Merge pull request #78 from kmnhan/manager-thumbnails

.github/workflows/ci.yml

@@ -47,23 +47,23 @@ jobs:
           export QT_API=${{ matrix.qt-api }}
 
       - name: Test with pytest
-        if: matrix.python-version != '3.12' || matrix.qt-api != 'pyqt6'
+        if: matrix.python-version != '3.12' || matrix.qt-api != 'pyside6'
         run: |
           python -u -m pytest -v
 
       - name: Test with pytest with coverage
-        if: matrix.python-version == '3.12' && matrix.qt-api == 'pyqt6'
+        if: matrix.python-version == '3.12' && matrix.qt-api == 'pyside6'
         run: |
           python -u -m pytest -v --cov erlab --junitxml=junit.xml
 
       - name: Upload coverage to Codecov
-        if: matrix.python-version == '3.12' && matrix.qt-api == 'pyqt6'
+        if: matrix.python-version == '3.12' && matrix.qt-api == 'pyside6'
         uses: codecov/codecov-action@v5
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
 
       - name: Upload test results to Codecov
-        if: ${{ matrix.python-version == '3.12' && matrix.qt-api == 'pyqt6' && !cancelled() }}
+        if: ${{ matrix.python-version == '3.12' && matrix.qt-api == 'pyside6' && !cancelled() }}
         uses: codecov/test-results-action@v1
         with:
           token: ${{ secrets.CODECOV_TOKEN }}

docs/source/conf.py

@@ -68,6 +68,7 @@
 if os.getenv("READTHEDOCS"):
     extensions.append("sphinxcontrib.googleanalytics")
     googleanalytics_id = "G-DDZYWTBV4S"
+    html_baseurl = "https://erlabpy.readthedocs.io/en/stable/"  # Canonical URL
 
 # templates_path = ["_templates"]
 # exclude_patterns = []

docs/source/index.rst

@@ -65,14 +65,14 @@ analysis.
 
 .. image:: images/imagetool_light.png
     :align: center
-    :alt: Imagetool
+    :alt: ImageTool window in light mode
     :class: only-light
 
 .. only:: format_html
 
     .. image:: images/imagetool_dark.png
         :align: center
-        :alt: Imagetool
+        :alt: ImageTool window in dark mode
         :class: only-dark
 
 .. toctree::

docs/source/user-guide/filtering.ipynb

@@ -249,14 +249,14 @@
     "\n",
     ".. image:: ../images/dtool_light.png\n",
     "    :align: center\n",
-    "    :alt: DerivativeTool\n",
+    "    :alt: DerivativeTool window in light mode\n",
     "    :class: only-light\n",
     "\n",
     ".. only:: format_html\n",
     "\n",
     "    .. image:: ../images/dtool_dark.png\n",
     "        :align: center\n",
-    "        :alt: DerivativeTool\n",
+    "        :alt: DerivativeTool window in dark mode\n",
     "        :class: only-dark\n",
     "\n",
     "- The first section interpolates the data to a grid prior to smoothing.\n",

docs/source/user-guide/imagetool.rst

@@ -6,14 +6,14 @@ The ImageTool window
 
 .. image:: ../images/imagetool_light.png
     :align: center
-    :alt: Imagetool
+    :alt: ImageTool window in light mode
     :class: only-light
 
 .. only:: format_html
 
     .. image:: ../images/imagetool_dark.png
         :align: center
-        :alt: Imagetool
+        :alt: ImageTool window in dark mode
         :class: only-dark
 
 Inspired by *Image Tool* for Igor Pro written by the Advanced Light Source at Lawrence
@@ -185,4 +185,32 @@ Features
   Toggle the console with :kbd:`⌃+`` (Mac) or :kbd:`Ctrl+`` (Windows/Linux) or through
   the ``View`` menu.
 
+- Toggle the ``Preview on Hover`` option in the ``View`` menu to show a preview of the
+  main image when hovering over each tool.
+
 - Explore the menubar for more features!
+
+
+Tips
+~~~~
+
+- Sometimes, you may wish to send data from the manager to a jupyter notebook for
+  further analysis. You can do this by using the manager console and the `%store
+  <https://ipython.readthedocs.io/en/stable/config/extensions/storemagic.html>`_ magic
+  command.
+
+  For example, if you want to store the data displayed in a tool with index 0, run the
+  following in the manager console:
+
+  .. code-block:: python
+
+      my_data = tools[0].data
+      %store my_data
+
+  In the notebook, you can retrieve the data by running:
+
+  .. code-block:: python
+
+      %store -r my_data
+
+  after which the data will be available as ``my_data`` in the notebook.

docs/source/user-guide/io.ipynb

@@ -737,6 +737,7 @@
     "\n",
     ".. image:: ../images/flowchart_single.pdf\n",
     "   :align: center\n",
+    "   :alt: Flowchart for loading data from a single file\n",
     "\n",
     "If only all data formats were as simple as this! Unfortunately, there are some setups\n",
     "where data that belongs to a single scan is saved over multiple files. In this case, the\n",
@@ -748,6 +749,7 @@
     "\n",
     ".. image:: ../images/flowchart_multiple.pdf\n",
     "   :align: center\n",
+    "   :alt: Flowchart for loading data from multiple files\n",
     "\n",
     "In this case, the method :meth:`identify <erlab.io.dataloader.LoaderBase.identify>`\n",
     "should resolve *all* files that belong to the given sequence number, and return a *list*\n",

src/erlab/accessors/fit.py

@@ -14,9 +14,7 @@
 from collections.abc import Collection, Hashable, Iterable, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Literal, cast
 
-import lazy_loader as _lazy
 import numpy as np
-import tqdm.auto
 import xarray as xr
 from xarray.core.dataarray import _THIS_ARRAY
 
@@ -28,9 +26,15 @@
     # Avoid importing until runtime for initial import performance
     import joblib
     import lmfit
+    import tqdm.auto as tqdm
 else:
+    import lazy_loader as _lazy
+
+    from erlab.utils.misc import LazyImport
+
     lmfit = _lazy.load("lmfit")
     joblib = _lazy.load("joblib")
+    tqdm = LazyImport("tqdm.auto")
 
 
 def _nested_dict_vals(d):
@@ -556,7 +560,7 @@ def _output_wrapper(name, da, out=None) -> dict:
             parallel_obj = joblib.Parallel(**parallel_kw)
 
             if parallel_obj.return_generator:
-                out_dicts = tqdm.auto.tqdm(  # type: ignore[call-overload]
+                out_dicts = tqdm.tqdm(  # type: ignore[call-overload]
                     parallel_obj(
                         itertools.starmap(
                             joblib.delayed(_output_wrapper), self._obj.data_vars.items()
@@ -578,7 +582,7 @@ def _output_wrapper(name, da, out=None) -> dict:
 
         else:
             result = type(self._obj)()
-            for name, da in tqdm.auto.tqdm(self._obj.data_vars.items(), **tqdm_kw):  # type: ignore[call-overload]
+            for name, da in tqdm.tqdm(self._obj.data_vars.items(), **tqdm_kw):  # type: ignore[call-overload]
                 _output_wrapper(name, da, result)
 
         result = result.assign_coords(

src/erlab/accessors/general.py

@@ -22,6 +22,7 @@
     ERLabDatasetAccessor,
     either_dict_or_kwargs,
 )
+from erlab.utils.array import sort_coord_order
 from erlab.utils.formatting import format_html_table
 from erlab.utils.misc import emit_user_level_warning
 
@@ -486,7 +487,6 @@ def __call__(
         DataArray
             The selected and averaged data.
 
-
         Note
         ----
         Unlike :meth:`xarray.DataArray.sel`, this method treats all dimensions without
@@ -506,6 +506,8 @@ def __call__(
         """
         indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "qsel")
 
+        coord_order = list(self._obj.coords.keys())
+
         # Bin widths for each dimension, zero if width not specified
         bin_widths: dict[Hashable, float] = {}
 
@@ -554,12 +556,12 @@ def __call__(
 
         unindexed_dims: list[Hashable] = [
             k for k in slices | scalars if k not in self._obj.indexes
-        ]
+        ]  # Unindexed dimensions, i.e. dimensions without coordinates
 
         if len(unindexed_dims) >= 1:
             out = self._obj.assign_coords(
                 {k: np.arange(self._obj.sizes[k]) for k in unindexed_dims}
-            )
+            )  # Assign temporary coordinates
         else:
             out = self._obj
 
@@ -594,7 +596,9 @@ def __call__(
 
             print(out_str)
 
-        return out.drop_vars(unindexed_dims, errors="ignore")
+        out = out.drop_vars(unindexed_dims, errors="ignore")
+
+        return sort_coord_order(out, keys=coord_order, dims_first=True)
 
     def around(
         self, radius: float | dict[Hashable, float], *, average: bool = True, **sel_kw

src/erlab/analysis/fit/models.py

@@ -98,16 +98,11 @@ def fit_edges_linear(x, data, len_fit) -> tuple[float, float, float, float]:
     n1, m1
         The coefficients of the linear fit for the right edge.
     """
-    n0, m0 = fit_poly_jit(
-        np.array(x[:len_fit], dtype=np.float64),
-        np.array(data[:len_fit], dtype=np.float64),
-        deg=1,
-    )
-    n1, m1 = fit_poly_jit(
-        np.array(x[-len_fit:], dtype=np.float64),
-        np.array(data[-len_fit:], dtype=np.float64),
-        deg=1,
-    )
+    na_idx = np.isnan(np.asarray(data))
+    xv = np.array(x[~na_idx], dtype=np.float64)
+    yv = np.array(data[~na_idx], dtype=np.float64)
+    n0, m0 = fit_poly_jit(xv[:len_fit], yv[:len_fit], deg=1)
+    n1, m1 = fit_poly_jit(xv[-len_fit:], yv[-len_fit:], deg=1)
     return n0, m0, n1, m1