def test_FRM(self):
import swig_frm
from sh_alignment.frm import frm_align
from pytom_volume import vol, rotate, shift
from pytom.basic.structures import Rotation, Shift
from pytom.tools.maths import rotation_distance
v = vol(32,32,32)
v.setAll(0)
vMod = vol(32,32,32)
vRot = vol(32,32,32)
v.setV(1,10,10,10)
v.setV(1,20,20,20)
v.setV(1,15,15,15)
v.setV(1,7,21,7)
rotation = Rotation(10,20,30)
shiftO = Shift(1,-3,5)
rotate(v,vRot,rotation.getPhi(),rotation.getPsi(),rotation.getTheta())
shift(vRot,vMod,shiftO.getX(),shiftO.getY(),shiftO.getZ())
pos, ang, score = frm_align(vMod, None, v, None, [4, 64], 10)
rotdist = rotation_distance(ang1=rotation, ang2=ang)
diffx = shiftO[0] - (pos[0] - 16)
diffy = shiftO[1] - (pos[1] - 16)
diffz = shiftO[2] - (pos[2] - 16)
self.assertTrue( rotdist < 5., msg='Rotations are different')
self.assertTrue( diffx < .5, msg='x-difference > .5')
self.assertTrue( diffy < .5, msg='y-difference > .5')
self.assertTrue( diffz < .5, msg='z-difference > .5')
def frm_proxy(p, ref, freq, offset, binning, mask):
from pytom_volume import read, pasteCenter, vol
from pytom.basic.transformations import resize
from pytom.basic.structures import Shift, Rotation
from sh_alignment.frm import frm_align
import time
v = p.getVolume(binning)
if mask.__class__ == str:
maskBin = read(mask, 0, 0, 0, 0, 0, 0, 0, 0, 0, binning, binning,
binning)
if v.sizeX() != maskBin.sizeX() or v.sizeY() != maskBin.sizeY(
) or v.sizeZ() != maskBin.sizeZ():
mask = vol(v.sizeX(), v.sizeY(), v.sizeZ())
mask.setAll(0)
pasteCenter(maskBin, mask)
else:
mask = maskBin
pos, angle, score = frm_align(v, p.getWedge(), ref.getVolume(), None,
[4, 64], freq, offset, mask)
return (Shift([
pos[0] - v.sizeX() // 2, pos[1] - v.sizeY() // 2,
pos[2] - v.sizeZ() // 2
]), Rotation(angle), score, p.getFilename())
def run(self, verbose=False):
from sh_alignment.frm import frm_align
from pytom.basic.structures import Shift, Rotation
from pytom.tools.ProgressBar import FixedProgBar
while True:
# get the job
try:
job = self.get_job()
except:
if verbose:
print(self.node_name + ': end')
break # get some non-job message, break it
if verbose:
prog = FixedProgBar(0,
len(job.particleList) - 1,
self.node_name + ':')
i = 0
ref = job.reference[0].getVolume()
# run the job
for p in job.particleList:
if verbose:
prog.update(i)
i += 1
v = p.getVolume()
pos, angle, score = frm_align(v, p.getWedge(), ref, None,
job.bw_range, job.freq,
job.peak_offset,
job.mask.getVolume())
p.setShift(
Shift([
pos[0] - v.sizeX() / 2, pos[1] - v.sizeY() / 2,
pos[2] - v.sizeZ() / 2
]))
p.setRotation(Rotation(angle))
p.setScore(FRMScore(score))
# average the particle list
name_prefix = os.path.join(
self.destination, self.node_name + '_' + str(job.max_iter))
self.average_sub_pl(job.particleList, name_prefix, job.weighting)
# send back the result
self.send_result(
FRMResult(name_prefix, job.particleList, self.mpi_id))
pytom_mpi.finalise()
def FRMAlignmentWrapper(particle,wedgeParticle, reference, wedgeReference,bandwidth, highestFrequency, mask = None , peakPrior = None):
"""
FRMAlignmentWrapper: Wrapper for frm_align to handle PyTom objects.
@param particle: The particle
@type particle: L{pytom.basic.structures.Particle}
@param wedgeParticle: Wedge object of particle
@type wedgeParticle: L{pytom.basic.structures.Wedge}
@param reference: Reference used for alignment
@type reference: L{pytom.basic.structures.Reference}
@param wedgeReference: Information about reference wedge
@type wedgeReference: L{pytom.basic.structures.Wedge}
@param bandwidth: The bandwidth of the spherical harmonics - lowestBand used, highestBand used
@type bandwidth: [lowestBand,highestBand]
@param highestFrequency: Highest frequency for lowpass filter in fourierspace
@type highestFrequency: int
@param mask: Mask that is applied to the particle
@type mask: L{pytom.basic.structures.Mask}
@param peakPrior: Maximum distance of peak from origin
@type peakPrior: L{pytom.score.score.PeakPrior} or an integer
@return: Returns a list of [L{pytom.basic.structures.Shift}, L{pytom.basic.structures.Rotation}, scoreValue]
"""
from pytom.basic.structures import Particle,Reference,Mask,Wedge,Shift,Rotation
from pytom.score.score import PeakPrior
from sh_alignment.frm import frm_align
if particle.__class__ == Particle:
particle = particle.getVolume()
if reference.__class__ == Reference:
reference = reference.getVolume()
if mask.__class__ == Mask:
mask = mask.getVolume()
else:
mask = None
if bandwidth.__class__ == list and len(bandwidth) != 2:
raise RuntimeError('Bandwidth parameter must be a list of two integers!')
if peakPrior and peakPrior.__class__ == PeakPrior:
peakPrior = int(peakPrior.getRadius())
if not peakPrior or peakPrior < 0.0001:
peakPrior = None
pos, angle, score = frm_align(particle, wedgeParticle.getWedgeObject(), reference*mask, wedgeReference.getWedgeObject(), bandwidth, int(highestFrequency), peakPrior)
return [Shift([pos[0]-particle.sizeX()/2, pos[1]-particle.sizeY()/2, pos[2]-particle.sizeZ()/2]), Rotation(angle), score]
def calculate_difference_map(v1,
band1,
v2,
band2,
mask=None,
focus_mask=None,
align=True,
sigma=None,
threshold=0.4):
"""mask if for alignment, while focus_mask is for difference map.
"""
from pytom_volume import vol, power, abs, limit, transformSpline, variance, mean, max, min
from pytom.basic.normalise import mean0std1
from pytom.basic.filter import lowpassFilter
# do lowpass filtering first
lv1 = lowpassFilter(v1, band1, band1 / 10.)[0]
lv2 = lowpassFilter(v2, band2, band2 / 10.)[0]
# do alignment of two volumes, if required. v1 is used as reference.
if align:
from sh_alignment.frm import frm_align
band = int(band1 if band1 < band2 else band2)
pos, angle, score = frm_align(lv2, None, lv1, None, [4, 64], band,
lv1.sizeX() // 4, mask)
shift = [
pos[0] - v1.sizeX() // 2, pos[1] - v1.sizeY() // 2,
pos[2] - v1.sizeZ() // 2
]
# transform v2
lvv2 = vol(lv2)
transformSpline(lv2, lvv2, -angle[1], -angle[0], -angle[2],
lv2.sizeX() // 2,
lv2.sizeY() // 2,
lv2.sizeZ() // 2, -shift[0], -shift[1], -shift[2], 0,
0, 0)
else:
lvv2 = lv2
# do normalization
mean0std1(lv1)
mean0std1(lvv2)
# only consider the density beyond certain sigma
if sigma is None or sigma == 0:
pass
elif sigma < 0: # negative density counts
assert min(lv1) < sigma
assert min(lvv2) < sigma
limit(lv1, 0, 0, sigma, 0, False, True)
limit(lvv2, 0, 0, sigma, 0, False, True)
else: # positive density counts
assert max(lv1) > sigma
assert max(lvv2) > sigma
limit(lv1, sigma, 0, 0, 0, True, False)
limit(lvv2, sigma, 0, 0, 0, True, False)
# if we want to focus on specific area only
if focus_mask:
lv1 *= focus_mask
lvv2 *= focus_mask
# calculate the STD map
avg = (lv1 + lvv2) / 2
var1 = avg - lv1
power(var1, 2)
var2 = avg - lvv2
power(var2, 2)
std_map = var1 + var2
power(std_map, 0.5)
# calculate the coefficient of variance map
# std_map = std_map/abs(avg)
if focus_mask:
std_map *= focus_mask
# threshold the STD map
mv = mean(std_map)
threshold = mv + (max(std_map) - mv) * threshold
limit(std_map, threshold, 0, threshold, 1, True, True)
# do a lowpass filtering
std_map1 = lowpassFilter(std_map, v1.sizeX() // 4, v1.sizeX() / 40.)[0]
if align:
std_map2 = vol(std_map)
transformSpline(std_map1, std_map2, angle[0], angle[1], angle[2],
v1.sizeX() // 2,
v1.sizeY() // 2,
v1.sizeZ() // 2, 0, 0, 0, shift[0], shift[1], shift[2])
else:
std_map2 = std_map1
limit(std_map1, 0.5, 0, 1, 1, True, True)
limit(std_map2, 0.5, 0, 1, 1, True, True)
# return the respective difference maps
return (std_map1, std_map2)
def run(self, verbose=False):
from sh_alignment.frm import frm_align
from sh_alignment.constrained_frm import frm_constrained_align, AngularConstraint
from pytom.basic.structures import Shift, Rotation
from pytom.tools.ProgressBar import FixedProgBar
from pytom.basic.transformations import resize, resizeFourier
binningType = 'Fourier'
while True:
# get the job
try:
job = self.get_job()
except:
if verbose:
print(self.node_name + ': end')
break # get some non-job message, break it
if verbose:
prog = FixedProgBar(0,
len(job.particleList) - 1,
self.node_name + ':')
i = 0
ref = job.reference.getVolume()
if job.binning > 1:
ref = resize(volume=ref,
factor=1. / job.binning,
interpolation=binningType)
if type(ref) == tuple:
ref = ref[0]
# re-set max frequency in case it exceeds Nyquist - a bit brute force
job.freq = min(job.freq, ref.sizeX() // 2 - 1)
# run the job
for p in job.particleList:
if verbose:
prog.update(i)
i += 1
v = p.getVolume()
if job.binning > 1:
v = resize(volume=v,
factor=1. / job.binning,
interpolation=binningType)
if type(v) == tuple:
v = v[0]
mask = job.mask.getVolume()
if job.binning > 1:
mask = resize(volume=mask,
factor=1. / job.binning,
interpolation='Spline')
if type(mask) == tuple:
mask = mask[0]
if job.constraint:
constraint = job.constraint
if job.constraint.type == AngularConstraint.ADP_ANGLE: # set the constraint around certain angle
rot = p.getRotation()
constraint.setAngle(rot.getPhi(), rot.getPsi(),
rot.getTheta())
#pos, angle, score = frm_constrained_align(v, p.getWedge(), ref, None, job.bw_range, job.freq, job.peak_offset, job.mask.getVolume(), constraint)
if job.binning > 1:
pos, angle, score = frm_constrained_align(
v, p.getWedge(), ref, None, job.bw_range, job.freq,
job.peak_offset / job.binning, mask, constraint)
else:
pos, angle, score = frm_constrained_align(
v, p.getWedge(), ref, None, job.bw_range, job.freq,
job.peak_offset, mask, constraint)
else:
#pos, angle, score = frm_align(v, p.getWedge(), ref, None, job.bw_range, job.freq, job.peak_offset, job.mask.getVolume())
#if job.binning >1:
# print(job.peak_offset)
# print(type(job.peak_offset))
# print(job.peak_offset/job.binning)
# print(type(job.binning))
# pos, angle, score = frm_align(v, p.getWedge(), ref, None, job.bw_range, job.freq,
# job.peak_offset/job.binning, mask)
#else:
pos, angle, score = frm_align(v, p.getWedge(), ref, None,
job.bw_range, job.freq,
job.peak_offset, mask)
if job.binning > 1:
pos[0] = job.binning * (pos[0] - v.sizeX() / 2)
pos[1] = job.binning * (pos[1] - v.sizeY() / 2)
pos[2] = job.binning * (pos[2] - v.sizeZ() / 2)
p.setShift(Shift([pos[0], pos[1], pos[2]]))
else:
p.setShift(
Shift([
pos[0] - v.sizeX() / 2, pos[1] - v.sizeY() / 2,
pos[2] - v.sizeZ() / 2
]))
p.setRotation(Rotation(angle))
p.setScore(FRMScore(score))
# average the particle list
name_prefix = os.path.join(
job.destination, self.node_name + '_' + str(job.max_iter))
self.average_sub_pl(job.particleList, name_prefix, job.weighting)
# send back the result
self.send_result(
FRMResult(name_prefix, job.particleList, self.mpi_id))
pytom_mpi.finalise()
def start(self, job, verbose=False):
if self.mpi_id == 0:
from pytom.basic.structures import ParticleList, Reference
from pytom.basic.resolution import bandToAngstrom
from pytom.basic.filter import lowpassFilter
from math import ceil
# randomly split the particle list into 2 half sets
if len(job.particleList.splitByClass()) != 2:
import numpy as np
n = len(job.particleList)
labels = np.random.randint(2, size=(n, ))
print(self.node_name + ': Number of 1st half set:',
n - np.sum(labels), 'Number of 2nd half set:',
np.sum(labels))
for i in range(n):
p = job.particleList[i]
p.setClass(labels[i])
self.destination = job.destination
new_reference = job.reference
old_freq = job.freq
new_freq = job.freq
# main node
for i in range(job.max_iter):
if verbose:
print(self.node_name + ': starting iteration %d ...' % i)
# construct a new job by updating the reference and the frequency
new_job = FRMJob(job.particleList, new_reference, job.mask,
job.peak_offset, job.sampleInformation,
job.bw_range, new_freq, job.destination,
job.max_iter - i, job.r_score, job.weighting)
# distribute it
self.distribute_job(new_job, verbose)
# get the result back
all_even_pre = None # the 1st set
all_even_wedge = None
all_odd_pre = None # the 2nd set
all_odd_wedge = None
pl = ParticleList()
for j in range(self.num_workers):
result = self.get_result()
pl += result.pl
pre, wedge = self.retrieve_res_vols(result.name)
if self.assignment[result.worker_id] == 0:
if all_even_pre:
all_even_pre += pre
all_even_wedge += wedge
else:
all_even_pre = pre
all_even_wedge = wedge
else:
if all_odd_pre:
all_odd_pre += pre
all_odd_wedge += wedge
else:
all_odd_pre = pre
all_odd_wedge = wedge
# write the new particle list to the disk
pl.toXMLFile('aligned_pl_iter' + str(i) + '.xml')
# create the averages separately
if verbose:
print(self.node_name + ': determining the resolution ...')
even = self.create_average(all_even_pre, all_even_wedge)
odd = self.create_average(all_odd_pre, all_odd_wedge)
# apply symmetries if any
even = job.symmetries.applyToParticle(even)
odd = job.symmetries.applyToParticle(odd)
# determine the transformation between even and odd
# here we assume the wedge from both sets are fully sampled
from sh_alignment.frm import frm_align
pos, angle, score = frm_align(odd, None, even, None,
job.bw_range, new_freq,
job.peak_offset)
print(self.node_name +
'Transform of even set to match the odd set - shift: ' +
str(pos) + ' rotation: ' + str(angle))
# transform the odd set accordingly
from pytom_volume import vol, transformSpline
from pytom.basic.fourier import ftshift
from pytom_volume import reducedToFull
from pytom_freqweight import weight
transformed_odd_pre = vol(odd.sizeX(), odd.sizeY(),
odd.sizeZ())
full_all_odd_wedge = reducedToFull(all_odd_wedge)
ftshift(full_all_odd_wedge)
odd_weight = weight(
full_all_odd_wedge) # the funny part of pytom
transformed_odd = vol(odd.sizeX(), odd.sizeY(), odd.sizeZ())
transformSpline(all_odd_pre, transformed_odd_pre, -angle[1],
-angle[0], -angle[2],
odd.sizeX() / 2,
odd.sizeY() / 2,
odd.sizeZ() / 2, -(pos[0] - odd.sizeX() / 2),
-(pos[1] - odd.sizeY() / 2),
-(pos[2] - odd.sizeZ() / 2), 0, 0, 0)
odd_weight.rotate(-angle[1], -angle[0], -angle[2])
transformed_odd_wedge = odd_weight.getWeightVolume(True)
transformSpline(odd, transformed_odd, -angle[1], -angle[0],
-angle[2],
odd.sizeX() / 2,
odd.sizeY() / 2,
odd.sizeZ() / 2, -(pos[0] - odd.sizeX() / 2),
-(pos[1] - odd.sizeY() / 2),
-(pos[2] - odd.sizeZ() / 2), 0, 0, 0)
all_odd_pre = transformed_odd_pre
all_odd_wedge = transformed_odd_wedge
odd = transformed_odd
# determine resolution
resNyquist, resolutionBand, numberBands = self.determine_resolution(
even, odd, job.fsc_criterion, None, job.mask, verbose)
# write the half set to the disk
even.write(
os.path.join(self.destination,
'fsc_' + str(i) + '_even.em'))
odd.write(
os.path.join(self.destination,
'fsc_' + str(i) + '_odd.em'))
current_resolution = bandToAngstrom(
resolutionBand, job.sampleInformation.getPixelSize(),
numberBands, 1)
if verbose:
print(
self.node_name + ': current resolution ' +
str(current_resolution), resNyquist)
# create new average
all_even_pre += all_odd_pre
all_even_wedge += all_odd_wedge
average = self.create_average(all_even_pre, all_even_wedge)
# apply symmetries
average = job.symmetries.applyToParticle(average)
# filter average to resolution
average_name = os.path.join(self.destination,
'average_iter' + str(i) + '.em')
average.write(average_name)
# update the references
new_reference = [
Reference(
os.path.join(self.destination,
'fsc_' + str(i) + '_even.em')),
Reference(
os.path.join(self.destination,
'fsc_' + str(i) + '_odd.em'))
]
# low pass filter the reference and write it to the disk
filtered = lowpassFilter(average, ceil(resolutionBand),
ceil(resolutionBand) / 10)
filtered_ref_name = os.path.join(
self.destination, 'average_iter' + str(i) + '_res' +
str(current_resolution) + '.em')
filtered[0].write(filtered_ref_name)
# if the position/orientation is not improved, break it
# change the frequency to a higher value
new_freq = int(ceil(resolutionBand)) + 1
if new_freq <= old_freq:
if job.adaptive_res is not False: # two different strategies
print(
self.node_name +
': Determined resolution gets worse. Include additional %f percent frequency to be aligned!'
% job.adaptive_res)
new_freq = int((1 + job.adaptive_res) * old_freq)
else: # always increase by 1
print(
self.node_name +
': Determined resolution gets worse. Increase the frequency to be aligned by 1!'
)
new_freq = old_freq + 1
old_freq = new_freq
else:
old_freq = new_freq
if new_freq >= numberBands:
print(self.node_name +
': New frequency too high. Terminate!')
break
if verbose:
print(self.node_name + ': change the frequency to ' +
str(new_freq))
# send end signal to other nodes and terminate itself
self.end(verbose)
else:
# other nodes
self.run(verbose)
def run(self, verbose=False):
from sh_alignment.frm import frm_align
from pytom.basic.structures import Shift, Rotation
from pytom.tools.ProgressBar import FixedProgBar
from pytom.basic.fourier import convolute
from pytom_volume import read, power
while True:
# get the job
try:
job = self.get_job()
except:
if verbose:
print(self.node_name + ': end')
break # get some non-job message, break it
if verbose:
prog = FixedProgBar(0,
len(job.particleList) - 1,
self.node_name + ':')
i = 0
ref = []
ref.append(job.reference[0].getVolume())
ref.append(job.reference[1].getVolume())
# convolute with the approximation of the CTF
if job.sum_ctf_sqr:
ctf = read(job.sum_ctf_sqr)
power(ctf,
0.5) # the number of CTFs should not matter, should it?
ref0 = ref[0]
ref1 = ref[1]
ref0 = convolute(ref0, ctf, True)
ref1 = convolute(ref1, ctf, True)
ref = [ref0, ref1]
if job.bfactor and job.bfactor != 'None':
# restore_kernel = create_bfactor_restore_vol(ref.sizeX(), job.sampleInformation.getPixelSize(), job.bfactor)
from pytom_volume import vol, read
bfactor_kernel = read(job.bfactor)
unit = vol(bfactor_kernel)
unit.setAll(1)
restore_kernel = unit / bfactor_kernel
# run the job
for p in job.particleList:
if verbose:
prog.update(i)
i += 1
v = p.getVolume()
# if weights is None: # create the weights according to the bfactor
# if job.bfactor == 0:
# weights = [1 for k in xrange(job.freq)]
# else:
# restore_fnc = create_bfactor_restore_fnc(ref.sizeX(), job.sampleInformation.getPixelSize(), job.bfactor)
# # cut out the corresponding part and square it to get the weights!
# weights = restore_fnc[1:job.freq+1]**2
if job.bfactor and job.bfactor != 'None':
v = convolute(v, restore_kernel,
True) # if bfactor is set, restore it
pos, angle, score = frm_align(v, p.getWedge(),
ref[int(p.getClass())], None,
job.bw_range, job.freq,
job.peak_offset,
job.mask.getVolume())
p.setShift(
Shift([
pos[0] - v.sizeX() / 2, pos[1] - v.sizeY() / 2,
pos[2] - v.sizeZ() / 2
]))
p.setRotation(Rotation(angle))
p.setScore(FRMScore(score))
# average the particle list
name_prefix = self.node_name + '_' + str(job.max_iter)
pair = ParticleListPair('', job.ctf_conv_pl, None, None)
pair.set_phase_flip_pl(job.particleList)
self.average_sub_pl(
pair.get_ctf_conv_pl(),
name_prefix) # operate on the CTF convoluted projection!
# send back the result
self.send_result(
FRMResult(name_prefix, job.particleList, self.mpi_id))
pytom_mpi.finalise()
def start(self, job, verbose=False):
if self.mpi_id == 0:
from pytom.basic.structures import ParticleList, Reference
from pytom.basic.resolution import bandToAngstrom
from pytom.basic.filter import lowpassFilter
from math import ceil
from pytom.basic.fourier import convolute
from pytom_volume import vol, power, read
# randomly split the particle list into 2 half sets
import numpy as np
num_pairs = len(job.particleList.pairs)
for i in range(num_pairs):
# randomize the class labels to indicate the two half sets
pl = job.particleList.pairs[i].get_phase_flip_pl()
n = len(pl)
labels = np.random.randint(2, size=(n, ))
print(self.node_name + ': Number of 1st half set:',
n - np.sum(labels), 'Number of 2nd half set:',
np.sum(labels))
for j in range(n):
p = pl[j]
p.setClass(labels[j])
new_reference = job.reference
old_freq = job.freq
new_freq = job.freq
# main node
for i in range(job.max_iter):
if verbose:
print(self.node_name + ': starting iteration %d ...' % i)
# construct a new job by updating the reference and the frequency
# here the job.particleList is actually ParticleListSet
new_job = MultiDefocusJob(job.particleList, new_reference,
job.mask, job.peak_offset,
job.sampleInformation, job.bw_range,
new_freq, job.destination,
job.max_iter - i, job.r_score,
job.weighting, job.bfactor)
# distribute it
num_all_particles = self.distribute_job(new_job, verbose)
# calculate the denominator
sum_ctf_squared = None
for pair in job.particleList.pairs:
if sum_ctf_squared is None:
sum_ctf_squared = pair.get_ctf_sqr_vol() * pair.snr
else:
sum_ctf_squared += pair.get_ctf_sqr_vol() * pair.snr
# get the result back
all_even_pre = None
all_even_wedge = None
all_odd_pre = None
all_odd_wedge = None
pls = []
for j in range(len(job.particleList.pairs)):
pls.append(ParticleList())
for j in range(self.num_workers):
result = self.get_result()
pair_id = self.assignment[result.worker_id]
pair = job.particleList.pairs[pair_id]
pl = pls[pair_id]
pl += result.pl
even_pre, even_wedge, odd_pre, odd_wedge = self.retrieve_res_vols(
result.name)
if all_even_pre:
all_even_pre += even_pre * pair.snr
all_even_wedge += even_wedge
all_odd_pre += odd_pre * pair.snr
all_odd_wedge += odd_wedge
else:
all_even_pre = even_pre * pair.snr
all_even_wedge = even_wedge
all_odd_pre = odd_pre * pair.snr
all_odd_wedge = odd_wedge
# write the new particle list to the disk
for j in range(len(job.particleList.pairs)):
pls[j].toXMLFile('aligned_pl' + str(j) + '_iter' + str(i) +
'.xml')
# correct for the number of particles in wiener filter
sum_ctf_squared = sum_ctf_squared / num_all_particles
# all_even_pre = all_even_pre/(num_all_particles/2)
# all_odd_pre = all_odd_pre/(num_all_particles/2)
# bfactor
if job.bfactor and job.bfactor != 'None':
# bfactor_kernel = create_bfactor_vol(sum_ctf_squared.sizeX(), job.sampleInformation.getPixelSize(), job.bfactor)
bfactor_kernel = read(job.bfactor)
bfactor_kernel_sqr = vol(bfactor_kernel)
power(bfactor_kernel_sqr, 2)
all_even_pre = convolute(all_even_pre, bfactor_kernel,
True)
all_odd_pre = convolute(all_odd_pre, bfactor_kernel, True)
sum_ctf_squared = sum_ctf_squared * bfactor_kernel_sqr
# create averages of two sets
if verbose:
print(self.node_name + ': determining the resolution ...')
even = self.create_average(
all_even_pre, sum_ctf_squared, all_even_wedge
) # assume that the CTF sum is the same for the even and odd
odd = self.create_average(all_odd_pre, sum_ctf_squared,
all_odd_wedge)
# determine the transformation between even and odd
# here we assume the wedge from both sets are fully sampled
from sh_alignment.frm import frm_align
pos, angle, score = frm_align(odd, None, even, None,
job.bw_range, new_freq,
job.peak_offset)
print(
self.node_name +
': transform of even set to match the odd set - shift: ' +
str(pos) + ' rotation: ' + str(angle))
# transform the odd set accordingly
from pytom_volume import vol, transformSpline
from pytom.basic.fourier import ftshift
from pytom_volume import reducedToFull
from pytom_freqweight import weight
transformed_odd_pre = vol(odd.sizeX(), odd.sizeY(),
odd.sizeZ())
full_all_odd_wedge = reducedToFull(all_odd_wedge)
ftshift(full_all_odd_wedge)
odd_weight = weight(
full_all_odd_wedge) # the funny part of pytom
transformed_odd = vol(odd.sizeX(), odd.sizeY(), odd.sizeZ())
transformSpline(all_odd_pre, transformed_odd_pre, -angle[1],
-angle[0], -angle[2], int(odd.sizeX() / 2),
int(odd.sizeY() / 2), int(odd.sizeZ() / 2),
-(pos[0] - odd.sizeX() / 2),
-(pos[1] - odd.sizeY() / 2),
-(pos[2] - odd.sizeZ() / 2), 0, 0, 0)
odd_weight.rotate(-angle[1], -angle[0], -angle[2])
transformed_odd_wedge = odd_weight.getWeightVolume(True)
transformSpline(odd, transformed_odd, -angle[1], -angle[0],
-angle[2], int(odd.sizeX() / 2),
int(odd.sizeY() / 2), int(odd.sizeZ() / 2),
-(pos[0] - odd.sizeX() / 2),
-(pos[1] - odd.sizeY() / 2),
-(pos[2] - odd.sizeZ() / 2), 0, 0, 0)
all_odd_pre = transformed_odd_pre
all_odd_wedge = transformed_odd_wedge
odd = transformed_odd
# apply symmetries before determine resolution
# with gold standard you should be careful about applying the symmetry!
even = job.symmetries.applyToParticle(even)
odd = job.symmetries.applyToParticle(odd)
resNyquist, resolutionBand, numberBands = self.determine_resolution(
even, odd, job.fsc_criterion, None, job.mask, verbose)
# write the half set to the disk
even.write('fsc_' + str(i) + '_even.em')
odd.write('fsc_' + str(i) + '_odd.em')
current_resolution = bandToAngstrom(
resolutionBand, job.sampleInformation.getPixelSize(),
numberBands, 1)
if verbose:
print(
self.node_name + ': current resolution ' +
str(current_resolution), resNyquist)
# create new average
all_even_pre += all_odd_pre
all_even_wedge += all_odd_wedge
# all_even_pre = all_even_pre/2 # correct for the number of particles in wiener filter
average = self.create_average(all_even_pre, sum_ctf_squared,
all_even_wedge)
# apply symmetries
average = job.symmetries.applyToParticle(average)
# filter average to resolution and update the new reference
average_name = 'average_iter' + str(i) + '.em'
average.write(average_name)
# update the references
new_reference = [
Reference('fsc_' + str(i) + '_even.em'),
Reference('fsc_' + str(i) + '_odd.em')
]
# low pass filter the reference and write it to the disk
filtered = lowpassFilter(average, ceil(resolutionBand),
ceil(resolutionBand) / 10)
filtered_ref_name = 'average_iter' + str(i) + '_res' + str(
current_resolution) + '.em'
filtered[0].write(filtered_ref_name)
# change the frequency to a higher value
new_freq = int(ceil(resolutionBand)) + 1
if new_freq <= old_freq:
if job.adaptive_res is not False: # two different strategies
print(
self.node_name +
': Determined resolution gets worse. Include additional %f percent frequency to be aligned!'
% job.adaptive_res)
new_freq = int((1 + job.adaptive_res) * old_freq)
else: # always increase by 1
print(
self.node_name +
': Determined resolution gets worse. Increase the frequency to be aligned by 1!'
)
new_freq = old_freq + 1
old_freq = new_freq
else:
old_freq = new_freq
if new_freq >= numberBands:
print(self.node_name +
': Determined frequency too high. Terminate!')
break
if verbose:
print(self.node_name + ': change the frequency to ' +
str(new_freq))
# send end signal to other nodes and terminate itself
self.end(verbose)
else:
# other nodes
self.run(verbose)
