend-select = [
     "UP", # pyupgrade
 ]
+allowed-confusables = [
+    "×",
+    "−",
+    "𝑎",
+    "𝒂",
+    "𝑏",
+    "𝒃",
+    "𝑐",
+    "𝑥",
+    "𝑦",
+    "𝑧",
+    "𝛼",
+    "γ",
+    "𝛾",
+    "ν",
+    "α",
+]
 
 [tool.ruff.format]
 quote-style = "double"
@@ -176,3 +194,37 @@ profile = "black"
 addopts = ["--import-mode=importlib"]
 pythonpath = "src"
 testpaths = "tests"
+
+[tool.mypy]
+plugins = ["numpy.typing.mypy_plugin"]
+warn_unused_configs = true
+warn_redundant_casts = true
+warn_unused_ignores = true
+allow_redefinition = true
+exclude = [
+    "^docs/",
+    "^tests/",
+    "_deprecated/",
+    "interactive/fermiedge.py",
+    "io/",
+]
+
+[[tool.mypy.overrides]]
+module = [
+    "astropy.*",
+    "h5netcdf.*",
+    "igor2.*",
+    "iminuit.*",
+    "ipywidgets.*",
+    "joblib.*",
+    "lmfit.*",
+    "mpl_toolkits.*",
+    "numba.*",
+    "pyperclip.*",
+    "pyqtgraph.*",
+    "qtawesome.*",
+    "scipy.*",
+    "uncertainties.*",
+    "varname.*",
+]
+ignore_missing_imports = true
diff --git a/requirements.txt b/requirements.txt
index ce4088e1..40a1c36d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,6 @@
 dask>=2024.4.1
 h5netcdf>=1.2.0
 igor2>=0.5.6
-iminuit>=2.25.2
 joblib>=1.3.2
 lmfit>=1.2.0,!=1.3.0
 matplotlib>=3.8.0
@@ -14,8 +13,7 @@ pyqtgraph>=0.13.1
 qtawesome>=1.3.1
 qtpy>=2.4.1
 scipy>=1.12.0
-superqt>=0.6.2
 tqdm>=4.66.2
-uncertainties>=3.0.1
+uncertainties>=3.1.4
 varname>=0.13.0
 xarray>=2024.02.0
diff --git a/src/erlab/accessors/fit.py b/src/erlab/accessors/fit.py
index eaa540d7..094aefcd 100644
--- a/src/erlab/accessors/fit.py
+++ b/src/erlab/accessors/fit.py
@@ -18,7 +18,11 @@
 import tqdm.auto
 import xarray as xr
 
-from erlab.accessors.utils import _THIS_ARRAY, ERLabAccessor
+from erlab.accessors.utils import (
+    _THIS_ARRAY,
+    ERLabDataArrayAccessor,
+    ERLabDatasetAccessor,
+)
 from erlab.parallel import joblib_progress
 
 if TYPE_CHECKING:
@@ -33,7 +37,7 @@ def _nested_dict_vals(d):
             yield v
 
 
-def _broadcast_dict_values(d: Mapping[str, Any]) -> Mapping[str, xr.DataArray]:
+def _broadcast_dict_values(d: dict[str, Any]) -> dict[str, xr.DataArray]:
     to_broadcast = {}
     for k, v in d.items():
         if isinstance(v, xr.DataArray | xr.Dataset):
@@ -41,16 +45,20 @@ def _broadcast_dict_values(d: Mapping[str, Any]) -> Mapping[str, xr.DataArray]:
         else:
             to_broadcast[k] = xr.DataArray(v)
 
-    for k, v in zip(to_broadcast.keys(), xr.broadcast(*to_broadcast.values())):
+    for k, v in zip(
+        to_broadcast.keys(), xr.broadcast(*to_broadcast.values()), strict=True
+    ):
         d[k] = v
     return d
 
 
-def _concat_along_keys(d: Mapping[str, xr.DataArray], dim_name: str) -> xr.DataArray:
+def _concat_along_keys(d: dict[str, xr.DataArray], dim_name: str) -> xr.DataArray:
     return xr.concat(d.values(), d.keys()).rename(concat_dim=dim_name)
 
 
-def _parse_params(d: Mapping[str, Any], dask: bool) -> xr.DataArray | _ParametersWraper:
+def _parse_params(
+    d: dict[str, Any] | lmfit.Parameters, dask: bool
+) -> xr.DataArray | _ParametersWraper:
     if isinstance(d, lmfit.Parameters):
         # Input to apply_ufunc cannot be a Mapping, so wrap in a class
         return _ParametersWraper(d)
@@ -65,7 +73,7 @@ def _parse_params(d: Mapping[str, Any], dask: bool) -> xr.DataArray | _Parameter
     return _ParametersWraper(lmfit.create_params(**d))
 
 
-def _parse_multiple_params(d: Mapping[str, Any], as_str: bool) -> xr.DataArray:
+def _parse_multiple_params(d: dict[str, Any], as_str: bool) -> xr.DataArray:
     for k in d.keys():
         if isinstance(d[k], int | float | complex | xr.DataArray):
             d[k] = {"value": d[k]}
@@ -111,7 +119,7 @@ def __init__(self, params: lmfit.Parameters):
 
 
 @xr.register_dataset_accessor("modelfit")
-class ModelFitDatasetAccessor(ERLabAccessor):
+class ModelFitDatasetAccessor(ERLabDatasetAccessor):
     """`xarray.Dataset.modelfit` accessor for fitting lmfit models."""
 
     def __call__(
@@ -121,9 +129,10 @@ def __call__(
         reduce_dims: Dims = None,
         skipna: bool = True,
         params: lmfit.Parameters
-        | Mapping[str, float | dict[str, Any]]
+        | dict[str, float | dict[str, Any]]
         | xr.DataArray
         | xr.Dataset
+        | _ParametersWraper
         | None = None,
         guess: bool = False,
         errors: Literal["raise", "ignore"] = "raise",
@@ -374,10 +383,15 @@ def _wrapper(Y, *args, **kwargs):
 
             x = np.squeeze(x)
 
-            if n_coords == 1:
-                indep_var_kwargs = {model.independent_vars[0]: x}
+            if model.independent_vars is not None:
+                if n_coords == 1:
+                    indep_var_kwargs = {model.independent_vars[0]: x}
+                else:
+                    indep_var_kwargs = dict(
+                        zip(model.independent_vars[:n_coords], x, strict=True)
+                    )
             else:
-                indep_var_kwargs = dict(zip(model.independent_vars[:n_coords], x))
+                raise ValueError("Independent variables not defined in model")
 
             if guess:
                 if isinstance(model, lmfit.model.CompositeModel):
@@ -534,7 +548,7 @@ def _output_wrapper(name, da, out=None) -> dict:
             parallel_obj = joblib.Parallel(**parallel_kw)
 
             if parallel_obj.return_generator:
-                out_dicts = tqdm.auto.tqdm(
+                out_dicts = tqdm.auto.tqdm(  # type: ignore[call-overload]
                     parallel_obj(
                         joblib.delayed(_output_wrapper)(name, da)
                         for name, da in self._obj.data_vars.items()
@@ -548,13 +562,13 @@ def _output_wrapper(name, da, out=None) -> dict:
                         for name, da in self._obj.data_vars.items()
                     )
             result = type(self._obj)(
-                dict(itertools.chain.from_iterable(d.items() for d in out_dicts))
+                dict(itertools.chain.from_iterable(d.items() for d in out_dicts))  # type: ignore[call-overload]
             )
             del out_dicts
 
         else:
             result = type(self._obj)()
-            for name, da in tqdm.auto.tqdm(self._obj.data_vars.items(), **tqdm_kw):
+            for name, da in tqdm.auto.tqdm(self._obj.data_vars.items(), **tqdm_kw):  # type: ignore[call-overload]
                 _output_wrapper(name, da, result)
 
         result = result.assign_coords(
@@ -572,23 +586,22 @@ def _output_wrapper(name, da, out=None) -> dict:
 
 
 @xr.register_dataarray_accessor("modelfit")
-class ModelFitDataArrayAccessor(ERLabAccessor):
+class ModelFitDataArrayAccessor(ERLabDataArrayAccessor):
     """`xarray.DataArray.modelfit` accessor for fitting lmfit models."""
 
     def __call__(self, *args, **kwargs) -> xr.Dataset:
         return self._obj.to_dataset(name=_THIS_ARRAY).modelfit(*args, **kwargs)
 
     __call__.__doc__ = (
-        ModelFitDatasetAccessor.__call__.__doc__.replace(
-            "Dataset.curvefit", "DataArray.curvefit"
-        )
+        str(ModelFitDatasetAccessor.__call__.__doc__)
+        .replace("Dataset.curvefit", "DataArray.curvefit")
         .replace("Dataset.polyfit", "DataArray.polyfit")
         .replace("[var]_", "")
     )
 
 
 @xr.register_dataarray_accessor("parallel_fit")
-class ParallelFitDataArrayAccessor(ERLabAccessor):
+class ParallelFitDataArrayAccessor(ERLabDataArrayAccessor):
     """
     `xarray.DataArray.parallel_fit` accessor for fitting lmfit models in parallel along
     a single dimension.
@@ -647,7 +660,7 @@ def __call__(self, dim: str, model: lmfit.Model, **kwargs) -> xr.Dataset:
         fitres = ds.modelfit(set(self._obj.dims) - {dim}, model, **kwargs)
 
         drop_keys = []
-        concat_vars = {}
+        concat_vars: dict[Hashable, list[xr.DataArray]] = {}
         for k in ds.data_vars.keys():
             for var in self._VAR_KEYS:
                 key = f"{k}_{var}"
diff --git a/src/erlab/accessors/kspace.py b/src/erlab/accessors/kspace.py
index 13b6eebd..ab950519 100644
--- a/src/erlab/accessors/kspace.py
+++ b/src/erlab/accessors/kspace.py
@@ -6,20 +6,20 @@
 import functools
 import time
 import warnings
-from collections.abc import Callable, ItemsView, Iterable, Iterator
-from typing import Literal
+from collections.abc import Hashable, ItemsView, Iterable, Iterator, Mapping
+from typing import Literal, cast
 
 import numpy as np
 import xarray as xr
 
-from erlab.accessors.utils import ERLabAccessor
+from erlab.accessors.utils import ERLabDataArrayAccessor
 from erlab.analysis.interpolate import interpn
 from erlab.analysis.kspace import AxesConfiguration, get_kconv_func, kz_func
 from erlab.constants import rel_kconv, rel_kzconv
-from erlab.interactive.kspace import ktool
+from erlab.interactive.kspace import KspaceTool, ktool
 
 
-def only_angles(method: Callable | None = None):
+def only_angles(method=None):
     """
     A decorator that ensures the data is in angle space before executing the decorated
     method.
@@ -28,7 +28,7 @@ def only_angles(method: Callable | None = None):
     `ValueError` is raised.
     """
 
-    def wrapper(method: Callable):
+    def wrapper(method):
         @functools.wraps(method)
         def _impl(self, *args, **kwargs):
             if "kx" in self._obj.dims or "ky" in self._obj.dims:
@@ -44,7 +44,7 @@ def _impl(self, *args, **kwargs):
     return wrapper
 
 
-def only_momentum(method: Callable | None = None):
+def only_momentum(method=None):
     """
     A decorator that ensures the data is in momentum space before executing the
     decorated method.
@@ -53,7 +53,7 @@ def only_momentum(method: Callable | None = None):
     present), a `ValueError` is raised.
     """
 
-    def wrapper(method: Callable):
+    def wrapper(method):
         @functools.wraps(method)
         def _impl(self, *args, **kwargs):
             if not ("kx" in self._obj.dims or "ky" in self._obj.dims):
@@ -111,10 +111,7 @@ def __init__(self, xarray_obj: xr.DataArray):
             if k + "_offset" not in self._obj.attrs:
                 self[k] = 0.0
 
-    def __len__(self) -> int:
-        return len(self._obj.kspace.valid_offset_keys)
-
-    def __iter__(self) -> Iterator[str, float]:
+    def __iter__(self) -> Iterator[tuple[str, float]]:
         for key in self._obj.kspace.valid_offset_keys:
             yield key, self.__getitem__(key)
 
@@ -132,7 +129,10 @@ def __setitem__(self, key: str, value: float) -> None:
             self._obj.attrs[key + "_offset"] = float(value)
 
     def __eq__(self, other: object) -> bool:
-        return dict(self) == dict(other)
+        if isinstance(other, Mapping):
+            return dict(self) == dict(other)
+        else:
+            return False
 
     def __repr__(self) -> str:
         return dict(self).__repr__()
@@ -152,13 +152,13 @@ def _repr_html_(self) -> str:
 
     def update(
         self,
-        other: dict | Iterable[tuple[str, float]] | None = None,
+        other: dict[str, float] | Iterable[tuple[str, float]] | None = None,
         **kwargs,
     ) -> "OffsetView":
         """Updates the offset view with the provided key-value pairs."""
         if other is not None:
             for k, v in other.items() if isinstance(other, dict) else other:
-                self[k] = v
+                self[str(k)] = v
         for k, v in kwargs.items():
             self[k] = v
         return self
@@ -175,7 +175,7 @@ def reset(self) -> "OffsetView":
 
 
 @xr.register_dataarray_accessor("kspace")
-class MomentumAccessor(ERLabAccessor):
+class MomentumAccessor(ERLabDataArrayAccessor):
     """`xarray.DataArray.kspace` accessor for momentum conversion related utilities.
 
     This class provides convenient access to various momentum-related properties of a
@@ -198,7 +198,7 @@ def configuration(self) -> AxesConfiguration:
                 "Configuration not found in data attributes! "
                 "Data attributes may have been discarded since initial import."
             )
-        return AxesConfiguration(int(self._obj.attrs.get("configuration")))
+        return AxesConfiguration(int(self._obj.attrs.get("configuration", 0)))
 
     @configuration.setter
     def configuration(self, value: AxesConfiguration | int):
@@ -294,7 +294,7 @@ def angle_resolution(self, value: float):
         self._obj.attrs["angle_resolution"] = float(value)
 
     @property
-    def slit_axis(self) -> str:
+    def slit_axis(self) -> Literal["kx", "ky"]:
         """Returns the momentum axis parallel to the slit.
 
         Returns
@@ -309,7 +309,7 @@ def slit_axis(self) -> str:
                 return "ky"
 
     @property
-    def other_axis(self) -> str:
+    def other_axis(self) -> Literal["kx", "ky"]:
         """Returns the momentum axis perpendicular to the slit.
 
         Returns
@@ -325,7 +325,7 @@ def other_axis(self) -> str:
 
     @property
     @only_angles
-    def momentum_axes(self) -> tuple[str, ...]:
+    def momentum_axes(self) -> tuple[Literal["kx", "ky", "kz"], ...]:
         """Returns the momentum axes of the data after conversion.
 
         Returns
@@ -529,9 +529,9 @@ def best_kz_resolution(self) -> float:
         kin = self.kinetic_energy.values
         c1, c2 = 641.0, 0.096
         imfp = (c1 / (kin**2) + c2 * np.sqrt(kin)) * 10
-        return np.amin(1 / imfp)
+        return float(np.amin(1 / imfp))
 
-    def _get_transformed_coords(self) -> dict[str, xr.DataArray]:
+    def _get_transformed_coords(self) -> dict[Literal["kx", "ky", "kz"], xr.DataArray]:
         kx, ky = self._forward_func(self.alpha, self.beta)
         if "hv" in kx.dims:
             kz = kz_func(self.kinetic_energy, self.inner_potential, kx, ky)
@@ -539,7 +539,7 @@ def _get_transformed_coords(self) -> dict[str, xr.DataArray]:
         else:
             return {"kx": kx, "ky": ky}
 
-    def estimate_bounds(self) -> dict[str, tuple[float, float]]:
+    def estimate_bounds(self) -> dict[Literal["kx", "ky", "kz"], tuple[float, float]]:
         """
         Estimates the bounds of the data in momentum space based on the available
         parameters.
@@ -605,7 +605,7 @@ def estimate_resolution(
         else:
             raise ValueError(f"`{axis}` is not a valid momentum axis.")
 
-        if from_numpoints:
+        if from_numpoints and (lims is not None):
             return float((lims[1] - lims[0]) / len(self._obj[dim]))
         elif axis == "kz":
             return self.best_kz_resolution
@@ -637,7 +637,7 @@ def _inverse_broadcast(self, kx, ky, kz=None) -> dict[str, xr.DataArray]:
         if self.has_eV:
             out_dict["eV"] = self.binding_energy
 
-        if kz is not None:
+        if kzval is not None:
             out_dict["hv"] = (
                 rel_kzconv * (kxval**2 + kyval**2 + kzval**2)
                 - self.inner_potential
@@ -645,7 +645,16 @@ def _inverse_broadcast(self, kx, ky, kz=None) -> dict[str, xr.DataArray]:
                 - self.binding_energy
             )
 
-        return dict(zip(out_dict.keys(), xr.broadcast(*out_dict.values())))
+        return cast(
+            dict[str, xr.DataArray],
+            dict(
+                zip(
+                    cast(list[str], out_dict.keys()),
+                    xr.broadcast(*out_dict.values()),
+                    strict=True,
+                )
+            ),
+        )
 
     @only_angles
     def convert_coords(self) -> xr.DataArray:
@@ -662,7 +671,7 @@ def convert_coords(self) -> xr.DataArray:
         return self._obj.assign_coords(self._get_transformed_coords())
 
     @only_angles
-    def _get_coord_for_conversion(self, name: str) -> xr.DataArray:
+    def _get_coord_for_conversion(self, name: Hashable) -> xr.DataArray:
         """
         Get the coordinte array for given dimension name. This just ensures that the
         energy coordinates are given as binding energy.
@@ -779,6 +788,12 @@ def convert(
                     print(f"Data spans {lims[1] - lims[0]:.3f} Å⁻¹ of {k}")
                 momentum_coords[k] = np.array([(lims[0] + lims[1]) / 2])
 
+        for k, v in coords.items():
+            if k in self.momentum_axes:
+                momentum_coords[k] = v
+            else:
+                raise ValueError(f"Dimension `{k}` is not a momentum axis")
+
         if not silent:
             print("Calculating destination coordinates")
 
@@ -803,13 +818,13 @@ def convert(
         dim_mapping: dict[str, str] = {}
         for d in coords_for_transform.dims:
             if d == self.slit_axis:
-                dim_mapping["alpha"] = d
+                dim_mapping["alpha"] = str(d)
             elif d == self.other_axis:
-                dim_mapping["beta"] = d
+                dim_mapping["beta"] = str(d)
             elif d == "kz":
-                dim_mapping["hv"] = d
+                dim_mapping["hv"] = str(d)
             else:
-                dim_mapping[d] = d
+                dim_mapping[str(d)] = str(d)
 
         # Delete keys not in the input data, e.g. "beta" for cuts
         for k in list(dim_mapping.keys()):
@@ -828,8 +843,10 @@ def _wrap_interpn(arr, *args):
             return interpn(points, arr, xi, bounds_error=False).squeeze()
 
         input_core_dims = [input_dims]
-        input_core_dims.extend([[d] for d in input_dims])
-        input_core_dims.extend([target_dict[d].dims for d in input_dims])
+        input_core_dims.extend([(d,) for d in input_dims])
+        input_core_dims.extend(
+            [cast(tuple[str, ...], target_dict[d].dims) for d in input_dims]
+        )
 
         out = xr.apply_ufunc(
             _wrap_interpn,
@@ -853,7 +870,7 @@ def _wrap_interpn(arr, *args):
 
         return out
 
-    def interactive(self, **kwargs) -> ktool:
+    def interactive(self, **kwargs) -> KspaceTool:
         """Open the interactive momentum space conversion tool."""
         if self._obj.ndim < 3:
             raise ValueError("Interactive tool requires three-dimensional data.")
diff --git a/src/erlab/accessors/utils.py b/src/erlab/accessors/utils.py
index af4e7169..607f211b 100644
--- a/src/erlab/accessors/utils.py
+++ b/src/erlab/accessors/utils.py
@@ -21,10 +21,17 @@
 _THIS_ARRAY: str = "<this-array>"
 
 
-class ERLabAccessor:
+class ERLabDataArrayAccessor:
     """Base class for accessors."""
 
-    def __init__(self, xarray_obj: xr.DataArray | xr.Dataset):
+    def __init__(self, xarray_obj: xr.DataArray):
+        self._obj = xarray_obj
+
+
+class ERLabDatasetAccessor:
+    """Base class for accessors."""
+
+    def __init__(self, xarray_obj: xr.Dataset):
         self._obj = xarray_obj
 
 
@@ -52,7 +59,7 @@ def either_dict_or_kwargs(
 
 
 @xr.register_dataarray_accessor("qplot")
-class PlotAccessor(ERLabAccessor):
+class PlotAccessor(ERLabDataArrayAccessor):
     """`xarray.DataArray.qplot` accessor for plotting data."""
 
     def __call__(self, *args, **kwargs):
@@ -83,10 +90,10 @@ def __call__(self, *args, **kwargs):
 
 
 @xr.register_dataarray_accessor("qshow")
-class ImageToolAccessor(ERLabAccessor):
+class ImageToolAccessor(ERLabDataArrayAccessor):
     """`xarray.DataArray.qshow` accessor for interactive visualization."""
 
-    def __call__(self, *args, **kwargs) -> ImageTool:
+    def __call__(self, *args, **kwargs) -> ImageTool | list[ImageTool] | None:
         if len(self._obj.dims) >= 2:
             return itool(self._obj, *args, **kwargs)
         else:
@@ -94,7 +101,7 @@ def __call__(self, *args, **kwargs) -> ImageTool:
 
 
 @xr.register_dataarray_accessor("qsel")
-class SelectionAccessor(ERLabAccessor):
+class SelectionAccessor(ERLabDataArrayAccessor):
     """
     `xarray.DataArray.qsel` accessor for conveniently selecting and averaging
     data.
@@ -102,7 +109,7 @@ class SelectionAccessor(ERLabAccessor):
 
     def __call__(
         self,
-        indexers: dict[str, float | slice] | None = None,
+        indexers: Mapping[Hashable, float | slice] | None = None,
         *,
         verbose: bool = False,
         **indexers_kwargs,
@@ -151,28 +158,39 @@ def __call__(
         indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "qsel")
 
         # Bin widths for each dimension, zero if width not specified
-        bin_widths: dict[str, float] = {}
+        bin_widths: dict[Hashable, float] = {}
 
         for dim in indexers:
-            if not dim.endswith("_width"):
-                bin_widths[dim] = indexers.get(f"{dim}_width", 0.0)
+            if not str(dim).endswith("_width"):
+                width = indexers.get(f"{dim}_width", 0.0)
+                if isinstance(width, slice):
+                    raise ValueError(
+                        f"Slice not allowed for width of dimension `{dim}`"
+                    )
+                else:
+                    bin_widths[dim] = float(width)
                 if dim not in self._obj.dims:
                     raise ValueError(f"Dimension `{dim}` not found in data.")
 
-        scalars: dict[str, float] = {}
-        slices: dict[str, slice] = {}
-        avg_dims: list[str] = []
+        scalars: dict[Hashable, float] = {}
+        slices: dict[Hashable, slice] = {}
+        avg_dims: list[Hashable] = []
 
         for dim, width in bin_widths.items():
+            value = indexers[dim]
+
             if width == 0.0:
-                if isinstance(indexers[dim], slice):
-                    slices[dim] = indexers[dim]
+                if isinstance(value, slice):
+                    slices[dim] = value
                 else:
-                    scalars[dim] = float(indexers[dim])
+                    scalars[dim] = float(value)
             else:
-                slices[dim] = slice(
-                    indexers[dim] - width / 2, indexers[dim] + width / 2
-                )
+                if isinstance(value, slice):
+                    raise ValueError(
+                        f"Slice not allowed for value of dimension `{dim}` "
+                        "with width specified"
+                    )
+                slices[dim] = slice(value - width / 2, value + width / 2)
                 avg_dims.append(dim)
 
         if len(scalars) >= 1:
@@ -182,12 +200,14 @@ def __call__(
                         f"Selected value {v} for `{k}` is outside coordinate bounds",
                         stacklevel=2,
                     )
-            out = self._obj.sel(**scalars, method="nearest")
+            out = self._obj.sel(
+                {str(k): v for k, v in scalars.items()}, method="nearest"
+            )
         else:
             out = self._obj
 
         if len(slices) >= 1:
-            out = out.sel(**slices)
+            out = out.sel(slices)
 
             lost_coords = {k: out[k].mean() for k in avg_dims}
             out = out.mean(dim=avg_dims, keep_attrs=True)
diff --git a/src/erlab/analysis/correlation.py b/src/erlab/analysis/correlation.py
index c3c65ae5..30f46be9 100644
--- a/src/erlab/analysis/correlation.py
+++ b/src/erlab/analysis/correlation.py
@@ -91,7 +91,7 @@ def acf2(arr, mode: str = "full", method: str = "fft"):
         acf,
         {
             d: autocorrelation_lags(n, mode) * s
-            for s, n, d in zip(steps, arr.shape, out.dims)
+            for s, n, d in zip(steps, arr.shape, out.dims, strict=True)
         },
         attrs=out.attrs,
     )
@@ -114,14 +114,14 @@ def acf2stack(arr, stack_dims=("eV",), mode: str = "full", method: str = "fft"):
 
         out_list = joblib.Parallel(n_jobs=-1, pre_dispatch="3 * n_jobs")(
             joblib.delayed(nanacf)(
-                np.squeeze(arr.isel(dict(zip(stack_dims, vals))).values),
+                np.squeeze(arr.isel(dict(zip(stack_dims, vals, strict=True))).values),
                 mode,
                 method,
             )
             for vals in itertools.product(*stack_iter)
         )
         acf_dims = tuple(filter(lambda d: d not in stack_dims, arr.dims))
-        acf_sizes = dict(zip(acf_dims, out_list[0].shape))
+        acf_sizes = dict(zip(acf_dims, out_list[0].shape, strict=True))
         acf_steps = tuple(arr[d].values[1] - arr[d].values[0] for d in acf_dims)
 
         out_sizes = stack_sizes | acf_sizes
@@ -137,12 +137,14 @@ def acf2stack(arr, stack_dims=("eV",), mode: str = "full", method: str = "fft"):
             out = out.assign_coords({d: arr[d] for d in stack_dims})
 
         for i, vals in enumerate(itertools.product(*stack_iter)):
-            out.loc[{s: arr[s][v] for s, v in zip(stack_dims, vals)}] = out_list[i]
+            out.loc[{s: arr[s][v] for s, v in zip(stack_dims, vals, strict=True)}] = (
+                out_list[i]
+            )
 
         out = out.assign_coords(
             {
                 d: autocorrelation_lags(len(arr[d]), mode) * s
-                for s, d in zip(acf_steps, acf_dims)
+                for s, d in zip(acf_steps, acf_dims, strict=True)
             }
         )
         if all(i in out.dims for i in ["kx", "ky"]):
diff --git a/src/erlab/analysis/fit/functions/dynamic.py b/src/erlab/analysis/fit/functions/dynamic.py
index c374f2eb..909b0fe8 100644
--- a/src/erlab/analysis/fit/functions/dynamic.py
+++ b/src/erlab/analysis/fit/functions/dynamic.py
@@ -13,7 +13,8 @@
 ]
 import functools
 import inspect
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
+from typing import Any, TypedDict, no_type_check, ClassVar
 
 import numpy as np
 import numpy.typing as npt
@@ -30,7 +31,12 @@
 from erlab.constants import kb_eV
 
 
-def get_args_kwargs(func) -> tuple[list[str], dict[str, object]]:
+class PeakArgs(TypedDict):
+    args: list[str]
+    kwargs: dict[str, Any]
+
+
+def get_args_kwargs(func: Callable) -> tuple[list[str], dict[str, Any]]:
     """Get all argument names and default values from a function signature.
 
     Parameters
@@ -72,6 +78,11 @@ def get_args_kwargs(func) -> tuple[list[str], dict[str, object]]:
     return args, args_default
 
 
+def get_args_kwargs_dict(func: Callable) -> PeakArgs:
+    args, kwargs = get_args_kwargs(func)
+    return {"args": args, "kwargs": kwargs}
+
+
 class DynamicFunction:
     """Base class for dynamic functions.
 
@@ -81,7 +92,7 @@ class DynamicFunction:
 
     @property
     def __name__(self) -> str:
-        return self.__class__.__name__
+        return str(self.__class__.__name__)
 
     @property
     def argnames(self) -> list[str]:
@@ -91,7 +102,8 @@ def argnames(self) -> list[str]:
     def kwargs(self) -> dict[str, int | float]:
         return {}
 
-    def __call__(self, x: npt.NDArray[np.float64], **params) -> npt.NDArray[np.float64]:
+    @no_type_check
+    def __call__(self, **kwargs):
         raise NotImplementedError("Must be overloaded in child classes")
 
 
@@ -149,7 +161,7 @@ class MultiPeakFunction(DynamicFunction):
 
     """
 
-    PEAK_SHAPES: dict[Callable, list[str]] = {
+    PEAK_SHAPES: ClassVar[dict[Callable, list[str]]] = {
         lorentzian_wh: ["lorentzian", "lor", "l"],
         gaussian_wh: ["gaussian", "gauss", "g"],
     }
@@ -180,20 +192,20 @@ def __init__(
 
         self._peak_shapes = peak_shapes
 
-        self._peak_funcs = [None] * self.npeaks
-        for i, name in enumerate(self._peak_shapes):
+        self._peak_funcs: list[Callable] = []
+        for name in self._peak_shapes:
             for fcn, aliases in self.PEAK_SHAPES.items():
                 if name in aliases:
-                    self._peak_funcs[i] = fcn
+                    self._peak_funcs.append(fcn)
 
-        if None in self._peak_funcs:
+        if len(self._peak_funcs) != self.npeaks:
             raise ValueError("Invalid peak name")
 
     @functools.cached_property
-    def peak_all_args(self) -> dict[Callable, dict[str, list | dict]]:
-        res = {}
+    def peak_all_args(self) -> dict[Callable, PeakArgs]:
+        res: dict[Callable, PeakArgs] = {}
         for func in self.PEAK_SHAPES:
-            res[func] = dict(zip(("args", "kwargs"), get_args_kwargs(func)))
+            res[func] = get_args_kwargs_dict(func)
         return res
 
     @functools.cached_property
@@ -201,12 +213,12 @@ def peak_argnames(self) -> dict[Callable, list[str]]:
         res = {}
         for func in self.PEAK_SHAPES:
             res[func] = self.peak_all_args[func]["args"][1:] + list(
-                self.peak_all_args[func]["kwargs"].keys()
+                dict(self.peak_all_args[func]["kwargs"]).keys()
             )
         return res
 
     @property
-    def peak_funcs(self) -> list[Callable]:
+    def peak_funcs(self) -> Sequence[Callable]:
         return self._peak_funcs
 
     @property
@@ -232,7 +244,7 @@ def kwargs(self):
             kws += [("resolution", 0.02)]
 
         for i, func in enumerate(self.peak_funcs):
-            for arg, val in self.peak_all_args[func]["kwargs"].items():
+            for arg, val in dict(self.peak_all_args[func]["kwargs"]).items():
                 kws.append((f"p{i}_{arg}", val))
         return kws
 
@@ -252,7 +264,7 @@ def amplitude_expr(self, index: int, prefix: str) -> str | None:
         else:
             return None
 
-    def eval_peak(self, index: int, x: npt.NDArray[np.float64], **params: dict):
+    def eval_peak(self, index: int, x, **params):
         return self.peak_funcs[index](
             x,
             **{
@@ -262,12 +274,10 @@ def eval_peak(self, index: int, x: npt.NDArray[np.float64], **params: dict):
             },
         )
 
-    def eval_bkg(self, x: npt.NDArray[np.float64], **params: dict):
+    def eval_bkg(self, x, **params):
         return params["lin_bkg"] * x + params["const_bkg"]
 
-    def pre_call(
-        self, x: npt.NDArray[np.float64], **params: dict
-    ) -> npt.NDArray[np.float64]:
+    def pre_call(self, x, **params):
         x = np.asarray(x).copy()
         y = np.zeros_like(x)
 
@@ -284,9 +294,7 @@ def pre_call(
 
         return y
 
-    def __call__(
-        self, x: npt.NDArray[np.float64], **params: dict
-    ) -> npt.NDArray[np.float64]:
+    def __call__(self, x, **params):
         if isinstance(x, xr.DataArray):
             return x * 0.0 + self.__call__(x.values, **params)
 
@@ -319,7 +327,7 @@ def kwargs(self):
             ("resolution", 0.02),
         ]
 
-    def pre_call(self, eV, alpha, **params: dict):
+    def pre_call(self, eV, alpha, **params):
         center = self.poly(
             np.asarray(alpha),
             *[params.pop(f"c{i}") for i in range(self.poly.degree + 1)],
@@ -328,15 +336,20 @@ def pre_call(self, eV, alpha, **params: dict):
             1 + np.exp((1.0 * eV - center) / max(TINY, params["temp"] * kb_eV))
         ) + params["offset"]
 
-    def __call__(self, eV, alpha, **params: dict):
+    def __call__(
+        self,
+        eV: npt.NDArray[np.float64] | xr.DataArray,
+        alpha: npt.NDArray[np.float64] | xr.DataArray,
+        **params,
+    ):
         if isinstance(eV, xr.DataArray) and isinstance(alpha, xr.DataArray):
             out = eV * alpha * 0.0
             return out + self.__call__(eV.values, alpha.values, **params).reshape(
                 out.shape
             )
-        if isinstance("eV", xr.DataArray):
+        if isinstance(eV, xr.DataArray):
             eV = eV.values
-        if isinstance("alpha", xr.DataArray):
+        if isinstance(alpha, xr.DataArray):
             alpha = alpha.values
         if "resolution" not in params:
             raise TypeError("Missing parameter `resolution` required for convolution")
diff --git a/src/erlab/analysis/fit/functions/general.py b/src/erlab/analysis/fit/functions/general.py
index ba13f087..7a6b2335 100644
--- a/src/erlab/analysis/fit/functions/general.py
+++ b/src/erlab/analysis/fit/functions/general.py
@@ -132,13 +132,17 @@ def do_convolve(
 
 def do_convolve_2d(
     x: npt.NDArray[np.float64],
-    y: npt.NDArray[np.float64],
+    y: npt.NDArray[np.float64] | float,
     func: Callable,
     resolution: float,
     pad: int = 5,
     **kwargs,
 ) -> npt.NDArray[np.float64]:
     idx_x = None
+
+    if not np.iterable(y):
+        y = np.asarray([y])
+
     try:
         # check if x is a meshgrid
         shape_x, idx_x, x = _infer_meshgrid_shape(np.ascontiguousarray(x))
@@ -153,9 +157,6 @@ def do_convolve_2d(
         np.asarray(np.squeeze(x), dtype=np.float64), resolution, pad=pad
     )
 
-    if not np.iterable(y):
-        y = [y]
-
     convolved = np.vstack(
         [
             np.convolve(func(xn, yi, **kwargs), g, mode="valid")
diff --git a/src/erlab/analysis/fit/minuit.py b/src/erlab/analysis/fit/minuit.py
index c3d986e5..3cdae183 100644
--- a/src/erlab/analysis/fit/minuit.py
+++ b/src/erlab/analysis/fit/minuit.py
@@ -1,13 +1,18 @@
 from __future__ import annotations
 
+import importlib
 from collections.abc import Iterable, Sequence
 from typing import TYPE_CHECKING
 
+if not importlib.util.find_spec("iminuit"):
+    raise ImportError("`erlab.analysis.fit.minuit` requires `iminuit` to be installed.")
+
 import iminuit.cost
 import iminuit.util
 import matplotlib.pyplot as plt
 import numpy as np
 import numpy.typing as npt
+import xarray
 from iminuit.util import _detect_log_spacing, _smart_sampling
 
 import erlab.plotting.general
@@ -27,7 +32,7 @@ def visualize(
         if self._ndim > 1:
             raise ValueError("visualize is not implemented for multi-dimensional data")
 
-        plt.grid(visible="both")
+        plt.grid(visible=True, axis="both")
         x, y, ye = self._masked.T
         plt.errorbar(
             x, y, ye, fmt="o", lw=0.75, ms=3, mfc="w", zorder=2, c="0.4", capsize=0
@@ -43,7 +48,10 @@ def visualize(
             ym = self.model(xm, *args)
         else:
             xm, ym = _smart_sampling(
-                lambda x: self.model(x, *args), x[0], x[-1], start=len(x)
+                lambda x: self.model(x, *args),
+                x[0],
+                x[-1],
+                start=len(x),
             )
         plt.plot(xm, ym, "r-", lw=1, zorder=3)
         return (x, y, ye), (xm, ym)
@@ -100,17 +108,19 @@ class Minuit(iminuit.Minuit):
     def from_lmfit(
         cls,
         model: lmfit.Model,
-        data: npt.ArrayLike,
-        ivars: npt.ArrayLike | Sequence[npt.ArrayLike],
-        yerr: float | npt.ArrayLike | None = None,
+        data: npt.NDArray | xarray.DataArray,
+        ivars: npt.NDArray
+        | xarray.DataArray
+        | Sequence[npt.NDArray | xarray.DataArray],
+        yerr: float | npt.NDArray | None = None,
         return_cost: bool = False,
         **kwargs,
     ) -> Minuit | tuple[LeastSq, Minuit]:
         if len(model.independent_vars) == 1:
-            if len(ivars) != 1:
+            if isinstance(ivars, np.ndarray | xarray.DataArray):
                 ivars = [ivars]
 
-        x = [np.asarray(a) for a in ivars]
+        x: npt.NDArray | list[npt.NDArray] = [np.asarray(a) for a in ivars]
 
         if len(x) != len(model.independent_vars):
             raise ValueError("Number of independent variables does not match model.")
@@ -173,12 +183,16 @@ def from_lmfit(
         if len(model.independent_vars) == 1:
 
             def _temp_func(x, *fargs):
-                return model.func(x, **dict(zip(model._param_root_names, fargs)))
+                return model.func(
+                    x, **dict(zip(model._param_root_names, fargs, strict=True))
+                )
 
         else:
 
             def _temp_func(x, *fargs):
-                return model.func(*x, **dict(zip(model._param_root_names, fargs)))
+                return model.func(
+                    *x, **dict(zip(model._param_root_names, fargs, strict=True))
+                )
 
         c = LeastSq(x, data, yerr, _temp_func)
         m = cls(c, name=param_names, **values)
diff --git a/src/erlab/analysis/fit/models.py b/src/erlab/analysis/fit/models.py
index e251bb55..85e6d654 100644
--- a/src/erlab/analysis/fit/models.py
+++ b/src/erlab/analysis/fit/models.py
@@ -115,8 +115,11 @@ class FermiEdgeModel(lmfit.Model):
     """
     Fermi-dirac function with linear background above and below the fermi level,
     convolved with a gaussian kernel.
+
     """
 
+    __doc__ = __doc__ + lmfit.models.COMMON_INIT_DOC
+
     @staticmethod
     def LinearBroadFermiDirac(
         x,
@@ -165,7 +168,6 @@ def guess(self, data, x, **kwargs):
 
         return lmfit.models.update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lmfit.models.COMMON_INIT_DOC
     guess.__doc__ = COMMON_GUESS_DOC
 
 
@@ -199,7 +201,7 @@ def guess(self, data, x, **kwargs):
 
         return lmfit.models.update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lmfit.models.COMMON_INIT_DOC
+    __doc__ = lmfit.models.COMMON_INIT_DOC
     guess.__doc__ = COMMON_GUESS_DOC
 
 
@@ -220,7 +222,7 @@ def guess(self, data, x=None, **kwargs):
                 pars[f"{self.prefix}c{i}"].set(value=coef)
         return lmfit.models.update_param_vals(pars, self.prefix, **kwargs)
 
-    __init__.doc = lmfit.models.COMMON_INIT_DOC
+    __doc__ = lmfit.models.COMMON_INIT_DOC
     guess.__doc__ = COMMON_GUESS_DOC
 
 
@@ -326,7 +328,7 @@ class FermiEdge2dModel(lmfit.Model):
     :math:`c` convolved with a gaussian, where :math:`\omega` is the binding energy and
     :math:`\alpha` is the detector angle.
 
-    """
+    """ + lmfit.models.COMMON_INIT_DOC.replace("['x']", "['eV', 'alpha']")
 
     def __init__(
         self,
@@ -381,7 +383,6 @@ def fit(self, data, *args, **kwargs):
         # Ensure flat fit
         return super().fit(data.ravel(), *args, **kwargs)
 
-    __init__.__doc__ = lmfit.models.COMMON_INIT_DOC.replace("['x']", "['eV', 'alpha']")
     guess.__doc__ = COMMON_GUESS_DOC.replace("x : ", "eV, alpha : ")
 
 
@@ -392,7 +393,7 @@ def __init__(self, **kwargs):
         self.set_param_hint("tc", min=0.0)
 
     __doc__ = bcs_gap.__doc__
-    __init__.doc = lmfit.models.COMMON_INIT_DOC
+    __init__.__doc__ = lmfit.models.COMMON_INIT_DOC
 
 
 class DynesModel(lmfit.Model):
@@ -402,4 +403,4 @@ def __init__(self, **kwargs):
         self.set_param_hint("delta", min=0.0)
 
     __doc__ = dynes.__doc__
-    __init__.doc = lmfit.models.COMMON_INIT_DOC
+    __init__.__doc__ = lmfit.models.COMMON_INIT_DOC
diff --git a/src/erlab/analysis/fit/spline.py b/src/erlab/analysis/fit/spline.py
index f2e759c0..47e449e7 100644
--- a/src/erlab/analysis/fit/spline.py
+++ b/src/erlab/analysis/fit/spline.py
@@ -4,8 +4,7 @@
     import csaps
 except ImportError as e:
     raise ImportError(
-        "The `csaps` package is required for this module. "
-        "Please install it using `pip install csaps`."
+        "`erlab.analysis.fit.spline` requires `csaps` to be installed."
     ) from e
 
 
diff --git a/src/erlab/analysis/gold.py b/src/erlab/analysis/gold.py
index 2f878161..fb2a5bb3 100644
--- a/src/erlab/analysis/gold.py
+++ b/src/erlab/analysis/gold.py
@@ -10,9 +10,10 @@
     "spline_from_edge",
 ]
 
-from collections.abc import Callable, Sequence
+from collections.abc import Callable
 
 import joblib
+import lmfit
 import lmfit.model
 import matplotlib
 import matplotlib.figure
@@ -142,7 +143,7 @@ def correct_with_edge(
 
 
 def edge(
-    gold: xr.DataArray | xr.Dataset,
+    gold: xr.DataArray,
     angle_range: tuple[float, float],
     eV_range: tuple[float, float],
     bin_size: tuple[int, int] = (1, 1),
@@ -158,7 +159,7 @@ def edge(
     parallel_obj: joblib.Parallel | None = None,
     return_full: bool = False,
     **kwargs,
-) -> tuple[npt.NDArray, npt.NDArray] | xr.Dataset:
+) -> tuple[xr.DataArray, xr.DataArray] | list[lmfit.model.ModelResult]:
     """
     Fit a Fermi edge to the given gold data.
 
@@ -211,9 +212,10 @@ def edge(
         `True`.
 
     """
+
     if fast:
         params = lmfit.create_params()
-        model_cls = StepEdgeModel
+        model_cls: lmfit.Model = StepEdgeModel
     else:
         if temp is None:
             temp = gold.attrs["temp_sample"]
@@ -230,12 +232,12 @@ def edge(
 
     if any(b != 1 for b in bin_size):
         gold_binned = gold.coarsen(alpha=bin_size[0], eV=bin_size[1], boundary="trim")
-        gold = gold_binned.mean()
+        gold = gold_binned.mean()  # type: ignore[attr-defined]
 
     gold_sel = gold.sel(alpha=slice(*angle_range), eV=slice(*eV_range))
 
     # Assuming Poisson noise, the weights are the square root of the counts.
-    weights = 1 / np.sqrt(gold_sel.sum("eV").values)
+    weights = 1 / np.sqrt(np.asarray(gold_sel.sum("eV").values))
 
     n_fits = len(gold_sel.alpha)
 
@@ -273,7 +275,7 @@ def _fit(data, w):
     tqdm_kw = {"desc": "Fitting", "total": n_fits, "disable": not progress}
 
     if parallel_obj.return_generator:
-        fitresults = tqdm.auto.tqdm(
+        fitresults = tqdm.auto.tqdm(  # type: ignore[call-overload]
             parallel_obj(
                 joblib.delayed(_fit)(gold_sel.isel(alpha=i), weights[i])
                 for i in range(n_fits)
@@ -296,7 +298,7 @@ def _fit(data, w):
     if return_full:
         return list(fitresults)
 
-    xval = []
+    xval: list[npt.NDArray] = []
     res_vals = []
 
     for i, r in enumerate(fitresults):
@@ -310,13 +312,10 @@ def _fit(data, w):
                 xval.append(gold_sel.alpha.values[i])
                 res_vals.append([center_ufloat.nominal_value, center_ufloat.std_dev])
 
-    xval = np.asarray(xval)
+    coords = {"alpha": np.asarray(xval)}
     yval, yerr = np.asarray(res_vals).T
 
-    return (
-        xr.DataArray(yval, coords={"alpha": xval}),
-        xr.DataArray(yerr, coords={"alpha": xval}),
-    )
+    return xr.DataArray(yval, coords=coords), xr.DataArray(yerr, coords=coords)
 
 
 def poly_from_edge(
@@ -336,7 +335,7 @@ def poly_from_edge(
 
 
 def spline_from_edge(
-    center, weights: Sequence[float] | None = None, lam: float | None = None
+    center, weights: npt.ArrayLike | None = None, lam: float | None = None
 ) -> scipy.interpolate.BSpline:
     spl = scipy.interpolate.make_smoothing_spline(
         center.alpha.values,
@@ -448,7 +447,7 @@ def _plot_gold_fit(fig, gold, angle_range, eV_range, center_arr, center_stderr,
 
 
 def poly(
-    gold: xr.DataArray | xr.Dataset,
+    gold: xr.DataArray,
     angle_range: tuple[float, float],
     eV_range: tuple[float, float],
     bin_size: tuple[int, int] = (1, 1),
@@ -501,7 +500,7 @@ def poly(
 
 
 def spline(
-    gold: xr.DataArray | xr.Dataset,
+    gold: xr.DataArray,
     angle_range: tuple[float, float],
     eV_range: tuple[float, float],
     bin_size: tuple[int, int] = (1, 1),
@@ -543,7 +542,7 @@ def spline(
 
 
 def resolution(
-    gold: xr.DataArray | xr.Dataset,
+    gold: xr.DataArray,
     angle_range: tuple[float, float],
     eV_range_edge: tuple[float, float],
     eV_range_fit: tuple[float, float] | None = None,
diff --git a/src/erlab/analysis/image.py b/src/erlab/analysis/image.py
index c3d09bf2..ae8dbe79 100644
--- a/src/erlab/analysis/image.py
+++ b/src/erlab/analysis/image.py
@@ -7,7 +7,7 @@
 unlike the scipy default of 'reflect'.
 """
 
-from collections.abc import Sequence
+from collections.abc import Collection, Mapping, Sequence, Sized, Hashable
 
 import numpy as np
 import numpy.typing as npt
@@ -19,13 +19,13 @@
 
 def gaussian_filter(
     darr: xr.DataArray,
-    sigma: float | dict[str, float] | Sequence[float],
-    order: int | Sequence[int] | dict[str, int] = 0,
-    mode: str | Sequence[str] | dict[str, str] = "nearest",
+    sigma: float | Collection[float] | Mapping[Hashable, float],
+    order: int | Sequence[int] | Mapping[Hashable, int] = 0,
+    mode: str | Sequence[str] | Mapping[Hashable, str] = "nearest",
     cval: float = 0.0,
     truncate: float = 4.0,
     *,
-    radius: None | float | Sequence[float] | dict[str, float] = None,
+    radius: None | float | Collection[float] | Mapping[Hashable, float] = None,
 ) -> xr.DataArray:
     """Coordinate-aware wrapper around `scipy.ndimage.gaussian_filter`.
 
@@ -99,48 +99,58 @@ def gaussian_filter(
     Dimensions without coordinates: x, y
 
     """
-    if np.isscalar(sigma):
-        sigma = dict.fromkeys(darr.dims, sigma)
-    elif not isinstance(sigma, dict):
-        sigma = dict(zip(darr.dims, sigma))
+    if isinstance(sigma, Mapping):
+        sigma_dict = dict(sigma)
+    elif np.isscalar(sigma):
+        sigma_dict = dict.fromkeys(darr.dims, sigma)
+    elif isinstance(sigma, Collection):
+        sigma_dict = dict(zip(darr.dims, sigma, strict=True))
+    else:
+        raise TypeError("`sigma` must be a scalar, sequence, or mapping")
 
     # Get the axis indices to apply the filter
-    axes = tuple(darr.get_axis_num(d) for d in sigma.keys())
+    axes = tuple(darr.get_axis_num(d) for d in sigma_dict.keys())
 
     # Convert arguments to tuples acceptable by scipy
-    if isinstance(order, dict):
-        order = tuple(order.get(d, 0) for d in sigma.keys())
-    if isinstance(mode, dict):
-        mode = tuple(mode[d] for d in sigma.keys())
-    if radius is not None:
-        if len(radius) != len(sigma):
-            raise ValueError("`radius` does not match dimensions of `sigma`")
+    if isinstance(order, Mapping):
+        order = tuple(order.get(str(d), 0) for d in sigma_dict.keys())
+    if isinstance(mode, Mapping):
+        mode = tuple(mode[str(d)] for d in sigma_dict.keys())
 
-        if np.isscalar(radius):
-            radius = dict.fromkeys(sigma.keys(), radius)
-        elif not isinstance(radius, dict):
-            radius = dict(zip(sigma.keys(), radius))
+    if radius is not None:
+        if isinstance(radius, Mapping):
+            radius_dict = dict(radius)
+        elif isinstance(radius, Sized):
+            if len(radius) != len(sigma_dict):
+                raise ValueError("`radius` does not match dimensions of `sigma`")
+            radius_dict = dict(zip(sigma_dict.keys(), radius, strict=True))
+        elif np.isscalar(radius):
+            radius_dict = dict.fromkeys(sigma_dict.keys(), radius)
+        else:
+            raise TypeError("`radius` must be a scalar, sequence, or mapping")
 
         # Calculate radius in pixels
-        radius: tuple[int, ...] = tuple(
+        radius_pix: tuple[int, ...] | None = tuple(
             round(r / (darr[d].values[1] - darr[d].values[0]))
-            for d, r in radius.items()
+            for d, r in radius_dict.items()
         )
+    else:
+        radius_pix = None
 
     # Calculate sigma in pixels
-    sigma: tuple[float, ...] = tuple(
-        val / (darr[d].values[1] - darr[d].values[0]) for d, val in sigma.items()
+    sigma_pix: tuple[float, ...] = tuple(
+        val / (darr[d].values[1] - darr[d].values[0]) for d, val in sigma_dict.items()
     )
 
     return darr.copy(
         data=scipy.ndimage.gaussian_filter(
             darr.values,
-            sigma=sigma,
+            sigma=sigma_pix,
             order=order,
             mode=mode,
             cval=cval,
             truncate=truncate,
-            radius=radius,
+            radius=radius_pix,
             axes=axes,
         )
     )
@@ -148,8 +158,8 @@ def gaussian_filter(
 
 def gaussian_laplace(
     darr: xr.DataArray,
-    sigma: float | dict[str, float] | Sequence[float],
-    mode: str | Sequence[str] | dict[str, str] = "nearest",
+    sigma: float | Collection[float] | Mapping[str, float],
+    mode: str | Sequence[str] | Mapping[str, str] = "nearest",
     cval: float = 0.0,
     **kwargs,
 ) -> xr.DataArray:
@@ -195,28 +205,35 @@ def gaussian_laplace(
     :func:`scipy.ndimage.gaussian_laplace` : The underlying function used to apply the
         filter.
     """
-    if np.isscalar(sigma):
-        sigma = dict.fromkeys(darr.dims, sigma)
-    elif not isinstance(sigma, dict):
-        sigma = dict(zip(darr.dims, sigma))
 
-    if len(sigma) != darr.ndim:
+    if isinstance(sigma, Mapping):
+        sigma_dict = dict(sigma)
+    elif np.isscalar(sigma):
+        sigma_dict = dict.fromkeys(darr.dims, sigma)
+    elif isinstance(sigma, Collection):
+        sigma_dict = dict(zip(darr.dims, sigma, strict=True))
+    else:
+        raise TypeError("`sigma` must be a scalar, sequence, or mapping")
+
+    if len(sigma_dict) != darr.ndim:
+        required_dims = set(darr.dims) - set(sigma_dict.keys())
         raise ValueError(
-            "`sigma` must be provided for every dimension of the DataArray"
+            "`sigma` missing for the following dimension"
+            f"{'' if len(required_dims) == 1 else 's'}: {required_dims}"
         )
 
     # Convert mode to tuple acceptable by scipy
     if isinstance(mode, dict):
-        mode = tuple(mode[d] for d in sigma.keys())
+        mode = tuple(mode[d] for d in sigma_dict.keys())
 
     # Calculate sigma in pixels
-    sigma: tuple[float, ...] = tuple(
-        val / (darr[d].values[1] - darr[d].values[0]) for d, val in sigma.items()
+    sigma_pix: tuple[float, ...] = tuple(
+        val / (darr[d].values[1] - darr[d].values[0]) for d, val in sigma_dict.items()
     )
 
     return darr.copy(
         data=scipy.ndimage.gaussian_laplace(
-            darr.values, sigma=sigma, mode=mode, cval=cval, **kwargs
+            darr.values, sigma=sigma_pix, mode=mode, cval=cval, **kwargs
         )
     )
 
diff --git a/src/erlab/analysis/interpolate.py b/src/erlab/analysis/interpolate.py
index c6b08b9a..991aa43f 100644
--- a/src/erlab/analysis/interpolate.py
+++ b/src/erlab/analysis/interpolate.py
@@ -350,5 +350,5 @@ def _get_interpolator_nd_fast(method, **kwargs):
         return _get_interpolator_nd_original(method, **kwargs)
 
 
-xarray.core.missing._get_interpolator = _get_interpolator_fast
+xarray.core.missing._get_interpolator = _get_interpolator_fast  # type: ignore[assignment]
 xarray.core.missing._get_interpolator_nd = _get_interpolator_nd_fast
diff --git a/src/erlab/analysis/kspace.py b/src/erlab/analysis/kspace.py
index 8f90eb1f..ef875ae6 100644
--- a/src/erlab/analysis/kspace.py
+++ b/src/erlab/analysis/kspace.py
@@ -12,6 +12,7 @@
 
 import numpy as np
 import numpy.typing as npt
+import xarray
 
 import erlab.constants
 import erlab.io
@@ -64,7 +65,7 @@ def kz_func(kinetic_energy, inner_potential, kx, ky):
 
 
 def get_kconv_func(
-    kinetic_energy: float | npt.NDArray,
+    kinetic_energy: float | npt.NDArray | xarray.DataArray,
     configuration: AxesConfiguration,
     angle_params: dict[str, float],
 ) -> tuple[Callable, Callable]:
@@ -122,7 +123,7 @@ def get_kconv_func(
 
     match configuration:
         case AxesConfiguration.Type1:
-            func = _kconv_func_type1
+            func: Callable = _kconv_func_type1
         case AxesConfiguration.Type2:
             func = _kconv_func_type2
         case AxesConfiguration.Type1DA:
@@ -135,13 +136,7 @@ def get_kconv_func(
     return func(k_tot, **angle_params)
 
 
-def _kconv_func_type1(
-    k_tot: float | npt.NDArray,
-    delta: float = 0.0,
-    xi: float = 0.0,
-    xi0: float = 0.0,
-    beta0: float = 0.0,
-):
+def _kconv_func_type1(k_tot, delta=0.0, xi=0.0, xi0=0.0, beta0=0.0):
     cd, sd = np.cos(np.deg2rad(delta)), np.sin(np.deg2rad(delta))  # δ
     cx, sx = np.cos(np.deg2rad(xi - xi0)), np.sin(np.deg2rad(xi - xi0))  # ξ - ξ0
 
@@ -179,13 +174,7 @@ def _inverse_func(kx, ky, kz=None):
     return _forward_func, _inverse_func
 
 
-def _kconv_func_type2(
-    k_tot: float | npt.NDArray,
-    delta: float = 0.0,
-    xi: float = 0.0,
-    xi0: float = 0.0,
-    beta0: float = 0.0,
-):
+def _kconv_func_type2(k_tot, delta=0.0, xi=0.0, xi0=0.0, beta0=0.0):
     cd, sd = np.cos(np.deg2rad(delta)), np.sin(np.deg2rad(delta))  # δ
     cx, sx = np.cos(np.deg2rad(xi - xi0)), np.sin(np.deg2rad(xi - xi0))  # ξ - ξ0
 
@@ -223,14 +212,7 @@ def _inverse_func(kx, ky, kz=None):
     return _forward_func, _inverse_func
 
 
-def _kconv_func_type1_da(
-    k_tot: float | npt.NDArray,
-    delta: float = 0.0,
-    chi: float = 0.0,
-    chi0: float = 0.0,
-    xi: float = 0.0,
-    xi0: float = 0.0,
-):
+def _kconv_func_type1_da(k_tot, delta=0.0, chi=0.0, chi0=0.0, xi=0.0, xi0=0.0):
     _fwd_2, _inv_2 = _kconv_func_type2_da(k_tot, delta, chi, chi0, xi, xi0)
 
     def _forward_func(alpha, beta):
@@ -243,14 +225,7 @@ def _inverse_func(kx, ky, kz=None):
     return _forward_func, _inverse_func
 
 
-def _kconv_func_type2_da(
-    k_tot: float | npt.NDArray,
-    delta: float = 0.0,
-    chi: float = 0.0,
-    chi0: float = 0.0,
-    xi: float = 0.0,
-    xi0: float = 0.0,
-):
+def _kconv_func_type2_da(k_tot, delta=0.0, chi=0.0, chi0=0.0, xi=0.0, xi0=0.0):
     cd, sd = np.cos(np.deg2rad(delta)), np.sin(np.deg2rad(delta))  # δ, azimuth
     cx, sx = np.cos(np.deg2rad(xi - xi0)), np.sin(np.deg2rad(xi - xi0))  # ξ
     cc, sc = np.cos(np.deg2rad(chi - chi0)), np.sin(np.deg2rad(chi - chi0))  # χ
@@ -300,7 +275,7 @@ def _inverse_func(kx, ky, kz=None):
             k_sq = kx**2 + ky**2 + kz**2
             k = np.sqrt(k_sq)
 
-        kperp = _kperp_func(k_sq, kx, ky)  # sqrt(k² − k_x² − k_y²)
+        kperp = _kperp_func(k_sq, kx, ky)  # sqrt(k² - k_x² - k_y²)
 
         proj1 = t11 * kx + t12 * ky + t13 * kperp
         proj2 = t21 * kx + t22 * ky + t23 * kperp
diff --git a/src/erlab/analysis/mask/polygon.py b/src/erlab/analysis/mask/polygon.py
index e4574872..42e59843 100644
--- a/src/erlab/analysis/mask/polygon.py
+++ b/src/erlab/analysis/mask/polygon.py
@@ -160,6 +160,8 @@ def bounded_side_bool(
             return True
         case Side.ON_BOUNDARY:
             return boundary
+        case _:
+            return False
 
 
 @numba.njit(nogil=True, cache=True)
diff --git a/src/erlab/analysis/utilities.py b/src/erlab/analysis/utilities.py
index a70a006e..b3bb5131 100644
--- a/src/erlab/analysis/utilities.py
+++ b/src/erlab/analysis/utilities.py
@@ -2,6 +2,7 @@
 
 import itertools
 import warnings
+from typing import cast
 
 import numpy as np
 import scipy.ndimage
@@ -85,7 +86,7 @@ def shift(
                 f"Dimension {dim} in shift array has different size than input array"
             )
 
-    domain_indices: list[int] = [darr.get_axis_num(ax) for ax in shift.dims]
+    domain_indices: tuple[int, ...] = darr.get_axis_num(shift.dims)
 
     # `along` must be evenly spaced and monotonic increasing
     out = darr.sortby(along).copy()
@@ -96,7 +97,7 @@ def shift(
 
     if shift_coords:
         # We first apply the integer part of the average shift to the coords
-        rigid_shift = np.round(shift.values.mean())
+        rigid_shift: float = np.round(shift.values.mean())
         shift = shift - rigid_shift
 
         # Apply coordinate shift
@@ -104,7 +105,7 @@ def shift(
 
         # The bounds of the remaining shift values are used to pad the data
         nshift_min, nshift_max = shift.values.min(), shift.values.max()
-        pads: tuple[int] = min(0, round(nshift_min)), max(0, round(nshift_max))
+        pads: tuple[int, int] = min(0, round(nshift_min)), max(0, round(nshift_max))
 
         # Construct new coordinate array
         new_along = np.linspace(
@@ -114,21 +115,24 @@ def shift(
         )
 
         # Pad the data and assign new coordinates
-        out = out.pad({along: np.abs(pads)}, mode="constant", constant_values=np.nan)
+        out = out.pad(
+            {along: tuple(np.abs(pads))}, mode="constant", constant_values=np.nan
+        )
         out = out.assign_coords({along: new_along})
 
     for idxs in itertools.product(*[range(darr.shape[i]) for i in domain_indices]):
         # Construct slices for indexing
-        slices = [slice(None)] * darr.ndim
-        for domain_index, i in zip(domain_indices, idxs):
-            slices[domain_index] = i
-        slices = tuple(slices)
+        _slices: list[slice | int] = [slice(None)] * darr.ndim
+        for domain_index, i in zip(domain_indices, idxs, strict=True):
+            _slices[domain_index] = i
+
+        slices: tuple[slice | int, ...] = tuple(_slices)
 
         # Initialize arguments to `scipy.ndimage.shift`
         input = out[slices]
-        shifts = [0] * input.ndim
-        shift_val: float = float(shift.isel(dict(zip(shift.dims, idxs))))
-        shifts[input.get_axis_num(along)] = shift_val
+        shifts: list[float] = [0.0] * input.ndim
+        shift_val: float = float(shift.isel(dict(zip(shift.dims, idxs, strict=True))))
+        shifts[cast(int, input.get_axis_num(along))] = shift_val
 
         # Apply shift
         out[slices] = scipy.ndimage.shift(input.values, shifts, **shift_kwargs)
diff --git a/src/erlab/characterization/__init__.py b/src/erlab/characterization/__init__.py
index aab49998..cbf7ee98 100644
--- a/src/erlab/characterization/__init__.py
+++ b/src/erlab/characterization/__init__.py
@@ -1,15 +1,8 @@
-"""
-Data import and analysis for characterization experiments.
-
-.. currentmodule:: erlab.characterization
-
-Modules
-=======
-
-.. autosummary::
-   :toctree: generated
-
-   xrd
-   resistance
-
-"""
+import warnings
+from erlab.io.characterization import xrd, resistance  # noqa: F401
+
+warnings.warn(
+    "`erlab.characterization` is deprecated. Use `erlab.io.characterization` instead",
+    DeprecationWarning,
+    stacklevel=2,
+)
diff --git a/src/erlab/interactive/bzplot.py b/src/erlab/interactive/bzplot.py
index 6b3829f7..b1fcfabb 100644
--- a/src/erlab/interactive/bzplot.py
+++ b/src/erlab/interactive/bzplot.py
@@ -49,11 +49,20 @@ def __init__(
             param_type = "bvec"
 
         if param_type == "lattice":
+            if len(params) != 6:
+                raise TypeError("Lattice parameters must be a 6-tuple.")
+
             bvec = to_reciprocal(abc2avec(*params))
-        elif param_type == "avec":
-            bvec = to_reciprocal(params)
-        elif param_type == "bvec":
-            bvec = params
+        else:
+            if not isinstance(params, np.ndarray):
+                raise TypeError("Lattice vectors must be a numpy array.")
+            if params.shape != (3, 3):
+                raise TypeError("Lattice vectors must be a 3 by 3 numpy array.")
+
+            if param_type == "avec":
+                bvec = to_reciprocal(params)
+            elif param_type == "bvec":
+                bvec = params
 
         self.controls = None
         self.plot = BZPlotWidget(bvec)
diff --git a/src/erlab/interactive/colors.py b/src/erlab/interactive/colors.py
index 4df7987c..a506a3de 100644
--- a/src/erlab/interactive/colors.py
+++ b/src/erlab/interactive/colors.py
@@ -16,14 +16,17 @@
 
 import weakref
 from collections.abc import Iterable, Sequence
-from typing import Literal
+from typing import TYPE_CHECKING, Literal
 
-import matplotlib.colors as mcolors
+import matplotlib.colors
 import numpy as np
 import numpy.typing as npt
 import pyqtgraph as pg
 from qtpy import QtCore, QtGui, QtWidgets
 
+if TYPE_CHECKING:
+    from matplotlib.typing import ColorType
+
 EXCLUDED_CMAPS: tuple[str, ...] = (
     "prism",
     "tab10",
@@ -156,16 +159,16 @@ class ColorMapGammaWidget(QtWidgets.QWidget):
 
     def __init__(
         self,
-        parent: QtWidgets.QWidget = None,
+        parent: QtWidgets.QWidget | None = None,
         value: float = 1.0,
         slider_cls: type | None = None,
         spin_cls: type | None = None,
     ):
         super().__init__(parent=parent)
-        self.setLayout(QtWidgets.QHBoxLayout(self))
-        self.layout().setContentsMargins(0, 0, 0, 0)
-
-        self.layout().setSpacing(3)
+        layout = QtWidgets.QHBoxLayout(self)
+        self.setLayout(layout)
+        layout.setContentsMargins(0, 0, 0, 0)
+        layout.setSpacing(3)
 
         if slider_cls is None:
             slider_cls = QtWidgets.QSlider
@@ -202,9 +205,9 @@ def __init__(
         )
         self.slider.valueChanged.connect(self.slider_changed)
 
-        self.layout().addWidget(self.label)
-        self.layout().addWidget(self.spin)
-        self.layout().addWidget(self.slider)
+        layout.addWidget(self.label)
+        layout.addWidget(self.spin)
+        layout.addWidget(self.slider)
 
     def value(self) -> float:
         return self.spin.value()
@@ -219,13 +222,13 @@ def spin_changed(self, value: float):
         self.slider.blockSignals(False)
         self.valueChanged.emit(value)
 
-    def slider_changed(self, value: float):
+    def slider_changed(self, value: float | int):
         self.spin.setValue(self.gamma_scale_inv(value))
 
     def gamma_scale(self, y: float) -> int:
         return round(1e4 * np.log10(y))
 
-    def gamma_scale_inv(self, x: int) -> float:
+    def gamma_scale_inv(self, x: float | int) -> float:
         return np.power(10, x * 1e-4)
 
 
@@ -248,7 +251,7 @@ class BetterImageItem(pg.ImageItem):
 
     sigColorChanged = QtCore.Signal()  #: :meta private:
 
-    def __init__(self, image: npt.NDArray = None, **kwargs):
+    def __init__(self, image: npt.NDArray | None = None, **kwargs):
         super().__init__(image, **kwargs)
 
     def set_colormap(
@@ -279,7 +282,7 @@ class BetterColorBarItem(pg.PlotItem):
     def __init__(
         self,
         parent: QtWidgets.QWidget | None = None,
-        image: Sequence[BetterImageItem] | BetterImageItem | None = None,
+        image: Iterable[BetterImageItem] | BetterImageItem | None = None,
         autoLevels: bool = False,
         limits: tuple[float, float] | None = None,
         pen: QtGui.QPen | str = "c",
@@ -386,15 +389,14 @@ def setLimits(self, limits: tuple[float, float] | None):
         if self._primary_image is not None:
             self.limit_changed()
 
-    def addImage(self, image: Sequence[BetterImageItem] | BetterImageItem):
-        # if isinstance(image, BetterImageItem):
-        if not np.iterable(image):
+    def addImage(self, image: Iterable[BetterImageItem] | BetterImageItem):
+        if not isinstance(image, Iterable):
             self._images.add(weakref.ref(image))
         else:
             for img in image:
                 self._images.add(weakref.ref(img))
 
-    def removeImage(self, image: Sequence[BetterImageItem] | BetterImageItem):
+    def removeImage(self, image: Iterable[BetterImageItem] | BetterImageItem):
         if isinstance(image, Iterable):
             for img in image:
                 self._images.remove(weakref.ref(img))
@@ -403,7 +405,7 @@ def removeImage(self, image: Sequence[BetterImageItem] | BetterImageItem):
 
     def setImageItem(
         self,
-        image: Sequence[BetterImageItem] | BetterImageItem,
+        image: Iterable[BetterImageItem] | BetterImageItem,
         insert_in: pg.PlotItem | None = None,
     ):
         self.addImage(image)
@@ -527,9 +529,7 @@ def mouseDragEvent(self, ev):
         ev.ignore()
 
 
-def color_to_QColor(
-    c: str | tuple[float, ...], alpha: float | None = None
-) -> QtGui.QColor:
+def color_to_QColor(c: ColorType, alpha: float | None = None) -> QtGui.QColor:
     """Convert a matplotlib color to a :class:`PySide6.QtGui.QColor`.
 
     Parameters
@@ -546,7 +546,7 @@ def color_to_QColor(
     PySide6.QtGui.QColor
 
     """
-    return QtGui.QColor.fromRgbF(*mcolors.to_rgba(c, alpha=alpha))
+    return QtGui.QColor.fromRgbF(*matplotlib.colors.to_rgba(c, alpha=alpha))
 
 
 def pg_colormap_names(
@@ -700,5 +700,5 @@ def pg_colormap_to_QPixmap(
     cmap_arr = cmap.getLookupTable(0, 1, w, alpha=True)[:, None]
 
     # print(cmap_arr.shape)
-    img = QtGui.QImage(cmap_arr, w, 1, QtGui.QImage.Format_RGBA8888)
+    img = QtGui.QImage(cmap_arr, w, 1, QtGui.QImage.Format.Format_RGBA8888)
     return QtGui.QPixmap.fromImage(img).scaled(w, h)
diff --git a/src/erlab/interactive/curvefittingtool.py b/src/erlab/interactive/curvefittingtool.py
index c4d62f9f..6e66d41a 100644
--- a/src/erlab/interactive/curvefittingtool.py
+++ b/src/erlab/interactive/curvefittingtool.py
@@ -1,5 +1,6 @@
 import copy
 import sys
+from typing import cast
 
 import lmfit
 import pyqtgraph as pg
@@ -54,7 +55,7 @@
 
 
 class SinglePeakWidget(ParameterGroup):
-    VALID_LINESHAPE = ["lorentzian", "gaussian"]
+    VALID_LINESHAPE: tuple[str, ...] = ("lorentzian", "gaussian")
 
     def __init__(self, peak_index):
         self.peak_index = peak_index
@@ -96,7 +97,7 @@ def param_dict(self):
 
     @property
     def peak_shape(self) -> str:
-        return self.values["Peak Shape"]
+        return str(self.values["Peak Shape"])
 
 
 class PlotPeakItem(pg.PlotCurveItem):
@@ -200,7 +201,7 @@ def __init__(self, data, n_bands: int = 1, parameters=None, *args, **kwargs):
         self.qapp = QtCore.QCoreApplication.instance()
         if not self.qapp:
             self.qapp = QtWidgets.QApplication(sys.argv)
-        self.qapp.setStyle("Fusion")
+        cast(QtWidgets.QApplication, self.qapp).setStyle("Fusion")
         super().__init__()
         self.resize(720, 360)
 
@@ -299,8 +300,8 @@ def __init__(self, data, n_bands: int = 1, parameters=None, *args, **kwargs):
         self.fitplot = self.plotwidget.plot()
         self.fitplot.setPen(pg.mkPen("c"))
 
-        self.peakcurves = []
-        self.peaklines = []
+        self.peakcurves: list[PlotPeakItem] = []
+        self.peaklines: list[PlotPeakPosition] = []
 
         self.refresh_n_peaks()
 
@@ -427,7 +428,7 @@ def set_params(self, params: dict):
             }
         )
         for i in range(self.n_bands):
-            self._params_peak.widget(i).set_values(
+            self._params_peak.widget(i).set_values(  # type: ignore[union-attr]
                 **{k[3:]: v for k, v in params.items() if k.startswith(f"p{i}")}
             )
 
@@ -455,7 +456,7 @@ def __init__(self, data, n_bands: int = 1, parameters=None, *args, **kwargs):
         self.qapp = QtCore.QCoreApplication.instance()
         if not self.qapp:
             self.qapp = QtWidgets.QApplication(sys.argv)
-        self.qapp.setStyle("Fusion")
+        cast(QtWidgets.QApplication, self.qapp).setStyle("Fusion")
         super().__init__()
         self.resize(720, 360)
 
@@ -534,8 +535,8 @@ def __init__(self, data, n_bands: int = 1, parameters=None, *args, **kwargs):
         self.fitplot = self.plotwidget.plot()
         self.fitplot.setPen(pg.mkPen("c"))
 
-        self.peakcurves = []
-        self.peaklines = []
+        self.peakcurves: list[PlotPeakItem] = []
+        self.peaklines: list[PlotPeakPosition] = []
 
         self.refresh_n_peaks()
 
@@ -660,7 +661,7 @@ def set_params(self, params: dict):
             }
         )
         for i in range(self.n_bands):
-            self._params_peak.widget(i).set_values(
+            self._params_peak.widget(i).set_values(  # type: ignore[union-attr]
                 **{k[3:]: v for k, v in params.items() if k.startswith(f"p{i}")}
             )
 
diff --git a/src/erlab/interactive/derivative.py b/src/erlab/interactive/derivative.py
index 471b270f..89e619f8 100644
--- a/src/erlab/interactive/derivative.py
+++ b/src/erlab/interactive/derivative.py
@@ -5,6 +5,7 @@
 import functools
 import os
 import sys
+from typing import TYPE_CHECKING, cast
 
 import numpy as np
 import pyqtgraph as pg
@@ -26,14 +27,17 @@
     xImageItem,
 )
 
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
 
 class DerivativeTool(
-    *uic.loadUiType(os.path.join(os.path.dirname(__file__), "dtool.ui"))
+    *uic.loadUiType(os.path.join(os.path.dirname(__file__), "dtool.ui"))  # type: ignore[misc]
 ):
     def __init__(self, data: xr.DataArray, *, data_name: str | None = None):
         if data_name is None:
             try:
-                data_name = varname.argname("data", func=self.__init__, vars_only=False)
+                data_name = varname.argname("data", func=self.__init__, vars_only=False)  # type: ignore[misc]
             except varname.VarnameRetrievingError:
                 data_name = "data"
 
@@ -50,8 +54,8 @@ def __init__(self, data: xr.DataArray, *, data_name: str | None = None):
         self.data: xr.DataArray = parse_data(data)
         self._result: xr.DataArray = self.data.copy()
 
-        self.xdim: str = self.data.dims[1]
-        self.ydim: str = self.data.dims[0]
+        self.xdim: Hashable = self.data.dims[1]
+        self.ydim: Hashable = self.data.dims[0]
 
         self.xinc: float = abs(float(self.data[self.xdim][1] - self.data[self.xdim][0]))
         self.yinc: float = abs(float(self.data[self.ydim][1] - self.data[self.ydim][0]))
@@ -138,11 +142,11 @@ def processed_data(self) -> xr.DataArray:
         if self.interp_group.isChecked():
             out = self.data.interp(
                 {
-                    self.xdim: np.linspace(
-                        *self.data[self.xdim][[0, -1]], self.nx_spin.value()
+                    self.xdim: np.linspace(  # type: ignore[call-overload]
+                        *self.data[self.xdim].values[[0, -1]], self.nx_spin.value()
                     ),
-                    self.ydim: np.linspace(
-                        *self.data[self.ydim][[0, -1]], self.ny_spin.value()
+                    self.ydim: np.linspace(  # type: ignore[call-overload]
+                        *self.data[self.ydim].values[[0, -1]], self.ny_spin.value()
                     ),
                 }
             )
@@ -221,7 +225,9 @@ def copy_code(self):
             arg_dict = {
                 dim: f"|np.linspace(*{data_name}['{dim}'][[0, -1]], {n})|"
                 for dim, n in zip(
-                    [self.xdim, self.ydim], [self.nx_spin.value(), self.ny_spin.value()]
+                    [self.xdim, self.ydim],
+                    [self.nx_spin.value(), self.ny_spin.value()],
+                    strict=True,
                 )
             }
             lines.append(
@@ -240,6 +246,7 @@ def copy_code(self):
                             np.round(s.value(), s.decimals())
                             for s in (self.sx_spin, self.sy_spin)
                         ],
+                        strict=True,
                     )
                 )
             }
@@ -310,7 +317,7 @@ def dtool(data, data_name: str | None = None, *, execute: bool | None = None):
     if not qapp:
         qapp = QtWidgets.QApplication(sys.argv)
 
-    qapp.setStyle("Fusion")
+    cast(QtWidgets.QApplication, qapp).setStyle("Fusion")
 
     win = DerivativeTool(data, data_name=data_name)
     win.show()
diff --git a/src/erlab/interactive/fermiedge.py b/src/erlab/interactive/fermiedge.py
index e18d571c..ca195580 100644
--- a/src/erlab/interactive/fermiedge.py
+++ b/src/erlab/interactive/fermiedge.py
@@ -18,6 +18,7 @@
     ParameterGroup,
     ROIControls,
     gen_function_code,
+    xImageItem,
 )
 from erlab.parallel import joblib_progress_qt
 
@@ -156,6 +157,9 @@ def __init__(
                 self._argnames["data_corr"] = "data_corr"
 
         self.data_corr = data_corr
+        self.hists: pg.HistogramLUTItem
+        self.axes: list[pg.PlotItem]
+        self.images: list[xImageItem]
 
         self.axes[1].setVisible(False)
         self.hists[1].setVisible(False)
@@ -169,7 +173,7 @@ def __init__(
 
         self.params_roi = ROIControls(self.add_roi(0))
         self.params_edge = ParameterGroup(
-            **{
+            {
                 "T (K)": {"qwtype": "dblspin", "value": temp, "range": (0.0, 400.0)},
                 "Fix T": {"qwtype": "chkbox", "checked": True},
                 "Bin x": {"qwtype": "spin", "value": 1, "minimum": 1},
@@ -195,7 +199,7 @@ def __init__(
         self.params_edge.widgets["Fast"].stateChanged.connect(self._toggle_fast)
 
         self.params_poly = ParameterGroup(
-            **{
+            {
                 "Degree": {"qwtype": "spin", "value": 4, "range": (1, 20)},
                 "Method": {"qwtype": "combobox", "items": LMFIT_METHODS},
                 "Scale cov": {"qwtype": "chkbox", "checked": True},
@@ -220,7 +224,7 @@ def __init__(
         )
 
         self.params_spl = ParameterGroup(
-            **{
+            {
                 "Auto": {"qwtype": "chkbox", "checked": True},
                 "lambda": {
                     "qwtype": "dblspin",
@@ -299,18 +303,18 @@ def __init__(
         self.axes[0].disableAutoRange()
 
         # Setup time calculation
-        self.start_time: float | None = None
-        self.step_times: list[float] = []
+        self.start_time: float
+        self.step_times: list[float]
 
         # Setup progress bar
-        self.progress = QtWidgets.QProgressDialog(
+        self.progress: QtWidgets.QProgressDialog = QtWidgets.QProgressDialog(
             labelText="Fitting...",
             minimum=0,
             parent=self,
             minimumDuration=0,
             windowModality=QtCore.Qt.WindowModal,
         )
-        self.pbar = QtWidgets.QProgressBar()
+        self.pbar: QtWidgets.QProgressBar = QtWidgets.QProgressBar()
         self.progress.setBar(self.pbar)
         self.progress.setFixedSize(self.progress.size())
         self.progress.setCancelButtonText("Abort!")
@@ -360,7 +364,7 @@ def iterated(self, n: int):
     @QtCore.Slot()
     def perform_edge_fit(self):
         self.start_time = time.perf_counter()
-        self.step_times: list[float] = [0.0]
+        self.step_times = [0.0]
 
         self.progress.setVisible(True)
         self.params_roi.draw_button.setChecked(False)
diff --git a/src/erlab/interactive/imagetool/__init__.py b/src/erlab/interactive/imagetool/__init__.py
index 2b40fdcb..cd24f379 100644
--- a/src/erlab/interactive/imagetool/__init__.py
+++ b/src/erlab/interactive/imagetool/__init__.py
@@ -21,8 +21,10 @@
 
 import gc
 import sys
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, cast
 
+import numpy as np
+import numpy.typing as npt
 import xarray as xr
 from qtpy import QtCore, QtWidgets
 
@@ -38,23 +40,16 @@
 if TYPE_CHECKING:
     from collections.abc import Callable, Sequence
 
-    import numpy as np
-    import numpy.typing as npt
-
     from erlab.interactive.imagetool.slicer import ArraySlicer
 
 
 def itool(
-    data: (
-        Sequence[xr.DataArray | npt.ArrayLike[np.floating]]
-        | xr.DataArray
-        | npt.ArrayLike[np.floating]
-    ),
+    data: Sequence[xr.DataArray | npt.NDArray] | xr.DataArray | npt.NDArray,
     link: bool = False,
     link_colors: bool = True,
     execute: bool | None = None,
     **kwargs,
-):
+) -> ImageTool | list[ImageTool] | None:
     """Create and display an ImageTool window.
 
     Parameters
@@ -78,7 +73,7 @@ def itool(
 
     Returns
     -------
-    ImageTool or tuple of ImageTool
+    ImageTool or list of ImageTool
         The created ImageTool window(s).
 
     Notes
@@ -93,29 +88,32 @@ def itool(
     >>> itool(data_list, link=True)
     """
 
-    qapp: QtWidgets.QApplication = QtWidgets.QApplication.instance()
+    qapp = QtWidgets.QApplication.instance()
     if not qapp:
         qapp = QtWidgets.QApplication(sys.argv)
-    qapp.setStyle("Fusion")
-
-    if isinstance(data, list | tuple):
-        win = ()
-        for d in data:
-            win += (ImageTool(d, **kwargs),)
-        for w in win:
-            w.show()
-        win[-1].activateWindow()
-        win[-1].raise_()
-
-        if link:
-            linker = SlicerLinkProxy(  # noqa: F841
-                *[w.slicer_area for w in win], link_colors=link_colors
-            )
-    else:
-        win = ImageTool(data, **kwargs)
-        win.show()
-        win.raise_()
-        win.activateWindow()
+
+    if isinstance(qapp, QtWidgets.QApplication):
+        qapp.setStyle("Fusion")
+
+    if isinstance(data, np.ndarray | xr.DataArray):
+        data = cast(list[npt.NDArray | xr.DataArray], [data])
+
+    itool_list = [ImageTool(d, **kwargs) for d in data]
+
+    for w in itool_list:
+        w.show()
+
+    if len(itool_list) == 0:
+        raise ValueError("No data provided")
+
+    itool_list[-1].activateWindow()
+    itool_list[-1].raise_()
+
+    if link:
+        linker = SlicerLinkProxy(  # noqa: F841
+            *[w.slicer_area for w in itool_list], link_colors=link_colors
+        )
+
     if execute is None:
         execute = True
         try:
@@ -127,13 +125,14 @@ def itool(
                 start_event_loop_qt4(qapp)
         except NameError:
             pass
+
     if execute:
         qapp.exec()
-        del win
+        del itool_list
         gc.collect()
 
         return None
-    return win
+    return itool_list
 
 
 class BaseImageTool(QtWidgets.QMainWindow):
@@ -231,7 +230,7 @@ def colorAct(self):
         )
 
     def _generate_menu_kwargs(self) -> dict:
-        menu_kwargs = {
+        menu_kwargs: dict[str, Any] = {
             "fileMenu": {
                 "title": "&File",
                 "actions": {
@@ -340,6 +339,7 @@ def _generate_menu_kwargs(self) -> dict:
                 ),
                 (1, 1, 0, 0) * 2,
                 (1, -1, 1, -1, 10, -10, 10, -10),
+                strict=True,
             )
         ):
             menu_kwargs["viewMenu"]["actions"]["cursorMoveMenu"]["actions"][
@@ -373,6 +373,7 @@ def _generate_menu_kwargs(self) -> dict:
                 ),
                 (1, 1, 0, 0) * 2,
                 (1, -1, 1, -1, 10, -10, 10, -10),
+                strict=True,
             )
         ):
             menu_kwargs["viewMenu"]["actions"]["cursorMoveMenu"]["actions"][
@@ -402,7 +403,9 @@ def refreshMenus(self):
         self.action_dict["snapCursorAct"].blockSignals(False)
 
         cmap_props = self.slicer_area.colormap_properties
-        for ca, k in zip(self.colorAct, ["reversed", "highContrast", "zeroCentered"]):
+        for ca, k in zip(
+            self.colorAct, ["reversed", "highContrast", "zeroCentered"], strict=True
+        ):
             ca.blockSignals(True)
             ca.setChecked(cmap_props[k])
             ca.blockSignals(False)
diff --git a/src/erlab/interactive/imagetool/_deprecated/imagetool_mpl.py b/src/erlab/interactive/imagetool/_deprecated/imagetool_mpl.py
index c8b3af92..6c1fee6d 100644
--- a/src/erlab/interactive/imagetool/_deprecated/imagetool_mpl.py
+++ b/src/erlab/interactive/imagetool/_deprecated/imagetool_mpl.py
@@ -839,7 +839,9 @@ def update_spans(self):
                     span.set_xy(get_xy_y(*domain))
                 span.set_visible(self.visible)
             if self.useblit:
-                for i, span in list(zip(self.span_ax_index[axis], self.spans[axis])):
+                for i, span in list(
+                    zip(self.span_ax_index[axis], self.spans[axis], strict=True)
+                ):
                     self.axes[i].draw_artist(span)
 
     def get_index_of_value(self, axis, val):
@@ -968,7 +970,9 @@ def _update(self):
                 # self.pool(delayed(self.axes[i].draw_artist)(art) for i, art in list(zip(
                 #     (0, 1, 4, 0, 2, 5, 3, 5, 4), self.cursors)))
             else:
-                for i, art in list(zip(self.ax_index, self.all + self.scaling_axes)):
+                for i, art in list(
+                    zip(self.ax_index, self.all + self.scaling_axes, strict=True)
+                ):
                     self.axes[i].draw_artist(art)
             if any(self.averaged):
                 self.update_spans()
diff --git a/src/erlab/interactive/imagetool/_deprecated/imagetool_old.py b/src/erlab/interactive/imagetool/_deprecated/imagetool_old.py
index 5189482c..17c805e0 100644
--- a/src/erlab/interactive/imagetool/_deprecated/imagetool_old.py
+++ b/src/erlab/interactive/imagetool/_deprecated/imagetool_old.py
@@ -1154,7 +1154,7 @@ def _initialize_layout(
             )
         else:
             raise NotImplementedError("Only supports 2D, 3D, and 4D arrays.")
-        for i, (p, sel) in enumerate(zip(self.axes, valid_selection)):
+        for i, (p, sel) in enumerate(zip(self.axes, valid_selection, strict=True)):
             p.setDefaultPadding(0)
             for axis in ["left", "bottom", "right", "top"]:
                 p.getAxis(axis).setTickFont(font)
diff --git a/src/erlab/interactive/imagetool/controls.py b/src/erlab/interactive/imagetool/controls.py
index 12b4515a..e069c93a 100644
--- a/src/erlab/interactive/imagetool/controls.py
+++ b/src/erlab/interactive/imagetool/controls.py
@@ -13,44 +13,47 @@
 import pyqtgraph as pg
 import qtawesome as qta
 from qtpy import QtCore, QtGui, QtWidgets
-
+import types
 from erlab.interactive.colors import ColorMapComboBox, ColorMapGammaWidget
 from erlab.interactive.utilities import BetterSpinBox
 
 if TYPE_CHECKING:
     import xarray as xr
+    from collections.abc import Mapping
 
     from erlab.interactive.imagetool.core import ImageSlicerArea
     from erlab.interactive.imagetool.slicer import ArraySlicer
 
 
 class IconButton(QtWidgets.QPushButton):
-    ICON_ALIASES = {
-        "invert": "mdi6.invert-colors",
-        "invert_off": "mdi6.invert-colors-off",
-        "contrast": "mdi6.contrast-box",
-        "lock": "mdi6.lock",
-        "unlock": "mdi6.lock-open-variant",
-        "bright_auto": "mdi6.brightness-auto",
-        "bright_percent": "mdi6.brightness-percent",
-        "colorbar": "mdi6.gradient-vertical",
-        "transpose_0": "mdi6.arrow-top-left-bottom-right",
-        "transpose_1": "mdi6.arrow-up-down",
-        "transpose_2": "mdi6.arrow-left-right",
-        "transpose_3": "mdi6.axis-z-arrow",
-        "snap": "mdi6.grid",
-        "snap_off": "mdi6.grid-off",
-        "palette": "mdi6.palette-advanced",
-        "styles": "mdi6.palette-swatch",
-        "layout": "mdi6.page-layout-body",
-        "zero_center": "mdi6.format-vertical-align-center",
-        "table_eye": "mdi6.table-eye",
-        "plus": "mdi6.plus",
-        "minus": "mdi6.minus",
-        "reset": "mdi6.backup-restore",
-        # all_cursors="mdi6.checkbox-multiple-outline",
-        "all_cursors": "mdi6.select-multiple",
-    }
+    ICON_ALIASES: Mapping[str, str] = types.MappingProxyType(
+        {
+            "invert": "mdi6.invert-colors",
+            "invert_off": "mdi6.invert-colors-off",
+            "contrast": "mdi6.contrast-box",
+            "lock": "mdi6.lock",
+            "unlock": "mdi6.lock-open-variant",
+            "bright_auto": "mdi6.brightness-auto",
+            "bright_percent": "mdi6.brightness-percent",
+            "colorbar": "mdi6.gradient-vertical",
+            "transpose_0": "mdi6.arrow-top-left-bottom-right",
+            "transpose_1": "mdi6.arrow-up-down",
+            "transpose_2": "mdi6.arrow-left-right",
+            "transpose_3": "mdi6.axis-z-arrow",
+            "snap": "mdi6.grid",
+            "snap_off": "mdi6.grid-off",
+            "palette": "mdi6.palette-advanced",
+            "styles": "mdi6.palette-swatch",
+            "layout": "mdi6.page-layout-body",
+            "zero_center": "mdi6.format-vertical-align-center",
+            "table_eye": "mdi6.table-eye",
+            "plus": "mdi6.plus",
+            "minus": "mdi6.minus",
+            "reset": "mdi6.backup-restore",
+            # all_cursors="mdi6.checkbox-multiple-outline",
+            "all_cursors": "mdi6.select-multiple",
+        }
+    )
 
     def __init__(self, on: str | None = None, off: str | None = None, **kwargs):
         self.icon_key_on = None
@@ -88,18 +91,22 @@ def refresh_icons(self):
         if self.icon_key_on is not None:
             self.setIcon(self.get_icon(self.icon_key_on))
 
-    def changeEvent(self, evt: QtCore.QEvent):  # handles dark mode
-        if evt.type() == QtCore.QEvent.Type.PaletteChange:
+    def changeEvent(self, evt: QtCore.QEvent | None):  # handles dark mode
+        if evt is not None and evt.type() == QtCore.QEvent.Type.PaletteChange:
             qta.reset_cache()
             self.refresh_icons()
         super().changeEvent(evt)
 
 
-def clear_layout(layout: QtWidgets.QLayout):
+def clear_layout(layout: QtWidgets.QLayout | None) -> None:
+    if layout is None:
+        return
     while layout.count():
         child = layout.takeAt(0)
-        if child.widget():
-            child.widget().deleteLater()
+        if child is not None:
+            w = child.widget()
+            if w is not None:
+                w.deleteLater()
 
 
 class ItoolControlsBase(QtWidgets.QWidget):
@@ -108,7 +115,7 @@ def __init__(
     ):
         super().__init__(*args, **kwargs)
         self._slicer_area = slicer_area
-        self.sub_controls = []
+        self.sub_controls: list[QtWidgets.QWidget] = []
         self.initialize_layout()
         self.initialize_widgets()
         self.connect_signals()
diff --git a/src/erlab/interactive/imagetool/core.py b/src/erlab/interactive/imagetool/core.py
index 832ee263..56998947 100644
--- a/src/erlab/interactive/imagetool/core.py
+++ b/src/erlab/interactive/imagetool/core.py
@@ -10,7 +10,7 @@
 import os
 import time
 import weakref
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal, TypedDict, cast
 
 import numpy as np
 import numpy.typing as npt
@@ -32,6 +32,14 @@
     from pyqtgraph.graphicsItems.ViewBox import ViewBoxMenu
     from pyqtgraph.GraphicsScene import mouseEvents
 
+    class ColorMapProperties(TypedDict):
+        cmap: str | pg.ColorMap
+        gamma: float
+        reversed: bool
+        highContrast: bool
+        zeroCentered: bool
+
+
 suppressnanwarning = np.testing.suppress_warnings()
 suppressnanwarning.filter(RuntimeWarning, r"All-NaN (slice|axis) encountered")
 
@@ -118,11 +126,13 @@ def link_slicer(
     indices
         If `True`, the input argument named `value` given to `func` are interpreted as
         indices, and will be converted to appropriate values for other instances of
-        `ImageSlicerArea`. The behavior of this conversion is determined by `steps`.
+        `ImageSlicerArea`. The behavior of this conversion is determined by `steps`. If
+        `True`, An input argument named `axis` of type integer must be present in the
+        decorated method to determine the axis along which the index is to be changed.
     steps
-        If `False`, considers `value` as an absolute index. If `True`, considers
-        `value` as a relative value such as the number of steps or bins. See the
-        implementation of `SlicerLinkProxy` for more information.
+        If `False`, considers `value` as an absolute index. If `True`, considers `value`
+        as a relative value such as the number of steps or bins. See the implementation
+        of `SlicerLinkProxy` for more information.
     color
         Boolean whether the decorated method is related to visualization, such as
         colormap control.
@@ -167,7 +177,7 @@ class SlicerLinkProxy:
 
     """
 
-    def __init__(self, *slicers: list[ImageSlicerArea], link_colors: bool = True):
+    def __init__(self, *slicers: ImageSlicerArea, link_colors: bool = True):
         self.link_colors = link_colors
         self._slicers: set[ImageSlicerArea] = set()
         for s in slicers:
@@ -228,20 +238,18 @@ def convert_args(
         steps: bool,
     ):
         if indices:
-            axis: int | None = args.get("axis")
-            index: int | None = args.get("value")
+            index: int | None = args.get("value", None)
 
             if index is not None:
+                axis: int | None = args.get("axis")
+
                 if axis is None:
-                    args["value"] = [
-                        self.convert_index(source, target, a, i, steps)
-                        for (a, i) in zip(axis, index)
-                    ]
-                else:
-                    args["value"] = self.convert_index(
-                        source, target, axis, index, steps
+                    raise ValueError(
+                        "Axis argument not found in decorated method with `indices=True`"
                     )
 
+                args["value"] = self.convert_index(source, target, axis, index, steps)
+
         args["__slicer_skip_sync"] = True  # passed onto the decorator
         return args
 
@@ -309,7 +317,7 @@ class ImageSlicerArea(QtWidgets.QWidget):
 
     """
 
-    COLORS: list[QtGui.QColor] = [
+    COLORS: tuple[QtGui.QColor, ...] = (
         pg.mkColor(0.8),
         pg.mkColor("y"),
         pg.mkColor("m"),
@@ -317,7 +325,7 @@ class ImageSlicerArea(QtWidgets.QWidget):
         pg.mkColor("g"),
         pg.mkColor("r"),
         pg.mkColor("b"),
-    ]  #: List of :class:`PySide6.QtGui.QColor` containing colors for multiple cursors.
+    )  #: :class:`PySide6.QtGui.QColor`\ s for multiple cursors.
 
     sigDataChanged = QtCore.Signal()  #: :meta private:
     sigCurrentCursorChanged = QtCore.Signal(int)  #: :meta private:
@@ -362,9 +370,11 @@ def __init__(
 
         self.bench = bench
 
-        self.setLayout(QtWidgets.QHBoxLayout())
-        self.layout().setContentsMargins(0, 0, 0, 0)
-        self.layout().setSpacing(0)
+        layout = QtWidgets.QHBoxLayout()
+        self.setLayout(layout)
+
+        layout.setContentsMargins(0, 0, 0, 0)
+        layout.setSpacing(0)
 
         self._splitters = (
             QtWidgets.QSplitter(QtCore.Qt.Orientation.Vertical),
@@ -383,7 +393,7 @@ def __init__(
             # s.setPalette(palette)
             # print(s.handleWidth())
             # pass
-        self.layout().addWidget(self._splitters[0])
+        layout.addWidget(self._splitters[0])
         for i, j in ((0, 1), (1, 2), (1, 3), (0, 4), (4, 5), (4, 6)):
             self._splitters[i].addWidget(self._splitters[j])
         _sync_splitters(self._splitters[1], self._splitters[4])
@@ -391,11 +401,11 @@ def __init__(
         self.cursor_colors: list[QtGui.QColor] = [self.COLORS[0]]
 
         self._colorbar = ItoolColorBar(self)
-        self.layout().addWidget(self._colorbar)
+        layout.addWidget(self._colorbar)
         self._colorbar.setVisible(False)
 
         pkw = {"image_cls": image_cls, "plotdata_cls": plotdata_cls}
-        self.manual_limits: dict[str | list[float]] = {}
+        self.manual_limits: dict[str, list[list[float]]] = {}
         self._plots: tuple[ItoolGraphicsLayoutWidget, ...] = (
             ItoolGraphicsLayoutWidget(self, image=True, display_axis=(0, 1), **pkw),
             ItoolGraphicsLayoutWidget(self, display_axis=(0,), **pkw),
@@ -414,7 +424,7 @@ def __init__(
         for i in (5, 2):
             self._splitters[6].addWidget(self._plots[i])
 
-        self.qapp: QtWidgets.QApplication = QtWidgets.QApplication.instance()
+        self.qapp = cast(QtWidgets.QApplication, QtWidgets.QApplication.instance())
         self.qapp.aboutToQuit.connect(self.on_close)
 
         cmap_reversed = False
@@ -426,7 +436,7 @@ def __init__(
             if cmap.startswith("cet_CET"):
                 cmap = cmap[4:]
 
-        self.colormap_properties: dict[str, str | pg.ColorMap | float | bool] = {
+        self.colormap_properties: ColorMapProperties = {
             "cmap": cmap,
             "gamma": gamma,
             "reversed": cmap_reversed,
@@ -488,15 +498,18 @@ def slices(self) -> tuple[ItoolPlotItem, ...]:
             return tuple(self.get_axes(ax) for ax in (4, 5))
         elif self.data.ndim == 4:
             return tuple(self.get_axes(ax) for ax in (4, 5, 7))
+        else:
+            raise ValueError("Data must have 2 to 4 dimensions")
 
     @property
     def profiles(self) -> tuple[ItoolPlotItem, ...]:
         if self.data.ndim == 2:
-            profile_axes = (1, 2)
+            profile_axes = [1, 2]
         elif self.data.ndim == 3:
-            profile_axes = (1, 2, 3)
+            profile_axes = [1, 2, 3]
         else:
-            profile_axes = (1, 2, 3, 6)
+            profile_axes = [1, 2, 3, 6]
+
         return tuple(self.get_axes(ax) for ax in profile_axes)
 
     @property
@@ -639,7 +652,7 @@ def set_data(
         if hasattr(self, "_array_slicer"):
             self._array_slicer.set_array(self._data, reset=True)
         else:
-            self._array_slicer = ArraySlicer(self._data)
+            self._array_slicer: ArraySlicer = ArraySlicer(self._data)
 
         while self.n_cursors != n_cursors_old:
             self.array_slicer.add_cursor(update=False)
@@ -813,7 +826,7 @@ def set_colormap(
 
     @QtCore.Slot(bool)
     def lock_levels(self, lock: bool):
-        self.levels_locked: bool = lock
+        self.levels_locked = lock
 
         if self.levels_locked:
             levels = self.array_slicer.limits
@@ -870,7 +883,7 @@ def adjust_layout(
         font = QtGui.QFont()
         font.setPointSizeF(float(font_size))
 
-        valid_axis: tuple[tuple[bool, bool, bool, bool]] = (
+        valid_axis: tuple[tuple[Literal[0, 1], ...], ...] = (
             (1, 0, 0, 1),
             (1, 1, 0, 0),
             (0, 0, 1, 1),
@@ -905,7 +918,7 @@ def adjust_layout(
         ]
         if self.data.ndim == 4:
             sizes[3] = (0, 0, (r0 + r1 - d))
-        for split, sz in zip(self._splitters, sizes):
+        for split, sz in zip(self._splitters, sizes, strict=True):
             split.setSizes(tuple(round(s * scale) for s in sz))
 
         for i, sel in enumerate(valid_axis):
@@ -940,22 +953,23 @@ def toggle_snap(self, value: bool | None = None):
         self.array_slicer.snap_to_data = value
         self.sigViewOptionChanged.emit()
 
-    def changeEvent(self, evt: QtCore.QEvent):
-        if evt.type() == QtCore.QEvent.Type.PaletteChange:
-            self.qapp.setStyle(self.qapp.style().name())
+    def changeEvent(self, evt: QtCore.QEvent | None):
+        if evt is not None and evt.type() == QtCore.QEvent.Type.PaletteChange:
+            style = self.qapp.style()
+            if style is not None:
+                self.qapp.setStyle(style.name())
         super().changeEvent(evt)
 
 
 class ItoolCursorLine(pg.InfiniteLine):
     def __init__(self, *args, **kargs):
         super().__init__(*args, **kargs)
-        self.qapp: QtWidgets.QApplication = QtWidgets.QApplication.instance()
 
     @property
     def plotItem(self) -> ItoolPlotItem:
         return self.parentItem().parentItem().parentItem()
 
-    def setBounds(self, bounds: Sequence[float], value: float | None = None):
+    def setBounds(self, bounds: Sequence[np.floating], value: float | None = None):
         if bounds[0] > bounds[1]:
             bounds = list(bounds)
             bounds.reverse()
@@ -970,7 +984,7 @@ def value(self) -> float:
     def mouseDragEvent(self, ev: mouseEvents.MouseDragEvent):
         if (
             QtCore.Qt.KeyboardModifier.ControlModifier
-            not in self.qapp.keyboardModifiers()
+            not in QtWidgets.QApplication.keyboardModifiers()
         ):
             if self.movable and ev.button() == QtCore.Qt.MouseButton.LeftButton:
                 if ev.isStart():
@@ -1001,7 +1015,7 @@ def mouseDragEvent(self, ev: mouseEvents.MouseDragEvent):
     def mouseClickEvent(self, ev: mouseEvents.MouseClickEvent):
         if (
             QtCore.Qt.KeyboardModifier.ControlModifier
-            not in self.qapp.keyboardModifiers()
+            not in QtWidgets.QApplication.keyboardModifiers()
         ):
             super().mouseClickEvent(ev)
         else:
@@ -1011,7 +1025,7 @@ def mouseClickEvent(self, ev: mouseEvents.MouseClickEvent):
     def hoverEvent(self, ev):
         if (
             QtCore.Qt.KeyboardModifier.ControlModifier
-            not in self.qapp.keyboardModifiers()
+            not in QtWidgets.QApplication.keyboardModifiers()
         ):
             super().hoverEvent(ev)
         else:
@@ -1038,7 +1052,7 @@ def __init__(self, axes, cursor: int | None = None):
         if cursor is None:
             cursor = 0
         self._cursor_index = int(cursor)
-        self.qapp: QtGui.QGuiApplication = QtGui.QGuiApplication.instance()
+        self.qapp = QtGui.QGuiApplication.instance()
 
     @property
     def display_axis(self):
@@ -1117,13 +1131,19 @@ def refresh_data(self):
         )
 
     def mouseDragEvent(self, ev: mouseEvents.MouseDragEvent):
-        if QtCore.Qt.KeyboardModifier.ControlModifier in self.qapp.keyboardModifiers():
+        if (
+            QtCore.Qt.KeyboardModifier.ControlModifier
+            in QtWidgets.QApplication.keyboardModifiers()
+        ):
             ev.ignore()
         else:
             super().mouseDragEvent(ev)
 
     def mouseClickEvent(self, ev: mouseEvents.MouseClickEvent):
-        if QtCore.Qt.KeyboardModifier.ControlModifier in self.qapp.keyboardModifiers():
+        if (
+            QtCore.Qt.KeyboardModifier.ControlModifier
+            in QtWidgets.QApplication.keyboardModifiers()
+        ):
             ev.ignore()
         else:
             super().mouseClickEvent(ev)
@@ -1181,14 +1201,16 @@ def __init__(
             slot=self.process_drag,
         )
         if self.slicer_area.bench:
-            self._time_start = None
-            self._time_end = None
-            self._single_queue = collections.deque([0], maxlen=9)
-            self._next_queue = collections.deque([0], maxlen=9)
+            self._time_start: float | None = None
+            self._time_end: float | None = None
+            self._single_queue = collections.deque([0.0], maxlen=9)
+            self._next_queue = collections.deque([0.0], maxlen=9)
 
     @property
-    def axis_dims(self) -> list[str]:
-        dim_list = [self.slicer_area.data.dims[ax] for ax in self.display_axis]
+    def axis_dims(self) -> list[str | None]:
+        dim_list: list[str | None] = [
+            str(self.slicer_area.data.dims[ax]) for ax in self.display_axis
+        ]
         if not self.is_image:
             if self.slicer_data_items[-1].is_vertical:
                 dim_list = [None, *dim_list]
@@ -1204,7 +1226,10 @@ def refresh_manual_range(self):
         if self.is_independent:
             return
         for dim, auto, rng in zip(
-            self.axis_dims, self.vb.state["autoRange"], self.vb.state["viewRange"]
+            self.axis_dims,
+            self.vb.state["autoRange"],
+            self.vb.state["viewRange"],
+            strict=True,
         ):
             if dim is not None:
                 if auto:
@@ -1218,7 +1243,7 @@ def update_manual_range(self):
         self.set_range_from(self.slicer_area.manual_limits)
 
     def set_range_from(self, limits: dict[str, list[float]], **kwargs):
-        for dim, key in zip(self.axis_dims, ("xRange", "yRange")):
+        for dim, key in zip(self.axis_dims, ("xRange", "yRange"), strict=True):
             if dim is not None:
                 try:
                     kwargs[key] = limits[dim]
@@ -1252,7 +1277,7 @@ def process_drag(
         self, sig: tuple[mouseEvents.MouseDragEvent, QtCore.Qt.KeyboardModifier]
     ):
         if self.slicer_area.bench:
-            if self._time_end is not None:
+            if self._time_end is not None and self._time_start is not None:
                 self._single_queue.append(1 / (self._time_end - self._time_start))
             self._time_end = self._time_start
             self._time_start = time.perf_counter()
@@ -1352,7 +1377,7 @@ def add_cursor(self, update=True):
 
         self.cursor_lines.append({})
         self.cursor_spans.append({})
-        for c, s, ax in zip(cursors, spans, self.display_axis):
+        for c, s, ax in zip(cursors, spans, self.display_axis, strict=False):
             self.cursor_lines[-1][ax] = c
             self.cursor_spans[-1][ax] = s
             self.addItem(c)
@@ -1400,7 +1425,9 @@ def remove_cursor(self, index: int):
         item = self.slicer_data_items.pop(index)
         self.removeItem(item)
         for line, span in zip(
-            self.cursor_lines.pop(index).values(), self.cursor_spans.pop(index).values()
+            self.cursor_lines.pop(index).values(),
+            self.cursor_spans.pop(index).values(),
+            strict=True,
         ):
             self.removeItem(line)
             self.removeItem(span)
@@ -1449,7 +1476,9 @@ def refresh_labels(self):
         if self.is_image:
             label_kw = {
                 a: self._get_label_unit(i)
-                for a, i in zip(("top", "bottom", "left", "right"), (0, 0, 1, 1))
+                for a, i in zip(
+                    ("top", "bottom", "left", "right"), (0, 0, 1, 1), strict=True
+                )
                 if self.getAxis(a).isVisible()
             }
         else:
@@ -1540,7 +1569,7 @@ def array_slicer(self) -> ArraySlicer:
 
 
 class ItoolColorBarItem(BetterColorBarItem):
-    def __init__(self, slicer_area: ImageSlicerArea | None = None, **kwargs):
+    def __init__(self, slicer_area: ImageSlicerArea, **kwargs):
         self._slicer_area = slicer_area
         kwargs.setdefault(
             "axisItems",
@@ -1572,14 +1601,14 @@ def setImageItem(self, *args, **kwargs):
 
 
 class ItoolColorBar(pg.PlotWidget):
-    def __init__(self, slicer_area: ImageSlicerArea | None = None, **cbar_kw):
+    def __init__(self, slicer_area: ImageSlicerArea, **cbar_kw):
         super().__init__(
             parent=slicer_area, plotItem=ItoolColorBarItem(slicer_area, **cbar_kw)
         )
         self.scene().sigMouseClicked.connect(self.mouseDragEvent)
 
     @property
-    def cb(self) -> BetterColorBarItem:
+    def cb(self) -> ItoolColorBarItem:
         return self.plotItem
 
     def set_dimensions(
diff --git a/src/erlab/interactive/imagetool/fastbinning.py b/src/erlab/interactive/imagetool/fastbinning.py
index 6fadf7ad..15990525 100644
--- a/src/erlab/interactive/imagetool/fastbinning.py
+++ b/src/erlab/interactive/imagetool/fastbinning.py
@@ -6,7 +6,7 @@
 
 __all__ = ["fast_nanmean"]
 
-from collections.abc import Iterable
+from collections.abc import Collection
 
 import numba
 import numba.core.registry
@@ -315,8 +315,8 @@ def _nanmean_4_123(a: npt.NDArray[np.float32 | np.float64]) -> npt.NDArray[np.fl
 
 
 def fast_nanmean(
-    a: npt.NDArray[np.float32 | np.float64], axis: int | Iterable[int] | None = None
-) -> npt.NDArray[np.float32 | np.float64] | float:
+    a: npt.NDArray[np.float32 | np.float64], axis: int | Collection[int] | None = None
+) -> npt.NDArray[np.float32 | np.float64] | np.float64:
     """A fast, parallelized arithmetic mean for floating point arrays that ignores NaNs.
 
     Parameters
@@ -345,8 +345,8 @@ def fast_nanmean(
     if a.ndim == 1 or axis is None:
         return _nanmean_all(a)
     elif a.ndim > 4:
-        return np.ascontiguousarray(numbagg.nanmean(a, axis))
-    if hasattr(axis, "__iter__"):
+        return np.ascontiguousarray(numbagg.nanmean(a, axis))  # type: ignore[arg-type]
+    if isinstance(axis, Collection):
         if len(axis) == a.ndim:
             return _nanmean_all(a)
         axis = frozenset(x % a.ndim for x in axis)
@@ -356,8 +356,8 @@ def fast_nanmean(
 
 
 def _fast_nanmean_skipcheck(
-    a: npt.NDArray[np.float32 | np.float64], axis: int | Iterable[int]
-) -> npt.NDArray[np.float32 | np.float64] | float:
+    a: npt.NDArray[np.float32 | np.float64], axis: int | Collection[int]
+) -> npt.NDArray[np.float32 | np.float64] | np.float64:
     """A version of `fast_nanmean` with near-zero overhead. Meant for internal use.
 
     Strict assumptions on the input parameters allow skipping some checks.
@@ -377,18 +377,8 @@ def _fast_nanmean_skipcheck(
         The calculated mean. The output array is always C-contiguous.
 
     """
-    if hasattr(axis, "__iter__"):
+    if isinstance(axis, Collection):
         if len(axis) == a.ndim:
             return _nanmean_all(a)
         axis = frozenset(axis)
     return nanmean_funcs[a.ndim][axis](a).astype(a.dtype)
-
-
-if __name__ == "__main__":
-    for nd, funcs in nanmean_funcs.items():
-        x = np.random.RandomState(42).randn(*((30,) * nd))
-        for axis, func in funcs.items():
-            if isinstance(axis, frozenset):
-                axis = tuple(axis)
-            if not np.allclose(np.nanmean(x, axis), fast_nanmean(x, axis)):
-                print(func)
diff --git a/src/erlab/interactive/imagetool/slicer.py b/src/erlab/interactive/imagetool/slicer.py
index 0e1a3b04..633d81db 100644
--- a/src/erlab/interactive/imagetool/slicer.py
+++ b/src/erlab/interactive/imagetool/slicer.py
@@ -15,7 +15,7 @@
 from erlab.interactive.imagetool.fastbinning import _fast_nanmean_skipcheck
 
 if TYPE_CHECKING:
-    from collections.abc import Sequence
+    from collections.abc import Sequence, Hashable
 
     import xarray as xr
 
@@ -54,8 +54,8 @@ def _array_rect(
     y = lims[j][0] - incs[j]
     w = lims[i][-1] - x
     h = lims[j][-1] - y
-    x += 0.5 * incs[i]
-    y += 0.5 * incs[j]
+    x += np.float32(0.5 * incs[i])
+    y += np.float32(0.5 * incs[j])
     return x, y, w, h
 
 
@@ -101,7 +101,9 @@ def _is_uniform(arr: npt.NDArray[np.float32]) -> bool:
     ],
     cache=True,
 )
-def _index_of_value_nonuniform(arr: npt.NDArray[np.float32], val: np.float32) -> int:
+def _index_of_value_nonuniform(
+    arr: npt.NDArray[np.float32], val: np.float32
+) -> np.int_:
     return np.searchsorted((arr[:-1] + arr[1:]) / 2, val)
 
 
@@ -146,7 +148,6 @@ class ArraySlicer(QtCore.QObject):
 
     def __init__(self, xarray_obj: xr.DataArray):
         super().__init__()
-        self._obj: xr.DataArray | None = None
         self.set_array(xarray_obj, validate=True, reset=True)
 
     @property
@@ -205,29 +206,29 @@ def data_vals_T(self) -> npt.NDArray[np.floating]:
     # Benchmarks result in 10~20x slower speeds for bottleneck and numbagg compared to
     # numpy on arm64 mac with Accelerate BLAS. Needs confirmation on intel systems.
     @functools.cached_property
-    def nanmax(self) -> np.floating:
-        return np.nanmax(self._obj.values)
+    def nanmax(self) -> float:
+        return float(np.nanmax(self._obj.values))
 
     @functools.cached_property
-    def nanmin(self) -> np.floating:
-        return np.nanmin(self._obj.values)
+    def nanmin(self) -> float:
+        return float(np.nanmin(self._obj.values))
 
     @functools.cached_property
-    def absnanmax(self) -> np.floating:
+    def absnanmax(self) -> float:
         return max(abs(self.nanmin), abs(self.nanmax))
 
     @functools.cached_property
-    def absnanmin(self) -> np.floating:
+    def absnanmin(self) -> float:
         mn, mx = self.nanmin, self.nanmax
         if mn * mx <= np.float32(0.0):
-            return np.float32(0.0)
+            return 0.0
         elif mn < np.float32(0.0):
             return -mx
         else:
             return mn
 
     @property
-    def limits(self) -> tuple[np.floating, np.floating]:
+    def limits(self) -> tuple[float, float]:
         """Returns the global minima and maxima of the data."""
         return self.nanmin, self.nanmax
 
@@ -265,7 +266,7 @@ def validate_array(data: xr.DataArray) -> xr.DataArray:
             # if data has kx and ky axis, transpose
             if "eV" in data.dims:
                 new_dims += ("eV",)
-            new_dims += tuple(d for d in data.dims if d not in new_dims)
+            new_dims += tuple(str(d) for d in data.dims if d not in new_dims)
             data = data.transpose(*new_dims)
 
         nonuniform_dims: list[str] = [
@@ -304,13 +305,14 @@ def clear_cache(self):
     def set_array(
         self, xarray_obj: xr.DataArray, validate: bool = True, reset: bool = False
     ) -> None:
-        del self._obj
+        if hasattr(self, "_obj"):
+            del self._obj
 
         if validate:
             self._obj: xr.DataArray = self.validate_array(xarray_obj)
         else:
-            self._obj: xr.DataArray = xarray_obj
-        self._nonuniform_axes: list[str] = [
+            self._obj = xarray_obj
+        self._nonuniform_axes: list[int] = [
             i for i, d in enumerate(self._obj.dims) if str(d).endswith("_idx")
         ]
 
@@ -324,11 +326,11 @@ def set_array(
                 [s // 2 - (1 if s % 2 == 0 else 0) for s in self._obj.shape]
             ]
             self._values: list[list[np.float32]] = [
-                [c[i] for c, i in zip(self.coords, self._indices[0])]
+                [c[i] for c, i in zip(self.coords, self._indices[0], strict=True)]
             ]
             self.snap_to_data: bool = False
 
-    def values_of_dim(self, dim: str) -> npt.NDArray[np.float32]:
+    def values_of_dim(self, dim: Hashable) -> npt.NDArray[np.float32]:
         """Fast equivalent of :code:`self._obj[dim].values`.
 
         Returns the cached pointer of the underlying coordinate array, achieving a ~80x
@@ -353,13 +355,13 @@ def values_of_dim(self, dim: str) -> npt.NDArray[np.float32]:
         do the trick.
 
         """
-        return self._obj._coords[dim]._data.array._data
+        return self._obj._coords[dim]._data.array._data  # type: ignore[union-attr]
 
     def add_cursor(self, like_cursor: int = -1, update: bool = True) -> None:
         self._bins.append(list(self.get_bins(like_cursor)))
         new_ind = self.get_indices(like_cursor)
         self._indices.append(list(new_ind))
-        self._values.append([c[i] for c, i in zip(self.coords, new_ind)])
+        self._values.append([c[i] for c, i in zip(self.coords, new_ind, strict=True)])
         if update:
             self.sigCursorCountChanged.emit(self.n_cursors)
 
@@ -580,7 +582,8 @@ def isel_args(
     ) -> dict[str, slice | int]:
         axis = sorted(set(range(self._obj.ndim)) - set(disp))
         return {
-            self._obj.dims[ax]: self._bin_slice(cursor, ax, int_if_one) for ax in axis
+            str(self._obj.dims[ax]): self._bin_slice(cursor, ax, int_if_one)
+            for ax in axis
         }
 
     def qsel_args(self, cursor: int, disp: Sequence[int]) -> dict:
@@ -638,17 +641,17 @@ def isel_code(self, cursor: int, disp: Sequence[int]) -> str:
         return f".isel({dict_repr})"
 
     def xslice(self, cursor: int, disp: Sequence[int]) -> xr.DataArray:
-        isel_kw: dict[str, slice] = self.isel_args(cursor, disp, int_if_one=False)
+        isel_kw = self.isel_args(cursor, disp, int_if_one=False)
         binned_coord_average: dict[str, xr.DataArray] = {
-            k: self._obj[k][isel_kw[k]].mean()
-            for k, v in zip(self._obj.dims, self.get_binned(cursor))
+            str(k): self._obj[k][isel_kw[str(k)]].mean()
+            for k, v in zip(self._obj.dims, self.get_binned(cursor), strict=True)
             if v
         }
         return (
-            self._obj.isel(**isel_kw)
+            self._obj.isel(isel_kw)
             .squeeze()
             .mean(binned_coord_average.keys())
-            .assign_coords(**binned_coord_average)
+            .assign_coords(binned_coord_average)
         )
 
     @QtCore.Slot(int, tuple, result=np.ndarray)
@@ -673,6 +676,8 @@ def extract_avg_slice(
 
     def span_bounds(self, cursor: int, axis: int) -> npt.NDArray[np.float32]:
         slc = self._bin_slice(cursor, axis)
+        if isinstance(slc, int):
+            return self.coords_uniform[axis][slc : slc + 1]
         lb = max(0, slc.start)
         ub = min(self._obj.shape[axis] - 1, slc.stop - 1)
         return self.coords_uniform[axis][[lb, ub]]
diff --git a/src/erlab/interactive/kspace.py b/src/erlab/interactive/kspace.py
index f2f5ad5d..cd26472c 100644
--- a/src/erlab/interactive/kspace.py
+++ b/src/erlab/interactive/kspace.py
@@ -24,7 +24,7 @@
 
 
 class KspaceToolGUI(
-    *uic.loadUiType(os.path.join(os.path.dirname(__file__), "ktool.ui"))
+    *uic.loadUiType(os.path.join(os.path.dirname(__file__), "ktool.ui"))  # type: ignore[misc]
 ):
     def __init__(self):
         # Start the QApplication if it doesn't exist
@@ -155,7 +155,9 @@ def __init__(self, data: xr.DataArray, *, data_name: str | None = None):
         if data_name is None:
             try:
                 self._argnames["data"] = varname.argname(
-                    "data", func=self.__init__, vars_only=False
+                    "data",
+                    func=self.__init__,  # type: ignore[misc]
+                    vars_only=False,
                 )
             except varname.VarnameRetrievingError:
                 self._argnames["data"] = "data"
@@ -260,11 +262,11 @@ def show_converted(self):
         wait_dialog.setLayout(QtWidgets.QVBoxLayout())
         wait_dialog.layout().addWidget(QtWidgets.QLabel("Converting..."))
         wait_dialog.open()
-        itool = ImageTool(
+        self._itool = ImageTool(
             self.data.kspace.convert(bounds=self.bounds, resolution=self.resolution)
         )
         wait_dialog.close()
-        itool.show()
+        self._itool.show()
 
     def copy_code(self):
         arg_dict = {}
@@ -302,7 +304,10 @@ def copy_code(self):
     def bounds(self) -> dict[str, tuple[float, float]] | None:
         if self.bounds_group.isChecked():
             return {
-                k: tuple(self._bound_spins[f"{k}{j}"].value() for j in range(2))
+                k: (
+                    self._bound_spins[f"{k}0"].value(),
+                    self._bound_spins[f"{k}1"].value(),
+                )
                 for k in self.data.kspace.momentum_axes
             }
         else:
@@ -425,5 +430,7 @@ def ktool(data: xr.DataArray, *, data_name: str | None = None) -> KspaceTool:
 
 
 if __name__ == "__main__":
-    dat = erlab.io.load_hdf5("/Users/khan/2210_ALS_f0008.h5")
+    from typing import cast
+
+    dat = cast(xr.DataArray, erlab.io.load_hdf5("/Users/khan/2210_ALS_f0008.h5"))
     win = ktool(dat)
diff --git a/src/erlab/interactive/masktool.py b/src/erlab/interactive/masktool.py
index 18221878..7bc4a1e7 100644
--- a/src/erlab/interactive/masktool.py
+++ b/src/erlab/interactive/masktool.py
@@ -1,7 +1,5 @@
-import sys
-
 import pyqtgraph as pg
-from pyqtgraph.Qt import QtCore, QtWidgets
+from pyqtgraph.Qt import QtCore
 
 from erlab.interactive.utilities import AnalysisWindow, ParameterGroup
 
@@ -85,23 +83,3 @@ def update_cursor(self, change):
         # self.images[0].setImage(self.data.isel({dim_z:self.cursor.widgets["slider"].value()}).values)
 
         # self.cursor.values["slider"]
-
-
-if __name__ == "__main__":
-    import erlab.io
-
-    qapp = QtWidgets.QApplication.instance()
-    if not qapp:
-        qapp = QtWidgets.QApplication(sys.argv)
-    qapp.setStyle("Fusion")
-
-    ds = erlab.io.load_igor_h5(
-        "/Users/khan/Documents/ERLab/CsV3Sb5/220630_ALS_Kagome_nesting/maps.h5"
-    )
-    map3 = ds["Map3"].rename(phony_dim_0="kx", phony_dim_1="ky", phony_dim_2="eV")
-    # map6 = ds["Map6"].rename(phony_dim_0="kx", phony_dim_3="ky", phony_dim_4="eV")
-    ct = masktool(map3)
-    ct.show()
-    ct.activateWindow()
-    ct.raise_()
-    qapp.exec()
diff --git a/src/erlab/interactive/utilities.py b/src/erlab/interactive/utilities.py
index 9cbc9789..6296bcda 100644
--- a/src/erlab/interactive/utilities.py
+++ b/src/erlab/interactive/utilities.py
@@ -4,8 +4,9 @@
 
 import re
 import sys
+import types
 import warnings
-from typing import Any, Literal
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 import numpy as np
 import numpy.typing as npt
@@ -13,10 +14,12 @@
 import pyqtgraph as pg
 import xarray as xr
 from qtpy import QtCore, QtGui, QtWidgets
-from superqt import QDoubleSlider
 
 from erlab.interactive.colors import BetterImageItem, pg_colormap_powernorm
 
+if TYPE_CHECKING:
+    from collections.abc import Mapping
+
 __all__ = [
     "AnalysisWidgetBase",
     "AnalysisWindow",
@@ -251,7 +254,9 @@ def __init__(
         self._updateWidth()
 
         if self.isReadOnly():
-            self.lineEdit().setReadOnly(True)
+            line_edit = self.lineEdit()
+            if line_edit is not None:
+                line_edit.setReadOnly(True)
             self.setButtonSymbols(self.ButtonSymbols.NoButtons)
         self.setValue(self.value())
 
@@ -516,6 +521,7 @@ def labelString(self):
                     for k, v in zip(
                         ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "-"),
                         ("⁰", "¹", "²", "³", "⁴", "⁵", "⁶", "⁷", "⁸", "⁹", "⁻"),
+                        strict=True,
                     ):
                         units = units.replace(k, v)
                     units = f"10{units}"
@@ -565,8 +571,9 @@ def __init__(
         super().__init__()
         if spin_kw is None:
             spin_kw = {}
-        self.layout = QtWidgets.QHBoxLayout(self)
-        self.layout.setContentsMargins(0, 0, 0, 0)
+        layout = QtWidgets.QHBoxLayout(self)
+        self.setLayout(layout)
+        layout.setContentsMargins(0, 0, 0, 0)
 
         self.param_name = name
         self._prefix = ""
@@ -591,14 +598,15 @@ def __init__(
         self.spin_ub.setSizePolicy(
             QtWidgets.QSizePolicy.Policy.Minimum, QtWidgets.QSizePolicy.Policy.Fixed
         )
-        self.check = QtWidgets.QCheckBox(toolTip="Fixed")
+        self.check = QtWidgets.QCheckBox()
+        self.check.setToolTip("Fix parameter")
 
         if show_label:
-            self.layout.addWidget(self.label)
-        self.layout.addWidget(self.spin_value)
-        self.layout.addWidget(self.spin_lb)
-        self.layout.addWidget(self.spin_ub)
-        self.layout.addWidget(self.check)
+            layout.addWidget(self.label)
+        layout.addWidget(self.spin_value)
+        layout.addWidget(self.spin_lb)
+        layout.addWidget(self.spin_ub)
+        layout.addWidget(self.check)
 
         for spin in (self.spin_value, self.spin_lb, self.spin_ub):
             spin.valueChanged.connect(lambda: self.sigParamChanged.emit())
@@ -811,31 +819,44 @@ class ParameterGroup(QtWidgets.QGroupBox):
 
     """
 
-    VALID_QWTYPE: dict[str, QtWidgets.QWidget] = {
-        "spin": QtWidgets.QSpinBox,
-        "dblspin": QtWidgets.QDoubleSpinBox,
-        "btspin": BetterSpinBox,
-        "slider": QtWidgets.QSlider,
-        "dblslider": QDoubleSlider,
-        "chkbox": QtWidgets.QCheckBox,
-        "pushbtn": QtWidgets.QPushButton,
-        "chkpushbtn": QtWidgets.QPushButton,
-        "combobox": QtWidgets.QComboBox,
-        "fitparam": FittingParameterWidget,
-    }  # : Dictionary of valid widgets that can be added.
+    VALID_QWTYPE: Mapping[str, type[QtWidgets.QWidget]] = types.MappingProxyType(
+        {
+            "spin": QtWidgets.QSpinBox,
+            "dblspin": QtWidgets.QDoubleSpinBox,
+            "btspin": BetterSpinBox,
+            "slider": QtWidgets.QSlider,
+            "chkbox": QtWidgets.QCheckBox,
+            "pushbtn": QtWidgets.QPushButton,
+            "chkpushbtn": QtWidgets.QPushButton,
+            "combobox": QtWidgets.QComboBox,
+            "fitparam": FittingParameterWidget,
+        }
+    )  # : Dictionary of valid widgets that can be added.
 
     sigParameterChanged: QtCore.SignalInstance = QtCore.Signal(dict)  #: :meta private:
 
-    def __init__(self, ncols: int = 1, groupbox_kw: dict | None = None, **kwargs):
+    def __init__(
+        self,
+        widgets: dict[str, dict] | None = None,
+        ncols: int = 1,
+        groupbox_kw: dict | None = None,
+        **widgets_kwargs,
+    ):
         if groupbox_kw is None:
             groupbox_kw = {}
         super().__init__(**groupbox_kw)
-        self.setLayout(QtWidgets.QGridLayout(self))
+        layout = QtWidgets.QGridLayout(self)
+        self.setLayout(layout)
 
         self.labels = []
         self.untracked = []
         self.widgets: dict[str, QtWidgets.QWidget] = {}
 
+        if widgets is not None:
+            kwargs = widgets
+        else:
+            kwargs = widgets_kwargs
+
         j = 0
         for i, (k, v) in enumerate(kwargs.items()):
             if isinstance(v, dict):
@@ -859,12 +880,12 @@ def __init__(self, ncols: int = 1, groupbox_kw: dict | None = None, **kwargs):
             self.labels.append(QtWidgets.QLabel(str(showlabel)))
             self.labels[i].setBuddy(self.widgets[k])
             if showlabel:
-                self.layout().addWidget(self.labels[i], j // ncols, 2 * (j % ncols))
-                self.layout().addWidget(
+                layout.addWidget(self.labels[i], j // ncols, 2 * (j % ncols))
+                layout.addWidget(
                     self.widgets[k], j // ncols, 2 * (j % ncols) + 1, 1, 2 * ind_eff - 1
                 )
             else:
-                self.layout().addWidget(
+                layout.addWidget(
                     self.widgets[k], j // ncols, 2 * (j % ncols), 1, 2 * ind_eff
                 )
             j += ind_eff
@@ -879,7 +900,6 @@ def getParameterWidget(
                 "dblspin",
                 "btspin",
                 "slider",
-                "dblslider",
                 "chkbox",
                 "pushbtn",
                 "chkpushbtn",
@@ -1054,7 +1074,6 @@ def values(self) -> dict[str, float | int | bool]:
     # "spin": QtWidgets.QSpinBox,
     # "dblspin": QtWidgets.QDoubleSpinBox,
     # "slider": QtWidgets.QSlider,
-    # "dblslider": QDoubleSlider,
     # "chkbox": QtWidgets.QCheckBox,
     # "pushbtn": QtWidgets.QPushButton,
     # "chkpushbtn": QtWidgets.QPushButton,
@@ -1134,7 +1153,7 @@ def update_pos(self):
         self.widgets["y0"].setMaximum(self.widgets["y1"].value())
         self.widgets["x1"].setMinimum(self.widgets["x0"].value())
         self.widgets["y1"].setMinimum(self.widgets["y0"].value())
-        for pos, spin in zip(self.roi_limits, self.roi_spin):
+        for pos, spin in zip(self.roi_limits, self.roi_spin, strict=True):
             spin.blockSignals(True)
             spin.setValue(pos)
             spin.blockSignals(False)
@@ -1142,7 +1161,9 @@ def update_pos(self):
     def modify_roi(self, x0=None, y0=None, x1=None, y1=None, update=True):
         lim_new = (x0, y0, x1, y1)
         lim_old = self.roi_limits
-        x0, y0, x1, y1 = ((f if f is not None else i) for i, f in zip(lim_old, lim_new))
+        x0, y0, x1, y1 = (
+            (f if f is not None else i) for i, f in zip(lim_old, lim_new, strict=True)
+        )
         xm, ym, xM, yM = self.roi.maxBounds.getCoords()
         x0, y0, x1, y1 = max(x0, xm), max(y0, ym), min(x1, xM), min(y1, yM)
         self.roi.setPos((x0, y0), update=False)
@@ -1224,13 +1245,6 @@ def mouseDragEventCustom(ev, axis=None):
         vb.mouseDragEvent = mouseDragEventCustom  # set to modified mouseDragEvent
 
 
-class PostInitCaller(type(QtWidgets.QMainWindow)):
-    def __call__(cls, *args, **kwargs):
-        obj = type.__call__(cls, *args, **kwargs)
-        obj.__post_init__()
-        return obj
-
-
 class AnalysisWindow(QtWidgets.QMainWindow):
     def __init__(
         self,
@@ -1318,9 +1332,9 @@ def addParameterGroup(self, *args, **kwargs):
         self.controls.addWidget(group)
         return group
 
-    def closeEvent(self, event: QtGui.QCloseEvent) -> None:
-        cb = QtWidgets.QApplication.instance().clipboard()
-        if cb.text(cb.Mode.Clipboard) != "":
+    def closeEvent(self, event: QtGui.QCloseEvent | None) -> None:
+        cb = cast(QtWidgets.QApplication, QtWidgets.QApplication.instance()).clipboard()
+        if event is not None and cb is not None and cb.text(cb.Mode.Clipboard) != "":
             pyperclip.copy(cb.text(cb.Mode.Clipboard))
         return super().closeEvent(event)
 
@@ -1368,10 +1382,12 @@ def __init__(
             if link in ("y", "both"):
                 self.axes[i].setYLink(self.axes[0])
 
-    def initialize_layout(self, nax):
-        self.hists = [pg.HistogramLUTItem() for _ in range(nax)]
-        self.axes = [pg.PlotItem() for _ in range(nax)]
-        self.images = [xImageItem(axisOrder="row-major") for _ in range(nax)]
+    def initialize_layout(self, nax: int):
+        self.hists: pg.HistogramLUTItem = [pg.HistogramLUTItem() for _ in range(nax)]
+        self.axes: list[pg.PlotItem] = [pg.PlotItem() for _ in range(nax)]
+        self.images: list[xImageItem] = [
+            xImageItem(axisOrder="row-major") for _ in range(nax)
+        ]
         cmap = pg_colormap_powernorm("terrain", 1.0, N=6)
         for i in range(nax):
             self.addItem(self.axes[i], *self.get_axis_pos(i))
@@ -1504,7 +1520,7 @@ def refresh_all(self):
 
 
 class DictMenuBar(QtWidgets.QMenuBar):
-    def __init__(self, parent: QtWidgets.QWidget | None = ..., **kwargs) -> None:
+    def __init__(self, parent: QtWidgets.QWidget | None = None, **kwargs) -> None:
         super().__init__(parent)
 
         self.menu_dict: dict[str, QtWidgets.QMenu] = {}
@@ -1517,7 +1533,7 @@ def __getattribute__(self, __name: str) -> Any:
             return super().__getattribute__(__name)
         except AttributeError:
             try:
-                out = self.menu_dict[__name]
+                out: Any = self.menu_dict[__name]
             except KeyError:
                 out = self.action_dict[__name]
             warnings.warn(
@@ -1596,7 +1612,9 @@ def parse_action(actopts: dict):
 if __name__ == "__main__":
     from scipy.ndimage import gaussian_filter  # , uniform_filter
 
-    qapp = QtWidgets.QApplication.instance()
+    qapp: QtWidgets.QApplication = cast(
+        QtWidgets.QApplication, QtWidgets.QApplication.instance()
+    )
     if not qapp:
         qapp = QtWidgets.QApplication(sys.argv)
     qapp.setStyle("Fusion")
diff --git a/src/erlab/io/__init__.py b/src/erlab/io/__init__.py
index 0cd82616..41d7177f 100644
--- a/src/erlab/io/__init__.py
+++ b/src/erlab/io/__init__.py
@@ -16,6 +16,7 @@
    utilities
    igor
    exampledata
+   characterization
 
 
 For a single session, it is very common to use only one type of loader for a single
diff --git a/src/erlab/io/characterization/__init__.py b/src/erlab/io/characterization/__init__.py
new file mode 100644
index 00000000..b8ec9f36
--- /dev/null
+++ b/src/erlab/io/characterization/__init__.py
@@ -0,0 +1,14 @@
+"""Data import for characterization experiments.
+
+.. currentmodule:: erlab.io.characterization
+
+Modules
+=======
+
+.. autosummary::
+   :toctree:
+
+   xrd
+   resistance
+
+"""
diff --git a/src/erlab/characterization/resistance.py b/src/erlab/io/characterization/resistance.py
similarity index 97%
rename from src/erlab/characterization/resistance.py
rename to src/erlab/io/characterization/resistance.py
index 427ddb06..69fdb543 100644
--- a/src/erlab/characterization/resistance.py
+++ b/src/erlab/io/characterization/resistance.py
@@ -1,4 +1,4 @@
-"""Functions related to analyzing temperature-dependent resistance data.
+"""Functions related to loading temperature-dependent resistance data.
 
 Currently only supports loading raw data from ``.dat`` and ``.csv`` files output by
 physics lab III equipment.
diff --git a/src/erlab/characterization/xrd.py b/src/erlab/io/characterization/xrd.py
similarity index 86%
rename from src/erlab/characterization/xrd.py
rename to src/erlab/io/characterization/xrd.py
index 477e10d3..a142f38d 100644
--- a/src/erlab/characterization/xrd.py
+++ b/src/erlab/io/characterization/xrd.py
@@ -1,4 +1,4 @@
-"""Functions related to analyzing x-ray diffraction spectra.
+"""Functions related to loading x-ray diffraction spectra.
 
 Currently only supports loading raw data from igor ``.itx`` files.
 
@@ -57,9 +57,12 @@ def load_xrd_itx(path: str, **kwargs):
     kwargs.setdefault("encoding", "windows-1252")
     with open(path, **kwargs) as file:
         content = file.read()
-    head, data = re.search(
-        r"IGOR\nWAVES/O\s(.*?)\nBEGIN\n(.+?)\nEND", content, re.DOTALL
-    ).groups()
+
+    search = re.search(r"IGOR\nWAVES/O\s(.*?)\nBEGIN\n(.+?)\nEND", content, re.DOTALL)
+    if search is None:
+        raise ValueError("Failed to parse .itx file.")
+
+    head, data = search.groups()
     head = head.split(", ")
 
     data = np.array(
diff --git a/src/erlab/io/dataloader.py b/src/erlab/io/dataloader.py
index 792545f5..273f1749 100644
--- a/src/erlab/io/dataloader.py
+++ b/src/erlab/io/dataloader.py
@@ -23,7 +23,7 @@
 import itertools
 import os
 import warnings
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, ClassVar, Self, cast
 
 import joblib
 import numpy as np
@@ -32,7 +32,9 @@
 import xarray as xr
 
 if TYPE_CHECKING:
-    from collections.abc import Iterable, Sequence
+    from collections.abc import Iterable, Mapping
+
+    DataFromSingleFile = xr.DataArray | xr.Dataset | list[xr.DataArray]
 
 
 def _is_uniform(arr: npt.NDArray) -> bool:
@@ -40,7 +42,7 @@ def _is_uniform(arr: npt.NDArray) -> bool:
     return np.allclose(dif, dif[0], rtol=3e-05, atol=3e-05, equal_nan=True)
 
 
-def _is_monotonic(arr: npt.NDArray) -> bool:
+def _is_monotonic(arr: npt.NDArray) -> np.bool_:
     dif = np.diff(arr)
     return np.all(dif >= 0) or np.all(dif <= 0)
 
@@ -53,19 +55,26 @@ class ValidationWarning(UserWarning):
     """This warning is issued when the loaded data fails validation checks."""
 
 
+class LoaderNotFoundError(Exception):
+    """This exception is raised when a loader is not found in the registry."""
+
+    def __init__(self, key: str):
+        super().__init__(f"Loader for name or alias {key} not found in the registry")
+
+
 class LoaderBase:
     """Base class for all data loaders."""
 
-    name: str = None
+    name: str
     """
     Name of the loader. Using a unique and descriptive name is recommended. For easy
     access, it is recommended to use a name that passes :func:`str.isidentifier`.
     """
 
-    aliases: list[str] | None = None
+    aliases: Iterable[str] | None = None
     """List of alternative names for the loader."""
 
-    name_map: dict[str, str | Iterable[str]] = {}
+    name_map: ClassVar[dict[str, str | Iterable[str]]] = {}
     """
     Dictionary that maps **new** coordinate or attribute names to **original**
     coordinate or attribute names. If there are multiple possible names for a single
@@ -83,7 +92,7 @@ class LoaderBase:
     consistency.
     """
 
-    additional_attrs: dict[str, str | int | float] = {}
+    additional_attrs: ClassVar[dict[str, str | int | float]] = {}
     """Additional attributes to be added to the data."""
 
     always_single: bool = True
@@ -107,7 +116,7 @@ def name_map_reversed(self) -> dict[str, str]:
         return self.reverse_mapping(self.name_map)
 
     @staticmethod
-    def reverse_mapping(mapping: dict[str, str | Iterable[str]]) -> dict[str, str]:
+    def reverse_mapping(mapping: Mapping[str, str | Iterable[str]]) -> dict[str, str]:
         """Reverse the given mapping dictionary to form a one-to-one mapping.
 
         Parameters
@@ -268,6 +277,7 @@ def formatter(cls, val: object):
             )
 
         elif np.issubdtype(type(val), np.floating):
+            val = cast(np.floating, val)
             if val.is_integer():
                 return cls.formatter(np.int64(val))
             else:
@@ -311,8 +321,8 @@ def get_styler(cls, df: pandas.DataFrame) -> pandas.io.formats.style.Styler:
 
     def load(
         self,
-        identifier: str | os.PathLike | int | None,
-        data_dir: str | os.PathLike | None = None,
+        identifier: str | int,
+        data_dir: str | None = None,
         **kwargs,
     ) -> xr.DataArray | xr.Dataset | list[xr.DataArray]:
         """Load ARPES data.
@@ -400,8 +410,6 @@ def load(
         if not self.skip_validate:
             self.validate(data)
 
-        data.attrs["data_loader_name"] = str(self.name)
-
         return data
 
     def summarize(
@@ -467,19 +475,19 @@ def summarize(
         styled = self.get_styler(df)
 
         try:
-            shell = get_ipython().__class__.__name__  # type: ignore
+            shell = get_ipython().__class__.__name__  # type: ignore[name-defined]
             if display and (
                 shell in ["ZMQInteractiveShell", "TerminalInteractiveShell"]
             ):
-                from IPython.display import display
+                from IPython.display import display  # type: ignore[assignment]
 
                 with pandas.option_context(
                     "display.max_rows", len(df), "display.max_columns", len(df.columns)
                 ):
-                    display(styled)
+                    display(styled)  # type: ignore[misc]
 
                 if importlib.util.find_spec("ipywidgets"):
-                    display(self.isummarize(df=df))
+                    display(self.isummarize(df=df))  # type: ignore[misc]
 
                 return None
 
@@ -513,12 +521,10 @@ def isummarize(self, df: pandas.DataFrame | None = None, **kwargs):
                 "ipywidgets and IPython is required for interactive summaries"
             )
         if df is None:
-            kwargs.setdefault("display", False)
-            df = self.summarize(**kwargs)
+            kwargs["display"] = False
+            df = cast(pandas.DataFrame, self.summarize(**kwargs))
 
         import matplotlib.pyplot as plt
-        import erlab.plotting.erplot as eplt
-
         from ipywidgets import (
             HTML,
             Button,
@@ -532,6 +538,8 @@ def isummarize(self, df: pandas.DataFrame | None = None, **kwargs):
         )
         from ipywidgets.widgets.interaction import show_inline_matplotlib_plots
 
+        import erlab.plotting.erplot as eplt
+
         self._temp_data: xr.DataArray | None = None
 
         def _format_data_info(series) -> str:
@@ -755,7 +763,7 @@ def identify(
         """
         raise NotImplementedError("method must be implemented in the subclass")
 
-    def infer_index(self, name: str) -> tuple[int | None, dict | None]:
+    def infer_index(self, name: str) -> tuple[int | None, dict[str, Any]]:
         """Infer the index for the given file name.
 
         This method takes a file name and tries to infer the scan index from it. If the
@@ -802,9 +810,11 @@ def generate_summary(self, data_dir: str | os.PathLike) -> pandas.DataFrame:
 
     def combine_multiple(
         self,
-        data_list: list[xr.DataArray | xr.Dataset],
-        coord_dict: dict[str, Sequence],
-    ) -> xr.DataArray | xr.Dataset | Sequence[xr.DataArray | xr.Dataset]:
+        data_list: list[xr.DataArray | xr.Dataset | list[xr.DataArray]],
+        coord_dict: dict[str, Iterable],
+    ) -> (
+        xr.DataArray | xr.Dataset | list[xr.DataArray | xr.Dataset | list[xr.DataArray]]
+    ):
         if len(coord_dict) == 0:
             try:
                 # Try to merge the data without conflicts
@@ -814,16 +824,25 @@ def combine_multiple(
                 return data_list
         else:
             for i in range(len(data_list)):
-                data_list[i] = data_list[i].assign_coords(
-                    {k: v[i] for k, v in coord_dict.items()}
+                if isinstance(data_list[i], list):
+                    data_list[i] = self.combine_multiple(data_list[i], coord_dict={})
+
+                if not isinstance(data_list[i], list):
+                    data_list[i] = data_list[i].assign_coords(
+                        {k: v[i] for k, v in coord_dict.items()}
+                    )
+            try:
+                return xr.concat(
+                    data_list,
+                    dim=next(iter(coord_dict.keys())),
+                    coords="different",
                 )
-            return xr.concat(
-                data_list, dim=next(iter(coord_dict.keys())), coords="different"
-            )
+            except:  # noqa: E722
+                return data_list
 
     def post_process_general(
-        self, data: xr.DataArray | xr.Dataset | list[xr.DataArray | xr.Dataset]
-    ) -> xr.DataArray | xr.Dataset | list[xr.DataArray | xr.Dataset]:
+        self, data: xr.DataArray | xr.Dataset | list[xr.DataArray]
+    ) -> xr.DataArray | xr.Dataset | list[xr.DataArray]:
         if isinstance(data, xr.DataArray):
             return self.post_process(data)
 
@@ -868,11 +887,15 @@ def process_keys(
 
     def post_process(self, data: xr.DataArray) -> xr.DataArray:
         data = self.process_keys(data)
-        data = data.assign_attrs(self.additional_attrs)
+        data = data.assign_attrs(
+            self.additional_attrs | {"data_loader_name": str(self.name)}
+        )
         return data
 
     @classmethod
-    def validate(cls, data: xr.DataArray | xr.Dataset):
+    def validate(
+        cls, data: xr.DataArray | xr.Dataset | list[xr.DataArray | xr.Dataset]
+    ) -> None:
         """Validate the input data to ensure it is in the correct format.
 
         Checks for the presence of all required coordinates and attributes. If the data
@@ -918,7 +941,7 @@ def validate(cls, data: xr.DataArray | xr.Dataset):
 
     def load_multiple_parallel(
         self, file_paths: list[str], n_jobs: int | None = None
-    ) -> list[xr.DataArray | xr.Dataset]:
+    ) -> list[xr.DataArray | xr.Dataset | list[xr.DataArray]]:
         """Load multiple files in parallel.
 
         Parameters
@@ -958,7 +981,7 @@ class RegistryBase:
     registry is created and used throughout the application.
     """
 
-    __instance = None
+    __instance: RegistryBase | None = None
 
     def __new__(cls):
         if not isinstance(cls.__instance, cls):
@@ -966,16 +989,16 @@ def __new__(cls):
         return cls.__instance
 
     @classmethod
-    def instance(cls) -> LoaderRegistry:
+    def instance(cls) -> Self:
         """Returns the registry instance."""
         return cls()
 
 
 class LoaderRegistry(RegistryBase):
-    loaders: dict[str, LoaderBase | type[LoaderBase]] = {}
+    loaders: ClassVar[dict[str, LoaderBase | type[LoaderBase]]] = {}
     """Registered loaders \n\n:meta hide-value:"""
 
-    alias_mapping: dict[str, str] = {}
+    alias_mapping: ClassVar[dict[str, str]] = {}
     """Mapping of aliases to loader names \n\n:meta hide-value:"""
 
     current_loader: LoaderBase | None = None
@@ -996,15 +1019,18 @@ def register(self, loader_class: type[LoaderBase]):
 
     def get(self, key: str) -> LoaderBase:
         loader_name = self.alias_mapping.get(key)
+        if loader_name is None:
+            raise LoaderNotFoundError(key)
+
         loader = self.loaders.get(loader_name)
 
         if loader is None:
-            raise KeyError(f"Loader for {key} not found")
+            raise LoaderNotFoundError(key)
 
         if not isinstance(loader, LoaderBase):
             # If not an instance, create one
-            self.loaders[loader_name] = loader()
-            loader = self.loaders[loader_name]
+            loader = loader()
+            self.loaders[loader_name] = loader
 
         return loader
 
@@ -1014,10 +1040,10 @@ def __getitem__(self, key: str) -> LoaderBase:
     def __getattr__(self, key: str) -> LoaderBase:
         try:
             return self.get(key)
-        except KeyError as e:
-            raise AttributeError(f"Loader for {key} not found") from e
+        except LoaderNotFoundError as e:
+            raise AttributeError(str(e)) from e
 
-    def set_loader(self, loader: str | LoaderBase):
+    def set_loader(self, loader: str | LoaderBase | None):
         """Set the current data loader.
 
         All subsequent calls to `load` will use the loader set here.
@@ -1094,7 +1120,7 @@ def loader_context(
             if data_dir is not None:
                 self.set_data_dir(old_data_dir)
 
-    def set_data_dir(self, data_dir: str | os.PathLike):
+    def set_data_dir(self, data_dir: str | os.PathLike | None):
         """Set the default data directory for the data loader.
 
         All subsequent calls to `load` will use the `data_dir` set here unless
@@ -1111,7 +1137,7 @@ def set_data_dir(self, data_dir: str | os.PathLike):
         directly, it will not use the default data directory.
 
         """
-        if not os.path.isdir(data_dir):
+        if data_dir is not None and not os.path.isdir(data_dir):
             raise FileNotFoundError(f"Directory {data_dir} not found")
         self.default_data_dir = data_dir
 
diff --git a/src/erlab/io/exampledata.py b/src/erlab/io/exampledata.py
index 869d1c9b..c7ac81f6 100644
--- a/src/erlab/io/exampledata.py
+++ b/src/erlab/io/exampledata.py
@@ -58,7 +58,7 @@ def generate_data(
     Eres: float = 2.0e-3,
     noise: bool = True,
     seed: int | None = None,
-    count: int = 1e8,
+    count: int = 100000000,
     ccd_sigma: float = 0.6,
 ) -> xr.DataArray:
     """Generate simulated data for a given shape in momentum space.
@@ -108,12 +108,12 @@ def generate_data(
     if isinstance(krange, dict):
         kx = np.linspace(*krange["kx"], shape[0])
         ky = np.linspace(*krange["ky"], shape[1])
-    elif not np.iterable(krange):
-        kx = np.linspace(-krange, krange, shape[0])
-        ky = np.linspace(-krange, krange, shape[1])
-    else:
+    elif isinstance(krange, tuple):
         kx = np.linspace(*krange, shape[0])
         ky = np.linspace(*krange, shape[1])
+    else:
+        kx = np.linspace(-krange, krange, shape[0])
+        ky = np.linspace(-krange, krange, shape[1])
 
     eV = np.linspace(*Erange, shape[2])
 
@@ -169,7 +169,7 @@ def generate_data_angles(
     Eres: float = 10.0e-3,
     noise: bool = True,
     seed: int | None = None,
-    count: int = 1e8,
+    count: int = 100000000,
     ccd_sigma: float = 0.6,
     assign_attributes: bool = False,
 ) -> xr.DataArray:
@@ -228,12 +228,12 @@ def generate_data_angles(
     if isinstance(angrange, dict):
         alpha = np.linspace(*angrange["alpha"], shape[0])
         beta = np.linspace(*angrange["beta"], shape[1])
-    elif not np.iterable(angrange):
-        alpha = np.linspace(-angrange, angrange, shape[0])
-        beta = np.linspace(-angrange, angrange, shape[1])
-    else:
+    elif isinstance(angrange, tuple):
         alpha = np.linspace(*angrange, shape[0])
         beta = np.linspace(*angrange, shape[1])
+    else:
+        alpha = np.linspace(-angrange, angrange, shape[0])
+        beta = np.linspace(-angrange, angrange, shape[1])
 
     if not isinstance(configuration, erlab.analysis.kspace.AxesConfiguration):
         configuration = erlab.analysis.kspace.AxesConfiguration(configuration)
@@ -307,7 +307,7 @@ def generate_gold_edge(
     angres: float = 0.1,
     edge_coeffs: Sequence[float] = (0.04, 1e-5, -3e-4),
     background_coeffs: Sequence[float] = (1.0, 0.0, -2e-3),
-    count: int = 1e6,
+    count: int = 1000000,
     noise: bool = True,
     seed: int | None = None,
     ccd_sigma: float = 0.6,
@@ -384,19 +384,3 @@ def generate_gold_edge(
         )
 
     return data.assign_attrs(temp_sample=temp)
-
-
-if __name__ == "__main__":
-    # out = generate_data(
-    #     shape=(201, 202, 203),
-    #     krange=1.4,
-    #     Erange=(-0.45, 0.09),
-    #     temp=30,
-    #     bandshift=-0.2,
-    #     count=1000,
-    #     noise=True,
-    # )
-    out = generate_data_angles()
-    import erlab.plotting.erplot as eplt
-
-    eplt.itool([out, out.kspace.convert()])
diff --git a/src/erlab/io/igor.py b/src/erlab/io/igor.py
index 3e86f610..480aa7ce 100644
--- a/src/erlab/io/igor.py
+++ b/src/erlab/io/igor.py
@@ -4,6 +4,7 @@
 import igor2.binarywave
 import igor2.packed
 import igor2.record
+from typing import Any
 import numpy as np
 import xarray as xr
 
@@ -18,9 +19,9 @@ def _load_experiment_raw(
     ignore: list[str] | None = None,
     recursive: bool = False,
     **kwargs,
-) -> xr.Dataset:
+) -> dict[str, xr.DataArray]:
     if folder is None:
-        folder = []
+        split_path: list[Any] = []
     if ignore is None:
         ignore = []
 
@@ -31,13 +32,13 @@ def _load_experiment_raw(
         except ValueError:
             continue
 
-    waves = {}
+    waves: dict[str, xr.DataArray] = {}
     if isinstance(folder, str):
-        folder = folder.split("/")
-    folder = [n.encode() for n in folder]
+        split_path = folder.split("/")
+    split_path = [n.encode() for n in split_path]
 
     expt = expt["root"]
-    for dirname in folder:
+    for dirname in split_path:
         expt = expt[dirname]
 
     def unpack_folders(expt):
@@ -216,7 +217,7 @@ def get_dim_name(index):
     dims = [get_dim_name(i) for i in range(_MAXDIM)]
     coords = {
         dims[i]: np.linspace(b, b + a * (c - 1), c)
-        for i, (a, b, c) in enumerate(zip(sfA, sfB, shape))
+        for i, (a, b, c) in enumerate(zip(sfA, sfB, shape, strict=True))
         if c != 0
     }
 
diff --git a/src/erlab/io/plugins/da30.py b/src/erlab/io/plugins/da30.py
index 96d0a3e8..8efe41dd 100644
--- a/src/erlab/io/plugins/da30.py
+++ b/src/erlab/io/plugins/da30.py
@@ -7,7 +7,7 @@
 import os
 import tempfile
 import zipfile
-
+from typing import ClassVar
 import numpy as np
 import xarray as xr
 
@@ -16,19 +16,18 @@
 
 
 class DA30Loader(LoaderBase):
-    name: str = "da30"
-    aliases: list[str] = ["DA30"]
+    name = "da30"
+    aliases = ("DA30",)
 
-    name_map: dict[str, str] = {
+    name_map: ClassVar[dict] = {
         "eV": ["Kinetic Energy [eV]", "Energy [eV]"],
         "alpha": ["Y-Scale [deg]", "Thetax [deg]"],
         "beta": ["Thetay [deg]"],
         "hv": ["BL Energy", "Excitation Energy"],
     }
-    coordinate_attrs: tuple[str, ...] = ()
-    additional_attrs: dict[str, str | int | float] = {}
-    always_single: bool = True
-    skip_validate: bool = True
+    additional_attrs: ClassVar[dict] = {}
+    always_single = True
+    skip_validate = True
 
     def load_single(self, file_path: str | os.PathLike) -> xr.DataArray:
         ext = os.path.splitext(file_path)[-1]
diff --git a/src/erlab/io/plugins/kriss.py b/src/erlab/io/plugins/kriss.py
index 4f8b230f..4abfd68d 100644
--- a/src/erlab/io/plugins/kriss.py
+++ b/src/erlab/io/plugins/kriss.py
@@ -3,19 +3,20 @@
 import os
 import re
 from collections.abc import Iterable
+from typing import ClassVar
 
 import erlab.io.utilities
 from erlab.io.plugins.da30 import DA30Loader
 
 
 class KRISSLoader(DA30Loader):
-    name: str = "kriss"
+    name = "kriss"
 
-    aliases: list[str] = ["KRISS"]
+    aliases = ("KRISS",)
 
-    coordinate_attrs: tuple[str, ...] = ("beta", "chi", "xi", "hv", "x", "y", "z")
+    coordinate_attrs = ("beta", "chi", "xi", "hv", "x", "y", "z")
 
-    additional_attrs: dict[str, str | int | float] = {"configuration": 4}
+    additional_attrs: ClassVar[dict] = {"configuration": 4}
 
     @property
     def name_map(self):
diff --git a/src/erlab/io/plugins/merlin.py b/src/erlab/io/plugins/merlin.py
index 52d9f66c..82909a50 100644
--- a/src/erlab/io/plugins/merlin.py
+++ b/src/erlab/io/plugins/merlin.py
@@ -4,6 +4,7 @@
 import glob
 import os
 import re
+from typing import Any, ClassVar
 
 import numpy as np
 import numpy.typing as npt
@@ -16,10 +17,11 @@
 
 
 class BL403Loader(LoaderBase):
-    name: str = "merlin"
-    aliases: list[str] = ["ALS_BL4", "als_bl4", "BL403", "bl403"]
+    name = "merlin"
 
-    name_map: dict[str, str | list[str]] = {
+    aliases = ("ALS_BL4", "als_bl4", "BL403", "bl403")
+
+    name_map: ClassVar[dict] = {
         "alpha": "deg",
         "beta": ["Polar", "Polar Compens"],
         "delta": "Azimuth",
@@ -32,7 +34,7 @@ class BL403Loader(LoaderBase):
         "temp_sample": "Temperature Sensor B",
         "mesh_current": "Mesh Current",
     }
-    coordinate_attrs: tuple[str, ...] = (
+    coordinate_attrs = (
         "beta",
         "delta",
         "xi",
@@ -43,11 +45,11 @@ class BL403Loader(LoaderBase):
         "polarization",
         "mesh_current",
     )
-    additional_attrs: dict[str, str | int | float] = {
+    additional_attrs: ClassVar[dict] = {
         "configuration": 1,
         "sample_workfunction": 4.44,
     }
-    always_single: bool = False
+    always_single = False
 
     def load_single(self, file_path: str | os.PathLike) -> xr.DataArray:
         if os.path.splitext(file_path)[1] == ".ibw":
@@ -59,9 +61,7 @@ def load_single(self, file_path: str | os.PathLike) -> xr.DataArray:
 
         return self.process_keys(data)
 
-    def identify(
-        self, num: int, data_dir: str | os.PathLike
-    ) -> tuple[list[str], dict[str, npt.NDArray[np.float64]]]:
+    def identify(self, num: int, data_dir: str | os.PathLike):
         coord_dict: dict[str, npt.NDArray[np.float64]] = {}
 
         # Look for scans
@@ -74,9 +74,12 @@ def identify(
             files = glob.glob(f"*_{str(num).zfill(3)}_R*.pxt", root_dir=data_dir)
             files.sort()
         elif len(files) > 1:
-            prefix: str = re.match(
+            match_prefix = re.match(
                 r"(.*?)_" + str(num).zfill(3) + r"(?:_S\d{3})?.pxt", files[0]
-            ).group(1)
+            )
+            if match_prefix is None:
+                raise RuntimeError(f"Failed to match prefix in {files[0]}")
+            prefix: str = match_prefix.group(1)
 
             motor_file = os.path.join(
                 data_dir, f"{prefix}_{str(num).zfill(3)}_Motor_Pos.txt"
@@ -104,16 +107,20 @@ def identify(
 
         return files, coord_dict
 
-    def infer_index(self, name: str) -> tuple[int | None, dict]:
+    def infer_index(self, name: str) -> tuple[int | None, dict[str, Any]]:
         try:
-            scan_num: str = re.match(r".*?(\d{3})(?:_S\d{3})?", name).group(1)
-        except (AttributeError, IndexError):
-            return None, None
+            match_scan = re.match(r".*?(\d{3})(?:_S\d{3})?", name)
+            if match_scan is None:
+                return None, {}
+
+            scan_num: str = match_scan.group(1)
+        except IndexError:
+            return None, {}
 
         if scan_num.isdigit():
             return int(scan_num), {}
         else:
-            return None, None
+            return None, {}
 
     def post_process(self, data: xr.DataArray) -> xr.DataArray:
         data = super().post_process(data)
diff --git a/src/erlab/io/plugins/ssrl52.py b/src/erlab/io/plugins/ssrl52.py
index a13acc0f..6b832d29 100644
--- a/src/erlab/io/plugins/ssrl52.py
+++ b/src/erlab/io/plugins/ssrl52.py
@@ -3,10 +3,10 @@
 import datetime
 import os
 import re
+from typing import ClassVar
 
 import h5netcdf
 import numpy as np
-import numpy.typing as npt
 import pandas as pd
 import xarray as xr
 
@@ -15,10 +15,10 @@
 
 
 class SSRL52Loader(LoaderBase):
-    name: str = "ssrl"
-    aliases: list[str] = ["ssrl52", "bl5-2"]
+    name = "ssrl"
+    aliases = ("ssrl52", "bl5-2")
 
-    name_map: dict[str, str] = {
+    name_map: ClassVar[dict] = {
         "eV": "Kinetic Energy",
         "alpha": "ThetaX",
         "beta": ["ThetaY", "YDeflection", "DeflectionY"],
@@ -32,17 +32,10 @@ class SSRL52Loader(LoaderBase):
         "temp_sample": ["TB", "sample_stage_temperature"],
         "sample_workfunction": "WorkFunction",
     }
-    coordinate_attrs: tuple[str, ...] = (
-        "beta",
-        "delta",
-        "chi",
-        "xi",
-        "hv",
-        "x",
-        "y",
-        "z",
-    )
-    additional_attrs: dict[str, str | int | float] = {
+
+    coordinate_attrs = ("beta", "delta", "chi", "xi", "hv", "x", "y", "z")
+
+    additional_attrs: ClassVar[dict] = {
         "configuration": 3,
         "sample_workfunction": 4.5,
     }
@@ -123,7 +116,7 @@ def identify(
         num: int,
         data_dir: str | os.PathLike,
         zap: bool = False,
-    ) -> tuple[list[str], dict[str, npt.NDArray[np.float64]]]:
+    ):
         if zap:
             target_files = erlab.io.utilities.get_files(
                 data_dir, extensions=(".h5",), contains="zap"
diff --git a/src/erlab/io/utilities.py b/src/erlab/io/utilities.py
index b9437074..502f463c 100644
--- a/src/erlab/io/utilities.py
+++ b/src/erlab/io/utilities.py
@@ -204,7 +204,7 @@ def save_as_hdf5(
         # IGORWaveScaling order: chunk row column layer
         scaling = [[1, 0]]
         for i in range(data.ndim):
-            coord: npt.NDArray = data[data.dims[i]].values
+            coord: npt.NDArray = np.asarray(data[data.dims[i]].values)
             delta = coord[1] - coord[0]
             scaling.append([delta, coord[0]])
         if data.ndim == 4:
diff --git a/src/erlab/plotting/__init__.py b/src/erlab/plotting/__init__.py
index e8c96789..366374b9 100644
--- a/src/erlab/plotting/__init__.py
+++ b/src/erlab/plotting/__init__.py
@@ -62,6 +62,10 @@ def load_igor_ct(fname: str, name: str) -> None:
 
     """
     file = pkgutil.get_data(__package__, "IgorCT/" + fname)
+
+    if file is None:
+        raise FileNotFoundError(f"Could not find file {fname}")
+
     if fname.endswith(".txt"):
         values = np.genfromtxt(io.StringIO(file.decode()))
     elif fname.endswith(".ibw"):
diff --git a/src/erlab/plotting/annotations.py b/src/erlab/plotting/annotations.py
index 6d9d1137..f7864318 100644
--- a/src/erlab/plotting/annotations.py
+++ b/src/erlab/plotting/annotations.py
@@ -23,9 +23,13 @@
 
 import io
 import re
-from typing import TYPE_CHECKING, Literal
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 import matplotlib
+import matplotlib.backends.backend_pdf
+import matplotlib.backends.backend_svg
+import matplotlib.figure
+import matplotlib.mathtext
 import matplotlib.pyplot as plt
 import matplotlib.ticker
 import matplotlib.transforms as mtransforms
@@ -264,6 +268,7 @@ def copy_mathtext(
         rcparams = {}
     parser = matplotlib.mathtext.MathTextParser("path")
     width, height, depth, _, _ = parser.parse(s, dpi=72, prop=fontproperties)
+
     fig = matplotlib.figure.Figure(figsize=(width / 72, height / 72))
     fig.patch.set_facecolor("none")
     fig.text(0, depth / height, s, fontproperties=fontproperties)
@@ -285,11 +290,11 @@ def copy_mathtext(
             rcparams.setdefault("svg.fonttype", "path" if outline else "none")
             rcparams.setdefault("svg.image_inline", True)
             with plt.rc_context(rcparams):
-                fig.canvas.print_svg(buffer)
+                fig.canvas.print_svg(buffer)  # type: ignore[attr-defined]
         else:
             rcparams.setdefault("pdf.fonttype", 3 if outline else 42)
             with plt.rc_context(rcparams):
-                fig.canvas.print_pdf(buffer)
+                fig.canvas.print_pdf(buffer)  # type: ignore[attr-defined]
         pyperclip.copy(buffer.getvalue().decode("utf-8"))
 
 
@@ -308,7 +313,7 @@ def fancy_labels(ax=None, deg2rad=False):
 
 
 def label_subplot_properties(
-    axes: matplotlib.axes.Axes | Sequence[matplotlib.axes.Axes],
+    axes: matplotlib.axes.Axes | Iterable[matplotlib.axes.Axes],
     values: dict,
     decimals: int | None = None,
     si: int = 0,
@@ -352,7 +357,7 @@ def label_subplot_properties(
     kwargs.setdefault("suffix", "")
     kwargs.setdefault("loc", "upper right")
 
-    strlist = []
+    strlist: Any = []
     for k, v in values.items():
         if not isinstance(v, tuple | list | np.ndarray):
             v = [v]
@@ -364,14 +369,14 @@ def label_subplot_properties(
                 for val in v
             ]
         )
-    strlist = list(zip(*strlist))
+    strlist = list(zip(*strlist, strict=True))
     strlist = ["\n".join(strlist[i]) for i in range(len(strlist))]
     label_subplots(axes, strlist, order=order, **kwargs)
 
 
 def label_subplots(
-    axes: matplotlib.axes.Axes | Sequence[matplotlib.axes.Axes],
-    values: Sequence[int | str] | None = None,
+    axes: matplotlib.axes.Axes | Iterable[matplotlib.axes.Axes],
+    values: Iterable[int | str] | None = None,
     startfrom: int = 1,
     order: Literal["C", "F", "A", "K"] = "C",
     loc: Literal[
@@ -467,10 +472,12 @@ def label_subplots(
 
     axlist = np.array(axes, dtype=object).flatten(order=order)
     if values is None:
-        values = np.array([i + startfrom for i in range(len(axlist))], dtype=np.int64)
+        value_arr = np.array(
+            [i + startfrom for i in range(len(axlist))], dtype=np.int64
+        )
     else:
-        values = np.array(values).flatten(order=order)
-        if not (axlist.size == values.size):
+        value_arr = np.array(values).flatten(order=order)
+        if not (axlist.size == value_arr.size):
             raise IndexError(
                 "The number of given values must match the number of given axes."
             )
@@ -479,16 +486,14 @@ def label_subplots(
         bbox_to_anchor = axlist[i].bbox
         if fontsize is None:
             if isinstance(axlist[i], matplotlib.figure.Figure):
-                fs = "large"
+                fontsize = "large"
             else:
-                fs = "medium"
-        else:
-            fs = fontsize
+                fontsize = "medium"
 
         bbox_transform = matplotlib.transforms.ScaledTranslation(
             offset[0] / 72, offset[1] / 72, axlist[i].get_figure().dpi_scale_trans
         )
-        label_str = _alph_label(values[i], prefix, suffix, numeric, capital)
+        label_str = _alph_label(value_arr[i], prefix, suffix, numeric, capital)
         with plt.rc_context({"text.color": axes_textcolor(axlist[i])}):
             at = matplotlib.offsetbox.AnchoredText(
                 label_str,
@@ -496,7 +501,7 @@ def label_subplots(
                 frameon=False,
                 pad=0,
                 borderpad=0.5,
-                prop=dict(fontsize=fs, **kwargs),
+                prop=dict(fontsize=fontsize, **kwargs),
                 bbox_to_anchor=bbox_to_anchor,
                 bbox_transform=bbox_transform,
                 clip_on=False,
@@ -587,16 +592,18 @@ def label_subplots_nature(
 
     axlist = np.array(axes, dtype=object).flatten(order=order)
     if values is None:
-        values = np.array([i + startfrom for i in range(len(axlist))], dtype=np.int64)
+        value_arr = np.array(
+            [i + startfrom for i in range(len(axlist))], dtype=np.int64
+        )
     else:
-        values = np.array(values).flatten(order=order)
-        if not (axlist.size == values.size):
+        value_arr = np.array(values).flatten(order=order)
+        if not (axlist.size == value_arr.size):
             raise IndexError(
                 "The number of given values must match the number of given axes."
             )
 
     for i in range(len(axlist)):
-        label_str = _alph_label(values[i], prefix, suffix, numeric, capital)
+        label_str = _alph_label(value_arr[i], prefix, suffix, numeric, capital)
         trans = matplotlib.transforms.ScaledTranslation(
             offset[0] / 72, offset[1] / 72, axlist[i].get_figure().dpi_scale_trans
         )
@@ -624,7 +631,7 @@ def mark_points(
     literal: bool = False,
     roman: bool = True,
     bar: bool = False,
-    ax: matplotlib.axes.Axes | Iterable[matplotlib.axes.Axes] = None,
+    ax: matplotlib.axes.Axes | Iterable[matplotlib.axes.Axes] | None = None,
     **kwargs,
 ):
     """Mark points above the horizontal axis.
@@ -654,23 +661,32 @@ def mark_points(
     """
     if ax is None:
         ax = plt.gca()
+
     if np.iterable(ax):
         for a in np.asarray(ax, dtype=object).flatten():
             mark_points(points, labels, y, pad, literal, roman, bar, a, **kwargs)
     else:
+        ax = cast(matplotlib.axes.Axes, ax)  # to appease mypy
+        fig = ax.get_figure()
+
+        if fig is None:
+            raise ValueError("Given axes does not belong to a figure")
+
         for k, v in {"ha": "center", "va": "baseline", "fontsize": "small"}.items():
             kwargs.setdefault(k, v)
+
         if not np.iterable(y):
-            y = [y] * len(points)
+            y = [y] * len(points)  # type: ignore[list-item]
+
         with plt.rc_context({"font.family": "serif"}):
-            for xi, yi, label in zip(points, y, labels):
+            for xi, yi, label in zip(points, y, labels, strict=True):
                 ax.text(
                     xi,
                     yi,
                     label if literal else parse_point_labels(label, roman, bar),
                     transform=ax.transData
                     + mtransforms.ScaledTranslation(
-                        pad[0] / 72, pad[1] / 72, ax.figure.dpi_scale_trans
+                        pad[0] / 72, pad[1] / 72, fig.dpi_scale_trans
                     ),
                     **kwargs,
                 )
@@ -682,7 +698,7 @@ def mark_points_outside(
     axis: Literal["x", "y"] = "x",
     roman: bool = True,
     bar: bool = False,
-    ax: matplotlib.axes.Axes | Iterable[matplotlib.axes.Axes] = None,
+    ax: matplotlib.axes.Axes | Iterable[matplotlib.axes.Axes] | None = None,
 ):
     """Mark points above the horizontal axis.
 
@@ -712,6 +728,8 @@ def mark_points_outside(
         for a in np.asarray(ax, dtype=object).flatten():
             mark_points_outside(points, labels, axis, roman, bar, a)
     else:
+        ax = cast(matplotlib.axes.Axes, ax)  # to appease mypy
+
         if axis == "x":
             label_ax = ax.twiny()
             label_ax.set_xlim(ax.get_xlim())
@@ -775,7 +793,7 @@ def plot_hv_text_right(ax, val, x=1 - 0.025, y=0.975, **kwargs):
     )
 
 
-def property_label(key, value, decimals=None, si=0, name=None, unit=None):
+def property_label(key, value, decimals=None, si=0, name=None, unit=None) -> str:
     if name == "":
         delim = ""
     else:
@@ -883,7 +901,7 @@ def scale_units(
 def set_titles(axes, labels, order="C", **kwargs):
     axlist = np.array(axes, dtype=object).flatten(order=order)
     labels = np.asarray(labels)
-    for ax, label in zip(axlist.flat, labels.flat):
+    for ax, label in zip(axlist.flat, labels.flat, strict=True):
         ax.set_title(label, **kwargs)
 
 
@@ -892,7 +910,7 @@ def set_xlabels(axes, labels, order="C", **kwargs):
     if isinstance(labels, str):
         labels = [labels] * len(axlist)
     labels = np.asarray(labels)
-    for ax, label in zip(axlist.flat, labels.flat):
+    for ax, label in zip(axlist.flat, labels.flat, strict=True):
         ax.set_xlabel(label, **kwargs)
 
 
@@ -901,7 +919,7 @@ def set_ylabels(axes, labels, order="C", **kwargs):
     if isinstance(labels, str):
         labels = [labels] * len(axlist)
     labels = np.asarray(labels)
-    for ax, label in zip(axlist.flat, labels.flat):
+    for ax, label in zip(axlist.flat, labels.flat, strict=True):
         ax.set_ylabel(label, **kwargs)
 
 
diff --git a/src/erlab/plotting/atoms.py b/src/erlab/plotting/atoms.py
index 92df4320..b187d959 100644
--- a/src/erlab/plotting/atoms.py
+++ b/src/erlab/plotting/atoms.py
@@ -9,17 +9,18 @@
 import contextlib
 import functools
 import itertools
-from collections.abc import Callable, Sequence
-from typing import Literal
+from collections.abc import Callable, Iterable, Mapping, Sequence
+from typing import Literal, cast
 
 import matplotlib.collections
-import matplotlib.colors as mcolors
+import matplotlib.colors
 import matplotlib.pyplot as plt
 import mpl_toolkits.mplot3d
 import mpl_toolkits.mplot3d.art3d
 import mpl_toolkits.mplot3d.proj3d
 import numpy as np
 import numpy.typing as npt
+from matplotlib.typing import ColorType
 
 __all__ = ["Atom3DCollection", "Bond3DCollection", "CrystalProperty"]
 
@@ -78,7 +79,9 @@ def projected_length_pos(ax: mpl_toolkits.mplot3d.Axes3D, length, position):
         return np.asarray(
             [
                 projected_length_pos(ax, d, p)
-                for d, p in zip(np.asarray(length).flat, np.asarray(position))
+                for d, p in zip(
+                    np.asarray(length).flat, np.asarray(position), strict=True
+                )
             ]
         )
     rc = np.asarray(position).reshape(-1, 1)
@@ -100,7 +103,7 @@ def _zalpha(colors, zs):
         return np.zeros((0, 4))
     norm = plt.Normalize(min(zs), max(zs))
     sats = 1 - norm(zs) * 0.7
-    rgba = np.broadcast_to(mcolors.to_rgba_array(colors), (len(zs), 4))
+    rgba = np.broadcast_to(matplotlib.colors.to_rgba_array(colors), (len(zs), 4))
     rgba = rgba.T * sats
     rgba += 1 - sats
     rgba = rgba.T
@@ -131,7 +134,7 @@ def _maybe_depth_shade_and_sort_colors(self, color_array):
         )
         if len(color_array) > 1:
             color_array = color_array[self._z_markers_idx]
-        return mcolors.to_rgba_array(color_array, self._alpha)
+        return matplotlib.colors.to_rgba_array(color_array, self._alpha)
 
     def set_sizes(self, sizes: np.ndarray, dpi: float = 72.0):
         super().set_sizes(sizes, dpi)
@@ -240,15 +243,15 @@ def draw(self, renderer):
 class CrystalProperty:
     def __init__(
         self,
-        atom_pos: dict[
-            str, npt.NDArray[np.float64] | Sequence[npt.NDArray[np.float64]]
+        atom_pos: Mapping[
+            str, Iterable[float | np.floating | npt.NDArray[np.floating]]
         ],
         avec: npt.NDArray[np.float64],
-        offset: npt.NDArray[np.float64] | Sequence[float] = (0.0, 0.0, 0.0),
-        radii: Sequence[float] | None = None,
-        colors: Sequence[str | tuple[float, ...]] | None = None,
+        offset: Iterable[float] = (0.0, 0.0, 0.0),
+        radii: Iterable[float] | None = None,
+        colors: Iterable[ColorType] | None = None,
         repeat: tuple[int, int, int] = (1, 1, 1),
-        bounds: dict[Literal["x", "y", "z"], tuple[float, float]] | None = None,
+        bounds: Mapping[Literal["x", "y", "z"], tuple[float, float]] | None = None,
         mask: Callable | None = None,
         r_factor: float = 0.4,
     ):
@@ -296,16 +299,18 @@ def __init__(
 
         if radii is None:
             radii = [1.0] * len(self.atoms)
-        self.atom_radii: dict[str, float] = dict(zip(self.atoms, radii))
+        self.atom_radii: dict[str, float] = dict(zip(self.atoms, radii, strict=True))
 
         if colors is None:
             colors = [f"C{i}" for i in range(len(self.atoms))]
+
         self.atom_color: dict[str, str] = {
-            k: mcolors.to_hex(v) for k, v in zip(self.atoms, colors)
+            k: matplotlib.colors.to_hex(v)
+            for k, v in zip(self.atoms, colors, strict=True)
         }
 
         self.repeat: tuple[int, int, int] = repeat
-        self._bounds: dict[Literal["x", "y", "z"], tuple[float, float]] = (
+        self._bounds: Mapping[Literal["x", "y", "z"], tuple[float, float]] = (
             {} if bounds is None else bounds
         )
         self.mask: Callable | None = mask
@@ -326,9 +331,14 @@ def from_fractional(
         *args,
         **kwargs,
     ):
-        atom_pos = {}
+        atom_pos: dict[str, list[npt.NDArray[np.float64]]] = {}
         for k, v in frac_pos.items():
-            atom_pos[k] = [x[0] * avec[0] + x[1] * avec[1] + x[2] * avec[2] for x in v]
+            atom_pos[k] = [
+                np.asarray(
+                    x[0] * avec[0] + x[1] * avec[1] + x[2] * avec[2], dtype=np.float64
+                )
+                for x in v
+            ]
         return cls(atom_pos, avec, *args, **kwargs)
 
     @property
@@ -336,7 +346,7 @@ def bounds(self) -> list[tuple[float, float]]:
         bound_list = []
         for dim in ("x", "y", "z"):
             try:
-                bound_list.append(self._bounds[dim])
+                bound_list.append(self._bounds[cast(Literal["x", "y", "z"], dim)])
             except KeyError:
                 bound_list.append((-np.inf, np.inf))
         return bound_list
@@ -386,7 +396,7 @@ def atom_pos(self) -> dict[str, npt.NDArray[np.float64]]:
         return masked_atom_pos
 
     @property
-    def _color_array(self) -> tuple[npt.NDArray[np.str_]]:
+    def _color_array(self) -> npt.NDArray[np.str_]:
         return np.asarray(
             [
                 self.atom_color[k]
@@ -396,7 +406,7 @@ def _color_array(self) -> tuple[npt.NDArray[np.str_]]:
         )
 
     @property
-    def _size_array(self) -> tuple[npt.NDArray[np.float64]]:
+    def _size_array(self) -> npt.NDArray[np.float64]:
         return np.asarray(
             [
                 self.atom_radii[k]
@@ -464,6 +474,7 @@ def plot(
 
         if ax is None:
             ax = plt.gcf().add_subplot(projection="3d")
+        ax = cast(mpl_toolkits.mplot3d.Axes3D, ax)
 
         if clean_axes:
             ax.set_facecolor("none")
diff --git a/src/erlab/plotting/bz.py b/src/erlab/plotting/bz.py
index 5ffd364c..0031bab7 100644
--- a/src/erlab/plotting/bz.py
+++ b/src/erlab/plotting/bz.py
@@ -22,7 +22,7 @@
 def get_bz_edge(
     basis: npt.NDArray[np.float64],
     reciprocal: bool = True,
-    extend: tuple[int, int, int] | tuple[int, int] | None = None,
+    extend: tuple[int, ...] | None = None,
 ) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:
     """Calculates the edge of the first Brillouin zone (BZ) from lattice vectors.
 
@@ -47,18 +47,21 @@ def get_bz_edge(
         Vertices of the BZ.
 
     """
-    if not (basis.shape == (2, 2) or basis.shape == (3, 3)):
+    if basis.shape == (2, 2):
+        ndim = 2
+    elif basis.shape == (3, 3):
+        ndim = 3
+    else:
         raise ValueError("Shape of `basis` must be (N, N) where N = 2 or 3.")
+
     if not reciprocal:
         basis = 2 * np.pi * np.linalg.inv(basis).T
 
-    ndim = basis.shape[-1]
-
     if extend is None:
         extend = (1,) * ndim
 
     points = (
-        np.tensordot(basis, np.mgrid[[slice(-1, 2) for _ in range(ndim)]], axes=[0, 0])
+        np.tensordot(basis, np.mgrid[[slice(-1, 2) for _ in range(ndim)]], axes=(0, 0))
         .reshape((ndim, 3**ndim))
         .T
     )
@@ -71,7 +74,7 @@ def get_bz_edge(
     lines = []
     vertices = []
 
-    for pointidx, simplex in zip(vor.ridge_points, vor.ridge_vertices):
+    for pointidx, simplex in zip(vor.ridge_points, vor.ridge_vertices, strict=True):
         simplex = np.asarray(simplex)
         if zero_ind in pointidx:
             # If the origin is included in the ridge, add the vertices
@@ -79,8 +82,8 @@ def get_bz_edge(
             vertices.append(vor.vertices[simplex])
 
     # Remove duplicates
-    lines_new = []
-    vertices_new = []
+    lines_new: list[npt.NDArray] = []
+    vertices_new: list[npt.NDArray] = []
 
     for line in lines:
         for i in range(line.shape[0] - 1):
@@ -94,8 +97,8 @@ def get_bz_edge(
         if not any(np.allclose(v, vn) for vn in vertices_new):
             vertices_new.append(v)
 
-    lines = np.asarray(lines_new)
-    vertices = np.asarray(vertices_new)
+    lines_arr = np.asarray(lines_new)
+    vertices_arr = np.asarray(vertices_new)
 
     # Extend the BZ
     additional_lines = []
@@ -103,12 +106,12 @@ def get_bz_edge(
     for vals in itertools.product(*[range(-n + 1, n) for n in extend]):
         if vals != (0,) * ndim:
             displacement = np.dot(vals, basis)
-            additional_lines.append(lines + displacement)
-            additional_verts.append(vertices + displacement)
-    lines = np.r_[lines, *additional_lines]
-    vertices = np.r_[vertices, *additional_verts]
+            additional_lines.append(lines_arr + displacement)
+            additional_verts.append(vertices_arr + displacement)
+    lines_arr = np.r_[lines_arr, *additional_lines]
+    vertices_arr = np.r_[vertices_arr, *additional_verts]
 
-    return lines, vertices
+    return lines_arr, vertices_arr
 
 
 def plot_hex_bz(
diff --git a/src/erlab/plotting/colors.py b/src/erlab/plotting/colors.py
index 2ee6c11d..7035d4f7 100644
--- a/src/erlab/plotting/colors.py
+++ b/src/erlab/plotting/colors.py
@@ -47,18 +47,20 @@
 
 from collections.abc import Iterable, Sequence
 from numbers import Number
-from typing import Any, Literal
+from typing import Any, Literal, cast
 
 import matplotlib
 import matplotlib.axes
 import matplotlib.cm
 import matplotlib.collections
+import matplotlib.colorbar
 import matplotlib.colors
 import matplotlib.image
 import matplotlib.pyplot as plt
 import matplotlib.transforms
 import numpy as np
 import numpy.typing as npt
+from matplotlib.typing import ColorType
 
 
 class InversePowerNorm(matplotlib.colors.Normalize):
@@ -469,7 +471,8 @@ def get_mappable(
     image_only
         Only consider images as a valid mappable, by default `False`.
     silent
-        If `False`, raises a `RuntimeError`. If `True`, silently returns `None`.
+        If `False`, raises a `RuntimeError` when no mappable is found. If `True`,
+        silently returns `None`.
 
     Returns
     -------
@@ -478,7 +481,7 @@ def get_mappable(
     """
     if not image_only:
         try:
-            mappable = ax.collections[-1]
+            mappable: Any = ax.collections[-1]
         except (IndexError, AttributeError):
             mappable = None
 
@@ -488,13 +491,14 @@ def get_mappable(
         except (IndexError, AttributeError):
             mappable = None
 
-    if not silent and mappable is None:
-        raise RuntimeError(
-            "No mappable was found to use for colorbar "
-            "creation. First define a mappable such as "
-            "an image (with imshow) or a contour set ("
-            "with contourf)."
-        )
+    if mappable is None:
+        if not silent:
+            raise RuntimeError(
+                "No mappable was found to use for colorbar "
+                "creation. First define a mappable such as "
+                "an image (with imshow) or a contour set ("
+                "with contourf)."
+            )
     return mappable
 
 
@@ -517,21 +521,29 @@ def unify_clim(
         If `True`, only consider mappables that are images. Default is `False`.
 
     """
+    vmn: float | None
+    vmx: float | None
+
     if target is None:
-        vmn, vmx = [], []
+        vmn_list, vmx_list = [], []
         for ax in axes.flat:
-            mappable = get_mappable(ax, image_only=image_only)
-            vmn.append(mappable.norm.vmin)
-            vmx.append(mappable.norm.vmax)
-        vmn, vmx = min(vmn), max(vmx)
-
+            mappable = get_mappable(ax, image_only=image_only, silent=True)
+            if mappable is not None:
+                if mappable.norm.vmin is not None:
+                    vmn_list.append(mappable.norm.vmin)
+                if mappable.norm.vmax is not None:
+                    vmx_list.append(mappable.norm.vmax)
+        vmn, vmx = min(vmn_list), max(vmx_list)
     else:
-        mappable = get_mappable(target, image_only=image_only)
-        vmn, vmx = mappable.norm.vmin, mappable.norm.vmax
+        mappable = get_mappable(target, image_only=image_only, silent=True)
+        if mappable is not None:
+            vmn, vmx = mappable.norm.vmin, mappable.norm.vmax
 
+    # Apply color limits
     for ax in axes.flat:
-        mappable = get_mappable(ax, image_only=image_only)
-        mappable.norm.vmin, mappable.norm.vmax = vmn, vmx
+        mappable = get_mappable(ax, image_only=image_only, silent=True)
+        if mappable is not None:
+            mappable.norm.vmin, mappable.norm.vmax = vmn, vmx
 
 
 def proportional_colorbar(
@@ -597,7 +609,9 @@ def proportional_colorbar(
             ax = plt.gca()
             if mappable is None:
                 mappable = get_mappable(ax)
-    elif isinstance(ax, np.ndarray):
+    elif isinstance(ax, Iterable):
+        if not isinstance(ax, np.ndarray):
+            ax = np.array(ax, dtype=object)
         i = 0
         while mappable is None and i < len(ax.flat):
             mappable = get_mappable(ax.flatten()[i], silent=(i != (len(ax.flat) - 1)))
@@ -605,8 +619,13 @@ def proportional_colorbar(
     elif mappable is None:
         mappable = get_mappable(ax)
 
+    if mappable is None:
+        raise RuntimeError("No mappable was found to use for colorbar creation")
+
     if mappable.colorbar is None:
         plt.colorbar(mappable=mappable, cax=cax, ax=ax, **kwargs)
+        mappable.colorbar = cast(matplotlib.colorbar.Colorbar, mappable.colorbar)
+
     ticks = mappable.colorbar.get_ticks()
     if cax is None:
         mappable.colorbar.remove()
@@ -718,6 +737,8 @@ def _ez_inset(
     **kwargs,
 ) -> matplotlib.axes.Axes:
     fig = parent_axes.get_figure()
+    if fig is None:
+        raise RuntimeError("Parent axes is not attached to a figure")
     locator = InsetAxesLocator(parent_axes, width, height, pad, loc)
     ax_ = fig.add_axes(locator(parent_axes, None).bounds, **kwargs)
     ax_.set_axes_locator(locator)
@@ -808,7 +829,7 @@ def _gen_cax(ax, width=4.0, aspect=7.0, pad=3.0, horiz=False, **kwargs):
 
 # TODO: fix colorbar size properly
 def nice_colorbar(
-    ax: matplotlib.axes.Axes | None = None,
+    ax: matplotlib.axes.Axes | Iterable[matplotlib.axes.Axes] | None = None,
     mappable: matplotlib.cm.ScalarMappable | None = None,
     width: float = 5.0,
     aspect: float = 5.0,
@@ -874,7 +895,9 @@ def nice_colorbar(
         )
 
     else:
-        if np.iterable(ax):
+        if isinstance(ax, Iterable):
+            if not isinstance(ax, np.ndarray):
+                ax = np.array(ax, dtype=object)
             bbox = matplotlib.transforms.Bbox.union(
                 [
                     x.get_window_extent().transformed(
@@ -884,9 +907,12 @@ def nice_colorbar(
                 ]
             )
         else:
-            bbox = ax.get_window_extent().transformed(
-                ax.figure.dpi_scale_trans.inverted()
-            )
+            fig = ax.get_figure()
+
+            if fig is None:
+                raise RuntimeError("Axes is not attached to a figure")
+
+            bbox = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
 
         if orientation == "horizontal":
             kwargs["anchor"] = (1, 1)
@@ -951,10 +977,21 @@ def flatten_transparency(rgba: npt.NDArray, background: Sequence[float] | None =
     return rgb.reshape(original_shape[:-1] + (3,))
 
 
+def _get_segment_for_color(
+    cmap: matplotlib.colors.LinearSegmentedColormap,
+    color: Literal["red", "green", "blue", "alpha"],
+) -> Any:
+    if hasattr(cmap, "_segmentdata"):
+        if color in cmap._segmentdata:
+            return cmap._segmentdata[color]
+    return None
+
+
 def _is_segment_iterable(cmap: matplotlib.colors.Colormap) -> bool:
     if not isinstance(cmap, matplotlib.colors.LinearSegmentedColormap):
         return False
-    if any(callable(cmap._segmentdata[c]) for c in ["red", "green", "blue"]):
+
+    if any(callable(_get_segment_for_color(cmap, c)) for c in ["red", "green", "blue"]):  # type: ignore[arg-type]
         return False
     return True
 
@@ -1000,15 +1037,25 @@ def combined_cmap(
         cmap2 = matplotlib.colormaps[cmap2]
 
     if all(_is_segment_iterable(c) for c in (cmap1, cmap2)):
-        segnew = {}
+        cmap1 = cast(
+            matplotlib.colors.LinearSegmentedColormap, cmap1
+        )  # to appease mypy
+        cmap2 = cast(
+            matplotlib.colors.LinearSegmentedColormap, cmap2
+        )  # to appease mypy
+
+        segnew: dict[
+            Literal["red", "green", "blue", "alpha"], Sequence[tuple[float, ...]]
+        ] = {}
+
         for c in ["red", "green", "blue"]:
             seg1_c, seg2_c = (
-                np.asarray(cmap1._segmentdata[c]),
-                np.asarray(cmap2._segmentdata[c]),
+                np.asarray(_get_segment_for_color(cmap1, c)),  # type: ignore[arg-type]
+                np.asarray(_get_segment_for_color(cmap2, c)),  # type: ignore[arg-type]
             )
             seg1_c[:, 0] = seg1_c[:, 0] * 0.5
             seg2_c[:, 0] = seg2_c[:, 0] * 0.5 + 0.5
-            segnew[c] = np.r_[seg1_c, seg2_c]
+            segnew[c] = np.r_[seg1_c, seg2_c]  # type: ignore[index]
         cmap = matplotlib.colors.LinearSegmentedColormap(
             name=name, segmentdata=segnew, N=N
         )
@@ -1029,11 +1076,11 @@ def combined_cmap(
 def gen_2d_colormap(
     ldat,
     cdat,
-    cmap: matplotlib.colors.Colormap | str = None,
+    cmap: matplotlib.colors.Colormap | str | None = None,
     *,
     lnorm: plt.Normalize | None = None,
     cnorm: plt.Normalize | None = None,
-    background: Any = None,
+    background: ColorType | None = None,
     N: int = 256,
 ):
     """Generate a 2D colormap image from lightness and color data.
@@ -1081,9 +1128,9 @@ def gen_2d_colormap(
         cnorm = plt.Normalize()
 
     if background is None:
-        background: tuple[float, float, float] = (1, 1, 1, 1)
+        background_arr: tuple[float, float, float, float] = (1.0, 1.0, 1.0, 1.0)
     else:
-        background: tuple[float, float, float] = matplotlib.colors.to_rgba(background)
+        background_arr = matplotlib.colors.to_rgba(background)
 
     ldat_masked = np.ma.masked_invalid(ldat)
     cdat_masked = np.ma.masked_invalid(cdat)
@@ -1097,20 +1144,21 @@ def gen_2d_colormap(
 
     img = cmap(c_vals)
     img *= l_vals
-    img += (1 - l_vals) * background
+    img += (1 - l_vals) * background_arr
 
-    l_linear = lnorm(np.linspace(lnorm.vmin, lnorm.vmax, N))[:, np.newaxis, np.newaxis]
-    cmap_img = np.repeat(
-        cmap(cnorm(np.linspace(cnorm.vmin, cnorm.vmax, N)))[np.newaxis, :], N, 0
-    )
+    lmin, lmax = cast(float, lnorm.vmin), cast(float, lnorm.vmax)  # to appease mypy
+    cmin, cmax = cast(float, cnorm.vmin), cast(float, cnorm.vmax)
+
+    l_linear = lnorm(np.linspace(lmin, lmax, N))[:, np.newaxis, np.newaxis]
+    cmap_img = np.repeat(cmap(cnorm(np.linspace(cmin, cmax, N)))[np.newaxis, :], N, 0)
     cmap_img *= l_linear
-    cmap_img += (1 - l_linear) * background
+    cmap_img += (1 - l_linear) * background_arr
 
     return cmap_img, img
 
 
-def color_distance(c1, c2) -> float:
-    """Calculate the color distance between two RGB colors.
+def color_distance(c1: ColorType, c2: ColorType) -> float:
+    """Calculate the color distance between two matplotlib colors.
 
     Parameters
     ----------
@@ -1143,7 +1191,7 @@ def color_distance(c1, c2) -> float:
     return np.sqrt((2 + r) * dR2 + 4 * dG2 + (2 + 255 / 256 - r) * dB2)
 
 
-def close_to_white(c) -> bool:
+def close_to_white(c: ColorType) -> bool:
     """Check if a given color is closer to white than black.
 
     Parameters
@@ -1188,7 +1236,9 @@ def image_is_light(
     return close_to_white(prominent_color(im))
 
 
-def axes_textcolor(ax: matplotlib.axes.Axes, light="k", dark="w"):
+def axes_textcolor(
+    ax: matplotlib.axes.Axes, light: ColorType = "k", dark: ColorType = "w"
+):
     """Determine the text color based on the color of the mappable in an axes.
 
     Parameters
diff --git a/src/erlab/plotting/general.py b/src/erlab/plotting/general.py
index e621673b..d3cf85f1 100644
--- a/src/erlab/plotting/general.py
+++ b/src/erlab/plotting/general.py
@@ -17,10 +17,11 @@
 
 import contextlib
 import copy
-from typing import TYPE_CHECKING, Any, Literal
+from collections.abc import Iterable
+from typing import TYPE_CHECKING, Any, Literal, Union, cast
 
 import matplotlib
-import matplotlib.colors as mcolors
+import matplotlib.colors
 import matplotlib.image
 import matplotlib.patches
 import matplotlib.path
@@ -40,7 +41,7 @@
 )
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Iterable, Sequence
+    from collections.abc import Callable, Collection, Sequence
 
 figure_width_ref = {
     "aps": [3.4, 7.0],
@@ -119,20 +120,22 @@ def __init__(
         textOn: bool = True,
         useblit: bool = True,
         textprops: dict | None = None,
-        **lineprops: dict,
+        **lineprops,
     ):
         super().__init__(ax)
 
         if textprops is None:
             textprops = {}
 
-        self.connect_event("motion_notify_event", self.onmove)
-        self.connect_event("draw_event", self.clear)
+        self.connect_event("motion_notify_event", self.onmove)  # type: ignore[arg-type]
+        self.connect_event("draw_event", self.clear)  # type: ignore[arg-type]
 
         self.visible = True
         self.horizOn = horizOn
         self.vertOn = vertOn
         self.textOn = textOn
+        if self.canvas is None:
+            raise RuntimeError("No canvas found to attach to")
         self.useblit = useblit and self.canvas.supports_blit
 
         if self.useblit:
@@ -291,14 +294,14 @@ def plot_array(
     colorbar: bool = False,
     colorbar_kw: dict | None = None,
     gamma: float = 1.0,
-    norm: mcolors.Normalize | None = None,
+    norm: matplotlib.colors.Normalize | None = None,
     xlim: float | tuple[float, float] | None = None,
     ylim: float | tuple[float, float] | None = None,
     crop: bool = False,
     rad2deg: bool | Iterable[str] = False,
     func: Callable | None = None,
     func_args: dict | None = None,
-    **improps: dict,
+    **improps,
 ) -> matplotlib.image.AxesImage:
     """Plots a 2D :class:`xarray.DataArray` using :func:`matplotlib.pyplot.imshow`.
 
@@ -340,12 +343,16 @@ def plot_array(
 
     if isinstance(arr, np.ndarray):
         arr = xr.DataArray(arr)
+
     if ax is None:
         ax = plt.gca()
-    if xlim is not None and not np.iterable(xlim):
+
+    if xlim is not None and not isinstance(xlim, Iterable):
         xlim = (-xlim, xlim)
-    if ylim is not None and not np.iterable(ylim):
+
+    if ylim is not None and not isinstance(ylim, Iterable):
         ylim = (-ylim, ylim)
+
     if rad2deg is not False:
         if np.iterable(rad2deg):
             conv_dims = rad2deg
@@ -368,7 +375,7 @@ def plot_array(
         colorbar_kw.setdefault("extend", "max")
 
     if norm is None:
-        norm = copy.deepcopy(mcolors.PowerNorm(gamma, **norm_kw))
+        norm = copy.deepcopy(matplotlib.colors.PowerNorm(gamma, **norm_kw))
 
     improps_default = {
         "interpolation": "none",
@@ -385,19 +392,24 @@ def plot_array(
             arr = arr.copy(deep=True).sel({arr.dims[1]: slice(*xlim)})
         if ylim is not None:
             arr = arr.copy(deep=True).sel({arr.dims[0]: slice(*ylim)})
+
     if func is not None:
         img = ax.imshow(func(arr.values, **func_args), norm=norm, **improps)
     else:
         img = ax.imshow(arr.values, norm=norm, **improps)
-    ax.set_xlabel(arr.dims[1])
-    ax.set_ylabel(arr.dims[0])
+
+    ax.set_xlabel(str(arr.dims[1]))
+    ax.set_ylabel(str(arr.dims[0]))
     fancy_labels(ax)
+
     if xlim is not None:
         ax.set_xlim(*xlim)
     if ylim is not None:
         ax.set_ylim(*ylim)
+
     if colorbar:
         nice_colorbar(ax=ax, **colorbar_kw)
+
     return img
 
 
@@ -409,16 +421,16 @@ def plot_array_2d(
     normalize_with_larr: bool = False,
     xlim: float | tuple[float, float] | None = None,
     ylim: float | tuple[float, float] | None = None,
-    cmap: mcolors.Colormap | str = None,
-    lnorm: mcolors.Normalize | None = None,
-    cnorm: mcolors.Normalize | None = None,
+    cmap: matplotlib.colors.Colormap | str | None = None,
+    lnorm: matplotlib.colors.Normalize | None = None,
+    cnorm: matplotlib.colors.Normalize | None = None,
     background: Any = None,
     colorbar: bool = True,
     cax: matplotlib.axes.Axes | None = None,
     colorbar_kw: dict | None = None,
     imshow_kw: dict | None = None,
     N: int = 256,
-    **indexers_kwargs: dict,
+    **indexers_kwargs,
 ):
     if lnorm is None:
         lnorm = plt.Normalize()
@@ -446,16 +458,19 @@ def plot_array_2d(
     larr = larr.qsel(**indexers_kwargs).copy(deep=True)
     carr = carr.qsel(**indexers_kwargs).copy(deep=True)
     sel_kw = {}
+
     if xlim is not None:
-        if not np.iterable(xlim):
+        if not isinstance(xlim, Iterable):
             xlim = (-xlim, xlim)
         sel_kw[larr.dims[1]] = slice(*xlim)
+
     if ylim is not None:
-        if not np.iterable(ylim):
+        if not isinstance(ylim, Iterable):
             ylim = (-ylim, ylim)
         sel_kw[larr.dims[0]] = slice(*ylim)
-    larr = larr.sel(**sel_kw)
-    carr = carr.sel(**sel_kw)
+
+    larr = larr.sel(sel_kw)
+    carr = carr.sel(sel_kw)
 
     if normalize_with_larr:
         carr = carr / larr
@@ -478,23 +493,34 @@ def plot_array_2d(
 
     if colorbar:
         if cax is None:
+            fig = ax.get_figure()
+            if fig is None:
+                raise ValueError(
+                    "Cannot create colorbar without a figure. Please provide `cax`."
+                )
+
             colorbar_kw.setdefault("aspect", 2)
             colorbar_kw.setdefault("anchor", (0, 1))
             colorbar_kw.setdefault("panchor", (0, 1))
-            cb = ax.get_figure().colorbar(plt.cm.ScalarMappable(), ax=ax, **colorbar_kw)
+
+            cb = fig.colorbar(plt.cm.ScalarMappable(), ax=ax, **colorbar_kw)
             cax = cb.ax
             cax.clear()
 
+        lmin, lmax = cast(float, lnorm.vmin), cast(float, lnorm.vmax)  # to appease mypy
+        cmin, cmax = cast(float, cnorm.vmin), cast(float, cnorm.vmax)
+
         cax.imshow(
             cmap_img.transpose(1, 0, 2),
-            extent=(lnorm.vmin, lnorm.vmax, cnorm.vmin, cnorm.vmax),
+            extent=(lmin, lmax, cmin, cmax),
             origin="lower",
             aspect="auto",
         )
 
     im = ax.imshow(img, extent=array_extent(larr), **imshow_kw)
-    ax.set_xlabel(larr.dims[0])
-    ax.set_ylabel(larr.dims[1])
+    ax.set_xlabel(str(larr.dims[0]))
+    ax.set_ylabel(str(larr.dims[1]))
+    fancy_labels(ax)
 
     if colorbar:
         return im, cb
@@ -503,11 +529,11 @@ def plot_array_2d(
 
 
 def gradient_fill(
-    x: Sequence[int | float],
-    y: Sequence[int | float],
+    x: Collection[int | float],
+    y: Collection[int | float],
     y0: float | None = None,
     color: str | tuple[float, float, float] | tuple[float, float, float, float] = "C0",
-    cmap: str | mcolors.Colormap | None = None,
+    cmap: str | matplotlib.colors.Colormap | None = None,
     transpose: bool = False,
     reverse: bool = False,
     ax: matplotlib.axes.Axes | None = None,
@@ -543,8 +569,8 @@ def gradient_fill(
     kwargs.setdefault("norm", InversePowerNorm(0.5))
     kwargs.setdefault("alpha", 0.75)
     if cmap is None:
-        cmap = mcolors.LinearSegmentedColormap.from_list(
-            "", colors=[(1, 1, 1, 0), mcolors.to_rgba(color)], N=1024
+        cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
+            "", colors=[(1, 1, 1, 0), matplotlib.colors.to_rgba(color)], N=1024
         )
     if isinstance(cmap, str):
         cmap = matplotlib.colormaps[cmap]
@@ -559,6 +585,8 @@ def gradient_fill(
 
     if y0 is None:
         y0 = min(y)
+
+    x = np.asarray(x)
     xn = np.r_[x[0], x, x[-1]]
     yn = np.r_[y0, y, y0]
     patch = matplotlib.patches.PathPatch(
@@ -569,7 +597,7 @@ def gradient_fill(
     im = matplotlib.image.AxesImage(
         ax, cmap=cmap, interpolation="bicubic", origin="lower", zorder=0, **kwargs
     )
-    im.use_sticky_edges = False
+    im.use_sticky_edges = False  # type: ignore[attr-defined]
     ax.add_artist(im)
     if transpose:
         im.set_data(np.linspace(0, 1, 1024).reshape(1024, 1).T)
@@ -598,8 +626,15 @@ def plot_slices(
     colorbar: Literal["none", "right", "rightspan", "all"] = "none",
     hide_colorbar_ticks: bool = True,
     annotate: bool = True,
-    cmap: str | mcolors.Colormap | Iterable[mcolors.Colormap | str] | None = None,
-    norm: mcolors.Normalize | Iterable[mcolors.Normalize] | None = None,
+    cmap: str
+    | matplotlib.colors.Colormap
+    | Iterable[
+        str | matplotlib.colors.Colormap | Iterable[matplotlib.colors.Colormap | str]
+    ]
+    | None = None,
+    norm: matplotlib.colors.Normalize
+    | Iterable[matplotlib.colors.Normalize | Iterable[matplotlib.colors.Normalize]]
+    | None = None,
     order: Literal["C", "F"] = "C",
     cmap_order: Literal["C", "F"] = "C",
     norm_order: Literal["C", "F"] | None = None,
@@ -608,9 +643,9 @@ def plot_slices(
     subplot_kw: dict | None = None,
     annotate_kw: dict | None = None,
     colorbar_kw: dict | None = None,
-    axes: npt.NDArray[matplotlib.axes.Axes] | None = None,
-    **values: dict,
-) -> tuple[matplotlib.figure.Figure, npt.NDArray[matplotlib.axes.Axes]]:
+    axes: Iterable[matplotlib.axes.Axes] | None = None,
+    **values,
+) -> tuple[matplotlib.figure.Figure, Iterable[matplotlib.axes.Axes]]:
     """Automated comparison plot of slices.
 
     Parameters
@@ -766,18 +801,18 @@ def plot_slices(
         slice_levels = slice_kw[slice_dim]
         slice_width = kwargs.pop(slice_dim + "_width", None)
 
-    plot_dims = [d for d in dims if d != slice_dim]
+    plot_dims: list[str] = [str(d) for d in dims if d != slice_dim]
 
     if len(plot_dims) not in (1, 2):
         raise ValueError("The data to plot must be 1D or 2D")
 
-    if not np.iterable(slice_levels):
+    if not isinstance(slice_levels, Iterable):
         slice_levels = [slice_levels]
 
-    if xlim is not None and not np.iterable(xlim):
+    if xlim is not None and not isinstance(xlim, Iterable):
         xlim = (-xlim, xlim)
 
-    if ylim is not None and not np.iterable(ylim):
+    if ylim is not None and not isinstance(ylim, Iterable):
         ylim = (-ylim, ylim)
 
     auto_gradient_color = all(k not in gradient_kw for k in ("c", "color"))
@@ -796,10 +831,16 @@ def plot_slices(
 
     cmap_name = cmap
     cmap_norm = norm
+
     if axes is None:
         fig, axes = plt.subplots(nrow, ncol, figsize=figsize, **subplot_kw)
-
+        axes = cast(npt.NDArray[Any], axes)
     else:
+        if not isinstance(axes, np.ndarray):
+            if not isinstance(axes, Iterable):
+                raise TypeError("axes must be an iterable of matplotlib.axes.Axes")
+            axes = np.array(axes, dtype=object)
+
         fig = axes.flat[0].get_figure()
 
     if nrow == 1:
@@ -808,7 +849,7 @@ def plot_slices(
     if ncol == 1:
         axes = axes[:, np.newaxis].reshape(-1, 1)
 
-    qsel_kw = {}
+    qsel_kw: dict[str, Any] = {}
 
     if crop:
         if len(plot_dims) == 1:
@@ -825,7 +866,7 @@ def plot_slices(
             if ylim is not None:
                 qsel_kw[plot_dims[0]] = slice(*ylim)
 
-    if slice_width is not None:
+    if slice_width is not None and slice_dim is not None:
         qsel_kw[slice_dim + "_width"] = slice_width
 
     for i in range(len(slice_levels)):
@@ -840,17 +881,26 @@ def plot_slices(
             elif order == "C":
                 ax = axes[j, i]
 
-            if np.iterable(cmap_name) and not isinstance(cmap_name, str):
+            if isinstance(cmap_name, Iterable) and not isinstance(cmap_name, str):
+                cmap_name = list(cmap_name)
                 if cmap_order == "F":
-                    if isinstance(cmap_name[i], str):
+                    if isinstance(cmap_name[i], str | matplotlib.colors.Colormap):
                         cmap = cmap_name[i]
                     else:
-                        cmap = cmap_name[i][j]
+                        cmap = list(
+                            cast(
+                                Iterable[str | matplotlib.colors.Colormap], cmap_name[i]
+                            )
+                        )[j]
                 elif cmap_order == "C":
-                    if isinstance(cmap_name[j], str):
+                    if isinstance(cmap_name[j], str | matplotlib.colors.Colormap):
                         cmap = cmap_name[j]
                     else:
-                        cmap = cmap_name[j][i]
+                        cmap = list(
+                            cast(
+                                Iterable[str | matplotlib.colors.Colormap], cmap_name[j]
+                            )
+                        )[i]
             else:
                 cmap = cmap_name
 
@@ -884,21 +934,24 @@ def plot_slices(
                         )
 
             elif len(plot_dims) == 2:
-                if np.iterable(cmap_norm):
+                if isinstance(cmap_norm, Iterable):
+                    cmap_norm = list(cmap_norm)
                     if norm_order == "F":
                         try:
-                            norm = cmap_norm[i][j]
+                            norm = list(cast(Iterable[plt.Normalize], cmap_norm[i]))[j]
                         except TypeError:
                             norm = cmap_norm[i]
 
                     elif norm_order == "C":
                         try:
-                            norm = cmap_norm[j][i]
+                            norm = list(cast(Iterable[plt.Normalize], cmap_norm[j]))[i]
                         except TypeError:
                             norm = cmap_norm[j]
                 else:
                     norm = copy.deepcopy(cmap_norm)
-                plot_array(dat_sel, ax=ax, norm=norm, cmap=cmap, **kwargs)
+                plot_array(
+                    dat_sel, ax=ax, norm=cast(plt.Normalize, norm), cmap=cmap, **kwargs
+                )
 
     if same_limits and len(plot_dims) == 2:
         vmn, vmx = [], []
@@ -939,12 +992,15 @@ def plot_slices(
     return fig, axes
 
 
+MultipleLine2D = list[Union[matplotlib.lines.Line2D, "MultipleLine2D"]]
+
+
 def fermiline(
     ax: matplotlib.axes.Axes | None = None,
     value: float = 0.0,
     orientation: Literal["h", "v"] = "h",
     **kwargs,
-) -> matplotlib.lines.Line2D:
+) -> matplotlib.lines.Line2D | MultipleLine2D:
     """Plots a constant energy line to denote the Fermi level.
 
     Parameters
diff --git a/src/erlab/plotting/plot3d.py b/src/erlab/plotting/plot3d.py
index 8e916fa5..69fda946 100644
--- a/src/erlab/plotting/plot3d.py
+++ b/src/erlab/plotting/plot3d.py
@@ -63,7 +63,7 @@ def set_3d_properties(self, verts, zs=0, zdir="z"):
     self._segment3d = np.asarray(
         [
             (*np.dot(_transform_zdir(zdir), (x, y, 0)), 0, 0, z)
-            for ((x, y), z) in zip(verts, zs)
+            for ((x, y), z) in zip(verts, zs, strict=True)
         ]
     )
 
diff --git a/tests/accessors/test_fit.py b/tests/accessors/test_fit.py
index cbe6c83f..3eb60b5b 100644
--- a/tests/accessors/test_fit.py
+++ b/tests/accessors/test_fit.py
@@ -130,7 +130,9 @@ def sine(t, a, f, p):
 
     # params as DataArray of JSON strings
     params = []
-    for a, p, f in zip(a_guess, p_guess, np.full_like(da.x, 2, dtype=float)):
+    for a, p, f in zip(
+        a_guess, p_guess, np.full_like(da.x, 2, dtype=float), strict=True
+    ):
         params.append(lmfit.create_params(a=a, p=p, f=f).dumps())
     params = xr.DataArray(params, coords=[da.x])
     fit = da.modelfit(
diff --git a/tests/analysis/test_fit_functions_dynamic.py b/tests/analysis/test_fit_functions_dynamic.py
index 30966595..ba53abcd 100644
--- a/tests/analysis/test_fit_functions_dynamic.py
+++ b/tests/analysis/test_fit_functions_dynamic.py
@@ -33,7 +33,7 @@ def test_poly_func_call():
     x = np.arange(5, dtype=np.float64)
     coeffs = RAND_STATE.randn(3)
     expected_result = np.polyval(np.asarray(list(reversed(coeffs))), x)
-    params = dict(zip([f"c{i}" for i in range(3)], coeffs))
+    params = dict(zip([f"c{i}" for i in range(3)], coeffs, strict=True))
     result = PolynomialFunction(degree=2)(x, **params)
     assert np.allclose(result, expected_result)
 
diff --git a/tests/analysis/test_kspace.py b/tests/analysis/test_kspace.py
index 7ec8062e..a450807e 100644
--- a/tests/analysis/test_kspace.py
+++ b/tests/analysis/test_kspace.py
@@ -18,6 +18,7 @@ def _generate_funclist() -> list[tuple[Callable, Callable]]:
             [0, 30.0, -30.0],
             [0.0, 10.0, -10.0],
             [0.0, 10.0, -10.0],
+            strict=True,
         ):
             funcs.append(kconv_func(k_tot, delta, xi, xi0, beta0))
     for kconv_func in (
@@ -30,6 +31,7 @@ def _generate_funclist() -> list[tuple[Callable, Callable]]:
             [0.0, 10.0, -10.0],
             [0.0, 10.0, -10.0],
             [0.0, 10.0, -10.0],
+            strict=True,
         ):
             funcs.append(kconv_func(k_tot, delta, chi, chi0, xi, xi0))
     return funcs
diff --git a/tests/io/test_dataloader.py b/tests/io/test_dataloader.py
index 2c8d9658..7b438f4c 100644
--- a/tests/io/test_dataloader.py
+++ b/tests/io/test_dataloader.py
@@ -4,12 +4,13 @@
 import os
 import re
 import tempfile
+from typing import ClassVar
 
 import erlab.io
 import numpy as np
 import pandas as pd
-from erlab.io.exampledata import generate_data_angles
 from erlab.io.dataloader import LoaderBase
+from erlab.io.exampledata import generate_data_angles
 
 
 def make_data(beta=5.0, temp=20.0, hv=50.0, bandshift=0.0):
@@ -88,9 +89,9 @@ def test_loader():
     class ExampleLoader(LoaderBase):
         name = "example"
 
-        aliases = ["Ex"]
+        aliases = ("Ex",)
 
-        name_map = {
+        name_map: ClassVar[dict] = {
             "eV": "BindingEnergy",
             "alpha": "ThetaX",
             "beta": [
@@ -107,7 +108,7 @@ class ExampleLoader(LoaderBase):
             "temp_sample": "TB",
         }
 
-        coordinate_attrs: tuple[str, ...] = (
+        coordinate_attrs = (
             "beta",
             "delta",
             "xi",
@@ -121,7 +122,7 @@ class ExampleLoader(LoaderBase):
         # Attributes to be used as coordinates. Place all attributes that we don't want to
         # lose when merging multiple file scans here.
 
-        additional_attrs = {
+        additional_attrs: ClassVar[dict] = {
             "configuration": 1,  # Experimental geometry. Required for momentum conversion
             "sample_workfunction": 4.3,
         }  # Any additional metadata you want to add to the data