def test_is_power_of_2():
for x, is_pow_2 in [
(0,True),
(1,True),
(2,True),
(3,False),
(4,True),
(256, True),
(8., True),
(-2, True),
(-3,False),
("2",False)]:
yield lambda x,y: is_power_of_2(x) == y, x, is_pow_2
def calculate_wt_power(self,data):
"""For given EEG-data, calculate baseline-corrected Wavelet-power"""
#Checks
data_len = dataset.samples_original.shape[1]
if not is_power_of_2(data_len*2/3):
raise ValueError("length of data must be 1.5 x a power of 2")
# Shorthands
nwtt = self._num_wts_to_take
# Calculate
rv = np.zeros((data.shape),"d")
for i_s in range(data.shape[0]):
for i_ch in range(data.shape[2]):
#TODO: Noch nicht implementiert
rv[i_s,:,i_ch] = wt_power()
def calculate_wt_power(self, data):
"""For given EEG-data, calculate baseline-corrected Wavelet-power"""
#Checks
data_len = dataset.samples_original.shape[1]
if not is_power_of_2(data_len * 2 / 3):
raise ValueError("length of data must be 1.5 x a power of 2")
# Shorthands
nwtt = self._num_wts_to_take
# Calculate
rv = np.zeros((data.shape), "d")
for i_s in range(data.shape[0]):
for i_ch in range(data.shape[2]):
#TODO: Noch nicht implementiert
rv[i_s, :, i_ch] = wt_power()
def test_is_power_of_2_wrong_argument_type():
is_power_of_2("abc")
def wavepower_lin(x,wavelet,mode="per",level=None,return_sl=False,normalise=True,norm_fraction=None,as_dB=True):
"""Performs a wavelet-decomposition using pywt.wavedec, but returns the power
as one 1d-array.
Uses different default-values for mode and level.
If normalise==False, len(x) must be a power of two.
If normalise==True, len(x)*2/3 must be a power of two.
In this case two Wavelet-transforms are performed, one from 0 to 2/3
and one from 1/3 to 1. The center-value of the first wt-power is then used
for normalization of the second wt-power. The normalization is performed for each
scale separately.
norm_fraction: Not used anymore!
If as_dB, convert normalised power to dB. Ignored if normalise=False
"""
assert len(x.shape) in [1,2], "Only 1d and 2d-arrays supported up to now!"
if normalise:
assert is_power_of_2(len(x)*2/3), "len(x)*2/3 must be a power of 2"
if level == None:
level = int(round(n.log2(x.shape[0]*2/3)))
else:
assert is_power_of_2(len(x)), "len(x) must be a power of 2"
if level == None:
level = int(round(n.log2(x.shape[0])))
lx = x.shape[0]
if len(x.shape) == 1:
if normalise:
wts_bl = wavedec(x[:lx*2/3],wavelet,mode=mode,level=level)
wts = wavedec(x[lx/3:],wavelet,mode=mode,level=level)
else:
wts = wavedec(x[:],wavelet,mode=mode,level=level)
sc_lengths = [len(x) for x in wts]
rv = n.zeros((sum(sc_lengths)),"d")
for j in range(len(wts)):
wts_power = wts[j]**2
if normalise:
wts_power /= (wts_bl[j][len(wts_bl[j])/2])**2
if as_dB:
wts_power[:] = 10 * n.log(wts_power)
offset = sum(sc_lengths[:j])
rv[offset:offset+sc_lengths[j]]=wts_power[:]
else: #len(x.shape)==2
# create array of return-values
if normalise:
wts = wavedec(x[lx/3:,0],wavelet,mode=mode,level=level)
else:
wts = wavedec(x[:,0],wavelet,mode=mode,level=level)
sc_lengths = [len(i) for i in wts]
rv = n.zeros((sum(sc_lengths),x.shape[1]),"d")
#iterate over dim1
for i in range(x.shape[1]):
if i>0:
# for ch0, wts was already calculated
if normalise:
wts = wavedec(x[lx/3:,i],wavelet,mode=mode,level=level)
else:
wts = wavedec(x[:,i],wavelet,mode=mode,level=level)
for j in range(len(wts)):
wts_power = wts[j]**2
if normalise:
wts_bl = wavedec(x[:lx*2/3,i],wavelet,mode=mode,level=level)
wts_power /= (wts_bl[j][len(wts_bl[j])/2])**2
if as_dB:
wts_power[:] = 10 * n.log(wts_power)
offset = sum(sc_lengths[:j])
rv[offset:offset+sc_lengths[j],i]=wts_power[:]
if return_sl:
return rv, sc_lengths
else:
return rv
def test_is_power_of_2_wrong_argument_type():
is_power_of_2("abc")
def test_is_power_of_2():
for x, is_pow_2 in [(0, True), (1, True), (2, True), (3, False), (4, True),
(256, True), (8., True), (-2, True), (-3, False),
("2", False)]:
yield lambda x, y: is_power_of_2(x) == y, x, is_pow_2
def wavepower_lin(x,
wavelet,
mode="per",
level=None,
return_sl=False,
normalise=True,
norm_fraction=None,
as_dB=True):
"""Performs a wavelet-decomposition using pywt.wavedec, but returns the power
as one 1d-array.
Uses different default-values for mode and level.
If normalise==False, len(x) must be a power of two.
If normalise==True, len(x)*2/3 must be a power of two.
In this case two Wavelet-transforms are performed, one from 0 to 2/3
and one from 1/3 to 1. The center-value of the first wt-power is then used
for normalization of the second wt-power. The normalization is performed for each
scale separately.
norm_fraction: Not used anymore!
If as_dB, convert normalised power to dB. Ignored if normalise=False
"""
assert len(x.shape) in [1, 2], "Only 1d and 2d-arrays supported up to now!"
if normalise:
assert is_power_of_2(len(x) * 2 / 3), "len(x)*2/3 must be a power of 2"
if level == None:
level = int(round(n.log2(x.shape[0] * 2 / 3)))
else:
assert is_power_of_2(len(x)), "len(x) must be a power of 2"
if level == None:
level = int(round(n.log2(x.shape[0])))
lx = x.shape[0]
if len(x.shape) == 1:
if normalise:
wts_bl = wavedec(x[:lx * 2 / 3], wavelet, mode=mode, level=level)
wts = wavedec(x[lx / 3:], wavelet, mode=mode, level=level)
else:
wts = wavedec(x[:], wavelet, mode=mode, level=level)
sc_lengths = [len(x) for x in wts]
rv = n.zeros((sum(sc_lengths)), "d")
for j in range(len(wts)):
wts_power = wts[j]**2
if normalise:
wts_power /= (wts_bl[j][len(wts_bl[j]) / 2])**2
if as_dB:
wts_power[:] = 10 * n.log(wts_power)
offset = sum(sc_lengths[:j])
rv[offset:offset + sc_lengths[j]] = wts_power[:]
else: #len(x.shape)==2
# create array of return-values
if normalise:
wts = wavedec(x[lx / 3:, 0], wavelet, mode=mode, level=level)
else:
wts = wavedec(x[:, 0], wavelet, mode=mode, level=level)
sc_lengths = [len(i) for i in wts]
rv = n.zeros((sum(sc_lengths), x.shape[1]), "d")
#iterate over dim1
for i in range(x.shape[1]):
if i > 0:
# for ch0, wts was already calculated
if normalise:
wts = wavedec(x[lx / 3:, i],
wavelet,
mode=mode,
level=level)
else:
wts = wavedec(x[:, i], wavelet, mode=mode, level=level)
for j in range(len(wts)):
wts_power = wts[j]**2
if normalise:
wts_bl = wavedec(x[:lx * 2 / 3, i],
wavelet,
mode=mode,
level=level)
wts_power /= (wts_bl[j][len(wts_bl[j]) / 2])**2
if as_dB:
wts_power[:] = 10 * n.log(wts_power)
offset = sum(sc_lengths[:j])
rv[offset:offset + sc_lengths[j], i] = wts_power[:]
if return_sl:
return rv, sc_lengths
else:
return rv
