def selection_changed(self, widget):
"""单击词条时触发
1、发音
2、显示详细的解释
"""
meaning_detail = self.builder.get_object("textview_word_detail")
select_word = ''
# 发音
(tree_model, tree_iter) = widget.get_selected()
if tree_iter :
select_word = tree_model.get_value(tree_iter, COLUMN_WORD)
if not self.is_mute:
read(select_word)
# print select_word
# 寻找单词的完整解释、例句
for word in self.words_list:
if select_word == word['name']:
word_dic = word
# print word_dic
sentence_en_3 = [(word_dic['sen_en_1'], word_dic['sen_zh_1']),
(word_dic['sen_en_2'], word_dic['sen_zh_2']),
(word_dic['sen_en_3'], word_dic['sen_zh_3'])
]
if word_dic['sen_en_1']:
sen_display = "\n例句与用法:"
i = 1 # 例句标号
for item in sentence_en_3:
if item[0]:
sen_display += '\n\n' + str(i) + '. ' + item[0] + '\n ' + item[1]
i += 1
else:
sen_display = ''
detial = gtk.TextBuffer()
detial.set_text(
word_dic['name']\
+ "\n" + '[' + word_dic['pronounce'] + ']' \
+ "\n" + word_dic['meaning']
+ "\n" \
+ sen_display)
meaning_detail.set_buffer(detial)
def display_now_word(self):
"""显示新单词
"""
self.now_word = self.words_typing.pop()
if self.read_word:
read(self.now_word['name'])
self.character_pos = 0
self.word_len = len(self.now_word['name'])
self.entry_follow_input.set_text("") # 这个同样会引发insert-text事件
self.label_follow_word.set_label(self.now_word['name'])
self.label_follow_meaning.set_label(self.now_word['meaning'])
self.entry_follow_input.set_max_length (self.word_len)
# self.label_follow_times.set_label("第%d次" % self.now_times)
self.progressbar_follow.set_text("第%d个 （共%d个）" % (self.now_turn, self.words_len))
self.progressbar_follow.set_fraction(float(self.now_turn)/self.words_len)
def __display_next_word(self):
"""显示下一单词
"""
self.now = random.sample(self.pool, 1)[0]
self.pool.remove(self.now)
if self.read_word:
read(self.now['name'])
self.label_word.set_label(self.now['name'])
self.progressbar_meaning.set_text("第%d个(共%d个)" % (len(self.passed_words) + 1, len(self.queue)))
self.progressbar_meaning.set_fraction(float(len(self.passed_words)) / len(self.queue))
answer_index = random.randint(0, 3)
self.buttons[answer_index].set_label(self.now['meaning'].replace('\n', ' '))
filling = random.sample(self.__withou_now_word(), 4)
for button in self.buttons:
if self.buttons.index(button) != answer_index:
random_mean = filling[self.buttons.index(button)]
button.set_label(random_mean['meaning'].replace('\n', ' '))
def display_now_word(self):
"""显示新单词
"""
self.now_word = self.words_typing.pop()
if self.read_word:
read(self.now_word['name'])
self.character_pos = 0
self.word_len = len(self.now_word['name'])
self.entry_follow_input.set_text("") # 这个同样会引发insert-text事件
self.label_follow_word.set_label(self.now_word['name'])
self.label_follow_meaning.set_label(self.now_word['meaning'])
self.entry_follow_input.set_max_length(self.word_len)
# self.label_follow_times.set_label("第%d次" % self.now_times)
self.progressbar_follow.set_text("第%d个 （共%d个）" %
(self.now_turn, self.words_len))
self.progressbar_follow.set_fraction(
float(self.now_turn) / self.words_len)
duration = 2
Fe=44100
frame_size = duration*Fe//8192*8192
mdct = Mdct()
mp = MatchingPursuit(mdct,apf)
mp.buildMask(frame_size)
# pour chaque musique de la bdd on prend 3 extraits
extracts_k = [1./4,2./4,3./4]
counter = np.zeros(len(noises))
i = 0
for track in music_list:
print(track)
wavdata = sound.read(music_dir + "/" + track)
lenght = wavdata.getnframes()
fs = wavdata.getframerate()
for coef in extracts_k:
for k in range(len(database)):
for q in range(len(noises)):
wavdata.rewind()
wavdata.readframes(int(lenght*coef/(5*Fe*8192))*(5*Fe*8192))
db = database[k]
j=0
result = None
offsets = {}
while result == None and j < 30:
mp = MatchingPursuit(mdct, atoms_per_frame)
biggest_atom_size = mdct.sizes[-1]
Fe = 44100
frame_size = int(frame_duration*Fe/biggest_atom_size)*biggest_atom_size
mp.buildMask(frame_size)
for track in music_list:
query = []
track_title = track[:-4]
database = Database()
track_id = database.addTrack(track_title)
del database
print("=> Processing %s, id: %d" % (track_title, track_id))
wavdata = sound.read(track)
Fe = wavdata.getframerate()
frame_number = int(wavdata.getnframes()/frame_size)
print("%d frames to process for this track" % frame_number)
progress = 0
for i in range(frame_number):
s = wavdata.readframes(frame_size)
s = np.frombuffer(s, dtype='<i2')
y = mp.sparse(s)
keys = mp.extractKeys(y)
query.extend([(Binary(hash_key)[0:5],track_id, int(i*frame_size+offset)) for (hash_key,offset) in keys])
for file in os.listdir(music_dir):
if file.endswith(".mp3"):
music_list.append(file)
shaz = Shazam(100)
duration = 2
# pour chaque musique de la bdd on prend 3 extraits
extracts_k = [1./4, 2./4, 3./4]
counter = [0, 0, 0, 0, 0]
for track in music_list:
print track
wavdata = sound.read(music_dir + "/" + track)
lenght = wavdata.getnframes()
fs = wavdata.getframerate()
for coef in extracts_k:
wavdata.rewind()
wavdata.readframes(int(lenght*coef))
keys = shaz.processTrack(wavdata, int(duration*fs))
found_keys = {}
for index,db in enumerate(database):
for key in keys:
string = str(key[0]) + '-' + str(key[1]) + '-' + str(key[2])
offset = int(key[3])
key_hash = hashlib.sha1(string).digest()
mp = MatchingPursuit(mdct, atoms_per_frame)
biggest_atom_size = mdct.sizes[-1]
Fe = 44100
frame_size = int(frame_duration * Fe / biggest_atom_size) * biggest_atom_size
mp.buildMask(frame_size)
for track in music_list:
query = []
track_title = track[:-4]
database = Database()
track_id = database.addTrack(track_title)
del database
print("=> Processing %s, id: %d" % (track_title, track_id))
wavdata = sound.read(track)
Fe = wavdata.getframerate()
frame_number = int(wavdata.getnframes() / frame_size)
print("%d frames to process for this track" % frame_number)
progress = 0
for i in range(frame_number):
s = wavdata.readframes(frame_size)
s = np.frombuffer(s, dtype='<i2')
y = mp.sparse(s)
keys = mp.extractKeys(y)
query.extend([(Binary(hash_key)[0:5], track_id,
int(i * frame_size + offset))
for (hash_key, offset) in keys])
