Beispiel #1
0
class BaseProgram(object):
    '''
        Base program which implements all possible steps of a force field
        fitting program. The actual sequence of the steps are defined in the
        deriving classes.
    '''
    def __init__(self, system, ai, **kwargs):
        '''
            **Arguments**

            system
                a Yaff `System` object defining the system

            ai
                a `Reference` instance corresponding to the ab initio input data

            **Keyword Arguments**

            ffrefs
                a list of `Reference` objects corresponding to a priori determined
                contributions to the force field (such as eg. electrostatics
                or van der Waals contributions)

            fn_yaff
                the name of the file to write the final parameters to in Yaff
                format. The default is `pars.txt`.

            fn_charmm22_prm
                the name of a CHARMM parameter file. If not given, the file is not written

            fn_charmm22_psf
                the name of a CHARMM topology file. If not given, the file is not written

            fn_sys
                the name of the file to write the system to. The default is
                `system.chk`.

            fn_traj
                a cPickle filename to read/write the perturbation trajectories
                from/to. If the file exists, the trajectories are read from the
                file. If the file does not exist, the trajectories are written
                to the file.

            only_traj
                specifier to determine for which terms a perturbation trajectory
                needs to be constructed. If ONLY_TRAJ is a single string, it is
                interpreted as a task (only terms that have this task in their
                tasks attribute will get a trajectory). If ONLY_TRAJ is a list
                of strings, each string is interpreted as the basename of the
                term for which a trajectory will be constructed.

            plot_traj
                if set to True, all energy contributions along each perturbation
                trajectory will be plotted using the final force field.

            xyz_traj
                if set to True, each perturbation trajectory will be written to
                an XYZ file.
        '''
        with log.section('PROG', 2, timer='Initializing'):
            log.dump('Initializing program')
            self.system = system
            self.ai = ai
            self.kwargs = kwargs
            self.valence = ValenceFF(system)
            self.perturbation = RelaxedStrain(system, self.valence)
            self.trajectories = None

    def reset_system(self):
        '''
            routine to reset the system coords to the ai equilbrium
        '''
        log.dump('Resetting system coordinates to ab initio ref')
        self.system.pos = self.ai.coords0.copy()
        self.valence.dlist.forward()
        self.valence.iclist.forward()

    def update_trajectory_terms(self):
        '''
            Routine to make ``self.valence.terms`` and the term attribute of each
            trajectory in ``self.trajectories`` consistent again. This is usefull
            if the trajectory were read from a file and the ``valenceFF`` instance
            was modified.
        '''
        log.dump('Updating terms of trajectories to current valenceFF terms')
        with log.section('PTUPD', 3):
            #update the terms in the trajectories to match the terms in
            #self.valence
            for traj in self.trajectories:
                found = False
                for term in self.valence.iter_terms():
                    if traj.term.get_atoms() == term.get_atoms():
                        if found:
                            raise ValueError(
                                'Found two terms for trajectory %s with atom indices %s'
                                % (traj.term.basename,
                                   str(traj.term.get_atoms())))
                        traj.term = term
                        if 'PT_ALL' not in term.tasks:
                            log.dump(
                                'PT_ALL not in tasks of %s-%i, deactivated PT'
                                % (term.basename, term.index))
                            traj.active = False
                        found = True
                if not found:
                    log.warning(
                        'No term found for trajectory %s with atom indices %s'
                        % (traj.term.basename, str(traj.term.get_atoms())))
            #check if every term with task PT_ALL has a trajectory associated
            #with it. It a trajectory is missing, generate it.
            for term in self.valence.iter_terms():
                if 'PT_ALL' not in term.tasks: continue
                found = False
                for traj in self.trajectories:
                    if term.get_atoms() == traj.term.get_atoms():
                        if found:
                            raise ValueError(
                                'Found two trajectories for term %s with atom indices %s'
                                % (term.basename, str(term.get_atoms())))
                        found = True
                if not found:
                    log.warning(
                        'No trajectory found for term %s with atom indices %s. Generating it now.'
                        % (term.basename, str(term.get_atoms())))
                    trajectory = self.perturbation.prepare([term])[term.index]
                    self.perturbation.generate(trajectory)
                    self.trajectories.append(trajectory)

    def average_pars(self):
        '''
            Average force field parameters over master and slaves.
        '''
        log.dump('Averaging force field parameters over master and slaves')
        for master in self.valence.iter_masters():
            npars = len(self.valence.get_params(master.index))
            pars = np.zeros([len(master.slaves) + 1, npars], float)
            pars[0, :] = np.array(self.valence.get_params(master.index))
            for i, islave in enumerate(master.slaves):
                pars[1 + i, :] = np.array(self.valence.get_params(islave))
            if master.kind == 0:  #harmonic
                fc, rv = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv)
            elif master.kind == 1:
                a0, a1, a2, a3 = pars.mean(axis=0)
                self.valence.set_params(master.index,
                                        a0=a0,
                                        a1=a1,
                                        a2=a2,
                                        a3=a3)
                for islave in master.slaves:
                    self.valence.set_params(islave, a0=a0, a1=a1, a2=a2, a3=a3)
            elif master.kind == 3:  #cross
                fc, rv0, rv1 = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv0, rv1=rv1)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv0, rv1=rv1)
            elif master.kind == 4:  #cosine
                assert pars[:,
                            0].std() < 1e-6, 'dihedral multiplicity not unique'
                m, fc, rv = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv, m=m)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv, m=m)
            else:
                raise NotImplementedError

    def make_output(self):
        '''
            Dump Yaff parameters, Yaff system, plot energy contributions along
            perturbation trajectories and dump perturbation trajectories to XYZ
            files.
        '''
        fn_yaff = self.kwargs.get('fn_yaff', None)
        if fn_yaff is None:
            fn_yaff = 'pars_cov%s.txt' % (self.kwargs.get('suffix', ''))
        self.valence.dump_yaff(fn_yaff)
        fn_charmm22_prm = self.kwargs.get('fn_charmm22_prm')
        if fn_charmm22_prm is not None:
            dump_charmm22_prm(self.valence, fn_charmm22_prm)
        fn_charmm22_psf = self.kwargs.get('fn_charmm22_psf')
        if fn_charmm22_psf is not None:
            dump_charmm22_psf(self.system, self.valence, fn_charmm22_psf)
        fn_sys = self.kwargs.get('fn_sys', None)
        if fn_sys is None:
            fn_sys = 'system%s.chk' % (self.kwargs.get('suffix', ''))
        self.system.to_file(fn_sys)
        self.plot_trajectories(do_valence=True)

    def plot_trajectories(self, do_valence=False):
        '''
            Plot energy contributions along perturbation trajectories and dump
            perturbation trajectories to XYZ files.
        '''
        only = self.kwargs.get('only_traj', 'PT_ALL')
        if not isinstance(only, list): only = [only]
        with log.section('PLOT', 3, timer='PT plot energy'):
            if self.kwargs.get('plot_traj', False):
                ffrefs = self.kwargs.get('ffrefs', [])
                valence = None
                if do_valence: valence = self.valence
                for trajectory in self.trajectories:
                    if trajectory is None: continue
                    for pattern in only:
                        if pattern == 'PT_ALL' or pattern in trajectory.term.basename:
                            trajectory.plot(self.ai,
                                            ffrefs=ffrefs,
                                            valence=valence)
        with log.section('XYZ', 3, timer='PT dump XYZ'):
            if self.kwargs.get('xyz_traj', False):
                for trajectory in self.trajectories:
                    if trajectory is None: continue
                    for pattern in only:
                        if pattern == 'PT_ALL' or pattern in trajectory.term.basename:
                            trajectory.to_xyz()

    def do_pt_generate(self):
        'Generate perturbation trajectories.'
        with log.section('PTGEN', 2, timer='PT Generate'):
            #read if an existing file was specified through fn_traj
            fn_traj = self.kwargs.get('fn_traj', None)
            if fn_traj is not None and os.path.isfile(fn_traj):
                self.trajectories = cPickle.load(open(fn_traj, 'r'))
                log.dump('Trajectories read from file %s' % fn_traj)
                self.update_trajectory_terms()
                newname = 'updated_' + fn_traj.split('/')[-1]
                cPickle.dump(self.trajectories, open(newname, 'w'))
                return
            #configure
            self.reset_system()
            only = self.kwargs.get('only_traj', 'PT_ALL')
            if isinstance(only, str):
                do_terms = [
                    term for term in self.valence.terms if only in term.tasks
                ]
            else:
                do_terms = []
                for pattern in only:
                    for term in self.valence.iter_terms(pattern):
                        do_terms.append(term)
            trajectories = self.perturbation.prepare(do_terms)
            #compute
            log.dump('Constructing trajectories')
            self.trajectories = paracontext.map(
                self.perturbation.generate,
                [traj for traj in trajectories if traj.active])
            #write the trajectories to the non-existing file fn_traj
            if fn_traj is not None:
                assert not os.path.isfile(fn_traj)
                cPickle.dump(self.trajectories, open(fn_traj, 'w'))
                log.dump('Trajectories stored to file %s' % fn_traj)

    def do_pt_estimate(self, do_valence=False, logger_level=3):
        '''
            Estimate force constants and rest values from the perturbation
            trajectories

            **Optional Arguments**

            do_valence
                if set to True, the current valence force field will be used to
                estimate the contribution of all other valence terms.
        '''
        with log.section('PTEST', 2, timer='PT Estimate'):
            self.reset_system()
            message = 'Estimating FF parameters from perturbation trajectories'
            if do_valence: message += ' with valence reference'
            log.dump(message)
            ffrefs = self.kwargs.get('ffrefs', [])
            #compute fc and rv from trajectory
            for traj in self.trajectories:
                if traj is None: continue
                self.perturbation.estimate(traj,
                                           self.ai,
                                           ffrefs=ffrefs,
                                           do_valence=do_valence)
            #set force field parameters to computed fc and rv
            for traj in self.trajectories:
                if traj is None: continue
                self.valence.set_params(traj.term.index,
                                        fc=traj.fc,
                                        rv0=traj.rv)
            #output
            self.valence.dump_logger(print_level=logger_level)
            #do not add average here since the fluctuation on the parameters is
            #required for do_pt_postprocess. Average will be done at the end of
            #do_pt_postprocess

    def do_pt_postprocess(self):
        '''
            Do some first post processing of the ff parameters estimated from
            the perturbation trajectories including:

                * detecting bend patterns with rest values of 90 and 180 deg
                * detecting bend patterns with rest values only close to 180 deg
                * averaging parameters
        '''
        with log.section('PTPOST', 2, timer='PT Post process'):
            self.do_squarebend()
            self.do_bendcharm()
            #self.do_sqoopdist_to_oopdist()
            self.average_pars()

    def do_eq_setrv(self, tasks, logger_level=3):
        '''
            Set the rest values to their respective AI equilibrium values.
        '''
        with log.section('EQSET', 2, timer='Equil Set RV'):
            self.reset_system()
            log.dump(
                'Setting rest values to AI equilibrium values for tasks %s' %
                ' '.join(tasks))
            for term in self.valence.terms:
                vterm = self.valence.vlist.vtab[term.index]
                if np.array([task in term.tasks for task in tasks]).any():
                    if term.kind == 3:  #cross term
                        ic0 = self.valence.iclist.ictab[vterm['ic0']]
                        ic1 = self.valence.iclist.ictab[vterm['ic1']]
                        self.valence.set_params(term.index,
                                                rv0=ic0['value'],
                                                rv1=ic1['value'])
                    elif term.kind == 4 and term.ics[
                            0].kind == 4:  #Cosine of DihedAngle
                        ic = self.valence.iclist.ictab[vterm['ic0']]
                        m = self.valence.get_params(term.index, only='m')
                        rv = ic['value'] % (360.0 * deg / m)
                        with log.section('EQSET', 4, timer='Equil Set RV'):
                            log.dump(
                                'Set rest value of %s(%s) (eq=%.3f deg) to %.3f deg'
                                % (term.basename, '.'.join([
                                    str(at) for at in term.get_atoms()
                                ]), ic['value'] / deg, rv / deg))
                        self.valence.set_params(term.index, rv0=rv)
                    else:
                        rv = self.valence.iclist.ictab[vterm['ic0']]['value']
                        self.valence.set_params(term.index, rv0=rv)
            self.valence.dump_logger(print_level=logger_level)
            self.average_pars()

    def do_hc_estimatefc(self, tasks, logger_level=3):
        '''
            Refine force constants using Hessian Cost function.

            **Arguments**

            tasks
                A list of strings identifying which terms should have their
                force constant estimated from the hessian cost function. Using
                such a flag, one can distinguish between for example force
                constant refinement (flag=HC_FC_DIAG) of the diagonal terms and
                force constant estimation of the cross terms (flag=HC_FC_CROSS).
                If the string 'all' is present in tasks, all fc's will be
                estimated.

            **Optional Arguments**

            logger_level
                print level at which the resulting parameters should be dumped to
                the logger. By default, the parameters will only be dumped at
                the highest log level.
        '''
        with log.section('HCEST', 2, timer='HC Estimate FC'):
            self.reset_system()
            log.dump(
                'Estimating force constants from Hessian cost for tasks %s' %
                ' '.join(tasks))
            ffrefs = self.kwargs.get('ffrefs', [])
            term_indices = []
            for index in xrange(self.valence.vlist.nv):
                term = self.valence.terms[index]
                flagged = False
                for flag in tasks:
                    if flag in term.tasks:
                        flagged = True
                        break
                if flagged:
                    #first check if all rest values and multiplicities have been defined
                    if term.kind == 0: self.valence.check_params(term, ['rv'])
                    if term.kind == 1:
                        self.valence.check_params(term,
                                                  ['a0', 'a1', 'a2', 'a3'])
                    if term.kind == 3:
                        self.valence.check_params(term, ['rv0', 'rv1'])
                    if term.kind == 4:
                        self.valence.check_params(term, ['rv', 'm'])
                    if term.is_master():
                        term_indices.append(index)
                else:
                    #first check if all pars have been defined
                    if term.kind == 0:
                        self.valence.check_params(term, ['fc', 'rv'])
                    if term.kind == 1:
                        self.valence.check_params(term,
                                                  ['a0', 'a1', 'a2', 'a3'])
                    if term.kind == 3:
                        self.valence.check_params(term, ['fc', 'rv0', 'rv1'])
                    if term.kind == 4:
                        self.valence.check_params(term, ['fc', 'rv', 'm'])
            cost = HessianFCCost(self.system,
                                 self.ai,
                                 self.valence,
                                 term_indices,
                                 ffrefs=ffrefs)
            fcs = cost.estimate()
            for index, fc in zip(term_indices, fcs):
                master = self.valence.terms[index]
                assert master.is_master()
                self.valence.set_params(index, fc=fc)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc)
            self.valence.dump_logger(print_level=logger_level)

    def do_cross_init(self):
        '''
            Set the rest values of cross terms to the rest values of the
            corresponding diagonal terms. The force constants are initialized
            to zero.
        '''
        with log.section('VAL', 2, 'Initializing'):
            self.reset_system()
            self.valence.init_cross_terms()
            for index in xrange(self.valence.vlist.nv):
                term = self.valence.vlist.vtab[index]
                if term['kind'] != 3: continue
                rv0, rv1 = None, None
                for index2 in xrange(self.valence.vlist.nv):
                    term2 = self.valence.vlist.vtab[index2]
                    if term2['kind'] == 3: continue
                    if term['ic0'] == term2['ic0']:
                        assert rv0 is None
                        rv0 = self.valence.get_params(index2, only='rv')
                    if term['ic1'] == term2['ic0']:
                        assert rv1 is None
                        rv1 = self.valence.get_params(index2, only='rv')
                if rv0 is None or rv1 is None:
                    raise ValueError('No rest values found for %s' %
                                     self.valence.terms[index].basename)
                self.valence.set_params(index, fc=0.0, rv0=rv0, rv1=rv1)

    def do_squarebend(self, thresshold=10 * deg):
        '''
            Identify bend patterns in which 4 atoms of type A surround a central
            atom of type B with A-B-A angles of 90/180 degrees. A simple
            harmonic pattern will not be adequate since a rest value of 90 and
            180 degrees is possible for the same A-B-A term. Therefore, a
            cosine term with multiplicity of 4 is used:

                  V = K/2*[1-cos(4*theta)]

            To identify the patterns, it is assumed that the rest values have
            already been estimated from the perturbation trajectories. For each
            master and slave of a BENDAHARM term, its rest value is computed and
            checked if it lies either the interval [90-thresshold,90+thresshold]
            or [180-thresshold,180]. If this is the case, the new cosine term
            is used.

            **Optional arguments**

            thresshold
                the (half) the width of the interval around 180 deg (90 degrees)
                to check if a square BA4
        '''
        for master in self.valence.iter_masters(label='BendAHarm'):
            rvs = np.zeros([len(master.slaves) + 1], float)
            rvs[0] = self.valence.get_params(master.index, only='rv')
            for i, islave in enumerate(master.slaves):
                rvs[1 + i] = self.valence.get_params(islave, only='rv')
            n90 = 0
            n180 = 0
            nother = 0
            for i, rv in enumerate(rvs):
                if 90 * deg - thresshold <= rv and rv <= 90 * deg + thresshold:
                    n90 += 1
                elif 180 * deg - thresshold <= rv and rv <= 180 * deg + thresshold:
                    n180 += 1
                else:
                    nother += 1
            if n90 > 0 and n180 > 0:
                log.dump(
                    '%s has rest values around 90 deg and 180 deg, converted to BendCos with m=4'
                    % master.basename)
                #modify master and slaves
                indices = [master.index]
                for slave in master.slaves:
                    indices.append(slave)
                for index in indices:
                    term = self.valence.terms[index]
                    self.valence.modify_term(
                        index, Cosine, [BendAngle(*term.get_atoms())],
                        term.basename.replace('BendAHarm', 'BendCos'),
                        ['HC_FC_DIAG'], ['au', 'kjmol', 'deg'])
                    self.valence.set_params(index, rv0=0.0, m=4)
                    for traj in self.trajectories:
                        if traj.term.index == index:
                            traj.active = False
                            traj.fc = None
                            traj.rv = None

    def do_bendcharm(self, thresshold=2 * deg):
        '''
            No Harmonic bend can have a rest value equal to are large than
            180 deg - thresshold. If a master (or its slaves) has such a rest
            value, convert master and all slaves to BendCharm with
            cos(phis0)=-1.
        '''
        for master in self.valence.iter_masters(label='BendAHarm'):
            indices = [master.index]
            for slave in master.slaves:
                indices.append(slave)
            found = False
            for index in indices:
                rv = self.valence.get_params(index, only='rv')
                if rv >= 180.0 * deg - thresshold:
                    found = True
                    break
            if found:
                log.dump(
                    '%s has rest value > 180-%.0f deg, converted to BendCHarm with cos(phi0)=-1'
                    % (master.basename, thresshold / deg))
                for index in indices:
                    term = self.valence.terms[index]
                    self.valence.modify_term(
                        index, Harmonic, [BendCos(*term.get_atoms())],
                        term.basename.replace('BendAHarm', 'BendCHarm'),
                        ['HC_FC_DIAG'], ['kjmol', 'au'])
                    self.valence.set_params(index, fc=0.0, rv0=-1.0)
                    for traj in self.trajectories:
                        if traj.term.index == index:
                            traj.rv = None
                            traj.fc = None
                            traj.active = False

    def do_sqoopdist_to_oopdist(self, thresshold=1e-4 * angstrom):
        '''
            Transform a SqOopdist term with a rest value that has been set to
            zero, to a term Oopdist (harmonic in Oopdist instead of square of
            Oopdist) with a rest value of 0.0 A.
        '''
        for master in self.valence.iter_masters(label='SqOopdist'):
            indices = [master.index]
            for slave in master.slaves:
                indices.append(slave)
            found = False
            for index in indices:
                rv = self.valence.get_params(index, only='rv')
                if rv <= thresshold:
                    found = True
                    break
            if found:
                log.dump(
                    '%s has rest value <= %.0f A^2, converted to Oopdist with d0=0'
                    % (master.basename, thresshold / angstrom))
                for index in indices:
                    term = self.valence.terms[index]
                    self.valence.modify_term(
                        index, Harmonic, [OopDist(*term.get_atoms())],
                        term.basename.replace('SqOopdist', 'Oopdist'),
                        ['HC_FC_DIAG'], ['kjmol/A**2', 'A'])
                    self.valence.set_params(index, fc=0.0, rv0=0.0)

    def run(self):
        '''
            Sequence of instructions, should be implemented in the inheriting
            classes. The various inheriting classes distinguish themselves by
            means of the instructions implemented in this routine.
        '''
        raise NotImplementedError
Beispiel #2
0
class BaseProgram(object):
    '''
        Base program which implements all possible steps of a force field
        fitting program. The actual sequence of the steps are defined in the
        deriving classes.
    '''
    def __init__(self, system, ai, settings, ffrefs=[]):
        '''
            **Arguments**

            system
                a Yaff `System` instance defining the system

            ai
                a `Reference` instance corresponding to the ab initio input data

            settings
                a `Settings` instance defining all QuickFF settings

            **Optional Arguments**

            ffrefs
                a list of `Reference` instances defining the a-priori force
                field contributions.
        '''
        with log.section('INIT', 1, timer='Initializing'):
            log.dump('Initializing program')
            self.settings = settings
            self.system = system
            self.ai = ai
            self.ffrefs = ffrefs
            self.valence = ValenceFF(system, settings)
            self.perturbation = RelaxedStrain(system, self.valence, settings)
            self.trajectories = None
            self.print_system()

    def print_system(self):
        '''
            dump overview of atoms (and associated parameters) in the system
        '''
        with log.section('SYS', 3, timer='Initializing'):
            log.dump('Atomic configuration of the system:')
            log.dump('')
            log.dump('  index  |  x [A]  |  y [A]  |  z [A]  | ffatype |    q    |  R [A]  ')
            log.dump('---------------------------------------------------------------------')
            for i in range(len(self.system.numbers)):
                x, y, z = self.system.pos[i,0], self.system.pos[i,1], self.system.pos[i,2]
                if self.system.charges is not None:
                    q = self.system.charges[i]
                else:
                    q = np.nan
                if self.system.radii is not None:
                    R = self.system.radii[i]
                else:
                    R = np.nan
                log.dump('  %4i   | % 7.3f | % 7.3f | % 7.3f |  %6s | % 7.3f | % 7.3f ' %(
                    i, x/angstrom, y/angstrom, z/angstrom,
                    self.system.ffatypes[self.system.ffatype_ids[i]],
                    q, R/angstrom
                ))


    def reset_system(self):
        '''
            routine to reset the system coords to the ai equilbrium
        '''
        log.dump('Resetting system coordinates to ab initio ref')
        self.system.pos = self.ai.coords0.copy()
        self.valence.dlist.forward()
        self.valence.iclist.forward()


    def update_trajectory_terms(self):
        '''
            Routine to make ``self.valence.terms`` and the term attribute of each
            trajectory in ``self.trajectories`` consistent again. This is usefull
            if the trajectory were read from a file and the ``valenceFF`` instance
            was modified.
        '''
        log.dump('Updating terms of trajectories to current valenceFF terms')
        with log.section('PTUPD', 3):
            #update the terms in the trajectories to match the terms in
            #self.valence
            for traj in self.trajectories:
                found = False
                for term in self.valence.iter_terms():
                    if traj.term.get_atoms()==term.get_atoms():
                        if found: raise ValueError('Found two terms for trajectory %s with atom indices %s' %(traj.term.basename, str(traj.term.get_atoms())))
                        traj.term = term
                        if 'PT_ALL' not in term.tasks:
                            log.dump('PT_ALL not in tasks of %s-%i, deactivated PT' %(term.basename, term.index))
                            traj.active = False
                        found = True
                if not found:
                    log.warning('No term found for trajectory %s with atom indices %s, deactivating trajectory' %(traj.term.basename, str(traj.term.get_atoms())))
                    traj.active = False
            #check if every term with task PT_ALL has a trajectory associated
            #with it. It a trajectory is missing, generate it.
            for term in self.valence.iter_terms():
                if 'PT_ALL' not in term.tasks: continue
                found = False
                for traj in self.trajectories:
                    if term.get_atoms()==traj.term.get_atoms():
                        if found: raise ValueError('Found two trajectories for term %s with atom indices %s' %(term.basename, str(term.get_atoms())))
                        found =True
                if not found:
                    log.warning('No trajectory found for term %s with atom indices %s. Generating it now.' %(term.basename, str(term.get_atoms())))
                    trajectory = self.perturbation.prepare([term])[term.index]
                    self.perturbation.generate(trajectory)
                    self.trajectories.append(trajectory)

    def average_pars(self):
        '''
            Average force field parameters over master and slaves.
        '''
        log.dump('Averaging force field parameters over master and slaves')
        for master in self.valence.iter_masters():
            npars = len(self.valence.get_params(master.index))
            pars = np.zeros([len(master.slaves)+1, npars], float)
            pars[0,:] = np.array(self.valence.get_params(master.index))
            for i, islave in enumerate(master.slaves):
                pars[1+i,:] = np.array(self.valence.get_params(islave))
            if master.kind in [0,2,11,12]:#harmonic,fues,MM3Quartic,MM3Bend
                fc, rv = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv)
            elif master.kind==1:
                a0, a1, a2, a3 = pars.mean(axis=0)
                self.valence.set_params(master.index, a0=a0, a1=a1, a2=a2, a3=a3)
                for islave in master.slaves:
                    self.valence.set_params(islave, a0=a0, a1=a1, a2=a2, a3=a3)
            elif master.kind==3:#cross
                fc, rv0, rv1 = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv0, rv1=rv1)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv0, rv1=rv1)
            elif master.kind==4:#cosine
                assert pars[:,0].std()<1e-6, 'dihedral multiplicity not unique'
                m, fc, rv = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv, m=m)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv, m=m)
            elif master.kind in [5, 6, 7, 8, 9]:#chebychev
                assert pars.shape[1]==2
                fc = pars[:,0].mean()
                self.valence.set_params(master.index, fc=fc)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc)
            else:
                raise NotImplementedError

    def make_output(self):
        '''
            Dump Yaff parameters, Yaff system, plot energy contributions along
            perturbation trajectories and dump perturbation trajectories to XYZ
            files.
        '''
        if self.settings.fn_yaff is not None:
            dump_yaff(self.valence, self.settings.fn_yaff)
        if self.settings.fn_charmm22_prm is not None:
            dump_charmm22_prm(self.valence, self.settings.fn_charmm22_prm)
        if self.settings.fn_charmm22_psf is not None:
            dump_charmm22_psf(self.system, self.valence, self.settings.fn_charmm22_psf)
        if self.settings.fn_sys is not None:
            self.system.to_file(self.settings.fn_sys)
        if self.settings.plot_traj is not None and self.settings.plot_traj.lower() in ['Ehc3', 'final', 'all']:
            self.plot_trajectories(do_valence=True, suffix='_Ehc3')
        if self.settings.xyz_traj:
            self.write_trajectories()

    def plot_trajectories(self, do_valence=False, suffix=''):
        '''
            Plot energy contributions along perturbation trajectories and
        '''
        only = self.settings.only_traj
        if not isinstance(only, list): only = [only]
        with log.section('PLOT', 3, timer='PT plot energy'):
            valence = None
            if do_valence: valence=self.valence
            for trajectory in self.trajectories:
                if trajectory is None: continue
                for pattern in only:
                    if pattern=='PT_ALL' or pattern in trajectory.term.basename:
                        log.dump('Plotting trajectory for %s' %trajectory.term.basename)
                        trajectory.plot(self.ai, ffrefs=self.ffrefs, valence=valence, suffix=suffix)

    def write_trajectories(self):
        '''
            Write perturbation trajectories to XYZ files.
        '''
        only = self.settings.only_traj
        if not isinstance(only, list): only = [only]
        with log.section('XYZ', 3, timer='PT dump XYZ'):
            for trajectory in self.trajectories:
                if trajectory is None: continue
                for pattern in only:
                    if pattern=='PT_ALL' or pattern in trajectory.term.basename:
                        log.dump('Writing XYZ trajectory for %s' %trajectory.term.basename)
                        trajectory.to_xyz()

    def do_pt_generate(self):
        '''
            Generate perturbation trajectories.
        '''
        with log.section('PTGEN', 2, timer='PT Generate'):
            #read if an existing file was specified through fn_traj
            fn_traj = self.settings.fn_traj
            if fn_traj is not None and os.path.isfile(fn_traj):
                self.trajectories = pickle.load(open(fn_traj, 'rb'))
                log.dump('Trajectories read from file %s' %fn_traj)
                self.update_trajectory_terms()
                newname = 'updated_'+fn_traj.split('/')[-1]
                pickle.dump(self.trajectories, open(newname, 'wb'))
                return
            #configure
            self.reset_system()
            only = self.settings.only_traj
            if only is None or only=='PT_ALL' or only=='pt_all':
                do_terms = [term for term in self.valence.terms if term.kind in [0,2,11,12]]
            else:
                if isinstance(only, str): only = [only]
                do_terms = []
                for pattern in only:
                    for term in self.valence.iter_terms(pattern):
                        if term.kind in [0,2,11,12]:
                            do_terms.append(term)
            trajectories = self.perturbation.prepare(do_terms)
            #compute
            log.dump('Constructing trajectories')
            self.trajectories = paracontext.map(self.perturbation.generate, [traj for traj in trajectories if traj.active])
            #write the trajectories to the non-existing file fn_traj
            if fn_traj is not None:
                assert not os.path.isfile(fn_traj)
                pickle.dump(self.trajectories, open(fn_traj, 'wb'))
                log.dump('Trajectories stored to file %s' %fn_traj)

    def do_pt_estimate(self, do_valence=False, energy_noise=None, logger_level=3):
        '''
            Estimate force constants and rest values from the perturbation
            trajectories

            **Optional Arguments**

            do_valence
                if set to True, the current valence force field will be used to
                estimate the contribution of all other valence terms.
        '''
        with log.section('PTEST', 2, timer='PT Estimate'):
            self.reset_system()
            message = 'Estimating FF parameters from perturbation trajectories'
            if do_valence: message += ' with valence reference'
            log.dump(message)
            #compute fc and rv from trajectory
            only = self.settings.only_traj
            for traj in self.trajectories:
                if traj is None: continue
                if not (only is None or only=='PT_ALL' or only=='pt_all'):
                    if isinstance(only, str): only = [only]
                    basename = self.valence.terms[traj.term.master].basename
                    if basename not in only: continue
                self.perturbation.estimate(traj, self.ai, ffrefs=self.ffrefs, do_valence=do_valence, energy_noise=energy_noise)
            #set force field parameters to computed fc and rv
            for traj in self.trajectories:
                if traj is None: continue
                if not (only is None or only=='PT_ALL' or only=='pt_all'):
                    if isinstance(only, str): only = [only]
                    basename = self.valence.terms[traj.term.master].basename
                    if basename not in only: continue
                self.valence.set_params(traj.term.index, fc=traj.fc, rv0=traj.rv)
            #output
            self.valence.dump_logger(print_level=logger_level)
            #do not add average here since the fluctuation on the parameters is
            #required for do_pt_postprocess. Average will be done at the end of
            #do_pt_postprocess

    def do_pt_postprocess(self):
        '''
            Do some first post processing of the ff parameters estimated from
            the perturbation trajectories including:

                * detecting bend patterns with rest values of 90 and 180 deg
                * detecting bend patterns with rest values only close to 180 deg
                * transforming SqOopDist with rv=0.0 to OopDist
                * averaging parameters
        '''
        with log.section('PTPOST', 2, timer='PT Post process'):
            if self.settings.do_squarebend:
                self.do_squarebend()
            if self.settings.do_bendclin:
                self.do_bendclin()
            if self.settings.do_sqoopdist_to_oopdist:
                self.do_sqoopdist_to_oopdist()
            self.average_pars()

    def do_eq_setrv(self, tasks, logger_level=3):
        '''
            Set the rest values to their respective AI equilibrium values.
        '''
        with log.section('EQSET', 2, timer='Equil Set RV'):
            self.reset_system()
            log.dump('Setting rest values to AI equilibrium values for tasks %s' %' '.join(tasks))
            for term in self.valence.terms:
                vterm = self.valence.vlist.vtab[term.index]
                if np.array([task in term.tasks for task in tasks]).any():
                    if term.kind==3:#cross term
                        ic0 = self.valence.iclist.ictab[vterm['ic0']]
                        ic1 = self.valence.iclist.ictab[vterm['ic1']]
                        self.valence.set_params(term.index, rv0=ic0['value'], rv1=ic1['value'])
                    elif term.kind==4 and term.ics[0].kind==4:#Cosine of DihedAngle
                        ic = self.valence.iclist.ictab[vterm['ic0']]
                        m = self.valence.get_params(term.index, only='m')
                        rv = ic['value']%(360.0*deg/m)
                        with log.section('EQSET', 4, timer='Equil Set RV'):
                            log.dump('Set rest value of %s(%s) (eq=%.3f deg) to %.3f deg' %(
                                term.basename,
                                '.'.join([str(at) for at in term.get_atoms()]),
                                ic['value']/deg, rv/deg
                            ))
                        self.valence.set_params(term.index, rv0=rv)
                    else:
                        rv = self.valence.iclist.ictab[vterm['ic0']]['value']
                        self.valence.set_params(term.index, rv0=rv)
            self.valence.dump_logger(print_level=logger_level)
            self.average_pars()

    def do_hc_estimatefc(self, tasks, logger_level=3, do_svd=False, svd_rcond=0.0, do_mass_weighting=True):
        '''
            Refine force constants using Hessian Cost function.

            **Arguments**

            tasks
                A list of strings identifying which terms should have their
                force constant estimated from the hessian cost function. Using
                such a flag, one can distinguish between for example force
                constant refinement (flag=HC_FC_DIAG) of the diagonal terms and
                force constant estimation of the cross terms (flag=HC_FC_CROSS).
                If the string 'all' is present in tasks, all fc's will be
                estimated.

            **Optional Arguments**

            logger_level
                print level at which the resulting parameters should be dumped to
                the logger. By default, the parameters will only be dumped at
                the highest log level.

            do_svd
                whether or not to do an SVD decomposition before solving the
                set of equations and explicitly throw out the degrees of
                freedom that correspond to the lowest singular values.

            do_mass_weighting
                whether or not to apply mass weighing to the ab initio hessian
                and the force field contributions before doing the fitting.
        '''
        with log.section('HCEST', 2, timer='HC Estimate FC'):
            self.reset_system()
            log.dump('Estimating force constants from Hessian cost for tasks %s' %' '.join(tasks))
            term_indices = []
            for index in range(self.valence.vlist.nv):
                term = self.valence.terms[index]
                flagged = False
                for flag in tasks:
                    if flag in term.tasks:
                        flagged = True
                        break
                if flagged:
                    #first check if all rest values and multiplicities have been defined
                    if term.kind==0: self.valence.check_params(term, ['rv'])
                    if term.kind==1: self.valence.check_params(term, ['a0', 'a1', 'a2', 'a3'])
                    if term.kind==3: self.valence.check_params(term, ['rv0','rv1'])
                    if term.kind==4: self.valence.check_params(term, ['rv', 'm'])
                    if term.is_master():
                        term_indices.append(index)
                else:
                    #first check if all pars have been defined
                    if term.kind==0: self.valence.check_params(term, ['fc', 'rv'])
                    if term.kind==1: self.valence.check_params(term, ['a0', 'a1', 'a2', 'a3'])
                    if term.kind==3: self.valence.check_params(term, ['fc', 'rv0','rv1'])
                    if term.kind==4: self.valence.check_params(term, ['fc', 'rv', 'm'])
            if len(term_indices)==0:
                log.dump('No terms (with task in %s) found to estimate FC from HC' %(str(tasks)))
                return
            # Try to estimate force constants; if the remove_dysfunctional_cross
            # keyword is True, a loop is performed which checks whether there
            # are cross terms for which corresponding diagonal terms have zero
            # force constants. If this is the case, those cross terms are removed
            # from the fit and we try again until such cases do no longer occur
            max_iter = 100
            niter = 0
            while niter<max_iter:
                cost = HessianFCCost(self.system, self.ai, self.valence, term_indices, ffrefs=self.ffrefs, do_mass_weighting=do_mass_weighting)
                fcs = cost.estimate(do_svd=do_svd, svd_rcond=svd_rcond)
                # No need to continue, if cross terms with corresponding diagonal
                # terms with negative force constants are allowed
                if self.settings.remove_dysfunctional_cross is False: break
                to_remove = []
                for index, fc in zip(term_indices, fcs):
                    term = self.valence.terms[index]
                    if term.basename.startswith('Cross'):
                        # Find force constants of corresponding diagonal terms
                        diag_fcs = np.zeros((2))
                        for idiag in range(2):
                            diag_index = term.diag_term_indexes[idiag]
                            if diag_index in term_indices:
                                fc_diag = fcs[term_indices.index(diag_index)]
                            else:
                                fc_diag = self.valence.get_params(diag_index, only='fc')
                            diag_fcs[idiag] = fc_diag
                        # If a force constant from any corresponding diagonal term is negative,
                        # we remove the cross term for the next iteration
                        if np.any(diag_fcs<=0.0):
                            to_remove.append(index)
                            self.valence.set_params(index, fc=0.0)
                            log.dump('WARNING! Dysfunctional cross term %s detected, removing from the hessian fit.'%term.basename)
                if len(to_remove)==0: break
                else:
                    for index in to_remove:
                        term_indices.remove(index)
                niter += 1
            assert niter<max_iter, "Could not remove all dysfunctional cross terms in %d iterations, something is seriously wrong"%max_iter
            for index, fc in zip(term_indices, fcs):
                master = self.valence.terms[index]
                assert master.is_master()
                self.valence.set_params(index, fc=fc)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc)
            self.valence.dump_logger(print_level=logger_level)

    def do_cross_init(self):
        '''
            Add cross terms to the valence list and initialize parameters.
        '''
        with log.section('VAL', 2, 'Initializing'):
            self.reset_system()
            self.valence.init_cross_angle_terms()
            if self.settings.do_cross_DSS or self.settings.do_cross_DSD or self.settings.do_cross_DAD or self.settings.do_cross_DAA:
                self.valence.init_cross_dihed_terms()
            self.update_cross_pars()

    def update_cross_pars(self):
        '''
            Set the rest values of cross terms to the rest values of the
            corresponding diagonal terms. Set the force constants to zero.
        '''
        with log.section('VAL', 2, 'Initializing'):
            def find_rest_value(iterm):
                term = self.valence.terms[iterm]
                if term.basename.startswith('TorsCheby') or term.basename.startswith('BendCheby'):
                    return -self.valence.get_params(iterm, only='sign')
                else:
                    return self.valence.get_params(iterm, only='rv')

            # Bond-Bond Cross terms
            cases = [('Cross','bb',3),('Cross','b0a',3),('Cross','b1a',3)]

            # Bond-Dihedral Cross terms
            for m in [1,2,3,4,6]:
                for suffix in ['bb','b0d','b1d','b2d']:
                    case = ('CrossBondDih%i'%m,suffix,4)
                    cases.append(case)

            # Angle-Dihedral Cross terms
            for m in [1,2,3,4,6]:
                for suffix in ['aa','a0d','a1d']:
                    case = ('CrossBendDih%i'%m,suffix,4)
                    cases.append(case)

                for suffix in ['a0d','a1d']:
                    case = ('CrossCBendDih%i'%m,suffix,4)
                    cases.append(case)

            # Loop over all cases
            for prefix, suffix, ntypes in cases:
                # Loop over all cross terms belonging to this case
                for term in self.valence.iter_masters('^%s/.*/%s$'%(prefix,suffix), use_re=True):
                    types = term.basename.split('/')[1].split('.')
                    assert len(types)==ntypes, 'Found cross term with %d atom types, expected %d'%(len(types),ntype)
                    rv0 = find_rest_value(term.diag_term_indexes[0])
                    rv1 = find_rest_value(term.diag_term_indexes[1])
                    self.valence.set_params(term.index, fc=0.0, rv0=rv0, rv1=rv1)
                    for index in term.slaves: self.valence.set_params(index, fc=0.0, rv0=rv0, rv1=rv1)

    def do_squarebend(self, thresshold=20*deg):
        '''
            Identify bend patterns in which 4 atoms of type A surround a central
            atom of type B with A-B-A angles of 90/180 degrees. A simple
            harmonic pattern will not be adequate since a rest value of 90 and
            180 degrees is possible for the same A-B-A term. Therefore, a
            cosine term with multiplicity of 4 is used (which corresponds to a
            chebychev4 potential with sign=-1):

                  V = K/2*[1-cos(4*theta)]

            To identify the patterns, it is assumed that the rest values have
            already been estimated from the perturbation trajectories. For each
            master and slave of a BENDAHARM term, its rest value is computed and
            checked if it lies either the interval [90-thresshold,90+thresshold]
            or [180-thresshold,180]. If this is the case, the new cosine term
            is used.

            **Optional arguments**

            thresshold
                the (half) the width of the interval around 180 deg (90 degrees)
                to check if a square BA4
        '''
        for master in self.valence.iter_masters(label='BendAHarm'):
            rvs = np.zeros([len(master.slaves)+1], float)
            rvs[0] = self.valence.get_params(master.index, only='rv')
            for i, islave in enumerate(master.slaves):
                rvs[1+i] = self.valence.get_params(islave, only='rv')
            n90 = 0
            n180 = 0
            nother = 0
            for i, rv in enumerate(rvs):
                if 90*deg-thresshold<=rv and rv<=90*deg+thresshold: n90 += 1
                elif 180*deg-thresshold<=rv and rv<=180*deg+thresshold: n180 += 1
                else: nother += 1
            if n90>0 and n180>0:
                log.dump('%s has rest values around 90 deg and 180 deg, converted to BendCheby4' %master.basename)
                #modify master and slaves
                indices = [master.index]
                for slave in master.slaves: indices.append(slave)
                for index in indices:
                    term = self.valence.terms[index]
                    self.valence.modify_term(
                        index,
                        Chebychev4, [BendCos(*term.get_atoms())],
                        term.basename.replace('BendAHarm', 'BendCheby4'),
                        ['HC_FC_DIAG'], ['kjmol', 'au']
                    )
                    self.valence.set_params(index, sign=-1)
                    for traj in self.trajectories:
                        if traj.term.index==index:
                            traj.active = False
                            traj.fc = None
                            traj.rv = None

    def do_bendclin(self, thresshold=5*deg):
        '''
            No Harmonic bend can have a rest value equal to are large than
            180 deg - thresshold. If a master (or its slaves) has such a rest
            value, convert master and all slaves to BendCLin (which corresponds
            to a chebychev1 potential with sign=+1):

                0.5*K*[1+cos(theta)]
        '''
        for master in self.valence.iter_masters(label='BendAHarm'):
            indices = [master.index]
            for slave in master.slaves: indices.append(slave)
            found = False
            for index in indices:
                rv = self.valence.get_params(index, only='rv')
                if rv>=180.0*deg-thresshold:
                    found = True
                    break
            if found:
                log.dump('%s has rest value > 180-%.0f deg, converted to BendCheby1' %(master.basename, thresshold/deg))
                for index in indices:
                    term = self.valence.terms[index]
                    self.valence.modify_term(
                        index,
                        Chebychev1, [BendCos(*term.get_atoms())],
                        term.basename.replace('BendAHarm', 'BendCheby1'),
                        ['HC_FC_DIAG'], ['kjmol', 'au']
                    )
                    self.valence.set_params(index, fc=0.0, sign=1.0)
                    for traj in self.trajectories:
                        if traj.term.index==index:
                            traj.rv = None
                            traj.fc = None
                            traj.active = False

    def do_sqoopdist_to_oopdist(self, thresshold=1e-4*angstrom):
        '''
            Transform a SqOopdist term with a rest value that has been set to
            zero, to a term Oopdist (harmonic in Oopdist instead of square of
            Oopdist) with a rest value of 0.0 A.
        '''
        for master in self.valence.iter_masters(label='SqOopdist'):
            indices = [master.index]
            for slave in master.slaves: indices.append(slave)
            found = False
            for index in indices:
                rv = self.valence.get_params(index, only='rv')
                if rv<=thresshold:
                    found = True
                    break
            if found:
                log.dump('%s has rest value <= %.0f A^2, converted to Oopdist with d0=0' %(master.basename, thresshold/angstrom))
                for index in indices:
                    term = self.valence.terms[index]
                    self.valence.modify_term(
                        index,
                        Harmonic, [OopDist(*term.get_atoms())],
                        term.basename.replace('SqOopdist', 'Oopdist'),
                        ['HC_FC_DIAG'], ['kjmol/A**2', 'A']
                    )
                    self.valence.set_params(index, fc=0.0, rv0=0.0)

    def run(self):
        '''
            Sequence of instructions, should be implemented in the inheriting
            classes. The various inheriting classes distinguish themselves by
            means of the instructions implemented in this routine.
        '''
        raise NotImplementedError
Beispiel #3
0
class BaseProgram(object):
    '''
        Base program which implements all possible steps of a force field
        fitting program. The actual sequence of the steps are defined in the
        deriving classes.
    '''
    def __init__(self, system, ai, settings, ffrefs=[]):
        '''
            **Arguments**

            system
                a Yaff `System` instance defining the system

            ai
                a `Reference` instance corresponding to the ab initio input data

            settings
                a `Settings` instance defining all QuickFF settings

            **Optional Arguments**

            ffrefs
                a list of `Reference` instances defining the a-priori force
                field contributions.
        '''
        with log.section('INIT', 1, timer='Initializing'):
            log.dump('Initializing program')
            self.settings = settings
            self.system = system
            self.ai = ai
            self.ffrefs = ffrefs
            self.valence = ValenceFF(system, settings)
            self.perturbation = RelaxedStrain(system, self.valence, settings)
            self.trajectories = None
            self.print_system()

    def print_system(self):
        '''
            dump overview of atoms (and associated parameters) in the system
        '''
        with log.section('SYS', 3, timer='Initializing'):
            log.dump('Atomic configuration of the system:')
            log.dump('')
            log.dump(
                '  index  |  x [A]  |  y [A]  |  z [A]  | ffatype |    q    |  R [A]  '
            )
            log.dump(
                '---------------------------------------------------------------------'
            )
            for i in range(len(self.system.numbers)):
                x, y, z = self.system.pos[i, 0], self.system.pos[
                    i, 1], self.system.pos[i, 2]
                if self.system.charges is not None:
                    q = self.system.charges[i]
                else:
                    q = np.nan
                if self.system.radii is not None:
                    R = self.system.radii[i]
                else:
                    R = np.nan
                log.dump(
                    '  %4i   | % 7.3f | % 7.3f | % 7.3f |  %6s | % 7.3f | % 7.3f '
                    % (i, x / angstrom, y / angstrom, z / angstrom,
                       self.system.ffatypes[self.system.ffatype_ids[i]], q,
                       R / angstrom))

    def reset_system(self):
        '''
            routine to reset the system coords to the ai equilbrium
        '''
        log.dump('Resetting system coordinates to ab initio ref')
        self.system.pos = self.ai.coords0.copy()
        self.valence.dlist.forward()
        self.valence.iclist.forward()

    def update_trajectory_terms(self):
        '''
            Routine to make ``self.valence.terms`` and the term attribute of each
            trajectory in ``self.trajectories`` consistent again. This is usefull
            if the trajectory were read from a file and the ``valenceFF`` instance
            was modified.
        '''
        log.dump('Updating terms of trajectories to current valenceFF terms')
        with log.section('PTUPD', 3):
            #update the terms in the trajectories to match the terms in
            #self.valence
            for traj in self.trajectories:
                found = False
                for term in self.valence.iter_terms():
                    if traj.term.get_atoms() == term.get_atoms():
                        if found:
                            raise ValueError(
                                'Found two terms for trajectory %s with atom indices %s'
                                % (traj.term.basename,
                                   str(traj.term.get_atoms())))
                        traj.term = term
                        if 'PT_ALL' not in term.tasks:
                            log.dump(
                                'PT_ALL not in tasks of %s-%i, deactivated PT'
                                % (term.basename, term.index))
                            traj.active = False
                        found = True
                if not found:
                    log.warning(
                        'No term found for trajectory %s with atom indices %s, deactivating trajectory'
                        % (traj.term.basename, str(traj.term.get_atoms())))
                    traj.active = False
            #check if every term with task PT_ALL has a trajectory associated
            #with it. It a trajectory is missing, generate it.
            for term in self.valence.iter_terms():
                if 'PT_ALL' not in term.tasks: continue
                found = False
                for traj in self.trajectories:
                    if term.get_atoms() == traj.term.get_atoms():
                        if found:
                            raise ValueError(
                                'Found two trajectories for term %s with atom indices %s'
                                % (term.basename, str(term.get_atoms())))
                        found = True
                if not found:
                    log.warning(
                        'No trajectory found for term %s with atom indices %s. Generating it now.'
                        % (term.basename, str(term.get_atoms())))
                    trajectory = self.perturbation.prepare([term])[0]
                    self.perturbation.generate(trajectory)
                    self.trajectories.append(trajectory)

    def average_pars(self):
        '''
            Average force field parameters over master and slaves.
        '''
        log.dump('Averaging force field parameters over master and slaves')
        for master in self.valence.iter_masters():
            npars = len(self.valence.get_params(master.index))
            pars = np.zeros([len(master.slaves) + 1, npars], float)
            pars[0, :] = np.array(self.valence.get_params(master.index))
            for i, islave in enumerate(master.slaves):
                pars[1 + i, :] = np.array(self.valence.get_params(islave))
            if master.kind in [0, 2, 11,
                               12]:  #harmonic,fues,MM3Quartic,MM3Bend
                fc, rv = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv)
            elif master.kind == 1:
                a0, a1, a2, a3 = pars.mean(axis=0)
                self.valence.set_params(master.index,
                                        a0=a0,
                                        a1=a1,
                                        a2=a2,
                                        a3=a3)
                for islave in master.slaves:
                    self.valence.set_params(islave, a0=a0, a1=a1, a2=a2, a3=a3)
            elif master.kind == 3:  #cross
                fc, rv0, rv1 = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv0, rv1=rv1)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv0, rv1=rv1)
            elif master.kind == 4:  #cosine
                assert pars[:,
                            0].std() < 1e-6, 'dihedral multiplicity not unique'
                m, fc, rv = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv, m=m)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv, m=m)
            elif master.kind in [5, 6, 7, 8, 9]:  #chebychev
                assert pars.shape[1] == 2
                fc = pars[:, 0].mean()
                self.valence.set_params(master.index, fc=fc)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc)
            else:
                raise NotImplementedError

    def make_output(self):
        '''
            Dump Yaff parameters, Yaff system, plot energy contributions along
            perturbation trajectories and dump perturbation trajectories to XYZ
            files.
        '''
        if self.settings.fn_yaff is not None:
            dump_yaff(self.valence, self.settings.fn_yaff)
        if self.settings.fn_charmm22_prm is not None:
            dump_charmm22_prm(self.valence, self.settings.fn_charmm22_prm)
        if self.settings.fn_charmm22_psf is not None:
            dump_charmm22_psf(self.system, self.valence,
                              self.settings.fn_charmm22_psf)
        if self.settings.fn_sys is not None:
            self.system.to_file(self.settings.fn_sys)
        if self.settings.plot_traj is not None and self.settings.plot_traj.lower(
        ) in ['Ehc3', 'final', 'all']:
            self.plot_trajectories(do_valence=True, suffix='_Ehc3')
        if self.settings.xyz_traj:
            self.write_trajectories()

    def plot_trajectories(self, do_valence=False, suffix=''):
        '''
            Plot energy contributions along perturbation trajectories and
        '''
        only = self.settings.only_traj
        if not isinstance(only, list): only = [only]
        with log.section('PLOT', 3, timer='PT plot energy'):
            valence = None
            if do_valence: valence = self.valence
            for trajectory in self.trajectories:
                if trajectory is None: continue
                for pattern in only:
                    if pattern in ['PT_ALL', 'pt_all', None
                                   ] or pattern in trajectory.term.basename:
                        log.dump('Plotting trajectory for %s' %
                                 trajectory.term.basename)
                        trajectory.plot(self.ai,
                                        ffrefs=self.ffrefs,
                                        valence=valence,
                                        suffix=suffix)

    def write_trajectories(self):
        '''
            Write perturbation trajectories to XYZ files.
        '''
        only = self.settings.only_traj
        if not isinstance(only, list): only = [only]
        with log.section('XYZ', 3, timer='PT dump XYZ'):
            for trajectory in self.trajectories:
                if trajectory is None: continue
                for pattern in only:
                    if pattern in ['PT_ALL', 'pt_all', None
                                   ] or pattern in trajectory.term.basename:
                        log.dump('Writing XYZ trajectory for %s' %
                                 trajectory.term.basename)
                        trajectory.to_xyz()

    def do_pt_generate(self):
        '''
            Generate perturbation trajectories.
        '''
        with log.section('PTGEN', 2, timer='PT Generate'):
            #read if an existing file was specified through fn_traj
            fn_traj = self.settings.fn_traj
            if fn_traj is not None and os.path.isfile(fn_traj):
                self.trajectories = pickle.load(open(fn_traj, 'rb'))
                log.dump('Trajectories read from file %s' % fn_traj)
                self.update_trajectory_terms()
                newname = 'updated_' + fn_traj.split('/')[-1]
                pickle.dump(self.trajectories, open(newname, 'wb'))
                return
            #configure
            self.reset_system()
            only = self.settings.only_traj
            dont_traj = self.settings.dont_traj

            if sum([only is None, dont_traj is None]) == 0:
                raise AssertionError(
                    'The settings only_traj and dont_traj cannot be specified both'
                )
            if (only is None or only == 'PT_ALL' or only == 'pt_all'
                ) and dont_traj is None:  # only=None is equivalent to PT_ALL
                do_terms = [
                    term for term in self.valence.terms
                    if term.kind in [0, 2, 11, 12]
                ]
            elif only is None and dont_traj is not None:
                kind2string = {
                    0: 'bond',
                    2: 'bend',
                    11: 'oopdist',
                    12: 'dihedral'
                }
                ffatypes = [
                    self.system.ffatypes[fid]
                    for fid in self.system.ffatype_ids
                ]

                dont_patterns = dont_traj.split(',')  # split patterns
                dont_terms = []
                for term in self.valence.terms:
                    if term.kind in [0, 2, 11, 12]:
                        types = term.basename.split('/')[1].split('.')
                        option1 = '.'.join(types)
                        option2 = '.'.join(types[::-1])
                        for dp in dont_patterns:
                            pattern = re.compile(dp, re.IGNORECASE)
                            if pattern.match(option1) or pattern.match(
                                    option2):
                                dont_terms.append(term)

                do_terms = [
                    term for term in self.valence.terms
                    if term.kind in [0, 2, 11, 12] and term not in dont_terms
                ]
                with log.section('PTNOT', 3):
                    for term in dont_terms:
                        log.dump(
                            'Taking AI equilibrium rest value instead of generating perturbation trajectory for %s'
                            % term.basename)
                        vterm = self.valence.vlist.vtab[term.index]
                        self.valence.set_params(term.index,
                                                fc=0,
                                                rv0=self.valence.iclist.ictab[
                                                    vterm['ic0']]['value'])
            else:
                if isinstance(only, str): only = [only]
                do_terms = []
                for pattern in only:
                    for term in self.valence.iter_terms(pattern):
                        if term.kind in [0, 2, 11, 12]:
                            do_terms.append(term)
            trajectories = self.perturbation.prepare(do_terms)
            #compute
            log.dump('Constructing trajectories')
            self.trajectories = paracontext.map(
                self.perturbation.generate,
                [traj for traj in trajectories if traj.active])
            #write the trajectories to the non-existing file fn_traj
            if fn_traj is not None:
                assert not os.path.isfile(fn_traj)
                pickle.dump(self.trajectories, open(fn_traj, 'wb'))
                log.dump('Trajectories stored to file %s' % fn_traj)

    def do_pt_estimate(self,
                       do_valence=False,
                       energy_noise=None,
                       logger_level=3):
        '''
            Estimate force constants and rest values from the perturbation
            trajectories

            **Optional Arguments**

            do_valence
                if set to True, the current valence force field will be used to
                estimate the contribution of all other valence terms.
        '''
        with log.section('PTEST', 2, timer='PT Estimate'):
            self.reset_system()
            message = 'Estimating FF parameters from perturbation trajectories'
            if do_valence: message += ' with valence reference'
            log.dump(message)
            #compute fc and rv from trajectory
            only = self.settings.only_traj
            for traj in self.trajectories:
                if traj is None: continue
                if not (only is None or only == 'PT_ALL' or only == 'pt_all'):
                    if isinstance(only, str): only = [only]
                    basename = self.valence.terms[traj.term.master].basename
                    if basename not in only: continue
                self.perturbation.estimate(traj,
                                           self.ai,
                                           ffrefs=self.ffrefs,
                                           do_valence=do_valence,
                                           energy_noise=energy_noise)
            #set force field parameters to computed fc and rv
            for traj in self.trajectories:
                if traj is None: continue
                if not (only is None or only == 'PT_ALL' or only == 'pt_all'):
                    if isinstance(only, str): only = [only]
                    basename = self.valence.terms[traj.term.master].basename
                    if basename not in only: continue
                self.valence.set_params(traj.term.index,
                                        fc=traj.fc,
                                        rv0=traj.rv)
            #output
            self.valence.dump_logger(print_level=logger_level)
            #do not add average here since the fluctuation on the parameters is
            #required for do_pt_postprocess. Average will be done at the end of
            #do_pt_postprocess

    def do_pt_postprocess(self):
        '''
            Do some first post processing of the ff parameters estimated from
            the perturbation trajectories including:

                * detecting bend patterns with rest values of 90 and 180 deg
                * detecting bend patterns with rest values only close to 180 deg
                * transforming SqOopDist with rv=0.0 to OopDist
                * averaging parameters
        '''
        with log.section('PTPOST', 2, timer='PT Post process'):
            if self.settings.do_squarebend:
                self.do_squarebend()
            if self.settings.do_bendclin:
                self.do_bendclin()
            if self.settings.do_sqoopdist_to_oopdist:
                self.do_sqoopdist_to_oopdist()
            self.average_pars()

    def do_eq_setrv(self, tasks, logger_level=3):
        '''
            Set the rest values to their respective AI equilibrium values.
        '''
        with log.section('EQSET', 2, timer='Equil Set RV'):
            self.reset_system()
            log.dump(
                'Setting rest values to AI equilibrium values for tasks %s' %
                ' '.join(tasks))
            for term in self.valence.terms:
                vterm = self.valence.vlist.vtab[term.index]
                if np.array([task in term.tasks for task in tasks]).any():
                    if term.kind == 3:  #cross term
                        ic0 = self.valence.iclist.ictab[vterm['ic0']]
                        ic1 = self.valence.iclist.ictab[vterm['ic1']]
                        self.valence.set_params(term.index,
                                                rv0=ic0['value'],
                                                rv1=ic1['value'])
                    elif term.kind == 4 and term.ics[
                            0].kind == 4:  #Cosine of DihedAngle
                        ic = self.valence.iclist.ictab[vterm['ic0']]
                        m = self.valence.get_params(term.index, only='m')
                        rv = ic['value'] % (360.0 * deg / m)
                        with log.section('EQSET', 4, timer='Equil Set RV'):
                            log.dump(
                                'Set rest value of %s(%s) (eq=%.3f deg) to %.3f deg'
                                % (term.basename, '.'.join([
                                    str(at) for at in term.get_atoms()
                                ]), ic['value'] / deg, rv / deg))
                        self.valence.set_params(term.index, rv0=rv)
                    else:
                        rv = self.valence.iclist.ictab[vterm['ic0']]['value']
                        self.valence.set_params(term.index, rv0=rv)
            self.valence.dump_logger(print_level=logger_level)
            self.average_pars()

    def do_hc_estimatefc(self,
                         tasks,
                         logger_level=3,
                         do_svd=False,
                         svd_rcond=0.0,
                         do_mass_weighting=True):
        '''
            Refine force constants using Hessian Cost function.

            **Arguments**

            tasks
                A list of strings identifying which terms should have their
                force constant estimated from the hessian cost function. Using
                such a flag, one can distinguish between for example force
                constant refinement (flag=HC_FC_DIAG) of the diagonal terms and
                force constant estimation of the cross terms (flag=HC_FC_CROSS).
                If the string 'all' is present in tasks, all fc's will be
                estimated.

            **Optional Arguments**

            logger_level
                print level at which the resulting parameters should be dumped to
                the logger. By default, the parameters will only be dumped at
                the highest log level.

            do_svd
                whether or not to do an SVD decomposition before solving the
                set of equations and explicitly throw out the degrees of
                freedom that correspond to the lowest singular values.

            do_mass_weighting
                whether or not to apply mass weighing to the ab initio hessian
                and the force field contributions before doing the fitting.
        '''
        with log.section('HCEST', 2, timer='HC Estimate FC'):
            self.reset_system()
            log.dump(
                'Estimating force constants from Hessian cost for tasks %s' %
                ' '.join(tasks))
            term_indices = []
            for index in range(self.valence.vlist.nv):
                term = self.valence.terms[index]
                flagged = False
                for flag in tasks:
                    if flag in term.tasks:
                        flagged = True
                        break
                if flagged:
                    #first check if all rest values and multiplicities have been defined
                    if term.kind == 0: self.valence.check_params(term, ['rv'])
                    if term.kind == 1:
                        self.valence.check_params(term,
                                                  ['a0', 'a1', 'a2', 'a3'])
                    if term.kind == 3:
                        self.valence.check_params(term, ['rv0', 'rv1'])
                    if term.kind == 4:
                        self.valence.check_params(term, ['rv', 'm'])
                    if term.is_master():
                        term_indices.append(index)
                else:
                    #first check if all pars have been defined
                    if term.kind == 0:
                        self.valence.check_params(term, ['fc', 'rv'])
                    if term.kind == 1:
                        self.valence.check_params(term,
                                                  ['a0', 'a1', 'a2', 'a3'])
                    if term.kind == 3:
                        self.valence.check_params(term, ['fc', 'rv0', 'rv1'])
                    if term.kind == 4:
                        self.valence.check_params(term, ['fc', 'rv', 'm'])
            if len(term_indices) == 0:
                log.dump(
                    'No terms (with task in %s) found to estimate FC from HC' %
                    (str(tasks)))
                return
            # Try to estimate force constants; if the remove_dysfunctional_cross
            # keyword is True, a loop is performed which checks whether there
            # are cross terms for which corresponding diagonal terms have zero
            # force constants. If this is the case, those cross terms are removed
            # from the fit and we try again until such cases do no longer occur
            max_iter = 100
            niter = 0
            while niter < max_iter:
                cost = HessianFCCost(self.system,
                                     self.ai,
                                     self.valence,
                                     term_indices,
                                     ffrefs=self.ffrefs,
                                     do_mass_weighting=do_mass_weighting)
                fcs = cost.estimate(do_svd=do_svd, svd_rcond=svd_rcond)
                # No need to continue, if cross terms with corresponding diagonal
                # terms with negative force constants are allowed
                if self.settings.remove_dysfunctional_cross is False: break
                to_remove = []
                for index, fc in zip(term_indices, fcs):
                    term = self.valence.terms[index]
                    if term.basename.startswith('Cross'):
                        # Find force constants of corresponding diagonal terms
                        diag_fcs = np.zeros((2))
                        for idiag in range(2):
                            diag_index = term.diag_term_indexes[idiag]
                            if diag_index in term_indices:
                                fc_diag = fcs[term_indices.index(diag_index)]
                            else:
                                fc_diag = self.valence.get_params(diag_index,
                                                                  only='fc')
                            diag_fcs[idiag] = fc_diag
                        # If a force constant from any corresponding diagonal term is negative,
                        # we remove the cross term for the next iteration
                        if np.any(diag_fcs <= 0.0):
                            to_remove.append(index)
                            self.valence.set_params(index, fc=0.0)
                            log.dump(
                                'WARNING! Dysfunctional cross term %s detected, removing from the hessian fit.'
                                % term.basename)
                if len(to_remove) == 0: break
                else:
                    for index in to_remove:
                        term_indices.remove(index)
                niter += 1
            assert niter < max_iter, "Could not remove all dysfunctional cross terms in %d iterations, something is seriously wrong" % max_iter
            for index, fc in zip(term_indices, fcs):
                master = self.valence.terms[index]
                assert master.is_master()
                self.valence.set_params(index, fc=fc)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc)
            self.valence.dump_logger(print_level=logger_level)

    def do_cross_init(self):
        '''
            Add cross terms to the valence list and initialize parameters.
        '''
        with log.section('VAL', 2, 'Initializing'):
            self.reset_system()
            self.valence.init_cross_angle_terms()
            if self.settings.do_cross_DSS or self.settings.do_cross_DSD or self.settings.do_cross_DAD or self.settings.do_cross_DAA:
                self.valence.init_cross_dihed_terms()
            self.update_cross_pars()

    def update_cross_pars(self):
        '''
            Set the rest values of cross terms to the rest values of the
            corresponding diagonal terms. Set the force constants to zero.
        '''
        with log.section('VAL', 2, 'Initializing'):

            def find_rest_value(iterm):
                term = self.valence.terms[iterm]
                if term.basename.startswith(
                        'TorsCheby') or term.basename.startswith('BendCheby'):
                    return -self.valence.get_params(iterm, only='sign')
                else:
                    return self.valence.get_params(iterm, only='rv')

            # Bond-Bond Cross terms
            cases = [('Cross', 'bb', 3), ('Cross', 'b0a', 3),
                     ('Cross', 'b1a', 3)]

            # Bond-Dihedral Cross terms
            for m in [1, 2, 3, 4, 6]:
                for suffix in ['bb', 'b0d', 'b1d', 'b2d']:
                    case = ('CrossBondDih%i' % m, suffix, 4)
                    cases.append(case)

            # Angle-Dihedral Cross terms
            for m in [1, 2, 3, 4, 6]:
                for suffix in ['aa', 'a0d', 'a1d']:
                    case = ('CrossBendDih%i' % m, suffix, 4)
                    cases.append(case)

                for suffix in ['a0d', 'a1d']:
                    case = ('CrossCBendDih%i' % m, suffix, 4)
                    cases.append(case)
Beispiel #4
0
class BaseProgram(object):
    '''
        Base program which implements all possible steps of a force field
        fitting program. The actual sequence of the steps are defined in the
        deriving classes.
    '''
    def __init__(self, system, ai, settings, ffrefs=[]):
        '''
            **Arguments**

            system
                a Yaff `System` instance defining the system

            ai
                a `Reference` instance corresponding to the ab initio input data

            settings
                a `Settings` instance defining all QuickFF settings

            **Optional Arguments**

            ffrefs
                a list of `Reference` instances defining the a-priori force
                field contributions.
        '''
        with log.section('PROG', 2, timer='Initializing'):
            log.dump('Initializing program')
            self.settings = settings
            self.system = system
            self.ai = ai
            self.ffrefs = ffrefs
            self.valence = ValenceFF(system, settings)
            self.perturbation = RelaxedStrain(system, self.valence, settings)
            self.trajectories = None

    def reset_system(self):
        '''
            routine to reset the system coords to the ai equilbrium
        '''
        log.dump('Resetting system coordinates to ab initio ref')
        self.system.pos = self.ai.coords0.copy()
        self.valence.dlist.forward()
        self.valence.iclist.forward()

    def update_trajectory_terms(self):
        '''
            Routine to make ``self.valence.terms`` and the term attribute of each
            trajectory in ``self.trajectories`` consistent again. This is usefull
            if the trajectory were read from a file and the ``valenceFF`` instance
            was modified.
        '''
        log.dump('Updating terms of trajectories to current valenceFF terms')
        with log.section('PTUPD', 3):
            #update the terms in the trajectories to match the terms in
            #self.valence
            for traj in self.trajectories:
                found = False
                for term in self.valence.iter_terms():
                    if traj.term.get_atoms() == term.get_atoms():
                        if found:
                            raise ValueError(
                                'Found two terms for trajectory %s with atom indices %s'
                                % (traj.term.basename,
                                   str(traj.term.get_atoms())))
                        traj.term = term
                        if 'PT_ALL' not in term.tasks:
                            log.dump(
                                'PT_ALL not in tasks of %s-%i, deactivated PT'
                                % (term.basename, term.index))
                            traj.active = False
                        found = True
                if not found:
                    log.warning(
                        'No term found for trajectory %s with atom indices %s, deactivating trajectory'
                        % (traj.term.basename, str(traj.term.get_atoms())))
                    traj.active = False
            #check if every term with task PT_ALL has a trajectory associated
            #with it. It a trajectory is missing, generate it.
            for term in self.valence.iter_terms():
                if 'PT_ALL' not in term.tasks: continue
                found = False
                for traj in self.trajectories:
                    if term.get_atoms() == traj.term.get_atoms():
                        if found:
                            raise ValueError(
                                'Found two trajectories for term %s with atom indices %s'
                                % (term.basename, str(term.get_atoms())))
                        found = True
                if not found:
                    log.warning(
                        'No trajectory found for term %s with atom indices %s. Generating it now.'
                        % (term.basename, str(term.get_atoms())))
                    trajectory = self.perturbation.prepare([term])[term.index]
                    self.perturbation.generate(trajectory)
                    self.trajectories.append(trajectory)

    def average_pars(self):
        '''
            Average force field parameters over master and slaves.
        '''
        log.dump('Averaging force field parameters over master and slaves')
        for master in self.valence.iter_masters():
            npars = len(self.valence.get_params(master.index))
            pars = np.zeros([len(master.slaves) + 1, npars], float)
            pars[0, :] = np.array(self.valence.get_params(master.index))
            for i, islave in enumerate(master.slaves):
                pars[1 + i, :] = np.array(self.valence.get_params(islave))
            if master.kind in [0, 2, 11,
                               12]:  #harmonic,fues,MM3Quartic,MM3Bend
                fc, rv = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv)
            elif master.kind == 1:
                a0, a1, a2, a3 = pars.mean(axis=0)
                self.valence.set_params(master.index,
                                        a0=a0,
                                        a1=a1,
                                        a2=a2,
                                        a3=a3)
                for islave in master.slaves:
                    self.valence.set_params(islave, a0=a0, a1=a1, a2=a2, a3=a3)
            elif master.kind == 3:  #cross
                fc, rv0, rv1 = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv0, rv1=rv1)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv0, rv1=rv1)
            elif master.kind == 4:  #cosine
                assert pars[:,
                            0].std() < 1e-6, 'dihedral multiplicity not unique'
                m, fc, rv = pars.mean(axis=0)
                self.valence.set_params(master.index, fc=fc, rv0=rv, m=m)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc, rv0=rv, m=m)
            elif master.kind in [5, 6, 7, 8, 9]:  #chebychev
                assert pars.shape[1] == 2
                fc = pars[:, 0].mean()
                self.valence.set_params(master.index, fc=fc)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc)
            else:
                raise NotImplementedError

    def make_output(self):
        '''
            Dump Yaff parameters, Yaff system, plot energy contributions along
            perturbation trajectories and dump perturbation trajectories to XYZ
            files.
        '''
        if self.settings.fn_yaff is not None:
            dump_yaff(self.valence, self.settings.fn_yaff)
        if self.settings.fn_charmm22_prm is not None:
            dump_charmm22_prm(self.valence, self.settings.fn_charmm22_prm)
        if self.settings.fn_charmm22_psf is not None:
            dump_charmm22_psf(self.system, self.valence,
                              self.settings.fn_charmm22_psf)
        if self.settings.fn_sys is not None:
            self.system.to_file(self.settings.fn_sys)
        if self.settings.plot_traj is not None and self.settings.plot_traj.lower(
        ) in ['final', 'all']:
            self.plot_trajectories(do_valence=True, suffix='_Ehc3')
        if self.settings.xyz_traj:
            self.write_trajectories()

    def plot_trajectories(self, do_valence=False, suffix=''):
        '''
            Plot energy contributions along perturbation trajectories and
        '''
        only = self.settings.only_traj
        if not isinstance(only, list): only = [only]
        with log.section('PLOT', 3, timer='PT plot energy'):
            valence = None
            if do_valence: valence = self.valence
            for trajectory in self.trajectories:
                if trajectory is None: continue
                for pattern in only:
                    if pattern == 'PT_ALL' or pattern in trajectory.term.basename:
                        trajectory.plot(self.ai,
                                        ffrefs=self.ffrefs,
                                        valence=valence,
                                        suffix=suffix)

    def write_trajectories(self):
        '''
            Write perturbation trajectories to XYZ files.
        '''
        only = self.settings.only_traj
        if not isinstance(only, list): only = [only]
        with log.section('XYZ', 3, timer='PT dump XYZ'):
            for trajectory in self.trajectories:
                if trajectory is None: continue
                for pattern in only:
                    if pattern == 'PT_ALL' or pattern in trajectory.term.basename:
                        trajectory.to_xyz()

    def do_pt_generate(self):
        '''
            Generate perturbation trajectories.
        '''
        with log.section('PTGEN', 2, timer='PT Generate'):
            #read if an existing file was specified through fn_traj
            fn_traj = self.settings.fn_traj
            if fn_traj is not None and os.path.isfile(fn_traj):
                self.trajectories = pickle.load(open(fn_traj, 'rb'))
                log.dump('Trajectories read from file %s' % fn_traj)
                self.update_trajectory_terms()
                newname = 'updated_' + fn_traj.split('/')[-1]
                pickle.dump(self.trajectories, open(newname, 'wb'))
                return
            #configure
            self.reset_system()
            only = self.settings.only_traj
            if only is None or only == 'PT_ALL' or only == 'pt_all':
                do_terms = [
                    term for term in self.valence.terms
                    if term.kind in [0, 2, 11, 12]
                ]
            else:
                if isinstance(only, str): only = [only]
                do_terms = []
                for pattern in only:
                    for term in self.valence.iter_terms(pattern):
                        if term.kind in [0, 2, 11, 12]:
                            do_terms.append(term)
            trajectories = self.perturbation.prepare(do_terms)
            #compute
            log.dump('Constructing trajectories')
            self.trajectories = paracontext.map(
                self.perturbation.generate,
                [traj for traj in trajectories if traj.active])
            #write the trajectories to the non-existing file fn_traj
            if fn_traj is not None:
                assert not os.path.isfile(fn_traj)
                pickle.dump(self.trajectories, open(fn_traj, 'wb'))
                log.dump('Trajectories stored to file %s' % fn_traj)

    def do_pt_estimate(self, do_valence=False, logger_level=3):
        '''
            Estimate force constants and rest values from the perturbation
            trajectories

            **Optional Arguments**

            do_valence
                if set to True, the current valence force field will be used to
                estimate the contribution of all other valence terms.
        '''
        with log.section('PTEST', 2, timer='PT Estimate'):
            self.reset_system()
            message = 'Estimating FF parameters from perturbation trajectories'
            if do_valence: message += ' with valence reference'
            log.dump(message)
            #compute fc and rv from trajectory
            only = self.settings.only_traj
            for traj in self.trajectories:
                if traj is None: continue
                if not (only is None or only == 'PT_ALL' or only == 'pt_all'):
                    if isinstance(only, str): only = [only]
                    basename = self.valence.terms[traj.term.master].basename
                    if basename not in only: continue
                self.perturbation.estimate(traj,
                                           self.ai,
                                           ffrefs=self.ffrefs,
                                           do_valence=do_valence)
            #set force field parameters to computed fc and rv
            for traj in self.trajectories:
                if traj is None: continue
                if not (only is None or only == 'PT_ALL' or only == 'pt_all'):
                    if isinstance(only, str): only = [only]
                    basename = self.valence.terms[traj.term.master].basename
                    if basename not in only: continue
                self.valence.set_params(traj.term.index,
                                        fc=traj.fc,
                                        rv0=traj.rv)
            #output
            self.valence.dump_logger(print_level=logger_level)
            #do not add average here since the fluctuation on the parameters is
            #required for do_pt_postprocess. Average will be done at the end of
            #do_pt_postprocess

    def do_pt_postprocess(self):
        '''
            Do some first post processing of the ff parameters estimated from
            the perturbation trajectories including:

                * detecting bend patterns with rest values of 90 and 180 deg
                * detecting bend patterns with rest values only close to 180 deg
                * transforming SqOopDist with rv=0.0 to OopDist
                * averaging parameters
        '''
        with log.section('PTPOST', 2, timer='PT Post process'):
            if self.settings.do_squarebend:
                self.do_squarebend()
            if self.settings.do_bendclin:
                self.do_bendclin()
            if self.settings.do_sqoopdist_to_oopdist:
                self.do_sqoopdist_to_oopdist()
            self.average_pars()

    def do_eq_setrv(self, tasks, logger_level=3):
        '''
            Set the rest values to their respective AI equilibrium values.
        '''
        with log.section('EQSET', 2, timer='Equil Set RV'):
            self.reset_system()
            log.dump(
                'Setting rest values to AI equilibrium values for tasks %s' %
                ' '.join(tasks))
            for term in self.valence.terms:
                vterm = self.valence.vlist.vtab[term.index]
                if np.array([task in term.tasks for task in tasks]).any():
                    if term.kind == 3:  #cross term
                        ic0 = self.valence.iclist.ictab[vterm['ic0']]
                        ic1 = self.valence.iclist.ictab[vterm['ic1']]
                        self.valence.set_params(term.index,
                                                rv0=ic0['value'],
                                                rv1=ic1['value'])
                    elif term.kind == 4 and term.ics[
                            0].kind == 4:  #Cosine of DihedAngle
                        ic = self.valence.iclist.ictab[vterm['ic0']]
                        m = self.valence.get_params(term.index, only='m')
                        rv = ic['value'] % (360.0 * deg / m)
                        with log.section('EQSET', 4, timer='Equil Set RV'):
                            log.dump(
                                'Set rest value of %s(%s) (eq=%.3f deg) to %.3f deg'
                                % (term.basename, '.'.join([
                                    str(at) for at in term.get_atoms()
                                ]), ic['value'] / deg, rv / deg))
                        self.valence.set_params(term.index, rv0=rv)
                    else:
                        rv = self.valence.iclist.ictab[vterm['ic0']]['value']
                        self.valence.set_params(term.index, rv0=rv)
            self.valence.dump_logger(print_level=logger_level)
            self.average_pars()

    def do_hc_estimatefc(self,
                         tasks,
                         logger_level=3,
                         do_svd=False,
                         do_mass_weighting=True):
        '''
            Refine force constants using Hessian Cost function.

            **Arguments**

            tasks
                A list of strings identifying which terms should have their
                force constant estimated from the hessian cost function. Using
                such a flag, one can distinguish between for example force
                constant refinement (flag=HC_FC_DIAG) of the diagonal terms and
                force constant estimation of the cross terms (flag=HC_FC_CROSS).
                If the string 'all' is present in tasks, all fc's will be
                estimated.

            **Optional Arguments**

            logger_level
                print level at which the resulting parameters should be dumped to
                the logger. By default, the parameters will only be dumped at
                the highest log level.

            do_svd
                whether or not to do an SVD decomposition before solving the
                set of equations and explicitly throw out the degrees of
                freedom that correspond to the lowest singular values.

            do_mass_weighting
                whether or not to apply mass weighing to the ab initio hessian
                and the force field contributions before doing the fitting.
        '''
        with log.section('HCEST', 2, timer='HC Estimate FC'):
            self.reset_system()
            log.dump(
                'Estimating force constants from Hessian cost for tasks %s' %
                ' '.join(tasks))
            term_indices = []
            for index in range(self.valence.vlist.nv):
                term = self.valence.terms[index]
                flagged = False
                for flag in tasks:
                    if flag in term.tasks:
                        flagged = True
                        break
                if flagged:
                    #first check if all rest values and multiplicities have been defined
                    if term.kind == 0: self.valence.check_params(term, ['rv'])
                    if term.kind == 1:
                        self.valence.check_params(term,
                                                  ['a0', 'a1', 'a2', 'a3'])
                    if term.kind == 3:
                        self.valence.check_params(term, ['rv0', 'rv1'])
                    if term.kind == 4:
                        self.valence.check_params(term, ['rv', 'm'])
                    if term.is_master():
                        term_indices.append(index)
                else:
                    #first check if all pars have been defined
                    if term.kind == 0:
                        self.valence.check_params(term, ['fc', 'rv'])
                    if term.kind == 1:
                        self.valence.check_params(term,
                                                  ['a0', 'a1', 'a2', 'a3'])
                    if term.kind == 3:
                        self.valence.check_params(term, ['fc', 'rv0', 'rv1'])
                    if term.kind == 4:
                        self.valence.check_params(term, ['fc', 'rv', 'm'])
            if len(term_indices) == 0:
                log.dump(
                    'No terms (with task in %s) found to estimate FC from HC' %
                    (str(tasks)))
                return
            cost = HessianFCCost(self.system,
                                 self.ai,
                                 self.valence,
                                 term_indices,
                                 ffrefs=self.ffrefs,
                                 do_mass_weighting=do_mass_weighting)
            fcs = cost.estimate(do_svd=do_svd)
            for index, fc in zip(term_indices, fcs):
                master = self.valence.terms[index]
                assert master.is_master()
                self.valence.set_params(index, fc=fc)
                for islave in master.slaves:
                    self.valence.set_params(islave, fc=fc)
            self.valence.dump_logger(print_level=logger_level)

    def do_cross_init(self):
        '''
            Set the rest values of cross terms to the rest values of the
            corresponding diagonal terms. The force constants are initialized
            to zero.
        '''
        with log.section('VAL', 2, 'Initializing'):
            self.reset_system()
            self.valence.init_cross_angle_terms()

            #function to find rest value
            def find_rest_value(label):
                candidates = [
                    cand for cand in self.valence.iter_masters(label=label,
                                                               use_re=True)
                ]
                assert len(
                    candidates) < 2, 'Multiple masters found for %s: %s' % (
                        label, ','.join([cand.basename
                                         for cand in candidates]))
                if len(candidates) == 0:
                    if label.startswith('^Bond') or label.startswith(
                            '^Bend') or label.startswith('^Tors'):
                        sublabels = label.split('|')
                        prefix0, types0 = sublabels[0].lstrip('^').rstrip(
                            '$').split('/')
                        label = '^' + prefix0 + '/' + '\.'.join(
                            types0.split('.')[::-1]) + '$'
                        if len(sublabels) > 1:
                            prefix1, types1 = sublabels[1].lstrip('^').rstrip(
                                '$').split('/')
                            label += '|'
                            label += '^' + prefix1 + '/' + '\.'.join(
                                types1.split('.')[::-1]) + '$'
                        candidates = [
                            cand
                            for cand in self.valence.iter_masters(label=label,
                                                                  use_re=True)
                        ]
                        assert len(
                            candidates
                        ) < 2, 'Multiple masters found for %s: %s' % (
                            label, ','.join(
                                [cand.basename for cand in candidates]))
                        if len(candidates) == 0:
                            return None
                can = candidates[0]
                if can.basename.startswith(
                        'TorsCheby') or can.basename.startswith('BendCheby'):
                    return -self.valence.get_params(can.index, only='sign')
                else:
                    return self.valence.get_params(can.index, only='rv')

            #set rest values and initialize fc for bond-bond cross
            for term in self.valence.iter_masters('^Cross/.*/bb$',
                                                  use_re=True):
                types = term.basename.split('/')[1].split('.')
                assert len(
                    types
                ) == 3, 'Found angle cross terms with more/less than 3 atom types'
                rv0 = find_rest_value('^Bond.*/%s$' % ('.'.join(types[:2])))
                rv1 = find_rest_value('^Bond.*/%s$' % ('.'.join(types[1:])))
                assert rv0 is not None, 'Rest value of BondHarm/%s not found' % (
                    '.'.join(types[:2]))
                assert rv1 is not None, 'Rest value of BondHarm/%s not found' % (
                    '.'.join(types[1:]))
                self.valence.set_params(term.index, fc=0.0, rv0=rv0, rv1=rv1)
                for index in term.slaves:
                    self.valence.set_params(index, fc=0.0, rv0=rv0, rv1=rv1)

            #set rest values and initialize fc for bond-angle cross
            for term in self.valence.iter_masters('^Cross/.*/b0a$',
                                                  use_re=True):
                types = term.basename.split('/')[1].split('.')
                assert len(
                    types
                ) == 3, 'Found angle cross terms with more/less than 3 atom types'
                rv0 = find_rest_value('^Bond.*/%s$' % ('.'.join(types[:2])))
                rv1 = find_rest_value('^BendAHarm/%s$|^BendMM3/%s$' %
                                      ('.'.join(types), '.'.join(types)))
                assert rv0 is not None, 'Rest value of BondHarm/%s not found' % (
                    '.'.join(types[:2]))
                assert rv1 is not None, 'Rest value of BendAHarm|BendMM3/%s not found' % (
                    '.'.join(types))
                self.valence.set_params(term.index, fc=0.0, rv0=rv0, rv1=rv1)
                for index in term.slaves:
                    self.valence.set_params(index, fc=0.0, rv0=rv0, rv1=rv1)

            for term in self.valence.iter_masters('^Cross/.*/b1a$',
                                                  use_re=True):
                types = term.basename.split('/')[1].split('.')
                assert len(
                    types
                ) == 3, 'Found angle cross terms with more/less than 3 atom types'
                rv0 = find_rest_value('^Bond.*/%s$' % ('.'.join(types[1:])))
                rv1 = find_rest_value('^BendAHarm/%s$|^BendMM3/%s$' %
                                      ('.'.join(types), '.'.join(types)))
                assert rv0 is not None, 'Rest value of BondHarm/%s not found' % (
                    '.'.join(types[1:]))
                assert rv1 is not None, 'Rest value of BendAHarm|BendMM3/%s not found' % (
                    '.'.join(types))
                self.valence.set_params(term.index, fc=0.0, rv0=rv0, rv1=rv1)
                for index in term.slaves:
                    self.valence.set_params(index, fc=0.0, rv0=rv0, rv1=rv1)

            if self.settings.do_cross_DSS or self.settings.do_cross_DSD or self.settings.do_cross_DAD or self.settings.do_cross_DAA:
                self.valence.init_cross_dihed_terms()
                #set rest values and initialize fc for bond-bond cross
                for m in [1, 2, 3, 4, 6]:
                    for term in self.valence.iter_masters(
                            '^CrossBondDih%i/.*/bb$' % m, use_re=True):
                        types = term.basename.split('/')[1].split('.')
                        assert len(
                            types
                        ) == 4, 'Found angle cross terms with more/less than 4 atom types'
                        rv0 = find_rest_value('^Bond.*/%s$' %
                                              ('.'.join(types[:2])))
                        rv1 = find_rest_value('^Bond.*/%s$' %
                                              ('.'.join(types[2:])))
                        self.valence.set_params(term.index,
                                                fc=0.0,
                                                rv0=rv0,
                                                rv1=rv1)
                        for index in term.slaves:
                            self.valence.set_params(index,
                                                    fc=0.0,
                                                    rv0=rv0,
                                                    rv1=rv1)

                #set rest values and initialize fc for bond-dihed cross
                for m in [1, 2, 3, 4, 6]:
                    for term in self.valence.iter_masters(
                            '^CrossBondDih%i/.*/b0d$' % m, use_re=True):
                        types = term.basename.split('/')[1].split('.')
                        assert len(
                            types
                        ) == 4, 'Found angle cross terms with more/less than 4 atom types'
                        rv0 = find_rest_value('^Bond.*/%s$' %
                                              ('.'.join(types[:2])))
                        rv1 = find_rest_value('^TorsCheby%i/%s$' %
                                              (m, '.'.join(types)))
                        self.valence.set_params(term.index,
                                                fc=0.0,
                                                rv0=rv0,
                                                rv1=rv1)
                        for index in term.slaves:
                            self.valence.set_params(index,
                                                    fc=0.0,
                                                    rv0=rv0,
                                                    rv1=rv1)

                    for term in self.valence.iter_masters(
                            '^CrossBondDih%i/.*/b1d$' % m, use_re=True):
                        types = term.basename.split('/')[1].split('.')
                        assert len(
                            types
                        ) == 4, 'Found angle cross terms with more/less than 4 atom types'
                        rv0 = find_rest_value('^Bond.*/%s$' %
                                              ('.'.join(types[1:3])))
                        rv1 = find_rest_value('^TorsCheby%i/%s$' %
                                              (m, '.'.join(types)))
                        self.valence.set_params(term.index,
                                                fc=0.0,
                                                rv0=rv0,
                                                rv1=rv1)
                        for index in term.slaves:
                            self.valence.set_params(index,
                                                    fc=0.0,
                                                    rv0=rv0,
                                                    rv1=rv1)

                    for term in self.valence.iter_masters(
                            '^CrossBondDih%i/.*/b2d$' % m, use_re=True):
                        types = term.basename.split('/')[1].split('.')
                        assert len(
                            types
                        ) == 4, 'Found angle cross terms with more/less than 4 atom types'
                        rv0 = find_rest_value('^Bond.*/%s$' %
                                              ('.'.join(types[2:])))
                        rv1 = find_rest_value('^TorsCheby%i/%s$' %
                                              (m, '.'.join(types)))
                        self.valence.set_params(term.index,
                                                fc=0.0,
                                                rv0=rv0,
                                                rv1=rv1)
                        for index in term.slaves:
                            self.valence.set_params(index,
                                                    fc=0.0,
                                                    rv0=rv0,
                                                    rv1=rv1)

                #set rest values and initialize fc for angle-angle cross
                for m in [1, 2, 3, 4, 6]:
                    for term in self.valence.iter_masters(
                            '^CrossBendDih%i/.*/aa$' % m, use_re=True):
                        types = term.basename.split('/')[1].split('.')
                        assert len(
                            types
                        ) == 4, 'Found angle cross terms with more/less than 4 atom types'
                        rv0 = find_rest_value(
                            '^BendAHarm/%s$|^BendMM3/%s$' %
                            ('.'.join(types[:3]), '.'.join(types[:3])))
                        rv1 = find_rest_value(
                            '^BendAHarm/%s$|^BendMM3/%s$' %
                            ('.'.join(types[1:]), '.'.join(types[1:])))
                        self.valence.set_params(term.index,
                                                fc=0.0,
                                                rv0=rv0,
                                                rv1=rv1)
                        for index in term.slaves:
                            self.valence.set_params(index,
                                                    fc=0.0,
                                                    rv0=rv0,
                                                    rv1=rv1)

                #set rest values and initialize fc for angle-dihed cross
                for m in [1, 2, 3, 4, 6]:
                    for term in self.valence.iter_masters(
                            '^CrossBendDih%i/.*/a0d$' % m, use_re=True):
                        types = term.basename.split('/')[1].split('.')
                        assert len(
                            types
                        ) == 4, 'Found angle cross terms with more/less than 4 atom types'
                        rv0 = find_rest_value(
                            '^BendAHarm/%s$|^BendMM3/%s$' %
                            ('.'.join(types[:3]), '.'.join(types[:3])))
                        rv1 = find_rest_value('^TorsCheby%i/%s$' %
                                              (m, '.'.join(types)))
                        self.valence.set_params(term.index,
                                                fc=0.0,
                                                rv0=rv0,
                                                rv1=rv1)
                        for index in term.slaves:
                            self.valence.set_params(index,
                                                    fc=0.0,
                                                    rv0=rv0,
                                                    rv1=rv1)

                    for term in self.valence.iter_masters(
                            '^CrossBendDih%i/.*/a1d$' % m, use_re=True):
                        types = term.basename.split('/')[1].split('.')
                        assert len(
                            types
                        ) == 4, 'Found angle cross terms with more/less than 4 atom types'
                        rv0 = find_rest_value(
                            '^BendAHarm/%s$|^BendMM3/%s$' %
                            ('.'.join(types[1:]), '.'.join(types[1:])))
                        rv1 = find_rest_value('^TorsCheby%i/%s$' %
                                              (m, '.'.join(types)))
                        self.valence.set_params(term.index,
                                                fc=0.0,
                                                rv0=rv0,
                                                rv1=rv1)
                        for index in term.slaves:
                            self.valence.set_params(index,
                                                    fc=0.0,
                                                    rv0=rv0,
                                                    rv1=rv1)

                    for term in self.valence.iter_masters(
                            '^CrossCBendDih%i/.*/a0d$' % m, use_re=True):
                        types = term.basename.split('/')[1].split('.')
                        assert len(
                            types
                        ) == 4, 'Found angle cross terms with more/less than 4 atom types'
                        rv0 = find_rest_value('^BendCLin/%s$' %
                                              ('.'.join(types[:3])))
                        rv1 = find_rest_value('^TorsCheby%i/%s$' %
                                              (m, '.'.join(types)))
                        self.valence.set_params(term.index,
                                                fc=0.0,
                                                rv0=rv0,
                                                rv1=rv1)
                        for index in term.slaves:
                            self.valence.set_params(index,
                                                    fc=0.0,
                                                    rv0=rv0,
                                                    rv1=rv1)

                    for term in self.valence.iter_masters(
                            '^CrossCBendDih%i/.*/a1d$' % m, use_re=True):
                        types = term.basename.split('/')[1].split('.')
                        assert len(
                            types
                        ) == 4, 'Found angle cross terms with more/less than 4 atom types'
                        rv0 = find_rest_value('^BendCLin/%s$' %
                                              ('.'.join(types[1:])))
                        rv1 = find_rest_value('^TorsCheby%i/%s$' %
                                              (m, '.'.join(types)))
                        self.valence.set_params(term.index,
                                                fc=0.0,
                                                rv0=rv0,
                                                rv1=rv1)
                        for index in term.slaves:
                            self.valence.set_params(index,
                                                    fc=0.0,
                                                    rv0=rv0,
                                                    rv1=rv1)

    def do_squarebend(self, thresshold=10 * deg):
        '''
            Identify bend patterns in which 4 atoms of type A surround a central
            atom of type B with A-B-A angles of 90/180 degrees. A simple
            harmonic pattern will not be adequate since a rest value of 90 and
            180 degrees is possible for the same A-B-A term. Therefore, a
            cosine term with multiplicity of 4 is used (which corresponds to a
            chebychev4 potential with sign=-1):

                  V = K/2*[1-cos(4*theta)]

            To identify the patterns, it is assumed that the rest values have
            already been estimated from the perturbation trajectories. For each
            master and slave of a BENDAHARM term, its rest value is computed and
            checked if it lies either the interval [90-thresshold,90+thresshold]
            or [180-thresshold,180]. If this is the case, the new cosine term
            is used.

            **Optional arguments**

            thresshold
                the (half) the width of the interval around 180 deg (90 degrees)
                to check if a square BA4
        '''
        for master in self.valence.iter_masters(label='BendAHarm'):
            rvs = np.zeros([len(master.slaves) + 1], float)
            rvs[0] = self.valence.get_params(master.index, only='rv')
            for i, islave in enumerate(master.slaves):
                rvs[1 + i] = self.valence.get_params(islave, only='rv')
            n90 = 0
            n180 = 0
            nother = 0
            for i, rv in enumerate(rvs):
                if 90 * deg - thresshold <= rv and rv <= 90 * deg + thresshold:
                    n90 += 1
                elif 180 * deg - thresshold <= rv and rv <= 180 * deg + thresshold:
                    n180 += 1
                else:
                    nother += 1
            if n90 > 0 and n180 > 0:
                log.dump(
                    '%s has rest values around 90 deg and 180 deg, converted to BendCheby4'
                    % master.basename)
                #modify master and slaves
                indices = [master.index]
                for slave in master.slaves:
                    indices.append(slave)
                for index in indices:
                    term = self.valence.terms[index]
                    self.valence.modify_term(
                        index, Chebychev4, [BendCos(*term.get_atoms())],
                        term.basename.replace('BendAHarm', 'BendCheby4'),
                        ['HC_FC_DIAG'], ['kjmol', 'au'])
                    self.valence.set_params(index, sign=-1)
                    for traj in self.trajectories:
                        if traj.term.index == index:
                            traj.active = False
                            traj.fc = None
                            traj.rv = None

    def do_bendclin(self, thresshold=5 * deg):
        '''
            No Harmonic bend can have a rest value equal to are large than
            180 deg - thresshold. If a master (or its slaves) has such a rest
            value, convert master and all slaves to BendCLin (which corresponds
            to a chebychev1 potential with sign=+1):

                0.5*K*[1+cos(theta)]
        '''
        for master in self.valence.iter_masters(label='BendAHarm'):
            indices = [master.index]
            for slave in master.slaves:
                indices.append(slave)
            found = False
            for index in indices:
                rv = self.valence.get_params(index, only='rv')
                if rv >= 180.0 * deg - thresshold:
                    found = True
                    break
            if found:
                log.dump(
                    '%s has rest value > 180-%.0f deg, converted to BendCheby1'
                    % (master.basename, thresshold / deg))
                for index in indices:
                    term = self.valence.terms[index]
                    self.valence.modify_term(
                        index, Chebychev1, [BendCos(*term.get_atoms())],
                        term.basename.replace('BendAHarm', 'BendCheby1'),
                        ['HC_FC_DIAG'], ['kjmol', 'au'])
                    self.valence.set_params(index, fc=0.0, sign=1.0)
                    for traj in self.trajectories:
                        if traj.term.index == index:
                            traj.rv = None
                            traj.fc = None
                            traj.active = False

    def do_sqoopdist_to_oopdist(self, thresshold=1e-4 * angstrom):
        '''
            Transform a SqOopdist term with a rest value that has been set to
            zero, to a term Oopdist (harmonic in Oopdist instead of square of
            Oopdist) with a rest value of 0.0 A.
        '''
        for master in self.valence.iter_masters(label='SqOopdist'):
            indices = [master.index]
            for slave in master.slaves:
                indices.append(slave)
            found = False
            for index in indices:
                rv = self.valence.get_params(index, only='rv')
                if rv <= thresshold:
                    found = True
                    break
            if found:
                log.dump(
                    '%s has rest value <= %.0f A^2, converted to Oopdist with d0=0'
                    % (master.basename, thresshold / angstrom))
                for index in indices:
                    term = self.valence.terms[index]
                    self.valence.modify_term(
                        index, Harmonic, [OopDist(*term.get_atoms())],
                        term.basename.replace('SqOopdist', 'Oopdist'),
                        ['HC_FC_DIAG'], ['kjmol/A**2', 'A'])
                    self.valence.set_params(index, fc=0.0, rv0=0.0)

    def run(self):
        '''
            Sequence of instructions, should be implemented in the inheriting
            classes. The various inheriting classes distinguish themselves by
            means of the instructions implemented in this routine.
        '''
        raise NotImplementedError