def initialize(pl, settings):
from pytom.basic.structures import Particle
# from pytom.alignment.alignmentFunctions import average2
from pytom.basic.filter import lowpassFilter
print("Initializing the class centroids ...")
pl = pl.copy()
pl.sortByScore()
if settings["noise"]:
pl = pl[:int((1-settings["noise"])*len(pl))]
K = settings["ncluster"]
freq = settings["frequency"]
kn = len(pl)//K
references = {}
frequencies = {}
# get the first class centroid
pp = pl[:kn]
# avg, fsc = average2(pp, norm=True, verbose=False)
pp.setClassAllParticles('0')
res, tmp, tmp2 = calculate_averages(pp, settings["binning"], None, outdir=settings["output_directory"])
avg = res['0']
avg = lowpassFilter(avg, freq, freq/10.)[0]
avg.write(os.path.join(settings['output_directory'], 'initial_0.em') )
p = Particle(os.path.join(settings['output_directory'], 'initial_0.em'))
p.setClass('0')
references['0'] = p
frequencies['0'] = freq
for k in range(1, K):
distances = [4]*len(pl)
for c, ref in references.items():
args = list(zip(pl, [ref]*len(pl), [freq]*len(pl), [settings["fmask"]]*len(pl), [settings["binning"]]*len(pl)))
dist = mpi.parfor(distance, args)
for i in range(len(pl)):
if distances[i] > dist[i]:
distances[i] = dist[i]
distances = np.asarray(distances)
print('sum distances: ', distances.sum())
distances = distances/np.sum(distances)
idx = np.random.choice(len(pl), kn, replace=False, p=distances)
pp = ParticleList()
for i in idx:
pp.append(pl[int(i)])
# avg, fsc = average2(pp, norm=True, verbose=False)
pp.setClassAllParticles('0')
res, tmp, tmp2 = calculate_averages(pp, settings["binning"], None, outdir=settings["output_directory"])
avg = res['0']
avg = lowpassFilter(avg, freq, freq/10.)[0]
kname = os.path.join(settings['output_directory'], 'initial_{}.em'.format(k))
avg.write(kname)
p = Particle(kname)
p.setClass(str(k))
references[str(k)] = p
frequencies[str(k)] = freq
return references, frequencies
