def distance(p, ref, freq, mask, binning):
from pytom.basic.correlation import nxcc
from pytom_volume import vol, initSphere, read, pasteCenter
from pytom.basic.filter import lowpassFilter
from pytom.basic.transformations import resize
v = p.getTransformedVolume(binning)
w = p.getWedge()
r = ref.getVolume()
a = lowpassFilter(w.apply(v, p.getRotation().invert()), freq)[0]
b = lowpassFilter(w.apply(r, p.getRotation().invert()), freq)[0]
if not mask:
mask = vol(r)
initSphere(mask,
r.sizeX() // 2 - 3, 3, 0,
r.sizeX() // 2,
r.sizeY() // 2,
r.sizeZ() // 2)
else:
#THE MASK is binning (sampled every n-points). This does lead to a reduction of the smoothing of the edges.
maskBin = read(mask, 0, 0, 0, 0, 0, 0, 0, 0, 0, binning, binning,
binning)
if a.sizeX() != maskBin.sizeX() or a.sizeY() != maskBin.sizeY(
) or a.sizeZ() != maskBin.sizeZ():
mask = vol(a.sizeX(), a.sizeY(), a.sizeZ())
mask.setAll(0)
pasteCenter(maskBin, mask)
else:
mask = maskBin
s = nxcc(a, b, mask)
d2 = 2 * (1 - s)
return d2
def initSphere(cubeSize,
radius,
smoothing=0,
centerX=None,
centerY=None,
centerZ=None):
"""
initSphere: Initilizes a volume with a sphere
@param cubeSize: The size of the whole volume
@param radius: Radius of the sphere
@param smoothing: Smoothing at the edges of the sphere
@param centerX: Center of shpere along X axis
@param centerY: Center of shpere along Y axis
@param centerZ: Center of shpere along Z axis
"""
from pytom_volume import vol, initSphere
sphere = vol(cubeSize, cubeSize, cubeSize)
sphere.setAll(0)
if centerX is None:
centerX = cubeSize / 2 - 1
if centerY is None:
centerY = cubeSize / 2 - 1
if centerZ is None:
centerZ = cubeSize / 2 - 1
initSphere(sphere, radius, smoothing, 0, centerX, centerY, centerZ)
return sphere
def setUp(self):
from helper_functions import create_RandomParticleList
from pytom.tompy.io import read_size
from pytom_volume import vol, initSphere
self.reffile = './testData/ribo.em'
self.pl_filename = 'pl.xml'
self.pdir = './testparticles'
self.pl = create_RandomParticleList(reffile=self.reffile,
pl_filename=self.pl_filename,
pdir=self.pdir,
nparticles=10)
# set parameters for GLocal
self.settings = {}
self.settings["binning"] = 4
self.settings["niteration"] = 1
self.settings["mask"] = './testData/ribo_mask.em'
dims = read_size(self.reffile)
maskvol = vol(int(dims[0]), int(dims[1]), int(dims[2]))
initSphere(maskvol, 30, 5, 0, int(dims[0] / 2), int(dims[1] / 2),
int(dims[2] / 2))
maskvol.write(self.settings["mask"])
self.settings["destination"] = './'
#self.settings["score"] = 'nxcf'
self.settings["score"] = 'flcf'
self.settings["pixelsize"] = 2.
self.settings["diameter"] = 250.
self.settings["job"] = './myGLocal.xml'
def initVolume(self, sizeX, sizeY, sizeZ):
"""
initVolume:
@param sizeX:
@param sizeY:
@param sizeZ:
@return:
@author: Thomas Hrabe
"""
from pytom_volume import vol, initSphere
self._weight = vol(sizeX, sizeY, sizeZ)
if self._radius > 0 or self._smooth > 0:
initSphere(self._weight, self._radius, self._smooth, 0.0,
sizeX / 2, sizeY / 2, sizeZ / 2)
else:
self._weight.setAll(1)
def initSphere(sizeX,
sizeY,
sizeZ,
radius,
smooth=0,
maxradius=0,
cent=None,
filename=''):
"""
initSphere: Initializes a sphere
@param sizeX: X size of cube
@type sizeX: int
@param sizeY: Y size of cube
@type sizeY: int
@param sizeZ: Z size of cube
@type sizeZ: int
@param radius: Radius of sphere mask
@type radius: float
@param smooth: Smooth of sphere mask
@type smooth: float
@param maxradius: maximum radius - data will be set to zero beyond this radius - default 0 does not do anything
@param cent: centre vector
@type cent: array (3-dim)
@param filename: If specified by the user, the spherical mask will be written to disk.
"""
from pytom_volume import initSphere, vol
v = vol(sizeX, sizeY, sizeZ)
if cent:
initSphere(v, radius, smooth, maxradius, cent[0], cent[1], cent[2])
else:
initSphere(v, radius, smooth, maxradius, sizeX / 2, sizeY / 2,
sizeZ / 2)
if filename != '':
v.write(filename)
return v
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.)
def create_bfactor_vol(size, ps, bfactor, FSC=None, apply_range=None):
"""Create a B-factor volume in Frequency space.
@param size: The size of the volume, assuming it is a cube
@param ps: The pixel size in angstrom
@param bfactor: B factor
@param FSC: Fourier Shell Correlation
@param apply_range: The apply range (also in angstrom) of the B factor correction
"""
if FSC is None:
FSC = np.ones(size / 2)
# transfer the pixel size to angstrom
x = (ps * size) / np.arange(1, size / 2 + 1, 1) # starts from 1!
if apply_range is None:
apply_range_pixel = [1, size / 2] # starts from 1!
else:
assert apply_range[0] > apply_range[1]
apply_range_pixel = [
size * ps / apply_range[0], size * ps / apply_range[1]
]
# create the FSC weight
FSC_weight = np.sqrt((2 * np.array(FSC)) / (1 + np.array(FSC)))
# calculate the decay function
decay = FSC_weight * np.exp(-bfactor / (np.power(x, 2) * 4))
# make the decay volume
v = sph2cart(decay, size)
# transfer to the volume format and multiple with the mask
from pytom_volume import vol, initSphere
from pytom_numpy import npy2vol
vv = npy2vol(np.array(v, dtype='float32', order='F'), 3)
if apply_range_pixel[0] == 1:
mask = vol(size, size, size)
initSphere(mask, apply_range_pixel[1] - 1, 0, 0, size / 2, size / 2,
size / 2) # minus 1 to make it consistent with python
else:
mask1 = vol(size, size, size)
mask2 = vol(size, size, size)
initSphere(mask1, apply_range_pixel[0] - 1, 0, 0, size / 2, size / 2,
size / 2)
initSphere(mask2, apply_range_pixel[1] - 1, 0, 0, size / 2, size / 2,
size / 2)
mask = mask2 - mask1
return vv * mask
def sag_fine_grained_alignment(vf,
wf,
vg,
wg,
max_freq,
ang=[0, 0, 0],
loc=[0, 0, 0],
mask=None,
B,
alpha,
maxIter,
lambda1):
"""SAG-based fine-grained alignment between experimental data and reference data.
Parameters
vf: Experimental data
pytom_volume.vol
wf: Mask of vf in Fourier space.
pytom.basic.structures.Wedge. If none, no missing wedge.
vg: Reference data
pytom_volume.vol
wg: Mask of vg in Fourier space.
pytom.basic.structures.Wedge. If none, no missing wedge.
max_freq: Maximal frequency involved in calculation.
Integer.
ang: Initial rotation angle
loc: Initial translation value
mask: Mask volume in real space.
pytom_volume.vol
B: Batch number
alpha: Step size
maxIter: The max iteration number
lambda1: Regularization parameter
Returns
-------
(Optimal rotation angle and translation value.
(best_translation, best_rotation, correlation_score)
"""
from pytom_volume import vol, rotateSpline, peak, sum, power
from pytom.basic.transformations import shift
from pytom.basic.filter import lowpassFilter
from pytom.basic.structures import Mask, SingleTiltWedge, Rotation
from pytom_volume import initSphere
from pytom_numpy import vol2npy
import math
import random
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 wf is None:
wf = SingleTiltWedge(0)
else:
vf = wf.apply(vf)
if wg is None:
wg = SingleTiltWedge(0)
else:
vg = wg.apply(vg)
if mask is None:
m = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(m,
vf.sizeX() / 2, 0, 0,
vf.sizeX() / 2,
vf.sizeY() / 2,
vf.sizeZ() / 2)
mask = m
old_value = -1
max_pos = [-1, -1, -1]
max_ang = None
max_value = -1.0
ang_epsilon = np.ones(3) * (math.pi * (1.0 / 180))
loc_epsilon = np.ones(3) * 1.0
n = vf.sizeX()
vf0_n = vol2npy(vf)
if maxIter is None:
maxIter = n / 2
iVals = np.int32(np.ceil((n - B) * np.random.random(maxIter)))
if lambda1 is None:
lambda1 = 1 / n
eps = np.finfo(np.float32).eps
Lmax = 0.25 * np.max(np.sum(vf0_n**2)) + lambda1
if alpha is None:
alpha = 1 / Lmax
d = np.zeros(6)
g = np.zeros([n, 6])
covered = np.int32(np.zeros(n))
nCovered = 0
grad = np.zeros(6)
deri = np.zeros(6)
vg2 = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
mask2 = vol(mask.sizeX(), mask.sizeY(), mask.sizeZ())
for i in range(n):
if (covered[i] != 0):
nCovered += 1
for k in range(maxIter):
i = iVals[k] - 1
if k == 0:
rotateSpline(vg, vg2, ang[0], ang[1], ang[2])
rotateSpline(mask, mask2, ang[0], ang[1], ang[2])
vg2 = wf.apply(vg2)
vg2 = lowpassFilter(vg2, max_freq, max_freq / 10.)[0]
vg2_s = transform_single_vol(vg2, mask2)
vf2 = shift(vf,
-loc[0] + vf.sizeX() / 2,
-loc[1] + vf.sizeY() / 2,
-loc[2] + vf.sizeZ() / 2,
imethod='spline')
vf2 = lowpassFilter(vf2, max_freq, max_freq / 10.)[0]
vf2 = wg.apply(vf2, Rotation(ang))
vf2_s = transform_single_vol(vf2, mask2)
i = 0
ri = np.sum(
vol2npy(vf2_s)[i:i + B, :, :] - vol2npy(vg2_s)[i:i + B, :, :])
vg2_p = vol(n, n, n)
vg2_m = vol(n, n, n)
mask2_p = vol(n, n, n)
mask2_m = vol(n, n, n)
for dim_i in range(3):
if abs(ang_epsilon[dim_i]) > eps:
ang_epsilon_t = np.zeros(3)
ang_epsilon_t[dim_i] = ang_epsilon[dim_i]
angle = ang + ang_epsilon_t
rotateSpline(vg, vg2_p, angle[0], angle[1], angle[2])
rotateSpline(mask, mask2_p, angle[0], angle[1], angle[2])
vg2_p = wf.apply(vg2_p)
vg2_p = lowpassFilter(vg2_p, max_freq, max_freq / 10.)[0]
vg2_pf = transform_single_vol(vg2_p, mask2_p)
angle = ang - ang_epsilon_t
rotateSpline(vg, vg2_m, angle[0], angle[1], angle[2])
rotateSpline(mask, mask2_m, angle[0], angle[1], angle[2])
vg2_m = wf.apply(vg2_m)
vg2_m = lowpassFilter(vg2_m, max_freq, max_freq / 10.)[0]
vg2_mf = transform_single_vol(vg2_m, mask2_m)
vg2_ang_deri = (vg2_pf - vg2_mf) / (2 * ang_epsilon[dim_i])
vg2_ang_deri_n = vol2npy(vg2_ang_deri)
deri[dim_i] = np.sum(vg2_ang_deri_n[i:i + B, :, :])
del vg2_pf, vg2_mf, vg2_ang_deri, vg2_ang_deri_n, angle
del vg2_p, vg2_m, mask2_p, mask2_m
vf1_ps = vol(n, n, n)
vf1_ms = vol(n, n, n)
ang_f = [ang[0], ang[1], ang[2]]
for dim_i in range(3):
if abs(loc_epsilon[dim_i]) > eps:
loc_epsilon_t = np.zeros(3)
loc_epsilon_t[dim_i] = ang_epsilon[dim_i]
vf1_ps.copyVolume(vf)
vf1_ms.copyVolume(vf)
loc_r = loc + loc_epsilon_t
vf1_tp = shift(vf1_ps, -loc_r[0] + vf1_ps.sizeX() / 2,
-loc_r[1] + vf1_ps.sizeY() / 2,
-loc_r[2] + vf1_ps.sizeZ() / 2, 'spline')
vf1_tp = lowpassFilter(vf1_tp, max_freq, max_freq / 10.)[0]
vf1_tp = wg.apply(vf1_tp, Rotation(ang_f))
loc_r = loc - loc_epsilon_t
vf1_tm = shift(vf1_ms, -loc_r[0] + vf1_ms.sizeX() / 2,
-loc_r[1] + vf1_ms.sizeY() / 2,
-loc_r[2] + vf1_ms.sizeZ() / 2, 'spline')
vf1_tm = lowpassFilter(vf1_tm, max_freq, max_freq / 10.)[0]
vf1_tm = wg.apply(vf1_tm, Rotation(ang_f))
vf1_tpf = transform_single_vol(vf1_tp, mask2)
vf1_tmf = transform_single_vol(vf1_tm, mask2)
vf1_loc_deri = (vf1_tpf - vf1_tmf) / (2 * ang_epsilon[dim_i])
vf1_loc_deri_n = vol2npy(vf1_loc_deri)
deri[dim_i + 3] = np.sum(vf1_loc_deri_n[i:i + B, :, :])
del vf1_tp, vf1_tm, vf1_tpf, vf1_tmf, vf1_loc_deri, vf1_loc_deri_n
del vf1_ps, vf1_ms, ang_f
for dim_i in range(6):
grad[dim_i] = ri * deri[dim_i] / B
for dim_i in range(6):
d[dim_i] += grad[dim_i] - np.sum(g[i:i + B, dim_i])
for dim_i in range(6):
g[i:i + B, dim_i] = grad[dim_i]
for j0 in range(i, i + B + 1):
if (covered[j0] == 0):
covered[j0] = 1
nCovered += 1
for dim_i in range(6):
opt_beta[dim_i] -= alpha * d[dim_i] / nCovered
ang = opt_beta[:3]
loc = opt_beta[3:]
rotateSpline(vg, vg2, ang[0], ang[1], ang[2])
rotateSpline(mask, mask2, ang[0], ang[1], ang[2])
vg2 = wf.apply(vg2)
vg2 = lowpassFilter(vg2, max_freq, max_freq / 10.)[0]
vg2_s = transform_single_vol(vg2, mask2)
vf2 = shift(vf,
-loc[0] + vf.sizeX() / 2,
-loc[1] + vf.sizeY() / 2,
-loc[2] + vf.sizeZ() / 2,
imethod='spline')
vf2 = lowpassFilter(vf2, max_freq, max_freq / 10.)[0]
vf2 = wg.apply(vf2, Rotation(ang))
vf2_s = transform_single_vol(vf2, mask2)
ri = np.sum(
vol2npy(vf2_s)[i:i + B, :, :] - vol2npy(vg2_s)[i:i + B, :, :])
from pytom.basic.correlation import nxcc
val = nxcc(vf2_s, vg2_s, mask)
if val > max_value:
max_pos = loc
max_ang = ang
max_value = val
if abs(max_value - old_value) <= eps:
break
else:
old_value = max_value
del vg2_s, vf2, vf2_s
del d, g, grad, deri
return max_pos, max_ang, max_value
def extractPeaks(volume,
reference,
rotations,
scoreFnc=None,
mask=None,
maskIsSphere=False,
wedgeInfo=None,
**kwargs):
'''
Created on May 17, 2010
@param volume: target volume
@type volume: L{pytom_volume.vol}
@param reference: reference
@type reference: L{pytom_volume.vol}
@param rotations: rotation angle list
@type rotations: L{pytom.angles.globalSampling.GlobalSampling}
@param scoreFnc: score function that is used
@type scoreFnc: L{pytom.basic.correlation}
@param mask: mask volume
@type mask: L{pytom_volume.vol}
@param maskIsSphere: flag to indicate whether the mask is sphere or not
@type maskIsSphere: boolean
@param wedgeInfo: wedge information
@type wedgeInfo: L{pytom.basic.structures.WedgeInfo}
@return: both the score volume and the corresponding rotation index volume
@rtype: L{pytom_volume.vol}
@author: chen
'''
# from pytom.tools.timing import timing
# t = timing(); t.start()
# parse the parameters
nodeName = kwargs.get('nodeName', '')
verbose = kwargs.get('verboseMode', True)
if verbose not in [True, False]:
verbose = True
moreInfo = kwargs.get('moreInfo', False)
if moreInfo not in [True, False]:
moreInfo = False
from pytom.basic.correlation import FLCF
from pytom.basic.structures import WedgeInfo, Wedge
from pytom_volume import vol, pasteCenter
from pytom_volume import rotateSpline as rotate # for more accuracy
from pytom_volume import updateResFromIdx
from pytom.basic.files import write_em
if scoreFnc == None:
scoreFnc = FLCF
# only FLCF needs mask
if scoreFnc == FLCF:
if mask.__class__ != vol: # construct a sphere mask by default
from pytom_volume import initSphere
mask = vol(reference.sizeX(), reference.sizeY(), reference.sizeZ())
mask.setAll(0)
initSphere(mask,
reference.sizeX() / 2, 0, 0,
reference.sizeX() / 2,
reference.sizeX() / 2,
reference.sizeX() / 2)
maskIsSphere = True
# result volume which stores the score
result = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
result.setAll(-1)
# result orientation of the peak value (index)
orientation = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
orientation.setAll(0)
currentRotation = rotations.nextRotation()
index = 0
if verbose == True:
from pytom.tools.ProgressBar import FixedProgBar
max = rotations.numberRotations() - 1
prog = FixedProgBar(0, max, nodeName)
if moreInfo:
sumV = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
sumV.setAll(0)
sqrV = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
sqrV.setAll(0)
else:
sumV = None
sqrV = None
if wedgeInfo.__class__ == WedgeInfo or wedgeInfo.__class__ == Wedge:
print('Applied wedge to volume')
volume = wedgeInfo.apply(volume)
while currentRotation != [None, None, None]:
if verbose == True:
prog.update(index)
# rotate the reference
ref = vol(reference.sizeX(), reference.sizeY(), reference.sizeZ())
rotate(reference, ref, currentRotation[0], currentRotation[1],
currentRotation[2])
# apply wedge
if wedgeInfo.__class__ == WedgeInfo or wedgeInfo.__class__ == Wedge:
ref = wedgeInfo.apply(ref)
# rotate the mask if it is asymmetric
if scoreFnc == FLCF:
if maskIsSphere == False: # if mask is not a sphere, then rotate it
m = vol(mask.sizeX(), mask.sizeY(), mask.sizeZ())
rotate(mask, m, currentRotation[0], currentRotation[1],
currentRotation[2])
else:
m = mask
# compute the score
# if mask is sphere and it is the first run, compute the standard deviation of the volume under mask for late use
if scoreFnc == FLCF and index == 0 and maskIsSphere == True:
# compute standard deviation of the volume under mask
maskV = m
if volume.sizeX() != m.sizeX() or volume.sizeY() != m.sizeY(
) or volume.sizeZ() != m.sizeZ():
maskV = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
maskV.setAll(0)
pasteCenter(m, maskV)
from pytom_volume import sum
p = sum(m)
from pytom.basic.correlation import meanUnderMask, stdUnderMask
meanV = meanUnderMask(volume, maskV, p)
stdV = stdUnderMask(volume, maskV, p, meanV)
# ref.write('template_cpu.em')
if scoreFnc == FLCF:
if maskIsSphere == True:
score = scoreFnc(volume, ref, m, stdV, wedge=1)
else:
score = scoreFnc(volume, ref, m)
else: # not FLCF, so doesn't need mask as parameter and perhaps the reference should have the same size
_ref = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
_ref.setAll(0)
pasteCenter(ref, _ref)
score = scoreFnc(volume, _ref)
# update the result volume and the orientation volume
updateResFromIdx(result, score, orientation, index)
if moreInfo:
sumV = sumV + score
sqrV = sqrV + score * score
currentRotation = rotations.nextRotation()
index = index + 1
# if moreInfo:
# sumV = sumV/rotations.numberRotations()
# sqrV = sqrV/rotations.numberRotations()
# time = t.end(); print 'The overall execution time: %f' % time
return [result, orientation, sumV, sqrV]
def frm_constrained_align(vf,
wf,
vg,
wg,
b,
max_freq,
peak_offset=None,
mask=None,
constraint=None,
weights=None,
position=None,
num_seeds=5,
pytom_volume=None):
"""Find the best alignment (translation & rotation) of volume vg (reference) to match vf.
For details, please check the paper.
Parameters
----------
vf: Volume Nr. 1
pytom_volume.vol
wf: Mask of vf in Fourier space.
pytom.basic.structures.Wedge. If none, no missing wedge.
vg: Volume Nr. 2 / Reference
pytom_volume.vol
wg: Mask of vg in Fourier space.
pytom.basic.structures.Wedge. If none, no missing wedge.
b: Bandwidth range of spherical harmonics.
None -> [4, 64]
List -> [b_min, b_max]
Integer -> [b, b]
max_freq: Maximal frequency involved in calculation.
Integer.
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.
pytom_volume.vol / Integer. By default is half of the volume radius.
mask: Mask volume for vg in real space.
pytom_volume.vol
constraint: Angular constraint
sh_alignment.constrained_frm.AngularConstraint
weights: Obsolete.
position: If the translation is already known or not. If provided, no translational search will be conducted.
List: [x,y,z], default None.
num_seeds: Number of threads for the expectation maximization procedure. The more the better, yet slower.
Integer, default is 5.
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 FLCF
from pytom.basic.filter import lowpassFilter
from pytom.basic.structures import Mask, SingleTiltWedge
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 wf is None:
wf = SingleTiltWedge(0)
if wg is None:
wg = SingleTiltWedge(0)
if peak_offset is None:
peak_offset = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(peak_offset,
vf.sizeX() / 4, 0, 0,
vf.sizeX() / 2,
vf.sizeY() / 2,
vf.sizeZ() / 2)
elif peak_offset.__class__ == 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
m = vol(vf.sizeX(), vf.sizeY(), vf.sizeZ())
initSphere(m,
vf.sizeX() / 2, 0, 0,
vf.sizeX() / 2,
vf.sizeY() / 2,
vf.sizeZ() / 2)
vf = vf * m
vg = vg * m
if mask is None:
mask = m
else:
vg = vg * mask
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
numerator, denominator1, denominator2 = frm_correlate(
vf, wf, vg, wg, b, max_freq, weights, True, None, None, False)
score = numerator / (denominator1 * denominator2)**0.5
res = frm_find_topn_constrained_angles_interp(
score, num_seeds,
get_adaptive_bw(max_freq, b) / 16., constraint)
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, 'fourier')
score = frm_correlate(vf2, wf, vg, wg, b, max_freq, weights, ps=False)
orientation, max_value = frm_find_best_constrained_angle_interp(
score, constraint=constraint)
return position, orientation, max_value
# iteratively refine the position & orientation
from pytom.tools.maths import euclidianDistance
max_iter = 10 # maximal number of iterations
mask2 = vol(mask.sizeX(), mask.sizeY(),
mask.sizeZ()) # store the rotated mask
vg2 = vol(vg.sizeX(), vg.sizeY(), vg.sizeZ())
lowpass_vf = lowpassFilter(vf, max_freq, max_freq / 10.)[0]
max_position = None
max_orientation = None
max_value = -1.0
for i in xrange(num_seeds):
old_pos = [-1, -1, -1]
lm_pos = [-1, -1, -1]
lm_ang = None
lm_value = -1.0
orientation = res[i][0] # initial orientation
for j in xrange(max_iter):
rotateSpline(vg, vg2, orientation[0], orientation[1],
orientation[2]) # first rotate
rotateSpline(mask, mask2, orientation[0], orientation[1],
orientation[2]) # rotate the mask as well
vg2 = wf.apply(vg2) # then apply the wedge
vg2 = lowpassFilter(vg2, max_freq, max_freq / 10.)[0]
score = FLCF(lowpass_vf, vg2, mask2) # find the position
pos = peak(score, peak_offset)
pos, val = find_subpixel_peak_position(vol2npy(score), pos)
if val > lm_value:
lm_pos = pos
lm_ang = orientation
lm_value = val
if euclidianDistance(lm_pos, old_pos) <= 1.0:
# terminate this thread
if lm_value > max_value:
max_position = lm_pos
max_orientation = lm_ang
max_value = lm_value
break
else:
old_pos = lm_pos
# 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, -lm_pos[0] + vf.sizeX() / 2,
-lm_pos[1] + vf.sizeY() / 2,
-lm_pos[2] + vf.sizeZ() / 2, 'fourier')
score = frm_correlate(vf2, wf, vg, wg, b, max_freq, weights, False,
denominator1, denominator2, True)
orientation, val = frm_find_best_constrained_angle_interp(
score, constraint=constraint)
else: # no converge after the specified iteration, still get the best result as we can
if lm_value > max_value:
max_position = lm_pos
max_orientation = lm_ang
max_value = lm_value
# print max_value # for show the convergence of the algorithm
return max_position, max_orientation, max_value
def simpleSimulation(volume,
rotation,
shiftV,
wedgeInfo=None,
SNR=0.1,
mask=None):
"""
simpleSimulation: Simulates an ET by applying rotation,shift,wedge and noise to an volume
@param volume: the volume used for simulations
@param rotation: the rotation applied to volume
@param shiftV: shift vector applied to volume
@param wedgeInfo: wedge applied to volume
@param SNR: noise level applied to volume
@param mask: Apodisation mask
@return: a simple cryo em simulation of volume
"""
from pytom_volume import vol, rotate, shift, initSphere
from pytom.simulation import whiteNoise
if not rotation == [0, 0, 0]:
#print '---ROTATE---'
#print 'EMSimulation simpleSimulation: in rotation 1 ' + str(rotation)
rotatedCopy = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
rotate(volume, rotatedCopy, rotation[0], rotation[1], rotation[2])
else:
rotatedCopy = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
rotatedCopy.copyVolume(volume)
#print 'EMSimulation simpleSimulation: after rotation '
if not mask:
#print 'EMSimulation simpleSimulation: in mask 1'
mask = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
initSphere(mask,
volume.sizeX() // 2 - 1, 0, 0,
volume.sizeX() // 2,
volume.sizeX() // 2,
volume.sizeX() // 2)
maskedCopy = rotatedCopy * mask
if not mask.__class__ == vol:
#print 'EMSimulation simpleSimulation: in mask 2'
mask = mask.getVolume(rotation)
maskedCopy = rotatedCopy * mask
else:
#print 'EMSimulation simpleSimulation: in mask 3'
maskedCopy = rotatedCopy * mask
#print 'EMSimulation simpleSimulation: after mask'
if not shiftV == [0, 0, 0]:
#print '--SHIFT---'
shiftedCopy = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
shift(maskedCopy, shiftedCopy, shiftV[0], shiftV[1], shiftV[2])
else:
shiftedCopy = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
shiftedCopy.copyVolume(maskedCopy)
if (shiftV == [0, 0, 0]) and (rotation == [0, 0, 0]):
#no shift and no rotation -> simply take the original volume
c = vol(maskedCopy.sizeX(), volume.sizeY(), volume.sizeZ())
c.copyVolume(maskedCopy)
noisyCopy = whiteNoise.add(c, SNR)
else:
noisyCopy = whiteNoise.add(shiftedCopy, SNR)
if wedgeInfo:
#print '---WEDGE---'
result = wedgeInfo.apply(noisyCopy)
else:
result = noisyCopy
#print 'EMSimulation simpleSimulation: end function'
if result.__class__ == list:
return result[0]
else:
return result
def maskOut(self, mask, center, size):
"""
maskOut: Set part of mask volume to all zero. The region is specified by center and size.
@param mask: volume that you handle with
@type mask: L{pytom_volume.vol}
@param center: center of the region
@type center: [x,y,z]
@param size: size of the region
@type size: [sizeX, sizeY, sizeZ] or radius
"""
from pytom_volume import vol, putSubVolume
if size.__class__ == list:
p_sizeX = size[0]
p_sizeY = size[1]
p_sizeZ = size[2]
elif size.__class__ == vol:
mm = size
p_sizeX = mm.sizeX()
p_sizeY = mm.sizeY()
p_sizeZ = mm.sizeZ()
else:
radius = size
p_sizeX = radius * 2
p_sizeY = radius * 2
p_sizeZ = radius * 2
maskSize = [mask.sizeX(), mask.sizeY(), mask.sizeZ()]
if maskSize < center:
raise RuntimeError('Center out of range!')
# [)
# mask out double size. CHANGED!!!
startX = int(center[0] - p_sizeX / 2)
endX = int(center[0] + p_sizeX / 2)
startY = int(center[1] - p_sizeY / 2)
endY = int(center[1] + p_sizeY / 2)
startZ = int(center[2] - p_sizeZ / 2)
endZ = int(center[2] + p_sizeZ / 2)
# only used for radius
sub_startX = 0
sub_startY = 0
sub_startZ = 0
if startX < 0:
sub_startX = -startX
startX = 0
if endX > maskSize[0]:
endX = maskSize[0]
if startY < 0:
sub_startY = -startY
startY = 0
if endY > maskSize[1]:
endY = maskSize[1]
if startZ < 0:
sub_startZ = -startZ
startZ = 0
if endZ > maskSize[2]:
endZ = maskSize[2]
sizeX = endX - startX
sizeY = endY - startY
sizeZ = endZ - startZ
if size.__class__ == list:
subV = vol(sizeX, sizeY, sizeZ)
subV.setAll(0)
elif size.__class__ == vol:
from pytom_volume import limit, subvolume
subV = (mm - 1) / -1
limit(subV, 0.999, 0, 0, 0, True, False)
subV = subvolume(subV, sub_startX, sub_startY, sub_startZ, sizeX,
sizeY, sizeZ)
tempV = subvolume(mask, startX, startY, startZ, sizeX, sizeY,
sizeZ)
subV = subV * tempV # AND operation
else:
from pytom_volume import initSphere, subvolume
subV = vol(radius * 2, radius * 2, radius * 2)
initSphere(subV, radius, 0, 0, radius, radius, radius)
tempV = vol(radius * 2, radius * 2, radius * 2)
tempV.setAll(1)
subV = tempV - subV
subV = subvolume(subV, sub_startX, sub_startY, sub_startZ, sizeX,
sizeY, sizeZ)
tempV = subvolume(mask, startX, startY, startZ, sizeX, sizeY,
sizeZ)
subV = subV * tempV # AND operation
putSubVolume(subV, mask, startX, startY, startZ)
def FLCF(volume, template, mask=None, stdV=None):
'''
Created on Apr 13, 2010
FLCF: compute the fast local correlation function
This functions works only when the mask is in the middle.
@param volume: target volume
@type volume: L{pytom_volume.vol}
@param template: template to be searched. It can have smaller size then target volume.
@type template: L{pytom_volume.vol}
@param mask: template mask. If not given, a default sphere mask will be generated which has the same size with the given template.
@type mask: L{pytom_volume.vol}
@param stdV: standard deviation of the target volume under mask, which do not need to be calculated again when the mask is identical.
@type stdV: L{pytom_volume.vol}
@return: the local correlation function
@rtype: L{pytom_volume.vol}
@author: Yuxiang Chen
'''
from pytom_volume import vol, pasteCenter
from pytom.basic.fourier import fft, ifft, iftshift
from pytom_volume import conjugate
from pytom.basic.structures import Mask
from pytom_volume import sum
from pytom.basic.files import write_em
if volume.__class__ != vol and template.__class__ != vol:
raise RuntimeError('Wrong input type!')
if volume.sizeX()<template.sizeX() or volume.sizeY()<template.sizeY() or volume.sizeZ()<template.sizeZ():
raise RuntimeError('Template size is bigger than the target volume')
# generate the mask
if mask.__class__ != vol:
from pytom_volume import initSphere
mask = vol(template.sizeX(), template.sizeY(), template.sizeZ())
mask.setAll(0)
initSphere(mask, template.sizeX()/2,0,0,template.sizeX()/2, template.sizeY()/2, template.sizeZ()/2)
else:
if template.sizeX()!=mask.sizeX() and template.sizeY()!=mask.sizeY() and template.sizeZ()!=mask.sizeZ():
raise RuntimeError('Template and mask size are not consistent!')
# calculate the non-zeros
p = sum(mask)
# normalize the template under mask
meanT = meanValueUnderMask(template, mask, p)
stdT = stdValueUnderMask(template, mask, meanT, p)
temp = (template - meanT)/stdT
temp = temp * mask
# construct both the template and the mask which has the same size as target volume
tempV = temp
if volume.sizeX() != temp.sizeX() or volume.sizeY() != temp.sizeY() or volume.sizeZ() != temp.sizeZ():
tempV = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
tempV.setAll(0)
pasteCenter(temp, tempV)
maskV = mask
if volume.sizeX() != mask.sizeX() or volume.sizeY() != mask.sizeY() or volume.sizeZ() != mask.sizeZ():
maskV = vol(volume.sizeX(), volume.sizeY(), volume.sizeZ())
maskV.setAll(0)
pasteCenter(mask, maskV)
# calculate the mean and std of volume
if stdV.__class__ != vol:
meanV = meanUnderMask(volume, maskV, p)
stdV = stdUnderMask(volume, maskV, p, meanV)
size = volume.numelem()
fT = fft(tempV)
conjugate(fT)
result = iftshift(ifft(fT*fft(volume)))/stdV
result.shiftscale(0,1/(size*p))
return result
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]
