def check_accuracy(pmode, mmode, wavelet):
# max RMSE
epsilon = 1.0e-10
w = pywt.Wavelet(wavelet)
data_size = list(range(w.dec_len, 40)) + [100, 200, 500, 1000, 50000]
np.random.seed(1234)
for N in data_size:
data = np.random.random(N)
# PyWavelets result
pa, pd = pywt.dwt(data, wavelet, pmode)
# Matlab result
ma, md = mlab.dwt(data, wavelet, 'mode', mmode, nout=2)
ma = ma.flat
md = md.flat
# calculate error measures
rms_a = np.sqrt(np.mean((pa-ma)**2))
rms_d = np.sqrt(np.mean((pd-md)**2))
msg = ('[RMS_A > EPSILON] for Mode: %s, Wavelet: %s, '
'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_a))
assert_(rms_a < epsilon, msg=msg)
msg = ('[RMS_D > EPSILON] for Mode: %s, Wavelet: %s, '
'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_d))
assert_(rms_d < epsilon, msg=msg)
def test_accuracy(families, wavelets, modes, epsilon=1.0e-10):
print "Testing decomposition".upper()
for pmode, mmode in modes:
for wavelet in wavelets:
print "Wavelet: %-8s Mode: %s" % (wavelet, pmode)
w = pywt.Wavelet(wavelet)
data_size = range(w.dec_len, 40) + [100, 200, 500, 1000, 50000]
for N in data_size:
data = numpy.random.random(N)
# PyWavelets result
pa, pd = pywt.dwt(data, wavelet, pmode)
# Matlab result
ma, md = mlab.dwt(data, wavelet, 'mode', mmode, nout=2)
ma = ma.flat; md = md.flat
# calculate error measures
mse_a, mse_d = mse(pa, ma), mse(pd, md)
rms_a, rms_d = math.sqrt(mse_a), math.sqrt(mse_d)
if rms_a > epsilon:
print '[RMS_A > EPSILON] for Mode: %s, Wavelet: %s, Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_a)
if rms_d > epsilon:
print '[RMS_D > EPSILON] for Mode: %s, Wavelet: %s, Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_d)
def check_accuracy(pmode, mmode, wavelet):
# max RMSE
epsilon = 1.0e-10
w = pywt.Wavelet(wavelet)
data_size = list(range(w.dec_len, 40)) + [100, 200, 500, 1000, 50000]
np.random.seed(1234)
for N in data_size:
data = np.random.random(N)
# PyWavelets result
pa, pd = pywt.dwt(data, wavelet, pmode)
# Matlab result
ma, md = mlab.dwt(data, wavelet, 'mode', mmode, nout=2)
ma = ma.flat
md = md.flat
# calculate error measures
rms_a = np.sqrt(np.mean((pa - ma)**2))
rms_d = np.sqrt(np.mean((pd - md)**2))
msg = ('[RMS_A > EPSILON] for Mode: %s, Wavelet: %s, '
'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_a))
assert_(rms_a < epsilon, msg=msg)
msg = ('[RMS_D > EPSILON] for Mode: %s, Wavelet: %s, '
'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_d))
assert_(rms_d < epsilon, msg=msg)
def test_accuracy(families, wavelets, modes, epsilon=1.0e-10):
print "Testing decomposition".upper()
for pmode, mmode in modes:
for wavelet in wavelets:
print "Wavelet: %-8s Mode: %s" % (wavelet, pmode)
w = pywt.Wavelet(wavelet)
data_size = range(w.dec_len, 40) + [100, 200, 500, 1000, 50000]
for N in data_size:
data = numpy.random.random(N)
# PyWavelets result
pa, pd = pywt.dwt(data, wavelet, pmode)
# Matlab result
ma, md = mlab.dwt(data, wavelet, 'mode', mmode, nout=2)
ma = ma.flat
md = md.flat
# calculate error measures
mse_a, mse_d = mse(pa, ma), mse(pd, md)
rms_a, rms_d = math.sqrt(mse_a), math.sqrt(mse_d)
if rms_a > epsilon:
print '[RMS_A > EPSILON] for Mode: %s, Wavelet: %s, '\
'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_a)
if rms_d > epsilon:
print '[RMS_D > EPSILON] for Mode: %s, Wavelet: %s, '\
'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_d)
