def target(self, dxyz):
self.move_on = False
chi_score = 1e30
if (True):
for ii in range(50):
dxyz = self.pdb_obj.beads.relax(self.restraints, dxyz)
print ii, ' ',
if (self.pdb_obj.beads.restraint(self.restraints, dxyz) <
self.threshold):
self.new_xyz = self.pdb_obj.perturb(dxyz)
self.move_on = True
break
if (self.move_on):
t1 = time.time()
self.she_engine.engine.update_coord(self.new_xyz, self.new_indx)
new_I = self.she_engine.engine.I()
self.time_she += (time.time() - t1)
var = self.expt_s
s, o = she.linear_fit(new_I, self.expt_I, var)
chi_score = flex.sum(
flex.pow2((self.expt_I - (s * new_I + o))) / self.expt_s)
#restraint=self.pdb_obj.beads.restraint(self.restraints, self.new_xyz)
#tot = chi_score + restraint*self.restraint_weight
print chi_score
self.counter += 1
return chi_score
def compute_score(self, nstruct, prefix):
new_indx = flex.int(self.she_obj.natom)
files = []
results = flex.double()
pdb_files = glob.glob(prefix+"*.pdb") # including the input pdb model, avoiding no entry case
for pdb_file_name in pdb_files:
new_xyz = extract_xyz(pdb_file_name)
self.she_obj.engine.update_coord(new_xyz,new_indx)
new_i = self.she_obj.engine.I()
s,o = she.linear_fit(new_i, self.obs.i, self.obs.s)
chi2= flex.mean( flex.pow2( (self.obs.i-(s*new_i+o)) /self.obs.s ))
files.append( pdb_file_name )
results.append( chi2 )
self.min_indx = flex.min_index( results )
self.min_file = files[ self.min_indx ]
self.min_score= results[ self.min_indx ]
return files, results
def compute_score(self, nstruct, prefix):
new_indx = flex.int(self.she_obj.natom)
files = []
results = flex.double()
for ii in range(1, nstruct + 1):
pdb_file_name = prefix + str(ii) + '.pdb'
while (not os.path.exists(pdb_file_name)):
time.sleep(0.02)
time.sleep(0.05)
new_xyz = extract_xyz(pdb_file_name)
self.she_obj.engine.update_coord(new_xyz, new_indx)
new_i = self.she_obj.engine.I()
s, o = she.linear_fit(new_i, self.obs.i, self.obs.s)
chi2 = flex.mean(
flex.pow2((self.obs.i - (s * new_i + o)) / self.obs.s))
files.append(pdb_file_name)
results.append(chi2)
return files, results
def target(self, vector):
self.counter += 1
result = 0
length = flex.sum(flex.pow(vector, 2))
if (length > self.Rmax2):
result = 1e30
else:
new_coord = self.pdb.NMPerturb(self.modes, vector)
t1 = time.time()
self.she_engine.engine.update_coord(flex.vec3_double(new_coord),
self.new_indx)
new_I = self.she_engine.engine.I()
self.time_she += (time.time() - t1)
var = self.expt_s
s, o = she.linear_fit(new_I, self.expt_I, var)
result = flex.sum(
flex.pow2((self.expt_I - (s * new_I + o)) / self.expt_s))
return result
def target(self, vector):
self.counter += 1
result = 0
length = flex.sum_sq(vector)
if (length > self.Rmax2):
result = 1e30
else:
new_coord = self.pdb.NMPerturb(self.modes, vector)
self.she_engine.engine.update_coord(new_coord, self.new_indx)
new_I = self.she_engine.engine.I()
var = self.expt_s
s, o = she.linear_fit(new_I[:5], self.expt_I[:5], var[:5])
result = flex.sum(
flex.pow2((self.expt_I - (s * new_I + o)) / self.expt_s))
# result = flex.sum_sq( (self.expt_I-new_I) /self.expt_s )
#print self.pdb.r, result
return result
def estimate_init_weight(self):
## initial chi-score ##
new_I = self.she_engine.engine.I()
var = self.expt_s
s, o = she.linear_fit(new_I, self.expt_I, var)
chi_score = flex.sum(
flex.pow2((self.expt_I - (s * new_I + o)) / self.expt_s))
n_restraint = self.nbeads**2 / 20
tot_res = 0
for ii in range(
10): ## perturb 10 times to estimate initial restraint ##
vector = flex.random_double(self.nbeads * 3) * self.step_size
self.restraints = self.build_restraint(n_restraint)
self.new_xyz = self.pdb_obj.perturb(vector)
restraint = self.pdb_obj.beads.restraint(self.restraints,
self.new_xyz)
tot_res += restraint
mean_res = tot_res / 10.0
self.restraint_weight = chi_score / mean_res