def __init__(self, f, start=0, step=1000, flush=None):
"""
**Argument:**
f
A h5.File object to write the restart information to.
**Optional arguments:**
start
The first iteration at which this hook should be called.
step
The hook will be called every `step` iterations.
flush
Flush the h5.File object every `flush` iterations so it can be
read up to the latest flush. This is useful to avoid data loss
for long calculations or to monitor running calculations.
Note that flushing too often might impact performance of hdf5.
"""
self.f = f
self.flush = flush
self.state = None
self.default_state = None
Hook.__init__(self, start, step)
def __init__(self, start=0, step=1):
"""
**Optional arguments:**
start
The first iteration at which this hook should be called.
step
The hook will be called every `step` iterations.
"""
self.econs_correction = 0.0
Hook.__init__(self, start=0, step=1)
def __init__(self, start=0, step=1):
"""
**Optional arguments:**
start
The first iteration at which this hook should be called.
step
The hook will be called every `step` iterations.
"""
self.econs_correction = 0.0
Hook.__init__(self, start=0, step=1)
def __init__(self, f, start=0, step=1):
"""
**Argument:**
f
A h5.File object to write the trajectory to.
**Optional arguments:**
start
The first iteration at which this hook should be called.
step
The hook will be called every `step` iterations.
"""
self.f = f
Hook.__init__(self, start, step)
def __init__(self, f, start=0, step=1000):
"""
**Argument:**
f
A h5.File object to write the restart information to.
**Optional arguments:**
start
The first iteration at which this hook should be called.
step
The hook will be called every `step` iterations.
"""
self.f = f
self.state = None
self.default_state = None
Hook.__init__(self, start, step)
def __init__(self, fn_xyz, select=None, start=0, step=1):
"""
**Argument:**
fn_xyz
A filename to write the XYZ trajectory too.
**Optional arguments:**
select
A list of atom indexes that should be written to the trajectory
output. If not given, all atoms are included.
start
The first iteration at which this hook should be called.
step
The hook will be called every `step` iterations.
"""
self.fn_xyz = fn_xyz
self.select = select
self.xyz_writer = None
Hook.__init__(self, start, step)
def __init__(self, fn_xyz, select=None, start=0, step=1):
"""
**Argument:**
fn_xyz
A filename to write the XYZ trajectory too.
**Optional arguments:**
select
A list of atom indexes that should be written to the trajectory
output. If not given, all atoms are included.
start
The first iteration at which this hook should be called.
step
The hook will be called every `step` iterations.
"""
self.fn_xyz = fn_xyz
self.select = select
self.xyz_writer = None
Hook.__init__(self, start, step)
def __init__(self, f, start=0, step=1, flush=None):
"""
**Argument:**
f
A h5.File object to write the trajectory to.
**Optional arguments:**
start
The first iteration at which this hook should be called.
step
The hook will be called every `step` iterations.
flush
Flush the h5.File object every `flush` iterations so it can be
read up to the latest flush. This is useful to avoid data loss
for long calculations or to monitor running calculations.
Note that flushing too often might impact performance of hdf5.
"""
self.f = f
self.flush = flush
Hook.__init__(self, start, step)
def __init__(self, start=0, step=1):
Hook.__init__(self, start, step)
self.time0 = None
def __init__(self, start=0, step=1):
Hook.__init__(self, start, step)
self.time0 = None
def __init__(self, ff, cv, sigma, K, f=None, start=0, step=1,
restart_file=None, tempering=0, periodicities=None, comlist=None):
"""
**Arguments:**
ff
A ForceField instance
cv
A single ``CollectiveVariable`` or a list of ``CollectiveVariable``
instances.
sigma
The width of the Gaussian or a NumPy array [Ncv] specifying the
widths of the Gaussians
K
The prefactor of the Gaussian hills.
**Optional arguments:**
f
A h5.File object to write the Gaussian hills to.
start
The first iteration at which a Gaussian hill should be added.
step
A Gaussian hill will be added every `step` iterations.
restart_files
A single h5 file containing hills added during previous
simulations. Gaussian hills present in restart_files will
be added at the start of the simulation.
tempering
Perform a well-tempered metadynamics simulation
periodicities
The periodicity of the single collective variable or a [Ncv]
NumPy array specifying the periodicity of each
collective variable. Specifying None means the CV is not
periodic.
comlist
An optional layer to derive centers of mass from the atomic positions.
These centers of mass are used as input for the first layer, the relative
vectors.
"""
self.hills = GaussianHills(cv, sigma, periodicities=periodicities)
self.K = K
self.f = f
self.tempering = tempering
# Add the bias part to the force field
part = ForcePartBias(ff.system, comlist=comlist)
part.add_term(self.hills)
ff.add_part(part)
# Initialize hills from restart files
if restart_file is not None:
if not 'hills' in restart_file:
raise ValueError("Could not read hills group from %s"%(restart_file))
if not self.tempering==restart_file['hills'].attrs['tempering']:
raise ValueError("Inconsistent tempering between runs")
if 'hills/periodicities' in restart_file:
if not np.all(self.hills.periodicities==restart_file['hills/periodicities']):
raise ValueError("Inconsistent periodicities between runs")
elif self.hills.periodicities is not None:
raise ValueError("Inconsistent periodicities between runs")
q0 = restart_file['hills/q0'][:]
K = restart_file['hills/K'][:]
self.hills.add_hills(q0, K)
if self.f is not None:
for istep in range(q0.shape[0]):
self.dump_h5(q0[istep], K[istep])
Hook.__init__(self, start, step)
def __init__(self,
system,
timestep=0.0,
restart=0,
fn='plumed.dat',
kernel=None,
fn_log='plumed.log'):
r'''Initialize a PLUMED ForcePart. More information on the interface
between PLUMED and MD codes can be found on
http://tcb.ucas.ac.cn/plumed2/developer-doc/html/_how_to_plumed_your_m_d.html
Unfortunately, PLUMED partially breaks the orthogonality of the pes
and the sampling modules in Yaff: PLUMED sometimes requires
information from the integrator. For example, in metadynamics
PLUMED needs to know when a time integration step has been
completed (which is not necessarily after each force calculation).
ForcePartPlumed therefore also inherits from Hook and
by attaching this hook to the integrator, it is possible to obtain
the necessary information from the integrator. Problems within
PLUMED for this approach to work, particularly in the VES module,
have been resolved; see the discussion at
https://groups.google.com/forum/?fromgroups=#!topic/plumed-users/kPZu_tNZtgk
**Arguments:**
system
An instance of the System class
**Optional Arguments:**
timestep
The timestep (in au) of the integrator
restart
Set to a value different from 0 to let PLUMED know that this
is a restarted run
fn
A filename from which the PLUMED instructions are read, default
is plumed.dat
kernel
Path to the PLUMED library (something like
/path/to/libplumedKernel.so). If None is provided, the
environment variable $PLUMED_KERNEL should point to the library
file.
fn_log
Path to the file where PLUMED logs output, default is
plumed.log
'''
self.system = system
self.fn = fn
self.kernel = kernel
self.fn_log = fn_log
self.plumedstep = 0
# TODO In the LAMMPS-PLUMED interface (src/USER-PLUMED/fix_plumed.cpp)
# it is mentioned that biasing is not possible when tailcorrections are
# included. Maybe this should be checked...
# Check cell dimensions, only 0D and 3D systems supported
if not self.system.cell.nvec in [0, 3]:
raise NotImplementedError
# Setup PLUMED by sending commands to the PLUMED API
self.setup_plumed(timestep, restart)
# PLUMED requires masses to be set...
if self.system.masses is None:
self.system.set_standard_masses()
# Initialize the ForcePart
ForcePart.__init__(self, 'plumed', self.system)
# Initialize the Hook, can't see a reason why start and step could
# differ from default values
Hook.__init__(self, start=0, step=1)
self.hooked = False
if log.do_warning:
log.warn("When using PLUMED as a hook for your integrator "
"and PLUMED adds time-dependent forces (for instance "
"when performing metadynamics), there is no energy "
"conservation. The conserved quantity reported by "
"YAFF is irrelevant in this case.")
