updated links in all documentation sources

README.rst

@@ -40,6 +40,8 @@ The following are *in the works* (probably), so stay tuned!
 
 #. D-Optimal designs
 
+*See the* `package homepage`_ *for details on usage and other notes*
+
 Requirements
 ============
 
@@ -49,7 +51,7 @@ Requirements
 Installation and download
 =========================
 
-See the `package documentation`_ for helpful hints relating to downloading
+See the `package homepage`_ for helpful hints relating to downloading
 and installing pyDOE.
 
 Source Code
@@ -90,4 +92,4 @@ References
 .. _Central composite designs: http://en.wikipedia.org/wiki/Central_composite_design
 .. _Plackett-Burman designs: http://en.wikipedia.org/wiki/Plackett-Burman_design
 .. _Latin-Hypercube designs: http://en.wikipedia.org/wiki/Latin_hypercube_sampling
-.. _package documentation: http://pythonhosted.org/pyDOE
+.. _package homepage: http://pythonhosted.org/pyDOE

doc/_build/html/_sources/factorial.txt

@@ -1,16 +1,26 @@
+.. _factorial:
+
 ================================================================================
 Factorial Designs
 ================================================================================
 
 In this section, the following kinds of *factorial designs* will be described:
 
-- General Full-Factorial
-- 2-Level Full-Factorial
-- 2-Level Fractional-Factorial
-- Plackett-Burman
+- :ref:`General Full-Factorial <general_full_factorial>`
+- :ref:`2-Level Full-Factorial <2_level_full_factorial>`
+- :ref:`2-Level Fractional-Factorial <fractional_factorial>`
+- :ref:`Plackett-Burman <plackett_burman>`
+
+.. hint::
+   All available designs can be accessed after a simple import statement::
+
+    >>> from pyDOE import *
+
 
 .. index:: General Full-Factorial
 
+.. _general_full_factorial:
+
 General Full-Factorial (``fullfact``)
 =====================================
 
@@ -34,6 +44,8 @@ items in the input array.
 
 .. index:: 2-Level Full Factorial
 
+.. _2_level_full_factorial:
+
 2-Level Full-Factorial (``ff2n``)
 =================================
 
@@ -53,6 +65,8 @@ create a design for::
 
 .. index:: 2-Level Fractional Factorial
 
+.. _fractional_factorial:
+
 2-Level Fractional-Factorial (``fracfact``)
 ===========================================
 
@@ -164,6 +178,8 @@ array to the keyword ``columns``::
 
 .. index:: Plackett-Burman
 
+.. _plackett_burman:
+
 Plackett-Burman (``pbdesign``)
 ==============================
 
@@ -208,7 +224,8 @@ consult the following articles on Wikipedia:
 - `Plackett-Burman designs`_
 
 There is also a wealth of information on the `NIST`_ website about the
-various design matrices that can be created.
+various design matrices that can be created as well as detailed information
+about designing/setting-up/running experiments in general.
 
 Any questions, comments, bug-fixes, etc. can be forwarded to the `author`_.
 

doc/_build/html/_sources/index.txt

@@ -11,31 +11,36 @@ The ``pyDOE`` package is designed to help the
 **scientist, engineer, statistician,** etc., to construct appropriate 
 **experimental designs**.
 
+.. hint::
+   All available designs can be accessed after a simple import statement::
+
+    >>> from pyDOE import *
+
 Capabilities
 ============
 
 The package currently includes functions for creating designs for any 
 number of factors:
 
-- *Factorial Designs*
+- :ref:`Factorial Designs <factorial>`
 
-  #. **General Full-Factorial** (``fullfact``)
+  #. :ref:`General Full-Factorial <general_full_factorial>` (``fullfact``)
 
-  #. **2-level Full-Factorial** (``ff2n``)
+  #. :ref:`2-Level Full-Factorial <2_level_full_factorial>` (``ff2n``)
 
-  #. **2-level Fractional Factorial** (``fracfact``)
+  #. :ref:`2-Level Fractional-Factorial <fractional_factorial>` (``fracfact``)
 
-  #. **Plackett-Burman** (``pbdesign``)
+  #. :ref:`Plackett-Burman <plackett_burman>` (``pbdesign``)
 
-- *Response-Surface Designs* 
+- :ref:`Response-Surface Designs <response_surface>`
 
-  #. **Box-Behnken** (``bbdesign``)
+  #. :ref:`Box-Behnken <box_behnken>` (``bbdesign``)
 
-  #. **Central-Composite** (``ccdesign``)
+  #. :ref:`Central-Composite <central_composite>` (``ccdesign``)
 
-- *Randomized Designs*
+- :ref:`Randomized Designs <randomized>`
 
-  #. **Latin-Hypercube** (``lhs``)
+  #. :ref:`Latin-Hypercube <latin_hypercube>` (``lhs``)
 
 The following are *in the works* (probably), so stay tuned!
 
@@ -171,6 +176,10 @@ References
 - `Central composite designs`_
 - `Latin-Hypercube designs`_
 
+There is also a wealth of information on the `NIST`_ website about the
+various design matrices that can be created as well as detailed information
+about designing/setting-up/running experiments in general.
+
 .. _author: mailto:tisimst@gmail.com
 .. _Factorial designs: http://en.wikipedia.org/wiki/Factorial_experiment
 .. _Box-Behnken designs: http://en.wikipedia.org/wiki/Box-Behnken_design
@@ -179,4 +188,4 @@ References
 .. _Latin-Hypercube designs: http://en.wikipedia.org/wiki/Latin_hypercube_sampling
 .. _setuptools: http://pypi.python.org/pypi/setuptools
 .. _download: http://pypi.python.org/pypi/pyDOE/#downloads
-
+.. _NIST: http://www.itl.nist.gov/div898/handbook/pri/pri.htm

doc/_build/html/_sources/randomized.txt

@@ -1,5 +1,7 @@
 .. index:: Randomized Designs
 
+.. _randomized:
+
 ================================================================================
 Randomized Designs
 ================================================================================
@@ -9,8 +11,15 @@ be described:
 
 - Latin-Hypercube
 
+.. hint::
+   All available designs can be accessed after a simple import statement::
+
+    >>> from pyDOE import *
+
 .. index:: Latin-Hypercube
 
+.. _latin_hypercube:
+
 Latin-Hypercube (``lhs``)
 =========================
 
@@ -29,7 +38,8 @@ the design space).
 The output design scales all the variable ranges from zero to one which
 can then be transformed as the user wishes (like to a specific statistical
 distribution using the `scipy.stats.distributions`_ ``ppf`` (inverse
-cumulative distribution) function. An example of this is shown below.
+cumulative distribution) function. An example of this is :ref:`shown below
+<statistical_distribution_usage>`.
 
 For example, if I wanted to transform the uniform distribution of 5 samples
 to a normal distribution (mean=0, standard deviation=1), I would do 
@@ -68,9 +78,12 @@ Let's say we want more samples, like 10::
            [ 0.85,  0.75,  0.85,  0.85],
            [ 0.65,  0.15,  0.65,  0.95]])
 
-.. Using Custom Distributions:
+.. _statistical_distribution_usage:
+
+Customizing with Statistical Distributions
+------------------------------------------
 
-Now, let's say we want to translate these designs to be normally
+Now, let's say we want to transform these designs to be normally
 distributed with means = [1, 2, 3, 4] and standard deviations = [0.1,
 0.5, 1, 0.25]::
 
@@ -109,10 +122,12 @@ consult the following articles on Wikipedia:
 - `Latin-Hypercube designs`_
 
 There is also a wealth of information on the `NIST`_ website about the
-various design matrices that can be created.
+various design matrices that can be created as well as detailed information
+about designing/setting-up/running experiments in general.
 
 Any questions, comments, bug-fixes, etc. can be forwarded to the `author`_.
 
 .. _author: mailto:tisimst@gmail.com
 .. _Latin-Hypercube designs: http://en.wikipedia.org/wiki/Latin_hypercube_sampling
-.. _NIST: http://www.itl.nist.gov/div898/handbook/pri/pri.htm
+.. _NIST: http://www.itl.nist.gov/div898/handbook/pri/pri.htm
+.. _scipy.stats.distributions: http://docs.scipy.org/doc/scipy/reference/stats.html

doc/_build/html/_sources/rsm.txt

@@ -1,17 +1,26 @@
 .. index:: Response Surface Designs, RSM
 
+.. _response_surface:
+
 ================================================================================
 Response Surface Designs
 ================================================================================
 
 In this section, the following kinds of *response surface designs* will 
 be described:
 
-- Box-Behnken
-- Central Composite
+- :ref:`Box-Behnken <box_behnken>`
+- :ref:`Central Composite <central_composite>`
+
+.. hint::
+   All available designs can be accessed after a simple import statement::
 
+    >>> from pyDOE import *
+
 .. index:: Box-Behnken
 
+.. _box_behnken:
+
 Box-Behnken (``bbdesign``)
 ==========================
 
@@ -79,6 +88,8 @@ A customized design with four factors, but only a single center point::
 
 .. index:: Central Composite
 
+.. _central_composite:
+
 Central Composite (``ccdesign``)
 ================================
 
@@ -168,7 +179,8 @@ consult the following articles on Wikipedia:
 - `Central composite designs`_
 
 There is also a wealth of information on the `NIST`_ website about the
-various design matrices that can be created.
+various design matrices that can be created as well as detailed information
+about designing/setting-up/running experiments in general.
 
 Any questions, comments, bug-fixes, etc. can be forwarded to the `author`_.
 

doc/_build/html/factorial.html

@@ -107,16 +107,23 @@ <h3>Documentation license</h3>
           <div class="body">
 
   <div class="section" id="factorial-designs">
-<h1>Factorial Designs<a class="headerlink" href="#factorial-designs" title="Permalink to this headline">¶</a></h1>
+<span id="factorial"></span><h1>Factorial Designs<a class="headerlink" href="#factorial-designs" title="Permalink to this headline">¶</a></h1>
 <p>In this section, the following kinds of <em>factorial designs</em> will be described:</p>
 <ul class="simple">
-<li>General Full-Factorial</li>
-<li>2-Level Full-Factorial</li>
-<li>2-Level Fractional-Factorial</li>
-<li>Plackett-Burman</li>
+<li><a class="reference internal" href="#general-full-factorial"><em>General Full-Factorial</em></a></li>
+<li><a class="reference internal" href="#level-full-factorial"><em>2-Level Full-Factorial</em></a></li>
+<li><a class="reference internal" href="#fractional-factorial"><em>2-Level Fractional-Factorial</em></a></li>
+<li><a class="reference internal" href="#plackett-burman"><em>Plackett-Burman</em></a></li>
 </ul>
+<div class="admonition hint">
+<p class="first admonition-title">Hint</p>
+<p>All available designs can be accessed after a simple import statement:</p>
+<div class="last highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pyDOE</span> <span class="kn">import</span> <span class="o">*</span>
+</pre></div>
+</div>
+</div>
 <div class="section" id="general-full-factorial-fullfact">
-<span id="index-0"></span><h2>General Full-Factorial (<tt class="docutils literal"><span class="pre">fullfact</span></tt>)<a class="headerlink" href="#general-full-factorial-fullfact" title="Permalink to this headline">¶</a></h2>
+<span id="general-full-factorial"></span><span id="index-0"></span><h2>General Full-Factorial (<tt class="docutils literal"><span class="pre">fullfact</span></tt>)<a class="headerlink" href="#general-full-factorial-fullfact" title="Permalink to this headline">¶</a></h2>
 <p>This kind of design offers full flexibility as to the number of discrete
 levels for each factor in the design. Its usage is simple:</p>
 <div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="n">fullfact</span><span class="p">(</span><span class="n">levels</span><span class="p">)</span>
@@ -136,7 +143,7 @@ <h1>Factorial Designs<a class="headerlink" href="#factorial-designs" title="Perm
 items in the input array.</p>
 </div>
 <div class="section" id="level-full-factorial-ff2n">
-<span id="index-1"></span><h2>2-Level Full-Factorial (<tt class="docutils literal"><span class="pre">ff2n</span></tt>)<a class="headerlink" href="#level-full-factorial-ff2n" title="Permalink to this headline">¶</a></h2>
+<span id="level-full-factorial"></span><span id="index-1"></span><h2>2-Level Full-Factorial (<tt class="docutils literal"><span class="pre">ff2n</span></tt>)<a class="headerlink" href="#level-full-factorial-ff2n" title="Permalink to this headline">¶</a></h2>
 <p>This function is a convenience wrapper to <tt class="docutils literal"><span class="pre">fullfact</span></tt> that forces all the
 factors to have two levels each, you simple tell it how many factors to
 create a design for:</p>
@@ -153,7 +160,7 @@ <h1>Factorial Designs<a class="headerlink" href="#factorial-designs" title="Perm
 </div>
 </div>
 <div class="section" id="level-fractional-factorial-fracfact">
-<span id="index-2"></span><h2>2-Level Fractional-Factorial (<tt class="docutils literal"><span class="pre">fracfact</span></tt>)<a class="headerlink" href="#level-fractional-factorial-fracfact" title="Permalink to this headline">¶</a></h2>
+<span id="fractional-factorial"></span><span id="index-2"></span><h2>2-Level Fractional-Factorial (<tt class="docutils literal"><span class="pre">fracfact</span></tt>)<a class="headerlink" href="#level-fractional-factorial-fracfact" title="Permalink to this headline">¶</a></h2>
 <p>This function requires a little more knowledge of how the <em>confounding</em>
 will be allowed (this means that some factor effects get muddled with
 other interaction effects, so it&#8217;s harder to distinguish between them).</p>
@@ -259,7 +266,7 @@ <h1>Factorial Designs<a class="headerlink" href="#factorial-designs" title="Perm
 </div>
 </div>
 <div class="section" id="plackett-burman-pbdesign">
-<span id="index-3"></span><h2>Plackett-Burman (<tt class="docutils literal"><span class="pre">pbdesign</span></tt>)<a class="headerlink" href="#plackett-burman-pbdesign" title="Permalink to this headline">¶</a></h2>
+<span id="plackett-burman"></span><span id="index-3"></span><h2>Plackett-Burman (<tt class="docutils literal"><span class="pre">pbdesign</span></tt>)<a class="headerlink" href="#plackett-burman-pbdesign" title="Permalink to this headline">¶</a></h2>
 <p>Another way to generate fractional-factorial designs is through the use
 of <strong>Plackett-Burman</strong> designs. These designs are unique in that the
 number of trial conditions (rows) expands by multiples of four (e.g. 4,
@@ -297,7 +304,8 @@ <h1>Factorial Designs<a class="headerlink" href="#factorial-designs" title="Perm
 <li><a class="reference external" href="http://en.wikipedia.org/wiki/Plackett-Burman_design">Plackett-Burman designs</a></li>
 </ul>
 <p>There is also a wealth of information on the <a class="reference external" href="http://www.itl.nist.gov/div898/handbook/pri/pri.htm">NIST</a> website about the
-various design matrices that can be created.</p>
+various design matrices that can be created as well as detailed information
+about designing/setting-up/running experiments in general.</p>
 <p>Any questions, comments, bug-fixes, etc. can be forwarded to the <a class="reference external" href="mailto:tisimst&#37;&#52;&#48;gmail&#46;com">author</a>.</p>
 </div>
 </div>

doc/_build/html/index.html

@@ -120,25 +120,32 @@ <h1><tt class="docutils literal"><span class="pre">pyDOE</span></tt>: The experi
 <p>The <tt class="docutils literal"><span class="pre">pyDOE</span></tt> package is designed to help the
 <strong>scientist, engineer, statistician,</strong> etc., to construct appropriate
 <strong>experimental designs</strong>.</p>
+<div class="admonition hint">
+<p class="first admonition-title">Hint</p>
+<p>All available designs can be accessed after a simple import statement:</p>
+<div class="last highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pyDOE</span> <span class="kn">import</span> <span class="o">*</span>
+</pre></div>
+</div>
+</div>
 <div class="section" id="capabilities">
 <h2>Capabilities<a class="headerlink" href="#capabilities" title="Permalink to this headline">¶</a></h2>
 <p>The package currently includes functions for creating designs for any
 number of factors:</p>
 <ul class="simple">
-<li><em>Factorial Designs</em><ol class="arabic">
-<li><strong>General Full-Factorial</strong> (<tt class="docutils literal"><span class="pre">fullfact</span></tt>)</li>
-<li><strong>2-level Full-Factorial</strong> (<tt class="docutils literal"><span class="pre">ff2n</span></tt>)</li>
-<li><strong>2-level Fractional Factorial</strong> (<tt class="docutils literal"><span class="pre">fracfact</span></tt>)</li>
-<li><strong>Plackett-Burman</strong> (<tt class="docutils literal"><span class="pre">pbdesign</span></tt>)</li>
+<li><a class="reference internal" href="factorial.html#factorial"><em>Factorial Designs</em></a><ol class="arabic">
+<li><a class="reference internal" href="factorial.html#general-full-factorial"><em>General Full-Factorial</em></a> (<tt class="docutils literal"><span class="pre">fullfact</span></tt>)</li>
+<li><a class="reference internal" href="factorial.html#level-full-factorial"><em>2-Level Full-Factorial</em></a> (<tt class="docutils literal"><span class="pre">ff2n</span></tt>)</li>
+<li><a class="reference internal" href="factorial.html#fractional-factorial"><em>2-Level Fractional-Factorial</em></a> (<tt class="docutils literal"><span class="pre">fracfact</span></tt>)</li>
+<li><a class="reference internal" href="factorial.html#plackett-burman"><em>Plackett-Burman</em></a> (<tt class="docutils literal"><span class="pre">pbdesign</span></tt>)</li>
 </ol>
 </li>
-<li><em>Response-Surface Designs</em><ol class="arabic">
-<li><strong>Box-Behnken</strong> (<tt class="docutils literal"><span class="pre">bbdesign</span></tt>)</li>
-<li><strong>Central-Composite</strong> (<tt class="docutils literal"><span class="pre">ccdesign</span></tt>)</li>
+<li><a class="reference internal" href="rsm.html#response-surface"><em>Response-Surface Designs</em></a><ol class="arabic">
+<li><a class="reference internal" href="rsm.html#box-behnken"><em>Box-Behnken</em></a> (<tt class="docutils literal"><span class="pre">bbdesign</span></tt>)</li>
+<li><a class="reference internal" href="rsm.html#central-composite"><em>Central-Composite</em></a> (<tt class="docutils literal"><span class="pre">ccdesign</span></tt>)</li>
 </ol>
 </li>
-<li><em>Randomized Designs</em><ol class="arabic">
-<li><strong>Latin-Hypercube</strong> (<tt class="docutils literal"><span class="pre">lhs</span></tt>)</li>
+<li><a class="reference internal" href="randomized.html#randomized"><em>Randomized Designs</em></a><ol class="arabic">
+<li><a class="reference internal" href="randomized.html#latin-hypercube"><em>Latin-Hypercube</em></a> (<tt class="docutils literal"><span class="pre">lhs</span></tt>)</li>
 </ol>
 </li>
 </ul>
@@ -249,6 +256,9 @@ <h2>References<a class="headerlink" href="#references" title="Permalink to this
 <li><a class="reference external" href="http://en.wikipedia.org/wiki/Central_composite_design">Central composite designs</a></li>
 <li><a class="reference external" href="http://en.wikipedia.org/wiki/Latin_hypercube_sampling">Latin-Hypercube designs</a></li>
 </ul>
+<p>There is also a wealth of information on the <a class="reference external" href="http://www.itl.nist.gov/div898/handbook/pri/pri.htm">NIST</a> website about the
+various design matrices that can be created as well as detailed information
+about designing/setting-up/running experiments in general.</p>
 </div>
 </div>