Skip to main content

Cython Based Fast FES Calculation Toolkit.

Project description

CyFES:GPU加速高性能数据透视工具

Hex.pm Hex.pm Hex.pm Hex.pm Hex.pm

介绍

这是一个使用Cython+CUDA+Python编写的高性能FES计算软件,可以用于加速数据透视的计算:

$$ F_{avg}(x)=-kT\log\left(\frac{\sum_{j=1}^Ne^{\frac{V_j}{kT}}e^{-\frac{(x-n_j)^2}{2\sigma^2}}}{N\Pi_i\sqrt{2\pi}\sigma_i}\right)-F_{min} $$

$$ V(x)=w\sum_t\prod_ie^{-\frac{(x_i-\mu_t)^2}{2\sigma^2}} $$

$$ F(x)=-V-F_{min} $$

安装

pip安装

本项目可以直接使用pip进行安装:

$ python3 -m pip install cyfes --user --upgrade -i https://pypi.org/simple

源码安装

首先将本仓库clone到本地:

$ git clone https://gitee.com/dechin/cy-fes.git && cd cy-fes/

然后直接运行setup.py进行安装:

$ python3 -m pip install .

安装测试

在本仓库的test路径下存放了一个测试用例,用于测试cyfes是否安装成功。用户可以直接简单的运行:

$ python3 tests/test_path_fes.py 
[0.00902854 0.         0.09338432 0.02065182]
[0.03961432 0.         0.01514649 0.02259541]
[0.00129778 0.         0.02988457 0.0869869 ]
[0.01712827 0.01378975 0.         0.02229569]
[0.0114323  0.03356422 0.         0.0328276 ]

若输出为多个数组,则表示安装成功。也可以使用单元测试运行,但是这需要在本地先安装pytest

$ python3 -m pip install pytest

然后直接在仓库的根目录下运行:

$ py.test
============================ test session starts =============================
platform linux -- Python 3.7.5, pytest-7.4.4, pluggy-1.2.0
rootdir: /home/cy-fes
collected 5 items                                                            

tests/test_path_fes.py .....                                           [100%]

============================= 5 passed in 14.23s =============================

没有报错,则表示安装成功。

使用方法

在安装成功后,可以直接在Python脚本中调用:

import numpy as np
from cyfes import PathFES
np.random.seed(0)

def test_path_fes():
    atoms = 4
    cvs = 10000
    crd = np.random.random((atoms, 3))
    cv = np.random.random((cvs, 3))
    bw = np.random.random(3)
    bias = np.random.random(cvs)-1

    fes = np.asarray(PathFES(crd, cv, bw, bias))
    print (fes)

if __name__ == '__main__':
    test_path_fes()

还可以使用命令行模式:

$ python3 -m cyfes --help
usage: __main__.py [-h] [-i I] [-ic IC] [-ib IB] [-s S] [-e E] [-g G] [-o O]
                   [-no_bias NO_BIAS] [-f32 F32] [-sigma SIGMA]
                   [-device DEVICE]

optional arguments:
  -h, --help        show this help message and exit
  -i I              Set the input record file path.
  -ic IC            Set the cv index of input record file. Default: 0,1,2
  -ib IB            Set the bias index of input record file. Default: 3
  -s S              CV length. Default: None
  -e E              Edge length. Default: 1.0
  -g G              Grid numbers. Default: 10,10,10
  -o O              Set the output FES file path.
  -no_bias NO_BIAS  Do not use the bias from input file. Default: false
  -f32 F32          Use float32. Default: false
  -sigma SIGMA      Sigma value when calculating FES. Default: 0.3
  -device DEVICE    Set the device ids separated with commas. Default: 0

假如我们有一个三维的CV,那么最简单的运行方式为:

$ python3 -m cyfes -i /home/Data/xyz_bias.txt -o ./work_dir/z.cub

那么最后产生的文件内容为:

$ head -n 10 work_dir/z.cub
Generated by CyFES
Total	1000	grids
1	21.6622	19.8498	42.3652
10	6.40465	0	0
10	0	7.02147	0
10	0	0	6.33118
1	1.000000	53.6854	54.9571	74.0211
450	450	450	450	450	450	
450	450	450	450	450	450	
450	450	70.0855	70.848	450	450	

该cube格式的文件可以在支持的软件(如VMD)中进行可视化操作。

已知问题

  1. 使用numpy==1.22.2的版本中会出现ImportError: numpy.core.multiarray failed to import问题。解决方案:升级numpy版本:python3 -m pip install numpy --upgrade
  2. 执行python3 -m cyfes --help报错ModuleNotFoundError: No module named 'cyfes.wrapper',这是因为执行命令的目录下存在名为cyfes的文件夹,需要切换执行命令的位置。
  3. 使用cyfes出现Segmentation fault段错误问题,是因为找不到编译好的动态链接库文件,大概率是系统环境下权限不足,没有site路径的权限,可以使用如下脚本进行检查:
# check_dynamics.py
import os
import site
from pathlib import Path

site_path = Path(site.getsitepackages()[0])
site_file_path = site_path.parent.parent.parent / 'cyfes' / 'libcufes.so'
site_dynamics_path = str(site_file_path)

user_site_path = Path(site.USER_SITE)
user_file_path = user_site_path.parent.parent.parent / 'cyfes' / 'libcufes.so'
user_dynamics_path = str(user_file_path)

if not os.path.exists(site_dynamics_path) and not os.path.exists(user_dynamics_path):
    print ('Check dynamics complete, no libcufes.so file founded!')
else:
    print ('Installation of CyFES success!')

使用python3运行该脚本,即可判断动态链接库是否被正确安装。

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cyfes-4.3.tar.gz (349.4 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

cyfes-4.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

cyfes-4.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

cyfes-4.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

cyfes-4.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

File details

Details for the file cyfes-4.3.tar.gz.

File metadata

  • Download URL: cyfes-4.3.tar.gz
  • Upload date:
  • Size: 349.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.21

File hashes

Hashes for cyfes-4.3.tar.gz
Algorithm Hash digest
SHA256 1d201af9316d8a096bc49685f7607a4960c355585048b7282b7b5f08f49a81e8
MD5 11eb037c449ac778870d654b0c5619cd
BLAKE2b-256 c72a7d9cf5cab55c45a4f1ea8a46fcaa415cb70138bc80cd33c95b2bd0cbcbc4

See more details on using hashes here.

File details

Details for the file cyfes-4.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for cyfes-4.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 c7be3c09b854a7978bfe0d774c466d19474050ebadf47900605422e7356c47ac
MD5 6939b3c809e34b5f2a699e6f4e24bb71
BLAKE2b-256 66e522dafe3272a5c4980ca87966e266b4a7493056c22d75d7250eee36872314

See more details on using hashes here.

File details

Details for the file cyfes-4.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for cyfes-4.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 e8518b808e6530cfc80e5ed50be2c73d801a940594c36fca6257724711985842
MD5 6ac1a12cbbb703d8d384dfcc2b6236f7
BLAKE2b-256 df341874845e36d79a9b65056f502c7dfa9357f4e2595659686c7098d68f837c

See more details on using hashes here.

File details

Details for the file cyfes-4.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for cyfes-4.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0517ab6c95437b073344a8697aabf2f2486e2e05dcaf8da7dfa2e0c87ca1f815
MD5 eb6799605ad8c475ad98dde981f53eb5
BLAKE2b-256 c9acd75101ab03fc34a64a1b576ca41fbb5cfdb899ec2049e79d244e1c66051a

See more details on using hashes here.

File details

Details for the file cyfes-4.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for cyfes-4.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6d3c8d4468e268a0640b403e57382333ed7a55360e0cfce726a23aeb5206471f
MD5 721c434711dab5a25897e463bcb57d53
BLAKE2b-256 59eeb73295d5a8db59d6f61822d9dd23df51739b946c2bf5b82d50ab124083c1

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page