Doc: Doctest for Tree generator

mapytex/calculus/core/random/__init__.py

@@ -38,18 +38,34 @@ Tree with RdLeaf replaced by generated values
 :example:
 
 >>> from ..tree import Tree
->>> rd_t = Tree("+", RdLeaf("a"), RdLeaf("a*k"))
+>>> rd_t = Tree("/", RdLeaf("a"), RdLeaf("a*k"))
 >>> print(rd_t)
-+
+/
  > {a}
  > {a*k}
->>> extract_rdleaf(rd_t)
+>>> leafs = extract_rdleaf(rd_t)
+>>> leafs
 ['a', 'a*k']
+>>> rd_varia = extract_rv(leafs)
+>>> rd_varia # doctest: +SKIP
+{'a', 'k'}
+>>> generated = random_generator(rd_varia, conditions=['a%2+1'])
+>>> generated # doctest: +SKIP
+{'a': 7, 'k': 4}
+>>> computed = compute_leafs(leafs, generated)
+>>> computed # doctest: +SKIP
+{'a': 7, 'a*k': 28}
+>>> replaced = replace_rdleaf(rd_t, computed)
+>>> print(replaced) # doctest: +SKIP
+/
+ > 7
+ > 28
 
 
 """
 
 from random import choice
+from functools import reduce
 from .leaf import RdLeaf
 
 def extract_rdleaf(tree):
@@ -125,7 +141,6 @@ def replace_rdleaf(tree, computed_leafs):
             return leaf
     return tree.map_on_leaf(replace)
 
-
 def random_generator(rd_variables,
                      conditions = [],
                      rejected = [0],
@@ -170,6 +185,10 @@ def random_generator(rd_variables,
                             ).difference(complete_scope[v]["rejected"]))
                     for v in rd_variables}
 
+    # quantity_choices = reduce(lambda x,y : x*y,
+    #                           [len(choices_list[v]) for v in choices_list])
+    # TODO: améliorer la méthode de rejet avec un cache |dim. mai 12 17:04:11 CEST 2019
+
     generate_variable = {v: choice(choices_list[v]) for v in rd_variables}
 
     while not all([eval(c, __builtins__, generate_variable) for c in conditions]):