Update Docstrings for PEP 257.

README.rst

@@ -3,27 +3,28 @@ mixedvines Package for Python
 =============================
 
 Package for canonical vine copula trees with mixed continuous and discrete
-marginals.  If you use this software for publication, please cite [ONKEN2016]_.
+marginals.  If you use this software for publication, please cite
+[ONKEN2016]_.
 
 
 Description
 -----------
 
 This package contains a complete framework based on canonical vine copulas for
-modelling multivariate data that are partly discrete and partly continuous.  The
-resulting multivariate distributions are flexible with rich dependence
+modelling multivariate data that are partly discrete and partly continuous.
+The resulting multivariate distributions are flexible with rich dependence
 structures and marginals.
 
 For continuous marginals, implementations of the normal and the gamma
 distributions are provided.  For discrete marginals, Poisson, binomial and
 negative binomial distributions are provided.  As bivariate copula building
-blocks, the Gaussian, Frank and Clayton families as well as rotation transformed
-families are provided.  Additional marginal and pair copula distributions can be
-added easily.
+blocks, the Gaussian, Frank and Clayton families as well as rotation
+transformed families are provided.  Additional marginal and pair copula
+distributions can be added easily.
 
-The package includes methods for sampling, likelihood calculation and inference,
-all of which have quadratic complexity.  These procedures are combined to
-estimate entropy by means of Monte Carlo integration.
+The package includes methods for sampling, likelihood calculation and
+inference, all of which have quadratic complexity.  These procedures are
+combined to estimate entropy by means of Monte Carlo integration.
 
 Please see [ONKEN2016]_ for a more detailed description of the framework.
 
@@ -55,11 +56,11 @@ To install the mixedvines package, run::
 Usage
 -----
 
-Suppose that data are given in a NumPy array ``samples`` with shape ``(n, d)``,
-where ``n`` is the number of samples and ``d`` is the number of elements per
-sample.  First, specify which of the elements are continuous.  If, for instance,
-the distribution has three elements and the first and last element are
-continuous whereas the second element is discrete:
+Suppose that data are given in a NumPy array ``samples`` with shape
+``(n, d)``, where ``n`` is the number of samples and ``d`` is the number of
+elements per sample.  First, specify which of the elements are continuous.
+If, for instance, the distribution has three elements and the first and last
+element are continuous whereas the second element is discrete:
 
 .. code-block:: python
 
@@ -72,8 +73,9 @@ To fit a mixed vine to the samples:
     from mixedvines.mixedvine import MixedVine
     vine = MixedVine.fit(samples, is_continuous)
 
-``vine`` is now a ``MixedVine`` object.  To draw samples from the distribution,
-calculate their density and estimate the distribution entropy in units of bits:
+``vine`` is now a ``MixedVine`` object.  To draw samples from the
+distribution, calculate their density and estimate the distribution entropy in
+units of bits:
 
 .. code-block:: python
 

docs/conf.py

@@ -77,7 +77,7 @@ def __getattr__(cls, name):
 # The short X.Y version.
 version = u'1.3'
 # The full version, including alpha/beta/rc tags.
-release = u'1.3.0'
+release = u'1.3.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -151,7 +151,7 @@ def __getattr__(cls, name):
 # The name for this set of Sphinx documents.
 # "<project> v<release> documentation" by default.
 #
-# html_title = u'mixedvines v1.3.0'
+# html_title = u'mixedvines v1.3.1'
 
 # A shorter title for the navigation bar.  Default is the same as html_title.
 #

docs/intro.rst

@@ -3,17 +3,17 @@ Introduction
 
 
 This packge contains a complete framework based on canonical vine copulas for
-modelling multivariate data that are partly discrete and partly continuous.  The
-resulting multivariate distributions are flexible with rich dependence
+modelling multivariate data that are partly discrete and partly continuous.
+The resulting multivariate distributions are flexible with rich dependence
 structures and marginals.
 
 For continuous marginals, implementations of the normal and the gamma
 distributions are provided.  For discrete marginals, Poisson, binomial and
 negative binomial distributions are provided.  As bivariate copula building
-blocks, the Gaussian, Frank and Clayton families as well as rotation transformed
-families are provided.  Additional marginal and pair copula distributions can be
-added easily.
+blocks, the Gaussian, Frank and Clayton families as well as rotation
+transformed families are provided.  Additional marginal and pair copula
+distributions can be added easily.
 
-The package includes methods for sampling, likelihood calculation and inference,
-all of which have quadratic complexity.  These procedures are combined to
-estimate entropy by means of Monte Carlo integration.
+The package includes methods for sampling, likelihood calculation and
+inference, all of which have quadratic complexity.  These procedures are
+combined to estimate entropy by means of Monte Carlo integration.

docs/usage.rst

@@ -2,11 +2,11 @@ Usage
 =====
 
 
-Suppose that data are given in a NumPy array ``samples`` with shape ``(n, d)``,
-where ``n`` is the number of samples and ``d`` is the number of elements per
-sample.  First, specify which of the elements are continuous.  If, for instance,
-the distribution has three elements and the first and last element are
-continuous whereas the second element is discrete:
+Suppose that data are given in a NumPy array ``samples`` with shape
+``(n, d)``, where ``n`` is the number of samples and ``d`` is the number of
+elements per sample.  First, specify which of the elements are continuous.
+If, for instance, the distribution has three elements and the first and last
+element are continuous whereas the second element is discrete:
 
 .. code-block:: python
 
@@ -21,19 +21,19 @@ To fit a mixed vine to the samples:
 
 ``vine`` is now a ``MixedVine`` object.  Note that for the canonical vine, the
 order of elements is important.  Elements should be sorted according to the
-importance of their dependencies to other elements where elements with important
-dependencies to many other elements should come first.  A heuristic way to
-select the order of elements is to calculate Kendall's tau between all element
-pairs, to obtain a score for each element by summing the taus of the pairs the
-element occurs in and to sort elements in descending order according to their
-scores.  This is what the ``MixedVine.fit`` method does internally by default to
-construct an improved canonical vine tree.  This internal sorting is used to
-construct the vine tree only, so the order of elements is not changed in a user
-visible way.  To prevent this internal sorting, set the ``keep_order`` argument
-to ``True``.
-
-To draw samples from the distribution, calculate their density and estimate the
-distribution entropy in units of bits:
+importance of their dependencies to other elements where elements with
+important dependencies to many other elements should come first.  A heuristic
+way to select the order of elements is to calculate Kendall's tau between all
+element pairs, to obtain a score for each element by summing the taus of the
+pairs the element occurs in and to sort elements in descending order according
+to their scores.  This is what the ``MixedVine.fit`` method does internally by
+default to construct an improved canonical vine tree.  This internal sorting
+is used to construct the vine tree only, so the order of elements is not
+changed in a user visible way.  To prevent this internal sorting, set the
+``keep_order`` argument to ``True``.
+
+To draw samples from the distribution, calculate their density and estimate
+the distribution entropy in units of bits:
 
 .. code-block:: python
 

mixedvines/__init__.py

@@ -15,6 +15,24 @@
 #
 # You should have received a copy of the GNU General Public License along with
 # this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+mixedvines
+==========
+
+Provides canonical vine copula trees with mixed continuous and discrete
+marginals.  The main class is `MixedVine` implementing a copula vine model with
+mixed marginals.
+
+Modules
+-------
+mixedvine
+    Copula vine model with mixed marginals.
+copula
+    Bivariate copula distributions.
+marginal
+    Univariate marginal distributions.
+
+"""
 from . import marginal
 from . import copula
 from . import mixedvine