Merge pull request #270 from choderalab/quickdocfix

Doc updates for 3.0.3

devtools/conda-recipe/meta.yaml

@@ -1,21 +1,20 @@
 package:
   name: pymbar-dev
-  version: !!str 3.0.1.dev0
+  version: !!str 3.0.3.dev0
 
 source:
   path: ../../
 
 requirements:
   build:
     - python
-    - numpy
-    - scipy
     - setuptools
-    - numexpr
     - six
+    - numpy x.x
+    - toolchain
   run:
     - python
-    - numpy
+    - numpy x.x
     - scipy
     - numexpr
     - six

docs/conf.py

@@ -71,7 +71,7 @@
 
 # General information about the project.
 project = u'pymbar'
-copyright = u'2014, John D. Chodera, Michael Shirts, Kyle A. Beauchamp'
+copyright = u'2017, John D. Chodera, Michael Shirts, Kyle A. Beauchamp, Levi N. Naden'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
@@ -84,8 +84,8 @@
 #release = version
 
 # The full version, including alpha/beta/rc tags.
-version = '2.1.0'
-release = '2.1.0'
+version = '3.0.3'
+release = '3.0.3'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -254,7 +254,7 @@
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
   ('index', 'pymbar.tex', u'pymbar Documentation',
-   u'John D. Chodera, Michael Shirts, Kyle A. Beauchamp', 'manual'),
+   u'John D. Chodera, Michael Shirts, Kyle A. Beauchamp, Levi N. Naden', 'manual'),
 ]
 
 # The name of an image file (relative to this directory) to place at the top of
@@ -284,7 +284,7 @@
 # (source start file, name, description, authors, manual section).
 man_pages = [
     ('index', 'pymbar', u'pymbar Documentation',
-     [u'John D. Chodera, Michael Shirts, Kyle A. Beauchamp'], 1)
+     [u'John D. Chodera, Michael Shirts, Kyle A. Beauchamp, Levi N. Naden'], 1)
 ]
 
 # If true, show URL addresses after external links.
@@ -298,7 +298,7 @@
 #  dir menu entry, description, category)
 texinfo_documents = [
   ('index', 'pymbar', u'pymbar Documentation',
-   u'John D. Chodera, Michael Shirts, Kyle A. Beauchamp', 'pymbar', 'One line description of project.',
+   u'John D. Chodera, Michael Shirts, Kyle A. Beauchamp, Levi N. Naden', 'pymbar', 'One line description of project.',
    'Miscellaneous'),
 ]
 

docs/timeseries.rst

@@ -1,46 +1,58 @@
 .. currentmodule:: pymbar.timeseries
 
-The :mod:`timeseries` module: :module:`pymbar.timeseries`:
-=========================================================
+The timeseries module :mod:`pymbar.timeseries`
+==============================================
 
 The :mod:`pymbar.timeseries` module contains tools for dealing with timeseries data.
-The `MBAR <http://www.alchemistry.org/wiki/Multistate_Bennett_Acceptance_Ratio>`_ method is only applicable to uncorrelated samples from probability distributions, so we provide a number of tools that can be used to decorrelate simulation data.
+The `MBAR <http://www.alchemistry.org/wiki/Multistate_Bennett_Acceptance_Ratio>`_ method is only applicable to
+uncorrelated samples from probability distributions, so we provide a number of tools that can be used to decorrelate
+simulation data.
 
 Automatically identifying the equilibrated production region
 ------------------------------------------------------------
 
-Most simulations start from initial conditions that are highly unrepresentative of equilibrated samples that occur late in the simulation.
+Most simulations start from initial conditions that are highly unrepresentative of equilibrated samples that occur
+late in the simulation.
 We can improve our estimates by discarding these initial regions to "equilibration" (also known as "burn-in").
-We recommend a simple scheme described in Ref. :cite:`chodera:jctc:2016:automatic-equilibration-detection`, which identifies the production region as the final contiguous region containing the *largest number of uncorrelated samples.
+We recommend a simple scheme described in Ref. :cite:`chodera:jctc:2016:automatic-equilibration-detection`, which
+identifies the production region as the final contiguous region containing the *largest* number of uncorrelated samples.
 This scheme is implemented in the :func:`detectEquilibration` method:
 
-.. python
+.. code-block:: python
 
    from pymbar import timeseries
    [t0, g, Neff_max] = timeseries.detectEquilibration(A_t) # compute indices of uncorrelated timeseries
    A_t_equil = A_t[t0:]
    indices = timeseries.subsampleCorrelatedData(A_t_equil, g=g)
    A_n = A_t_equil[indices]
 
-In this example, the :func:`detectEquilibration` method is used on the correlated timeseries ``A_t`` to identify the sample index corresponding to the beginning of the production region, ``t_0``, the statistical inefficiency of the production region ``[t0:]``, ``g``, and the effective number of uncorrelated samples in the production region, ``Neff_max``.
-The production (equilibrated) region of the timeseries is extracted as ``A_t_equil`` and then subsampled using the :func:`subsampleCorrelatedData` method with the provided statistical inefficiency ``g``.
+In this example, the :func:`detectEquilibration` method is used on the correlated timeseries ``A_t`` to identify the
+sample index corresponding to the beginning of the production region, ``t_0``, the statistical inefficiency of the
+production region ``[t0:]``, ``g``, and the effective number of uncorrelated samples in the production
+region, ``Neff_max``.
+The production (equilibrated) region of the timeseries is extracted as ``A_t_equil`` and then subsampled using
+the :func:`subsampleCorrelatedData` method with the provided statistical inefficiency ``g``.
 Finally, the decorrelated samples are stored in ``A_n``.
 
-Note that, by default, the statistical inefficiency is computed for every time origin in a call to :func:`detectEquilibration`, which can be slow.
-If your dataset is more than a few hundred samples, you may want to evaluate only every ``nskip`` samples as potential time origins.
-This may result in discarding slightly more data than strictly necessary, but may not have a significant impact if the timeseries is long.
+Note that, by default, the statistical inefficiency is computed for every time origin in a call to
+:func:`detectEquilibration`, which can be slow.
+If your dataset is more than a few hundred samples, you may want to evaluate only every ``nskip`` samples as potential
+time origins.
+This may result in discarding slightly more data than strictly necessary, but may not have a significant impact if the
+timeseries is long.
 
-.. python
+.. code-block:: python
 
    nskip = 10 # only try every 10 samples for time origins
    [t0, g, Neff_max] = timeseries.detectEquilibration(A_t, nskip=nskip)
 
 Subsampling timeseries data
 ---------------------------
 
-If there is no need to discard the initial transient to equilibration, the :func:`subsampleCorrelatedData` method can be used directly to identify an effectively uncorrelated subset of data.
+If there is no need to discard the initial transient to equilibration, the :func:`subsampleCorrelatedData` method can be
+used directly to identify an effectively uncorrelated subset of data.
 
-.. python
+.. code-block:: python
 
    from pymbar import timeseries
    indices = timeseries.subsampleCorrelatedData(A_t_equil)
@@ -51,6 +63,7 @@ Here, the statistical inefficiency ``g`` is computed automatically.
 Other utility timeseries functions
 ----------------------------------
 
-A number of other useful functions for computing autocorrelation functions from one or more timeseries sampled from the same process are also provided.
+A number of other useful functions for computing autocorrelation functions from one or more timeseries sampled from the
+same process are also provided.
 
 .. automodule:: pymbar.timeseries