IMLCV.base.bias =============== .. py:module:: IMLCV.base.bias Exceptions ---------- .. autoapisummary:: IMLCV.base.bias.EnergyError IMLCV.base.bias.BiasError Classes ------- .. autoapisummary:: IMLCV.base.bias.EnergyResult IMLCV.base.bias.Energy IMLCV.base.bias.EnergyFn IMLCV.base.bias.PlumedEnerg IMLCV.base.bias.Bias IMLCV.base.bias.CompositeBias IMLCV.base.bias.BiasModify IMLCV.base.bias.BiasF IMLCV.base.bias.NoneBias Functions --------- .. autoapisummary:: IMLCV.base.bias._zero_fun IMLCV.base.bias._constant Module Contents --------------- .. py:class:: EnergyResult(*args, **kwargs) Bases: :py:obj:`IMLCV.base.datastructures.MyPyTreeNode` Base class for dataclasses that should act like a JAX pytree node. .. py:attribute:: energy :type: jax.Array .. py:attribute:: gpos :type: jax.Array | None .. py:attribute:: vtens :type: jax.Array | None .. py:method:: __add__(other: EnergyResult) -> EnergyResult .. py:exception:: EnergyError Bases: :py:obj:`Exception` Common base class for all non-exit exceptions. .. py:class:: Energy .. py:attribute:: external_callback :value: True .. py:attribute:: manual_vtens :value: False .. py:property:: nl :type: IMLCV.base.CV.NeighbourList | None .. py:property:: cell :type: jax.Array | None :abstractmethod: .. py:property:: coordinates :type: jax.Array | None :abstractmethod: .. py:property:: sp :type: IMLCV.base.CV.SystemParams | None .. py:method:: _compute_coor(sp: IMLCV.base.CV.SystemParams, nl: IMLCV.base.CV.NeighbourList | None, gpos=False, vir=False) -> EnergyResult :abstractmethod: .. py:method:: _handle_exception(e=None) .. py:method:: get_vtens_finite_difference(sp: IMLCV.base.CV.SystemParams, nl: IMLCV.base.CV.NeighbourList | None, eps=1e-05, gpos=False) .. py:method:: 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 .. py:method:: save(filename: str | pathlib.Path) .. py:method:: load(filename) -> Energy :staticmethod: .. py:class:: EnergyFn(*args, **kwargs) Bases: :py:obj:`Energy`, :py:obj:`IMLCV.base.datastructures.MyPyTreeNode` Base class for dataclasses that should act like a JAX pytree node. .. py:attribute:: external_callback :value: False .. py:attribute:: f :type: Callable .. py:attribute:: _sp :type: IMLCV.base.CV.SystemParams | None :value: None .. py:attribute:: _nl :type: IMLCV.base.CV.NeighbourList | None :value: None .. py:attribute:: kwargs :type: dict .. py:attribute:: static_kwargs :type: dict .. py:property:: nl .. py:property:: cell :type: jax.Array | None .. py:property:: coordinates :type: jax.Array | None .. py:property:: sp :type: IMLCV.base.CV.SystemParams | None .. py:method:: _compute_coor(sp: IMLCV.base.CV.SystemParams, nl: IMLCV.base.CV.NeighbourList | None, gpos=False, vir=False) -> EnergyResult .. py:class:: PlumedEnerg Bases: :py:obj:`Energy` .. py:exception:: BiasError Bases: :py:obj:`Exception` Common base class for all non-exit exceptions. .. py:class:: Bias(*args, **kwargs) Bases: :py:obj:`abc.ABC`, :py:obj:`IMLCV.base.datastructures.MyPyTreeNode` base class for biased MD runs. .. py:attribute:: collective_variable :type: IMLCV.base.CV.CollectiveVariable .. py:attribute:: start :type: int | None .. py:attribute:: step :type: int | None .. py:attribute:: finalized :type: bool .. py:attribute:: slice_exponent :type: float .. py:attribute:: log_exp_slice :type: bool .. py:attribute:: slice_mean :type: bool .. py:method:: create(*args, **kwargs) :staticmethod: :abstractmethod: .. py:method:: update_bias(md: IMLCV.base.MdEngine.MDEngine) -> Bias update the bias. Can only change the properties from _get_args .. py:method:: _update_bias() -> tuple[bool, typing_extensions.Self] update the bias. Can only change the properties from _get_args .. py:method:: 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. .. py:method:: 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 .. py:method:: _compute(cvs) -> jax.Array :abstractmethod: function that calculates the bias potential. .. py:method:: 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) .. py:method:: resample(cv_grid: IMLCV.base.CV.CV | None = None, n=40, margin=0.3) -> Bias .. py:method:: save(filename: str | pathlib.Path, cv_file: pathlib.Path | None = None) .. py:method:: load(filename) -> Bias :staticmethod: .. py:method:: __getstate__() .. py:method:: __setstate__(statedict: dict) .. py:method:: bounds_from_bias(T, sign=1.0, margin=1e-10, n=50) .. py:method:: kl_divergence(other: Bias, T: float, symmetric=True, margin=0.2, sign=1.0, n=100) .. py:method:: 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]] .. py:method:: apply(cvs: list[IMLCV.base.CV.CV], shmap=False, macro_chunk_size=10000) .. py:class:: CompositeBias(*args, **kwargs) Bases: :py:obj:`Bias` Class that combines several biases in one single bias. .. py:attribute:: biases :type: list[Bias] .. py:attribute:: fun :type: Callable .. py:method:: create(biases: list[Bias], fun=jnp.sum) -> CompositeBias :classmethod: .. py:method:: _compute(cvs) function that calculates the bias potential. .. py:method:: update_bias(md: IMLCV.base.MdEngine.MDEngine) -> Bias update the bias. Can only change the properties from _get_args .. py:function:: _zero_fun(cvs: IMLCV.base.CV.CV) .. py:function:: _constant(cvs: IMLCV.base.CV.CV, val: float = 0.0) .. py:class:: BiasModify(*args, **kwargs) Bases: :py:obj:`Bias` Bias according to CV. .. py:attribute:: fun :type: Callable .. py:attribute:: bias :type: Bias .. py:attribute:: kwargs :type: dict .. py:attribute:: static_kwargs :type: dict .. py:method:: create(fun: Callable, bias: Bias, kwargs: dict = {}, static_kwargs: dict = {}) -> BiasModify :classmethod: .. py:method:: _compute(cvs) function that calculates the bias potential. .. py:method:: update_bias(md: IMLCV.base.MdEngine.MDEngine) -> Bias update the bias. Can only change the properties from _get_args .. py:method:: __getstate__() .. py:method:: __setstate__(statedict: dict) .. py:class:: BiasF(*args, **kwargs) Bases: :py:obj:`Bias` Bias according to CV. .. py:attribute:: g :type: Callable .. py:attribute:: static_kwargs :type: dict .. py:attribute:: kwargs :type: dict .. py:method:: create(cvs: IMLCV.base.CV.CollectiveVariable, g: Callable = _constant, kwargs: dict = {}, static_kwargs: dict = {}) -> BiasF :staticmethod: .. py:method:: _compute(cvs) function that calculates the bias potential. .. py:class:: NoneBias(*args, **kwargs) Bases: :py:obj:`BiasF` Bias according to CV. .. py:method:: create(collective_variable: IMLCV.base.CV.CollectiveVariable) -> NoneBias :staticmethod: