Re: [IMP-users] Sampling and writing to pym/rmf

Hi Josh,Â

I think get_children() is only a member function of Hierarchy, so you should do something like

IMP.atom.Hierarchy(p)

c = p.get_children()

'cs' seems to be a ConfigurationSet object - are you sure that you expect it to have children?

What do you want to do?

Cheers,

Barak

On Wed, Jul 2, 2014 at 1:42 PM, Josh Bullock <" target="_blank">> wrote:

ah sorry ! this line gives me the error

h= IMP.atom.Hierarchy.get_children(cs)

thanks

josh

On 2 July 2014 17:45, <" target="_blank">> wrote:

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Â Â1. Re: Sampling and writing to pym/rmf (Barak Raveh) (Barak Raveh)

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Message: 1
Date: Wed, 2 Jul 2014 09:45:30 -0700
From: Barak Raveh <" target="_blank">>
To: Help and discussion for users of IMP <" target="_blank">>
Subject: Re: [IMP-users] Sampling and writing to pym/rmf (Barak Raveh)
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Â Â Â Â <CAHp+_Uo19VasJDJYi+2CoUUu=" target="_blank">u_6duKCraVetU4dW45+>
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Which lines throws the error?

On Wed, Jul 2, 2014 at 4:56 AM, Josh Bullock <" target="_blank">> wrote:

> 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()):
> Â Â JOSH = cs.load_configuration(i)
> Â Â S= IMP.atom.Selection
> Â Â h= IMP.atom.Hierarchy.get_children(cs)
> Â Â tfn = IMP.create_temporary_file_name("josh%d"%i, ".rmf")
> Â Â rh = RMF.create_rmf_file(tfn)
>
> On 1 July 2014 17:31, <" target="_blank">> wrote:
>
>> Send IMP-users mailing list submissions to
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>>
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>> than "Re: Contents of IMP-users digest..."
>>
>>
>> Today's Topics:
>>
>> Â Â1. Re: Sampling and writing to pym/rmf (Barak Raveh)
>>
>>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Tue, 1 Jul 2014 09:31:33 -0700
>> From: Barak Raveh <" target="_blank">>
>> To: Help and discussion for users of IMP <" target="_blank">>
>> Subject: Re: [IMP-users] Sampling and writing to pym/rmf
>> Message-ID:
>> Â Â Â Â <CAHp+_UowiBwJozbwOfi8yFEVt7Z8o2tEZ=
>> " target="_blank">>
>> 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 <" target="_blank">>
>> 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),
>> > Â Â Â Â Â Â Â Â Â Â Â Â Â Â ÂIMP.algebra.Vector3D(300, 300, 300))
>> >
>> >
>> > # this function creates the molecular hierarchies for the various
>> involved
>> > proteins
>> > def create_representation():
>> > Â Â m= IMP.Model()
>> > Â Â all=IMP.atom.Hierarchy.setup_particle(IMP.Particle(m))
>> > Â Â all.set_name("the universe")
>> > Â Â # create a protein, represented as a set of connected balls of
>> > appropriate
>> > Â Â # radii and number, chose by the resolution parameter and the
>> number of
>> > Â Â # amino acids.
>> >
>> > Â Â def create_protein_from_pdbs(name, files):
>> >
>> > Â Â Â Â def create_from_pdb(file):
>> > Â Â Â Â Â Â sls=IMP.SetLogState(IMP.NONE)
>> > Â Â Â Â Â Â datadir = os.getcwd()
>> > Â Â Â Â Â Â print datadir
>> > Â Â t=IMP.atom.read_pdb( datadir+'/' + file, m,
>> > Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â ÂIMP.atom.ATOMPDBSelector())
>> > Â Â Â Â Â Â del sls
>> > Â Â Â Â Â Â #IMP.atom.show_molecular_hierarchy(t)
>> > Â Â Â Â Â Â c=IMP.atom.Chain(IMP.atom.get_by_type(t,
>> > IMP.atom.CHAIN_TYPE)[0])
>> > Â Â Â Â Â Â if c.get_number_of_children()==0:
>> > Â Â Â Â Â Â Â Â IMP.atom.show_molecular_hierarchy(t)
>> > Â Â Â Â Â Â # there is no reason to use all atoms, just approximate the
>> > pdb shape instead
>> > Â Â Â Â Â Â s=IMP.atom.create_simplified_along_backbone(c,
>> >
>> resolution/300.0)
>> > Â Â Â Â Â Â IMP.atom.destroy(t)
>> > Â Â Â Â Â Â # make the simplified structure rigid
>> > Â Â Â Â Â Â rb=IMP.atom.create_rigid_body(s)
>> > # Â Â Â Â Â Ârb=IMP.atom.create_rigid_body(c)
>> > Â Â Â Â Â Â rb.set_coordinates_are_optimized(True)
>> > Â Â Â Â Â Â return s
>> > # Â Â Â Â Â Âreturn c
>> >
>> > Â Â Â Â h= create_from_pdb(files[0])
>> > Â Â Â Â h.set_name(name)
>> > Â Â Â Â all.add_child(h)
>> >
>> > Â Â create_protein_from_pdbs("A", [Firstpdb])
>> > Â Â create_protein_from_pdbs("B", [Secondpdb])
>> > Â Â create_protein_from_pdbs("C", [Thirdpdb])
>> > Â Â create_protein_from_pdbs("D", [Fourthpdb])
>> > Â Â #create_protein_from_pdbs("C", ["rpt3_imp.pdb"])
>> > Â Â return (m, all)
>> >
>> > # create the needed restraints and add them to the model
>> >
>> > def create_restraints(m, all):
>> > Â Â def add_connectivity_restraint(s):
>> >
>> > Â Â Â Â tr= IMP.core.TableRefiner()
>> > Â Â Â Â rps=[]
>> > Â Â Â Â for sc in s:
>> > Â Â Â Â Â Â ps= sc.get_selected_particles()
>> > Â Â Â Â Â Â rps.append(ps[0])
>> > Â Â Â Â Â Â tr.add_particle(ps[0], ps)
>> >
>> > Â Â Â Â # duplicate the IMP.atom.create_connectivity_restraint
>> > functionality
>> >
>> > Â Â Â Â score=
>> >
>> IMP.core.KClosePairsPairScore(IMP.core.HarmonicSphereDistancePairScore(0,1),tr)
>> >
>> > Â Â Â Â r= IMP.core.MSConnectivityRestraint(m,score)
>> >
>> > Â Â Â Â iA = r.add_type([rps[0]])
>> > Â Â Â Â iB = r.add_type([rps[1]])
>> > Â Â Â Â iC = r.add_type([rps[2]])
>> > Â Â Â Â iD = r.add_type([rps[3]])
>> > Â Â Â Â n1 = r.add_composite([iA, iB, iC, iD])
>> > Â Â Â Â n2 = r.add_composite([iA, iB], n1)
>> > Â Â Â Â n3 = r.add_composite([iC, iD], n1)
>> > Â Â Â Â n4 = r.add_composite([iB, iC, iD], n1)
>> >
>> > Â Â Â Â m.add_restraint(r)
>> >
>> > Â Â evr=IMP.atom.create_excluded_volume_restraint([all])
>> > Â Â m.add_restraint(evr)
>> > Â Â # a Selection allows for natural specification of what the
>> restraints
>> > act on
>> > Â Â S= IMP.atom.Selection
>> > Â Â sA=S(hierarchy=all, molecule="A")
>> > Â Â sB=S(hierarchy=all, molecule="B")
>> > Â Â sC=S(hierarchy=all, molecule="C")
>> > Â Â sD=S(hierarchy=all, molecule="D")
>> > Â Â add_connectivity_restraint([sA, sB, sC, sD])
>> >
>> >
>> > # find acceptable conformations of the model
>> > def get_conformations(m):
>> > Â Â sampler= IMP.core.MCCGSampler(m)
>> > Â Â sampler.set_bounding_box(bb)
>> > Â Â # magic numbers, experiment with them and make them large enough for
>> > things to work
>> > Â Â sampler.set_number_of_conjugate_gradient_steps(100)
>> > Â Â sampler.set_number_of_monte_carlo_steps(20)
>> > Â Â sampler.set_number_of_attempts(models)
>> > Â Â # We don't care to see the output from the sampler
>> > Â Â sampler.set_log_level(IMP.SILENT)
>> > Â Â # return the IMP.ConfigurationSet storing all the found
>> configurations
>> > that
>> > Â Â # meet the various restraint maximum scores.
>> > Â Â cs= sampler.create_sample()
>> > Â Â return cs
>> >
>> >
>> > # cluster the conformations and write them to a file
>> > def analyze_conformations(cs, all, gs):
>> > Â Â # we want to cluster the configurations to make them easier to
>> > understand
>> > Â Â # in the case, the clustering is pretty meaningless
>> > Â Â embed= IMP.statistics.ConfigurationSetXYZEmbedding(cs,
>> >
>> > ÂIMP.container.ListSingletonContainer(IMP.atom.get_leaves(all)), True)
>> > Â Â cluster= IMP.statistics.create_lloyds_kmeans(embed, 10, 10000)
>> > Â Â # dump each cluster center to a file so it can be viewed.
>> > Â Â for i in range(cluster.get_number_of_clusters()):
>> > Â Â Â Â center= cluster.get_cluster_center(i)
>> > Â Â Â Â cs.load_configuration(i)
>> > Â Â Â Â w= IMP.display.PymolWriter("cluster.%d.pym"%i)
>> > Â Â Â Â for g in gs:
>> > Â Â Â Â Â Â 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()):
>> > Â Â color= IMP.display.get_display_color(i)
>> > Â Â n= all.get_child(i)
>> > Â Â name= n.get_name()
>> > Â Â g= IMP.atom.HierarchyGeometry(n)
>> > Â Â g.set_color(color)
>> > Â Â 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()):
>> > Â Â Â Â cs.load_configuration(i)
>> > Â Â Â Â # print the configuration
>> > Â Â Â Â print "solution number: ",i,"scored :", m.evaluate(False)
>> > Â Â Â Â 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()):
>> > Â Â JOSH = cs.load_configuration(i)
>> > Â Â S= IMP.atom.Selection
>> > Â Â h= IMP.atom.Hierarchy.get_children(cs)
>> > Â Â tfn = IMP.create_temporary_file_name("josh%d"%i, ".rmf")
>> > Â Â rh = RMF.create_rmf_file(tfn)
>> >
>> > Â Â # add the hierarchy to the file
>> > Â Â IMP.rmf.add_hierarchies(rh, h)
>> >
>> > Â Â # add the current configuration to the file as frame 0
>> > Â Â IMP.rmf.save_frame(rh)
>> >
>> > Â Â for g in gs:
>> > Â Â Â Â w.add_geometry(g)
>> >
>> > analyze_conformations(cs, all, gs)
>> >
>> >
>> > _______________________________________________
>> > IMP-users mailing list
>> > " target="_blank">
>> > https://salilab.org/mailman/listinfo/imp-users
>> >
>> >
>>
>>
>> --
>> Barak
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