def test_TrajCluster(self):
"""TrajCluster test"""
from Biskit.EnsembleTraj import traj2ensemble
traj = T.load( T.testRoot()+'/lig_pcr_00/traj.dat')
traj = traj2ensemble( traj )
aMask = traj.ref.mask( lambda a: a['name'] in ['CA','CB','CG'] )
traj = traj.thin( 1 )
traj.fit( aMask, verbose=self.local )
self.tc = TrajCluster( traj, verbose=self.local )
## check how many clusters that are needed with the given criteria
n_clusters = self.tc.calcClusterNumber( min_clst=3, max_clst=15,
rmsLimit=0.7, aMask=aMask )
## cluster
self.tc.cluster( n_clusters, aMask=aMask )
if self.local:
member_frames = self.tc.memberFrames()
print 'There are %i clusters where the members are:'%n_clusters
for i in range(n_clusters):
print 'Cluster %i (%i members): %s'%( i+1,
len(member_frames[i]),
member_frames[i] )
def test_TrajCluster(self):
"""TrajCluster test"""
from Biskit.EnsembleTraj import traj2ensemble
traj = T.load(T.testRoot() + '/lig_pcr_00/traj.dat')
traj = traj2ensemble(traj)
aMask = traj.ref.mask(lambda a: a['name'] in ['CA', 'CB', 'CG'])
traj = traj.thin(1)
traj.fit(aMask, verbose=self.local)
self.tc = TrajCluster(traj, verbose=self.local)
## check how many clusters that are needed with the given criteria
n_clusters = self.tc.calcClusterNumber(min_clst=3,
max_clst=15,
rmsLimit=0.7,
aMask=aMask)
## cluster
self.tc.cluster(n_clusters, aMask=aMask)
if self.local:
member_frames = self.tc.memberFrames()
print 'There are %i clusters where the members are:' % n_clusters
for i in range(n_clusters):
print 'Cluster %i (%i members): %s' % (
i + 1, len(member_frames[i]), member_frames[i])
def loadTraj( self, ftraj ):
"""
Load trajectories from disc.
@param ftraj: path to trajectory
@type ftraj: str
@return: ensemble trajectory object
@rtype: EnsembleTraj
"""
t = T.load( T.absfile( ftraj ) )
return traj2ensemble( t )
def loadTraj(self, ftraj):
"""
Load trajectories from disc.
@param ftraj: path to trajectory
@type ftraj: str
@return: ensemble trajectory object
@rtype: EnsembleTraj
"""
t = T.load(T.absfile(ftraj))
return traj2ensemble(t)
def test_FlexMaster(self):
"""TrajFlexMaster test"""
from Biskit.MatrixPlot import MatrixPlot
from numpy.random.mtrand import random_sample as random
assert len(hosts.cpus_all) > 0,\
'Master requires at least 1 PVM node for initialisation.'
traj_1 = T.load(T.testRoot() + '/lig_pcr_00/traj.dat')
traj_1 = traj2ensemble(traj_1)
## create fake second trajectory by adding
## increasing noise to first
frames = []
for i in range(len(traj_1)):
f = traj_1.frames[i]
d = N0.zeros(N0.shape(f), N0.Float32)
if i > 0:
d = random(N0.shape(f)) * ((i / 10) + 1)
frames += [f + d]
traj_2 = traj_1.clone()
traj_2.frames = frames
master = TrajFlexMaster(traj_1,
traj_2,
hosts=hosts.cpus_all,
show_output=self.local,
add_hosts=1,
log=None,
slaveLog=None,
verbose=self.local,
only_cross_member=0)
r = master.calculateResult(mirror=0)
if self.local:
p = MatrixPlot(r, palette='plasma2', legend=1)
p.show()
def test_FlexMaster(self):
"""TrajFlexMaster test"""
from Biskit.MatrixPlot import MatrixPlot
from numpy.oldnumeric.random_array import random
assert len(hosts.cpus_all) > 0,\
'Master requires at least 1 PVM node for initialisation.'
traj_1 = T.load( T.testRoot() + '/lig_pcr_00/traj.dat' )
traj_1 = traj2ensemble( traj_1 )
## create fake second trajectory by adding
## increasing noise to first
frames = []
for i in range( len( traj_1 ) ):
f = traj_1.frames[i]
d = N.zeros( N.shape( f ), N.Float32)
if i > 0:
d = random( N.shape( f ) ) * ((i / 10) + 1)
frames += [f + d]
traj_2 = traj_1.clone()
traj_2.frames = frames
master = TrajFlexMaster( traj_1, traj_2,
hosts=hosts.cpus_all,
show_output= self.local,
add_hosts=1,
log=None,
slaveLog=None,
verbose= self.local,
only_cross_member=0 )
r = master.calculateResult( mirror=0 )
if self.local:
p = MatrixPlot( r, palette='plasma2', legend=1 )
p.show()
def load(options):
f_traj = options['i']
start = int(options['s'])
traj = T.load(f_traj)
if traj.__class__ != EnsembleTraj:
traj = traj2ensemble(traj)
end = traj.lenFrames()
if options.has_key('e'):
end = int(options['e'])
step = int(options['step'])
traj = traj.takeFrames(range(start, end))
if step != 1:
traj = traj.thin(step)
traj.fit()
return TrajCluster(traj)
def load( options ):
f_traj = options['i']
start = int( options['s'] )
traj = T.load( f_traj )
if traj.__class__ != EnsembleTraj:
traj = traj2ensemble( traj )
end = traj.lenFrames()
if options.has_key('e'):
end = int( options['e'] )
step = int( options['step'] )
traj = traj.takeFrames( range( start, end ) )
if step != 1:
traj = traj.thin( step )
traj.fit()
return TrajCluster( traj )
syntax()
## get and clean up options
o = options()
o['z'] = float( o['z'] )
o['step'] = int( o['step'] )
o['i'] = absfile( o['i'] )
o['o'] = o.get('o',
'%s/%s_rms.eps' % (osp.dirname(o['i']), stripFilename(o['i'])))
flushPrint( "Loading..." )
t = load( o['i'] )
flushPrint( "done." )
if not isinstance( t, EnsembleTraj ):
t = traj2ensemble( t )
if o['step'] != 1:
t = t.thin( o['step'] )
flushPrint( "Fitting..." )
calcRmsd( t )
flushPrint( "done." )
p = plot( t, o.get( 'title', stripFilename(o['i']) ) )
flushPrint( "Getting outliers ..." )
markOutliers( t, o['z'], p )
flushPrint( "done." )
p.show()
## 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")
Default options:
"""
for key, value in o.items():
print "\t-",key, "\t",value
sys.exit(0)
##########
## MAIN ##
if len( sys.argv) < 2:
_use()
o = cmdDict( o )
print "Loading trajectory .. "
traj = load( absfile(o['i']) )
traj = traj2ensemble( traj )
print "Removing frames ..."
thin_traj = traj.thin( step=int(o['step']) )
print "Dumping thinned trajectory with %i frames (%i frames removed)..."\
%(thin_traj.lenFrames(), traj.lenFrames()-thin_traj.lenFrames())
dump( thin_traj, absfile( o['i'] ) )
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']))
i - pickled Trajectory object
step - int, 1..keep all frames, 2..skip first and every second, ..
Default options:
"""
for key, value in o.items():
print "\t-", key, "\t", value
sys.exit(0)
##########
## MAIN ##
if len(sys.argv) < 2:
_use()
o = cmdDict(o)
print "Loading trajectory .. "
traj = load(absfile(o['i']))
traj = traj2ensemble(traj)
print "Removing frames ..."
thin_traj = traj.thin(step=int(o['step']))
print "Dumping thinned trajectory with %i frames (%i frames removed)..."\
%(thin_traj.lenFrames(), traj.lenFrames()-thin_traj.lenFrames())
dump(thin_traj, absfile(o['i']))
syntax()
## get and clean up options
o = options()
o['z'] = float( o['z'] )
o['step'] = int( o['step'] )
o['i'] = absfile( o['i'] )
o['o'] = o.get('o',
'%s/%s_rms.eps' % (osp.dirname(o['i']), stripFilename(o['i'])))
flushPrint( "Loading..." )
t = load( o['i'] )
flushPrint( "done." )
if not isinstance( t, EnsembleTraj ):
t = traj2ensemble( t )
if o['step'] != 1:
t = t.thin( o['step'] )
flushPrint( "Fitting..." )
calcRmsd( t )
flushPrint( "done." )
p = plot( t, o.get( 'title', stripFilename(o['i']) ) )
flushPrint( "Getting outliers ..." )
markOutliers( t, o['z'], p )
flushPrint( "done." )
p.show()
## 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")
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'] ) )
