def proccess_data(FILE_, DATA, NUM_SAMPLES, LABEL):
# Remove header, Nan and trash
data = np.load('DB_1d/' + FILE_ + '.npy')
Xc_1 = data[:10000, 0]
Xc_2 = data[10000:, 0]
print '\nXc_1 cleaned shape ', Xc_1.shape
print 'Xc_2 cleaned shape ', Xc_2.shape
#Create temporal serie
NUM_SAMPLES = 50
#NUM_COLS = Xc_1.shape[1]
Xc_1 = mls.generate_envelope(Xc_1, NUM_SAMPLES)
Xc_2 = mls.generate_envelope(Xc_2, NUM_SAMPLES)
#Labeling the type of movement
C = (np.ones(len(Xc_1)) * 0).reshape((len(Xc_1), 1))
Xc_1 = np.hstack((Xc_1.reshape(Xc_1.shape), C.reshape((len(Xc_1), 1))))
C = (np.ones(len(Xc_2)) * 1).reshape((len(Xc_2), 1))
Xc_2 = np.hstack((Xc_2.reshape(Xc_2.shape), C.reshape((len(Xc_2), 1))))
print 'Xc labeled shape ', Xc_1.shape
print 'Xc labeled shape ', Xc_2.shape
# Salving in file on the folder <classifier_data>
np.save('./preprocessed_data/' + FILE_ + 'op', Xc_1, allow_pickle=False)
print FILE_ + 'op.npy'
np.save('./preprocessed_data/' + FILE_ + 'pp', Xc_2, allow_pickle=False)
print FILE_ + 'pp.npy'
DATA.append(FILE_ + 'op.npy')
DATA.append(FILE_ + 'pp.npy')
def proccess_data(FILE_, DATA, NUM_SAMPLES, LABEL):
Xc = np.genfromtxt('DB_nd/' + FILE_ + '.txt',
delimiter=",",
usecols=(1, 2, 3, 4))
print '\nXc shape ', Xc.shape
#Create temporal serie
Xc = mls.generate_envelope(Xc, NUM_SAMPLES)
print 'Xc temporal-serie shape ', Xc.shape
#Labeling the type of movement
C = (np.ones(len(Xc)) * LABEL).reshape((len(Xc), 1))
Xc = np.hstack((Xc.reshape(Xc.shape), C.reshape((len(Xc), 1))))
print 'Xc labeled shape ', Xc.shape
np.save('./preprocessed_data/' + FILE_, Xc, allow_pickle=False)
print FILE_ + '.npy'
DATA.append(FILE_ + '.npy')
def test_generate_envelope(self):
v1 = np.array([1, 2, 3])
r1 = generate_envelope(v1, 2)
expeted = np.array([[1, 2], [2, 3]])
self.assertTrue((r1 == expeted).all())
def test_generate_evnelop_two_channels(self):
v1 = np.array([[1, 2], [3, 4], [5, 6]])
r1 = generate_envelope(v1, 2)
#expected = np.array([[1,3, 2, 4],[3,5, 4,6]])
expected = np.array([[1, 2, 3, 4], [3, 4, 5, 6]])
self.assertTrue((r1 == expected).all())