Ciao a tutti ho un problemino che non riesco a risolvere.
Ho queste 2 classi Cwt e Morlet:
class Cwt:
fourierwl=1.00
def _log2(self, x):
# utility function to return (integer) log2
return int( NP.log(float(x))/ NP.log(2.0)+0.0001 )
def __init__(self, data, largestscale=1, notes=0, order=2,
scaling='linear'):
"""
Continuous wavelet transform of data
data: data in array to transform, length must be power of 2
notes: number of scale intervals per octave
largestscale: largest scale as inverse fraction of length
of data array
scale = len(data)/largestscale
smallest scale should be >= 2 for meaningful data
order: Order of wavelet basis function for some families
scaling: Linear or log
"""
ndata = len(data)
self.order=order
self.scale=largestscale
self._setscales(ndata,largestscale,notes,scaling)
self.cwt= NP.zeros((self.nscale,ndata), NP.complex64)
omega=
NP.array(range(0,ndata/2)+range(-ndata/2,0))*(2.0*NP.pi/ndata)
datahat=NP.fft.fft(data)
self.fftdata=datahat
for scaleindex in range(self.nscale):
currentscale=self.scales[scaleindex]
self.currentscale=currentscale # for internal use
s_omega = omega*currentscale
psihat=self.wf(s_omega)
psihat = psihat * NP.sqrt(2.0*NP.pi*currentscale)
convhat = psihat * datahat
W = NP.fft.ifft(convhat)
self.cwt[scaleindex,0:ndata] = W
return
def _setscales(self,ndata,largestscale,notes,scaling):
if scaling=="log":
if notes<=0: notes=1
# adjust nscale so smallest scale is 2
noctave=self._log2( ndata/largestscale/2 )
self.nscale=notes*noctave
self.scales=NP.zeros(self.nscale,float)
for j in range(self.nscale):
self.scales[j] =
ndata/(self.scale*(2.0**(float(self.nscale-1-j)/notes)))
elif scaling=="linear":
nmax=ndata/largestscale/2
self.scales=NP.arange(float(2),float(nmax))
self.nscale=len(self.scales)
else: raise ValueError, "scaling must be linear or log"
return
def getdata(self):
return self.cwt
def getcoefficients(self):
return self.cwt
def getpower(self):
return (self.cwt* NP.conjugate(self.cwt)).real
def getscales(self):
return self.scales
def getnscale(self):
return self.nscale
class Morlet(Cwt):
_omega0=5
fourierwl=4* NP.pi/(_omega0+ NP.sqrt(2.0+_omega0**2))
def wf(self, s_omega):
H=(s_omega>0)*1.
xhat=0.75112554*( NP.exp(-(s_omega-self._omega0)**2/2.0))*H
return xhat
Da un altro file importo la classe Morlet con:
from nomefile import Morlet
e poi la richiamo col comando
cw=Morlet(A,maxscale,notes,scaling=scaling)
e tutto funziona!!
Però avrei la necessità di passare il parametro _omega0=xxx quando
chiamo la classe.... tipo così:
cw=Morlet(A,maxscale,notes,scaling=scaling,_omega0=5)
ho fatto un po di prove ma non riesco.
Ogni aiuto è ben accetto.
Grazie
Matteo
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