Re: [IMP-users] Sampling and writing to pym/rmf (Barak Raveh)

2 Jul 2014


      Hi Barek,
So I'm not giving hierarchy.get_children the correct input:
TypeError: unbound method get_children() must be called with Hierarchy
instance as first argument (got ConfigurationSet instance instead)
I'm not sure which argument is the hierarchy instance.
Thanks,
Josh
-------------------------------------------------
cs= get_conformations(m)
for i in range(0, cs.get_number_of_configurations()):
    JOSH = cs.load_configuration(i)
    S= IMP.atom.Selection
    h= IMP.atom.Hierarchy.get_children(cs)
    tfn = IMP.create_temporary_file_name("josh%d"%i, ".rmf")
    rh = RMF.create_rmf_file(tfn)
On 1 July 2014 17:31, imp-users-request@salilab.org wrote:
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>    1. Re: Sampling and writing to pym/rmf (Barak Raveh)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Tue, 1 Jul 2014 09:31:33 -0700
> From: Barak Raveh barak.raveh@gmail.com
> To: Help and discussion for users of IMP imp-users@salilab.org
> Subject: Re: [IMP-users] Sampling and writing to pym/rmf
> Message-ID:
>         <CAHp+_UowiBwJozbwOfi8yFEVt7Z8o2tEZ=
> LvYPnJh-LjpC2cSA@mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hi Josh, from a very superficial look, your code to write the RMF files
> seems fine - do you get an output RMF file? Could you load it in Chimera?
>
>
> On Tue, Jul 1, 2014 at 2:40 AM, Josh Bullock jma.bullock@gmail.com
> wrote:
>
> > Hello,
> >
> > I'm relatively new to all this so please let me know if i'm making any
> > obvious errors ...
> >
> > Essentially all i'm trying to do is generate an ensemble of models made
> > from four subunits - constrained by MS connectivity restraints. The
> models
> > get scored but nothing seems to write to the pymol file. Ideally i'd like
> > to write to an .rmf but i haven't worked that one out either ...
> >
> > Is this a reasonable way to go about my problem ?
> >
> > Many thanks,
> >
> > Josh
> >
> > -------------------------------------------
> >
> > import IMP
> > import IMP.atom
> > import IMP.rmf
> > import inspect
> > import IMP.container
> > import IMP.display
> > import IMP.statistics
> > #import IMP.example
> > import sys, math, os, optparse
> > import RMF
> >
> > from optparse import OptionParser
> >
> >
> > # Convert the arguments into strings and number
> > Firstpdb = str(sys.argv[1])
> > Secondpdb = str(sys.argv[2])
> > Thirdpdb = str(sys.argv[3])
> > Fourthpdb = str(sys.argv[4])
> > models = float(sys.argv[5])
> >
> > #*****************************************
> >
> > # the spring constant to use, it doesnt really matter
> > k=100
> > # the target resolution for the representation, this is used to specify
> > how detailed
> > # the representation used should be
> > resolution=300
> > # the box to perform everything
> > bb=IMP.algebra.BoundingBox3D(IMP.algebra.Vector3D(0,0,0),
> >                              IMP.algebra.Vector3D(300, 300, 300))
> >
> >
> > # this function creates the molecular hierarchies for the various
> involved
> > proteins
> > def create_representation():
> >     m= IMP.Model()
> >     all=IMP.atom.Hierarchy.setup_particle(IMP.Particle(m))
> >     all.set_name("the universe")
> >     # create a protein, represented as a set of connected balls of
> > appropriate
> >     # radii and number, chose by the resolution parameter and the number
> of
> >     # amino acids.
> >
> >     def create_protein_from_pdbs(name, files):
> >
> >         def create_from_pdb(file):
> >             sls=IMP.SetLogState(IMP.NONE)
> >             datadir = os.getcwd()
> >             print datadir
> >     t=IMP.atom.read_pdb( datadir+'/' + file, m,
> >                                  IMP.atom.ATOMPDBSelector())
> >             del sls
> >             #IMP.atom.show_molecular_hierarchy(t)
> >             c=IMP.atom.Chain(IMP.atom.get_by_type(t,
> > IMP.atom.CHAIN_TYPE)[0])
> >             if c.get_number_of_children()==0:
> >                 IMP.atom.show_molecular_hierarchy(t)
> >             # there is no reason to use all atoms, just approximate the
> > pdb shape instead
> >             s=IMP.atom.create_simplified_along_backbone(c,
> >                                                         resolution/300.0)
> >             IMP.atom.destroy(t)
> >             # make the simplified structure rigid
> >             rb=IMP.atom.create_rigid_body(s)
> > #            rb=IMP.atom.create_rigid_body(c)
> >             rb.set_coordinates_are_optimized(True)
> >             return s
> > #            return c
> >
> >         h= create_from_pdb(files[0])
> >         h.set_name(name)
> >         all.add_child(h)
> >
> >     create_protein_from_pdbs("A", [Firstpdb])
> >     create_protein_from_pdbs("B", [Secondpdb])
> >     create_protein_from_pdbs("C", [Thirdpdb])
> >     create_protein_from_pdbs("D", [Fourthpdb])
> >     #create_protein_from_pdbs("C", ["rpt3_imp.pdb"])
> >     return (m, all)
> >
> > # create the needed restraints and add them to the model
> >
> > def create_restraints(m, all):
> >     def add_connectivity_restraint(s):
> >
> >         tr= IMP.core.TableRefiner()
> >         rps=[]
> >         for sc in s:
> >             ps= sc.get_selected_particles()
> >             rps.append(ps[0])
> >             tr.add_particle(ps[0], ps)
> >
> >         # duplicate the IMP.atom.create_connectivity_restraint
> > functionality
> >
> >         score=
> >
> IMP.core.KClosePairsPairScore(IMP.core.HarmonicSphereDistancePairScore(0,1),tr)
> >
> >         r= IMP.core.MSConnectivityRestraint(m,score)
> >
> >         iA = r.add_type([rps[0]])
> >         iB = r.add_type([rps[1]])
> >         iC = r.add_type([rps[2]])
> >         iD = r.add_type([rps[3]])
> >         n1 = r.add_composite([iA, iB, iC, iD])
> >         n2 = r.add_composite([iA, iB], n1)
> >         n3 = r.add_composite([iC, iD], n1)
> >         n4 = r.add_composite([iB, iC, iD], n1)
> >
> >         m.add_restraint(r)
> >
> >     evr=IMP.atom.create_excluded_volume_restraint([all])
> >     m.add_restraint(evr)
> >     # a Selection allows for natural specification of what the restraints
> > act on
> >     S= IMP.atom.Selection
> >     sA=S(hierarchy=all, molecule="A")
> >     sB=S(hierarchy=all, molecule="B")
> >     sC=S(hierarchy=all, molecule="C")
> >     sD=S(hierarchy=all, molecule="D")
> >     add_connectivity_restraint([sA, sB, sC, sD])
> >
> >
> > # find acceptable conformations of the model
> > def get_conformations(m):
> >     sampler= IMP.core.MCCGSampler(m)
> >     sampler.set_bounding_box(bb)
> >     # magic numbers, experiment with them and make them large enough for
> > things to work
> >     sampler.set_number_of_conjugate_gradient_steps(100)
> >     sampler.set_number_of_monte_carlo_steps(20)
> >     sampler.set_number_of_attempts(models)
> >     # We don't care to see the output from the sampler
> >     sampler.set_log_level(IMP.SILENT)
> >     # return the IMP.ConfigurationSet storing all the found
> configurations
> > that
> >     # meet the various restraint maximum scores.
> >     cs= sampler.create_sample()
> >     return cs
> >
> >
> > # cluster the conformations and write them to a file
> > def analyze_conformations(cs, all, gs):
> >     # we want to cluster the configurations to make them easier to
> > understand
> >     # in the case, the clustering is pretty meaningless
> >     embed= IMP.statistics.ConfigurationSetXYZEmbedding(cs,
> >
> >  IMP.container.ListSingletonContainer(IMP.atom.get_leaves(all)), True)
> >     cluster= IMP.statistics.create_lloyds_kmeans(embed, 10, 10000)
> >     # dump each cluster center to a file so it can be viewed.
> >     for i in range(cluster.get_number_of_clusters()):
> >         center= cluster.get_cluster_center(i)
> >         cs.load_configuration(i)
> >         w= IMP.display.PymolWriter("cluster.%d.pym"%i)
> >         for g in gs:
> >             w.add_geometry(g)
> >
> >
> >
> >
> #******************************************************************************************
> > # now do the actual work
> >
> > (m,all)= create_representation()
> > IMP.atom.show_molecular_hierarchy(all)
> > create_restraints(m, all)
> >
> > # in order to display the results, we need something that maps the
> > particles onto
> > # geometric objets. The IMP.display.Geometry objects do this mapping.
> > # IMP.display.XYZRGeometry map an IMP.core.XYZR particle onto a sphere
> > gs=[]
> > for i in range(all.get_number_of_children()):
> >     color= IMP.display.get_display_color(i)
> >     n= all.get_child(i)
> >     name= n.get_name()
> >     g= IMP.atom.HierarchyGeometry(n)
> >     g.set_color(color)
> >     gs.append(g)
> >
> > cs= get_conformations(m)
> >
> > print "found", cs.get_number_of_configurations(), "solutions"
> >
> > ListScores = []
> > for i in range(0, cs.get_number_of_configurations()):
> >         cs.load_configuration(i)
> >         # print the configuration
> >         print "solution number: ",i,"scored :", m.evaluate(False)
> >         ListScores.append(m.evaluate(False))
> >
> > f1 = open("out_scores.csv", "w")
> > f1.write("\n".join(map(lambda x: str(x), ListScores)))
> > f1.close()
> >
> > # for each of the configuration, dump it to a file to view in pymol
> > for i in range(0, cs.get_number_of_configurations()):
> >     JOSH = cs.load_configuration(i)
> >     S= IMP.atom.Selection
> >     h= IMP.atom.Hierarchy.get_children(cs)
> >     tfn = IMP.create_temporary_file_name("josh%d"%i, ".rmf")
> >     rh = RMF.create_rmf_file(tfn)
> >
> >     # add the hierarchy to the file
> >     IMP.rmf.add_hierarchies(rh, h)
> >
> >     # add the current configuration to the file as frame 0
> >     IMP.rmf.save_frame(rh)
> >
> >     for g in gs:
> >         w.add_geometry(g)
> >
> > analyze_conformations(cs, all, gs)
> >
> >
> > _______________________________________________
> > IMP-users mailing list
> > IMP-users@salilab.org
> > https://salilab.org/mailman/listinfo/imp-users
> >
> >
>
>
> --
> Barak
>