deepmd.dpmodel.utils package

class deepmd.dpmodel.utils.AtomExcludeMask(ntypes: int, exclude_types: List[int] = [])[source]

Bases: object

Computes the type exclusion mask for atoms.

Methods

build_type_exclude_mask(atype)

Compute type exclusion mask for atoms.

build_type_exclude_mask(atype: ndarray)[source]

Compute type exclusion mask for atoms.

Parameters

atype: The extended aotm types. shape: nf x natom

Returns

mask: The type exclusion mask for atoms. shape: nf x natom Element [ff,ii] being 0 if type(ii) is excluded, otherwise being 1.

deepmd.dpmodel.utils.EmbeddingNet: alias of EN

class deepmd.dpmodel.utils.EnvMat(rcut, rcut_smth)[source]

Bases: NativeOP

Methods

`__call__`(args, *kwargs)	Forward pass in NumPy implementation.
`call`(coord_ext, atype_ext, nlist[, davg, ...])	Compute the environment matrix.

deserialize
serialize

call(coord_ext: ndarray, atype_ext: ndarray, nlist: ndarray, davg: Optional[ndarray] = None, dstd: Optional[ndarray] = None, radial_only: bool = False) → ndarray[source]

Compute the environment matrix.

Parameters

nlist: The neighbor list. shape: nf x nloc x nnei
coord_ext: The extended coordinates of atoms. shape: nf x (nallx3)
atype_ext: The extended aotm types. shape: nf x nall
davg: The data avg. shape: nt x nnei x (4 or 1)
dstd: The inverse of data std. shape: nt x nnei x (4 or 1)
radial_only: Whether to only compute radial part of the environment matrix. If True, the output will be of shape nf x nloc x nnei x 1. Otherwise, the output will be of shape nf x nloc x nnei x 4. Default: False.

Returns

env_mat: The environment matrix. shape: nf x nloc x nnei x (4 or 1)
switch: The value of switch function. shape: nf x nloc x nnei

classmethod deserialize(data: dict) → EnvMat[source]

serialize() → dict[source]

deepmd.dpmodel.utils.FittingNet: alias of FN

class deepmd.dpmodel.utils.NativeLayer(num_in, num_out, bias: bool = True, use_timestep: bool = False, activation_function: Optional[str] = None, resnet: bool = False, precision: str = 'float64')[source]

Bases: NativeOP

Native representation of a layer.

Parameters

wnp.ndarray, optional: The weights of the layer.
bnp.ndarray, optional: The biases of the layer.
idtnp.ndarray, optional: The identity matrix of the layer.
activation_functionstr, optional: The activation function of the layer.
resnetbool, optional: Whether the layer is a residual layer.

Methods

`__call__`(args, *kwargs)	Forward pass in NumPy implementation.
`call`(x)	Forward pass.
`deserialize`(data)	Deserialize the layer from a dict.
`serialize`()	Serialize the layer to a dict.

check_shape_consistency
check_type_consistency
dim_in
dim_out

call(x: ndarray) → ndarray[source]

Forward pass.

Parameters

xnp.ndarray: The input.

Returns

np.ndarray: The output.

check_shape_consistency()[source]

check_type_consistency()[source]

classmethod deserialize(data: dict) → NativeLayer[source]

Deserialize the layer from a dict.

Parameters

datadict: The dict to deserialize from.

dim_in() → int[source]

dim_out() → int[source]

serialize() → dict[source]

Serialize the layer to a dict.

Returns

dict: The serialized layer.

deepmd.dpmodel.utils.NativeNet: alias of NN

class deepmd.dpmodel.utils.NetworkCollection(ndim: int, ntypes: int, network_type: str = 'network', networks: ~typing.List[~typing.Union[~deepmd.dpmodel.utils.network.make_multilayer_network.<locals>.NN, dict]] = [])[source]

Bases: object

A collection of networks for multiple elements.

The number of dimesions for types might be 0, 1, or 2. - 0: embedding or fitting with type embedding, in () - 1: embedding with type_one_side, or fitting, in (type_i) - 2: embedding without type_one_side, in (type_i, type_j)

Parameters

ndimint: The number of dimensions.
network_typestr, optional: The type of the network.
networksdict, optional: The networks to initialize with.

Methods

`check_completeness`()	Check whether the collection is complete.
`deserialize`(data)	Deserialize the networks from a dict.
`serialize`()	Serialize the networks to a dict.

NETWORK_TYPE_MAP: ClassVar[Dict[str, type]] = {'embedding_network': <class 'deepmd.dpmodel.utils.network.make_embedding_network.<locals>.EN'>, 'fitting_network': <class 'deepmd.dpmodel.utils.network.make_fitting_network.<locals>.FN'>, 'network': <class 'deepmd.dpmodel.utils.network.make_multilayer_network.<locals>.NN'>}

check_completeness()[source]

Check whether the collection is complete.

Raises

RuntimeError: If the collection is incomplete.

classmethod deserialize(data: dict) → NetworkCollection[source]

Deserialize the networks from a dict.

Parameters

datadict: The dict to deserialize from.

serialize() → dict[source]

Serialize the networks to a dict.

Returns

dict: The serialized networks.

class deepmd.dpmodel.utils.PairExcludeMask(ntypes: int, exclude_types: List[Tuple[int, int]] = [])[source]

Bases: object

Computes the type exclusion mask for atom pairs.

Methods

build_type_exclude_mask(nlist, atype_ext)

Compute type exclusion mask for atom pairs.

build_type_exclude_mask(nlist: ndarray, atype_ext: ndarray)[source]

Compute type exclusion mask for atom pairs.

Parameters

nlist: The neighbor list. shape: nf x nloc x nnei
atype_ext: The extended aotm types. shape: nf x nall

Returns

mask: The type exclusion mask for pair atoms of shape: nf x nloc x nnei. Element [ff,ii,jj] being 0 if type(ii), type(nlist[ff,ii,jj]) is excluded, otherwise being 1.

deepmd.dpmodel.utils.build_multiple_neighbor_list(coord: ndarray, nlist: ndarray, rcuts: List[float], nsels: List[int]) → Dict[str, ndarray][source]

Input one neighbor list, and produce multiple neighbor lists with different cutoff radius and numbers of selection out of it. The required rcuts and nsels should be smaller or equal to the input nlist.

Parameters

coordnp.ndarray: exptended coordinates of shape [batch_size, nall x 3]
nlistnp.ndarray: Neighbor list of shape [batch_size, nloc, nsel], the neighbors should be stored in an ascending order.
rcutsList[float]: list of cut-off radius in ascending order.
nselsList[int]: maximal number of neighbors in ascending order.

Returns

nlist_dictDict[str, np.ndarray]: A dict of nlists, key given by get_multiple_nlist_key(rc, nsel) value being the corresponding nlist.

deepmd.dpmodel.utils.build_neighbor_list(coord1: ndarray, atype: ndarray, nloc: int, rcut: float, sel: Union[int, List[int]], distinguish_types: bool = True) → ndarray[source]

Build neightbor list for a single frame. keeps nsel neighbors.

Parameters

coord1np.ndarray: exptended coordinates of shape [batch_size, nall x 3]
atypenp.ndarray: extended atomic types of shape [batch_size, nall]
nlocint: number of local atoms.
rcutfloat: cut-off radius
selint or List[int]: maximal number of neighbors (of each type). if distinguish_types==True, nsel should be list and the length of nsel should be equal to number of types.
distinguish_typesbool: distinguish different types.

Returns

neighbor_listnp.ndarray: Neighbor list of shape [batch_size, nloc, nsel], the neighbors are stored in an ascending order. If the number of neighbors is less than nsel, the positions are masked with -1. The neighbor list of an atom looks like |------ nsel ------| xx xx xx xx -1 -1 -1 if distinguish_types==True and we have two types |---- nsel[0] -----| |---- nsel[1] -----| xx xx xx xx -1 -1 -1 xx xx xx -1 -1 -1 -1

deepmd.dpmodel.utils.extend_coord_with_ghosts(coord: ndarray, atype: ndarray, cell: Optional[ndarray], rcut: float)[source]

Extend the coordinates of the atoms by appending peridoc images. The number of images is large enough to ensure all the neighbors within rcut are appended.

Parameters

coordnp.ndarray: original coordinates of shape [-1, nloc*3].
atypenp.ndarray: atom type of shape [-1, nloc].
cellnp.ndarray: simulation cell tensor of shape [-1, 9].
rcutfloat: the cutoff radius

Returns

extended_coord: np.ndarray: extended coordinates of shape [-1, nall*3].
extended_atype: np.ndarray: extended atom type of shape [-1, nall].
index_mapping: np.ndarray: maping extended index to the local index

deepmd.dpmodel.utils.get_multiple_nlist_key(rcut: float, nsel: int) → str[source]

deepmd.dpmodel.utils.inter2phys(coord: ndarray, cell: ndarray) → ndarray[source]

Convert internal(direct) coordinates to physical coordinates.

Parameters

coordnp.ndarray: internal coordinates of shape [*, na, 3].
cellnp.ndarray: simulation cell tensor of shape [*, 3, 3].

Returns

phys_coord: np.ndarray: the physical coordinates

deepmd.dpmodel.utils.load_dp_model(filename: str) → dict[source]

Load a DP model from a file in the native format.

Parameters

filenamestr: The filename to load from.

Returns

dict: The loaded model dict, including meta information.

deepmd.dpmodel.utils.make_embedding_network(T_Network, T_NetworkLayer)[source]

deepmd.dpmodel.utils.make_fitting_network(T_EmbeddingNet, T_Network, T_NetworkLayer)[source]

deepmd.dpmodel.utils.make_multilayer_network(T_NetworkLayer, ModuleBase)[source]

deepmd.dpmodel.utils.nlist_distinguish_types(nlist: ndarray, atype: ndarray, sel: List[int])[source]: Given a nlist that does not distinguish atom types, return a nlist that distinguish atom types.

deepmd.dpmodel.utils.normalize_coord(coord: ndarray, cell: ndarray) → ndarray[source]

Apply PBC according to the atomic coordinates.

Parameters

coordnp.ndarray: orignal coordinates of shape [*, na, 3].
cellnp.ndarray: simulation cell shape [*, 3, 3].

Returns

wrapped_coord: np.ndarray: wrapped coordinates of shape [*, na, 3].

deepmd.dpmodel.utils.phys2inter(coord: ndarray, cell: ndarray) → ndarray[source]

Convert physical coordinates to internal(direct) coordinates.

Parameters

coordnp.ndarray: physical coordinates of shape [*, na, 3].
cellnp.ndarray: simulation cell tensor of shape [*, 3, 3].

Returns

inter_coord: np.ndarray: the internal coordinates

deepmd.dpmodel.utils.save_dp_model(filename: str, model_dict: dict) → None[source]

Save a DP model to a file in the native format.

Parameters

filenamestr: The filename to save to.
model_dictdict: The model dict to save.

deepmd.dpmodel.utils.to_face_distance(cell: ndarray) → ndarray[source]

Compute the to-face-distance of the simulation cell.

Parameters

cellnp.ndarray: simulation cell tensor of shape [*, 3, 3].

Returns

dist: np.ndarray: the to face distances of shape [*, 3]

deepmd.dpmodel.utils.traverse_model_dict(model_obj, callback: callable, is_variable: bool = False)[source]

Traverse a model dict and call callback on each variable.

Parameters

model_objobject: The model object to traverse.
callbackcallable(): The callback function to call on each variable.
is_variablebool, optional: Whether the current node is a variable.

Returns

object: The model object after traversing.

Submodules

deepmd.dpmodel.utils.batch_size module

class deepmd.dpmodel.utils.batch_size.AutoBatchSize(initial_batch_size: int = 1024, factor: float = 2.0)[source]

Bases: AutoBatchSize

Automatic batch size for NumPy.

Methods

`execute`(callable, start_index, natoms)	Excuate a method with given batch size.
`execute_all`(callable, total_size, natoms, ...)	Excuate a method with all given data.
`is_gpu_available`()	Check if GPU is available.
`is_oom_error`(e)	Check if the exception is an OOM error.

is_gpu_available() → bool[source]

Check if GPU is available.

Returns

bool: True if GPU is available

is_oom_error(e: Exception) → bool[source]

Check if the exception is an OOM error.

Parameters

eException: Exception

deepmd.dpmodel.utils.env_mat module

class deepmd.dpmodel.utils.env_mat.EnvMat(rcut, rcut_smth)[source]

Bases: NativeOP

Methods

`__call__`(args, *kwargs)	Forward pass in NumPy implementation.
`call`(coord_ext, atype_ext, nlist[, davg, ...])	Compute the environment matrix.

deserialize
serialize

call(coord_ext: ndarray, atype_ext: ndarray, nlist: ndarray, davg: Optional[ndarray] = None, dstd: Optional[ndarray] = None, radial_only: bool = False) → ndarray[source]

Compute the environment matrix.

Parameters

nlist: The neighbor list. shape: nf x nloc x nnei
coord_ext: The extended coordinates of atoms. shape: nf x (nallx3)
atype_ext: The extended aotm types. shape: nf x nall
davg: The data avg. shape: nt x nnei x (4 or 1)
dstd: The inverse of data std. shape: nt x nnei x (4 or 1)
radial_only: Whether to only compute radial part of the environment matrix. If True, the output will be of shape nf x nloc x nnei x 1. Otherwise, the output will be of shape nf x nloc x nnei x 4. Default: False.

Returns

env_mat: The environment matrix. shape: nf x nloc x nnei x (4 or 1)
switch: The value of switch function. shape: nf x nloc x nnei

classmethod deserialize(data: dict) → EnvMat[source]

serialize() → dict[source]

deepmd.dpmodel.utils.env_mat.compute_smooth_weight(distance: ndarray, rmin: float, rmax: float)[source]: Compute smooth weight for descriptor elements.

deepmd.dpmodel.utils.exclude_mask module

class deepmd.dpmodel.utils.exclude_mask.AtomExcludeMask(ntypes: int, exclude_types: List[int] = [])[source]

Bases: object

Computes the type exclusion mask for atoms.

Methods

build_type_exclude_mask(atype)

Compute type exclusion mask for atoms.

build_type_exclude_mask(atype: ndarray)[source]

Compute type exclusion mask for atoms.

Parameters

atype: The extended aotm types. shape: nf x natom

Returns

mask: The type exclusion mask for atoms. shape: nf x natom Element [ff,ii] being 0 if type(ii) is excluded, otherwise being 1.

class deepmd.dpmodel.utils.exclude_mask.PairExcludeMask(ntypes: int, exclude_types: List[Tuple[int, int]] = [])[source]

Bases: object

Computes the type exclusion mask for atom pairs.

Methods

build_type_exclude_mask(nlist, atype_ext)

Compute type exclusion mask for atom pairs.

build_type_exclude_mask(nlist: ndarray, atype_ext: ndarray)[source]

Compute type exclusion mask for atom pairs.

Parameters

nlist: The neighbor list. shape: nf x nloc x nnei
atype_ext: The extended aotm types. shape: nf x nall

Returns

mask: The type exclusion mask for pair atoms of shape: nf x nloc x nnei. Element [ff,ii,jj] being 0 if type(ii), type(nlist[ff,ii,jj]) is excluded, otherwise being 1.

deepmd.dpmodel.utils.neighbor_stat module

class deepmd.dpmodel.utils.neighbor_stat.NeighborStat(ntypes: int, rcut: float, mixed_type: bool = False)[source]

Bases: NeighborStat

Neighbor statistics using pure NumPy.

Parameters

ntypesint: The num of atom types
rcutfloat: The cut-off radius
mixed_typebool, optional, default=False: Treat all types as a single type.

Methods

`get_stat`(data)	Get the data statistics of the training data, including nearest nbor distance between atoms, max nbor size of atoms.
`iterator`(data)	Abstract method for producing data.

iterator(data: DeepmdDataSystem) → Iterator[Tuple[ndarray, float, str]][source]

Abstract method for producing data.

Yields

np.ndarray: The maximal number of neighbors
float: The squared minimal distance between two atoms
str: The directory of the data system

class deepmd.dpmodel.utils.neighbor_stat.NeighborStatOP(ntypes: int, rcut: float, mixed_types: bool)[source]

Bases: NativeOP

Class for getting neighbor statics data information.

Parameters

ntypes: The num of atom types
rcut: The cut-off radius
mixed_typesbool, optional: If True, treat all types as a single type.

Methods

`__call__`(args, *kwargs)	Forward pass in NumPy implementation.
`call`(coord, atype, cell)	Calculate the neareest neighbor distance between atoms, maximum nbor size of atoms and the output data range of the environment matrix.

call(coord: ndarray, atype: ndarray, cell: Optional[ndarray]) → Tuple[float, ndarray][source]

Calculate the neareest neighbor distance between atoms, maximum nbor size of atoms and the output data range of the environment matrix.

Parameters

coord: The coordinates of atoms.
atype: The atom types.
cell: The cell.

Returns

float: The minimal squared distance between two atoms
np.ndarray: The maximal number of neighbors

deepmd.dpmodel.utils.network module

Native DP model format for multiple backends.

See issue #2982 for more information.

class deepmd.dpmodel.utils.network.Counter[source]

Bases: object

A callable counter.

Examples

>>> counter = Counter()
>>> counter()
0
>>> counter()
1

Methods

__call__()

Call self as a function.

deepmd.dpmodel.utils.network.EmbeddingNet: alias of EN

deepmd.dpmodel.utils.network.FittingNet: alias of FN

class deepmd.dpmodel.utils.network.NativeLayer(num_in, num_out, bias: bool = True, use_timestep: bool = False, activation_function: Optional[str] = None, resnet: bool = False, precision: str = 'float64')[source]

Bases: NativeOP

Native representation of a layer.

Parameters

wnp.ndarray, optional: The weights of the layer.
bnp.ndarray, optional: The biases of the layer.
idtnp.ndarray, optional: The identity matrix of the layer.
activation_functionstr, optional: The activation function of the layer.
resnetbool, optional: Whether the layer is a residual layer.

Methods

`__call__`(args, *kwargs)	Forward pass in NumPy implementation.
`call`(x)	Forward pass.
`deserialize`(data)	Deserialize the layer from a dict.
`serialize`()	Serialize the layer to a dict.

check_shape_consistency
check_type_consistency
dim_in
dim_out

call(x: ndarray) → ndarray[source]

Forward pass.

Parameters

xnp.ndarray: The input.

Returns

np.ndarray: The output.

check_shape_consistency()[source]

check_type_consistency()[source]

classmethod deserialize(data: dict) → NativeLayer[source]

Deserialize the layer from a dict.

Parameters

datadict: The dict to deserialize from.

dim_in() → int[source]

dim_out() → int[source]

serialize() → dict[source]

Serialize the layer to a dict.

Returns

dict: The serialized layer.

deepmd.dpmodel.utils.network.NativeNet: alias of NN

class deepmd.dpmodel.utils.network.NetworkCollection(ndim: int, ntypes: int, network_type: str = 'network', networks: ~typing.List[~typing.Union[~deepmd.dpmodel.utils.network.make_multilayer_network.<locals>.NN, dict]] = [])[source]

Bases: object

A collection of networks for multiple elements.

The number of dimesions for types might be 0, 1, or 2. - 0: embedding or fitting with type embedding, in () - 1: embedding with type_one_side, or fitting, in (type_i) - 2: embedding without type_one_side, in (type_i, type_j)

Parameters

ndimint: The number of dimensions.
network_typestr, optional: The type of the network.
networksdict, optional: The networks to initialize with.

Methods

`check_completeness`()	Check whether the collection is complete.
`deserialize`(data)	Deserialize the networks from a dict.
`serialize`()	Serialize the networks to a dict.

NETWORK_TYPE_MAP: ClassVar[Dict[str, type]] = {'embedding_network': <class 'deepmd.dpmodel.utils.network.make_embedding_network.<locals>.EN'>, 'fitting_network': <class 'deepmd.dpmodel.utils.network.make_fitting_network.<locals>.FN'>, 'network': <class 'deepmd.dpmodel.utils.network.make_multilayer_network.<locals>.NN'>}

check_completeness()[source]

Check whether the collection is complete.

Raises

RuntimeError: If the collection is incomplete.

classmethod deserialize(data: dict) → NetworkCollection[source]

Deserialize the networks from a dict.

Parameters

datadict: The dict to deserialize from.

serialize() → dict[source]

Serialize the networks to a dict.

Returns

dict: The serialized networks.

deepmd.dpmodel.utils.network.load_dp_model(filename: str) → dict[source]

Load a DP model from a file in the native format.

Parameters

filenamestr: The filename to load from.

Returns

dict: The loaded model dict, including meta information.

deepmd.dpmodel.utils.network.make_embedding_network(T_Network, T_NetworkLayer)[source]

deepmd.dpmodel.utils.network.make_fitting_network(T_EmbeddingNet, T_Network, T_NetworkLayer)[source]

deepmd.dpmodel.utils.network.make_multilayer_network(T_NetworkLayer, ModuleBase)[source]

deepmd.dpmodel.utils.network.save_dp_model(filename: str, model_dict: dict) → None[source]

Save a DP model to a file in the native format.

Parameters

filenamestr: The filename to save to.
model_dictdict: The model dict to save.

deepmd.dpmodel.utils.network.traverse_model_dict(model_obj, callback: callable, is_variable: bool = False)[source]

Traverse a model dict and call callback on each variable.

Parameters

model_objobject: The model object to traverse.
callbackcallable(): The callback function to call on each variable.
is_variablebool, optional: Whether the current node is a variable.

Returns

object: The model object after traversing.

deepmd.dpmodel.utils.nlist module

deepmd.dpmodel.utils.nlist.build_multiple_neighbor_list(coord: ndarray, nlist: ndarray, rcuts: List[float], nsels: List[int]) → Dict[str, ndarray][source]

Input one neighbor list, and produce multiple neighbor lists with different cutoff radius and numbers of selection out of it. The required rcuts and nsels should be smaller or equal to the input nlist.

Parameters

coordnp.ndarray: exptended coordinates of shape [batch_size, nall x 3]
nlistnp.ndarray: Neighbor list of shape [batch_size, nloc, nsel], the neighbors should be stored in an ascending order.
rcutsList[float]: list of cut-off radius in ascending order.
nselsList[int]: maximal number of neighbors in ascending order.

Returns

nlist_dictDict[str, np.ndarray]: A dict of nlists, key given by get_multiple_nlist_key(rc, nsel) value being the corresponding nlist.

deepmd.dpmodel.utils.nlist.build_neighbor_list(coord1: ndarray, atype: ndarray, nloc: int, rcut: float, sel: Union[int, List[int]], distinguish_types: bool = True) → ndarray[source]

Build neightbor list for a single frame. keeps nsel neighbors.

Parameters

coord1np.ndarray: exptended coordinates of shape [batch_size, nall x 3]
atypenp.ndarray: extended atomic types of shape [batch_size, nall]
nlocint: number of local atoms.
rcutfloat: cut-off radius
selint or List[int]: maximal number of neighbors (of each type). if distinguish_types==True, nsel should be list and the length of nsel should be equal to number of types.
distinguish_typesbool: distinguish different types.

Returns

neighbor_listnp.ndarray: Neighbor list of shape [batch_size, nloc, nsel], the neighbors are stored in an ascending order. If the number of neighbors is less than nsel, the positions are masked with -1. The neighbor list of an atom looks like |------ nsel ------| xx xx xx xx -1 -1 -1 if distinguish_types==True and we have two types |---- nsel[0] -----| |---- nsel[1] -----| xx xx xx xx -1 -1 -1 xx xx xx -1 -1 -1 -1

deepmd.dpmodel.utils.nlist.extend_coord_with_ghosts(coord: ndarray, atype: ndarray, cell: Optional[ndarray], rcut: float)[source]

Extend the coordinates of the atoms by appending peridoc images. The number of images is large enough to ensure all the neighbors within rcut are appended.

Parameters

coordnp.ndarray: original coordinates of shape [-1, nloc*3].
atypenp.ndarray: atom type of shape [-1, nloc].
cellnp.ndarray: simulation cell tensor of shape [-1, 9].
rcutfloat: the cutoff radius

Returns

extended_coord: np.ndarray: extended coordinates of shape [-1, nall*3].
extended_atype: np.ndarray: extended atom type of shape [-1, nall].
index_mapping: np.ndarray: maping extended index to the local index

deepmd.dpmodel.utils.nlist.get_multiple_nlist_key(rcut: float, nsel: int) → str[source]

deepmd.dpmodel.utils.nlist.nlist_distinguish_types(nlist: ndarray, atype: ndarray, sel: List[int])[source]: Given a nlist that does not distinguish atom types, return a nlist that distinguish atom types.

deepmd.dpmodel.utils.region module

deepmd.dpmodel.utils.region.b_to_face_distance(cell)[source]

deepmd.dpmodel.utils.region.inter2phys(coord: ndarray, cell: ndarray) → ndarray[source]

Convert internal(direct) coordinates to physical coordinates.

Parameters

coordnp.ndarray: internal coordinates of shape [*, na, 3].
cellnp.ndarray: simulation cell tensor of shape [*, 3, 3].

Returns

phys_coord: np.ndarray: the physical coordinates

deepmd.dpmodel.utils.region.normalize_coord(coord: ndarray, cell: ndarray) → ndarray[source]

Apply PBC according to the atomic coordinates.

Parameters

coordnp.ndarray: orignal coordinates of shape [*, na, 3].
cellnp.ndarray: simulation cell shape [*, 3, 3].

Returns

wrapped_coord: np.ndarray: wrapped coordinates of shape [*, na, 3].

deepmd.dpmodel.utils.region.phys2inter(coord: ndarray, cell: ndarray) → ndarray[source]

Convert physical coordinates to internal(direct) coordinates.

Parameters

coordnp.ndarray: physical coordinates of shape [*, na, 3].
cellnp.ndarray: simulation cell tensor of shape [*, 3, 3].

Returns

inter_coord: np.ndarray: the internal coordinates

deepmd.dpmodel.utils.region.to_face_distance(cell: ndarray) → ndarray[source]

Compute the to-face-distance of the simulation cell.

Parameters

cellnp.ndarray: simulation cell tensor of shape [*, 3, 3].

Returns

dist: np.ndarray: the to face distances of shape [*, 3]

deepmd.dpmodel.utils.update_sel module

class deepmd.dpmodel.utils.update_sel.UpdateSel[source]

Bases: BaseUpdateSel

Attributes

neighbor_stat

Methods

get_min_nbor_dist
get_nbor_stat
get_rcut
get_sel
get_type_map
hook
parse_auto_sel
parse_auto_sel_ratio
update_one_sel
wrap_up_4

hook(min_nbor_dist, max_nbor_size)[source]

property neighbor_stat: Type[NeighborStat]