def cwtMS(X, scales, sampleScale = 1.0, wlet = 'DOG', maxClip = 1000.):
'''
X is the INTERPOLATED intensity array from a mass spectrum.
interpolation IS necessary especially for TOF data as the m/z domain is non-linear.
'''
ans = W.cwt_a(X, scales, sampling_scale = sampleScale)#, wavelet = wlet)
scaledCWT=N.clip(N.fabs(ans.real), 0., maxClip)#N.fabs get the element-wise absolute values
return scaledCWT
def cwtMS(X, scales, sampleScale=1.0, wlet='DOG', maxClip=1000.):
'''
X is the INTERPOLATED intensity array from a mass spectrum.
interpolation IS necessary especially for TOF data as the m/z domain is non-linear.
'''
ans = W.cwt_a(X, scales, sampling_scale=sampleScale) #, wavelet = wlet)
scaledCWT = N.clip(N.fabs(ans.real), 0.,
maxClip) #N.fabs get the element-wise absolute values
return scaledCWT
# print newdl,dl
return data[0:newdl]
t1 = time.clock()
ms = P.load('N3_Norm.txt')
#ms = P.load('J15.csv', delimiter = ',')
#x, ms = interpolate_spectrum(ms)
#ms = normalize(topHat(ms, 0.01))
#ms = roundLen(ms)
print len(ms)
s = N.arange(2, 32, 2) #changed to 8 from 32
a = W.cwt_a(ms, s**2, sampling_scale=1.0)
plotcwt = N.clip(N.fabs(a.real), 0., 1000.)
print "wavelet complete"
print time.clock() - t1, 'seconds'
fig1 = P.figure()
ax = fig1.add_subplot(211)
im = ax.imshow(plotcwt, vmax=100, cmap=P.cm.jet, aspect='auto')
ax2 = fig1.add_subplot(212, sharex=ax)
##ax3 = fig1.add_subplot(313,sharex=ax)
#ax.plot(plotcwt[0])
#ax2.plot(ms, 'r')
##ax2.plot(plotcwt[1], ':r')
##
##
##mNoise = plotcwt[0].mean()
