# Copyright 2021-2023 @ Shenzhen Bay Laboratory &
# Peking University &
# Huawei Technologies Co., Ltd
#
# This code is a part of MindSPONGE:
# MindSpore Simulation Package tOwards Next Generation molecular modelling.
#
# MindSPONGE is open-source software based on the AI-framework:
# MindSpore (https://www.mindspore.cn/)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Vector"""
from mindspore import ops
from mindspore.common import Tensor
from mindspore.ops import functional as F
from .atoms import AtomsBase
from .get import get_atoms
from ...function import get_integer, check_broadcast, all_none, any_not_none
[文档]class Vector(AtomsBase):
r"""Vector between specific atoms or virtual atoms.
Args:
atoms (AtomsBase): Atoms of shape `(..., 2, D)` to form a vector of shape `(..., D)` or `(..., 1, D)`.
Cannot be used with `atoms0` or `atoms1`.
Default: ``None``. `D` means Spatial dimension of the simulation system. Usually is 3.
atoms0 (AtomsBase): The initial point of atoms of shape `(..., D)` to form a vector of shape `(..., D)`.
Must be used with `atoms1`, and cannot be used with `atoms`.
Default: ``None``.
atoms1 (AtomsBase): The terminal point of atoms of shape `(..., D)` to form a vector of shape `(..., D)`.
Must be used with `atoms0`, and cannot be used with `atoms`.
Default: ``None``.
batched (bool): Whether the first dimension of index is the batch size.
Default: ``False``.
use_pbc (bool): Whether to calculate distance under periodic boundary condition.
Default: ``None``.
keepdims (bool): If this is set to True, the axis which is take from the `atoms` will be left,
and the shape of the vector will be `(..., 1, D)`
If this is set to False, the shape of the vector will be `(..., D)`
if None, its value will be determined according to the rank (number of dimension) of
the input atoms: False if the rank is greater than 2, otherwise True.
It only works when initialized with `atoms`.
Default: ``None``.
axis (int): Axis along which the coordinate of atoms are take, of which the dimension must be 2.
It only works when initialized with `atoms`.
Default: -2.
name (str): Name of the Colvar. Default: 'vector'.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore as ms
>>> import numpy as np
>>> from mindspore import Tensor
>>> from sponge.colvar import Vector
>>> crd = Tensor(np.random.random((4, 3)), ms.float32)
>>> crd
Tensor(shape=[4, 3], dtype=Float32, value=
[[ 2.47492954e-01, 9.78153408e-01, 1.44034222e-01],
[ 2.36211464e-01, 3.35842371e-01, 8.39536846e-01],
[ 8.82235169e-01, 5.98322928e-01, 6.68052316e-01],
[ 7.17712820e-01, 4.72498119e-01, 1.69098437e-01]])
>>> vc02 = Vector(atoms0=[0], atoms1=[2])
>>> vc02(crd)
Tensor(shape=[1, 3], dtype=Float32, value=
[[ 6.34742200e-01, -3.79830480e-01, 5.24018109e-01]])
"""
def __init__(self,
atoms: AtomsBase = None,
atoms0: AtomsBase = None,
atoms1: AtomsBase = None,
batched: bool = False,
use_pbc: bool = None,
keepdims: bool = None,
axis: int = -2,
name: str = 'vector',
):
super().__init__(
keep_in_box=False,
name=name,
)
if all_none([atoms, atoms0, atoms1]):
raise ValueError('No input atoms!')
self.atoms = None
self.atoms0 = None
self.atoms1 = None
self.split2 = None
self.squeeze = None
if atoms is None:
if atoms0 is None:
raise ValueError('atoms0 cannot be None when atoms1 is given!')
if atoms1 is None:
raise ValueError('atoms1 cannot be None when atoms0 is given!')
# (..., D)
self.atoms0 = get_atoms(atoms0, batched, False)
self.atoms1 = get_atoms(atoms1, batched, False)
if self.atoms0.ndim > self.atoms1.ndim:
new_shape = (1,) * (self.atoms0.ndim - self.atoms1.ndim)
self.atoms1.reshape(new_shape)
if self.atoms0.ndim < self.atoms1.ndim:
new_shape = (1,) * (self.atoms1.ndim - self.atoms0.ndim)
self.atoms0.reshape(new_shape)
# (..., D)
self._set_shape(check_broadcast(self.atoms0.shape, self.atoms1.shape))
else:
if any_not_none([atoms0, atoms1]):
raise ValueError('When atoms is given, atoms0 and atoms1 must be None!')
# (..., 2, D)
self.atoms = get_atoms(atoms, batched, False)
axis = get_integer(axis)
# (1, ..., 2, D)
shape = (1,) + self.atoms.shape
if shape[axis] != 2:
raise ValueError(f'The dimension at axis must be 2 but got: {shape[axis]}')
self.split2 = ops.Split(axis, 2)
if keepdims is None:
if self.atoms.ndim > 2:
keepdims = False
else:
keepdims = True
if keepdims:
# (1, ..., 1, D) <- (1, ..., 2, D)
shape = shape[:axis] + (1,) + shape[axis+1:]
else:
# (1, ..., D) <- (1, ..., 2, D)
shape = shape[:axis] + shape[axis+1:]
self.squeeze = ops.Squeeze(axis)
# (..., D) <- (1, ..., D)
self._set_shape(shape[1:])
self.set_pbc(use_pbc)
@property
def ndim(self) -> int:
"""rank (number of dimensions) of the vector"""
return self._ndim
@property
def shape(self) -> tuple:
"""shape of the vector"""
return self._shape
def construct(self, coordinate: Tensor, pbc_box: Tensor = None):
r"""get vector between specific atoms or virtual atoms.
Args:
coordinate (Tensor): Tensor of shape `(B, A, D)`. Data type is float.
`B` means batchsize, i.e. number of walkers in simulation.
`A` means number of atoms in system.
pbc_box (Tensor): Tensor of shape `(B, D)`. Data type is float.
Default: ``None``.
Returns:
vector (Tensor): Tensor of shape `(B, ..., D)`. Data type is float.
"""
if self.atoms is None:
# (..., D)
atoms0 = self.atoms0(coordinate, pbc_box)
atoms1 = self.atoms1(coordinate, pbc_box)
else:
# (B, ..., 2, D)
atoms = self.atoms(coordinate, pbc_box)
# (B, ..., 1, D) <- (B, ..., 2, D)
atoms0, atoms1 = self.split2(atoms)
if self.squeeze is not None:
# (B, ..., D) <- (B, ..., 1, D)
atoms0 = self.squeeze(atoms0)
atoms1 = self.squeeze(atoms1)
# (B, ..., D) or (B, ..., 1, D)
vector = self.get_vector(atoms0, atoms1, pbc_box)
if self.do_reshape:
new_shape = coordinate.shape[0] + self._shape
vector = F.reshape(vector, new_shape)
# (B, ..., D) or (B, ..., 1, D)
return vector