/output.py

"""@namespace IMP.pmi1.output
   Classes for writing output files and processing them.
"""

from __future__ import print_function, division
import IMP
import IMP.atom
import IMP.core
import IMP.pmi1
import IMP.pmi1.tools
import IMP.pmi1.io
import os
import sys
import ast
import RMF
import numpy as np
import operator
import string
try:
    import cPickle as pickle
except ImportError:
    import pickle

class _ChainIDs(object):
    """Map indices to multi-character chain IDs.
       We label the first 26 chains A-Z, then we move to two-letter
       chain IDs: AA through AZ, then BA through BZ, through to ZZ.
       This continues with longer chain IDs."""
    def __getitem__(self, ind):
        chars = string.ascii_uppercase
        lc = len(chars)
        ids = []
        while ind >= lc:
            ids.append(chars[ind % lc])
            ind = ind // lc - 1
        ids.append(chars[ind])
        return "".join(reversed(ids))


class ProtocolOutput(object):
    """Base class for capturing a modeling protocol.
       Unlike simple output of model coordinates, a complete
       protocol includes the input data used, details on the restraints,
       sampling, and clustering, as well as output models.
       Use via IMP.pmi1.representation.Representation.add_protocol_output()
       (for PMI 1) or
       IMP.pmi1.topology.System.add_protocol_output() (for PMI 2).

       @see IMP.pmi1.mmcif.ProtocolOutput for a concrete subclass that outputs
            mmCIF files.
    """
    pass

def _flatten(seq):
    l = []
    for elt in seq:
        t = type(elt)
        if t is tuple or t is list:
            for elt2 in _flatten(elt):
                l.append(elt2)
        else:
            l.append(elt)
    return l

class Output(object):
    """Class for easy writing of PDBs, RMFs, and stat files

    \note Model should be updated prior to writing outputs.
    """
    def __init__(self, ascii=True,atomistic=False):
        self.dictionary_pdbs = {}
        self.dictionary_rmfs = {}
        self.dictionary_stats = {}
        self.dictionary_stats2 = {}
        self.best_score_list = None
        self.nbestscoring = None
        self.suffixes = []
        self.replica_exchange = False
        self.ascii = ascii
        self.initoutput = {}
        self.residuetypekey = IMP.StringKey("ResidueName")
        # 1-character chain IDs, suitable for PDB output
        self.chainids = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"
        # Multi-character chain IDs, suitable for mmCIF output
        self.multi_chainids = _ChainIDs()
        self.dictchain = {}  # keys are molecule names, values are chain ids
        self.particle_infos_for_pdb = {}
        self.atomistic=atomistic

    def get_pdb_names(self):
        return list(self.dictionary_pdbs.keys())

    def get_rmf_names(self):
        return list(self.dictionary_rmfs.keys())

    def get_stat_names(self):
        return list(self.dictionary_stats.keys())

    def _init_dictchain(self, name, prot, multichar_chain=False):
        self.dictchain[name] = {}

        chainids = self.multi_chainids if multichar_chain else self.chainids
        for n, i in enumerate(self.dictionary_pdbs[name].get_children()):
            self.dictchain[name][i.get_name()] = chainids[n]

    def init_pdb(self, name, prot):
        """Init PDB Writing.
        @param name The PDB filename
        @param prot The hierarchy to write to this pdb file
        \note if the PDB name is 'System' then will use Selection to get molecules
        """
        flpdb = open(name, 'w')
        flpdb.close()
        self.dictionary_pdbs[name] = prot
        self._init_dictchain(name, prot)

    def write_psf(self,filename,name):
        flpsf=open(filename,'w')
        flpsf.write("PSF CMAP CHEQ"+"\n")
        index_residue_pair_list={}
        (particle_infos_for_pdb, geometric_center)=self.get_particle_infos_for_pdb_writing(name)
        nparticles=len(particle_infos_for_pdb)
        flpsf.write(str(nparticles)+" !NATOM"+"\n")
        for n,p in enumerate(particle_infos_for_pdb):
            atom_index=n+1
            residue_type=p[2]
            chain=p[3]
            resid=p[4]
            flpsf.write('{0:8d}{1:1s}{2:4s}{3:1s}{4:4s}{5:1s}{6:4s}{7:1s}{8:4s}{9:1s}{10:4s}{11:14.6f}{12:14.6f}{13:8d}{14:14.6f}{15:14.6f}'.format(atom_index," ",chain," ",str(resid)," ",'"'+residue_type.get_string()+'"'," ","C"," ","C",1.0,0.0,0,0.0,0.0))
            flpsf.write('\n')
            #flpsf.write(str(atom_index)+" "+str(chain)+" "+str(resid)+" "+str(residue_type).replace('"','')+" C C "+"1.0 0.0 0 0.0 0.0\n")
            if chain not in index_residue_pair_list:
                index_residue_pair_list[chain]=[(atom_index,resid)]
            else:
                index_residue_pair_list[chain].append((atom_index,resid))


        #now write the connectivity
        indexes_pairs=[]
        for chain in sorted(index_residue_pair_list.keys()):

            ls=index_residue_pair_list[chain]
            #sort by residue
            ls=sorted(ls, key=lambda tup: tup[1])
            #get the index list
            indexes=[x[0] for x in ls]
            # get the contiguous pairs
            indexes_pairs+=list(IMP.pmi1.tools.sublist_iterator(indexes,lmin=2,lmax=2))
        nbonds=len(indexes_pairs)
        flpsf.write(str(nbonds)+" !NBOND: bonds"+"\n")

        sublists=[indexes_pairs[i:i+4] for i in range(0,len(indexes_pairs),4)]

        # save bonds in fized column format
        for ip in sublists:
            if len(ip)==4:
                flpsf.write('{0:8d}{1:8d}{2:8d}{3:8d}{4:8d}{5:8d}{6:8d}{7:8d}'.format(ip[0][0],ip[0][1],
                             ip[1][0],ip[1][1],ip[2][0],ip[2][1],ip[3][0],ip[3][1]))
            elif len(ip)==3:
                flpsf.write('{0:8d}{1:8d}{2:8d}{3:8d}{4:8d}{5:8d}'.format(ip[0][0],ip[0][1],ip[1][0],
                             ip[1][1],ip[2][0],ip[2][1]))
            elif len(ip)==2:
                flpsf.write('{0:8d}{1:8d}{2:8d}{3:8d}'.format(ip[0][0],ip[0][1],ip[1][0],ip[1][1]))
            elif len(ip)==1:
                flpsf.write('{0:8d}{1:8d}'.format(ip[0][0],ip[0][1]))
            flpsf.write('\n')

        del particle_infos_for_pdb
        flpsf.close()

    def write_pdb(self,name,
                  appendmode=True,
                  translate_to_geometric_center=False,
                  write_all_residues_per_bead=False):
        if appendmode:
            flpdb = open(name, 'a')
        else:
            flpdb = open(name, 'w')

        (particle_infos_for_pdb,
         geometric_center) = self.get_particle_infos_for_pdb_writing(name)

        if not translate_to_geometric_center:
            geometric_center = (0, 0, 0)

        for n,tupl in enumerate(particle_infos_for_pdb):
            (xyz, atom_type, residue_type,
             chain_id, residue_index, all_indexes, radius) = tupl
            if atom_type is None:
                atom_type = IMP.atom.AT_CA
            if ( (write_all_residues_per_bead) and (all_indexes is not None) ):
                for residue_number in all_indexes:
                    flpdb.write(IMP.atom.get_pdb_string((xyz[0] - geometric_center[0],
                                                        xyz[1] - geometric_center[1],
                                                        xyz[2] - geometric_center[2]),
                                                        n+1, atom_type, residue_type,
                                                        chain_id, residue_number,' ',1.00,radius))
            else:
                flpdb.write(IMP.atom.get_pdb_string((xyz[0] - geometric_center[0],
                                                    xyz[1] - geometric_center[1],
                                                    xyz[2] - geometric_center[2]),
                                                    n+1, atom_type, residue_type,
                                                    chain_id, residue_index,' ',1.00,radius))
        flpdb.write("ENDMDL\n")
        flpdb.close()

        del particle_infos_for_pdb

    def get_prot_name_from_particle(self, name, p):
        """Get the protein name from the particle.
           This is done by traversing the hierarchy."""
        return IMP.pmi1.tools.get_prot_name_from_particle(
                                   p, self.dictchain[name])

    def get_particle_infos_for_pdb_writing(self, name):
        # index_residue_pair_list={}

        # the resindexes dictionary keep track of residues that have been already
        # added to avoid duplication
        # highest resolution have highest priority
        resindexes_dict = {}

        # this dictionary dill contain the sequence of tuples needed to
        # write the pdb
        particle_infos_for_pdb = []

        geometric_center = [0, 0, 0]
        atom_count = 0
        atom_index = 0

        ps = IMP.atom.get_leaves(self.dictionary_pdbs[name])

        for n, p in enumerate(ps):
            protname, is_a_bead = self.get_prot_name_from_particle(name, p)

            if protname not in resindexes_dict:
                resindexes_dict[protname] = []

            if IMP.atom.Atom.get_is_setup(p) and self.atomistic:
                residue = IMP.atom.Residue(IMP.atom.Atom(p).get_parent())
                rt = residue.get_residue_type()
                resind = residue.get_index()
                atomtype = IMP.atom.Atom(p).get_atom_type()
                xyz = list(IMP.core.XYZ(p).get_coordinates())
                radius = IMP.core.XYZR(p).get_radius()
                geometric_center[0] += xyz[0]
                geometric_center[1] += xyz[1]
                geometric_center[2] += xyz[2]
                atom_count += 1
                particle_infos_for_pdb.append((xyz,
                                               atomtype, rt, self.dictchain[name][protname], resind, None, radius))
                resindexes_dict[protname].append(resind)

            elif IMP.atom.Residue.get_is_setup(p):

                residue = IMP.atom.Residue(p)
                resind = residue.get_index()
                # skip if the residue was already added by atomistic resolution
                # 0
                if resind in resindexes_dict[protname]:
                    continue
                else:
                    resindexes_dict[protname].append(resind)
                rt = residue.get_residue_type()
                xyz = IMP.core.XYZ(p).get_coordinates()
                radius = IMP.core.XYZR(p).get_radius()
                geometric_center[0] += xyz[0]
                geometric_center[1] += xyz[1]
                geometric_center[2] += xyz[2]
                atom_count += 1
                particle_infos_for_pdb.append((xyz, None,
                                               rt, self.dictchain[name][protname], resind, None, radius))

            elif IMP.atom.Fragment.get_is_setup(p) and not is_a_bead:
                resindexes = IMP.pmi1.tools.get_residue_indexes(p)
                resind = resindexes[len(resindexes) // 2]
                if resind in resindexes_dict[protname]:
                    continue
                else:
                    resindexes_dict[protname].append(resind)
                rt = IMP.atom.ResidueType('BEA')
                xyz = IMP.core.XYZ(p).get_coordinates()
                radius = IMP.core.XYZR(p).get_radius()
                geometric_center[0] += xyz[0]
                geometric_center[1] += xyz[1]
                geometric_center[2] += xyz[2]
                atom_count += 1
                particle_infos_for_pdb.append((xyz, None,
                                               rt, self.dictchain[name][protname], resind, resindexes, radius))

            else:
                if is_a_bead:
                    rt = IMP.atom.ResidueType('BEA')
                    resindexes = IMP.pmi1.tools.get_residue_indexes(p)
                    if len(resindexes) > 0:
                        resind = resindexes[len(resindexes) // 2]
                        xyz = IMP.core.XYZ(p).get_coordinates()
                        radius = IMP.core.XYZR(p).get_radius()
                        geometric_center[0] += xyz[0]
                        geometric_center[1] += xyz[1]
                        geometric_center[2] += xyz[2]
                        atom_count += 1
                        particle_infos_for_pdb.append((xyz, None,
                                                       rt, self.dictchain[name][protname], resind, resindexes, radius))

        if atom_count > 0:
            geometric_center = (geometric_center[0] / atom_count,
                                geometric_center[1] / atom_count,
                                geometric_center[2] / atom_count)

        # sort by chain ID, then residue index. Longer chain IDs (e.g. AA)
        # should always come after shorter (e.g. Z)
        particle_infos_for_pdb = sorted(particle_infos_for_pdb,
                                        key=lambda x: (len(x[3]), x[3], x[4]))

        return (particle_infos_for_pdb, geometric_center)


    def write_pdbs(self, appendmode=True):
        for pdb in self.dictionary_pdbs.keys():
            self.write_pdb(pdb, appendmode)

    def init_pdb_best_scoring(self,
                              suffix,
                              prot,
                              nbestscoring,
                              replica_exchange=False):
        # save only the nbestscoring conformations
        # create as many pdbs as needed

        self.suffixes.append(suffix)
        self.replica_exchange = replica_exchange
        if not self.replica_exchange:
            # common usage
            # if you are not in replica exchange mode
            # initialize the array of scores internally
            self.best_score_list = []
        else:
            # otherwise the replicas must cominucate
            # through a common file to know what are the best scores
            self.best_score_file_name = "best.scores.rex.py"
            self.best_score_list = []
            best_score_file = open(self.best_score_file_name, "w")
            best_score_file.write(
                "self.best_score_list=" + str(self.best_score_list))
            best_score_file.close()

        self.nbestscoring = nbestscoring
        for i in range(self.nbestscoring):
            name = suffix + "." + str(i) + ".pdb"
            flpdb = open(name, 'w')
            flpdb.close()
            self.dictionary_pdbs[name] = prot
            self._init_dictchain(name, prot)

    def write_pdb_best_scoring(self, score):
        if self.nbestscoring is None:
            print("Output.write_pdb_best_scoring: init_pdb_best_scoring not run")

        # update the score list
        if self.replica_exchange:
            # read the self.best_score_list from the file
            exec(open(self.best_score_file_name).read())

        if len(self.best_score_list) < self.nbestscoring:
            self.best_score_list.append(score)
            self.best_score_list.sort()
            index = self.best_score_list.index(score)
            for suffix in self.suffixes:
                for i in range(len(self.best_score_list) - 2, index - 1, -1):
                    oldname = suffix + "." + str(i) + ".pdb"
                    newname = suffix + "." + str(i + 1) + ".pdb"
                    # rename on Windows fails if newname already exists
                    if os.path.exists(newname):
                        os.unlink(newname)
                    os.rename(oldname, newname)
                filetoadd = suffix + "." + str(index) + ".pdb"
                self.write_pdb(filetoadd, appendmode=False)

        else:
            if score < self.best_score_list[-1]:
                self.best_score_list.append(score)
                self.best_score_list.sort()
                self.best_score_list.pop(-1)
                index = self.best_score_list.index(score)
                for suffix in self.suffixes:
                    for i in range(len(self.best_score_list) - 1, index - 1, -1):
                        oldname = suffix + "." + str(i) + ".pdb"
                        newname = suffix + "." + str(i + 1) + ".pdb"
                        os.rename(oldname, newname)
                    filenametoremove = suffix + \
                        "." + str(self.nbestscoring) + ".pdb"
                    os.remove(filenametoremove)
                    filetoadd = suffix + "." + str(index) + ".pdb"
                    self.write_pdb(filetoadd, appendmode=False)

        if self.replica_exchange:
            # write the self.best_score_list to the file
            best_score_file = open(self.best_score_file_name, "w")
            best_score_file.write(
                "self.best_score_list=" + str(self.best_score_list))
            best_score_file.close()

    def init_rmf(self, name, hierarchies, rs=None, geometries=None, listofobjects=None):
        """
        This function initialize an RMF file

        @param name          the name of the RMF file
        @param hierarchies   the hiearchies to be included (it is a list)
        @param rs            optional, the restraint sets (it is a list)
        @param geometries    optional, the geometries (it is a list)
        @param listofobjects optional, the list of objects for the stat (it is a list)
        """
        rh = RMF.create_rmf_file(name)
        IMP.rmf.add_hierarchies(rh, hierarchies)
        cat=None
        outputkey_rmfkey=None

        if rs is not None:
            IMP.rmf.add_restraints(rh,rs)
        if geometries is not None:
            IMP.rmf.add_geometries(rh,geometries)
        if listofobjects is not None:
            cat = rh.get_category("stat")
            outputkey_rmfkey={}
            for l in listofobjects:
                if not "get_output" in dir(l):
                    raise ValueError("Output: object %s doesn't have get_output() method" % str(l))
                output=l.get_output()
                for outputkey in output:
                    rmftag=RMF.string_tag
                    if type(output[outputkey]) is float:
                        rmftag=RMF.float_tag
                    elif type(output[outputkey]) is int:
                        rmftag=RMF.int_tag
                    elif type(output[outputkey]) is str:
                        rmftag = RMF.string_tag
                    else:
                        rmftag = RMF.string_tag
                    rmfkey=rh.get_key(cat, outputkey, rmftag)
                    outputkey_rmfkey[outputkey]=rmfkey
            outputkey_rmfkey["rmf_file"]=rh.get_key(cat, "rmf_file", RMF.string_tag)
            outputkey_rmfkey["rmf_frame_index"]=rh.get_key(cat, "rmf_frame_index", RMF.int_tag)

        self.dictionary_rmfs[name] = (rh,cat,outputkey_rmfkey,listofobjects)

    def add_restraints_to_rmf(self, name, objectlist):
        flatobjectlist=_flatten(objectlist)
        for o in flatobjectlist:
            try:
                rs = o.get_restraint_for_rmf()
            except:
                rs = o.get_restraint()
            IMP.rmf.add_restraints(
                self.dictionary_rmfs[name][0],
                rs.get_restraints())

    def add_geometries_to_rmf(self, name, objectlist):
        for o in objectlist:
            geos = o.get_geometries()
            IMP.rmf.add_geometries(self.dictionary_rmfs[name][0], geos)

    def add_particle_pair_from_restraints_to_rmf(self, name, objectlist):
        for o in objectlist:

            pps = o.get_particle_pairs()
            for pp in pps:
                IMP.rmf.add_geometry(
                    self.dictionary_rmfs[name][0],
                    IMP.core.EdgePairGeometry(pp))

    def write_rmf(self, name):
        IMP.rmf.save_frame(self.dictionary_rmfs[name][0])
        if self.dictionary_rmfs[name][1] is not None:
            cat=self.dictionary_rmfs[name][1]
            outputkey_rmfkey=self.dictionary_rmfs[name][2]
            listofobjects=self.dictionary_rmfs[name][3]
            for l in listofobjects:
                output=l.get_output()
                for outputkey in output:
                    rmfkey=outputkey_rmfkey[outputkey]
                    try:
                        self.dictionary_rmfs[name][0].get_root_node().set_value(rmfkey,output[outputkey])
                    except NotImplementedError:
                        continue
            rmfkey = outputkey_rmfkey["rmf_file"]
            self.dictionary_rmfs[name][0].get_root_node().set_value(rmfkey, name)
            rmfkey = outputkey_rmfkey["rmf_frame_index"]
            nframes=self.dictionary_rmfs[name][0].get_number_of_frames()
            self.dictionary_rmfs[name][0].get_root_node().set_value(rmfkey, nframes-1)
        self.dictionary_rmfs[name][0].flush()

    def close_rmf(self, name):
        rh = self.dictionary_rmfs[name][0]
        del self.dictionary_rmfs[name]
        del rh

    def write_rmfs(self):
        for rmfinfo in self.dictionary_rmfs.keys():
            self.write_rmf(rmfinfo[0])

    def init_stat(self, name, listofobjects):
        if self.ascii:
            flstat = open(name, 'w')
            flstat.close()
        else:
            flstat = open(name, 'wb')
            flstat.close()

        # check that all objects in listofobjects have a get_output method
        for l in listofobjects:
            if not "get_output" in dir(l):
                raise ValueError("Output: object %s doesn't have get_output() method" % str(l))
        self.dictionary_stats[name] = listofobjects

    def set_output_entry(self, key, value):
        self.initoutput.update({key: value})

    def write_stat(self, name, appendmode=True):
        output = self.initoutput
        for obj in self.dictionary_stats[name]:
            d = obj.get_output()
            # remove all entries that begin with _ (private entries)
            dfiltered = dict((k, v) for k, v in d.items() if k[0] != "_")
            output.update(dfiltered)

        if appendmode:
            writeflag = 'a'
        else:
            writeflag = 'w'

        if self.ascii:
            flstat = open(name, writeflag)
            flstat.write("%s \n" % output)
            flstat.close()
        else:
            flstat = open(name, writeflag + 'b')
            cPickle.dump(output, flstat, 2)
            flstat.close()

    def write_stats(self):
        for stat in self.dictionary_stats.keys():
            self.write_stat(stat)

    def get_stat(self, name):
        output = {}
        for obj in self.dictionary_stats[name]:
            output.update(obj.get_output())
        return output

    def write_test(self, name, listofobjects):
#       write the test:
#       output=output.Output()
#       output.write_test("test_modeling11_models.rmf_45492_11Sep13_veena_imp-020713.dat",outputobjects)
#       run the test:
#       output=output.Output()
#       output.test("test_modeling11_models.rmf_45492_11Sep13_veena_imp-020713.dat",outputobjects)
        flstat = open(name, 'w')
        output = self.initoutput
        for l in listofobjects:
            if not "get_test_output" in dir(l) and not "get_output" in dir(l):
                raise ValueError("Output: object %s doesn't have get_output() or get_test_output() method" % str(l))
        self.dictionary_stats[name] = listofobjects

        for obj in self.dictionary_stats[name]:
            try:
                d = obj.get_test_output()
            except:
                d = obj.get_output()
            # remove all entries that begin with _ (private entries)
            dfiltered = dict((k, v) for k, v in d.items() if k[0] != "_")
            output.update(dfiltered)
        #output.update({"ENVIRONMENT": str(self.get_environment_variables())})
        #output.update(
        #    {"IMP_VERSIONS": str(self.get_versions_of_relevant_modules())})
        flstat.write("%s \n" % output)
        flstat.close()

    def test(self, name, listofobjects, tolerance=1e-5):
        output = self.initoutput
        for l in listofobjects:
            if not "get_test_output" in dir(l) and not "get_output" in dir(l):
                raise ValueError("Output: object %s doesn't have get_output() or get_test_output() method" % str(l))
        for obj in listofobjects:
            try:
                output.update(obj.get_test_output())
            except:
                output.update(obj.get_output())
        #output.update({"ENVIRONMENT": str(self.get_environment_variables())})
        #output.update(
        #    {"IMP_VERSIONS": str(self.get_versions_of_relevant_modules())})

        flstat = open(name, 'r')

        passed=True
        for l in flstat:
            test_dict = ast.literal_eval(l)
        for k in test_dict:
            if k in output:
                old_value = str(test_dict[k])
                new_value = str(output[k])
                try:
                    float(old_value)
                    is_float = True
                except ValueError:
                    is_float = False

                if is_float:
                    fold = float(old_value)
                    fnew = float(new_value)
                    diff = abs(fold - fnew)
                    if diff > tolerance:
                        print("%s: test failed, old value: %s new value %s; "
                              "diff %f > %f" % (str(k), str(old_value),
                                                str(new_value), diff,
                                                tolerance), file=sys.stderr)
                        passed=False
                elif test_dict[k] != output[k]:
                    if len(old_value) < 50 and len(new_value) < 50:
                        print("%s: test failed, old value: %s new value %s"
                              % (str(k), old_value, new_value), file=sys.stderr)
                        passed=False
                    else:
                        print("%s: test failed, omitting results (too long)"
                              % str(k), file=sys.stderr)
                        passed=False

            else:
                print("%s from old objects (file %s) not in new objects"
                      % (str(k), str(name)), file=sys.stderr)
        return passed

    def get_environment_variables(self):
        import os
        return str(os.environ)

    def get_versions_of_relevant_modules(self):
        import IMP
        versions = {}
        versions["IMP_VERSION"] = IMP.get_module_version()
        try:
            import IMP.pmi1
            versions["PMI_VERSION"] = IMP.pmi1.get_module_version()
        except (ImportError):
            pass
        try:
            import IMP.isd2
            versions["ISD2_VERSION"] = IMP.isd2.get_module_version()
        except (ImportError):
            pass
        try:
            import IMP.isd_emxl
            versions["ISD_EMXL_VERSION"] = IMP.isd_emxl.get_module_version()
        except (ImportError):
            pass
        return versions

#-------------------
    def init_stat2(
        self,
        name,
        listofobjects,
        extralabels=None,
            listofsummedobjects=None):
        # this is a new stat file that should be less
        # space greedy!
        # listofsummedobjects must be in the form [([obj1,obj2,obj3,obj4...],label)]
        # extralabels

        if listofsummedobjects is None:
            listofsummedobjects = []
        if extralabels is None:
            extralabels = []
        flstat = open(name, 'w')
        output = {}
        stat2_keywords = {"STAT2HEADER": "STAT2HEADER"}
        stat2_keywords.update(
            {"STAT2HEADER_ENVIRON": str(self.get_environment_variables())})
        stat2_keywords.update(
            {"STAT2HEADER_IMP_VERSIONS": str(self.get_versions_of_relevant_modules())})
        stat2_inverse = {}

        for l in listofobjects:
            if not "get_output" in dir(l):
                raise ValueError("Output: object %s doesn't have get_output() method" % str(l))
            else:
                d = l.get_output()
                # remove all entries that begin with _ (private entries)
                dfiltered = dict((k, v)
                                 for k, v in d.items() if k[0] != "_")
                output.update(dfiltered)

        # check for customizable entries
        for l in listofsummedobjects:
            for t in l[0]:
                if not "get_output" in dir(t):
                    raise ValueError("Output: object %s doesn't have get_output() method" % str(t))
                else:
                    if "_TotalScore" not in t.get_output():
                        raise ValueError("Output: object %s doesn't have _TotalScore entry to be summed" % str(t))
                    else:
                        output.update({l[1]: 0.0})

        for k in extralabels:
            output.update({k: 0.0})

        for n, k in enumerate(output):
            stat2_keywords.update({n: k})
            stat2_inverse.update({k: n})

        flstat.write("%s \n" % stat2_keywords)
        flstat.close()
        self.dictionary_stats2[name] = (
            listofobjects,
            stat2_inverse,
            listofsummedobjects,
            extralabels)

    def write_stat2(self, name, appendmode=True):
        output = {}
        (listofobjects, stat2_inverse, listofsummedobjects,
         extralabels) = self.dictionary_stats2[name]

        # writing objects
        for obj in listofobjects:
            od = obj.get_output()
            dfiltered = dict((k, v) for k, v in od.items() if k[0] != "_")
            for k in dfiltered:
                output.update({stat2_inverse[k]: od[k]})

        # writing summedobjects
        for l in listofsummedobjects:
            partial_score = 0.0
            for t in l[0]:
                d = t.get_output()
                partial_score += float(d["_TotalScore"])
            output.update({stat2_inverse[l[1]]: str(partial_score)})

        # writing extralabels
        for k in extralabels:
            if k in self.initoutput:
                output.update({stat2_inverse[k]: self.initoutput[k]})
            else:
                output.update({stat2_inverse[k]: "None"})

        if appendmode:
            writeflag = 'a'
        else:
            writeflag = 'w'

        flstat = open(name, writeflag)
        flstat.write("%s \n" % output)
        flstat.close()

    def write_stats2(self):
        for stat in self.dictionary_stats2.keys():
            self.write_stat2(stat)


class OutputStatistics(object):
    """Collect statistics from ProcessOutput.get_fields().
       Counters of the total number of frames read, plus the models that
       passed the various filters used in get_fields(), are provided."""
    def __init__(self):
        self.total = 0
        self.passed_get_every = 0
        self.passed_filterout = 0
        self.passed_filtertuple = 0


class ProcessOutput(object):
    """A class for reading stat files (either rmf or ascii v1 and v2)"""
    def __init__(self, filename):
        self.filename = filename
        self.isstat1 = False
        self.isstat2 = False
        self.isrmf = False

        # open the file
        if not self.filename is None:
            f = open(self.filename, "r")
        else:
            raise ValueError("No file name provided. Use -h for help")

        try:
            #let's see if that is an rmf file
            rh = RMF.open_rmf_file_read_only(self.filename)
            self.isrmf=True
            cat=rh.get_category('stat')
            rmf_klist=rh.get_keys(cat)
            self.rmf_names_keys=dict([(rh.get_name(k),k) for k in rmf_klist])
            del rh

        except IOError:
            # try with an ascii stat file
            # get the keys from the first line
            for line in f.readlines():
                d = ast.literal_eval(line)
                self.klist = list(d.keys())
                # check if it is a stat2 file
                if "STAT2HEADER" in self.klist:
                    self.isstat2 = True
                    for k in self.klist:
                        if "STAT2HEADER" in str(k):
                            # if print_header: print k, d[k]
                            del d[k]
                    stat2_dict = d
                    # get the list of keys sorted by value
                    kkeys = [k[0]
                             for k in sorted(stat2_dict.items(), key=operator.itemgetter(1))]
                    self.klist = [k[1]
                                  for k in sorted(stat2_dict.items(), key=operator.itemgetter(1))]
                    self.invstat2_dict = {}
                    for k in kkeys:
                        self.invstat2_dict.update({stat2_dict[k]: k})
                else:
                    IMP.handle_use_deprecated("statfile v1 is deprecated. "
                                              "Please convert to statfile v2.\n")
                    self.isstat1 = True
                    self.klist.sort()

                break
            f.close()


    def get_keys(self):
        if self.isrmf:
            return sorted(self.rmf_names_keys.keys())
        else:
            return self.klist

    def show_keys(self, ncolumns=2, truncate=65):
        IMP.pmi1.tools.print_multicolumn(self.get_keys(), ncolumns, truncate)

    def get_fields(self, fields, filtertuple=None, filterout=None, get_every=1,
                   statistics=None):
        '''
        Get the desired field names, and return a dictionary.
        Namely, "fields" are the queried keys in the stat file (eg. ["Total_Score",...])
        The returned data structure is a dictionary, where each key is a field and the value
        is the time series (ie, frame ordered series)
        of that field (ie, {"Total_Score":[Score_0,Score_1,Score_2,Score_3,...],....} )

        @param fields (list of strings) queried keys in the stat file (eg. "Total_Score"....)
        @param filterout specify if you want to "grep" out something from
                         the file, so that it is faster
        @param filtertuple a tuple that contains
                     ("TheKeyToBeFiltered",relationship,value)
                     where relationship = "<", "==", or ">"
        @param get_every only read every Nth line from the file
        @param statistics if provided, accumulate statistics in an
                          OutputStatistics object
        '''

        if statistics is None:
            statistics = OutputStatistics()
        outdict = {}
        for field in fields:
            outdict[field] = []

        # print fields values
        if self.isrmf:
            rh = RMF.open_rmf_file_read_only(self.filename)
            nframes=rh.get_number_of_frames()
            for i in range(nframes):
                statistics.total += 1
                # "get_every" and "filterout" not enforced for RMF
                statistics.passed_get_every += 1
                statistics.passed_filterout += 1
                IMP.rmf.load_frame(rh, RMF.FrameID(i))
                if not filtertuple is None:
                    keytobefiltered = filtertuple[0]
                    relationship = filtertuple[1]
                    value = filtertuple[2]
                    datavalue=rh.get_root_node().get_value(self.rmf_names_keys[keytobefiltered])
                    if self.isfiltered(datavalue,relationship,value): continue

                statistics.passed_filtertuple += 1
                for field in fields:
                    outdict[field].append(rh.get_root_node().get_value(self.rmf_names_keys[field]))

        else:
            f = open(self.filename, "r")
            line_number = 0

            for line in f.readlines():
                statistics.total += 1
                if not filterout is None:
                    if filterout in line:
                        continue
                statistics.passed_filterout += 1
                line_number += 1

                if line_number % get_every != 0:
                    if line_number == 1 and self.isstat2:
                        statistics.total -= 1
                        statistics.passed_filterout -= 1
                    continue
                statistics.passed_get_every += 1
                #if line_number % 1000 == 0:
                #    print "ProcessOutput.get_fields: read line %s from file %s" % (str(line_number), self.filename)
                try:
                    d = ast.literal_eval(line)
                except:
                    print("# Warning: skipped line number " + str(line_number) + " not a valid line")
                    continue

                if self.isstat1:

                    if not filtertuple is None:
                        keytobefiltered = filtertuple[0]
                        relationship = filtertuple[1]
                        value = filtertuple[2]
                        datavalue=d[keytobefiltered]
                        if self.isfiltered(datavalue, relationship, value): continue

                    statistics.passed_filtertuple += 1
                    [outdict[field].append(d[field]) for field in fields]

                elif self.isstat2:
                    if line_number == 1:
                        statistics.total -= 1
                        statistics.passed_filterout -= 1
                        statistics.passed_get_every -= 1
                        continue

                    if not filtertuple is None:
                        keytobefiltered = filtertuple[0]
                        relationship = filtertuple[1]
                        value = filtertuple[2]
                        datavalue=d[self.invstat2_dict[keytobefiltered]]
                        if self.isfiltered(datavalue, relationship, value): continue

                    statistics.passed_filtertuple += 1
                    [outdict[field].append(d[self.invstat2_dict[field]]) for field in fields]

            f.close()

        return outdict

    def isfiltered(self,datavalue,relationship,refvalue):
        dofilter=False
        try:
            fdatavalue=float(datavalue)
        except ValueError:
            raise ValueError("ProcessOutput.filter: datavalue cannot be converted into a float")

        if relationship == "<":
            if float(datavalue) >= refvalue:
                dofilter=True
        if relationship == ">":
            if float(datavalue) <= refvalue:
                dofilter=True
        if relationship == "==":
            if float(datavalue) != refvalue:
                dofilter=True
        return dofilter


class RMFHierarchyHandler(IMP.atom.Hierarchy):
    """ class to allow more advanced handling of RMF files.
    It is both a container and a IMP.atom.Hierarchy.
     - it is iterable (while loading the corresponding frame)
     - Item brackets [] load the corresponding frame
     - slice create an iterator
     - can relink to another RMF file
     """
    def __init__(self,model,rmf_file_name):
        """
        @param model: the IMP.Model()
        @param rmf_file_name: str, path of the rmf file
        """
        self.model=model
        try:
            self.rh_ref = RMF.open_rmf_file_read_only(rmf_file_name)
        except TypeError:
            raise TypeError("Wrong rmf file name or type: %s"% str(rmf_file_name))
        hs = IMP.rmf.create_hierarchies(self.rh_ref, self.model)
        IMP.rmf.load_frame(self.rh_ref, RMF.FrameID(0))
        self.root_hier_ref = hs[0]
        IMP.atom.Hierarchy.__init__(self, self.root_hier_ref)
        self.model.update()
        self.ColorHierarchy=None


    def link_to_rmf(self,rmf_file_name):
        """
        Link to another RMF file
        """
        self.rh_ref = RMF.open_rmf_file_read_only(rmf_file_name)
        IMP.rmf.link_hierarchies(self.rh_ref, [self])
        if self.ColorHierarchy:
            self.ColorHierarchy.method()
        RMFHierarchyHandler.set_frame(self,0)

    def set_frame(self,index):
        try:
            IMP.rmf.load_frame(self.rh_ref, RMF.FrameID(index))
        except:
            print("skipping frame %s:%d\n"%(self.current_rmf, index))
        self.model.update()

    def get_number_of_frames(self):
        return self.rh_ref.get_number_of_frames()

    def __getitem__(self,int_slice_adaptor):
        if type(int_slice_adaptor) is int:
            self.set_frame(int_slice_adaptor)
            return int_slice_adaptor
        elif type(int_slice_adaptor) is slice:
            return self.__iter__(int_slice_adaptor)
        else:
            raise TypeError("Unknown Type")

    def __len__(self):
        return self.get_number_of_frames()

    def __iter__(self,slice_key=None):
        if slice_key is None:
            for nframe in range(len(self)):
                yield self[nframe]
        else:
            for nframe in list(range(len(self)))[slice_key]:
                yield self[nframe]

class CacheHierarchyCoordinates(object):
    def __init__(self,StatHierarchyHandler):
        self.xyzs=[]
        self.nrms=[]
        self.rbs=[]
        self.nrm_coors={}
        self.xyz_coors={}
        self.rb_trans={}
        self.current_index=None
        self.rmfh=StatHierarchyHandler
        rbs,xyzs=IMP.pmi1.tools.get_rbs_and_beads([self.rmfh])
        self.model=self.rmfh.get_model()
        self.rbs=rbs
        for xyz in xyzs:
            if IMP.core.NonRigidMember.get_is_setup(xyz):
                nrm=IMP.core.NonRigidMember(xyz)
                self.nrms.append(nrm)
            else:
                fb=IMP.core.XYZ(xyz)
                self.xyzs.append(fb)

    def do_store(self,index):
        self.rb_trans[index]={}
        self.nrm_coors[index]={}
        self.xyz_coors[index]={}
        for rb in self.rbs:
            self.rb_trans[index][rb]=rb.get_reference_frame()
        for nrm in self.nrms:
            self.nrm_coors[index][nrm]=nrm.get_internal_coordinates()
        for xyz in self.xyzs:
            self.xyz_coors[index][xyz]=xyz.get_coordinates()
        self.current_index=index

    def do_update(self,index):
        if self.current_index!=index:
            for rb in self.rbs:
                rb.set_reference_frame(self.rb_trans[index][rb])
            for nrm in self.nrms:
                nrm.set_internal_coordinates(self.nrm_coors[index][nrm])
            for xyz in self.xyzs:
                xyz.set_coordinates(self.xyz_coors[index][xyz])
            self.current_index=index
            self.model.update()

    def get_number_of_frames(self):
        return len(self.rb_trans.keys())

    def __getitem__(self,index):
        if type(index) is int:
            if index in self.rb_trans.keys():
                return True
            else:
                return False
        else:
            raise TypeError("Unknown Type")

    def __len__(self):
        return self.get_number_of_frames()




class StatHierarchyHandler(RMFHierarchyHandler):
    """ class to link stat files to several rmf files """
    def __init__(self,model=None,stat_file=None,number_best_scoring_models=None,score_key=None,StatHierarchyHandler=None,cache=None):
        """

        @param model: IMP.Model()
        @param stat_file: either 1) a list or 2) a single stat file names (either rmfs or ascii, or pickled data or pickled cluster), 3) a dictionary containing an rmf/ascii
            stat file name as key and a list of frames as values
        @param number_best_scoring_models:
        @param StatHierarchyHandler: copy constructor input object
        @param cache: cache coordinates and rigid body transformations.
        """

        if not StatHierarchyHandler is None:
            #overrides all other arguments
            #copy constructor: create a copy with different RMFHierarchyHandler
            self.model=StatHierarchyHandler.model
            self.data=StatHierarchyHandler.data
            self.number_best_scoring_models=StatHierarchyHandler.number_best_scoring_models
            self.is_setup=True
            self.current_rmf=StatHierarchyHandler.current_rmf
            self.current_frame=None
            self.current_index=None
            self.score_threshold=StatHierarchyHandler.score_threshold
            self.score_key=StatHierarchyHandler.score_key
            self.cache=StatHierarchyHandler.cache
            RMFHierarchyHandler.__init__(self, self.model,self.current_rmf)
            if self.cache:
                self.cache=CacheHierarchyCoordinates(self)
            else:
                self.cache=None
            self.set_frame(0)

        else:
            #standard constructor
            self.model=model
            self.data=[]
            self.number_best_scoring_models=number_best_scoring_models
            self.cache=cache

            if score_key is None:
                self.score_key="Total_Score"
            else:
                self.score_key=score_key
            self.is_setup=None
            self.current_rmf=None
            self.current_frame=None
            self.current_index=None
            self.score_threshold=None

            if type(stat_file) is str:
                self.add_stat_file(stat_file)
            elif type(stat_file) is list:
                for f in stat_file:
                    self.add_stat_file(f)

    def add_stat_file(self,stat_file):
        try:
            import cPickle as pickle
        except ImportError:
            import pickle

        try:
            '''check that it is not a pickle file with saved data from a previous calculation'''
            self.load_data(stat_file)

            if self.number_best_scoring_models:
                scores = self.get_scores()
                max_score = sorted(scores)[0:min(len(self), self.number_best_scoring_models)][-1]
                self.do_filter_by_score(max_score)

        except pickle.UnpicklingError:
            '''alternatively read the ascii stat files'''
            try:
                scores,rmf_files,rmf_frame_indexes,features = self.get_info_from_stat_file(stat_file, self.score_threshold)
            except KeyError:
                # in this case check that is it an rmf file, probably without stat stored in
                try:
                    # let's see if that is an rmf file
                    rh = RMF.open_rmf_file_read_only(stat_file)
                    nframes = rh.get_number_of_frames()
                    scores=[0.0]*nframes
                    rmf_files=[stat_file]*nframes
                    rmf_frame_indexes=range(nframes)
                    features={}
                except:
                    return


            if len(set(rmf_files)) > 1:
                raise ("Multiple RMF files found")

            if not rmf_files:
                print("StatHierarchyHandler: Error: Trying to set none as rmf_file (probably empty stat file), aborting")
                return

            for n,index in enumerate(rmf_frame_indexes):
                featn_dict=dict([(k,features[k][n]) for k in features])
                self.data.append(IMP.pmi1.output.DataEntry(stat_file,rmf_files[n],index,scores[n],featn_dict))

            if self.number_best_scoring_models:
                scores=self.get_scores()
                max_score=sorted(scores)[0:min(len(self),self.number_best_scoring_models)][-1]
                self.do_filter_by_score(max_score)

        if not self.is_setup:
            RMFHierarchyHandler.__init__(self, self.model,self.get_rmf_names()[0])
            if self.cache:
                self.cache=CacheHierarchyCoordinates(self)
            else:
                self.cache=None
            self.is_setup=True
            self.current_rmf=self.get_rmf_names()[0]

        self.set_frame(0)

    def save_data(self,filename='data.pkl'):
        try:
            import cPickle as pickle
        except ImportError:
            import pickle
        fl=open(filename,'wb')
        pickle.dump(self.data,fl)

    def load_data(self,filename='data.pkl'):
        try:
            import cPickle as pickle
        except ImportError:
            import pickle
        fl=open(filename,'rb')
        data_structure=pickle.load(fl)
        #first check that it is a list
        if not type(data_structure) is list:
            raise TypeError("%filename should contain a list of IMP.pmi1.output.DataEntry or IMP.pmi1.output.Cluster" % filename)
        # second check the types
        if all(isinstance(item, IMP.pmi1.output.DataEntry) for item in data_structure):
            self.data=data_structure
        elif all(isinstance(item, IMP.pmi1.output.Cluster) for item in data_structure):
            nmodels=0
            for cluster in data_structure:
                nmodels+=len(cluster)
            self.data=[None]*nmodels
            for cluster in data_structure:
                for n,data in enumerate(cluster):
                    index=cluster.members[n]
                    self.data[index]=data
        else:
            raise TypeError("%filename should contain a list of IMP.pmi1.output.DataEntry or IMP.pmi1.output.Cluster" % filename)

    def set_frame(self,index):
        if self.cache is not None and self.cache[index]:
            self.cache.do_update(index)
        else:
            nm=self.data[index].rmf_name
            fidx=self.data[index].rmf_index
            if nm != self.current_rmf:
                self.link_to_rmf(nm)
                self.current_rmf=nm
                self.current_frame=-1
            if fidx!=self.current_frame:
                RMFHierarchyHandler.set_frame(self, fidx)
                self.current_frame=fidx
            if self.cache is not None:
                self.cache.do_store(index)

        self.current_index = index

    def __getitem__(self,int_slice_adaptor):
        if type(int_slice_adaptor) is int:
            self.set_frame(int_slice_adaptor)
            return self.data[int_slice_adaptor]
        elif type(int_slice_adaptor) is slice:
            return self.__iter__(int_slice_adaptor)
        else:
            raise TypeError("Unknown Type")

    def __len__(self):
        return len(self.data)

    def __iter__(self,slice_key=None):
        if slice_key is None:
            for i in range(len(self)):
                yield self[i]
        else:
            for i in range(len(self))[slice_key]:
                yield self[i]

    def do_filter_by_score(self,maximum_score):
        self.data=[d for d in self.data if d.score<=maximum_score]

    def get_scores(self):
        return [d.score for d in self.data]

    def get_feature_series(self,feature_name):
        return [d.features[feature_name] for d in self.data]

    def get_feature_names(self):
        return self.data[0].features.keys()

    def get_rmf_names(self):
        return [d.rmf_name for d in self.data]

    def get_stat_files_names(self):
        return [d.stat_file for d in self.data]

    def get_rmf_indexes(self):
        return [d.rmf_index for d in self.data]

    def get_info_from_stat_file(self, stat_file, score_threshold=None):
        po=ProcessOutput(stat_file)
        fs=po.get_keys()
        models = IMP.pmi1.io.get_best_models([stat_file],
                                            score_key=self.score_key,
                                            feature_keys=fs,
                                            rmf_file_key="rmf_file",
                                            rmf_file_frame_key="rmf_frame_index",
                                            prefiltervalue=score_threshold,
                                            get_every=1)



        scores = [float(y) for y in models[2]]
        rmf_files = models[0]
        rmf_frame_indexes = models[1]
        features=models[3]
        return scores, rmf_files, rmf_frame_indexes,features


class DataEntry(object):
    '''
    A class to store data associated to a model
    '''
    def __init__(self,stat_file=None,rmf_name=None,rmf_index=None,score=None,features=None):
        self.rmf_name=rmf_name
        self.rmf_index=rmf_index
        self.score=score
        self.features=features
        self.stat_file=stat_file

    def __repr__(self):
        s= "IMP.pmi1.output.DataEntry\n"
        s+="---- stat file %s \n"%(self.stat_file)
        s+="---- rmf file %s \n"%(self.rmf_name)
        s+="---- rmf index %s \n"%(str(self.rmf_index))
        s+="---- score %s \n"%(str(self.score))
        s+="---- number of features %s \n"%(str(len(self.features.keys())))
        return s


class Cluster(object):
    '''
    A container for models organized into clusters
    '''
    def __init__(self,cid=None):
        self.cluster_id=cid
        self.members=[]
        self.precision=None
        self.center_index=None
        self.members_data={}

    def add_member(self,index,data=None):
        self.members.append(index)
        self.members_data[index]=data
        self.average_score=self.compute_score()

    def compute_score(self):
        try:
            score=sum([d.score for d in self])/len(self)
        except AttributeError:
            score=None
        return score

    def __repr__(self):
        s= "IMP.pmi1.output.Cluster\n"
        s+="---- cluster_id %s \n"%str(self.cluster_id)
        s+="---- precision %s \n"%str(self.precision)
        s+="---- average score %s \n"%str(self.average_score)
        s+="---- number of members %s \n"%str(len(self.members))
        s+="---- center index %s \n"%str(self.center_index)
        return s

    def __getitem__(self,int_slice_adaptor):
        if type(int_slice_adaptor) is int:
            index=self.members[int_slice_adaptor]
            return self.members_data[index]
        elif type(int_slice_adaptor) is slice:
            return self.__iter__(int_slice_adaptor)
        else:
            raise TypeError("Unknown Type")

    def __len__(self):
        return len(self.members)

    def __iter__(self,slice_key=None):
        if slice_key is None:
            for i in range(len(self)):
                yield self[i]
        else:
            for i in range(len(self))[slice_key]:
                yield self[i]

    def __add__(self, other):
        self.members+=other.members
        self.members_data.update(other.members_data)
        self.average_score=self.compute_score()
        self.precision=None
        self.center_index=None
        return self


def plot_clusters_populations(clusters):
    indexes=[]
    populations=[]
    for cluster in clusters:
        indexes.append(cluster.cluster_id)
        populations.append(len(cluster))

    import matplotlib.pyplot as plt
    fig, ax = plt.subplots()
    ax.bar(indexes, populations, 0.5, color='r') #, yerr=men_std)
    ax.set_ylabel('Population')
    ax.set_xlabel(('Cluster index'))
    plt.show()

def plot_clusters_precisions(clusters):
    indexes=[]
    precisions=[]
    for cluster in clusters:
        indexes.append(cluster.cluster_id)

        prec=cluster.precision
        print(cluster.cluster_id,prec)
        if prec is None:
            prec=0.0
        precisions.append(prec)

    import matplotlib.pyplot as plt
    fig, ax = plt.subplots()
    ax.bar(indexes, precisions, 0.5, color='r') #, yerr=men_std)
    ax.set_ylabel('Precision [A]')
    ax.set_xlabel(('Cluster index'))
    plt.show()

def plot_clusters_scores(clusters):
    indexes=[]
    values=[]
    for cluster in clusters:
        indexes.append(cluster.cluster_id)
        values.append([])
        for data in cluster:
            values[-1].append(data.score)

    plot_fields_box_plots("scores.pdf", values, indexes, frequencies=None,
                          valuename="Scores", positionname="Cluster index", xlabels=None,scale_plot_length=1.0)

class CrossLinkIdentifierDatabase(object):
    def __init__(self):
        self.clidb=dict()

    def check_key(self,key):
        if key not in self.clidb:
            self.clidb[key]={}

    def set_unique_id(self,key,value):
        self.check_key(key)
        self.clidb[key]["XLUniqueID"]=str(value)

    def set_protein1(self,key,value):
        self.check_key(key)
        self.clidb[key]["Protein1"]=str(value)

    def set_protein2(self,key,value):
        self.check_key(key)
        self.clidb[key]["Protein2"]=str(value)

    def set_residue1(self,key,value):
        self.check_key(key)
        self.clidb[key]["Residue1"]=int(value)

    def set_residue2(self,key,value):
        self.check_key(key)
        self.clidb[key]["Residue2"]=int(value)

    def set_idscore(self,key,value):
        self.check_key(key)
        self.clidb[key]["IDScore"]=float(value)

    def set_state(self,key,value):
        self.check_key(key)
        self.clidb[key]["State"]=int(value)

    def set_sigma1(self,key,value):
        self.check_key(key)
        self.clidb[key]["Sigma1"]=str(value)

    def set_sigma2(self,key,value):
        self.check_key(key)
        self.clidb[key]["Sigma2"]=str(value)

    def set_psi(self,key,value):
        self.check_key(key)
        self.clidb[key]["Psi"]=str(value)

    def get_unique_id(self,key):
        return self.clidb[key]["XLUniqueID"]

    def get_protein1(self,key):
        return self.clidb[key]["Protein1"]

    def get_protein2(self,key):
        return self.clidb[key]["Protein2"]

    def get_residue1(self,key):
        return self.clidb[key]["Residue1"]

    def get_residue2(self,key):
        return self.clidb[key]["Residue2"]

    def get_idscore(self,key):
        return self.clidb[key]["IDScore"]

    def get_state(self,key):
        return self.clidb[key]["State"]

    def get_sigma1(self,key):
        return self.clidb[key]["Sigma1"]

    def get_sigma2(self,key):
        return self.clidb[key]["Sigma2"]

    def get_psi(self,key):
        return self.clidb[key]["Psi"]

    def set_float_feature(self,key,value,feature_name):
        self.check_key(key)
        self.clidb[key][feature_name]=float(value)

    def set_int_feature(self,key,value,feature_name):
        self.check_key(key)
        self.clidb[key][feature_name]=int(value)

    def set_string_feature(self,key,value,feature_name):
        self.check_key(key)
        self.clidb[key][feature_name]=str(value)

    def get_feature(self,key,feature_name):
        return self.clidb[key][feature_name]

    def write(self,filename):
        import pickle
        with open(filename, 'wb') as handle:
            pickle.dump(self.clidb,handle)

    def load(self,filename):
        import pickle
        with open(filename, 'rb') as handle:
            self.clidb=pickle.load(handle)

def plot_fields(fields, framemin=None, framemax=None):
    import matplotlib as mpl
    mpl.use('Agg')
    import matplotlib.pyplot as plt

    plt.rc('lines', linewidth=4)
    fig, axs = plt.subplots(nrows=len(fields))
    fig.set_size_inches(10.5, 5.5 * len(fields))
    plt.rc('axes', color_cycle=['r'])

    n = 0
    for key in fields:
        if framemin is None:
            framemin = 0
        if framemax is None:
            framemax = len(fields[key])
        x = list(range(framemin, framemax))
        y = [float(y) for y in fields[key][framemin:framemax]]
        if len(fields) > 1:
            axs[n].plot(x, y)
            axs[n].set_title(key, size="xx-large")
            axs[n].tick_params(labelsize=18, pad=10)
        else:
            axs.plot(x, y)
            axs.set_title(key, size="xx-large")
            axs.tick_params(labelsize=18, pad=10)
        n += 1

    # Tweak spacing between subplots to prevent labels from overlapping
    plt.subplots_adjust(hspace=0.3)
    plt.show()


def plot_field_histogram(
    name, values_lists, valuename=None, bins=40, colors=None, format="png",
        reference_xline=None, yplotrange=None, xplotrange=None,normalized=True,
        leg_names=None):

    '''Plot a list of histograms from a value list.
    @param name the name of the plot
    @param value_lists the list of list of values eg: [[...],[...],[...]]
    @param valuename the y-label
    @param bins the number of bins
    @param colors If None, will use rainbow. Else will use specific list
    @param format output format
    @param reference_xline plot a reference line parallel to the y-axis
    @param yplotrange the range for the y-axis
    @param normalized whether the histogram is normalized or not
    @param leg_names names for the legend
    '''

    import matplotlib as mpl
    mpl.use('Agg')
    import matplotlib.pyplot as plt
    import matplotlib.cm as cm
    fig = plt.figure(figsize=(18.0, 9.0))

    if colors is None:
        colors = cm.rainbow(np.linspace(0, 1, len(values_lists)))
    for nv,values in enumerate(values_lists):
        col=colors[nv]
        if leg_names is not None:
            label=leg_names[nv]
        else:
            label=str(nv)
        h=plt.hist(
            [float(y) for y in values],
            bins=bins,
            color=col,
            normed=normalized,histtype='step',lw=4,
            label=label)

    # plt.title(name,size="xx-large")
    plt.tick_params(labelsize=12, pad=10)
    if valuename is None:
        plt.xlabel(name, size="xx-large")
    else:
        plt.xlabel(valuename, size="xx-large")
    plt.ylabel("Frequency", size="xx-large")

    if not yplotrange is None:
        plt.ylim()
    if not xplotrange is None:
        plt.xlim(xplotrange)

    plt.legend(loc=2)

    if not reference_xline is None:
        plt.axvline(
            reference_xline,
            color='red',
            linestyle='dashed',
            linewidth=1)

    plt.savefig(name + "." + format, dpi=150, transparent=True)
    plt.show()


def plot_fields_box_plots(name, values, positions, frequencies=None,
                          valuename="None", positionname="None", xlabels=None,scale_plot_length=1.0):
    '''
    Plot time series as boxplots.
    fields is a list of time series, positions are the x-values
    valuename is the y-label, positionname is the x-label
    '''

    import matplotlib as mpl
    mpl.use('Agg')
    import matplotlib.pyplot as plt
    from matplotlib.patches import Polygon

    bps = []
    fig = plt.figure(figsize=(float(len(positions))*scale_plot_length, 5.0))
    fig.canvas.set_window_title(name)

    ax1 = fig.add_subplot(111)

    plt.subplots_adjust(left=0.1, right=0.990, top=0.95, bottom=0.4)

    bps.append(plt.boxplot(values, notch=0, sym='', vert=1,
                           whis=1.5, positions=positions))

    plt.setp(bps[-1]['boxes'], color='black', lw=1.5)
    plt.setp(bps[-1]['whiskers'], color='black', ls=":", lw=1.5)

    if frequencies is not None:
        for n,v in enumerate(values):
            plist=[positions[n]]*len(v)
            ax1.plot(plist, v, 'gx', alpha=0.7, markersize=7)

    # print ax1.xaxis.get_majorticklocs()
    if not xlabels is None:
        ax1.set_xticklabels(xlabels)
    plt.xticks(rotation=90)
    plt.xlabel(positionname)
    plt.ylabel(valuename)

    plt.savefig(name+".pdf",dpi=150)
    plt.show()


def plot_xy_data(x,y,title=None,out_fn=None,display=True,set_plot_yaxis_range=None,
                 xlabel=None,ylabel=None):
    import matplotlib as mpl
    mpl.use('Agg')
    import matplotlib.pyplot as plt
    plt.rc('lines', linewidth=2)

    fig, ax  = plt.subplots(nrows=1)
    fig.set_size_inches(8,4.5)
    if title is not None:
        fig.canvas.set_window_title(title)

    #plt.rc('axes', color='r')
    ax.plot(x,y,color='r')
    if set_plot_yaxis_range is not None:
        x1,x2,y1,y2=plt.axis()
        y1=set_plot_yaxis_range[0]
        y2=set_plot_yaxis_range[1]
        plt.axis((x1,x2,y1,y2))
    if title is not None:
        ax.set_title(title)
    if xlabel is not None:
        ax.set_xlabel(xlabel)
    if ylabel is not None:
        ax.set_ylabel(ylabel)
    if out_fn is not None:
        plt.savefig(out_fn+".pdf")
    if display:
        plt.show()
    plt.close(fig)

def plot_scatter_xy_data(x,y,labelx="None",labely="None",
                         xmin=None,xmax=None,ymin=None,ymax=None,
                         savefile=False,filename="None.eps",alpha=0.75):

    import matplotlib as mpl
    mpl.use('Agg')
    import matplotlib.pyplot as plt
    import sys
    from matplotlib import rc
    #rc('font', **{'family':'serif','serif':['Palatino']})
    rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
    #rc('text', usetex=True)

    fig, axs = plt.subplots(1)

    axs0 = axs

    axs0.set_xlabel(labelx, size="xx-large")
    axs0.set_ylabel(labely, size="xx-large")
    axs0.tick_params(labelsize=18, pad=10)

    plot2 = []

    plot2.append(axs0.plot(x,  y,   'o', color='k',lw=2, ms=0.1, alpha=alpha,  c="w"))

    axs0.legend(
        loc=0,
        frameon=False,
        scatterpoints=1,
        numpoints=1,
        columnspacing=1)

    fig.set_size_inches(8.0, 8.0)
    fig.subplots_adjust(left=0.161, right=0.850, top=0.95, bottom=0.11)
    if (not ymin is None) and (not ymax is None):
        axs0.set_ylim(ymin,ymax)
    if (not xmin is None) and (not xmax is None):
        axs0.set_xlim(xmin,xmax)

    #plt.show()
    if savefile:
        fig.savefig(filename, dpi=300)


def get_graph_from_hierarchy(hier):
    graph = []
    depth_dict = {}
    depth = 0
    (graph, depth, depth_dict) = recursive_graph(
        hier, graph, depth, depth_dict)

    # filters node labels according to depth_dict
    node_labels_dict = {}
    node_size_dict = {}
    for key in depth_dict:
        node_size_dict = 10 / depth_dict[key]
        if depth_dict[key] < 3:
            node_labels_dict[key] = key
        else:
            node_labels_dict[key] = ""
    draw_graph(graph, labels_dict=node_labels_dict)


def recursive_graph(hier, graph, depth, depth_dict):
    depth = depth + 1
    nameh = IMP.atom.Hierarchy(hier).get_name()
    index = str(hier.get_particle().get_index())
    name1 = nameh + "|#" + index
    depth_dict[name1] = depth

    children = IMP.atom.Hierarchy(hier).get_children()

    if len(children) == 1 or children is None:
        depth = depth - 1
        return (graph, depth, depth_dict)

    else:
        for c in children:
            (graph, depth, depth_dict) = recursive_graph(
                c, graph, depth, depth_dict)
            nameh = IMP.atom.Hierarchy(c).get_name()
            index = str(c.get_particle().get_index())
            namec = nameh + "|#" + index
            graph.append((name1, namec))

        depth = depth - 1
        return (graph, depth, depth_dict)


def draw_graph(graph, labels_dict=None, graph_layout='spring',
               node_size=5, node_color=None, node_alpha=0.3,
               node_text_size=11, fixed=None, pos=None,
               edge_color='blue', edge_alpha=0.3, edge_thickness=1,
               edge_text_pos=0.3,
               validation_edges=None,
               text_font='sans-serif',
               out_filename=None):

    import matplotlib as mpl
    mpl.use('Agg')
    import networkx as nx
    import matplotlib.pyplot as plt
    from math import sqrt, pi

    # create networkx graph
    G = nx.Graph()

    # add edges
    if type(edge_thickness) is list:
        for edge,weight in zip(graph,edge_thickness):
            G.add_edge(edge[0], edge[1], weight=weight)
    else:
        for edge in graph:
            G.add_edge(edge[0], edge[1])

    if node_color==None:
        node_color_rgb=(0,0,0)
        node_color_hex="000000"
    else:
        cc=IMP.pmi1.tools.ColorChange()
        tmpcolor_rgb=[]
        tmpcolor_hex=[]
        for node in G.nodes():
            cctuple=cc.rgb(node_color[node])
            tmpcolor_rgb.append((cctuple[0]/255,cctuple[1]/255,cctuple[2]/255))
            tmpcolor_hex.append(node_color[node])
        node_color_rgb=tmpcolor_rgb
        node_color_hex=tmpcolor_hex

    # get node sizes if dictionary
    if type(node_size) is dict:
        tmpsize=[]
        for node in G.nodes():
            size=sqrt(node_size[node])/pi*10.0
            tmpsize.append(size)
        node_size=tmpsize

    for n,node in enumerate(G.nodes()):
        color=node_color_hex[n]
        size=node_size[n]
        nx.set_node_attributes(G, "graphics", {node : {'type': 'ellipse','w': size, 'h': size,'fill': '#'+color, 'label': node}})
        nx.set_node_attributes(G, "LabelGraphics", {node : {'type': 'text','text':node, 'color':'#000000', 'visible':'true'}})

    for edge in G.edges():
        nx.set_edge_attributes(G, "graphics", {edge : {'width': 1,'fill': '#000000'}})

    for ve in validation_edges:
        print(ve)
        if (ve[0],ve[1]) in G.edges():
            print("found forward")
            nx.set_edge_attributes(G, "graphics", {ve : {'width': 1,'fill': '#00FF00'}})
        elif (ve[1],ve[0]) in G.edges():
            print("found backward")
            nx.set_edge_attributes(G, "graphics", {(ve[1],ve[0]) : {'width': 1,'fill': '#00FF00'}})
        else:
            G.add_edge(ve[0], ve[1])
            print("not found")
            nx.set_edge_attributes(G, "graphics", {ve : {'width': 1,'fill': '#FF0000'}})

    # these are different layouts for the network you may try
    # shell seems to work best
    if graph_layout == 'spring':
        print(fixed, pos)
        graph_pos = nx.spring_layout(G,k=1.0/8.0,fixed=fixed,pos=pos)
    elif graph_layout == 'spectral':
        graph_pos = nx.spectral_layout(G)
    elif graph_layout == 'random':
        graph_pos = nx.random_layout(G)
    else:
        graph_pos = nx.shell_layout(G)


    # draw graph
    nx.draw_networkx_nodes(G, graph_pos, node_size=node_size,
                           alpha=node_alpha, node_color=node_color_rgb,
                           linewidths=0)
    nx.draw_networkx_edges(G, graph_pos, width=edge_thickness,
                           alpha=edge_alpha, edge_color=edge_color)
    nx.draw_networkx_labels(
        G, graph_pos, labels=labels_dict, font_size=node_text_size,
        font_family=text_font)
    if out_filename:
        plt.savefig(out_filename)
        nx.write_gml(G,'out.gml')
    plt.show()


def draw_table():

    # still an example!

    from ipyD3 import d3object
    from IPython.display import display

    d3 = d3object(width=800,
                  height=400,
                  style='JFTable',
                  number=1,
                  d3=None,
                  title='Example table with d3js',
                  desc='An example table created created with d3js with data generated with Python.')
    data = [
        [1277.0,
         654.0,
         288.0,
         1976.0,
         3281.0,
         3089.0,
         10336.0,
         4650.0,
         4441.0,
         4670.0,
         944.0,
         110.0],
        [1318.0,
         664.0,
         418.0,
         1952.0,
         3581.0,
         4574.0,
         11457.0,
         6139.0,
         7078.0,
         6561.0,
         2354.0,
         710.0],
        [1783.0,
         774.0,
         564.0,
         1470.0,
         3571.0,
         3103.0,
         9392.0,
         5532.0,
         5661.0,
         4991.0,
         2032.0,
         680.0],
        [1301.0,
         604.0,
         286.0,
         2152.0,
         3282.0,
         3369.0,
         10490.0,
         5406.0,
         4727.0,
         3428.0,
         1559.0,
         620.0],
        [1537.0,
         1714.0,
         724.0,
         4824.0,
         5551.0,
         8096.0,
         16589.0,
         13650.0,
         9552.0,
         13709.0,
         2460.0,
         720.0],
        [5691.0,
         2995.0,
         1680.0,
         11741.0,
         16232.0,
         14731.0,
         43522.0,
         32794.0,
         26634.0,
         31400.0,
         7350.0,
         3010.0],
        [1650.0,
         2096.0,
         60.0,
         50.0,
         1180.0,
         5602.0,
         15728.0,
         6874.0,
         5115.0,
         3510.0,
         1390.0,
         170.0],
        [72.0, 60.0, 60.0, 10.0, 120.0, 172.0, 1092.0, 675.0, 408.0, 360.0, 156.0, 100.0]]
    data = [list(i) for i in zip(*data)]
    sRows = [['January',
              'February',
              'March',
              'April',
              'May',
              'June',
              'July',
              'August',
              'September',
              'October',
              'November',
              'Deecember']]
    sColumns = [['Prod {0}'.format(i) for i in range(1, 9)],
                [None, '', None, None, 'Group 1', None, None, 'Group 2']]
    d3.addSimpleTable(data,
                      fontSizeCells=[12, ],
                      sRows=sRows,
                      sColumns=sColumns,
                      sRowsMargins=[5, 50, 0],
                      sColsMargins=[5, 20, 10],
                      spacing=0,
                      addBorders=1,
                      addOutsideBorders=-1,
                      rectWidth=45,
                      rectHeight=0
                      )
    html = d3.render(mode=['html', 'show'])
    display(html)