# 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.
# ============================================================================
"""
Atom group
"""
from typing import Union, List, Tuple
import mindspore
from mindspore import ops
from mindspore.ops import functional as F
from mindspore import Tensor
from mindspore.nn import CellList
from .colvar import Colvar
from ..function import get_integer
[docs]class ColvarGroup(Colvar):
r"""Concatenate a group of `Colvar` classes into one `Colvar` class
Args:
colvar (list or tuple): Array of `Colvar` to be concatenated.
axis (int): Axis to be concatenated. NOTE: This refers to the axis of the output Tensor
with the shape `(B, S_1, S_2, ..., S_n)`. Default: -1.
use_pbc (bool): Whether to use periodic boundary condition. Default: ``None``.
name (str): Name of the collective variables. Default: 'colvar_group'.
Supported Platforms:
``Ascend`` ``GPU``
"""
def __init__(self,
colvar: Union[List[Colvar], Tuple[Colvar]],
axis: int = -1,
use_pbc: bool = None,
name: str = 'colvar_group',
):
super().__init__(name=name)
if isinstance(colvar, Colvar):
colvar = [colvar]
elif not isinstance(colvar, (list, tuple)):
raise TypeError(f'The type of "colvar" must be list of Colvar but got: {type(colvar)}')
self.num_colvar = len(colvar)
axis = get_integer(axis)
if axis == 0:
raise ValueError(f'The axis ({axis}) cannot be 0 (the dimension of batch size)!')
shape = None
dim = 0
periodic = ()
colvar_ = []
for i, cv in enumerate(colvar):
shape_ = (1,) + cv.shape
dim += shape_[axis]
if axis == -1:
shape_ = shape_[:-1] + (None,)
else:
shape_ = shape_[:axis] + (None,) + shape_[axis+1:]
if i > 0 and shape_ != shape:
raise ValueError(f'The shape of the No.{i} colvar {cv.shape} cannot be '
f'concatenate with the shape of the colvar group: {shape}')
shape = shape_
if use_pbc is not None:
cv.set_pbc(use_pbc)
colvar_.append(cv)
periodic += (F.expand_dims(cv.periodic, 0),)
self.colvar: List[Colvar] = CellList(colvar_)
if axis == -1:
shape = shape[:-1] + (dim,)
else:
shape = shape[:axis] + (dim,) + shape[axis+1:]
self._shape = shape[1:]
self._ndim = len(self._shape)
self.concat = ops.Concat(axis)
periodic_int = [p.astype(mindspore.int32) for p in periodic]
self._periodic = F.squeeze(self.concat(periodic_int), 0).astype(mindspore.bool_)
[docs] def set_pbc(self, use_pbc: bool):
"""set whether to use periodic boundary condition"""
self._use_pbc = use_pbc
self.get_vector.set_pbc(use_pbc)
for i in range(self.num_colvar):
self.colvar[i].set_pbc(use_pbc)
return self
def construct(self, coordinate: Tensor, pbc_box: Tensor = None):
r"""get position coordinates of colvar group
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 colvar in system.
D means Dimension of the simulation system. Usually is 3.
Position coordinate of colvar in system.
pbc_box (Tensor): Tensor of shape `(B, D)`. Data type is float.
Tensor of PBC box. Default: ``None``.
Returns:
position (Tensor): Tensor of shape `(B, S_1, S_2, ..., S_n)`. Data type is float.
"""
colvar = ()
for i in range(self.num_colvar):
# (B, a_1'(i), a_2, ..., a_n, D)
colvar += (self.colvar[i](coordinate, pbc_box),)
# (B, a_1, a_2, ..., a_n, D) <- (B, a_1'(i), a_2, ..., a_n, D)
colvar = self.concat(colvar)
if self.do_reshape:
new_shape = coordinate.shape[0] + self._shape
colvar = F.reshape(colvar, new_shape)
return colvar