MRG, DOC: Integrate manual parts (mne-tools#6767)

* DOC: Integrate manual parts

* integrate SSP-api into tutorial

* add URL for MNE-C manual PDF

* fix: missing newline at EOF breaks RST rendering?

* fix dead crossref

* cleanups/tweaks

* STY: PEP8

* typos

* [skip travis][skip azp] tweaks to install doc sequence

doc/glossary.rst

@@ -143,7 +143,7 @@ general neuroimaging concepts. If you think a term is missing, please consider
         one anatomy to another. It is commonly referred as realignment in fMRI
         literature. This operation is necessary for group studies (to get the
         data in a common space for statistical analysis).
-        See :ref:`c_legacy_ch_morph` for more details.
+        See :ref:`ch_morph` for more details.
 
     pick
         An integer that is the index of a channel in the measurement info.

doc/install/freesurfer.rst

@@ -19,7 +19,7 @@ need to install ``tcsh`` for FreeSurfer to work; ``tcsh`` is usually
 pre-installed with macOS, and is available in the package repositories for
 Linux-based systems (e.g., ``sudo apt install tcsh`` on Ubuntu-like systems).
 
-**Next:** :doc:`mne_c`
+**Next:** :doc:`advanced`
 
 .. LINKS
 

doc/install/index.rst

@@ -3,7 +3,7 @@
 Installation — contents
 =======================
 
-The MNE software products are open-source tools for processing, analysis, and
+MNE-Python is an open-source Python module for processing, analysis, and
 visualization of functional neuroimaging data (EEG, MEG, sEEG, ECoG, and
 fNIRS). Pages describing the installation procedure are listed below. The
 :ref:`contributing guide <contributing>` has advanced installation instructions
@@ -15,5 +15,12 @@ for (future) contributors to MNE-Python.
     pre_install
     mne_python
     freesurfer
-    mne_c
     advanced
+
+Installation instructions are also provided for MNE-C (the Unix command-line
+tools originally developed by Matti Hämäläinen).
+
+.. toctree::
+    :maxdepth: 2
+
+    mne_c

doc/install/mne_c.rst

@@ -20,9 +20,8 @@ disk space. MATLAB is an optional dependency; the free `MATLAB runtime`_ is
 sufficient. If MATLAB is not present, the utilities ``mne_convert_mne_data``,
 ``mne_epochs2mat``, ``mne_raw2mat``, and ``mne_simu`` will not work.
 
-For boundary-element model (BEM) mesh generation (see :doc:`Creating the BEM
-meshes <../manual/appendix/bem_model>`), and for accessing the ``tkmedit``
-program from ``mne_analyze`` (see :ref:`CACCHCBF`), MNE-C needs access to a
+For boundary-element model (BEM) mesh generation, and for accessing the ``tkmedit``
+program from ``mne_analyze``, MNE-C needs access to a
 working installation of :doc:`FreeSurfer <freesurfer>`, including the
 environment variables ``FREESURFER_HOME``, ``SUBJECTS_DIR``, and ``SUBJECT``.
 
@@ -183,8 +182,6 @@ a typical 64-bit Ubuntu-like system this would be accomplished by:
 If you encounter other errors installing MNE-C, please send a message to the
 `MNE mailing list`_.
 
-**Next:** :doc:`advanced`
-
 .. links
 
 .. _MNE-C download page: http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/MNE_register/index.php

doc/install/pre_install.rst

@@ -10,16 +10,17 @@ Before you install
 Overview of the MNE tools suite
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-- :doc:`MNE-C <../manual/c_reference>` was the initial stage of this project,
+- MNE-C was the initial stage of this project,
   providing a set of interrelated command-line and GUI programs focused on
   computing cortically constrained Minimum Norm Estimates from MEG and EEG
-  data. These tools were written in C by Matti Hämäläinen.
+  data. These tools were written in C by Matti Hämäläinen, and are
+  documented `here <MNE-C manual_>`_.
 
 - :doc:`MNE-Python <../python_reference>` reimplements the functionality of
   MNE-C, and extends considerably the analysis and visualization capabilities.
   MNE-Python is collaboratively developed and has more than 150 contributors.
 
-- The :doc:`../manual/matlab` provides a MATLAB interface to the .fif file
+- The :ref:`mne_matlab` provides a MATLAB interface to the .fif file
   format and other MNE data structures, and provides example MATLAB
   implementations of some of the core analysis functionality of MNE-C. It is
   distributed alongside MNE-C, and can also be downloaded from the `MNE-MATLAB
@@ -54,23 +55,20 @@ What should I install?
 ^^^^^^^^^^^^^^^^^^^^^^
 
 If you intend only to perform ERP, ERF, or other sensor-level analyses,
-:doc:`MNE-Python <mne_python>` is all you need. If you prefer MATLAB
-over Python, probably all you need is :doc:`MNE-C <mne_c>` — the MNE
-MATLAB toolbox is distributed with it — although note that the MATLAB toolbox
-is less actively developed than the MNE-Python module, and hence the MATLAB
-code is considerably less feature-complete.
+:doc:`MNE-Python <mne_python>` is all you need. If you prefer to work with
+shell scripts and the Unix command line, or prefer MATLAB over Python, probably
+all you need is :doc:`MNE-C <mne_c>` — the MNE MATLAB toolbox is distributed
+with it — although note that the C tools and the MATLAB toolbox are less
+actively developed than the MNE-Python module, and hence are considerably less
+feature-complete.
 
 If you want to transform sensor recordings into estimates of localized brain
-activity, you will most likely need:
+activity, you will most likely also need :doc:`FreeSurfer <freesurfer>` to
+convert structural MRI scans into models of the scalp, inner/outer skull, and
+cortical surfaces (specifically, for command-line functions
+:ref:`gen_mne_flash_bem`, :ref:`gen_mne_watershed_bem`, and
+:ref:`gen_mne_make_scalp_surfaces`).
 
-- :doc:`FreeSurfer <freesurfer>` to convert structural MRI scans into
-  models of the scalp, inner/outer skull, and cortical surfaces
-
-- :doc:`MNE-C <mne_c>` for constructing and solving a boundary-element
-  model of tissue conductance, and for aligning coordinate frames between the
-  structural MRI and the digitizations of M/EEG sensor locations
-
-- :doc:`MNE-Python <mne_python>` can be used for everything else
 
 Getting help
 ^^^^^^^^^^^^

doc/links.inc

@@ -16,6 +16,7 @@
 .. _`mne-python sample dataset`: https://s3.amazonaws.com/mne-python/datasets/MNE-sample-data-processed.tar.gz
 .. _`mne command line utilities`: http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/MNE_register/
 .. _`mne-scripts`: https://github.com/mne-tools/mne-scripts/
+.. _`MNE-C manual`: https://mne.tools/mne-c-manual/MNE-manual-2.7.3.pdf
 .. _`MNE mailing list`: http://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
 .. _`GitHub issues page`: https://github.com/mne-tools/mne-python/issues/
 .. _`MNE gitter channel`: https://gitter.im/mne-tools/mne-python