user guide updates

doc/index.rst

@@ -27,21 +27,23 @@ Welcome to LumiSpy's documentation!
 .. _DOI: https://doi.org/10.5281/zenodo.4640445
 
 **LumiSpy** is a Python package extending the functionality for multi-dimensional
-data analysis provided by the `HyperSpy <https://hyperspy.org/>`_ library. It is
-aimed at helping with the analysis of luminescence spectroscopy data
+data analysis provided by the `HyperSpy <https://hyperspy.org/>`_ library. **LumiSpy**
+is aimed at helping with the analysis of luminescence spectroscopy data
 (cathodoluminescence, photoluminescence, electroluminescence, Raman, SNOM).
+Reading and writing of data from many relevant file types is provided by the
+library `RosettaSciIO <https://hyperspy.org/rosettasciio>`_.
 
-Check out the :ref:`installation` section for further information, including
-how to start using this project.
+Check out the :ref:`installation` section for further information on how to
+start using **LumiSpy**.
 
 Complementing this documentation, the `LumiSpy Demos <https://github.com/LumiSpy/lumispy-demos>`_
 repository contains curated Jupyter notebooks to provide tutorials and exemplary
 workflows.
 
 .. note::
 
-   This project is under active development. Everyone is welcome to contribute.
-   Please read our:ref:`contributing_label` guidelines and get started!
+   **LumiSpy** is under active development. Everyone is welcome to contribute.
+   Please read our :ref:`contributing_label` guidelines and get started!
 
 Contents
 ========

doc/user_guide/introduction.rst

@@ -38,13 +38,15 @@ Notable features that **HyperSpy** provides are:
 
 - :external+hyperspy:ref:`base signal classes <signal-label>`
   for the handling of (multidimensional) spectral data,
-- the necessary tools for loading :external+hyperspy:ref:`various data file formats
-  <io>`,
+- the necessary tools for  :external+hyperspy:ref:`loading <io>`
+  :external+rsciio:ref:`various data file formats <supported_formats>` using the
+  library `RosettaSciIO <https://hyperspy.org/rosettasciio>`_,
 - :external+hyperspy:ref:`analytical tools <signal1D-label>`
   that exploit the multidimensionality of datasets,
 - a user-friendly and powerful framework for :external+hyperspy:ref:`model fitting
-  <model-label>` that
-  provides many standard functions and can be easily extended to custom ones,
+  <model-label>` that provides many :external+hyperspy:ref:`standard functions
+  <model_components-label>` and can be easily extended to
+  :external+hyperspy:ref:`custom ones <expression_component-label>`,
 - :external+hyperspy:ref:`machine learning <ml-label>`
   algorithms that can be useful, e.g. for denoising data,
 - efficient handling of :external+hyperspy:ref:`big datasets <big-data-label>`,
@@ -55,7 +57,7 @@ Notable features that **HyperSpy** provides are:
   :external+hyperspy:ref:`regions of interest <roi-label>` and a powerful
   numpy-style :external+hyperspy:ref:`indexing mechanism <signal.indexing>`,
 - handling of :external+hyperspy:ref:`non-uniform data axes <Axes_types>`
-  (introduced in the :external+hyperspy:ref:`v1.7 release 
+  (introduced in the :external+hyperspy:ref:`1.7.0 release 
   <changes_1.7.0>`).
 
 **LumiSpy** provides in particular:
@@ -124,16 +126,16 @@ The hierarchy of the LumiSpy signal types and their inheritance from HyperSpy
 is summarized in the following diagram:
 
 |   └── :external:class:`hyperspy.signal.BaseSignal`
-|       ├── :external:class:`hyperspy._signals.signal1d.Signal1D`
-|       │   └── :class:`~.signals.luminescence_spectrum.LumiSpectrum`
-|       │   │   ├── :class:`~.signals.cl_spectrum.CLSpectrum`
-|       │   │   │   ├── :class:`~.signals.cl_spectrum.CLSEMSpectrum` 
-|       │   │   │   └── :class:`~.signals.cl_spectrum.CLSTEMSpectrum` 
-|       │   │   ├── :class:`~.signals.el_spectrum.ELSpectrum`
-|       │   │   └── :class:`~.signals.pl_spectrum.PLSpectrum`
-|       │   └── :class:`~.signals.luminescence_transient.LumiTransient`
-|       └── :class:`hyperspy.signal.Signal2D`
-|           └── :class:`~.signals.luminescence_transientspec.LumiTransientSpectrum`
+|           ├── :external:class:`hyperspy._signals.signal1d.Signal1D`
+|           │       └── :class:`~.signals.luminescence_spectrum.LumiSpectrum`
+|           │       │       ├── :class:`~.signals.cl_spectrum.CLSpectrum`
+|           │       │       │       ├── :class:`~.signals.cl_spectrum.CLSEMSpectrum` 
+|           │       │       │       └── :class:`~.signals.cl_spectrum.CLSTEMSpectrum` 
+|           │       │       ├── :class:`~.signals.el_spectrum.ELSpectsrum`
+|           │       │       └── :class:`~.signals.pl_spectrum.PLSpectrum`
+|           │       └── :class:`~.signals.luminescence_transient.LumiTransient`
+|           └── :class:`hyperspy.signal.Signal2D`
+|                   └── :class:`~.signals.luminescence_transientspec.LumiTransientSpectrum`
 |
 |
 
@@ -142,16 +144,17 @@ Where are we heading?
 =====================
 
 LumiSpy is under active development, and as a user-driven project, we welcome
-contributions (see :ref:`contributing_label`) to the code and documentation,
-but also bug reports and feature requests from any users. Don't hesitate
+:ref:`contributions <contributing_label>` to the code and documentation,
+but also bug reports and feature requests from any user. Don't hesitate
 to join the discussions!
 
 Currrently, we have implemented the base functionality that extends 
 :external+hyperspy:ref:`HyperSpy's capabilities <user_guide>`
 to additional signal classes. In the near future, the following functions
 should be developed:
 
-- handling of transient (time-resolved) data,
-- reading of common PL data formats (see :external+rsciio:ref:`supported-formats` of RosettaSciIO),
+- handling of transient (time-resolved) data with dedicated models,
+- extend the capabilities for reading relevant data formats
+  (see :external+rsciio:ref:`supported-formats` of RosettaSciIO),
 - more dedicated analysis functionalities,
 - ...

doc/user_guide/signal_axis.rst

@@ -87,8 +87,8 @@ Jacobian transformation
 When transforming the signal axis, the signal intensity is automatically
 rescaled (Jacobian transformation), unless the ``jacobian=False`` option is
 given. Only converting the signal axis, and leaving the signal intensity
-unchanged, implies that the integral of the signal over the same interval would
-lead to different results depending on the quantity on the axis (see e.g.
+unchanged, would implie that the integral of the signal over the same interval
+leads to different results depending on the quantity on the axis (see e.g.
 [Mooney]_ and [Wang]_).
 
 For the energy axis as example, if we require :math:`I(E)dE = I(\lambda)d\lambda`,
@@ -126,7 +126,7 @@ if the ``metadata.Signal.Noise_properties.variance`` attribute is a constant,
 it is converted into a :external:class:`hyperspy.api.signals.BaseSignal` object
 before the transformation.
 
-See :ref:`fitting_variance` for more general information on data variance
+See the section on :ref:`fitting_variance` for more general information on data variance
 in the context of model fitting and the HyperSpy documentation on `
 :external+hyperspy:ref:`setting the noise properties <signal.noise_properties>`.
 

doc/user_guide/signal_tools.rst

@@ -108,7 +108,7 @@ finding the position of the maximum intensity of a peak, useful in particular fo
 non-symmetric peaks with pronounced shoulders.
 It finds the centroid (center of mass) of a peak in the spectrum from the signal axis
 units (or pixel number) and the intensity at each pixel value. It basically represents a
-"weighted average" of the peak as such:
+"weighted average" of the peak defined as:
 
 .. math::
 
@@ -144,7 +144,7 @@ with the `kwargs` of :external:class:`scipy.interpolate.interp1d` function.
 
     This function only works for a single peak. If you have multiple peaks,
     slice the signal beforehand or use the slice parameter (which follows the
-    ``s.isig[:]`` convention).
+    ``s.isig[:]`` convention).s
 
 .. Note::
 
@@ -197,7 +197,7 @@ The default operational mode is ``inplace=False`` (a new signal object is return
 
 .. code-block:: python
 
-    >>> s.remove_negative(0.1)
+    >>> s2 = s.remove_negative(0.1)
 
 
 .. _crop_edges: