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differential_tone_coding.py
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differential_tone_coding.py
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#! /usr/bin/env python
# coding=utf-8
import sys, math, unicodedata
from collections import Counter, defaultdict
import Levenshtein
import re
import itertools
import csv
import codecs
# Installation of prerequisites
# sudo pip install python-Levenshtein
# Constant lists
code_seperator = u'_'
mode_indicators = u'+-'
mode_names = [u"insert",u"delete"]
def code_dispatcher(code, sel_en) :
lst = [""]
if not code : return lst
if not is_a_good_code(code) : print "(dispatcher) input code incorrect !" ; print code ; exit()
#if code[-1] == code_seperator : code = code[: -1]
# code_segments = code.split(code_seperator)
code_segments = split2(code,code_seperator)
# Filtering
def indexing(op) :
m,p,c = op
return 2 * int(p) + mode_indicators.index(m)
ops = [code_segments[i : i + 3] for i in range(0, len(code_segments), 3)]
if sel_en :
ops = sorted(ops, key=lambda op : indexing(op))
ops2 = list()
i_pre = -1
for op in ops :
i = indexing(op)
if i > i_pre :
ops2.append(op)
i_pre = i
else :
ops2 = ops
for op in ops2 :
m,p,c = op
lst[0] += \
u"{}{}{}{}{}{}".format(m, code_seperator, p, code_seperator, c, code_seperator)
lst2 = list()
for element in lst :
try :
if element[-1] == code_seperator or element[-1] == code_seperator.decode('utf-8') :
lst2.append(element[:-1])
else :
lst2.append(element)
except :
lst2.append(element)
for code in lst2 :
if not is_a_good_code(code):
print "(dispatcher) output code incorrect !"
print code
return lst2
def apply_filter_to_base_element(x, sel_en, show_approx_err = False) :
if isinstance(x, tuple) :
return (x[0], filter(x[0], x[1].decode("utf-8"), sel_en, show_approx_err).encode("utf-8"))
else :
return [apply_filter_to_base_element(element, sel_en, show_approx_err) for element in x]
def filter(token, tag, sel_en, show_approx_err = False) :
subcodes = code_dispatcher(tag, sel_en)
code2 = code_seperator.encode("utf-8").join([subcode for p, subcode in enumerate(subcodes)])
ret = code_resort(code2)
if ret != tag and show_approx_err :
# un prix d'approximation à payer :
# notre décomposition de la CRF paye bien un prix : la perte de l'ordre des caractères (de types différents) à insérer sur
# la même position par rapport qu token original, bien que ce soit rarissime d'insérer successivement un caractère non diacritique
# et un diacritique à la même position sur un token, ça peut arriver, donc générer une erreur observable si nous dés-commentons
# les lignes de code ci-dessous
# par exemple,
# tag_gold = +_0_ɔ_+_0_̀_+_0_n_-_0_a
# tag_reconstitued = +_0_ɔ_+_0_n_+_0_̀_-_0_a
# pour rappel, cette erreur est comptée dans l'évaluation, et curieusement on n'a pas constaté de dégradation considérable
# dans l'exactitude du résultat, ça peut s'expliquer par ce qu'une insertion successive est souvent dfficile à apprendre
# en soi, (hypothèse à vérifier, donc), donc une perte d'ordre ne rend pas ce cas de figure plus catastrohpique quantitativement
print "Case of modeling error inherent to decomposition hypothesis : \n", ret, tag
return ret
def split2 (str_in, seperator) :
buf = ''
ret = []
for c in str_in :
if c != seperator :
buf += c
else :
if buf :
ret.append(buf)
buf = ''
else :
buf += c
if buf :
ret.append(buf)
return ret
def accuray2 (dataset1, dataset2, is_tone_mode = False) :
cnt_sucess = 0
cnt_fail = 0
if not is_tone_mode :
for sent1, sent2 in zip(dataset1, dataset2) :
for token1, token2 in zip(sent1, sent2) :
if token1 == token2 :
cnt_sucess += 1
else :
cnt_fail += 1
else :
for sent1, sent2 in zip(dataset1, dataset2) :
for token1, token2 in zip(sent1, sent2) :
is_identical = True
for syllabe1, syllabe2 in zip(token1, token2) :
if syllabe1 != syllabe2 : is_identical = False ; break;
if is_identical :
cnt_sucess += 1
else :
cnt_fail += 1
cnt_tot = cnt_sucess + cnt_fail
if not cnt_tot : return 0.0
else : return cnt_sucess / float(cnt_tot)
def get_sub_tone_code_of_sentence (sentence, sel_en) :
labels = list()
for i, token in enumerate(sentence) :
label = list()
for j, syllabe_code in enumerate(token) :
syllabe, code = syllabe_code
subcode = code_dispatcher(code.decode('utf-8'), sel_en)[0].encode('utf-8')
label.append(subcode)
labels.append(label)
return labels
def accumulate_tone_code_of_dataset (dataset_acc, dataset) :
for p, sent in enumerate(dataset_acc) :
for i, token in enumerate(sent) :
for j, syllabe_tag_acc in enumerate(token) :
syllabe_acc, tag_acc = syllabe_tag_acc
syllabe, tag = dataset[p][i][j]
if tag_acc and tag :
tag_acc += code_seperator.encode('utf-8') + tag
else :
tag_acc += tag
dataset_acc[p][i][j] = \
tuple([syllabe, code_resort(tag_acc.decode('utf-8')).encode('utf-8')])
return dataset_acc
def reshape_tokens_as_sentnece(tokens, sentnece) :
ret = list()
n = 0
for i, token in enumerate(sentnece) :
tmp = list()
for j, syllabe in enumerate(token) :
tmp.append(tokens[n])
n += 1
ret.append(tmp)
return ret
def make_tokens_from_sentence(sent, is_tone_mode = False) :
if is_tone_mode :
tokens = list()
labels = list()
for token in sent :
tokens.append(unzip(token)[0])
labels.append(unzip(token)[1])
else :
tokens = unzip(sent)[0]
labels = unzip(sent)[1]
return [tokens, labels]
def make_features_from_tokens(tokens, is_tone_mode = False) :
if is_tone_mode :
features_syllabe = list()
for i, token in enumerate(tokens) :
feature = list()
for j, syllabe_code in enumerate(token) :
feature.append(get_features_customised_tone(tokens, i, j))
features_syllabe.append(feature)
features = list(itertools.chain(*features_syllabe))
else :
features = list()
for i in range(len(tokens)) :
features.append(get_features_customised(tokens, i))
return features
def inspector_tokens(gold_tokens, predicted_tokens) :
for x,y in zip(gold_tokens, predicted_tokens) :
try :
if x[1] != y[1] :
print x[0],":",x[1].decode('utf-8'),"->",y[1].decode('utf-8') # ,"(",len(x[1]), len(y[1]),")"
else :
print "*",x[0],":",x[1].decode('utf-8'),"->",y[1].decode('utf-8') # ,"(",len(x[1]), len(y[1]),")"
except :
print type(x[0]),":",type(x[1]),"->",type(y[1])
def unzip(input) :
return [list(li) for li in zip(*input)]
def csv_export(filename, gold_set, test_set, is_tone_mode = False):
if not is_tone_mode :
csvfile = codecs.open(filename, 'wb')
writer = csv.writer(csvfile)
writer.writerow(["Token", "Golden", "Predicted", "Same"])
for gold_sent, test_sent in zip(gold_set, test_set) :
for gold_token, test_token in zip(gold_sent, test_sent) :
token = gold_token[0]
gold_code = gold_token[1]
test_code = test_token[-1]
# print token, gold_code, test_code
sameCodes = (gold_code == test_code)
if not repr(token.encode('utf-8')) :
sameCodes = u''
row = [\
(token.encode('utf-8')), \
gold_code, \
test_code, \
sameCodes]
writer.writerow(row)
csvfile.close()
else :
csvfile = codecs.open(filename, 'wb')
writer = csv.writer(csvfile)
writer.writerow(["Token", \
"Golden Form","Predicted Form", \
"Golden code", "Predicted code", "Same"])
enc = encoder_tones()
for gold_sent, test_sent in zip(gold_set, test_set) :
for gold_token, test_token in zip(gold_sent, test_sent) :
gold_code = ''
test_code = ''
gold_form = ''
test_form = ''
token = ''
for gold_syllabe, test_syllabe in zip(gold_token, test_token) :
token += gold_syllabe[0] + ' '
if gold_syllabe[1] :
gold_code += gold_syllabe[1] + ' '
else :
gold_code += 'NULL' + ' '
if test_syllabe[1] :
test_code += test_syllabe[1] + ' '
else :
test_code += 'NULL' + ' '
gold_form += enc.differential_decode(gold_syllabe[0], gold_syllabe[1].decode('utf-8')) + ' '
test_form += enc.differential_decode(gold_syllabe[0], test_syllabe[1].decode('utf-8')) + ' '
sameCodes = (gold_code == test_code)
sameForms = (gold_form == test_form)
sameCodes = (gold_code == test_code)
sameForms = (gold_form == test_form)
if not repr(token.encode('utf-8')) :
sameCodes = u''
row = [\
(token.encode('utf-8')), \
repr(gold_form.encode('utf-8')), \
repr(test_form.encode('utf-8')), \
repr(gold_code, spaces=True), \
repr(test_code, spaces=True), \
sameCodes]
writer.writerow(row)
csvfile.close()
def sampling(allsents, p, ratio = 1) :
train_set, eval_set = [], []
for i, sent in enumerate(allsents[0 : : int(1/float(ratio))]) :
p_approx = float(len(train_set) + 1) / float(len(eval_set) + len(train_set) + 1)
if p_approx <= p :
train_set.append(sent)
else:
eval_set.append(sent)
return [train_set, eval_set]
def get_duration(t1_secs, t2_secs) :
secs = abs(t1_secs - t2_secs)
days = secs // 86400
hours = secs // 3600 - days * 24
minutes = secs // 60 - hours * 60 - days * 60 * 24
secondes = int(secs) % 60
return '{:>02.0f}:{:>02.0f}:{:>02.0f}:{:>02d}'.format(days, hours, minutes, secondes)
def is_a_good_code(code) :
if not code : return True
code2 = code
# +_2__ is good, because -> + 2 _
if code2[-1] == code_seperator.decode('utf-8') or code2[-1] == code_seperator :
try :
if code2[-1] != code2[-2] :
return False
except IndexError:
return False
# code3 = code2.split(code_seperator.decode('utf-8'))
code3 = split2(code2,code_seperator.decode('utf-8'))
if len(code3) % 3 != 0 :
return False
else :
return True
def code_resort(code) :
ret = []
if not code : return code
if not is_a_good_code(code) : print "(resort) input code incorrect !" ; exit()
#if code[-1] == code_seperator : code = code[: -1]
#code_segments = code.split(code_seperator)
code_segments = split2(code,code_seperator)
for i in range(0, len(code_segments), 3) :
try :
m, p, c = code_segments[i : i + 3]
except :
print code
print code_segments;
print "Bug 1 !"
exit()
ret.append(u"{}{}{}{}{}{}".format(m, code_seperator, p, code_seperator, c, code_seperator))
ret = sorted(ret, key=lambda x : int(mode_indicators.index(split2(x, code_seperator)[0])) + 2 * int(split2(x, code_seperator)[1]))
ret = ''.join(ret)
if ret : ret = ret[:-1]
if not is_a_good_code(ret) : print ("(resort) ouptut code incorrect !") ; exit()
return ret
def get_features_customised(tokens, idx):
feature_list = []
if not tokens:
return feature_list
token = tokens[idx]
# Capitalization
if token[0].isupper():
feature_list.append(u'CAPITALIZATION')
# Number
if re.search(r'\d', token) is not None:
feature_list.append(u'IL_Y_A_UN_CHIFFRE')
# Punctuation
punc_cat = set([u"Pc", u"Pd", u"Ps", u"Pe", u"Pi", u"Pf", u"Po"])
if all (unicodedata.category(x) in punc_cat for x in token):
feature_list.append(u'PONCTUATION_PURE')
# Syllabes précédent et suivant
try :
feature_list.append(u'TOKEN_PRECEDENT_' + token[idx - 1])
except IndexError :
pass
try :
feature_list.append(u'TOKEN_SUIVANT_' + token[idx + 1])
except IndexError :
pass
feature_list.append(u'TOKEN_ACTUEL_' + (token))
# Suffix & prefix up to length 3
if len(token) > 1:
feature_list.append(u'SUF_' + token[-1:])
feature_list.append(u'PRE_' + token[:1])
if len(token) > 2:
feature_list.append(u'SUF_' + token[-2:])
feature_list.append(u'PRE_' + token[:2])
if len(token) > 3:
feature_list.append(u'SUF_' + token[-3:])
feature_list.append(u'PRE_' + token[:3])
return feature_list
def get_features_customised_tone(tokens, i, j) :
feature_list = []
if not tokens:
return feature_list
try :
syllabes = tokens[i]
syllabe = syllabes[j]
except IndexError :
raise
# Positions
feature_list.append(u'SYLLABE_ID_POSITIF_' + str(j))
feature_list.append(u'SYLLABE_ID_NEGATIF_' + str(len(syllabes) - j - 1))
feature_list.append(u'TOKEN_ID_POSITIF_' + str(i))
feature_list.append(u'TOKEN_ID_NEGATIF_' + str(len(tokens) - i - 1))
# Châine de caractères au niveau du vocable actuel
feature_list.append(u'SYLLABE_ACTUELLE_' + syllabe)
feature_list.append(u'PREFIXE_ACTUEL_' + syllabes[0])
feature_list.append(u'SUFFIXE_ACTUEL_' + syllabes[-1])
try : feature_list.append(u'SYLLABE_QUI_PRECEDE_' + syllabes[j - 1])
except : pass
try : feature_list.append(u'SYLLABE_QUI_PRECEDE2_' + syllabes[j - 2])
except : pass
try : feature_list.append(u'SYLLABE_QUI_PRECEDE3_' + syllabes[j - 3])
except : pass
try : feature_list.append(u'SYLLABE_QUI_PRECEDE4_' + syllabes[j - 4])
except : pass
try : feature_list.append(u'SYLLABE_QUI_SUIT_' + syllabes[j + 1])
except : pass
try : feature_list.append(u'SYLLABE_QUI_SUIT2_' + syllabes[j + 2])
except : pass
try : feature_list.append(u'SYLLABE_QUI_SUIT3_' + syllabes[j + 3])
except : pass
try : feature_list.append(u'SYLLABE_QUI_SUIT4_' + syllabes[j + 4])
except : pass
# châine de caractères au niveau du vocable qui précède et celui qui suit
try : feature_list.append(u'PREFIXE_DU_TOKEN_QUI_PRECEDE_' + tokens[i-1][0])
except : pass
try : feature_list.append(u'SUFFIXE_DU_TOKEN_QUI_PRECEDE_' + tokens[i-1][-1])
except : pass
try : ffeature_list.append(u'PREFIXE_DU_TOKEN_QUI_SUIT_' + tokens[i+1][0])
except : pass
try : ffeature_list.append(u'SUFFIXE_DU_TOKEN_QUI_SUIT_' + tokens[i+1][-1])
except : pass
# châine de caractères au niveau d'une phrase
feature_list.append(u'TOKEN_ACTUEL_' + ''.join(syllabes))
try : feature_list.append(u'TOKEN_QUI_PRECEDE_' + ''.join(tokens[i - 1]))
except : pass
try : feature_list.append(u'TOKEN_QUI_SUIT_' + ''.join(tokens[i + 1]))
except : pass
# Capitalization
if syllabe[0].isupper():
feature_list.append(u'CAPITALIZATION')
# Number
if re.search(r'\d', syllabe) is not None:
feature_list.append(u'IL_Y_A_UN_CHIFFRE')
# Punctuation
punc_cat = set([u"Pc", u"Pd", u"Ps", u"Pe", u"Pi", u"Pf", u"Po"])
if all (unicodedata.category(x) in punc_cat for x in syllabe):
feature_list.append(u'PONCTUATION_PURE')
return feature_list
def repr (c, null = "", spaces = False) :
if not c : return null
else :
if spaces :
return " " + rm_sep(c) + " "
else:
return rm_sep(c)
def rm_sep(str_in, seprator_in = code_seperator, replacing = u''):
try :
return str_in.replace(seprator_in, u"")
except:
try :
return str_in.decode('utf-8').replace(seprator_in, replacing).encode('utf-8')
except :
try :
return str_in.encode('utf-8').replace(seprator_in, replacing).decode('utf-8')
except :
raise
def chunking (token, mode) :
chunks = []
if mode == 0 :
# sans segmenteur
chunks.append(token)
# segmentation à intervalle régulier
else :
token2 = unicodedata.normalize('NFD', token)
seg = ""
for c in token2 :
seg += c
if len(seg) == mode :
chunks.append(seg)
seg = ""
if seg :
chunks.append(seg)
return chunks
def reshaping (token) :
"""
référence :
http://stackoverflow.com/questions/517923/what-is-the-best-way-to-remove-accents-in-a-python-unicode-string
"""
token = unicodedata.normalize('NFD', token)
#return token.lower()
return token
def mode_position_encoder (token, position, mode_id, chunks, offset = 0, code_seperator_in = code_seperator) :
mode_indicator = mode_indicators[mode_id]
caracter_position_in_token = position + offset
caracter_position_in_chunk = 0
chunk_id = 0
if mode_id == mode_names.index('insert') :
chunk_position_limit_offset = 1
else :
chunk_position_limit_offset = 0
chunk_position_limit = len(chunks[chunk_id]) + chunk_position_limit_offset
for i in range(len(token) + 1):
if i == caracter_position_in_token:
mp_code = mode_indicator + code_seperator_in + str(caracter_position_in_chunk)
return [mp_code, chunk_id]
caracter_position_in_chunk += 1
if caracter_position_in_chunk == chunk_position_limit :
chunk_id += 1
caracter_position_in_chunk = chunk_position_limit_offset
chunk_position_limit = len(chunks[chunk_id]) + chunk_position_limit_offset
return [None, None]
def entropy2 (dic, cnty, cntx, mode = 'token', unit = 'shannon') :
# cntx : compteur pour la distribution des tokens
# cnty : compteur pour la distribution des formes tonales
# dic : dicionnaire de correspondances entre chacun (en string)
# des tokens et la liste contenant chacune de ses formes tonales
averaged_entropy = 0.0
n = 0
for n, token in enumerate(dic.keys()) :
forms = dic[token]
form_cnt = {form_tonal : cnty[form_tonal] for form_tonal in forms}
if mode == 'occurrence' :
averaged_entropy += entropy(form_cnt, unit) * cntx[token]
else : # mode == "token"
averaged_entropy += entropy(form_cnt, unit)
averaged_entropy /= float(n + 1)
if mode == 'occurrence' :
averaged_entropy /= float(sum(cntx.values()))
return averaged_entropy
def entropy (cnt, unit = 'shannon') :
"""
Reférence
http://stackoverflow.com/questions/15450192/fastest-way-to-compute-entropy-in-python
"""
base = {
'shannon' : 2.,
'natural' : math.exp(1),
'hartley' : 10.
}
if len(cnt) <= 1:
return 0
len_data = sum(cnt.values())
probs = [float(c) / len_data for c in cnt.values()]
probs = [p for p in probs if p > 0.]
ent = 0
for p in probs:
if p > 0.:
ent -= p * math.log(p, base[unit])
return ent
def sprint_cnt(cnt, prefix = "", num = -1, min = -1) :
lst = cnt.most_common()
if num > 0 :
try :
lst = lst[:num]
except IndexError :
pass
if min > 0 :
lst = [element for element in lst if element[1] >= min]
try :
return u"".join([prefix + ' ' + itm[0].encode('utf-8') + u' : ' + str(itm[1]).encode('utf-8') + u'\n' for itm in lst if itm])
except :
return u"".join([prefix + ' ' + itm[0] + u' : ' + str(itm[1]) + u'\n' for itm in lst if itm])
class statistique () :
def __init__(self) :
self.form_non_tonal = Counter()
self.form_tonal = Counter()
self.code = Counter()
self.code2 = Counter()
self.segment_code = Counter()
self.dict_code = defaultdict()
self.dict_form_tonal= defaultdict()
self.num = 0
self.err_cnt = 0
self.cnt_ops = 0
self.mode = Counter()
self.src_delete = Counter()
self.dst_insert = Counter()
def __str__(self) :
ret = u""
ret += u"Over a corpus of {} word(s)\n".format(str(self.num))
ret += u"Global Entropy\n"
ret += u"\tE(Token) = {:<6.2f} \n".format(entropy(self.form_non_tonal))
ret += u"\tE(Tonalized Forme) = {:<6.2f} \n".format(entropy(self.form_tonal))
ret += u"\tE(Resulting Code) = {:<6.2f} \n".format(entropy(self.code2))
ret += u"\tr_E(Resulting Code) = {:<6.2f} \n".format(entropy(self.form_tonal)/entropy(self.code2))
ret += u"Entropy by token (by average)\n"
ret += u"\tE(Tonalized Form) = {:<6.2f} \n".\
format(entropy2(self.dict_form_tonal, cnty = self.form_tonal, cntx = self.form_non_tonal))
ret += u"\tE(Resulting Code) = {:<6.2f} \n".\
format(entropy2(self.dict_code, cnty = self.code, cntx = self.form_non_tonal))
ret += u"Levanshtein Distance between a tonalized form and its token (by average) = {:<6.2f} \n".format(self.cnt_ops / float(self.num))
ret += u"Distribution over : \n"
ret += u"\tEdit Operations : \n" + sprint_cnt(self.mode, u"\t\t",-1,20)
ret += u"\tTotality of codes by chunk : \n" + sprint_cnt(self.code, u"\t\t",-1,20)
ret += u"\tTotality of codes by edit operaiton : \n" + sprint_cnt(self.segment_code, u"\t\t",-1,20)
ret += u"\tTotality of caracters deleted : \n" + sprint_cnt(self.src_delete, u"\t\t",-1,20)
ret += u"\tTotality of caracters inserted : \n" + sprint_cnt(self.dst_insert, u"\t\t",-1,20)
if self.err_cnt :
ret += u"\nError : error rate in tone coding given by auto-verification = {}\n".format(self.err_cnt)
return ret
class encoder_tones () :
def __init__ (self) :
self.src = ""
self.dst = ""
self.p_src = 0
self.p_dst = 0
self.ret = ""
self.chunks = []
self.stat = statistique()
def delete(self) :
mode_id = mode_names.index("delete")
[mp_code, chunk_id] = mode_position_encoder(self.src,self.p_src, mode_id, self.chunks)
segment = mp_code + code_seperator
caracter_src = self.src[self.p_src]
segment += caracter_src + code_seperator
self.ret[chunk_id] += segment
self.stat.cnt_ops += 1
self.stat.mode["delete"] += 1
self.stat.src_delete[caracter_src] += 1
self.stat.segment_code[repr(segment)] += 1
def insert(self) :
mode_id = mode_names.index("insert")
[mp_code, chunk_id] = mode_position_encoder(self.src,self.p_src, mode_id, self.chunks)
segment = mp_code + code_seperator
caracter_dst = self.dst[self.p_dst]
segment += caracter_dst + code_seperator
self.ret[chunk_id] += segment
self.stat.cnt_ops += 1
self.stat.mode["insert"] += 1
self.stat.dst_insert[caracter_dst] += 1
self.stat.segment_code[repr(segment)] += 1
def differential_encode (self, form_non_tonal, form_tonal, chunk_mode) :
self.p_src = -1
self.p_dst = -1
self.src = reshaping(form_non_tonal)
if not self.src :
return [[u""], [form_non_tonal]]
self.chunks = chunking(self.src, chunk_mode)
self.ret = [u"" for i in range(len(self.chunks))]
self.dst = reshaping(form_tonal)
ops = Levenshtein.editops(self.src, self.dst)
self.stat.form_non_tonal[self.src] += 1
self.stat.form_tonal [self.dst] += 1
self.stat.dict_form_tonal.setdefault(self.src, []).append(self.dst)
for op in ops :
mode, self.p_src, self.p_dst = op
if mode == "delete" :
self.delete()
elif mode == "insert" :
self.insert()
else : # mode == "replace"
self.insert()
self.delete()
# enlèvement du séparateur du code à la fin du chunk
tmp = []
for ret2 in self.ret :
try :
if ret2[-1] == code_seperator :
ret2 = ret2[:-1]
except IndexError:
pass
tmp.append(ret2)
self.ret = tmp
self.stat.num += 1
repr_code = repr(u"".join(self.ret))
self.stat.code[repr_code] += 1
for chunk_code in self.ret :
self.stat.code2[chunk_code] += 1
self.stat.dict_code.setdefault(self.src, []).append(repr_code)
# internal auto-check
form_tonal_reproduced = repr(''.join([self.differential_decode(chunk, code) for code, chunk in zip(self.ret,self.chunks)]))
if form_tonal_reproduced :
form1 = reshaping(repr(unicodedata.normalize('NFD', form_tonal_reproduced)))
form2 = reshaping(repr(unicodedata.normalize('NFD', form_tonal)))
if form1 != form2 :
self.stat.err_cnt += 1
for code in self.ret :
if not is_a_good_code :
print "(encode) ouput code incorrect !";
print code ;
exit ()
return [self.ret, self.chunks]
def report (self) :
print self.stat.__str__()
def differential_decode (self, chunk, code) :
chunk = reshaping(chunk)
if len(code.strip()) == 0 : return chunk
if not is_a_good_code(code) : print "(decode) input code incorrect !" ; print chunk ; print code ; exit()
# if code[-1] == code_seperator : code = code[: -1]
# code_segments = code.split(code_seperator)
code_segments = split2(code,code_seperator)
if len(code_segments) % 3 != 0 : print code ; print (code_segments) ; print ("input code incorrect !"); exit(1)
p_offset = 0
for i in range(0,len(code_segments),3) :
try :
m, p, c = code_segments[i:i+3]
except :
print (u"Bug 2 : {}".format(code))
exit(1)
p_eff = int(p) + p_offset
if m == mode_indicators[mode_names.index('delete')] :
try : l = chunk[: p_eff]
except IndexError : l = ''
try : r = chunk[p_eff + 1 :]
except IndexError : r = ''
chunk = l + r
p_offset -= 1
else : # elif m == mode_indicators[mode_names.index('insert')] :
try : l = chunk[: p_eff]
except IndexError : l = ''
try : r = chunk[p_eff :]
except IndexError : r = ''
chunk = l + c + r
p_offset += 1
return chunk
if __name__ == "__main__" : main()