Fragment the result set documentation.

Closes #985.

Also tweaked some minor stylistic issues.

Author: drvinceknight

docs/tutorials/advanced/index.rst

@@ -12,6 +12,7 @@ Contents:
    classification_of_strategies.rst
    strategy_transformers.rst
    making_tournaments.rst
+   tournament_results.rst
    reading_and_writing_interactions.rst
    parallel_processing.rst
    using_the_cache.rst

docs/tutorials/getting_started/tournament_results.rst renamed to docs/tutorials/advanced/tournament_results.rst

@@ -370,31 +370,3 @@ of the cooperation rating::
     [0.57..., 0.0, 0.57..., 0.57...]
 
 For more information about these see :ref:`morality-metrics`.
-
-Summarising a tournament
-------------------------
-
-The results set can also return a list of named tuples, ordered by strategy rank
-that summarises the results of the tournament::
-
-    >>> summary = results.summarise()
-    >>> pprint.pprint(summary)
-    [Player(Rank=0, Name='Defector', Median_score=2.6..., Cooperation_rating=0.0, Wins=3.0, Initial_C_rate=0.0, CC_rate=...),
-     Player(Rank=1, Name='Tit For Tat', Median_score=2.3..., Cooperation_rating=0.7, Wins=0.0, Initial_C_rate=1.0, CC_rate=...),
-     Player(Rank=2, Name='Grudger', Median_score=2.3..., Cooperation_rating=0.7, Wins=0.0, Initial_C_rate=1.0, CC_rate=...),
-     Player(Rank=3, Name='Cooperator', Median_score=2.0..., Cooperation_rating=1.0, Wins=0.0, Initial_C_rate=1.0, CC_rate=...)]
-
-It is also possible to write this data directly to a csv file using the
-`write_summary` method::
-
-    >>> results.write_summary('summary.csv')
-    >>> import csv
-    >>> with open('summary.csv', 'r') as outfile:
-    ...     csvreader = csv.reader(outfile)
-    ...     for row in csvreader:
-    ...         print(row)
-    ['Rank', 'Name', 'Median_score', 'Cooperation_rating', 'Wins', 'Initial_C_rate', 'CC_rate', 'CD_rate', 'DC_rate', 'DD_rate', 'CC_to_C_rate', 'CD_to_C_rate', 'DC_to_C_rate', 'DD_to_C_rate']
-    ['0', 'Defector', '2.6...', '0.0', '3.0', '0.0', '0.0', '0.0', '0.4...', '0.6...', '0', '0', '0', '0']
-    ['1', 'Tit For Tat', '2.3...', '0.7', '0.0', '1.0', '0.66...', '0.03...', '0.0', '0.3...', '1.0', '0', '0', '0']
-    ['2', 'Grudger', '2.3...', '0.7', '0.0', '1.0', '0.66...', '0.03...', '0.0', '0.3...', '1.0', '0', '0', '0']
-    ['3', 'Cooperator', '2.0...', '1.0', '0.0', '1.0', '0.66...', '0.33...', '0.0', '0.0', '1.0', '1.0', '0', '0']

docs/tutorials/further_topics/approximate_moran_processes.rst

@@ -26,8 +26,9 @@ Now let us create an Approximate Moran Process::
     >>> amp.population_distribution()
     Counter({'Random: 0.5': 3})
 
-We see that the :code:`Random: 0.5` won this Moran process. This is not what happens in a
-standard Moran process where the `Random: 0.5` player will not win::
+We see that the :code:`Random: 0.5` won this Moran process. This is not what
+happens in a standard Moran process where the :code:`Random: 0.5` player will
+not win::
 
     >>> axl.seed(2)
     >>> amp = axl.MoranProcess(players)

docs/tutorials/further_topics/morality_metrics.rst

@@ -4,7 +4,7 @@ Morality Metrics
 ================
 
 Tyler Singer-Clark's June 2014 paper, "Morality Metrics On Iterated Prisoner’s
-Dilemma Players" [Tyler-Clark2014]), describes several interesting metrics which
+Dilemma Players" [Singer-Clark2014]_), describes several interesting metrics which
 may be used to analyse IPD tournaments all of which are available within the
 ResultSet class. (Tyler's paper is available here:
 http://www.scottaaronson.com/morality.pdf).

docs/tutorials/getting_started/index.rst

@@ -12,7 +12,7 @@ Contents:
    installation.rst
    match.rst
    tournament.rst
-   tournament_results.rst
+   summarising_tournaments.rst
    interactions.rst
    visualising_results.rst
    moran.rst

docs/tutorials/getting_started/summarising_tournaments.rst

@@ -0,0 +1,43 @@
+.. _tournament-results-summary:
+
+Summarising tournament results
+==============================
+
+
+As shown in :ref:`creating_tournaments` let us create a tournament::
+
+    >>> import axelrod as axl
+    >>> players = [axl.Cooperator(), axl.Defector(),
+    ...            axl.TitForTat(), axl.Grudger()]
+    >>> tournament = axl.Tournament(players, turns=10, repetitions=3)
+    >>> results = tournament.play()
+
+The results set can return a list of named tuples, ordered by strategy rank
+that summarises the results of the tournament::
+
+    >>> summary = results.summarise()
+    >>> import pprint
+    >>> pprint.pprint(summary)
+    [Player(Rank=0, Name='Defector', Median_score=2.6..., Cooperation_rating=0.0, Wins=3.0, Initial_C_rate=0.0, CC_rate=...),
+     Player(Rank=1, Name='Tit For Tat', Median_score=2.3..., Cooperation_rating=0.7, Wins=0.0, Initial_C_rate=1.0, CC_rate=...),
+     Player(Rank=2, Name='Grudger', Median_score=2.3..., Cooperation_rating=0.7, Wins=0.0, Initial_C_rate=1.0, CC_rate=...),
+     Player(Rank=3, Name='Cooperator', Median_score=2.0..., Cooperation_rating=1.0, Wins=0.0, Initial_C_rate=1.0, CC_rate=...)]
+
+It is also possible to write this data directly to a csv file using the
+`write_summary` method::
+
+    >>> results.write_summary('summary.csv')
+    >>> import csv
+    >>> with open('summary.csv', 'r') as outfile:
+    ...     csvreader = csv.reader(outfile)
+    ...     for row in csvreader:
+    ...         print(row)
+    ['Rank', 'Name', 'Median_score', 'Cooperation_rating', 'Wins', 'Initial_C_rate', 'CC_rate', 'CD_rate', 'DC_rate', 'DD_rate', 'CC_to_C_rate', 'CD_to_C_rate', 'DC_to_C_rate', 'DD_to_C_rate']
+    ['0', 'Defector', '2.6...', '0.0', '3.0', '0.0', '0.0', '0.0', '0.4...', '0.6...', '0', '0', '0', '0']
+    ['1', 'Tit For Tat', '2.3...', '0.7', '0.0', '1.0', '0.66...', '0.03...', '0.0', '0.3...', '1.0', '0', '0', '0']
+    ['2', 'Grudger', '2.3...', '0.7', '0.0', '1.0', '0.66...', '0.03...', '0.0', '0.3...', '1.0', '0', '0', '0']
+    ['3', 'Cooperator', '2.0...', '1.0', '0.0', '1.0', '0.66...', '0.33...', '0.0', '0.0', '1.0', '1.0', '0', '0']
+
+
+The result set class computes a large number of detailed outcomes read about
+those in :ref:`tournament-results`.