理解pipenv in Python

本文介绍Pipenv这一Python包管理工具的基本使用方法。Pipenv整合了Pipfile和Pipfile.lock文件来管理依赖项,并自动创建和管理项目的虚拟环境。通过简单的命令行操作,用户可以安装、卸载依赖包并生成确定性的构建环境。
  • Prerequisite

    理解Pipfile vs. setup.py || applications vs. libraries

  • Pipenv (Homepage)

    requests作者 K e n R e i t z Ken Reitz KenReitz发布于2017.1.22.

    Windows is a first-class citizen, in our world.

    Pipenv is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yern, etc.) to the Python world.

    It harnesses Pipfile, pip, and virtualenv into one single toolchain.

    It automatically creates and manages a virtualenv for you projects, as well as adds/removes packages from your Pipfile as you install/uninstall packages. It also generates the ever important Pipfile.lock, which is used to produce deterministic builds.

    Pipenv is primarily meant to provide users and developers of applications with an easy method to setup a working environment.

    Pipenv 集成包管理+虚拟环境,通过Pipfile,Pipfile.lock以实现.针对虚拟环境,不再需要activate and deactivate

  • Installing Pipenv

    pipx

    pipenv

    pip install --user pipx 
    pipx ensurepath
    pipx install pipenv 
    
  • 基础用法

    详情参见:

    Announcing Pipenv!

    Basic Usage if Pipenv

    Pipenv & Virtual Environments

    Pipenv CLI Reference

    Pipenv manages dependencies on a per-project basis. To install packages, change into your projects’s directory (or just an empty directory for this tutorial) and run:

    cd project_folder
    pipenv install requests
    pipenv shell # activate environment
    touch script.py
    pipenv run python script.py
    pipenv uninstall <package>
    pipenv --rm # remove the virtualenv
    

    Pipenv will install requests library and create a Pipfile for you in your project’s directory. The Pipfile is used to track which dependencies your project needs in case you need to re-install them, such as when you share your projects with others.

    If it already has a requirements.txt file, it will generate a Pipfile file with the requirements and a virtual environment folder, otherwise, it will generate an empty Pipfile file.

    If you disliked or changed your mind about somthing that you have installed:

    pipenv uninstall <package>

    To activate the virtual environment that pipenv generated:

    pipenv shell

    to leave the environment:

    exit

    Using pipenv run ensures that your installed packages are available to your scripts. It’s also possible to spawn a new shell that ensures all commands have access to your installed packages with pipenv shell.

  • References

  1. How are Pipfile and Pipfile.lock used?
【负荷预测】基于VMD-CNN-LSTM的负荷预测研究(Python代码实现)内容概要:本文介绍了基于变分模态分解(VMD)、卷积神经网络(CNN)和长短期记忆网络(LSTM)相结合的VMD-CNN-LSTM模型在负荷预测中的研究与应用,采用Python代码实现。该方法首先利用VMD对原始负荷数据进行分解,降低序列复杂性并提取不同频率的模态分量;随后通过CNN提取各模态的局部特征;最后由LSTM捕捉时间序列的长期依赖关系,实现高精度的负荷预测。该模型有效提升了预测精度,尤其适用于非平稳、非线性的电力负荷数据,具有较强的鲁棒性和泛化能力。; 适合人群:具备一定Python编程基础和深度学习背景,从事电力系统、能源管理或时间序列预测相关研究的科研人员及工程技术人员,尤其适合研究生、高校教师及电力行业从业者。; 使用场景及目标:①应用于日前、日内及实时负荷预测场景,支持智慧电网调度与能源优化管理;②为研究复合型深度学习模型在非线性时间序列预测中的设计与实现提供参考;③可用于学术复现、课题研究或实际项目开发中提升预测性能。; 阅读建议:建议读者结合提供的Python代码,深入理解VMD信号分解机制、CNN特征提取原理及LSTM时序建模过程,通过实验调试参数(如VMD的分解层数K、惩罚因子α等)优化模型性能,并可进一步拓展至风电、光伏等其他能源预测领域。
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