def dumpMembers( self, traj, fname ):
"""
Dump ensemble member trajectories
@param traj: Trajectory to dump
@type traj: Trajectory
@param fname: trajectory file name - used to derrive name for members
@type fname: str'
@return: list of trajectory files
@rtype: [str]
"""
fname = T.stripSuffix( T.absfile( fname, resolveLinks=0 ) )
members = range( traj.n_members )
r = []
for n in members:
f = fname + '_member_%02i.traj' % n
if os.path.exists( f ):
self.log.add('using existing ' + f )
else:
self.log.write('saving ' + f + '...')
m = traj.takeMember( n )
T.dump( m, f )
self.log.add('done')
r += [ f ]
return r
def test_Analyzer( self):
"""Dock.Analyzer test """
from Biskit import Trajectory
from Biskit.Dock import ComplexList
## create a minimal 1-frame receptor trajectory from a pdb file
self.t_rec = Trajectory( [t.testRoot()+'/rec/1A2P.pdb'],
verbose=self.local)
t.dump( self.t_rec, self.f_out )
## load a complex list
cl = t.load( t.testRoot() + '/dock/hex/complexes.cl')
self.a= Analyzer( rec = self.f_out,
lig = t.testRoot()+'/lig_pcr_00/traj.dat',
ref = t.testRoot()+'/com/ref.complex',
verbose = self.local)
## shuffle this list five times
shuff_lst = self.a.shuffledLists( 5, range(8) )
## create two random contact matrices
rand_mat = self.a.random_contacts( cl[0].atomContacts(), 2 )
self.assertEqual( N0.shape(rand_mat[1]), (1075, 876) )
def saveProtocols( self ):
"""
Save protocol to file.
"""
f_prot = T.stripSuffix( T.absfile(self.fout) ) + '_protocols.dat'
self.log.write( 'Saving parameters to %s...' % f_prot )
T.dump( self.protocols, f_prot )
def done(self):
"""
Write result to file.
"""
tree = self.getResult()
self.log.add("Saving result to %s..." % self.fout)
T.dump( tree, self.fout )
self.log.add( "Done" )
def saveRst( self, fname ):
"""
Pickle data necessary for a restart of the running calculation.
@param fname: file name
@type fname: str
"""
T.dump( self.getRst(), fname )
def updateModelDic( f ):
""" Call update() on all models in a dict to make them aware of the
new profiles."""
print 'Updating ', f
d = T.load( T.absfile( f ) )
for m in d.values():
m.update( updateMissing=1 )
T.dump( d, T.absfile( f ) )
def write_PDBModels(self, pdb_list, output_file = None):
"""
Pickles the list of PDBModels to disc.
@param pdb_list: list of models
@type pdb_list: ModelList
@param output_file: output file
(default: None S{->} outFolder/L{F_PDBModels_OUT})
@type output_file: str
"""
output_file = output_file or self.outFolder + self.F_PDBModels_OUT
T.dump(pdb_list, '%s'%(output_file))
def dump(self, o):
"""
Try to pickle an object to the currently valid path.
@return: the absolute path to which o was pickled
@rtype: str
"""
try:
f = self.local()
T.dump(f, o)
return f
except:
T.errWriteln("Couldn't dump to %s (constructed from %s)" %\
self.formatted(), self.local() )
raise
def dump( self, o ):
"""
Try to pickle an object to the currently valid path.
@return: the absolute path to which o was pickled
@rtype: str
"""
try:
f = self.local()
T.dump( f, o )
return f
except:
T.errWriteln("Couldn't dump to %s (constructed from %s)" %\
self.formatted(), self.local() )
raise
def changeModel(inFile, prefix, sourceModel):
print '\nget ' + os.path.basename(inFile) + '..',
model = PDBModel(inFile)
model.update()
model = model.sort()
eq = model.equals(sourceModel)
if not eq[0] and eq[1]:
raise ConvertError('source and other models are not equal: ' + str(eq))
# model.validSource()
model.setSource(sourceModel.validSource())
#model.atomsChanged = 0
for k in model.atoms:
model.atoms[k, 'changed'] = N0.all(model[k] == sourceModel[k])
model.xyzChanged = (0 != N0.sum(N0.ravel(model.xyz - sourceModel.xyz)))
model.update(updateMissing=1)
if model.xyzChanged:
doper = PDBDope(model)
if 'MS' in sourceModel.atoms.keys():
doper.addSurfaceRacer(probe=1.4)
if 'density' in sourceModel.atoms.keys():
doper.addDensity()
if 'foldX' in sourceModel.info.keys():
doper.addFoldX()
if 'delphi' in sourceModel.info.keys():
doper.addDelphi()
outFile = os.path.dirname( inFile ) + '/' + prefix +\
T.stripFilename( inFile ) + '.model'
T.dump(model, outFile)
print '-> ' + os.path.basename(outFile)
def pickleError(self, o):
"""
Pickle object to disc.
@param o: object to pickle
@type o: any
"""
try:
fname = self.ferror + '_dat'
if not os.path.exists(fname):
T.dump(o, fname)
except Exception, why:
f = open('ErrorReportError_ContactSlave', 'a')
f.write('Could not pickle error infos\n')
f.write(str(why))
f.close()
def pickleError( self, o ):
"""
Pickle object to disc.
@param o: object to pickle
@type o: any
"""
try:
fname = self.ferror + '_dat'
if not os.path.exists( fname ):
T.dump( o, fname )
except Exception, why:
f = open('ErrorReportError_ContactSlave','a')
f.write('Could not pickle error infos\n')
f.write( str( why ) )
f.close()
def changeModel( inFile, prefix, sourceModel ):
print '\nget ' + os.path.basename( inFile ) + '..',
model = PDBModel( inFile )
model.update()
model = model.sort()
eq = model.equals( sourceModel )
if not eq[0] and eq[1]:
raise ConvertError('source and other models are not equal: ' + str(eq))
# model.validSource()
model.setSource( sourceModel.validSource() )
#model.atomsChanged = 0
for k in model.atoms:
model.atoms[k,'changed'] = N0.all( model[k] == sourceModel[k] )
model.xyzChanged = ( 0 != N0.sum( N0.ravel( model.xyz - sourceModel.xyz)) )
model.update( updateMissing=1 )
if model.xyzChanged:
doper = PDBDope( model )
if 'MS' in sourceModel.atoms.keys():
doper.addSurfaceRacer( probe=1.4 )
if 'density' in sourceModel.atoms.keys():
doper.addDensity()
## if 'foldX' in sourceModel.info.keys():
## doper.addFoldX()
if 'delphi' in sourceModel.info.keys():
doper.addDelphi()
outFile = os.path.dirname( inFile ) + '/' + prefix +\
T.stripFilename( inFile ) + '.model'
T.dump( model, outFile )
print '-> ' + os.path.basename( outFile )
def dumpMissing( self, o, fname ):
"""
Pickle *o* to path *fname*, if it is not already there.
@param o: object to dump
@type o: any
@param fname: file name
@type fname: str
@return: file name
@rtype: str
"""
if os.path.exists( fname ):
self.log.add('using existing ' + fname )
else:
self.log.add('Saving ' + fname )
T.dump( o, fname )
return fname
def repair( f, old, new ):
"""
Load and repickle with corrected path
"""
try:
o = load( f )
print 'working on %s...' % f
if isinstance( o, PDBModel) : replace_in_PDBModel( o, old, new )
if isinstance( o, Complex ) : replace_in_Complex( o, old, new )
if isinstance( o, Trajectory): replace_in_Traj( o, old, new )
if isinstance( o, dict ) : replace_in_dict( o, old, new )
if isinstance( o, list ) : replace_in_list( o, old, new )
if isinstance( o, ComplexList): o = replace_in_ComplexList(o,old,new)
dump( o, f )
print
except InvalidTargetPath, e:
print e
def dumpMissing(self, o, fname):
"""
Pickle *o* to path *fname*, if it is not already there.
@param o: object to dump
@type o: any
@param fname: file name
@type fname: str
@return: file name
@rtype: str
"""
if os.path.exists(fname):
self.log.add('using existing ' + fname)
else:
self.log.add('Saving ' + fname)
T.dump(o, fname)
return fname
def compile_standard_dict( fpdb, fout, fmask=None, skip_first=True,
take_chain=True):
"""
@param fpdb: str, pdb file containing standard residues
@param fout: str, file name for pickled dictionary
@param fmask: func, method to mask standard residues
"""
m = B.PDBModel( fpdb )
if take_chain:
m = m.takeChains( [0] )
if skip_first:
m.removeRes( 0 )
if fmask is not None:
m = m.compress( fmask(m) )
d = extract_unique_residues( m )
for name in d:
d[name] = normalize_residue( d[name] )
T.dump( d, fout )
return d
def compile_standard_dict(fpdb,
fout,
fmask=None,
skip_first=True,
take_chain=True):
"""
@param fpdb: str, pdb file containing standard residues
@param fout: str, file name for pickled dictionary
@param fmask: func, method to mask standard residues
"""
m = B.PDBModel(fpdb)
if take_chain:
m = m.takeChains([0])
if skip_first:
m.removeRes(0)
if fmask is not None:
m = m.compress(fmask(m))
d = extract_unique_residues(m)
for name in d:
d[name] = normalize_residue(d[name])
T.dump(d, fout)
return d
def __dumpFrames(self, traj, outFolder, prefix ):
"""
@param traj: Trajectory
@type traj: Trajectory
@param outFolder: folder for pickled arrays
@type outFolder: str
@param prefix: file name prefix
@type prefix: str
@return: { (int,int) : str } OR None, if traj is None
@rtype: {(int,int) : str}
"""
if traj is None:
return None
if self.verbose: self.log.write('dumping frame chunks...')
n_frames = self.__windowSize( 20, len( self.hosts ), len( traj ) )
i_windows = self.__getFrameWindows( traj, n_frames )
r = {}
for i in range( len(i_windows) ):
w = i_windows[i]
a = traj.frames[ w[0]:w[1] ]
f = outFolder + '/%s_%i_to_%i.dat' % ((prefix,) + w)
T.dump( a, f )
r[w] = f
if self.verbose and i % (len(i_windows)/50 + 1) == 0:
self.log.write('#')
if self.verbose: self.log.add('done')
return r
def __dumpFrames(self, traj, outFolder, prefix):
"""
@param traj: Trajectory
@type traj: Trajectory
@param outFolder: folder for pickled arrays
@type outFolder: str
@param prefix: file name prefix
@type prefix: str
@return: { (int,int) : str } OR None, if traj is None
@rtype: {(int,int) : str}
"""
if traj is None:
return None
if self.verbose: self.log.write('dumping frame chunks...')
n_frames = self.__windowSize(20, len(self.hosts), len(traj))
i_windows = self.__getFrameWindows(traj, n_frames)
r = {}
for i in range(len(i_windows)):
w = i_windows[i]
a = traj.frames[w[0]:w[1]]
f = outFolder + '/%s_%i_to_%i.dat' % ((prefix, ) + w)
T.dump(a, f)
r[w] = f
if self.verbose and i % (len(i_windows) / 50 + 1) == 0:
self.log.write('#')
if self.verbose: self.log.add('done')
return r
options = tools.cmdDict( {'o':'traj.dat'} )
## get all PDBs and models directly from a directory (avoids shell limits)
if 'd' in options:
d = tools.absfile( options['d'] )
l = os.listdir( d )
l = [ x for x in l if x[-4:].upper() == '.PDB' or x[-6:] == '.MODEL' \
or x[-7:].upper() == '.PDB.GZ' ]
l = [ os.path.join( d, f ) for f in l ]
options['i'] = l
if 'e' in options:
t_class = EnsembleTraj
else:
t_class = Trajectory
traj = t_class( options['i'],
options.get('r', None),
rmwat =options.has_key('wat'),
castAll =options.has_key('c') )
## remove dependencies to source
traj.ref.disconnect()
if options.has_key('f'):
traj.fit()
tools.dump( traj, options['o'] )
import Biskit.tools as T t = T.load( 'com_fake.etraj' ) x = t.takeFrames( range(0, t.n_members * 5) ) x.ref.disconnect() T.dump( x, 'extract.etraj' )
except cPickle.UnpicklingError:
print "Trying to load %s in sloppy mode..." % f
return PickleUpgrader(open(T.absfile(f))).load()
#########
## Main
#########
__use()
fs = sys.argv[1:]
for f in fs:
try:
o = sloppyload( f )
## don't slim PDBModels that are their own source
if isinstance( o, PDBModel ) and str( o.source ) == T.absfile( f ):
o.forcePickle = 1
T.flushPrint('Dumping %s\n' % f )
T.dump( o, T.absfile( f ) )
except:
print "Error with ", f
print T.lastError()
## MAIN ##
use()
o = T.cmdDict({'n': 10})
f_in = T.absfile(o['i'])
f_out = T.absfile(o.get('o', f_in))
n = int(o['n'])
T.flushPrint("Loading...")
t = T.load(f_in)
T.flushPrint("Converting %i frames..." % len(t))
if isinstance(t, EnsembleTraj):
T.flushPrint("Nothing to be done!\n")
sys.exit(0)
t = traj2ensemble(t, n)
if 'pdb' in o:
t.ref.pdbCode = o['pdb']
if f_in == f_out:
os.rename(f_in, f_in + '_backup')
T.flushPrint("Saving...")
T.dump(t, f_out)
T.flushPrint("Done.\n")
niceness=nice_dic,
outFile=subFile,
com_version=version,
show_output=show_x,
add_hosts=add_hosts)
t.flushPrint('Start job processing .. ')
master.start()
## wait until master is finished
while not master.isFinished():
time.sleep(5)
## subList contains no info to be updated
else:
t.dump(subLst, subFile)
print '\nCollecting final results...'
complex_lst = ComplexList()
for f in subFile_names:
sub = t.load(f)
complex_lst += sub
os.unlink(f)
t.dump(complex_lst, options['o'])
else:
subLst = checkListStatus(complex_lst, update, force, version)
if subLst:
def cluster(tc, options):
n_cluster = int(options['n'])
allowedAtoms = T.toList(options.get('a', []))
if allowedAtoms:
mask = tc.traj.ref.mask(lambda a: a['name'] in allowedAtoms)
else:
mask = selectedAtoms(tc.traj.ref)
saveIn = T.absfile(options['o']) + '/'
conv = float(options['conv'])
tc.cluster(n_cluster, aMask=mask, converged=conv)
## collect center frame index for each cluster
frames = [members[0] for members in tc.memberFrames()]
result = tc.traj.takeFrames(frames) ## trajectory of cluster centers
model_dic = {}
dic_index = 1
if options.has_key('ref'):
## use user-provided reference structure
if os.path.isfile(T.absfile(options['ref'])):
print '\nUsing user specified reference pdb'
m = PDBModel(options['ref'])
m.remove(m.maskH2O())
## use reference in trajectory
else:
print '\nUsing reference in trajectory'
m = tc.traj.ref
m = dumpModel(m, options, saveIn + m.getPdbCode() + '_ref.model')
## add ref as first model in dictionary
model_dic[dic_index] = m
dic_index += 1
## save the individual models and add them to the model dictionary
for i in range(0, result.lenFrames()):
m = result.getPDBModel(i)
m = dumpModel(
m, options,
saveIn + T.stripFilename(result.frameNames[i]) + '.model')
model_dic[dic_index] = m
dic_index += 1
## save model dictionary
fdic = options['dic'] or m.getPdbCode() + '_models.dic'
T.dump(model_dic, T.absfile(fdic))
## REDUNDANT CODE AS MULTIDOCK NOW WRITES THE HEX PDB FILES
##
## ## save all models in the dictionary as HEX pdb files
## for k in model_dic.keys():
## m = model_dic[k]
## ## remove hydrogens and sort atoms in standard order
## m.remove( m.maskH() )
## m = molUtils.sortAtomsOfModel(m)
## setChainID( m )
## ## save single hex pdbs
## if options['hex']:
## fhex = options['hex'] + '_%03d' %(k)
## else:
## fhex = m.getPdbCode() + '_%03d_hex.pdb'%(k)
## hexTools.createHexPdb_single( m, T.absfile( fhex ) )
# fhex = options['hex'] or m.getPdbCode() + '_hex.pdb'
# hexTools.createHexPdb( model_dic, T.absfile( fhex ) )
return result
############
options = T.cmdDict( {'o':'ref.complex', 'lo':'lig.model', 'ro':'rec.model' } )
if len (sys.argv) < 3:
_use( options )
## create a reference complex
print "Loading..."
ref_com = PDBModel( options['c'] )
print "Removing water..."
ref_com.remove( lambda a: a['residue_name'] in ['TIP3','HOH','WAT'] )
## extract rec and lig chains
rec_chains = T.toIntList( options['r'] )
lig_chains = T.toIntList( options['l'] )
print "Extracting rec and lig..."
ref_rec = ref_com.takeChains( rec_chains )
ref_lig = ref_com.takeChains( lig_chains )
## create Protein complex
com = ProteinComplex( ref_rec, ref_lig )
print "Saving..."
ref_lig.saveAs( T.absfile( options['lo'] ) )
ref_rec.saveAs( T.absfile( options['ro'] ) )
T.dump( com, T.absfile( options['o']) )
return result
def test():
options = defOptions()
options['i'] = '/home/Bis/raik/data/tb/interfaces/c11/lig_pcr_00/traj.dat'
options['step'] = '3'
options['s'] = '0'
options['o'] = '~johan/dock/scripts'
options['ref'] = ''
return options
if __name__ == '__main__':
if len(sys.argv) < 2:
_use()
## options = test()
options = T.cmdDict( defOptions() )
tc = load( options )
r = cluster( tc, options )
## r=rmsdLimitedClustering( tc, options )
if options.has_key('co'):
T.dump( tc, options['co'] )
report( tc )
#!/usr/bin/env python
import Biskit.tools as T
## pickle to raw data
t = T.load('traj.dat')
t.ref.writePdb('traj_ref.pdb')
T.dump(t.frameNames, 'traj_framenames.list') ## for sorting by time and member
t.writeCrd('traj.crd')
rec_dic = T.load( T.absfile( options['rdic'] ) )
lig_dic = T.load( T.absfile( options['ldic'] ) )
for f in fs:
T.flushPrint('Loading %s ...' % f )
cl = T.load( f )
cl = reduceComplexList( cl )
result += cl
T.flushPrint('done\n')
T.flushPrint('correct model numbers...')
correct_model_numbers( result, rec_dic, lig_dic )
T.flushPrint( '\ncasting all rec models...' )
pairwise_cast( result.models.recModels() )
T.flushPrint( '\ncasting all lig models...')
pairwise_cast( result.models.ligModels() )
T.flushPrint('done\n')
save_changed_models( result, T.absfile( options['mo']) )
T.flushPrint('Dumping result to %s' % T.absfile( options['o'] ) )
T.dump( result, T.absfile( options['o'] ) )
relacements = tools.toList(options['r'])
repl = [r.split(':') for r in relacements]
for f in files:
try:
print "Re-localizing ", f
f = tools.absfile(f)
o = tools.load(f)
result = 0
if o.__class__ in [PCRModel, PDBModel]:
result = localizeModel(o, repl, f)
else:
if o.__class__ in [Complex]:
result = localizeComplex(o, repl)
else:
print "unknown class", o.__class__
if result:
f_bak = f + '__old'
os.rename(f, f_bak)
tools.dump(o, f)
print "..done"
else:
print "..skipped"
except Exception, why:
print "ERROR converting %s: %s" % (f, str(why))
rec_dic = T.load(T.absfile(options['rdic']))
lig_dic = T.load(T.absfile(options['ldic']))
for f in fs:
T.flushPrint('Loading %s ...' % f)
cl = T.load(f)
cl = reduceComplexList(cl)
result += cl
T.flushPrint('done\n')
T.flushPrint('correct model numbers...')
correct_model_numbers(result, rec_dic, lig_dic)
T.flushPrint('\ncasting all rec models...')
pairwise_cast(result.models.recModels())
T.flushPrint('\ncasting all lig models...')
pairwise_cast(result.models.ligModels())
T.flushPrint('done\n')
save_changed_models(result, T.absfile(options['mo']))
T.flushPrint('Dumping result to %s' % T.absfile(options['o']))
T.dump(result, T.absfile(options['o']))
#!/usr/bin/env python
import Biskit.tools as T
## pickle to raw data
t = T.load( 'traj.dat' )
t.ref.writePdb('traj_ref.pdb')
T.dump(t.frameNames, 'traj_framenames.list') ## for sorting by time and member
t.writeCrd('traj.crd')
def cluster( tc, options ):
n_cluster = int( options['n'] )
allowedAtoms = T.toList( options.get('a',[]) )
if allowedAtoms:
mask = tc.traj.ref.mask( lambda a: a['name'] in allowedAtoms )
else:
mask = selectedAtoms( tc.traj.ref )
saveIn = T.absfile( options['o'] ) + '/'
conv = float( options['conv'] )
tc.cluster( n_cluster, aMask=mask, converged=conv )
## collect center frame index for each cluster
frames = [ members[0] for members in tc.memberFrames() ]
result = tc.traj.takeFrames( frames ) ## trajectory of cluster centers
model_dic = {}
dic_index = 1
if options.has_key('ref'):
## use user-provided reference structure
if os.path.isfile( T.absfile(options['ref']) ):
print '\nUsing user specified reference pdb'
m = PDBModel( options['ref'] )
m.remove( m.maskH2O() )
## use reference in trajectory
else:
print '\nUsing reference in trajectory'
m = tc.traj.ref
m = dumpModel( m, options, saveIn+m.getPdbCode()+'_ref.model')
## add ref as first model in dictionary
model_dic[dic_index] = m
dic_index += 1
## save the individual models and add them to the model dictionary
for i in range(0, result.lenFrames() ):
m = result.getPDBModel(i)
m = dumpModel(m, options, saveIn +
T.stripFilename(result.frameNames[i]) +'.model' )
model_dic[dic_index] = m
dic_index += 1
## save model dictionary
fdic = options['dic'] or m.getPdbCode() + '_models.dic'
T.dump( model_dic, T.absfile( fdic ) )
## REDUNDANT CODE AS MULTIDOCK NOW WRITES THE HEX PDB FILES
##
## ## save all models in the dictionary as HEX pdb files
## for k in model_dic.keys():
## m = model_dic[k]
## ## remove hydrogens and sort atoms in standard order
## m.remove( m.maskH() )
## m = molUtils.sortAtomsOfModel(m)
## setChainID( m )
## ## save single hex pdbs
## if options['hex']:
## fhex = options['hex'] + '_%03d' %(k)
## else:
## fhex = m.getPdbCode() + '_%03d_hex.pdb'%(k)
## hexTools.createHexPdb_single( m, T.absfile( fhex ) )
# fhex = options['hex'] or m.getPdbCode() + '_hex.pdb'
# hexTools.createHexPdb( model_dic, T.absfile( fhex ) )
return result
except cPickle.UnpicklingError:
print "Trying to load %s in sloppy mode..." % f
return PickleUpgrader(open(T.absfile(f))).load()
#########
## Main
#########
__use()
fs = sys.argv[1:]
for f in fs:
try:
o = sloppyload( f )
## don't slim PDBModels that are their own source
if isinstance( o, PDBModel ) and str( o.source ) == T.absfile( f ):
o.forcePickle = 1
T.flushPrint('Dumping %s\n' % f )
T.dump( o, T.absfile( f ) )
except:
print "Error with ", f
print T.lastError()
else:
options[k] = 0
for k in ['chains', 'ex1', 'ex2', 'ex']:
if k in options:
options[k] = t.toIntList(options[k])
if 'atoms' in options:
options['atoms'] = t.toList(options['atoms'])
if 'ex1' in options and 'ex2' in options:
options['ex'] = (options['ex1'], options['ex2'])
else:
options['ex'] = options.get('ex', options.get('ex1', None))
if 'log' in options:
options['log'] = LogFile(options['log'])
f_in = options['i']
del options['i']
a = AmberEntropist(f_in, **options)
a.run()
t.dump(a.result, options['o'])
print "Dumped detailed result to %s. (for python unpickling)" % options['o']
print "Entropy in cal/mol-kelvin (total, vibrational): ",
print a.result['S_total'], ',', a.result['S_vibes']
print
else:
options[k] = 0
for k in ['chains','ex1', 'ex2', 'ex']:
if k in options:
options[k] = t.toIntList( options[k] )
if 'atoms' in options:
options['atoms'] = t.toList( options['atoms'] )
if 'ex1' in options and 'ex2' in options:
options['ex'] = ( options['ex1'], options['ex2'] )
else:
options['ex'] = options.get( 'ex', options.get('ex1', None) )
if 'log' in options:
options['log'] = LogFile( options['log'] )
f_in = options['i']
del options['i']
a = AmberEntropist( f_in, **options )
a.run()
t.dump( a.result, options['o'] )
print "Dumped detailed result to %s. (for python unpickling)" % options['o']
print "Entropy in cal/mol-kelvin (total, vibrational): ",
print a.result['S_total'], ',', a.result['S_vibes']
print
return result
def test():
options = defOptions()
options['i'] = '/home/Bis/raik/data/tb/interfaces/c11/lig_pcr_00/traj.dat'
options['step'] = '3'
options['s'] = '0'
options['o'] = '~johan/dock/scripts'
options['ref'] = ''
return options
if __name__ == '__main__':
if len(sys.argv) < 2:
_use()
## options = test()
options = T.cmdDict(defOptions())
tc = load(options)
r = cluster(tc, options)
## r=rmsdLimitedClustering( tc, options )
if options.has_key('co'):
T.dump(tc, options['co'])
report(tc)
options = tools.cmdDict({'o': 'traj.dat'})
## get all PDBs and models directly from a directory (avoids shell limits)
if 'd' in options:
d = tools.absfile(options['d'])
l = os.listdir(d)
l = [ x for x in l if x[-4:].upper() == '.PDB' or x[-6:] == '.MODEL' \
or x[-7:].upper() == '.PDB.GZ' ]
l = [os.path.join(d, f) for f in l]
options['i'] = l
if 'e' in options:
t_class = EnsembleTraj
else:
t_class = Trajectory
traj = t_class(options['i'],
options.get('r', None),
rmwat=options.has_key('wat'),
castAll=options.has_key('c'))
## remove dependencies to source
traj.ref.disconnect()
if options.has_key('f'):
traj.fit()
tools.dump(traj, options['o'])
import Biskit.tools as T
import Biskit as B
import numpy as N
com = T.load( 'com.traj' )
# re-order frames into 4 parallel trajectories
frames = N.zeros( len(com), int )
for i in range( 11 ):
N.put( frames, range(i*4,i*4+4), N.arange(i,44,11) )
etraj = B.EnsembleTraj( n_members=4 )
etraj.frames = com.takeFrames( frames ).frames
etraj.ref = com.ref
etraj.resetFrameNames()
etraj.ref.disconnect()
# separate protein and DNA into two chains
etraj.ref.chainIndex(breaks=True, force=True, cache=True)
etraj.ref.addChainId()
## extract only some residues for speed
t1 = etraj.takeAtoms( etraj.ref.res2atomIndices(range(10)) )
t2 = etraj.takeChains( [1] )
etraj = t1.concatAtoms( t2 )
T.dump( etraj, 'com_fake.etraj' )
for xyz in t.frames:
result_xyz.append( xyz.astype('f') )
for fname in t.frameNames:
result_frameNames.append( fname )
T.flushPrint('#')
print " Done"
result = Trajectory()
result.ref = result_ref
result.ref.disconnect()
if 'pdb' in o:
result.ref.pdbCode = o['pdb']
result.frames = N0.array( result_xyz, 'f' )
result.frameNames = result_frameNames
del result_xyz
## too much memory required for this
## result = trajLst[0].concat( *trajLst[1:] )
T.flushPrint("Converting to EnsembleTraj...")
result = traj2ensemble( result, len(inLst))
T.flushPrint( "Done\nDumping ensemble traj to " + o['o'] )
T.dump( result, T.absfile( o['o'] ) )
## MAIN ##
use()
o = T.cmdDict( {'n':10} )
f_in = T.absfile( o['i'] )
f_out = T.absfile( o.get('o', f_in) )
n = int( o['n'] )
T.flushPrint("Loading...")
t = T.load( f_in )
T.flushPrint("Converting %i frames..." % len(t) )
if isinstance(t, EnsembleTraj ):
T.flushPrint( "Nothing to be done!\n")
sys.exit(0)
t = traj2ensemble( t, n )
if 'pdb' in o:
t.ref.pdbCode = o['pdb']
if f_in == f_out:
os.rename( f_in, f_in + '_backup')
T.flushPrint("Saving...")
T.dump( t, f_out )
T.flushPrint("Done.\n")
import Biskit.tools as T
import Biskit as B
import numpy as N
com = T.load('com.traj')
# re-order frames into 4 parallel trajectories
frames = N.zeros(len(com), int)
for i in range(11):
N.put(frames, range(i * 4, i * 4 + 4), N.arange(i, 44, 11))
etraj = B.EnsembleTraj(n_members=4)
etraj.frames = com.takeFrames(frames).frames
etraj.ref = com.ref
etraj.resetFrameNames()
etraj.ref.disconnect()
# separate protein and DNA into two chains
etraj.ref.chainIndex(breaks=True, force=True, cache=True)
etraj.ref.addChainId()
## extract only some residues for speed
t1 = etraj.takeAtoms(etraj.ref.res2atomIndices(range(10)))
t2 = etraj.takeChains([1])
etraj = t1.concatAtoms(t2)
T.dump(etraj, 'com_fake.etraj')
for key in ['indi', 'exdi', 'salt', 'ionrad', 'prbrad',
'bndcon', 'scale', 'perfil']:
if key in options: options[key] = float( options[key] )
if 'log' in options:
options['log'] = LogFile( options['log'] )
## create a complex
com = inputComplex( options )
dg = DelphiBindingEnergy( com, **options )
r = dg.run()
print "Saving result complex to ", f_ocom
T.dump( dg.delphicom, f_ocom )
print "Final Result"
print "============"
print report( dg.delphicom )
f = open( f_out, 'w' )
f.write( report( dg.delphicom ) )
f.close()
print "energy values written to ", f_out
except KeyError, why:
print 'Insufficient options. Missing: ', (str(why))
_use( options )
except Exception, why:
for xyz in t.frames:
result_xyz.append(xyz.astype('f'))
for fname in t.frameNames:
result_frameNames.append(fname)
T.flushPrint('#')
print " Done"
result = Trajectory()
result.ref = result_ref
result.ref.disconnect()
if 'pdb' in o:
result.ref.pdbCode = o['pdb']
result.frames = N.array(result_xyz, 'f')
result.frameNames = result_frameNames
del result_xyz
## too much memory required for this
## result = trajLst[0].concat( *trajLst[1:] )
T.flushPrint("Converting to EnsembleTraj...")
result = traj2ensemble(result, len(inLst))
T.flushPrint("Done\nDumping ensemble traj to " + o['o'])
T.dump(result, T.absfile(o['o']))
'indi', 'exdi', 'salt', 'ionrad', 'prbrad', 'bndcon', 'scale',
'perfil'
]:
if key in options: options[key] = float(options[key])
if 'log' in options:
options['log'] = LogFile(options['log'])
## create a complex
com = inputComplex(options)
dg = DelphiBindingEnergy(com, **options)
r = dg.run()
print "Saving result complex to ", f_ocom
T.dump(dg.delphicom, f_ocom)
print "Final Result"
print "============"
print report(dg.delphicom)
f = open(f_out, 'w')
f.write(report(dg.delphicom))
f.close()
print "energy values written to ", f_out
except KeyError, why:
print 'Insufficient options. Missing: ', (str(why))
_use(options)
except Exception, why:
if len (sys.argv) < 2:
_use( default )
options = T.cmdDict( default )
## where to run the calculation
base_folder = T.absfile( options['r'] )+'/'
## template gly-xxx-gly
template = os.path.abspath( options['i'] )
## label the result dictionaty files
label = '_' + options['l']
## mask to used to delete glycines
mask = [ int(i) for i in T.toList( options['mask'] )]
## create random prptides from template
#randomPeptides( template, base_folder )
## collect average surfaces
MS, AS, MS_sd, AS_sd = randomSurfaces( base_folder, label, mask )
## save dictionary with all 20 amino acids
T.dump( MS, base_folder + 'MS%s.dic'%label)
T.dump( AS, base_folder + 'AS%s.dic'%label )
T.dump( MS_sd, base_folder + 'MS_sd%s.dic'%label )
T.dump( AS_sd, base_folder + 'AS_sd%s.dic'%label )
for f in files:
try:
print "Re-localizing ", f
f = tools.absfile( f )
o = tools.load( f )
result = 0
if o.__class__ in [ PCRModel, PDBModel ]:
result = localizeModel( o, repl, f )
else:
if o.__class__ in [ Complex ]:
result = localizeComplex( o, repl )
else:
print "unknown class", o.__class__
if result:
f_bak = f + '__old'
os.rename( f, f_bak )
tools.dump( o, f )
print "..done"
else:
print "..skipped"
except Exception, why:
print "ERROR converting %s: %s" % (f, str(why))
niceness = nice_dic,
outFile = subFile,
com_version = version,
show_output = show_x,
add_hosts=add_hosts)
t.flushPrint('Start job processing .. ')
master.start()
## wait until master is finished
while not master.isFinished():
time.sleep( 5 )
## subList contains no info to be updated
else:
t.dump( subLst, subFile )
print '\nCollecting final results...'
complex_lst = ComplexList()
for f in subFile_names:
sub = t.load( f )
complex_lst += sub
os.unlink( f )
t.dump( complex_lst, options['o'] )
else:
subLst = checkListStatus(complex_lst, update, force, version )
if subLst:
import Biskit.tools as T
t = T.load('com_fake.etraj')
x = t.takeFrames(range(0, t.n_members * 5))
x.ref.disconnect()
T.dump(x, 'extract.etraj')
if len (sys.argv) < 2:
_use( default )
options = T.cmdDict( default )
## where to run the calculation
base_folder = T.absfile( options['r'] )+'/'
## template gly-xxx-gly
template = os.path.abspath( options['i'] )
## label the result dictionaty files
label = '_' + options['l']
## mask to used to delete glycines
mask = [ int(i) for i in T.toList( options['mask'] )]
## create random prptides from template
#randomPeptides( template, base_folder )
## collect average surfaces
MS, AS, MS_sd, AS_sd = randomSurfaces( base_folder, label, mask )
## save dictionary with all 20 amino acids
T.dump( MS, base_folder + 'MS%s.dic'%label)
T.dump( AS, base_folder + 'AS%s.dic'%label )
T.dump( MS_sd, base_folder + 'MS_sd%s.dic'%label )
T.dump( AS_sd, base_folder + 'AS_sd%s.dic'%label )
