PhysicsExp

USTC Physics Experiments Data Processing Tools

中科大大物实验数据处理工具

The package is also released on pypi.

For readers from pypi, here please.

关于本人的实验数据和数据处理脚本、图片、计算结果（从一级到四级），请见 USTCPhysExpData 项目。 My experiment data, data processing scripts, figures, and some results(from experiment level I to IV), please visit the USTCPhysExpData project.

About

Don't want to use OriginLab or Excel? Try Python!

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matplotlib绘图库、简单拟合、文件输入、docx生成，简化重复劳动。

Now I only wrapped matplotlib plotting library, implemented simple regression, easy file input, and docx generation. To simplify repetious works.

相关博客：USTC LUG上的页面 本人主页上的页面

Installation

Install the package

Use TUNA mirror to accelerate. Depencencies like numpy and matplotlib will be installed automatically.

pip install -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple physicsexp

Test the installation(Optional)

>>> from physicsexp.mainfunc import *
>>> from physicsexp.gendocx import *
>>>

If no error then you are ready! If error occurs feel free to open an issue.

Run the example script(Recommended)

python3 ./physicsexp/example/plot.py

You'll see graphs poped out and saved to .png, a generated gen.docx ready to print, and calculations printed to output, in the ./physicsexp/example directory. You can also clone USTCPhysExpData to try some real-life cases. Then you can modify the code or write your own code to process your data!

Example Script Explained

It is a real-case example of input several lines of data, plot the data and do linear regression, and generate a printable docx document containing plot and analyse results.

If you really want to know, the experiment is about verifying the relativistic kinetic energy vs. momentum relationship of electron(beta-ray) and measuring the extraction of beta-ray by aluminum pieces of different thickness.

First, put your data in data.txt, like this:

# 位置x
e -2
23.     24.2    25.5    26.5    27.7    29.     30.5    31.8
# 峰位N
245.77  291.79  336.40  378.52  417.94  456.14  510.12  544.95
# 铝片数量M
0       1       2       3       4       5
# 选区计数N
43901   34258   28725   23670   19386   16866

You can use # to add some comment lines, and e * to specify the order of magnitude -- thus be able to directly write down the original on-paper data without conversion.

Then it's time to write python

Headers & imports

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

from physicsexp.mainfunc import *
from physicsexp.gendocx import *

Read the file easily with the readoneline function

fin = open('./data.txt', 'r', encoding='utf-8')
pos = readoneline(fin)
N = readoneline(fin)
Al_num = readoneline(fin)
Cnt = readoneline(fin)
fin.close()

Calculate and print some results. This is python, you can do whatever you like easily. (This part is not related to the library, you can skip this)

a = 2.373e-3
b = -.0161
dEk = .20

c0 = 299792458.
MeV = 1e6 * electron

Emeasure = a * N + b + dEk
x0 = .10
R = (pos - x0) / 2
B = 640.01e-4
Momentum = 300 * B * R
Eclassic = ((Momentum * MeV)**2 / (2 * me * c0**2)) / MeV
Erela = np.array([math.sqrt((i * MeV)**2 + (me * c0**2)**2) - me * c0**2 for i in Momentum]) / MeV
print('pos\t', pos)
print('R\t', R*100)
print('pc\t', Momentum)
print('N\t', N)
print('Eclas\t', Eclassic)
print('Erela\t', Erela)
print('Emes\t', Emeasure)

Now, plot!

First graph: three curve in one figure. Using simple_plot. You can use LaTeX in plot labels. Graph is saved to 1.png. Use show=0 to plot multiple lines on one figure.

simple_plot(Momentum, Emeasure, show=0, issetrange=0, dot='+', lab='测量动能')
simple_plot(Momentum, Eclassic, show=0, issetrange=0, dot='*', lab='经典动能')
simple_plot(Momentum, Erela, dot='o', save='1.png', issetrange=0, xlab='$pc/MeV$', ylab='$E/MeV$', title='电子动能随动量变化曲线', lab='相对论动能')

Second graph, a simple curve, saved to 2.png:

Len = 150
Cnt = Cnt / Len
simple_plot(Al_num, Cnt, xlab='铝片数', ylab='选区计数率(射线强度)', title='$\\beta$射线强度随铝片数衰减曲线', save='2.png')

Third graph, a curve with a linear fit, using simple_linear_plot, saved to 3.png:

CntLn = np.log(Cnt)
d = 50
Al_Real = Al_num * d
slope, intercept = simple_linear_plot(Al_Real, CntLn, xlab='质量厚度$g/cm^{-2}$', ylab='选区计数率对数(射线强度)', title='半对数曲线曲线', save='3.png')
print(-slope)
print(math.log(1e4) / (-slope))
print((math.log(Cnt[0]) - 4 * math.log(10) - intercept) / slope)

Don't bother putting pictures in documents yourself!

With a single line of code, generate a printable docx document with the above three pictures and the fit results.

gendocx('gen.docx', '1.png', '2.png', '3.png', 'slope, intercept: %f %f' % (slope, intercept))

Results

Output:

pos	 [0.23  0.242 0.255 0.265 0.277 0.29  0.305 0.318]
R	 [ 6.5   7.1   7.75  8.25  8.85  9.5  10.25 10.9 ]
pc	 [1.2480195  1.3632213  1.48802325 1.58402475 1.69922655 1.8240285
 1.96803075 2.0928327 ]
N	 [245.77 291.79 336.4  378.52 417.94 456.14 510.12 544.95]
Eclas	 [1.52375616 1.81804848 2.16616816 2.45469003 2.82471934 3.25488743
 3.78910372 4.28491053]
Erela	 [0.83752628 0.94478965 1.0622588  1.15334615 1.26333503 1.3831891
 1.52222218 1.64324566]
Emes	 [0.76711221 0.87631767 0.9821772  1.08212796 1.17567162 1.26632022
 1.39441476 1.47706635]
0.0038199159787357996
2411.136900195471
2402.45428200782

Generated docx:

Don't forget to change my name to yours.

JupyterHub

Later I found that using jupyter notebook is WAY MUCH better than coding in python editor:

And, Jupyterhub can provide jupyter notebook access for a group of users: the common case when several people want to share their experiment data processing script with each other.

In ./jupyterhub is my config being used to run jupyterhub: docker spawner with a shared strorage, dummy authenication. Literally no security or access control but acceptable for a group of trustable people on a private server.

Detailed Usage

Wanna know how to use after reading the example?

You can:

• Have a look at my programs in USTCPhysExpData.

However, they are not intended to run directly on your machine and magically give you correct answer without any change, but, if you really want to run them, maybe a git reset on this repository and dive into the dark history is the last resort.

• Read the source code yourself. Especially physicsexp/mainfunc.py and physicsexp/gendocx.py -- with the example covering most use cases, you just need to check out function declaration and extra available options(they make life easier).

• Or open an issue. If you are also a USTC student just contact me with QQ/Email. Contacts are on my website.

But don't be frustrated if none of these works. The project comes with absolutely no warrenty.

And can using these tools boost your efficiency? I don't know, but probably can't.

**However, if these can make you feel better despite spending more time, use it. **

Finally, think twice before wasting time on this project, instead, enjoy your life, learn some real physics, and find a (boy|girl)friend.

Misc Information

Here.