Metadata-Version: 2.1
Name: physicsexp
Version: 0.0.1
Summary: A simple wrapper of numpy and matplotlib to make physics experiment data analyse easier
Home-page: https://github.com/ustcpetergu/PhysicsExp
Author: ustcpetergu
Author-email: guyimin@mail.ustc.edu.cn
License: UNKNOWN
Description: # PhysicsExp
        ### USTC Physics Experiments Data Processing Tools
        
        ### 大物实验数据+数据处理工具
        
        #### Comes with NO WARRENTY
        
        最终目的是建造一套用于自动化处理大物实验数据、绘制图像、生成可打印文档、将文档提交到在线打印系统的工具；针对常用数据处理需求实现简化和自动化，只要简单的几行代码，就能完成通用的绘图、拟合、不确定度计算等大物实验常用任务。
        理想与现实差距还很大，目前仅仅包装了一些matplotlib绘图库和文件输入简化重复劳动。
        
        ### A Simple Guide
        
        Assuming you are using Windows. 
        
        **Build**
        
        ```
        python setup.py sdist bdist_wheel
        ```
        
        Then the packaged wheel file can be found at `./dist/physicsexp-0.0.1-py3-none-any.whl`(Name may be different)
        
        **Install**
        
        **This package haven't been tested as it should and I don't know what will happen after installation.**
        
        **Use a virtualenv is recommended. **
        
        Create a virtualenv
        
        ```
        python -m venv test-env
        ```
        
        Activate it
        
        ```
        ./test-env/Scripts/activate.bat
        ```
        
        Install the wheel (Use USTC mirror to accelerate, for it will download and install other packages)
        
        ```
        pip install -i https://mirrors.ustc.edu.cn/pypi/web/simple path\to\physicsexp-0.0.1-py3-none-any.whl
        ```
        
        Wait a moment and installation will finish.
        
        **Use**
        
        Launch python in your venv
        
        ```
        python
        ```
        
        Import the package (`from xxx import *` may be bad, don't imitate me)
        
        ```
        >>> from physicsexp.mainfunc import *
        >>> from physicsexp.gendocx import *
        >>>
        ```
        
        Enjoy. 
        
        Wanna know how to use? Read source code yourself, see examples at `Experiments/`(Most of them are already outdated and cannot be run), or contact developer.
        
        But most of the time neither of these works. 
        
        And can using these tools boost your efficiency? I don't know, but likely can't. 
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
