Mid-way working through things.

docs/source/explanations/tractography.rst

@@ -12,6 +12,14 @@ The pyAFQ software implements a simplified programming interface to the myriad
 methods that are implemented in DIPY. Please refer to the detailed
 documentation of the :mod:`AFQ.tractography.tractography` module for details.
 
+.. note::
+
+  Tractography is concerned with the visualization and mapping of white matter
+  tracts in the brain, while tractometry involves the quantitative analysis of
+  the structural properties of these tracts. Both techniques are valuable in
+  understanding the brain's connectivity and can provide insights into various
+  neurological conditions and cognitive processes.
+
 
 References
 ~~~~~~~~~~~

docs/source/howto/usage/index.rst

@@ -1,26 +1,6 @@
 How to use `pyAFQ`
 ==================
 
-`pyAFQ` can be used in two different ways.
-
-1. The first is as a program run in the command line. After installing the software, and organizing the data, run::
-
-    pyAFQ /path/to/config.toml
-
-pointing the program to the location of a configuration file (see :ref:`config`
-for an explanation of this file). This will run whole-brain tractography,
-segment the tracts, and extract tract-profiles for each tract, generating a CSV
-file under `study/derivatives/afq/tract_profiles.csv` that contains the tract
-profiles for all participants/tracts/statistics. The csv file has the same
-format as `nodes.csv in AFQ Browser <https://yeatmanlab.github.io/AFQ-Browser/dataformat.html>`_.
-
-
-2. The second is to write a program that uses `pyAFQ` as a software library.
-This is because `pyAFQ` provides a programmatic application programming
-interface (API) that can be used to integrate pyAFQ functionality into Python
-programs or into interactive analysis sessions (e.g., using `Jupyter <https://jupyter.org>`_ notebooks).
-Examples of using the API are provided in the :ref:`examples` documentation section.
-
 .. toctree::
     :maxdepth: 2
 

docs/source/index.rst

@@ -10,8 +10,10 @@ processing pipeline for tractometry, from raw DTI data to white matter tract ide
 , as well as quantification of tissue properties along the length of the
 major long-range brain white matter connections.
 
-- To get started, please refer to the `getting started`_ page.
-- What is the difference between tractography and tractometry?
+- To get started, please refer to the `getting started <tutorials/index.html>`_ page.
+- What is the difference between tractography and tractometry? See in the `explanations <explanations/index.html>`_ page.
+- For more detailed information on the variety of uses of pyAFQ, see the `how to <howto/index.html>`_ page.
+- For a detailed description of the methods and objects used in pyAFQ, see the `API reference <reference/index.html>`_ page.
 
 .. todo::
   create and link to page that lists the `major fiber tracts` supported by
@@ -21,15 +23,6 @@ major long-range brain white matter connections.
   create and link to page that identifies the supported `tissue properties`
   (believe this includes both dti and dki? fa, md, cfa?, and pdd?)
 
-.. note::
-
-  Tractography is concerned with the visualization and mapping of white matter
-  tracts in the brain, while tractometry involves the quantitative analysis of
-  the structural properties of these tracts. Both techniques are valuable in
-  understanding the brain's connectivity and can provide insights into various
-  neurological conditions and cognitive processes. More information can be found in
-  the Explanations page.
-
 .. toctree::
     :maxdepth: 2
     :hidden:
@@ -42,20 +35,6 @@ major long-range brain white matter connections.
 
 .. grid:: 2
 
-    .. grid-item-card::
-        :link: howto/index.html
-
-        :octicon:`rocket;3em;sd-text-center`
-
-        How To
-        ^^^^^^
-
-        User's guide to pyAFQ. This guide assumes you know
-        the basics and walks through more commonly used examples.
-
-        +++
-
-
     .. grid-item-card::
         :link: tutorials/index.html
 
@@ -69,6 +48,18 @@ major long-range brain white matter connections.
 
         +++
 
+    .. grid-item-card::
+        :link: howto/index.html
+
+        :octicon:`rocket;3em;sd-text-center`
+
+        How To
+        ^^^^^^
+
+        User's guide to pyAFQ. This guide assumes you know
+        the basics and walks through more commonly used examples.
+
+        +++
 
     .. grid-item-card::
         :link: explanations/index.html

docs/source/tutorials/index.rst

@@ -1,5 +1,30 @@
-Tutorials
----------
+Getting started with pyAFQ
+--------------------------
+
+After you :ref:`_installation_guide`_ the software, you can start using pyAFQ to analyze your data.
+
+pyAFQ assumes that preprocessed diffusion MRI data is organized according to
+the BIDS standard.
+
+There are two ways to use pyAFQ:
+
+1. The first is as a program run in the command line. After installing the software, and organizing the data, run::
+
+    pyAFQ /path/to/config.toml
+
+pointing the program to the location of a configuration file (see :ref:`config`
+for an explanation of this file). This will run whole-brain tractography,
+segment the tracts, and extract tract-profiles for each tract, generating a CSV
+file under `study/derivatives/afq/tract_profiles.csv` that contains the tract
+profiles for all participants/tracts/statistics. The csv file has the same
+format as `nodes.csv in AFQ Browser <https://yeatmanlab.github.io/AFQ-Browser/dataformat.html>`_.
+
+2. The second is to write a program that uses `pyAFQ` as a software library.
+This is because `pyAFQ` provides a programmatic application programming
+interface (API) that can be used to integrate pyAFQ functionality into Python
+programs or into interactive analysis sessions (e.g., using `Jupyter <https://jupyter.org>`_ notebooks).
+Examples of using the API are provided in the :ref:`tutorial_examples` documentation section.
+
 
 .. toctree::
     :maxdepth: 2

examples/tutorial_examples/README.txt

@@ -3,4 +3,7 @@
 Examples
 ========
 
-Tutorial examples.
+These examples provide detailed walk-throughs of code that runs.
+
+.. toctree::
+    :maxdepth: 2