Merge pull request #1126 from DimitriPapadopoulos/http

DOC: http:// → https://

Dockerfile

@@ -138,7 +138,7 @@ ARG VCS_REF
 LABEL org.label-schema.build-date=$BUILD_DATE \
       org.label-schema.name="MRIQC" \
       org.label-schema.description="MRIQC - Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and functional MRI of the brain" \
-      org.label-schema.url="http://mriqc.readthedocs.io" \
+      org.label-schema.url="https://mriqc.readthedocs.io" \
       org.label-schema.vcs-ref=$VCS_REF \
       org.label-schema.vcs-url="https://github.com/nipreps/mriqc" \
       org.label-schema.version=$VERSION \

NOTICE

@@ -9,7 +9,7 @@ Psychology at Stanford University, Stanford, CA, US.
 
 This software contains code ultimately derived from the
 PCP Quality Assessment Protocol (QAP;
-http://preprocessed-connectomes-project.github.io/quality-assessment-protocol)
+http://preprocessed-connectomes-project.org/quality-assessment-protocol)
 by C. Craddock, S. Giavasis, D. Clark, Z. Shezhad, and J. Pellman.
 
 This software is also distributed as a Docker container image.

README.rst

@@ -11,7 +11,7 @@ MRIQC is an open-source project, developed under the following
 software engineering principles:
 
 #. **Modularity and integrability**: MRIQC implements a
-   `nipype <http://nipype.readthedocs.io>`_ workflow to integrate modular
+   `nipype <https://nipype.readthedocs.io>`_ workflow to integrate modular
    sub-workflows that rely upon third party software toolboxes such as
    ANTs and AFNI.
 
@@ -20,8 +20,8 @@ software engineering principles:
    processed derivatives.
 
 #. **Interoperability and standards**: MRIQC follows the the `brain imaging data structure
-   (BIDS) <http://bids.neuroimaging.io>`_, and it adopts the `BIDS-App
-   <http://bids-apps.neuroimaging.io>`_ standard.
+   (BIDS) <https://bids.neuroimaging.io>`_, and it adopts the `BIDS-App
+   <https://bids-apps.neuroimaging.io>`_ standard.
 
 #. **Reliability and robustness**: the software undergoes frequent vetting sprints
    by testing its robustness against data variability (acquisition parameters,
@@ -39,7 +39,7 @@ Citation
 
 Support and communication
 -------------------------
-The documentation of this project is found here: http://mriqc.readthedocs.io/.
+The documentation of this project is found here: https://mriqc.readthedocs.io/.
 
 Users can get help using the `mriqc-users google group <https://groups.google.com/forum/#!forum/mriqc-users>`_.
 
@@ -54,7 +54,7 @@ of the *NiPreps framework*.
 
 *MRIQC* originally derives from, and hence is heavily influenced by, the
 `PCP Quality Assessment Protocol
-<http://preprocessed-connectomes-project.github.io/quality-assessment-protocol>`__.
+<http://preprocessed-connectomes-project.org/quality-assessment-protocol/>`__.
 Please check the ``NOTICE`` file for further information.
 
 License

docs/source/conf.py

@@ -322,7 +322,7 @@
     "nibabel": ("https://nipy.org/nibabel/", None),
     "nipype": ("https://nipype.readthedocs.io/en/latest/", None),
     "numpy": ("https://numpy.org/doc/stable/", None),
-    "pandas": ("http://pandas.pydata.org/pandas-docs/dev", None),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/dev/", None),
     "python": ("https://docs.python.org/3/", None),
     "scipy": ("https://docs.scipy.org/doc/scipy/reference", None),
 }

docs/source/docker.rst

@@ -7,11 +7,11 @@ Preliminaries
 -------------
 #. Get the Docker Engine (https://docs.docker.com/engine/installation/)
 #. Have your data organized in BIDS
-   (`get BIDS specification <http://bids.neuroimaging.io/>`_,
-   `see BIDS paper <http://dx.doi.org/10.1038/sdata.2016.44>`_).
-#. Validate your data (http://incf.github.io/bids-validator/). You can
-   safely use the BIDS-validator since no data is uploaded to the server,
-   works locally in your browser.
+   (`get BIDS specification <https://bids.neuroimaging.io/>`_,
+   `see BIDS paper <https://doi.org/10.1038/sdata.2016.44>`_).
+#. Validate your data (https://bids-standard.github.io/bids-validator/).
+   You can safely use the BIDS-validator since no data is uploaded to the
+   server, it works locally in your browser.
 
 .. warning ::
 

mriqc/data/tests/ds000005/README

@@ -14,9 +14,9 @@ Release history:
 
 This dataset is made available under the Public Domain Dedication and License 
 v1.0, whose full text can be found at 
-http://www.opendatacommons.org/licenses/pddl/1.0/. 
+https://opendatacommons.org/licenses/pddl/1-0/. 
 We hope that all users will follow the ODC Attribution/Share-Alike 
-Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/); 
+Community Norms (https://opendatacommons.org/norms/odc-by-sa/); 
 in particular, while not legally required, we hope that all users 
 of the data will acknowledge the OpenfMRI project and NSF Grant 
 OCI-1131441 (R. Poldrack, PI) in any publications.

mriqc/interfaces/diffusion.py

@@ -514,7 +514,7 @@ def _rms(estimator, X):
     Callable to pass to GridSearchCV that will calculate a distance score.
 
     To consider: using `MDL
-    <http://erikerlandson.github.io/blog/2016/08/03/x-medoids-using-minimum-description-length-to-identify-the-k-in-k-medoids/>`__
+    <https://erikerlandson.github.io/blog/2016/08/03/x-medoids-using-minimum-description-length-to-identify-the-k-in-k-medoids/>`__
 
     """
     if len(np.unique(estimator.cluster_centers_)) < estimator.n_clusters:

mriqc/qc/__init__.py

@@ -45,7 +45,7 @@
 
   Most of the :abbr:`IQMs (image quality metrics)` in this module are adapted, derived or
   reproduced from the :abbr:`QAP (quality assessment protocols)` project [QAP]_.
-  We particularly thank Steve Giavasis (`@sgiavasis <http://github.com/sgiavasis>`_) and
+  We particularly thank Steve Giavasis (`@sgiavasis <https://github.com/sgiavasis>`_) and
   Krishna Somandepali for their original implementations of the code in this module that
   we took from the [QAP]_.
   The [QAP]_ has a very good description of the :abbr:`IQMs (image quality metrics)`
@@ -66,7 +66,7 @@
     <https://github.com/oesteban/quality-assessment-protocol/blob/enh/SmartQCWorkflow/qap/temporal_qc.py#L16>`_.
 
   .. [QAP-measures] `The Quality Assessment Protocols website: Taxonomy of QA Measures
-    <http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/#taxonomy-of-qa-measures>`_.
+    <http://preprocessed-connectomes-project.org/quality-assessment-protocol/#taxonomy-of-qa-measures>`_.
 
 
 """

mriqc/qc/anatomical.py

@@ -171,7 +171,7 @@
 
   .. [Dietrich2007] Dietrich et al., *Measurement of SNRs in MR images: influence
     of multichannel coils, parallel imaging and reconstruction filters*, JMRI 26(2):375--385.
-    2007. doi:`10.1002/jmri.20969 <http://dx.doi.org/10.1002/jmri.20969>`_.
+    2007. doi:`10.1002/jmri.20969 <https://doi.org/10.1002/jmri.20969>`_.
 
   .. [Ganzetti2016] Ganzetti et al., *Intensity inhomogeneity correction of structural MR images:
     a data-driven approach to define input algorithm parameters*. Front Neuroinform 10:10. 2016.
@@ -180,15 +180,15 @@
   .. [Magnota2006] Magnotta, VA., & Friedman, L., *Measurement of signal-to-noise
     and contrast-to-noise in the fBIRN multicenter imaging study*.
     J Dig Imag 19(2):140-147, 2006. doi:`10.1007/s10278-006-0264-x
-    <http://dx.doi.org/10.1007/s10278-006-0264-x>`_.
+    <https://doi.org/10.1007/s10278-006-0264-x>`_.
 
   .. [Mortamet2009] Mortamet B et al., *Automatic quality assessment in
     structural brain magnetic resonance imaging*, Mag Res Med 62(2):365-372,
-    2009. doi:`10.1002/mrm.21992 <http://dx.doi.org/10.1002/mrm.21992>`_.
+    2009. doi:`10.1002/mrm.21992 <https://doi.org/10.1002/mrm.21992>`_.
 
   .. [Tustison2010] Tustison NJ et al., *N4ITK: improved N3 bias correction*,
     IEEE Trans Med Imag, 29(6):1310-20,
-    2010. doi:`10.1109/TMI.2010.2046908 <http://dx.doi.org/10.1109/TMI.2010.2046908>`_.
+    2010. doi:`10.1109/TMI.2010.2046908 <https://doi.org/10.1109/TMI.2010.2046908>`_.
 
   .. [Shehzad2015] Shehzad Z et al., *The Preprocessed Connectomes Project
      Quality Assessment Protocol - a resource for measuring the quality of MRI data*,

mriqc/qc/functional.py

@@ -170,41 +170,41 @@
   .. [Atkinson1997] Atkinson et al., *Automatic correction of motion artifacts
     in magnetic resonance images using an entropy
     focus criterion*, IEEE Trans Med Imag 16(6):903-910, 1997.
-    doi:`10.1109/42.650886 <http://dx.doi.org/10.1109/42.650886>`_.
+    doi:`10.1109/42.650886 <https://doi.org/10.1109/42.650886>`_.
 
   .. [Friedman2008] Friedman, L et al., *Test--retest and between‐site reliability in a multicenter
     fMRI study*. Hum Brain Mapp, 29(8):958--972, 2008. doi:`10.1002/hbm.20440
-    <http://dx.doi.org/10.1002/hbm.20440>`_.
+    <https://doi.org/10.1002/hbm.20440>`_.
 
   .. [Giannelli2010] Giannelli et al., *Characterization of Nyquist ghost in
     EPI-fMRI acquisition sequences implemented on two clinical 1.5 T MR scanner
     systems: effect of readout bandwidth and echo spacing*. J App Clin Med Phy,
     11(4). 2010.
-    doi:`10.1120/jacmp.v11i4.3237 <http://dx.doi.org/10.1120/jacmp.v11i4.3237>`_.
+    doi:`10.1120/jacmp.v11i4.3237 <https://doi.org/10.1120/jacmp.v11i4.3237>`_.
 
   .. [Jenkinson2002] Jenkinson et al., *Improved Optimisation for the Robust and
     Accurate Linear Registration and Motion Correction of Brain Images*.
     NeuroImage, 17(2), 825-841, 2002.
-    doi:`10.1006/nimg.2002.1132 <http://dx.doi.org/10.1006/nimg.2002.1132>`_.
+    doi:`10.1006/nimg.2002.1132 <https://doi.org/10.1006/nimg.2002.1132>`_.
 
   .. [Kruger2001] Krüger et al., *Physiological noise in oxygenation-sensitive
     magnetic resonance imaging*, Magn. Reson. Med. 46(4):631-637, 2001.
-    doi:`10.1002/mrm.1240 <http://dx.doi.org/10.1002/mrm.1240>`_.
+    doi:`10.1002/mrm.1240 <https://doi.org/10.1002/mrm.1240>`_.
 
   .. [Nichols2013] Nichols, `Notes on Creating a Standardized Version of DVARS
-    <http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/scripts/fsl/standardizeddvars.pdf>`_,
+    <https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/scripts/fsl/standardizeddvars.pdf>`_,
     2013.
 
   .. [Power2012] Power et al., *Spurious but systematic correlations in
     functional connectivity MRI networks arise from subject motion*,
     NeuroImage 59(3):2142-2154,
     2012, doi:`10.1016/j.neuroimage.2011.10.018
-    <http://dx.doi.org/10.1016/j.neuroimage.2011.10.018>`_.
+    <https://doi.org/10.1016/j.neuroimage.2011.10.018>`_.
 
   .. [Saad2013] Saad et al. *Correcting Brain-Wide Correlation Differences
     in Resting-State FMRI*, Brain Conn 3(4):339-352,
     2013, doi:`10.1089/brain.2013.0156
-    <http://dx.doi.org/10.1089/brain.2013.0156>`_.
+    <https://doi.org/10.1089/brain.2013.0156>`_.
 """
 import os.path as op
 

mriqc/reports/__init__.py

@@ -33,7 +33,7 @@
 :abbr:`IQMs (image quality metrics)`, so it is particularly easy to identify the cases that
 are outliers for each metric. The plots are interactive, such that clicking on any particular
 sample opens the corresponding individual report of that case. Examples of group and individual
-reports for the ABIDE dataset are available online at `mriqc.org <http://mriqc.org>`_.
+reports for the ABIDE dataset are available online at `mriqc.org <https://mriqc.readthedocs.io/>`_.
 
 .. toctree::
     :maxdepth: 3