Merge pull request #26 from josephburkhart/fix-docs-typos

Fix typos in documentation and readme

Author: AlainKadar

README.rst

@@ -5,7 +5,7 @@ StructuralGT
 Overview
 ========
 A python package for the extraction and analysis of graphs from 2D and 3D experimental micrographs. Image processing techniques taken from `StructuralGT <https://github.com/drewvecchio/StructuralGT>`__.
-*StructuralGT* is available as an API based package, or GUI. Use `conda-forge <https://anaconda.org/conda-forge/structuralgt>`__ or the main GitHub branch for the API version, and the `GUI branch <https://github.com/compass-stc/StructuralGT/tree/DicksonOwuor-GUI>`__ for the GUI version.
+*StructuralGT* is available as an API-based or GUI-based package. Use `conda-forge <https://anaconda.org/conda-forge/structuralgt>`__ or the main GitHub branch for the API version, and the `GUI branch <https://github.com/compass-stc/StructuralGT/tree/DicksonOwuor-GUI>`__ for the GUI version.
 
 Installation guide
 ==================
@@ -16,12 +16,12 @@ platforms. Install with
 
    conda install conda-forge::structuralgt
 
-**StructuralGT** can also be built from source, via the
+**StructuralGT** can also be built from source via the
 `public repository <https://github.com/compass-stc/StructuralGT>`__.
 Prior to install, you will need to install some dependencies into your conda
 environment. Note that installation will most likely be
 successful if carried out in a new conda environment. While it is posisble to
-obtain some of these dependencies with pip, installation will most likely be
+obtain some of these dependencies with pip, installation is most likely to be
 sucessful when all dependencies are obtained with conda.
 
 .. code:: bash
@@ -57,7 +57,7 @@ included tests:
 Quickstart and examples repository
 ==================================
 
-To showcase the API, here is a simple example script used to predict the sheet resistance of silver nanowires, used in our recent publication :cite:`WuKadar2025`
+To showcase the API, here is a simple example script used to predict the sheet resistance of silver nanowires, used in our recent publication :cite:`WuKadar2025`.
 To learn how to implement this yourself, consult our
 `examples repository <https://github.com/compass-stc/StructuralGT-Examples>`__.
 
@@ -111,7 +111,9 @@ To build the documentation, run the following commands in the source directory:
     cd doc
     make html
     # Then open build/html/index.html
+
+or
+
 .. code:: bash
 
-   conda install sphinx furo nbsphinx jupyter_sphinx sphinxcontrib-bibtex sphinx-copybutton
    sphinx-build -b html doc html

doc/source/gettingstarted/introduction.rst

@@ -2,8 +2,8 @@
 Introduction
 ============
 
-StructuralGT is a Python package for graph theoretic analysis of image data. Originally prototyped as a GUI based package for microscopy data, :cite:`Vecchio2021` the package outlined in this documentation is designed for terminal based use. Addtionally, this package offers the possibility of analysing 3-dimensional datasets and property predictions. Initial graph theoretic studies of microscopy datasets have shown structural differences betweeen disordered architectures of various nanoscale materials. This API offers the users the possiblility of performing analysis beyond what has been reported in the literature. Some basic examples of analyses are given in Examples.
+StructuralGT is a Python package for graph theoretic analysis of image data. Originally prototyped as a GUI based package for microscopy data, :cite:`Vecchio2021` the package outlined in this documentation is designed for terminal-based use. Addtionally, this package offers the possibility of analysing 3-dimensional datasets and property predictions. Initial graph theoretic studies of microscopy datasets have shown structural differences betweeen disordered architectures of various nanoscale materials. This API offers the users the possiblility of performing analysis beyond what has been reported in the literature. Some basic examples of analyses are given in Examples.
 
-All analyses carried out with StructuralGT involve a :class:`Network` class, which represents the system being studied. To motivate it's construction, we first outline the conceptual differecne between a `network` and a `graph`. Networks are real-world objects which exhibit a structure akin to points linked by connections. Their study belongs to the discipline of `network science` and examples may include social networks, internet networks, or material networks. The mathematical objects which abstract these structures are graphs, and the discipline which examines them is `graph theory`. A graph may be defined only by it's nodes and connecting edges. While the nodes and edges may contain attributes, to give further detail, a large part of the network's description is lost as a result of it's abstraction into a graph (most notably, its geometric features). Hence we construct the :class:`Network` class to contain a conventional graph, and all other information that would be otherwise lost. Specifically, after the appropriate graph extraction methods have been called, the :class:`Network` class is populated with a :attr:`graph` attribute.
+All analyses carried out with StructuralGT involve a :class:`Network` class, which represents the system being studied. To motivate its construction, we first outline the conceptual difference between a `network` and a `graph`. Networks are real-world objects which exhibit a structure akin to points linked by connections. Their study belongs to the discipline of `network science` and examples may include social networks, internet networks, or material networks. The mathematical objects which abstract these structures are graphs, and the discipline which examines them is `graph theory`. A graph may be defined only by its nodes and connecting edges. While the nodes and edges may contain attributes, to give further detail, a large part of the network's description is lost as a result of its abstraction into a graph (most notably, its geometric features). Hence we construct the :class:`Network` class to contain a conventional graph, and all other information that would be otherwise lost. Specifically, after the appropriate graph extraction methods have been called, the :class:`Network` class is populated with a :attr:`graph` attribute.
 
-The second component required for analysis in StructuralGT is a :class:`Compute` class, which represents the calculations being carried out on the system (i.e. :class:`Network` class). All compute methods may be instantiated with a weight type, to specify whether graph weights are to be used in the calculations. Compute modules contain a :meth:`compute` method, which populates the object with attributes containing the results of the calculations. Generally, compute modules call a mixture of python functions and fast graph theoretic computations, via the :code:`igraph` C library :cite:`Csardi2005`. When the user wishes to carry out their own custom analysis with C-level speed, they may implement their own C routines, which expose the results to the Python API. Instructions on this implementation are given in Adding C++ Scripts.
+The second component required for analysis in StructuralGT is a :class:`Compute` class, which represents the calculations being carried out on the system (i.e. :class:`Network` class). All compute methods may be instantiated with a weight type, to specify whether graph weights are to be used in the calculations. Compute modules contain a :meth:`compute` method, which populates the object with attributes containing the results of the calculations. Generally, compute modules call a mixture of python functions and fast graph theoretic computations, via the :code:`igraph` C library :cite:`Csardi2005`. When the user wishes to carry out their own custom analysis with C-level speed, they may implement their own C routines, which expose the results to the Python API. Instructions on this implementation are given in :doc:`../reference/addingcppscripts`.

doc/source/reference/addingcppscripts.rst

@@ -7,7 +7,7 @@ Adding C++ Scripts
 Python Wrapper Method
 =====================
 
-This is the called by the user and should be a method of a :class:`Compute` subclass. These methods should create a deep copy of the graph, to pass to the C++ script. The graph is passed to the C++ script via its pointer, which can be accessed from igraph's :meth:`_raw_pointer` method.
+This is called by the user and should be a method of a :class:`Compute` subclass. These methods should create a deep copy of the graph to pass to the C++ script. The graph is passed to the C++ script via its pointer, which can be accessed from igraph's :meth:`_raw_pointer` method.
 
 Cython Module
 =============