print()
rhos = np.array(rhos)
average_rho = rhosum / nmaps
saxs.write_mrc(average_rho, side, output + "_avg.mrc")
print("%s_avg.mrc written." % output)
"""
#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(args.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(nmaps):
fscs.append(saxs.calc_fsc(rhos[map], average_rho, side))
fscs = np.array(fscs)
fsc = np.mean(fscs, axis=0)
np.savetxt(args.output + '_fsc.dat',
fsc,
delimiter=" ",
fmt="%.5e",
header="1/resolution, FSC")
x = np.linspace(fsc[0, 0], fsc[-1, 0], 100)
y = np.interp(x, fsc[:, 0], fsc[:, 1])
resi = np.argmin(y >= 0.5)
resx = np.interp(0.5, [y[resi + 1], y[resi]], [x[resi + 1], x[resi]])
resn = round(float(1. / resx), 1)
print('Resolution: %.1f' % resn, '\u212B')
logging.info('Resolution: %.1f ' + '\u212B', resn)
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',
fsc,
delimiter=" ",
fmt="%.5e",
header="1/resolution, FSC")
x = np.linspace(fsc[0, 0], fsc[-1, 0], 100)
y = np.interp(x, fsc[:, 0], fsc[:, 1])
resi = np.argmin(y >= 0.5)
resx = np.interp(0.5, [y[resi + 1], y[resi]], [x[resi + 1], x[resi]])
resn = round(float(1. / resx), 1)
print("Resolution: %.1f A" % resn)
superlogger.info('Resolution: %.1f ', resn)
rhos.append(rho)
if rhosum is None:
rhosum = rho
else:
rhosum += rho
print()
rhos = np.array(rhos)
average_rho = rhosum / nmaps
saxs.write_mrc(average_rho, side, output + "_avg.mrc")
print("%s_avg.mrc written." % output)
#rather than compare two halves, average all fsc's to the reference
fscs = []
resns = []
for calc_map in range(nmaps):
fsc_map = saxs.calc_fsc(rhos[calc_map], average_rho, side)
fscs.append(fsc_map)
resn_map = saxs.fsc2res(fsc_map)
resns.append(resn_map)
fscs = np.array(fscs)
resns = np.array(resns)
fsc = np.mean(fscs, axis=0)
resn, x, y, resx = saxs.fsc2res(fsc, return_plot=True)
resn_sd = np.std(resns)
if np.min(fsc[:, 1]) > 0.5:
print("Resolution: < %.1f +- %.1f A (maximum possible)" %
(resn, resn_sd))
else:
print("Resolution: %.1f +- %.1f A " % (resn, resn_sd))
if args.output is None:
basename, ext = os.path.splitext(args.file)
output = basename + '_fsc'
else:
output = args.output
rho, side = saxs.read_mrc(args.file)
refrho, refside = saxs.read_mrc(args.ref)
if rho.shape[0] != refrho.shape[0]:
print "Shape of rho and ref are not equal."
sys.exit()
if side != refside:
print "Side length of rho and ref are not equal."
sys.exit()
fsc = saxs.calc_fsc(rho, refrho, side)
np.savetxt(output + '.dat', fsc, delimiter=' ', fmt='% .5e')
x = np.linspace(fsc[0, 0], fsc[-1, 0], 100)
y = np.interp(x, fsc[:, 0], fsc[:, 1])
resi = np.argmin(y >= 0.5)
resx = np.interp(0.5, [y[resi + 1], y[resi]], [x[resi + 1], x[resi]])
resn = round(float(1. / resx), 1)
print "Resolution: %.1f" % resn, u'\u212B'.encode('utf-8')
if args.plot:
plt.plot(fsc[:, 0], fsc[:, 0] * 0 + 0.5, 'k--')
plt.plot(fsc[:, 0], fsc[:, 1], 'o')
plt.plot(x, y, 'k-')
plt.plot([resx], [0.5],
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))
superlogger.info('Mean Density of Avg Map (all voxels): %3.5f', np.mean(average_rho))
superlogger.info('Std. Dev. of Density (all voxels): %3.5f', np.std(average_rho))
superlogger.info('RMSD of Density (all voxels): %3.5f', np.sqrt(np.mean(np.square(average_rho))))
idx = np.where(np.abs(average_rho)>0.01*average_rho.max())
superlogger.info('Modified Mean Density (voxels >0.01*max): %3.5f', np.mean(average_rho[idx]))
superlogger.info('Modified Std. Dev. of Density (voxels >0.01*max): %3.5f', np.std(average_rho[idx]))
superlogger.info('Modified RMSD of Density (voxels >0.01*max): %3.5f', np.sqrt(np.mean(np.square(average_rho[idx]))))
saxs.write_mrc(average_rho, sides[0], output+'_avg.mrc')
#rather than compare two halves, average all fsc's to the reference
fscs = []
resns = []
for calc_map in range(len(aligned)):
fsc_map = saxs.calc_fsc(aligned[calc_map],refrho,sides[0])
fscs.append(fsc_map)
resn_map = saxs.fsc2res(fsc_map)
resns.append(resn_map)
fscs = np.array(fscs)
#save a file containing all fsc curves
fscs_header = ['res(1/A)']
for i in kept_ids:
ioutput = output+"_"+str(i)+"_aligned"
fscs_header.append(ioutput)
#add the resolution as the first column
fscs_for_file = np.vstack((fscs[0,:,0],fscs[:,:,1])).T
np.savetxt(output+'_allfscs.dat',fscs_for_file,delimiter=" ",fmt="%.5e",header=",".join(fscs_header))
