mindsponge.common.make_transform_from_reference

mindsponge.common.make_transform_from_reference(point_a, point_b, point_c)[源代码]

使用施密特正交化方法构造骨架的旋转矩阵和平移向量。

计算旋转矩阵和平移满足

a）’N’原子是原始点

b）’CA’原子位于x轴上

c）平面CA-N-C在x-y平面上。

\begin{array}{r} \begin{aligned} {\vec{v}}_{1} = {\vec{x}}_{3} - {\vec{x}}_{2} \\ {\vec{v}}_{2} = {\vec{x}}_{1} - {\vec{x}}_{2} \\ {\vec{e}}_{1} = {\vec{v}}_{1} / | | {\vec{v}}_{1} | | \\ {\vec{u}}_{2} = {\vec{v}}_{2} - {\vec{e}}_{1} ({\vec{e}}_{1}^{T} {\vec{v}}_{2}) \\ {\vec{e}}_{2} = {\vec{u}}_{2} / | | {\vec{u}}_{2} | | \\ {\vec{e}}_{3} = {\vec{e}}_{1} \times {\vec{e}}_{2} \\ r o t a t i o n = ({\vec{e}}_{1}, {\vec{e}}_{2}, {\vec{e}}_{3}) \\ t r a n s l a t i o n = ({\vec{x}}_{2}) \end{aligned} \end{array}

参数：

point_a (float, tensor) -> (tensor) - ‘N’原子空间位置信息，shape为: $[. . ., N_{r e s}, 3]$ 。
point_b (float, tensor) -> (tensor) - ‘CA’原子空间位置信息，shape为: $[. . ., N_{r e s}, 3]$ 。
point_c (float, tensor) -> (tensor) - ‘C’原子空间位置信息，shape为: $[. . ., N_{r e s}, 3]$ 。

返回：

旋转矩阵(tuple) $(x x, x y, x z, y x, y y, y z, z x, z y, z z)$ ，每个元素shape为 $(. . ., N_{r e s})$ 。平移向量(tuple) $(x, y, z)$ 每个元素shape为 $(. . ., N_{r e s})$ 。

支持平台：

Ascend GPU

样例：

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import Tensor
>>> from mindsponge.common.geometry import make_transform_from_reference
>>> input_0 = Tensor(np.ones((4, 256, 3)), ms.float32)
>>> input_1 = Tensor(np.ones((4, 256, 3)), ms.float32)
>>> input_2 = Tensor(np.ones((4, 256, 3)), ms.float32)
>>> rots, trans = make_transform_from_reference(input_0, input_1, input_2)
>>> print(len(rots), rots[0].shape, len(trans), trans[0].shape)
9, (4, 256), 3, (4, 256)