Merge branch 'no_yeatmanlab' of github.com:tractometry/pyAFQ into no_…

…yeatmanlab

.zenodo.json

@@ -69,7 +69,7 @@
   "license": "BSD-3-Clause",
   "related_identifiers": [
     {
-      "identifier": "http://tractometry.github.io/pyAFQ",
+      "identifier": "http://tractometry.org/pyAFQ",
       "relation": "isPartOf",
       "scheme": "url"
     }

AFQ/tasks/viz.py

@@ -404,7 +404,7 @@ def init_viz_backend(viz_backend_spec="plotly_no_gif",
     viz_backend_spec : str, optional
         Which visualization backend to use.
         See Visualization Backends page in documentation for details:
-        https://tractometry.github.io/pyAFQ/usage/viz_backend.html
+        https://tractometry.org/pyAFQ/usage/viz_backend.html
         One of {"fury", "plotly", "plotly_no_gif"}.
         Default: "plotly_no_gif"
     """

README.md

@@ -1,7 +1,7 @@
 # pyAFQ
 Automated Fiber Quantification ... in Python.
 
-For details, see [Documentation](https://tractometry.github.io/pyAFQ)
+For details, see [Documentation](https://tractometry.org/pyAFQ)
 
 For further analysis of results, see [AFQ-Insight](https://github.com/richford/AFQ-Insight)
 

bin/pyAFQ

@@ -23,7 +23,7 @@ usage = \
 Runs full AFQ processing as specified in the configuration file.
 
 For details about configuration, see instructions in:
-https://tractometry.github.io/pyAFQ/usage/config.html
+https://tractometry.org/pyAFQ/usage/config.html
 
 The default configuration file looks like:
 
@@ -104,7 +104,7 @@ def parse_cli(arg_dict):
         logger.info(
             "Your use of pyAFQ is being recorded using Google Analytics. "
             "For more information, please refer to the pyAFQ documentation: "
-            "https://tractometry.github.io/pyAFQ/usage.html#usage-tracking-with-google-analytics. "  # noqa
+            "https://tractometry.org/pyAFQ/usage.html#usage-tracking-with-google-analytics. "  # noqa
             "To turn this off, use the `--notrack` "
             "flag when using the pyAFQ CLI")
         import popylar

docs/source/explanations/modeling.rst

@@ -11,7 +11,7 @@ For an interesting perspective on modeling of tissue properties from diffusion
 MRI data, please refer to a recent paper by Novikov and colleagues
 [Novikov2018]_.
 
-`This page <https://tractometry.github.io/pyAFQ/reference/methods.rst>` includes
+`This page <https://tractometry.org/pyAFQ/reference/methods.rst>` includes
 a list of the model parameters that are accessible through the
 :class:`AFQ.api.group.GroupAFQ` and :class:`AFQ.api.participant.ParticipantAFQ`
 objects.

docs/source/howto/developing/index.rst

@@ -20,14 +20,14 @@ As an overview, the pyAFQ code can roughly be divided into 5 sections.
 3 - Code which describes tasks within the tractometry workflow.
     **For most contributors, this is a good place to start**. The user-facing API/CLI
     use a workflow automatically constructed from these discrete tasks,
-    as described in `tasks <https://tractometry.github.io/pyAFQ/developing/tasks.html>`_ .
+    as described in `tasks <https://tractometry.org/pyAFQ/developing/tasks.html>`_ .
     For shorter tasks, one may only need to edit the relevant task file.
     For longer tasks, one can add to the files containing low-level
     functionality.
 
 4 - Code which describes 'definitions', classes which help users describe
     inputs to pyAFQ. These have particular requirements and must be
-    BIDS compatible, see `here <https://tractometry.github.io/pyAFQ/developing/definitions.html>`_ .
+    BIDS compatible, see `here <https://tractometry.org/pyAFQ/developing/definitions.html>`_ .
 
 5 - Code which contains tests, such as in the tests folder and utils/tests.
     It is important when adding new functionality to add a corresponding test.

docs/source/howto/usage/kwargs.rst

@@ -108,7 +108,7 @@ brain_mask_definition: instance from `AFQ.definitions.image`, optional
 bundle_info: strings, dict, or BundleDict, optional
 	List of bundle names to include in segmentation,
 	or a bundle dictionary (see BundleDict for inspiration),
-	or a BundleDict. See `Defining Custom Bundle Dictionaries <https://tractometry.github.io/pyAFQ/reference/bundledict.html>`_
+	or a BundleDict. See `Defining Custom Bundle Dictionaries <https://tractometry.org/pyAFQ/reference/bundledict.html>`_
 	in the `usage` section of pyAFQ's documentation for details.
 	If None, will get all appropriate bundles for the chosen
 	segmentation algorithm.
@@ -228,7 +228,7 @@ n_points_indiv: int or None
 viz_backend_spec: str, optional
 	Which visualization backend to use.
 	See Visualization Backends page in documentation for details:
-	https://tractometry.github.io/pyAFQ/usage/viz_backend.html
+	https://tractometry.org/pyAFQ/usage/viz_backend.html
 	One of {"fury", "plotly", "plotly_no_gif"}.
 	Default: "plotly_no_gif"
 

docs/source/reference/kwargs.rst

@@ -258,7 +258,7 @@ n_points_indiv: int or None
 viz_backend_spec: str, optional
 	Which visualization backend to use.
 	See Visualization Backends page in documentation for details:
-	https://tractometry.github.io/pyAFQ/usage/viz_backend.html
+	https://tractometry.org/pyAFQ/usage/viz_backend.html
 	One of {"fury", "plotly", "plotly_no_gif"}.
 	Default: "plotly_no_gif"
 

examples/howto_examples/cloudknot_hcp_example.py

@@ -8,7 +8,7 @@
 `here <https://wiki.humanconnectome.org/display/PublicData/How+To+Connect+to+Connectome+Data+via+AWS>`_.
 We will use the ``Cloudknot`` library to run our AFQ analysis in the AWS
 Batch service (see also
-`this example <http://tractometry.github.io/pyAFQ/auto_examples/cloudknot_example.html>`_).
+`this example <http://tractometry.org/pyAFQ/auto_examples/cloudknot_example.html>`_).
 In the following we will use ``Cloudknot`` to run multiple
 configurations of pyAFQ on the HCP dataset. Specifically, here we will run
 pyAFQ with different tractography seeding strategies.
@@ -147,7 +147,7 @@ def attach_keys(list_of_arg_lists):
 
 ##########################################################################
 # Define the :meth:`Knot` object to run your jobs on. See
-# `this example <http://tractometry.github.io/pyAFQ/auto_examples/cloudknot_example.html>`_ for more
+# `this example <http://tractometry.org/pyAFQ/auto_examples/cloudknot_example.html>`_ for more
 # details about the arguments to the object.
 knot = ck.Knot(
     name='afq-hcp-tractography-201110-0',

examples/tutorial_examples/plot_002_bids_layout.py

@@ -213,7 +213,7 @@
 # which we already have clear definitions of the information
 # needed to segment them (e.g., waypoint ROIs and probability maps).
 # For an example that includes custom definition of bundle info, see the
-# `plot_callosal_tract_profile example <http://tractometry.github.io/pyAFQ/auto_examples/plot_callosal_tract_profile.html>`_.
+# `plot_callosal_tract_profile example <http://tractometry.org/pyAFQ/auto_examples/plot_callosal_tract_profile.html>`_.
 
 bundle_info = abd.default18_bd()[
     "Left Superior Longitudinal",