def preWhitenCube(**kwargs):
'''
Pre-whitenening using noise estimates from a cube taken from the difference map.
Returns a the pre-whitened volume and various spectra. (Alp Kucukelbir, 2013)
'''
print '\n= Pre-whitening the Cubes'
tStart = time()
n = kwargs.get('n', 0)
vxSize = kwargs.get('vxSize', 0)
elbowAngstrom = kwargs.get('elbowAngstrom', 0)
rampWeight = kwargs.get('rampWeight', 1.0)
dataF = kwargs.get('dataF', 0)
dataBGF = kwargs.get('dataBGF', 0)
dataBGSpect = kwargs.get('dataBGSpect', 0)
epsilon = 1e-10
pWfilter = createPreWhiteningFilter(n=n,
spectrum=dataBGSpect,
elbowAngstrom=elbowAngstrom,
rampWeight=rampWeight,
vxSize=vxSize)
# Apply the pre-whitening filter to the inside cube
dataF = np.multiply(pWfilter['pWfilter'], dataF)
dataPWFabs = np.abs(dataF)
dataPWFabs = dataPWFabs - np.min(dataPWFabs)
dataPWFabs = dataPWFabs / np.max(dataPWFabs)
dataPWSpect = sphericalAverage(dataPWFabs**2) + epsilon
dataPW = np.real(fftpack.ifftn(fftpack.ifftshift(dataF)))
del dataF
# Apply the pre-whitening filter to the outside cube
dataBGF = np.multiply(pWfilter['pWfilter'], dataBGF)
dataPWBGFabs = np.abs(dataBGF)
dataPWBGFabs = dataPWBGFabs - np.min(dataPWBGFabs)
dataPWBGFabs = dataPWBGFabs / np.max(dataPWBGFabs)
dataPWBGSpect = sphericalAverage(dataPWBGFabs**2) + epsilon
dataBGPW = np.real(fftpack.ifftn(fftpack.ifftshift(dataBGF)))
del dataBGF
m, s = divmod(time() - tStart, 60)
print " :: Time elapsed: %d minutes and %.2f seconds" % (m, s)
return {
'dataPW': dataPW,
'dataBGPW': dataBGPW,
'dataPWSpect': dataPWSpect,
'dataPWBGSpect': dataPWBGSpect,
'peval': pWfilter['peval'],
'pcoef': pWfilter['pcoef']
}
def preWhitenVolumeSoftBG(**kwargs):
'''
Pre-whitenening using noise estimates from a soft mask of the background.
Returns a the pre-whitened volume and various spectra. (Alp Kucukelbir, 2013)
'''
print '\n= Pre-whitening'
tStart = time()
n = kwargs.get('n', 0)
elbowAngstrom = kwargs.get('elbowAngstrom', 0)
dataBGSpect = kwargs.get('dataBGSpect', 0)
dataF = kwargs.get('dataF', 0)
softBGmask = kwargs.get('softBGmask', 0)
vxSize = kwargs.get('vxSize', 0)
rampWeight = kwargs.get('rampWeight', 1.0)
epsilon = 1e-10
pWfilter = createPreWhiteningFilter(n=n,
spectrum=dataBGSpect,
elbowAngstrom=elbowAngstrom,
rampWeight=rampWeight,
vxSize=vxSize)
# Apply the pre-whitening filter
dataF = np.multiply(pWfilter['pWfilter'], dataF)
dataPWFabs = np.abs(dataF)
dataPWFabs = dataPWFabs - np.min(dataPWFabs)
dataPWFabs = dataPWFabs / np.max(dataPWFabs)
dataPWSpect = sphericalAverage(dataPWFabs**2) + epsilon
dataPW = np.real(fftpack.ifftn(fftpack.ifftshift(dataF)))
del dataF
dataPWBG = np.multiply(dataPW, softBGmask)
dataPWBG = np.array(fftpack.fftshift(
fftpack.fftn(dataPWBG, overwrite_x=True)),
dtype='complex64')
dataPWBGFabs = np.abs(dataPWBG)
del dataPWBG
dataPWBGFabs = dataPWBGFabs - np.min(dataPWBGFabs)
dataPWBGFabs = dataPWBGFabs / np.max(dataPWBGFabs)
dataPWBGSpect = sphericalAverage(dataPWBGFabs**2) + epsilon
m, s = divmod(time() - tStart, 60)
print " :: Time elapsed: %d minutes and %.2f seconds" % (m, s)
return {
'dataPW': dataPW,
'dataPWSpect': dataPWSpect,
'dataPWBGSpect': dataPWBGSpect,
'peval': pWfilter['peval']
}
def preWhitenCube(**kwargs):
'''
Pre-whitenening using noise estimates from a cube taken from the difference map.
Returns a the pre-whitened volume and various spectra. (Alp Kucukelbir, 2013)
'''
print '\n= Pre-whitening the Cubes'
tStart = time()
n = kwargs.get('n', 0)
vxSize = kwargs.get('vxSize', 0)
elbowAngstrom = kwargs.get('elbowAngstrom', 0)
rampWeight = kwargs.get('rampWeight',1.0)
dataF = kwargs.get('dataF', 0)
dataBGF = kwargs.get('dataBGF', 0)
dataBGSpect = kwargs.get('dataBGSpect', 0)
epsilon = 1e-10
pWfilter = createPreWhiteningFilter(n = n,
spectrum = dataBGSpect,
elbowAngstrom = elbowAngstrom,
rampWeight = rampWeight,
vxSize = vxSize)
# Apply the pre-whitening filter to the inside cube
dataF = np.multiply(pWfilter['pWfilter'],dataF)
dataPWFabs = np.abs(dataF)
dataPWFabs = dataPWFabs-np.min(dataPWFabs)
dataPWFabs = dataPWFabs/np.max(dataPWFabs)
dataPWSpect = sphericalAverage(dataPWFabs**2) + epsilon
dataPW = np.real(fftpack.ifftn(fftpack.ifftshift(dataF)))
del dataF
# Apply the pre-whitening filter to the outside cube
dataBGF = np.multiply(pWfilter['pWfilter'],dataBGF)
dataPWBGFabs = np.abs(dataBGF)
dataPWBGFabs = dataPWBGFabs-np.min(dataPWBGFabs)
dataPWBGFabs = dataPWBGFabs/np.max(dataPWBGFabs)
dataPWBGSpect = sphericalAverage(dataPWBGFabs**2) + epsilon
dataBGPW = np.real(fftpack.ifftn(fftpack.ifftshift(dataBGF)))
del dataBGF
m, s = divmod(time() - tStart, 60)
print " :: Time elapsed: %d minutes and %.2f seconds" % (m, s)
return {'dataPW':dataPW, 'dataBGPW':dataBGPW, 'dataPWSpect': dataPWSpect, 'dataPWBGSpect': dataPWBGSpect, 'peval': pWfilter['peval'], 'pcoef': pWfilter['pcoef'] }
def preWhitenVolumeSoftBG(**kwargs):
'''
Pre-whitenening using noise estimates from a soft mask of the background.
Returns a the pre-whitened volume and various spectra. (Alp Kucukelbir, 2013)
'''
print '\n= Pre-whitening'
tStart = time()
n = kwargs.get('n', 0)
elbowAngstrom = kwargs.get('elbowAngstrom', 0)
dataBGSpect = kwargs.get('dataBGSpect', 0)
dataF = kwargs.get('dataF', 0)
softBGmask = kwargs.get('softBGmask', 0)
vxSize = kwargs.get('vxSize', 0)
rampWeight = kwargs.get('rampWeight',1.0)
epsilon = 1e-10
pWfilter = createPreWhiteningFilter(n = n,
spectrum = dataBGSpect,
elbowAngstrom = elbowAngstrom,
rampWeight = rampWeight,
vxSize = vxSize)
# Apply the pre-whitening filter
dataF = np.multiply(pWfilter['pWfilter'],dataF)
dataPWFabs = np.abs(dataF)
dataPWFabs = dataPWFabs-np.min(dataPWFabs)
dataPWFabs = dataPWFabs/np.max(dataPWFabs)
dataPWSpect = sphericalAverage(dataPWFabs**2) + epsilon
dataPW = np.real(fftpack.ifftn(fftpack.ifftshift(dataF)))
del dataF
dataPWBG = np.multiply(dataPW,softBGmask)
dataPWBG = np.array(fftpack.fftshift(fftpack.fftn(dataPWBG,overwrite_x=True)), dtype='complex64')
dataPWBGFabs = np.abs(dataPWBG)
del dataPWBG
dataPWBGFabs = dataPWBGFabs-np.min(dataPWBGFabs)
dataPWBGFabs = dataPWBGFabs/np.max(dataPWBGFabs)
dataPWBGSpect = sphericalAverage(dataPWBGFabs**2) + epsilon
m, s = divmod(time() - tStart, 60)
print " :: Time elapsed: %d minutes and %.2f seconds" % (m, s)
return {'dataPW':dataPW, 'dataPWSpect': dataPWSpect, 'dataPWBGSpect': dataPWBGSpect, 'peval': pWfilter['peval'] }
def calculatePowerSpectrum(data):
epsilon = 1e-10
dataF = np.array(fftpack.fftshift(fftpack.fftn(data)), dtype='complex64')
dataFabs = np.abs(dataF)
dataFabs = dataFabs - np.min(dataFabs)
dataFabs = dataFabs / np.max(dataFabs)
dataPowerSpectrum = sphericalAverage(dataFabs**2) + epsilon
del dataFabs
return (dataF, dataPowerSpectrum)
def calculatePowerSpectrum(data): epsilon = 1e-10 dataF = np.array(fftpack.fftshift(fftpack.fftn(data)), dtype='complex64') dataFabs = np.abs(dataF) dataFabs = dataFabs-np.min(dataFabs) dataFabs = dataFabs/np.max(dataFabs) dataPowerSpectrum = sphericalAverage(dataFabs**2) + epsilon del dataFabs return (dataF, dataPowerSpectrum)
