src/sage/game_theory: Update imports for pygambit

src/sage/game_theory/gambit_docs.py

@@ -18,8 +18,8 @@
 
 Here is an example that constructs the Prisoner's Dilemma::
 
-    In [1]: import gambit
-    In [2]: g = gambit.Game.new_table([2,2])
+    In [1]: import pygambit
+    In [2]: g = pygambit.Game.new_table([2,2])
     In [3]: g.title = "A prisoner's dilemma game"
     In [4]: g.players[0].label = "Alphonse"
     In [5]: g.players[1].label = "Gaston"
@@ -79,16 +79,16 @@
 
 Here is how to use the ``ExternalEnumPureSolver``::
 
-    In [21]: solver = gambit.nash.ExternalEnumPureSolver()
+    In [21]: solver = pygambit.nash.ExternalEnumPureSolver()
     In [22]: solver.solve(g)
     Out[22]: [<NashProfile for 'A prisoner's dilemma game': [Fraction(0, 1), Fraction(1, 1), Fraction(0, 1), Fraction(1, 1)]>]
 
 Note that the above finds the equilibria by investigating all potential pure
 pure strategy pairs. This will fail to find all Nash equilibria in certain
 games.  For example here is an implementation of Matching Pennies::
 
-    In [1]: import gambit
-    In [2]: g = gambit.Game.new_table([2,2])
+    In [1]: import pygambit
+    In [2]: g = pygambit.Game.new_table([2,2])
     In [3]: g[0, 0][0] = 1
     In [4]: g[0, 0][1] = -1
     In [5]: g[0, 1][0] = -1
@@ -97,13 +97,13 @@
     In [8]: g[1, 0][1] = 1
     In [9]: g[1, 1][0] = 1
     In [10]: g[1, 1][1] = -1
-    In [11]: solver = gambit.nash.ExternalEnumPureSolver()
+    In [11]: solver = pygambit.nash.ExternalEnumPureSolver()
     In [12]: solver.solve(g)
     Out[12]: []
 
 If we solve this with the ``LCP`` solver we get the expected Nash equilibrium::
 
-    In [13]: solver = gambit.nash.ExternalLCPSolver()
+    In [13]: solver = pygambit.nash.ExternalLCPSolver()
     In [14]: solver.solve(g)
     Out[14]: [<NashProfile for '': [0.5, 0.5, 0.5, 0.5]>]
 
@@ -117,8 +117,8 @@
 showing the Battle of the Sexes::
 
     sage: # optional - pygambit
-    sage: import gambit
-    sage: g = gambit.Game.new_table([2,2])
+    sage: import pygambit
+    sage: g = pygambit.Game.new_table([2,2])
     sage: g[int(0), int(0)][int(0)] = int(2)
     sage: g[int(0), int(0)][int(1)] = int(1)
     sage: g[int(0), int(1)][int(0)] = int(0)
@@ -127,7 +127,7 @@
     sage: g[int(1), int(0)][int(1)] = int(0)
     sage: g[int(1), int(1)][int(0)] = int(1)
     sage: g[int(1), int(1)][int(1)] = int(2)
-    sage: solver = gambit.nash.ExternalLCPSolver()
+    sage: solver = pygambit.nash.ExternalLCPSolver()
     sage: solver.solve(g)
     [<NashProfile for '': [[1.0, 0.0], [1.0, 0.0]]>,
      <NashProfile for '': [[0.6666666667, 0.3333333333], [0.3333333333, 0.6666666667]]>,

src/sage/game_theory/normal_form_game.py

@@ -431,7 +431,7 @@
 It is also possible to generate a Normal form game from a gambit Game::
 
     sage: # optional - pygambit
-    sage: from gambit import Game
+    sage: from pygambit import Game
     sage: gambitgame= Game.new_table([2, 2])
     sage: gambitgame[int(0), int(0)][int(0)] = int(8)
     sage: gambitgame[int(0), int(0)][int(1)] = int(8)
@@ -584,7 +584,7 @@
     sage: degenerate_game.is_degenerate()
     True
 
-Note the 'negative' `-0.0` output by gambit. This is due to the numerical
+Note the 'negative' `-0.0` output by pygambit. This is due to the numerical
 nature of the algorithm used.
 
 Here is an example with the trivial game where all payoffs are 0::
@@ -652,8 +652,8 @@
 from sage.cpython.string import bytes_to_str
 
 try:
-    from gambit import Game
-    from gambit.nash import ExternalLPSolver, ExternalLCPSolver
+    from pygambit import Game
+    from pygambit.nash import ExternalLPSolver, ExternalLCPSolver
 except ImportError:
     Game = None
     ExternalLPSolver = None
@@ -719,7 +719,7 @@ def __init__(self, generator=None):
         Can initialise a game from a gambit game object::
 
             sage: # optional - pygambit
-            sage: from gambit import Game
+            sage: from pygambit import Game
             sage: gambitgame= Game.new_table([2, 2])
             sage: gambitgame[int(0), int(0)][int(0)] = int(5)
             sage: gambitgame[int(0), int(0)][int(1)] = int(8)
@@ -980,7 +980,7 @@ def _gambit_game(self, game):
         TESTS::
 
             sage: # optional - pygambit
-            sage: from gambit import Game
+            sage: from pygambit import Game
             sage: testgame = Game.new_table([2, 2])
             sage: testgame[int(0), int(0)][int(0)] = int(8)
             sage: testgame[int(0), int(0)][int(1)] = int(8)
@@ -1021,7 +1021,7 @@ def _gambit_(self, as_integer=False, maximization=True):
         TESTS::
 
             sage: # optional - pygambit
-            sage: from gambit import Game
+            sage: from pygambit import Game
             sage: A = matrix([[2, 1], [1, 2.5]])
             sage: g = NormalFormGame([A])
             sage: gg = g._gambit_(); gg

src/sage/game_theory/parser.py

@@ -196,9 +196,9 @@ def format_gambit(self, gambit_game):
         Here we construct a two by two game in gambit::
 
             sage: # optional - pygambit
-            sage: import gambit
+            sage: import pygambit
             sage: from sage.game_theory.parser import Parser
-            sage: g = gambit.Game.new_table([2,2])
+            sage: g = pygambit.Game.new_table([2,2])
             sage: g[int(0), int(0)][int(0)] = int(2)
             sage: g[int(0), int(0)][int(1)] = int(1)
             sage: g[int(0), int(1)][int(0)] = int(0)
@@ -207,7 +207,7 @@ def format_gambit(self, gambit_game):
             sage: g[int(1), int(0)][int(1)] = int(0)
             sage: g[int(1), int(1)][int(0)] = int(1)
             sage: g[int(1), int(1)][int(1)] = int(2)
-            sage: solver = gambit.nash.ExternalLCPSolver()
+            sage: solver = pygambit.nash.ExternalLCPSolver()
 
         Here is the output of the LCP algorithm::
 
@@ -228,7 +228,7 @@ def format_gambit(self, gambit_game):
         Here is another game::
 
             sage: # optional - pygambit
-            sage: g = gambit.Game.new_table([2,2])
+            sage: g = pygambit.Game.new_table([2,2])
             sage: g[int(0), int(0)][int(0)] = int(4)
             sage: g[int(0), int(0)][int(1)] = int(8)
             sage: g[int(0), int(1)][int(0)] = int(0)
@@ -237,7 +237,7 @@ def format_gambit(self, gambit_game):
             sage: g[int(1), int(0)][int(1)] = int(3)
             sage: g[int(1), int(1)][int(0)] = int(1)
             sage: g[int(1), int(1)][int(1)] = int(0)
-            sage: solver = gambit.nash.ExternalLCPSolver()
+            sage: solver = pygambit.nash.ExternalLCPSolver()
 
         Here is the LCP output::
 
@@ -254,7 +254,7 @@ def format_gambit(self, gambit_game):
         Here is a larger degenerate game::
 
             sage: # optional - pygambit
-            sage: g = gambit.Game.new_table([3,3])
+            sage: g = pygambit.Game.new_table([3,3])
             sage: g[int(0), int(0)][int(0)] = int(-7)
             sage: g[int(0), int(0)][int(1)] = int(-9)
             sage: g[int(0), int(1)][int(0)] = int(-5)
@@ -273,7 +273,7 @@ def format_gambit(self, gambit_game):
             sage: g[int(2), int(1)][int(1)] = int(6)
             sage: g[int(2), int(2)][int(0)] = int(1)
             sage: g[int(2), int(2)][int(1)] = int(-10)
-            sage: solver = gambit.nash.ExternalLCPSolver()
+            sage: solver = pygambit.nash.ExternalLCPSolver()
 
         Here is the LCP output::