def convolute(v, k, kernel_in_fourier=False):
"""
@param v: the volume to be convolute
@type v: L{pytom_volume.vol}
@param k: the convolution kernel in real space
@type k: L{pytom_volume.vol}
@param kernel_in_fourier: the given kernel is already in Fourier space or not (full size, shifted in the center! \
or reduced size). Default is False.
@type kernel_in_fourier: L{bool}
@return: Volume convoluted with kernel
@rtype: L{pytom_volume.vol}
"""
fv = fft(v)
if not kernel_in_fourier:
fk = fft(k)
res = fv*fk
else:
from pytom_volume import complexRealMult, fullToReduced
if k.sizeZ() == k.sizeX():
fk = fullToReduced(ftshift(k, inplace=False))
else:
fk = k
res = complexRealMult(fv, fk)
out = ifft(res)
out.shiftscale(0.0,1/float(out.sizeX()*out.sizeY()*out.sizeZ()))
return out
def create_average(self, sum_ctf_conv, sum_ctf_squared, wedge_weight):
"""For the master node, this function is rewritten.
"""
from pytom_volume import vol, complexDiv, fullToReduced, initSphere, complexRealMult, limit
from pytom.basic.fourier import fft, ifft, ftshift
from pytom.basic.normalise import mean0std1
# limit(wedge_weight, 0.1, 0, 0,0,True,False) # set all the values below the specified value to 0
# for mask out the outside area
# mask = vol(sum_ctf_conv)
# mask.setAll(0)
# initSphere(mask, sum_ctf_conv.sizeX()/2-1, 0,0, sum_ctf_conv.sizeX()/2, sum_ctf_conv.sizeX()/2, sum_ctf_conv.sizeX()/2)
# mask = fullToReduced(ftshift(mask, inplace=False))
# Wiener filter
numerator = fft(sum_ctf_conv)
sum_ctf_squared = fullToReduced(ftshift(sum_ctf_squared, inplace=False))
denominator = (sum_ctf_squared+1)*wedge_weight
r = complexDiv(numerator, denominator)
# average = ifft(complexRealMult(r, mask))
average = ifft(r)
average.shiftscale(0.0,1/float(average.sizeX()*average.sizeY()*average.sizeZ()))
# nomalize the average
try:
average = mean0std1(average, True)
except:
average *= 1000 # in case the average volume is too small to normalize
average = mean0std1(average, True)
return average
def rotateWeighting(weighting, z1, z2, x, mask=None, isReducedComplex=None, returnReducedComplex=False, binarize=False):
"""
rotateWeighting: Rotates a frequency weighting volume around the center. If the volume provided is reduced complex, it will be rescaled to full size, ftshifted, rotated, iftshifted and scaled back to reduced size.
@param weighting: A weighting volume
@type weighting: L{pytom_volume.vol}
@param z1: Z1 rotation angle
@type z1: float
@param z2: Z2 rotation angle
@type z2: float
@param x: X rotation angle
@type x: float
@param mask:=None is there a rotation mask? A mask with all = 1 will be generated otherwise. Such mask should be \
provided anyway.
@type mask: L{pytom_volume.vol}
@param isReducedComplex: Either set to True or False. Will be determined otherwise
@type isReducedComplex: bool
@param returnReducedComplex: Return as reduced complex? (Default is False)
@type returnReducedComplex: bool
@param binarize: binarize weighting
@type binarize: bool
@return: weight as reduced complex volume
@rtype: L{pytom_volume.vol_comp}
"""
from pytom_volume import vol, limit, vol_comp
from pytom_volume import rotate
assert type(weighting) == vol or type(weighting) == vol_comp, "rotateWeighting: input neither vol nor vol_comp"
isReducedComplex = isReducedComplex or int(weighting.sizeX()/2)+1 == weighting.sizeZ();
if isReducedComplex:
#scale weighting to full size
from pytom_fftplan import fftShift
from pytom_volume import reducedToFull
weighting = reducedToFull(weighting)
fftShift(weighting, True)
if not mask:
mask = vol(weighting.sizeX(),weighting.sizeY(),weighting.sizeZ())
mask.setAll(1)
weightingRotated = vol(weighting.sizeX(),weighting.sizeY(),weighting.sizeZ())
rotate(weighting,weightingRotated,z1,z2,x)
weightingRotated = weightingRotated * mask
if returnReducedComplex:
from pytom_fftplan import fftShift
from pytom_volume import fullToReduced
fftShift(weightingRotated,True)
returnVolume = fullToReduced(weightingRotated)
else:
returnVolume = weightingRotated
if binarize:
limit(returnVolume,0.5,0,0.5,1,True,True)
return returnVolume
def shift(volume,shiftX,shiftY,shiftZ,imethod='fourier',twice=False):
"""
shift: Performs a shift on a volume
@param volume: the volume
@param shiftX: shift in x direction
@param shiftY: shift in y direction
@param shiftZ: shift in z direction
@param imethod: Select interpolation method. Real space : linear, cubic, spline . Fourier space: fourier
@param twice: Zero pad volume into a twice sized volume and perform calculation there.
@return: The shifted volume.
@author: Yuxiang Chen and Thomas Hrabe
"""
if imethod == 'fourier':
from pytom_volume import vol_comp,reducedToFull,fullToReduced,shiftFourier
from pytom.basic.fourier import fft,ifft,ftshift, iftshift
fvolume = fft(volume)
fullFVolume = reducedToFull(fvolume)
destFourier = vol_comp(fullFVolume.sizeX(),fullFVolume.sizeY(),fullFVolume.sizeZ())
shiftFourier(fullFVolume,destFourier,shiftX,shiftY,shiftZ)
resFourier = fullToReduced(destFourier)
return ifft(resFourier)/volume.numelem()
else:
from pytom_volume import vol
if imethod == 'linear':
from pytom_volume import transform
elif imethod == 'cubic':
from pytom_volume import transformCubic as transform
elif imethod == 'spline':
from pytom_volume import transformSpline as transform
# now results should be consistent with python2
centerX = int(volume.sizeX()/2)
centerY = int(volume.sizeY()/2)
centerZ = int(volume.sizeZ()/2)
res = vol(volume.sizeX(),volume.sizeY(),volume.sizeZ())
transform(volume,res,0,0,0,centerX,centerY,centerZ,shiftX,shiftY,shiftZ,0,0,0)
return res