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']))
## 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'] ) )