def calc_feat_df(cough_dir_list, WIN_SIZE):
feat_col_Names = get_colNames()
df = pd.DataFrame(columns=feat_col_Names)
k = 0.0
for dir in cough_dir_list:
coughs_list = glob(dir + "/*.wav")
coughs_list = natsort(coughs_list)
feat_matrix = extract_features(dir, coughs_list, meta_data, WIN_SIZE)
temp_df = pd.DataFrame(feat_matrix, columns=feat_col_Names)
df = df.append(temp_df)
k += 1
printProgress(k, len(cough_dir_list), prefix="Progress:", suffix="Complete", barLength=50)
return df
def calc_feat_df(cough_dir_list,WIN_SIZE): feat_col_Names = get_colNames() df = pd.DataFrame(columns = feat_col_Names) k = 0.0 # Init progress bar printProgress(k,len(cough_dir_list),prefix = "Progress:",suffix = "Complete",barLength = 50) for dir in cough_dir_list: coughs_list = glob(dir + "/*.wav") # first numerically sort the coughs list coughs_list = natsorted(coughs_list) # extract spectral features for all coughs in dir feat_matrix = extract_features(dir,coughs_list, meta_data,WIN_SIZE) temp_df = pd.DataFrame(feat_matrix,columns = feat_col_Names) df = df.append(temp_df) k += 1 printProgress(k,len(cough_dir_list),prefix = "Progress:",suffix = "Complete",barLength = 50) return df
def write(filename, c, totalsize):
"""this function write <filename> to socket <c>"""
f = open(filename, 'rb')
bytes_sent = 0
pb.printProgress(bytes_sent, totalsize, prefix='Sent', barLength=50)
while bytes_sent < totalsize:
data = f.read(BUFFER_SIZE)
c.send(data)
bytes_sent += len(data)
pb.printProgress(bytes_sent, totalsize, prefix='Sent', barLength=50)
print('Completed!')
f.close()
def train(self, train_steps, epoch = 0):
# do not do restart here, continue from testing
#self._restartRandom()
# play given number of steps
import time
start_time = time.time()
for i in xrange(train_steps):
# perform game step
action, reward, screen, terminal = self.step(self._explorationRate())
printProgress(i, train_steps, start_time)
self.mem.add(action, reward, screen, terminal)
# train after every train_frequency steps
if self.mem.count > self.mem.batch_size and i % self.train_frequency == 0:
# train for train_repeat times
for j in xrange(self.train_repeat):
# sample minibatch
minibatch = self.mem.getMinibatch()
# train the network
self.net.train(minibatch, epoch)
# increase number of training steps for epsilon decay
self.total_train_steps += 1
def train(self, train_steps, epoch=0):
# do not do restart here, continue from testing
#self._restartRandom()
# play given number of steps
import time
start_time = time.time()
for i in xrange(train_steps):
# perform game step
action, reward, screen, terminal = self.step(
self._explorationRate())
printProgress(i, train_steps, start_time)
self.mem.add(action, reward, screen, terminal)
# train after every train_frequency steps
if self.mem.count > self.mem.batch_size and i % self.train_frequency == 0:
# train for train_repeat times
for j in xrange(self.train_repeat):
# sample minibatch
minibatch = self.mem.getMinibatch()
# train the network
self.net.train(minibatch, epoch)
# increase number of training steps for epsilon decay
self.total_train_steps += 1
def run(self):
global userinput, received
while True:
#start receive data
process_lock.acquire()
process_lock.release()
recv_lock.acquire()
try:
received = str(self.sock.recv(1024), 'UTF-8')
finally:
recv_lock.release()
if process_lock.acquire(False):
# this message is for you
fileinfo = received
j = json.loads(fileinfo)
filename = j['filename']
size = int(j['size'])
check = '{} is goint to send "{}" with size {} bytes. Receive it? [Y/N] '.format(
self.ip, filename, size)
print(check, end='')
sys.stdout.flush()
try:
while True:
if input_lock.acquire():
if userinput is None:
input_lock.release()
else:
input_lock.release()
break
agreement = userinput
while True:
if (agreement == 'Y'):
new_filename = input(
"NOTE: The file will be downed at the current directory.\n"
+ "How do you want to name this file? " +
"(Leave it blank if your don't want to change it): \n"
)
filename = filename if new_filename.strip(
) == '' else new_filename
f = open(filename, 'wb') #open in binary
self.sock.send(b'Yes')
print('Downloading data...')
bytes_rev = 0
pb.printProgress(bytes_rev,
size,
prefix='Downloaded',
barLength=50)
while (bytes_rev < size):
data = self.sock.recv(BUFFER_SIZE)
f.write(data)
bytes_rev += len(data)
pb.printProgress(bytes_rev,
size,
prefix='Downloaded',
barLength=50)
print('Completed!')
sys.stdout.flush()
f.close()
break
elif (agreement == 'N'):
self.sock.send(b'No')
break
else:
agreement = input(check)
finally:
process_lock.release()
else:
#sombody else in under control, the data is not for you
pass
f.close()
list_rst_files = files_read.split('\n')
for file_rst in list_rst_files:
f = open(file_rst, 'r')
f_read = f.read()
f.close()
print "Traduciendo %s" % (file_rst)
# Separar cadena por linias e ignorar algunas
list_file = f_read.split('\n')
init_list = 0
len_list = len(list_file) - 1
printProgress(init_list,
len_list,
prefix='Progress:',
suffix='Complete',
barLength=50)
for n, line in enumerate(list_file):
if line[:3] not in (".. ", " ", "* :", "", "===", "---"):
text = translator.translate(line)
if text.split(':')[0] == "MYMEMORY WARNING":
raise Exception(
"MYMEMORY WARNING QUOTAREACHED: VISIT HTTP://MYMEMORY.TRANSLATED.NET/DOC/QUOTAREACHED"
)
list_file[n] = text
printProgress(n,
len_list,
prefix='Progreso:',
suffix='Completado',
barLength=50)
