A simple wrapper of numpy and matplotlib to make physics experiment data analyse easier

# PhysicsExp

### 中科大大物实验数据处理工具

The package is released on pypi.

My experiment data, data processing scripts, figures, and some results(from experiment level I to IV), please visit the USTCPhysExpData project.

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

Imagination and expectation are far from reality. Now I only wrapped matplotlib plotting library, implemented simple regression, easy file input, and docx generation. To simplify repetious works.

This project comes with NO WARRENTY.

## 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 comment a line, and e * to specify the order of magnitude.

Then it's time to write python

#!/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')
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.

## 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.

• 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.

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

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

Here.

## Project details

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