def PeakJobMsg_Test(self):
from pytom.localization.peak_job_msg import PeakJobMsg
from pytom.localization.peak_job import PeakJob
from pytom.basic.structures import Mask, Reference, WedgeInfo
from pytom.localization.structures import Volume
from pytom.score.score import FLCFScore
from pytom.angles.angleList import AngleList
v = Volume(self.testfilename)
ref = Reference(self.testfilename)
m = Mask(self.testfilename)
w = WedgeInfo(30)
s = FLCFScore()
rot = AngleList([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
j = PeakJob(v, ref, m, w, rot, s)
a = PeakJobMsg(str(0), str(1))
a.setJob(j)
xmlObj = a.toXML()
b = PeakJobMsg()
b.fromXML(xmlObj)
self.assertTrue(b.getSender() == a.getSender(), msg='')
self.assertTrue(b.getRecipient() == a.getRecipient(), msg='')
def PeakJob_Test(self):
from pytom.localization.peak_job import PeakJob
from pytom.basic.structures import Mask, Reference, WedgeInfo
from pytom.localization.structures import Volume
from pytom.score.score import FLCFScore
from pytom.angles.angleList import AngleList
v = Volume(self.testfilename)
ref = Reference(self.testfilename)
m = Mask(self.testfilename)
w = WedgeInfo(30)
s = FLCFScore()
rot = AngleList([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
a = PeakJob(v, ref, m, w, rot, s, 1)
xmlObj = a.toXML()
b = PeakJob()
b.fromXML(xmlObj)
self.assertTrue(b.volume.getFilename() == a.volume.getFilename(),
msg='')
self.assertTrue(b.jobID == a.jobID, msg='')
self.assertTrue(b.reference.getReferenceFilename() ==
a.reference.getReferenceFilename(),
msg='')
self.assertTrue(b.mask.getFilename() == a.mask.getFilename(), msg='')
self.assertTrue(b.wedge.getWedgeAngle() == a.wedge.getWedgeAngle(),
msg='')
self.assertTrue(b.score.getScoreFunc() == a.score.getScoreFunc(),
msg='')
def simulationDescriptionToParticleList(directory,prefix = ''):
"""
simulationDescriptionToParticleList:
"""
lenDir = len(directory)
if not directory[lenDir-1] == '/':
directory = directory + '/'
xmlFile = directory + 'desc.xml'
from lxml import etree
simulationXML = etree.parse(xmlFile)
#print etree.tostring(simulationXML,pretty_print=True)
particles = simulationXML.xpath('particle')
parameters = simulationXML.xpath('Simulation_Parameters')
wedge = int(parameters[0].get('wangleEnd'))/2
from pytom.basic.structures import Particle,ParticleList,WedgeInfo
wi = WedgeInfo(wedge,[0.0,0.0,0.0])
pl = ParticleList(directory)
for particle in particles:
filename = prefix + particle.get('filename')
rotation = particle.get('rotation')
rotation = rotation.replace('[','')
rotation = rotation.replace(']','')
rotation = rotation.split(',')
rotation = [float(rotation[0]),float(rotation[1]),float(rotation[2])]
shift = particle.get('shift')
shift = shift.replace('[','')
shift = shift.replace(']','')
shift = shift.split(',')
shift = [int(round(float(shift[0]))),int(round(float(shift[1]))),int(round(float(shift[2])))]
p = Particle(filename,rotation = rotation,shift = shift,wedge=wi)
pl.append(p)
#al.toXMLFile(directory+'AlignmentList.xml')
return pl
def setUp(self):
"""set up"""
from pytom_volume import vol, initSphere
from pytom.basic.structures import WedgeInfo
from pytom.simulation.EMSimulation import simpleSimulation
self.wedge = 0.
self.shift = [-1, 2, 3]
self.rotation = [0, 0, 0]
# create sphere
self.v = vol(32, 32, 32)
self.mask = vol(32, 32, 32)
initSphere(self.v, 10, 2, 0, 15, 15, 15)
# there is a slight inconsistency when smoothing > 0 -
# cleaner implementation would be multipliction with sqrt(mask) in corr function
initSphere(self.mask, 13, 0, 0, 16, 16, 16)
self.wi = WedgeInfo(wedgeAngle=self.wedge,
rotation=[10.0, 20.0, 30.0],
cutoffRadius=0.0)
self.s = simpleSimulation(volume=self.v,
rotation=self.rotation,
shiftV=self.shift,
wedgeInfo=self.wi,
SNR=10.)
from pytom.basic.structures import WedgeInfo
from pytom.localization.extractPeaks import extractPeaks
from pytom.angles.fromFile import AngleListFromEM
from pytom.basic.normalise import mean0std1
from pytom.simulation.whiteNoise import add
from pytom.basic.correlation import nxcc
v = read('/fs/home/ychen/matlab/template/binning/temp80SRibosome_bin2.em')
b = 32
v2 = vol(v)
w = np.array(create_wedge_sf(-60, 60, b))
m = np.ones(4 * b**2)
mask = read('/fs/home/ychen/matlab/test_frm/mask_11.em')
wedge = WedgeInfo(30.0)
angles = AngleListFromEM("")
angles.readRotationsFromEMFile("angles_12.85_7112.em")
# angles.readRotationsFromEMFile("angles_17.86_3040.em")
def frm_fourier_constrained_vol(vf, mf, vg, mg):
radius = vf.sizeX() / 2 - 3 # intepolation in the outer part is nonsense
from pytom.basic.fourier import fft, ifft, ftshift, iftshift
vf = ftshift(reducedToFull(fft(iftshift(vf, inplace=False))),
inplace=False)
vg = ftshift(reducedToFull(fft(iftshift(vg, inplace=False))),
inplace=False)
def weightedXCC(volume, reference, numberOfBands, wedgeAngle=-1, gpu=False):
"""
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
"""
if gpu:
import cupy as xp
else:
import numpy as xp
from pytom.tompy.transform import fft, fourier_reduced2full
from pytom.basic.structures import WedgeInfo
result = 0
numberVoxels = 0
#volume.write('vol.em');
#reference.write('ref.em');
wedge = WedgeInfo(wedgeAngle)
wedgeVolume = wedge.returnWedgeVolume(volume.shape[0], volume.shape[1],
volume.shape[2])
increment = int(volume.shape[0] / 2 * 1 / numberOfBands)
band = [0, 100]
for i in range(0, volume.shape[0] / 2, increment):
band[0] = i
band[1] = i + increment
r = bandCC(volume, reference, band, gpu=gpu)
cc = r[0]
#print cc;
filter = r[1]
#get bandVolume
bandVolume = filter.getWeightVolume(True)
filterVolumeReduced = bandVolume * wedgeVolume
filterVolume = fourier_reduced2full(filterVolumeReduced)
#determine number of voxels != 0
N = (filterVolume != 0).sum()
w = xp.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))
def interpretRequestParameters(parameters):
from pytom.localization.peak_job import PeakJob
from pytom.localization.structures import Volume
from pytom.basic.structures import Mask, Reference, WedgeInfo
from pytom.angles.globalSampling import GlobalSampling
from pytom.basic.structures import BandPassFilter
from pytom.frontend.serverpages.serverMessages import FileMessage
if 'tomo' in parameters:
vol = Volume(parameters['tomo'])
else:
raise RuntimeError('Parameter missing in request!')
if 'ref' in parameters:
ref = Reference(parameters['ref'])
else:
raise RuntimeError('Parameter missing in request!')
if 'mask' in parameters:
mask = Mask(parameters['mask'])
else:
raise RuntimeError('Parameter missing in request!')
if 'angle' in parameters:
ang = GlobalSampling(parameters['angle'])
else:
raise RuntimeError('Parameter missing in request!')
if 'dest' in parameters:
dest = parameters['dest']
else:
dest = './'
if 'low' in parameters:
low = float(parameters['low'])
else:
raise RuntimeError('Parameter missing in request!')
if 'high' in parameters:
high = float(parameters['high'])
else:
raise RuntimeError('Parameter missing in request!')
if 'smooth' in parameters:
smooth = float(parameters['smooth'])
else:
raise RuntimeError('Parameter missing in request!')
if 'w1' in parameters:
w1 = float(parameters['w1'])
else:
raise RuntimeError('Parameter missing in request!')
if 'w2' in parameters:
w2 = float(parameters['w2'])
else:
raise RuntimeError('Parameter missing in request!')
if 'x' in parameters:
x = float(parameters['x'])
else:
x = 0
if 'y' in parameters:
y = float(parameters['y'])
else:
y = 0
if 'z' in parameters:
z = float(parameters['z'])
else:
z = 0
bp = BandPassFilter(low, high, smooth)
wedge = WedgeInfo([w1, w2])
from pytom.score.score import FLCFScore
sc = FLCFScore()
job = PeakJob(vol,
ref,
mask,
wedge,
ang,
dstDir=dest,
score=sc,
bandpass=bp)
jobXMLFile = ''
if 'jobFile' in parameters:
jobXMLFile = parameters['jobFile']
job.toXMLFile(jobXMLFile)
if jobXMLFile[-4:] == '.xml':
jobRunFile = jobXMLFile[0:-4]
else:
jobRunFile = jobXMLFile
createRunscripts(jobRunFile + '.sh', jobXMLFile, x, y, z)
return FileMessage('LocalizationJob', jobXMLFile, 'created')
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))
if not dest_dir:
dest_dir = '.'
from pytom_volume import read, subvolume
v = read(vol_filename)
from pytom.basic.structures import ParticleList, Particle, WedgeInfo
pl = ParticleList("./")
pl.fromXMLFile(pl_filename)
def regulaize(xx, dim):
if xx*binning-radius < 0:
if 2*radius > dim:
raise Exception("Volume size to be cut is too big!")
return 0
if xx*binning+radius > dim:
if dim-2*radius < 0:
raise Exception("Volume size to be cut is too big!")
return dim-2*radius
return xx*binning-radius
res = ParticleList(dest_dir)
for p in pl:
x,y,z = p.getPickPosition().toVector()
x = regulaize(int(x), v.sizeX()); y = regulaize(int(y), v.sizeY()); z = regulaize(int(z), v.sizeZ())
new_vol = subvolume(v, x, y, z, 2*radius, 2*radius, 2*radius)
name = dest_dir+'/'+p.getFilename()
new_vol.write(name) # write the subtomograms to the disk
res.append(Particle(name, p.getRotation(), None, WedgeInfo(w), 1, p.getPickPosition(), p.getScore())) # append it to the particle list for alignment
res.toXMLFile(dest_dir+'/'+res_name)
def xu_align_vol(vf, wf, vg, wg, b, radius=None, mask=None, peak_offset=None):
"""Implementation of Xu's approach for alignment. For detail, please check the paper.
Parameters
----------
vf: The volume you want to match.
pytom_volume.vol
wf: The single tilt wedge information of volume vf.
[missing_wedge_angle1, missing_wedge_angle2]. Note this is defined different with frm_align im frm.py!
vg: The reference volume.
pytom_volume.vol
wg: The single tilt wedge information of volume vg.
[missing_wedge_angle1, missing_wedge_angle2]. Note this is defined different with frm_align im frm.py!
b: The adaptive bandwidth of spherical harmonics.
List [min_bandwidth, max_bandwidth], min_bandwidth, max_bandwidth in the range [4, 64].
Or integer, which would then mean to use fixed bandwidth: min_bandwidth = max_bandwidth = integer.
radius: The maximal radius in the Fourier space, which is equal to say the maximal frequency involved in calculation.
Integer. By default is half of the volume size.
peak_offset: The maximal offset which allows the peak of the score to be.
Or simply speaking, the maximal distance allowed to shift vg to match vf.
This parameter is needed to prevent shifting the reference volume out of the frame.
Integer. By default is half of the volume size.
Returns
-------
The best translation and rotation (Euler angle, ZXZ convention [Phi, Psi, Theta]) to transform vg to match vf.
(best_translation, best_rotation, correlation_score)
"""
from pytom_volume import vol, rotateSpline, peak
from pytom.basic.transformations import shift
from pytom.basic.correlation import nXcf
from pytom.basic.filter import lowpassFilter
from pytom.basic.structures import Mask
from pytom_volume import initSphere
from pytom_numpy import vol2npy
if vf.sizeX()!=vg.sizeX() or vf.sizeY()!=vg.sizeY() or vf.sizeZ()!=vg.sizeZ():
raise RuntimeError('Two volumes must have the same size!')
if mask is None:
mask = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(mask, vf.sizeX()/2, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
elif mask.__class__ == vol:
pass
elif mask.__class__ == Mask:
mask = mask.getVolume()
elif isinstance(mask, int):
mask_radius = mask
mask = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(mask, mask_radius, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
else:
raise RuntimeError('Given mask has wrong type!')
if peak_offset is None:
peak_offset = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(peak_offset, vf.sizeX()/2, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
elif isinstance(peak_offset, int):
peak_radius = peak_offset
peak_offset = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(peak_offset, peak_radius, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
elif peak_offset.__class__ == vol:
pass
else:
raise RuntimeError('Peak offset is given wrong!')
# cut out the outer part which normally contains nonsense
vf = vf*mask
position = None
if position is None: # if position is not given, we have to find it ourself
# first roughtly determine the orientation (only according to the energy info)
# get multiple candidate orientations
orientations = frm_determine_orientation(vf, wf, vg, wg, b, radius, None, None, False)
else:
# the position is given by the user
vf2 = shift(vf, -position[0]+vf.sizeX()/2, -position[1]+vf.sizeY()/2, -position[2]+vf.sizeZ()/2, 'spline')
res = frm_fourier_adaptive_wedge_vol(vf2, wf, vg, wg, b, radius, None, None, False)
orientation, max_value = frm_find_best_angle_interp(res)
return position, orientation, max_value
from pytom.basic.structures import WedgeInfo
from pytom.tools.maths import euclidianDistance
max_iter = 1 # maximal number of iterations
wedge = WedgeInfo([90+wf[0], 90-wf[1]])
old_pos = [-1, -1, -1]
vg2 = vol(vg.sizeX(), vg.sizeY(), vg.sizeZ())
lowpass_vf = lowpassFilter(vf, radius, 0)[0]
peak_value = 0.0
position = None
ang = None
for orientation in orientations:
orientation = orientation[0]
rotateSpline(vg, vg2, orientation[0], orientation[1], orientation[2]) # first rotate
vg2 = wedge.apply(vg2) # then apply the wedge
vg2 = lowpassFilter(vg2, radius, 0)[0]
res = nXcf(lowpass_vf, vg2) # find the position
pos = peak(res, peak_offset)
# val = res(pos[0], pos[1], pos[2])
pos, val = find_subpixel_peak_position(vol2npy(res), pos)
if val > peak_value:
position = pos
ang = orientation
peak_value = val
return position, ang, peak_value
def bart_align_vol(vf, wf, vg, wg, b, radius=None, peak_offset=None):
"""Implementation of Bartesaghi's approach for alignment. For detail, please check the paper.
Parameters
----------
vf: The volume you want to match.
pytom_volume.vol
wf: The single tilt wedge information of volume vf.
[missing_wedge_angle1, missing_wedge_angle2]. Note this is defined different with frm_align im frm.py!
vg: The reference volume.
pytom_volume.vol
wg: The single tilt wedge information of volume vg.
[missing_wedge_angle1, missing_wedge_angle2]. Note this is defined different with frm_align im frm.py!
b: The bandwidth of spherical harmonics.
Integer in the range [4, 64]
radius: The maximal radius in the Fourier space, which is equal to say the maximal frequency involved in calculation.
Integer. By default is half of the volume size.
peak_offset: The maximal offset which allows the peak of the score to be.
Or simply speaking, the maximal distance allowed to shift vg to match vf.
This parameter is needed to prevent shifting the reference volume out of the frame.
Integer. By default is half of the volume size.
Returns
-------
The best translation and rotation (Euler angle, ZXZ convention [Phi, Psi, Theta]) to transform vg to match vf.
(best_translation, best_rotation, correlation_score)
"""
from pytom_volume import vol, rotateSpline, max, peak
from pytom.basic.correlation import nXcf
from pytom.basic.filter import lowpassFilter
from pytom.basic.structures import WedgeInfo
from pytom_volume import initSphere
if not radius: # set the radius
radius = vf.sizeX()/2
if peak_offset is None:
peak_offset = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(peak_offset, vf.sizeX()/2, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
elif isinstance(peak_offset, int):
peak_radius = peak_offset
peak_offset = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(peak_offset, peak_radius, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
elif peak_offset.__class__ == vol:
pass
else:
raise RuntimeError('Peak offset is given wrong!')
from pytom.basic.fourier import fft, ifft, ftshift, iftshift
from pytom_volume import vol, reducedToFull, rescale, abs, real
from .vol2sf import vol2sf
from pytom_numpy import vol2npy
from math import log, ceil, pow
# IMPORTANT!!! Should firstly do the IFFTSHIFT on the volume data (NOT FFTSHIFT since for odd-sized data it matters!),
# and then followed by the FFT.
ff = abs(ftshift(reducedToFull(fft(iftshift(vf, inplace=False))), inplace=False))
ff = real(ff)
gg = abs(ftshift(reducedToFull(fft(iftshift(vg, inplace=False))), inplace=False))
gg = real(gg)
sf = None
sg = None
mf = create_wedge_sf(wf[0], wf[1], b)
mg = create_wedge_sf(wg[0], wg[1], b)
for r in range(3, radius+1): # Should start from 3 since the interpolation in the first 2 bands is not accurate.
if sf is None:
sf = vol2sf(ff, r, b)
sg = vol2sf(gg, r, b)
else:
sf += vol2sf(ff, r, b)
sg += vol2sf(gg, r, b)
corr = frm_constrained_corr(sf, mf, sg, mg)
ang, val = frm_find_best_angle_interp(corr)
tmp = vol(vg.sizeX(),vg.sizeY(),vg.sizeZ())
rotateSpline(vg, tmp, ang[0], ang[1], ang[2])
wedge_f = WedgeInfo(90+wf[0], 90-wf[1])
wedge_g = WedgeInfo(90+wg[0], 90-wg[1])
cc = nXcf(lowpassFilter(wedge_g.apply(vf), radius, 0)[0], lowpassFilter(wedge_f.apply(tmp), radius, 0)[0])
pos = peak(cc, peak_offset)
pos, score = find_subpixel_peak_position(vol2npy(cc), pos)
return (pos, ang, score)
def frm_align_vol_rscore(vf, wf, vg, wg, b, radius=None, mask=None, peak_offset=None, weights=None, position=None):
"""Obsolete.
"""
from pytom_volume import vol, rotateSpline, peak
from pytom.basic.transformations import shift
from pytom.basic.correlation import xcf
from pytom.basic.filter import lowpassFilter
from pytom.basic.structures import Mask
from pytom_volume import initSphere
from pytom_numpy import vol2npy
if vf.sizeX()!=vg.sizeX() or vf.sizeY()!=vg.sizeY() or vf.sizeZ()!=vg.sizeZ():
raise RuntimeError('Two volumes must have the same size!')
if mask is None:
mask = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(mask, vf.sizeX()/2, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
elif mask.__class__ == vol:
pass
elif mask.__class__ == Mask:
mask = mask.getVolume()
elif isinstance(mask, int):
mask_radius = mask
mask = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(mask, mask_radius, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
else:
raise RuntimeError('Given mask has wrong type!')
if peak_offset is None:
peak_offset = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(peak_offset, vf.sizeX()/2, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
elif isinstance(peak_offset, int):
peak_radius = peak_offset
peak_offset = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(peak_offset, peak_radius, 0,0, vf.sizeX()/2,vf.sizeY()/2,vf.sizeZ()/2)
elif peak_offset.__class__ == vol:
pass
else:
raise RuntimeError('Peak offset is given wrong!')
# cut out the outer part which normally contains nonsense
vf = vf*mask
# # normalize them first
# from pytom.basic.normalise import mean0std1
# mean0std1(vf)
# mean0std1(vg)
if position is None: # if position is not given, we have to find it ourself
# first roughtly determine the orientation (only according to the energy info)
# get multiple candidate orientations
orientations = frm_determine_orientation_rscore(vf, wf, vg, wg, b, radius, weights)
else:
# the position is given by the user
vf2 = shift(vf, -position[0]+vf.sizeX()/2, -position[1]+vf.sizeY()/2, -position[2]+vf.sizeZ()/2, 'spline')
res = frm_fourier_adaptive_wedge_vol_rscore(vf2, wf, vg, wg, b, radius, weights)
orientation, max_value = frm_find_best_angle_interp(res)
return position, orientation, max_value
# iteratively refine the position & orientation
from pytom.basic.structures import WedgeInfo
from pytom.tools.maths import euclidianDistance
max_iter = 10 # maximal number of iterations
wedge = WedgeInfo([90+wf[0], 90-wf[1]])
old_pos = [-1, -1, -1]
vg2 = vol(vg.sizeX(), vg.sizeY(), vg.sizeZ())
lowpass_vf = lowpassFilter(vf, radius, 0)[0]
for i in range(max_iter):
peak_value = 0.0
position = None
for orientation in orientations:
orientation = orientation[0]
rotateSpline(vg, vg2, orientation[0], orientation[1], orientation[2]) # first rotate
vg2 = wedge.apply(vg2) # then apply the wedge
vg2 = lowpassFilter(vg2, radius, 0)[0]
res = xcf(lowpass_vf, vg2) # find the position
pos = peak(res, peak_offset)
# val = res(pos[0], pos[1], pos[2])
pos, val = find_subpixel_peak_position(vol2npy(res), pos)
if val > peak_value:
position = pos
peak_value = val
if euclidianDistance(position, old_pos) <= 1.0:
break
else:
old_pos = position
# here we shift the target, not the reference
# if you dont want the shift to change the energy landscape, use fourier shift
vf2 = shift(vf, -position[0]+vf.sizeX()/2, -position[1]+vf.sizeY()/2, -position[2]+vf.sizeZ()/2, 'fourier')
res = frm_fourier_adaptive_wedge_vol_rscore(vf2, wf, vg, wg, b, radius, weights)
orientations = frm_find_topn_angles_interp(res)
return position, orientations[0][0], orientations[0][1]
@param splitType: split type of the job
@type splitType: "Ang" or "Vol"
"""
def __init__(self, jobID=-1, originalJobID=-1, splitType=None):
self.jobID = jobID
self.originalJobID = originalJobID
self.splitType = splitType
if __name__ == '__main__':
from pytom.basic.structures import Mask, Reference, WedgeInfo
from pytom.localization.structures import Volume
from pytom.score.score import FLCFScore, xcfScore
from pytom.localization.peak_job import PeakJob
v = Volume('/fs/pool/pool-foerster/apps/src/molmatch/test/testvol.em')
ref = Reference('/fs/pool/pool-foerster/apps/src/molmatch/test/templ.em')
m = Mask('/fs/pool/pool-foerster/apps/src/molmatch/test/mask_15.em', True)
w = WedgeInfo(0)
s = FLCFScore()
from pytom.angles.globalSampling import GlobalSampling
r = GlobalSampling(
'/fs/home/ychen/develop/pytom/trunk/pytom/pytomc/libs/libtomc/data/common_data/angles_90_26.em'
)
job = PeakJob(v, ref, m, w, r, s)
job.toXMLFile('JobInfo.xml')
job.toHTMLFile('JobInfo.html')