def playing_with_data():
#folder_data = '/home/maja/PhDProject/human_data/data/'
folder_data = '/home/maja/PhDProject/data/'
folder_data='/home/maja/PhDProject/human_data/data/'
folder_data ='/home/maja/PhDProject/data/'
folder_specific = '2013_07_31/' #'HT_2013_04_02/'
folder_specific = 'others/'
folder_specific = '2013_08_10/'
file_data = folder_specific + '2013_07_31_0002.abf' #2013_04_02_0013.abf'
file_data = folder_specific + '2013_07_03 PR1_0000.abf'
file_data = folder_specific + '2013_09_03_0002.abf'
#file_data = folder_specific + '2013_09_03_0006.abf'
file_data = folder_specific + '2013_09_05_0009_afterNBQX.abf'
file_data = folder_specific + '2013_09_05_0019_synch.abf'
file_data = folder_specific + '2013_09_05_0017.abf'
file_data = folder_specific + '2013_10_08_0002.abf'
folder_save = '/home/maja/PhDProject/data/2013_07_31/saved/'
folder_save = '/home/maja/PhDProject/human_data/data/others/'
file_save = folder_save + 'all_data_gabaB.npz'
#file_save = folder_save + 'data.dat'
data, scale, fs = dh_temp.read_data(folder_data, file_data)
dh_temp.save_data(folder_save, file_save, data, scale, fs)
del data, scale, fs
display.plot_data(folder_save, file_save, x_scale = 'ms')
def do_POST(self):
# Header
self.send_response(200)
self.end_headers()
# Extract data from request
content_len = int(self.headers['Content-Length'])
data = self.rfile.read(content_len).decode('utf-8')
# Extract dictionary with params
data = urllib.parse.parse_qs(data)
print('[INFO] Data decoded: ', data)
# Extract college name, the param with key 'name'
new_name = str(data['name'][0])
# Load database
names = load_data()
# Check if name already exists in database
if new_name in names:
self.wfile.write(
bytes('[ERR] This name already exists in database', "utf-8"))
# If not exists, save new name into database
else:
names.append(new_name)
save_data(names) # Sort and save
self.wfile.write(
bytes('[INFO] Added {} to database'.format(new_name), "utf-8"))
def save_code_features_test_train(self, path_id=""):
train_features_path = "features_train%s.csv"%(path_id)
test_features_path = "features_test%s.csv"%(path_id)
datasets = data_handler.load_reuters_dataset(0, path_id)
train_set_x, train_set_y = datasets[0]
test_set_x, test_set_y = datasets[2]
features, labels = data_handler.load_full_data()
for dA in self.dA_layers:
train_set_x = dA.get_hidden_values(train_set_x)
test_set_x = dA.get_hidden_values(test_set_x)
print features.eval()
print train_set_x.shape.eval(), test_set_x.shape.eval()
data_handler.save_data(train_set_x.eval(), train_set_y.get_value(borrow=True), train_features_path)
data_handler.save_data(test_set_x.eval(), test_set_y.get_value(borrow=True), test_features_path)
def save_code_features_gpu(self, datasets, features_path="features.csv", experiment="id" ,feature_size=1000, finetune_lr=0.1, pretrain_lr=0.1, noise_level=0.1, best_validation_loss=0, test_score=0):
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
for dA in self.dA_layers:
train_set_x = dA.get_hidden_values(train_set_x)
valid_set_x = dA.get_hidden_values(valid_set_x)
test_set_x = dA.get_hidden_values(test_set_x)
# merge all results into single feature matrix
train_set_x = train_set_x.eval()
valid_set_x = valid_set_x.eval()
test_set_x = test_set_x.eval()
x = numpy.concatenate((train_set_x, valid_set_x, test_set_x))
# merge all labels into single array
train_set_y = train_set_y.get_value(borrow=True)
valid_set_y = valid_set_y.get_value(borrow=True)
test_set_y = test_set_y.get_value(borrow=True)
y = numpy.concatenate((train_set_y, valid_set_y, test_set_y))
data_handler.save_data(x, y, features_path)
# apply svm classification
try:
clf = LinearSVC()
scores = cross_validation.cross_val_score(clf, x, y, cv=10)
accuracy = scores.mean() * 100
# save the accuracy
#threadLock.acquire()
file = open('deep.csv','a')
file.write("%s,%d,%f,%f,%f,%f,%f,%f\n" %(experiment, feature_size, noise_level, pretrain_lr, finetune_lr, accuracy, best_validation_loss * 100., test_score * 100.))
file.close()
#threadLock.release()
except:
pass
def do_DELETE(self):
self.send_response(200)
self.end_headers()
# Extract data from request
content_len = int(self.headers['Content-Length'])
data = self.rfile.read(content_len).decode('utf-8')
data = urllib.parse.parse_qs(data)
college_name = str(data['name'][0])
# Search for this name into the database
# Load database
names = load_data()
# Check if name already exists in database
if college_name in names:
names.remove(college_name)
self.wfile.write(bytes('[INFO] Deleting name in database',
"utf-8"))
save_data(names) # Sort and save
# If not exists, return an error
else:
self.wfile.write(
bytes(
'[ERR] {} was not found in database'.format(college_name),
"utf-8"))
def do_PUT(self):
self.send_response(200)
self.end_headers()
# Extract data from request
content_len = int(self.headers['Content-Length'])
data = self.rfile.read(content_len).decode('utf-8')
# Extract dict with parameters
data = urllib.parse.parse_qs(data)
# Extract params from dict
old_name = str(data['name'][0])
new_name = str(data['new_name'][0])
# Search for this name into the database
names = load_data()
if old_name in names:
names.remove(old_name)
names.append(new_name)
self.wfile.write(bytes('[INFO] Updating name in database',
"utf-8"))
save_data(names) # Sort and save
else:
self.wfile.write(
bytes('[ERR] {} was not found in database'.format(old_name),
"utf-8"))
def save_data():
data = request.get_json()
psw = util.hash_password(data["password"])
data_handler.save_data(data["username"], data["email"], psw)
return "done"
def save_data():
data = request.get_json()
psw = util.hash_password(data['password'])
data_handler.save_data(data['username'], data['email'], psw)
return 'done'