def main():
# print(nltk.corpus.treebank.parsed_sents('wsj_0001.mrg')[0])
# nltk.corpus.treebank.parsed_sents('wsj_0001.mrg')[0].draw()
# print("Induce PCFG grammar from treebank data:")
#
productions = []
print(len(treebank.fileids()))
for item in treebank.fileids(): # Goes through all trees
for tree in treebank.parsed_sents(item):
# perform optional tree transformations, e.g.:
tree.collapse_unary(collapsePOS = False)# Remove branches A-B-C into A-B+C
tree.chomsky_normal_form(horzMarkov = 2)# Remove A->(B,C,D) into A->B,C+D->D
productions += tree.productions()
# #
# # print(type(productions[0]))
# #
S = Nonterminal('S')
grammar = induce_pcfg(S, productions)
# # # print(grammar) # This is a PCFG
# pickle.dump(grammar, open("tbank-grammar.p", "wb"))
# t = time.time()
# grammar = pickle.load(open("tbank-grammar.p", "rb"))
# textf = open("lexicon.txt", "w")
# n = textf.write(str(reduce(lambda a, b: a + "\n" + b, list(filter(lambda x: "'" in x, str(grammar).split("\n"))))))
# textf.close()
# print(time.time()-t)
parser = ViterbiParser(grammar)
# pickle.dump(parser, open("cky-parser.p", "wb"))
# parser = pickle.load(open("cky-parser.p", "rb"))
parser.trace(0)
sent = "John will join the board"
tokens = sent.split()
try:
grammar.check_coverage(tokens)
print("All words covered")
parses = parser.parse_all(tokens)
if parses:
lp = len(parses)
print(lp)
print(parses[0].label())
# parses[0].draw()
p = reduce(lambda a,b:a+b.prob(), list(filter(lambda x: x.label() == 'S', parses)), 0.0)
else:
p = 0
print("Probability:", p)
except:
print("Some words not covered")
def test():
"""
A test to check if the changes I made have the intended
effect
"""
import nltk
from nltk.parse import ViterbiParser
sent = 'I saw the man with my telescope'
tokens = sent.split()
grammar = nltk.toy_pcfg1
parser = ViterbiParser(grammar)
parser.trace(3)
parses = parser.nbest_parse(tokens)
print(parses)
def test(): """ A test to check if the changes I made have the intended effect """ import nltk from nltk.parse import ViterbiParser sent = 'I saw the man with my telescope' tokens = sent.split() grammar = nltk.toy_pcfg1 parser = ViterbiParser(grammar) parser.trace(3) parses = parser.nbest_parse(tokens) print(parses)
def parse_sentence(self, sent):
"""
Parse sent using induced grammar
Visualize the most likely parse tree for sent
:return: None. Save parsing results to pcfg.txt
"""
if self.grammar is None:
raise ValueError("PCFG hasn't been induced yet.")
# other parser option(s): e.g., parser = pchart.InsideChartParser(self.grammar)
parser = ViterbiParser(self.grammar)
parser.trace(3)
# http://www.nltk.org/api/nltk.parse.html
sys.stdout = open('pcfg.txt', 'w')
parses = parser.parse(sent)
for parse in parses:
print(parse)
# visualize the tree:
print(parse.draw())
def Parser_Section():
demos = [('I saw John through the telescope', toy_pcfg1)]
sent, grammar = demos[0]
# print(grammar)
# Tokenize the sentence.
tokens = sent.split()
parser = ViterbiParser(grammar)
parser.trace(0) # Use this to change verbosity
t = time.time()
parses = parser.parse_all(tokens)
print("Time:", time.time()-t)
if parses:
lp = len(parses)
p = reduce(lambda a,b:a+b.prob(), parses, 0.0)
else:
p = 0
print("Probability:", p)
def demo():
"""
A demonstration of the probabilistic parsers. The user is
prompted to select which demo to run, and how many parses should
be found; and then each parser is run on the same demo, and a
summary of the results are displayed.
"""
import sys, time
from nltk import tokenize
from nltk.parse import ViterbiParser
from nltk.grammar import toy_pcfg1, toy_pcfg2
# Define two demos. Each demo has a sentence and a grammar.
demos = [
('I saw the man with my telescope', toy_pcfg1),
('the boy saw Jack with Bob under the table with a telescope',
toy_pcfg2),
]
# Ask the user which demo they want to use.
print()
for i in range(len(demos)):
print('%3s: %s' % (i + 1, demos[i][0]))
print(' %r' % demos[i][1])
print()
print('Which demo (%d-%d)? ' % (1, len(demos)), end=' ')
try:
snum = int(sys.stdin.readline().strip()) - 1
sent, grammar = demos[snum]
except:
print('Bad sentence number')
return
# Tokenize the sentence.
tokens = sent.split()
parser = ViterbiParser(grammar)
all_parses = {}
print('\nsent: %s\nparser: %s\ngrammar: %s' % (sent, parser, grammar))
parser.trace(3)
t = time.time()
parses = parser.parse_all(tokens)
time = time.time() - t
average = (reduce(lambda a, b: a + b.prob(), parses, 0) /
len(parses) if parses else 0)
num_parses = len(parses)
for p in parses:
all_parses[p.freeze()] = 1
# Print some summary statistics
print()
print('Time (secs) # Parses Average P(parse)')
print('-----------------------------------------')
print('%11.4f%11d%19.14f' % (time, num_parses, average))
parses = all_parses.keys()
if parses:
p = reduce(lambda a, b: a + b.prob(), parses, 0) / len(parses)
else:
p = 0
print('------------------------------------------')
print('%11s%11d%19.14f' % ('n/a', len(parses), p))
# Ask the user if we should draw the parses.
print()
print('Draw parses (y/n)? ', end=' ')
if sys.stdin.readline().strip().lower().startswith('y'):
from nltk.draw.tree import draw_trees
print(' please wait...')
draw_trees(*parses)
# Ask the user if we should print the parses.
print()
print('Print parses (y/n)? ', end=' ')
if sys.stdin.readline().strip().lower().startswith('y'):
for parse in parses:
print(parse)
def demo():
"""
A demonstration of the probabilistic parsers. The user is
prompted to select which demo to run, and how many parses should
be found; and then each parser is run on the same demo, and a
summary of the results are displayed.
"""
import sys, time
import nltk
from nltk import tokenize
from nltk.parse import ViterbiParser
# Define two demos. Each demo has a sentence and a grammar.
demos = [('I saw the man with my telescope', nltk.toy_pcfg1),
('the boy saw Jack with Bob under the table with a telescope', nltk.toy_pcfg2)]
# Ask the user which demo they want to use.
print
for i in range(len(demos)):
print '%3s: %s' % (i+1, demos[i][0])
print ' %r' % demos[i][1]
print
print 'Which demo (%d-%d)? ' % (1, len(demos)),
try:
snum = int(sys.stdin.readline().strip())-1
sent, grammar = demos[snum]
except:
print 'Bad sentence number'
return
# Tokenize the sentence.
tokens = sent.split()
parser = ViterbiParser(grammar)
all_parses = {}
print '\nsent: %s\nparser: %s\ngrammar: %s' % (sent,parser,grammar)
parser.trace(3)
t = time.time()
parses = parser.nbest_parse(tokens)
time = time.time()-t
if parses:
average = reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
else:
average = 0
num_parses = len(parses)
for p in parses:
all_parses[p.freeze()] = 1
# Print some summary statistics
print
print 'Time (secs) # Parses Average P(parse)'
print '-----------------------------------------'
print '%11.4f%11d%19.14f' % (time, num_parses, average)
parses = all_parses.keys()
if parses:
p = reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
else: p = 0
print '------------------------------------------'
print '%11s%11d%19.14f' % ('n/a', len(parses), p)
# Ask the user if we should draw the parses.
print
print 'Draw parses (y/n)? ',
if sys.stdin.readline().strip().lower().startswith('y'):
from nltk.draw.tree import draw_trees
print ' please wait...'
draw_trees(*parses)
# Ask the user if we should print the parses.
print
print 'Print parses (y/n)? ',
if sys.stdin.readline().strip().lower().startswith('y'):
for parse in parses:
print parse
def create_viterbi_parser(grammar, pickle_it=False, filename="viterbi"):
parser = ViterbiParser(grammar)
parser.trace(0)
if pickle_it:
pickle.dump(parser, open("%s%s-parser.p" % (var_dir, filename), "wb"))
return parser
def demo():
"""
A demonstration of the probabilistic parsers. The user is
prompted to select which demo to run, and how many parses should
be found; and then each parser is run on the same demo, and a
summary of the results are displayed.
"""
import sys, time
from nltk import tokenize
from nltk.parse import ViterbiParser
from nltk.grammar import toy_pcfg1, toy_pcfg2
from nltk.draw.tree import draw_trees
from nltk import Tree
from nltk.draw.util import CanvasFrame
from nltk.draw import TreeWidget
# Define two demos. Each demo has a sentence and a grammar.
# demos = [('move the green sphere to the bottom left corner', learned_pcfg),
# ('move the green ball over the red block', learned_pcfg),
# ('take the green pyramid and put it in the top left corner', learned_pcfg),
# ('put the green pyramid on the red block', learned_pcfg),
# ('move the red cylinder and place it on top of the blue cylinder that is on top of a green cylinder', learned_pcfg),]
# Ask the user which demo they want to use.
# print()
# for i in range(len(demos)):
# print('%3s: %s' % (i+1, demos[i][0]))
# print(' %r' % demos[i][1])
# print()
# print('Which demo (%d-%d)? ' % (1, len(demos)), end=' ')
# try:
# snum = int(sys.stdin.readline().strip())-1
# sent, grammar = demos[snum]
# except:
# print('Bad sentence number')
# return
max_scene = 1
if max_scene<10: sc = '0000'+str(max_scene)
elif max_scene<100: sc = '000'+str(max_scene)
elif max_scene<1000: sc = '00'+str(max_scene)
elif max_scene<10000: sc = '0'+str(max_scene)
g = 'grammar_'+sc+'.txt'
learned_pcfg = load('/home/omari/Dropbox/robot_modified/AR/grammar/'+g)
grammar = learned_pcfg
file1 = open('/home/omari/Dropbox/robot_modified/AR/hypotheses/matched_commands.txt', 'r')
g1 = [i for i in file1.readlines()]
for line in g1:
line = unicode(line,encoding='utf-8')
sent = line.split('\n')[0].split('-')[-1]
scene = line.split('\n')[0].split('-')[0]
sent_num = line.split('\n')[0].split('-')[1]
print(line)
if scene == '239' and sent_num == '0': continue
# Tokenize the sentence.
tokens = sent.split()
parser = ViterbiParser(grammar)
all_parses = {}
# print('\nsent: %s\nparser: %s\ngrammar: %s' % (sent,parser,grammar))
parser.trace(3)
parses = parser.parse_all(tokens)
average = (reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
if parses else 0)
num_parses = len(parses)
for p in parses:
all_parses[p.freeze()] = 1
# Print some summary statistics
# print()
# print('Time (secs) # Parses Average P(parse)')
# print('-----------------------------------------')
# print('%11.4f%11d%19.14f' % (time, num_parses, average))
parses = all_parses.keys()
if parses:
p = reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
else: p = 0
# print('------------------------------------------')
# print('%11s%11d%19.14f' % ('n/a', len(parses), p))
# Ask the user if we should draw the parses.
# print()
# print('Draw parses (y/n)? ', end=' ')
# if sys.stdin.readline().strip().lower().startswith('y'):
# print(' please wait...')
# draw_trees(*parses)
cf = CanvasFrame()
# t = Tree(parses)
t = Tree.fromstring('(S (CH_POS_PREPOST move) (PRE_POST (PRE (the the) (_entity (F_HSV green) (F_SHAPE sphere))) (PREPOST_connect (to to) (the the)) (POST (_F_POS (F_POS (_bottom_left (bottom bottom) (left left)))) (corner corner))))')
tc = TreeWidget(cf.canvas(), t, draggable=1,
node_font=('helvetica', -14),
leaf_font=('helvetica', -12),
roof_fill='white', roof_color='black',
leaf_color='green4', node_color='blue4')
cf.add_widget(tc,10,10)
# tc = TreeWidget(cf.canvas(),t)
# cf.add_widget(tc,10,10) # (10,10) offsets
cf.print_to_file('/home/omari/Dropbox/robot_modified/trees/scene-'+scene+'-'+sent_num+'.ps')
cf.destroy()
############ create PCFG from the productions #######
from nltk import Nonterminal
from nltk import induce_pcfg
S = Nonterminal('SENT')
grammar = induce_pcfg(S, productions)
print(grammar)
######### Parser with CYK dynamic algorithm ########
from nltk.parse import pchart
from nltk.parse import ViterbiParser
from nltk.treetransforms import un_chomsky_normal_form
parser = ViterbiParser(grammar)
parser.trace(2)
parses_bank = []
test_file = open(args.test_dir, 'wb')
test_output_file = open(args.output_dir, 'wb')
for i in range(valid_idx, test_idx + 1):
# take the leaves of each tree of testset and store
# them in the test file
tokens = treebank[i][0].leaves()
sentence = u" ".join(tokens)
test_file.write((sentence + u"\n").encode('utf-8'))
print 'parsing :', sentence
# we will use lexicon knowledge to replace the
def demo():
"""
A demonstration of the probabilistic parsers. The user is
prompted to select which demo to run, and how many parses should
be found; and then each parser is run on the same demo, and a
summary of the results are displayed.
"""
import sys
import time
from nltk import tokenize
from nltk.grammar import PCFG
from nltk.parse import ViterbiParser
toy_pcfg1 = PCFG.fromstring("""
S -> NP VP [1.0]
NP -> Det N [0.5] | NP PP [0.25] | 'John' [0.1] | 'I' [0.15]
Det -> 'the' [0.8] | 'my' [0.2]
N -> 'man' [0.5] | 'telescope' [0.5]
VP -> VP PP [0.1] | V NP [0.7] | V [0.2]
V -> 'ate' [0.35] | 'saw' [0.65]
PP -> P NP [1.0]
P -> 'with' [0.61] | 'under' [0.39]
""")
toy_pcfg2 = PCFG.fromstring("""
S -> NP VP [1.0]
VP -> V NP [.59]
VP -> V [.40]
VP -> VP PP [.01]
NP -> Det N [.41]
NP -> Name [.28]
NP -> NP PP [.31]
PP -> P NP [1.0]
V -> 'saw' [.21]
V -> 'ate' [.51]
V -> 'ran' [.28]
N -> 'boy' [.11]
N -> 'cookie' [.12]
N -> 'table' [.13]
N -> 'telescope' [.14]
N -> 'hill' [.5]
Name -> 'Jack' [.52]
Name -> 'Bob' [.48]
P -> 'with' [.61]
P -> 'under' [.39]
Det -> 'the' [.41]
Det -> 'a' [.31]
Det -> 'my' [.28]
""")
# Define two demos. Each demo has a sentence and a grammar.
demos = [
("I saw the man with my telescope", toy_pcfg1),
("the boy saw Jack with Bob under the table with a telescope",
toy_pcfg2),
]
# Ask the user which demo they want to use.
print()
for i in range(len(demos)):
print(f"{i + 1:>3}: {demos[i][0]}")
print(" %r" % demos[i][1])
print()
print("Which demo (%d-%d)? " % (1, len(demos)), end=" ")
try:
snum = int(sys.stdin.readline().strip()) - 1
sent, grammar = demos[snum]
except:
print("Bad sentence number")
return
# Tokenize the sentence.
tokens = sent.split()
parser = ViterbiParser(grammar)
all_parses = {}
print(f"\nsent: {sent}\nparser: {parser}\ngrammar: {grammar}")
parser.trace(3)
t = time.time()
parses = parser.parse_all(tokens)
time = time.time() - t
average = (reduce(lambda a, b: a + b.prob(), parses, 0) /
len(parses) if parses else 0)
num_parses = len(parses)
for p in parses:
all_parses[p.freeze()] = 1
# Print some summary statistics
print()
print("Time (secs) # Parses Average P(parse)")
print("-----------------------------------------")
print("%11.4f%11d%19.14f" % (time, num_parses, average))
parses = all_parses.keys()
if parses:
p = reduce(lambda a, b: a + b.prob(), parses, 0) / len(parses)
else:
p = 0
print("------------------------------------------")
print("%11s%11d%19.14f" % ("n/a", len(parses), p))
# Ask the user if we should draw the parses.
print()
print("Draw parses (y/n)? ", end=" ")
if sys.stdin.readline().strip().lower().startswith("y"):
from nltk.draw.tree import draw_trees
print(" please wait...")
draw_trees(*parses)
# Ask the user if we should print the parses.
print()
print("Print parses (y/n)? ", end=" ")
if sys.stdin.readline().strip().lower().startswith("y"):
for parse in parses:
print(parse)
def demo():
"""
A demonstration of the probabilistic parsers. The user is
prompted to select which demo to run, and how many parses should
be found; and then each parser is run on the same demo, and a
summary of the results are displayed.
"""
import sys, time
from nltk import tokenize
from nltk.parse import ViterbiParser
from nltk.grammar import toy_pcfg1, toy_pcfg2
# Define two demos. Each demo has a sentence and a grammar.
demos = [('حرك الكرة الخضراء في أسفل الزاوية اليسرى', learned_pcfg),
('حرك الكرة', learned_pcfg),
('take the green pyramid and put it in the top left corner', learned_pcfg),
('move the pink triangle on top of the black square', learned_pcfg),
('move the red block and place it on top of the blue block that is on top of a green block', learned_pcfg),
('move the green block on top of the blue block', learned_pcfg)]
# Ask the user which demo they want to use.
print()
for i in range(len(demos)):
print('%3s: %s' % (i+1, demos[i][0]))
# print(' %r' % demos[i][1])
print()
print('Which demo (%d-%d)? ' % (1, len(demos)), end=' ')
try:
snum = int(sys.stdin.readline().strip())-1
sent, grammar = demos[snum]
except:
print('Bad sentence number')
return
# Tokenize the sentence.
tokens = sent.split()
print(grammar)
parser = ViterbiParser(grammar)
all_parses = {}
# print('\nsent: %s\nparser: %s\ngrammar: %s' % (sent,parser,grammar))
parser.trace(3)
t = time.time()
parses = parser.parse_all(tokens)
time = time.time()-t
average = (reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
if parses else 0)
num_parses = len(parses)
for p in parses:
all_parses[p.freeze()] = 1
# Print some summary statistics
print()
print('Time (secs) # Parses Average P(parse)')
print('-----------------------------------------')
print('%11.4f%11d%19.14f' % (time, num_parses, average))
parses = all_parses.keys()
if parses:
p = reduce(lambda a,b:a+b.prob(), parses, 0)/len(parses)
else: p = 0
print('------------------------------------------')
print('%11s%11d%19.14f' % ('n/a', len(parses), p))
# Ask the user if we should draw the parses.
print()
print('Draw parses (y/n)? ', end=' ')
if sys.stdin.readline().strip().lower().startswith('y'):
from nltk.draw.tree import draw_trees
print(' please wait...')
draw_trees(*parses)
# Ask the user if we should print the parses.
print()
print('Print parses (y/n)? ', end=' ')
if sys.stdin.readline().strip().lower().startswith('y'):
for parse in parses:
print(parse)
def parseAllTestXmls(fileList, grammar, allTestSolutionsDict, verbose=False, displayTrees=False):
testPitchLists = []
testIntervalLists = []
totalCorrect = 0
totalCorrectNonN = 0
totalProductions = 0
totalLeaves = 0
parseTreeStrings = {}
for filepath in fileList:
curPitchList = getPitchListFromFile(filepath)
testPitchLists.append(curPitchList)
testIntervalLists.append(getIntervalStringsFromPitchList(curPitchList, verbose))
if verbose:
print(testIntervalLists[-1])
listLen = len(testIntervalLists[-1])
if verbose:
print(tree)
parser = ViterbiParser(grammar)
if verbose:
parser.trace(0)#3
else:
parser.trace(0)
try:
parses = parser.parse_all(testIntervalLists[-1])
except Exception as errorMsg:
print("error parsing file " + filepath)
print(errorMsg)
numTrees = sum(1 for _ in parses)
if numTrees > 0 and displayTrees == True:
from nltk.draw.tree import draw_trees
draw_trees(*parses)
if numTrees == 0:
print("Couldn't find a valid parse, this is bad, very very bad")
return 0,0
numCorrect = 0
numCorrectNonN = 0
bottomCorrect = 0
bottomCorrectNonN = 0
solutionTree = None
try:
solutionTreeStr = allTestSolutionsDict[filepath]
solutionTree = Tree.fromstring(solutionTreeStr)
except Exception as errorMsg:
print("couldn't find solution for file " + filepath)
print(errorMsg)
if solutionTree != None and solutionTree != '':
parseTreeStrings[filepath] = str(parses[0])
numCorrect, numCorrectNonN = validate_tree.compareTrees(solutionTree, parses[0])
numProductions = len(solutionTree.productions())
totalProductions += numProductions
#solutionTree.draw()
#parses[0].draw()
bottomCorrect, bottomCorrectNonN = validate_tree.compareTreesBottomUp(solutionTree, parses[0])
parseTreeStrings[filepath+'_afterComparison'] = str(parses[0])
totalLeaves += len(solutionTree.leaves())
#parses[0].draw()
totalCorrect += bottomCorrect
totalCorrect += numCorrect
totalCorrectNonN += numCorrectNonN
totalCorrectNonN += bottomCorrectNonN
return totalCorrect, totalCorrectNonN, totalProductions, totalLeaves, parseTreeStrings
print(t)
### try RandomChartParser, UnsortedChartParser, LongestChartParser
# In[ ]:
parser = nltk.parse.EarleyChartParser(grammar)
for t in parser.parse(tokens):
print(t)
# In[ ]:
### CYK parser gets the most probable parse
from nltk.parse import ViterbiParser
parser = ViterbiParser(grammar)
parser.trace(3)
parsed_sent = list(parser.parse_all(tokens)) # to convert generator to list
parsed_sent[0].draw()
for t in parsed_sent:
print(t)
# In[ ]:
### CYK parser gets the most probable parse
from nltk.parse import ViterbiParser
parser = ViterbiParser(grammar)
parser.trace(3)
parsed_sent = list(parser.parse(tokens)) # to convert generator to list
parsed_sent[0].draw()
for t in parsed_sent:
def demo():
"""
A demonstration of the probabilistic parsers. The user is
prompted to select which demo to run, and how many parses should
be found; and then each parser is run on the same demo, and a
summary of the results are displayed.
"""
import sys
import time
from nltk import tokenize
from nltk.grammar import toy_pcfg1, toy_pcfg2
from nltk.parse import ViterbiParser
# Define two demos. Each demo has a sentence and a grammar.
demos = [
("I saw the man with my telescope", toy_pcfg1),
("the boy saw Jack with Bob under the table with a telescope",
toy_pcfg2),
]
# Ask the user which demo they want to use.
print()
for i in range(len(demos)):
print(f"{i + 1:>3}: {demos[i][0]}")
print(" %r" % demos[i][1])
print()
print("Which demo (%d-%d)? " % (1, len(demos)), end=" ")
try:
snum = int(sys.stdin.readline().strip()) - 1
sent, grammar = demos[snum]
except:
print("Bad sentence number")
return
# Tokenize the sentence.
tokens = sent.split()
parser = ViterbiParser(grammar)
all_parses = {}
print(f"\nsent: {sent}\nparser: {parser}\ngrammar: {grammar}")
parser.trace(3)
t = time.time()
parses = parser.parse_all(tokens)
time = time.time() - t
average = (reduce(lambda a, b: a + b.prob(), parses, 0) /
len(parses) if parses else 0)
num_parses = len(parses)
for p in parses:
all_parses[p.freeze()] = 1
# Print some summary statistics
print()
print("Time (secs) # Parses Average P(parse)")
print("-----------------------------------------")
print("%11.4f%11d%19.14f" % (time, num_parses, average))
parses = all_parses.keys()
if parses:
p = reduce(lambda a, b: a + b.prob(), parses, 0) / len(parses)
else:
p = 0
print("------------------------------------------")
print("%11s%11d%19.14f" % ("n/a", len(parses), p))
# Ask the user if we should draw the parses.
print()
print("Draw parses (y/n)? ", end=" ")
if sys.stdin.readline().strip().lower().startswith("y"):
from nltk.draw.tree import draw_trees
print(" please wait...")
draw_trees(*parses)
# Ask the user if we should print the parses.
print()
print("Print parses (y/n)? ", end=" ")
if sys.stdin.readline().strip().lower().startswith("y"):
for parse in parses:
print(parse)
