def FSCSum(volume,reference,numberOfBands,wedgeAngle=-1):
"""
FSCSum: Determines the sum of the Fourier Shell Correlation coefficient for a volume and reference.
@param volume: A volume
@type volume: L{pytom_volume.vol}
@param reference: A reference of same size as volume
@type reference: L{pytom_volume.vol}
@param numberOfBands: Number of bands
@param wedgeAngle: A optional wedge angle
@return: The sum FSC coefficient
@rtype: float
@author: Thomas Hrabe
"""
from pytom.basic.correlation import bandCC
import pytom_volume
from pytom.basic.fourier import fft
from math import sqrt
import pytom_freqweight
result = 0
numberVoxels = 0
#volume.write('vol.em');
#reference.write('ref.em');
fvolume = fft(volume)
freference = fft(reference)
numelem = volume.numelem()
fvolume.shiftscale(0,1/float(numelem))
freference.shiftscale(0,1/float(numelem))
#print '-----'
for i in range(numberOfBands):
#process bandCorrelation
band = []
band[0] = i*volume.sizeX()/numberOfBands
band[1] = (i+1)*volume.sizeX()/numberOfBands
r = bandCC(fvolume,freference,band)
cc = r[0]
#print cc
result = result + cc
#print '-----'
return result*(1/float(numberOfBands))
def FSC(volume1,volume2,numberBands,mask=None,verbose=False, filename=None):
"""
FSC - Calculates the Fourier Shell Correlation for two volumes
@param volume1: volume one
@type volume1: L{pytom_volume.vol}
@param volume2: volume two
@type volume2: L{pytom_volume.vol}
@param numberBands: number of shells for FSC
@type numberBands: int
@param filename: write FSC to ascii file if specified
@type filename: string
@return: Returns a list of cc values
@author: Thomas Hrabe
@rtype: list[floats]
"""
from pytom.basic.correlation import bandCC
from pytom.tools.macros import volumesSameSize
from pytom.basic.structures import Mask
import pytom_volume
if not volumesSameSize(volume1, volume2):
raise RuntimeError('Volumes must have the same size!')
if mask:
if mask.__class__ == pytom_volume.vol:
volume1 = volume1 * mask
volume2 = volume2 * mask
elif mask.__class__ == Mask:
mask = mask.getVolume()
volume1 = volume1 * mask
volume2 = volume2 * mask
elif mask.__class__ == str:
mask = pytom_volume.read(mask)
volume1 = volume1 * mask
volume2 = volume2 * mask
else:
raise RuntimeError('FSC: Mask must be a volume OR a Mask object OR a string path to a mask!')
fscResult = []
band = [-1,-1]
increment = int(volume1.sizeX()/2 * 1/numberBands)
for i in range(0,volume1.sizeX()//2, increment):
band[0] = i
band[1] = i + increment
if verbose:
print('Band : ' ,band)
res = bandCC(volume1,volume2,band,verbose)
if i == 0 and increment == 1:
#force a 1 for correlation of the zero frequency
res[0] = 1
if verbose:
print('Correlation ' ,res[0])
fscResult.append(res[0])
if filename:
f = open(filename,'w')
for item in fscResult:
f.write("%s\n" % item)
f.close()
return fscResult
def weightedXCC(volume,reference,numberOfBands,wedgeAngle=-1):
"""
weightedXCC: Determines the band weighted correlation coefficient for a volume and reference. Notation according Steward/Grigorieff paper
@param volume: A volume
@type volume: L{pytom_volume.vol}
@param reference: A reference of same size as volume
@type reference: L{pytom_volume.vol}
@param numberOfBands: Number of bands
@param wedgeAngle: A optional wedge angle
@return: The weighted correlation coefficient
@rtype: float
@author: Thomas Hrabe
"""
import pytom_volume
from pytom.basic.fourier import fft
from math import sqrt
import pytom_freqweight
result = 0
numberVoxels = 0
#volume.write('vol.em');
#reference.write('ref.em');
fvolume = fft(volume)
freference = fft(reference)
numelem = volume.numelem()
fvolume.shiftscale(0,1/float(numelem))
freference.shiftscale(0,1/float(numelem))
from pytom.basic.structures import WedgeInfo
wedge = WedgeInfo(wedgeAngle)
wedgeVolume = wedge.returnWedgeVolume(volume.sizeX(),volume.sizeY(),volume.sizeZ())
increment = int(volume.sizeX()/2 * 1/numberOfBands)
band = [0,100]
for i in range(0,volume.sizeX()/2, increment):
band[0] = i
band[1] = i + increment
r = bandCC(volume,reference,band)
cc = r[0]
#print cc;
filter = r[1]
#get bandVolume
bandVolume = filter.getWeightVolume(True)
filterVolumeReduced = bandVolume * wedgeVolume
filterVolume = pytom_volume.reducedToFull(filterVolumeReduced)
#determine number of voxels != 0
N = pytom_volume.numberSetVoxels(filterVolume)
w = sqrt(1/float(N))
#add to number of total voxels
numberVoxels=numberVoxels + N
#print 'w',w;
#print 'cc',cc;
#print 'N',N;
cc2 = cc*cc
#print 'cc2',cc2;
if cc <= 0.0:
cc = cc2
else:
cc = cc2/(cc+w)
#print 'cc',cc;
cc = cc *cc *cc; #no abs
#print 'cc',cc;
#add up result
result = result + cc*N
return result*(1/float(numberVoxels))