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33 changes: 33 additions & 0 deletions .github/workflows/sphinx.yml
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name: Sphinx Docs Build

on:
push:
branches: [ "master", "development" ]

jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v3
with:
fetch-depth: 0
ref: ${{ github.ref }}

- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.9'

- name: Install dependencies
run: pip install -r docs/requirements.txt

- name: Build documentation
run: sphinx-build -b html docs/source docs/_build/html

- name: Commit and push changes
run: |
git config --global user.name "github-actions[bot]"
git config --global user.email "github-actions[bot]@users.noreply.github.com"
git add docs/_build/html
git diff --quiet && echo "No changes to commit" || (git commit -m "Automated documentation update" && git push origin HEAD:${GITHUB_REF#refs/heads/})
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208 changes: 104 additions & 104 deletions docs/model_template.rst → docs/_templates/model_template.rst
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@@ -1,104 +1,104 @@
Model: <model_name> (<model_abbreviation>)
==========================================

Description:
------------
<A paragraph describing the stochastic model. Use math if it is helpful.>

Sample math... :math:`S = 1500`

Sample math...

.. math::

\frac{ \sum_{t=0}^{N}f(t,k) }{N}

Sources of Randomness:
----------------------
<The number and nature of sources of randomness.>

Model Factors:
--------------
* <factor1name>: <short description>

* Default: <default value>

* <factor2name>: <short description>

* Default: <default value>

* <factor3name>: <short description>

* Default: <default value>

Responses:
----------
* <response1name>: <short description>

* <response2name>: <short description>

* <response3name>: <short description>


References:
===========
This model is adapted from the article <article name with full citation + hyperlink to journal/arxiv page>




Optimization Problem: <problem_name> (<problem_abbrev>)
========================================================

Decision Variables:
-------------------
* <dv1name that matches model factor name>
* <dv2name that matches model factor name>

Objectives:
-----------
<Description using response names. Use math if it is helpful.>

Constraints:
------------
<Description using response names. Use math if it is helpful.>

Problem Factors:
----------------
* <factor1name>: <short description>

* Default: <default value>

* <factor2name>: <short description>

* Default: <default value>

Fixed Model Factors:
--------------------
* <factor1name>: <fixed value>

* <factor2name>: <fixed value>

Starting Solution:
------------------
* <dv1name>: <dv1initialvalue>

* <dv2name>: <dv2initialvalue>

Random Solutions:
------------------
<description of how to generate random solutions>

Optimal Solution:
-----------------
<if known, otherwise unknown>

Optimal Objective Function Value:
---------------------------------
<if known, otherwise unknown>


Optimization Problem: <problem_name> (<problem_abbrev>)
========================================================

...
Model: <model_name> (<model_abbreviation>)
==========================================
Description:
------------
<A paragraph describing the stochastic model. Use math if it is helpful.>
Sample math... :math:`S = 1500`
Sample math...
.. math::
\frac{ \sum_{t=0}^{N}f(t,k) }{N}
Sources of Randomness:
----------------------
<The number and nature of sources of randomness.>
Model Factors:
--------------
* <factor1name>: <short description>
* Default: <default value>
* <factor2name>: <short description>
* Default: <default value>
* <factor3name>: <short description>
* Default: <default value>
Responses:
----------
* <response1name>: <short description>
* <response2name>: <short description>
* <response3name>: <short description>
References:
===========
This model is adapted from the article <article name with full citation + hyperlink to journal/arxiv page>
Optimization Problem: <problem_name> (<problem_abbrev>)
========================================================
Decision Variables:
-------------------
* <dv1name that matches model factor name>
* <dv2name that matches model factor name>
Objectives:
-----------
<Description using response names. Use math if it is helpful.>
Constraints:
------------
<Description using response names. Use math if it is helpful.>
Problem Factors:
----------------
* <factor1name>: <short description>
* Default: <default value>
* <factor2name>: <short description>
* Default: <default value>
Fixed Model Factors:
--------------------
* <factor1name>: <fixed value>
* <factor2name>: <fixed value>
Starting Solution:
------------------
* <dv1name>: <dv1initialvalue>
* <dv2name>: <dv2initialvalue>
Random Solutions:
------------------
<description of how to generate random solutions>
Optimal Solution:
-----------------
<if known, otherwise unknown>
Optimal Objective Function Value:
---------------------------------
<if known, otherwise unknown>
Optimization Problem: <problem_name> (<problem_abbrev>)
========================================================
...
82 changes: 41 additions & 41 deletions docs/solver_template.rst → docs/_templates/solver_template.rst
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@@ -1,42 +1,42 @@
Solver: <solver_name> (<solver_abbreviation>)
=============================================

Description:
------------
<A paragraph describing the solver algorithm. Use math if it is helpful.>

Sample math... :math:`S = 1500`

Sample math...

.. math::

\frac{ \sum_{t=0}^{N}f(t,k) }{N}


Modifications & Implementation:
-------------------------------
<What changed from the original algorithm? Other implementation choices like how initial points are chosen, etc.>

Scope:
------
<The types of models that can be solved (i.e. objective_type, constraint_type, and variable_type).>

Solver Factors:
---------------
* <factor1name>: <short description>

* Default: <default value>

* <factor2name>: <short description>

* Default: <default value>

* <factor3name>: <short description>

* Default: <default value>


References:
===========
Solver: <solver_name> (<solver_abbreviation>)
=============================================
Description:
------------
<A paragraph describing the solver algorithm. Use math if it is helpful.>
Sample math... :math:`S = 1500`
Sample math...
.. math::
\frac{ \sum_{t=0}^{N}f(t,k) }{N}
Modifications & Implementation:
-------------------------------
<What changed from the original algorithm? Other implementation choices like how initial points are chosen, etc.>
Scope:
------
<The types of models that can be solved (i.e. objective_type, constraint_type, and variable_type).>
Solver Factors:
---------------
* <factor1name>: <short description>
* Default: <default value>
* <factor2name>: <short description>
* Default: <default value>
* <factor3name>: <short description>
* Default: <default value>
References:
===========
This solver is adapted from the article <article name with full citation + hyperlink to journal/arxiv page>
14 changes: 7 additions & 7 deletions docs/GUI.rst → docs/source/GUI.rst
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@@ -1,7 +1,7 @@
GUI module
==========

.. automodule:: GUI
:members:
:undoc-members:
:show-inheritance:
GUI module
==========
.. automodule:: GUI
:members:
:undoc-members:
:show-inheritance:
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