IMLCV.base.bias

Exceptions

EnergyError	Common base class for all non-exit exceptions.
BiasError	Common base class for all non-exit exceptions.

Classes

EnergyResult	Base class for dataclasses that should act like a JAX pytree node.
Energy
EnergyFn	Base class for dataclasses that should act like a JAX pytree node.
PlumedEnerg
Bias	base class for biased MD runs.
CompositeBias	Class that combines several biases in one single bias.
BiasModify	Bias according to CV.
BiasF	Bias according to CV.
NoneBias	Bias according to CV.

Functions

_zero_fun(cvs)
_constant(cvs[, val])

Module Contents

class IMLCV.base.bias.EnergyResult(*args, **kwargs)

Bases: IMLCV.base.datastructures.MyPyTreeNode

Base class for dataclasses that should act like a JAX pytree node.

energy: jax.Array

gpos: jax.Array | None

vtens: jax.Array | None

__add__(other: EnergyResult) → EnergyResult

exception IMLCV.base.bias.EnergyError

Bases: Exception

Common base class for all non-exit exceptions.

class IMLCV.base.bias.Energy

external_callback = True

manual_vtens = False

property nl: IMLCV.base.CV.NeighbourList | None

property cell: jax.Array | None

Abstractmethod:

property coordinates: jax.Array | None

Abstractmethod:

property sp: IMLCV.base.CV.SystemParams | None

abstract _compute_coor(sp: IMLCV.base.CV.SystemParams, nl: IMLCV.base.CV.NeighbourList | None, gpos=False, vir=False) → EnergyResult

_handle_exception(e=None)

get_vtens_finite_difference(sp: IMLCV.base.CV.SystemParams, nl: IMLCV.base.CV.NeighbourList | None, eps=1e-05, gpos=False)

compute_from_system_params(sp: IMLCV.base.CV.SystemParams, gpos=False, vir=False, nl: IMLCV.base.CV.NeighbourList | None = None, manual_vir=None, shmap=False, shmap_kwarg=ShmapKwargs.create()) → EnergyResult

save(filename: str | pathlib.Path)

static load(filename) → Energy

class IMLCV.base.bias.EnergyFn(*args, **kwargs)

Bases: Energy, IMLCV.base.datastructures.MyPyTreeNode

Base class for dataclasses that should act like a JAX pytree node.

external_callback = False

f: Callable

_sp: IMLCV.base.CV.SystemParams | None = None

_nl: IMLCV.base.CV.NeighbourList | None = None

kwargs: dict

static_kwargs: dict

property nl

property cell: jax.Array | None

property coordinates: jax.Array | None

property sp: IMLCV.base.CV.SystemParams | None

_compute_coor(sp: IMLCV.base.CV.SystemParams, nl: IMLCV.base.CV.NeighbourList | None, gpos=False, vir=False) → EnergyResult

class IMLCV.base.bias.PlumedEnerg

Bases: Energy

exception IMLCV.base.bias.BiasError

Bases: Exception

Common base class for all non-exit exceptions.

class IMLCV.base.bias.Bias(*args, **kwargs)

Bases: abc.ABC, IMLCV.base.datastructures.MyPyTreeNode

base class for biased MD runs.

collective_variable: IMLCV.base.CV.CollectiveVariable

start: int | None

step: int | None

finalized: bool

slice_exponent: float

log_exp_slice: bool

slice_mean: bool

static create(*args, **kwargs)

Abstractmethod:

update_bias(md: IMLCV.base.MdEngine.MDEngine) → Bias

update the bias.

Can only change the properties from _get_args

_update_bias() → tuple[bool, typing_extensions.Self]

update the bias.

Can only change the properties from _get_args

compute_from_system_params(sp: IMLCV.base.CV.SystemParams, nl: IMLCV.base.CV.NeighbourList | None = None, gpos=False, vir=False, chunk_size: int | None = None, shmap=False, use_jac=False, push_jac=False, rel=False, shmap_kwargs=ShmapKwargs.create()) → tuple[IMLCV.base.CV.CV, EnergyResult]

Computes the bias, the gradient of the bias wrt the coordinates and the virial.

compute_from_cv(cvs: IMLCV.base.CV.CV, diff=False, chunk_size=None, shmap=False, shmap_kwargs=ShmapKwargs.create()) → tuple[jax.Array, IMLCV.base.CV.CV | None]

compute the energy and derivative.

If map==False, the cvs are assumed to be already mapped

abstract _compute(cvs) → jax.Array

function that calculates the bias potential.

plot(name: str | None = None, traj: list[IMLCV.base.CV.CV] | None = None, dlo_kwargs=None, dlo=None, vmax=100 * kjmol, map=False, inverted=False, margin=0.1, dpi=300, T=300 * kelvin, **kwargs)

resample(cv_grid: IMLCV.base.CV.CV | None = None, n=40, margin=0.3) → Bias

save(filename: str | pathlib.Path, cv_file: pathlib.Path | None = None)

static load(filename) → Bias

__getstate__()

__setstate__(statedict: dict)

bounds_from_bias(T, sign=1.0, margin=1e-10, n=50)

kl_divergence(other: Bias, T: float, symmetric=True, margin=0.2, sign=1.0, n=100)

slice(T, inverted=True, vmax=None, n_max_bias=100000.0, margin=0.2, macro_chunk=10000, offset=True) → dict[int, dict[tuple[int], Bias]]

apply(cvs: list[IMLCV.base.CV.CV], shmap=False, macro_chunk_size=10000)

class IMLCV.base.bias.CompositeBias(*args, **kwargs)

Bases: Bias

Class that combines several biases in one single bias.

biases: list[Bias]

fun: Callable

classmethod create(biases: list[Bias], fun=jnp.sum) → CompositeBias

_compute(cvs)

function that calculates the bias potential.

update_bias(md: IMLCV.base.MdEngine.MDEngine) → Bias

update the bias.

Can only change the properties from _get_args

IMLCV.base.bias._zero_fun(cvs: IMLCV.base.CV.CV)

IMLCV.base.bias._constant(cvs: IMLCV.base.CV.CV, val: float = 0.0)

class IMLCV.base.bias.BiasModify(*args, **kwargs)

Bases: Bias

Bias according to CV.

fun: Callable

bias: Bias

kwargs: dict

static_kwargs: dict

classmethod create(fun: Callable, bias: Bias, kwargs: dict = {}, static_kwargs: dict = {}) → BiasModify

_compute(cvs)

function that calculates the bias potential.

update_bias(md: IMLCV.base.MdEngine.MDEngine) → Bias

update the bias.

Can only change the properties from _get_args

__getstate__()

__setstate__(statedict: dict)

class IMLCV.base.bias.BiasF(*args, **kwargs)

Bases: Bias

Bias according to CV.

g: Callable

static_kwargs: dict

kwargs: dict

static create(cvs: IMLCV.base.CV.CollectiveVariable, g: Callable = _constant, kwargs: dict = {}, static_kwargs: dict = {}) → BiasF

_compute(cvs)

function that calculates the bias potential.

class IMLCV.base.bias.NoneBias(*args, **kwargs)

Bases: BiasF

Bias according to CV.

static create(collective_variable: IMLCV.base.CV.CollectiveVariable) → NoneBias