👽 github.com → github.io

Ref https://github.blog/changelog/2021-01-29-github-pages-will-stop-redirecting-pages-sites-from-github-com-after-april-15-2021/

Author: shnizzedy

.circleci/config.yml

@@ -52,7 +52,7 @@ commands:
       - run:
           name: 🔧 Configuring git user
           command: |
-            git config --global user.email "[email protected].com"
+            git config --global user.email "[email protected].io"
             git config --global user.name "ci-build"
       - run:
           name: 🔀 Combining new docs with existing docs

CONTRIBUTING.md

@@ -47,7 +47,7 @@ Steps to build a release:
 - Only write a document once, and liberally use the [reStructured Text `.. include::` directive](https://docutils.sourceforge.io/docs/ref/rst/directives.html#include) to include that document where appropriate.
 - Use absolute paths for `.. include::`s. That way the path will resolve correctly regardless of differences in nesting levels.
 - Include any source documents that you want built in at least one [`toctree`](https://www.sphinx-doc.org/en/1.8/usage/restructuredtext/directives.html#directive-toctree). Use the `:hidden:` option if you don't want it linked in an actual table of contents in the document with the `toctree`.
-- Use consistent section title indicators throughout a sourcetree. [fcp-indi.github.com/docs/user](https://fcp-indi.github.com/docs/user) currently has the following hierarchy (top to bottom):
+- Use consistent section title indicators throughout a sourcetree. [fcp-indi.github.io/docs/user](https://fcp-indi.github.io/docs/user) currently has the following hierarchy (top to bottom):
   ```
   =
   ^
@@ -66,12 +66,12 @@ Steps to build a release:
 #### Let CircleCI build your drafts / works-in-progress
 * Build environment will match actual docs build environment
 * CircleCI takes ~2 minutes to build
-1. Fork https://github.com/FCP-INDI/fcp-indi.github.com
+1. Fork https://github.com/FCP-INDI/fcp-indi.github.io
 1. In your fork's settings, set the GitHub Pages `source` to `master` branch
     ![GitHub Pages settings example screenshot](./images/github-pages-settings-example.png)
 1. Add your project on CircleCI
 1. Merge your draft / work-in-progress into your fork's `source` branch. Make sure you push to your fork and not the main repository's `source` branch.
-1. Your fork will publish at `https://[your_GitHub_username].github.io/fcp-indi.github.com/`.
+1. Your fork will publish at `https://[your_GitHub_username].github.io/fcp-indi.github.io/`.
 
 <a id="markdown-build-locally-c-pac-≥-v180" name="build-locally-c-pac-≥-v180"></a>
 #### Build locally (C-PAC ≥ v1.8.0)
@@ -122,14 +122,14 @@ where `{width}` and `{height}` are the values already present in the existing `w
 <a id="markdown-references-and-citations" name="references-and-citations"></a>
 ## References and citations
 
-[sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/) is installed and configured. This extension creates links between the citations and the reference in the reference list and formats citations in referenced BibTeX files using built-in or [custom styles](https://github.com/FCP-INDI/fcp-indi.github.com/blob/source/docs/_sources/references/style.py). To use this Sphinx extension, 
+[sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/) is installed and configured. This extension creates links between the citations and the reference in the reference list and formats citations in referenced BibTeX files using built-in or [custom styles](https://github.com/FCP-INDI/fcp-indi.github.io/blob/source/docs/_sources/references/style.py). To use this Sphinx extension, 
 
-1. Include your citations in a BibTeX file (see the `*.bib` files in [docs/_sources/references](https://github.com/FCP-INDI/fcp-indi.github.com/blob/source/docs/_sources/references) for examples).
+1. Include your citations in a BibTeX file (see the `*.bib` files in [docs/_sources/references](https://github.com/FCP-INDI/fcp-indi.github.io/blob/source/docs/_sources/references) for examples).
 2. Using the key (the text between the opening `{` and the first `,` in a BibTeX entry) use the ReStructuredText syntax `` :cite:`key` `` to cite your reference in a ReStructuredText file.
 3. Include a `.. bibliography::` directive somewhere on any page that you want to use this extension to format references and create two-way links between the references and citations. Specify the (one) BibTeX file for this reference list any formatting for the reference list in this directive. Both `:cite:` and `.. bibliography::` need to be rendered on the same page for the links to generate.
 4. If you will (or might) use more than one `.. bibliography::` directive on a single rendered page (including `.. include::` directives), choose a prefix for the keys and include that prefix in both the `:cite:` role (like `` :cite:`prefix-key` ``) and the bibliography directive (like `:keyprefix: prefix-`).
 5. If you want to include a header over a reference list, use the `.. rubric::` directive above its `.. bibliography` directive.
-6. If the entry type (e.g., `book`, `article`, `misc`) of any of the entries in your BibTeX file(s) is not included in [docs/_sources/references/style.py](https://github.com/FCP-INDI/fcp-indi.github.com/blob/source/docs/_sources/references/style.py), add a `get_{entry_type}_template` [Pybtex](https://pybtex.org) method to `CPAC_DocsStyle`.
+6. If the entry type (e.g., `book`, `article`, `misc`) of any of the entries in your BibTeX file(s) is not included in [docs/_sources/references/style.py](https://github.com/FCP-INDI/fcp-indi.github.io/blob/source/docs/_sources/references/style.py), add a `get_{entry_type}_template` [Pybtex](https://pybtex.org) method to `CPAC_DocsStyle`.
 
 For example, if you have a BibTeX file called `cpac_citation.bib` that contains
 
@@ -182,5 +182,5 @@ The rendered file should look something like
       :shell:
       :ellipsis: 0,9
    ```
-* :heavy_plus_sign: Check [`.circleci/config.yml`](https://github.com/FCP-INDI/fcp-indi.github.com/blob/source/.circleci/config.yml) of the branch you're working from for build dependencies.
+* :heavy_plus_sign: Check [`.circleci/config.yml`](https://github.com/FCP-INDI/fcp-indi.github.io/blob/source/.circleci/config.yml) of the branch you're working from for build dependencies.
 * :octocat: Set an environment variable `GITHUBTOKEN` to a [personal access token](https://help.github.com/en/github/authenticating-to-github/creating-a-personal-access-token-for-the-command-line) to increase [your API rate limit](https://developer.github.com/v3/#rate-limiting) from 60 to 5000 requests per hour (for getting [release notes from GitHub](https://github.com/FCP-INDI/C-PAC/releases)).

GUI/index.html

@@ -1,5 +1,5 @@
 <!DOCTYPE html>
 <meta charset="utf-8">
 <script>
-window.location.href = "https://fcp-indi.github.com/C-PAC_GUI" + window.location.href.split('/GUI')[1]
+window.location.href = "https://fcp-indi.github.io/C-PAC_GUI" + window.location.href.split('/GUI')[1]
 </script>

README.md

@@ -1,4 +1,4 @@
-# fcp-indi.github.com
+# fcp-indi.github.io
 
 GitHub Pages repo for FCP-INDI
 

docs/_sources/user/alff.rst

@@ -9,7 +9,7 @@ ALFF is defined as the total power within the frequency range between 0.01 and 0
 
 Computation and Analysis Considerations
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-All computations are performed in a subject's native space. After transforming voxel time series frequency information into the power domain, calculation of these measures is relatively simple. ALFF is calculated as the sum of amplitudes within a specific low frequency range. f/ALFF is calculated as a fraction of the sum of amplitudes across the entire frequency range detectable in a given signal. For both measures, amplitudes in subject-level maps are transformed into Z-scores to create standardized subject-level maps. Anatomical images and Z-score maps are then transformed into MNI152 standard space. For more detail on how CPAC computes these steps, please see the `ALFF and f/ALFF Page of the developer documentation <http://fcp-indi.github.com/docs/developer/workflows/alff.html>`_.
+All computations are performed in a subject's native space. After transforming voxel time series frequency information into the power domain, calculation of these measures is relatively simple. ALFF is calculated as the sum of amplitudes within a specific low frequency range. f/ALFF is calculated as a fraction of the sum of amplitudes across the entire frequency range detectable in a given signal. For both measures, amplitudes in subject-level maps are transformed into Z-scores to create standardized subject-level maps. Anatomical images and Z-score maps are then transformed into MNI152 standard space. For more detail on how CPAC computes these steps, please see the `ALFF and f/ALFF Page of the developer documentation <http://fcp-indi.github.io/docs/developer/workflows/alff.html>`_.
 
 Though both ALFF and f/ALFF are sensitive mostly to signal from gray matter, ALFF is more prone to noise from physiological sources, particularly near the ventricles and large blood vessels (Zuo et al., 2008;2010). The figure below (from Zuo et al., 2010) shows areas in which ALFF shows higher amplitude than f/ALFF, as well as the relative sensitivity of these measures to gray matter.
 

docs/_sources/user/appendix.rst

@@ -411,7 +411,7 @@ Installing C-PAC
 """"""""""""""""
 Congratulations, you are now ready to install C-PAC itself!
 
-C-PAC is available for download from the `C-PAC Homepage <http://fcp-indi.github.com/>`__. Click the button labeled "Download as tar.gz". Unpack the downloaded archive and navigate to the new directory. To install C-PAC, run the command ``sudo python setup.py install``. C-PAC will be installed alongside your other python packages. If this fails, check to make sure that you have all the dependencies installed.  You may also install C-PAC using the commands below:
+C-PAC is available for download from the `C-PAC Homepage <http://fcp-indi.github.io/>`__. Click the button labeled "Download as tar.gz". Unpack the downloaded archive and navigate to the new directory. To install C-PAC, run the command ``sudo python setup.py install``. C-PAC will be installed alongside your other python packages. If this fails, check to make sure that you have all the dependencies installed.  You may also install C-PAC using the commands below:
 
  .. code-block:: console
 
@@ -432,7 +432,7 @@ Once you are able to successfully ``import CPAC`` it is safe to delete any setup
 
 Updating C-PAC
 """"""""""""""
-C-PAC is being actively developed, and new versions (containing bug fixes and new features) are often released. To update to the latest version, simply download it from the `C-PAC Homepage <http://fcp-indi.github.com/>`__ and repeat the instructions in the `Installing C-PAC` section above. A list of previous versions and the changes they contain is available on the :doc:`Release Notes Page </user/rnotes>`.
+C-PAC is being actively developed, and new versions (containing bug fixes and new features) are often released. To update to the latest version, simply download it from the `C-PAC Homepage <http://fcp-indi.github.io/>`__ and repeat the instructions in the `Installing C-PAC` section above. A list of previous versions and the changes they contain is available on the :doc:`Release Notes Page </user/rnotes>`.
 
 .. note::
 

docs/_sources/user/centrality.rst

@@ -19,7 +19,7 @@ Computation and Analysis Considerations
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 For each subject, the timeseries for each node is extracted and used to calculate a temporal correlation matrix which represents the connectivity between all nodes. A threshold is then applied to the correlation matrix to create an adjacency matrix which describes all nodes that have survived thresholding. In CPAC, users can choose to apply a threshold based on either sparsity, significance, or correlation strength. Threshold values should be chosen carefully, as they can strongly influence the results of centrality analysis (Zuo et al., 2012).
 
-Of the three centrality measures computed by CPAC, Degree Centrality is the simplest, and is computed by counting the number of significant correlations between the timeseries of a given node and that of all other nodes in the adjacency matrix. Local Functional Connectivity Density should be faster because it only calculates neighboring connections which have weights above the threshold. On the other hand, Eigenvector Centrality is computed by identifying the first eigenvector of the adjacency matrix, which corresponds to the largest eigenvalue. Since EC is a recursive measure that takes into account the properties of connected nodes, the EC of a given node is proportional to the sum of the EC of all directly neighboring nodes. After computing the centrality measures, centrality maps are generated and standardized using Z-scores for use in group analysis (Zuo et al., 2012). For more detail on how CPAC computes these steps, please see the `Network Centrality Page of the developer documentation <http://fcp-indi.github.com/docs/developer/workflows/network_centrality.html>`_.
+Of the three centrality measures computed by CPAC, Degree Centrality is the simplest, and is computed by counting the number of significant correlations between the timeseries of a given node and that of all other nodes in the adjacency matrix. Local Functional Connectivity Density should be faster because it only calculates neighboring connections which have weights above the threshold. On the other hand, Eigenvector Centrality is computed by identifying the first eigenvector of the adjacency matrix, which corresponds to the largest eigenvalue. Since EC is a recursive measure that takes into account the properties of connected nodes, the EC of a given node is proportional to the sum of the EC of all directly neighboring nodes. After computing the centrality measures, centrality maps are generated and standardized using Z-scores for use in group analysis (Zuo et al., 2012). For more detail on how CPAC computes these steps, please see the `Network Centrality Page of the developer documentation <http://fcp-indi.github.io/docs/developer/workflows/network_centrality.html>`_.
 
 Applications and Recommendations 
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

docs/_sources/user/futuredocs/basc.rst

@@ -29,7 +29,7 @@ Computation and Analysis Considerations
 
 #. The group stability matrix is fed into a clustering algorithm to derive stable clusters. 
 
-The figure below (taken from Garcia-Garcia et al.) shows a visual representation of these steps. For more detail on how CPAC computes these steps, please see the `BASC Page of the developer documentation <http://fcp-indi.github.com/docs/developer/workflows/basc.html>`_.
+The figure below (taken from Garcia-Garcia et al.) shows a visual representation of these steps. For more detail on how CPAC computes these steps, please see the `BASC Page of the developer documentation <http://fcp-indi.github.io/docs/developer/workflows/basc.html>`_.
 
 .. figure:: /_images/basc_manu_schematic.png
 

docs/_sources/user/group_mdmr.rst

@@ -17,7 +17,7 @@ Computation steps for CWAS are listed below as described by Shehzad and colleagu
 
 #. The significance of these similarities and differences is assessed with a permutation test. This identifies brain regions whose whole-brain pattern of connectivity is significantly predicted by a particular phenotypic variable.
 
-The figure below (taken from Shehzad et al., 2010) outlines these steps. For more detail on how C-PAC handles these computations, please see the `CWAS section of the developer documentation <http://fcp-indi.github.com/docs/developer/workflows/cwas.html>`_.
+The figure below (taken from Shehzad et al., 2010) outlines these steps. For more detail on how C-PAC handles these computations, please see the `CWAS section of the developer documentation <http://fcp-indi.github.io/docs/developer/workflows/cwas.html>`_.
 
 .. figure:: /_images/cwas_shehzad_schematic.png
 

docs/_sources/user/reho.rst

@@ -11,7 +11,7 @@ The figure above (taken from Yan and Zang, 2010) shows the default mode network
 
 Computation and Analysis Considerations
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-KCC is computed for every voxel in a subject, and is based on the time series of each voxel, the number of time points within a time series, and the number of voxels within a cluster (Zang et al, 2004). Depending on whether neighboring voxels are taken to include those on the side, edge, or corner of a given voxel, cluster size can be 7, 19, or 27 voxels, respectively. Values of KCC range from 0 to 1, with higher values indicating greater similarity between the activation pattern of a given voxel and that of its neighbors. Voxel-based maps are generated based on KCC values and then standardized using Z-scores in order to perform group analysis. For more detail on how CPAC handles these computations, please see the `ReHo section of the developer documentation <http://fcp-indi.github.com/docs/developer/workflows/reho.html>`_.
+KCC is computed for every voxel in a subject, and is based on the time series of each voxel, the number of time points within a time series, and the number of voxels within a cluster (Zang et al, 2004). Depending on whether neighboring voxels are taken to include those on the side, edge, or corner of a given voxel, cluster size can be 7, 19, or 27 voxels, respectively. Values of KCC range from 0 to 1, with higher values indicating greater similarity between the activation pattern of a given voxel and that of its neighbors. Voxel-based maps are generated based on KCC values and then standardized using Z-scores in order to perform group analysis. For more detail on how CPAC handles these computations, please see the `ReHo section of the developer documentation <http://fcp-indi.github.io/docs/developer/workflows/reho.html>`_.
 
 .. figure:: /_images/reho_voxel_schematic.png