Small update for documentation issues with read the docs
Hard coded some versions

Hard coded some versions
Removed scikit-learn dependence
the required code from scikit-learn is now included in mloop. When scikit-learn releases the latest dev version, this code will be removed and mloop will require scikit-learn as a dependency. But this is a better solution than requiring installation of scikit-learn from source in the short term. Interface is no longer run as a seperate process but just a seperate thread. This is to improve compatibility with matplotlib on MacOS and Windows.
michaelhush · michaelhush · michaelhush · michaelhush · michaelhush · michaelhush
diff --git a/.travis.yml b/.travis.yml
@@ -0,0 +1,15 @@
+language: python
+python:
+  - "2.7.12"
+  - "3.5"
+install: 
+  - pip install --upgrade pip
+  - python --version
+  - pip --version
+  - pip install -r requirements.txt
+# command to run tests
+script: python setup.py test
+os:
+  - linux
+  
+  
diff --git a/docs/api/controllers.rst b/docs/api/controllers.rst
@@ -1,7 +1,7 @@
 .. _api-controllers:
 
 controllers
------------
+===========
 
 .. automodule:: mloop.controllers
     :members:
diff --git a/docs/api/index.rst b/docs/api/index.rst
@@ -1,5 +1,6 @@
 .. _sec-api:
 
+==========
 M-LOOP API
 ==========
 
diff --git a/docs/api/interfaces.rst b/docs/api/interfaces.rst
@@ -1,5 +1,5 @@
 interfaces
-----------
+==========
 
 .. automodule:: mloop.interfaces
     :members:
diff --git a/docs/api/launchers.rst b/docs/api/launchers.rst
@@ -1,5 +1,5 @@
 launchers
----------
+=========
 
 .. automodule:: mloop.launchers
     :members:
diff --git a/docs/api/learners.rst b/docs/api/learners.rst
@@ -1,7 +1,7 @@
 .. _api-learners:
 
 learners
----------
+========
 
 .. automodule:: mloop.learners
     :members:
diff --git a/docs/api/mloop.rst b/docs/api/mloop.rst
@@ -1,4 +1,4 @@
 mloop
------
+=====
 
 .. automodule:: mloop
diff --git a/docs/api/t_esting.rst b/docs/api/t_esting.rst
@@ -1,5 +1,5 @@
 testing
--------
+=======
 
 .. automodule:: mloop.testing
     :members:
diff --git a/docs/api/utilities.rst b/docs/api/utilities.rst
@@ -1,5 +1,5 @@
 utilities
----------
+=========
 
 .. automodule:: mloop.utilities
     :members:
diff --git a/docs/api/visualizations.rst b/docs/api/visualizations.rst
@@ -1,5 +1,5 @@
 visualizations
---------------
+==============
 
 .. automodule:: mloop.visualizations
     :members:
diff --git a/docs/conf.py b/docs/conf.py
@@ -15,7 +15,6 @@
 
 import sys
 import os
-import mloop
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
@@ -71,9 +70,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = mloop.__version__
+version = '2.1'
 # The full version, including alpha/beta/rc tags.
-release = mloop.__version__
+release = '2.1.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/docs/contributing.rst b/docs/contributing.rst
@@ -1,19 +1,20 @@
 .. _sec-contributing:
 
+============
 Contributing
 ============
 
 If you use M-LOOP please consider contributing to the project. There are many quick and easy ways to help out.
 
-- If you use M-LOOP be sure to cite paper where it first used: `'Fast machine-learning online optimization of ultra-cold-atom experiments', Sci Rep 6, 25890 (2016) <http://www.nature.com/srep/>`_. 
-- Star and watch the `M-LOOP github <https://github.com/michaelhush/M-LOOP/watchers>`_.
-- Make a suggestion on what features you would like added, or report an issue, on the `github <https://github.com/michaelhush/M-LOOP/watchers>`_ or by `email <mailto:MichaelRHush@gmail.com>`_. 
-- Contribute your own code to the `M-LOOP github <https://github.com/michaelhush/M-LOOP/watchers>`_, this could be the interface you designed, more options or a completely new solver.
+- If you use M-LOOP be sure to cite the paper where it first used: `'Fast machine-learning online optimization of ultra-cold-atom experiments', Sci Rep 6, 25890 (2016) <http://www.nature.com/srep/>`_. 
+- Star and watch the `M-LOOP GitHub <https://github.com/michaelhush/M-LOOP/watchers>`_.
+- Make a suggestion on what features you would like added, or report an issue, on the `GitHub <https://github.com/michaelhush/M-LOOP/watchers>`_ or by `email <mailto:MichaelRHush@gmail.com>`_. 
+- Contribute your own code to the `M-LOOP GitHub <https://github.com/michaelhush/M-LOOP/watchers>`_, this could be the interface you designed, more options or a completely new solver.
 
 Finally spread the word! Let others know the success you have had with M-LOOP and recommend they try it too.
 
 Contributors
-------------
+============
 
 M-LOOP is written and maintained by `Michael R Hush <http://www.michaelhush.com>`_ <MichaelRHush@gmail.com>
 
diff --git a/docs/data.rst b/docs/data.rst
@@ -1,12 +1,13 @@
 .. _sec-data:
 
+====
 Data
 ====
 
 M-LOOP saves all data produced by the experiment in archives which are saved to disk during and after the optimization run. The archives also contain information derived from the data, including the machine learning model for how the experiment works. Here we explain how to interpret the file archives. 
 
 File Formats
-------------
+============
 
 M-LOOP currently supports three file formats for all file input and output. 
 
@@ -15,7 +16,7 @@ M-LOOP currently supports three file formats for all file input and output.
 - 'pkl' pickle files: a serialization of a python dictionary made with `pickle <https://docs.python.org/3/library/pickle.html>`. Your data can be retrieved from this dictionary using the appropriate keywords. 
 
 File Keywords
--------------
+=============
 
 The archives contain a set of keywords/variable names with associated data. The quickest way to understand what the values mean for a particular keyword is to :ref:`search` the documentation for a description. 
 
@@ -26,7 +27,7 @@ For the controller archive see :ref:`api-controllers`.
 For the learner archive see :ref:`api-learners`. The generic keywords are described in the class Learner, with learner specific options described in the derived classes, for example GaussianProcessLearner.
 
 Converting files
-----------------
+================
 
 If for whatever reason you want to convert files between the formats you can do so using the utilities module of M-LOOP. For example the following python code will convert the file controller_archive_2016-08-18_12-18.pkl from a 'pkl' file to a 'mat' file::
 
diff --git a/docs/examples.rst b/docs/examples.rst
@@ -1,5 +1,6 @@
 .. _sec-examples:
 
+========
 Examples
 ========
 
@@ -10,24 +11,33 @@ The options available are also comprehensively documented in the :ref:`sec-api`
 Each of the example files is used when running tests of M-LOOP. So please copy and modify them elsewhere if you use them as a starting point for your configuration file. 
 
 Interfaces
-----------
+==========
 
-There is currently one interface supported: 'file'. You can specify which interface you want with the option::
+There are currently two interfaces supported: 'file' and 'shell'. You can specify which interface you want with the option::
 
    interface_type = [name]
 
 The default will be 'file'. The specific options for each of the interfaces are described below.
 
 File Interface
-~~~~~~~~~~~~~~
+--------------
 
-You can change the names of the files used for the file interface and their type. The file interface options are described in *file_interface_config.txt*.
+The file interface exchanges information with the experiment by writing files to disk. You can change the names of the files used for the file interface and their type. The file interface options are described in *file_interface_config.txt*.
 
 .. include:: ../examples/file_interface_config.txt
    :literal:
 
+Shell Interface
+---------------
+
+The shell interface is for experiments that can be run through a command executed in a shell. Information is then piped between M-LOOP and the experiment through the shell. You can change the command to run the experiment and the way the parameters are formatted. The shell interface options are described in *shell_interface_config.txt*
+
+.. include:: ../examples/shell_interface_config.txt
+   :literal:
+
+   
 Controllers
------------
+===========
 
 There are currently three controller types supported: 'gaussian_process', 'random' and 'nelder_mead'. The default is 'gaussian_process'. You can set which interface you want to use with the option::
 
@@ -38,8 +48,8 @@ Each of the controllers and their specific options are described below. There is
 .. include:: ../examples/controller_config.txt
    :literal:
    
-Gaussian Process
-~~~~~~~~~~~~~~~~
+Gaussian process
+----------------
 
 The Gaussian-process controller is the default controller and is the currently the most sophisticated machine learner algorithm. It uses a `Link Gaussian process <http://scikit-learn.org/dev/modules/gaussian_process.html>`_ to develop a model for how the parameters relate to the measured cost, effectively creating a model for how the experiment operates. This model is then used when picking new points to test. 
 
@@ -52,9 +62,25 @@ There are two example files for the Gaussian-process controller: *gaussian_proce
 
 .. include:: ../examples/gaussian_process_complete_config.txt
    :literal:
+
+Differential evolution
+----------------------
+
+The differential evolution (DE) controller uses a `Link DE algorithm <https://en.wikipedia.org/wiki/Differential_evolution>`_ for optimization. DE is a type of evolutionary algorithm, and is historically the most commonly used in automated optimization. DE will eventually find a global solution, however it can take many experiments before it does so. 
+
+There are two example files for the differential evolution controller: *differential_evolution_simple_config.txt* which contains the basic options.
+
+.. include:: ../examples/differential_evolution_simple_config.txt
+   :literal:
+   
+*differential_evolution_complete_config.txt* which contains a comprehensive list of options.
+
+.. include:: ../examples/differential_evolution_complete_config.txt
+   :literal:
+   
    
 Nelder Mead
-~~~~~~~~~~~
+-----------
 
 The Nelder Mead controller implements the `Link Nelder-Mead method <https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method>`_ for optimization. You can control the starting point and size of the initial simplex of the method with the configuration file.
 
@@ -69,7 +95,7 @@ There are two example files for the Nelder-Mead controller: *nelder_mead_simple_
    :literal:
    
 Random
-~~~~~~
+------
 
 The random optimization algorithm picks parameters randomly from a uniform distribution from within the parameter bounds or trust region. 
 
@@ -84,15 +110,15 @@ There are two example files for the random controller: *random_simple_config.txt
    :literal:
    
 Logging
--------
+=======
 
 You can control the filename of the logs and also the level which is reported to the file and the console. For more information see `Link logging levels <https://docs.python.org/3.6/library/logging.html#levels>`_. The logging options are described in *logging_config.txt*.
 
 .. include:: ../examples/logging_config.txt
    :literal:
 
 Extras
-------
+======
 
 Extras refers to options related to post processing your data once the optimization is complete. Currently the only extra option is for visualizations. The extra options are described in *extras_config.txt*.
 
diff --git a/docs/index.rst b/docs/index.rst
@@ -1,6 +1,6 @@
-######
+======
 M-LOOP
-######
+======
 
 The Machine-Learner Online Optimization Package is designed to automatically and rapidly optimize the parameters of a scientific experiment or computer controller system. 
 
@@ -13,25 +13,24 @@ Using M-LOOP is simple, once the parameters of your experiment is computer contr
 
 M-LOOP not only finds an optimal set of parameters for the experiment it also provides a model of how the parameters are related to the costs which can be used to improve the experiment. 
 
-If you use M-LOOP please cite our publication where we first used the package to optimise the production of a Bose-Einstein Condensate:
+If you use M-LOOP please cite our publication where we first used the package to optimize the production of a Bose-Einstein Condensate:
 
 Fast Machine-Learning Online Optimization of Ultra-Cold-Atom Experiments. *Scientific Reports* **6**, 25890 (2016). DOI: `Link 10.1038/srep25890 <http://dx.doi.org/10.1038/srep25890>`_
 
 http://www.nature.com/articles/srep25890
 
 Quick Start
------------
+===========
 
-To get the M-LOOP running as soon as possible follow the :ref:'sec-installation' instructions and :ref:'_sec-tutorial'. 
+To get M-LOOP running follow the :ref:`sec-installation` instructions and :ref:`sec-tutorial`. 
 
 Contents
---------
+========
 
 .. toctree::
-   :maxdepth: 2
    
    install
-   tutorial
+   tutorials
    interfaces
    data
    visualizations
@@ -40,7 +39,7 @@ Contents
    api/index
    
 Indices
--------
+=======
 
 * :ref:`genindex`
 * :ref:`modindex`