Switch docs theme

.github/workflows/gh-ci.yaml

@@ -155,7 +155,7 @@ jobs:
       with:
         micromamba: true
         full-deps: true
-        extra-pip-deps: "docutils<0.18 sphinx<7.0 sphinx-sitemap sphinx_rtd_theme msmb_theme==1.2.0 sphinxcontrib-bibtex pybtex pybtex-docutils"
+        extra-pip-deps: "docutils sphinx<7 sphinx-sitemap mdanalysis-sphinx-theme>=1.0.1 sphinxcontrib-bibtex pybtex pybtex-docutils"
 
     - name: build_srcs
       uses: ./.github/actions/build-src

maintainer/conda/environment.yml

@@ -6,13 +6,14 @@ dependencies:
   - chemfiles>=0.10
   - codecov
   - cython
-  - docutils <0.18
+  - docutils
   - fasteners
   - griddataformats
   - gsd
   - h5py>=2.10
   - hypothesis
   - joblib>=0.12
+  - mdanalysis-sphinx-theme >=1.0.1
   - matplotlib>=3.2.2
   - mmtf-python
   - mock
@@ -28,11 +29,9 @@ dependencies:
   - tidynamics>=1.0.0
   - tqdm>=4.43.0
   - sphinxcontrib-bibtex
-  - sphinx_rtd_theme
   - pip:
       - duecredit
       - parmed
-      - msmb_theme==1.2.0
-      - sphinx-sitemap==1.0.2
+      - sphinx-sitemap
       - packaging
       - pyedr>=0.7.0

package/MDAnalysis/analysis/bat.py

@@ -31,11 +31,11 @@
 
 This module contains classes for interconverting between Cartesian and an
 internal coordinate system, Bond-Angle-Torsion (BAT) coordinates
-:cite:p:`Chang2003`, for a given set of atoms or residues. This coordinate
+:footcite:p:`Chang2003`, for a given set of atoms or residues. This coordinate
 system is designed to be complete, non-redundant, and minimize correlations
 between degrees of freedom. Complete and non-redundant means that for N atoms
 there will be 3N Cartesian coordinates and 3N BAT coordinates. Correlations are
-minimized by using improper torsions, as described in :cite:p:`Hikiri2016`.
+minimized by using improper torsions, as described in :footcite:p:`Hikiri2016`.
 
 More specifically, bond refers to the bond length, or distance between
 a pair of bonded atoms. Angle refers to the bond angle, the angle between
@@ -56,7 +56,7 @@
 :math:`\phi`, and azimuthal angle, :math:`\theta`. :math:`\omega` is a third angle
 that describes the rotation of the third atom about the axis.
 
-This module was adapted from AlGDock :cite:p:`Minh2020`.
+This module was adapted from AlGDock :footcite:p:`Minh2020`.
 
 
 See Also
@@ -129,7 +129,7 @@ class to calculate dihedral angles for a given set of atoms or residues
 
 Analysis classes
 ----------------
- .. autoclass:: BAT
+.. autoclass:: BAT
     :members:
     :inherited-members:
 
@@ -154,13 +154,7 @@ class to calculate dihedral angles for a given set of atoms or residues
 References
 ----------
 
-.. bibliography::
-    :filter: False
-    :style: MDA
-
-    Chang2003
-    Hikiri2016
-    Minh2020
+.. footbibliography::
 
 """
 import logging

package/MDAnalysis/analysis/contacts.py

@@ -113,7 +113,7 @@
 
 Analyze a single DIMS transition of AdK between its closed and open
 conformation and plot the trajectory projected on q1-q2
-:cite:p:`Franklin2007` ::
+:footcite:p:`Franklin2007` ::
 
 
     import MDAnalysis as mda
@@ -134,7 +134,7 @@
     f.show()
 
 Compare the resulting pathway to the `MinActionPath result for AdK`_
-:cite:p:`Franklin2007`.
+:footcite:p:`Franklin2007`.
 
 .. _MinActionPath result for AdK:
    http://lorentz.dynstr.pasteur.fr/joel/adenylate.php
@@ -204,6 +204,9 @@ def is_any_closer(r, r0, dist=2.5):
 .. autoclass:: Contacts
    :members:
 
+.. rubric:: References
+.. footbibliography::
+
 """
 import os
 import errno
@@ -228,7 +231,7 @@ def is_any_closer(r, r0, dist=2.5):
 def soft_cut_q(r, r0, beta=5.0, lambda_constant=1.8):
     r"""Calculate fraction of native contacts *Q* for a soft cut off
 
-    The native contact function is defined as :cite:p:`Best2013`
+    The native contact function is defined as :footcite:p:`Best2013`
 
     .. math::
 
@@ -254,14 +257,6 @@ def soft_cut_q(r, r0, beta=5.0, lambda_constant=1.8):
     -------
     Q : float
       fraction of native contacts
-
-    References
-    ----------
-    .. bibliography::
-        :filter: False
-        :style: MDA
-
-        Best2013
     """
     r = np.asarray(r)
     r0 = np.asarray(r0)
@@ -313,14 +308,6 @@ def radius_cut_q(r, r0, radius):
     Q : float
         fraction of contacts
 
-    References
-    ----------
-    .. bibliography::
-        :filter: False
-        :style: MDA
-
-        Franklin2007
-
     """
     return hard_cut_q(r, radius)
 
@@ -521,7 +508,7 @@ def q1q2(u, select='all', radius=4.5):
     """Perform a q1-q2 analysis.
 
     Compares native contacts between the starting structure and final structure
-    of a trajectory :cite:p:`Franklin2007`.
+    of a trajectory :footcite:p:`Franklin2007`.
 
     Parameters
     ----------

package/MDAnalysis/analysis/data/filenames.py

@@ -38,7 +38,7 @@
    Reference Ramachandran histogram for
    :class:`MDAnalysis.analysis.dihedrals.Ramachandran`.  The data were
    calculated on a data set of 500 PDB structures taken from
-   :cite:p:`Lovell2003`. This is a numpy array in the :math:`\phi` and
+   :footcite:p:`Lovell2003`. This is a numpy array in the :math:`\phi` and
    :math:`\psi` backbone dihedral angles.
 
    Load and plot it with ::
@@ -71,7 +71,7 @@
 
    Reference Janin histogram for :class:`MDAnalysis.analysis.dihedrals.Janin`.
    The data were calculated on a data set of 500 PDB structures taken from
-   :cite:p:`Lovell2003`. This is a numpy array in the :math:`\chi_1` and
+   :footcite:p:`Lovell2003`. This is a numpy array in the :math:`\chi_1` and
    :math:`\chi_2` sidechain dihedral angles.
 
    Load and plot it with ::

package/MDAnalysis/analysis/diffusionmap.py

@@ -42,7 +42,7 @@
 reduction.
 
 More details about diffusion maps are in
-:cite:p:`deLaPorte2008,Coifman-Lafon,Ferguson2011,Clementi2011`.
+:footcite:p:`deLaPorte2008,Coifman-Lafon,Ferguson2011,Clementi2011`.
 
 .. _Diffusion-Map-tutorial:
 
@@ -63,15 +63,15 @@
    from MDAnalysis.tests.datafiles import PSF, DCD
 
 Given a universe or atom group, we can create and eigenvalue decompose
-the Diffusion Matrix from that trajectory using :class:`DiffusionMap`:: and get
+the Diffusion Matrix from that trajectory using :class:`DiffusionMap` and get
 the corresponding eigenvalues and eigenvectors.
 
 .. code-block:: python
 
    u = mda.Universe(PSF,DCD)
 
 We leave determination of the appropriate scale parameter epsilon to the user,
-:cite:p:`Clementi2011` uses a complex method involving the k-nearest-neighbors
+:footcite:p:`Clementi2011` uses a complex method involving the k-nearest-neighbors
 of a trajectory frame, whereas others simple use a trial-and-error approach
 with a constant epsilon. Currently, the constant epsilon method is implemented
 by MDAnalysis.
@@ -124,24 +124,16 @@
 ----------
 
 If you use this Dimension Reduction method in a publication, please
-cite :cite:p:`Coifman-Lafon`.
+cite :footcite:p:`Coifman-Lafon`.
 
 If you choose the default metric, this module uses the fast QCP algorithm
-:cite:p:`Theobald2005` to calculate the root mean square distance (RMSD)
+:footcite:p:`Theobald2005` to calculate the root mean square distance (RMSD)
 between two coordinate sets (as implemented in
 :func:`MDAnalysis.lib.qcprot.CalcRMSDRotationalMatrix`).  When using this
-module in published work please :cite:p:`Theobald2005`.
+module in published work please :footcite:p:`Theobald2005`.
 
 
-.. bibliography::
-    :filter: False
-    :style: MDA
-
-    Coifman-Lafon
-    deLaPorte2008
-    Clementi2011
-    Ferguson2011
-    Theobald2005
+.. footbibliography::
 """
 import logging
 import warnings
@@ -329,7 +321,7 @@ def __init__(self, u, epsilon=1, **kwargs):
         epsilon : Float
             Specifies the method used for the choice of scale parameter in the
             diffusion map. More information in
-            :cite:p:`Coifman-Lafon,Ferguson2011,Clementi2011`, Default: 1.
+            :footcite:p:`Coifman-Lafon,Ferguson2011,Clementi2011`, Default: 1.
         **kwargs
             Parameters to be passed for the initialization of a
             :class:`DistanceMatrix`.
@@ -395,7 +387,7 @@ def transform(self, n_eigenvectors, time):
         Return
         ------
         diffusion_space : array (n_frames, n_eigenvectors)
-            The diffusion map embedding as defined by :cite:p:`Ferguson2011`.
+            The diffusion map embedding as defined by :footcite:p:`Ferguson2011`.
         """
         return (self._eigenvectors[1:n_eigenvectors+1,].T *
                 (self.eigenvalues[1:n_eigenvectors+1]**time))

package/MDAnalysis/analysis/dihedrals.py

@@ -72,7 +72,7 @@
 ~~~~~~~~~~~~~~~~~~~~~
 
 The :class:`~MDAnalysis.analysis.dihedrals.Ramachandran` class allows for the
-quick calculation of classical Ramachandran plots :cite:p:`Ramachandran1963` in
+quick calculation of classical Ramachandran plots :footcite:p:`Ramachandran1963` in
 the backbone :math:`phi` and :math:`psi` angles. Unlike the
 :class:`~MDanalysis.analysis.dihedrals.Dihedral` class which takes a list of
 `atomgroups`, this class only needs a list of residues or atoms from those
@@ -117,7 +117,7 @@
 Janin analysis
 ~~~~~~~~~~~~~~
 
-Janin plots :cite:p:`Janin1978` for side chain conformations (:math:`\chi_1`
+Janin plots :footcite:p:`Janin1978` for side chain conformations (:math:`\chi_1`
 and :math:`chi_2` angles) can be created with the
 :class:`~MDAnalysis.analysis.dihedrals.Janin` class. It works in the same way,
 only needing a list of residues; see the :ref:`Janin plot figure
@@ -158,12 +158,12 @@
 (:data:`~MDAnalysis.analysis.data.filenames.Rama_ref`) and Janin reference data
 (:data:`~MDAnalysis.analysis.data.filenames.Janin_ref`) were made using data
 obtained from a large selection of 500 PDB files, and were analyzed using these
-classes :cite:p:`Mull2018`. The allowed and marginally allowed regions of the
+classes :footcite:p:`Mull2018`. The allowed and marginally allowed regions of the
 Ramachandran reference plot have cutoffs set to include 90% and 99% of the data
 points, and the Janin reference plot has cutoffs for 90% and 98% of the data
 points. The list of PDB files used for the reference plots was taken from
-:cite:p:`Lovell2003` and information about general Janin regions was taken from
-:cite:p:`Janin1978`.
+:footcite:p:`Lovell2003` and information about general Janin regions was taken from
+:footcite:p:`Janin1978`.
 
 
 
@@ -236,14 +236,7 @@
 References
 ----------
 
-.. bibliography::
-    :filter: False
-    :style: MDA
-
-    Ramachandran1963
-    Janin1978
-    Lovell2003
-    Mull2018
+.. footbibliography::
 
 """
 import numpy as np

package/MDAnalysis/analysis/encore/similarity.py

@@ -30,8 +30,8 @@
 .. versionadded:: 0.16.0
 
 The module contains implementations of similarity measures between protein
-ensembles described in :cite:p:`b-LindorffLarsen2009`. The implementation and
-examples are described in :cite:p:`b-Tiberti2015`.
+ensembles described in :footcite:p:`LindorffLarsen2009`. The implementation and
+examples are described in :footcite:p:`Tiberti2015`.
 
 The module includes facilities for handling ensembles and trajectories through
 the :class:`Universe` class, performing clustering or dimensionality reduction
@@ -52,18 +52,11 @@
 + **Clustering based convergence evaluation** : :func:`ces_convergence`
 + **Dimensionality-reduction based convergence evaluation** : :func:`dres_convergence`
 
-When using this module in published work please cite :cite:p:`b-Tiberti2015`.
+When using this module in published work please cite :footcite:p:`Tiberti2015`.
 
 .. rubric:: References
 
-.. bibliography::
-    :filter: False
-    :style: MDA
-    :keyprefix: b-
-    :labelprefix: ᵇ
-
-    Tiberti2015
-    LindorffLarsen2009
+.. footbibliography::
 
 .. _Examples:
 Examples
@@ -256,7 +249,7 @@ def harmonic_ensemble_similarity(sigma1,
                                  x2):
     """
     Calculate the harmonic ensemble similarity measure
-    as defined in :cite:p:`b-Tiberti2015`.
+    as defined in :footcite:p:`Tiberti2015`.
 
     Parameters
     ----------
@@ -729,7 +722,7 @@ def hes(ensembles,
     r"""Calculates the Harmonic Ensemble Similarity (HES) between ensembles.
 
     The HES is calculated with the symmetrized version of Kullback-Leibler
-    divergence as described in :cite:p:`b-Tiberti2015`.
+    divergence as described in :footcite:p:`Tiberti2015`.
 
     Parameters
     ----------
@@ -972,7 +965,7 @@ def ces(ensembles,
 
     Calculates the Clustering Ensemble Similarity (CES) between ensembles
     using the Jensen-Shannon divergence as described in
-    :cite:p:`b-Tiberti2015`.
+    :footcite:p:`Tiberti2015`.
 
     Parameters
     ----------
@@ -1240,7 +1233,7 @@ def dres(ensembles,
 
     Calculates the Dimensional Reduction Ensemble Similarity (DRES) between
     ensembles using the Jensen-Shannon divergence as described in
-    :cite:p:`b-Tiberti2015`.
+    :footcite:p:`Tiberti2015`.
 
 
     Parameters

package/MDAnalysis/analysis/gnm.py

@@ -34,7 +34,7 @@
 
 
 Analyse a trajectory using elastic network models, following the approach of
-:cite:p:`Hall2007`.
+:footcite:p:`Hall2007`.
 
 An example is provided in the MDAnalysis Cookbook_, listed as GNMExample_.
 
@@ -58,15 +58,11 @@
 
 
 The results are found in :attr:`GNMAnalysis.results`, which can be
-used for further processing (see :cite:p:`Hall2007`).
+used for further processing (see :footcite:p:`Hall2007`).
 
 .. rubric:: References
 
-.. bibliography::
-    :filter: False
-    :style: MDA
-
-    Hall2007
+.. footbibliography::
 
 
 Analysis tasks