Add link and fix some headers

docs/source/tutorial/example1Analysis.rst

@@ -13,21 +13,21 @@ First, **load the data file**:
 
 
 Voicing
-^^^^^^
+^^^^^^^
 
 A plot of the basic voicing effect, by vowel:
 
 
 ::
 
-    ggplot(aes(x=following_consonant_voicing, y=vowel_duration), data=cvc) + geom_boxplot() + 
-    facet_wrap(~vowel, scales = "free_y") + xlab("Consonant voicing") + ylab("Vowel duration (sec)") 
+    ggplot(aes(x=following_consonant_voicing, y=vowel_duration), data=cvc) + geom_boxplot() +
+    facet_wrap(~vowel, scales = "free_y") + xlab("Consonant voicing") + ylab("Vowel duration (sec)")
 
 
 .. figure:: figure/ex1Plot1-1.png
-    :alt: 
+    :alt:
+
 
-
 
 It looks like there is generally an effect in the expected direction, but the size of the effect may differ by vowel.
 
@@ -40,15 +40,15 @@ A plot of the basic speech rate effect, divided up by consonant voicing:
 ::
 
     ggplot(aes(x=speech_rate, y=vowel_duration), data=cvc) +
-     geom_smooth(aes(color=following_consonant_voicing)) + 
+     geom_smooth(aes(color=following_consonant_voicing)) +
     geom_point(aes(color=following_consonant_voicing), alpha=0.1, size=1) +
      xlab("Speech rate (sylls/sec)") + ylab("Vowel duration")
 
 
 .. figure:: figure/ex1Plot2-1.png
-    :alt: 
+    :alt:
+
 
-
 
 There is a large (and possibly nonlinear) speech rate effect. The size of the voicing effect is small compared to speech rate, and the voicing effect may be modulated by speech rate.
 
@@ -60,39 +60,39 @@ A plot of the basic frequency effect, divided up by consonant voicing:
 
 ::
 
-    ggplot(aes(x=word_frequency, y=vowel_duration), data=cvc) + 
-    geom_smooth(aes(color=following_consonant_voicing), method="lm") + 
+    ggplot(aes(x=word_frequency, y=vowel_duration), data=cvc) +
+    geom_smooth(aes(color=following_consonant_voicing), method="lm") +
     geom_point(aes(color=following_consonant_voicing), alpha=0.1, size=1) +
      xlab("Word frequency (log)") + ylab("Vowel duration") + scale_x_log10()
 
 
 .. figure:: figure/ex1Plot3-1.png
-    :alt: 
+    :alt:
+
 
-
 
 (Note that we have forced a linear trend here, to make the effect clearer given the presence of more tokens for more frequent words. This turns out to be what the "real" effect looks like, once token frequency is accounted for.)
 
 The basic frequency effect is as expected: shorter duration for higher frequency words. The voicing effect is (again) small in comparison, and may be modulated by word frequency: more frequent words (more reduced?) show a smaller effect.
 
 Neighborhood density
-^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^
 
 In contrast, there is no clear effect of neighborhood density:
 
 
 ::
 
-    ggplot(aes(x=word_neighborhood_density, y=vowel_duration), data=cvc) + 
-    geom_smooth(aes(color=following_consonant_voicing)) + 
-    geom_point(aes(color=following_consonant_voicing), alpha=0.1, size=1) + 
+    ggplot(aes(x=word_neighborhood_density, y=vowel_duration), data=cvc) +
+    geom_smooth(aes(color=following_consonant_voicing)) +
+    geom_point(aes(color=following_consonant_voicing), alpha=0.1, size=1) +
     xlab("Neighborhood density") + ylab("Vowel duration")
 
 
 .. figure:: figure/ex1Plot4-1.png
-    :alt: 
+    :alt:
+
 
-
 
 This turns out to be not unexpected, given previous work: while word duration and vowel quality (e.g., centralization) depend on neighborhood density (e.g. Gahl & Yao, 2011), vowel *duration* has not been consistently found to depend on neighborhood density (e.g. Munson, 2007).
 

docs/source/tutorial/installation.rst

@@ -1,4 +1,4 @@
-.. _installation_tutorial:
+.. _installation_tutorial2:
 
 Tutorial: Installation Setup
 ############################
@@ -7,6 +7,8 @@ Tutorial: Installation Setup
 
 .. _GitHub repository: https://https://github.com/MontrealCorpusTools/speechcorpustools
 
+.. _`SCT releases`: https://github.com/MontrealCorpusTools/speechcorpustools/releases
+
 Installing Neo4j
 ****************
 

docs/source/tutorial/tutorial.rst

@@ -1,7 +1,7 @@
 .. _tutintroduction:
 
 Tutorial
-########################
+########
 
 .. _PGDB website: http://montrealcorpustools.github.io/PolyglotDB/
 
@@ -10,13 +10,13 @@ Tutorial
 Speech Corpus Tools is a system for going from a raw speech corpus to a data file (CSV) ready for further analysis (e.g. in R), which conceptually consists of a pipeline of four steps:
 
 1. **Import** the corpus into SCT
-	* Result: a structured database of linguistic objects (words, phones, discourses).
+    * Result: a structured database of linguistic objects (words, phones, discourses).
 
 2. **Enrich** the database
-    * Result: Further linguistic objects (utterances, syllables), and information about objects (e.g. speech rate, word frequencies). 
+    * Result: Further linguistic objects (utterances, syllables), and information about objects (e.g. speech rate, word frequencies).
 
 3. **Query** the database
-    * Result: A set of linguistic objects of interest (e.g. utterance-final words ending with a stop), 
+    * Result: A set of linguistic objects of interest (e.g. utterance-final words ending with a stop),
 
 4. **Export** the results
     * Result: A CSV file containing information about the set of objects of interest
@@ -33,10 +33,11 @@ This tutorial is structured as follows:
   been completed , either by using a :ref:`premade <premade>` version,
   or doing the import and enrichment steps :ref:`yourself <buildownlibrispeech>`.
 
-* :ref:`Examples <vignetteMain>`: 
+* :ref:`Examples <vignetteMain>`:
       * Two worked examples (:ref:`1 <example1>`, :ref:`2 <example2>`)
-	illustrating the *Query* and *Export* steps, as well as
-	(optional) basic analysis of the resulting data files (CSV's) in R.
+        illustrating the *Query* and *Export* steps, as well as
+        (optional) basic analysis of the resulting data files (CSV's) in R.
+
       * One additional example (:ref:`3 <example3>`) left as an exercise.
 
 
@@ -48,16 +49,12 @@ This tutorial is structured as follows:
 .. _installation_tutorial:
 
 Installation
-********
-
-.. _PGDB website: http://montrealcorpustools.github.io/PolyglotDB/
-
-.. _GitHub repository: https://https://github.com/MontrealCorpusTools/speechcorpustools
+************
 
 .. _neo4j_install:
 
 Installing Neo4j
-========
+================
 
 SCT currently requires that `Neo4j <https://neo4j.com/>`_ version 3.0 be installed locally and running.
 To install Neo4j, please use the following links:
@@ -197,15 +194,15 @@ doing the following **once**, before running SCT:
 .. _sct_install:
 
 Installing SCT
-=========
+==============
 
 Once Neo4j is set up as above, the latest version of SCT can be downloaded from
 the `SCT releases
 <https://github.com/MontrealCorpusTools/speechcorpustools/releases>`_
 page. As of 12 July 2016, the most current release is v0.5.
 
 Windows
-------
+-------
 
 1. Download the zip archive for Windows
 2. Extract the folder
@@ -235,7 +232,7 @@ Mac
 .. _librispeech:
 
 LibriSpeech database
-*******
+********************
 
 The examples in this tutorial use a subset of the `LibriSpeech ASR
 corpus <http://www.openslr.org/12/>`_, a corpus of read English speech
@@ -267,15 +264,17 @@ tutorial, just use the pre-made copy.)
 .. _premade:
 
 Use pre-made database
-=======
+=====================
+
+.. _`librispeechDatabase.zip`: https://github.com/MontrealCorpusTools/speechcorpustools/releases/download/v0.5/librispeechDatabase.zip
 
 Make sure you have opened the SCT application and started Neo4j, at least once.  This creates
 folders for Neo4j databases and for all SCT's local files (including SQL databases):
 
 * OS X: ``/Users/username/Documents/Neo4j``, ``/Users/username/Documents/SCT``
 * Windows: ``C:\Users\username\Documents\Neo4j``, ``C:\Users\username\Documents\SCT``
 
-Unzip the ``librispeechDatabases.zip`` file.  It contains two folders,
+Unzip the `librispeechDatabase.zip`_ file.  It contains two folders,
 ``librispeech.graphdb`` and ``LibriSpeech``. Move these (using Finder on
 OS X, or File Explorer on Windows) to the ``Neo4j`` and ``SCT`` folders.
 After doing so, these directories should exist:
@@ -296,7 +295,7 @@ Some important information about the database (to replicate if you are building
 .. _buildownlibrispeech:
 
 Building your own Librispeech database
-========
+======================================
 
 **Coming soon!**  Some general information on building a database in
 SCT (= importing data) is :any:`here <../additional/buildown>`.
@@ -306,7 +305,7 @@ SCT (= importing data) is :any:`here <../additional/buildown>`.
 .. _vignetteMain:
 
 Examples
-******
+********
 
 Several worked examples follow, which demonstrate the workflow of SCT
 and how to construct queries and exports. You should be able to
@@ -327,10 +326,10 @@ visualization in R are given.
 .. _example1:
 
 Example 1: Factors affecting vowel duration
-=======
+===========================================
 
 Motivation
--------
+----------
 
 A number of factors affect the duration of vowels, including:
 
@@ -347,7 +346,7 @@ voicing is compared to other factors.
 
 
 Step 1: Creating a query profile
--------
+--------------------------------
 
 Based on the motivation above, we want to make a query for:
 
@@ -429,7 +428,7 @@ You should now:
 
 
 Step 2: Creating an export profile
--------
+----------------------------------
 
 The next step is to export information about each vowel token as a CSV file.  We would like the vowel's *duration* and *identity*, as well as the following factors which are expected to affect the vowel's duration:
 
@@ -499,7 +498,7 @@ You can now:
 
 
 Step 3: Examine the data
--------
+------------------------
 
 
 Here are the first few rows of the resulting data file, in Excel:
@@ -517,11 +516,11 @@ Here are the first few rows of the resulting data file, in Excel:
 .. _example2:
 
 Example 2: Polysyllabic shortening
-=========
+==================================
 
 Motivation
---------
-  
+----------
+
 *Polysyllabic shortening* refers to the "same" rhymic unit (syllable or vowel) becoming shorter as the size of the containing domain (word or prosodic domain) increases. Two classic examples:
 
 * English: *stick*, *stick*\y, *stick*\iness (Lehiste, 1972)
@@ -541,7 +540,7 @@ We show (1) here, and leave (2) as an exercise.
 
 
 Step 1: Query profile
------------
+---------------------
 
 In this case, we want to make a query for:
 
@@ -579,7 +578,7 @@ You should **input this query profile**, then **run it** (optionally
 saving first).
 
 Step 2: Export profile
---------
+----------------------
 
 This query has found all word-initial stressed syllables for words in utterance-final position. We now want to export information about these linguistic objects to a CSV file, for which we again need to construct a query profile.  (You should now **Start a new export profile**.)
 
@@ -603,7 +602,7 @@ The following export profile contains these nine variables:
 After you **enter these rows** in the export profile, **run the export** (optionally saving the export profile first).  I exported it as ``librispeechCvc.csv``.
 
 Step 3: Examine the data
-------------
+------------------------
 
 .. include:: example2Analysis.rst
 
@@ -614,7 +613,7 @@ Step 3: Examine the data
 .. _example3:
 
 Example 3: Menzerath's Law
-========
+==========================
 
 **Motivation**: Menzerath's Law (Menzerath 1928, 1954) refers to the general finding that segments and syllables are shorter in longer words, both in terms of
 
@@ -642,8 +641,8 @@ data file which lets you test Menzerath's law for the LibriSpeech corpus.  For e
 .. [#f1] Note that it is also possible to input some of these rows automatically, using the checkboxes in the `Simple exports` tab.
 
 .. [#f2] Technically, this database consists of two sub-databases: a
-	 Neo4j database (which contains the hierarchical
-	 representation of discourses), and a SQL database (which contains lexical and featural information, and cached acoustic measurements). 
+     Neo4j database (which contains the hierarchical
+     representation of discourses), and a SQL database (which contains lexical and featural information, and cached acoustic measurements).