Python wrappers for ease of use for lab stuff

Built With

vim :^)

Latest Version

New versions of the functions will be available as soon as they're pushed. This might take up to a few days, depending on the internet connection used.

Authors

Some weeb
That weeaboo's waifu
Justin Pieber
This total nerd
Some furry

Acknowledgments

Hat tip to my m'lady

Dependencies

Most functions only require basic Python and numpy. Linear regression additionally requires matplotlib.pyplot and scipy.optimize. Ideally, one should also have uncertainties installed. Every function should also be usable without uncertainties (fit_pm currently isn't).

Functions

linreg

Simple linear regression function that utilizes outputs from scipy.optimize's curve_fit and plots the result with matplotlib.pyplot's plot. Uses a*x+b as its fit function. Use unumpy=True if the xarr and yarr arrays are uarrays.

linreg(xarr, yarr, markeraus='-', grenz=True, markergrenz='--', color='orange', labelaus='Lineare Regression',
      labelgrenz=None, unumpy=False, first=0, last=-1, xnum=100, xfitrestrictl=None, xfitrestrictr=None,
      yfitrestrictl=None, yfitrestrictr=None, subplot=None)

xarr and yarr are the input arrays. By default, these are parsed as lists. If unumpy is set to True, unp.nominal_values will be used.

markeraus sets the marker used for the linear regression.

grenz sets whether or not Grenzgeraden (I can't figure out the English term) should be used.

markergrenz sets the marker used for the Grenzgeraden.

color sets the color used by both the Grenzgeraden and the Ausgleichsgerade.

labelaus and labelgrenz set the labels for the Ausgleichs- and Grenzgeraden.

first and last set how much of the xarr should be used in the end.

xnum sets the size of the array used for fitting. The default, 100, is already overkill, since this is just a straight line.

x/yfitrestrictl/r are restrictions for curve_fit.

mean

Simply calculates the sum of an array over its length.

meanDeviation and meandev

Computes the mean deviation of a given list. The latter allows entering a unumpy uarray with uncertainties=True.

nmean

Combines mean and meandev to output one ufloat. Input can be uarray with uncertainties=True, but output will always be a ufloat.

gewMitt

Weighted mean calculation. Requires input of both nominal values and uncertainties.

omega

Turns a periodic time into angular frequency.

a, b, s, Da, Db

Calculate linear regression "by hand". Requires x and y inputs. Getting the values from pyplot is probably faster, but at least you know exactly what's being done in this case.

t

Find out if values are good or not. Optimal values are t < 2.

s_t

Calculate uncertainty for certain devices. I don't know what this is for exactly.

csvtex

csvtex(filename: str, style=None, caption="CAPTION", label="Tab:X", index=None, line_break=True, dec_comma=False):

Takes a csv file and outputs code for a LaTeX table. More in depth explanation can be found in ThonkTools/textools.py.

hi

Step by step instructions to solving coupled systems of non-linear inhomogeneous differential equations.

Installation

$ git clone https://github.com/BigThonkers/ThonkTools.git
$ cd ThonkTools
$ pip install .

$ pip install git+https://github.com/BigThonkers/ThonkTools.git

The former, however, is probably better, as stability isn't exactly something you can expect from this package. You can also just put this in the right directory. I've yet to put this in pip's repository. Windows users can place the ThonkTools folder in appdata > Local > Programs > Python3.7 > site-packages or something like that.

BigThonkers / ThonkTools

readme