gambit: Re-create as a pip package pygambit (help wanted!)

build/pkgs/pygambit/SPKG.rst

@@ -0,0 +1,18 @@
+pygambit: The package for computation in game theory
+====================================================
+
+Description
+-----------
+
+The package for computation in game theory
+
+License
+-------
+
+GPL2+
+
+Upstream Contact
+----------------
+
+https://pypi.org/project/pygambit/
+

build/pkgs/gambit/dependencies → build/pkgs/pygambit/dependencies

@@ -1,4 +1,4 @@
-cython | $(PYTHON_TOOLCHAIN) $(PYTHON)
+numpy scipy | $(PYTHON_TOOLCHAIN) cython $(PYTHON)
 
 ----------
 All lines of this file are ignored except the first.

build/pkgs/pygambit/requirements.txt

@@ -0,0 +1 @@
+pygambit

build/pkgs/pygambit/type

@@ -0,0 +1 @@
+optional

src/doc/en/developer/sage_manuals.rst

@@ -248,6 +248,10 @@ by Sage, you can link toward it without specifying its full path:
      - ``:ppl:`Linear_Expression <classParma__Polyhedra__Library_1_1 Linear__Expression>```
      - :ppl:`Linear_Expression <classParma__Polyhedra__Library_1_1Linear__Expression>`
 
+   * - :ref:`pygambit <spkg_pygambit>`
+     - ``:pygambit:`pygambit.nash.lcp_solve```
+     - :pygambit:`pygambit.nash.lcp_solve`
+
    * - :ref:`QEPCAD <spkg_qepcad>`
      - ``:qepcad:`QEPCAD: Entering formulas <user/EnterForm>```
      - :qepcad:`QEPCAD: Entering formulas <user/EnterForm>`

src/sage/features/gambit.py

@@ -0,0 +1,42 @@
+# sage_setup: distribution = sagemath-environment
+r"""
+Check for pygambit
+"""
+
+# ****************************************************************************
+#       Copyright (C) 2024 Matthias Koeppe
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#                  https://www.gnu.org/licenses/
+# ****************************************************************************
+
+from . import PythonModule
+
+
+class pygambit(PythonModule):
+    r"""
+    A :class:`sage.features.Feature` describing the presence of the
+    Python package :ref:`pygambit <spkg_pygambit>`.
+
+    EXAMPLES::
+
+        sage: from sage.features.gambit import pygambit
+        sage: pygambit().is_present()                           # optional - pygambit
+        FeatureTestResult('pygambit', True)
+    """
+    def __init__(self):
+        r"""
+        TESTS::
+
+            sage: from sage.features.gambit import pygambit
+            sage: isinstance(pygambit(), pygambit)
+            True
+        """
+        PythonModule.__init__(self, 'pygambit', spkg="pygambit")
+
+
+def all_features():
+    return [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]>]
 
@@ -116,9 +116,9 @@
 converted to Python integers (due to the preparser). Here is an example
 showing the Battle of the Sexes::
 
-    sage: # optional - gambit
-    sage: import gambit
-    sage: g = gambit.Game.new_table([2,2])
+    sage: # optional - pygambit
+    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]]>,