def test():
print("# TEST %02d: %s" % (num, spec))
# initialize reweighting class
rew = bme.Reweight()
# load data
rew.load(exp_train1, calc_train1)
rew.load(exp_train2, calc_train2)
rew.load(exp_train3, calc_train3)
# do optimization using theta=2
chib, chia, srel = rew.optimize(theta=50)
# compare NOE before and after optimization .
# output is written to files with prefix 'example1_noe'
chi2_b, chi2_a = rew.weight_exp(exp_train1, calc_train1,
'%s/test_%02d_train1' % (outdir, num))
chi2_b, chi2_a = rew.weight_exp(exp_train2, calc_train2,
'%s/test_%02d_train2' % (outdir, num))
chi2_b, chi2_a = rew.weight_exp(exp_train3, calc_train3,
'%s/test_%02d_train3' % (outdir, num))
ww = rew.get_weights()
fh = open("%s/test_%02d_weights.dat" % (outdir, num), "w")
fh.write(" ".join(["%8.4f " % x for x in ww]))
fh.close()
comp("%s/test_%02d_weights.dat" % (refdir, num))
comp("%s/test_%02d_train1.stats.dat" % (refdir, num))
comp("%s/test_%02d_train2.stats.dat" % (refdir, num))
comp("%s/test_%02d_train3.stats.dat" % (refdir, num))
print("# TEST %02d: %s" % (num, spec))
print("# DONE #")
def test():
print("# TEST %02d: %s" % (num,spec))
# initialize reweighting class, use non-uniform initial weights
bias = [float(line.split()[1]) for line in open(bias_file)]
rew = bme.Reweight(w0=bias,kbt=kbt)
# load data
rew.load(exp_train,calc_train)
# do optimization using theta=2
chib,chia, srel = rew.optimize(theta=1)
# compare NOE before and after optimization .
# output is written to files with prefix 'example1_noe'
chi2_b,chi2_a = rew.weight_exp(exp_train,calc_train, '%s/test_%02d_train' % (outdir,num))
chi2_b,chi2_a = rew.weight_exp(exp_test,calc_test, '%s/test_%02d_test' % (outdir,num))
ww = rew.get_weights()
fh = open("%s/test_%02d_weights.dat" % (outdir,num) ,"w")
fh.write(" ".join(["%8.4f " % x for x in ww]) )
fh.close()
comp("%s/test_%02d_weights.dat" % (refdir,num))
comp("%s/test_%02d_train.stats.dat" % (refdir,num))
comp("%s/test_%02d_test.stats.dat" % (refdir,num))
print("# TEST %02d: %s" % (num,spec))
print("# DONE #")
def main():
args = parse()
bmea = bme.Reweight(verbose=(not args.quiet))
assert len(args.exp_files) == len(args.calc_files)
for j in range(len(args.exp_files)):
bmea.load(args.exp_files[j], args.calc_files[j])
bmea.optimize(theta=args.theta)
def test():
print("# TEST %02d: %s" % (num,spec))
# initialize reweighting class
rew_bme = bme.Reweight()
rows = range(0,20000,100)
# load data
rew_bme.load(exp_train,calc_train,rows=rows)
# do optimization using theta=2, method MAXENT (default)
chib,chia, srel = rew_bme.optimize(theta=2,method="MAXENT")
ww_bme = rew_bme.get_weights()
# initialize reweighting class
rew_eros = bme.Reweight()
# load data
rew_eros.load(exp_train,calc_train,rows=rows)
# do optimization using theta=2
chib,chia, srel = rew_eros.optimize(theta=2,method="BER")
ww_ber = rew_eros.get_weights()
diff=np.sqrt(np.sum(((ww_ber-ww_bme)**2))/len(ww_ber))
print(" rms difference between BER and BME: %10.5e" % diff)
fh = open("%s/test_%02d_weights_bme.dat" % (outdir,num) ,"w")
fh.write(" ".join(["%10.5e \n" % x for x in ww_bme]) )
fh.close()
fh = open("%s/test_%02d_weights_ber.dat" % (outdir,num) ,"w")
fh.write(" ".join(["%10.5e \n" % x for x in ww_ber]) )
fh.close()
comp("%s/test_%02d_weights_ber.dat" % (refdir,num))
comp("%s/test_%02d_weights_bme.dat" % (refdir,num))
print("# TEST %02d: %s" % (num,spec))
print("# DONE #")
import sys bme_path = os.getcwd()[:-8] # here append the path to the bme script sys.path.append(bme_path) import bme_reweight as bme # define name of experimental datafiles exp_couplings = '../data/couplings_exp.dat' exp_noe='../data/NOE_exp.dat' # define name of experimental quantities # calculated from the trajectory calc_couplings = '../data/couplings_calc.dat' calc_noe='../data/NOE_calc.dat' # initialize reweighting class rew = bme.Reweight() # load data rew.load(exp_couplings,calc_couplings) # do optimization using theta=2 chib,chia, srel = rew.optimize(theta=2) # compare NOE before and after optimization . # output is written to files with prefix 'example1_noe' chi2_b,chi2_a = rew.weight_exp(exp_noe,calc_noe, 'example1_noe') # print chisquared print srel print chib, chia print chi2_a, chi2_b
beta = model.intercept_ #Offset
ab.append(np.array([c, alpha, beta]))
#Applying scale factor and offset obtained in this cycle to the Pepsi-SAXS ensemble and write to file to feed BME
calc = alpha * calc + beta
new_calc = open(dir_ + '/tmp_t' + str(t), 'w')
new_calc.write('#\n')
for i in range(0, np.shape(calc)[0]):
new_calc.write('frame' + str(i) + ' ' +
' '.join(str(n) for n in calc[i]) + '\n')
new_calc.close()
#Running BME with the scaled/shifted ensemble
if len(sys.argv) == 6:
rew = bme.Reweight(w0=w_in)
rew.load(exp_file, dir_ + '/tmp_t' + str(t))
chi2_before, chi2_after, srel = rew.optimize(theta=t)
data.append(np.array([c, t, chi2_before, chi2_after, np.exp(srel)]))
print(data[-1])
weights.append(rew.get_weights())
else:
rew = bme.Reweight()
rew.load(exp_file, dir_ + '/tmp_t' + str(t))
chi2_before, chi2_after, srel = rew.optimize(theta=t)
data.append(np.array([c, t, chi2_before, chi2_after, np.exp(srel)]))
print(data[-1])
weights.append(rew.get_weights())
#Weighted average of the Pepsi-SAXS profiles with BME weights
Iav = np.average(calc, axis=0, weights=weights[-1])
