Tidy up

docs/source/CHANGELOG.rst

@@ -20,7 +20,7 @@ Changed
 * Add new endstation
 * Logging and endstation plugin can be selected from local_config.py
 
-Miror
+Minor
 ~~~~~
 
 * Recommend to use uv, instead of rye.
@@ -36,7 +36,7 @@ The name change
 * corrections -> correction
 
      This is somehow backwards incopatibilities.  However, the effect for most is really minor,
-     because this functionalites are not so frequently used. Thus the major version number has not been changed.
+     because this functionalities are not so frequently used. Thus the major version number has not been changed.
 
 * New UI
 
@@ -71,7 +71,7 @@ Changed
     * Remove G.extent
     * Remove overlapped_stack_dispersion_plot
         - use stack_dispersion_plot_with_appropriate_args
-    * Revise the k-conversion.  The origianl version is correct from the view of the coding, but incorrect from the physics!
+    * Revise the k-conversion.  The original version is correct from the view of the coding, but incorrect from the physics!
     * introduce new attrs, "energy_notation". if not specified, attrs["energy_notation"] = "Binding" is assumed to keep the consistency from the previous version.
 
     * see Changes.md for others
@@ -120,7 +120,7 @@ New
    and the content available greatly expanded.
 
    * Tutorials for common types of analysis are available as Jupyter notebooks.
-   * An organized API documentation page is availabe.
+   * An organized API documentation page is available.
    * Docstrings have been massively expanded to cover the public API
      and most of the internal API.
    * The documentation build process has been simplified.
@@ -446,7 +446,7 @@ New:
 ~~~~
 
 1. Improved API documentation.
-2. Most recent interative plot context is saved to
+2. Most recent interactive plot context is saved to
    ``arpes.config.CONFIG['CURRENT_CONTEXT']``. This allows simple and
    transparent recovery in case you forget to save the context and
    performed a lot of work in an interactive session. Additionally, this
@@ -459,7 +459,7 @@ New:
 Changed:
 ~~~~~~~~
 
-1. Metadata reworked to a common format accross all endstations. This is
+1. Metadata reworked to a common format across all endstations. This is
    now documented appropriately with the data model.
 
 .. _fixed-8:
@@ -494,7 +494,7 @@ New:
    ARPES experiments from inside PyARPES.
 
    1. As an example: After conducting nano-XPS, you can use PCA to
-      select your sample region and export a scan sequnce just over the
+      select your sample region and export a scan sequence just over the
       sample ROI or over the border between your sample and another
       area.
 

docs/source/conf.py

@@ -30,7 +30,7 @@
 # The full version, including alpha/beta/rc tags
 release = arpes.__version__
 
-# supress some output information for nbconvert, don't open tools
+# suppress some output information for nbconvert, don't open tools
 
 nbsphinx_allow_errors = True
 

docs/source/contributing.rst

@@ -27,7 +27,7 @@ repo <https://gitlab.com/lanzara-group/python-arpes>`__.
 What you’ll need
 ----------------
 
-Here’s a summary of what you’ll need to do, if you’are already familar
+Here’s a summary of what you’ll need to do, if you’are already familiar
 with contributing to open source. If you are less familiar, much more
 detail on this is described in the :doc:`developer’s guide </dev-guide>`.
 

docs/source/curve-fitting.rst

@@ -20,7 +20,7 @@ complicated models using operators like ``+`` and ``*``, but also makes
 the process of curve fitting transparent and simple.
 
 Here we will prepare an EDC with a step edge, and fit it with a linear
-density of states multipled by the Fermi distribution and convolved with
+density of states multiplied by the Fermi distribution and convolved with
 Gaussian instrumental broadening (``AffineBroadenedFD``). In general in
 PyARPES, we use extensions of the models available in ``lmfit``, which
 provides an ``xarray`` compatible and unitful fitting function

docs/source/data-provenance.rst

@@ -126,7 +126,7 @@ appropriately.
 Additionally, because PyARPES includes recent Juypyter cell evaluations
 in the record, you shouldn’t find the need to be very defensive in your
 use of manual provenance tracking, although you should wrap code that
-forms part of your more permanent analysis repetoire.
+forms part of your more permanent analysis repertoire.
 
 .. figure:: _static/decorator-provenance.png
    :alt: Example data provenance in PyARPES

docs/source/dev-guide.rst

@@ -8,7 +8,7 @@ Installing an editable copy of PyARPES
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 1. Install `uv <https://docs.astral.sh/uv/guides/projects/>` to make an isolated environment for development.
-2. Clone the respository
+2. Clone the repository
 
 .. code:: bash
 

docs/source/faq.rst

@@ -4,7 +4,7 @@ Frequently Asked Questions
 Igor Installation
 -----------------
 
-Using the suggested invokation I get a pip error
+Using the suggested invocation I get a pip error
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Pip on Windows appears not to like certain archival formats. While

docs/source/index.rst

@@ -19,7 +19,7 @@ I recommend igor to analyze, especially for students.
 
 
 
-**December 2020, V3 Release**: The current relase focuses on improving
+**December 2020, V3 Release**: The current release focuses on improving
 usage and workflow for less experienced Python users, lifting version
 incompatibilities with dependencies, and ironing out edges in the user
 experience.

docs/source/installation.rst

@@ -21,7 +21,7 @@ available either from the main repository at
 `GitHub <https://github.com/arafune/arpes>`.
 
 1. Install `uv <https://docs.astral.sh/uv/guides/projects/>`__
-2. Clone or otherwise download the respository
+2. Clone or otherwise download the repository
 
 .. code:: bash
 

docs/source/interactive.rst

@@ -65,7 +65,7 @@ Laying Lineshapes for Curve Fitting
 -----------------------------------
 
 Use ``.S.show_band_tool()`` to get an interactive lineshape/band layer
-to set inital locations and model structure for complicated curve fits.
+to set initial locations and model structure for complicated curve fits.
 
 Examining Fits
 --------------

docs/source/notebooks/basic-data-exploration.ipynb

@@ -255,7 +255,7 @@
    "source": [
     "### PyARPES philosophy on interactive tools\n",
     "\n",
-    "Instead of one large interactive application where you perform analysis, PyARPES has many small interactive utilities which are built for a single purpose. From Ver 4.0.1 (pragmatically, from 4.0), the Qt based interactive tools are depricated. Thus, the following tools cannot be used:\n",
+    "Instead of one large interactive application where you perform analysis, PyARPES has many small interactive utilities which are built for a single purpose. From Ver 4.0.1 (pragmatically, from 4.0), the Qt based interactive tools are deprecated. Thus, the following tools cannot be used:\n",
     "\n",
     "1. A data browser: `arpes.plotting.qt_tool`\n",
     "2. A momentum offset browser: `arpes.plotting.qt_ktool.ktool`\n",

docs/source/notebooks/converting-to-kspace.ipynb

@@ -305,7 +305,7 @@
    "source": [
     "Excellent, this enables all kinds of analysis which we frequently want to perform.\n",
     "\n",
-    "## Exactracting a Momentum Cut Passing Thorugh Known Angular Coordinates\n",
+    "## Exactracting a Momentum Cut Passing Through Known Angular Coordinates\n",
     "\n",
     "For instance, we can determine a momentum cut passing through our point of interest."
    ]

docs/source/notebooks/curve-fitting.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "PyARPES uses `lmfit` in order to provide a user friendly, compositional API for curve fitting. This allows users to define more complicated models using operators like `+` and `*`, but also makes the process of curve fitting transparent and simple.\n",
     "\n",
-    "Here we will prepare an EDC with a step edge, and fit it with a linear density of states multipled by the Fermi distribution and convolved with Gaussian instrumental broadening (`AffineBroadenedFD`). In general in PyARPES, we use extensions of the models available in `lmfit`, which provides an xarray compatible and unitful fitting function `guess_fit`. This has more or less the same call signature as `fit except that we do not need to pass the X and Y data separately, the X data is provided by the dataset coordinates."
+    "Here we will prepare an EDC with a step edge, and fit it with a linear density of states multiplied by the Fermi distribution and convolved with Gaussian instrumental broadening (`AffineBroadenedFD`). In general in PyARPES, we use extensions of the models available in `lmfit`, which provides an xarray compatible and unitful fitting function `guess_fit`. This has more or less the same call signature as `fit except that we do not need to pass the X and Y data separately, the X data is provided by the dataset coordinates."
    ]
   },
   {
@@ -79,7 +79,7 @@
    "id": "6",
    "metadata": {},
    "source": [
-    "## Influencing the fit by setting parametrs\n",
+    "## Influencing the fit by setting parameters\n",
     "\n",
     "Using the `params=` keyword you can provide initial guess with `value`, enforce a `max` or `min`, and request that a parameter be allowed to `vary` or not. In this case, we will force a fit with the step edge at 10 millivolts, obtaining a substantially worse result.\n",
     "\n",
@@ -290,7 +290,7 @@
    "source": [
     "## Interactively inspecting fits\n",
     "\n",
-    "There's no substitute for inspecting fits by eye. PyARPES has holoviews based interactive fit inspection tools. This is very much like `profile_view` which we have already seen with the adddition that the marginal shows the curve fitting information for a broadcast fit. \n",
+    "There's no substitute for inspecting fits by eye. PyARPES has holoviews based interactive fit inspection tools. This is very much like `profile_view` which we have already seen with the addition that the marginal shows the curve fitting information for a broadcast fit. \n",
     "\n",
     "Additionally, you can use the tool to copy any given marginal's parameters to a hint dictionary which you can pass into the curve fit\n",
     "for refinement."

docs/source/notebooks/custom-dot-t-functionality.ipynb

@@ -140,7 +140,7 @@
    "source": [
     "## Functional Programming Primitives: `filter` and `map`\n",
     "\n",
-    "You can `filter` or conditionally remove some of a datasets contents. To do this over coordinates on a dataset according to a function/sieve which accepts the coordinate and data value, you can use `filter_coord`. The sieving function should accept two arguments, the coordinate and the cut at that coordinate respectively. You can specify which coordinate or coordinates are iterated across when filtering using the `coordinate_name` paramter.\n",
+    "You can `filter` or conditionally remove some of a datasets contents. To do this over coordinates on a dataset according to a function/sieve which accepts the coordinate and data value, you can use `filter_coord`. The sieving function should accept two arguments, the coordinate and the cut at that coordinate respectively. You can specify which coordinate or coordinates are iterated across when filtering using the `coordinate_name` parameter.\n",
     "\n",
     "As a simple, example, we can remove all the odd valued coordinates along Y:"
    ]

docs/source/notebooks/full-analysis-xps.ipynb

@@ -457,8 +457,8 @@
    "source": [
     "## Exercises\n",
     "\n",
-    "1. Peform a selection of the data according to the fitting values and plot the corresponding mask. What does the collection of points for which **Lower Peak BE** > -34.4eV look like?\n",
-    "2. Peform a selection of the data for the decompositions above and plot their corresponding average XPS curves. How do these results compare to the PCA results we found before?\n",
+    "1. Perform a selection of the data according to the fitting values and plot the corresponding mask. What does the collection of points for which **Lower Peak BE** > -34.4eV look like?\n",
+    "2. Perform a selection of the data for the decompositions above and plot their corresponding average XPS curves. How do these results compare to the PCA results we found before?\n",
     "3. What might you conclude about the sample and experimental conditions given the extracted peak width map?"
    ]
   }

docs/source/notebooks/jupyter-crash-course.ipynb

@@ -37,7 +37,7 @@
    "id": "4",
    "metadata": {},
    "source": [
-    "You can use this with library functions, PyARPES funtions, or your own."
+    "You can use this with library functions, PyARPES functions, or your own."
    ]
   },
   {

docs/source/spectra.rst

@@ -42,7 +42,7 @@ cases, this imaged axis will always be treated in the same role as the
 high-resolution motion axis of a nano-ARPES system.
 
 Working in two coordinate systems is frustrating, and it makes comparing
-data cumbersome. In PyARPES x,y,z is always the total inferrable
+data cumbersome. In PyARPES x,y,z is always the total inferable
 coordinate value, i.e. (+/- long range +/- high resolution) as
 appropriate. You can still access the underlying coordinates in this
 case as ``long_{dim}`` and ``short_{dim}``.

docs/source/stack-plots.rst

@@ -63,7 +63,7 @@ axes with ``cbarmap=``. Utilities for colorbars are
 
 Finally, as an example of how you might use the code in a real
 situation, we can do some preprocessing of the data before creating the
-figure. Here we subract and normalize by the low temperature data, which
+figure. Here we subtract and normalize by the low temperature data, which
 highlights the Fermi edge width changing.
 
 .. figure:: _static/flat-stack-difference.png

docs/source/writing-interactive-tools.rst

@@ -191,7 +191,7 @@ data (``xy``) and the transformed data (``f(xy)``). To do this we used
 the utility function ``generate_marginal_for`` that can be used to
 create browsable marginal plots for high dimensional data sets. Here we
 do not want to integrate out any dimensions so we passed an tuple as the
-first argument. With the rest of the invokation we specify to add the
+first argument. With the rest of the invocation we specify to add the
 plot to the layout ``self.content_layout`` in the locations (0,0) and
 (1,0). Because we are not linking plots we don’t need cursors.
 

src/arpes/analysis/band_analysis.py

@@ -162,16 +162,25 @@ def dataarray_for_value(
             *,
             is_value: bool,
         ) -> xr.DataArray | None:
-            """Return DataArray representing the fit results.
+            """Return a DataArray representing the fit results for a specific parameter.
 
-            Args:
-                param_name (Literal["center", "amplitude", "sigma", "gamma"]): [TODO:description]
-                i (int): index for band names in identified_band_results.
-                is_value (bool): if True, return the value, else return stderr.
+            This function retrieves the values (or standard errors) of a specified fit parameter
+            (such as "center", "amplitude", "sigma", or "gamma") for each band in the
+            `identified_band_results`. The result is returned as an `xr.DataArray`. If the parameter
+            is not available in the fitting results for a given band, `None` is returned.
 
-            Returns: xr.DataArray | None
-                DataArray storing the fitting data. if the corresponding parameter name is not used,
-                returns None.
+            Args:
+                param_name (Literal["center", "amplitude", "sigma", "gamma"]): The name of the fit
+                    parameter whose values are being retrieved (e.g., "center", "amplitude", etc.).
+                i (int): Index for band names in the `identified_band_results` list. It is used to
+                    identify the correct band in the results.
+                is_value (bool): If `True`, the function returns the fit parameter's value; if
+                    `False`, it returns the standard error (stderr) of the fit.
+
+            Returns:
+                xr.DataArray | None: An `xr.DataArray` containing the fit parameter values
+                    (or stderr). Returns `None` if the corresponding parameter is not found for the
+                    given index.
             """
             values: NDArray[np.float64] = np.zeros_like(
                 band_results.values,
@@ -303,13 +312,25 @@ def _modelresult_to_array(
     prefix: str = "",
     weights: tuple[float, float, float] = (2, 0, 10),
 ) -> NDArray[np.float64]:
-    """Convert ModelResult to NDArray.
+    """Convert ModelResult to a weighted NDArray of fit parameter values.
+
+    This function extracts the values and standard errors for the parameters
+    "sigma", "gamma", "amplitude", and "center" from the `model_fit` object,
+    applies weights for each parameter (sigma, amplitude, center), and
+    returns the result as a NumPy array.
+
+    If any parameter is missing from `model_fit`, a default value and
+    standard error are assigned. The weights are applied to the parameters
+    during the conversion process.
 
     Args:
-        model_fit (ModelResult): [TODO:description]
-        prefix (str): Prefix in ModelResult
-        weights (tuple[float, float, float]): Weight for (sigma, amplitude, center)
+        model_fit (ModelResult): The model fitting result containing the parameters.
+        prefix (str): Prefix to be added to parameter names for identification.
+        weights (tuple[float, float, float]): Weights for the parameters in the order
+            (sigma, amplitude, center). Default is (2, 0, 10).
 
+    Returns:
+        NDArray[np.float64]: A NumPy array containing the weighted parameter values.
     """
     parameter_names: set[str] = set(model_fit.params.keys())
     if prefix + "sigma" in parameter_names:
@@ -358,34 +379,32 @@ def fit_patterned_bands(  # noqa: PLR0913
     interactive: bool = True,
     dataset: bool = True,
 ) -> XrTypes:
-    """Fits bands and determines dispersion in some region of a spectrum.
+    """Fits bands and determines dispersion in a region of a spectrum.
 
     The dimensions of the dataset are partitioned into three types:
 
-    1. Fit directions, these are coordinates along the 1D (or maybe later 2D) marginals
-    2. Broadcast directions, these are directions used to interpolate against the patterned
-    3. Free directions, these are broadcasted but they are not used to extract initial values of the
-       directions
-       fit parameters
-
-    For instance, if you laid out band patterns in a E, k_p, delay spectrum at delta_t=0, then if
-    you are using MDCs, k_p is the fit direction, E is the broadcast direction, and delay is a free
-    direction.
+    1. Fit directions: Coordinates along the 1D (or later 2D) marginals, e.g., energy (E).
+    2. Broadcast directions: Directions used to interpolate against the patterned, e.g., k.
+    3. Free directions: Broadcasted directions not used to extract the initial parameter values.
 
-    In general we can recover the free directions and the broadcast directions implicitly by
-    examining the band_set passed as a pattern.
+    For example, in a spectrum at delta_t=0, if using MDCs, `k_p` could be the fit direction,
+    `E` the broadcast direction, and `delay` a free direction.
 
     Args:
-        arr (xr.DataArray): [ToDo: description]
-        band_set: dictionary with bands and points along the spectrum
-        fit_direction (str): [ToDo: description]
-        stray (float, optional): [ToDo: description]
-        background (bool): [ToDo: description]
-        interactive(bool): [ToDo: description]
-        dataset(bool): if true, return as xr.Dataset.
-
-    Returns: XrTypes
-        Dataset or DataArray, as controlled by the parameter "dataset"
+        arr (xr.DataArray): The data array containing the spectrum to fit.
+        band_set (dict[Incomplete, Incomplete]): A dictionary defining the bands and points along
+            the spectrum.
+        fit_direction (str): The direction to fit the data (e.g., "energy").
+        stray (float, optional): A parameter used for adjusting fits. Defaults to None.
+        background (bool | type[Band]): If True, includes background fitting, otherwise specifies
+            the background band class.
+        interactive (bool): If True, show an interactive progress bar.
+        dataset (bool): If True, return the results as an `xr.Dataset`. If False, return just the
+            `band_results`.
+
+    Returns:
+        XrTypes: Either an `xr.DataArray` or an `xr.Dataset` depending on the `dataset` argument.
+        The returned object contains fitting results, residuals, and normalized residuals.
     """
     if background:
         from arpes.models.band import BackgroundBand