def choose_node_index_with_leaf_probability(tree, node_selection_leaf_probability):
'''
Returns an index into tree, choosing a leaf with probability
node-selection-leaf-probability.
'''
if type(tree) == list:
if random_push.lrand() > node_selection_leaf_probability:
allItems = util.all_items(tree)
itemsWithCount = []
for i in range(len(allItems)):
itemsWithCount.apped([allItems[i], i])
listItems = []
for i in itemsWithCount:
if type(i) == list:
listItems.append(i)
return(random_push.lrand_nth(listItems)[1])
else:
return 0
print('FINAL:')
print('======')
pushstate.state_pretty_print(final_state)
'''
#'''
# Testing Code Generation #
###########################
import random_push
import interpreter
import Pysh.pushstate
#print random_push.decompose(100, 100)
atom_generators = Pysh.pushstate.registered_instructions
atom_generators.append([random_push.lrand_int(100), random_push.lrand()])
#print atom_generators
random_code = random_push.random_code(50, atom_generators)
print random_code
print
starting_state = Pysh.pushstate.make_push_state()
final_state = interpreter.run_push(random_code, starting_state, True, True, True, False)
#'''
'''
starting_state = pushstate.make_push_state()
starting_code = '("Hello World" string_contained)'
final_state = interpreter.run_push(starting_code, starting_state, True, True, True, True)
'''
