def determine_resolution(self,
even,
odd,
criterion,
numberBands,
mask,
verbose=False):
"""For the master node, determine the resolution.
"""
from pytom.basic.correlation import FSC, determineResolution
if not numberBands:
numberBands = even.sizeX() / 2
fsc = FSC(even, odd, numberBands, mask, verbose=False)
if verbose:
print(self.node_name + ': FSC: ' + str(fsc))
return determineResolution(fsc, criterion, verbose=False)
def determine_resolution(self,
even,
odd,
criterion,
numberBands,
mask,
verbose=False):
"""For the master node, determine the resolution.
@param even: particle list even
@type even: L{pytom.basic.structures.ParticleList}
@param odd: particle list odd
@type odd: L{pytom.basic.structures.ParticleList}
"""
from pytom.basic.correlation import FSC, determineResolution
if not numberBands:
numberBands = even.sizeX() / 2
fsc = FSC(even, odd, numberBands, mask, verbose=False)
if verbose:
print(self.node_name + ': FSC: ' + str(fsc))
return determineResolution(fsc, criterion, verbose=False)
def calculate_averages(pl, binning, mask, outdir='./'):
"""
calcuate averages for particle lists
@param pl: particle list
@type pl: L{pytom.basic.structures.ParticleList}
@param binning: binning factor
@type binning: C{int}
last change: Jan 18 2020: error message for too few processes, FF
"""
import os
from pytom_volume import complexDiv, vol, pasteCenter
from pytom.basic.fourier import fft, ifft
from pytom.basic.correlation import FSC, determineResolution
from pytom_fftplan import fftShift
from pytom_volume import reducedToFull
pls = pl.copy().splitByClass()
res = {}
freqs = {}
wedgeSum = {}
for pp in pls:
# ignore the -1 class, which is used for storing the trash class
class_label = pp[0].getClass()
if class_label != '-1':
assert len(pp) > 3
if len(pp) >= 4 * mpi.size:
spp = mpi._split_seq(pp, mpi.size)
else: # not enough particle to do averaging on one node
spp = [None] * 2
spp[0] = pp[:len(pp) // 2]
spp[1] = pp[len(pp) // 2:]
args = list(
zip(spp, [True] * len(spp), [binning] * len(spp),
[False] * len(spp), [outdir] * len(spp)))
avgs = mpi.parfor(paverage, args)
even_a, even_w, odd_a, odd_w = None, None, None, None
even_avgs = avgs[1::2]
odd_avgs = avgs[::2]
for a, w in even_avgs:
if even_a is None:
even_a = a.getVolume()
even_w = w.getVolume()
else:
even_a += a.getVolume()
even_w += w.getVolume()
os.remove(a.getFilename())
os.remove(w.getFilename())
for a, w in odd_avgs:
if odd_a is None:
odd_a = a.getVolume()
odd_w = w.getVolume()
else:
odd_a += a.getVolume()
odd_w += w.getVolume()
os.remove(a.getFilename())
os.remove(w.getFilename())
# determine the resolution
# raise error message in case even_a == None - only one processor used
if even_a == None:
from pytom.basic.exceptions import ParameterError
raise ParameterError(
'cannot split odd / even. Likely you used only one processor - use: mpirun -np 2 (or higher!)?!'
)
if mask and mask.__class__ == str:
from pytom_volume import read, pasteCenter, vol
maskBin = read(mask, 0, 0, 0, 0, 0, 0, 0, 0, 0, binning,
binning, binning)
if even_a.sizeX() != maskBin.sizeX() or even_a.sizeY(
) != maskBin.sizeY() or even_a.sizeZ() != maskBin.sizeZ():
mask = vol(even_a.sizeX(), even_a.sizeY(), even_a.sizeZ())
mask.setAll(0)
pasteCenter(maskBin, mask)
else:
mask = maskBin
fsc = FSC(even_a, odd_a, int(even_a.sizeX() // 2), mask)
band = determineResolution(fsc, 0.5)[1]
aa = even_a + odd_a
ww = even_w + odd_w
fa = fft(aa)
r = complexDiv(fa, ww)
rr = ifft(r)
rr.shiftscale(0.0, 1. / (rr.sizeX() * rr.sizeY() * rr.sizeZ()))
res[class_label] = rr
freqs[class_label] = band
ww2 = reducedToFull(ww)
fftShift(ww2, True)
wedgeSum[class_label] = ww2
print('done')
return res, freqs, wedgeSum
if not numberBands:
numberBands = int(v1.sizeX() // 2)
f = FSC(v1, v2, numberBands, mask, verbose)
#f = [1, 0.9999816330959427, 0.9998727543638058, 0.9986706311763134, 0.9967503089610698, 0.9945473896086455, 0.9953391452631559, 0.9926167503040506, 0.9886499997836082, 0.9846988786130074, 0.9850170987613799, 0.9849222409268831, 0.9820779872082366, 0.981161445747785, 0.978102618561267, 0.9749670311874213, 0.9710488103484851, 0.9686564997495193, 0.9643854526532541, 0.9621314730670771, 0.9568606791204185, 0.9488084842261655, 0.9416015322427522, 0.9316703960630233, 0.9106972097966776, 0.8912878863048055, 0.8795187076235272, 0.8713474813842144, 0.8508750246010772, 0.8195820950483931, 0.8065990902773463, 0.7823660922709397, 0.7521861621134768, 0.7236765089946452, 0.6967229052025852, 0.6815563485825349, 0.6606856188994277, 0.6513326589007892, 0.6340321380485363, 0.6057709663336085, 0.584793117868313, 0.5612341962238455, 0.5623548376402193, 0.5564192235463952, 0.5397585867441497, 0.5089376925831242, 0.4647300273454635, 0.4310334881504598, 0.41741429413879966, 0.4195952490948154, 0.4066609275881065, 0.37019375197265786, 0.31936001816491055, 0.275903152373691, 0.2538399661514517, 0.24197349348826183, 0.20730706794873432, 0.1823204187105925, 0.17041895753522998, 0.16153106667416953, 0.13872290716093824, 0.12428131231796732, 0.09594749780671366, 0.09281895056407187, 0.0950512406930502, 0.07845013400157819, 0.06778918720241832, 0.05699422426625805, 0.04004787096713291, 0.035285330785697615, 0.02761223114687527, 0.029854265039150632, 0.030802679570736933, 0.02855574408867763, 0.04062783248396335, 0.046982827702621556, 0.044667285930674615, 0.03327190294513204, 0.028879433147898908, 0.019113096081122542, 0.018889519864393182, 0.03363102079214279, 0.030416314115916717, 0.015045702588444513, 0.007700419599421394, 0.013662921155622407, 0.02549288977161008, 0.01648898979277964, 0.004577992397744576, 0.003687537468279412, 0.015624522796941348, 0.012150048589636583, 0.013236997547964386, 0.024818980351894827, 0.017881736272355488, 0.008875703095090339, 0.004009836930167128, 0.005169522148403328, 0.013778610598594218, 0.024255111798589142]
if verbose: print('FSC:\n', f)
import pytom.tompy.correlation as correlation
from pytom_numpy import vol2npy
import numpy as np
from pytom.tompy.io import write
if randomize is None:
for (ii, fscel) in enumerate(f):
f[ii] = 2. * fscel / (1. + fscel)
r = determineResolution(f, fscCriterion, verbose)
else:
randomizationFrequency = np.floor(
determineResolution(np.array(f), randomize, verbose)[1])
oddVolumeRandomizedPhase = correlation.randomizePhaseBeyondFreq(
vol2npy(v1), randomizationFrequency)
evenVolumeRandomizedPhase = correlation.randomizePhaseBeyondFreq(
vol2npy(v2), randomizationFrequency)
write(os.path.join(outdir, 'randOdd.mrc'),
oddVolumeRandomizedPhase)
write(os.path.join(outdir, 'randEven.mrc'),
evenVolumeRandomizedPhase)
oddVolumeRandomizedPhase = read(os.path.join(
outdir, 'randOdd.mrc'))
evenVolumeRandomizedPhase = read(
os.path.join(outdir, 'randEven.mrc'))