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properties.py
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"""Holds the class which computes important properties of the system, and
prepares them for output.
Copyright (C) 2013, Joshua More and Michele Ceriotti
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http.//www.gnu.org/licenses/>.
Classes:
Properties: This is the class that holds all the algorithms to calculate
the important properties that should be output.
Trajectories: This class deals with outputting all position data in the
appropriate format.
Functions:
getkey: This function strips the units and argument list specification
from a string specifying an output parameter.
getall: This function gives the keyword, units and argument list
specification from a string specifying an output parameter.
help_latex: This returns a string that can be used in the manual to
specify the different available outputs.
"""
__all__ = ['Properties', 'Trajectories', 'getkey', 'getall', 'help_latex']
import os
import numpy as np
from ipi.utils.messages import verbosity, info, warning
from ipi.utils.depend import *
from ipi.utils.units import Constants, unit_to_internal, unit_to_user
from ipi.utils.mathtools import logsumlog, h2abc_deg
from ipi.utils.io import *
from ipi.engine.atoms import *
from ipi.engine.cell import *
from ipi.engine.ensembles import *
from ipi.engine.forces import *
def getkey(pstring):
"""Strips units and argument lists from a property/trajectory keyword.
Args:
pstring: The string input by the user that specifies an output,
which in general will specify units and argument lists.
Returns: A string giving the keyword for the property, stripped of the
argument lists and units key words.
"""
pa = pstring.find('(')
if pa < 0:
pa = len(pstring)
pu = pstring.find('{')
if pu < 0:
pu = len(pstring)
return pstring[0:min(pa,pu)].strip()
def getall(pstring):
"""Returns the keyword, units and argument list separately.
Args:
pstring: The string input by the user that specifies an output,
which in general will specify units and argument lists.
Returns: A tuple giving the keyword for the property, and its units
argument list and key word argument list.
"""
unit = ""
arglist = ()
kwarglist = {}
unstart = len(pstring)
argstart = unstart
if '}' in pstring:
# the property has a user-defined unit
unstart = pstring.find('{')
unstop = pstring.find('}', unstart)
if unstop == -1:
raise ValueError("Incorrect format in units specification " + pstring)
unit = pstring[unstart+1:unstop]
if '(' in pstring:
# If the property has additional arguments
argstart = pstring.find('(')
argstop = pstring.find(')', argstart)
if argstop == -1:
raise ValueError("Incorrect format in argument list " + pstring)
argstr = pstring[argstart:argstop+1]
arglist = io_xml.read_tuple(argstr, delims="()", split=";", arg_type=str)
for arg in arglist:
# If a keyword argument is used
equals = arg.find('=')
if equals >= 0:
kwarglist[arg[0:equals].strip()] = arg[equals+1:].strip()
arglist = tuple(a for a in arglist if not a == arg)
pstring = pstring[0:min(unstart,argstart)].strip() # strips the arguments from pstring name
return (pstring, unit, arglist, kwarglist)
def help_latex(idict, standalone=True):
"""Function to generate a LaTeX formatted file.
Args:
idict: Either property_dict or traj_dict, to be used to
generate the help file.
standalone: A boolean giving whether the latex file produced will be a
stand-alone document, or will be intended as a section of a larger
document with cross-references between the different sections.
Returns:
A LaTeX formatted string.
"""
rstr = ""
if standalone:
#assumes that it is a stand-alone document, so must have document
#options.
rstr += r"\documentclass[12pt,fleqn]{report}"
rstr += r"""
\usepackage{etoolbox}
\usepackage{suffix}
\newcommand{\ipiitem}[3]{%
\ifblank{#1}{}{\ifstrequal{#1}{\underline{}}{}{
{\noindent\textbf{#1}:\rule{0.0pt}{1.05\baselineskip}\quad}}}% uses a strut to add a bit of vertical space
{#2}\parskip=0pt\par
\ifblank{#3}{}%
{ {\hfill\raggedleft\textit{\small #3}\par} }
}
"""
rstr += "\n\\begin{document}\n"
rstr += "The following are the different allowable ouputs:\n\\par"
for out in sorted(idict):
rstr += "\\ipiitem{" + out + "}"
if "longhelp" in idict[out]:
rstr += "{" + idict[out]['longhelp'] +"}"
else:
rstr += "{" + idict[out]['help'] +"}"
#see if there are additional attributes to print out
xstr = ""
if "dimension" in idict[out] and idict[out]['dimension'] != "undefined": #doesn't print out dimension if not necessary.
xstr += "dimension: " + idict[out]['dimension'] + '; '
if "size" in idict[out]:
xstr += "size: " + str(idict[out]['size']) +"; "
rstr += "{" + xstr + "}"
if standalone:
#ends the created document if it is not part of a larger document
rstr += "\\end{document}"
# Some escape characters are necessary for the proper latex formatting
rstr = rstr.replace('_', '\\_')
rstr = rstr.replace('\\\\_', '\\_')
rstr = rstr.replace('...', '\\ldots ')
rstr = rstr.replace('<', '$<$')
rstr = rstr.replace('>', '$>$')
rstr = rstr.replace('[', '$[$')
rstr = rstr.replace(']', '$]$')
return rstr
class Properties(dobject):
"""A proxy to compute and output properties of the system.
Takes the fundamental properties calculated during the simulation, and
prepares them for output. It also contains simple algorithms to calculate
other properties not calculated during the simulation itself, so that
these can also be output.
Attributes:
fd_delta: A float giving the size of the finite difference
parameter used in the Yamamoto kinetic energy estimator. Defaults
to _DEFAULT_FINDIFF.
_DEFAULT_FDERROR: A float giving the size of the minimum precision
allowed for the finite difference calculation in the Yamamoto kinetic
energy estimator.
_DEFAULT_MINFID: A float giving the maximum displacement in the Yamamoto
kinetic energy estimator.
dbeads: A dummy Beads object used in the Yamamoto kinetic energy
estimator.
dforces: A dummy Forces object used in the Yamamoto kinetic energy
estimator.
simul: The Simulation object containing the data to be output.
ensemble: An ensemble object giving the objects necessary for producing
the correct ensemble.
beads: A beads object giving the atoms positions.
nm: A normal modes object giving the normal mode representation.
cell: A cell object giving the system box.
forces: A forcefield object giving the force calculator for each
replica of the system.
property_dict: A dictionary containing all the properties that can be
output.
"""
_DEFAULT_FINDIFF = 1e-5
_DEFAULT_FDERROR = 1e-9
_DEFAULT_MINFID = 1e-12
def __init__(self):
"""Initialises Properties."""
self.property_dict = {
"step": { "dimension" : "number",
"help" : "The current simulation time step.",
'func': (lambda: (1 + self.simul.step))},
"time": { "dimension": "time",
"help": "The elapsed simulation time.",
'func': (lambda: (1 + self.simul.step)*self.ensemble.dt)},
"temperature": {"dimension": "temperature",
"help": "The current temperature, as obtained from the MD kinetic energy.",
"longhelp" : """The current temperature, as obtained from the MD kinetic energy of the (extended)
ring polymer. Takes a single, optional argument 'atom', which can be either an
atom label or an index (zero-based) to specify which species or individual atom
to output the temperature of. If not specified, all atoms are used and averaged.""",
'func': self.get_temp },
"density": { "dimension": "density",
"help": "The mass density of the physical system.",
'func': (lambda: self.beads.m.sum()/self.cell.V)},
"volume": { "dimension": "volume",
"help": "The volume of the cell box.",
'func': (lambda: self.cell.V) },
"cell_h": { "dimension" : "length",
"help": "The simulation cell as a matrix. Returns the 6 non-zero components in the form [xx, yy, zz, xy, xz, yz].",
"size": 6,
"func": (lambda: self.tensor2vec(self.cell.h))},
"cell_abcABC": {"dimension" : "undefined",
"help": "The lengths of the cell vectors and the angles between them in degrees as a list of the form [a, b, c, A, B, C]",
"longhelp": """The lengths of the cell vectors and the angles between them in degrees as a list of the
form [a, b, c, A, B, C], where A is the angle between the sides of length b and c in degrees, and B and C
are defined similarly. Since the output mixes different units, a, b and c can only be output in bohr.""",
"size": 6,
'func': (lambda: np.asarray(h2abc_deg(self.cell.h)))},
"conserved": { "dimension": "energy",
"help": "The value of the conserved energy quantity per bead.",
'func': (lambda: self.ensemble.econs/float(self.beads.nbeads))},
"potential": { "dimension" : "energy",
"help": "The physical system potential energy.",
'func': (lambda: self.forces.pot/self.beads.nbeads)},
"spring": { "dimension" : "energy",
"help": "The total spring potential energy between the beads of all the ring polymers in the system.",
'func': (lambda: self.beads.vpath*self.nm.omegan2/self.beads.nbeads)},
"kinetic_md": {"dimension" : "energy",
"help": "The kinetic energy of the (extended) classical system.",
"longhelp" : """The kinetic energy of the (extended) classical system. Takes an argument 'atom',
which can be either an atom label or index (zero based) to specify which species to find the
kinetic energy of. If not specified, all atoms are used.""",
'func': self.get_kinmd},
"kinetic_cv": {"dimension" : "energy",
"help": "The centroid-virial quantum kinetic energy of the physical system.",
"longhelp": """The centroid-virial quantum kinetic energy of the physical system.
Takes an argument 'atom', which can be either an atom label or index (zero based)
to specify which species to find the kinetic energy of. If not specified, all atoms are used.""",
'func': self.get_kincv},
"kinetic_tens":{"dimension" : "energy",
"help" : "The centroid-virial quantum kinetic energy tensor of the physical system.",
"longhelp" : """The centroid-virial quantum kinetic energy tensor of the physical system.
Returns the 6 independent components in the form [xx, yy, zz, xy, xz, yz]. Takes an
argument 'atom', which can be either an atom label or index (zero based) to specify
which species to find the kinetic tensor components of. If not specified, all atoms are used.""",
"size" : 6,
"func" : self.get_ktens},
"kinetic_ij": {"dimension" : "energy",
"help" : "The centroid-virial off-diagonal quantum kinetic energy tensor of the physical system.",
"longhelp" : """The centroid-virial off-diagonal quantum kinetic energy tensor of the physical system.
This computes the cross terms between atoms i and atom j, whose average is <p_i*p_j/(2*sqrt(m_i*m_j))>.
Returns the 6 independent components in the form [xx, yy, zz, xy, xz, yz]. Takes arguments 'i' and 'j',
which give the indices of the two desired atoms.""",
"size" : 6,
"func" : self.get_kij},
"r_gyration": { "dimension" : "length",
"help" : "The average radius of gyration of the selected ring polymers.",
"longhelp" : """The average radius of gyration of the selected ring polymers. Takes an
argument 'atom', which can be either an atom label or index (zero based) to specify which
species to find the radius of gyration of. If not specified, all atoms are used and averaged.""",
"func": self.get_rg},
"atom_x": { "dimension" : "length",
"help": "The position (x,y,z) of a particle given its index.",
"longhelp" : """The position (x,y,z) of a particle given its index. Takes arguments index
and bead (both zero based). If bead is not specified, refers to the centroid.""",
"size" : 3,
"func" : (lambda atom="", bead="-1": self.get_atom_vec(self.beads.q, atom=atom, bead=bead))},
"atom_v": { "dimension" : "velocity",
"help": "The velocity (x,y,z) of a particle given its index.",
"longhelp": """The velocity (x,y,z) of a particle given its index. Takes arguments index
and bead (both zero based). If bead is not specified, refers to the centroid.""",
"size" : 3,
"func" : (lambda atom="", bead="-1": self.get_atom_vec(self.beads.p/self.beads.m3, atom=atom, bead=bead))},
"atom_p": { "dimension" : "momentum",
"help": "The momentum (x,y,z) of a particle given its index.",
"longhelp": """The momentum (x,y,z) of a particle given its index. Takes arguments index
and bead (both zero based). If bead is not specified, refers to the centroid.""",
"size" : 3,
"func" : (lambda atom="", bead="-1": self.get_atom_vec(self.beads.p, atom=atom, bead=bead))},
"atom_f": { "dimension" : "force",
"help": "The force (x,y,z) acting on a particle given its index.",
"longhelp": """The force (x,y,z) acting on a particle given its index. Takes arguments index
and bead (both zero based). If bead is not specified, refers to the centroid.""",
"size" : 3,
"func" : (lambda atom="", bead="-1": self.get_atom_vec(self.forces.f, atom=atom, bead=bead))},
"stress_md": { "dimension": "pressure",
"size" : 6,
"help": "The total stress tensor of the (extended) classical system.",
"longhelp": """The total stress tensor of the (extended) classical system. Returns the 6
independent components in the form [xx, yy, zz, xy, xz, yz].""",
"func": (lambda: self.tensor2vec((self.forces.vir + self.nm.kstress)/self.cell.V))},
"pressure_md": {"dimension": "pressure",
"help": "The pressure of the (extended) classical system.",
"func": (lambda: np.trace((self.forces.vir + self.nm.kstress)/(3.0*self.cell.V)))},
"kstress_md": {"dimension": "pressure",
"size" : 6,
"help": "The kinetic stress tensor of the (extended) classical system.",
"longhelp": """The kinetic stress tensor of the (extended) classical system. Returns the 6
independent components in the form [xx, yy, zz, xy, xz, yz].""",
"func": (lambda: self.tensor2vec(self.nm.kstress/self.cell.V))},
"virial_md": { "dimension": "pressure",
"size" : 6,
"help": "The virial tensor of the (extended) classical system.",
"longhelp": """The virial tensor of the (extended) classical system. Returns the 6
independent components in the form [xx, yy, zz, xy, xz, yz].""",
"func": (lambda: self.tensor2vec(self.forces.vir/self.cell.V))},
"stress_cv": { "dimension": "pressure",
"size" : 6,
"help": "The total quantum estimator for the stress tensor of the physical system.",
"longhelp": """The total quantum estimator for the stress tensor of the physical system. Returns the
6 independent components in the form [xx, yy, zz, xy, xz, yz].""",
"func": (lambda: self.tensor2vec(self.forces.vir + self.kstress_cv())/(self.cell.V*self.beads.nbeads))},
"pressure_cv": {"dimension": "pressure",
"help": "The quantum estimator for pressure of the physical system.",
"func": (lambda: np.trace(self.forces.vir + self.kstress_cv())/(3.0*self.cell.V*self.beads.nbeads))},
"kstress_cv": {"dimension": "pressure",
"size" : 6,
"help": "The quantum estimator for the kinetic stress tensor of the physical system.",
"longhelp": """The quantum estimator for the kinetic stress tensor of the physical system.
Returns the 6 independent components in the form [xx, yy, zz, xy, xz, yz].""",
"func": (lambda: self.tensor2vec(self.kstress_cv()/(self.cell.V*self.beads.nbeads)))},
"virial_cv": { "dimension": "pressure",
"size" : 6,
"help": "The quantum estimator for the virial stress tensor of the physical system.",
"longhelp": """The quantum estimator for the virial stress tensor of the physical system.
Returns the 6 independent components in the form [xx, yy, zz, xy, xz, yz].""",
"func": (lambda: self.tensor2vec(self.forces.vir/(self.cell.V*self.beads.nbeads)))},
"displacedpath": { "dimension": "undefined",
"help": "The displaced path end-to-end distribution estimator",
"longhelp": """This is the estimator for the end-to-end distribution, that can be used to calculate the
particle momentum distribution as described in in L. Lin, J. A. Morrone, R. Car and M. Parrinello,
105, 110602 (2010), Phys. Rev. Lett. Takes arguments 'ux', 'uy' and 'uz', which are the components of
the path opening vector. Also takes an argument 'atom', which can be either an atom label or index
(zero based) to specify which species to find the end-to-end distribution estimator for. If not
specified, all atoms are used. Note that one atom is computed at a time, and that each path opening
operation costs as much as a PIMD step. Returns the average over the selected atoms of the estimator of
exp(-U(u)) for each frame.""",
"func": self.get_linlin},
"scaledcoords": { "dimension": "undefined",
"help" : "The scaled coordinates estimators that can be used to compute energy and heat capacity",
"longhelp": """Returns the estimators that are required to evaluate the scaled-coordinates estimators
for total energy and heat capacity, as described in T. M. Yamamoto,
J. Chem. Phys., 104101, 123 (2005). Returns eps_v and eps_v', as defined in that paper.
As the two estimators have a different dimensions, this can only be output in atomic units.
Takes one argument, 'fd_delta', which gives the value of the finite difference parameter used -
which defaults to """+ str(-self._DEFAULT_FINDIFF) + """. If the value of 'fd_delta' is negative,
then its magnitude will be reduced automatically by the code if the finite difference error
becomes too large.""",
'func': self.get_yama_estimators,
"size": 2},
"isotope_scfep": {"dimension": "undefined",
"size": 7,
'func': self.get_isotope_yama,
"help": "The scaled-coordinates free energy perturbation scaled mass KE estimator.",
"longhelp" : """Returns the (many) terms needed to compute the scaled-coordinates free energy
perturbation scaled mass KE estimator (M. Ceriotti, T. Markland, J. Chem. Phys. 138, 014112 (2013)).
Takes two arguments, 'alpha' and 'atom', which give the
scaled mass parameter and the atom of interest respectively, and default to '1.0' and ''. The
'atom' argument can either be the label of a particular kind of atom, or an index (zero based)
of a specific atom. This property computes, for each atom in the selection, an estimator for
the kinetic energy it would have had if it had the mass scaled by alpha. The 7 numbers output
are the average over the selected atoms of the log of the weights <h>, the average of the
squares <h**2>, the average of the un-weighted scaled-coordinates kinetic energies <T_CV>
and of the squares <T_CV**2>, the log sum of the weights LW=ln(sum(e**(-h))), the sum of the
re-weighted kinetic energies, stored as a log modulus and sign, LTW=ln(abs(sum(T_CV e**(-h))))
STW=sign(sum(T_CV e**(-h))). In practice, the best estimate of the estimator can be computed
as [sum_i exp(LTW_i)*STW_i]/[sum_i exp(LW_i)]. The other terms can be used to compute diagnostics
for the statistical accuracy of the re-weighting process. Note that evaluating this estimator costs
as much as a PIMD step for each atom in the list. The elements that are output have different
units, so the output can be only in atomic units.""" },
"isotope_tdfep": {"dimension" : "undefined",
"size" : 7,
'func': self.get_isotope_thermo,
"help": "The thermodynamic free energy perturbation scaled mass KE estimator.",
"longhelp" : """Returns the (many) terms needed to compute the thermodynamic free energy
perturbation scaled mass KE estimator (M. Ceriotti, T. Markland, J. Chem. Phys. 138, 014112 (2013)).
Takes two arguments, 'alpha' and 'atom', which give the
scaled mass parameter and the atom of interest respectively, and default to '1.0' and ''. The
'atom' argument can either be the label of a particular kind of atom, or an index (zero based)
of a specific atom. This property computes, for each atom in the selection, an estimator for
the kinetic energy it would have had if it had the mass scaled by alpha. The 7 numbers output
are the average over the selected atoms of the log of the weights <h>, the average of the
squares <h**2>, the average of the un-weighted scaled-coordinates kinetic energies <T_CV>
and of the squares <T_CV**2>, the log sum of the weights LW=ln(sum(e**(-h))), the sum of the
re-weighted kinetic energies, stored as a log modulus and sign, LTW=ln(abs(sum(T_CV e**(-h))))
STW=sign(sum(T_CV e**(-h))). In practice, the best estimate of the estimator can be computed
as [sum_i exp(LTW_i)*STW_i]/[sum_i exp(LW_i)]. The other terms can be used to compute diagnostics
for the statistical accuracy of the re-weighting process. Evaluating this estimator is inexpensive,
but typically the statistical accuracy is worse than with the scaled coordinates estimator.
The elements that are output have different
units, so the output can be only in atomic units.""" }
}
def bind(self, simul):
"""Binds the necessary objects from the simulation to calculate the
required properties.
Args:
simul: The Simulation object to be bound.
"""
self.ensemble = simul.ensemble
self.beads = simul.beads
self.nm = simul.nm
self.cell = simul.cell
self.forces = simul.forces
self.simul = simul
# dummy beads and forcefield objects so that we can use scaled and
# displaced path estimators without changing the simulation bead
# coordinates
self.dbeads = simul.beads.copy()
self.dforces = Forces()
self.dforces.bind(self.dbeads, self.simul.cell, self.simul.flist)
def __getitem__(self, key):
"""Retrieves the item given by key.
Note that if the key contains a string (arg1; arg2; ... )
then it will pass the appropriate positional arguments to the
calculation function of the property. Note the brackets and
the semi-colon separators. If instead we have the syntax
(arg1=val1;arg2; ... ), then the keyword/value pair (arg1,val1)
will be added to the keyword argument list. The appropriate key word
arguments will then be passed to the calculation function instead.
Similarly, if the key contains a string {unit}, then it will take
the string 'unit' and use it to define the units that the property
is output in.
Args:
key: A string contained in property_dict.
Returns:
The property labelled by the keyword key, along with its unit
keyword, and the argument lists for the function used to calculate
the property specified by the keyword key.
"""
(key, unit, arglist, kwarglist) = getall(key)
pkey = self.property_dict[key]
#pkey["func"](*arglist,**kwarglist) gives the value of the property
#in atomic units. unit_to_user() returns the value in the user
#specified units.
if "dimension" in pkey and unit != "":
return unit_to_user(pkey["dimension"], unit, pkey["func"](*arglist,**kwarglist))
else:
return pkey["func"](*arglist,**kwarglist)
def tensor2vec(self, tensor):
"""Takes a 3*3 symmetric tensor and returns it as a 1D array,
containing the elements [xx, yy, zz, xy, xz, yz].
"""
return np.array([tensor[0,0], tensor[1,1], tensor[2,2], tensor[0,1], tensor[0,2], tensor[1,2]])
def get_atom_vec(self, prop_vec, atom="", bead="-1"):
"""Gives a vector for one atom.
Args:
prop_vec: An array from which to take the atomic vector from.
atom: The index of the atom for which the vector will
be output.
bead: The index of the replica of the atom for which the
vector will be output. If less than 0, then the centroid is used.
"""
if atom == "":
raise IndexError("Must specify the index for atom_vec property")
atom = int(atom)
bead = int(bead)
if atom >= self.beads.natoms:
raise IndexError("Cannot output atom_vec property as atom index %d is larger than the number of atoms" % atom)
if bead >= self.beads.nbeads:
raise IndexError("Cannot output atom_vec property as bead index %d is larger than the number of beads" % bead)
if bead < 0:
atom_vec = np.zeros(3)
for b in range(self.beads.nbeads):
atom_vec += prop_vec[b,3*atom:3*(atom+1)]
return atom_vec/float(self.beads.nbeads)
else:
return prop_vec[bead,3*atom:3*(atom+1)]
def get_temp(self, atom=""):
"""Calculates the MD kinetic temperature.
Note that in the case that the centre of mass constraint there will be
3 fewer degrees of freedom than without, so this has to be taken into
account when calculating the kinetic temperature.
Args:
atom: If given, specifies the atom to give the temperature
for. If not, then the simulation temperature.
"""
if self.ensemble.fixcom:
mdof = 3
else:
mdof = 0
if atom == "":
# use the KE computed in the NM representation in order to avoid problems when mass scaling is used
kedof = self.get_kinmd()/(3*self.beads.natoms*self.beads.nbeads - mdof)
else:
try:
#iatom gives the index of the atom to be studied
iatom = int(atom)
latom = ""
if iatom >= self.beads.natoms:
raise IndexError("Cannot output temperature as atom index %d is larger than the number of atoms" % iatom)
except ValueError:
#here 'atom' is a label rather than an index which is stored in latom
iatom = -1
latom = atom
ncount = 0
for i in range(self.beads.natoms):
if (iatom == i or latom == self.beads.names[i]):
ncount += 1
if ncount == 0:
raise IndexError("Couldn't find an atom which matched the argument of temperature")
# "spreads" the COM removal correction evenly over all the atoms...
kedof = self.get_kinmd(atom)/ncount*(self.beads.natoms/(3.0*self.beads.natoms*self.beads.nbeads - mdof))
return kedof/(0.5*Constants.kb)
def get_kincv(self, atom=""):
"""Calculates the quantum centroid virial kinetic energy estimator.
Args:
atom: If given, specifies the atom to give the kinetic energy
for. If not, the system kinetic energy is given.
"""
try:
#iatom gives the index of the atom to be studied
iatom = int(atom)
latom = ""
if iatom >= self.beads.natoms:
raise IndexError("Cannot output kinetic energy as atom index %d is larger than the number of atoms" % iatom)
except ValueError:
#here 'atom' is a label rather than an index which is stored in latom
iatom = -1
latom = atom
q = depstrip(self.beads.q)
qc = depstrip(self.beads.qc)
f = depstrip(self.forces.f)
acv = 0.0
ncount = 0
for i in range(self.beads.natoms):
if (atom != "" and iatom != i and latom != self.beads.names[i]):
continue
kcv = 0.0
k = 3*i
for b in range(self.beads.nbeads):
kcv += (q[b,k] - qc[k])* f[b,k] + (q[b,k+1] - qc[k+1])* f[b,k+1] + (q[b,k+2] - qc[k+2])* f[b,k+2]
kcv *= -0.5/self.beads.nbeads
kcv += 1.5*Constants.kb*self.ensemble.temp
acv += kcv
ncount += 1
if ncount == 0:
warning("Couldn't find an atom which matched the argument of kinetic energy, setting to zero.", verbosity.medium)
return acv
def get_kinmd(self, atom=""):
"""Calculates the classical kinetic energy of the simulation (p^2/2m)
Args:
atom: If given, specifies the atom to give the kinetic energy
for. If not, the simulation kinetic energy is given.
"""
if atom == "":
return self.nm.kin/self.beads.nbeads
else:
try:
#iatom gives the index of the atom to be studied
iatom = int(atom)
latom = ""
if iatom >= self.beads.natoms:
raise IndexError("Cannot output kinetic energy as atom index %d is larger than the number of atoms" % iatom)
except ValueError:
#here 'atom' is a label rather than an index which is stored in latom
iatom = -1
latom = atom
pnm = depstrip(self.nm.pnm)
dm3 = depstrip(self.nm.dynm3)
kmd = 0.0
ncount = 0
for i in range(self.beads.natoms):
if (atom != "" and iatom != i and latom != self.beads.names[i]):
continue
k = 3*i
for b in range(self.beads.nbeads):
kmd += (pnm[b,k]**2 + pnm[b,k+1]**2 + pnm[b,k+2]**2)/(2.0*dm3[b,k])
ncount += 1
if ncount == 0:
warning("Couldn't find an atom which matched the argument of kinetic energy, setting to zero.", verbosity.medium)
return kmd/self.beads.nbeads
def get_ktens(self, atom=""):
"""Calculates the quantum centroid virial kinetic energy
TENSOR estimator.
Args:
atom: The index of the atom for which the kinetic energy tensor
is to be output, or the index of the type of atoms for which
it should be output.
"""
try:
#iatom gives the index of the atom to be studied
iatom = int(atom)
latom = ""
if iatom >= self.beads.natoms:
raise IndexError("Cannot output kinetic tensor as atom index %d is larger than the number of atoms" % iatom)
except ValueError:
#here 'atom' is a label rather than an index which is stored in latom
iatom = -1
latom = atom
tkcv = np.zeros((6),float)
ncount = 0
for i in range(self.beads.natoms):
if (atom != "" and iatom != i and latom != self.beads.names[i]):
continue
tkcv += self.get_kij(str(i), str(i))
ncount += 1
if ncount == 0:
warning("Couldn't find an atom which matched the argument of kinetic tensor, setting to zero.", verbosity.medium)
return tkcv
def get_kij(self, ni="0", nj="0"):
"""Calculates the quantum centroid virial kinetic energy
TENSOR estimator for two possibly different atom indices.
Args:
ni: The index of atom i.
nj: The index of atom j.
Returns:
The contribution to the kinetic energy tensor estimator from
the interactions between atom i and atom j.
"""
i = int(ni)
j = int(nj)
if i >= self.beads.natoms:
raise IndexError("Cannot output kinetic_ij as atom index %d is larger than the number of atoms" % i)
if j >= self.beads.natoms:
raise IndexError("Cannot output kinetic_ij as atom index %d is larger than the number of atoms" % j)
mi = self.beads.m[i]
mj = self.beads.m[j]
ai = 3*i
aj = 3*j
q = depstrip(self.beads.q)
qc = depstrip(self.beads.qc)
f = depstrip(self.forces.f)
# I implement this for the most general case. In practice T_ij = <p_i p_j>/(2sqrt(m_i m_j))
kcv = np.zeros((6),float)
for b in range(self.beads.nbeads):
kcv[0] += mi*(q[b,ai] - qc[ai]) *f[b,aj] + mj*(q[b,aj] - qc[aj]) *f[b,ai] #Txx
kcv[1] += mi*(q[b,ai+1] - qc[ai+1])*f[b,aj+1] + mj*(q[b,aj+1] - qc[aj+1])*f[b,ai+1] #Tyy
kcv[2] += mi*(q[b,ai+2] - qc[ai+2])*f[b,aj+2] + mj*(q[b,aj+2] - qc[aj+2])*f[b,ai+2] #Tzz
kcv[3] += mi*(q[b,ai] - qc[ai])* f[b,aj+1] + mj*(q[b,aj+1] - qc[aj+1])*f[b,ai] #Txy
kcv[4] += mi*(q[b,ai] - qc[ai])* f[b,aj+2] + mj*(q[b,aj+2] - qc[aj+2])*f[b,ai] #Txz
kcv[5] += mi*(q[b,ai+1] - qc[ai+1])*f[b,aj+2] + mj*(q[b,aj+2] - qc[aj+2])*f[b,ai+1] #Tyz
kcv *= -0.5/(self.beads.nbeads*2*np.sqrt(mi*mj))
if i == j:
kcv[0:3] += 0.5*Constants.kb*self.ensemble.temp
return kcv
def get_rg(self, atom=""):
"""Calculates the radius of gyration of the ring polymers.
Args:
atom: If given, specifies the atom to give the gyration radius
for. If not, the system average gyration radius is given.
"""
try:
#iatom gives the index of the atom to be studied
iatom = int(atom)
latom = ""
if iatom >= self.beads.natoms:
raise IndexError("Cannot output gyration radius as atom index %d is larger than the number of atoms" % iatom)
except ValueError:
#here 'atom' is a label rather than an index which is stored in latom
iatom = -1
latom = atom
q = depstrip(self.beads.q)
qc = depstrip(self.beads.qc)
nat = self.beads.natoms
nb = self.beads.nbeads
rg_tot = 0.0
ncount = 0
for i in range(nat):
if (atom != "" and iatom != i and latom != self.beads.names[i]):
continue
rg_at = 0.0
for j in range(nb):
dq = q[j,3*i:3*(i+1)] - qc[3*i:3*(i+1)]
rg_at += np.dot(dq, dq)
ncount += 1
rg_tot += np.sqrt(rg_at/float(nb))
if ncount == 0:
raise IndexError("Couldn't find an atom which matched the argument of r_gyration")
return rg_tot/float(ncount)
def kstress_cv(self):
"""Calculates the quantum centroid virial kinetic stress tensor
estimator.
Note that this is not divided by the volume or the number of beads.
Returns:
A 3*3 tensor with all the components of the tensor.
"""
kst = np.zeros((3,3),float)
q = depstrip(self.beads.q)
qc = depstrip(self.beads.qc)
pc = depstrip(self.beads.pc)
m = depstrip(self.beads.m)
fall = depstrip(self.forces.f)
na3 = 3*self.beads.natoms
for b in range(self.beads.nbeads):
for i in range(3):
for j in range(i,3):
kst[i,j] -= np.dot(q[b,i:na3:3] - qc[i:na3:3],
fall[b,j:na3:3])
# return the CV estimator MULTIPLIED BY NBEADS -- again for consistency with the virial, kstress_MD, etc...
for i in range(3):
kst[i,i] += self.beads.nbeads * ( np.dot(pc[i:na3:3],pc[i:na3:3]/m) )
return kst
def opening(self, bead):
"""Path opening function, used in linlin momentum distribution
estimator.
Args:
bead: The index of the bead to shift.
"""
return bead/float(self.beads.nbeads) + 0.5*(1.0/self.beads.nbeads - 1)
def get_linlin(self, ux="0", uy="0", uz="0", atom=""):
"""Calculates the end-to-end distribution for a particular path opening
vector.
Args:
ux: The x-component of the path opening vector.
uy: The y-component of the path opening vector.
uz: The z-component of the path opening vector.
atom: If given, specifies the atom to give the kinetic energy
for. If not, the simulation kinetic energy is given.
"""
try:
#iatom gives the index of the atom to be studied
iatom = int(atom)
latom = ""
if iatom >= self.beads.natoms:
raise IndexError("Cannot output linlin estimator as atom index %d is larger than the number of atoms" % iatom)
except ValueError:
#here 'atom' is a label rather than an index which is stored in latom
iatom = -1
latom = atom
beta = 1.0/(self.ensemble.temp*Constants.kb)
u = np.array([float(ux), float(uy), float(uz)])
u_size = np.dot(u,u)
q = depstrip(self.beads.q)
nat = self.beads.natoms
nb = self.beads.nbeads
nx_tot = 0.0
ncount = 0
for i in range(nat):
if (atom != "" and iatom != i and latom != self.beads.names[i]):
continue
mass = self.beads.m[i]
self.dbeads.q[:] = q
for b in range(nb):
self.dbeads.q[b,3*i:3*(i+1)] += self.opening(b)*u
dV = self.dforces.pot - self.forces.pot
n0 = np.exp(-mass*u_size/(2.0*beta*Constants.hbar**2))
nx_tot += n0*np.exp(-dV*beta/float(self.beads.nbeads))
ncount += 1
if ncount == 0:
raise IndexError("Couldn't find an atom which matched the argument of linlin")
return nx_tot/float(ncount)
def get_yama_estimators(self, fd_delta= - _DEFAULT_FINDIFF):
"""Calculates the quantum scaled coordinate kinetic energy estimator.
Uses a finite difference method to calculate the estimators
needed to calculate the energy and heat capacity of the system, as
shown in Takeshi M. Yamamoto, Journal of Chemical Physics,
104101, 123 (2005). Returns both eps_v and eps_v' as defined in
the above article. Note that heat capacity is calculated as
beta**2*kboltzmann*(<eps_v**2> - <eps_v>**2 - <eps_v'>), and the
energy of the system as <eps_v>.
Args:
fd_delta: the relative finite difference in temperature to apply in
computing finite-difference quantities. If it is negative, will be
scaled down automatically to avoid discontinuities in the potential.
"""
dbeta = abs(float(fd_delta))
beta = 1.0/(Constants.kb*self.ensemble.temp)
qc = depstrip(self.beads.centroid.q)
q = depstrip(self.beads.q)
v0 = self.forces.pot/self.beads.nbeads
while True:
splus = np.sqrt(1.0 + dbeta)
sminus = np.sqrt(1.0 - dbeta)
for b in range(self.beads.nbeads):
self.dbeads[b].q = qc*(1.0 - splus) + splus*q[b,:]
vplus = self.dforces.pot/self.beads.nbeads
for b in range(self.beads.nbeads):
self.dbeads[b].q = qc*(1.0 - sminus) + sminus*q[b,:]
vminus = self.dforces.pot/self.beads.nbeads
if (fd_delta < 0 and abs((vplus + vminus)/(v0*2) - 1.0) > self._DEFAULT_FDERROR and dbeta > self._DEFAULT_MINFID):
dbeta *= 0.5
info("Reducing displacement in Yamamoto kinetic estimator", verbosity.low)
continue
else:
eps = ((1.0 + dbeta)*vplus - (1.0 - dbeta)*vminus)/(2*dbeta)
eps += 0.5*(3*self.beads.natoms)/beta
eps_prime = ((1.0 + dbeta)*vplus + (1.0 - dbeta)*vminus - 2*v0)/(dbeta**2*beta)
eps_prime -= 0.5*(3*self.beads.natoms)/beta**2
break
return np.asarray([eps, eps_prime])
def get_isotope_yama(self, alpha="1.0", atom=""):
"""Gives the components of the yamamoto scaled-mass KE estimator
for a given atom index.
Args:
alpha: m'/m the mass ratio
atom: the index of the atom to compute the isotope fractionation
pair for, or a label
Returns:
a tuple from which one can reconstruct all that is needed to
compute the SMKEE, and its statistical accuracy:
(sum_deltah, sum_ke, log(sum(weights)), log(sum(weight*ke)),
sign(sum(weight*ke)) )
"""
try:
#iatom gives the index of the atom to be studied
iatom = int(atom)
latom = ""
if iatom >= self.beads.natoms:
raise IndexError("Cannot output scaled-mass kinetic energy estimator as atom index %d is larger than the number of atoms" % iatom)
except ValueError:
#here 'atom' is a label rather than an index which is stored in latom
iatom = -1
latom = atom
alpha = float(alpha)
atcv = 0.0
atcv2 = 0.0
alogr = 0.0
alogr2 = 0.0
law = 0.0
lawke = 0.0
sawke = 1.0
ni = 0
# strips dependency control since we are not gonna change the true beads in what follows
q = depstrip(self.beads.q)
f = depstrip(self.forces.f)
qc = depstrip(self.beads.qc)
for i in range(self.beads.natoms):
# selects only the atoms we care about
if (atom != "" and iatom != i and latom != self.beads.names[i]):
continue
ni += 1
# arranges coordinate-scaled beads in a auxiliary beads object
self.dbeads.q[:] = q[:]
for b in range(self.beads.nbeads):
self.dbeads.q[b,3*i:3*(i+1)] = ( qc[3*i:3*(i+1)]+
np.sqrt(1.0/alpha)*(q[b,3*i:3*(i+1)]-qc[3*i:3*(i+1)]) )
tcv = 0.0
for b in range(self.beads.nbeads):
tcv += np.dot( (self.dbeads.q[b,3*i:3*(i+1)]-self.dbeads.qc[3*i:3*(i+1)]),
self.dforces.f[b,3*i:3*(i+1)] )
tcv *= -0.5/self.beads.nbeads
tcv += 1.5*Constants.kb*self.simul.ensemble.temp
logr = (self.dforces.pot-self.forces.pot)/(Constants.kb*self.simul.ensemble.temp*self.beads.nbeads)
atcv += tcv
atcv2 += tcv*tcv
alogr += logr
alogr2 += logr*logr;
#accumulates log averages in a way which preserves accuracy
if (ni == 1):
law = -logr
else:
(law, drop) = logsumlog( (law,1.0), (-logr,1.0))
#here we need to take care of the sign of tcv, which might as well be
#negative... almost never but...
if (ni == 1):
lawke = -logr + np.log(abs(tcv))
sawke = np.sign(tcv);
else:
(lawke, sawke) = logsumlog( (lawke, sawke), (-logr+np.log(abs(tcv)), np.sign(tcv)) )
if ni == 0:
raise IndexError("Couldn't find an atom which matched the argument of isotope_y")
return np.asarray([alogr/ni, alogr2/ni, atcv/ni, atcv2/ni, law, lawke, sawke])
def get_isotope_thermo(self, alpha="1.0", atom=""):
"""Gives the components of the thermodynamic scaled-mass KE
estimator for a given atom index.
Args:
alpha: m'/m the mass ratio
atom: the index of the atom to compute the isotope fractionation
pair for, or a label
Returns:
a tuple from which one can reconstruct all that is needed to
compute the SMKEE:
(sum_deltah, sum_ke, log(sum(weights)), log(sum(weight*ke)),
sign(sum(weight*ke)) )
"""
try:
#iatom gives the index of the atom to be studied
iatom = int(atom)
latom = ""
if iatom >= self.beads.natoms:
raise IndexError("Cannot output scaled-mass kinetic energy estimator as atom index %d is larger than the number of atoms" % iatom)
except ValueError:
#here 'atom' is a label rather than an index which is stored in latom
iatom = -1
latom = atom
alpha = float(alpha)
atcv = 0.0
alogr = 0.0
atcv2 = 0.0
alogr2 = 0.0
law = 0.0
lawke = 0.0
sawke = 1.0
ni = 0
# strips dependency control since we are not gonna change the true beads in what follows
q = depstrip(self.beads.q)
f = depstrip(self.forces.f)
qc = depstrip(self.beads.qc)
for i in range(self.beads.natoms):
# selects only the atoms we care about
if (atom != "" and iatom != i and latom != self.beads.names[i]):
continue
ni += 1
spr = 0.0
for b in range(1,self.beads.nbeads):
for j in range(3*i,3*(i+1)):
spr += (q[b,j]-q[b-1,j])**2
for j in range(3*i,3*(i+1)):
spr += (q[self.beads.nbeads-1,j]-q[0,j])**2
spr *= 0.5*self.beads.m[i]*self.nm.omegan2
# centroid virial contribution from atom i
tcv = 0.0
for b in range(self.beads.nbeads):
tcv += np.dot( (q[b,3*i:3*(i+1)]-qc[3*i:3*(i+1)]), f[b,3*i:3*(i+1)])
tcv *= -0.5/self.beads.nbeads
tcv += 1.5*Constants.kb*self.simul.ensemble.temp
logr = (alpha-1)*spr/(Constants.kb*self.simul.ensemble.temp*self.beads.nbeads)
atcv += tcv
atcv2 += tcv*tcv
alogr += logr
alogr2 += logr*logr
#accumulates log averages in a way which preserves accuracy
if (ni == 1):
law = -logr
else:
(law, drop) = logsumlog( (law,1.0), (-logr,1.0))
#here we need to take care of the sign of tcv, which might as well be
#negative... almost never but...
if (ni == 1):
lawke = -logr + np.log(abs(tcv))
sawke = np.sign(tcv)
else:
(lawke, sawke) = logsumlog( (lawke, sawke), (-logr+np.log(abs(tcv)), np.sign(tcv)) )
if ni == 0:
raise IndexError("Couldn't find an atom which matched the argument of isotope_y")
return np.asarray([alogr/ni, alogr2/ni, atcv/ni, atcv2/ni, law, lawke, sawke])
class Trajectories(dobject):
"""A simple class to take care of output of trajectory data.
Attributes:
simul: The simulation object from which the position data will be
obtained.
fatom: A dummy beads object used so that individual replica trajectories
can be output.
traj_dict: A dictionary containing all the trajectories that can be
output.
"""
def __init__(self):
"""Initialises a Trajectories object."""
self.traj_dict = {
# Note that here we want to return COPIES of the different arrays, so we make sure to make an operation in order not to return a reference.
"positions": { "dimension" : "length",
"help": "The atomic coordinate trajectories. Will print out one file per bead, unless the bead attribute is set by the user.",
'func': (lambda : 1.0*self.simul.beads.q)},
"velocities": {"dimension" : "velocity",
"help": "The velocity trajectories. Will print out one file per bead, unless the bead attribute is set by the user.",
'func': (lambda : self.simul.beads.p/self.simul.beads.m3)},
"momenta": {"dimension" : "momentum",
"help": "The momentum trajectories. Will print out one file per bead, unless the bead attribute is set by the user.",
'func': (lambda : 1.0*self.simul.beads.p)},
"forces": { "dimension" : "force",
"help": "The force trajectories. Will print out one file per bead, unless the bead attribute is set by the user.",
'func': (lambda : 1.0*self.simul.forces.f)},
"x_centroid": {"dimension" : "length",
"help": "The centroid coordinates.",
'func': (lambda : 1.0*self.simul.beads.qc)},
"v_centroid": {"dimension" : "velocity",
"help": "The centroid velocity.",
'func': (lambda : self.simul.beads.pc/self.simul.beads.m3[0])},
"p_centroid": {"dimension" : "momentum",
"help": "The centroid momentum.",
'func': (lambda : 1.0*self.simul.beads.pc)},
"f_centroid": {"dimension" : "force",
"help": "The force acting on the centroid.",
'func': (lambda : np.sum(self.simul.forces.f,0)/float(self.simul.beads.nbeads))},
"kinetic_cv": {"dimension" : "energy",
"help": "The centroid virial quantum kinetic energy estimator for each atom, resolved into Cartesian components [xx, yy, zz]",
'func': self.get_akcv},
"kinetic_od": {"dimension" : "energy",
"help": "The off diagonal elements of the centroid virial quantum kinetic energy tensor [xy, xz, yz]",
'func': self.get_akcv_od},
"r_gyration": {"dimension" : "length",
"help": "The radius of gyration of the ring polymer, for each atom and resolved into Cartesian components [xx, yy, zz]",
'func': self.get_rg},
"extras": { "help": """The additional data returned by the client code, printed verbatim. Will print
out one file per bead, unless the bead attribute is set by the user.""",
'func': (lambda : self.simul.forces.extras)}
}
def bind(self, simul):
""" Binds to a simulation object to fetch atomic and force data.
Args:
simul: The simulation object that will be managed by this Trajectories.
"""
self.simul = simul
self.fatom = simul.beads[0].copy()
def get_akcv(self):
"""Calculates the contribution to the kinetic energy due to each degree
of freedom.
"""
rv = np.zeros(self.simul.beads.natoms*3)
for b in range(self.simul.beads.nbeads):
rv[:] += (self.simul.beads.q[b]-self.simul.beads.qc)*self.simul.forces.f[b]
rv *= -0.5/self.simul.beads.nbeads
rv += 0.5*Constants.kb*self.simul.ensemble.temp
return rv
def get_akcv_od(self):
"""Calculates the "off-diagonal" contribution to the kinetic energy tensor
due to each atom.
"""
rv = np.zeros((self.simul.beads.natoms,3))
# helper arrays to make it more obvious what we are computing
dq = np.zeros((self.simul.beads.natoms,3))
f = np.zeros((self.simul.beads.natoms,3))
for b in range(self.simul.beads.nbeads):
dq[:] = (self.simul.beads.q[b]-self.simul.beads.qc).reshape((self.simul.beads.natoms,3))
f[:] = self.simul.forces.f[b].reshape((self.simul.beads.natoms,3))
rv[:,0] += dq[:,0]*f[:,1] + dq[:,1]*f[:,0]
rv[:,1] += dq[:,0]*f[:,2] + dq[:,2]*f[:,0]
rv[:,2] += dq[:,1]*f[:,2] + dq[:,2]*f[:,1]
rv *= 0.5
rv *= -0.5/self.simul.beads.nbeads
return rv.reshape(self.simul.beads.natoms*3)
def get_rg(self):
"""Calculates the radius of gyration of the ring polymers.
Computes separately the x, y, z contributions so that the actual
gyration radius can be recovered as sqrt(rx^2+ry^2+rz^2).
"""
q = depstrip(self.simul.beads.q)
qc = depstrip(self.simul.beads.qc)
nat = self.simul.beads.natoms
nb = self.simul.beads.nbeads
rg = np.zeros(3*nat)
for i in range(nb):
for j in range(nat):
dq = q[i,3*j:3*(j+1)] - qc[3*j:3*(j+1)]
rg[3*j:3*(j+1)] += dq*dq
return np.sqrt(rg/float(nb))
def __getitem__(self, key):
"""Retrieves the item given by key.
Note that if the key contains a string (arg1; arg2; ... )
then it will pass the appropriate positional arguments to the
calculation function of the property. Note the brackets and
the semi-colon separators. If instead we have the syntax
(arg1=val1;arg2; ... ), then the keyword/value pair (arg1,val1)
will be added to the keyword argument list. The appropriate key word
arguments will then be passed to the calculation function instead.
Similarly, if the key contains a string {unit}, then it will take
the string 'unit' and use it to define the units that the trajectory
is output in.
Args:
key: A string contained in trajectory_dict.
Returns:
The trajectory labelled by the keyword key, along with its unit
keyword, and the argument lists for the function used to calculate
the trajectory specified by the keyword key.
"""
(key, unit, arglist, kwarglist) = getall(key)
pkey = self.traj_dict[key]
#pkey["func"](*arglist,**kwarglist) gives the value of the trajectory
#in atomic units. unit_to_user() returns the value in the user
#specified units.
if "dimension" in pkey and unit != "":
return unit_to_user(pkey["dimension"], unit, 1.0) * pkey["func"](*arglist,**kwarglist)
else:
return pkey["func"](*arglist,**kwarglist)
def print_traj(self, what, stream, b=0, format="pdb", cell_units="atomic_unit", flush=True):
"""Prints out a frame of a trajectory for the specified quantity and bead.
Args:
what: A string specifying what to print.
b: The bead index. Defaults to 0.
stream: A reference to the stream on which data will be printed.
format: The output file format.
cell_units: The units used to specify the cell parameters.
flush: A boolean which specifies whether to flush the output buffer
after each write to file or not.
"""
cq = self[what]
if getkey(what) in [ "extras" ] :
stream.write(" #*EXTRAS*# Step: %10d Bead: %5d \n" % (self.simul.step+1, b) )
stream.write(cq[b])
stream.write("\n")
if flush :
stream.flush()
os.fsync(stream)
return
elif getkey(what) in [ "positions", "velocities", "forces" ] :
self.fatom.q[:] = cq[b]
else:
self.fatom.q[:] = cq
fcell = Cell()
fcell.h = self.simul.cell.h*unit_to_user("length", cell_units, 1.0)
if format == "pdb":
io_pdb.print_pdb(self.fatom, fcell, stream, title=("Traj: %s Step: %10d Bead: %5d " % (what, self.simul.step+1, b) ) )
elif format == "xyz":
io_xyz.print_xyz(self.fatom, fcell, stream, title=("Traj: %s Step: %10d Bead: %5d " % (what, self.simul.step+1, b) ) )
elif format == "bin":
io_binary.print_bin(self.fatom, fcell, stream, title=("Traj: %s Step: %10d Bead: %5d " % (what, self.simul.step+1, b) ) )
if flush :
stream.flush()
os.fsync(stream)

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