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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Today's Topics:

   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)
>
>
> _______________________________________________
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> IMP-users@salilab.org
> https://salilab.org/mailman/listinfo/imp-users
>
>


--
Barak
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