Release v0.2.1

Author: sertalpbilal

.gitlab-ci.yml

@@ -0,0 +1,20 @@
+py37:
+  image: python:3.7
+  script:
+  - python setup.py install
+  - cd tests
+  - python test_generators.py
+
+#py36:
+#  image: python:3.6
+#  script:
+#  - python setup.py install
+#  - cd tests
+#  - python test_generators.py
+#
+#py35:
+#  image: python:3.5
+#  script:
+#  - python setup.py install
+#  - cd tests
+#  - python test_generators.py

README.md

@@ -32,7 +32,15 @@ To use **sasoptpy**, you need to have:
 
 ### Installation
 
-**sasoptpy** can be installed from [project releases](https://github.com/sassoftware/sasoptpy/releases) page.
+**sasoptpy** can be installed using `pip`:
+
+    pip install sasoptpy
+
+or using `conda`:
+
+    conda install -c sas-institute sasoptpy
+
+Old releases can be found on the GitHub repository: https://github.com/sassoftware/sasoptpy/releases
 Download the release and install it using `pip`:
 
     pip install vX.X.X.tar.gz
@@ -47,7 +55,7 @@ Alternatively, use:
 
 ## Getting Started
 
-* The source code is currently hosted on Github at https://github.com/sassoftware/sasoptpy
+* The source code is currently hosted on GitHub at https://github.com/sassoftware/sasoptpy
 * Online documentation is at https://sassoftware.github.io/sasoptpy/
 * For the latest release go to https://github.com/sassoftware/sasoptpy/releases/latest
 

doc/_static/css/sasoptpy.css

@@ -9,3 +9,10 @@ table.field-list {
     border-spacing: 10px;
 }
 
+.http-example div[class^='highlight'] {
+    margin-top: -20px;
+}
+
+.row-even td {
+    background-color: #D9F3BA;
+}

doc/api/functions.rst

@@ -35,6 +35,7 @@ Utility Functions
    tuple_unpack
    union
    wrap
+   utils._evaluate
 
 
 .. _math-functions:

doc/conf.py

@@ -23,6 +23,7 @@
 import os
 import sys
 sys.path.insert(0, os.path.abspath('..'))
+import matplotlib
 
 # -- General configuration ------------------------------------------------
 
@@ -34,8 +35,8 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    'matplotlib.sphinxext.only_directives',
-    'matplotlib.sphinxext.plot_directive',
+    #'matplotlib.sphinxext.only_directives',
+    #'matplotlib.sphinxext.plot_directive',
     'sphinx.ext.autodoc',
     'sphinx.ext.doctest',
     'sphinx.ext.intersphinx',
@@ -47,6 +48,8 @@
     'sphinx.ext.imgmath',
     'sphinxcontrib.fulltoc',
     'numpydoc',
+    'sphinxcontrib.httpdomain',
+    'sphinxcontrib.excel_table',
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -63,17 +66,17 @@
 
 # General information about the project.
 project = 'sasoptpy'
-copyright = '2017-2018, SAS Institute'
-author = 'SAS Institute (Sertalp B. Cay)'
+copyright = '2017-2019, SAS Institute'
+author = 'Sertalp B. Cay (SAS Institute)'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = '0.2.0'
+version = '0.2.1'
 # The full version, including alpha/beta/rc tags.
-release = '0.2.0'
+release = '0.2.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -117,7 +120,7 @@
 # documentation.
 #
 html_theme_options = {
-    "sidebarwidth": "400"
+    "sidebarwidth": "320"
 }
 
 # Add any paths that contain custom static files (such as style sheets) here,

doc/examples.rst

@@ -32,6 +32,7 @@ Viya Examples / Concrete
    Decentralization <examples/decentralization.rst>
    SAS/OR Blog: Optimal Wedding <examples/optimal_wedding.rst>
    SAS/OR Blog: Kidney Exchange <examples/kidney_exchange.rst>
+   Multiobjective <examples/multiobjective.rst>
 
 Viya Examples / Abstract
 ------------------------
@@ -50,3 +51,11 @@ SAS (saspy) Examples
    :maxdepth: 1
 
    Decentralization <examples/decentralization_saspy.rst>
+
+RESTful API Examples
+--------------------
+
+.. toctree::
+   :maxdepth: 1
+   
+   Knapsack <examples/knapsack.rst>

doc/examples/knapsack.rst

@@ -0,0 +1,25 @@
+
+.. _examples/rest_knapsack:
+
+Knapsack
+========
+
+Model
+-----
+
+.. literalinclude:: ../../examples/rest_knapsack.py
+
+Output
+------
+
+.. ipython:: python
+   :suppress:
+
+   import sasoptpy
+   sasoptpy.reset_globals()
+
+
+.. ipython:: python
+
+   from examples.rest_knapsack import test
+   test(hostname, port)

doc/examples/multiobjective.rst

@@ -0,0 +1,50 @@
+
+.. _examples/multiobjective:
+
+Multiobjective
+==============
+
+Reference
+---------
+
+https://go.documentation.sas.com/?cdcId=pgmsascdc&cdcVersion=9.4_3.4&docsetId=ormpug&docsetTarget=ormpug_lsosolver_examples07.htm&locale=en
+
+http://support.sas.com/documentation/onlinedoc/or/ex_code/143/lsoe10.html
+
+
+Model
+-----
+
+.. literalinclude:: ../../examples/multiobjective.py
+
+Output
+------
+
+.. ipython:: python
+   :suppress:
+   
+   import sasoptpy
+   sasoptpy.reset_globals()
+
+.. ipython:: python
+
+   from examples.multiobjective import test
+   sols = test(cas_conn, sols=True)
+
+.. ipython:: python
+
+   import matplotlib.pyplot as plt
+   tr = sols.transpose()
+   scvalues = tr.iloc[2:]
+   scvalues = scvalues.astype({0: float, 1: float})
+   x = sasoptpy.get_obj_by_name('x')
+   f1 = sasoptpy.get_obj_by_name('f1')
+   f2 = sasoptpy.get_obj_by_name('f2')
+   x[1].set_value(scvalues[0])
+   x[2].set_value(scvalues[1])
+   scvalues['f1'] = f1.get_value()
+   scvalues['f2'] = f2.get_value()
+   f = scvalues.plot.scatter(x='f1', y='f2')
+   f.set_title('Multiobjective: Plot of Pareto-Optimal Set');
+   @savefig multiobj.png
+   f

doc/examples/refinery_optimization.rst

@@ -25,6 +25,7 @@ Output
 
 
 .. ipython:: python
+   :okwarning:
 
    from examples.refinery_optimization import test
    test(cas_conn)

doc/index.rst

@@ -19,14 +19,14 @@ solve them using
 
 *sasoptpy* can handle linear optimization, mixed integer linear optimization,
 and nonlinear optimization problems. Users can benefit from native Python
-structures like dictionaries, tuples, and list to define an optimization
+structures like dictionaries, tuples, and lists to define an optimization
 problem. *sasoptpy* supports `Pandas <http://pandas.pydata.org/>`_
 objects extensively.
 
-Under the hood, *sasoptpy* uses
-`swat package <https://sassoftware.github.io/python-swat/>`_ to communicate
-SAS Viya, and uses
-`saspy package <https://sassoftware.github.io/saspy/>`_ to communicate SAS 9.4
+Under the hood, *sasoptpy* uses the
+`swat package <https://sassoftware.github.io/python-swat/>`_ to communicate with
+SAS Viya, and uses the
+`saspy package <https://sassoftware.github.io/saspy/>`_ to communicate with SAS 9.4
 installations.
 
 *sasoptpy* is an interface to SAS Optimization solvers. Check
@@ -51,4 +51,5 @@ See our SAS Global Forum paper: `Optimization Modeling with Python and SAS Viya
    workflow
    examples
    api/api
+   web-api
 

doc/input-data.rst

@@ -145,7 +145,8 @@ CASTable
 ~~~~~~~~
 
 When a data is available on the server-side, a reference to the object can be
-passed. Note that, using CASTable and Abstract Data requires SAS Viya version
+passed. Note that, using :class:`swat.cas.table.CASTable`
+and Abstract Data requires SAS Viya version
 3.4.
 
 .. ipython:: python

doc/install.rst

@@ -18,38 +18,22 @@ It depends on the following packages:
 * swat
 * pandas
 
-Getting swat
-------------
-
-`swat <https://github.com/sassoftware/python-swat>`_ package is a requirement
-to use SAS Viya solvers.
-
-swat releases are listed at
-`<https://github.com/sassoftware/python-swat/releases>`_.
-After downloading the platform-specific release file, it can be installed using
-pip::
-
-	pip install python-swat-X.X.X-platform.tar.gz
-
-Getting saspy
--------------
+Getting sasoptpy
+----------------
 
-`saspy <https://github.com/sassoftware/saspy>`_ package is a requirement to use
-SAS 9.4 solvers.
+*sasoptpy* can be installed using `pip` or `conda`::
 
-saspy releases are listed at
-`<https://github.com/sassoftware/saspy/releases>`_.
-The easiest way to download the latest stable version of saspy is to use::
+   pip install sasoptpy
 
-        pip install saspy
+   conda install -c sas-institute sasoptpy
 
-	
-Getting sasoptpy
-----------------
+Any dependencies should be installed automatically.
+   
+GitHub repository
++++++++++++++++++
 
-The latest available version of *sasoptpy* can be obtained from the online
-repository.
-Call::
+You can also get stable and development versions of *sasoptpy* from the GitHub repository.
+To get the latest version,  call::
 
   git clone https://github.com/sassoftware/sasoptpy.git
 
@@ -62,52 +46,3 @@ Alternatively, you can use::
   python setup.py install
 
 
-Step-by-step installation
--------------------------
-
-#. **Installing pandas and numpy**
-   
-   First, download and install numpy and pandas using pip:
-
-   .. code-block:: bash
-
-      pip install numpy
-      pip install pandas
-
-      
-#. **Installing the swat package**
-
-   First, check the
-   `swat release page <https://github.com/sassoftware/python-swat/releases>`_
-   to find the latest release of the SAS-SWAT package for your environment.
-
-   Then install it using
-
-   .. code-block:: bash
-
-      pip install python-swat-X.X.X.platform.tar.gz
-
-   As an example, run
-
-   .. code-block:: bash
-
-      wget https://github.com/sassoftware/python-swat/releases/download/v1.2.1/python-swat-1.2.1-linux64.tar.gz
-      pip install python-swat-1.2.1-linux64.tar.gz
-
-   to install the version 1.2.1 of the swat package for 64-bit Linux
-   environments.
-
-#. **Installing sasoptpy**
-
-   Finally you can install *sasoptpy* by downloading the latest archive file
-   and install via pip.
-
-   .. code-block:: bash
-
-      wget *url-to-sasoptpy.tar.gz*
-      pip install sasoptpy.tar.gz
-
-   Latest release file is available at
-   `Github releases <https://github.com/sassoftware/sasoptpy/releases/latest>`_
-   page.
-