saxs.write_mrc(aligned[i], sides[0], ioutput + ".mrc")
print("%s.mrc written. Score = %0.3f %s " %
(ioutput, scores[i], filtered[i]))
logging.info('Correlation score to reference: %s.mrc %.3f %s', ioutput,
scores[i], filtered[i])
aligned = aligned[scores > threshold]
average_rho = np.mean(aligned, axis=0)
superlogger.info('Mean of correlation scores: %.3f', mean)
superlogger.info('Standard deviation of the scores: %.3f', std)
superlogger.info('Total number of input maps for alignment: %i',
allrhos.shape[0])
superlogger.info('Number of aligned maps accepted: %i', aligned.shape[0])
superlogger.info('Correlation score between average and reference: %.3f',
-saxs.rho_overlap_score(average_rho, refrho))
saxs.write_mrc(average_rho, sides[0], output + '_avg.mrc')
"""
#split maps into 2 halves--> enan, align, average independently with same refrho
avg_rho1 = np.mean(aligned[::2],axis=0)
avg_rho2 = np.mean(aligned[1::2],axis=0)
fsc = saxs.calc_fsc(avg_rho1,avg_rho2,sides[0])
np.savetxt(output+'_fsc.dat',fsc,delimiter=" ",fmt="%.5e",header="qbins, FSC")
"""
#rather than compare two halves, average all fsc's to the reference
fscs = []
for calc_map in range(len(aligned)):
fscs.append(saxs.calc_fsc(aligned[calc_map], refrho, sides[0]))
fscs = np.array(fscs)
fsc = np.mean(fscs, axis=0)
np.savetxt(output + '_fsc.dat',
else:
filtered[i] = ' '
basename, ext = os.path.splitext(args.files[i])
ioutput = basename+"_aligned"
saxs.write_mrc(aligned[i], sides[0], ioutput+'.mrc')
print("%s.mrc written. Score = %0.3f %s " % (ioutput,scores[i],filtered[i]))
logging.info('Correlation score to reference: %s.mrc %.3f %s', ioutput, scores[i], filtered[i])
aligned = aligned[scores>threshold]
average_rho = np.mean(aligned,axis=0)
logging.info('Mean of correlation scores: %.3f', mean)
logging.info('Standard deviation of the scores: %.3f', std)
logging.info('Total number of input maps for alignment: %i',allrhos.shape[0])
logging.info('Number of aligned maps accepted: %i', aligned.shape[0])
logging.info('Correlation score between average and reference: %.3f', 1/saxs.rho_overlap_score(average_rho, refrho))
saxs.write_mrc(average_rho, sides[0], output+'_avg.mrc')
logging.info('END')
"""
#split maps into 2 halves--> enan, align, average independently with same refrho
avg_rho1 = np.mean(aligned[::2],axis=0)
avg_rho2 = np.mean(aligned[1::2],axis=0)
fsc = saxs.calc_fsc(avg_rho1,avg_rho2,sides[0])
np.savetxt(output+'_fsc.dat',fsc,delimiter=" ",fmt="%.5e",header="qbins, FSC")
"""
#rather than compare two halves, average all fsc's to the reference
fscs = []
for map in range(len(aligned)):
fscs.append(saxs.calc_fsc(aligned[map],refrho,sides[0]))
fscs = np.array(fscs)
