diff --git a/.doctrees/encore/similarity.doctree b/.doctrees/encore/similarity.doctree index d44b0f9..d6d6b91 100644 Binary files a/.doctrees/encore/similarity.doctree and b/.doctrees/encore/similarity.doctree differ diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle index 17f9608..e526b31 100644 Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ diff --git a/.doctrees/index.doctree b/.doctrees/index.doctree index c7575ed..9d28b94 100644 Binary files a/.doctrees/index.doctree and b/.doctrees/index.doctree differ diff --git a/_modules/mdaencore/similarity.html b/_modules/mdaencore/similarity.html index 5a7b6a7..80a8109 100644 --- a/_modules/mdaencore/similarity.html +++ b/_modules/mdaencore/similarity.html @@ -138,11 +138,11 @@

Source code for mdaencore.similarity

 described in :footcite:p:`Tiberti2015`.
 
 The module includes facilities for handling ensembles and trajectories through
-the :class:`Universe` class, performing clustering or dimensionality reduction
-of the ensemble space, estimating multivariate probability distributions from
-the input data, and more. ENCORE can be used to compare experimental and
-simulation-derived ensembles, as well as estimate the convergence of
-trajectories from time-dependent simulations.
+the :class:`~MDAnalysis.core.universe.Universe` class, performing clustering
+or dimensionality reduction of the ensemble space, estimating multivariate
+probability distributions from the input data, and more. ENCORE can be used to
+compare experimental and simulation-derived ensembles, as well as estimate the
+convergence of trajectories from time-dependent simulations.
 
 ENCORE includes three different methods for calculations of similarity measures
 between ensembles implemented in individual functions:
@@ -1188,13 +1188,13 @@ 
Source code for mdaencore.similarity
     --------
     To calculate the Clustering Ensemble similarity, two ensembles are
     created as Universe object using a topology file and two trajectories. The
-    topology- and trajectory files used are obtained from the MDAnalysis
-    test suite for two different simulations of the protein AdK.
-    To use a different clustering method, set the parameter clustering_method
-    (Note that the sklearn module must be installed). Likewise, different parameters
-    for the same clustering method can be explored by adding different
-    instances of the same clustering class.
-    Here the simplest case of just two instances of :class:`Universe` is illustrated:
+    topology- and trajectory files used are obtained from the MDAnalysis test
+    suite for two different simulations of the protein AdK. To use a different
+    clustering method, set the parameter clustering_method (Note that the
+    sklearn module must be installed). Likewise, different parameters for the
+    same clustering method can be explored by adding different instances of
+    the same clustering class. Here the simplest case of just two instances
+    of :class:`~MDAnalysis.core.universe.Universe` is illustrated:
 
         >>> from MDAnalysis import Universe
         >>> import mdaencore as encore
@@ -1471,8 +1471,8 @@ 
Source code for mdaencore.similarity
     To use a different dimensional reduction methods, simply set the
     parameter dimensionality_reduction_method. Likewise, different parameters
     for the same clustering method can be explored by adding different
-    instances of the same method  class.
-    Here the simplest case of comparing just two instances of :class:`Universe` is
+    instances of the same method  class. Here the simplest case of comparing
+    just two instances of :class:`~MDAnalysis.core.universe.Universe` is
     illustrated:
 
         >>> from MDAnalysis import Universe
@@ -1487,7 +1487,7 @@ 
Source code for mdaencore.similarity
 
     In addition to the quantitative similarity estimate, the dimensional
     reduction can easily be visualized, see the ``Example`` section in
-    :mod:`mdaencore.dimensionality_reduction.reduce_dimensionality``
+    :mod:`mdaencore.dimensionality_reduction.reduce_dimensionality`
 
     """
 
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
index 4540722..c14f24c 100644
--- a/_sources/index.rst.txt
+++ b/_sources/index.rst.txt
@@ -19,7 +19,7 @@ ensembles described in :footcite:p:`LindorffLarsen2009`. The implementation and
 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
+the :class:`~MDAnalysis.core.universe.Universe` class, performing clustering or dimensionality reduction
 of the ensemble space, estimating multivariate probability distributions from
 the input data, and more. ENCORE can be used to compare experimental and
 simulation-derived ensembles, as well as estimate the convergence of
diff --git a/encore/similarity.html b/encore/similarity.html
index 6beb497..8285f03 100644
--- a/encore/similarity.html
+++ b/encore/similarity.html
@@ -151,11 +151,11 @@
 The implementation and examples are also further
 described in [2].

 
The module includes facilities for handling ensembles and trajectories through
-the Universe class, performing clustering or dimensionality reduction
-of the ensemble space, estimating multivariate probability distributions from
-the input data, and more. ENCORE can be used to compare experimental and
-simulation-derived ensembles, as well as estimate the convergence of
-trajectories from time-dependent simulations.

+the Universe class, performing clustering
+or dimensionality reduction of the ensemble space, estimating multivariate
+probability distributions from the input data, and more. ENCORE can be used to
+compare experimental and simulation-derived ensembles, as well as estimate the
+convergence of trajectories from time-dependent simulations.

 
ENCORE includes three different methods for calculations of similarity measures
 between ensembles implemented in individual functions: