Merge pull request #26 from kmnhan/dev

.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

.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!

.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:

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

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

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
 

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
 =============

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

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,

docs/source/reference.rst

@@ -2,19 +2,19 @@
 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
 ===========
 
 ========================   ========================
 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
 ==========
@@ -35,15 +35,13 @@ 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::
    :hidden:
 
    erlab.lattice
    erlab.constants
-   erlab.accessors
    erlab.parallel

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,

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)"
             ]
         },
         {