Merge pull request #138 from jhlegarreta/AddMiscStatusBadgesToREADME

DOC: Add status badges to ``README`` file

README.rst

@@ -6,39 +6,55 @@
    :target: https://doi.org/10.5281/zenodo.4680599
    :alt: DOI
 
+.. image:: https://img.shields.io/pypi/l/eddymotion.svg
+   :target: https://pypi.python.org/pypi/eddymotion/
+   :alt: License
+
+.. image:: https://img.shields.io/pypi/v/eddymotion.svg
+   :target: https://pypi.python.org/pypi/eddymotion/
+   :alt: Latest Version
+
+.. image:: https://github.com/nipreps/eddymotion/actions/workflows/pages/pages-build-deployment/badge.svg
+   :target: https://www.nipreps.org/eddymotion/main/index.html
+   :alt: Documentation
+
+.. image:: https://github.com/nipreps/eddymotion/actions/workflows/pythonpackage.yml/badge.svg
+   :target: https://github.com/nipreps/eddymotion/actions/workflows/pythonpackage.yml
+   :alt: Python package
+
 Retrospective estimation of head-motion between diffusion-weighted images (DWI) acquired within
 diffusion MRI (dMRI) experiments renders exceptionally challenging1 for datasets including
 high-diffusivity (or “high b”) images.
 These “high b” (b > 1000s/mm2) DWIs enable higher angular resolution, as compared to more traditional
 diffusion tensor imaging (DTI) schemes.
-UNDISTORT [1]_ (Using NonDistorted Images to Simulate a Template Of the Registration Target)
+UNDISTORT [#r1]_ (Using NonDistorted Images to Simulate a Template Of the Registration Target)
 was the earliest method addressing this issue, by simulating a target DW image without motion
 or distortion from a DTI (b=1000s/mm2) scan of the same subject.
-Later, Andersson and Sotiropoulos [2]_ proposed a similar approach (widely available within the
+Later, Andersson and Sotiropoulos [#r2]_ proposed a similar approach (widely available within the
 FSL ``eddy`` tool), by predicting the target DW image to be registered from the remainder of the
 dMRI dataset and modeled with a Gaussian process.
 Besides the need for less data, ``eddy`` has the advantage of implicitly modeling distortions due
 to Eddy currents.
-More recently, Cieslak et al. [3]_ integrated both approaches in *SHORELine*, by
+More recently, Cieslak et al. [#r3]_ integrated both approaches in *SHORELine*, by
 (i) setting up a leave-one-out prediction framework as in eddy; and
-(ii) replacing eddy’s general-purpose Gaussian process prediction with the SHORE [4]_ diffusion model.
+(ii) replacing eddy’s general-purpose Gaussian process prediction with the SHORE [#r4]_ diffusion model.
 
 *Eddymotion* is an open implementation of eddy-current and head-motion correction that builds upon
 the work of ``eddy`` and *SHORELine*, while generalizing these methods to multiple acquisition schemes
-(single-shell, multi-shell, and diffusion spectrum imaging) using diffusion models available with DIPY [5]_.
+(single-shell, multi-shell, and diffusion spectrum imaging) using diffusion models available with DIPY [#r5]_.
 
 
-.. image:: docs/_static/eddymotion-flowchart.svg
+.. image:: https://raw.githubusercontent.com/nipreps/eddymotion/507fc9bab86696d5330fd6a86c3870968243aea8/docs/_static/eddymotion-flowchart.svg
    :alt: The eddymotion flowchart
 
 
-.. [1] S. Ben-Amitay et al., Motion correction and registration of high b-value diffusion weighted images, Magnetic
+.. [#r1] S. Ben-Amitay et al., Motion correction and registration of high b-value diffusion weighted images, Magnetic
    Resonance in Medicine 67:1694–1702 (2012)
-.. [2] J. L. R. Andersson. et al., An integrated approach to correction for off-resonance effects and subject movement
+.. [#r2] J. L. R. Andersson. et al., An integrated approach to correction for off-resonance effects and subject movement
    in diffusion MR imaging, NeuroImage 125 (2016) 1063–1078
-.. [3] M. Cieslak et al., QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI data.
+.. [#r3] M. Cieslak et al., QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI data.
    Nature Methods, 18(7), 775–778 (2021)
-.. [4] E. Ozarslan et al., Simple Harmonic Oscillator Based Reconstruction and Estimation for Three-Dimensional Q-Space
+.. [#r4] E. Ozarslan et al., Simple Harmonic Oscillator Based Reconstruction and Estimation for Three-Dimensional Q-Space
    MRI. in Proc. Intl. Soc. Mag. Reson. Med. vol. 17 1396 (2009)
-.. [5] E. Garyfallidis et al., Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinformatics 8, 8
+.. [#r5] E. Garyfallidis et al., Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinformatics 8, 8
    (2014)