def __init__(self, order=20, size=1024):
self.size = size
self.half = A.nblock2(size)
self.wlen = self.half * 2
self.wvlt_i = wvlt.daubechies(order)
self.wwrk_i = wvlt.workspace(self.wlen)
self.tail_i = N.zeros(self.half)
self.wvlt_q = wvlt.daubechies(order)
self.wwrk_q = wvlt.workspace(self.wlen)
self.tail_q = N.zeros(self.half)
def __init__(self,
order = 20,
size = 1024):
self.size = size
self.half = A.nblock2(size)
self.wlen = self.half*2
self.wvlt_i = wvlt.daubechies(order)
self.wwrk_i = wvlt.workspace(self.wlen)
self.tail_i = N.zeros(self.half)
self.wvlt_q = wvlt.daubechies(order)
self.wwrk_q = wvlt.workspace(self.wlen)
self.tail_q = N.zeros(self.half)
def transform(data):
w = wavelet.daubechies(4)
# If no workspace is given, the wrapper will allocate one itself for the
# transform. I am not sure how much of an performance impact that is.
result = w.transform_forward(data);
# If you perfer you could have the workspace and the transform stored
# somewhere, and then perhaps your code is faster
# work = wavelet.wavelet_workspace(n)
# result = w.transform_forward(data, work)
return result
def transform(data):
w = wavelet.daubechies(4)
# If no workspace is given, the wrapper will allocate one itself for the
# transform. I am not sure how much of an performance impact that is.
result = w.transform_forward(data)
# If you perfer you could have the workspace and the transform stored
# somewhere, and then perhaps your code is faster
# work = wavelet.wavelet_workspace(n)
# result = w.transform_forward(data, work)
return result
def run(array):
# Initalise the wavelet and the workspace
w = wavelet.daubechies(4)
ws = wavelet.workspace(len(array))
# Transform forward
result = w.transform_forward(array, ws)
# Select the largest 20 coefficients
abscoeff = numx.absolute(result)
indices = numx.argsort(abscoeff) # ascending order
tmp = numx.zeros(result.shape, numx.float_)
for i in indices[-20:]:
tmp[i] = result[i] # Set all others to zero
# And back
result2 = w.transform_inverse(tmp, ws)
def run(array):
# Initalise the wavelet and the workspace
w = wavelet.daubechies(4)
ws = wavelet.workspace(len(array))
# Transform forward
result = w.transform_forward(array, ws)
# Select the largest 20 coefficients
abscoeff = numx.absolute(result)
indices = numx.argsort(abscoeff) # ascending order
tmp = numx.zeros(result.shape, numx.float_)
for i in indices[-20:]:
tmp[i] = result[i] # Set all others to zero
# And back
result2 = w.transform_inverse(tmp, ws)
